Repository: rhiever/Data-Analysis-and-Machine-Learning-Projects
Branch: master
Commit: 4723494d12ce
Files: 37
Total size: 3.9 MB

Directory structure:
gitextract_qtvjqcg8/

├── .gitignore
├── README.md
├── environment.yml
├── example-data-science-notebook/
│   ├── Example Machine Learning Notebook.ipynb
│   ├── iris-data-clean.csv
│   ├── iris-data.csv
│   └── iris_dtc.dot
├── follower-factory/
│   └── Twitter Follower Factory.ipynb
├── optimal-road-trip/
│   ├── Computing the optimal road trip across the U.S..ipynb
│   ├── OptimalRoadTripHtmlSaveAndDisplay.py
│   └── my-waypoints-dist-dur.tsv
├── pareto-optimized-road-trip/
│   ├── my-waypoints-dist-dur.tsv
│   └── optimized-state-capitols-trip.ipynb
├── pct-bachelors-degrees-conferred-women-usa/
│   ├── Percentage of Bachelor’s degrees conferred to women in the US, by major.ipynb
│   └── gender_degree_data.tsv
├── python-baseball-simulator/
│   └── Simulating baseball in Python.ipynb
├── rethinking-population-pyramid/
│   └── Rethinking the population pyramid.ipynb
├── revisiting-vaccine-visualizations/
│   └── Revisiting the vaccine visualizations.ipynb
├── tpot-demo/
│   └── TPOT - A Python tool for automating data science.ipynb
├── traveling-salesman-portrait/
│   └── traveling-salesman-portrait.py
├── us-weather-history/
│   ├── KCLT.csv
│   ├── KCQT.csv
│   ├── KHOU.csv
│   ├── KIND.csv
│   ├── KJAX.csv
│   ├── KMDW.csv
│   ├── KNYC.csv
│   ├── KPHL.csv
│   ├── KPHX.csv
│   ├── KSAF.csv
│   ├── KSEA.csv
│   ├── README.md
│   ├── visualize_weather.py
│   ├── wunderground_parser.py
│   └── wunderground_scraper.py
└── wheres-waldo-path-optimization/
    ├── Where's Waldo path optimization.ipynb
    └── wheres-waldo-locations.csv

================================================
FILE CONTENTS
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FILE: .gitignore
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*/.ipynb_checkpoints/*


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FILE: README.md
================================================
![Python 2.7](https://img.shields.io/badge/python-2.7-blue.svg)
![Python 3.5](https://img.shields.io/badge/python-3.5-blue.svg)
![License](https://img.shields.io/badge/license-MIT%20License-blue.svg)

# Randy Olson's data analysis and machine learning projects

© 2016 - current, Randal S. Olson

This is a repository of teaching materials, code, and data for my data analysis and machine learning projects.

Each repository will (usually) correspond to one of the blog posts on my [web site](http://www.randalolson.com/blog/).

Be sure to check the documentation (usually in IPython Notebook format) in the directory you're interested in for the notes on the analysis, data usage terms, etc.

If you don't have the necessary software installed to run IPython Notebook, don't fret. You can use [nbviewer](http://nbviewer.ipython.org/) to view a notebook on the web.

For example, if you want to view the notebook in the `wheres-waldo-path-optimization` directory, copy the [full link](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/wheres-waldo-path-optimization/Where's%20Waldo%20path%20optimization.ipynb) to the notebook then paste it into [nbviewer](http://nbviewer.ipython.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/wheres-waldo-path-optimization/Where%27s%20Waldo%20path%20optimization.ipynb).

## License

### Instructional Material

All instructional material in this repository is made available under the [Creative Commons Attribution license](https://creativecommons.org/licenses/by/4.0/). The following is a human-readable summary of (and not a substitute for) the [full legal text of the CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/legalcode).

You are free to:

* **Share**—copy and redistribute the material in any medium or format
* **Adapt**—remix, transform, and build upon the material

for any purpose, even commercially.

The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

* **Attribution**—You must give appropriate credit (mentioning that your work is derived from work that is © Randal S. Olson and, where practical, linking to http://www.randalolson.com/), provide a [link to the license](https://creativecommons.org/licenses/by/4.0/), and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

**No additional restrictions**—You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

**Notices:**

* You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
* No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

### Software

Except where otherwise noted, the example programs and other software provided in this repository are made available under the [OSI](http://opensource.org/)-approved [MIT license](http://opensource.org/licenses/mit-license.html).

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


================================================
FILE: environment.yml
================================================
channels:
  - conda-forge
  - defaults
dependencies:
  - numpy
  - pandas
  - scikit-learn
  - matplotlib
  - seaborn
  - watermark
  - tqdm
  - joblib
  - tpot
  - pip:
    - twitter
    - googlemaps


================================================
FILE: example-data-science-notebook/Example Machine Learning Notebook.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An example machine learning notebook\n",
    "\n",
    "### Notebook by [Randal S. Olson](http://www.randalolson.com/)\n",
    "#### Supported by [Jason H. Moore](http://www.epistasis.org/)\n",
    "#### [University of Pennsylvania Institute for Bioinformatics](http://upibi.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It is recommended to [view this notebook in nbviewer](http://nbviewer.ipython.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb) for the best viewing experience.**\n",
    "\n",
    "**You can also [execute the code in this notebook on Binder](https://mybinder.org/v2/gh/rhiever/Data-Analysis-and-Machine-Learning-Projects/master?filepath=example-data-science-notebook%2FExample%20Machine%20Learning%20Notebook.ipynb) - no local installation required.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "\n",
    "1. [Introduction](#Introduction)\n",
    "\n",
    "2. [License](#License)\n",
    "\n",
    "3. [Required libraries](#Required-libraries)\n",
    "\n",
    "4. [The problem domain](#The-problem-domain)\n",
    "\n",
    "5. [Step 1: Answering the question](#Step-1:-Answering-the-question)\n",
    "\n",
    "6. [Step 2: Checking the data](#Step-2:-Checking-the-data)\n",
    "\n",
    "7. [Step 3: Tidying the data](#Step-3:-Tidying-the-data)\n",
    "\n",
    "    - [Bonus: Testing our data](#Bonus:-Testing-our-data)\n",
    "\n",
    "8. [Step 4: Exploratory analysis](#Step-4:-Exploratory-analysis)\n",
    "\n",
    "9. [Step 5: Classification](#Step-5:-Classification)\n",
    "\n",
    "    - [Cross-validation](#Cross-validation)\n",
    "\n",
    "    - [Parameter tuning](#Parameter-tuning)\n",
    "\n",
    "10. [Step 6: Reproducibility](#Step-6:-Reproducibility)\n",
    "\n",
    "11. [Conclusions](#Conclusions)\n",
    "\n",
    "12. [Further reading](#Further-reading)\n",
    "\n",
    "13. [Acknowledgements](#Acknowledgements)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "In the time it took you to read this sentence, terabytes of data have been collectively generated across the world — more data than any of us could ever hope to process, much less make sense of, on the machines we're using to read this notebook.\n",
    "\n",
    "In response to this massive influx of data, the field of Data Science has come to the forefront in the past decade. Cobbled together by people from a diverse array of fields — statistics, physics, computer science, design, and many more — the field of Data Science represents our collective desire to understand and harness the abundance of data around us to build a better world.\n",
    "\n",
    "In this notebook, I'm going to go over a basic Python data analysis pipeline from start to finish to show you what a typical data science workflow looks like.\n",
    "\n",
    "In addition to providing code examples, I also hope to imbue in you a sense of good practices so you can be a more effective — and more collaborative — data scientist.\n",
    "\n",
    "I will be following along with the data analysis checklist from [The Elements of Data Analytic Style](https://leanpub.com/datastyle), which I strongly recommend reading as a free and quick guidebook to performing outstanding data analysis.\n",
    "\n",
    "**This notebook is intended to be a public resource. As such, if you see any glaring inaccuracies or if a critical topic is missing, please feel free to point it out or (preferably) submit a pull request to improve the notebook.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely usable and shareable as possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Required libraries\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "If you don't have Python on your computer, you can use the [Anaconda Python distribution](http://continuum.io/downloads) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
    "\n",
    "This notebook uses several Python packages that come standard with the Anaconda Python distribution. The primary libraries that we'll be using are:\n",
    "\n",
    "* **NumPy**: Provides a fast numerical array structure and helper functions.\n",
    "* **pandas**: Provides a DataFrame structure to store data in memory and work with it easily and efficiently.\n",
    "* **scikit-learn**: The essential Machine Learning package in Python.\n",
    "* **matplotlib**: Basic plotting library in Python; most other Python plotting libraries are built on top of it.\n",
    "* **Seaborn**: Advanced statistical plotting library.\n",
    "* **watermark**: A Jupyter Notebook extension for printing timestamps, version numbers, and hardware information.\n",
    "\n",
    "To make sure you have all of the packages you need, install them with `conda`:\n",
    "\n",
    "    conda install numpy pandas scikit-learn matplotlib seaborn\n",
    "    \n",
    "    conda install -c conda-forge watermark\n",
    "\n",
    "`conda` may ask you to update some of them if you don't have the most recent version. Allow it to do so.\n",
    "\n",
    "**Note:** I will not be providing support for people trying to run this notebook outside of the Anaconda Python distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The problem domain\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "For the purposes of this exercise, let's pretend we're working for a startup that just got funded to create a smartphone app that automatically identifies species of flowers from pictures taken on the smartphone. We're working with a moderately-sized team of data scientists and will be building part of the data analysis pipeline for this app.\n",
    "\n",
    "We've been tasked by our company's Head of Data Science to create a demo machine learning model that takes four measurements from the flowers (sepal length, sepal width, petal length, and petal width) and identifies the species based on those measurements alone.\n",
    "\n",
    "<img src=\"images/petal_sepal.jpg\" />\n",
    "\n",
    "We've been given a [data set](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/raw/master/example-data-science-notebook/iris-data.csv) from our field researchers to develop the demo, which only includes measurements for three types of *Iris* flowers:\n",
    "\n",
    "### *Iris setosa*\n",
    "\n",
    "<img src=\"images/iris_setosa.jpg\" />\n",
    "\n",
    "### *Iris versicolor*\n",
    "<img src=\"images/iris_versicolor.jpg\" />\n",
    "\n",
    "### *Iris virginica*\n",
    "<img src=\"images/iris_virginica.jpg\" />\n",
    "\n",
    "The four measurements we're using currently come from hand-measurements by the field researchers, but they will be automatically measured by an image processing model in the future.\n",
    "\n",
    "**Note:** The data set we're working with is the famous [*Iris* data set](https://archive.ics.uci.edu/ml/datasets/Iris) — included with this notebook — which I have modified slightly for demonstration purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Answering the question\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "The first step to any data analysis project is to define the question or problem we're looking to solve, and to define a measure (or set of measures) for our success at solving that task. The data analysis checklist has us answer a handful of questions to accomplish that, so let's work through those questions.\n",
    "\n",
    ">Did you specify the type of data analytic question (e.g. exploration, association causality) before touching the data?\n",
    "\n",
    "We're trying to classify the species (i.e., class) of the flower based on four measurements that we're provided: sepal length, sepal width, petal length, and petal width.\n",
    "\n",
    ">Did you define the metric for success before beginning?\n",
    "\n",
    "Let's do that now. Since we're performing classification, we can use [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision) — the fraction of correctly classified flowers — to quantify how well our model is performing. Our company's Head of Data has told us that we should achieve at least 90% accuracy.\n",
    "\n",
    ">Did you understand the context for the question and the scientific or business application?\n",
    "\n",
    "We're building part of a data analysis pipeline for a smartphone app that will be able to classify the species of flowers from pictures taken on the smartphone. In the future, this pipeline will be connected to another pipeline that automatically measures from pictures the traits we're using to perform this classification.\n",
    "\n",
    ">Did you record the experimental design?\n",
    "\n",
    "Our company's Head of Data has told us that the field researchers are hand-measuring 50 randomly-sampled flowers of each species using a standardized methodology. The field researchers take pictures of each flower they sample from pre-defined angles so the measurements and species can be confirmed by the other field researchers at a later point. At the end of each day, the data is compiled and stored on a private company GitHub repository.\n",
    "\n",
    ">Did you consider whether the question could be answered with the available data?\n",
    "\n",
    "The data set we currently have is only for three types of *Iris* flowers. The model built off of this data set will only work for those *Iris* flowers, so we will need more data to create a general flower classifier.\n",
    "\n",
    "<hr />\n",
    "\n",
    "Notice that we've spent a fair amount of time working on the problem without writing a line of code or even looking at the data.\n",
    "\n",
    "**Thinking about and documenting the problem we're working on is an important step to performing effective data analysis that often goes overlooked.** Don't skip it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Checking the data\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "The next step is to look at the data we're working with. Even curated data sets from the government can have errors in them, and it's vital that we spot these errors before investing too much time in our analysis.\n",
    "\n",
    "Generally, we're looking to answer the following questions:\n",
    "\n",
    "* Is there anything wrong with the data?\n",
    "* Are there any quirks with the data?\n",
    "* Do I need to fix or remove any of the data?\n",
    "\n",
    "Let's start by reading the data into a pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "0              5.1             3.5              1.4             0.2   \n",
       "1              4.9             3.0              1.4             0.2   \n",
       "2              4.7             3.2              1.3             0.2   \n",
       "3              4.6             3.1              1.5             0.2   \n",
       "4              5.0             3.6              1.4             0.2   \n",
       "\n",
       "         class  \n",
       "0  Iris-setosa  \n",
       "1  Iris-setosa  \n",
       "2  Iris-setosa  \n",
       "3  Iris-setosa  \n",
       "4  Iris-setosa  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "iris_data = pd.read_csv('iris-data.csv')\n",
    "iris_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're in luck! The data seems to be in a usable format.\n",
    "\n",
    "The first row in the data file defines the column headers, and the headers are descriptive enough for us to understand what each column represents. The headers even give us the units that the measurements were recorded in, just in case we needed to know at a later point in the project.\n",
    "\n",
    "Each row following the first row represents an entry for a flower: four measurements and one class, which tells us the species of the flower.\n",
    "\n",
    "**One of the first things we should look for is missing data.** Thankfully, the field researchers already told us that they put a 'NA' into the spreadsheet when they were missing a measurement.\n",
    "\n",
    "We can tell pandas to automatically identify missing values if it knows our missing value marker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data = pd.read_csv('iris-data.csv', na_values=['NA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voilà! Now pandas knows to treat rows with 'NA' as missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, it's always a good idea to look at the distribution of our data — especially the outliers.\n",
    "\n",
    "Let's start by printing out some summary statistics about the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.644627</td>\n",
       "      <td>3.054667</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.236552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.312781</td>\n",
       "      <td>0.433123</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.755058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.055000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.700000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm\n",
       "count       150.000000      150.000000       150.000000      145.000000\n",
       "mean          5.644627        3.054667         3.758667        1.236552\n",
       "std           1.312781        0.433123         1.764420        0.755058\n",
       "min           0.055000        2.000000         1.000000        0.100000\n",
       "25%           5.100000        2.800000         1.600000        0.400000\n",
       "50%           5.700000        3.000000         4.350000        1.300000\n",
       "75%           6.400000        3.300000         5.100000        1.800000\n",
       "max           7.900000        4.400000         6.900000        2.500000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see several useful values from this table. For example, we see that five `petal_width_cm` entries are missing.\n",
    "\n",
    "If you ask me, though, tables like this are rarely useful unless we know that our data should fall in a particular range. It's usually better to visualize the data in some way. Visualization makes outliers and errors immediately stand out, whereas they might go unnoticed in a large table of numbers.\n",
    "\n",
    "Since we know we're going to be plotting in this section, let's set up the notebook so we can plot inside of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This line tells the notebook to show plots inside of the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's create a **scatterplot matrix**. Scatterplot matrices plot the distribution of each column along the diagonal, and then plot a scatterplot matrix for the combination of each variable. They make for an efficient tool to look for errors in our data.\n",
    "\n",
    "We can even have the plotting package color each entry by its class to look for trends within the classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 823.5x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We have to temporarily drop the rows with 'NA' values\n",
    "# because the Seaborn plotting function does not know\n",
    "# what to do with them\n",
    "sb.pairplot(iris_data.dropna(), hue='class')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the scatterplot matrix, we can already see some issues with the data set:\n",
    "\n",
    "1. There are five classes when there should only be three, meaning there were some coding errors.\n",
    "\n",
    "2. There are some clear outliers in the measurements that may be erroneous: one `sepal_width_cm` entry for `Iris-setosa` falls well outside its normal range, and several `sepal_length_cm` entries for `Iris-versicolor` are near-zero for some reason.\n",
    "\n",
    "3. We had to drop those rows with missing values.\n",
    "\n",
    "In all of these cases, we need to figure out what to do with the erroneous data. Which takes us to the next step..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Tidying the data\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Now that we've identified several errors in the data set, we need to fix them before we proceed with the analysis.\n",
    "\n",
    "Let's walk through the issues one-by-one.\n",
    "\n",
    ">There are five classes when there should only be three, meaning there were some coding errors.\n",
    "\n",
    "After talking with the field researchers, it sounds like one of them forgot to add `Iris-` before their `Iris-versicolor` entries. The other extraneous class, `Iris-setossa`, was simply a typo that they forgot to fix.\n",
    "\n",
    "Let's use the DataFrame to fix these errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[iris_data['class'] == 'versicolor', 'class'] = 'Iris-versicolor'\n",
    "iris_data.loc[iris_data['class'] == 'Iris-setossa', 'class'] = 'Iris-setosa'\n",
    "\n",
    "iris_data['class'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better! Now we only have three class types. Imagine how embarrassing it would've been to create a model that used the wrong classes.\n",
    "\n",
    ">There are some clear outliers in the measurements that may be erroneous: one `sepal_width_cm` entry for `Iris-setosa` falls well outside its normal range, and several `sepal_length_cm` entries for `Iris-versicolor` are near-zero for some reason.\n",
    "\n",
    "Fixing outliers can be tricky business. It's rarely clear whether the outlier was caused by measurement error, recording the data in improper units, or if the outlier is a real anomaly. For that reason, we should be judicious when working with outliers: if we decide to exclude any data, we need to make sure to document what data we excluded and provide solid reasoning for excluding that data. (i.e., \"This data didn't fit my hypothesis\" will not stand peer review.)\n",
    "\n",
    "In the case of the one anomalous entry for `Iris-setosa`, let's say our field researchers know that it's impossible for `Iris-setosa` to have a sepal width below 2.5 cm. Clearly this entry was made in error, and we're better off just scrapping the entry than spending hours finding out what happened."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This line drops any 'Iris-setosa' rows with a separal width less than 2.5 cm\n",
    "iris_data = iris_data.loc[(iris_data['class'] != 'Iris-setosa') | (iris_data['sepal_width_cm'] >= 2.5)]\n",
    "iris_data.loc[iris_data['class'] == 'Iris-setosa', 'sepal_width_cm'].hist()\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent! Now all of our `Iris-setosa` rows have a sepal width greater than 2.5.\n",
    "\n",
    "The next data issue to address is the several near-zero sepal lengths for the `Iris-versicolor` rows. Let's take a look at those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.067</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.060</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.057</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.055</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.055</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "77            0.067             3.0              5.0             1.7   \n",
       "78            0.060             2.9              4.5             1.5   \n",
       "79            0.057             2.6              3.5             1.0   \n",
       "80            0.055             2.4              3.8             1.1   \n",
       "81            0.055             2.4              3.7             1.0   \n",
       "\n",
       "              class  \n",
       "77  Iris-versicolor  \n",
       "78  Iris-versicolor  \n",
       "79  Iris-versicolor  \n",
       "80  Iris-versicolor  \n",
       "81  Iris-versicolor  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['class'] == 'Iris-versicolor') &\n",
    "              (iris_data['sepal_length_cm'] < 1.0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about that? All of these near-zero `sepal_length_cm` entries seem to be off by two orders of magnitude, as if they had been recorded in meters instead of centimeters.\n",
    "\n",
    "After some brief correspondence with the field researchers, we find that one of them forgot to convert those measurements to centimeters. Let's do that for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['class'] == 'Iris-versicolor') &\n",
    "              (iris_data['sepal_length_cm'] < 1.0),\n",
    "              'sepal_length_cm'] *= 100.0\n",
    "\n",
    "iris_data.loc[iris_data['class'] == 'Iris-versicolor', 'sepal_length_cm'].hist()\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phew! Good thing we fixed those outliers. They could've really thrown our analysis off.\n",
    "\n",
    ">We had to drop those rows with missing values.\n",
    "\n",
    "Let's take a look at the rows with missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "7               5.0             3.4              1.5             NaN   \n",
       "8               4.4             2.9              1.4             NaN   \n",
       "9               4.9             3.1              1.5             NaN   \n",
       "10              5.4             3.7              1.5             NaN   \n",
       "11              4.8             3.4              1.6             NaN   \n",
       "\n",
       "          class  \n",
       "7   Iris-setosa  \n",
       "8   Iris-setosa  \n",
       "9   Iris-setosa  \n",
       "10  Iris-setosa  \n",
       "11  Iris-setosa  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['sepal_length_cm'].isnull()) |\n",
    "              (iris_data['sepal_width_cm'].isnull()) |\n",
    "              (iris_data['petal_length_cm'].isnull()) |\n",
    "              (iris_data['petal_width_cm'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's not ideal that we had to drop those rows, especially considering they're all `Iris-setosa` entries. Since it seems like the missing data is systematic — all of the missing values are in the same column for the same *Iris* type — this error could potentially bias our analysis.\n",
    "\n",
    "One way to deal with missing data is **mean imputation**: If we know that the values for a measurement fall in a certain range, we can fill in empty values with the average of that measurement.\n",
    "\n",
    "Let's see if we can do that here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_data.loc[iris_data['class'] == 'Iris-setosa', 'petal_width_cm'].hist()\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the petal widths for `Iris-setosa` fall within the 0.2-0.3 range, so let's fill in these entries with the average measured petal width."
   ]
  },
  {
   "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>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "7               5.0             3.4              1.5            0.25   \n",
       "8               4.4             2.9              1.4            0.25   \n",
       "9               4.9             3.1              1.5            0.25   \n",
       "10              5.4             3.7              1.5            0.25   \n",
       "11              4.8             3.4              1.6            0.25   \n",
       "\n",
       "          class  \n",
       "7   Iris-setosa  \n",
       "8   Iris-setosa  \n",
       "9   Iris-setosa  \n",
       "10  Iris-setosa  \n",
       "11  Iris-setosa  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_petal_width = iris_data.loc[iris_data['class'] == 'Iris-setosa', 'petal_width_cm'].mean()\n",
    "\n",
    "iris_data.loc[(iris_data['class'] == 'Iris-setosa') &\n",
    "              (iris_data['petal_width_cm'].isnull()),\n",
    "              'petal_width_cm'] = average_petal_width\n",
    "\n",
    "iris_data.loc[(iris_data['class'] == 'Iris-setosa') &\n",
    "              (iris_data['petal_width_cm'] == average_petal_width)]"
   ]
  },
  {
   "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>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [sepal_length_cm, sepal_width_cm, petal_length_cm, petal_width_cm, class]\n",
       "Index: []"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['sepal_length_cm'].isnull()) |\n",
    "              (iris_data['sepal_width_cm'].isnull()) |\n",
    "              (iris_data['petal_length_cm'].isnull()) |\n",
    "              (iris_data['petal_width_cm'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now we've recovered those rows and no longer have missing data in our data set.\n",
    "\n",
    "**Note:** If you don't feel comfortable imputing your data, you can drop all rows with missing data with the `dropna()` call:\n",
    "\n",
    "    iris_data.dropna(inplace=True)\n",
    "\n",
    "After all this hard work, we don't want to repeat this process every time we work with the data set. Let's save the tidied data file *as a separate file* and work directly with that data file from now on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data.to_csv('iris-data-clean.csv', index=False)\n",
    "\n",
    "iris_data_clean = pd.read_csv('iris-data-clean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's take a look at the scatterplot matrix now that we've tidied the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dUsqwY08CoLW1Ve7evdvJh9SsGj/yPob9C7vOmv749IL233f9voL2J1Oi0gdQsngth8EB7USnt9gEtJNcTovNcCKM9i3t2PXursy2mWfOxANzH0BSTSLkCyGSiEAIoH1LR95+a69Yy5WRhzBma43VOFrwJFR/PaKpWF7bWFWqiCQiCPqC6H2vF4/sewTPvfEcljYtxbV/cm1mTHVsrfh4Yry6neU8vgGAGEol84VKvu5LOBHGulfWYe7kuZg6fip6T/Ri88HNWPTBRYaYtTqnOBDbFY9XKp6jNS/pi5SvWv2/EGKtlLLdyeckohKymRdtlcfs9/gz75Q1+BugSpU5+zT2WI2jugatxbGi/TGW/UeZIhSEfCG0rGtBUiYz2x966SEsvmhx5v85nmhElvN4PaC31i7Tei9BbxAPvfQQ7t97f2abV3ix+KLFefsxtsmK0wX7I/lImZ+PiKCldenv6ha0KJjeYjP7HTs9LzrrZBdNRrFkxpK8d9MGU4NQ1SSCvnpEE2FACDSf0Wx4N635jGYMpgaRUlOZd5OD3iA8isfJl4CotFRV69SU+y62qgLxdK3L0deAbXcDLz+ttaodHNDGUTwC1RfM+wRGrw/Y9e4uzDtnHm6cfiOmjp+KSCKSGStLZiwx/CGo1w/wk0zKsJrHEzGtlnGkT15MYlsVGNU5xepckRuzw+0HwPC8+v5c9HLs4G+XqMbZ7atvyqzFpkledMBTl5effPW0qxFPxtG+dZn2vFuXIZlK5eXy33vZvQjHw+znT9XLsjYsZVrrgs+uA1qu07YPs95LwBNA95xuLG1aio7mDmw+uBnvhN8xjJW2aW1Y2rQ0r2aGKMMXBNoeNc7jn1un1b2MtM6LSWyrgydGfU4JeCxqWXJrXiz2k1Ianrc/3s+1j8YgR2teRnwyIV6UUl5c7OOw5oUKUPH81krnYxedO2z1jnL2c8QH0G6Se79q1ip86oefMmy7/4rvQIXMvEsmpTTN219z+Ro0+MuTyuAyYz5mq84wNS3YeM2I28NLf432F7tNx2jQG8zUtnztkq9h9c7Vefvdd/l9CPlCiCaiCPlC5X7XmfHqdoMDwI4HgA9+Gjj9Au0TwLrxwA+XjFjPaBbb4WV70b7zjlGdU+yej6z2yz2n/PivfoxVO1YVciwVj1cqXrnTxhg0RGVWdO6w3uoVsMyLDlrk3p/dcHbetjpvIPPHVb2v3rIOhv38qWoMU9NiZ7vVei96+ote2zJ1/FTT/ep99WhZ14I9i/YwXYby+UPAti7ghbuGtq3ss7fOi0lsB8dPHvU5xe75yGq/3HPK2Q1nszZmDCr3LHdfmZ+PaMyz21e/IDlrv0QTYdPneHvg7fznTRibDw63pgVRVYhHtBqWm3ZofxTetGOopsVsLY2c7VbrvehjVB/DvSd6Tfc7GT9Z/Jim2pG7Nlc8nB+f/e/aW+dFr5fJEj1xcNTnFLvnI6v9cs8pbw+87fz5jVzP0YsXIcT5QohHhBA/FUJs0b/0/5dSft/J53PalNiTBX0RVQOzNSOKyos3yYEOwpO/XsXsboz3j8/Z1mX6DptZ33++c0ZVwxfUalh+0jlU29JyHXDkNeAz38mvGfPXG2rJgq88Y7neCzA0hjcf3IzbL709b6zsemcXa11IY1Z/BeTHp816RrP9goFTR31OsXs+stpvfJ3xnDK+bryz5zeqCk6v8/ISgO9CW5gyU20rpbS1UKVdpcpvnfLVHxe0/4FvfWrknbKw5qUiKp6q6IZ87FF3GzNjlt/f3gP13ZcRPXcOgnWnIDp4EsHfbQPO/wSiMpHpNhb0BqEo+dmqKTWFaDLKbmMaxmy1sap5+fNurbvYnFu1WoN4GPA3DHUhy6olM+s2lj1G9TEc8AQMYyXgDWAwNVjJDkuMVzexmJ/xo478+LzmB2XvNgbYPx+Z7QfkP6/ZtmGOpeLxSsVzuuYlKaV80OHHJKIiKULJFC8W3EI198Rllt9/2geg9vVCpi84pOKB6g/B6/WjXmhdZOr947SHMzkheRRPpjh/jBbpUzWzqnk5/QKtLfLLT2vvWn/9yNC6GulaMlWqiCoCAQBmbyZmjxcAEEJk1kwSQrDGhYws5mfT+PQF8tPEzJjVPdrs5mX1xlTu+cjqgsbsvGV3G9UuRy5ehBCN6X/+SAhxE4AfAhjU/19K2efE81S7fW8crPQhEBVGT0F46gbtZDd5FrDgX/PWDEjGIzh+9gws37oMPYd60HxGM7pmd+G0VAJeb93Qw6XbNndu68zs1z2nG42BRv4RRtXLah2No68Zv4+HgbpxmU36eHjq9afw6amfxspfrjSMi9PqTsPxweN548VqO8cRmcbi8TfN43OwH9j410Nz+9WPAaGJ5p++ZLE7j6fUFPpifVi+fbnhvNAYaDR8ss7zAhXKqajYA2A3gOsB/D2AX6a36duJqBolItqFy4HtgJrUbn/1ENBmzIGOKcDy7cux691dSMokdr27C8u3L0dMTRgeLpqMonNbp2G/zm2dLK6k6mZWP9D2KPDKs0Pf/+UDgDCmQurjYe7kuVj5y5Wm48JqvHAckSmzWAydZhKfj2lzefbc/tQN2pw/ArvxF01GTc8LZvsxnqkQjnzyIqU8BwCEEAEpZSz7/4QQAfN7EZHrmaUgbOsC5nxFWw8gnUoWsmiVnNvuuOi2zURupCjaO9aZMREG9j6prasx5yvaJzCbbweuethwN308WLVAHm5ccRyRqbxYjAwV4Ru2BbW5PJtZq2QTdudxnheoVJyuefklgNxFKM22EZEL5eUdSwF59fcQnToHobpTEBk8iWDvNniSg4b76e2OsxcK09sdCyEyj6cIBUtmLMHcyXMxdfxU9J7oxeaDmxFNRpmrTNUpuyYMAKTUvl79EfBc59B+U2ZrfzRmrZWU2wLZbPzo4+XcU89FOBFGyKvVDnTP6cYtP7vFsD/HEVnKrVvR23XnppLlxKiZaDJqOY8DMCxAXMx5YTA1CFWqzjSaoZriSBQIIc4UQrQACAohmoUQF6e/LgPAleaIqoCed9y+pR0t61rQvqUdEajomzILHT+7BS3rWtDxs1vQN2UWUjJlaMUZgJLX7rh7djdSMmV4vHAijIUXLMTqnavRur4Vq3euRtu0NgQ8dSMfIJHbmLWlDR8BfveCeYtkk3ecrVog337p7fjde79D27Q2rN65Gi3rWrBs6zK8E34H619dj6ZJTbjnY/ewPSwZmcVk5Ii2PZsvqKU25qY6+kaOoYAnkInL7HlcSmmY71Myhe7Z3SOeF+KpOK6edrXh8a6edjXiqbhhv75YH1SbjQKotjnSKlkIcT2AvwHQCmONSz+A70spNxX9JFmqtVUyVo0vcP8The1PZireFrFa2niGE2G0b2k3vEv28wU/x9+98HeGbTPPnIk1l92LhtWTh+48ZTaS1z6NmJrIdJXxKB4s3bw0776rZq3Cp374KcO2tZevQT27jOkYs9ViNC2Sc+S2QA76guh9rxeP7HsEN06/Eat3rs4bQ1+75GtYvXM11ly+BiFfqNLvSjNe3cQqJhduNH6iMjgA7HhAS208/QIttfGVZ4FZN434yYvZucJqbr9/7v1Iqalhzws//qsfY9WOVfbOFVesLfbTxYrHKxXPqZqXxwE8LoRok1I+7cRjElF5meUdj/OPM89Z9o8zbMPBHfB6fGhIdxbTW7ma3ffshrPztgVzF0YjqgajaZGcI7sdbMgXQsu6FiRlEgCw+qOrTceQXiMT8oUM9yeyjMncWhZ/SKt5eeGuoW2KF/jYrSM+hVWNitncXuepg+LVYt/qvHB2w9n2zxX8dJHgfM3LB4QQt+RsOwFgj5Ryr8PPRUQmi4eZvrub7rUf9IUQTffaV5T8zke5eccD8QHTXORIfABi6a8RnDAN0WO/RfCVZ6BY5PPn5ju/PfC24Xmbz2hGNN3hJvv4IIRzC2sSlUJ2W9oL24Y+aYmd0L5/+WlgznLtXe66hvRilAFEkzEE0nGdvf6FPgbv33s/AFjWwvQn+rFkxhJEEpHMukiOLkRL1Sse0WIu9xOV3FqWeARq2/fyFhZWErERF64saG7PqcMyu+/bA2+bPl5frA+b5m8asQ5Gf1zG/tjh9G+3FcCXAJyd/loM4DIAjwghOoe5HxEVymZus5rutd++tUPLHd7aoeUOqynDfmZ5zF7Fa5rbrChetL/YjZb1rWh/sRt9M78ANSdXWs/nN+Q7z+nGeP/4vBzopJoyHF9/fCCv/ob5zuQ6elvay24D5q4EftIJ3DkJ+MF12vefXQe0XAdsvAa4YyLU/VvQFzuOda+uxzvhd9CRjvmOrR2ZWpa2aW1Y2rQUXuHF5oOb82rJbr/0dmz8v41arZhXa+ZpVq/G8TJG+YJazOmx+JNO7fuc+Vn1BdB3ziy0p+sZ2392C/rOmQU1p57R7JxS56nLi0t9/Zbc+T73kxKz88L4uvF52+697F4ICFt1MP3xfsb+GONIzUvmwYR4HkCblHIg/X0DgKcA/BW0T18+6MTzsOaFClDx/NaS5WPbzG0OxwfQvrUjL584t87ELI+52Fxks3eDIaXhUyBFKFi65WZbz+tAvnM1qN2YrUWqCsQHtAuU3LG44EnD9vBX30T7z27J1KyMVMsSSUQQ8AYQS8YMtTDPvfGcVnt2+Ro0+BssaxDKNF4Yr25S7HnhY/ei/luTh73vQHwA619dn/eJ/LV/cq2hi5jVJyCm5wUYPz0BYLuupsDamIrHKxXP6bSxyQDiWd8nAHxAShkVQgxa3IeIRsNmbnPQak0IG732i81Fzs7Hz5xIBDIXTfUF5kAz35lcR29BazYWc7YH604Zdl2X3FoWPSUstxZG319fL4PrZFBGseeFuvx6xtz7hnwhPPTSQ5n0RgDwCi8WX7Q4c7Ey3EWz6Xkh69/1vvqCaiZZGzP2OJ029iSAXwkhviGE+AaAXwDYIISoB/CKw89FNLaoqvaumkzfxsNavn02vU9/lmh6DZZs2XUmmf3SucjZ9Fzk3Pta5TaPhtnxvT3wNpbMWIJN8zdh76K92DR/E5bMWMIVl8l99HG54jBw0w6t1gXQxuLggFZ/AEC9sA2RRBi7F+1Gf6LfdFzpNS7RZBSqVBFOhKFKNbPeS+7+kfQYNhu7xYxJqmJ6zctNO4CVfdrtnOX2zwuD/cbHMzmn6PGYOz9Hcs4pxdDrv3Kfw+zc4+T5iKqDoxcvUso7oNW5vAetUP9LUsrbpZRhKeVfO/lcRGOKWX2LmtTy7e2sJZG3BkuXrVzkxkCjvdzm2V0IegKj+tHMjq8x0GhaaxMY5XMQlYQ+LjdeM1RfMHelVgPzme8Av/ou0HId1M+uQ98nVqFj6zK0rmvFxv/baFrLsvngZnTP6UbAEzDUsHRs7TDUwujjUB/DVvVlfPd5DLJZ8xL0BszPC4pvxHNKwGu+zoteg+UEq7VkzOpqxteNZ+yPMY7WvACAEMID4AxkpaRJKQ86+RyseaECVDy/1ZF4tcpjvuYHI3aGAex1GwPyc5EBYN0r6/JymxdN/QwQO2HsNvbhkdcHsJJ7fBCikjn8lVYbMTsWWI3Lzz0B/ORWrdvYlNkIX7MB7VuXGeJ5adNSXPsn1xq6jcVSsczYM4v/7FqYoDcIT9YYrmC3McarmxSwzou6f0t+t7HzLgcghj2nlKPGarjnAFBMt7GKxysVz9GaFyFEO4BvADgEIAUtSCSAi5x8HqIxxyqP2RcYWj9imAsHRfEY6kwA6z92cvOOTXObp98I5Z//WNsPABQv1NlfQTQ+MOIFkp3js8p35rtpVBFWLcmtxmUg/UbVTdqaL0EhMvE875x5uHH6jZg6fipiqRgAZGpb6hVt7FnVsOg1LkIICGH8G8yqjoBqXG5sWsSk6gsiGu9H0FePaCKMoC8I5ekvoF7V6qj0edywJpHFOSXoDWJScJKhjfFj+x5zdH4ero7LrK6GsT+2OF2wvwzABVLKYw4/Ltkw/fHpBe2/7/p9JToSclz2ehI6PRd5FJ926K1VO7d1oudQD5rPaEb3nG40BhoN71hZ9fOPnjiI7FOE+rHl6IsdR+f2rMdLp5fZvYDJZvm8OWsGEJWcnhr21A3aH4WTZ2mpNKGJ2h+NZuOC3dDyAAAgAElEQVTyxFta+th/3gwc3IFo+240n9GM04Ono6O5Ayt/uXJU4+73/b/HZ/7jM5b3ozHGLDYX/GteTFrOz23fg/Lvi4Yez+Y5ZTA1iI6LO7DiFysyj3fnR+7EYGrQsQsYngNoOE7Pem9Bq3UhIifp60mMkItsVzQZRee2Tux6dxeSMold7+5C57bOvCLHoAS6L73DmE/8kbsQrDvVcCzRD38JndtzHm/78lEXTTKHn1wjEdH+ODywXaszO7Bd+z4RMR+XbY8CnoB24ZK+T3DzHei+9A7c3HQzVv5y5cjjziT+7/zInfjO3u8Mez8aY8xi81cPaTFoZ36edvmozimqVLHiFysMj7fiFyscXVuF5wAajtOfvPQCeEEI8WMAmdbIUsp7HH4eorFFUbR3ehduHLG+xQ67rVUVXxCNP/oy1s7pHKpveX4llL/6LvDn3ZkVnIP+BlvtmO1ShILGQCPWXrGWqyZTZQ3XelaYjcsgIIThPsrLT6NReNB41UP2xp1J/N+x4w4898Zzw96Pxhiz2NzWBcy5xeb8XD+qc0o5WnPzHEDDcfri5WD6y5/+skUIcSqARwFcCK1G5v9JKXcMfy+isUWFRFQAQSB9K0f90antj+QHB6BMmIb69Dtq9VIFJkzT3vHLfrz4AJbMWJJX2B9NRAwLYRZCkUC9qjUUqVelNjOw1JLKbaSUTX2dF0C7VVNa0XTufU6flmmVnL3YpN7yOOQNQklEM39AZtewAMDh6GHDYTGFpsZZ1Vlls4rN4weBB4ba6Ee/etB8vk9EgNxziprS2vDXNWhx7K8HclJ/9TbGefO9w/HIOi6y4nSr5H+UUv4jgLv1f6e/H8l9AP5LSvnHAGYAeNXJ4yKqdqqa0lqnbu1Ay7oWtG/tQF+sD6qaGtXj2f5I3h8yabt5PZBKGrYFhNfZ1plmraEjR7TtROXkC+al4aDt0bzWswC0C5fwEa1F8me+k7mPevlt6Jv5hUyr5NU7V6OjuQNLm5bi9ktvx/pX16Nv4G2ov3rANM6ZQjPG2J3/rNKJQ6cZtgWFYtoWOammcs4px6HGTmqtv++YqN2Gj2hxncWqjTFb2VO5ONoqWQgxC8BjABqklJOFEDMALJFS3jTMfU4B8BKAqdLmwbBVsjkW7Juq+Hv1TsRrOD6A9q0d+W0jL18z6k82bLVWtWq7+RdrgLVDC5yFl+1F+847nGudabfdZ22qiZitGYMDwI4HgA9+OpOGg1eeBWaZtAbX//A7sF1brHLOrcDpFyCcjOS1Sp555kzcc9k9+Oavv4nn3nhOGy8Xd6L+x7eaxnkFWyGPhPHqtELmP7NPaIC8bSrS8ZPuNqYID5ZuuTl/zr7k66i/r8n4vAueBAKnZDaVo1VyCVU8Xql4TqeNfRvAlQCeAQAp5UtCiDkj3GcqgCMAvpe+2NkDYJmUMpy9kxBiMbQFMDF58mSHD5vIWU7Ha9AXcrSmBLD5kbxVvv9pHzAe3/jJzuZAD1dnQCXBOdaCP6TVEbxw19A2xQt87Nb8fesahuL25ae1L8WL4NePmI6Pcb5xmTqWnkM9CE6YZhnnTKExqul4LWT+y01b1OVsU6Cg3j8OAFDvH2fdjn58zmt5cEfeBVM5al6IhuP42zZSyrdyNo2U1+IFcDGAB6WUzQDCAL5q8rgPSylbpZStEydOdOZgiUrE6XiNJiJoPqPZsC2Ts1xKek51tsmzgONvGo/vxEEsmbEEm+Zvwt5Fe7Fp/iYsmbHEvBuSqmrvLMr0rVkqmNXzxkv8845hnGMtWMWiWQzrtS45+0YTYdPx23uiF/POmYdN8zdh96LdiMT7oX5sOePchpqO1zLMf5bnlBM5a4rrsZ5933TNi635nqgEnP7k5S0hxKUApBDCD6ADI9ev/B7A76WUv05//xRMLl7IeUwzqx5BbxDds7vQuX25oU9/yd/p0nOqc9e48Pi1dIL0tkDodLRNa8PyrOPrmt2FgKfO+HjDrZmRXYxq9bxFfNJENCpmsdj2qFbXsq3LGMP+eu3/nv6iYV+z8ds1uwt7D+/NX/dldhcafUHn31mk6lGG+S/oDZicU7oRhMcwt6PtUS2uswQ8dfbme6IScbrm5XRoxfcfh5ZX+FNoKWDDLlophNgO4ItSyteEEKsA1Esp/95qf9a8mCv0YqRQVXrxUvH8VqfiVVVT6ZzlwlewL/KJR8ypDgvYq8kpNpd7lK2hq0zNxGzNyI7FwQHtwiU7jSw7hs26NSWiUH/1AKIfnJ9pOR448jqi512BDrNxUx21AzrGaymUev4bHIC6fwui585BsO4URAdPIvi7bVDO/wSQig/bbawUNZhlVPF4peI5+smLlPIogL8exV3bAfxr+tOaXgBfcPK4iGqBongyJwZHThAmJ8dUuqgz5KtHJBFG0BuEEAqiitDaaSoCQaHl32fnVAet8qdz3yl0Ipe7xFRVIpJIIeT3IBJPIeTzQFF4vrNS06+XPkZ8QS1FTDfnFq2Af9vdWl1LdgwrnqHiZv3WH4Lysy7Ub9UueOoBQPEiZFELw9qB0quZuLXR2tiUPwTl6S+gXk0CGIpJfP0I4Et3Dcsq0s82XA3mQHwAIV8IkfQbbB6H32BzceMKKiNHfuNCiLVCiDVWXyPdX0q5N527epGU8i+llMedOC4ismDSijOVCKMvdhwdW5ehZV0LOrYuw/HYe+iP96N9S7vWTnNLu9aiOWclZds1OS6vZVFViWPhOG58fDfOv+053Pj4bhwLx6Gqzn1CXUtq+vXSx8iOB4ATbxnbx574vdZxbO5KravYSDFsEfcRi3ETSbpjPNQq18et3VbJemvuEVobmypiLraa7wcSA+hIt17uSLfzT42ynb8ZVarakgEjnI+o9jl1ubobWpcwqy8icpNERMunPrAdUJPAge2IQsXy7cux691dSMokdr27C53bO3Fi8IRx27bOvMJMPac/dx2BvHeQrdYlcEktSySRQseGHuzoPYakKrGj9xg6NvQgknDuBFxLavr10sfIBz8N/OfNhrGC/7x5aPvlK0aOYYu4V4SCOz9yp2Hc3PmRO6Gw4qWkXB+3JvMznrohb3FgxMNafVX2fk9/Uds+kiLmYvP5vhtPvvqk4VyxfPtyR4v4o8koOrd1jng+otrnSNqYlPJxO/sJIdZKKdudeE4iKoJJ+lbIP840FeDshrPztuVelCiKB42BRqy9fM3wNTmKohU2L9zoylqWkN+DXQf6DNt2HehDyF+G2qIqVNOvlz5GTr/APNVR3944BZAYPoYt4j4ggDU71uBrl3wts1L5mhfX4Juzv1nKn2zMc33c2k2vzW7Nnb2fnTTbIuZis/k+4A3ioZceMuzXc6gHIUebDLBFM2nK/RfDR8r8fEQE7eP2cCI8dJuI5aUMROL9pqkAbw+8nbctmoyatIoVCKoSAkBQlbCsi9RrWfS6mTJduKiqxMBgEqpM35qkiETiKcyc0mjYNnNKIyLx0b8ja+d5q5GqSoQHk5avV+7PnUqp7nsdhmvbrafVHH1taKxc2AbctEOrC4iHgRWH021k5ciPmRP3qgAiiQgORw/jqmeuQtO6Jlz1zFU4HD2MSKlboI8xubEYGTQf57FEKi9GKzJ+4xFgznIt1lb2abdzTFpoDw6Y75cc1BZMlap2m65ryVPEXKzXYCpCQb2/AdFk1DwFMhExnnuKSPGyeg5+8jL2uOPtTiIqGdM84VQE6ufWGVIGglJBl0kqwHj/+Px0MFU15GPLyBGI2AkoGxdC3DFRuw0fgWq2hksF2M1xD/k8WLOwGbOmToBXEZg1dQLWLGxGyDe6d2Rdn1s/SvrP9b2fv4GutovyXq+gVzH83P+yvdd9r8NIdQV6Ws0rzwKf+Q5w2W1ajctPOrNqX97SOo/pdQY2axX0MbnjDzvyxlzX7C4EvIEKvCC1yWwMplQVaxY2GeL2u9dejPBgMidGB9EfS5Q/bn1BoOU6LdbunKTdtlynbc/mD+Xv9+G/BWIncupgjlpfwDgk6A2anj9SMuVYjUrQG0T3nG7jc8zp5icvY5CjrZJHfDIhXpRSXlzs47BVsjm2SjZV8fYxlW7jGU6E0b6lPb+t5aV3oT56XEt/Ofoa8MqzSH10GaJqfKjbmPBC/HKtocVr8JVnoFx4NbA26x2wKbOBv1iTt01dsAFKYFwZf1pzA+k/Snb0DnVtnzV1Ah65vhUNdcbsWSe7EBXyvFlcH7PZP9f8GX+EpZefh/MmNSAST6Le70UkkTL83M9/eQ5WPfObQl+H0rLTtju721g8rP0hmLv/n3drfzQueFJ7B9tGK/DsMfnVmV/Fp879FE7xn4JIIoKANwCvUqHXZHRcHa9WY/Cxv2mFKpEZ55DAjU/k77f6qum47O4XDNtKHrd2W8qb7bf8APBvi/Lvu+BJy+5hTkml2/nr3cY8igdLNy91tBW4A93GKh6vVLxyz5AMGqIys8wTrp8E/PMfD21UvPB87FY0eLWFxhr847S0A5MWr5j9FeOTHNwBnPaBvG2izh1rVRSS464oIvOHSbF/oLg+t36Usn+uZ176A5556Q/wKgKv3zUPihB5P/d5kxrc9zrYqSvIbdc9XO2Lvp+NWoXsMfmtXd/Ct3Z9C17hxZ5Fe9j21WFWYzDg80AR2p8kDXVeqFKa7vf+xlDetpLHrd2aF7P9AuNHXwdTJI/iQUO6jX+DvwGqVQv9Ij4pUYSSufCporWQyGHlniXvK/PzEY15lnnCJw4ad5w8C0jEjPn6gwNIXf09DHz1TajfOI6Br76J1NXfA46/mX9fk21y0EbXmzIoRS2Lm5+31Eb6uXL/f//hAfe9DlatYrPrVHLrV8z212tiBgds1SqoUkUkEcHuRbuxaf4mzDtnHoDK5u7n1cTVUOtZu2PQar+3+iJ520oet5ZtjMPGeIyH8/eLnbCO61zD1Xw5IJqMontON7Yv2I6XrnsJ2xdsR/ecbtaoUNGcWuflR0KIZ6y+9P2klN934vnGiumPTy/oi8hM0BMwb2McONXYJvPz64B4v3HtF8WLvimz0PGzW7Te/T+7BX1TZiE17kzDfWXbY5DB03K2PQr43ZGL7HQti9uft9RG+rly///5l9/BfQua3PU6mLWK/cx3tBqWSLqGJbt+5XcvAG2P5u//yrPadn9oxFoFvdalY2sHWte1YvXO1eho7sDSpqUVy92v9bUz7I5B8/2acFrIV/64tWpjrCaN9VRqMn8/xZsfp3p8ZrO7lkwR6jx1aJrUhFte0M4ft7xwC5omNaHOU+fYc9DY5EjNixDiY8P9v5TyZ0U/SZaxUvMy/ZzJhT1+ibHmZXQqXfOCwQGov3ogv25l1s3aO256m0xIYMNCQ670wNcOouOFv8vLWV5z2bfRcOL3Q/Uy498P7HhQW/siq4YGH/5SyfOs7arUitqjeN6qiNmRfq7c/w96FUSTqrtWNFdVIJ5elfzoa8C2u4GXnx6qEciucblphxbTl3wRCJ4KDPYD/gbtne43fg6cd4W23zC1Clb1Z2suX4OQL1SRlDHLmjj7dQmuj1e7Y9BsPwAVmTcy9Vb6/CwU4MnP5cfWNT8wzuNCAL/dDJzzUS1Oo+9p8XnuZca52G5dTREG4tqilWbxrqeXVUDF45WK59Q6L45enBCRg/whKGZ1K3Nu1U6IgHaykqrttV9C/gbggazUhG8cB7Z1AS/cNbRN8QIfu7UEP9DoOFnLUg3PW2oj/Vxm/9/gUSz3rwi9puWOicZuTHqNQPZ4OP0CYNtsYM5X8vdXvFr7ZP2+2bJqFazqzyp14TLcMdVSBye7Y9Bqv4qM39x6K5P5GQd3AL5A/jz+9Bes41Nnt66mCCFfyDLeiYrh6GwphJgmhHhKCPGKEKJX/3LyOYgoi1nOspoy9vhPxs3z8HPrW0zyp63WfonE+43HUUiedVE/rnvWTHHTsbhB9uvRH0sgparV8bpk1xfo67jo67esOKR9/8kuLcZXHLaOdb0eYcVh7T4XtmX9n1Y34cZ1Ktx4TOWQSqnojyWG4jXlsjS53Lk9EbM3j1vVZuXOxZZ1Nc6tMRRJRCzXfiEqhtNv9XwPwIMAkgAuB/AEgHUOPwcRAeY5y4mwtuZEdo//RBRoud6Yh3/JjXn1LWb500Hkr/3SNbsLQSg286yd6wbjpjVT3HQsbpD7eix+Yg/ePh7LrO/i6tdFry/IXsflzknpdVx+DxzZD3zoL4EfXKdt3/lofqzr9QgbrxkaY3NXao959WPac8Cd61S48ZhKLZVScSwcx+In9mTi9Vg47p4LGLO5Pd6vzdsjzeMyBbQ9NnLNi1VdjYOfipit/dI1u6umY4vKw9F1XoQQe6SULUKIfVLK6elt26WUsx17ErDmpVJY8zI6Jat5sdvjv70H+FHHyNvM8qd9IaQgDb37g94gPBDGfGxfCIDU3n2ua9COzV8PKM4Vto5yzZSSKPGxVF3MWr0eq+Z/CKue+U1l13OxQ699MVvH5XNPaBcu2dsvu02r56prGL4eYcGTWl1M1srlDqxT4bgij6nq4rU/lsDiJ/bkxevD17VgXMBXikMsjFU9Su5aWlbz+FWPArG+kesPc+tqfCFDrDohd+2XoDcIj4PnhVGoeLxS8Zw+m8SEEAqA3wohbgbwNoBJDj8HEQH2e/yf9gF728zypwF4AEPv/ozsfGxAOxHq9xUKij1HpFIqIokU6uu8CA8mUV/ntb1WiN0C3dznCPk88HhGPnnX6voto2X1eujru4T8HiSTKqLJwl/rstDrC8zGRPDU/O3burR6LqEMX49Q1wDkvD/oxnUq3HhMpWQ1l9TXeaGq0jBX5MZt0OuB11viuLWqR8ldS8tqHm+YCNxzwdC2CtYf5q79QuQEp0fglwGEAHQAaAGwCMD1Dj8HEQHmOctm+fjH37S3rZh8Z4fbbpqldQzEkrbWa7Cb0lVM6kitrt8yWpFB89dDX9/l4LEI+iJxfO/nb7gzTQewrgGIvjdyDYHVffsOON5+looXHjSfS/pjScNckUyq6IsY54i+SBzJZIl/n1bxlLuWltU8brYOV27NSxlaJROViqMXL1LKXVLKAQAnAXRIKa+SUv7KyecgojSznGWz2pPgafn7hUy2FZPvnIgAT92gpS+oSe32qRu07aMQSaSwbONe7Og9hqQqsaP3GL7/izdw38KR1wqJJFLo2NBjuG/Hhh5EEqkRn2PZxr15+5mp1fVbRktRgH/67EWG16Or7SI8//I76Gq7CPf89+tYtnEvrrzwrIJf67KxWvfljZ+PXM9ldd+tdxY1Dqg0fIrIW3fovgVNSKZShrkimjSfI6LJEsetVT1K6DR783jOmlum9YcOz9lE5eRo2pgQohVa0f649PcnAPw/KeUeJ5+HiKD9xRiaqPXlz85Z9oW0XPvs2hOI/P2A/G2jzXd2uO2mWVrHmi37sfSK8/DI9a3DpoPZTekaLnVkJIoiMKHeP+KxjBUBnwd3P/8aVs3/EM6b1IBIPImgz4MrLzwLd//0NTzz0h/gVQTOmzSUNmL3tS6b3PGkj53Q6Vo6Ze6Yys7bz9x3gzaOjr4GbL5dWzNG8TrafpaK5/d58Nzut/DgtRfjlKAPJ6MJ/Ofet3Hth6cY5opi5oiiWM3tgM1tcvh4BcrSKpmoVJwegf8C4CYp5XYAEEJ8FNrFzEUOPw8RAflrAej0wszsAk2z/cy2jYae5pBdOKqnoY3isfW0juyCWj0tSy+otSoAj8RT6LjiPFx54Vk4b1ID9h8ewPMvv4NIPGW4j9VzhAeTtop2a3X9ltGIxFM4dHIQV357GwDg+S/PwfMvv4MrLzwL936+CUsvPw/Pv/wO9h8eSl0p5LUum+zxpI+d3O+tFl1V0nVeT3zGsXFApRGJp3AiksChk4MYF/Dh0MlBnIgkMmmO+lxR7BxRFKu53e62keLV4TmbqJycrnnp1y9cAEBK+XMA/cPsT0S1wOG2myGfxzStw05aVtCrYMElk7Hqmd/gghXPYdUzv8GCSyYjmFNkW8xzkFFuGl3vkX7T30Hvkf7afq3L0H6Wimc1R/Qe6Tekfwa95nNE0FsDcctYpSrmdKvke6EV7G+A1mPl8wCOA3gaAKSULzrxPGyVXBlslTw6JWuV7DYOt90cbSewQtoYj/Y5SqwqYza7w1t4MGnZitZlr7XzytB+1mWqLl6t5oiHr2tBvd9b+W5j5TL2YhVwQbxS8ZzOdWhK334jZ/ul0C5mrnD4+YjILazSHEbJ41EwLv3HbSEpGoW0MR7tc1C+7DS64WoFFCFq+7V2eByQ86zmCD0+s3m9CsZ5a3SOYKxSlXK629jlw3zxwoXI7VRVK/CU6dsKts1UVYmBwSRUmb5Vpem2XKVoY2zneUmjqtKyFW3VtZJ20Xig0csdv1atvasuPgvBWKYa4nS3sTMAfBPAH0kp5wkhPghglpTyMSefxzUKTAMjcjW97/9TN2hdZybPSrfnnFj2VAJ9rZaODT3YdaAPM6c0Ys3CJvg9Cr60/sWsbc2YUO83pHno9RfG+46+jbH5seQ/Lw29VrsPHMN9C5qwbOPezGt234ImBKopTcxF44FGz2z8fvfai3HfwiYs21DF8VkIxjLVGKej9vsAngfwR+nvX4e2cCURuZ2L+v6br9WyF8cjiRHXb8luY/z6XfPwyPWtRV1o2F03hoZeq6kTx2HjzoNYNf9DeO3OeVg1/0PYuPNg6dfHcJKLxgONntn4/dL6FyElqjs+C8FYphrjdM3L6VLKHwghvgYAUsqkEKJGZwOiGuOivv9WOenvbwzlbTOrZXGyjXEhNTRjnf5anTepAZ/ash/3/M9vM//nVQRunjutgkdXIBeNBxo9q/F7ekMd/vSbmzPbqi4+C8FYphrj9MVLWAgxAVpxPoQQHwZwwuHnIKJC2ekqU8G+/9mdqiLxFCBhulbLW33Gdwqz12QY7vFCPg9UVY6qa5BeQ2O25sxYX98ll/5a7T88kPn9nTuxHgODSZwS8GldxvweRBNqJo0v9/fkSCqeE12UuA5GTbAavydjCexd+WeZRSp3/O4owoNJADB0xAOQ15FQCFGauB3JaOOasUw1xukz7y0AngFwrhDiFwAmArja4ecYM/a9cbCg/d3WWplcwm6+s973P3e/Evf9N8tJf+S6Fiy4ZHJezUTI78GsqROGzVO3erxIPJX3eI0h/4gXME7X0NSygEfBfQuasOfNPiy4ZDI27jyIv2x+H5Y//b+Z166r7SL8R8/vcf2lUxBPqejIqjtwpJbIqfz+Co0HcpbZ+LWaD3yKwBe+v3v4/RY2oc5G7Z3jiolrxjLVGKfXefkstJqX9wNoA/CnAL7u1PouOtes8xK4xvFjKEapL164zsvoVHydl8EBYMMC47tuU2YDCzfmv+tWgb7/ZmsuvHDrZfjapn156zDc87kZOBlLGj6N+cJHzzG0MDV7vL0r/wx/u/5F03Ud7LQ/Nfskp4R/qFRtzPbHEvjez9/AFz56DhY/sQer5n8Iq575Td7rvmr+h1DnVUx/x2br8RSkkHgfydhcB6NQro/X3PErpTRdh+jBay9G0+3/ndlmNW+svmo6Lrv7BcO2ouN2JMXGNWNZV/F4peI5PdK+LqX8dyHEaQA+DuCfATwI7SJmWEKIAwD6AaQAJKWUrQ4fG5E7FXNSsXPfQvKdi+j7b/cP/Nz9gj4FZ5xSh+e/PCdzUfK+04KmeeoTG+oQ9HsgBHDGKXUYH/Khvs6LgXQ6UiSeMs1xPyXos1zXwQ4na2hqjf77DPq0uLvp8vMQTaSw/ot/img8hTNOqTPsr9fE6P/O/b+ia4mGi/e88RIEElHr8cN1MGpC7vhVpcybcx58YT9OCfoM26zmDbPau6BPMc5Dxb7BoaaAeFiLu8EB7baYuhXGMtUQpy+79eL8TwH4rpTyPwH4C7j/5VLKJl640JihpwJsWADcMVG7jRyx14Pf7n31fOdser6zYz+Glqp14+O7cf5tz+HGx3fjWDietx6K2X79sSQ6P/nHWPXMb3DBiuew6pnfoC8cR8cV5xnuu3ZhE/oicfzt+hdx/m3P4W/Xv4h5F56FwUTK8HgDsfw1Rk5GE6brOug57jQ6+u/zX7b34u3jMXzv52/gD+/FsPiJPbhgxXO48YnduPXKCzB/xh9l7jNzSiOOR+J4qy9SmrU2LOM9nD9ewkeAHQ8UPvaoqsXiKdx65QWGOefWKy9ANJ4ybDObS2ZOacThkzHDto4rzrM1/9mmprTY3HiNFpsbrwFi/SWfx4mqhdNpY88CeBvapy4tAKIAdkopZ9i47wEArVLKoyPtayelodAUsHIodZoZ08ZMVfwj4mHjtZhUALv3LUOPf7NULbNUikJSxL67qAVfWrcnk1f+0HUtWGKS6vHdRS2Y8Y8/zWy75ePTsOBPJxvWcCim5qUC3B2zWfTfp54eZpUmtvqq6fj4PT/DzCmN+KfPXoTTQn4kU2p5a17844AnP5c/Xv68G3hg1tD3o0kvG9uqJl51/bGEadrYPZ+bgVnf2pLZdsvHp+XV3unx+8XHh2pjHlrUgiXr8h9v1KlksZPaBUt2rF52G9ByPfA012opUsXjlYrndP7D5wB8EsDdUsr3hBBnAfh7m/eVAH4qhJAAHpJSPpz9n0KIxQAWA8DkySxMJ3ezHa/FtLC0e19F0U5wCzeWLN/Zbjths/3e3xgyvW9DnRer5n8ok8LRUOc13W9cwDiNrdmyH0uvOA+PXN9qSOEIeD14+LqWgruNjRWjmWOzWyNn32bbdaAPkyeE8Nqd87D/8ADufv413PP5JkBqsZH7eyq6lsgq3gXMx8vpFxi/Z/vYqlDM3wT1FnPJpFMChm1rtuzHTZefZ5iH9Pg1xK3T7dTNUsS2dQFzvlLSeZyoWjga9VLKiJRyk5Tyt+nv35FS/nSk+6V9REp5MYB5AJYKIebkPPbDUspWKWXrxHdTXQsAACAASURBVIkTnTxsIsfZjtdiUrqGS48ZHACkqt2qKlQBhBUBFelbh9970tuRZps5pRHxRAr9sQRUKdEfSyBmsp9V+tBbfRFc+e1tOPcffoIrv73NMvXrZDSRty2aUNFQ54UitFx3RRHwehWMC/igCIFxAR8vXHKMZo7Nbo2cfZtt5pRG/PbQQOb3eOjkIMKDSSiKyNQiZP+eHKHn94v0raJYj5ejrxm/LzINR5Uqwomw4ZacV0i8plKqYR4KD5qng5m1Yv/98ahhHjp0cjDTJl2PW6v5b9QpkIMD1nN7blyXAWOa3MY1Z28p5R/St4cB/BDAJZU9IqIy0FtYTpkNKF7t1m4LS6v7qklDXr86eAJ9sT60b2lHy7oWtG9pR1+sz9ETUNCrtcidNXUCvIrQUiaua8HJWBKLn9iD8297Douf2IOBeBLfvfZiw36nhXxYs9B43zULm3BqyGfY5lVE3nPct6DJ8P/afdnGuFz0NrTPv/wOutouytzm/o6ef/kdw/dBbwV+P74g0Paocby0PQq88mzhY8+CKtWSjzUqTCql4lg4bpiHUqrEfTlzzn0LmzA+6Mubh04L5W7Ln1/0ceDYPOQPmcdqBT4VZEyTGzla8zLqgxCiHoAipexP//u/Adwupfwvs/1Z82KONS+mKp7fOmK8OtltTCh5ef3hZXvRvvMO7Hp3V2bbzDNnYu0Va1Hvqx/tj2UwMJjEv2zvNSwqedapAdMalYeva4EQwpAqBOQvViilzFscTt/PFQvGlU7FD76QGoLsbmP66x9JpFDv9+K3hwew43dHMevc03HepAZE4hVM1xsc0IrzP/hpLVXs6GvAkf3AuZdp72I7kIYTToTRvqW9pGPNhVwdr1b1LY9e3wpVSq1bYSyJF9/sw6aeP+CWPzsfkyeEhp2b7HRRLGoeGhwA9m8BzvkoEDwViL4HvPFz4Lwryl6PVYMxXfF4peK5pefnGQB+KIQAtGN60urChajmFNPCMve+Us3LlQ6On4yeQz2GbT2HehD0Bkd7xHlCfg/WbNmPe/7nt5ltvav/3LI9saKNdUMxa34rYoFx6QUos9diMdvGNsaVk92Gtr5O4PzbnsPrd83D+bc9h2RWtyWvIvD6XfMyv/uy84e0uoEX7hrapniBrx8ZSsMpUtAbLPlYo8JY1bcE/R5Iibw4/cm+d/D6XfNGmJvyOdpO3R8Cnv6C9il65gnSsVpmjGlyI1ec6aWUvQBG7EhGRCPQ8/qzPnmJnjiI5jOaDe+cNZ/RjGgy6tg7Z5F4CmsXNmHWuafjlKAPJ6MJRNN54NnveOrtie0sDJlKqXmfsng8rsl0JRN6HIQHk5kC/fu37sczL/0h87vP/j2WdfFPk7GRqXFx6N3saDJa8rFGhdHrW3LnoYGYdmHw+l3zMBDT4vJQfyxzcT2QjtWKfIpbhli1izFNbuSKixdyxr43Dlb6EKjS9DqYrDaxwcCp6J7Tjc5tneg51IPmM5rRPafb0XfOAh4FLR9oxN+uf3GoFfHCJjx47cXGbTbrHfQ89dzWxhPq/byAcTE9DhY/MdTiuqvtIpw3sR4LLpmMn//2CFo+0IgJ9X4IIXAsHEfHhh5nWyVbMRkbxda45Ap6gyUfa1SYoNeD+xY05c0ligBuzIrTB6+9GB5Fwd/9m8Otu0ejDLFqF2Oa3MgVNS+FYs1Lhaw6UekjGI2K57cWugZB0UxqaFShvYMW9AYzt4pw7iLAKq98zcIm+DxK5tOYHb87itnnTxoxrcLq8R6+rsXWpzZVrmpj1ur39tCiFmx68fdY9aNXDHVPdtYGclQx9WV2n0KqJR1rLuTqeB0YTGL764cNnwr7PQpusLneVEnjcThliFXbh1JbMV3xeKXi8ZMXolpjUkOjAJmP+EvxUb9VXnljfR3O/YefZLbpdQ/9scSw6WBWj1fPmhbXUlU57O9t1Y9eMXyv/zt331GvjWFHMfVldp9CKCUda1SYkN+D9g17DXUtZvV4VutNlTQeh1OGWLV9KIxpcpmqvXQmIvcoZN2EgZz2ycfCcaRSxrabVo8XHkyC3EdVJY6F45Zr8ew/PGD4PjyYdH5tDCITZnHWH8ufX6zWm2I8ErkP38YsoymxJwva321pZtMfn17Q/lXaWplGIeQzyStf2IQ6j4JZUycYtn3/F29kUjN29B7Dso17tXSwrE9fTB9vQRPXb3GpSCKFjg09uPJDZ5j+3jbuPAivIjBzSiP+6bMXwSMEAum1MXJrXvg7JieFTOLMqwj802cvwt//+/9mtjUEPLhvYROWbdjLeCRyOV68EFHRPB4FE+r9ePi6lrw1WB65vjXTTSroU7Bmy37Dfc3Swawej8X67hTye7DrQF/movTBay/GKUEfIoMpPP3iW7jywrOw9Ipp2H94AHc//xru+XwTFCEwod5viI8aWKOHXEZR8uMs6Pfgth++hlXzP5RZl+rOZ1/FP39uBh65rhWhOsYjkZvx4oWIHOHxKHlrsKiqsSFIJJ5CxxXnGRazfP7ld0zbJwshkF77yfDvXGVtt0umr3ckkd8WGwAkJE5EErjy29sy22ZNnYBIPIWGOq+za2MQWciNs/5YAlNPN9ZuTD29HpF4ynKeISL34NmCiEpCr4PITtd45LoWLLhkcl5aUW77ZLP7mrUttbsfOcPq9W4M+bBmYTN2HziW3zJ7QRMAYM2W/UzFIVcIej2m85BHCNzw+G7OJUQux1bJLua6mpdzJlf6EAxs1tRU/KxT9lbJLjEwmMxrhWu3HanZfYvZr8q4NmaHe71DPg/C8aRlq+T6Oi8i8STq/V7+MVhbKv7LLHSOtWrp/eC1F6Pp9v82bKvyuYTyVTxeqXhMICeiktDrILLZbUdqdt9i9iNnDPd6K4qwbJXcEPDighXPob6OFy5UeVZxekrQl7eNcwmR+/DihYhKwqxFqd12pHbb6LLdbnmN9HpbtbjWWyjz90JuMFyc5m5jzBK5Dy9eiKgk9Bals6ZOgFcRmDV1Ak4L+bBmYZNhm1kNhNl9i9mPnDHS6623uM7+//sWNGHH747y90KuEfSax6n+b84lRO7GmhcXY83L8Fjz4n5mnakA2OoOZreLWA12G6v4wQ8XsyO93qmUikgilWlxHfR5EEuqtfB7IXMV/6WOZo5NJlVEk1lx6tXis8bmEsrHX2gNYBUaEZWMVStcO+1x7bbRZbvd8hrp9TZrmd3A9XnIZbxeBeO8xjgF7M1NRFRZPKMQUcFUVWJgMAlVpm9VabrN7n2p+qVSKvpjCahSoj+WQDKl8vdLrsV4JapefGuBiApittbHd6+9GPGUio4Ne7kuyxiUSqk4Fo7nrZux580+tE6ZwN8vuQrjlai68ZMXIipIJJFCx4Ye7Og9hqQqsaP3GI5HEujYsNewrWNDDyKJ1Ij3NduPqkskkcKyjcbf/7KNezHr3NP5+yXXYbwSVTdevBBRQcqxfgtVl+HWzeDvl9yG8UpU3XjxQkQFKcf6LVRduL4LVRPGK1F148UL2bbvjYMFfVFtKsf6LVRduL4LVRPGK1F1Y8E+ERVEUQQm1PvxyPWteeu35G7LLXq1ui+LY6ubx6NgQr0fD1/XYljfZfb5k/j7JddhvBJVN168EFHByrF+C1UXru9C1YTxSlS9OFKJiIiIiKgq8OKFiIiIiIiqAi9eiIiIiIioKvDihYiIiIiIqoKQUlb6GAomhDgC4M1KH0eFnQ7gaKUPwkWsXo+jUspPlvtgshUQr7XyO+XPUZxqilmnuT123H58QPmP0Y3xWg2/J7tq5Wdxy89R8Xil4lXlxQsBQojdUsrWSh+HW9TC61ELPwPAn4NGz+2vuduPD6iOYyy1WnoNauVnqZWfg9yBaWNERERERFQVePFCRERERERVgRcv1evhSh+Ay9TC61ELPwPAn4NGz+2vuduPD6iOYyy1WnoNauVnqZWfg1yANS9ERERERFQV+MkLERERERFVBV68EBERERFRVeDFCxERERERVQVevBARERERUVXgxQsREREREVUFXrwQEREREVFV4MULERERERFVBV68EBERERFRVeDFCxERERERVQVevBARERERUVXgxQsREREREVUFXrwQEREREVFV4MULERERERFVBV68EBERERFRVeDFCxERERERVYWqvHj55Cc/KQHwi192viqO8cqvAr8qjjHLrwK+Ko7xyq8CvqgGlOXiRQjhEUL0CCGeNfm/vxFCHBFC7E1/fXGkxzt69GhpDpSoBBivVG0Ys1RNGK9EY4u3TM+zDMCrAE6x+P9/k1LeXKZjISIiIiKiKlTyT16EEO8D8CkAj5b6uYiIiIiIqHaVI23s2wA6AajD7NMmhPhfIcRTQoj3l+GYiIiIiIioypT04kUI8WkAh6WUe4bZ7UcApkgpLwLwPwAet3isxUKI3UKI3UeOHCnB0RI5h/FK1YYxS9WE8Uo0dpX6k5ePAJgvhDgAYCOAK4QQ67N3kFIek1IOpr99BECL2QNJKR+WUrZKKVsnTpxYymMmKhrjlaoNY5aqCeOVaOwq6cWLlPJrUsr3SSmnAFgAYIuU8trsfYQQZ2V9Ox9aYf+YoqoSA4NJqDJ9q7KbHxFVN85rVAsYx0TuU65uYwZCiNsB7JZSPgOgQwgxH0ASQB+Av6nEMVWKqkocC8fRsaEHuw70YeaURqxZ2IwJ9X4oiqj04RHZNv3x6QXtv+/6fSU6Eqo0zmtUCxjHRO5UtkUqpZQvSCk/nf73yvSFi/7pzIeklDOklJdLKf+vXMfkBpFECh0berCj9xiSqsSO3mPo2NCDSCJV6UMjIhoVzmtUCxjHRO5UtosXMhfye7DrQJ9h264DfQj5PRU6IiKi4nBeo1rAOCZyJ168VFgknsLMKY2GbTOnNCIS5zs7RFSdOK9RLWAcE7kTL14qLOTzYM3CZsyaOgFeRWDW1AlYs7AZIR/f2SGi6sR5jWoB45jInSpSsE9DFEVgQr0fj1zfipDfg0g8hZDPw2JAIqpanNeoFjCOidyJFy8uoCgCDXXar0K/JSKqZpzXqBYwjonch2ljRERERERUFXjxQkREREREVYEXL0REREREVBV48VImqioxMJiEKtO3qqz0IREROY5zHVUDxilR9WL1WRmoqsSxcBwdG3qw60AfZk5pxJqFzZhQ72fXEiKqGZzrqBowTomqGz95KYNIIoWODT3Y0XsMSVViR+8xdGzoQSTBha6IqHZwrqNqwDglqm68eCmDkN+DXQf6DNt2HehDyM+FroiodnCuo2rAOCWqbrx4KYNIPIWZUxoN22ZOaUQkznd5iKh2cK6jasA4JapuvHgpkp2iv5DPgzULmzFr6gR4FYFZUydgzcJmhHx8l4eIaof5XNcERYAF0VQxuefpoFfhOZmoirFgvwh2i/4URWBCvR+PXN+KkN+DSDyFkM/DwkAiqimZue66VoTqPDh4LIK7fvwqDp0cZEE0VYTVebox5OM5mahK8ZOXIhRS9KcoAg11XigifctJkohqkKIIQAB//civcdndL+A/9v6BBdFUMVbn6WhS5TmZqErx4qUILPojIsrHuZHcgrFIVHt48VIEFv0REeXj3EhuwVgkqj28eCkCC/GJiPJxbiS3YCwS1Z6yFOwLITwAdgN4W0r56Zz/qwPwxP9n79zjoyjv/f95Zi/ZSwIkIQJFEalc5ZIIWFFDUc5pta3UFi/gjfZ4vGBLELWhHjwef9Tf8SRVkejxTk8FK7SKrXjl16oI9lDLJUEUrVAKKHJPCMneZ+f5/TE7m93Zmd3ZzV5mN9/365XXZmeeefbZ7DefmWdnPp8BMBnACQDXcM735WNcvSVXRnxJ4vCGwmQkJAjC9OjpFYWUEGZArxYBoDsgUn0SRBGSr7SxhQA+BdBPY91NADo452czxuYAaAJwTZ7G1WsUIz6A6GNvMJpgRhAEUWhS6VU2tZEgMkVdi7SfJYjiJueXjTHGTgfwXQDP6TT5PoDnI7+/DGAmY6zPqkc6CWYEQRCFhPSKKEaobgmiuMmH5+VRAI0AJJ31QwF8AQCccxFAJ4BqdSPG2C2Msa2Msa3Hjh3L1VgLDiWjlAZ9pV6J0iGTmiW9IgpFbzSW6pYgipucTl4YY98DcJRzvi1ZM41lCbdi5pw/wzmfwjmfUlNTk7Uxmg1KRikN+kq9EqVDJjVLekUUit5oLNUtQRQ3ub4Q+UIAsxhj3wHgANCPMfYC5/z6mDZfAjgDwJeMMSuA/gDaE7sqPsJhCd5QGO4yKzwBES6bBRZL8vmikoyivhaXklGIUmPC8xPSar9z3s4cjYTIlFi9GtSvDHf80ygMq3bBGwhDknicf4CCSAizkM5+NpP9OEEQuSWnkxfO+T0A7gEAxtgMAHerJi4AsA7APACbAVwJ4F3OecKZl2IjHJZwwhPEwjVtUXFcPqcW1W57UuGjlB6CIIoFRa9W/GgKPAERDavbNA3QZJAmzITR/Wym+3GCIHJLQf77GGNLGWOzIk9XAKhmjO0BcCeAnxdiTNnGGwpj4Zq2OEPgwjVthgyBSjKKwCKPtHMnCMKkCAKDxIGG1W26BmgySBNmw8h+tjf7cYIgckfe8is55xsAbIj8fl/Mcj+Aq/I1jnzhLrNqGgLdFBlKEESJkcoATQZpohih/ThBmBM675kjPAFR0xDoCYgFGhFBEERuSGWAJoM0UYzQfpwgzInhyQtjbABjrIEx9ghjrEX5yeXgihmXzYLlc2oxbUQ1rALDtBHVWD6nloz3BEGUHIoBOlbvYg3QqdYThBmh/ThBmJN0zn2+CeAvAHZC/54tfQJRlOATe9JHnFYLrNb4eaDFIqDabcczN05OmVJCKTyJSFyCT/TBaXVGHwUmpFxHEET+0TJAO60CvKEwnDYB3mAY1eWyHsbqHAB0B0TSvjxAmhqP1n6Xc56QLFblit+PO62UNpYORmurL9YgkTnpTF4cnPM7czaSIkEUJbR7E9NHqlx2zQlMRUTkKhw2zf4ohScRiUto97ejcWMjWo+0om5QHZqnN6PKIZ++11tHQkcQhUMxQAPyN9YnPEGs/nA/rqg7HYvXfhTVt6bZE/GH1i8x74LhCIYl3YQyInuQpsajvd+thc0iYP4L26PLnrz+XISoRjMmWd3F1pbRdgShkE5VrGKM3cwYG8IYq1J+cjYyk+ITtdNHfGJm125TCk8iPtGHxo2N2HJ4C0QuYsvhLWjc2Aif6Eu6jiAIc6Do2rfHD8HitR/F6dvitR/h2+OHoMMbSppQRmQP0tR4tPe7bTjpDcUtO0k12iuM1lZfrEGid6Rz5iUI4JcAlgBQ7sPCAYzI9qDMTLbTRyiFJxGn1YnWI61xy1qPtMJpdUZ/11tHEEThUXTt7NPKNfXt7NPKo7+r1/Vl7csVpKnx6O13z6hyxS07o8pFNdoLUtVduu0IQiGdMy93Ajibcz6cc35W5KdPTVyA7KePUApPIj7Rh7pBdXHL6gbVRb8l1FtHEIQ5UHRtz9FuTX3bc7QbX7R7SfvyBGlqPHr73S/avXHLqEZ7h9Ha6os1SPSOdCYvnwDwpmxV4jit2ukjTqsFksTRHRAhcfkxHJbinksST+iPUngScVqdaJ7ejKmDp8LKrJg6eCqapzfDaXUmXUcQhDlw2Sx49sbJGNzfgd/c/A1suHsGrqj9GqaNqEbT7IlY//EhVLpsaJlbS9qXB0hT43HZLHjq+nOx4e4Z+Pt/fgcb7p6Bp64/FwNctrh6HKBZo5Q2ZhSjtdUXa5DoHYzzxANqzYaM/R7AOQDeAxBQlnPOG3IzNH2mTJnCt27dmu+XBRBJKAmKECWOfk4bTvlCsAoMTpsF7d5QnAFw+ZxarPnrAbS8uyep0Y/SxhLJYjJOwf+QhazXfDLh+Qk57X/nvJ057d9EFH3NagWbtMythdtuRZlNgC8kRQ8ASfvyQw7Txgr+gaVbr7JhPxBnxH/oqkk42uXD2adVxCWLeYIiOrwhnFHlwhftXlS6bKhw2KhODWLCtDH64EqAdIwaf4j89Gm8oTBuXrkNm/eeiC6bNqIaz9w4OWoABBA18t8/6xw88qfdUaPfs/OmRBN5FGJTetTr+ioCE+C2uQEg+mhkHUEQhSc22ARA1BD9zI2TYREElJf1HJSQ9uUH0tQeZMN+fH3e/dIO3D/rHNyychuenTcFFQ4bugMibnthe8L+Xms/TmhjtLb6Wg0SvSOd/76XAfg552EAYIxZAJTlZFQmRs/op2fkV4ypynMy+hEEUepkO9iEILKJ3n5cCZhQ9tMUqEMQ5iSdc3LvAIi9ANEJ4E/ZHY750TP66Rn59xztjntORj+CIEqdbAebEEQ20duPKwETyn6aAnUIwpykM3lxcM6jR+KR311J2pckyQz26uXL59Ri/ceHyIxKEESfIlmwCUEUGq39tRIkEbufpkAdgjAn6ZzD9zDGzuWcbwcAxthkAEWVY5epMT5uu1AYVS4bnp03JaGfarc9brnTKuBf6kfgpzNH9ikzah6NdwRB5JlUOipJHP6whCqXHc/cODnO/Gy1kg7kGtLf1AgCQ6XTFl+fNgv+pX5EXD1r7df7yn68mKCa73ukM3m5A8BLjLGvIs+HALgm+0PKDXK6SDAuDUwv/cvwdozFmfa0jPflFiHueakjcQnt/nY0bmxE65FW1A2qQ/P0ZlQ5qkhMCKLISaWjmeoskR1If40RDiem4S2fU6tZpxSoY26o5vsmhj9ZzvkWAGMAzAdwO4CxnPNtynrG2D9nf3jZQ04XkdPARIlH07+8oeTXrma6XV/FJ/rQuLERWw5vgchFbDm8BY0bG+lmUwRRAqTSQ9LLwkL6awxvqCcNT6nThWvaqE6LEKr5vkla01LOeYhz/jHnfCfnPKRa3ZTFcWWdTFNDKG0kPZxWJ1qPtMYtaz3SSjebIogSIJUekl4WFtJfY1AaXulANd83yeY5NVNfE5BpagiljaSHT/ShblBd3LK6QXX0LQhBlACp9JD0srCQ/hqD0vBKB6r5vgnjnGenI8a2c87PVS1zANgI+X4wVgAvc87/Q9XmRwB+CeBgZNHjnPPnkr1WJnd/7o3nxRsUIUoc/Zw2nPKFYLcICHMOd5kV/mAYEudwlVmTmvkyDQsoNkx4/WnB/8i9vVt5oZjw/IRCDyGOnfN2FnoI+cK0NZtMRzmXNc5dZkW3X4TLZkF3UESFQ9ZGdwqNJHpPgfS34B9muhobDkvwhcJx+3WrwOCwWuATw1ETv8tmgcVi7O/WV/bxZiODmqcPpQTI9TnSAIBLOOfdjDEbgA8YY29xzv+iavdbzvlPczmQTFNDOOfwBsMJxr5t+9ux/pMjuPvbo/Gzlz5KOiHqSyZWgQmoclThsUseo+QPgigx9HSUc1nj1Dp58KQX1W4HFq9NrpFEdiD9NQbnSNivP3n9uWj3BbFwdaKJP9UEpi/t480G1XzfJJuf7j71Ai6j3BvGFvnJzqmeDFBSQ5SUMCOiomfsm/b1gZg/42z87KWPUppT+5qJVWAC3DZ33CNBEKWBlo7q6eTXayqweG1qjSSyB+lvanxiYr2e9IawcHVmJv6+to83G1TzfY+0zrwwxi4AMDx2O875ysjjD3W2sQDYBuBsAP/NOf9Qo9lsxth0AJ8DWMQ5/0Kjn1sA3AIAw4YNS2fYvULP2NfPaUOFw2bInEom1r5HoeqVIDKlNzWrp5PlDu3lpH1Eb8l2vZ5R5crYxE/7eILIL4anp4yxVQAeAnARgKmRnymptuOchznntQBOB3AeY2y8qslrAIZzzicC+BOA53X6eYZzPoVzPqWmpsbosHuNnrHvlC+EPUe7DZlTycTa9yhUvRJEpvSmZvV0stuvvZy0j+gt2a7XL9q9GZv4aR9PEPnFsGGfMfYpgHG8Fw5/xth/APBwzh/SWW8B0M4575+sn0wN0MkMdeGwFDWbxhr1wmFJ81pum1VAP4cNnoAIXyiMgeVl+KLdi0qXfEZGEFi0T5fdIvcRcy1txtfDShIQ8gJ2FxD0AjYXIAgJd5h1WBzwh/10DagJzHlk2M8OZNjPH5kYoBN0cm4t+pVZ0RUQ0RCnfbVwl1nhsMrfSpecyVlHo9Pupng0veAfWLr1KooSuoMiTnpDOKPKhS/avRhYbpcvf1R5Xqpc9pQm/pLyvGSpfrMyFNX/gBJ/rF6W5v9BkX0ghBbpXDb2MYDBAA4Z3YAxVgMgxDk/yRhzAvgnqO4HwxgbwjlX+pwF4NM0xmSYVCk5WhOUarcdgHxK+Mnrz0U/pw1dfhEWBty8clu07S+vmoi7fteGI6cCaJlbCyBxZ95wydl4+obJKHf0InFHkgDvMeDlm4ADm4Fh04ArV0ByDUR7oCMubaOpvglrd6/F0zueNkPiF0EQJYzFIqDabcczN06Opo1JnOPHv96KQf3K8OAPJ2BYtQsnugMAB57buBfzLhiOYFhSTWyK9IBPQUej4apJ6wBQK0GJND27BMMS7nllZ9xk2yYgWsOnfCE4rALavdrHBrETmEwDgUxHluo3K0PRSRGzCTYs2rDILGmmRIFI+Wkzxl5jjK0DMBDALsbYesbYOuUnxeZDALzHGPsIwBYAf+Scv84YW8oYmxVp08AY+4QxtgNAA4AfZf529ElmqEt2t11vKIybV25D7dI/YsQ9b+Jwpx83r9wW1/ZnL32E+TPOjvTZptnnI3/ajVtXbYMnIBoOC0gg5JVFZd8mQBLlx5dv0rzD7OJNizFz2Ey64yxBEHlBOZi77tkPIXGO+S9sx+a9J/CHtq8w46ENuO7ZD2GzCGhY04Zvjx+CDm8IDSqDdNGbnHU0GiFvWt2QpucWnxhONOevboPVYsEtK7fhlC+E2qV/hF+UdI8N1GQSCGQ6slS/2UDrf6BxYyM6A50Jy+j/oO9h5MyL5iVeRuCcfwSgTmP5fTG/3wPgnkxfwyipDHXJjHqx684+rVyz7dmnlafVZ0bYn+rNIgAAIABJREFUXfK3IbEc2AynzaV5h9kR/UfEPac7zhIEkUsUI3Q/p3aYibI8Vi/VbYra5Kyj0bC70upG767hpOnZQS9gIrZ+AejWca/242YmS/WbDfT+B4aWD01YRv8HfY+UZ1445+9zzt8H8B3l99hluR9idkhmqEt2t131Oj2T/p6j3Yb7zJigVz6NG8uwafCFvJp3mN3buTfuOX07QRBELlF075QvpBt0ouilnkG6qE3OOhqNYPpnXkjTc0ey/bNSpwB067hX+3Ezk6X6zQZ6/wMHuw8mLKP/g75HOhcJ/rPGssuyNZBc47JZ0DK3DtNGVMMqMEwbUY2WuXVw2Sxw2SxYPqc2bt3yObWa69Z/fCih7S+vmognN+wx3GfG2Fzy9afD6wHBKj9euQJOqxPN05sxdfBUWJkVUwdPRVN9E9458E70efP0Zvp2giCInKLo3ua/H9fUv81/P46m2ROx/uNDqHTZ0DK3VlOTixYdjYYt/TMvpOm5w2nV3j8f6/Jj+ZxavNp2MLou6/txM5Ol+s0GWv8DzdOb0b+sf8Iy+j/oe6RMG2OMzQdwO4ARAP4es6oCwJ8559fnbnja5DNtDJCTSWLTRpxWS9xzC2NwpNlnxhhMGyuzlMEv+uGyueANeeG0OmARUp/qDkth+ERfzHZOWISiFuqCX3hMaWPZgdLG8kdvalatl4reOm0W+FT6yxjrA2ljTiDkSzu9SS9tzGFxaGq0VjJTnkzMBf/AMqlXvf26TWCw23rqkXOesB8vybpVkMJA0AOUlQOBbsDuBrJ8DGC0Vo2mjXHO0zluKZEPqm9j5MLNFwG8BeBBAD+PWd7FOW/X3sScKIY6ANFHBYtFQEVkYlHhsEWXSxJHhy8Ul1K2fE4t1vz1AFre3RNNyHHYLIb77OWbkEUF6HlEzx1mAcBpccgpHZsW9yRy1DfJiRxJRCgshdHub8fimO2aItsV+QSGyAM7/3EgrfYTzqKbd5YaWnrZMrcOVS4b2r2Jy6vddl1NLlpiNdrmyji9KVbTlUcHHJoaXemoxMnAyYRkJkph0kavTmOT7nrqkcXtx0sqFlmNJAHe4zlNG9NLEdOqVa3/gdjf3TY3Hbf0UYxUowXAKQA/AdAV8wPGWFWS7UoCrZSyhZG0HLMm5PhEHxo3LY5P5Ni0OOV1oT7Rh8Wq7RYb2I4gCAJInuqot7ykyXJ6k55G+0W/ZjITabc2vanHkq7lPKSN6aWIZVqrdNzSNzHyVdc2ABzyqbZhADoivw8AcADAWTkbnQnQSylT0nKU52ZKyNFLH3OmuG7VpbOdqwDXuxIEUXzo6aVeupOZdDMnZDm9KZlGa2o+eQE0SZU+mqttTU8e0sb0UsQyrVU6bumbpJy8cM7PAgDG2FMA1nHO34w8vwzyTSdLGiWlbPPeE9FlseliynNvMGyayx6U9LEth7dEl9UNqoMv5IXbXq67nVdnO2/Ii/Ik2xHFgek8LGleZkaYHz29VFKc1MvNpJs5QUlv2repZ5mS3lSWvqYm02hNzRd9cZfbEDJ6dWqkHnuzrenJcr1qoaSIZatW6bilb5LORYxTlYkLAHDO3wLwzewPqXdIEkd3QL67c3dAhCRx3XWiKKHLH4LEObr8IYTDUkJ/ckqZKm1kbi3Wf3woPwk5kiSb5njkUUocoxqn1Ynm+qZoIsdPan+CRy9+FE6bC55gN8KSCE/IA4lL0Udlu6aY7aYOnormejnJwxPywCf6eraRwrrjiu03tn+CIEoPRVfDkgQB0EgQq4WFMfzm5m9gw90zcEXt10ojWUyPWM1mQk9604SrgAWtwLx1AHhUM9V6GZbCuvqpp9EWwYLnvvUcPpjzAVpvaMUbP3gDy2YsozMvOmjt11vmGksRS5Zcamq0jiXUy2xOw2ljkhSGJ9gt12mwWz4m0HpZVX07LA7NFDGtWhUlEd2R1+gOdkOUEiOqtf4nmuqbqPZLnJRpY9GGjK0HsAnAC5AvI7sewHTO+bdzNzxt9JJFkhnpACSsUxvvl8+pRbXbHpcIJknyxKbDG8IZVS580e5FpcsGm1WAw5bjpBFJytjsKUVSwxxWJzr8HWjc1BhnZlu7ey2e3vF0glkuNm2sO9SNFz99MdrugQsfQMv2Fhz1HZUDALb8D4T3m+LGJTEYNuPliYI7KM2SNpbrMy85P5Nyf2du+zcPRVOziuau/nA/rqg7HX9o/RJzzxsGTzAc1csBLhtW/u++mICTWrjLrHBYSyihSUFLs69eJR8ccjFByyXXQLQHOqJ6eeukWzF75Ow483GsfkpcgjfkRZiHUWGvQHeoG1ziWPT+omj7pRcsxet7X8eVo67Ml+4W/ENMV2NFUUJ3UMTJmP36AJcN5XYrrFYDSXBJkktNiVZdXrMKCAc1ji8GpkzHkyIm+VShQHrm/MqySvjD/qRpY6IkosPfoRlOYVUlp6aZkmriD4owSjqqNhdADYDfA/gDgNMiy0xDMiOdEeP9wjVtCaY7byiM217YjhkPbcDX/+1NzHhoA257YTskDghMTi/LmWj1wjwnCBa47eXwiz40bmpMMLPNHDZT0yxnESwot5fDJ/pwx3t34L/b/jva7t4/34ubJtzUEwAwblbCuLJtxiMIwrwouvrt8UOweO1H+Pb4IWhY0xanl/Nf2K4KOGmT9dPMB3uZoqXZv7sBYExTy9V6OXPYzATzcax++kQfGt5rwEVrLsKklZNwxHMEi95fFNf+vv+9DzOHzSTdTYJPDGO+ar8+/4Xt8InGTPdKcmnOjwGyhVZdejt0ji988iViLJKap/FFqdFQIL3jAX/YD7fNHU0T05pg+0W/bjiFGuW4RWACyu3llDLWBzB8gWYkFnlhDsfSa1IZ6YwY792qa1YLas7LgnlOz7w/ov+IuOfqU6x6pjplu9YjrXBWj0wYlzOyTr0dncIliNJD0cezTyuPe4zF7AEnWUVPs50DNJer9XlE/xFJ9VOty3rtleWku9roBUio9/8lg1ZdVp6Z8fGF0VCg3pjzyYhPJCPlmRfG2KORx9cYY+vUP7kfonEUI10sipFOb53aeO8JxF9TmazPnKOY52JRzHMGUcz7sdQNqsPezr1xz7W+MUm2Xd2gOvhO7E4Yl9529A0gQZQeij7uOdod9xiLXsBJSaKn2b6TmsvV+ry3c29S/VTrq157ZTnprjZKgEQsWvv/kkGrLjv2Z3x8oXdc4VNdFdKb4wGvzmt4sxjbTBQvRi4bWxV5fAjAwxo/piGZkU5r3fI58cb75XMSDXsFNefZXIbNc3qozfuKme2dA+8kNcs5rc4EU90DFz6AFTtXREyiTXDuWpcwLq3t9Mx4BEEUN4o+rv/4EJpmT4w+JtPZojA3Z4qWZs9+DvjHB8D3H0/QcrVevnPgnURDfox+Gmm/9IKleOfAO6S7SXBaLVg+pzahTp3WPlSXrsqMjy+0jiuaNUzyvTkecFgdmkZ8h9WR8Z+BKB3SMexfAuAvnPOCT3uTmfOSGenCYQneUBjuMis8ARFOqwU+UX7uD4YhcQ5XmRXeYBgOixBd5wmIsDAGRyHMeZIkX6+axDyXirAkwif6o2Y2h9WBQDigaZaTJFFeZnMjIPohQYLT6oJX9EKAAIfVEe3DGvJrjkviUrRfPTNeHin4xchk2M8SZNjPG+nUrKK5Tpsg62NEJxXtVJ47bRb4QrJ+xoailAyxWh3oBuxu2T9gc/REzSrLg57o+rDNoanPDosjzoTssDrgj2nntDrhD/vhsDii5ufY5XnUXdPXq3rf77JZwDni9vFOqwX+sGR+832mSGG57mLrEFxVmy55ImMA9XGF0+qARWNbreMBAIaOEURJjKt5h9UBgQm9Pb4owQ+375HOJ/4jAG2Msc2MsWbG2OWMscocjStj9Ix0ksTR7g3hlpXbMGrJW7hl5TZ0+EJw260ABzzBMG6OrNv0+VG0e4NxbbsDIqQwz785TxBSmueSIXEJHYGTaHivAZNXTUbDew04GTgZ/YePNctJkoh2fwcWvLcQk1dNxk/e/Sl8ITke2Rfy4afv/jTaR4e/A2GbQ3Ncsf3qmfEIgigNFM21CAIqHDZYBAFOqwXHuwK4ZeU2jL73bdyychvaPUF8sPsY2r2huAj7kkBJc1o9B/hFDbDmWsB7XI6e9Z6QnyvLPceAvzwFPHAapL88gQ5/R4I+OywOdAR6lr/w6QsJ7ToCHdFUJUVnFbMy6W4P4bCEE574/fkJTxB+MRy37KtOP361aS9OeIIlWp/H4+swcArwqGvzuDzJSdWdxnFFR+Ck5m0R1McDgJxIuuDdBZi8ajIWvLsA7f72hG0lLuGkxrFLV7Ar5bZE6WNY3TjnN3LORwGYDeBLAP8N4FiuBpZt0kkim/b1gVi4pi0hmcxoEomZSCf9Sy9BxC/6NFM/fBqpHwRBED4xrKmh074+MKq7JYVeMmTQk7h87b8C474HSCJ842Ylam5En9NJISP08Ya0a1H5XXmMpuX1lfr0dgBrNWoz6EnZXW9SRY1uq9euM9BJ/weE8bQxxtj1AOoBTABwHMDjkO/7UhSkk0TWz2krmSSSdNI+nDa3TrqH3nJK/SAIIhG9NCdFW0subUwvZaysXHv5wNEAAGf1SF1tTSeFjNAnWS2qlylpeX2iPvXSxspS35W+NyliRrfVaze0fGhGr0uUFumcV34UQC2AZwE0cM6bOeebU2xjGtJJIjvlC5VMEkk6aR++kEcn3UNvecHtTwRBmBC9NCdFW0subUwvZSzQrb38+N8AAL4Tu3W1NZ0UMkKfZLWoXqak5fWJ+tRLGwt0IxW9SREzuq1eu4PdBzN6XaK0MGzYBwDG2DkApgO4CMBIAH/jnN+QpL0DwEYAZZDP8rzMOf8PVZsyACsBTAZwAsA1nPN9ycaRiWFfuRN0w+pWbNnXHrnTcx2q3XYAQJc/hI7I3XbbPQHYLALmv7A92nb5nFpUue3wi2kY+pKZ7WPMczzkk9uWucEDspFTUMzvkTvHOm0u+BTTZozZXgBDmdUBX8SoKahuzqR1h9tlM5bBwiw9JjjBBqvFBkkMoiscQGewE0PLh+Jg90H0t/eH2+7WvNNtlaMSFsGaYMiLNZCmY6iL7ScQDkDiUjZM/wU355FhP0uQYT9vGKlZtdY6rQJ8ohQ17rvLrDhwwotH//Q5jpwKYPmcWmzb344pw6tQ7S4rTlO0ouk2p8r87AICXfKlOJXDIob88p427fsg7fkjfKO+BeeAM+ENdsNpcyEo+hHkYXQGOnF6xenwhDxwWV3Rg7GG9xrQeqQVt066FbNHzo7T4ObpzXBZXXEhKorR34j+ZjFYpeAfZLJ6VTwvC9e0xe3Py8usONoVwBlVLnzR7kWFw4q//uMELjy7BuUOa/7DeTJF6zgDUC1zys8lEXD0B/ydgKVMrtm1N8lnXIZNA2avgOSuhk8d6MPj+5NsTnhFH8I8jAp7BbqCXdFjCnUNaR0fdAQ64o5Jmqc3o7KsMq5uHRYHukPd6AzEHI+U9QcALNqwKG7bKkdVOrVr8g+UMEI6aWP9AFwI4JuQLx8bCDl9bF6SbRgAN+e8mzFmA/ABgIWc87/EtLkdwETO+W2MsTkAfsA5vybZWPSEKtkERZnAaE1swmEJJ7xBLFwdI25za+G2W+G0W+AJiPhg9zEsWN2W0KcuioHz5RhhuHIF4KqBnBBwTL6+tGIwMPM+4A+3R9vx2SvA3XK7dn87GlWThrW71+LpHU+jblAdHrjwAbRsb8FR31E01zfJ/8QaE5jYSYEn6EHjpsa4PisP7oAQ9KL9rAvi1v3nRf+JR7c9ipnDZuL8r50Pt80dlyyinhzp7WRTiUtsP6c5T0PDuQ2498/39kagFAouVDR5yRI0eckbqWpWrbUNl5yNOecNw5q/HsAVdadj8dqPYjS4Fi67FWUWAV+e9KHSZUOFw2b+g0I1iqZvfR6YdDXw6k/jDvpgtQMfPiuv2/G7uDbSNxejfeqP47R86QVL8emJT1F3Wm3C8tf3vo7ZI2ejwl4Bu8WOrmAX3DY3PCEPKuwV8IpehMNhLHp/UZyOf9X9Fb5W/rWU+qv1pVapamw4LMEXCkOUOPo5bTjlC8FhFXAqIMbt85+8/lyEwhIaYpYZ2tcXEr3jDIsd+O0NPcuuWQWIwYSJCmwu+Vik8kygYz8kdw3aeSCxLmCBENOfeN3L6Ah1JXyhWemohDUmcUyvzhwWB9r97XFfkloES3SyXjeoDi0Xt8Av+uOOR5rrmzHAMUA3LdUgJv0wiXRIZ/LyEeTJxwcANnLOv0zrhRhzRbadzzn/MGb5egD3c843M8asAA4DqOFJBqYnVN0BETc/vxWb956ILps2ohrPzpuC8iR+lS6/nEKm3u7J68/FkVMB3L/uk7T7RKBbTp7ZF2MLGl4PzF0DcElO9ti3Cbh9M/BmY0I7ac5q+ASGBe81YMvhLdFVUwdPxT3n3YMfrvthwvOpg6fisYtb4LbrX7PaHexGg0afLRcvB5PCWPD+nbqvN3XwVDx2yWPRxBAA8IQ8WPDugug2r8x6BQ/+9cGEPtTbqYntJ9M+dCi4UNHkJUvQ5CVvpKpZtdauv2M67l/3Ce6fdY6mXt4/6xx8+9GN0ecp9dOMKJr+nWZNzcblLUDYL69TtfH85EMs2N6coGmPzHgEd27Q1twH//ogWi5u0dFr7eXLL16Ohe8tTKmdat3Wa2cQU9er1v697b5/xvwXtsct23D3DNzzys709/WFRO844/IW4LGYS64WtAKvNaRs51nYhgV//UViXZz373Avr40u6/63L9GgUWctF7egPOb4Q6/O7p92P777++/GLVs2YxkuWnNRdNkHcz7Aog2LUr5GBhS8XoneY/g/knM+Mdl6xthjnPMFGsstALYBOBvAf8dOXCIMBfBF5DVExlgngGrIoQCx/dwC4BYAGDZsmOYYUpny9Uhm6KtwaJv3Uxr69AycdlfP74Bs3NRox8rccAKahrUR/UdoPm890gpnChO92giqbOeK7LCSvZ4RU12mxtLYfkrBnGqkXgnCTKRTs2qtVYzOymMsyvLY50VpiFY0XUezUXlmz++qNnrG/H72frqaq2XcV9brLXfrhKsYNUOXosZq7d+1QnnOqHJltq8vJHrHGUotKuiZ81XtnP2HaddF//i/r9EQn3RM9xX2irhlFfYKCgoidMlmEPyFWgs552HOeS2A0wGcxxgbr2qiNQtOOOvCOX+Gcz6Fcz6lpqZGcwDJTPnJSGboUwx86fapa+AMeuNNnMf/ptmOBzzwqUybgGxO29u5V/N53aA6+FKY6NVGUGU7b8gDX+BU0tczYqrL1Fga208pmFON1CtBmIl0alattYpO6unlnqPdcc+L0hCtaLqOZqNjf886VRs9Y/6poL7mahn3lfV6yz064SpGzdClqLFa+3etUJ4v2r2Z7esLid5xRsf++GV65nxVO1/nAe266Iw/q240xCcd031XsCtuWVewi4KCCF3ydhcrzvlJABsAXKpa9SWAMwAgctlYfwDtyACXzYKWuXWYNqIaVoFh2ohqtMytg8uW/JsTl82C5XNq47ZbPqcWm/9+HE9u2INfXjUx7T5hc8nXng6vl+9YO7xefm5zyXe2nf2cvGzTI8AVT8S147NXAHY3nFYnmuubMHXwVFiZFVMHT0VTfRPeOfBO9PkDFz6AFTtXYOrgqWiub0r5zZnD6kCTRp+O/R/C+feNCa8X1//0Zs1v8JqnN0e3eefAOwn9a22nJrafFTtX4IELH0i7D4Ig8oNaa9d/fAjL59Ri/ceH0DR7YoKWrv/4UHr6aUYUTd/1OvD9x+O1ffYKwFXZs07VxrlrXYK2Lr1gKbYc2pKgl0svWBrVUV291lm+9+ReQ/qr1u1S1lit/btVYAnLBrhsaJlbm/6+vpDoHWe4KuOXuSrlGlXXrDO+nbNsgHZdOAbEtXMwm25dxqJXZ/3t/RO2tTBL3DILs2i+RinWKJE+aaWNJe2Ise2c83NVy2oAhDjnJxljTgD/D0AT5/z1mDY/ATAhxrD/Q8751cleK5U5zxuSk248AREumwUWS+o5mno7p9UCf1iCy26BPxSGJAGuMkt6CSRJ08ZEeVlZObgYAMKhaHINt7ujpnvNtDHRH30uMKEnbQwCBJsjmgbii02c4Sy6TrTJffSkjVlhtdiBQDckuwu+2P4FC8osZWml1qjTbgQmpOxD3Q+ljWUf8rwUDUVRs0bSxtRaatoEp2RardUuoqUoKwdCPjk9MjZ5LOiVv6SKSSSL1VZvJB3SL/riNF1ZrmieRbAgHNkHRPU6kipWZimL13GdtDG99LG+kjYGaB8XcC7fTDVapzYLgmEps319IdFMG5N66lOpScDQMokJiXWhShuDzQURUkL9xZr1o8PTqDOJJ24raLwu5zyu9pX/iV5i8g+UMEI2XWhaBTEEwPMR34sA4Hec89cZY0sBbOWcrwOwAsAqxtgeyGdc5mQ6AEniaPeGdNPGkmGxCKiITHIqHPLNq8qt8nOXvefPlJZxTxB6bvgUe+MnSQK8J+SEkIrBYKq0MaakkgkCBMESNeC7rS7AewzuSLKIe9g0+azN396Ce/Rl0T60km2aL/gFql67H0LXIVivXIFyVw3AgXLlDtCR1xauXAG3qwZgQpzxP5mJU2BCdH30UXBHRctook1sP7HfrmRgICWKgOH+F9Nqvy83wyAyRBBYVA+VRxdj+omPjJnT+JwsGVI9gREE+eBQaa+RFonZzwHbVgIbm+KeCxubZM1W9Ffo0VjlsVz1CAAWQY6gTZYOprRXDh7dQo+OJttOrdulitb+HQDcAsOJ7sR6ddkt5qxVLdTHGZIIeI7LaaaxNekYIAcFxSaQhYMJdS+4ahLrgiHuNcJSWOf2CVUJkwutOout2XKN44zY19VqRxDZvGxsuXoB5/wjznkd53wi53w853xpZPl9kYkLOOd+zvlVnPOzOefncc73qvsxijcURsPqVmzeewKixLF57wk0rG6FN2Sya1ZDXlkw9m0C6u+Ud3z7Nsmis2+TvE7rus7Y7ZS2f7gdmHBVXB++cbPQuGkxthzeApGL2HJ4Cxr/99/hm35XfP9a/em9dgYoE5e4cWxsLKrrqgmCME7RaHAs6epgKv1e+6/AuO/pP89AYzPVUtLg5BRlvaYi6JVrTl2T4WD8Mm9Hxvt/n+jDYtUxxuJNi6muiLyR8qsFxthr0DDQK3DOZ0Uef529YWVGpmljeSc2IUQvucaukaihlyziHGAo2cZZPTKxf6OvnQGlkGhDEIRxikaDY0mVDJmsvZ5+Dxyd/HmaGpuplpIGJ6co6zUVZeXaNVmmOnOhl0BmoDaTpeARRD4wcublIQAPJ/kxDZmmjeWd2IQQveSaoMa3H3rJIr6ThpJtfCd2x/efLBEtC5RCog1BEMYpGg2OJV0dNKLfx/+W/HmaGpuplpIGJ6co6zUVsWmmCsOmyctj0UsgM1CbyVLwCCIfZM2wn0/0zHnquz6b9g65koRwyAMfJLjsFfCGPHCKIiyOfvJdbstr4BOERCOlJAGBTvl0b+SOuHBWAo4KSP4u+AIn4ew/DD7PUfCyiugdbDsCHXBZXXBYHfAGu+CEAAuzANayyLWxOtd6GzWx6r1NjbvrLpuxDFbBmg2TqFEK/sEXq2E/1wb8tD0v//Xd1I1Kg6Kt2aLR4FiMeF6iRn1njwm/fR+w549AjN9Q1/PCAal8YCR8xS1rvtUFf9ifYLx3Wh2wqIzPEpfgDXkR5mFU2CvQFeyChcleGLV+qg3SoiRi0YZFSX2HvTTvF/yD7VP1qiZhP+0A/F2AT3WcYHcBnQd7lpXXQJJE+PyR44bOA3A6BkAo6yeHUCTZ74elMLqD3egMdmJo+VAc7D6I/vb+cNvdCIQDKU33jLGEegNgqAazEDRRJB8skQzDkxfG2EgADwIYByCah8c5H6G7UY5IJlTqBBwzpoWEpTDa/e2JZredvwfzn0w02ys7G47EnewPnoL02RtoP2cWGv+8BK1HWnHrpFsxe+TsuP4fuPABtGxvwVHfUfm19m2G5eUfA9MXA+ffCpRVxAtVOibWJKgTxDwhj2EDf5Yo+IdPkxdtaPKiS1HXbDFocAJJkyEjWrj1eWDS1cCrP43XxLL+sp9ASWwSrEDXYfkgMdANQIK0dxPaz5oWp+tLL1iKT098itrTajWMz5VxExjdfYbKIK31hVHz9Ga4bW7dxEe9bdLQ5YJ/uH2uXhW09tPXrALEYPyXkrNXAFY78Nsbosuka1ahnYfRuCnmc69vRhWzQIhpp7Xf1/tiMiSFEurIxmxY9H7P5HnZN5chxEOGttWaZPeyVgET1CvRe9I5YvwfAE8CEAFcDGAlgFW5GFRvUBJwlGQbM4qQrtlt0tXaZnvFYKllLP39bfBNugaNf14S3WbmsJkJ/d/753tx04Sbel5rxHS5jw3/F1hzXU9coiJQWTLzK0kjSjQnmUcJorQpBg1OQElsYkK8DgI9Wjjue/LERa2Jkgg4+snbMgF48WrgsTpgaRXQdRD47Q3wfX16gq7f97/3YeqQqTrGZ3/c8IwapPUM+hKX4rTYyDZ9RZeLsl4VtPbT3g554hJn2L9JXh6zzOc/icZNqs99UyN8/pMp9/taNdMZ6NSso85gZ3y7YGI7vW2N1ndfqVWih3QmL07O+TuQz9bs55zfD+CS3AyrtNE1u5X10zfbW526xlJnWb+4bUb0H6HZx4j+I+JeK7aPBJNeuiZWA5B5lCCIokPRQiPhKmrdjGyj1mhA1r5+du3lauOzUYN0JhpLulzEaO2n9Yz4lWfGLXL2H6b9ufcflritPXWdDS0fqtnf0PKhKdvpbat1c1WqVQJIb/LiZ4wJAHYzxn7KGPsBgNNyNK6SRtfsFjilb7YXfbrGUl/gVNw2ezv3avaxt3Nv3GvF9pFg0suBmZ/MowSCZR8tAAAgAElEQVRBFB2KFhoJV1HrZmQbtUYDsvadCmovVxufjRqkM9FY0uUiRms/rWfE79gft8jXeUD7c+9UXS6ssd/XqpmD3Qc1+zvYfTBlO71ttc68UK0SQHqTlzsAuAA0AJgM4AYA83IxqKJCkuTrmnnkUZJSbuK0OtFU34Spg6fCyqyYOngqmuqb4NzxOzh3rcOyby7DGz94A203tOGNH7yBZTOWyd8s2Fzy9afD6+XrqofXA1c8AeeO36L5gl9E+3vnwDsJ/T9w4QNYsXNFz2vt3djTx5UrInfljUHrtbTapYHT6kTz9Oa4cTVPb6ZvTQiCMCdSWNb2eesARxVwxZOJmqiEqQCJurnrdWD2c3D+fSOaVZrcPL0ZjLNEvf+mHGoicQmekAcSl/T3GRrfTKersaTLJsXIsYXNJXtcFrQC97XLj+U1sscltk5nrwBclXHLnI4BiTVZ3wSnY0DK/b5WzfQv649lMxKPXfrb+8e3s/fX3NZIDVKtEgppp40xxvoB4JzzrtwMKTVmMUBnbGpXp40Fu+FkVlhsDoTFADpCngQTXaVjgGzgjDOWeuQdp80JKeSDjyGawME510wbi6bUQIBgcyRPEetl2pjmW+99Uki6FPwCZrPUKxn2iwaqWTMghQHPsfg7lV+zKuKLqZDTxjb8p2zO10tpDHRH9VOyu+AT/XDaXPiy60s80fYEhvcfjitHXpmg9y/vfhlP73g6zpCsldqkvps5kJnG9tW0MdNi9NhCz7APxKeSuirlyUj3sbhlUllFtCZ9kZoSwFLu9yUuoSvYhc5AT9pYlaMKftGfUMsDHANUaXqUNkb0nnTSxqZANu1XRBZ1AvgXzvm2HI1NF9MIVaAbWD1HNrUpDK8H5q5JvCGUwe26GUfDewux5fCW6Kqpg6ei5eLlKLdXaHSWiCfkwYJ3FyT0cc959+CH636IqYOn4rFLHoPb5jb8VouYgguVWeqVJi9FA9WsGfCfAtZcm6jTV68Efndj+rqPRG1+ZdYrePCvD+pqtfLc5HpN9ZptjB5baLVb0Aq81pC47eUtcpBEsv4MonWM8cYP3sD9m+/XOHZpQbk9/dfIIQWvV6L3WFM3ifIrALdzzjcBAGPsIsiTmYm5GFhRkKmpPcl2LkDHmGl8x6Vnaos17NNpVoIgiCTo3ancOSDjMBO1NqcKV1Gek173MYweW/TCsN+bAJ50DPvqUAmCyAbpnGvrUiYuAMA5/wBAwS4dMwWZmtqTbOcNeXSMmR7Dw9IztcUa9sngRhAEkQS9O5X7TmYcZqLW5lThKspz0us+htFji14Y9nsTwJOOYV8dKkEQ2SCdyctfGWNPM8ZmMMa+yRh7AsAGxti5jLFzczVAU5OpqT3JdkaNmcnQMrXFGvbJ4EYQBJECuxuY/ZzK+Pwc8I8PgO8/nlGYiVqbtcJVmuqb8M6Bd8iQ3Jcxemyh1c5Vqb2tyrDfmwAePcN+c31zQi07rI7UHRJEmqTjeXkvyWrOOc/bPV9MdX2rUVN7QjsnEPLFGO8tQMRAH7Y5IsZMN7whT8SYmXiFXzLjmnqdwATdOyybkSwa+wt+fatZ6pU8L0UD1Wy+SKXfUljWZ7tbfiwrjzPho6y8ZzvA0L5ArW1llrI4Q7PD6kAgHEjQvnyGnaT5WlSvuUCpvWjNuQGNgAZIYk8tKrUJIaEWJciBD3HmfK3+jA5Po0YkLiXUslXj2KU3r5GFmi94vRK9x3AVcM4vTvLTd29WmezOzApKIsjqOcAvauRH73F558Yhi8yLV8vrNj8Bi+c4yl+cC+EXNSh/cS4s3hMJMYkSl9Dub8eCdxdg8qrJWPDuArT72yFxuV3s3ZTdNnf0n17rDstmI9V7IwiC6DWaunwsXmsFC2Avl/V6zbVyuzXXAt4T8nJF94HUfSldqrTZKlhRbi+HwASU28thFaxx65WJS740kfTXBEiSRs0dT6wnKQx4VO08xwHwuOMSiQHtgQ4seK9B/kzfa0B7oKNXn6m6jgUmaNZyxn8CqkMiCcazEBkbxBhbwRh7K/J8HGPsptwNrYQIeeUow32b5G9J9m2Sn4e8ievGfU+O5tRqG4NP9KFxYyO2HN4CkYvYcngLGjc2lsS10aX83giCMAnJdDnddkb7ypB8aiLprwkwWk9BT+Lxwtp/lZfHUIyfaTGOmcgf6XwF/2sA6wF8LfL8c8g3riRSkSw5RL1u4GhDKSN6iWKlcG10Kb83giBMQm8SndTtMk2eNEg+NZH01wQYrSe9RDxV/HExfqbFOGYif6RzTm8g5/x3jLF7AIBzLjLGwjkaV2mhJILE5q7HJn3Erjv+N/22MYKkpH3EZqorqTQmvh+AIUr5vRUSs3lYck3aHp95O3M0EsKUJNPl2IM/I+2M9pUh+dRE0l8TYLSelEQ8dbtAN+DoF11UjJ9pMY6ZyB/pnHnxMMaqIbs0wBg7H/KNKnVhjJ3BGHuPMfYpY+wTxthCjTYzGGOdjLG2yM99ab2DHCFxCZ6QJ+6xZ6UkiwOPPGpc1xyHXnIIEwCbE9LVq+BZ2AbpvnZ4nJWQrl6VMhVEK+0jNpVGksLwBLvl8Qe7IUnG5plJ33eeSPXeCIIgkmJEo40kOkmSrNOp2sX0JU24WtbzeevgEViihkbGFpZEdEc0ujvYjXASjdbTRMUPk02tJv01Abq16ZBvnsol+dHu0k7Es7vi6t9pcWh/phaHoWMZreMCo8cKmR5TOK1OLJuxDG/84A203dCGN37wBpbNWEZ1SABIL23sXACPARgP4GMANQCu5Jx/lGSbIQCGcM63M8YqAGwDcAXnfFdMmxkA7uacf8/ooHOdLKIYxRo3NqL1SCvqBtWheXozqhxVEDhkI+bLN8mnZ4dNi8QQ1mib9YHIzqoT8HbIN4rq2A84K4G/PgvpxG60f3spGv+8JP61LC4IkfQxo6k10VQaKSyPf9Pinj7rm+TxJ0kXSfq+82zyp7SxHHB//5x2n+szL+mmjRXxmReq2d6gGPGNaHSytDGln63PA1PmAQFPj367KoGy/qpkMgmS6Ed72KuvoZE+wwc+RPvwaVgco9FNEY226Gi0WhNFScSiDYtyotWUNmYCEmrTIZvx1/5rT13Pfg5wD0xMG/OeSKh/yTUQvrC/5zO1OCB4j6f8P9E6Llg2YxlCUijlsUJvjilyeDxS8Holek86FfB1AJcBuACy92U3Ulx2xjk/xDnfHvm9C8CnAIZmNtT8kdQolokxM+QFfnsD8FgdsLRKfvzdDcC478E3/S40/nlJ4msxnjzBDNppH9Hxb1oc3+emxSmNbmYyyOm9N4IgiKSko9HJ0iKVfsZ9T36M1e/f3pDYnyDAx3hyDY306RsxHYtVGr04hUartXDRhkU502rSXxOgrs2gV8ec75UvEWOC/Bjya9a/EPLFf6Yhn6H/E63jgs5Ap6Fjhd4cU5jpeIQwH+ko0r9zzk8BqATwTwCeAfCk0Y0ZY8MB1AH4UGP1NMbYDsbYW4yxc3S2v4UxtpUxtvXYsWNpDDt9khrFMjFm6m0zcDSc1SOzbkpz2lzafaa4IRUZ5LJHPuuVILJBydRstszzSj8GQ1QAAxoa6dNV1k+zncvgTQNJq0uoXo1i0Jyf1SAKaNfa0PKhhuqvN3VKNU4kI53Ji3JB7ncBPMU5fxWA3ciGjLFyAGsB3BGZAMWyHcCZnPNJkC9L+4NWH5zzZzjnUzjnU2pqatIYdvooRrFYFKNY1EgXS6z5Xgu9bY7/Db4Tu/VfK9Pxh7zafaaI7Uz6vom0yGe9EkQ2KJmazUSjk/WjhKgY6C+lhkb69AZOabbzGoxWJq0uoXo1imLOj0Ux58ditP4NttOqtYPdBw3VX2/qlGqcSEY6k5eDjLGnAVwN4E3GWJmR7RljNsgTl99wzl9Rr+ecn+Kcd0d+fxOAjTE2MI1xZZ2khkUjJk81WtvMfg7Y9TqcGx9G8wW/yKo50ml1orm+Kb7P+qaUfZJRkyCIoicTjU7Wz67Xge8/bqi/lBoa6dO5dyOaVBrdZECjDb8OUXrY3TrmfFXyltH6N9hOq9b6l/U3VH+9qVOqcSIZ6Rj2XQAuBbCTc747YsafwDn/f0m2YQCeB9DOOde8JwxjbDCAI5xzzhg7D8DLkM/E6A4sH+Y8SQrLpjabC76QVzYsKkbKZCZP3Q7FRFNdyA/YXZBCfvgYz4Y53dj4k22XPaO8WSi4Oc80ZtIiN+xXjP15Wu27Pv2vtNqnGwiQQ6hme4ueRqfS7tj1gW75wDDo6WmveA+SaH5KDY28RtjmgE/0w2VzwRvRaD2zfkavkz+oXnOBFJZrL3rM4AbAE48jtO5ib/QYxWA7rVoDYKj+elOnOarxgtcr0XsM3+eFc+4F8ErM80MADqXY7EIANwDYyRhriyz7NwDDIn08BeBKAPMZYyIAH4A5ySYueUGSIHiPwx1J4XCrUzgUIx1gLMNfCuukhNQATIBgd0H57iRb+eWCYIHbLo9NeTS0XcTMl82xEARB5BUtjU6VQqa1/vuPAzt+J6eNuWp6zNPJXjqVhkbGZgFQHtHm8jQ02vDrEMWLFAY8x1THDCsAq10Oi0iVomf0GMVgO71aM1J/valTqnFCj5x+TcM5/4BzzjjnEznntZGfNznnT0UmLuCcP845P4dzPolzfj7n/H9zOSZDZJIoloygRyclxJPdcRMEQRDapNJ1rfWv/rQnbSxT/SeIdNE8ZrhJvt1Cto5LCKKIKerrgXJGttJqFIymhBAEQRC5IZWuJ0mF7JX+E0S66B0zVJ6ZuIzqkuiDGL5srE+hpHDs29SzTEnhyGTCoaSEqPsLdMu57ARBpCRdDwtBxJFK1/XWK2ljmeo/QaSL3jFDx/74dlSXRB+FJi9aKCkcyrXP0xcD59/aY+I0YtCPRUkJUXte1Ckh2SSTUAGCIIhSxeYCrlklX3pTeaZ8IOiq7ElXUut+rOflyhWy34VL+npKmktkC7sbuHoV4IupVWelbDUfXh/vedFK0dMy+6cRBkEQZocmL1oIgmyCm7sGsDlls/2a61Kb5HRhsnhcswpw9Af8nZGEkByFXqQyphIEQfRFwkHgtYZ4XVSI1f3YtLFp82WPwYtX6+spaS6RVRggadSqa2BPfepOorXM/pGAIJrAECUCqaoeSgpHyCcb5Xpjkgt5gdVzgabhwP+plB9Xz82d0S7bgQMEQRDFjhFdVHSfCfIlvYIFAJMTnpJtR5pLZBPdevL11GdZufbEmAKCiD4ATV5SkQ3zfrYDAMz2egRBEGYnU100sh1pLpFNelNPFBBE9AFo8pIKxcQZi2KSy2cf6ZDv1yMIgjA7meqike1Ic4ls0pt6Usz+6m0D3dkbH0EUGJq8pEIxcQ6vl30qw+v1TXK57CPfYyYIgiglMtVFI9uR5hLZpDf1pAQExW6b64AggsgzZNhPhdrEmUmKTDb6iCBJYfhEH5w2F3whL5xWJwTBEp90E/IaM/YRBEH0FTLVYUGQ9XTOi6r0JkHVRsPsH9J/DYlLspZbndFHgZFGEzBWc7rbWmRzfsK2iWZ93eMJgjA5NHkxgmLiBDK/bjQLfUhSGO3+djRuWozWI62oG1SH5vomVDkqIXhPaCfdMIGudS1V7u9f6BEQRHGRiQ5LEuA9njpJTBDkiYondeqYxCVZyzc29mj59GZUOapoAkMYrzk9BEvPPeR07iWnfzxRRRMYwvSQShYRPtGHxk2LseXwFohcxJbDW9C4aTF8oo+SbgiCIHJBOkliBtv6RB8aNzbGa/nGRlnLCSIP6XVJjycIwuTQ5KWIcNpcaD3SGres9UgrnDY3Jd0QBEHkgnSSnwy2dVqd2lpudWZjxESxk4f0Ov3jCTpuIMwPTV6KCF/Ii7pBdXHL6gbVwRfyUNINQRBELkgn+clgW5/o09Zy+tabAPKSXqd/PEHHDYT5oclLEeG0OtFc34Spg6fCyqyYOngqmuub5G/rKOmGIAgi+6ST/GSwrdPqRPP05ngtn95MZ14ImTyk1yU9niAIk0OG/SJCECyoclThsYtbEtNBspRmRhAEQcSQTkqZwbYCE2Qtv+QxShsjEsliQqn+SyQ5niAIk0OTlyJDECxw2+WUHOUxsqL3iWgEQRBEIunoq8G2AhPgtsn33lAeCSJKHvbpuscTBGFyaPJCECZhwvMT0mq/M0fjIAiCIAiCMCt0jpogCIIgCIIgiKIgp5MXxtgZjLH3GGOfMsY+YYwt1GjDGGMtjLE9jLGPGGPn5nJMGSFJ8l1qeeRRkgo9IoIgCKK3kLYTZoVqkyB0yfWZFxHAXZzzsQDOB/ATxtg4VZvLAIyM/NwC4Mkcjyk9JAnwHgNWzwF+USM/eo+RkBAEQRQzpO2EWaHaJIik5NTzwjk/BOBQ5PcuxtinAIYC2BXT7PsAVnLOOYC/MMYGMMaGRLYtPLF3ugV67nQ7dw0Z44mssvMfBwo9BILoO5C2E2aFapMgkpI3zwtjbDiAOgAfqlYNBfBFzPMvI8vU29/CGNvKGNt67NixXA0zkTzc6ZYoPQpWrwSRIX2uZknbi5qSrleqTYJISl4mL4yxcgBrAdzBOT+lXq2xCU9YwPkznPMpnPMpNTU1uRimNnm40y1RehSsXgkiQ/pczZK2FzUlXa9UmwSRlJxPXhhjNsgTl99wzl/RaPIlgDNinp8O4Ktcj8swebjTLUEQBJFnSNsJs0K1SRBJyannhTHGAKwA8Cnn/BGdZusA/JQxtgbANwB0msbvAuTlTrcEQRSe4T9/I6f97/uv7+a0fyJNSNsJs0K1mXe2bdt2mtVqfQ7AeNBtRMyCBOBjURT/dfLkyUdjV+T6JpUXArgBwE7GWFtk2b8BGAYAnPOnALwJ4DsA9gDwAvhxjseUPnT3eoIgiNKDtJ0wK1SbecVqtT43ePDgsTU1NR2CICRYF4j8I0kSO3bs2LjDhw8/B2BW7Lpcp419AG1PS2wbDuAnuRwHQRAEQRAEQegwniYu5kIQBF5TU9N5+PDh8ep1uT7zQhBEkTLc/2Khh0AQBEEQ+UCgiYv5iHwmCZfx0XV9BEEQBEEQBEEUBTR5IQiCIAiCIIgi4c477/zafffdN6jQ4ygUNHkhCIIgCIIgCKIoKErPy7Zt244zxvYXehwFZiCA44UehInQ+3u8zTm/NN+DiSWNejXZZ/q9TDc02fvImKy+D9ZkuGkx1Wy2MXvtmH18QP7HaMZ6LYbPySil8l7M8j4KXq+Z8Pjjj1e3tLQMYoxh7NixvhEjRgSUdQ8//PDA//mf/6kJhUJs+PDhgZdffvkfFRUV0q9+9avKBx988GuCIPCKiorw1q1b/7Z161bHj3/847NCoRCTJAlr1679+4QJEwLJXtuMMDnsiyg2GGNbOedTCj0Os1AKf49SeA8AvQ8ic8z+Nzf7+IDiGGOuKaW/Qam8F7O/jx07duybNGmSGSZXCWzdutVx5ZVXnr158+bPhgwZIh45csTS1NQ0qLy8PLx06dIjhw8ftgwePDgMAA0NDV8bNGiQuGTJkqOjRo0at379+t1nnXVW6Pjx45aBAweG582bd8b555/vmT9/frvf72eiKKK8vNzUE4EdO3YMnDRp0vDYZXTZGEEQBEEQBEGYkPXr1/e7/PLLO4YMGSICwKBBg8Kx67dt2+acPHny6FGjRo1bu3Zt9SeffOIAgClTpnRfd911wx9++OGBoigCAKZNm+Z5+OGHhyxZsmTw7t277WafuOhBkxeCIAiCIAiCMCGcczDGdCcZt9xyy1mPP/74gc8//3zX4sWLvwoEAgIAvPjiiwceeOCBr7744gt7bW3tOYcPH7bcdttt7a+++uoep9MpXXbZZaPWrVtXkb93kj1o8lK8PFPoAZiMUvh7lMJ7AOh9EJlj9r+52ccHFMcYc00p/Q1K5b2UyvvIO5deeumpdevWVR0+fNgCAEeOHLHErvd6vcKwYcNCgUCArVmzpkpZ/sknn5RdcsklnkcfffSryspKce/evfZdu3bZx44dG7j33nuPfutb3zrZ1tbmzPf7yQZFadgnAM45CUEMpfD3KIX3AND7IDLH7H9zs48PKI4x5ppS+huUynsplfdRCKZMmeK/6667DtXX148RBIGPHz/ee+aZZwaV9T//+c+/Ou+888YOHTo0OHbsWG93d7cFABYtWnT6vn37yjjn7KKLLjp1/vnn+5YsWTL4pZdeqrZarbympib04IMPflW4d5Y5ZNgnCIIgCIIg+ixmNuz3dciwTxAEQRAEQRBE0UKTF4IgCIIgCIIgigKavBAEQRAEQRAEURTQ5IUgCIIgCIIgiKKAJi8EQRAEQRAEQRQFNHkhCIIgCIIgCKIooMkLQRAEQRAEQRQQl8tVp7eurq5uTK5e9+c///ngXPWdK2jyQhAEQRAEQRAGkSRe1R0QJ0icT+4OiBMkiVel3ip9RFEEALS2tn6Wi/4BoKWlZUiu+s4VNHkhCIIgCIIgCANIEq864QmcefPzW+2jlryFm5/faj/hCZyZrQnM66+/XvGNb3xj1OWXX37W6NGjzwF6zsrs37/fNmXKlNFjxowZN3LkyHPefvvtcvX2W7dudUyYMGHsmDFjxo0aNWrczp07ywDgiSeeqFKWX3vttWeKoojbb799aCAQEMaMGTNu1qxZZwHA/fffP2jkyJHnjBw58pylS5eeBgCnTp0SZsyYcfbo0aPHjRw58pxnn322EgDuvvvuIePHjx87cuTIc+bOnXumJEnZ+BOkxJqXVyEIgiAIgiCIIscbCg9tWN0mbN57AgCwee8JNKxuE56dN2VoeZm1PRuv8dFHH7lbW1s/GTNmTDB2+a9+9auqmTNndjY1NR0WRRFdXV0JJyEee+yxmttvv/3I/Pnz2/1+PxNFEdu3b3e8/PLLVVu3bv2srKyMX3/99cOeeuqp6ieeeOLgr3/969M+++yzXQCwadMm14svvli9bdu2TznnmDx58tiZM2d27d69u2zw4MGhDRs27AGAEydOWADgZz/72dGHHnroEABcccUVZ61Zs6b/tdde25mNv0EyaPJCEARBEARBEAZw2S32Lfvi5yhb9rXDZbfYs/UaEydO9KgnLgBw/vnne2699dbhoVBIuPLKKzsuuOACn7rNtGnTPA899NCQL7/80j5nzpyOCRMmBN5+++2Kjz/+2DVp0qSxAOD3+4XTTjtNVG+7YcOG8u985zsn+/XrJwHAd7/73Y733nuvYtasWZ1Lliw5Y/78+UO///3vd1566aXdAPDWW29VPPLII4P9fr9w8uRJ67hx43wAcj55ocvGCIIgCIIgCMIA3mA4OHV4/BViU4dXwRsMJ0w2MsXlcmlef3XZZZd1b9y48W9Dhw4N/uhHPzrr8ccfr165cuWAMWPGjBszZsy4jRs3um677bb2V199dY/T6ZQuu+yyUevWravgnLOrrrrqxGeffbbrs88+27Vv376PH3nkka/U/XPONcczceLEwPbt23dNmDDBt2TJkqF33333EK/Xy+66664zX3nllb9//vnnu66//vrjfr8/L/MKmrwQBEEQBEEQhAFcNsvBlrm10rQR1bAKDNNGVKNlbq3kslkO5vq1P//8c/vQoUNDd9111/Hrr7/++Pbt21033njjSWVSMn36dO+uXbvsY8eODdx7771Hv/Wtb51sa2tzXnrppadef/31yoMHD1oB4MiRI5bPP//cDgBWq5UHAgEGAJdcckn3m2++OaCrq0s4deqU8Oabb1ZefPHFXfv27bNVVFRIt99+e/sdd9xxpK2tzeX1egUAGDx4sNjZ2Sm89tprlbl+/wp02RhBEARBEARBGEAQWHu1uwzPzpsy1GW32L3BcNBlsxwUBJYVv0sy1q9fX9HS0jLYarVyl8sV/s1vfvMPdZtVq1ZVvfTSS9VWq5XX1NSEHnzwwa8GDRoUvvfeew/OnDlzlCRJsNlsvKWl5cCoUaOC11133bGxY8eOGz9+vHfdunX/uPbaa0+ce+65YwHghhtuOHbhhRf61q5d2++ee+45XRAEWK1W/sQTT+wfOHBg+Lrrrjs2bty4c04//fTgpEmTPLl+/wpM7xSRmbn00kv522+/XehhEMUBK/QAqF6JNKGaJYoJqleimNCs1x07duybNGnS8XwPhkjNjh07Bk6aNGl47LKivGzs+HGqL6J4oHolig2qWaKYoHoliL5FUU5eCIIgCIIgCILoe9DkhSAIgiAIgiCIosAUkxfG2GjGWFvMzynG2B2FHhdBEARBEARBEObBFGljnPO/AagFAMaYBcBBAL8v6KAIgiAIgiAIgjAVpjjzomImgL9zzvcXeiAEQRAEQRAEQZgHM05e5gBYXehBEIVF4hI8IU/cI5Fd6G9MEARBmJW+to9yuVx1euvq6urG5HMsWnzzm988+/jx45Z0t7vzzju/dt999w3K5lhMNXlhjNkBzALwksa6WxhjWxljW48dO5b/wRF5Q+IS2v3tWPDuAkxeNRkL3l2Adn97UQmX2eu1FP7GRHYxe80SRCxUr6WN6fdRklSFQNcEcGkyAl0TIElVuXgZURQBAK2trZ/lon81oVBId93777+/Z+DAgeFCjkHBVJMXAJcB2M45P6JewTl/hnM+hXM+paampgBDI/KFT/ShcWMjthzeApGL2HJ4Cxo3NsIn+go9NMOYvV5L4W9MZBez1yxBxEL1WtqYeh8lSVXwHjsTq+fa8YsaYPVcO7zHzszWBOb111+v+MY3vjHq8ssvP2v06NHnAD1nZfbv32+bMmXK6DFjxowbOXLkOW+//Xa5evuJEyeO2bp1q0N5ft55543etGmT69SpU8JVV101fPz48WPHjh077oUXXhgAAC0tLdWXXXbZiEsuueTs+vr6UXqvMXTo0AmHDv1/9u49Porq7h/45+wtu5uES5CLQhFQAVFuEsCgoGj7VItaBapiAe1PwWol3vpAtZamVFGwFacTZVwAACAASURBVA1qi0r7CCraAvXGoz6tgFBFIdxEARERqQpySYBc9r7n98dkNjs7s8kku5vdzX7erxevkNm5JTlzZuac8/2egzYAePLJJzv17dt3QL9+/QZcffXVvQFgz549jpKSkr59+/YdUFJS0vfzzz93xJ7bBx984Bo8eHD/vn37DvjBD35wxpEjR6zqOd5xxx3dhw8f3u/BBx9sspcm015eJoFDxnKey+bC1u+2apZt/W4rXDZXms6o7eHvmIiIMlVG36MCtd2x/GYL9q8HwkFg/3pg+c0WBGq7J+sQH3/8cf6jjz76zRdffPFp9PK//OUvRZdeeumJ3bt379y1a9enI0eOrIvddsKECZUvvvhiEaC87Bw+fNg+evTouvvvv//UsWPHnvzkk092rV+//rMHHnigx8mTJy0AsGXLloJly5Z9+eGHH+5p6hgVFRXOP/zhD6e+9957ez777LOdixYtOgAAP//5z3vecMMNx/bs2bPzuuuuO3bbbbd9L/bcbrrppt5z5879es+ePTvPOeccz6xZs05TPzt+/Lh106ZNn/3ud7/TdWDEypiXFyGEG8APAKxM97m0hlwby9kcnqAHQ7tqh34O7To0M1pc2ojm/I7bSlltKz8HEVG2MFvvxq6X0c8BjnwHDmzQLjuwQVmeJIMGDart37+/P3b5+eefX7ts2bJT7rnnntM2btzo6tixo+4XOnXq1KrXX3+9IwAsWbKk45VXXlkFAGvXrm23YMGCU/v37z/gwgsv7Ofz+cTevXsdADB69OiTXbt2DZk5xjvvvNPuyiuvrDr11FODAKBut3Xr1vzp06dXAsBtt91WuXnzZk2v0LFjx6zV1dXWcePG1QDAtGnTjn344YeRdSZNmlRp9veTMS8vUso6KWUnKeWJdJ9LqmX8WM40c1qdmDd6HoZ3Gw6bsGF4t+GYN3oenFZn0xuTKS6bC/PHzNf8juePma9r1WorZbWt/BxERNnCbL1rtF4wHDR1j0oLf60fPUu0y3qWKMuTxO12G96cLr/88pp169Z91r17d/9NN93U+8knn+y0ZMmSDv379x/Qv3//AevWrXP37t070KFDh+BHH33kWrlyZdGUKVMqAUBKieXLl+/dvXv3zt27d+88ePDgjvPOO88bezyjY0Sfg5QSQgiZrJ9VVVhYaPqGnDEvL7kko8dyppHa4uINebHi8xW4b8R9qJhcgftG3IcVn6+AN+RN9ym2GRZhQZGzCAsvWYjNUzZj4SULUeQsgkVoq4TmlNVM7tngNUdE1Loaq3dje1li17t77d3It+c3eY9KC3v+N5i4OIxeowGLDeg1Gpi4OAx7/jepPvSePXsc3bt3D9x7771HJ0+efHTLli3uqVOnHldfSMaMGVMHABMnTqycO3dut+rqauuIESM8ADB27NiTf/zjH7uGw8q9+f333zd8EzQ6RvTnl1122cnXX3+96NChQ1YA+O6776wAMHTo0NrnnnuuIwAsWrSoqLi4uCZ6u06dOoXatWsXUmNoFi9e3KmkpESzjlkZMUllrsnosZxpora8zFw3E8/+4Fks2r4IT217KvK5TdgwfdD0NJ5h22MRFuTb8wEg8jWW2bIa/ffb+t1WDO06FPPHzM+Ymw2vOSKi1tVYvXvL/90SuVc891/PGa6XZ82L3D/i3aPSwmKphLszMGlZdzjyHfDX+mHP/wYWi+lhTy31zjvvFJaXl3ez2WzS7XaHXnzxxS+N1ps8eXLVb37zm5533nnnt+qyRx555Nvp06f37N+//wAppejRo4dvzZo1e5t7jOLiYu+99957cPTo0f0tFos899xz61asWLH/T3/604Ebb7yx1xNPPNGtU6dOwSVLluyP3fdf//rXL2+77bbTS0tLLT179vQtW7ZMt44ZQsqk9/ykXHFxsayoqEj3abRYbaAWM1bPwKZDmyLLhncbjoWXLMysC7QVRf9OVl61Eg9vfDhZvx+R1BNtgWwur2bLaqaX6Uw/vxhZUWZ7/WpVs/a5/5FxiZwSZa6sKK/U+uLVu2UlZRj3j4b6YNU1q1C2oay16mfD8rp9+/b9gwcPPprsg1Hitm/ffsrgwYN7RS9Lf5NoDjIbb5BLoltont3xLOaMmsPfTwYwW1YzvWeD1xwRUeuKV+8+ve1pzXpPb3ua9TM1C4eNpUF0vIHL5oIn6IHL5sqI4TXpomYW2XRoE9768i0AQFlJGXoU9uDvJ43MltXov59KzQyTCT0bvOaIiFqXUb1rERYc9hzWrHfYczgS38L6mcxgyUgTNd4g+msui22hOeo5CrddiRGL/f1kcmB4W2SmrLJng4iImuKwOAzvFXnWPD4TkWnseaGMYLZlPNMDw3NVpvdssNwQEbWuePVux7yOGXuvoOzA0kIp1ZxeEjMt/Ex5mxmM/q6Z3JvIckNElDxm7u3x6l1vyJux9wrKDiwxlDKpmBgw0wPDc0E2TvjIckNElBxm7wGsdylV+PJCKZOK1m41MDyaGhhOrSMbezFYboiIksPsPYD1bvO43e6h8T4bOnRo/0T3/+KLL7a///77uzV3OzPHvu66607fvHmzs2Vn1nx8eaGUSUWrCwPD0y8bW9NYboiIksPsPaAt17thGS6qDdQODMvwsPqvRak4TjAYBABs3bp1d6L7+ulPf3pi7ty5h2KXBwKBRrczc+xXXnnlq2HDhnkTOL1m4csLpUwqWl2iA8M3T9mMhZcsZNB1K8vG1jSWGyKi5DB7D2ir9W5YhosqvZWnz1g9w1E/bM5R6a08PVkvMG+++WbhyJEj+1555ZW9+/Xrdw7Q0Cvz1Vdf2YuLi/v1799/wFlnnXXO22+/XRC7/aBBg/pXVFREekFGjBjRb/369e7y8vJOU6dO7QkAEyZM6HXLLbf0GDlyZN/bb7+9x7fffmsbNWrUWQMGDDj7hhtuOP20004bePDgQVv0sd98883CESNG9Lvsssv69O7d+5yrrrqqdzgcjhxj3bp1bgBYvnx5uwEDBpzdr1+/ASUlJX0BYM2aNe6hQ4f2P/vsswcMHTq0//bt2/MS+R1ldwmijKQG8LlsLjw+9nH8YsgvIq0uCy5eoFknXpxEY8GAmRwY3hbF/i2cVqdha5rT6kxLCmuzSSFYboiIEhevR8XoHpBIvWu2bm/t6RM8QU/3metmWmKGzVk8QU/3ZB3j448/zn/00Ue/+eKLLz6NXv6Xv/yl6NJLLz2xe/funbt27fp05MiRdbHbTpgwofLFF18sApSXncOHD9tHjx6tW++LL75wvv/++3ueffbZr3/1q1+ddtFFF1Xv3Llz1/jx46sOHjzoMDqvXbt2uZ566qn/7N2799MDBw7k/fOf/9S8PH377be2O+64o9fKlSu/+Oyzz3a++uqrXwDA4MGDvRs3bty9a9eunb/97W+/mTlzZo9Efj9MlUxJZZgacfR8TBs4Df6wH7WBWty99u5G09UyrW3mMJvq0ml1ospX1ep/M5YVIqLWZZQaP9n3ALN1ezruAS6byxFn2JzhA39LDBo0qLZ///7+2OXnn39+7a233torEAhYJk6cWDVq1CjdkIepU6dWff/73++7YMGCb5csWdLxyiuvrDI6xvjx46tsNuU1YOPGjQWvvvrqXgCYOHHiyXbt2oWMthk4cGDtGWecEQCAc845p+6LL77Q/Mxr167NHzFiRLV67l27dg0BQGVlpfW6667rvX//fqcQQgYCAdGsX0iMjLi7CyE6CCGWCyF2CyF2CSFK0n1O2S5dEzl6gh4s37Mc9424DxWTK3DfiPuw/PPl8Ia8CMuw6SC/bAsIb6vMprr0hrym/2ZGZbOl5ZVlhYgo/ZpzDzCjOUkBWvse4Al6/HGGzeleNlrK7XYb3gQvv/zymnXr1n3WvXt3/0033dT7ySef7LRkyZIO/fv3H9C/f/8B69atc/fu3TvQoUOH4EcffeRauXJl0ZQpUyqN9lVQUBA5hpTS1Hnl5eVFVrRarQgGg5qXECklhNC/l8yaNav7RRddVP35559/+sYbb+z1+/0JvX9kxMsLgCcAvC2l7A9gMIBdaT6frJbOVLZOqxNX9LkCD298GMUvFOPhjQ/jij5XwGl1NivIL9sCwtuqZP/N4pXNan91i8orywoRUesyqseTXRdn8vOCy+b6Zv6Y+eGYYXNhl831TcoOWm/Pnj2O7t27B+69996jkydPPrplyxb31KlTj+/evXvn7t27d44ZM6YOACZOnFg5d+7cbtXV1dYRI0Y0+SY3YsSImqVLlxYBwMqVK9udPHnS2pLzGzt2bO1HH31UuHv3bgcAfPfdd1YAOHnypLVHjx5+AFi0aNEpLdl3tLS/vAgh2gEYA2AxAEgp/VLK4+k9q+yWrJaIlow39QQ9mP3BbM2xZ38wG56gx3SQXzYGhLdVyf6bGfbM7VmOE74TLSqvLCtERK3L6Bnj6+qvTdfFZie4vHXwrVh51Upsm7INK69aiVsH36rbX12gzvC4dQFdiEfSWISlsshZ9NXCSxb66xMR+IucRV9ZhMWwhyOZ3nnnncIBAwacc/bZZw947bXXOs6cOfM7o/UmT55ctWrVqqIf//jHps7pkUce+Xb16tXtBgwYcPaqVavad+7cOdChQwfDoWONOe2004Ll5eX7r7nmmjP79es34JprrukDALNmzTpUVlbW47zzzusfCjV7tzrCbFdRqgghhgB4BsBOKL0umwHcKaWsjVlvOoDpANCzZ89hX331VWufatYIyzCGLR2GoAxGltmEDZunbDY9BrSl4003T9kc99gA0jGGNaFxlS3VVsqr2b9FKBxCpbcSs9bPiqw3b/Q8FDmLYLVYNesdrD2I2R/Mjqw3Z9QcdMvvhqFLG25AZstrG415yYoy2+tXq5q1//2PjGvxuVFGy4rySslj9Iwxrvc4/HL4L5N2f0/0nnJq/qma9aIYltft27fvHzx48NHEfzvZyePxCJvNJu12O/71r3/l33HHHafv3r17Z7rPCwC2b99+yuDBg3tFL8uEl5diAB8CuEBK+ZEQ4gkAJ6WUv4m3TXFxsayoqGi1c8w2tYFazFg9A5sObYosG95tOBZeshD59vxID4kaaOeyuSKVhvoZgEb3Ee9YK69aiXcPvItLe16KPu37YN+JfXj3wLuYMmBKk8eOZnY9E9JyY42WCeU1kd+nmW1rA7XYW7UXfTr0Qb49H7WBWuw7vg9ndjxTU15q/DUoXVOqK1cLLl6AC1++ULMstqyl4mfLUFlRZvnyQvWyorySOWbre6Png6cufQphGU7KtoD5Z5ClO5fGfeYwwJcXAzt27Mi79tprzwiHw7Db7fKpp5766qKLLkpd91UzGL28ZMId/msAX0spP6r/fjmA89J4PlmvsYmhGouHif7MaXW2aLzpxoMbMeGsCZqYlwlnTYDTqqQcN5s2kWltkyfRGCgzf4s8ax5OKzgNd665E8OWDsOda+7EaQWnIc+qTeXutrsNy1Who7DFE5mxrBARJc7svcJpdWLe6HmaOnve6HlwWBxN1sVGMSpdXF0iLzXNjaFx2VyY2Hei5pljYt+JjHtspoEDB/p27dq187PPPtv5ySef7MqUF5d40n6Xl1IeAvAfIUS/+kWXQhlCRi3U2MRQjcXDeIIebP1uKx67+DEIIfDa1a/h8t6XR/arjl+NHqMaG3Mw4tQRmLV+lmb/s9bPgjfUahOvUozWyMbiDXqN/+5B7d89XoyKL+RD+dhybJ6yGeVjy9EhrwNfQoiIWpHZe4U35MWKz1doYhdXfL7C1H3eKJbl3uJ7WxxDYxEWdMzrqLl/dMzr2JL7RzgcDqe9F5G06v8mupbWTHk6mAHgRSHExwCGAJib5vPJevFaoxtrzciz5mFIlyG4Z+09GLZ0GMo2lOGu8+7CuN7jMLzbcDx4wYP4/Ybfa1pjYnt5+nTow+xPGaY1srHE61Fx292aZUYtdgsuWoBafy1K15Ri2NJhKF1TiipvFULhxIP6iIjInOb0dizavgjjXx+PIUuHYPzr47Fo+yJT9xSn1akbnSGEQBdXF816//7634a9O+ooDlVYhlHlq9LeP3xVLcmu+smRI0fa8wUmc4TDYXHkyJH2AD6J/SwjJqmUUm4DUJzu88gFast39DhStTVDShlpPQeATYc24YH3H8DCSxbimOcYHt/yON768i0AwMx1MyNjT6Mnq1Izfxjt30z8AiVfY3/zZP1N4v3d6wJ1KHA0TMAb3WKnjk8WFoGZ783UlLtZ62ehfGy5ZlsiIkods/eKRO4p3pBX95wxc91MlJWUYdWXDXF0F/a4UHevWPH5CiWWxaI9F7XXJnp/ZmMmVcFg8JZDhw49d+jQoXOROQ37uS4M4JNgMHhL7AcZ8fJCLdNYYF0oHIIn6IHb7kZdoA4WYYHT5oSUEuVjy1G6plST6UNtMYnX6vLjV3+sySwS3Rqj9u4ASgv8/DHzdZlE2POSPmrvWOzfxGl1ojZQ22Sge2xZctlcEEJoyp7TpvSoxGaGcdq0x3BanVi0fRGe2vZUZP/bp26P22tj5vyIiKhxZgLx490rjHpeWnqfd9lc6OLqgpVXrYy8lCzesRjdC7pj1TWr0L2gO76p+QbdC7rr7hU2YcMtA29Bjb8mcj+K1+vf3GeOYcOGHQZwVbM2orRJ+suLEKIDgKkAekXvX0pZmuxj5bLG0g1KKXUpBh+84EGUbyjHYc9hzBs9D09f+jQcVoemEqvx18RtPTfbyhIdb8OHzsxg9DdxWp2o8lW1OF2ly+bSvQB3dCrjjtWbitPmxHHfce0xRs/HrYNv1dyQqv3VhuWrJlCDu9bc1ZZSIBMRtTqz6Ymbc/+2W+woKymLvGzYLXZT5+IL+VB6XikeeP+ByLk8OuZRVPmqULahrNF7xfwx81HlrdLcjx4f+zhHe+SgVDwF/C+UF5cdUOZsUf9REjUVeB8dPH2K6xQEw0HMHT03ElgXDAd1+7RarHjwggc1Y0wfvOBBWCwWzB9tnL3MCLM/ZZ7Yv4k35DUVmBlbltQhXSEZ0m0bW6Z8IZ/+GOtn4oazb9CUJauw6sY2zx89Hy/teimlSQaIiHJBc5K2mLl/e4IebDy4Ee3y2kEIgXZ57bDx4EZT9XNYhvHA+w9ozqU2UGvqXnH+aefr7kcv7XqpWc8n1DakYtiYU0p5Twr2S1GaCqxTP7u89+UoHVqqm8DJZXNh2NJhmhaYPGseyreUa8aYlm8px0MXPoQlny/B42MfR4G9gL0pbYDZwMzGUhtH+/73vo9qf7WmRey5/3rOcNsCe4GuZc9lc2l6bdSA0KbOj4iIGpfspC3RyX2ie+RjU+ObPZfuBd1N3yti11u0fRGmDZzG0R45JhV/3aVCiGlCiFOFEEXqvxQcJ6fFppy9vPfleO3q1wAowdO3Dr4VADBt4DTM/mC2pqVi9gezUResM+yxOew5rMkgcthzGPtO7MO+E/tw3Hs8LT8rJV+8lMWxLWfqkMHY9ar91Zpl484Yp2sRU4eDxW5bF6jTtexZLVYUOApgERYUOArgDXl16TRvHXwre16IiJrJbH1vltnU+EbU55Pour3SWxk3hX7sceP9HBztkVtS0fPiB/AogF8DkPXLJIA+KThWzooOmOvi6qIbQzpv9DwAQJ/2xqmLo8eCRrfAxAbhzRk1B+/95z1d7w3jD7JbcwIzjQLxrcKK4d2GR5YVOgp15Szfnh83iL8pajpN3bbWprclygQDnx/YrPV33LgjRWdCuS6RAHsjZlPjG3HajOv2BRcvwN1r744sW3DxgshwsuhzXnDRAtz93t2abdkjn3uElLLptZqzQyG+ADBSSnk0qTuOUlxcLCsqKlK1+6yhZg8BgBmrZ2gC1oZ3G47yseUQQhh+dt+I+zD+9fGR79W0gtEZSb6u/hpPbnsS0wZOw8MbH9bto7mpCNMk7TnbM7W8GmWfAaBbJqVsMtuYlBKla0o1ZeTf1/8by3Yvw6U9L40MQ3z3wLuYfPZkCCEa7eJXZ1vO0jKXqKwos71+tarRz2Ptf2RcIqeUdXLo5SUrymuuM5NtzKwaf42uvo9+5mjsGGa3BYyfaxZeshBSSs39yGqxNuf0015eKXGpaDb/FEBdCvZLMdQu0njjWd12N/KseYYTPb174F3D4LbYtMdHPUfj9t6wtSO7xQZmAkCltxIzVs/AsKXDIpORCiE0Q7qsFqtuW7WHJrqcFToKDScyc9vdumPETijWGhNrEhHlimQm0jGq79XRHk3V7Y312sTeU+LdA2LvR5R7UjFsLARgmxBiDYDIgEWmSk6dpiaejJ3o6duabzH57MmYPmh6pOVCrciiW2dcNheeuvQpeENepiLMAYlM9mW1WHWpkv0hv2G5+br66yaP0RoTaxIRUfNZLVYUOYs09b3NYsPt796uq9ufuvQphGU40qNiERZTdbvZiY8pN6Wi5+VVAA8B+ABMldwq1PGsRqkC3Xa3pvX72R3PopOrE0rXlGLY0mEoXVOKKl8VwjIcyQUf3XJSG6iF0+qMu39qOxLp7QiFQ6jyVmnKVbW/GuVjy3Xl5ultTzd5jMbKNBERpVdskhWH1aG7f3RxdYkMAY5+plhw8YIm63Z14uPY3h0zMZPU9qWi52U5AK+UMgQAQggrgKbz51GLNTaxVOzEk9HZxwBtyzeAuC3vnHiy7UuktyN6PhgAkewz5WPLNeXGIiw47Dms2dboGJzslIgoexjdP24fcnvcZ4qm6nZv0KsbNbLi8xWYfPZk9rxQSl5e3gXwfQA19d+7APwfgFEpOFabFAwH4Q164ba74Q16EUYYbptbucitTlgCHsDhBvx1gN0NWCyauAU18L42UAu33Y3Hxz6Ol3a9hEXbF6FPh8bjVxhnkLtcNhcWXLwAJ3wnIrMmt89rjzxrHmr8NVEBkk5YLdqqw213o4urC1ZetTJyo1m8YzHcdrcmHafD4jCd9Sa2TBMRUfIYBvFLAIE63TNGU9s6rU7d/aNHYY+4zxTqy0q8ul0dNfLUtqciy2zChumDpqM2UMtGrRyXir+4U0qpvrig/v9N588jAMqLizr85v719+O47zhKV5dqA+A+fBr4fWdg2fVA3REgrA2Iix3+ddeauzDxrImomFwBTyB+vvfGcsEbBXLHBuJRdpNSIhAKoGxDGYpfKEbZhjIEQgHU+ms1w8EqvVUIhYOabb1BL0rPK8XDGx9G8QvFeHjjwyg9rxTeoFdTbqp8VXBanSgrKUPF5AqUlZTBbrGn6ScmIspNRsPEK72VCPtOKM8WzXjGUOt2SGjuHzWBmhbPLxPveSR2GBqfRXJTKl5eaoUQ56nfCCGGAWiypAoh9gshdgghtgkhcirnodpLEpZhzeRPNw+8GQ+8/4BmIqiZ62ehbuR0hB84jNpxf0B48/NKK0kUT9CD5XuW474R96FicgXuG3Efln++HN6QF1aLFQ9e8KBmHOmDFzwIi7DEjTOwCEuk6zd2YktqPdHlRP1qVigcQo2/BmEZRo2/BqFwSLeOJ+jBzPUxf+f1M3HCf0I3GZknZjIyCakrqw+8/wDqgnW6clPprcS4f4zDkKVDMO4f43D32rtZloiIWlF0ghbNfd17HNi/HggHla/LbwYCtYCvBpBhwFcTd9vYe8VLu17Sxa3MvXCuqZ4Si7Dg0TGPYtU1q7BtyjasumYVHh3zKLxBL59FKCXDxu4C8HchxLf1358K4DqT245N5fwwmUhtwVCH0WyesjnSzRo3RXFeIYYtLVaG3JSUocju0ryFOq1OXNHnCs2kknNGzYHT6oQQAuVbyjXjSMu3lGPu6LmwSKAIViwc8Ru42veE58QBuGAFrHkcTpZmseWkOROFhsIhVHordZOCFTmLNGkm46Ww7F7QXbcsdjKyeMH+HfM6mtofyxIRUeuJm6ClfU/tigc2AI584PmrlP/3LIHrxtdN3SsWbV+EaQOnoaykLDKUzCZscFgcTZ6fw+LA8dBxlG0oi9y3HrrgIXR2d9afM+8fOSfpPS9Syk0A+gO4DcDtAM6WUkayjQkhfpDsY2YjtfUcUFICnuI6BUEZxEn/yUhX6b4T+wy7Tfcd34egDOIU1ynKhDpCaFriPUFPJChfbZ2Y/cHsyNCww57Dmrk3DnsOKy0XgTpYXpmC/CeGwDKnSPn6yhR46lMWxp4HWztaT9xWMpPd72pvnrb3RLttXZy/8zc13+iW1cX29jVjW6NlLEtERK0nXn1fF6jRrtizBKjcr+mN8Rz/ynR9f8RzBL6QMmuGL+TD8s+Xm75v/fr9X2vuW79+/9eR56boY/D+kXtSEuUkpQxIKT+RUu6QUgZiPp4XbzMA/yeE2CyEmB77oRBiuhCiQghRceTIkaSfc2uKHS9atqEMpUNLcXnvy7Hqi1WRbtbFOxbrhnjNGTUHz+54Fpf3vhylQ0tRtqFMN/azsUmgGk1B63ArLSvRDmxQtonp+p0/eh5cVqYsjCfZ5TWRNMaNlYdoRqkp54+ej/aO9k2mqzTedh7a57VvehlTIGeEtlTHUtvH8poYl82FOaPm6J4vXPYCoNdowGJTvk5cDKydq9k27+gXpu4V88fMh03YNLGQV/S5IqH7VqGjkPcPgpBStu4BhdgqpRxqsPw0KeW3QoguAP4JYIaUcp3RPoqLi2VFRfaGxagBZ9EpBYd3G477RtyH8a+Px2MXPYaS00oaso3Vv5DUBeqw4dsN6NW+F87ocAa+rv4aT257Em99+VZkHwsvWQgpJUrXlOr2Xz62HAWOAuMMI8KijGlddr3SwqLqNRqYtAxhXy08YT9c7brDc+xzuHa+Dsv5twN5GZ+yUKT7BJJRXuOVGTMTSNb4axotD9HHWLpzKS7teWlkSOG7B97FpH6TIIVEO0c7nPSfxKaDmzCq+yjNcWv9Nfjg2w0YfupwzXoXnlaC8Imv4ep0VqTcYFQpPDIIl90NjzpJKmdJjpYV5dE0zwAAIABJREFUZbbXr1Y1a5/7HxmXyCml3cDnB6Z0/ztu3JHS/adQVpTXnBcOa7KI1Qpg6a4XdPX9lLMnI1+iIduYsAAvXat5Lqi57wA2HNyoq+8v6H6B5nnFIiy4Y/UdTd57jDR2zwOQSLaxtJdXSlw68ssZvi1JKb+t/3oYwD8AjGjNk2pN8VrR+7Tvg+HdhmNo16Fw292wSMDtq0bBS9fD8vvOcO1djSFdhuDhjQ/remzUfTitTryw6wVdq8icUXMirRNqCtrorwCUlIgTF2tbXa5+GnjzblhW3oJ8GYZl5TTkPzUSlvfmKZUbtYpEJm10xZnsyxXTe+KyuTQTmo5/fTz2n9iPgAzgnrX3YNjSYbhn7T04u9PZcFpje15cOLvT2br1HDanUl7mFClfj+6BxXcC+fVlOv+l62GpO6rLZkNEREkSDitZw6KyiLlCAUzsc5WmV2Ri7x8p95S8AuWlJa8AsDl1zwUue4FhfZ9nzdNMXOm0OU31+htp7J5n+PxCOSUVAfvNJoTIB2CRUlbX//+/AMxJ82mlTLzJAL0hb2TiJouwAP4aJdNHfYuHt3Nf3USAsz+YjftG3Ie3vnxLiYc5sS+SF/2xix9Dob0Q+07sw5v73lRaVBpr7bBYAPcpwPUvKZWW9wSw/RVgx9+Vz1+7A/jRfOCTFco4WF+Nsl4jueApORKZtNEa8KJo/waUX/QY3HntUOc7Cde+dbCecYmm58zsJGOzP5iN8rFPoECKSOucR0A3+ensD2aj/OLHUXD7BuCUfsDRzwBnkaZMR7LZTFoGQDQ5twAREUWJ6VExrDsDdbp61/LKFBRduxQLL3oMrrx28PhOwvXFOljO6KC8uETvz90ZmPRyQ30fFVcLRN8XtD0qiUx8zImKqTHpeHnZb7CsK4B/CCEA5ZxeklK+3Zon1ZrUFgWjifo0F2ZMDIqr01mN9tjMGTUH5VvLAdRn+Rg0DcVqVjKDlnadcBioO6pUcvVZRfDjJ4GvNyovLAfqH0J7jQYmPAd8+Gdg3TxlvYmLlQqOD5wp0+JJG+1uWHuORMHLk4EDG1Cg/r0MMobFlst4k4y57drsM+442WfcjgLgf2c2lKeJi4HCbtrzM8hmw/JEmWbHlweatf7A3j2bXokoEWqPSvQ926juNIpnLewGS9iP/L8p2+b3LFHu60IoPTSx+1MbuvIK4JZhUz0qjT3rmMGJiimelLy8CCFGAegVvX8p5ZL6r+Nj15dS7gMwOBXnkolMtyj465TKo761xHPs87g9NmUlZSjfWh6JfxnadSg8gTpsnrK5oUXlzEsaj1ExaJ3R9bYE6pSemQ//DKx9qGG95TcrLTOZHwOTeywWXcuZUeucUblUs4jpWs4CtciPKidq9hndese/0qyH5TcDV5Y39OYB2mw20euxPBERxWd0zzaqO301mmcJAMDF9+u3XXGLUj83sb+6OPeFukCdpueFvSeUKkkvQUKIpQD+AOBCAMPr/xUn+zjZLm7cSbSYGBTXztcNsn49Aqc1D26bE0c9RxuWl5TB/fqdsPyuI/IfOR2WFT9riFEJhxsmnPLXAb5q5f9xso1FelsmLgbs+Uoltm6efj3GwGQui0U7jjlOj0ZsuXTZnMaZ5mJazlyr5xqPT16tzVKDAxuAol5NZrNheSIiakK8e7bDrZlUEo58pZ6dsRWYXal87Xi68bYdTzfeXxSzcZSAyWcdomZKRc9LMYABsrXTmLVFMS3mFn8diuyuhlYMfzVcHy6C5b15KLpoFhZevAAuR6GSwWnD07B8sqJhXz1LGlrc1W7mwm7ApbOBV29XKqhfbNK3zqi9LZNebmitN2rFUffPlvI2xRLwoOjLDfpx0d3O1a7X6SwUyZgJTqUVlk5naXfYswQ4eVDpzVPjYMISqD6kX4/liYgovpjRGQDq41GrgZd/2jD0a9LLQCgAvFHasGzCc8CYWQ0jKNRtq77SHsOgLrZabChydkT52PJIZjGXzQmrJSPCqCkHpKKkfQKgG4CDKdh3q4mbTrhZO2kkkE7zWS0grIDdqV9PbTEHgLwCWMIh5IdCQLgW+ct+Gqm0LGseQv6X64BJy5APACOmAV+ui6qoFgN2lzYJwO0blBcXteJb86CSXUx9mYnERuRrW+rVHqHYcbYmMohQAswEZsbbtKXlWVhh6XEe8uvjZfLVsmRzNrTeVX0F5HeC5eWfRoaI5QNKr8r1LwL71+nLSVCZtAxWJ5CXH6c8uepbDRnET0SkY3QvnrAY+HCRduiX9wTwj5/rh4hdF1M/T3hOud9H1+3ujgjbXfAEajX3D6vFFhki1lTaY6JkS9rLixDiDShpkAsB7BRCbATgUz+XUl6VrGOlmjqJZGyQWZGzyPwLTGOBdID+s6ufBt6do7RAxwtWDoeA2iNKpTP1NeMuX7sbWPJjZR83/E15IfJVK5XZunnAA4cbtjuln3Yfn6xQhhVNWqZ0M8d7YDQZQ0FJZDYw02jTRMqzLQ+w2pVx0FEvKjh5UNuKd+PrxuUxrzCmnLiUpBDR205crGS5M1qvBT8vEVFOMLwXu/TDugu7GdfPzkJt3W7PB4J1mvo5fN1SVPqqEnseIkqyZJa8PwD4I4AyAFcDmFv/vfova3iCnkh62KAMYtOhTZi5biY8QY/5nUQH0oWDQEEXwFerTI/krwEqnm/4bP96pbdj9D0NAXKBOv0+/bXKi8v+9cpwm54l2s97lijL969X9h8OKstrjwFHdyvfR2939DOl2/j2Dco42Ns3AP2vAsyM+DMZQ0FJElue1HIS9GrHNhvMl9JoeW5iW/hrleMsHArMKVK++qqBV2/Tnov3hHF59NVolwV9xj9HwKMtTwFPnPUMrgsiIlIEvfr7evUh4/q5cr+2bq89oqt3Pd7jiT8PESVZ0p44pZTvSSnfA/Aj9f/Ry5J1nNYQbxJJs+n9AGgD6c6doMSWvFGqTBD18g3A4GuV5So1MF79v1Gwcl5Bwz7X/UFJYxwd+PzjJ5Xl505Q9v/yDcrx3ihVjn/uBO12X64Hhk1VUtk+2EX52nOEkkmsfiIr1B3hBIKZIE6qS/irNROPGf29Gi3PTWyrKXORZe0MlhUal0dHvvYY/uo4qZJjyntjgahERGQ4+ST8Ncqw8ej7ut2tDCdrKlGKQRC/q33PxJ+HiJIsFc3lPzBYdnkKjpMy6sRK0dSJlUxTA+kAYMwvlZTD0a3Ir92hLFepvSbq//0GLcxqoDygDPF6d47S5fubI8D455Tl458FfvQHYPvf9Me78gnlmN9+rKQ7HnJDQ0+Out6KW4ABV7C1O9NElydVdKrLRv5eccvz8a+a7tkwOm7VV/qWvZqjSpn70XxlaOKP5ivf+2v0x7j4fu3+jMq70XHjXRdERLkobo+8T1sXb3xWGVERvczu1idKqfpKV+96ThxI/HmIKMmS9vIihLhNCLEDQD8hxMdR/74E8HGyjtMa1ImVdGlfm9PSEJ3mODa2BGjoaVFbQa5+Glj/WFRKYoMWZke+ElCntp7UHFZaomVYGY722h1KS8vfphr37NjdDb0rjnzlX7zziv6erd3pF5M2G71GKymHTfROxC3PRmmMY//WRsct7KbvsRMCKP6Zdtnga5UyFnsMo1TJseXd6LhMCkFE1CBej7xF6OviglOAp0uUIWJPlwDbXtI+T/QaDbg66updl7ND4s9DREmWzGxjLwF4C8DDAH4VtbxaSlmZxOOkXFImVooOpAvESWfor1V6TdRsY+OfaTz43WIF8jsrvSZ5BQ352wOehh4UQD+5pHo8NR5mxS3KeanLY89L7QGKnCdT1qadUWCmv9ZUymrD8iwFLNUxCQGN/tZGxw0H9eVtxc3ANYu0KZC3/w0YONHgGLVNJ3tgUggiosYZpUo2mnzytTuA65Zqt931BnDe1JjnCTcAi6betdjdKBLgRJOUUZIZ83JCSrkfwC8AVEf9gxDCnqzjtJakTKykBrXb8/WtyNctVVqrAcBibwiul2EoSdvqRU8o6asBIABnOyWw2dlOeaFpbHLJ2HgY9TOH27h1e8JzwM432dqdiWKTJBiVqzh/L115tjnjbOvSB/HHHtcoDubABqDdqUrqY0D5OnIa4O5ocIx8c8kemBSCiCg+u1t5loiefDJej7yzvf5eb7Ur9StQ/9ViWO9yoknKNKmY52ULgO8BqIIymKkDgINCiMMApkkpN6fgmJktthU54FUCl1+ZApx9JTDgx0pLdnSu9fzOAIS59LjxJqpSe3Yq9yvxMdG9MGoLu1GaxZLbgYt+ydbuTJdI70S8FJtm0hPHm6TUVx0zCdpiJa0ye0+IiFIj5NfWu9e/GP95ILaXpe4Y09FTVkpFCX0bSsaxU6SUnaAE6/8NwO0Ank7B8bJDdGuGDDd06w78CbB5iTaQbvOS+qFkUNIrF3RpPKg6XnyAo0DpxMnLV+JjjFrnda0sVrZ2Z5NEeidit42Xnjg2HbPVocRoRZe3q58G/J6Y5A83N7wkszwREZkXO+rCKOunUcC+3wNM/Ku2N2biXxtGa6hfA16mo6eslYqel2Ip5c/Vb6SU/yeEmCulvEcIkZeC42Wf6GFezvZKMN1rdzS0fqgpZn/fueF7QOk5MQqqbqoFnrEDZEZj6ZhjW+c+e0sb3/LuHCXmJdqBDYyVIiJqLrOTEhvV2QWn6CcRvvpPgLtIux7T0VMWS8UTbKUQYpYQ4vT6fzMBVAkhrADiThgihLAKIbYKId5MwTmlXzgEeE82tKKo6Qj9NcZplP212pSzalrlMbMaWmP8dcpQHRlWWkvsbuMWbsYOtA1GLXFmWufMbhtdLlXx0jGf+QNt5prqQ0qazWhGk1QSEVHj4qVANpPK3mgS4VdvU54pmtqW6egpS6TiKfYGAD0AvArgNQA965dZAVzbyHZ3AtiVgvNJv3BImblWnTTywz83pCh0FBq3fuQVaNMcntIXuPjXSoral28AVk5X4hOWTeKEkrnAaDKyuiOA70TTE00abes7oV8mQ0oLnZl0zEbpjvPyjSepJCIi88z2ithd+nTHzvbxnyk02zIdPWWvpA8bk1IeBTAjzsd7jRYKIXoAGAfgIQD3JPucUipc3+vR2JAsf602tezah5Sv1y4BAnHS3R7Zre2Juf4l4PyfKy8u+9crEwO+ers2HeLym5XhYRyq0/ZEt8QBDX/vK8ubLgNG29ZVKcMKope9MgX4yfNKuXR1ADzHlVY8w+D8Gu1QRGEBPnhSnyq55HaWRyKi5oiXFCXgUSabVOtdyIaYWbXePXkwfp0tLNpnFQ4ppyyV9FIqhOgrhHhGCPF/QojV6r8mNnscwEw0PqxsuhCiQghRceTIkaSec4vFaw2Pbfk2Si27bp7SQvLm3frg5+i0xkBDq0n0fuJNfMnxqhkh6eU1Xktcx9P1y2LLgNG2HU833p+7ozLJ6e87K19lWN8bo/aoRA9FtDmB4hu1E6MV38hWvCySkXUsURxturw68pV6NrZXxF+jfd5w5CvPEtFDeP/1W32Pilpnxz6rABxSTlkpFSX17wC2AngAwH9H/TMkhLgCwOGmUihLKZ+RUhZLKYs7d+6czPNtObPjUo1iCXqWAN4TSpCzEMrX3xxReli2/60hrbG6rq9GO0b16Gccr5rBkl5e441PNoozCXibjmWp+sp4f5X7teX5lSnKPETR2fC2/01pAYwWnTTiN0eUr0y5mVUyso4liqNNl9eAR6lno+vdUBCoeF67zHtSX49XHwLCUl9n+04ysxi1Gal4sghKKf8kpdwopdys/mtk/QsAXCWE2A/gZQCXCCFeSMF5JZ/ZcakOt35c6oTngI//prSWLDgXeGJQ/br5SoyLUatJ9BjV9Y/pe2w4XrXtijc+OXYSyOuWKtnBmoplyYszweXaudrjHtgAFHYx16PCxBBERImzu/U92YXdlGeD6GV5hfoemqv/BNjztOsNm6o8b0TjSA3KYqlIlfyGEOJ2AP8A4FMXSikrjVaWUt4H4D4AEEJcDOCXUsrJKTiv5Is7OWRdTMyBVz8udfMSYMAV+u2AhhYXo9iB2MkuJy1TXmw4XrVti5cOG9Aug1R6S2JjWcY/py1TFc8Do+6IiVsRSqtdNKP4FpYzIqLUMarv/bUNmUkB5WvVV8CO5dq6fdsypW6PnpDS6gB2vaE9htGzClGWSMXLy431X6OHikkAfVJwrPRSW8Njc7HHtko73MCxPdplx/YolY3Fpt+u+Mb4+1Rbt9X9qlgBtX3Rf/vov3f0MhlWWuhu39BwM1v/mNJ7svIWbZmyORteQvIKlKx4E55Tkkuo6014Tnk5tlj1xyUiotbhyNeP9Fg7F/jhQ/rnhei63dlOicM186xClCVSkW2sdwLbrgWwNmknk2pNTQ6pCniBS2cr2cEik0Y9rcxc/psjnFSSkideWQt4my5TFiuQ31nbYhf94kJERKlnNEnlhMXKPG9qtlJA6Sl3FJqo200+qxBliVRkG3MLIR4QQjxT//1Z9UH5bZOZcf4y1JDWODJp1O1KS3dL90lkJF5ZkyFzZcpiVVrqhEX5Gu/FxezkmERE1DxGyYBW3Aycf6s+TtHm1NftRvUznyuoDUnFsLG/AtgMYFT9919DyUD2ZgqOlR2MunsPbFCW/75zQxcuszNRohora8li1CrI8ktElBzxkgHlmehlYf1MOSAVJfkMKeV8AAEAkFJ6AIgUHKf1JNrKHC/N7dHPlFaVgi6Ar1b5LbEVO/cksxcjXlkzSqHd0uOaTRFORETN11g93lTvCetnygGp6HnxCyFcUIL0IYQ4A1FZx7JOMloxjAL7f/wk8O4c4NwJSozCa3ewlSQXJbuVzGwSiUSOazZFOBGZNvD5gc3eZseNO1JwJpR2dqdx8hS7s+ltWT9TDkjF0/FvAbwN4HtCiBcBvAtgZgqO0zqS0YoRO4Ff9ESUY37ZkP6QrSS5J9mtZGYni0zkuM3p3SEioubx1zVMr6BONLl5ibk6lvUz5YBUZBv7pxBiC4DzoQyEulNKeTTZx2k1yWrF0KQ4LlDSIe9fp6SzZStJ7kpFK1m8lMrJOq7Z3h0iImq+vAJg3TxtZjGLDbjol01vy/qZckDSXl6EEOfFLDpY/7WnEKKnlHJLso7VKsLh+lZoqaQnHHBFw7wZO99MbHKn6NbxgMmJLqltMjvRaTxqOY2duDJ2WWzPSyLHZdpNIqLU8dUYP3f4apQskI1h/Uw5IJk9L39s5DMJ4JIkHiu1ouMBzr4SGDbVYOypK7FjqK3jnDwqtyXSShYvbsXqAF6Z0ngsS6Ktc2Z6d4iIqPkcbuPnDrM98qyfqY1L2suLlHKsmfWEED+QUv4zWcdNieh4gB/NVyoQtYV6/3rl+0kvJ6dSYCtJbkvk7x9dToGGuJUry/XLYssryx0RUWby1xk/d1z/UtM9L0Q5IB1PKvPScMzmiY4HaI2YFE4eldta+vePF7fS8XT9MqPyynJHRJR58grizPPCXhQiID0vL5k/50t0to6jnzFzB2WmeFllqr7SL2N5JSLKDr4a47rdV5Oe8yHKMOl4eZFpOGbzqPEAvUYD6x8Drn5a+b/FpnxlTAplguhyGl023R1ZXomIspUjX4lxia7HJzynLCeilExSmf1i4wECXmDSMqXiYGwAZYp4cSsAY1mIiLKVxQrkd1ZiXPIKlB4XR76ynIjS0vOyP3aBEMIphNgohNguhPhUCPG7NJyXVnQ8gMMN5BU2xAYASmUiw8rXcDi950q5yyhuxWhZOMwyS0SULSxWJThfWJSvFivrcaJ6yZznZXxjn0spV9Z/NVrPB+ASKWWNEMIO4N9CiLeklB8m6/ySJl56WqNZzIkyAcssEVF2Yz1OFJHMEn9lI/+uaGxDqVAj0ez1/zIzNiY6PW042JCKNsCAaMpQLLNERNmN9ThRRDLneflZItsLIawANgM4E8BTUsqPYj6fDmA6APTs2TORQyUmXnraZKZOpqyXMeUVYJklUzKqzBI1IefKK+txooiU9DUKIcYJIWYKIWar/5raRkoZklIOAdADwAghxLkxnz8jpSyWUhZ37tw5FadtTrz0tExFS1EyprwCLLNkSkaVWaIm5Fx5ZT1OFJH0bGNCiD8DcAMYC+A5ABMBbDS7vZTyuBBiLYDLAHyS7PNLmJqeNnbcKVPRUqZimSVqdTu+PNCs9Qf2zoHeA2o51uNEEalIlTxKSjlICPGxlPJ3Qog/AljZ2AZCiM4AAvUvLi4A3wcwLwXnlrh46WkZMEeZimWWiCi7sR4nikjFy4un/mudEOI0AMcA9G5im1MBPF8f92IB8Dcp5ZspOLfksESlTFa/EmUyllkiouzGepwIQGpeXt4UQnQA8CiALVCyhj3X2AZSyo8BDE3BuRARUQbo9atVzVp//yPjUnQmRESUzVLx8jJfSukDsEII8SYAJwBvCo5DREREREQ5JBWDJSO5/KSUPinliehlRERERERELZG0nhchRDcA3QG4hBBDAYj6j9pByT5GRERERETUYskcNvZDADdBmaflsajlJwHcn8TjEBERERFRDkray4uU8nkoGcMmSClXJGu/REREREREQGpiXt4XQiwWQrwFAEKIAUKIm1NwHCIiIiIiyiGpeHn5K4B3AJxW//0eAHel4DhERERERJRDUpEq+RQp5d+EEPcBgJQyKIQIpeA4REREOWvg8wObtf6OG3ek6EyIiFpPKnpeaoUQnaBMTgkhxPkATqTgOERERERElENS0fNyD4DXAfQRQrwPoDOAiSk4DhERERER5ZBUvLzsBPAPAHUAqgG8CiXuhYiIiIiIqMVSMWxsCYD+AOYCWAjgLABLU3AcIiIiIiLKIanoeeknpRwc9f0aIcT2FByHiIiIiIhySCp6XrbWB+kDAIQQIwG8H29lIcT3hBBrhBC7hBCfCiHuTME5ERERERFRlktFz8tIAFOFEAfqv+8JYJcQYgcAKaUcFLN+EMC9UsotQohCAJuFEP+UUu5MwbkREREREVGWSsXLy2XNWVlKeRDAwfr/VwshdgHoDiXwv9WEwxJ1gRDcDivq/CG47VZYLCLhdYmoZZJ9nfG6JSKV2fqA9QZR5kn6y4uU8quWbiuE6AVgKICPDD6bDmA6APTs2bOlhzAUDkscq/WjdNlWbNpfieG9ilA+aSg65Tt0lVRz1qXclcrymguSfZ3xum0ayyxlk0TKq9n6gPUGUWZKRcxLiwghCgCsAHCXlPJk7OdSymeklMVSyuLOnTsn9dh1gRBKl23Fhn3HEAxLbNh3DKXLtqIuEEpoXcpdqSyvuSDZ1xmv26axzFI2SaS8mq0PWG8QZaZUDBtrNiGEHcqLy4tSypWtfXy3w4pN+ys1yzbtr4TbYU1oXSJqmWRfZ7xuSaesfbrPgNLEbH3AeoMoM6W950UIIQAsBrBLSvlYOs6hzh/C8F5FmmXDexWhzm/Q89KMdYmoZZJ9nfG6JSKV2fqA9QZRZkr7ywuACwBMAXCJEGJb/b8fteYJuO1WlE8aipI+nWCzCJT06YTySUPhthv0vNitKJ80JGbdIYbrhkJhVHsDCEuJam8AoVDY8PjhsESNL4iwrP8alkn/GYkyWew14LJZTF9nZsS7bl02C689ohxjtj6IVw+x3iBKr7QPG5NS/htAWiPfLBaBTvkOPHtjsamMIg6rBQ+PH4jvFbnxn8o6OKz6d8BQKIxjtX7c+fK2SKDfE9cPQad8B6xR6zMgkHKd8TUwBC67tcnrrDmMrtsaXxA/f2ELrz2iHGOuPtDXQy67FZV1fpQu28Z6gyhNMqHnJW2iW3u9gRCkVFpPpJSR/wNAMNjQg1LrD+JYrQ++oNKL4guG8fwH+w0D/e58eZsm0O/Ol7cxIJDaLKMeRDPLvEGja2Abav0h3XXmDYZa1OJZFwjh+Q/26/ZXVRfgtUeUY+oCIRyr9aFTgQNCAJ0KHDhW6zOoD/T1UK0/hNJl23T1RkvrJiJqvrT3vKRLdGtv13Z5+OUP++G///6xrpdESqCyrqEHpfSSM3H9iJ6aHpV5EwbBZde+B+bn2QwD/fLztL9yBgRSW2DUe/LnyefBHwrHtFAOgcNq0bVudm2Xp9lf13Z5sAig7PVPG9a7fghqfcEWtXi67BZcPbQHZq34WHPdntbBqVmP1x5R2+e0WlCQZ8f0JZs19/wit0OznmE9FKe+amndRETNl1MvL9GTTdX6g5HW3nfuGoP//vvH2LDvGABgw75j2PxVJS48qzMERKQHBQB+eO6pmu837DuGWSs+xrNTi2ERYYSlhDvPhlpfEAsnDUGfzoU4s0sB9h6uwTufHEStL4hCpz1yTmpAoLo/oCEgsCAvp/48lMWiexAB5bqoqgtg18ET+NPk89DOZcdJTwAbvjiKs09tr1mvdNk2PDx+IF7d9m1kf3d9vy8q9ldqtg3Uvwhpt92KZ28s1l0rwWAYnmAI+fXXolUIzFrxse66/dPk8zTbxbv2EpmojpPcEaVXbH0gALy88QDKrjoncn9+eeMB/OzC3njnrjGRZe2cNiz7SLveso8O4K7v99XVV2brJiJKXM5cVbEtw589eHmkx+PMLgWa3o+yKwdg2OlFmL5kM164ZaTms9h1AaW11mm34NBJr6735uWNBzBu9d7I9y6btlXXZbPgieuH6GJjXLacHtFHWcaoB7FHBxfcDitui+plMWrd3LS/Ej07uVHSp1NkvR4d9du+OG2kqV7KYDCs6S2N11q6aX8l2rnsmuMaJepIJC6NMW1E6WVUH7xwywjDnli3w6rpZYm3XveOTk290bOTmyMoiFpRm3t5idfKWRcIoWL/sUhLbo03iNJLzsRj//ocew/XoPSSM/HDc0/FmV0KUOsL4n/e/xIb9h3TfVbtDUS2U6mttbG9N3e+vA1lV52Dx/71OToX5qHOH8IphQI1vmDkvDzBsGEL0P8b3QcFCQYoE6VK7HUBbXe4AAAgAElEQVQGCc11svdwDXyhsK6X8s6Xt+G5G4s1rZvvfHIQXn8Iz0wdFmkZ9Qf12x44VmfYS1nrC0IIETkXKaVuW6PeHXXbphJ1GPUqlS7bisU3FSMs0aJt2SJL1Do8wZCuPqj1hfDq1q81991Xt36Nn13YW7PMGwgb9tg+M3WYpt6o83EEBVFralNXVWOtnE6bBcNOL9K1AgPAh/uOGsax7D1Siw1f6D9Ttyuv71GZN2EQCpzGMS5ndinAVYNPwy//q5+m9UY9L7fDivLVezUvQzaLwB2XntV6vziiZjC6zhbfWKy7TuKNDa/zBzWtm3+afF79MM7Gt338X3tQPmmIZj21tXTycxub7KGJ7d1Rt7ValEaCeA8ZRr1KZse4M6aNKL2M4k/z86ymel7i9djm59lgEcp1XpBnQzgsUT5pqO7Zo6Wp3YmocW2qab+xzF2eONm/fnZhb0w473u6z2at+Bi/GHsmSs44xXC7qaN64bMHL0fZVefg1a1f46QnYDiZ1d7DNfjF2DMjrTex58VJsCjbGF1n/qheluhMPXd9v69m2+ix4ep6x+sCBtl79Nt+d9IHt8OGsqvO0Vx71d6gZtsab9Dwmqr2BnXbegLGcy9pfl6Da9To5zDKVMbrmyi9an36+qDOH9Ldk2et+Bi1vlCT9ZDaYxsterqFPQ9djmdvLObQUKIUalM9L26HFZed21UT5Pvatm8irZyNZf+K12sS77NCpx39HngLw3sV4dGfDMJr277Boz8ZZBjz8otLzkLXdnmaoTJ/WrtXOS8JtthQVjHqTWjnspvq7TAaG/69IuPx4rHbPnH9EORZLejaLg9CKL0fN47qpcvgt+VApWEcmVWXNWhoZLK5xoZ+qZPYRl+jZse4G23L65vSZeDzA5u1/o4bd6ToTFqPy2bV1QfxsoEWOG26ZUb1UGzsKqC8wKi9txwqRpRabeoK8wdCuPzcU3VDw/yBEAJhaTgm9aQnEPm/fryqkpUk3md7Hroc353wIiwlJp/fC4dOePDYtYPRtb0Tdb4QAImrhnSHLxDSpWJ+9CeD4A2E4HbYmjVBJlG6GWXIq67v7dBdJ76gJpalzqdf7z+VxrEsx2p8moaIylofAOheSsLQtoIueu9L/M9NxZrjumzKNRV9nblsFlTWBZoMpjeaxNbsGPfmToBLRMnlDYXxzfE6LJoyDAVOG2q8wUhvTLzngehltb6gph6yWQSvX6I0a1PDxgJhaTjEKxCWcNuV1peSPp1gswiU9OmEJ64fgle3foNXt35j+Nm/Pz8CTyCER38ySPPZoz8ZhDp/CPe8sg0hKfHLv3+Mfg+8hV/+/WPYrBZAAhDAtCWbcfEf1uJYrT8SzK+e13///WOE60esqC02FtHQcsPJrihTqb0J0deEzSIMrxOLECh02jVfY9crcFrxxCTt9Vc+aQjsVgtue2EL+v76Ldz2whZ0LnQaXt/R56BsOxR2m1VzXJvNorvOPMGw8TBTv36yudht3Q797yBej0rstnzwIWo9brsVPTrm49alm9H312/h1qWbYbcIw3t+bF3y6E8GIRAMY8icf6LPff+LIXP+iWlLNnMiW6I0a1M9L41NDGkRSgtodGvsyi1fo+yNnZF11dYVtaV2dN8ucDusuOeVbZoMJH945zM8dt0QPHbdEHj9ITw7dRjceTZNq2r00JrTOriMh5jk6R90mFqVMp1Rb4LLYcWv//GZ4XUSzemw4g8x6z345i788drBut4JKaXmem3s+m5Jz0bcYPo8K3767EfN7o1hjwpR5jG6Vh12K96q+I9uiPlPR56uq8P+eK22DmPCDaL0a1MvL/G6gtWJIa1WCwrr0w8LIfD2J99F1it7Yyfe+fQ7PHtjcWQSyQKbBdXeAL476cMPH18XWbekT6fIPt1RQ0Sih4tED63Ze7im0SEm8SbPBJhalTJT7PjuGl/Q8DqJHUZV28h6kesusr6IXK+FTjuqvYFGr2/ttk2LN0HsgWN1pq4/jnEnyk61viDe/uQ7/Pb1hsbLkj6dcFHfLrq66T+VdZptmQKZKP3a1LCxeEPDjIZyGA19MRr24XZYMW+CdpiLmlKxqXNR9/+ntXt1Q2XUY6k9LdOer0DfX78Ft8O4dZktPZTJ1MlWY6+92MlWW3o9Ac27vs0wrgOG4PF/7dGsx+uPKHvF3mOnPV8RCeLX1CWThqCj266rD/TLmHCDKN0youlACPEXAFcAOCylPLel+7FaLbqhYW67Fdb61lvNxHqBEIrc9iaHfXgCYcPJrP7f6D4oyIv/7hfbVe0NhPDs1GK487THqvFpe1qa6qUhykRmJ1tt6fUExL++hRBNZgwzYjScxCKUlMzReP0RZS+jiWJr/EFdEP8XR6rRr2s73TMBAA4PJcowmXI3/h8ATwJYkuiOooeGqUNJgCZiSaIC5WO57VZMGnl6i1KdRg8rcTuMh5fFjrt/as1ezJswSDehJVt6KJOZnWw1kesJ0F/ficaIxQ794mRzRG2LUWzbtv9UYcCp7XHr0s2azIUOqwU2m37SWg4PJcosGXElSinXCSF6pfIYRq0vZmJJUh2YGzvu/vXt3+LMzvmR1mW29FA2iBc/kurUwS29ruNhIH4WK2uf7jOgDGRUN/XqVBC/p9jWpkbTE7VJWXOVCiGmCyEqhBAVR44cafb2cTMLmRjLnspUp0bj7ieNPB35DqZWzWaJltdsYzaGDEju9ZTIdR1PrqY2zrUyS9nNbHk1qpt6dnKjfPVe/PDxdTjj/v/FDx9fh/LVexnbRpQlMqLnxQwp5TMAngGA4uLiZk98YrZluLWxpbdtSrS8Zpt0leNMva6zUa6VWcpuZstrIpPMElFmypqel0Q1p2W4teVqSy+1Lekox5l8XRNRZkhkklkiyjw508TAHg6itofXNRE1F+sNouyWET0vQohlADYA6CeE+FoIcXMqjsMeDqK2h9c1ETUX6w2i7JURPS9SyknpPgciIiIiIspsGdHzQkRERERE1BS+vBARERERUVbgywsREREREWUFvrwQEREREVFWEFJm31xkQogjAL5K93mk2SkAjqb7JDJIvN/HUSnlZa19MtGaUV7byt+UP0disqnMJluml51MPz+g9c8xE8trNvydzGorP0um/BxpL6+UuKx8eSFACFEhpSxO93lkirbw+2gLPwPAn4NaLtN/55l+fkB2nGOqtaXfQVv5WdrKz0GZgcPGiIiIiIgoK/DlhYiIiIiIsgJfXrLXM+k+gQzTFn4fbeFnAPhzUMtl+u88088PyI5zTLW29DtoKz9LW/k5KAMw5oWIiIiIiLICe16IiIiIiCgr8OWFiIiIiIiyAl9eiIiIiIgoK/DlhYiIiIiIsgJfXoiIiIiIKCvw5YWIiIiIiLICX16IiIiIiCgr8OWFiIiIiIiyAl9eiIiIiIgoK/DlhYiIiIiIsgJfXoiIiIiIKCvw5YWIiIiIiLICX16IiIiIiCgr8OWFiIiIiIiyAl9eiIiIiIgoK9jSfQItcdlll8m333473adB2UGk+wRYXqmZWGYpm2RNee31q1XN2u/+R8a19JQoc6W9vFLisrLn5ejRo+k+BSLTWF4p27DMUjZheSXKLVn58kJERERERLmHLy9ERERERJQV+PJCRERERERZgS8vRERERESUFVL68iKE+J4QYo0QYpcQ4lMhxJ0G61wshDghhNhW/292Ks+JiIiIiIiyU6p7XoIA7pVSng3gfAC/EEIMMFhvvZRySP2/OSk+p5wQlmHUBmo1X1O5XbL3QbklFA6hxl+DsAyjxl+DUDhkeluWN8ol8cq7+n30tcTrgYjaopS+vEgpD0opt9T/vxrALgDdU3lMUm5ild5KzFg9A8OWDsOM1TNQ6a1s8ibW0u2SvQ/KLaFwCJXeSpSuKcWwpcNQuqYUld5KUy8wLG+US+KVd/UaWrpzKQ7WHoxcS7weiKgtarWYFyFELwBDAXxk8HGJEGK7EOItIcQ5rXVObZUn6MHMdTOx6dAmBGUQmw5twsx1M+EJelKyXbL3QbnFE/Rg1vpZmjIza/0sU2WG5Y1ySWPlfea6mbi056WY/cFsXg9E1Ka1ysuLEKIAwAoAd0kpT8Z8vAXA6VLKwQAWAng1zj6mCyEqhBAVR44cSe0JZzmXzYWt323VLNv63Va4bK6UbJfsfbQFLK/mue1uwzLjtrub3JblLXlYZjNfvPKuXkN92vfJmeuB5ZUod6X85UUIYYfy4vKilHJl7OdSypNSypr6//8vALsQ4hSD9Z6RUhZLKYs7d+6c6tPOap6gB0O7DtUsG9p1qKmel5Zsl+x9tAUsr+bVBeoMy0xdoK7JbVnekodlNvPFK+/qNbTvxL6cuR5YXolyV6qzjQkAiwHsklI+FmedbvXrQQgxov6cjqXyvDJdogHILpsLCy5egFXXrMK2Kduw6ppVWHDxAlM9L/PHzMfwbsNhEzYM7zYc88fMb3bPS6L7oMySSHk0s63L5sK80fM0ZWbe6Hlw2pymtmV5o2xm9voKhUMQEHHL+/wx8/HugXcxZ9QcXg9E1KbZUrz/CwBMAbBDCLGtftn9AHoCgJTyzwAmArhNCBEE4AFwvZRSpvi8MpYakDlz3Uxs/W4rhnYdivlj5qPIWQSLMP+uGQgHULahTLMPM+wWO8pKytC9oDu+qfkGdou9WedvERYUOYuw8JKFcNlc8AQ9cNlczTp3yhyJlEez21otVhQ5i1A+thxuuxt1gTo4bU4c9x1vcluWN8pmZq8RNSB/1vpZ6OLqgrKSMvQo7KEp70XOIkwZMAVOqzNyLfF6IKK2SGTje0JxcbGsqKhI92mkRG2gFjNWz8CmQ5siy4Z3G46FlyxEvj0/pftIxrEzkEj3CWRzeU2kTKRr2zaAZTZHmC3nNf4alK4p1a1XPrYcBY6CVj1nA1lTXnv9alWz9rv/kXEtPSXKXGkvr5Q4NsdkmHQGzTP4mWIlUibStS1RtjBbzhNJakFE1Nbw5SXDpDNonsHPFCuRMpGubYmyhdlynkhSCyKitoYvLxkmnUHzDH6mWImUiXRtS5QtzJbzeEkteD0QUS5izEsGCstwJNDSbMBl7DZ51jx4g95IALTNYoPD6mhyf6FwCJ6gJ7Kd1WJFnjUPnqAHAgJOmxN1gTq4bC5YLdZU/PiG55HA8dI+vjUTyqtRmQJgqpyZLY9GfzP1GNHLJKSmbDptTtgs+twhSSwD2YZlNgdEX1fqdRB7XajfG9XpTpsTvpAPTqsT3pA3cn3Gft+SgP1m3oOyprwy5oWQAeWVEseelwxkERbk2/M1XxujZqyZsXoGhi0dhhmrZ6DKW4UXdr2AYUuHoXRNKSq9lbh//f2YsXoGKr2Vhuk4wzKMKl8VSteURrar8lZFtjvuO477198f2V8oHErJz69m1ok+j1Qer60zKh+V3kpU+6v/f3vvHidHVeb/v5/unpmeS0gyEJQNxBBAd1nQBBI0YJCLF+SyqKAkrFz2i+KNRFndxAvLDxEvybILBFcQiAJBEhAQkbCyCGQTMEgCCQTBXWIIIYASMpNJZqbn0tPn90dV91TXpbu6p3v6Ms/79epXd506derpPs9zuk/X+TzlKfPzizD+6NdnnX2ddA92Z5V1D3azu2+3p14ylfTY7PHF/s6C04YrSjXijsl0HKTH7DteuoPOvmH/T4+/zbHmTJzNumMWy19c7hvby19cnjeuw9pWTBuKoijlRCcvdUAimWDhmoWs/8t6kibJ+r+sZ9HaRZw85eTM9mVPXsZFR17E+r+sZ+Gahb7aAb92nMc5Xy9au6hs+oNEMsGitYs870f1DsXh168L1yykq7/LU1bsZ+zXZwvXes/R1d/FwrVeX+1L9oWyWX1AqQfyjdknTznZG0+2/zuP86vnHvsLjRuNPUVRqh2dvNQBQRlrpo2f5rsdlLUpXzvu1+XKdKOZdUpLUL9ObpvsKSt2DX1Qn7nPMbltcqi+1WxjSj2Tb6ydNn5aYJw4y4Pqucf+0chWqSiKMlro5KUOCMpYs7Vrq+92UNamfO24X5cr041m1iktQf36evfrnrJi/10N6jP3OV7vfj1U32q2MaWeyTfWbu3aGhgnzvKgeu6xfzSyVSqKoowWOnmpA/wy1iyes5hHtz+atX3w+INZ9clVXHPCNUQkQsqk6Bnsyaxl9mvnquOuYtnmZZ7X5cx0o5l1SktQRqPxTeNDZfNy+onTX9zncPfZkjnec4xvGs+SOV5fjcfioWweiQ+EeR+KMhrkG7Mf3f6oN56OX0I0EuWWj97Cqk+u4rSDT/Ot5x77w8SNMyYiEtFMf4qiVDWabaxOcGeHcWac6R7s5s6X7uSnz/2UGe+YwZI5S7jn5XuGt49fQnu8PTOhcbYTkcioZxtLmRS9g70MmSHGNY5j78BeohKlpaGl4Kw5VEFmkWrw12KzjaXFuwvXLGTjXzd6/MVZz6/PmmPNnsxHKZMKlW2smKx7ud5/mPdRJajPjgEKzTaWTCW5dPWlWf5rjOGRbY/wwQM/yIHjDiwq25hfbFxzwjXEIjHNNqbZxuqRivurMnJ08lLn9Az2MP+x+az/y/pM2ax3zuJbx3yLTz3wqcz29SddT2tDa6XMzCLI5iJtrPhAVcv+GrYvStxnJafa7XOhPqtkUc5xvASxUTP+qpMXhSrwV2XkeP/uVOqKsGL+aloSoILR6iFsX1R7n1W7fYqSi3KO4xob9cuRtx1ZUP3NF2wukyWKUlqqbr2EUlrCivmrSYypgtHqIWxfVHufVbt9ipKLco7jGhuKotQaOnmpc3yFz3OWBAo63aLmRDKRWZsdJHYulRA6fXw8GveIulUwWhnCCuebY80sPXEpT8x9gufOf44n5j7B0hOXEo/GQ/lGucX05UgAoCjlxBkLxhiuOeEaj/9OaJrAaQefltlO6xYLaV/HW0VRag1dNjYGaIg0cMXsK5jcNpnXu1+nIdrABX9/ARe/9+IsMaafcPOq465i9fbVnDDlBC578jKP2BkoiRDafe4vvO8LXHvitbQ1tI1YrK2MDI//RBo8ddIT3EVrFw2Lfj90DZ39naHE/uUW00ckQnu8netPur4kCQAUpZz4xcTiOYtZPGcx7fF2KwW5gXtfvpdvzPoGxhiuXn81byXeChU7Ot4qilLL6OhU5ySSCS5dfSmn/eo0pi+fzmm/Oo1LV1+aSYnZ2tCa+ZLyu7PyZU9exqmHnMplT17me8flUt2N2d3Of276T772+NdIJBNZNiqjS5D/uPu3L9nnudN310BXKN8YrTt6O/1dfUqpZvxiYtHaRezu352Jw4VrF3LylJNZuGYhu/t3s+qVVaFjR8dbRVFqGb3yUucUIsYMqrtP4z452yiF2FNFo9VJ2H5x3/kbYHLb5LoQ+yvKaBNWoD9t/LSihPsac4qi1DL690qdU4gYM6junoE9gW2USuypotHqJGy/uO/8DfB69+t1IfZXlNEmrEB/a9fWooT7GnOKotQyOnmpcwoRKvvVveq4q3jozw9x1XFX+bZRKiG0Cqqrk7D9Eo/FPXf6Ht84PrTYX/teUYbxi4nFcxZnJVq58tgreXT7o57yMLGjMacoSi1T1ptUishBwO3AO4EUcJMx5jpXHQGuA04FeoELjTHP5mq3Hm6gNpQaIpFMZO6mHIvEaIw2lkUs6b5TeVO0KetOzs2xZqKRqG/diERoijbRP9RPyqR8xc6luhN62HYKPF/Fb0hVDf46kj5KppKeO3/HIt4Vp371IhLxnBcIVVbqdfel8tNRQH22jnH6YXr8TSQTxGPxTPwkkgni0Th9Q32Zes796eP6hvqy6hUybjqPyzW+h6Bm/HWs3aRS7/PiS8X9VRk55f7mTgJfN8b8HfAB4CsicrirzseBw+zHxcANZbap4gylhujo62DB4ws4evnRLHh8AXsH9rLypZXMf2w+HX0dJU0V6xQox6NxOvs6s87d0dfBUGrIU7e1oTXzJdYcaw4UO5dKCB2mnXSWnPmPzefo5UeX5fOqN0bymQ2lhjz+0tnXmfEX5zl29+/Oqre7fzdAVp8Cvra465Vj4qJ+o1Qatx8ueHwBb/a8ybo31mXF2fzH5tPZ35kZf9sa24hFYrQ1tmW2o5EorQ2tmedCx810+wA9gz0aG4qi1AxlnbwYY95MX0UxxuwFXgImu6qdCdxuLJ4CJojIAeW0q9KkU8q6M8mcesipZcu0lO/ctbLWebQyU9UTI/nMwvpL2HNUqv/Ub5RqwM8PL//95cw6YJYnzkrpn7n8X2NDUZRaY9SyjYnIVGAG8AfXrsnAa47tHXbZm67jL8a6MsOUKVPKZeao4JeZKZ3VK/26XGuPg87d0tBSlvOVmlrJklNN/jqSzyysv4Q9R6X6r1b8ppJUk8/WK8VmdCzXeUuZMXK0UX/Nz+ZXtlfaBEUpC6Oy4FtE2oB7ga8ZY/a4d/sc4hHiGGNuMsbMNMbMnDRpUjnMHDX8MjOls3qlX5frX6+gc/cO9pblfKWmVrLkVJO/juQzC+svYc9Rqf6rFb+pJNXks/VKMRkdy3neUmaMHG3UXxVl7FLQ5EVEJorIe0XkqPQjxDENWBOXXxhj7vOpsgM4yLF9IPBGIXbVGs2xZk9mpsVzFvPQnx9i1jtncc0J12CMIWVSdA90e/QFhZIyKXoGe0iZFNFIlCVzvFlsRutfNqct6edC0Cw5hVPIZ5ZMJeke6M74XjwW55oPXcOqT65i03mbWPXJVVzzoWuKzhhWqf5Tv1GqAV8/nLMEY4yvf0YkUvAY6TfG5vJ/jQ1FUWqN0NnGROR7wIXAnxm+MmKMMSflOEaA24AOY8zXAuqcBlyClW3s/cBSY8wxuWyph0w4QdnG+of66RnoYeHahWz860ZmvGMGi+cspj3enskIVghpoebCNcPtXXPCNUQl6pttrJz42bLk+CW0x9sLEmhrtrHCCfOZJVNJOvs6WbR20XD/zFlCQ7SBS1dfmrfPypQtrmRotrHwVIPP1itOP+xL9tGb7GXhmoXs37w/X57+ZQ4cdyBv9b7Ftc9cy1uJtwoaI3ONsRCc0W+EsVEz/jrWso1xxfgC63eVx47qouL+qoycQr65PwMcYow5wRhzov0InLjYHAecB5wkIpvsx6ki8kUR+aJd5yFgK7AFuBn4cqFvohaJRqJZmWPSaWWHUkMsXLuwZIJ6PzHmpasvRUSystaMBqUShpYqu9lYIsxn1pfs84qG1y6kq78rVJ+F7ZdK9Z/6jVINOP0vZVKZMXHVK6s47Ven8bn//hx7B/ay6pVVBY+RucbYXP6vsaEoSi1RiGD/BWAC8FbYA4wxT5BnlmusSz9fKcCOuqbUgvpqEipXky2KlyDfm9w22VOmfaYoIyco5qaNn5a1HTbedIxVFGUsUMjfKz8ENorIwyLyQPpRLsPGKqUW1FeTGLOabFG8BPne692ve8q0zxRl5ATF3NaurVnbhVx50TFWUZR6p5DJy23AYuBHwL87HkoJCRLzF/vPWTWJMavJFsVLPBb3+N6SOUsY3zRe+0xRykDQeP/o9keLijcdYxVFGQsUItj/H2PMh8psTyjqXUzqFPP3JfswJkVzQwuJwV7isTh9Q/0ZYWVEIjRFmzyvm6JN9CX7Mm2kTCoj0I/H4vQ72iihcNO3Dadd8WicvqG+0RRNV1ycVw3+GrZfk6lkxm/SviJIVnKJ9A8hd5nBeI6NSMRzXoyxXts+3RxrJjJKuqsaQX22DhlKJUk44iMdE/FY3BM3zvHZOZY797vH0vR2PBrPxOYojbk1468q2M9XXwX7Sm1QyAj2jIj8UERmF5IqWSmctJgfY+gd7GH+4ws4evnRzH98AR19nSx/cbm1/dh8Ovs6+fbab/u+vuOlO/j22m+zu383C+w2Fjy+gE5XGx19HaRMKpOpZv5j8z37wuLXRpZd/Z2ZL08Vho4OYfs1ZVIeX+kZ7KGzvzPbf/o76R7oziobTA3S2dfp8bPewV7PefcOdLt8uoPUCNOBK0o1M5RK0uGKjzd73mTdG+s8cbO7f3dmjIxH475xtaVzS1ZML39xeWZ75h0zWWDHVTwap7O/c0RjuqIoSrVRyC/HGcAHgB8wvGTs6nIYpVgkkgkWurI/LVq7iJOnnJzZvuzJy7joyIs8r9P1LjryIi578rKcbaSz0ZQiG5hfG067iskupoyMsP3qV6+rv8v32K6B7Axkg6lBT6ayRWsXMWSG8h67cATZ9BSlFkj4ZPK7/PeXM+uAWd4Mf47YTCQTvnE1bcK0rPKTp5wc2E4pMjwqiqJUE6GzjRljTiynIYqX5pCZaNLbuV7nayO9FGikmWqCst04bdH116NL2AxEfvUmt00OlYGstaHVt964xnF5j9341400F5lNT1FqgaCsYvs07pMzNoOOc8fbtPHTArNUavYxRVHqjfB3oRL5gYhMcGxPFJGrymOWApAImYkmve33emvX1tDZbEqRqSaoDadd+q/f6BK2X/3qvd79eqgMZD2DPb719g7szXvsjHfMIFFkNj1FqQWCsortGdiTMzaDjnPHW9A4H3S8jsGKotQyhSwb+7gxZnd6wxjTCZxaepOqg5RJ0TPYk/U82jTHmlmSJxPNVcddxbLNyzyv0/WWbV7GVcddFSqbTSky1fi14bRLM9+MPmH71a/e+KbxvseOb8zOQNYQafDNmhSVaN5jl4wgm56iVBt+3x3NPpn8rjz2Sta/ud6b4c8Rm0HZyLbu3ppV/uj2RwPb0exjiqLUG4VkG3semGWM6be3m4ENxpi/L6N9vpQ7E05a4LxwzUI2/nUjM94xgyXHL6E93j7qAvOUnXlspNnGDMaTlcaTfSaVItXfRaJvN83jp5Do2k5zfAKRpvEQCf++c2UbG4XsYm4qnlmk0pmbUibF3oG9dPV3MbltMq93v874pvGMaxzn6Qu/rGSAp8zYGcOGMyfFMUNJ+tLfBdMAACAASURBVMwgLQ2t9A72EJcGItEGEi5f02xjeRnzPlur5PruMCblzTY22E18+3r69zuE5gnv8h0jndknw2Qbc4/rpcggmYea8VfNNpavvmYbU2qD0JoX4A7gURH5OWCA/4d175e6wylyBDIix+tPup7WhtZRtSUSidLa2AYw/Byxus1pi9/rNrt+i0NPkN7XGmn1HMdgL5G7zqN121prH8DUOTBvJTS1hbfZziSWy0Zl9EgkE1y6+tKMPwPMeucsX3/O13eZMhn2r7bGNujvhhVzabN9pw0yvtNq+47zWLdPK0o9kO+7IxMzBrj9zEy8xACmzqF13kpwTSwy2ScZjrlY+jvANY77jetBMa0oilKrFCLYX2Jfffkw1sz1e8aYh8tmWQUJK3CuOxpbYPu67LLt66xypWYZFX9W31GU8LGm8aIoilI0BV07Nsb81hjzDWPM190TFxFZF3RcrVEK4XpNMtALU2Znl02ZbZUrNcuo+LP6jqKEjzWNF0VRlKIp5cLXeAnbqihjVuTY0AJnL7OW+0Ri1vPZy6xypWYZFX9W31GU8LGm8aIoilI0hWhe8hFO+V8DRCRCe7yd60+6vpwix+JIDcFAj6VB6e+GxlbwEzunUjDYay1DGOi1vhTzie4jEWiZZGlcCjnOferyC0SVAhixP4fxpUiEVMt+JM5d6RLij+AcULgPK0oFiUiE9qaJXH/iUkccxIkM+PhxeqxtaB4e0wvwcx1nFUUZq+hIF0Ba5Oh8rjipIejZCSvPhe9Nsp57dlrlWfVS0LsTVsy16q2Ya22nQqR7jkSsL1Gxn4uYuHT0dTD/sfkcvfxo5j82n46+joqkmlaGKdqfQ/pSyqTo6O9k/uMLrH5/fAEd/Z3h+t3vHP1dxfuwolSKVIpI79u03jmXyPcmWc89b8O6n3j9OBKxJiq9bw+P6SH9XMdZRVHGMqX8Ra7p58rNQA/c+znYthZSSev53s9Z5U4Ge+Gei7Lr3XORVV5mnNl2kiaZybZT93qheiWkL42o3/3O0dtZMR9WlKLx8+V7PweHn+7vx0WO1TrOKooylinl5OW8Eral+NHU5p+hxp3GuIKZbMZsprZ6JaQvjajf/c4x8V2ajUmpPYLiZb/3ZG+n/bjIsVrHWUVRxjKhJy8i8ikReVlEukRkj4jsFZE96f3GmBfKY6KSob/bP0NNf3d2WQUz2YzZTG31SkhfGlG/+52j81XNxqTUHkHx8vb/Zm+n/bjIsVrHWUVRxjKFCPaXAGcYY14Ke4CI/Aw4HXjLGHOEz/4TgF8Dr9hF9xljrizApuoml9A5rKDeWS/aSOozy0n076Z5/BQSXdtpbppApLE1ux6G1Ll3k0gN0tw0jkT/XpqJEGlotiY6Dc0wmCjMrpD2prPtuO8wrf8IjpyUfaftou5MX0wCB4CGFlLnLCfR5/C5+IRhX7Lba24I6PdoE/TtyU4wgbhsabYyLd1zkfWv85TZ0DLRW6bZmJRqISie0lnEnH77meVW3cs7rEl52yQwKSueBeLn/5rEYDctDW2O+Mp/5UXHWUVRxiqFTF7+WsjExeZW4MfA7TnqrDXGnF5gu9VPWoTs/vHVMsnaH7TP+YPS1UbqrJ/TcfCxLHz6e8NfWHOW0I4h0vu2q95sFq5d5Ki3mPb1dxJJdMHR51vrsEPbtZ8lKs1nL1Weqa2GSaWG6Ojr8PZpvD3/BCaXL+aZwKQwdJik1+f69xC567xMe5FzltNuolx/zL8OT3KkiUjPLrjXcd6zbrEmMCvmeX3MneUORpz5TlFKTs6xPQWRRjhjqbX0sXsnDA3Ar76QFQOp7U/TcdB07nnlIU6fdjqX//7yrElIu+ReFqHjrKIoY5m8I529XOxTwAYRuUtE5qXL7PJAjDFrgI5SGVtT5BJihhVpuuolDjmehWtdIs21tkjTU2+Rq94iEu87xxKOukX/+ewa6ClIVFqVmdpqnEQy4d+nxQriQ4rf/c+7kETfbo/APnL3ebReN53Ile3Wc89Oa+LiFi+nX2fZkvBmuRth5jtFKQu54mmgF+4+D66fAVe2Q1+HbwwkDjmeheuu4OQpJ3P57y8vSniv46yiKGOVMFdeznC87gU+6tg2wH0jtGG2iDwHvAF8wxjzR79KInIxcDHAlClTRnjKUSCfEDOMSNPVRnPTPv4izYbWcPWa9oH9xhVuV1CiABVPB1Jqf21uaAno+xB9MIIEDoHnHe96T34C+yDRfXx8UbYo5aXmxthKUcjYvt97fOumx+hp46ep8L5I1F8VZeyS968aY8w/GWP+Cbgl/dpRtmyE538WeJcx5n3A9cD9Oey4yRgz0xgzc9KkSSM87SiQS4gZVqTpqpfo3+Mv0hzsCVevf48lHC3UrqBEASqeDqTU/poY7A3o+xB9MIIEDoHn7dqeXdFPYB8kuu/rKsoWpbzU3BhbKXLFk3usDBhv02P01q6tKrwvEvVXRRm7FHKd+fqQZaExxuwxxnTbrx8CGkRkv5G0OeqkUtYXlrGf0zcXSws3p86BSMx6TguOc+1z4qrX/Oc1LJmzmFnvnEVMYnxl+le49sRraW5opefcFaQ+vdy33qx3zmLJnMU0P3cXvPigpTtIn/uE78DcX1j/GkrEa9dZyyyNwtxfWHWd9krE+76VstAca/bv0zD/0Ab4W6qhmZ7BHlImlXl2+3NzLO5z3iU0xydkt9cy0RImz99oCZPnb4TWSZb/uP0p/TqX7ytKNeA3vgeO33GINgyPr0d+GprGwwUPWPFw5KczdZtThiXHXsWj2x/lymOvzI6vAOG9M04z8aooijIGEWNM7gois4Fjga8B1zh27QN80r5qkuv4qcCDAdnG3omVCMCIyDHAPVhXYnIaNXPmTLNhw4acdo8K+YTQpc42NtBLqiFOItlHPNZMZ18nC9cudAm4JxIZ7MvUszJT9dAsDURijcPZnQYT1nPP29mC6nRmnHHvhP698IebYM1iW2i6DFr3s/ankuAQbIcVgFeAit88tVT+WspsY6mGZjr6Oz3ZitqJZgnxOWc5KfBmG4vELDHyxHdZV1jS/pIlzl8GTeNg71+G67VMtH7wOY9tmWj9yKs+36kUdeOzNU2+pCtZ43fcHks/B393Bkw/d1gb4zw2ZeCRy2DvX6wsftEG4rbgvqWhJVB4nzIpK2GHO17j7dWgdakZf536zVUFtbvtR6cVa1J1cMX4/HWy6nflr+PgyNuOLKj+5gs2F1S/TFTcX5WRE2bUawTasPQx4xyPPcDZuQ4UkRXAOuA9IrJDRC4SkS+KyBftKmcDL9ial6XA3HwTl6oinxA6l+A4rBjZVS8SidHa2EZfstdHvG8LuB31IhKhtXEckYa441xR63kw4RWT3n0e9HdZyx3u+iys/r5DaGoLqxFr4qJ3Px9VIpGoo0/bwk9crIOz/Cgx1Od/h24/If5dLiH+XedZk4+0KPn6GTDU7yNMvsgqd9bzO/au89R3lOoj1/juHr8HeocTofzXQuh6zf/Yvg7Y/EvYttaKKwPRSJS2dFwHCO8TyYR/vOryMkVRxiB5BfvGmP8B/kdEbjXGvFpI48aYeXn2/xgrlXJtUsk72Te0Bov3w5LvbtAjTTigVC2Bd+gOI8Tfvs4qdxIfH16c7z5WfUepRgoZ391JTQKE+pmxNVdbPgTGqwr7FUUZg4RJlfwbEXkAuF5EHnA/RsHG6qWSd7If7AkW74cl192gixH2q+i6Zgi8Q3cYIf6U2Va5k76u8OJ897HqO0o1Usg4F1Koz9v/m78tHwLjVa+8KIoyBgmzbOxq4N+BV4AEcLP96AZeKJ9pNUBY4X0ZGJGAO42f/WfdYon61/4HfOInI0s4oFQt6Tt0e4TCfkJ8v75umZhdFollJ4JI+5KfON99rPqOUo0UMs41tmb7vzsxinNsLcLvA+NVr7woijIGySvYz1QUWWOMOT5f2WhQVWLS0ML7Ietmj01t1r906fpNbZDsh6HB7H2R/LfgSaWStoC71RJwE7G0LU5RfmOLdV6JWqJST+IAH7sG+4afzZD1xVxswoHKU3FxXln91dN/rZamyVPP218pISMQzgiFjfG2hxn21bSPQMiyiNdPoFZ8p1LUt89WI0HjWbo8PXY2tnrjI9lnxaGzbKAne4x3j/lF+H3KpLzxWnmxPtSQv6pgP199FewrtUEhI98kEZmW3hCRgwFNrh5GeJ8agp6dsPJc+N4keOpGKzPNynPh6ZutpTXpfSvPtfalkrnPm0oR6d1F653ziNx3Ma29HUTu/IzVxoq51vnW/cTenge9b8N9F1v7endaX8qplFXuPHfvLutLVSLWl23TuJElHFDKh9uvVp5rbaeGXPXsrEkr5g77R+9OIobsO3QbvP7Qvwd6drnOsQv69np9djDh9SXw+on6jlJNBMQHqZTlm+lMYk/daAnxnT6+5w1rIuIs63pteIx/6sbseGgs3u+dgv4gYb+iKMpYoJDR71JgtYisFpHVwONY6ZOVfAz0DGeiSSXh8NOHt4/8dPa+bWut7XxroZ2ZcOb8M9z/ZW8bh58+vH3/l616zow5+bKlKdWN268yvuPSPYXtZ796vZ3+WcQSnd7zpl+rLym1RL74SGcSO/x0+PUl2fXu/5KVItxZ9utLhsd45xis8aAoilIS8q9NsjHG/FZEDgP+1i76kzGmvzxm1Rm5MtE0T/DPStPUlrtNZyacsJltnFnENGtY7eP2K/D3nbBZk/zqhc02FpRZTH1JqXbyxUc6zoLGWb9YSNctMruYoiiKEkyYbGMn2c+fAk4DDrEfp9llSj5yZaJJ7PbPStPfnbtNZyacsJlt0tuaNaw+cPsV+PtO2H72qxc221hQZjH1JaXayRcf6TgLGmf9YiFdt8jsYoqiKEowYZaNfch+PsPncXqZ7KovGlutO9fP3wiXd0C83br7+NQ51g3L/LLSRBvApKBvj1fDAJYu5Ry7zf3enT+zzSd+YmUQ06xh9YM7w1G63xtd9/oJ7Oe45V9pP2uIe+u1TBz21cw5lkHzRB+fbRr28fkbLf9UX1KqnVzjYCpllZ2z3Bq33fU+cYOlC3QfG2+3xvwXH7SWBs/fCBc8ABirTUVRFKVoQmcbqyZqLhNOWhB6z0XW0oEps60vQ4kOZ/IySWvZTV8XSAyeugHWLLbqnnULtE7KziLlbvP4RfCBL1hfpAVlG6uZrGHFUvHMItWZbcwWId/7uWGfPOsWaNoH9v7FWgrT+ao1eYnEoHtndlnaz5yZlHp3Zfv42cugZVK9+dNoUN8+W434jYNgjbEbboPp8yx9y7h3woe/C/scYI3VTeOg+21obLZed2yD1T+wYujsZVY89e+p97ioGX/VbGP56mu2MaU2CK15EZE/A08Ba4E1xpgXy2ZVveEUhIL1fNd5MG+lnaFp3vA+sP69O3UJrP7+sBh67p0Q3ye4zdXfh21rrDbTmofM87jh49x6iHTmJ799SvUTiQ77hdM/PPVc/dy3Z1jsD8N+ds5yuN5xM7ypc+CMpd6yeSuzz9vf7fXxey7K9kdFqVb8xsG0T5+6xJq4pH17zj/DbV/wjtnuOLnnImvc1rhQFEUpKYX89XM48FNgX+BqEdkqIr8qj1l1Ri5BaNA+t9CzWBG2ovgRJPb3E937CZLDiP3VH5VaJu3TbqF+IcL9oDjTuFAURSmaQiYvQ8Cg/ZwC/gq8VQ6j6o5cgtCgfW6hZ7EibEXxI0js7ye69xMkhxH7qz8qtUzap91C/UKE+0FxpnGhKIpSNIVMXvYA1wKvABcYY2YbY75QHrPqjFyCUL99brF9QSJs/UdPCUGQ2D/92ulTLRPz+5n6o1JvpH36xQfhzB8P+/aLD3pjJyhOGls1LhRFUUpMaMG+iJwJfBA4BhgAfo+lfXm0fOb5U3ExaTEid4+wugUG+4afzdCweD8trC9YhF13YvtSUHFx3qj7q6/42HiF/eBTJv7C5TB+pv5YKsaez5aakfpi+viGZitGGluHY8WTECVPnNR/XNSMv6pgP199FewrtUHoEdQY82tjzL8AXwAeAi4EHiyTXdVLOsvXirnwvUnWc+/O3OkvUynofRtWnmsds/JcK9PTup9Y23d+xvpyM1hfjpGYJYKWiPXsN3GBYZGp2M/19YWoFIOff/Z3Wf6W5X87rfpuP/PzqbB+pv6oVAPFjNFBx1+1vxUvXa/BUzdacdPQYsVK2DjRuFAURSkpoUdREbnXzjh2HdAKnA9MLJdhVYszy1cqOZw9ZjDHGma/Y+79HBx+evg2FCUMfr7W2wn3+vjfQE+lrVWU0lPMGJ3v+F9fYo3XOk4riqJUnNCpkoEfAc8aY3zumAgi8hFjzCOlMauKKSarUtiMYpqBRhkpfr428V3+/qepWpV6ZKSZ73KN1zpOK4qiVJxClo2tD5q42CwugT3VTzFZlcJmFNMMNMpI8fO1zlf9/c+dwU5R6oGRZr7LNV7rOK0oilJxCrnykg+PCEpEfgacDrxljDnCZ79gLUM7FegFLjTGPFtCm4rHKbJMDsDQgL1uWaxsMe47JufKHtPQYt38r7dz+C7lzRPh6Zstfcvxi+ADX7DO1d/tLwZNr5Ouf/Hn2CNsnwbdBTyrrNnra22T4Kxl1tKxtM+edcuwvxUjzleUaiWdJcxvjPZLnJKVHMVObnHBA9CxDVb/APb+xco29tzdVjsiYFIaH4qiKBWilJMXv7RltwI/Bm4POObjwGH24/3ADfZzZUkLNu+5CP7uDDj8TEsjkP4i/MxymLfCkR0sxBfY0AD8ZkH2l+mxl8DxX7fF1P+Y/cPymdthzeLhui2TrHbSdjnbaZmkX6C1itPXcvWpX71zllt+5T420pjta2ctg9Z9rbt9O3+k9e7yHhtthLvOU/9SapdIxPLZeSu9Gfd6dmaP5c6x9vhFcPT52fvPXgaNbRCLw4x/tNq/8xyND0VRlApS1hHXGLMG6MhR5UzgdmPxFDBBRA4op02hcAo2j/y09WXmFG/efR4YEz57TJCA1KSsKyx+Ymo/Mf9IhahK9RG2T4OE+H7HJjpd/nSR9QPOmVlssM//2N5O9S+l9vHL8DXQ4x3LnWPt4ad7999zkXW15vYzrax9Ov4qiqJUnFJOXrYVccxk4DXH9g67zIOIXCwiG0Rkw86dO4s4VQE4BZvNE0Yuds4lIC1EzD9SIaoyaoT217B9WogQf+K7vGVufw06r9+x6l9jglEdYytBU1vusTYtyHfvTx8XtF/joyLUvb8qihJI3smLiHwq1yNdzxjzqVztBDXvU+Z710xjzE3GmJnGmJmTJk0q4lQF4BRsJnaPXOycS0BaiJh/pEJUZdQI7a9h+7QQIX7nq94yt78GndfvWPWvMcGojrGVoL8791ibFuS796ePC9qv8VER6t5fFUUJJMyVlzNyPE4f4fl3AAc5tg8E3hhhmyMnLficOgc2/9JaFz11jiWunzrHFju3Ftdeuo20gNRv31m3wIsPhqubL1mAUt2E7VO/ei0T/Y9tnpjfX4PO2zJR/UupTxpb/cfy9Fj74oPe/Wcvs447e5m1/8wfa3woiqJUGDHG90JH6U4gMhV4MCDb2GnAJVjZxt4PLDXGHJOvzZkzZ5oNGzaU2FIXQdnG+rutL7Ogu96HaS9vBjHNNlZC/K7ujSp5/bWk2cZsYXJWRqUAfw3bnvrXaFP9PlurhMk2Ntjn9f90rDQ0Dx+v8ZGmZvx16jdXFdTuth+dVqxJ1cEV4wus31VQ9SNvO7Kg+psv2FwN7VfcX5WRU1C2MXuy8fdAPF1mjLkyR/0VwAnAfiKyA/j/gAb7uBuBh7AmLluwUiX/U2Hml5G04BOgIW49wBI7j7Q9t/7Ab18hdZXaJmyfBtXzK0v7aS5/LaQ9RakHIlFvbLif/fzfGSvueoqiKMqoEnryIiI3Ai3AicAtwNnA07mOMcbMy7PfAF8Ja4OiKIqiKIqiKGOXQq53H2uMOR/oNMZ8F5hNtl5FURRFURRFURSlbBQyeUnYz70i8jfAIHBw6U1SFEVRFEVRFEXxUojm5UERmQD8G/AsVkrjW8pilaIoiqIoiqIoiotCJi9LjDH9wL0i8iCWaL+vPGYpiqIoiqIoiqJkU8iyscythY0x/caYLmeZoiiKoiiKoihKOcl75UVE3glMBppFZAbDObL3wco+piiKoiiKoiiKUnbCLBv7GHAhcCDwH47yPcC3y2CToiiKoiiKoiiKh7yTF2PMbcBtInKWMebeUbBJURRFURRFURTFQyGalydFZJmI/BeAiBwuIheVyS5FURRFURRFUZQsCpm8/Bx4GPgbe/v/gK+V3CJFURRFURRFURQfCpm87GeMuRtIARhjksBQWaxSFEVRFEVRFEVxUcjkpUdE9sW6OSUi8gGgqyxWKYqiKIqiKIqiuCjkJpX/DDwATBORJ4FJwNllsUpRFEVRFEVRFMVFIZOXF4FfAb3AXuB+LN1L3ZNKGXoHh2hpjNI7MERLQ5RIRPIfqChKSdFYrD20zxRFUZRSUsjk5Xase7v8wN6eBywHPl1qo6qJVMqwq2eABSs2sn5bB7OmtrN03gz2bW3UL2BFGUU0FmsP7TNFURSl1BSieXmPMeZzxpjH7cfFwLvLZVi10Ds4xIIVG1m3dRfJlGHd1l0sWLGR3kHNVaAoo4nGYu2hfaYoiqKUmkImLxttkT4AIvJ+4MnSm1RdtDRGWb+tI6ts/bYOWhqjFbJIUcYmGou1h/aZoiiKUmoKmby8H/i9iGwTkW3AOuBDIrJZRJ4vi3VVQO/AELOmtmeVzZraTu+A/nOoKKOJxmLtoX2mKIqilJpCJi+nAAcDH7IfBwOnAqcDZwQdJCKniMj/isgWEfmmz/4LRWSniGyyH58r7C2Ul5aGKEvnzWD2tH2JRYTZ0/Zl6bzpRARSxtDdnySVMpU2U1FqmlTKjqUcMeUfizNoadB/8auVnOOnjpuKoihKEYQW7BtjXi20cRGJAv8JfATYAawXkQeMMS+6qt5ljLmk0PZHg0hE2Le1kZsvmGlly+kfYiiV4qJbN6gAVVFKQFhRtycWNXNV1ZPps/Nn0tIUZfuuXr6/6iX+uqdfx01FGWMceduRlTZBqRMKufJSDMcAW4wxW40xA8BK4Mwyn7PkRCJCW1OMiAgIfPGOZ1WAqiglohBRtzMW25pi+sO3BohErHHzH2/+AydcvZr7N72h46aiKIpSNOWevEwGXnNs77DL3JwlIs+LyD0icpBfQyJysYhsEJENO3fuLIetoVABqhKGavHXWkBjqjoop89qHyulRsdYRRm7lHvy4ve3qHuh82+AqcaY9wK/A27za8gYc5MxZqYxZuakSZNKbGZ4VICqhKFa/LUW0JiqDsrps9rHSqnRMVZRxi6F3KSyGHYAzispBwJvOCsYY3Y5Nm8GFpfZphGRFqC61+eraFhRikNjqv7RPlZqkanfXFVQ/W0/Oq1MlthcMb687StKjVDuyct64DARORh4HZgLnOusICIHGGPetDf/AXipzDYVTCpl6B0cskTCg0NMbG7gpvOPprUpRk9/kuaYv2g4mUyRSA5l1YvFvBe7stpXEbIyxggS4htj2NuXzMRPS0OUaDTcxWK/mAI8ZcZY9Yo5R1jGSny732c8Ghke/waStLda46a7T7r7k3X/2SiKoiilo6yTF2NMUkQuAR4GosDPjDF/FJErgQ3GmAeABSLyD0AS6AAuLKdNheLOhLTgpEOZe8wUvrpyU+YfxOvmTmff1sasHz3JZIqO3gFPvfaWxqwJTNhMS4pSz6SF+ABtTTGGhlLs6vHGjzvO/PCLqRs/exQDQykWrNjkiLPpNEQjfOmOZws+R1jGSny73+f186Zz9Lvas/pv8Vnv5f6NO/jEjAO5f+MOLjh2qk+f1N9noyiKopSWcmteMMY8ZIx5tzHmEGPM9+2yy+2JC8aYbxlj/t4Y8z5jzInGmD+V26ZCcGdC+tgRB/DVlZuyMiN9deUmT9acRHLIt14iOZSzfc3CoyhWXISJs6Bj3THV2TvIghWbXHG2id29g0Wdo5D3MRbi2/0+Zx+yn6f/Ft37PB874oDMs3+f1N9noyiKopSWci8bq3ncWXIO3b/NN2tOa1P2R9naFAtVT7PwKIqXsPHjh19MHdTe4tveQe0tRZ0jLGMlvt3vc5/mBt/3nR4/D92/LVPmrlNvn42iBKIaFkUpirJfeal13FlytrzV7Zs1p6c/mVXW058MVU+z8CiKl7Dx44dfTL3W0evb3msdvUWdIyxjJb7d73NPYtD3fafHzy1vdQf2Sb19NoqiKEppGVNXXnIJZ4P2tTREufGzR9HZO8hB7S283d3PdfOm89UV2Wvx3VlzmmNRrps73bNmvzmWXc/d/msdvUxsadAsPErNUUphektDcPzs7RvMKbD3y2w1saWBpfOm+2peZk/bN2csj4SxkmXLOY4dOLGZxOAQv/j8+9m+q5drf/d//HVPf0bzsnTedFIGWhujPn1Sf5+NoiiKUlrGzOQll3AWyLlvYCjFt+7bnNl3w2eP4sbzjmZcPMaexCAxnx9osViE9pZGT1Yyv2xj7vaXzpte3g9DUUpMqYXpIkJLY5QbPnsU+zQ3sCcxSDwW8U2C4RbY+2Uvi0cjmIEkP/zUkZk/CRqiEdoaY1kxWupsY0GZ1OpRkD4wlOKBTa/ziRkHsuje57PGs5bGGPGGCJ+eeRDfX/USf93Tz9J5VgKTsfDZKEotcORtR1baBEUJxZhZNpZLOJt/X7ao9Et3PMtfuvqY9q2HmH7lI3z+9md8RaaxWIRx8QYiIoyLN/hOXPzaX7CitKJhRSk3pRam9w4O8fnbn2H6lY9k4qwvmQot4k9nL4uI9ZxIDvGlO57lhKtXc8i3H+KEq1fzpTueJZEcyorRUqdJ9rOlHn+cp8extCDfPZ7t6Ezw2VueZk9fkvs3vZEpTyRTdf/Z/pDveAAAFutJREFUKIqiKKWl7q68BC7/yiOcLXRfWnCa3vYT+YZZRjNWBL1KfeHx7Rx+XMx9PFoao5xyxDuyrrwEicDDxN5IEgAo+Un3f1BCE7dQP12u45yiKIpSKHV15SW9dOXzt23g3d/5Lz5/2wZ29QxYP2RyCGd7+/33/bWrj5f/6i/Q3/JWd9a2W+SbyxYnY0XQq9QPfr7d3ecvsO/uS+aNAT8GBof4+BEH8KU7nuXd3/kvvnTHs6FF/H72jSQBgJKf9DgWlNDEKdR3lus4pyiKohRKXU1eci1dSQtnZ0/bl1hEmD1t34w4NBKBf/v0e7P2/dun30vKGP7z8S0sPit733Vzp/PwC29mbbtFpmGX0eSyS1GqET/fvvXJV7hu3vTsOJk3nVuffKWopWSDKeNZIvbEyzu5bu50TyyGib2wxyrFkR7HHn7hTc94ufis9/LwC296xk0d5xRFUZRiEGPy/wtabcycOdNs2LDBU54yhuXrtnHm9MmZpSa/3vQ6582eSkSEZDJFIjmUJaBPb+9NDCL22vTuviTPbu9gzmH7c8i3H2LV/OOYsm9r5jjBYJDMdkNEaGyI0jcwRMoYWppi9PYPce+zr/GBaftx6P5t7O7tJxaJMq7ZKwx2L3FpjkVIJFMqYi0NFf/ggvy1VkkZw7u/818kHVdQYhHhT1ee4omvb9zzHF864VAO3b+NLW91c8PqLVx99vs89SIRyY6Bhgh/enNPVtxt39XDe96xj+dYEWvCkhHdN0b5+t3hzuunQwubNa2U2dVcVLXPut93PBohkRzKjKfOcbXHuWQwM75Z+1saoohIuT5DZfSoeIeFHWOnfnNVWe3Y9qPTCjugyu7zcuTBUyptwojYfMHmMNUq7q/KyKmrBd/OpSbObEQDg0PEIv6ZilY+vZ2tb/fwjY+9h3/55fNZ+3b39nPrhTOZNC7Oxbc/4zlu6WNbMtsvvrKLQ/cf52lj5dPbeaqlgY8fcQCX3PmMb5aktKAXrH8wS5m1SVFKTXqJ0LqtuzJl18+b7o2vedP519MP55I7h335lguO9tS74bNHMTiU8qQxnjyxJTvu5k2neyDpie+WxmhWvRs+exQLT/lbvn73c5myf//M+zzH+sVV2Kxppc6uViu43/eCkw5l7jFTeH13L5MntPDMqx0c/a72rP5Np0j+xIwDuX/jDuYeM4Xu/kH2bW1iwNPv9f8ZKoqiKCOjrpaN+S01+erKTQymDInkkO++jx1xAF864VD+5ZfPe/bFolGOsr+I/Y5zbh81pd23jY8dcQBnTp8cOktSqbM2KUqp8Vvq+MHDJnl9fMUm9vYls8qGUnjq7e4d9M24t7t30NOep2zlpsxrZ3tfv/u5rLKv3/2c51i/uAobf2M1Tt3v+2NHHMBXV27ikEnj+OrKTcw+ZD9P/y669/lMFrJ0/Unj4nT69nv9f4aKoijKyKirKy/5Mgrlyhrmt29cPP9x6e22uP+5D92/DRH/NvwyHWn2MaXa8b13SYDfHtTeklXmFycHtbeEOjaobJ/mhqLbc8dV2Pgbq3Hqft/pLGLpfg3KCOfMNpYe+1oa/cfMev8MFaVa2fzK9oLq1/oyM6V2qasrL7kyCgXt2/JWd2CGnD2JwZzHObeDsi1teaubPYnB0JmONPuYUgu4710SFCevdfRmlfnFyWsdvaGODSrbkxgsuj13XIWNv7Eap+73nR470/0aNNY5s42lx76gfqr3z1BRFEUZGXU1eWlpiAZmFGqO+e97+IU3uWH1Fk+2sZvPP5qIfZdvTxalgGxjQe3/etPrOTMdpVKG7v4kKWOICCx1nU+z8ijVjm98zZvO/uOa2HT5R9j6w1PZdPlHaIqKp96ElgYfn5/OhJYGT3uesrnTM6/ztTfRdaxfXAVl/2uORTIx2t2fpDkWGZNZAt2fTzqL2J937uW6udNZ9+e3Pf2bzjbmzDq2c28fE337qf4/Q0VRFGVk1FW2MYChoVR25iFHVq9c2cYSA0MkU4Zx8Rh9A0N09yczotMFJx3KhccdTFvce1xPf5InXt7J/BWbsuq5s4YNDA4xmDIeu/yEvzd+9iiikQgtTZqBpwRU/IOrt2xjfgwNpUgMWjGUzvQXj0XY05f0JMlob2nMm22spSFKytaqOesB+bONBWSxAorKItYci9DRO+gR57e3NJQrK2BV+2z6vlktTVG27+pl265uph80kbbGmJVtrCFKwpX9rad/iNamKImBIeKxKH1DqYL6RKlqKt5hmm2sMlTbsjHNNjZ2qCvNC0A0GmGcPVkZF89eCx+LRRgXy943zv5H9fO3bchkT9p0+UcyolOA//jdy6zb2sFN5x/tOe7i25/x1Lv5gpmZ7GFtti3xxhhx2w6nXU4BLMC6rbv44h3PcvMFMzNLchSl2ukdHMqKBfDGUVpgnxVHjljIxIz9HImIJ14B/zKfmHe3F1Tmxpn9r60pRnd/0hOjC1ZszI7zMRSnkYiAwD/e/Ies/p49bV9uOv9ozlv2ND897+jA/bFYhDZHiuqx+BkqiqIoxVNXy8aKxS1CDRKdugX2pRDtjlXhr1Jf+CXLCBtH1Y7GqJegzyTtB0EJTGqt7xVFUZTqQycveEWoYQX2pRDtjlXhr1Jf+An2C0lUUc1ojHoJ+kzSfhCUwKTW+l5RFEWpPso+eRGRU0Tkf0Vki4h802d/k4jcZe//g4hMLbdNbtwiVD/RqVNgH3RcMYLTUrShKJXGL2FFLOIV5/vFUbWjMeol12eydN6MjIDf3fdp3ZKiKIqiFEtZBfsiEgX+D/gIsANYD8wzxrzoqPNl4L3GmC+KyFzgk8aYc3K1Ww4BtFukG49GsoTBTuF/ruOKEZyWog0lkIp/kGNBsA/+yTLAK6b3i6NqZ5RjtCZ8NugzSZe7x9DmWJRYrPb6XslLTfgrqGC/1KhgX6kU5V6AfAywxRizFUBEVgJnAi866pwJXGG/vgf4sYiIGeU0aG6RLvgLg8McV4pzK0qtEZQsIyiBRi2hMeol6DNxlocZQxVFURSlEMr9N9hk4DXH9g67zLeOMSYJdAH7ltkuRVEURVEURVFqjHL/heh3ec59RSVMHUTkYuBigClTqutSpaK4UX9Vag31WaWWqEZ/LXRZ2rZ4/jrVzOZXthdUv9qWmSm1S7mvvOwADnJsHwi8EVRHRGLAeKDDVQdjzE3GmJnGmJmTJk0qk7mKUhrUX5VaQ31WqSXUXxVl7FLuyct64DAROVhEGoG5wAOuOg8AF9ivzwYeG229i6IoiqIoiqIo1U9Zl40ZY5IicgnwMBAFfmaM+aOIXAlsMMY8ACwDlovIFqwrLnPLaZOiKIqiKIqiKLVJWVMllwsR2Qm8Wmk7Ksx+wNuVNqKKCPo83jbGnDLaxjgpwF/rpU/1fYyMWvLZUlPtvlPt9sHo21iN/loL/RSWenkv1fI+Ku6vysipycmLAiKywRgzs9J2VAv18HnUw3sAfR9K8VT7Z17t9kFt2Fhu6ukzqJf3Ui/vQ6kO9I5hiqIoiqIoiqLUBDp5URRFURRFURSlJtDJS+1yU6UNqDLq4fOoh/cA+j6U4qn2z7za7YPasLHc1NNnUC/vpV7eh1IFqOZFURRFURRFUZSaQK+8KIqiKIqiKIpSE+jkRVEURVEURVGUmkAnLzWGiEwQkXtE5E8i8pKIzK60TZVGRC4VkT+KyAsiskJE4pW2KSwicpCIPG735R9F5KuVtqlYRCQuIk+LyHP2e/lupW0qFhGJishGEXmw0rbUO7UUA9XuF/r9ACJyioj8r4hsEZFvVtqeYhGRn4nIWyLyQqVtKZZaim2lttDJS+1xHfBbY8zfAu8DXqqwPRVFRCYDC4CZxpgjgCgwt7JWFUQS+Lox5u+ADwBfEZHDK2xTsfQDJxlj3gdMB04RkQ9U2KZi+SpjPLZGkVqKgWr3izH9/SAiUeA/gY8DhwPzqtiX8nErUOs3U6yl2FZqCJ281BAisg9wPLAMwBgzYIzZXVmrqoIY0CwiMaAFeKPC9oTGGPOmMeZZ+/VerB8bkytrVXEYi257s8F+1FxGEBE5EDgNuKXStowFaiUGqt0v9PsBgGOALcaYrcaYAWAlcGaFbSoKY8waoKPSdoyEWoltpfbQyUttMQ3YCfzcXrpwi4i0VtqoSmKMeR24GtgOvAl0GWP+u7JWFYeITAVmAH+orCXFYy+r2QS8BTxijKnF93ItsBBIVdqQsUaVx0C1+4V+P1g/jF9zbO9AfyxXBVUe20qNoZOX2iIGHAXcYIyZAfQANbumtxSIyESsf9YOBv4GaBWRz1bWqsIRkTbgXuBrxpg9lbanWIwxQ8aY6cCBwDEickSlbSoEETkdeMsY80ylbRlrVHMM1Ihf6PcDiE9ZzV39rTeqObaV2kQnL7XFDmCH49/se7C+rMYyHwZeMcbsNMYMAvcBx1bYpoIQkQasgf0Xxpj7Km1PKbCXq6ym9tZsHwf8g4hsw1pycpKI3FFZk+qfGoiBWvAL/X6wPoODHNsHUkPLiOuRGohtpQbRyUsNYYz5C/CaiLzHLjoZeLGCJlUD24EPiEiLiAjWZ1IzIlXb5mXAS8aY/6i0PSNBRCaJyAT7dTPWxPJPlbWqMIwx3zLGHGiMmYqV+OExY0zNXcmrJWohBmrBL/T7AYD1wGEicrCINGL11QMVtmnMUguxrdQmOnmpPeYDvxCR57EyOv2gwvZUFPtfxnuAZ4HNWD59U0WNKozjgPOw/sndZD9OrbRRRXIA8Ljtm+uxNC9VmVJWqSrqKQYqzZj+fjDGJIFLgIex/sS62xjzx8paVRwisgJYB7xHRHaIyEWVtqkINLaVsiDG6HJQRVEURVEURVGqH73yoiiKoiiKoihKTaCTF0VRFEVRFEVRagKdvCiKoiiKoiiKUhPo5EVRFEVRFEVRlJpAJy+KoiiKoiiKotQEOnlRFEVRFEVRFKUm0MlLhRGRE0Qk8F4YInKhiPy4DOe9UET+xrG9TUT2K/V5lPoln++GOH6miCwN2LdNRPYTkQki8uVSnVOpH9xjWI56t4rI2Tn2rxaRmSW2Tf1WCaRUvhvi+CtF5MM+5Rl/tF8fW6pzKspooJOXscuFQN7BU1HKhTFmgzFmQZ5qE4Av56mjjE0upHrHMPVbJRcXMgq+a4y53BjzuzzVTgCOzVNHUaoKnbyEQERaRWSViDwnIi+IyDkicrSI/I+IPCMiD4vIAXbd1SJyrYj83q57jF1+jF220X5+TxF2TBKRe0Vkvf04zi6/QkR+Zp97q4gscBzzryLyJxF5RERWiMg37H9VZmLdiXmTiDTb1eeLyLMisllE/jaHHW0i8nO73vMicpZd3i0ii+3P5Hf2e07b9A+Fvl9l5FTSd23/mCAWu0TkfLt8uYh82PXv374i8t/2OX4KiN3Mj4BDbD/9N7usTUTusf36FyIi3rNnbJhl2/yciDwtIuPsfz3vF5HfiMgrInKJiPyzfe6nRKS9uE9bGQkiMtXu09vsceUeEWnx81e/MUxELrfHxRdE5KZcfpHDho+KyDp7HPyliLTZ5dtE5Lvu8dEekx+xy38qIq+KdQVb/XYMUQnftcfl++zXZ4pIQkQaRSQuIlvt8sxVFBE5xbbxCeBTabuBLwKX2rbMsZs/3va/rZLnKoyILLRj4jkR+ZFdtlpErhGRNSLyku3P94nIyyJyVTGfsaJkYYzRR54HcBZws2N7PPB7YJK9fQ7wM/v16nRd4HjgBfv1PkDMfv1h4F779QnAgznOfSHwY/v1ncAH7ddTgJfs11fY9jQB+wG7gAasAXIT0AyMA14GvuGwc6bjPNuA+fbrLwO35LBpMXCtY3ui/WyAj9uvfwX8t23H+4BNle7HsfiosO/eCJwGHAGsd7T9MtDmPB5YClxuvz7N9qX9gKlpOxzn7AIOxPrzZV06JnzO3whsBWY534cdU1vsmJhkt/dFu841wNcq3W9j8WH3tQGOs7d/BvxLHn91jmHtjtfLgTPs17cCZ+c472qssXI/YA3QapcvcvjkNnzGR+DHwLfs16eo347NRyV81/aJV+zXV2ONsccBHwJWOI8H4sBrwGFYfwzdzfDYewX27wLHMb+0/fRwYEuO9/1x+z22ON+H/f4W26+/CrwBHID1G2UHsG+l+0wftf2IoYRhM3C1iCwGHgQ6sX6QPWL/QRIF3nTUXwFgjFkjIvuIyASsL5zbROQwrEGuoQg7Pgwc7vhTZh8RGWe/XmWM6Qf6ReQt4B3AB4FfG2MSACLymzzt32c/P4P9z0wOO+amN4wxnfbLAeC39uvNQL8xZlBENmMN7sroU0nfXYs1CXoVuAG4WEQmAx3GmG7Xn4vHY/ucMWaViHS6G3PwtDFmB4CIbMLyrSd86r0HeNMYs95ud499DMDjxpi9wF4R6QLSsbEZeG/I96eUnteMMU/ar+8Avk1uf3VyoogsBFqAduCPDPdrGD6A9WPtSftcjViTjDR+4+MHgU8CGGN+q347phlV3zXGJEVki4j8HXAM8B9Y42gUa+x18rdYE52XAUTkDuDiHM3fb4xJAS+KyDty1Psw8HNjTK9tU4dj3wP282bgj8aYN+1zbwUOwvqTVVGKQicvITDG/J+IHA2cCvwQeAQrGGcHHeKz/T2sL55P2pdqVxdhSgSYnZ6MpLEHxn5H0RBW3xa6bCLdRvr4IATvewQYNMaky1Pp9owxKRFRX6sAFfbdNcBXsK4SfgfrR97ZeL9Yg84dhJ+v+xHkp+42Uo7tVI72lPLj7q+95PZXAEQkDvwE69/s10TkCqx/mwtBgEeMMfMC9vuNj4WMseq39U0lfHct1tWPQeB3WFdNosA3QtiXC6ef5fLxML7q9NP0tvqqMiJU8xICsbKC9Bpj7sC6PPt+YJKIzLb3N4jI3zsOOccu/yDQZYzpwlqu87q9/8IiTflv4BKHXdPz1H8COMNeA9uGtRwnzV6sf9RLYcfEIttRykwlfdcY8xrWEprDjDFbsfzxG/hPXtYA/2if++NA2qdG4qd/Av5GRGbZ7Y7TSXTVMyXtm8A84CmC/dXpG+kfe2/bY10x2ZKeAo4TkUPtc7WIyLvzHPME8Bm7/kdRvx3LVMJ31wBfA9YZY3YC+2JdZfmjq96fgINF5BCHfWlG+lvg/4lIC4Co7koZJXTyEo4jgaftS/3fAS7HGmAWi8hzWLoSZ7aOThH5Pdaa/4vssiXAD0XkSax/RophATBTLEHgi1hCu0DsZQcPAM9hLXnYgLVOGqx/aG6UbMF+WK4CJoolLnwOOLHA45XRo9K++wfg/+zXa4HJ+C+V+S6WSPRZ4KPAdgBjzC6sZTwvyLDwORTGmAGsydj19nt9hML/jVdGl5eAC0TkeazlM9cT7K+3Yo9hWP/s3oy1ROV+rPX/BWH/+LsQWGGf/ymsH4K5+C7wUdtvP461LGiv+u2YpBK++wesJeJr7O3ngecdKyAAMMb0YS0TWyWWYP9Vx+7fAJ+UbMF+KIwxv8X6jbHBfi9+V3wUpeSIy8eVESIiq7HEbxsqbQtYmcFsfUEL1gB3sTHm2UrbpVQf1ea7ytjCXpL4oDHmiAqbEhoRaQKGbP3BbOAGY0y+K+JKnVGLvqsotYxeiq5/bhKRw7H+ubtNJy6KoiglYwpwt4hEsBKWfL7C9iiKotQ9euWlShCRf8JKKejkSWPMVyphD1SnTUr1UQ1+IiK/Ag52FS8yxjw8WjYo1U01+kg12qRUH5X2ExE5EiuFs5N+Y8z7R+P8iuJGJy+KoiiKoiiKotQEKthXFEVRFEVRFKUm0MmLoiiKoiiKoig1gU5eFEVRFEVRFEWpCXTyoiiKoiiKoihKTfD/AzQYfHVF7Dw0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 823.5x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.pairplot(iris_data_clean, hue='class')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, I purposely inserted numerous errors into this data set to demonstrate some of the many possible scenarios you may face while tidying your data.\n",
    "\n",
    "The general takeaways here should be:\n",
    "\n",
    "* Make sure your data is encoded properly\n",
    "\n",
    "* Make sure your data falls within the expected range, and use domain knowledge whenever possible to define that expected range\n",
    "\n",
    "* Deal with missing data in one way or another: replace it if you can or drop it\n",
    "\n",
    "* Never tidy your data manually because that is not easily reproducible\n",
    "\n",
    "* Use code as a record of how you tidied your data\n",
    "\n",
    "* Plot everything you can about the data at this stage of the analysis so you can *visually* confirm everything looks correct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Testing our data\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "At SciPy 2015, I was exposed to a great idea: We should test our data. Just how we use unit tests to verify our expectations from code, we can similarly set up unit tests to verify our expectations about a data set.\n",
    "\n",
    "We can quickly test our data using `assert` statements: We assert that something must be true, and if it is, then nothing happens and the notebook continues running. However, if our assertion is wrong, then the notebook stops running and brings it to our attention. For example,\n",
    "\n",
    "```Python\n",
    "assert 1 == 2\n",
    "```\n",
    "\n",
    "will raise an `AssertionError` and stop execution of the notebook because the assertion failed.\n",
    "\n",
    "Let's test a few things that we know about our data set now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We know that we should only have three classes\n",
    "assert len(iris_data_clean['class'].unique()) == 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We know that sepal lengths for 'Iris-versicolor' should never be below 2.5 cm\n",
    "assert iris_data_clean.loc[iris_data_clean['class'] == 'Iris-versicolor', 'sepal_length_cm'].min() >= 2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We know that our data set should have no missing measurements\n",
    "assert len(iris_data_clean.loc[(iris_data_clean['sepal_length_cm'].isnull()) |\n",
    "                               (iris_data_clean['sepal_width_cm'].isnull()) |\n",
    "                               (iris_data_clean['petal_length_cm'].isnull()) |\n",
    "                               (iris_data_clean['petal_width_cm'].isnull())]) == 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And so on. If any of these expectations are violated, then our analysis immediately stops and we have to return to the tidying stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Exploratory analysis\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Now after spending entirely too much time tidying our data, we can start analyzing it!\n",
    "\n",
    "Exploratory analysis is the step where we start delving deeper into the data set beyond the outliers and errors. We'll be looking to answer questions such as:\n",
    "\n",
    "* How is my data distributed?\n",
    "\n",
    "* Are there any correlations in my data?\n",
    "\n",
    "* Are there any confounding factors that explain these correlations?\n",
    "\n",
    "This is the stage where we plot all the data in as many ways as possible. Create many charts, but don't bother making them pretty — these charts are for internal use.\n",
    "\n",
    "Let's return to that scatterplot matrix that we used earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.pairplot(iris_data_clean)\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data is normally distributed for the most part, which is great news if we plan on using any modeling methods that assume the data is normally distributed.\n",
    "\n",
    "There's something strange going on with the petal measurements. Maybe it's something to do with the different `Iris` types. Let's color code the data by the class again to see if that clears things up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 823.5x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.pairplot(iris_data_clean, hue='class')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sure enough, the strange distribution of the petal measurements exist because of the different species. This is actually great news for our classification task since it means that the petal measurements will make it easy to distinguish between `Iris-setosa` and the other `Iris` types.\n",
    "\n",
    "Distinguishing `Iris-versicolor` and `Iris-virginica` will prove more difficult given how much their measurements overlap.\n",
    "\n",
    "There are also correlations between petal length and petal width, as well as sepal length and sepal width. The field biologists assure us that this is to be expected: Longer flower petals also tend to be wider, and the same applies for sepals.\n",
    "\n",
    "We can also make **violin plots** of the data to compare the measurement distributions of the classes. Violin plots contain the same information as [box plots](https://en.wikipedia.org/wiki/Box_plot), but also scales the box according to the density of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "for column_index, column in enumerate(iris_data_clean.columns):\n",
    "    if column == 'class':\n",
    "        continue\n",
    "    plt.subplot(2, 2, column_index + 1)\n",
    "    sb.violinplot(x='class', y=column, data=iris_data_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enough flirting with the data. Let's get to modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Classification\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Wow, all this work and we *still* haven't modeled the data!\n",
    "\n",
    "As tiresome as it can be, tidying and exploring our data is a vital component to any data analysis. If we had jumped straight to the modeling step, we would have created a faulty classification model.\n",
    "\n",
    "Remember: **Bad data leads to bad models.** Always check your data first.\n",
    "\n",
    "<hr />\n",
    "\n",
    "Assured that our data is now as clean as we can make it — and armed with some cursory knowledge of the distributions and relationships in our data set — it's time to make the next big step in our analysis: Splitting the data into training and testing sets.\n",
    "\n",
    "A **training set** is a random subset of the data that we use to train our models.\n",
    "\n",
    "A **testing set** is a random subset of the data (mutually exclusive from the training set) that we use to validate our models on unforseen data.\n",
    "\n",
    "Especially in sparse data sets like ours, it's easy for models to **overfit** the data: The model will learn the training set so well that it won't be able to handle most of the cases it's never seen before. This is why it's important for us to build the model with the training set, but score it with the testing set.\n",
    "\n",
    "Note that once we split the data into a training and testing set, we should treat the testing set like it no longer exists: We cannot use any information from the testing set to build our model or else we're cheating.\n",
    "\n",
    "Let's set up our data first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data_clean = pd.read_csv('iris-data-clean.csv')\n",
    "\n",
    "# We're using all four measurements as inputs\n",
    "# Note that scikit-learn expects each entry to be a list of values, e.g.,\n",
    "# [ [val1, val2, val3],\n",
    "#   [val1, val2, val3],\n",
    "#   ... ]\n",
    "# such that our input data set is represented as a list of lists\n",
    "\n",
    "# We can extract the data in this format from pandas like this:\n",
    "all_inputs = iris_data_clean[['sepal_length_cm', 'sepal_width_cm',\n",
    "                             'petal_length_cm', 'petal_width_cm']].values\n",
    "\n",
    "# Similarly, we can extract the class labels\n",
    "all_labels = iris_data_clean['class'].values\n",
    "\n",
    "# Make sure that you don't mix up the order of the entries\n",
    "# all_inputs[5] inputs should correspond to the class in all_labels[5]\n",
    "\n",
    "# Here's what a subset of our inputs looks like:\n",
    "all_inputs[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our data is ready to be split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "(training_inputs,\n",
    " testing_inputs,\n",
    " training_classes,\n",
    " testing_classes) = train_test_split(all_inputs, all_labels, test_size=0.25, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our data split, we can start fitting models to our data. Our company's Head of Data is all about decision tree classifiers, so let's start with one of those.\n",
    "\n",
    "Decision tree classifiers are incredibly simple in theory. In their simplest form, decision tree classifiers ask a series of Yes/No questions about the data — each time getting closer to finding out the class of each entry — until they either classify the data set perfectly or simply can't differentiate a set of entries. Think of it like a game of [Twenty Questions](https://en.wikipedia.org/wiki/Twenty_Questions), except the computer is *much*, *much* better at it.\n",
    "\n",
    "Here's an example decision tree classifier:\n",
    "\n",
    "<img src=\"iris_dtc.png\" />\n",
    "\n",
    "Notice how the classifier asks Yes/No questions about the data — whether a certain feature is <= 1.75, for example — so it can differentiate the records. This is the essence of every decision tree.\n",
    "\n",
    "The nice part about decision tree classifiers is that they are **scale-invariant**, i.e., the scale of the features does not affect their performance, unlike many Machine Learning models. In other words, it doesn't matter if our features range from 0 to 1 or 0 to 1,000; decision tree classifiers will work with them just the same.\n",
    "\n",
    "There are several [parameters](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) that we can tune for decision tree classifiers, but for now let's use a basic decision tree classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97368421052631582"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Create the classifier\n",
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "# Train the classifier on the training set\n",
    "decision_tree_classifier.fit(training_inputs, training_classes)\n",
    "\n",
    "# Validate the classifier on the testing set using classification accuracy\n",
    "decision_tree_classifier.score(testing_inputs, testing_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heck yeah! Our model achieves 97% classification accuracy without much effort.\n",
    "\n",
    "However, there's a catch: Depending on how our training and testing set was sampled, our model can achieve anywhere from 80% to 100% accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1Hk+vzSEO91hM/rmc9QWLKV0EWcPoAsY5fO+Cz+sWGfdjwJO0zxO0vrXASa19OvA4R7iQe6zXyehiz6+39mvbL0yA1/H9F2Of4Ni9GLuaOucO1sXoAuBXgVNnXdMq6jwdeFlr/wnw/tY+Ffhy+/1d29rHXJ2rrPG4eW2O1TLP4S/GXs73X4z93LSey5k/EFN8gC8DvsjoCO69re/9wFvHxrwPuO6Q7X4CeKj9Aj4EbJ11Laupk9GFrX9u9dwPvHls2/e27R4DLp11LdOok9G53kda/xeAn591Laus820t4L4IfOhg8LV1v8Hoovpe4JpZ1zLpGo/D1+bHGJ06/B9GR+lbgXcA72jrw+iPNH2p1bNpWs+ln4yVpM71eo5ektQY9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kde7/AC40cbqbQG9XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_accuracies = []\n",
    "\n",
    "for repetition in range(1000):\n",
    "    (training_inputs,\n",
    "     testing_inputs,\n",
    "     training_classes,\n",
    "     testing_classes) = train_test_split(all_inputs, all_labels, test_size=0.25)\n",
    "    \n",
    "    decision_tree_classifier = DecisionTreeClassifier()\n",
    "    decision_tree_classifier.fit(training_inputs, training_classes)\n",
    "    classifier_accuracy = decision_tree_classifier.score(testing_inputs, testing_classes)\n",
    "    model_accuracies.append(classifier_accuracy)\n",
    "    \n",
    "plt.hist(model_accuracies)\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's obviously a problem that our model performs quite differently depending on the subset of the data it's trained on. This phenomenon is known as **overfitting**: The model is learning to classify the training set so well that it doesn't generalize and perform well on data it hasn't seen before.\n",
    "\n",
    "### Cross-validation\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "This problem is the main reason that most data scientists perform ***k*-fold cross-validation** on their models: Split the original data set into *k* subsets, use one of the subsets as the testing set, and the rest of the subsets are used as the training set. This process is then repeated *k* times such that each subset is used as the testing set exactly once.\n",
    "\n",
    "10-fold cross-validation is the most common choice, so let's use that here. Performing 10-fold cross-validation on our data set looks something like this:\n",
    "\n",
    "(each square is an entry in our data set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "def plot_cv(cv, features, labels):\n",
    "    masks = []\n",
    "    for train, test in cv.split(features, labels):\n",
    "        mask = np.zeros(len(labels), dtype=bool)\n",
    "        mask[test] = 1\n",
    "        masks.append(mask)\n",
    "    \n",
    "    plt.figure(figsize=(15, 15))\n",
    "    plt.imshow(masks, interpolation='none', cmap='gray_r')\n",
    "    plt.ylabel('Fold')\n",
    "    plt.xlabel('Row #')\n",
    "\n",
    "plot_cv(StratifiedKFold(n_splits=10), all_inputs, all_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that we used **Stratified *k*-fold cross-validation** in the code above. Stratified *k*-fold keeps the class proportions the same across all of the folds, which is vital for maintaining a representative subset of our data set. (e.g., so we don't have 100% `Iris setosa` entries in one of the folds.)\n",
    "\n",
    "We can perform 10-fold cross-validation on our model with the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "# cross_val_score returns a list of the scores, which we can visualize\n",
    "# to get a reasonable estimate of our classifier's performance\n",
    "cv_scores = cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10)\n",
    "plt.hist(cv_scores)\n",
    "plt.title('Average score: {}'.format(np.mean(cv_scores)))\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a much more consistent rating of our classifier's general classification accuracy.\n",
    "\n",
    "### Parameter tuning\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Every Machine Learning model comes with a variety of parameters to tune, and these parameters can be vitally important to the performance of our classifier. For example, if we severely limit the depth of our decision tree classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decision_tree_classifier = DecisionTreeClassifier(max_depth=1)\n",
    "\n",
    "cv_scores = cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10)\n",
    "plt.hist(cv_scores)\n",
    "plt.title('Average score: {}'.format(np.mean(cv_scores)))\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the classification accuracy falls tremendously.\n",
    "\n",
    "Therefore, we need to find a systematic method to discover the best parameters for our model and data set.\n",
    "\n",
    "The most common method for model parameter tuning is **Grid Search**. The idea behind Grid Search is simple: explore a range of parameters and find the best-performing parameter combination. Focus your search on the best range of parameters, then repeat this process several times until the best parameters are discovered.\n",
    "\n",
    "Let's tune our decision tree classifier. We'll stick to only two parameters for now, but it's possible to simultaneously explore dozens of parameters if we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.959731543624161\n",
      "Best parameters: {'max_depth': 3, 'max_features': 3}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "parameter_grid = {'max_depth': [1, 2, 3, 4, 5],\n",
    "                  'max_features': [1, 2, 3, 4]}\n",
    "\n",
    "cross_validation = StratifiedKFold(n_splits=10)\n",
    "\n",
    "grid_search = GridSearchCV(decision_tree_classifier,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(all_inputs, all_labels)\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's visualize the grid search to see how the parameters interact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_visualization = grid_search.cv_results_['mean_test_score']\n",
    "grid_visualization.shape = (5, 4)\n",
    "sb.heatmap(grid_visualization, cmap='Blues', annot=True)\n",
    "plt.xticks(np.arange(4) + 0.5, grid_search.param_grid['max_features'])\n",
    "plt.yticks(np.arange(5) + 0.5, grid_search.param_grid['max_depth'])\n",
    "plt.xlabel('max_features')\n",
    "plt.ylabel('max_depth')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a better sense of the parameter space: We know that we need a `max_depth` of at least 2 to allow the decision tree to make more than a one-off decision.\n",
    "\n",
    "`max_features` doesn't really seem to make a big difference here as long as we have 2 of them, which makes sense since our data set has only 4 features and is relatively easy to classify. (Remember, one of our data set's classes was easily separable from the rest based on a single feature.)\n",
    "\n",
    "Let's go ahead and use a broad grid search to find the best settings for a handful of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9664429530201343\n",
      "Best parameters: {'criterion': 'entropy', 'max_depth': 3, 'max_features': 3, 'splitter': 'best'}\n"
     ]
    }
   ],
   "source": [
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "parameter_grid = {'criterion': ['gini', 'entropy'],\n",
    "                  'splitter': ['best', 'random'],\n",
    "                  'max_depth': [1, 2, 3, 4, 5],\n",
    "                  'max_features': [1, 2, 3, 4]}\n",
    "\n",
    "cross_validation = StratifiedKFold(n_splits=10)\n",
    "\n",
    "grid_search = GridSearchCV(decision_tree_classifier,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(all_inputs, all_labels)\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can take the best classifier from the Grid Search and use that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=3,\n",
       "            max_features=3, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "            min_impurity_split=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_classifier = grid_search.best_estimator_\n",
    "decision_tree_classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even visualize the decision tree with [GraphViz](http://www.graphviz.org/) to see how it's making the classifications:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.tree as tree\n",
    "from sklearn.externals.six import StringIO\n",
    "\n",
    "with open('iris_dtc.dot', 'w') as out_file:\n",
    "    out_file = tree.export_graphviz(decision_tree_classifier, out_file=out_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"iris_dtc.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(This classifier may look familiar from earlier in the notebook.)\n",
    "\n",
    "Alright! We finally have our demo classifier. Let's create some visuals of its performance so we have something to show our company's Head of Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P/aKkVyW9IunnMb7Yo8qs9FnS+ijza5K+4yjzrZJ2R/s8IumyIY/9kqSW6LbMe+bofdEXvfZnb/O85r3gOe5STCWdg/fFjUp/wmq7pLWSxnrOrPRfLL+RtCM6/n4Z4+v8B6XnwfuUPjteLumrkr4abTdJD0c/03ZJ84c8Nm/HH984BADH+MYhADhGSQOAY5Q0ADhGSQOAY5Q0ADhGSQOAY5Q0ADhGSQOAY/8F1NyQCK+nzg8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_scores = cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10)\n",
    "\n",
    "sb.boxplot(dt_scores)\n",
    "sb.stripplot(dt_scores, jitter=True, color='black')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm... that's a little boring by itself though. How about we compare another classifier to see how they perform?\n",
    "\n",
    "We already know from previous projects that Random Forest classifiers usually work better than individual decision trees. A common problem that decision trees face is that they're prone to overfitting: They complexify to the point that they classify the training set near-perfectly, but fail to generalize to data they have not seen before.\n",
    "\n",
    "**Random Forest classifiers** work around that limitation by creating a whole bunch of decision trees (hence \"forest\") — each trained on random subsets of training samples (drawn with replacement) and features (drawn without replacement) — and have the decision trees work together to make a more accurate classification.\n",
    "\n",
    "Let that be a lesson for us: **Even in Machine Learning, we get better results when we work together!**\n",
    "\n",
    "Let's see if a Random Forest classifier works better here.\n",
    "\n",
    "The great part about scikit-learn is that the training, testing, parameter tuning, etc. process is the same for all models, so we only need to plug in the new classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9664429530201343\n",
      "Best parameters: {'criterion': 'gini', 'max_features': 1, 'n_estimators': 25}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features=1, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=25, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "random_forest_classifier = RandomForestClassifier()\n",
    "\n",
    "parameter_grid = {'n_estimators': [10, 25, 50, 100],\n",
    "                  'criterion': ['gini', 'entropy'],\n",
    "                  'max_features': [1, 2, 3, 4]}\n",
    "\n",
    "cross_validation = StratifiedKFold(n_splits=10)\n",
    "\n",
    "grid_search = GridSearchCV(random_forest_classifier,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(all_inputs, all_labels)\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compare their performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_forest_classifier = grid_search.best_estimator_\n",
    "\n",
    "rf_df = pd.DataFrame({'accuracy': cross_val_score(random_forest_classifier, all_inputs, all_labels, cv=10),\n",
    "                       'classifier': ['Random Forest'] * 10})\n",
    "dt_df = pd.DataFrame({'accuracy': cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10),\n",
    "                      'classifier': ['Decision Tree'] * 10})\n",
    "both_df = rf_df.append(dt_df)\n",
    "\n",
    "sb.boxplot(x='classifier', y='accuracy', data=both_df)\n",
    "sb.stripplot(x='classifier', y='accuracy', data=both_df, jitter=True, color='black')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about that? They both seem to perform about the same on this data set. This is probably because of the limitations of our data set: We have only 4 features to make the classification, and Random Forest classifiers excel when there's hundreds of possible features to look at. In other words, there wasn't much room for improvement with this data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Reproducibility\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Ensuring that our work is reproducible is the last and — arguably — most important step in any analysis. **As a rule, we shouldn't place much weight on a discovery that can't be reproduced**. As such, if our analysis isn't reproducible, we might as well not have done it.\n",
    "\n",
    "Notebooks like this one go a long way toward making our work reproducible. Since we documented every step as we moved along, we have a written record of what we did and why we did it — both in text and code.\n",
    "\n",
    "Beyond recording what we did, we should also document what software and hardware we used to perform our analysis. This typically goes at the top of our notebooks so our readers know what tools to use.\n",
    "\n",
    "[Sebastian Raschka](http://sebastianraschka.com/) created a handy [notebook tool](https://github.com/rasbt/watermark) for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Randal S. Olson Thu Jul 12 2018 \n",
      "\n",
      "CPython 3.6.6\n",
      "IPython 6.4.0\n",
      "\n",
      "numpy 1.12.1\n",
      "pandas 0.23.1\n",
      "sklearn 0.19.1\n",
      "matplotlib 2.2.2\n",
      "seaborn 0.8.1\n",
      "\n",
      "compiler   : GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)\n",
      "system     : Darwin\n",
      "release    : 17.6.0\n",
      "machine    : x86_64\n",
      "processor  : i386\n",
      "CPU cores  : 8\n",
      "interpreter: 64bit\n"
     ]
    }
   ],
   "source": [
    "%watermark -a 'Randal S. Olson' -nmv --packages numpy,pandas,sklearn,matplotlib,seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's extract the core of our work from Steps 1-5 and turn it into a single pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.2  3.6  6.1  2.5]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
      "[ 7.4  2.8  6.1  1.9]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
      "[ 5.8  2.7  5.1  1.9]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
      "[ 6.1  2.6  5.6  1.4]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
      "[ 6.9  3.1  5.4  2.1]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
      "[ 4.9  3.   1.4  0.2]\t-->\tIris-setosa\t(Actual: Iris-setosa)\n",
      "[ 5.1  3.7  1.5  0.4]\t-->\tIris-setosa\t(Actual: Iris-setosa)\n",
      "[ 5.   3.2  1.2  0.2]\t-->\tIris-setosa\t(Actual: Iris-setosa)\n",
      "[ 5.8  2.6  4.   1.2]\t-->\tIris-versicolor\t(Actual: Iris-versicolor)\n",
      "[ 5.5  2.6  4.4  1.2]\t-->\tIris-versicolor\t(Actual: Iris-versicolor)\n"
     ]
    },
    {
     "data": {
      "image/png": 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GeQuV73kdknTLiPWbSjZqAZPp5cpMhDfq4w8BPj9inzpJZdH9ZyR9O7o/T1J1dH+apH2SvhDDBwCjzjxin4cV3weHxRznafr4A9o6ZU7qZueZU9H+M6Plf5X0La95s7ZvkbQ6jnOiBMf4AUlvR49Rocwc8B84z5ySNCO6v0RSmzKfXcR1vBuU/4PDu5T7wWFLtH66pN9EP4fTovvTS5apwOB3KlOw+yX9bbTu25L+MLp/X1QM+yT9c1ZhfFXSjuh/0g5Jj8Z4sEeVecRjPKyYSrrI43yTpJ3Rcd4p6RveM484P3ZK+pGkSud5G5T5a3lZXMe3yPMiJen7kvZI2i1p/TWQuSrKuluZF8QbYsz8E2Xm7i8o8+74G5Iek/RYtN0k/VP0nHZKWpb1Z/9cUkd0+7NS5uIbhwDgGN84BADHKGkAcIySBgDHKGkAcIySBgDHKGkAcIySBgDHKGkAcOz/AaiRFRRi6qcdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "\n",
    "# We can jump directly to working with the clean data because we saved our cleaned data set\n",
    "iris_data_clean = pd.read_csv('iris-data-clean.csv')\n",
    "\n",
    "# Testing our data: Our analysis will stop here if any of these assertions are wrong\n",
    "\n",
    "# We know that we should only have three classes\n",
    "assert len(iris_data_clean['class'].unique()) == 3\n",
    "\n",
    "# We know that sepal lengths for 'Iris-versicolor' should never be below 2.5 cm\n",
    "assert iris_data_clean.loc[iris_data_clean['class'] == 'Iris-versicolor', 'sepal_length_cm'].min() >= 2.5\n",
    "\n",
    "# We know that our data set should have no missing measurements\n",
    "assert len(iris_data_clean.loc[(iris_data_clean['sepal_length_cm'].isnull()) |\n",
    "                               (iris_data_clean['sepal_width_cm'].isnull()) |\n",
    "                               (iris_data_clean['petal_length_cm'].isnull()) |\n",
    "                               (iris_data_clean['petal_width_cm'].isnull())]) == 0\n",
    "\n",
    "all_inputs = iris_data_clean[['sepal_length_cm', 'sepal_width_cm',\n",
    "                             'petal_length_cm', 'petal_width_cm']].values\n",
    "\n",
    "all_labels = iris_data_clean['class'].values\n",
    "\n",
    "# This is the classifier that came out of Grid Search\n",
    "random_forest_classifier = RandomForestClassifier(criterion='gini', max_features=3, n_estimators=50)\n",
    "\n",
    "# All that's left to do now is plot the cross-validation scores\n",
    "rf_classifier_scores = cross_val_score(random_forest_classifier, all_inputs, all_labels, cv=10)\n",
    "sb.boxplot(rf_classifier_scores)\n",
    "sb.stripplot(rf_classifier_scores, jitter=True, color='black')\n",
    "\n",
    "# ...and show some of the predictions from the classifier\n",
    "(training_inputs,\n",
    " testing_inputs,\n",
    " training_classes,\n",
    " testing_classes) = train_test_split(all_inputs, all_labels, test_size=0.25)\n",
    "\n",
    "random_forest_classifier.fit(training_inputs, training_classes)\n",
    "\n",
    "for input_features, prediction, actual in zip(testing_inputs[:10],\n",
    "                                              random_forest_classifier.predict(testing_inputs[:10]),\n",
    "                                              testing_classes[:10]):\n",
    "    print('{}\\t-->\\t{}\\t(Actual: {})'.format(input_features, prediction, actual))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There we have it: We have a complete and reproducible Machine Learning pipeline to demo to our company's Head of Data. We've met the success criteria that we set from the beginning (>90% accuracy), and our pipeline is flexible enough to handle new inputs or flowers when that data set is ready. Not bad for our first week on the job!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "I hope you found this example notebook useful for your own work and learned at least one new trick by reading through it.\n",
    "\n",
    "If you've spotted any errors or would like to contribute to this notebook, please don't hestitate to get in touch. I can be reached in the following ways:\n",
    "\n",
    "* [Email](http://www.randalolson.com/contact/) me\n",
    "\n",
    "* [Tweet](https://twitter.com/randal_olson/) at me\n",
    "\n",
    "* [Submit an issue](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues) on GitHub\n",
    "\n",
    "* Fork the [notebook repository](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/), make the fix/addition yourself, then send over a pull request"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further reading\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "This notebook covers a broad variety of topics but skips over many of the specifics. If you're looking to dive deeper into a particular topic, here's some recommended reading.\n",
    "\n",
    "**Data Science**: William Chen compiled a [list of free books](http://www.wzchen.com/data-science-books/) for newcomers to Data Science, ranging from the basics of R & Python to Machine Learning to interviews and advice from prominent data scientists.\n",
    "\n",
    "**Machine Learning**: /r/MachineLearning has a useful [Wiki page](https://www.reddit.com/r/MachineLearning/wiki/index) containing links to online courses, books, data sets, etc. for Machine Learning. There's also a [curated list](https://github.com/josephmisiti/awesome-machine-learning) of Machine Learning frameworks, libraries, and software sorted by language.\n",
    "\n",
    "**Unit testing**: Dive Into Python 3 has a [great walkthrough](https://www.diveinto.org/python3/unit-testing.html) of unit testing in Python, how it works, and how it should be used\n",
    "\n",
    "**pandas** has [several tutorials](http://pandas.pydata.org/pandas-docs/stable/tutorials.html) covering its myriad features.\n",
    "\n",
    "**scikit-learn** has a [bunch of tutorials](http://scikit-learn.org/stable/tutorial/index.html) for those looking to learn Machine Learning in Python. Andreas Mueller's [scikit-learn workshop materials](https://github.com/amueller/scipy_2015_sklearn_tutorial) are top-notch and freely available.\n",
    "\n",
    "**matplotlib** has many [books, videos, and tutorials](http://matplotlib.org/resources/index.html) to teach plotting in Python.\n",
    "\n",
    "**Seaborn** has a [basic tutorial](http://stanford.edu/~mwaskom/software/seaborn/tutorial.html) covering most of the statistical plotting features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acknowledgements\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Many thanks to [Andreas Mueller](http://amueller.github.io/) for some of his [examples](https://github.com/amueller/scipy_2015_sklearn_tutorial) in the Machine Learning section. I drew inspiration from several of his excellent examples.\n",
    "\n",
    "The photo of a flower with annotations of the petal and sepal was taken by [Eric Guinther](https://commons.wikimedia.org/wiki/File:Petal-sepal.jpg).\n",
    "\n",
    "The photos of the various *Iris* flower types were taken by [Ken Walker](http://www.signa.org/index.pl?Display+Iris-setosa+2) and [Barry Glick](http://www.signa.org/index.pl?Display+Iris-virginica+3)."
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
   "version": "3.6.6"
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 "nbformat": 4,
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================================================
FILE: example-data-science-notebook/iris-data-clean.csv
================================================
sepal_length_cm,sepal_width_cm,petal_length_cm,petal_width_cm,class
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.24999999999999997,Iris-setosa
4.4,2.9,1.4,0.24999999999999997,Iris-setosa
4.9,3.1,1.5,0.24999999999999997,Iris-setosa
5.4,3.7,1.5,0.24999999999999997,Iris-setosa
4.8,3.4,1.6,0.24999999999999997,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
5.7,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.8,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
5.6,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,2.3,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginica


================================================
FILE: example-data-science-notebook/iris-data.csv
================================================
sepal_length_cm,sepal_width_cm,petal_length_cm,petal_width_cm,class
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5,3.4,1.5,NA,Iris-setosa
4.4,2.9,1.4,NA,Iris-setosa
4.9,3.1,1.5,NA,Iris-setosa
5.4,3.7,1.5,NA,Iris-setosa
4.8,3.4,1.6,NA,Iris-setosa
4.8,3,1.4,0.1,Iris-setosa
5.7,3,1.1,0.1,Iris-setosa
5.8,4,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setossa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5,3,1.6,0.2,Iris-setosa
5,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5,3.3,1.4,0.2,Iris-setosa
7,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5,2,3.5,1,Iris-versicolor
5.9,3,4.2,1.5,Iris-versicolor
6,2.2,4,1,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
0.067,3,5,1.7,Iris-versicolor
0.06,2.9,4.5,1.5,Iris-versicolor
0.057,2.6,3.5,1,Iris-versicolor
0.055,2.4,3.8,1.1,Iris-versicolor
0.055,2.4,3.7,1,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6,2.8,5.1,1.6,Iris-versicolor
5.4,3,4.5,1.5,Iris-versicolor
6,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3,4.1,1.3,Iris-versicolor
5.5,2.5,4,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3,4.6,1.4,Iris-versicolor
5.8,2.6,4,1.2,Iris-versicolor
5,2.3,3.3,1,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3,4.2,1.2,versicolor
5.7,2.9,4.2,1.3,versicolor
6.2,2.9,4.3,1.3,versicolor
5.1,2.5,3,1.1,versicolor
5.7,2.8,4.1,1.3,versicolor
6.3,3.3,6,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3,5.8,2.2,Iris-virginica
7.6,3,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3,5.5,2.1,Iris-virginica
5.7,2.5,5,2,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6,2.2,5,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2,Iris-virginica
5.6,2.8,6.7,2,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6,3,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3,5.2,2.3,Iris-virginica
6.3,2.5,5,2.3,Iris-virginica
6.5,3,5.2,2,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3,5.1,1.8,Iris-virginica

================================================
FILE: example-data-science-notebook/iris_dtc.dot
================================================
digraph Tree {
node [shape=box] ;
0 [label="X[2] <= 2.45\nentropy = 1.585\nsamples = 149\nvalue = [49, 50, 50]"] ;
1 [label="entropy = 0.0\nsamples = 49\nvalue = [49, 0, 0]"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="X[3] <= 1.75\nentropy = 1.0\nsamples = 100\nvalue = [0, 50, 50]"] ;
0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
3 [label="X[3] <= 1.35\nentropy = 0.445\nsamples = 54\nvalue = [0, 49, 5]"] ;
2 -> 3 ;
4 [label="entropy = 0.0\nsamples = 28\nvalue = [0, 28, 0]"] ;
3 -> 4 ;
5 [label="entropy = 0.706\nsamples = 26\nvalue = [0, 21, 5]"] ;
3 -> 5 ;
6 [label="X[2] <= 4.85\nentropy = 0.151\nsamples = 46\nvalue = [0, 1, 45]"] ;
2 -> 6 ;
7 [label="entropy = 0.918\nsamples = 3\nvalue = [0, 1, 2]"] ;
6 -> 7 ;
8 [label="entropy = 0.0\nsamples = 43\nvalue = [0, 0, 43]"] ;
6 -> 8 ;
}

================================================
FILE: follower-factory/Twitter Follower Factory.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Twitter Follower Factory analysis script\n",
    "\n",
    "This Python script was inspired by the New York Times' [Follower Factory article](https://www.nytimes.com/interactive/2018/01/27/technology/social-media-bots.html), which showed that Twitter accounts can be analyzed to discover whether someone has purchased fake followers for that account. This script enables you to analyze any Twitter account and generate a similar scatter plot visualization of their followers. You can typically tell when someone purchased fake followers for an account when there are solid lines of followers that were created around the same time, as shown in the New York Times article.\n",
    "\n",
    "**Note that you can [execute the code in this notebook on Binder](https://mybinder.org/v2/gh/rhiever/Data-Analysis-and-Machine-Learning-Projects/master?filepath=follower-factory%2FTwitter%2520Follower%2520Factory.ipynb) - no local installation required.**\n",
    "\n",
    "## Dependencies\n",
    "\n",
    "You will need to install Python's [python-twitter](https://github.com/sixohsix/twitter/) library:\n",
    "\n",
    "    pip install twitter\n",
    "\n",
    "You will also need to create an app account on https://dev.twitter.com/apps\n",
    "\n",
    "1. Sign in with your Twitter account\n",
    "2. Create a new app account\n",
    "3. Modify the settings for that app account to allow read & write\n",
    "4. Generate a new OAuth token with those permissions\n",
    "\n",
    "Following these steps will create 4 tokens that you will need to place in the script, as discussed below.\n",
    "\n",
    "## Usage\n",
    "\n",
    "Before you can run this script, you need to fill in the following 5 variables in the file:\n",
    "\n",
    "```Python\n",
    "USER_TO_ANALYZE = ''\n",
    "OAUTH_TOKEN = ''\n",
    "OAUTH_SECRET = ''\n",
    "CONSUMER_KEY = ''\n",
    "CONSUMER_SECRET = ''\n",
    "```\n",
    "\n",
    "`USER_TO_ANALYZE` is straightforward: If you want to analyze my Twitter account, you would enter `randal_olson` in between the two single quotes.\n",
    "\n",
    "Once you've created the Twitter app account as described under the Dependencies section, you should be able to find the OAUTH and CONSUMER keys on the \"Key and Access Tokens\" section, as shown in the image below.\n",
    "\n",
    "<img src='images/twitter-app-example.png' />\n",
    "\n",
    "Once you've filled out those 5 variables, the script below will handle the rest.\n",
    "\n",
    "If you're analyzing an account with 100,000s or more of followers, running the script may take a while as the script follows the Twitter API's usage restrictions. A progress bar will keep you updated on the progress of the analysis.\n",
    "\n",
    "Once the script finishes, it will save an image to the directory that you ran the script in. That image will contain the scatter plot visualization for the account you targeted.\n",
    "\n",
    "## Security concerns\n",
    "\n",
    "Although you should be fine with this notebook, you should beware of placing private API keys and security-related information into scripts like this one. If you're particularly paranoid about the security of your Twitter account, you are welcome to review the code below and all of the open source libraries that it relies upon.\n",
    "\n",
    "If it looks like anything fishy is going on with the API keys, please don't hesitate to [file an issue on this repository](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues/new) and raise your concerns.\n",
    "\n",
    "## Questions and Comments\n",
    "\n",
    "If you have any questions or comments, please [file an issue on this repository](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues/new) and I'll get back to you as soon as I can. If you'd like to submit a pull request to improve this script in any way, please file an issue first to discuss your change(s). I'm generally open to accepting pull requests on this repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function\n",
    "import time\n",
    "from datetime import datetime\n",
    "import os\n",
    "\n",
    "from twitter import Twitter, OAuth, TwitterHTTPError\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "USER_TO_ANALYZE = ''\n",
    "OAUTH_TOKEN = ''\n",
    "OAUTH_SECRET = ''\n",
    "CONSUMER_KEY = ''\n",
    "CONSUMER_SECRET = ''\n",
    "\n",
    "twitter_connection = Twitter(auth=OAuth(OAUTH_TOKEN, OAUTH_SECRET, CONSUMER_KEY, CONSUMER_SECRET))\n",
    "\n",
    "pbar = tqdm()\n",
    "pbar.write('Collecting list of Twitter followers for @{}'.format(USER_TO_ANALYZE))\n",
    "\n",
    "rl_status = twitter_connection.application.rate_limit_status()\n",
    "if rl_status['resources']['followers']['/followers/ids']['remaining'] <= 0:\n",
    "    sleep_until = rl_status['resources']['followers']['/followers/ids']['reset']\n",
    "    sleep_for = int(sleep_until - time.time()) + 10 # wait a little extra time just in case\n",
    "    if sleep_for > 0:\n",
    "        pbar.write('Sleeping for {} seconds...'.format(sleep_for))\n",
    "        time.sleep(sleep_for)\n",
    "        pbar.write('Awake!')\n",
    "\n",
    "followers_status = twitter_connection.followers.ids(screen_name=USER_TO_ANALYZE)\n",
    "followers = followers_status['ids']\n",
    "next_cursor = followers_status['next_cursor']\n",
    "pbar.update(len(followers))\n",
    "\n",
    "while next_cursor != 0:\n",
    "    rl_status = twitter_connection.application.rate_limit_status()\n",
    "    if rl_status['resources']['followers']['/followers/ids']['remaining'] <= 0:\n",
    "        sleep_until = rl_status['resources']['followers']['/followers/ids']['reset']\n",
    "        sleep_for = int(sleep_until - time.time()) + 10 # wait a little extra time just in case\n",
    "        if sleep_for > 0:\n",
    "            pbar.write('Sleeping for {} seconds...'.format(sleep_for))\n",
    "            time.sleep(sleep_for)\n",
    "            pbar.write('Awake!')\n",
    "\n",
    "    followers_status = twitter_connection.followers.ids(screen_name=USER_TO_ANALYZE, cursor=next_cursor)\n",
    "    # Prevent duplicate Twitter user IDs\n",
    "    more_followers = [follower for follower in followers_status['ids'] if follower not in followers]\n",
    "    followers += more_followers\n",
    "    next_cursor = followers_status['next_cursor']\n",
    "\n",
    "    pbar.update(len(more_followers))\n",
    "\n",
    "pbar.close()\n",
    "\n",
    "pbar = tqdm(total=len(followers))\n",
    "pbar.write('Collecting join dates of Twitter followers for @{}'.format(USER_TO_ANALYZE))\n",
    "followers_created = list()\n",
    "\n",
    "rl_status = twitter_connection.application.rate_limit_status()\n",
    "remaining_calls = rl_status['resources']['users']['/users/lookup']['remaining']\n",
    "\n",
    "for base_index in range(0, len(followers), 100):\n",
    "    if remaining_calls == 50:\n",
    "        # Update the remaining calls count just in case\n",
    "        rl_status = twitter_connection.application.rate_limit_status()\n",
    "        remaining_calls = rl_status['resources']['users']['/users/lookup']['remaining']\n",
    "\n",
    "    if remaining_calls <= 0:\n",
    "        sleep_until = rl_status['resources']['users']['/users/lookup']['reset']\n",
    "        sleep_for = int(sleep_until - time.time()) + 10 # wait a little extra time just in case\n",
    "        if sleep_for > 0:\n",
    "            pbar.write('Sleeping for {} seconds...'.format(sleep_for))\n",
    "            time.sleep(sleep_for)\n",
    "            pbar.write('Awake!')\n",
    "            rl_status = twitter_connection.application.rate_limit_status()\n",
    "            remaining_calls = rl_status['resources']['users']['/users/lookup']['remaining']\n",
    "\n",
    "    remaining_calls -= 1\n",
    "\n",
    "    # 100 users per request\n",
    "    user_info = twitter_connection.users.lookup(user_id=list(followers[base_index:base_index + 100]))\n",
    "    followers_created += [x['created_at'] for x in user_info]\n",
    "\n",
    "    pbar.update(len(followers[base_index:base_index + 100]))\n",
    "\n",
    "pbar.close()\n",
    "print('Creating Follower Factory visualization for @{}'.format(USER_TO_ANALYZE))\n",
    "\n",
    "days_since_2006 = [(x.year - 2006) * 365 + x.dayofyear for x in pd.to_datetime(followers_created)]\n",
    "\n",
    "mpl_style_url = 'https://gist.githubusercontent.com/rhiever/d0a7332fe0beebfdc3d5/raw/1b807615235ff6f4c919b5b70b01a609619e1e9c/tableau10.mplstyle'\n",
    "alpha = 0.1 * min(9, 80000. / len(days_since_2006))\n",
    "with plt.style.context(mpl_style_url):\n",
    "    plt.figure(figsize=(9, 12))\n",
    "    plt.scatter(x=range(len(days_since_2006)), y=days_since_2006[::-1], s=2, alpha=alpha)\n",
    "    plt.yticks(range(0, 365 * (datetime.today().year + 1 - 2006), 365), range(2006, datetime.today().year + 1))\n",
    "    plt.xlabel('Follower count for @{}'.format(USER_TO_ANALYZE))\n",
    "    plt.ylabel('Date follower joined Twitter')\n",
    "    plt.savefig('{}-follower-factory.png'.format(USER_TO_ANALYZE))\n",
    "\n",
    "print('Follower Factory visualization saved to {}'.format(os.getcwd()))"
   ]
  }
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================================================
FILE: optimal-road-trip/Computing the optimal road trip across the U.S..ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing the optimal road trip across the U.S."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides the methodology and code used in the blog post, [Computing the optimal road trip across the U.S.](http://www.randalolson.com/2015/03/08/computing-the-optimal-road-trip-across-the-u-s/)\n",
    "\n",
    "### Notebook by [Randal S. Olson](http://www.randalolson.com)\n",
    "\n",
    "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely useable and shareable as possible.\n",
    "\n",
    "The code in this notebook is also available as a single Python script [here](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/optimal-road-trip/OptimalRoadTripHtmlSaveAndDisplay.py) courtesy of [Andrew Liesinger](https://github.com/AndrewLiesinger)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Required Python libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you don't have Python on your computer, you can use the [Anaconda Python distribution](https://www.anaconda.com/download/) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
    "\n",
    "This code uses base Python libraries except for `googlemaps` and `pandas` packages. You can install these packages using `pip` by typing the following commands into your command line:\n",
    "\n",
    "> pip install pandas\n",
    "\n",
    "> pip install googlemaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct a list of road trip waypoints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to decide where you want to stop on your road trip.\n",
    "\n",
    "Make sure you look all of the locations up on [Google Maps](http://maps.google.com) first so you have the correct address, city, state, etc. If the text you use to look up the location doesn't work on Google Maps, then it won't work here either.\n",
    "\n",
    "Add all of your waypoints to the list below. Make sure they're formatted the same way as in the example below.\n",
    "\n",
    "*Technical note: Due to daily usage limitations of the Google Maps API, you can only have a maximum of 70 waypoints. You will have to pay Google for an increased API limit if you want to add more waypoints.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_waypoints = [\"USS Alabama, Battleship Parkway, Mobile, AL\",\n",
    "                 \"Grand Canyon National Park, Arizona\",\n",
    "                 \"Toltec Mounds, Scott, AR\",\n",
    "                 \"San Andreas Fault, San Benito County, CA\",\n",
    "                 \"Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\",\n",
    "                 \"Pikes Peak, Colorado\",\n",
    "                 \"The Mark Twain House & Museum, Farmington Avenue, Hartford, CT\",\n",
    "                 \"New Castle Historic District, Delaware\",\n",
    "                 \"White House, Pennsylvania Avenue Northwest, Washington, DC\",\n",
    "                 \"Cape Canaveral, FL\",\n",
    "                 \"Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\",\n",
    "                 \"Craters of the Moon National Monument & Preserve, Arco, ID\",\n",
    "                 \"Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\",\n",
    "                 \"West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\",\n",
    "                 \"Terrace Hill, Grand Avenue, Des Moines, IA\",\n",
    "                 \"C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\",\n",
    "                 \"Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\",\n",
    "                 \"French Quarter, New Orleans, LA\",\n",
    "                 \"Acadia National Park, Maine\",\n",
    "                 \"Maryland State House, 100 State Cir, Annapolis, MD 21401\",\n",
    "                 \"USS Constitution, Boston, MA\",\n",
    "                 \"Olympia Entertainment, Woodward Avenue, Detroit, MI\",\n",
    "                 \"Fort Snelling, Tower Avenue, Saint Paul, MN\",\n",
    "                 \"Vicksburg National Military Park, Clay Street, Vicksburg, MS\",\n",
    "                 \"Gateway Arch, Washington Avenue, St Louis, MO\",\n",
    "                 \"Glacier National Park, West Glacier, MT\",\n",
    "                 \"Ashfall Fossil Bed, Royal, NE\",\n",
    "                 \"Hoover Dam, NV\",\n",
    "                 \"Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\",\n",
    "                 \"Congress Hall, Congress Place, Cape May, NJ 08204\",\n",
    "                 \"Carlsbad Caverns National Park, Carlsbad, NM\",\n",
    "                 \"Statue of Liberty, Liberty Island, NYC, NY\",\n",
    "                 \"Wright Brothers National Memorial Visitor Center, Manteo, NC\",\n",
    "                 \"Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\",\n",
    "                 \"Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\",\n",
    "                 \"Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\",\n",
    "                 \"Columbia River Gorge National Scenic Area, Oregon\",\n",
    "                 \"Liberty Bell, 6th Street, Philadelphia, PA\",\n",
    "                 \"The Breakers, Ochre Point Avenue, Newport, RI\",\n",
    "                 \"Fort Sumter National Monument, Sullivan's Island, SC\",\n",
    "                 \"Mount Rushmore National Memorial, South Dakota 244, Keystone, SD\",\n",
    "                 \"Graceland, Elvis Presley Boulevard, Memphis, TN\",\n",
    "                 \"The Alamo, Alamo Plaza, San Antonio, TX\",\n",
    "                 \"Bryce Canyon National Park, Hwy 63, Bryce, UT\",\n",
    "                 \"Shelburne Farms, Harbor Road, Shelburne, VT\",\n",
    "                 \"Mount Vernon, Fairfax County, Virginia\",\n",
    "                 \"Hanford Site, Benton County, WA\",\n",
    "                 \"Lost World Caverns, Lewisburg, WV\",\n",
    "                 \"Taliesin, County Road C, Spring Green, Wisconsin\",\n",
    "                 \"Yellowstone National Park, WY 82190\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next you'll have to register this script with the Google Maps API so they know who's hitting their servers with hundreds of Google Maps routing requests.\n",
    "\n",
    "1) Enable the Google Maps Distance Matrix API on your Google account. Google explains how to do that [here](https://github.com/googlemaps/google-maps-services-python#api-keys).\n",
    "\n",
    "2) Copy and paste the API key they had you create into the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import googlemaps\n",
    "\n",
    "gmaps = googlemaps.Client(key=\"PASTE YOUR API KEY HERE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're going to query the Google Maps API for the shortest route between all of the waypoints.\n",
    "\n",
    "This is equivalent to doing Google Maps directions lookups on the Google Maps site, except now we're performing hundreds of lookups automatically using code.\n",
    "\n",
    "If you get an error on this part, that most likely means one of the waypoints you entered couldn't be found on Google Maps. Another possible reason for an error here is if it's not possible to drive between the points, e.g., finding the driving directions between Hawaii and Florida will return an error until we invent flying cars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gather the distance traveled on the shortest route between all waypoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import combinations\n",
    "\n",
    "waypoint_distances = {}\n",
    "waypoint_durations = {}\n",
    "\n",
    "for (waypoint1, waypoint2) in combinations(all_waypoints, 2):\n",
    "    try:\n",
    "        route = gmaps.distance_matrix(origins=[waypoint1],\n",
    "                                      destinations=[waypoint2],\n",
    "                                      mode=\"driving\", # Change this to \"walking\" for walking directions,\n",
    "                                                      # \"bicycling\" for biking directions, etc.\n",
    "                                      language=\"English\",\n",
    "                                      units=\"metric\")\n",
    "\n",
    "        # \"distance\" is in meters\n",
    "        distance = route[\"rows\"][0][\"elements\"][0][\"distance\"][\"value\"]\n",
    "\n",
    "        # \"duration\" is in seconds\n",
    "        duration = route[\"rows\"][0][\"elements\"][0][\"duration\"][\"value\"]\n",
    "\n",
    "        waypoint_distances[frozenset([waypoint1, waypoint2])] = distance\n",
    "        waypoint_durations[frozenset([waypoint1, waypoint2])] = duration\n",
    "    \n",
    "    except Exception as e:\n",
    "        print(\"Error with finding the route between %s and %s.\" % (waypoint1, waypoint2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the routes between all of our waypoints, let's save them to a text file so we don't have to bother Google about them again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"my-waypoints-dist-dur.tsv\", \"w\") as out_file:\n",
    "    out_file.write(\"\\t\".join([\"waypoint1\",\n",
    "                              \"waypoint2\",\n",
    "                              \"distance_m\",\n",
    "                              \"duration_s\"]))\n",
    "    \n",
    "    for (waypoint1, waypoint2) in waypoint_distances.keys():\n",
    "        out_file.write(\"\\n\" +\n",
    "                       \"\\t\".join([waypoint1,\n",
    "                                  waypoint2,\n",
    "                                  str(waypoint_distances[frozenset([waypoint1, waypoint2])]),\n",
    "                                  str(waypoint_durations[frozenset([waypoint1, waypoint2])])]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use a genetic algorithm to optimize the order to visit the waypoints in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of exhaustively looking at every possible solution, genetic algorithms start with a handful of random solutions and continually tinkers with these solutions — always trying something slightly different from the current solutions and keeping the best ones — until they can’t find a better solution any more.\n",
    "\n",
    "Below, all you need to do is make sure that the file name above matches the file name below (both currently `my-waypoints-dist-dur.tsv`) and run the code. The code will read in your route information and use a genetic algorithm to discover an optimized driving route."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "waypoint_distances = {}\n",
    "waypoint_durations = {}\n",
    "all_waypoints = set()\n",
    "\n",
    "waypoint_data = pd.read_csv(\"my-waypoints-dist-dur.tsv\", sep=\"\\t\")\n",
    "\n",
    "for i, row in waypoint_data.iterrows():\n",
    "    waypoint_distances[frozenset([row.waypoint1, row.waypoint2])] = row.distance_m\n",
    "    waypoint_durations[frozenset([row.waypoint1, row.waypoint2])] = row.duration_s\n",
    "    all_waypoints.update([row.waypoint1, row.waypoint2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def compute_fitness(solution):\n",
    "    \"\"\"\n",
    "        This function returns the total distance traveled on the current road trip.\n",
    "        \n",
    "        The genetic algorithm will favor road trips that have shorter\n",
    "        total distances traveled.\n",
    "    \"\"\"\n",
    "    \n",
    "    solution_fitness = 0.0\n",
    "    \n",
    "    for index in range(len(solution)):\n",
    "        waypoint1 = solution[index - 1]\n",
    "        waypoint2 = solution[index]\n",
    "        solution_fitness += waypoint_distances[frozenset([waypoint1, waypoint2])]\n",
    "        \n",
    "    return solution_fitness\n",
    "\n",
    "def generate_random_agent():\n",
    "    \"\"\"\n",
    "        Creates a random road trip from the waypoints.\n",
    "    \"\"\"\n",
    "    \n",
    "    new_random_agent = list(all_waypoints)\n",
    "    random.shuffle(new_random_agent)\n",
    "    return tuple(new_random_agent)\n",
    "\n",
    "def mutate_agent(agent_genome, max_mutations=3):\n",
    "    \"\"\"\n",
    "        Applies 1 - `max_mutations` point mutations to the given road trip.\n",
    "        \n",
    "        A point mutation swaps the order of two waypoints in the road trip.\n",
    "    \"\"\"\n",
    "    \n",
    "    agent_genome = list(agent_genome)\n",
    "    num_mutations = random.randint(1, max_mutations)\n",
    "    \n",
    "    for mutation in range(num_mutations):\n",
    "        swap_index1 = random.randint(0, len(agent_genome) - 1)\n",
    "        swap_index2 = swap_index1\n",
    "\n",
    "        while swap_index1 == swap_index2:\n",
    "            swap_index2 = random.randint(0, len(agent_genome) - 1)\n",
    "\n",
    "        agent_genome[swap_index1], agent_genome[swap_index2] = agent_genome[swap_index2], agent_genome[swap_index1]\n",
    "            \n",
    "    return tuple(agent_genome)\n",
    "\n",
    "def shuffle_mutation(agent_genome):\n",
    "    \"\"\"\n",
    "        Applies a single shuffle mutation to the given road trip.\n",
    "        \n",
    "        A shuffle mutation takes a random sub-section of the road trip\n",
    "        and moves it to another location in the road trip.\n",
    "    \"\"\"\n",
    "    \n",
    "    agent_genome = list(agent_genome)\n",
    "    \n",
    "    start_index = random.randint(0, len(agent_genome) - 1)\n",
    "    length = random.randint(2, 20)\n",
    "    \n",
    "    genome_subset = agent_genome[start_index:start_index + length]\n",
    "    agent_genome = agent_genome[:start_index] + agent_genome[start_index + length:]\n",
    "    \n",
    "    insert_index = random.randint(0, len(agent_genome) + len(genome_subset) - 1)\n",
    "    agent_genome = agent_genome[:insert_index] + genome_subset + agent_genome[insert_index:]\n",
    "    \n",
    "    return tuple(agent_genome)\n",
    "\n",
    "def generate_random_population(pop_size):\n",
    "    \"\"\"\n",
    "        Generates a list with `pop_size` number of random road trips.\n",
    "    \"\"\"\n",
    "    \n",
    "    random_population = []\n",
    "    for agent in range(pop_size):\n",
    "        random_population.append(generate_random_agent())\n",
    "    return random_population\n",
    "    \n",
    "def run_genetic_algorithm(generations=5000, population_size=100):\n",
    "    \"\"\"\n",
    "        The core of the Genetic Algorithm.\n",
    "        \n",
    "        `generations` and `population_size` must be a multiple of 10.\n",
    "    \"\"\"\n",
    "    \n",
    "    population_subset_size = int(population_size / 10.)\n",
    "    generations_10pct = int(generations / 10.)\n",
    "    \n",
    "    # Create a random population of `population_size` number of solutions.\n",
    "    population = generate_random_population(population_size)\n",
    "\n",
    "    # For `generations` number of repetitions...\n",
    "    for generation in range(generations):\n",
    "        \n",
    "        # Compute the fitness of the entire current population\n",
    "        population_fitness = {}\n",
    "\n",
    "        for agent_genome in population:\n",
    "            if agent_genome in population_fitness:\n",
    "                continue\n",
    "\n",
    "            population_fitness[agent_genome] = compute_fitness(agent_genome)\n",
    "\n",
    "        # Take the top 10% shortest road trips and produce offspring each from them\n",
    "        new_population = []\n",
    "        for rank, agent_genome in enumerate(sorted(population_fitness,\n",
    "                                                   key=population_fitness.get)[:population_subset_size]):\n",
    "            \n",
    "            if (generation % generations_10pct == 0 or generation == generations - 1) and rank == 0:\n",
    "                print(\"Generation %d best: %d | Unique genomes: %d\" % (generation,\n",
    "                                                                       population_fitness[agent_genome],\n",
    "                                                                       len(population_fitness)))\n",
    "                print(agent_genome)\n",
    "                print(\"\")\n",
    "\n",
    "            # Create 1 exact copy of each of the top road trips\n",
    "            new_population.append(agent_genome)\n",
    "\n",
    "            # Create 2 offspring with 1-3 point mutations\n",
    "            for offspring in range(2):\n",
    "                new_population.append(mutate_agent(agent_genome, 3))\n",
    "                \n",
    "            # Create 7 offspring with a single shuffle mutation\n",
    "            for offspring in range(7):\n",
    "                new_population.append(shuffle_mutation(agent_genome))\n",
    "\n",
    "        # Replace the old population with the new population of offspring \n",
    "        for i in range(len(population))[::-1]:\n",
    "            del population[i]\n",
    "\n",
    "        population = new_population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try running the genetic algorithm a few times to see the different solutions it comes up with. It should take about a minute to finish running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 0 best: 90432537 | Unique genomes: 100\n",
      "('Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'Statue of Liberty, Liberty Island, NYC, NY', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Gateway Arch, Washington Avenue, St Louis, MO', 'USS Alabama, Battleship Parkway, Mobile, AL', \"Fort Sumter National Monument, Sullivan's Island, SC\", 'Glacier National Park, West Glacier, MT', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Graceland, Elvis Presley Boulevard, Memphis, TN', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'The Breakers, Ochre Point Avenue, Newport, RI', 'Hoover Dam, NV', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'New Castle Historic District, Delaware', 'USS Constitution, Boston, MA', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'Acadia National Park, Maine', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'San Andreas Fault, San Benito County, CA', 'Ashfall Fossil Bed, Royal, NE', 'French Quarter, New Orleans, LA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Liberty Bell, 6th Street, Philadelphia, PA', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Carlsbad Caverns National Park, Carlsbad, NM', 'Pikes Peak, Colorado', 'Toltec Mounds, Scott, AR', 'Grand Canyon National Park, Arizona', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'Yellowstone National Park, WY 82190', 'Cape Canaveral, FL', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Craters of the Moon National Monument & Preserve, Arco, ID')\n",
      "\n",
      "Generation 1000 best: 22613362 | Unique genomes: 95\n",
      "('Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Glacier National Park, West Glacier, MT', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR', 'Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \"Fort Sumter National Monument, Sullivan's Island, SC\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Statue of Liberty, Liberty Island, NYC, NY', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN')\n",
      "\n",
      "Generation 2000 best: 22324940 | Unique genomes: 98\n",
      "('Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \"Fort Sumter National Monument, Sullivan's Island, SC\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Statue of Liberty, Liberty Island, NYC, NY', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Glacier National Park, West Glacier, MT', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR')\n",
      "\n",
      "Generation 3000 best: 22324940 | Unique genomes: 95\n",
      "('Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \"Fort Sumter National Monument, Sullivan's Island, SC\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Statue of Liberty, Liberty Island, NYC, NY', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Glacier National Park, West Glacier, MT', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR')\n",
      "\n",
      "Generation 4000 best: 22324940 | Unique genomes: 97\n",
      "('Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \"Fort Sumter National Monument, Sullivan's Island, SC\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Statue of Liberty, Liberty Island, NYC, NY', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Glacier National Park, West Glacier, MT', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR')\n",
      "\n",
      "Generation 4999 best: 22324940 | Unique genomes: 99\n",
      "('Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \"Fort Sumter National Monument, Sullivan's Island, SC\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Statue of Liberty, Liberty Island, NYC, NY', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Glacier National Park, West Glacier, MT', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "run_genetic_algorithm(generations=5000, population_size=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize your road trip on a Google map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have an ordered list of the waypoints, we should put them on a Google map so we can see the trip from a high level and make any extra adjustments.\n",
    "\n",
    "There's no easy way to make this visualization in Python, but the Google Maps team provides a nice JavaScript library for visualizing routes on a Google Map.\n",
    "\n",
    "Here's an example map with the route between 50 waypoints visualized: [link](http://rhiever.github.io/optimal-roadtrip-usa/major-landmarks.html)\n",
    "\n",
    "The tricky part here is that the JavaScript library only plots routes with a maximum of 10 waypoints. If we want to plot a route with >10 waypoints, we need to call the route plotting function multiple times.\n",
    "\n",
    "Thanks to some optimizations by [Nicholas Clarke](https://github.com/nicholasgodfreyclarke) to my original map, this is a simple operation:\n",
    "\n",
    "1) Copy the final route generated by the genetic algorithm above.\n",
    "\n",
    "2) Place brackets (`[` & `]`) around the route, e.g.,\n",
    "\n",
    "    ['Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \"Fort Sumter National Monument, Sullivan's Island, SC\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Statue of Liberty, Liberty Island, NYC, NY', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Glacier National Park, West Glacier, MT', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR']\n",
    "\n",
    "3) Paste the final route with brackets into [line 93](https://github.com/rhiever/optimal-roadtrip-usa/blob/gh-pages/major-landmarks.html#L93) of my road trip map code. It should look like this:\n",
    "\n",
    "    optimal_route = [ ... ]\n",
    "    \n",
    "where `...` is your optimized road trip.\n",
    "\n",
    "That's all it takes! Now you have your own optimized road trip ready to show off to the world."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some technical notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As I mentioned in the [original article](http://www.randalolson.com/2015/03/08/computing-the-optimal-road-trip-across-the-u-s/), by the end of 5,000 generations, the genetic algorithm will very likely find a *good* but probably not the *absolute best* solution to the optimal routing problem. It is in the nature of genetic algorithms that we never know if we found the absolute best solution.\n",
    "\n",
    "However, there exist some brilliant analytical solutions to the optimal routing problem such as the [Concorde TSP solver](http://en.wikipedia.org/wiki/Concorde_TSP_Solver). If you're interested in learning more about Concorde and how it's possible to find a perfect solution to the routing problem, I advise you check out [Bill Cook's article](http://www.math.uwaterloo.ca/tsp/usa50/index.html) on the topic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If you have any questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please feel free to:\n",
    "\n",
    "* [Email me](http://www.randalolson.com/contact/),\n",
    "\n",
    "* [Tweet](https://twitter.com/randal_olson) at me, or\n",
    "\n",
    "* comment on the [blog post](http://www.randalolson.com/2015/03/08/computing-the-optimal-road-trip-across-the-u-s/)\n",
    "\n",
    "I'm usually pretty good about getting back to people within a day or two."
   ]
  }
 ],
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================================================
FILE: optimal-road-trip/OptimalRoadTripHtmlSaveAndDisplay.py
================================================
"""
Randy Olson's Shortest Route Program modified By Andrew Liesinger to:
    1: Detect waypoints file at runtime - if found use it, otherwise look up distances via google calls (and then save to waypoint file)
    2: Dynamically create and open an HTML file showing the route when a shorter route is found
    3: Make it easier to tinker with the Generation / Population parameters
"""
from __future__ import print_function
from itertools import combinations
import googlemaps
import pandas as pd
import numpy as np
import os.path
import random
import webbrowser

GOOGLE_MAPS_API_KEY = "Your Key Here"
waypoints_file = "my-waypoints-dist-dur.tsv"

#This is the general filename - as shorter routes are discovered the Population fitness score will be inserted into the filename
#so that interim results are saved for comparision.  The actual filenames using the default below will be:
#Output_<Population Fitness Score>.html 
output_file = 'Output.html'

#parameters for the Genetic algoritim
thisRunGenerations=5000
thisRunPopulation_size=100


all_waypoints = ["USS Alabama, Battleship Parkway, Mobile, AL",
                 "Grand Canyon National Park, Arizona",
                 "Toltec Mounds, Scott, AR",
                 "San Andreas Fault, San Benito County, CA",
                 "Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108",
                 "Pikes Peak, Colorado",
                 "The Mark Twain House & Museum, Farmington Avenue, Hartford, CT",
                 "New Castle Historic District, Delaware",
                 "White House, Pennsylvania Avenue Northwest, Washington, DC",
                 "Cape Canaveral, FL",
                 "Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA",
                 "Craters of the Moon National Monument & Preserve, Arco, ID",
                 "Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL",
                 "West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN",
                 "Terrace Hill, Grand Avenue, Des Moines, IA",
                 "C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS",
                 "Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY",
                 "French Quarter, New Orleans, LA",
                 "Acadia National Park, Maine",
                 "Maryland State House, 100 State Cir, Annapolis, MD 21401",
                 "USS Constitution, Boston, MA",
                 "Olympia Entertainment, Woodward Avenue, Detroit, MI",
                 "Fort Snelling, Tower Avenue, Saint Paul, MN",
                 "Vicksburg National Military Park, Clay Street, Vicksburg, MS",
                 "Gateway Arch, Washington Avenue, St Louis, MO",
                 "Glacier National Park, West Glacier, MT",
                 "Ashfall Fossil Bed, Royal, NE",
                 "Hoover Dam, NV",
                 "Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH",
                 "Congress Hall, Congress Place, Cape May, NJ 08204",
                 "Carlsbad Caverns National Park, Carlsbad, NM",
                 "Statue of Liberty, Liberty Island, NYC, NY",
                 "Wright Brothers National Memorial Visitor Center, Manteo, NC",
                 "Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND",
                 "Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH",
                 "Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086",
                 "Columbia River Gorge National Scenic Area, Oregon",
                 "Liberty Bell, 6th Street, Philadelphia, PA",
                 "The Breakers, Ochre Point Avenue, Newport, RI",
                 "Fort Sumter National Monument, Sullivan's Island, SC",
                 "Mount Rushmore National Memorial, South Dakota 244, Keystone, SD",
                 "Graceland, Elvis Presley Boulevard, Memphis, TN",
                 "The Alamo, Alamo Plaza, San Antonio, TX",
                 "Bryce Canyon National Park, Hwy 63, Bryce, UT",
                 "Shelburne Farms, Harbor Road, Shelburne, VT",
                 "Mount Vernon, Fairfax County, Virginia",
                 "Hanford Site, Benton County, WA",
                 "Lost World Caverns, Lewisburg, WV",
                 "Taliesin, County Road C, Spring Green, Wisconsin",
                 "Yellowstone National Park, WY 82190"]

def CreateOptimalRouteHtmlFile(optimal_route, distance, display=True):
    optimal_route = list(optimal_route)
    optimal_route += [optimal_route[0]]

    Page_1 = """
    <!DOCTYPE html>
    <html lang="en">
      <head>
        <meta charset="utf-8">
        <meta name="viewport" content="initial-scale=1.0, user-scalable=no">
        <meta name="description" content="Randy Olson uses machine learning to find the optimal road trip across the U.S.">
        <meta name="author" content="Randal S. Olson">
        
        <title>The optimal road trip across the U.S. according to machine learning</title>
        <style>
          html, body, #map-canvas {
            height: 100%;
            margin: 0px;
            padding: 0px
          }
          #panel {
            position: absolute;
            top: 5px;
            left: 50%;
            margin-left: -180px;
            z-index: 5;
            background-color: #fff;
            padding: 10px;
            border: 1px solid #999;
          }
        </style>
        <script src="https://maps.googleapis.com/maps/api/js?v=3.exp&signed_in=true"></script>
        <script>
            var routes_list = []
            var markerOptions = {icon: "http://maps.gstatic.com/mapfiles/markers2/marker.png"};
            var directionsDisplayOptions = {preserveViewport: true,
                                            markerOptions: markerOptions};
            var directionsService = new google.maps.DirectionsService();
            var map;

            function initialize() {
              var center = new google.maps.LatLng(39, -96);
              var mapOptions = {
                zoom: 5,
                center: center
              };
              map = new google.maps.Map(document.getElementById('map-canvas'), mapOptions);
              for (i=0; i<routes_list.length; i++) {
                routes_list[i].setMap(map); 
              }
            }

            function calcRoute(start, end, routes) {
              
              var directionsDisplay = new google.maps.DirectionsRenderer(directionsDisplayOptions);

              var waypts = [];
              for (var i = 0; i < routes.length; i++) {
                waypts.push({
                  location:routes[i],
                  stopover:true});
                }
              
              var request = {
                  origin: start,
                  destination: end,
                  waypoints: waypts,
                  optimizeWaypoints: false,
                  travelMode: google.maps.TravelMode.DRIVING
              };

              directionsService.route(request, function(response, status) {
                if (status == google.maps.DirectionsStatus.OK) {
                    directionsDisplay.setDirections(response);      
                }
              });

              routes_list.push(directionsDisplay);
            }

            function createRoutes(route) {
                // Google's free map API is limited to 10 waypoints so need to break into batches
                route.push(route[0]);
                var subset = 0;
                while (subset < route.length) {
                    var waypointSubset = route.slice(subset, subset + 10);

                    var startPoint = waypointSubset[0];
                    var midPoints = waypointSubset.slice(1, waypointSubset.length - 1);
                    var endPoint = waypointSubset[waypointSubset.length - 1];

                    calcRoute(startPoint, endPoint, midPoints);

                    subset += 9;
                }
            }
    """
    Page_2 = """
            
            createRoutes(optimal_route);

            google.maps.event.addDomListener(window, 'load', initialize);

        </script>
      </head>
      <body>
        <div id="map-canvas"></div>
      </body>
    </html>
    """

    localoutput_file = output_file.replace('.html', '_' + str(distance) + '.html')
    with open(localoutput_file, 'w') as fs:
        fs.write(Page_1)
        fs.write("\t\t\toptimal_route = {0}".format(str(optimal_route)))
        fs.write(Page_2)

    if display:
        webbrowser.open_new_tab(localoutput_file)


def compute_fitness(solution):
    """
        This function returns the total distance traveled on the current road trip.
        
        The genetic algorithm will favor road trips that have shorter
        total distances traveled.
    """
    
    solution_fitness = 0.0
    
    for index in range(len(solution)):
        waypoint1 = solution[index - 1]
        waypoint2 = solution[index]
        solution_fitness += waypoint_distances[frozenset([waypoint1, waypoint2])]
        
    return solution_fitness

def generate_random_agent():
    """
        Creates a random road trip from the waypoints.
    """
    
    new_random_agent = list(all_waypoints)
    random.shuffle(new_random_agent)
    return tuple(new_random_agent)

def mutate_agent(agent_genome, max_mutations=3):
    """
        Applies 1 - `max_mutations` point mutations to the given road trip.
        
        A point mutation swaps the order of two waypoints in the road trip.
    """
    
    agent_genome = list(agent_genome)
    num_mutations = random.randint(1, max_mutations)
    
    for mutation in range(num_mutations):
        swap_index1 = random.randint(0, len(agent_genome) - 1)
        swap_index2 = swap_index1

        while swap_index1 == swap_index2:
            swap_index2 = random.randint(0, len(agent_genome) - 1)

        agent_genome[swap_index1], agent_genome[swap_index2] = agent_genome[swap_index2], agent_genome[swap_index1]
            
    return tuple(agent_genome)

def shuffle_mutation(agent_genome):
    """
        Applies a single shuffle mutation to the given road trip.
        
        A shuffle mutation takes a random sub-section of the road trip
        and moves it to another location in the road trip.
    """
    
    agent_genome = list(agent_genome)
    
    start_index = random.randint(0, len(agent_genome) - 1)
    length = random.randint(2, 20)
    
    genome_subset = agent_genome[start_index:start_index + length]
    agent_genome = agent_genome[:start_index] + agent_genome[start_index + length:]
    
    insert_index = random.randint(0, len(agent_genome) + len(genome_subset) - 1)
    agent_genome = agent_genome[:insert_index] + genome_subset + agent_genome[insert_index:]
    
    return tuple(agent_genome)

def generate_random_population(pop_size):
    """
        Generates a list with `pop_size` number of random road trips.
    """
    
    random_population = []
    for agent in range(pop_size):
        random_population.append(generate_random_agent())
    return random_population
    
def run_genetic_algorithm(generations=5000, population_size=100):
    """
        The core of the Genetic Algorithm.
        
        `generations` and `population_size` must be a multiple of 10.
    """
    
    current_best_distance = -1
    population_subset_size = int(population_size / 10.)
    generations_10pct = int(generations / 10.)
    
    # Create a random population of `population_size` number of solutions.
    population = generate_random_population(population_size)

    # For `generations` number of repetitions...
    for generation in range(generations):
        
        # Compute the fitness of the entire current population
        population_fitness = {}

        for agent_genome in population:
            if agent_genome in population_fitness:
                continue

            population_fitness[agent_genome] = compute_fitness(agent_genome)

        # Take the top 10% shortest road trips and produce offspring each from them
        new_population = []
        for rank, agent_genome in enumerate(sorted(population_fitness,
                                                   key=population_fitness.get)[:population_subset_size]):
            if (generation % generations_10pct == 0 or generation == generations - 1) and rank == 0:
                current_best_genome = agent_genome
                print("Generation %d best: %d | Unique genomes: %d" % (generation,
                                                                       population_fitness[agent_genome],
                                                                       len(population_fitness)))
                print(agent_genome)                
                print("")

                # If this is the first route found, or it is shorter than the best route we know,
                # create a html output and display it
                if population_fitness[agent_genome] < current_best_distance or current_best_distance < 0:
                    current_best_distance = population_fitness[agent_genome]
                    CreateOptimalRouteHtmlFile(agent_genome, current_best_distance, False)
                    

            # Create 1 exact copy of each of the top road trips
            new_population.append(agent_genome)

            # Create 2 offspring with 1-3 point mutations
            for offspring in range(2):
                new_population.append(mutate_agent(agent_genome, 3))
                
            # Create 7 offspring with a single shuffle mutation
            for offspring in range(7):
                new_population.append(shuffle_mutation(agent_genome))

        # Replace the old population with the new population of offspring 
        for i in range(len(population))[::-1]:
            del population[i]

        population = new_population
    return current_best_genome


if __name__ == '__main__':
    # If this file exists, read the data stored in it - if not then collect data by asking google
    print("Begin finding shortest route")
    file_path = waypoints_file
    if os.path.exists(file_path):
        print("Waypoints exist")
        #file exists used saved results
        waypoint_distances = {}
        waypoint_durations = {}
        all_waypoints = set()

        waypoint_data = pd.read_csv(file_path, sep="\t")

        for i, row in waypoint_data.iterrows():
            waypoint_distances[frozenset([row.waypoint1, row.waypoint2])] = row.distance_m
            waypoint_durations[frozenset([row.waypoint1, row.waypoint2])] = row.duration_s
            all_waypoints.update([row.waypoint1, row.waypoint2])

    else:
        # File does not exist - compute results       
        print("Collecting Waypoints")
        waypoint_distances = {}
        waypoint_durations = {}


        gmaps = googlemaps.Client(GOOGLE_MAPS_API_KEY)
        for (waypoint1, waypoint2) in combinations(all_waypoints, 2):
            try:
                route = gmaps.distance_matrix(origins=[waypoint1],
                                              destinations=[waypoint2],
                                              mode="driving", # Change to "walking" for walking directions,
                                                              # "bicycling" for biking directions, etc.
                                              language="English",
                                              units="metric")

                # "distance" is in meters
                distance = route["rows"][0]["elements"][0]["distance"]["value"]

                # "duration" is in seconds
                duration = route["rows"][0]["elements"][0]["duration"]["value"]

                waypoint_distances[frozenset([waypoint1, waypoint2])] = distance
                waypoint_durations[frozenset([waypoint1, waypoint2])] = duration
        
            except Exception as e:
                print("Error with finding the route between %s and %s." % (waypoint1, waypoint2))
        
        print("Saving Waypoints")
        with open(waypoints_file, "w") as out_file:
            out_file.write("\t".join(["waypoint1",
                                      "waypoint2",
                                      "distance_m",
                                      "duration_s"]))
        
            for (waypoint1, waypoint2) in waypoint_distances.keys():
                out_file.write("\n" +
                               "\t".join([waypoint1,
                                          waypoint2,
                                          str(waypoint_distances[frozenset([waypoint1, waypoint2])]),
                                          str(waypoint_durations[frozenset([waypoint1, waypoint2])])]))

    print("Search for optimal route")
    optimal_route = run_genetic_algorithm(generations=thisRunGenerations, population_size=thisRunPopulation_size)

    # This is probably redundant now that the files are created in run_genetic_algorithm,
    # but leaving it active to ensure  the final result is not lost
    CreateOptimalRouteHtmlFile(optimal_route, 1, True)


================================================
FILE: optimal-road-trip/my-waypoints-dist-dur.tsv
================================================
waypoint1	waypoint2	distance_m	duration_s
Taliesin, County Road C, Spring Green, Wisconsin	Glacier National Park, West Glacier, MT	2218019	74837
French Quarter, New Orleans, LA	Fort Snelling, Tower Avenue, Saint Paul, MN	1916146	63591
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	The Breakers, Ochre Point Avenue, Newport, RI	3057999	101536
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	2347381	78156
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Gateway Arch, Washington Avenue, St Louis, MO	455098	15430
Maryland State House, 100 State Cir, Annapolis, MD 21401	Graceland, Elvis Presley Boulevard, Memphis, TN	1476772	48116
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Carlsbad Caverns National Park, Carlsbad, NM	874048	32551
Liberty Bell, 6th Street, Philadelphia, PA	Cape Canaveral, FL	1618906	52203
Liberty Bell, 6th Street, Philadelphia, PA	The Breakers, Ochre Point Avenue, Newport, RI	441382	16915
San Andreas Fault, San Benito County, CA	French Quarter, New Orleans, LA	3481242	111261
USS Alabama, Battleship Parkway, Mobile, AL	Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	2975383	98743
Graceland, Elvis Presley Boulevard, Memphis, TN	Yellowstone National Park, WY 82190	2487165	87170
Shelburne Farms, Harbor Road, Shelburne, VT	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	1280422	46354
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1667022	56534
Liberty Bell, 6th Street, Philadelphia, PA	Terrace Hill, Grand Avenue, Des Moines, IA	1737865	57267
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Statue of Liberty, Liberty Island, NYC, NY	577249	21127
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Ashfall Fossil Bed, Royal, NE	976321	34111
Liberty Bell, 6th Street, Philadelphia, PA	Bryce Canyon National Park, Hwy 63, Bryce, UT	3687679	120631
USS Alabama, Battleship Parkway, Mobile, AL	Congress Hall, Congress Place, Cape May, NJ 08204	1862015	62154
Maryland State House, 100 State Cir, Annapolis, MD 21401	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1297516	44127
Taliesin, County Road C, Spring Green, Wisconsin	The Breakers, Ochre Point Avenue, Newport, RI	1855961	64286
Toltec Mounds, Scott, AR	Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	2344430	75072
Mount Vernon, Fairfax County, Virginia	Congress Hall, Congress Place, Cape May, NJ 08204	324587	13291
Acadia National Park, Maine	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	2101168	71928
New Castle Historic District, Delaware	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1264487	42249
Hanford Site, Benton County, WA	Columbia River Gorge National Scenic Area, Oregon	202197	7339
Graceland, Elvis Presley Boulevard, Memphis, TN	The Breakers, Ochre Point Avenue, Newport, RI	2057815	69018
Hanford Site, Benton County, WA	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	4548872	147740
Hoover Dam, NV	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	2925806	92712
Liberty Bell, 6th Street, Philadelphia, PA	The Alamo, Alamo Plaza, San Antonio, TX	2800673	90492
Fort Snelling, Tower Avenue, Saint Paul, MN	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	3156193	102831
Mount Vernon, Fairfax County, Virginia	Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	2868913	94308
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	The Breakers, Ochre Point Avenue, Newport, RI	2700531	92033
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Toltec Mounds, Scott, AR	1872657	59896
Mount Vernon, Fairfax County, Virginia	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	829832	28982
Carlsbad Caverns National Park, Carlsbad, NM	Craters of the Moon National Monument & Preserve, Arco, ID	1875346	66294
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	3794883	121470
Bryce Canyon National Park, Hwy 63, Bryce, UT	Ashfall Fossil Bed, Royal, NE	1693953	58524
Lost World Caverns, Lewisburg, WV	Statue of Liberty, Liberty Island, NYC, NY	770142	25955
Craters of the Moon National Monument & Preserve, Arco, ID	Congress Hall, Congress Place, Cape May, NJ 08204	3853634	125539
Hanford Site, Benton County, WA	New Castle Historic District, Delaware	4363313	142580
San Andreas Fault, San Benito County, CA	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	3667715	117791
Toltec Mounds, Scott, AR	Glacier National Park, West Glacier, MT	3041488	98106
USS Alabama, Battleship Parkway, Mobile, AL	Maryland State House, 100 State Cir, Annapolis, MD 21401	1613845	52212
Lost World Caverns, Lewisburg, WV	Fort Sumter National Monument, Sullivan's Island, SC	690404	24395
Toltec Mounds, Scott, AR	Shelburne Farms, Harbor Road, Shelburne, VT	2274624	78824
Maryland State House, 100 State Cir, Annapolis, MD 21401	New Castle Historic District, Delaware	100318	4328
Toltec Mounds, Scott, AR	Ashfall Fossil Bed, Royal, NE	1276957	43608
Mount Vernon, Fairfax County, Virginia	Olympia Entertainment, Woodward Avenue, Detroit, MI	869309	29349
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Columbia River Gorge National Scenic Area, Oregon	1747344	56909
USS Alabama, Battleship Parkway, Mobile, AL	Graceland, Elvis Presley Boulevard, Memphis, TN	582585	21366
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Fort Sumter National Monument, Sullivan's Island, SC	1885895	62411
Lost World Caverns, Lewisburg, WV	French Quarter, New Orleans, LA	1441808	46167
USS Alabama, Battleship Parkway, Mobile, AL	Olympia Entertainment, Woodward Avenue, Detroit, MI	1585831	51531
Mount Vernon, Fairfax County, Virginia	Acadia National Park, Maine	1159086	41873
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	The Breakers, Ochre Point Avenue, Newport, RI	2264052	77724
Acadia National Park, Maine	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	2293420	77193
Hoover Dam, NV	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	2972025	94061
Hoover Dam, NV	Ashfall Fossil Bed, Royal, NE	2042375	68608
Fort Sumter National Monument, Sullivan's Island, SC	French Quarter, New Orleans, LA	1223403	39567
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Yellowstone National Park, WY 82190	1152968	43043
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	3143831	103818
Wright Brothers National Memorial Visitor Center, Manteo, NC	Gateway Arch, Washington Avenue, St Louis, MO	1619422	53683
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	553214	19824
Lost World Caverns, Lewisburg, WV	Craters of the Moon National Monument & Preserve, Arco, ID	3375573	109217
Statue of Liberty, Liberty Island, NYC, NY	Columbia River Gorge National Scenic Area, Oregon	4503584	145711
Terrace Hill, Grand Avenue, Des Moines, IA	Yellowstone National Park, WY 82190	1736070	60164
Hanford Site, Benton County, WA	Taliesin, County Road C, Spring Green, Wisconsin	2865645	92549
The Alamo, Alamo Plaza, San Antonio, TX	Ashfall Fossil Bed, Royal, NE	1582862	53486
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Statue of Liberty, Liberty Island, NYC, NY	1993400	64754
Ashfall Fossil Bed, Royal, NE	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	957962	33654
San Andreas Fault, San Benito County, CA	The Alamo, Alamo Plaza, San Antonio, TX	2614335	83174
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Wright Brothers National Memorial Visitor Center, Manteo, NC	2070353	68518
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Acadia National Park, Maine	370453	16119
Lost World Caverns, Lewisburg, WV	Olympia Entertainment, Woodward Avenue, Detroit, MI	751740	27138
Toltec Mounds, Scott, AR	Mount Vernon, Fairfax County, Virginia	1628719	53196
Cape Canaveral, FL	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1787806	57333
Hanford Site, Benton County, WA	Craters of the Moon National Monument & Preserve, Arco, ID	804532	27912
Ashfall Fossil Bed, Royal, NE	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1348425	46160
Shelburne Farms, Harbor Road, Shelburne, VT	Olympia Entertainment, Woodward Avenue, Detroit, MI	1075881	37603
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Statue of Liberty, Liberty Island, NYC, NY	2772422	91153
Lost World Caverns, Lewisburg, WV	Glacier National Park, West Glacier, MT	3442230	115082
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Craters of the Moon National Monument & Preserve, Arco, ID	2830185	93012
Hoover Dam, NV	Shelburne Farms, Harbor Road, Shelburne, VT	4286375	141744
Maryland State House, 100 State Cir, Annapolis, MD 21401	Taliesin, County Road C, Spring Green, Wisconsin	1461552	50064
Liberty Bell, 6th Street, Philadelphia, PA	Craters of the Moon National Monument & Preserve, Arco, ID	3705218	120341
Lost World Caverns, Lewisburg, WV	Fort Snelling, Tower Avenue, Saint Paul, MN	1626905	54854
Terrace Hill, Grand Avenue, Des Moines, IA	Statue of Liberty, Liberty Island, NYC, NY	1787020	59289
Hanford Site, Benton County, WA	Ashfall Fossil Bed, Royal, NE	2197917	74421
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	White House, Pennsylvania Avenue Northwest, Washington, DC	809619	28300
Taliesin, County Road C, Spring Green, Wisconsin	Columbia River Gorge National Scenic Area, Oregon	3013796	97923
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Statue of Liberty, Liberty Island, NYC, NY	192047	7989
Toltec Mounds, Scott, AR	French Quarter, New Orleans, LA	670161	24369
USS Constitution, Boston, MA	Acadia National Park, Maine	443026	16367
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Columbia River Gorge National Scenic Area, Oregon	3571513	116405
San Andreas Fault, San Benito County, CA	Ashfall Fossil Bed, Royal, NE	2748296	91875
Toltec Mounds, Scott, AR	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	2503709	84931
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Bryce Canyon National Park, Hwy 63, Bryce, UT	1828713	63879
Wright Brothers National Memorial Visitor Center, Manteo, NC	Bryce Canyon National Park, Hwy 63, Bryce, UT	3890985	126573
Bryce Canyon National Park, Hwy 63, Bryce, UT	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	2651338	85582
Toltec Mounds, Scott, AR	Vicksburg National Military Park, Clay Street, Vicksburg, MS	342172	13312
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	2168326	72590
Toltec Mounds, Scott, AR	Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	612075	20648
Mount Vernon, Fairfax County, Virginia	Shelburne Farms, Harbor Road, Shelburne, VT	838747	31297
Cape Canaveral, FL	Congress Hall, Congress Place, Cape May, NJ 08204	1697361	55802
Maryland State House, 100 State Cir, Annapolis, MD 21401	Bryce Canyon National Park, Hwy 63, Bryce, UT	3616879	118534
Maryland State House, 100 State Cir, Annapolis, MD 21401	Statue of Liberty, Liberty Island, NYC, NY	346519	12361
Maryland State House, 100 State Cir, Annapolis, MD 21401	Acadia National Park, Maine	1120643	39839
Liberty Bell, 6th Street, Philadelphia, PA	Carlsbad Caverns National Park, Carlsbad, NM	3152378	105074
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Columbia River Gorge National Scenic Area, Oregon	4663563	150807
Toltec Mounds, Scott, AR	Cape Canaveral, FL	1567778	51965
White House, Pennsylvania Avenue Northwest, Washington, DC	The Alamo, Alamo Plaza, San Antonio, TX	2579805	82868
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Bryce Canyon National Park, Hwy 63, Bryce, UT	2718623	88932
New Castle Historic District, Delaware	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1794302	58751
Maryland State House, 100 State Cir, Annapolis, MD 21401	Fort Sumter National Monument, Sullivan's Island, SC	911048	29592
Pikes Peak, Colorado	Olympia Entertainment, Woodward Avenue, Detroit, MI	2190092	71624
Toltec Mounds, Scott, AR	Statue of Liberty, Liberty Island, NYC, NY	1983218	65145
Olympia Entertainment, Woodward Avenue, Detroit, MI	French Quarter, New Orleans, LA	1716724	55770
Hoover Dam, NV	Gateway Arch, Washington Avenue, St Louis, MO	2550203	80416
Hanford Site, Benton County, WA	White House, Pennsylvania Avenue Northwest, Washington, DC	4241452	137764
Mount Vernon, Fairfax County, Virginia	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1638902	52805
Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	Fort Snelling, Tower Avenue, Saint Paul, MN	2146870	73185
Lost World Caverns, Lewisburg, WV	The Alamo, Alamo Plaza, San Antonio, TX	2277257	72943
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	The Alamo, Alamo Plaza, San Antonio, TX	652795	22214
Wright Brothers National Memorial Visitor Center, Manteo, NC	New Castle Historic District, Delaware	422162	17408
Fort Sumter National Monument, Sullivan's Island, SC	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1080930	36445
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Fort Sumter National Monument, Sullivan's Island, SC	1407416	47697
New Castle Historic District, Delaware	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4651287	150314
Lost World Caverns, Lewisburg, WV	White House, Pennsylvania Avenue Northwest, Washington, DC	416487	13963
Pikes Peak, Colorado	Fort Sumter National Monument, Sullivan's Island, SC	2763349	91159
San Andreas Fault, San Benito County, CA	Pikes Peak, Colorado	2028101	70650
Hanford Site, Benton County, WA	Fort Snelling, Tower Avenue, Saint Paul, MN	2476769	78773
Hanford Site, Benton County, WA	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	3537439	115348
Maryland State House, 100 State Cir, Annapolis, MD 21401	Columbia River Gorge National Scenic Area, Oregon	4394752	141872
USS Alabama, Battleship Parkway, Mobile, AL	Mount Vernon, Fairfax County, Virginia	1538446	49634
Taliesin, County Road C, Spring Green, Wisconsin	Bryce Canyon National Park, Hwy 63, Bryce, UT	2408644	77823
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Olympia Entertainment, Woodward Avenue, Detroit, MI	1245101	40896
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Grand Canyon National Park, Arizona	1651973	56937
Olympia Entertainment, Woodward Avenue, Detroit, MI	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	625402	22359
Craters of the Moon National Monument & Preserve, Arco, ID	Gateway Arch, Washington Avenue, St Louis, MO	2387912	77468
Maryland State House, 100 State Cir, Annapolis, MD 21401	Gateway Arch, Washington Avenue, St Louis, MO	1357038	46244
Congress Hall, Congress Place, Cape May, NJ 08204	Statue of Liberty, Liberty Island, NYC, NY	254433	9322
Graceland, Elvis Presley Boulevard, Memphis, TN	Columbia River Gorge National Scenic Area, Oregon	3466308	113158
Terrace Hill, Grand Avenue, Des Moines, IA	Lost World Caverns, Lewisburg, WV	1444979	48126
Mount Vernon, Fairfax County, Virginia	San Andreas Fault, San Benito County, CA	4600219	148058
Wright Brothers National Memorial Visitor Center, Manteo, NC	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1641490	55760
Glacier National Park, West Glacier, MT	Grand Canyon National Park, Arizona	1671390	57638
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Liberty Bell, 6th Street, Philadelphia, PA	2722541	89251
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Glacier National Park, West Glacier, MT	2204537	75486
Yellowstone National Park, WY 82190	Ashfall Fossil Bed, Royal, NE	1235995	47419
Cape Canaveral, FL	Olympia Entertainment, Woodward Avenue, Detroit, MI	1869664	62291
Ashfall Fossil Bed, Royal, NE	Grand Canyon National Park, Arizona	1845322	64321
Pikes Peak, Colorado	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1765180	59041
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Acadia National Park, Maine	3512958	116635
Carlsbad Caverns National Park, Carlsbad, NM	Pikes Peak, Colorado	922977	35011
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	3254574	107170
Hoover Dam, NV	Olympia Entertainment, Woodward Avenue, Detroit, MI	3287993	104894
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	199119	8955
Wright Brothers National Memorial Visitor Center, Manteo, NC	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	1179764	41494
Pikes Peak, Colorado	Congress Hall, Congress Place, Cape May, NJ 08204	2949957	99167
USS Constitution, Boston, MA	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1863224	61974
Fort Sumter National Monument, Sullivan's Island, SC	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	363482	13635
Toltec Mounds, Scott, AR	New Castle Historic District, Delaware	1783543	59257
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Columbia River Gorge National Scenic Area, Oregon	3726654	119687
Mount Vernon, Fairfax County, Virginia	Statue of Liberty, Liberty Island, NYC, NY	386403	14398
Mount Vernon, Fairfax County, Virginia	Graceland, Elvis Presley Boulevard, Memphis, TN	1414540	45977
Statue of Liberty, Liberty Island, NYC, NY	Fort Snelling, Tower Avenue, Saint Paul, MN	1934123	64833
Taliesin, County Road C, Spring Green, Wisconsin	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	3320261	105749
Yellowstone National Park, WY 82190	Fort Snelling, Tower Avenue, Saint Paul, MN	1822887	63288
White House, Pennsylvania Avenue Northwest, Washington, DC	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1114274	36642
Graceland, Elvis Presley Boulevard, Memphis, TN	Bryce Canyon National Park, Hwy 63, Bryce, UT	2504346	83235
Shelburne Farms, Harbor Road, Shelburne, VT	New Castle Historic District, Delaware	751134	27927
Wright Brothers National Memorial Visitor Center, Manteo, NC	Acadia National Park, Maine	1492775	55252
Terrace Hill, Grand Avenue, Des Moines, IA	Fort Snelling, Tower Avenue, Saint Paul, MN	394558	12643
Pikes Peak, Colorado	Ashfall Fossil Bed, Royal, NE	948929	35456
Mount Vernon, Fairfax County, Virginia	Cape Canaveral, FL	1373791	43281
Cape Canaveral, FL	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4607140	147726
Acadia National Park, Maine	Ashfall Fossil Bed, Royal, NE	2988611	101499
USS Constitution, Boston, MA	Vicksburg National Military Park, Clay Street, Vicksburg, MS	2347308	77140
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Gateway Arch, Washington Avenue, St Louis, MO	1957477	64076
The Breakers, Ochre Point Avenue, Newport, RI	Acadia National Park, Maine	561691	21320
Lost World Caverns, Lewisburg, WV	Pikes Peak, Colorado	2378452	78521
White House, Pennsylvania Avenue Northwest, Washington, DC	Fort Sumter National Monument, Sullivan's Island, SC	854668	27805
Statue of Liberty, Liberty Island, NYC, NY	Grand Canyon National Park, Arizona	3838968	124490
USS Alabama, Battleship Parkway, Mobile, AL	USS Constitution, Boston, MA	2267282	75766
The Alamo, Alamo Plaza, San Antonio, TX	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1800120	58441
New Castle Historic District, Delaware	Olympia Entertainment, Woodward Avenue, Detroit, MI	969661	33480
Terrace Hill, Grand Avenue, Des Moines, IA	Fort Sumter National Monument, Sullivan's Island, SC	1936485	62907
Bryce Canyon National Park, Hwy 63, Bryce, UT	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	2375844	76706
Liberty Bell, 6th Street, Philadelphia, PA	Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	2943788	96463
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Graceland, Elvis Presley Boulevard, Memphis, TN	1905830	63373
Mount Vernon, Fairfax County, Virginia	USS Constitution, Boston, MA	729479	26780
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	5079239	165660
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Gateway Arch, Washington Avenue, St Louis, MO	1568984	50208
Liberty Bell, 6th Street, Philadelphia, PA	Taliesin, County Road C, Spring Green, Wisconsin	1520502	52001
French Quarter, New Orleans, LA	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	3655745	115357
Glacier National Park, West Glacier, MT	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	2627806	88466
Statue of Liberty, Liberty Island, NYC, NY	Fort Sumter National Monument, Sullivan's Island, SC	1222041	40861
USS Alabama, Battleship Parkway, Mobile, AL	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	2109747	70257
Mount Vernon, Fairfax County, Virginia	Fort Snelling, Tower Avenue, Saint Paul, MN	1810094	60656
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Gateway Arch, Washington Avenue, St Louis, MO	879834	29469
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Craters of the Moon National Monument & Preserve, Arco, ID	2302132	74104
Cape Canaveral, FL	Ashfall Fossil Bed, Royal, NE	2651598	86966
Terrace Hill, Grand Avenue, Des Moines, IA	Graceland, Elvis Presley Boulevard, Memphis, TN	1008442	33550
Craters of the Moon National Monument & Preserve, Arco, ID	Vicksburg National Military Park, Clay Street, Vicksburg, MS	2984500	96748
Shelburne Farms, Harbor Road, Shelburne, VT	Cape Canaveral, FL	2212383	74063
Graceland, Elvis Presley Boulevard, Memphis, TN	Wright Brothers National Memorial Visitor Center, Manteo, NC	1513958	50106
Acadia National Park, Maine	Fort Snelling, Tower Avenue, Saint Paul, MN	2666567	90398
New Castle Historic District, Delaware	French Quarter, New Orleans, LA	1900188	62217
Pikes Peak, Colorado	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	2110257	72715
The Breakers, Ochre Point Avenue, Newport, RI	Congress Hall, Congress Place, Cape May, NJ 08204	545003	20300
Liberty Bell, 6th Street, Philadelphia, PA	Shelburne Farms, Harbor Road, Shelburne, VT	603814	22908
San Andreas Fault, San Benito County, CA	Bryce Canyon National Park, Hwy 63, Bryce, UT	1153959	39401
Carlsbad Caverns National Park, Carlsbad, NM	Fort Sumter National Monument, Sullivan's Island, SC	2529332	84159
Toltec Mounds, Scott, AR	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	3179769	101423
Mount Vernon, Fairfax County, Virginia	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1094658	35633
Columbia River Gorge National Scenic Area, Oregon	Glacier National Park, West Glacier, MT	844416	29826
Graceland, Elvis Presley Boulevard, Memphis, TN	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	622132	20233
Terrace Hill, Grand Avenue, Des Moines, IA	Cape Canaveral, FL	2229372	72962
Taliesin, County Road C, Spring Green, Wisconsin	White House, Pennsylvania Avenue Northwest, Washington, DC	1422941	49023
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Columbia River Gorge National Scenic Area, Oregon	3653599	116971
Olympia Entertainment, Woodward Avenue, Detroit, MI	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	725616	23888
Hanford Site, Benton County, WA	Vicksburg National Military Park, Clay Street, Vicksburg, MS	3685723	118499
Graceland, Elvis Presley Boulevard, Memphis, TN	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	482758	15622
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Hanford Site, Benton County, WA	3007742	95841
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Grand Canyon National Park, Arizona	1966377	66850
Wright Brothers National Memorial Visitor Center, Manteo, NC	Glacier National Park, West Glacier, MT	4052231	135932
Fort Sumter National Monument, Sullivan's Island, SC	Columbia River Gorge National Scenic Area, Oregon	4507989	144600
Graceland, Elvis Presley Boulevard, Memphis, TN	Acadia National Park, Maine	2553219	85419
Yellowstone National Park, WY 82190	Bryce Canyon National Park, Hwy 63, Bryce, UT	962304	36167
The Breakers, Ochre Point Avenue, Newport, RI	New Castle Historic District, Delaware	588702	21933
Terrace Hill, Grand Avenue, Des Moines, IA	Bryce Canyon National Park, Hwy 63, Bryce, UT	1980517	63788
Carlsbad Caverns National Park, Carlsbad, NM	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	2081182	70149
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Congress Hall, Congress Place, Cape May, NJ 08204	2857920	94583
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Fort Sumter National Monument, Sullivan's Island, SC	1007099	33020
Terrace Hill, Grand Avenue, Des Moines, IA	Congress Hall, Congress Place, Cape May, NJ 08204	1886282	62465
USS Constitution, Boston, MA	Bryce Canyon National Park, Hwy 63, Bryce, UT	4079429	133189
Hoover Dam, NV	Vicksburg National Military Park, Clay Street, Vicksburg, MS	2492392	79680
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1717447	60333
Liberty Bell, 6th Street, Philadelphia, PA	Mount Vernon, Fairfax County, Virginia	242317	9386
Maryland State House, 100 State Cir, Annapolis, MD 21401	Carlsbad Caverns National Park, Carlsbad, NM	2992105	99733
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	581412	21547
The Breakers, Ochre Point Avenue, Newport, RI	Vicksburg National Military Park, Clay Street, Vicksburg, MS	2284060	75766
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	2170327	72448
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Yellowstone National Park, WY 82190	3684032	124272
Congress Hall, Congress Place, Cape May, NJ 08204	Fort Snelling, Tower Avenue, Saint Paul, MN	2033385	68009
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Lost World Caverns, Lewisburg, WV	508306	18411
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	2701523	87464
Lost World Caverns, Lewisburg, WV	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4297208	137228
White House, Pennsylvania Avenue Northwest, Washington, DC	New Castle Historic District, Delaware	145742	6395
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Pikes Peak, Colorado	1931715	64442
San Andreas Fault, San Benito County, CA	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	3619999	116303
Pikes Peak, Colorado	Gateway Arch, Washington Avenue, St Louis, MO	1390792	46772
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Gateway Arch, Washington Avenue, St Louis, MO	1734279	58145
Cape Canaveral, FL	Statue of Liberty, Liberty Island, NYC, NY	1759380	57091
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	USS Constitution, Boston, MA	167183	6166
New Castle Historic District, Delaware	Grand Canyon National Park, Arizona	3741677	122144
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	310125	10608
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	White House, Pennsylvania Avenue Northwest, Washington, DC	2202900	71934
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Taliesin, County Road C, Spring Green, Wisconsin	770373	26747
Graceland, Elvis Presley Boulevard, Memphis, TN	Gateway Arch, Washington Avenue, St Louis, MO	469145	15247
Taliesin, County Road C, Spring Green, Wisconsin	Statue of Liberty, Liberty Island, NYC, NY	1570383	53903
Carlsbad Caverns National Park, Carlsbad, NM	Congress Hall, Congress Place, Cape May, NJ 08204	3232567	109057
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Fort Snelling, Tower Avenue, Saint Paul, MN	1277557	42826
Terrace Hill, Grand Avenue, Des Moines, IA	Grand Canyon National Park, Arizona	2126579	69490
Taliesin, County Road C, Spring Green, Wisconsin	Fort Snelling, Tower Avenue, Saint Paul, MN	402693	14609
Craters of the Moon National Monument & Preserve, Arco, ID	Statue of Liberty, Liberty Island, NYC, NY	3754373	122363
Mount Vernon, Fairfax County, Virginia	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4551979	146197
Taliesin, County Road C, Spring Green, Wisconsin	Acadia National Park, Maine	2310920	79386
Shelburne Farms, Harbor Road, Shelburne, VT	Lost World Caverns, Lewisburg, WV	1199298	41645
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Bryce Canyon National Park, Hwy 63, Bryce, UT	2490228	83656
Maryland State House, 100 State Cir, Annapolis, MD 21401	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	1105058	36238
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Graceland, Elvis Presley Boulevard, Memphis, TN	2399554	77775
Shelburne Farms, Harbor Road, Shelburne, VT	Hanford Site, Benton County, WA	4564119	151892
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Wright Brothers National Memorial Visitor Center, Manteo, NC	1136995	39795
Graceland, Elvis Presley Boulevard, Memphis, TN	Ashfall Fossil Bed, Royal, NE	1310105	46863
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Fort Snelling, Tower Avenue, Saint Paul, MN	1667139	56039
Liberty Bell, 6th Street, Philadelphia, PA	Wright Brothers National Memorial Visitor Center, Manteo, NC	582053	22681
Liberty Bell, 6th Street, Philadelphia, PA	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1171045	40708
Pikes Peak, Colorado	Bryce Canyon National Park, Hwy 63, Bryce, UT	998488	37341
Terrace Hill, Grand Avenue, Des Moines, IA	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	1011679	35365
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Taliesin, County Road C, Spring Green, Wisconsin	1208996	40460
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Glacier National Park, West Glacier, MT	3369066	109995
Terrace Hill, Grand Avenue, Des Moines, IA	Acadia National Park, Maine	2529755	84694
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Cape Canaveral, FL	2330047	77079
USS Alabama, Battleship Parkway, Mobile, AL	Ashfall Fossil Bed, Royal, NE	1889577	67851
Graceland, Elvis Presley Boulevard, Memphis, TN	Congress Hall, Congress Place, Cape May, NJ 08204	1717234	57440
The Alamo, Alamo Plaza, San Antonio, TX	Bryce Canyon National Park, Hwy 63, Bryce, UT	2013900	69315
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Acadia National Park, Maine	597379	21385
Hoover Dam, NV	Terrace Hill, Grand Avenue, Des Moines, IA	2328938	73873
San Andreas Fault, San Benito County, CA	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	3397300	109030
Toltec Mounds, Scott, AR	White House, Pennsylvania Avenue Northwest, Washington, DC	1627313	53061
USS Alabama, Battleship Parkway, Mobile, AL	Glacier National Park, West Glacier, MT	3545115	123166
Shelburne Farms, Harbor Road, Shelburne, VT	Fort Sumter National Monument, Sullivan's Island, SC	1672509	57554
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Fort Sumter National Monument, Sullivan's Island, SC	922894	31784
Liberty Bell, 6th Street, Philadelphia, PA	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	337338	12880
Carlsbad Caverns National Park, Carlsbad, NM	Statue of Liberty, Liberty Island, NYC, NY	3284729	109318
Graceland, Elvis Presley Boulevard, Memphis, TN	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	3375034	106909
Glacier National Park, West Glacier, MT	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	1852892	68182
Cape Canaveral, FL	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	377410	13220
The Alamo, Alamo Plaza, San Antonio, TX	Gateway Arch, Washington Avenue, St Louis, MO	1454861	48938
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Mount Vernon, Fairfax County, Virginia	1776793	60250
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Pikes Peak, Colorado	1138992	41032
White House, Pennsylvania Avenue Northwest, Washington, DC	French Quarter, New Orleans, LA	1743678	56192
The Breakers, Ochre Point Avenue, Newport, RI	Yellowstone National Park, WY 82190	3809631	129558
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	The Breakers, Ochre Point Avenue, Newport, RI	1317092	46456
Carlsbad Caverns National Park, Carlsbad, NM	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1858566	62840
Maryland State House, 100 State Cir, Annapolis, MD 21401	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	848229	29340
Yellowstone National Park, WY 82190	The Alamo, Alamo Plaza, San Antonio, TX	2356077	82050
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	San Andreas Fault, San Benito County, CA	2527020	83368
New Castle Historic District, Delaware	Gateway Arch, Washington Avenue, St Louis, MO	1440290	50020
French Quarter, New Orleans, LA	Grand Canyon National Park, Arizona	2486793	79674
Statue of Liberty, Liberty Island, NYC, NY	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1477357	50369
Hoover Dam, NV	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	3441584	113235
Maryland State House, 100 State Cir, Annapolis, MD 21401	Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	2265577	74139
Hanford Site, Benton County, WA	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	3072833	99361
Fort Sumter National Monument, Sullivan's Island, SC	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4455913	142569
Terrace Hill, Grand Avenue, Des Moines, IA	Gateway Arch, Washington Avenue, St Louis, MO	567564	19773
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Lost World Caverns, Lewisburg, WV	1869023	61109
Pikes Peak, Colorado	The Alamo, Alamo Plaza, San Antonio, TX	1435151	49274
Graceland, Elvis Presley Boulevard, Memphis, TN	Vicksburg National Military Park, Clay Street, Vicksburg, MS	382413	12329
Liberty Bell, 6th Street, Philadelphia, PA	Hanford Site, Benton County, WA	4339013	140742
USS Alabama, Battleship Parkway, Mobile, AL	Fort Sumter National Monument, Sullivan's Island, SC	1002499	32623
Liberty Bell, 6th Street, Philadelphia, PA	Columbia River Gorge National Scenic Area, Oregon	4452738	143085
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	702803	22713
The Alamo, Alamo Plaza, San Antonio, TX	Columbia River Gorge National Scenic Area, Oregon	3174721	106519
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	827545	27595
Wright Brothers National Memorial Visitor Center, Manteo, NC	Columbia River Gorge National Scenic Area, Oregon	4743875	154046
Columbia River Gorge National Scenic Area, Oregon	Grand Canyon National Park, Arizona	1682141	56226
Terrace Hill, Grand Avenue, Des Moines, IA	Ashfall Fossil Bed, Royal, NE	460092	17005
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Shelburne Farms, Harbor Road, Shelburne, VT	2658016	91689
Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4384469	140971
Craters of the Moon National Monument & Preserve, Arco, ID	Acadia National Park, Maine	4497107	147768
White House, Pennsylvania Avenue Northwest, Washington, DC	Fort Snelling, Tower Avenue, Saint Paul, MN	1778589	60035
Gateway Arch, Washington Avenue, St Louis, MO	Acadia National Park, Maine	2328971	79163
Toltec Mounds, Scott, AR	Gateway Arch, Washington Avenue, St Louis, MO	568157	20503
Wright Brothers National Memorial Visitor Center, Manteo, NC	Yellowstone National Park, WY 82190	3764344	127510
San Andreas Fault, San Benito County, CA	The Breakers, Ochre Point Avenue, Newport, RI	5103126	166192
Gateway Arch, Washington Avenue, St Louis, MO	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	158520	5593
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Yellowstone National Park, WY 82190	2591982	89869
Shelburne Farms, Harbor Road, Shelburne, VT	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	192042	8293
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Fort Snelling, Tower Avenue, Saint Paul, MN	956302	31450
Liberty Bell, 6th Street, Philadelphia, PA	Fort Sumter National Monument, Sullivan's Island, SC	1077955	35849
Liberty Bell, 6th Street, Philadelphia, PA	Gateway Arch, Washington Avenue, St Louis, MO	1425131	48171
Yellowstone National Park, WY 82190	Acadia National Park, Maine	4264590	144658
New Castle Historic District, Delaware	Columbia River Gorge National Scenic Area, Oregon	4478003	145647
Congress Hall, Congress Place, Cape May, NJ 08204	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1311091	45188
Carlsbad Caverns National Park, Carlsbad, NM	Lost World Caverns, Lewisburg, WV	2627620	87418
Terrace Hill, Grand Avenue, Des Moines, IA	Carlsbad Caverns National Park, Carlsbad, NM	1775662	59430
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Cape Canaveral, FL	3635791	117123
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	French Quarter, New Orleans, LA	2529104	82743
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Ashfall Fossil Bed, Royal, NE	1604535	55497
The Breakers, Ochre Point Avenue, Newport, RI	White House, Pennsylvania Avenue Northwest, Washington, DC	653295	24507
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1901493	61580
Hoover Dam, NV	Craters of the Moon National Monument & Preserve, Arco, ID	1149083	35910
Toltec Mounds, Scott, AR	Grand Canyon National Park, Arizona	2072393	65425
Yellowstone National Park, WY 82190	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	3446653	117372
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Toltec Mounds, Scott, AR	756195	24900
USS Alabama, Battleship Parkway, Mobile, AL	Vicksburg National Military Park, Clay Street, Vicksburg, MS	370688	13387
Liberty Bell, 6th Street, Philadelphia, PA	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	1233782	40742
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Vicksburg National Military Park, Clay Street, Vicksburg, MS	2200235	71786
Hoover Dam, NV	Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	2084706	67807
USS Alabama, Battleship Parkway, Mobile, AL	Wright Brothers National Memorial Visitor Center, Manteo, NC	1489642	47392
USS Alabama, Battleship Parkway, Mobile, AL	Grand Canyon National Park, Arizona	2615332	85785
Hoover Dam, NV	Wright Brothers National Memorial Visitor Center, Manteo, NC	4004005	128209
Graceland, Elvis Presley Boulevard, Memphis, TN	Fort Snelling, Tower Avenue, Saint Paul, MN	1288825	43921
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1032820	34455
French Quarter, New Orleans, LA	Bryce Canyon National Park, Hwy 63, Bryce, UT	2723632	91296
Craters of the Moon National Monument & Preserve, Arco, ID	Columbia River Gorge National Scenic Area, Oregon	845463	29100
Carlsbad Caverns National Park, Carlsbad, NM	Taliesin, County Road C, Spring Green, Wisconsin	2204052	73510
Olympia Entertainment, Woodward Avenue, Detroit, MI	Ashfall Fossil Bed, Royal, NE	1419146	48026
The Alamo, Alamo Plaza, San Antonio, TX	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	2788838	87269
Maryland State House, 100 State Cir, Annapolis, MD 21401	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1103913	37085
White House, Pennsylvania Avenue Northwest, Washington, DC	Grand Canyon National Park, Arizona	3663661	116717
Yellowstone National Park, WY 82190	Gateway Arch, Washington Avenue, St Louis, MO	2149556	74278
Craters of the Moon National Monument & Preserve, Arco, ID	Ashfall Fossil Bed, Royal, NE	1480088	56115
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	New Castle Historic District, Delaware	2958272	98658
Graceland, Elvis Presley Boulevard, Memphis, TN	Olympia Entertainment, Woodward Avenue, Detroit, MI	1204474	40007
Maryland State House, 100 State Cir, Annapolis, MD 21401	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	917186	32349
Liberty Bell, 6th Street, Philadelphia, PA	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1369812	46284
Taliesin, County Road C, Spring Green, Wisconsin	USS Constitution, Boston, MA	1881312	64293
Cape Canaveral, FL	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1250556	40658
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Terrace Hill, Grand Avenue, Des Moines, IA	1038320	32938
Toltec Mounds, Scott, AR	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	2177479	71850
Bryce Canyon National Park, Hwy 63, Bryce, UT	Columbia River Gorge National Scenic Area, Oregon	1502684	48423
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1085667	36886
Hoover Dam, NV	Yellowstone National Park, WY 82190	1260922	45106
White House, Pennsylvania Avenue Northwest, Washington, DC	Yellowstone National Park, WY 82190	3376611	114295
Lost World Caverns, Lewisburg, WV	Acadia National Park, Maine	1555610	53471
Liberty Bell, 6th Street, Philadelphia, PA	USS Constitution, Boston, MA	495038	18425
Terrace Hill, Grand Avenue, Des Moines, IA	USS Constitution, Boston, MA	2099559	69476
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	White House, Pennsylvania Avenue Northwest, Washington, DC	2836410	93842
Graceland, Elvis Presley Boulevard, Memphis, TN	Taliesin, County Road C, Spring Green, Wisconsin	1107487	36315
Statue of Liberty, Liberty Island, NYC, NY	Acadia National Park, Maine	779355	28628
Olympia Entertainment, Woodward Avenue, Detroit, MI	Bryce Canyon National Park, Hwy 63, Bryce, UT	2939571	94810
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Pikes Peak, Colorado	989094	33954
Cape Canaveral, FL	Fort Sumter National Monument, Sullivan's Island, SC	642615	21283
Lost World Caverns, Lewisburg, WV	Yellowstone National Park, WY 82190	3138421	106104
Hanford Site, Benton County, WA	San Andreas Fault, San Benito County, CA	1398524	51329
French Quarter, New Orleans, LA	Columbia River Gorge National Scenic Area, Oregon	3922705	128082
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	2866081	93302
Carlsbad Caverns National Park, Carlsbad, NM	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	3711294	126359
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Congress Hall, Congress Place, Cape May, NJ 08204	825912	29227
Graceland, Elvis Presley Boulevard, Memphis, TN	Statue of Liberty, Liberty Island, NYC, NY	1767750	57903
Mount Vernon, Fairfax County, Virginia	Yellowstone National Park, WY 82190	3398494	115127
Statue of Liberty, Liberty Island, NYC, NY	French Quarter, New Orleans, LA	2098917	68067
Carlsbad Caverns National Park, Carlsbad, NM	Bryce Canyon National Park, Hwy 63, Bryce, UT	1371003	50784
Hoover Dam, NV	Bryce Canyon National Park, Hwy 63, Bryce, UT	470908	16041
Carlsbad Caverns National Park, Carlsbad, NM	Acadia National Park, Maine	4029017	136409
USS Alabama, Battleship Parkway, Mobile, AL	Yellowstone National Park, WY 82190	3112156	106169
Lost World Caverns, Lewisburg, WV	Columbia River Gorge National Scenic Area, Oregon	4123092	131961
Carlsbad Caverns National Park, Carlsbad, NM	Cape Canaveral, FL	2624095	87210
Liberty Bell, 6th Street, Philadelphia, PA	Congress Hall, Congress Place, Cape May, NJ 08204	135319	5538
Toltec Mounds, Scott, AR	Acadia National Park, Maine	2768590	92702
Fort Sumter National Monument, Sullivan's Island, SC	Gateway Arch, Washington Avenue, St Louis, MO	1376724	44982
Fort Sumter National Monument, Sullivan's Island, SC	Grand Canyon National Park, Arizona	3346258	107267
Hoover Dam, NV	Columbia River Gorge National Scenic Area, Oregon	1586117	53673
New Castle Historic District, Delaware	Fort Sumter National Monument, Sullivan's Island, SC	1004882	33412
Lost World Caverns, Lewisburg, WV	Gateway Arch, Washington Avenue, St Louis, MO	991827	32343
Craters of the Moon National Monument & Preserve, Arco, ID	Grand Canyon National Park, Arizona	1092664	37411
Wright Brothers National Memorial Visitor Center, Manteo, NC	Craters of the Moon National Monument & Preserve, Arco, ID	4003167	130557
Pikes Peak, Colorado	Fort Snelling, Tower Avenue, Saint Paul, MN	1621098	52897
Terrace Hill, Grand Avenue, Des Moines, IA	Craters of the Moon National Monument & Preserve, Arco, ID	1969397	63464
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Yellowstone National Park, WY 82190	2747124	93151
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	USS Constitution, Boston, MA	2773664	92931
Lost World Caverns, Lewisburg, WV	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	1353504	45969
Taliesin, County Road C, Spring Green, Wisconsin	Gateway Arch, Washington Avenue, St Louis, MO	640177	21609
Shelburne Farms, Harbor Road, Shelburne, VT	Fort Snelling, Tower Avenue, Saint Paul, MN	2108269	74013
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1944125	62449
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Lost World Caverns, Lewisburg, WV	1442759	47179
Carlsbad Caverns National Park, Carlsbad, NM	Olympia Entertainment, Woodward Avenue, Detroit, MI	2557663	85968
Wright Brothers National Memorial Visitor Center, Manteo, NC	Olympia Entertainment, Woodward Avenue, Detroit, MI	1296121	44397
Maryland State House, 100 State Cir, Annapolis, MD 21401	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1700468	54930
Mount Vernon, Fairfax County, Virginia	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	571779	21234
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Gateway Arch, Washington Avenue, St Louis, MO	847491	27193
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4838236	155507
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	San Andreas Fault, San Benito County, CA	2485901	83726
Hoover Dam, NV	French Quarter, New Orleans, LA	2725797	87321
Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	Grand Canyon National Park, Arizona	2462148	77824
Hanford Site, Benton County, WA	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	4790512	157993
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	524146	17801
Mount Vernon, Fairfax County, Virginia	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	956980	34372
Maryland State House, 100 State Cir, Annapolis, MD 21401	Lost World Caverns, Lewisburg, WV	479163	16168
Wright Brothers National Memorial Visitor Center, Manteo, NC	Ashfall Fossil Bed, Royal, NE	2531422	86090
Lost World Caverns, Lewisburg, WV	Cape Canaveral, FL	1230767	40942
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Wright Brothers National Memorial Visitor Center, Manteo, NC	3283326	109628
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	897371	30973
Graceland, Elvis Presley Boulevard, Memphis, TN	Carlsbad Caverns National Park, Carlsbad, NM	1530542	52124
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Grand Canyon National Park, Arizona	2732563	86585
Hanford Site, Benton County, WA	Grand Canyon National Park, Arizona	1641209	55038
Lost World Caverns, Lewisburg, WV	Wright Brothers National Memorial Visitor Center, Manteo, NC	630856	21882
The Breakers, Ochre Point Avenue, Newport, RI	Fort Sumter National Monument, Sullivan's Island, SC	1509644	51864
Maryland State House, 100 State Cir, Annapolis, MD 21401	Ashfall Fossil Bed, Royal, NE	2139243	72176
Lost World Caverns, Lewisburg, WV	Bryce Canyon National Park, Hwy 63, Bryce, UT	3263699	104829
USS Alabama, Battleship Parkway, Mobile, AL	Liberty Bell, 6th Street, Philadelphia, PA	1783560	58556
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Ashfall Fossil Bed, Royal, NE	1048083	38446
Fort Sumter National Monument, Sullivan's Island, SC	Glacier National Park, West Glacier, MT	3936065	130151
USS Constitution, Boston, MA	Fort Snelling, Tower Avenue, Saint Paul, MN	2236960	75305
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1166316	37905
New Castle Historic District, Delaware	Ashfall Fossil Bed, Royal, NE	2222494	75952
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Glacier National Park, West Glacier, MT	788472	29371
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Ashfall Fossil Bed, Royal, NE	2408053	81112
USS Alabama, Battleship Parkway, Mobile, AL	Acadia National Park, Maine	2697479	90984
San Andreas Fault, San Benito County, CA	Taliesin, County Road C, Spring Green, Wisconsin	3458781	111318
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1749501	58448
Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	Grand Canyon National Park, Arizona	3274814	105668
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	USS Constitution, Boston, MA	3083351	101542
Toltec Mounds, Scott, AR	Yellowstone National Park, WY 82190	2419546	81262
Congress Hall, Congress Place, Cape May, NJ 08204	Gateway Arch, Washington Avenue, St Louis, MO	1573547	53369
Maryland State House, 100 State Cir, Annapolis, MD 21401	San Andreas Fault, San Benito County, CA	4594279	149454
San Andreas Fault, San Benito County, CA	Statue of Liberty, Liberty Island, NYC, NY	4774120	155696
Pikes Peak, Colorado	Yellowstone National Park, WY 82190	1010111	40861
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Mount Vernon, Fairfax County, Virginia	2656789	87096
Statue of Liberty, Liberty Island, NYC, NY	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1480247	49443
San Andreas Fault, San Benito County, CA	Craters of the Moon National Monument & Preserve, Arco, ID	1361109	46565
The Alamo, Alamo Plaza, San Antonio, TX	Grand Canyon National Park, Arizona	1780052	58087
Wright Brothers National Memorial Visitor Center, Manteo, NC	The Alamo, Alamo Plaza, San Antonio, TX	2548833	81099
Columbia River Gorge National Scenic Area, Oregon	Ashfall Fossil Bed, Royal, NE	2359209	79778
Bryce Canyon National Park, Hwy 63, Bryce, UT	Glacier National Park, West Glacier, MT	1475714	47994
The Alamo, Alamo Plaza, San Antonio, TX	French Quarter, New Orleans, LA	875710	28421
Craters of the Moon National Monument & Preserve, Arco, ID	Pikes Peak, Colorado	1310714	47209
San Andreas Fault, San Benito County, CA	Olympia Entertainment, Woodward Avenue, Detroit, MI	3989459	128044
Graceland, Elvis Presley Boulevard, Memphis, TN	Craters of the Moon National Monument & Preserve, Arco, ID	2718789	90414
Fort Sumter National Monument, Sullivan's Island, SC	Bryce Canyon National Park, Hwy 63, Bryce, UT	3649551	117677
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Bryce Canyon National Park, Hwy 63, Bryce, UT	1801093	58970
Toltec Mounds, Scott, AR	The Breakers, Ochre Point Avenue, Newport, RI	2273878	76157
The Breakers, Ochre Point Avenue, Newport, RI	Craters of the Moon National Monument & Preserve, Arco, ID	4042971	132612
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Vicksburg National Military Park, Clay Street, Vicksburg, MS	2575996	84758
Liberty Bell, 6th Street, Philadelphia, PA	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	722539	26017
Carlsbad Caverns National Park, Carlsbad, NM	Gateway Arch, Washington Avenue, St Louis, MO	1705580	57853
Yellowstone National Park, WY 82190	Olympia Entertainment, Woodward Avenue, Detroit, MI	2695125	91185
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1761665	61810
Terrace Hill, Grand Avenue, Des Moines, IA	Wright Brothers National Memorial Visitor Center, Manteo, NC	2072573	69466
Vicksburg National Military Park, Clay Street, Vicksburg, MS	French Quarter, New Orleans, LA	331934	11302
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Craters of the Moon National Monument & Preserve, Arco, ID	4157603	137649
Shelburne Farms, Harbor Road, Shelburne, VT	Ashfall Fossil Bed, Royal, NE	2420015	85093
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Cape Canaveral, FL	2130718	68401
Hoover Dam, NV	New Castle Historic District, Delaware	3984959	129829
Cape Canaveral, FL	Yellowstone National Park, WY 82190	3827870	127325
Cape Canaveral, FL	Grand Canyon National Park, Arizona	3497485	112423
Graceland, Elvis Presley Boulevard, Memphis, TN	Glacier National Park, West Glacier, MT	2982190	101527
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1553662	49942
Hanford Site, Benton County, WA	Lost World Caverns, Lewisburg, WV	4081860	130602
Olympia Entertainment, Woodward Avenue, Detroit, MI	Glacier National Park, West Glacier, MT	2895149	97734
Olympia Entertainment, Woodward Avenue, Detroit, MI	Acadia National Park, Maine	1587595	54638
Shelburne Farms, Harbor Road, Shelburne, VT	The Alamo, Alamo Plaza, San Antonio, TX	3222592	109018
Hoover Dam, NV	Fort Snelling, Tower Avenue, Saint Paul, MN	2719740	86231
Cape Canaveral, FL	Gateway Arch, Washington Avenue, St Louis, MO	1684309	53709
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Taliesin, County Road C, Spring Green, Wisconsin	760692	25343
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Hanford Site, Benton County, WA	1586051	51552
Shelburne Farms, Harbor Road, Shelburne, VT	White House, Pennsylvania Avenue Northwest, Washington, DC	815727	30501
Toltec Mounds, Scott, AR	Fort Sumter National Monument, Sullivan's Island, SC	1309910	43611
Shelburne Farms, Harbor Road, Shelburne, VT	San Andreas Fault, San Benito County, CA	4988674	165123
Craters of the Moon National Monument & Preserve, Arco, ID	New Castle Historic District, Delaware	3799076	125459
Shelburne Farms, Harbor Road, Shelburne, VT	Columbia River Gorge National Scenic Area, Oregon	4676038	154286
French Quarter, New Orleans, LA	Ashfall Fossil Bed, Royal, NE	1916780	65306
The Breakers, Ochre Point Avenue, Newport, RI	Olympia Entertainment, Woodward Avenue, Detroit, MI	1201574	41180
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	1271563	41645
Mount Vernon, Fairfax County, Virginia	Ashfall Fossil Bed, Royal, NE	2121840	71736
Taliesin, County Road C, Spring Green, Wisconsin	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1485999	48318
Ashfall Fossil Bed, Royal, NE	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	2609776	86305
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Congress Hall, Congress Place, Cape May, NJ 08204	440711	16089
The Breakers, Ochre Point Avenue, Newport, RI	Grand Canyon National Park, Arizona	4129628	135501
Shelburne Farms, Harbor Road, Shelburne, VT	Glacier National Park, West Glacier, MT	3923595	134241
Carlsbad Caverns National Park, Carlsbad, NM	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1374729	46713
Pikes Peak, Colorado	Grand Canyon National Park, Arizona	973994	38556
Wright Brothers National Memorial Visitor Center, Manteo, NC	Pikes Peak, Colorado	3006046	99860
Craters of the Moon National Monument & Preserve, Arco, ID	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	2367353	77196
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Wright Brothers National Memorial Visitor Center, Manteo, NC	905467	34613
USS Alabama, Battleship Parkway, Mobile, AL	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	2494702	83431
Hanford Site, Benton County, WA	Fort Sumter National Monument, Sullivan's Island, SC	4461509	143963
Liberty Bell, 6th Street, Philadelphia, PA	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1339772	44555
The Breakers, Ochre Point Avenue, Newport, RI	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	1632118	55615
The Breakers, Ochre Point Avenue, Newport, RI	The Alamo, Alamo Plaza, San Antonio, TX	3223524	105975
Wright Brothers National Memorial Visitor Center, Manteo, NC	Congress Hall, Congress Place, Cape May, NJ 08204	448155	23747
Shelburne Farms, Harbor Road, Shelburne, VT	Carlsbad Caverns National Park, Carlsbad, NM	3483464	120037
Cape Canaveral, FL	Bryce Canyon National Park, Hwy 63, Bryce, UT	3739594	124152
Pikes Peak, Colorado	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1787044	60791
San Andreas Fault, San Benito County, CA	Lost World Caverns, Lewisburg, WV	4233272	135783
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	New Castle Historic District, Delaware	1876337	64324
Terrace Hill, Grand Avenue, Des Moines, IA	San Andreas Fault, San Benito County, CA	3029552	97044
Toltec Mounds, Scott, AR	Fort Snelling, Tower Avenue, Saint Paul, MN	1399583	44781
Craters of the Moon National Monument & Preserve, Arco, ID	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	1222590	40995
The Breakers, Ochre Point Avenue, Newport, RI	Glacier National Park, West Glacier, MT	4038048	135310
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	New Castle Historic District, Delaware	484456	17754
Cape Canaveral, FL	Acadia National Park, Maine	2532824	84632
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Statue of Liberty, Liberty Island, NYC, NY	1973392	66712
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Craters of the Moon National Monument & Preserve, Arco, ID	1100123	41870
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Terrace Hill, Grand Avenue, Des Moines, IA	333113	11215
San Andreas Fault, San Benito County, CA	Wright Brothers National Memorial Visitor Center, Manteo, NC	4699698	152179
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Graceland, Elvis Presley Boulevard, Memphis, TN	805016	26751
Craters of the Moon National Monument & Preserve, Arco, ID	USS Constitution, Boston, MA	4066911	132549
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Olympia Entertainment, Woodward Avenue, Detroit, MI	1731916	57583
Yellowstone National Park, WY 82190	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	2134234	74013
Glacier National Park, West Glacier, MT	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	2894639	99476
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	1954380	62884
Congress Hall, Congress Place, Cape May, NJ 08204	Fort Sumter National Monument, Sullivan's Island, SC	1160225	39754
Shelburne Farms, Harbor Road, Shelburne, VT	Grand Canyon National Park, Arizona	4087112	135156
Hanford Site, Benton County, WA	USS Constitution, Boston, MA	4699823	153033
Graceland, Elvis Presley Boulevard, Memphis, TN	Hanford Site, Benton County, WA	3425764	112518
USS Constitution, Boston, MA	Ashfall Fossil Bed, Royal, NE	2559004	86405
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Shelburne Farms, Harbor Road, Shelburne, VT	3167088	107665
USS Alabama, Battleship Parkway, Mobile, AL	Statue of Liberty, Liberty Island, NYC, NY	1924035	63443
Taliesin, County Road C, Spring Green, Wisconsin	The Alamo, Alamo Plaza, San Antonio, TX	2046331	65654
The Breakers, Ochre Point Avenue, Newport, RI	French Quarter, New Orleans, LA	2389577	79078
Shelburne Farms, Harbor Road, Shelburne, VT	French Quarter, New Orleans, LA	2527076	83700
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	San Andreas Fault, San Benito County, CA	3796749	122084
Terrace Hill, Grand Avenue, Des Moines, IA	The Breakers, Ochre Point Avenue, Newport, RI	2075619	69538
Graceland, Elvis Presley Boulevard, Memphis, TN	New Castle Historic District, Delaware	1570606	51937
San Andreas Fault, San Benito County, CA	Glacier National Park, West Glacier, MT	2190913	74514
Terrace Hill, Grand Avenue, Des Moines, IA	Shelburne Farms, Harbor Road, Shelburne, VT	1961166	68468
Gateway Arch, Washington Avenue, St Louis, MO	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1303092	43755
Liberty Bell, 6th Street, Philadelphia, PA	Statue of Liberty, Liberty Island, NYC, NY	151962	6043
Carlsbad Caverns National Park, Carlsbad, NM	USS Constitution, Boston, MA	3599410	121316
San Andreas Fault, San Benito County, CA	Fort Snelling, Tower Avenue, Saint Paul, MN	3294713	108401
Wright Brothers National Memorial Visitor Center, Manteo, NC	White House, Pennsylvania Avenue Northwest, Washington, DC	462796	16916
Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	3288988	105207
Toltec Mounds, Scott, AR	Graceland, Elvis Presley Boulevard, Memphis, TN	229031	7951
Statue of Liberty, Liberty Island, NYC, NY	Gateway Arch, Washington Avenue, St Louis, MO	1532676	52256
Graceland, Elvis Presley Boulevard, Memphis, TN	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	1963583	64635
Mount Vernon, Fairfax County, Virginia	Grand Canyon National Park, Arizona	3665067	116851
Wright Brothers National Memorial Visitor Center, Manteo, NC	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4874201	156275
USS Constitution, Boston, MA	French Quarter, New Orleans, LA	2454093	80405
White House, Pennsylvania Avenue Northwest, Washington, DC	Statue of Liberty, Liberty Island, NYC, NY	362725	13529
Ashfall Fossil Bed, Royal, NE	Glacier National Park, West Glacier, MT	1691765	60397
Terrace Hill, Grand Avenue, Des Moines, IA	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	2891033	91475
Carlsbad Caverns National Park, Carlsbad, NM	Glacier National Park, West Glacier, MT	2483417	83906
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	New Castle Historic District, Delaware	2359410	77959
Hoover Dam, NV	Statue of Liberty, Liberty Island, NYC, NY	4078427	131726
Shelburne Farms, Harbor Road, Shelburne, VT	Bryce Canyon National Park, Hwy 63, Bryce, UT	3938537	131628
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	French Quarter, New Orleans, LA	1413656	47943
USS Alabama, Battleship Parkway, Mobile, AL	Terrace Hill, Grand Avenue, Des Moines, IA	1568964	54581
Hanford Site, Benton County, WA	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	4385252	142720
French Quarter, New Orleans, LA	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1196556	38645
Hoover Dam, NV	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	2703190	85402
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	French Quarter, New Orleans, LA	2293165	74916
Hanford Site, Benton County, WA	Cape Canaveral, FL	4766469	152673
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	White House, Pennsylvania Avenue Northwest, Washington, DC	2624980	86273
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Olympia Entertainment, Woodward Avenue, Detroit, MI	1220596	42219
Carlsbad Caverns National Park, Carlsbad, NM	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	2281669	80307
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Grand Canyon National Park, Arizona	2254207	71839
USS Constitution, Boston, MA	Olympia Entertainment, Woodward Avenue, Detroit, MI	1157988	39544
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Wright Brothers National Memorial Visitor Center, Manteo, NC	2302762	76129
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Grand Canyon National Park, Arizona	1591868	52162
Mount Vernon, Fairfax County, Virginia	Fort Sumter National Monument, Sullivan's Island, SC	833917	26773
Statue of Liberty, Liberty Island, NYC, NY	The Alamo, Alamo Plaza, San Antonio, TX	2933460	94860
Hanford Site, Benton County, WA	Acadia National Park, Maine	5129430	168127
USS Alabama, Battleship Parkway, Mobile, AL	The Alamo, Alamo Plaza, San Antonio, TX	1069098	34228
Columbia River Gorge National Scenic Area, Oregon	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	3504228	113055
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	1508650	52479
Liberty Bell, 6th Street, Philadelphia, PA	Lost World Caverns, Lewisburg, WV	637355	21587
USS Alabama, Battleship Parkway, Mobile, AL	Bryce Canyon National Park, Hwy 63, Bryce, UT	2859726	96881
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	996021	33100
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Yellowstone National Park, WY 82190	2674069	90435
Hoover Dam, NV	San Andreas Fault, San Benito County, CA	763736	27404
Gateway Arch, Washington Avenue, St Louis, MO	Glacier National Park, West Glacier, MT	2628822	89235
Mount Vernon, Fairfax County, Virginia	Bryce Canyon National Park, Hwy 63, Bryce, UT	3597733	117900
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Ashfall Fossil Bed, Royal, NE	2649100	91200
Bryce Canyon National Park, Hwy 63, Bryce, UT	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	1328462	43497
San Andreas Fault, San Benito County, CA	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	4209966	136875
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Taliesin, County Road C, Spring Green, Wisconsin	1440063	46409
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Fort Snelling, Tower Avenue, Saint Paul, MN	1402360	44596
Hoover Dam, NV	USS Constitution, Boston, MA	4427850	143274
Liberty Bell, 6th Street, Philadelphia, PA	White House, Pennsylvania Avenue Northwest, Washington, DC	222250	8641
Gateway Arch, Washington Avenue, St Louis, MO	Fort Snelling, Tower Avenue, Saint Paul, MN	847947	30144
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Glacier National Park, West Glacier, MT	4152680	140348
Fort Snelling, Tower Avenue, Saint Paul, MN	Grand Canyon National Park, Arizona	2517491	81762
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Gateway Arch, Washington Avenue, St Louis, MO	555304	18644
Congress Hall, Congress Place, Cape May, NJ 08204	Grand Canyon National Park, Arizona	3879839	125602
Vicksburg National Military Park, Clay Street, Vicksburg, MS	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1049651	34167
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Congress Hall, Congress Place, Cape May, NJ 08204	2014264	67825
Statue of Liberty, Liberty Island, NYC, NY	Bryce Canyon National Park, Hwy 63, Bryce, UT	3768500	122799
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	1533836	51383
Taliesin, County Road C, Spring Green, Wisconsin	Yellowstone National Park, WY 82190	2075224	72149
Shelburne Farms, Harbor Road, Shelburne, VT	The Breakers, Ochre Point Avenue, Newport, RI	470924	17258
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	French Quarter, New Orleans, LA	3023850	97576
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	New Castle Historic District, Delaware	1187971	40241
San Andreas Fault, San Benito County, CA	Acadia National Park, Maine	5557263	181349
Toltec Mounds, Scott, AR	Terrace Hill, Grand Avenue, Des Moines, IA	1007426	32538
Toltec Mounds, Scott, AR	Wright Brothers National Memorial Visitor Center, Manteo, NC	1728198	57317
Toltec Mounds, Scott, AR	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	849822	27914
Pikes Peak, Colorado	Glacier National Park, West Glacier, MT	1632053	57705
USS Constitution, Boston, MA	The Alamo, Alamo Plaza, San Antonio, TX	3289321	107282
Terrace Hill, Grand Avenue, Des Moines, IA	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	545245	17997
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1160395	39050
Hanford Site, Benton County, WA	Carlsbad Caverns National Park, Carlsbad, NM	2485831	86610
USS Alabama, Battleship Parkway, Mobile, AL	Gateway Arch, Washington Avenue, St Louis, MO	1043023	36000
Columbia River Gorge National Scenic Area, Oregon	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	1044669	35450
New Castle Historic District, Delaware	USS Constitution, Boston, MA	641379	23278
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	3385471	114071
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Pikes Peak, Colorado	754485	28424
Olympia Entertainment, Woodward Avenue, Detroit, MI	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	3850939	122474
Craters of the Moon National Monument & Preserve, Arco, ID	The Alamo, Alamo Plaza, San Antonio, TX	2521886	85067
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	2932400	96249
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Shelburne Farms, Harbor Road, Shelburne, VT	2954965	100452
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	USS Constitution, Boston, MA	1696634	56942
Graceland, Elvis Presley Boulevard, Memphis, TN	The Alamo, Alamo Plaza, San Antonio, TX	1179943	38509
Toltec Mounds, Scott, AR	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	696215	22929
USS Alabama, Battleship Parkway, Mobile, AL	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1064711	34283
Carlsbad Caverns National Park, Carlsbad, NM	Fort Snelling, Tower Avenue, Saint Paul, MN	2166013	71753
Hanford Site, Benton County, WA	Bryce Canyon National Park, Hwy 63, Bryce, UT	1460920	47057
Toltec Mounds, Scott, AR	Taliesin, County Road C, Spring Green, Wisconsin	1206666	41628
Mount Vernon, Fairfax County, Virginia	The Breakers, Ochre Point Avenue, Newport, RI	674006	25401
The Breakers, Ochre Point Avenue, Newport, RI	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1770817	60421
Maryland State House, 100 State Cir, Annapolis, MD 21401	Terrace Hill, Grand Avenue, Des Moines, IA	1679749	55332
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	White House, Pennsylvania Avenue Northwest, Washington, DC	1754476	59507
Gateway Arch, Washington Avenue, St Louis, MO	Grand Canyon National Park, Arizona	2307961	72717
Maryland State House, 100 State Cir, Annapolis, MD 21401	Grand Canyon National Park, Arizona	3659127	118247
White House, Pennsylvania Avenue Northwest, Washington, DC	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1065777	36173
Acadia National Park, Maine	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	2253691	76984
Shelburne Farms, Harbor Road, Shelburne, VT	Statue of Liberty, Liberty Island, NYC, NY	477387	18879
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Congress Hall, Congress Place, Cape May, NJ 08204	2437518	82535
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Cape Canaveral, FL	1541612	49003
Liberty Bell, 6th Street, Philadelphia, PA	Glacier National Park, West Glacier, MT	3700295	123040
Hoover Dam, NV	Hanford Site, Benton County, WA	1545185	52485
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	1144699	36459
Statue of Liberty, Liberty Island, NYC, NY	Yellowstone National Park, WY 82190	3524053	119175
Liberty Bell, 6th Street, Philadelphia, PA	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	918887	31359
Hoover Dam, NV	Congress Hall, Congress Place, Cape May, NJ 08204	4119298	132839
Yellowstone National Park, WY 82190	Grand Canyon National Park, Arizona	1142593	44149
White House, Pennsylvania Avenue Northwest, Washington, DC	Gateway Arch, Washington Avenue, St Louis, MO	1318428	45204
Liberty Bell, 6th Street, Philadelphia, PA	Olympia Entertainment, Woodward Avenue, Detroit, MI	944184	31505
The Alamo, Alamo Plaza, San Antonio, TX	Fort Snelling, Tower Avenue, Saint Paul, MN	2008901	63971
Congress Hall, Congress Place, Cape May, NJ 08204	Ashfall Fossil Bed, Royal, NE	2345131	79089
The Alamo, Alamo Plaza, San Antonio, TX	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1607847	53924
San Andreas Fault, San Benito County, CA	Congress Hall, Congress Place, Cape May, NJ 08204	4814991	156809
Gateway Arch, Washington Avenue, St Louis, MO	Bryce Canyon National Park, Hwy 63, Bryce, UT	2276197	73341
USS Alabama, Battleship Parkway, Mobile, AL	Hoover Dam, NV	2861891	92906
Carlsbad Caverns National Park, Carlsbad, NM	New Castle Historic District, Delaware	3085939	103553
Carlsbad Caverns National Park, Carlsbad, NM	The Alamo, Alamo Plaza, San Antonio, TX	758015	26423
Carlsbad Caverns National Park, Carlsbad, NM	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	3434325	115391
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	USS Constitution, Boston, MA	251490	9163
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	The Breakers, Ochre Point Avenue, Newport, RI	145604	6359
Shelburne Farms, Harbor Road, Shelburne, VT	Gateway Arch, Washington Avenue, St Louis, MO	1780820	62923
Hoover Dam, NV	Pikes Peak, Colorado	1346909	47426
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Hanford Site, Benton County, WA	2663870	85877
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Hoover Dam, NV	2223982	70608
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Bryce Canyon National Park, Hwy 63, Bryce, UT	1417261	48534
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Hoover Dam, NV	1715316	56972
Liberty Bell, 6th Street, Philadelphia, PA	French Quarter, New Orleans, LA	1966566	63823
Graceland, Elvis Presley Boulevard, Memphis, TN	Fort Sumter National Monument, Sullivan's Island, SC	1084382	36047
San Andreas Fault, San Benito County, CA	Cape Canaveral, FL	4432637	143630
Carlsbad Caverns National Park, Carlsbad, NM	Columbia River Gorge National Scenic Area, Oregon	2526763	87797
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	French Quarter, New Orleans, LA	995629	31618
Olympia Entertainment, Woodward Avenue, Detroit, MI	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1575841	53083
White House, Pennsylvania Avenue Northwest, Washington, DC	Congress Hall, Congress Place, Cape May, NJ 08204	300705	12240
USS Alabama, Battleship Parkway, Mobile, AL	Hanford Site, Benton County, WA	4050755	131517
Graceland, Elvis Presley Boulevard, Memphis, TN	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	937381	34311
Hoover Dam, NV	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	4515460	147852
Mount Vernon, Fairfax County, Virginia	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1280757	43906
White House, Pennsylvania Avenue Northwest, Washington, DC	Bryce Canyon National Park, Hwy 63, Bryce, UT	3578269	117494
Acadia National Park, Maine	French Quarter, New Orleans, LA	2884290	95624
Liberty Bell, 6th Street, Philadelphia, PA	Yellowstone National Park, WY 82190	3474172	117273
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Statue of Liberty, Liberty Island, NYC, NY	2406284	81083
Terrace Hill, Grand Avenue, Des Moines, IA	Columbia River Gorge National Scenic Area, Oregon	2715601	86700
Craters of the Moon National Monument & Preserve, Arco, ID	Fort Sumter National Monument, Sullivan's Island, SC	3760470	121856
Pikes Peak, Colorado	Columbia River Gorge National Scenic Area, Oregon	1989641	67397
USS Constitution, Boston, MA	Fort Sumter National Monument, Sullivan's Island, SC	1565117	53243
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Vicksburg National Military Park, Clay Street, Vicksburg, MS	2672008	86961
Shelburne Farms, Harbor Road, Shelburne, VT	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1933249	66415
Shelburne Farms, Harbor Road, Shelburne, VT	Congress Hall, Congress Place, Cape May, NJ 08204	705691	26152
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Bryce Canyon National Park, Hwy 63, Bryce, UT	4170118	138148
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	3363863	107141
San Andreas Fault, San Benito County, CA	Columbia River Gorge National Scenic Area, Oregon	1228419	42635
Hoover Dam, NV	Cape Canaveral, FL	3741759	120177
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Congress Hall, Congress Place, Cape May, NJ 08204	1939504	64109
Terrace Hill, Grand Avenue, Des Moines, IA	Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	1452974	46585
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	USS Alabama, Battleship Parkway, Mobile, AL	2485277	84139
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Lost World Caverns, Lewisburg, WV	2685723	88506
Craters of the Moon National Monument & Preserve, Arco, ID	Yellowstone National Park, WY 82190	413219	17526
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Craters of the Moon National Monument & Preserve, Arco, ID	1957641	63753
The Breakers, Ochre Point Avenue, Newport, RI	Pikes Peak, Colorado	3199746	109066
Maryland State House, 100 State Cir, Annapolis, MD 21401	Olympia Entertainment, Woodward Avenue, Detroit, MI	886410	29704
Toltec Mounds, Scott, AR	Hoover Dam, NV	2312537	73064
Craters of the Moon National Monument & Preserve, Arco, ID	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	2762301	89737
Carlsbad Caverns National Park, Carlsbad, NM	Ashfall Fossil Bed, Royal, NE	1584981	59676
Shelburne Farms, Harbor Road, Shelburne, VT	Acadia National Park, Maine	544911	23436
Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	Bryce Canyon National Park, Hwy 63, Bryce, UT	3573294	116435
USS Alabama, Battleship Parkway, Mobile, AL	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	3851355	120882
Gateway Arch, Washington Avenue, St Louis, MO	Olympia Entertainment, Woodward Avenue, Detroit, MI	857616	28721
USS Constitution, Boston, MA	Gateway Arch, Washington Avenue, St Louis, MO	1899364	64069
Maryland State House, 100 State Cir, Annapolis, MD 21401	Craters of the Moon National Monument & Preserve, Arco, ID	3647102	118406
San Andreas Fault, San Benito County, CA	Fort Sumter National Monument, Sullivan's Island, SC	4281410	138473
Wright Brothers National Memorial Visitor Center, Manteo, NC	Grand Canyon National Park, Arizona	3764546	120973
White House, Pennsylvania Avenue Northwest, Washington, DC	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1637791	52725
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Pikes Peak, Colorado	3109610	103980
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	The Alamo, Alamo Plaza, San Antonio, TX	2558847	84752
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Columbia River Gorge National Scenic Area, Oregon	3048674	97029
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	White House, Pennsylvania Avenue Northwest, Washington, DC	1066109	35425
USS Alabama, Battleship Parkway, Mobile, AL	Carlsbad Caverns National Park, Carlsbad, NM	1744227	59939
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	The Alamo, Alamo Plaza, San Antonio, TX	1645458	53310
USS Alabama, Battleship Parkway, Mobile, AL	The Breakers, Ochre Point Avenue, Newport, RI	2214605	74421
Hoover Dam, NV	The Alamo, Alamo Plaza, San Antonio, TX	2012743	63275
Carlsbad Caverns National Park, Carlsbad, NM	French Quarter, New Orleans, LA	1626516	54348
Toltec Mounds, Scott, AR	San Andreas Fault, San Benito County, CA	3004953	97302
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Craters of the Moon National Monument & Preserve, Arco, ID	2873247	93460
Carlsbad Caverns National Park, Carlsbad, NM	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	2122087	70306
Gateway Arch, Washington Avenue, St Louis, MO	Columbia River Gorge National Scenic Area, Oregon	3129086	100814
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	New Castle Historic District, Delaware	868799	30943
Hanford Site, Benton County, WA	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	3463296	111867
Congress Hall, Congress Place, Cape May, NJ 08204	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4775270	153549
Maryland State House, 100 State Cir, Annapolis, MD 21401	Shelburne Farms, Harbor Road, Shelburne, VT	799522	29333
Taliesin, County Road C, Spring Green, Wisconsin	Grand Canyon National Park, Arizona	2555807	83764
USS Constitution, Boston, MA	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1670972	56710
Fort Sumter National Monument, Sullivan's Island, SC	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1483499	48668
Taliesin, County Road C, Spring Green, Wisconsin	Pikes Peak, Colorado	1659414	54899
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Hanford Site, Benton County, WA	1661609	56332
Liberty Bell, 6th Street, Philadelphia, PA	New Castle Historic District, Delaware	162565	6109
Toltec Mounds, Scott, AR	Maryland State House, 100 State Cir, Annapolis, MD 21401	1690140	55321
San Andreas Fault, San Benito County, CA	White House, Pennsylvania Avenue Northwest, Washington, DC	4598813	147923
Congress Hall, Congress Place, Cape May, NJ 08204	Olympia Entertainment, Woodward Avenue, Detroit, MI	1092600	36702
Terrace Hill, Grand Avenue, Des Moines, IA	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1848155	63009
USS Alabama, Battleship Parkway, Mobile, AL	Fort Snelling, Tower Avenue, Saint Paul, MN	1867680	64758
Wright Brothers National Memorial Visitor Center, Manteo, NC	Fort Snelling, Tower Avenue, Saint Paul, MN	2236906	75704
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	2335379	76633
Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1147157	39324
New Castle Historic District, Delaware	Acadia National Park, Maine	1071576	38496
Hoover Dam, NV	Mount Vernon, Fairfax County, Virginia	3904526	124088
Taliesin, County Road C, Spring Green, Wisconsin	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	921910	31814
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	USS Constitution, Boston, MA	2359929	78386
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Grand Canyon National Park, Arizona	4039492	130415
Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1406589	47379
San Andreas Fault, San Benito County, CA	Vicksburg National Military Park, Clay Street, Vicksburg, MS	3189360	103045
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Fort Sumter National Monument, Sullivan's Island, SC	1155865	37713
Terrace Hill, Grand Avenue, Des Moines, IA	Pikes Peak, Colorado	1230185	40625
Statue of Liberty, Liberty Island, NYC, NY	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1278590	44793
Fort Sumter National Monument, Sullivan's Island, SC	Fort Snelling, Tower Avenue, Saint Paul, MN	2120739	69923
Gateway Arch, Washington Avenue, St Louis, MO	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	3309548	105479
Maryland State House, 100 State Cir, Annapolis, MD 21401	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4568737	146417
Maryland State House, 100 State Cir, Annapolis, MD 21401	Mount Vernon, Fairfax County, Virginia	75403	3165
Mount Vernon, Fairfax County, Virginia	Hanford Site, Benton County, WA	4263334	138596
New Castle Historic District, Delaware	The Alamo, Alamo Plaza, San Antonio, TX	2736315	88893
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Olympia Entertainment, Woodward Avenue, Detroit, MI	2168837	70848
Wright Brothers National Memorial Visitor Center, Manteo, NC	Fort Sumter National Monument, Sullivan's Island, SC	715393	23430
USS Constitution, Boston, MA	Grand Canyon National Park, Arizona	4225504	136164
Taliesin, County Road C, Spring Green, Wisconsin	Cape Canaveral, FL	2250465	72627
French Quarter, New Orleans, LA	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1245550	40104
Mount Vernon, Fairfax County, Virginia	Lost World Caverns, Lewisburg, WV	420070	14282
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Grand Canyon National Park, Arizona	4316197	141264
New Castle Historic District, Delaware	Pikes Peak, Colorado	2811795	95709
Lost World Caverns, Lewisburg, WV	USS Constitution, Boston, MA	1126003	38377
Terrace Hill, Grand Avenue, Des Moines, IA	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	2190251	74576
USS Alabama, Battleship Parkway, Mobile, AL	Pikes Peak, Colorado	2157571	74324
USS Alabama, Battleship Parkway, Mobile, AL	French Quarter, New Orleans, LA	232384	7544
Craters of the Moon National Monument & Preserve, Arco, ID	White House, Pennsylvania Avenue Northwest, Washington, DC	3677215	120643
Graceland, Elvis Presley Boulevard, Memphis, TN	Lost World Caverns, Lewisburg, WV	1111929	36026
Carlsbad Caverns National Park, Carlsbad, NM	The Breakers, Ochre Point Avenue, Newport, RI	3575389	120329
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	509622	16928
Mount Vernon, Fairfax County, Virginia	Pikes Peak, Colorado	2712486	91592
Congress Hall, Congress Place, Cape May, NJ 08204	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1418227	48154
Graceland, Elvis Presley Boulevard, Memphis, TN	Grand Canyon National Park, Arizona	2265379	71607
USS Alabama, Battleship Parkway, Mobile, AL	San Andreas Fault, San Benito County, CA	3676539	116761
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	1616472	51634
Lost World Caverns, Lewisburg, WV	Congress Hall, Congress Place, Cape May, NJ 08204	719625	25492
Cape Canaveral, FL	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1528374	49277
Wright Brothers National Memorial Visitor Center, Manteo, NC	Cape Canaveral, FL	1253439	39835
New Castle Historic District, Delaware	Congress Hall, Congress Place, Cape May, NJ 08204	84730	8914
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Glacier National Park, West Glacier, MT	1154489	41962
Graceland, Elvis Presley Boulevard, Memphis, TN	USS Constitution, Boston, MA	2123612	70326
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Shelburne Farms, Harbor Road, Shelburne, VT	2221537	77379
New Castle Historic District, Delaware	Bryce Canyon National Park, Hwy 63, Bryce, UT	3700130	122310
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	908130	33755
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Bryce Canyon National Park, Hwy 63, Bryce, UT	3928478	127896
Maryland State House, 100 State Cir, Annapolis, MD 21401	Cape Canaveral, FL	1449191	45859
White House, Pennsylvania Avenue Northwest, Washington, DC	Ashfall Fossil Bed, Royal, NE	2100633	71136
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Fort Snelling, Tower Avenue, Saint Paul, MN	2337354	80120
Olympia Entertainment, Woodward Avenue, Detroit, MI	Columbia River Gorge National Scenic Area, Oregon	3674656	117721
Taliesin, County Road C, Spring Green, Wisconsin	Wright Brothers National Memorial Visitor Center, Manteo, NC	1869857	64810
Hanford Site, Benton County, WA	Yellowstone National Park, WY 82190	1214950	42730
Acadia National Park, Maine	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	5418743	175779
Craters of the Moon National Monument & Preserve, Arco, ID	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	3747256	123720
West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	354930	15229
Cape Canaveral, FL	New Castle Historic District, Delaware	1544583	49962
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	The Alamo, Alamo Plaza, San Antonio, TX	1949778	63675
Congress Hall, Congress Place, Cape May, NJ 08204	Columbia River Gorge National Scenic Area, Oregon	4589082	149141
USS Alabama, Battleship Parkway, Mobile, AL	Shelburne Farms, Harbor Road, Shelburne, VT	2387575	79031
The Alamo, Alamo Plaza, San Antonio, TX	Glacier National Park, West Glacier, MT	2995591	98170
Statue of Liberty, Liberty Island, NYC, NY	Ashfall Fossil Bed, Royal, NE	2245869	75913
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	3751821	121023
Lost World Caverns, Lewisburg, WV	The Breakers, Ochre Point Avenue, Newport, RI	1060206	37070
Shelburne Farms, Harbor Road, Shelburne, VT	Pikes Peak, Colorado	3189307	108703
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	1432857	47614
Maryland State House, 100 State Cir, Annapolis, MD 21401	Congress Hall, Congress Place, Cape May, NJ 08204	284500	11072
Mount Vernon, Fairfax County, Virginia	Craters of the Moon National Monument & Preserve, Arco, ID	3630343	118186
Liberty Bell, 6th Street, Philadelphia, PA	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4626854	148352
Toltec Mounds, Scott, AR	Olympia Entertainment, Woodward Avenue, Detroit, MI	1402648	46020
Lost World Caverns, Lewisburg, WV	Ashfall Fossil Bed, Royal, NE	1903828	64750
Congress Hall, Congress Place, Cape May, NJ 08204	French Quarter, New Orleans, LA	2045021	67422
Taliesin, County Road C, Spring Green, Wisconsin	New Castle Historic District, Delaware	1544803	53839
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Fort Snelling, Tower Avenue, Saint Paul, MN	1071728	34851
Hanford Site, Benton County, WA	Glacier National Park, West Glacier, MT	683123	24469
Hanford Site, Benton County, WA	Congress Hall, Congress Place, Cape May, NJ 08204	4474392	146074
New Castle Historic District, Delaware	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1187638	40989
Graceland, Elvis Presley Boulevard, Memphis, TN	Pikes Peak, Colorado	1792648	59301
Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	Ashfall Fossil Bed, Royal, NE	2270381	77013
San Andreas Fault, San Benito County, CA	USS Constitution, Boston, MA	5127066	166130
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Mount Vernon, Fairfax County, Virginia	2204704	71995
San Andreas Fault, San Benito County, CA	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	5217759	171230
Maryland State House, 100 State Cir, Annapolis, MD 21401	Pikes Peak, Colorado	2729245	91812
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	947062	32347
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	San Andreas Fault, San Benito County, CA	2905229	93739
San Andreas Fault, San Benito County, CA	Yellowstone National Park, WY 82190	1747331	61356
Wright Brothers National Memorial Visitor Center, Manteo, NC	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1643008	52482
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Craters of the Moon National Monument & Preserve, Arco, ID	3984636	130620
Graceland, Elvis Presley Boulevard, Memphis, TN	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	671337	22049
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Fort Sumter National Monument, Sullivan's Island, SC	3179558	103575
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Pikes Peak, Colorado	3418392	114811
Graceland, Elvis Presley Boulevard, Memphis, TN	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	2291550	77952
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Vicksburg National Military Park, Clay Street, Vicksburg, MS	810507	26740
Mount Vernon, Fairfax County, Virginia	New Castle Historic District, Delaware	169236	6985
Pikes Peak, Colorado	French Quarter, New Orleans, LA	2019630	68626
Mount Vernon, Fairfax County, Virginia	The Alamo, Alamo Plaza, San Antonio, TX	2579868	82894
Ashfall Fossil Bed, Royal, NE	Fort Snelling, Tower Avenue, Saint Paul, MN	607553	22708
New Castle Historic District, Delaware	Fort Snelling, Tower Avenue, Saint Paul, MN	1900450	64852
Mount Vernon, Fairfax County, Virginia	Carlsbad Caverns National Park, Carlsbad, NM	2930231	97369
USS Alabama, Battleship Parkway, Mobile, AL	New Castle Historic District, Delaware	1707248	56147
Congress Hall, Congress Place, Cape May, NJ 08204	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1518228	51482
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Statue of Liberty, Liberty Island, NYC, NY	2983852	98722
Hanford Site, Benton County, WA	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	3530581	115217
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Ashfall Fossil Bed, Royal, NE	1399554	47258
The Breakers, Ochre Point Avenue, Newport, RI	USS Constitution, Boston, MA	120154	5338
Maryland State House, 100 State Cir, Annapolis, MD 21401	Liberty Bell, 6th Street, Philadelphia, PA	206045	7473
Lost World Caverns, Lewisburg, WV	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1336291	42854
Graceland, Elvis Presley Boulevard, Memphis, TN	Cape Canaveral, FL	1341318	44354
Taliesin, County Road C, Spring Green, Wisconsin	Craters of the Moon National Monument & Preserve, Arco, ID	2398626	77738
Hoover Dam, NV	Fort Sumter National Monument, Sullivan's Island, SC	3585717	114503
Yellowstone National Park, WY 82190	Glacier National Park, West Glacier, MT	905478	36382
Shelburne Farms, Harbor Road, Shelburne, VT	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1532580	55702
Maryland State House, 100 State Cir, Annapolis, MD 21401	Hanford Site, Benton County, WA	4280062	138804
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Grand Canyon National Park, Arizona	2861597	90877
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Cape Canaveral, FL	2059873	67236
White House, Pennsylvania Avenue Northwest, Washington, DC	Acadia National Park, Maine	1136168	41070
Toltec Mounds, Scott, AR	The Alamo, Alamo Plaza, San Antonio, TX	974447	31908
White House, Pennsylvania Avenue Northwest, Washington, DC	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1259332	43180
Fort Sumter National Monument, Sullivan's Island, SC	Yellowstone National Park, WY 82190	3522910	118615
San Andreas Fault, San Benito County, CA	Gateway Arch, Washington Avenue, St Louis, MO	3243113	103924
Craters of the Moon National Monument & Preserve, Arco, ID	French Quarter, New Orleans, LA	3174475	105249
Toltec Mounds, Scott, AR	USS Constitution, Boston, MA	2338394	77484
Pikes Peak, Colorado	Statue of Liberty, Liberty Island, NYC, NY	2909086	98054
Cape Canaveral, FL	French Quarter, New Orleans, LA	1112328	34804
New Castle Historic District, Delaware	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1381193	47996
Statue of Liberty, Liberty Island, NYC, NY	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4676008	150374
Hoover Dam, NV	Lost World Caverns, Lewisburg, WV	3537579	111813
Maryland State House, 100 State Cir, Annapolis, MD 21401	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1170057	38211
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	The Breakers, Ochre Point Avenue, Newport, RI	3281541	108734
USS Alabama, Battleship Parkway, Mobile, AL	Taliesin, County Road C, Spring Green, Wisconsin	1681531	57125
Shelburne Farms, Harbor Road, Shelburne, VT	USS Constitution, Boston, MA	352908	12461
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Acadia National Park, Maine	3724389	124204
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Acadia National Park, Maine	1826373	62529
The Breakers, Ochre Point Avenue, Newport, RI	Gateway Arch, Washington Avenue, St Louis, MO	1823335	63268
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	2011410	65799
Taliesin, County Road C, Spring Green, Wisconsin	French Quarter, New Orleans, LA	1729998	55959
Fort Sumter National Monument, Sullivan's Island, SC	Acadia National Park, Maine	1994724	68336
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1675183	56120
Gateway Arch, Washington Avenue, St Louis, MO	Ashfall Fossil Bed, Royal, NE	973284	33919
Mount Vernon, Fairfax County, Virginia	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1078689	36453
Maryland State House, 100 State Cir, Annapolis, MD 21401	The Alamo, Alamo Plaza, San Antonio, TX	2642482	85073
West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	3683936	117748
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	2879277	91764
USS Alabama, Battleship Parkway, Mobile, AL	Craters of the Moon National Monument & Preserve, Arco, ID	3412760	112517
Hoover Dam, NV	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	3101055	98114
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	White House, Pennsylvania Avenue Northwest, Washington, DC	550976	20506
Terrace Hill, Grand Avenue, Des Moines, IA	French Quarter, New Orleans, LA	1631859	53421
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Fort Sumter National Monument, Sullivan's Island, SC	2942338	94545
Wright Brothers National Memorial Visitor Center, Manteo, NC	USS Constitution, Boston, MA	1063168	40159
Fort Sumter National Monument, Sullivan's Island, SC	The Alamo, Alamo Plaza, San Antonio, TX	2061745	66283
USS Constitution, Boston, MA	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4988547	160560
Wright Brothers National Memorial Visitor Center, Manteo, NC	French Quarter, New Orleans, LA	1710546	54335
Cape Canaveral, FL	Fort Snelling, Tower Avenue, Saint Paul, MN	2528087	83138
Mount Vernon, Fairfax County, Virginia	Taliesin, County Road C, Spring Green, Wisconsin	1444824	49855
Taliesin, County Road C, Spring Green, Wisconsin	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	488221	16708
Toltec Mounds, Scott, AR	Bryce Canyon National Park, Hwy 63, Bryce, UT	2310372	77039
White House, Pennsylvania Avenue Northwest, Washington, DC	Columbia River Gorge National Scenic Area, Oregon	4356142	140831
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Cape Canaveral, FL	1328279	42300
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	The Alamo, Alamo Plaza, San Antonio, TX	3129292	101592
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Pikes Peak, Colorado	1210842	43490
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1255436	41765
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Yellowstone National Park, WY 82190	2069144	70493
Terrace Hill, Grand Avenue, Des Moines, IA	Olympia Entertainment, Woodward Avenue, Detroit, MI	961951	31389
Cape Canaveral, FL	Glacier National Park, West Glacier, MT	4307136	142282
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Vicksburg National Military Park, Clay Street, Vicksburg, MS	861072	27740
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Carlsbad Caverns National Park, Carlsbad, NM	2045404	70443
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	1811850	62418
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Liberty Bell, 6th Street, Philadelphia, PA	1865848	62627
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Acadia National Park, Maine	2776915	92233
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Cape Canaveral, FL	3247298	103255
Terrace Hill, Grand Avenue, Des Moines, IA	Taliesin, County Road C, Spring Green, Wisconsin	431273	14664
Maryland State House, 100 State Cir, Annapolis, MD 21401	Glacier National Park, West Glacier, MT	3601333	121768
USS Alabama, Battleship Parkway, Mobile, AL	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	625397	21721
The Breakers, Ochre Point Avenue, Newport, RI	Wright Brothers National Memorial Visitor Center, Manteo, NC	1008190	38506
Liberty Bell, 6th Street, Philadelphia, PA	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1861049	60510
Acadia National Park, Maine	Grand Canyon National Park, Arizona	4655701	151382
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1953916	67074
Fort Snelling, Tower Avenue, Saint Paul, MN	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1110505	38784
Hanford Site, Benton County, WA	Pikes Peak, Colorado	1948710	66209
Hoover Dam, NV	Graceland, Elvis Presley Boulevard, Memphis, TN	2504838	78843
New Castle Historic District, Delaware	Statue of Liberty, Liberty Island, NYC, NY	298132	10955
Toltec Mounds, Scott, AR	Craters of the Moon National Monument & Preserve, Arco, ID	2720150	87610
Hoover Dam, NV	Grand Canyon National Park, Arizona	390505	12874
Yellowstone National Park, WY 82190	French Quarter, New Orleans, LA	2943174	101546
Statue of Liberty, Liberty Island, NYC, NY	Olympia Entertainment, Woodward Avenue, Detroit, MI	993338	33526
Fort Sumter National Monument, Sullivan's Island, SC	Ashfall Fossil Bed, Royal, NE	2351786	78304
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	New Castle Historic District, Delaware	931480	33116
Mount Vernon, Fairfax County, Virginia	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	1079835	35606
Hanford Site, Benton County, WA	The Alamo, Alamo Plaza, San Antonio, TX	3128728	105141
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	1231682	41511
Shelburne Farms, Harbor Road, Shelburne, VT	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4850154	159553
French Quarter, New Orleans, LA	Glacier National Park, West Glacier, MT	3565117	118390
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Lost World Caverns, Lewisburg, WV	2460677	80618
USS Alabama, Battleship Parkway, Mobile, AL	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	864616	27305
Pikes Peak, Colorado	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1489713	50155
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	1986021	69191
USS Alabama, Battleship Parkway, Mobile, AL	White House, Pennsylvania Avenue Northwest, Washington, DC	1557762	50504
Toltec Mounds, Scott, AR	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	722344	25610
Craters of the Moon National Monument & Preserve, Arco, ID	Bryce Canyon National Park, Hwy 63, Bryce, UT	849848	26970
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	2207410	72609
Acadia National Park, Maine	Glacier National Park, West Glacier, MT	4450701	151090
Toltec Mounds, Scott, AR	Liberty Bell, 6th Street, Philadelphia, PA	1850866	60901
Liberty Bell, 6th Street, Philadelphia, PA	Graceland, Elvis Presley Boulevard, Memphis, TN	1634964	53535
Terrace Hill, Grand Avenue, Des Moines, IA	New Castle Historic District, Delaware	1764053	59412
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	New Castle Historic District, Delaware	2746841	91089
The Breakers, Ochre Point Avenue, Newport, RI	Cape Canaveral, FL	2049951	68069
Terrace Hill, Grand Avenue, Des Moines, IA	Vicksburg National Military Park, Clay Street, Vicksburg, MS	1340289	44533
Carlsbad Caverns National Park, Carlsbad, NM	Yellowstone National Park, WY 82190	1844673	67814
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Yellowstone National Park, WY 82190	3925672	134524
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Ashfall Fossil Bed, Royal, NE	1415710	49510
USS Constitution, Boston, MA	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1823494	61766
Shelburne Farms, Harbor Road, Shelburne, VT	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	1582765	56311
Maryland State House, 100 State Cir, Annapolis, MD 21401	Fort Snelling, Tower Avenue, Saint Paul, MN	1817199	61076
Taliesin, County Road C, Spring Green, Wisconsin	Ashfall Fossil Bed, Royal, NE	751773	27471
Gateway Arch, Washington Avenue, St Louis, MO	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	381136	12903
Craters of the Moon National Monument & Preserve, Arco, ID	Olympia Entertainment, Woodward Avenue, Detroit, MI	2929304	94463
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Glacier National Park, West Glacier, MT	3061692	103519
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	San Andreas Fault, San Benito County, CA	4976755	161077
USS Constitution, Boston, MA	Columbia River Gorge National Scenic Area, Oregon	4814513	156100
Maryland State House, 100 State Cir, Annapolis, MD 21401	USS Constitution, Boston, MA	689767	24684
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Gateway Arch, Washington Avenue, St Louis, MO	2009904	69030
USS Constitution, Boston, MA	Yellowstone National Park, WY 82190	3834982	129564
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Yellowstone National Park, WY 82190	1725271	60529
Wright Brothers National Memorial Visitor Center, Manteo, NC	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	974305	32187
Yellowstone National Park, WY 82190	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	2524697	86519
Wright Brothers National Memorial Visitor Center, Manteo, NC	Statue of Liberty, Liberty Island, NYC, NY	720092	27777
Craters of the Moon National Monument & Preserve, Arco, ID	Fort Snelling, Tower Avenue, Saint Paul, MN	2074907	66319
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Olympia Entertainment, Woodward Avenue, Detroit, MI	418716	14381
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1000446	32351
Graceland, Elvis Presley Boulevard, Memphis, TN	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	787078	25987
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Glacier National Park, West Glacier, MT	2707726	86377
Lost World Caverns, Lewisburg, WV	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1013896	34420
The Breakers, Ochre Point Avenue, Newport, RI	Fort Snelling, Tower Avenue, Saint Paul, MN	2222722	75082
Congress Hall, Congress Place, Cape May, NJ 08204	Bryce Canyon National Park, Hwy 63, Bryce, UT	3823059	125963
Olympia Entertainment, Woodward Avenue, Detroit, MI	Fort Snelling, Tower Avenue, Saint Paul, MN	1111015	37041
The Alamo, Alamo Plaza, San Antonio, TX	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1693831	55844
Maryland State House, 100 State Cir, Annapolis, MD 21401	Yellowstone National Park, WY 82190	3415222	115336
Hoover Dam, NV	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	4276900	137981
Liberty Bell, 6th Street, Philadelphia, PA	Fort Snelling, Tower Avenue, Saint Paul, MN	1884969	62812
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	The Alamo, Alamo Plaza, San Antonio, TX	1939427	67437
Acadia National Park, Maine	Columbia River Gorge National Scenic Area, Oregon	5244120	171194
The Breakers, Ochre Point Avenue, Newport, RI	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4964607	160622
Lost World Caverns, Lewisburg, WV	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	666822	22049
USS Alabama, Battleship Parkway, Mobile, AL	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	1170385	37497
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Maryland State House, 100 State Cir, Annapolis, MD 21401	1793552	60470
Bryce Canyon National Park, Hwy 63, Bryce, UT	Grand Canyon National Park, Arizona	232427	10298
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	French Quarter, New Orleans, LA	1301278	41736
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Bryce Canyon National Park, Hwy 63, Bryce, UT	1875561	60523
Toltec Mounds, Scott, AR	Lost World Caverns, Lewisburg, WV	1326109	43245
Columbia River Gorge National Scenic Area, Oregon	Fort Snelling, Tower Avenue, Saint Paul, MN	2638062	84130
The Alamo, Alamo Plaza, San Antonio, TX	Acadia National Park, Maine	3718928	122376
Mount Vernon, Fairfax County, Virginia	Columbia River Gorge National Scenic Area, Oregon	4377863	140930
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	USS Constitution, Boston, MA	3294781	109111
Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	Columbia River Gorge National Scenic Area, Oregon	4426183	143908
Mount Vernon, Fairfax County, Virginia	Glacier National Park, West Glacier, MT	3625420	120884
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Wright Brothers National Memorial Visitor Center, Manteo, NC	3071895	102059
Cape Canaveral, FL	Pikes Peak, Colorado	3065349	99886
Hoover Dam, NV	Taliesin, County Road C, Spring Green, Wisconsin	2756482	87940
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Acadia National Park, Maine	2790125	93605
Yellowstone National Park, WY 82190	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	1608811	55787
Hoover Dam, NV	Acadia National Park, Maine	4857457	158367
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Craters of the Moon National Monument & Preserve, Arco, ID	1183571	39177
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Acadia National Park, Maine	2126830	72160
The Breakers, Ochre Point Avenue, Newport, RI	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	370511	14011
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	The Alamo, Alamo Plaza, San Antonio, TX	3516822	114874
The Breakers, Ochre Point Avenue, Newport, RI	Ashfall Fossil Bed, Royal, NE	2534468	86162
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	USS Alabama, Battleship Parkway, Mobile, AL	1368815	49143
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	The Alamo, Alamo Plaza, San Antonio, TX	1309345	42088
Toltec Mounds, Scott, AR	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	1001984	33121
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	2183781	71315
New Castle Historic District, Delaware	Yellowstone National Park, WY 82190	3498473	119111
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	San Andreas Fault, San Benito County, CA	2345929	78179
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Lost World Caverns, Lewisburg, WV	965974	32687
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	2050914	69431
Toltec Mounds, Scott, AR	Congress Hall, Congress Place, Cape May, NJ 08204	1929321	64500
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	1523445	51973
White House, Pennsylvania Avenue Northwest, Washington, DC	Olympia Entertainment, Woodward Avenue, Detroit, MI	847800	28664
The Breakers, Ochre Point Avenue, Newport, RI	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1569250	55804
Toltec Mounds, Scott, AR	Carlsbad Caverns National Park, Carlsbad, NM	1327080	45836
San Andreas Fault, San Benito County, CA	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	212841	10052
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	2885548	97975
Taliesin, County Road C, Spring Green, Wisconsin	Olympia Entertainment, Woodward Avenue, Detroit, MI	755368	26029
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Columbia River Gorge National Scenic Area, Oregon	2704802	87065
USS Alabama, Battleship Parkway, Mobile, AL	Columbia River Gorge National Scenic Area, Oregon	4091687	132705
Olympia Entertainment, Woodward Avenue, Detroit, MI	Grand Canyon National Park, Arizona	3086485	100490
Graceland, Elvis Presley Boulevard, Memphis, TN	White House, Pennsylvania Avenue Northwest, Washington, DC	1414096	45911
Wright Brothers National Memorial Visitor Center, Manteo, NC	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1293271	44236
West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	Grand Canyon National Park, Arizona	2684847	85097
Mount Vernon, Fairfax County, Virginia	Gateway Arch, Washington Avenue, St Louis, MO	1336076	45794
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Olympia Entertainment, Woodward Avenue, Detroit, MI	1957406	63279
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Grand Canyon National Park, Arizona	1903087	63470
Toltec Mounds, Scott, AR	Pikes Peak, Colorado	1600474	53363
Cape Canaveral, FL	Columbia River Gorge National Scenic Area, Oregon	4807400	153861
Carlsbad Caverns National Park, Carlsbad, NM	Wright Brothers National Memorial Visitor Center, Manteo, NC	3016475	98927
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Ashfall Fossil Bed, Royal, NE	614858	21957
Graceland, Elvis Presley Boulevard, Memphis, TN	Shelburne Farms, Harbor Road, Shelburne, VT	2060445	71605
Terrace Hill, Grand Avenue, Des Moines, IA	Glacier National Park, West Glacier, MT	2098158	71965
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	USS Constitution, Boston, MA	1396766	47436
White House, Pennsylvania Avenue Northwest, Washington, DC	Glacier National Park, West Glacier, MT	3562723	120728
USS Alabama, Battleship Parkway, Mobile, AL	Toltec Mounds, Scott, AR	703118	26278
USS Constitution, Boston, MA	Glacier National Park, West Glacier, MT	4021094	135997
Cape Canaveral, FL	The Alamo, Alamo Plaza, San Antonio, TX	1949042	61487
Taliesin, County Road C, Spring Green, Wisconsin	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	754858	27771
Hoover Dam, NV	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	938239	31500
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Taliesin, County Road C, Spring Green, Wisconsin	1730362	59000
Hanford Site, Benton County, WA	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	1214774	44145
Shelburne Farms, Harbor Road, Shelburne, VT	Vicksburg National Military Park, Clay Street, Vicksburg, MS	2421559	80387
Hoover Dam, NV	Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	1831327	59398
Taliesin, County Road C, Spring Green, Wisconsin	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	1972002	69252
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Taliesin, County Road C, Spring Green, Wisconsin	1461512	48261
Hanford Site, Benton County, WA	Gateway Arch, Washington Avenue, St Louis, MO	3088155	99626
Shelburne Farms, Harbor Road, Shelburne, VT	Yellowstone National Park, WY 82190	3699278	128423
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Yellowstone National Park, WY 82190	856780	33369
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Cape Canaveral, FL	1947118	64246
Maryland State House, 100 State Cir, Annapolis, MD 21401	Wright Brothers National Memorial Visitor Center, Manteo, NC	519176	18703
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Carlsbad Caverns National Park, Carlsbad, NM	1589047	55377
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Fort Sumter National Monument, Sullivan's Island, SC	1792618	60835
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Gateway Arch, Washington Avenue, St Louis, MO	448421	16253
Congress Hall, Congress Place, Cape May, NJ 08204	Acadia National Park, Maine	1028018	36728
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	1484285	50922
Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	Glacier National Park, West Glacier, MT	3925919	132329
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Olympia Entertainment, Woodward Avenue, Detroit, MI	1065861	36337
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Carlsbad Caverns National Park, Carlsbad, NM	2257948	75758
Mount Vernon, Fairfax County, Virginia	Terrace Hill, Grand Avenue, Des Moines, IA	1662991	55112
Maryland State House, 100 State Cir, Annapolis, MD 21401	The Breakers, Ochre Point Avenue, Newport, RI	637090	23339
Hoover Dam, NV	Liberty Bell, 6th Street, Philadelphia, PA	3970882	127641
Vicksburg National Military Park, Clay Street, Vicksburg, MS	Olympia Entertainment, Woodward Avenue, Detroit, MI	1582788	52125
Maryland State House, 100 State Cir, Annapolis, MD 21401	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	533335	19200
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Fort Sumter National Monument, Sullivan's Island, SC	1827656	59817
USS Alabama, Battleship Parkway, Mobile, AL	Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	1180006	39965
Terrace Hill, Grand Avenue, Des Moines, IA	White House, Pennsylvania Avenue Northwest, Washington, DC	1642191	54596
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	2450621	83486
USS Constitution, Boston, MA	Statue of Liberty, Liberty Island, NYC, NY	349747	13535
Glacier National Park, West Glacier, MT	Fort Snelling, Tower Avenue, Saint Paul, MN	1798041	61737
White House, Pennsylvania Avenue Northwest, Washington, DC	USS Constitution, Boston, MA	705972	25852
The Breakers, Ochre Point Avenue, Newport, RI	Bryce Canyon National Park, Hwy 63, Bryce, UT	4054078	133182
Lost World Caverns, Lewisburg, WV	Grand Canyon National Park, Arizona	3298120	104576
Mount Vernon, Fairfax County, Virginia	French Quarter, New Orleans, LA	1744419	56118
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Glacier National Park, West Glacier, MT	2940288	97891
Hoover Dam, NV	The Breakers, Ochre Point Avenue, Newport, RI	4369087	142738
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Graceland, Elvis Presley Boulevard, Memphis, TN	787287	28159
Fort Snelling, Tower Avenue, Saint Paul, MN	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	793789	27595
Carlsbad Caverns National Park, Carlsbad, NM	White House, Pennsylvania Avenue Northwest, Washington, DC	2929429	97527
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Terrace Hill, Grand Avenue, Des Moines, IA	1012008	32273
Hanford Site, Benton County, WA	French Quarter, New Orleans, LA	3881773	126894
Columbia River Gorge National Scenic Area, Oregon	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	3113765	100548
Shelburne Farms, Harbor Road, Shelburne, VT	Wright Brothers National Memorial Visitor Center, Manteo, NC	1170622	44500
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Ashfall Fossil Bed, Royal, NE	548999	20170
Mount Vernon, Fairfax County, Virginia	Wright Brothers National Memorial Visitor Center, Manteo, NC	441414	16045
Terrace Hill, Grand Avenue, Des Moines, IA	Hanford Site, Benton County, WA	2621379	85403
Terrace Hill, Grand Avenue, Des Moines, IA	Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	940705	30634
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	French Quarter, New Orleans, LA	2681513	88071
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	3174040	106502
Craters of the Moon National Monument & Preserve, Arco, ID	Cape Canaveral, FL	4128473	133673
Hanford Site, Benton County, WA	Wright Brothers National Memorial Visitor Center, Manteo, NC	4702943	152858
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Fort Snelling, Tower Avenue, Saint Paul, MN	723916	23333
Liberty Bell, 6th Street, Philadelphia, PA	San Andreas Fault, San Benito County, CA	4666575	151611
Congress Hall, Congress Place, Cape May, NJ 08204	Yellowstone National Park, WY 82190	3609552	122605
Carlsbad Caverns National Park, Carlsbad, NM	Grand Canyon National Park, Arizona	1133513	39315
USS Alabama, Battleship Parkway, Mobile, AL	Cape Canaveral, FL	880086	27420
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS	Carlsbad Caverns National Park, Carlsbad, NM	1466457	49870
Lost World Caverns, Lewisburg, WV	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	665677	22896
Shelburne Farms, Harbor Road, Shelburne, VT	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	387386	13408
Lost World Caverns, Lewisburg, WV	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	951633	33294
Maryland State House, 100 State Cir, Annapolis, MD 21401	Hoover Dam, NV	3901708	126053
Vicksburg National Military Park, Clay Street, Vicksburg, MS	The Alamo, Alamo Plaza, San Antonio, TX	931061	31591
Yellowstone National Park, WY 82190	Columbia River Gorge National Scenic Area, Oregon	1256183	44089
Shelburne Farms, Harbor Road, Shelburne, VT	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1724831	60967
Toltec Mounds, Scott, AR	Columbia River Gorge National Scenic Area, Oregon	3399077	107798
Craters of the Moon National Monument & Preserve, Arco, ID	Glacier National Park, West Glacier, MT	794318	29136
Hanford Site, Benton County, WA	The Breakers, Ochre Point Avenue, Newport, RI	4674471	153027
Hoover Dam, NV	Carlsbad Caverns National Park, Carlsbad, NM	1372972	46551
Terrace Hill, Grand Avenue, Des Moines, IA	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	1949612	64573
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Columbia River Gorge National Scenic Area, Oregon	1808374	61862
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	2608579	87006
San Andreas Fault, San Benito County, CA	Grand Canyon National Park, Arizona	1082921	37113
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Vicksburg National Military Park, Clay Street, Vicksburg, MS	2187279	71449
Carlsbad Caverns National Park, Carlsbad, NM	San Andreas Fault, San Benito County, CA	1947584	66210
White House, Pennsylvania Avenue Northwest, Washington, DC	Pikes Peak, Colorado	2685136	90854
Liberty Bell, 6th Street, Philadelphia, PA	Acadia National Park, Maine	924645	33519
Toltec Mounds, Scott, AR	Hanford Site, Benton County, WA	3358145	106610
Maryland State House, 100 State Cir, Annapolis, MD 21401	White House, Pennsylvania Avenue Northwest, Washington, DC	51172	2473
Bryce Canyon National Park, Hwy 63, Bryce, UT	Fort Snelling, Tower Avenue, Saint Paul, MN	2371319	76146
The Alamo, Alamo Plaza, San Antonio, TX	Olympia Entertainment, Woodward Avenue, Detroit, MI	2353130	76673
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD	Maryland State House, 100 State Cir, Annapolis, MD 21401	2663590	87313
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Statue of Liberty, Liberty Island, NYC, NY	1341458	44603
Taliesin, County Road C, Spring Green, Wisconsin	Fort Sumter National Monument, Sullivan's Island, SC	1766175	59104
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	White House, Pennsylvania Avenue Northwest, Washington, DC	933392	33517
Terrace Hill, Grand Avenue, Des Moines, IA	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	878386	30913
San Andreas Fault, San Benito County, CA	New Castle Historic District, Delaware	4676829	153351
The Breakers, Ochre Point Avenue, Newport, RI	Statue of Liberty, Liberty Island, NYC, NY	294397	11884
Liberty Bell, 6th Street, Philadelphia, PA	Ashfall Fossil Bed, Royal, NE	2196715	73892
Toltec Mounds, Scott, AR	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1238466	42013
The Breakers, Ochre Point Avenue, Newport, RI	Columbia River Gorge National Scenic Area, Oregon	4790491	155356
Mount Vernon, Fairfax County, Virginia	White House, Pennsylvania Avenue Northwest, Washington, DC	23912	1729
Pikes Peak, Colorado	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	2684344	89867
Maryland State House, 100 State Cir, Annapolis, MD 21401	Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	2875021	94882
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Statue of Liberty, Liberty Island, NYC, NY	1023174	35317
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN	259939	10000
Liberty Bell, 6th Street, Philadelphia, PA	Grand Canyon National Park, Arizona	3731423	120405
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Acadia National Park, Maine	3203271	108025
Cape Canaveral, FL	White House, Pennsylvania Avenue Northwest, Washington, DC	1394370	44355
Shelburne Farms, Harbor Road, Shelburne, VT	Craters of the Moon National Monument & Preserve, Arco, ID	3928519	131542
Taliesin, County Road C, Spring Green, Wisconsin	Lost World Caverns, Lewisburg, WV	1273680	44029
Hanford Site, Benton County, WA	Olympia Entertainment, Woodward Avenue, Detroit, MI	3573878	114770
Wright Brothers National Memorial Visitor Center, Manteo, NC	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	1290669	47751
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Olympia Entertainment, Woodward Avenue, Detroit, MI	725571	24642
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Fort Snelling, Tower Avenue, Saint Paul, MN	1124962	37663
French Quarter, New Orleans, LA	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	857117	29161
Cape Canaveral, FL	USS Constitution, Boston, MA	2102628	69414
The Breakers, Ochre Point Avenue, Newport, RI	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1768017	61380
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Fort Snelling, Tower Avenue, Saint Paul, MN	2086009	70012
Taliesin, County Road C, Spring Green, Wisconsin	Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA	1869248	62674
Pikes Peak, Colorado	Acadia National Park, Maine	3757896	124929
New Castle Historic District, Delaware	Glacier National Park, West Glacier, MT	3684584	125544
Graceland, Elvis Presley Boulevard, Memphis, TN	French Quarter, New Orleans, LA	626412	19970
Hoover Dam, NV	Glacier National Park, West Glacier, MT	1774950	56934
Statue of Liberty, Liberty Island, NYC, NY	Glacier National Park, West Glacier, MT	3749449	125061
Hoover Dam, NV	White House, Pennsylvania Avenue Northwest, Washington, DC	3903362	124350
USS Alabama, Battleship Parkway, Mobile, AL	Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL	1197210	41107
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	Liberty Bell, 6th Street, Philadelphia, PA	2302327	76937
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Pikes Peak, Colorado	1833065	62315
Hanford Site, Benton County, WA	Statue of Liberty, Liberty Island, NYC, NY	4388894	142644
Shelburne Farms, Harbor Road, Shelburne, VT	Taliesin, County Road C, Spring Green, Wisconsin	1745609	63151
Liberty Bell, 6th Street, Philadelphia, PA	Pikes Peak, Colorado	2801541	93969
Terrace Hill, Grand Avenue, Des Moines, IA	The Alamo, Alamo Plaza, San Antonio, TX	1618550	51648
USS Constitution, Boston, MA	Congress Hall, Congress Place, Cape May, NJ 08204	598411	21635
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Glacier National Park, West Glacier, MT	3870144	130704
Taliesin, County Road C, Spring Green, Wisconsin	Congress Hall, Congress Place, Cape May, NJ 08204	1655882	57333
Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	Grand Canyon National Park, Arizona	1257737	41233
Congress Hall, Congress Place, Cape May, NJ 08204	The Alamo, Alamo Plaza, San Antonio, TX	2882943	94397
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086	French Quarter, New Orleans, LA	1043911	34380
Carlsbad Caverns National Park, Carlsbad, NM	Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	1998091	67238
Acadia National Park, Maine	Bryce Canyon National Park, Hwy 63, Bryce, UT	4509036	148282
Maryland State House, 100 State Cir, Annapolis, MD 21401	French Quarter, New Orleans, LA	1805839	58242
Gateway Arch, Washington Avenue, St Louis, MO	French Quarter, New Orleans, LA	1091490	34833
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY	Congress Hall, Congress Place, Cape May, NJ 08204	1312237	44340
Fort Sumter National Monument, Sullivan's Island, SC	Olympia Entertainment, Woodward Avenue, Detroit, MI	1329533	45974
Graceland, Elvis Presley Boulevard, Memphis, TN	San Andreas Fault, San Benito County, CA	3200531	102813
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Congress Hall, Congress Place, Cape May, NJ 08204	1054409	36769
White House, Pennsylvania Avenue Northwest, Washington, DC	Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108	4524629	145459
Lost World Caverns, Lewisburg, WV	New Castle Historic District, Delaware	572997	19988
Pikes Peak, Colorado	USS Constitution, Boston, MA	3327699	109711
Congress Hall, Congress Place, Cape May, NJ 08204	Glacier National Park, West Glacier, MT	3848711	128237
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT	Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	391642	13719
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH	Bryce Canyon National Park, Hwy 63, Bryce, UT	2818397	91042
USS Alabama, Battleship Parkway, Mobile, AL	Lost World Caverns, Lewisburg, WV	1302307	41498
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH	Columbia River Gorge National Scenic Area, Oregon	4905203	161060
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND	Congress Hall, Congress Place, Cape May, NJ 08204	3069351	102152

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FILE: pareto-optimized-road-trip/my-waypoints-dist-dur.tsv
================================================
waypoint1	waypoint2	distance_m	duration_s
2 E Main St, Madison, WI 53703	Wyoming State Capitol, Cheyenne, WY 82001	1484756	13.459166666666667
L St & 10th St, Sacramento, CA 95814	Virginia State Capitol, Richmond, VA 23219	4496033	40.22388888888889
Michigan State Capitol, Lansing, MI 48933	North Dakota State Capitol, Bismarck, ND 58501	1702226	15.2425
Michigan State Capitol, Lansing, MI 48933	Oklahoma State Capitol, Oklahoma City, OK 73105	1577431	14.1075
New Jersey State House, Trenton, NJ 08608	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1050714	9.756944444444445
Maryland State House, 100 State Cir, Annapolis, MD 21401	West Virginia State Capitol, Charleston, WV 25317	624275	5.811111111111111
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Missouri State Capitol, Jefferson City, MO 65101	1933062	17.850277777777777
402 S Monroe St, Tallahassee, FL 32301	Vermont State House, 115 State Street, Montpelier, VT 05633	2270947	20.70388888888889
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Virginia State Capitol, Richmond, VA 23219	3620386	32.61944444444445
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	4310134	38.85166666666667
Nevada State Capitol, Carson City, NV 89701	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	4417580	39.431666666666665
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Missouri State Capitol, Jefferson City, MO 65101	1654361	15.761111111111111
Maryland State House, 100 State Cir, Annapolis, MD 21401	Nevada State Capitol, Carson City, NV 89701	4238501	38.34916666666667
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	3965649	35.45805555555555
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Michigan State Capitol, Lansing, MI 48933	3125553	28.08
300 SW 10th Ave, Topeka, KS 66612	Virginia State Capitol, Richmond, VA 23219	1817890	16.451666666666668
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	207363	2.6302777777777777
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Oklahoma State Capitol, Oklahoma City, OK 73105	873294	7.731944444444444
New Jersey State House, Trenton, NJ 08608	Vermont State House, 115 State Street, Montpelier, VT 05633	609928	5.969722222222222
Minnesota State Capitol, St Paul, MN 55155	North Dakota State Capitol, Bismarck, ND 58501	701087	6.226111111111111
700 W Jefferson St, Boise, ID 83720	Rhode Island State House, 82 Smith Street, Providence, RI 02903	4262023	38.382222222222225
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	1410057	12.826666666666666
Maryland State House, 100 State Cir, Annapolis, MD 21401	Michigan State Capitol, Lansing, MI 48933	984795	9.165
New Jersey State House, Trenton, NJ 08608	West Virginia State Capitol, Charleston, WV 25317	775909	7.272222222222222
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	Wyoming State Capitol, Cheyenne, WY 82001	2836858	25.635833333333334
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Vermont State House, 115 State Street, Montpelier, VT 05633	3858914	35.9
Oklahoma State Capitol, Oklahoma City, OK 73105	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1708829	15.338333333333333
Louisiana State Capitol, Baton Rouge, LA 70802	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	4032674	36.91388888888889
Minnesota State Capitol, St Paul, MN 55155	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1215946	11.09361111111111
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1177643	10.660277777777777
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Indiana State Capitol, Indianapolis, IN 46204	2734360	24.705277777777777
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	State House, 107 North Main Street, Concord, NH 03303	2090541	19.531666666666666
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	500 E Capitol Ave, Pierre, SD 57501	2545226	23.341944444444444
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Wyoming State Capitol, Cheyenne, WY 82001	1012033	9.015
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	200 E Colfax Ave, Denver, CO 80203	1556144	13.736944444444445
L St & 10th St, Sacramento, CA 95814	Nevada State Capitol, Carson City, NV 89701	209218	2.620277777777778
L St & 10th St, Sacramento, CA 95814	Indiana State Capitol, Indianapolis, IN 46204	3518613	31.3025
New Mexico State Capitol, Santa Fe, NM 87501	500 E Capitol Ave, Pierre, SD 57501	1398667	13.206666666666667
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	300 SW 10th Ave, Topeka, KS 66612	1939553	18.27861111111111
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	1209098	11.276388888888889
Missouri State Capitol, Jefferson City, MO 65101	Rhode Island State House, 82 Smith Street, Providence, RI 02903	2036038	19.129722222222224
Louisiana State Capitol, Baton Rouge, LA 70802	2 E Main St, Madison, WI 53703	1648573	14.640555555555556
402 S Monroe St, Tallahassee, FL 32301	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	1991155	17.946944444444444
700 W Jefferson St, Boise, ID 83720	Massachusetts State House, Boston, MA 02108	4283333	38.500277777777775
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	North Carolina State Capitol, Raleigh, NC 27601	3499526	31.790555555555557
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Massachusetts State House, Boston, MA 02108	611507	6.089722222222222
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Louisiana State Capitol, Baton Rouge, LA 70802	552559	6.1066666666666665
Indiana State Capitol, Indianapolis, IN 46204	Wyoming State Capitol, Cheyenne, WY 82001	1769573	15.93138888888889
700 W Jefferson St, Boise, ID 83720	North Dakota State Capitol, Bismarck, ND 58501	1653552	14.968055555555555
L St & 10th St, Sacramento, CA 95814	New Jersey State House, Trenton, NJ 08608	4526276	40.489444444444445
Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	West Virginia State Capitol, Charleston, WV 25317	4182133	37.61138888888889
Georgia State Capitol, Atlanta, GA 30334	Nevada State Capitol, Carson City, NV 89701	3827390	34.82694444444444
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	3016589	27.24
200 E Colfax Ave, Denver, CO 80203	Utah State Capitol, Salt Lake City, UT 84103	841152	7.91
South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	Utah State Capitol, Salt Lake City, UT 84103	3334079	30.226666666666667
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	West Virginia State Capitol, Charleston, WV 25317	265168	2.6933333333333334
200 E Colfax Ave, Denver, CO 80203	Vermont State House, 115 State Street, Montpelier, VT 05633	3117244	28.791666666666668
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	2180753	20.04
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	Vermont State House, 115 State Street, Montpelier, VT 05633	2344034	22.141111111111112
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Utah State Capitol, Salt Lake City, UT 84103	774035	6.633333333333334
300 SW 10th Ave, Topeka, KS 66612	Maine State House, Augusta, ME 04330	2649936	24.170833333333334
Michigan State Capitol, Lansing, MI 48933	West Virginia State Capitol, Charleston, WV 25317	679517	6.6338888888888885
New Mexico State Capitol, Santa Fe, NM 87501	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	2920958	26.529444444444444
400-498 N West St, Jackson, MS 39201	2 E Main St, Madison, WI 53703	1365734	12.152222222222223
State House, 107 North Main Street, Concord, NH 03303	Rhode Island State House, 82 Smith Street, Providence, RI 02903	198603	1.9908333333333332
Massachusetts State House, Boston, MA 02108	Oklahoma State Capitol, Oklahoma City, OK 73105	2710778	24.665277777777778
Illinois State Capitol, Springfield, IL 62756	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	1213082	11.370833333333334
200 E Colfax Ave, Denver, CO 80203	West Virginia State Capitol, Charleston, WV 25317	2179520	19.378888888888888
Maine State House, Augusta, ME 04330	North Dakota State Capitol, Bismarck, ND 58501	3181562	28.85638888888889
400-498 N West St, Jackson, MS 39201	North Carolina State Capitol, Raleigh, NC 27601	1266179	11.560555555555556
Illinois State Capitol, Springfield, IL 62756	Wyoming State Capitol, Cheyenne, WY 82001	1414419	12.964166666666667
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Nevada State Capitol, Carson City, NV 89701	2565429	23.017222222222223
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	832772	7.963055555555556
Maryland State House, 100 State Cir, Annapolis, MD 21401	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2513292	22.845833333333335
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Louisiana State Capitol, Baton Rouge, LA 70802	2386413	21.665555555555557
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	North Dakota State Capitol, Bismarck, ND 58501	2555394	22.523611111111112
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	North Carolina State Capitol, Raleigh, NC 27601	3628835	33.34361111111111
Maine State House, Augusta, ME 04330	Utah State Capitol, Salt Lake City, UT 84103	4056973	36.73361111111111
Georgia State Capitol, Atlanta, GA 30334	2 E Main St, Madison, WI 53703	1396771	12.522777777777778
Vermont State House, 115 State Street, Montpelier, VT 05633	2 E Main St, Madison, WI 53703	1777622	17.235833333333332
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	4722133	43.030277777777776
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Nevada State Capitol, Carson City, NV 89701	3735753	34.01277777777778
Massachusetts State House, Boston, MA 02108	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1246212	11.47888888888889
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	300 SW 10th Ave, Topeka, KS 66612	780685	7.1033333333333335
Louisiana State Capitol, Baton Rouge, LA 70802	Nevada State Capitol, Carson City, NV 89701	3319161	30.426666666666666
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Maryland State House, 100 State Cir, Annapolis, MD 21401	3696277	33.754444444444445
Nevada State Capitol, Carson City, NV 89701	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	1086169	11.026111111111112
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	334062	3.2086111111111113
402 S Monroe St, Tallahassee, FL 32301	West Virginia State Capitol, Charleston, WV 25317	1133547	11.511111111111111
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Louisiana State Capitol, Baton Rouge, LA 70802	1998869	18.3225
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	3894102	35.33111111111111
700 W Jefferson St, Boise, ID 83720	North Carolina State Capitol, Raleigh, NC 27601	3930023	35.38611111111111
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Vermont State House, 115 State Street, Montpelier, VT 05633	4207835	38.82194444444445
200 E Colfax Ave, Denver, CO 80203	500 E Capitol Ave, Pierre, SD 57501	844954	8.028333333333334
402 S Monroe St, Tallahassee, FL 32301	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	1078970	10.289722222222222
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Missouri State Capitol, Jefferson City, MO 65101	554812	5.858888888888889
New Jersey State House, Trenton, NJ 08608	Oklahoma State Capitol, Oklahoma City, OK 73105	2251698	20.604166666666668
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	North Carolina State Capitol, Raleigh, NC 27601	1779142	16.510277777777777
Illinois State Capitol, Springfield, IL 62756	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	697787	6.743888888888889
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	North Dakota State Capitol, Bismarck, ND 58501	2668685	24.16583333333333
300 SW 10th Ave, Topeka, KS 66612	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	2925507	26.209444444444443
Georgia State Capitol, Atlanta, GA 30334	Rhode Island State House, 82 Smith Street, Providence, RI 02903	1687443	15.93
Louisiana State Capitol, Baton Rouge, LA 70802	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	2390708	21.575277777777778
Nevada State Capitol, Carson City, NV 89701	Utah State Capitol, Salt Lake City, UT 84103	880839	7.743611111111111
402 S Monroe St, Tallahassee, FL 32301	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	570645	6.329722222222222
North Carolina State Capitol, Raleigh, NC 27601	North Dakota State Capitol, Bismarck, ND 58501	2649871	24.302222222222223
200 E Colfax Ave, Denver, CO 80203	North Carolina State Capitol, Raleigh, NC 27601	2688370	24.213333333333335
500 E Capitol Ave, Pierre, SD 57501	West Virginia State Capitol, Charleston, WV 25317	2059330	18.691388888888888
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	State House, 107 North Main Street, Concord, NH 03303	2364261	22.52111111111111
Louisiana State Capitol, Baton Rouge, LA 70802	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1553207	14.011944444444444
400-498 N West St, Jackson, MS 39201	Nevada State Capitol, Carson City, NV 89701	3284019	30.118333333333332
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Illinois State Capitol, Springfield, IL 62756	1053119	9.519444444444444
Missouri State Capitol, Jefferson City, MO 65101	Virginia State Capitol, Richmond, VA 23219	1532730	13.918333333333333
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Utah State Capitol, Salt Lake City, UT 84103	3490982	31.87527777777778
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1059373	9.633333333333333
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	300 SW 10th Ave, Topeka, KS 66612	1442611	12.900833333333333
Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	Wyoming State Capitol, Cheyenne, WY 82001	2580236	23.32777777777778
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1909649	17.545833333333334
Indiana State Capitol, Indianapolis, IN 46204	Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	758123	7.004444444444444
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Massachusetts State House, Boston, MA 02108	163033	1.6980555555555557
300 SW 10th Ave, Topeka, KS 66612	Rhode Island State House, 82 Smith Street, Providence, RI 02903	2321198	21.662777777777777
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Illinois State Capitol, Springfield, IL 62756	2502392	22.606944444444444
Nevada State Capitol, Carson City, NV 89701	2 E Main St, Madison, WI 53703	3065319	27.260277777777777
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	400-498 N West St, Jackson, MS 39201	2366978	20.807222222222222
Indiana State Capitol, Indianapolis, IN 46204	Rhode Island State House, 82 Smith Street, Providence, RI 02903	1446931	13.763333333333334
Maryland State House, 100 State Cir, Annapolis, MD 21401	500 E Capitol Ave, Pierre, SD 57501	2446586	22.220277777777778
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Massachusetts State House, Boston, MA 02108	4261483	38.669444444444444
500 E Capitol Ave, Pierre, SD 57501	Vermont State House, 115 State Street, Montpelier, VT 05633	2804324	26.13222222222222
Vermont State House, 115 State Street, Montpelier, VT 05633	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4928200	45.08638888888889
Illinois State Capitol, Springfield, IL 62756	Utah State Capitol, Salt Lake City, UT 84103	2121657	19.371388888888887
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	2 E Main St, Madison, WI 53703	2099306	19.149166666666666
North Dakota State Capitol, Bismarck, ND 58501	Rhode Island State House, 82 Smith Street, Providence, RI 02903	2907302	26.524444444444445
700 W Jefferson St, Boise, ID 83720	West Virginia State Capitol, Charleston, WV 25317	3421174	30.551666666666666
300 SW 10th Ave, Topeka, KS 66612	Wyoming State Capitol, Cheyenne, WY 82001	1016304	8.745555555555555
Georgia State Capitol, Atlanta, GA 30334	Maryland State House, 100 State Cir, Annapolis, MD 21401	1082137	9.9925
South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1870907	16.675833333333333
North Carolina State Capitol, Raleigh, NC 27601	500 E Capitol Ave, Pierre, SD 57501	2566101	23.543055555555554
Maine State House, Augusta, ME 04330	Wyoming State Capitol, Cheyenne, WY 82001	3349736	30.32638888888889
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	3364264	30.154166666666665
L St & 10th St, Sacramento, CA 95814	State House, 107 North Main Street, Concord, NH 03303	4819339	44.10638888888889
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Nevada State Capitol, Carson City, NV 89701	3008237	27.350833333333334
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	1875100	17.163611111111113
L St & 10th St, Sacramento, CA 95814	Maryland State House, 100 State Cir, Annapolis, MD 21401	4434054	39.71194444444444
Louisiana State Capitol, Baton Rouge, LA 70802	Vermont State House, 115 State Street, Montpelier, VT 05633	2643406	24.621944444444445
700 W Jefferson St, Boise, ID 83720	Michigan State Capitol, Lansing, MI 48933	3051430	27.051388888888887
Michigan State Capitol, Lansing, MI 48933	500 E Capitol Ave, Pierre, SD 57501	1631881	14.58611111111111
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Maryland State House, 100 State Cir, Annapolis, MD 21401	1340229	12.249444444444444
Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	Utah State Capitol, Salt Lake City, UT 84103	2629654	23.787777777777777
Maryland State House, 100 State Cir, Annapolis, MD 21401	New Jersey State House, Trenton, NJ 08608	262813	2.633611111111111
Nevada State Capitol, Carson City, NV 89701	State House, 107 North Main Street, Concord, NH 03303	4648498	42.49944444444444
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Nevada State Capitol, Carson City, NV 89701	4507409	40.7575
Maine State House, Augusta, ME 04330	West Virginia State Capitol, Charleston, WV 25317	1458292	13.589166666666667
200 E Colfax Ave, Denver, CO 80203	North Dakota State Capitol, Bismarck, ND 58501	1115101	10.553333333333333
Nevada State Capitol, Carson City, NV 89701	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	832440	8.6625
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	4892402	44.316944444444445
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	New Mexico State Capitol, Santa Fe, NM 87501	1899972	16.586111111111112
New Jersey State House, Trenton, NJ 08608	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	4712435	42.61805555555556
Illinois State Capitol, Springfield, IL 62756	Missouri State Capitol, Jefferson City, MO 65101	335367	3.391111111111111
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2969380	26.964444444444446
402 S Monroe St, Tallahassee, FL 32301	Indiana State Capitol, Indianapolis, IN 46204	1253894	11.976666666666667
Maine State House, Augusta, ME 04330	Minnesota State Capitol, St Paul, MN 55155	2483764	22.751666666666665
Maryland State House, 100 State Cir, Annapolis, MD 21401	Massachusetts State House, Boston, MA 02108	686203	6.848611111111111
400-498 N West St, Jackson, MS 39201	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	2126538	19.275555555555556
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	2 E Main St, Madison, WI 53703	781082	7.281944444444444
200 E Colfax Ave, Denver, CO 80203	Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	3023952	27.3175
Michigan State Capitol, Lansing, MI 48933	Missouri State Capitol, Jefferson City, MO 65101	960368	8.884722222222223
Georgia State Capitol, Atlanta, GA 30334	Minnesota State Capitol, St Paul, MN 55155	1803693	16.06972222222222
300 SW 10th Ave, Topeka, KS 66612	New Mexico State Capitol, Santa Fe, NM 87501	1113558	10.723333333333333
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Minnesota State Capitol, St Paul, MN 55155	1917773	17.89361111111111
Georgia State Capitol, Atlanta, GA 30334	Massachusetts State House, Boston, MA 02108	1737475	16.463055555555556
New Mexico State Capitol, Santa Fe, NM 87501	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1948400	17.435833333333335
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Maine State House, Augusta, ME 04330	2338330	21.378055555555555
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Oklahoma State Capitol, Oklahoma City, OK 73105	2249555	20.97888888888889
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	993281	9.273055555555555
200 E Colfax Ave, Denver, CO 80203	Rhode Island State House, 82 Smith Street, Providence, RI 02903	3149582	28.52111111111111
L St & 10th St, Sacramento, CA 95814	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	4330179	38.74888888888889
402 S Monroe St, Tallahassee, FL 32301	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	3703198	33.8
400-498 N West St, Jackson, MS 39201	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	1371664	13.203611111111112
Louisiana State Capitol, Baton Rouge, LA 70802	Massachusetts State House, Boston, MA 02108	2546224	23.336111111111112
L St & 10th St, Sacramento, CA 95814	North Carolina State Capitol, Raleigh, NC 27601	4499451	40.45944444444444
Michigan State Capitol, Lansing, MI 48933	Minnesota State Capitol, St Paul, MN 55155	1004428	9.137777777777778
Oklahoma State Capitol, Oklahoma City, OK 73105	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1092426	9.8425
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	1369160	12.65638888888889
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Massachusetts State House, Boston, MA 02108	1995567	18.72
300 SW 10th Ave, Topeka, KS 66612	North Carolina State Capitol, Raleigh, NC 27601	1821308	16.68722222222222
200 E Colfax Ave, Denver, CO 80203	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	2072303	19.064722222222223
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	905401	8.324722222222222
Maine State House, Augusta, ME 04330	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	3405003	31.066388888888888
400-498 N West St, Jackson, MS 39201	Vermont State House, 115 State Street, Montpelier, VT 05633	2379235	22.322222222222223
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	2651879	23.929444444444446
200 E Colfax Ave, Denver, CO 80203	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	2024609	18.102777777777778
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Virginia State Capitol, Richmond, VA 23219	1111807	10.0725
Vermont State House, 115 State Street, Montpelier, VT 05633	West Virginia State Capitol, Charleston, WV 25317	1307877	12.747777777777777
200 E Colfax Ave, Denver, CO 80203	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	2564693	22.948333333333334
South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	West Virginia State Capitol, Charleston, WV 25317	569053	5.368888888888889
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	400-498 N West St, Jackson, MS 39201	399927	4.009444444444444
Louisiana State Capitol, Baton Rouge, LA 70802	Minnesota State Capitol, St Paul, MN 55155	1900670	17.549166666666668
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Vermont State House, 115 State Street, Montpelier, VT 05633	1492754	14.696666666666667
New Mexico State Capitol, Santa Fe, NM 87501	2 E Main St, Madison, WI 53703	2012902	18.82138888888889
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	473386	4.486944444444444
State House, 107 North Main Street, Concord, NH 03303	500 E Capitol Ave, Pierre, SD 57501	2843784	26.504166666666666
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Minnesota State Capitol, St Paul, MN 55155	1857595	16.41888888888889
Maryland State House, 100 State Cir, Annapolis, MD 21401	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	1980198	18.176388888888887
Indiana State Capitol, Indianapolis, IN 46204	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	267015	2.671388888888889
Maine State House, Augusta, ME 04330	Missouri State Capitol, Jefferson City, MO 65101	2364114	21.656388888888888
200 E Colfax Ave, Denver, CO 80203	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1857910	16.49972222222222
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	North Dakota State Capitol, Bismarck, ND 58501	2615571	23.998333333333335
300 SW 10th Ave, Topeka, KS 66612	Oklahoma State Capitol, Oklahoma City, OK 73105	473488	4.159722222222222
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Minnesota State Capitol, St Paul, MN 55155	2649346	24.74138888888889
Rhode Island State House, 82 Smith Street, Providence, RI 02903	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1729221	15.988611111111112
North Carolina State Capitol, Raleigh, NC 27601	Utah State Capitol, Salt Lake City, UT 84103	3456831	31.470833333333335
402 S Monroe St, Tallahassee, FL 32301	Georgia State Capitol, Atlanta, GA 30334	420539	4.3533333333333335
200 E Colfax Ave, Denver, CO 80203	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	779919	6.818888888888889
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	New Mexico State Capitol, Santa Fe, NM 87501	2160641	19.406111111111112
Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	917431	8.471944444444444
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	Wyoming State Capitol, Cheyenne, WY 82001	713490	6.3675
700 W Jefferson St, Boise, ID 83720	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	782531	7.341388888888889
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Nevada State Capitol, Carson City, NV 89701	4337141	39.470555555555556
Indiana State Capitol, Indianapolis, IN 46204	North Dakota State Capitol, Bismarck, ND 58501	1640234	14.748055555555556
Massachusetts State House, Boston, MA 02108	Wyoming State Capitol, Cheyenne, WY 82001	3101206	28.11722222222222
Maine State House, Augusta, ME 04330	Oklahoma State Capitol, Oklahoma City, OK 73105	2959307	26.874444444444446
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	Utah State Capitol, Salt Lake City, UT 84103	1420727	12.774444444444445
Indiana State Capitol, Indianapolis, IN 46204	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	2624064	23.901944444444446
Illinois State Capitol, Springfield, IL 62756	500 E Capitol Ave, Pierre, SD 57501	1322600	11.902777777777779
300 SW 10th Ave, Topeka, KS 66612	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	1992901	18.201944444444443
Michigan State Capitol, Lansing, MI 48933	2 E Main St, Madison, WI 53703	597506	5.591111111111111
Minnesota State Capitol, St Paul, MN 55155	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	1708022	15.726944444444445
400-498 N West St, Jackson, MS 39201	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1270368	11.52361111111111
L St & 10th St, Sacramento, CA 95814	New Mexico State Capitol, Santa Fe, NM 87501	1836427	16.634166666666665
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	2950421	26.576666666666668
Missouri State Capitol, Jefferson City, MO 65101	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1412471	12.889166666666666
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	299648	3.1172222222222223
300 SW 10th Ave, Topeka, KS 66612	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1697631	15.422222222222222
South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	500 E Capitol Ave, Pierre, SD 57501	2535022	22.758055555555554
402 S Monroe St, Tallahassee, FL 32301	Virginia State Capitol, Richmond, VA 23219	1225602	10.708333333333334
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1711975	15.538055555555555
Maryland State House, 100 State Cir, Annapolis, MD 21401	400-498 N West St, Jackson, MS 39201	1637909	14.934722222222222
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Missouri State Capitol, Jefferson City, MO 65101	711404	6.59
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	561239	5.164444444444444
Georgia State Capitol, Atlanta, GA 30334	West Virginia State Capitol, Charleston, WV 25317	804715	7.476944444444444
Louisiana State Capitol, Baton Rouge, LA 70802	New Mexico State Capitol, Santa Fe, NM 87501	1713974	15.636388888888888
Minnesota State Capitol, St Paul, MN 55155	State House, 107 North Main Street, Concord, NH 03303	2197183	21.084444444444443
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1326907	12.3975
300 SW 10th Ave, Topeka, KS 66612	2 E Main St, Madison, WI 53703	885264	8.190833333333334
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Rhode Island State House, 82 Smith Street, Providence, RI 02903	2069524	19.095277777777778
Maine State House, Augusta, ME 04330	State House, 107 North Main Street, Concord, NH 03303	276259	2.623611111111111
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	300 SW 10th Ave, Topeka, KS 66612	412567	3.7730555555555556
Illinois State Capitol, Springfield, IL 62756	New Jersey State House, Trenton, NJ 08608	1409179	13.11138888888889
Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	Utah State Capitol, Salt Lake City, UT 84103	2078970	20.267222222222223
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Utah State Capitol, Salt Lake City, UT 84103	3078983	27.696666666666665
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	3618995	32.575
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	2 E Main St, Madison, WI 53703	1450674	12.872222222222222
Louisiana State Capitol, Baton Rouge, LA 70802	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	950242	8.470555555555556
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	400-498 N West St, Jackson, MS 39201	998879	9.043611111111112
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Maine State House, Augusta, ME 04330	410528	3.8625
Maine State House, Augusta, ME 04330	Rhode Island State House, 82 Smith Street, Providence, RI 02903	348608	3.3652777777777776
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1987338	18.030555555555555
Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	2 E Main St, Madison, WI 53703	1001345	8.963055555555556
Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	Wyoming State Capitol, Cheyenne, WY 82001	2041131	18.416944444444443
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Virginia State Capitol, Richmond, VA 23219	827652	7.616388888888889
Louisiana State Capitol, Baton Rouge, LA 70802	North Carolina State Capitol, Raleigh, NC 27601	1500710	13.439722222222223
Oklahoma State Capitol, Oklahoma City, OK 73105	Rhode Island State House, 82 Smith Street, Providence, RI 02903	2630826	24.329722222222223
Massachusetts State House, Boston, MA 02108	Virginia State Capitol, Richmond, VA 23219	888608	8.704166666666667
Maryland State House, 100 State Cir, Annapolis, MD 21401	Rhode Island State House, 82 Smith Street, Providence, RI 02903	636172	6.315555555555555
402 S Monroe St, Tallahassee, FL 32301	Oklahoma State Capitol, Oklahoma City, OK 73105	1613562	15.234444444444444
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	2 E Main St, Madison, WI 53703	2729787	25.70916666666667
L St & 10th St, Sacramento, CA 95814	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	861505	8.210833333333333
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Minnesota State Capitol, St Paul, MN 55155	1205997	11.106111111111112
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Maryland State House, 100 State Cir, Annapolis, MD 21401	1672128	15.351666666666667
Louisiana State Capitol, Baton Rouge, LA 70802	Michigan State Capitol, Lansing, MI 48933	1767813	15.742222222222223
700 W Jefferson St, Boise, ID 83720	New Jersey State House, Trenton, NJ 08608	3956849	35.416111111111114
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Rhode Island State House, 82 Smith Street, Providence, RI 02903	1475019	13.7275
402 S Monroe St, Tallahassee, FL 32301	Utah State Capitol, Salt Lake City, UT 84103	3417917	31.342777777777776
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	3317609	29.146944444444443
Minnesota State Capitol, St Paul, MN 55155	Wyoming State Capitol, Cheyenne, WY 82001	1404993	12.471111111111112
South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	Vermont State House, 115 State Street, Montpelier, VT 05633	1641637	15.272222222222222
Georgia State Capitol, Atlanta, GA 30334	New Mexico State Capitol, Santa Fe, NM 87501	2222203	20.036666666666665
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Michigan State Capitol, Lansing, MI 48933	1163752	10.827222222222222
Indiana State Capitol, Indianapolis, IN 46204	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	283280	2.6772222222222224
New Mexico State Capitol, Santa Fe, NM 87501	Vermont State House, 115 State Street, Montpelier, VT 05633	3522278	32.493611111111115
Oklahoma State Capitol, Oklahoma City, OK 73105	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	3141325	28.153333333333332
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	2895779	26.269444444444446
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	New Jersey State House, Trenton, NJ 08608	3802612	34.59861111111111
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	New Jersey State House, Trenton, NJ 08608	188117	1.8747222222222222
Indiana State Capitol, Indianapolis, IN 46204	North Carolina State Capitol, Raleigh, NC 27601	1014212	9.731944444444444
Missouri State Capitol, Jefferson City, MO 65101	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	3273298	29.595555555555556
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	826451	7.4655555555555555
700 W Jefferson St, Boise, ID 83720	Indiana State Capitol, Indianapolis, IN 46204	2949186	26.229166666666668
200 E Colfax Ave, Denver, CO 80203	Missouri State Capitol, Jefferson City, MO 65101	1219182	10.786388888888888
L St & 10th St, Sacramento, CA 95814	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	3675274	32.87861111111111
300 SW 10th Ave, Topeka, KS 66612	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	997131	9.106666666666667
200 E Colfax Ave, Denver, CO 80203	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	1272393	11.210833333333333
State House, 107 North Main Street, Concord, NH 03303	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1227308	11.992222222222223
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	400-498 N West St, Jackson, MS 39201	1325240	12.133333333333333
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1063689	9.881944444444445
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	2 E Main St, Madison, WI 53703	1510851	14.346666666666666
200 E Colfax Ave, Denver, CO 80203	Virginia State Capitol, Richmond, VA 23219	2684952	23.977777777777778
400-498 N West St, Jackson, MS 39201	New Mexico State Capitol, Santa Fe, NM 87501	1678832	15.328055555555556
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	2072368	19.018333333333334
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	891342	8.227777777777778
Louisiana State Capitol, Baton Rouge, LA 70802	500 E Capitol Ave, Pierre, SD 57501	2163883	20.43027777777778
Missouri State Capitol, Jefferson City, MO 65101	Oklahoma State Capitol, Oklahoma City, OK 73105	671161	6.1819444444444445
300 SW 10th Ave, Topeka, KS 66612	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	2138101	19.470833333333335
402 S Monroe St, Tallahassee, FL 32301	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	4649068	42.4975
Michigan State Capitol, Lansing, MI 48933	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	3808788	34.27444444444444
700 W Jefferson St, Boise, ID 83720	Oklahoma State Capitol, Oklahoma City, OK 73105	2381578	20.9625
Missouri State Capitol, Jefferson City, MO 65101	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	3220845	29.23138888888889
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Vermont State House, 115 State Street, Montpelier, VT 05633	2157152	20.068055555555556
Minnesota State Capitol, St Paul, MN 55155	Rhode Island State House, 82 Smith Street, Providence, RI 02903	2209294	20.433888888888887
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Indiana State Capitol, Indianapolis, IN 46204	939579	8.519166666666667
Michigan State Capitol, Lansing, MI 48933	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	409545	4.043333333333333
Massachusetts State House, Boston, MA 02108	West Virginia State Capitol, Charleston, WV 25317	1209763	11.38
Missouri State Capitol, Jefferson City, MO 65101	New Jersey State House, Trenton, NJ 08608	1658410	15.402222222222223
North Carolina State Capitol, Raleigh, NC 27601	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4695729	42.50138888888889
700 W Jefferson St, Boise, ID 83720	2 E Main St, Madison, WI 53703	2666733	23.79361111111111
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Virginia State Capitol, Richmond, VA 23219	1775724	16.274722222222223
New Mexico State Capitol, Santa Fe, NM 87501	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	3311587	29.805555555555557
700 W Jefferson St, Boise, ID 83720	Maine State House, Augusta, ME 04330	4530828	40.665
700 W Jefferson St, Boise, ID 83720	New Mexico State Capitol, Santa Fe, NM 87501	1550056	14.786944444444444
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Maine State House, Augusta, ME 04330	859002	8.254166666666666
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	North Dakota State Capitol, Bismarck, ND 58501	1067013	9.260277777777778
Massachusetts State House, Boston, MA 02108	State House, 107 North Main Street, Concord, NH 03303	122084	1.2683333333333333
200 E Colfax Ave, Denver, CO 80203	Wyoming State Capitol, Cheyenne, WY 82001	163100	1.6005555555555555
Michigan State Capitol, Lansing, MI 48933	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	865309	7.960277777777778
Massachusetts State House, Boston, MA 02108	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1483905	13.944166666666666
New Mexico State Capitol, Santa Fe, NM 87501	West Virginia State Capitol, Charleston, WV 25317	2475742	22.078055555555554
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	New Mexico State Capitol, Santa Fe, NM 87501	3392652	30.780277777777776
Massachusetts State House, Boston, MA 02108	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	2396299	21.946944444444444
Georgia State Capitol, Atlanta, GA 30334	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	3310362	29.805
200 E Colfax Ave, Denver, CO 80203	400-498 N West St, Jackson, MS 39201	1929525	17.800833333333333
Minnesota State Capitol, St Paul, MN 55155	Oklahoma State Capitol, Oklahoma City, OK 73105	1265804	11.178055555555556
Minnesota State Capitol, St Paul, MN 55155	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1404656	12.47888888888889
Nevada State Capitol, Carson City, NV 89701	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	3498150	31.13888888888889
Rhode Island State House, 82 Smith Street, Providence, RI 02903	2 E Main St, Madison, WI 53703	1807993	16.895555555555557
North Dakota State Capitol, Bismarck, ND 58501	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2117504	20.38722222222222
Illinois State Capitol, Springfield, IL 62756	Maine State House, Augusta, ME 04330	2107868	19.36722222222222
New Mexico State Capitol, Santa Fe, NM 87501	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	2564803	22.93166666666667
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	West Virginia State Capitol, Charleston, WV 25317	1187217	10.837222222222222
Maine State House, Augusta, ME 04330	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	876031	8.362777777777778
Minnesota State Capitol, St Paul, MN 55155	Utah State Capitol, Salt Lake City, UT 84103	2018129	18.496111111111112
Oregon State Capitol, 900 Court St NE, Salem, OR 97301	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	3379043	31.99888888888889
700 W Jefferson St, Boise, ID 83720	Utah State Capitol, Salt Lake City, UT 84103	544207	4.828333333333333
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	New Jersey State House, Trenton, NJ 08608	292803	3.0347222222222223
Nevada State Capitol, Carson City, NV 89701	New Jersey State House, Trenton, NJ 08608	4355435	38.8825
North Carolina State Capitol, Raleigh, NC 27601	Oklahoma State Capitol, Oklahoma City, OK 73105	1956768	17.853055555555557
North Carolina State Capitol, Raleigh, NC 27601	Rhode Island State House, 82 Smith Street, Providence, RI 02903	1107472	10.61138888888889
Rhode Island State House, 82 Smith Street, Providence, RI 02903	500 E Capitol Ave, Pierre, SD 57501	2842367	25.890555555555554
700 W Jefferson St, Boise, ID 83720	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	765315	7.364444444444445
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	1438743	13.418055555555556
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	West Virginia State Capitol, Charleston, WV 25317	722915	6.932777777777778
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Wyoming State Capitol, Cheyenne, WY 82001	2954014	26.755
Indiana State Capitol, Indianapolis, IN 46204	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	3707961	33.49333333333333
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	West Virginia State Capitol, Charleston, WV 25317	1055647	9.713055555555556
Illinois State Capitol, Springfield, IL 62756	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	3352808	30.52611111111111
Louisiana State Capitol, Baton Rouge, LA 70802	New Jersey State House, Trenton, NJ 08608	2125065	19.19333333333333
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Oklahoma State Capitol, Oklahoma City, OK 73105	1551801	14.149722222222222
Georgia State Capitol, Atlanta, GA 30334	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4308684	38.86666666666667
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Wyoming State Capitol, Cheyenne, WY 82001	1112354	9.662222222222223
200 E Colfax Ave, Denver, CO 80203	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	2168898	19.80527777777778
Louisiana State Capitol, Baton Rouge, LA 70802	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	1926794	17.361666666666668
402 S Monroe St, Tallahassee, FL 32301	Maine State House, Augusta, ME 04330	2356857	21.520555555555557
400-498 N West St, Jackson, MS 39201	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	3080815	28.288055555555555
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	400-498 N West St, Jackson, MS 39201	410924	4.3116666666666665
Oklahoma State Capitol, Oklahoma City, OK 73105	2 E Main St, Madison, WI 53703	1345956	12.126388888888888
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Michigan State Capitol, Lansing, MI 48933	1344347	12.05
400-498 N West St, Jackson, MS 39201	North Dakota State Capitol, Bismarck, ND 58501	2327096	21.09027777777778
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	2758173	24.61138888888889
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Maine State House, Augusta, ME 04330	4508978	40.833888888888886
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Indiana State Capitol, Indianapolis, IN 46204	914961	8.330833333333333
200 E Colfax Ave, Denver, CO 80203	Massachusetts State House, Boston, MA 02108	3170892	28.639166666666668
North Dakota State Capitol, Bismarck, ND 58501	Vermont State House, 115 State Street, Montpelier, VT 05633	2874965	26.795
Massachusetts State House, Boston, MA 02108	Rhode Island State House, 82 Smith Street, Providence, RI 02903	80922	0.9569444444444445
Oregon State Capitol, 900 Court St NE, Salem, OR 97301	Virginia State Capitol, Richmond, VA 23219	4686106	42.285555555555554
Georgia State Capitol, Atlanta, GA 30334	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	4256232	38.5025
Indiana State Capitol, Indianapolis, IN 46204	State House, 107 North Main Street, Concord, NH 03303	1494753	14.639166666666666
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	3606514	32.85805555555555
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Wyoming State Capitol, Cheyenne, WY 82001	1475490	13.96
Illinois State Capitol, Springfield, IL 62756	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	623156	5.752777777777778
500 E Capitol Ave, Pierre, SD 57501	2 E Main St, Madison, WI 53703	1044717	9.381388888888889
Minnesota State Capitol, St Paul, MN 55155	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	700086	6.300833333333333
Rhode Island State House, 82 Smith Street, Providence, RI 02903	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4958571	44.74611111111111
Georgia State Capitol, Atlanta, GA 30334	Vermont State House, 115 State Street, Montpelier, VT 05633	1899060	17.810833333333335
Massachusetts State House, Boston, MA 02108	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	627502	6.153611111111111
Maine State House, Augusta, ME 04330	Michigan State Capitol, Lansing, MI 48933	1501673	13.915
300 SW 10th Ave, Topeka, KS 66612	West Virginia State Capitol, Charleston, WV 25317	1312458	11.852777777777778
Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1158389	10.587777777777777
Michigan State Capitol, Lansing, MI 48933	North Carolina State Capitol, Raleigh, NC 27601	1188366	11.468333333333334
300 SW 10th Ave, Topeka, KS 66612	State House, 107 North Main Street, Concord, NH 03303	2369020	22.538611111111113
Georgia State Capitol, Atlanta, GA 30334	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	1601210	14.720277777777778
300 SW 10th Ave, Topeka, KS 66612	Louisiana State Capitol, Baton Rouge, LA 70802	1280134	12.777222222222223
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Nevada State Capitol, Carson City, NV 89701	3479126	31.49888888888889
Georgia State Capitol, Atlanta, GA 30334	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	344713	3.0719444444444446
Louisiana State Capitol, Baton Rouge, LA 70802	State House, 107 North Main Street, Concord, NH 03303	2641198	24.147777777777776
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	Vermont State House, 115 State Street, Montpelier, VT 05633	1193740	11.68138888888889
402 S Monroe St, Tallahassee, FL 32301	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	790850	7.7125
New Jersey State House, Trenton, NJ 08608	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	785681	7.418333333333333
Massachusetts State House, Boston, MA 02108	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	3916863	35.80138888888889
200 E Colfax Ave, Denver, CO 80203	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	1864193	16.6325
L St & 10th St, Sacramento, CA 95814	Massachusetts State House, Boston, MA 02108	4852761	43.57388888888889
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	500 E Capitol Ave, Pierre, SD 57501	2159926	20.214166666666667
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Maryland State House, 100 State Cir, Annapolis, MD 21401	1692816	15.496944444444445
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Michigan State Capitol, Lansing, MI 48933	1314610	11.880555555555556
Maine State House, Augusta, ME 04330	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	5288124	47.888333333333335
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	North Carolina State Capitol, Raleigh, NC 27601	1000236	9.589444444444444
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	3599401	33.15222222222222
200 E Colfax Ave, Denver, CO 80203	Maryland State House, 100 State Cir, Annapolis, MD 21401	2704297	24.6175
Louisiana State Capitol, Baton Rouge, LA 70802	Oklahoma State Capitol, Oklahoma City, OK 73105	1015864	9.211666666666666
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2611932	23.9675
Michigan State Capitol, Lansing, MI 48933	New Mexico State Capitol, Santa Fe, NM 87501	2352926	21.808333333333334
New Mexico State Capitol, Santa Fe, NM 87501	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	2308293	21.89527777777778
Georgia State Capitol, Atlanta, GA 30334	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	1615880	14.783333333333333
Maine State House, Augusta, ME 04330	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	2025749	18.705277777777777
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4661429	42.1975
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Missouri State Capitol, Jefferson City, MO 65101	427240	4.4225
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	State House, 107 North Main Street, Concord, NH 03303	3879141	36.28
Louisiana State Capitol, Baton Rouge, LA 70802	Utah State Capitol, Salt Lake City, UT 84103	2687549	25.205277777777777
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	Utah State Capitol, Salt Lake City, UT 84103	3544096	32.043055555555554
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1319361	11.98638888888889
700 W Jefferson St, Boise, ID 83720	Maryland State House, 100 State Cir, Annapolis, MD 21401	3864627	34.63861111111111
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Virginia State Capitol, Richmond, VA 23219	1547668	14.019444444444444
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Georgia State Capitol, Atlanta, GA 30334	2974348	26.195833333333333
Missouri State Capitol, Jefferson City, MO 65101	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	705688	6.440555555555555
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	West Virginia State Capitol, Charleston, WV 25317	3161299	28.406388888888888
L St & 10th St, Sacramento, CA 95814	500 E Capitol Ave, Pierre, SD 57501	2363213	21.469722222222224
Minnesota State Capitol, St Paul, MN 55155	New Jersey State House, Trenton, NJ 08608	1904120	17.467777777777776
300 SW 10th Ave, Topeka, KS 66612	New Jersey State House, Trenton, NJ 08608	1943570	17.935555555555556
State House, 107 North Main Street, Concord, NH 03303	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	5020691	46.1925
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	402 S Monroe St, Tallahassee, FL 32301	1549915	14.0425
Indiana State Capitol, Indianapolis, IN 46204	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1001496	9.178055555555556
Illinois State Capitol, Springfield, IL 62756	2 E Main St, Madison, WI 53703	425309	3.9405555555555556
North Carolina State Capitol, Raleigh, NC 27601	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	363112	3.348611111111111
Nevada State Capitol, Carson City, NV 89701	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	4159337	37.14194444444445
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	1772295	16.54138888888889
New Mexico State Capitol, Santa Fe, NM 87501	Wyoming State Capitol, Cheyenne, WY 82001	792157	7.053333333333334
Georgia State Capitol, Atlanta, GA 30334	700 W Jefferson St, Boise, ID 83720	3498032	31.39
Missouri State Capitol, Jefferson City, MO 65101	500 E Capitol Ave, Pierre, SD 57501	1190637	10.608055555555556
Massachusetts State House, Boston, MA 02108	North Carolina State Capitol, Raleigh, NC 27601	1157235	11.136111111111111
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	400-498 N West St, Jackson, MS 39201	1736549	16.05638888888889
Illinois State Capitol, Springfield, IL 62756	North Dakota State Capitol, Bismarck, ND 58501	1530029	13.591944444444444
Nevada State Capitol, Carson City, NV 89701	Vermont State House, 115 State Street, Montpelier, VT 05633	4628271	42.11944444444445
402 S Monroe St, Tallahassee, FL 32301	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	1994047	18.71527777777778
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Rhode Island State House, 82 Smith Street, Providence, RI 02903	561475	5.556666666666667
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1278074	11.519166666666667
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Wyoming State Capitol, Cheyenne, WY 82001	2783745	25.468333333333334
200 E Colfax Ave, Denver, CO 80203	Nevada State Capitol, Carson City, NV 89701	1595181	15.340833333333334
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	State House, 107 North Main Street, Concord, NH 03303	2057414	19.745833333333334
North Carolina State Capitol, Raleigh, NC 27601	West Virginia State Capitol, Charleston, WV 25317	509177	4.908055555555555
Maine State House, Augusta, ME 04330	Massachusetts State House, Boston, MA 02108	260531	2.618888888888889
Georgia State Capitol, Atlanta, GA 30334	Michigan State Capitol, Lansing, MI 48933	1252890	11.448333333333334
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Georgia State Capitol, Atlanta, GA 30334	1585527	14.938055555555556
Indiana State Capitol, Indianapolis, IN 46204	500 E Capitol Ave, Pierre, SD 57501	1545420	13.984722222222222
Michigan State Capitol, Lansing, MI 48933	Virginia State Capitol, Richmond, VA 23219	1107187	10.313055555555556
Illinois State Capitol, Springfield, IL 62756	Nevada State Capitol, Carson City, NV 89701	2967815	26.966666666666665
L St & 10th St, Sacramento, CA 95814	Minnesota State Capitol, St Paul, MN 55155	3086551	27.525277777777777
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	North Dakota State Capitol, Bismarck, ND 58501	987502	9.234166666666667
700 W Jefferson St, Boise, ID 83720	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	4018994	35.965
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	2 E Main St, Madison, WI 53703	1681120	15.633611111111112
Oregon State Capitol, 900 Court St NE, Salem, OR 97301	Utah State Capitol, Salt Lake City, UT 84103	1303708	11.963611111111112
Michigan State Capitol, Lansing, MI 48933	Utah State Capitol, Salt Lake City, UT 84103	2577637	23.119722222222222
Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2 E Main St, Madison, WI 53703	1902526	17.63888888888889
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	1827872	15.870833333333334
State House, 107 North Main Street, Concord, NH 03303	2 E Main St, Madison, WI 53703	1809409	17.50888888888889
North Dakota State Capitol, Bismarck, ND 58501	Utah State Capitol, Salt Lake City, UT 84103	1494479	14.052777777777777
300 SW 10th Ave, Topeka, KS 66612	Minnesota State Capitol, St Paul, MN 55155	804823	7.222777777777778
Maryland State House, 100 State Cir, Annapolis, MD 21401	Oklahoma State Capitol, Oklahoma City, OK 73105	2150915	19.857222222222223
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Virginia State Capitol, Richmond, VA 23219	329515	3.4183333333333334
Missouri State Capitol, Jefferson City, MO 65101	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	1462312	13.661666666666667
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Georgia State Capitol, Atlanta, GA 30334	1183433	11.198888888888888
Maryland State House, 100 State Cir, Annapolis, MD 21401	Wyoming State Capitol, Cheyenne, WY 82001	2685105	24.346666666666668
400-498 N West St, Jackson, MS 39201	Utah State Capitol, Salt Lake City, UT 84103	2652406	24.896944444444443
Georgia State Capitol, Atlanta, GA 30334	Louisiana State Capitol, Baton Rouge, LA 70802	847414	7.3630555555555555
Missouri State Capitol, Jefferson City, MO 65101	Vermont State House, 115 State Street, Montpelier, VT 05633	2063633	19.625555555555554
Georgia State Capitol, Atlanta, GA 30334	Utah State Capitol, Salt Lake City, UT 84103	3025080	27.3475
Minnesota State Capitol, St Paul, MN 55155	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1937523	17.61861111111111
Indiana State Capitol, Indianapolis, IN 46204	300 SW 10th Ave, Topeka, KS 66612	875152	8.04888888888889
Indiana State Capitol, Indianapolis, IN 46204	Louisiana State Capitol, Baton Rouge, LA 70802	1361626	12.204722222222221
Oregon State Capitol, 900 Court St NE, Salem, OR 97301	Vermont State House, 115 State Street, Montpelier, VT 05633	4989186	45.78805555555556
200 E Colfax Ave, Denver, CO 80203	300 SW 10th Ave, Topeka, KS 66612	868171	7.5925
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	New Jersey State House, Trenton, NJ 08608	1764351	16.129166666666666
Georgia State Capitol, Atlanta, GA 30334	300 SW 10th Ave, Topeka, KS 66612	1388708	12.551666666666666
Indiana State Capitol, Indianapolis, IN 46204	Vermont State House, 115 State Street, Montpelier, VT 05633	1474526	14.259166666666667
Louisiana State Capitol, Baton Rouge, LA 70802	West Virginia State Capitol, Charleston, WV 25317	1551493	14.017222222222221
New Jersey State House, Trenton, NJ 08608	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2735269	24.786666666666665
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	1502867	13.77138888888889
402 S Monroe St, Tallahassee, FL 32301	Louisiana State Capitol, Baton Rouge, LA 70802	713508	6.373333333333333
Illinois State Capitol, Springfield, IL 62756	North Carolina State Capitol, Raleigh, NC 27601	1354089	12.807222222222222
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	402 S Monroe St, Tallahassee, FL 32301	1952010	17.781666666666666
200 E Colfax Ave, Denver, CO 80203	Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	1079887	9.487222222222222
200 E Colfax Ave, Denver, CO 80203	Minnesota State Capitol, St Paul, MN 55155	1472312	12.963055555555556
Maine State House, Augusta, ME 04330	Maryland State House, 100 State Cir, Annapolis, MD 21401	936447	8.990833333333333
Minnesota State Capitol, St Paul, MN 55155	North Carolina State Capitol, Raleigh, NC 27601	1946878	18.148333333333333
Massachusetts State House, Boston, MA 02108	Michigan State Capitol, Lansing, MI 48933	1253144	11.705833333333333
Massachusetts State House, Boston, MA 02108	2 E Main St, Madison, WI 53703	1828313	16.995833333333334
South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	2 E Main St, Madison, WI 53703	1530438	14.185
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	State House, 107 North Main Street, Concord, NH 03303	1512981	15.07638888888889
Missouri State Capitol, Jefferson City, MO 65101	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	1852941	16.93777777777778
L St & 10th St, Sacramento, CA 95814	300 SW 10th Ave, Topeka, KS 66612	2735540	24.093055555555555
Georgia State Capitol, Atlanta, GA 30334	Wyoming State Capitol, Cheyenne, WY 82001	2317843	20.940555555555555
Nevada State Capitol, Carson City, NV 89701	Oklahoma State Capitol, Oklahoma City, OK 73105	2470617	22.605555555555554
Missouri State Capitol, Jefferson City, MO 65101	New Mexico State Capitol, Santa Fe, NM 87501	1436727	13.885277777777778
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1165605	10.690833333333334
Georgia State Capitol, Atlanta, GA 30334	North Carolina State Capitol, Raleigh, NC 27601	652791	6.0777777777777775
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4831698	43.48416666666667
L St & 10th St, Sacramento, CA 95814	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	4588421	41.03861111111111
New Mexico State Capitol, Santa Fe, NM 87501	Rhode Island State House, 82 Smith Street, Providence, RI 02903	3494683	31.9975
Maine State House, Augusta, ME 04330	400-498 N West St, Jackson, MS 39201	2529620	23.155555555555555
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	3864119	35.05833333333333
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Missouri State Capitol, Jefferson City, MO 65101	2219599	20.138055555555557
700 W Jefferson St, Boise, ID 83720	Virginia State Capitol, Richmond, VA 23219	3926605	35.15027777777778
Georgia State Capitol, Atlanta, GA 30334	Virginia State Capitol, Richmond, VA 23219	853715	7.815277777777778
300 SW 10th Ave, Topeka, KS 66612	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1157547	10.576666666666666
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Utah State Capitol, Salt Lake City, UT 84103	1719270	15.421944444444444
Rhode Island State House, 82 Smith Street, Providence, RI 02903	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1433934	13.457222222222223
Minnesota State Capitol, St Paul, MN 55155	New Mexico State Capitol, Santa Fe, NM 87501	1931932	17.900555555555556
402 S Monroe St, Tallahassee, FL 32301	State House, 107 North Main Street, Concord, NH 03303	2204336	20.1675
Indiana State Capitol, Indianapolis, IN 46204	Virginia State Capitol, Richmond, VA 23219	1010794	9.49611111111111
Massachusetts State House, Boston, MA 02108	Missouri State Capitol, Jefferson City, MO 65101	2115585	19.447222222222223
Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	500 E Capitol Ave, Pierre, SD 57501	2341717	21.20138888888889
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	2 E Main St, Madison, WI 53703	1563965	14.514444444444445
Minnesota State Capitol, St Paul, MN 55155	Nevada State Capitol, Carson City, NV 89701	2864288	26.09138888888889
New Jersey State House, Trenton, NJ 08608	Rhode Island State House, 82 Smith Street, Providence, RI 02903	398312	4.088888888888889
Vermont State House, 115 State Street, Montpelier, VT 05633	Wyoming State Capitol, Cheyenne, WY 82001	3050515	28.357222222222223
200 E Colfax Ave, Denver, CO 80203	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1478566	13.878333333333334
Indiana State Capitol, Indianapolis, IN 46204	Massachusetts State House, Boston, MA 02108	1528174	14.106666666666667
Missouri State Capitol, Jefferson City, MO 65101	North Carolina State Capitol, Raleigh, NC 27601	1536148	14.154166666666667
Maine State House, Augusta, ME 04330	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1494741	13.688055555555556
Massachusetts State House, Boston, MA 02108	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	5039595	45.67916666666667
State House, 107 North Main Street, Concord, NH 03303	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	257015	3.1944444444444446
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Rhode Island State House, 82 Smith Street, Providence, RI 02903	4180240	38.32611111111111
700 W Jefferson St, Boise, ID 83720	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	3099564	27.6725
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	Oklahoma State Capitol, Oklahoma City, OK 73105	2446430	22.183611111111112
L St & 10th St, Sacramento, CA 95814	Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	4705821	42.25222222222222
Illinois State Capitol, Springfield, IL 62756	Louisiana State Capitol, Baton Rouge, LA 70802	1226097	10.945555555555556
New Jersey State House, Trenton, NJ 08608	North Dakota State Capitol, Bismarck, ND 58501	2605873	23.586111111111112
Louisiana State Capitol, Baton Rouge, LA 70802	Wyoming State Capitol, Cheyenne, WY 82001	2123471	19.44972222222222
402 S Monroe St, Tallahassee, FL 32301	700 W Jefferson St, Boise, ID 83720	3890868	35.385
Illinois State Capitol, Springfield, IL 62756	Vermont State House, 115 State Street, Montpelier, VT 05633	1806724	17.35527777777778
Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2537644	22.948888888888888
Georgia State Capitol, Atlanta, GA 30334	Missouri State Capitol, Jefferson City, MO 65101	1103516	10.033888888888889
Maryland State House, 100 State Cir, Annapolis, MD 21401	2 E Main St, Madison, WI 53703	1412212	13.225277777777778
Rhode Island State House, 82 Smith Street, Providence, RI 02903	Wyoming State Capitol, Cheyenne, WY 82001	3080887	28.016944444444444
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	2326097	21.381944444444443
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	Utah State Capitol, Salt Lake City, UT 84103	2767412	24.85333333333333
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	2078837	19.298055555555557
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	500 E Capitol Ave, Pierre, SD 57501	2715494	24.628611111111113
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Virginia State Capitol, Richmond, VA 23219	3617475	33.053333333333335
Minnesota State Capitol, St Paul, MN 55155	2 E Main St, Madison, WI 53703	417349	3.953888888888889
Maryland State House, 100 State Cir, Annapolis, MD 21401	North Dakota State Capitol, Bismarck, ND 58501	2516932	22.876666666666665
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Illinois State Capitol, Springfield, IL 62756	715903	7.0344444444444445
700 W Jefferson St, Boise, ID 83720	Louisiana State Capitol, Baton Rouge, LA 70802	3230224	29.836666666666666
Illinois State Capitol, Springfield, IL 62756	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	601548	5.588333333333333
L St & 10th St, Sacramento, CA 95814	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	1630833	14.535833333333333
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Vermont State House, 115 State Street, Montpelier, VT 05633	773092	7.4375
Illinois State Capitol, Springfield, IL 62756	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1308331	12.036944444444444
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	200 E Colfax Ave, Denver, CO 80203	1319137	12.647222222222222
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	State House, 107 North Main Street, Concord, NH 03303	2432697	22.41861111111111
Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	3473890	32.7875
500 E Capitol Ave, Pierre, SD 57501	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1828065	16.27888888888889
300 SW 10th Ave, Topeka, KS 66612	Utah State Capitol, Salt Lake City, UT 84103	1694356	15.054722222222223
Indiana State Capitol, Indianapolis, IN 46204	Maine State House, Augusta, ME 04330	1775669	16.27111111111111
402 S Monroe St, Tallahassee, FL 32301	North Carolina State Capitol, Raleigh, NC 27601	992856	8.732222222222223
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	Rhode Island State House, 82 Smith Street, Providence, RI 02903	2376371	21.870555555555555
North Carolina State Capitol, Raleigh, NC 27601	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	867080	8.14
Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	West Virginia State Capitol, Charleston, WV 25317	631657	5.870555555555556
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	3794792	34.92333333333333
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1496159	13.354166666666666
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	493300	4.680555555555555
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Maine State House, Augusta, ME 04330	2243062	20.884722222222223
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	182763	1.7986111111111112
L St & 10th St, Sacramento, CA 95814	402 S Monroe St, Tallahassee, FL 32301	4246543	37.35444444444445
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	470503	4.606944444444444
North Carolina State Capitol, Raleigh, NC 27601	Virginia State Capitol, Richmond, VA 23219	273743	2.4966666666666666
400-498 N West St, Jackson, MS 39201	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4113442	37.221944444444446
Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	3913258	35.306666666666665
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	North Carolina State Capitol, Raleigh, NC 27601	1038859	9.794444444444444
Nevada State Capitol, Carson City, NV 89701	West Virginia State Capitol, Charleston, WV 25317	3819760	34.018055555555556
300 SW 10th Ave, Topeka, KS 66612	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	269993	2.882222222222222
Missouri State Capitol, Jefferson City, MO 65101	2 E Main St, Madison, WI 53703	697472	6.839722222222222
Minnesota State Capitol, St Paul, MN 55155	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	2871909	25.724444444444444
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	Virginia State Capitol, Richmond, VA 23219	2029907	18.575277777777778
Illinois State Capitol, Springfield, IL 62756	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	1596033	14.6675
200 E Colfax Ave, Denver, CO 80203	Georgia State Capitol, Atlanta, GA 30334	2253063	20.075
Georgia State Capitol, Atlanta, GA 30334	Illinois State Capitol, Springfield, IL 62756	999216	9.17
200 E Colfax Ave, Denver, CO 80203	Illinois State Capitol, Springfield, IL 62756	1465413	13.14
New Mexico State Capitol, Santa Fe, NM 87501	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	2404888	22.635833333333334
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Maryland State House, 100 State Cir, Annapolis, MD 21401	530919	5.235
Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	Virginia State Capitol, Richmond, VA 23219	354652	3.5616666666666665
Nevada State Capitol, Carson City, NV 89701	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2710776	25.432222222222222
L St & 10th St, Sacramento, CA 95814	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	1115233	10.574444444444444
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	New Jersey State House, Trenton, NJ 08608	1090072	10.113888888888889
402 S Monroe St, Tallahassee, FL 32301	Maryland State House, 100 State Cir, Annapolis, MD 21401	1454024	12.885277777777778
Missouri State Capitol, Jefferson City, MO 65101	Nevada State Capitol, Carson City, NV 89701	2835852	25.671944444444446
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	L St & 10th St, Sacramento, CA 95814	1212739	10.910555555555556
Maryland State House, 100 State Cir, Annapolis, MD 21401	North Carolina State Capitol, Raleigh, NC 27601	499503	4.728611111111111
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Louisiana State Capitol, Baton Rouge, LA 70802	1282198	11.503333333333334
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Minnesota State Capitol, St Paul, MN 55155	1381467	12.380555555555556
402 S Monroe St, Tallahassee, FL 32301	Illinois State Capitol, Springfield, IL 62756	1392053	13.165277777777778
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	North Dakota State Capitol, Bismarck, ND 58501	2785840	25.285
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Louisiana State Capitol, Baton Rouge, LA 70802	2327122	20.377777777777776
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	State House, 107 North Main Street, Concord, NH 03303	706481	6.901111111111111
700 W Jefferson St, Boise, ID 83720	500 E Capitol Ave, Pierre, SD 57501	1700787	16.372222222222224
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Utah State Capitol, Salt Lake City, UT 84103	3661251	33.16222222222222
Missouri State Capitol, Jefferson City, MO 65101	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	872387	8.043611111111112
Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	Rhode Island State House, 82 Smith Street, Providence, RI 02903	578364	5.651666666666666
Georgia State Capitol, Atlanta, GA 30334	Maine State House, Augusta, ME 04330	1984970	18.6275
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	1894156	17.088055555555556
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	North Dakota State Capitol, Bismarck, ND 58501	1074658	9.473333333333333
402 S Monroe St, Tallahassee, FL 32301	2 E Main St, Madison, WI 53703	1789607	16.518055555555556
Utah State Capitol, Salt Lake City, UT 84103	2 E Main St, Madison, WI 53703	2194529	19.756666666666668
Maryland State House, 100 State Cir, Annapolis, MD 21401	New Mexico State Capitol, Santa Fe, NM 87501	3015312	27.569444444444443
L St & 10th St, Sacramento, CA 95814	Illinois State Capitol, Springfield, IL 62756	3161763	28.34111111111111
Maryland State House, 100 State Cir, Annapolis, MD 21401	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	567996	5.439444444444445
L St & 10th St, Sacramento, CA 95814	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	3810136	33.81611111111111
Illinois State Capitol, Springfield, IL 62756	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	2406938	21.828611111111112
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Minnesota State Capitol, St Paul, MN 55155	2088042	19.18027777777778
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	1838597	17.153055555555557
Nevada State Capitol, Carson City, NV 89701	Virginia State Capitol, Richmond, VA 23219	4325191	38.61694444444444
Michigan State Capitol, Lansing, MI 48933	Vermont State House, 115 State Street, Montpelier, VT 05633	1228009	11.509444444444444
300 SW 10th Ave, Topeka, KS 66612	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	990848	8.973888888888888
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	Nevada State Capitol, Carson City, NV 89701	2266886	20.369722222222222
L St & 10th St, Sacramento, CA 95814	Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	2761755	24.421666666666667
Louisiana State Capitol, Baton Rouge, LA 70802	Virginia State Capitol, Richmond, VA 23219	1701633	15.176944444444445
Minnesota State Capitol, St Paul, MN 55155	Virginia State Capitol, Richmond, VA 23219	1935517	17.858055555555556
700 W Jefferson St, Boise, ID 83720	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	3760751	33.675555555555555
Missouri State Capitol, Jefferson City, MO 65101	West Virginia State Capitol, Charleston, WV 25317	1027298	9.319722222222222
Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4457921	40.05694444444445
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Virginia State Capitol, Richmond, VA 23219	731609	7.1575
Louisiana State Capitol, Baton Rouge, LA 70802	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1183500	10.326944444444445
Maryland State House, 100 State Cir, Annapolis, MD 21401	Missouri State Capitol, Jefferson City, MO 65101	1555721	14.639444444444445
Maryland State House, 100 State Cir, Annapolis, MD 21401	Vermont State House, 115 State Street, Montpelier, VT 05633	847788	8.196388888888889
North Carolina State Capitol, Raleigh, NC 27601	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	625512	5.963055555555556
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	New Jersey State House, Trenton, NJ 08608	1572177	14.505277777777778
New Jersey State House, Trenton, NJ 08608	Utah State Capitol, Salt Lake City, UT 84103	3481284	31.463055555555556
Indiana State Capitol, Indianapolis, IN 46204	New Jersey State House, Trenton, NJ 08608	1069303	10.036111111111111
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Michigan State Capitol, Lansing, MI 48933	1083435	10.286666666666667
700 W Jefferson St, Boise, ID 83720	State House, 107 North Main Street, Concord, NH 03303	4249912	39.033055555555556
Massachusetts State House, Boston, MA 02108	New Jersey State House, Trenton, NJ 08608	449801	4.581388888888889
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	2845972	25.66027777777778
402 S Monroe St, Tallahassee, FL 32301	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4701521	42.861666666666665
Illinois State Capitol, Springfield, IL 62756	West Virginia State Capitol, Charleston, WV 25317	845239	7.972777777777778
Maryland State House, 100 State Cir, Annapolis, MD 21401	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	179018	1.898611111111111
Louisiana State Capitol, Baton Rouge, LA 70802	Maryland State House, 100 State Cir, Annapolis, MD 21401	1901317	17.226111111111113
Illinois State Capitol, Springfield, IL 62756	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	3405261	30.89027777777778
Minnesota State Capitol, St Paul, MN 55155	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	1966265	18.016666666666666
400-498 N West St, Jackson, MS 39201	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	887636	8.437777777777777
Georgia State Capitol, Atlanta, GA 30334	State House, 107 North Main Street, Concord, NH 03303	1832449	17.274444444444445
Maine State House, Augusta, ME 04330	2 E Main St, Madison, WI 53703	2076842	19.205
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Missouri State Capitol, Jefferson City, MO 65101	1157419	10.383055555555556
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Louisiana State Capitol, Baton Rouge, LA 70802	591309	5.184444444444445
200 E Colfax Ave, Denver, CO 80203	State House, 107 North Main Street, Concord, NH 03303	3137471	29.171666666666667
300 SW 10th Ave, Topeka, KS 66612	North Dakota State Capitol, Bismarck, ND 58501	1239183	11.00388888888889
L St & 10th St, Sacramento, CA 95814	Missouri State Capitol, Jefferson City, MO 65101	3030263	27.0325
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1760444	15.849444444444444
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	2633957	23.764166666666668
700 W Jefferson St, Boise, ID 83720	300 SW 10th Ave, Topeka, KS 66612	2166113	19.01972222222222
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1232677	11.606111111111112
State House, 107 North Main Street, Concord, NH 03303	New Mexico State Capitol, Santa Fe, NM 87501	3556272	32.89055555555556
Oklahoma State Capitol, Oklahoma City, OK 73105	500 E Capitol Ave, Pierre, SD 57501	1174982	11.439166666666667
North Dakota State Capitol, Bismarck, ND 58501	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	2174863	19.649722222222223
Virginia State Capitol, Richmond, VA 23219	West Virginia State Capitol, Charleston, WV 25317	507154	4.714166666666666
New Mexico State Capitol, Santa Fe, NM 87501	Oklahoma State Capitol, Oklahoma City, OK 73105	866244	7.8213888888888885
Georgia State Capitol, Atlanta, GA 30334	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	1151965	10.569722222222222
Massachusetts State House, Boston, MA 02108	New Mexico State Capitol, Santa Fe, NM 87501	3575175	32.37722222222222
200 E Colfax Ave, Denver, CO 80203	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	2614534	23.720833333333335
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Vermont State House, 115 State Street, Montpelier, VT 05633	2358711	22.27027777777778
402 S Monroe St, Tallahassee, FL 32301	400-498 N West St, Jackson, MS 39201	694704	6.720555555555555
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1620221	14.949444444444444
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	West Virginia State Capitol, Charleston, WV 25317	322220	3.0175
State House, 107 North Main Street, Concord, NH 03303	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4959988	45.359722222222224
402 S Monroe St, Tallahassee, FL 32301	North Dakota State Capitol, Bismarck, ND 58501	2894327	26.169444444444444
Rhode Island State House, 82 Smith Street, Providence, RI 02903	Utah State Capitol, Salt Lake City, UT 84103	3788124	34.424166666666665
Illinois State Capitol, Springfield, IL 62756	400-498 N West St, Jackson, MS 39201	945911	8.484166666666667
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Massachusetts State House, Boston, MA 02108	2337722	21.607222222222223
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	North Dakota State Capitol, Bismarck, ND 58501	1948713	17.15972222222222
Michigan State Capitol, Lansing, MI 48933	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	2686056	24.39666666666667
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Maryland State House, 100 State Cir, Annapolis, MD 21401	105428	1.3294444444444444
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	2702432	24.357777777777777
Michigan State Capitol, Lansing, MI 48933	State House, 107 North Main Street, Concord, NH 03303	1218614	12.2075
State House, 107 North Main Street, Concord, NH 03303	North Carolina State Capitol, Raleigh, NC 27601	1250547	11.9375
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	2892479	26.149722222222223
Oklahoma State Capitol, Oklahoma City, OK 73105	Utah State Capitol, Salt Lake City, UT 84103	1910174	16.99861111111111
300 SW 10th Ave, Topeka, KS 66612	Massachusetts State House, Boston, MA 02108	2402441	22.00611111111111
400-498 N West St, Jackson, MS 39201	Rhode Island State House, 82 Smith Street, Providence, RI 02903	2232241	20.464444444444446
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	State House, 107 North Main Street, Concord, NH 03303	4228062	39.20194444444444
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Maine State House, Augusta, ME 04330	1772326	16.46388888888889
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	2088428	18.80666666666667
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	307125	2.8447222222222224
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	Vermont State House, 115 State Street, Montpelier, VT 05633	254720	3.1572222222222224
Georgia State Capitol, Atlanta, GA 30334	500 E Capitol Ave, Pierre, SD 57501	2226023	19.879166666666666
400-498 N West St, Jackson, MS 39201	State House, 107 North Main Street, Concord, NH 03303	2377027	21.84777777777778
Georgia State Capitol, Atlanta, GA 30334	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	913039	8.318055555555556
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Rhode Island State House, 82 Smith Street, Providence, RI 02903	3891251	35.629444444444445
Georgia State Capitol, Atlanta, GA 30334	Oklahoma State Capitol, Oklahoma City, OK 73105	1366266	12.402777777777779
Massachusetts State House, Boston, MA 02108	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	3156474	28.857222222222223
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	200 E Colfax Ave, Denver, CO 80203	2283660	20.398611111111112
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1591468	14.635
300 SW 10th Ave, Topeka, KS 66612	500 E Capitol Ave, Pierre, SD 57501	908563	8.276111111111112
L St & 10th St, Sacramento, CA 95814	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2834432	25.199166666666667
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Vermont State House, 115 State Street, Montpelier, VT 05633	2037187	19.36611111111111
Oklahoma State Capitol, Oklahoma City, OK 73105	West Virginia State Capitol, Charleston, WV 25317	1611885	14.410277777777777
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	1873972	17.040555555555557
Indiana State Capitol, Indianapolis, IN 46204	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	3686093	32.94722222222222
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2750503	24.789166666666667
Nevada State Capitol, Carson City, NV 89701	North Dakota State Capitol, Bismarck, ND 58501	2234268	20.314166666666665
Maine State House, Augusta, ME 04330	Virginia State Capitol, Richmond, VA 23219	1137137	10.913333333333334
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Louisiana State Capitol, Baton Rouge, LA 70802	1605426	14.594722222222222
South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	711401	6.652777777777778
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	2255678	21.27111111111111
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	1568253	14.388333333333334
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	North Dakota State Capitol, Bismarck, ND 58501	1903795	17.210833333333333
Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	2 E Main St, Madison, WI 53703	3161453	28.25111111111111
Virginia State Capitol, Richmond, VA 23219	2 E Main St, Madison, WI 53703	1533610	14.348055555555556
500 E Capitol Ave, Pierre, SD 57501	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	2125997	19.844722222222224
North Carolina State Capitol, Raleigh, NC 27601	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2187972	19.727777777777778
Oklahoma State Capitol, Oklahoma City, OK 73105	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	625486	5.748888888888889
Indiana State Capitol, Indianapolis, IN 46204	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	1064665	9.761111111111111
Illinois State Capitol, Springfield, IL 62756	New Mexico State Capitol, Santa Fe, NM 87501	1727925	16.314444444444444
500 E Capitol Ave, Pierre, SD 57501	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1798030	17.05472222222222
700 W Jefferson St, Boise, ID 83720	Illinois State Capitol, Springfield, IL 62756	2592335	23.267777777777777
Maryland State House, 100 State Cir, Annapolis, MD 21401	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	3500762	32.03055555555556
State House, 107 North Main Street, Concord, NH 03303	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1870532	17.2975
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4714543	42.365
Michigan State Capitol, Lansing, MI 48933	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	879693	8.182222222222222
South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	Virginia State Capitol, Richmond, VA 23219	596292	5.276666666666666
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	1039311	9.755833333333333
300 SW 10th Ave, Topeka, KS 66612	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	1747472	16.19472222222222
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	New Jersey State House, Trenton, NJ 08608	2071197	18.904444444444444
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	500 E Capitol Ave, Pierre, SD 57501	2598339	23.509444444444444
Massachusetts State House, Boston, MA 02108	Utah State Capitol, Salt Lake City, UT 84103	3808444	34.52444444444444
Missouri State Capitol, Jefferson City, MO 65101	State House, 107 North Main Street, Concord, NH 03303	2083860	20.005555555555556
Indiana State Capitol, Indianapolis, IN 46204	400-498 N West St, Jackson, MS 39201	1081440	9.743055555555555
Nevada State Capitol, Carson City, NV 89701	Wyoming State Capitol, Cheyenne, WY 82001	1584035	14.022777777777778
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1622824	14.474444444444444
Missouri State Capitol, Jefferson City, MO 65101	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	2274975	20.53388888888889
Illinois State Capitol, Springfield, IL 62756	Michigan State Capitol, Lansing, MI 48933	628469	5.753611111111111
Louisiana State Capitol, Baton Rouge, LA 70802	North Dakota State Capitol, Bismarck, ND 58501	2494502	23.158333333333335
300 SW 10th Ave, Topeka, KS 66612	Vermont State House, 115 State Street, Montpelier, VT 05633	2348793	22.15888888888889
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	North Carolina State Capitol, Raleigh, NC 27601	910884	8.335
New Jersey State House, Trenton, NJ 08608	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1356014	12.425555555555556
L St & 10th St, Sacramento, CA 95814	Vermont State House, 115 State Street, Montpelier, VT 05633	4799113	43.72638888888889
New Jersey State House, Trenton, NJ 08608	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4651731	41.78527777777778
New Jersey State House, Trenton, NJ 08608	New Mexico State Capitol, Santa Fe, NM 87501	3116096	28.316388888888888
402 S Monroe St, Tallahassee, FL 32301	300 SW 10th Ave, Topeka, KS 66612	1781545	16.546666666666667
L St & 10th St, Sacramento, CA 95814	West Virginia State Capitol, Charleston, WV 25317	3990601	35.625
Georgia State Capitol, Atlanta, GA 30334	New Jersey State House, Trenton, NJ 08608	1314085	12.248055555555556
Maine State House, Augusta, ME 04330	500 E Capitol Ave, Pierre, SD 57501	3111216	28.2
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	700 W Jefferson St, Boise, ID 83720	1480533	13.941944444444445
North Dakota State Capitol, Bismarck, ND 58501	Wyoming State Capitol, Cheyenne, WY 82001	960446	9.149166666666666
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Oklahoma State Capitol, Oklahoma City, OK 73105	1296244	11.693888888888889
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	400-498 N West St, Jackson, MS 39201	2124093	19.399722222222223
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Massachusetts State House, Boston, MA 02108	2090835	19.213333333333335
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Oklahoma State Capitol, Oklahoma City, OK 73105	2340718	20.198055555555555
State House, 107 North Main Street, Concord, NH 03303	New Jersey State House, Trenton, NJ 08608	543114	5.3825
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	2 E Main St, Madison, WI 53703	1135904	10.694166666666666
Maryland State House, 100 State Cir, Annapolis, MD 21401	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1134038	10.484444444444444
200 E Colfax Ave, Denver, CO 80203	Louisiana State Capitol, Baton Rouge, LA 70802	1966426	18.144166666666667
L St & 10th St, Sacramento, CA 95814	200 E Colfax Ave, Denver, CO 80203	1881205	16.975555555555555
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	591688	5.702222222222222
Maryland State House, 100 State Cir, Annapolis, MD 21401	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4562790	41.075833333333335
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Utah State Capitol, Salt Lake City, UT 84103	2380471	21.142222222222223
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	North Carolina State Capitol, Raleigh, NC 27601	831070	7.851944444444444
300 SW 10th Ave, Topeka, KS 66612	Michigan State Capitol, Lansing, MI 48933	1223735	11.0925
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	1942564	17.828055555555554
North Dakota State Capitol, Bismarck, ND 58501	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	2102664	18.569444444444443
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	New Jersey State House, Trenton, NJ 08608	3586077	32.66305555555556
Maine State House, Augusta, ME 04330	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	520684	4.886388888888889
Oregon State Capitol, 900 Court St NE, Salem, OR 97301	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	3859065	34.80777777777778
Oregon State Capitol, 900 Court St NE, Salem, OR 97301	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	4520252	40.81055555555555
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	L St & 10th St, Sacramento, CA 95814	3151067	28.136666666666667
Rhode Island State House, 82 Smith Street, Providence, RI 02903	Virginia State Capitol, Richmond, VA 23219	838637	8.217222222222222
Georgia State Capitol, Atlanta, GA 30334	Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	1448964	13.422222222222222
South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	Wyoming State Capitol, Cheyenne, WY 82001	2626841	23.81972222222222
L St & 10th St, Sacramento, CA 95814	Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	4532569	40.880833333333335
300 SW 10th Ave, Topeka, KS 66612	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	3022102	26.950277777777778
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Georgia State Capitol, Atlanta, GA 30334	834382	7.7091666666666665
Georgia State Capitol, Atlanta, GA 30334	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	402403	3.7736111111111112
300 SW 10th Ave, Topeka, KS 66612	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1096352	9.781944444444445
North Carolina State Capitol, Raleigh, NC 27601	2 E Main St, Madison, WI 53703	1545151	14.650833333333333
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	300 SW 10th Ave, Topeka, KS 66612	1849376	17.73027777777778
L St & 10th St, Sacramento, CA 95814	400-498 N West St, Jackson, MS 39201	3427740	30.878611111111113
Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	West Virginia State Capitol, Charleston, WV 25317	590933	5.471666666666667
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4362587	39.215833333333336
402 S Monroe St, Tallahassee, FL 32301	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	1577371	14.174444444444445
Michigan State Capitol, Lansing, MI 48933	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	1165493	10.542222222222222
700 W Jefferson St, Boise, ID 83720	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2619542	24.863888888888887
State House, 107 North Main Street, Concord, NH 03303	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	720814	6.955
Maine State House, Augusta, ME 04330	Nevada State Capitol, Carson City, NV 89701	4903131	44.32888888888889
Michigan State Capitol, Lansing, MI 48933	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1247121	11.903611111111111
Louisiana State Capitol, Baton Rouge, LA 70802	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	692703	6.487777777777778
300 SW 10th Ave, Topeka, KS 66612	Maryland State House, 100 State Cir, Annapolis, MD 21401	1837235	17.09138888888889
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Vermont State House, 115 State Street, Montpelier, VT 05633	321861	3.0213888888888887
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	609426	5.7011111111111115
402 S Monroe St, Tallahassee, FL 32301	New Mexico State Capitol, Santa Fe, NM 87501	2364174	21.85611111111111
Oregon State Capitol, 900 Court St NE, Salem, OR 97301	500 E Capitol Ave, Pierre, SD 57501	2250703	21.171944444444446
Rhode Island State House, 82 Smith Street, Providence, RI 02903	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	3108475	28.349722222222223
New Mexico State Capitol, Santa Fe, NM 87501	North Dakota State Capitol, Bismarck, ND 58501	1744158	16.00611111111111
L St & 10th St, Sacramento, CA 95814	Wyoming State Capitol, Cheyenne, WY 82001	1754877	15.629722222222222
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	785111	7.170277777777778
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	2249106	20.584722222222222
Oklahoma State Capitol, Oklahoma City, OK 73105	Virginia State Capitol, Richmond, VA 23219	2078855	18.6975
Maine State House, Augusta, ME 04330	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	5227420	47.05555555555556
Rhode Island State House, 82 Smith Street, Providence, RI 02903	West Virginia State Capitol, Charleston, WV 25317	1161764	10.8725
Nevada State Capitol, Carson City, NV 89701	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	3639294	32.20916666666667
L St & 10th St, Sacramento, CA 95814	Rhode Island State House, 82 Smith Street, Providence, RI 02903	4831450	43.455555555555556
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Oklahoma State Capitol, Oklahoma City, OK 73105	547112	4.926666666666667
Missouri State Capitol, Jefferson City, MO 65101	Wyoming State Capitol, Cheyenne, WY 82001	1282456	11.669444444444444
Oklahoma State Capitol, Oklahoma City, OK 73105	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	2057100	18.861666666666668
Maine State House, Augusta, ME 04330	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	4165392	38.010555555555555
200 E Colfax Ave, Denver, CO 80203	700 W Jefferson St, Boise, ID 83720	1314104	11.952222222222222
Minnesota State Capitol, St Paul, MN 55155	500 E Capitol Ave, Pierre, SD 57501	637790	6.874722222222222
Massachusetts State House, Boston, MA 02108	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	272155	2.6772222222222224
700 W Jefferson St, Boise, ID 83720	Vermont State House, 115 State Street, Montpelier, VT 05633	4229685	38.653055555555554
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	700 W Jefferson St, Boise, ID 83720	4134203	37.20444444444445
500 E Capitol Ave, Pierre, SD 57501	Utah State Capitol, Salt Lake City, UT 84103	1321211	12.447777777777778
700 W Jefferson St, Boise, ID 83720	Minnesota State Capitol, St Paul, MN 55155	2349986	21.033333333333335
700 W Jefferson St, Boise, ID 83720	Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	2192328	19.348333333333333
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	783537	7.793333333333333
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	New Mexico State Capitol, Santa Fe, NM 87501	1234237	12.029722222222222
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1051234	9.8125
Illinois State Capitol, Springfield, IL 62756	Massachusetts State House, Boston, MA 02108	1860373	17.20277777777778
L St & 10th St, Sacramento, CA 95814	North Dakota State Capitol, Bismarck, ND 58501	2405110	21.92111111111111
Utah State Capitol, Salt Lake City, UT 84103	Virginia State Capitol, Richmond, VA 23219	3454401	31.113333333333333
North Dakota State Capitol, Bismarck, ND 58501	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1913954	17.184166666666666
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	455898	4.509444444444444
500 E Capitol Ave, Pierre, SD 57501	Wyoming State Capitol, Cheyenne, WY 82001	683179	6.741666666666666
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1048210	9.455833333333333
Indiana State Capitol, Indianapolis, IN 46204	Michigan State Capitol, Lansing, MI 48933	410164	3.794722222222222
Indiana State Capitol, Indianapolis, IN 46204	West Virginia State Capitol, Charleston, WV 25317	505363	4.8975
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	4775247	43.19777777777778
400-498 N West St, Jackson, MS 39201	Virginia State Capitol, Richmond, VA 23219	1467102	13.298055555555555
Oregon State Capitol, 900 Court St NE, Salem, OR 97301	West Virginia State Capitol, Charleston, WV 25317	4180675	37.68666666666667
Illinois State Capitol, Springfield, IL 62756	Minnesota State Capitol, St Paul, MN 55155	832231	7.4875
Illinois State Capitol, Springfield, IL 62756	Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	541419	4.966944444444445
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Georgia State Capitol, Atlanta, GA 30334	259130	2.347777777777778
Maine State House, Augusta, ME 04330	Vermont State House, 115 State Street, Montpelier, VT 05633	289475	3.777222222222222
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	981864	8.9975
Indiana State Capitol, Indianapolis, IN 46204	New Mexico State Capitol, Santa Fe, NM 87501	2049550	18.461388888888887
402 S Monroe St, Tallahassee, FL 32301	Michigan State Capitol, Lansing, MI 48933	1653544	15.526666666666667
Nevada State Capitol, Carson City, NV 89701	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	4204933	37.5875
L St & 10th St, Sacramento, CA 95814	2 E Main St, Madison, WI 53703	3236161	28.86722222222222
North Dakota State Capitol, Bismarck, ND 58501	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	2049899	18.350555555555555
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	987798	8.910277777777777
400-498 N West St, Jackson, MS 39201	Wyoming State Capitol, Cheyenne, WY 82001	2107645	19.017222222222223
New Jersey State House, Trenton, NJ 08608	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	201751	1.9908333333333332
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	New Mexico State Capitol, Santa Fe, NM 87501	2130566	19.22222222222222
200 E Colfax Ave, Denver, CO 80203	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	2906553	26.10388888888889
Maryland State House, 100 State Cir, Annapolis, MD 21401	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	4623494	41.908611111111114
New Mexico State Capitol, Santa Fe, NM 87501	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1106403	10.647777777777778
Indiana State Capitol, Indianapolis, IN 46204	Nevada State Capitol, Carson City, NV 89701	3322968	29.933611111111112
West Virginia State Capitol, Charleston, WV 25317	2 E Main St, Madison, WI 53703	1036808	9.813333333333333
L St & 10th St, Sacramento, CA 95814	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	3668991	32.74611111111111
North Dakota State Capitol, Bismarck, ND 58501	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	2635531	23.70916666666667
700 W Jefferson St, Boise, ID 83720	Nevada State Capitol, Carson City, NV 89701	704765	6.965
Minnesota State Capitol, St Paul, MN 55155	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	1684917	15.38
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	North Dakota State Capitol, Bismarck, ND 58501	2376757	22.615833333333335
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Illinois State Capitol, Springfield, IL 62756	1404632	13.471388888888889
Oklahoma State Capitol, Oklahoma City, OK 73105	Vermont State House, 115 State Street, Montpelier, VT 05633	2658420	24.825555555555557
200 E Colfax Ave, Denver, CO 80203	New Jersey State House, Trenton, NJ 08608	2810632	25.461666666666666
North Carolina State Capitol, Raleigh, NC 27601	Wyoming State Capitol, Cheyenne, WY 82001	2749593	25.06388888888889
Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	Vermont State House, 115 State Street, Montpelier, VT 05633	3178782	29.566111111111113
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	1718292	15.6325
Maryland State House, 100 State Cir, Annapolis, MD 21401	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	684897	6.671666666666667
North Dakota State Capitol, Bismarck, ND 58501	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	2406031	21.8175
Illinois State Capitol, Springfield, IL 62756	300 SW 10th Ave, Topeka, KS 66612	601069	5.632777777777778
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	Virginia State Capitol, Richmond, VA 23219	770599	7.291944444444445
Michigan State Capitol, Lansing, MI 48933	Wyoming State Capitol, Cheyenne, WY 82001	1870400	16.7125
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	402 S Monroe St, Tallahassee, FL 32301	339009	3.7333333333333334
402 S Monroe St, Tallahassee, FL 32301	New Jersey State House, Trenton, NJ 08608	1685972	15.141111111111112
Massachusetts State House, Boston, MA 02108	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4978891	44.84638888888889
Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	Vermont State House, 115 State Street, Montpelier, VT 05633	1803150	17.30361111111111
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Wyoming State Capitol, Cheyenne, WY 82001	2371746	21.289722222222224
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Minnesota State Capitol, St Paul, MN 55155	393856	3.536388888888889
Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	West Virginia State Capitol, Charleston, WV 25317	2007288	18.133055555555554
Massachusetts State House, Boston, MA 02108	Nevada State Capitol, Carson City, NV 89701	4654602	42.11972222222222
Indiana State Capitol, Indianapolis, IN 46204	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	463791	4.415555555555556
Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	Wyoming State Capitol, Cheyenne, WY 82001	1637527	15.243333333333334
Michigan State Capitol, Lansing, MI 48933	Rhode Island State House, 82 Smith Street, Providence, RI 02903	1242182	11.545277777777779
402 S Monroe St, Tallahassee, FL 32301	Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	1841801	17.4175
Illinois State Capitol, Springfield, IL 62756	Maryland State House, 100 State Cir, Annapolis, MD 21401	1302845	12.267222222222221
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	3708217	33.0375
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Utah State Capitol, Salt Lake City, UT 84103	2632968	23.90361111111111
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	1025967	9.347222222222221
400-498 N West St, Jackson, MS 39201	Missouri State Capitol, Jefferson City, MO 65101	874641	8.697777777777778
Michigan State Capitol, Lansing, MI 48933	New Jersey State House, Trenton, NJ 08608	1075790	9.922777777777778
Maine State House, Augusta, ME 04330	North Carolina State Capitol, Raleigh, NC 27601	1405764	13.345277777777778
Michigan State Capitol, Lansing, MI 48933	400-498 N West St, Jackson, MS 39201	1486311	13.251111111111111
Maryland State House, 100 State Cir, Annapolis, MD 21401	Utah State Capitol, Salt Lake City, UT 84103	3392343	30.753611111111113
Louisiana State Capitol, Baton Rouge, LA 70802	Missouri State Capitol, Jefferson City, MO 65101	1157480	11.186111111111112
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	300 SW 10th Ave, Topeka, KS 66612	2218253	20.36777777777778
Louisiana State Capitol, Baton Rouge, LA 70802	Rhode Island State House, 82 Smith Street, Providence, RI 02903	2496412	22.764166666666668
Utah State Capitol, Salt Lake City, UT 84103	Wyoming State Capitol, Cheyenne, WY 82001	713245	6.519166666666667
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	4078845	37.13194444444444
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	Oklahoma State Capitol, Oklahoma City, OK 73105	696496	6.364444444444445
Massachusetts State House, Boston, MA 02108	Minnesota State Capitol, St Paul, MN 55155	2235235	20.5425
North Dakota State Capitol, Bismarck, ND 58501	500 E Capitol Ave, Pierre, SD 57501	336454	3.381388888888889
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	500 E Capitol Ave, Pierre, SD 57501	1127755	10.738055555555556
Illinois State Capitol, Springfield, IL 62756	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1482225	14.017222222222221
402 S Monroe St, Tallahassee, FL 32301	Massachusetts State House, Boston, MA 02108	2109362	19.355833333333333
Oregon State Capitol, 900 Court St NE, Salem, OR 97301	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	4565848	41.25611111111111
402 S Monroe St, Tallahassee, FL 32301	Nevada State Capitol, Carson City, NV 89701	3969361	36.64666666666667
L St & 10th St, Sacramento, CA 95814	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	4312359	38.533055555555556
200 E Colfax Ave, Denver, CO 80203	Maine State House, Augusta, ME 04330	3418388	30.80361111111111
Indiana State Capitol, Indianapolis, IN 46204	Oklahoma State Capitol, Oklahoma City, OK 73105	1185153	10.749166666666667
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	New Mexico State Capitol, Santa Fe, NM 87501	3113952	28.691111111111113
Louisiana State Capitol, Baton Rouge, LA 70802	Maine State House, Augusta, ME 04330	2793719	25.500555555555554
Maine State House, Augusta, ME 04330	New Mexico State Capitol, Santa Fe, NM 87501	3823704	34.58638888888889
State House, 107 North Main Street, Concord, NH 03303	Virginia State Capitol, Richmond, VA 23219	981920	9.505555555555556
402 S Monroe St, Tallahassee, FL 32301	Minnesota State Capitol, St Paul, MN 55155	2196529	20.065
Minnesota State Capitol, St Paul, MN 55155	West Virginia State Capitol, Charleston, WV 25317	1438029	13.313888888888888
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	2748473	24.67
Utah State Capitol, Salt Lake City, UT 84103	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	1399410	12.69138888888889
State House, 107 North Main Street, Concord, NH 03303	Wyoming State Capitol, Cheyenne, WY 82001	3082303	28.630555555555556
Missouri State Capitol, Jefferson City, MO 65101	North Dakota State Capitol, Bismarck, ND 58501	1521256	13.33611111111111
L St & 10th St, Sacramento, CA 95814	Georgia State Capitol, Atlanta, GA 30334	3969099	35.581944444444446
700 W Jefferson St, Boise, ID 83720	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	3240708	28.74277777777778
Illinois State Capitol, Springfield, IL 62756	Virginia State Capitol, Richmond, VA 23219	1350670	12.571666666666667
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	500 E Capitol Ave, Pierre, SD 57501	1836021	16.49972222222222
Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	Wyoming State Capitol, Cheyenne, WY 82001	1922417	17.38083333333333
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Rhode Island State House, 82 Smith Street, Providence, RI 02903	1945535	18.18722222222222
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	Virginia State Capitol, Richmond, VA 23219	770231	7.3625
Missouri State Capitol, Jefferson City, MO 65101	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	565824	5.4494444444444445
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Indiana State Capitol, Indianapolis, IN 46204	1344056	12.471111111111112
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Oklahoma State Capitol, Oklahoma City, OK 73105	1274629	11.588611111111112
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	3389980	30.9225
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	700 W Jefferson St, Boise, ID 83720	3551935	31.739166666666666
North Carolina State Capitol, Raleigh, NC 27601	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	771396	7.4944444444444445
Illinois State Capitol, Springfield, IL 62756	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	600913	5.644166666666667
Indiana State Capitol, Indianapolis, IN 46204	Missouri State Capitol, Jefferson City, MO 65101	589960	5.5311111111111115
402 S Monroe St, Tallahassee, FL 32301	Rhode Island State House, 82 Smith Street, Providence, RI 02903	2059331	18.823055555555555
Minnesota State Capitol, St Paul, MN 55155	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	2746945	24.42527777777778
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	New Mexico State Capitol, Santa Fe, NM 87501	774427	7.361111111111111
New Mexico State Capitol, Santa Fe, NM 87501	North Carolina State Capitol, Raleigh, NC 27601	2813969	25.462222222222223
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	500 E Capitol Ave, Pierre, SD 57501	660935	6.571111111111111
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1316090	12.180277777777778
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2999476	27.310555555555556
L St & 10th St, Sacramento, CA 95814	Michigan State Capitol, Lansing, MI 48933	3620857	32.124722222222225
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	700 W Jefferson St, Boise, ID 83720	3963934	35.91777777777778
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	West Virginia State Capitol, Charleston, WV 25317	3119986	28.509166666666665
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	L St & 10th St, Sacramento, CA 95814	3878584	34.79833333333333
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	1138460	10.850833333333334
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	North Carolina State Capitol, Raleigh, NC 27601	598142	5.8502777777777775
Rhode Island State House, 82 Smith Street, Providence, RI 02903	Vermont State House, 115 State Street, Montpelier, VT 05633	367228	3.4922222222222223
Maryland State House, 100 State Cir, Annapolis, MD 21401	Minnesota State Capitol, St Paul, MN 55155	1819133	16.771944444444443
402 S Monroe St, Tallahassee, FL 32301	500 E Capitol Ave, Pierre, SD 57501	2618860	23.874166666666667
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	Rhode Island State House, 82 Smith Street, Providence, RI 02903	1165413	11.170277777777779
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	737764	6.850555555555555
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	743518	6.871666666666667
Indiana State Capitol, Indianapolis, IN 46204	Minnesota State Capitol, St Paul, MN 55155	942436	8.643333333333333
Utah State Capitol, Salt Lake City, UT 84103	West Virginia State Capitol, Charleston, WV 25317	2948970	26.514444444444443
400-498 N West St, Jackson, MS 39201	New Jersey State House, Trenton, NJ 08608	1860894	16.893333333333334
Maryland State House, 100 State Cir, Annapolis, MD 21401	State House, 107 North Main Street, Concord, NH 03303	781177	7.66
Utah State Capitol, Salt Lake City, UT 84103	Vermont State House, 115 State Street, Montpelier, VT 05633	3757481	34.615833333333335
Missouri State Capitol, Jefferson City, MO 65101	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1200610	11.5825
Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	Utah State Capitol, Salt Lake City, UT 84103	3287474	29.73472222222222
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	1013497	9.551666666666666
Louisiana State Capitol, Baton Rouge, LA 70802	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4129269	37.654444444444444
400-498 N West St, Jackson, MS 39201	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	957477	8.536666666666667
700 W Jefferson St, Boise, ID 83720	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	861910	8.105
Minnesota State Capitol, St Paul, MN 55155	400-498 N West St, Jackson, MS 39201	1616982	15.023333333333333
L St & 10th St, Sacramento, CA 95814	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	2461787	21.753611111111113
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Utah State Capitol, Salt Lake City, UT 84103	1062245	10.053888888888888
New Jersey State House, Trenton, NJ 08608	2 E Main St, Madison, WI 53703	1501153	13.934722222222222
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	402 S Monroe St, Tallahassee, FL 32301	1080808	10.475833333333334
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	2133342	19.453333333333333
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	New Mexico State Capitol, Santa Fe, NM 87501	1403049	12.560555555555556
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Maine State House, Augusta, ME 04330	2585218	23.77166666666667
700 W Jefferson St, Boise, ID 83720	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	3105847	27.80527777777778
North Dakota State Capitol, Bismarck, ND 58501	Virginia State Capitol, Richmond, VA 23219	2633526	23.948611111111113
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Maryland State House, 100 State Cir, Annapolis, MD 21401	934431	8.569722222222222
400-498 N West St, Jackson, MS 39201	West Virginia State Capitol, Charleston, WV 25317	1287322	11.7175
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	West Virginia State Capitol, Charleston, WV 25317	1524476	13.97638888888889
State House, 107 North Main Street, Concord, NH 03303	West Virginia State Capitol, Charleston, WV 25317	1303076	12.18138888888889
Massachusetts State House, Boston, MA 02108	500 E Capitol Ave, Pierre, SD 57501	2862687	25.990833333333335
Nevada State Capitol, Carson City, NV 89701	500 E Capitol Ave, Pierre, SD 57501	2192372	19.86277777777778
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	1826496	16.678055555555556
Maryland State House, 100 State Cir, Annapolis, MD 21401	Virginia State Capitol, Richmond, VA 23219	230875	2.296666666666667
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	3769670	34.43888888888889
North Carolina State Capitol, Raleigh, NC 27601	Vermont State House, 115 State Street, Montpelier, VT 05633	1319088	12.4925
Louisiana State Capitol, Baton Rouge, LA 70802	400-498 N West St, Jackson, MS 39201	283870	2.6305555555555555
New Mexico State Capitol, Santa Fe, NM 87501	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	2331032	20.911666666666665
New Jersey State House, Trenton, NJ 08608	500 E Capitol Ave, Pierre, SD 57501	2535528	22.92972222222222
700 W Jefferson St, Boise, ID 83720	400-498 N West St, Jackson, MS 39201	3256677	29.30611111111111
Indiana State Capitol, Indianapolis, IN 46204	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	1263835	11.571388888888889
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	593674	5.222222222222222
North Dakota State Capitol, Bismarck, ND 58501	2 E Main St, Madison, WI 53703	1115357	10.044166666666667
402 S Monroe St, Tallahassee, FL 32301	Missouri State Capitol, Jefferson City, MO 65101	1496353	14.028888888888888
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	North Carolina State Capitol, Raleigh, NC 27601	1426809	13.190555555555555
Indiana State Capitol, Indianapolis, IN 46204	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	873206	8.295277777777779
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Massachusetts State House, Boston, MA 02108	1524831	14.299444444444445
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	1655113	15.069444444444445
Georgia State Capitol, Atlanta, GA 30334	North Dakota State Capitol, Bismarck, ND 58501	2501491	22.174444444444443
700 W Jefferson St, Boise, ID 83720	Wyoming State Capitol, Cheyenne, WY 82001	1185449	10.55638888888889
300 SW 10th Ave, Topeka, KS 66612	400-498 N West St, Jackson, MS 39201	1158756	11.0025
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	2 E Main St, Madison, WI 53703	799075	7.559166666666667
Louisiana State Capitol, Baton Rouge, LA 70802	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	1539070	15.27138888888889
Nevada State Capitol, Carson City, NV 89701	New Mexico State Capitol, Santa Fe, NM 87501	1693243	15.817222222222222
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	3648222	33.21222222222222
200 E Colfax Ave, Denver, CO 80203	New Mexico State Capitol, Santa Fe, NM 87501	628035	5.675277777777778
Michigan State Capitol, Lansing, MI 48933	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	3748085	33.44166666666667
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	Oklahoma State Capitol, Oklahoma City, OK 73105	1468075	13.293333333333333
400-498 N West St, Jackson, MS 39201	500 E Capitol Ave, Pierre, SD 57501	1996477	18.362222222222222
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	Indiana State Capitol, Indianapolis, IN 46204	1065356	10.381944444444445
Maine State House, Augusta, ME 04330	New Jersey State House, Trenton, NJ 08608	698330	6.790555555555556
L St & 10th St, Sacramento, CA 95814	700 W Jefferson St, Boise, ID 83720	894846	8.514444444444445
200 E Colfax Ave, Denver, CO 80203	2 E Main St, Madison, WI 53703	1554292	13.9325
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	West Virginia State Capitol, Charleston, WV 25317	1044556	9.495
Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	Virginia State Capitol, Richmond, VA 23219	2367739	21.315277777777776
Indiana State Capitol, Indianapolis, IN 46204	Maryland State House, 100 State Cir, Annapolis, MD 21401	962969	9.191944444444445
South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4617683	41.74583333333333
400-498 N West St, Jackson, MS 39201	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	666003	5.996944444444445
Virginia State Capitol, Richmond, VA 23219	Wyoming State Capitol, Cheyenne, WY 82001	2747570	24.87
Maine State House, Augusta, ME 04330	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1732435	16.153333333333332
Massachusetts State House, Boston, MA 02108	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1777219	16.496111111111112
200 E Colfax Ave, Denver, CO 80203	Oklahoma State Capitol, Oklahoma City, OK 73105	1083636	9.535277777777777
Georgia State Capitol, Atlanta, GA 30334	400-498 N West St, Jackson, MS 39201	614336	5.541388888888889
New Jersey State House, Trenton, NJ 08608	Virginia State Capitol, Richmond, VA 23219	455416	4.516944444444444
Massachusetts State House, Boston, MA 02108	Vermont State House, 115 State Street, Montpelier, VT 05633	289370	2.7591666666666668
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	700 W Jefferson St, Boise, ID 83720	2853423	25.184444444444445
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	2 E Main St, Madison, WI 53703	815071	7.6075
Nevada State Capitol, Carson City, NV 89701	North Carolina State Capitol, Raleigh, NC 27601	4328609	38.8525
North Dakota State Capitol, Bismarck, ND 58501	West Virginia State Capitol, Charleston, WV 25317	2136037	19.404444444444444
New Jersey State House, Trenton, NJ 08608	North Carolina State Capitol, Raleigh, NC 27601	724043	6.948888888888889
Indiana State Capitol, Indianapolis, IN 46204	2 E Main St, Madison, WI 53703	535514	5.096388888888889
500 E Capitol Ave, Pierre, SD 57501	Virginia State Capitol, Richmond, VA 23219	2562885	23.285555555555554
State House, 107 North Main Street, Concord, NH 03303	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1577218	14.745555555555555
New Jersey State House, Trenton, NJ 08608	Wyoming State Capitol, Cheyenne, WY 82001	2774047	25.05611111111111
400-498 N West St, Jackson, MS 39201	Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	1662623	15.061666666666667
Minnesota State Capitol, St Paul, MN 55155	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1888668	16.8
Georgia State Capitol, Atlanta, GA 30334	Indiana State Capitol, Indianapolis, IN 46204	861058	7.981388888888889
700 W Jefferson St, Boise, ID 83720	Missouri State Capitol, Jefferson City, MO 65101	2460836	21.95916666666667
North Dakota State Capitol, Bismarck, ND 58501	Oklahoma State Capitol, Oklahoma City, OK 73105	1494640	14.765277777777778
200 E Colfax Ave, Denver, CO 80203	Indiana State Capitol, Indianapolis, IN 46204	1742380	15.568333333333333
Missouri State Capitol, Jefferson City, MO 65101	Utah State Capitol, Salt Lake City, UT 84103	1989694	18.076666666666668
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	500 E Capitol Ave, Pierre, SD 57501	1618094	14.431944444444444
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Nevada State Capitol, Carson City, NV 89701	1439435	12.973055555555556
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	2912212	26.12638888888889
Indiana State Capitol, Indianapolis, IN 46204	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1760623	15.893611111111111
300 SW 10th Ave, Topeka, KS 66612	Missouri State Capitol, Jefferson City, MO 65101	352120	3.2605555555555554
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	Wyoming State Capitol, Cheyenne, WY 82001	2060174	18.44611111111111
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	500 E Capitol Ave, Pierre, SD 57501	809818	7.162222222222222
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	451917	4.066666666666666
Georgia State Capitol, Atlanta, GA 30334	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1534179	13.697777777777778
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	1365529	12.602222222222222
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Rhode Island State House, 82 Smith Street, Providence, RI 02903	140938	1.5366666666666666
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	2 E Main St, Madison, WI 53703	474235	4.506666666666667
Oklahoma State Capitol, Oklahoma City, OK 73105	Wyoming State Capitol, Cheyenne, WY 82001	1232122	10.689444444444444
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Oklahoma State Capitol, Oklahoma City, OK 73105	2528255	23.06833333333333
Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	3998563	36.00805555555556
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Wyoming State Capitol, Cheyenne, WY 82001	1702419	14.832777777777778
Vermont State House, 115 State Street, Montpelier, VT 05633	Virginia State Capitol, Richmond, VA 23219	1050640	10.086944444444445
400-498 N West St, Jackson, MS 39201	Oklahoma State Capitol, Oklahoma City, OK 73105	980722	8.903333333333334
Illinois State Capitol, Springfield, IL 62756	Indiana State Capitol, Indianapolis, IN 46204	331633	3.2391666666666667
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	Illinois State Capitol, Springfield, IL 62756	1683332	15.560555555555556
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	1832197	16.561944444444446
State House, 107 North Main Street, Concord, NH 03303	Utah State Capitol, Salt Lake City, UT 84103	3789540	35.0375
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	1994652	18.935
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Wyoming State Capitol, Cheyenne, WY 82001	1925731	17.496388888888887
200 E Colfax Ave, Denver, CO 80203	Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	2802811	25.78611111111111
Maine State House, Augusta, ME 04330	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	2644828	24.156111111111112
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	New Jersey State House, Trenton, NJ 08608	1916564	17.464166666666667
Minnesota State Capitol, St Paul, MN 55155	Missouri State Capitol, Jefferson City, MO 65101	816553	7.780833333333334
Oregon State Capitol, 900 Court St NE, Salem, OR 97301	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	257602	2.6061111111111113
Illinois State Capitol, Springfield, IL 62756	State House, 107 North Main Street, Concord, NH 03303	1826951	17.735277777777778
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	North Carolina State Capitol, Raleigh, NC 27601	2033325	18.810833333333335
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Rhode Island State House, 82 Smith Street, Providence, RI 02903	2287910	21.03527777777778
200 E Colfax Ave, Denver, CO 80203	402 S Monroe St, Tallahassee, FL 32301	2618682	23.936944444444446
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	1791330	16.66583333333333
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	1282062	12.008611111111112
New Mexico State Capitol, Santa Fe, NM 87501	Virginia State Capitol, Richmond, VA 23219	2934829	26.29083333333333
Minnesota State Capitol, St Paul, MN 55155	Vermont State House, 115 State Street, Montpelier, VT 05633	2176956	20.704722222222223
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	West Virginia State Capitol, Charleston, WV 25317	1270293	11.675833333333333
Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	1202866	11.0975
Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	2897903	26.289166666666667
200 E Colfax Ave, Denver, CO 80203	Michigan State Capitol, Lansing, MI 48933	1938989	17.19
Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1381842	12.518888888888888
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	Michigan State Capitol, Lansing, MI 48933	858932	7.764444444444444
New York State Capitol, State St. and Washington Ave, Albany, NY 12224	Rhode Island State House, 82 Smith Street, Providence, RI 02903	261924	2.602222222222222
Massachusetts State House, Boston, MA 02108	400-498 N West St, Jackson, MS 39201	2281091	20.94638888888889
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	3997144	36.134166666666665
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	3611622	32.29694444444444
Oklahoma State Capitol, Oklahoma City, OK 73105	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	3237920	28.89388888888889
Michigan State Capitol, Lansing, MI 48933	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	987696	9.139722222222222
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	500 E Capitol Ave, Pierre, SD 57501	1811441	16.343611111111112
State House, 107 North Main Street, Concord, NH 03303	North Dakota State Capitol, Bismarck, ND 58501	2914129	27.160555555555554
L St & 10th St, Sacramento, CA 95814	Louisiana State Capitol, Baton Rouge, LA 70802	3538731	31.1025
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	402 S Monroe St, Tallahassee, FL 32301	3034934	26.629444444444445
State House, 107 North Main Street, Concord, NH 03303	Vermont State House, 115 State Street, Montpelier, VT 05633	179010	1.801111111111111
L St & 10th St, Sacramento, CA 95814	Oklahoma State Capitol, Oklahoma City, OK 73105	2613800	23.4225
Oregon State Capitol, 900 Court St NE, Salem, OR 97301	Wyoming State Capitol, Cheyenne, WY 82001	1944950	17.691388888888888
402 S Monroe St, Tallahassee, FL 32301	Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215	1332540	12.580833333333333
Nevada State Capitol, Carson City, NV 89701	Rhode Island State House, 82 Smith Street, Providence, RI 02903	4660609	41.84861111111111
Indiana State Capitol, Indianapolis, IN 46204	Utah State Capitol, Salt Lake City, UT 84103	2476810	22.338333333333335
Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243	Virginia State Capitol, Richmond, VA 23219	988289	9.033333333333333
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	West Virginia State Capitol, Charleston, WV 25317	1052764	9.833333333333334
Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	2148020	19.5825
Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	2 E Main St, Madison, WI 53703	1307343	12.206388888888888
Massachusetts State House, Boston, MA 02108	North Dakota State Capitol, Bismarck, ND 58501	2933033	26.647222222222222
West Virginia State Capitol, Charleston, WV 25317	Wyoming State Capitol, Cheyenne, WY 82001	2241251	20.226388888888888
402 S Monroe St, Tallahassee, FL 32301	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	1400273	12.708055555555555
Michigan State Capitol, Lansing, MI 48933	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	2165494	19.401666666666667
L St & 10th St, Sacramento, CA 95814	Maine State House, Augusta, ME 04330	5100256	45.73833333333334
700 W Jefferson St, Boise, ID 83720	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	3806347	34.12111111111111
North Carolina State Capitol, Raleigh, NC 27601	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	4687982	42.62555555555556
Oregon State Capitol, 900 Court St NE, Salem, OR 97301	2 E Main St, Madison, WI 53703	3286034	29.595555555555556
Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106	State House, 107 North Main Street, Concord, NH 03303	258007	2.5094444444444446
700 W Jefferson St, Boise, ID 83720	Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509	1892359	16.68
Illinois State Capitol, Springfield, IL 62756	Oklahoma State Capitol, Oklahoma City, OK 73105	952777	8.616666666666667
400-498 N West St, Jackson, MS 39201	Oregon State Capitol, 900 Court St NE, Salem, OR 97301	4016847	36.48111111111111
Louisiana State Capitol, Baton Rouge, LA 70802	Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601	3232764	29.059722222222224
State House, 107 North Main Street, Concord, NH 03303	Oklahoma State Capitol, Oklahoma City, OK 73105	2691874	25.178333333333335
New Jersey State House, Trenton, NJ 08608	New York State Capitol, State St. and Washington Ave, Albany, NY 12224	330136	3.2127777777777777
300 SW 10th Ave, Topeka, KS 66612	Nevada State Capitol, Carson City, NV 89701	2462119	22.726666666666667
Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	Michigan State Capitol, Lansing, MI 48933	663503	6.138055555555556
Oregon State Capitol, 900 Court St NE, Salem, OR 97301	Rhode Island State House, 82 Smith Street, Providence, RI 02903	5021523	45.5175
Maryland State House, 100 State Cir, Annapolis, MD 21401	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	826173	7.536666666666667
Virginia State Capitol, Richmond, VA 23219	Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504	4684188	42.19861111111111
Michigan State Capitol, Lansing, MI 48933	Nevada State Capitol, Carson City, NV 89701	3423796	30.715
New Mexico State Capitol, Santa Fe, NM 87501	Utah State Capitol, Salt Lake City, UT 84103	1009968	10.115833333333333
Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130	500 E Capitol Ave, Pierre, SD 57501	2279926	20.22833333333333
State House, 107 North Main Street, Concord, NH 03303	Texas Capitol, 1100 Congress Avenue, Austin, TX 78701	3249787	29.65861111111111
Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319	New Mexico State Capitol, Santa Fe, NM 87501	1539423	14.454444444444444
Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120	Vermont State House, 115 State Street, Montpelier, VT 05633	725358	7.509444444444444
402 S Monroe St, Tallahassee, FL 32301	Wyoming State Capitol, Cheyenne, WY 82001	2710679	24.935555555555556
Georgia State Capitol, Atlanta, GA 30334	Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601	659468	6.027222222222222
Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007	Nevada State Capitol, Carson City, NV 89701	1149381	11.2775
L St & 10th St, Sacramento, CA 95814	Utah State Capitol, Salt Lake City, UT 84103	1051681	9.350555555555555
Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901	South Carolina State House, 1100 Gervais Street, Columbia, SC 29201	924813	8.658333333333333
Illinois State Capitol, Springfield, IL 62756	Rhode Island State House, 82 Smith Street, Providence, RI 02903	1786807	16.83888888888889

================================================
FILE: pareto-optimized-road-trip/optimized-state-capitols-trip.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing optimal road trips on a limited budget\n",
    "\n",
    "This notebook provides the methodology and code used in the blog post, [Computing optimal road trips on a limited budget](http://www.randalolson.com/2016/06/05/computing-optimal-road-trips-on-a-limited-budget/).\n",
    "\n",
    "### Notebook by [Randal S. Olson](http://www.randalolson.com)\n",
    "\n",
    "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely useable and shareable as possible.\n",
    "\n",
    "### Required Python libraries\n",
    "\n",
    "If you don't have Python on your computer, you can use the [Anaconda Python distribution](http://continuum.io/downloads) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
    "\n",
    "This code uses base Python libraries except for the `googlemaps`, `pandas`, `deap`, and `tqdm` packages. You can install these packages using `pip` by typing the following commands into your command line:\n",
    "\n",
    "> pip install googlemaps pandas deap tqdm\n",
    "\n",
    "### Construct a list of road trip waypoints\n",
    "\n",
    "The first step is to decide where you want to stop on your road trip.\n",
    "\n",
    "Make sure you look all of the locations up on [Google Maps](http://maps.google.com) first so you have the correct address, city, state, etc. If the text you use to look up the location doesn't work on Google Maps, then it won't work here either.\n",
    "\n",
    "Add all of your waypoints to the list below. Make sure they're formatted the same way as in the example below.\n",
    "\n",
    "*Technical note: Due to daily usage limitations of the Google Maps API, you can only have a maximum of 70 waypoints. You will have to pay Google for an increased API limit if you want to add more waypoints.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://en.wikipedia.org/wiki/List_of_state_capitols_in_the_United_States\n",
    "\n",
    "all_waypoints = ['Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130',\n",
    "                 #'Alaska State Capitol, Juneau, AK',\n",
    "                 'Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007',\n",
    "                 'Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201',\n",
    "                 'L St & 10th St, Sacramento, CA 95814',\n",
    "                 '200 E Colfax Ave, Denver, CO 80203',\n",
    "                 'Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106',\n",
    "                 'Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901',\n",
    "                 '402 S Monroe St, Tallahassee, FL 32301',\n",
    "                 'Georgia State Capitol, Atlanta, GA 30334',\n",
    "                 #'Hawaii State Capitol, 415 S Beretania St, Honolulu, HI 96813'\n",
    "                 '700 W Jefferson St, Boise, ID 83720',\n",
    "                 'Illinois State Capitol, Springfield, IL 62756',\n",
    "                 'Indiana State Capitol, Indianapolis, IN 46204',\n",
    "                 'Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319',\n",
    "                 '300 SW 10th Ave, Topeka, KS 66612',\n",
    "                 'Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601',\n",
    "                 'Louisiana State Capitol, Baton Rouge, LA 70802',\n",
    "                 'Maine State House, Augusta, ME 04330',\n",
    "                 'Maryland State House, 100 State Cir, Annapolis, MD 21401',\n",
    "                 'Massachusetts State House, Boston, MA 02108',\n",
    "                 'Michigan State Capitol, Lansing, MI 48933',\n",
    "                 'Minnesota State Capitol, St Paul, MN 55155',\n",
    "                 '400-498 N West St, Jackson, MS 39201',\n",
    "                 'Missouri State Capitol, Jefferson City, MO 65101',\n",
    "                 'Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601',\n",
    "                 'Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509',\n",
    "                 'Nevada State Capitol, Carson City, NV 89701',\n",
    "                 'State House, 107 North Main Street, Concord, NH 03303',\n",
    "                 'New Jersey State House, Trenton, NJ 08608',\n",
    "                 'New Mexico State Capitol, Santa Fe, NM 87501',\n",
    "                 'New York State Capitol, State St. and Washington Ave, Albany, NY 12224',\n",
    "                 'North Carolina State Capitol, Raleigh, NC 27601',\n",
    "                 'North Dakota State Capitol, Bismarck, ND 58501',\n",
    "                 'Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215',\n",
    "                 'Oklahoma State Capitol, Oklahoma City, OK 73105',\n",
    "                 'Oregon State Capitol, 900 Court St NE, Salem, OR 97301',\n",
    "                 'Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120',\n",
    "                 'Rhode Island State House, 82 Smith Street, Providence, RI 02903',\n",
    "                 'South Carolina State House, 1100 Gervais Street, Columbia, SC 29201',\n",
    "                 '500 E Capitol Ave, Pierre, SD 57501',\n",
    "                 'Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243',\n",
    "                 'Texas Capitol, 1100 Congress Avenue, Austin, TX 78701',\n",
    "                 'Utah State Capitol, Salt Lake City, UT 84103',\n",
    "                 'Vermont State House, 115 State Street, Montpelier, VT 05633',\n",
    "                 'Virginia State Capitol, Richmond, VA 23219',\n",
    "                 'Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504',\n",
    "                 'West Virginia State Capitol, Charleston, WV 25317',\n",
    "                 '2 E Main St, Madison, WI 53703',\n",
    "                 'Wyoming State Capitol, Cheyenne, WY 82001']\n",
    "\n",
    "len(all_waypoints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next you'll have to register this script with the Google Maps API so they know who's hitting their servers with hundreds of Google Maps routing requests.\n",
    "\n",
    "1) Enable the Google Maps Distance Matrix API on your Google account. Google explains how to do that [here](https://github.com/googlemaps/google-maps-services-python#api-keys).\n",
    "\n",
    "2) Copy and paste the API key they had you create into the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import googlemaps\n",
    "\n",
    "gmaps = googlemaps.Client(key='ENTER YOUR GOOGLE MAPS KEY HERE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're going to query the Google Maps API for the shortest route between all of the waypoints.\n",
    "\n",
    "This is equivalent to doing Google Maps directions lookups on the Google Maps site, except now we're performing hundreds of lookups automatically using code.\n",
    "\n",
    "If you get an error on this part, that most likely means one of the waypoints you entered couldn't be found on Google Maps. Another possible reason for an error here is if it's not possible to drive between the points, e.g., finding the driving directions between Hawaii and Florida will return an error until we invent flying cars.\n",
    "\n",
    "### Gather the distance traveled on the shortest route between all waypoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import combinations\n",
    "\n",
    "waypoint_distances = {}\n",
    "waypoint_durations = {}\n",
    "\n",
    "for (waypoint1, waypoint2) in combinations(all_waypoints, 2):\n",
    "    try:\n",
    "        route = gmaps.distance_matrix(origins=[waypoint1],\n",
    "                                      destinations=[waypoint2],\n",
    "                                      mode='driving', # Change this to 'walking' for walking directions,\n",
    "                                                      # 'bicycling' for biking directions, etc.\n",
    "                                      language='English',\n",
    "                                      units='metric')\n",
    "\n",
    "        # 'distance' is in meters\n",
    "        distance = route['rows'][0]['elements'][0]['distance']['value']\n",
    "\n",
    "        # 'duration' is in seconds\n",
    "        duration = route['rows'][0]['elements'][0]['duration']['value']\n",
    "\n",
    "        waypoint_distances[frozenset([waypoint1, waypoint2])] = distance\n",
    "        waypoint_durations[frozenset([waypoint1, waypoint2])] = duration\n",
    "    \n",
    "    except Exception as e:\n",
    "        print('Error with finding the route between {} and {}.'.format(waypoint1, waypoint2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the routes between all of our waypoints, let's save them to a text file so we don't have to bother Google about them again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('my-waypoints-dist-dur.tsv', 'w') as out_file:\n",
    "    out_file.write('\\t'.join(['waypoint1',\n",
    "                              'waypoint2',\n",
    "                              'distance_m',\n",
    "                              'duration_s']))\n",
    "    \n",
    "    for (waypoint1, waypoint2) in waypoint_distances.keys():\n",
    "        out_file.write('\\n' +\n",
    "                       '\\t'.join([waypoint1,\n",
    "                                  waypoint2,\n",
    "                                  str(waypoint_distances[frozenset([waypoint1, waypoint2])]),\n",
    "                                  str(waypoint_durations[frozenset([waypoint1, waypoint2])])]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use a genetic algorithm to optimize the order to visit the waypoints in\n",
    "\n",
    "Instead of exhaustively looking at every possible solution, genetic algorithms start with a handful of random solutions and continually tinkers with these solutions — always trying something slightly different from the current solutions and keeping the best ones — until they can’t find a better solution any more.\n",
    "\n",
    "Below, all you need to do is make sure that the file name above matches the file name below (both currently `my-waypoints-dist-dur.tsv`) and run the code. The code will read in your route information and use a genetic algorithm to discover an optimized driving route."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "waypoint_distances = {}\n",
    "waypoint_durations = {}\n",
    "all_waypoints = set()\n",
    "\n",
    "waypoint_data = pd.read_csv('my-waypoints-dist-dur.tsv', sep='\\t')\n",
    "\n",
    "for i, row in waypoint_data.iterrows():\n",
    "    # Distance = meters\n",
    "    waypoint_distances[frozenset([row.waypoint1, row.waypoint2])] = row.distance_m\n",
    "    \n",
    "    # Duration = hours\n",
    "    waypoint_durations[frozenset([row.waypoint1, row.waypoint2])] = row.duration_s / (60. * 60.)\n",
    "    all_waypoints.update([row.waypoint1, row.waypoint2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import copy\n",
    "from tqdm import tqdm\n",
    "\n",
    "from deap import algorithms\n",
    "from deap import base\n",
    "from deap import creator\n",
    "from deap import tools\n",
    "\n",
    "creator.create('FitnessMulti', base.Fitness, weights=(1.0, -1.0))\n",
    "creator.create('Individual', list, fitness=creator.FitnessMulti)\n",
    "\n",
    "toolbox = base.Toolbox()\n",
    "toolbox.register('waypoints', random.sample, all_waypoints, random.randint(2, 20))\n",
    "toolbox.register('individual', tools.initIterate, creator.Individual, toolbox.waypoints)\n",
    "toolbox.register('population', tools.initRepeat, list, toolbox.individual)\n",
    "\n",
    "def eval_capitol_trip(individual):\n",
    "    \"\"\"\n",
    "        This function returns the total distance traveled on the current road trip\n",
    "        as well as the number of waypoints visited in the trip.\n",
    "        \n",
    "        The genetic algorithm will favor road trips that have shorter\n",
    "        total distances traveled and more waypoints visited.\n",
    "    \"\"\"\n",
    "    trip_length = 0.\n",
    "    individual = list(individual)\n",
    "    \n",
    "    # Adding the starting point to the end of the trip forces it to be a round-trip\n",
    "    individual += [individual[0]]\n",
    "    \n",
    "    for index in range(1, len(individual)):\n",
    "        waypoint1 = individual[index - 1]\n",
    "        waypoint2 = individual[index]\n",
    "        trip_length += waypoint_distances[frozenset([waypoint1, waypoint2])]\n",
    "        \n",
    "    return len(set(individual)), trip_length\n",
    "\n",
    "def pareto_selection_operator(individuals, k):\n",
    "    \"\"\"\n",
    "        This function chooses what road trips get copied into the next generation.\n",
    "        \n",
    "        The genetic algorithm will favor road trips that have shorter\n",
    "        total distances traveled and more waypoints visited.\n",
    "    \"\"\"\n",
    "    return tools.selNSGA2(individuals, int(k / 5.)) * 5\n",
    "\n",
    "def mutation_operator(individual):\n",
    "    \"\"\"\n",
    "        This function applies a random change to one road trip:\n",
    "        \n",
    "            - Insert: Adds one new waypoint to the road trip\n",
    "            - Delete: Removes one waypoint from the road trip\n",
    "            - Point: Replaces one waypoint with another different one\n",
    "            - Swap: Swaps the places of two waypoints in the road trip\n",
    "    \"\"\"\n",
    "    possible_mutations = ['swap']\n",
    "    \n",
    "    if len(individual) < len(all_waypoints):\n",
    "        possible_mutations.append('insert')\n",
    "        possible_mutations.append('point')\n",
    "    if len(individual) > 2:\n",
    "        possible_mutations.append('delete')\n",
    "    \n",
    "    mutation_type = random.sample(possible_mutations, 1)[0]\n",
    "    \n",
    "    # Insert mutation\n",
    "    if mutation_type == 'insert':\n",
    "        waypoint_to_add = individual[0]\n",
    "        while waypoint_to_add in individual:\n",
    "            waypoint_to_add = random.sample(all_waypoints, 1)[0]\n",
    "            \n",
    "        index_to_insert = random.randint(0, len(individual) - 1)\n",
    "        individual.insert(index_to_insert, waypoint_to_add)\n",
    "    \n",
    "    # Delete mutation\n",
    "    elif mutation_type == 'delete':\n",
    "        index_to_delete = random.randint(0, len(individual) - 1)\n",
    "        del individual[index_to_delete]\n",
    "    \n",
    "    # Point mutation\n",
    "    elif mutation_type == 'point':\n",
    "        waypoint_to_add = individual[0]\n",
    "        while waypoint_to_add in individual:\n",
    "            waypoint_to_add = random.sample(all_waypoints, 1)[0]\n",
    "        \n",
    "        index_to_replace = random.randint(0, len(individual) - 1)\n",
    "        individual[index_to_replace] = waypoint_to_add\n",
    "        \n",
    "    # Swap mutation\n",
    "    elif mutation_type == 'swap':\n",
    "        index1 = random.randint(0, len(individual) - 1)\n",
    "        index2 = index1\n",
    "        while index2 == index1:\n",
    "            index2 = random.randint(0, len(individual) - 1)\n",
    "            \n",
    "        individual[index1], individual[index2] = individual[index2], individual[index1]\n",
    "    \n",
    "    return individual,\n",
    "\n",
    "\n",
    "toolbox.register('evaluate', eval_capitol_trip)\n",
    "toolbox.register('mutate', mutation_operator)\n",
    "toolbox.register('select', pareto_selection_operator)\n",
    "\n",
    "def pareto_eq(ind1, ind2):\n",
    "    return np.all(ind1.fitness.values == ind2.fitness.values)\n",
    "\n",
    "pop = toolbox.population(n=1000)\n",
    "hof = tools.ParetoFront(similar=pareto_eq)\n",
    "stats = tools.Statistics(lambda ind: (int(ind.fitness.values[0]), round(ind.fitness.values[1], 2)))\n",
    "stats.register('Minimum', np.min, axis=0)\n",
    "stats.register('Maximum', np.max, axis=0)\n",
    "# This stores a copy of the Pareto front for every generation of the genetic algorithm\n",
    "stats.register('ParetoFront', lambda x: copy.deepcopy(hof))\n",
    "# This is a hack to make the tqdm progress bar work\n",
    "stats.register('Progress', lambda x: pbar.update())\n",
    "\n",
    "# How many iterations of the genetic algorithm to run\n",
    "# The more iterations you allow it to run, the better the solutions it will find\n",
    "total_gens = 5000\n",
    "\n",
    "pbar = tqdm(total=total_gens)\n",
    "pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0., mutpb=1.0, ngen=total_gens, \n",
    "                               stats=stats, halloffame=hof, verbose=False)\n",
    "pbar.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Animated road trip map\n",
    "\n",
    "Now that we've optimized the road trip, let's visualize it!\n",
    "\n",
    "The function below will take the results of the genetic algorithm and generate an animated map showing the Pareto optimized road trips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_animated_road_trip_map(optimized_routes):\n",
    "    \"\"\"\n",
    "        This function takes a list of optimized road trips and generates\n",
    "        an animated map of them using the Google Maps API.\n",
    "    \"\"\"\n",
    "    \n",
    "    # This line makes the road trips round trips\n",
    "    optimized_routes = [list(route) + [route[0]] for route in optimized_routes]\n",
    "\n",
    "    Page_1 = \"\"\"\n",
    "    <!DOCTYPE html>\n",
    "    <html lang=\"en\">\n",
    "      <head>\n",
    "        <meta charset=\"utf-8\">\n",
    "        <meta name=\"viewport\" content=\"initial-scale=1.0, user-scalable=no\">\n",
    "        <meta name=\"description\" content=\"Randy Olson uses machine learning to find the optimal road trip across the U.S.\">\n",
    "        <meta name=\"author\" content=\"Randal S. Olson\">\n",
    "        \n",
    "        <title>An optimized road trip across the U.S. according to machine learning</title>\n",
    "        <style>\n",
    "          html, body, #map-canvas {\n",
    "              height: 100%;\n",
    "              margin: 0px;\n",
    "              padding: 0px\n",
    "          }\n",
    "          #panel {\n",
    "              position: absolute;\n",
    "              top: 5px;\n",
    "              left: 50%;\n",
    "              margin-left: -180px;\n",
    "              z-index: 5;\n",
    "              background-color: #fff;\n",
    "              padding: 10px;\n",
    "              border: 1px solid #999;\n",
    "          }\n",
    "        </style>\n",
    "        <script src=\"https://maps.googleapis.com/maps/api/js?v=3\"></script>\n",
    "        <script>\n",
    "            var routesList = [];\n",
    "            var markerOptions = {icon: \"http://maps.gstatic.com/mapfiles/markers2/marker.png\"};\n",
    "            var directionsDisplayOptions = {preserveViewport: true,\n",
    "                                            markerOptions: markerOptions};\n",
    "            var directionsService = new google.maps.DirectionsService();\n",
    "            var map;\n",
    "            var mapNum = 0;\n",
    "            var numRoutesRendered = 0;\n",
    "            var numRoutes = 0;\n",
    "            \n",
    "            function initialize() {\n",
    "                var center = new google.maps.LatLng(39, -96);\n",
    "                var mapOptions = {\n",
    "                    zoom: 5,\n",
    "                    center: center\n",
    "                };\n",
    "                map = new google.maps.Map(document.getElementById(\"map-canvas\"), mapOptions);\n",
    "                for (var i = 0; i < routesList.length; i++) {\n",
    "                    routesList[i].setMap(map); \n",
    "                }\n",
    "            }\n",
    "            function calcRoute(start, end, routes) {\n",
    "                var directionsDisplay = new google.maps.DirectionsRenderer(directionsDisplayOptions);\n",
    "                var waypts = [];\n",
    "                for (var i = 0; i < routes.length; i++) {\n",
    "                    waypts.push({\n",
    "                        location:routes[i],\n",
    "                        stopover:true});\n",
    "                    }\n",
    "\n",
    "                var request = {\n",
    "                    origin: start,\n",
    "                    destination: end,\n",
    "                    waypoints: waypts,\n",
    "                    optimizeWaypoints: false,\n",
    "                    travelMode: google.maps.TravelMode.DRIVING\n",
    "                };\n",
    "                directionsService.route(request, function(response, status) {\n",
    "                    if (status == google.maps.DirectionsStatus.OK) {\n",
    "                        directionsDisplay.setDirections(response);\n",
    "                        directionsDisplay.setMap(map);\n",
    "                        numRoutesRendered += 1;\n",
    "                        \n",
    "                        if (numRoutesRendered == numRoutes) {\n",
    "                            mapNum += 1;\n",
    "                            if (mapNum < 47) {\n",
    "                                setTimeout(function() {\n",
    "                                    return createRoutes(allRoutes[mapNum]);\n",
    "                                }, 5000);\n",
    "                            }\n",
    "                        }\n",
    "                    }\n",
    "                });\n",
    "                \n",
    "                routesList.push(directionsDisplay);\n",
    "            }\n",
    "            function createRoutes(route) {\n",
    "                // Clear the existing routes (if any)\n",
    "                for (var i = 0; i < routesList.length; i++) {\n",
    "                    routesList[i].setMap(null);\n",
    "                }\n",
    "                routesList = [];\n",
    "                numRoutes = Math.floor((route.length - 1) / 9 + 1);\n",
    "                numRoutesRendered = 0;\n",
    "            \n",
    "                // Google's free map API is limited to 10 waypoints so need to break into batches\n",
    "                var subset = 0;\n",
    "                while (subset < route.length) {\n",
    "                    var waypointSubset = route.slice(subset, subset + 10);\n",
    "                    var startPoint = waypointSubset[0];\n",
    "                    var midPoints = waypointSubset.slice(1, waypointSubset.length - 1);\n",
    "                    var endPoint = waypointSubset[waypointSubset.length - 1];\n",
    "                    calcRoute(startPoint, endPoint, midPoints);\n",
    "                    subset += 9;\n",
    "                }\n",
    "            }\n",
    "            \n",
    "            allRoutes = [];\n",
    "            \"\"\"\n",
    "    Page_2 = \"\"\"\n",
    "            createRoutes(allRoutes[mapNum]);\n",
    "            google.maps.event.addDomListener(window, \"load\", initialize);\n",
    "        </script>\n",
    "      </head>\n",
    "      <body>\n",
    "        <div id=\"map-canvas\"></div>\n",
    "      </body>\n",
    "    </html>\n",
    "    \"\"\"\n",
    "\n",
    "    with open('us-state-capitols-animated-map.html', 'w') as output_file:\n",
    "        output_file.write(Page_1)\n",
    "        for route in optimized_routes:\n",
    "            output_file.write('allRoutes.push({});'.format(str(route)))\n",
    "        output_file.write(Page_2)\n",
    "\n",
    "create_animated_road_trip_map(reversed(hof))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!open us-state-capitols-animated-map.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Individual road trip maps\n",
    "\n",
    "We can also visualize single trips at a time instead of"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_individual_road_trip_maps(optimized_routes):\n",
    "    \"\"\"\n",
    "        This function takes a list of optimized road trips and generates\n",
    "        individual maps of them using the Google Maps API.\n",
    "    \"\"\"\n",
    "    \n",
    "    # This line makes the road trips round trips\n",
    "    optimized_routes = [list(route) + [route[0]] for route in optimized_routes]\n",
    "\n",
    "    for route_num, route in enumerate(optimized_routes):\n",
    "        Page_1 = \"\"\"\n",
    "        <!DOCTYPE html>\n",
    "        <html lang=\"en\">\n",
    "          <head>\n",
    "            <meta charset=\"utf-8\">\n",
    "            <meta name=\"viewport\" content=\"initial-scale=1.0, user-scalable=no\">\n",
    "            <meta name=\"description\" content=\"Randy Olson uses machine learning to find the optimal road trip across the U.S.\">\n",
    "            <meta name=\"author\" content=\"Randal S. Olson\">\n",
    "\n",
    "            <title>An optimized road trip across the U.S. according to machine learning</title>\n",
    "            <style>\n",
    "              html, body, #map-canvas {\n",
    "                  height: 100%;\n",
    "                  margin: 0px;\n",
    "                  padding: 0px\n",
    "              }\n",
    "              #panel {\n",
    "                  position: absolute;\n",
    "                  top: 5px;\n",
    "                  left: 50%;\n",
    "                  margin-left: -180px;\n",
    "                  z-index: 5;\n",
    "                  background-color: #fff;\n",
    "                  padding: 10px;\n",
    "                  border: 1px solid #999;\n",
    "              }\n",
    "            </style>\n",
    "            <script src=\"https://maps.googleapis.com/maps/api/js?v=3\"></script>\n",
    "            <script>\n",
    "                var routesList = [];\n",
    "                var markerOptions = {icon: \"http://maps.gstatic.com/mapfiles/markers2/marker.png\"};\n",
    "                var directionsDisplayOptions = {preserveViewport: true,\n",
    "                                                markerOptions: markerOptions};\n",
    "                var directionsService = new google.maps.DirectionsService();\n",
    "                var map;\n",
    "\n",
    "                function initialize() {\n",
    "                    var center = new google.maps.LatLng(39, -96);\n",
    "                    var mapOptions = {\n",
    "                        zoom: 5,\n",
    "                        center: center\n",
    "                    };\n",
    "                    map = new google.maps.Map(document.getElementById(\"map-canvas\"), mapOptions);\n",
    "                    for (var i = 0; i < routesList.length; i++) {\n",
    "                        routesList[i].setMap(map); \n",
    "                    }\n",
    "                }\n",
    "                function calcRoute(start, end, routes) {\n",
    "                    var directionsDisplay = new google.maps.DirectionsRenderer(directionsDisplayOptions);\n",
    "                    var waypts = [];\n",
    "                    for (var i = 0; i < routes.length; i++) {\n",
    "                        waypts.push({\n",
    "                            location:routes[i],\n",
    "                            stopover:true});\n",
    "                        }\n",
    "\n",
    "                    var request = {\n",
    "                        origin: start,\n",
    "                        destination: end,\n",
    "                        waypoints: waypts,\n",
    "                        optimizeWaypoints: false,\n",
    "                        travelMode: google.maps.TravelMode.DRIVING\n",
    "                    };\n",
    "                    directionsService.route(request, function(response, status) {\n",
    "                        if (status == google.maps.DirectionsStatus.OK) {\n",
    "                            directionsDisplay.setDirections(response);\n",
    "                            directionsDisplay.setMap(map);\n",
    "                        }\n",
    "                    });\n",
    "\n",
    "                    routesList.push(directionsDisplay);\n",
    "                }\n",
    "                function createRoutes(route) {\n",
    "                    // Google's free map API is limited to 10 waypoints so need to break into batches\n",
    "                    var subset = 0;\n",
    "                    while (subset < route.length) {\n",
    "                        var waypointSubset = route.slice(subset, subset + 10);\n",
    "                        var startPoint = waypointSubset[0];\n",
    "                        var midPoints = waypointSubset.slice(1, waypointSubset.length - 1);\n",
    "                        var endPoint = waypointSubset[waypointSubset.length - 1];\n",
    "                        calcRoute(startPoint, endPoint, midPoints);\n",
    "                        subset += 9;\n",
    "                    }\n",
    "                }\n",
    "\n",
    "                \"\"\"\n",
    "        Page_2 = \"\"\"\n",
    "                createRoutes(optimized_route);\n",
    "                google.maps.event.addDomListener(window, \"load\", initialize);\n",
    "            </script>\n",
    "          </head>\n",
    "          <body>\n",
    "            <div id=\"map-canvas\"></div>\n",
    "          </body>\n",
    "        </html>\n",
    "        \"\"\"\n",
    "\n",
    "        with open('optimized-us-capitol-trip-{}-states.html'.format(route_num + 2), 'w') as output_file:\n",
    "            output_file.write(Page_1)\n",
    "            output_file.write('optimized_route = {};'.format(str(route)))\n",
    "            output_file.write(Page_2)\n",
    "\n",
    "create_individual_road_trip_maps(reversed(hof))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!open optimized-us-capitol-trip-48-states.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some technical notes\n",
    "\n",
    "As I mentioned in the [original article](http://www.randalolson.com/2015/03/08/computing-the-optimal-road-trip-across-the-u-s/), by the end of 5,000 generations, the genetic algorithm will very likely find a *good* but probably not the *absolute best* solution to the optimal routing problem. It is in the nature of genetic algorithms that we never know if we found the absolute best solution.\n",
    "\n",
    "However, there exist some brilliant analytical solutions to the optimal routing problem such as the [Concorde TSP solver](http://en.wikipedia.org/wiki/Concorde_TSP_Solver). If you're interested in learning more about Concorde and how it's possible to find a perfect solution to the routing problem, I advise you check out [Nathan Brixius' article](https://nathanbrixius.wordpress.com/2016/06/16/finding-optimal-state-capitol-tours-on-the-cloud-with-neos/) on the topic.\n",
    "\n",
    "### If you have any questions\n",
    "\n",
    "Please feel free to:\n",
    "\n",
    "* [Email me](http://www.randalolson.com/contact/),\n",
    "\n",
    "* [Tweet](https://twitter.com/randal_olson) at me, or\n",
    "\n",
    "* comment on the [blog post](http://www.randalolson.com/2016/06/05/computing-optimal-road-trips-on-a-limited-budget/)\n",
    "\n",
    "I'm usually pretty good about getting back to people within a day or two."
   ]
  }
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================================================
FILE: pct-bachelors-degrees-conferred-women-usa/Percentage of Bachelor’s degrees conferred to women in the US, by major.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Percentage of Bachelor’s degrees conferred to women in the U.S., by major\n",
    "\n",
    "This notebook provides the methodology and code used in the blog post, [Percentage of Bachelor’s degrees conferred to women in the U.S., by major (1970-2014)](http://www.randalolson.com/2014/06/14/percentage-of-bachelors-degrees-conferred-to-women-by-major-1970-2012/).\n",
    "\n",
    "### Notebook by [Randal S. Olson](http://www.randalolson.com)\n",
    "\n",
    "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely useable and shareable as possible.\n",
    "\n",
    "### Required Python libraries\n",
    "\n",
    "If you don't have Python on your computer, you can use the [Anaconda Python distribution](http://continuum.io/downloads) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
    "\n",
    "This code uses base Python libraries except for the `BeautifulSoup`, `pandas`, and `matplotlib` packages. You can install these packages using `pip` by typing the following command into your command line:\n",
    "\n",
    "> pip install beautifulsoup4 pandas matplotlib \n",
    "\n",
    "If you're on a Mac, Linux, or Unix machine, you may need to type `sudo` before the command to install the package with administrator privileges.\n",
    "\n",
    "### Scraping the NCES database\n",
    "\n",
    "To acquire the data used in the blog post, we need to scrape the [NCES database](http://nces.ed.gov/programs/digest/current_tables.asp). The NCES database is available for download as Excel files, but we didn't want to deal with a bunch of Excel files. Instead, let's scrape the NCES database web pages directly using [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/).\n",
    "\n",
    "Running the code below will create a file called `gender_degree_data.tsv` that contains all data about the gender breakdowns of various degree majors in the US."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "\n",
    "with open('gender_degree_data.tsv', 'w') as out_file:\n",
    "\n",
    "    out_file.write('\\t'.join(['Year', 'Degree_Major',\n",
    "                          'Total_Bachelors',\n",
    "                          'Percent_change_Bachelors',\n",
    "                          'Male_Bachelors', 'Female_Bachelors', 'Female_percent_Bachelors',\n",
    "                          'Total_Masters', 'Male_Masters', 'Female_Masters',\n",
    "                          'Total_Doctorates', 'Male_Doctorates', 'Female_Doctorates']) + '\\n')\n",
    "\n",
    "    table_list_response = requests.get('http://nces.ed.gov/programs/digest/current_tables.asp')\n",
    "    table_list_response = BeautifulSoup(table_list_response.text, 'lxml')\n",
    "\n",
    "    for link in table_list_response.find_all('a', href=True):\n",
    "        # We only want the tables that stratify the data by degree and gender, which are in table group 325\n",
    "        if 'dt15_325' in link['href'] and int(link.text.split('.')[1]) % 5 == 0:\n",
    "            url = 'http://nces.ed.gov/programs/digest/{}'.format(link['href'])\n",
    "            url_response = requests.get(url)\n",
    "            url_response = BeautifulSoup(url_response.text, 'lxml')\n",
    "            degree_major = url_response.find('title').text.split('Degrees in')[1].split('conferred')[0].strip()\n",
    "\n",
    "            all_trs = url_response.find_all('tr')\n",
    "            for tr in all_trs:\n",
    "                # We only want to parse entries that correspond to a certain year\n",
    "                year_header = tr.find('th')\n",
    "                if year_header is None:\n",
    "                    continue\n",
    "\n",
    "                # Stop parsing after all of the years are listed\n",
    "                if 'Percent change' in year_header.text:\n",
    "                    break\n",
    "\n",
    "                # Years always have a dash (-) in them\n",
    "                if '-' not in year_header.text:\n",
    "                    continue\n",
    "\n",
    "                year = str(int(year_header.text.split('-')[0]) + 1)\n",
    "                year_vals = [x.text.replace(',', '').replace('†', '0').replace('#', '0') for x in tr.find_all('td')]\n",
    "\n",
    "                out_text = '\\t'.join([year, degree_major] + year_vals) + '\\n'\n",
    "                out_file.write(out_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the gender breakdowns for the various degree programs\n",
    "\n",
    "Next, let's use [matplotlib](http://matplotlib.org) to visualize the trends in the gender breakdowns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/randal_olson/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n",
      "/Users/randal_olson/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cY8aMEYKT+fPnCw8+6urq8M0332DatGkICAhAYGCg8Pfss88iLS1NaNr36aef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\niWTW6KoWxeY1z8fchIahNcGS84VjUMUlI1KlJkM4olDd4qa5x0cokvq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p7fMTVcDli7Cv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gDLdjXh8QVRCDB7tJVIozxR7C2bazbz4MoHaXBJtru5Ubm8ePaLRxUU3oCX3Q272Vq3la21W9le\ntx2bzxbWR6/Sc2XOldww8ob+SUD3OqSVksoN4GoOfy9yOKRMlZzjuunMFQgGWLxnMa9tew2/6O/2\nMHRKXWcR05XtcYf3IzQRmDVmEowJJ82gQKb7OH1OilqKKGwpDLPqbvG0dOo7JmYMV+dezfnp56NT\n6U7BaGVkZGROX2SBIjNgCIoiq/c209ju2DUhw0z6AK8SPxiod9bz4MoH2VK7BQCT2sTTM59mTtoc\nbF4b2+u2s61uG1tqt7C7YfdR65VE66K5Kvcqfjnil0TqIvt+oG0VUuX32l1SFfhDKDWQOEESJqaE\nXu9+T8Me/rzhzxS2FOLyu/pgwEcnw5LBQ2c8xPSk6f16nNOJVk8ra6vWhkwguoMoitQ4akKrI+W2\nckR69pVn0Vq4POtyrsy9stfFVGVkBiK3fHsL2ZHZPHzmw6d6KDIynZAFisyAYXeZjcIqKb8gLVbH\npMz+c5863fAH/SzYuoDFexaH2jIsGZS0lYS5b3UkxZTCxPiJTIybyMT4iaSb0/vekUsUofkglPwI\nTUXh7xnjJFGSOEFKgu9DfEEfDq8Dm8+G3WvH7rNj89qweW2h54fa27xtYX3sPjt2r/24NWkA5g6b\nywOTHyDJlNSn4z+d6E5tnd6gVWrJjMwMOeMdqsvjD/r5pPATPi74OLTyCJK194zkGfxixC+YkTQD\npULZZ2PpiMPnoNpeTZWjihpHDVX2KmxeG7NTZ3NW8llDymJdBh5d/SifF32OIAgcmo4JgsCHF33Y\nfzl97bR521AJqn43D1m4fSHflX7Hfy75T78eR2ZoIQsUmQFBdZOb9QWtAJgNKmaPsspJ8f3A0tKl\nPLrm0VDl9UMICORE5YQEyYS4Cf1bP0QMQv1+SZh0dOMSFBA7ClKnSuFcA3gy5gv4QgLH5rOFRM2B\nlgMs3r04NJnWKXXcOuZWbh59M1ql9hSPevBwtNo6vSHFlBKqxXPoZ1pE2jFFhi/gY2n5Uj7Y/0Fo\n9fEQyaZkrsy5ksuyL8Oqs3ZrDP6gP/S30uRuotpeTbWj/dH+vMpRFTJ16IqJcRO5b9J9/eeSJ3PS\neXT1o9S76nn2rGfpOB2L0kX1OkzUH/SjGkDFaRduX8j3Zd/z6cWfnuqhyAwiui1Qhg8fTmlpaaf2\nCy+8kC+++AKAJ554gkWLFtHc3MyZZ57Ja6+9xsiRh+1N77//ft577z1MJhPPPvssv/zlL0PvffHF\nFzz//POsXLnyRM9JZpDhcPv5cVcTvoCISilwzmgrJv3A+XAdapS2lfLM+mfwBDxMjJfEyPi48Zg1\n5v4/eDAg2QSXrABH7eF2hRqSp8Cws6A/wsdOMnXOOl7c8iJLDi4JtaWYUnjojIc4O/XsUzewQcDR\nautMjp/MLaNvISMyo0f7i9RGYlQbT2hMhc2F/Dv/33xR9EXYKo5aoeaC9AtIt6Rj97avtrWvsIVW\n59qf9zasUCWoUAiKsNDLs1PP5rcTfktWVNYJnZfMqefR1Y/S6mnllTmvdPm+L+DjxS0v8nXx19h9\ndnKtuTww+QEmxE0AYFPNJm799lZem/MaC3csJL8pn5fOeYlZKbNYXr6chTsWUtRSRKw+lp8M/wl3\njL8jZAByZIhXo6uRJ9Y+wfrq9UTro7lr/F28s/sd5qXP445xdwAw9r2xPDbtMdZVrWNV5SqiddHc\nNeEuLsq46KjneDyB8uXBL/nn3n9S3FaMVqllcvxkHjrjIeIMcWHnuGjeIhZsXUBhSyEZlgwen/Y4\nedF5of38p/A/LNyxkBZPC9OTpjMtcRrPbHiGnTfuDI3jyJWczw58xp83/JkN124ApBsjz296nl0N\nu3D4HKSb07l7wt3MSpkV2qY718nutfO3zX/jx/If8QQ8jIweye8m/45R0aNC7z+z4RnWVq3F4XMQ\nZ4jj2rxruTbv2qNex9ONbs8CN2/eTCAQCL2uqqpi0qRJXH311QA899xzvPTSS7z33nvk5OTw5JNP\nMnfuXAoKCjAajXzxxRd88MEH/PDDD+Tn53PLLbdwwQUXYLVasdvt3H///Xz55Zd9f4YyA5pAUGRj\nYSu+gKSTJ2aYZXHSzwwzD+Pv8/5+cg8a8EH1FihZCe4Oie8qHaROg9TpnWyCBzNxhjj+ctZfuCL7\nCp7d+CwFzQVU2Cv4zbLfcFbyWTx0xkMMMw871cMcUOyo38FbO99iecXysPZ+r63TDbKjsnl06qPc\nN+k+viz6kg/yP+BAywF8QR9fHPzihPZtUptINCWSaDzi0d4Wq4/F5rXx9u63eX/f+3iDXpaXL2dF\n+Qp+lvkz7hp/lxxCOIR5YcsLfF/yPU/PfJpkUzLv7XmP27+/nSWXLyFGHxPqN3/rfB6Y/ABp5jSM\nKiNrKtfw8KqHefjMh5kUP4kqexV/Wv8n/EE/90++v8tj/XH1H2l0N/LO+e+gUWp4fvPz1DhqOvV7\nc+eb3DfxPu6ddC+fFn7KY2seY3L8ZBKMvcsR9Af93DXhLoZbhtPibuGlLS/x0MqHWHzB4rB+C7Yu\n4L5J9xGjj+EvG//Cw6se5r+X/heA7XXbeWLdE9w38T7OTTuXzbWbeXnry51CIrsKkezY5vQ5OSvl\nLH478bdolBq+LfmW+368j08u/oR0S3q3r9OdS+/EorHw+nmvY9aY+bzoc2779jY+v+xzYvQxLNi2\ngKKWIl4/73WiddFU2Cpo9hxhCnOa0+2ZYHR0dNjrRYsWYbFYuPLKKwF4+eWXefjhh7n00ksBeO+9\n94iLi+P999/ntttuY//+/Zx99tlMmDCBCRMmcO+991JcXIzVauWRRx7hhhtuIDe3f+MtZQYeO0ts\ntDikhOjMBAPJ0bJbzpDC74aKDVC2Grwdwso0JkibCSln9nl+yUBicsJk/n3Rv/kw/0Ne3f4qNq+N\nVZWrWP/Zem4adRO/GvOr07p45KHaOm/teotNNZtC7Seltk4vMKqNXD3iaq7KvYottVv4d/6/+aH0\nB/yiH61SG3J5C/3UmMIc38waMya1iUhtJAnGBJJMSURoIo573EhdJL+b/DuuzbuWhTsW8t8D/yUo\nBvm86HO+Lv6aa0Zcw21jbiNKF3USroL0eytqKeoUKno8Yg2xJJuS+2lUg5fVlas5859nhl5Pip/E\n6+e9jsvv4sP8D3lqxlPMTJ4JwGPTHmNjzUY+2P8Bd0+4O7TNnePvZFrStNDrRbsWcfPom7k482JA\nCku8d+K9PLzq4S4FSnFrMWur1vL+he8zOmY0AE/PeJoLPrmgU9+fZfyMn2b8FIC7x9/NP/f9ky21\nW7gw48Jenf+lWZeGniebknlk6iNc+t9LqXPWhVZRAO6ecDeTEyYDcPu427nxmxtDfd7f/z7Tk6Zz\n0+ibAEgzp7GrYRefFvYsrCzXmhv2mfOrMb/ix/If+b70e24be1u3rtOG6g0UNBew8uqVaJQaAO4a\nfxfLy5fzZdGX3DT6Jqod1eRF54VWVHor7oYyvb5V/c4773D99dej1WopLi6mpqaGuXPnht7X6XTM\nmjWLtWvXcttttzFu3DgWLVpES0sLRUVFuN1usrKyWL9+PcuXL2fr1q19ckIyg4eyehcldVLYg9Wk\nZnTa0LmDftrj90DpSsmVy98hmVwXBemzpOKKytPDPlqlUPHLvF9yfvr5LNi2gE8LP8UX9LFo1yI+\nL/qcB6c8yLxh84Zc8rMoirgD7k45OodCn9q8bXxX8l2n2jqXZF3CzaNu7p/aOn2EIAhMTpjM5ITJ\neANS6NWhiUh/kmBM4MnpT3LjyBtZsG0BS8uW4gv6+L+9/8d/Cv/DTaNu4vqR1/eb6C1uLWbJwSV8\nVfwV5bbyXu1j3rB5/GbCb0J3o2VgUsIknpj2RMhhTqeUbtqU28oJiAHGxx5ePVQICsbFjuNg68FQ\nmyAIoYnuIfY27mVPwx7e3vV2qE1ExBvw0uBqCFt9AShpLUEpKBkZfTgsP8GYQKwhttN4c6JyQs+V\nCiVR2iia3E29OfXQWN/Y8Qb5Tfm0elsRRRFBEKh2VIcEiiAIZEdlh7aJNcQiiiJN7ibiDHGUtJZ0\nCp8dGzO2xwLF5XexcPtCVlaspN5Vjz/oxxv0khsliZbuXKd9jftw+V2c9cFZYfv2Br2h/5urc6/m\n/uX3s6dhD9OSpjE7ZXZIfMlI9EqgfPfdd5SUlHDbbbcBUFNTgyAIxMeHJ9XGx8dTVVUFwLx587ju\nuuuYMmUKBoOBf/zjHxiNRn7961/zxhtv8Pbbb/Pyyy9jNBpZsGAB06ZN63RcmaFDm9PP9uI2ADQq\ngTNyLCgUQ2uCdtrSWAD7/gPuDrUljPGQPhvix0I/uR8NdKL10Tw5/UmuyL6CP2/4M7sbd1PrrOWB\nFQ9wRsIZ/G7y78K+9AY6noCHopaiUD2RAy0HaHY3hzmddbfujF6l56qcq7hh1A1hd0wHAydDmBxJ\nRmQG88+Zz876nczfOp9NNZuw++y8uv1V/rX/X/x63K+5IvsK1H1wE6DB1cDXxV/z5cEv2du494T3\n913pdywtW8pl2Zdxx7g7Bt3vuz/QK/WkRKSc2D6OKMAriiK3j7udeenzOvXtrrHD0TgyAV8QhKO6\nQR4Pl9/FHT/cwbSkaTx71rNYdVaaPc3c+PWN+ALh5hgdi+cKSPOFnhy3o1PaIfzB8M+ov236G2ur\n1vLAlAdIi0hDp9LxyKpHemTUESRIjC6G937yXidbc5NauhE7M3km31/xPasqV7GhegN3Lb2L89PP\n56kZT3X7OEOdXgmURYsWMWXKFEaPHt2j7R577DEee+yx0OtnnnmGGTNmYDabefzxx9m5cyc7duzg\nqquuori4GJWq8/AaGxtpaGjo1B4TE9MpDE3uPzD7+wJB8uuUKNorgE/JtqDXKAfN+OX+R+nvc0nJ\n7/V7iLEYiDbrwZwM6edC7AgQFAN7/CepvwYNC6YuYFXzKuZvmU+zp5mNNRu5+surmR01m8uTL+8U\nBnMqx19fX0+ts5aS1hJK2koobSulSqyimupOkwO/3U/AFui0H2WEEpWp8+e5yWvi/NjzuTDjQsxa\nM83lzTTTPKB+XwO5/9jYsbw9723WVK1h/pb57CnfQ1V1FY8XP85fVX9lmGUYw8zDGDtsLJMyJpEV\nmYWpQ67X0favM+vY7tjOkoNL2FCzIfR7PvT7zYrM4uzUs0N1YSxWC5aozrbwrc2ttDZJ7owBMcDK\nipWsKF+BIkLBxwUf82XRl1ybdy03j74Zi9Zyyq9nf/Xvbfh6akQqKkHFtrptIQETFIPsqN9xzKR0\ngLzoPIpbi7tdu2e4ZThBguxt3BsKXapx1FDvrO/V2LtLcWsxLZ4W7plwTyiX6kDpgR6vKKdb0tnd\nsDusbWfDzrDXVp2100rPvqZ9Ya+31W/j4syLmZM2B5BuxJTbykMrft25TnnWPBrdjQiCQIrp6MLT\norVwUcZFXJRxETOSZ/CHlX/gf6f9b5gQO53psUCpr6/n888/Z+HChaG2hIQERFGktraWlJTDv4za\n2loSErqOqysoKGDx4sVs27aNd999l9mzZxMXF8fcuXPxeDzk5+czatSoTttFR0d3+cFwNOT+A6u/\nKIpsKmxFoZOKreWlGImzaI/av7/HI/fvo/51e2D/V6CzQWq0VFwx+yeQfIZkHTzQx38K+l8eczlz\n0ubw+vbX+TD/Q/yinxXNK1jTsobLsy/n9nG3dxle0RfjCbNI9too9BayrXRb2OpHvauewuZCClsK\ncfgcx9yvWqEmw5JBXHIcEZqIsLyLCPXhXIyw/Ay1CYPa0CMr1cH0+z1Z/QVBYGbyTKYnTeeb4m94\nZdsrVNgr8OOniCKK3EUsy18G+VL/ZFNyuOVygmS5LIoiqytXs6R4Ccs3Le9UEDPFlMKFYy/kwowL\nGW4Z3r0TOCLd5DquI78pn5e3vsyqylW4A27e3v02HxV8xK1jbuWXI355yq/nyex/PPQqPVflXsX8\nLfOJ1EaSHJHMP/b8gyZ3E1flXhXq15UZ6+1jb+fuZXeTaErk/GHno1QoOdBygI2zCgEAACAASURB\nVF0Nu7h/UucclHRLOtOSpvHUuqd4dOqjaJQaXtz8IjqVLrRacSJ4/B7ym/LD2nQqHYnGRDQKDe/v\ne59rRlzDwdaDvLb9tU7bH89w9tq8a7np65t4d/e7oST5ZWXLwvpMTphMq6eVRTsXccHwC9hUs4kf\nSn8I65NuTmdp2VLOTj0bpULJGzveCHPQ6851mpY0jfFx47ln2T3cN+k+hluGU++sZ23VWqYlTWNC\n3ARe2/4aedY8siKz8Af9/FD6A6kRqbI46UCPBcrixYvR6XRcc801obbhw4eTkJDA999/z6RJkwBw\nu92sWrWKF154ocv9/PrXv+aFF14gIiKCYDCIzyctn4miiM/nC3MMkxk6HKx1UdkkffHFWTTkJp+Y\n/afMKcZjg/wvoG7X4bboHMi7bEjYBfc3Fq2Fh898mOvyruPV7a/yVfFX+EU/HxZ8yBcHv+C6vOu4\nafRNvbaALmsrY0XFClZXrg4V/LP77D2qxn4kicZEcqJywmuKmNPkL9ZTjEJQ8NOMnzJ32Fy+Kv6K\nbXXbQgKzo8Vxpb2SSnsly8uXh9o0Cg1apRabL7wGi1Vn5fx0aYVrbMzYPsmTyrXm8vp5r7O5ZjMv\nbX2JnfU7afO28dKWl/jnvn9y57g7uSTrkpNSx0MURXxB3ykJ0+su90+6H0EQeGztY9i8NkZYR/DG\neW+E5ZB09XuZnjyd1+a8xps73+S9Pe+hElQMMw/jkqxLDm93hPB4ZsYzPLHuCW799lasOit3jr+T\nCntFWP2m3v4NVNgruOrLq8LaRlpH8q+L/sWfZv6JV7a+wr/z/01OVA4PTnmQO364I6zv8dy3xsWO\n4/Hpj/P69td5fcfrnJlwJreMviVM7GRYMnh06qO8test3tr1FrNTZ3Pb2Nt4Zdthi+cHJz/I42sf\n56ZvbsKsNXNd3nWdQs26c51en/M6r2x7hSfXPUmTq4lofTTj48aHTAs0Cg2vbHuFSnslWqWWsbFj\nWXDugl5c2aFLjws15ubmcs455/DGG2+Etf/1r3/l2Wef5Z133iE7O5unn36a1atXk5+fj9EYPgl9\n6623+Prrr/nkk08A2LJlC3PmzGHJkiVs376dp556irKyMrRauajZUKLJ5mXl3mZEEfQaBeeMiUar\n7l0hKplTjChCzXYo+EIK7QJQ6SHnIqny+xBL+D5Z7G/az/yt81lTuSbUZtFa+NXoX3HNiGvQHcfx\nzBf0sa12GysqVrCyYiUlbSW9HkuEJoJMS2aYGMmKyjo59XJk+oygGKTSVklBcwEFLVK+UGFzIaVt\npZ3i40G6a39O6jlcmHEh05Km9avwFEWRH8t/ZMHWBRS1FoXa083p3DPxHs5LO6/PzSM8AQ8bqzeG\n/kfqnfVcM+IafjPhN6e1o15XtLhbOPejc3l+1vPMGTbnVA+nxzy38Tk21mzkk4s/6dfjDPbrNFDp\nkUBZvnw5c+bMYePGjaGVko489dRTvPnmm0ct1AhQV1fH1KlTWbt2bVj413PPPccLL7yA2Wxm4cKF\nYY5gMoMfjy/Ij7sacXmDCALMGhmFNWLg3rWSOQbuFtj3X2jssFwfNxpyLwbt8S1TZY7PpppNzN8y\nPyyGOt4Qz53j7+TizIvD7i43u5tZXbmaFRUrWFu5ttNdcIAkYxKjY0YToYmQrG41pk6WuIdscU1q\n6XGsKusygx+X30VRSxGFzYUUNBfQ6mllevJ0zk0996RP1APBAJ8Xfc7rO14PqycxOno056efHxLI\nMfqYXgmWWkctqypXsaJiBRuqN3RZNDPFlMKT05/kjMQzTuhcBjMbqzfi8DnIjsqm0d3IK1tfoaSt\nhC8v+/K4N0cGAu/ufpdpSdMwqAysq17H85ue595J9/Z58cPBfp0GCz1eQZGR6Q07S9ooqpG+FMam\nR5CZIN+pGnSIQajcBIVfw6EQIY0Jci+B+J4ZZsgcH1EUWVa+jAVbF4RZimZYMvjVmF9R66xlRfkK\ndjbs7JSsrhAUjI8dz6yUWcxOmU1mZOaQszGWGXp4Ah4+2P8Bi3YtotXT2un9KG1UWGhhdlQ2mZGZ\nnRysgmKQPQ17QqskRyZCg+RENSV+Cg6fI+xGwBU5V3D/pPu7VZ/meGyv285bu97i1TmvnvC+TgZr\nK9fyty1/o9JWiU6lY1zsOB6c8mC3E+1PNQ+ueJDNtZuxe+0km5K5MvfKfqnMPtiv02BBFigy/Y7X\nH+SbrfUEghBr1jAjL1KeLJ1KxCA0HQi3AT7uNkDtDmg+PFEmcYIU0iWHRfQr/qCfL4q+4LXtr1Hr\nrD1qvwh1BDOTZzIrdRYzk2YSKecAyQxSbF4bi3cv5tPCT2l0Nx6zr4BAmjmNnKgcsiKzqHZUs6pi\nVZfbWXXWkGifljQNo9pIIBjgg/wPeHnry6GVlThDHI9Pe5xZKbN6PHZRFFlbtZa3dr3F5trNAOy6\ncddxtpKRkTkSWaDI9Dv5lXb2lksuQDNGRBIXKecWnRKCASlvpGQFnIh1pNYiJcHHDJwK36cDXd1d\nHm4ZzuyU2cxKmcX4uPFyovopRCro5sYrevEGPaGHp8PzQw+loCRem0yCLhmzSr5hcywaXA2hMLRD\nP4taisKclY5FnjUvJEpGxYw6qmtcua2cJ9c9yYbqDaG2CzMu5KEpDxGlizrucYJikKVlS1m0c1HY\nio1aoWbr9ae+ELXN38r7FX/n8sQbiNXGd3p9PN6v+DujIyYw1jLlJIxWRkYWKDL9TCAo8u22Bjy+\nIBaDinPGWOUv45NNwAuVm6FsVc9WTTohSLbB2ReAHGd7yrB5beys30lqROqArrY+VBBFEU/Qjc3f\nht3fhj3Q/tPfhs3fhiNgwx1wE6R3zpMGpZEEbQqJOukRpY7pkfXy6Yg/6KfMVhYmWgqbC6m0V6JT\n6piaOJVZqbOYlTyLeOPxJ9+HEEWRTws/5W+b/4bdZwekVZeHz3yY84ed3+V3ly/oY8nBJby96+0w\nU4qeFB/9seFrCuy7ERDCjAvitUlcmtg3IUo2fyv/qljEZYnXE6uNRxRF3EEnOoWhW9/JskCROdnI\nAkWmXympc7LtoJS0OznLTGqM/jhbyPQZfjeUr4ey1dCxjoXWDGkzIX4M9MTfXqWVhYnMkCMgBnD6\n7dg6CI9D4sMesGH3t+EXu19F+lhoBA0ahRZ30H3UfWoELfG6JBK1KSToUojTJqAUOlvuBsVg1ys1\novRTo9ASrY4lUh19Uix7BwJOnxO1Qo1aeWIribWOWp5e/zTLK5aH2s5NPZdHpz4aqk3k8rv4tPBT\n3t3zLjWOGjRKNVqVBqvByoXDf8qs1LNQq1R4gx5Gmyce83g/NnyN02/n3NgLoYNAUaBEq+ybz9ye\nrpgciSxQZE42p8enVg/wB0Qcbj8WoxwqcaKIokhhlROQbIWTrfLk9qTgsUH5GkmcdKx3obdC+mxI\nnAinyYRFRsYb9Bxe/Qitgtiw+Vux+204A/Yu7XaPhRIlJpUZkyoCk8qMTmFAo9BK9UQUuvbn0mvp\npw6NoAndqQ6KQRq9dVS7K6jxVFDtrsQdlD4rvaKHclcx5a7i0LGiNXEggDd4OHysu6JJQMCitmJV\nxxCticWqiSVaHYtJZR5yq9m9cR8TRRHfEWF5XsHLPWfewVnDz2RZxTKC+AkoHfxtz5/Ijc7BF/TS\n4m1GqVDys/Fno1Ud6UjpYnXLd6FXxxMoAEpBiV7Z9fjfLHmeWdHzqHCVUuY6iEFpYHLkTLJNh11S\naz1VrG78gWZfI1Z1DFOiZvJV7cf8LOEaknSdk7ePFCxBMci6ph856CzAE3ChVxrJMuVxZtThPBy/\n6Gdlw3cUOfahVmgZY57IOMvp63om07/Is5QOVDe52VFiQxRh7vhoVEp5mf1EqGnxYndLYQ+ZiQYU\niqH1ZTjgcDVD6Sqo2gRB/+F2UwKkny1ZAcvWsTJDHFEUafTWUejYS5EjH0egs+3y8dAp9IcFiNJM\nhMrc/lp66LsZFnM0FIKCWG0CsdoExjIZURRp9TdT464IiZY2v5RnFCBAnbe618cSEWnxNdLia+Sg\n87A1uEbQEKVpFy3qWKI1ccRo4of8asuhv49SVxFlzoPUe2uOLlAVMCEtL6zJhwsUEKk6ufWAtras\n48yo2ZwZNYt99p0sb/iGRF0qJlUEvqCXb2r/Q6o+nXNjLsQRsLOuadlxK8B3fH9X2xZKnAeYG/sz\nTCoLDr+NFn9TWP9dbVuYHDmD8ZYzKHMdZE3TUhJ0KcRrk/rlnGVOb4b2J1EP8fpFXF7JrrOgysnI\nVNMpHtHgprBKCitSKQXSY+XQrj4nGABnA9hrpJokNTskh65DWIbB8LMhOlcunCgz5GnztXDAsY9C\nxz5afEd3flKgwNguPA6tgIQESHubWnFyazQJgkCk2kqk2sqIiLEAOPx2qj0V1LgraPTWoRRUnVdm\nBG37io2mw3vS+66AkyZvA42+epq80sPeQax5RS+1nipqPVUdro2SOG0CCboUErXSxLOvQoxOJb6g\nj0p3KWXOIkpdB3EG7D3aXoECUNDmbsPpc+H1e1ErtGSYMxgWkY5OqUej0KIN+x20P4Tu/S2Vu4p5\np3R+hxaBUeYJoRWMbNMoskySWJoSOZPdbVupdpeTbRpJoWMvIDI75nyUgooooplgmcqyhiXHPGZH\nYWb3t2FRR5GgSwHApIognnDhkaJPZ5R5AgCj1RPZ3baVSleZLFBk+gVZoHQgLVZHca2TZoefwioH\nw2J1GHXyJeoNTTYfjTYpBGF4nB61Sl6N6jWiCF6bJETsNWBr/+moA7GLxNzobEg/ByLTZWEiM6Rx\nB1wUOfZzwLGPGk9lp/cTtSmk6jNCAiRCZUavNA6KJHSjykSWagRZxhG92l6vNGLVxJLF4RUAT8BN\nk6+BJm89jd56mnz1NHkb8ImSI1aQADWeSmo8lWxHcrOyqmNJ1KW0i5ZkjKqTV4w1KAYREHq1WmXz\nt1HmLKLMdZBKdxkB0d+pj1UdQ7J+GAalKVxcCOEiQyWoEASBNm8bnx34jPSodGYmz+zTELlEXQqz\nos8Pa9MoDjteWjWxoecKQYFOoccVkMICW3xNRKljwnKV4rSJPQpdzDWNZkntR3xQ8RYp+nTS9Bmk\n6oeHnWN0hzEAGJSm0BhkZPoaefbdAUEQGJMewco9zQRF2FVqZ2quXEugNxRWS6sngiCFd8l0EzEI\n9lpoqzgsSOw14DvOl4CggNiRUiiXOfmkDFVG5lTgC/oodRVRaN9LhauYIOFFKq3qGLJNI8k05hFx\nksNwBjpapY5EpeQWdghRFLH726jz1lDTHl7W6K0PTW6bfJKQ2WPbBkCEytKewJ9MrCaBKE10l0n8\nvcHubwuFuNW4K2nyNaBAcUQ+T9ci4lCfRm89pc4imnydrdSVKEnSpzFMn0maIYMIlaVH4zNrzFw/\n8vo+OdcjUQlqzOqjzzekVZzDSMKh7zyOYrTx/DLlfyh3lVDpLuXHhq+I1sRyUcLVxxgDfToGGZmO\nyALlCKIjNKTG6ChvcFPd7KGu1UOcRa7b0RPsbj9VTVJydmq0Dr1Gzns4KgGfJEZaSqGlGFrLJPet\nY6ExSXklHR/GODhB5xoZmYGKL+il0l1KsaOQYmcBviMSxE3KCLKMeWSZRna6yytzbARBIEJtIUJt\nIdMo1TbyBj3UuKvahUIFdZ5qAu02yjZ/KzZ/KwWOPdL2CESqo7FqYohWS0n4Vk0sJmXEMVcYRFHK\njan2tOfduCuxB9o69QsSxB104Q66enV+BqWRNH0mwwyZJOvSTnr43skgUh1NoX0vAdEfEot1nurj\n5qAciVqhIcOYQ4Yxh1zTaP5T/f9o9TVjUR+/DoyMTF8jC5QuGJVmoqrJQyAosqvEzjljNSjkUJlu\nU1R9+G5/dpLxFI5kAOJ3t4uREunRVhGe0N4RhQqM8UeIkXjQnrwQCxmZU4XN10qpq4hSZxFV7vJO\ndUY0Ci0ZhlyyTSNJ1KYMOUeqU4lGoSXNMJw0w3AAAqKfek8N1e5KSbR4KvEGpZtQIiLNvgaafQ0U\nsf/wPgQtVk1MyDXMqolFEIR2I4BKaj2VRxUdKkFFnDaRuPbchiMLXUoPb8hW+UhiNQkMM2SSps8k\nRhM3KP42AmIAZ8AR1iYgHNXZqyPZxjw2Na9mRcO3TLBMxRGwsa11fWgf3WFn62YMSiPRmjgUgoJC\n+140Ci1Gpfx9I3NqkAVKF+g1SnKTjewtt9Pm8lNc6yIzQQ5T6g4eX5DSeulLJ96iwWw4zf/EAl5o\nyIfmYkmQ2Gs46pK43irljUSmQ+Qw0EfLrlsypw1BMUitpyqUyNzsa+jURymoSNNnkG0cSZpheJ+F\nFskcG6WgIqE9DwXORBQlUdIYlstSj6ND8rlX9ITyWY6HVqEjQZscSs6P0cajFLr32SeKIt4ONsEG\npQG9cvDdGKt0l/L/yheGXouImJQRXJt6+3FFhlqh4Sfxl7Oq8Xs+qf4HUepoJkfO4Lv6z8Ku45H7\n6fhardCwo20Tbb5mQCBGG8dP4644jqvbwBd+MoMXuVDjUQgERZbuaMThCaBWCswdH4NWPfATK081\n+yvs7KuQ7gLNzIsi1jL0ltOPSzAATQegZjvU75VESicEaUUkKv2wKNHK8fIypxeegJtyVzGlriLK\nXcV4gp3DG41KUyhEJ0mXhlohhzIOVNwBV3gSvreeJl9Dp5otJmVESIwk6FKIUkcPilWOwUSJs5Dv\n6j7jhtS70CllF02ZwYd8++koKBUCo4eZ2FDQii8gsq/Czvjh8gTyWASCIkU1UniXxaAixnwaTSRE\nEdrKJVFSuwu8R9hYKlRgTglfIZGrssucZkh5B02hGhQ1noounYbitIlSIrM+g+hBEqIjAzqlniRl\nalhhQFEUafO30OStJ0CQeG1ij5PTZY5PgX03EapITKoImrwNrG36kWGGLFmcyAxaZIFyDBKjtMRa\nNNS3eimudTE8Ti9XmD8GZfUuvH5pspGddGKFzAYNjnpJlNTsANcRtRcUKojNg4TxYM2Wk9iPgTvg\notZThTNgP4pLjwaVoD49/qaGGAHRT7W7glJnEWWuolABwo6oBQ2p+nTSDJmk6YcPyhAdma4RBAGL\nOkpOtO5nnAEnm1vW4gw42o0BMsKqwMvIDDaGdIiXKIrUe2uI0yb2eh9tTj/LdjYiAjFmNTPzouRJ\nUheIosj3OxpxuAPoNQrmTYgZusYCHhvU7pSESVvFEW8KYM2UREncKHmV5CgcthOtpMZdQVMX+QZH\n0qXdaBdWo10WS2vvO9SrZA8UnAEHZc6DlLmKqHCVdHLdAjCrIttDtzJI1KV2O+dARkZGRmboM6S/\nrffYtrGmaSnjzWcwJeqsXhXnMhtUZCToKapx0dDmo6rJQ3K0POk8kupmDw635LKTlWgYmuJEDELB\nV1C+lk6J7hHJkihJGCvnkhzBYTvRynYHn4ou7USPx4najYJUKduqiSEvYhzZxrwhaTl6KgiKQRq9\ndZS5DlLmPEidt7pTHwGBRF1KKJ/EopJv9sjIyMjIdM2QXUHxBr38q+LvoclMki6VObE/w9CL0AGv\nP8j32xvw+kUMGgXnjY9BqZC/WDuyYncTTXYfaqXA+RNjUCuHmKGAKELhEihbc7hNb4WEcZIwMcad\nurENQJwBBwfs+6h2l1NzDDtRpaAiTpPYXqk6mUi1Fd8h+9DgYWceT7udaNd2o+0PsSszgqOjETTk\nmEYzMmI8UZrovjjt0wZ/0E+9tyZUVK/WXdnl9dcqdKTpM0gzZJKqS0erlG/uyMjIyMgcnyErUABa\nfE18X/dZKHzEoDRyXuzFYVV0u0txrZPtxTYA8lKMjEgx9elYBzONNi8r9zQDkJNkYFTaEPRNP7gU\nDv4gPTdEw8grwZJ2qJSuDNJKSY2nkr227Rx05Heq8A0d7USTSdCmEKtN6LPQnnC7UXeYwDn0cAWd\nFDsKsAdsYdsm69IYGTGBdENWr1ZahzqegJtaTxXVocJ9NZ3qkhzCqo4hzZDJMH0Gcdok+XrKyMjI\nyPSYIS1QAHxBH6sbvw+rejs16mzGmCf1KLxAFEV+3NVEq9OPUgHnjYvBoJVjpgHW57dQ3exBEOCC\nCTHohlrl+LI1UPCl9FxrgSm3gy7y1I5pAOELeil07GVP23aafPVh7xmVEdLqiDaZRF0KUeqYUx7W\nExSDlLmK2NO2jQp3adh7RqWJvIhxjDCNxag6fW9CuAJOqtxl7RW+K2g84vfaEbMqst0yNplk3TAi\n1LJDk4yMjIzMiTHkBQpI4mKffSdrGpeG7vplGHKYHXMBGoW22/tpaPOyaq+0UpASrWVKtjxJtbn8\n/LBDcq9Ki9UxKXOITU6qtsDej6XnaiNM/jUYY0/tmAYIzd5G9tq2U2DfHRbeo0BBhjGXURHjidcm\nn3JBcixafE3stW0n37Y7rCK1AgXphmxGmSecFlXKRVGkyVdPaXtie62n6qh9ozVx7fUrpFWw01nI\nycjIyMj0D6eFQDlEvaeG7+o+CyXoWlRRzIu7BKum+xPOjQUtVDZJE5mzRkYRYx6YSbaiKJ6USdX2\ng20U10n5BXPGRg+tyvF1e2DnPwFRcuOaeBuYk071qE4pATFAqfMAe2zbqXKXhb1nUkaQFzGeERFj\nepXrdSrxBb0ccOxnj20bjd66sPei1DHkmEaRZczDpBo64Yv+oI8qd3l7TZKiTmFvAEqUxGrbc4S0\nKcTrktD24KaOjIyMjIxMbzitBApI9RaWNSyh3FUMgEpQMyt6Htmmkd3a3ukJ8P32BoKiVIzwnDHW\nAXd3dX+FnfxKB7nJ/Zsr4/EF+WZrPUER4iM1TB8xhHzuGw/A9ndBDIBCDRNvkQosnmYExSDOgB27\nv41Kdxl7bTtwBsKLUKbo0hllHk+aPnPQ5xuIokitp4q9tu0UOfI75Vkk6VLJNo5kuCFnUCZ8O/w2\nSl0HKXMWUekuxS/6O/WJVEczrD2xPV6biFIYQjcdZGRkZGQGBaedQAFpErK1dR2bWw47Mo2MGM90\n6znd+jLeV25nf6UDgPHDIxgeb+i3sfaUgkoHe8qlCaQgSKsaEfr+mWB0vA4z86KItQzM1aQe01IK\n296BgBcEJYy7HmJy+2TXATHQIWn7iITuDi5VACaVGZPSjEllJkJlRqvQ9bkY9gW92P1t2Pw27IFW\n7H5b++s27P42HAFbl5W+NQotuabRjIqYMGQLsLkCTvbbdpFv30WrvznsPSVK0gwZZBlHMsyQMSAn\n8QExQKuviSZvAw3eOirdJTQcsToEUjhbki61vUhixpD9fcrIyAwsHBs3UnbTzeSsXYMysv9C5utf\nfQ3bd9+R8fln/XYMmb7ntBQoh6hwlbC0/suQBWqsJoG5cRcToTp2HoU/IPLDjgZc3iAalcDc8TFo\nVKf+znFHp7FDJFm1nJnT9//4/oDIt9vq8fpFIo0qzh498FaSeoWtGrb8HfxuQIAxv4D4MbgDLspd\nxZS6imj01tHTfxuf6MUb9OLvomBdd1EJakyqiDDRYmp/GJUmAqL/sCVvmAhyt4sfb/v7kiByBux4\ngu4ejSFGE8+oiAlkGkegVqh7fS6DCVEUafDWUujYywH7PlxBZ9j7GkFLhjGXbGMeibrUHv0fHBKs\nohhEo9CiFFQ9/j8SRRFnwE6jt4EmXx1N3gYavfW0+Bq7dFID0CsMpBkyGKbPJFmfjkauByMjc0rx\nNzbS8Mab2FeswF9Tg9JqRZubg/XaazHNGpoV4UW/n0BrK6rovrF591VWcuC8uaR//BH6UaNC7UGX\nC9HrRWkZYjmyQ5zTWqCAVNH6+7rPQ4XFtAod58ZcSJoh45jbVTS42XSgFYDMBANj009tbHp5g4vN\nB6TcGrVKINKopr5VSlyePSoKa0TfTkAO1jjZUSKJoSlZFlJiBl+4SyecDbD5TfDaEYHmEfMoizBQ\n6pSShrtaSegPVIIaUQwSOIqNa38iIGBUmkLCx6QyE9EuiMzqyNO+uF5QDFLpLuWAfR/FzoJOFdKN\nygiyjCMwqcxdiMVD9VoOPz8yxEqBQqp639VD0KJRaNAqtCgEJa2+Zhq99TT56o8rNAUEojWxoSKJ\nsZqE0/r3KCMzkPBVVlLyi1+iiIgg9p570OXmIAZFHOvW0vT2O2QtW3qqhzgo8FZUUjRvHukffRgm\nUGQGJ6e9QAHpLub6puXstm0NtWUaRzA16uyjJsWKosiqvc002nwIApw7NhpzP4VSHY+aZg/rC1oQ\nRVApBGaMjEKrUvDDDilXJjpCzVkj+25i2XEFyaBVMHd8zOCvHO9uxb/5DaoVDkoNakojzdjpXHhO\nI2hJ1KWg6uHqgVpQH2XCeeihQavQoVZoUApKRFHEHXSGQq3sfpv0PHDodVu3K6ofbdKrV+gxqSyd\nVmIGex7JycIX9FHqKuKAfS/lruKjrlacbAxKI1Z1DFZNLFZNLNHqWCLV0agUAy8MTUZGBsr+53/w\nFBSS+c3XKHThN/sCdjtKkwlfdTU1zzyDc916AIzTpxP/6B9Rx8cD7WFM336L9dZbaHjlVfzNzZh/\ncgGJTzxB80cf0bjoLUSXC8ullxL/h4dC+z8w5zwsP78cX3kFtu++Q2E2E//7BzHOnEnN409gX74c\nVWws8Y/9L6YZM4D20KwbbyJn3dpQaNaRqxeH+qQtfoe6l17CU1CINjOTxKeeRDdy5FH349q+nbr5\nL+PauRNBqUQ3ehTJf/0rqthY7KtW0/DmG3gKDyAAujFjiH/kYbQZ0g3lfXkjpdj29mmt4YwzGPbe\nu9S/8qoU4vXF50D7qvjChbR89DGBxkY06enE3vtbIs49N+xckl+eT8sH/8a5bRvq5CQSHnkE4/Tp\n0j78fmr/8hy2774j0NKCMiYay0U/I+7++/r2j+M0Rv7GApSCkhnRc4jXJrGi8Vv8oo8ix35KnUVM\nipzGGPOkTjHmgiAwNj2CH3c1IYqwq8TG9BGRJ/2uZEOblw3t4kQhwNTcYOGdtQAAIABJREFUSKwm\nafKckWDgQLWTRpuPmhYviVF9476zt9yOyytNxnKSjINWnATEAHZ/G1X2QspqVlCRJOBXHBKkh8VJ\npMoaCoeJ1yX3WWHBYyEIAnqlEb3SSJw2scs+vqAPR8CGzd+KM+BAJajRKDRh4kfby7AhmeOjVqjJ\nMo4gyzgCV8DJQUc+Bxz7qPFUhvqoBFUXYvSwKD30XIHiiBWWw0UmO67C+DrYOSsFVbsQicGqjiW6\nXZDolQMnJ05GRubYBFpbcaxeQ+x993YSJwBKkwlRFCm/8y4Uej3D/u8fkpnHU3+i4u7fMPyjD0N9\nfZWV2Jf9SOqbb+CrraXyN/fgr6tHFRdL2ttv4y0+SMW996GfNBHz3Lmh7Zr/8X/E3nsvMXfcTvMH\n/6bqDw9jnDoV84U/Jfa+e2l4802qHvoD2cuWImjaozG6+k7poq3upZeIf+ABlDEx1D7zZyp//3sy\nv/yyy23c+/dTetPNWC69hPiH/4Cg0eDasgUxIEUTBF1OrDfeiG7ECIIuF41vvEH5HXeQuWQJgkpF\n+ocfUnLVVaS9/Rba3FwEtfrwMTocp+m992ha/C6JTz6BbtQoWj/7nIrf3MPwTz9Bl3s437T+5QXE\n//5BEp54nIbXF1L5uwfIWrYUhV5P0z/+D9uypSTPfwl1UhL+mho8xSXH/F3L9AxZoHQgy5RHgi6Z\ndU3LOejMxy/62NC8kv22XcyInkOqfnhY/0ijmvQ4PSV1LupavX0qArpDs93HuvwWgiIIwBk5kWGJ\n6jlJRkrrXPgCInvKbMRHak5YTDTZvBTVSDH41gjp/AcqnqAntNpw6GHrsALh6OhGpQfpKkorDom6\nlFA4zEBNGlYr1EQqrESqrad6KKc9eqWBUeYJjDJPwB1wISK255T0rZgNikF87blMeqVRXu2SkRnk\neMvKQBRDqwBd4Vi7Fk9hIVnff4c6UbphlfTC3yiadz6O9esxTp0KSCsDSc/+GYXRiDYrC+NZZ+Hc\nvJnshcsRVCq0GcMxTJiAc8PGMIFinDmTqGuuBiD27rtoWrwYTfowLBdfLLXdcQetn3yKu7Dw2KFT\nXQTkxP32tximTAEg5s47Kb3uOny1dajj4zr1bXz7HXR5eSQ+8USoTTv88LzLPG9eWP/Ep58mf8oZ\nuHbuwjBxAiqr9F2tsFiOmdfStPhdom+9BfNPfyqd3z2/wbl5M03vvEPSc8+F+llvuhHT7NlSn/vu\no/Wzz3Dv249h4gR81dVo09MxTJwIgDohAf348Ue/NjI9RhYoR2BSmZkbdzGVrlLWNC2l2ddIq7+Z\nr2o/Jt2QxbSoczCrDyedj0w1UdnoxhcQ2VVqI86iQano/7vVbU4/a/c34w9IHwiTssydxJFWrSAn\nyciecjs2V4CyevcJCYpAUGTrQSnPRSHAxAzzgLozX+upYkfrRlp9zdj9bWHFA4+Hzh8kTYxgWOK5\nJBuGy7UeZHqNTtl/ol0hKNAqdWgZAjlfMjIyXU7qj8R7sBhVXGxInABoUlJQxcXhOVAUEijqxEQU\nxsM1qFTR0WjS0xFUh6d6qphoAk2NYfvX5uaEnisMBgS9Hm12dqhNGRMDQKCpqWfnJghocw7vWxUX\nB6JIoKmxS4Hi3reXiA7C6Ui85eXUz38Z165d0liCQRBFfNVVwIRuDSlgd+Cvq0M/Pry/ftJEHCtX\nhbXpOoz90HgPXbvIyy6l7JZbKTr/AowzZmCaPQvjrFkDak402JEFylFI1g/j50k3sqdtG5tb1uAT\nvZQ4D1DuKmG8+QzGW85ApVCjVSsYkWJiV6kNhztAUY2TnKT+LVLncAdYs68Zr1/6YBuXHkFqTNeT\nosxEAwdrnbi8QfaV20mJ1qFS9u4fqKDSgc0lLbWOSDH1m31xbyh2FLC0YQmBLuo6HEKBAmO7C1aE\nyoSpoQSTrQGrx0+sOQfFmGtB0f/hWzIyMjIyMgCaYcNAEPAUHSRizpye76DD13lHISI1CJ3bEBCD\n4flygkp9RA+g43aHJt3t2wkKaeW2Ywqz6O/6u7fj8Q/t5sjjd5fyX9+OOjGRxKeeRBUXj6BSUnTh\nRYi+3rtjhnGkuOh07Q6PXTdyJFnLlmJfvRrn+vVU/eFhdHkjSHvnnb4Zi4wsUI6FUlAy1jKZLOMI\nNjSvpMCxh4DoZ0vrWgoce5gWdQ7phiwy4vWU1DmxuQLkVzhIi9Gh0/TPRNftlcSJ2yf9k4xMNZGR\ncPSYc6VCIC/FxNaDbbh9QYpqnOQm91xAtTp85FdJNU8sBhXZiQMnzn1321bWNEkuJwICqfrhRKgs\nnSx5QyExYhB2/xtq23MFojJh9C9kcSIjIyMjc1JRWiwYZ86g+Z//xHr9dSj04TcbAzYb2swM/HX1\n+KqqUCclAdJqgr+uDm1Wdle77d8xW60givjr61FFSWFV7n37us5L6QG6vJE412+A33Z+L9DSgre4\nmIQnHsd4xhkAuPbsgY7C6FDOyTEEkNJkRBUXh2vbVoxTzwy1u7ZsRZuZ2aPxKgwGzPPmYZ43D8ul\nl1Jy9TV4S0sl0SlzwsgBzN3AoDJxTuxPuSThF0RrpGU+m7+V7+r/y1e1H9MWaGbMMCm52h8U2VNm\nP9bueo3XH2TNvhYcHmkVIzvRQE7S8YVCWqwOs0HSogVVDjy+nt29CIpSaJfYnusyMdOM4iSEsR0P\nURTZ0LwyJE5UgpoL4n7OT+J/zszo8xhvOTOUV2RURbSLExH2fwa1O6WdmFOlQozK06Omh4yMjIzM\nwCLhscdAFCm+4kravvkWT3ExnoPFNP/rXxRfcinG6dPR5mRT+eDvce3eg2vXbqoe/D260aMxnnnG\nSR+vJi0NVWICDa++hrekBPvqNTS88Wbnjt0xie3QJ/rWW3Dv20f1Y4/jzs/HU1xMy8cf46upQWGx\noIyKouWjj/GWleHYuJGaJ58KD1+LjkbQ6XCsXo2/sZGAveu5WPStt9D4zmJalyzBW1JC/YIFOLdu\nxXrrLd2+Bo3vvkvrkiV4Dh7EW1pK6xdfooiIQJWQ0O19yBwbWaD0gARdCpcnXs9M63loFVIMeIW7\nhI8qF3MgsJqYKGniX9bgpsnWR0uO7fgDQdbub6HNJd0tSI/TMyrN1K14R0EQGJVqat+PSH579ffu\nUlTtpMUhHTcryUCk8dRP5gNigB8bvmJ76wZAKjx3ccI1pBmGH30jUYQD30DlRum1MR4m3AQqOd9E\nRkamfxFFkbbvv+fgZZeTP+UMSm++mfpXX8Oxfj1BV/csw2WGJpqUFIZ/+gnG6dOpe/FFii+9jLKb\nb8a2dBnxjzwMQOrrr6OyRlF2002U3Xwzqrg4Ul555cQP3k03ro5tgkpFyosv4q0o5+Bll9Pw2mtd\n2+t2Z98dXutGSCFS3uJiSq75BSXX/IK2r75GUElOlMkvvYQnP5+DF19C7dPPEPvbew67igGCUknC\no3+k5aOPKZx9NhV33d3lKUddfz3Rt95C3QsvcPDiS7AtXUbKggVhOSdHHXt7u8JopOntdyi56mqK\nr7gST34+aYv+jkIrzyf6CrkOSi9xBZxsal7FPvvOUJtKUKO15aF3jCLaaGD2qL6pPRIIiqzb30J9\nm5T0nRytZUqWpUf7FkWR1fuaaWiT6rbMHReNUXf8CD+728+ynY0EgmDUKZkzNvqkmAAcC2/Qw3d1\nn1HpLgXAoorip/FXhJkXdEnxcij6Vnqut8LkX4PW3L+DlZGROe1xbNxI/Qsv4tqxo+sOKhW6USMx\nTJyEYfIk9BMnhkJnZGRkZE5HZIFygtR7aljfvIIqd1moTQjoMDrGMD1hMsPjTCe0/2BQZGNhK9XN\nHgDiIzVMzYnsVYhVs93H8t2SC0dKtI4p2ZZj9u8oagDOGhlFjLlvK9L3FIffztd1n9DorQMgTpPI\nBfGXH7/2Q8V6KbQLJFEy+deSSJGRkZHpJ9z79lH34ks4Vh12B1JYLJhmzcK9ezfe4uKjbqvJysQw\naTKGSRMxTJ4cyj2QkZGROR2QBUofIIoiFe4SNjavpKF94gygDJiYEXsWuREje1WvIBgU2XSglaom\nSZxER6iZPiKq1y5cABsLW6hslPZ39mgrUaajh2sV1zrZXmwDYHicnvEZp3a1odnbyFe1H2MPSFbH\nw/SZzIn9GerjVXWv3gZ7PgJEUBtg0v+AKb7/BywjI3Na4i0vp/7lBbR1KEgn6HRYb7iB6F/ditIs\nfZb6m5pwbtmCa/MWnFu34t67F/4/e2ceH0V5//H33nc298WRkISE+z7kUPBAQVFbb9C2tmqrrW3V\nnmqtVm3VtlbppVZ/rfQQj7ZaQQFFBATkvklIyEECue/sfc7vj2ezm80BCSQQcN6v17x2d3Z25pnZ\n3Xmez/O9QkXpOmOcNo2Eb30L09w5cipTGRmZCx5ZoPQjkiRR4izk84ZNOKXW8Pp4TSIz4i5huCGr\n1x1LUJLYdbSVypA4iTOpmTM6Do36zMKG7G4/6/Y3IkmQFKNlzujYbtvk8gZYt78Rf0DCoFVy+YSE\nMz72mVDjrmRN3X/xBN0AjDJP4OKEBacWfvUFcOCfInOXSgdT74aYoWehxTIyMl80/A0NNLz0Ms1v\nvw3tqU9VKmJvvonE+77dbe2HjgQdDlz79+PctVsIl/37kdzuqG30Y8aQ8M1vYllwBQqVnHlQRkbm\nwkQWKANAIOjn/aLtNGj2ElRFgh9TdUOZGXcJqfohJ/18UJLYXdzKiZClIzYkTrT9JBD2H2ujtEa0\na/aoWFJio4O6JEliW1ErNSG3sll5saTGnbvArzLHUT5pWBWucTItdg5TrLNOLfaaSmDf6xD0g1IN\nk78OcT1X65WRkZE5HQJ2O01//SuNry9HcjrD6y2LFpL8/e+jzcw8rf1KPh/u/Hza1qyl5a23CHbY\nt3bECBLuvhvrtYujAoVlZGRkLgRUTzzxxBPnuhEXGkqFkhR9GnXlw1BIGgLaRiRFEHugjUL7QRo8\ndcRpEzGqutYjEeKkbcDECUCcSUNZnYugBK1OPyOSDVGD/comD0WhTF9DE/SnVTelvzjctpcNjasJ\nEkSBgnkJC5lgnXZqcdJ6HPb9DYI+UChh4h2QcPbzxcvIyFy4BB0OmlesoPL7D4g4k5DVxDR7NkNe\neIGEr34FVewpknecBIVKhSY1FfPcOcTdeisKgwFPYSGSx0OgpQX7+vW0vPc/FEolutxcFJpzn2FR\nZnDgOnSY4nnzib3hy6gslnPdnH6j+PIrkIJBjJMndfta5sJBtqAMILtLWqmodxNUuIkdXkyZZz8B\nIv7FGYZsJlsvIkUvgh8lSWJ3SRvHG4RJ32pUM3dM/4qTdo6csFNwQoiQqdkxDE8SxaE8viDr9jfg\n9Uto1QqumJiITnP2Xbt8QS+7WrZwoG0XIDKkLUi6juHGXlhA7LWw+xXwuQAFjLsFUuWbl4yMTP/g\nKSmhecWbtL73HsEOtRb048aR/IOHMM2aNWDHDjocNL/zDk1/ex1/bW14vSoujvivfZW4pUvDMS4y\nX1xchw5z7JZbyFn3cY8JFsq/+jV0ubmk/uzRs9y606f48iuIu+MOEr5+JwD+5maURqOc3vcCRK4k\nP4CMHWamqtGDP6hHqpvILaOnsaftc4rsh5CQKHeVUO4qIV0/jEkxM6mpiuNEg7CcWI39bznpSE6a\nibJaF25fkPzjdoYk6FEpFRwst+H1C806IdNy1sVJQApQYNvPnpbPcQWFO4NeaWRRyg0k69JOvQNn\nE+z5v5A4AUZd37M48fvBbhf1UbRasajVZ1wNV0ZG5sJD8vmwrf+U5hUrcG7bFvWeNjOTpAcewHLV\nlQMewK40mUi4807ili6l9X//o/G11/CVVxBobqb+xWU0vvoacUuXEHfHHWhS5GQgMuceye+PKqjY\nn8jpuC9cZIEygOi1KvKGmjhcYcfmClDXaGB+2kImW2eyr3UHRfbDBAlQ5T5Olfs4aikBo24cSaos\n5oyOG1BxoFYpGDXUxL4yGy5vkNIaJxaDOmy9SY3VMjRBP2DH74wkSRQ7CtjZshmbP5JgIFGbzBVJ\n12HV9OIm5GmDvf8HXhtIQMYVoMuCslKw2YQYsdvBHnreXXE0pTIiVsKLLvq1TgdmM1gs4lFvkEWN\njMwFiq+ujpZ33qHlrbfx19VFvWeaM4e4pUswz5s3YAOwnlBqtcTdfDOxN9yAbe1aGv7yKp4jRwg6\nHDS++hqNr76GNiMDw9SpGKeK+iqa4cPlDGCDDPtnm2l45WU8R4tRAPrx40l55GF0WcJbwFdZSfEV\nCxiy7EVa3nwL5969aIakk/rII5hmz+6wn8+ofeZZfJWVGMaPJ/a22/rcFsnrpfa3v6Xtw9UEbTb0\no0aR/JMfY5wyBRD1fCq+die5n28Nuy62ty/z3+9gGDs2vM2wV16m/o9/wnPkCEP/8HtcBw9hW7uW\nxPvupe7FZQQaGzHOuoj0p58O78t18BD1L76IOz8fyedDl5dHyo9+iGFSzx4QnS0qzW++RdPrr+Or\nrkZpNKIfN45hr7yMQqmk6uFHCDQ3Y5w2lcbly5HcHuKWLCHpwQdo+OOfaH7zTVAqSPja10i4++4+\nXz+Z/kUWKANMdqqRY3UuHO4AR044GJZowKqJY17iVUyLnc3+1p0cbttPUOHHr2mkLW4jCvVBjnlm\nkqMejUoxcFlaMpINFFc7sbsDFFY6wumL1SoFk0bEnJWOTJIkjrvK2NG8iUZffXh9jNrK9NiLyTaN\n6l07XHZYvxyaVOBLg4AWSouAor41KBgEt1ssvUWlEkLFbAFL6NHc8dEstpGRkTkvkCQJ586dNL+x\nAtu6dcLaGkIZE0Psl79M3JLbTjv4vT9RqFTEXH01lkWLcGzaRMMrf8G1Zw8A3vJyvOXltP73vwCo\nkhJFMciQYNHl5cmZwM4xQZeT+K99Df2oUQRdLhpffpnj991H9gcfRIne+mW/J+XHPyL1icdp+PNL\nVP7gh+Ss/wSlwYCvpoYT93+X2FtvIW7JEjyFRdQ++2yf21L7m99iW7uW9Gd+hWbIUJr+9jeO330P\n2R+tRZ2YKDbqZeX5uud/R8pPfoxmeAZKkxHXwUP4KitpW72GYX/6I0Gnk8oHH6LuxRdJC4VCBx0O\nrF+6npSQy1nzv97g+LfuJfujtaisJ6/bBkLg1Dz9NOnPPYtxyhQCbTac26Otnc5du1CnpZLx97/j\nKSig8oc/wl1QgH7MGDLf+BeOz7dR84tfYJo9G/2YMX27gDL9iixQBhiVUsH4DAvbClvwBSTyj9uZ\nHKonYlSZ0bdMJb4hG6fpCG7jEYJKL63+JjY0rGZX8xYmWqczyjwe9alqfZwGSoWCscPNbC9qxReQ\n8AWEa9e44WYMuoHvtGrdVWxv3kS153h4nUFpZErsLEZbJvZOnLW0wOGDUHAIAmoiP+luQqtUqojV\no6OAUKnA6xWLxxN57vWCt/Nrb9f9BgLQ2iqWnkhNhTFjIStbFisyMoOUoMNBy3vv0bxiBd7ikqj3\ndGNGE790KTHXXIPSYDhHLewZhUKBed48zPPm4TpwAMeWLTh378G1dy9Bh4g3DNQ3YFu7FtvatYBw\nFzNMnoxxmhAthkmT5ED7s0zMlVdGvU57+mkKp8/AdeAgximTw+vj7/wa5nnzAEh68EFa//c/3AVH\nME6ZTPMbK9CkC6sKgG7ECLzHyqj//R963Y6gy0XLm2+S9qtfYr74YgBSf/EEju3baX7jDZK+972e\nP9xNKHPSd++PsvAASMEg6c8+g9IkEu/E3nILre++G37fdNHMqO1THn0E29q12Dd9hvXaxac8B1+N\nsJpYLr0UpdGIJi0NfV5u1DbKGAupP/85CoUC3YgRNP71b/gbGkh+8AEAtBkZNL76Ko7tO2SBco6R\nBcpZIDVWS7JVS12rl2N1LkakGLAa1ewts1Fe70aJnrTAVC4aMo8S90EOtO3EGXBgD7SxpekTdrd8\nzriYyWQZ84jVxPerZSMtTke8RUOTTWSfSbBoyEwe2M632dvIjpZNHHMWh9dpFFomWWcwPmYqGuUp\nUmYGg1BeDocPQeWJ6Pc0wJBMsMR0tWro9WfuiiVJwrrisIOtg7uY3RZ53Z3rWE2NWLZugVGjhVi5\ngDKryMic79g2bKDm8SeiAs8VWi0xixYRt3QJ+gkTzhv3KMOECRgmTACE/7+7sBDX7t04d+/BuXs3\ngYYGQAgyx+bNODZvBkBpsWCaOwfL/PmYLrlE9u8/AyRJwltWhnP3buJuvrnH7bzHj1P/4jJcBw8S\naGoS/Zsk4auuAiICRZ8bGWi319MJNDWKfZSVYpg4MWq/J3OL6rYdFceRAgEMkyPHVCiVGCZNxNNJ\nrJ8ShQL92LFdVmvS08PiBECdnIy/qSn82t/URP2Ly3Du2IG/sRECAYJeL77q6l4d1jx7Npr0dIov\nvwLT3LmY5swmZsGCqGPqsnOi/sfqhASU1uikEqrEhPC1lTl3yALlLKBQKJiQaeGTA6JA4oFjNiwG\nNeV1YiBrMaiYOzoOvVbFRN10xlomU+Q4zP7W7bT5W3EHnexq2cKuli3EqK0MN2STYcwmTT8UleLM\nvkKFQlh4PjvchEqpYHLWwLl22f1t7GrZQpH9MFLIwqFExdiYSUy2XoRBZTz5DpxOOFIA+YchNCMY\nxuCCZBVcejfoBjAtskIBBoNYEpO638bvjxYwTU1wtCjiOrZvL+zfB8MzYOxYGDpMjmGRkTlH+Jub\nqf3VM7StXBlepxkyhLglt2G98cbzfpCuUKsxjB2LYexY4r/6VSRJwldejjMsWHbhK68AIGizYVu9\nBtvqNaBQYJg0CfP8+Zjnz0eXO/KsCDR/Q4MoVLlnN56jR9GkpKLLy0OXOxJ9Xl7E1WiQIfn9uAsK\nQkU2d+HavYdAczPASQXK8W/diyYtjbQnf4E6OQWFWkXJNYuR2gt9ttNNjJMUDPbrOfRI6HtXKEVc\nbMfkr1IH98eOdGdl7BKnpVAIQRai6ic/JdDURMqjj6BJT0eh1VLxtTuRfN14LnR3TJOJEf/9D86d\nu3Bs3Urjq69R/8KLjPj3O6iTknpsg0IdbTVUoDh711amR2SBcpawGNRkpRgpqXHSaPPRGLJYmPUR\ncdKOWqlmjGUio8zjKXUUsrd1O02h+Iw2fyuHbHs4ZNuDRqFhqCGT4YZshhuzuq2r0hvizRqunJSI\nUqno98B8f9BPhauUYkc+5c5SgqE0ywoUjDSPZVrsHCzqk6TElCSoqYbDh0Wwe8ebhkYNxhaIaQOT\nFqZ/Z2DFSW9Rq8EaK5Z2Zl4EpSXC6lNbK86r/JhYYqxCqOTmCSuPzMAgSdDUCCggPl4WhV9wJEnC\ntnYtNU89TaBRzJYqzWaSf/wjYm+88YKNzVAoFGgzM9FmZhJ7442ASATg3LYN+4aN2D/7jKDNBpKE\na+9eXHv3Uv/CC6jT07CExIpx5sx+SesqSRK+ioqwUHLt2o23vPykn1HFx6PLy0Wfm4cuN1eIl5xs\nlGf53hl0OnEdOBARJPsPRBXp7A2Blha8ZWWkPvE4phkzAHAdPhwV89QbtFnZ2D7+OGqda9++vu1j\n+DAUajWuPXvQDh0KCAHk2rcf62LhXqWKjwdJwl9fHxbu7oKCfruXuvbsIeVnPwu7mPkbGvDX15/i\nU9EolEpMM2dgmjmDpPu/Q9Gcudg2bDipSJQZnMgC5SwyaqiJ4w2ucBpfs17FxWOixUlHlAolOebR\nZJtG0epvptxZQoWrhGr3CSQkfJKPMudRypxHoRGStWkMN2aRYcgmQZvcp9mu/ow5kSSJKvdxih35\nlDqK8EqeqPczDTlMj7uYeO1JZsLa2sTg/UiBsEB0JCEBRmZC0xoIuEChggl3gDG+386h31GpYGSu\nWBrqheAqPio6orZW+Hwr7NgOOSNh7DhI6sE6I9N3mpvFtS4+Kn5XACaTsGANz4AhQ0D2u/9C4aur\no/app7B9vC68zjx/PqlPPI4mNfUctuzcoElOxnrddVivuw7J58O5d68QKxs24C0tBcBfVU3zGyto\nfmMFCoMB06xZ6LKzUcZYUFksKM0WVBYzSktM6FGsU5qM4b5ICgTwFBaGXc2cu3cRqG/ooVEadDk5\n+KurCbS0hFcHmppwfr4N5+cdgp+VSrQZGSHBkos+T4gXzZAh4Vn/MyHo9eItKcFdWIjnSCHOPXtw\n5+f3KCTUKSkipmfaVIxTp/W4X6XViioujpZ3/o0mNRVfTQ11v32+zxnh4m67labXX6fmV78KB8k3\nv/V2n/ahNBiIW3Ibdb99HlVsLJqhQ2n62+sEGhuJW7oEAO3w4ajTUmn4459IfuhBvCcqaXj5la47\nO83yetrMTNpWvo9hwniCTqe4FtpTuHx3wLZhA76K4xinT0NlteLYtp2g04kuO+e02iNzbpEFyllE\nq1YyIdPCruI2YTk5iTjpiEKhIFYTT6w1nonW6XgCbo67j1HhLKHCVYonKDJO1XmrqfNWs6tlCyaV\nmTzzeMbFTDm161Q/IEkSTb56jtrzKXYU4AjYo97XKLSMMI5ktGUiqfohXXcQDIoYjYpyEV/S0hz9\nvlIpAszHjoP4GNj1shAnAKO/DHEjBujMBoDEJJg3Hy6aBUWFwqrS2iqC7QuPiCU+AZKTxGNCgniU\nrSu9x+GAkmLhWtfQzQDI4YCCfLGoVEKktAsWOTbogkWSJFrffY/aZ58lGBKrqthYUh59lJjF15w3\nMSYDiUKjwTRjBqYZM0j58Y/wlpdj3yjEimPnLvD5kFwu7OvXY1+//tQ7VKlQms2ozGYCra1RhS07\n0jFg3zBlCoYJE1Dq9UihGXtP0VE8hYV4iopwFxXhLS6OuEEFg3jLyvCWlYUTAAAojUYhWjoJl54K\nWUqShL+qCndREZ7CotCxCvGWHRP35x7QZmWFM6MZpk5DMyS9V78lhULBkBdeoPaXv6T0uuvRDh9O\n8k9+TOX3vt95w+4+HH6qSUtj6B9+T+2zz9Hy9jvox44l+Qc/oOpMiC6DAAAgAElEQVTHPz55A4LB\nKEth8g9+ACioevRRgm029KNHM+y1V8NudQq1mqG/+x3VTz5J6ZdvEGmIH3qQ4/fed+r29oK0X/2K\nmp//nLKbbkadnETS/ffT2NxpLNB53x1eq2JiaPrkExpeeomg24122DDSnn4qKtlAr5DvA4MCuZL8\nOcDpCaDXKFEqz/xPEJSC1HmqKHeVUuEsockXPRhTKzSMtkxgQsx0zOr+H3jZ/K0U2ws46iigudOx\nlSgZZshipHk0GYbsrpnI3G44XiFESUVF9xmyLBYYPQZGjQKDEYIB2Pc6NIUC7DPnQ85V/X5eZxVJ\ngspKyD8Ex471PPtkNAnXpISEyGNsnJwVrB2PB8rKoLgIqqq6XsekZBg5UnQ+5eVQVRntMthOfAJk\nhMRKcrIQxzLnPb6qKqp//ng4IBwg5upFpDz6KOqEhLPSBmebl8ZKO35fkPg0IzEJBhT90A+cLQJ2\nO44tW4VY2boVf0PDSQfuJ0OVmCgG9VOnYpg6BX1eXp8sB5LPh7e8XFg1QuLFXVSIv+rUAdXqtDT0\nIeGiTk7GU1oi9lFUJNzbToZGg37UqIggmTIFdfwgtt6fhJKFi4i9+SYS7rrrXDdFRqYLskC5wLD5\nWil3lVBkP0y9tya8XomSXPNYJlln9q7o4Ulo87VQ7iqh1FFEjedEl/dTdUMZaR5NljEPvapDoJwk\nCXebinLhvtUei9GZlNTIALFjrIAkwZH3oHKHeJ00FiYsBcUFNIC028WsfnUVNDZ2L9o6olSC1Rqx\nssSHxIvZ3D8Zy9raRNxGY6MQlImJkJYm4mYGwyxTICBE7tGj4jfVebAUYxWiJGckxMZGv+fzwYkT\nUHFMCOTu/Mf1evE7nDIlOqZI5rxBCgZpfvNN6n/7PMHQd6xOSiL18Z9jueKKATmm3xegudpJwwk7\njZWRxWWLDnxW61QkpJtIGGIOLeK53nR+uBxKkoTkchGw2QnabQRtNvHc1hZeF7DZCIaeK7RaDJMm\nYZw6FU1GxoBYrAJtbXiOHg0Jl4glJNg5sUovUKemhuJdctG1x7yMyOyT29FgxN/QgH3DBqp//jgj\n/v2OnE5XZlAiC5QLFMnlolKqZW/bdqrcFeH1ChSMMOYy2TqTRF1Kr/YVlILUeCqpcJZQ7iqlxdc1\n/V6cJpGRptHkmEdjUXcqqNSefavwSCQGoCNarchklZEBw4aLDFndUbEFilaJ55Z0mPYtUJ3fHcVJ\nkSThitQuEJqaxPOWlu5n/jui1UbESkIH4dJTx+p2i303NYWOFXreU7Cm0Shqu6SmiSUhYeAtDYGA\nELgdr0ddbVcRZzBAdo4QJknJvRNSkiRcwcqPCQHdOTBTpYLpM2D8BNmich7hPXaM6p89hnPXrvA6\n6403kPLjH/eq8FtvsDd7qK9oo7HSTsMJB01Vdlpqnafrhg+AKVYXFivtS3yaEaVK/u2dDpIk4aus\nEoKlKOQmVliEt6wMgkHhCjZyZChbWC76PGFd6a/fyGCj+MqrQAEJd95J3JIl57o5MjLdIguUC41g\nED7bJARBcgrMvZjaGB/7WrZzzFUctekwwwgmWy8iTT+0y27cARfHXWVUuEqpcJXiDXq6bGNWWcg2\njWKkeQzxmqTo2TBJgtoaEV9RWtp1QB0bK2amMzKExeRUbkoNR2Df3wEJdDEw/dugvzA7j1MSCAiR\n0nGg3tgIzl7MEFosEbESDIb20dS7z6pUPbtzaDTie0wLCZbk5G7TYvYKSRKWpO6EWU+3K7UaRmQJ\nS8nQoWcuIhwOYZk5dkyIlnaSkmH+fHENZQYlkiTh2rWL5hUraPvo47DI1qSnk/rkk5jnzumX4/h9\nAba9V8r+9ce7rQvbjlKtID7NREJ6xEqi1qloqnJ0sK448LpOnrlJo1eRmmUlPcdKWk4sKZkxqHsR\nwyjTM0GPh0BLC+qkpH4JppeRkek/ZIFyISFJsOFTEXjdkdFjYMZMmpQ29rXuoNhREK5DApCqG8Jk\n60WY1TFUuEood5ZS66mM2qadFF16qA5LVldRAsJt5miRyFLVudBRfALk5QlR0hd3GXsN7HwZAh5Q\naoTlJKabQPsvOm53ZDDfGytIT/RkfVGrxT6rq0OFJ6u7d4sCIRCSksHYx6KfLpdoc29c22LjRPuG\nD4eMzIHLxFVdBRs3iEQG7ceePEUscvzPoCFgd9C28n2a31iB5+jRqPfibr+d5IcejCrYdiY0Vtn5\n+P/yaayMDvi2xOu7WD6sKQZUp7B8SJKEvdkTESwn7DRWOWiucSIFu++ilSoFyRkW0nJiSc+JJTXb\net64hsnIyMicClmgXChIEmzaAEeOiNdmsxg8tlsudDqYMRNGjaYt0Mb+tp0U2g4S4OQBjlqFlqGG\nEWQYsxhmyOo5I1hzsyigWFQYPbgMZ98aK2bY++pz7LHBzj+DO5RmcsLtkDyub/v4ItMxjqSj+1Zr\na2iQHyvER3v8SkKCSMHbW7eotraQYAkt7YP4/sRkihZKCQlC4J5NceD3w66dcGB/xIoTHw/zLhXW\nIplzhufoUZpXrKD1vf+FY0xAZKOKuXoRcXd8BcP4/rlnSJLEwQ2VbP1vMQGfuLcmZ8Yw+8vZJA4z\nozP2r0AI+II01zqor7BRXdxKVXELraECv90Rn24iPSeWtJFWho2Ox2Du4NLp9wvLoM0mLJT2Do82\nO3g9IhGHxQxmi+hD2h8tFuHWORjizmQGF++8Jfr4k6RTHlB27RRjD7cb5l8q6onJXBDIAuVCQJJg\n00bh1gUQFwfXXieyGm3ZAieOR7ZNTIQ5F0NqKk6/nQNtu8m37cMnRUSFVR0XrqeSqh+KStHDQDAQ\nEO4vhw+LjEgdMZtD2bdGi47tdAj4YM9r0BqKocm5SmTtkjlz/H4x2OjvQb7TKYRKdbWID+mr9Uaj\niQiRdsHUDwXh+o36OmGlbK/No1DAhIkwbfrpu7TJ9BnJ58O2bh3Nb6zAuXNn1Hua9HRil9xG7I03\n9mt2JWebl/V/L6D8UMgyrICpCzOYvnjEKS0k/Ymj1UNNcQtVR1uoLm6hodLRreejRg2XTfWTExsS\nIn0sItgFpVJMFlg6iJf0dEgfIguX/qChHv77H0hJgeu/3LvPFB6BLZvhG3cPbNtOxqkESuERcc9U\nKMRYxWgUbsAXXQSWkxRp7g1NTeL4Vy0UE6BarWzVvoCQBcr5jiSJmJOCfPE6NiRO2kWBJAk/+s+3\niJmzdnJzYeYsMBrxBNwUOwoIEmSYYQSxmpN06g5HZABaVtY1dmHoMGEtGZ5xZnEAkgSH34aaUDXc\ntMkw5ma5I5Q59wQCsG8v7NkdsVBaraK2TVr6OW3ahY6vtpaWt96m+Z23owv8KRSYLp5L3JIlmC+5\npN+rwB872MD6vxeEs3CZ43Us+PoY0keeWUbELvj9IatGu2UjJC4cdvB4hXXa6xGPod+eN6CkxqGn\n2m6gymGk1qEnIEXuvROTmpg1pB5Vd7dOozEiNrRaIWLaLSuncrNsJzYWxowVM9dnezIhGARbW3Ss\nWiAAWp04n86LruP60HONZnD0K59tEn1mUSF8+QbRl5+MYFC4U58PAmXLZlhyu+jXW1qEt4dKDTed\nZp/ePmw9dgw+XgvfvPd0Wy8IBuXkJ4MQWaCcz0gSbP5MmDdBdBTXXt+9xcLvF4OqfXsjgc4ajZj5\nHTuu+1kHSRIuO+2CpKa65yxceaOEMOmvVKwntouUwgCxmTDlLlDKM9Qyg4imRtiwQVhV2hkzFmZe\n1HO2NJnTIuj1Uv/iMpqWL49K1KCyWrHeeCNxt92Kdvjwfj+u3xtg67slHPw0kk595LRk5i3N67s7\nlyQJN5TO4qOju5WrZ/et3hIIKihrNbHheCqegLivpyXClQtMmFOs0S5cJxNyHk902zq3t3PaXrUa\nRuaKfiAh8YzPowvt8WlNjZHkHs2nEWPXHR0FS4+CpoOwiYkRS38Nav1++MdyYTk5uF8cY9bsyPs2\nG7zxT7j8CigoENbpmbNgy2cRy4RCIURCd0LB7RbbVleL79VigYmTRL/dzsr/CVGk04kJT4VCfJ8d\n2+FyiXi8yhMiW+KUaaK9vREoHUVU8VFY/wncukRM7ni98PlW4ZHh90NSElw0Wzx23McVC2D7NiFy\nMjNFAp6O5//Ne8XzPbuFR4nLJfY/fQZkjuj5Wl40S/x+t2yGK64UE7p2u0i4cunlwgtlx3axv4xM\nMRnV/t85XgF79ojfIojYy9lzhCdLx+MtuAoKDov4TYsFZs8V+2+npRm2bRMxj5IkPAgumSe8CUC4\n7x/YJ8Zg7fXhJkzs8Sd1ISCP+M5XJEn8mdrFidUKi6/r2Z1KrRZiJDdP3AiOlYmA9s+3ij/y7LnC\nXN/Y0CGmoKbnDlOlEr73uXkipWt/BijbayLphHUxIu5EFicyg434BPjSl+HgAdi5Qwyc8w+LNMWz\n54gOcTDMzJ7neEpKqPzhj/AUFITX6cePJ27pUmIWLUSp1w/IcRtO2Pn4r4dpqhIDcY1exbwleeTO\nSOm+fkcgIAbt9g4D+c7xHqczmG63cuj0vRo8q7RacrRaktsCrHn1sIhfaYC3V3m58q5khg7ppdub\nTieWnopYut1i4Jh/WAya/H4xsC3IFynIx4wVA9e+WrO6Syfe1Ng7F7WYGHEdvN7IcqqU7BDZti+o\n1WIQ2jmhSE9p8k9GaYlwd4qPh5F5sO4jMdHRWQDt2C4G7vMvDQ3Mg+Les+R2QAJ1D/1wIACJSTBp\nCmg1ov7TZ5uEUB3SIeFM8VGRSv1LN4ixwCfrxIA7J0e8/+l6Yc1bfK04/62dPDN6i1IVaRfA6g/E\nb23RNeKx8Aisel8ImPYxTSAghMDF88Q1NhpFWYJNG+GrdxJOpXfwgIgVvHieEDhFhfDRWrjx5ujf\ncsdrqVQKERIICMF1+QLx/KO1YlGr4cqF4jf/0RqRnbRdHPj8MGGCEOV+n2jjmtVw623R39/OHTBr\nFsy9RAioTz6GpXeIsZPDAf97T7i+Lb5OXIO6uoilqCAfdu2CuXPF99jUFLJCqcQE8wWKPOo7H5Ek\ncWM4fEi8tlqF5aQ3GWpiYoS/ZkUFbN0sLCTNzfDBSvEn7KkD1Wqj08gmJQ2Mz33ACwdXQNAPKGDc\nraA19/9xZL6QSD4fQU/XlNknQ6nV9lyYTakUM5GZmWJmsbpaDEQ/WisGG5MmCwEvuw/0GUmSaHnz\nTWqffQ4p9J3px40j9eePYZgwYeCOG5Q48OkJPn+3hIBfDG5Ts2K44utjsSZ1Gnw2NsL2z8WA4TQK\nAaJSRYLQzZ2C0y0WcU8/TXe1mCS44UdT+Oyto+RvrsJl8/H+sn3MvD6LKVdmnHkFe71e/PYnTITj\nxyH/EJSXi/dqasTy+VYRhzh6jDifjnRMJ94xgcfJ0om3o9NFx6nFx0NcN3WeJEn0ad5O7nEdF083\n6zpv2117/H5RL6lzzSSjsatoSTyFRanwiHC7BjFRqFYL96WsrOjtxo2PXtd+vqcSRSaT+K7aGT0G\nKiuFIOkoUOLixUQmiHFFQb6wluTkiO/leIWYlElJFdvMvwxW/Ovkx+6M3Q7794HJLLw+Kk+I7/5r\nX4/81qfPEL+lo0WRdksSzL04+lp2d/4H9ovPtIuq6TPEhOv+fXDZ5ZHtOl/L8DEuEecOYh+HDgoB\n1O6+mJEpYm7bBUrnfcybD3/7PyEwUlMj6ydMEK7vIBIWFRWK805NFWM5jQYWXBnpK2I6xOfs2S1i\ndkaEjmWxiL7l8CFZoMgMItrFyaGD4nWMVcSc9DV95vDhMORWMduwe5e42XYUJ+2BbGmhJS7+7Ayy\nilaBI+QyM+IyiMs6+fYyMt0gSRL+2lo8hYW4O1ST9pSW9nkWW6HVEnfHHSR977s9z9ZbQ+6VBfnC\nBcHrFYOu9Z+ILDMTJwlroxxI3yv8TU1UP/oz7J9+KlYoFCTccw9J370fxUClk0YEoK9fXkBFflP7\nYZl2zQimLcroWiSx8oQQoiebedfrQ2Kju8xYZtAbBtTKptaouPSOUaRmWdm4opCAL8i290qpKWnl\n8jvH9E9aYoVC9CfDh4t4kPx8YZV3u4UFfu8e4VqckSEC6ttrOPU6nXhstBCJ70OmQYVCDPw0mr73\nke2ERU5IsLjdoXPokNK946SH0ymWjslpvnVfz/tvbRVi7vIFkXU5I6GwoOvgt93l6XTOYe8eKCkR\ncaOBgLAsdY6ZS+hkXTOawB3yomhpjqSPb6ddRJ8Knw/++ppoR7s158qrxP4aGsT1Xf636M8EAtDW\nISukQtGzNa8dr1dMFKSkRq9PTROTsh3p7lqqVBFxAmIcZDBEx1YZjeJatNPWBju3C0HidkfErN0G\ndGhHx9pZ7des3UOlsUG0sbsxlsslRN2mjcLq1U4weMFb6PvUW9bU1PDTn/6UDz/8EJvNRnZ2Ni+9\n9BIXX3xxeJsnnniCV199lebmZmbOnMmf/vQnxowZE37/oYceYvny5ZjNZp555hmWLl0afm/lypX8\n5je/YdOmTch0gySJGamwOIkJiZPTtDCoVEKF54wUQsXrjVQHj4k5+z/+mgNQGcrIEzsCRlx6do8v\nc14SdDjwHD2Ku12EFBbiPnqUYD+lPJa8Xpr++ldsn6wj/emnMU6f3v2GCoVwa8nOEULlwH7RubS1\niY5l9y4x6zZ6jByjchLsn31G1cOPEGgQQfDqtDTSn3sW04wZA3rcysJm1v7fYVxtYtBsSdCz4Btj\nScvupiBs8VHh7tLuPpSdIwY2YUtISIgMEkE6enYaScPNrH7lEG31Lo4dbOSdZ3ay8JvjSRpuOfUO\neoslRrgmTZsuBsP5h6C2NpKs5dixnj9rMnUVIrFnOZ14d0SJnNC6jgN7SRKD/samaLe0lubeuZcd\nKRD7+Nc/ur7nsEf37z25cJ2K/ftEHz97jriuGo2YSHF3cuFW9hCL2pHTGRdoNHDTLaAADMbo/4Uk\niXXXf6n7z7WjUp3ZmKTzR7u7lt3tvzvR0PGSrP5A/NcvmRcSzkp4+82u3323++lNCHhom0vmdRVe\nFzi9vnu2trYyZ84cLrnkElavXk1iYiKlpaUkd6gB8Nxzz/HCCy+wfPlycnNz+cUvfsGCBQsoKirC\nZDKxcuVK3nzzTdatW0dhYSHf+MY3WLhwIfHx8djtdh566CFWrVo1ICc6qPB4xJ+tL52XJMG2z8VN\nBkLi5HrREZ4pZnN0INy5wNUEBf8VzzUG4drV3c1S5oIlYHcQaGwgYLMTtNsI2GwE22yh53aCNhsB\nu41g+H07gaYmfJWVp9y3OikJXV4eutxc1ImJve/oJIm2NWtwHziAr7yC8q98lbilS0h66AeozD3M\nHOp0QviPGy8CG/fvE7NpTqf4D+/dI8zy4yeIGXYZQFT1rnv+eZr/HhmoWRYtJO2JJ1BZuxEJ/YQk\nSez9qIJt75WExwu5M1OYd1seWkM39+j9+8T3CGLQcellYpJnkJM41MItD0/jk+UFlO1voK3BzX9+\nvZtLluQyZk4/Z59TqYTLUm6uSJ97+LAQdX6/GHTGxUcLkcGWTrwvKBRCRJjMworUTiAArS1CsPRE\nMChcfWbMFBamjqz/BAoLYcrUnj+vUvVukFtTLdyLRuZG1rW2gq4PEyWxceJYdbWRgbKtm2QJPdHR\nZakjiUngcgoBcaZph7VaIRJqa6Jd12qqI0Hr/Um7Ne3iecI1D4TLX2+EaUcSEsX/o7tsYgajOKfW\n1ujv7wtAr0fIzz33HOnp6fztbxEzXEanP9SyZct4+OGH+dKXhBJevnw5ycnJvPHGG9xzzz0cOXKE\n+fPnM3nyZCZPnswDDzxAWVkZ8fHxPPLII3z1q18lL+8CLLIjSeJHW35MBNCGZgbR63vwPQ496vWR\nDBXbt4kZWRCfWXxd/4iTwUAwAAffFJXiAcbcBPqBG5DInH2kYBB/fQP+6ip81dX4qqrwVYUeq6vx\nVVf3i8VDYTCgy8lBl5eLPlcIEl1eLuoz6Jziv/ZVmpb/nfply5A8HprfWIFtwwbSfvEk5ovn9vxB\ntRrGjYPRo6GkGPbuFbOqHo/wKT6wX1hTJk48fSvoBYK7qIiqH/4IT1ERAEqjkZTHHsP6peu7D0jv\nJzxOX3jADqDSKLnkth4G7O0W7PZJIq1WuKkMGdp120GKzqhh0b3j2ftxBdveKyXgD/LpP45QXdLK\nvNtyUWsHYFIoMUn45c+eIwZ0ZvMF75oCCPHQXgC3JyrKxTUZPaarQMvOEQkITiZQzBYhhE6cELEZ\nanX3E5/WWBGIX1Mtki0cPiTc8XR9yLYWGyvKCGzaBJdcItIEf771zK2EQ4cKz401a4T1LS4WHCEX\nuaFDhUdHX5g4SbjVxsSEguSLhAvdjTedWTu7Q6cT47SCfCEiHA4Rk9ZXd/ix48Q+Pv4IpkwRWdzq\n6yJCftp0kRRJqxVCMxgUwt/hgMlT+v+8Bgm9/mX973//Y9GiRdx22218+umnpKenc/fdd/Od73wH\ngLKyMmpqaliwIOJHqdfrueSSS9i6dSv33HMPEydO5NVXX6WlpYWSkhLcbjc5OTls27aNDRs2sGfP\nnv4/w3OF1yt8lMvLxU2ou2xYbrdYOgfZtaNWi5u5Viv8G0GIk2uv7xpweD5T8jG0hfx1h82CpDEn\n315m0BF0u8PCw99RgLQ/r6kRfshngMJoRGWxoLSYUZktKC0WVBYL2hEjQoIkF82wYf1eA0OhUpHw\nja9jufwyqn/2GM6dO/FXVXP8nnuwfulLpPz0J6hiT5JeW6US8Scjc0X2vL17xH/e7xeD3cOHxPuT\nJkf7P38BkCSJ5n/+i7rf/AYpFI+gnziBIb/5zYCkDe5I/XEba/4iXJ4AYhL1Pbs8+f3Cpau0RLw2\nmuDqa07tEz8IUSgUTLkyg5TMGNa+Jlzajmytpr7CxvyleaRmDdBvsN1NSibCkSNipr8761FWtsg0\ndeKEuC90J+pSU4W4+eRjMfHRU5rhKVOFtWP1h+J+lDdK3I/aU+P2lksvE9mjVq0UA/Op07q6iZ0O\ni64RWa4+2yjGSgaDOLfTqQo/brzoa7ZvC6UZjhUpfjsKxf4SyAqFCGzfshn+/bYQRbNmi9i0ztt1\n99l2TCa47kvCMrvyffFefLxw6wKRaEKtEdbbnTvEdxgXLybALmB6XQfFYDCgUCh48MEHueWWW9i3\nbx/3338/zz33HN/+9rf5/PPPmTt3LuXl5QztkNv5rrvuoqqqitWrVwPw5JNP8o9//AOj0chTTz3F\n1VdfzdSpU3nppZc4ePAgy5Ytw2Qy8fvf/55Zs2YNzFkPFG1tQoyUl4ssD92Z+RIShRlYpeqaA/9U\nwbtmM1x3/ZmbQQcTjUWwN2SVM6fB9PtAJXdigw3J78ddWCgESGfrR1UVgaY+dnSIWXLNkHTU6elo\n0tLQpKWjTk5GFWNBabaIR4sFldmM0mxGMQj8+aVgkJa336buN78lGHJtUCUmkvrzx4i58spe7kQS\nGXT27oaqqsh6hUIMSiZPHpg6EgOMv6GBQHPzqTcMEfR6qV+2DMemz8QKpZLEe79F4n33DWggPEDB\n1io2rigi4BP36MwJiVxx5+jua5t4PLB2jahPAMLV5eprLohJIkerh7WvHqK6OGK9TB8Zy9SFGQwb\nEz+g1isZGRmZk9FrgaLT6ZgxYwafffZZeN2jjz7Ke++9x+HDh3stUDrzy1/+ksrKSr797W9zxRVX\ncODAAfbv3883vvENysrKUA+CQUkXgkHhM2kLiYuGBiFMuuuc1WoxQzI8UwiTntyyTlXES6OBeZf2\n7Md5PuKxwfbfg9cOKi3MuB9Mp5mlRGbAsG/aRO0vf4W3PYVob1AoUCcmoklPR52ehiY9HU1aunhM\nT0OTloYyJua8HQD5qqupfvzxyOAasFx1FamP/UzEuPSW2hrh+lV+LHr98AwhVPrq3nAOkHw+6n73\nAk2vv97LoM+uaNLTSf/NrzFOPYk7Sz/g9wX47M0i8rdUA0ITXvSlbCYvGN592l27HT5cFbm3p6bC\nVYsuqNihYCDI9pVl7FtXQdAf+f6ShluYujCDrElJZ56SWEZGRqaP9FqgZGZmcuWVV/KXv/wlvO6f\n//wn9913HzabjbKyMrKzs9m5cydTO3QyixcvJikpKSp2pZ2ioiKuvvpq9u7dy+uvv87mzZt56623\nAEhOTubTTz9l7NixUZ9pbGykoT2GowOJiYkkdGNuP63tq6tDqQId4ZSBiSoVCSpVpIJuyDrSaLfT\nYO8UJGY0kjh6NAkTJoi0ih1E1llp//mwfUI8CRXvQ1OxWDHmJkifev60/wuwvbe6msZX/oJz+zbi\nVGpiO7hOKXQ6NGlp2OPjabXGoElORp2UjDopCXVKMql5eSSldR1cD+bz7ev2kiRhX7+e+pdfIdbl\nIlalQmW1kvLIw8Rcdx0KhaL3+29qhL17ady/jwabPbI+KQlGjyFx/HgSuhE+5/r6+GrrqHzoIVy7\ndwPQEgjQHOhqCe78+2mnJRDAd/HFJN7/HVQd0pUORPttTW4+e6uI5honJn0MSUmJXHn3OIbmxXW7\nPS3NIrWn00Wi2UTC+AmijsIFej93tnk58nk1Rbtq0SvNmENxgLEpRqZclUHuzBRaWpoHbfsH8/YX\nZGytjMwA02uBcvvtt3PixAk2btwYXvfYY4/x7rvvcuiQKBiYnp7O9773PX76058C4Ha7SUlJ4fnn\nn+fuu+/uss9LL72UBx54gOuvv55ly5axceNG/vvf/yJJEvHx8WzcuJEJA1iQqwvFxbDlM2HJ6Csp\nKWLWMyNT+A6epzPDZ41jG6F4jXieOgnG3iJfs0FC0OWi8dXXaHzttXBcgNJsJvG++zDOmIFmSDqq\nuLjz1vrR3/jr66l56mlsH30UXmeaO5eURx9BN2JE33bW1gb794rsPe1VlkEEwE6aAiNGDJqij47t\nO6j8wQ/C6YB1o0aRcM/dfYoB0qSnD2jRxXaOHWhg3ev5eLZFTIEAACAASURBVJxCPKVlW7ny7nGY\n43rIHFVVBWtXR+p0jB0ngrwHybUfSNwOHwc+PcGBT4/jcUTEpjlOx+QrhzN6TjqagQiml5GRkelA\nrwXKrl27mDNnDo8//ji33nore/bs4Z577uHZZ5/l3nvvBeDXv/41zzzzDH/9618ZOXIkTz/9NJs3\nb6awsBBTp2I+r732GqtXr+Y///kPALt37+byyy/ngw8+YN++fTz55JNUVFSgO5upB8uPwZpuXNEU\nClGcp7uMW+1VgOW6Br2ntQJ2vQJSEAwJMPO7oD5PU0xeQEiShG3dOuqeeRZfh9gI65e/TPIPHuqb\n69IXkLa1H1Hz1FPhATsaDQl33knivd9C2dcicQ6HCKDPPxydXMBqFcH0I3PPWX0ISZJofO016l94\nMWxJtt54A6mPPdZzIctzRDAoseP9UnavibgnTrx8GLNuyEbVufBiOyXFIsVrewzhzItEZqAvmCD3\nuv3kb65i38cVOFojBRUNFg0TLhvG+HlDuo/ZkZGRkekHei1QAFavXs3DDz9MUVERw4cP57vf/W44\ni1c7Tz75JK+88kqPhRoB6urquOiii9i6dSupqZHCM8899xzPP/88MTExvPTSS1EZwc4KbW2i4m1n\n8WE0nvtiURcKPhds/wO4m0GhEkHxMUNO/TmZAcVTWkrt07/EsXVreJ1+zBhSHvsZxsmTz2HLzi8C\nLS3UPf87Wv7973A8hjolhZSf/BjLokV9tzp5PKIw66GD0ZZdk0nEp7TfozreswZwUifQ1kbVw49g\n/+QTABRaLak/f4zYmwYghecZ4mjx8PHf8qksFPEjGr2Ky74ympypyeK78XgiCUraY/7a2kSmNRDW\nknmXiloeX2ACviCF22vYvbY8nPEMQKtXkZptRWtQozWo0XV6jH6uEs/1ajmeRUZGplf0SaDIyJwR\nkgQHV0DdQfF65DWQcZI6EjIDTsDuoOGlP9O0/O/hLHIqq5WkBx8k9uab+j1l7xcF18GD1Dz1NO4D\nB8LrjDNmkPKzR9GfzoDX54tUpz9VYTSttuski9ks3MRiT78ejPvIEU587/v4KioA0AwbxtBlL6If\nM0BpwSVJ/Ca9HvB4hbuVzxtdxbkHSovsfPpBHW6XsILExylZOAfilPZIcpOTZU3UaESNk6HD+ulk\nzn+CgSAle+rZvaacxkr7qT/QDXqThnHzhjDhsqEYzLLXQV/4033rWfjNcWRPTj71xsCOVWWU7Klj\nyc9nDnDLZGQGBlmgyJw9KndAwbvieWIeTPzaF85tor8Julw4tm3DtW8/Cq1G1Akxh2qFhJ6rLGaU\nMTGozOZw+lZJkmhb9QF1v/41/vY6PAoFsbfeQtL3v39GhQ1lBFIwSOu771L3/O8iaZhVKuLvuJ3E\n++9HdTppagMBOFoER4+KQmsdEnb0isREUfU8J6dPxSFb/vsuNb/4BZJHFFM1X3op6c8+c3oV3oNB\nUV27plpYLDweIT7CS4fXfeyefAEFWyqTOdwYqUuTF9/KvKG1aFSn2JdSKURdXBxMn35epno+G0iS\nRPmhRvI3V+Fo8eBx+fG6/HhdAQL+3v0W1VolY+cOYdKCYZjjBpdb4Lngk+X5HNlWE36tN2lIHRHD\n7BtziEsV7qHONi86k7pn18RO7FhVRuneOm57TBYoMucnskCROTvYa2HHnyDoA12MiDvRfrGrZ58u\nvupq7Bs3Yv90A45t28KDxt6g0OtRWswoVGr8NZEO0TBpEimP/QxDp6x5MmdOoLWV+j/8keY33giL\nCVVCAsk//CHW669DcSaB151TnrenJg+nKbdFAr07kz5ExLKMGNGjW1jQ46H26adpeeffYoVSSdID\nD5Bw9129b7ffLwrN1lRDdbVIrXyGRTu7o96p46NjabR4xLloVQHmD6tlZJxNbKDXd4oh7OQeZzDI\nEyZniN8XwOsK4HX5w8LF4/TjdfvxOPwU766lrtwW3l6pUpB3USpTrswgNsV42sdtqXNSfrCRivxG\nggGJhCFmEoaYSBhiJj7NhHqQB/V/sjwfR6uXBV8fgySJ+jRb/1OMo9XL0sdPT2DIAkXmfEcWKDID\nj88lxImrEVDAlLsgPvtct+q8QQoEcB88iG3DBuwbNuI5cqTrRgrFadWg6LeBsswpcRcWUvvU0zh3\n7QqvOxvCUPJ4cO/cibKqEo3TgbKzoFWpICMDcnIjRWQB74kTVH7v+7jz88VmCQkMef55TBedYsDj\n8UTESE011Nf3bOXRaECnF25pWi3otKDVRV6Hl/Z1mi4iQgpK7N3ayPb1dQRDic/SMowsuGEIllhN\nyOXNIlcxHwRIksSJwmZ2ry4PxwaB+EqzpyYz5aoMkoad2rIYCASpLm7l2MEGyg820lLr7HFbhQKs\nycYo0ZIwxExMgn7QxMN8sjwft8PPNd+OZLQ7drCBD/98gG/9fj4qjbKLi1djpZ3N7xylpqQVlVbJ\niAmJXHxLLlqDSIPdWaBIksSuD4+Rv7kKl81HbIqBmddlMWJipPZYTVkrm1YU0VTtICHdxMzrslj5\nx/18+aHJpI+M45+Pfc64eUOYdMXw8Gdaap3864lt3PLI9F59dzIyvWUQVkGUuaCQgnDozZA4AbIu\nl8VJLwjY7Tg2b8G+YQP2TZu6rdSuSkrEPG8elvnzMc2ahUKnI2CzEbTbCdpsBGx2gnabWNdmI2C3\nEQyvs6PLyiL+63eenquRTJ/R5+Ux/B9/p+2DD4VrXV0drn37OHbTzcTedCOxt92GfsyYfkvfHGhr\no/W992he8SbesrJIO1KSsY7KIyYvF7XJJNzGSkuhtJSAz4ezuQWXy03zuk8ItrUBYJw2jfSnnkQT\nEyMqqnd0yWp30XK7ob4OuvmthrFaRXB/Wpp4jIk5I6uFvdnNutcLwoNdpVLB9GtHMOWqDJSDZPAp\nE0GhUDBsVDzDRsVTU9bKnjXllO1vQJKgeFcdxbvqyBiXwJSFGaTnxEZ91mX3UnGokWMHG6nIb8Lr\n6hpDZEnQozWoaa5xhItOSpIYRLfUOinZE9lWo1MRn24iPs2E3qTpENivQmvUiMdQYL/OqEajV5+1\n35TX7eforloShppRabpOHPm8AVb+fh8pWVZufng6boePT/95hPX/KGDhN8d3u8/9nxxn37rjzL89\nj6ThFgq317D6lUPc8sh0Eoea8XkCfPjnAwwbE8+Cb4zB3uJh89tH6XjGo+ekUbC1OkqgFGytJmmY\nRRYnMv2OLFBkBpaSj6GxSDxPGgMjLj237RnEBOwO7J+so3XVBzi2bevWDUY/dizm+fMxz5+PfuyY\nLlYPdVyc8KGXGZQoFAqsi6/BPH8+jS+/ROPyv4PPR8s7/6blnX+jzcrCeu1iYhYvRjvs9AK03QUF\nNL+xgtZVq5Bcrq7v19bhrq2jdtNmTMOGETM6D0tONiqtFpVGgyU5CQuQcPttoFCg1GqFaPpoTV9P\nFhISogWJ8fTdeDpTsqeOT/95JFzbxJpkYME3xpIyIqbfjiEzcKSOsHL1fRNorLKzZ205R3fWIQVF\nfEv5oUbScqyMnz+UtgYXxw40UlPW2iVBgkIBqdlWMscnkjE+gfg0EwqFgkAgSGuti8ZKOw2VdppC\nj/amiPXQ5wlQW9ZGbVlbr9us0au6zVYWFjbdrleTMOTU7swVhxr5y/dFnTmfN4AlTs/i+yd2u23R\n9hr8viBXfH1MuCbN/NvzeO+FvbTWu7AmGbp8Zt+640xeMJyR01IAmHltFtVHW9j3cQVXfH0Mhdtr\nkIJw2R2jUWmUxKWamLoog3V/zQ/vY/TsdHasKqO2rI2UETFIQYnC7TVMW5TR62soI9NbZIEiM3DU\nHoRjG8RzU3KoGKPsRtQRyefDvnkzbStXYVu/HqlTkVCFwYBpzmxhJbnkEjTJvcvgIjO4UZlNwrXu\nhhuoe+7X2DdtAknCW1pK/bLfU7/s9xgmTSLm2sXELFqEOj7+pPsLer3Y1q6l+Y0VuPbujXpPkzGc\nuNuWYJw2jaDDQcDW1sGSZsNjs+OqrUcLGM0m9NYYFEolqr7UNFEqhStVXHxIjKRCSuqA1Ifyuv1s\nfucoBVuqw+tGz05j7i0j0erlLu18IyHdzIKvj2XmtVns/aiCgq3VBPzChau6uLXL9jqjmuFj4skY\nn0jG2AT05q6ueyqVUlhH0k2MnJ4SXu9x+miscoQEi4PGE3ZaG1x4nf5eBfj73AF87gA09z7uD+A7\nL192ym3Sc2O59I5RIgO208ehjZW8v2wvN/10epeCos21ThKGmKMKZqZmW1EoFDRXO7oIFK/bj6PV\nQ2p2dFKLtJxYyg8J74aWWifx6aYoi01KpjVKExpjtGSOS6RgaxUpI2IoP9yIx+lj5IxUZGT6G/lu\nLjMw2GsgPxRYq9bDxK/IxRhDSJKEa+9eWleuxLZ6DYGWlqj3VXFxWBZeheWyyzHOmI7ybBYrlTmr\n6LKyGPbKy/iqq2n78ENa31+Jp7AQANe+fbj27aP2mWcxzZmNdfG1WC6/DGUHK4SvspLmN9+i5T//\niXYDVCoxz59P3NKlmGbP6lt8kcsFpSXQUA/qUAyHrnNcSKfX6rPTldQea+Pjvx6mtU5YhnRGNfNv\nHyVqm8ic18QkGpi3NI9p12Sy/5PjHNpUKcQAEJdqJGN8IiMmJJCaZUXZy0xWndEZNaTnxHZxHwNR\n7yWckcwdCu7vEOzfnqnM4/KFHiPr258HA6cf0qvWqohJbBcWBi69w8KrD27i8OZKZl6b1fsd9dEL\nra8elqPnpLHub/nMvWUkR7ZWkzUpCZ1BHkrK9D/yr0qm//E5Yf8/IOAFFDDuVjDKKTs9JSW0rlxJ\n26oP8J04EfWewmDAcvnlWK9djGn27HA6YJkvBpq0NBLuuouEu+7CXVRE28pVtH6wCn9VNfj9ODZu\nwrFxEwqjEcsVl2O6aBa2jz/GvnFjVAC6Kj6e2JtvJu6Wm9EMOc0CqAYDjB3XT2fWf+RvrmLjG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BSLvrTpGcCOOSFJbobHLRXNFN1bZ2GvbZ5G8oYOY5Ocy5KA+VenxM9xQEQRDGFpGgnGy8PbDz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\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hF3pID2drxbs31GAsttG95U+oB3sMVF9w6B/CYPfk5HgPQggxIZVKEQwGczMix5NQ7PF4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\nIiKiBoMBChERERERNRj/D6kSljdXPIoEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116eb8f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# This is my custom style that does most of the plot formatting\n",
    "plt.style.use('https://gist.githubusercontent.com/rhiever/a4fb39bfab4b33af0018/raw/b25b4ba478c2e163dd54fd5600e80ca7453459da/tableau20.mplstyle')\n",
    "\n",
    "degree_gender_data = pd.read_csv('gender_degree_data.tsv', sep='\\t')\n",
    "degree_gender_data = degree_gender_data[degree_gender_data['Year'] >= 1970]\n",
    "degree_gender_data.set_index('Year', inplace=True)\n",
    "\n",
    "# Create a list of the degree majors ranked by their last value in the time series\n",
    "# We'll use this list to determine what colors the degree majors are assigned\n",
    "degree_major_order = degree_gender_data.groupby('Degree_Major')['Female_percent_Bachelors'].last()\n",
    "degree_major_order = degree_major_order.sort_values(ascending=False).index.values\n",
    "degree_major_order_dict = dict(zip(degree_major_order, range(len(degree_major_order))))\n",
    "\n",
    "degree_gender_data['Degree_Major_Order'] = degree_gender_data[\n",
    "    'Degree_Major'].apply(lambda major: degree_major_order_dict[major])\n",
    "\n",
    "degree_gender_data.groupby('Degree_Major_Order')['Female_percent_Bachelors'].plot(figsize=(10, 12))\n",
    "\n",
    "plt.xlabel('')\n",
    "plt.yticks(range(0, 91, 10), ['{}%'.format(x) for x in range(0, 91, 10)])\n",
    "\n",
    "plt.xlim(1969, 2014)\n",
    "plt.ylim(-1, 90)\n",
    "\n",
    "plt.title('Percentage of Bachelor\\'s degrees conferred to women'\n",
    "          'in the U.S.A., by major (1970-2014)\\n', fontsize=14)\n",
    "plt.grid(False, axis='x')\n",
    "\n",
    "degree_major_pcts = dict(degree_gender_data.groupby(\n",
    "        'Degree_Major')['Female_percent_Bachelors'].last().iteritems())\n",
    "\n",
    "degree_major_color_map = [x['color'] for x in plt.rcParams['axes.prop_cycle']]\n",
    "\n",
    "# We use this dictionary to rename the degree majors to shorter names\n",
    "degree_major_name_map = {\n",
    "    'the social sciences and history': 'Social Sciences and History',\n",
    "    'the health professions and related programs': 'Health Professions',\n",
    "    'visual and performing arts': 'Art and Performance',\n",
    "    'foreign languages and literatures': 'Foreign Languages',\n",
    "    'engineering and engineering technologies': 'Engineering',\n",
    "    'the biological and biomedical sciences': 'Biology',\n",
    "    'mathematics and statistics': 'Math and Statistics',\n",
    "    'agriculture and natural resources': 'Agriculture',\n",
    "    'the physical sciences and science technologies': 'Physical Sciences',\n",
    "    'communication, journalism, and related '\n",
    "    'programs and in communications technologies': 'Communications\\nand Journalism',\n",
    "    'public administration and social services': 'Public Administration',\n",
    "    'psychology': 'Psychology',\n",
    "    'English language and literature/letters': 'English',\n",
    "    'computer and information sciences': 'Computer Science',\n",
    "    'education': 'Education',\n",
    "    'business': 'Business',\n",
    "    'architecture and related services': 'Architecture',\n",
    "}\n",
    "\n",
    "# We use these offsets to prevent the degree major labels from overlapping\n",
    "degree_major_offset_map = {\n",
    "    'foreign languages and literatures': 1.0,\n",
    "    'English language and literature/letters': -0.5,\n",
    "    'agriculture and natural resources': 0.5,\n",
    "    'business': -0.5,\n",
    "    'architecture and related services': 0.75,\n",
    "    'mathematics and statistics': -0.75,\n",
    "    'engineering and engineering technologies': 0.75,\n",
    "    'computer and information sciences': -0.75,\n",
    "}\n",
    "\n",
    "# Draw the degree major labels at the end of the time series lines\n",
    "for degree_major in degree_major_pcts:\n",
    "    plt.text(2014.5, degree_major_pcts[degree_major] - 0.5 + degree_major_offset_map.get(degree_major, 0),\n",
    "             degree_major_name_map[degree_major],\n",
    "             color=degree_major_color_map[degree_major_order_dict[degree_major]])\n",
    "\n",
    "plt.text(1967, -9,\n",
    "         '\\nData source: nces.ed.gov/programs/digest/current_tables.asp (Tables 325.*)\\n'\n",
    "         'Author: Randy Olson (randalolson.com / @randal_olson)',\n",
    "         ha='left', fontsize=10)\n",
    "\n",
    "plt.savefig('pct-bachelors-degrees-women-usa-1970-2014.png')\n",
    ";"
   ]
  }
 ],
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================================================
FILE: pct-bachelors-degrees-conferred-women-usa/gender_degree_data.tsv
================================================
Year	Degree_Major	Total_Bachelors	Percent_change_Bachelors	Male_Bachelors	Female_Bachelors	Female_percent_Bachelors	Total_Masters	Male_Masters	Female_Masters	Total_Doctorates	Male_Doctorates	Female_Doctorates
1971	agriculture and natural resources	12672	0	12136	536	4.2	2457	2313	144	1086	1055	31
1972	agriculture and natural resources	13516	6.7	12779	737	5.5	2680	2490	190	971	945	26
1973	agriculture and natural resources	14756	9.2	13661	1095	7.4	2807	2588	219	1059	1031	28
1974	agriculture and natural resources	16253	10.1	14684	1569	9.7	2928	2640	288	930	897	33
1975	agriculture and natural resources	17528	7.8	15061	2467	14.1	3067	2703	364	991	958	33
1976	agriculture and natural resources	19402	10.7	15845	3557	18.3	3340	2862	478	928	867	61
1977	agriculture and natural resources	21467	10.6	16690	4777	22.3	3724	3177	547	893	831	62
1978	agriculture and natural resources	22650	5.5	17069	5581	24.6	4023	3268	755	971	909	62
1979	agriculture and natural resources	23134	2.1	16854	6280	27.1	3994	3187	807	950	877	73
1980	agriculture and natural resources	22802	-1.4	16045	6757	29.6	3976	3082	894	991	879	112
1981	agriculture and natural resources	21886	-4.0	15154	6732	30.8	4003	3061	942	1067	940	127
1982	agriculture and natural resources	21029	-3.9	14443	6586	31.3	4163	3114	1049	1079	925	154
1983	agriculture and natural resources	20909	-0.6	14085	6824	32.6	4254	3129	1125	1149	1004	145
1984	agriculture and natural resources	19317	-7.6	13206	6111	31.6	4178	2989	1189	1172	1001	171
1985	agriculture and natural resources	18107	-6.3	12477	5630	31.1	3928	2846	1082	1213	1036	177
1986	agriculture and natural resources	16823	-7.1	11544	5279	31.4	3801	2701	1100	1158	966	192
1987	agriculture and natural resources	14991	-10.9	10314	4677	31.2	3522	2460	1062	1049	871	178
1988	agriculture and natural resources	14222	-5.1	9744	4478	31.5	3479	2427	1052	1142	926	216
1989	agriculture and natural resources	13492	-5.1	9298	4194	31.1	3245	2231	1014	1183	950	233
1990	agriculture and natural resources	12900	-4.4	8822	4078	31.6	3382	2239	1143	1295	1038	257
1991	agriculture and natural resources	13124	1.7	8832	4292	32.7	3295	2160	1135	1185	953	232
1992	agriculture and natural resources	15113	15.2	9867	5246	34.7	3730	2409	1321	1205	955	250
1993	agriculture and natural resources	16769	11.0	11079	5690	33.9	3959	2474	1485	1159	869	290
1994	agriculture and natural resources	18056	7.7	11746	6310	34.9	4110	2512	1598	1262	969	293
1995	agriculture and natural resources	19832	9.8	12686	7146	36.0	4234	2541	1693	1256	955	301
1996	agriculture and natural resources	21425	8.0	13531	7894	36.8	4551	2642	1909	1259	926	333
1997	agriculture and natural resources	22597	5.5	13791	8806	39.0	4505	2601	1904	1202	875	327
1998	agriculture and natural resources	23276	3.0	13806	9470	40.7	4464	2545	1919	1290	924	366
1999	agriculture and natural resources	24179	3.9	14045	10134	41.9	4376	2360	2016	1249	869	380
2000	agriculture and natural resources	24238	0.2	13843	10395	42.9	4360	2356	2004	1168	803	365
2001	agriculture and natural resources	23370	-3.6	12840	10530	45.1	4272	2251	2021	1127	741	386
2002	agriculture and natural resources	23331	-0.2	12630	10701	45.9	4503	2340	2163	1148	760	388
2003	agriculture and natural resources	23348	0.1	12343	11005	47.1	4492	2232	2260	1229	790	439
2004	agriculture and natural resources	22835	-2.2	11889	10946	47.9	4783	2306	2477	1185	758	427
2005	agriculture and natural resources	23002	0.7	11987	11015	47.9	4746	2288	2458	1173	763	410
2006	agriculture and natural resources	23053	0.2	12063	10990	47.7	4640	2280	2360	1194	710	484
2007	agriculture and natural resources	23133	0.3	12309	10824	46.8	4623	2174	2449	1272	768	504
2008	agriculture and natural resources	24113	4.2	12634	11479	47.6	4684	2180	2504	1257	742	515
2009	agriculture and natural resources	24982	3.6	13096	11886	47.6	4878	2328	2550	1328	741	587
2010	agriculture and natural resources	26343	5.4	13524	12819	48.7	5215	2512	2703	1149	626	523
2011	agriculture and natural resources	28630	8.7	14678	13952	48.7	5766	2746	3020	1246	675	571
2012	agriculture and natural resources	30972	8.2	15485	15487	50.0	6390	3026	3364	1333	721	612
2013	agriculture and natural resources	33592	8.5	16618	16974	50.5	6336	2912	3424	1411	767	644
2014	agriculture and natural resources	35116	4.5	17249	17867	50.9	6544	2966	3578	1407	739	668
1950	architecture and related services	2563	0	2441	122	4.8	166	159	7	1	1	0
1960	architecture and related services	1801	0	1744	57	3.2	319	305	14	17	17	0
1968	architecture and related services	3057	0	2931	126	4.1	1021	953	68	15	15	0
1970	architecture and related services	4105	0	3888	217	5.3	1427	1260	167	35	33	2
1971	architecture and related services	5570	35.7	4906	664	11.9	1705	1469	236	36	33	3
1972	architecture and related services	6440	15.6	5667	773	12.0	1899	1626	273	50	43	7
1973	architecture and related services	6962	8.1	6042	920	13.2	2307	1943	364	58	54	4
1974	architecture and related services	7822	12.4	6665	1157	14.8	2702	2208	494	69	65	4
1975	architecture and related services	8226	5.2	6791	1435	17.4	2938	2343	595	69	58	11
1976	architecture and related services	9146	11.2	7396	1750	19.1	3215	2545	670	82	69	13
1977	architecture and related services	9222	0.8	7249	1973	21.4	3213	2489	724	73	62	11
1978	architecture and related services	9250	0.3	7054	2196	23.7	3115	2304	811	73	57	16
1979	architecture and related services	9273	0.2	6876	2397	25.8	3113	2226	887	96	74	22
1980	architecture and related services	9132	-1.5	6596	2536	27.8	3139	2245	894	79	66	13
1981	architecture and related services	9455	3.5	6800	2655	28.1	3153	2234	919	93	73	20
1982	architecture and related services	9728	2.9	6825	2903	29.8	3327	2242	1085	80	58	22
1983	architecture and related services	9823	1.0	6403	3420	34.8	3357	2224	1133	97	74	23
1984	architecture and related services	9186	-6.5	5895	3291	35.8	3223	2197	1026	84	62	22
1985	architecture and related services	9325	1.5	6019	3306	35.5	3275	2148	1127	89	66	23
1986	architecture and related services	9119	-2.2	5824	3295	36.1	3260	2129	1131	73	56	17
1987	architecture and related services	8950	-1.9	5617	3333	37.2	3163	2086	1077	92	66	26
1988	architecture and related services	8603	-3.9	5271	3332	38.7	3159	2042	1117	98	66	32
1989	architecture and related services	9150	6.4	5545	3605	39.4	3383	2192	1191	86	63	23
1990	architecture and related services	9364	2.3	5703	3661	39.1	3499	2228	1271	103	73	30
1991	architecture and related services	9781	4.5	5788	3993	40.8	3490	2244	1246	135	101	34
1992	architecture and related services	8753	-10.5	5805	2948	33.7	3640	2271	1369	132	93	39
1993	architecture and related services	9167	4.7	5940	3227	35.2	3808	2376	1432	148	105	43
1994	architecture and related services	8975	-2.1	5764	3211	35.8	3943	2428	1515	161	111	50
1995	architecture and related services	8756	-2.4	5741	3015	34.4	3923	2310	1613	141	95	46
1996	architecture and related services	8352	-4.6	5340	3012	36.1	3993	2361	1632	141	96	45
1997	architecture and related services	7944	-4.9	5090	2854	35.9	4034	2336	1698	135	93	42
1998	architecture and related services	7652	-3.7	4966	2686	35.1	4347	2537	1810	131	80	51
1999	architecture and related services	8245	7.7	5145	3100	37.6	4235	2435	1800	119	76	43
2000	architecture and related services	8462	2.6	5193	3269	38.6	4268	2508	1760	129	85	44
2001	architecture and related services	8480	0.2	5086	3394	40.0	4302	2515	1787	153	83	70
2002	architecture and related services	8808	3.9	5224	3584	40.7	4566	2606	1960	183	117	66
2003	architecture and related services	9056	2.8	5331	3725	41.1	4925	2832	2093	152	83	69
2004	architecture and related services	8838	-2.4	5059	3779	42.8	5424	3049	2375	173	94	79
2005	architecture and related services	9237	4.5	5222	4015	43.5	5674	3180	2494	179	110	69
2006	architecture and related services	9515	3.0	5414	4101	43.1	5743	3165	2578	201	108	93
2007	architecture and related services	9717	2.1	5393	4324	44.5	5951	3304	2647	178	104	74
2008	architecture and related services	9805	0.9	5579	4226	43.1	6065	3252	2813	199	103	96
2009	architecture and related services	10119	3.2	5797	4322	42.7	6587	3657	2930	212	113	99
2010	architecture and related services	10051	-0.7	5694	4357	43.3	7280	4012	3268	210	116	94
2011	architecture and related services	9831	-2.2	5698	4133	42.0	7788	4265	3523	205	110	95
2012	architecture and related services	9727	-1.1	5566	4161	42.8	8448	4504	3944	255	147	108
2013	architecture and related services	9757	0.3	5581	4176	42.8	8095	4261	3834	247	134	113
2014	architecture and related services	9144	-6.3	5173	3971	43.4	8048	4122	3926	247	134	113
1952	the biological and biomedical sciences	11094	0	8212	2882	26.0	2307	1908	399	764	680	84
1954	the biological and biomedical sciences	9279	0	6710	2569	27.7	1610	1287	323	1077	977	100
1956	the biological and biomedical sciences	12423	0	9515	2908	23.4	1759	1379	380	1025	908	117
1958	the biological and biomedical sciences	14308	0	11159	3149	22.0	1852	1448	404	1125	987	138
1960	the biological and biomedical sciences	15576	0	11654	3922	25.2	2154	1668	486	1205	1086	119
1962	the biological and biomedical sciences	16915	0	12136	4779	28.3	2642	1982	660	1338	1179	159
1964	the biological and biomedical sciences	22723	0	16321	6402	28.2	3296	2348	948	1625	1432	193
1966	the biological and biomedical sciences	26916	0	19368	7548	28.0	4232	3085	1147	2097	1792	305
1968	the biological and biomedical sciences	31826	0	22986	8840	27.8	5506	3959	1547	2784	2345	439
1970	the biological and biomedical sciences	34034	0	23919	10115	29.7	5800	3975	1825	3289	2820	469
1971	the biological and biomedical sciences	35705	4.9	25319	10386	29.1	5625	3782	1843	3603	3018	585
1972	the biological and biomedical sciences	37269	4.4	26314	10955	29.4	5989	4056	1933	3587	2981	606
1973	the biological and biomedical sciences	42207	13.2	29625	12582	29.8	6156	4317	1839	3583	2892	691
1974	the biological and biomedical sciences	48244	14.3	33217	15027	31.1	6408	4512	1896	3358	2684	674
1975	the biological and biomedical sciences	51609	7.0	34580	17029	33.0	6429	4554	1875	3334	2612	722
1976	the biological and biomedical sciences	54154	4.9	35498	18656	34.4	6457	4466	1991	3347	2631	716
1977	the biological and biomedical sciences	53464	-1.3	34178	19286	36.1	6953	4670	2283	3335	2627	708
1978	the biological and biomedical sciences	51360	-3.9	31673	19687	38.3	6651	4353	2298	3255	2481	774
1979	the biological and biomedical sciences	48713	-5.2	29173	19540	40.1	6638	4198	2440	3459	2593	866
1980	the biological and biomedical sciences	46254	-5.0	26797	19457	42.1	6339	4042	2297	3568	2651	917
1981	the biological and biomedical sciences	43078	-6.9	24124	18954	44.0	5766	3602	2164	3640	2620	1020
1982	the biological and biomedical sciences	41501	-3.7	22722	18779	45.2	5679	3384	2295	3662	2611	1051
1983	the biological and biomedical sciences	39924	-3.8	21572	18352	46.0	5711	3298	2413	3386	2306	1080
1984	the biological and biomedical sciences	38593	-3.3	20565	18028	46.7	5489	3123	2366	3496	2416	1080
1985	the biological and biomedical sciences	38354	-0.6	20071	18283	47.7	5109	2775	2334	3465	2335	1130
1986	the biological and biomedical sciences	38395	0.1	20000	18395	47.9	5064	2733	2331	3405	2273	1132
1987	the biological and biomedical sciences	38074	-0.8	19684	18390	48.3	4995	2646	2349	3469	2268	1201
1988	the biological and biomedical sciences	36688	-3.6	18267	18421	50.2	4871	2530	2341	3688	2389	1299
1989	the biological and biomedical sciences	36068	-1.7	17998	18070	50.1	5034	2598	2436	3617	2299	1318
1990	the biological and biomedical sciences	37304	3.4	18363	18941	50.8	4941	2509	2432	3922	2478	1444
1991	the biological and biomedical sciences	39482	5.8	19418	20064	50.8	4834	2417	2417	4152	2618	1534
1992	the biological and biomedical sciences	42892	8.6	20816	22076	51.5	4862	2437	2425	4442	2749	1693
1993	the biological and biomedical sciences	47009	9.6	22870	24139	51.3	5026	2540	2486	4749	2866	1883
1994	the biological and biomedical sciences	51296	9.1	25071	26225	51.1	5462	2681	2781	4891	2910	1981
1995	the biological and biomedical sciences	55983	9.1	26734	29249	52.2	5873	2920	2953	5069	3012	2057
1996	the biological and biomedical sciences	61014	9.0	28921	32093	52.6	6593	3212	3381	5250	3062	2188
1997	the biological and biomedical sciences	63973	4.8	29562	34411	53.8	6986	3419	3567	5313	3014	2299
1998	the biological and biomedical sciences	65917	3.0	29663	36254	55.0	6848	3336	3512	5474	3123	2351
1999	the biological and biomedical sciences	65310	-0.9	28507	36803	56.4	6966	3279	3687	5250	3010	2240
2000	the biological and biomedical sciences	63630	-2.6	26579	37051	58.2	6850	3171	3679	5463	3068	2395
2001	the biological and biomedical sciences	60576	-4.8	24600	35976	59.4	7017	3075	3942	5225	2923	2302
2002	the biological and biomedical sciences	60309	-0.4	23694	36615	60.7	7011	3033	3978	5104	2836	2268
2003	the biological and biomedical sciences	61294	1.6	23356	37938	61.9	7050	3015	4035	5268	2866	2402
2004	the biological and biomedical sciences	62624	2.2	23691	38933	62.2	7732	3271	4461	5538	2975	2563
2005	the biological and biomedical sciences	65915	5.3	25104	40811	61.9	8284	3361	4923	5935	3025	2910
2006	the biological and biomedical sciences	70607	7.1	27183	43424	61.5	8781	3709	5072	6162	3138	3024
2007	the biological and biomedical sciences	76832	8.8	30600	46232	60.2	8898	3639	5259	6764	3440	3324
2008	the biological and biomedical sciences	79829	3.9	32401	47428	59.4	9689	4094	5595	7400	3645	3755
2009	the biological and biomedical sciences	82828	3.8	33707	49121	59.3	10018	4250	5768	7499	3549	3950
2010	the biological and biomedical sciences	86391	4.3	35866	50525	58.5	10730	4612	6118	7672	3603	4069
2011	the biological and biomedical sciences	89984	4.2	36888	53096	59.0	11324	4869	6455	7693	3648	4045
2012	the biological and biomedical sciences	95850	6.5	39542	56308	58.7	12419	5378	7041	7935	3708	4227
2013	the biological and biomedical sciences	100397	4.7	41556	58841	58.6	13300	5783	7517	7939	3689	4250
2014	the biological and biomedical sciences	104633	4.2	43427	61206	58.5	13960	6072	7888	8302	3884	4418
1956	business	42813	0	38706	4107	9.6	3280	3118	162	129	127	2
1958	business	51991	0	48063	3928	7.6	4223	4072	151	110	105	5
1960	business	51076	0	47262	3814	7.5	4643	4476	167	135	133	2
1962	business	49017	0	45184	3833	7.8	7691	7484	207	226	221	5
1964	business	55474	0	51056	4418	8.0	9251	9008	243	275	268	7
1966	business	62721	0	57516	5205	8.3	12959	12628	331	387	370	17
1968	business	79074	0	72126	6948	8.8	17795	17186	609	441	427	14
1970	business	105580	0	96346	9234	8.7	21561	20792	769	620	610	10
1971	business	115396	9.3	104936	10460	9.1	26490	25458	1032	774	753	21
1972	business	121917	5.7	110331	11586	9.5	30509	29317	1192	876	857	19
1973	business	126717	3.9	113337	13380	10.6	31208	29689	1519	917	864	53
1974	business	132304	4.4	115363	16941	12.8	32691	30557	2134	922	873	49
1975	business	133639	1.0	111983	21656	16.2	36315	33274	3041	939	900	39
1976	business	143171	7.1	114986	28185	19.7	42592	37654	4938	906	856	50
1977	business	152010	6.2	116394	35616	23.4	46505	39852	6653	839	785	54
1978	business	160775	5.8	117103	43672	27.2	48347	40224	8123	834	760	74
1979	business	172392	7.2	119765	52627	30.5	50397	40766	9631	852	752	100
1980	business	186264	8.0	123639	62625	33.6	55008	42744	12264	767	650	117
1981	business	200521	7.7	126798	73723	36.8	57888	43411	14477	808	686	122
1982	business	215190	7.3	130693	84497	39.3	61251	44230	17021	826	676	150
1983	business	226442	5.2	131451	94991	41.9	64741	45987	18754	770	638	132
1984	business	229013	1.1	129296	99717	43.5	66129	46167	19962	926	727	199
1985	business	232282	1.4	127467	104815	45.1	66981	46199	20782	827	685	142
1986	business	236700	1.9	128415	108285	45.7	66676	45927	20749	923	720	203
1987	business	240346	1.5	128506	111840	46.5	67093	44913	22180	1062	808	254
1988	business	242859	1.0	129467	113392	46.7	69230	45980	23250	1063	810	253
1989	business	246262	1.4	131098	115164	46.8	73065	48540	24525	1100	800	300
1990	business	248568	0.9	132284	116284	46.8	76676	50585	26091	1093	818	275
1991	business	249165	0.2	131557	117608	47.2	78255	50883	27372	1185	876	309
1992	business	256298	2.9	135263	121035	47.2	84517	54609	29908	1242	953	289
1993	business	256473	0.1	135368	121105	47.2	89425	57504	31921	1346	969	377
1994	business	246265	-4.0	128946	117319	47.6	93285	59223	34062	1364	980	384
1995	business	233895	-5.0	121663	112232	48.0	93540	58931	34609	1391	1011	380
1996	business	226623	-3.1	116545	110078	48.6	93554	58400	35154	1366	972	394
1997	business	225934	-0.3	116023	109911	48.6	97204	59333	37871	1336	947	389
1998	business	232079	2.7	119379	112700	48.6	101652	62357	39295	1290	885	405
1999	business	239924	3.4	121741	118183	49.3	106830	64271	42559	1216	848	368
2000	business	256070	6.7	128521	127549	49.8	111532	67078	44454	1194	812	382
2001	business	263515	2.9	132275	131240	49.8	115602	68471	47131	1180	783	397
2002	business	278217	5.6	138343	139874	50.3	119725	70463	49262	1156	746	410
2003	business	293391	5.5	145075	148316	50.6	127685	75239	52446	1252	820	432
2004	business	307149	4.7	152513	154636	50.3	139347	80858	58489	1481	960	521
2005	business	311574	1.4	155940	155634	50.0	142617	82151	60466	1498	901	597
2006	business	318042	2.1	159683	158359	49.8	146406	83550	62856	1711	1049	662
2007	business	327531	3.0	166350	161181	49.2	150211	84115	66096	2029	1188	841
2008	business	335254	2.4	170978	164276	49.0	155637	86258	69379	2084	1250	834
2009	business	348056	3.8	177924	170132	48.9	168404	91991	76413	2123	1302	821
2010	business	358119	2.9	183272	174847	48.8	177748	96742	81006	2249	1338	911
2011	business	365133	2.0	187116	178017	48.8	187178	101440	85738	2286	1357	929
2012	business	367235	0.6	190180	177055	48.2	191606	103250	88356	2538	1461	1077
2013	business	360887	-1.7	187843	173044	47.9	188617	101599	87018	2828	1605	1223
2014	business	358079	-0.8	188418	169661	47.4	189328	101043	88285	3039	1722	1317
1971	communication, journalism, and related programs and in communications technologies	10802	0	6989	3813	35.3	1856	1214	642	145	126	19
1972	communication, journalism, and related programs and in communications technologies	12340	14.2	7964	4376	35.5	2200	1443	757	111	96	15
1973	communication, journalism, and related programs and in communications technologies	14317	16.0	9074	5243	36.6	2406	1546	860	139	114	25
1974	communication, journalism, and related programs and in communications technologies	17096	19.4	10536	6560	38.4	2640	1668	972	175	146	29
1975	communication, journalism, and related programs and in communications technologies	19248	12.6	11455	7793	40.5	2794	1618	1176	165	119	46
1976	communication, journalism, and related programs and in communications technologies	21282	10.6	12458	8824	41.5	3126	1818	1308	204	154	50
1977	communication, journalism, and related programs and in communications technologies	23214	9.1	12932	10282	44.3	3091	1719	1372	171	130	41
1978	communication, journalism, and related programs and in communications technologies	25400	9.4	13480	11920	46.9	3296	1673	1623	191	138	53
1979	communication, journalism, and related programs and in communications technologies	26457	4.2	13266	13191	49.9	2882	1483	1399	192	138	54
1980	communication, journalism, and related programs and in communications technologies	28616	8.2	13656	14960	52.3	3082	1527	1555	193	121	72
1981	communication, journalism, and related programs and in communications technologies	31282	9.3	14179	17103	54.7	3105	1448	1657	182	107	75
1982	communication, journalism, and related programs and in communications technologies	34222	9.4	14917	19305	56.4	3327	1578	1749	200	136	64
1983	communication, journalism, and related programs and in communications technologies	38647	12.9	16213	22434	58.0	3600	1660	1940	208	123	85
1984	communication, journalism, and related programs and in communications technologies	40203	4.0	16662	23541	58.6	3620	1578	2042	216	129	87
1985	communication, journalism, and related programs and in communications technologies	42102	4.7	17233	24869	59.1	3657	1574	2083	232	141	91
1986	communication, journalism, and related programs and in communications technologies	43145	2.5	17681	25464	59.0	3808	1603	2205	218	116	102
1987	communication, journalism, and related programs and in communications technologies	45521	5.5	18201	27320	60.0	3881	1584	2297	275	158	117
1988	communication, journalism, and related programs and in communications technologies	46916	3.1	18672	28244	60.2	3916	1568	2348	233	133	100
1989	communication, journalism, and related programs and in communications technologies	48889	4.2	19357	29532	60.4	4249	1734	2515	248	137	111
1990	communication, journalism, and related programs and in communications technologies	51572	5.5	20374	31198	60.5	4353	1705	2648	272	145	127
1991	communication, journalism, and related programs and in communications technologies	53047	2.9	20806	32241	60.8	4327	1711	2616	272	150	122
1992	communication, journalism, and related programs and in communications technologies	55144	4.0	21601	33543	60.8	4463	1692	2771	255	132	123
1993	communication, journalism, and related programs and in communications technologies	54907	-0.4	22154	32753	59.7	5179	1969	3210	301	146	155
1994	communication, journalism, and related programs and in communications technologies	52033	-5.2	21484	30549	58.7	5388	2088	3300	345	174	171
1995	communication, journalism, and related programs and in communications technologies	48969	-5.9	20501	28468	58.1	5559	2086	3473	321	162	159
1996	communication, journalism, and related programs and in communications technologies	48173	-1.6	19868	28305	58.8	5561	2153	3408	345	190	155
1997	communication, journalism, and related programs and in communications technologies	47894	-0.6	19771	28123	58.7	5552	1989	3563	300	155	145
1998	communication, journalism, and related programs and in communications technologies	50263	4.9	20103	30160	60.0	6097	2369	3728	359	171	188
1999	communication, journalism, and related programs and in communications technologies	52397	4.2	20943	31454	60.0	5582	2001	3581	348	182	166
2000	communication, journalism, and related programs and in communications technologies	57058	8.9	22152	34906	61.2	5525	2030	3495	357	168	189
2001	communication, journalism, and related programs and in communications technologies	59191	3.7	22542	36649	61.9	5645	1964	3681	370	190	180
2002	communication, journalism, and related programs and in communications technologies	64036	8.2	23692	40344	63.0	5980	2169	3811	383	168	215
2003	communication, journalism, and related programs and in communications technologies	69828	9.0	25338	44490	63.7	6495	2301	4194	398	179	219
2004	communication, journalism, and related programs and in communications technologies	73002	4.5	25813	47189	64.6	6900	2329	4571	426	186	240
2005	communication, journalism, and related programs and in communications technologies	75238	3.1	26926	48312	64.2	7195	2535	4660	468	195	273
2006	communication, journalism, and related programs and in communications technologies	76936	2.3	28142	48794	63.4	7745	2611	5134	464	207	257
2007	communication, journalism, and related programs and in communications technologies	78420	1.9	29009	49411	63.0	7272	2485	4787	480	188	292
2008	communication, journalism, and related programs and in communications technologies	81048	3.4	30384	50664	62.5	7546	2580	4966	496	209	287
2009	communication, journalism, and related programs and in communications technologies	83084	2.5	31207	51877	62.4	7517	2446	5071	535	225	310
2010	communication, journalism, and related programs and in communications technologies	86062	3.6	32048	54014	62.8	8093	2656	5437	573	225	348
2011	communication, journalism, and related programs and in communications technologies	88089	2.4	33018	55071	62.5	8804	2819	5985	578	207	371
2012	communication, journalism, and related programs and in communications technologies	88754	0.8	33525	55229	62.2	9502	3068	6434	567	242	325
2013	communication, journalism, and related programs and in communications technologies	89805	1.2	33670	56135	62.5	9337	3021	6316	612	246	366
2014	communication, journalism, and related programs and in communications technologies	92591	3.1	34370	58221	62.9	9928	3129	6799	614	267	347
1971	computer and information sciences	2388	0	2064	324	13.6	1588	1424	164	128	125	3
1972	computer and information sciences	3402	42.5	2941	461	13.6	1977	1752	225	167	155	12
1973	computer and information sciences	4304	26.5	3664	640	14.9	2113	1888	225	196	181	15
1974	computer and information sciences	4756	10.5	3976	780	16.4	2276	1983	293	198	189	9
1975	computer and information sciences	5033	5.8	4080	953	18.9	2299	1961	338	213	199	14
1976	computer and information sciences	5652	12.3	4534	1118	19.8	2603	2226	377	244	221	23
1977	computer and information sciences	6407	13.4	4876	1531	23.9	2798	2332	466	216	197	19
1978	computer and information sciences	7201	12.4	5349	1852	25.7	3038	2471	567	196	181	15
1979	computer and information sciences	8719	21.1	6272	2447	28.1	3055	2480	575	236	206	30
1980	computer and information sciences	11154	27.9	7782	3372	30.2	3647	2883	764	240	213	27
1981	computer and information sciences	15121	35.6	10202	4919	32.5	4218	3247	971	252	227	25
1982	computer and information sciences	20267	34.0	13218	7049	34.8	4935	3625	1310	251	230	21
1983	computer and information sciences	24565	21.2	15641	8924	36.3	5321	3813	1508	262	228	34
1984	computer and information sciences	32439	32.1	20416	12023	37.1	6190	4379	1811	251	225	26
1985	computer and information sciences	39121	20.6	24737	14384	36.8	7101	5064	2037	248	223	25
1986	computer and information sciences	42337	8.2	27208	15129	35.7	8070	5658	2412	344	299	45
1987	computer and information sciences	39767	-6.1	25962	13805	34.7	8481	5985	2496	374	322	52
1988	computer and information sciences	34651	-12.9	23414	11237	32.4	9197	6726	2471	428	380	48
1989	computer and information sciences	30560	-11.8	21143	9417	30.8	9414	6775	2639	551	466	85
1990	computer and information sciences	27347	-10.5	19159	8188	29.9	9677	6960	2717	627	534	93
1991	computer and information sciences	25159	-8.0	17771	7388	29.4	9324	6563	2761	676	584	92
1992	computer and information sciences	24821	-1.3	17685	7136	28.7	9655	6980	2675	772	669	103
1993	computer and information sciences	24519	-1.2	17606	6913	28.2	10353	7557	2796	805	689	116
1994	computer and information sciences	24527	0	17528	6999	28.5	10568	7836	2732	810	685	125
1995	computer and information sciences	24737	0.9	17684	7053	28.5	10595	7805	2790	887	726	161
1996	computer and information sciences	24506	-0.9	17757	6749	27.5	10579	7729	2850	869	743	126
1997	computer and information sciences	25422	3.7	18527	6895	27.1	10513	7526	2987	857	721	136
1998	computer and information sciences	27829	9.5	20372	7457	26.8	11765	8343	3422	858	718	140
1999	computer and information sciences	30552	9.8	22289	8263	27.0	12843	8866	3977	806	656	150
2000	computer and information sciences	37788	23.7	27185	10603	28.1	14990	9978	5012	779	648	131
2001	computer and information sciences	44142	16.8	31923	12219	27.7	16911	11195	5716	768	632	136
2002	computer and information sciences	50365	14.1	36462	13903	27.6	17173	11447	5726	752	581	171
2003	computer and information sciences	57433	14.0	41950	15483	27.0	19509	13267	6242	816	648	168
2004	computer and information sciences	59488	3.6	44585	14903	25.1	20143	13868	6275	909	709	200
2005	computer and information sciences	54111	-9.0	42125	11986	22.2	18416	13136	5280	1119	905	214
2006	computer and information sciences	47480	-12.3	37705	9775	20.6	17055	12470	4585	1416	1109	307
2007	computer and information sciences	42170	-11.2	34342	7828	18.6	16232	11985	4247	1595	1267	328
2008	computer and information sciences	38476	-8.8	31694	6782	17.6	17087	12513	4574	1698	1323	375
2009	computer and information sciences	37992	-1.3	31213	6779	17.8	17907	13063	4844	1580	1226	354
2010	computer and information sciences	39593	4.2	32414	7179	18.1	17955	13019	4936	1599	1250	349
2011	computer and information sciences	43066	8.8	35477	7589	17.6	19516	14010	5506	1588	1267	321
2012	computer and information sciences	47406	10.1	38796	8610	18.2	20925	15132	5793	1698	1332	366
2013	computer and information sciences	50961	7.5	41874	9087	17.8	22782	16539	6243	1834	1480	354
2014	computer and information sciences	55367	8.6	45393	9974	18.0	24532	17484	7048	1982	1566	416
1950	education	61472	0	31398	30074	48.9	20069	12025	8044	953	797	156
1960	education	89002	0	25556	63446	71.3	33433	18057	15376	1591	1279	312
1968	education	133965	0	31926	102039	76.2	63399	30672	32727	4078	3250	828
1970	education	163964	0	40420	123544	75.3	78020	34832	43188	5588	4479	1109
1971	education	176307	7.5	44896	131411	74.5	87666	38365	49301	6041	4771	1270
1972	education	190880	8.3	49344	141536	74.1	96668	41141	55527	6648	5104	1544
1973	education	193984	1.6	51300	142684	73.6	103777	43298	60479	6857	5191	1666
1974	education	184907	-4.7	48997	135910	73.5	110402	44112	66290	6757	4974	1783
1975	education	166758	-9.8	44463	122295	73.3	117841	44430	73411	6975	4856	2119
1976	education	154437	-7.4	42004	112433	72.8	126061	44831	81230	7202	4826	2376
1977	education	143234	-7.3	39867	103367	72.2	124267	42308	81959	7338	4832	2506
1978	education	135821	-5.2	37410	98411	72.5	116916	37662	79254	7018	4281	2737
1979	education	125873	-7.3	33743	92130	73.2	109866	34410	75456	7170	4174	2996
1980	education	118038	-6.2	30901	87137	73.8	101819	30300	71519	7314	4100	3214
1981	education	108074	-8.4	27039	81035	75.0	96713	27548	69165	7279	3843	3436
1982	education	100932	-6.6	24380	76552	75.8	91601	25339	66262	6999	3612	3387
1983	education	97908	-3.0	23651	74257	75.8	83254	22824	60430	7063	3550	3513
1984	education	92310	-5.7	22200	70110	76.0	75700	21164	54536	6914	3448	3466
1985	education	88078	-4.6	21254	66824	75.9	74667	20539	54128	6614	3174	3440
1986	education	87147	-1.1	20982	66165	75.9	74816	20302	54514	6610	3088	3522
1987	education	86788	-0.4	20705	66083	76.1	72619	18955	53664	5905	2745	3160
1988	education	90928	4.8	20947	69981	77.0	75270	18777	56493	5568	2530	3038
1989	education	96740	6.4	21643	75097	77.6	79793	19616	60177	5884	2522	3362
1990	education	105112	8.7	23007	82105	78.1	84890	20469	64421	6503	2776	3727
1991	education	110807	5.4	23417	87390	78.9	87352	20448	66904	6189	2614	3575
1992	education	107836	-2.7	22655	85181	79.0	91225	20897	70328	6423	2652	3771
1993	education	107578	-0.2	23199	84379	78.4	94497	21857	72640	6581	2712	3869
1994	education	107440	-0.1	24424	83016	77.3	97427	22656	74771	6450	2555	3895
1995	education	105929	-1.4	25619	80310	75.8	99835	23511	76324	6475	2490	3985
1996	education	105384	-0.5	26214	79170	75.1	104936	24955	79981	6246	2404	3842
1997	education	105116	-0.3	26242	78874	75.0	108720	25518	83202	6297	2367	3930
1998	education	105833	0.7	26285	79548	75.2	113374	26814	86560	6261	2334	3927
1999	education	107372	1.5	26321	81051	75.5	118226	28077	90149	6471	2297	4174
2000	education	108034	0.6	26103	81931	75.8	123045	29081	93964	6409	2295	4114
2001	education	105458	-2.4	24580	80878	76.7	127829	29997	97832	6284	2237	4047
2002	education	106295	0.8	24049	82246	77.4	135189	31907	103282	6549	2211	4338
2003	education	105845	-0.4	22604	83241	78.6	147883	34033	113850	6832	2314	4518
2004	education	106278	0.4	22802	83476	78.5	162345	37843	124502	7088	2403	4685
2005	education	105451	-0.8	22513	82938	78.7	167490	38863	128627	7681	2557	5124
2006	education	107238	1.7	22448	84790	79.1	174620	40700	133920	7584	2664	4920
2007	education	105641	-1.5	22516	83125	78.7	176572	40164	136408	8261	2681	5580
2008	education	102582	-2.9	21828	80754	78.7	175880	40055	135825	8491	2773	5718
2009	education	101716	-0.8	21163	80553	79.2	178538	40312	138226	9028	2956	6072
2010	education	101287	-0.4	20739	80548	79.5	182165	41284	140881	9237	3023	6214
2011	education	104008	2.7	21206	82802	79.6	185127	42043	143084	9642	3070	6572
2012	education	105656	1.6	21714	83942	79.4	179047	41364	137683	10118	3262	6856
2013	education	104698	-0.9	21824	82874	79.2	164652	37816	126836	10572	3418	7154
2014	education	98854	-5.6	20353	78501	79.4	154636	35984	118652	10920	3464	7456
1950	engineering and engineering technologies	52246	0	52071	175	0.3	4496	4481	15	417	416	1
1960	engineering and engineering technologies	37679	0	37537	142	0.4	7159	7133	26	786	783	3
1970	engineering and engineering technologies	44479	0	44149	330	0.7	15593	15421	172	3681	3657	24
1971	engineering and engineering technologies	50182	12.8	49775	407	0.8	16947	16734	213	3688	3663	25
1972	engineering and engineering technologies	51258	2.1	50726	532	1.0	17299	17009	290	3708	3685	23
1973	engineering and engineering technologies	51384	0.2	50766	618	1.2	16988	16694	294	3513	3459	54
1974	engineering and engineering technologies	50412	-1.9	49611	801	1.6	15851	15470	381	3374	3318	56
1975	engineering and engineering technologies	47131	-6.5	46105	1026	2.2	15837	15426	411	3181	3113	68
1976	engineering and engineering technologies	46676	-1.0	45184	1492	3.2	16800	16174	626	2874	2805	69
1977	engineering and engineering technologies	49482	6.0	47238	2244	4.5	16659	15891	768	2622	2547	75
1978	engineering and engineering technologies	56150	13.5	52353	3797	6.8	16887	15940	947	2483	2424	59
1979	engineering and engineering technologies	62898	12.0	57603	5295	8.4	16012	14971	1041	2545	2459	86
1980	engineering and engineering technologies	69387	10.3	62877	6510	9.4	16765	15535	1230	2546	2447	99
1981	engineering and engineering technologies	75355	8.6	67573	7782	10.3	17216	15761	1455	2608	2499	109
1982	engineering and engineering technologies	80632	7.0	71305	9327	11.6	18475	16747	1728	2676	2532	144
1983	engineering and engineering technologies	89811	11.4	78673	11138	12.4	19949	18038	1911	2871	2742	129
1984	engineering and engineering technologies	95295	6.1	82841	12454	13.1	21197	18916	2281	3032	2864	168
1985	engineering and engineering technologies	97099	1.9	83991	13108	13.5	22124	19688	2436	3269	3055	214
1986	engineering and engineering technologies	97122	0	84050	13072	13.5	22146	19545	2601	3456	3220	236
1987	engineering and engineering technologies	93560	-3.7	80543	13017	13.9	23101	20137	2964	3854	3585	269
1988	engineering and engineering technologies	89406	-4.4	76886	12520	14.0	23839	20815	3024	4237	3941	296
1989	engineering and engineering technologies	85982	-3.8	74020	11962	13.9	25066	21731	3335	4572	4160	412
1990	engineering and engineering technologies	82480	-4.1	70859	11621	14.1	25294	21753	3541	5030	4576	454
1991	engineering and engineering technologies	79751	-3.3	68482	11269	14.1	25450	21780	3670	5330	4834	496
1992	engineering and engineering technologies	78036	-2.2	67086	10950	14.0	26373	22397	3976	5499	4967	532
1993	engineering and engineering technologies	78619	0.7	67214	11405	14.5	29103	24721	4382	5870	5300	570
1994	engineering and engineering technologies	78580	0	66867	11713	14.9	30102	25394	4708	5954	5288	666
1995	engineering and engineering technologies	78483	-0.1	66157	12326	15.7	29949	25028	4921	6108	5378	730
1996	engineering and engineering technologies	77997	-0.6	65362	12635	16.2	28843	23840	5003	6354	5559	795
1997	engineering and engineering technologies	75659	-3.0	62994	12665	16.7	27016	22047	4969	6166	5408	758
1998	engineering and engineering technologies	74557	-1.5	61880	12677	17.0	27244	21800	5444	5966	5230	736
1999	engineering and engineering technologies	72796	-2.4	59859	12937	17.8	26689	21348	5341	5413	4643	770
2000	engineering and engineering technologies	73323	0.7	59668	13655	18.6	26648	21047	5601	5367	4539	828
2001	engineering and engineering technologies	72869	-0.6	59489	13380	18.4	27187	21341	5846	5547	4630	917
2002	engineering and engineering technologies	74588	2.4	60417	14171	19.0	26987	21212	5775	5181	4285	896
2003	engineering and engineering technologies	77231	3.5	62821	14410	18.7	30583	24097	6486	5252	4353	899
2004	engineering and engineering technologies	78079	1.1	63401	14678	18.8	35053	27561	7492	5859	4821	1038
2005	engineering and engineering technologies	79544	1.9	65033	14511	18.2	34988	27049	7939	6467	5263	1204
2006	engineering and engineering technologies	81406	2.3	66866	14540	17.9	33389	25568	7821	7318	5848	1470
2007	engineering and engineering technologies	81854	0.6	68081	13773	16.8	31989	24746	7243	7928	6285	1643
2008	engineering and engineering technologies	83608	2.1	69540	14068	16.8	34430	26461	7969	7977	6263	1714
2009	engineering and engineering technologies	84404	1.0	70504	13900	16.5	38008	29458	8550	7803	6123	1680
2010	engineering and engineering technologies	88735	5.1	73838	14897	16.8	39391	30554	8837	7773	5986	1787
2011	engineering and engineering technologies	93097	4.9	77080	16017	17.2	43179	33372	9807	8425	6548	1877
2012	engineering and engineering technologies	98654	6.0	81364	17290	17.5	45116	34712	10404	8856	6838	2018
2013	engineering and engineering technologies	102997	4.4	84645	18352	17.8	45328	34496	10832	9467	7305	2162
2014	engineering and engineering technologies	108969	5.8	88938	20031	18.4	47326	35777	11549	10117	7820	2297
1950	English language and literature/letters	17240	0	8221	9019	52.3	2259	1320	939	230	181	49
1960	English language and literature/letters	20128	0	7580	12548	62.3	2931	1458	1473	397	314	83
1968	English language and literature/letters	47977	0	15700	32277	67.3	7916	3434	4482	977	717	260
1970	English language and literature/letters	56410	0	18650	37760	66.9	8517	3326	5191	1213	837	376
1971	English language and literature/letters	63914	13.3	22005	41909	65.6	10441	4126	6315	1554	1107	447
1972	English language and literature/letters	63707	-0.3	22580	41127	64.6	10412	4066	6346	1734	1173	561
1973	English language and literature/letters	60607	-4.9	22022	38585	63.7	10035	3988	6047	1817	1189	628
1974	English language and literature/letters	54190	-10.6	20082	34108	62.9	9573	3824	5749	1755	1142	613
1975	English language and literature/letters	47062	-13.2	17689	29373	62.4	9178	3463	5715	1595	974	621
1976	English language and literature/letters	41452	-11.9	15898	25554	61.6	8599	3290	5309	1514	895	619
1977	English language and literature/letters	37343	-9.9	14135	23208	62.1	7824	2907	4917	1373	768	605
1978	English language and literature/letters	34799	-6.8	12972	21827	62.7	7444	2623	4821	1272	698	574
1979	English language and literature/letters	33218	-4.5	12085	21133	63.6	6503	2307	4196	1186	639	547
1980	English language and literature/letters	32187	-3.1	11237	20950	65.1	6026	2181	3845	1196	635	561
1981	English language and literature/letters	31922	-0.8	11082	20840	65.3	5742	2026	3716	1040	497	543
1982	English language and literature/letters	33078	3.6	11300	21778	65.8	5593	1916	3677	986	467	519
1983	English language and literature/letters	31327	-5.3	10699	20628	65.8	4866	1653	3213	877	419	458
1984	English language and literature/letters	32296	3.1	11007	21289	65.9	4814	1681	3133	899	413	486
1985	English language and literature/letters	32686	1.2	11195	21491	65.7	4987	1723	3264	915	414	501
1986	English language and literature/letters	34083	4.3	11657	22426	65.8	5335	1811	3524	895	390	505
1987	English language and literature/letters	35667	4.6	12133	23534	66.0	5298	1819	3479	853	367	486
1988	English language and literature/letters	38106	6.8	12687	25419	66.7	5366	1796	3570	858	380	478
1989	English language and literature/letters	41786	9.7	13729	28057	67.1	5716	1930	3786	929	405	524
1990	English language and literature/letters	46803	12.0	15437	31366	67.0	6317	2125	4192	986	444	542
1991	English language and literature/letters	51064	9.1	16891	34173	66.9	6784	2203	4581	1056	469	587
1992	English language and literature/letters	54250	6.2	18314	35936	66.2	7215	2441	4774	1142	484	658
1993	English language and literature/letters	55289	1.9	19007	36282	65.6	7537	2570	4967	1201	495	706
1994	English language and literature/letters	53150	-3.9	18214	34936	65.7	7611	2620	4991	1205	512	693
1995	English language and literature/letters	51170	-3.7	17581	33589	65.6	7612	2672	4940	1393	589	804
1996	English language and literature/letters	49928	-2.4	17007	32921	65.9	7657	2727	4930	1395	535	860
1997	English language and literature/letters	48641	-2.6	16325	32316	66.4	7487	2650	4837	1431	610	821
1998	English language and literature/letters	49016	0.8	16280	32736	66.8	7587	2568	5019	1489	611	878
1999	English language and literature/letters	49877	1.8	16332	33545	67.3	7326	2452	4874	1412	554	858
2000	English language and literature/letters	50106	0.5	16124	33982	67.8	7022	2315	4707	1470	611	859
2001	English language and literature/letters	50569	0.9	15997	34572	68.4	6763	2160	4603	1330	533	797
2002	English language and literature/letters	52375	3.6	16457	35918	68.6	7097	2270	4827	1291	532	759
2003	English language and literature/letters	53699	2.5	16738	36961	68.8	7428	2433	4995	1246	492	754
2004	English language and literature/letters	53984	0.5	16792	37192	68.9	7956	2459	5497	1207	479	728
2005	English language and literature/letters	54379	0.7	17154	37225	68.5	8468	2615	5853	1212	494	718
2006	English language and literature/letters	55096	1.3	17316	37780	68.6	8845	2860	5985	1254	510	744
2007	English language and literature/letters	55122	0	17475	37647	68.3	8742	2867	5875	1178	478	700
2008	English language and literature/letters	55038	0	17681	37357	67.9	9161	3027	6134	1262	453	809
2009	English language and literature/letters	55465	0.8	17973	37492	67.6	9262	3000	6262	1271	464	807
2010	English language and literature/letters	53229	-4.0	17050	36179	68.0	9202	3006	6196	1334	523	811
2011	English language and literature/letters	52754	-0.9	16917	35837	67.9	9475	3137	6338	1344	529	815
2012	English language and literature/letters	53765	1.9	16976	36789	68.4	9938	3403	6535	1427	548	879
2013	English language and literature/letters	52401	-2.5	16508	35893	68.5	9755	3213	6542	1377	554	823
2014	English language and literature/letters	50404	-3.8	15809	34595	68.6	9292	3113	6179	1393	557	836
1960	foreign languages and literatures	5462	0	2090	3372	61.7	1125	590	535	229	166	63
1968	foreign languages and literatures	19254	0	5253	14001	72.7	4849	2068	2781	707	503	204
1970	foreign languages and literatures	21109	0	5613	15496	73.4	5137	1917	3220	869	579	290
1971	foreign languages and literatures	20988	-0.6	5508	15480	73.8	5480	1961	3519	1084	714	370
1972	foreign languages and literatures	19890	-5.2	5196	14694	73.9	5283	1919	3364	1134	742	392
1973	foreign languages and literatures	20170	1.4	5119	15051	74.6	5068	1909	3159	1347	829	518
1974	foreign languages and literatures	20197	0.1	5063	15134	74.9	4851	1788	3063	1248	703	545
1975	foreign languages and literatures	19103	-5.4	4723	14380	75.3	4721	1672	3049	1220	665	555
1976	foreign languages and literatures	17068	-10.7	4270	12798	75.0	4432	1581	2851	1245	643	602
1977	foreign languages and literatures	15496	-9.2	3965	11531	74.4	4056	1365	2691	1103	574	529
1978	foreign languages and literatures	14334	-7.5	3684	10650	74.3	3624	1194	2430	1002	490	512
1979	foreign languages and literatures	13211	-7.8	3391	9820	74.3	3248	1092	2156	960	471	489
1980	foreign languages and literatures	12480	-5.5	3226	9254	74.2	3067	1026	2041	857	412	445
1981	foreign languages and literatures	11638	-6.7	3013	8625	74.1	2934	1039	1895	931	460	471
1982	foreign languages and literatures	11175	-4.0	2919	8256	73.9	2892	997	1895	869	407	462
1983	foreign languages and literatures	11170	0	3048	8122	72.7	2706	1001	1705	790	362	428
1984	foreign languages and literatures	10985	-1.7	3098	7887	71.8	2814	984	1830	779	353	426
1985	foreign languages and literatures	11436	4.1	3186	8250	72.1	2708	932	1776	761	342	419
1986	foreign languages and literatures	11550	1.0	3374	8176	70.8	2690	878	1812	768	338	430
1987	foreign languages and literatures	11706	1.4	3374	8332	71.2	2574	847	1727	769	332	437
1988	foreign languages and literatures	11515	-1.6	3223	8292	72.0	2680	931	1749	725	330	395
1989	foreign languages and literatures	12403	7.7	3432	8971	72.3	2837	955	1882	727	317	410
1990	foreign languages and literatures	13133	5.9	3625	9508	72.4	3018	987	2031	816	348	468
1991	foreign languages and literatures	13937	6.1	4008	9929	71.2	3049	1018	2031	889	396	493
1992	foreign languages and literatures	14634	5.0	4225	10409	71.1	3229	1074	2155	984	434	550
1993	foreign languages and literatures	15305	4.6	4435	10870	71.0	3513	1182	2331	977	417	560
1994	foreign languages and literatures	15242	-0.4	4573	10669	70.0	3612	1199	2413	1033	418	615
1995	foreign languages and literatures	14558	-4.5	4496	10062	69.1	3439	1124	2315	1081	479	602
1996	foreign languages and literatures	14832	1.9	4514	10318	69.6	3443	1141	2302	1020	446	574
1997	foreign languages and literatures	14487	-2.3	4388	10099	69.7	3361	1104	2257	1064	450	614
1998	foreign languages and literatures	15279	5.5	4585	10694	70.0	3181	1033	2148	1118	473	645
1999	foreign languages and literatures	15835	3.6	4738	11097	70.1	3109	976	2133	1049	443	606
2000	foreign languages and literatures	15886	0.3	4616	11270	70.9	3037	944	2093	1086	446	640
2001	foreign languages and literatures	16128	1.5	4695	11433	70.9	3035	969	2066	1078	420	658
2002	foreign languages and literatures	16258	0.8	4685	11573	71.2	3075	958	2117	1003	418	585
2003	foreign languages and literatures	16912	4.0	4996	11916	70.5	3049	874	2175	1042	424	618
2004	foreign languages and literatures	17754	5.0	5215	12539	70.6	3124	957	2167	1031	410	621
2005	foreign languages and literatures	18386	3.6	5370	13016	70.8	3407	1056	2351	1027	410	617
2006	foreign languages and literatures	19410	5.6	5842	13568	69.9	3539	1049	2490	1074	436	638
2007	foreign languages and literatures	20275	4.5	6173	14102	69.6	3443	1058	2385	1059	437	622
2008	foreign languages and literatures	20977	3.5	6254	14723	70.2	3565	1128	2437	1078	431	647
2009	foreign languages and literatures	21169	0.9	6305	14864	70.2	3592	1211	2381	1111	426	685
2010	foreign languages and literatures	21507	1.6	6607	14900	69.3	3756	1254	2502	1091	446	645
2011	foreign languages and literatures	21705	0.9	6719	14986	69.0	3727	1256	2471	1158	477	681
2012	foreign languages and literatures	21756	0.2	6629	15127	69.5	3827	1280	2547	1231	497	734
2013	foreign languages and literatures	21647	-0.5	6842	14805	68.4	3708	1235	2473	1304	531	773
2014	foreign languages and literatures	20335	-6.1	6266	14069	69.2	3482	1222	2260	1231	498	733
1971	the health professions and related programs	25223	0	5785	19438	77.1	5330	2165	3165	15988	14863	1125
1972	the health professions and related programs	28611	13.4	7005	21606	75.5	6811	2749	4062	16538	15373	1165
1973	the health professions and related programs	33562	17.3	7752	25810	76.9	7978	3189	4789	18215	16870	1345
1974	the health professions and related programs	41421	23.4	9347	32074	77.4	9232	3444	5788	20094	18287	1807
1975	the health professions and related programs	49002	18.3	10844	38158	77.9	10277	3686	6591	22191	19808	2383
1976	the health professions and related programs	53885	10.0	11386	42499	78.9	12164	3837	8327	25267	21980	3287
1977	the health professions and related programs	57222	6.2	11896	45326	79.2	12627	3865	8762	24972	21022	3950
1978	the health professions and related programs	59445	3.9	11600	47845	80.5	14027	3972	10055	26516	21622	4894
1979	the health professions and related programs	62095	4.5	11214	50881	81.9	15110	4155	10955	27766	22194	5572
1980	the health professions and related programs	63848	2.8	11330	52518	82.3	15374	4060	11314	28190	22157	6033
1981	the health professions and related programs	63665	-0.3	10531	53134	83.5	16176	4024	12152	29595	22792	6803
1982	the health professions and related programs	63660	0	10110	53550	84.1	16212	3743	12469	30096	22968	7128
1983	the health professions and related programs	65642	3.1	10247	55395	84.4	16941	4138	12803	30800	22920	7880
1984	the health professions and related programs	65305	-0.5	10068	55237	84.6	17351	4124	13227	31655	22851	8804
1985	the health professions and related programs	65331	0	9741	55590	85.1	17442	4046	13396	31493	22045	9448
1986	the health professions and related programs	65309	0	9629	55680	85.3	18603	4355	14248	31922	22069	9853
1987	the health professions and related programs	63963	-2.1	9137	54826	85.7	18442	3818	14624	29500	19686	9814
1988	the health professions and related programs	61614	-3.7	8955	52659	85.5	18774	4004	14770	30060	19853	10207
1989	the health professions and related programs	59850	-2.9	8878	50972	85.2	19493	4197	15296	30546	19893	10653
1990	the health professions and related programs	58983	-1.4	9075	49908	84.6	20406	4486	15920	30101	19118	10983
1991	the health professions and related programs	59875	1.5	9619	50256	83.9	21354	4423	16931	29842	18492	11350
1992	the health professions and related programs	62779	4.9	10330	52449	83.5	23671	4794	18877	31479	19362	12117
1993	the health professions and related programs	68434	9.0	11605	56829	83.0	26190	5249	20941	31089	18446	12643
1994	the health professions and related programs	75890	10.9	13377	62513	82.4	28442	5813	22629	30959	17988	12971
1995	the health professions and related programs	81596	7.5	14812	66784	81.8	31770	6718	25052	32124	18463	13661
1996	the health professions and related programs	86087	5.5	15942	70145	81.5	33920	7017	26903	32678	18495	14183
1997	the health professions and related programs	87997	2.2	16440	71557	81.3	36162	7536	28626	34971	19619	15352
1998	the health professions and related programs	86843	-1.3	15700	71143	81.9	39567	8644	30923	35369	19370	15999
1999	the health professions and related programs	84989	-2.1	15191	69798	82.1	40628	9152	31476	35939	19673	16266
2000	the health professions and related programs	80863	-4.9	13342	67521	83.5	42593	9500	33093	37829	19984	17845
2001	the health professions and related programs	75933	-6.1	12514	63419	83.5	43623	9711	33912	39019	20260	18759
2002	the health professions and related programs	72887	-4.0	10869	62018	85.1	43560	9588	33972	39435	19760	19675
2003	the health professions and related programs	71261	-2.2	10096	61165	85.8	42748	9280	33468	39799	19493	20306
2004	the health professions and related programs	73934	3.8	10017	63917	86.5	44939	9670	35269	41861	19587	22274
2005	the health professions and related programs	80685	9.1	10858	69827	86.5	46703	9816	36887	44201	19697	24504
2006	the health professions and related programs	91973	14.0	12914	79059	86.0	51380	10630	40750	45677	19640	26037
2007	the health professions and related programs	101810	10.7	14325	87485	85.9	54531	10636	43895	48943	20522	28421
2008	the health professions and related programs	111478	9.5	16286	95192	85.4	58120	11010	47110	51675	21616	30059
2009	the health professions and related programs	120420	8.0	17776	102644	85.2	62642	11848	50794	54846	22678	32168
2010	the health professions and related programs	129623	7.6	19309	110314	85.1	69112	12874	56238	57750	23946	33804
2011	the health professions and related programs	143463	10.7	21540	121923	85.0	75571	14034	61537	60221	25386	34835
2012	the health professions and related programs	163675	14.1	24905	138770	84.8	84355	15675	68680	62097	26074	36023
2013	the health professions and related programs	181149	10.7	28208	152941	84.4	90933	16747	74186	64192	26852	37340
2014	the health professions and related programs	198770	9.7	30931	167839	84.4	97403	17749	79654	67448	28084	39364
1950	mathematics and statistics	6382	0	4942	1440	22.6	974	784	190	160	151	9
1960	mathematics and statistics	11399	0	8293	3106	27.2	1757	1422	335	303	285	18
1968	mathematics and statistics	23513	0	14782	8731	37.1	5527	4199	1328	947	895	52
1970	mathematics and statistics	27442	0	17177	10265	37.4	5636	3966	1670	1236	1140	96
1971	mathematics and statistics	24801	-9.6	15369	9432	38.0	5191	3673	1518	1199	1106	93
1972	mathematics and statistics	23713	-4.4	14454	9259	39.0	5198	3655	1543	1128	1039	89
1973	mathematics and statistics	23067	-2.7	13796	9271	40.2	5028	3525	1503	1068	966	102
1974	mathematics and statistics	21635	-6.2	12791	8844	40.9	4834	3337	1497	1031	931	100
1975	mathematics and statistics	18181	-16.0	10586	7595	41.8	4327	2905	1422	975	865	110
1976	mathematics and statistics	15984	-12.1	9475	6509	40.7	3857	2547	1310	856	762	94
1977	mathematics and statistics	14196	-11.2	8303	5893	41.5	3695	2396	1299	823	714	109
1978	mathematics and statistics	12569	-11.5	7398	5171	41.1	3373	2228	1145	805	681	124
1979	mathematics and statistics	11806	-6.1	6899	4907	41.6	3036	1985	1051	730	608	122
1980	mathematics and statistics	11378	-3.6	6562	4816	42.3	2860	1828	1032	724	624	100
1981	mathematics and statistics	11078	-2.6	6342	4736	42.8	2567	1692	875	728	614	114
1982	mathematics and statistics	11599	4.7	6593	5006	43.2	2727	1821	906	681	587	94
1983	mathematics and statistics	12294	6.0	6888	5406	44.0	2810	1838	972	697	581	116
1984	mathematics and statistics	13087	6.5	7290	5797	44.3	2723	1773	950	695	569	126
1985	mathematics and statistics	15009	14.7	8080	6929	46.2	2859	1858	1001	699	590	109
1986	mathematics and statistics	16122	7.4	8623	7499	46.5	3131	2028	1103	742	618	124
1987	mathematics and statistics	16257	0.8	8673	7584	46.7	3283	1995	1288	723	598	125
1988	mathematics and statistics	15712	-3.4	8408	7304	46.5	3413	2052	1361	750	625	125
1989	mathematics and statistics	15017	-4.4	8081	6936	46.2	3405	2061	1344	866	700	166
1990	mathematics and statistics	14276	-4.9	7674	6602	46.2	3624	2172	1452	917	754	163
1991	mathematics and statistics	14393	0.8	7580	6813	47.3	3549	2096	1453	978	790	188
1992	mathematics and statistics	14468	0.5	7668	6800	47.0	3558	2151	1407	1048	825	223
1993	mathematics and statistics	14384	-0.6	7566	6818	47.4	3644	2151	1493	1138	867	271
1994	mathematics and statistics	14171	-1.5	7594	6577	46.4	3682	2237	1445	1125	880	245
1995	mathematics and statistics	13494	-4.8	7154	6340	47.0	3820	2289	1531	1181	919	262
1996	mathematics and statistics	12713	-5.8	6847	5866	46.1	3651	2178	1473	1158	919	239
1997	mathematics and statistics	12401	-2.5	6649	5752	46.4	3504	2055	1449	1134	861	273
1998	mathematics and statistics	11795	-4.9	6247	5548	47.0	3409	1985	1424	1215	903	312
1999	mathematics and statistics	12011	1.8	6206	5805	48.3	3304	1912	1392	1107	812	295
2000	mathematics and statistics	11418	-4.9	5955	5463	47.8	3208	1749	1459	1075	803	272
2001	mathematics and statistics	11171	-2.2	5791	5380	48.2	3209	1857	1352	997	715	282
2002	mathematics and statistics	11950	7.0	6333	5617	47.0	3350	1913	1437	923	658	265
2003	mathematics and statistics	12505	4.6	6784	5721	45.7	3620	1996	1624	1007	734	273
2004	mathematics and statistics	13327	6.6	7203	6124	46.0	4191	2302	1889	1060	762	298
2005	mathematics and statistics	14351	7.7	7937	6414	44.7	4477	2525	1952	1176	841	335
2006	mathematics and statistics	14770	2.9	8115	6655	45.1	4730	2712	2018	1293	911	382
2007	mathematics and statistics	14954	1.2	8360	6594	44.1	4884	2859	2025	1351	949	402
2008	mathematics and statistics	15192	1.6	8490	6702	44.1	4980	2860	2120	1360	938	422
2009	mathematics and statistics	15507	2.1	8801	6706	43.2	5211	3064	2147	1535	1059	476
2010	mathematics and statistics	16029	3.4	9087	6942	43.3	5639	3378	2261	1596	1118	478
2011	mathematics and statistics	17182	7.2	9782	7400	43.1	5866	3459	2407	1586	1132	454
2012	mathematics and statistics	18841	9.7	10722	8119	43.1	6246	3695	2551	1669	1198	471
2013	mathematics and statistics	20449	8.5	11599	8850	43.3	6957	4178	2779	1823	1292	531
2014	mathematics and statistics	20980	2.6	11967	9013	43.0	7273	4256	3017	1863	1325	538
1960	the physical sciences and science technologies	16007	0	14013	1994	12.5	3376	3049	327	1838	1776	62
1968	the physical sciences and science technologies	19380	0	16739	2641	13.6	5499	4869	630	3593	3405	188
1970	the physical sciences and science technologies	21439	0	18522	2917	13.6	5908	5069	839	4271	4038	233
1971	the physical sciences and science technologies	21410	-0.1	18457	2953	13.8	6336	5495	841	4324	4082	242
1972	the physical sciences and science technologies	20743	-3.1	17661	3082	14.9	6268	5390	878	4075	3805	270
1973	the physical sciences and science technologies	20692	-0.2	17622	3070	14.8	6230	5388	842	3961	3698	263
1974	the physical sciences and science technologies	21170	2.3	17669	3501	16.5	6019	5157	862	3558	3312	246
1975	the physical sciences and science technologies	20770	-1.9	16986	3784	18.2	5782	4949	833	3577	3284	293
1976	the physical sciences and science technologies	21458	3.3	17349	4109	19.1	5428	4622	806	3388	3097	291
1977	the physical sciences and science technologies	22482	4.8	17985	4497	20.0	5281	4411	870	3295	2981	314
1978	the physical sciences and science technologies	22975	2.2	18083	4892	21.3	5507	4583	924	3073	2763	310
1979	the physical sciences and science technologies	23197	1.0	17976	5221	22.5	5418	4438	980	3061	2717	344
1980	the physical sciences and science technologies	23407	0.9	17861	5546	23.7	5167	4210	957	3044	2669	375
1981	the physical sciences and science technologies	23936	2.3	18052	5884	24.6	5246	4172	1074	3105	2733	372
1982	the physical sciences and science technologies	24045	0.5	17861	6184	25.7	5446	4274	1172	3246	2804	442
1983	the physical sciences and science technologies	23374	-2.8	16988	6386	27.3	5250	4131	1119	3214	2767	447
1984	the physical sciences and science technologies	23645	1.2	17112	6533	27.6	5541	4249	1292	3269	2789	480
1985	the physical sciences and science technologies	23694	0.2	17065	6629	28.0	5752	4425	1327	3349	2808	541
1986	the physical sciences and science technologies	21711	-8.4	15750	5961	27.5	5860	4443	1417	3521	2946	575
1987	the physical sciences and science technologies	20060	-7.6	14365	5695	28.4	5586	4193	1393	3629	3004	625
1988	the physical sciences and science technologies	17797	-11.3	12385	5412	30.4	5696	4300	1396	3758	3085	673
1989	the physical sciences and science technologies	17179	-3.5	12071	5108	29.7	5691	4180	1511	3795	3046	749
1990	the physical sciences and science technologies	16056	-6.5	11026	5030	31.3	5410	3996	1414	4116	3328	788
1991	the physical sciences and science technologies	16334	1.7	11170	5164	31.6	5281	3823	1458	4248	3417	831
1992	the physical sciences and science technologies	16970	3.9	11443	5527	32.6	5397	3935	1462	4378	3433	945
1993	the physical sciences and science technologies	17577	3.6	11853	5724	32.6	5392	3840	1552	4372	3426	946
1994	the physical sciences and science technologies	18474	5.1	12271	6203	33.6	5718	4069	1649	4652	3657	995
1995	the physical sciences and science technologies	19247	4.2	12556	6691	34.8	5798	4058	1740	4486	3443	1043
1996	the physical sciences and science technologies	19716	2.4	12634	7082	35.9	5910	4031	1879	4589	3543	1046
1997	the physical sciences and science technologies	19594	-0.6	12285	7309	37.3	5616	3799	1817	4501	3479	1022
1998	the physical sciences and science technologies	19454	-0.7	11999	7455	38.3	5411	3484	1927	4592	3451	1141
1999	the physical sciences and science technologies	18448	-5.2	11119	7329	39.7	5241	3454	1787	4229	3206	1023
2000	the physical sciences and science technologies	18427	-0.1	11019	7408	40.2	4888	3167	1721	4017	3002	1015
2001	the physical sciences and science technologies	18025	-2.2	10628	7397	41.0	5134	3276	1858	3968	2914	1054
2002	the physical sciences and science technologies	17890	-0.7	10349	7541	42.2	5082	3186	1896	3824	2766	1058
2003	the physical sciences and science technologies	18038	0.8	10625	7413	41.1	5196	3284	1912	3939	2854	1085
2004	the physical sciences and science technologies	18131	0.5	10577	7554	41.7	5714	3470	2244	3937	2855	1082
2005	the physical sciences and science technologies	19104	5.4	11065	8039	42.1	5823	3569	2254	4248	3071	1177
2006	the physical sciences and science technologies	20522	7.4	11978	8544	41.6	6063	3666	2397	4642	3258	1384
2007	the physical sciences and science technologies	21291	3.7	12604	8687	40.8	6012	3675	2337	5041	3454	1587
2008	the physical sciences and science technologies	22179	4.2	13143	9036	40.7	6061	3762	2299	4994	3513	1481
2009	the physical sciences and science technologies	22691	2.3	13465	9226	40.7	5862	3576	2286	5237	3554	1683
2010	the physical sciences and science technologies	23381	3.0	13866	9515	40.7	6066	3654	2412	5065	3406	1659
2011	the physical sciences and science technologies	24705	5.7	14778	9927	40.2	6386	3907	2479	5295	3608	1687
2012	the physical sciences and science technologies	26664	7.9	15972	10692	40.1	6911	4299	2612	5370	3609	1761
2013	the physical sciences and science technologies	28053	5.2	17142	10911	38.9	7014	4377	2637	5514	3646	1868
2014	the physical sciences and science technologies	29304	4.5	17802	11502	39.3	6984	4312	2672	5806	3873	1933
1950	psychology	9569	0	6055	3514	36.7	1316	948	368	283	241	42
1960	psychology	8061	0	4773	3288	40.8	1406	981	425	641	544	97
1968	psychology	23819	0	13792	10027	42.1	3479	2321	1158	1268	982	286
1970	psychology	33679	0	19077	14602	43.4	5158	2975	2183	1962	1505	457
1971	psychology	38187	13.4	21227	16960	44.4	5717	3395	2322	2144	1629	515
1972	psychology	43433	13.7	23352	20081	46.2	6764	3934	2830	2277	1694	583
1973	psychology	47940	10.4	25117	22823	47.6	7619	4325	3294	2550	1797	753
1974	psychology	52139	8.8	25868	26271	50.4	8796	4983	3813	2872	1987	885
1975	psychology	51245	-1.7	24284	26961	52.6	9394	5035	4359	2913	1979	934
1976	psychology	50278	-1.9	22898	27380	54.5	10167	5136	5031	3157	2115	1042
1977	psychology	47861	-4.8	20627	27234	56.9	10859	5293	5566	3386	2127	1259
1978	psychology	44879	-6.2	18422	26457	59.0	10282	4670	5612	3164	1974	1190
1979	psychology	42697	-4.9	16540	26157	61.3	10132	4405	5727	3228	1895	1333
1980	psychology	42093	-1.4	15440	26653	63.3	9938	4096	5842	3395	1921	1474
1981	psychology	41068	-2.4	14332	26736	65.1	10223	4066	6157	3576	2002	1574
1982	psychology	41212	0.4	13645	27567	66.9	9947	3823	6124	3461	1856	1605
1983	psychology	40460	-1.8	13131	27329	67.5	9981	3647	6334	3602	1838	1764
1984	psychology	39955	-1.2	12812	27143	67.9	9525	3400	6125	3535	1774	1761
1985	psychology	39900	-0.1	12706	27194	68.2	9891	3452	6439	3447	1739	1708
1986	psychology	40628	1.8	12605	28023	69.0	9845	3347	6498	3593	1724	1869
1987	psychology	43152	6.2	13395	29757	69.0	11000	3516	7484	4062	1801	2261
1988	psychology	45371	5.1	13579	31792	70.1	10488	3256	7232	3973	1783	2190
1989	psychology	49083	8.2	14265	34818	70.9	11329	3465	7864	4143	1773	2370
1990	psychology	53952	9.9	15336	38616	71.6	10730	3377	7353	3811	1566	2245
1991	psychology	58655	8.7	16067	42588	72.6	11349	3329	8020	3932	1520	2412
1992	psychology	63683	8.6	17062	46621	73.2	11659	3335	8324	3814	1490	2324
1993	psychology	66931	5.1	17942	48989	73.2	12518	3380	9138	4100	1570	2530
1994	psychology	69419	3.7	18668	50751	73.1	13723	3763	9960	4021	1497	2524
1995	psychology	72233	4.1	19570	52663	72.9	15378	4210	11168	4252	1562	2690
1996	psychology	73416	1.6	19836	53580	73.0	15152	4090	11062	4141	1380	2761
1997	psychology	74308	1.2	19408	54900	73.9	15769	4155	11614	4507	1495	3012
1998	psychology	74107	-0.3	18976	55131	74.4	15142	3978	11164	4541	1470	3071
1999	psychology	73747	-0.5	18376	55371	75.1	15560	3959	11601	4678	1528	3150
2000	psychology	74194	0.6	17451	56743	76.5	15740	3821	11919	4731	1529	3202
2001	psychology	73645	-0.7	16585	57060	77.5	16539	3892	12647	5091	1598	3493
2002	psychology	76775	4.3	17284	59491	77.5	16357	3814	12543	4759	1503	3256
2003	psychology	78650	2.4	17514	61136	77.7	17161	3839	13322	4835	1483	3352
2004	psychology	82098	4.4	18193	63905	77.8	17898	3789	14109	4827	1496	3331
2005	psychology	85614	4.3	19000	66614	77.8	18830	3900	14930	5106	1466	3640
2006	psychology	88134	2.9	19865	68269	77.5	19770	4079	15691	4921	1347	3574
2007	psychology	90039	2.2	20343	69696	77.4	21037	4265	16772	5153	1382	3771
2008	psychology	92587	2.8	21202	71385	77.1	21431	4356	17075	5296	1440	3856
2009	psychology	94273	1.8	21490	72783	77.2	23415	4789	18626	5477	1478	3999
2010	psychology	97215	3.1	22262	74953	77.1	23763	4799	18964	5540	1478	4062
2011	psychology	100906	3.8	23230	77676	77.0	25062	5127	19935	5851	1481	4370
2012	psychology	109099	8.1	25420	83679	76.7	27052	5482	21570	5936	1525	4411
2013	psychology	114446	4.9	26814	87632	76.6	27787	5715	22072	6326	1628	4698
2014	psychology	117298	2.5	27304	89994	76.7	27966	5731	22235	6634	1680	4954
1971	public administration and social services	5466	0	1726	3740	68.4	7785	3893	3892	174	132	42
1972	public administration and social services	7508	37.4	2588	4920	65.5	8756	4537	4219	193	150	43
1973	public administration and social services	10690	42.4	3998	6692	62.6	10068	5271	4797	198	160	38
1974	public administration and social services	11966	11.9	4266	7700	64.3	11415	6028	5387	201	154	47
1975	public administration and social services	13661	14.2	4630	9031	66.1	13617	7200	6417	257	192	65
1976	public administration and social services	15440	13.0	5706	9734	63.0	15209	7969	7240	292	192	100
1977	public administration and social services	16136	4.5	5544	10592	65.6	17026	8810	8216	292	197	95
1978	public administration and social services	16607	2.9	5096	11511	69.3	17337	8513	8824	357	237	120
1979	public administration and social services	17328	4.3	4938	12390	71.5	17306	8051	9255	315	215	100
1980	public administration and social services	16644	-3.9	4451	12193	73.3	17560	7866	9694	342	216	126
1981	public administration and social services	16707	0.4	4248	12459	74.6	17803	7460	10343	362	212	150
1982	public administration and social services	16495	-1.3	4176	12319	74.7	17416	6975	10441	372	205	167
1983	public administration and social services	14414	-12.6	3343	11071	76.8	16046	5961	10085	347	184	163
1984	public administration and social services	12570	-12.8	2998	9572	76.1	15060	5634	9426	420	230	190
1985	public administration and social services	11754	-6.5	2829	8925	75.9	15575	5573	10002	431	213	218
1986	public administration and social services	11887	1.1	2966	8921	75.0	15692	5594	10098	382	171	211
1987	public administration and social services	12328	3.7	2993	9335	75.7	16432	5673	10759	398	216	182
1988	public administration and social services	12385	0.5	2923	9462	76.4	16424	5631	10793	470	238	232
1989	public administration and social services	13162	6.3	3214	9948	75.6	17020	5615	11405	428	210	218
1990	public administration and social services	13908	5.7	3334	10574	76.0	17399	5634	11765	508	235	273
1991	public administration and social services	14350	3.2	3215	11135	77.6	17905	5679	12226	430	190	240
1992	public administration and social services	15987	11.4	3479	12508	78.2	19243	5769	13474	432	204	228
1993	public administration and social services	16775	4.9	3801	12974	77.3	20634	6105	14529	459	215	244
1994	public administration and social services	17815	6.2	3919	13896	78.0	21833	6406	15427	519	238	281
1995	public administration and social services	18586	4.3	3935	14651	78.8	23501	6870	16631	556	274	282
1996	public administration and social services	19849	6.8	4205	15644	78.8	24229	6927	17302	499	220	279
1997	public administration and social services	20649	4.0	4177	16472	79.8	24781	6957	17824	518	243	275
1998	public administration and social services	20408	-1.2	3881	16527	81.0	25144	7025	18119	499	223	276
1999	public administration and social services	20323	-0.4	3799	16524	81.3	25038	6621	18417	534	240	294
2000	public administration and social services	20185	-0.7	3816	16369	81.1	25594	6808	18786	537	227	310
2001	public administration and social services	19447	-3.7	3670	15777	81.1	25268	6544	18724	574	263	311
2002	public administration and social services	19392	-0.3	3706	15686	80.9	25448	6505	18943	571	250	321
2003	public administration and social services	19900	2.6	3726	16174	81.3	25903	6391	19512	599	265	334
2004	public administration and social services	20552	3.3	3793	16759	81.5	28250	7001	21249	649	275	374
2005	public administration and social services	21769	5.9	4209	17560	80.7	29552	7370	22182	673	272	401
2006	public administration and social services	21986	1.0	4126	17860	81.2	30510	7572	22938	704	285	419
2007	public administration and social services	23147	5.3	4354	18793	81.2	31131	7758	23373	726	253	473
2008	public administration and social services	23493	1.5	4202	19291	82.1	33029	8140	24889	760	269	491
2009	public administration and social services	23852	1.5	4373	19479	81.7	33934	8346	25588	812	306	506
2010	public administration and social services	25421	6.6	4578	20843	82.0	35740	8868	26872	838	323	515
2011	public administration and social services	26799	5.4	4913	21886	81.7	38614	9791	28823	851	327	524
2012	public administration and social services	29695	10.8	5386	24309	81.9	41737	10494	31243	890	343	547
2013	public administration and social services	31950	7.6	5664	26286	82.3	43591	10864	32727	979	351	628
2014	public administration and social services	33483	4.8	5917	27566	82.3	44490	10833	33657	1047	346	701
1971	the social sciences and history	155324	0	98173	57151	36.8	16539	11833	4706	3660	3153	507
1972	the social sciences and history	158060	1.8	100895	57165	36.2	17445	12540	4905	4081	3483	598
1973	the social sciences and history	155970	-1.3	99735	56235	36.1	17477	12605	4872	4234	3573	661
1974	the social sciences and history	150320	-3.6	95650	54670	36.4	17293	12321	4972	4124	3383	741
1975	the social sciences and history	135190	-10.1	84826	50364	37.3	16977	11875	5102	4212	3334	878
1976	the social sciences and history	126396	-6.5	78691	47705	37.7	15953	10918	5035	4157	3262	895
1977	the social sciences and history	117040	-7.4	71128	45912	39.2	15533	10413	5120	3802	2957	845
1978	the social sciences and history	112952	-3.5	67217	45735	40.5	14718	9845	4873	3594	2722	872
1979	the social sciences and history	108059	-4.3	62852	45207	41.8	12963	8395	4568	3371	2501	870
1980	the social sciences and history	103662	-4.1	58511	45151	43.6	12176	7794	4382	3230	2357	873
1981	the social sciences and history	100513	-3.0	56131	44382	44.2	11945	7457	4488	3122	2274	848
1982	the social sciences and history	99705	-0.8	55196	44509	44.6	12002	7468	4534	3061	2237	824
1983	the social sciences and history	95228	-4.5	52771	42457	44.6	11205	6974	4231	2931	2042	889
1984	the social sciences and history	93323	-2.0	52154	41169	44.1	10577	6551	4026	2911	2030	881
1985	the social sciences and history	91570	-1.9	51226	40344	44.1	10503	6475	4028	2851	1933	918
1986	the social sciences and history	93840	2.5	52724	41116	43.8	10564	6419	4145	2955	1970	985
1987	the social sciences and history	96342	2.7	53949	42393	44.0	10506	6373	4133	2916	2026	890
1988	the social sciences and history	100460	4.3	56377	44083	43.9	10412	6310	4102	2781	1849	932
1989	the social sciences and history	108151	7.7	60121	48030	44.4	11023	6599	4424	2885	1949	936
1990	the social sciences and history	118083	9.2	65887	52196	44.2	11634	6898	4736	3010	2019	991
1991	the social sciences and history	125107	5.9	68701	56406	45.1	12233	7016	5217	3012	1956	1056
1992	the social sciences and history	133974	7.1	73001	60973	45.5	12702	7237	5465	3218	2126	1092
1993	the social sciences and history	135703	1.3	73589	62114	45.8	13471	7671	5800	3460	2203	1257
1994	the social sciences and history	133680	-1.5	72006	61674	46.1	14561	8152	6409	3627	2317	1310
1995	the social sciences and history	128154	-4.1	68139	60015	46.8	14845	8207	6638	3725	2319	1406
1996	the social sciences and history	126479	-1.3	65872	60607	47.9	15012	8093	6919	3760	2339	1421
1997	the social sciences and history	124891	-1.3	64115	60776	48.7	14787	7830	6957	3989	2479	1510
1998	the social sciences and history	125040	0.1	63537	61503	49.2	14938	7960	6978	4127	2445	1682
1999	the social sciences and history	124815	-0.2	61843	62972	50.5	14396	7440	6956	3873	2290	1583
2000	the social sciences and history	127101	1.8	62062	65039	51.2	14066	7024	7042	4095	2407	1688
2001	the social sciences and history	128036	0.7	61749	66287	51.8	13791	6816	6975	3930	2302	1628
2002	the social sciences and history	132874	3.8	64170	68704	51.7	14112	6941	7171	3902	2219	1683
2003	the social sciences and history	143256	7.8	69517	73739	51.5	14630	7202	7428	3850	2196	1654
2004	the social sciences and history	150357	5.0	73834	76523	50.9	16110	7810	8300	3811	2188	1623
2005	the social sciences and history	156892	4.3	77702	79190	50.5	16952	8256	8696	3819	2184	1635
2006	the social sciences and history	161485	2.9	80799	80686	50.0	17369	8415	8954	3914	2218	1696
2007	the social sciences and history	164183	1.7	82417	81766	49.8	17665	8577	9088	3844	2110	1734
2008	the social sciences and history	167363	1.9	84868	82495	49.3	18495	9349	9146	4059	2194	1865
2009	the social sciences and history	168517	0.7	85202	83315	49.4	19241	9605	9636	4234	2353	1881
2010	the social sciences and history	172782	2.5	87404	85378	49.4	20234	9967	10267	4238	2292	1946
2011	the social sciences and history	177169	2.5	89809	87360	49.3	21085	10578	10507	4390	2331	2059
2012	the social sciences and history	178534	0.8	90628	87906	49.2	21891	10987	10904	4597	2464	2133
2013	the social sciences and history	177767	-0.4	90143	87624	49.3	21591	10811	10780	4610	2461	2149
2014	the social sciences and history	173096	-2.6	88233	84863	49.0	21475	10750	10725	4724	2494	2230
1971	visual and performing arts	30394	0	12256	18138	59.7	6675	3510	3165	621	483	138
1972	visual and performing arts	33831	11.3	13580	20251	59.9	7537	4049	3488	572	428	144
1973	visual and performing arts	36017	6.5	14267	21750	60.4	7254	4005	3249	616	449	167
1974	visual and performing arts	39730	10.3	15821	23909	60.2	8001	4325	3676	585	440	145
1975	visual and performing arts	40782	2.6	15532	25250	61.9	8362	4448	3914	649	446	203
1976	visual and performing arts	42138	3.3	16491	25647	60.9	8817	4507	4310	620	447	173
1977	visual and performing arts	41793	-0.8	16166	25627	61.3	8636	4211	4425	662	447	215
1978	visual and performing arts	40951	-2.0	15572	25379	62.0	9036	4327	4709	708	448	260
1979	visual and performing arts	40969	0	15380	25589	62.5	8524	3933	4591	700	454	246
1980	visual and performing arts	40892	-0.2	15065	25827	63.2	8708	4067	4641	655	413	242
1981	visual and performing arts	40479	-1.0	14798	25681	63.4	8629	4056	4573	654	396	258
1982	visual and performing arts	40422	-0.1	14819	25603	63.3	8746	3866	4880	670	380	290
1983	visual and performing arts	39804	-1.5	14695	25109	63.1	8763	4013	4750	692	404	288
1984	visual and performing arts	40131	0.8	15089	25042	62.4	8526	3897	4629	730	406	324
1985	visual and performing arts	38285	-4.6	14518	23767	62.1	8720	3896	4824	696	407	289
1986	visual and performing arts	37241	-2.7	14236	23005	61.8	8420	3775	4645	722	396	326
1987	visual and performing arts	36873	-1.0	13980	22893	62.1	8508	3756	4752	793	447	346
1988	visual and performing arts	37150	0.8	14225	22925	61.7	7939	3442	4497	727	424	303
1989	visual and performing arts	38420	3.4	14698	23722	61.7	8267	3611	4656	753	446	307
1990	visual and performing arts	39934	3.9	15189	24745	62.0	8481	3706	4775	849	472	377
1991	visual and performing arts	42186	5.6	15761	26425	62.6	8657	3830	4827	838	466	372
1992	visual and performing arts	46522	10.3	17616	28906	62.1	9353	4078	5275	906	504	402
1993	visual and performing arts	47761	2.7	18610	29151	61.0	9440	4099	5341	882	478	404
1994	visual and performing arts	49053	2.7	19538	29515	60.2	9925	4229	5696	1054	585	469
1995	visual and performing arts	48690	-0.7	19781	28909	59.4	10277	4374	5903	1080	545	535
1996	visual and performing arts	49296	1.2	20126	29170	59.2	10280	4361	5919	1067	524	543
1997	visual and performing arts	50083	1.6	20729	29354	58.6	10627	4470	6157	1060	525	535
1998	visual and performing arts	52077	4.0	21483	30594	58.7	11145	4596	6549	1163	566	597
1999	visual and performing arts	54446	4.5	22270	32176	59.1	10762	4544	6218	1117	567	550
2000	visual and performing arts	58791	8.0	24003	34788	59.2	10918	4672	6246	1127	537	590
2001	visual and performing arts	61148	4.0	24967	36181	59.2	11404	4788	6616	1167	568	599
2002	visual and performing arts	66773	9.2	27130	39643	59.4	11595	4912	6683	1114	490	624
2003	visual and performing arts	71482	7.1	27922	43560	60.9	11982	4975	7007	1293	613	680
2004	visual and performing arts	77181	8.0	30037	47144	61.1	12906	5531	7375	1282	572	710
2005	visual and performing arts	80955	4.9	31355	49600	61.3	13183	5646	7537	1278	594	684
2006	visual and performing arts	83297	2.9	32117	51180	61.4	13530	5801	7729	1383	639	744
2007	visual and performing arts	85186	2.3	32729	52457	61.6	13767	5910	7857	1364	625	739
2008	visual and performing arts	87703	3.0	33862	53841	61.4	14164	5998	8166	1453	675	778
2009	visual and performing arts	89143	1.6	35055	54088	60.7	14918	6325	8593	1569	726	843
2010	visual and performing arts	91798	3.0	35768	56030	61.0	15562	6531	9031	1599	700	899
2011	visual and performing arts	93939	2.3	36342	57597	61.3	16277	6881	9396	1646	770	876
2012	visual and performing arts	95806	2.0	37164	58642	61.2	17307	7320	9987	1728	790	938
2013	visual and performing arts	97799	2.1	38063	59736	61.1	17869	7610	10259	1814	850	964
2014	visual and performing arts	97246	-0.6	38081	59165	60.8	17863	7711	10152	1778	869	909


================================================
FILE: python-baseball-simulator/Simulating baseball in Python.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulating baseball in Python\n",
    "\n",
    "This notebook provides the methodology and code used in the blog post, [How much does batting order matter in Major League Baseball? A simulation approach](http://www.randalolson.com/2018/07/04/does-batting-order-matter-in-major-league-baseball-a-simulation-approach).\n",
    "\n",
    "### Notebook by [Randal S. Olson](http://www.randalolson.com)\n",
    "\n",
    "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely useable and shareable as possible.\n",
    "\n",
    "### Required Python libraries\n",
    "\n",
    "If you don't have Python on your computer, you can use the [Anaconda Python distribution](https://www.anaconda.com/download/) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
    "\n",
    "This code uses base Python libraries except for `seaborn`, `tqdm`, and `joblib` packages. You can install these packages using `pip` by typing the following commands into your command line:\n",
    "\n",
    "> pip install seaborn tqdm joblib\n",
    "\n",
    "### Using the baseball simulator\n",
    "\n",
    "Below is the Python code used to simulate baseball in my blog post, run the simulations, and generate the data visualizations shown in my blog post. When I get more time, I plan to clean up and comment this code better than it currently is.\n",
    "\n",
    "For the data visualizations, you will need to place [this tableau10.mplstyle](https://gist.github.com/rhiever/d0a7332fe0beebfdc3d5) in your `~/.matplotlib/stylelib/` directory for the visualizations to show up as they do in my blog post. Otherwise, you will have to use [other matplotlib styles](https://matplotlib.org/users/style_sheets.html).\n",
    "\n",
    "If you have any comments or questions about this project, I prefer that you [file an issue](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues/new) on this GitHub repository. If you don't feel comfortable with GitHub, feel free to [contact me by email](http://www.randalolson.com/contact/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import numpy as np\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "from joblib import Parallel, delayed\n",
    "import time\n",
    "\n",
    "# ParallelExecutor code taken and modified from https://gist.github.com/MInner/12f9cf961059aed1a60e72c5531a697f\n",
    "def text_progessbar(seq, total=None):\n",
    "    step = 1\n",
    "    tick = time.time()\n",
    "    while True:\n",
    "        time_diff = time.time() - tick\n",
    "        avg_speed = time_diff / step\n",
    "        total_str = 'of {}'.format(total if total else '')\n",
    "        print('step', step, '{}'.format(round(time_diff, 2)), 'avg: {} iter/sec'.format(round(avg_speed)), total_str)\n",
    "        step += 1\n",
    "        yield next(seq)\n",
    "\n",
    "all_bar_funcs = {\n",
    "    'tqdm': lambda args: lambda x: tqdm(x, **args),\n",
    "    'txt': lambda args: lambda x: text_progessbar(x, **args),\n",
    "    'False': lambda args: iter,\n",
    "    'None': lambda args: iter,\n",
    "}\n",
    "\n",
    "def ParallelExecutor(use_bar='tqdm', **joblib_args):\n",
    "    def aprun(bar=use_bar, **tq_args):\n",
    "        def tmp(op_iter):\n",
    "            if str(bar) in all_bar_funcs.keys():\n",
    "                bar_func = all_bar_funcs[str(bar)](tq_args)\n",
    "            else:\n",
    "                raise ValueError('Value {} not supported as bar type'.format(bar))\n",
    "            return Parallel(**joblib_args)(bar_func(op_iter))\n",
    "        return tmp\n",
    "    return aprun\n",
    "\n",
    "def simulate_game(batters, return_stats=False):\n",
    "    '''Simulates the batting side of a Major League Baseball game\n",
    "    \n",
    "    This is a simplified simulation of a baseball game, where each batter performs randomly\n",
    "    according to their corresponding batting average. This simulation incorporates\n",
    "    different types of hits, such as singles, doubles, triples, and home runs, and uses\n",
    "    2017-2018 Major League averages for the probabilities of those hit types occurring.\n",
    "    This simulation leaves out other aspects of the game, such as individual-level hit type\n",
    "    tendencies, double plays, stolen bases, errors, and so forth.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    batters: list\n",
    "        A list of batting averages for 9 batters in the desired batting order\n",
    "        Example input: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25]\n",
    "\n",
    "    Returns\n",
    "    ----------\n",
    "    runs_scored: int\n",
    "        The number of runs scored by the batters in one simulated game\n",
    "    batting_stats: dict\n",
    "        Dictionary containing batting statistics for each batter\n",
    "    '''\n",
    "    runs_scored = 0\n",
    "    batter_num = 0\n",
    "\n",
    "    # NOTE: Earned Bases is the total number of bases that the batter advanced themselves\n",
    "    #       AND their teammates through batting\n",
    "    batting_stats = {}\n",
    "    for batter in range(len(batters)):\n",
    "        batting_stats[batter] = {\n",
    "            'At Bat': 0, 'Single': 0, 'Double': 0, 'Triple': 0, 'Home Run': 0, 'Out': 0,\n",
    "            'RBI': 0, 'Earned Bases': 0, 'Players On Base': 0, 'Bases Loaded': 0, 'Grand Slam': 0\n",
    "        }\n",
    "\n",
    "    # Assume the game lasts for only 9 innings (no extra innings)\n",
    "    for inning in range(9):\n",
    "        bases = [\n",
    "            False, # First base, index 0\n",
    "            False, # Second base, index 1\n",
    "            False  # Third base, index 2\n",
    "        ]\n",
    "        batters_out = 0\n",
    "\n",
    "        while batters_out < 3:\n",
    "            batting_stats[batter_num]['At Bat'] += 1\n",
    "            if bases[2] and bases[1] and bases[0]:\n",
    "                batting_stats[batter_num]['Bases Loaded'] += 1\n",
    "\n",
    "            if bases[2]:\n",
    "                batting_stats[batter_num]['Players On Base'] += 1\n",
    "            if bases[1]:\n",
    "                batting_stats[batter_num]['Players On Base'] += 1\n",
    "            if bases[0]:\n",
    "                batting_stats[batter_num]['Players On Base'] += 1\n",
    "\n",
    "            if np.random.random() < batters[batter_num]:\n",
    "                # Batting estimates from MLB.com statistics in 2017/2018 seasons:\n",
    "                #     Single base hit: 64% of hits\n",
    "                #     Double base hit: 20% of hits\n",
    "                #     Triple base hit: 2% of hits\n",
    "                #     Home run: 14% of hits\n",
    "                hit_type = np.random.choice(['Single', 'Double', 'Triple', 'Home Run'], p=[.64, .2, .02, .14])\n",
    "\n",
    "                if hit_type == 'Single':\n",
    "                    batting_stats[batter_num]['Single'] += 1\n",
    "\n",
    "                    # All base runners advance 1 base\n",
    "                    if bases[2]:\n",
    "                        runs_scored += 1\n",
    "                        batting_stats[batter_num]['RBI'] += 1\n",
    "                        batting_stats[batter_num]['Earned Bases'] += 1\n",
    "                        bases[2] = False\n",
    "                    if bases[1]:\n",
    "                        bases[2] = True\n",
    "                        bases[1] = False\n",
    "                        batting_stats[batter_num]['Earned Bases'] += 1\n",
    "                    if bases[0]:\n",
    "                        bases[1] = True\n",
    "                        batting_stats[batter_num]['Earned Bases'] += 1\n",
    "\n",
    "                    bases[0] = True\n",
    "                    batting_stats[batter_num]['Earned Bases'] += 1\n",
    "\n",
    "                elif hit_type == 'Double':\n",
    "                    batting_stats[batter_num]['Double'] += 1\n",
    "\n",
    "                    # All base runners advance 2 bases\n",
    "                    if bases[2]:\n",
    "                        runs_scored += 1\n",
    "                        batting_stats[batter_num]['RBI'] += 1\n",
    "                        batting_stats[batter_num]['Earned Bases'] += 1\n",
    "                        bases[2] = False\n",
    "                    if bases[1]:\n",
    "                        runs_scored += 1\n",
    "                        batting_stats[batter_num]['RBI'] += 1\n",
    "                        batting_stats[batter_num]['Earned Bases'] += 2\n",
    "                        bases[1] = False\n",
    "                    if bases[0]:\n",
    "                        bases[2] = True\n",
    "                        batting_stats[batter_num]['Earned Bases'] += 2\n",
    "                        bases[0] = False\n",
    "\n",
    "                    bases[1] = True\n",
    "                    batting_stats[batter_num]['Earned Bases'] += 2\n",
    "\n",
    "                elif hit_type == 'Triple':\n",
    "                    batting_stats[batter_num]['Triple'] += 1\n",
    "\n",
    "                    # All base runners advance 3 bases\n",
    "                    if bases[2]:\n",
    "                        runs_scored += 1\n",
    "                        batting_stats[batter_num]['RBI'] += 1\n",
    "                        batting_stats[batter_num]['Earned Bases'] += 1\n",
    "                        bases[2] = False\n",
    "                    if bases[1]:\n",
    "                        runs_scored += 1\n",
    "                        batting_stats[batter_num]['RBI'] += 1\n",
    "                        batting_stats[batter_num]['Earned Bases'] += 2\n",
    "                        bases[1] = False\n",
    "                    if bases[0]:\n",
    "                        runs_scored += 1\n",
    "                        batting_stats[batter_num]['RBI'] += 1\n",
    "                        batting_stats[batter_num]['Earned Bases'] += 3\n",
    "                        bases[0] = False\n",
    "\n",
    "                    bases[2] = True\n",
    "                    batting_stats[batter_num]['Earned Bases'] += 3\n",
    "\n",
    "                elif hit_type == 'Home Run':\n",
    "                    batting_stats[batter_num]['Home Run'] += 1\n",
    "\n",
    "                    # Check if a Grand Slam was scored\n",
    "                    if bases[0] and bases[1] and bases[2]:\n",
    "                        batting_stats[batter_num]['Grand Slam'] += 1\n",
    "\n",
    "                    # All base runners and the hitter score a run\n",
    "                    if bases[2]:\n",
    "                        runs_scored += 1\n",
    "                        batting_stats[batter_num]['RBI'] += 1\n",
    "                        batting_stats[batter_num]['Earned Bases'] += 1\n",
    "                        bases[2] = False\n",
    "                    if bases[1]:\n",
    "                        runs_scored += 1\n",
    "                        batting_stats[batter_num]['RBI'] += 1\n",
    "                        batting_stats[batter_num]['Earned Bases'] += 2\n",
    "                        bases[1] = False\n",
    "                    if bases[0]:\n",
    "                        runs_scored += 1\n",
    "                        batting_stats[batter_num]['RBI'] += 1\n",
    "                        batting_stats[batter_num]['Earned Bases'] += 3\n",
    "                        bases[0] = False\n",
    "\n",
    "                    runs_scored += 1\n",
    "                    batting_stats[batter_num]['RBI'] += 1\n",
    "                    batting_stats[batter_num]['Earned Bases'] += 4\n",
    "\n",
    "            else:\n",
    "                # Batter struck out, flew out, or grounded out\n",
    "                batters_out += 1\n",
    "                batting_stats[batter_num]['Out'] += 1\n",
    "\n",
    "            batter_num = (batter_num + 1) % len(batters)\n",
    "\n",
    "    return runs_scored, batting_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a48d044db65b42529e0cff1ac86648fa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "designated_hitter_spot_scores = {}\n",
    "num_simulated_games = 1000000\n",
    "for team_avg in tqdm([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\n",
    "    for designated_hitter_spot in range(9):\n",
    "        batters = [team_avg] * 9\n",
    "        batters[designated_hitter_spot] = 0.35\n",
    "        aprun = ParallelExecutor(n_jobs=-1, use_bar=False)\n",
    "        designated_hitter_spot_scores[(team_avg, designated_hitter_spot)] = [runs_scored for runs_scored, _ in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dh_batting_avgs = []\n",
    "for team_avg in reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\n",
    "    dh_spot_avgs = []\n",
    "    for designated_hitter_spot in range(9):\n",
    "        dh_spot_avgs.append(np.mean(designated_hitter_spot_scores[(team_avg, designated_hitter_spot)]))\n",
    "    dh_spot_avgs = np.array(dh_spot_avgs) / np.mean(dh_spot_avgs)\n",
    "    dh_batting_avgs.append(dh_spot_avgs)\n",
    "\n",
    "plt.figure(figsize=(9, 9))\n",
    "sb.heatmap(dh_batting_avgs, cmap='PuOr', center=1., annot=True, fmt='.3f', cbar=False)\n",
    "plt.xticks([x + 0.5 for x in range(9)], [str(x) for x in range(1, 10)], fontsize=12)\n",
    "plt.xlabel('DH Batting Position (BA=0.35)', fontsize=14)\n",
    "plt.yticks([y + 0.5 for y in range(6)], reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]), fontsize=12, va='center')\n",
    "plt.ylabel('Team Batting Average (BA)', fontsize=14)\n",
    "plt.title('Batting order matters when one player is much\\nbetter than their teammates\\n\\n', fontsize=20)\n",
    "plt.text(4.5, -0.1, 'Measured: Relative runs scored based on the DH batting position\\n>1 means more runs scored, <1 means fewer runs scored', fontsize=12, ha='center')\n",
    "plt.text(-0.7, 6.8, 'Data source: League averages & custom baseball simulations\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\n",
    "plt.savefig('mlb-batting-order-dh.png', bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sig diff: team avg=0.1, dh pos=1 vs. dh pos=3 [p=1.993213882431106e-34]\n",
      "sig diff: team avg=0.1, dh pos=1 vs. dh pos=4 [p=1.812699459453188e-33]\n",
      "sig diff: team avg=0.1, dh pos=1 vs. dh pos=5 [p=4.841408611226961e-105]\n",
      "sig diff: team avg=0.1, dh pos=1 vs. dh pos=6 [p=2.1989383913301317e-231]\n",
      "sig diff: team avg=0.1, dh pos=1 vs. dh pos=7 [p=9.216188457618672e-207]\n",
      "sig diff: team avg=0.1, dh pos=1 vs. dh pos=8 [p=3.667389122454236e-218]\n",
      "sig diff: team avg=0.1, dh pos=1 vs. dh pos=9 [p=3.4321976744637874e-290]\n",
      "sig diff: team avg=0.1, dh pos=2 vs. dh pos=3 [p=8.683361888508758e-21]\n",
      "sig diff: team avg=0.1, dh pos=2 vs. dh pos=4 [p=4.275214713813377e-20]\n",
      "sig diff: team avg=0.1, dh pos=2 vs. dh pos=5 [p=2.307786280302862e-79]\n",
      "sig diff: team avg=0.1, dh pos=2 vs. dh pos=6 [p=6.56752502068817e-192]\n",
      "sig diff: team avg=0.1, dh pos=2 vs. dh pos=7 [p=1.2862878707847538e-169]\n",
      "sig diff: team avg=0.1, dh pos=2 vs. dh pos=8 [p=6.758757959239213e-180]\n",
      "sig diff: team avg=0.1, dh pos=2 vs. dh pos=9 [p=1.7225195850226612e-245]\n",
      "sig diff: team avg=0.1, dh pos=3 vs. dh pos=5 [p=1.8386948714134494e-21]\n",
      "sig diff: team avg=0.1, dh pos=3 vs. dh pos=6 [p=1.6438493001481296e-90]\n",
      "sig diff: team avg=0.1, dh pos=3 vs. dh pos=7 [p=1.8420283773808343e-75]\n",
      "sig diff: team avg=0.1, dh pos=3 vs. dh pos=8 [p=2.922048694446345e-82]\n",
      "sig diff: team avg=0.1, dh pos=3 vs. dh pos=9 [p=8.729968564474104e-128]\n",
      "sig diff: team avg=0.1, dh pos=4 vs. dh pos=5 [p=7.157137876264341e-22]\n",
      "sig diff: team avg=0.1, dh pos=4 vs. dh pos=6 [p=4.583023030013289e-91]\n",
      "sig diff: team avg=0.1, dh pos=4 vs. dh pos=7 [p=4.8140938524114977e-76]\n",
      "sig diff: team avg=0.1, dh pos=4 vs. dh pos=8 [p=7.650725116291236e-83]\n",
      "sig diff: team avg=0.1, dh pos=4 vs. dh pos=9 [p=2.4714887218395744e-128]\n",
      "sig diff: team avg=0.1, dh pos=5 vs. dh pos=6 [p=2.198816819980043e-26]\n",
      "sig diff: team avg=0.1, dh pos=5 vs. dh pos=7 [p=1.0330372720533307e-18]\n",
      "sig diff: team avg=0.1, dh pos=5 vs. dh pos=8 [p=4.768588761976132e-22]\n",
      "sig diff: team avg=0.1, dh pos=5 vs. dh pos=9 [p=2.1756207162931975e-47]\n",
      "sig diff: team avg=0.1, dh pos=7 vs. dh pos=9 [p=1.8820378340149334e-08]\n",
      "sig diff: team avg=0.1, dh pos=8 vs. dh pos=9 [p=1.532389060082728e-06]\n",
      "sig diff: team avg=0.15, dh pos=1 vs. dh pos=3 [p=3.6557297985692933e-06]\n",
      "sig diff: team avg=0.15, dh pos=1 vs. dh pos=5 [p=1.4410639275113839e-18]\n",
      "sig diff: team avg=0.15, dh pos=1 vs. dh pos=6 [p=2.813016210280305e-69]\n",
      "sig diff: team avg=0.15, dh pos=1 vs. dh pos=7 [p=2.1148242931535942e-89]\n",
      "sig diff: team avg=0.15, dh pos=1 vs. dh pos=8 [p=3.304295742713236e-120]\n",
      "sig diff: team avg=0.15, dh pos=1 vs. dh pos=9 [p=2.137620551842133e-178]\n",
      "sig diff: team avg=0.15, dh pos=2 vs. dh pos=3 [p=9.408419520887769e-10]\n",
      "sig diff: team avg=0.15, dh pos=2 vs. dh pos=5 [p=9.070076759034137e-25]\n",
      "sig diff: team avg=0.15, dh pos=2 vs. dh pos=6 [p=4.013501898130506e-81]\n",
      "sig diff: team avg=0.15, dh pos=2 vs. dh pos=7 [p=7.944112339150383e-103]\n",
      "sig diff: team avg=0.15, dh pos=2 vs. dh pos=8 [p=8.693675681304036e-136]\n",
      "sig diff: team avg=0.15, dh pos=2 vs. dh pos=9 [p=2.4995069467551577e-197]\n",
      "sig diff: team avg=0.15, dh pos=3 vs. dh pos=6 [p=2.1216224794592715e-38]\n",
      "sig diff: team avg=0.15, dh pos=3 vs. dh pos=7 [p=1.3510161214298604e-53]\n",
      "sig diff: team avg=0.15, dh pos=3 vs. dh pos=8 [p=9.038350203657884e-78]\n",
      "sig diff: team avg=0.15, dh pos=3 vs. dh pos=9 [p=1.6883407647869588e-125]\n",
      "sig diff: team avg=0.15, dh pos=4 vs. dh pos=5 [p=9.32637132490468e-16]\n",
      "sig diff: team avg=0.15, dh pos=4 vs. dh pos=6 [p=2.516207750135432e-63]\n",
      "sig diff: team avg=0.15, dh pos=4 vs. dh pos=7 [p=1.513573427948481e-82]\n",
      "sig diff: team avg=0.15, dh pos=4 vs. dh pos=8 [p=4.0313322280118733e-112]\n",
      "sig diff: team avg=0.15, dh pos=4 vs. dh pos=9 [p=2.808888831509607e-168]\n",
      "sig diff: team avg=0.15, dh pos=5 vs. dh pos=6 [p=2.2369635603850538e-18]\n",
      "sig diff: team avg=0.15, dh pos=5 vs. dh pos=7 [p=4.441855163805096e-29]\n",
      "sig diff: team avg=0.15, dh pos=5 vs. dh pos=8 [p=3.653180674858543e-47]\n",
      "sig diff: team avg=0.15, dh pos=5 vs. dh pos=9 [p=3.159014991272495e-85]\n",
      "sig diff: team avg=0.15, dh pos=6 vs. dh pos=8 [p=1.504718632116115e-08]\n",
      "sig diff: team avg=0.15, dh pos=6 vs. dh pos=9 [p=3.424427465554104e-27]\n",
      "sig diff: team avg=0.15, dh pos=7 vs. dh pos=9 [p=7.239966785374588e-17]\n",
      "sig diff: team avg=0.15, dh pos=8 vs. dh pos=9 [p=2.632610106337814e-07]\n",
      "sig diff: team avg=0.2, dh pos=1 vs. dh pos=3 [p=4.975863769272197e-06]\n",
      "sig diff: team avg=0.2, dh pos=1 vs. dh pos=6 [p=2.65483444031005e-28]\n",
      "sig diff: team avg=0.2, dh pos=1 vs. dh pos=7 [p=1.8135853092597406e-47]\n",
      "sig diff: team avg=0.2, dh pos=1 vs. dh pos=8 [p=1.150233689898291e-57]\n",
      "sig diff: team avg=0.2, dh pos=1 vs. dh pos=9 [p=7.223424302190309e-92]\n",
      "sig diff: team avg=0.2, dh pos=2 vs. dh pos=6 [p=1.0411706910861108e-19]\n",
      "sig diff: team avg=0.2, dh pos=2 vs. dh pos=7 [p=5.1747610796208696e-36]\n",
      "sig diff: team avg=0.2, dh pos=2 vs. dh pos=8 [p=6.258135807210273e-45]\n",
      "sig diff: team avg=0.2, dh pos=2 vs. dh pos=9 [p=1.5616448083207673e-75]\n",
      "sig diff: team avg=0.2, dh pos=3 vs. dh pos=6 [p=9.158553336775526e-11]\n",
      "sig diff: team avg=0.2, dh pos=3 vs. dh pos=7 [p=3.301865172490029e-23]\n",
      "sig diff: team avg=0.2, dh pos=3 vs. dh pos=8 [p=2.1319152939525667e-30]\n",
      "sig diff: team avg=0.2, dh pos=3 vs. dh pos=9 [p=4.16454555014728e-56]\n",
      "sig diff: team avg=0.2, dh pos=4 vs. dh pos=6 [p=7.091753492774976e-27]\n",
      "sig diff: team avg=0.2, dh pos=4 vs. dh pos=7 [p=1.3363453468048125e-45]\n",
      "sig diff: team avg=0.2, dh pos=4 vs. dh pos=8 [p=1.3048721867324426e-55]\n",
      "sig diff: team avg=0.2, dh pos=4 vs. dh pos=9 [p=2.650185348750165e-89]\n",
      "sig diff: team avg=0.2, dh pos=5 vs. dh pos=6 [p=1.4343465725467364e-11]\n",
      "sig diff: team avg=0.2, dh pos=5 vs. dh pos=7 [p=2.2522684790558515e-24]\n",
      "sig diff: team avg=0.2, dh pos=5 vs. dh pos=8 [p=1.0047752339155035e-31]\n",
      "sig diff: team avg=0.2, dh pos=5 vs. dh pos=9 [p=7.322521942273125e-58]\n",
      "sig diff: team avg=0.2, dh pos=6 vs. dh pos=8 [p=6.891044968687213e-07]\n",
      "sig diff: team avg=0.2, dh pos=6 vs. dh pos=9 [p=1.8501164693310642e-20]\n",
      "sig diff: team avg=0.2, dh pos=7 vs. dh pos=9 [p=5.55812122235324e-09]\n",
      "sig diff: team avg=0.25, dh pos=1 vs. dh pos=6 [p=1.3831070831091773e-08]\n",
      "sig diff: team avg=0.25, dh pos=1 vs. dh pos=7 [p=3.31894013383353e-18]\n",
      "sig diff: team avg=0.25, dh pos=1 vs. dh pos=8 [p=1.7330019936918686e-26]\n",
      "sig diff: team avg=0.25, dh pos=1 vs. dh pos=9 [p=2.313896122609819e-28]\n",
      "sig diff: team avg=0.25, dh pos=2 vs. dh pos=7 [p=2.165570151778953e-08]\n",
      "sig diff: team avg=0.25, dh pos=2 vs. dh pos=8 [p=4.208549321541679e-14]\n",
      "sig diff: team avg=0.25, dh pos=2 vs. dh pos=9 [p=1.871896887582137e-15]\n",
      "sig diff: team avg=0.25, dh pos=3 vs. dh pos=7 [p=3.2875112864625612e-06]\n",
      "sig diff: team avg=0.25, dh pos=3 vs. dh pos=8 [p=3.877797203112571e-11]\n",
      "sig diff: team avg=0.25, dh pos=3 vs. dh pos=9 [p=2.481145930009218e-12]\n",
      "sig diff: team avg=0.25, dh pos=4 vs. dh pos=7 [p=1.1449304045033806e-12]\n",
      "sig diff: team avg=0.25, dh pos=4 vs. dh pos=8 [p=1.226266128442354e-19]\n",
      "sig diff: team avg=0.25, dh pos=4 vs. dh pos=9 [p=3.0013494742914022e-21]\n",
      "sig diff: team avg=0.25, dh pos=5 vs. dh pos=7 [p=3.756596993806079e-07]\n",
      "sig diff: team avg=0.25, dh pos=5 vs. dh pos=8 [p=1.9972127491978356e-12]\n",
      "sig diff: team avg=0.25, dh pos=5 vs. dh pos=9 [p=1.0843693706190971e-13]\n",
      "sig diff: team avg=0.25, dh pos=6 vs. dh pos=8 [p=6.095539057877671e-07]\n",
      "sig diff: team avg=0.25, dh pos=6 vs. dh pos=9 [p=7.246783482517967e-08]\n",
      "sig diff: team avg=0.3, dh pos=1 vs. dh pos=7 [p=3.590625549191536e-06]\n",
      "sig diff: team avg=0.3, dh pos=1 vs. dh pos=8 [p=4.0136192312912695e-11]\n",
      "sig diff: team avg=0.3, dh pos=1 vs. dh pos=9 [p=1.7985941375427346e-10]\n",
      "sig diff: team avg=0.3, dh pos=2 vs. dh pos=8 [p=7.586368508725617e-10]\n",
      "sig diff: team avg=0.3, dh pos=2 vs. dh pos=9 [p=3.0993959675870173e-09]\n",
      "sig diff: team avg=0.3, dh pos=3 vs. dh pos=8 [p=7.627671059317768e-08]\n",
      "sig diff: team avg=0.3, dh pos=3 vs. dh pos=9 [p=2.632896126595547e-07]\n",
      "sig diff: team avg=0.3, dh pos=4 vs. dh pos=7 [p=5.161961870945482e-07]\n",
      "sig diff: team avg=0.3, dh pos=4 vs. dh pos=8 [p=2.699565677716188e-12]\n",
      "sig diff: team avg=0.3, dh pos=4 vs. dh pos=9 [p=1.3251648748904703e-11]\n",
      "sig diff: team avg=0.3, dh pos=5 vs. dh pos=8 [p=7.733403379191462e-07]\n",
      "sig diff: team avg=0.3, dh pos=5 vs. dh pos=9 [p=2.435910255012577e-06]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ranksums\n",
    "from itertools import product\n",
    "\n",
    "for team_avg1, designated_hitter_spot1, team_avg2, designated_hitter_spot2 in product([0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9), [0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9)):\n",
    "    if team_avg1 != team_avg2:\n",
    "        continue\n",
    "    if designated_hitter_spot1 > designated_hitter_spot2:\n",
    "        continue\n",
    "    if team_avg1 == team_avg2 and designated_hitter_spot1 == designated_hitter_spot2:\n",
    "        continue\n",
    "    statistic, pval = ranksums(designated_hitter_spot_scores[(team_avg1, designated_hitter_spot1)], designated_hitter_spot_scores[(team_avg2, designated_hitter_spot2)])\n",
    "    if pval < 1e-5:\n",
    "        print('sig diff: team avg={}, dh pos={} vs. dh pos={} [p={}]'.format(team_avg1, designated_hitter_spot1 + 1, designated_hitter_spot2 + 1, pval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7090098ae5344608824b5159a98774c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "pitcher_spot_scores = {}\n",
    "num_simulated_games = 1000000\n",
    "for team_avg in tqdm([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\n",
    "    for pitcher_spot in range(9):\n",
    "        batters = [team_avg] * 9\n",
    "        batters[pitcher_spot] = 0.1\n",
    "        aprun = ParallelExecutor(n_jobs=-1, use_bar=False)\n",
    "        pitcher_spot_scores[(team_avg, pitcher_spot)] = [runs_scored for runs_scored, _ in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_batting_avgs = []\n",
    "for team_avg in reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\n",
    "    p_spot_avgs = []\n",
    "    for pitcher_spot in range(9):\n",
    "        p_spot_avgs.append(np.mean(pitcher_spot_scores[(team_avg, pitcher_spot)]))\n",
    "    p_spot_avgs = np.array(p_spot_avgs) / np.mean(p_spot_avgs)\n",
    "    p_batting_avgs.append(p_spot_avgs)\n",
    "\n",
    "plt.figure(figsize=(9, 9))\n",
    "sb.heatmap(p_batting_avgs, cmap='PuOr', center=1., annot=True, fmt='.3f', cbar=False)\n",
    "plt.xticks([x + 0.5 for x in range(9)], [str(x) for x in range(1, 10)], fontsize=12)\n",
    "plt.xlabel('Pitcher Batting Position (BA=0.1)', fontsize=14)\n",
    "plt.yticks([y + 0.5 for y in range(6)], reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]), fontsize=12, va='center')\n",
    "plt.ylabel('Team Batting Average (BA)', fontsize=14)\n",
    "plt.title('Batting order matters when one player is much\\nworse than their teammates\\n\\n', fontsize=20)\n",
    "plt.text(4.5, -0.1, 'Measured: Relative runs scored based on the Pitcher batting position\\n>1 means more runs scored, <1 means fewer runs scored', fontsize=12, ha='center')\n",
    "plt.text(-0.7, 6.8, 'Data source: League averages & custom baseball simulations\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\n",
    "plt.savefig('mlb-batting-order-pitcher.png', bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=5 [p=3.8851625783791845e-06]\n",
      "sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=6 [p=1.6567387490416848e-09]\n",
      "sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=7 [p=3.521864556215601e-13]\n",
      "sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=8 [p=2.2218326239175095e-14]\n",
      "sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=9 [p=7.785499313865129e-19]\n",
      "sig diff: team avg=0.15, pitcher pos=2 vs. pitcher pos=6 [p=2.0124828886376083e-06]\n",
      "sig diff: team avg=0.15, pitcher pos=2 vs. pitcher pos=7 [p=2.0015496834873097e-09]\n",
      "sig diff: team avg=0.15, pitcher pos=2 vs. pitcher pos=8 [p=1.995996488992889e-10]\n",
      "sig diff: team avg=0.15, pitcher pos=2 vs. pitcher pos=9 [p=3.2618519915831955e-14]\n",
      "sig diff: team avg=0.15, pitcher pos=3 vs. pitcher pos=7 [p=1.0796284981609147e-06]\n",
      "sig diff: team avg=0.15, pitcher pos=3 vs. pitcher pos=8 [p=1.6098095825185125e-07]\n",
      "sig diff: team avg=0.15, pitcher pos=3 vs. pitcher pos=9 [p=1.0132397815959589e-10]\n",
      "sig diff: team avg=0.15, pitcher pos=4 vs. pitcher pos=6 [p=7.396386504689107e-07]\n",
      "sig diff: team avg=0.15, pitcher pos=4 vs. pitcher pos=7 [p=5.706121239186367e-10]\n",
      "sig diff: team avg=0.15, pitcher pos=4 vs. pitcher pos=8 [p=5.3022601322627294e-11]\n",
      "sig diff: team avg=0.15, pitcher pos=4 vs. pitcher pos=9 [p=6.745193973135644e-15]\n",
      "sig diff: team avg=0.2, pitcher pos=1 vs. pitcher pos=6 [p=3.736793590540399e-21]\n",
      "sig diff: team avg=0.2, pitcher pos=1 vs. pitcher pos=7 [p=2.524976319110312e-18]\n",
      "sig diff: team avg=0.2, pitcher pos=1 vs. pitcher pos=8 [p=6.746491316929479e-35]\n",
      "sig diff: team avg=0.2, pitcher pos=1 vs. pitcher pos=9 [p=6.403222171714512e-45]\n",
      "sig diff: team avg=0.2, pitcher pos=2 vs. pitcher pos=6 [p=2.745377273205046e-20]\n",
      "sig diff: team avg=0.2, pitcher pos=2 vs. pitcher pos=7 [p=1.592850476032973e-17]\n",
      "sig diff: team avg=0.2, pitcher pos=2 vs. pitcher pos=8 [p=9.402831597164582e-34]\n",
      "sig diff: team avg=0.2, pitcher pos=2 vs. pitcher pos=9 [p=1.3256041606779473e-43]\n",
      "sig diff: team avg=0.2, pitcher pos=3 vs. pitcher pos=6 [p=3.052850586857199e-19]\n",
      "sig diff: team avg=0.2, pitcher pos=3 vs. pitcher pos=7 [p=1.4702483430295165e-16]\n",
      "sig diff: team avg=0.2, pitcher pos=3 vs. pitcher pos=8 [p=2.2604947523265107e-32]\n",
      "sig diff: team avg=0.2, pitcher pos=3 vs. pitcher pos=9 [p=5.097574771619022e-42]\n",
      "sig diff: team avg=0.2, pitcher pos=4 vs. pitcher pos=6 [p=3.0892533399419725e-19]\n",
      "sig diff: team avg=0.2, pitcher pos=4 vs. pitcher pos=7 [p=1.5077781232512707e-16]\n",
      "sig diff: team avg=0.2, pitcher pos=4 vs. pitcher pos=8 [p=2.1263766982882966e-32]\n",
      "sig diff: team avg=0.2, pitcher pos=4 vs. pitcher pos=9 [p=4.537418406485668e-42]\n",
      "sig diff: team avg=0.2, pitcher pos=5 vs. pitcher pos=6 [p=1.8903311286109317e-12]\n",
      "sig diff: team avg=0.2, pitcher pos=5 vs. pitcher pos=7 [p=2.4052778093024993e-10]\n",
      "sig diff: team avg=0.2, pitcher pos=5 vs. pitcher pos=8 [p=2.987994710899591e-23]\n",
      "sig diff: team avg=0.2, pitcher pos=5 vs. pitcher pos=9 [p=1.6689098012995852e-31]\n",
      "sig diff: team avg=0.2, pitcher pos=6 vs. pitcher pos=9 [p=3.2940442949580448e-06]\n",
      "sig diff: team avg=0.2, pitcher pos=7 vs. pitcher pos=9 [p=8.444971822549267e-08]\n",
      "sig diff: team avg=0.25, pitcher pos=1 vs. pitcher pos=6 [p=1.263607488343873e-23]\n",
      "sig diff: team avg=0.25, pitcher pos=1 vs. pitcher pos=7 [p=4.516058690648763e-49]\n",
      "sig diff: team avg=0.25, pitcher pos=1 vs. pitcher pos=8 [p=1.868709972805515e-46]\n",
      "sig diff: team avg=0.25, pitcher pos=1 vs. pitcher pos=9 [p=9.124875977854823e-82]\n",
      "sig diff: team avg=0.25, pitcher pos=2 vs. pitcher pos=6 [p=9.121582635686449e-20]\n",
      "sig diff: team avg=0.25, pitcher pos=2 vs. pitcher pos=7 [p=2.658571220387695e-43]\n",
      "sig diff: team avg=0.25, pitcher pos=2 vs. pitcher pos=8 [p=7.532218740635377e-41]\n",
      "sig diff: team avg=0.25, pitcher pos=2 vs. pitcher pos=9 [p=3.792046725058025e-74]\n",
      "sig diff: team avg=0.25, pitcher pos=3 vs. pitcher pos=6 [p=8.383966192001412e-09]\n",
      "sig diff: team avg=0.25, pitcher pos=3 vs. pitcher pos=7 [p=1.4330997662660238e-25]\n",
      "sig diff: team avg=0.25, pitcher pos=3 vs. pitcher pos=8 [p=1.0181059424978879e-23]\n",
      "sig diff: team avg=0.25, pitcher pos=3 vs. pitcher pos=9 [p=5.150512722206992e-50]\n",
      "sig diff: team avg=0.25, pitcher pos=4 vs. pitcher pos=6 [p=2.4556456675838104e-22]\n",
      "sig diff: team avg=0.25, pitcher pos=4 vs. pitcher pos=7 [p=3.8858858801144415e-47]\n",
      "sig diff: team avg=0.25, pitcher pos=4 vs. pitcher pos=8 [p=1.405260832926898e-44]\n",
      "sig diff: team avg=0.25, pitcher pos=4 vs. pitcher pos=9 [p=3.7388299589130853e-79]\n",
      "sig diff: team avg=0.25, pitcher pos=5 vs. pitcher pos=6 [p=1.0041981665787318e-16]\n",
      "sig diff: team avg=0.25, pitcher pos=5 vs. pitcher pos=7 [p=1.1186354391340145e-38]\n",
      "sig diff: team avg=0.25, pitcher pos=5 vs. pitcher pos=8 [p=2.2474110418558506e-36]\n",
      "sig diff: team avg=0.25, pitcher pos=5 vs. pitcher pos=9 [p=4.689603775373094e-68]\n",
      "sig diff: team avg=0.25, pitcher pos=6 vs. pitcher pos=7 [p=2.6120598419790005e-06]\n",
      "sig diff: team avg=0.25, pitcher pos=6 vs. pitcher pos=9 [p=6.531623482124316e-20]\n",
      "sig diff: team avg=0.25, pitcher pos=7 vs. pitcher pos=9 [p=8.779138829657236e-06]\n",
      "sig diff: team avg=0.25, pitcher pos=8 vs. pitcher pos=9 [p=1.194392607523925e-06]\n",
      "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=2 [p=8.353757123256407e-08]\n",
      "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=3 [p=1.6153351773311131e-22]\n",
      "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=4 [p=2.6074603335403655e-06]\n",
      "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=5 [p=6.0048940289536404e-15]\n",
      "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=6 [p=1.7188273321673662e-52]\n",
      "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=7 [p=6.956087996395689e-82]\n",
      "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=8 [p=3.4107621024146694e-109]\n",
      "sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=9 [p=4.971738054338718e-173]\n",
      "sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=3 [p=9.959224425730982e-06]\n",
      "sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=6 [p=4.3000326686945074e-23]\n",
      "sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=7 [p=2.0450594381375726e-43]\n",
      "sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=8 [p=1.025315766940147e-63]\n",
      "sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=9 [p=6.1794625367623775e-114]\n",
      "sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=4 [p=4.4498404478852373e-07]\n",
      "sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=6 [p=4.320334730364867e-08]\n",
      "sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=7 [p=6.30824649646141e-21]\n",
      "sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=8 [p=2.0209047768680628e-35]\n",
      "sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=9 [p=2.6572390367461014e-74]\n",
      "sig diff: team avg=0.3, pitcher pos=4 vs. pitcher pos=6 [p=6.026023750905654e-26]\n",
      "sig diff: team avg=0.3, pitcher pos=4 vs. pitcher pos=7 [p=2.881407355644203e-47]\n",
      "sig diff: team avg=0.3, pitcher pos=4 vs. pitcher pos=8 [p=2.215982775474632e-68]\n",
      "sig diff: team avg=0.3, pitcher pos=4 vs. pitcher pos=9 [p=4.880235566606631e-120]\n",
      "sig diff: team avg=0.3, pitcher pos=5 vs. pitcher pos=6 [p=1.1352532168880723e-13]\n",
      "sig diff: team avg=0.3, pitcher pos=5 vs. pitcher pos=7 [p=9.404064042439993e-30]\n",
      "sig diff: team avg=0.3, pitcher pos=5 vs. pitcher pos=8 [p=8.688559264804496e-47]\n",
      "sig diff: team avg=0.3, pitcher pos=5 vs. pitcher pos=9 [p=1.391575735005446e-90]\n",
      "sig diff: team avg=0.3, pitcher pos=6 vs. pitcher pos=8 [p=3.578976447437263e-12]\n",
      "sig diff: team avg=0.3, pitcher pos=6 vs. pitcher pos=9 [p=2.2812745629023015e-37]\n",
      "sig diff: team avg=0.3, pitcher pos=7 vs. pitcher pos=9 [p=8.020784190788164e-19]\n",
      "sig diff: team avg=0.3, pitcher pos=8 vs. pitcher pos=9 [p=6.172240291460689e-09]\n",
      "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=2 [p=3.813780983491385e-15]\n",
      "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=3 [p=1.3830330447057497e-36]\n",
      "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=4 [p=2.5780721611446785e-27]\n",
      "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=5 [p=1.1216694099222578e-27]\n",
      "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=6 [p=3.872729624040197e-90]\n",
      "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=7 [p=2.626956916522825e-176]\n",
      "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=8 [p=9.715274614129921e-230]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=9 [p=3.220267321795051e-281]\n",
      "sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=3 [p=1.6139670212100365e-06]\n",
      "sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=6 [p=8.299306585791126e-35]\n",
      "sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=7 [p=1.8586225058566005e-93]\n",
      "sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=8 [p=2.603919487805058e-133]\n",
      "sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=9 [p=4.022886588831997e-173]\n",
      "sig diff: team avg=0.35, pitcher pos=3 vs. pitcher pos=6 [p=6.614034101392958e-14]\n",
      "sig diff: team avg=0.35, pitcher pos=3 vs. pitcher pos=7 [p=1.7637445840810704e-55]\n",
      "sig diff: team avg=0.35, pitcher pos=3 vs. pitcher pos=8 [p=7.739828991638169e-87]\n",
      "sig diff: team avg=0.35, pitcher pos=3 vs. pitcher pos=9 [p=2.1719000592863943e-119]\n",
      "sig diff: team avg=0.35, pitcher pos=4 vs. pitcher pos=6 [p=1.2942527134733691e-20]\n",
      "sig diff: team avg=0.35, pitcher pos=4 vs. pitcher pos=7 [p=1.2835707549227605e-68]\n",
      "sig diff: team avg=0.35, pitcher pos=4 vs. pitcher pos=8 [p=3.6300281863188245e-103]\n",
      "sig diff: team avg=0.35, pitcher pos=4 vs. pitcher pos=9 [p=1.8965909045113902e-138]\n",
      "sig diff: team avg=0.35, pitcher pos=5 vs. pitcher pos=6 [p=2.730228869895967e-20]\n",
      "sig diff: team avg=0.35, pitcher pos=5 vs. pitcher pos=7 [p=5.1291556746202074e-68]\n",
      "sig diff: team avg=0.35, pitcher pos=5 vs. pitcher pos=8 [p=2.0013757721704826e-102]\n",
      "sig diff: team avg=0.35, pitcher pos=5 vs. pitcher pos=9 [p=1.3596282572447242e-137]\n",
      "sig diff: team avg=0.35, pitcher pos=6 vs. pitcher pos=7 [p=2.1711619378971672e-16]\n",
      "sig diff: team avg=0.35, pitcher pos=6 vs. pitcher pos=8 [p=1.0883133428675732e-34]\n",
      "sig diff: team avg=0.35, pitcher pos=6 vs. pitcher pos=9 [p=4.59793707847946e-56]\n",
      "sig diff: team avg=0.35, pitcher pos=7 vs. pitcher pos=9 [p=3.626139910490934e-14]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ranksums\n",
    "from itertools import product\n",
    "\n",
    "for team_avg1, pitcher_spot1, team_avg2, pitcher_spot2 in product([0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9), [0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9)):\n",
    "    if team_avg1 != team_avg2:\n",
    "        continue\n",
    "    if pitcher_spot1 > pitcher_spot2:\n",
    "        continue\n",
    "    if team_avg1 == team_avg2 and pitcher_spot1 == pitcher_spot2:\n",
    "        continue\n",
    "    statistic, pval = ranksums(pitcher_spot_scores[(team_avg1, pitcher_spot1)], pitcher_spot_scores[(team_avg2, pitcher_spot2)])\n",
    "    if pval < 1e-5:\n",
    "        print('sig diff: team avg={}, pitcher pos={} vs. pitcher pos={} [p={}]'.format(team_avg1, pitcher_spot1 + 1, pitcher_spot2 + 1, pval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96d6c555d55e45c59744678efa8bf796",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "hitter_spot_scores = {}\n",
    "num_simulated_games = 1000000\n",
    "for hitter_ba in tqdm([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\n",
    "    for hitter_spot in range(9):\n",
    "        batters = [0.25] * 9\n",
    "        batters[hitter_spot] = hitter_ba\n",
    "        aprun = ParallelExecutor(n_jobs=-1, use_bar=False)\n",
    "        hitter_spot_scores[(hitter_ba, hitter_spot)] = [runs_scored for runs_scored, _ in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hitter_batting_avgs = []\n",
    "for hitter_ba in reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\n",
    "    hitter_spot_avgs = []\n",
    "    for hitter_spot in range(9):\n",
    "        hitter_spot_avgs.append(np.mean(hitter_spot_scores[(hitter_ba, hitter_spot)]))\n",
    "    hitter_spot_avgs = np.array(hitter_spot_avgs) / np.mean(hitter_spot_avgs)\n",
    "    hitter_batting_avgs.append(hitter_spot_avgs)\n",
    "\n",
    "plt.figure(figsize=(9, 9))\n",
    "sb.heatmap(hitter_batting_avgs, cmap='PuOr', center=1., annot=True, fmt='.3f', cbar=False)\n",
    "plt.xticks([x + 0.5 for x in range(9)], [str(x) for x in range(1, 10)], fontsize=12)\n",
    "plt.xlabel('Hitter Batting Position (Team BA=0.25)', fontsize=14)\n",
    "plt.yticks([y + 0.5 for y in range(6)], reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]), fontsize=12, va='center')\n",
    "plt.ylabel('Hitter Batting Average (BA)', fontsize=14)\n",
    "plt.title('Exceptional batters should lead the batting line-up,\\npoor batters should conclude the line-up\\n\\n', fontsize=20)\n",
    "plt.text(4.5, -0.1, 'Measured: Relative runs scored based on the Hitter batting position & BA\\n>1 means more runs scored, <1 means fewer runs scored', fontsize=12, ha='center')\n",
    "plt.text(-0.7, 6.8, 'Data source: League averages & custom baseball simulations\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\n",
    "plt.savefig('mlb-batting-order-varying-hitter.png', bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sig diff: batter avg=0.1, batter pos=1 vs. batter pos=6 [p=2.3356714160801088e-24]\n",
      "sig diff: batter avg=0.1, batter pos=1 vs. batter pos=7 [p=1.9333496033653533e-42]\n",
      "sig diff: batter avg=0.1, batter pos=1 vs. batter pos=8 [p=6.10815448231743e-53]\n",
      "sig diff: batter avg=0.1, batter pos=1 vs. batter pos=9 [p=1.575713429661131e-97]\n",
      "sig diff: batter avg=0.1, batter pos=2 vs. batter pos=6 [p=2.0215081818800176e-15]\n",
      "sig diff: batter avg=0.1, batter pos=2 vs. batter pos=7 [p=4.017891266089287e-30]\n",
      "sig diff: batter avg=0.1, batter pos=2 vs. batter pos=8 [p=5.341388537747882e-39]\n",
      "sig diff: batter avg=0.1, batter pos=2 vs. batter pos=9 [p=5.060705124564318e-78]\n",
      "sig diff: batter avg=0.1, batter pos=3 vs. batter pos=6 [p=1.9653904111195653e-11]\n",
      "sig diff: batter avg=0.1, batter pos=3 vs. batter pos=7 [p=2.7407260526466543e-24]\n",
      "sig diff: batter avg=0.1, batter pos=3 vs. batter pos=8 [p=2.816306052006508e-32]\n",
      "sig diff: batter avg=0.1, batter pos=3 vs. batter pos=9 [p=3.0275119028564315e-68]\n",
      "sig diff: batter avg=0.1, batter pos=4 vs. batter pos=6 [p=5.661422005059482e-20]\n",
      "sig diff: batter avg=0.1, batter pos=4 vs. batter pos=7 [p=1.6915555936193104e-36]\n",
      "sig diff: batter avg=0.1, batter pos=4 vs. batter pos=8 [p=2.9356014190839377e-46]\n",
      "sig diff: batter avg=0.1, batter pos=4 vs. batter pos=9 [p=2.9250009360182003e-88]\n",
      "sig diff: batter avg=0.1, batter pos=5 vs. batter pos=6 [p=1.4549070522486695e-13]\n",
      "sig diff: batter avg=0.1, batter pos=5 vs. batter pos=7 [p=1.828373272957227e-27]\n",
      "sig diff: batter avg=0.1, batter pos=5 vs. batter pos=8 [p=5.822777629859619e-36]\n",
      "sig diff: batter avg=0.1, batter pos=5 vs. batter pos=9 [p=1.0934504454814966e-73]\n",
      "sig diff: batter avg=0.1, batter pos=6 vs. batter pos=8 [p=2.9773549237136997e-07]\n",
      "sig diff: batter avg=0.1, batter pos=6 vs. batter pos=9 [p=4.8361947840322014e-27]\n",
      "sig diff: batter avg=0.1, batter pos=7 vs. batter pos=9 [p=2.7297346879156197e-13]\n",
      "sig diff: batter avg=0.1, batter pos=8 vs. batter pos=9 [p=1.5922744179739228e-08]\n",
      "sig diff: batter avg=0.15, batter pos=1 vs. batter pos=6 [p=4.366179130152077e-06]\n",
      "sig diff: batter avg=0.15, batter pos=1 vs. batter pos=7 [p=1.934107684620965e-12]\n",
      "sig diff: batter avg=0.15, batter pos=1 vs. batter pos=8 [p=7.78624198027531e-20]\n",
      "sig diff: batter avg=0.15, batter pos=1 vs. batter pos=9 [p=1.3890943563058296e-38]\n",
      "sig diff: batter avg=0.15, batter pos=2 vs. batter pos=6 [p=4.209619788129184e-08]\n",
      "sig diff: batter avg=0.15, batter pos=2 vs. batter pos=7 [p=2.28957523663897e-15]\n",
      "sig diff: batter avg=0.15, batter pos=2 vs. batter pos=8 [p=1.5958028806387556e-23]\n",
      "sig diff: batter avg=0.15, batter pos=2 vs. batter pos=9 [p=1.0540870392388548e-43]\n",
      "sig diff: batter avg=0.15, batter pos=3 vs. batter pos=7 [p=5.888897525539422e-07]\n",
      "sig diff: batter avg=0.15, batter pos=3 vs. batter pos=8 [p=1.5522832890635303e-12]\n",
      "sig diff: batter avg=0.15, batter pos=3 vs. batter pos=9 [p=7.454010617532868e-28]\n",
      "sig diff: batter avg=0.15, batter pos=4 vs. batter pos=6 [p=1.5887629310380087e-08]\n",
      "sig diff: batter avg=0.15, batter pos=4 vs. batter pos=7 [p=5.738765364593026e-16]\n",
      "sig diff: batter avg=0.15, batter pos=4 vs. batter pos=8 [p=2.814964152068496e-24]\n",
      "sig diff: batter avg=0.15, batter pos=4 vs. batter pos=9 [p=9.416275387342214e-45]\n",
      "sig diff: batter avg=0.15, batter pos=5 vs. batter pos=7 [p=5.789746845829661e-07]\n",
      "sig diff: batter avg=0.15, batter pos=5 vs. batter pos=8 [p=1.5306117655840685e-12]\n",
      "sig diff: batter avg=0.15, batter pos=5 vs. batter pos=9 [p=7.237481516249173e-28]\n",
      "sig diff: batter avg=0.15, batter pos=6 vs. batter pos=8 [p=5.940356459903494e-06]\n",
      "sig diff: batter avg=0.15, batter pos=6 vs. batter pos=9 [p=4.351690466180415e-17]\n",
      "sig diff: batter avg=0.15, batter pos=7 vs. batter pos=9 [p=2.610584051321233e-09]\n",
      "sig diff: batter avg=0.2, batter pos=1 vs. batter pos=9 [p=3.76689328874521e-08]\n",
      "sig diff: batter avg=0.2, batter pos=2 vs. batter pos=8 [p=5.205859940162342e-06]\n",
      "sig diff: batter avg=0.2, batter pos=2 vs. batter pos=9 [p=1.2174761546450417e-10]\n",
      "sig diff: batter avg=0.2, batter pos=3 vs. batter pos=9 [p=7.448777734010058e-07]\n",
      "sig diff: batter avg=0.2, batter pos=4 vs. batter pos=9 [p=4.803472139153211e-08]\n",
      "sig diff: batter avg=0.2, batter pos=5 vs. batter pos=7 [p=2.4459594614199587e-06]\n",
      "sig diff: batter avg=0.2, batter pos=5 vs. batter pos=8 [p=7.626491206096117e-07]\n",
      "sig diff: batter avg=0.2, batter pos=5 vs. batter pos=9 [p=8.845036615126957e-12]\n",
      "sig diff: batter avg=0.3, batter pos=1 vs. batter pos=7 [p=4.087726662547383e-09]\n",
      "sig diff: batter avg=0.3, batter pos=1 vs. batter pos=8 [p=1.3205738706976753e-09]\n",
      "sig diff: batter avg=0.3, batter pos=1 vs. batter pos=9 [p=1.0290332194299952e-15]\n",
      "sig diff: batter avg=0.3, batter pos=2 vs. batter pos=7 [p=9.480606656615423e-07]\n",
      "sig diff: batter avg=0.3, batter pos=2 vs. batter pos=8 [p=3.633806148920251e-07]\n",
      "sig diff: batter avg=0.3, batter pos=2 vs. batter pos=9 [p=1.8419919798882447e-12]\n",
      "sig diff: batter avg=0.3, batter pos=3 vs. batter pos=9 [p=2.3167418309385654e-10]\n",
      "sig diff: batter avg=0.3, batter pos=4 vs. batter pos=7 [p=7.55298575627675e-06]\n",
      "sig diff: batter avg=0.3, batter pos=4 vs. batter pos=8 [p=3.1216536518845004e-06]\n",
      "sig diff: batter avg=0.3, batter pos=4 vs. batter pos=9 [p=3.5434170853752895e-11]\n",
      "sig diff: batter avg=0.3, batter pos=5 vs. batter pos=8 [p=5.911151637054599e-06]\n",
      "sig diff: batter avg=0.3, batter pos=5 vs. batter pos=9 [p=8.765394985228765e-11]\n",
      "sig diff: batter avg=0.35, batter pos=1 vs. batter pos=6 [p=1.7269273863841656e-12]\n",
      "sig diff: batter avg=0.35, batter pos=1 vs. batter pos=7 [p=1.0342126228400915e-19]\n",
      "sig diff: batter avg=0.35, batter pos=1 vs. batter pos=8 [p=2.8883476180027313e-26]\n",
      "sig diff: batter avg=0.35, batter pos=1 vs. batter pos=9 [p=1.0533809552536307e-44]\n",
      "sig diff: batter avg=0.35, batter pos=2 vs. batter pos=7 [p=6.177648266053254e-08]\n",
      "sig diff: batter avg=0.35, batter pos=2 vs. batter pos=8 [p=3.9592132643227364e-12]\n",
      "sig diff: batter avg=0.35, batter pos=2 vs. batter pos=9 [p=3.60635063988907e-25]\n",
      "sig diff: batter avg=0.35, batter pos=3 vs. batter pos=7 [p=2.092547777615177e-06]\n",
      "sig diff: batter avg=0.35, batter pos=3 vs. batter pos=8 [p=3.601328161213642e-10]\n",
      "sig diff: batter avg=0.35, batter pos=3 vs. batter pos=9 [p=3.1411817736944074e-22]\n",
      "sig diff: batter avg=0.35, batter pos=4 vs. batter pos=6 [p=3.8732703941556835e-10]\n",
      "sig diff: batter avg=0.35, batter pos=4 vs. batter pos=7 [p=1.1262407382894222e-16]\n",
      "sig diff: batter avg=0.35, batter pos=4 vs. batter pos=8 [p=1.0286309709980345e-22]\n",
      "sig diff: batter avg=0.35, batter pos=4 vs. batter pos=9 [p=5.3593257566914e-40]\n",
      "sig diff: batter avg=0.35, batter pos=5 vs. batter pos=7 [p=4.938191614284398e-10]\n",
      "sig diff: batter avg=0.35, batter pos=5 vs. batter pos=8 [p=9.606821714815804e-15]\n",
      "sig diff: batter avg=0.35, batter pos=5 vs. batter pos=9 [p=5.513759189984902e-29]\n",
      "sig diff: batter avg=0.35, batter pos=6 vs. batter pos=9 [p=2.7471407448075344e-12]\n",
      "sig diff: batter avg=0.35, batter pos=7 vs. batter pos=9 [p=7.161732780696792e-07]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ranksums\n",
    "from itertools import product\n",
    "\n",
    "for hitter_avg1, hitter_spot1, hitter_avg2, hitter_spot2 in product([0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9), [0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9)):\n",
    "    if hitter_avg1 != hitter_avg2:\n",
    "        continue\n",
    "    if hitter_spot1 > hitter_spot2:\n",
    "        continue\n",
    "    if hitter_avg1 == hitter_avg2 and hitter_spot1 == hitter_spot2:\n",
    "        continue\n",
    "    statistic, pval = ranksums(hitter_spot_scores[(hitter_avg1, hitter_spot1)], hitter_spot_scores[(hitter_avg2, hitter_spot2)])\n",
    "    if pval < 1e-5:\n",
    "        print('sig diff: batter avg={}, batter pos={} vs. batter pos={} [p={}]'.format(hitter_avg1, hitter_spot1 + 1, hitter_spot2 + 1, pval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "batters = [0.25] * 9\n",
    "num_simulated_games = 1000000\n",
    "aprun = ParallelExecutor(n_jobs=-1, use_bar=False)\n",
    "average_team_stats = [game_stats for _, game_stats in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batting_stat = 'At Bat'\n",
    "hitter_spot_avgs = []\n",
    "for hitter_spot in range(9):\n",
    "    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\n",
    "\n",
    "with plt.style.context('tableau10'):\n",
    "    plt.figure()\n",
    "    plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD')\n",
    "    batting_stat += 's per Game'\n",
    "    plt.ylabel(batting_stat)\n",
    "    plt.xticks(range(9), [str(x) for x in range(1, 10)])\n",
    "    plt.xlabel('Batting Position')\n",
    "    plt.title('Earlier batters have more At Bats on average')\n",
    "    plt.text(-1.3, -0.75, 'Data source: League averages & custom baseball simulations\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\n",
    "    plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4.419502,\n",
       " 4.31038,\n",
       " 4.212098,\n",
       " 4.118346,\n",
       " 4.0211,\n",
       " 3.913748,\n",
       " 3.79499,\n",
       " 3.667696,\n",
       " 3.53981]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batting_stat = 'At Bat'\n",
    "hitter_spot_avgs = []\n",
    "for hitter_spot in range(9):\n",
    "    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\n",
    "hitter_spot_avgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batting_stat = 'Players On Base'\n",
    "hitter_spot_avgs = []\n",
    "for hitter_spot in range(9):\n",
    "    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\n",
    "\n",
    "with plt.style.context('tableau10'):\n",
    "    plt.figure()\n",
    "    plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD')\n",
    "    batting_stat += ' per Game'\n",
    "    plt.ylabel(batting_stat)\n",
    "    plt.xticks(range(9), [str(x) for x in range(1, 10)])\n",
    "    plt.xlabel('Batting Position')\n",
    "    plt.title('Middle batters tend to have more players on base when batting')\n",
    "    plt.text(-1.5, -0.4, 'Data source: League averages & custom baseball simulations\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\n",
    "    plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batting_stat = 'RBI'\n",
    "hitter_spot_avgs = []\n",
    "for hitter_spot in range(9):\n",
    "    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\n",
    "\n",
    "with plt.style.context('tableau10'):\n",
    "    plt.figure()\n",
    "    plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD')\n",
    "    batting_stat += ' per Game'\n",
    "    plt.ylabel(batting_stat)\n",
    "    plt.xticks(range(9), [str(x) for x in range(1, 10)])\n",
    "    plt.xlabel('Batting Position')\n",
    "    plt.title('Middle batters tend to contribute more RBI')\n",
    "    plt.text(-1.6, -0.06, 'Data source: League averages & custom baseball simulations\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\n",
    "    plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.300596,\n",
       " 0.305068,\n",
       " 0.311784,\n",
       " 0.321964,\n",
       " 0.317941,\n",
       " 0.299361,\n",
       " 0.292952,\n",
       " 0.287004,\n",
       " 0.27537]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batting_stat = 'RBI'\n",
    "hitter_spot_avgs = []\n",
    "for hitter_spot in range(9):\n",
    "    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\n",
    "hitter_spot_avgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batting_stat = 'Bases Loaded'\n",
    "hitter_spot_avgs = []\n",
    "for hitter_spot in range(9):\n",
    "    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\n",
    "\n",
    "with plt.style.context('tableau10'):\n",
    "    plt.figure()\n",
    "    plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD')\n",
    "    batting_stat += ' per Game'\n",
    "    plt.ylabel(batting_stat)\n",
    "    plt.xticks(range(9), [str(x) for x in range(1, 10)])\n",
    "    plt.xlabel('Batting Position')\n",
    "    plt.title('The 6th batter is most likely to face a Bases Loaded situation')\n",
    "    plt.text(-1.6, -0.013, 'Data source: League averages & custom baseball simulations\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\n",
    "    plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batting_stat = 'Grand Slam'\n",
    "hitter_spot_avgs = []\n",
    "for hitter_spot in range(9):\n",
    "    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\n",
    "\n",
    "with plt.style.context('tableau10'):\n",
    "    plt.figure()\n",
    "    plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD')\n",
    "    batting_stat += 's per Game'\n",
    "    plt.ylabel(batting_stat)\n",
    "    plt.xticks(range(9), [str(x) for x in range(1, 10)])\n",
    "    plt.xlabel('Batting Position')\n",
    "    plt.title('The 6th batter is most likely to hit a Grand Slam')\n",
    "    plt.text(-1.85, -0.0005, 'Data source: League averages & custom baseball simulations\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\n",
    "    plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.001948,\n",
       " 0.001837,\n",
       " 0.001915,\n",
       " 0.001929,\n",
       " 0.002284,\n",
       " 0.002683,\n",
       " 0.002059,\n",
       " 0.001988,\n",
       " 0.002107]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batting_stat = 'Grand Slam'\n",
    "hitter_spot_avgs = []\n",
    "for hitter_spot in range(9):\n",
    "    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\n",
    "hitter_spot_avgs"
   ]
  },
  {
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}


================================================
FILE: rethinking-population-pyramid/Rethinking the population pyramid.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rethinking the population pyramid\n",
    "\n",
    "This notebook provides the methodology and code used in the blog post, [Rethinking the population pyramid](http://www.randalolson.com/2015/07/14/rethinking-the-population-pyramid/).\n",
    "\n",
    "### Notebook by [Randal S. Olson](http://www.randalolson.com/)\n",
    "\n",
    "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is widely useable and shareable as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age_Range</th>\n",
       "      <th>Total_Pop_2010</th>\n",
       "      <th>Males_2010</th>\n",
       "      <th>Females_2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt;5</td>\n",
       "      <td>20201362</td>\n",
       "      <td>10319427</td>\n",
       "      <td>9881935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5-9</td>\n",
       "      <td>20348657</td>\n",
       "      <td>10389638</td>\n",
       "      <td>9959019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10-14</td>\n",
       "      <td>20677194</td>\n",
       "      <td>10579862</td>\n",
       "      <td>10097332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15-19</td>\n",
       "      <td>22040343</td>\n",
       "      <td>11303666</td>\n",
       "      <td>10736677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20-24</td>\n",
       "      <td>21585999</td>\n",
       "      <td>11014176</td>\n",
       "      <td>10571823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age_Range  Total_Pop_2010  Males_2010  Females_2010\n",
       "0        <5        20201362    10319427       9881935\n",
       "1       5-9        20348657    10389638       9959019\n",
       "2     10-14        20677194    10579862      10097332\n",
       "3     15-19        22040343    11303666      10736677\n",
       "4     20-24        21585999    11014176      10571823"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# This is a custom matplotlib style that I use for most of my charts\n",
    "plt.style.use('https://gist.githubusercontent.com/rhiever/d0a7332fe0beebfdc3d5/raw/205e477cf231330fe2f265070f7c37982fd3130c/tableau10.mplstyle')\n",
    "\n",
    "age_gender_data = pd.read_csv('http://www.randalolson.com/wp-content/uploads/us-age-gender-breakdown.csv')\n",
    "age_gender_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Problems with the population pyramid\n",
    "\n",
    "###1) Violates the [standard expectation](http://mathbench.umd.edu/modules/visualization_graph/page02.htm) of having the causal variable on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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miDNnzuT000/n0EMP5ZxzzqF3796UlZWxePFiVq9ezUsvvZTpzg6hIDcv7IQW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\naYbKysqUUkplZGSoTZs2haQhMTGxwW9vLS8vVx06dFAHDx5USin1+uu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44QfExsbWG8P7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\nEV3FBSIREREREREBAP4fFr3zea97x3YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109fd0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 7))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "for (i, row) in age_gender_data.iterrows():\n",
    "    plt.bar([i, i], [row['Females_2010'], -row['Males_2010']],\n",
    "            color=['#CC6699', '#008AB8'], width=0.8, align='center', edgecolor='none')\n",
    "    \n",
    "plt.xlim(-0.6, 20.6)\n",
    "plt.ylim(-12.1e6, 12.1e6)\n",
    "#plt.grid(False, axis='x')\n",
    "plt.xticks(np.arange(0, 21), age_gender_data['Age_Range'], fontsize=11)\n",
    "plt.yticks(np.arange(-12e6, 13e6, 2e6),\n",
    "           ['{}m'.format(int(abs(x) / 1e6)) if x != 0 else 0 for x in np.arange(-12e6, 13e6, 2e6)])\n",
    "plt.xlabel('Age group')\n",
    "plt.ylabel('Number of people (millions)')\n",
    "\n",
    "plt.savefig('pop_pyramid_rotated.pdf')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###2) Doesn't allow direct comparisons between the two categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AQCsX8E1qKisr5XQ6lZCQoHPOOScYmST90Nz9+c9/1uOPP+61rWPHjh6PLRaL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b26cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 7))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "for (i, row) in age_gender_data.iterrows():\n",
    "    plt.bar([i - 0.2, i + 0.2], [row['Females_2010'], row['Males_2010']],\n",
    "            color=['#CC6699', '#008AB8'], width=0.4, align='center', edgecolor='none')\n",
    "    \n",
    "plt.xlim(-0.6, 20.6)\n",
    "plt.xticks(range(0, 21), age_gender_data['Age_Range'], fontsize=11)\n",
    "plt.grid(False, axis='x')\n",
    "plt.yticks(np.arange(0, 13e6, 1e6),\n",
    "           ['{}m'.format(int(x / 1e6)) if x > 0 else 0 for x in np.arange(0, 13e6, 1e6)])\n",
    "plt.xlabel('Age group')\n",
    "plt.ylabel('Number of people (millions)')\n",
    "\n",
    "plt.savefig('pop_pyramid_grouped.pdf')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###3) Relative trends between the categories can be masked by displaying absolute values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6dNCyZcs8FhAAAAAAYA9GZyAXLVqksWPHqmfPnrpw4UK+fREREUpNTfVENgAA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\nknT27FkNGTJEGzduVGpqqla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Bx/7222+Wj4/PNU74f3bu3GlFRkZa7733ntW4cWOrcePG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d38b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_gender_data['Male_Pct'] = age_gender_data['Males_2010'] / age_gender_data['Total_Pop_2010']\n",
    "age_gender_data['Female_Pct'] = age_gender_data['Females_2010'] / age_gender_data['Total_Pop_2010']\n",
    "\n",
    "plt.figure(figsize=(15, 7))\n",
    "\n",
    "for (i, row) in age_gender_data.iterrows():\n",
    "    plt.bar([i], [row['Male_Pct']],\n",
    "            color=['#008AB8'], width=0.9, align='center', edgecolor='none')\n",
    "    plt.bar([i], [row['Female_Pct']], bottom=[row['Male_Pct']],\n",
    "            color=['#CC6699'], width=0.9, align='center', edgecolor='none')\n",
    "    \n",
    "plt.xlim(-0.6, 20.6)\n",
    "plt.ylim(0, 1)\n",
    "plt.xticks(range(0, 21), age_gender_data['Age_Range'], fontsize=11)\n",
    "plt.grid(False)\n",
    "plt.yticks(np.arange(0, 1.01, 0.25),\n",
    "           ['{}%'.format(int(x * 100)) for x in np.arange(0, 1.01, 0.25)])\n",
    "plt.xlabel('Age group')\n",
    "plt.ylabel('Percentage of age group')\n",
    "\n",
    "plt.plot([-0.425, 20.425], [0.5, 0.5], lw=2, color='black')\n",
    "plt.plot([-0.425, 20.425], [0.25, 0.25], lw=2, color='black')\n",
    "\n",
    "plt.savefig('pop_pyramid_stacked.pdf')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: revisiting-vaccine-visualizations/Revisiting the vaccine visualizations.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Revisiting the vaccine visualizations\n",
    "\n",
    "This notebook provides the methodology and code used in the blog post, [Revisiting the vaccine visualizations]( http://www.randalolson.com/2016/03/04/revisiting-the-vaccine-visualizations/).\n",
    "\n",
    "### Notebook by [Randal S. Olson](http://www.randalolson.com/)\n",
    "\n",
    "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is widely useable and shareable as possible.\n",
    "\n",
    "### Related resources\n",
    "\n",
    "Here is the original WSJ article that inspired this rework: http://graphics.wsj.com/infectious-diseases-and-vaccines/\n",
    "\n",
    "Here is an article showing how you can remake the charts in R: http://www.r-bloggers.com/recreating-the-vaccination-heatmaps-in-r/\n",
    "\n",
    "### How to download the data\n",
    "\n",
    "This notebook assumes that you have downloaded the Project Tycho data and placed it in a directory called `data` in the directory you ran the notebook. To download the data (instructions from [here](http://www.r-bloggers.com/recreating-the-vaccination-heatmaps-in-r/)):\n",
    "\n",
    "1) Register and login to [\"Project Tycho\"](https://www.tycho.pitt.edu/)\n",
    "\n",
    "2) Go to level 1 data, then Search and retrieve data\n",
    "\n",
    "3) Now change a couple of options: geographic level := state; disease outcome := incidence\n",
    "\n",
    "4) Add all states (highlight all at once with Ctrl+A (or Cmd+A on Macs)\n",
    "\n",
    "5) Hit submit and scroll down to Click here to download results to excel\n",
    "\n",
    "6) Open in excel and export to CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import all of the libraries we'll be using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Vendor:  Continuum Analytics, Inc.\n",
      "Package: mkl\n",
      "Message: trial mode expires in 29 days\n",
      "/Users/randal_olson/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "sb.set_style('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Take a quick look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"Weekly Polio Incidence, 1928-1969\"\r\n",
      "\"Data provided by Project Tycho, Data Version 1.0.0, released 28 November 2013.\"\r\n",
      "YEAR,WEEK,ALABAMA,ALASKA,ARIZONA,ARKANSAS,CALIFORNIA,COLORADO,CONNECTICUT,DELAWARE,DISTRICT OF COLUMBIA,FLORIDA,GEORGIA,HAWAII,IDAHO,ILLINOIS,INDIANA,IOWA,KANSAS,KENTUCKY,LOUISIANA,MAINE,MARYLAND,MASSACHUSETTS,MICHIGAN,MINNESOTA,MISSISSIPPI,MISSOURI,MONTANA,NEBRASKA,NEVADA,NEW HAMPSHIRE,NEW JERSEY,NEW MEXICO,NEW YORK,NORTH CAROLINA,NORTH DAKOTA,OHIO,OKLAHOMA,OREGON,PENNSYLVANIA,RHODE ISLAND,SOUTH CAROLINA,SOUTH DAKOTA,TENNESSEE,TEXAS,UTAH,VERMONT,VIRGINIA,WASHINGTON,WEST VIRGINIA,WISCONSIN,WYOMING\r\n",
      "1928,1,0.00,-,0.00,0.00,0.17,0.39,0.00,0.00,-,0.00,0.03,-,0.00,0.03,0.03,0.08,0.00,0.00,0.00,0.00,0.06,0.14,0.04,0.00,0.00,0.03,0.18,0.07,-,-,0.08,0.00,0.08,0.00,-,0.02,0.00,0.64,0.00,0.00,0.06,0.00,0.04,0.05,0.00,0.00,-,0.26,0.06,0.03,0.00\r\n",
      "1928,2,0.00,-,0.00,0.00,0.15,0.20,0.00,0.00,-,0.00,0.00,-,0.00,0.01,0.03,-,0.22,0.00,0.05,0.13,0.06,0.14,0.04,0.04,0.00,0.06,0.00,0.07,-,-,0.03,0.00,0.05,0.03,0.45,-,0.04,0.43,0.03,0.00,0.06,0.00,0.04,0.04,0.00,0.00,-,0.39,0.24,0.03,0.00\r\n",
      "1928,3,0.04,-,0.00,0.00,0.11,0.00,0.06,0.00,-,0.00,-,-,0.00,0.03,0.03,-,0.00,0.00,0.00,0.00,0.00,0.07,0.02,0.00,0.00,0.03,0.18,0.00,-,-,0.00,0.00,0.03,0.00,0.00,0.06,0.00,1.07,0.02,0.00,0.35,0.00,0.00,0.00,0.00,0.00,-,0.13,0.12,0.03,0.00\r\n",
      "1928,4,0.00,-,0.24,0.11,0.07,0.20,0.06,0.00,0.00,0.00,0.00,-,0.00,0.05,0.12,0.00,0.00,0.00,0.00,0.00,0.00,0.02,0.02,0.00,0.00,0.06,0.00,0.00,-,0.00,0.03,0.00,0.06,0.00,0.15,0.00,0.09,0.53,0.02,0.00,0.23,0.00,0.04,0.05,0.00,0.00,-,0.06,0.12,0.00,0.00\r\n",
      "1928,5,0.00,-,0.24,0.00,0.32,0.00,0.13,0.00,0.00,0.00,0.00,-,0.00,0.04,0.00,0.04,0.00,0.00,0.00,0.38,0.12,0.02,0.04,0.00,0.00,0.00,0.00,0.15,-,0.00,0.03,0.48,0.07,0.00,0.00,0.03,0.00,0.32,0.00,0.00,0.17,0.15,0.00,0.05,0.00,0.00,-,0.13,0.06,0.03,0.00\r\n",
      "1928,6,0.00,-,0.00,0.00,0.22,0.10,0.00,0.00,0.00,0.00,0.00,-,-,0.03,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.05,0.06,0.00,0.00,0.00,0.00,0.07,-,0.00,0.00,0.00,0.03,0.00,0.00,0.05,0.04,0.21,0.04,0.00,0.06,0.29,0.04,0.00,0.20,0.00,0.04,0.06,0.00,0.14,0.00\r\n",
      "1928,7,0.08,-,0.00,0.00,0.13,0.00,0.00,0.00,0.00,0.00,0.00,-,0.22,0.01,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.09,0.00,0.00,0.00,0.00,0.00,0.00,-,0.21,0.03,0.24,0.02,0.03,0.15,0.05,0.04,0.32,0.01,0.00,0.00,0.00,0.00,0.00,0.40,0.00,-,0.00,0.00,0.07,0.00\r\n"
     ]
    }
   ],
   "source": [
    "!head data/POLIO_Incidence_1928-1969_20160304121200.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize the Polio data as a heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ttm1bWrZsSZs2bTAzM8PU1JTRo0cDj2a/qVKlCn379mXYsGGsWbOGDRs20KJF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\nVSIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153c2160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "polio_data = pd.read_csv('data/POLIO_Incidence_1928-1969_20160304121200.csv', skiprows=2, na_values='-')\n",
    "\n",
    "polio_years = list(polio_data['YEAR'].unique())\n",
    "polio_states = polio_data.drop(['YEAR', 'WEEK'], axis=1).columns.values\n",
    "polio_states = [state.title() for state in polio_states]\n",
    "\n",
    "polio_data.drop(['WEEK'], axis=1, inplace=True)\n",
    "polio_data = polio_data.groupby('YEAR').sum()\n",
    "polio_data = polio_data.transpose().values\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "sb.heatmap(polio_data, cmap='Reds', robust=True,\n",
    "           xticklabels=[year if year % 5 == 0 or year == max(polio_years) else '' for year in polio_years],\n",
    "           yticklabels=polio_states)\n",
    "plt.plot([polio_years.index(1955) + 0.425, polio_years.index(1955) + 0.425],\n",
    "         [0, 51],\n",
    "         color='black', lw=1.5)\n",
    "\n",
    "cax = plt.gcf().axes[-1]\n",
    "cax.set_yticklabels([10, 20, 30, 40, '50+'])\n",
    "cax.tick_params(labelsize=12)\n",
    "\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.text(0, 51.5, 'Polio cases in the United States', fontsize=14)\n",
    "plt.text(polio_years.index(1955) + 0.15, 51.5, 'Vaccine introduced', fontsize=12, weight='bold')\n",
    "plt.text(-8, -3, 'Data source: Project Tycho (tycho.pitt.edu) '\n",
    "         '| Author: Randy Olson (randalolson.com / @randal_olson)',\n",
    "         fontsize=10)\n",
    "\n",
    "plt.savefig('polio-cases-heatmap-sequential-colormap.png', bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Same process for the Measles data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dPBm66dIZWADC0HUYapsnLcGYl3oaSvei5fUm/VnazBBD7ZLXNn1eHYPX/b3P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Bw5koCAAJydnQkODiY/v5JzVIUQQggh7nd6Xc1/3cauXbvo0KEDjo6OADg7O/PRRx+xdetWfH19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\nhBBCAP8HFMuSQ3mVUoYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153ce3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "measles_data = pd.read_csv('data/MEASLES_Incidence_1928-2003_20160304120254.csv', skiprows=2, na_values='-')\n",
    "\n",
    "measles_years = list(measles_data['YEAR'].unique())\n",
    "measles_states = measles_data.drop(['YEAR', 'WEEK'], axis=1).columns.values\n",
    "measles_states = [state.title() for state in measles_states]\n",
    "\n",
    "measles_data.drop(['WEEK'], axis=1, inplace=True)\n",
    "measles_data = measles_data.groupby('YEAR').sum()\n",
    "measles_data = measles_data.transpose().values\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "sb.heatmap(measles_data, cmap='Reds', robust=True,\n",
    "           xticklabels=[year if year % 10 == 0 else '' for year in measles_years],\n",
    "           yticklabels=measles_states)\n",
    "plt.plot([polio_years.index(1963) + 0.425, polio_years.index(1963) + 0.425],\n",
    "         [0, 51],\n",
    "         color='black', lw=1.5)\n",
    "\n",
    "cax = plt.gcf().axes[-1]\n",
    "cax.set_yticklabels([0, 200, 400, 600, 800, '1,000+'])\n",
    "cax.tick_params(labelsize=12)\n",
    "\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.text(0, 51.5, 'Measles cases in the United States', fontsize=14)\n",
    "plt.text(polio_years.index(1963) + 0.05, 51.5, 'Vaccine introduced', fontsize=12, weight='bold')\n",
    "plt.text(-14.5, -3, 'Data source: Project Tycho (tycho.pitt.edu) '\n",
    "         '| Author: Randy Olson (randalolson.com / @randal_olson)',\n",
    "         fontsize=10)\n",
    "\n",
    "plt.savefig('measles-cases-heatmap-sequential-colormap.png', bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize the Polio data as a line chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iiMVAjogoAEIYUIqskVNVlukKlixLDIaJgsBAjogoAMIQkGSOyJlNliUIgyNy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\nIpGcoEhDTiKRSCQSieQE5f8B5ef9lNJTc+UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d3c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "polio_data = pd.read_csv('data/POLIO_Incidence_1928-1969_20160304121200.csv', skiprows=2, na_values='-').fillna(0.)\n",
    "\n",
    "polio_data.drop(['WEEK'], axis=1, inplace=True)\n",
    "polio_data = polio_data.groupby('YEAR').sum()\n",
    "\n",
    "polio_data.plot(color='#3F5D7D', alpha=0.25, lw=0.5, legend=False, figsize=(10, 7))\n",
    "plt.plot([1955, 1955], [0, 160], color='black', lw=1.5)\n",
    "polio_data.transpose().median().plot(color='#3F5D7D', lw=2)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('# cases per 100,000 people', fontsize=14)\n",
    "\n",
    "plt.text(1928.5, 163, 'Polio cases in the United States', fontsize=14)\n",
    "plt.text(1954.75, 163, 'Vaccine introduced', fontsize=12, weight='bold')\n",
    "plt.text(1924, -17, 'Data source: Project Tycho (tycho.pitt.edu) '\n",
    "         '| Author: Randy Olson (randalolson.com / @randal_olson)',\n",
    "         fontsize=10)\n",
    "\n",
    "plt.savefig('polio-cases-line-chart-raw.png', bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summarize Polio data with median + 95% CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/randal_olson/anaconda/lib/python3.5/site-packages/numpy/lib/function_base.py:3151: RuntimeWarning: Invalid value encountered in median for 2 results\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bpigsQkFxOaQyD7t3MGguNtAlsh0TNiKiNqiishI5+UUwQgyZ3NMpMegNJuj1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117862978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "polio_data = pd.read_csv('data/POLIO_Incidence_1928-1969_20160304121200.csv', skiprows=2, na_values='-').fillna(0.)\n",
    "\n",
    "polio_years = list(polio_data['YEAR'].unique())\n",
    "polio_states = polio_data.drop(['YEAR', 'WEEK'], axis=1).columns.values\n",
    "polio_states = [state.title() for state in polio_states]\n",
    "\n",
    "polio_data.drop(['WEEK'], axis=1, inplace=True)\n",
    "polio_data = polio_data.groupby('YEAR').sum()\n",
    "polio_data = pd.rolling_mean(polio_data, 3)\n",
    "polio_data = polio_data.transpose().values\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "sb.tsplot(data=polio_data, ci=95, estimator=np.median, color='#3F5D7D')\n",
    "plt.plot([polio_years.index(1955), polio_years.index(1955)], [0, 35], color='black', lw=1.5)\n",
    "\n",
    "plt.yticks(fontsize=12)\n",
    "plt.xticks(range(len(polio_years)), [year if year % 5 == 0 else '' for year in polio_years], fontsize=12)\n",
    "plt.ylabel('# cases per 100,000 people', fontsize=14)\n",
    "\n",
    "plt.text(0.3, 35.5, 'Polio cases in the United States', fontsize=14)\n",
    "plt.text(polio_years.index(1955) - 0.3, 35.5, 'Vaccine introduced', fontsize=12, weight='bold')\n",
    "plt.text(-3.5, -3.5, 'Data source: Project Tycho (tycho.pitt.edu) '\n",
    "         '| Author: Randy Olson (randalolson.com / @randal_olson)',\n",
    "         fontsize=10)\n",
    "\n",
    "plt.savefig('polio-cases-line-chart-statistics.png', bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Display the Polio data as small multiples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DwwN2dnaws7NDx44dkZeXh+joaFy7dg3e3t68NX8+VJMmTSAnJ4fTp0+jVatW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jYWR44cwc8//1yfIRIpuXLlSqOrBVFYWAh1dXU4Ojri/PnzdX68ZcuWISwsTOQ+N2/exKZN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\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pLl26xKFDh7STwL/Yw8jU1JRq1apRu3Ztihcvjkaj4e7du2zYsIGwsDDKlStHYmIid+7c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\nCCGEEEJv/T8pAcFD2Oa7YAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11be582b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "polio_data = pd.read_csv('data/POLIO_Incidence_1928-1969_20160304121200.csv', skiprows=2, na_values='-').fillna(0.)\n",
    "\n",
    "polio_data.drop(['WEEK'], axis=1, inplace=True)\n",
    "polio_data = polio_data.groupby('YEAR').sum()\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "column_count = 0\n",
    "for column in polio_data.columns.values:\n",
    "    if 'DISTRICT' in column:\n",
    "        column_num = 53\n",
    "        state_name = 'Washington D.C.'\n",
    "    else:\n",
    "        column_count += 1\n",
    "        column_num = column_count\n",
    "        state_name = column.title()\n",
    "    \n",
    "    plt.subplot(11, 5, column_num)\n",
    "    polio_data[column].fillna(0.).plot(color='black', lw=0.5)\n",
    "    plt.plot([1955, 1955], [0, plt.ylim()[1]], color='black', lw=1)\n",
    "    \n",
    "    if column_num >= 46 and column_num != 48:\n",
    "        plt.xticks(range(1930, 1971, 10))\n",
    "    else:\n",
    "        plt.xticks([])\n",
    "    \n",
    "    plt.yticks([])\n",
    "    plt.xlabel('')\n",
    "    plt.title(state_name, fontsize=11)\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.text(1732.5, -220, 'Data source: Project Tycho (tycho.pitt.edu)\\n'\n",
    "         'Author: Randy Olson (randalolson.com / @randal_olson)',\n",
    "         fontsize=10)\n",
    "\n",
    "plt.text(1853, 1850, 'Polio cases per 100,000 residents across the United States\\n'\n",
    "         '(vertical line = Polio vaccine introduced)',\n",
    "         ha='center', fontsize=18)\n",
    "\n",
    "plt.savefig('polio-cases-small-multiples.png', bbox_inches='tight')\n",
    ";"
   ]
  }
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================================================
FILE: tpot-demo/TPOT - A Python tool for automating data science.ipynb
================================================
{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TPOT: A Python tool for automating data science\n",
    "\n",
    "### Notebook by [Randal S. Olson](http://www.randalolson.com/)\n",
    "\n",
    "This notebook is a demo for the [TPOT](https://github.com/rhiever/tpot) data science automation tool under development at the [Penn Institute for Biomedical Informatics](http://upibi.org/).\n",
    "\n",
    "Below are code samples demonstrating why designing machine learning pipelines is difficult, and how TPOT automates that process.\n",
    "\n",
    "## License\n",
    "\n",
    "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely usable and shareable as possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why automate data science?\n",
    "\n",
    "### Model hyperparameter tuning is important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "mnist_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/mnist.csv.gz', sep='\\t', compression='gzip')\n",
    "mnist_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
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X0nfme0CfN5mtSbTEViiO0JiZmVn0ihKhUevw/fbbDwilqKLkO4AxY8YAyW56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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cde0358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "for record_num in range(1, 65):\n",
    "    plt.subplot(8, 8, record_num)\n",
    "    \n",
    "    digit_features = mnist_data.iloc[record_num].drop('class').values\n",
    "    sb.heatmap(digit_features.reshape((28, 28)),\n",
    "               cmap='Greys',\n",
    "               square=True, cbar=False,\n",
    "               xticklabels=[], yticklabels=[])\n",
    "\n",
    "plt.tight_layout()\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.9420414   0.9620217   0.94888635  0.94000857  0.95071429  0.94427775\n",
      "  0.94955702  0.94240389  0.93467696  0.95554603]\n",
      "0.947013394966\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(RandomForestClassifier(n_estimators=10, n_jobs=-1),\n",
    "                            X=mnist_data.drop('class', axis=1).values,\n",
    "                            y=mnist_data.loc[:, 'class'].values,\n",
    "                            cv=10)\n",
    "\n",
    "print(cv_scores)\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.96445396  0.97829812  0.97015991  0.96629053  0.96828571  0.96170882\n",
      "  0.97084881  0.96698585  0.96483705  0.97641509]\n",
      "0.968828385431\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1),\n",
    "                            X=mnist_data.drop('class', axis=1).values,\n",
    "                            y=mnist_data.loc[:, 'class'].values,\n",
    "                            cv=10)\n",
    "\n",
    "print(cv_scores)\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model selection is important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X92</th>\n",
       "      <th>X93</th>\n",
       "      <th>X94</th>\n",
       "      <th>X95</th>\n",
       "      <th>X96</th>\n",
       "      <th>X97</th>\n",
       "      <th>X98</th>\n",
       "      <th>X99</th>\n",
       "      <th>X100</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1317.265789</td>\n",
       "      <td>1315.220951</td>\n",
       "      <td>1312.770581</td>\n",
       "      <td>1309.834252</td>\n",
       "      <td>1306.315588</td>\n",
       "      <td>1302.099102</td>\n",
       "      <td>1297.046401</td>\n",
       "      <td>1290.991646</td>\n",
       "      <td>1283.736109</td>\n",
       "      <td>1275.041652</td>\n",
       "      <td>...</td>\n",
       "      <td>1327.575109</td>\n",
       "      <td>1327.575350</td>\n",
       "      <td>1327.575552</td>\n",
       "      <td>1327.575719</td>\n",
       "      <td>1327.575859</td>\n",
       "      <td>1327.575976</td>\n",
       "      <td>1327.576074</td>\n",
       "      <td>1327.576155</td>\n",
       "      <td>1327.576223</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7329.967624</td>\n",
       "      <td>7379.907443</td>\n",
       "      <td>7441.799231</td>\n",
       "      <td>7518.503422</td>\n",
       "      <td>7613.565031</td>\n",
       "      <td>7731.377492</td>\n",
       "      <td>7877.385707</td>\n",
       "      <td>8058.337694</td>\n",
       "      <td>8282.596458</td>\n",
       "      <td>8560.526497</td>\n",
       "      <td>...</td>\n",
       "      <td>7121.300474</td>\n",
       "      <td>7121.300438</td>\n",
       "      <td>7121.300410</td>\n",
       "      <td>7121.300387</td>\n",
       "      <td>7121.300368</td>\n",
       "      <td>7121.300353</td>\n",
       "      <td>7121.300341</td>\n",
       "      <td>7121.300331</td>\n",
       "      <td>7121.300323</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>809.421410</td>\n",
       "      <td>809.780119</td>\n",
       "      <td>810.207191</td>\n",
       "      <td>810.715653</td>\n",
       "      <td>811.321016</td>\n",
       "      <td>812.041748</td>\n",
       "      <td>812.899834</td>\n",
       "      <td>813.921452</td>\n",
       "      <td>815.137768</td>\n",
       "      <td>816.585886</td>\n",
       "      <td>...</td>\n",
       "      <td>807.545134</td>\n",
       "      <td>807.544181</td>\n",
       "      <td>807.543381</td>\n",
       "      <td>807.542709</td>\n",
       "      <td>807.542144</td>\n",
       "      <td>807.541670</td>\n",
       "      <td>807.541272</td>\n",
       "      <td>807.540937</td>\n",
       "      <td>807.540656</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45334.208880</td>\n",
       "      <td>45334.213560</td>\n",
       "      <td>45334.219060</td>\n",
       "      <td>45334.225500</td>\n",
       "      <td>45334.233050</td>\n",
       "      <td>45334.241910</td>\n",
       "      <td>45334.252300</td>\n",
       "      <td>45334.264480</td>\n",
       "      <td>45334.278760</td>\n",
       "      <td>45334.295520</td>\n",
       "      <td>...</td>\n",
       "      <td>47550.921710</td>\n",
       "      <td>47224.457710</td>\n",
       "      <td>46946.072760</td>\n",
       "      <td>46708.686150</td>\n",
       "      <td>46506.259970</td>\n",
       "      <td>46333.645520</td>\n",
       "      <td>46186.452370</td>\n",
       "      <td>46060.936670</td>\n",
       "      <td>45953.905930</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>...</td>\n",
       "      <td>1.790275</td>\n",
       "      <td>1.794794</td>\n",
       "      <td>1.798296</td>\n",
       "      <td>1.801010</td>\n",
       "      <td>1.803114</td>\n",
       "      <td>1.804744</td>\n",
       "      <td>1.806008</td>\n",
       "      <td>1.806987</td>\n",
       "      <td>1.807746</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             X1            X2            X3            X4            X5  \\\n",
       "0   1317.265789   1315.220951   1312.770581   1309.834252   1306.315588   \n",
       "1   7329.967624   7379.907443   7441.799231   7518.503422   7613.565031   \n",
       "2    809.421410    809.780119    810.207191    810.715653    811.321016   \n",
       "3  45334.208880  45334.213560  45334.219060  45334.225500  45334.233050   \n",
       "4      1.810359      1.810359      1.810359      1.810359      1.810359   \n",
       "\n",
       "             X6            X7            X8            X9           X10  \\\n",
       "0   1302.099102   1297.046401   1290.991646   1283.736109   1275.041652   \n",
       "1   7731.377492   7877.385707   8058.337694   8282.596458   8560.526497   \n",
       "2    812.041748    812.899834    813.921452    815.137768    816.585886   \n",
       "3  45334.241910  45334.252300  45334.264480  45334.278760  45334.295520   \n",
       "4      1.810359      1.810359      1.810359      1.810359      1.810359   \n",
       "\n",
       "   ...             X92           X93           X94           X95  \\\n",
       "0  ...     1327.575109   1327.575350   1327.575552   1327.575719   \n",
       "1  ...     7121.300474   7121.300438   7121.300410   7121.300387   \n",
       "2  ...      807.545134    807.544181    807.543381    807.542709   \n",
       "3  ...    47550.921710  47224.457710  46946.072760  46708.686150   \n",
       "4  ...        1.790275      1.794794      1.798296      1.801010   \n",
       "\n",
       "            X96           X97           X98           X99          X100  class  \n",
       "0   1327.575859   1327.575976   1327.576074   1327.576155   1327.576223      0  \n",
       "1   7121.300368   7121.300353   7121.300341   7121.300331   7121.300323      1  \n",
       "2    807.542144    807.541670    807.541272    807.540937    807.540656      1  \n",
       "3  46506.259970  46333.645520  46186.452370  46060.936670  45953.905930      1  \n",
       "4      1.803114      1.804744      1.806008      1.806987      1.807746      0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "hill_valley_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/Hill_Valley_without_noise.csv.gz', sep='\\t', compression='gzip')\n",
    "hill_valley_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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wpemPpekbTtM3lKZvODV2McNEZRE/TbVRltdEWV4bZVVjKbXloSXZQ7pcybRFa1eMtq4R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\nRSFMZ162AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d1bc240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "with plt.style.context('seaborn-notebook'):\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    for record_num in range(1, 11):\n",
    "        plt.subplot(10, 1, record_num)\n",
    "        hv_record_features = hill_valley_data.loc[record_num].drop('class').values\n",
    "        plt.plot(hv_record_features)\n",
    "        plt.yticks([])\n",
    "        plt.xticks([])\n",
    "\n",
    "    plt.tight_layout()\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.6147541   0.63934426  0.62809917  0.60330579  0.6446281   0.5785124\n",
      "  0.66942149  0.55371901  0.49586777  0.66115702]\n",
      "0.608880910446\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1),\n",
    "                            X=hill_valley_data.drop('class', axis=1).values,\n",
    "                            y=hill_valley_data.loc[:, 'class'].values,\n",
    "                            cv=10)\n",
    "\n",
    "print(cv_scores)\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.          1.          0.99173554  1.          1.          1.          1.\n",
      "  0.99173554  1.          1.        ]\n",
      "0.998347107438\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(LogisticRegression(),\n",
    "                            X=hill_valley_data.drop('class', axis=1).values,\n",
    "                            y=hill_valley_data.loc[:, 'class'].values,\n",
    "                            cv=10)\n",
    "\n",
    "print(cv_scores)\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature preprocessing is important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X92</th>\n",
       "      <th>X93</th>\n",
       "      <th>X94</th>\n",
       "      <th>X95</th>\n",
       "      <th>X96</th>\n",
       "      <th>X97</th>\n",
       "      <th>X98</th>\n",
       "      <th>X99</th>\n",
       "      <th>X100</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.02</td>\n",
       "      <td>36.49</td>\n",
       "      <td>38.20</td>\n",
       "      <td>38.85</td>\n",
       "      <td>39.38</td>\n",
       "      <td>39.74</td>\n",
       "      <td>37.02</td>\n",
       "      <td>39.53</td>\n",
       "      <td>38.81</td>\n",
       "      <td>38.79</td>\n",
       "      <td>...</td>\n",
       "      <td>36.62</td>\n",
       "      <td>36.92</td>\n",
       "      <td>38.80</td>\n",
       "      <td>38.52</td>\n",
       "      <td>38.07</td>\n",
       "      <td>36.73</td>\n",
       "      <td>39.46</td>\n",
       "      <td>37.50</td>\n",
       "      <td>39.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.83</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.78</td>\n",
       "      <td>...</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68177.69</td>\n",
       "      <td>66138.42</td>\n",
       "      <td>72981.88</td>\n",
       "      <td>74304.33</td>\n",
       "      <td>67549.66</td>\n",
       "      <td>69367.34</td>\n",
       "      <td>69169.41</td>\n",
       "      <td>73268.61</td>\n",
       "      <td>74465.84</td>\n",
       "      <td>72503.37</td>\n",
       "      <td>...</td>\n",
       "      <td>73438.88</td>\n",
       "      <td>71053.35</td>\n",
       "      <td>71112.62</td>\n",
       "      <td>74916.48</td>\n",
       "      <td>72571.58</td>\n",
       "      <td>66348.97</td>\n",
       "      <td>71063.72</td>\n",
       "      <td>67404.27</td>\n",
       "      <td>74920.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44889.06</td>\n",
       "      <td>39191.86</td>\n",
       "      <td>40728.46</td>\n",
       "      <td>38576.36</td>\n",
       "      <td>45876.06</td>\n",
       "      <td>47034.00</td>\n",
       "      <td>46611.43</td>\n",
       "      <td>37668.32</td>\n",
       "      <td>40980.89</td>\n",
       "      <td>38466.15</td>\n",
       "      <td>...</td>\n",
       "      <td>42625.67</td>\n",
       "      <td>40684.20</td>\n",
       "      <td>46960.73</td>\n",
       "      <td>44546.80</td>\n",
       "      <td>45410.53</td>\n",
       "      <td>47139.44</td>\n",
       "      <td>43095.68</td>\n",
       "      <td>40888.34</td>\n",
       "      <td>39615.19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.70</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.28</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.27</td>\n",
       "      <td>5.61</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.34</td>\n",
       "      <td>5.87</td>\n",
       "      <td>...</td>\n",
       "      <td>5.17</td>\n",
       "      <td>5.67</td>\n",
       "      <td>5.60</td>\n",
       "      <td>5.94</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.22</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1        X2        X3        X4        X5        X6        X7  \\\n",
       "0     39.02     36.49     38.20     38.85     39.38     39.74     37.02   \n",
       "1      1.83      1.71      1.77      1.77      1.68      1.78      1.80   \n",
       "2  68177.69  66138.42  72981.88  74304.33  67549.66  69367.34  69169.41   \n",
       "3  44889.06  39191.86  40728.46  38576.36  45876.06  47034.00  46611.43   \n",
       "4      5.70      5.40      5.28      5.38      5.27      5.61      6.00   \n",
       "\n",
       "         X8        X9       X10  ...         X92       X93       X94  \\\n",
       "0     39.53     38.81     38.79  ...       36.62     36.92     38.80   \n",
       "1      1.70      1.75      1.78  ...        1.80      1.79      1.77   \n",
       "2  73268.61  74465.84  72503.37  ...    73438.88  71053.35  71112.62   \n",
       "3  37668.32  40980.89  38466.15  ...    42625.67  40684.20  46960.73   \n",
       "4      5.38      5.34      5.87  ...        5.17      5.67      5.60   \n",
       "\n",
       "        X95       X96       X97       X98       X99      X100  class  \n",
       "0     38.52     38.07     36.73     39.46     37.50     39.10      0  \n",
       "1      1.74      1.74      1.80      1.78      1.75      1.69      1  \n",
       "2  74916.48  72571.58  66348.97  71063.72  67404.27  74920.24      1  \n",
       "3  44546.80  45410.53  47139.44  43095.68  40888.34  39615.19      0  \n",
       "4      5.94      5.73      5.22      5.30      5.73      5.91      0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "hill_valley_noisy_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/Hill_Valley_with_noise.csv.gz', sep='\\t', compression='gzip')\n",
    "hill_valley_noisy_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WFF+9vICco/vxSVlJYVS36+kfHSM2PIgf3FRKSVb0jCdpapyWXVWDdI/YGBy188x7LVjG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4vfP1dPc6+ZMI0Sk83D0JiDxfOTZv3dRFHkme2d7KgdZsTkIjxURW5q1NR2Ob3873N1DBudqJXy\nKYbEqQgLC/nFTNtPW6BEMdhx767XsbNOd0ytZ1fK4APXgs7k5LaLClmQHTd589Piw9nbOELn0Dir\nK1IJUSmmfHZXvY72wXFyUiOn/HD+gMADrzTi8Qb4/g0VLCtJZm1lGhcumkd1UQLrKtNYX5XOhup0\nQkOU1HUaOdw+RlluHBFaNXsaRnh+RxeJ0aH8+xcrWVuRxqDBQVOvmf3No9R1Gnl6eyfbjwxR32Vi\n1OwiMSaUNRWpXL8uj7LceFxuH20D4xxpH2N/0yjZKRHERX481v35zpH2MV7Y0QUEwxnLFiSf1fP3\njth49t1OslMiuXljPvJjlqFcJmN+Vgx7Gkep7zZSVZhAhHaqGIiiSNewlZfe7+bRt9o43D5GU6+Z\n9sFxenQ2+kbsjJpcLCme/vIP6O388vFDDBqc5KZF8s2rFnDR4gxWlaVgc3pp6jGzq36EPY0juDx+\nNlbP45tXLWBdZTqOCS+NPWZ21+vwB0SSY7UntYCtTi+PvNlGfJSGWy4snGyLTCYjXKvmUJsBt9fP\nwoKEyc+8uqeXHp2N2y8qYmjMQeeQlTUzvKf+gMBjW9p49+gwSTGh/OjWquBvMGBhxYKU07KkDzTr\nefNAP0UZ0fz09mpyUiJRKuTERmhwuf009JgQRCjOimVsfIL/e7kRjVrBj2+txmh109xrJjU+7JRe\niT8g8Ma+Ph7a3ILZ7mFpSRLJsVqaes10DVlPKVKCKHK0w8hfXm7gcNsYGrWSC6rSGdA7ONRmYNTs\nojgrFpVSjiCIDBudHGoz8PCbreys02Gf8FKUEc2I2UXXsI0VpckzhuA+zJF2A398oZ6+ETt5aVF8\n/8YKLlyUQW5aFBFa9bRnTKWUs2h+Iv16O009Zhq6TWw50I/V4WVDVTr/dl05/aN2GnvMTHgCs4Zl\n364Z5I39faTFhxEIiLQNWFhZlopG/cGz8OQ77fSM2JABA3oHayvT5rwg5hkJVH2H4d5BvYOeEStt\nA+PsbxrlyXc62HKgn9Z+C0NjDmo7x6j8kHdyIgdb9bx1YICSrBiuX5835QdUyGUo5DLqukwo5DKK\ns2In99W06nn4jVaaes1oNSpy0z5Q6x21wxxo0bN+YTpLSz7orFRKOdHhIUSFhxAZpiZCq6Y4Kxa1\nSn4sv2BArVLw1LYOtCFKfnhTJXFRoWjUSpaWJKNWKWjoMmGyukmK01KeF8/ayjRuWJ/PF1ZmU5wV\nS2ykhtT4MJYvSGFpcRIyoKXfzN7GUWRyGflpUZPXOKC38/yOLl7e1UPfiA2X2482RPmpx3s/S5ht\nbv74Qj0AqXFauoZtVOTFn3ZIWG928ecXGzDZ3BRmRE/eI1EUefiNVoxWN1/bVDwtJKXVqEiMCeVg\ni57OISsrS5NRyOWMml3saRjhX28H34ehMSfJsVouXZrJmopU1laksrF6HgbLBE29ZtISwkmL/yD0\n4/MH+OML9VjsXu64pIhbNhYQe8zAUSsVLCxIIDslko5BCwlRodx9TRmry1NRKuSolHIq8oP7W/uD\nnc62w4N0D1tRKGQkxoROC79sPRh8Z69clTPlXQJIidNyuH2Mtv5xlpYkERaqwun28fDmViLD1Hzp\n0iIUchn1XSZCVAqKTrCM/QGBv77SxKE2A9kpEfzgpkriIjWEqhUc6RjDLwiU587Ni7LYPfzpxQbk\nchnfu6FiijcHUJAezcEWPY09Zsrz4nnynXb0lgnuuLiI/HnRZCVHsKN2mB6djXWVqbMWf4yYnPzh\n+XoOthqIDg/h61eUcNmyLCoLEtCZnDT2mOketlI9g0gNG51sOzTIY1ta2Vmvw+MNTBoOFfkJLJmf\nSM+IjaYeMzWteuq6jDy1vYPth4do7DHh9vhZVZbCN76wgAsXzWNs3B3c7p1dHCD4HjyypZXX9vQh\nCHDdulxuv7homsE0E0qFnEVFiYyYnLQNjBOqVvCNK0q4cHEwhFyeF09dl5H6LiORYWqyU6aGSA+2\n6Hni7XZiIkK45+ZKoiNCONphxOHyTho0x0PUGYnhrF+YRmOPmRC1gsJ50dPaY3N6CVFPNXJmE6iT\nFkls+v5r03aGhigpzYmlPC8evdnF63v7iIsM4Yc3VZIYo51yrN3l5Sf/OIjXF+CXX11C4gzxaK8v\nwD1/24/XF+B3dy0nPFTF0JiDXz9xBGQQolJgd3n57rVllOXG4/b6+Y+/7cfjF/jtncuInGN4Y2fd\nME9sbUckKIzfv6GCoszpLqjT7UMuk51WHLp9wMJDm1uw2D0UZUSzfmE679cN09JnAYIPiD8gTB4f\nH6WhNCeO8rw4ijJiUH/IIpUIIggi9z9bS9vAOLdeVEhClIbfP1/PoqJE7rpywbTjxx0eIsPUyD9k\nRfaO2PjjC/XYXT4A1i9M4+aNBchlMpp6Tfz+uXoW5MTyvesrZm3LY2+1sateR+G8aKxOL6NmFxB8\nlioLElhfmTZF+I6jt7j42T9rCA1R8uuvLZmshHv+vS621gywtjKN2y4qnP03OEUl4YTHz8FWPXsb\nR+getgEQqVVx15ULJkMsXl+AH/x1H6Iocv83V0zrHCDYCf399WZWlaXwpUvn8+b+Pl7a2cN163K5\nZEkmbq+fHzywD4VCxn13LUetUkzmf/Y0jlCSFcO3ry6bPHdAEPjxQwew2D389hvLiYk4uUEhiiJ/\nerGBhm4Tt15UyLrKtBmPO36/tCFKXB4/FXnx3H1N6eTvc/x3vWZNDpcty5r2HbsbRnh6ewden8DK\n0hRuvCB/iofnDwg8+GoTtZ1GslMiSI0LwxcQ8PkFjFY3g8dy3qEhCqoKErlkaca0nJM/IPDanl62\n7O9HJGgA5KRGkpMaRXlu3KQhAuD2+vnV44cZMbn49tWlUzxYCIYQ3zk0wOZ9fXh9AnlpUXzp0qIz\nKpIJCAKH28bIT4+a0gYIhpr/64nDOCf8XLEii6hwNRq1ErfXz1PbOlAp5fzoi1WkJ4YjCCK/fPwQ\nA3oHP7plIdkpkfzisUMMjzn5ya1VpMaHcc+D+wD43V3Lp/Slr+7u4fW9fdxzU+WU/ne2IomTelB2\nh+fe8rw4Fs9PYvmCZDZWz+OmDfksnp/EvMRwijJjUCpkHOkwcqRjjIq8+Emrxx8QePKdDnp0Nq5Z\nk0v5LLHr47Hk+m4TKqWcjKRw7nu2DpvTy52bSli3MI19TaMc7RijPDeePY0j1HebuGxp5qznnIms\n5EhS4rT0jNi4eUM+lR96EI6jVirm7JYeJz4qlBWlKYyaXTT1mjnUZmBs3M38zBhuvaiQL186n6rC\nRFLitKgUckZMLjqHrBxo0fPOoUF6R2wUZkTPmqf4MAFBmNYJfx556+AAu+qDiefr1+WRGBNKXZeR\ntoHp+ZJ3jwxx/7N1HO0YIyxURUpcGDKZjOZeM394vp4Jj5/r1uZic3qp7zZhsXsoy43jb681Y3V6\n+eaVC2asYjvO/KwYjh7LU3r9Acrz4rlkSSa3XVzIytIU4qNDZxSR8FAVchnUdRpxTvipyI+nfcDC\nE1vbSYwJ5dtXlZ40lCSTnbx0WqWUk5UcyeryVBbPT0StUtA2MM6+plEiQlVkp0Syu2GEQ20GLlw0\nj9Lcma301LgwDrUZaBsYZ/H8JP71Tjsy4OubSlAp5SgVclweP829ZuKiNGQlR/Lanl62HR4iKzmC\n/3d9+ZTnVy6TEaJScLTDiCCKp6zo29M4wts1gxRnxXDzhvxZrzkxRotxfIKeERthGiXfu74czQkd\nYPB6dbQOjLOqNAWNWok/IDDu8PDoW228dWCAEJWCr24q5vLlWdPedblcRlVhAkNjDlr7xxk0OBg2\nOhk1u3BO+CjPjefKVdnccXER1UWJM3ow8mPRoDUVqVy+PIuLl2ROesQfNnyVCjmFGdHsbRyhrstE\nZX48dpePbp2Nhm4Tj77VxqE2A1qNkls2FnLTxnwi5+A1zYRcJiM9IXxG4ztMoyI/LZp9zaO09Fuo\n7zJxpH2M+i4TMpmM715bNul5y2Qy0hPD2d0wQv+oHa8vwIEWPavLU1i/MB2VUk5AEGnoNhEaoqDg\nmBf1fu0wz+/oJiFawxUrs1ErPzCUzsiDmmuZ+daDAzy/o4sIrYq4SA1muweb0wtAZnIEP72t6qRj\nLdxeP/c8uB9BEMlJi6Spx8wlSzO4bm0eEAz3/e21ZuIiNTjdPlRKOf9z57Izqrb5OBFFkZ31OvpG\n7KyrTJs1qe4PCHQPW6nvNlHfZWTE5JpmCX74vIMGR7CU9Fj4IS0+jBsuyGf+DF7g54EenY3/fvII\n4VoVv/zy4smO4PizsLo8lTsuCZYU13aMBXMRIQo8XgFBFEmK1bKwIJ53agaRyWTceUUxVYWJOCZ8\n/O9zdfSP2slICmdA72BpcRJfv2J6BduHsTq9DI85yE+PQqWcu9frDwj88rFDDI05+c61ZTz1Tgdm\nu5sf31I1Ldx2NmgfsPDAK004JnysrUilbWAco3WC3921/KSh0f3No/xjcwvxURqMVjcXLprHjRfk\nT+632D3c8+A+EmNC2bhoHk9sbSchWsNPbq2eMZLhDwj86O8HsLm8/O4by2Y0AARRpGvIyp9erEcU\n4VdfWXLKIhjHhI+nt3ewYkEKJdmx0/bvqB3mX2+3ozlWKez1fxC9KEiP4mubSk75HaIoYhifQC6T\noVLKUSvlqFWK0x7KMld2N+h4dEvbtO0yGayvTOeq1dmfSGpgbHyCIYMDtzeA2+vH7Q1QMC96xuf0\nkTdb2dM4AgQNsd98femk0Tjh8XPPg/uQyWT87q5ltPWP85eXGwjTqPjJrVUkxU6Ntn3s46C2HR7k\nhR1dyGQyYiJCiI0IIT4qlMtXZM0Y2vswb+zr4+VdPQCUZMXw/66vmJI0fH1vL6/u7gXgpg35bKye\nN9emndOIosh9zwTDWN+6qpSqwqmenf5YBZdhfAIAGZAaH8aw0Qkw6V18+Iaf6xwPec70wvfobPzh\n+Tqcbj/fv7GCkhNyk4Ig8uN/HMBsc/PbbyzHbHdz39O1IIP/+OJCtCFKthzoZ2/jKAFBJDREwXeu\nKZtSUeRy+/nDC3V0D9tQyGX8+utL5/SMfhS6dVZ+88QRAETg8uWZXL0692P7PuP4BH9+qZGhsWBI\nakVpMl+5rPiknwkIAj/9x0H0lmDH/NtvLJvWkT/8Rgv7mkaBYKc0U2dzIsfFYl1lGusq0xBEEUEU\nMVk91HcZaeg2YjsWer3jkiJWl6d+lMuevI4HXm5CZ3ISGqIM5n1DlOSnR7Ghet6cihE+aY5XQXYP\nW0mIDiUxJpSE6FCykiOmpU7OFWxOLz9+6AAuj3/Ge3e8z15aksTR9jEAfnhz5bTqP/iEBup+lNHc\nEx4/P3roAGqlnJ/dsWhaglQURZ59twudycl3rik77TDcucyIycnPH6khQqvmv766ZNIzdEz4+PW/\njqA3u1g8P5GKvHiKs2OJ1KonK886h6wo5DKuXZvLRYszPuUrmRsut4/fPl2Lxe7h2rW5rCxLmQxZ\ntvZb+PNLDXh9Ab50yXxWlqVM+/zOumEe39rO4vmJtPZbcEz4+M41ZVNCviarm33NoyzMj5+xosvt\n9fPM9k6ykiNYtzD947vYE3h6WwfbjwyRkRTOT2+r/tis8eN4vAEe2RIsNPrxrVVTijRmY2/jCP98\ns3VWr3JAb+feRw+hVspn7WxOxOcX+I+/78di98y4P1KroiwvnurCRMpmCT9KnLs0dBvpGrZx5ars\naWkHlzvoRbk8fmQyuPvqslmHHXzqM0nMBZvLS4hKMa2M9XzgePJwQ3U6N28owB8Q+P1zdbQNjHPJ\nkgyuW5c37TOiKHKkfYyntncErZlbq07ZYXza+PwCf3g+eF0yGYgi5KZFcuuFhZhsbh58tRkQufOK\nEqoKE2c9xz1/24fVEQwj33ZRIWtnSaqfS3h8AXYcHWZRUeLHNuB4JgKCcMrpjI4jiCI1rXoWZMdN\nMxKPc7jNQFyUZlq112y0D1ioaTMgR4ZMFszRaEOUlOTEkp0SeV7kU89X3jrYzxkF27QAACAASURB\nVIs7urn1FO/oZ0Kgzmd8/gA/+2cNhvEJ/vP2at6vHZ4sEPjW1aUnfYnbByz89ulaUuK03PulRaeV\nHzlTRFE8qac8U/WZKIr8Y3MLB1r0LCxI4KYL8nluRxeH2wzIZCBDhlIp4+6ry2bMLZzItkODPPNu\n55RcpYSExLmHY8I3q7FzHEmgPgO09pm579k6wkNVOCZ8ZCSF86MvVs1YFvxhnnynnfeODnPZskyu\nWfPx5TYA9jeN8vT2DtYvTOfKVdnThGp4zMH/PleHWqVgdXkqK0pTiApT89LObt7c309uWiQ/vLFy\nsry+qdfEU9s6cbi8fPfacvLST+0FiqLI2PgECbNUz0lISHx2kATqM8I/Njezv1lPdLia/7x90SnH\njxzH7fXzs3/WYLZ5+OntVWQlfxB+cbl9+APinMeMzYYgiLz4fjdbj83PBnDp0kyuWZMzKRLDRif3\nPX0Um8s3Of5LIZeRmxZFx+A4STGh/PjWqmnluYIg4g8I0pgwCYnzEEmgPiM4Jny8sa+PlWUppJ/m\nJJItfWbuf7aO9IQwfnbHIvpH7eyoHQ7OoKGU86uvLpmz4H0Yl9vH315vpqnHTFKsltsuKuSJrW3o\nLROTIqUzuSbF6ZYLC1hanMT+Zj276nUMGhxEaINVX+dqVZKEhMSngyRQ5wmPb21jZ52OqHD1ZBFB\nZJgam9NLVWEC37qq9LTPOe7w8Nuna9GbXZTmxHHnFcVoNSosdg+/e/ooessEq8tTqesyYnN6ueXC\nAtafUBl3fP7GMI1y2gh2CQkJidkE6vNTqy0BwPXr8oiP0mB3+qgqTOD7N1bw+2+tIC89iiPtY9R1\nGk/7nG/XDKA3u9hQnc53ry2bHDAYnJtrIUkxoeyq12FzevnixqniBMFCiXmJ4ZI4SUhInBaSB/U5\nxOkOLlh24pQow2MO7n30ENHhan711SVznlbJ4wvwgwf2opDLuO+bK2Ycf2axe3jynXYq8uJZdRYG\nWkpISJxfSB7UeUSYRjVtvq60hHAuXpKByebhtT29cz5XTYsep9vP6orUWQdHx0SEcPc1ZZI4SUhI\nnFUkgTqP2LQ8OO3UtkND9I/aT3m8KIq8d3QYmQzWlJ/7A2ElJCQ+X0gCdR6hVim49eJCBFHkibfb\nEE4S3gXoGbHRr7dTkRf/ic58ICEhIQGSQJ13lGTFsqQ4id4RO/VdJy+YeO/IMADrqz6ZueokJCQk\nTkQSqPOQy5dlAvD2wYFZj7G5vBxq05Mcq6X4c7qkh4SExLmNJFDnIWkJ4ZTlxtExZKVbZ53xmN31\nOvwBkXWVadJUQhISEp8KkkCdpxxfmmMmL0oQRN6v1aFWyVlRmvxJN01CQkICkATqvKUoI5rMpAiO\ndIxhsLim7DvYqsdkc7OsJPkTWcVTQkJCYiYkgTpPkclkXLwkA1GEbYeGJrc39Zh4dEsraqX8c7Nq\nsYSExGcTSaDOY6qLEoiLDGF3ow7HhI/WPjN/ebkRmUzGd64tI3UOK7BKSEhIfFxIAnUeo5DL2bgo\nA69P4ImtbfzppQZEUeTbV5dSnHXyBQMlJCQkPm4kgTrPWVWWQmiIksPtYwQCIt+8spTSnLhPu1kS\nEhISJ58sVkJCQkJC4tNC8qAkJCQkJM5JJIGSkJCQkDgnkQRKQkJCQuKcRBIoCQkJCYlzEkmgJCQk\nJCTOSSSBkpCQkJA4J5EESkJCQkLinEQSKAkJCQmJcxJJoCQkJCQkzkkkgZKQkJCQOCdRnmzn2Jj9\nrM6DZHV4CAtVoVRIungcw/gEL77fzbVrc0mMDj0r53xzfx8eX4CrVuXMeTVcf0CY9b5MePw09Zqp\nyItDpVSclTZKfDo43T50Ric5qZEo5NJ7KHFukJAQMWNHdVKBOpvojE7ufbSGdZXp3LQhf8ZjBvR2\nADKSIs7Kd3p9ARq6TVQWxJ+1l7G5z0zn4Dhen4DHH8DrC5CRFMGGqvQzWhr9jX19HG4z4PMF+O51\n5R+5feMOD6/s6kUQRcJD1Vy46ORrOomiyJPbOthdP8KG6nQ2Lc8iNOSDx6Kpx8TjW9sw2TysX5jG\nLRcWfuQ2nouIosjWgwN0DVtRKuQoFTIUCjk5qZGsrUib9TNev0CI6sxEW292satBR5hGRV5aFNkp\nEaiUCkRRZNDgoLHHREufhYJ50XxhZfYZX1tAEGjqMbO3aZS6TiP+gEBafBg3bcg/5az1/oDAi+93\nU54bx/xZjj3cZkCpkFORH3/GbZSQmIk5C5Q/IOCc8BEVHnJGX7T9yBD+gMjuBh1Xr84hRD31pXZM\n+PjNk0fw+gSWFidx9Zoc4qM+mkexeV8fb+7v56pV2WxaMf0Fd7n9PL+jizUVqWSnRJ7yfMbxCf70\nQj3+wFTHcm/jKMZxNzdekHdaIuVy+6lp1QNQ322itd/C/MyYOX9+JvY3jSKIIjIZPP9eFxmJ4RTN\nck5RFHlhRzc7jg4jk8HWgwPsaxzhqtU5VBUm8vx7XexpHEEhlxGhVbGjdpjV5alnZEBsPzxIXZcR\nx4Rv8i8jMYJ7bq781D1qQRT519vt7KzTTdu3p2GEzKSIGZ+PF3d2896RYe790iKSYrVz/r4hg4M3\n9vdxqM3AiXM1KxUyMpIiMFndWJ3eye2t/RaKMqIpzDj9Z6OmVc/T2zuxHTtfSpyWtIRwjrQZuP/Z\nOhYWJHD9+rxZvfc9DSO8c2iQg616/vvrS9Gop3YZA3o7D77ahAjctCFfWuTyPKd9wMLBFj1Xr8kl\nPPSjr8atuPfee2fd6XJ57xVFkSPtY/zfy428tqeX8tx4ok9TpJxuH/98s4WAIOIPiCTGhJL5oU5u\n+5EhGnvMRGhV9Ohs7KjV4fUHyE6JRKU8/Q7M5w/w0OYWvH6BnhEbyxckT/EMAJ7a1s6u+hF0Rier\nylNPec6ntnUwoHdwzZocrlmTy4bqdNZVptE+OE59lxGfX6A4K2bOIrWrXkddp5HqokR0RifDY05W\nV6Se9PP+gMBLu7oBpnUqoijy6FtteHwBvnV1KYfaDNR3G1kyP2natUPQe3tjfz8pcVp+dvsiwrUq\n2vrHOdIxxjuHBunX28lICuffritnQU4s+5v0DBudrCxNOS0hbu4z89DrLYyNu3H7AmhUCtRKBcNG\nJzIZMwpoj87GzrphkuO00zrFs4kgiDz6Viu7G0bISAznJ7dVcemyLDZWp5OfHsWhNgMGi4vlC5Kn\nXPOw0cnDm1vxBwT8AYGKvFN7D3aXl39sbuHp7Z0MG51kJIZz04Z8Fs1PIjpMjS8g0j9qR6WUU1WQ\nwKVLM1ldkcrBZj3dOhury1NRyOf+u+84OsSjW9pABmvKU7nlwkKuXp3D4vlJVOTFozM6ae41836t\njszkiGki6/UFeOCVRtzeAB5vAJlMNsWAEkWRv73WjMnmRhuipLbTiFolJz89esp5dEYnDd0m+vV2\nBkbt9OvtWJ1eEmNCzyjqIPHJ4PML/OWlBjbv62PY6MTjCxChVU9zLo7jmPDx26eP0jFopaXPwqL5\niajnmBIICwv5xUzbTypQDZ1j9/7ttSbeOTSIy+NHEMHu8rF4ftKcvvQ4O44O09BtYt3CNPpH7Nhd\nvimCEBAEHt4cFLDffmM5aQlhdOtsNHSbqO0cY1VZCooZrOyAIGB1eGfsfA806znYoictPoxxhxeb\n00tVYeLk/tZ+C89s7wTAbPdQkhVLbKRm1mvoH7Xz5LYOMhLD+eqmYmIjNUSGqYkMU1NVkEBdl4m6\nLiOCKFKUcWqREkWRx95qw+n284MbKrA6vTT3mkmK0TIvMXzWzz21rYNth4do7DaxtjJtinh3D9t4\n6+AAi4sSuWxZFuGhKg63jdExOM7yBclTwpzbDg/ywvvdxEVquOfmSuKiNBTOi2ZFaQpOtw/DuJsr\nVmTx5cvmExOhITFGy6DBEWxj7PQ2OiZ8KJXyadft8wv8+cUGnG4fP7ujmls2FrJx0TxWlaVwoGWU\nxh4zFXnxUzzzEZOT3z5dS3OvmR1Hh/H4AmQln5mhcjICgsA/32xlf5Oe7JQIfnBTJdHhIWjUCkJD\nlKTGh9Gjs9HSZyE3LYqkmGAHLooiD7/Rgt4ygUatoH/UwerylJMKqc3l5f5namkfHCcnNZLbLirk\nhvV5pCeEkxYfRmluHGsr0rh0aQaXLM2kujCR9MRwkmK02F1eGnvMKBWyOXtRb+7v49n3uojUqrjn\npkpWlacSExEyeX+iw0NYWZpCcpyW2s4xmnvNrCxLQX1CuHL7kSGOtI9xwcJ0xh0eWvstLCtJQqsJ\nWsaH2gy8XTNIRV48d121gKMdYxxpH0Mug/x50TR0m3jqnXaee6+L2k4jdZ1G6rqCfwda9NhdPkpz\n4s6KSA0ZHCgUsjl3iBKn5ultHRxs0eNy++kdsXG4fYytNQN0DY1TmZ8w7X18fGsb3TobafFhDI45\naOu3sKgoaU7v7RkJ1Dfv23Gvxe6hqjCBu68uo3ck+LJWFSQQGaaecuygwcEjb7aSHKslJuKDzkYQ\nRP6xuQVfQOC715YzaHDQNjBOdeEH56jtMLKzXseqshQWzU9iXmIEayvTsDq8tPRZEESmxcoFQeQP\nz9fz3HudlObGTfPqHt/ajtXh4ce3VdM1ZKWp10xxVgxxkRo8vgB/fL4el8fP9evyaO4z43T7ZxVe\nURT5xxstGK1uvrqpeLKjOo5GrQyKVKeR2k4jAUGkMCMa+UlevG6djTf391NVkMCq8lQykyPYUauj\nZ8TK2oq0GQV5V72OV3f3olbKmfAGUCpkU7yP1/b0MqB3cOMF+SREh5KVHIHJ5qaxx0zbwDitfRYO\ntOjZ0zjCe0eGiQpTc88XK0k4wRMLDVGysCCBS5dmUJgRg/wEiz0nJZL363R0DI6zpiIVlVKO1xfg\n5d09PPByEz06G9WFiVOs/K0H+znUZuCChemsPsEoUSnlpMaFsa9plJ4RGyvLUpDLZdhdXn73TC1W\np5d1C9MwWYPt31k3jNcXoGckaLgc7RijvttE3DFD4XQRRJGH32jlYIue3LRIvnd9JWGa6SGJtPgw\ndtbpGDQ4WVMZ9G4buk1s3tdHSVYMG6rnUddpRKmQz5rPsTm93PdsLcNjTi5YmM63rlpAclzYjB2z\nQj5d5PPSotnbNEJzb9AqPVnoRBRFXtzZzet7+4iNDOGemxeSljCzwSOTyUhPCEehkFPbaST4rgeN\nuAmPn7++2oRCIePb15QRH6nhUJsBq9NLdVEiHl+A/3upAZ9f4O5ry0iO1VJZkEBth5GjnUZ21evY\nWadjbNxN4bxoLl0WFN2FBQksLEjAaHVT321ieMxJZf5HyxF3D1v51ROH6RyynrZ3f77h8wtz8sL3\nNIzwyu4e0hPC+fXXllKZH09iTCheX4D2QSt9ozYWz0+a7B8auo28+H4PWckR/Oft1Zjtbhq6zbQP\nWFhUlHhKkTojgWrpNt775cvmc/GSTMJDVUSFqTnYosfp9lFd9IE34vMHuP/ZumNej5GlxUmT1mR9\nl4kdtcOsWJDMkuKgmh5qM6BSyllwbOXWJ99px2h18+XL5k92Nsdf+IMt+qCVnT/Vyn5jXx+7G0YQ\nROgbsbOqPGVSEHp0Nl7f20tFXjzrF6aTlhDO7oYRBvUOVlek8srOHuq7TVy0eB5fWJlNQ7eJtn4L\nS4uTZnz5m3vNbN7Xx4Ls2FmT1cc79qMdY9R3mahpNRAdpiYlTjvjC/PKrh4GDQ5u2lhAYnQoWo0K\nt9dPY4+ZELWCgnlTwyTdOisPvtpEaIiSH99axdGOMdoHxllVnopGrcDt9fPIljaiw0K4aUM+MpkM\nmUxGaU4sbf3jdA1bGRpzMmJyYbBMEBMRwvduqCA1LmzG65mpzVqNClEQqe82IQgiCrmMP7xQT32X\nCYVCzqjZhc7opKowAblMhsnq5sHXmtBqlHz76tJpFYCJMdpJAVKr5GSnRPKnF+oZNDi5fHkWN67P\nZ21lGhq1go4hK829Zlr6LHQOWekdsdM3YmdnnY4Jr5/c1KjT8rC2HOhn2+Eh8tKj+N71FWg1M3s/\nUeEhGCwTtPQFvduUOC1/eamBCU+Au68ppSgjhl31Orp1NtYvTJvWBpvTy33P1DJsdLKhKp2bN+af\ndgeqUsqJj9RwsFWPzuicDDfqzS521g2z/fAgO2qH2VozwOt7emnutZAcq+Xfb144xfiYjZzUSJp7\nzTT2mElPCCc1Poy3DvbT0G3i8uVZlObEkZYQRmOPmeZjht7+Zj21nUYuWpIxadiFaVQsLIinvsuE\n1ell+YIUvnzZfC5blkV2SiQZSRGTf0vmJ9Gjs9LYY6ZjYJzKggT8fpGGbiPbDg+y9UA/ogjzksJP\nGfL+04sN2Jw+zDYPybFa0k8SgTif6R2x8bNHDuKY8LEge/ZVs/tH7fzfy41o1Ap+eFMF0REhxEZq\nKJgXzYrSZAZG7TT1mtFbXCwsTMDtDfCH5+vx+QX+7bpyYiJCqMiLZ2x8goYeM639Frz+AFaHF69P\nQKWUT/N0z0igSrNi7o07IeyVFBNKfZeJ1r6gKkZog2Ly0vs91HUZSY0PY2zcTe+InaUlQXV9cls7\nY+NuvnzpfKLCQ0iMCeX9umEGDQ42VM9jxOTkhfe7KcqI5pKlmVO+X6mQkxKvZV/TKH2jdlaVBS3Y\njsFx/vlmK7GRIRRnxtI6YCFcoyI3LSrYnp3dDBoc3Lwxn8QYLbGRGgwWF029ZpwTfrYfGSQhWsNd\nV5aiVMg/WPJcECn/UC5BEEQeeKUJu8vLN69acNIikdAQJYuLk/D4ArT2WahpM9DQbSIhOnRKR+F0\n+3hkSxvxkRpuvOCDDis7JYJd9SO0D4wzPzOWCK0KuVyG1eHh/mfrmPD4ufuaMvLSoghRyTnaacTn\nD1CWG8+BZj2H2wxsrE5nfuYHlrxCLmdFWQorSlO4ZEkmly3LZNPyLC5ZknlGBS/ZKZEcaNHT2Gti\nb+MoLo+fCxfN4+5ryugbtdPYY2LMMkFlfgKPbW1jyODk1gsLJ+/NhynIiGZf0yiNPSb6Ru209Fuo\nLkrklgsLkMlkKBVyCuZFTxayrChNZt3CNC6snseC7Fh6Rqw0dJvZ3zxKbKSG1FkMghNp67fwzzdb\niYkI4Z6bK0+ZzM1MDmdH7TC9I3b8AYHD7WOsX5jOimOhZ19ApLHHRIQ2WI13HIs9eN90RicbqtMn\nDYczISVOS9+oneZeM0arm9f39PLyrh5a+y3oTC6MVjeCIBKmUVGYEcO3riolOmJu91cuk5GfHsWu\n+hFa+8xU5CfwyJZWNGold15RgupY6DYtIYzdDSP06mzUdxkJD1Vx15ULpoiyVqNiVXkqFy3KYHFx\n0qzPmEopZ/H8JEbNLhp7zOyuH+H1vX3UtBroH7Vjsnmo6zLS2GMiPSF81vD7mwf6qWk1UJkfj8nm\npmvYypqK1E+98ObTwB8QaOo1Ex+tmRa9EQSRv7zUiNHqpnvYRkxECJnJ04udHBM+7numFpfbx7eu\nXkBO6tT3Vi6TUVmQQPvgOI09wf60qc9MW7+FTcuzWFIcNFZkMhmV+QnoLRM09Zpp6jFT02pgZ52O\nd2oGp6VUzkigXC7vlJ0ymYzIMDU1rQYmPH6qChPpGBzn8a1tJMaE8vM7FqE/9sB5fAFiIzU8914X\nBfOiuWxZVvAC5TLsLh8tfRbS4sM43Gagb9TODevzSY2fbs0nxmgxHLtIbYiSpFgt9z9bh8cr8N3r\nylhemsLueh3N/RaWlyTjCwg8uqWNhOipnX9OahQ763R0DlsB+OaVC0g55j0kx2nZ3zRK51Dw4T6x\nbHhf0yg763SsWJDMuoXps/5Wx9GoFZTnxbOkOAm7y0tzn4V9TaMYLC4KM2JQqxTsrNNR32Xi0qWZ\nUzwllVKBShkMt+yq17HlQD9H28fYVT+C0ermurW5LC9NAWBeYjgHW/W0HvP8XtvTi8nm4SuXzZ/M\nEZx438I0KkJDlISoFJMdzpmgUMhJiA7lYIue9IQwvnNNOavKU1GrFFQVJtA2YAlaxcce4Lz0qJN2\nzGqlgqSYUA4069FbJshOieQ715RO62DUKgWp8WEkxWonw3opcWGsKU9FLpfR3Bt8AToGx5mXGD5r\nxzju8HD/c3X4/QL/dn355DNwMrQaFU53cCxYa7+FMI2Sb11dOpmvSYsP472jwwwYHFxQlY5cLqN7\n2Mp9z9ZiHHezsXoeN11w5uIEwXuYlxbFznodfaN2HBPB/M3ly7O45cJCrluXy2XLsthQPY/F85Nm\nTWTPRoRWjUoZNHoONI/i8gS4anXOlBBybKQGvdlFa7+FgCByy4UF0zowCBqWc/FmFXIZVccs8B6d\njczkcFaXp3Ltmlw2Lc86lpe1sLthBINlgry0yCl5vlGzi7+/1kxEmIof3FiBDBn13SZgekrgaMcY\n/Xo7qbOEVj8PvL63l8e3tuOY8FGeO9XQfu/oMLsbRijNicM54eNoxxjFHxIJm8vLg682MWhw8IWV\n2ayZZXiFUiFnYUECDd0m6ruDhmVqfBhf21QyJXwok8moKkigPC+ekuxYclIiSY7TkhIXRnVRIpoT\nntGzIlAASbFajnaM0dpvoTI/nr+/3ozL4+e715STGBPKgpw4ajuDYa6uIStWp5cb1udNEZ/YyBDe\nOzqMxe6huc9MdHgIt1xUMGvOpmBeFHsaRmjqM9M1FAxVXbk6h2UlyYSoFESEqjjcPsbY+ERQFHrN\nXLEie4o1e7yQorXfwsqyFC5clDG5Ty6TIZMFw5FqpZyizBhEUaSm1cCz73UiCHD3NaUzFmPMRnio\niuqiRCry4uk/5hLvbRolITqUt2sGcLn9fPXy4mkdSVZKJEmxWiK1KkCGzuTE5vSyqChxiuDK5UFj\n4VCbgQG9nY4hK/MzY9h4inFPZ4PkOC0rFiRz0ZIM4qI+eMCVCjnVhQk091ro1tmQyeA715Sdsuoz\nJS4Mp9uHPyDwb9eWEXYa5akKRfB+LS5OYmx8guY+CzvrdFjsHrJTI6e8BAFB4M8vNKAzubhxfT6L\nTghTn4qslEjerxvGHxC5fl0eRScUK6hVCuwuH819ZhJjQunX2/nrq024vQFuXJ/HF1Zmn5VOUav5\n/+zdZ3gb15Xw8T8qQZAEe++9U6R6tSTLlrvlFvc4PXGcTTZ9N7vZXSebN5veHdtJ3LvcJMuWLFlW\nrxR772AvKAQLCBBEmfcDJNo0SYmSKJty7u959EEAMZgZzMy599wzd1RkJgSRkxjM56/LYt3pkn9f\nH+VZxzvnKiVGR237IAMWO0H+ar5yY860sdDkaB0HK3tJigrgnqsyLnq7ZDIZeSmh3LAqkSsWxZKV\nEEyIToNW4z1/shOD6RwYpbbd28vS+ionb3N4dFsNhiE7X7o+m8QoHSnROo7V9FPXbmFFbiR+GhUu\nt4cX9jaxdV8LpY1GShqNBPn7EBXyQU+7f9DGocpe6jsspMcHXZYBzOF08/j2WpwuD+19o+j81JO3\nRgxbHfzlzRpUSjnfv6doMmtR1WJmRU4kGrWCk3UD/PE177lRlB7G/ddknnU/qJUKitLDKW00Mj7h\n4lu3FxA2QzpZJpMRHOBDbJgfaXGBFKSGUpQRPuW8hNkDlEySZp8sYraZJIrrB3hsey2+PkrsDhfX\nrUzgMxvSJt/vM4/x02dKcEy4CdH58MsHV00bBP3FC2U0dQ0B8JkNqdPSex91vKafv79dB0B2YjDf\nu6twcoBOkiR++WI5TV1Dk72f335j9bSehMcjUdFiIi85ZEq1EsD4hIsf/PUYMpmMr23J5Y2Dbej7\nRlAqZNy9KZ0r59B7mo3b42F3cRfbDutxuT0ALM+O4MEteXP6rGl4nLBAzbR96JEkfvr0KToHrAB8\n9aYcVuZGXfB6zpdR2wRPvFNPVkIw165IOPcH5lFNm5mX97XQaxpDo1aQFhdISIAPwQHeNO/x2gGW\nZkXw9S25530hKq4foKHDwn2bM6b9Fubhcf798eOolHLGJ9z4aZQ8uCWP3OSz3wi70AwM2vjLG9Xc\ntCZp1qIhy6hjstLx4+DxSOwv7+H1g62MT7hJjwskKyGYHcfaKUwL45u350/+lifrBnj8rVrvRXZz\nJo9uq6GlZ5i4cD+SonUcre5DkrzBOCshmMoWEz2mscnveuiWvCnj65eL90u7eeG9JtbkR1HZYsbu\ncPH9uwvJTAjm7ztqOV47wP2bMyavY3tOdfHy+80kRweg06qpbDWjVsm57YpUrjqdBZgLq93JkNVB\n3CyFOHM120wS592DAm+L91SDAcuog9gwP752c96Url2AVk10iJbSRiNb1iaT9pH7IgDkcihrMqFS\nyvnKTbnTAsZHxYX70Wu2MeF08693LJoyqC2TyUiJ0XGwohen28O6RTEzHmQymYzoUL8ZK+SUCjnj\nE940zvGafoasDpZlRfAvtxecdUBxLrw5/iCWZIbT0T/KsHWCB67JOmtZ+4c/6++rmrGFLJPJCAvS\ncLx2AF8fJV+4LmvGbfu4+agUrMyNIi1u5nGnSykiWMv6whgC/dS09o7QNWClY8BKY+cQ3cYxIkO0\nfPuOgguasik23J9FaWEz/hZajZKBQTvt/aPEhvnxg3uK5nTz90Lj76uaLCyaja+Pct5L/s/mzPm9\nOi8a8/A4NXpvClmjVvDtzyya0hCNDfOjocNCbbuFo9V9DFjsrMiJ5Fu3F7AsK4Ll2RGT1cHN3cOM\nT7gpSAllQ1Esde0WuoxWNhbFXla9KI9H4m87aplweosUchKDOVbTT3mziQCtmh3H2kmMCuBz12R9\naMhDh3l4nKo2b485OzGY79xZSEHq+ZX9q1WKC6qi/ah57UGBt6zwpfdb+PqW3FlnFrCNO/H1Uc64\nwRNONz9/vpSC1FBuuyL1nBsA3p6SR5JmLUl953g7O46289+fXzbjeNa5DI9N8PBTxYQH+nLnlWlT\nUoTzxSNJWO1OdNqL/1HBu092HG0nPMiXVXmffO9pobE7XAxZHQyOOhgaLg3cAgAAIABJREFUdZCd\nGDynhsGFsNqdnGowsDJn5hujhflR3mzk7WPtXL0snpU504/5zoFRfvL0KWTIuHNjKlcvi592Dero\nH8Uy6iArMWhyXOupnd4btr9yUw6rFkAmYq5KGgz8dVsN6wtj+Ny1WQAcKO/h2d2NAMiAH39u6bQG\nk9Pl5tUDrcSH+7O24JMtz5+tB3XBAWqhOtukp3MhSdJl1XoSBGG6uvZBtBolSVFz78Wahu386PET\nhAZq+NmXV1wWlYCSJPGzZ0tp7xvhZ19ZMaXo57k9jewv62FDYQwPnA5cC9UnPlnsx+ViDyoRnATh\n8neuSXBnEhboy/rCGPaV9XCspn/KjeULlfeewBGK0sOmVaTee1U6SzPCSY+fPsRyuVj4TQRBEISP\nyQ2rklAp5ew4qsfp8nzSqzNFeZORJ3fWU9JgwDHhBrwTPANcs3x6MZJCLic7KeSy6AnO5lPXgxIE\nQbhQwQE+XLk4lt3FXRyq7GXTkguv3p1P+8u6eX5PExLeaYjUSu9MOxUtJlJjdKR/AgVJHwcRoARB\nED7kupWJHCjvZcdR720hKTE6EiMDzllpfLFae4Yxj4yzKC1s8naZM0VQ247oCdCqeOCaTDoGRilt\nNFLRYgLg2hUJn9qhiU9dkYQgCMLF2nWyg1f3t07+XyH3TqybnRhMTnIw6XFBF/ygypl0G6z87NkS\nJlweNGoFy7IiWJMfzal6A++XdRMWqOF7dxVOeSRKj2kM05D9vEvDF6J/mio+QRCE+WAYstPWO0xb\n7wj63hE6BkYnH1Z65tEnD1yTOacJec/G7nDxv8+U0D9oY21+NHUdgwyOOCbfjwv34zt3Fk55SsSn\njQhQgiAIF8HhdNPcPURdu4U6/SCdBiuZ8UH88N6iC+7BSJLE42/VUlxvYPOyeO7elI5HkmjssHC0\nph+ny8MD12bO+CiYTxMRoARBEObRn1+vorzZxBevz2ZtQfQFLWN/WTfP7WkiLTaQH95bdFlX3F2M\n2QLUP+feEARBuEj3XZ2Bj0rB1v0tjNomzvvz+r4RXnq/GX9fFQ9uyf2nDU5nI/aIIAjCBQjRabh1\nXTJWu5Ot+1rm/DmPR+JgRQ9/eLUSt1viqzflXLLpty53osxcEAThAm1aGsex2n6O1vSzJj96yvOz\nZlLTZuaV/S30GMdQq+Tcvzlj8sniwnRiDEoQBOEi6PtG+NkzJUSEaPnpF5fPONP7kNXB07saqGo1\nIwPWFERz67qUT3Vl3vkQRRKCIAiXyAvvNfF+aTfpp58g/eFJaitaTDz5Tj1Wu5OshCDu3pQ+6xMg\n/lmJACUIgnCJ2B0u/vF2HeXNJmTA6rwoblqTxHsl3bxf2o1SIeeuK9O4cvHl9aypj4sIUIIgCJdY\nXfsgr+xroctgnXwtOlTLg1vyiI+4uKfOfpqJACUIgvAx8HgkjlT3sfNEB7lJIdx5Zdq8Tov0aSQC\nlCAIgrAgXVCAEgRBEIRPirhRVxAEQViQRIASBEEQFiQRoARBEIQFSQQoQRAEYUESAUoQBEFYkESA\nEgRBEBYkEaAEQRCEBUkEKEEQBGFBEgFKEARBWJBEgBIEQRAWpLM+UVfMxScIgiBcarPNxSd6UIIg\nCMKCdNYelCBcDtweD/tKe9hX1s2SzAhuWJWIr88Hh7YkSVS3DfLuyQ6W50SyoTD2E1xbQRDmSjxu\nQ7isNXRYeGFvEz3GscnXArQqblmXwhWLoukcsPLq/hYaOocAUMhl/PiBpSRGiUduC8JCIZ4HJXyq\n2MZdPLu7geJ6AzJg3aJoblqdzPHaft450YFjwk1wgA+WUQcA+SmhFKSG8sJ7TcSE+fHfn1uKWjxE\nThAWhNkClEjxCZel1w+2UlxvIDk6gPuuziQlRgfAjauTWFcQzbYjeg5V9pIUFcBnNqaRnRgMQL/Z\nxvtl3bx+sI17rkr/JDdBEIRzED0o4bIzYLHx47+fJCxQw/9+eQVKxcy1Pm6PB7lMhkz2QePM4XTz\nk6dO0T9o4/t3F5KTFPJxrbYgCLMQVXzCp8a2w3rcHonb1qfOGpwAFHL5lOAE4KNS8JWbcpDLZDzx\nTj22ceelXl1BEC6QCFDCZaVzYJSTdQMkRgWwJDP8gpaRHK3j5jVJWEYdvLKvZZ7XUBCE+SIClHBZ\nef1gGwB3rE9FLpsxKzAnN6xOJDbMj6PV/ZiG7PO1eoIgzCMRoITLRmOnheo2M9mJweQkBV/UshRy\nOdetTMAjSew+1TVPaygIwnwSAUq4LEiSxGsHWwG4fX3qtLGlC7E8O5JQnQ+HK3sZtU1c9PIEQZhf\nIkAJC55t3MW2w3pae0ZYkhE+WVJ+sZQKOZuXJTDh8rCvrGdelikIwvwR90EJC5Zl1MF7JV0crOjB\n7nCj9VFy2/qUef2OdYuieeuonvdLu7l2eQI+au/Nu+MTLh7fXsuQdYJv3VFAcIDPvH6vIAjnJu6D\nEj4R5c1G+s02VuZGTbv495nH2HWik+O1/bg9EoF+aq5aGsfGoli0GtW8r8ubh9rYcayde69K56ql\n8YyNO/nD1kpae0cAiAjy5Yf3FhGi08z7dwuCIKY6EhaQUdsEP3j0GBNO7420RRlhbCiKxU+jZOfx\nDkobjUhAVIiWa1cksCo3CpXy0mWjR2wT/PCvxwjQqvnPB5bwh62VdBqsrMqNJESn4Z3jHYQFavjh\nvUWEBfpesvUQhH9WIkAJF0WSpFkLE2zjTh7dXktEkC/XrUw450V82+E23jrazvLsCPrNNjoN1inv\nJ0YFcOOqRIoywi+qlPx8PL+nkX1lPfhplIyNu9hQGMP912QiA7Yf0fPW0XZCdd4gFR4kgpQgzKcF\nGaAcTjcqhRy5/OO5CM2H4bEJFHIZ/r7zn2q6UAODNnx9lOj81Jdk+VWtZp7eVc9nN2dSlDH95tit\n+1p4t7gT8M4WviY/iutXJRExw4V8fMLFD/56DJlMxq+/vhq1Sk5b3wgHy3sZG3eycXEsuUkh81Kl\ndz6MQ3Z+9PgJPJLENcvjuXNj2pR1eOuonm2H9YTqfPiPzy6d05iUbdxFZauJpZnhqJTnPzGttzik\njcgQLZuWxJ335y+U0+W5pD3Wy8XZGmXC/JotQCkefvjhWT9ks03M/uZFMlhs/Nc/iukzj7F4hose\nwBuHWilvNpGfEnqpVmPOJEliX1kPf3i1kpIGAxuKYucUWHcc1fPUrgaSY3SEBEwfw7DanRyu6iPI\nXz3lGUZntPYM8+LeZpQKOdGhftPeb+kZ5mfPlnC8doBVeVH4zPMM3cNjE/zulQpGxpw0dFhYWxAz\nZRZwg8XGP96pIzhAwz2b0ukxjVHXbmFfaQ8yGWQmTL1f6f3SbsqbTVy/MpHcZG8gCgnQUJQRzoqc\nSCKCtZ/IRcFPoyI0UENhWhjXrUyctg6ZCcHIZVDWbKKu3cLK3MizXsQtow5+83IFB8p7cLo85CWf\n3zHc2jvMb1+uoEY/SHWbmbhwP2LCpv/+feYxeoxjhM1Tr25vSRe/erEcP41q3qolP6qkwcDW/S1k\nxAfNeMx/mMcj0dg5xNGafqJCtGjUH09d184THfzxtSoWpYXO2vA7nwAmSRLvneqiy2glNswfxWXU\nKP84+Pn5/GSm1z+RAOVye/j91koMQ3a6jVZW50Xh95HB7z7zGI9tr6Wtd4RlWREEaC9N72AuRsYm\neGx7LXtLuvF4JMbGXYQFas75TKGTdQM8/14TY+MuiusMpMTopqSHugxWfv1SOSfrBthX1sPYuJOE\nqAB8VApGxiZ44b0mXniviT6zjdJGIynROiKCtZOfHxwZ59cvV2B3uBmfcNM1MMrKnKh5u8BLksRj\n22vpMlhJidHRP2jHandSlP5Bg+KZdxvoNo7xwDWZrM6L4sqiWKJCtej7RihrNhEWqCEh0rufnC4P\nj22vQQK+dnPugnvcRUJkAIlRAbPuv4z4IEZtTqpazbT3j7A8O3LGRkqfeYxfvVjOgMWGUiFH3zfK\n6rwotJpzX1w9ksSuk538Y0cdtnEXGxfH0msao6LZxJLMiCk998ZOC798qZxDlb0E+qtJirrwgOL2\neHjxvSbeOtqOJHm3YdPSuHlvLIyNO/ntyxV0G8eoaPFu00eDlCRJtPaMsOtkJ0/vamBfWQ+NnUOY\nhsZZnh05r+szE8uog7++WY3D6aa9f4S1BdHTUs0VzSZ+90oFUSF+RIZoZ1nSB1p7R3h0ey2VLWaO\nVvehVMiJj/BDIZ+9keN0eWjrHaahw0JZk5FDVb0cruqjRj9IS/cwnQOjGIfsuNwSWh/lWeelXOgu\nKEC9faTt4bjws+/E2ThdHo7X9OPvq5p2AG7d30Jpk5GoEC1WuxMZsmm9pNcOtNI54B2bUCrk5H0M\nvSiPR+Kto+2UNhpp6x2my2BF3zfC42/V0mmwkpsUzINb8jhW00eXwcrGs/SiOvpH+fPrVaiUcu5Y\nn0pt+yAn6gaICfMnJsyPkgYDf3ytklGbk3UF0YzanNToB9lf3oPRYue5PU209Y4QF+7PTWuSqOuw\ncKrBQHZiMCE6DRNON7/dWonBYufuK9NQyGVU6wcByEq8uFkWzjhY0cueU11kJwbzg3uKqGgxUd02\nSGZ8EGFBvjR3D7F1fyupMTru3pSO7PTM4XHh/hSkhnG8pp+yJiPZiSGE6DQcre7jRN0AVy2Jm7XX\nvJDJZN7jtHPASnXbIOaRcYrSw6ZcxFt7hvnNyxUMj01w6xUprMqNpLTRiN3hmjE9+mFWu5O/vF7F\nwYpedH5qvnlbPpuWxBMWqOFkvYGGTgtr8qJRKuTUtg/yx1ercJ++OJU2GKc0Bs6H3eHiL29Wc7LO\nQGy4H8nROvR9o2TGB837eNsbB9to6BwiMSqAXtMYla1mlmaGT/aM+gdt/O2tWt441Ia+bwSlQsaq\n3CgA6jst5CWHXPJqylf2NdPWN0pYoIZekw1ftZK0uMDJ9/vMY/x+q/fcbewa4opFMedMib5+sI1u\no5WlWRF0G61UNJs4XNmHxkdBYuT0RpFxyM4vXihj18lOyptNNHYN0W0cY8Bip8tgpaVnmLp2C2VN\nJg5V9rLzeAfHavrQ946QER8075mUS+2CAtSPHj32cGmjkaTogBnTU7Nxezw8tr2WnSc6OFLVR1iQ\nhthwfwAqW0y8uLeZqBAt//nAEk7UDtDSO8yVRbGTeXrLqIOndtUTHuSLTCajy2DlqqXxl7xbfKJ2\ngBf3NqPvG6Gxa+h0emUQt0fizo1p3Lc5k5AADUNjE9TqLYQH+c54QRixTfCbl8qx2l18/ZY81hbE\nkBYbyKkGIyfq+uk1jbH9iB6lQs6DW/K4flUiG4ti0WlVtPUM09w9jFop584r0/j8dZmkxgYSF+7H\nyVoDpY0GClJD2bq/lVr9IGsLorl9fSp5KaGcajBQ0WwiNeaDnpbb46G+w4LBYpvcn3PRZx7jkTeq\n0agVfPeuQvx8VSRFBXC4qpeWnhGuKIjm8bfqGLI6+PoteYR+5KLh76siMSqA4zUDVLQYWZIZwXO7\nGxmfcPPglrxzpnYWKplMRmF6GHXt3mmXRm1O+gdtFNcPcKC8h22H9TicHj5/XRZXL4snLtyf0iYj\nde2DLM0MnzVd1Gsa49cvldPeP0pBaijfvauQuNPnTFyEP1abk8pWM+bhcZQKOX9+vRoJiW/cls9V\nS+I41WCguMFAdKh28lwD77nU3D1M/6CNgdP/+kxjNHcPU6M3U9po5LWDrbT1jpCfEsp37lxEZLCW\nI9V9eDwSSzIjzrlPPJKEJHHOY8swZOcfb9cREqDh4S8sw+2RqGg2UdVqZlFaKLuLu/j7jloGLHby\nkkO4b3MG92/OZHFGODFhfhyp7sNgsbMmf/6yBB/VYxrjmXcbiAnz49/vX8Lxmj6q9YMsy/L2Xscn\nXPz2lUosow6yEoLoNo7hcnvO2oC22p08tbOB8GBf/v3eItYvikUmh+buYcqajHQZrOQmh0xmFNp6\nR/jNS+WYRxysyo3kysVxXLUsni1rk7l1XQrrC2NYkRNJUVo4GfGBhAf6olbJGRxx0NY3gtPpoSD1\nkxkWeeNQG396vYriugHqO4foMlixjbuICj176v6CAtSAyfpwddsgR6r6cDjdpMcFojhHN9IjSTz5\nTj2nGgwkRPgzYp/gZJ0B05Cd6FAtf3q9Go8E37u7kLBAX9weiepWM/6+6slWytvH22nqGub2DamE\n6Hyo77AQF+435cS7ULPljV1uD4+8Wc2Ey8337ipkTX40Relh5CWHcPv6VArTwyc/Fx/uz/ul3XQb\nrWxcHDul++9ye/jz61V0Gca4ZV0yGwpjAQgP8iUnKYTSRgPt/d7W2Q/uLprs7SjkMlJiAtlQFEtc\nuD93X5VOdmLw5LKjQ/0IPd2SPlrTR+eAlbTYQL5+Sx4KhRy1SkFaXCBHa/qobDUTG+7PvrIentrZ\nwP7yHo7XDlDXYSEu3P+cA/wut4c/vlqFecTBl2/MIS3W+7sEB/hgG3dR1WqmvtNCW+8Iy7Mj2Lws\nYcblRAT74ueroqTRyInaASyjDtYVRE+2iC9XSoWcovQwSpsM1LZbqNUP0tY7Qv/pYpWvbcllRY43\nFXVmjO1knXf7z7z+YdVtZn6/1dvrumFVIp+/LmvaWEtOUjB17d4GU3HdAEqFjG/eUUBBahiB/j5k\nJwZTXD9AcZ0BtUrBqQYDr+xr4fWDrZyoG+Dkh/4V1xuoaDFR3+H9Da12J5uWxPGlG7NRqxSE6Hw4\nWW+gtWeEK5fEop6lwMPl9rC/vIc/v1ZFeYuJ1XlRZ626fObdRnpOp4MTIgPISQxmfMJNZYuZvaXd\nNHYNEejvw5duyOa2K1KIDNFOZihCAzXo+0aoa7eQEhM4p7TahXh6ZwP9gza+cF02SVEBhAX6crJu\ngM6BUdbkRfP3t+tp7BziqqVxfOmGbIrrDNToBynKCCdwlsbHvrIeqtvM3LQqkbS4IHzUCnKTQliT\nH03nwCg1em92JTlaR/vpzIt9ws19V2dwx4Y0kqJ1RAT54qdRoVLK8dOoCAnQEBWqJTlaR0FqKGvy\no7l6WTzHa/tp6vb26jTqj7cXdbCih1f3t+KjVGC1u+gyWGnqGuJUg4FRm5OC1NBZg9QFBai06ICH\nsxKCaOoaprLVTEmjkdzkkFkr2CRJ4vndjRyp7ic1RscP7y1iZW4ULT3DVLd501cTTg/3XZ3OorQw\nAGLC/NhX2kOXwcqmJXE4nG7+tqMWrY+SL92QTVigL/vKerA7XKzOiz7PXfYBjyRNDnwGaNXTxo/2\nlXVzst7ApiXxbFwcR3iQLzFhfiRGBUwb//L1UTJknaBWP0hksC/xEd5l2R0untpZT2WrmcUZ4dy3\nOWPKDxIc4ENRehj+viq+cH32jAPbSoWcuHD/GbvoCZEBaNQKqlrNBAf48IN7iqaMawQH+OCvVVPS\nYOBE3QBtvd4UyZr8aEJ0Gmr1gxyu7MU8PE5KjG7GAeemriEe215De7933OSm1UlT3k+PC+REbT89\nJu/4yjdvyz/rzbPJ0QGM2Jw0dQ0hk8GDN+ctqArIC+WjVrA8K4LIEC1r82O4bmUCt16Rwpa1ydOK\nWSJDfKnvsFDXbiEnKXiyt+l0eXjvVBdP7qxHkmR86cZsNi9LmPEklstl5CWHcLy2H5DxnTsXTXnY\nYnCAD5nxwZysH6Cq1Uxb7wiOCTfZScGszosiPzWU3OQQcpNDyE8JZWVOJOsLY9m83NsyX54dORlc\nZDIZE043NfpBQnUakqOnjm1JkkRJo5FH3qzmRO0ADqeHwRHHtFTYh7X0DPPKvhaSo3Xcc9UH6eDc\n5BDGJ9y0941yzbIEHro1j4QZUl4AceH+HCjvocc0xvrCmDn3osYnXLxf2k2feYzEs6RAGzstvH6w\njYz4IG5fn4JMJiMmzI9uo5Ua/SANnUNUt5nJiAvkKzflolIqiArRcqy2ny7DKGsLoqetk0eSeOLt\neiZcHr58Y86UcVdfHyWrcqNQyGVUtpg5Ut3HqXoDSoWch27NZ3Xe+TXk5HIZSoWM8mYTchnkJn98\nD+OsbR/k8e11+Pmq+K/PLeXOjWlcsSiGgtRQugxWKlvNTLg85CQFz/i7XXCRRFigL+sKYnBMuKlq\nM3OyboD0uKBpeWBJkti6v4X3S3tIiPDne3cXovVR4e+rYm1+NG6PREv3MEuzIvjMhg8m+1QrFQyP\nTVDXbiE6TEtj5xCVLWZuWJVEdmIIAVo1de3eg2OmYoq5sNqdPLathv3lvbjcEtVtZnISP8hl2x0u\n/rqtBplMxkO35s0pfxsX7s++sm66jWNcWRRLW98Iv32lgubuYZKjdfzLbfkztjwDtGqyEoMvuEAg\nLTaQ1FgdN6xMnLEnlBQVgEeSCNFpuO2KVB64NpOi9HCWZ0eSER9Ex+kW296Sbho7h7DanfhplNgc\nLp7Z1cCrB1oZsk6wIieSz27OnJZbVyrkRIVoOVk3wPWrEs6ZAjpzERq1TVCUEX5Zjj3NRqNWkhSl\nIybMjyB/H3xUihlPPplMRnSIH4er+ugftBEX7s87x9t58p16KlpMBPqp+e5diyhIDTvr9/n6KFmd\nF82Vi+OIi5ieTQjRachOCibY34eb1yRx/+ZM1uZHk5kQTHpc0OS/tNhAEiIDiAzREuTvM2NDJTzI\nl/dOdTNknWBDUezk67ZxF7/bWsnu4k7GJ9xsLIrlSzfkcKrBQK1+kOXZEfh9pAHiLbapwTLq4MEt\nuVPuk5PJZOSlhHL9ykTyU0PPOtCv81NjHLJ7G4YhWuIj/HG5PRyu6uXpnQ2UNBrweLzrrlLKcbo8\n7C/r5q9v1lDWZKKi2YROq5oWcM+s46PbaydT1h++vmXGB3Hk9G8X6K/mB3cXTTbKIoK19JjGqNUP\nEhzgM61Qpa7dwt7SblblRLJyhsyBTCYjMyGY7KRg6totqFVyvn93EVkJFzaOHBfux+HKPpp7htlY\nNHvvdzZnbjs6nxRqj2mM371SiSRJfPsziyYbGL4+SsKDfFmSEU5Fi4mKFhNyuWxaZS9cZBWfUiEn\nPzWU4AAfShqMHK/rJybUW/bqkSQqWkyn03pGokO1/ODeIgJ8P+h1yOUycpJC2FAYw5q86GmFBVEh\nvrxf2o1xyE59hwVJgq9tyZ3cuXKZt1WgUSvITjy/VoG+b4TfvFSBvn+UnKRgbl+fSkmjgapWMyty\nIvH1UbLrZAdVrWZuXJXEonNcJM7QapRYRh3U6gfpHLDy+oE2bOMurl+ZyFduyrmkg5QRwdpZx3Bk\nMhnZiSEsyYwgKlQ7ZV+HB/lyRWEMwf4+WEYdtPQMU6sfZF9Zz+kWps2bNrw1j6uXxs868BsZomVd\nQfSUtOfZyOUyFqWFkRkfdGEb/CkQotPQ0T9KXbuFQ5W96PtG8fVRsrEoli/ekD1jCflMNGrFWcfv\nQgI0ZCUGEx7ke1Fjthq1kvb+URq7hihK96YRHU43f3i1kubuYQrTwvjW7QWszosmQKsmJEBDcb2B\nHtMYq/OmjhGdrB9gb0k3SzLCuW5l4ozfN9d7IRMi/dlf3kN73ygKhYxHt9dwonYAq92JwWKnvNnE\neyVddPaP8vrBNorrDSCTsXlpPAMWGyUNRmLDppfsH6nuY395D8uyIti8LH7avog8HYi+emPutM+m\nxQZysLLXextGfvTkfI7gLQjrM9t44NrMs47jh+o0XLk4lquWxl1UEciZgraqVjM+SsWMwWAmkiRx\nsm6AP71Wxfaj7ZQ1GWjqGqLXNIZKqZh1WGBkbIJfv1TOyNgEX74xZzIz9mE+agVF6WGUNRkpazLh\nq1aQGju1pz0vZeaJUQEkResobTRyorYf27iLVw+0sLekG8uog6L0MB7ckkeQ/8wbo1ErZ+7e+aro\nNlhp6BxifMLNpo9UeUWGaNlX1kOvaYyrlsbNaXYBl9vDrpMd/ONtb7nuzWuS+Px12cRF+KNRKyht\nMtLaO0x+Sih/e6sWjVrJg1tyz+sGxbgwP/aV9dBnthEU4MM3by/gisKYBX3jsVwmIylax4aiWDYU\nxnjvyVDI0foouXtTOndemTanE8TXZ+bfUphdQqQ/9R0WshKDuXNjKvdvziA/JXTBFoyolXKK6w0o\nFHJykoL5yxvVNHQMsTw7gq/fkjcl9R0b5kd7/yi1+kHCAr3FQ06Xh22H23hlXwtKhYx/uS3/otO7\nWo0Kq91FjX6QqlYzbrfEpiVxPHRrHhsXxxHgq8I84i0Mcbo8XLXU+97ijHCyE4M5UTdASYOB9Dhv\nJap5eJyn321g5/EOlAo537ht5hR0TJgfVy6eOXj4+ihRKxWUN5s41eCthIwI8sUy6uC53Y3ER/pz\n67qUc54vcrnsgiqmPyouwo8D5T209Y5w5eLYc5afdxmsPLqthj0l3bjcEiE6H3pN3hleGjqHOFTZ\ni8FiJzX2g2EBt8fDocpeHnurFsuogy1rk7lqafys3+Hro2RRehglDQZKGo3TqjFnC1AXNJNEe/8I\nf3i1ipGxCeQyGStyIrh+ZeJFFTE0dw/xf8+XoZDL+MXXVhEaOPVAeG5PI/vLevjW7QUUpofhdHlo\n7x9h1OYkPS5wysnS0T/KU7vq6RywotOq+PJNOVNulJQkib/tqONk3QCB/mqGrRPcsymdq5fNvoNn\ncya3fcu6lE/F2IognOFye/j+X4/hdnvITgympNFIfkoo37w9f8aLnnl4nB8/cRKlXMbXtuSydV8L\n3cYxwgI1fOmG7Dm35s/Fanfytx21xIX7c83yhGnFCZIk0Wu24a9REviRxnJt+yB/2FqJWiVnfWEs\n+8q6mXB6SI3Vcf/Vmee8t3E2Ho/EtiN6dh7vwCNJk0UKe0518blrM1lfGHvuhcyjt47o2XZEz50b\n07h2xfQiplHbBM3dw1S1mjhc1YckQVF6GHdvSic8yBe3x4NpeJxuwxhvH2unY2AUjVrBlrXJhAX6\n8sahVvrMNtQqOdevTOSm1UlzarD2mcfYXdzFreuSp/w28z7VkWm65un2AAAgAElEQVTYTnG9gWVZ\nEfNyr4QkSby4t5mQAJ8Z0wAd/aP85OlTxIb54eeroq13BJfb490IvAUEuckhuNwe7w21ksTa/Gju\nvDJtxsDhcLr5+XOldBmshOp8+PlXV4npXQThI7bub+Hdk95prDLiAvnOXYVnTV+/X9rNC+81Tf5/\nQ2EMn9mYtqB6icX1Azy+vRYJ77jWZzaksuocFYhz1d4/wpPv1NNtHAPA10fBb7+x5mObAeMMq93J\nDx49hkal4Ks352IZHcc8PI5xaJzW3mH6zLbJv40M9uXeqzNmnbHH45E4VNnL6wdbGRt3Ad5MzBWL\norl5bfKsGbPzsSDn4jtf//tMCfq+EWRAfIQ/6fFB6LQq6jssNHcP4/Z4VzcsUMPnrssiN+ns41XG\nITvP7W5k8/L4856KRhD+GfSZx/ifJ4uJDfOfVjU6E48k8ftXKugftPHAtVkLYpqymRTXD9BntnH1\n0vg5zfBxPlxuD28fa+ed4x1cuyKB29enzuvy5+rV/S3sOt24+DAftYK0GB3p8WcKZwLnNAuF1e5k\n+xE9Y3YnN61JmnHqtQv1qQhQllEHveYxkqN00w6q8QkXTV1D3gq07MgpA5WCIFw405CdQH+fOWcY\nPJKEjPOrBPs0mnC6USnln9h+sI072XZEj49KQahOQ2ighhCdhqgQ33kZ65pPn4oAJQiCIHz6XFCA\nEgRBEIRPysLq5wmCIAjCaSJACYIgCAuSCFCCIAjCgiQClCAIgrAgiQAlCIIgLEgiQAmCIAgLkghQ\ngiAIwoIkApQgCIKwIIkAJQiCICxIZ50lUUx1JAiCIFxqs011JHpQgiAIwoIkApQgCIKwIIkAJQiC\nICxIC+cxl8JlxzHhZtQ2wajdiW3cRUqMbkE9OVUQhMubuJoI562jf5Q/vV6FZdQx5fWlmeE8dGv+\nJ7RWgiB82ogAJZwXq93JI29WYxl1kJscgk6rIkCrpkY/SEmjkS6DlfgI/096NQVB+BQQAUqYM48k\n8Y+36zANj3PzmiRuWZcy+V5Vq4k/vFrFjqN60YsSBGFeiCIJYc7ePtpOVauZvOQQbl6TPOW9/JRQ\nkqICKGk00m20fkJrKAjCp4kIUMKcVLeZ2X5ET6hOw1dvzkUun3pfnUwm4+a13qC142j7lPcmnG6e\n39PI7uLOj2t1BUH4FBApPuGc6toH+dtbtSgUMh66NQ9/X9WMf7coNZTEqABKGgz0mMaIDfPDNu7k\nj69V0dw9jEIuY1lWBCE6zce8BYIgXI5ED0qYVWvPML9+qZzfvFzB2LiL+zdnkhytm/XvZTIZN69J\nQgJ2HNUzbHXwixfKae4eJjpUi9sj8X5Z98e3AYIgXNZED0qYZnhsgmffbaC82QR4x5duuyKFxKiA\nc362MC2MhEh/TtUbaO0ZxjziYOPiWO7cmMa/PXqMg+W93LQ6CY1aHHqCIJyd6EEJ07ywp5HyZhNp\ncYH8+32L+c6di+YUnOBMLyoZCTCPOLh5TRL3X52Bj0rBhqJYbA4XR6v7L+0GCILwqSAClDBFS88w\nJY1GUmJ0/Oi+xWTEB533MorSw7hxdSJfuiGbW9alIJN5Cyo2Lo5DqZDzXkkXHumDifI9Holn323g\n58+X4nC6521bBEG4vIkAJUySJImt+1sAuHNj2mRgOV8ymYzbrkhlTX70lNcD/dSszI3EYLFT2WKa\n/M7n9zRyoKKXlu5hdp8UlX6CIHiJACVMKmsy0dI9TFF62AX1nOZi89J4AN471QXAG4faOFDRS3yE\nPzo/NTtPdkybQmkmdoeLGr15Sk9MEIRPFxGgFjiPJHG0uu+S3/zqcnt47UALcpmMOzakXrLviYvw\nJzcpmIbOIZ55t4F3jncQEezLd+8q5LYrUphwenjjUOusn7c7XOw41s4PHz3G716p5O1j7ZdsXQVB\n+GSJALXAlTQYeOKden7y1CleO9DKxCUaozlY0cuAxc76ohiiQ/0uyXeccfWyhMnvDPJX8/27Cgn0\nU7M2P5q4cH+OVffT0T865TMOp5t3jnsD05uH2gDw91Xx9rEOBgZt5/xO07CdP7xayYHynnnfHkEQ\nLo3LOkB1G63sLenC7fFckuU7nG5++vQpXtzbdEmWD3Cspo//+NsJmruHpr0nSRJvH+tAJoMgfzU7\nT3Tw308UU9s+OK/rYHe4eOuoHh+1gi0fmcLoUshLCSExKgB/XxXfu6uQsCBfAORyGXdtSkMCXn6/\nGel0+q6mzcx//eMkrx9sQ5Lg1nXJ/Orrq/nsNZm43B6e29M4+bcz0feN8P+eLaWq1cxzuxupbjOf\n9zqP2iZ4cmc9//d8Kebh8Qva7suBy+1hT3HnZbWNTpeHv++oZfsRPU7XpbkWXA7a+0f49UvlGCzn\nbrBdLhQPP/zwrG/abBOzv/kJsztc/Py5Uk41GDBY7CxOD7/gQf3ZvHuyk+O1A+j7RliVG4nfLDMo\nXKh9Zd08vasRq92JvneE9YUxyD+0DZUtZt4r6WJlbiTfvbMQt1uiqs3MsZp+6jssjNon0GpUBPiq\nkIDOASvHavrZfkRPSYMRtUpOZLB21v0iSRK1+kGe3NnAgMXOzauTKEgNm9dtnIlMJmNlTiSblyUQ\n/JFZJSKCfGnvG6G23UJQgA+7i7t47UAr4xNurl2RwDduzScvJRSVUk5MqBZ93yi1+kGiQrTEzTCL\nekWLiT++Vol93MXmZfF0DFipaDaxNCt8Tr+nJEkcq+nnT69X09IzzOCIg7ImI4VpoRd9PFjtTv78\nehVVrWbsDhcBWtVZn6fV2GnhkTdreO9UFyqlnNhwv2lTTl2sd0508NqBVoxDdlbkRM7rsi+V/WXd\n7DrZSWPnEBXNRlJiAgny9/mkV+tjJUkSf3mjhubuYcbGnSzJjJiXZf797ToqWkwUpoVd0PVVkiSs\ndic+KsVZ/87Pz+cnM73+iQQo27iLk3UDbDusp6TBQL/Zxti4C4VCjtZHOacd8dLeJho6h/DTKNH3\njWIcslM0j0FqeGyCR7fXIHlAksDlkViUdu6Ld61+kLImI/GR/igVs3dQd53o4OX3W9BpVWQmBNPS\nM0yAr4qUmEDA+8M+8U49llEHX705lxCdhtzkEArTwugz22juGqK23cL+sh6OVvez60QHe051Uddu\nwTg0Tv+gjeJ6A0eq+3BMuPHXqplwunG6JTweD/UdQ/zj7Tp2nuhkyOpgSWY4n9mYdtZ1nk9KhXzW\n70qMCuBgRS8VzSZ6jGMkR+v41zsKWJ0XjUr5wWdkMhlpsYEcquilodPCukUxqJXeE8HjkXi/tJsn\n3qlHIZPx0C15XL0snqAANacaDDR0WlidF33W7e01jfH4W7XsOdUFMrhjQyoZcYGUNZsoaTSQnxJK\ngFYNeJ+R9ey7jTy1s56mriGcbg+hgRrUZzkxX9nfQnG9d1qoihYTe051UVw/gMFiRy6XERLgg1wu\nY3hsgud2N/LyvhZGrBPYJ1yUNZk4VtOPWiknNtwfxTwEKtOQnce31+L2SPQP2liSGY7OTz3t70Zs\nE3g80pTf4mIcr+nnty+X09Y7gssjEaLzmfwdz2V8wsUjb9Ygk8lYlRtFjX6QI1V9SBKkxQbOewBf\nqEobjbxX4i086jXZWJkXhZ9megOq22jF10c5p+OltNHIm4f1dBmsKBRyMi+gcOrFvc38dVsNDqeb\nrMSgKQ3wD/vEA5QkSVS0mHjjYBvPvNtIaZOR/kEbfWYbDZ1DFNcb2FvSTXv/KMtzIs8aaOrbB3lx\nbzOx4X789+eX0dQ1RHXbIObhcQrT5x7pPR6JqlYzAVrVtBPi1f0ttPQMc/dV6QwM2mjqGuaKRdGz\nzoDgcnt47WArz+1upLZ9kFP1BmLD/Ag/nb768H5483Ab2w7rCdH58MN7F7M8J5KDFb00dg6xdlE0\nPioFDR0W3jneQVF6GFefrnwDCPL3YW1BNBuLYokN90OpkNNjGkOpkLMkM5zrViTw2WsyWZkbCTIZ\nbX0j1OgH2V/Ww3sl3ewu7mTniU5O1A1gGXVQlB7G127O5epl8R9bcDqXAK0al1uix2jlzo1pPHBN\nJkEBM7eI/TQqZDKoaDFjH3cRFqjh3eJOntxZz6kGIzqtiu/dXUR2UggAiZEBjNgmqGo1YxyysyRz\naqPG7fFQ3mTkxb1NvLKvBdPwOAWpoXz7jkUUpIaRmRCMr4+SkkYjpxoMhOo0vHGojZf3tdA/aEPn\np6ZzwDoZcFp6hkmLDZx2segcGOWZdxuICtHyw3uKiArVIpfJ6DJYae4e5nhNP3tKumjpHubV/a3o\n+0ZIjArgm7cXcNPqZDweiabuIcqbTZyqH6AwLQztDBekD7OMOthf3sPu4i7iI/wng+sZT7xTT49p\njJW5kXQbxxifcE1riZuG7Pz4Hyd5t7iLEesEESG+s87NOBcjtgn+sLUS67iLXrONsiYje4q7aO4a\nIjrUj+BZfvczdp3ooLLVzI2rkrhnUzqpsToaOi1UNJvYV9ZNU9cQ5hFvulLnp56XQP5xkCSJI9V9\nFNcPkBkfdNZA6/Z4eOTNGuzjLm5ak0RD5xAu9/QG9Ym6fn7zcgWtPcOsyIk86zJdbg+PvFnN+IQb\nnZ+ayhYTWQlBhAV+cD2TJIl9ZT0cqOghIz5o2jX0/dJuth/RA977K1u6hylIDZ2xNzVbgJKdLXe/\n+2ibZBoexzw8jmnYjo9KwV2b0gmcoVVVqx/kpfebWZsfzdXL4lDIP7jY2R0unt7VwKkGAwAxYX6s\nyIlkRXYEKqWCLoOVbqOV0kYD+r5R7t6UzuZl8dO+A7yPGf+vJ05iHhnnxw8sJTlah23cxW9fKUff\nN8q6gmgeuDZzyvfPxDLq4O87amnoHCIqRMv37iokNNCbbuo1jfHfTxQTEezLT7+0nMNVfTy3u5Hr\nViTwmY1p05Y1ODLOY9traekZJjLYl9zkEPaX9yBJsDY/mpvWJNE5MEptu4VavRnj0DgRQb58/57C\nyR/8vZIuXtrbzPrCGD53bRa/fqmc+g4L//W5pWed/+4MSZJmDMx2h4sTdQN09I/idHlwuT04XR78\nfJVcvTSehMi5zRDxSZhtmz7K5fbwk6dO0WMam3xN66NkeXYE169KnHJSnfn7X71UTkv3MFkJQfj7\nqlAo5Mhk0NBhYcg6AUBGfBCbl8VTNEOj50BFD8+928iZsyc1Vscta1PISQrGODxOaYOBUw0G2vtH\niQrR8p8PLJkMUpIk8csXy2nqGuI7dy4iPyV0crlOl4em7iGqWsxUtZkZGLTh66Pk9vUpbCiMnXJR\nGbI6eOtoOwfKewgO8OEH9xQRFaKdsp5Ol4fSJgNHq/upax/kzOmu06r4/j1FxIV706KVLSb++FoV\nGfFB/PDeIv7niWL6zDZ+8bWVk2OEkiTxu62V1OoH8dMoGRt3IQMWpYVx4+okUmLOfZx+1JPv1HOk\nuo97NqWTkxRMWbOJimYj+r5R5DIZ169K5OY1STM2nqx2J//22DEUcjm/fHDVZHrUNu5i+xE9lS0m\nDEP2yb/X+anZsjaZdQVn7zlLkkRz9zC95jFWZEeeNe16Rnv/CCdqB1hbED25Ty+Uy+3hpb3N7D9d\n0FOQGspDt+TN2hs/UNHDs+82sqEwhvs2Z/AffzuBZXSCX3191WSqc9jq4Mf/OMnYuAuAdQXRfP66\nrFnPr31l3Ty/p4mNi2NZkR3Jr14sR+en4uEvLkd3OhvzzLsNHK8dACAyRMu37ygg8vTxV9Nm5vev\nVhLgq+L7dxfxxqE2KlpMhOp8+MZt+SRFTT1WwsMDZlyRs/agvvPHQw/X6Adp6x2hf9BGt3GM+vZB\nlp8OLGfo+0b4/dZKhqwOatsHqWo1kxKtI9Dfh47+UX7zcgXN3d6W5LfuKOCWdclkJgTj5+vNuUeG\naEmPC6IoPZwj1X1Ut5lZmhk+rYUHsHV/CzX6Qa5bmTB5I6hKKWdZVgS17RaqWs3U6QfJPH3hmUlF\ni4nfb62k12wjNtyPPrONkkYDBanelM1TOxvoG7TxheuziA3zJy7cj8OVfbT0DLOxKHbKtle1mvnd\n1kr6B20sz47gX+9YxJLMCBalhaLvHaFaP8jeku7Ji5UkSRSlh/P1W/KmzOqdGBlAWZORmrZBArQq\n9pX1kJsUzPWrkmb9fT5stgNNpZSTHK2jMD2MJZnhLMuKYEVOJIszwglc4Hn6ufaE5XIZiZEBVLWa\nyU4M5rYrUvjcdZkszoiYsVchl8soSAmlotlEx4CVXrONHuMY3cYxZDJYvyiWL16fxfUrE4kO9Ztx\nPZKidJMT4N53dQa3XZFCxOnxPj+NivS4INYXxuKYcFPRYkLfOzLZai2uN7DnVBeFaWHTnqulkMuI\nCPIlPyWUq5bEsSY/ihtWJpERHzRtPTRqJYvSwlAr5ZQ2eXt0eSkh6PzUeDzesbNH3qzmSFU/xiE7\nqTE6blydRE5iMKVNJk41GMhNDkGrUfLH16oYd7j51h0FBPr54KtWUtpkxO2RJsclj9f2s7u4i7zk\nEP7nC8uICfNjcNRBfYeFo9V96LRqkubQmDqjpXuYF/Y2ER/hz+evzyLQ34fMeO9+y4gLpKHTQmWL\nmYoWE6kxumnH67bDeho6h7htfSpZCcGTr6uUcu/+WxrP+sIYUmMC0WnV6PtGKGsyUtxgINhfTXTo\n1PFZjyRR0WziyV317DjaTmWLmaPVfQT6q4kLn34cSJJEQ4eFZ99t4LUDbbT2jlDSaKAwLWzatUuS\nJCpbzXQZrPio5PjOMpQxNu7kL29Uc6rBQFy4P/GR/tS0DdLaO8KSzPBpgdXhdPPIm9VIksQ3bstH\n66NCpZBT3mxCJpORmxwyOZbUabByx4ZUxsZdVLWaUasUpMdNT9vZHS7++mY1yGR849Z8YsP8UChk\nlJ9OuWfGB/G7rZXU6AdJjdGxJDOc6lYzJ2r7SY3R4XB6+P3WCiRJxnfvKiQpWsey7AjkchkVzSaO\nVvdTlBE2JX18QT2oF3fWScEBPoQGagjVadh22HtTZXpcIN+9qxAflYKBQRs/f74Uq93JF67LpqHT\nwrGafuQyGcuyIyhtNOByS1y/MpFb1iWfM41U2mjgkTdrSI7W8R+fXTylJ9TUNcQvXygjMkTLT764\nbEqgALCNO3l+TxMn6gZQq+TcvSmd9YtikMlkuNweeoxjHKnu4/3SbpQKOXdvSmNjUSy7Tnby2oFW\n/H1V3LgqkZf3tZB5uiV55iDadaKDVw+0ctsVKdy4OgmPJPHWET1vHW1HqZBzz1XpbCiMmXLQudwe\n9pZ0U9cxSGpMILnJISRHB8zau6trH+Q3L1dM/v/f7i0i80MnnjC/3B4Pdocbj0fC7ZFwezzotOqz\njhudL49H4pE3qylvNrGuIJp7rkrnP/9+klGbk599eTkRwdpzL2QO3i/t5oX3mvD3VXHLumT2l/VM\npn43FMWwsSh2yu0Dhyp7eWZXA1qNkoLUUI7XDnDN8njuujId8B67P3r8BCO2CX799dUA/OffT+By\nS/zvl5dP6ZXW6gd5/K1arHYnGxfHcs+m9Cnn+ZmbqeUfSaX+9OkSugxW/uP+JaTFBU7bJrvDxSv7\nmjlU2YdCLmPdohiuWR5PZLAWy6iDf3/8OAFaFf/31ZXTrgUzGT7d4zxY0YtHkgjVadD5qdFqlPhp\nlHQZrPSZvRVwi1JDiYvwZ8+pLpwuDxnxQXxmYyput0SveYx+s43GrqHJ2yGyE4NJidHxzvEOgvzV\n/Pv9S4g43fO0O1w8tauBktMZJPD25lKidUSFaAnQqvD3VaHxUfLGoTYGBm0UpoXx1ZtzUMjlPLa9\nZnJuzG/fsQit5oMe3TvH23n9YBs3rk7ktiu89y86XW5++Ohxxp1ufvPQaqpazfx9Rx1ZCUF8/54i\nhq0T/OzZEiyjDh66JY+lWVPTuNsOt/HW0Xa2rE1my+lnvHkkid+/UkFtuwW1Us6Ey8Oa/CgeuCYT\nlVLBocpentvdCIC/VsWwdYKv3pzDypyoKcuuajXxXkk3n92cMeXYn60HddYAZTSOTnnT45H4245a\niuu9A8SfuzaTX7xQhml4nAeuzWRDYSzg7d49824j5pFx/H1VfPnGHApSQ2f8jpn8bUctJ2oHuPWK\nFG5anYTd4eLt4+28d6obt9vDj2Y5oM84WTfAc7sbsTlcpMcF4nJLdBmsuNzeEtToUC0Pbskj/kNV\nXwdPd5PPbPBHU2t2h4sf/PUYCoWMh7+wnKd3NVDdZiYsUMNDt+ZN67JeqEfeqKa0yUhabCA/un/x\nvFcmCh8/x4SbX7xQRsfAKPER/nQZrFMuKPPlcGUvT+9qQAJkMliTH82WNcmTqeuPOlrdx5M765Ek\n720M/+8rK6eks84EvRtXJ2IcGudk3QD3XJU+ZUz0DNOQnT+9Xk230UpWQhC3r09F3zdCY+cQjV1D\nuD0e1hfGcvXSeIIDfNhb0sWLe71DAl+8Ifus21XVaub5PY2YhseRAYszwvFIEuXNJr5wXdb/b+88\n4+Mqz7x9TR+NNJJGGpVRr1a1LcsdV0w3hJqlLRASUjfJL2+SN9lN2V2SfZNfsskm2ZRlA4QklAAm\nmGbAYGyDcZFsq/fepRlpZjQaTW/n/TBYMKja2EbAub7NnDlnnjlzznO3/3Mftq1OO6vzZLS6eO5I\nLx2Dk7i8AQLB8F0vk0rYWJrC1RuzZtJ0EzY3Tx3smuns/17OjOWaTdkz6c3XTw7y1KFu9HFqvnfX\nWpxuP394vhmT1UVhRhwVBXp6x+z0jdmx2ufumHLNxixu2ZE/k84NBEM8vK+Vk23jpCZoKM6KJ0kX\nRWKsmr/u70AmlfCzL22OMFz7qwfZc7ibSyvTOdlqIhAU+PF9G2Zq4kPjDn76eA2hkMA9VxWxukBP\nTJSCKYeXf/ljFSqljJ99aVNEzX3K6eP+R05id/m4bVchV6zLiJif2gcm+cNzTTg9Aa7fksON2/KW\n/J+cFwN15mT99tlGmnutKBVSfP5QhKU9g8cXmDFkixU634/T4+dfHw57mdddksOh2mGmXX50WhV3\nXFY4y+LPhdXu4c+vtNHSP4lMKiEjOYbcVC25hlg2lKSgUs72uE61j/PQSy1sLkvls7tn3zR7j/Sw\n7/jAzO8uz0vgi58q+0BF4rnG/ehrHVy/Jfeccvoiy5PJae+M16rTqvjpFzbNeQ1+UGo6JmjqtXDl\n+kzS9IsvuK5qNfLM4R7uunIFawqTIrZ5/UG++8Bx3N4ggWCIvLRYvn/X2nmL6x5fgIf3tVHbORHx\nfkKsikBQwO70IZOGlxjUdk0gQcJPv7hpTqXg+wmGQtR0TPBq9eBM1GJI1PDj+zYsWm9eDH8giMsT\nQC6Xzql8g3CNrrrNhE6rwpAQjUGvwZAQHWEUzvDi0T6eP9qHPk6N3enDFwhx9cYsbt6eFxFZTjm8\nmO0eHC4/0y4/024fGUkxETXJM4RCAo8f6Jxzofntuwq4ckNWxHtub4DvPnB8puZ095UruLQyI+Iz\njT3huqMghI1tjkGLTCale3hqzs9D+Dp2eQOkz3NtmW1uesfsrCtOnlexNxfnzUBB+ML9r6fr6R6e\nYkdFGvdcVXTePf3GHgu/eaYBAJVCxu7N2Vy5PnNRPf17EQQB85SH+BjlklIAEP5jVUrZnCfX7vTx\n3QeO4wuEuH5LDtdvyf3EyFhFPjiDpmkeP9DJ9VtyKM9dekbhw2Tf8X72HulFJpVw/2fXk76IACAk\nhOX9w+MOCjLiKM7SoY9TEwgKnGgxsr96EOM7nT/mmwQXQhAE2gdtnGg2hutL6fNnUj4sBEHgmTd7\n2F89SJRKzn3XllC5ImnxHZeA1xdkwuZm3OZmfNJNMBTiqg1Zc5ZOzqTqSrJ1fPv2ijnntBGzk7rO\nCVr6rHSPTBEMCaQkaPiP+zZcVFXveTVQEPaWuoenKM1JuGCT9P7qQax2D7s3Zy+bhXc9I1MAy/LG\nEBE537g8fv7r6Xo2laZyxTzK2rMhJAg0dJuZsHm4fG3Gx9bBE95JQWalxMxSkV4sPL4AB04Ps32V\nYUmCKLc3QPfIFGmJ0fOmhS8U591AiYiIiIiInA/mM1DLY2WmiIiIiIjI+1gwghIREREREfmwECMo\nEREREZFliWigRERERESWJaKBEhERERFZlogGSkRERERkWSIaKBERERGRZYlooEREREREliWigRIR\nERERWZaIBkpEREREZFkiGigRERERkWXJgs8yFnvxiYiIiIhcaMRefCIiIiIiHylEA/UenB4/v3u2\nkWfe7Mbp8X/YwxERERH5RCM+buMdgqEQv3mmkZY+KwDRajnXbs7hsrXpEQ87XOiBhiIiIiIiZ4/4\nPKhFeOpgF6+fGmJVfiLFWTr2He/H5Q2QGKsiOzUW85Qby5QHpydAsi6Kf7qxnKwU7Yc9bBEREZGP\nPKKBWoBjTWP86eU2DIkafnD3OjRqOQ63n1dODPBGzTCBYAilXEpinJpYjZKOIRtKuZTPXF3M5vLU\nD3v4IiIiIh9pRAM1Dz2jU/z8iTqUcin/+pl1pCRoIrY7PX4CQYFYjQLJO2m9uq4JHt7XitsbZFdl\nOrdfVohcJpbzRERERM6FT6SKr3PIxg8eqqK61TTn9hGzk9/vbSIYCvHlG8tmGSeAaLWCuGjljHEC\nWFOYxL99Zj3pSdEcqh3hl0/W4fEFLtjvEBEREfkk8rE2UK9UDTBmcfHHF1t44Wgf740Wazsn+H+P\nnmbK4eP2XYWU5yae1bFTEjT88O51rCtKonN4ij/sbcIfCJ3vnyAiIiLyieVja6CmHF6ae60YEjXo\n49S8cLSPB19qxesP8vzbvfx+bxOCIPDlG8q4Yn3mOX2HSinji9eXUVGgp6V/kgdfbCEYetdIBYIh\n3qwb4W8HOkXjJSIiInKWLNhJ4qPMiRYTIUFgV2UG64uT+f3eJqpbTTT3WnB6Aujj1Hzt5pUfWIkn\nl0n5yo1l/HpPAzWdE/z11Q7uvaaY0x3jPHekF9OkGwhHXP0L2DEAAB4rSURBVJetzTgfP01ERETk\nE8HHUiQhCAL/9shJTFYXv/raVmKiFPgDQf78ajtVLSZKsnV85cZyYqIU5+073d4Av3iyjn7jNDqt\nislpLzKphG2rDBxvMRKtVvCzL21GIf/YBq0iIiIi58R8IomPZQQ1aHIwMuFkbVHSjBFSyGV84bpS\nrt2UjSExGqn0/C60jVLJ+eatq/n53+oYNTvZUJLMTdvzSNFpUCllvHZyiKNNY1y6Jj1iv8lpLwdO\nDVGcraM8N+G8j0tERETko8rH0kAdbRoDYMtKQ8T7EomE9KSYC/a9Wo2SH96zFrvTR7LuXUXg1Ruz\nOVQ7wisn+tm2yjAjSfcHgvx+byN9Y9PsPzlIQqyKrSsNbF1lQB8XdcHGKSIiIvJRYNkaqJ6RKV6p\nGiA6SsFnrymOkHkvRCAYorrVRKxGQXluwgUe5WzUSjlqZeRpjYtWsrMinQOnhzjWNMaOinAU9cSB\nTvrGpllXlER0lIKqVhMvHuvnpWP9pCZqyEmNJcegJdcQS65Bi0wqpgdFREQ+OSw7A9U9MsWLR/to\nfqcnHkB5bgIbSlKWtH9DtwWH28+V6zOX1eLZazZlcbhuhJdPDLBlpYFjTWMcaRgjKyWGz19XilIh\n47ZdBZxqH6eqxUTfmJ0xi5ETLUYAdFoVO9eks2N1GrHRyg/514iIiIhceM5ZJBEIhugZmWJFZvyS\no5uFCIZCPPhiK6faxwEozopn++o0HnmlHa1GwU+/sAmVUrbIUeB3zzZS12XmR5/bQGbyhUvnnQtP\nHOjkYM0wuyrTOdIwikoh49/vXY8+fnY6LyQImKwu+sem6RiyUd1mwusLIpdJWFeczM3b8ubcD8Bk\ndTHl9JGfHntOUZcgCDjcfrQa0RCKiIhceOYTScjuv//+eXf6z8dr7l9TmIRsjsL93iO9/OXVdtKT\nYkjXRy9pEB5fAJlUMqdBe/atHt6qHyUvLZYvfqqUG7bmkZEcgz8YpLHHgkQioSRbt+Dx7S4fj73W\nQWZSDDdszV3SmC4mGUnRHKodpmfUjiDA125ZSY4hds7PSiQStBolmckxVBTquawyA51Wxfikm/YB\nG72jdravTpu1nz8Q5N8eOcXBmmEO144wYnYiCAKJseolR5RHGkb5+RN15Bpi5+yucbEIBEO4fQEc\nLj82hxevP4hGff6UlxebkCDQO2YnLlr5ke6Gb7K6CIYE1EtwGOfC4fbTM2onaR4HS2T5MDzhwOm5\n8M5qdLTqR3O9v6CB+tWTtfcnxanJTo1cK+T1BXnwxRb8wRB2p4+tqwzzHOFdxixOvv9QNR1DNtYU\n6iPk1nVdEzxxoItkXRT/8o9rMSS+a/DyDHEcbzbS2j/JxrIUoheYoPYd76djyMbuTdnkp8ctOqaL\nTZRKjt3pp2/Mzi078ti6craBmQ+FXEpeWiy7KtPpN07TPmhjZV4iOq0q4nMnmk1UtZrISdXiCwTp\nGp7iVPs4x5rGWF2gX1RaLwgCD+1rZdrtZ2jCwc6K9PMSIZ8tz77Vw6+fbuCVqkFePzXEodoR3jg9\nTH5abIQAZakEgiH+9kYnNodv1vV8MXB6/PzxhRaeOdyDyxNgVf7ZdS5ZLky7fHz/oSoO1QwTG60k\nKznmrK4Pnz/Iz/9Wy6tVg5Rk60iMU1/A0X68ONY0xhMHOsk1xF6UNP/IhIP/+OtpjjSOsa4oec65\n43jzGG/WjVL2ARXI52Sg9rzRef+Y1cWllZGT1JGGUWo6J5BJJZinPKwrSlr0hD19qJv+sWkmbG6a\ney1UFOpRK+WMT7r49Z5GpBL49u1r0L/vgpXLpOi0Kk62jWOZ8rCxdO5a1OsnB3nu7T4SYlXcfVUR\nSsW5eXcXmrJcHRUFetYXJ5/TxC+RSNBGKznRbMQXCLK2KHlmmyAIPPJKGw6Xnx/cs5brt+ayukCP\nUi6ldWCS2s4JKlckLWjku0emeLV6EJlUwpTTR7IuiszkyAl9wDjNz5+oRamQXZDJvqrVyFMHu9Fp\nVZRk68hJ1ZJjiGVkwknHoI3tqw1nXV9s6Dbz9KFu6rvNONx+ynMTLprhHTBO84sn6+gdsyORhF+v\nL04+J6/U6w8SDApL/v2NPRb+/Go7eWmxS/o+tzfA397oRBejIj5GNWv7saYx6rrMBIIC9V1mBk0O\nirN1S46mHn2tg6becH3Z7vKxqeyDPw3A5fHzvQersNq9lOd9NA3/YgRDIf77742Mml1UtRrJMcSS\nfAEjUH8gyK/2NGBz+AgGBTqHbGxdmRpRMqjtnOCPL7TQZ5xGIoHiRTJcCzGfgVrwKt9UmsKYxUVD\nt3nmvZAgcOD0MHKZhDsvLwTgcN3Igl8+bnNT1WIiXR/Nzoo0Bscd/OTRGgaM0/zPc824vQHuvqpo\n3prR+uJkVmTGU9dlprnXMmv7odphnjrUTXyMku/csea8LsA938ikUnINsR9ocizN1pGmj+ZU2zg2\nh3fm/c4hG0PjDiqLkkiIVSOVSMg1xHLnFSv4h535TE57+cWTdVjtnnmP/Vb9KAD3XlOMXCbluSN9\nEW2a3N4ADzzfjGnSzWOvddA9MrWkMY/b3ExOexf93PCEg7+82o5aKeM7d6zh67es4ovXl/G53SVc\nvTELi93D82/3Lek738ux5rDYRB+n5mDNML97tvGiNPg90jDKTx6rwTzl4bpLsvnyDeUEQwJ/f7Pn\nrI7j8gR48Wgf3/r9Mb734AlGzM5F9xEEgWfe7KZzyMav9tQv+L+fYd/xft6qH2XP4e45t1e1mJAA\nP7h7LcVZ8dR3m/nXh6s50jCK27vw+Xy7YZS3G8fITtFSkB5HY4+FoXHHomNajGNNRqx2Lwdrhpmw\nuc/pGC6Pn/FJ17zbzVNuXjzWh+s8Pmnb5Qng9QWX9NnGHgs2h4+8tFj8gRC/2dPA2w2jH3gMbm+A\nUGi21OCZwz0MTzjZuSadnRVpDI07ePLgu9fEgHGaB19qQaGQEh+j5OUTAwwYpz/weN7PghFUjEp2\n/+G6Eax2L9veqXc09Vo4WDPM5rJUbtiWy9sNo/SNTXPZ2ox5vbq/v9lDv3GaOy4vZPembGQyKbWd\nExypH2XK6WP76jSu3zJ/zUgikZCVEsNbDaO09k9id/mQSiXotCqONo3x6GsdxEYr+e6da0hNWFo9\n7KOMRCJBKpFQ321GpZDNeC5PvtGF0eriM1cXkRgbGYkWZsQDUNdlpqHHwvqipFlyeKfHz59faUcf\np+az1xTj8gRo7rOi1SjIT4tDEAT+9HIbncNTVBToMVpdNPdauaQ8FdUCEWv7wCQ/eayGk20mdlak\nI5vnOnF7A/zyqXrsTh9fur6MFZnxEdsL0uM42T5OU6+FVfmz05vzYXf5eHR/BxlJMfzwnnUMmKZp\n6rXS1BM+TpTqwohZXzjax9OHuolSyfinG8u5dE0GaYkaWgcmae2fXFKKy+0NsP/kIH98oZmmXitK\nuZRpt59TbeOU5SYQN0eUc4aeETuvVA0QF6PENu2juc/KhpKUebML5ik3D73URkgQME952FASGeWN\n29zsOdxNaY6O6y7JYXN5Khq1gqZeC7WdExw4PcSgaRqpRII+Th3xPw+apvnDc82oFWHHI02voarV\nhMcXiMgCQNh7b+i2oNOqFo0UBUHg4ZfbcLr9CEI4hVhRmLTgPu8lGApxuHaE3+9t4rWT4QXz7/9P\nAsEQv3yqnurWcUyT7nPOfpzB4fbz/Nt9PPBCMw3dFratNixak9xzqBvTpJtvfHoVG0pSqO2c4GT7\nOIFgiOJs3TmNZ2TCwQ8erqaqxUhaomamJtjQbebJg10YEjV89aaVrMxLpL7bQmOPBUOiBo1awX8+\nWYvLE+ArN5azviSFY01Gukem2LYqbU7NwmKcU4pPBvf3j9lpG5ikNEdHYqyax1/vYMLm4XO7S9Bp\n1Xh8QVr6rCTGqclJnV3wt9o9PPJyG8nxUdxzVTFSqYSizHgSY9U0dFvIStXy1ZvKF1WbxcWoUMil\nNPdZ6RyycbzZyOunh6jtmCAmSsF37lhDun55qfYuJIbEaA7XjjBomuaytZlY7R4ef72TnFQtN23L\nm/OCLcqMxx8MUd9lprnXyqbS1Iha4JH6URq6LezenE1hRjzZqVrerBuhZ2SKnRXpHGs28krVIAUZ\ncXzz1tXIZFLquswMjzvYWJYy53f2G+38ak8DvkAItzdIlFo+YyzfiyAI/PHFVrqHp7h6YxZXrJvd\nwFcmk5KeFMOxJiN9Y3a2rTIsKe99pGGUxh4LuzdlU5ytY0NJMtMuHw09Flr6rOd8Uy3Eq9UD7D3S\nS1K8mu/dtZa8tHBNVCKRkJYYzduNY4yYnWxfbZh3cjFNuvjJo6ep7TSjlEu5fksOX7q+jKR4Nafb\nxznZNk5RVjwJsXMbuWff6mV4wsHXb16JRi2nodtC1/AUG0pT5pz4nzjQyaDJQeWKJMYsLqQSCSvf\nUys7VDNM+6CNT12SS1aKFolEQn56HJtLw7XhyWkvnUPhmuf+k0O0DViZsLkJBEM8/HIr0y4/X7mp\nnPy0OJJ1UdR2TtA+YGNzeepM2jkUEnjg+WZePNbPiRYjSXFRETXp99M2MMmBU0NsKk0hEBRoH7Sx\nuSx1Vhq7pd/KqTYTDnc4AopSyekYnOR3e5s41mRELpMQDAo09VrY/D6H69m3eqjpmEAplzI84USn\nVc051y2GxxfglaoB/veFZtoHbUiASYeX+BgVufOIpSA8hz5+oJNcg5brt+SSGKdm7Yokmnot1HeZ\n6RqeojQn4awcrUAwxH8/04jF7sHp9nOs2cio2Yk+Ts0DLzQTCgl869YKEmPDjkZxdjzHmo00dJtp\n7DEzbvNw66UFbFudRnJ8FHanj8YeC4IgUJpz9utPz8lAuVy++xNi1RxtGsPh8pORFM3Th3soyozn\n2ktyAEjRaXjj9DAWu4edFWmzbrbnjvTSM2rn1l0FEX9qVoqWrasMXFaZseR6UWFGPFetz6QgI57o\nKAVTTh8yqYRv3VbxiXv8ulwmxeHx09I/SYouivpuMz2jdv5hZwGZ85wLiURCabYOh9tPY4+FwfFp\nNpaEDYsgCPxlfzsuT4DPX1eKSilDpZARDAk09liw2j28fmqIKKWM79y+hmi1gsKMOPrGpmnus4Yd\nj6zIHPSYxckvnqzH7Qtw7zXFdA3b6ByaYseaNJTyyP98f/UgB2uGKc6K577rSub1KJPio7DaPeFo\nQiFlRWY80y4fg6ZpekftJL3Pcwd47LUOHG4/911bgkopQyqVsCo/EbvTR1OvlWAoRNk53FRdwzbe\nrBshNloZUYM9WDM8U0P75zsrZ6nVEmLVjJqdtPRbSdNHz9ndZMzi5D//VsfktI9rNmbx1ZtXUpqT\ngEIuJSc1XH841T5OdZuJwvS4WZ1HHO5wNJyki+L2XQWU5yVimnTT1GtheNzBuuLkCOM+YJzm8QOd\nZCXH8M1bK3i7cZR+o53L14bXEwqCwF/3d+D1B/nc7pIIx0ajVlCUpWNXZTqVK5KIUstxewP0jdrp\nGLJxosWEyxPg2s3Z7KoMN0yWSCSoVXJqOiYIhQRW5euBcK36WJMRQ6IGq91LVauJAeM0+emxcyo4\n9xzqZszi4jNXF5OZHMPpjolZUVRNxzi//XsjrQOTnGwbD3d1qRrgaJMRh8vPtlUGvnbLKrRRCmo7\nIx2u9oFJHt3fQXJ8FN+9s5LqVhMN3RYqVySdVQ3R4fbz74+coq7LjFop5+Yd+dx91QqONI7ROWRj\nR0UaCvnc8+CBU0O0D9q4cVveTM03JkrBprJUxiwumvusHGsaI0WnIW2JiuoXj/Vxsm2crSsNfHZ3\nCcPjDpr7rBxpGMXnD3HbrkIqV7x7DrUaJfo4NdWt49hdfravNnDLjvyZ+b4oK57qVhONPZY5xVvv\nJRAMzXIsP4CBUtHSZ6VtYBKj1YV5ysMdlxfOeDVRKjlDEw7aB22U5yVGeHNTTh8P72tFF6Pk3mtK\nZg0qSiWfN90zHzKZlJQEDavyE7l8XSZXb8yes5j7SSAlIYo3aobD0vPBSaLVCu7dXbxgVCGRSCjP\nTaR3zE5zrxWvPxh+PWrn5RMDrC1KjlBlZqdqebthlJ5RO6GQwFduKCc3LXbmWCvzEjnZZqK+y4w/\nEMLlCSCVSvB4A/ziqTqmnD4+c3UROyrSZ9KSCFD2ni4fjT1m/vxKO/ExSr59+5pFPcEVmfEcazLS\n1GPhwKkhXjzWz9HGMU63jzNuc7O2KGnmxhk0TfPC0X5W5+vZXvGualIikVCUFc/JtvBNVZaTMCsS\nGZ5wMDg+jT4+KsLxCoUEXjreH053Dk1xuG6EjsFJVAoZXcM2Hnu9k9hoJf98Z+W8isPsVC2Ha0fo\nG7Ozc016RAQ3anbyiyfD5+72XQVcvzV3VpPhzOQY0pOiOdkWjqTeP2EerhuhqdfCdZuzKcgIr1Ws\nKNDTO2anqddK64CV8tyw131GuWme8vD5T5WSmqDB4w3Q0jeJ/p3MyIBpOnx9rEhic/ncwgaJREJc\njIqynAQuXZPOZWszKEiPIz5GRWFmPLdsz484j2l6DSeajXQOT7FjdRrHW4w8d6QXQ6KGH9y9ls1l\nqYyanTT3WXmrYZR0fXRENGW1e3jstU4yU2K4aVse6foYTraNR0RR3SNT/O7ZJuQyKfdcXURhehw6\nrSocjeuj+fKNZVy6JgO1UkZ+ehz9xnD6VyGXkp4UzX89XY/XH+Ibt64iO0VLii6KqlYTXcNTs0QD\nC/HY6x10DNm4dE063/j0KoqzdGjUCiSEmwsEgsKcAo9QSODhl1sJCXDftSURka9SIWNDSTJxMSoa\neixUtZqwObyUZusWnFf7jXYe3tdGQqyKr9+yiqT4qJn2ar1jdspzE7htV8GsYCMjKQaZVEKyLoq7\nriyK+O1ymZTM5HB2o2NwkpQEDUlx7943ghAWWjy6v4NHXmmjPDfyfjtnAyWRSIiJUnCybRzzlIek\neDV3XVEUMXhtlILjzUYCQYG1Re9a3ZeO9dExZOPTO/KXpez7o45GrWB43EHHkI1AUOCaTVmUZC8e\nCUgkElbnJ1Lbaaah24I+Tk1NxwSD4w7uuLwwQh0kl0lRKWQ09li4cn0ml78v9aZUyChIj6eq1Uj7\noI1T7eMcrBnmwOlh3N4gt+zI44r1WQBkp8ZwvNlI24CNLStTiVLJGbM4+fWeBgQkfPv2ClKXsO5K\nKZfNRI2x0SoK0+OoKNATCIZo6Z8kIfbdpRGvVg/SM2rnlh35s7xLuUxKdoqWY41jdAzZ2LYqbSZa\neLN+lD+8k/6pbjUhk0pI00cz7fLz+72NHG0ykhCr4pYd+fgDoZnf3tBtmUk5L+TNRqsVuDwBmnqt\ntPRZMFndOD1+7C4/v/17A3aXnzsvL5w5d3ORpo8mNUFDdauJjiEbW1YaZsb/p5fb8PpDfOFTpTMZ\nCqlUwtoVSZinwhHo8WYjmckxmCZd7Ds+QHleAje8UwtOSYjMjOyvHqJ31M6nd+STmrg0mb9SIcOQ\nGE15XiJlc6gmpRIJUmnYaRmecHCoZoQYjYLv3llJfIwKrUbJJeWpJOuiaOyxcrpjnNKchBnv/LWT\ng7QP2rhpex45qWHhUbRaPhNFGRKj+eVT9fj8Ib5280o2lKRQmBFP5Yoktq9OY3N5aoRzK5FIKMtN\noLrVRF2Xmc6hKUbNTm7Ymsvmd9SGafpobA4vjT0WPL4gK5egGmzpt/L0oW6yUmL42s0rIzJGuQYt\nVS0mWvsn2VCSMkvg1dRr5XDdCFtXGlj3vlrdmTHnGmKpXJFE9/AUjT2Wd5byJM2ZmfIHgvx6TyN2\nl4+v3rRy5hoN1/m1XLU+c8Ea24rMeCoK9HM6wfr4KLz+cP2wqsXEWw2j2J0+HG4/j77WwUvH+xm3\nuSnJ1rG9Ii0ijXrOBgrCF2t12zhOt58btuRSkBFpbPRxak62jdM9YkMmk9LQbeFk2zhHG8eI0Si4\n79oSsY/cBSIuWjmTQ//ip8qW1G0Dwt3dy3MTONFspLZzghGzk8RYFbdfVjjr4sw1xLKhJJlNpXPX\nmXRaFTsq0ijJ1pGVrCUuRolCLuWytRns3pQ9s49MKkWjUlDTOYHLG2BFZjy/eLIOm8PH568rWdLN\nfgZDYjTXbs7h8rUZbCxNoSw3gfLcRI43j9HQbWZNoR6NWs6fXm5DKZfxmauL5rypEuPUeHyBmQmn\nKCuev77azr4TA2jUCiqLkugZmaK+28KbdSO8WT/KqMXFmkI937y1gqIsHVtWGsI3NRJCgsBXbixb\nUso5xxBLz6id3hE73SNTnO6Y4HizEa8/xN1XruCytYs/SDM9KYZpVzj/P+X0saYwifaBSV4/PczG\n0uSZifUMcpmUtSuSiItWUt9t5nizkeY+K/5giK/dtHImVRmlkjP8TmakNCeBF472IZdJuGee83iu\npCdF81b9KMMTTmQyKd+6rSJCzSuRSMhM1pKRHMOJFiP1XROsLUpGpZDx0L5WpBIJ9+1+N7JI00fP\nRFF1XWZsDh93X1007/KU96NSyMhLi+VYkxGL3UNBehyfvbY4IuVckq2jtnOCxh4Lbm+ArFTtvCIh\nrz/Ib55pwOMN8o1Pr0anjYzSZdKFl9HsOdyN0erinquKFkybxWqUbF2Zyvikm6ZeK/XdZioKwvfA\ne3n2zV7qusxcVpkx5/PpJJK5GyksldIcHavy9cikEgZNDtoGJqnpmMA67aWiQM9915Zw3SU5s87X\nBzJQEokEQ4KGYCjEDVtzZxVYJZLwjdnYY6G1f5Ku4SkGTNMEQwK3XlogRk8XkMRYNXanj3XFKRFp\ns6UQE6UgO1XLiWYTwZDANRuzKMqcey2DVqNc8MJVKWSk6DQUZMTNeKiFGbPbYGUkxVDTMUFrv5W2\nARvDE052b8rmyg3zRwpLRaOWk5qg4USLic4hG1qNkuPNRravTmN1gX7e/Yoy46npCE84pzsmaBuY\nJNeg5Tu3r2HbqjS2V6SjUkjpN07j8QW44/IV3LarIOIm02qUrMpPZGdF+pJTzkqFjK2rDFy1IZPy\n3ATS9THExSi57pKcWZ34F6IkO4GmHgtNveFouLptnFGzk7uvmq3mhHe97vK8RFr6rdgc4cX2Z5oY\nnyHmnfPXOWzDaveydaWBNWehkFsKcpkUuVxK+8Akn7+udF4nJTVBQ0yUgtMdE7T0hdWM1a3j7FyT\nFjGm90ZRLm+47rV7U/ZZjSkxVk1stBKbw8eXbihDGxVZa5LLpBRlxdPQY6a5z8rh2hF8gSBZKdpZ\ntdW9R3pp7LFw1casef9TQ6KGjkEbLf1WCtLDAhIAm8M7k8K8cWvuooZDJpVSWZSExxeOYk61myjN\nScDnD3KkYZSnDnZxumOCFF0U/3TzygvSq1QiCaurVxfouWJ9BpnJ4bTo3VcWccX6zHkFPfMZqPP2\nwMJgKERdpxmZLJwSjIlSoNUol/WaJJEwRxpGebNuhP9z62piL0L/vfpuM7/9eyMAq/MT+fotq86r\nV/7E650crB1GLpMSCIb493vXL7qguG/Mzk8erSEkCOysSOOOy1fMqvv4A0HcvuBFOUdny/ikix/9\n5RTBkEAwKJCaqOHHn9uw6KTm9Pg51T7OxpKUWbU/QRD44cPVjFnC64O+d1flnArM84HXH1xwqcIZ\nnjrYxeunhmZe/+QLG2ep/EIhgf99sYUErWrOWsr5wh8IcaRhlH3H+5ly+ohSyVlfnMTqfD2lOQkY\nrS5+/NdT6OPU/Pi+jQv+vgHjND/+yylkMgkJWjUJsSr8gRA9o3buvnIFl1ae3dO491cPsudwN3KZ\nhEAwPI1LJRJKsuO57bJCMi7gY4fOhfl68X0sn6grsrwRBIEHXmjB5vDyzX9Yfd7XIfkDQX7yaA2D\n4w4ykmL40efWL2mSaug2ExKE8x4lXCxOt4/zP883A/CPV6yYM4VzthysGeaJA53o49T8/MubP5S2\nV+8lFBL4n+ebqe2coCRbx3fuWPOhjgfCxvVw7Qj7qwewu8IydrlMSpRKxrTLz/+9vWJJ0utDtcMc\nbRzDOu3F7vQBEK2W8/MvXzIrVbcUqlqN/O1AF9mpWtYXJ7OmUL9sG0CLBkpkWSEIwgWd7IxWF3/Y\n28S1l2SzqfSDt9P5qLD3SA+1nWa+f9fac5rU3o/bG+A3zzSwZaVhzubEHwZef5CXT/SzoTiFjGX0\nxIJQKNwMuKE7LD4annCwfXUa915TfNbHCgRD2Ka9KJWyZRmxn29EAyUiIiJyEXG4/WjU8o905/qL\nxTkZKBERERERkQ8LUfstIiIiIrIsEQ2UiIiIiMiyRDRQIiIiIiLLEtFAiYiIiIgsS0QDJSIiIiKy\nLBENlIiIiIjIsuT/AwYuWcOKMBlBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ce6ecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "with plt.style.context('seaborn-notebook'):\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    for record_num in range(1, 11):\n",
    "        plt.subplot(10, 1, record_num)\n",
    "        hv_noisy_record_features = hill_valley_noisy_data.loc[record_num].drop('class').values\n",
    "        plt.plot(hv_noisy_record_features)\n",
    "        plt.yticks([])\n",
    "        plt.xticks([])\n",
    "\n",
    "    plt.tight_layout()\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.53278689  0.53278689  0.55737705  0.59016393  0.62295082  0.55737705\n",
      "  0.58333333  0.55833333  0.6         0.56666667]\n",
      "0.570177595628\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1),\n",
    "                            X=hill_valley_noisy_data.drop('class', axis=1).values,\n",
    "                            y=hill_valley_noisy_data.loc[:, 'class'].values,\n",
    "                            cv=10)\n",
    "\n",
    "print(cv_scores)\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.95901639  0.98360656  0.87704918  0.98360656  0.92622951  0.93442623\n",
      "  0.90833333  0.925       0.95833333  0.96666667]\n",
      "0.942226775956\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(make_pipeline(PCA(n_components=10), RandomForestClassifier(n_estimators=100, n_jobs=-1)),\n",
    "                            X=hill_valley_noisy_data.drop('class', axis=1).values,\n",
    "                            y=hill_valley_noisy_data.loc[:, 'class'].values,\n",
    "                            cv=10)\n",
    "\n",
    "print(cv_scores)\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automating data science with TPOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X92</th>\n",
       "      <th>X93</th>\n",
       "      <th>X94</th>\n",
       "      <th>X95</th>\n",
       "      <th>X96</th>\n",
       "      <th>X97</th>\n",
       "      <th>X98</th>\n",
       "      <th>X99</th>\n",
       "      <th>X100</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.02</td>\n",
       "      <td>36.49</td>\n",
       "      <td>38.20</td>\n",
       "      <td>38.85</td>\n",
       "      <td>39.38</td>\n",
       "      <td>39.74</td>\n",
       "      <td>37.02</td>\n",
       "      <td>39.53</td>\n",
       "      <td>38.81</td>\n",
       "      <td>38.79</td>\n",
       "      <td>...</td>\n",
       "      <td>36.62</td>\n",
       "      <td>36.92</td>\n",
       "      <td>38.80</td>\n",
       "      <td>38.52</td>\n",
       "      <td>38.07</td>\n",
       "      <td>36.73</td>\n",
       "      <td>39.46</td>\n",
       "      <td>37.50</td>\n",
       "      <td>39.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.83</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.78</td>\n",
       "      <td>...</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68177.69</td>\n",
       "      <td>66138.42</td>\n",
       "      <td>72981.88</td>\n",
       "      <td>74304.33</td>\n",
       "      <td>67549.66</td>\n",
       "      <td>69367.34</td>\n",
       "      <td>69169.41</td>\n",
       "      <td>73268.61</td>\n",
       "      <td>74465.84</td>\n",
       "      <td>72503.37</td>\n",
       "      <td>...</td>\n",
       "      <td>73438.88</td>\n",
       "      <td>71053.35</td>\n",
       "      <td>71112.62</td>\n",
       "      <td>74916.48</td>\n",
       "      <td>72571.58</td>\n",
       "      <td>66348.97</td>\n",
       "      <td>71063.72</td>\n",
       "      <td>67404.27</td>\n",
       "      <td>74920.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44889.06</td>\n",
       "      <td>39191.86</td>\n",
       "      <td>40728.46</td>\n",
       "      <td>38576.36</td>\n",
       "      <td>45876.06</td>\n",
       "      <td>47034.00</td>\n",
       "      <td>46611.43</td>\n",
       "      <td>37668.32</td>\n",
       "      <td>40980.89</td>\n",
       "      <td>38466.15</td>\n",
       "      <td>...</td>\n",
       "      <td>42625.67</td>\n",
       "      <td>40684.20</td>\n",
       "      <td>46960.73</td>\n",
       "      <td>44546.80</td>\n",
       "      <td>45410.53</td>\n",
       "      <td>47139.44</td>\n",
       "      <td>43095.68</td>\n",
       "      <td>40888.34</td>\n",
       "      <td>39615.19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.70</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.28</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.27</td>\n",
       "      <td>5.61</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.34</td>\n",
       "      <td>5.87</td>\n",
       "      <td>...</td>\n",
       "      <td>5.17</td>\n",
       "      <td>5.67</td>\n",
       "      <td>5.60</td>\n",
       "      <td>5.94</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.22</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1        X2        X3        X4        X5        X6        X7  \\\n",
       "0     39.02     36.49     38.20     38.85     39.38     39.74     37.02   \n",
       "1      1.83      1.71      1.77      1.77      1.68      1.78      1.80   \n",
       "2  68177.69  66138.42  72981.88  74304.33  67549.66  69367.34  69169.41   \n",
       "3  44889.06  39191.86  40728.46  38576.36  45876.06  47034.00  46611.43   \n",
       "4      5.70      5.40      5.28      5.38      5.27      5.61      6.00   \n",
       "\n",
       "         X8        X9       X10  ...         X92       X93       X94  \\\n",
       "0     39.53     38.81     38.79  ...       36.62     36.92     38.80   \n",
       "1      1.70      1.75      1.78  ...        1.80      1.79      1.77   \n",
       "2  73268.61  74465.84  72503.37  ...    73438.88  71053.35  71112.62   \n",
       "3  37668.32  40980.89  38466.15  ...    42625.67  40684.20  46960.73   \n",
       "4      5.38      5.34      5.87  ...        5.17      5.67      5.60   \n",
       "\n",
       "        X95       X96       X97       X98       X99      X100  class  \n",
       "0     38.52     38.07     36.73     39.46     37.50     39.10      0  \n",
       "1      1.74      1.74      1.80      1.78      1.75      1.69      1  \n",
       "2  74916.48  72571.58  66348.97  71063.72  67404.27  74920.24      1  \n",
       "3  44546.80  45410.53  47139.44  43095.68  40888.34  39615.19      0  \n",
       "4      5.94      5.73      5.22      5.30      5.73      5.91      0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from tpot import TPOTClassifier\n",
    "\n",
    "hill_valley_noisy_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/Hill_Valley_with_noise.csv.gz', sep='\\t', compression='gzip')\n",
    "hill_valley_noisy_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:   9%|▉         | 100/1100 [00:00<36:47,  2.21s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1 - Current best internal CV score: 0.9472727662812096\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  18%|█▊        | 201/1100 [07:15<50:37,  3.38s/pipeline]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 2 - Current best internal CV score: 0.9495601844270386\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  27%|██▋       | 301/1100 [08:12<04:03,  3.29pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 3 - Current best internal CV score: 0.9495601844270386\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  36%|███▋      | 401/1100 [09:28<05:49,  2.00pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 4 - Current best internal CV score: 0.9495601844270386\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  46%|████▌     | 501/1100 [12:45<21:05,  2.11s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 5 - Current best internal CV score: 0.9495601844270386\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  54%|█████▍    | 596/1100 [00:00<01:19,  6.31pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 6 - Current best internal CV score: 0.950678752659701\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  63%|██████▎   | 695/1100 [00:00<02:43,  2.47pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 7 - Current best internal CV score: 0.9529158891250255\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  73%|███████▎  | 801/1100 [16:28<01:18,  3.83pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 8 - Current best internal CV score: 0.9529158891250255\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  80%|████████  | 884/1100 [00:00<02:46,  1.30pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 9 - Current best internal CV score: 0.9529158891250255\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  91%|█████████ | 1001/1100 [19:09<00:31,  3.17pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 10 - Current best internal CV score: 0.9529158891250255\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best pipeline: LogisticRegression(input_matrix, 0.57999999999999996, 25, False)\n",
      "0.983631993007\n"
     ]
    }
   ],
   "source": [
    "X = hill_valley_noisy_data.drop('class', axis=1).values\n",
    "y = hill_valley_noisy_data.loc[:, 'class'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, test_size=0.25)\n",
    "\n",
    "my_tpot = TPOTClassifier(generations=10, verbosity=2)\n",
    "my_tpot.fit(X_train, y_train)\n",
    "print(my_tpot.score(X_test, y_test))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: traveling-salesman-portrait/traveling-salesman-portrait.py
================================================
'''
This is a script written by Randal S. Olson (randalolson.com) for the Traveling Salesman Portrait project.

More information on the project can be found on my blog:

http://www.randalolson.com/2018/04/11/traveling-salesman-portrait-in-python/

Please check my project repository for information on how this script can be used and shared:

https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects
'''

import os
import math
import matplotlib.pyplot as plt
import numpy as np
import urllib.request
from PIL import Image
from tsp_solver.greedy_numpy import solve_tsp
from scipy.spatial.distance import pdist, squareform

image_url = 'http://www.randalolson.com/wp-content/uploads/Frankenstein.jpg'
image_path = 'Frankenstein.jpg'

if not os.path.exists(image_path):
    urllib.request.urlretrieve(image_url, image_path)

original_image = Image.open(image_path)
bw_image = original_image.convert('1', dither=Image.NONE)

bw_image_array = np.array(bw_image, dtype=np.int)
black_indices = np.argwhere(bw_image_array == 0)
chosen_black_indices = black_indices[np.random.choice(black_indices.shape[0], replace=False, size=10000)]

distances = pdist(chosen_black_indices)
distance_matrix = squareform(distances)

optimized_path = solve_tsp(distance_matrix)

optimized_path_points = [chosen_black_indices[x] for x in optimized_path]

plt.figure(figsize=(8, 10), dpi=100)
plt.plot([x[1] for x in optimized_path_points], [x[0] for x in optimized_path_points], color='black', lw=1)
plt.xlim(0, 600)
plt.ylim(0, 800)
plt.gca().invert_yaxis()
plt.xticks([])
plt.yticks([])
plt.savefig('traveling-salesman-portrait.png', bbox_inches='tight')


================================================
FILE: us-weather-history/KCLT.csv
================================================
date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation
2014-7-1,81,70,91,67,89,56,104,1919,2012,0.00,0.10,5.91
2014-7-2,85,74,95,68,89,56,101,2008,1931,0.00,0.10,1.53
2014-7-3,82,71,93,68,89,56,99,2010,1931,0.14,0.11,2.50
2014-7-4,75,64,86,68,89,55,99,1933,1955,0.00,0.10,2.63
2014-7-5,72,60,84,68,89,57,100,1967,1954,0.00,0.10,1.65
2014-7-6,74,61,87,68,89,57,99,1964,1948,0.00,0.10,1.95
2014-7-7,79,67,91,68,89,55,100,1972,1954,0.00,0.11,2.37
2014-7-8,83,72,94,68,89,58,101,1892,2010,0.00,0.11,1.87
2014-7-9,80,71,89,68,89,57,101,1891,1986,0.15,0.12,3.71
2014-7-10,78,71,85,68,89,53,101,1961,1926,0.0,0.11,2.45
2014-7-11,78,68,87,68,89,55,100,1961,1986,0.00,0.12,3.10
2014-7-12,79,67,91,68,89,59,102,1953,1930,0.00,0.11,1.95
2014-7-13,81,69,92,68,89,56,100,1918,1986,0.00,0.12,2.09
2014-7-14,85,74,96,68,89,59,102,2007,1954,0.00,0.12,2.15
2014-7-15,81,67,94,68,89,58,100,2001,1893,0.87,0.12,4.61
2014-7-16,76,66,85,68,89,58,100,2001,1887,0.00,0.12,1.47
2014-7-17,74,63,84,68,89,62,100,1886,1986,0.00,0.12,4.14
2014-7-18,77,70,83,68,89,62,102,1896,1887,0.0,0.12,1.82
2014-7-19,70,66,74,68,89,60,103,2009,1986,0.17,0.12,2.30
2014-7-20,74,65,83,68,89,60,101,1910,1986,0.0,0.12,1.25
2014-7-21,74,70,77,68,89,57,103,1890,1986,1.30,0.12,3.83
2014-7-22,79,72,85,68,89,58,103,2001,1926,0.0,0.12,2.56
2014-7-23,80,69,90,68,89,56,101,2007,1987,0.00,0.13,6.88
2014-7-24,80,71,89,68,89,59,100,1947,1952,0.06,0.12,2.83
2014-7-25,77,68,86,68,89,62,101,1947,2010,0.00,0.13,2.34
2014-7-26,78,66,89,68,89,60,100,1904,2005,0.00,0.13,2.84
2014-7-27,80,70,90,68,89,57,103,1920,1940,0.18,0.13,2.46
2014-7-28,79,68,90,68,89,54,102,1962,1952,0.06,0.13,2.98
2014-7-29,73,62,83,68,89,59,103,1920,1952,0.00,0.14,2.04
2014-7-30,74,63,85,68,89,58,101,1914,2011,0.00,0.14,4.24
2014-7-31,72,65,78,68,89,56,100,1914,1999,1.45,0.14,1.45
2014-8-1,68,65,70,68,89,58,98,1966,1957,2.39,0.14,2.39
2014-8-2,69,65,72,68,89,59,98,1895,1942,0.00,0.14,2.00
2014-8-3,77,67,86,68,89,60,98,1998,2011,0.00,0.15,4.51
2014-8-4,78,69,87,68,89,58,98,1912,1935,0.00,0.15,2.59
2014-8-5,77,65,88,68,89,55,100,1912,1954,0.00,0.15,3.01
2014-8-6,78,68,88,68,88,57,102,1912,1956,0.17,0.14,3.19
2014-8-7,78,67,88,68,88,50,100,2004,1963,0.00,0.13,1.53
2014-8-8,77,73,81,68,88,55,102,2004,2007,0.0,0.13,1.37
2014-8-9,76,68,84,68,88,56,104,2002,2007,0.00,0.13,1.26
2014-8-10,70,67,73,68,88,56,104,1879,2007,0.01,0.13,2.26
2014-8-11,78,71,85,68,88,58,98,1879,1956,0.58,0.13,3.04
2014-8-12,81,72,89,68,88,57,100,1890,1925,0.31,0.13,2.25
2014-8-13,75,65,84,68,88,57,102,1979,1956,0.00,0.14,1.27
2014-8-14,71,59,83,68,88,57,98,1967,1925,0.00,0.14,2.00
2014-8-15,73,60,86,68,88,55,99,1964,2007,0.00,0.14,3.17
2014-8-16,77,68,86,68,88,57,102,1924,1954,0.00,0.14,2.51
2014-8-17,78,67,89,67,88,55,97,1979,1911,0.00,0.14,2.92
2014-8-18,81,71,90,67,88,59,101,1976,1988,0.0,0.13,1.76
2014-8-19,80,70,90,67,87,59,100,1981,1988,0.0,0.14,1.96
2014-8-20,79,67,91,67,87,56,103,1918,1925,0.00,0.14,1.84
2014-8-21,80,67,93,67,87,56,103,1998,1983,0.00,0.13,2.22
2014-8-22,83,70,95,67,87,57,101,1886,1983,0.00,0.15,2.81
2014-8-23,82,73,91,67,87,53,103,1930,1983,0.08,0.13,2.74
2014-8-24,74,66,81,67,87,55,97,1888,1968,0.14,0.14,2.35
2014-8-25,72,60,83,66,87,56,98,1952,1943,0.00,0.14,3.16
2014-8-26,72,60,84,66,87,55,100,1942,1954,0.00,0.14,5.36
2014-8-27,72,57,87,66,86,55,101,1952,1954,0.00,0.13,4.39
2014-8-28,77,61,92,66,86,55,98,1905,1924,0.00,0.13,4.64
2014-8-29,79,69,88,66,86,54,100,1986,1948,0.00,0.13,1.42
2014-8-30,79,68,90,66,86,53,101,1965,1932,0.00,0.12,2.54
2014-8-31,83,74,91,65,86,53,99,1946,1957,0.06,0.12,3.40
2014-9-1,82,71,93,65,85,51,99,1999,1957,0.00,0.11,2.93
2014-9-2,82,69,94,65,85,53,99,1967,1932,0.0,0.12,1.20
2014-9-3,81,68,93,65,85,52,98,1967,1925,0.63,0.11,2.67
2014-9-4,77,69,85,64,85,54,100,1952,1925,0.82,0.11,1.15
2014-9-5,79,69,89,64,84,54,101,1967,1954,0.01,0.11,2.46
2014-9-6,80,70,90,64,84,53,104,1924,1954,0.00,0.11,3.47
2014-9-7,79,70,88,64,84,53,100,1924,1925,0.09,0.11,2.38
2014-9-8,70,67,73,63,84,54,99,1988,1925,0.0,0.11,1.96
2014-9-9,72,66,77,63,83,46,101,1998,1939,0.00,0.11,1.92
2014-9-10,72,65,79,63,83,47,98,1998,1939,0.00,0.10,3.64
2014-9-11,77,64,89,62,83,49,96,1956,1925,0.09,0.10,1.80
2014-9-12,81,72,90,62,83,45,95,1917,1925,0.08,0.09,1.76
2014-9-13,76,66,86,62,82,49,94,1917,1962,1.56,0.11,4.12
2014-9-14,66,62,69,61,82,47,97,1985,1897,0.0,0.10,3.23
2014-9-15,76,65,86,61,81,47,96,2000,1956,0.37,0.11,4.06
2014-9-16,76,65,86,61,81,47,96,2000,1956,0.37,0.11,4.06
2014-9-17,69,63,74,60,81,46,96,1984,1954,0.00,0.11,3.14
2014-9-18,71,62,80,60,81,48,99,2001,1896,0.00,0.11,4.84
2014-9-19,73,67,79,59,80,46,97,1984,1954,0.02,0.10,2.72
2014-9-20,71,59,82,59,80,45,98,1929,1925,0.00,0.11,2.82
2014-9-21,71,57,85,59,80,43,97,1918,1895,0.00,0.11,2.74
2014-9-22,69,61,76,58,79,43,98,1918,1895,0.00,0.10,2.92
2014-9-23,59,53,65,58,79,39,97,1999,1931,0.02,0.11,1.69
2014-9-24,59,53,64,57,79,43,95,1999,1895,0.19,0.10,3.89
2014-9-25,66,61,70,57,78,40,94,1887,2010,0.01,0.11,1.31
2014-9-26,67,62,71,57,78,42,94,1950,1961,0.0,0.11,2.54
2014-9-27,69,59,78,56,78,43,93,1940,1900,0.00,0.11,1.94
2014-9-28,68,60,76,56,77,41,93,1947,1954,0.00,0.12,3.38
2014-9-29,67,64,70,55,77,40,93,1942,1941,0.09,0.11,3.59
2014-9-30,71,59,83,55,77,38,91,1888,1926,0.00,0.12,3.65
2014-10-1,70,56,84,54,76,39,92,1899,1954,0.00,0.12,3.80
2014-10-2,72,59,85,54,76,36,92,1899,1954,0.00,0.11,1.60
2014-10-3,70,61,79,54,76,36,91,1974,1954,0.03,0.12,2.15
2014-10-4,55,43,67,53,75,33,95,1974,1954,0.00,0.12,4.21
2014-10-5,52,38,65,53,75,38,97,1974,1954,0.00,0.12,1.16
2014-10-6,62,47,76,52,75,38,98,1935,1954,0.00,0.12,1.98
2014-10-7,67,53,80,52,74,34,91,1935,1951,0.00,0.11,2.70
2014-10-8,72,58,86,52,74,34,93,1889,1941,0.00,0.12,3.82
2014-10-9,66,52,80,51,74,31,92,2000,2007,0.00,0.12,3.80
2014-10-10,72,60,84,51,74,30,89,2000,1939,0.00,0.11,4.18
2014-10-11,78,68,87,51,73,29,90,2000,1939,0.08,0.12,2.58
2014-10-12,63,58,68,50,73,31,87,1906,1919,0.02,0.12,2.82
2014-10-13,66,59,72,50,73,35,91,1964,1954,0.04,0.12,1.93
2014-10-14,73,64,82,49,72,32,87,2006,1940,0.45,0.11,2.27
2014-10-15,66,58,73,49,72,30,88,2006,1985,0.24,0.11,1.83
2014-10-16,61,50,71,49,72,35,87,1939,1925,0.0,0.11,4.76
2014-10-17,59,44,74,48,71,35,85,2001,1962,0.00,0.11,1.90
2014-10-18,65,53,76,48,71,31,87,2001,1938,0.00,0.12,2.34
2014-10-19,57,45,69,48,71,30,87,2009,1938,0.00,0.10,1.20
2014-10-20,59,46,71,47,71,30,86,1972,1926,0.0,0.11,2.33
2014-10-21,61,47,75,47,70,30,88,1952,1943,0.00,0.11,1.31
2014-10-22,56,44,68,47,70,29,85,1952,1939,0.00,0.10,4.52
2014-10-23,55,40,70,46,70,31,88,1998,1941,0.00,0.11,1.72
2014-10-24,54,39,69,46,70,28,88,2006,1939,0.00,0.10,0.85
2014-10-25,57,39,74,46,69,27,84,2006,1931,0.00,0.09,2.65
2014-10-26,66,48,83,45,69,27,86,2013,1940,0.00,0.10,1.85
2014-10-27,62,43,80,45,69,24,86,1962,1939,0.00,0.09,2.03
2014-10-28,68,52,84,45,68,26,87,2001,1940,0.00,0.10,1.23
2014-10-29,69,58,79,44,68,27,82,1976,1984,0.00,0.10,1.65
2014-10-30,57,45,68,44,68,26,84,1952,1950,0.00,0.10,2.51
2014-10-31,50,39,61,44,68,29,85,2008,1950,0.49,0.10,1.68
2014-11-1,47,40,53,43,67,29,84,1954,1950,0.28,0.10,1.14
2014-11-2,44,30,57,43,67,27,85,1954,1961,0.00,0.10,1.82
2014-11-3,45,24,65,43,67,24,84,1954,1961,0.00,0.10,1.69
2014-11-4,53,39,67,43,66,22,83,1966,1961,0.00,0.10,1.97
2014-11-5,60,49,70,42,66,24,81,2006,2003,0.00,0.10,0.97
2014-11-6,60,48,71,42,66,26,82,1976,2003,0.04,0.10,1.34
2014-11-7,50,39,61,42,65,26,78,1998,1888,0.00,0.10,1.71
2014-11-8,45,31,58,41,65,25,78,1967,1934,0.00,0.11,2.79
2014-11-9,51,38,63,41,65,24,82,1976,2005,0.00,0.10,2.76
2014-11-10,51,32,69,41,64,28,83,1973,2006,0.00,0.10,1.43
2014-11-11,57,39,74,41,64,24,78,1973,2006,0.00,0.09,1.82
2014-11-12,59,41,77,40,64,24,81,1894,1989,0.00,0.10,1.54
2014-11-13,53,46,60,40,63,21,81,2013,1931,0.0,0.10,1.93
2014-11-14,37,28,46,40,63,21,80,2013,1879,0.01,0.11,1.23
2014-11-15,35,25,45,39,63,20,82,1969,1993,0.00,0.10,1.32
2014-11-16,41,29,53,39,62,22,82,1969,1958,0.00,0.10,2.26
2014-11-17,47,36,57,39,62,20,84,1883,1958,0.63,0.11,1.90
2014-11-18,30,20,39,38,62,20,78,2014,1942,0.00,0.10,1.93
2014-11-19,29,14,44,38,61,14,78,2014,1942,0.00,0.11,2.04
2014-11-20,47,34,59,38,61,18,79,1951,1942,0.00,0.11,1.36
2014-11-21,44,30,58,38,61,19,78,1914,1942,0.00,0.11,3.26
2014-11-22,39,22,55,37,60,13,76,2008,2011,0.00,0.11,1.68
2014-11-23,54,42,65,37,60,18,78,1880,1900,1.72,0.11,1.72
2014-11-24,67,59,74,37,59,16,75,1970,1958,0.00,0.11,1.68
2014-11-25,58,49,66,37,59,13,76,1970,1921,0.22,0.10,2.42
2014-11-26,46,40,52,36,59,11,75,1950,2001,0.89,0.11,2.36
2014-11-27,43,34,52,36,58,18,77,1903,2001,0.00,0.11,1.54
2014-11-28,36,25,47,36,58,19,77,2013,1990,0.00,0.11,2.93
2014-11-29,44,31,56,35,58,20,76,1955,2001,0.00,0.12,1.37
2014-11-30,54,39,68,35,57,15,76,1929,1991,0.00,0.11,2.09
2014-12-1,57,41,73,35,57,15,76,1964,1946,0.00,0.11,1.21
2014-12-2,49,42,55,35,57,20,76,1924,1991,0.00,0.11,1.85
2014-12-3,53,42,64,34,56,20,75,1960,1998,0.0,0.12,2.96
2014-12-4,56,49,63,34,56,18,76,2000,1956,0.0,0.11,1.93
2014-12-5,47,42,51,34,56,18,75,1969,1998,0.04,0.11,2.06
2014-12-6,47,42,52,34,55,18,78,1895,1998,0.12,0.10,2.37
2014-12-7,48,39,56,33,55,12,76,2010,1998,0.00,0.10,1.56
2014-12-8,40,36,43,33,55,10,76,1882,1998,0.14,0.11,1.15
2014-12-9,46,37,55,33,54,14,78,2006,1943,0.0,0.10,1.78
2014-12-10,43,32,53,33,54,13,80,1917,2007,0.00,0.10,1.91
2014-12-11,37,23,51,33,54,13,79,1957,2007,0.00,0.11,1.69
2014-12-12,41,22,60,32,54,7,79,1962,2007,0.00,0.11,1.57
2014-12-13,46,25,66,32,53,2,77,1962,2007,0.00,0.10,1.78
2014-12-14,46,29,63,32,53,12,73,1962,1948,0.00,0.10,2.38
2014-12-15,43,26,60,32,53,10,73,2010,1948,0.00,0.10,2.46
2014-12-16,48,37,58,32,53,12,75,1958,1971,0.09,0.10,1.58
2014-12-17,46,31,60,32,52,14,73,1901,1956,0.00,0.10,1.30
2014-12-18,43,30,56,31,52,11,75,1953,1984,0.00,0.10,1.74
2014-12-19,43,30,56,31,52,11,76,1963,1984,0.00,0.11,0.85
2014-12-20,44,40,48,31,52,12,79,1884,1931,0.03,0.10,2.38
2014-12-21,47,42,51,31,52,10,73,1901,2013,0.0,0.10,2.19
2014-12-22,42,38,46,31,51,11,72,1960,1998,0.26,0.11,2.27
2014-12-23,42,37,47,31,51,8,73,1989,1990,0.29,0.11,2.12
2014-12-24,53,45,60,31,51,6,71,1983,1982,0.91,0.11,1.47
2014-12-25,45,32,57,30,51,4,77,1983,1955,0.00,0.10,2.32
2014-12-26,43,27,59,30,51,6,76,1983,1889,0.00,0.11,1.72
2014-12-27,48,32,64,30,51,15,72,1970,1971,0.00,0.10,1.25
2014-12-28,55,49,60,30,51,10,77,1925,1971,0.04,0.11,1.67
2014-12-29,51,44,58,30,51,2,74,1894,1984,0.59,0.10,1.82
2014-12-30,39,32,45,30,50,-5,76,1880,1984,0.06,0.10,1.21
2014-12-31,37,27,46,30,50,0,70,1890,1996,0.00,0.10,2.15
2015-1-1,40,26,53,30,50,10,74,1928,1985,0.00,0.11,1.54
2015-1-2,47,42,52,30,50,6,78,1928,1952,0.12,0.10,2.10
2015-1-3,50,47,52,30,50,8,74,1887,2004,0.25,0.11,1.93
2015-1-4,56,47,65,30,50,8,75,1887,1950,0.26,0.11,1.32
2015-1-5,45,36,54,30,50,10,72,1920,1950,0.00,0.12,1.77
2015-1-6,41,24,57,30,50,5,73,1884,1950,0.00,0.11,3.45
2015-1-7,29,16,41,29,50,6,77,2014,1890,0.00,0.12,1.76
2015-1-8,18,8,28,29,50,8,74,1970,1946,0.00,0.11,1.47
2015-1-9,34,22,45,29,50,4,72,1970,2008,0.00,0.11,1.91
2015-1-10,30,19,41,29,50,4,76,1970,1949,0.00,0.12,2.35
2015-1-11,31,15,46,29,50,0,78,1886,1949,0.08,0.12,1.78
2015-1-12,41,37,45,29,50,-1,75,1886,1890,0.98,0.11,2.22
2015-1-13,41,35,47,29,50,3,75,1895,1960,0.04,0.12,1.68
2015-1-14,35,30,40,29,50,7,77,1912,1907,0.0,0.11,1.45
2015-1-15,34,26,42,29,50,12,77,1994,1907,0.0,0.11,0.96
2015-1-16,43,30,56,29,50,5,74,1893,1974,0.00,0.11,1.69
2015-1-17,41,24,57,29,51,5,75,1977,1943,0.00,0.11,2.38
2015-1-18,49,37,61,29,51,10,71,2003,1937,0.02,0.11,2.51
2015-1-19,49,33,64,30,51,6,69,1994,1929,0.00,0.11,2.28
2015-1-20,53,39,67,30,51,-2,71,1985,1951,0.00,0.11,1.98
2015-1-21,50,36,64,30,51,-5,73,1985,1935,0.00,0.11,2.28
2015-1-22,41,25,57,30,51,5,74,1970,1937,0.00,0.11,1.79
2015-1-23,41,36,45,30,51,9,75,2003,1927,0.88,0.11,1.48
2015-1-24,43,33,53,30,51,7,74,1963,1967,0.16,0.10,1.79
2015-1-25,43,28,57,30,51,11,76,1963,1950,0.00,0.11,2.27
2015-1-26,45,37,53,30,51,8,75,1940,1950,0.01,0.10,1.43
2015-1-27,39,29,48,30,52,6,75,1940,1890,0.00,0.10,1.51
2015-1-28,36,23,49,30,52,5,79,1986,1944,0.00,0.11,1.27
2015-1-29,38,25,50,30,52,8,79,1897,2002,0.0,0.10,1.21
2015-1-30,38,26,49,30,52,4,78,1966,2002,0.00,0.11,1.13
2015-1-31,35,19,51,30,52,4,77,1966,1975,0.00,0.11,1.75
2015-2-1,47,36,57,31,52,10,76,1900,1950,0.01,0.11,1.78
2015-2-2,42,26,57,31,52,10,80,1917,1989,0.39,0.11,2.30
2015-2-3,34,22,46,31,53,8,81,1917,1989,0.00,0.12,1.25
2015-2-4,41,28,53,31,53,11,76,1886,1927,0.00,0.11,1.93
2015-2-5,40,27,52,31,53,6,78,1886,1890,0.00,0.11,2.00
2015-2-6,32,18,45,31,53,13,79,1977,1927,0.00,0.10,2.91
2015-2-7,47,28,65,31,53,10,74,1895,1999,0.00,0.11,1.65
2015-2-8,59,47,70,31,54,1,74,1895,2009,0.00,0.10,1.12
2015-2-9,56,51,61,32,54,6,77,1895,1994,0.87,0.11,1.81
2015-2-10,45,37,53,32,54,8,76,1934,1938,0.13,0.10,2.22
2015-2-11,42,28,55,32,54,12,75,1885,1965,0.00,0.11,1.19
2015-2-12,42,26,57,32,54,11,73,1899,1999,0.00,0.11,1.65
2015-2-13,31,21,40,32,55,2,74,1899,1951,0.00,0.12,1.88
2015-2-14,40,23,56,33,55,-5,77,1899,1949,0.00,0.11,1.53
2015-2-15,25,14,36,33,55,12,80,1943,1989,0.00,0.13,1.65
2015-2-16,29,24,33,33,55,11,76,1905,1976,0.51,0.12,2.28
2015-2-17,29,23,34,33,55,7,79,1958,1927,0.15,0.13,1.47
2015-2-18,32,21,42,33,56,5,78,1958,1891,0.0,0.12,1.89
2015-2-19,19,12,26,34,56,8,76,1958,1939,0.00,0.13,2.08
2015-2-20,19,7,31,34,56,7,77,2015,2014,0.00,0.13,1.82
2015-2-21,34,25,42,34,56,6,75,1896,2011,0.0,0.13,1.44
2015-2-22,47,36,57,34,57,12,74,1963,1990,0.18,0.13,1.53
2015-2-23,44,35,52,34,57,19,76,1963,1962,0.02,0.13,1.72
2015-2-24,32,28,35,35,57,16,80,1967,1982,0.07,0.13,1.90
2015-2-25,36,29,42,35,57,7,82,1967,1930,0.48,0.13,1.78
2015-2-26,37,31,42,35,58,7,81,1967,1977,0.21,0.12,1.82
2015-2-27,38,26,50,35,58,7,82,1963,2011,0.00,0.13,1.70
2015-2-28,37,29,44,36,58,14,78,2002,2011,0.00,0.13,2.09
2015-3-1,36,31,40,36,59,15,82,1980,1918,0.16,0.13,2.18
2015-3-2,55,39,70,36,59,14,80,1980,1887,0.0,0.14,1.59
2015-3-3,46,40,51,36,59,4,84,1980,1976,0.06,0.12,3.04
2015-3-4,56,42,69,36,59,15,82,1943,1976,0.0,0.13,1.70
2015-3-5,52,36,68,37,60,12,84,1960,1955,0.54,0.14,1.65
2015-3-6,35,25,44,37,60,10,78,1960,1956,0.00,0.13,2.46
2015-3-7,42,23,61,37,60,14,85,1899,1974,0.00,0.14,1.38
2015-3-8,52,32,71,37,61,16,84,1920,1974,0.00,0.14,1.87
2015-3-9,58,51,65,38,61,16,83,1996,1974,0.01,0.14,1.78
2015-3-10,66,53,78,38,61,17,82,1932,1974,0.0,0.13,1.35
2015-3-11,71,58,83,38,62,22,83,1969,1990,0.15,0.14,1.04
2015-3-12,61,51,70,38,62,19,86,1969,1990,0.00,0.14,2.17
2015-3-13,50,42,58,39,62,18,86,1960,1990,0.19,0.13,2.31
2015-3-14,53,41,64,39,62,16,83,1926,2012,0.35,0.14,1.81
2015-3-15,63,49,76,39,63,16,86,1993,1967,0.00,0.14,4.24
2015-3-16,62,43,81,39,63,18,83,1970,1945,0.00,0.13,1.84
2015-3-17,63,41,84,40,63,23,90,1900,1945,0.00,0.13,2.00
2015-3-18,54,47,61,40,64,21,85,1967,2011,0.00,0.13,1.52
2015-3-19,49,42,55,40,64,21,86,1902,1945,0.21,0.13,2.65
2015-3-20,50,42,58,40,64,23,85,1923,1948,0.0,0.12,2.11
2015-3-21,56,40,71,41,65,18,87,1965,1935,0.00,0.12,1.79
2015-3-22,62,54,70,41,65,24,89,1965,1907,0.01,0.12,0.88
2015-3-23,58,51,65,41,65,20,91,1888,1907,0.0,0.11,2.00
2015-3-24,59,45,72,41,66,24,88,1940,1929,0.00,0.12,1.62
2015-3-25,62,54,69,42,66,22,90,1940,1929,0.01,0.12,1.89
2015-3-26,65,56,74,42,66,22,86,1972,1929,0.00,0.13,2.86
2015-3-27,54,43,65,42,67,19,85,1955,1950,0.24,0.12,1.22
2015-3-28,43,34,51,42,67,25,90,1982,1907,0.00,0.13,1.95
2015-3-29,42,29,55,42,67,26,89,2013,1907,0.00,0.12,2.20
2015-3-30,55,39,70,43,67,24,86,1964,1998,0.26,0.13,2.36
2015-3-31,56,35,77,43,68,21,86,1964,1986,0.00,0.12,2.21
2015-4-1,61,49,73,43,68,24,86,1923,1986,0.00,0.11,1.26
2015-4-2,66,58,73,43,68,28,86,1881,1978,0.04,0.12,2.99
2015-4-3,70,58,82,44,69,28,90,1972,1946,0.0,0.10,1.18
2015-4-4,58,45,71,44,69,29,87,1936,1999,0.0,0.11,1.58
2015-4-5,52,36,67,44,69,26,88,1891,1942,0.00,0.10,1.86
2015-4-6,64,51,77,44,70,28,90,1944,1929,0.0,0.10,3.84
2015-4-7,65,57,72,45,70,25,89,2007,1929,0.51,0.11,1.74
2015-4-8,73,63,82,45,70,21,88,2007,1929,0.26,0.10,1.33
2015-4-9,77,65,88,45,70,25,89,1972,1893,0.02,0.10,2.07
2015-4-10,75,67,82,45,71,28,90,1985,2001,0.0,0.11,1.98
2015-4-11,67,56,78,46,71,28,88,1961,1930,0.00,0.10,3.20
2015-4-12,62,50,74,46,71,29,92,1973,1930,0.00,0.09,1.77
2015-4-13,66,59,73,46,71,28,90,1940,1930,0.11,0.10,1.75
2015-4-14,73,65,80,46,72,27,89,1950,1941,0.16,0.09,1.65
2015-4-15,58,50,65,47,72,28,89,1907,1936,1.26,0.09,1.73
2015-4-16,51,47,54,47,72,29,89,1962,1896,0.40,0.10,1.60
2015-4-17,62,54,69,47,72,30,94,1949,1896,0.03,0.10,1.51
2015-4-18,70,60,79,48,73,32,93,2001,1896,0.04,0.10,1.70
2015-4-19,67,64,69,48,73,30,89,1983,1955,2.65,0.10,2.65
2015-4-20,67,60,74,48,73,28,91,1983,1896,0.47,0.11,1.47
2015-4-21,58,45,71,48,73,30,90,1983,1963,0.00,0.10,2.39
2015-4-22,60,43,77,49,74,33,91,1978,1957,0.00,0.11,2.28
2015-4-23,62,49,74,49,74,35,91,1986,1960,0.00,0.11,1.58
2015-4-24,54,38,70,49,74,36,96,1893,1925,0.00,0.10,2.33
2015-4-25,56,51,61,49,74,31,93,2005,1925,0.19,0.10,1.78
2015-4-26,53,48,58,50,75,34,92,1910,1915,0.0,0.10,2.34
2015-4-27,55,39,71,50,75,37,90,1972,1981,0.00,0.10,1.35
2015-4-28,54,38,69,50,75,36,90,1967,1943,0.00,0.10,2.23
2015-4-29,55,45,64,51,75,33,91,1973,1888,0.0,0.09,2.19
2015-4-30,63,49,76,51,75,37,91,2008,1962,0.0,0.09,2.34
2015-5-1,57,47,67,51,76,37,92,1963,1962,0.00,0.09,1.84
2015-5-2,61,45,76,52,76,32,91,1963,1959,0.00,0.10,1.36
2015-5-3,63,46,80,52,76,39,94,1981,1902,0.00,0.10,3.67
2015-5-4,69,55,83,52,76,34,92,1971,1959,0.00,0.10,1.62
2015-5-5,69,56,82,52,76,36,91,1940,1955,0.00,0.10,2.40
2015-5-6,69,53,84,53,77,39,93,1891,1950,0.00,0.09,1.60
2015-5-7,70,58,81,53,77,38,95,1891,1940,0.00,0.10,2.58
2015-5-8,71,57,85,53,77,35,96,1989,1940,0.00,0.10,2.41
2015-5-9,72,60,84,54,77,39,94,1984,1889,0.0,0.10,1.35
2015-5-10,74,65,82,54,78,38,94,1906,1889,0.00,0.10,0.86
2015-5-11,76,61,90,54,78,39,95,1977,1916,0.00,0.10,1.12
2015-5-12,77,66,88,55,78,39,94,1969,1889,0.0,0.10,1.92
2015-5-13,73,60,85,55,78,38,93,1960,1881,0.00,0.10,2.46
2015-5-14,67,58,75,55,78,41,95,1917,1956,0.00,0.10,1.34
2015-5-15,73,63,82,56,79,40,94,1888,1962,0.00,0.10,2.11
2015-5-16,75,62,87,56,79,42,94,1978,1941,0.00,0.10,1.79
2015-5-17,75,63,86,56,79,43,93,1973,1896,0.00,0.09,1.09
2015-5-18,78,67,89,56,79,34,94,1973,1960,0.00,0.10,4.85
2015-5-19,77,65,89,57,80,40,95,2009,1962,0.0,0.10,2.40
2015-5-20,76,61,91,57,80,40,95,2002,1964,0.00,0.10,3.81
2015-5-21,75,64,86,57,80,41,97,1894,1941,0.00,0.10,2.41
2015-5-22,64,52,76,58,80,42,98,2002,1941,0.00,0.10,3.50
2015-5-23,65,47,82,58,81,37,98,2002,1941,0.00,0.10,3.16
2015-5-24,68,54,82,58,81,43,95,1931,1939,0.00,0.11,1.03
2015-5-25,73,59,86,59,81,41,95,2013,1953,0.00,0.11,2.24
2015-5-26,78,67,88,59,81,45,96,1979,1995,0.32,0.11,3.03
2015-5-27,75,65,84,59,82,41,96,1961,1916,0.0,0.11,1.55
2015-5-28,78,67,88,60,82,42,97,1961,1941,0.00,0.12,2.70
2015-5-29,78,67,88,60,82,48,98,1894,1941,0.00,0.12,1.18
2015-5-30,76,65,87,60,82,47,95,1894,1918,0.00,0.11,2.52
2015-5-31,78,67,89,61,83,42,97,1984,1953,0.00,0.12,1.77
2015-6-1,77,66,88,61,83,45,98,1889,1918,0.0,0.13,2.87
2015-6-2,73,64,81,61,83,46,99,1966,1951,0.76,0.13,2.28
2015-6-3,67,64,69,61,83,51,100,1966,1895,0.02,0.13,3.78
2015-6-4,69,61,77,62,84,50,98,1988,1911,0.0,0.13,2.12
2015-6-5,72,59,84,62,84,50,102,1954,1943,0.00,0.13,2.45
2015-6-6,76,62,90,62,84,48,98,1976,1943,0.00,0.13,2.76
2015-6-7,78,70,86,63,84,45,97,2000,1899,0.17,0.14,2.22
2015-6-8,79,69,88,63,85,47,99,1977,1899,0.01,0.13,1.98
2015-6-9,79,67,90,63,85,50,99,1912,2008,0.0,0.13,3.26
2015-6-10,79,69,89,63,85,46,99,1977,1947,0.0,0.12,2.18
2015-6-11,79,68,90,64,85,47,100,1913,1914,0.00,0.14,2.85
2015-6-12,81,71,90,64,85,45,98,1972,1956,0.0,0.13,2.73
2015-6-13,83,72,94,64,86,49,97,1903,1958,0.00,0.13,3.77
2015-6-14,83,68,97,64,86,53,99,1903,1958,0.00,0.13,3.16
2015-6-15,84,68,99,65,86,51,99,1933,2015,0.00,0.14,1.16
2015-6-16,84,69,99,65,86,52,99,1961,2015,0.00,0.13,3.32
2015-6-17,86,73,99,65,87,53,100,1917,1943,0.00,0.12,2.76
2015-6-18,87,74,100,65,87,52,100,1961,2015,0.0,0.13,1.86
2015-6-19,84,72,96,66,87,54,102,1965,1944,0.0,0.13,1.50
2015-6-20,83,71,95,66,87,54,102,1879,1887,0.01,0.12,1.67
2015-6-21,84,71,97,66,87,54,99,2003,1964,0.00,0.13,1.98
2015-6-22,83,65,100,66,87,53,100,2003,2015,0.00,0.12,1.31
2015-6-23,87,73,100,66,88,53,100,1947,2015,0.00,0.11,1.82
2015-6-24,88,75,100,66,88,55,102,1972,1930,0.00,0.12,1.25
2015-6-25,86,74,98,67,88,53,102,1889,1914,0.00,0.11,2.76
2015-6-26,85,70,100,67,88,55,102,1979,1952,1.21,0.11,1.98
2015-6-27,82,71,92,67,88,56,103,2012,1954,0.55,0.11,1.86
2015-6-28,76,66,85,67,88,53,101,1968,1959,0.00,0.11,1.83
2015-6-29,73,59,87,67,88,54,104,1974,2012,0.00,0.11,2.39
2015-6-30,83,71,94,67,89,54,104,1962,2012,0.00,0.11,2.75


================================================
FILE: us-weather-history/KCQT.csv
================================================
date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation
2014-7-1,70,63,77,63,82,52,107,1886,1985,0.00,0.00,0.09
2014-7-2,70,62,77,63,82,51,102,1887,1985,0.00,0.00,0.00
2014-7-3,72,63,81,63,82,52,100,1902,1985,0.00,0.00,0.00
2014-7-4,72,62,82,63,82,53,103,1900,1907,0.00,0.00,0.07
2014-7-5,75,64,86,63,82,50,100,1886,1907,0.00,0.00,0.02
2014-7-6,73,64,81,63,82,51,94,1894,1992,0.00,0.00,0.03
2014-7-7,77,64,89,63,83,51,96,1894,1954,0.00,0.00,0.02
2014-7-8,77,68,86,63,83,51,95,1894,1886,0.00,0.00,0.13
2014-7-9,75,67,82,63,83,52,100,1901,1985,0.00,0.00,0.05
2014-7-10,76,66,85,63,83,55,103,1903,1959,0.00,0.00,0.00
2014-7-11,73,65,80,64,83,52,97,1878,1959,0.00,0.00,0.04
2014-7-12,74,65,82,64,83,49,98,1888,1953,0.00,0.00,0.05
2014-7-13,75,67,82,64,83,51,98,1888,1990,0.00,0.00,0.02
2014-7-14,74,68,80,64,83,52,98,1908,1984,0.00,0.00,0.24
2014-7-15,76,69,82,64,83,50,98,1893,1886,0.00,0.00,0.00
2014-7-16,74,66,81,64,83,52,96,1894,1930,0.00,0.01,0.02
2014-7-17,73,65,80,64,83,52,98,1894,1998,0.00,0.00,0.01
2014-7-18,72,64,79,64,84,53,97,1884,1936,0.00,0.00,0.36
2014-7-19,71,66,75,64,84,52,97,1884,1960,0.0,0.00,0.02
2014-7-20,73,66,80,64,84,52,100,1892,1960,0.00,0.00,0.00
2014-7-21,74,66,81,64,84,53,96,1903,1960,0.00,0.00,0.00
2014-7-22,75,65,85,64,84,52,101,1880,2006,0.00,0.00,0.10
2014-7-23,77,65,88,64,84,53,97,1903,1890,0.00,0.00,0.08
2014-7-24,79,65,92,64,84,52,103,1882,1891,0.00,0.00,0.00
2014-7-25,79,69,88,64,84,52,109,1903,1891,0.00,0.00,0.04
2014-7-26,77,68,86,64,84,52,100,1886,1891,0.00,0.00,0.04
2014-7-27,76,67,84,64,84,52,102,1903,1972,0.0,0.00,0.00
2014-7-28,78,68,88,64,84,52,101,1883,1995,0.0,0.00,0.01
2014-7-29,78,68,88,64,84,53,100,1909,1995,0.00,0.00,0.00
2014-7-30,79,68,90,64,84,54,100,1894,1980,0.00,0.00,0.02
2014-7-31,77,67,87,64,84,52,100,1885,1972,0.00,0.00,0.01
2014-8-1,76,66,85,64,84,54,99,1888,1959,0.00,0.00,0.01
2014-8-2,78,65,90,64,84,51,98,1888,1997,0.02,0.00,0.02
2014-8-3,79,72,85,64,84,52,100,1913,1918,0.02,0.00,0.02
2014-8-4,76,65,87,64,84,52,94,1912,1971,0.00,0.00,0.05
2014-8-5,73,63,82,64,84,52,100,1887,1997,0.00,0.00,0.09
2014-8-6,70,63,77,64,84,50,105,1883,1983,0.00,0.01,0.02
2014-8-7,73,64,81,64,84,54,99,1894,1884,0.00,0.00,0.11
2014-8-8,74,65,82,64,84,54,100,1916,1881,0.00,0.00,0.02
2014-8-9,75,67,82,64,84,53,100,1889,1998,0.00,0.00,0.19
2014-8-10,76,66,85,64,84,54,99,1897,1882,0.00,0.00,0.04
2014-8-11,75,65,85,64,84,53,98,1894,1879,0.00,0.00,0.02
2014-8-12,75,66,84,64,84,52,104,1894,1994,0.00,0.00,0.32
2014-8-13,73,65,81,64,84,54,102,1892,1994,0.00,0.00,0.13
2014-8-14,74,65,83,64,84,54,97,1887,1885,0.00,0.01,0.01
2014-8-15,76,63,89,64,84,52,98,1894,1994,0.00,0.00,0.40
2014-8-16,78,66,90,64,84,52,98,1883,1890,0.00,0.00,0.19
2014-8-17,78,66,89,64,85,52,104,1887,1885,0.00,0.00,2.06
2014-8-18,76,65,86,64,85,52,102,1887,1885,0.00,0.00,0.44
2014-8-19,76,69,82,64,85,54,106,1916,1885,0.00,0.00,0.14
2014-8-20,76,67,84,64,85,51,97,1900,1986,0.00,0.00,0.03
2014-8-21,73,65,80,64,85,53,96,1899,1982,0.00,0.00,0.00
2014-8-22,74,66,81,64,85,52,102,1901,1972,0.00,0.01,0.04
2014-8-23,76,66,85,64,85,54,100,1905,1968,0.00,0.00,0.00
2014-8-24,75,67,83,64,85,53,96,1900,1931,0.00,0.00,0.05
2014-8-25,72,64,80,64,85,52,102,1909,1985,0.00,0.00,0.01
2014-8-26,75,65,84,64,85,53,98,1885,1981,0.00,0.00,0.10
2014-8-27,79,66,91,64,85,53,103,1902,1981,0.00,0.00,0.96
2014-8-28,80,68,91,64,85,52,99,1902,1995,0.00,0.01,0.10
2014-8-29,78,65,90,64,85,51,102,1901,1884,0.00,0.00,0.08
2014-8-30,77,65,89,64,85,49,101,1901,1998,0.00,0.00,0.21
2014-8-31,78,69,87,64,85,52,103,1895,1967,0.00,0.00,0.61
2014-9-1,78,70,85,64,85,49,110,1901,1955,0.00,0.00,0.41
2014-9-2,76,68,84,64,85,51,108,1881,1955,0.00,0.00,0.02
2014-9-3,75,67,83,64,85,51,103,1892,1988,0.00,0.00,0.05
2014-9-4,73,66,80,64,85,52,110,1892,1988,0.00,0.01,0.30
2014-9-5,75,66,83,64,85,50,105,1898,1984,0.00,0.00,0.32
2014-9-6,76,65,86,64,84,48,102,1898,1955,0.00,0.00,0.17
2014-9-7,80,68,92,64,84,50,103,1884,1949,0.00,0.00,0.00
2014-9-8,80,71,88,64,84,47,103,1901,1984,0.01,0.00,0.19
2014-9-9,80,68,91,64,84,48,102,1884,1956,0.00,0.01,0.01
2014-9-10,76,64,87,64,84,52,103,1893,1983,0.00,0.00,1.74
2014-9-11,78,67,89,64,84,48,101,1901,1983,0.00,0.01,0.33
2014-9-12,80,68,91,64,84,48,102,1884,1971,0.00,0.01,0.13
2014-9-13,82,69,94,64,84,48,106,1884,1971,0.00,0.00,0.07
2014-9-14,85,71,98,63,84,48,100,1898,2012,0.00,0.01,0.04
2014-9-15,84,73,94,63,83,49,103,1895,2012,0.00,0.01,0.03
2014-9-16,90,76,103,63,83,48,103,1884,1909,0.00,0.01,0.07
2014-9-17,84,72,96,63,83,46,108,1884,1913,0.00,0.01,0.42
2014-9-18,74,67,81,63,83,48,103,1884,1939,0.00,0.01,1.39
2014-9-19,75,67,82,63,83,49,104,1907,1939,0.00,0.01,0.27
2014-9-20,72,66,78,63,83,49,107,1900,1939,0.00,0.01,0.29
2014-9-21,72,65,79,63,82,47,108,1880,1885,0.00,0.01,0.15
2014-9-22,72,63,80,63,82,48,104,1895,1883,0.00,0.02,0.40
2014-9-23,74,63,84,62,82,44,104,1880,1978,0.00,0.01,0.28
2014-9-24,75,62,88,62,82,45,106,1880,1978,0.00,0.01,1.95
2014-9-25,76,67,85,62,82,48,107,1879,1978,0.00,0.01,3.96
2014-9-26,74,65,83,62,82,48,109,1893,1963,0.00,0.01,0.24
2014-9-27,70,63,77,62,81,46,113,1901,2010,0.00,0.02,0.68
2014-9-28,72,64,79,62,81,49,106,1901,1963,0.00,0.01,0.74
2014-9-29,70,61,78,62,81,44,102,1880,1999,0.00,0.01,0.49
2014-9-30,75,64,85,62,81,48,105,1886,1906,0.00,0.02,1.58
2014-10-1,73,64,82,61,81,49,100,1901,1991,0.00,0.01,0.74
2014-10-2,81,64,98,61,81,44,101,1884,1995,0.00,0.02,0.53
2014-10-3,83,67,98,61,80,44,108,1884,1987,0.00,0.02,0.37
2014-10-4,81,65,97,61,80,44,108,1884,1987,0.00,0.01,0.24
2014-10-5,80,65,95,61,80,46,101,1884,1971,0.00,0.02,1.15
2014-10-6,78,64,92,61,80,44,102,1881,1971,0.00,0.02,0.30
2014-10-7,79,64,93,60,80,43,98,1884,1971,0.00,0.02,0.17
2014-10-8,75,63,87,60,80,43,100,1881,1994,0.00,0.02,0.42
2014-10-9,74,67,80,60,79,46,102,1879,1988,0.00,0.01,0.44
2014-10-10,72,64,79,60,79,46,107,1890,1991,0.00,0.02,0.71
2014-10-11,73,64,81,60,79,42,100,1879,1971,0.00,0.02,0.34
2014-10-12,74,67,81,60,79,43,99,1886,1950,0.00,0.02,0.43
2014-10-13,74,65,82,59,79,42,104,1886,1950,0.00,0.02,1.09
2014-10-14,71,66,76,59,79,44,103,1881,1961,0.00,0.02,1.75
2014-10-15,71,63,78,59,79,43,102,1881,1961,0.00,0.01,0.27
2014-10-16,69,61,77,59,79,44,104,1881,1958,0.00,0.02,0.35
2014-10-17,69,62,76,59,78,44,102,1881,1958,0.00,0.03,1.72
2014-10-18,70,61,78,58,78,44,97,1881,1940,0.00,0.02,0.48
2014-10-19,70,62,77,58,78,43,99,1881,1940,0.00,0.02,0.79
2014-10-20,71,63,79,58,78,43,96,1886,1964,0.00,0.02,3.16
2014-10-21,71,64,78,58,78,45,100,1882,1929,0.00,0.02,0.53
2014-10-22,72,60,84,58,78,40,100,1892,1965,0.00,0.02,0.77
2014-10-23,73,60,85,57,78,41,98,1892,1965,0.00,0.03,1.87
2014-10-24,74,59,88,57,78,43,99,1886,1909,0.00,0.02,0.55
2014-10-25,71,61,80,57,77,43,96,1886,1983,0.00,0.03,1.04
2014-10-26,70,61,78,57,77,45,95,1897,1879,0.00,0.02,1.74
2014-10-27,70,62,77,57,77,41,99,1886,1890,0.00,0.03,1.37
2014-10-28,69,59,78,56,77,45,94,1903,1931,0.00,0.03,0.56
2014-10-29,72,59,85,56,77,43,94,1878,1931,0.00,0.03,0.84
2014-10-30,71,60,81,56,77,41,99,1971,1939,0.00,0.03,1.02
2014-10-31,66,60,71,56,76,42,96,1886,1918,0.25,0.03,1.64
2014-11-1,61,55,67,56,76,38,100,1886,1966,0.18,0.03,0.44
2014-11-2,61,51,70,55,76,36,99,1886,1997,0.00,0.03,0.35
2014-11-3,65,52,77,55,76,41,97,1886,2010,0.00,0.02,0.35
2014-11-4,64,51,77,55,76,44,96,1923,2010,0.00,0.03,0.42
2014-11-5,69,53,85,55,75,41,94,1881,1898,0.00,0.03,0.56
2014-11-6,76,61,90,54,75,40,95,1879,2006,0.00,0.02,1.07
2014-11-7,75,60,89,54,75,41,97,1890,2006,0.00,0.03,3.85
2014-11-8,74,59,89,54,75,40,93,1888,1956,0.00,0.03,1.82
2014-11-9,68,58,77,54,75,43,94,1886,1996,0.00,0.03,3.41
2014-11-10,66,62,70,53,74,42,93,1880,1990,0.00,0.02,1.95
2014-11-11,66,62,69,53,74,39,94,1881,1900,0.00,0.03,1.55
2014-11-12,64,58,70,53,74,38,93,1978,1974,0.00,0.04,1.88
2014-11-13,63,55,70,53,74,35,96,1880,1933,0.0,0.03,0.99
2014-11-14,67,60,73,52,73,36,93,1880,1933,0.00,0.03,1.33
2014-11-15,65,58,72,52,73,40,93,1968,2008,0.00,0.03,1.64
2014-11-16,65,55,74,52,73,35,93,1880,1924,0.00,0.04,2.01
2014-11-17,65,53,76,52,73,36,91,1886,1945,0.00,0.04,1.58
2014-11-18,65,52,77,51,72,34,94,1881,1895,0.00,0.03,1.50
2014-11-19,64,53,74,51,72,34,92,1886,2006,0.00,0.04,3.61
2014-11-20,62,53,70,51,72,37,91,1906,2006,0.0,0.03,1.38
2014-11-21,64,55,73,51,71,39,93,1905,1924,0.00,0.04,2.97
2014-11-22,63,52,73,50,71,36,89,1882,1950,0.00,0.04,3.51
2014-11-23,69,57,80,50,71,39,91,2003,1933,0.00,0.05,0.91
2014-11-24,67,53,80,50,71,36,94,1895,1933,0.00,0.04,2.03
2014-11-25,66,52,80,50,70,36,92,1879,1977,0.00,0.04,0.78
2014-11-26,69,53,85,50,70,40,90,1880,1903,0.00,0.04,1.28
2014-11-27,74,58,89,49,70,37,90,1897,1903,0.00,0.05,0.99
2014-11-28,67,53,81,49,70,36,90,1906,1924,0.00,0.04,3.51
2014-11-29,63,51,74,49,69,37,87,1878,1995,0.00,0.04,2.43
2014-11-30,63,60,66,49,69,37,88,1906,1964,0.30,0.05,1.96
2014-12-1,63,57,69,49,69,38,85,1880,1972,0.00,0.05,1.40
2014-12-2,62,58,66,49,69,36,88,1884,1893,1.21,0.05,1.21
2014-12-3,63,59,67,48,69,36,89,1897,1958,0.31,0.05,1.59
2014-12-4,64,57,71,48,69,37,91,1909,1979,0.00,0.05,1.59
2014-12-5,63,57,69,48,68,38,89,1891,1979,0.00,0.06,1.81
2014-12-6,63,55,71,48,68,36,88,1897,1938,0.00,0.06,1.01
2014-12-7,65,54,76,48,68,32,88,1978,1940,0.00,0.06,2.78
2014-12-8,67,55,78,48,68,30,92,1978,1938,0.00,0.07,2.29
2014-12-9,65,54,76,48,68,32,85,1951,1973,0.00,0.06,1.70
2014-12-10,63,53,73,48,68,36,87,1951,1950,0.00,0.06,1.58
2014-12-11,64,60,67,48,68,34,86,1898,1895,0.01,0.07,1.65
2014-12-12,59,52,65,47,68,34,86,1878,1895,1.60,0.06,4.30
2014-12-13,57,48,66,47,67,31,83,1901,1952,0.00,0.07,1.67
2014-12-14,57,47,66,47,67,30,85,1878,1886,0.00,0.07,0.96
2014-12-15,56,47,65,47,67,36,85,1901,1998,0.0,0.07,2.26
2014-12-16,57,54,60,47,67,37,89,1897,1980,0.41,0.07,1.96
2014-12-17,58,51,65,47,67,34,85,1895,1998,0.15,0.08,1.97
2014-12-18,55,48,62,47,67,35,86,1885,1917,0.00,0.08,2.18
2014-12-19,58,49,66,47,67,32,86,1897,1917,0.00,0.08,2.80
2014-12-20,57,50,64,47,67,35,83,1887,1985,0.00,0.08,4.19
2014-12-21,59,50,68,47,67,30,85,1897,1917,0.00,0.08,1.57
2014-12-22,63,50,75,47,67,33,80,1990,1960,0.00,0.09,2.63
2014-12-23,67,51,83,47,67,33,86,1905,1989,0.00,0.08,1.84
2014-12-24,64,53,75,47,67,33,87,1879,1985,0.00,0.09,2.64
2014-12-25,58,50,65,47,67,30,85,1879,1980,0.00,0.09,3.01
2014-12-26,53,42,63,47,68,35,85,1987,1980,0.00,0.09,1.20
2014-12-27,51,39,63,47,68,36,85,1916,2013,0.00,0.10,1.81
2014-12-28,53,43,63,47,68,36,87,1954,1919,0.00,0.10,5.55
2014-12-29,52,41,63,47,68,35,89,1882,1897,0.00,0.11,3.84
2014-12-30,52,47,56,47,68,37,89,1945,1980,0.19,0.10,1.85
2014-12-31,49,41,57,47,68,36,86,1882,1897,0.00,0.10,4.86
2015-1-1,48,37,59,47,68,35,84,1884,1898,0.00,0.11,3.12
2015-1-2,50,39,61,47,68,32,85,1886,1994,0.00,0.10,1.57
2015-1-3,52,40,63,47,68,33,87,1949,1902,0.00,0.11,2.27
2015-1-4,56,42,69,47,68,28,87,1949,1902,0.00,0.10,3.24
2015-1-5,66,50,81,48,68,31,85,1949,1902,0.00,0.10,2.11
2015-1-6,71,57,84,48,68,30,85,1913,2006,0.00,0.10,2.30
2015-1-7,69,53,85,48,68,28,87,1913,1996,0.00,0.10,3.52
2015-1-8,63,51,75,48,68,32,90,1888,1923,0.00,0.10,1.19
2015-1-9,64,56,72,48,68,31,88,1888,1923,0.00,0.10,2.58
2015-1-10,58,52,63,48,68,31,87,1888,1986,0.48,0.11,3.30
2015-1-11,60,56,63,48,68,32,89,1949,1996,0.50,0.10,1.57
2015-1-12,60,53,66,48,68,33,88,1949,2009,0.00,0.11,1.40
2015-1-13,61,54,68,48,68,34,86,1950,1986,0.00,0.10,1.45
2015-1-14,60,47,73,48,68,34,88,2013,1975,0.00,0.10,1.56
2015-1-15,64,51,76,48,68,32,85,1888,2009,0.00,0.11,2.13
2015-1-16,64,50,77,48,68,31,86,1888,1976,0.00,0.10,2.74
2015-1-17,64,50,78,48,68,34,90,1888,1971,0.00,0.11,2.74
2015-1-18,64,53,75,48,68,37,95,1892,1971,0.00,0.10,2.30
2015-1-19,61,50,72,48,68,32,92,1889,1971,0.00,0.09,2.26
2015-1-20,58,53,63,48,68,30,85,1883,2009,0.00,0.10,3.30
2015-1-21,63,54,71,48,68,32,81,1937,1912,0.00,0.09,2.98
2015-1-22,61,47,75,48,68,34,84,1937,1994,0.00,0.09,3.69
2015-1-23,63,50,75,48,68,33,84,1937,1968,0.00,0.10,1.05
2015-1-24,66,49,82,48,68,36,85,1937,1992,0.00,0.09,2.04
2015-1-25,66,48,83,48,68,36,86,1898,1986,0.00,0.09,3.43
2015-1-26,69,57,80,48,68,31,86,1898,1987,0.08,0.10,5.71
2015-1-27,66,57,74,48,68,32,87,1898,1986,0.00,0.09,2.28
2015-1-28,66,56,75,48,68,35,90,1957,1971,0.00,0.10,2.81
2015-1-29,68,60,75,48,68,30,88,1880,1971,0.0,0.10,1.96
2015-1-30,65,59,70,48,68,35,85,1880,1992,0.03,0.11,2.19
2015-1-31,64,56,71,48,68,32,91,1880,2003,0.00,0.11,3.03
2015-2-1,63,49,76,48,68,34,88,1880,1995,0.00,0.12,2.26
2015-2-2,65,51,78,48,68,34,94,1901,1995,0.00,0.11,2.84
2015-2-3,62,51,72,49,68,30,94,1883,1995,0.00,0.12,1.84
2015-2-4,64,56,72,49,68,32,91,1883,1995,0.00,0.12,2.55
2015-2-5,68,53,82,49,68,33,89,1899,2007,0.00,0.13,2.30
2015-2-6,65,54,75,49,68,28,86,1883,1987,0.00,0.13,2.39
2015-2-7,62,54,69,49,68,32,89,1883,1996,0.02,0.13,1.60
2015-2-8,68,59,77,49,68,36,85,1899,1996,0.00,0.13,2.43
2015-2-9,66,58,73,49,68,33,85,1891,2006,0.00,0.14,1.92
2015-2-10,66,53,78,49,68,37,88,1890,1988,0.00,0.13,2.55
2015-2-11,70,55,85,49,68,35,88,1894,1971,0.00,0.13,2.12
2015-2-12,71,56,85,49,68,35,91,1949,1971,0.00,0.14,2.45
2015-2-13,73,57,88,49,69,35,90,1903,1985,0.00,0.14,2.71
2015-2-14,71,56,86,49,69,32,90,1903,1985,0.00,0.14,2.50
2015-2-15,67,54,80,49,69,33,88,1903,1985,0.00,0.14,2.78
2015-2-16,63,56,70,49,69,35,88,1903,1896,0.00,0.15,3.03
2015-2-17,64,57,71,50,69,33,89,1889,1890,0.00,0.16,2.18
2015-2-18,65,55,75,50,69,32,89,1882,1981,0.00,0.15,4.26
2015-2-19,65,57,72,50,69,34,91,1880,1995,0.00,0.15,2.32
2015-2-20,63,56,70,50,69,34,95,1882,1995,0.00,0.15,3.60
2015-2-21,63,57,69,50,69,35,88,1897,2002,0.00,0.15,2.08
2015-2-22,59,53,64,50,69,35,90,1897,2002,0.70,0.15,2.19
2015-2-23,58,50,65,50,69,35,90,1894,1954,0.0,0.14,2.11
2015-2-24,60,46,74,50,69,35,94,1890,1986,0.00,0.13,4.80
2015-2-25,62,51,72,50,69,40,92,1962,1921,0.00,0.14,2.33
2015-2-26,61,51,70,50,69,37,87,1894,2002,0.00,0.13,1.08
2015-2-27,61,55,66,50,69,38,86,1894,1992,0.00,0.13,2.17
2015-2-28,59,52,65,50,69,39,88,1962,1901,0.11,0.12,2.85
2015-3-1,58,50,65,50,69,36,88,1894,1967,0.66,0.11,3.42
2015-3-2,56,49,63,50,69,35,87,1896,1994,0.21,0.11,5.88
2015-3-3,58,48,67,50,69,38,85,1894,1931,0.00,0.12,3.12
2015-3-4,60,48,72,51,70,35,94,1976,1972,0.00,0.10,2.54
2015-3-5,64,51,77,51,70,35,87,1896,1899,0.00,0.11,1.98
2015-3-6,66,49,83,51,70,39,90,1882,1899,0.00,0.11,0.88
2015-3-7,70,54,86,51,70,40,89,1893,1914,0.00,0.10,2.52
2015-3-8,66,53,78,51,70,35,93,1882,2004,0.00,0.09,1.71
2015-3-9,65,57,73,51,70,31,90,1893,1934,0.00,0.10,2.67
2015-3-10,68,55,80,51,70,38,96,1893,1916,0.00,0.09,2.22
2015-3-11,67,60,73,51,70,41,93,1969,1916,0.0,0.09,1.45
2015-3-12,71,56,86,51,70,36,93,1893,2007,0.00,0.09,1.44
2015-3-13,76,61,90,51,70,40,90,1907,2015,0.00,0.09,2.23
2015-3-14,78,62,93,51,70,39,93,1895,2015,0.00,0.08,1.74
2015-3-15,79,66,92,51,70,37,92,1880,2015,0.00,0.08,4.10
2015-3-16,78,66,90,51,70,36,91,1880,1914,0.00,0.08,2.53
2015-3-17,73,63,82,51,70,37,94,1881,1914,0.00,0.06,1.28
2015-3-18,69,62,75,51,70,38,87,1898,1997,0.0,0.07,1.03
2015-3-19,68,60,76,51,70,37,97,1886,1997,0.00,0.07,1.45
2015-3-20,69,59,78,51,70,39,93,1898,1997,0.00,0.06,2.42
2015-3-21,69,62,75,51,71,35,93,1894,1931,0.00,0.06,1.34
2015-3-22,66,59,72,51,71,37,88,1894,1986,0.00,0.06,1.11
2015-3-23,65,57,73,51,71,36,88,1898,1926,0.00,0.06,2.08
2015-3-24,67,58,75,51,71,37,94,1898,1988,0.00,0.06,1.39
2015-3-25,72,60,84,51,71,37,96,1902,1988,0.00,0.06,2.19
2015-3-26,77,61,92,51,71,39,98,1907,1988,0.00,0.06,1.53
2015-3-27,77,62,91,52,71,37,91,1884,1986,0.00,0.05,1.98
2015-3-28,71,61,81,52,71,37,90,1892,1879,0.00,0.06,1.62
2015-3-29,67,56,78,52,71,41,99,1880,1879,0.00,0.05,1.04
2015-3-30,68,57,78,52,71,36,89,1892,2003,0.00,0.05,1.27
2015-3-31,65,57,73,52,71,40,92,1897,2011,0.00,0.05,1.65
2015-4-1,66,58,74,52,71,40,93,1897,1985,0.00,0.04,1.01
2015-4-2,65,54,75,52,71,39,96,1906,1985,0.00,0.05,1.43
2015-4-3,72,57,86,52,71,40,94,1892,1961,0.00,0.04,1.14
2015-4-4,72,59,84,52,72,43,100,1892,1989,0.00,0.04,1.44
2015-4-5,64,57,71,52,72,42,105,1921,1989,0.00,0.04,2.74
2015-4-6,60,53,67,52,72,40,106,1975,1989,0.00,0.04,0.84
2015-4-7,59,51,67,52,72,39,100,1975,1989,0.13,0.04,1.21
2015-4-8,60,49,70,53,72,36,92,1901,1989,0.00,0.04,2.19
2015-4-9,65,53,76,53,72,40,94,1900,1890,0.00,0.03,1.71
2015-4-10,63,54,71,53,72,41,96,1927,1904,0.00,0.04,0.63
2015-4-11,63,53,72,53,72,40,90,1896,1940,0.00,0.03,1.97
2015-4-12,67,57,77,53,72,42,97,1887,1888,0.00,0.03,1.90
2015-4-13,67,58,75,53,73,41,99,1893,1898,0.00,0.02,0.49
2015-4-14,66,58,73,53,73,39,95,1883,1964,0.00,0.03,0.83
2015-4-15,69,57,80,53,73,43,99,1917,1966,0.00,0.03,0.81
2015-4-16,68,54,81,54,73,42,91,1917,2011,0.00,0.03,2.10
2015-4-17,69,56,81,54,73,40,90,1896,1999,0.00,0.03,1.24
2015-4-18,69,56,81,54,73,40,91,1882,1939,0.00,0.03,1.27
2015-4-19,65,55,75,54,73,40,94,1880,2009,0.00,0.02,0.86
2015-4-20,63,55,70,54,73,38,100,1896,2009,0.00,0.03,1.28
2015-4-21,63,58,67,54,73,38,95,1896,2009,0.00,0.03,0.89
2015-4-22,63,57,69,54,73,44,99,1882,1910,0.00,0.02,0.96
2015-4-23,62,55,69,55,74,42,100,1896,1910,0.00,0.02,0.98
2015-4-24,62,57,67,55,74,44,93,1900,2001,0.00,0.03,0.41
2015-4-25,61,56,66,55,74,42,94,1882,1898,0.0,0.02,0.50
2015-4-26,66,54,77,55,74,42,99,1894,2004,0.00,0.02,1.62
2015-4-27,70,57,82,55,74,41,102,1900,2004,0.00,0.02,0.82
2015-4-28,77,61,92,55,74,41,96,1900,2008,0.00,0.03,1.02
2015-4-29,75,60,89,55,74,44,98,1901,1981,0.00,0.02,0.89
2015-4-30,73,60,86,56,74,44,96,1915,1996,0.00,0.02,0.96
2015-5-1,71,57,84,56,74,41,92,1915,2014,0.00,0.02,0.76
2015-5-2,70,59,80,56,74,44,98,1915,2004,0.00,0.02,0.54
2015-5-3,66,58,74,56,74,42,101,1883,2004,0.00,0.01,1.76
2015-5-4,63,56,69,56,74,44,93,1899,1987,0.00,0.01,0.66
2015-5-5,63,58,67,56,74,46,101,1920,1990,0.00,0.01,0.90
2015-5-6,63,58,67,56,74,43,97,1892,1990,0.00,0.02,1.53
2015-5-7,60,54,65,56,74,42,97,1892,1941,0.03,0.01,1.00
2015-5-8,57,50,63,56,74,43,97,1879,1984,0.18,0.01,2.02
2015-5-9,62,55,69,57,74,44,99,1879,1923,0.00,0.01,0.75
2015-5-10,63,54,72,57,74,43,95,1922,1934,0.00,0.00,0.15
2015-5-11,67,57,76,57,74,43,93,1890,1996,0.00,0.01,0.29
2015-5-12,65,57,72,57,74,44,94,1887,1979,0.00,0.01,1.18
2015-5-13,64,58,69,57,74,43,98,1880,1979,0.00,0.01,0.84
2015-5-14,61,53,68,57,74,45,99,1882,2014,0.69,0.01,0.97
2015-5-15,61,55,66,57,74,42,102,1883,2014,0.03,0.01,1.46
2015-5-16,64,56,71,57,74,40,102,1883,1967,0.00,0.00,0.04
2015-5-17,65,58,72,57,74,42,96,1880,1892,0.00,0.01,0.18
2015-5-18,66,60,71,57,75,45,99,1880,1892,0.0,0.01,0.26
2015-5-19,65,56,73,57,75,45,95,1893,1892,0.00,0.00,0.35
2015-5-20,65,57,72,58,75,45,99,1879,1942,0.00,0.01,0.84
2015-5-21,64,57,71,58,75,43,100,1902,1883,0.00,0.01,1.03
2015-5-22,63,57,69,58,75,44,94,1879,1967,0.00,0.00,0.67
2015-5-23,64,57,71,58,75,41,92,1881,1932,0.00,0.01,0.10
2015-5-24,64,58,70,58,75,45,95,1888,1896,0.00,0.00,0.48
2015-5-25,64,57,71,58,75,47,103,1916,1896,0.00,0.01,0.42
2015-5-26,64,59,69,58,75,46,96,1917,1896,0.00,0.01,0.21
2015-5-27,65,58,72,58,75,47,97,1879,1880,0.00,0.00,0.57
2015-5-28,66,57,75,58,75,48,101,1895,1973,0.00,0.01,1.14
2015-5-29,68,58,77,58,75,46,99,1881,1978,0.00,0.00,0.09
2015-5-30,68,59,77,58,75,48,95,1878,1972,0.00,0.01,0.14
2015-5-31,66,59,73,58,75,47,98,1908,1909,0.00,0.00,0.09
2015-6-1,67,59,75,58,75,47,100,1908,1879,0.00,0.01,0.57
2015-6-2,68,60,76,59,76,47,104,1895,1879,0.00,0.00,0.58
2015-6-3,68,61,74,59,76,49,99,1908,1879,0.0,0.01,0.49
2015-6-4,66,61,70,59,76,47,100,1885,1895,0.0,0.00,0.33
2015-6-5,67,60,73,59,76,50,98,1999,1890,0.0,0.01,0.76
2015-6-6,65,58,72,59,76,47,102,1887,1890,0.00,0.00,0.02
2015-6-7,69,60,77,59,76,48,105,1887,1890,0.00,0.01,0.10
2015-6-8,73,60,85,59,76,49,99,1894,1890,0.00,0.00,0.02
2015-6-9,74,62,86,59,77,50,94,1879,1979,0.01,0.01,0.32
2015-6-10,68,62,73,60,77,46,102,1892,1979,0.00,0.00,0.22
2015-6-11,69,62,75,60,77,48,105,1892,1979,0.00,0.01,0.11
2015-6-12,68,63,72,60,77,47,100,1894,1979,0.00,0.00,0.63
2015-6-13,68,63,72,60,77,46,99,1894,1896,0.00,0.00,0.53
2015-6-14,70,63,77,60,78,49,100,1894,1917,0.00,0.01,0.01
2015-6-15,70,62,78,60,78,48,102,1892,1981,0.00,0.00,0.22
2015-6-16,70,61,79,60,78,49,105,1895,1981,0.00,0.00,0.38
2015-6-17,70,62,78,61,78,49,105,1885,1917,0.00,0.01,0.02
2015-6-18,72,62,81,61,79,47,104,1885,1957,0.00,0.00,0.11
2015-6-19,70,58,82,61,79,48,101,1892,1973,0.00,0.00,0.01
2015-6-20,74,63,85,61,79,48,106,1881,1973,0.00,0.00,0.05
2015-6-21,73,64,82,61,79,50,105,1882,1973,0.00,0.00,0.07
2015-6-22,73,62,83,61,80,51,97,1885,1949,0.00,0.00,0.05
2015-6-23,74,63,84,61,80,49,97,1893,1976,0.00,0.00,0.03
2015-6-24,73,63,83,62,80,50,102,1882,1976,0.00,0.00,0.01
2015-6-25,72,62,81,62,80,49,98,1893,1883,0.00,0.01,0.00
2015-6-26,69,60,78,62,80,48,112,1894,1990,0.00,0.00,0.19
2015-6-27,70,62,77,62,81,49,109,1894,1990,0.0,0.00,0.39
2015-6-28,77,67,86,62,81,52,98,1887,1980,0.0,0.00,0.00
2015-6-29,80,68,91,62,81,50,98,1893,1996,0.00,0.00,0.03
2015-6-30,78,67,89,62,81,49,100,1894,1985,0.0,0.00,0.01


================================================
FILE: us-weather-history/KHOU.csv
================================================
date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation
2014-7-1,84,74,94,75,93,66,102,1985,1980,0.00,0.16,1.40
2014-7-2,84,74,94,75,93,66,104,1924,1894,0.00,0.16,5.43
2014-7-3,84,73,94,75,93,66,100,1977,1980,0.0,0.16,1.29
2014-7-4,82,72,91,75,93,66,101,1910,2009,0.48,0.15,3.49
2014-7-5,82,73,90,75,93,66,102,1924,2009,0.31,0.16,3.04
2014-7-6,81,72,89,75,93,63,101,1970,2005,0.01,0.15,1.73
2014-7-7,84,74,93,75,93,62,100,1972,1980,0.08,0.15,2.32
2014-7-8,83,74,92,75,93,68,104,1892,1980,0.00,0.15,1.49
2014-7-9,85,74,95,75,93,64,102,1905,1980,0.00,0.14,2.39
2014-7-10,83,73,93,75,93,65,101,1893,1980,0.0,0.14,1.75
2014-7-11,83,72,94,75,93,64,103,1905,1980,0.0,0.13,3.08
2014-7-12,84,74,94,75,93,65,104,1891,1980,0.00,0.12,5.19
2014-7-13,87,77,96,75,94,66,102,1895,1980,0.0,0.13,5.40
2014-7-14,86,76,96,75,94,62,103,1990,1980,0.00,0.12,2.94
2014-7-15,82,75,89,75,94,64,103,1967,2000,0.06,0.12,4.32
2014-7-16,82,73,90,75,94,66,104,1895,1980,0.00,0.12,2.04
2014-7-17,83,76,89,75,94,67,104,1974,1980,0.39,0.10,2.23
2014-7-18,81,75,87,75,94,67,102,1974,1980,0.04,0.11,2.85
2014-7-19,79,72,85,75,94,68,102,1974,1980,0.00,0.11,1.51
2014-7-20,83,73,93,75,94,69,102,1970,2000,0.01,0.11,2.08
2014-7-21,87,77,96,75,94,65,101,1989,2000,0.00,0.10,1.09
2014-7-22,87,77,96,75,94,67,101,1988,2000,0.00,0.10,2.78
2014-7-23,86,76,95,75,94,64,101,1970,1980,0.07,0.11,2.36
2014-7-24,84,73,94,75,94,68,101,1972,1954,0.01,0.10,2.09
2014-7-25,85,74,95,75,94,65,102,1894,1964,0.00,0.10,3.74
2014-7-26,87,78,95,75,94,68,105,1972,1954,0.00,0.11,2.35
2014-7-27,87,77,97,75,95,66,101,1902,1995,0.00,0.10,5.22
2014-7-28,88,77,99,75,95,66,103,1891,1995,0.00,0.10,2.52
2014-7-29,83,74,92,75,95,64,101,1994,1907,0.03,0.10,4.70
2014-7-30,83,72,94,75,95,68,102,1994,1914,0.00,0.09,2.11
2014-7-31,84,73,94,75,95,67,103,1936,1998,3.91,0.09,3.91
2014-8-1,80,73,87,75,95,67,103,1984,1998,0.01,0.10,2.10
2014-8-2,77,71,82,75,95,68,104,1891,1998,0.00,0.10,1.65
2014-8-3,81,72,89,75,95,66,103,1949,1998,0.00,0.10,1.77
2014-8-4,83,73,92,75,95,63,102,1976,1951,0.0,0.11,2.94
2014-8-5,83,74,92,75,95,64,103,1976,1964,0.0,0.10,2.81
2014-8-6,85,75,94,75,95,64,101,1891,1947,0.0,0.11,1.20
2014-8-7,87,79,95,75,95,63,104,1891,2003,0.00,0.11,2.61
2014-8-8,87,77,97,75,95,68,104,1990,1962,0.10,0.10,1.66
2014-8-9,86,77,94,75,95,62,106,1989,1962,0.00,0.11,1.83
2014-8-10,86,78,94,75,95,62,104,1989,1962,0.00,0.11,2.88
2014-8-11,86,75,97,75,95,65,102,1989,2011,0.25,0.10,2.72
2014-8-12,87,76,97,75,95,64,105,1989,1962,0.00,0.11,1.41
2014-8-13,84,73,94,75,95,64,104,2004,1962,0.00,0.11,1.82
2014-8-14,83,73,93,75,95,65,102,2004,2007,0.02,0.11,2.80
2014-8-15,86,75,96,75,95,61,101,2004,1994,0.00,0.11,2.74
2014-8-16,89,80,98,75,95,63,102,2004,1925,0.00,0.12,3.95
2014-8-17,88,78,97,75,95,60,104,1992,1909,0.0,0.12,3.57
2014-8-18,86,76,96,75,95,62,108,1992,1909,0.00,0.13,6.69
2014-8-19,85,76,94,75,95,69,107,2000,1909,0.0,0.13,1.85
2014-8-20,85,74,96,75,95,65,107,1976,1909,0.01,0.13,1.80
2014-8-21,87,76,98,75,94,63,106,1940,1909,0.00,0.13,2.00
2014-8-22,87,75,98,75,94,64,106,1976,1980,0.00,0.13,3.25
2014-8-23,87,75,98,75,94,64,107,1973,1980,0.00,0.13,2.46
2014-8-24,86,74,98,75,94,54,105,1891,1980,0.00,0.14,2.48
2014-8-25,87,76,97,75,94,55,103,1891,1924,0.00,0.13,1.90
2014-8-26,86,74,97,74,94,57,104,1891,2011,0.00,0.14,1.66
2014-8-27,85,75,94,74,94,59,109,1891,2011,0.00,0.14,8.32
2014-8-28,83,76,90,74,94,63,107,1992,2011,0.00,0.14,3.88
2014-8-29,83,75,91,74,93,61,107,1992,2011,0.02,0.15,2.89
2014-8-30,84,75,92,74,93,62,104,1986,2000,0.0,0.15,2.56
2014-8-31,79,74,84,74,93,62,107,1893,2000,0.83,0.16,5.98
2014-9-1,84,74,93,74,93,63,107,1915,2000,0.36,0.15,2.22
2014-9-2,85,76,94,73,93,60,104,1893,2000,0.00,0.14,1.81
2014-9-3,84,75,93,73,93,60,105,1987,2000,0.00,0.14,3.20
2014-9-4,85,76,93,73,92,59,109,1974,2000,0.00,0.14,2.75
2014-9-5,84,75,93,73,92,55,108,1891,2000,0.03,0.14,2.07
2014-9-6,85,75,95,73,92,56,99,1974,1909,0.16,0.14,2.90
2014-9-7,84,73,95,72,92,59,98,1891,1963,0.42,0.15,4.55
2014-9-8,82,72,92,72,92,58,100,1988,1907,0.00,0.15,0.87
2014-9-9,85,74,95,72,91,59,100,1988,1909,0.00,0.15,4.22
2014-9-10,87,78,95,72,91,59,99,1988,1993,0.00,0.16,1.82
2014-9-11,87,76,97,71,91,56,99,1907,1893,0.00,0.15,4.81
2014-9-12,82,75,89,71,91,56,101,1907,1909,0.02,0.15,2.25
2014-9-13,72,67,77,71,90,56,102,1907,2011,0.01,0.15,7.73
2014-9-14,71,65,77,70,90,55,100,1940,2011,0.00,0.15,4.11
2014-9-15,80,68,92,70,90,55,98,1892,1980,0.0,0.14,3.23
2014-9-16,83,75,91,70,90,53,98,1979,1995,0.12,0.13,1.72
2014-9-17,82,75,89,70,89,55,100,2000,1995,0.25,0.13,1.85
2014-9-18,77,73,81,69,89,54,99,2000,1927,2.31,0.13,2.53
2014-9-19,79,73,84,69,89,51,97,1981,1995,0.20,0.12,6.92
2014-9-20,81,73,89,69,89,54,99,1901,1999,0.00,0.13,7.66
2014-9-21,82,73,91,68,88,53,100,1983,2005,0.00,0.13,3.07
2014-9-22,83,72,93,68,88,49,100,1983,2005,0.0,0.12,3.14
2014-9-23,74,64,84,68,88,49,96,1999,1993,0.00,0.13,2.37
2014-9-24,75,64,85,67,88,48,95,1975,2013,0.00,0.13,2.22
2014-9-25,74,68,80,67,87,50,99,1994,2005,0.0,0.13,3.05
2014-9-26,77,68,86,67,87,49,99,2000,2005,0.00,0.13,2.86
2014-9-27,79,71,86,66,87,45,100,1942,2005,0.0,0.13,3.46
2014-9-28,81,72,89,66,87,48,98,1909,1998,0.00,0.13,1.25
2014-9-29,80,70,89,66,86,47,96,1908,2011,0.00,0.12,2.51
2014-9-30,80,71,89,65,86,47,95,1890,1998,0.0,0.13,2.55
2014-10-1,83,72,93,65,86,44,99,1984,1900,0.09,0.15,4.62
2014-10-2,82,71,93,65,86,47,97,1984,1938,0.78,0.16,2.25
2014-10-3,74,63,84,65,85,45,95,1975,1994,0.32,0.17,3.24
2014-10-4,68,59,77,64,85,43,95,1975,2002,0.00,0.17,3.60
2014-10-5,69,53,85,64,85,41,94,1975,2006,0.00,0.17,2.90
2014-10-6,75,65,84,64,85,44,94,1975,1928,0.69,0.17,4.42
2014-10-7,78,65,90,63,84,46,95,1889,1956,0.00,0.17,5.76
2014-10-8,80,69,90,63,84,42,96,1952,1962,0.06,0.17,2.07
2014-10-9,79,68,90,63,84,40,93,1976,1962,0.00,0.17,3.60
2014-10-10,83,75,91,63,84,43,95,1908,1962,0.03,0.17,3.50
2014-10-11,76,66,85,62,83,44,94,1990,1962,0.02,0.18,3.02
2014-10-12,76,65,87,62,83,42,96,1977,1991,0.35,0.18,3.37
2014-10-13,72,59,85,62,83,36,94,1977,1991,0.61,0.18,2.55
2014-10-14,67,54,79,61,83,40,92,1977,1954,0.00,0.19,4.91
2014-10-15,68,51,84,61,82,40,92,1978,1947,0.00,0.20,4.87
2014-10-16,72,57,87,61,82,41,92,1893,1989,0.00,0.21,5.17
2014-10-17,73,60,86,61,82,40,93,1977,1895,0.00,0.20,4.31
2014-10-18,75,63,86,60,82,39,96,1978,1895,0.50,0.21,4.04
2014-10-19,72,62,81,60,81,36,94,1989,2004,0.00,0.21,1.23
2014-10-20,71,59,83,60,81,32,93,1989,2004,0.00,0.20,2.54
2014-10-21,69,55,82,60,81,33,93,1976,2004,0.00,0.21,2.40
2014-10-22,72,61,82,59,80,41,91,1898,2004,0.00,0.20,3.70
2014-10-23,69,58,80,59,80,40,91,1996,1988,0.00,0.20,3.79
2014-10-24,68,54,82,59,80,39,92,1908,1931,0.00,0.19,2.13
2014-10-25,70,55,85,59,80,39,91,1980,1950,0.00,0.20,9.25
2014-10-26,71,56,85,58,79,39,92,1892,2010,0.00,0.20,2.43
2014-10-27,72,58,86,58,79,36,94,1898,2010,0.00,0.18,2.72
2014-10-28,73,60,86,58,79,36,90,1898,1991,0.00,0.18,8.04
2014-10-29,73,61,84,57,78,39,90,1910,1991,0.00,0.18,2.45
2014-10-30,67,55,79,57,78,33,89,1917,2004,0.00,0.17,5.30
2014-10-31,67,55,79,57,78,29,88,1993,2004,0.00,0.16,3.84
2014-11-1,55,45,65,57,77,35,89,1993,2010,0.00,0.15,6.25
2014-11-2,57,41,72,56,77,34,89,1991,1978,0.00,0.14,4.58
2014-11-3,67,53,80,56,77,30,87,1999,1992,0.00,0.15,3.09
2014-11-4,72,62,82,56,76,28,89,1991,1988,0.00,0.16,3.20
2014-11-5,69,64,74,55,76,31,88,1991,1963,1.12,0.16,5.22
2014-11-6,62,58,66,55,76,36,88,1993,1963,0.17,0.15,1.67
2014-11-7,57,49,65,55,75,29,88,1993,2002,0.00,0.15,1.46
2014-11-8,63,50,75,55,75,34,89,1991,1989,0.0,0.15,1.47
2014-11-9,60,47,72,54,75,29,88,1991,1969,0.00,0.15,3.08
2014-11-10,63,45,80,54,74,30,88,1977,1988,0.00,0.15,2.85
2014-11-11,60,49,71,54,74,32,87,1977,1978,0.01,0.16,1.88
2014-11-12,51,43,58,53,74,32,85,1987,2003,0.0,0.15,4.68
2014-11-13,41,36,45,53,73,29,86,1907,2005,0.20,0.14,4.53
2014-11-14,41,34,47,53,73,28,89,1969,1921,0.00,0.15,1.40
2014-11-15,48,43,53,52,73,25,88,1969,1978,0.30,0.14,2.80
2014-11-16,50,42,57,52,72,25,85,1970,1948,0.65,0.14,3.03
2014-11-17,44,36,52,52,72,31,85,1997,2013,0.00,0.14,5.19
2014-11-18,44,32,55,51,72,28,84,1959,1973,0.00,0.14,3.36
2014-11-19,50,30,69,51,71,30,85,2014,1985,0.00,0.14,2.10
2014-11-20,61,46,76,51,71,27,84,1969,1977,0.04,0.13,1.85
2014-11-21,64,61,66,50,71,29,84,1937,1999,0.57,0.14,1.54
2014-11-22,63,58,68,50,70,30,87,1975,1973,0.11,0.13,3.09
2014-11-23,71,61,80,50,70,24,85,1975,1973,0.23,0.14,3.81
2014-11-24,56,45,66,49,70,26,84,1970,1910,0.00,0.13,3.35
2014-11-25,54,41,66,49,69,29,86,1950,1967,0.00,0.14,1.70
2014-11-26,58,43,73,49,69,27,86,1975,1967,0.00,0.14,3.17
2014-11-27,55,43,67,49,69,26,84,1975,1989,0.00,0.14,3.12
2014-11-28,55,39,71,48,68,26,83,1976,1977,0.00,0.14,1.82
2014-11-29,69,60,78,48,68,22,83,1976,2006,0.00,0.15,3.47
2014-11-30,71,61,80,48,68,19,82,1976,1950,0.00,0.15,1.41
2014-12-1,57,45,69,47,67,26,83,1979,2012,0.03,0.15,5.80
2014-12-2,50,44,56,47,67,28,84,1979,2012,0.00,0.13,3.28
2014-12-3,59,52,66,47,67,25,85,1929,1995,0.0,0.14,2.50
2014-12-4,63,53,72,47,67,27,83,1897,2012,0.0,0.13,1.96
2014-12-5,72,65,79,46,66,26,83,2009,1998,0.07,0.13,2.02
2014-12-6,65,58,71,46,66,20,82,1950,1951,0.0,0.13,3.08
2014-12-7,57,54,59,46,66,24,84,1950,1998,0.00,0.13,4.95
2014-12-8,57,46,67,46,66,25,83,1927,2007,0.00,0.13,1.95
2014-12-9,57,43,70,45,65,25,82,1917,2007,0.00,0.13,1.69
2014-12-10,56,42,69,45,65,20,83,1978,1939,0.00,0.12,4.41
2014-12-11,64,56,72,45,65,26,83,1957,2007,0.02,0.12,2.85
2014-12-12,66,57,75,45,65,25,82,1989,1948,0.0,0.13,2.57
2014-12-13,64,54,74,45,65,19,82,1989,1921,0.00,0.12,2.85
2014-12-14,67,58,76,45,64,25,83,1985,1995,0.0,0.12,5.64
2014-12-15,70,59,80,44,64,21,82,1901,1995,0.0,0.12,2.19
2014-12-16,57,48,66,44,64,19,82,1989,1971,0.00,0.12,3.05
2014-12-17,54,44,64,44,64,20,80,1932,1984,0.0,0.12,2.28
2014-12-18,64,57,70,44,64,24,82,1996,1980,0.80,0.11,2.14
2014-12-19,56,52,59,44,64,20,81,1929,1978,3.06,0.12,3.06
2014-12-20,52,50,54,44,63,21,81,1996,1951,0.00,0.12,2.02
2014-12-21,55,48,61,44,63,21,82,1973,2010,0.00,0.12,3.41
2014-12-22,64,55,72,44,63,13,81,1989,1981,0.0,0.11,2.21
2014-12-23,54,48,60,44,63,7,80,1989,1996,0.69,0.13,1.01
2014-12-24,46,37,55,43,63,11,82,1989,1948,0.00,0.11,2.04
2014-12-25,50,34,66,43,63,11,82,1983,1964,0.00,0.12,2.09
2014-12-26,61,51,71,43,63,18,78,1983,2005,0.04,0.11,2.06
2014-12-27,60,46,74,43,63,19,81,1892,1971,0.22,0.11,1.38
2014-12-28,44,41,46,43,63,15,80,1894,1996,0.67,0.11,2.70
2014-12-29,44,39,48,43,63,15,81,1894,1984,0.00,0.10,2.15
2014-12-30,46,38,53,43,63,19,83,1983,1974,0.00,0.10,1.36
2014-12-31,42,37,46,43,63,18,82,1983,1974,0.00,0.10,3.35
2015-1-1,44,41,47,43,63,18,81,1928,2006,0.15,0.10,3.12
2015-1-2,51,47,54,43,63,16,81,1928,2000,0.63,0.10,2.26
2015-1-3,47,41,53,43,62,16,83,1911,1989,0.63,0.10,3.44
2015-1-4,46,36,56,43,62,18,79,1911,1997,0.00,0.11,2.23
2015-1-5,43,31,54,43,62,22,80,1972,1998,0.00,0.11,1.39
2015-1-6,50,34,65,43,62,22,80,1972,1989,0.00,0.11,3.70
2015-1-7,51,39,62,43,62,19,79,1970,1989,0.00,0.10,2.01
2015-1-8,35,28,42,43,62,19,79,1996,1957,0.01,0.11,5.89
2015-1-9,39,37,40,43,63,19,80,1976,1974,0.06,0.10,2.49
2015-1-10,39,37,41,43,63,18,82,1977,1963,0.08,0.11,1.43
2015-1-11,44,39,49,43,63,12,81,1982,1995,0.66,0.10,2.78
2015-1-12,46,43,49,43,63,11,81,1918,2000,0.0,0.11,2.27
2015-1-13,45,43,46,43,63,17,81,1912,2000,0.05,0.10,1.51
2015-1-14,42,39,45,43,63,19,82,1982,1971,0.0,0.11,1.45
2015-1-15,47,39,54,43,63,21,79,1892,1996,0.0,0.11,2.82
2015-1-16,47,33,61,43,63,22,81,1972,1936,0.00,0.11,2.60
2015-1-17,53,37,69,43,63,15,80,1930,1974,0.00,0.11,2.39
2015-1-18,57,43,71,43,63,5,81,1930,2000,0.00,0.12,2.83
2015-1-19,53,34,72,43,63,10,81,1940,2000,0.00,0.11,2.00
2015-1-20,61,45,76,43,63,20,81,1985,2012,0.00,0.11,1.83
2015-1-21,62,57,67,43,63,16,81,1985,1911,0.0,0.11,2.05
2015-1-22,52,44,60,43,63,15,80,1930,1972,0.87,0.12,2.52
2015-1-23,42,39,44,43,63,14,83,1940,1972,0.03,0.11,2.62
2015-1-24,49,35,62,43,63,18,82,1963,1971,0.00,0.11,1.94
2015-1-25,55,40,70,44,63,19,81,1894,1971,0.00,0.11,2.83
2015-1-26,56,44,67,44,64,23,84,1943,1975,0.00,0.11,2.58
2015-1-27,64,47,81,44,64,19,83,1897,1975,0.00,0.11,2.86
2015-1-28,61,43,79,44,64,19,82,1897,1975,0.00,0.12,0.75
2015-1-29,62,49,75,44,64,22,82,1897,1975,0.00,0.11,1.93
2015-1-30,55,45,65,44,64,18,84,1949,1911,0.00,0.12,1.64
2015-1-31,53,42,63,44,64,14,82,1949,1911,0.0,0.11,2.12
2015-2-1,63,48,77,44,64,15,82,1951,1989,0.01,0.12,3.69
2015-2-2,43,36,50,44,64,14,83,1951,1911,0.00,0.11,1.70
2015-2-3,44,41,46,44,64,23,80,1951,2008,0.17,0.10,1.82
2015-2-4,52,42,61,45,65,22,81,1996,1957,0.16,0.10,2.19
2015-2-5,48,41,55,45,65,23,83,1996,1957,0.0,0.10,1.80
2015-2-6,49,41,56,45,65,24,81,1989,1969,0.00,0.11,1.09
2015-2-7,53,37,69,45,65,11,80,1895,1957,0.00,0.11,1.46
2015-2-8,66,54,77,45,65,10,82,1895,1932,0.00,0.11,1.84
2015-2-9,69,54,83,45,65,15,86,1933,1962,0.00,0.11,2.00
2015-2-10,59,43,74,46,65,22,85,1973,1957,0.00,0.12,2.22
2015-2-11,60,44,75,46,66,20,82,1981,1999,0.00,0.11,1.88
2015-2-12,56,46,66,46,66,6,84,1899,1922,0.00,0.12,2.70
2015-2-13,53,39,66,46,66,6,83,1899,1962,0.00,0.11,2.99
2015-2-14,61,43,78,46,66,10,83,1899,1911,0.00,0.11,1.90
2015-2-15,61,51,71,47,66,18,83,1905,2000,0.02,0.13,2.00
2015-2-16,53,37,68,47,66,13,82,1895,1927,0.15,0.12,2.05
2015-2-17,44,34,54,47,67,16,86,1895,1907,0.0,0.12,0.87
2015-2-18,49,33,64,47,67,19,87,1936,1986,0.00,0.13,2.09
2015-2-19,51,37,64,47,67,20,88,1900,1986,0.00,0.12,1.30
2015-2-20,61,52,69,48,67,25,91,1908,1986,0.01,0.12,2.60
2015-2-21,70,65,75,48,67,24,89,1978,1996,0.0,0.12,1.98
2015-2-22,63,45,80,48,68,21,90,1978,1996,0.0,0.12,1.96
2015-2-23,40,34,46,48,68,27,88,1976,1996,0.02,0.11,1.60
2015-2-24,38,33,43,48,68,26,87,1914,1927,0.0,0.12,1.38
2015-2-25,42,33,50,49,68,26,85,1974,1918,0.10,0.10,2.10
2015-2-26,43,31,55,49,69,26,86,1974,1986,0.00,0.12,1.51
2015-2-27,42,37,46,49,69,22,86,2002,1954,0.00,0.11,1.85
2015-2-28,47,37,57,49,69,28,90,2002,1940,0.02,0.12,1.44
2015-3-1,54,49,59,50,69,28,84,1980,1975,0.22,0.11,3.32
2015-3-2,54,48,60,50,70,22,91,1980,1909,0.01,0.12,1.55
2015-3-3,68,56,80,50,70,21,86,1943,1893,0.00,0.11,1.03
2015-3-4,63,43,83,50,70,22,86,2002,1893,0.0,0.12,4.56
2015-3-5,44,36,51,50,70,25,84,1989,1998,0.15,0.12,2.20
2015-3-6,44,30,57,51,71,25,86,1989,1991,0.00,0.12,1.44
2015-3-7,48,35,60,51,71,28,90,1989,1911,0.0,0.12,1.58
2015-3-8,49,45,53,51,71,27,88,1971,1911,1.49,0.11,2.05
2015-3-9,58,50,66,51,71,27,88,1932,2000,1.96,0.11,1.96
2015-3-10,57,53,60,51,72,27,89,1932,1980,0.00,0.11,3.23
2015-3-11,58,53,62,52,72,27,87,1948,1911,0.12,0.11,2.81
2015-3-12,59,55,62,52,72,24,89,1948,1904,0.33,0.11,4.28
2015-3-13,61,55,66,52,72,27,88,1932,1942,0.34,0.10,1.96
2015-3-14,64,51,77,52,73,29,88,1993,2008,0.00,0.11,1.82
2015-3-15,64,55,73,52,73,32,89,1999,2008,0.00,0.11,2.06
2015-3-16,67,57,77,53,73,34,87,1913,1908,0.00,0.11,2.83
2015-3-17,70,58,82,53,73,30,88,1900,1908,0.00,0.10,2.57
2015-3-18,74,65,82,53,74,27,90,1892,2013,0.11,0.10,1.60
2015-3-19,73,66,79,53,74,27,89,1923,1908,0.49,0.09,4.20
2015-3-20,74,66,82,53,74,24,90,1923,1907,0.02,0.11,7.47
2015-3-21,65,64,66,54,74,30,87,1996,1907,1.01,0.10,1.38
2015-3-22,68,60,76,54,75,32,89,1970,1916,0.00,0.12,2.01
2015-3-23,68,55,80,54,75,35,89,1989,1929,0.00,0.11,2.27
2015-3-24,67,53,80,54,75,32,87,1897,1907,0.00,0.11,1.46
2015-3-25,68,56,80,54,75,36,93,2006,1928,0.00,0.12,7.36
2015-3-26,62,54,69,55,75,30,94,1955,1928,0.09,0.12,2.23
2015-3-27,62,44,80,55,76,31,89,1955,1935,0.00,0.11,3.00
2015-3-28,65,47,82,55,76,33,89,1931,1972,0.00,0.11,1.99
2015-3-29,70,57,82,55,76,35,90,1894,1974,0.00,0.10,1.75
2015-3-30,72,61,83,55,76,33,96,1987,1946,0.00,0.11,2.90
2015-3-31,74,64,83,56,77,30,88,1987,1946,0.00,0.10,1.44
2015-4-1,74,69,79,56,77,39,89,1987,1936,0.01,0.10,2.26
2015-4-2,77,69,84,56,77,38,89,1996,1939,0.00,0.10,4.75
2015-4-3,77,71,82,56,77,31,87,1987,2006,0.0,0.10,3.09
2015-4-4,67,61,73,56,77,34,88,1987,1897,0.00,0.10,3.35
2015-4-5,62,57,67,57,78,38,91,1973,1982,0.02,0.10,2.61
2015-4-6,75,67,82,57,78,37,88,1970,1909,0.0,0.10,1.90
2015-4-7,79,70,87,57,78,32,91,1971,1907,0.00,0.10,2.28
2015-4-8,78,71,84,57,78,35,92,1971,1986,0.00,0.10,3.26
2015-4-9,78,70,86,58,78,36,92,1973,1963,0.00,0.10,2.68
2015-4-10,68,61,74,58,79,31,92,1973,1963,1.74,0.11,2.57
2015-4-11,70,63,77,58,79,38,89,1989,1934,0.13,0.10,1.67
2015-4-12,74,68,80,58,79,41,89,1900,1981,0.14,0.10,4.04
2015-4-13,79,71,86,59,79,39,92,1940,1996,0.32,0.10,6.43
2015-4-14,70,64,76,59,79,39,89,1980,1996,0.12,0.11,3.52
2015-4-15,71,60,81,59,80,38,90,1928,1944,0.0,0.10,3.05
2015-4-16,74,65,82,59,80,38,92,1983,1987,1.66,0.11,4.70
2015-4-17,74,64,83,60,80,38,92,1999,2006,1.10,0.11,1.90
2015-4-18,72,66,78,60,80,42,94,1999,1987,0.09,0.11,8.16
2015-4-19,72,58,86,60,80,40,92,1901,1987,0.54,0.11,2.77
2015-4-20,67,59,74,61,81,39,93,2013,1987,0.00,0.11,2.70
2015-4-21,68,56,79,61,81,42,90,1978,1987,0.00,0.12,3.76
2015-4-22,76,67,84,61,81,43,90,1993,2009,0.00,0.12,1.50
2015-4-23,79,69,88,61,81,44,91,1998,1999,0.00,0.11,2.40
2015-4-24,78,71,85,62,81,46,92,1995,1894,0.0,0.12,2.24
2015-4-25,78,67,88,62,82,41,90,1910,1987,0.21,0.13,2.50
2015-4-26,76,67,85,62,82,46,91,1978,1988,0.0,0.12,3.52
2015-4-27,72,66,78,63,82,47,93,1978,1933,0.02,0.13,2.72
2015-4-28,64,56,71,63,82,47,95,1978,1987,0.0,0.12,3.76
2015-4-29,65,52,78,63,82,46,92,2008,1987,0.00,0.13,2.86
2015-4-30,68,53,82,64,83,42,92,1908,1981,0.00,0.14,3.20
2015-5-1,71,57,84,64,83,45,94,1908,1947,0.00,0.15,4.11
2015-5-2,70,57,83,64,83,45,96,1976,1964,0.00,0.15,3.48
2015-5-3,70,57,83,64,83,47,93,1978,1890,0.00,0.16,5.10
2015-5-4,73,61,84,65,84,42,94,2013,1890,0.00,0.16,1.72
2015-5-5,73,63,82,65,84,49,94,1970,1906,0.13,0.17,3.60
2015-5-6,78,73,83,65,84,49,94,2013,1906,0.41,0.17,5.00
2015-5-7,80,74,85,65,84,47,94,1891,1998,0.0,0.17,2.61
2015-5-8,81,77,85,66,85,45,96,1892,1967,0.0,0.17,2.34
2015-5-9,82,76,87,66,85,46,94,1984,1998,0.0,0.17,2.42
2015-5-10,82,77,87,66,85,51,94,1984,1927,0.03,0.17,5.89
2015-5-11,75,69,80,67,85,47,94,1981,1890,0.28,0.17,2.16
2015-5-12,74,69,79,67,85,50,92,1895,1967,0.56,0.16,5.27
2015-5-13,75,68,81,67,86,50,94,1895,1907,1.55,0.16,3.44
2015-5-14,80,72,87,67,86,47,93,1971,1995,0.05,0.16,2.44
2015-5-15,79,73,84,68,86,49,92,1890,2003,0.15,0.18,4.64
2015-5-16,81,73,89,68,86,47,93,1973,2003,0.00,0.17,2.80
2015-5-17,78,71,85,68,87,52,95,1970,1894,0.98,0.17,4.20
2015-5-18,80,73,86,68,87,50,95,1976,1890,0.24,0.15,6.16
2015-5-19,80,73,86,68,87,50,95,1976,2003,0.02,0.16,6.87
2015-5-20,81,73,89,69,87,48,95,1894,2008,0.31,0.16,4.17
2015-5-21,77,71,82,69,88,53,96,1892,2005,1.08,0.15,5.45
2015-5-22,72,65,79,69,88,48,96,1892,2005,0.00,0.16,3.10
2015-5-23,79,73,85,69,88,45,95,1892,1996,0.72,0.16,3.99
2015-5-24,75,69,81,70,88,54,95,1892,1955,0.56,0.16,3.96
2015-5-25,77,66,88,70,88,56,95,1979,1927,4.34,0.16,4.34
2015-5-26,76,66,85,70,89,57,98,1979,2011,0.77,0.17,2.91
2015-5-27,76,65,86,70,89,50,98,1901,1928,1.41,0.17,3.99
2015-5-28,80,70,90,70,89,54,98,1901,1906,0.00,0.17,2.83
2015-5-29,80,70,89,71,89,58,99,1984,1996,0.01,0.17,4.33
2015-5-30,80,69,91,71,89,53,97,1984,1998,0.57,0.17,5.01
2015-5-31,78,69,87,71,90,50,98,1889,1998,0.00,0.17,3.45
2015-6-1,78,66,89,71,90,53,98,1984,1998,0.00,0.18,1.32
2015-6-2,79,68,90,71,90,55,100,1892,2011,0.00,0.17,4.03
2015-6-3,80,69,91,72,90,52,98,1970,1960,0.00,0.18,3.27
2015-6-4,80,69,90,72,90,59,98,1970,2011,0.00,0.17,3.72
2015-6-5,82,72,92,72,90,56,105,1970,2011,0.00,0.18,5.33
2015-6-6,83,72,93,72,91,57,105,1970,2011,0.00,0.18,2.66
2015-6-7,83,72,93,72,91,58,99,1970,1903,0.00,0.18,2.29
2015-6-8,83,72,93,72,91,60,99,1977,1980,0.00,0.19,8.13
2015-6-9,84,74,93,73,91,56,97,1996,1956,0.00,0.19,2.92
2015-6-10,84,74,94,73,91,57,100,1955,1902,0.00,0.19,2.92
2015-6-11,84,73,94,73,91,61,101,1976,1902,0.01,0.21,5.63
2015-6-12,83,74,92,73,91,55,101,1979,1978,0.0,0.22,3.05
2015-6-13,80,74,86,73,91,57,101,1979,1911,0.35,0.21,3.58
2015-6-14,81,74,88,73,91,55,102,1903,1998,0.52,0.21,3.81
2015-6-15,83,75,90,73,92,55,100,1903,2011,0.14,0.21,5.68
2015-6-16,78,75,80,74,92,57,100,1989,1963,2.26,0.21,2.71
2015-6-17,80,76,84,74,92,62,100,1989,1902,0.70,0.21,1.51
2015-6-18,81,76,86,74,92,64,103,1969,1918,0.0,0.22,3.29
2015-6-19,83,76,90,74,92,64,101,1912,1934,0.00,0.22,4.43
2015-6-20,84,77,90,74,92,62,101,1976,1902,0.20,0.21,1.99
2015-6-21,86,78,93,74,92,59,100,1976,1998,0.00,0.21,2.04
2015-6-22,84,76,92,74,92,66,101,1912,1933,0.00,0.22,3.13
2015-6-23,84,76,92,74,92,60,101,1904,2009,0.00,0.20,2.22
2015-6-24,84,74,93,74,92,61,104,1904,2009,0.00,0.21,4.47
2015-6-25,85,75,94,75,92,60,102,1974,2009,0.00,0.20,2.82
2015-6-26,86,77,94,75,92,59,105,1974,2012,0.13,0.19,10.34
2015-6-27,83,73,93,75,92,57,102,1974,1980,2.38,0.19,2.50
2015-6-28,82,73,90,75,93,59,102,1974,2009,2.24,0.19,2.98
2015-6-29,84,76,92,75,93,59,107,1974,2013,0.00,0.19,2.50
2015-6-30,82,72,92,75,93,61,103,1985,1980,2.26,0.19,2.26


================================================
FILE: us-weather-history/KIND.csv
================================================
date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation
2014-7-1,76,66,86,66,85,48,97,1885,1970,0.13,0.16,5.09
2014-7-2,71,63,79,66,85,50,99,1904,1970,0.00,0.16,4.50
2014-7-3,67,60,73,66,85,49,100,1968,1911,0.0,0.15,1.89
2014-7-4,67,54,79,66,85,48,103,1968,1911,0.00,0.15,2.40
2014-7-5,69,59,79,66,85,49,103,1972,2012,0.00,0.16,3.27
2014-7-6,74,64,83,66,85,48,105,2001,2012,0.03,0.15,1.64
2014-7-7,74,68,79,66,85,54,105,1894,2012,0.0,0.16,4.43
2014-7-8,74,67,81,66,85,51,104,1984,1936,0.32,0.16,2.60
2014-7-9,72,62,81,66,85,52,103,1952,1936,0.00,0.16,2.49
2014-7-10,71,61,80,66,85,50,105,1963,1936,0.00,0.16,1.52
2014-7-11,71,60,81,66,85,52,104,1945,1936,0.00,0.16,2.49
2014-7-12,74,67,80,66,85,53,103,1975,1936,0.07,0.16,2.70
2014-7-13,78,72,84,66,85,49,103,1976,1936,0.00,0.17,2.78
2014-7-14,75,65,85,66,85,48,106,1975,1936,0.39,0.16,2.96
2014-7-15,64,56,71,66,85,51,103,1967,1988,0.00,0.17,2.05
2014-7-16,63,53,72,66,85,49,98,1945,1988,0.00,0.15,2.32
2014-7-17,65,53,77,66,85,51,101,1976,2012,0.00,0.15,3.71
2014-7-18,68,57,78,66,85,51,100,1976,1954,0.00,0.15,2.61
2014-7-19,68,58,77,66,85,50,98,1984,1930,0.00,0.14,1.31
2014-7-20,71,60,81,66,85,49,103,1947,1934,0.00,0.13,4.00
2014-7-21,74,62,85,66,85,46,106,1944,1934,0.00,0.14,1.89
2014-7-22,78,67,88,66,85,49,106,1947,1901,0.00,0.14,1.82
2014-7-23,69,62,76,66,85,46,102,1947,1934,1.61,0.14,1.61
2014-7-24,67,58,76,66,85,52,105,1947,1934,0.00,0.14,1.98
2014-7-25,65,56,74,66,85,51,105,1911,1934,0.0,0.13,2.61
2014-7-26,75,67,82,66,85,51,98,1911,1941,0.10,0.13,2.41
2014-7-27,76,66,85,66,85,51,100,1962,1916,0.22,0.12,1.86
2014-7-28,65,57,73,66,85,51,101,2013,1930,0.00,0.12,2.95
2014-7-29,64,52,75,66,85,51,100,1881,1941,0.21,0.13,0.80
2014-7-30,68,57,78,66,84,51,103,1965,1940,0.02,0.13,1.63
2014-7-31,70,59,80,66,84,49,98,1895,1913,0.00,0.12,2.84
2014-8-1,72,61,82,65,84,53,97,1971,1887,0.01,0.11,2.86
2014-8-2,73,63,82,65,84,52,98,1927,1888,0.0,0.11,2.53
2014-8-3,73,61,84,65,84,50,98,1965,1897,0.00,0.11,1.72
2014-8-4,74,62,85,65,84,49,99,1912,1887,0.0,0.10,3.81
2014-8-5,73,65,81,65,84,51,103,1974,1918,0.00,0.10,1.65
2014-8-6,71,63,79,65,84,50,99,1994,1918,0.00,0.11,4.46
2014-8-7,71,64,78,65,84,49,98,1989,1930,0.00,0.10,1.08
2014-8-8,70,65,74,65,84,48,98,1989,2012,0.03,0.09,1.51
2014-8-9,76,68,83,65,84,50,99,1884,1930,0.0,0.10,2.36
2014-8-10,77,70,84,65,84,50,99,1972,1911,0.13,0.10,1.25
2014-8-11,75,68,82,65,84,52,99,1967,1941,0.10,0.09,2.07
2014-8-12,65,58,71,65,84,47,101,2004,1881,0.0,0.10,1.93
2014-8-13,67,55,79,65,84,48,98,1964,1936,0.00,0.09,3.08
2014-8-14,70,59,81,65,84,48,95,1964,1871,0.0,0.09,2.72
2014-8-15,63,49,77,65,84,45,96,1979,2010,0.00,0.10,2.26
2014-8-16,67,59,75,65,84,50,102,1979,1988,0.09,0.10,1.43
2014-8-17,73,65,81,65,84,52,99,1963,1988,0.03,0.11,3.18
2014-8-18,76,65,86,65,84,48,98,1963,1936,0.00,0.11,1.61
2014-8-19,76,65,86,64,84,50,100,1896,1936,0.50,0.10,1.65
2014-8-20,74,64,83,64,84,50,99,1897,1983,0.0,0.11,2.22
2014-8-21,78,70,86,64,84,47,100,1950,1936,0.16,0.10,1.23
2014-8-22,80,73,86,64,84,46,101,1950,1936,0.02,0.10,1.90
2014-8-23,79,70,88,64,84,46,96,1890,1936,0.81,0.10,2.07
2014-8-24,78,70,86,64,84,49,98,1927,1936,0.00,0.09,1.75
2014-8-25,81,71,90,64,84,49,96,1887,1948,0.78,0.10,1.41
2014-8-26,81,69,93,63,84,48,97,1945,1948,0.26,0.11,1.90
2014-8-27,79,70,88,63,84,48,97,1968,1948,0.00,0.10,2.60
2014-8-28,75,66,84,63,83,43,96,1986,1953,0.00,0.10,2.44
2014-8-29,78,68,87,63,83,41,97,1965,1953,0.0,0.10,1.38
2014-8-30,75,71,78,63,83,42,95,1986,1953,0.02,0.10,2.16
2014-8-31,76,69,83,62,83,44,97,1915,1951,0.01,0.10,2.43
2014-9-1,77,71,82,62,83,44,100,1987,1953,0.00,0.10,7.20
2014-9-2,72,65,79,62,82,45,100,1885,1953,1.00,0.10,2.10
2014-9-3,74,64,83,61,82,45,100,1952,2011,0.00,0.10,2.63
2014-9-4,77,66,87,61,82,46,98,1952,1954,0.00,0.09,6.80
2014-9-5,80,70,90,61,82,45,100,1974,1954,0.02,0.10,1.54
2014-9-6,67,60,73,60,81,44,99,1988,1954,0.0,0.10,1.19
2014-9-7,65,54,76,60,81,42,98,1988,1939,0.00,0.11,2.71
2014-9-8,68,56,79,60,81,38,97,1986,1939,0.00,0.10,2.61
2014-9-9,69,57,81,59,80,41,98,1883,1939,0.00,0.10,2.34
2014-9-10,75,67,82,59,80,40,96,1969,2013,0.90,0.11,2.60
2014-9-11,64,56,71,58,80,39,95,1917,1897,0.0,0.11,1.78
2014-9-12,59,53,64,58,79,40,96,1955,1897,0.0,0.11,1.08
2014-9-13,55,47,63,58,79,39,99,1975,1939,0.0,0.11,1.80
2014-9-14,57,46,68,57,79,37,100,1964,1939,0.00,0.11,2.93
2014-9-15,57,49,65,57,78,42,100,1974,1939,0.54,0.11,2.21
2014-9-16,59,50,67,56,78,39,96,1984,1939,0.01,0.09,2.53
2014-9-17,56,45,67,56,77,40,93,1984,1955,0.00,0.10,1.07
2014-9-18,61,49,72,55,77,39,95,1959,1953,0.00,0.09,1.64
2014-9-19,63,53,73,55,76,38,93,1956,1940,0.00,0.11,2.29
2014-9-20,67,54,80,54,76,36,94,1991,1940,0.02,0.10,2.20
2014-9-21,65,56,74,54,76,35,96,1875,2010,0.04,0.11,1.61
2014-9-22,57,47,66,53,75,37,93,1999,1895,0.00,0.11,1.22
2014-9-23,60,47,72,53,75,34,94,1974,2010,0.00,0.11,2.11
2014-9-24,62,47,76,53,74,34,92,1887,2007,0.00,0.10,1.29
2014-9-25,66,54,78,52,74,35,92,1950,1891,0.00,0.11,2.17
2014-9-26,69,55,82,52,73,37,91,2001,1891,0.00,0.10,1.62
2014-9-27,69,56,81,51,73,35,93,1991,1998,0.00,0.11,2.59
2014-9-28,71,58,83,51,72,33,91,1942,1939,0.00,0.11,1.41
2014-9-29,68,54,82,50,72,34,97,1970,1953,0.00,0.10,2.45
2014-9-30,62,52,71,50,72,30,89,1899,1971,0.00,0.11,1.88
2014-10-1,63,49,76,50,71,32,89,1946,1897,0.00,0.10,2.74
2014-10-2,71,60,81,49,71,29,89,1974,1953,0.14,0.11,1.39
2014-10-3,59,44,73,49,70,27,90,1974,1954,0.28,0.11,2.61
2014-10-4,45,40,49,48,70,31,90,1987,1951,0.00,0.11,1.55
2014-10-5,50,40,59,48,69,31,87,1968,1922,0.13,0.10,2.93
2014-10-6,55,42,67,48,69,30,89,1980,2007,0.02,0.11,1.45
2014-10-7,54,46,62,47,69,27,90,1952,2007,0.33,0.10,1.65
2014-10-8,56,43,68,47,68,28,91,1987,2007,0.00,0.10,1.02
2014-10-9,51,45,56,47,68,30,88,1989,1939,0.02,0.11,1.25
2014-10-10,52,49,54,46,67,27,88,1964,2010,0.01,0.10,3.88
2014-10-11,50,42,57,46,67,26,86,1906,2010,0.00,0.10,1.56
2014-10-12,54,45,62,46,67,26,86,1988,1879,0.30,0.10,3.01
2014-10-13,67,58,76,45,66,24,86,1988,1975,0.57,0.10,0.92
2014-10-14,61,55,67,45,66,24,86,1979,1899,0.56,0.10,1.71
2014-10-15,55,53,56,45,66,27,87,1876,1897,0.18,0.10,1.14
2014-10-16,56,51,61,45,65,26,85,1991,1897,0.00,0.10,1.65
2014-10-17,59,50,68,44,65,26,86,1948,1950,0.00,0.10,1.45
2014-10-18,50,46,54,44,65,22,84,1948,1953,0.09,0.10,2.15
2014-10-19,50,43,56,44,64,25,85,1992,1953,0.00,0.10,1.33
2014-10-20,57,46,68,44,64,23,86,1972,1953,0.01,0.10,1.33
2014-10-21,51,44,57,43,64,23,86,1952,1953,0.02,0.09,1.43
2014-10-22,48,38,57,43,63,23,85,1976,1947,0.00,0.10,1.84
2014-10-23,49,37,60,43,63,24,86,1969,1963,0.00,0.09,1.57
2014-10-24,53,40,66,42,62,20,81,1981,1963,0.00,0.09,1.52
2014-10-25,63,50,76,42,62,25,82,1887,1963,0.00,0.09,1.54
2014-10-26,55,42,68,42,62,20,82,1962,1963,0.00,0.10,2.45
2014-10-27,66,51,80,42,61,24,83,1976,1940,0.00,0.10,1.71
2014-10-28,58,46,70,41,61,20,82,1976,1927,0.31,0.10,1.72
2014-10-29,47,39,54,41,61,22,81,1952,1922,0.00,0.10,1.16
2014-10-30,44,34,53,41,60,22,82,1895,1950,0.0,0.11,1.30
2014-10-31,40,33,47,41,60,24,82,1988,1950,0.20,0.10,2.01
2014-11-1,36,29,43,40,59,24,81,1949,1950,0.00,0.12,1.95
2014-11-2,37,24,50,40,59,16,78,1951,1961,0.00,0.11,3.86
2014-11-3,54,42,66,40,59,11,77,1951,1987,0.00,0.12,0.65
2014-11-4,51,44,57,39,58,16,78,1951,2003,0.30,0.11,1.60
2014-11-5,47,40,54,39,58,15,76,1991,1975,0.00,0.12,1.92
2014-11-6,43,39,47,39,57,20,77,1982,1975,0.09,0.12,1.20
2014-11-7,40,33,46,38,57,16,76,1971,1915,0.00,0.11,3.51
2014-11-8,42,36,48,38,56,13,76,1991,1999,0.0,0.12,1.23
2014-11-9,40,29,51,38,56,17,76,1991,1999,0.00,0.11,1.15
2014-11-10,50,38,62,37,55,17,75,1957,1949,0.00,0.11,1.54
2014-11-11,48,34,61,37,55,18,76,1995,1927,0.06,0.12,1.63
2014-11-12,31,27,34,37,54,14,75,1911,1949,0.00,0.12,1.64
2014-11-13,24,20,27,36,54,10,77,1986,1955,0.0,0.13,2.02
2014-11-14,25,17,32,36,53,11,76,1986,1971,0.0,0.14,4.15
2014-11-15,26,17,34,36,53,12,74,1933,1971,0.0,0.13,2.55
2014-11-16,32,28,35,35,52,10,74,1883,1952,0.22,0.13,1.68
2014-11-17,21,13,29,35,52,4,77,1959,1958,0.0,0.13,2.48
2014-11-18,14,9,19,34,51,5,73,1880,1941,0.0,0.14,4.30
2014-11-19,26,14,37,34,51,-3,76,1880,1930,0.0,0.13,2.74
2014-11-20,24,17,30,33,50,8,75,1914,1942,0.0,0.13,1.73
2014-11-21,23,12,33,33,49,2,73,1880,1990,0.00,0.14,3.71
2014-11-22,43,30,56,33,49,-5,74,1880,1900,0.02,0.13,1.68
2014-11-23,50,44,55,32,48,1,73,1880,1999,1.91,0.13,1.91
2014-11-24,46,36,55,32,48,-1,65,1950,1973,0.18,0.13,1.23
2014-11-25,31,26,36,31,47,-2,69,1950,1908,0.00,0.12,2.19
2014-11-26,30,26,34,31,47,3,70,1898,1984,0.00,0.12,1.16
2014-11-27,28,24,31,31,46,1,70,1930,1990,0.0,0.12,2.03
2014-11-28,27,19,34,30,46,2,69,1930,1909,0.0,0.12,1.82
2014-11-29,44,30,57,30,45,-1,70,1872,1927,0.00,0.12,1.00
2014-11-30,52,39,64,29,45,-2,68,1958,1927,0.02,0.12,1.81
2014-12-1,33,26,39,29,44,4,70,1886,1970,0.02,0.10,1.39
2014-12-2,30,26,33,29,44,-3,74,1886,1982,0.01,0.11,1.84
2014-12-3,36,28,43,28,43,-2,69,1929,1982,0.01,0.10,3.46
2014-12-4,32,29,35,28,43,5,70,1991,1998,0.06,0.10,0.98
2014-12-5,37,34,40,27,42,8,70,1886,2001,0.98,0.11,1.14
2014-12-6,35,30,40,27,42,-1,73,1977,1998,0.26,0.10,1.05
2014-12-7,33,26,39,27,41,-7,65,1882,1951,0.00,0.10,1.15
2014-12-8,36,31,41,26,41,-10,66,1882,1966,0.06,0.11,1.58
2014-12-9,36,32,39,26,41,-15,65,1876,1952,0.01,0.11,1.39
2014-12-10,33,30,35,26,40,-8,66,1917,1971,0.0,0.10,1.37
2014-12-11,31,25,37,25,40,-9,66,1977,1931,0.00,0.10,1.87
2014-12-12,29,22,35,25,40,-7,63,1962,1927,0.00,0.10,1.06
2014-12-13,40,34,46,25,39,-4,65,1962,1975,0.00,0.10,1.73
2014-12-14,45,41,48,25,39,-4,68,1901,1984,0.0,0.09,2.12
2014-12-15,43,41,44,24,39,-10,69,1951,1948,0.01,0.10,1.45
2014-12-16,39,31,46,24,38,-15,65,1951,1948,0.19,0.10,0.81
2014-12-17,27,23,31,24,38,-10,64,1989,1984,0.0,0.10,1.64
2014-12-18,25,22,27,24,38,-10,65,1884,1877,0.0,0.09,2.05
2014-12-19,24,19,29,23,38,-12,67,1884,1877,0.00,0.10,1.49
2014-12-20,29,25,32,23,37,-12,66,1963,1949,0.00,0.10,0.82
2014-12-21,28,20,36,23,37,-21,67,1989,1967,0.00,0.11,2.57
2014-12-22,37,27,46,23,37,-23,61,1989,1949,0.04,0.11,1.62
2014-12-23,50,45,55,23,37,-15,64,1989,1933,0.17,0.11,1.71
2014-12-24,40,34,45,22,37,-17,68,1983,1889,0.52,0.10,1.54
2014-12-25,36,31,41,22,36,-15,64,1983,1893,0.01,0.11,1.36
2014-12-26,41,30,52,22,36,-5,67,1983,1875,0.00,0.11,1.22
2014-12-27,40,33,46,22,36,-7,68,1925,2008,0.20,0.10,1.95
2014-12-28,28,22,33,22,36,-13,65,1924,1984,0.0,0.10,2.02
2014-12-29,29,20,37,22,36,-13,66,1880,1889,0.00,0.10,1.36
2014-12-30,23,15,31,21,36,-14,67,1983,1965,0.00,0.10,1.94
2014-12-31,16,9,23,21,36,-9,68,1967,1951,0.00,0.10,1.62
2015-1-1,26,18,34,21,36,-12,69,1968,1876,0.00,0.10,3.81
2015-1-2,32,25,39,21,36,-12,65,1887,2006,0.00,0.10,1.58
2015-1-3,43,33,52,21,36,-22,65,1879,1950,0.79,0.10,2.82
2015-1-4,28,12,44,21,36,-15,66,1879,1998,0.01,0.10,2.44
2015-1-5,9,4,14,21,35,-25,61,1884,1955,0.11,0.10,2.70
2015-1-6,14,9,19,21,35,-20,63,1884,2008,0.08,0.09,1.48
2015-1-7,3,-6,12,21,35,-17,68,1970,2008,0.0,0.09,1.58
2015-1-8,7,-7,20,21,35,-13,63,1970,1965,0.0,0.10,1.41
2015-1-9,11,1,21,21,35,-18,66,1875,1880,0.0,0.09,1.15
2015-1-10,11,-3,24,20,35,-16,62,1982,1975,0.00,0.09,1.29
2015-1-11,28,21,34,20,35,-16,70,1977,1890,0.43,0.08,1.16
2015-1-12,25,19,31,20,35,-19,69,1918,1890,0.19,0.09,1.28
2015-1-13,16,8,23,20,35,-10,64,1997,1950,0.0,0.09,1.47
2015-1-14,13,5,21,20,35,-7,65,1957,1928,0.0,0.08,1.77
2015-1-15,25,13,36,20,35,-17,66,1972,1932,0.00,0.08,1.68
2015-1-16,32,21,42,20,35,-20,66,1972,1990,0.00,0.08,1.42
2015-1-17,39,27,51,20,35,-20,67,1977,1952,0.0,0.09,1.11
2015-1-18,36,28,44,20,35,-22,64,1994,1929,0.01,0.08,1.96
2015-1-19,35,23,47,20,35,-27,69,1994,1907,0.00,0.08,0.94
2015-1-20,40,30,50,20,36,-22,70,1985,1906,0.0,0.08,1.26
2015-1-21,37,30,43,20,36,-21,68,1984,1906,0.0,0.08,2.25
2015-1-22,31,28,33,20,36,-17,64,1936,1964,0.0,0.07,1.87
2015-1-23,29,24,34,20,36,-18,67,1936,1909,0.00,0.08,0.90
2015-1-24,30,20,39,20,36,-16,70,1963,1967,0.00,0.08,1.06
2015-1-25,29,20,37,20,36,-14,71,1897,1950,0.04,0.08,0.92
2015-1-26,22,17,26,20,36,-12,67,1897,1944,0.0,0.08,1.41
2015-1-27,25,20,29,21,36,-10,66,1936,1916,0.01,0.08,1.14
2015-1-28,25,15,34,21,36,-18,65,1963,2002,0.00,0.08,2.00
2015-1-29,38,30,45,21,36,-13,66,1873,2002,0.0,0.08,1.50
2015-1-30,26,20,31,21,36,-11,65,1966,2013,0.01,0.08,1.47
2015-1-31,26,16,36,21,37,-11,66,2004,1917,0.13,0.08,1.26
2015-2-1,30,27,33,21,37,-11,63,1951,1911,0.29,0.08,1.21
2015-2-2,20,13,27,21,37,-19,59,1951,1911,0.01,0.09,1.24
2015-2-3,26,16,36,21,37,-11,62,1996,1992,0.00,0.08,2.50
2015-2-4,29,15,42,21,37,-12,69,1996,1890,0.07,0.08,1.68
2015-2-5,15,5,24,22,37,-16,66,1979,1927,0.00,0.09,1.83
2015-2-6,25,12,37,22,38,-12,64,1977,1925,0.00,0.07,1.14
2015-2-7,42,28,55,22,38,-11,66,1977,1925,0.00,0.08,1.53
2015-2-8,47,36,57,22,38,-14,68,1895,1937,0.0,0.08,0.78
2015-2-9,31,26,36,22,38,-18,66,1899,1943,0.0,0.08,2.04
2015-2-10,27,18,36,23,39,-21,73,1982,1932,0.00,0.09,1.56
2015-2-11,32,25,39,23,39,-9,75,1885,1999,0.0,0.08,1.87
2015-2-12,20,13,27,23,39,-10,66,1899,1984,0.0,0.08,2.67
2015-2-13,20,9,31,23,39,-17,67,1899,1938,0.00,0.08,1.48
2015-2-14,18,6,30,24,40,-14,65,1905,1954,0.0,0.09,1.37
2015-2-15,12,4,20,24,40,-8,72,1875,1954,0.00,0.08,1.50
2015-2-16,11,6,16,24,40,-7,72,1943,1883,0.14,0.08,1.58
2015-2-17,14,1,26,24,41,-9,69,1903,1911,0.01,0.08,0.91
2015-2-18,9,0,17,25,41,-11,66,1936,1961,0.07,0.08,1.58
2015-2-19,2,-5,9,25,41,-8,69,1979,1930,0.00,0.08,1.21
2015-2-20,6,-6,18,25,42,-8,67,1885,1983,0.0,0.08,3.02
2015-2-21,26,16,35,26,42,-4,68,1963,1930,0.47,0.08,1.53
2015-2-22,22,15,29,26,43,-9,70,1963,1922,0.0,0.08,2.09
2015-2-23,11,3,19,26,43,-1,71,1889,2000,0.01,0.09,1.10
2015-2-24,10,-6,25,26,43,-6,71,2015,2000,0.00,0.08,1.05
2015-2-25,24,14,33,27,44,-7,76,1900,2000,0.00,0.09,2.35
2015-2-26,16,7,25,27,44,-6,68,1963,1998,0.04,0.09,2.49
2015-2-27,10,2,18,27,45,-7,73,1963,1996,0.00,0.09,1.11
2015-2-28,16,7,25,28,45,2,68,1993,1895,0.23,0.09,1.17
2015-3-1,27,22,31,28,45,-4,71,1960,1976,0.53,0.10,1.90
2015-3-2,24,14,34,28,46,-7,74,1980,1992,0.00,0.09,1.56
2015-3-3,33,26,40,29,46,-5,77,1943,1992,0.28,0.09,2.01
2015-3-4,27,19,35,29,47,2,78,1873,1983,0.07,0.10,3.00
2015-3-5,16,8,23,29,47,-1,75,1978,1956,0.00,0.10,3.06
2015-3-6,16,2,30,30,47,-6,75,1960,1973,0.00,0.11,1.21
2015-3-7,32,21,42,30,48,-1,78,1943,1974,0.00,0.11,1.45
2015-3-8,38,27,49,30,48,-6,80,1943,1974,0.00,0.10,2.28
2015-3-9,40,31,48,31,49,-1,72,1984,1878,0.00,0.11,1.61
2015-3-10,45,40,49,31,49,8,74,1934,2009,0.40,0.11,2.54
2015-3-11,49,34,64,31,50,3,77,1948,1990,0.00,0.10,2.01
2015-3-12,46,31,60,32,50,-5,76,1948,1990,0.00,0.11,1.97
2015-3-13,48,42,53,32,51,3,80,1960,2007,0.51,0.11,1.51
2015-3-14,51,43,59,32,51,10,81,1891,2012,0.43,0.11,3.02
2015-3-15,49,33,65,33,51,6,80,1975,2012,0.00,0.12,3.06
2015-3-16,61,47,74,33,52,3,79,1900,1945,0.00,0.11,1.36
2015-3-17,47,33,60,33,52,2,77,1900,2012,0.00,0.12,1.35
2015-3-18,37,27,46,34,53,7,80,1941,2012,0.00,0.13,2.03
2015-3-19,45,40,50,34,53,7,82,1923,2012,0.02,0.12,1.90
2015-3-20,44,39,48,34,54,3,83,1885,2012,0.03,0.12,2.13
2015-3-21,49,34,63,34,54,11,84,1876,2012,0.00,0.12,1.06
2015-3-22,44,36,52,35,54,9,82,1888,1907,0.00,0.13,2.36
2015-3-23,36,30,41,35,55,12,80,1885,1939,0.03,0.12,1.48
2015-3-24,35,26,44,35,55,2,84,1974,1910,0.04,0.13,2.76
2015-3-25,48,42,54,36,56,5,81,1974,2007,0.63,0.13,4.70
2015-3-26,40,34,45,36,56,8,80,1955,1907,0.50,0.12,1.98
2015-3-27,28,21,35,36,56,11,84,1955,1910,0.0,0.13,2.27
2015-3-28,29,17,40,37,57,16,82,1934,1910,0.00,0.12,1.75
2015-3-29,38,24,51,37,57,16,82,1887,1895,0.06,0.13,1.46
2015-3-30,49,36,61,37,58,17,82,1923,1986,0.00,0.13,1.56
2015-3-31,54,44,63,38,58,13,85,1923,1981,0.00,0.13,1.56
2015-4-1,53,37,69,38,58,20,82,1923,2010,0.00,0.13,2.85
2015-4-2,62,52,72,38,59,20,81,1961,2010,0.29,0.12,2.08
2015-4-3,50,39,61,39,59,21,84,1879,2012,0.11,0.11,1.92
2015-4-4,43,30,55,39,59,23,80,1975,1929,0.00,0.12,1.48
2015-4-5,49,34,64,39,60,20,84,1995,1988,0.00,0.12,1.87
2015-4-6,55,47,62,40,60,22,83,1979,2010,0.0,0.12,1.90
2015-4-7,61,56,66,40,61,19,83,1982,2001,0.32,0.12,2.08
2015-4-8,64,55,72,40,61,18,83,1972,2001,0.74,0.13,2.15
2015-4-9,71,61,80,41,61,18,84,1997,2001,0.24,0.13,1.26
2015-4-10,56,42,69,41,62,23,84,1989,1930,0.04,0.13,1.97
2015-4-11,50,38,62,41,62,24,89,1973,1930,0.00,0.12,1.72
2015-4-12,57,43,70,42,62,20,86,1940,1930,0.00,0.12,0.80
2015-4-13,64,56,71,42,63,18,86,1950,1941,0.04,0.12,1.13
2015-4-14,58,51,64,42,63,24,85,1943,1883,0.00,0.13,1.55
2015-4-15,58,46,69,43,63,24,84,1928,2002,0.07,0.12,1.42
2015-4-16,61,53,68,43,64,21,87,1875,1896,0.20,0.12,2.08
2015-4-17,61,46,76,43,64,19,87,1875,1896,0.00,0.13,1.90
2015-4-18,64,51,76,44,64,19,87,1875,1977,0.00,0.13,2.29
2015-4-19,62,56,67,44,65,23,85,1983,2002,0.93,0.13,3.06
2015-4-20,52,45,58,44,65,23,85,1897,1915,0.30,0.12,2.66
2015-4-21,52,41,62,45,65,28,84,1907,1985,0.00,0.13,1.85
2015-4-22,44,37,50,45,66,27,85,1936,1960,0.10,0.13,2.34
2015-4-23,45,32,57,45,66,26,88,1986,1925,0.00,0.13,1.46
2015-4-24,47,32,62,46,66,28,90,1910,1925,0.0,0.13,1.65
2015-4-25,50,44,56,46,67,27,87,1919,1915,0.62,0.14,2.56
2015-4-26,49,39,58,46,67,31,87,1976,1915,0.00,0.13,1.48
2015-4-27,47,35,58,47,67,30,84,1976,1962,0.00,0.13,1.56
2015-4-28,51,39,63,47,68,28,86,1976,1894,0.00,0.14,1.71
2015-4-29,54,41,67,47,68,31,86,1976,1942,0.00,0.14,1.71
2015-4-30,53,44,61,48,68,31,90,1948,1942,0.0,0.14,1.66
2015-5-1,55,39,71,48,68,29,89,1963,1942,0.00,0.16,1.76
2015-5-2,62,49,75,48,69,31,87,1961,1959,0.00,0.15,2.93
2015-5-3,66,53,78,49,69,29,86,2005,1949,0.0,0.16,1.56
2015-5-4,67,58,76,49,69,30,88,1976,1949,0.13,0.16,1.47
2015-5-5,73,62,83,49,70,35,92,1957,1952,0.00,0.16,1.79
2015-5-6,74,61,86,49,70,31,89,1968,1949,0.00,0.16,2.56
2015-5-7,75,64,86,50,70,31,87,1974,1926,0.00,0.16,2.10
2015-5-8,74,63,85,50,70,31,90,1976,1896,0.0,0.16,2.34
2015-5-9,73,66,80,50,71,29,93,1947,1895,0.05,0.16,1.08
2015-5-10,74,64,83,51,71,28,92,1966,1896,0.05,0.16,1.92
2015-5-11,70,61,78,51,71,31,89,1945,1896,0.06,0.17,1.95
2015-5-12,57,50,64,51,72,33,88,1976,1881,0.00,0.17,2.15
2015-5-13,55,43,67,52,72,35,88,1878,1956,0.00,0.18,1.96
2015-5-14,60,49,70,52,72,33,88,1895,1881,0.0,0.17,1.84
2015-5-15,71,60,82,52,72,35,88,1973,2001,0.12,0.16,1.97
2015-5-16,68,64,71,53,73,32,90,1997,1900,0.53,0.17,1.60
2015-5-17,72,64,79,53,73,36,90,1973,1962,0.00,0.17,2.30
2015-5-18,71,62,79,53,73,35,90,1973,1962,0.0,0.17,2.36
2015-5-19,57,48,65,54,74,32,90,2002,1977,0.00,0.17,1.36
2015-5-20,48,43,52,54,74,38,90,1894,1977,0.0,0.16,1.68
2015-5-21,56,47,64,54,74,36,92,2002,1941,0.00,0.18,2.69
2015-5-22,60,44,76,55,75,35,93,1883,1941,0.00,0.16,2.16
2015-5-23,67,55,79,55,75,37,92,1963,1939,0.00,0.16,3.13
2015-5-24,72,59,85,55,75,37,90,1925,1965,0.00,0.17,1.55
2015-5-25,73,67,79,56,76,34,93,1925,1965,0.01,0.16,1.35
2015-5-26,72,64,80,56,76,35,94,1961,1911,0.07,0.16,2.33
2015-5-27,73,63,82,56,76,31,94,1961,1911,0.07,0.15,1.62
2015-5-28,72,60,84,57,76,39,96,1907,1911,0.00,0.16,1.50
2015-5-29,75,65,84,57,77,36,93,1965,1895,0.0,0.16,2.19
2015-5-30,75,66,84,57,77,37,94,1947,1895,1.28,0.15,3.80
2015-5-31,61,55,67,58,77,36,96,1889,1895,0.08,0.16,2.61
2015-6-1,58,52,64,58,78,39,96,2003,1934,0.0,0.14,1.95
2015-6-2,60,49,70,58,78,39,100,1956,1934,0.00,0.15,3.00
2015-6-3,61,51,71,59,78,40,100,1929,1895,0.00,0.14,2.89
2015-6-4,70,57,82,59,79,43,95,1946,2011,0.00,0.14,2.87
2015-6-5,75,63,87,59,79,40,98,1945,1925,0.00,0.15,1.51
2015-6-6,69,61,76,60,79,39,96,1894,1933,0.00,0.15,2.62
2015-6-7,75,62,88,60,80,41,98,1894,1933,0.00,0.14,3.80
2015-6-8,70,60,79,60,80,44,95,1913,1933,1.90,0.15,2.14
2015-6-9,70,58,82,60,80,41,94,1885,1914,0.00,0.14,2.11
2015-6-10,78,65,90,61,81,44,96,1988,1933,0.00,0.14,2.21
2015-6-11,80,69,90,61,81,41,96,1980,1933,0.00,0.14,2.90
2015-6-12,81,72,89,61,81,42,96,1903,1894,0.21,0.13,3.04
2015-6-13,80,71,88,62,82,45,95,1903,1894,0.00,0.14,2.20
2015-6-14,78,70,86,62,82,45,94,1955,1954,0.13,0.14,2.12
2015-6-15,79,70,88,62,82,45,94,1917,1952,0.54,0.13,1.82
2015-6-16,78,71,84,63,82,41,96,1917,1913,0.50,0.13,1.98
2015-6-17,76,72,79,63,83,46,95,1980,1913,0.75,0.14,1.73
2015-6-18,77,68,86,63,83,45,96,1960,1962,0.67,0.14,2.05
2015-6-19,73,68,78,63,83,48,97,1980,1953,0.67,0.14,1.75
2015-6-20,75,70,79,64,83,44,100,1980,1953,0.0,0.14,2.03
2015-6-21,75,65,84,64,84,41,98,1992,1988,0.27,0.14,1.95
2015-6-22,78,69,86,64,84,37,99,1992,1988,0.00,0.15,1.92
2015-6-23,76,68,83,64,84,44,96,1918,1930,0.0,0.14,2.81
2015-6-24,71,62,80,64,84,49,98,1887,1933,0.0,0.15,0.92
2015-6-25,75,66,83,65,84,46,102,1972,1988,0.85,0.15,3.73
2015-6-26,71,66,76,65,85,49,102,1979,1954,1.57,0.14,3.09
2015-6-27,66,60,72,65,85,48,100,1926,1934,0.0,0.14,2.79
2015-6-28,67,55,78,65,85,50,104,1950,2012,0.09,0.15,1.65
2015-6-29,70,63,77,65,85,48,103,1923,2012,0.21,0.14,1.63
2015-6-30,73,64,82,65,85,46,97,1943,1933,0.0,0.14,1.50


================================================
FILE: us-weather-history/KJAX.csv
================================================
date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation
2014-7-1,82,72,91,72,91,65,100,1981,1990,0.00,0.21,7.26
2014-7-2,81,73,89,72,91,65,100,2000,1902,0.00,0.21,2.30
2014-7-3,84,71,96,72,92,63,99,2000,1970,0.41,0.22,4.87
2014-7-4,81,70,91,72,92,65,99,2000,1954,0.00,0.21,1.40
2014-7-5,82,73,90,72,92,66,100,2000,1877,0.01,0.21,1.85
2014-7-6,79,70,88,72,92,66,100,1986,1969,0.00,0.21,3.49
2014-7-7,81,70,92,72,92,65,101,1988,1902,0.0,0.21,2.07
2014-7-8,82,72,91,72,92,61,100,1972,1879,0.00,0.21,3.78
2014-7-9,83,71,94,72,92,63,101,2006,1941,0.00,0.22,2.07
2014-7-10,82,73,91,72,92,65,102,2000,1879,0.31,0.21,1.45
2014-7-11,81,72,89,73,92,65,104,1963,1879,0.27,0.21,3.14
2014-7-12,76,72,79,73,92,66,102,1890,1879,0.38,0.20,3.97
2014-7-13,80,71,88,73,92,66,102,1974,1980,0.02,0.21,2.35
2014-7-14,83,73,92,73,92,63,102,1974,1951,0.00,0.20,2.11
2014-7-15,82,72,91,73,92,62,102,1974,1981,0.0,0.20,3.16
2014-7-16,81,72,89,73,92,67,102,2001,1981,0.62,0.20,3.66
2014-7-17,79,69,89,73,92,64,103,1980,1981,0.00,0.21,2.20
2014-7-18,79,71,87,73,92,68,100,2011,1887,0.71,0.21,1.10
2014-7-19,82,74,89,73,92,65,101,2011,2000,0.00,0.22,2.17
2014-7-20,83,73,92,73,92,68,103,2006,2000,0.0,0.22,3.17
2014-7-21,81,73,88,73,92,66,102,1929,1942,0.00,0.23,1.80
2014-7-22,81,72,89,73,92,67,101,1965,1942,0.00,0.22,2.50
2014-7-23,83,72,93,73,92,64,99,2007,1952,0.00,0.22,3.19
2014-7-24,83,72,93,73,92,68,100,1977,1952,0.00,0.23,2.32
2014-7-25,84,74,94,73,92,64,101,1977,1875,0.02,0.22,2.14
2014-7-26,83,73,92,73,92,67,102,2000,1926,0.0,0.22,2.94
2014-7-27,86,76,95,73,92,67,100,1978,1872,0.00,0.20,3.01
2014-7-28,87,78,96,73,92,67,104,1920,1872,0.00,0.20,4.46
2014-7-29,85,75,94,73,92,69,100,2000,1875,0.00,0.20,2.88
2014-7-30,81,70,92,73,92,68,102,1894,2010,0.00,0.20,1.74
2014-7-31,81,69,93,73,92,69,102,1914,1999,0.00,0.21,2.03
2014-8-1,84,74,93,73,92,68,102,1966,1999,0.00,0.21,3.73
2014-8-2,82,73,91,73,92,68,101,1997,1999,0.59,0.20,3.38
2014-8-3,81,73,89,73,92,69,99,1974,1957,0.70,0.21,2.72
2014-8-4,80,75,85,73,92,68,99,2001,2011,0.01,0.19,4.02
2014-8-5,83,74,92,73,92,69,99,1989,2011,0.00,0.20,3.23
2014-8-6,84,74,94,73,92,67,99,1969,1956,0.00,0.19,3.35
2014-8-7,85,74,95,73,92,66,100,1950,1956,0.00,0.20,3.30
2014-8-8,84,74,94,73,92,64,98,2002,2007,0.0,0.19,2.66
2014-8-9,84,73,95,73,92,63,101,2002,1987,0.00,0.20,2.04
2014-8-10,83,74,92,73,92,66,99,1918,1987,0.0,0.21,1.92
2014-8-11,82,72,92,73,91,68,98,2002,2011,0.70,0.21,1.46
2014-8-12,82,75,89,73,91,65,100,1989,1874,0.01,0.20,3.15
2014-8-13,80,74,86,73,91,64,99,1890,1954,0.08,0.21,2.34
2014-8-14,82,73,91,73,91,66,97,1890,1938,0.04,0.21,3.62
2014-8-15,81,73,89,73,91,67,99,1983,1956,0.00,0.21,3.06
2014-8-16,83,73,93,73,91,67,97,1954,1957,0.00,0.20,3.83
2014-8-17,83,73,93,73,91,67,99,1993,1954,0.25,0.20,1.40
2014-8-18,84,74,93,73,91,67,98,1945,1954,0.02,0.21,3.26
2014-8-19,84,73,94,73,91,64,99,1889,1900,0.0,0.21,3.22
2014-8-20,84,73,95,73,91,68,101,1960,1900,0.00,0.22,4.44
2014-8-21,85,73,97,73,91,66,101,1961,1901,0.00,0.22,3.83
2014-8-22,86,73,99,73,90,64,100,1930,1900,0.00,0.22,6.80
2014-8-23,87,75,98,73,90,65,98,1886,1966,0.0,0.24,2.51
2014-8-24,82,73,91,73,90,66,99,1996,1987,0.0,0.25,4.46
2014-8-25,80,74,86,72,90,65,98,1997,1968,0.49,0.25,3.63
2014-8-26,78,70,85,72,90,64,98,1997,1872,0.14,0.26,3.74
2014-8-27,77,66,88,72,90,64,99,1984,2011,0.00,0.26,3.42
2014-8-28,77,64,90,72,90,64,97,2014,1959,0.00,0.25,7.82
2014-8-29,80,67,92,72,90,63,99,1984,1990,0.00,0.26,2.73
2014-8-30,82,72,91,72,90,64,98,1896,1948,0.00,0.25,3.88
2014-8-31,82,71,93,72,89,64,98,1999,1970,0.00,0.26,2.93
2014-9-1,84,73,94,72,89,62,98,1999,1912,0.00,0.28,6.40
2014-9-2,83,73,92,72,89,57,98,1999,1970,0.10,0.29,4.13
2014-9-3,81,70,91,72,89,64,99,1972,1912,0.00,0.29,1.81
2014-9-4,79,70,88,72,89,66,97,1972,1951,0.0,0.29,4.08
2014-9-5,79,70,88,72,89,63,98,1997,1999,1.27,0.29,5.56
2014-9-6,80,73,86,71,89,61,98,1997,1875,0.58,0.29,5.92
2014-9-7,76,73,78,71,88,63,96,1997,1951,1.31,0.29,3.85
2014-9-8,80,71,88,71,88,61,98,2011,1990,1.42,0.28,3.11
2014-9-9,79,71,86,71,88,62,97,2011,1990,0.55,0.27,6.10
2014-9-10,80,71,88,71,88,62,97,1956,1945,0.0,0.27,6.01
2014-9-11,80,70,90,71,88,62,96,1998,1954,0.00,0.27,4.12
2014-9-12,81,71,90,71,88,61,96,1917,1991,0.00,0.28,5.43
2014-9-13,82,72,91,70,88,61,97,1917,1991,0.00,0.28,2.96
2014-9-14,82,72,91,70,87,62,96,1956,1991,0.00,0.29,1.90
2014-9-15,82,72,92,70,87,62,96,1996,1972,0.12,0.29,3.85
2014-9-16,77,72,82,70,87,57,97,2001,1990,0.74,0.28,6.61
2014-9-17,80,71,88,70,87,60,96,2001,1896,0.00,0.28,3.82
2014-9-18,81,69,92,69,87,58,95,1981,1972,0.00,0.28,3.02
2014-9-19,77,70,84,69,86,48,96,1981,1942,0.37,0.29,2.84
2014-9-20,73,68,78,69,86,48,95,1981,1925,0.23,0.28,4.68
2014-9-21,76,64,87,69,86,49,97,1897,1990,0.00,0.26,5.27
2014-9-22,76,62,89,68,86,49,96,1897,1990,0.01,0.26,4.24
2014-9-23,75,69,81,68,86,53,95,1897,1925,0.05,0.26,5.10
2014-9-24,68,63,72,68,85,53,94,1990,1925,0.00,0.26,4.46
2014-9-25,71,63,78,68,85,51,94,1990,1961,0.00,0.26,4.04
2014-9-26,74,66,82,67,85,55,96,1940,1961,0.00,0.25,9.82
2014-9-27,75,71,79,67,85,54,94,1940,1970,0.00,0.26,5.26
2014-9-28,80,73,86,67,85,56,96,1909,1900,0.00,0.24,4.80
2014-9-29,76,72,80,66,84,53,92,1993,1936,3.05,0.24,4.73
2014-9-30,74,70,77,66,84,50,94,1967,1959,0.00,0.24,3.25
2014-10-1,76,70,82,66,84,48,94,1920,1958,0.00,0.21,5.14
2014-10-2,78,70,85,65,84,43,93,1876,1977,0.0,0.20,3.57
2014-10-3,78,68,88,65,83,45,94,1876,1911,0.61,0.20,7.83
2014-10-4,68,56,80,65,83,47,94,2011,1990,0.15,0.19,2.24
2014-10-5,60,47,73,64,83,46,94,1987,1951,0.00,0.18,4.51
2014-10-6,64,50,78,64,83,49,95,1979,1951,0.00,0.17,3.48
2014-10-7,72,60,84,64,82,47,95,2010,1951,0.00,0.18,6.14
2014-10-8,76,63,89,63,82,44,95,1987,1949,0.00,0.17,3.91
2014-10-9,76,64,88,63,82,46,94,2000,1941,0.00,0.17,1.80
2014-10-10,76,65,87,63,82,46,93,2000,2009,0.00,0.16,3.56
2014-10-11,75,62,88,62,82,45,91,1906,1959,0.00,0.16,4.30
2014-10-12,74,62,86,62,81,46,92,1971,2009,0.00,0.15,2.31
2014-10-13,76,66,86,62,81,43,89,1977,1986,0.03,0.14,1.82
2014-10-14,79,70,88,61,81,40,91,1977,1912,1.04,0.12,1.99
2014-10-15,69,59,78,61,81,40,90,1977,1990,0.04,0.13,2.24
2014-10-16,66,53,78,61,80,39,90,1978,1925,0.00,0.12,6.47
2014-10-17,66,52,80,60,80,37,91,1977,1989,0.00,0.11,3.86
2014-10-18,69,54,84,60,80,36,91,1977,1980,0.00,0.11,7.47
2014-10-19,67,57,76,59,80,43,91,1981,1980,0.00,0.10,9.21
2014-10-20,69,56,82,59,80,40,89,1981,2006,0.00,0.09,3.87
2014-10-21,71,59,83,59,79,36,89,1989,1993,0.00,0.09,3.18
2014-10-22,63,52,74,58,79,39,92,1976,2006,0.00,0.09,5.26
2014-10-23,61,49,73,58,79,40,88,1976,2007,0.00,0.08,3.60
2014-10-24,61,46,75,58,79,37,87,2006,1949,0.00,0.09,4.69
2014-10-25,62,47,77,57,78,38,89,2006,2010,0.00,0.08,4.76
2014-10-26,65,47,83,57,78,38,89,1980,2010,0.00,0.07,1.79
2014-10-27,71,54,87,57,78,40,90,1926,2010,0.00,0.07,2.64
2014-10-28,70,55,84,56,78,37,89,2008,2010,0.00,0.08,1.93
2014-10-29,72,58,85,56,78,33,87,2008,1984,0.00,0.07,1.76
2014-10-30,70,59,81,56,77,37,86,1910,1951,0.00,0.08,3.97
2014-10-31,63,50,75,55,77,38,86,1993,1896,0.00,0.07,2.84
2014-11-1,50,42,57,55,77,33,88,1993,1961,0.00,0.07,2.75
2014-11-2,50,37,62,55,77,35,87,1993,1961,0.00,0.08,1.37
2014-11-3,52,36,68,54,76,30,86,1954,2004,0.00,0.07,2.50
2014-11-4,61,47,74,54,76,31,87,1966,1992,0.00,0.08,2.68
2014-11-5,67,56,78,54,76,35,87,1976,2003,0.0,0.07,1.51
2014-11-6,69,56,82,53,76,32,86,1976,2003,0.00,0.08,1.92
2014-11-7,55,43,67,53,76,36,87,1976,1986,0.00,0.07,1.64
2014-11-8,56,41,70,53,75,30,85,1976,1986,0.0,0.08,1.86
2014-11-9,62,54,70,53,75,25,88,1976,1986,0.0,0.08,1.28
2014-11-10,59,52,65,52,75,33,84,1976,1948,0.00,0.07,2.29
2014-11-11,66,52,80,52,75,35,87,1977,1986,0.00,0.07,2.79
2014-11-12,61,45,77,52,74,31,87,2011,1986,0.00,0.07,1.30
2014-11-13,64,55,73,52,74,31,86,1987,1889,0.00,0.07,0.86
2014-11-14,53,44,61,51,74,31,84,1984,2008,0.00,0.07,2.21
2014-11-15,50,40,60,51,74,29,85,1969,1958,0.00,0.07,1.50
2014-11-16,62,47,77,51,73,25,85,1940,2011,0.00,0.07,2.10
2014-11-17,67,52,81,50,73,29,85,1970,1957,0.95,0.06,1.28
2014-11-18,43,33,52,50,73,32,85,2008,1938,0.0,0.07,1.26
2014-11-19,40,27,53,50,73,27,84,2014,1948,0.00,0.07,1.21
2014-11-20,45,24,65,50,73,24,86,2014,1988,0.00,0.06,2.17
2014-11-21,51,33,68,49,72,26,84,1937,1962,0.00,0.06,1.16
2014-11-22,62,51,72,49,72,25,84,2000,1948,0.0,0.06,2.08
2014-11-23,73,68,78,49,72,28,84,2000,1992,1.11,0.06,2.01
2014-11-24,78,70,86,49,72,25,86,1970,2014,0.00,0.06,1.21
2014-11-25,66,61,71,48,71,21,85,1970,1992,1.69,0.07,1.69
2014-11-26,52,41,63,48,71,27,84,1950,1946,0.21,0.06,1.48
2014-11-27,51,40,61,48,71,30,83,1903,1973,0.00,0.07,0.94
2014-11-28,44,35,53,48,70,26,85,1903,1948,0.00,0.08,3.61
2014-11-29,51,34,68,47,70,30,85,1959,1978,0.00,0.08,2.24
2014-11-30,58,41,74,47,70,26,84,1959,1982,0.00,0.08,1.81
2014-12-1,63,49,77,47,70,28,84,1979,1981,0.00,0.08,3.43
2014-12-2,66,52,79,47,69,25,84,1876,1991,0.00,0.09,1.12
2014-12-3,66,56,76,47,69,24,83,1876,1991,0.00,0.09,2.70
2014-12-4,65,54,76,46,69,25,84,1876,1994,0.00,0.09,3.81
2014-12-5,63,60,65,46,69,25,83,1876,2013,0.00,0.09,1.28
2014-12-6,70,60,79,46,69,26,83,1876,2013,0.00,0.09,1.38
2014-12-7,59,54,63,46,68,21,83,1937,2013,0.01,0.09,2.03
2014-12-8,53,49,56,45,68,24,83,2010,1978,0.00,0.08,1.80
2014-12-9,50,40,60,45,68,28,84,1984,2004,0.00,0.09,3.29
2014-12-10,48,36,59,45,68,25,82,1974,1961,0.00,0.08,2.86
2014-12-11,47,34,59,45,67,23,83,1981,1961,0.00,0.09,1.34
2014-12-12,50,35,64,45,67,22,83,1962,1961,0.00,0.09,2.72
2014-12-13,50,31,68,44,67,12,83,1962,1956,0.00,0.09,2.86
2014-12-14,51,31,71,44,67,20,82,2010,1956,0.00,0.08,2.07
2014-12-15,52,34,70,44,67,21,84,2010,2009,0.00,0.09,1.59
2014-12-16,55,34,76,44,66,26,83,1968,1956,0.00,0.09,1.86
2014-12-17,53,40,66,44,66,26,81,1943,1961,0.00,0.09,3.96
2014-12-18,52,39,64,43,66,25,81,1988,1961,0.00,0.10,1.54
2014-12-19,56,41,70,43,66,23,83,1975,1967,0.0,0.10,2.14
2014-12-20,60,51,69,43,66,19,82,1981,1990,0.0,0.10,2.86
2014-12-21,57,56,58,43,66,20,83,1901,2013,0.16,0.10,4.69
2014-12-22,63,56,70,43,65,25,84,1973,2013,2.59,0.10,2.89
2014-12-23,68,62,74,43,65,24,84,1989,1956,0.74,0.09,1.41
2014-12-24,72,64,79,42,65,18,81,1983,1981,0.22,0.10,1.65
2014-12-25,53,42,64,42,65,11,81,1983,1984,0.00,0.09,2.72
2014-12-26,53,38,67,42,65,13,83,1983,1987,0.00,0.10,1.59
2014-12-27,62,49,74,42,65,23,81,1935,2008,0.00,0.08,1.63
2014-12-28,68,54,82,42,65,20,83,2010,1971,0.00,0.09,2.61
2014-12-29,69,58,79,42,65,14,82,1894,1990,0.02,0.08,2.06
2014-12-30,64,56,71,42,65,19,83,1909,1990,0.0,0.09,1.12
2014-12-31,53,49,57,42,65,19,82,1917,1973,0.00,0.09,1.83
2015-1-1,55,48,61,41,65,21,81,1918,1967,0.0,0.09,1.20
2015-1-2,53,48,57,42,65,21,82,1928,1988,0.00,0.09,1.83
2015-1-3,71,60,81,41,64,19,81,1979,2015,0.0,0.09,1.97
2015-1-4,72,65,79,41,64,22,82,1887,1973,0.27,0.09,1.74
2015-1-5,56,45,66,41,64,23,81,1999,1972,0.00,0.09,2.40
2015-1-6,56,40,71,41,64,21,83,1999,2007,0.00,0.09,1.97
2015-1-7,52,39,65,41,64,21,82,1924,2007,0.00,0.10,2.96
2015-1-8,34,27,40,41,64,21,82,1970,2005,0.0,0.09,3.09
2015-1-9,45,35,55,41,64,19,83,1970,1974,0.00,0.11,2.10
2015-1-10,47,38,56,41,64,21,82,1970,1957,0.00,0.10,2.11
2015-1-11,56,48,63,41,64,17,83,1982,1972,0.07,0.11,2.69
2015-1-12,62,57,66,41,64,15,82,1886,1949,1.07,0.11,2.68
2015-1-13,58,52,64,41,64,13,82,1981,1972,0.0,0.11,2.23
2015-1-14,50,47,53,41,65,23,81,1964,1888,0.00,0.11,2.35
2015-1-15,45,42,48,41,65,22,83,1979,1989,0.19,0.11,0.89
2015-1-16,49,38,60,41,65,22,84,1927,1947,0.0,0.10,1.67
2015-1-17,51,35,66,41,65,21,84,1977,1943,0.00,0.11,2.43
2015-1-18,56,42,69,41,65,21,83,1977,1937,0.00,0.11,0.62
2015-1-19,51,33,68,41,65,19,83,1977,1937,0.00,0.11,2.90
2015-1-20,54,36,71,41,65,19,83,1985,1990,0.00,0.11,1.61
2015-1-21,62,48,75,41,65,7,83,1985,1921,0.0,0.12,1.71
2015-1-22,59,45,72,42,65,16,82,1985,1999,0.00,0.12,1.21
2015-1-23,60,52,67,42,65,25,82,1960,1974,1.34,0.11,2.33
2015-1-24,55,40,69,42,65,19,83,2003,1937,0.12,0.12,2.18
2015-1-25,49,35,63,42,65,20,83,2003,1990,0.00,0.12,2.64
2015-1-26,52,44,60,42,65,17,82,1905,1950,0.02,0.12,2.31
2015-1-27,50,36,63,42,66,17,84,1940,1962,0.00,0.11,1.11
2015-1-28,45,34,55,42,66,16,83,1986,1957,0.00,0.12,2.06
2015-1-29,49,31,67,42,66,22,84,1897,1957,0.00,0.11,1.63
2015-1-30,55,42,67,42,66,20,85,1966,2013,0.00,0.12,1.14
2015-1-31,48,36,60,42,66,20,84,1966,1982,0.00,0.11,3.43
2015-2-1,56,38,74,43,66,24,83,1977,1957,0.00,0.11,2.82
2015-2-2,55,41,68,43,66,23,84,1979,1990,0.32,0.11,3.68
2015-2-3,44,31,56,43,66,16,86,1917,1990,0.00,0.11,4.93
2015-2-4,51,45,56,43,67,23,84,1981,1957,0.42,0.11,0.99
2015-2-5,53,42,64,43,67,19,87,1996,1957,0.87,0.10,2.00
2015-2-6,47,37,57,43,67,23,85,2009,1989,0.00,0.11,3.06
2015-2-7,50,34,66,43,67,25,80,1988,1904,0.00,0.10,1.89
2015-2-8,57,40,73,44,67,14,84,1895,1957,0.00,0.11,2.49
2015-2-9,55,42,68,44,67,19,88,1895,1937,0.13,0.11,1.64
2015-2-10,53,45,60,44,67,26,84,1979,1957,0.00,0.10,1.22
2015-2-11,52,40,64,44,68,24,84,1971,1939,0.00,0.11,1.80
2015-2-12,56,41,70,44,68,24,83,2012,1965,0.00,0.11,1.64
2015-2-13,42,32,51,44,68,10,85,1899,1959,0.00,0.10,1.86
2015-2-14,46,29,62,45,68,16,86,1899,1959,0.00,0.11,2.66
2015-2-15,53,42,63,45,68,23,83,1943,2001,0.00,0.11,2.65
2015-2-16,59,43,74,45,68,25,86,1991,1990,0.00,0.12,2.46
2015-2-17,56,45,66,45,69,23,83,2007,1956,0.62,0.12,2.44
2015-2-18,46,35,57,45,69,18,85,1900,1956,0.00,0.12,2.07
2015-2-19,35,27,42,45,69,24,86,1958,1891,0.00,0.12,2.66
2015-2-20,36,24,47,46,69,24,86,2015,1961,0.00,0.12,3.19
2015-2-21,50,30,70,46,69,25,85,1958,1997,0.00,0.12,1.77
2015-2-22,65,50,79,46,70,29,85,1958,1961,0.00,0.13,2.53
2015-2-23,65,53,76,46,70,25,85,1978,1962,0.00,0.12,1.59
2015-2-24,51,49,53,46,70,27,88,1989,1962,0.32,0.12,1.47
2015-2-25,56,47,65,47,70,24,85,1989,2001,0.0,0.13,3.20
2015-2-26,57,47,67,47,70,22,88,1967,1962,0.62,0.12,1.19
2015-2-27,49,44,53,47,70,25,86,1974,1962,0.00,0.12,3.28
2015-2-28,51,48,54,47,71,25,87,2002,1962,0.02,0.12,1.98
2015-3-1,56,49,63,47,71,28,87,2002,1997,0.20,0.14,4.36
2015-3-2,67,53,81,47,71,27,86,1890,1997,0.00,0.13,3.30
2015-3-3,69,57,81,48,71,23,88,1980,2012,0.00,0.15,2.38
2015-3-4,74,62,85,48,71,24,86,1980,1953,0.00,0.14,1.31
2015-3-5,72,58,85,48,72,28,87,1971,1985,0.15,0.14,3.00
2015-3-6,55,45,64,48,72,28,88,1960,1961,0.0,0.15,1.60
2015-3-7,50,39,61,48,72,29,87,1899,1956,0.00,0.14,2.71
2015-3-8,54,38,70,48,72,26,88,1899,1945,0.00,0.13,0.93
2015-3-9,61,45,77,49,72,27,88,1996,1974,0.00,0.13,4.87
2015-3-10,69,52,86,49,73,26,91,1932,1974,0.00,0.13,1.69
2015-3-11,75,64,86,49,73,31,89,1934,1967,0.0,0.13,2.66
2015-3-12,65,54,76,49,73,32,91,2011,1967,0.00,0.13,1.41
2015-3-13,72,64,80,49,73,32,88,1998,1982,0.01,0.14,1.74
2015-3-14,77,66,87,49,73,28,89,1926,1956,0.0,0.12,1.40
2015-3-15,72,61,83,50,74,27,90,1993,1967,0.00,0.13,2.63
2015-3-16,68,51,84,50,74,30,89,1988,1945,0.00,0.13,2.35
2015-3-17,71,54,88,50,74,30,88,1890,2002,0.00,0.12,2.63
2015-3-18,67,59,74,50,74,32,89,1971,1963,0.00,0.13,3.02
2015-3-19,69,59,78,50,74,29,89,1892,1955,0.01,0.12,1.51
2015-3-20,70,61,79,50,75,34,89,1923,1945,0.00,0.12,3.04
2015-3-21,73,64,82,51,75,30,90,1971,1907,0.53,0.12,0.95
2015-3-22,70,62,77,51,75,31,90,1876,1935,0.08,0.11,1.77
2015-3-23,63,59,67,51,75,33,91,1986,1935,0.47,0.11,2.34
2015-3-24,62,57,66,51,75,35,91,1968,1907,0.03,0.11,3.31
2015-3-25,67,59,75,51,75,34,90,2008,1954,0.00,0.12,2.34
2015-3-26,68,58,77,51,76,32,88,1979,1949,0.53,0.12,1.65
2015-3-27,64,51,76,51,76,32,89,1894,1991,0.28,0.13,3.91
2015-3-28,53,42,63,52,76,35,89,2013,1994,0.00,0.12,7.12
2015-3-29,51,39,62,52,76,34,89,1955,1991,0.00,0.12,1.92
2015-3-30,59,40,78,52,76,36,89,1964,1961,0.00,0.12,1.43
2015-3-31,68,55,81,52,76,32,88,1964,1954,0.00,0.12,4.31
2015-4-1,71,58,84,52,77,34,89,1987,1974,0.05,0.11,2.24
2015-4-2,68,54,81,52,77,36,90,1987,2012,0.00,0.10,3.24
2015-4-3,73,59,87,52,77,39,89,1962,2012,0.00,0.11,7.35
2015-4-4,71,59,83,53,77,38,90,1992,1974,0.12,0.10,1.24
2015-4-5,68,60,75,53,77,35,89,1975,1880,0.00,0.10,2.46
2015-4-6,70,60,80,53,77,34,90,1891,1947,0.00,0.09,1.80
2015-4-7,73,60,86,53,78,38,92,1974,1967,0.46,0.10,1.82
2015-4-8,75,63,86,53,78,31,91,2007,1978,0.00,0.10,1.92
2015-4-9,74,62,85,53,78,37,92,2000,2011,0.00,0.10,1.75
2015-4-10,77,65,89,54,78,37,92,2000,2011,0.00,0.10,3.83
2015-4-11,74,65,82,54,78,37,91,1996,2011,0.00,0.09,2.80
2015-4-12,75,69,81,54,79,41,92,1989,1965,0.48,0.10,1.70
2015-4-13,76,69,83,54,79,35,92,1940,2001,0.22,0.09,1.68
2015-4-14,78,67,88,54,79,40,90,1907,1922,0.0,0.10,2.42
2015-4-15,76,67,85,54,79,39,90,2004,1972,0.11,0.08,2.08
2015-4-16,67,64,70,55,79,40,90,2008,1972,0.0,0.09,0.91
2015-4-17,70,62,78,55,79,38,93,1950,1967,0.00,0.09,1.00
2015-4-18,75,63,87,55,80,37,94,2001,1967,0.27,0.08,2.60
2015-4-19,77,68,86,55,80,36,92,2001,1968,0.00,0.09,4.53
2015-4-20,70,63,77,55,80,40,92,1983,1968,0.04,0.09,2.46
2015-4-21,72,62,81,56,80,38,95,1983,1968,0.00,0.08,2.29
2015-4-22,70,54,85,56,80,42,93,1950,1968,0.00,0.08,1.21
2015-4-23,74,60,88,56,81,37,92,1993,1970,0.00,0.08,2.40
2015-4-24,68,61,75,56,81,39,93,1986,1958,0.00,0.07,1.04
2015-4-25,70,60,79,57,81,40,92,2005,1958,0.91,0.07,1.89
2015-4-26,80,70,90,57,81,40,92,1974,2011,0.00,0.07,0.86
2015-4-27,71,65,76,57,82,42,94,1977,1986,0.00,0.07,1.81
2015-4-28,68,65,71,57,82,43,94,1977,1991,0.01,0.07,2.65
2015-4-29,71,62,79,58,82,43,93,1973,1991,0.00,0.07,1.50
2015-4-30,68,57,79,58,82,42,93,1874,1991,0.00,0.07,2.39
2015-5-1,66,53,78,58,82,47,96,2000,1990,0.00,0.06,1.99
2015-5-2,63,49,77,59,83,47,95,1941,1990,0.00,0.06,2.28
2015-5-3,66,51,81,59,83,47,94,1981,2002,0.00,0.06,2.17
2015-5-4,67,54,80,59,83,45,93,1971,2006,0.00,0.06,4.21
2015-5-5,70,59,80,59,83,46,94,1976,1898,0.00,0.07,0.95
2015-5-6,71,61,80,60,84,45,96,1973,2012,0.0,0.07,1.64
2015-5-7,73,59,86,60,84,49,94,1992,1977,0.0,0.07,3.79
2015-5-8,76,64,87,60,84,45,96,1992,1955,0.0,0.07,1.29
2015-5-9,76,62,89,61,84,48,96,1928,1962,0.00,0.07,1.24
2015-5-10,78,63,92,61,84,49,93,1984,1899,0.00,0.07,1.18
2015-5-11,80,67,92,61,85,47,95,1986,1973,0.00,0.07,2.42
2015-5-12,80,67,92,62,85,47,97,1981,1967,0.00,0.07,1.90
2015-5-13,80,67,92,62,85,47,100,1941,1967,0.00,0.06,8.18
2015-5-14,76,67,85,62,85,47,99,2013,1967,0.00,0.07,2.02
2015-5-15,76,70,81,62,85,52,97,2002,1967,0.00,0.07,2.18
2015-5-16,71,60,82,63,86,52,96,1951,1995,0.00,0.06,0.63
2015-5-17,75,63,86,63,86,46,96,1973,1995,0.00,0.07,1.36
2015-5-18,76,65,87,63,86,46,96,2011,1899,0.00,0.07,4.94
2015-5-19,81,69,93,64,86,51,97,1977,1960,0.14,0.07,2.14
2015-5-20,80,67,92,64,86,46,99,1894,1962,0.00,0.07,5.07
2015-5-21,82,69,94,64,87,50,99,2002,1938,0.00,0.09,2.61
2015-5-22,75,64,86,65,87,47,99,1981,1938,0.00,0.09,7.59
2015-5-23,73,63,83,65,87,48,97,1993,1953,0.00,0.09,2.87
2015-5-24,77,70,84,65,87,52,98,2001,1953,0.0,0.10,4.09
2015-5-25,79,70,87,66,87,55,99,1892,1953,0.0,0.10,2.74
2015-5-26,80,71,88,66,87,47,99,1979,1953,0.00,0.10,2.40
2015-5-27,78,69,86,66,88,49,98,1979,1962,0.00,0.11,5.40
2015-5-28,73,62,84,66,88,52,99,1988,1967,0.00,0.10,4.47
2015-5-29,74,62,85,67,88,53,97,1988,1967,0.00,0.11,2.83
2015-5-30,73,65,81,67,88,51,97,1971,1945,0.94,0.12,1.82
2015-5-31,77,67,86,67,88,50,98,1984,1945,0.0,0.13,3.38
2015-6-1,78,66,89,67,88,47,99,1984,1945,0.72,0.16,2.80
2015-6-2,75,64,85,68,88,53,100,1984,1985,0.03,0.17,3.13
2015-6-3,78,68,88,68,89,57,100,1966,1985,0.24,0.17,5.31
2015-6-4,77,67,86,68,89,61,100,1966,1985,0.0,0.17,5.85
2015-6-5,77,67,86,68,89,59,100,1972,1943,0.02,0.17,3.31
2015-6-6,78,66,89,69,89,60,99,1971,1985,0.00,0.18,5.92
2015-6-7,77,66,87,69,89,59,100,2006,1993,0.00,0.19,4.72
2015-6-8,79,68,89,69,89,58,99,1998,1993,0.00,0.19,3.42
2015-6-9,82,73,91,69,89,58,101,1987,1872,0.00,0.20,2.36
2015-6-10,78,70,86,69,89,58,100,1913,1954,0.92,0.20,5.11
2015-6-11,78,67,89,70,90,57,99,1960,2010,0.00,0.21,3.44
2015-6-12,80,68,92,70,90,54,99,1913,1998,0.0,0.22,3.38
2015-6-13,82,71,93,70,90,57,100,1979,1977,0.00,0.23,6.07
2015-6-14,81,68,94,70,90,57,101,1972,1998,0.00,0.23,4.15
2015-6-15,81,68,94,70,90,57,102,1995,1981,0.00,0.23,3.60
2015-6-16,83,69,96,70,90,62,102,2002,1981,0.00,0.24,2.45
2015-6-17,85,72,97,70,90,61,100,1961,1944,0.00,0.24,2.49
2015-6-18,85,72,97,71,90,59,100,2012,1950,0.00,0.24,2.78
2015-6-19,86,73,98,71,90,60,103,1899,1998,0.71,0.24,4.37
2015-6-20,86,74,97,71,90,60,100,1899,1990,0.0,0.23,2.15
2015-6-21,86,76,96,71,91,60,99,1965,1950,0.06,0.23,2.85
2015-6-22,84,70,97,71,91,62,99,1965,1970,1.70,0.24,2.11
2015-6-23,81,71,91,71,91,63,99,1879,1981,0.25,0.23,2.27
2015-6-24,85,74,95,71,91,63,101,2003,1914,0.00,0.23,4.60
2015-6-25,81,73,89,71,91,64,99,1889,1950,1.16,0.23,7.36
2015-6-26,82,73,90,72,91,63,101,1966,1952,0.02,0.23,5.20
2015-6-27,84,75,92,72,91,65,102,1936,1950,0.0,0.24,1.39
2015-6-28,82,73,91,72,91,63,101,2012,1954,0.23,0.23,1.37
2015-6-29,79,71,86,72,91,63,100,1981,1998,0.23,0.24,5.90
2015-6-30,79,69,89,72,91,66,100,1974,1990,0.03,0.24,5.91


================================================
FILE: us-weather-history/KMDW.csv
================================================
date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation
2014-7-1,76,68,84,67,84,49,103,1982,1956,0.04,0.11,1.23
2014-7-2,66,58,74,67,84,48,99,1930,1970,0.12,0.10,2.50
2014-7-3,66,57,75,67,84,50,102,1940,1949,0.05,0.11,1.38
2014-7-4,70,60,79,67,84,49,101,1940,2012,0.00,0.11,0.94
2014-7-5,71,63,79,67,84,46,103,1972,2012,0.01,0.13,1.18
2014-7-6,78,66,89,67,84,49,105,1983,2012,0.00,0.12,3.93
2014-7-7,81,72,89,67,85,48,102,1983,1936,0.48,0.13,1.30
2014-7-8,77,70,84,68,85,49,106,1934,1936,0.48,0.13,2.41
2014-7-9,73,64,81,68,85,51,100,1941,1936,0.00,0.12,2.43
2014-7-10,71,63,78,68,85,51,106,1941,1936,0.00,0.13,0.98
2014-7-11,73,63,83,68,85,51,107,1978,1936,0.0,0.13,6.16
2014-7-12,76,70,81,68,85,55,100,1975,1936,3.31,0.13,6.16
2014-7-13,79,71,86,68,85,50,106,1940,1995,0.04,0.12,1.49
2014-7-14,73,64,81,68,85,52,104,1930,1936,0.37,0.13,1.52
2014-7-15,64,57,71,68,85,49,103,1930,1988,0.05,0.13,1.36
2014-7-16,65,56,73,68,85,49,100,1945,1931,0.00,0.13,2.48
2014-7-17,68,58,78,68,85,51,100,1940,1942,0.00,0.12,5.72
2014-7-18,73,64,81,68,84,53,101,1979,1930,0.00,0.13,2.36
2014-7-19,71,62,80,68,84,55,104,1947,1930,0.00,0.13,3.69
2014-7-20,75,66,84,68,84,50,103,1941,1934,0.00,0.13,3.03
2014-7-21,78,68,87,68,84,51,108,1970,1934,0.00,0.14,1.43
2014-7-22,82,72,91,68,84,49,104,1947,1934,0.19,0.14,1.82
2014-7-23,70,64,76,68,84,51,109,1947,1934,0.00,0.14,2.31
2014-7-24,68,60,75,68,84,55,107,2000,1934,0.00,0.14,4.77
2014-7-25,69,60,77,68,84,58,105,2013,1934,0.0,0.13,1.83
2014-7-26,78,70,85,68,84,53,102,1962,1936,0.00,0.14,1.77
2014-7-27,78,66,89,68,84,51,100,1937,1955,0.04,0.14,4.71
2014-7-28,68,62,73,68,84,53,100,1981,1983,0.00,0.14,1.25
2014-7-29,71,60,81,68,84,50,101,1981,1941,0.00,0.14,1.47
2014-7-30,71,61,80,68,84,51,104,1936,1999,0.00,0.14,2.43
2014-7-31,74,63,85,67,83,50,99,1936,2006,0.00,0.15,1.20
2014-8-1,75,67,83,67,83,52,100,1947,1988,0.21,0.15,2.55
2014-8-2,74,63,84,67,83,56,102,1965,1991,0.00,0.14,3.58
2014-8-3,77,66,87,67,83,52,101,1950,1930,0.00,0.13,1.95
2014-8-4,78,68,88,67,83,52,100,1929,1947,1.79,0.14,2.17
2014-8-5,74,68,80,67,83,52,100,1974,1947,0.07,0.13,1.11
2014-8-6,73,65,81,67,83,50,101,1948,1947,0.00,0.13,2.17
2014-8-7,75,69,81,67,83,53,96,1976,1984,0.00,0.14,1.67
2014-8-8,76,70,82,67,83,52,103,1976,1934,0.00,0.13,2.35
2014-8-9,74,68,80,67,83,51,104,1972,1934,0.00,0.13,2.13
2014-8-10,77,71,82,67,83,49,99,1972,1944,0.00,0.13,2.70
2014-8-11,76,68,84,67,83,50,100,1982,1941,1.16,0.12,2.36
2014-8-12,67,63,70,67,83,50,100,1930,1995,0.16,0.14,1.18
2014-8-13,71,59,83,67,83,52,101,1967,1936,0.00,0.13,1.09
2014-8-14,67,61,73,67,82,48,99,1964,1944,0.00,0.14,1.79
2014-8-15,67,55,79,67,82,48,98,1929,1944,0.00,0.14,2.07
2014-8-16,73,64,81,67,82,49,101,1976,1988,0.00,0.13,3.79
2014-8-17,69,66,72,67,82,48,99,1976,1988,0.00,0.13,2.60
2014-8-18,76,67,85,66,82,49,99,1963,1936,0.00,0.13,1.80
2014-8-19,80,73,86,66,82,49,99,1933,1983,0.13,0.12,1.39
2014-8-20,78,68,87,66,82,47,98,1950,1947,0.00,0.12,1.47
2014-8-21,77,71,83,66,82,46,99,1950,1955,1.29,0.13,1.58
2014-8-22,81,74,87,66,82,51,103,1928,1936,3.17,0.12,3.17
2014-8-23,79,71,87,66,82,52,97,1940,1947,0.50,0.11,2.49
2014-8-24,81,73,89,65,82,48,100,1942,1947,0.00,0.13,1.20
2014-8-25,83,73,92,65,81,47,95,1934,1991,0.78,0.11,1.88
2014-8-26,79,72,86,65,81,48,97,1982,1973,0.00,0.12,3.66
2014-8-27,76,70,81,65,81,46,97,1982,1973,0.00,0.13,1.57
2014-8-28,73,69,77,65,81,46,97,1982,1955,0.02,0.12,1.57
2014-8-29,81,73,88,64,81,43,98,1965,1953,0.00,0.12,2.02
2014-8-30,80,74,86,64,81,47,96,1946,1953,0.0,0.12,1.43
2014-8-31,79,71,87,64,80,46,99,1935,1953,0.00,0.13,1.62
2014-9-1,80,74,85,63,80,45,101,1949,1953,0.0,0.12,2.92
2014-9-2,77,69,85,63,80,44,101,1946,1953,0.00,0.12,1.37
2014-9-3,76,67,85,63,80,47,97,1974,1953,0.00,0.11,1.92
2014-9-4,80,73,87,62,79,43,95,1938,1960,0.22,0.12,3.05
2014-9-5,78,67,89,62,79,48,97,1988,1954,0.43,0.11,1.89
2014-9-6,69,62,76,62,79,46,98,1984,1990,0.00,0.12,0.83
2014-9-7,69,58,79,61,79,46,101,1986,1939,0.00,0.12,3.51
2014-9-8,70,60,79,61,78,45,98,1986,1933,0.00,0.11,1.37
2014-9-9,74,66,81,61,78,47,95,1975,1983,0.01,0.11,3.25
2014-9-10,70,58,82,60,78,44,96,1943,2013,1.61,0.11,1.61
2014-9-11,55,52,58,60,77,44,95,1943,1952,0.00,0.11,4.16
2014-9-12,52,46,57,59,77,44,96,1955,1952,0.10,0.11,2.43
2014-9-13,51,42,60,59,77,40,99,1953,1939,0.01,0.11,3.62
2014-9-14,57,46,67,58,76,42,101,1949,1939,0.00,0.11,2.19
2014-9-15,58,54,62,58,76,41,102,1949,1939,0.0,0.11,1.07
2014-9-16,58,47,69,57,75,39,93,1984,1931,0.00,0.11,1.70
2014-9-17,61,49,72,57,75,39,93,1937,1955,0.00,0.10,1.20
2014-9-18,63,53,72,57,75,39,94,1929,1955,0.00,0.11,1.95
2014-9-19,67,57,76,56,74,37,93,1929,1955,0.00,0.12,0.94
2014-9-20,72,63,80,56,74,38,92,1956,1931,0.12,0.12,1.12
2014-9-21,61,52,70,55,73,40,92,1930,1970,0.0,0.11,1.67
2014-9-22,59,48,69,55,73,34,93,1974,1937,0.00,0.11,1.09
2014-9-23,63,50,76,54,73,35,93,1974,1937,0.00,0.11,2.09
2014-9-24,66,54,77,54,72,30,91,1928,2007,0.00,0.11,1.85
2014-9-25,68,58,78,53,72,30,91,1942,1986,0.00,0.12,2.00
2014-9-26,70,59,81,53,71,30,93,1928,1998,0.00,0.10,2.72
2014-9-27,70,61,79,52,71,31,91,1942,1971,0.00,0.10,1.43
2014-9-28,69,58,79,52,70,29,92,1942,1953,0.00,0.10,3.65
2014-9-29,71,57,84,52,70,33,99,1942,1953,0.0,0.10,1.13
2014-9-30,56,51,61,51,69,34,92,1984,1971,0.0,0.09,0.76
2014-10-1,63,52,73,51,69,32,92,1974,1971,0.0,0.10,0.73
2014-10-2,71,66,75,50,68,28,91,1974,1971,0.71,0.09,3.80
2014-10-3,57,46,68,50,68,29,91,1974,1954,1.05,0.10,3.95
2014-10-4,43,37,49,50,68,34,90,1989,1951,0.05,0.11,1.89
2014-10-5,49,42,56,49,67,31,89,1980,1997,0.0,0.10,2.01
2014-10-6,56,48,63,49,67,28,94,1952,1963,0.01,0.10,1.57
2014-10-7,59,49,69,48,66,31,88,2000,2007,0.00,0.10,1.26
2014-10-8,58,48,67,48,66,30,88,1952,2007,0.00,0.09,1.32
2014-10-9,57,50,64,48,66,32,87,1964,2010,0.00,0.11,2.27
2014-10-10,52,44,59,48,65,27,87,1964,1938,0.00,0.10,3.94
2014-10-11,50,40,60,47,65,29,88,2009,1928,0.00,0.10,1.47
2014-10-12,55,45,64,47,64,30,86,1987,2008,0.01,0.10,1.40
2014-10-13,65,59,71,46,64,30,88,1987,1975,0.44,0.10,2.43
2014-10-14,61,54,67,46,64,28,89,1937,1975,1.18,0.11,1.18
2014-10-15,56,53,58,46,63,29,88,1937,1947,0.07,0.10,1.66
2014-10-16,56,53,58,46,63,30,87,1944,1963,0.0,0.10,0.81
2014-10-17,56,49,63,45,62,26,86,1948,1950,0.01,0.10,1.48
2014-10-18,46,39,52,45,62,20,87,1948,1950,0.02,0.11,3.18
2014-10-19,47,36,57,45,62,24,84,1930,1971,0.00,0.10,1.41
2014-10-20,58,50,66,44,61,20,86,1930,1953,0.00,0.10,0.86
2014-10-21,52,49,54,44,61,26,87,1952,1953,0.00,0.11,2.24
2014-10-22,50,43,56,44,60,24,87,1930,1953,0.00,0.11,1.77
2014-10-23,52,41,62,44,60,25,85,1981,1963,0.0,0.11,1.48
2014-10-24,61,54,68,43,60,20,83,1981,1963,0.00,0.10,1.38
2014-10-25,64,52,75,43,59,25,79,1962,1989,0.00,0.11,1.87
2014-10-26,55,44,66,43,59,22,84,1942,1963,0.00,0.11,1.49
2014-10-27,67,54,79,42,58,24,82,1942,1940,0.02,0.11,1.56
2014-10-28,57,48,66,42,58,25,81,1976,1999,0.03,0.12,0.65
2014-10-29,45,40,49,42,58,22,79,1980,1999,0.00,0.11,1.02
2014-10-30,45,37,53,41,57,25,85,1988,1950,0.0,0.12,1.24
2014-10-31,42,34,49,41,57,27,84,1996,1950,0.04,0.11,2.63
2014-11-1,39,33,44,41,56,22,81,1930,1950,0.0,0.12,2.45
2014-11-2,41,29,53,40,56,14,78,1951,1974,0.00,0.11,1.20
2014-11-3,55,45,64,40,55,11,76,1951,1987,0.00,0.11,1.72
2014-11-4,53,45,61,40,55,15,74,1951,1981,0.02,0.12,1.83
2014-11-5,51,39,63,39,54,11,79,1951,1978,0.0,0.12,1.26
2014-11-6,46,40,52,39,54,19,75,1991,1975,0.0,0.11,1.05
2014-11-7,41,37,45,38,53,16,74,1991,1945,0.00,0.12,1.17
2014-11-8,44,39,49,38,53,14,74,1991,1999,0.0,0.11,1.17
2014-11-9,45,36,53,38,52,16,75,1991,1999,0.00,0.11,2.69
2014-11-10,55,44,65,37,52,17,71,1933,1999,0.00,0.11,3.49
2014-11-11,47,33,61,37,51,15,74,1950,1964,0.23,0.11,1.30
2014-11-12,30,27,33,36,51,10,70,1986,1949,0.0,0.12,0.70
2014-11-13,29,26,31,36,50,7,74,1986,1989,0.0,0.12,1.09
2014-11-14,27,24,29,35,50,12,78,1940,1971,0.0,0.12,1.60
2014-11-15,27,21,33,35,49,8,72,1933,1990,0.0,0.12,0.89
2014-11-16,29,25,33,34,48,5,73,1933,1952,0.0,0.12,1.29
2014-11-17,19,13,25,34,48,3,74,1959,1975,0.0,0.11,1.26
2014-11-18,17,12,21,34,47,12,72,2014,1999,0.0,0.12,2.23
2014-11-19,26,19,33,33,47,17,77,1951,1930,0.0,0.11,0.74
2014-11-20,24,17,30,33,46,14,69,1964,1930,0.00,0.11,1.23
2014-11-21,25,13,36,32,46,7,66,1964,1990,0.0,0.12,1.35
2014-11-22,44,34,54,32,45,2,67,1929,2010,0.05,0.11,1.30
2014-11-23,52,50,54,31,45,-1,70,1950,1931,0.62,0.12,1.98
2014-11-24,39,27,51,31,44,-2,69,1950,1966,0.35,0.12,0.75
2014-11-25,26,22,30,30,43,0,61,1950,2006,0.0,0.10,0.76
2014-11-26,27,22,32,30,43,2,68,1930,1990,0.0,0.11,1.20
2014-11-27,25,19,30,29,42,0,70,1930,1990,0.0,0.11,3.34
2014-11-28,28,20,36,29,42,-3,69,1930,1998,0.0,0.11,1.56
2014-11-29,44,33,54,28,41,-1,69,1929,1998,0.00,0.11,1.31
2014-11-30,43,29,56,28,41,-1,66,1947,1962,0.0,0.11,0.60
2014-12-1,25,21,29,28,40,5,68,1930,1970,0.0,0.10,1.04
2014-12-2,28,22,33,27,40,-1,69,1942,1982,0.0,0.10,2.78
2014-12-3,33,26,39,27,40,-8,72,1940,2012,0.00,0.10,0.68
2014-12-4,31,25,37,26,39,3,68,1991,1998,0.00,0.09,1.19
2014-12-5,38,35,41,26,39,7,68,2005,2001,0.00,0.10,0.95
2014-12-6,39,34,43,26,38,0,65,1972,1951,0.00,0.09,1.38
2014-12-7,35,30,39,25,38,-2,61,1936,1946,0.00,0.09,1.26
2014-12-8,37,35,39,25,37,0,64,1958,1946,0.10,0.09,0.77
2014-12-9,37,33,40,24,37,-8,61,1958,1946,0.02,0.09,1.31
2014-12-10,34,31,36,24,37,-8,60,1978,1971,0.0,0.10,2.18
2014-12-11,30,26,34,24,36,-5,61,1972,1949,0.00,0.09,1.73
2014-12-12,32,25,39,24,36,-7,62,1962,1991,0.0,0.08,1.76
2014-12-13,43,37,49,23,36,-3,67,2000,1975,0.00,0.08,1.24
2014-12-14,50,47,53,23,35,-4,66,1985,1975,0.0,0.08,1.00
2014-12-15,46,43,48,23,35,-8,64,1989,,0.12,0.08,1.14
2014-12-16,40,32,47,22,35,-12,63,1951,1984,0.02,0.08,0.47
2014-12-17,29,26,32,22,35,-7,60,1951,1977,0.0,0.08,0.44
2014-12-18,28,25,31,22,34,-9,59,1983,1939,0.0,0.07,1.06
2014-12-19,32,29,34,22,34,-12,56,1983,1988,0.0,0.08,0.80
2014-12-20,34,32,35,21,34,-9,59,1963,1988,0.0,0.08,1.12
2014-12-21,36,34,38,21,34,-13,62,1989,1967,0.00,0.07,1.81
2014-12-22,38,31,44,21,33,-12,57,1989,1957,0.28,0.08,0.98
2014-12-23,46,42,50,21,33,-17,60,1983,1982,0.12,0.08,1.12
2014-12-24,41,38,43,21,33,-20,59,1983,1982,0.12,0.07,2.61
2014-12-25,40,35,44,20,33,-13,64,1983,1982,0.00,0.08,1.80
2014-12-26,45,38,51,20,33,-9,55,1933,1971,0.00,0.07,0.80
2014-12-27,41,32,50,20,33,-12,61,1933,1982,0.0,0.07,2.05
2014-12-28,31,26,36,20,32,-3,67,1961,1984,0.00,0.08,1.13
2014-12-29,30,24,35,20,32,-8,64,1976,1984,0.00,0.07,0.78
2014-12-30,19,13,24,19,32,-8,60,1983,2002,0.00,0.07,1.69
2014-12-31,13,6,20,19,32,-10,60,1967,1962,0.00,0.08,1.41
2015-1-1,25,17,32,19,32,-10,52,1969,2000,0.00,0.08,1.33
2015-1-2,28,20,36,19,32,-13,61,1979,2000,0.00,0.08,1.22
2015-1-3,34,31,37,19,32,-12,60,1979,1950,0.55,0.07,0.79
2015-1-4,21,5,36,19,32,-8,64,1999,1997,0.10,0.08,1.59
2015-1-5,5,-1,10,19,32,-15,56,1999,1998,0.11,0.08,0.80
2015-1-6,9,5,12,19,32,-15,60,2014,2008,0.0,0.07,0.75
2015-1-7,2,-4,8,18,32,-14,65,1942,2008,0.00,0.08,0.69
2015-1-8,7,-7,20,18,32,-12,63,1942,1965,0.05,0.07,1.48
2015-1-9,10,3,16,18,31,-17,57,1982,1939,0.0,0.08,0.76
2015-1-10,13,0,26,18,31,-22,60,1982,1975,0.00,0.07,2.29
2015-1-11,30,25,34,18,31,-11,54,1977,2013,0.12,0.07,0.34
2015-1-12,25,19,30,18,31,-9,62,1974,2005,0.03,0.07,2.76
2015-1-13,18,13,23,18,31,-10,56,1929,1932,0.0,0.07,1.37
2015-1-14,13,5,21,18,31,-14,56,1979,1952,0.00,0.07,1.00
2015-1-15,27,17,36,18,31,-19,60,1979,1949,0.00,0.06,0.39
2015-1-16,29,22,35,18,31,-19,56,1977,1990,0.00,0.07,0.43
2015-1-17,37,30,44,18,31,-17,58,1982,1952,0.00,0.07,0.68
2015-1-18,37,31,43,18,31,-20,61,1994,1996,0.00,0.06,1.39
2015-1-19,35,27,43,18,31,-21,57,1985,1933,0.0,0.07,1.09
2015-1-20,37,33,40,18,31,-25,53,1985,1954,0.01,0.06,0.99
2015-1-21,34,31,36,18,31,-17,57,1984,1934,0.0,0.06,0.96
2015-1-22,32,30,34,18,31,-17,58,1936,1964,0.00,0.06,1.33
2015-1-23,32,30,34,18,31,-19,59,1936,1967,0.00,0.06,0.73
2015-1-24,39,32,45,18,31,-16,65,1936,1967,0.00,0.05,0.73
2015-1-25,32,26,38,18,32,-10,67,1936,1950,0.06,0.06,1.30
2015-1-26,25,21,29,18,32,-10,62,1936,1944,0.04,0.06,1.66
2015-1-27,29,24,33,18,32,-10,61,1955,2002,0.0,0.06,0.74
2015-1-28,30,24,36,18,32,-13,55,1977,1970,0.03,0.05,0.52
2015-1-29,35,31,39,18,32,-16,61,1966,2013,0.0,0.06,0.99
2015-1-30,28,23,32,18,32,-15,55,1966,1988,0.0,0.05,1.25
2015-1-31,31,23,39,19,32,-9,67,1996,1989,0.13,0.06,0.70
2015-2-1,27,21,33,19,32,-9,56,1951,1968,0.12,0.05,0.95
2015-2-2,16,9,22,19,32,-15,51,1951,1992,0.0,0.06,1.22
2015-2-3,19,8,30,19,33,-17,60,1996,1992,0.05,0.06,0.43
2015-2-4,21,12,30,19,33,-12,54,1996,1993,0.0,0.05,0.69
2015-2-5,10,3,17,19,33,-17,56,1979,1946,0.00,0.06,0.68
2015-2-6,24,15,32,20,33,-12,56,1982,1938,0.00,0.05,1.96
2015-2-7,35,25,44,20,33,-5,59,1972,2009,0.00,0.06,0.67
2015-2-8,37,31,42,20,34,-12,61,1933,1990,0.0,0.07,0.81
2015-2-9,29,26,31,20,34,-20,57,1933,1999,0.0,0.06,0.77
2015-2-10,29,25,33,20,34,-12,62,1982,2009,0.0,0.06,0.98
2015-2-11,30,21,38,21,35,-8,71,1955,1999,0.0,0.07,0.82
2015-2-12,16,11,21,21,35,-10,61,1981,1984,0.0,0.07,0.50
2015-2-13,18,7,29,21,35,-5,63,1988,1938,0.0,0.07,0.78
2015-2-14,17,4,29,21,35,-5,62,1936,1954,0.0,0.06,0.94
2015-2-15,9,0,17,22,36,-6,69,1936,1954,0.0,0.07,0.78
2015-2-16,14,8,19,22,36,-7,53,1958,1981,0.00,0.07,0.75
2015-2-17,17,11,22,22,36,-10,58,1936,2011,0.0,0.07,0.84
2015-2-18,6,0,11,23,37,-16,62,1936,1961,0.0,0.07,0.84
2015-2-19,1,-5,7,23,37,-8,65,1936,1930,0.00,0.07,0.78
2015-2-20,10,-1,20,23,37,-9,65,1929,1983,0.0,0.08,1.35
2015-2-21,26,19,33,23,38,-6,66,1963,1930,0.00,0.07,3.25
2015-2-22,19,7,30,24,38,-7,63,1963,1984,0.00,0.08,1.40
2015-2-23,8,0,15,24,39,0,66,1939,2000,0.00,0.09,0.90
2015-2-24,19,6,32,24,39,-5,64,1967,1930,0.0,0.08,1.36
2015-2-25,22,16,28,25,39,-5,73,1967,2000,0.06,0.09,0.62
2015-2-26,15,7,22,25,40,-1,64,1963,2000,0.01,0.08,1.76
2015-2-27,9,0,17,25,40,-6,75,1934,1976,0.00,0.09,0.79
2015-2-28,12,1,23,26,40,1,59,2015,1965,0.0,0.08,0.77
2015-3-1,23,18,27,26,41,-5,71,1962,1992,0.01,0.09,1.09
2015-3-2,25,16,34,26,41,-4,71,1913,1974,0.00,0.08,1.02
2015-3-3,32,27,37,27,42,-6,80,1873,1974,0.10,0.09,1.21
2015-3-4,24,14,33,27,42,-4,74,2002,1983,0.00,0.08,1.42
2015-3-5,14,8,19,27,42,0,76,1978,1983,0.00,0.09,0.99
2015-3-6,17,6,27,28,43,2,71,1960,1983,0.00,0.08,0.49
2015-3-7,36,26,46,28,43,-2,79,1943,2000,0.00,0.09,1.27
2015-3-8,37,31,42,28,44,-7,79,1943,2000,0.0,0.09,2.02
2015-3-9,40,28,52,29,44,3,69,1932,1974,0.00,0.08,1.41
2015-3-10,46,38,54,29,44,5,70,2003,1955,0.00,0.08,1.12
2015-3-11,45,36,54,29,45,3,76,1979,1990,0.00,0.08,0.92
2015-3-12,46,33,58,30,45,1,81,1948,1990,0.00,0.08,1.30
2015-3-13,53,41,65,30,46,11,74,1948,2007,0.00,0.08,1.02
2015-3-14,51,40,62,30,46,8,81,1993,2012,0.00,0.09,1.62
2015-3-15,49,34,64,31,46,12,81,1979,2012,0.00,0.08,1.66
2015-3-16,59,44,74,31,47,5,81,1941,2012,0.00,0.09,1.12
2015-3-17,38,31,44,31,47,0,80,1941,2012,0.00,0.08,1.42
2015-3-18,40,30,50,32,48,3,81,1941,2012,0.00,0.09,1.04
2015-3-19,46,41,50,32,48,11,79,1965,2012,0.00,0.09,2.50
2015-3-20,46,39,52,32,48,8,85,1965,2012,0.00,0.09,0.78
2015-3-21,43,35,50,33,49,12,86,1951,2012,0.00,0.09,0.93
2015-3-22,34,30,37,33,49,6,84,1940,2012,0.0,0.09,1.15
2015-3-23,31,30,32,33,50,6,79,1940,1939,0.15,0.09,0.93
2015-3-24,33,25,40,34,50,5,79,1974,1939,0.04,0.08,1.26
2015-3-25,41,38,43,34,50,10,78,1940,2007,0.13,0.09,1.95
2015-3-26,40,34,46,34,51,11,79,1955,2007,0.0,0.09,1.40
2015-3-27,30,25,34,35,51,13,82,1965,1945,0.01,0.09,1.06
2015-3-28,29,21,37,35,52,9,78,1934,1968,0.00,0.10,1.26
2015-3-29,40,28,51,35,52,17,86,1969,1986,0.04,0.09,1.33
2015-3-30,51,37,64,36,52,12,83,1969,1986,0.00,0.10,1.36
2015-3-31,49,41,57,36,53,12,85,,1986,0.00,0.11,2.50
2015-4-1,58,41,74,36,53,24,82,1954,2010,0.00,0.11,1.58
2015-4-2,59,50,68,37,54,22,82,1961,1963,0.27,0.12,1.67
2015-4-3,45,33,57,39,54,19,81,1954,1956,0.0,0.11,1.08
2015-4-4,46,31,61,38,55,16,80,1975,1929,0.00,0.11,1.90
2015-4-5,58,47,69,38,55,18,85,1979,1988,0.00,0.11,2.58
2015-4-6,53,41,64,38,55,16,84,1982,1991,0.07,0.12,0.94
2015-4-7,45,40,49,39,56,10,83,1982,2001,0.04,0.12,0.78
2015-4-8,49,43,54,39,56,20,82,1972,2001,0.08,0.12,1.38
2015-4-9,60,46,74,39,57,21,78,1972,1952,0.95,0.12,1.58
2015-4-10,54,42,65,40,57,22,91,1989,1930,0.0,0.13,1.15
2015-4-11,52,39,64,40,57,25,91,1973,1930,0.00,0.12,2.26
2015-4-12,59,47,71,41,58,21,86,1957,1977,0.00,0.12,1.16
2015-4-13,61,53,69,41,58,21,83,1950,1941,0.14,0.12,1.84
2015-4-14,57,46,67,41,59,23,85,1943,2003,0.00,0.12,1.21
2015-4-15,56,44,67,42,59,21,89,1928,2002,0.00,0.12,1.26
2015-4-16,56,47,64,42,60,26,88,1962,2002,0.34,0.13,1.33
2015-4-17,65,49,80,42,60,29,88,1983,1976,0.00,0.12,1.87
2015-4-18,59,50,68,43,60,25,89,1983,2002,0.00,0.13,3.83
2015-4-19,60,51,69,43,61,24,86,1983,1985,0.42,0.12,1.52
2015-4-20,50,43,56,43,61,27,87,1983,1987,0.0,0.12,1.27
2015-4-21,50,41,58,44,62,28,86,1943,1985,0.00,0.13,1.08
2015-4-22,43,37,49,44,62,25,91,1936,1980,0.0,0.12,1.50
2015-4-23,45,34,56,44,62,28,88,1936,1960,0.00,0.12,0.83
2015-4-24,48,35,60,45,63,29,89,1930,1990,0.29,0.12,2.05
2015-4-25,45,40,49,45,63,27,89,1934,1990,0.34,0.12,1.62
2015-4-26,48,39,57,45,64,29,91,1930,1986,0.00,0.13,0.84
2015-4-27,50,43,56,46,64,28,89,1928,1986,0.00,0.13,2.13
2015-4-28,49,38,60,46,64,30,84,1950,1951,0.00,0.13,1.63
2015-4-29,53,42,64,46,65,31,89,1958,1942,0.0,0.13,1.62
2015-4-30,47,42,52,47,65,31,92,1931,1942,0.00,0.12,2.15
2015-5-1,53,40,65,47,65,30,90,1943,1951,0.00,0.12,2.05
2015-5-2,66,53,79,47,66,30,91,1978,1959,0.00,0.12,1.26
2015-5-3,71,60,82,48,66,32,92,1971,1955,0.20,0.13,0.76
2015-5-4,66,57,74,48,66,37,82,2005,1999,0.0,0.12,0.93
2015-5-5,56,51,60,48,67,34,94,1954,1949,0.79,0.13,0.83
2015-5-6,67,51,82,49,67,34,93,1989,1934,0.00,0.12,1.71
2015-5-7,72,57,87,49,67,32,89,1974,1936,0.00,0.12,2.01
2015-5-8,75,66,84,49,68,33,93,1977,2014,0.28,0.12,1.09
2015-5-9,58,49,67,49,68,31,96,1983,1934,0.38,0.13,2.33
2015-5-10,53,47,58,50,68,29,91,1966,2011,0.16,0.12,2.84
2015-5-11,61,50,71,50,69,32,89,1981,1987,0.14,0.13,3.30
2015-5-12,53,47,58,50,69,28,92,1981,1956,0.0,0.13,1.92
2015-5-13,49,45,53,51,69,30,90,1938,1982,0.00,0.14,1.46
2015-5-14,58,46,69,51,70,37,92,1973,1982,0.13,0.13,1.95
2015-5-15,70,60,79,51,70,34,91,1937,2001,0.03,0.13,0.85
2015-5-16,75,67,82,52,70,35,94,1940,1962,0.0,0.13,1.38
2015-5-17,77,69,84,52,71,36,94,1973,1962,0.0,0.13,1.40
2015-5-18,66,51,81,52,71,34,93,2002,1996,0.00,0.15,0.79
2015-5-19,49,44,54,52,71,37,95,1937,1934,0.00,0.14,1.54
2015-5-20,47,43,51,53,72,36,95,1981,1977,0.02,0.14,2.54
2015-5-21,59,47,70,53,72,34,94,2002,1934,0.0,0.14,1.79
2015-5-22,59,50,68,53,72,40,93,1929,1977,0.00,0.14,1.11
2015-5-23,68,53,83,54,72,35,92,1931,1977,0.00,0.14,1.41
2015-5-24,69,62,76,54,73,36,93,1935,1950,0.44,0.14,1.31
2015-5-25,75,68,81,54,73,35,90,1934,1950,0.06,0.14,2.65
2015-5-26,73,69,77,55,73,34,93,1934,1967,0.42,0.14,1.49
2015-5-27,71,64,77,55,74,35,97,1961,2012,0.06,0.13,1.49
2015-5-28,72,60,84,55,74,40,94,1930,2012,0.00,0.15,2.20
2015-5-29,78,71,84,56,74,36,95,1930,1942,0.18,0.14,0.83
2015-5-30,59,45,73,56,75,36,95,1930,1942,0.79,0.14,1.60
2015-5-31,51,44,57,57,75,41,102,1928,1934,0.05,0.15,2.97
2015-6-1,55,48,62,57,75,39,107,2003,1934,0.00,0.15,1.88
2015-6-2,58,45,71,57,76,44,92,1946,1944,0.00,0.14,2.35
2015-6-3,67,55,79,58,76,37,98,1945,1934,0.00,0.15,1.15
2015-6-4,72,62,82,58,76,35,97,1945,1977,0.00,0.15,1.00
2015-6-5,65,56,73,58,77,37,98,1945,1934,0.00,0.15,0.75
2015-6-6,64,54,73,59,77,44,97,1998,1971,0.00,0.15,2.73
2015-6-7,72,60,83,59,77,45,101,1944,1933,0.44,0.15,3.11
2015-6-8,75,63,86,60,78,45,97,1978,1985,0.10,0.15,2.07
2015-6-9,74,61,87,60,78,42,96,1977,1968,0.00,0.14,2.58
2015-6-10,83,71,94,60,78,41,101,1928,1933,0.09,0.14,2.97
2015-6-11,72,65,79,61,79,41,100,1972,1933,0.20,0.14,2.08
2015-6-12,66,58,74,61,79,48,97,1985,1956,0.17,0.14,1.33
2015-6-13,73,58,88,62,79,44,95,1942,1990,0.19,0.15,4.64
2015-6-14,78,70,85,62,80,40,99,1933,1987,0.98,0.14,2.58
2015-6-15,77,71,83,62,80,45,95,1933,1994,0.24,0.13,1.93
2015-6-16,67,60,74,63,80,44,95,1947,1952,0.0,0.14,1.78
2015-6-17,65,58,72,63,81,42,96,1980,1987,0.33,0.13,3.16
2015-6-18,71,59,82,63,81,48,98,1930,1954,0.48,0.13,1.12
2015-6-19,63,57,68,64,81,44,102,1936,1953,0.00,0.14,1.79
2015-6-20,70,57,82,64,82,45,104,1980,1953,0.22,0.13,2.56
2015-6-21,79,70,87,64,82,45,101,1940,1988,0.0,0.13,1.53
2015-6-22,79,72,85,65,82,46,98,1963,1988,0.04,0.14,1.25
2015-6-23,74,67,81,65,82,46,99,1972,1930,0.00,0.12,2.20
2015-6-24,73,66,79,65,83,48,99,1936,1937,0.00,0.13,2.31
2015-6-25,74,65,83,65,83,46,104,1986,1988,0.16,0.12,4.58
2015-6-26,66,61,70,66,83,52,98,1945,1936,0.70,0.12,2.57
2015-6-27,68,59,77,66,83,50,102,1981,1933,0.0,0.12,2.88
2015-6-28,70,58,81,66,83,49,104,1938,1934,0.0,0.12,1.66
2015-6-29,71,66,75,66,84,50,97,1943,1954,0.03,0.11,1.91
2015-6-30,71,61,80,66,84,45,101,1943,1931,0.00,0.11,2.59


================================================
FILE: us-weather-history/KNYC.csv
================================================
date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation
2014-7-1,81,72,89,68,83,52,100,1943,1901,0.00,0.12,2.17
2014-7-2,82,72,91,68,83,56,100,2001,1966,0.96,0.13,1.79
2014-7-3,78,69,87,68,83,54,103,1933,1966,1.78,0.12,2.80
2014-7-4,70,65,74,68,84,55,102,1986,1949,0.14,0.13,1.76
2014-7-5,72,63,81,68,84,53,101,1979,1999,0.00,0.12,3.07
2014-7-6,75,66,84,68,84,54,103,1979,2010,0.00,0.13,1.97
2014-7-7,81,72,90,68,84,56,100,1914,2010,0.04,0.13,3.13
2014-7-8,81,71,91,69,84,56,100,1894,1993,0.39,0.14,1.80
2014-7-9,80,71,88,69,84,54,106,1963,1936,0.09,0.14,1.09
2014-7-10,78,72,83,69,84,55,102,1890,1993,0.00,0.15,1.79
2014-7-11,79,71,86,69,84,57,98,1898,1988,0.00,0.15,1.94
2014-7-12,78,71,85,69,84,57,99,1926,1966,0.00,0.15,2.68
2014-7-13,78,72,83,69,84,54,101,1888,1966,0.03,0.17,3.16
2014-7-14,78,72,84,69,84,58,100,1926,1954,0.46,0.16,1.60
2014-7-15,79,72,86,69,84,57,102,1930,1995,1.30,0.15,2.33
2014-7-16,75,68,81,69,84,56,99,1946,1980,0.0,0.16,1.38
2014-7-17,74,67,81,69,84,57,100,1892,1953,0.00,0.15,3.13
2014-7-18,73,64,81,69,84,57,101,1925,1953,0.00,0.15,1.76
2014-7-19,72,68,76,69,84,57,102,1924,1977,0.00,0.16,1.82
2014-7-20,72,66,78,69,84,55,101,1890,1980,0.0,0.15,1.97
2014-7-21,76,67,85,69,84,55,104,1890,1977,0.00,0.15,2.26
2014-7-22,79,71,86,69,84,55,104,1873,2011,0.00,0.14,1.86
2014-7-23,80,72,88,69,84,58,100,1890,2011,0.19,0.15,2.41
2014-7-24,75,70,80,69,84,56,97,1893,1999,0.00,0.15,3.75
2014-7-25,74,66,82,69,84,57,97,1953,1999,0.00,0.15,1.64
2014-7-26,75,69,81,69,84,55,98,1920,1940,0.00,0.16,3.80
2014-7-27,78,71,85,69,84,55,98,1920,1963,0.02,0.16,2.65
2014-7-28,75,68,82,69,84,57,97,1903,1999,0.19,0.17,3.11
2014-7-29,70,64,76,69,84,59,99,1914,1949,0.00,0.17,3.47
2014-7-30,72,63,80,69,84,57,98,1956,1988,0.00,0.17,3.56
2014-7-31,75,68,82,69,84,57,102,1914,1933,0.00,0.17,2.29
2014-8-1,78,71,84,69,84,59,100,1964,1933,0.00,0.16,2.85
2014-8-2,69,63,74,69,84,58,100,1924,1955,0.41,0.16,2.49
2014-8-3,71,66,76,69,84,55,97,1927,2005,0.07,0.17,2.71
2014-8-4,77,70,84,69,84,56,100,1886,1944,0.00,0.16,3.25
2014-8-5,81,71,90,69,84,56,101,1951,1944,0.00,0.16,1.44
2014-8-6,77,70,83,69,84,57,97,1994,1955,0.00,0.16,3.31
2014-8-7,75,66,83,69,84,57,104,1994,1918,0.00,0.15,2.18
2014-8-8,74,65,83,69,84,54,99,1903,2001,0.00,0.15,2.60
2014-8-9,77,66,87,69,84,57,103,1989,2001,0.00,0.15,4.10
2014-8-10,78,68,88,69,83,55,98,1879,1949,0.00,0.14,4.64
2014-8-11,79,71,87,69,83,56,102,1962,1944,0.00,0.14,2.39
2014-8-12,75,70,79,68,83,55,97,1889,1944,0.19,0.14,3.62
2014-8-13,75,68,82,68,83,55,99,1930,2005,0.53,0.14,2.70
2014-8-14,70,63,77,68,83,54,99,1964,1988,0.00,0.15,5.81
2014-8-15,67,61,73,68,83,54,97,1964,1988,0.00,0.14,1.52
2014-8-16,71,63,78,68,83,55,96,1880,1944,0.00,0.15,4.80
2014-8-17,74,66,82,68,83,56,95,1979,1944,0.01,0.14,2.86
2014-8-18,72,63,81,68,83,55,94,1915,2002,0.00,0.14,3.95
2014-8-19,73,63,83,68,83,55,94,1924,2002,0.00,0.15,2.53
2014-8-20,77,70,84,68,82,55,97,1949,1955,0.00,0.14,3.63
2014-8-21,74,65,83,68,82,53,96,1922,1955,0.35,0.15,4.19
2014-8-22,72,65,79,67,82,52,95,1895,1916,0.06,0.14,1.85
2014-8-23,72,67,77,67,82,51,92,1923,1916,0.01,0.14,3.03
2014-8-24,72,64,80,67,82,52,94,1890,1972,0.00,0.14,3.61
2014-8-25,76,64,88,67,82,52,95,1940,1948,0.00,0.14,1.86
2014-8-26,80,70,89,67,81,53,103,1887,1948,0.00,0.12,3.24
2014-8-27,80,70,90,67,81,50,101,1885,1948,0.00,0.13,4.16
2014-8-28,74,66,82,66,81,50,100,1885,1948,0.00,0.13,3.99
2014-8-29,71,61,80,66,81,50,99,1986,1953,0.00,0.12,2.68
2014-8-30,73,65,80,66,81,50,98,1965,1973,0.00,0.12,2.30
2014-8-31,82,73,90,66,80,50,100,1976,1953,0.62,0.12,3.76
2014-9-1,82,75,88,66,80,51,97,1872,1953,0.00,0.13,3.84
2014-9-2,85,77,92,65,80,51,102,1886,1953,0.00,0.13,2.12
2014-9-3,79,72,86,65,80,50,99,1893,1929,0.00,0.13,3.32
2014-9-4,78,69,87,65,79,47,97,1883,1929,0.00,0.14,3.48
2014-9-5,80,72,87,64,79,51,94,1963,1985,0.00,0.13,2.45
2014-9-6,79,67,91,64,79,48,97,1924,1881,0.11,0.13,3.26
2014-9-7,73,65,81,64,78,46,101,1888,1881,0.00,0.14,2.07
2014-9-8,70,65,75,64,78,54,93,1955,1919,0.00,0.13,4.86
2014-9-9,68,63,73,63,78,48,94,1883,1915,0.00,0.13,0.86
2014-9-10,72,63,80,63,77,43,97,1883,1983,0.00,0.13,1.38
2014-9-11,76,69,83,63,77,43,99,1917,1983,0.00,0.13,2.90
2014-9-12,70,62,78,62,77,46,94,1917,1961,0.00,0.14,2.35
2014-9-13,64,58,69,62,76,46,94,1963,1952,0.26,0.14,3.94
2014-9-14,62,53,71,62,76,46,93,1975,1931,0.00,0.14,3.82
2014-9-15,63,55,71,61,76,45,92,1913,1927,0.00,0.15,4.16
2014-9-16,64,58,70,61,75,47,93,1966,1915,0.37,0.15,5.02
2014-9-17,64,55,73,61,75,45,93,1986,1991,0.00,0.15,3.37
2014-9-18,67,57,76,60,75,44,91,1990,1891,0.00,0.15,3.92
2014-9-19,60,54,66,60,74,44,94,1929,1983,0.00,0.15,4.30
2014-9-20,66,57,75,59,74,44,93,1993,1983,0.00,0.15,2.32
2014-9-21,71,67,75,59,73,40,95,1871,1895,0.15,0.15,5.54
2014-9-22,63,55,71,59,73,41,95,1904,1914,0.0,0.15,2.34
2014-9-23,62,52,71,58,73,41,97,1947,1895,0.00,0.14,8.28
2014-9-24,65,58,71,58,72,40,89,1963,1959,0.00,0.15,2.26
2014-9-25,61,57,64,57,72,40,90,1887,1970,0.32,0.14,2.36
2014-9-26,68,58,77,57,71,42,91,1940,1970,0.00,0.15,2.34
2014-9-27,72,60,83,57,71,41,90,1957,1933,0.00,0.15,3.13
2014-9-28,74,64,84,56,70,41,88,1947,1881,0.00,0.16,3.84
2014-9-29,73,67,79,56,70,42,88,1942,1945,0.00,0.16,2.18
2014-9-30,67,62,71,55,70,39,89,1912,1986,0.0,0.16,2.64
2014-10-1,63,61,65,55,69,36,88,1947,1927,0.02,0.15,4.98
2014-10-2,66,61,70,55,69,39,90,1886,1927,0.00,0.14,2.16
2014-10-3,64,56,71,54,68,38,87,1974,1919,0.00,0.14,1.55
2014-10-4,61,52,69,54,68,37,88,1888,1941,1.18,0.13,4.05
2014-10-5,54,46,61,54,68,35,94,1881,1941,0.00,0.15,1.99
2014-10-6,60,50,69,53,67,36,90,1881,1941,0.00,0.14,2.39
2014-10-7,67,63,71,53,67,39,88,1999,1944,0.06,0.15,4.09
2014-10-8,68,62,73,52,67,37,87,1988,2007,0.04,0.14,4.30
2014-10-9,62,55,68,52,66,37,86,2000,1916,0.00,0.15,7.33
2014-10-10,58,52,64,52,66,35,91,1888,1939,0.0,0.16,2.16
2014-10-11,55,50,60,51,65,34,85,1964,1949,0.33,0.15,3.06
2014-10-12,56,48,63,51,65,35,86,1876,1954,0.00,0.14,4.26
2014-10-13,59,52,65,51,65,33,87,1875,1954,0.05,0.14,2.75
2014-10-14,70,63,76,50,64,37,84,1988,1920,0.00,0.15,1.76
2014-10-15,73,69,77,50,64,32,84,1876,1956,0.69,0.14,1.70
2014-10-16,66,61,71,50,64,34,87,1876,1897,1.11,0.14,2.15
2014-10-17,65,59,71,50,63,33,90,1886,1938,0.00,0.14,2.28
2014-10-18,63,56,70,49,63,35,82,1974,1928,0.00,0.14,2.45
2014-10-19,50,44,56,49,63,30,83,1940,1963,0.00,0.14,4.35
2014-10-20,51,42,60,49,62,31,80,1974,1969,0.00,0.14,2.78
2014-10-21,61,55,67,48,62,32,84,1974,1920,0.11,0.14,2.17
2014-10-22,54,50,58,48,62,30,88,1940,1979,1.51,0.15,1.51
2014-10-23,52,50,53,48,61,32,85,1969,1947,0.61,0.14,2.97
2014-10-24,57,51,63,48,61,31,79,1969,2001,0.00,0.14,2.51
2014-10-25,59,50,67,47,61,29,79,1879,1963,0.01,0.15,3.30
2014-10-26,58,53,63,47,61,30,78,1879,1964,0.0,0.13,3.40
2014-10-27,56,48,63,47,60,28,82,1936,1963,0.00,0.14,1.88
2014-10-28,63,53,72,47,60,29,83,1976,1919,0.00,0.13,2.49
2014-10-29,62,51,72,46,60,31,78,1925,1971,0.0,0.14,3.67
2014-10-30,53,47,59,46,59,31,82,1925,1961,0.00,0.13,1.64
2014-10-31,50,45,55,46,59,29,81,1925,1946,0.05,0.14,2.41
2014-11-1,45,42,47,46,59,30,84,1885,1950,0.35,0.14,1.69
2014-11-2,45,41,48,45,58,30,83,1887,1950,0.00,0.14,1.70
2014-11-3,50,39,61,45,58,28,79,1875,2003,0.00,0.13,2.60
2014-11-4,61,53,68,45,58,25,78,1879,1975,0.00,0.13,1.44
2014-11-5,60,56,64,45,57,23,78,1879,1961,0.00,0.13,1.94
2014-11-6,53,48,57,44,57,27,74,1879,1948,0.37,0.14,1.47
2014-11-7,47,40,53,44,57,29,78,1930,1938,0.00,0.13,2.96
2014-11-8,42,36,48,44,57,29,76,1886,1975,0.00,0.12,7.40
2014-11-9,52,46,57,44,56,24,75,1976,1975,0.00,0.13,3.65
2014-11-10,53,44,61,43,56,27,73,1914,1985,0.00,0.13,1.70
2014-11-11,57,49,64,43,56,28,74,1933,1949,0.00,0.12,1.41
2014-11-12,56,47,65,43,55,26,76,1926,1879,0.00,0.12,2.39
2014-11-13,42,36,48,43,55,22,73,1873,1931,0.20,0.13,2.06
2014-11-14,39,35,42,42,55,20,72,1905,1993,0.06,0.13,2.23
2014-11-15,38,33,42,42,54,20,80,1967,1993,0.00,0.13,2.43
2014-11-16,40,35,45,42,54,17,72,1933,1928,0.03,0.13,2.39
2014-11-17,46,40,52,41,53,19,71,1933,1953,1.54,0.14,1.54
2014-11-18,35,24,45,41,53,18,73,1936,1928,0.00,0.14,1.24
2014-11-19,29,22,36,41,53,18,72,1936,1921,0.00,0.14,1.95
2014-11-20,38,31,45,40,52,20,77,1873,1985,0.00,0.13,3.37
2014-11-21,33,28,37,40,52,16,74,1879,1900,0.00,0.14,1.33
2014-11-22,36,28,44,40,52,13,72,1880,1931,0.00,0.14,2.03
2014-11-23,50,43,57,40,51,14,72,1880,1931,0.00,0.14,1.84
2014-11-24,61,53,69,39,51,14,73,1880,1979,0.70,0.14,1.95
2014-11-25,60,51,68,39,50,19,73,1938,1979,0.00,0.13,1.36
2014-11-26,43,34,51,39,50,16,67,1938,1946,1.24,0.14,1.91
2014-11-27,36,34,38,38,50,12,72,1932,1896,0.02,0.13,2.15
2014-11-28,33,29,37,38,49,15,70,1930,2011,0.00,0.14,2.14
2014-11-29,36,27,45,38,49,15,69,1872,1990,0.00,0.14,2.01
2014-11-30,50,45,55,37,49,7,70,1875,1991,0.00,0.15,1.11
2014-12-1,54,42,65,37,48,9,70,1875,2006,0.09,0.14,1.72
2014-12-2,39,35,43,37,48,12,66,1875,1970,0.08,0.14,2.16
2014-12-3,44,41,46,36,47,9,69,1976,1998,0.06,0.15,1.63
2014-12-4,41,37,45,36,47,10,74,1882,1998,0.00,0.14,1.84
2014-12-5,39,34,44,35,47,11,70,1926,2001,0.51,0.14,1.28
2014-12-6,45,39,50,35,46,11,71,1871,2001,1.22,0.13,1.60
2014-12-7,36,30,42,35,46,10,75,1926,1998,0.04,0.14,1.98
2014-12-8,31,24,37,34,45,10,65,1882,1927,0.00,0.14,1.54
2014-12-9,39,36,42,34,45,7,66,1876,1966,2.54,0.13,2.54
2014-12-10,36,32,40,34,45,3,70,1876,1946,0.08,0.14,1.62
2014-12-11,35,31,38,33,44,6,64,1880,1879,0.01,0.14,2.41
2014-12-12,35,32,38,33,44,5,68,1988,1931,0.00,0.13,1.60
2014-12-13,39,34,44,33,44,8,64,1960,1923,0.00,0.13,3.03
2014-12-14,42,38,46,32,43,12,67,1976,1881,0.00,0.13,2.22
2014-12-15,43,37,48,32,43,6,67,1874,2008,0.00,0.13,1.34
2014-12-16,44,38,49,32,43,7,63,1876,1971,0.20,0.12,2.25
2014-12-17,48,42,54,32,42,1,62,1919,2000,0.02,0.13,2.28
2014-12-18,40,37,42,31,42,-1,63,1919,1984,0.00,0.12,1.30
2014-12-19,35,31,38,31,42,-1,58,1884,1931,0.00,0.12,1.58
2014-12-20,32,30,33,31,42,-4,60,1942,2002,0.00,0.12,1.82
2014-12-21,34,31,36,30,41,-1,65,1942,2013,0.0,0.13,2.49
2014-12-22,40,35,44,30,41,2,71,1872,2013,0.04,0.12,2.18
2014-12-23,45,43,46,30,41,-1,66,1883,1990,0.16,0.13,1.61
2014-12-24,51,44,58,30,41,6,63,1983,1996,0.80,0.13,1.42
2014-12-25,53,44,62,29,40,-1,64,1980,1982,0.09,0.13,1.30
2014-12-26,45,40,50,29,40,3,63,1914,1982,0.00,0.12,1.66
2014-12-27,50,44,55,29,40,6,63,1872,1949,0.00,0.12,2.14
2014-12-28,49,43,54,29,40,8,65,1917,1982,0.10,0.12,1.35
2014-12-29,39,34,44,28,39,-6,70,1917,1984,0.00,0.11,2.52
2014-12-30,31,28,34,28,39,-13,65,1917,1984,0.00,0.12,1.69
2014-12-31,30,27,32,28,39,-7,63,1917,1965,0.00,0.11,2.31
2015-1-1,33,27,39,28,39,-4,62,1918,1966,0.00,0.12,2.05
2015-1-2,39,35,42,28,39,2,68,1918,1876,0.00,0.12,1.92
2015-1-3,38,33,42,28,39,-4,64,1879,2000,0.71,0.12,2.42
2015-1-4,49,41,56,27,39,-3,66,1918,1950,0.30,0.12,2.73
2015-1-5,35,21,49,27,38,-4,64,1904,1993,0.00,0.12,1.50
2015-1-6,21,19,22,27,38,-2,72,1896,2007,0.05,0.13,1.65
2015-1-7,16,9,23,27,38,4,64,2014,1907,0.00,0.12,2.67
2015-1-8,15,8,21,27,38,2,65,1968,1998,0.00,0.11,1.25
2015-1-9,26,19,33,27,38,-1,64,1968,1937,0.07,0.13,1.42
2015-1-10,20,16,23,27,38,-6,60,1875,1876,0.00,0.12,1.72
2015-1-11,28,18,37,27,38,3,63,1968,1975,0.00,0.12,1.46
2015-1-12,37,35,39,27,38,2,64,1981,1890,0.36,0.13,2.35
2015-1-13,27,17,36,27,38,-3,68,1914,1932,0.00,0.12,1.44
2015-1-14,24,16,32,27,38,-5,70,1914,1932,0.00,0.12,2.06
2015-1-15,30,25,35,27,38,0,67,1957,1932,0.00,0.13,1.27
2015-1-16,32,20,43,27,38,1,58,2004,1995,0.00,0.12,1.44
2015-1-17,25,17,32,27,38,-2,63,1977,1990,0.00,0.12,1.36
2015-1-18,37,31,42,27,38,0,66,1982,1990,2.10,0.11,2.10
2015-1-19,39,36,42,27,38,-3,64,1875,1951,0.00,0.12,2.39
2015-1-20,36,32,40,27,38,0,61,1994,2006,0.00,0.11,1.41
2015-1-21,31,25,36,27,38,-2,63,1985,2006,0.00,0.12,3.45
2015-1-22,36,31,40,27,38,0,61,1888,1959,0.00,0.12,1.70
2015-1-23,34,28,39,27,38,-3,62,1936,1906,0.00,0.11,2.55
2015-1-24,36,33,39,27,38,-6,68,1882,1967,0.72,0.11,2.18
2015-1-25,37,31,42,27,38,2,60,1945,1967,0.00,0.11,1.80
2015-1-26,27,22,31,27,39,3,72,1927,1950,0.48,0.11,2.19
2015-1-27,25,20,30,27,39,-1,69,1927,1916,0.36,0.11,1.94
2015-1-28,25,16,34,27,39,-2,66,1925,1916,0.00,0.11,1.87
2015-1-29,28,19,36,27,39,0,69,1873,2002,0.02,0.11,1.03
2015-1-30,29,19,38,27,39,2,64,1873,2006,0.06,0.11,1.19
2015-1-31,20,13,26,27,39,-1,63,1920,1947,0.00,0.12,1.51
2015-2-1,28,20,36,27,39,-2,67,1920,1989,0.03,0.11,2.12
2015-2-2,24,14,34,27,39,-3,59,1881,1988,1.02,0.11,2.98
2015-2-3,20,13,26,27,39,0,64,1955,1991,0.00,0.11,1.55
2015-2-4,34,24,43,28,40,0,68,1918,1991,0.00,0.10,2.10
2015-2-5,28,14,42,28,40,-6,70,1918,1991,0.00,0.11,1.43
2015-2-6,20,12,27,28,40,-4,68,1895,2008,0.00,0.11,2.74
2015-2-7,33,25,40,28,40,1,54,1910,1938,0.02,0.11,2.96
2015-2-8,33,29,37,28,40,-7,61,1934,1965,0.00,0.11,1.15
2015-2-9,27,25,29,28,40,-15,63,1934,1990,0.09,0.10,1.74
2015-2-10,33,26,40,28,41,-6,61,1899,2001,0.01,0.11,2.63
2015-2-11,28,22,34,28,41,-2,65,1899,1960,0.00,0.10,2.74
2015-2-12,28,16,40,28,41,-3,62,1914,1999,0.0,0.11,1.66
2015-2-13,15,8,21,29,41,-1,64,1914,1951,0.00,0.10,2.42
2015-2-14,24,16,32,29,41,2,63,1916,1946,0.02,0.11,1.59
2015-2-15,15,4,25,29,42,-8,73,1943,1949,0.0,0.10,1.73
2015-2-16,12,3,21,29,42,1,71,1888,1954,0.00,0.12,1.40
2015-2-17,21,14,27,29,42,-5,67,1896,1976,0.14,0.11,1.49
2015-2-18,26,19,33,29,42,0,68,1979,1981,0.0,0.11,1.50
2015-2-19,18,8,27,30,42,1,66,1936,1997,0.00,0.11,2.15
2015-2-20,11,2,19,30,43,2,69,2015,1939,0.00,0.11,3.07
2015-2-21,23,13,32,30,43,4,68,1896,1930,0.61,0.11,1.86
2015-2-22,38,32,43,30,43,8,69,1963,1997,0.10,0.11,2.39
2015-2-23,23,8,38,30,43,5,70,1889,1985,0.00,0.12,1.38
2015-2-24,14,4,24,30,44,-4,75,1873,1985,0.00,0.12,1.69
2015-2-25,29,20,37,31,44,1,75,1914,1930,0.00,0.11,2.11
2015-2-26,27,21,32,31,44,7,65,1990,1890,0.00,0.12,1.87
2015-2-27,24,18,30,31,44,5,72,1900,1997,0.00,0.12,1.56
2015-2-28,21,13,29,31,45,5,67,1934,1976,0.00,0.12,1.21
2015-3-1,28,24,31,31,45,5,73,1884,1972,0.52,0.13,2.95
2015-3-2,33,27,39,32,45,9,72,1891,1972,0.00,0.12,2.41
2015-3-3,30,22,37,32,45,11,65,2003,1991,0.67,0.12,2.25
2015-3-4,40,35,45,32,46,7,70,1943,1974,0.25,0.13,1.65
2015-3-5,30,19,40,32,46,3,72,1872,1880,0.76,0.13,1.81
2015-3-6,20,12,27,32,46,6,68,1872,1935,0.00,0.13,2.63
2015-3-7,28,18,38,33,47,7,74,1890,1946,0.00,0.13,1.87
2015-3-8,43,37,49,33,47,8,76,1883,1987,0.00,0.14,1.78
2015-3-9,47,40,54,33,47,11,69,1996,2000,0.01,0.13,1.82
2015-3-10,46,39,53,33,48,12,74,1929,2006,0.46,0.13,1.62
2015-3-11,52,44,59,34,48,14,73,1960,1977,0.01,0.13,2.94
2015-3-12,42,36,47,34,48,8,71,1888,1890,0.00,0.13,2.33
2015-3-13,37,31,43,34,49,6,85,1888,1990,0.00,0.12,3.86
2015-3-14,46,40,51,35,49,15,75,1896,1946,0.81,0.14,1.02
2015-3-15,40,36,44,35,49,14,77,1993,1990,0.00,0.14,1.81
2015-3-16,44,35,52,35,50,13,82,1911,1990,0.00,0.14,2.03
2015-3-17,46,34,57,35,50,9,75,1916,1945,0.02,0.15,1.42
2015-3-18,34,29,39,36,50,7,77,1916,1989,0.00,0.14,3.10
2015-3-19,36,29,43,36,51,8,76,1967,1918,0.00,0.15,2.19
2015-3-20,34,29,38,36,51,11,83,1885,1945,0.40,0.15,1.93
2015-3-21,38,29,47,37,51,10,84,1885,1921,0.0,0.15,2.37
2015-3-22,36,28,43,37,52,12,78,1885,2012,0.00,0.15,3.44
2015-3-23,31,23,38,37,52,12,76,1875,1923,0.00,0.16,1.60
2015-3-24,36,26,45,37,52,12,76,1888,1988,0.00,0.15,2.05
2015-3-25,42,34,49,38,53,13,79,1878,1963,0.05,0.15,4.25
2015-3-26,52,42,62,38,53,20,76,1960,1922,0.32,0.15,1.42
2015-3-27,43,39,46,38,54,20,83,1894,1998,0.27,0.16,1.79
2015-3-28,33,26,40,39,54,13,84,1923,1945,0.0,0.15,2.98
2015-3-29,36,25,46,39,54,10,86,1923,1945,0.00,0.15,2.03
2015-3-30,45,36,54,39,55,16,82,1887,1998,0.0,0.16,2.45
2015-3-31,41,35,47,40,55,14,86,1923,1998,0.17,0.15,2.20
2015-4-1,42,32,51,40,56,12,83,1923,1917,0.00,0.16,1.89
2015-4-2,54,41,67,40,56,22,81,1919,1967,0.00,0.16,1.93
2015-4-3,62,59,64,41,56,24,81,1954,1981,0.04,0.16,1.90
2015-4-4,51,42,60,41,57,21,80,1874,1892,0.01,0.16,1.99
2015-4-5,52,42,61,41,57,20,80,1874,1928,0.00,0.16,2.76
2015-4-6,53,42,63,42,58,21,79,1982,1947,0.00,0.15,2.52
2015-4-7,52,42,62,42,58,21,92,1982,2010,0.15,0.16,1.35
2015-4-8,41,37,45,42,58,25,90,1982,1991,0.00,0.15,1.93
2015-4-9,40,37,43,43,59,25,86,1977,1991,0.00,0.16,3.42
2015-4-10,48,39,56,43,59,28,86,1997,1922,0.17,0.17,4.31
2015-4-11,51,44,57,43,60,24,84,1909,1955,0.00,0.16,1.10
2015-4-12,55,43,66,44,60,22,90,1874,1977,0.00,0.16,2.12
2015-4-13,59,50,68,44,60,25,88,1874,1977,0.00,0.16,1.26
2015-4-14,60,55,65,44,61,26,85,1950,1941,0.01,0.16,2.72
2015-4-15,62,51,72,45,61,28,87,1943,1941,0.00,0.15,7.57
2015-4-16,58,52,64,45,62,29,92,1928,2002,0.00,0.14,3.29
2015-4-17,63,55,71,45,62,28,96,1875,2002,0.08,0.15,1.29
2015-4-18,70,59,80,46,62,25,96,1875,1976,0.00,0.14,2.19
2015-4-19,56,48,64,46,63,21,92,1875,1976,0.00,0.15,1.96
2015-4-20,52,46,57,46,63,24,90,1897,1927,1.37,0.14,1.96
2015-4-21,59,52,65,47,63,26,87,1875,1923,0.20,0.14,2.28
2015-4-22,59,48,69,47,64,33,86,1947,2001,0.05,0.14,2.45
2015-4-23,47,41,52,47,64,32,86,1930,2007,0.00,0.14,2.34
2015-4-24,46,39,52,48,64,31,87,1930,2001,0.00,0.14,2.17
2015-4-25,50,38,62,48,65,29,91,1919,1915,0.00,0.14,1.68
2015-4-26,55,46,64,48,65,31,92,1919,2009,0.00,0.14,1.88
2015-4-27,56,49,62,48,65,36,92,1932,1915,0.00,0.14,2.04
2015-4-28,61,50,71,49,66,34,90,1934,1990,0.00,0.15,2.74
2015-4-29,64,50,78,49,66,32,89,1874,1974,0.00,0.14,0.91
2015-4-30,58,48,67,49,66,30,91,1875,1942,0.00,0.13,4.97
2015-5-1,56,49,63,50,67,35,87,1880,2001,0.00,0.12,2.48
2015-5-2,61,48,74,50,67,37,90,1903,2001,0.00,0.12,1.10
2015-5-3,66,51,80,50,67,36,90,1874,2001,0.00,0.13,1.66
2015-5-4,71,57,85,50,68,38,92,1917,2001,0.00,0.13,2.02
2015-5-5,76,66,85,51,68,34,90,1891,1980,0.00,0.13,1.55
2015-5-6,66,59,73,51,68,32,92,1891,1986,0.0,0.13,1.46
2015-5-7,68,56,80,51,68,37,93,1891,2000,0.00,0.14,3.82
2015-5-8,69,56,82,52,69,37,91,1947,2000,0.00,0.12,3.02
2015-5-9,64,57,70,52,69,35,94,1947,1979,0.00,0.13,1.42
2015-5-10,72,61,83,52,69,36,94,1966,1979,0.00,0.13,2.10
2015-5-11,77,70,84,52,70,36,92,1913,1993,0.0,0.12,1.67
2015-5-12,75,64,86,53,70,40,93,1907,1881,0.00,0.13,1.84
2015-5-13,62,53,70,53,70,39,89,1895,1956,0.00,0.12,1.66
2015-5-14,62,50,73,53,70,40,88,1878,1900,0.00,0.12,3.38
2015-5-15,65,55,75,54,71,42,90,1947,1900,0.00,0.13,1.16
2015-5-16,66,57,75,54,71,42,90,1878,1951,0.30,0.13,2.66
2015-5-17,74,64,83,54,71,39,92,1956,1974,0.02,0.13,1.05
2015-5-18,68,60,75,55,71,41,90,1973,1936,0.00,0.13,2.18
2015-5-19,70,59,80,55,72,38,99,1976,1962,0.0,0.13,2.02
2015-5-20,61,54,67,55,72,43,96,2002,1996,0.00,0.14,2.03
2015-5-21,57,52,62,55,72,40,93,1907,1996,0.00,0.14,1.94
2015-5-22,65,55,74,56,72,44,96,1990,1941,0.00,0.15,1.25
2015-5-23,60,49,70,56,73,43,94,1963,1964,0.00,0.15,2.70
2015-5-24,69,56,81,56,73,39,93,1963,1975,0.00,0.15,2.07
2015-5-25,75,64,85,57,73,41,95,1925,1880,0.00,0.15,0.86
2015-5-26,78,67,88,57,74,42,95,1967,1880,0.00,0.15,1.28
2015-5-27,78,70,85,57,74,41,96,1961,1880,0.08,0.14,2.62
2015-5-28,78,70,85,58,74,43,94,1961,1959,0.00,0.14,1.16
2015-5-29,75,65,85,58,74,43,97,1902,1969,0.00,0.16,3.99
2015-5-30,76,67,85,58,75,42,97,1884,1987,0.00,0.15,2.19
2015-5-31,72,57,87,59,75,46,96,1938,1939,1.46,0.15,3.13
2015-6-1,55,51,58,59,75,44,96,1945,1895,0.72,0.16,2.60
2015-6-2,53,50,55,59,76,48,96,1946,1895,0.37,0.16,2.79
2015-6-3,61,52,70,60,76,45,95,1929,1895,0.00,0.16,3.01
2015-6-4,60,54,65,60,76,48,99,1926,1925,0.00,0.16,2.75
2015-6-5,63,55,70,60,76,47,99,1945,1925,0.13,0.16,2.80
2015-6-6,68,60,76,61,77,47,98,1945,1925,0.19,0.16,2.62
2015-6-7,65,55,74,61,77,47,96,1897,1925,0.00,0.16,4.16
2015-6-8,71,63,79,61,77,47,95,1932,1933,0.06,0.16,1.47
2015-6-9,76,68,83,62,78,47,97,1980,1933,0.01,0.16,2.55
2015-6-10,74,65,82,62,78,49,96,1972,2008,0.00,0.16,2.07
2015-6-11,81,72,89,62,78,46,95,1972,1973,0.00,0.16,1.14
2015-6-12,81,73,88,63,78,48,93,1979,1973,0.00,0.16,2.18
2015-6-13,79,72,86,63,79,51,96,1953,1961,0.00,0.16,1.71
2015-6-14,77,66,88,63,79,49,99,1875,1956,0.36,0.16,2.54
2015-6-15,74,64,83,64,79,48,96,1933,1994,0.57,0.15,1.13
2015-6-16,72,65,79,64,80,52,97,1927,1891,0.27,0.15,1.31
2015-6-17,74,66,82,64,80,51,96,1926,1957,0.0,0.16,1.82
2015-6-18,68,64,71,65,80,48,95,1950,1929,0.00,0.14,2.30
2015-6-19,78,68,87,65,80,52,98,1920,1994,0.00,0.14,2.16
2015-6-20,70,64,75,65,81,49,98,1914,1923,0.0,0.13,1.39
2015-6-21,80,71,88,65,81,49,97,1897,1988,0.64,0.14,1.70
2015-6-22,79,70,87,66,81,52,98,1940,1988,0.00,0.13,1.96
2015-6-23,83,75,90,66,81,49,96,1918,1888,0.02,0.13,1.75
2015-6-24,76,68,84,66,82,52,96,1932,1888,0.00,0.13,1.46
2015-6-25,74,65,83,66,82,50,99,1873,1952,0.00,0.14,1.28
2015-6-26,75,69,81,67,82,56,100,1979,1952,0.00,0.13,4.29
2015-6-27,65,58,71,67,82,55,101,1940,1966,1.12,0.12,2.11
2015-6-28,68,62,73,67,83,54,96,1995,1991,0.29,0.13,1.69
2015-6-29,70,63,76,67,83,52,101,1919,1934,0.00,0.12,2.57
2015-6-30,75,68,82,67,83,53,99,1919,1964,0.00,0.13,3.07


================================================
FILE: us-weather-history/KPHL.csv
================================================
date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation
2014-7-1,83,72,93,68,86,52,102,1988,1901,0.00,0.13,1.04
2014-7-2,86,75,96,68,86,52,103,1965,1901,0.21,0.13,1.73
2014-7-3,83,74,92,68,87,54,104,1957,1966,0.09,0.12,3.66
2014-7-4,73,68,78,68,87,52,103,1986,1966,0.04,0.13,2.08
2014-7-5,74,64,83,69,87,55,100,1963,1999,0.00,0.13,4.38
2014-7-6,75,64,86,69,87,52,102,1960,2010,0.00,0.13,1.36
2014-7-7,84,74,93,69,87,55,103,1968,2010,0.00,0.13,2.97
2014-7-8,84,72,95,69,87,53,100,1960,1993,0.17,0.14,2.28
2014-7-9,80,71,89,69,87,53,103,1963,1936,0.0,0.15,1.79
2014-7-10,81,74,87,69,87,53,104,1963,1936,0.0,0.14,2.99
2014-7-11,80,70,90,69,87,55,100,1963,1988,0.0,0.14,1.92
2014-7-12,80,70,89,69,87,55,97,1999,1936,0.00,0.15,4.68
2014-7-13,82,72,91,69,87,57,98,1998,1994,0.05,0.14,2.80
2014-7-14,82,74,90,69,87,52,101,1940,1954,1.18,0.15,2.89
2014-7-15,82,73,91,70,87,56,103,1940,1995,0.81,0.15,2.96
2014-7-16,78,72,83,70,87,55,102,1946,1988,0.0,0.15,1.89
2014-7-17,75,65,84,70,87,56,102,1946,1988,0.00,0.15,2.06
2014-7-18,75,65,84,70,87,57,100,1976,2012,0.00,0.15,3.45
2014-7-19,75,68,81,70,87,58,100,1962,1930,0.0,0.14,1.93
2014-7-20,76,69,82,70,87,57,99,1890,1930,0.0,0.14,2.32
2014-7-21,76,66,86,70,87,51,103,1966,1930,0.00,0.14,3.45
2014-7-22,79,70,88,70,87,56,103,1944,2011,0.00,0.14,2.98
2014-7-23,83,72,93,70,87,58,101,1950,2011,0.05,0.14,3.05
2014-7-24,77,70,83,70,87,57,98,1985,2011,0.0,0.14,1.45
2014-7-25,74,63,84,70,87,56,96,1953,1999,0.00,0.14,1.35
2014-7-26,75,69,81,70,87,56,101,1920,1892,0.22,0.15,1.97
2014-7-27,81,70,91,70,87,53,100,1962,1940,1.25,0.15,2.16
2014-7-28,77,69,84,70,87,53,100,1962,1941,0.23,0.14,8.02
2014-7-29,70,64,76,70,87,59,98,1914,2002,0.00,0.14,3.53
2014-7-30,71,62,80,69,87,58,98,1981,1954,0.00,0.14,3.36
2014-7-31,76,65,86,69,87,57,102,1895,1954,0.00,0.14,3.00
2014-8-1,77,70,83,69,87,56,99,1964,2002,0.19,0.13,2.01
2014-8-2,73,68,77,69,87,58,99,1947,1955,0.36,0.12,2.86
2014-8-3,72,67,77,69,87,56,98,1959,1975,0.23,0.12,5.63
2014-8-4,78,71,84,69,87,52,98,1965,1995,0.00,0.12,2.74
2014-8-5,80,69,90,69,87,52,98,1951,1930,0.00,0.12,1.60
2014-8-6,76,69,82,69,86,53,103,1957,1918,0.0,0.11,2.74
2014-8-7,75,66,84,69,86,54,106,1964,1918,0.00,0.12,3.70
2014-8-8,73,63,82,69,86,55,100,1989,2001,0.00,0.12,4.40
2014-8-9,77,67,86,69,86,54,101,1964,2001,0.00,0.12,2.36
2014-8-10,78,67,88,69,86,54,98,1964,1949,0.00,0.12,2.24
2014-8-11,76,66,85,69,86,55,101,1972,1900,0.00,0.11,2.05
2014-8-12,74,68,79,69,86,53,99,1962,1900,1.08,0.11,3.29
2014-8-13,77,69,85,69,86,51,99,1941,2002,0.0,0.11,5.21
2014-8-14,71,62,80,68,86,50,99,1964,2002,0.25,0.12,4.84
2014-8-15,69,60,77,68,86,50,98,1964,1988,0.00,0.11,1.54
2014-8-16,71,61,81,68,86,53,98,1979,1997,0.00,0.12,2.25
2014-8-17,75,65,84,68,85,50,96,1979,1995,0.0,0.11,2.76
2014-8-18,73,63,83,68,85,52,97,1961,1995,0.00,0.11,2.54
2014-8-19,77,66,87,68,85,51,94,1963,2002,0.00,0.12,3.73
2014-8-20,79,71,86,68,85,52,99,1949,1983,0.0,0.12,1.87
2014-8-21,76,68,83,68,85,54,96,1949,1916,0.00,0.11,1.77
2014-8-22,74,68,79,67,85,52,97,1940,1916,0.08,0.12,2.73
2014-8-23,69,64,74,67,85,53,94,1952,1916,0.25,0.12,3.74
2014-8-24,74,64,83,67,84,50,93,1957,1972,0.00,0.10,1.70
2014-8-25,75,63,86,67,84,47,97,1963,1968,0.00,0.11,3.03
2014-8-26,80,69,90,67,84,51,100,1963,1948,0.00,0.10,2.52
2014-8-27,78,67,89,66,84,51,98,1963,1948,0.00,0.11,4.77
2014-8-28,74,66,81,66,84,49,99,1986,1948,0.00,0.09,2.33
2014-8-29,71,61,81,66,84,44,98,1986,1991,0.00,0.10,1.88
2014-8-30,71,60,81,66,83,47,100,1986,1953,0.00,0.10,2.32
2014-8-31,83,72,93,66,83,45,101,1965,1953,1.11,0.10,3.41
2014-9-1,82,74,89,65,83,49,97,1963,1953,0.01,0.10,1.80
2014-9-2,84,74,93,65,83,47,100,1963,1953,0.18,0.12,1.89
2014-9-3,78,71,85,65,82,51,100,1958,1953,0.0,0.12,3.37
2014-9-4,78,68,87,65,82,52,93,1997,1973,0.00,0.12,3.25
2014-9-5,81,72,89,64,82,53,92,1994,1961,0.00,0.12,2.45
2014-9-6,83,72,93,64,81,48,95,1962,1983,0.01,0.11,2.19
2014-9-7,75,66,83,64,81,47,102,1984,1881,0.00,0.12,1.22
2014-9-8,71,67,75,63,81,45,96,1962,1939,0.00,0.12,3.65
2014-9-9,72,67,76,63,81,49,94,1956,1884,0.00,0.12,1.59
2014-9-10,75,67,82,63,80,45,97,1956,1983,0.00,0.11,1.59
2014-9-11,76,67,84,62,80,43,98,1917,1983,0.00,0.13,2.10
2014-9-12,71,63,78,62,80,45,95,1940,1931,0.00,0.12,4.60
2014-9-13,66,59,72,62,79,44,95,1958,1952,0.36,0.13,2.80
2014-9-14,61,53,69,61,79,44,92,1975,1995,0.00,0.13,4.69
2014-9-15,63,53,72,61,78,45,95,1895,1927,0.00,0.13,2.74
2014-9-16,67,59,74,60,78,45,94,1984,1991,0.20,0.13,6.63
2014-9-17,66,56,75,60,78,44,94,1959,1991,0.00,0.13,3.48
2014-9-18,65,56,74,60,77,42,90,1959,1891,0.00,0.14,1.91
2014-9-19,65,58,72,59,77,43,92,1959,1983,0.00,0.13,1.95
2014-9-20,68,56,79,59,77,45,90,1956,1983,0.00,0.13,3.04
2014-9-21,72,65,79,58,76,38,97,1956,1895,0.0,0.13,2.44
2014-9-22,65,57,73,58,76,39,97,1962,1895,0.00,0.13,4.65
2014-9-23,61,50,72,58,75,40,97,1983,1895,0.00,0.12,3.72
2014-9-24,64,55,73,57,75,40,95,1963,1970,0.04,0.14,2.08
2014-9-25,62,59,64,57,75,35,92,1963,1970,0.89,0.13,1.66
2014-9-26,67,55,78,56,74,38,92,1963,1970,0.00,0.13,2.79
2014-9-27,72,59,84,56,74,36,91,1947,1998,0.00,0.14,1.85
2014-9-28,73,60,86,55,73,37,91,1957,1886,0.00,0.13,3.22
2014-9-29,70,64,76,55,73,37,89,1942,1921,0.0,0.14,1.87
2014-9-30,71,63,79,54,72,38,88,1942,1881,0.00,0.13,2.41
2014-10-1,68,62,73,54,72,34,89,1947,1927,0.0,0.11,3.00
2014-10-2,68,62,73,54,72,39,87,1966,2002,0.00,0.11,1.52
2014-10-3,66,59,72,53,71,33,90,1974,1919,0.06,0.12,1.11
2014-10-4,60,50,70,53,71,34,88,1974,1941,0.17,0.10,2.73
2014-10-5,52,43,61,52,70,31,96,1961,1941,0.00,0.12,1.71
2014-10-6,60,47,72,52,70,32,95,1965,1941,0.00,0.11,1.48
2014-10-7,67,63,71,52,70,37,93,1954,1941,0.38,0.10,1.60
2014-10-8,66,56,75,51,69,32,89,1964,2007,0.0,0.11,5.53
2014-10-9,61,51,70,51,69,35,86,1978,1939,0.00,0.11,2.17
2014-10-10,57,53,61,50,69,33,90,1979,1939,0.0,0.11,2.10
2014-10-11,54,50,57,50,68,28,86,1964,1949,0.48,0.10,2.04
2014-10-12,55,46,64,50,68,28,88,1964,1954,0.00,0.11,1.62
2014-10-13,61,56,66,49,68,32,87,1988,1954,0.02,0.10,0.90
2014-10-14,72,65,79,49,67,30,88,1988,1975,0.00,0.11,1.66
2014-10-15,72,67,77,49,67,32,87,1876,1975,0.66,0.10,1.04
2014-10-16,66,60,72,48,67,31,88,1876,1897,0.24,0.10,1.60
2014-10-17,65,57,73,48,66,34,89,1961,1938,0.00,0.10,1.87
2014-10-18,63,54,72,48,66,32,85,1982,1908,0.00,0.10,1.11
2014-10-19,51,45,56,47,66,29,80,1976,1947,0.00,0.09,3.59
2014-10-20,51,39,63,47,65,27,80,1974,1938,0.00,0.10,2.72
2014-10-21,61,52,70,47,65,28,86,1952,1947,0.0,0.10,2.21
2014-10-22,55,52,58,46,65,26,83,1940,1920,0.44,0.10,2.71
2014-10-23,54,51,57,46,64,31,87,1969,1947,0.05,0.10,1.92
2014-10-24,62,53,70,46,64,25,83,1969,2001,0.00,0.09,2.26
2014-10-25,58,46,70,45,64,25,76,1962,1993,0.00,0.10,3.82
2014-10-26,59,50,67,45,63,29,78,1960,1920,0.00,0.09,2.11
2014-10-27,57,46,67,45,63,29,80,1936,1963,0.00,0.09,1.45
2014-10-28,63,49,76,44,63,28,85,1976,1919,0.00,0.10,2.03
2014-10-29,60,50,70,44,62,30,79,1965,1918,0.03,0.10,2.68
2014-10-30,53,45,60,44,62,27,81,1962,1946,0.00,0.10,1.77
2014-10-31,47,40,54,44,62,26,82,1966,1946,0.0,0.10,1.32
2014-11-1,49,45,52,43,61,30,84,1968,1950,0.92,0.10,1.81
2014-11-2,46,42,49,43,61,27,81,1976,1950,0.0,0.10,1.65
2014-11-3,48,36,60,43,61,28,80,1951,1990,0.0,0.10,1.58
2014-11-4,60,49,71,43,60,26,81,1951,1974,0.00,0.09,1.36
2014-11-5,61,52,69,42,60,26,80,1879,1961,0.01,0.10,1.48
2014-11-6,53,49,56,42,60,26,79,1962,1948,0.52,0.09,1.41
2014-11-7,46,38,53,42,59,20,76,1962,1938,0.0,0.10,3.99
2014-11-8,42,34,50,41,59,25,78,1960,1975,0.00,0.09,3.07
2014-11-9,50,42,57,41,59,23,78,1976,1975,0.00,0.09,1.57
2014-11-10,50,37,63,41,58,23,73,1961,1999,0.00,0.09,1.46
2014-11-11,57,43,70,41,58,21,74,1961,1949,0.00,0.09,1.03
2014-11-12,58,46,70,40,58,26,75,1960,1879,0.0,0.08,1.90
2014-11-13,41,35,47,40,57,24,74,1996,1902,0.16,0.09,1.83
2014-11-14,39,35,43,40,57,19,76,1986,1955,0.0,0.09,2.64
2014-11-15,38,32,44,39,56,19,81,1942,1993,0.00,0.10,2.00
2014-11-16,39,33,45,39,56,20,76,1933,2005,0.09,0.09,1.46
2014-11-17,46,40,51,39,56,21,72,1933,1987,1.05,0.10,1.58
2014-11-18,34,24,44,39,55,20,75,1936,1921,0.00,0.10,1.07
2014-11-19,28,20,36,38,55,20,75,1936,1921,0.00,0.11,2.13
2014-11-20,41,32,50,38,54,21,76,1984,1985,0.00,0.10,2.59
2014-11-21,32,27,37,38,54,19,74,1879,1931,0.00,0.10,2.30
2014-11-22,35,23,46,37,54,14,73,1880,1883,0.0,0.11,1.66
2014-11-23,48,38,57,37,53,10,72,1880,1931,0.0,0.10,1.80
2014-11-24,64,56,72,37,53,16,72,1880,2014,0.37,0.11,2.22
2014-11-25,58,47,68,36,52,20,73,2013,1973,0.00,0.11,3.46
2014-11-26,42,34,50,36,52,18,70,1938,1999,0.95,0.10,1.69
2014-11-27,36,30,42,36,52,16,73,1932,1896,0.0,0.11,1.95
2014-11-28,34,28,39,35,51,17,71,1930,1990,0.00,0.11,2.78
2014-11-29,36,26,45,35,51,15,70,1955,1927,0.00,0.12,1.68
2014-11-30,51,42,59,35,50,8,73,1875,1933,0.00,0.12,0.96
2014-12-1,54,42,65,34,50,11,72,1875,2001,0.05,0.12,1.38
2014-12-2,41,36,45,34,49,11,66,1875,1970,0.20,0.13,1.48
2014-12-3,44,40,48,34,49,9,68,1976,1950,0.11,0.12,1.60
2014-12-4,39,32,45,34,49,12,73,1966,1998,0.00,0.13,1.69
2014-12-5,41,36,46,33,48,13,71,1886,2001,0.07,0.12,1.52
2014-12-6,46,42,50,33,48,15,72,1901,2001,1.02,0.13,1.16
2014-12-7,40,32,48,33,48,15,73,1926,1998,0.0,0.12,3.71
2014-12-8,32,27,37,32,47,14,67,1882,1980,0.00,0.12,1.22
2014-12-9,41,37,45,32,47,6,70,1876,1966,0.66,0.13,2.13
2014-12-10,40,36,43,32,46,4,71,1876,1966,0.0,0.13,1.45
2014-12-11,36,33,38,31,46,8,65,1880,1971,0.04,0.12,2.72
2014-12-12,38,34,42,31,46,8,65,1988,1931,0.0,0.13,1.45
2014-12-13,39,35,42,31,45,8,65,1960,1923,0.00,0.12,2.90
2014-12-14,42,37,47,30,45,10,69,1960,1881,0.00,0.12,2.02
2014-12-15,42,36,48,30,45,7,66,1960,1971,0.00,0.11,1.13
2014-12-16,43,32,53,30,44,9,68,1876,1971,0.12,0.12,1.54
2014-12-17,46,39,53,30,44,7,65,1919,2000,0.00,0.11,2.23
2014-12-18,38,34,41,29,44,4,70,1919,2006,0.00,0.11,1.72
2014-12-19,38,33,42,29,44,4,65,1884,1929,0.00,0.11,1.70
2014-12-20,34,33,35,29,43,1,64,1942,1957,0.00,0.11,1.32
2014-12-21,35,29,40,29,43,1,67,1942,2013,0.00,0.11,1.40
2014-12-22,36,27,45,28,43,6,68,1989,2013,0.02,0.11,1.77
2014-12-23,48,44,52,28,43,7,66,1989,1990,0.11,0.12,1.62
2014-12-24,56,47,64,28,42,3,64,1983,1990,0.82,0.11,1.74
2014-12-25,52,39,65,28,42,1,68,1980,1964,0.03,0.11,1.11
2014-12-26,42,34,50,28,42,3,68,1983,1964,0.00,0.11,1.90
2014-12-27,45,32,57,27,42,8,64,1989,1949,0.00,0.10,1.55
2014-12-28,48,39,56,27,42,9,66,1950,2008,0.02,0.10,1.45
2014-12-29,43,37,48,27,41,-1,72,1917,1984,0.00,0.10,2.80
2014-12-30,33,28,38,27,41,-5,65,1880,1990,0.00,0.09,2.30
2014-12-31,29,25,33,27,41,0,66,1880,1884,0.00,0.09,1.09
2015-1-1,35,28,42,27,41,4,64,1881,2005,0.00,0.09,1.60
2015-1-2,37,29,44,26,41,7,67,1968,1876,0.00,0.10,1.68
2015-1-3,36,28,44,26,41,-3,63,1879,2000,0.72,0.09,1.50
2015-1-4,50,42,58,26,41,2,68,1918,1950,0.37,0.10,1.51
2015-1-5,37,24,50,26,40,-2,66,1904,1997,0.00,0.10,2.24
2015-1-6,20,16,24,26,40,4,73,1896,2007,0.12,0.09,2.00
2015-1-7,19,13,24,26,40,4,65,2014,1950,0.0,0.10,2.49
2015-1-8,15,9,21,26,40,2,69,1970,1998,0.00,0.09,1.20
2015-1-9,29,21,36,26,40,1,69,1970,1930,0.01,0.10,1.54
2015-1-10,21,16,25,26,40,-5,63,1875,1950,0.00,0.10,1.28
2015-1-11,28,15,40,26,40,1,66,1982,1975,0.00,0.10,1.29
2015-1-12,36,33,38,25,40,1,72,1981,1890,0.59,0.10,2.19
2015-1-13,30,21,38,25,40,0,70,1981,1932,0.00,0.10,1.46
2015-1-14,26,20,32,25,40,0,73,1914,1932,0.00,0.10,2.27
2015-1-15,33,29,36,25,40,2,71,1964,1932,0.00,0.10,1.23
2015-1-16,34,25,43,25,40,0,62,1977,2007,0.00,0.10,1.09
2015-1-17,26,18,33,25,40,-7,63,1982,1913,0.00,0.10,1.66
2015-1-18,34,24,43,25,40,-4,64,1982,1990,1.84,0.10,1.84
2015-1-19,38,30,45,25,40,-5,65,1994,1951,0.00,0.09,1.40
2015-1-20,38,29,46,25,40,-3,65,1984,1951,0.00,0.10,1.89
2015-1-21,31,27,34,25,40,-6,62,1985,1959,0.06,0.10,1.32
2015-1-22,36,30,41,25,40,-7,70,1984,1906,0.03,0.09,1.03
2015-1-23,34,25,42,25,40,-2,71,1936,1906,0.16,0.10,2.32
2015-1-24,36,33,39,25,40,0,69,1882,1950,0.55,0.10,1.61
2015-1-25,40,33,46,25,40,-2,66,1963,1967,0.0,0.10,1.83
2015-1-26,31,27,34,26,40,4,74,1961,1950,0.0,0.09,1.57
2015-1-27,28,21,34,26,41,4,69,1927,1916,0.07,0.10,1.64
2015-1-28,28,20,35,26,41,3,67,1961,1916,0.00,0.09,1.63
2015-1-29,28,21,34,26,41,-5,72,1963,2002,0.0,0.11,1.23
2015-1-30,31,20,42,26,41,8,70,1948,1947,0.0,0.10,1.79
2015-1-31,22,14,29,26,41,3,65,1948,1947,0.00,0.10,1.30
2015-2-1,30,21,39,26,41,2,70,1920,2002,0.06,0.10,2.92
2015-2-2,32,23,41,26,41,-4,61,1961,1988,0.93,0.10,1.49
2015-2-3,25,20,30,26,41,4,62,1881,1991,0.00,0.09,1.64
2015-2-4,35,23,47,26,42,2,69,1886,1991,0.00,0.10,1.34
2015-2-5,29,17,41,26,42,-2,69,1918,1991,0.0,0.10,1.62
2015-2-6,23,14,32,26,42,-3,68,1895,2008,0.00,0.10,3.86
2015-2-7,33,21,44,27,42,5,67,1910,2008,0.00,0.09,1.35
2015-2-8,42,33,51,27,42,-2,63,1934,1925,0.00,0.09,1.15
2015-2-9,33,28,37,27,42,-11,64,1934,1990,0.03,0.10,1.17
2015-2-10,35,28,42,27,43,-6,65,1899,1925,0.0,0.09,1.10
2015-2-11,33,26,39,27,43,-6,69,1899,2009,0.00,0.09,1.85
2015-2-12,31,20,41,27,43,2,70,1979,1999,0.00,0.09,1.35
2015-2-13,17,10,24,27,43,3,66,1983,1951,0.00,0.09,2.04
2015-2-14,25,18,32,28,44,2,67,1979,1990,0.04,0.09,1.26
2015-2-15,15,9,21,28,44,3,74,1943,1949,0.0,0.10,1.09
2015-2-16,10,3,17,28,44,2,73,1888,1954,0.02,0.09,1.84
2015-2-17,21,13,28,28,44,-2,68,1896,1976,0.24,0.09,1.10
2015-2-18,22,11,32,28,44,0,69,1979,1991,0.0,0.10,1.35
2015-2-19,16,8,23,28,45,3,68,1903,1948,0.00,0.09,1.65
2015-2-20,10,2,18,29,45,1,70,1979,1939,0.00,0.09,2.40
2015-2-21,22,9,34,29,45,6,72,1968,1930,0.76,0.09,1.44
2015-2-22,40,33,47,29,46,7,68,1963,1974,0.28,0.10,1.88
2015-2-23,25,13,37,29,46,6,75,1963,1874,0.0,0.10,1.65
2015-2-24,16,7,25,29,46,2,74,1889,1985,0.00,0.09,1.84
2015-2-25,27,15,38,30,46,4,79,1894,1930,0.00,0.10,0.90
2015-2-26,29,26,32,30,47,10,69,1970,1890,0.0,0.10,2.16
2015-2-27,27,22,31,30,47,6,74,1900,1997,0.0,0.09,1.55
2015-2-28,24,16,32,30,47,9,68,1934,1976,0.00,0.10,1.40
2015-3-1,27,21,32,30,48,9,76,1980,1972,0.52,0.10,1.46
2015-3-2,35,29,40,31,48,8,77,1886,1972,0.00,0.11,1.22
2015-3-3,30,25,34,31,48,10,73,1886,1991,0.61,0.10,2.20
2015-3-4,38,33,43,31,49,7,74,1943,1974,0.49,0.11,1.61
2015-3-5,30,20,40,31,49,5,79,1872,1976,0.78,0.10,1.40
2015-3-6,19,11,26,32,49,10,71,1978,1935,0.00,0.11,2.06
2015-3-7,26,11,41,32,50,9,74,1960,1974,0.00,0.12,1.71
2015-3-8,38,26,50,32,50,12,80,1986,2000,0.00,0.12,1.51
2015-3-9,45,32,57,32,50,12,73,1960,2000,0.00,0.11,0.94
2015-3-10,46,32,59,33,51,7,76,1984,2006,0.71,0.12,1.43
2015-3-11,53,44,61,33,51,12,72,1960,1977,0.0,0.11,1.69
2015-3-12,45,37,52,33,51,12,83,1900,1990,0.00,0.11,2.64
2015-3-13,40,30,49,33,52,8,84,1888,1990,0.08,0.12,2.57
2015-3-14,45,38,51,34,52,12,85,1888,1990,0.95,0.12,1.56
2015-3-15,44,40,48,34,52,11,81,1993,1990,0.00,0.13,2.79
2015-3-16,44,33,55,34,53,16,82,1916,1945,0.0,0.13,1.72
2015-3-17,53,37,68,35,53,10,86,1885,1945,0.0,0.13,1.04
2015-3-18,39,32,45,35,53,10,78,1916,1989,0.00,0.13,1.13
2015-3-19,37,28,46,35,54,9,78,1876,1918,0.00,0.13,2.27
2015-3-20,35,32,37,35,54,8,83,1885,1945,0.70,0.13,1.76
2015-3-21,42,31,53,36,54,6,85,1885,1948,0.00,0.14,2.24
2015-3-22,41,34,48,36,55,11,80,1885,1948,0.00,0.13,1.90
2015-3-23,35,27,43,36,55,14,80,1958,2012,0.00,0.12,1.36
2015-3-24,36,31,40,37,56,15,76,1896,1988,0.00,0.14,1.24
2015-3-25,38,28,47,37,56,16,82,1878,1910,0.05,0.13,2.72
2015-3-26,53,39,67,37,56,21,80,1960,1921,0.34,0.13,1.62
2015-3-27,44,39,48,38,57,21,83,1894,1921,0.20,0.14,1.78
2015-3-28,34,27,41,38,57,18,83,1923,1945,0.00,0.13,1.98
2015-3-29,36,26,46,38,57,14,87,1923,1945,0.00,0.13,1.72
2015-3-30,46,34,57,39,58,18,86,1887,1998,0.03,0.13,1.59
2015-3-31,48,37,59,39,58,21,81,1923,1998,0.06,0.13,1.57
2015-4-1,45,34,56,39,59,14,81,1923,1978,0.00,0.13,1.48
2015-4-2,52,37,67,40,59,25,84,1964,1967,0.00,0.14,2.76
2015-4-3,60,57,63,40,59,27,87,1894,1963,0.12,0.13,2.28
2015-4-4,53,42,63,40,60,25,80,1943,1892,0.0,0.13,1.26
2015-4-5,53,42,64,41,60,22,81,1981,1942,0.00,0.13,1.23
2015-4-6,57,39,75,41,60,24,87,1982,1942,0.00,0.12,1.76
2015-4-7,57,44,69,41,61,19,90,1982,1929,0.24,0.12,1.16
2015-4-8,42,39,44,42,61,23,90,1982,1929,0.03,0.12,2.67
2015-4-9,41,39,43,42,62,26,84,1977,1959,0.02,0.12,2.07
2015-4-10,49,41,57,42,62,22,89,1985,2013,0.07,0.13,1.78
2015-4-11,55,47,63,43,62,28,84,1966,1887,0.00,0.12,1.03
2015-4-12,53,38,67,43,63,18,92,1874,1977,0.00,0.12,1.83
2015-4-13,60,44,75,43,63,26,89,1940,1977,0.00,0.12,1.88
2015-4-14,58,52,63,44,63,24,91,1950,1941,0.12,0.12,2.28
2015-4-15,59,48,70,44,64,27,88,1943,1941,0.00,0.12,4.19
2015-4-16,61,50,71,44,64,27,90,1943,2002,0.00,0.11,3.11
2015-4-17,65,55,74,45,65,27,95,1962,2002,0.15,0.12,1.16
2015-4-18,68,54,81,45,65,25,94,1875,1976,0.00,0.11,2.16
2015-4-19,58,49,67,45,65,20,91,1875,1896,0.0,0.11,1.44
2015-4-20,63,50,76,46,66,27,92,1897,1941,2.01,0.12,2.01
2015-4-21,59,51,66,46,66,27,89,1875,1976,0.82,0.11,2.11
2015-4-22,60,47,73,46,66,30,90,1875,1985,0.0,0.11,1.87
2015-4-23,48,43,52,47,67,33,91,1982,1960,0.0,0.12,1.55
2015-4-24,48,39,57,47,67,33,86,1930,1960,0.00,0.11,1.80
2015-4-25,48,37,58,47,67,33,92,1956,1960,0.00,0.11,2.11
2015-4-26,55,45,64,48,68,35,93,1967,2009,0.00,0.11,2.42
2015-4-27,54,48,60,48,68,36,93,1967,1915,0.00,0.12,1.59
2015-4-28,61,51,71,48,68,34,90,1946,1990,0.00,0.11,2.19
2015-4-29,64,51,77,49,69,29,90,1874,1974,0.00,0.12,0.85
2015-4-30,61,50,72,49,69,34,91,1874,1888,0.00,0.10,4.42
2015-5-1,57,49,64,49,69,34,90,1978,1942,0.00,0.10,1.77
2015-5-2,61,47,74,50,70,35,89,1960,2010,0.00,0.11,2.49
2015-5-3,65,49,81,50,70,35,90,1966,2001,0.00,0.11,1.36
2015-5-4,70,54,85,50,70,35,91,1957,2001,0.00,0.11,1.88
2015-5-5,74,62,85,51,71,33,90,1966,1928,0.00,0.11,1.80
2015-5-6,67,57,77,51,71,36,90,1891,1986,0.0,0.12,2.00
2015-5-7,69,57,81,51,71,35,94,1970,1930,0.00,0.11,2.69
2015-5-8,69,55,83,51,71,37,93,1947,1936,0.00,0.12,1.42
2015-5-9,70,60,80,52,72,33,93,1947,1936,0.03,0.11,1.57
2015-5-10,76,67,84,52,72,33,93,1966,1896,0.0,0.12,1.35
2015-5-11,77,69,84,52,72,28,93,1966,1896,0.0,0.12,1.82
2015-5-12,77,66,88,53,73,37,91,1962,1881,0.00,0.11,1.58
2015-5-13,63,54,72,53,73,37,92,1967,1881,0.00,0.12,1.40
2015-5-14,61,50,72,53,73,40,92,1996,1956,0.00,0.12,1.48
2015-5-15,65,54,76,54,74,39,93,1966,1900,0.00,0.12,1.40
2015-5-16,73,59,86,54,74,40,90,1959,1951,0.33,0.13,2.03
2015-5-17,76,68,84,54,74,37,92,1956,1974,0.01,0.12,1.33
2015-5-18,74,61,87,55,74,41,94,1973,1962,0.03,0.12,1.47
2015-5-19,72,61,83,55,75,41,96,1882,1962,0.01,0.12,2.36
2015-5-20,67,58,75,55,75,43,94,2002,1962,0.00,0.13,1.64
2015-5-21,55,51,58,56,75,41,95,2002,1934,0.18,0.13,2.94
2015-5-22,65,53,77,56,76,43,96,1895,1941,0.00,0.13,1.67
2015-5-23,61,51,71,56,76,41,96,1961,1925,0.00,0.13,1.77
2015-5-24,67,52,82,57,76,40,90,1963,1964,0.00,0.12,2.49
2015-5-25,75,63,87,57,77,40,96,1963,1991,0.00,0.12,1.77
2015-5-26,77,65,89,57,77,43,96,1961,1880,0.00,0.13,1.70
2015-5-27,80,71,89,58,77,42,95,1961,1991,0.60,0.12,3.16
2015-5-28,80,70,89,58,77,41,94,1961,1941,0.0,0.12,2.28
2015-5-29,77,66,87,58,78,42,95,1884,1991,0.00,0.12,2.25
2015-5-30,78,68,87,59,78,41,97,1961,1991,0.00,0.13,1.74
2015-5-31,81,70,92,59,78,42,97,1965,1991,0.00,0.13,1.65
2015-6-1,67,58,76,59,79,44,97,1967,1895,1.96,0.12,1.96
2015-6-2,56,53,58,60,79,46,98,1907,1925,0.30,0.12,2.96
2015-6-3,63,55,70,60,79,48,97,1986,1919,0.01,0.12,1.60
2015-6-4,64,57,70,60,80,47,98,1977,1925,0.0,0.12,1.96
2015-6-5,66,56,76,61,80,48,100,1990,1925,0.13,0.13,2.31
2015-6-6,73,64,81,61,80,45,100,1945,1925,0.05,0.12,1.95
2015-6-7,68,58,77,61,80,47,98,1958,1925,0.00,0.13,3.50
2015-6-8,75,64,86,62,81,44,97,1977,2011,0.88,0.12,2.00
2015-6-9,78,69,86,62,81,44,99,1957,2011,0.02,0.12,2.03
2015-6-10,76,65,86,62,81,47,98,1988,2008,0.00,0.12,2.59
2015-6-11,81,70,92,63,82,44,95,1972,1986,0.00,0.11,2.04
2015-6-12,85,75,95,63,82,48,95,1979,1947,0.00,0.11,3.05
2015-6-13,83,77,88,63,82,50,95,1979,1956,0.00,0.11,2.21
2015-6-14,81,72,90,64,83,48,96,1965,1988,0.09,0.12,1.88
2015-6-15,82,74,89,64,83,47,100,1884,1994,0.0,0.11,2.27
2015-6-16,83,74,91,64,83,49,100,1963,1991,0.0,0.11,3.30
2015-6-17,78,69,86,64,83,44,98,1964,1957,0.0,0.10,2.41
2015-6-18,70,65,74,65,84,48,96,1950,1957,0.65,0.11,1.93
2015-6-19,79,70,87,65,84,53,100,1946,1994,0.27,0.10,1.64
2015-6-20,79,71,86,65,84,51,98,1956,1931,0.48,0.11,1.64
2015-6-21,82,73,91,66,84,48,99,1968,1923,0.30,0.11,1.62
2015-6-22,82,73,91,66,85,48,100,1963,1988,0.00,0.11,2.47
2015-6-23,83,70,95,66,85,47,97,1963,1888,0.55,0.11,2.12
2015-6-24,77,69,85,66,85,52,99,1947,1923,0.00,0.11,2.61
2015-6-25,76,68,84,67,85,52,98,1979,1952,0.13,0.11,2.55
2015-6-26,76,68,84,67,85,51,100,1960,1952,0.0,0.12,2.77
2015-6-27,67,63,71,67,86,50,97,1965,1963,1.34,0.10,3.27
2015-6-28,73,66,79,67,86,54,97,1961,1963,0.22,0.11,2.48
2015-6-29,72,63,81,68,86,52,102,1888,1934,0.00,0.12,4.62
2015-6-30,77,67,86,68,86,52,100,1988,1964,1.50,0.12,1.56


================================================
FILE: us-weather-history/KPHX.csv
================================================
date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation
2014-7-1,98,86,109,82,107,65,115,1927,1990,0.00,0.02,2.68
2014-7-2,98,86,109,82,107,65,118,1911,2011,0.00,0.01,2.81
2014-7-3,94,79,108,82,107,64,117,1916,1907,0.0,0.02,0.22
2014-7-4,90,81,98,83,107,63,118,1912,1989,0.00,0.02,0.22
2014-7-5,94,84,103,83,107,63,116,1912,1983,0.01,0.02,0.18
2014-7-6,95,84,105,83,107,65,116,1902,1942,0.00,0.02,0.60
2014-7-7,98,88,108,83,107,66,115,1915,1905,0.00,0.02,0.75
2014-7-8,96,86,105,83,107,67,115,1955,1985,0.0,0.03,0.53
2014-7-9,92,83,101,83,107,66,116,1926,1958,0.0,0.02,0.44
2014-7-10,96,87,105,83,107,67,115,1926,1958,0.00,0.03,0.64
2014-7-11,98,88,107,84,107,68,118,1944,1958,0.00,0.03,0.48
2014-7-12,99,88,110,84,107,69,115,1944,2005,0.00,0.03,1.02
2014-7-13,95,80,110,84,107,66,114,1944,1989,0.03,0.03,1.30
2014-7-14,92,81,103,84,106,68,116,1962,2003,0.02,0.04,1.24
2014-7-15,95,86,103,84,106,66,117,1944,1998,0.0,0.04,0.72
2014-7-16,96,84,107,84,106,68,118,1907,1925,0.00,0.04,1.48
2014-7-17,96,86,105,84,106,65,116,1908,2005,0.00,0.04,1.31
2014-7-18,94,83,105,84,106,68,115,1946,1989,0.00,0.04,1.04
2014-7-19,96,88,104,84,106,68,116,1913,1989,0.0,0.04,0.80
2014-7-20,96,85,106,84,106,67,114,1900,1978,0.00,0.04,1.70
2014-7-21,96,85,107,84,106,65,118,1924,2006,0.00,0.05,1.54
2014-7-22,101,89,113,84,106,66,116,1924,2006,0.00,0.04,1.15
2014-7-23,104,94,114,84,106,69,114,1913,2006,0.00,0.04,0.41
2014-7-24,105,93,116,84,106,68,116,1913,2014,0.0,0.04,1.66
2014-7-25,98,87,109,84,106,67,115,1897,1943,0.0,0.05,1.10
2014-7-26,96,83,108,84,105,65,116,1952,1995,0.0,0.04,1.38
2014-7-27,94,83,104,84,105,65,118,1913,1995,0.0,0.04,1.23
2014-7-28,96,85,107,84,105,66,121,1913,1995,0.00,0.05,1.90
2014-7-29,101,92,110,84,105,68,115,1913,1995,0.00,0.04,2.05
2014-7-30,102,92,111,84,105,66,115,1913,1934,0.00,0.04,1.16
2014-7-31,98,87,109,84,105,70,115,1921,1972,0.0,0.04,1.33
2014-8-1,90,81,99,84,105,68,116,1950,1972,0.0,0.03,1.48
2014-8-2,90,78,102,84,105,70,113,1946,1918,0.0,0.04,1.09
2014-8-3,92,80,103,84,105,66,114,1956,1975,0.0,0.04,2.12
2014-8-4,92,79,105,84,105,68,116,1956,1975,0.00,0.03,0.78
2014-8-5,94,83,105,84,105,69,114,1965,1969,0.00,0.04,1.16
2014-8-6,96,84,108,84,105,68,114,1949,1995,0.00,0.03,2.01
2014-8-7,94,82,105,83,105,66,112,1928,1905,0.00,0.04,0.87
2014-8-8,95,85,105,83,105,67,116,1903,2012,0.00,0.03,0.58
2014-8-9,94,84,104,83,105,70,114,1930,2012,0.0,0.03,0.45
2014-8-10,94,84,104,83,105,68,116,1949,2003,0.00,0.04,0.32
2014-8-11,98,87,109,83,105,65,113,1949,1962,0.00,0.03,0.54
2014-8-12,88,77,98,83,105,64,115,1949,2012,0.44,0.03,0.84
2014-8-13,84,73,95,83,105,69,115,1949,2012,0.11,0.04,0.30
2014-8-14,93,83,102,83,105,69,113,1925,1988,0.00,0.03,1.20
2014-8-15,94,83,104,83,105,66,112,1968,1992,0.00,0.03,1.84
2014-8-16,97,87,106,83,105,64,113,1918,1992,0.00,0.03,1.32
2014-8-17,99,88,109,83,104,64,114,1949,2013,0.0,0.03,0.89
2014-8-18,91,83,98,83,104,62,112,1918,2011,0.0,0.03,1.73
2014-8-19,83,74,91,83,104,63,113,1918,1986,0.43,0.03,0.78
2014-8-20,87,75,98,83,104,58,112,1917,1982,0.00,0.04,1.59
2014-8-21,81,71,90,82,104,62,110,1916,2007,0.19,0.03,0.72
2014-8-22,83,72,93,82,104,65,113,1917,2011,0.00,0.03,0.50
2014-8-23,87,76,97,82,104,61,114,1968,2011,0.00,0.03,0.80
2014-8-24,91,79,103,82,104,61,115,1965,1985,0.00,0.04,1.16
2014-8-25,92,83,101,82,104,65,113,1965,2011,0.00,0.03,0.90
2014-8-26,90,80,100,82,104,65,117,1928,2011,0.00,0.03,0.65
2014-8-27,93,83,103,82,104,64,113,1920,1981,0.00,0.03,2.43
2014-8-28,95,83,106,82,104,64,113,1920,1998,0.00,0.03,0.94
2014-8-29,96,83,108,82,104,64,113,1920,1981,0.00,0.03,0.68
2014-8-30,97,83,111,81,104,64,113,1920,2011,0.00,0.03,0.34
2014-8-31,96,83,109,81,104,62,113,1962,1950,0.00,0.02,0.61
2014-9-1,95,81,108,81,104,63,116,1962,1950,0.00,0.03,1.21
2014-9-2,96,82,109,81,103,64,112,1964,1982,0.00,0.02,1.36
2014-9-3,96,84,108,81,103,64,112,1964,1983,0.00,0.03,0.35
2014-9-4,95,83,107,80,103,60,112,1921,1948,0.0,0.02,2.91
2014-9-5,91,80,101,80,103,61,113,1912,1945,0.00,0.03,2.43
2014-9-6,96,86,105,80,103,63,111,1921,1986,0.00,0.02,0.43
2014-9-7,94,84,104,80,102,61,111,1895,1979,0.00,0.03,0.82
2014-9-8,81,71,90,80,102,62,110,1912,1979,3.29,0.02,3.29
2014-9-9,86,79,92,79,102,61,110,1920,1974,0.0,0.03,0.66
2014-9-10,88,79,97,79,102,59,111,1912,1990,0.00,0.02,1.01
2014-9-11,90,79,100,79,102,58,112,1912,1990,0.00,0.02,1.08
2014-9-12,91,78,104,78,101,58,110,1920,1971,0.00,0.02,1.90
2014-9-13,92,83,100,78,101,55,109,1952,2000,0.00,0.02,1.25
2014-9-14,92,81,102,78,101,58,112,1915,2000,0.00,0.02,1.50
2014-9-15,96,87,105,77,100,53,110,1915,2000,0.00,0.02,0.07
2014-9-16,86,80,92,77,100,56,109,1906,1928,0.01,0.03,1.33
2014-9-17,84,78,89,77,100,56,109,1906,1962,0.14,0.01,1.19
2014-9-18,86,76,96,76,99,58,109,1950,2010,0.00,0.02,1.46
2014-9-19,90,82,98,76,99,54,111,1965,2010,0.00,0.02,1.06
2014-9-20,90,81,99,76,99,47,107,1965,2010,0.00,0.02,0.49
2014-9-21,90,80,99,75,98,47,107,1965,2003,0.00,0.02,0.63
2014-9-22,91,80,101,75,98,47,109,1895,1989,0.00,0.02,0.41
2014-9-23,90,77,102,75,98,54,108,1965,1982,0.00,0.02,0.72
2014-9-24,91,77,104,74,97,55,108,1968,2002,0.00,0.02,0.68
2014-9-25,92,81,103,74,97,49,108,1913,1979,0.00,0.01,0.42
2014-9-26,92,82,101,73,96,50,108,1900,1989,0.00,0.02,2.62
2014-9-27,82,69,95,73,96,51,107,1920,2009,1.64,0.02,1.64
2014-9-28,79,71,87,72,96,50,108,1923,1992,0.02,0.02,1.31
2014-9-29,78,67,89,72,95,52,107,1923,2003,0.00,0.02,1.66
2014-9-30,78,66,89,72,95,49,107,1965,1980,0.00,0.02,0.31
2014-10-1,79,67,91,71,94,49,107,1927,1980,0.00,0.02,0.73
2014-10-2,81,69,92,71,94,49,107,1927,1980,0.00,0.02,0.60
2014-10-3,83,67,99,70,94,49,105,1907,1988,0.00,0.02,1.29
2014-10-4,84,69,99,70,93,46,103,1908,1993,0.00,0.02,0.91
2014-10-5,83,69,96,69,93,46,104,1908,1987,0.00,0.02,0.60
2014-10-6,82,70,93,69,92,46,105,1913,1917,0.00,0.02,1.17
2014-10-7,81,72,90,69,92,44,104,1913,1991,0.00,0.02,0.30
2014-10-8,76,69,82,68,92,47,104,1913,1950,0.05,0.02,1.04
2014-10-9,77,70,84,68,91,48,103,1949,1996,0.0,0.02,0.62
2014-10-10,79,69,89,67,91,44,105,1961,1991,0.00,0.01,0.56
2014-10-11,81,70,92,67,91,45,102,1920,1991,0.00,0.02,0.31
2014-10-12,82,70,94,66,90,44,103,1924,1950,0.00,0.02,0.32
2014-10-13,78,65,90,66,90,48,101,1969,1989,0.00,0.02,0.16
2014-10-14,80,65,94,66,89,43,100,1920,1973,0.00,0.02,2.32
2014-10-15,80,69,90,65,89,41,103,1899,1991,0.00,0.02,0.72
2014-10-16,80,67,92,65,89,40,101,1899,1991,0.00,0.01,0.32
2014-10-17,80,71,89,64,88,42,102,1938,2009,0.0,0.02,0.28
2014-10-18,79,68,90,64,88,43,103,1966,2003,0.00,0.02,0.75
2014-10-19,82,69,94,64,87,40,101,1917,2003,0.08,0.02,1.69
2014-10-20,78,66,90,63,87,40,103,1949,2003,0.00,0.02,0.41
2014-10-21,80,69,91,63,87,39,103,1949,2003,0.00,0.02,0.40
2014-10-22,80,67,93,62,86,37,102,1906,2003,0.00,0.02,0.38
2014-10-23,81,67,94,62,86,37,100,1906,2003,0.00,0.01,0.52
2014-10-24,82,68,96,62,85,41,96,1920,1959,0.00,0.02,0.22
2014-10-25,83,70,96,61,85,40,96,1956,1990,0.00,0.02,0.36
2014-10-26,80,71,89,61,85,42,98,1920,2001,0.00,0.02,0.33
2014-10-27,80,71,88,60,84,39,98,1918,2001,0.00,0.02,1.12
2014-10-28,77,65,88,60,84,41,97,1908,2007,0.00,0.01,0.92
2014-10-29,78,64,91,60,83,40,95,1971,2007,0.00,0.02,0.48
2014-10-30,79,64,94,59,83,34,94,1971,1990,0.00,0.02,1.00
2014-10-31,81,67,95,59,83,36,96,1900,1988,0.00,0.02,1.05
2014-11-1,76,69,83,58,82,37,96,1971,1924,0.00,0.02,1.25
2014-11-2,67,60,73,58,82,34,96,1956,1924,0.00,0.02,0.44
2014-11-3,64,53,75,58,81,36,96,1956,2009,0.00,0.02,0.31
2014-11-4,67,53,81,57,81,32,95,1956,2001,0.00,0.02,0.32
2014-11-5,73,60,85,57,80,32,93,1922,2007,0.00,0.02,0.63
2014-11-6,73,60,86,56,80,34,94,1922,2007,0.00,0.02,0.50
2014-11-7,72,59,84,56,79,34,92,1947,2007,0.00,0.02,0.39
2014-11-8,74,58,90,56,79,32,91,1897,1906,0.00,0.02,0.36
2014-11-9,74,61,87,55,79,31,88,1897,1973,0.00,0.02,0.55
2014-11-10,72,59,84,55,78,33,91,1948,2009,0.00,0.02,2.24
2014-11-11,71,58,84,54,78,35,91,1935,1934,0.00,0.01,1.41
2014-11-12,70,60,80,54,77,33,93,1898,1999,0.00,0.02,1.02
2014-11-13,71,61,80,54,77,31,91,1938,1999,0.00,0.02,0.96
2014-11-14,67,61,73,53,76,28,91,1916,1999,0.00,0.02,1.58
2014-11-15,67,57,77,53,76,33,90,1925,1999,0.00,0.02,0.58
2014-11-16,62,53,71,52,75,32,89,1916,1999,0.00,0.02,0.94
2014-11-17,59,47,71,52,75,32,87,1964,2008,0.00,0.01,0.78
2014-11-18,61,47,74,52,74,31,88,1958,2008,0.00,0.02,0.65
2014-11-19,62,50,74,51,74,30,88,1921,1897,0.00,0.02,0.65
2014-11-20,61,48,74,51,73,31,89,1956,2006,0.00,0.03,1.16
2014-11-21,63,53,72,51,73,30,88,1948,1924,0.00,0.02,0.60
2014-11-22,59,46,72,50,72,30,89,1953,1950,0.00,0.02,1.60
2014-11-23,61,48,74,50,72,27,87,1931,1950,0.00,0.03,0.98
2014-11-24,57,42,71,49,72,28,88,1931,1950,0.00,0.03,0.54
2014-11-25,58,46,69,49,71,32,88,1938,1950,0.00,0.03,0.64
2014-11-26,59,44,74,49,71,32,88,1966,1950,0.00,0.02,1.02
2014-11-27,67,47,87,48,70,31,87,1966,2014,0.00,0.03,1.29
2014-11-28,68,52,84,48,70,32,86,1896,1901,0.00,0.03,0.51
2014-11-29,66,51,80,48,70,29,85,1919,1999,0.00,0.03,0.72
2014-11-30,67,53,80,47,69,30,84,1911,1949,0.00,0.02,1.23
2014-12-1,62,51,73,47,69,31,83,1918,1949,0.00,0.03,0.40
2014-12-2,62,54,70,47,68,30,81,1913,1940,0.03,0.02,0.41
2014-12-3,68,60,76,47,68,30,81,1934,1940,0.01,0.03,0.69
2014-12-4,64,60,68,46,68,28,83,1909,1979,0.61,0.03,0.91
2014-12-5,65,57,72,46,68,25,82,1909,1965,0.00,0.03,0.67
2014-12-6,64,57,71,46,67,27,83,1960,1939,0.00,0.03,0.50
2014-12-7,64,51,77,46,67,27,83,1948,1939,0.00,0.02,1.08
2014-12-8,66,55,76,45,67,24,84,1916,1939,0.00,0.03,1.06
2014-12-9,66,54,78,45,67,24,84,1916,1981,0.00,0.03,0.77
2014-12-10,66,54,77,45,66,29,87,1967,1950,0.00,0.03,1.12
2014-12-11,66,56,76,45,66,26,81,1916,1950,0.00,0.03,0.87
2014-12-12,68,57,78,45,66,28,79,1971,2010,0.0,0.03,0.72
2014-12-13,59,51,66,45,66,26,82,1911,2010,0.08,0.03,1.04
2014-12-14,55,46,63,45,66,24,78,1901,2010,0.00,0.03,1.43
2014-12-15,55,46,64,44,66,26,79,1916,1952,0.00,0.03,0.86
2014-12-16,57,49,65,44,65,28,86,1931,1980,0.00,0.03,0.26
2014-12-17,56,51,61,44,65,28,82,1927,2013,0.07,0.03,1.35
2014-12-18,56,50,62,44,65,29,79,1933,1950,0.00,0.03,1.02
2014-12-19,55,45,64,44,65,25,79,1968,1917,0.00,0.03,0.98
2014-12-20,56,45,66,44,65,24,78,1897,1954,0.00,0.02,0.28
2014-12-21,56,47,65,44,65,27,77,1897,1985,0.00,0.03,0.52
2014-12-22,57,47,66,44,65,23,79,1897,1917,0.00,0.03,0.96
2014-12-23,58,47,68,44,65,26,79,1990,1950,0.00,0.02,0.83
2014-12-24,55,43,66,44,65,25,81,1953,1955,0.00,0.03,0.93
2014-12-25,55,45,64,44,65,27,78,1953,1950,0.00,0.03,0.63
2014-12-26,48,38,57,44,65,22,78,1911,1980,0.00,0.02,0.56
2014-12-27,48,35,60,44,65,24,82,1911,1980,0.00,0.03,1.05
2014-12-28,48,36,60,44,65,24,78,1954,1917,0.00,0.03,0.79
2014-12-29,49,36,61,44,65,26,85,1966,1980,0.00,0.03,0.47
2014-12-30,50,37,63,44,65,23,81,1895,1980,0.00,0.03,1.50
2014-12-31,43,36,50,44,66,22,79,1900,1897,0.11,0.03,0.80
2015-1-1,41,35,46,44,66,24,81,1919,1981,0.00,0.03,0.22
2015-1-2,43,31,54,45,66,23,81,1919,1981,0.00,0.03,0.99
2015-1-3,44,34,54,45,66,25,79,1950,1956,0.00,0.03,0.82
2015-1-4,50,35,65,45,66,23,83,1950,1927,0.00,0.03,0.65
2015-1-5,57,40,74,45,66,23,80,1950,1927,0.00,0.03,0.31
2015-1-6,62,44,79,45,66,17,81,1913,2006,0.00,0.03,0.76
2015-1-7,64,47,81,45,66,16,81,1913,2015,0.00,0.03,0.67
2015-1-8,63,54,71,45,66,19,81,1913,2002,0.00,0.03,1.22
2015-1-9,63,51,75,45,66,24,84,1964,1923,0.00,0.03,1.18
2015-1-10,60,54,65,45,67,25,84,1964,1990,0.0,0.03,1.51
2015-1-11,63,54,72,45,67,20,83,1964,1953,0.01,0.03,1.74
2015-1-12,62,56,67,45,67,22,80,1962,1996,0.02,0.03,1.04
2015-1-13,57,51,63,45,67,20,79,1963,1904,0.11,0.03,0.81
2015-1-14,57,46,67,45,67,22,81,1963,2000,0.00,0.04,0.77
2015-1-15,60,47,72,46,67,23,81,1964,2000,0.00,0.03,0.69
2015-1-16,62,49,74,46,67,22,83,1964,1976,0.00,0.03,1.05
2015-1-17,60,47,73,46,67,26,83,1964,1976,0.00,0.03,1.07
2015-1-18,62,48,75,46,68,27,80,1915,1971,0.00,0.03,0.51
2015-1-19,63,50,75,46,68,27,88,1915,1971,0.00,0.03,0.87
2015-1-20,62,48,75,46,68,22,84,1963,1950,0.00,0.03,0.77
2015-1-21,64,53,74,46,68,24,81,1922,2014,0.00,0.02,1.33
2015-1-22,57,48,65,46,68,21,81,1937,2013,0.00,0.03,0.35
2015-1-23,54,43,64,46,68,24,81,1937,2013,0.00,0.03,0.25
2015-1-24,64,50,77,46,68,24,80,1937,1951,0.00,0.02,0.51
2015-1-25,63,48,78,46,68,23,83,1898,1951,0.00,0.03,0.71
2015-1-26,62,53,71,46,68,26,81,1950,1987,0.01,0.03,1.18
2015-1-27,64,53,74,47,68,26,82,1950,2003,0.00,0.03,0.87
2015-1-28,64,52,75,47,69,30,83,1937,1971,0.00,0.02,0.57
2015-1-29,65,58,71,47,69,27,83,1948,1935,0.24,0.03,1.45
2015-1-30,58,55,60,47,69,28,82,1970,1935,0.35,0.03,1.19
2015-1-31,57,53,60,47,69,27,86,1949,2003,0.07,0.03,0.59
2015-2-1,60,50,69,47,69,28,83,1985,2003,0.00,0.03,1.13
2015-2-2,61,48,73,47,69,29,82,1922,1925,0.00,0.03,0.35
2015-2-3,63,50,76,47,69,28,86,1922,1925,0.00,0.03,1.06
2015-2-4,65,52,77,47,69,27,85,1955,1963,0.00,0.02,0.89
2015-2-5,67,52,82,47,69,28,87,1964,1963,0.00,0.03,0.82
2015-2-6,69,54,84,48,69,28,87,1955,1963,0.00,0.03,0.92
2015-2-7,67,53,81,48,70,24,89,1899,1963,0.00,0.03,0.74
2015-2-8,69,55,82,48,70,24,86,1933,1970,0.00,0.03,0.83
2015-2-9,69,55,83,48,70,28,84,1933,1987,0.00,0.03,0.88
2015-2-10,70,57,83,48,70,28,87,1939,1951,0.00,0.03,0.81
2015-2-11,68,54,81,48,70,26,83,1933,1951,0.00,0.03,0.77
2015-2-12,68,58,78,48,70,28,84,1965,1971,0.00,0.02,0.74
2015-2-13,68,55,80,48,70,27,88,1948,1957,0.00,0.03,1.42
2015-2-14,69,54,83,49,71,28,85,1966,1957,0.00,0.04,0.92
2015-2-15,71,63,79,49,71,26,86,1949,2014,0.00,0.03,0.79
2015-2-16,67,55,79,49,71,28,84,1964,1977,0.00,0.03,0.68
2015-2-17,66,55,77,49,71,26,88,1910,2014,0.00,0.04,0.57
2015-2-18,65,50,79,49,71,29,86,1964,1977,0.00,0.03,0.72
2015-2-19,66,52,80,49,71,29,88,1955,1977,0.00,0.04,0.93
2015-2-20,67,55,79,50,72,27,87,1955,1977,0.00,0.04,0.55
2015-2-21,67,54,79,50,72,29,86,1955,1982,0.00,0.04,0.73
2015-2-22,64,56,72,50,72,26,87,1955,1982,0.00,0.04,0.45
2015-2-23,65,59,70,50,72,29,89,1955,1989,0.01,0.03,0.94
2015-2-24,59,51,67,50,72,29,91,1964,1904,0.0,0.04,1.46
2015-2-25,60,47,72,50,72,29,92,1960,1921,0.00,0.04,0.81
2015-2-26,64,51,77,51,73,33,91,1919,1986,0.00,0.04,0.62
2015-2-27,64,50,78,51,73,28,92,1964,1986,0.00,0.03,0.57
2015-2-28,65,60,70,51,73,30,89,1962,1986,0.0,0.04,0.75
2015-3-1,69,61,77,51,73,31,89,1964,1986,0.00,0.04,0.94
2015-3-2,59,52,66,51,74,30,90,1971,1921,0.23,0.04,0.74
2015-3-3,56,47,65,51,74,28,91,1966,1921,0.07,0.04,1.98
2015-3-4,59,47,70,52,74,25,88,1966,1972,0.00,0.04,1.16
2015-3-5,61,46,75,52,74,31,93,1966,1972,0.00,0.03,0.97
2015-3-6,67,51,83,52,74,34,92,1967,1972,0.00,0.04,1.53
2015-3-7,69,55,83,52,75,34,91,1931,1972,0.00,0.04,0.71
2015-3-8,69,55,83,52,75,32,92,1969,1989,0.00,0.03,0.84
2015-3-9,70,56,84,52,75,29,94,1964,1972,0.00,0.04,0.98
2015-3-10,73,58,87,53,75,34,95,1964,1972,0.00,0.04,0.52
2015-3-11,73,59,87,53,76,33,94,1899,1900,0.00,0.03,1.40
2015-3-12,77,68,86,53,76,34,94,1909,1900,0.00,0.04,1.10
2015-3-13,74,60,88,53,76,32,92,1954,1972,0.00,0.04,0.51
2015-3-14,76,63,88,53,76,33,95,1969,2013,0.00,0.03,1.07
2015-3-15,76,63,89,53,77,31,92,1962,2007,0.00,0.04,0.79
2015-3-16,78,65,90,54,77,34,99,1917,2007,0.00,0.04,1.14
2015-3-17,78,64,91,54,77,31,99,1917,2007,0.00,0.03,0.54
2015-3-18,72,62,81,54,77,36,94,1954,2007,0.02,0.03,0.25
2015-3-19,65,60,70,54,78,34,92,1898,2004,0.01,0.04,0.54
2015-3-20,71,61,81,54,78,35,95,1898,2004,0.00,0.03,0.86
2015-3-21,73,60,85,54,78,35,97,1955,2004,0.00,0.02,0.27
2015-3-22,75,60,89,55,78,32,94,1897,1990,0.00,0.03,0.81
2015-3-23,75,61,88,55,79,31,93,1897,1990,0.00,0.03,0.52
2015-3-24,73,59,86,55,79,36,94,1949,1990,0.00,0.03,0.40
2015-3-25,75,62,88,55,79,34,93,1929,1990,0.00,0.02,0.64
2015-3-26,77,64,89,55,79,35,100,1913,1988,0.00,0.03,1.19
2015-3-27,79,63,95,55,80,33,98,1898,1986,0.00,0.02,0.65
2015-3-28,80,64,95,56,80,34,95,1898,1986,0.00,0.02,0.91
2015-3-29,81,65,97,56,80,36,97,1907,2015,0.00,0.02,0.42
2015-3-30,83,70,96,56,81,38,97,1898,2004,0.00,0.02,0.14
2015-3-31,82,68,95,56,81,32,95,1897,2015,0.00,0.02,0.27
2015-4-1,79,67,91,56,81,37,100,1904,2011,0.00,0.02,0.46
2015-4-2,76,64,87,57,81,37,98,1917,1943,0.00,0.01,0.68
2015-4-3,73,60,86,57,82,38,97,1906,1943,0.00,0.02,0.69
2015-4-4,76,62,89,57,82,39,98,1955,1961,0.00,0.02,0.43
2015-4-5,75,61,89,57,82,37,98,1921,1989,0.00,0.01,1.30
2015-4-6,74,61,86,57,82,38,102,1909,1989,0.00,0.02,0.56
2015-4-7,71,58,84,58,83,38,104,1922,1989,0.00,0.02,0.19
2015-4-8,70,61,78,58,83,39,104,1929,1989,0.00,0.01,0.99
2015-4-9,69,54,83,58,83,40,102,1953,1989,0.00,0.02,0.36
2015-4-10,72,57,87,58,84,35,100,1922,1989,0.00,0.01,0.83
2015-4-11,73,60,86,59,84,39,98,1979,1989,0.00,0.01,1.05
2015-4-12,76,65,86,59,84,40,99,1967,1936,0.00,0.01,0.27
2015-4-13,77,63,90,59,84,39,99,1927,1962,0.00,0.01,0.16
2015-4-14,81,67,94,60,85,40,103,1945,1925,0.00,0.01,0.38
2015-4-15,75,65,84,60,85,40,101,1970,1962,0.00,0.00,0.30
2015-4-16,66,56,75,60,85,40,101,1924,1984,0.00,0.01,0.67
2015-4-17,69,55,82,61,86,38,100,1924,1987,0.00,0.01,0.45
2015-4-18,75,59,90,61,86,38,100,1896,1962,0.00,0.00,0.21
2015-4-19,77,62,92,61,86,37,103,1968,1989,0.00,0.01,0.82
2015-4-20,79,65,93,61,87,38,105,1933,1989,0.00,0.01,0.41
2015-4-21,78,65,91,62,87,42,103,1967,1989,0.00,0.00,0.44
2015-4-22,77,65,89,62,87,39,105,1904,2012,0.00,0.01,0.90
2015-4-23,73,62,83,62,87,40,103,1923,2012,0.00,0.00,0.57
2015-4-24,70,62,78,63,88,41,99,1923,1987,0.02,0.00,0.17
2015-4-25,69,56,81,63,88,41,102,1964,1898,0.02,0.01,0.33
2015-4-26,65,54,75,63,88,42,101,1964,1992,0.14,0.00,0.20
2015-4-27,72,57,87,64,89,45,104,1963,1992,0.00,0.01,0.34
2015-4-28,81,68,93,64,89,45,104,1896,1992,0.00,0.00,0.65
2015-4-29,84,71,96,64,89,43,105,1970,1992,0.00,0.01,0.22
2015-4-30,85,70,99,65,90,39,102,1967,1943,0.00,0.00,0.17
2015-5-1,86,70,101,65,90,42,103,1915,1985,0.00,0.00,0.27
2015-5-2,85,72,98,65,90,40,107,1967,1947,0.00,0.01,0.47
2015-5-3,86,75,96,66,91,39,109,1899,1947,0.00,0.00,0.09
2015-5-4,75,65,85,66,91,45,106,1899,1947,0.24,0.01,0.91
2015-5-5,77,67,86,66,91,45,105,1964,1989,0.00,0.00,0.46
2015-5-6,80,69,90,67,92,44,105,1964,1947,0.00,0.00,0.12
2015-5-7,76,65,86,67,92,45,108,1896,1989,0.00,0.01,0.15
2015-5-8,68,61,75,67,92,40,110,1965,1989,0.0,0.00,0.12
2015-5-9,67,57,77,68,93,45,108,1950,1934,0.00,0.00,0.46
2015-5-10,75,61,89,68,93,46,111,1930,1934,0.00,0.01,0.78
2015-5-11,82,67,96,68,93,43,110,1905,1934,0.00,0.00,0.17
2015-5-12,81,70,91,68,94,48,109,1905,1996,0.00,0.01,0.22
2015-5-13,80,69,91,69,94,48,108,1962,1978,0.00,0.00,0.08
2015-5-14,79,70,87,69,94,50,107,1907,1927,0.00,0.00,0.21
2015-5-15,67,57,76,69,95,50,107,1968,1937,0.93,0.01,0.93
2015-5-16,66,57,75,70,95,49,106,1955,1997,0.0,0.00,0.08
2015-5-17,75,64,86,70,95,49,108,1943,1970,0.00,0.01,0.09
2015-5-18,78,65,90,70,95,44,107,1903,1970,0.00,0.00,0.06
2015-5-19,76,65,87,70,96,48,110,1903,2008,0.00,0.00,0.23
2015-5-20,80,67,93,71,96,49,108,1902,2008,0.00,0.01,0.73
2015-5-21,82,72,91,71,96,48,109,1902,2005,0.00,0.00,0.42
2015-5-22,74,66,82,71,97,48,109,1962,2000,0.00,0.00,0.28
2015-5-23,74,64,83,71,97,49,109,1965,2001,0.00,0.01,0.12
2015-5-24,76,66,85,72,97,52,109,1944,1983,0.00,0.00,0.34
2015-5-25,80,67,92,72,98,48,108,1965,2001,0.00,0.00,0.05
2015-5-26,84,71,96,72,98,48,112,1916,1951,0.00,0.01,0.14
2015-5-27,85,73,97,72,98,48,111,1917,1951,0.00,0.00,0.05
2015-5-28,85,71,98,73,99,53,113,1929,1983,0.00,0.00,0.27
2015-5-29,88,73,103,73,99,50,112,1918,1910,0.00,0.00,0.08
2015-5-30,90,74,105,73,99,51,114,1918,1910,0.00,0.01,0.01
2015-5-31,93,78,107,73,100,54,109,1918,2001,0.00,0.00,0.10
2015-6-1,92,78,105,74,100,50,111,1917,2012,0.00,0.00,0.02
2015-6-2,90,76,104,74,100,53,110,1917,1977,0.00,0.00,0.14
2015-6-3,88,74,102,74,101,51,112,1965,2006,0.00,0.00,0.41
2015-6-4,85,73,97,75,101,49,113,1908,1990,0.00,0.00,0.16
2015-6-5,81,72,90,75,101,55,112,1932,1990,0.16,0.00,0.16
2015-6-6,85,73,97,75,102,58,110,1967,2002,0.03,0.00,0.11
2015-6-7,86,71,100,75,102,56,115,1963,1985,0.00,0.00,0.06
2015-6-8,92,78,105,76,102,55,115,1950,1985,0.00,0.00,0.15
2015-6-9,89,83,95,76,103,54,114,1907,1985,0.0,0.00,0.07
2015-6-10,88,80,95,76,103,55,111,1965,1978,0.0,0.00,0.26
2015-6-11,91,82,100,76,103,55,114,1913,1918,0.00,0.00,0.02
2015-6-12,91,78,104,77,103,59,112,1913,1974,0.00,0.00,0.92
2015-6-13,93,80,105,77,104,59,114,1917,1936,0.00,0.00,0.04
2015-6-14,95,83,107,77,104,56,115,1922,1974,0.00,0.00,0.02
2015-6-15,99,86,112,78,104,54,115,1907,1974,0.00,0.00,0.08
2015-6-16,99,85,112,78,104,54,115,1907,1974,0.00,0.00,0.01
2015-6-17,100,86,114,78,105,54,114,1965,1896,0.00,0.00,0.19
2015-6-18,101,86,115,78,105,54,115,1921,1989,0.00,0.01,0.25
2015-6-19,100,85,114,79,105,61,115,1965,1968,0.00,0.00,0.19
2015-6-20,96,80,112,79,105,61,115,1964,1968,0.00,0.00,0.15
2015-6-21,99,88,109,79,105,57,115,1923,1968,0.00,0.00,0.27
2015-6-22,97,85,109,80,106,56,116,1965,1988,0.00,0.00,1.37
2015-6-23,99,87,111,80,106,61,116,1923,1974,0.00,0.00,0.84
2015-6-24,100,87,112,80,106,62,118,1945,1929,0.00,0.00,0.26
2015-6-25,100,90,110,80,106,60,120,1965,1990,0.00,0.00,0.34
2015-6-26,98,89,107,81,106,62,122,1963,1990,0.00,0.00,0.07
2015-6-27,99,90,108,81,106,55,118,1965,1990,0.01,0.00,0.04
2015-6-28,99,87,110,81,106,59,118,1965,1990,0.00,0.01,0.26
2015-6-29,98,86,110,81,107,59,119,1913,2013,0.05,0.00,0.09
2015-6-30,96,85,107,82,107,64,115,1913,1950,0.00,0.00,0.21


================================================
FILE: us-weather-history/KSAF.csv
================================================
date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation
2014-7-1,73,57,86,61,95,51,97,2004,1999,0.02
2014-7-2,70,57,82,62,91,51,96,2004,2012,0.17
2014-7-3,70,57,84,51,88,51,98,1980,2003,0.03
2014-7-4,68,52,84,52,92,51,99,2005,2003,0.00
2014-7-5,74,61,87,57,94,48,94,2004,1980,0.00
2014-7-6,74,57,91,59,98,53,98,2004,1980,0.00
2014-7-7,74,57,91,56,94,55,96,1997,2003,0.35
2014-7-8,72,57,86,55,92,51,96,2005,2005,0.26
2014-7-9,70,59,84,57,93,52,98,2015,2003,0.08
2014-7-10,74,62,87,58,94,52,94,2008,1980,0.00
2014-7-11,74,62,90,55,96,48,96,1999,1980,0.01
2014-7-12,71,55,89,58,96,53,96,2002,1980,0.00
2014-7-13,70,57,82,53,91,53,99,1980,2003,0.14
2014-7-14,73,60,87,54,88,54,98,1980,2003,0.05
2014-7-15,66,57,75,54,90,51,97,2002,2010,0.22
2014-7-16,68,57,81,60,95,51,96,2005,2007,0.21
2014-7-17,70,57,84,61,96,51,98,2013,2006,0.00
2014-7-18,74,60,89,56,97,52,97,1998,1980,0.00
2014-7-19,76,63,90,57,98,53,100,2000,2010,0.00
2014-7-20,74,57,95,57,94,54,99,1999,2010,0.13
2014-7-21,78,64,93,58,91,52,95,1999,2005,0.01
2014-7-22,78,62,93,54,91,51,95,2000,1996,0.06
2014-7-23,72,60,84,53,89,53,93,1980,2003,0.43
2014-7-24,74,61,89,52,88,52,96,1980,2003,0.00
2014-7-25,77,61,93,59,90,55,96,2004,2003,0.00
2014-7-26,80,64,96,59,96,51,96,2004,1980,0.00
2014-7-27,74,60,89,60,95,53,95,2004,1980,1.14
2014-7-28,70,59,81,61,96,52,96,2004,1980,0.01
2014-7-29,68,60,75,54,92,52,93,2004,2001,0.12
2014-7-30,72,60,84,57,92,50,93,2004,2008,0.00
2014-7-31,66,57,78,62,93,54,95,2003,2012,0.02
2014-8-1,64,55,73,52,95,52,95,1980,1980,0.27
2014-8-2,66,59,73,65,96,54,96,1998,1980,0.02
2014-8-3,66,55,78,57,91,54,95,1998,2008,0.00
2014-8-4,68,60,78,57,89,55,93,1998,2003,1.26
2014-8-5,68,55,82,61,91,53,95,1998,2003,0.00
2014-8-6,70,55,86,63,92,51,95,1998,2015,0.00
2014-8-7,68,57,80,53,92,53,95,1980,2011,0.00
2014-8-8,67,50,84,59,84,50,93,2014,2003,0.00
2014-8-9,70,55,84,58,93,52,93,1999,1980,0.00
2014-8-10,68,53,84,55,84,51,93,2000,2011,0.07
2014-8-11,66,52,80,58,85,51,96,2004,2012,0.00
2014-8-12,69,57,82,53,84,53,96,1980,2002,0.00
2014-8-13,71,63,80,56,76,51,95,1999,1996,0.01
2014-8-14,70,57,82,59,86,51,96,1998,1996,0.09
2014-8-16,72,55,89,51,82,51,93,1980,2002,0.00
2014-8-17,71,55,87,48,83,48,93,1980,2002,0.00
2014-8-18,72,57,87,53,88,46,93,2008,2015,0.00
2014-8-19,70,57,87,57,90,53,95,1997,2007,0.00
2014-8-20,72,61,84,50,82,50,95,1980,2007,0.00
2014-8-21,70,55,86,45,84,45,96,1980,2007,0.00
2014-8-22,62,55,68,47,90,47,95,1980,2007,0.10
2014-8-23,63,48,79,52,89,48,93,2014,2010,0.00
2014-8-24,66,50,84,54,92,48,92,2002,1980,0.00
2014-8-25,70,55,87,52,87,45,93,2004,2007,0.00
2014-8-26,66,55,79,56,84,46,93,2001,2002,0.22
2014-8-27,64,55,77,48,83,48,93,1980,2011,0.16
2014-8-28,62,48,78,47,84,46,91,2006,1997,0.00
2014-8-29,65,50,80,47,86,46,91,2005,1997,0.00
2014-8-30,68,53,86,48,85,48,95,1980,2011,0.00
2014-8-31,72,55,89,53,88,48,95,2002,2011,0.00
2014-9-1,70,51,89,46,85,45,91,1980,2013,0.00
2014-9-2,73,57,89,46,86,42,93,1980,2012,0.00
2014-9-3,72,55,90,47,88,44,90,1980,2011,0.00
2014-9-5,68,57,80,48,89,44,91,1980,2010,0.00
2014-9-6,65,55,77,52,84,48,90,1978,2010,0.00
2014-9-7,66,51,82,52,81,46,89,2003,2000,0.00
2014-9-8,70,57,82,48,81,39,87,2001,2007,0.00
2014-9-10,68,57,80,53,77,41,89,2001,2015,0.00
2014-9-12,56,46,66,45,78,43,88,1978,2004,0.00
2014-9-14,63,46,82,46,82,39,91,2005,1997,0.00
2014-9-15,68,53,84,48,81,37,89,2005,2010,0.00
2014-9-16,66,57,77,48,83,42,90,2002,2000,0.00
2014-9-17,60,55,66,45,84,37,89,2006,2010,0.00
2014-9-18,66,50,84,42,78,34,91,2006,2010,0.00
2014-9-19,69,57,82,40,83,30,89,2006,2010,0.00
2014-9-20,71,54,89,42,78,39,89,1978,2014,0.00
2014-9-21,68,55,82,47,76,39,88,2006,2010,0.00
2014-9-22,68,60,75,42,76,37,84,1980,2012,0.04
2014-9-23,70,59,81,42,75,36,84,2006,1998,0.00
2014-9-24,66,50,82,44,78,36,86,2006,1998,0.00
2014-9-25,66,52,81,43,71,28,86,2000,1998,0.00
2014-9-26,63,48,79,49,69,33,87,2000,1999,0.00
2014-9-27,64,53,77,48,72,32,88,1996,2010,0.00
2014-9-29,58,46,73,39,79,30,90,1999,2010,0.02
2014-9-30,56,42,72,40,83,37,84,1999,1978,0.00
2014-10-1,56,42,73,39,84,37,89,1980,2010,0.00
2014-10-2,52,39,68,42,84,37,87,2001,1980,0.00
2014-10-3,57,41,73,37,79,34,84,1980,2012,0.00
2014-10-4,58,37,78,34,77,33,80,1980,2007,0.00
2014-10-5,58,42,75,35,81,33,83,1980,1980,0.00
2014-10-7,60,46,77,33,79,33,81,1978,1980,0.00
2014-10-8,59,48,71,34,79,30,79,2012,1978,0.00
2014-10-10,51,48,54,35,77,32,81,2001,1999,0.26
2014-10-11,56,46,66,33,77,26,82,2001,2015,0.00
2014-10-12,52,39,66,33,77,28,81,1997,2007,0.01
2014-10-13,48,37,60,37,76,23,82,1997,2015,0.00
2014-10-14,50,32,68,29,66,27,81,1978,2015,0.00
2014-10-15,54,37,73,33,73,32,80,1997,2015,0.00
2014-10-16,58,42,75,33,69,30,80,1997,2011,0.00
2014-10-17,57,43,73,28,68,24,78,1980,1978,0.00
2014-10-18,56,48,64,28,66,21,82,1980,2003,0.01
2014-10-19,54,39,70,27,65,17,80,1980,2003,0.00
2014-10-20,56,42,73,25,72,21,81,1980,2003,0.08
2014-10-21,54,46,63,28,69,21,81,1996,2003,0.00
2014-10-22,58,46,70,30,59,5,80,1996,2003,0.00
2014-10-23,56,41,72,30,64,21,79,1996,2003,0.00
2014-10-25,60,42,79,29,57,19,79,1980,2014,0.00
2014-10-26,56,41,73,28,61,21,77,1997,2007,0.00
2014-10-27,57,45,69,32,55,21,70,2012,2001,0.00
2014-10-28,51,39,64,28,54,26,72,2012,2008,0.00
2014-10-29,52,35,69,25,54,22,73,1980,2001,0.00
2014-10-30,54,37,71,24,65,21,73,1980,2010,0.00
2014-10-31,52,39,64,32,65,26,104,1980,1996,0.00
2014-11-1,52,39,66,34,63,23,73,1977,2008,0.00
2014-11-3,42,32,53,34,64,18,69,2011,2010,0.00
2014-11-5,45,28,62,29,68,24,72,2003,1977,0.00
2014-11-6,46,30,61,35,67,19,72,2008,1977,0.00
2014-11-7,47,28,66,34,68,19,71,2000,1977,0.00
2014-11-8,44,28,60,31,52,15,71,2000,2007,0.00
2014-11-9,48,34,61,20,58,9,69,1977,2006,0.00
2014-11-10,52,37,66,21,52,10,70,1977,2005,0.00
2014-11-11,43,27,59,29,54,16,70,2012,1999,0.00
2014-11-12,23,15,30,28,52,15,70,2014,1999,0.00
2014-11-14,40,28,53,27,58,15,71,1997,1999,0.00
2014-11-15,44,34,55,27,54,9,66,1997,1999,0.00
2014-11-16,32,19,46,25,48,12,66,1997,1999,0.03
2014-11-17,24,12,37,24,53,12,68,2014,2008,0.00
2014-11-19,34,19,48,23,58,17,69,2000,2007,0.00
2014-11-20,38,23,53,26,57,20,66,1978,1996,0.00
2014-11-21,38,26,51,26,51,19,62,1998,1996,0.00
2014-11-22,40,26,55,31,49,17,66,1999,2006,0.00
2014-11-23,36,24,44,24,54,12,63,1999,1998,0.00
2014-11-24,26,12,39,29,54,12,62,2014,1998,0.00
2014-11-25,26,10,43,29,51,10,60,2014,1998,0.00
2014-11-26,42,30,53,24,59,10,63,2000,1998,0.00
2014-11-27,44,28,59,25,50,10,64,2001,1998,0.00
2014-11-28,42,26,57,21,46,10,62,2001,1999,0.00
2014-11-29,44,28,60,20,48,6,60,2004,2014,0.00
2014-11-30,40,21,60,24,47,1,63,2004,2003,0.00
2014-12-3,40,28,53,24,48,14,59,2004,2012,0.00
2014-12-4,41,36,46,20,54,12,62,2006,1977,0.61
2014-12-5,42,33,52,20,54,0,57,2011,2012,0.00
2014-12-6,42,32,51,14,52,-2,57,2011,2012,0.03
2014-12-8,42,30,55,18,55,-2,61,2005,1977,0.00
2014-12-9,42,32,52,16,58,6,59,2012,1977,0.00
2014-12-10,42,28,55,18,57,-2,60,2012,1977,0.00
2014-12-11,44,30,57,20,55,3,57,1997,1993,0.00
2014-12-12,44,32,55,18,48,3,57,1997,1977,0.00
2014-12-13,42,30,55,18,47,10,57,2001,2010,0.00
2014-12-14,34,25,43,18,49,9,59,1999,2010,0.02
2014-12-15,32,23,42,16,51,9,59,1999,2010,0.00
2014-12-16,32,25,39,10,35,10,54,1993,1998,0.00
2014-12-17,30,26,34,13,40,7,53,2005,1998,0.17
2014-12-18,29,19,39,14,39,1,53,1996,1998,0.00
2014-12-19,30,21,39,10,45,10,51,1993,2003,0.00
2014-12-20,30,21,39,16,41,10,54,1999,1998,0.00
2014-12-21,38,32,45,11,35,9,54,2012,2005,0.00
2014-12-22,41,28,52,17,34,3,55,1999,2005,0.00
2014-12-23,26,18,35,7,30,5,60,2004,2005,0.00
2014-12-25,35,25,45,9,43,3,55,1997,2005,0.00
2014-12-26,20,10,30,13,44,0,55,1997,2005,0.00
2014-12-27,16,3,30,18,41,1,51,1997,1999,0.00
2014-12-28,23,12,34,22,50,6,54,2007,2005,0.00
2014-12-29,23,9,37,15,48,5,57,2012,2005,0.00
2014-12-30,16,10,21,11,48,10,57,2014,1998,0.00
2014-12-31,16,8,25,17,49,0,57,2010,1996,0.00
2015-1-10,36,24,46,26,53,10,57,2000,2005,0.00
2015-1-11,36,24,48,32,50,8,54,2011,2000,0.00
2015-1-12,32,25,39,29,43,3,57,2013,2000,0.00
2015-1-13,30,26,33,22,48,-9,55,1999,2000,0.06
2015-1-14,29,24,34,19,47,3,55,2013,2003,0.05
2015-1-15,34,21,46,22,47,1,57,2013,2000,0.00
2015-1-16,34,23,46,32,36,3,61,2007,2000,0.00
2015-1-17,39,23,53,28,44,1,64,2008,2000,0.00
2015-1-18,34,19,50,28,40,6,57,2001,1999,0.00
2015-1-20,40,30,51,21,29,-4,61,2012,2005,0.00
2015-1-21,32,27,36,21,31,10,57,2006,2000,0.01
2015-1-22,21,12,30,22,38,3,55,2007,2003,0.00
2015-1-24,31,18,44,10,48,8,57,2014,2013,0.00
2015-1-25,38,23,52,7,30,7,55,1978,1999,0.00
2015-1-26,43,26,60,11,38,11,60,1978,2015,0.00
2015-1-28,46,36,57,23,47,10,62,2014,2003,0.00
2015-1-29,42,37,48,23,51,9,57,2000,2011,0.00
2015-1-30,32,28,37,23,53,10,59,2013,2016,0.11
2015-4-19,49,36,63,33,56,22,75,1973,1954,0.00
2015-5-1,62,46,81,37,66,28,81,2004,2015,0.00
2015-5-2,64,48,81,29,67,1,81,2001,2006,0.00
2015-5-3,59,43,75,36,69,21,84,2011,2000,0.00
2015-5-4,60,48,73,29,72,29,87,1980,2000,0.04
2015-5-6,56,46,70,36,75,25,84,1999,2000,0.00
2015-5-7,56,41,72,41,76,30,84,2007,2000,0.00
2015-5-9,48,37,55,32,67,27,84,1964,2004,0.00
2015-5-10,47,30,64,40,73,30,88,2003,2000,0.00
2015-5-11,54,37,71,38,76,28,82,2003,2000,0.00
2015-5-12,54,46,63,36,73,32,84,2005,2006,0.00
2015-5-13,56,45,69,34,74,29,84,1980,2003,0.02
2015-5-14,57,45,69,42,77,27,85,2014,1978,0.00
2015-5-15,50,42,57,41,79,28,89,2014,1978,0.00
2015-5-17,52,37,66,38,71,37,87,1978,2003,0.00
2015-5-18,57,42,73,36,67,35,84,1978,1998,0.00
2015-5-19,56,46,66,38,74,36,89,1978,2006,0.00
2015-5-20,56,42,72,40,81,30,90,2011,2005,0.00
2015-5-21,50,45,57,45,69,35,90,2003,2005,0.00
2015-5-22,55,41,70,44,76,32,91,2001,2005,0.00
2015-5-23,55,44,68,43,82,36,95,2008,2000,0.00
2015-5-24,50,39,64,39,80,37,93,1998,2000,0.00
2015-5-25,53,42,66,37,79,33,90,1980,2006,0.00
2015-5-26,55,37,75,38,80,37,90,2015,2001,0.00
2015-5-27,62,48,77,42,81,39,88,1998,2001,0.00
2015-5-28,64,48,80,35,79,33,91,1980,2000,0.00
2015-5-29,62,46,80,40,80,37,95,1980,2000,0.00
2015-5-31,66,52,81,39,85,34,95,1980,2002,0.00
2015-6-1,70,55,87,47,87,41,93,1980,2002,0.00
2015-6-2,68,50,86,44,84,35,91,1980,1998,0.00
2015-6-3,67,48,86,39,79,33,91,1980,2006,0.00
2015-6-5,70,61,80,44,86,36,96,1980,2006,0.00
2015-6-6,72,57,84,42,83,39,93,1999,2006,0.00
2015-6-7,68,53,84,42,82,37,91,2007,2004,0.00
2015-6-8,70,53,88,48,87,39,93,2007,2013,0.00
2015-6-9,71,60,84,55,85,39,91,1998,2001,0.00
2015-6-10,70,60,82,49,86,41,98,1999,2013,0.00
2015-6-12,65,48,82,50,90,43,97,1998,2013,0.00
2015-6-14,66,51,81,45,91,39,95,2001,2006,0.00
2015-6-15,66,51,82,45,92,39,97,1980,2008,0.00
2015-6-16,68,55,82,49,90,42,93,1997,2002,0.00
2015-6-17,68,51,86,55,91,46,96,2005,2008,0.00
2015-6-18,72,55,90,50,90,39,95,1998,2002,0.00
2015-6-20,74,55,93,44,89,42,97,1980,2012,0.00
2015-6-21,80,63,97,52,91,43,97,2003,2015,0.00
2015-6-22,78,60,99,46,90,45,99,2003,2015,0.00
2015-6-23,75,60,91,47,92,43,96,1980,2011,0.00
2015-6-24,75,61,89,53,95,47,99,1980,2012,0.00
2015-6-25,76,62,91,54,94,48,96,2003,2007,0.00
2015-6-26,73,55,91,49,95,48,97,1978,1980,0.00
2015-6-27,74,61,84,56,94,48,102,2006,2013,0.00
2015-6-28,72,55,91,54,91,46,98,2010,1998,0.00
2015-6-29,72,57,90,58,92,50,99,2005,1998,0.00
2015-6-30,72,55,91,53,84,50,99,1978,2012,0.00


================================================
FILE: us-weather-history/KSEA.csv
================================================
date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation
2014-7-1,77,60,94,54,73,45,94,1948,2014,0.00,0.03,0.75
2014-7-2,70,58,81,54,73,43,93,1954,2015,0.00,0.03,0.50
2014-7-3,64,57,71,54,74,44,92,1954,2015,0.00,0.03,0.47
2014-7-4,66,57,75,54,74,43,92,1949,2015,0.00,0.03,0.57
2014-7-5,66,56,76,55,74,47,91,1951,1958,0.00,0.03,0.64
2014-7-6,72,59,84,55,74,45,94,1949,1960,0.00,0.03,0.44
2014-7-7,73,64,81,55,74,47,90,1955,2010,0.00,0.03,0.58
2014-7-8,73,60,86,55,75,47,95,1950,2010,0.00,0.03,0.80
2014-7-9,69,57,80,55,75,48,93,1960,2010,0.00,0.02,0.81
2014-7-10,70,55,84,55,75,46,90,1960,1945,0.00,0.03,0.38
2014-7-11,74,59,88,55,75,49,98,1960,2007,0.00,0.03,0.25
2014-7-12,76,62,90,55,75,47,97,1954,1961,0.00,0.02,0.64
2014-7-13,72,59,85,56,76,50,93,2010,1961,0.0,0.02,0.85
2014-7-14,71,59,82,56,76,50,91,1982,1996,0.00,0.03,0.74
2014-7-15,73,57,88,56,76,49,92,1982,1958,0.00,0.02,0.11
2014-7-16,73,58,88,56,76,50,98,1968,1979,0.00,0.02,0.65
2014-7-17,69,57,80,56,76,48,96,1950,1979,0.00,0.02,0.35
2014-7-18,64,53,75,56,76,47,93,1952,1995,0.00,0.02,0.29
2014-7-19,69,59,78,56,77,48,95,1962,1988,0.00,0.02,0.45
2014-7-20,63,58,67,56,77,50,100,1953,1994,0.00,0.02,0.60
2014-7-21,66,56,75,56,77,48,97,1951,2006,0.00,0.01,0.29
2014-7-22,63,56,70,56,77,49,96,1973,2006,0.01,0.02,0.33
2014-7-23,61,56,66,56,77,47,99,1955,1991,0.76,0.02,0.76
2014-7-24,62,55,69,56,77,43,95,1953,2004,0.0,0.02,0.28
2014-7-25,64,54,73,56,77,47,94,1954,1988,0.00,0.02,0.40
2014-7-26,68,56,79,56,77,49,92,1952,1971,0.00,0.02,0.73
2014-7-27,71,59,83,57,77,46,95,1956,1958,0.00,0.01,0.30
2014-7-28,73,59,87,57,77,47,97,1953,1958,0.00,0.02,0.22
2014-7-29,73,60,86,57,77,45,103,1954,2009,0.00,0.02,0.17
2014-7-30,72,58,85,57,77,48,96,1951,2009,0.00,0.01,0.39
2014-7-31,76,64,87,57,77,47,93,1954,1959,0.00,0.02,0.26
2014-8-1,72,59,84,57,77,50,92,1954,1949,0.0,0.02,0.52
2014-8-2,73,60,85,57,77,46,89,1953,1977,0.02,0.02,0.76
2014-8-3,74,58,89,57,77,46,90,1956,1988,0.00,0.01,0.47
2014-8-4,76,61,91,57,77,44,95,1950,1993,0.00,0.02,0.61
2014-8-5,67,57,77,57,77,49,93,1950,2012,0.00,0.02,0.36
2014-8-6,69,59,79,57,77,50,91,1974,1972,0.00,0.02,0.57
2014-8-7,67,56,78,56,77,48,95,1948,1972,0.00,0.02,0.95
2014-8-8,67,56,78,56,77,47,98,1974,1960,0.00,0.02,0.13
2014-8-9,71,60,81,56,77,47,99,1950,1960,0.00,0.03,0.61
2014-8-10,72,57,87,56,77,46,98,1954,1981,0.00,0.02,0.28
2014-8-11,80,64,96,56,77,46,96,1954,2014,0.02,0.03,0.32
2014-8-12,72,63,81,56,77,49,96,1949,1977,0.50,0.02,0.56
2014-8-13,67,59,74,56,77,44,92,1955,2002,0.85,0.03,0.85
2014-8-14,67,63,70,56,77,44,95,1955,2010,0.0,0.03,1.60
2014-8-15,69,62,76,56,77,47,96,1955,2010,0.04,0.03,1.20
2014-8-16,69,60,78,56,77,50,98,1986,1967,0.00,0.03,0.41
2014-8-17,71,59,82,56,76,45,96,1951,1977,0.00,0.03,0.93
2014-8-18,73,60,85,56,76,47,88,1951,1991,0.00,0.03,1.63
2014-8-19,71,60,81,56,76,48,88,1959,1967,0.00,0.03,0.88
2014-8-20,64,57,71,56,76,47,87,1952,1966,0.00,0.03,0.70
2014-8-21,61,52,70,56,76,46,88,1973,1966,0.00,0.03,0.70
2014-8-22,66,56,75,56,76,45,86,1960,1956,0.00,0.03,1.41
2014-8-23,70,57,82,56,76,46,91,1955,1988,0.00,0.04,1.49
2014-8-24,67,56,77,55,76,45,88,1951,1982,0.00,0.03,1.00
2014-8-25,71,58,84,55,75,45,87,1954,1982,0.00,0.03,0.59
2014-8-26,74,60,88,55,75,46,91,1957,1986,0.00,0.04,0.67
2014-8-27,73,61,84,55,75,44,88,1952,1986,0.00,0.04,0.76
2014-8-28,66,58,74,55,75,46,95,1952,1967,0.00,0.03,0.47
2014-8-29,66,59,73,55,75,44,91,1951,1974,0.0,0.04,0.87
2014-8-30,62,59,64,55,75,45,88,1950,1987,0.33,0.04,0.37
2014-8-31,64,57,70,55,74,46,92,1950,1987,0.05,0.04,0.56
2014-9-1,65,55,74,55,74,46,91,1973,1987,0.00,0.04,0.55
2014-9-2,63,57,68,55,74,41,98,1960,1988,0.12,0.03,0.71
2014-9-3,62,55,69,54,74,45,92,1956,1988,0.00,0.04,0.46
2014-9-4,64,52,75,54,74,42,87,1952,1963,0.00,0.05,0.66
2014-9-5,70,57,82,54,74,44,88,1950,1973,0.00,0.04,1.09
2014-9-6,75,59,90,54,73,45,90,1948,2014,0.00,0.04,0.93
2014-9-7,70,56,83,54,73,45,94,1960,1981,0.00,0.04,0.26
2014-9-8,63,56,70,54,73,43,85,1950,1993,0.0,0.05,0.60
2014-9-9,64,56,71,53,73,40,89,1952,1963,0.0,0.04,0.61
2014-9-10,63,54,72,53,72,39,85,1952,2007,0.00,0.04,0.64
2014-9-11,66,55,76,53,72,40,93,1952,2013,0.00,0.05,0.58
2014-9-12,66,55,76,53,72,44,85,1949,1961,0.00,0.04,0.60
2014-9-13,67,50,83,53,72,39,87,1952,1999,0.00,0.05,0.51
2014-9-14,70,53,86,53,71,46,93,1955,1979,0.00,0.04,0.73
2014-9-15,71,54,87,52,71,42,93,1952,1967,0.00,0.05,0.69
2014-9-16,65,57,72,52,71,44,91,1952,1967,0.0,0.05,0.76
2014-9-17,66,58,73,52,70,42,85,1965,1991,0.02,0.04,1.49
2014-9-18,63,59,67,52,70,41,85,1955,1991,0.01,0.05,0.78
2014-9-19,68,61,75,52,70,40,82,1957,1999,0.00,0.05,0.79
2014-9-20,67,58,76,51,69,40,88,1949,1974,0.00,0.06,1.05
2014-9-21,67,55,79,51,69,39,88,1955,1952,0.00,0.05,0.94
2014-9-22,66,59,72,51,69,37,92,1955,1990,0.01,0.06,1.65
2014-9-23,62,58,66,51,68,40,83,1948,1957,0.72,0.05,0.72
2014-9-24,62,58,66,50,68,38,89,1948,1974,0.80,0.06,0.99
2014-9-25,65,58,71,50,68,36,85,1972,1991,0.17,0.06,0.63
2014-9-26,63,57,68,50,67,39,78,1972,1967,0.35,0.07,1.51
2014-9-27,61,53,69,50,67,35,89,1972,1967,0.00,0.06,1.04
2014-9-28,60,54,66,50,67,38,84,1950,2003,0.00,0.07,1.71
2014-9-29,57,52,62,49,66,37,81,1954,1992,0.03,0.06,0.92
2014-9-30,61,54,67,49,66,36,82,1950,1992,0.0,0.07,1.65
2014-10-1,59,52,65,49,65,35,89,1950,1987,0.00,0.07,0.56
2014-10-2,59,50,67,49,65,35,79,1950,1993,0.00,0.07,0.80
2014-10-3,60,48,72,48,65,39,80,1949,1993,0.00,0.07,0.67
2014-10-4,63,54,71,48,64,38,76,1957,1966,0.00,0.07,0.92
2014-10-5,64,53,75,48,64,38,82,1974,1980,0.00,0.08,1.26
2014-10-6,67,56,78,48,63,37,78,1955,1952,0.00,0.08,2.72
2014-10-7,62,57,66,48,63,37,75,1983,1951,0.00,0.09,0.71
2014-10-8,62,55,69,47,63,39,80,1949,1951,0.00,0.08,1.16
2014-10-9,58,52,63,47,62,35,79,1960,1996,0.0,0.09,1.27
2014-10-10,58,50,65,47,62,40,77,1972,1987,0.01,0.09,1.02
2014-10-11,59,53,65,47,61,37,83,1969,1991,0.29,0.09,0.89
2014-10-12,59,53,64,47,61,40,72,2002,2006,0.0,0.09,0.70
2014-10-13,60,50,70,46,61,35,75,1954,1961,0.30,0.10,0.66
2014-10-14,58,53,62,46,60,36,80,1949,1961,0.28,0.11,0.77
2014-10-15,57,53,61,46,60,35,77,1952,1991,0.34,0.10,0.93
2014-10-16,61,52,69,46,60,35,70,1980,1974,0.0,0.11,0.78
2014-10-17,58,53,62,46,59,34,74,1949,1989,0.13,0.11,1.24
2014-10-18,62,57,67,45,59,32,70,1949,1974,0.59,0.11,1.19
2014-10-19,64,55,72,45,58,28,72,1949,2014,0.00,0.12,1.05
2014-10-20,58,54,61,45,58,29,69,1949,1987,0.46,0.12,5.02
2014-10-21,57,53,61,45,58,32,72,1949,1965,0.04,0.13,1.29
2014-10-22,57,53,60,45,57,34,70,1950,1965,1.26,0.12,1.26
2014-10-23,53,47,58,45,57,32,73,1953,1965,0.37,0.14,1.24
2014-10-24,53,48,58,44,57,33,72,1954,1965,0.16,0.14,1.10
2014-10-25,55,47,62,44,57,31,67,1954,1965,0.24,0.14,1.16
2014-10-26,51,46,55,44,56,29,65,1954,1998,0.06,0.15,2.13
2014-10-27,52,44,60,44,56,31,67,1970,1968,0.03,0.15,1.23
2014-10-28,54,49,59,44,56,30,66,1971,1962,0.50,0.16,1.53
2014-10-29,58,53,62,43,55,31,65,1970,1986,0.02,0.16,1.24
2014-10-30,56,52,60,43,55,30,66,1991,1967,1.00,0.17,1.36
2014-10-31,51,47,55,43,55,30,71,2003,1949,0.67,0.17,1.04
2014-11-1,49,45,52,43,55,30,67,2003,1981,0.00,0.19,1.56
2014-11-2,51,45,56,43,54,31,72,1952,1970,0.07,0.19,2.18
2014-11-3,55,52,57,43,54,29,74,1953,2010,0.43,0.20,1.37
2014-11-4,55,51,58,42,54,30,74,1957,1949,0.16,0.20,1.91
2014-11-5,55,51,59,42,53,31,65,2003,1960,0.19,0.20,1.01
2014-11-6,57,51,62,42,53,30,65,2003,1976,0.16,0.21,3.29
2014-11-7,52,45,58,42,53,31,62,1952,2008,0.00,0.20,1.93
2014-11-8,47,39,55,42,53,27,64,1948,1952,0.00,0.22,0.85
2014-11-9,51,46,56,41,52,27,64,1948,1997,0.20,0.21,2.95
2014-11-10,47,42,52,41,52,30,63,1948,1997,0.00,0.22,1.08
2014-11-11,40,34,46,41,52,15,65,1955,1990,0.00,0.23,1.48
2014-11-12,38,32,44,41,52,13,60,1955,1953,0.00,0.23,1.52
2014-11-13,39,33,45,41,51,14,62,1955,1997,0.00,0.23,1.68
2014-11-14,37,28,45,40,51,9,65,1955,1995,0.00,0.23,2.61
2014-11-15,38,29,47,40,51,6,62,1955,2001,0.00,0.23,1.09
2014-11-16,39,28,49,40,51,21,68,1955,1965,0.00,0.23,1.08
2014-11-17,40,28,51,40,51,23,61,1955,1995,0.00,0.23,1.06
2014-11-18,38,31,45,40,50,29,59,2000,1965,0.00,0.23,2.06
2014-11-19,44,36,52,39,50,25,60,1977,2008,0.0,0.24,2.13
2014-11-20,47,42,52,39,50,25,64,1985,1987,0.14,0.23,3.41
2014-11-21,50,47,52,39,50,21,62,1977,1954,0.60,0.23,1.26
2014-11-22,47,44,49,39,49,14,58,1985,1954,0.02,0.22,1.76
2014-11-23,49,42,55,38,49,10,58,1985,1959,0.47,0.22,2.49
2014-11-24,47,40,53,38,49,14,59,2010,1974,0.05,0.23,2.93
2014-11-25,53,49,57,38,49,18,59,1985,1949,0.72,0.22,3.04
2014-11-26,57,54,59,38,48,25,61,1952,1949,0.01,0.22,1.34
2014-11-27,56,53,58,38,48,19,58,1985,2013,0.13,0.22,1.30
2014-11-28,47,38,55,37,48,17,59,1952,1999,1.35,0.21,1.55
2014-11-29,32,24,40,37,48,18,59,2006,1999,0.14,0.23,1.08
2014-11-30,30,23,37,37,48,21,59,1985,2012,0.00,0.22,1.50
2014-12-1,33,26,40,37,47,20,57,1985,1958,0.00,0.20,1.52
2014-12-2,34,26,42,37,47,25,57,1985,1958,0.00,0.20,1.75
2014-12-3,41,32,50,36,47,27,60,1994,1965,0.00,0.20,3.77
2014-12-4,43,39,47,36,47,21,57,1994,1989,0.03,0.19,1.41
2014-12-5,50,44,55,36,47,23,55,1956,1954,0.12,0.19,1.67
2014-12-6,50,46,53,36,46,19,57,1956,1965,0.29,0.18,1.39
2014-12-7,51,43,58,36,46,18,58,1972,2014,0.00,0.19,0.88
2014-12-8,53,48,58,36,46,13,59,1972,1957,0.36,0.18,1.61
2014-12-9,56,51,61,36,46,18,61,2009,2014,0.39,0.17,1.78
2014-12-10,58,50,66,36,46,16,66,2009,2014,0.51,0.17,1.55
2014-12-11,53,47,58,35,46,21,58,2009,2014,0.27,0.17,1.42
2014-12-12,49,45,52,35,46,22,60,1972,1960,0.0,0.16,2.19
2014-12-13,45,39,50,35,46,19,59,1972,1952,0.0,0.17,1.63
2014-12-14,45,35,55,35,45,22,59,2008,2002,0.00,0.17,1.67
2014-12-15,49,44,54,35,45,19,63,2008,1980,0.00,0.16,1.60
2014-12-16,49,47,50,35,45,10,57,1964,1974,0.0,0.17,1.32
2014-12-17,46,43,48,35,45,11,57,1964,1994,0.11,0.16,1.61
2014-12-18,47,44,49,35,45,18,54,1964,1958,0.51,0.16,1.23
2014-12-19,49,45,52,35,45,17,56,1990,1994,0.12,0.17,1.19
2014-12-20,50,44,55,35,45,14,57,1990,1973,0.77,0.16,1.93
2014-12-21,53,50,55,35,45,12,56,1990,2005,0.00,0.17,0.99
2014-12-22,47,43,51,35,45,14,57,1983,2005,0.00,0.17,1.28
2014-12-23,48,41,54,35,45,9,58,1983,1950,0.81,0.17,1.14
2014-12-24,42,39,45,35,45,16,62,1948,2005,0.21,0.17,0.75
2014-12-25,42,37,46,35,45,24,60,1995,1980,0.00,0.17,1.06
2014-12-26,39,35,42,36,45,22,62,1948,1980,0.00,0.17,2.14
2014-12-27,45,40,49,36,45,20,58,1968,1994,0.13,0.17,1.53
2014-12-28,41,37,44,36,45,12,56,1990,1980,0.16,0.16,0.89
2014-12-29,38,33,43,36,45,8,54,1968,1949,0.00,0.16,1.92
2014-12-30,33,28,38,36,45,6,56,1968,1958,0.00,0.16,1.06
2014-12-31,33,27,38,36,46,13,56,1968,1963,0.00,0.16,1.30
2015-1-1,34,26,42,36,46,10,58,1952,2006,0.00,0.18,1.37
2015-1-2,37,32,42,36,46,6,56,1950,2003,0.06,0.18,1.77
2015-1-3,38,35,41,36,46,6,58,1950,1984,0.0,0.19,1.01
2015-1-4,45,38,51,36,46,14,61,1950,1984,0.40,0.19,1.56
2015-1-5,52,49,54,36,46,15,55,1982,2006,0.32,0.18,1.33
2015-1-6,49,43,54,37,46,14,57,1982,2003,0.00,0.19,1.22
2015-1-7,44,42,46,37,46,19,59,1973,2002,0.00,0.19,2.29
2015-1-8,41,35,46,37,46,18,54,1949,1953,0.00,0.20,1.13
2015-1-9,44,38,50,37,46,13,56,1949,2003,0.01,0.19,2.83
2015-1-10,45,43,46,37,47,12,59,1949,1981,0.23,0.20,0.94
2015-1-11,47,45,49,37,47,12,59,1963,1987,0.06,0.19,1.26
2015-1-12,46,40,52,37,47,13,59,1963,1986,0.0,0.19,1.30
2015-1-13,43,37,49,37,47,11,56,1950,1986,0.00,0.18,1.63
2015-1-14,38,33,43,37,47,8,59,1950,1968,0.00,0.19,1.61
2015-1-15,40,34,46,37,47,15,58,1950,1974,0.38,0.19,1.68
2015-1-16,48,42,53,37,47,13,55,1950,2011,0.0,0.20,1.78
2015-1-17,47,38,56,37,47,15,56,1950,2015,1.03,0.19,2.39
2015-1-18,51,45,57,37,48,9,60,1950,2005,0.84,0.19,2.98
2015-1-19,47,43,50,37,48,18,62,1954,2005,0.02,0.18,2.26
2015-1-20,44,38,50,37,48,16,64,1954,1981,0.00,0.19,1.66
2015-1-21,38,31,45,37,48,13,62,1962,1981,0.00,0.17,1.64
2015-1-22,46,43,49,37,48,19,60,1962,1981,0.03,0.17,1.29
2015-1-23,51,47,54,37,48,16,56,1949,2001,0.23,0.17,1.36
2015-1-24,55,52,58,37,48,10,58,1950,1995,0.02,0.16,1.36
2015-1-25,54,45,63,37,48,7,63,1950,2015,0.00,0.17,0.93
2015-1-26,52,43,61,37,48,9,61,1957,2015,0.00,0.16,1.03
2015-1-27,50,47,52,37,48,11,58,1957,1952,0.03,0.16,1.61
2015-1-28,48,41,54,37,48,7,57,1950,1976,0.0,0.16,1.23
2015-1-29,46,38,54,37,49,6,60,1950,1992,0.00,0.16,1.63
2015-1-30,41,34,47,37,49,7,59,1950,1992,0.00,0.15,1.00
2015-1-31,42,38,45,37,49,0,61,1950,1960,0.00,0.16,1.93
2015-2-1,45,40,49,37,49,1,63,1950,1962,0.06,0.13,1.34
2015-2-2,47,41,52,37,49,8,64,1950,1962,0.29,0.13,1.32
2015-2-3,46,42,50,37,49,8,63,1950,1992,0.05,0.14,1.29
2015-2-4,46,40,51,37,49,7,63,1989,1993,0.33,0.13,0.91
2015-2-5,52,47,56,37,49,14,62,1989,1987,1.03,0.14,1.03
2015-2-6,54,50,58,37,49,18,64,1989,1993,0.68,0.13,1.54
2015-2-7,52,49,54,37,49,20,66,1989,1963,0.93,0.14,2.58
2015-2-8,53,47,59,37,49,22,68,1994,1963,0.14,0.13,3.06
2015-2-9,52,47,56,37,49,26,62,1989,1968,0.24,0.13,2.98
2015-2-10,51,47,55,37,49,24,65,1982,1963,0.01,0.13,0.96
2015-2-11,49,42,55,37,50,23,62,1948,1963,0.0,0.13,0.81
2015-2-12,56,49,62,37,50,21,66,1949,1963,0.04,0.12,1.23
2015-2-13,52,44,60,37,50,18,60,1949,2015,0.00,0.13,1.40
2015-2-14,51,44,58,37,50,22,63,1995,1991,0.01,0.11,0.74
2015-2-15,47,39,54,37,50,13,65,1956,1996,0.00,0.12,0.94
2015-2-16,51,42,59,37,50,13,59,1956,2015,0.00,0.12,1.83
2015-2-17,51,40,61,37,50,23,61,1990,2015,0.00,0.12,0.68
2015-2-18,47,40,54,37,50,20,62,1990,1977,0.00,0.11,1.63
2015-2-19,49,47,51,37,50,22,67,1986,1977,0.18,0.12,1.76
2015-2-20,49,45,52,37,50,25,62,1955,1977,0.03,0.12,0.74
2015-2-21,48,42,54,37,51,25,67,1957,1973,0.00,0.12,0.97
2015-2-22,46,38,53,37,51,30,64,2005,1966,0.00,0.12,0.71
2015-2-23,44,33,55,37,51,27,61,2011,1983,0.00,0.11,1.16
2015-2-24,44,36,52,37,51,24,62,2011,1949,0.00,0.12,1.55
2015-2-25,47,44,50,37,51,20,64,2011,1992,0.16,0.13,0.98
2015-2-26,50,46,53,37,51,18,67,1962,1992,0.37,0.12,1.19
2015-2-27,47,44,50,37,51,20,70,1962,1968,0.72,0.13,2.23
2015-2-28,46,38,54,38,51,24,68,1971,1968,0.00,0.12,1.46
2015-3-1,43,34,52,38,52,23,62,1971,1968,0.00,0.13,0.84
2015-3-2,46,40,52,38,52,22,64,1989,1990,0.00,0.14,1.16
2015-3-3,42,32,51,38,52,20,69,1989,1965,0.00,0.12,2.08
2015-3-4,43,31,55,38,52,11,68,1955,1965,0.00,0.13,0.92
2015-3-5,47,37,56,38,52,13,66,1955,1965,0.00,0.13,2.70
2015-3-6,49,38,59,38,52,26,68,1951,2007,0.00,0.12,1.18
2015-3-7,51,39,62,38,52,26,66,1951,2001,0.00,0.12,0.67
2015-3-8,51,39,63,38,53,28,67,1951,2005,0.00,0.12,1.27
2015-3-9,49,40,58,39,53,28,67,1951,1995,0.00,0.12,1.47
2015-3-10,49,41,56,39,53,20,68,1956,1979,0.03,0.12,0.87
2015-3-11,53,48,58,39,53,23,65,1956,2005,0.10,0.12,1.01
2015-3-12,57,49,64,39,53,25,68,1956,1998,0.00,0.13,0.99
2015-3-13,55,46,63,39,53,28,70,1967,1979,0.08,0.12,0.70
2015-3-14,53,49,57,39,53,26,64,1953,1996,0.67,0.13,0.76
2015-3-15,47,43,51,39,54,30,65,1955,2010,2.20,0.12,2.20
2015-3-16,50,43,57,39,54,29,66,1952,2007,0.04,0.12,1.09
2015-3-17,48,40,56,40,54,27,65,1951,1972,0.03,0.12,1.05
2015-3-18,53,45,60,40,54,29,70,1954,1996,0.00,0.12,1.86
2015-3-19,54,47,60,40,54,27,63,1965,1951,0.0,0.11,1.27
2015-3-20,53,48,57,40,54,26,69,1955,1968,0.16,0.12,0.76
2015-3-21,52,47,56,40,55,29,69,1952,1992,0.15,0.12,2.04
2015-3-22,48,43,53,40,55,28,68,1952,1979,0.04,0.13,1.81
2015-3-23,47,42,52,40,55,28,68,1954,1979,0.32,0.12,0.94
2015-3-24,49,43,55,40,55,30,68,1965,2010,0.30,0.11,0.98
2015-3-25,52,45,58,40,55,31,64,1954,1969,0.20,0.12,0.62
2015-3-26,60,50,69,40,55,32,70,1975,1969,0.00,0.11,1.51
2015-3-27,57,48,65,41,55,26,71,1975,1994,0.04,0.11,0.64
2015-3-28,55,49,60,41,56,26,73,1954,1994,0.00,0.11,0.87
2015-3-29,54,48,60,41,56,24,78,1954,2004,0.00,0.11,1.08
2015-3-30,58,51,64,41,56,27,72,1954,1995,0.07,0.11,0.65
2015-3-31,49,43,55,41,56,30,75,1949,1987,0.04,0.11,0.57
2015-4-1,49,42,55,41,56,29,82,1953,1987,0.20,0.11,1.13
2015-4-2,49,42,56,41,56,30,76,1963,1992,0.00,0.11,0.92
2015-4-3,47,41,52,41,56,32,71,1948,2004,0.06,0.11,1.46
2015-4-4,47,39,55,41,57,31,75,1948,1966,0.00,0.11,2.64
2015-4-5,50,37,62,41,57,29,73,1975,1966,0.00,0.10,0.79
2015-4-6,51,44,57,41,57,31,78,1956,2007,0.04,0.10,0.63
2015-4-7,51,44,58,41,57,29,78,1952,1996,0.02,0.10,1.54
2015-4-8,53,43,63,41,57,29,70,1952,2012,0.00,0.10,0.83
2015-4-9,53,43,63,41,57,34,73,1982,1959,0.00,0.10,0.96
2015-4-10,52,46,57,42,57,30,74,1953,2004,0.43,0.09,0.78
2015-4-11,48,42,53,42,58,35,80,1991,2004,0.0,0.10,1.04
2015-4-12,49,42,56,42,58,33,79,1968,2008,0.00,0.09,0.83
2015-4-13,46,39,53,42,58,33,75,1968,1980,0.55,0.09,1.45
2015-4-14,45,37,53,42,58,33,83,1955,1984,0.13,0.10,0.52
2015-4-15,48,38,57,42,58,33,74,1967,1999,0.00,0.09,0.75
2015-4-16,52,39,64,42,59,34,81,1960,1999,0.00,0.09,0.95
2015-4-17,55,43,66,42,59,33,74,1964,1983,0.00,0.09,0.73
2015-4-18,57,47,66,42,59,34,77,1950,1962,0.00,0.09,0.40
2015-4-19,59,47,70,42,59,33,74,1961,1950,0.00,0.09,1.85
2015-4-20,60,46,73,43,59,30,74,1961,2009,0.00,0.08,0.45
2015-4-21,54,44,63,43,59,29,76,1951,1982,0.22,0.09,0.87
2015-4-22,51,41,60,43,60,35,78,1950,1977,0.00,0.08,1.28
2015-4-23,49,44,54,43,60,31,72,1954,1977,0.12,0.08,1.71
2015-4-24,49,43,54,43,60,30,76,1950,1977,0.13,0.08,0.70
2015-4-25,49,42,56,43,60,34,77,1948,1992,0.05,0.07,0.52
2015-4-26,50,40,60,44,61,32,78,1948,1987,0.0,0.08,0.37
2015-4-27,64,51,77,44,61,31,81,1948,1987,0.01,0.08,0.70
2015-4-28,54,48,60,44,61,31,76,1955,1968,0.07,0.07,0.44
2015-4-29,53,45,61,44,61,31,80,1952,1976,0.00,0.07,1.06
2015-4-30,55,46,63,44,61,32,85,1954,1976,0.00,0.07,0.87
2015-5-1,57,48,65,44,62,28,85,1954,2014,0.00,0.07,0.81
2015-5-2,56,46,65,45,62,33,74,1952,1966,0.00,0.06,0.47
2015-5-3,58,46,69,45,62,35,77,1950,1992,0.00,0.07,1.31
2015-5-4,54,45,63,45,62,33,85,1962,1992,0.00,0.06,0.66
2015-5-5,52,45,58,45,63,36,86,1965,1953,0.24,0.07,0.59
2015-5-6,54,45,62,45,63,36,87,1965,2013,0.00,0.07,0.89
2015-5-7,56,43,69,46,63,33,81,1962,1987,0.00,0.06,0.66
2015-5-8,61,47,75,46,63,36,84,1999,1969,0.00,0.06,0.54
2015-5-9,65,49,80,46,63,38,81,1999,1976,0.00,0.06,0.74
2015-5-10,60,52,67,46,64,38,80,1999,1993,0.0,0.07,0.61
2015-5-11,54,50,57,46,64,35,87,1953,1971,0.0,0.06,0.47
2015-5-12,56,51,60,47,64,38,81,1964,1959,0.17,0.06,0.71
2015-5-13,52,50,54,47,64,37,84,1956,1973,0.16,0.06,0.44
2015-5-14,57,49,64,47,65,36,87,1964,1973,0.01,0.06,0.97
2015-5-15,59,49,68,47,65,38,85,1964,2007,0.00,0.06,0.81
2015-5-16,56,52,60,47,65,40,84,2003,1985,0.00,0.06,0.64
2015-5-17,59,51,67,48,65,39,90,1966,2008,0.00,0.06,0.72
2015-5-18,66,54,78,48,65,37,89,1950,1958,0.00,0.06,0.72
2015-5-19,62,53,71,48,65,38,88,1975,1963,0.00,0.06,0.70
2015-5-20,63,51,74,48,66,40,92,1960,1963,0.00,0.06,0.56
2015-5-21,66,53,78,48,66,39,93,1960,1963,0.00,0.06,0.55
2015-5-22,58,53,62,49,66,37,86,1960,1969,0.00,0.06,0.67
2015-5-23,57,53,61,49,66,37,90,1950,1969,0.0,0.06,0.70
2015-5-24,58,52,64,49,66,37,89,1950,1992,0.00,0.06,0.65
2015-5-25,56,52,60,49,66,37,84,1964,2001,0.00,0.06,0.41
2015-5-26,62,53,71,49,67,40,89,1952,2005,0.00,0.07,0.61
2015-5-27,65,53,76,49,67,37,89,1966,2005,0.00,0.06,0.83
2015-5-28,68,54,82,50,67,38,92,1954,1983,0.00,0.07,0.88
2015-5-29,67,55,79,50,67,35,85,1951,1973,0.00,0.06,1.83
2015-5-30,62,50,73,50,67,36,87,1951,1956,0.00,0.07,1.06
2015-5-31,65,53,77,50,67,35,90,1953,1964,0.00,0.06,0.97
2015-6-1,57,53,61,50,67,40,94,1951,1970,0.18,0.07,1.75
2015-6-2,60,55,64,50,68,40,94,1951,1970,0.02,0.06,0.48
2015-6-3,61,53,68,50,68,42,89,1962,2009,0.00,0.06,1.08
2015-6-4,63,53,73,50,68,41,91,1962,2009,0.00,0.07,0.83
2015-6-5,68,55,80,51,68,41,92,1953,1978,0.00,0.06,0.63
2015-6-6,71,56,85,51,68,42,90,1962,2003,0.00,0.06,1.54
2015-6-7,74,60,88,51,68,44,90,2002,2003,0.00,0.06,0.81
2015-6-8,73,58,87,51,69,42,87,2002,1948,0.00,0.07,0.81
2015-6-9,71,58,84,51,69,44,96,1971,1955,0.00,0.06,0.72
2015-6-10,65,52,78,51,69,44,83,2008,1982,0.00,0.06,0.88
2015-6-11,64,52,76,51,69,40,84,1952,1989,0.00,0.06,1.16
2015-6-12,61,53,68,51,69,38,85,1952,1999,0.00,0.06,0.60
2015-6-13,62,49,75,52,69,42,94,1952,2002,0.00,0.05,0.37
2015-6-14,68,53,82,52,70,43,86,1955,1988,0.00,0.06,0.39
2015-6-15,74,61,86,52,70,43,88,1949,1963,0.00,0.05,0.37
2015-6-16,63,52,73,52,70,42,88,1954,1961,0.00,0.06,0.52
2015-6-17,65,52,77,52,70,43,91,1954,1969,0.00,0.05,0.51
2015-6-18,67,57,76,52,70,43,94,1954,1982,0.0,0.05,0.41
2015-6-19,66,56,75,52,70,43,92,1951,1982,0.02,0.05,0.33
2015-6-20,66,55,77,53,71,43,88,1960,2004,0.00,0.05,1.14
2015-6-21,68,57,78,53,71,45,89,1960,1992,0.00,0.05,1.00
2015-6-22,66,55,77,53,71,45,92,1952,1992,0.00,0.04,0.62
2015-6-23,66,53,79,53,71,40,92,1952,1992,0.00,0.04,0.58
2015-6-24,70,61,78,53,72,45,92,1952,1992,0.0,0.05,1.00
2015-6-25,74,60,87,53,72,45,88,1949,2006,0.00,0.04,0.39
2015-6-26,77,64,89,53,72,46,90,1949,2006,0.00,0.03,0.32
2015-6-27,78,63,92,53,72,45,92,1949,2015,0.00,0.04,0.61
2015-6-28,74,65,83,54,72,45,91,1949,1995,0.01,0.04,0.79
2015-6-29,74,63,84,54,73,45,93,1949,1987,0.0,0.03,0.64
2015-6-30,73,59,87,54,73,43,96,1949,1995,0.00,0.04,0.84


================================================
FILE: us-weather-history/README.md
================================================
### U.S. weather history visualization

The raw data and code behind the story [What 12 Months Of Record-Setting Temperatures Looks Like Across The U.S.](http://fivethirtyeight.com/features/what-12-months-of-record-setting-temperatures-looks-like-across-the-u-s/)

#### Code

Code file | Description
---|---------
`wunderground_scraper.py` | Downloades weather data web pages from Weather Underground
`wunderground_parser.py` | Parses the weather data from Weather Underground into a flat CSV file
`visualize_weather.py` | Creates the visualization of the weather data

#### Data

Column | Description
---|---------
`date` | The date of the weather record, formatted YYYY-M-D
`actual_mean_temp` | The measured average temperature for that day
`actual_min_temp` | The measured minimum temperature for that day
`actual_max_temp` | The measured maximum temperature for that day
`average_min_temp` | The average minimum temperature on that day since 1880
`average_max_temp` | The average maximum temperature on that day since 1880
`record_min_temp` | The lowest ever temperature on that day since 1880
`record_max_temp` | The highest ever temperature on that day since 1880
`record_min_temp_year` | The year that the lowest ever temperature occurred
`record_max_temp_year` | The year that the highest ever temperature occurred
`actual_precipitation` | The measured amount of rain or snow for that day
`average_precipitation` | The average amount of rain or snow on that day since 1880
`record_precipitation` | The highest amount of rain or snow on that day since 1880

Source: [Weather Underground](http://wunderground.com)


================================================
FILE: us-weather-history/visualize_weather.py
================================================
import matplotlib.pyplot as plt
import pandas as pd
from datetime import datetime

'''
This is an example to generate the Philadelphia, PA weather chart.

If you want to make the chart for another city, you will have to modify
this code slightly to read that city's data in, change the title, and
likely change the y-axis of the chart to fit your city's temperature range.

I also use a custom matplotlib style as the basis for these charts, which you
can find here: https://gist.githubusercontent.com/rhiever/d0a7332fe0beebfdc3d5/raw/223d70799b48131d5ce2723cd5784f39d7a3a653/tableau10.mplstyle
'''

weather_data = pd.read_csv('KPHL.csv', parse_dates=['date'])
print(weather_data.describe())

# Generate a bunch of histograms of the data to make sure that all of the data
# is in an expected range.
with plt.style.context('https://gist.githubusercontent.com/rhiever/d0a7332fe0beebfdc3d5/raw/223d70799b48131d5ce2723cd5784f39d7a3a653/tableau10.mplstyle'):
    for column in weather_data.columns:
        if column in ['date']:
            continue
        plt.figure()
        plt.hist(weather_data[column].values)
        plt.title(column)
        plt.savefig('{}.png'.format(column))

    # Make sure we're only plotting temperatures for July 2014 - June 2015
    weather_data_subset = weather_data[weather_data['date'] >= datetime(year=2014, month=7, day=1)]
    weather_data_subset = weather_data_subset[weather_data_subset['date'] < datetime(year=2015, month=7, day=1)].copy()
    weather_data_subset['day_order'] = range(len(weather_data_subset))

    day_order = weather_data_subset['day_order']
    record_max_temps = weather_data_subset['record_max_temp'].values
    record_min_temps = weather_data_subset['record_min_temp'].values
    average_max_temps = weather_data_subset['average_max_temp'].values
    average_min_temps = weather_data_subset['average_min_temp'].values
    actual_max_temps = weather_data_subset['actual_max_temp'].values
    actual_min_temps = weather_data_subset['actual_min_temp'].values

    fig, ax1 = plt.subplots(figsize=(15, 7))

    # Create the bars showing all-time record highs and lows
    plt.bar(day_order, record_max_temps - record_min_temps, bottom=record_min_temps,
            edgecolor='none', color='#C3BBA4', width=1)

    # Create the bars showing average highs and lows
    plt.bar(day_order, average_max_temps - average_min_temps, bottom=average_min_temps,
            edgecolor='none', color='#9A9180', width=1)

    # Create the bars showing this year's highs and lows
    plt.bar(day_order, actual_max_temps - actual_min_temps, bottom=actual_min_temps,
            edgecolor='black', linewidth=0.5, color='#5A3B49', width=1)

    new_max_records = weather_data_subset[weather_data_subset.record_max_temp <= weather_data_subset.actual_max_temp]
    new_min_records = weather_data_subset[weather_data_subset.record_min_temp >= weather_data_subset.actual_min_temp]

    # Create the dots marking record highs and lows for the year
    plt.scatter(new_max_records['day_order'].values + 0.5,
                new_max_records['actual_max_temp'].values + 1.25,
                s=15, zorder=10, color='#d62728', alpha=0.75, linewidth=0)

    plt.scatter(new_min_records['day_order'].values + 0.5,
                new_min_records['actual_min_temp'].values - 1.25,
                s=15, zorder=10, color='#1f77b4', alpha=0.75, linewidth=0)

    plt.ylim(-15, 111)
    plt.xlim(-5, 370)

    plt.yticks(range(-10, 111, 10), [r'{}$^\circ$'.format(x)
                                     for x in range(-10, 111, 10)], fontsize=10)
    plt.ylabel(r'Temperature ($^\circ$F)', fontsize=12)

    month_beginning_df = weather_data_subset[weather_data_subset['date'].apply(lambda x: True if x.day == 1 else False)]
    month_beginning_indeces = list(month_beginning_df['day_order'].values)
    month_beginning_names = list(month_beginning_df['date'].apply(lambda x: x.strftime("%B")).values)
    month_beginning_names[0] += '\n\'14'
    month_beginning_names[6] += '\n\'15'

    # Add the last month label manually
    month_beginning_indeces += [weather_data_subset['day_order'].values[-1]]
    month_beginning_names += ['July']

    plt.xticks(month_beginning_indeces,
               month_beginning_names,
               fontsize=10)

    ax2 = ax1.twiny()
    plt.xticks(month_beginning_indeces,
               month_beginning_names,
               fontsize=10)

    plt.xlim(-5, 370)
    plt.grid(False)

    ax3 = ax1.twinx()
    plt.yticks(range(-10, 111, 10), [r'{}$^\circ$'.format(x)
                                     for x in range(-10, 111, 10)], fontsize=10)
    plt.ylim(-15, 111)
    plt.grid(False)

    plt.title('Philadelphia, PA\'s weather, July 2014 - June 2015\n\n', fontsize=20)

    plt.savefig('philadelphia-weather-july14-june15.png')


================================================
FILE: us-weather-history/wunderground_parser.py
================================================
from datetime import datetime, timedelta
from bs4 import BeautifulSoup
from urllib.request import urlopen


def parse_station(station):
    '''
    This function parses the web pages downloaded from wunderground.com
    into a flat CSV file for the station you provide it.
p
    Make sure to run the wunderground scraper first so you have the web
    pages downloaded.
    '''

    # Scrape between July 1, 2014 and July 1, 2015
    # You can change the dates here if you prefer to parse a different range
    current_date = datetime(year=2014, month=7, day=1)
    end_date = datetime(year=2015, month=7, day=1)

    with open('{}.csv'.format(station), 'w') as out_file:
        out_file.write('date,actual_mean_temp,actual_min_temp,actual_max_temp,'
                       'average_min_temp,average_max_temp,'
                       'record_min_temp,record_max_temp,'
                       'record_min_temp_year,record_max_temp_year,'
                       'actual_precipitation,average_precipitation,'
                       'record_precipitation\n')

        while current_date != end_date:
            try_again = False
            with open('{}/{}-{}-{}.html'.format(station,
                                                current_date.year,
                                                current_date.month,
                                                current_date.day)) as in_file:
                soup = BeautifulSoup(in_file.read(), 'html.parser')

                weather_data_rows = soup.find(id='historyTable').find_all('tr')
                weather_data = []
                for i in range(len(weather_data_rows)):
                    soup1 = weather_data_rows[i]
                    weather_data.append(soup1.find_all('span', class_='wx-value'))
                    weather_data = [x for x in weather_data if x != []]
                
                if len(weather_data[4]) < 2:
                    weather_data[4].append(None)
                    weather_data[4].append(None)


                weather_data_units = soup.find(id='historyTable').find_all('td')


                try:
                    actual_mean_temp = weather_data[0][0].text
                    actual_max_temp = weather_data[1][0].text
                    average_max_temp = weather_data[1][1].text
                    if weather_data[1][2]:
                        record_max_temp = weather_data[1][2].text
                    actual_min_temp = weather_data[2][0].text
                    average_min_temp = weather_data[2][1].text
                    record_min_temp = weather_data[2][2].text
                    record_max_temp_year = weather_data_units[
                        9].text.split('(')[-1].strip(')')
                    record_min_temp_year = weather_data_units[
                        13].text.split('(')[-1].strip(')')

                    actual_precipitation = weather_data[4][0].text
                    if actual_precipitation == 'T':
                        actual_precipitation = '0.0' 

                    #Test whether station collects average or record precipitation data
                    if weather_data[4][1]:
                        average_precipitation = weather_data[4][1].text
                    else: 
                        average_precipitation = None
                    if weather_data[4][2]:
                        record_precipitation = weather_data[4][2].text
                    else: 
                        record_precipitation = None

                    # Verify that the parsed data is valid
                    if (record_max_temp_year == '-1' or record_min_temp_year == '-1' or
                            int(record_max_temp) < max(int(actual_max_temp), int(average_max_temp)) or
                            int(record_min_temp) > min(int(actual_min_temp), int(average_min_temp)) or
                            ((record_precipitation is not None or average_precipitation is not None) and 
                            (float(actual_precipitation) > float(record_precipitation) or
                            float(average_precipitation) > float(record_precipitation)))):
                        raise Exception

                    out_file.write('{}-{}-{},'.format(current_date.year, current_date.month, current_date.day))
                    out_file.write(','.join([actual_mean_temp, actual_min_temp, actual_max_temp,
                                             average_min_temp, average_max_temp,
                                             record_min_temp, record_max_temp,
                                             record_min_temp_year, record_max_temp_year,
                                             actual_precipitation,]))
                    if average_precipitation:
                        out_file.write(',{}'.format(average_precipitation))
                    if record_precipitation:
                        out_file.write(',{}'.format(record_precipitation))

                    out_file.write('\n')
                    current_date += timedelta(days=1)
                    
                except:
                    # If the web page is formatted improperly, signal that the page may need
                    # to be downloaded again.
                    try_again = True

            # If the web page needs to be downloaded again, re-download it from
            # wunderground.com

            # If the parser gets stuck on a certain date, you may need to investigate
            # the page to find out what is going on. Sometimes data is missing, in
            # which case the parser will get stuck. You can manually put in the data
            # yourself in that case, or just tell the parser to skip this day.
            if try_again:
                print('Error with date {}'.format(current_date))

                lookup_URL = 'http://www.wunderground.com/history/airport/{}/{}/{}/{}/DailyHistory.html'
                formatted_lookup_URL = lookup_URL.format(station,
                                                         current_date.year,
                                                         current_date.month,
                                                         current_date.day)
                html = urlopen(formatted_lookup_URL).read().decode('utf-8')

                out_file_name = '{}/{}-{}-{}.html'.format(station,
                                                          current_date.year,
                                                          current_date.month,
                                                          current_date.day)

                with open(out_file_name, 'w') as out_file:
                    out_file.write(html)


# Parse the stations used in this article
for station in ['KCLT', 'KCQT', 'KHOU', 'KIND', 'KJAX',
                'KMDW', 'KNYC', 'KPHL', 'KPHX', 'KSEA', 'KSAF']:
    parse_station(station)


================================================
FILE: us-weather-history/wunderground_scraper.py
================================================
# coding: utf-8

from datetime import datetime, timedelta
from urllib.request import urlopen
import os


def scrape_station(station):
    '''
    This function scrapes the weather data web pages from wunderground.com
    for the station you provide it.

    You can look up your city's weather station by performing a search for
    it on wunderground.com then clicking on the "History" section.
    The 4-letter name of the station will appear on that page.
    '''

    # Scrape between July 1, 2014 and July 1, 2015
    # You can change the dates here if you prefer to scrape a different range
    current_date = datetime(year=2014, month=7, day=1)
    end_date = datetime(year=2015, month=7, day=1)

    # Make sure a directory exists for the station web pages
    os.mkdir(station)

    # Use .format(station, YYYY, M, D)
    lookup_URL = 'http://www.wunderground.com/history/airport/{}/{}/{}/{}/DailyHistory.html'

    while current_date != end_date:

        if current_date.day == 1:
            print(current_date)

        formatted_lookup_URL = lookup_URL.format(station,
                                                 current_date.year,
                                                 current_date.month,
                                                 current_date.day)
        html = urlopen(formatted_lookup_URL).read().decode('utf-8')

        out_file_name = '{}/{}-{}-{}.html'.format(station, current_date.year,
                                                  current_date.month,
                                                  current_date.day)

        with open(out_file_name, 'w') as out_file:
            out_file.write(html)

        current_date += timedelta(days=1)


# Scrape the stations used in this article
for station in ['KCLT', 'KCQT', 'KHOU', 'KIND', 'KJAX',
                'KMDW', 'KNYC', 'KPHL', 'KPHX', 'KSEA']:
    scrape_station(station)


================================================
FILE: wheres-waldo-path-optimization/Where's Waldo path optimization.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing the optimal path for finding Waldo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides the methodology and code used in the blog post, [Here’s Waldo: Computing the optimal search strategy for finding Waldo](http://www.randalolson.com/2015/02/03/heres-waldo-computing-the-optimal-search-strategy-for-finding-waldo/).\n",
    "\n",
    "### Notebook by [Randal S. Olson](http://www.randalolson.com/)\n",
    "\n",
    "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is widely useable and shareable as possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the data into a pandas DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we already have all 68 locations of Waldo from [Slate](http://www.slate.com/content/dam/slate/articles/arts/culturebox/2013/11/131111_heresWaldo920_1.jpg.CROP.original-original.jpg), we can jump right into analyzing them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Book</th>\n",
       "      <th>Page</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td> 68.000000</td>\n",
       "      <td> 68.000000</td>\n",
       "      <td> 68.000000</td>\n",
       "      <td> 68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>  3.514706</td>\n",
       "      <td>  6.058824</td>\n",
       "      <td>  6.700776</td>\n",
       "      <td>  3.875306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>  1.856756</td>\n",
       "      <td>  3.411492</td>\n",
       "      <td>  3.703276</td>\n",
       "      <td>  1.941349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>  1.000000</td>\n",
       "      <td>  1.000000</td>\n",
       "      <td>  0.625000</td>\n",
       "      <td>  0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>  2.000000</td>\n",
       "      <td>  3.000000</td>\n",
       "      <td>  3.513889</td>\n",
       "      <td>  2.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>  3.000000</td>\n",
       "      <td>  6.000000</td>\n",
       "      <td>  6.694444</td>\n",
       "      <td>  3.927083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>  5.000000</td>\n",
       "      <td>  9.000000</td>\n",
       "      <td> 10.062500</td>\n",
       "      <td>  5.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>  7.000000</td>\n",
       "      <td> 12.000000</td>\n",
       "      <td> 12.444444</td>\n",
       "      <td>  7.708333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Book       Page          X          Y\n",
       "count  68.000000  68.000000  68.000000  68.000000\n",
       "mean    3.514706   6.058824   6.700776   3.875306\n",
       "std     1.856756   3.411492   3.703276   1.941349\n",
       "min     1.000000   1.000000   0.625000   0.333333\n",
       "25%     2.000000   3.000000   3.513889   2.250000\n",
       "50%     3.000000   6.000000   6.694444   3.927083\n",
       "75%     5.000000   9.000000  10.062500   5.291667\n",
       "max     7.000000  12.000000  12.444444   7.708333"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import random\n",
    "import math\n",
    "\n",
    "sb.set_style(\"white\")\n",
    "plt.style.use(\"tableau10\")\n",
    "\n",
    "wheres_waldo_locations = pd.read_csv(\"wheres-waldo-locations.csv\")\n",
    "wheres_waldo_locations.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the dots according to the book they came from"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first basic visualization that we can make is to plot all of the points according to the book that they came from.\n",
    "\n",
    "The dashed line in the center represents the crease of the book, as \"Where's Waldo\" illustrations always stretched over two pages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UJFQDAEBBQjVAzeV5HmmaRp7nVbcCwAKEaoCay7Is0jSNLMuqbgWABQjVAABQkFANAAAF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\nZl+gygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b411050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12.75, 8))\n",
    "plt.plot([6.375, 6.375], [0, 8], \"--\", color=\"black\", alpha=0.4, lw=1.25)\n",
    "\n",
    "for book, group in wheres_waldo_locations.groupby(\"Book\"):\n",
    "    plt.plot(group.X, group.Y, \"o\", label=\"Book %d\" % (book))\n",
    "\n",
    "plt.xlim(0, 12.75)\n",
    "plt.ylim(0, 8)\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.legend(loc=\"upper center\", ncol=7, frameon=True, fancybox=True, bbox_to_anchor=(0.5, 1.1))\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kernel Density Estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can use the [Seaborn library](http://stanford.edu/~mwaskom/software/seaborn/examples/index.html) to perform a [kernel density estimation](http://en.wikipedia.org/wiki/Kernel_density_estimation) (KDE) of the points.\n",
    "\n",
    "A KDE will show us Waldo's hot spots, i.e., where he's most likely to appear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TVUJVH0L1rNqhKqIzViOF6usnRzI/+V5mz34jo2s/kZ3xO7U3KRQiNi9v0ao1WCNPV3/x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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b3bb1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.kdeplot(wheres_waldo_locations.X, wheres_waldo_locations.Y, shade=True, cmap=\"Blues\")\n",
    "plt.plot([6.375, 6.375], [0, 8], \"--\", color=\"black\", alpha=0.4, lw=1.25)\n",
    "plt.xlim(0, 12.75)\n",
    "plt.ylim(0, 8)\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Genetic Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now on to the real fun! I decided to approach this problem as a [traveling salesman problem](http://en.wikipedia.org/wiki/Travelling_salesman_problem): We need to check every possible location that Waldo could be at while taking as little time as possible. That means we need to cover as much ground as possible without any backtracking.\n",
    "\n",
    "In computer terms, that means we’re making a list of all 68 points that Waldo could be at, then sorting them based on the order that we’re going to visit them. We can use a [genetic algorithm](http://en.wikipedia.org/wiki/Genetic_algorithm) (GA) to try out hundreds of possible arrangements and continually build upon the best ones. Note that because GAs are stochastic, the end result will not always be the same each time you run it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reorganize the DataFrame Xs and Ys into a lookup table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Constantly looking up values in a DataFrame is quite slow, so it's better to use a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "waldo_location_map = {}\n",
    "\n",
    "for i, record in wheres_waldo_locations.iterrows():\n",
    "    key = \"B%dP%d\" % (record.Book, record.Page)\n",
    "    waldo_location_map[key] = (record.X, record.Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic functions for the Genetic Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_distance(x1, y1, x2, y2):\n",
    "    \"\"\"\n",
    "        Returns the Euclidean distance between points (x1, y1) and (x2, y2)\n",
    "    \"\"\"\n",
    "    return math.sqrt( (x2 - x1)**2 + (y2 - y1)**2 )\n",
    "\n",
    "def compute_fitness(solution):\n",
    "    \"\"\"\n",
    "        Computes the distance that the Waldo-seeking solution covers.\n",
    "        \n",
    "        Lower distance is better, so the GA should try to minimize this function.\n",
    "    \"\"\"\n",
    "    solution_fitness = 0.0\n",
    "    \n",
    "    for index in range(1, len(solution)):\n",
    "        w1 = solution[index]\n",
    "        w2 = solution[index - 1]\n",
    "        solution_fitness += calculate_distance(waldo_location_map[w1][0], waldo_location_map[w1][1],\n",
    "                                               waldo_location_map[w2][0], waldo_location_map[w2][1])\n",
    "        \n",
    "    return solution_fitness\n",
    "\n",
    "def generate_random_agent():\n",
    "    \"\"\"\n",
    "        Creates a random Waldo-seeking path.\n",
    "    \"\"\"\n",
    "    new_random_agent = waldo_location_map.keys()\n",
    "    random.shuffle(new_random_agent)\n",
    "    return tuple(new_random_agent)\n",
    "\n",
    "def mutate_agent(agent_genome, max_mutations=3):\n",
    "    \"\"\"\n",
    "        Applies 1 - `max_mutations` point mutations to the given Waldo-seeking path.\n",
    "        \n",
    "        A point mutation swaps the order of two locations in the Waldo-seeking path.\n",
    "    \"\"\"\n",
    "    agent_genome = list(agent_genome)\n",
    "    num_mutations = random.randint(1, max_mutations)\n",
    "    \n",
    "    for mutation in range(num_mutations):\n",
    "        swap_index1 = random.randint(0, len(agent_genome) - 1)\n",
    "        swap_index2 = swap_index1\n",
    "\n",
    "        while swap_index1 == swap_index2:\n",
    "            swap_index2 = random.randint(0, len(agent_genome) - 1)\n",
    "\n",
    "        agent_genome[swap_index1], agent_genome[swap_index2] = agent_genome[swap_index2], agent_genome[swap_index1]\n",
    "            \n",
    "    return tuple(agent_genome)\n",
    "\n",
    "def shuffle_mutation(agent_genome):\n",
    "    \"\"\"\n",
    "        Applies a single shuffle mutation to the given Waldo-seeking path.\n",
    "        \n",
    "        A shuffle mutation takes a random sub-section of the path and moves it to\n",
    "        another location in the path.\n",
    "    \"\"\"\n",
    "    agent_genome = list(agent_genome)\n",
    "    \n",
    "    start_index = random.randint(0, len(agent_genome) - 1)\n",
    "    length = random.randint(2, 20)\n",
    "    \n",
    "    genome_subset = agent_genome[start_index:start_index + length]\n",
    "    agent_genome = agent_genome[:start_index] + agent_genome[start_index + length:]\n",
    "    \n",
    "    insert_index = random.randint(0, len(agent_genome) + len(genome_subset) - 1)\n",
    "    agent_genome = agent_genome[:insert_index] + genome_subset + agent_genome[insert_index:]\n",
    "    \n",
    "    return tuple(agent_genome)\n",
    "\n",
    "def generate_random_population(pop_size):\n",
    "    \"\"\"\n",
    "        Generates a list with `pop_size` number of random Waldo-seeking paths.\n",
    "    \"\"\"\n",
    "    random_population = []\n",
    "    for agent in range(pop_size):\n",
    "        random_population.append(generate_random_agent())\n",
    "    return random_population\n",
    "\n",
    "def plot_trajectory(agent_genome):\n",
    "    \"\"\"\n",
    "        Create a visualization of the given Waldo-seeking path.\n",
    "    \"\"\"\n",
    "    agent_xs = []\n",
    "    agent_ys = []\n",
    "    agent_fitness = compute_fitness(agent_genome)\n",
    "\n",
    "    for waldo_loc in agent_genome:\n",
    "        agent_xs.append(waldo_location_map[waldo_loc][0])\n",
    "        agent_ys.append(waldo_location_map[waldo_loc][1])\n",
    "\n",
    "    plt.figure()\n",
    "    plt.title(\"Fitness: %f\" % (agent_fitness))\n",
    "    plt.plot(agent_xs[:18], agent_ys[:18], \"-o\", markersize=7)\n",
    "    plt.plot(agent_xs[17:35], agent_ys[17:35], \"-o\", markersize=7)\n",
    "    plt.plot(agent_xs[34:52], agent_ys[34:52], \"-o\", markersize=7)\n",
    "    plt.plot(agent_xs[51:], agent_ys[51:], \"-o\", markersize=7)\n",
    "    plt.plot(agent_xs[0], agent_ys[0], \"^\", color=\"#1f77b4\", markersize=15)\n",
    "    plt.plot(agent_xs[-1], agent_ys[-1], \"v\", color=\"#d62728\", markersize=15)\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.show()\n",
    "    \n",
    "def run_genetic_algorithm(generations=10000, population_size=100):\n",
    "    \"\"\"\n",
    "        The core of the Genetic Algorithm.\n",
    "        \n",
    "        `generations` and `population_size` must be a multiple of 10.\n",
    "    \"\"\"\n",
    "    \n",
    "    population_subset_size = int(population_size / 10.)\n",
    "    generations_10pct = int(generations / 10.)\n",
    "    \n",
    "    # Create a random population of `population_size` number of solutions.\n",
    "    population = generate_random_population(population_size)\n",
    "\n",
    "    # For `generations` number of repetitions...\n",
    "    for generation in range(int(generations)):\n",
    "        \n",
    "        # Compute the fitness of the entire current population\n",
    "        population_fitness = {}\n",
    "\n",
    "        for agent_genome in population:\n",
    "            if agent_genome in population_fitness:\n",
    "                continue\n",
    "\n",
    "            population_fitness[agent_genome] = compute_fitness(agent_genome)\n",
    "\n",
    "        # Take the top 10% shortest paths and produce offspring from each of them\n",
    "        new_population = []\n",
    "        for rank, agent_genome in enumerate(sorted(population_fitness,\n",
    "                                                   key=population_fitness.get)[:population_subset_size]):\n",
    "\n",
    "            if (generation % generations_10pct == 0 or generation == (generations - 1)) and rank == 0:\n",
    "                print(\"Generation %d best: %f\" % (generation, population_fitness[agent_genome]))\n",
    "                print(agent_genome)\n",
    "                plot_trajectory(agent_genome)\n",
    "\n",
    "            # Create 1 exact copy of each top path\n",
    "            new_population.append(agent_genome)\n",
    "\n",
    "            # Create 4 offspring with 1-3 mutations\n",
    "            for offspring in range(4):\n",
    "                new_population.append(mutate_agent(agent_genome, 3))\n",
    "                \n",
    "            # Create 5 offspring with a single shuffle mutation\n",
    "            for offspring in range(5):\n",
    "                new_population.append(shuffle_mutation(agent_genome))\n",
    "\n",
    "        # Replace the old population with the new population of offspring\n",
    "        for i in range(len(population))[::-1]:\n",
    "            del population[i]\n",
    "\n",
    "        population = new_population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 0 best: 283.706779\n",
      "('B3P8', 'B2P3', 'B4P3', 'B1P4', 'B4P8', 'B2P12', 'B2P1', 'B5P7', 'B4P11', 'B3P7', 'B4P6', 'B5P5', 'B5P6', 'B2P8', 'B7P7', 'B5P2', 'B4P9', 'B4P4', 'B5P3', 'B2P11', 'B4P7', 'B5P9', 'B7P6', 'B2P5', 'B6P2', 'B2P6', 'B1P10', 'B1P3', 'B3P2', 'B3P3', 'B3P6', 'B4P10', 'B3P9', 'B5P4', 'B6P11', 'B4P2', 'B1P2', 'B6P12', 'B4P1', 'B6P8', 'B6P5', 'B6P1', 'B1P11', 'B3P11', 'B1P9', 'B1P12', 'B2P2', 'B2P9', 'B3P4', 'B2P7', 'B5P1', 'B7P2', 'B2P4', 'B2P10', 'B1P8', 'B4P5', 'B5P10', 'B3P10', 'B1P5', 'B5P8', 'B3P5', 'B3P1', 'B5P11', 'B7P1', 'B1P7', 'B1P1', 'B7P3', 'B1P6')\n"
     ]
    },
    {
     "data": {
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C5O+/J/vAAf02mbk5jqNH4TxxIgpHRwNGJwhCVZVTqKb/9yeITM3DRC5jy+SO\ntK775L9vJEniWlwWuy7F8vvleBKyCorttzQ1oXcjVwY0d6drQxfMFJVijm61UbQwxN1q2Vno65eF\nR5OnymPw7sHE5sRibmLO1oCteNt7P/R184Pms+baGkzlpgSNDkIuEz8nT6pSfQZlcjl2fZ8DIP/C\nBVRxcQaNx7yeN7UXLsDrt21Yd+0CgFRYSNqq1YT17kPy0qVocnIfchZBEITSkySJT3ZcJTJVVz//\nTp+GZZKogq6NXxMPez7u14jTH/Zg0ysdGNHOE3tL3YhtvkrD7stxTFwXRNvZR/ho+1UCw1PRilZY\nRkEsDFG25l+YT2xOLADTWk0rVaIK/y4IUMu6lkhUy0il+yza9+un/zhr/34DRvIvy8aN8Vy+nLq/\n/oJlq1YAaHNySPl+MWF9+pC2di3awkIDRykIQlWw7UIMOy/p3qh38anBq13rl8t15HIZHeo5M3dw\nU85/3ItV49owoLk7lqa6R6CZ+So2noti+PJAOn31B3P2hRAcmylaYRmIVivdM2FOeHxn4s6w+cZm\nAFq7tmaU/6hSv7aobZVYEKDsVKoyANCNKoT37YcyIgLzRv7U277d0CEVI0kSucePk7RgIYWhofrt\nCjc3XN54HfuBA5EpRM2XIAiP7nZSDgGLT5Kv0lDDxpz907rgYmteoTHkFqo5EqJrhXX8ZjLq/4yq\n1nOx5vnmHgxo4Y53DesKja26Uqq1vLftMrsu3fu00crMhC2TO4oygEeQo8xh0O5BJOQmYKmw5LcB\nv1HHtk6pX997W28SchMIqBfAnC5zyjHS6qPSjazKZDLs+uqWXy28HkJheISBIypOJpNh060b3tt/\nw2P+d5jVrQuAOj6e+E8+Jbx/AFn79yNptQ85kyAIwr8KVBqmbLhIvkqDTAYLh7Wo8EQVwNpcwfMt\nPFg9vi3nP+7F7EFNaOftpN8fnpzLgiM36T7vLwb8cJKVJ8JJ/E/tq1B2sgtUvPTzOX2iaiIv3g3C\nTCGnQU0bQ4RWaX0b9K1+dPSd1u88UqKq1qpJyksCxMhqWao0ravupnBxIX39egBMHB2xbtfOwBHd\nSyaTYe7jg+PwYSjc3SgICUGbk4MmI4PsgwfJ/vNPTN3cMPX0fGCrGUEQBIAvf7/OH6G6P4KvP12f\nEe09DRwRWJqZ0Ky2A0Pa1GFomzq42pmTlqskOVtX9pSYVcjxW7pWWOci0lD90wrLwlS0wioLiVkF\njFp5josIxO3MAAAgAElEQVRRGQC09HTg2xebc/p2Kgq5jAK1lgKVlgY1bfBzszNwtJXD8ZjjfBf0\nHQAd3DrwYbsPH+lvdFJeEuuurwOgX71+NK7RuFzirG4qXRlAkfDBgym8HoKZtzf19u01+oRPW1hI\n+saNpP60HE16un67ZZvW1Hz7baxatzZgdIIgGLP9V+N5bf1FAFrXdWTTKx0wNTHeB2O3k7L/aYUV\nx53U4svAmprIeNq3Js+3cKenn6vo4fqYbiflMG71OWIz8gHo5e/K4hEt9Z/PQrWGjnP/IC1XSTtv\nJ7ZM7mjIcCuFzMJMBu8aTFJ+EjamNmwfsB03m0dbLvXvpL8Zu38sAEt6LqFr7a7lEWq1UylHVkE3\ngSn39Gk0GRnY9uyBwsXF0CE9kEyhwKpFCxyGD0dmbkbBtWtIKhXquHgyt28n/+pVzOvXN/r7EASh\nYkWn5TH+5/Mo1VrsLBT8OrE9jlZmhg7rgZyszelYvwbjO3nR3a8mVmYKYjLyyVNq0Eq6UoF9VxP4\n+VQEt5NyMDeVU9vR8p5H2ELJLtxJY/Sqs/oR7BHtPJk/tDnmd41YK+RyUnOVXLiTTmxGPv2bueFk\nXfFlI5XJl2e+5O/kvwH4pMMntHVr+8jnuJR0icN3DgMwselEnC2dyzTG6qrSJqumtWqRtlY31C63\nscGmc2cDR1Q6cjMzrNu1w2HoEJAk3WpYGg2qO3fI2LwFZXg45g0bih6tgiCg0miZsPa8fnRy0fAW\ntKrr9JBXGQ+ZTEYtewu6+brw8lPetPd2QiGXEZWWh1KtRaWRCE3IZtelONafjSImPR9bCwVudhZG\n/7TMUA5eS2Di2iByCzUAvNO7IR/19cNEfu9Iu4eDJWvP3AHA1EROt4ZiMOR+jkYd5fu/vwegi0cX\n3mnzzmN9D56MPcmZ+DOArt2VuYl4g1AWKm0ZAEDkqNHkX7iAws2NBkePICvhh9XYqRITSVn6Ixm/\n/QZqtW6jiQkOgwdR4/XXMXV7tEcQgiBUHV8fCOXHv8IAGNuxLjOeb2LgiMpGoVrDXzeS2X0pjiMh\niRSqi0849XCwJKC5O8+3cMevlq1IXP/xa+AdPtsVjFbSTaSaO7gpQ9s8ePLPiOWBnAlPxcHKlMCP\neop64RKkF6QzcNdA0grSsDWzZefzOx97idQ5Z+ewMXQjNqY2nBl5powjrb4q7cgqgLYgn9zjx9Hm\n5GDdqROm7u6GDumRmdjYYNv9aez79UOTkUHhrVug1VJw/TrpGzeiycjEonEj5JaWhg5VEIQKdPxm\nMh/vCAbA382OJSNboTDiOtVHoZDrZqj3a+bG+E5eNHCxoVCtJSY9H0mC7AI1QXfSWX82iv3B8WQV\nqHGzt9AvTlDdSJLEd4du8tWBUCR0K4n9NLY1/Zs9/G+euULO/uAEMdHqAT45+QnBqbqftS86fkEr\n11aPfa6tN7cSmRWJp50nw/2Gl1WI1V6lTlZNPTxIW7sWJAm5hQU23boZOqTHZuLggF2fPtj27oU6\nMRFlZCRoNORfvkzGxk1IhUosGjVCbi4eKQhCVZeUXcDY1efIU2qwMjPh14ntqWlrYeiwyoW5woRG\n7nYMaunBqPae1Ha0JKtATXymrt1Vaq6S02GprDkVybGbyRQoNXg4WmFtXj36Vas0Wj747So/n44E\nwNnajF9ebk+HeqWrhfSqYcWGs1HkqzSk5SkZ8pCR2OrmQMQBll1ZBkCPOj14s9WbTzSSvyZ4DSn5\nKfg7+9O/Xv+yCrPaq9TJqtzKivyLF1FFR6OKi8Np3NhKWQpwN0WNGtj374d1p86ooqJQxcUhqVTk\nnT9PxpYtIJdj0chfLCwgCFWUVivx6q8XCU3IBmDu4GY81aCGgaOqGFZmCprXcWBY2zq80Ko2Lrbm\npOYoSclRApCQWcBfN5NZdTKcoDvpaLQSdZysMFdUzUfbuYVqJv9ygf3Bup6fdZ2t2DipwyONjoqJ\nVveXkp/C60dfp1BTiIO5A0t7LcXa9MkWslj892IKNAW0d2vP03WeLptAhcqdrAJIGg05R48i5edj\n2ao1Zp6G7z1YFkzd3LAfNBDLli1RhoWhTk5GKiwk9/RpMrfvQG5phYWvLzKTqvlLWhCqq6V/hbHp\nfDQAg1t68HbvhgaOyDDsLU1p6+XE6A51ebZJLewsTInPLCCrQI0ERKXlcfh6IqtORnA9LguFXEZt\nR6sqUyqRnF3ImFXnOBeZBkCz2vZsmNQBd4dHLwkTE63uJUkSH574kBtpNwCY9dQsmrk0e6JzFqgL\n9JO0etTpQZtabZ44TkGn0ierprVr60oBNBpkJibY9upp6JDKjEwmw8zTE4ehQzBv2JDCmzfRpKej\nzc0l56+/yNy7FxMHR8x9GogJCIJQBVy4k8b0LZeRJPCuYc2KcW0wU1SN5OtJ1LAxp3ODGrzU2Yuu\nDV2wNJUTk55PvkqDRitxOymHPVfiWXs6kvCUXCzNTPBwsEReSVthRaTkMmJFILeScgB42teF1ePb\n4vCYLcscrc04G55GTHo+ESm5jO/kVWWS+sf1e/jvrA5eDcCzXs/yWovXnvicsTmxbAjdAMCA+gPw\nc/J74nMKOpU+WZWbm1Nw/TrK8HBUMTE4jRtX5R6Ry2QyzBvUx3H4cEzr1KEwJARtdjbazEyyDx8m\n+/ARFK61MPPyEkmrIFRSGXlKxqw6R2a+CjMTOWsntKO2o5WhwzIqMpkMdwdLuvvV5OWnvGnj5YRc\nJiM6LQ+lRotSo+V6fBY7/o5lw7lo4jLysbM0pVYlaoV1KTqDUSvPkvDPErVDWtdm0fCWTzyLX0y0\n+ldCbgJTj05FqVXibOHM0p5LsVA8eU14aFoov4f9DsAo/1F42Ho88TkFnUqfrAIgl5N94CCSUolF\n40aY169v6IjKhUwux8LfD4cRI1A4OVNw/TpSfj6a1FSy9u4l99QpTOt4YlZb/IAIQmUiSRJvbb7E\n39G6ZTM/7e9Pn8ZiXfEHkctl1HW25pnGtZjwlDeN3O1Qa7VEp+WjkSTylBouRWew+Xw0O/6OJTVH\niYutOc42xluv+UdoIhN+DiK7QNfGcGqPBnwW0KjEHqqPSky00pEkifeOvcftzNsAfN3la/yd/cvk\n3EGJQfwZ/ScAk5tNxt7cvkzOK0CVeA5g060bcivdCETW3n0Gjqb8yc3McBozmgaHD+Hy1lvIbW0B\nyL90iahx44ia8DL5V4MNHKUgCKX1S+AdDl5LBKB3I1fGdfIybECVjIWpCX2buvHTmDac/6QX37yo\nm5RWVAUQlZbHD3/epveC4zy78Dg//hVGTHreg09awTafj2LSugvkqzTIZTBrYBPe6eNbZiPC5goT\nhrSuDcC5iDRuJ2WXyXkrm99u/capuFOA7lF9d8/uZXbuhNwE/ceu1q5ldl6hioysykxNKbwdRuHN\nm6hiYnAcPQa5mXEvR1gWZKamWLVpg+OwoSCX61bDUqtRRUeTsXUrhTduYu7bEIVT5VnxRhCqm2tx\nmbyx/m80koS7vQVrJ7TD0qxqlTIBnI9MIyI5F0/n8i1tsDA1obG7PYNb1WZEO0/c7C3JyFeR+M9j\n9ZQcJadup7D6VCSnbqdQqNZSx9EKSzPDTFaVJInvj95m5t4QJEn3uH7pqFYMalW7zK9V3SdaxebE\nMu2Paai0Kmpa1WRxz8VlusLUnvA9hKSF4GThxMSmE8vsvEIVSVYBZGamZO3ZC2o15j4NsPD1NXRI\nFUZuYYF1x444vDAYSamkIDQUtFqU4eGkb9qEKjoGcz9/TOyqb42SIBij3EI1Y1adIyVXiYlcxurx\nbannYmPosMrFZzuDORySyPC2dSqsftTaXEFLT0dGtPNkYAsPnK3NSMouID1PBUBcRgF/hiax6mQE\nF6PSkdC1wqqoSW1qjZZPdl5jxYlwABysTFk3oR1dfMoniazOE620kpZ3/nqHyKxIAOZ1m4ePo0+Z\nXmNT6Cais6PxtvdmSMMhZXru6q7KJKtm7u6kb9iIVFiIpFRiH1D9mvHKra2x6dYV+wEBaDOzKLx5\nEySJwtBQ3WpYaWlY+Psjt36yPnKCIJSNj7Zf5XRYKqBb431gy7IfTTMGNxKymbn3OolZhTR0taWh\nq22Fx+BgZUb7es6M7ViXPo1dsbVQEJ+RT3ahGq0Ekal5HLyWyOpTEYQmZGNqIqe2oyWKcurdna/U\n8MaGi+y+HAfoRj03TupAE4/yrXOsrhOtNt3YxKYbmwB4wecFxjYeW+bXWHF1BemF6TSt0ZTnvJ8r\n8/NXZ1XmLZXMzAzbZ54BIOfUKdTp6QaOyHDMatfG/euvqLd7F7a9e+k2qlSk//ort/s8Q9L8BWgy\nMw0bpCBUUlETJhDi34gQ/0ZETZjw2OfZfjGG3y7GANC5gTOvPd2grEI0Oj8dC0OSdB9/d+gGao3W\nYLHIZDIau9vz0XP+nPygB1smd2RUe08crXRLuRaotOy5Es+kdUG0nXWED3+7wumwFDRaqcxiSMtV\nMnJlIEdCkgDdcrrbX+9Eg5rlP6rep7ErTta6MrkNZ6PK/XrGICorigUXFgDgbu3Oe23fK/NrSJKk\nr1l1s3Er8/NXd1VmZBVAbmVN5s6doNVi5umJZZPGhg7JoBTOztj17YtNt26oYmNRRUeDWk3+hQuk\nb9kCWkm3GpZp9VxvWxAeVdSECeSePqP/tyo6hoyt27Bq1xbTmjVLfZ7w5BwmrgtCpZH0y2faWlTN\nn8PYjHze33ZF/+/0PBW17C1pWtvwM6VlMhkejpb09Hfl5ae8aeXpiAzdhCyVRqJQrSU4LovfLsay\n+XwUCZkFOFqZUtPW/LFLGaLT8hi5IpCQeN0Ep84NnFk7oV2FrSqlkMtJq0YrWmm0Gt768y2is3UL\nbSzsvhBve+8yv062KpufrvwEQJ+6fWhRs0WZX6M6qzIjqwBWbVqjcNHV+mTt3WvgaIyHZdOmeK5e\nhefPP2PRXLdChzYri+QFC7jd5xnS1q9HUioNHKUgGL/cM4H3bFMnJhLz+hulPkeBSsOUDX+Tp9QA\nMH9YC2raPXmPR2P14o+n+e+Y5Cc7r3LhTppB4rkfUxM53f1qsnB4S4I+6cX3I1rSy78mpia6pDQx\nq5BVJyMY8MMpenx3jAWHbxKWnPNI1wiOzWTwj6cJT8kF4PkW7qwZ367C36gMb/fvSo8bz0VX6LUr\n2q8hv3Ix6SIAI/xG0M6tXblcJz4nXv9xLRvRdq6sValkVWZigl1fXZ1I3vnzqBKTDByRcbHu0B6v\nTZuovXQJ5j66wnJNSgqJM2cR9lxfMnbsRNJoDBylIBgnSalE/yz7CXy1P5Tr8VkATO5Wr8rOyC5Q\nafj5VATxmQX37NNKMG71eQNEVTpWZgoGNHdn5bi2nP+4F3MHN6VDPSeKBlMjUnJZdPQWPb87Rv/F\nJ1hxPJz4zPwHnvPErWSG/XSG5OxCACZ3rceCoS0MskKZdw1rOtV3BuC3izEUqKrm7/3wzHC+v6hb\n/tTT1pO3Wr1VbtdKzEvUf1zLSiSrZa1KlQEAmNjbk7F1GwCmbrWwbCGG4u8mk8kw9/bGYdhQzLy9\nKAgNRZuVhTY7m5yjR8k6eBCFSw3M6tWrNCu+CEJ5U8bEED35VdSJiffsM6lRgzrLfixVGcDBawnM\n2hsCQIs6Diwc1gKTSrok6P0kZBaw7FgYb22+xP7ghPsep9JoeamT9xOvzFTeLExNaOphz4ut6zCs\nbR1q2VmQkaci6Z+kMym7kBO3Ulh9KoLA8FSUaok6TpbF7mvH3zG8sf5vCtVaZDL4rH8j3uzpY9Df\nsWZVfKKVWqtm6h9Tic+NR4aMxT0X42nn+fAXPqaz8Wc5HnMcgNdbvI6NWdXs6mEoMkkqg6ECIyJJ\nEmHPPIsqKgqLZs3w3rLZ0CEZNUmpJGP7dlKWLEWdnKzfbtGkCS5vv4V1p04iaRWqtawDB4j/5FO0\nOf888lUoQK3W76/1xec4Dh/+0PPEZuTTd9EJMvNV2Foo2PdmF+o4VY3lVCVJ4mJUBj+fjmT/1XjU\npZyM9Gq3+nz4XOVcPz0sOYfdl+LYfTmOiH8e6xcxNZHRraELAc3dCUvO5fujtwAwM5Ezf1hz+jdz\nN0TIxRSqNXSc+wdpuUraeTmx5dWOhg6pTK28upJFFxcBMK7RON5t+265Xm/RxUWsvLoSE5kJF0Zf\nwERu3G/CKpsqVQYAupHDolKAgitXUEZVj9mOj0tmZobj8OHUP3SQmu+9i4m9btJDQXAw0S9PJGr8\nS+RfumTgKAWh4mkLCoj//Ati33pbn6g6jhlD3V9/ReHqCv+0NMr569hDz6XWaJm28W8y83X9Pb9+\noVmVSFQL1Rp2/B3DwCWneOHH0/x+OU6fqHo4WD709T+fjtA3669s6rvY8HbvhvzxTjd2T+nMxKe8\ncbXTTVRSaSSOhCQxbdMlfaIKsHp8W6NIVOE/K1pFVq0VrW6m32TJpSUAeNt7M6XllHK/ZlEngJpW\nNUWiWg6qXBkA6GbBp2/cqP/Yqk0bA0dk/GSmpli1aoXD8GHITBXkX7sOKhWq2Fgytv1GwfUQzBv4\noKjhbOhQBaHcFYaFET1xErnHdY/15Pb2eMz/Dudx4zCtVQvnl8ajio2jMCQEVUICTi+9hExx/1Wn\n5h++yc5Lun6ao9p78mq3+hVyH+UlKbuAFSfCeWvTZXb8HUtilu6RuKmJjOdbeDBrYBMOBCfo17i/\nH7VWIrdQQy//yrs0pUwmw9XOgq4NXZjQ2ZsO9ZzQShCacG/y9+eNJKLT8rC1MMXN3sLgT61qO1qx\n9nQkoOsS0M238tdPq7QqphydQlJeEnKZnCU9luBh61Hu110fsp643Dh8HH0Y5DOo3K9X3VS5kVUA\ncx8f/QQi0RXg0ZjY2uLy5ps0OHwIp3Fj9W2tcv74g4iBA4l9730xWm2Eyqr3Z3UnSRIZv/1GxItD\ndItqAJatWlFvx3Zse/YsdqxN16661xQUkHfu/pOFTt5KYclftwHwq2XLp/0blVP05e9ydAZvb75E\n56/+YOGRW6Tk6JLUGjZmTOvpw6kPerBgWAvupOYRm/HgCUdFtgZFE/6Is+qNlYlcRmM3e2LTS773\n9DwV689GMfSnMzz19R/M3RfCtbhMDFWNVxUnWq24soKQNF1d+MtNXqapS9MKuW58rq4bQC1rMbmq\nPFTJkVUATVYWeYGBaNLSsO3TB4WzGBF8FHIrK2y6dMF+4PNocnMpDL2hWw3r5k3SN25CnZSEhX8j\nTGzEaliGVla9P6s7TU4O8R9/QuqyZbqaVJkM51cn4z53rr485m4KV1dS16wBrRYTBwdsuna555jk\n7ELGrj5HbqEGS1MTfp3YrtK1qVJptOy9Gs9H26+w4MgtQhOyKSpJbephz4fP+fHVC814yscFa3MF\nkiTx1uZLpOaWrh2eVoKUHCX9mlX+RupxGfmMXBHI1VjdoivtvJ3YN60LU3o0oKGrLYVqDdHp+UgS\nZBequXAnnQ1no9h7NZ7MPBW17C1wsDKr0JiLJloVqiv/RKvrqdf5+OTHaNHi4+jDV12+qpBH8lpJ\ny/wL89FKWrrU7kJH96pV/2sMqtwEqyLK6GjCevcBwHnyZGq+XX4tK6qDwvAIkhd/T/b+A/ptMnNz\nHEePwnniRBSOjgaMrnoL8W9UYkslhasrPsf+qviAKqH84GvEvjMd1R3dUwOTGjXw+OZrrDt1euDr\n7owdR965c5jW9aTBwYPF9mm1EuN/Ps/xm7qJi9+80IyhbeuUzw2Ug9ScQjaei+KXwDv6x/wACrmM\n55q6Mb6TF608He55lH3keiIT1wU90rVkMtj9xlNGsVDA4wpNyGL86vMk/FOD26+pG98NbX5Pt4PU\nnEL2BSew+1Is5yPvXWmxeR0Hnm/uTv9mbhXyxkap1tJx7lFSK/lEK6VGybA9w7idcRuFTMGGfhvw\nd/avkGun5KfQfUt3AD5q9xEj/UdWyHWrkypZBgBgVqeOvgF+1r59BnvMUlWY1/Om9oIFeP22Det/\nRpCkwkLSVq0mrHcfkpcuRZOT+5CzCOVCfG8/NkmSSFu3jsgRI/SJqnXnztTbueOhiSqATTddKYDq\nThTKyMhi+5afCNcnqs+3cGdIm9plG3w5CY7N5N2tl+n41R/MO3RTn6g6WZsxpXsDTn7Qg8UjWtK6\nrmOJNZfLjoU98jUlCb4+EPrEsRvKmbBUhiw7o09Ux3fyYvGIliW25XK2MWdMh7psfbUTpz7swYfP\n+eF/12jm5egMZuy5Toe5Rxm1MpAt56P1E/PKg5lCzotVYKLV0ktLuZ2hK7d5pdkrFZaoQvEFAdys\nK/8TAmNUZUdWAdLWrSNxzlwAvDZvwrJ5cwNHVHXkBQWRNH8B+Rcv6reZODlRY/IrOAwfjty86i7f\nZ0ySlywhZfEP9+4wNcVzzWqsxeTC+1KnpxP/v4/J+fNP3QYTE1zemobzyy8jk5fufXzhrVuEBwwA\nwPV/H+E0diwAF6PSGbrsDGqtRF1nK/ZMfcqol1NVa7Qcup7Iz6ciORdZfGUpfzc7XursxYDm7kbf\nE9UQ9lyJY/rmyyg1WgA+es6PV7o+ep/qm4nZ7L4Ux67LsUSnFa95NTOR093PhedbeNDDr2aZfx0i\nUnLpPu8vACZ09uazgMpVV305+TJj949FK2nxd/Jnfb/1mMor7uft8J3DTP9rOgBb+m+p0ES5uqjS\nyaoqKYnbT3cHrRbHsWOo9b//GTqkKkWSJHKPHydp4SIKQ0L02xVubri88Tr2Awc+cIa08GRSflpO\n8oIFun/I5aDVFttv2bIlnitXILcWdcX/lRcUROy776FO0LWbMXV3x/27eVi1bPlI55Ekids9e6KO\ni8e6Uyc8V68iM19Fv+9PEJOej6mJjO2vdTbax9vpuUo2no/i1zN3iLtrpSm5DJ5pXIuXOnvT1qvk\nEVQBVp+MYObe60iSrhPCty82Z2DLJ5t5LkkSl6Iz2HUpjj1X4vWT2IrYmCvo09iV51t40Lm+MwqT\nsnlAOnJFIKfDUrG3NOXs/3pWmjcmBeoChvw+hMisSEzlpmzuvxkfR58KjWHdtXV8G/QtAMeHHcfR\nQpTFlbUqO8EKwMTamrzzQahiYlDFxeE0blypR0yEh5PJZJh5eeEwdAjm9etTGHoDTWYm2pwccv74\nk6x9+1E4O2NWv774Y1fGUlevIfm77wAwcamB+9y55F+8iNzaGvPGjVDHxqFOSCD/0iXsnn1W39Wh\nupM0GlJ/+om4j/6HNlv3uNO2d2/qLP8Jcy+vRz6fTCZDGRFBQfA11AkJOI4dw/Qd17kYlQHAx/0a\n8WwT45sdHJqQxbyDN3h362WO30whu1DXYsrBypTxnbxZOLwlI9p54uFoKX52S6DVSszdH8r8w7qO\nETbmClaObUufxk/+tZbJZLjZW/K0b00mdPairbcTCrmM6LQ8lBotSo2WkPhsdv4dy4ZzUcSm52Nn\n+eStsCrrRKsFFxZwLEbX63hqy6n0rtu7wmM4eOcgV5KvYGFiwdSWU8XPTDmo0iOrAOlbtpDw2ecA\neP78M9Yd2hs4oqpLUqvJ2LFDtxpWwr/LLJo38qfmW29h3aWL+CEuA3eXt5g4OVF33VrMGzTQ79cq\nlcRMnUruMV2PUOtOnaj949JqX5qhSkoi7v0PyAsMBHQLYrh+9CEOw4c/0fdl9h9/EvP66wDcnvY5\nU+/YAtDTryYrx7Uxmu95jVbiSIjuUf+Z8NRi+3xdbRnf2YuBLTywNKscI2qGUqjW8N7WK+y+rOub\n62Jrzs8vtaWxe/mOnheoNPx1I4ldl+I4GpqEUl38SUptR0sGNHfnTHgql6J1b5Y616/BrxNL9zev\nMk60upB4gZcOvISERLMazVj73FoU8op/mjf9r+kcvnMYLzsvfh/0e4Vfvzqo8smqJiODm126gkqF\nw5AhuM2cYeiQqjxtYSHpGzeS+tNyNOn/zna1bNOamm+/jVXr1gaMrnJL27CBxBkzATBxcMBz3Vos\nGja85zhtYSExr72mb2ll060btRd/j8zs0dviTDo0ibPxZwFo79aeFX1WPMEdGEbOiRPEffAhmjRd\nPaaZtzceC+Zj4ffkS31q8/K42aEjklLJgXodWdTsBWrZWbBvWhecrCu2DVFJMvNUbAmKZu2ZSGLu\n6v8pk0Evf1de6uxFx3rORpNUG7OsAhWv/nKB02G6ZL+eizVrX2pX4auRZRWoOHQtkV2XYjl1O4UH\nrW5by86ClePa0MTj4cn03H0h/HQ8HIAj07vSoKZtWYVc5r9H8lR5vPj7i0RnR2NuYs7WgK1423uX\nRaiPbOTekVxNuUp7t/as7LPSIDFUdVX+mbiJgwM2nTsDkHXoEJKydL3/hMcnNzfHefx46h8+TI2p\nU/Q1k/lBF7gzajRRr7xCwfXrBo6y8knfvEWfqMrt7fFcs7rERBV0X4PaS5Zg1bYtADnHjhEzfTqS\n6tFmFU86NInA+ECkf/4LjA+k59aeXE+tHF8/SaUiad48oie9ok9U7QcNwnvb1jJJVEHXk9i8je7z\n3Co+BDkSi4a3MHiieisxm493XKXD3KPM3heiT1RtLRRM6uLN8fe6s2JsGzrVryES1VJIzCpg6LIz\n+kS1lacDv73aySDL5tpZmPJi69r88nJ7zv6vF18ENKKVp0OJxyZkFTBxbelaiQ1v56n/eMPZ6DKJ\nFcrn98iCCwuIztbF+GbLNw2WqMK/S62KTgDlp8onqwB2/foBoM3MJOfUKQNHU32Y2Fjj8sYb1D9y\nGKcJE5D98xg69/gJIga/QOz06RRGRBg4ysoh47ftJHyuK2eR29riuWoVFv4PnnEqt7SkzrIfsfxn\n0lDOkaPEvv8+kvrBS2DerWgk5G5JeUm8cfSNR4jeMJQxMUSOHk3qylUAyKyscP/ma9znzinzSWd/\nOerKMGrmZ/BxIwva1zPMIiRarcTRkETGrDpL7wXHWX82ivx/ViWq72LNzIFNCPyoJx/3a2SQJKuy\nugNiN6wAACAASURBVJ2UzeClp/VLqPbyd2X9xA44GsHIuYutOeM7e/PBs0/+5qu8VrS63++RqX9M\nfazzBcYHsunGJgBa1WzF6Eajnyi+J6HSqEjJTwHE6lXlqVokq7Y9uiOz0DVXztq7z8DRVD8KR0dc\n33+P+ocO4jBsGPzTISBr337C+wcQ/+mnqOLjH3KW6itz927iP/kEALm1NZ4rV2DZpHGpXiu3tqbO\n8p+waKpbcjB7/wHiP/4YSfNkf4RS8lMYtmcYP/z9A5eTL6PRGtcyjVkHDhIxaDAFl68AYO7vj/dv\n27AfMKDMr7XrUizLC/8dURlQGFnm13iYrAIVq09G0P27v3h5bRAnbun+eMpkutrZX15ux5Hp3RjT\noS7W5qJDx6MIikzjhR/P6JePHdnek2WjWxlNbW9qTiHvbr3MsOWBJe4vKgMorRH/jK5m5qvYH/xk\nv5fTCtL48fKPSJRdtWGOMofPTn0GgKXCklmdZ/2fvbMObOruwvCTNHVXqBctDsMpFIZtuEuRttgY\nY/iEbTBBpt+Gjm2MIS1SdLgMh9IiQwfFpV6oayppcr8/LqR0LdQbWPP8Ra/knpQm99zzO+d9kUo0\nl8o8kT9Rvz9tZbXiqBLfWlJjY0y7dCb1wEHSjh9HlZmJ1NBQ02FVOXSrVcN+3ldYjxtL3PKfSd2/\nH5RKkrdtJ2X3HixHjMD63YnIrKw0HeorQ8r+/UR/8ikIAlIjI5xXrSqxXrCOqSkuf6wibMxYsm/d\nImX3HtDVxX7+/CLVMdrYt+FcTOE3wZsJN7mZcJOV/6zEQt+C9o7t6ejYEQ8HDywMCl+SrGhUWVk8\n+e47kjdvUW+zHD0au48+rJABs9D4DD778zoZJjbEmNpinxaH/NQpeGdCuV+rMB7EpeMfHMr2S5Fk\n5OQ9MJjoyxja0gnfdm642Wily0rLXyGPmRZwheynw0wfdK/LlC61X4m2CZVKYMvFCL47eFttGqAv\nk6KrIyX9qbpDdTMDzn3WtUSv+3bD6lgb65GQkUPA+QgGvlFyM4tHKY/wv+nP3gd7yVZmF3qMnZEd\ny7ssL/Fr/3jxR2IyxCR6VotZOJtp1hXuWQsAQHUjbWW1ovjPD1g9I+3oUSKniEsOjot+wqxXLw1H\npCXrzh3ili4j/fhx9TapkRFWY3yxGjsWHdPya+5/HUk99BdRH3wASiUSQ0NcVv2OURlE/nOTkgj3\n8SX73j0ALEZ4Uf2LL4q88Xbd1pVYeWy+bca6xjibOnM7saDrkFQipYlNEzydPPF09KSeVb1Kubln\nP3hA1MxZZN8V5YSk5uY4fL0Q027dKuZ6uUoG/xrMjahUALbnnsN433bQ0aHu2WB0zCpG+kelEjh9\nL461QaGceuqQ9YwaNsb4tnNlSEtnTLQV1DKx/lwYX+6+gUoAHamEbwc1ZljLV8Mu91ZMKnN2XldL\npAF0qWfHvH4NSclUqHtUiztY9W9KM2glCAIXn1zEL8RPLSX1jPpW9YlKjyI1J1W97Yr3lRJP7gdG\nBjL5mKi80ca+Db93/12jVVWAfQ/38WngpwDs7r+bmhY1NRrPf5Uqk6yqcnK4174DqrQ0TLp1xfnn\nQlx/tGgE+ZUrxC1egvzCBfU2HXNzrCdOxHLUSKQGFe+P/aqRduwYkdNnQG4uEn19nFeuLBfZtdz4\neMJ8fMl5KN6IrHx9sPvkk5cmkzcTbjL1+FQEQcBY15jQ1FBArGr0rtmboKggAqMCCY4OJkNR0HLX\n1tCWDo4d8HTypK19W0z1yvchRBAEUv7cyeOFCxEyxaVawzfewPGnH9F1cCjXaz3P/L03WRMk9ly/\n41mD6eZJREwQK6qOSxZj1qNHuV4vPTuXHZci8QsO5WF8/t9zx7q2jG3vRqc6tkilmq/6vc4IgsCP\nh++w4oRoG2uoq8Mvo5vT2d1Ow5GJfwNLjtxlbXAoyqcSAPbmBnzZtyFvN6xWbg+FJXG0UqgU/BX6\nF/4h/txKvJVvXyenTvg29KVltZbcSrzFO4ffUSesO/vtpLZl7cJeslBSslMYtHsQsZmxGOsas7Pf\nTuxNNL/s/sf1P1h6eSkA50eex0hX2wteEVSZZBUg+rM5pPz5JxJdXeoEnamwyoeWkiMIAhnBwcQt\nXkLWjRvq7TI7O2wmT8Zi8KAqI2yfdvIkkVOngUKBRE8Pp19/UStalAeKJ7GEeXujCA8HwPqdd7Cd\nNbNYN7pYeSwj94/kifwJAD92+pG33d4WX1ep4ErsFQKjAgmMDORBSkGPeJlExhvV3qCjY0c8nTyp\naV5yW8rnUaZn8HjePFL3PtU2lEiwnjgR26lTKtQ97ejNJ0zwF6tXTZ3M2TbJA5lSIUpYZWZiPnAg\nDt9+Uy7XCkvIwC84jG0XI9Ti/QBGejoMaeGETzs3atuZlMu1qjoKpYpPdlxnx+VIAKyN9VgzphVN\nnTXT1vIMQRA4dOMx8/be5HGq6DSmI5UwvkMNpnetUyF9yEU5WqXmpLLj7g423tqo/j4A0NfRp2+t\nvng38Kamef4q482EmwzfNxyA7zy/o3fN3sWO57PAz9j7UPycz/OYx6A6g0r71sqVhecWsuXOFsz0\nzAgaoR3griiqVLKaHhRExHix8mH/9ddYDH41/ti15CEIAmlHjhC3dBk5D/KSHV0XF2ynTsWsd6//\ntAtZeuAZIidPRlAokOjq4rTiZ0w6diz36yiiownz9kERFQWAzfvvYzt1SrHOvZt0F5+DPmQoMtCT\n6rH67dU0s2tW4Lio9CjORJ4hMCqQ8zHnyVJmFTjGwdhB3S7QqnqrElUlMkNCiJo1C0WYmHTr2Njg\n+MP3GHt4FPs1SkNMSiY9lwaSLFdgqi9j/zRPXKzFuCMmv0/68ePo2NhQ5/SpUv+tCoJA0P0E1gU/\n4tjtWJ7/lnaxMsKnnStDWzpjblg1HuAqg4zsXN7beJnTT1srXK2N8BvbWuM9v+EJcr7cc4MTd/Ja\nPlq4WrJwQCPqV6DL1N5r0UwNuALA4uFN1b2rUelRbLi5gT/v/Yk8V64+3srACq96Xgx3H46VQeFz\nB9nKbNpsbINSUDK20VhmtZhVrFiOhx9n+onpAHg6erKi64pXom8YYMqxKZyKPEVdy7rs6LdD0+H8\nZ6lSyaqQm8u9Tm+iTEhQ+3hreTURlEpS9uwlfvlyFNHR6u36detiO2MGJp3ffGW+rMqLjLNniZj0\nHkJ2Nujq4rR0KaZdOlfY9XIiIwkb7a12G7OdORObdycW69zgqGAmH5uMUlBiqW/Jhl4bcDFzeeHx\n2cpsLj6+yOnI05yOPE1kemSBY/SkerSq3kqdvL7o9QRBIGn9BmL/9z+1bqxx+/Y4fP8dMhubYsVf\nWnKVKkauOs+FUFGzdfmIN+jbNK/VIGnzFh4/dbB227692KoNz5Dn5LLzShTrgkK5F5ueb1/72taM\n9ahB53p26GiX+suVuLRsxq37m+tRKQA0cTJnzZhW2JhozvUtO1fJqtMPWX78vnrAy8JIl0971mNo\nC+cKb/f4t6PV3MHG+IX4cTT8KCohzz2rpnlNfBr40KdWH/R1iv59Ddw9kPvJ92nv0J7fuv9W5PFJ\nWUkM2D2AxKxETPVM2dV/F3ZGmm/JeMbgPYO5m3SXTk6d+Lmrtr2woqhSySrA4wULSdq4EaRS6pw+\nVeE3Ny1lQ5WTQ/LWbcT/9hvK+Hj1dsNmzbCdORPjNq01GF35kXH+AhHvvouQlQU6OmLPY/eK97jO\nCQ0lzNuH3DixamM3ezbWY8cU69wdd3fw1dmvAHA1c2VDzw3FUgEQBIGw1DB1u8DFJxdRqAqaFbiZ\nual7XVtWa4mejh65SUnEzJmbN5Sno4PtjOlYjx9fKRX3RUfusuyYOKA2orUz3w5qkm+/Ijqa+13E\n6WubaVOxfWrDWhQRiXLWnwtj84VwUrPylvoNdKUMau7EGA836lar2gOHFcWj+Ax811wgPFGsEr7p\nbsuKkc01KvEV/CCez3fd4EFcXm/y0BZOfNqrfqWaTXy9P4S1V/ehaxWIzCgs37429m3wbeBLe8f2\nJRpymn16NgceHcDG0IYTw04UefyHpz7kr9C/APimwzf0rdW3ZG+igmkf0J7UnFSGuw9nbtu5mg7n\nP0uVS1blly8TNnIUANXmzsVq9CgNR6SlOKjkchLXbyBh9WpUqXkTpcYeHtjOnIlh40YajK5syC9d\nIvydiQhyOUilOP70I2Y9e1ba9bMfPCDM20ft8FTt87lYjSre52Lp5aX8cV20F2xu15zf3/q9WNWV\n55Er5JyPOU9gVCCnI0/n6397hqHMkP4Z7vTxu4tegvj/L3Owx/GnnzB6anpQ0QQ/iGfUH+cRBKhb\nzYTd73coVGvzYd9+ZN+7h0HTJtTYsqWQVxIRBIFzDxNZF/yIIzef5LPMdLQwxKedK8NbOWNhpHnh\n+f8qVyOSGbfubxIzRGfDoS2c+GZQY3R1NNNqFJeWzTcHbrHzSpR6W91qJnw9sDGt3CpP0k+ukLPr\n/i7W3ljPY3neKohMIqNnjZ74NPShnlXpTAjW3FjD4kuLATg57CTWhi820DgUeoiPTn0EQBfnLizp\nvOSVWlGTK+S02SQOvk5vPp0JjStHsq4qUuW0TQybNUPmYE9udAypBw5ok9XXBKmRETbvTsTSazgJ\nq9eQuH49QmYmGcHBZAQHY9q9O7YzpqNfq5amQy0RmVevEvFcourw/feVmqgC6NeqhcvaNYT7+KJM\nSeHJgoVIdHWxHDasyHOnvjGVqLQoDoYe5HLsZT4P+pzvPL8rUaXFSNeIzi6d6ezSGUEQuJ98n9OR\npwmMCuRq7FVUylx6nMqgf+BFdJ4mdCENTYie3p129rk0UynQlVZs72ZCejYzNl9FEMRq588jXywK\nb9KpI9n37pH1z3VyExML6AZnKZTsvhrF2qBQtSPSM9rWtGKMRw261bdDpqGEqapw/PYT3t94Re3w\nNbVLbWZ1r6uRZEipEth0IZz/Hbqtrqwb6uowvVsdxneoUWnJc6w8loDbAWy9szWfzJSgNECS5sHu\nMbNxMS+bwoa7pbv633eS7uBhWHiPeXxmPF+f+xoAC30LPm/3+SuVqMK/NFa17lUVSpVLViVSKea9\nepHwx2oyL19GER1dofI2WsoXHXNz7GbNxMp7NPG/rSRp61ZQKEg7coS0Y8cw79cPmylT0HNy1HSo\nRZJ5/TrhE95BJZeDRIL9N19j3rePRmIxcHfHec1qwseOQ5WayuMvv0Kiq4fFwAEvPU8qkbKgwwIe\nyx9zJfYKBx8dxMnEiWnNp5UqDolEQh3LOtSxrMP4xuNJinpI6AczMbgqaqfm6IB/VymHm2fCo02s\nerQJU11T2jm0w9PJkw6OHbAxLN/WHpVK4INt14hNE8XNv+zb8KVL8sYdO4oWr4JAxpkzates6ORM\n1p8LI+BCOMnyvLYHfZmUAc0c8fVwo4GDVqGkMtjydzif7byBUiUglcD8/o0Y3dZVI7HciEphzq4b\nXIvI00zt3qAaX/ZtgJNl5cgg3Um8g/9Nfw48OkCuKq8NxdHEkeYW/dl0tBoI+lx6qMKljAsZ7lZ5\nyerdxLt4OBRMVgVBYP7Z+SRni7+TuW3nlvvnujzQGgJUHlUuWQUw691b7ReeeuAA1hO0pfvXDZmt\nLdU/n4vV2DHE/7yClD17QKUiZdcuUvbvx3LYMGwmvYvM1lbToRZKZkgI4eMnoEoXh2jsFy7AYsDL\nE8OKxrBhQ1xW/U74uPGoMjKImTMHiZ4u5r1fLi+jr6PP0s5L8T7oTVhqGKuur8LZ1JmBdQaWKZ70\nwDPEzZ6NwdP2BF03N1Rfvk8do0geRwVyPe46AgJpijQOhx3mcNhhABpYN8DT0RNPJ08aWTdCR1o2\nW8zVZx5x8ukkdp8m9ni1erkwvNEbbyA1MUGVnk76qdPcbdKBdUGhHAp5rNbGBNFdyLudKyNau1Rq\nH2JVRhAElh27z+Kj4sOPvkzK8hFv8FbDyk800rIU/HT4Lv5nQ9UtII4Whszr15BuDapV+PUFQSAo\nOgi/EL8CLnVNbZvi29CXLs5dUKok/BV8rEyOVs9jY2iDlYEViVmJ3Em6U+gx+x7u40SE2M/aw62H\nWh7vVeOZkxZoK6sVTZXrWQXxQ/qwdx9yHj5Ev359au78U9MhaSkj2ffvE7d0GWlHjqi3SQwNsfL2\nxnr8OHTMS+7iUlFk3bmjXnIHqP7VV1h6DddwVHnk66HV0cFx8SLM3nqryPPCU8MZdWAUydnJyCQy\nVnRbUWjVpCgEhYK4pUvVD5QA5gMGUP3zuUiN82SEErMS1YYEQVFB+ZYtn2Gpb0l7x/Z4OnqWygb2\nWkQyg38NJlcl4GJlxL5pHTAzKLrlIGzqdORHDiPXN2Joj69QPdcW0dLVkrHta/BWw2oa642siuQq\nVXy+O4SAC6LUmYWRLqt9W9HC1bJS4xAEgf3XY5i/96a6Wi+TSpjgWZNpXWtjpFexNaRsZTb7H+7H\nP8Q/nxayVCKlq0tXfBr4FJCie97R6sjMjtQp47DfxMMTORtzljqWdfizX/7775OMJwzcM5C0nDSs\nDazZ1X+Xxuybi2LF1RX8du03JEi4NPoSujpaKbmKokomqwBxK1YQv1yUmah5YD/6NbUWaf8FMq9f\nJ27xEjKCg9XbpGZmWI8fj5X3aKRGmnUXyb53jzAfX5RJScCrO+SXce68qE7wTEZr2VJMOxcto3Ul\n9goT/ppAjioHE10T/Hv6U8eyzotP8O8PD59aM9bsRE6XX4j+4AMyr10DQGJkhP2XX2Dev/9Lr5ur\nyuVG/A1OR57mTNSZAk46UHIb2NQsBb2XBRKRmIlMKmHHex5FisM/Sc1iw7kwIjZt491zGwGY5TmF\nB3Y16dvUgTEebjR2enUenKoKmTlKpgZc5ugt0TbY0cIQ//GtqWVbuWYKofEZfL77BoH38pRNWrtZ\nsXBgowpXe0jKSmLLnS0E3A4gMStRvd1QZsigOoMYVX8UzqaFrxqUxNGqOPx08SfWhaxDJpFxftR5\n9HT0ONS/Hc53xGX/G24SFo7QYWnnpXRx6VKma1Uknwd9zq77u7A1tOX4sONFn6Cl1FTZZDX70SMe\n9uwFgM3kydhOm6rhiLSUJxnnzhO7eBFZ1/5Rb9OxscFm0iQshw1Folf5y67ZDx+KiepTCS67T2Zj\nPWZMpcdRXNLPBBH53nt5BgW//opJh6KdtJ6f4K1uXJ2NvTYWrovo3x8enlT/mBphQMzfVqjEwWz0\n69XDcdEi9GvWKHHssfJYgqKCOB15mrMxZ4u0gW1n3w4TvbzERRAEpgRcYf8/4jLfnF71eadj4Q+0\ngiBwJSKZdUGhHLgeQ65KwDIrlU2H5gPwsMcwWi/4DFtTzWl2VmUSM3IY7/c3V8LFRKiBvRnrxrbC\nzqzybJyzc5X8dvIhK07eJ+epZqqVsR6f9qzHkBZOFTo4FJoSyvqb69nzYE8+Yw47IztG1R/FkLpD\nMNMrule6KEerkrD3wV4+O/MZANv6biN07Fhc7yTnOybRVILl0h9o4KGZPv7iMOHwBM7HnKeJTRM2\n9t6o6XD+01TZZBXg0aDBZN28iZ6bGzUPHnjlJg21lA1BEEg/cYK4xUvIvndPvV3X0RGbKVMw79cX\niU7Z+hmLSwE90w8/eC16pdNOnCBy2nTR+lVfH+eVKzFu26bI81ZfX82Sy0sAqG9Vn3U91hV0p/rK\nAhBQKeHJFXOS7+ct8VuOGoXdxx8h1S97gldcG9jm1Zqre13P35Hx2U7R9rezuy2rfVsVEGHPyVWx\n/3o064JCuRaZkm9fM2cLvtz/AwYP72pbjTRIRKIc3zUXeBgvPqx0qG3Dr6ObY1qMVo7y4sy9eD7f\nfYNH8XkPTCNaO/Px2/WwrKBeZUEQuPjkIv4h/pyKPIVA3m2+nlU9fBr40MOtR4mWrff9E82UTQUd\nrUrD3aS7DN4zGIBvmn5GLa/5FHb3TTKT4nEhpNTXqWj67uxLaGoo3V27s+jNRZoO5z9NlU5WE1av\nIfZ//wPAbcd2DBuWzG1Gy+uBoFSSeuAAccuWo4iIUG/Xq1UL2+nTMO3evUIfVHLCw8VE9YmoH2o7\nYzo2kyZV2PXKm9QjR4iaMROUSiSGhrj8sQqjFi1eeo4gCMw/N5/td7cD0MmpE0s7L80bdpInwg81\nyE6VERVsSXayeNOU6qqw7yTF7OeCy/jlRXFsYAWFJYp0d0xVjdg1zhcni7zl/9i0LDadD2fj+XDi\nnvYcAujqSOjd2B5fDzfecLEkbtky4n/5FYDap06iW63ih2a05HEjKoWx6/5W/x8NaObAD0Oaoier\nJBmo1CwW7L/F3mt5Dnz1qpvy9cBGtHCtGM1UhUrBkdAj+N3042bCzXz7PB098W3oS+vqrUv1ffdv\nR6utk9qVKc4R/2tFl0s5dLwtRSdHWehxr3KyKggCrTe2JkuZhXcDbz5u9bGmQ/pPU6WTVUVMDPc7\ni/0wVuPGUe3jjzQckZaKRFAoSN6xg/gVv6grnAAGjRphO3MGxh4e5Z605kRGEebjTW60uJz8urac\npB44QNSHH4FKhdTYGJc1qzFs2vSl5+SqcplybApB0UEAeLl78Vmbz5DkpCP49SPlzC0eXzJHUIrJ\ng6FNDo7tktDt+QF0mVPh7wkgKzeLi08uEhgZWKQNbC2TljwIdebYDSUKZd7Xpo2JHiPbuDK6jUu+\npeXMq1cJ9RoBPFV7GDKk4t+QFgAC78Uxaf0lMp4mQe92qsnst+tVuEUpiJqpG86F8eNfd0jLFmWg\njPR0mNW9LmM83CpEPzctJ40dd3ew8fbGfHJKelI9+tbqi08DH2palH0u49uDt1h5qvSDVqrMTFIP\nHCApYDNZN2689NgkMykWS75/ZdsAkrKS6LilIwAft/oY7wbeGo7ov02VTlYBQkePJvPiJWTVq1P7\n+LFKsWzUollUmZkkbdpEwu+r1BP5AEZt2mA3cwaGzZq95Ozio4iJIczbB0WkmABZT5yI7cwZr227\nScru3UR/8ikIAlJTU1zWrS1yNSI9Jx3fQ77cTRKlgj5qPoNRwTt4/GcIqWHP2gIErOunY9s4DfXQ\nfKfZ8OanUIm/K0EQmLbjMIcenERmcgc9k0eoyC1wnCrbhtx0d5wMmjOxVTcGvOGKvqxgO4mgVHKv\nfQeUycmYdu+O0/JllfE2qjw7r0Ty0bZ/yFUJSCTwRZ8GjG1f8r7n0vBPZDJzdt7gelTe90rPRtX5\nom8D7M0Ny/160enRbLi1gT/v/ZmvL9tS3xKvel4Mdx/+UoeoklLaQavsh49I3rKZ5J278jkQKiVw\nzV2P/h/9wj8zJ2KZKvbzvsoV1WfcSrjFsH2iccqiNxfR3bXi7bGrMlU+WU0KCODxPHEQwnXDeoxa\nttRwRFoqC2VaGolr15Kwzk+UaXqKSZcu2E6fjoF73VK/tuLJEzFRDRdlcqzGjcPuow9f20T1GUnb\ntvH48y8A0aDBxd8PA3f3l57zOOMxo/aPIjYzlhqPBb7ekYMsVUzudCzNcWibgokT0HkOHPkcMp9O\nKjcbBX2XQiXJwey9Fs3UALEnr7adCW83smDzjROkS28gM7mNVDelwDmGMkPa2LcRe10dPbE3sc+3\nP+qjj0nduxepsTF1zwZrZLCvqiAIAr+desj3h24DoKcjZfHwZvRuYl/EmWUnJVPBT4fvsP5cGM/u\nqM5Whszv14jO9QoZLiwjN+Jv4Bfix5GwIyiFvCX0GuY18GngQ5+afTCQVcwAWXEHrQSFgrTjJ0ja\nHID8bH4dV5mtLWGd3Zlvd5YkUwlHhxwl4drfJM+YDfBKV1SfcSL8BNNOiOYnm3ptorFtYw1H9N+m\nyieruYmJ3PPsCEolFiO8sP/yS02HpKWSyU1IIOH330naFICgeOosJJFg1qcPtlOnoOfiUqLXU8TG\nEu7jS05oKACWPt5U+/TT1z5RfUbixo08WbAQAB0rK1z9/dCvXful59yKvcGGBV4MP65E9+m91bhd\nWxx++CG/cUP8fdg4GJJCxZ9rdoZh/mBQsc5O4Qlyei0LJD27YCUVwMJIRq/mYFftEVcTznE19mq+\nJOEZtS1q09GpI56OnjS1a4r8wGGiP/wQAJd164o1nKal5ChVAvP3huB3NgwAUwMZq3xa0rZm+VUV\nC0MQBPZci2bBvlvEp4u9sbo6Et7tWIv3O9d+oSVvaVCqlJyMPIl/iD+XYy/n29e6emt8G/rSwbFD\niayOS0NRg1aKx49J3rqN5G3b8rVbARi1a4ul1whMu3TmQvxlJhwWh0xXdF1BR6eOFRp3ebPp1ia+\nvfAtACeGnXglHbb+S1T5ZBUgfMI7ZJw5g46lJXUCTyORVUljryqPIjqauF9+IeXPnaASl6OQybAY\nMhib9yajW63oCkluQgJhPr7kPBAnzi1HjqDa56+ep3VZSVi3jtjvvgdAx9YGV39/9GsUvtSqTEwk\nekJ/0m+Kkl1KCezpYoz3N3/ibF7Ig0B6HAQMh6hL4s/VGsOorWBWMbbImTlK6n9xqNB99e3NGNve\njX5NHfJVkFJzUjkbfZbAyEDORJ0hISuhwLmmuqa8adaC0R8fQ6IStH3xFUSWQsnMLVc5eEPs1bQ3\nN2Dd2Na4V69Y3dIHcel8sfsGQffz/u/b1rRi4YBG1LYrv2vLFXL2PNjD+pvrCU8LV2+XSWT0qNED\nnwY+1LeuX27XK4rCBq0ElYqMs2dJ3ryZtOMnQJn3ICc1M8Ni4AAshnvlk6FLzkrGc4snANObT2dC\n41dfHeV5Fl1axNoba5FJZVwafanCHxKqOtpkFUjeuYuYTz8FwHnVKkw8O2g4Ii2aJPvhI+KWLyPt\nYF4CI9HXx3L0KKwnTEBmWbjjTW5SEuG+Y8i+K/ZnWgwdSvV5X/1n+6Djf19F3CJRrkVWrRquG9aj\n55xfVFx+6RJR708gN1mcuM8xlTB/gJS7ThJqmNdgfc/1mOsXIpKfkwHbx8Pdg+LPZk4wejvYrFNy\nGAAAIABJREFUld9NOVmeQ8CFCPWy8TOkEni7YXXGeLjRuoZVkQ8aKkHFrcRboiFB5Bmux1/PJxU0\n3z+XelEQZ6fPP0snlJsNrBZIkSt4x/8iF0LF1pG61UxYN7Y1Dhbl3x/6jCyFkl9O3Oe3Uw/JUYoP\ntTYmeszpXZ8BzRzL7cE0Th5HwO0Att7dSkp2XguKqa4pQ9yHMLLeSI1ZfD4btDLJkbPDLQHZ/l3k\nhIXlO8agUSMsR3hh1qsXUsPC/z+6butKrDyWHm49+F+n/1VG6OXGx6c+5mDoQRxNHDk0uPCHXS3l\nhzZZBZTp6dzzaI+Qk4P5gAE4fPetpkPS8gqQdfMmsUuWkHE6UL1NamKC1bixWPn4omOSpwuqTE4m\nbOw4sm+JkkvmgwZhv3DBfzZRfUbc8p+JX7ECAF0HB1w3rEfXwQFBqSRh1Srili3jmfG5SQ0dHP7Y\nzZKYXawLWQdAq+qtWNltZeF6jyolHPgILj61XdU3B68NUKNsy4W3H6fiFxzKzitRZClU+faNbe/G\n+A41cLIsvdPZv21gu55IZsRp8Trvv6dDnIWkzDawWiAqOZMxay5wLzYdgNY1rFjl0xJzw4rrcT55\nJ5Yv94QQliD2uEskMKqNCx+9VQ9zo/K57t2ku/iH+HPg0QEUKoV6u6OJI6Prj2ZgnYEY6xq/5BUq\nFkEQeBR4gb0Lf6ZT5FX0VXmtMxJ9fcx698ZyhBeGjYvu4Zx8dDKBUYHUMK/BngF7KjLscsfnoA9X\nYq/QslpL1vZYq+lw/vNok9WnRE6dRtqRI0hNTKgTdKZcxMi1/DeQX7xI7OIlZF66pN6mY2WFzbsT\nsfDyQsjOJnzsOLJCxOlV8/79sP/mm0ozHNAkgiAQt2gxCatWAaDr4oLjokXE/vSjeqhCIhWwa6eD\n5U9/IbFwQiWo+PDUhxwJOwJA35p9+brD14VXpAQBgpbA0a/En6W6MOBXaDK0RHEqVQJHbz1hXVAo\nZx8WXLIHODjdk/r25dsbm6vKJSR4L3oTRLeeP96ScrhF/geYktrAahEfOMas+ZvHqWLFvndje34a\n1rRMrkov43FKFgv23WT/9Rj1toYOZnw9sDHNirDgLQ6CIBAcHYxfiB9nY87m29fEtgm+DXzp4tIF\nmVRzLWqqzExS9+8naVMAWTfza7jqurlh6TUciwED0LEo/u9j6eWl/HH9D6QSKedHnq+wobCK4K3t\nbxGTEUOfmn341lNb4KpotMnqU1IP/UXUjBkAOC5bitlbb2k4Ii2vEoIgkBEYSOziJerqKYDU1BRV\nWpr6Z7NevXD43w9VIlF9hiAIxH73PYl+fgX26Znm4thNisFHB8EqT+cxKzeL8YfH80+caIc7uelk\n3mv23osv8s822PUePKs0df0SOswsUtoqRa5g68UI/M6GEpmU+cLjlno1o38zx5e+VmkRBIH7HTuR\nGxeHbod2/DO7D4GRgaWygdUCZx8kMNH/olrDdIyHG1/0aVAhGqq5ShV+Z8NYdPiOWrPVRF/GB2/V\nxbuta5k1U3OUOex/uB//m/7cT76v3i6VSOnq0hWfBj40sysfKb3Skv3wIUmbN5Oyc1e+7zpBKiWo\nekP21/DA9/3BDGzu/JJXKZxDjw7x0WmxjzugdwCNbBqVW9wViVKlpMWGFigFJRMaT2B68+maDuk/\njzZZfYoqK4t7Hu1RyeWYvv02TkuXaDokLa8ggkpF2qFDxC1dVqBHC6DeP9eqpDyRoFBwu3GTfNuM\n7bNw6grSiQcK7TVNzEpk1P5RaiH+he0X0r92/xdf5FEgbB4Fz/r3Wo6Hnj+ATsFq0/3YNNYFh7Lj\nUhSZirxhD1MDGcNbOpMkV7DjsnjdYS2d+GHIyw0Oykr0nDmk7PgTiYEBdc+dRWpgUCob2JrmNat0\n1XXvtWg+2HpN3Sv6ac96TOxYMb+TK+FJzNl5g5sxebqgvZvY80WfBlQzK1sFMDkrma13txJwO4D4\nzHj1dkOZIQNrD2R0/dE4m5U8+SsvBIWCtGPHSQoIQH7+fL59Mjs7LIYNw3jgIDzX3CAhI4dWbpZs\nm+RR4us8THlI/13iZ/6rdl8xuO7gcom/onmc8Zju20Vd1c/bfs4w92Eajui/jzZZfY6ojz8mdc9e\nJPr61AkKyteTqEXL8yjT0rjbqnWB7foN6mM3YwbGnp5VJqlQREUR9cGHZF69mm+7vpUS1zWr0ann\n+cJzH6U8YvSB0aTmpCKTyljZbSWt7Qv+XtXE3oINQyD1qdNU3Z4wZDXoGaNSCZy8G8vaoFAC78Xn\nO62mrTFjPdwY1NyJG1EpjFh1DpUg6qnumdIeI72KXV5N/eswUdPF6ovzqt8x8Sz4OymODayDsYO6\nXaBV9VYY6Za+t/Z1Y/WZRyzYJy4/6+pI+N+Qpgx4o/yr4SlyBd//dZuAC+FqzVRXayMW9G9Ex7q2\nLz+5CMJSw1h/cz277+/O9/9rZ2jHiPojGFp3aOEDh5WEKDu1leRt2wvIThl7tMPCywvTzp2R6Ir9\nuWV1tFKqlLTd1JYsZRYj6o3gszaflc8bqWCuxl7F+6DoWPU6ym69jmiT1edIP3WKiHdFz3aHH77H\nvF8/DUek5VVElZlJxKT38lUcpCYmqNLT1T8btmiB3ayZGLVooYkQK43Uw4eJmfu52pVG3yIXiY6K\nrASxumzQqBEua9egY/rim9jFxxeZeGQiCpUCU11T1vdaTy2LWi+5aAxsHApPrgOgtH+DLXV+YuWl\nVPXgyzO61LNjjIcbHWrbIJVKSMzIodfSQB6nZqEnk7JnSnvqVa9YDVcQhzjvtm0HublYjh5N9bkv\nt5MtiQ2sp5MnHR07arQSV5GoVALfHrzFqsBHgLgM/9voFnSoU766loIg8OflKL45cIuEjBxANBZ4\n781avPdmrVL3wwqCwOXYy/iF+HEy4mQ+pQgQtXm39tla+JBhJSCoVGQEnyVpcwDpx0/kyfYBUnNz\nLAYMwMJreKHSdKHxGbxZCker5xmxbwQ3Em7Q3K45fj0LthK9ijzfvrC973bcrV5ujKKl7GiT1ecQ\nFArueXZEmZyMcaeOuKxcqemQtLxiqLKziXxvMhnBwQAYd+iA04qfERS5JPr7kbhmbb6k1bijJ3Yz\nZmDQoORf4q8yquxsYr//nqRNAeptlnWzsGuaiESmR3TU26SeFgfSDJs1w/mPP166UrH/4X4+CfwE\nEKeeN/Ta8HKR7axU5BtHYRRxGoAwlR1jFLN5JNhjoi9jSAsnfD3cqGGTd01BEJjgd5Fjt2MBWDig\nEaPbupb6d1BSwnzHID9/Hl1nZ2od/qvYlXdBEAhLDVO3C1x8cjHflPgz3Mzc1L2uLau1RE/n9W9H\nyc5V8tG2f9hzLRoAW1N91o1tRUOH8q0+3o9NY+6uG5x7mKje1qG2DfP7N6Smbel6hnNVuRwJO4Jf\niB8hCS+3DrUzsmN5l+U0sK6874ncpCRSdu4iactmFGHh+fYZNG6MpZcXZr16vlB26hmj/jhH0P2i\nHa1exFfBX7Hj3g5MdU0JGhH0WqxIrbuxjp8u/QRA0IggzPQq/oG3qqNNVv9FzJdfkbxlC8hk1Ak8\n/UJNTS1VD1VODpFTpqilrIzatcX511+RGuT1r+UmJZGw6g+SNm5EyM5Wbzfr1RObqVNfKJz/OpH9\n8BFRs2aRfVvUJ5WaGGHfIgEz+ySQ6MAwf4TabxM1axZpR44CYNSyJc6rfn/pjW/ltZX8fPVnABpZ\nN2JNjzUYyvIfr1IJBN6PZ23QI87cieFb2R8MlYkJawqmnGm1nI5d+2BqULBK9fwycq/G1Vkxsnml\n3hgTVq8h9n+ilmTNAwfyCaSXBLlCzvmY8wRGiVXXJ/InBY4pygb2dSA1S8Gk9ZcIfiCqN9S0NcZv\nbGucrcqv9SEzR8ny4/dYFfgQhVK8Fdqa6vN5nwb0bWJfqr+P9Jx0dtzbwcZbG4nJyFMP0JPq0bdW\nX/6892eB6iqAhb4Fp4afqlBxeUEQyPrnH5I2BZB68CBCTo56n8TAALPevbD0GoFh4+IPOhXlaFUU\nzztBHRp8CEeTihl0LE++u/AdG29txEhmxLmR516LBPt1R5us/ouMCxcI9/EFoPpXX2HpNVzDEWl5\nFRBycoicPoP0EycAMGrVCuffV74w+VI8eUL8r7+SvH0H5D7VIdTRwWLQQGwmT0bX/vVLHkA00Hi8\nYAGCXFxuN2xYF8f619CVJQESGLgSmoqfGSEnh8ip00g/dQoQe96cfv31hbJwgiDwRfAX7Lq/C4Au\nzl1Y9OYidKQ6ZGTnsuNyJOuCQ3kY9/wEvcAiu4MMSt0g/igzgEGroEH+Fp7rkSkM+jUIhVLAydKQ\n/dM8K1SPszCy79/nYZ++ANh9MhvrMWPK/JqCIHAv+R6BkYEERgUW2wZWV6qZJefi8iQ1C981F7j9\nWJw+b+5iwWrfVlgal1+1+PjtJ3yxO0StEiGVgHdbVz542x2zQh52iiImPYaNtzay494O0hV5qyuW\n+pYMrzec4e7DsTG0oYlfk0KTVQBXM1eG1R1G/9r9y7V3VSWXk7J/P0kBAWTfvJVvn16NGliO8MK8\nf390zEt+zecdrUozaHXpySXGHBoDwNLOS+ni0qXEMVQ2049P53jEcWqZ12LXgF2aDqdKoE1W/4Wg\nUnG/cxdynzzBqFUrXNf7azokLRpGUCjyVQkNW7TA5feVSI2LHsDLCQsj7ucVpO7bx7NpDYmuLpYj\nR2D97rvIrKwqNPbyQpmewZMF80nZnSfcbT16CLa6m5Fkisvq9FkMLcflO0+VnU3k5PfJCAoCxLYI\np59/RvoCxQSFSsHko5M5FyNqtPav4YV+6kC2/h2hlioCMNLTYXBzJ3w9XEVry8v+sHcGCEpAAj2+\nhbaiFFZaloI+y88QliBHJpWwdVI7mrtU/oqJIAg86NoNRXQ0xh7tcFmzptyvUVwb2HYO7fB08qSD\nY4dXztP8fmwavmv+JipZTCK7N6jGMq83MNQrHzm46ORM5u0N4a+QvIp0Eydzvh7QmMZOJU/WQuJD\n8Avx43DY4XwPCm5mbng38KZfrX759EPfOfyO+u/7RRjoGNCrZi+Guw8vU2tA9oMHJAVsJmX37nyy\nU8hkmHbtiuUIL4zatClzZbAsg1ZpOWl4BIgJ7uRmk3mv6Usk7F4Rhu8bzs2Em7R3aM9v3X/TdDhV\nAm2yWghPvvuexHXrQCKh9skT6FarpumQtGgIITeXqA8/Iu2QaKdn2LQpzqtXl1gpIuvOHeKWLiP9\n+HH1NqmREVZjfLEaO/alA0iaJuvWLaJmzFRLdelYW+PwxQeYhMzNm8rvvgDaTyv0fFVmJhHvTkJ+\n4QIAJl274rRksXqi+N+kZqcydM8oouWh4vUf90ORJN7MnK0M8W3nxtCWzgUro/eOwjZfyHla1Wr7\nPsJbC5i+Ja/n8ZOe9ZjU6SXDWxVMzLx5JAdsBl1d3M+dLdYDT2kpygb2GQ2sG6irrg2tG2rUBvZi\naCLj/S6Skin25I5q48L8/o3QKQcNVYVSxdqgRyw5eg/5U81UU30ZH/dwZ2Qb1xJdQyWoOBVxCr+b\nflx6cinfvlbVW+HbwBdPJ88XLuk3X99c3XdsZ2THzv472XN/D1vubCE0NTTfsU1sm+Dl7sVbbm+h\nr1O0WY2Qk0PasWMkBWxWf+aeIatWDYthQ7EYMhTdanbFfr9F8fyg1dj2bnzZt2GJzu+xowdR6VF0\nc+nG4s6Lyy2uiqLTlk4kZiUyuM5gvvL4StPhVAm0yWohZF6/TuhQUTetvJbrtLx+CEol0bM/Eaui\nFG+yvSjkV64Qt3hJvpuIjrk51hMnYjlqZL7+V00jCAJJGzcR+/33CArxxmrs0Q6Hzz9EtncUJIqV\nFDrNhs4vl5xRZWQQ/s5EMi9fBsC0Rw8cf/wfElmeZJQ8J5edV6JYFxTK/cQIjGr8glSWhiBIqKF8\nn2ntBtClnt3Lk4qYa6JSQLpYNYuwf5tuj0aQjR4d69qybkyrChGPLy5pJ04Q+d5kAJxW/Ixp166V\ndu1/28Cm5qQWOEaTNrB/hTxmWsAVsnPFafQP36rL+51rl0s/4KWwRObsvKFuKwDo38yBOb3rY2da\n/M9cZm4me+7vYf2t9YSl5uks60h0eNvtbXwb+hZZCX1erslAZoBfDz/1OSpBxfmY82y5s4UTESdQ\nCXmT+Zb6lgysM5ChdYfiZFqwL1QRE0PS1q0kb9+OMi6/dJuxhweWI0dg8uab+T5z5UlZBq2mHZ/G\niYgTOJs6c2DQgQqJr7zIVmbTckNLAN5v9j6Tmk7ScERVA22yWgiCIPDg7R4owsMxaNyYGtu2ajok\nLZWMoFIR89kcUnaJ/Uj6DerjunZtqXq6Cry2IJARHEzc4iVk3bih3i6zs8Nm8mQsBg96YdWxslAm\nJxM9dy7pR4+JG3R0sJ02DetRg5D494PYp9PNbSfD298U6SQFonxT+LjxZP0julaZ9e2Lw3ffEpWa\nzfqzYWz+O0JdUQMwMInGwHklSrIxlBmytsdaGloXo2KTHC5qscbfAeCCyp1P9T5ly4xe2Jho1kZZ\nlZnJ3TZtEXJysBg2DPv58zQSR64qlxvxN8Sqa9QZbiXeKnBMZdrArj8Xxpe7b6ASQEcq4btBjRna\nsuxSXEkZOXx38DZbLkaot9W0MWbBgEa0r1389of4zHgCbgew9c5WkrOT1dtNdE0YWncoI+uPpLpx\n9WK91p3EOwzZOwSAeR7zGFRnUKHHPc54zLa729hxd0e+dg4JEjydPPFy98LDvh2ZwedICggg/eTJ\ngrJTAwdi6TUcPTe3Yr/X0lKWQasVV1fw2zVxOf3cyHMY6766GufhqeH03tkbgAXtFzCg9gANR1Q1\n0CarLyB26VISfhU/PLX+OoSea+VJ3GjRLIJKRcwXX5CyfQcA+u7uuKxbW+7KEIIgkHbkiOiG9SDP\nvUjX2RnbaVMx690bibTiJoNfhPzyFaI+/IDcaHGSWWZvj+NPP2LUoDb494dosTpKcx/ou6xYieoz\nlCkphI0dqx7yCGnSkdk1+6Ak7306Whji3c4Vr1bOXE0IZvqJ6agEFTaGNmzstREHE4cir5OVmsDd\npX1pohSTarlZTYzG7gRLt2LHWlGEvzORjMBAZNWrU/vE8VdikjhWHsuZqDNF2sA+S1zb2rctFxtY\nQRD48fAdVpwQ//6N9HRYMao5nd3LtkStUglsvxzJtwdukSQXH4D0ZFKmdK7Nu51qoi8rXtXvXtI9\n/G/6s//h/nxyYQ7GDoxuMJpBdQaVOLHaemcrC84tAGBX/10v1xQGFEoFR8OPsvn2Zi7Hip89E7lA\n5+sCPa9KsUnMzXe8QZMmWI4YgVnPHpW6UlOWQaujYUeZeXImAOt7rte4xezLuBBzgfGHxwOw6q1V\ntLVvq+GIqgbaZPUFPD+5azt9GjbvvfpN31rKjiAIPJ43j+TNWwDQr1MbFz+/Ch2EEpRKUvbsJX75\nchTR0ert+nXrYjtjOiadO1dKQiOoVCSs+oO4ZctA+dQHvVtXHBYuRMdIDzYOgTBxUIpGg8Wp+xL2\nN2YplOwLvIXl3Bk4JEYBsN+tHT83HUSbmtaMbe9Gt/rV8nmub7y1ke8ufAeIU+3+Pf0x1Xt5K8ac\nndfZdv4BP+n+Sl+dp8MsxnYwcgs4Ni9RzOVN4voNPPn6awBq7N6FgfurJSheWTawCqWKT3ZcV9ve\nWhvrsWZMK5o6l6314M7jNObuus7foUnqbZ3q2jK/f0NcrYtOLAVB4GzMWfxD/AmKDsq3r7FNY3wa\n+tDNpRsyaemW0+ecmcOeB3sw1TXlzIgzxZaqEgSBO4F7CV33K/bnQ9F7TvQhWwbh7dyoOeY9GrXX\nnJlNaQetIlIj6LWzFwBz28xleL1XV4Vn9/3dzA2aC8C+gftwNdMWsioDbbL6Eh72H0D2nTvo1a5F\nzb17X4kKiJaKQxAEniz8mqSNGwHQq1kTV38/ZDaVMy2tyskhees24n/7DWX8c37hzZphO3Mmxm1e\nYkNaRnLj4oiePZuM4LOAqFhgN3s2lqNGIlEqYPNIuH9EPLhuTxi+HkrguBOdnMmGc2EEXAgnSa7A\nPDuNHwJ/xSVdVBIQBg2n/tdfvvAz9v2F79lwS5Snamvfll+6/fJC+aX9/8Tw/iaxAtXKxZwttQ4h\nPbtc3KlrBEP9oO5bxY69vMkJC+PB2z0AsJ01C5uJ72gsluJQUhvY1vatC+jj/puM7Fze23iZ03dF\nS09XayP8x7UuVjL5IuQ5uSw9do/VgY/IVYm3tWpm+nzRpyG9Glcv8vs7R5nDwUcH8b/pz92ku+rt\nEiR0cemCb0Nfmtk2K/N9oO/OvoSmhuLh4MHK7kUbz6gyMkjZt5+kzZvJvpW/XSPWVo/9TXM51ViC\n3ECMq6F1Q4a7D6dnjZ75VAgqg9IOWqkEFe02tUOeK2do3aF80e6LCoyybDyvB/33qL8r/XdcVdEm\nqy8h/vdVxC1aBLyaFRAt5YcgCMR+9z2JfqLdn56rKy7+/uU6MVtcVHI5ies3kLB6tdrGFMQhCduZ\nM0sk2F0c0oOCiP54NsoEsS9Oz9UVx8WLRNctZS5sHwu3nkpW1egII7eBbtFf0IIgcCksibVBoRwK\neYxSlfdVU93MgPH1Tej0yxcoI0T3HOsJ47H94INCkwGlSsnMkzM5ESHq3A6sPZB5HvMKHBuRKKfX\nskDSsnIxN9TlwHRPHC0M4cIqOPARIIjGBX0WQYsxpfhtlQ8PevQkJzQUw5YtcNuwQWNxlJRi28Da\nt8LTsXAb2Li0bMat+5vrUSmAKBu1ZkyrMvUTHw55zLy9N9VyV1IJjPGowczudQo1iHielOwUtt7Z\nSsDtAOIy49TbDWWGDKg9gNH1R+Ni5lLq2J4nOSsZzy2eALzX9D0mN5v8wmOz79/Pk516zhUPmQzT\nbt2w9PLCsHUrLsVeIuB2AMfDj+eTzjLTM2Ng7YEMcx9WbvEXh9IOWnkf8OZq3FWa2DZhY6+NFRxl\n6XnmuGWpb8lpr9OaDqfKoE1WX0JOZCQPunUHwHriROxmzdRwRFoqAkEQiFu0iIRVfwBiz6jren90\nqxdvYKKiUKakkLB6DYnr1yNkZqq3m3bvju30aejXrl2m1xcUCuKWLSfhjz/UGrDm/ftR7fMvRGku\nlQp2vw/XNoknOLUG752g//JexexcJXuvxbAu+BE3ovJPnLdwtWRsezfeblgdXR0pipgYwrx9UESK\nSY/N5PewnVa4BJZcIWf8X+O5kSAOpU19YyoTm0xU71coVQz97SxXI8QBmJXeLXi74XP/h7f2wY7x\nkPu0Muj5IXSZW6Ke2/LiybffkujnDzo61A0OKpfBvcqmNDawNjr1meB3lfBE0VTiTXdbVoxsjrF+\n6ZbUI5PkfLUnhKO3YtXbmjlb8PXARkVasoanhrP+5np2P9hNZm7e58vW0JaR9UcytO7QchXmBzgd\neZr3j70PwG/dfqO9Y/t8+4WcHNKOHhVlp/7+O98+WfXqT2WnhqBrV/AhOlYey467O9h2d1u+pBug\nvWN7vNy98HT0rHBpstIOWi08t5Atd7ZgKDPk3MhzFerkVRYmHZ1EUFQQ9a3qs7Wvdvi6stAmq0UQ\nOtyLzGvX0HVyotaRw9pWgP8gzw/T6To4iImq46tj+ZcbF0f8bytJ2roVnkpIIZVi3q8fNlOmoOdU\n8lgVUVFEffAhmVevAiAxNKT6F19gMfDpZKsgiJXIv1eJP1dvDL77wPDF/YRPUrPYeC6MTRfCiU/P\ns3HU05HSp6k9YzzcaOJU8PycyCjCvL3JjREHumxnTMdmUuFyMPGZ8Yw+MJqodLHf9TvP7+hdU5zM\nfb5fboyHG1/1K2QJMuJvCBgO8qfT1U28oN9ykJWfM1JxSA8KImL8BAAcFy/CrGfPSr1+RSBXyDkX\nc06dvBZmA4tKD0VGLZTp9ehR800WDX4TXZ2SJyU5uSpWn3nE0mN3yVKIE/BmBjI+6Vkfr1bOL5Qn\nEwSBK7FX8Avx40TEiXy6s3Us6+DbwJeeNXqip1Mxfw/Lryzn939+B/J7yiuio5/KTu3I1wIEYNy+\nvSg71alTsWSnFCoFJ8JPsOXOFi48zq+z6mDswFD3oQyqMwgrg4rpwy/toNXzg2evci/owN0DuZ98\nn87OnVnWZZmmw6kyaJPVIkj0X8+Tb74BwG1zAIbNXt0pRS0lJ27FCuKXi/1HsurVcd2wHj2nknlb\nVxY5kZHE/7yClD178iRqdHWxHDYMm0nvIrO1LdbrpB45QsycueoWA313dxwXL87vVX90HpwRW2Cw\nqQtjDoBJ4a9/JVxc6j9wPUbdKwiix/roNq6MbOOCrenLl3hzwsIIG+1NbpxYEbL7+GOsx40t9NgH\nyQ/wPuBNmiINXakuq95aRVqyM2PWipWoBvZm/DnZ48XLjwkPYMNgSHok/lyjk9iDa1B51U1VTg53\n27ZDkMsxHzAAh+++rbRrVwb/toG98uQKKlQFjqtjWUcc0iqBDez5hwnM3XWDe7F5S+ODmjvyWa/6\nL2wlyFXlcjT8KP4h/lyPv55vX3vH9vg28KWtfdsKL0ZMODyB8zHnqWVei539/iQjKIikTQGiJfFz\nslM65uaYDx6M5fBhZVKieZD8gC13trDnwZ58Cg+6Ul3ecnsLL3cvmto2Lff3XZpBq3/i/mHUgVEA\n/NTpJ95y01xf+ctou6ktGYoMRtQbwWdtXq4vraX80CarRZAbF8e9Tm+CSoWltzfV52j/OP8rxK/8\nnbjFoluKzM4O1/X+r4VEWfb9+8QtXUbakSPqbRJDQ6y8vbEeP+6FS8qq7Gxiv/+BpE2b1NssR47A\nbvZspPrP3eQDf4Jj88V/W7jA2ENgnr96m5Or4sD1GNYGh3ItIjnfvqbOFoxr70bPRvboyYpfNct+\n+JAwbx9172y1OXOw8h5d6LHnY84z6egkclW5mOqakxX2HokpFhjp6bBvagdq2hYhq5SsKag2AAAg\nAElEQVQRD5uGQ9RF8We7hjBqW4H3WZFEvD+F9GPH0LG2pk7gaY3IlFUGmy+EM2fP30gM7yIzuYO5\n9QPkyuQCxxVlA5uQns23B2+z/VJen2xtOxMW9G9Eu1rWhV47Q5HBjrs72HhrI9EZeUobulJd+tTs\ng08DH2pblq2dprgoVUo8AjzQSc1ganRDWp1LQhERke8Yw6ZNsRjhhVmP8pWdylBksP/hfgJuB3A/\n+X6+ffWs6uHl7kXPGj0x0jUql+uVZtBKrpDTdlNbBAQmNpnI1Demlkss5cnz1rCzWsxibKPCH6i1\nlD/aZLUYhI0di/zsOXRsbahz8iQSHc3ZEWopHxLWrCX2hx8A0LG1wdXPP39l8VXDvz88PCX+u2Yn\n8NlN5vXrxC1eQkZwsPowqZkZ1uPHY+U9GqlR3o0n++EjombNIvv2bfVx9gsXYPbWv6oX53+Hgx+J\n/zapDuMOMfrPWIIeiEuTrdysaF/Lhg3nw4hLy1afJpNK6N1EXOp/w6X0erRZd+8S7uOLMllMZqrP\nm4fl8GGFHvu8hIwqxwp56GR+GtyeQc2LWRnPkcOOCXBnv/izqQOM3g7VSmYVWVqStmzl8ZdfAuC2\nbSuGjRtXynUrC0EQWHbsPouPipP1+jIpy0e8QbcGdtxKuMXpqOLZwLa370BIqBk/HLqnNo0w0JUy\ntUsd3vGsWegD0eOMx2y8tZHtd7eTrsirwFroWzDcfThe9bwKJMMViSAI3Dm9h1PLPqXtbSGf7JTE\n0BDzPn2wHOElDjVWcByXYy+z5fYWjoQdIVfI02g11TWlf+3+DHcfjpu5W5mvVZpBq2dKCW86vcny\nrsvLHEN5cy/pHoP2iCYOP3T8gZ41Xv/2ndcFbbJaDJK3bydm7ucAuKxdg3G7dhqOSEtZSPT358k3\n4rKrjpUVrv5+ZR5WqlD8+8PDk/m3mTrAiABwaEbGufPELV5M5rVr6t06NjbYTJqE5bChpBw4wOP5\nCxDk4lCLYdOmOPz0U8Fe1ysbYffT6WQjaxhzgNF7UjhzP38P3fNYG+sxqo0Lo9q6Us2sfCpBWTdv\nEjZmrLpNwf7bb/N6af/FmJ0LuJQqDjmYS2pzdGRAyaRkVEo4ODuvN1ffTGwJqPlmGd5B8VDExHC/\ncxcAbKZMwXbK+xV+zcoiV6ni8903CLggVg4tjHRZ7duKFq4FH2SKYwOryjVGmVGX3HR32jt4sLBf\nG5ytClYBQxJC8A/x53Do4XyJmJuZG94NvOlbq2+RslrliSojg5S9+0TZqacPis/Qq1ULSy8vzAf0\nL5OFc2mJz4xXD2T9u7+4rX1bvOp50cmpU6n1ZJ8ftFo0rGmxHiI/OPkBh8MOY29sz+Ehh0t13Yrk\n+QG5V9284L+GNlktBsqUFO528ASFAouhQ7BfsEDTIWkpJYmbNvFkvvj/p2NhgYufHwbudTUc1UtQ\nKWG+NRRSecLUAT4QdRcFQSD9xAniFi8h+969F76c9TsTsJ02raCda8hO2D4OBJWYsPnuBYdm1Ph0\nP4V9Q8ikEr4f3ITeTexL5AFeXDL/+YfwseNQZWSAVIrDDz9g3qd3vmP+Dk3E6/ez6Fbbgq6FeFPs\n7tqdH//P3nkGNlX3bfhK0nTvXVpaoIO99ygge28oRRABcYsCKjyPoKjoqyIiIoogyN57L1kte2+6\noBQ66N4j67wfTps0dC+Kj72+0OSsf0La/M5v3He3H8s2SSwIcO4XOJar7SiVw9BfofnYyno5RfJw\nyFBygoMxbtaMulu3VPn1XgRZCjUfbLqmndB3tTZh7ZR2eJbUmoHOBvbvx6fZHXScZHV4gX2et4H1\nsfEhIDKANXfXcOXZFb19Wzu1ZmKjiXSr3e2FTpfnhIToZKcydL2iKilcayBn5KzlmLVr/1IM7Ko0\nKk4/Pc3mB5u5EH1Bb5uTqROjfUYz0mdkmTPR5Rm0Wn5rOUuuixnVwLGBla7GUFHyD4EdG3Ws1Ba7\nNVScmmC1lDx5513ST55EamWFT8AZJIYvdnq4hoqTv+wqtbLCY/VfGDdsWM2rKoLYB6Jk1M0tkB5T\n+D75gtU8BLWa1IMHifrk0wK7W/v54TyvEOH94KOi6L9GKYrmT9gF7qKFYN3ZBwoLk3G2NObCf3uW\n55WVmsxr14l44w0xIyyT4frTT1j2FdsWkjMVDFgcQFRKNoYGGlq228a9JDFgndR4EjPazCj7BW9v\nh93vgDpXyaDHXPCdWaXSVrELF4qSaRIJ3oEBGNgV3nv5TyExQ8Hk1Ze18mGNXCxZPaktjqXMuguC\nwOE7omZqTGo2EoMU5BbBeLo/IVFzl0xVQRvY55FJZPSp04eJjSbS2P7FtHSAKDuVeuwYyZs2k3lF\nP2g2cHFhf9McdtRPobFP51KZAVQHj1IesTVoK3tC95CmTNM+byA1oLd7b/wa+NHKsVWpg+yyDlqd\nfnKa90+8D8Cqvqto69y2nK+kavjl2i+suL0CqUTK1fFXy511rqHs/G929FcBlgPFrI4mJYX0wLMl\n7F3Dy0byjp26QNXCAveVK1++QDUrCS7/CSt6wG/t4ezi4gNV/00Fn5dKUaemQiHDOslbthA+egzp\nZ8+ivUd9FABbJ4iBqswQxm7UBqqCIGBrVvCmzNnSmD8ntin3yywtpq1aUnvZ70iMjUGtJnLmTNJO\nnEAQBD7ZfouoFFEvdc7Apizvu4S6VmLP8V93/2JrUDn0D5uOEgP1PFWAE1/D/o9EY4QqwrxrV/EH\nQSAjMLDKrvMieJKYycjfz2kD1S5e9mx5q0OpA9WIhEwmr77MOxuuEZMq/t+2cq3DntdmcMh/JYFj\nA1jZZyWD6w0u9jy1zGvRyLYRxgbGvIhcjDIykthFPxPySg+iZn6sF6ia+fri9ttSHPZvY1XrVFLM\nJTR3aF7layovda3qMqvdLI6PPs68jvNoYNsAELOvh8IP8frh1xm5byRbg7bqqQsUhX9bnRnBxksR\nJe5f31ZnvBOUGFSOV1C1xGSIf48dTR1rAtUXTE1mtZRoMjMJ7twFISsLy4EDcV34Y3UvqYZSkrJ3\nL1GzZoMgIDUzw33VSkyavyRfGGoVPDwJNzbAgwO6rF4eFrWg9US4sgrSn+meey6jCmK7SvScOaQd\nOy4+IZNh9+ZUpKamJP65EnVKinZf03btcBw/AJMrM0CRLro6+a2DBrpS+9bLT/h0xy29a7yIjOrz\npJ89y9N33kVQKJDI5dx/by7Tw8V+xb6NnVg2vjUSiYSnaU959eCrJGYnIpVI+bXHr/i6+Zb9grEP\nYMMoSMmd1PbuC6NWlWiGUB4EpZLgTp3RpKVhOWAArj8trPRrvAjuRKbw+l+XiU8Xh+6GtajFD6Oa\nl0oNIkelZsWZhyw5EUqOSpRvsjaV85/+DRjdWqeZGpYcxtp7a9kfth+FRlHcKbW4mrvSxbULXd26\n0ta5baX1qwpqNRmBgSRt2kz6mTP6slPW1liNHIGNnx+G7mKwVpIZwMuKIAjcjLvJlqAtHAk/omf6\nYCY3Y4jnEPzq++Fp7VnkOcoyaCUIAl02dyFVkcowr2F83fnlarmbfGQyl2Mu08KhBesGrKvu5fyr\nqAlWy0DkjJmkHjyIxMQEn7OBetPWNbycpBw4IJbENRqkpqbU/vNPTFu1rO5liQHRjQ1wa2vh2VPP\nntB2ihgoyQwg6gZs8he35Q5W5Sfz2nUiP56JKkoU1jdwccH1xwWYtm4NgDotjcS//iJh9RrtoBWA\nuWsWDk3TMZ78OzQbrX0+PD6DAb8EkKlQY2MqRy6TIpVI+HNiG5q4vvg+svTTp3ny/gegVJIjNWBe\nxynEeTfj4DRfrEx1/be3424z+chkstXZmBqYsqb/Gm12qEykRsPG0RCTq8np0kKUtjKvfPvdp9On\nk3boMFJLS3zOnS2V8PvLxJngON5Zf5UMhTji/la3eszq26BIYf78nAuLZ+7uO4TF6bJ0Y9q4Mbt/\nQ2zNDBEEgQvRF1h7by2BkfqZ5yZ2TZjYeCK9PHqh0qgqbANbGlSJiSTv2EHylq1a17U8TFq0wMZ/\nLBb9+ulLwVG0GcA/iYSsBHaF7mJb0DY9GTCAts5tGVt/LK+4v1JAK/fArWje23gNKN2gVV5A+DI6\nRA3YOYAnaU/oV6cfC7otqO7l/KuoCVbLQNqJEzx9V7w7rrXwR6wGDizhiBqqg4jJk8k4nzsokPvx\nlpiY4L78D0zbVmMPVGYi3NkBNzZC1LWC203toeV40bPetnQyWoJGQ8KfK4lbvBjUYrBg3rMntb6Z\nj8y6oFuUKiGBhMU/kLR9D4ImN5iQgOWgwTh88D6G7u4FbEvXTm5HV5/SGQ5UJbGHjhA7YwYyQUO2\nzBC+X0TLQT0K7Pf347+Zfmo6AgKOJo5sGLihfIMQOWmwdSKE/S0+tvaA8TvA3ruCr0Sf5F27if7P\nfwDw2LBee4Pxohj/50WtNFlnT3vWv9G+1MfuvPaUT7ffQqURkEjg80GNmNS55M9uXFoO3x68z67r\nkdrnfJzM+WZ4U9rWsUWpVnIo/BBr764lKElXDpYg4ZXarzCx8URaOrYstHeyLDawn6zPwP5eDBLA\nrGMH3FetKvR8Wdevk7RpM2mHDyModeeSmJrqZKeKaSuaenQqF6IvUM+qHnuG7Snx/XmZUWvUBEQG\nsDloM2cj9VviHEwcGOUzilE+o3A0FW/syjpo9f2l71l/fz2GUkMuvnrxpSm3awQNbda3QalRlr8v\nvoZyUxOslgGNQkFIF180qamY9+xJ7aW/VveSaniOiMmTyTh3vsDzzvO+wGZs1U93F0CtgrATYhY1\n6GDBMj+AR2doMxkaDgaD4p2e8qOKjyfq01lanVWJXI7jp59iM/7VogcgkiNgVX+U0dHE3zUnOdwc\n8lynDAywHjWSnY37suBqIlB6Qe8XwYwtN4jbf4BZlzcgQ0Bqaiq2dBTiKrf27loWXBEzHz42Pqzp\ntwZzw3KU8dVKsW/1+nrxsYkN+G/W9vVWBqr4eEK6iO0Kdm++ieOM6ZV27pIY/+fFAtJkeT3JxWXQ\nBUFg2emHfH9YlGMylElZ5NeCgc1cir2eWiOw8VIECw4/IDVb7AU2kcv4qJc3k7vUJVOVxrbgbWy8\nv1HP395YZsxQr6FMaDShzDacRdnAztmkplm4/tef0s4S28ULcGvTFXV6Bqn795G0aTM5Qfr9k4Ze\nntiM9cdq6JASZafUGjWdN3cmQ5nBcK/hfNX5qzKt/2UmIjWCrUFb2RW6S09yTCaR0cO9B2Prj6Wt\nc1u+O/xAO2h1dHpXfIoZtNoVsovPz4nKHLuG7Hphpg0lEZ8VzytbXwFgdrvZvNrw1Wpe0b+LmmC1\njER99hkpO3YikcvxPhuIzPKfV875X+Z+w0YUprVk4OSE9+lTL24hsffFDOqtLbpe0/wYWUELf2g9\nCRzLXqZOP3uWqFmztT7ihh4e1PppISaNiwks02Lgr/6QKH5p0G02OR5jiVvyC2mHDmt3y5EasK9e\nZ651HsKmj/tWiTRVWdl+9SkfbxN1ZN9WBDP00AqxB9nCAve//sKkif7rFgSBby9+y+agzQB0rtWZ\nJT2XlMrOswCCAKd/gFOi7TIyIxixHBoXrv1aHh6NGk32nTsYNWhAvd27Ku28JVGUNFlxvclqjcBX\n++6y5vxjACyMDVjxWhs61CteyeBOZAqf7b6j53jWp5ETXwxpjEYaz7r769gdupssVZZ2u72JPf4N\n/BnjMwZr44KVgrKiUasJCTrH3UuHaPDdLgq7pUs2hQfNbWhzPR2D7HwZWbkcy969sPH3x6RNm1JP\nxAcnBTNy70gA5nWcx0ifkRV+HS8b2apsDj06xOagzdxLuKe3rZ5VPXq7DefHnRagMS7xBvhewj38\n9vsB8J3vdwys93JUMO/E38H/gNiK9fMrP9PT/cX27v/beTny6/8grAYOJGXHTgSlkrRjx7Ae+b/3\nh6eGcqIt82+AqOuF71OrlZhFbTISDMve8yyoVMT9soSEFSu0QbnlkME4f/4FMnOz4te2brguUO34\nPnSfjZFEgtuiRWRPnUr0wkVknw3ESKNiVOhpRkVfIc0qHPlrE4s/dxUTFpfO3N13ALA3N2Tyh+9g\n2Nmd6M/moElLI2LKFDzWrMa4gS7ol0gkzGo3i+iMaE4/Pc3ZqLN8e/FbPu/wedm1LSUS6D4LrNxg\n3zRQ58C21yH1G+hYOUL+5l27kn3nDjkPHqB89gy5k1OlnLe8JGTkcCcypUB2NVupZvqWGxy6I/ZZ\nu1gZs3pSO+o7F50pS8tWsvBoMGvPh2uT+K7WJnw5pDH29tH8eP2//B3xt56LlZe1FxMbT2RA3QEY\nysonE6hKSCAnJISc4ODcf0PICQlBk5lJcTog1pnQ4XyS9nGClZSH3b2wGTWKjk36Y1pGvdGbcTqz\njpdZCaAiGBsYM9x7OMO9h3Mn/g6bHmzi8KPDKDQKHqY85I+UhVj5GJGd3IIdt32Z1a9BkTfBntae\nyCQy1IKaoKQgBvJyBKt5SgAALmbFVxBqqHxqMqtlRFCrCenWHXV8PGadOhba41RD9VFYG4CBkxNu\nvy0tPutYXtQqsafxxgYIOlR4mV9uCk1HQ5tJUKv8w13KqCgiZ35M1nUxEJaYmOA8dy5Ww4cVH4Bl\np8LaIboAutVEHm9PQfHoEVIrS2SWVsgsLbmepCYmNpn6yU9wzNJlv6QWFtj4jcHazw8DB4dK9Swv\niWylmmFLz/IgRtR8XDelHb7eYv9s0qZNxHwpllRlNjaiE5m3fj9ppjKT1w+/zv1EUT1heuvpTG4y\nufwLCv1b7GNV5GpQdngX+nxTqFRYWci6eZNwP7FNxfnrr7AZPbqEIyqHwtoA8pBIwK9NbWb2qY+D\nhREpmUqmrr3CpXCxRaS+kwWrJ7fFxarwCXtBEDhwO5qv9t0jNtea10AqYXIXD5r6RLA5eD234vTV\nJjrV6sTERhPpWKtjqW8q1OnpYjAaEkJOSKg2OFUnJpb2bSi4dgncqCvhaCsJ1zwlCPmGxRrbNdYa\nEjS2a4xMWnzlYU7gHPaE7cFCbkGgf+ALNSeoTpKzk9kdupstQVsKDLx5mDbhvTav0cu9F3JZwWrH\n8D3DCU0OpXOtzizrvexFLblY1t9bz/eXvwfgtN9pbI1tq3lF/y5qgtVyEDP/G5LWrwepFO/TpzBw\nqP7hkxp0hHTrjuqZrvRed89ujOvXL+aIcvDsnk60PyO28H0cG4lZ1GZjdNqd5STt+HGi/vuZ1oLU\nqH59XBf9hFG9esUfqMgUZZge5w5CNBkFI5aTcugwUTM/LvM6DD09qbdn9wubWP98zx3W5pab3+nu\nyax++i0TiWvW8Oz/vgNEi1mPdWsxqqs/4BObGcurB1/VZkYWdFtAvzr9yr+o6FuwYbROxaHhELEt\nQF5+WSRBrSakiy/qpCQsevfCbcmL80Xv8O3fWl3TwrAwMmBkazdOBcUSniAqSbSva8vy19pgZVJ4\nW0V4fAZz99whIEQXCLeua0rnFmEcfbqdyHTdYJVcKmdgvYG81ug1vG2KHl7TKBQoHj7UZUlzg1Jl\nVFSRx5QVmY0N1iNHYO3nR7qDWYk2sDZGNnR27Yyvqy+dXTsX6riU53ffqVanl9YMoCrRCBrORp5l\n4/1NoqKDRBdy2BnbMdJnJKN9RusNQc46M4uDjw5ib2LPyTEnq2PZBVhweQFr763FSGbE5VcvvxTu\nY/8maoLVcpB5/TqP/ccB4PTZZ9hOGF/NK6ohP1l37/LkzbdQJyQAYNapI7VXrqz4H5fSlPllhtB4\nuBik1m5fYfcjTU4OsT8sIGnDBu1z1v5jcZo1q+QMpypHdKYKzdVdrT8AxqwFmRxBpSK0V29UMUWY\nDhSDYZ06OHw4DYu+fZFUMKNYHIfvRPP2elE1oZW7NVve6ohcVvB68StWELfwJ0DMonusW6vVt8wj\nOCmY1w69RoYyA0OpISv7rqyYr3fyEzFgjcvVu63dHsZuArPyO1BFzZpFyp69SE1N8b5wHukLcsm7\nE5nCG2uuoBEEMpVq0rNVyKQS2njYcPFRwezkwKYuLBzTvNAybo5KzbJTD1l6KhRFrmaqjWUm7Zrf\n43bKET1XJCsjK8b4jGFcw3F6Vp6CWo3yyROytSX8UHJCQlCEh2sVL4pELgdlwcn/EpHLsX/nHeym\nTC4gOwWiKP7t+NsEPA0gIDKAB4kPCuyTZwPb1a0rvm6+1LepT6oilS6buwDwdvO3ea9F5bSN/FP5\nbP9ptgVtQ259GamBTkJPKpHySu1X8KvvR3uX9qy+u5pFVxcBcGrMKexMqt/ZbeapmRx9fBQPSw/2\nD99f3cv511ETrJYDQRAI69kLZVQUJi1aUGdzIU5CNVQ7MV99TdLGjQDU/mMZ5t26lf0kapUY7N3Y\nAMGHCy/zA9jUFQPUFq9WKGDJT86jR0TOmEnOfTEgklpY4DJ/vtZytMR1b38d7u8TH9ftBuO2glwX\n4MYvX0HcTz+Vej0SY2OEbF0GzqhRQxw/+ggzX99KzzI8TcpkwOIAUrNVWBobcGCaL7Vti+7xjVu6\nlPglojqHQS0X6qxbh9zVVW+fc1HneO/4e6gEFdZG1mwYsAF3S/fCTlc6spJhy3gIDxAf23qK0lal\nlB17npQDB7TZbve/VmHWsWP511ZOtlyOYNYOUVv2jS51MZBJWXY6TG8fX297vhjcCC9H/T7VwJB4\n5u65w6N4UTNVahSJj881YtQXUAu6INPdwp0JjSYwuN5gDJPSxYA0t580JziYnLAwhJyc4hcqk2FY\npw5GPt4YeXsjkcvJvnOXzKtXtUOHJSExNcVq8GBRdqpB2YYcn2U842zUWQKeBnA++nyhbk4OJg5I\nkBCbJVZefu/1O11cu5TpOv9rhMdn0P3HUyBR0qN1NDmmgQVaQepY1sHNwk2rq/tH7z/oVKt4uasX\nwasHX+VW3C3aO7fnz75/Vvdy/nXUBKvlROvpDXgeP46hm2sJR9TwolElJhLWtx+atDQM69UTy9fy\nUk6DP7unE+0vqswvkUGDAdBmihgMVmKWMWXPHqK//Eor4G/SvDm1Fi4s3edMo4E978LN3Juo2u1h\n/M4CDkzq5GTud+2OTFFCYABgYEDdnTvIPH+e+GV/oE7SDaCYtG6N44zplaYPqlRr8PvjPNcixL7Z\nZeNb0a9J8QMNgiAQ9/NiEv4Qy6zy2rXxWLcWubO+vurOkJ18cU603fWw9GB9//UVmzJX5cCe9+D2\nNvGxmQOM2wKuZX8v1MnJBHfQBajV0ROv0QiM/uM8Vx8nFbufTCrhtY4efNTThxyVmq8P3GffzShA\ng8w8GGvncyjkwdr9zbIEeqm8GURz3ONBkZstzWtrKQ65qytG3mJQauTjg5GPN4Z16yIolKQdOUzy\njp1kXStEt7gIjLy9sPb3x2rIEGTmFXclU6qVXI+9TkCkaEjwMOVhofs1sG3AwLoD8XXzpZ5VvX9t\nGfl5R6uHqUFsCdrCwYcHyVYXbEcZWG8g3/l+Vw0r1afn1p7EZsUy1HMo87vMr+7l/OuoCVbLSfaD\nBzwaNhwAh5kzsJ86tZpXVENhJKxcRewCUW/Tae4cbF8tRhsvMxFubxeD1OgbRe9n6SoK97ecAJaV\nOxWqycgg5uv5pOzerX3ObuobOEybVrpAWxDg4MdwOffO37kZTNwHJgUDsrtRKRyZMp3+jwrq0j6P\n9ZgxuHz1JQDq9AwS164hcdVfaNLTtfuYdfXF8aOPMG7UqOR1FsMPhx/w2ykxmzehgwdfD2tSquME\nQSD2+x9IXL0aENsVPNatLdBT/su1X1hxewUArRxbsbzPcoxkpde3LYBGAye+gkCxbIncFEb9BfXL\n1hf7wocDi+B+dCr9FwdoH8ukEn4a05wuXvYsPBbM5ksR2ql+LRIlcqtrWFgGUDs1jtpxAu5xAu5x\n4JVkiFlyyTdEMju7fEGpN8be3hh6eekFlIIgkHX1Ksk7dpJ65IieGxuA1NQUjVJZsBVALseyTx9s\n/Mdi0rp1lQaKT9OeEhgZqA1eC6OqbGD/CRTlaJWSk8Ke0D1sCdpCRFqE3jHNHZrjV9+PvnX6llsd\noiIoNUpar2uNgMBbzd7i/Zbvv/A1/NupCVbLiSAIPBw0GEVY2AvXRqyh9GgUCh4OGIjy6VNk1tZ4\nHj2ir42rVurK/EGHoRCnGxEJePUSLVC9eosWqJVM9oMHRE6fgeLRIwBktrbU+v57zH3LUDo8Pk8X\nNNn7wKRDYFZQaidbqWbQkkBywsJY9vePSCn6z4DE2BjPI0eQO+lbjaqSkkj480+S1m/QK9ta9O+H\nw7RpBQadSkNASByvrbqEIEADZwt2v9e5TDqvgiDwbP432h5fQy9PPNauxcBWN7mrETTMDpjNoUeH\nAOhfpz/fdf2u4lPal/+Eg5+AoAGJFAb8KH5eSsnLoBGs0Qj836H7rAh4pH1uUDMXfh3XSvv4XlQq\nY38LwDoxhrrp4dRVXKNOegQe8Sock6Gkd1FqaqrLkuYGpkbe3hjYFd0+o4yJIWX3HpJ37UT5WD+Q\nkVlZITExQcjJ0cv4A8hr1cLazw/rkSMwsC+b5FRFUWvUtNnQBpVGND9wM3erMhvYfxIKlYZO3/1N\nfHrhjlYaQcOFqAu8dfytAsfaGNkwwnsEo+uPxtX8xVUzo9Kj6LujLwBfdPyCUT6jXti1axCpCVYr\nQNxvvxH/izi1W+/Afow8Pat5RTUURurhI0R+9BEAtpMn4/TpJ/Dsrk60PyOu6IPNHMQMauuJYFOn\nStYnCAJJmzYR+933CAqxJ9a0Qwdq/fA9cscyeNGf+RFOfC3+bO0Bkw+DZa1Cd/1izx2tqPu6oA3Y\n3y9iYAwxs+s4c2aR25XPnhH/++8kb98BKvGLGZkMq+HDcHjvPeQupcs+x6ZlM2BxAPHpCkzkMvZ9\n0AUvx7KXaQWNhpgv5pG8TSzNGzVogMfqv/TsZxVqBVOPTuVarJjhmdp0KtNaTaed2UEAACAASURB\nVCvztQrw4CBsnwx5wvZdpkOPz0vVIlLdwWqOSs0n226x96Zuul4iaPDUpLP2FTsMn4ST/iCI6Ot3\nMIt9ilyjKfZ8ErkcQ0/PAtlSg1q1SpXZ1CgUpJ84QfKOnWScPStmsPMwMMDIxxuUKpSxsWhSUvJd\nWIJZV19sxo7FvGtXJLLqMbV43gxghPcIHqc+5szTMwREijaweYFsfupY1qGLaxd83Xxp49SmWjKJ\nVc13hx5oe6GLcrRaeGUhq++uBsDC0II0hW44T4KEbm7d8GvgR6danapcDuzqs6u8fvh1AJb1WkZn\n185Ver0aClITrFYAxePHhPUVS332776Dw7RK+LKrodIRBIHHr44n69o1JAYy6k2wwjDrTvEH1fEV\ndVEbDAaDqvuyUKekED1nLmnHjolPSKU4TPsAu6lTy/Yle2EZHJ4l/mzhImZUixj0ORkUy6S/LgPQ\n2sOGVQ2VRL39dpGn9jx+DEM3txKXoHj8mLhfl5K6f7826JLI5diM88furbf0spvPo9EITPzrklbq\naMGoZoxuU/4Mk6DREP3fz7TtFMaNG+P+1yq9rHpydjLjD43ncaoYtFeau9DTq7BxDGTmDvo0HQND\nl5b4OarONoDUbCUzl50g7vZ9PFJjaKqIo5kiAdmTcExVxZfwNUCMtQHhli6Em/jwxMaVDq+0ZfzI\nzpibl728nX3/vljm37cPdf4gFDFTbuDggCY9g+w7d/SCe5mNDdajRmLt51eqz2tVsz14O1+eF1tn\nCrMNLcoGNj8mBiZ0cOmg1XXNL+/0T0Y7aEXRls77wvbx38D/ArBxwEaepD1hS9AW7Q1mHrUtauNX\n349hXsMKlQ6rDA48PMDsgNkA7B66G0/rmsTUi6YmWK0geTaJcg93PA8f/tc2zb+05Jb5s/YvJ3yF\nKDdjUTsLt86FDJAYW4nT/K0ngYNPlS8t8/p1omZ+rNWJNHB2xnXhj2UfVLq+XhzyATC1EwNVh8J1\nZePTc+j3cwDx6TmYGco49GFXatuaENSqNUJWVqHHyBzscZ47F8s+pVAhALKDgohb/AvpJ05on5Oa\nmmL7+kRsJ00q1Ev9t1Oh/HBY9F8f3tKVn8Y0r/DvkqBWE/XJp6QePAiIQ2q1V67Uc+OKSI1g/MHx\nJOUkIZPI+K3nb3RyrYTJ48SHsH4UJOZO0tfxBb/1hfYO5ye/RrDE1JQG165WfC3PoRXRz53AT7sf\nRNLdB1hkp5V4bIIFPLGXEOEIT+ylODZtzYDub+Jq3YofjgSx67pOP9XJ0ojZ/RswtLkrUmnx/5eq\npCRS9x8geedOrfpFHlJLS1EZQRDIvnOngK6qSatW2Pj7Y9G3zwuT+yoNc8/OZXfobszl5pz1P1ts\n9k8QBEKSQ7TSWDdib+gpKOThbeONr6sYuDZ3bF4+++CXhLxBK0tjAy591qtAu0/+zPT8zvMZ6jUU\ngKBEcSBr/8P9eta8RjIj+tXph38DfxrbV+7N3crbK/n52s8AXBh3ATN59Tn6/VupCVYrSMKqv4j9\n4QcA6mzfXsCjvIZqIuaOOA2fr8wfed6a1Mei/JFHz3hMHXJlqFzbiLJTjYeXywK1rAgaDQl/riRu\n8WKtbqR5jx64fDMfAxubsp3s7i6x7CxowMgKXt8HLoVbOgqCwNS1Vzh+X1Q3yJ+9TNq8mZh5X+rt\nLzE01LYlAFj06YPz3DmlNsHIvH6duEU/k3npkvY5mZUVdm9OxebVV7U6sVcfJzHmj/OoNQJ17c3Y\n90EXzI0qpydYUCqJnDFTm7k2adMa9+XLkZrq/p9vxN5gypEpKDQKzORmrO2/Fh+bSrhZyUiATWPh\nae7rd2gI47eLtq1FkHX3LuFj/ECtxrR9OzzWrCn35TU5OToR/ZAQsnNF9FVR0SUeK7WyQupZl0Bp\nBresI3nqnMMTe8gwkSCXGDHMewivNXqNOlZ19I67+jiJr/bd5eZTXUa0pbs1XwxuTIva+oG6oFaT\nce4cyTt2kv733wj5h6IkEsw6dsSwjgfK2FjST5/RG5qSmppiOXQINmPHVr7hRyUxZPcQHqU8oqNL\nR5b3WV6mY1MVqZyLOkfA0wACIwNJzC6od2sht6CTayetIYF9GW1gq5uiBq3yUGqUtN/QHqVGyYRG\nE/i07ad629MUaewN28uWoC08Snmkt62JXRPGNhhL3zp9MTaouOPe/Avz2RK0BQtDC875n6vw+Woo\nOzXBagVRxsQQ+koPEARsJ03CadanJR9UQ9WQkSBKCN3YADG3CmxWZsgIO+iIoJZgbKeizqf9kbSb\nUmRwVxWo4uOJmjVb7MFDLJM7fvIJNhPGlz2TGHwUNvuDRiVOoE/YDe7ti9x9w8XHfLZLbH8Y0NSZ\npeNaaa+pycoixLer3nS/85fzkBob8+zb/9OWY6VWVjjNno3VsKGlWq8gCGScO0fcop/Fsm0uBo6O\n2L/7DpL+Qxj4+wUik7MwlEnZ+W6nAn70FUVQKHj64UeknxSdcEw7dKD2st/1TBWOhB/h49Oixqmz\nmTMbBmzA0bQM/cJFocyCHW/Ag1wRcQsXeHUbODct8pCIN98k40xAqQc3BbUaRUSEznI0N2OqePy4\nRBH9HJmcxxZOhFs6Y9ekIUOGdyXZ1ZwfgndwOvogSHU3KxqVOS2tBrFk0LvYGBd9U6XRCOy8Hsn3\nhx8Ql6ZrIRjZyo1Z/epjnRhD8q7dpOzerec0ByB3c8OiTx8kUgnpp0+TExKqt93I2xubcf5YDh6i\nlyF/2UjJSak0MwCNoOF+wn3ORJ4h8Gkgt+NvIxQyEFlWG9jqpqRBK4DR+0bzIPFBsdqmgiBwKeYS\nW4K2cCLihF5G2srIiuFewxlTfwy1LcrfVvTB3x9w6ukpvG282TlkZ7nPU0P5qQlWK4HH4yeQeeUK\nBs7OeJ34u0pdfWp4DrUSQo7livYfKWaaXyQ2rB4Jl0Utv1oLfsBq8OAXsUoAMs6dI3LWLNRxYi+j\n3MMd159+Kl8/4qMA0UZVlS26Zo3bCp6vFLl7WFw6A38JIFupwdnSmMMf+WJtql8yza8dbFinDvX2\n70NiYIAqPp6Y+d+Qdviwdl+zLl1w+XJeAeH9ohAEgbRjx4hb/AuKMJ3IfLK1I8s9e3HKrQWfD2nC\npM7lE9QvCY1CwdN33yMjUBQaN/P1xW3pr3pl41V3Vmldc0Ac4mjv0p4VfVZU8OJqOPJfuJjrcW5o\nAX5rwbNHobvHfPUVSRs3ITUzw+eKztZREARUz57pxPPzhPRLIaKvlkCUHTxxkJBQyxyvJuNZHGTN\nEyMbNBIpH/fxoUuTDJbf/IuAqJOQLxgyULmQFtsJVWoLTAyMOTajK242JVcg0nNULD0ZysqAR0hz\nsugSeYv+Ty7TKF5fh1RibIxl3z4YN25MTmgYKfv360tSyeVY9u0ryk61avWPaLUKeBrAu3+/C1S+\nGUBidmKl2MC+DJQ0aPVZ4GfsDduLtZE1Z/zOlPh/H5MRw46QHWwP3k58ls4cQoKEzq6d8W/gT+da\nncscyI/aO4qgpCB8XX35rddvZTq2hsqhJlitBPKXUD3WrcW0bdtqXtG/gJjbcCO3zJ9ZgmONzEgs\n8bedgtqmEWH9+qOOj8fA2RnPQweRmlStxqGgUhG35FcSli/XDoRYDh6M8xdflC879PQKrB0KinTR\nmMBvvWhOUARKtYaRv5/jVm5pdsMb7ensVbBkqIyJIbRXb1CpcP15EZb99HVCU48dI+arr7TBtsTU\nFMcZM7AZ51/qGzRBrSZl7z7ilyzR6z2Mc6hNi3mzsOjRo8qCEU12Nk/eepvMixcBMH/lFdwW/4wk\nN2AVBIFe23sRm6lvAuFo6siSHktoZFcB/VhBgPNL4ehn4mOpAQxZAi3GFdg1f2uR/fvvo4qP0wam\nmrSS+0rlrq5Ivery0FbFdvUlnjhIiLIFlYHufdUoLckI/S8yqcDEXmkEZR3gVvxN/SVn+jDScxxz\negzj8uMkxq0Q37deDZ34c2KbUrxkgaxr13i6aStZR45gqNQPqHN8GlF79DAkMhmp+/aTdV1fkULu\n6qqTnSpG1upl5Nfrv/LHLdGgInBsYJUFi6W1gW3u0Fzsdc21gX1ZAv6SBq3W3l3LgiuiTvbxUcdx\nMnMq1XmVGiUnIk6w+cFmrjy7orfN1dyV0T6jGeE9otgKQX66bO5CSk4KY3zGMLfj3FIdU0PlUhOs\nVgKqpCRCfLuCSoX1WD9c5s2r7iX9b5IRn6/Mf7vk/W09cy1Qx4GpbhI9ads2YuZ+DoDDRx9iX8wk\nfEVRRkUR+fEnWocdiYkJznPmYDViePm+MGLuwOoBkJ0CSGDkn9C0eM2/BUcesPSkmL2Y6luXzwYW\nHXRFzpiBIuIJdbZtLXR96pQUnv3wAyk7dKUwk1atcJk/H6N6pc+K3guPZ/nsnxh9/zi2OboAzKR5\ncxymT8esQ9HtDBVBk5lJxNQ3yboqDi5Z9OmD608LkRiIPbLN1jQrtMTqaOrI36P/rvgC7uyEXW9p\nbXs1nT4lx3kwObmOTjnBwWScK11PnMzeHiNvL500lLc39yzT2PZ0P8ceH0NZTJVBo7SElFdwcL1E\nQo6uh1UQZKhSmtPBfjjfD+6Hk6WuVWL6lhvaAaoVr7Whd6PCAwfls1hS9uwhZccOsRUhH6kmlhxx\nbcVte0+aJoQx4OkVzLJ0rSdIJJh37YrNOH/MunSpNtmpivLm0Tc5H32eelb12DNszwu7bmltYPPa\nBTq4dMDcsOIuXhVh/J8XCQyNL3TQ6mL0Rd44+gYAS3supatb1zKfPzQplC1BW9gbtpdMlS5jbyg1\npG+dvvg18KOZfbMi/x5nKjNpv1H8ezSt5TSmNqsxAKoOaoLVSiKvz0xmbY13wJnS23rWUDxqJYQc\nFTVRgw+L/ZnFITWABgPFILVuNyjkD5CgVvNo+AhygoORmJrideRwqYeGykLa8eNEfTZHqwFp5OOD\n66Kfyq/HGx8Cf/XX6cIOXiw6aRXDpUeJ+C0/jyBAQxdLdr/XCSODogOArBs30GRmYtap+In4jHPn\niJ77OcpIMXiRGBpi/9572E2eVOJnPyNHxeBfA3kYl4GJOodNdhGY7NykZ71p1qkTDtM/wqRp0b2d\n5UWdnk7ElClk3xT7mi0HDsTl++8IiD7L+ycKd6apaLAqKJUowsPFQafLJ8gJ3ENOEijTZUDxNy1S\nMzN9u9FczdI8KbCUnBT2he1jW/C2AlafxgbGZKsKWlgWWJ/aBEVSB5zpwfzBnenqU/D3IS4th54L\nT5GarcLV2oRjM7piaigG+YJCQdrJUyTv3EFGQGABTVTz7t2wHjYMtVrD/T/XYXn7qp4RRbaZJXZj\nRuP8qv8/3rpaI2jovKkz6cp0hnkN4+vOX1fLOkpjA2sgMaCVUyvRkMCtK3Wt6r7wrGtxg1bJ2cn4\nbvEF4MNWH/JG0zfKfZ0MZQb7w/azOWgzocn6vdANbRsytsFY+tftX8BN7GHKQ4buFpUIvu3yLYM9\nX1zrWA06aoLVSiJlzx6iZok6bLVXLMfc17eaV/QPJ+Z2rmj/1pLL/ABWtUXh/pYTwKJkLcKMc+eI\nmCy6C1mPHoXL15X3haJRKIj9YQFJ69drn7P2H4vTrFl6Qz1lIjkCVvWD1FxpoL7fQsfihzZSs5X0\n/zmAyOQsjAyk7PugS6Hi2+VFk5FB7OLFJK1br21vMGrUkFrz5xdrufrxtptsvyo6+czs7cMHPb1R\np6SQsHIVievW6UloWfTujcOH0zDy8irqdOVCnZpKxOuTyL53D4Bb7ez5pkcSQiFf1GVpAxA0GpRR\nUfo9pcHB5ISHF7QAfY48EX15rVpa2S9rPz+c531RIIAQBIGbcTfZFryNI+FHyFHrSuxyqZxeHr0Y\n4zOG1k6t6bmtF3FZ+q0NeWgUtigSfZGkteWdbg15p7tnsY5h6y88Zs5ucVDu7W6efFQXknfuJHXv\nPtTJyXr7Gnl7YTViJGadOpF+8gRJW7cWUCK4Y1eP/XU7cs6lKabmJszo7cOr7d0xkP1z+/5DkkIY\nsXcE8HK5HeW3gb0UfYlsdcGbmOqwgc0/aNXGw4bt7+jfKPfc1pPYzFj61enHgm4LKnw9QRC4+uwq\nW4K2cPzxcVSCLgFiYWjBMK9hjPEZo1W6OBd1jreOiW5aq/quoq1zTZtfdVATrFYS6vR0Qjp3QcjJ\nwWroUGp9/111L+mfR1nL/EjAu4+YRfXuDWVsmn/y1tuknz4NEgl1d+3EuEGD8q07HzmPHhE5cyY5\n90StSKmFBS5ff41lv77lP2lajBioJuXKs3T/D3SfXeJhH22+zu4bYl/ovMGNeL2Khpcyr18nes5c\n3eCUTIbdlCnYv/cuUiMjvX13XX/K9C1ib2QnTzvWTWmPLJ8Gpyoujvhlf5C0dasuuJNKsRoyBPv3\n36/UrFtYxA2iJ03FLlIsQx9rKWHzICsECaQrxeeKyqgKgoA6Pl4nC5U3hR8aWsCvvgASCYbu7hjV\n88Ao8wpGsqcYWasw9G6EZPx2sHAiuGMn1ElJBW6k0hRp7H+4n23B2whJCtE7rbuFO6N9RjPEawi2\nxrZoBA0HQk8y78wiFAaPn18FAMqUFrQ2fZuvhzannkPJ5WCNRmDcoqPYXzxN34jLeCXr24dKLSyw\nHDgA6xEj0GRnk7x5M6nHjheQnbIaNhRrv7E8tnLhq/13ORuaoN3u42TO54Ma08X7nyXFlEd+M4Cd\nQ3bibeNdzSsqSLYqmyvPrhDwVMy6VrcNbHGDVu8ef5eAyADqWtVl77C9lXrduMw4doTsYFvwtgK9\n6h1dOjK2wVgSshP46vxXABwccbBCqgI1lJ+aYLUSeTrtQ9KOHkVqZob32cDyZ9H+TagUujJ/yJGS\ny/wAZo7Q6jUxk2rtXu5L54SF8XDIUFHTsmMH3FetqlAJLGXvXmLmfYkmN1gxbt4M14ULK+amk5EA\nqwdCXK5Qesf3oc/8Qtsb8rPnRiQfbr4BQDcfB1ZPalul5T2NQkHCsmXEL1+htVw1rFsXl2/mY9pK\n9JV/GJfOoCWBZCrU2JkZcuhDXxwtC/8dUTyNJP7XX0nZu1dXUpbLsRkzBvu336pQ28azjGf8fvN3\ndoXuwjxdzRcb1dTOTd6b+Y8m/T0/PjgputEt6bGE+oa19XpK8wLU533oC8PA2VnPbtTI2xsjT0/d\n34acdNj2OoTmOphZucP47Tx673Oyb93CtEMH3P9axd2Eu2wN2srh8MN6QugGEgN6uPdgdP3RtHNu\nh1QiJUedw/6w/ay8vZon6eHafQVBgiqtKarUxhg5HENqJL7oTrU6saj7IkzlRU/4i5qo50nZtZOU\nY8eRPJclNuvUEavhIzDr2IHUw0dI2rwJRWiY3j5G9etj4z8Wy0GD9QYLBUHg2L1nzD9wn4hEXaDf\nu5ETnw1oSB37l1eiqjDKYgbwMiAIQqltYPN6XVs7ta5UG9jiBq0WX1vMn7f/RCqRcnHcxUrRTX0e\nlUbFqSen2By0mYvRF4vc7+r4q/+T9rf/BGqC1Uok9chRIj/8EADXxYux7Fs6x59/JdG3xAD19lbI\nTCh5f4C6XcUsav2BlWaBGvP1fJI2bADAbdnvWHTvXuZzaDIyiPl6vtbaE8DujSk4fPhhxXqXs1Nh\nzWCIFoNOWr8Og34uMVB9mpRJ/8UBpGWrsDUz5PBHvjhavJgbp+wHD4j+bA7Zd++KT0gk2Iwbh9W0\naYxac5O7UWJf6upJbelev2Qd05zQUOIW/6Kzo0UcUrOdMAG7KZORWZV+yjpVkcqq26tYf3+9tmwu\nlUgZY9ebUYtvookQs0sye3ushg4hJzSUnOAQVNGlE9E3zgtI8/pKvbxKtz61Cg5Mh2trxcfG1kQ+\n6kbq6csoHK355pPa3E/Ud3VyNXdllM8ohnkN04rBJ2YnsuXBFjYHbdYTkRfUhiiT26FI6oSgtEUq\nAb/2toTJFnM/Ufx/amLXhKW9lmJrrG+Jq4iIIHnnTlJ270EVE6O3LcbUhmPubWn31nj61TEnadPm\nArJTErkci379RNmpli2LvWHKUalZFRjOrydCyFCIWpmGMimTu9Tl/R5elWYUUdVUxAzgZaAiNrBT\nj07VBntllX0ratDq8KPDfHLmEwA2DdxEE/smFX2JxfIw5SFbg7ayJ3SPtsKSR/+6/RlbfywtHYv/\nLNdQ+dQEq5WIJjubkM5d0GRkYNGnD26/LK7uJb1cpMfllvk3wrPSlPkBY2vRArXNJLCv/HKaKimJ\nsD590aSlYVivHvX27C5TgJkdFETkR9NRPBJL9DJbW2p9/13Fe5YVmbB+JETkToY3HQ3D/yix1UGt\nEfBfcYFLj8Rgpbip7apCUKlIXLOGuF+WaPU/M6zt+bbhcK451eetrvX4z4CGZTpn1u3bxC36WW9S\nXmppid2UKdhOGK/nSPU8OeocNj/YzPJby0lVpCLRCDgnQX9NIwbQBLMniaQHBJRcvkfUBDXy8tIF\npHnDTg4OFfvyEgQ48yOcnM99Qzn379rS8KoMtQTGfyJDLZMgk8jo5taNMfXH0LFWR23G7mHKQ9bd\nW8e+sH16vasapRWKxM4ok9uBR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vO3q0QUv7JQqBW0Xi9+Jqe1nMbUZlPLdZ7CHK367ehH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6mILVaoZOpeJmx07oMjOx6d0bD6dVgJE/kdIGxqyGWq3A3PZfH+dzQ9Qh8UapVYHcHCYEQZ2q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\ns1IdtGfVmC0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b396d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1000 best: 73.095626\n",
      "('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B1P5', 'B3P4', 'B2P2', 'B2P8', 'B3P11', 'B4P7', 'B2P9', 'B2P7', 'B5P7', 'B1P8', 'B1P3', 'B1P9', 'B4P5', 'B4P1', 'B5P2', 'B4P9', 'B4P6', 'B1P12', 'B4P11', 'B6P12', 'B5P11', 'B7P7', 'B6P1', 'B1P6', 'B1P10', 'B1P11', 'B7P1', 'B5P3', 'B2P3', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B1P2', 'B3P1', 'B7P3', 'B3P3', 'B1P1', 'B6P2', 'B2P10', 'B5P9', 'B4P8', 'B4P10', 'B2P12', 'B5P5', 'B3P9', 'B3P2', 'B2P11', 'B1P7', 'B5P8', 'B3P10', 'B5P4', 'B7P6', 'B3P6', 'B1P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B2P4', 'B6P11', 'B4P4', 'B5P10')\n"
     ]
    },
    {
     "data": {
      "image/png": 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XtLOxY1r7aUxrNw29Tk+GMYOpe6bydejXGIyGErmmKBkbT15nX+RtQB0N9HV3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3VKjo+naNNmx15xp4VNeZGAO+9UpZ7UwlSGLxvOyaKTAIxtN5aHrn1IlVoURWF31m6WJS1jRdIK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\nEEIIIYRwWf8PxlsR5HLDkJQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bac48d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 2000 best: 64.163757\n",
      "('B5P1', 'B6P8', 'B7P2', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B2P7', 'B5P7', 'B5P2', 'B1P8', 'B1P3', 'B1P9', 'B4P1', 'B4P5', 'B4P9', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B7P7', 'B6P1', 'B3P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B3P10', 'B5P4', 'B7P6', 'B2P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zyu72AHg627BwSEPcHKwBmLLpHH9c+kvLeJpJyTAwdPFRToUnANC5jhef9agt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\nfVvqKFTsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd83450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 3000 best: 61.888180\n",
      "('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B2P7', 'B4P9', 'B5P2', 'B1P8', 'B1P3', 'B1P9', 'B4P1', 'B4P5', 'B5P7', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B1P2', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B7P6', 'B3P10', 'B5P4', 'B2P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\n"
     ]
    },
    {
     "data": {
      "image/png": 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GjCA0NJQPP/zwuvMPb2bixIkMGDCAn376ib///psmTZpgNpvZvn07BQUFdOnShf79+wPa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\n1v8D3rvPZbpqHfkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf6c450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 4000 best: 61.579230\n",
      "('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B2P7', 'B4P9', 'B5P7', 'B5P2', 'B1P8', 'B1P9', 'B4P1', 'B4P5', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B1P2', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B7P6', 'B3P10', 'B5P4', 'B2P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\n"
     ]
    },
    {
     "data": {
      "image/png": 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HjP6VAEhevpwzffuRc/GizsmEEDZLVSFysraQKu2SdqzhYBi4EjwqFctb2tvZ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\nfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ca6f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 5000 best: 60.948624\n",
      "('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B2P7', 'B4P9', 'B1P8', 'B5P2', 'B5P7', 'B4P5', 'B4P1', 'B1P9', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B7P6', 'B3P10', 'B5P4', 'B2P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\n"
     ]
    },
    {
     "data": {
      "image/png": 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4qG8TDDJPVVeVds7qjSgGA85t2mBfuzbpv/8BeXlk7NxJ7sWLuNzbHsVo1Dui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LFKFU2vqFrS0+1F6b59FOzfR+GBg9hyr36FprF6dTyaNrUXp82a4t648VU3NgieNtXRZ3qz34N/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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cd45d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 6000 best: 60.948624\n",
      "('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B2P7', 'B4P9', 'B1P8', 'B5P2', 'B5P7', 'B4P5', 'B4P1', 'B1P9', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B7P6', 'B3P10', 'B5P4', 'B2P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\n"
     ]
    },
    {
     "data": {
      "image/png": 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4qG8TDDJPVVeVds7qjSgGA85t2mBfuzbpv/8BeXlk7NxJ7sWLuNzbHsVo1Dui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LFKFU2vqFrS0+1F6b59FOzfR+GBg9hyr36FprF6dTyaNrUXp82a4t648VU3NgieNtXRZ3qz34N/\nP9atfn3SZ84k/ZvpKEVFZP3yK7lr1hLw2qv49e+PTq+dxXej3kjP2j1ZdHwR285uI8uUhZ9bxbiw\nztnYFBuzD8wGQI+eqp5VZUW1BOWZ84jaG8WCI/ar/ME+jmpih4lylb+waxMJ3gHwayRYTfD7s5CT\nCB1ftBe0osS0qWMvVhPOFRCfmU9wZU+1I5U7OsVJLl8rPH6c6HvtK2nVXnqJqs8OK9PXjx061HGB\nklf7doTMnu24z2YyUXj4MIX79ztWTs0JCVd9Lp2nJx5NmtiL0qZN8WjaDJfAgFL/Gq6lKDaWpHcn\nkbd5s+OYR4sWBE2YgHvDBiomu9yOxB08s+oZAN7p8A796/dXOZEozvrY9YxaPwqAl1u+zJAmNz5e\nTVzbpvhNvLv9XcdV/i56FyKbRvJ0k6dxNbiqnE5oTszfsPBR+/asAG2ehb6TQW9QN1c5sjc2k/7T\ntgLw6UPNeKBFsMqJyh+nKVZTPvuc9OnTAai7ehWutWqV2WsXNy5K7+eLV4eOmOPjKTx61LGT1BV0\nOtzq1cW9WTPHyqlbvXroDNr7QaEoCjmrVpP8/vuOVgEMBvyffJJqz4/UxM5gFpuF1vNbY7FZAGhX\nvR3f9v5W5VTiUoqi8Pjyx9mfuh8fFx9WDVwlp6RLQFpBGlN2TmFFzArHsRYBLZjQfgJhleQqf3EN\nyYdh3gDIOWu/3fg+6D8DXNzVzVVOmK02mr29ivwiK4+0rsUHA5qqHanccYpiVVEUTvXugzkuDvdm\nTanz009l+vpHGjW+4W3sDFWr2ovSpk3xaN4M9yZNMHg71y9qa24eaV99RcYPPzh6ao1BQQSOHYNP\nr17oVDyFFLkqku2Jl4/gunDRjmwRqQ27k3YzZKV9JfWZ25/hhRYvqJzIuSmKwh8n/+Dj3R+TXZQN\ngLeLNy+1fImBDQai12mnVUdoWFa8vWBNPWq/XbsTPDIfPCqpm6uceGLWDjafSCOsqhfrXu2mdpxy\nxymK1YL9+4l56GEAAse8gf/gwWX6+tcqVj3uuOP8iql91dRYo4aqxVxJKjx2jKQJEyn43/8cx7y6\ndiFo3LgyXdm+wKbYaD63uaNH71Jy8Y52jFgzgi0JW3DVu7Jy4EqqelRVO5LTOpN9hne2vcPOpJ2O\nYz1DejKm7RgCPNVtHRJOqCATFj4GsfZT1gSEw+O/gG8NdXOVA1FrT/DJ6uMA7BwbQYCvrFqXJKe4\nwCp72V/2D3Q6fPreWeav79W+3ZVtAD4+hHz7LR7Nm5V5nrLi3rAhtRfMJ+u330j56GOsWVnkbdzE\n6e33UHX4s/g//TR619LvkTuReYI/T//JX6f/KrZQFdpxLOMYWxK2ANC/fn8pVP8js83M94e+55t9\n32CymgAI8AhgbLuxRIREqJxOOC2PyvDE7/DbM3DkT0g5BDN7wRO/QbWGaqdzam3Dqjg+3hmTQb+m\n8gagJGn+/JFitZK9fDkAnq1bq3IhUsjs2RgDAy875tm6dbkuVC/Q6fVUGjiQsBXL8RvwAACKyUTq\nF18Sfd/95G27cketkpCcl8ycg3MYuGQgDyx5gO8OfkdyfnKxnyuzO7Vj1sFZAOh1egaHl+0ZkPLi\nQOoBHln6CF/884WjUH244cP8cf8fUqiKW+fiDg9+D63tF6qSHW/fPCC2mB0ixQ1rGuyHq9FeUu2U\neaslTvPFav6ePVhSUgDwvftu1XIET5uKMTAQ3flxUrnr1tkvrKogjJUrU+O996i9YD5uDezTAYqi\no4kdMpSEV17Fknrr+1DnmfNYfHIxkasi6fVLLz7Z8wnHMo857m8R0IK32r1FNY+LQ84vnP6XflX1\nxeXEObb47BPah1o+Zd8q4szyzflM2TmFQX8N4nim/XRimF8Yc++cy7h24/Bx9VE5oSg39Aa462Po\nMc5+u/AczL0PjixVN5cTc3cxcEcte/+vzFsteZpvA3C0ABiN+PTupVoOj/Bw6m/cQFFsLKf63gk2\nG2nTpxP82WeqZVKDZ4sW1Pn1FzJ+mEfqV1+h5OeTvWwZuRs3Uu3FF6n86CM3NenAbDOz7ew2lp5a\nyvq49RRaCy+7P9Q3lH5h/bgr7C5H8dOkahPH/vKyoqod3x/6HptiA+DpJk+rnMa5bIrfxKTtk0jM\nSwTOj6O6PZKnb5dxVKKU6HTQ5TX75gFLRoGlEH5+wl7EtpZ/v/9F2zr+7IjO4FhyDpl5RVT2kn+7\nJUXTF1gpZjMnOnfBeu4cXl27EHJ+dJXaEl5/newlf4JOR9iypbiFVcyxMebERJLfn0zO6tWOY+7h\n4QRNnIDH7bdf9XGKonAw7SBLTy9lRcwKMgovfxfq7+7PnXXupF9YP8KrhJebC9bKs7SCNPr+2heT\n1USnmp34uufXakdyCmkFaXy480OWxyx3HJNxVKLMHV8FiwaDOd9+u8vr0H2sbB5wk7acSOPxWfZd\n+2Y80ZLe4UEqJyo/NF2s5m7aRNywZwGoMeUD/O7TxvaappMnOX3PvaAo+N13HzWmfKB2JFXlbtxI\n0ruTMMfH2w/odFR65GEO7FpOzZP2QdRxDSsRPu9nlp1exrLTy4jJjrnsOdwN7nQP6c49YffQrkY7\nXPQuZfxViP/q3+PEvuvzHa2CWqmYSPtkHJXQnPg9sOBByE8H7ANwFHQccm/O7WM2qJvNSeQXWWg6\ncRUWm8LTnerwVj9pTyspmi5Wz44eTdbiJejc3Kj/9xZNzSuNf+FFclauBIOBuiuWqzLKSUtsBQWk\nTZ9O+qzZV90gId0HPhxoIDrI/m5dr9PTNqgt/er2IyIkAi8X9TcdEDdH5t7evNjsWN7Z9g47knY4\njsk4KqEJ6acoimqHK0WXHU7Bn+z+P1CvWSeVgjmP/tP+Zm/sOW6v6cef/yffr5Ki2bfvtsJCctbY\n52Z6d+2qqUIVoOpw+4ovVivp385UN4wG6D08CHjxRcIWL8azXbtiP6dKDrz+i5Xb/G/j1Vavsnrg\namb0nsG9de+VQtVJ7UjcccWxlPwUR0+xuMhsMzPzwEweWPKAo1AN8Ajg8+6f81n3z6RQFeqrUhej\ncuViQwAZeP/2BEv2nSWn8Cq7NQoA2tTxB+DQ2Sz5XpUgzRaruRs3YcvLA9SdAnA17o0a4d2tGwDn\nfv8dc1KSuoE0wi2sDiHfzb7qNFS9Ts+iexYxOHyw/HJ2YlmmLEZvGi1zb2/QwbSDPLr0URlHJZyW\nAoxauJeW765hyHc7+WlXLOm5JrVjaU67OvZ5qzYFdp/JVDlN+aHZaQDZf9mnAOi9vPDu2kXlNMWr\nOvxZcjdsALOZ9FmzCXpzrNqRNEGn0xHbsBK1j5277Himr55Kn09RKZUoKX8n/M34reNJyU8p9n6Z\ne3tRvjmfqL1RLDi6wDEpIcwvjIkdJnJHwB0qpxPiSofcm3O7ae9lx5IUf55XXgOgyGpj/bFU1h9L\nRa87QOtQf/o2CaJ3eBA1K3moEVlTWoZWRqez9/zujM6ge0NZlCkJmuxZtebmcqJjJxSTCb/77qXG\nFO0WOLFDh5K3dRs6NzfqrV2Dsars2HPB1jbhVM62/4LO9NXTYechlROJW5FvzufTPZ/y07GfHMe6\nBHfhcPph0grSANn29lKb4zczafskzuadBcCoNzLs9mEyjkpoXsrEOgRgn9KSgj8BE6MpstjYdjqd\nFQeTWH04ibTcoise1zTYjz7hQfQJD6JegLZa98rS3V9u5tDZbFrWrsyvIzqoHadc0OTKau66dSgm\n++kF37vuUjnNtVUZPpy8rdtQTCYy5swh4NVX1Y6kGZU+n0Lmi6MdHwvntTdlL29ueZO4nDgAPI2e\njG4zmv71+nMk44jMvb1EekE6U3ZNYXn0xXFUdwTcwcT2E2UclXAK2f1/gN+fcHwcALga9XRtUI2u\nDaox6f4m/BObyYqDSaw8lER8ZgEA++Oz2B+fxUcrj1EvwJu+5wvXJjV9eWLWTv4+ZX9T27FuVeY9\n01atL6/Uta1ThUNns9kff46CIiserjc+e1wUT3MrqxdWKgEwGrlt7z/oXLQ7xkhRFM48/gQFe/ag\n9/Sk7to1GCtXVjuWECWiyFrE1P9NZc6hOY7T2C0DWzKp4ySCfYJVTqctiqKw+NRiPt79MVkm+8g2\nGUclyjtFUTh0NpuVh+yF6/Hk3Cs+x82ox2SxXXYsyNedmYNb0aSmX1lFLTMrDiYxfN4eABY805YO\n9eSM663SVLF6WaF6njEwkOBpU/EID1cp1fXlbt5CXGQkAFWfe45qo+RKaOH8jmUcY8yWMZzIPAGA\nq96VUS1G8UTjJ6Tw+pfY7Fje2f7OZdMRIkIiGNNmDIFegSomE6JsnUrNPV+4JrMv7tw1PzfI153t\nY8vfBYYZeUW0eNe+Wc4LEfV5qVcDlRM5P00Vq0caNbZ3Jf+LMTCQ+hs3lH2gG6QoCjEPPkThwYPo\nfX2pt3YNBh/Zx1s4J4vNwpxDc5j6v6lYbBYAGvk3YnLnydStVFfldNpitpmZe2guX+/72nGVfzWP\narzZ9k0iape/X8JC3IzErAJWHUpmwpLir1fw9TDyz7heGA3l781v7882cjw5l3Zh/vw4rL3acZxe\n+fsbogKdTkfVEcMBsGVnk7lgocqJhPhvzmSfYfCKwXzxzxdYbBYMOgMjmo1g/t3zpVD9l0Nph3h0\n6aN8/s/njkL1oQYPsfj+xVKoCgFU9/NgcIdQOl3lNHh2gYU+n29ixcFENLRuViIuzFvdG3sOk8Wq\nchrnp6li1av9lcPkDVX8CZ42VYU0N8e7e3fcGjYEIGPOHGz5+SonEuLG2RQbC48uZOCSgexP3Q9A\nHb86zLtrHs81f062v71EvjmfD3d9yGN/PcaxzGOA/Xv1fd/veav9W/i4ylkVIS4175m2BPm6O277\nebhQ3c9++1RqHsPn/cP907ay9fwFWOVBm/PzVk0WG/vjs1RO4/w0VayGzJ6NMfDy/i6XmsG433ab\nSolunE6vd+xqZc3MJPPnn1VOJMSNScpL4tnVz/L+jvcptBYC8ETjJ/i53880qdpE5XTasiVhC/0X\n9+eHwz9gU2wY9UZGNBvBL/f8QovAFmrHE0KzZg5uRZCvO0G+7sx/pi3rX+3Gm3c1opKn/Y3wvrhz\nPPbtDp6YtYODCc5f3LU9v7IK9nmr4tZoqmcVoODQIeKfG4ktN9exg1XgW+PwHzRI5WTXp1itnO53\nD0XR0RirVaPumtXo3dzUjiVEsRRFYenppUzeMZkccw4ANbxqMKnTJFoHtVY5nbakF6Tz4a4P+Sv6\nL8exOwLuYEL7CdIeIcQtyC40M2PjaWZtiabAfPF0eb+m1Xmld0PqVHXerbi7fbSemPR8ujSoxtyh\nbdSO49Q0V6xeYCso4PQ992KOj0fv5UXYX8twCdT+VbXn/viDxDfGABA0YTyVH31U5URCXCmjMIN3\nt73Lmtg1jmP96/Xn9dav4+1acYd5A0SuinRc1d+2elv6hfXjo90fyTgqIUpRSk4hX607yYIdsVhs\n9rLEqNfxcOtajIqoT+AlbQTOou17a0jOsfezd6xbhfmRV7Y6ihuj2WIVIHfzZuIihwHg07s3wV9+\noXKi61PMZk7deRfm+HiMNapTb8UKdK6yW43QjnWx63h729tkFNpPTVVxr8LEDhPpVqubusE0IHJV\nJNsTt1/1fhlHJUTpOpOex6erj7P4f2cdx9xd9AzpWIfhXevi5+Ec/fOPz9zBlpOX9+CW59mypc0w\nceLEiWqHuBrX2rUpOh2N6cQJik6dwr1xOG516qgd65p0BgM6N3dyN2zAlpOLS3Aw7o0bqx1LCHKK\ncnh327t8/s/nFFjsO870qt2LaT2n0ahKI5XTacO4LeOKPa5Hz6fdPuW55s9V+JVnIUpTJU9X7mxS\nnV6NAzl7roCY9HwsNoXdMZks3BmLTgdNavppftzVK4v2XXEs12Rh3dEUnuksO9ndLG3/3wYCx7yB\n3tcXgKRJ7zr6WLXMr//9jgvF0mbMQLFYVE4kKrqdiTsZsGQAi08tBsDH1YfJnUfgIaMAABeVSURB\nVCfzSddPqOwuO65dTxWPKvSs3VPtGEJUGOE1/JgzpA0/DmtHi5BKAGQVmPlg+VG6frSehTtjsVht\n13kWUV5oemUVQO/lhcHXz7FSqZjNeHfqqHasa9IZDOgMBvI2b8aWlYVrnTDcG8oOFqLsFVgK+GT3\nJ0zaMYlcs30bxPbV2/NNr29oEdgCnU6nckJtWRmzkkxT5mXHAjwD+CriK6p5VlMplRAVV3BlTx5q\nVYsmNf04lpRNel4ReSYra4+ksHR/IlW93agf4K25n2UrDiaRllt02bELbQABTth/qzZN96xeoNhs\nnBn0OAV794LBQJ1FP2v+1LqtoICTPXthTU/HtV5dwpYsQafX/EK2KEcOpB5g7JaxxGTHAOBh9OCV\nlq/wUMOHNPeDXQv+Ov0XY7aMwaZcXK0J8Axg7YNrVUwlhLjAalP4fW8Cn60+TsK5Asfx22v6Mbrv\nbXSqX/zmA2Vty4k0hs7ZRdElK7/ldWvZsqL5lVWw7xDl3vR2zi36BaxWCg8dptKAAZou/nQuLqDY\nyNu6DWtGJm4NG+JWV0bciNJntpr5et/XvPX3W45VwmbVmjG913Q61uwohWoxVp9ZzRub38Cm2HAz\nuOHr6ouvmy9RPaJkRVUIjdDrdDSu4cugtiFU9nLlYEIWBWYrKTkmftubwO6YTOoFeKs6OWDPmUyG\nztlFocWGUW/fAKGSh6usqN4ip1hZvSDl089InzEDgMA338T/icdVTnRt1tw8TkZEYMvKwq1xI+r8\n+qsUCqJUncw8ydgtYzmScQQAo97IyOYjGRI+BIPeoHI6bdoQt4GX1r+ERbHgbnBnWs9pMmdWCCeQ\nU2hm5uZoZm4+TV7RxRmtd90exCu9G1K3WtleDHn4bDaPzNhGdqEFvQ6iHm3B3U2rl2mG8sqpilVb\nYaF99mpcnH326rKluAQFqR3rmlKnTiUt6isAak3/Bu+uXVVOJMojq83KD4d/IGpvFEU2e59Ug8oN\neL/T+zT0b6hyOu3amrCV59c9j9lmxlXvSlREFB1qdFA7lhDiJqTlmvhq3Unm7ziD2WovaQx6HQ+1\nCuaFiAYE+ZX+imZ0Wh4PfrPV0ac6ZcDtPNw6pNRft6JwqmIVIHfL38Q98wwAPr16ERz1pcqJrs2a\nlcXJHhHY8vLwaNaM2j8ulNVVUaLicuIYt2Uc/6T8A4Bep2dok6GMaDYCV4PM+L2aXUm7GLFmBCar\nCaPeyOfdPqdrLXkzKYSzisvI57PVx/n9fwlcqGzcjHqe6hjKiK51qeRZOj8Pz54r4MFvtjn6aMfd\n3UjGU5Uwp+hZvZRrSAhFMWcwHT9O0enTuDdurOnZq3p3d2x5eRTs2YMlORnP1q1wDQ5WO5YoBxRF\n4ZcTv/DC+heIy4kDIMQnhKgeUdxf73457X8Ne1P2MmLNCAqthRh0Bj7u+jE9QnqoHUsIcQv8PFzo\n0ySIvk2CSMoq5HRaHlabwp4zmczfEYuiQJOavriU4IzWtFwTj87YTmxGPgCjIuozsnu9Ent+Yed0\nK6sAlrQ0Tt11N7bsbIxBQYQtXYrBW7v7B1syMjjZIwKlsBDPtm2p/f0ctSMJJ5ean8r4rePZkrDF\ncezhhg/zcsuX8XTxVDGZ9h1IPUDk6kjyzHnodXo+6PwBd9a5U+1YQogStismgynLj7L7zMVxdNV8\n3Hghoj4Pt651y0VrVoGZR2ds53BiNgBPdQhlwj2N5expKXC6lVUAvacnhkqVyF2/HltuLorJhHfn\nTmrHuiq9hwfWzHMU7NuHOSEBrw4dcKkuTdfiv1kRvYKR60ZyIvMEYB+v9GnXTxnUeBAuBufYilAt\nRzOOErk6klxzLjp0TOo0iX5h/dSOJYQoBTUrefBgq2CaBvtxLCmH9Lwi8ousrDuawp/7zlLlFma0\n5hdZeOq7XeyPzwJgYMtgJt3fBL1eCtXS4JQrq3B+9uoTT1KwZw/o9YQu+hmP8HC1Y12VOTmFUz17\nopjNeHXpTMj5qQZC3KgsUxbvbX+P5THLHcf6hfXjjTZv4Ocme01fz4nMEwxdOZRzpnMAjG8/ngcb\nPKhyKiFEWbDaFJbsS+CTVceJz7w4ozW8hi+v972NLvWr3nDRarJYeeb73Ww+kQZA3/AgvnrsDs1v\nAevMnLZYBTCdPMnp/g+A2Yx7eDihP/2IzmhUO9ZVJb79NucW/ghA6C+/4NFEu8W10JbN8ZuZsHUC\nqQWpAFRyq8T49uPpVbuXysmcQ3RWNENWDCG9MB2AN9q8waBGg1ROJYQoayaLlYU7Yolad5L0vIs7\nTLUL82d039u4I+Ta209brDb+b+Felh9MAqBz/arMHNwKN6NcI1CanLIN4AKjvz9KkYmC3XuwpKZi\nqFQJj2bN1I51VW716pO5YAHYbFgzM/C96y61IwmNyzfn8/7O9/lo90fkW+wN/N2Cu/F1r6+5vert\nKqdzDnHZcQxdOZS0QvsqyCstX+HJ8CdVTiWEUINRr6d5SGUGtauNm9HAwYQsiqw24jML+GlXHEcS\ns2lU3Qd/L7crHmuzKYz+7QBL9p0FoGXtysx+qjUeLtpdJCsvnHplFc7PXr3vPsxnYtF7etpnr2q4\nH/Ts2DfJ+u03AOosWYx7gwYqJxJatSd5D29ueZOE3AQAvFy8GN16NPfXu18a+G/Q2dyzPLXiKRLz\nEgF4vvnzPNvsWZVTCSG0Ij3XxLQNp/hh2xnH9qh6nb0H9cWeDahRyQOwT195+8/DzNkaA0Dj6r4s\nHNYOPw+5TqAsOH2xCpC3dSuxQ5++eECnw6t9O0Jmz1Yv1FWYoqM5fXc/sNnwvftuan7ysdqRhAZE\nropkR+IOAFoHtaZxlcZ8f+h7FBTHsUkdJ1HDu4aaMZ1Kcl4yT614ivjceAAib49kVItRKqcSQmhR\nfGY+n685wW//xGM7XxW5GvVU83bl7LlCLi2Uwqp68fPw9lT1vnL1VZSOclGsAhzv1BlrWtplx4yB\ngQRPm6q5C68SXnmV7GXLQK8nbNlSTc+JFaUvclUk2xO3F3ufm8GNF1u8yGONHkOvk+b9G5VWkMaQ\nFUOIyY4BYHDjwbzS6hVZkRZCXNPx5Bw+WnmM1YeTi71fr4NZg1vT/baAMk5WsZWb337W9PQrjlmS\nk4l/bqQKaa6tyrPD7B/YbKR/O1PdMEJ1F1ZU/82oN/LzPT/zeOPHpVC9CZmFmUSuinQUqo80fEQK\nVSHEDWkQ6MO3T7bi1xHFb7tsU2DMbwfKOJWQ34AqcG/QAJ9e9qu4s5YsoSg+QeVEQosqu1UmzE+2\n7LsZWaYshq0exslzJwEYUH8AY9qOkUJVCHFTWtaujPzY0I5yU6x6tW93xbELbQBaVGX4+Ys8LBbS\nZ8nqakXWtnrbYo+7Glw5nnm8jNM4r9yiXIavHs7RjKMA3BN2D2+1e0tWpYUQ/0nHulWvOBbk687M\nwa1USFOxlZueVYDjHTpizcgAwFCpEg22b1M50bXFPvsseRs3oXNxoe6aNbgESg9MRRWxKIKU/BQA\nXPQumG1mAFz1rrze+nUeaviQrA5eQ745n+FrhrM3ZS8AfUL78EHnDzDqZaSMEOK/a/f+WpKyCwF7\nobp9bITKiSqmcrXkEPDaa46Pq47UXq/qv1UdPhwAxWwmQ4OTC0TZieoRRYBnAAGeAcztO5fnmj2H\nXqenyFbEpB2TeHnDy2SZstSOqUkFlgKeX/e8o1DtXqs7kztPlkJVCHHLZg5uRZCvu6yoqqxcrawW\n7NtHzMOPAFBrxnS8u3RROdH1nXlqCPnbt6Nzd6feurUY/f3VjiQ0YnfSbkZvHu1Yca3uVZ0pXaZw\nR8AdKifTDpPVxKh1o9h6disAnWp24ovuX+BqcFU5mRBCiJJSrlZWnZFjdbWwkIw536ucRmhJq6BW\n/HrPr3QL7gZAYl4iQ1YMYcb+GVhtVnXDaYDZaubVDa86CtV21dvxWbfPpFAVQohyRopVlXm2bYPH\nHfaVssz587GeO6dyIqElldwr8WWPL3mjzRu46F2wKlai9kYxbPUwx4prRWSxWRi9eTQb4jcA0DKw\nJV90/wJ3o7u6wYQQQpQ4KVZVptPpqDrCvrpqy8sjY958lRMJrdHpdAxqNIj5d80n1DcUgJ1JOxm4\nZCCb4jepG04FVpuVN7e8yeozqwFoWq0pUyOm4uniqXIyIYQQpUGKVQ3w6twZ98aNAcj44Qesubkq\nJxJa1KhKI37q9xP31b0PgExTJiPXjuSjXR9htppVTlc2bIqNCVsn8Ff0XwA0rtKYr3t+jZeLl8rJ\nhBBClBYpVjVAp9NR5cLqalYWmQsXqpxIaJWniyeTOk1icufJeBrtK4lzD8/l8eWPE5sdq3K60qUo\nCpO2T2LxqcUANKjcgOk9p+Pr6qtyMiGEEKVJilWN8ImIwK1+PQAyvpuDraBA5URCy/qF9ePne36m\ncRX7ivzh9MM8+OeDLD29VOVkpUNRFD7c9SGLji8CIMwvjBm9ZlDJvZLKyYQQQpQ2KVY1QqfXU+VZ\n++qqNSODc4sWqZxIaF1t39rMu3MeTzZ+EoB8Sz5jNo9h3JZx5JvzVU5XchRF4bN/PmPekXkAhPiE\nMLP3TKp4VFE5mRBCiLJQruasRj/4EIUHDgDg1qgRYb//pnKim6NYrZy+626KzpzBGBBA3TWr0bvK\nGB5xfZviNzFuyzgyTZkAhPqG8lHXj7jN/zaVk926af+bxtf7vgagpndN5vSdQ5BXkMqphBBClJVy\ns7IaO3Soo1AFMB05womu3Sg4dEjFVDdHZzBQZdgwACwpKWT99rvKiYSz6BLchV/u/YU2QW0AiMmO\n4bFljzH/yHyc+f3ozAMzHYVqoGcgM3vPlEJVCCEqmHKzsnqkUWMo5kvR+/gQHPUlrqGhGAMDNb+/\numI2c7JPHyxnE3GpWZO6K5ajc3FRO5ZwElablVkHZzHtf9OwKvaNA7rV6sa7Hd51uv7OuYfm8tHu\njwCo6lGVOX3nUNu3tsqphBBClLVyX6xeSufpiWtobdxC6+AaGoprnTr2P6GhGLy1M/omc+FCkt5+\nB4DqkydTqf/9KicSzmZvyl5GbxpNYl4iYF+V/KDzB7QKco69rX88+iPv7XgPAH93f2b3mU3dSnVV\nTiWEEEIN5aZYjR06lLyt2/7z443VqjkKV3sRG4pbnTq41KyJzmgsuaA3wGYycapnLyypqbiGhhK2\nbCk6g6FMMwjnl2XKYsLWCayNXQuAXqdneNPhDGs6DINeu3+ffj/xO+O3jgfAz82PWb1n0dC/ocqp\nhBBCqKXcFKsAJ7p2w5KcDIAxMJB669dhSUrCFB1NUXQMRTExFEVHUxQdjTkx8borsQC4uOBaq9b5\nQrY2bhdWY+vUwVC5cqm1FaTPmUPKB1MAqPnpJ/jedVepvI4o3xRF4edjP/Phrg8pshUB0CqwFZM7\nT9Zk7+fS00sZu3ksCgreLt7M7DOT8CrhascSQgihonJVrBYcOkT8cyMBCJ42FY/wq/+SsxUWUnQm\n1l68xtiLWdP5/9qys2/o9fS+vvYV2FD7Sqxr6PlCtnYIevdb26Pclp/PyYieWDMzcatfnzqL/0Cn\nLzfXw4kydizjGK9vep3TWacB+4rlpI6T6Farm7rBLrEyZiWvb3odm2LD0+jJjN4zaFatmdqxhBBC\nqKxcFaslQVEUrBkZjlVYU3Q0RTFn7EVtXByYb2BbS50Ol+rVL+uJvdBWYAwKuuGiM236DFI/+wyA\n4Klf4RMRcQtfmajo8s35TNk1hd9OXBzpNqjRIF5u+TKuBnVHpK2PXc/LG17GoljwMHrwdc+vaRnY\nUtVMQgghtEGK1ZugWCyY4+MxxcTY2wqiox1FrSU19YaeQ+fujmvt2hf7Yi+50Mvg43PZ51pzcznZ\nI+LiSq9Oh1f7doTMnl3CX5moSJZHL+ftbW+TZ84DwMvoRb7FvolA2+pt+bb3t2WaZ0vCFkatG4XZ\nZsZV78rUnlNpV71dmWYQQgihXVKslhBrbu7lfbEx0faiNuYMSv6N7SZkqFLFsQLrer61IGnCxCsK\nYWNg4HXbHIS4lricOEZvGs2BtANX3BfgGUBUjyjHVq6laXvidp5f+zwmqwmj3siX3b+kc3DnUn9d\nIYQQzkOK1VKmKAqW5GTHKuylF3uZExLAZvtPz2sMDKT+xg0lG1ZUKGarmRbzWhR7X4BnAGsfXFuq\nr78neQ8j1oygwFKAUWfkk26f0COkR6m+phBCCOdTtjOZKiCdTodLUBAuQUF4tW9/2X02kwlzbOzl\nfbHni1rruXMqJRYVhYvBBR06FMr+/eq+1H08t+Y5CiwF6HV6JneZLIWqEEKIYkmxqiK9mxtu9evj\nVr/+FfdZMjMpio4hcfx4ik6evOy+C20AQtyqttXbsj1x+2XHLrQBlJbD6YcZsXoE+ZZ8dOiY1HES\nfUP7ltrrCSGEcG7SBuAE/j0/Vk7/i5IUsSiClPwUx+3VA1eX2gzW45nHGbpyKFmmLAAmtp/IgAYD\nSuW1hBBClA8yuNMJBE+bijEwUFZURamI6hGFv7u/4/aGuA2l8jqnz50mclWko1Ad23asFKpCCCGu\nS1ZWhRDYFBu9FvUipSCFDjU6ML3X9BJ9/tjsWJ5a8RSpBfbJFq+2epXB4YNL9DWEEEKUT7KyKoRA\nr9M7drPambSTnKKcEnvuhNwEnl71tKNQHXXHKClUhRBC3DApVoUQAHQP6Q6AxWbh74S/S+Q5k/KS\neHrl0yTlJQHwbNNniWwaWSLPLYQQomKQYlUIAUCboDZ4Gj0BWB+3/pafLzU/lWdWPUNCbgIAQ8KH\nMLL5yFt+XiGEEBWLFKtCCABcDa50rNkRgM0JmzHbzP/5uTIKM4hcFcmZ7DMADGo0iJdavoROpyuR\nrEIIISoOKVaFEA7da9lbAXKKctiTvOc/PUeWKYvIVZGcyjoFwIMNHmR069FSqAohhPhPpFgVQjh0\nCe6CQWcAYH3szbcC5BTlMGz1MI5nHgfgvrr3Ma7dOClUhRBC/GdSrAohHPzc/GgZ2BKw963ezGS7\nPHMeI9aM4HD6YQDuDL2Ttzu8jV4nP2aEEEL8d/JbRAhxmQutAIl5iY4V0uspsBQwcu1I9qXuA6Bn\nSE/e6/weBr2h1HIKIYSoGKRYFUJc5sK8VYB1ceuu+/kmq4lR60Y5ely7BHfhwy4f4qJ3Ka2IQggh\nKhApVoUQlwn2CaZ+5frA9ftWzVYzL61/ie2J2wFoX709n3b7FBeDFKpCCCFKhhSrQogrXGgFOJJx\nxDHQ/9/MNjOvbXqNzQmbAWgV2IovenyBm8GtzHIKIYQo/6RYFUJcoUetHo6Pi9sgwGqzMnbzWNbG\nrgWgebXmTI2YiofRo8wyCiGEqBikWBVCXKFxlcYEeAYAsCFuw2X32RQb47eOZ0XMCgDCq4Qzrec0\nPF08yzqmEEKICkCKVSHEFXQ6naMVYGfSTnKKcgBQFIV3tr3DklNLAGhYuSHTe03Hx9VHtaxCCCHK\nNylWhRDFulCsWmwW/k74G0VR+GDnB/x64lcA6vrVZUbvGfi5+akZUwghRDlnVDuAEEKbWge1xsvF\nizxzHuvi1nEo/RALji4AINQ3lJl9ZuLv7q9ySiGEEOWdFKtCiGK5GlxxM7iRZ85jefRyx/Ga3jX5\ntve3VPWoqmI6IYQQFYW0AQghihW5KpKMwozLjul1et5o8wZBXkEqpRJCCFHRSLEqhCjWjsQdVxyz\nKTbe3f6uCmmEEEJUVFKsCiGEEEIIzZJiVQhRrLbV215xLMAzgKgeUSqkEUIIUVHpFEVR1A4hhNCm\niEURpOSnAPZCde2Da1VOJIQQoqKRlVUhxFVF9YgiwDNAVlSFEEKoRlZWhRBCCCGEZsnKqhBCCCGE\n0CwpVoUQQgghhGZJsSqEEEIIITRLilUhhBBCCKFZUqwKIYQQQgjNkmJVCCGEEEJolhSrQgghhBBC\ns6RYFUIIIYQQmiXFqhBCCCGE0CwpVoUQQgghhGZJsSqEEEIIITRLilUhhBBCCKFZUqwKIYQQQgjN\nkmJVCCGEEEJolhSrQgghhBBCs6RYFUIIIYQQmiXFqhBCCCGE0CwpVoUQQgghhGb9PxPQJ8jpzvSf\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cdef210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 7000 best: 60.934271\n",
      "('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B4P9', 'B2P7', 'B5P7', 'B5P2', 'B1P8', 'B4P5', 'B4P1', 'B1P9', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B7P6', 'B3P10', 'B5P4', 'B2P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\n"
     ]
    },
    {
     "data": {
      "image/png": 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dUxdTtIxn8/46dpGFu2IBaFi1LF3rVdI4kQApVm/LuVo1ghcvxrVuXQAyDh/m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\nCM2SYlUIIYQQQmiWFKtCCCGEEEKz/h/xvKjigNo4ygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cdef550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 8000 best: 60.772526\n",
      "('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B4P9', 'B2P7', 'B5P7', 'B5P2', 'B1P8', 'B4P5', 'B4P1', 'B1P9', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B7P6', 'B5P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B2P4', 'B3P10', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\n"
     ]
    },
    {
     "data": {
      "image/png": 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udd3LdltHe6M9XWt3JSU3hWNJx8gyZbHm7Br8nP0I8Qgp03OJitHrnpos2xtD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uBrgtespvzoV9DkbfpK+XklFx99jGsrV6KYzUUd/b7otXq6VLWsDuy9speUnBSVE4l/Y1bMLDpu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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf24e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 9000 best: 60.709435\n",
      "('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B2P9', 'B4P7', 'B4P9', 'B2P7', 'B5P7', 'B5P2', 'B1P8', 'B4P5', 'B4P1', 'B1P9', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B2P4', 'B7P6', 'B5P4', 'B3P10', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\n"
     ]
    },
    {
     "data": {
      "image/png": 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VU+V2zmphFBsbnJs1w75GDVK3/wnZ2aTt3o3h4kVcHm+BotdrHVEIYeX2x+9n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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b4fce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 9999 best: 60.324768\n",
      "('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B4P9', 'B2P7', 'B5P7', 'B5P2', 'B1P8', 'B4P5', 'B4P1', 'B1P9', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B3P1', 'B6P1', 'B7P3', 'B2P3', 'B1P6', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B2P4', 'B5P4', 'B7P6', 'B3P10', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\n"
     ]
    },
    {
     "data": {
      "image/png": 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H6RuohJNi9RZDqVIEfvstZYZoDbStiYlEDh3Kje/mUkKn9QohdBKdEk3/df35\n8dyPAJRxKsOcNnPoVa1Xvtyyf8DrAZa0X0JDv4YAnLxxkp5renL0uuzwV5Q1qVz2jsdNFiunoxML\nOU3RlZieyciV2r8FFwcDE7rUxk7mqeqqxM5ZvRPFzg7XRo1wrFSJ5G1/QmYmKbt2Ybp0Cbf/NUUx\nGvWOKIQo5vZH72fIxiFcTLwIQG3v2sxpM4cqXvm7raOT0Yl2ldpxM+MmJ26cINWcyuqw1fi7+RNS\nOiRf30sUjs51y7N0XyTJGWYAsn6vMZmt/H48mhZVfWTOZR6M/jmUPRfiAPjgqer870FvnRMJGVm9\nA/d27Qha8gNG/3IAJP72Gxd79yHz6lWdkwkhiitVVVl8cjGDNwwmLl37QdntwW7MazMPHxefAnlP\nezt73n/0fUY/OhqjYsRkNTFqxygmHZiExWopkPcUBWtOv/oYb40CNqpUhvfbVwPgRoqJnrP3cv5a\nsp7xbN4fp6+x/EAUAE0eKEPvhhV0TiRAitW7cqpWjeCVK3GpXx+A9BMnuNClK6kHD+qcTAhR3KSZ\n03hn+zt8uf9LLKoFBzsHxjQew+hGo7E32Bf4+3cL6cbMJ2bi4egBwLwT83h1y6skm6SwKWpqBHhQ\nydsVAA9newY1rcTbT1YFIDY5g16z93DhVs9QkVNCaibv/HQMADdHI+Pk9r/NkGL1HxhLl6bCvLl4\n9eoFgOXGDS72H0D88uU6JxNCFBeRSZH0XduXtRfWAuDn6seCtgt4psozhZqjYbmGLGm/hAc8HwBg\n++Xt9F7bm0uJlwo1h8h/Lz5emTef0HZpvJakFayXbqTqnMr2fPTbCWISMwAY3aEaAZ7OOicSWaRY\n/ReKvT1+H4zG75MxYG8PmZlEf/AhVz/+GNVk0jueEKII23l5Jz1W9+BMvLajVAO/Bixtv5QaZWvo\nkiewVCCL2i7i8fKPAxCeEE7PNT3Zc3WPLnlE/nm1ZRWGtdTmPV9NSKfn7D1ExUvBmmX9iWhWHb4M\nQPMQb7rVD9Q5kbidFKt55NW1KxUXzMdQVltteXPJUi4NfB7zjRs6JxNCFDWqqjLn+Bxe3PQiiSZt\nlXa/h/ox64lZlHEuo2s2Nwc3vm7+Nc/XeB6ARFMiL2x8gR9O/SCdUYq411tV4cXHKwNw+WYaPWfv\n4cpN6bEbl2LivVXHAXB3MvLFs7VkowwbI8XqPXCpW5fglStwqqGNeqQeOMCFLl1JP3lS52RCiKIi\n2ZTM61tekfK1AAAgAElEQVRfZ/KhyaioOBudGfe/cYxoMAKjnW10HDHYGXit3mt83vRzHOwcsKgW\nPt/3OWP2jCHTkql3PHGfFEVhZJsQBjfVNsGJjEuj1+w9RCek65xMX6N/DiU2WbtT+nHH6vjKbl82\nR4rVe2Tv50fFxYtwf/opAMxXrxLRqzcJa9bonEwIYevCE8LptbYXmy9tBqC8W3kWtV1E2+C2Oie7\nsw6VOrCg7QK8nbXWPSvPrmTIxiHEp8frnEzcL0VRGNWuGgOaBAEQcSOVXrP3cC2pZBasvx29wprj\nWqef1g/50qnOve0MJwqHFKv3wc7JCf8vv8Tn7bfBzg41PZ0rb47g2sRJqBZp9yKEyG3zpc30WtOL\nCwkXAHgs4DGWdlhq8z1Na5StwdIOS6lRRrujdCDmAD3X9ORs/Fmdk4n7pSgKH3R4iL6PVgQgPDaF\n3rP3EpucoXOywnUtKZ3Rv4QC4OViz9hnasrtfxslxep9UhSFMgP6Ezh7FnYeWruXG7NnE/nii1gS\nZacQIYTGYrUw5dAUXvvjNVIytZZBQ2sN5ZsW32S3irJ1Pi4+zHtyHu2C2wFwOfkyfdf2ZculLTon\nE/dLURQ+fro6PRtqC4nOXUumz5y9xKWUjIXDqqoy6qdQbqZq01o+7VQT71KyYYKtkmL1P3Jr0oTg\n5ctweECbtJ7y53YiunUnIzxc52RCCL0lZCTw8paXmX18NgCu9q5Mbj6ZVx5+BYOdQed098bJ6MQX\nTb9geN3hKCikmlMZ/sdwZh+bLQuviig7O4WxnWrSpV55AE5HJ9Fnzl5uphb/gvWnQ5fZdCoGgA61\nytG+VjmdE4l/IsVqPnCoWJGgpctwa9USAFNEBBHdupP0xx86JxNC6OVM3Bl6rO7Bzss7AajkUYkl\n7ZfQokILnZPdP0VRGFRzEFNaTMHF6ALAlMNTeHv726SbS+acx6LOzk7hy2dr8czD2lzNk1cT6fvd\nPhLSiu9CuqsJaXz02wkAyro58klHfVrFibyTYjWfGNxcKT9lCmVffhkAa3IyUS+9TOyMmTLqIEQJ\nszZ8LX3W9iEqWdu2sVWFVvzQ/geCPYJ1TpY/Hg98nMXtFhPgphU4v1/4nX7r+hGTEqNzMnE/DHYK\n47vU4qna/gAcv5xAv7n7SEovfgWrqqq8/eNxktLNAHzeuSZerg46pxL/RorVfKTY2eH96isETJmM\n4uICqsr1r7/m8htvYE2V5stCFHdmq5lx+8dpI42WdBQUhtcdzqTHJ+Fq76p3vHxVxasKS9ovoYFf\nAwBO3jhJjzU9OHb9mM7JxP0wGuyY1K02bWv4AXAk8ib95+0nJcOsc7L8tXR/JH+evQ5A57oBPPGQ\nr86JRF5IsVoA3Fu3JmjJEuzLa/OAkn5fR0Sv3piiLuucTAhRUG6k3WDIxiEsOrkIAA9HD2a0msGg\nmoOK7QpjLycvZj4xk24PdgMgNi2WAesG8FvYbzonE/fD3mDH5B4P06qaVsAdvBjPgPn7STUVj4I1\nMi6VT1drfdH93J348KnqOicSeSXFagFxCnmQoBXLcWn0KAAZp08T0bUrKXv36ZxMCJHfjl8/TvfV\n3dkfvR+AqqWrsrT9UhoHNNY5WcGzt7NndKPRvPfIexgUAyariVE7RjHp4CQsVmnlV9Q4GO2Y1vth\nmodovXX3XYhj0IIDpJmK9t+l1aoycuUxUm59Hl88WxMPZ3udU4m8kmK1ABm9vKgwezal+/UDwBIf\nz6WBA4n7/nuZxypEMfHTuZ+0+Zqpt1YWV+rAwrYLKV+qvM7JClePqj2Y+cTM7HZc80LnMeyPYSSb\nknVOJu6Vo9HA9D71aFpF2158V9gNhiw6QHpm0S1YF+25yO5wbXv0ng0DeTzER+dE4l4oqlRNheLm\nqp+J/vBDVJPWEsSjy7P4ffABdg4ysVuIomTwgobsVbU56N6KkWtoP8ANioG3GrxFr6q9iu1t/7yI\nTIzk1S2vEpYQBmhdEKa2mEoF9wo6Jys5Wn+1jbMxybSt4cf0PvXu+3XSMy0MnL+fXWFakfd4iDcz\n+9bD0Vi02q5FxKbQdvJ20jItBHg6s/71/+HmaBtbG4u8kZHVQuL5TCcqLl6E0Uf7bS5h5Y9ceq4f\nmdeu6ZxMCJFXgxc0ZA9pqIqCqijZhaq70ZU5refQu1rvEl2oAgS6B7K43WKalW8GaFvMdvy5I7UW\n1KLWgloM3jBY54Qir5zsDczpV5+GwaUB2HrmOi9/fwiT2apzsryzWFVGrDhK2q1R4fFda0mhWgRJ\nsVqInGvVImjlCpxr1wYg7cgRIrp2I+34cZ2TCSHyImtE9e8cTMnU96tfyGlsl5uDG5ObT2ZAjQEA\nmFUz6q3/7Lm6h5YrWnLyxkmdU4q8cHEwMq9/A+pX9AJg06lrDFtymExL0ShY5+28wIGL8QD0a1SR\nxpXL6pxI3A8pVguZvY8PFRYtxOPZzgCYY2K42LsPN3/+WedkQoh/lJ5w11N2MpkqF4OdgTfqvYFC\n7pHma6nXeHXLqzqkEvfD1dHIvAENeLiCJwDrTkTz2rIjmG28YD1/LZlx688AEFTGhbfbVtU5kbhf\nUqzqwM7BgXKfforv+++DwYBqMnH1nXeJ+fwLVHPxaBEiRLESsQOmN+GR9Ny7NPlYVKY2/lSHUEIU\nnlJO9iwY2JBa5bUFdGuOXeXNFUexWG3zNzWzxcqbK45iMltRFJjQtTYuDnL7v6iSYlUniqJQuk9v\nKnz3HQZP7bfVuAULiBwyBMvNmzqnE0IAYM6ADe/D/A6QEMns6Ov4mP9aEe1jUdk8MJSHqnbSMaRt\nq1o692iWj4sPU1tM1SGN+C/cnexZNPARHirnDsAvR64wcuUxrDZYsM78M5yjkdrP0kGPBVM/qLTO\nicR/IcWqzlwffYSglStxDAkBIGXXbi507Ub62bM6JxOihIsOhVnNYddUQAWjM7SbwNQmY/GxqDKi\nmkchpUNyPPZx8WFz1808VOYhnRKJ/8LDxZ7vBz1CVb9SAPx4KIpRq47bVMF6OjqRrzdpP0Mre7vy\nZuuQf3mGsHVSrNoAh/IBBC35gVJPPglAZmQkET16krhxo87JhCiBrBbYORlmN4drJ7Rj/g/D0D+h\n4WAeqvoMmweGyohqHmRYMth0cRMAjgZHGVEtJrxcHVg86BGq+LgB2hamH/waahP9wzMtVt5cfpRM\ni4qdAhO71cHJvmi12hK5SbFqI+xcXAj4ahLer70GioKamsrlV4dx/ZtpqFbbnsQubMDCjvCRp/Zn\nYUe90xRd8RdhwVOw8QOwmEAxQLO34fmN4P2g3umKnG2R20jO1DYFGPvYWBlRLUbKujny/eBHqOTt\nCsDiPZf4+LeTuhes32w5z4kriQC80KwydQI9dc0j8ocUqzZEURTKvjCU8tOmYeeqfQOI/eYbooYN\nw5KconM6YbMWdoTwrYCq/QnfChOrwZUj+uYqSlQVjvwA05vAxZ3asdKVYOB6aD4KDLIt4/1YE74G\nAFd71+y+q6L48CnlxJLBjxJUxgWA+bsiGLvmlG4Fa+jlBKb9cR6Aqn6lGN6qii45RP6TYtUGlWrR\nnKDly3CoWBGA5E2budizB6ZLl3ROJmxS+Lbcx5KuwJIehZ+lKEq5Acv7ws8vgilJO1Z/ILywAwIb\n6JutCEvISGD75e0AtKrQCiejk86JREHwdXfih8GPEljaGYA5Oy7w5bozhV6wZpgtvLH8CGaritFO\nYULX2kVupy1xd1Ks2ijHypUJWrEc16ZNAcg4d54LXbuRsmuXzslEkZEUDb+9prVdkqkkd3Z2A3z7\nKJz6TXvs6gO9VkCHr8DBVd9sRdzGixvJtGYC0L5Se53TiILk7+nMksGPEuCpFawztoXx1cbCXST8\n9aZznI3Rppy80uIBagR4FOr7i4IlxaoNM7i7EzhjOmUGPQ+ANSGBS4MGc2P+fN3nBQkbUulut1dV\nODgP5reHr2toLZiuHNFueZd0phRY/Tr80BVSbm15XLUDvLQHHmytb7ZiImsKgLezNw39GuqcRhS0\n8l4uLBn8KOU8tBH0KVvOM2XzuUJ570OX4pm5LQyAGgHuvNz8gUJ5X1F4pFi1cYrBgM+IEfhPmIDi\n6AhWK9e++JKr77yLNSND73jCFjz3Cyi3/VMu5QedZ0OV1mB3qwl24mWtBdOsZvBNA9j6BcSe1yev\n3qIOwIymcGCu9tihFHSaDt0Xg2sZfbMVE9Ep0RyMOQjAk8FPYrCT27ElQYUyWsHqU8oRgEkbz/Lt\n1oL9PpOeaWHEiqNYVXAw2DGxax3sDVLaFDfyN1pEeHRoT8UfvsdYrhwACb/8wsU+fcmMidE5mdCd\nKQXUW7f5HUtBz2VQqxv0XgFvnoX2k6Bik7+uv3EOtn4O39SDWY/Drm8g8You0QuVJRP++Ay+aw1x\n2igMFRrDizuhTi9Qcm8LKu7P7xd+R0UbwZcpACVLUFlXlgx5lLJuWsE6bt0ZZv8ZXmDvN379GcKv\nawuQX3uiCiG3+r+K4kVR5X5ykWK+cYOo4cNJO6CNWhi8y1J+8hRc6j6sczKhm5iTML2R9nHnOVCr\n652vS4iC0B/h+EqIPva3kwoEPQY1u0C1p8GlmO32EnsOfhoMVw5rjw0O0OJ9aPQKyKhfvuvyaxfO\nxJ8hyD2IXzv9iiK/CBSaPnP2suN8LABlXB04OPoJXXKci0mix6w93EgxAfBBh4cY+Fhwvr7Hvgtx\ndJ+1G1WFOoGerHyhEUYZVS2WpFgtglSTiejPPuPm0mXaAXt7yn34AZ5duugbTOjj9FpY2lP7+PlN\neVvBfv0shK6E4ysg7m+jHnb28EArrXANaQtLe/3VcaBSM23aQVGhqrB/DmwYDeY07ZhPdeg8C/xq\n6JutmDoff55nfn0GgJfrvMwLtV/QOVHJcXuhmsXP3Yk5/errsuDodHQiPWftIT5VW2in3PqvJpXL\nsnjQI//ptVMyzLSdvJ1Lcak4Gu1YO7wplb3d/ntoYZOkWC3C4pcuI/rTT8FsBsCrd29833kbxV56\nQpYou7+F9e9qH484D27eeX+uqsKVQ3D8R23UNTk653nF7q8pBllK+UPPJeBf57/lLmiJV+GXlyFs\n860DCjR+BZq/D/bSRqmgTD40mTnH5wCw9pm1BLoH6pyo5Ah+d80d10/6uTuxZ1TLwg8EnLiSQMdv\ndmL+23as/7WIHv1zKIv2XATg/fbVGNS00n/OKmyXjJcXYV49ulNxwXwMZbRFIfHff8+l5wdhjovT\nOZkoVPER2v/au4Jr2Xt7rqJAQD148jN44yQ89yvUfQ6cbv0A+XuhCrd6uPb8T5EL3IlV2tSIrELV\nIxD6/QatP5VCtQBZVStrw9cCUMu7lhSqgur+HlisuSvo6MR0Bi04cF+vufN8bHah2jCoNAOb5O/0\nAmF7pFgt4lzq1SN45QqcHtK2MEzdt4+ILl1JP3VK52Si0GQVq15B/22RkJ1Bu83/9FQYcQ56LPmH\ni230hkzaTfhpCKzoD2nx2rHaPbVFVMFNdY1WEhy5doQrKdpivfbBsrCqsFXxyX0bPGsEU1f5OGU5\nKT2TkSu1OffO9gbGd62FnZ3MiS7upFgtBuzLlaPi94tx79ABgMwrV4jo1ZvE33/XOZkoFLcXq/nF\n6AhV20Glx+983mAPlw/l3/vlhwvbte1Sj92ay+3sBV0XwDMz/hopFgUqq7eqQTHQJqiNzmlKlos3\nUrhyMz3Hsazb/3o3yG9SOfcdHw9n+/sqoseuOcXlm9r881HtqlKxjGzeURJIsVpM2Dk74z9+HD5v\nvQV2dqhpaVx+/Q2uffU1quxeVHxZrXBTux2Wr8Vqlud+0eaoZjHcmg998xJ89wRs/RIs5vx/33uR\nmQ7r34MFT0FilHbsgVZag//qnfTNVoJkWjJZf3E9AI/6P0oZZ+lZW1hMZivDlhwmOePW+gUXe9sY\nUb1l8aBH8HPPOf3Gy8WeqvfYZuqPM9dYuj8SgCYPlKH3IxXzLaOwbVKsFiOKolDm+YEEzpyBXSnt\nm8CNmTOJeullLElJOqcTBSI5Bsy3RlMKolgFbTFVKX/tz4D10GK0ttmA1QxbP4O5rbXWUHq4ekzr\nFbv7G0AFozO0nwi9V2qbI4hCs/PKThIyEgCZAlDYJm48w9Eo7Ws/sEkwhz9obRMjqreb068+fu5O\nlHLSNiqJuJHKT4cu5/n5CamZvPOjdvvfzdHIuC615fZ/CSLFajHk1rQpwSuW41C5MgDJW7cS0a07\nGeEXdE4m8l3WFAAouGLVvw68eUr7U74e/G8EDN4C3tW085cPajtC7Z2ljfQWBqsFdnwFs1vA9Vvz\ns/3rwgs7oMEgafCvg6wpAM5GZ1pW0GfleUn059nrzNymtZ+r7u/O221DdE50ZzUCPNgzqiX732uV\nvSXr5M3nMJnz9j3j499OEJOo7do4ukM1AjydCyyrsD1SrBZTDkFBBC1bilvz5gCYLlwgont3kv/8\nU+dkIl8VRrF6J+Vqw5CtWlN9FK2H6e9vweLOkJD30ZL7Eh8B89vDpo/AmgmKAR5/F57fAGVlT3A9\npGSmsDVyKwCPBz6Oi72LvoFKiOtJGbyx/CgALg4GpvZ8GEejbW9y4WRv4OXm2r/TyzfTWH4g8l+f\ns+FEND8d1r6vNA/xplt96TJR0kixWowZ3NwoP+0byryoNeW2JiUROfQFYmfPRtrrFhO3F6ueFQr3\nve2doM1Y6L8aPG69d/gfWsuo4yvz//1UFQ4v1hZRXdqtHStdGZ7fCI+/89d8WlHoNl/aTLpFm47S\noVIHndOUDFaryhvLjxCbrI02julYg0pFpCl+t/qBlPfSRka/2XKe9EzLXa+NSzExatVxANydjHzx\nbC3ZEa0EkmK1mFPs7PAZPpyAr79GcXYGVeX6xElceXME1rQ0veOJ/yqrWC3lr1//0KDHtNZQdfpo\nj9MT4MfnYcUASM2nnr8psbCsj9bk35SsHWswCF7Yrk1NELrKmgLg5ehFI/9GOqcpGebsCGf7OW23\nqk51/Hm2boDOifLOwWjHsBZVAK3f6tJ9l+567ehfQolN1rZs/bhjdXzdpU9ySSTFagnh/mQbgpYu\nwT5A+4aWuHYtEb17k3nlis7JxH9SEG2r7oeTO3SaBj1+AJdbbWpO/ATfNoJzm/7ba59dr73O6dXa\nYzdfbQFV+4ngIG1r9BabFsueq3sAaB3UGns7GeEuaEcjbzJu3RkAKpZx4ZNONYrcaGPnugEEldGm\ni0zbGkaaKffo6upjV1hz7CoArR/ypVOdolOQi/wlxWoJ4hQSQtDKFbg8ou3JnHHyFBe6dCV1/36d\nk4n7ZivFapaq7bWWUSHttMfJ0fD9s7D6dTCl3NtrZSTDb8Phh26Qck07Vu1p7fWrPJG/ucV9W3dh\nHdZbO53JFICCl5SeyatLDmO2qhjtFKb0eJhSTkXvFwSjwY7hrbTR1etJGSy+tSNVlutJGYz+ORTQ\n2lyNfaZmkSvIRf6RYrWEMXp5UWHObLz69gXAEhfHxQEDiV/yT7sVCZtkStWKQYDSNrTdoJu3NsLa\ncRo43OqjeGAuzHgMIvP4i1HkPu36g/O1x47u8MxM6LYQXEoXSGxxf7KmAAS4BVDbu7bOaYo3VVV5\n/+dQLsWlAjDyyRBqB3rqnOr+PV07gMre2t2R6dvCSLnVJ1ZVVUatOk58aiYAn3aqiXcpR91yCv1J\nsVoCKfb2+L03inJjx6LY24PZTPTHY7j6wYeoJpPe8URe3bxtnpetjKxmURR4uI82l7ViE+1YXLjW\nk3XzJ2C+y//PLJmw5VOY2wbib7Vaq3hrTmztHtKSysZcTLxI6A1t9KtdcDsZ+SpgPx66zC9HtKlb\n/3vQm0GPVdI50X9jsFN4rdWDgLaQav6uCABWHb7MxpMxAHSoVY72tcrpFVHYCClWSzDPZztTcdFC\njN7eANxcvpyL/Qdgjo3VOZnIE73aVt0Lr4rQbzW0/hQMDqBaYfsEmNMSrp3Kee31szCnFfw5XrvO\n4KA9r99vhd/pQORJ1qgqyBSAghZ+PZkPftF+MSjr5sjErsWjKX77muWyd7Ka9Wc452KS+PDXE4D2\neX7SsYae8YSNUFTpYVTiZcZcI+rVV0k/pu0OYvTzo/zUqTjXlG8SNm3PdFj3jvbxiHPg5qNvnn8T\ncxJ+GgIxWhsaDI7gUV4bcQVt1PTW3Ed8a0DnWeBbXZ+s4l+pqkqHVR24lHSJaqWrsfyp5XpHKrYy\nzBY6f7uLE1cSAVg4sCH/e9Bb51T5Z11oNC8sPpjr+Ky+9WhdXXaiEzKyKgB7Xx8qLlqIRydtH3Vz\ndDQX+/Qh4bffdE4m/lHWyKq9C7gWgR9cvg9pO1899gYodmDJgLgwQNX+ZBWqtXtq10mhatNCY0O5\nlKRNRWlfSbZXLUhf/H46u1Ad2qxSsSpUARbtich1zMneDn/ZpUrcIsWqAMDO0ZFyn3+G76h3wWBA\nzcjgylsjiRk3HtVy94bNQke3dwIoKnMFjQ7Q6kMYsO7u14RvA6MsprB1ay5oUwAUFJ4MelLnNMXX\n5lMxzNsZAUDtQE9GtLbN7VT/i11hN3IdS8+0MmjBAR3SCFskxarIpigKpZ97jgpzZmPw8AAgbu5c\nIocMxZKQoHM6kYutta26FxUeAYpIgS1yMVvN/H7hdwAa+jXE19VX50TFU0xiOm+t1KZnuTkamdrj\nYewN8mNblDzy/3qRi2ujRgStXIFjFa0HXsrOnVzo1o2M8+d1TiayqWrRLlYBKjXLfayUP/SUNmq2\nbu/VvcSla7uTyRSAgmGxqry29AhxKVrnjLHP1KDCrSb6xU2TymVzHfNzd2JOv/o6pBG2SIpVcUcO\ngYEELV1CqdatAci8eImIbt1J2rJF52QCgOQYMGt7sRfZYvW5X7TiNEspf3jzFPjX0S+TyJOsLgAO\ndg60qthK5zTF0/St59kdrt0e71qvPB2L8e5Niwc9gt9t26j6uTuxZ1RLagR46JhK2BIpVsVd2bm6\nEvD1V5Qd9ioA1tRUol56mevffotqteqcroQrCm2r8qLnEq1IlRHVIiPNnMbmS5sBaBbYjFJZGz+I\nfHPwYhxfbToHQCVvVz7uWPwXG87pVx8/dycZURV3ZNQ7gLBtip0d3i+9hFNICFfeGok1NZXYKVPJ\nOH0G/88/w85V9mbXRXEpVv3raKOposjYGrmVVLO2g1L7YJkCkN8S0jIZtuQIFquKg8GOqT0fxsWh\n+P+orhHgwZ5RLfWOIWyUjKyKPCnVsiVBy5ZiX1Frzp60YQMRPXthiozUOVkJdXuxKg3zRSFaG74W\ngFIOpWhavqnOaYoXVVV596djXL6ZBsC77apS3V9uhQshxarIM8cqVQhevhzXJtr2mRlnzxLRpSsp\nu3frnKwEyipWS5UDe+lFKArHzfSb7Li8A4DWFVvjYHDQOVHxsmRfJGuPRwPQsqoP/RsH6RtICBsh\nxaq4JwYPDwJnzqD0wIEAWBISuDRoMHELFyKboRWiot4JQBRJGy5uwKyaAekCkN/OxiTx8W/aNqO+\n7o6M71obpaj0TxaigEmxKu6ZYjTiO/It/Md9ieLgABYLMZ99ztVR72HNyNA7XskgxarQQVYXAF8X\nX+r51tM5TfGRnmnh1R8Ok2G2oijwVfc6lHaVUWshskixKu6bx9NPU/H77zH6aXs3J6xaxcXnniMz\n5prOyYq5zDRIuqp9LMWqKCRXkq9w6NohANoFt8NOkR8f+eXTNSc5E5MEwCvNH6DxHfqOClGSyXcb\n8Z8416xB8IrlONetC0D60WNEdOlC2pEjOicrxm5e+utjKVZFIVl7YW32xzIFIP+sC73K4j3av+l6\nFb0Y3rKKzomEsD1SrIr/zOjtTcX58/Ds2hUA8/XrXOz7HDd//EnnZMVUjrZVwbrFECWHqqrZUwAe\n8HyAB70e1DlR8XD5Zhojb22n6u5kZHKPOhhlO1Uhcin+zdtEoVAcHPAb8zFOD1UjeuxnqJmZXH3v\nPdJPn8Z35Fso9vZ6Ryw+ikuPVVFknI0/y/mb2nbL7Su1l4U//1GfOXvZGRbL7WtSv3y2FuW9iud2\nqkL8V/IrnMg3iqLg1bMnFefNxVC6NADxixZxafAQzPHxOqcrRrKKVaMzuPnoGkWUDFmjqqDNVxX3\nr8+cvew4n7NQdbY3EFhaClUh7kaKVZHvXBo0IHjFchyrVQMgdc8eIrp2I/3MGZ2TFRO3dwKQES5R\nwKyqNXu+al2fuvi7+eucqGjbGRab61hapoVBCw7okEaIokGKVVEg7AMCCPrhe9zbtQUgMyqKiB49\nSVy3XudkxYC0rRKF6GDMQWJSYwBZWFWQzFar3hGEsFlSrIoCY+fsjP/EiXi/+QYoCmpaGpdfe41r\nkyejyjfm+6OqUqyKQpU1BcCoGGldsbXOaYq+JndpS5VhtnIgIq6Q0whRNEixKgqUoiiUHTyYwBnT\nsStVCoAb02cQ9cqrWJKTdU5XBKVch8xU7WMpVkUBM1lMbLi4AYDHAh7D08lT50RF3+JBj+Dn7pT9\n2NXRAEBSuplec/ay5thVvaIJYbOkWBWFwq1ZM4KWLcMhWGu1lLxlCxHde2CKiNA3WFEjnQBEIdoe\ntZ0kk9asXqYA5J85/erj5+6En7sTy4Y04stna2KwUzCZrbz8wyFmbguT7auFuI0Uq6LQOFYKJmj5\nMtyaNQPAFBbGhW7dSd6+Q+dkRUjchb8+lmJVFLA1F7QpAC5GF5oFNtM5TfFRI8CDPaNasmdUS2oE\neNC9QQXm9W+Am6PWTfLz30/z/s+hmC0yXUoIkGJVFDJDqVKU/3YaZYYOBcCamEjk0KHc+O47GUnI\ni9tHVj0r6BZDFH9JpiS2RW4DoFXFVjgbnXVOVLz970FvVrzQKHuKwPd7LzF44QFSMsw6JxNCf1Ks\nikKnGAz4vP4aAV9NQnF2BquVa+MncOWtkVjT0/WOZ9uyilU3P3CQvoyi4Gy6uAmT1QRA+2CZAlAY\nqv19K1IAACAASURBVJVz5+eXm1CtnDsAf5y5TreZu4lJlO+LomSTYlXoxr1tW4J++B57f61vY+Lq\n1Vzs3YfMq7LA4K6kE4AoJFldAMo4laFhuYY6pyk5/DycWPFCI5o96A3AiSuJPDNtJ2eik3ROJoR+\npFgVunKqVo2glStwadAAgPQTJ7jQpSupBw/qnMxGSbEqCkFMSgz7ovcB0Da4LUY72Zm7MLk5GpnT\nrz49GwYCcCUhnS7Td7HzfO4NBYQoCaRYFbozli5Nhbnf4dW7NwCWGze42H8A8cuW65zMxmSmQ9IV\n7WMpVkUBWhexDhVtDrl0AdCHvcGOz56pycgnQwBIyjDTb+4+Vh6M0jmZEIVPilVhExR7e/xGv4/f\nJ2PA3h4yM4n+8EOufvQRqsmkdzzbcPPSXx9LsSoKUNYUgIruFaleprrOaUouRVF46fEHmNyjDg4G\nO8xWlRErjvLVxrOyIFWUKFKsCpvi1bUrFRcswFBW2+Xl5tJlXBw4EPONGzonswHSY1UUgvCb4ZyK\nOwVoC6sURdE5kehYJ4DFgx7Bw9kegMmbz/HmiqOYzNLaSpQM/2fvvuOjKrMGjv+mZNJ7TwghFCkB\nRHoREJBmw7LqsqKgEl1Bd1/76loQy+66uqsiukjEBTtiXwVBqvQuvSQkpJAQQirpM3PfP54UQoK0\nJHdmcr5+8uHOncnMmQjJyXPPc44kq8LhePW+grhFX+DRvTsAZVu3kfK7Wynbu1fnyHQmyapoAf87\n8r/aYykBcBz944L4atpgYoJUC7Gvtmcy5YPNFJZV6RyZEM1PklXhkNwiIoj96EP8J9wAgDUri6N3\nTKLwfz/oHJlOFkyAxY+rY4MRfCNa9rVnBKiPBRNa7nVFi9M0jR9TfgSgR0gP2vpJL19H0iHUh6+n\nDeHyGDX2dn3ySW79z3oy8kt1jkyI5iXJqnBYRg8PIv/+d8L+8iQYjWjl5Rx77DFyXn8dzWbTO7yW\ns2ACHFlVd1uzw7+6wbGdLfjamvo4sgpe79oyry1a3K8nfiXzVCYgq6qOKsTHnc8SBjI2PhyAQ8dP\ncdM769mdUahzZEI0H0lWhUMzGAwET5lCzNz3MPr7A3BybiLpDzyArahI5+hayJHVDc8VH4NPJ7r2\na4sWV1MCYDKYGNturM7RiLPxtJh4544+3DMkDoATxRXcNmcDy/cf1zkyIZqHJKvCKfgMGULcFwtx\n79QRgJI1v5B6621UJCfrHJmOKoqgqkzvKISLqLJXsTR1KQADIwcS4hmic0Tit5iMBp67vhvPX98N\ngwHKqmwkLNjKhxtS9Q5NiCYnyapwGpa2bYn99DN8rh4FQOXRo6TedjvFK1fqHFkzaz+88fOVp+Cd\nQfVLBJpauysbnjO7w+8/ab7XFLrYcGwD+RX5gJQAOJO7h8QxZ1IfPNyM2DV49tu9vPLjfux2aW0l\nXIckq8KpmHy8afPWW4Q8+CAA9pISMqZNJ/c/c1y37+Bd34JvVN1t71CIukId56eoutJvpkFpXtO/\ndq8/NDxnrYDkn5v+tYSuanqrepg8GNl2pM7RiAsxJj6Cz+4bRIiPBYD31hzhwU+3U17Vimr7hUuT\nZFU4HYPRSOiD04me9RYGLy/QNE688QaZDz+CvdRFd8VO/FQlrL5RcMcimLocxv4N3LzV/Ts/hrf7\nwa4voCmT9u0fqj8NRvCJAL9odXvFy5C8ouleR+iqtKqUlenqCsWImBF41/y9Ek6jV0wAX08bQodQ\n9f/ux93Z3JG4ibwSGaoinJ9Bc9nlKNEalB86RMb0B6lKTwfAvXNnjN7elO3YAYD3oIG0nTdPzxCb\nV0Ea/O8RSFpWd67j1XDtvyAw9tKeOzcJ3u6jjgf/Cca8CNl7IPFqsJaBZxDcvwYCYi7tdYSuEpYm\nsClrU+141bdHvs3wmLOUnriaBRPqNhG2H66uYji5wtIq7vtwK5tS1JWWdsFefHB3f+JC5BcQ4bxM\nM2bMmKF3EEJcLHNwMP43XE/5vn1UpWdgO3kSa1ZW7f1V6RkUfLEIr/79cAsL0zHSZuLhDz1uhdDL\n4Oh6qCqFvCOwfb6qLY3qA8aLvICyfhakbVDHN7wN3sHgEwb+MXDge5Wwpm2EyyeC0dx070m0mISl\nCWzM2ljv3NbjW+kX0Y9Qr1CdomohZ7aEy0+FbQtUnXZL9jFuYh5uJm7oFUVGXikHsospKKviu52Z\n9GsXSFSAp97hCXFRZGVVuATNaiXnn6+RN39+o/ebw8PptHpVywbV0krzYNlzsOPDunORl8P1b0FU\nrwt7LpsV3ugOxVkQMwDuXVr//h8ehS2J6rjvPXDdvy8tdqGLnvN71q6oni7MK4zlty7XIaIWNCMA\nGnnv+EbBo/tbPJympmka/1p2iFkrkgCwmI38+7ZeXNszUufIhLhwUrMqXILBbCb8qb9Aa55j7hUE\nE96Gyf+DoA7qXNavMHckLH0GKkvO/7mSV6hEFeCKSQ3vH/sKRPdVx1vnwc5PLy12IUSTMhgMPDqm\nM/+4pQcmo4FKq53pn2xnzupk192MKlyWJKvCpXgPGtjgnDk8nDbvzNYhGp3EDYUH1sOwx9Xlec2m\nLum/MxCSznMXf83qrJsXxN/U8H6zO9w2H7yC1e3/PazqWYXT2Jq9FQMNf7kL8wpj1shZOkTUwhpt\nCWeAQdNbPJTmdHu/tnwwpR8+7qpU52+LD/Dst3uw2uw6RybE+ZMyAOFS7JWVHOx5ee3tVnH5/7cc\n3wff/wkyttSd63EbjPsbeJ+l6XtJLrzeBexV0OsOuPGdsz//kVXw4U1qBGxgHNy3CjwDmvANiOaw\nN3cv9y69l5Kq+qvtreLyf40Th2B2v8bv63svjH0Z3FynxnN/VhF3f7CF7KJyAEZ2CWPWxCvwdpd6\nc+H4ZGVVuJSqo0drj41+fq1rRbUx4d3gnp/gmtfA4qvO7V4Ib/eFnZ803uZq1+cqUYXGSwBO1/4q\nGPmMOs5PgW8eALus2Diyw/mHuf/n+ympKsGAgQeveJAwr7DWs6Jao2bzIKjexaOeB3c10pmt78N7\nV8HxvbqE1hy6RvrxzfQhdI30A2DFgRxuf28DOdXJqxCOTFZWhUsp+mkpmX/+MwDtFn6OZ8+eOkfk\nQAoz4cfH4OCPdefihqvNUcHVNa6apqZindgPQe3hoe3nrgO22+GzP8Chxer2qOdg6KPN8x7EJUkr\nSmPyksnkluUC8OzAZ7mt8206R6WTrx+AXz9RCeqTqaprRkEafJkA6dUdEkzuMOYl6J/gMvXwpyqs\nTP94O6sPnQAgOsCTD+7ux2XhvjpHJsTZycqqcCmVKSm1x5a4OB0jcUD+0WpM6m0fqgb/ACmr4d3B\n8Mu/wFYFx7arRBXUqur5/IA2GuGm/6gyAIAVLzXvCFhXtmCC2qU+I0AdN6HskmwSlibUJqqP9nm0\n9SaqUJeQth1Q194toC1M+QGuekoNwrBVwOLH4dPfq/IYF+DjbiZxcl8m9lf9kTMLyrjl3fWsT3KN\n9ydckySrwqXUJKum0BBMvrJS0IDBAN1ugOmbVMspAGs5LH8B/hGrOgfUuHzi+T+vZwDc/iGYPVX9\n6qJ7oDCjaWN3dbV9PzX1cWQVvN4Vju285Kc+WXaShKUJHCs5BsD9Pe9nSvcpl/y8Tqv4uOpHDND2\njE2ZJjNc9Re4e7HqKQxwaIn6pc5Fpra5mYy8clMPnhjXGYDicit3zdvMom3yb1Y4JklWhUupqE5W\n3dvJqupv8gxQl//vXgIh6gdWg9ZWc0ddWKIU0aOu32rpSVg4GawVTROvq7JZIX0LrP5n46vRxcfg\no5vBfvEz3gsrCrl/2f2kFqUCMKnrJKb3cq0d7xcs/bRBCG0HNf6YtgPhj2vrumGcOq42Ey59BqzO\nP8LUYDAw7aqOvPn7XlhMRqx2jce++JU3fj4kra2Ew5FkVbgMTdNqV1Yt7dvrHI2TiB0Ef/yl8fuK\nj8GnF7C6CtBrYt2KbeZW+OnpS4vP1Wga5B6GzXPhszvg1fbw/tWw8qWzf07pSXi9M3z/f5C8UiW4\n56m0qpRpy6dxMP8gADd1vInH+z2OwUXqLy9aWk1NqgWiep/9cZ4B8LsPYMJscKseV7p+Frw/Wo0j\ndgETekXz0dQB+Hu6AfDGz4d57ItdVFplo6RwHLLBSrgM64kTHB46DICwvzxJ8JQp+gbkTJpymo+1\nAj4YD5nb1O2b3oPLb7/kEJ3WqRy1alrzUZTZ+OPcvNS43HPxDIKu10G3CWqDnMmt0YdV2CqY/vN0\nNmVvAmBM7BheHfYqJqPpot6GS3nvKji2o/HpbGeTmwRf3gtZ1Vcb3LzhmldVezcXSP6TT5xiygeb\nSc8rA2Bwh2DendSnNokVQk+SrAqXUbJ5M2l3TQYgZs5/8BneWNNv0agz56SDSlQnfnrho1oBCtJh\nzjAoy1N1rAnLITy+SUJ1eBWn4Oj6uuQ05yztj3zCVeuv9leppNM/WtWoFqu6Unyj4IF1cOAH2Pct\nHFkJ9jNWVT0CoEt14tr+KjBbAKiyV/HIykdYlbEKgKHRQ3lzxJu4nSWxbVUqTsHf26phGUP+DKNn\nnv/nWithxYuw/q26c/E3wXVvuER/4dxTFUydv5Wd6QUAXBbuw7wp/WgT6KVzZKK1k2RVuIz8zxeS\n/fzzAHRYthRLTIzOETmZMxOlS52PnrwCPrwZ0FQbrPtWgYf/JQbpgGxW1UUheaVKTjM2N0wqASw+\n0O7KugQ1tEvDFbljO+tKL878RaEsHw4uVolr8gqwnVE36e4PXa7B1vV6nspeyeKjPwHQN7wv7179\nLh5mj6Z5v87uyKq6TgsTP4PO4y/8OZJXwNd/VHWsoDZi3TxXldU4ubJKG//3+Q5+2qveW6ivOx9M\n6Uf3aBf8tyuchiSrwmUc/9vfyZs/H4PFQucd2zGY5HLnBfmtROlirf5nXT1ml+vg9o+c/5KppkHu\nobqV09S1UFHU8HEGE7TpV5ectul71kv2F6y8EA4uUYlr0s+qxRKqkOOFkCC+9PUBoId3G+aWGPFO\nWac+r/1wuOvbponBWa36O6z6mzp+IgW8gi7ueUpy4dvpqlMAqFZXw55QY45Nzj0VymbXePmH/cxb\np/YAeFlMvP2HKxjZJVznyERrJcmqcBlp999Pyeo1uHfqRPvvv9M7HAHVAwMm1v1Av3oGXPmwnhGd\n24IJcGS1Oq5J7oqz1bmaBLVmBfpMoV3rktPYweDh1/zxVhTDoZ/Q9n7NP3M38qGvumTbqbKSD7Jy\n8D9zotillHe4gpqSl5DO8ODmS3suTVOb5ZY+U/sLAzED4Za5qmerk/tgXQoz/7cPTQOjAV6Y0J07\nB8bqHZZohSRZFS4jafQYqtLT8R0zhjZvval3OKJGWb7a0JKfqlaf7vxGJYGOqLHaXaO58cv6AL6R\n9etO/SKbNbzf8u7Od3nn13cAiDW489/MY4RUlDT+4KYo83BGNquqV60qgd6T4Ya3zv055yN7j9p8\ndeKAuu3uD9e/Ad1vbprn19HSvdn86bMdlFepX3ruG9aev4zrgtHo5FdIhFOR1lXCJdgrK6nKVLus\nLe2lx6pD8QxUU7PMHqcNDDjLjni91ayonu70RNXiC5eNh/GvwvTN8Mh+Nb3r8t/rmqjO3zu/NlGN\n8I5g7s3fE/JoEiAJRT3Hd6tEFc7eX/ViRHRXNdn9pqrbFYWw6G5VJlBxquleRwdj4iP4/L5BhPio\nzXvvrTnCQ5/uoLzq4nv/CnGhJFkVLqHq6FF1yRlwlzGrjieyJ1z7L3VcmgtfTHasxuqaBod/ptH2\nXaCS1HuWwpMp8IfPYMD9ENrZIepvFx1axGtbXwMg2COYxDGJRPpEgsXr7CvYLrDid1HSTh8GMPDs\nj7sYbp5w7etqpLFnoDq34yPVFePYjqZ9rRZ2eUwAX08bQodQ1Wv2h91Z3JG4ibwSB/o3LFyaJKvC\nJVQcSak9tkiy6piuuAP6TFHHGVtg6V91DafWsR3q8v/HtzR+v28UTPmfmiHvYK2fFqcsZuYG1XrJ\nz+LHnNFziPU7rabwrm9V/LWqk+sNb8Oy52p/wWs10jaoP30iILBd87xGl2vhgfUQp3o+k5cMiaNh\n3VtO/fWOCfLiqweGMCBObUjbdjSfm99ZR2ruWUpNhGhCkqwKl1AzuQokWXVo4/4BUVeo483vwa6F\n+sWSnwqL7lX1tCnVl/8tvuDuW/eYmtpOB9yMtCp9FU//8jQaGl5mL969+l06B3Vu+MCJn6r34Rul\n2iv5VO/oXvcmfPMA2KpaNnC9aFrdymrbgc27Ku4XpWqzRz1fXfNcBcueVaNzi7Ob73Wbmb+XGwvu\n7c+NvdQvQKknS7npnXVsO5qnc2TC1UmyKlxCZcoRAEyhIZh8fc/xaKEbNw+4bUHdZdLv/wzH97Vs\nDKV5sOQpmNUX9ixS54xuMOCP8OedMPl/dcndxE9bNrbztClrE4+uehSrZsXd5M7bo96mZ2jPxh8c\n1Usl3I/uh563qolNQR3Ufbs+g09ud/q6yvOSn1LXF7Up61XPxmiCoY+o8pHA6l+gj6yEdwertmNO\nyt1s4t+39+KhkR0ByC+tYuLcTfywK0vnyIQrk24AwiWk3HY75bt24dW/P7EL5usdjjiXpJ/ho98B\nGgR3hISVzd/mqaoMNr4La/9dvy9q/M0w6lk1uMAJ7MzZyX3L7qPMWobZYObNkW8yrM2wC3uSklz4\n+FY1zAAgqjfc8QV4hzR9wI5i5ydqJRngvtUtu1peUQw/Pg6/nvbLT//71fQsN+cd1vD5ljSe/noP\nNrtKI9oFe3E0T40MHtIhhI+mDtAzPOFCZGVVOD1N02rLAKQEwEl0vBquekodn0yCb6epy7TNwW6D\n7R/CW71h+Qt1iWq7oZCwAm79wGkS1QN5B5i2fBpl1jKMBiN/H/b3C09UQSWlk79X/x9AJa3vj4G8\nlN/+PGdWU69q8YHw7i372u6+qmvEzYngXv1L2eY5MHck5DhvC7Hb+7Xlgyn98HFXQxBST5aiaeqf\n8tqkXAa+spw9mYU6R9l8JiVuIu6pH4h76gcmJW7SOxyXJiurwulZT5zg8FD1AzvsL08SPGWKvgGJ\n82O3w6e3w+Gl6vbomWpWe1PRNPXcP8+AnNNKDcK6wdUvQKfRDrGb/3ylFKYwZckU8spVfeDMwTO5\nqdNNl/aktir47qG6FT+jW12rLlebdvV2PzV5rP0IuOsb/eLIT4Uvp6pNhqB6D9f8GHbSr/n+rCLG\nv/lLo/eF+7mz6emrWzii5jcpcRNrk3LrnYvw8yBxcl8ZTdsMZGVVOL2K0zZXubd3jhUyARiNcNOc\nukk/P8+AlMZ/4F2wzG0w/3r45La6RNU3CibMhj+uhcvGOFWimnkqk4SlCbWJ6pP9nrz0RBVUd4Mb\n3637JcFehWrfpanhCK91dvq2S4Aqe8g9pI5bol71twS2g7uXqNGsoHoPn/41f72rGn3sRLpG+p21\no+/xogrGvbGGv3y5i882p3Egu6i2bMBZaZrGujMSVYDsonKmzt+qQ0Suz7kHGAsBVErbKuflFaQG\nBrw/Ro2rXHQ33L9G7aa+GHlHYPlM2Pt13Tl3f7jy/2DgA6oXppM5UXqChKUJHC9Vm4Me7PUgk7pN\naroXMBjUqva6t2jQZ/ZUNrw3Ql02D4yFgNgz/mwLFu+mi6W5pJ92ibap+6teDJMZRv4V1vyTBl/z\n4mPw6USnmzA2pGNIg5XGGgeyizmQXcxnW9IB8LaY6NHGn14xgfSKCeCKtgGE+zl27a6maew9VsSP\nu7P4cXfW2Toyi2YiyapwejX1qgaLBbeoi0xyhH6ieqlm6t89CCUn4Ispake+2XL+z1GSC6tfha3z\nqlcHAZMF+iXAsMdUUuyECsoLuG/ZfaQXqx/yd8ffzX0972vhKDQ1+en47sbv9g5tJImNVSuI/m0c\nozdtTb2qwQRt+uobi4v6aOoABr6ynOyicgDCfN156cbu7EwvYGd6AbsyCjlVoUpMSiptbDySx8Yj\ndS2vovw96NU2gF4xAfSKCaRHtD+eFpMu76WGpmnszizkh91ZLN6dTVr15rGzqSkDEE1PklXh9CpS\nqzdXxcZiMOn7zU1cpN53QsZm2L5ArYItexbG/+Pcn1dZAhvfgbVvQmVx3fket8LIZ5qv8XsLOFV5\nij/+/EeSCpIAuO2y23i4z8MYmqt8of1wdRn6dO5+0GGk+joXHIWCNLCW139MyQn1kdnI5U+DEfza\nnGVVNlb1fDW2QDVaTX/VyMsdayW4sa+5A7dMO5fEyX1rL4PX1G6OiY8AwGbXSD5xip1pBexIz2dH\nWgGHjhdTUxFwrLCcY7uz+XG36kNrMhroHO5bm8BeERNAh1AfjMbmLd/RNI1fMwprV1Az8ssaPKZX\nTACllVYOHa9r+Rbh58HGp0c1a2ytmWywEk4vafQYqtLT8R0zhjZvval3OOJiVZXDvDGQ9au6fcv7\n0ON3jT/WZoWdH8PKV9Sl6hpxw2H0C3WDB5xUmbWMB35+gG3HtwFwbftreeXKVzAamjmxe72rugwN\ndQMRTme3Q0kO5B9VyWv+UShIVX/mH4WijOoazPNkclelBKevxp6e0Nb0470UlaXw97ZqxX3gdBj3\nyqU/Z1PJ2gVzhtbdbuxr7sJKKqzszixUq69pagW2ZmW2Mb7uZnrG+NeuvvaKCSDU1/2S47DbNXak\nF7B4dxaL92STWdAwQe3dNoBrekQyvkckdrvG6H+vprzKjtloINjHwvuT+8nGqmYkK6vCqdkrKqjK\nzATA0l7qVZ1azcCAOcOhvEDtUg+Ph7CudY/RNDi4WG3Gyj1Ydz68u0pSO4xyqo1Tjam0VfLwqodr\nE9URMSN4cciLzZ+oglrR+3Ri3fGZjEbwjVAfbRvpoWmrgsKM0xLZM/4syTnj8RVw8rD6aIy7PwS2\nrUtkT1+VDWgLFq9zv6dj2+tKQxyhXvV0v7xed+wT5rQrqhfL293MwPbBDGwfXHsuu7Ccnen57KhO\nYHdnFlJaaQOguMLKuqSTrEs6Wfv46ABPerVVK6+9YgLoHu2Ph9u5r7DZ7Rrb0/L5cXc2i/dkkVVY\nP0k2GKBvbCDju0cyvkcEkf6q3l3TNKbO30p5lfql7OOpAxhwWvyiecjKqnBq5YcOkXLDBACi/vF3\n/CdM0DkicckOL1MN62u3MBjU5dIRz6h59mnr6x7r10Zd7u95m5oY5OSsditPrHmCZUeXATAochBv\nj3obi+kC6ncdWWWpKiUoOKpaOJ2Z0J4+rOF8eIc1XI2t+dOvDXx8S/3L7I8dVkmhIzhxEGYPADS4\nfKLqwyoasNrsHM45VW/19VBO8VnbMpuNBrpE+tZbfX3+uz2sT1YJbvcof/rEBrJkT3aDVVyDAfq1\nC+LaHpGM6x7R6Kavn/Zmc/+H6hfJ3/Vpw2u3Xt60b1g0SpJV4dSKflpK5p9V2512XyzEs0cPnSMS\nTeKNy9Xl5bPx8Iehj0H/+5x6AtDp7JqdZ9c9y3fJ3wHQK7QXc0bPwcvtPFYPXYGmQVn+2VdlC9LU\nSuylqKkHbcnpVWfz1f1q3C0GmL4ZQi/TOyKncarCyq6MgnoJbE7xxf3dMBqgf5xKUMfGRxD2G10J\nSiqsjP7Xao4VluPv6caKR4cT7HPpZQji3KQMQDi1ytN6rErbKhdScPTs9w1+CK58xGl3+DdG0zT+\nvvnvtYlq16CuzL56dutJVEEta3kFqY/Gao7tdlWf3GgiexSKMs9dL+sobaHyjsDuL9Rx/I2SqF4g\nH3czgzuEMLiDGg+saRpZheW1nQd2phWwK7Og9lJ9YywmI89d342x8RHnXff61vLDHKsuF/jL+C6S\nqLYgSVaFU6tMOQKAOTQUk4+PztGIZucTBmNe0juKJjdrxyw+PaDqFeP84/jP6P/gZ/HTOSoHYzSq\n/rt+URDbSGN/a6Xa4FWTvH7fhNPQmtraN0BTdZgMfUzfWFyAwWAgKsCTqABPrukRCUCVzc7B7GKu\nn7W20Z6oQd4WJg2MPe/XOJhdzPtr1eJI77YB3N43pilCF+dJJlgJp1aRkgrIqqrLaT+84TnfKPjD\nFy0fSzNL3J3I3N1zAYj2iWbu6LkEebjOqnGLMVsgqD10GAF9pkD7qxo+xhHaQhVmws5P1PFl4yGi\nu77xuCg3k5Hu0f4M6RjS4L4L7Ydqt2s8881urHYNk9HASzf2aPYWWqI+SVaF09I0jcojamVVklUX\nc9e3KrGoUdPSxxFqDZvQZwc+483tqt1aqGcoc8fMJdw7XOeoXISj/h1a/1Zdd4Jhsqra3D6aOoCI\n0+pQa/qhXkibqUXbM9iSmg/AlMHt6BYlVz1amiSrwmnZcnOxn1JNmd2lbZXrmfipSjAcYTWsGXyX\n/B0vb3oZgAD3AOaOmUuMr1xabFKO9nfoVA5s+686bj9Cpmm1kMTJfYnw87ioCVP5JZX87UdV4xzh\n58HDo6W+WA9SsyqcVsUR2Vzl0qJ66b8RpoklLE1gU5aaU69VV9L5uPkwZ/QcOgR00DM01+Rof4c2\nvF03AWzY4/rG0op0j/a/6OlS/1hygPxStRL+3PXd8HGXtEkP8lV3YWn33EPJBjVm0HvQQNrOm6dz\nRE2rXieA9u11jESIc0tYmsDGrI0Nzj/R7wm6BXfTISLRokrzYMv76rjtIGg3RN94xDltTc3jsy3p\nAAy/LJTx3SN0jqj1kjIAF5V2zz2UrN+gehdqGiXrN3Bo8BBKt23TO7QmU5OsGiwW3CIjdY5GiN9W\ns6J6prd3vt3CkQhdbJoDldWz5KVW1eFV2ew8880eANzNRmZOiMfg5NPxnJmsrLoQ26lTlG3fTunm\nzSpRPfP+vDyO3jEJc3g4lrg4LHHtcI+Lw9KuHZa4ONyiojCYnGcKUEV12ypLbKxTxS2EaGXKi2DT\nu+o46go1Flg4tP+uS+VAdjEA00d0JDbYW+eIWjdJVp2Y7dQpyrZto2TzZko3b6F8717VOPscCcV1\nvAAAIABJREFUrMePYz1+nNKN9S9JGtzccIttW53AxqmEtl07LHHtMAcGNtO7uHiVNW2rpARAOIEB\nkQMalAH4WfyYNXKWThGJFrMlEcoL1fGwx9UABOGwjhWU8e+fDwHQPsSb+4fLzxi9SbLqRM47OTUa\nMXp51e6Urz3t74/f+HHYS0qpTEmhMiUFe0lJ7f1aVRWVSclUJiU3eEpTQEDtCqxKYmNxj4vDLTYW\no6Xl55bbKyqoysgAwBLXrsVfX4gLNXfMXEZ9MYqc0pzac2ajmWifaB2jEs2ushQ2zFbHYfGqt6pw\naDO/30dppRra8OKN3XE3y5U7vUmy6sBsxcWUbttG6eYtlG7eTPm+fWdNTj3i4/Hq3w/v/v3x7NMH\nk48Ph4dfhfX4cQDM4eF0Wr2q3qdpmoYtN5eKlBQqU1JVApta/WdGBthsdbEUFFC2cydlO3c2eG23\n6OjaFdjTywrM4eHNVuNTefSoqscF3GVlVTiJWSNn8dCKh6iwVlBYWUheeR6vbX2NF4e8qHdoorls\nnw+luep46CNqEpdwWCsP5LBkbzYAE3pFNTpUQLQ8g6ZpjU0iEzqwFRdTunWrSk63bDl7cmoy4REf\nj3f/fnj1749n796Njhot27uXjGnTAWjzzmw84+PPOxatspLKjEwqU9UKbEVtIpuK7eTJ83oOg5cX\nlthY3OPanVFWEIfJ59Lqf4p+Wkrmn9U4xXZfLMSzR49Lej4hWpKmaUxbPo21mWsBSByTyIDIATpH\nJZqctQLevByKsyCoAzy4BYyySueoyiptjHljNel5Zfh6mFn+6HDCfD3O/Ymi2cnKagtprI2Uraio\n/srp/v2XlJyeyTM+vsFq6vkyWCy4t49rtNm+raiodgW2dlU2VX1oFRW1j9NKS6nYv5+K/Q37HJpD\nQ+uXFcS1w71dO9zatMFgPvdfy8rqzVUgPVaF8zEYDDw78Flu/PZGyqxlzNwwky9v+BIPs/xgdCk7\nP1aJKsDQRyVRdXCzVyaRnlcGwONjO0ui6kBkZbUF1LaROp3ZrC6zN/blN5nw6B6Pd//+Kjm9ovcl\nr0S2BM1ux5qVRUVN8lpdF1uZmkpVVlbj7/VMbm5YYmLqlxVUr8iagoIwGAz1vp4GNze67N7VvG9M\niGayYO8C/rn1nwAk9EjgT73/pHNEosnYqmBWbyhIA/+28KftYHLTOypxFkk5pxj/5hqqbBo92/jz\n9bQhmIyyEc5RSLLaAvZ37fbbiZqTJqcXwl5eTuXRo/XqYiuq62TtxcXn9RxGPz+w2xtsHDOHh19w\nmYMQjsBmt3HHj3ew9+RezAYzn133GZ2DOusdlmgKOz+Bbx5Qx9f+C/rdq2884qw0TeMPczex4chJ\nDAb4dvoQerYJ0DsscRpJVlvA2ZJVo7c30W+8gecVV7hccnq+NE3DlpfXoC62MiWFyvR0sFrP63ka\n20AmhDPYf3I/E3+YiE2z0TOkJwvGL8Akl4udm90GswfAycPgEwF//hXc5JKyo/pmRyb/97naPHzX\noFhmTuiuc0TiTFKz2gK8Bw1sUAZgDgujzbvvtPrVQIPBgDk4GHNwMF59+9a7T6uqoiozs15dbMHC\nhTpFKkTz6Brclbu63cUHez9gV+4uPj/4OX/o+ge9wxKXYt+3KlEFGPInSVQdWGFpFS/9sA+AEB93\nHh0jVzYckfTQaAFt583DHB5e75z/zTe1+kT1XAxubljatcN3xAiC77mbyJkv4D14UIPH1ZQBCOGs\nHuj1QG2/1Te3v0l2SbbOEYmLZrfDmtfUsVcw9Jmiazjit/1z6QFyT1UC8Ox1XfH3lLpiRyTJagtp\n885szOFhamMVcDLxfcoPHtQ5KudzZuJfc/lfEn/hzDzNnjw36DkASq2lvLzxZaRCy0kdWgI5e9Xx\noOlgaZ0lXs5gZ3oBH29KA2BIx2BuuDxK54jE2Uiy2kJUG6nVxC38HEwmsFrJeuZZtNMa74vzoxL/\ncFlRFS5lcNRgrm9/PQCrMlax7OgynSMSF0zTYI3q7oCHP/RL0DcecVY2u8Yz3+xG08BiMjJzQvdm\nG2IjLp0kqy3Mo1s3gu+5G4Dy3bvJW/ChzhE5n5r+sbKiKlzNY/0eI8Bd7UL+2+a/UVRZpHNE4oIc\nWQnHtqvj/veDh5++8Yiz+nBDKnsy1b+v+4e3p0PouXuXC/1IsqqDkOnTscTGAnDizTfVrnchRKsX\n5BHEE/2eACC3LJd/b/u3zhGJC1JTq+rmDQMf0DcWcVY5ReW8vvQQAG2DvJg+oqPOEYlzkWRVB0YP\nDyJenAmAVl5O9vPPS32aEAKA69pfx6BItZFw0aFFbDu+TeeIxHlJXQdH16njfveCV5C+8YizevGH\n/RRXqLaIL0yIx8NNWsU5OklWdeLdvz8Bt94KQMn6DRR+9bXOEQkhHIHBYODZQc/iYVLtjl7Y8AKV\ntkqdoxLn9Ev1qqrZAwY9qG8s4qx+OXyC7389BsA1PSIY0TlM54jE+ZBkVUdhjz+GOTQUgOOvvor1\nxAmdIxJCOIIY3xge6KUuI6cUppC4O1HniMRvytwGySvUce/J4Bv+248XuiivsvHct6pTg7fFxHPX\nyZ4HZyHJqo5Mfn5EPK/a1dgLC8l++RWdIxJCOIo7u91J50DVoHzu7rkkFyTrHJE4qzWvqz+NbmoI\ngHBIc1YfISW3BICHR19GhL8Ma3AWkqzqzPfqq/EdOxaA4iVLKF6+XOeIhBCOwM3oxozBMzAajFjt\nVl7Y8AJ2za53WOJM2Xvg4A/quNdE8G+jbzyiUam5JcxelQRA10g/pgxup29A4oJIsuoAIp75K0Z/\nfwCyX5iJrbhY54iEEI6ge0h3/tBFjV7dkbODRYcW6RyRaOCX6lVVgxGufFjfWESjNE3jue/2UmlV\nv+y9dGN3zCZJf5yJ/N9yAObQUMKfUO1qrDk55PzzNZ0jEkI4ioeueIhI70gAXtz4Ij3n9yRhqTSb\ndwi5h2Fv9ebYHrdCUHt94xGN+nF3NmsOqT0hE/vH0Cc2UOeIxIWSZNVB+N98E16DBgJQsHAhJZs3\n6xyREMIReLl54e/uX3tbQ2Nj1kZGfTGKfSf36RiZ4Jd/ARpggCsf0Tsa0Yji8ipe+F5tqgrytvDk\nuC46RyQuhiSrDsJgMBA5cyYGD1Xwnf3sc9jLy3WOSgjhCA7mHWxwLqc0h4dWPKRDNAKA/KOw63N1\n3PV6CJMkyBH9a9khcoorAHhqfBcCvCw6RyQuhiSrDsQSE0Pon9RO0sqjR8md/Y7OEQkhhGjUujdA\ns6njYY/pG4to1J7MQuavTwWgf7sgftdHNr85K0lWHUzQXXfi0b07ACfnzaN8n1zmE6K1GxA5oMG5\nMK8wZo2cpUM0gqJjsOMjddxpLERerm88ogG7XeOZb/Zg18BsNPDSTd0xGAx6hyUukiSrDsZgNhP5\n0otgNoPNRtYzz6JZrXqHJYTQ0dwxcwnzqj9pZ8agGXQL7qZTRK3c+llQM1VMVlUd0qdb0tiZXgDA\nvUPjuCzcV+eIxKWQZNUBeXTpQvC99wJQvm8fef/9r74BCSF0N2vkLEI9Q2tvr0hfoWM0rVhJLmz9\nQB3HDYeY/vrGIxrIPVXBPxYfACA6wJM/j+qkc0TiUkmy6qBCpj2AJS4OgBOz3qby6FGdIxJC6Klb\ncDdW3LaCIdFDAFiVvkqGBOhhw2ywlqljWVV1SK/8uJ+icnVF8vnru+FlMesckbhUkqw6KKO7O5Ev\nzgRAq6ggeew49nftRto99+gcmRBCTyNjRgKQW5bL7tzdOkfTypTlw+a56jhmALQbqm88ooGNR07y\n1fZMAK7uGs6Y+AidIxJNQZJVB+bVty/m8PC6E5pGyfoNHB5+FWV79+oXmBBCN1fFXFV7vCJNSgFa\n1Kb3oLJ6wuCwx0E27DiUSqudZ77ZA4Cnm4kZN0hNt6uQZNXBWXNyGp47fpyMadN1iEYIobcwrzB6\nhvQEYGX6Sp2jaUUqimFjdTvByMuh49X6xiMaSFx7hKScUwD8aVQn2gR66RyRaCqSrAohhJMZ0XYE\nACmFKaQUpugcTSuxdR6Uq93lsqrqeNLzSnlr+WEAOoX5cO+VcTpHJJqSJKsOzrt6BOvpzOHhtHln\ntg7RCCEcQU3dKsjqaouoKlPtqgBCu0Lna/WNR9SjaRozvttLeZXacPjSjd2xmCW9cSXyf9PBtZ03\nr37dqtlMp9Wr8IyP1y8oIYSu4vzjiPWLBaRutUVsXwAlJ9Tx0EfBKD86HcnSfcdZfkCVzN3Suw0D\n2gfrHJFoavIvzgm0eWc2Rm9vdcNqxZqbq29AQghdGQyG2tXVXSd2kVsm3xOajbUC1r2pjoPaQ/xN\n+sYj6impsPLCd2rDsb+nG09f00XniERzkGTVCXjGxxMz5z+1t0s2bNQxGiGEIxjZViWrGhqr0lfp\nG4yrWjABXgqHItUKiSsfAZP07HQkby0/zLHCcgCeHNeFYB93nSMSzUGSVSfhefnlGL3UzsaS9et1\njkYIobceIT0I8ggCpG61WSyYAEdWAVrduZUvw7GdekUkznAwu5j316oNhle0DeD3/WJ0jkg0F0lW\nnYTBzQ2vfv0AKNmwAU3TzvEZQghXZjKaGBGjugJsPLaR0qpSnSNyMUdWNzxXnAWfTmz5WEQDdrvG\nM9/sxmrXMBrUpiqjUTo0uCpJVp2I95DBAFizs6lMkXY1QrR2Nclqpb2SdcfW6RyNEC1n0fYMtqTm\nAzBlcBzxUf46RySakySrTsR70KDa45L1G3SMRAjhCAZEDsDT7AlIV4Am13ZAw3O+UTDx05aPRdST\nX1LJ337cD0C4nzuPjLlM54hEc5Nk1YlYOnbEHBoKSN2qEAI8zB4MiRoCwJqMNVTZq3SOyIV0vqb+\nbd8oeHQ/RPXSJx5R6x9LDpBfqv6uP3ddPD7usunN1Umy6kQMBgPeg9XqaummTWhWq84RCSH0VtMV\noKiyiO3Ht+scjQvZvUj9aTTLiqoD2XY0j8+2pAMw/LJQrukRoXNEoiVIsupkvAerulV7SQllu3br\nHI0QQm/D2gzDZDABUgrQZHIPQ/YudTzscVlRdRBWm52/fr0HAHezkZkT4jHI2NtWQZJVJ+M18LS6\n1Q1SCiBEa+fv7k+f8D6AamElnUKawJ4v646736JfHKLWpMRNdPzrYg5kFwMwfURHYoO9m+R54576\ngbinfmBS4qZLfj7RPKTQw8m4hYfh3qkjFYeTKFm/gdDp0/UOSQihsxExI9icvZmskiwO5B2ga3BX\nvUNyXppWl6xG9ISQTvrGI5iUuIm1SfWntCX+cgS7ptEm0Ouin3fO6mQO55yqvb02KZeBrywncXJf\nukdLdwFHIsmqE/IaNIiKw0mU/fortlMlmHwu/bdLIYTzGtF2BP/Y8g9Ara5KsnoJsndD7iF13ON3\n+sYiAFiX3HCccFG5lTd+Ptzkr5VdVM7U+VvZ+PSoJn9ucfGkDMAJ1dStYrVSumWzvsEIIXQX7RNN\nlyA1E13qVi/R6SUA8TfrF4cQopasrDoh7379wGwGq5WSDRvwHTFC75CEEDobETOCA3kHOJh/kMxT\nmUT7ROsdkvPRNNjzlTqOGQgBMr7TEQzpENKgDCDUx52/3dyDzhG+F/28D3++k61H8+udi/DzIHFy\n34t+TtE8ZGXVCRm9vfHsdTkg/VaFEEpNCyuAlWkrdYzEiWVsgcI0dSwbqxzGR1MHEOHnUXs7ws+D\nLc9czdXdwokJ8rroj0UPDK73vABfThss9aoOSJJVJ1VTClCZlEzV8eM6RyOE0FvnwM5EeUcBqm5V\nXISa3qoGI8TfqG8sop7EyX2J8PNo8pXPxMl9Cfa21N5en9SwPlboT5JVJ1Vv9OoGGb0qRGtnMBgY\n0VaVBG07vo3CikKdI3Iydhvs/Vodxw0DnzB94xH1dI/2Z+PTo9j49KgmXfnsHu3PpqdH4euhqiI3\nJJ9ssucWTUeSVSfl2aMHRh8fQEoBhBDKiBiVrNo0G2sy1ugcjZNJ/QVKctRxd+kC0JqYTUYGxAUD\nsD75pPQqdkCSrDopg9mM14ABgFpZlX9cQoje4b3xs/gB0hXggtV0ATC6Qdfr9I1FtLghHVWyml1U\nTkpuic7RiDNJsurEvAerUgDbiVwqDjd9vzkhhHNxM7oxrM0wANYdW0e5tVzniJyEtRL2faeOO40G\nz0B94xEtbnCHkNrj9VIK4HAkWXVi3oMG1x6XSt2qEIK6rgBl1jI2Zcn4yPOSvALKC9SxdAFolS4L\n96ndaLW+kSEEQl+SrDoxS1w7zJGRAJySulUhBDAkaggWo/qhuyJdSgHOy57qLgBuXtB5vL6xCF0Y\nDAYGdVClABuST2K3S2mdI5Fk1YkZDIbargClW7aiVVbqHJEQQm9ebl4MjBoIwKr0VdjsNp0jcnCV\npXDgR3V82TiwyPjq1qqmFCC/tIoD2cU6RyNOJ8mqk6vpt6qVllL26686RyOEcAQjY1QpQF55Hrty\nd+kcjYM7tASqqjfU9JAuAK3Z4OqVVZBSAEcjyaqT8x40sPZY+q0KIQCGxwzHgAGQaVa/acEEWHS3\nOjaYoePV+sYjdBUb7EWUv5poJf1WHYskq07OHByMe5cuAJSsk7pVIQSEeIZweagaybwifYW0tmvM\ngglwZFXdbc0Kb/aCYzt1C0noS9WtqlKATSl5WG12nSMSNSRZdQE1datlu3djK5Y6GyEEtdOsjhYd\nJaUwRedoHNCR1Q3PFR+DTye2fCzCYdT0Wz1VYWV3pkyBcxSSrLqAmrpV7HZKN0mrGiFEXd0qSFcA\nIc7XoHp1q1IK4CgkWXUBXn37YHBzA2T0qhBCaeffjjj/OEDqVhvVfnjDc25ecPuHLR+LcBiR/p60\nD1EdIWSTleOQZNUFGD098ezdG4CS9bLJSgih1Kyu7srdRU5pjs7ROJi7vgXfqPrnqkph9T+gUsZt\ntmY1q6tbU/Mpr5LWb45AklUXUVO3WpmaStWxYzpHI4RwBDV1q6B6roozTPxUJaw+4RBymTp3eCnM\nvx5KZFWttarpt1phtbMjrUDnaARIsuoyvIfUjV6VFlZCCIAeIT0I8VQ/eKVutRFRveDR/fDYIUhY\nCR1GqfOZ2+D9MZAnG9Nao4Htg2qPN0gpgEOQZNVFeHTrhtHfH5AWVkIIxWgwclXMVQBsztrMqcpT\n+gbkyNx94A+fw+XV3QDyklXCKq2sWp1gH3e6RPgCssnKUUiy6iIMJhPeAwYAULJxI5pd+sMJIerq\nVqvsVaw9tlbnaBycyQ1ufBeufFjdLsmB/14LybIq3drUlALsTC+gpMKqczRCklUXUtPCypaXR8XB\ngzpHI4RwBAMiB+Bl9gJgRZokXedkMMDVM2D8q4ABKk/Bx7fCroU6ByZaUk2/VatdY3Nqns7RCElW\nXYj34EG1x9IVQAgBYDFZuDL6SgDWZqylylalc0ROYsD9cOsHYLKA3QpfJcC6t0CmgbUK/eOCMBnV\nyGIZvao/SVZdiKVtW9zatAGk36oQos7ItqoUoLiqmC3Ht+gcjROJvwkmfQXuaj8Ay56Fn54GKbNy\neb4ebvSIVv/fpd+q/iRZdTE1LaxKt23DXlGhczRCCEcwtM1QzAYzIAMCLljcULhnMfhGqtsb34Ev\n7wWrfH91dYOr+63uPVZEQWmlztG0bpKsupiaFlZaeTllO3boHI0QwhH4WfzoG9EXgJXpK9HkUvaF\nCY+He5dBSGd1e+9X8NEtUC6z411ZzSYrTYONR6RuVU+SrLoYrwED1AYBpG5VCFFnRIwaEHC89Dj7\n8vbpHI0TCoiBe5ZAjOq6Quov8ME1UJSlb1yi2fSJDcRiUmmS9FvVlySrLsYcGIhHt26A1K0KIerU\n1K2CdAW4aF5Bakxrl+vU7eN7VC/WE4f0jUs0C0+LCQ83lSbN33CUSYmbdI6o9ZJk1QXVdAUo37sX\nW4GMimspCUsT6Dm/Jz3n9yRhaYLe4QhRT4R3BF2DugKqFEBcJDdPuG0B9L1H3S5Mg3ljIH2zvnGJ\nJndH4kaKyut6rK5NymXgK8vZkynlHy1NklUXVNNvFU2jZKP8JtgSEpYmsDFrI1r1fxuzNjJi4Qj2\n5u7VOzQhatWsrh7OP0x6cbrO0Tgxowmu/ReMeEbdLsuH+TfAgR/1jUtcsvS8UhZuSefhz3eyLqlh\ny6rsonKmzt+qQ2Stm1nvAETT8+zdG4O7O1pFBSXr1+M3bqzeIbms0qpS1mSsYWPWxgb35ZblMvGH\nifQI7UGngE50DOhIh4AOdArsRLBHMIbq2mIhWsqImBHM3jkbUF0B7oq/S+eInJjBAMMfB99w+P7/\nwFoGn98B1/0b+kzROzpxnrILy9lwJJf1SSfZcOQkGflleockGiHJqgsyurvj1acPJevXU7JBNlk1\ntXJrOWsz17IkdQlrMtZQZj37NzcNjV0ndrHrxK565/3d/ekY0LH2o0NABzoFdCLAI6C5wxet2GWB\nlxHtE03mqUxWpK+QZLUp9L4LfMJh4WSVsH7/Z7Xp6qq/1G52FY7jRHEFG4+oxHRD8klScksafZyv\nhxk3k5G8kvotqyL8PEic3LclQhWnMWjSw8QlnUxMJOe11wHosGwplpgYnSNyblW2KtYfW8+S1CWs\nTF9JSVX9b3AmgwmbZqt3ztfNl4GRAzlZfpLDBYcpriw+5+sEewTTMbAjnQI60SGgQ20y62PxadL3\nI1qvV7e8yof7PsRoMLLqtlUEegTqHZJrSN8Cn9wGZdUtjnpPVqUCJlkT0lN+SSWbUlRiuuHISQ4d\nP9Xo47wsJvrHBTGofTCDOgQTH+WPyWhg4CvLyS4qB1SiuvHpUS0ZvqgmyaqLKt+3j5SbbwEg4oUX\nCLz9Np0jcj5V9io2Z21mSeoSlqctb5Bsepm9uCrmKsa1G8eQ6CGM/2o8OaU5AIR5hbH81uW1j9U0\njRNlJ0jKTyKpQH0kFySTVJBEqbX0nLFEeEfUrr7WJLBx/nF4uXk17ZsWLm9L9hbu+UltDnpxyIvc\n2PFGnSNyIbmH4aOboSBN3e58DdzyPljk32lLKSqvYvORvNqV0/3ZRY1OyHU3G+nbLpDBHUIY2D6Y\nnm38cTM13MazJ7OwtkY1cXJfuldPtRItS5JVF6XZ7RweciW2/Hx8x46lzZtv6B2SU7DZbWw9vpUl\nqUv4+ejPFFTU76bgYfJgWJthjIsbx9DooXiYPWrv23dyHw+teAiAWSNn0S242zlfz67ZyS7Jrk1g\na5LZI4VHqLD99oQcAwaifaLpGNixXklBnH8cFpPlIt69aA2sdisjFo6goKKAETEjeGvkW3qH5FqK\ns+Hj30H2bnW7TX/4w+eq7ZW4JJMSN7Guut/pkA4hfDR1AKWVVrak5rM+OZeNySfZnVmIvZGsxmIy\n0qttAIPaBzO4QzC92gbgbja18DsQF0uSVReW+cgjFP24GJO/P53Wr8Ngkn+YjbFrdnbm7GRJ6hKW\nHV1Gbln95s9uRjeujL6S8XHjGd5meIusZtrsNjJOZdQmsMkFyRwuOExqUSpWu/U3P9dkMBHjG0On\nwE716mFj/GJwM7o1e+zC8f117V/5Lvk7PEwerPn9GjzNnnqH5FrKi+DzSZCyWt0O7gSTvoTAWH3j\ncmKTEjexNumM780mA3a7hq2RLMZkNHB5G38GdQhmUPsQ+sQG4mmRn4HOSpJVF5Z87bVUJh8BwKNH\nD+K+WKhzRI5D0zR25+5mSeoSlqYu5Xjp8Xr3mw1mBkUNYlzcOEbEjMDX4qtTpPVV2atIK0rjcMFh\nVUZQvRKbVpyGXbP/5ueajWbi/OPo6N+RjoF1SezMDTPZnK16RA6IHMDcMXNb4q0IHS1PW87/rfw/\nAN4c8Wa9gQGiiVgr4dtpsPsLddsnAiYtgoge+sblpOKe+qHRy/k1DAboHuXP4A7BDOwQTL92Qfi4\nS72wq5Bk1UWl3XNPg3Gr5vBw2rwzG8/4eJ2i0pemaezP21+boGaeyqx3v8lgon9Ef8bFjWNU21H4\nuztPbVKFrYKUwpQGK7FnvsfzEeYVdt5lDMI5lVaVMuzzYVTYKrix4428OORFvUNyTXY7LHsWNryt\nbrv7we0fQfvh+sblhOL+8gONJSteFhNv/v4K+scF4e8pV45clSSrLmp/12409muoOTycTqtXtXxA\nOjqcf5glqUv4KfUnjhYdrXefAQN9wvswPm48V8deTZCHa9WVlVaVcqTwSL162KSCpAYryWc6c4OY\ncD0PrXiIVemrCHQPZOVtKzEZ5RJps1n/Niz9qzo2usHNc6D7LfrG5ETWJeVy5/ubGtSi1rSRkk1P\nrk/WyIVLSilMUQlqyk8kFyY3uL9XaC/GxY1jdOxowrzCdIiwZXi5edE9pDvdQ7rXO19UWcSRgiPc\nufhOnSITehsZM5JV6avIr8hn54md9Anvo3dIrmvwg+AbAV//EexVsOgeKD4Og6bpHZnD++XwCabO\n39pooiptpFoPSVZdlPeggQ3KAEwhIbR5Z7ZOETW/9OJ0fkr9iSUpSziYf7DB/d2DuzMubhxjYscQ\n6ROpQ4SOw8/iR6+wXgyMHNjo9K2O/h2x2q2YjfItwlUNjxmO0WDErtlZkbZCktXm1uN34B0Cn02C\nymL46SkoPgZXzwSjTD5vzOpDJ0hYsJVKqx2T0cCjoy9jwQZ1dUwa87cuUgbgwg4Pvwrr8brLvdGz\n3sJv9GgdI2p62SXZtQnqnpN7GtzfObAz4+LGMbbdWGJ8ZTBCY0Z9Maq2P6zZYMaqqW4D4+PG88qV\nr0jC6sImL57M9pztxPjG8MNNP8gI4JaQtUu1tjpV/b25x20wYTaYpd3c6VYeyOH+j7ZRabVjNhp4\na+IVXNOjdS8ytGby65wLa/PObMxhdZe4y/fu1TGapnOi9AQf7/+YO3+8k9GLRvPa1tfqJaod/Dsw\nrdc0vrvxOxbdsIipPaZKovobZo2cRZhXGGFeYcwZPYcuQV0AWJyymL+u/Ss2u+0czyBj6aN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hWmBefOgfkvjo3T6XCtXw/3Jk1wb9IU92ZNcQkLQ6PVcq5rN1tRe6c/g3t5rNpMFhM9VvcgoyCD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\nEMJuSbEqhBBCCCHslhSrQgghhBDCbv0/Qet1IahPNOkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d0b3750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_genetic_algorithm(generations=10000, population_size=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some notes on the methodology for the nerdy folk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several people have pointed out that this method is very likely [overfit](http://en.wikipedia.org/wiki/Overfitting), i.e., that the path is trained so much on the 68 points that it likely wouldn't work well on a new 8th book. While this criticism is possibly true, it also misses the purpose of this project.\n",
    "\n",
    "**The purpose of this project is to find an optimal search path for the first 7 'Where's Waldo?' books only.**\n",
    "\n",
    "If the goal were to produce an optimal search path for any future books, it would become necessary to divide the 68 points into a training and testing set so some form of cross-validation could be performed. However, because the data is already quite sparse (68 points), this would likely prove to be a fruitless endeavor anyway.\n",
    "\n",
    "That said, please feel free to continue working on this data set and discover better methods to find Waldo efficiently. I've provided the data set along with this notebook if you would like to work on it with your own methods.\n",
    "\n",
    "If you find a better solution, please [contact me](http://www.randalolson.com/contact/) so I can take a look."
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}


================================================
FILE: wheres-waldo-path-optimization/wheres-waldo-locations.csv
================================================
Book,Page,X,Y
5,10,0.625,7.708333333
7,1,4.944444444,6.569444444
1,11,5.430555556,6.402777778
1,10,5.902777778,6.083333333
2,3,5.430555556,5.444444444
5,3,4.791666667,5.444444444
1,6,5.305555556,5.125
1,7,3.666666667,5.291666667
7,3,6.069444444,4.638888889
5,8,3.513888889,4.319444444
2,11,2.388888889,4.486111111
4,4,1.763888889,4.333333333
3,2,2.083333333,3.847222222
6,11,1.125,2.569444444
4,2,0.805555556,2.25
2,4,1.444444444,2.097222222
6,5,0.638888889,1.777777778
2,5,0.958333333,1.277777778
2,6,0.958333333,0.972222222
4,3,2.083333333,0.805555556
3,10,2.388888889,2.729166667
5,4,2.166666667,2.25
7,6,2.486111111,2.25
3,9,3.347222222,2.569444444
1,4,3.513888889,2.083333333
3,6,3.041666667,1.916666667
5,5,4.305555556,2.888888889
2,12,5.263888889,2.729166667
3,3,5.75,2.25
1,1,5.597222222,1.930555556
6,2,4.625,1.277777778
2,10,5.263888889,0.819444444
5,9,5.416666667,0.333333333
5,6,7.666666667,7.361111111
3,5,6.861111111,6.736111111
3,8,8.458333333,6.722222222
2,1,10.375,7.194444444
5,1,12.44444444,7.694444444
2,8,10.375,6.430555556
6,8,11.16666667,6.402777778
7,2,11.33333333,6.083333333
2,2,9.888888889,5.777777778
1,5,9.25,5.291666667
3,4,9.958333333,4.791666667
4,11,9.097222222,4.652777778
3,7,7.944444444,5.305555556
1,2,7.763888889,5
6,12,8,4.791666667
3,1,7.333333333,5.138888889
6,1,6.694444444,4.972222222
7,7,7.166666667,4.5
5,11,7.486111111,4.006944444
3,11,11.01388889,4.819444444
4,7,11.80555556,4.652777778
2,9,12.27777778,4.805555556
1,12,9.416666667,3.694444444
4,6,9.25,3.222222222
4,9,11.48611111,3.541666667
2,7,11.95833333,3.541666667
4,10,7.986111111,2.430555556
4,8,6.694444444,1.625
5,7,12.125,2.909722222
5,2,11.51388889,2.756944444
1,8,11.30555556,2.555555556
1,3,10.72222222,1.930555556
1,9,11.16666667,1.791666667
4,5,12.44444444,1.625
4,1,12.125,1.291666667