final_notebook1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [ES-21AC] State Prisons and County Jails\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "### Professor: Victoria Robinson \n",
    "### Data Science Fellow: Phillip Pierini\n",
    "\n",
    "The goal of this project is for students to understand that they can do social work using data. This notebook explores the incarceration trends and impacts of prison realignment in California. \n",
    "\n",
    "*Estimated Time: 60 minutes*\n",
    "\n",
    "---\n",
    "\n",
    "### Table of Contents\n",
    "\n",
    "[THE DATA](#sectiondata)<br>\n",
    "\n",
    "\n",
    "[CONTEXT](#sectioncontext)<br>\n",
    "\n",
    "\n",
    "[PRISONS](#section1)<br>\n",
    "1. [DATA](#subsection1)<br> \n",
    "2. [DATA ANALYSIS](#subsection2)<br>\n",
    "3. [GENERAL: DESIGNED & STAFFED CAPACITY](#subsection3)<br>\n",
    "4. [GENDER DIVISION](#subsection4)<br>\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "**Please run the cell below before you begin.**\n",
    "\n",
    "**Dependencies:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vx5qdR3kUEa2w/NnzeOYt3w6yHJAU/KflUaUS7ZvXo1EdD40MfiKRSOjXtSU/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\nAP8Pl83RByyuAAEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd14ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datascience import *\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "import ipywidgets as widgets\n",
    "%run Data_Cleaning_and_Helper_Functions.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## THE DATA <a id='sectiondata'></a>\n",
    "---\n",
    "\n",
    "We will be completing two notebooks. The first one will inform you about the state prisons and the second one about the county jails. \n",
    "\n",
    "In this notebook, you will use data from the CDCR (California Department of Corrections and Rehabilitation).\n",
    "\n",
    "The CDCR reports the number of people in state prisons across California. On this data set, you will find information including the capacity (staff & design) of each prison, and the current population. This will provide with a framework to understand prison overcrowding and the effectiveness of policy to reduce it. \n",
    "\n",
    "If you are interested in learning more please visit:\n",
    "\n",
    "[CDCR data and infromation](https://github.com/nrjones8/cdcr-population-data)\n",
    "\n",
    "---\n",
    "## CONTEXT  <a id='sectioncontext'></a>\n",
    "---\n",
    "\n",
    "During the course, we have learned about the policies of realignment, incarceration, and crime trends in California. By exploring these datasets we hope to get a clear picture of the magnitude of prison and county jail overcrowding per facility and region, and the effects of realignment policies in state prisons and county jails.\n",
    "\n",
    "\n",
    "\n",
    "The key difference between state prisons and jails involve the process of sentencing. Prisons are designed for long term sentences, while jails are for those who are unsentenced or have short term sentences. (Short-term sentences are generally one year or less.) Prisons are larger and controlled at the state level. In contrast, jails are smaller and handled by a city or county.\n",
    "\n",
    "\n",
    "\n",
    "The relationship between the two institutions is emphasized by mass incarceration. Through this activity, we will analyze how overcrowding within California’s state prisons influenced the size of jail populations after realignment policies were implemented.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# PRISONS<a id='section1'></a>\n",
    "---\n",
    "To observe how realignment influenced California's prisons and jails, we obtained data from 1996 to 2018, so that we could understand the trend over time. We will start by observing California's state prisons using the data we found through the CDCR (California Department of Corrections and Rehabilitation).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. DATA<a id='subsection1'></a>\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0.1  Data Dictionary \n",
    "\n",
    "Below you will find a data dictionary for future reference. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Column Name   | Description |\n",
    "|--------------|---------|\n",
    "|year |Year that the data was collected  |\n",
    "|month | Month that the data was collected |\n",
    "|institution_name |  Abbreviated Name of the State Prison|\n",
    "|population_felons | People imprisoned for committing felony (serious or violent crime) |\n",
    "|civil_addict | People imprisoned for drug related offenses |\n",
    "|total_population | Sum of civil addict and population felons columns|\n",
    "|designed_capacity | Max number of people the prison was built to hold|\n",
    "|percent_occupied | Percentage of people incarcerated out of designed capacity|\n",
    "|staffed_capacity | Max number of people the prison can hold based on the number of people employed|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0.2 Importing the Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the cell below, we will import the tabular data from `monthly_cdcr.csv` into a table, and save it into a new variable called `data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prisons = Table().read_table(\"monthly_cdcr.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we will try to understand trends over time, it might be helpful to **sort** the data by year. We will do this by running the cell below using the `sort` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Unnamed: 0</th> <th>year</th> <th>month</th> <th>institution_name</th> <th>population_felons</th> <th>civil_addict</th> <th>total_population</th> <th>designed_capacity</th> <th>percent_occupied</th> <th>staffed_capacity</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0         </td> <td>1996</td> <td>1    </td> <td>VSP (VALLEY SP)                   </td> <td>2294             </td> <td>0           </td> <td>2294            </td> <td>1980             </td> <td>115.9           </td> <td>1980            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1         </td> <td>1996</td> <td>1    </td> <td>SCC (SIERRA CONSERVATION CENTER)  </td> <td>322              </td> <td>0           </td> <td>322             </td> <td>320              </td> <td>100.6           </td> <td>320             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2         </td> <td>1996</td> <td>1    </td> <td>NCWF (NO CAL WOMEN'S FACIL)       </td> <td>786              </td> <td>4           </td> <td>790             </td> <td>400              </td> <td>197.5           </td> <td>760             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3         </td> <td>1996</td> <td>1    </td> <td>CCWF (CENTRAL CA WOMEN'S FAC)     </td> <td>2846             </td> <td>13          </td> <td>2859            </td> <td>2004             </td> <td>142.7           </td> <td>3224            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4         </td> <td>1996</td> <td>1    </td> <td>CRC (CAL REHAB CTR, WOMEN)        </td> <td>91               </td> <td>703         </td> <td>794             </td> <td>500              </td> <td>158.8           </td> <td>842             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>5         </td> <td>1996</td> <td>1    </td> <td>CIW (CA INSTITUTION FOR WOMEN)    </td> <td>1690             </td> <td>36          </td> <td>1726            </td> <td>1026             </td> <td>168.2           </td> <td>1646            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>6         </td> <td>1996</td> <td>1    </td> <td>WSP (WASCO SP)                    </td> <td>4475             </td> <td>62          </td> <td>4537            </td> <td>2484             </td> <td>182.6           </td> <td>4484            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7         </td> <td>1996</td> <td>1    </td> <td>SCC (SIERRA CONSERVATION CENTER)  </td> <td>6010             </td> <td>0           </td> <td>6010            </td> <td>3606             </td> <td>166.7           </td> <td>5884            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>8         </td> <td>1996</td> <td>1    </td> <td>SRTA (SANTA RITA CO. JAIL-RC)     </td> <td>811              </td> <td>0           </td> <td>811             </td> <td>395              </td> <td>205.3           </td> <td>750             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>9         </td> <td>1996</td> <td>1    </td> <td>RJD (RJ DONOVAN CORRECTIONAL FAC) </td> <td>4577             </td> <td>0           </td> <td>4577            </td> <td>2200             </td> <td>208             </td> <td>4566            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>10        </td> <td>1996</td> <td>1    </td> <td>PVSP (PLEASANT VALLEY SP)         </td> <td>4080             </td> <td>0           </td> <td>4080            </td> <td>2208             </td> <td>184.8           </td> <td>4008            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>11        </td> <td>1996</td> <td>1    </td> <td>PBSP (PELICAN BAY SP)             </td> <td>3509             </td> <td>1           </td> <td>3510            </td> <td>2280             </td> <td>153.9           </td> <td>3730            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>12        </td> <td>1996</td> <td>1    </td> <td>NKSP (NORTH KERN SP)              </td> <td>4525             </td> <td>1           </td> <td>4526            </td> <td>2492             </td> <td>181.6           </td> <td>4472            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>13        </td> <td>1996</td> <td>1    </td> <td>MCSP (MULE CREEK SP)              </td> <td>3530             </td> <td>0           </td> <td>3530            </td> <td>1700             </td> <td>207.6           </td> <td>3562            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>14        </td> <td>1996</td> <td>1    </td> <td>ISP (IRONWOOD SP)                 </td> <td>3874             </td> <td>0           </td> <td>3874            </td> <td>2200             </td> <td>176.1           </td> <td>4000            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>15        </td> <td>1996</td> <td>1    </td> <td>HDSP (HIGH DESERT SP)             </td> <td>2364             </td> <td>0           </td> <td>2364            </td> <td>1712             </td> <td>138.1           </td> <td>2224            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>16        </td> <td>1996</td> <td>1    </td> <td>FOL (FOLSOM SP) (MALE)            </td> <td>3757             </td> <td>0           </td> <td>3757            </td> <td>2064             </td> <td>182             </td> <td>3710            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>17        </td> <td>1996</td> <td>1    </td> <td>DVI (DEUEL VOCATIONAL INSTITUTION)</td> <td>3480             </td> <td>51          </td> <td>3531            </td> <td>1787             </td> <td>197.6           </td> <td>3396            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>18        </td> <td>1996</td> <td>1    </td> <td>CTF (CORRECTIONAL TRAINING FAC)   </td> <td>6359             </td> <td>3           </td> <td>6362            </td> <td>2981             </td> <td>213.4           </td> <td>6341            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>19        </td> <td>1996</td> <td>1    </td> <td>CVSP (CHUCKAWALLA VALLEY SP)      </td> <td>3514             </td> <td>0           </td> <td>3514            </td> <td>1738             </td> <td>202.2           </td> <td>3579            </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (9481 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prisons.sort(\"year\").show(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 1. DATA ANALYSIS<a id='subsection2'></a>\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When processing a new data set, it is a good idea to play around with the data, and try to combine it in different ways. To start it's good to think about how many times we expect each prison to occur in our dataset, and how many times they **actually** appear. \n",
    "\n",
    "Our dataset is made up of prisons in California from the year 1996 to 2018. The counts in our data set were done monthly, meaning for each year we expect there to be at most 12 counts for each prison. Let's calculate the number of times we expect each prison to appear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "276"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months = 12\n",
    "years = 2018 - 1996 + 1 # add 1 so that 1996 is included.\n",
    "\n",
    "months * years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would expect there to be a maximum number of times for a single institution to appear to be 276 times, which is equivalent to once a month for 23 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the number of times that each institution appears in our data set, we would have to group by the institution name. Essentially, we want our computer to make groups that only have values from the same prison. After we have made these groups, we want to group how many things we have in each group. \n",
    "\n",
    "The code that corresponds to this is to use the function `group()`. The `group()` function takes in a column name and puts all the similar values in that column into groups, then it counts the number of items in the group.\n",
    "\n",
    "Then, we can use the `sort()` function to order the counts in a specific order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>institution_name</th> <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>SCC (SIERRA CONSERVATION CENTER)    </td> <td>392  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>LAC (CA SP, LOS ANGELES COUNTY)     </td> <td>283  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>VSP (VALLEY SP)                     </td> <td>279  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>WSP (WASCO SP)                      </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SQ (SAN QUENTIN SP)                 </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SOL (CA SP, SOLANO)                 </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SAC (CA SP, SACRAMENTO)             </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>RJD (RJ DONOVAN CORRECTIONAL FAC)   </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>PVSP (PLEASANT VALLEY SP)           </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>PBSP (PELICAN BAY SP)               </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NKSP (NORTH KERN SP)                </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MCSP (MULE CREEK SP)                </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ISP (IRONWOOD SP)                   </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>HDSP (HIGH DESERT SP)               </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>FOL (FOLSOM SP) (MALE)              </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>DVI (DEUEL VOCATIONAL INSTITUTION)  </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CVSP (CHUCKAWALLA VALLEY SP)        </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CTF (CORRECTIONAL TRAINING FAC)     </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CRC (CAL REHAB CTR, MEN)            </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>COR (CA SP, CORCORAN)               </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CMF (CA MEDICAL FAC)                </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CMC (CA MEN'S COLONY)               </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CIW (CA INSTITUTION FOR WOMEN)      </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CIM (CA INSTITUTION FOR MEN)        </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CEN (CENTINELA SP)                  </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CCWF (CENTRAL CA WOMEN'S FAC)       </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CCI (CA CORRECTIONAL INSTITUTION)   </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CCC (CA CORRECTIONAL CENTER)        </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CAL (CALIPATRIA SP)                 </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ASP (AVENAL SP)                     </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SVSP (SALINAS VALLEY SP)            </td> <td>272  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SATF (CA SUBSTANCE ABUSE TREAT FAC) </td> <td>257  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>KVSP (KERN VALLEY SP)               </td> <td>163  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CRC (CAL REHAB CTR, WOMEN)          </td> <td>138  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NCWF (NO CAL WOMEN'S FACIL)         </td> <td>85   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>FOL (FOLSOM SP) (FEMALE)            </td> <td>72   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CHCF (CA HEALTH CARE FAC - STOCKTON)</td> <td>66   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SBRN (SAN BRUNO CO. JAIL)           </td> <td>20   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>FRCC (FRCCC BAKERSFIELD)            </td> <td>17   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SRTA (SANTA RITA CO. JAIL-RC)       </td> <td>5    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prisons.group(\"institution_name\").sort(\"count\", descending = True).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do we notice from this table? We can see that there are values in our table that went against what we had expected. We had expected all prisons to appear 276 times, but we can see that there are prisons with less than and greater than 276.\n",
    "\n",
    "*Possible Explanations*:\n",
    "- Less than 276: these prisons do not have data over all 23 years, they are may be missing information or their dates do not start in 1996.\n",
    "- Greater than 276: these prisons have multiple counts for some months.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The case where we have counts greater than 276 is interesting. Why would do we need two separate counts of the number of people in a prison? What is the significance of these counts, meaning what makes one count in a month different from the other count in that same month?\n",
    "\n",
    "To understand why we have different counts, we must research those interesting prisons. Thus, the prisons we will observe in the rest of this section are:\n",
    "- Valley State Prison (VSP)\n",
    "- Sierra Conservation Center (SCC)\n",
    "- Los Angeles County State Prison (LAC)\n",
    "\n",
    "---\n",
    "On the following section, we will explain what we think is the cause of \"inconsistency\" in the data, and how we decided to handle it. Then, we will read a data table with the \"cleaned\" data using `Table().read_table()` and assign it to a new variable.\n",
    "Though we denoted them as \"inconsistencies\", we urge you not to look at this as mistakes in the data but try to think how they fit into the major issues we have learned about in the class.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Case of Valley State Prison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "From outside research, we discovered that Valley State Prison changed from a female prison to a male prison in 2012 (October 2012 to be exact). However, all rows are labeled as a male institution. For future analysis, it would be better to separate VSP into its’ female and male years. Thus, we decided to split and relabeled the VSP data into male and female parts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Unnamed: 0</th> <th>year</th> <th>month</th> <th>institution_name</th> <th>population_felons</th> <th>civil_addict</th> <th>total_population</th> <th>designed_capacity</th> <th>percent_occupied</th> <th>staffed_capacity</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0         </td> <td>1996</td> <td>1    </td> <td>VALLEY SP (FEMALE)</td> <td>2294             </td> <td>0           </td> <td>2294            </td> <td>1980             </td> <td>115.9           </td> <td>1980            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>34        </td> <td>1996</td> <td>2    </td> <td>VALLEY SP (FEMALE)</td> <td>2096             </td> <td>0           </td> <td>2096            </td> <td>1980             </td> <td>105.9           </td> <td>2184            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>68        </td> <td>1996</td> <td>3    </td> <td>VALLEY SP (FEMALE)</td> <td>2326             </td> <td>7           </td> <td>2333            </td> <td>2380             </td> <td>98              </td> <td>2584            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>102       </td> <td>1996</td> <td>4    </td> <td>VALLEY SP (FEMALE)</td> <td>2498             </td> <td>13          </td> <td>2511            </td> <td>2380             </td> <td>105.5           </td> <td>2584            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>136       </td> <td>1996</td> <td>5    </td> <td>VALLEY SP (FEMALE)</td> <td>2562             </td> <td>9           </td> <td>2571            </td> <td>2380             </td> <td>108             </td> <td>2584            </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (274 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prisons_1 = Table().read_table(\"prisons1.csv\")\n",
    "prisons_1.where('institution_name', are.containing(\"VALLEY SP (\")).show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Case of Sierra Conservation Center (SCC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "For the Sierra Conservation Center, there were many double counts for each month. When we researched why there were separate counts for the same prison, it was unclear. One count had values in the thousands, while the other count was in the hundreds. Our best guess is that the prison counted the number of people within a fire camp nearby. We split and relabeled our data to show the difference between the smaller and larger population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Unnamed: 0</th> <th>year</th> <th>month</th> <th>institution_name</th> <th>population_felons</th> <th>civil_addict</th> <th>total_population</th> <th>designed_capacity</th> <th>percent_occupied</th> <th>staffed_capacity</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>7         </td> <td>1996</td> <td>1    </td> <td>SIERRA CONSERVATION CENTER (PRISON)</td> <td>6010             </td> <td>0           </td> <td>6010            </td> <td>3606             </td> <td>166.7           </td> <td>5884            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>41        </td> <td>1996</td> <td>2    </td> <td>SIERRA CONSERVATION CENTER (PRISON)</td> <td>5912             </td> <td>0           </td> <td>5912            </td> <td>3606             </td> <td>163.9           </td> <td>5884            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>75        </td> <td>1996</td> <td>3    </td> <td>SIERRA CONSERVATION CENTER (PRISON)</td> <td>5912             </td> <td>0           </td> <td>5912            </td> <td>3606             </td> <td>163.9           </td> <td>5884            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>109       </td> <td>1996</td> <td>4    </td> <td>SIERRA CONSERVATION CENTER (PRISON)</td> <td>5979             </td> <td>0           </td> <td>5979            </td> <td>3606             </td> <td>165.8           </td> <td>5884            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>143       </td> <td>1996</td> <td>5    </td> <td>SIERRA CONSERVATION CENTER (PRISON)</td> <td>5884             </td> <td>0           </td> <td>5884            </td> <td>3606             </td> <td>163.2           </td> <td>5884            </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (387 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prisons_2 = Table().read_table(\"prisons2.csv\")\n",
    "prisons_2.where('institution_name', are.containing(\"SIERRA\")).show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Case of Los Angeles County State Prison (LAC)\t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "In the case of Los Angeles County, we saw that less than a one year period from December 2005 to June 2006 was there a double count recorded in the prison population. Although we are unsure what caused the changes between the two groups, we saw that one group had a population size ranging in the four thousand while the other group was less than one thousand. We split and relabeled our data to show the difference between the smaller and larger population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Unnamed: 0</th> <th>year</th> <th>month</th> <th>institution_name</th> <th>population_felons</th> <th>civil_addict</th> <th>total_population</th> <th>designed_capacity</th> <th>percent_occupied</th> <th>staffed_capacity</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>23        </td> <td>1996</td> <td>1    </td> <td>LOS ANGELES COUNTY (STATE PRISON)</td> <td>3978             </td> <td>0           </td> <td>3978            </td> <td>2200             </td> <td>180.8           </td> <td>4000            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>57        </td> <td>1996</td> <td>2    </td> <td>LOS ANGELES COUNTY (STATE PRISON)</td> <td>3919             </td> <td>0           </td> <td>3919            </td> <td>2200             </td> <td>178.1           </td> <td>4000            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>91        </td> <td>1996</td> <td>3    </td> <td>LOS ANGELES COUNTY (STATE PRISON)</td> <td>3975             </td> <td>0           </td> <td>3975            </td> <td>2200             </td> <td>180.7           </td> <td>4000            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>125       </td> <td>1996</td> <td>4    </td> <td>LOS ANGELES COUNTY (STATE PRISON)</td> <td>4064             </td> <td>0           </td> <td>4064            </td> <td>2200             </td> <td>184.7           </td> <td>4000            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>160       </td> <td>1996</td> <td>5    </td> <td>LOS ANGELES COUNTY (STATE PRISON)</td> <td>4012             </td> <td>0           </td> <td>4012            </td> <td>2200             </td> <td>182.4           </td> <td>4000            </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (278 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prisons_3 = Table().read_table(\"prisons3.csv\")\n",
    "prisons_3.where('institution_name', are.containing(\"LOS\")).show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 2. GENERAL: DESIGNED & STAFFED CAPACITY<a id='subsection3'></a>\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Designed Capacity of California's Prisons over time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Context**: The Prison Industrial Complex has created our current issue with mass incarceration. Throughout the course, you have learned how prisons are designed to hold large numbers of people, and how creating these large institutions perpetuates the cycle of mass incarceration, because these prisons are made to be filled. In this next section, we will break down how the designed capacity of prisons is related to the actual population within a given institution. We will later compare the total population with the number of people employed to sustain this system.\n",
    "\n",
    "**Data Cleaning**: Let's look at how the size of prison populations has changed over time in regards to the number of inmates the prison is designed to hold. It is not necessary to look at the whole table, thus we will only look at the following variables: `year`, `institution_name`, `designed_capacity`. We use the function `select()` to cut the data table and save it to a new variable called `year_and_design_capacity` as we will be working with this later. Finally, we use `show` to limit the number of rows that appear in our table.  \n",
    "\n",
    "**BEFORE YOU CONTINUE**:\n",
    "\n",
    "From now on, we will use an updated version of the `prisons` table that incorporates the three data table changes we made above. We will call it this `prisons_updated`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prisons_updated = prisons_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>institution_name</th> <th>designed_capacity</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>NCWF (NO CAL WOMEN'S FACIL)  </td> <td>400              </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>CCWF (CENTRAL CA WOMEN'S FAC)</td> <td>2004             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>CRC (CAL REHAB CTR, WOMEN)   </td> <td>500              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (9498 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_and_design_capacity = prisons_updated.select(\"year\", \"institution_name\",'designed_capacity')\n",
    "year_and_design_capacity.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to see what is the `designed_capacity` for **all** the prisons in a given year. We will do this by grouping by **year** using the function `group` and summing over all the prisons in that year. Since we are only looking at *designed_capacity*, we can temporarily remove `institution_name` from the data table for this question. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>designed_capacity sum</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>873676               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>910797               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>956740               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "design_capacity_ca = year_and_design_capacity.drop(\"institution_name\").group(\"year\", sum)\n",
    "design_capacity_ca.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plotting**: Although we can spend a lot of time comparing the values for designed capacity for each year and notice a pattern, often times it is more useful to visually inspect the information as it might reveal useful insights and provide a context to the data we are looking at. \n",
    "\n",
    "Below we drew a line plot to reflect the **changes in design capacity of California's state prisons over time**.\n",
    "\n",
    "The format for plotting is: \n",
    "* **To draw plot**:\n",
    "*data_table*.`plot(`*x_variable*, *y_variable*`)`. Where the x and y variables stand for the name of the columns in our data table\n",
    "* **To label plot**: We use `plt.xlabel`(*x_axis name*), `plt.ylabel`(*y_axis name*), `plot.title`(*plot_title*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1104d0908>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ob9L/OKkMHToUXC4Xvr6+2LhxI/Ly8uDv749WrVpJ/C1raGhAIBBINZlEuQ+bbBurmry3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1104cd7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "design_capacity_ca.plot(\"year\", \"designed_capacity sum\")\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.title(\"Designed Capacity over Time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will focus on some years that mark important shifts as a result of realignment.\n",
    "\n",
    "Let's look at the year **2011**. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>designed_capacity sum</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2011</td> <td>1009197              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "year | designed_capacity sum\n",
       "2011 | 1009197"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "design_capacity_2011 = design_capacity_ca.where(\"year\", 2011)\n",
    "design_capacity_2011"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **To add red dots**: `plt.plot`(*x_coordinate*, *y_coordinate*, *line property* ). \n",
    "\n",
    "Using **total_capacity_2011**, can now assign **x_coordinate_2011** to the **year** column and the **y_coordinate_2011** to the **designed_capacity sum** column. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_coordinate_2011 = design_capacity_2011.column('year')\n",
    "y_coordinate_2011 = design_capacity_2011.column(\"designed_capacity sum\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you are ready to add the dot to the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1104bb358>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0794dzs7OACpH/g4ZMgRLlizB9u3bAQBLlizB8OHDme4gNzc3dOnSBXPnzsWm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1104bbac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "design_capacity_ca.plot(\"year\", \"designed_capacity sum\")\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.title(\"Design Capacity over time\")\n",
    "\n",
    "plt.plot(x_coordinate_2011, y_coordinate_2011, 'ro')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "\n",
    "**Looking at the graph produced in the section above how does it reflect the systematic changes of Califonia's potential prison population? Name a court case that is represented by a red dot on the plot above.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*double click this cell to type your response*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\n",
    "**What do you think happened in 2006 that caused that spike?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*double click this cell to type your response*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Total Population in California's Prisons over time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through the last section, we observed the change in the number of people California's prisons are designed to hold. Given this change over time, we wanted to compare it with the actual number of people within California's prisons. To do this, we will calculate the percent at which prisons were filled based on *how many people each prison is designed to hold*. Here is our original dataset for reference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Unnamed: 0</th> <th>year</th> <th>month</th> <th>institution_name</th> <th>population_felons</th> <th>civil_addict</th> <th>total_population</th> <th>designed_capacity</th> <th>percent_occupied</th> <th>staffed_capacity</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0         </td> <td>1996</td> <td>1    </td> <td>VSP (VALLEY SP)                 </td> <td>2294             </td> <td>0           </td> <td>2294            </td> <td>1980             </td> <td>115.9           </td> <td>1980            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1         </td> <td>1996</td> <td>1    </td> <td>SCC (SIERRA CONSERVATION CENTER)</td> <td>322              </td> <td>0           </td> <td>322             </td> <td>320              </td> <td>100.6           </td> <td>320             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2         </td> <td>1996</td> <td>1    </td> <td>NCWF (NO CAL WOMEN'S FACIL)     </td> <td>786              </td> <td>4           </td> <td>790             </td> <td>400              </td> <td>197.5           </td> <td>760             </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (9498 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prisons.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Cleaning**: Let's look at how the prison population changed over time in regards to the number of incarcerated people prison is designed to hold. It is not necessary to look at the whole table, thus we will only look at the following variables: `year`, `total_population`, `designed_capacity`. Just like in the last section, we use the function `select()` to cut the data table and save it to a new variable called `prison_pop_design` as we will be working with this later. Finally, we use `show` to limit the number of rows that appear in our table.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population</th> <th>designed_capacity</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>790             </td> <td>400              </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>2859            </td> <td>2004             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>794             </td> <td>500              </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>1726            </td> <td>1026             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>4537            </td> <td>2484             </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (9496 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_pop_and_design = prisons_updated.select(\"year\", \"total_population\", \"designed_capacity\")\n",
    "total_pop_and_design.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to see what is the `designed_capacity` and `total_population` for **all** the prisons in a given year. We will do this by grouping by **year** using the function `group` and summing over all the prisons in that year so that we can see how the population changed over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population sum</th> <th>designed_capacity sum</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>1623806             </td> <td>873676               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>1739423             </td> <td>910797               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>1788142             </td> <td>956740               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1999</td> <td>1818019             </td> <td>958881               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2000</td> <td>1820519             </td> <td>965721               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (18 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum_total_and_design = total_pop_and_design.group(\"year\", sum)\n",
    "sum_total_and_design.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculated the percentage of people within California's prisons with respect to the designed population size for **all** prisons within a certain year. Note: we are calculating the percentage, so we will be multiplying our proportion by 100.\n",
    "\n",
    "To do this, we will get the values from our table by using the `column()` function. The `column()` function gets all the values from a single column in our table, and make them an array.\n",
    "\n",
    "From class, we learned that despite their designed bed space, prisons are overcrowded. In this section, we will explore how much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 185.85905988,  190.97812136,  186.89947112,  189.59797931,\n",
       "        188.51397039,  186.46297264,  186.17592316,  188.84950734,\n",
       "        192.63564133,  190.09189001,  188.49532912,  195.52605312,\n",
       "        190.30095205,  183.36618572,  178.73669545,  173.23535445,\n",
       "        149.88321645,  144.95077023,  138.83530151,  132.72696534,\n",
       "        131.7232109 ,  132.02795138,  131.94811151])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_population = sum_total_and_design.column(\"total_population sum\") \n",
    "designed_sum = sum_total_and_design.column(\"designed_capacity sum\")\n",
    "\n",
    "capacity_percentage = (total_population / designed_sum) * 100\n",
    "capacity_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have these percentages, we must add them to our table, so that we can graph them. To add another column to a table, we use the function called `with_column()`. The `with_column()` function adds a column to a table when it is given a new column name and the column values. The format is:  *data_table*`.with_column(`*column_name, column_values*`)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population sum</th> <th>designed_capacity sum</th> <th>Design Percent</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>1623806             </td> <td>873676               </td> <td>185.859       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>1739423             </td> <td>910797               </td> <td>190.978       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>1788142             </td> <td>956740               </td> <td>186.899       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_and_design_and_percentages_table = sum_total_and_design.with_column(\"Design Percent\", capacity_percentage)\n",
    "total_and_design_and_percentages_table.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "**What can you tell about the design percent column in our table?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*double click this cell to type your response*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot how overcrowding has changed over time, by observing the change in the percentage of people within prisons in comparison to how many people prisons are designed to hold."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plotting**: Using the same technique as above, below we drew a line plot to reflect the **overcrowding of California's state prisons over time**.\n",
    "\n",
    "The format for plotting is: \n",
    "* **To draw plot**:\n",
    "*data_table*.`plot(`*x_variable*, *y_variable*`)`. Where the x and y variables stand for the name of the columns in our data table\n",
    "* **To label plot**: We use `plt.xlabel`(*x_axis name*), `plt.ylabel`(*y_axis name*), `plot.title`(*plot_title*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x110f480b8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RkZ+6JUuWxNzcXOu8cXFxHDp0CFtbW0aNGiXLV6NGDQYMGEB6erryieFVVlZWjBs3TiU9\nICCAJ0+e0KVLFxo0aCBbNnbs2Hx15vryyy9VnrD79u0LwNmzZ5Vp4eHh3Lp1Czc3N9q0aSPL369f\nPypVqqT1tvMSHh7O1atXqVevnsrTnaenJ+3bt+fRo0cEBAQU2DYB5W/t8ePHyrT8XivUdeQpXbq0\n7KlPnYCAAGUtVt26dWXLcvvT5KVkyZL89NNP6OrqKtOqVauGq6sr165dkzV7LV26FEmSmDt3ruw3\nB+Dn50ft2rUJCAjg6dOnwMtjEh0djaurq+ypEl5Wrzo7O7/xe70q93pvY2Oj8Wdy2djYqN2HLVq0\noFq1arLf9qtMTExUflMNGjSgc+fOPHnyRHYumZqaqu0rY2trS4cOHYiOjpZVlW/YsIGUlBT69eun\n0lQIFEhH2AEDBpCRkaHS1LFmzRqysrJkVfx50bqj17to0KABVatW5dChQzx69IiyZcsCL6uB0tPT\n8fPzQ6FQAC87dly8eBFzc3OWLl2qdn0GBgbcv39fVuWb6/V2WHhZ3ZGSkkLdunW1OgDq1gUQEREB\noLZKyMrKChcXF86ePcuNGzeoXr068LIKe82aNZw7d46GDRsSEhKCqakpjRs3xtXVVVkop6enEx4e\nTq1atWTfLSIiAoVCobaTi7u7u8bfKZeJiYnaH2ru/klKSlKm5baXurm5qeRv2LChVlXXAImJiSxY\nsIADBw5w584dUlNTZcvv37+v/P9z586Rk5ND48aNZc0b6miTN/cYurq6qjSpwMvCZdGiRcp8r6pZ\ns6baC09uXnXHw8TEhNq1a+fZ7pSX1y/8oP0x0tHRoVGjRty4cUOrbeflTec/vNx3u3fvJiIigm7d\nuhXINgFlX4d3uVZ06dKFpUuX0qtXLzp06ICHhweNGjXSuHPPm/ZzqVKlqFWrVp7H2NnZWe2N2avH\nM/e8DQ8PR09Pj927d7N7926Vz2RkZJCdnc3NmzepW7fuG889HR0dGjduXCRDViVJYvPmzcrq3qSk\nJFk/EXW/NXjZd0Fdc6S7uztbtmzh4sWL9OjRQ5l+8uRJli5dyunTp0lISCAjI0P2ufv37yubqc6c\nOQOg9gGroPj6+jJlyhRWr17NiBEjUCgUZGdns3btWoyNjfH19X3rOrS6ilpZWXHt2jViY2Pfmjc3\nj7W1tSy9Z8+eTJo0ib/++kvZqWXjxo0oFAr8/f2V+ZKSkpAkicTERKZPn/7GbT179kylUFY3fCG3\n88arT7WaUPf0DCg7j+S1PDeGVzuN5BbKISEhNGzYkKNHj9KsWTN0dHTw9PRk8uTJ3L17l9u3b5OW\nlqZyR5eSkoKJiYnawiCvON4krzv63Lv4V39Iud/X0tJSbf7Xj8GbJCUl4eXlxZ07d6hfvz5+fn6Y\nm5ujq6tLcnIyS5culXVc0+bYaZM3P8cw19vOC3X76U2fexN1x0nbY5TfbeflXfbdu8itfcu9qc/P\ntaJ+/frs27eP2bNns2fPHuVTtIODA1999RUDBgx443reZT9r85tLTEwkKytLo+/1rnG9Lvf45dWZ\n8E2++eYblixZgrW1Nd7e3pQvX15ZM5nbT0Gb+HK/z6sd9nbv3s3nn3+OkZERnp6eVKhQgZIlS6Kj\no8OxY8cICwvL9zUkv0qXLk2PHj1YuXIlISEheHp6cuDAAWJiYujTp49Gvfi1KpQbN25MaGgoQUFB\nfP7552/Mm/vE17hxY1m6r68vU6dOZcOGDQwZMoTr169z5swZ3N3dcXJyUubLvZN0cXHh+PHj2oQJ\n/HMX/arcHfLqE1h+1wX/xBgfH69StQT/VP+8elfs4eGBQqEgODgYHx8f7t+/ryx4c/8bHBys7LX5\neqFsYmJCUlIS6enpKgXzqx3QCkPuHWxCQoLKyZWdnU1iYqLGJ/zatWu5c+cO48ePV+lEcurUKZUn\nHm2OnTZ5Xz2G6qg7hrnedl4kJCSoXV6Yx+nVY1TY236XfZdfKSkpXLhwAUDZNJDfa0XDhg3ZtGkT\nGRkZXLx4kaCgIJYvX86oUaMoUaKE7CHhdUW1n01MTMjMzMyzEFOXv6Di+uSTTzA0NCQuLo7o6Ggq\nV66s0ecSEhJYtmwZLi4uajvibt26Nc/P5hVf7vd59Vz6+eefMTAwICgoSNZxFuCrr75SGTnx6nWh\nTp06Gn2X/PjPf/7DypUrWbVqFZ6enqxevRrgrTd6ubRqU+7Vq5eyd+6bevoePHiQc+fOUaZMGZV2\nDWtra1q0aEFkZCRRUVHKdsrX26RKlSqFi4sL0dHRsrajd1GlShVMTU25evUqMTEx77y+3AOrbnhJ\nQkICV65cwdjYWHYyW1paUr16dU6fPs2+ffuAl9V8ueszMzPj6NGjhISEoK+vr1I9VqdOHSRJUnvx\nUTd8pyDlDoE7ceKEyrLTp0+TlZWl8bpyq9A6dOigskzd96hfvz46OjqcPHlSZajZu+TN/U7h4eEq\nVV/wT5u/uurjvOSeF+q+x9OnT2XDpgram45RTk6O1sNicp/ecnJyVJa96fyH/O27t1mwYAFpaWnU\nrVtXWdX8rtcKAwMDGjRowNixY5U3g3v27HnjZ960n589e0ZkZKTWcajTsGFDnj59qvH63nTu5eTk\ncPLkSY23XaJECWV167Rp096aP/ep9Pbt2+Tk5ODl5aVSIMfGxnL79u081xEREaFsH39V7vd5dRju\nrVu3qFq1qkqBnNf3zL2JO3To0Fu/S17e9HvI5eLigpubGwEBAZw7d46DBw9St25dPvnkE422oVWh\n7OTkxJgxY8jMzMTPz0/tOMRjx44px2FNmzZNbZte7vCSdevWsXnzZoyNjVUKb4Bhw4aRmZnJ0KFD\nefLkicryp0+fKtsJNKGrq8vAgQN58eIFo0aNUhnXm5WVpdWdZO/evQGYM2eObLygJElMmjSJ58+f\n4+/vj76+vuxzzZs3Jz09nV9//RVbW1tloa2jo0OzZs04fPgw58+fp0GDBhgbG8s+m7vvfvjhB9LS\n0pTpSUlJzJo1S+PY8yN3tp65c+fK2jEzMjKYOnWqVuvKHU7wertbREQEc+fOVclftmxZunbtSnx8\nPN9++63KjyItLU15jmiT19bWFm9vb2JjY5k/f74s35UrV/j9998xNDSUtWO9Tdu2bTEzM2Pbtm0q\n5+eMGTPUjpktKI0bN6ZChQqcOHGCwMBA2bLVq1dr3Z6c2ySh7knN1dWVqlWrcvbsWZWOcCEhIeze\nvRsLCwvatm2r5bdQ9fz5c6ZPn87s2bMxNDRUqc7V9loRHh4u+/3kyv0dqxuP+6q2bdtiYmLCtm3b\nlE/uuWbNmlVgVfbDhg0DXj75qWs2fPHihezGwNXVlcqVKxMeHs7OnTtleVeuXKl1e/J3332Hra0t\nW7duZcKECWr3WVJSEhMnTlQ+Eeb+tk+ePCmrin/27BkjR4584817SkoKM2bMkKWdOXOG7du3Y2Zm\nJjuXHBwcuHXrlqxGTJIkfvnlF65evaqy7p49e2JiYsLq1as5evSoynJNmmXf9Ht41cCBA8nMzKR3\n797k5ORo/JQM+ejoNX78eF68eMG8efNo3rw5np6e1KhRg5ycHM6fP09YWBh6enrMnDkzzwtZmzZt\nMDc3Z+XKlWRmZqqMTc7Vq1cvIiIi+O2336hbty7e3t44ODiQnJzM3bt3OX78OF5eXiq9ud8W/9mz\nZzlw4AD16tXjs88+w8TEhNjYWEJCQhg5ciRDhw7VaF2NGjVi1KhRzJkzBzc3Nzp16oSJiQlBQUFE\nRETg4uLC999/r/K55s2bs2TJEhISElSqyHI7x+Tme123bt3Ytm0bgYGBuLm50a5dOzIzM9m9ezd1\n69bl5s2bGu8LbTVt2pR+/fqxevVq3Nzc8PHxwdDQkMDAQExMTChfvrxKT+e8+Pn5sWDBAiZMmEBo\naCgVK1bk5s2b7N+/Hx8fH7Zt26bymZkzZ3LlyhX++OMPwsLC8Pb2xsjIiLt373LkyBEWLVpE+/bt\ntc47Z84cPvvsM3766SeOHj1Kw4YNleOUc891bToGlipVivnz59O/f3/atWtH586dKV++PCdPnuTy\n5cs0adIkX00ymtDR0WHBggV069aN3r1706FDB+U45aCgIFq1asXBgwc1Pk5eXl7s2LGDkSNH0qFD\nB0qVKoWpqSmDBg1CoVCwZMkSOnXqxODBg9m+fbtynPKuXbswMDBg6dKlby3gXrdkyRLlNJvPnj3j\n1q1bHD9+nJSUFGxsbFi0aBGurq6yz2h7rZg/fz5Hjx7Fzc0NR0dHSpcuzY0bN9i/fz8lSpR464tB\nTExMmDVrFl9++SVt2rSRjVOOjIzE3d2dsLAwjfdzXjw8PPjhhx+YNGkS9evXp1WrVjg5OfHixQvu\n3bvH8ePHcXBwUN7cKhQKFi5cSOfOnenfv79snHJQUBAtW7bU6knRysqKnTt30rt3b5YsWcLmzZuV\nY/wzMzOJjo7m2LFjpKam8ttvvwEv26K7du3K1q1badasGV5eXqSkpBAUFISRkRG1atXK88nfzc2N\nP/74g7Nnz9K4cWPi4uLYvn07kiQxf/58WTkxdOhQ/ve//+Hh4UGHDh3Q09MjPDyca9eu8dlnnylr\nInNZWFiwfPlyPv/8czp16oS3tzc1a9bk2bNnXLlyhaioqDc+xcPL38OCBQuYOnUqV65cUTZbvj4p\nVocOHbCysiIuLg4TExPZXBNvo3WhrFAomDx5Mp06dWL58uWEhYVx/PhxFAoFtra2fPHFFwwePJiK\nFSvmuQ5DQ0O6devG8uXLAdWq61fNmDGD1q1bs3LlSo4dO8aTJ08wNTXFxsaGgQMHat2r08DAgC1b\ntrB69Wo2bdrEn3/+SXZ2NuXKlcPb2xsvLy+t1vf9999Tu3ZtfvvtN/766y/S09NxdHRkzJgxjBw5\nMs+ehHp6emRlZakUvK/+W12vVoVCwR9//MHcuXOVc86WK1eOnj17Mm7cuEKfn3fOnDlUrlyZ1atX\ns3r1asqUKUP79u2ZOHEiNWrUkPULeJPy5csTGBjI5MmTOXnyJEeOHKFy5crMnj2b5s2bqy2UzczM\nOHDgAEuXLmXbtm2sWbMGHR0dbGxs6N69u6yaVJu8jo6OBAcHM2vWLPbt28fJkycxNjbG3d2d//73\nv7IhaZrq2LEjW7duZfr06ezcuRMDAwOaNGnCwYMHmTt3bqEVygDNmjVj7969/Pjjjxw8eBB4WaW/\ne/du/vrrLwC156U6ffr0ISYmhi1btrB48WIyMzOxt7dX1obVq1eP4OBgZs6cSXBwMIcPH8bU1JR2\n7doxevRotbP+vU1uFbKuri6lSpXC2tqaVq1a0apVKzp06JBnIa/NtWLgwIGYm5tz9uxZwsPDyczM\npHz58vj5+TF8+HCNemH36NEDc3NzZs6cyY4dO2THeOLEiYDm+/lNRowYQePGjVm6dCknTpxg3759\nlCpVivLly9OjRw86d+4sy9+4cWMCAwP54YcfOHz4MIcPH6Z+/frs2bOHw4cPa119W6lSJUJDQ/nz\nzz/ZtWsXoaGhJCYmoq+vj4ODA35+fvTp00f2m1q4cCFOTk5s27aNFStWULZsWdq0acM333xDnz59\n8tyWk5MTc+fOZfLkyaxcuZKMjAzq1avH+PHjlc18ufr374+BgQFLlixh48aNGBkZ4ebmxqJFi9i1\na5dKoQwvJ7YKCQlh3rx5hISEEBwcjImJCZUqVVI7vPF1Xl5eTJs2jdWrV7NixQplbevrhbK+vj6+\nvr4sXLgQX19flRrPN1H8/yBpQXgnN2/epH79+nTt2pWVK1cWdzhCHj799FPOnDnD3bt3tbpQCJrL\nzs6mTp06ZGZmqkypK6gXGhqKj48P/v7+LFmypLjDKRBdunThyJEjHD9+XGWWyDcR71MWtBIfH6/S\nRvv8+XNlD+rcKmGh+KSlpcna/HOtX7+e8PBwWrRoIQrkApCcnKzyKklJkpg5cyYxMTHit/AvFhER\nwZEjR2jatKlWBTIU8eQhwofvt99+Y9OmTTRt2hRra2sePnzI0aNHiY2NpVWrVnTq1Km4Q/zXu3//\nPk2aNMHT0xNnZ2eysrKIjIzkxIkTmJqa8uOPPxZ3iB+F8+fP07dvX7y8vHBwcCA1NZXTp08TGRmJ\nnZ2dyrySXEUWAAAgAElEQVTxwsdv5cqV3L9/nw0bNqBQKPjmm2+0XocolAWteHh4EBERQVBQEE+e\nPEFPT4+KFSvy5ZdfMmTIkDzH7gpFx8LCgh49ehAWFsaxY8dIT0+nXLly9OrVizFjxqid3lTQnrOz\nM5999hknT57k4MGDZGVlYWNjw5dffsno0aPznMBD+HjNmzeP2NhYnJycWLRoUb5eLyralAVBEATh\nPSHalAVBEAThPSEKZUEQBEF4T2jUphwXF0dQUBBnzpzh/v37vHjxAgsLCypVqoS7uztNmzZ950Hy\ngiAIgvBv98Y25WPHjrFw4UIOHz5MdnY2tra2WFhYUKJECZ48eUJMTAzPnz/H2tqavn37MmzYsAKd\nfL4gaTOhuvAPsd+0J/ZZ/oj9pj2xzz4+eT4p9+jRg9DQUD777DNWrVpFkyZNVF4onZOTw+XLl9m3\nbx9btmxhxYoV/Pbbb3h7exd64IIgCILwscmzUK5YsSILFy5847SNOjo61KxZk5o1azJmzBgCAgIK\ndbJ9QRAEQfiY5Vko//LLL1qvrCDeBiMIgiAI/1aid5YgCIIgvCe0LpSTkpIYPHgwlStXplKlSgwa\nNIjExMTCiE0QBEEQ/lW0LpRHjx7N48ePWbhwIdOmTSMyMpKvvvqqMGITBEELMQ8eceHyTbKyst+e\nWRCE91Kebcrnzp2jXr16KunBwcFcvHhR+ZYZMzMzBgwYUHgRCoLwVqGno1i8djc5ORIOtpZMHNEL\nk1Lq3zssCML7K88n5Y4dOzJ69GiVV8CVLVuWs2fPKv997tw5ypYtW3gRCoLwRvGPk1ixKZCcnJdT\nDtyNTWDG0s2kZ2QWc2SCIGgrz0L52LFjxMXF0bBhQzZt2qRMHz9+PN27d8fLywtXV1dmzpwpXlEm\nCMVEkiSWrd/Li3R5ARx9O455v28nO1tUZQvChyTP6mtHR0c2btzIvn37+Prrr1mzZg1z586lS5cu\n1KhRg5CQEODlq/yqVatWZAELgvCPQ8fOE3X9jtpl56JusOLPfQzybyteqSkIH4i3dvTKfV9okyZN\n8Pb2ZvLkydjb2zNo0CAGDRokCmRBKCYJj5NYt+OwLO31svfI8Qi2BIQWYVSCILwLjXpfGxkZ8d13\n3xESEkJkZCSNGjViz549hR2bIAh5kCSJZRsCZNXWRob6TB3VF0sLU1neLYHHOHTsXFGHKAhCPuRZ\nKKenpzN9+nRatWpFs2bNGD16NKVLl2br1q38+OOPjB8/Hl9fX+7evVuU8QqCABwOO0/ktduytN6d\nvKlSwY4JQ3wpbVxCtmzFn/s4c/F6EUYoCEJ+5Fkojxs3jjVr1tC+fXv69OnDmTNn6N69OwCdOnXi\n1KlTVK5cmaZNmzJr1qwiC1gQ/u0SHiexdru82rpmFUdaNv0EAFvrsoz9sjsG+v90GZEkmL9qB9f/\njinSWAVB0E6ehfKuXbuYPXs2I0eOZNCgQWzatInIyEhu374NgLGxMT/++CP79u0jKCioqOIVhH81\nSZJYtlG12vrLXu1knbmqOtsxsn8nWRtzRmYWM5b+ReyDR0UZsiAIWsizUDYzM+PatWvKf1+7dg1J\nkjA1lbdXubi4sHfv3sKLUBCKiCRJHD97mZm//cWGnUEkpTwr7pBUHDl+gcirt2VpvTq1wMrCTCVv\ng9pVGOj7mSztaWoavyz5kyfJTwszTEEQ8inPIVHffPMNQ4cOZdOmTRgZGREVFcXw4cMxNzcvyvgE\noUhkZmbx+1/7OXI8AoAzRLP/6Bnat3ClvbcrJYwMizlCeJSYrLbaulVT1Zn3crVsWo/E5KdsDQxT\npiU8TuaXJX8y6b+9MS5pVGjxCoKgvTwL5e7du1OvXj2CgoLIyMhg+vTpNGrUqChjE4QikZTyjDkr\ntnHtlry99UV6JlsCj3Hw2Dm6tW1GC7e66OnpFkuMudXWaS8ylGnqqq3V6d7WgyfJz5Q3HAB3YuKZ\ns2IrXw/xRV8/z8uAIAhF7I2/xooVK1KxYsWiikUQitzNO3HMWr6VxKS8q3OTnz5n5Z/7CQw+jZ+P\nJ43qVC3yyTiCT0Zw8crfsrSeHb3UVlu/TqFQMND3M5JSUjkXdUOZHnX9DkvW7WFEv45ichFBeE/k\n2ab84MGDfK3w4cOH+Q5GEIpSSHgkk+atVSmQLS1MMSlVQiV/3MNE5qzYxvdz1nD15r2iCpPHT1JY\ns+2QLM2lsgOtm9XXeB26urqM7N+Jyk42svSws5dZt+NIgcQpCMK70/36668nq1tQpUoV4uPjcXR0\nxMLC4o0rSUtLY8eOHXz55Zekp6fj7u5eGLG+k8TExLd+j8JwPz6RFy/SP9i2u+Lab4UpOzubdTuO\nsGFnkPIlDrlqVXPi2+E9aevVCB0dBbfu3ic7J0eW53HSU4JPXuR2zAMcbcupvI2pIPeZJEnMX7WD\ne/f/6TFtaKDHhKG+lDbW7i1Qenq6NKhdhbOR13mamqZMv/53LCVLGFKlgm2BxJxfH+O5VtjEPvv4\n5Fl9HRAQwPfff0/jxo2pUaMGbm5u1KxZk7Jly2JoaEhSUhK3b9/m7NmzhIaGolAoGDlyJMOGDSvK\n+N9bkiSx8s99HDx2HoUC2nu70qtjC1FNWMyepqYxf9V2lR7M8PIY9ezgia7uy3ZjPx9PWjerz18B\nRwk6EYEkL785czGas5HRtGhSl+5tm2FuWrrA4w0+GcGFy7dkaf4dvLC2LJOv9ZmUKsmEoX5MnP0H\nSSmpyvQ1Ww9hVtoY9wY13ileQRDejSIpKUl6U4YLFy6wZs0aDh8+rDJ7l5GREfXr16dLly50796d\n0qXfflEKCwtj4cKFREREcP/+fRYtWkSvXr2Uy+Pj45k0aRJBQUEkJyfTpEkTZsyYIWvbTk9P57vv\nvmPr1q28ePECDw8PZs+eja1t3nf60dHRVK5c+a3xFQRJkli77RB7g07L0l3rVmX45x0w0NcvkjgK\nQlHut8J2Ny6eWb9t4eEj+etI9fV1GeTfFo9GtfL8bMyDR2zcFcSZi9Fqlxsa6NGuhSs+3o2Jjblb\nIPvs8ZMUxvz8G8/T/unc5VLZge//2+udb+5uxzxg8rx1so5jeno6TBjqR80qTu+07vz6mM61oiL2\n2cfnrd0u69atS926dQFISEjg/v37pKenU6ZMGRwcHNDXsoBJTU3FxcUFf39/Bg8eLFsmSRK9evVC\nR0eH9evXY2JiwqJFi+jYsSPh4eEYGxsDMGHCBAICAli5ciXm5uZ8++23+Pr6EhISonzKKU67Dp5Q\nKZABwi9c40nyBsYM6oZpaeNiiOzf61TENRat2aXyisMyZqUZ80VXKjra5PHJl+ysyzJ2UHeu3LjL\n+h1HiL4dJ1uenpHFtn1hHA47T+Pazjg6Ob3TzZckSfy2MUBWIBvo6/Flz4J545OTnTWjBnZl+tI/\nycp6WT2flZXDrN+2MOV/fXG0tXrnbQiCoL23PikXJltbW2bMmKF8Ur5x4wYNGjQgNDSUWrVePrXk\n5ORQpUoVvv/+e/r27UtycjKVKlVi0aJF9OjRA4CYmBhq1arFli1b8Pb2VrutorqjDDpxgaXrA96Y\nx9rSnAlDffNdBVmUPvQ7cUmS2BIQypbAYyrLqjrbMWpgF8xMSmm9zlMR19i4K4j78U9UlqempmJR\nxowGtavgXt+F2tWctR5KFXLyIovXyV/60q9bK9p4NtRqPW9z7MwlFq7eKUuzL1+WX8YNKPKhUh/6\nuVYcxD77+Gj0lqiikp6eDrysFs+lo6ODoaEhJ06cAF5Wp2dmZtKiRQtlHjs7O6pWrUp4eHjRBvya\n0xHXWLZBXiCXLGGAbTl5R4wHCU/4btYfhToP8f34RP7YcpDlmwIJPhlB3MPHSK83in7k0l6kM3v5\nVrUFsrd7XSaO6Kl1gQwvhxi51q3GrG8G8R/fTzEtrdrh6kV6JsdOX2L60r8Y9M08lq7fS8SVW2Rn\nZ791/YlJT/lj20FZWvVK9nzWvIHWsb5N0wY16N25hSzt3v1H/CVe9ygIxeK9mjWgSpUq2NnZMXXq\nVBYsWICxsTGLFy8mNjZWOdQqPj4eXV1dlR6HlpaWxMfHv3H90dHq2wMLwo27D1i26YCyKhBettH1\n69QU23IWrNp6hOg7/wwzS01NZfzPv9GrQzPqVHMqsDhepGdwICyC0NNXyMqW9xouZWxEBVsrKthb\nUcGuHHbWZdDToLq/MPdbYXn0JIWVW47wIEHefqyro6BzK1ea1KvE7dt/5/FpzVWwNmFEr9YEhUcR\ncuoS6RlZwMvjmys1NZU9h46z59BxShkbUbuqI5+4VMDZzgodHfl9sSRJrPjrMPEJico0fT1dWru5\ncOPGDQpDVfsyuFS05vTFm8q0jTsPYWlqiJOtZaFsMy8f4rlW3P6t++xjrSF4rwplfX191q1bx/Dh\nw6lQoQK6urp4enrSqlWrAnnKK6yDeDvmIX/t34WhYQkM/382RoUCRg3sSqM6VQGYWb0aSzfsJfRU\nlOyzWw6cpkQpU9p6NnyntkJJkgg+GcHGXcEkP32OoVEJXp8YUgJuxT7mVuxj4AoG+npUcrKhSgVb\nqjrbUcXZjlIl5eNzP8TqsYgrt1i+JZjU55nKfggApqVL8tWAzrhUdizwbdaq6ULf7s/Ydegk+4NP\nkpWjvhJKAiKuxRBxLQZz01K4fVIdt/rVqexki0KhICQ8kjv3n8ji7tu1JU1cNR+TnB9j7BwY+8ty\nHj/5Z8x2QOhFpn/9nyLrmPghnmvFTeyzj897VSjDy45lx44dIzk5mczMTMqWLYu3tzeffPLytXRW\nVlZkZ2fz+PFjypYtq/xcQkICbm5uRR7vw0dP+GXxJlKfp8vSv/BroyyQ4eUY0WF9fLCyMJXNQyxJ\nL4ejJDxOom+XlipPTpq4evMeq7cc4O972k3ckpGZxeXou1yO/qdXvX35slStaE9VZzuqOtt9UFXe\nT1PT2BZ4jMCQ0yrDl5zsyjF2UDfKljFV/+ECYGZSir5dWuJaw54shREnzl0m/MJVUp6lqc3/JPkZ\nAcGnCQg+jaWFKa51qxF04oIsT7WKdrQt4HZkdYxLGjHIvy2/LP5TmRb3MJFNu0Po26VloW9fEISX\n3rtCOVfu26hu3rzJ+fPn+fbbb4GXhba+vj5BQUHK9zvHxsZy7do1XF1dizTGpJRUfl60STbeE8DP\npzne7p+o5FcoFPRo1xzLMqYs37SP7FeqlwODz5CQmMx/+3XC0ECzJ5NHicls2BlE2NnLapdbmJfG\nvUEN/r73gOi/Y1V6Hqtz7/4j7t1/xKFj5wEoXVIf/44tadao5ns7lCs7O5tDYef5a2+obFKMXO71\nXfiyVzuN9+u70tHRoUZlR2pUcaR/99ZEXb/D8bOXOX3xmsrNW66Ex8nsOSzvE2Ggr8fgXu2LbGx7\nXZeKtGz6ifLYAwQEnaJRnapUq2hfJDEIwr9dnjN6FZZnz55x9epVHj58yNq1a3FxccHExISMjAxM\nTU3ZsWMH8fHxSJJEWFgYAwcOxMPDg9GjRwMvO4E9ePCAFStWUKNGDZKTk/nf//6HiYkJU6ZMyfNJ\ns6Bnvnmels6Pv24g5rV307bxbICfj+cbL6QV7K2p5GTDmcjrZGX90/En7mEikdf+pkGtKhgZGuT5\n+fSMTHbsP878VTu4HaP6dGygr0fnT5swsn9nPqlRCY9GtejQsjGN6lbFvrwlJY0MeP4iXTZGNS9P\nkp4See0Oh46d5/mLdGytLd6LNyblunD5JrOXbyEkPIqMzCzZMoUCenbwpG/XlkX6IolXzzUdHR2s\nLc1pWLsKbT0bUcnJBoWOgvjHSSpt/q/r1bkF9WpWKoqQlVwqORB29jLP0/65ebh68x5ebnUKfR+K\n2am0J/bZxyfPIVE+Pj6ar0ShYNeuXRrlDQ0NVbtuf39/lixZwtKlS1m4cCHx8fGUK1cOPz8/xo0b\nh4HBP4VU7uQhW7ZskU0eYmdnl+d2C7LtJSMzk+lLNhN1/Y4s3b2+i1aT+9+Oecj0pZtV5l4uV9aM\nr4f4YvNar+3c9/2u33lE1vb3egw9O3ppVE37KDGZq7diuHbzHldv3eNeXIJKtW9qaqqsfVNPTwe3\nei609WyIs0N5jb5nYYh7+Ji12w/LXrDwKksLUwb6fkZdl6J/oYom51pGZibnL93k+NnLnIu6oXJD\nUdXZjslf9c5Xc8a7irp+mx8WbJCltfFsQL9urQt1u6J9VHtin3188iyU27WTvxLuxo0bPHz4EAcH\nB6ysrIiPj+fu3btYW1tTqVIldu/eXWRB50dBnbw5OTnM+3074ReuydLrVHdm3JfdtX6aePwkhWlL\n/+RubIIsvbRxCcYM6qasNrx19z6rtxxUeb1gLie7cvTr1orqlRy02v6rUp+/IPp2LNf+v6C+cvMe\nT58+kxXKr6peyZ42ng1pWLtKkRUez56/bDfed/SsrPo/l5GhPp0/daetV8Niq27X9lxLe5HO2agb\nnDh3mb/vPcCmnAXD+vgUyrSdmvp98372Hz0rS5v4356FOtuXKGC0J/bZx0ejyUP27NnD119/zerV\nq2nQ4J+xkmfOnKF///5MmzaNdu3aFWqg76ogTl5Jkljx5z5ZmxtAJafyTBzR641Vzm/yPC2dOSu3\nqszHrK+vS/9urYm+HUfwSdW5l+Flj2L/Dp40d61d4AXjo8Rk/vhrL5du3s+zLRRePpW28WyIp2vt\nQnvxRnZ2NoePX2DznqNq240VCmjuWhs/n+bFWpjBx3GhfJGewbhfVsimJLW0MGXmhIGF1nzxMey3\noib22cdHo0K5SZMmjBgxAn9/f5Vl69evZ9GiRRw/frxQAiwoBXHybt4bIus5DWBbzoLJ/+uj8qYg\nbWVlZf//RB8XNcqvp6fDZ80b0uVT90J9A1V0dDT2Do6EhF8kMPi02hmscpUwMsCrcR0+bV6/QGcr\ni7hyizVbDxLz4LHa5dUq2vF511bFWp3+qo/lQnnlxl2mzF8nuxls2fQTvvBrUyjb+1j2W1ES++zj\no1Hv65s3b8qGH73K0tKSW7duqV32MQkMPq1SIFuYl+abYX7vXCDDyyFTg3u1w8rCjM17j74xb/1a\nlejTuSXlrYpmmk4jQwM+9WhA62b1OX/pBgFBp4m8dlslX9qLDAKCTxMYcpoGtarQsuknlDErjXEJ\nI4xLGmFooK9VT2JN2o17dWxB40+qibdvFYLqlRxo69lQNo/7oWPnaVSnKnWqOxdjZILw8dKoUHZ0\ndGTVqlW0atVKZdmqVatwcMh/O+aHIOzMJf7YKp/2sLRxCb4Z5l+g414VCgVd2zSlbBlTlm3Yq9Jm\namdtQZ8uLYul81JufPVqVqZezcrciY0nMPg0x85EkZkpnzpSkuD0xeucvnhdlq6jo8C4hBElSxhS\nsoThK/9vhPH//zc3/U7swze2G3dq3YR2LRq9t8O0Pha+Pp6cv3yTuIf/zDC2bMNeZk744oN9R7gg\nvM80KpTHjx/PF198gZubGx06dFB29Nq1axfXr19n+fLlhR1nsYm4covF63bLqvAMDfQYN7g7dtbq\naw/eVXPXWliYl2b+79tJeZaGcUlDurf1oFXTekU6tOdNHG2tGNyrHf4dPDl07Dz7Q8+S/Np47dfl\n5Eg8TU1T2yasqeautfDv4Fns7cb/FoYG+gzp3Z7v56xR/gYeP3nK2u2HGdzr/e5HIggfIo0K5a5d\nu2JhYcEvv/zC3LlzyczMRF9fn3r16rFt2zaaN29e2HEWC0mS2LgrSDafta6uDqMGdqVKhbyHXxWE\nmlWcWPTDcO7ExONga1VkE19oy7S0MV3bNKVDy8YcP3eFgKBTasdOv6uqznZ83rXlW1+xKBS8KhXs\n8GnZmF0HTyrTgk5E0KhOFerVFO2ZglCQtH51Y05ODo8fP8bCwqJYxlDmV347RCQ/TWX60s3cvHMf\ngOGfd6BZw5oFHd57S9v9JkkSV27c5ciJCB7EJ/I8LZ3UtBc8T0tXGYuribJlTOjVsQVu9ap/MO3G\nH2Pnm4zMTCZM/13W2c7ctBSzvv1CZb70/PoY91thE/vs46P1NJvPnz8nLS2N7OzsD6pQzi/T0sZM\nHNGT2Su28kmNSv+qAjk/FAoFLpUd1b70ITMzi+cv0kl9/oK0F+mkpr38/+dpL5Tpz9PSef4inays\nLKo62+PtXle0G78HDPT1GdrHh+9m/0FOzsv7+CfJz1i95SDD+3Yo5ugE4eOhcaG8b98+fv75Z6Ki\nXr7l6MiRI9StW5cRI0bg4eGhnIf6Y1TCyJBvhvr9K25CCpO+vh6m+nqYllY/GYnwfqvoaEPHVm5s\n3//P8MfQU1G41qlKw1deviIIQv5pVMrs2bOHnj17YmFhwZQpU2RvDnJ0dGTjxo2FFuD7QhTIggDd\n2jTD4bV3LC/fFEjKs+fFFJEgfFw0KmmmT59Or1692L59O0OGDJEtq169OleuXCmU4ARBeL/kvoJU\nV/efS0fy0+es2ry/GKMShI+HRoXy9evX6dKlC4BKZxszMzMSExPVfUwQhI+Qk501XT9rKks7fu4K\nJ86Jm3NBeFcaFcqlS5fm8WP1UxzevXs3z9m+BEH4OHVs5YaTXTlZ2so/96m8W1wQBO1oVCh7eXkx\nZ84ckpL+mZxeoVCQnp7O8uXLadmyZaEFKAjC+0dPT5ehfXzQ0/vnEvI0NY0VmwJlfU4EQdCORoXy\nd999R3x8PA0bNmTEiBEoFArmzZtHs2bNiIuL4+uvvy7sOAVBeM842lrRva2HLO30xeus+HOfKJgF\nIZ80KpQdHR0JCQnh008/JTg4GF1dXY4fP06DBg04dOgQ5cu/H2/nEQShaPl4u1LJSf77P3TsPL9t\nDBAFsyDkg8bjlG1tbfn1118LMxZBED4wurq6jPi8I9/PXSub+/zI8Zfv//6yZ9sPZiY2QXgfiMG3\ngiC8E2vLMkz6by/MTOSTwgSdiGDJuj3k5Ki+6UsQBPXyfFIeNmwYY8eOxcnJiWHDhr1xJQqFQjxF\nC8K/mK11WSaN7M3UBet5kvxMmR4SHokEDOnVTkzAIwgayLNQDg0NZfDgwQAcPXr0jVVQonpKEASb\nchZ8/99eKgXz0fBIJEliaO/2omAWhLfIs1C+ePGi8v8jIyOLJBhBED5sNuUsmPzVyyfmx0+eKtND\nT0Uh5eQwtI8PurrvxzvBBeF9JG5bBUEoUNaWZfj+v72wMC8tSz925jKL1uwmOzu7mCIThPefxoVy\ndnY269atY+TIkfTo0YORI0eyfv16rX9gYWFh+Pn5Ub16dczMzFi/fr1s+bNnzxg7diwuLi5YW1vT\noEEDFi1aJMuTnp7O2LFjcXZ2xsbGBj8/P2JjY7WKQxCEwmNtWYZJI3tTtoyJLD3s7GUW/rFTFMyC\nkAeNCuW7d+/SuHFjRowYweHDh0lISODw4cMMHz4cNzc37t69q/EGU1NTcXFxYdq0aZQoofpy9G+/\n/ZYDBw6wdOlSwsPDGT16NFOmTGHTpk3KPBMmTGD37t2sXLmSgIAAnj59iq+vr/ihC8J7pFxZcyaN\n7I2lhaks/cS5qyxYvZOsLPF7FYTXaVQojx07lqdPn7Jv3z6ioqIICgoiKiqKwMBAUlJSGDdunMYb\nbN26Nd9//z0dO3ZU2+nj1KlT+Pr64uHhgaOjI/7+/jRo0ICzZ88CkJyczNq1a5k6dSpeXl7UrVuX\nZcuWcenSJYKDgzWOQxCEwmdlYcak//ZSKZhPnr/KgtU7RMEsCK/RqFAODQ3l+++/x9XVVZbeuHFj\nJk6cSGhoaIEF1LhxY/bt20dMTAwA4eHhREVF4e3tDcCFCxfIzMykRYsWys/Y2dlRtWpVwsPDCywO\nQRAKhqWFGZNH9sbqtYI5/MI15q/aLgpmQXiFRjN6GRsbY2lpqXaZpaWl2mro/Jo+fTpfffUVNWvW\nRE/vZXgzZszgs88+AyA+Ph5dXV0sLCxU4oiPj3/juqOjowsszn8Tsd+0J/aZqr4d3Pl1fSCPn/wz\nXCro+HkSHj3i886e6Onqiv2WD//WfVa5cuXiDqFQaFQo9+jRg99//51WrVqpLFu1ahV+fn4FFtCy\nZcs4deoUGzduxN7enuPHjzNx4kQcHBze+W1UH+tBLEzR0dFiv2lJ7LO8zXF2ZuqC9TxIeKJM+zs2\nkd0hF/HxqE316tWKMboPjzjXPj4aFcrOzs7s3LmTJk2a4OPjg5WVFfHx8ezatYunT5/SqlUr1q5d\nq8zfp0+ffAWTlpbG1KlTWb16NW3atAGgZs2aREZGsnDhQlq2bImVlRXZ2dk8fvxY9h7nhIQE3Nzc\n8rVdQRCKhoW5yf/P/LWO+/H/FMxnI2/w6NFjfhpXCX19jafkF4SPjkZn/5gxYwCIjY3lypUrKstH\njx6t/H+FQpHvQjkzM5PMzEyVyQV0dXWV8+fWrVsXfX19goKC6N69uzKua9euqbR5C4Lw/iljVlo5\nJWfcw0Rl+uUbMfy6ZhdfDegsZgkU/rU0KpQjIiIKbIPPnj3j1q1bAOTk5BATE8PFixcxNzfH3t4e\nd3d3pkyZgrGxMfb29oSFhbFp0yamTJkCgKmpKX369GHSpElYWlpibm7Ot99+S40aNfD09CywOAVB\nKDzmpqX5/r+9+GHBBmIfPlamnzx/lT/3hODn41l8wQlCMVIkJSUV6UtPQ0ND8fHxUUn39/dnyZIl\nPHz4kClTphAUFMSTJ0+wt7enb9++DB8+XHn3nJ6eznfffceWLVt48eIFHh4ezJ49Gzs7uzy3K9pe\n8kfsN+2Jfaa5pJRnTJ2/ntiHj0lNTcXY+OWbpob2bk/zxrWLObr3nzjXPj5FXigXF3Hy5o/Yb9oT\n+0w7CY+T+HbWauIeJCgLZT09Hb4d5o9LZcdiju79Js61j49G1de1a9d+axtPQVZxC4Lw72FpYcbY\nQd0Z+/NSZVpWVg5zVmzjh9GfU96qTDFGJwhFS6NC2d3dXaVQTkxM5NSpUxgbG+Ph4VEowQmC8O9Q\nudt6AMoAACAASURBVIItPds3Y+vBM8q0p6lpzFi2mamjPqe0ccHNhSAI7zONCuUlS5aoTU9KSqJb\nt26ig5UgCO/sE5cK6BmW5M89R5VpcQ8TmbtiK98M80dPT7zyUfj4vdOrG83MzBgxYgQzZswoqHgE\nQfgX6/ypO80a1ZSlXYq+y4o/9yFJ/4ruL8K/3Du/T9nIyIi4uLiCiEUQhH85hULBl/5tqV7JXpYe\ndCKCXQdPFFNUglB08l0oZ2VlcfHiRaZNm0a1amJqPEEQCoa+vh6jBnbF2tJclr5hVzDhF64WU1SC\nUDQ0alM2NzfPs/d16dKl2bx5c4EGJQjCv5tJqZKMH9yD72avJvV5ujL91z92UdbchIqONsUYnSAU\nHo0K5XHjxqkUykZGRtjb29OyZUtMTU3z+KQgCEL+2JSzYNTArvy8aBPZ2S+n2c3IzGLGsr/4eWx/\nLMxNijlCQSh4GhXKEyZMKOw4BEEQVNSs4sRA389YtiFAmZaUksr0ZZuZ8lUfShgZFmN0glDwNGpT\nfvToETdu3FC77MaNGzx+/FjtMkEQhHfVokldfFrKXzZzJyaeBat3Kl9UIwgfC40K5dGjR/Prr7+q\nXbZ48WLZW6IEQRAKWq+OLWhYu4os7VzUDdZtP1xMEQlC4dCoUD558iQtWrRQu8zLy4vw8PACDUoQ\nBOFVCoWC4Z93oIJ9OVn63qDTHAg9W0xRCULB06hQTkpKyrMzl4mJCYmJiWqXCYIgFBQjQwPGD+5B\nGbPSsvRVfx3gwuWbxRSVIBQsjQplGxsbzpw5o3bZmTNnKFeunNplgiAIBcnctDTjB3fHyFBfmZaT\nIzF/1Xbu3U8oxsgEoWBoVCh37NiRuXPnsn//fln6/v37mTdvHp07dy6U4ARBEF7nZGfNf/t14tVR\nms/TMpixdDMZmZnFF5ggFACNCuVx48bh4uKCv78/1atXp0WLFlSvXh1/f39cXFwYP358YccpCIKg\nVL9WZfp0aSlLi3+czOmI68UUkSAUDI0K5ZIlS7J3717mz59PkyZNMDU1xd3dnYULF7J3715KlixZ\n2HEKgiDItPVsSNOGNWRpN26LefiFD5tGk4cA6Ovr06dPH/r06VOY8QiCIGhEoVDgWrcax05fUqbd\nune/GCMShHen0ZPy6dOn2b59u9plO3bsyLMTmCAIQmFytreW/fvvew/EhCLCB02jQnnKlClcuXJF\n7bJr164xZcqUAg1KEARBExbmJpiUKqH8d3pGFnHxYoim8OHSqFCOioqiYcOGapfVr1+fS5cuqV0m\nCIJQmBQKBRXsy8vSbt0VVdjCh0ujQjk9PT3PKqHs7GyeP39eoEEJgiBoytnhtSrsuw+KKRJBeHca\nFcpVqlQhMDBQ7bLAwEAqVaqk8QbDwsLw8/OjevXqmJmZsX79etlyMzMztX9jxoxR5klPT2fs2LE4\nOztjY2ODn58fsbGxGscgCMLHw9nhtSdl0dlL+IBpVCgPGDCAP/74g4kTJ3Ljxg2eP3/OzZs3mThx\nImvXrmXgwIEabzA1NRUXFxemTZtGiRIlVJZfu3ZN9rdp0yYAOnXqpMwzYcIEdu/ezcqVKwkICODp\n06f4+vqSnZ2tcRyCIHwcRGcv4WOi0ZCozz//nOjoaBYvXsyiRYuU6QqFgqFDh9KvXz+NN9i6dWta\nt24NwNChQ1WWvz5lZ0BAAJUqVaJp06YAJCcns3btWhYtWoSXlxcAy5Yto1atWgQHB+Pt7a1xLIIg\nfPhyO3ulPEsD/unsZWddtpgjEwTtaTxO+ccff2TAgAEEBwfz5MkTLCws8PT0xMnJqdCCe/bsGdu2\nbZPNGHbhwgUyMzNlb62ys7OjatWqhIeHi0JZEP5lcjt7RVy5pUy7dfe+KJSFD5LGhTKAs7Mzzs7O\nhRWLii1btvB/7d15XFRV/wfwzzCsouyjgmwuoEnkgklqjoI9uGCKWYFrYmq5lZYipo+ameCapWjm\njkv+ntwpNSsx0SiffEAsCwEVZREQhWQVZub3Bzk2snjBGWbh8369eMmcc+be7xwufrn3nnvOgwcP\nMGrUKGVZbm4uxGIx7O3tVdpKJBLk5ubWub2UlBSNxGno2G/1xz5rmIb2m6WpCMXFxcrXP/96CY62\n5uoKS6c11WPNw8ND2yFoxBOTskwmQ0xMDM6ePascTNWmTRv069cPQ4cOhVgs1lhwu3btwpAhQ+Dg\noJ6/eA31h6hJKSkp7Ld6Yp81zNP0W68SOX669Gj5xqIyeZP4GfBYMzx1JuUrV67gjTfeQGpqKoyM\njJTJ8fvvv8eOHTvQoUMH7NixA88++6zaA0tKSkJCQgIWLVqkUt6yZUvIZDLk5+erJOu8vDz06tVL\n7XEQke57fLDXjYyqwV5GRoLGshLpjFqP2OzsbAwfPhwymQw7duzArVu3lCOib926he3bt6OyshJB\nQUHIylL/JPC7du2Cm5sb+vfvr1LetWtXmJiYIDY2VlmWmZmJ5ORk+Pr6qj0OItJ9nNmLDEWtSXnN\nmjVo3rw5Tp8+jaCgIJWVoJo1a4YRI0bg9OnTsLKywtq1awXvsKioCElJSUhKSoJcLkdGRgaSkpJw\n69YtZZuSkhJ89dVXGDduHET/XDQVgLW1NcaNG4fFixfjzJkzuHTpEt566y14eXlVS+BE1DRwZi8y\nFLUm5VOnTmHWrFmwsbGp9c22trZ45513cOrUKcE7TEhIgFQqhVQqRWlpKSIiIiCVSrF8+XJlm0OH\nDqG4uBhjxoypcRsREREIDAxEaGgoBg0aBEtLS+zfv1+j97eJSLdxZi8yBLXeU87JyYGnp+cTN+Dp\n6YmcnBzBO+zbty8KCgrqbDN27FiMHTu21nozMzOsWrUKq1atErxfIjJsj8/slcYzZdJDtZ4pW1tb\n4/btJ/+lmZOTAysrK7UGRURUX7UN9iLSJ7Um5RdeeAHbt2+v880KhQLbt2/HCy+8oPbAiIjqg4O9\nyBDUmpRnzZqF+Ph4hIaGIi8vr1p9bm4uQkND8fPPP2PWrFkaDZKI6ElEIlH1xSl4CZv0TK33lLt3\n744NGzZg1qxZ+Oabb9CtWze4uroCAG7evImEhASIRCJ8+umn8PHxabSAiYhq09alNRKvqE63Ke3p\nrcWIiOqnzslDQkJC4OPjg02bNuHs2bNISkoCADg5OWHs2LF4++23BQ0GIyJqDNXPlDkCm/TLE6fZ\n9PDwqNdzyERE2sKZvUjf8UglIoPBwV6k75iUichgcLAX6TsmZSIyKG0fu4TNpEz6hEmZiAwKB3uR\nPmNSJiKDwpm9SJ8JSsrTp0/HjRs3aqy7efMmpk+frs6YiIgazN7WCtYtHq1qx8FepE8EJeV9+/Yh\nPz+/xrq7d+/iyy+/VGtQREQNVbWMo+rZclo67yuTfhB8+frxdY0fysnJgYWFRY11RETa8HhSvn6L\nSZn0Q62Th8TExODrr79Wvo6IiICdnZ1Km7KyMsTHx6NLly6ai5CIqJ442Iv0Va1JOSMjA/Hx8QCq\nzpIvX74MU1NTlTZmZmbo2bMnFi9erNkoiYjqgTN7kb6qNSlPnToVU6dOBQA899xz2Lt3L7y9ObE7\nEem+h4O9Cu+XAHg02Mu5tYOWIyOqm6A/G5OSkpiQiUhvcLAX6asnLkjxkFwux8WLF5GRkYGysrJq\n9aNGjVJrYERET+PxZRyv38pGP1+eXJBuE5SU//zzT4wZMwbXr1+HQqGoVi8SiZiUiUincLAX6SNB\nSfn9999HZWUlduzYAS8vr2oDvoiIdE37x5IyB3uRPhCUlJOSkhAVFYVhw4ZpOh4iIrWws2lRbbBX\nZk4+XBwlWo6MqHaC/mS0s7NT29nx+fPnERISgmeeeQY2NjbYu3dvtTapqakYO3YsXF1d4ejoCKlU\niuTkZGV9eXk55s6di3bt2sHJyQkhISHIzMxUS3xEZBhqGuzFS9ik6wQl5WnTpmHr1q2QyWRPvcPi\n4mJ07twZkZGRNc4EduPGDQwcOBBubm44duwY4uPjsXDhQlhaWirbzJ8/HzExMdi2bRuOHz+O+/fv\nIzg4WC3xEZHh4MxepG8EXb6+c+cOUlJS4OvrCz8/P9jY2KjUi0QifPDBB4J2GBAQgICAAABVyf5x\ny5Ytg7+/Pz7++GNlmbu7u/L7wsJC7N69G1FRUfDz8wMAbN68Gd7e3jhz5gwGDBggKA4iMnwc7EX6\nRlBSXr16tfL7tLS0avX1Scp1kcvlOHnyJGbNmoWRI0ciMTERrq6umDlzJl555RUAQGJiIioqKuDv\n7698n7OzMzp27IhffvmFSZmIlDjYi/SNoKR87949TccBAMjLy0NRURHWrl2LDz74AIsXL8bZs2cx\nefJkWFpaYuDAgcjNzYVYLIa9vb3KeyUSCXJzcxslTiLSDxzsRfpG8OQhjeHhQuRDhgzBjBkzAFRN\n8ZmYmIgtW7Zg4MCBT7X9lJSUp46xKWK/1R/7rGE00W/WlqbIup2nfB0X/yue9+6g9v1oS1M91jw8\nPLQdgkYITsoKhQInTpzATz/9hLt37yI8PByurq44d+4c2rdvD0dHxydv5Ans7e1hbGyMjh07qpR7\nenri0KFDAICWLVtCJpMhPz8fDg6P5rHNy8tDr1696ty+of4QNSklJYX9Vk/ss4bRVL/5dMnEzdsF\nytcP5GKD+fnwWDM8gm6sFBQUICAgAGPGjEF0dDT279+Pu3fvAgCio6PxySefqCUYU1NTdO/evdpf\nfqmpqXBxcQEAdO3aFSYmJoiNjVXWZ2ZmIjk5Gb6+vmqJg4gMx+P3lTnYi3SZoDPlf//738jMzMS3\n336L7t27QyJ5dD+mX79+WL9+veAdFhUV4dq1qvlo5XI5MjIykJSUBFtbW7i4uOCdd95BaGgoevfu\nDalUiri4OBw6dEj5PLO1tTXGjRuHxYsXQyKRwNbWFgsWLICXlxf69+9fj49ORE3B4yOwOdiLdJmg\no/L48eNYuHAhevbsCZFIpFLn7Oxcr4k7EhISIJVKIZVKUVpaioiICEilUixfvhwAMHToUKxbtw7r\n169H7969sXnzZnz++ecq95MjIiIQGBiI0NBQDBo0CJaWlti/fz/EYrHgOIioaXg42Ouhh4O9iHSR\noDPl4uJiODk51VhXXl5e4yIVtenbty8KCgrqbDNmzBiMGTOm1nozMzOsWrUKq1atErxfImqaHs7s\n9c8Vo67dvM0R2KSTBJ0pd+jQAadPn66x7vz58+jcubNagyIiUifO7EX6QtCZ8qRJkzB37lxYWVnh\n1VdfBVA1s9aePXuwZcsWrFu3TqNBEhE9DQ72In0hKClPmDABN27cQEREhPLe74gRI2BkZIR3330X\nr7/+ukaDJCJ6GhzsRfpC8HPKS5YswcSJE3HmzBnk5eXBzs4Ofn5+KvNSExHpIs7sRfqiXjN6ubq6\nYvz48ZqKhYhIIzjYi/SFoGs3e/bsQURERI11ERER2Ldvn1qDIiJSt8cvYXOwF+kiQUn5888/h52d\nXY11EokEmzZtUmtQRETq1u6xEdgc7EW6SFBSvn79Ojp16lRjnaenJ27cuKHOmIiI1K62wV5EukRQ\nUhaLxcq5rh+Xn8+ZcYhI93FmL9IHgpKyj48Ptm/fXmPd9u3b0a1bN7UGRUSkbg8He/0TL2GTrhE0\n+vr9999HUFAQBgwYgPHjx8PR0RHZ2dmIjo7GpUuXcPjwYU3HSUT01Nq5OqqMwL5+Kxv9fL21GBGR\nKkFJ+cUXX8SuXbswf/58zJo1S1nu6uqK6Oho9O3bV2MBEhGpCwd7ka4T/JxyYGAgAgMDkZKSgrt3\n78Le3h4dOnTQZGxERGrFmb1I1z3xSHzw4AGkUqlyQQoPDw/4+voyIROR3uFgL9J1T0zKpqamSE9P\n51rFRKT3ONiLdJ2gazZ+fn6IjY3VdCxERBrHmb1Ilwm6pzxlyhRMmTIFlZWVCAwMROvWrSESiVTa\ncGEKItIHHOxFukxQUg4MDAQAREVFYePGjTW2qW1yESIiXcLBXqTLBCXlqKgoTcdBRNQouIwj6TJB\nSXn06NGajoOIqFHUuIxjejaTMumEel2vkcvluHLlCs6dO4fi4mJNxUREpFHVBntl8L4y6QbBSXnL\nli3w9PREnz59MGzYMKSkpACoOov+/PPPNRYgEZG6cbAX6SpBSXnXrl0IDw9HYGAgdu7cCYVCoazr\n1asXjh07prEAiYjUjcs4kq4SlJSjoqIwY8YMfPrppxg6dKhKnaenJ1JTUwXv8Pz58wgJCcEzzzwD\nGxsb7N27V6V+6tSpsLGxUfl66aWXVNqUl5dj7ty5aNeuHZycnBASEoLMzEzBMRBR08aZvUhXCUrK\n6enp8Pf3r7GuWbNmKCwsFLzD4uJidO7cGZGRkbCwsKixTf/+/ZGcnKz8+uqrr1Tq58+fj5iYGGzb\ntg3Hjx/H/fv3ERwcDJlMJjgOImq6apzZK52TiJD2CRp9bW9vj5s3b9ZYl5qaCkdHxxrrahIQEICA\ngAAAwLRp02psY2ZmhlatWtVYV1hYiN27dyMqKgp+fn4AgM2bN8Pb2xtnzpzBgAEDBMdCRE1XtWUc\nM26jH57TYkREAs+UBw4ciJUrV+LGjRvKMpFIhPz8fGzcuFE5uYi6xMfHo0OHDvDx8cE777yDvLw8\nZV1iYiIqKipUztydnZ3RsWNH/PLLL2qNg4gM1+ODvRJ+T0NxSZmWoiGqIuhMeeHChYiLi0OvXr3g\n4+MDkUiE8PBwXL16FRKJBGFhYWoL6KWXXsLLL78MNzc33Lx5E8uWLcOwYcNw5swZmJmZITc3F2Kx\nGPb29irvk0gkyM3NrXPbD0eMU/2w3+qPfdYwjdpvslKVRzvTiosRHrEZb4X8C2amJo0Xx1Nqqsea\nh4eHtkPQCMGXr2NjY7Fp0yacPn0abdu2RWVlJSZPnoxp06bByspKbQGNHDlS+b2Xlxe6du0Kb29v\nfPvttxg2bNhTbdtQf4ialJKSwn6rJ/ZZw2ij3wKkN3D+4hXl69x7xTgSm4iwt16DqYnuJ2Yea4ZH\nUFIGgBYtWiAsLEytZ8VCODo6wsnJCdeuVd37admyJWQyGfLz8+Hg4KBsl5eXh169ejVqbESk36aM\nHoI79/5C8rUMZdnlP2/g0x1HMHviKzA25pK11LjqvKeckpKC8PBwBAcHY8qUKThx4kRjxaV0584d\nZGdnKwd+de3aFSYmJipLSWZmZiI5ORm+vr6NHh8R6S9zM1PMe/t1uDurDiz9NSkFm/bEqMzJQNQY\naj1TvnjxIoYOHYry8nLY29vj3r17OHDgANatW4fx48c3eIdFRUXKs165XI6MjAwkJSXB1tYWtra2\niIyMxLBhw9CqVSvcvHkTS5cuhUQiUT4fbW1tjXHjxmHx4sWQSCSwtbXFggUL4OXlhf79+zc4LiJq\nmiybmeOD6SFY+ukeZNx+9KzyuV+vwNzcDJOCB1VbqpZIU2o9U46IiEDbtm1x6dIlpKSkIC0tDQMH\nDsSyZcueaocJCQmQSqWQSqUoLS1FREQEpFIpli9fDrFYjCtXrmD06NHo0aMHpk6dig4dOuDUqVNo\n0aKFSmyBgYEIDQ3FoEGDYGlpif3790Ms5qUmIqo/6xaWWDBjNFraW6uUf38uAXuOnOYZMzUaUUFB\nQY1HW6dOnRAREYERI0Yoy65fvw4fHx/8/vvv9Xo2WRdwQETDsN/qj33WMLrQb7n5BVi0Nhr3CotU\nyl8PlGLk4Be1FFXtdKHPSL1qPVPOycmBq6urSpmrqysUCsUTHz0iItJHLe1tsHDmaFg1V51t8D/f\nnMU3sRe0FBU1JXUO9OJ9FCJqapxbO2DBjFGwbGamUh598Huc/ilRS1FRU1HnI1HvvvsumjdvXq18\n5syZsLS0VL4WiUQ4fvy4+qMjItICd+fWmPd2MJZHfYmy8gpl+RdfHoeZqQn69PDSYnRkyGo9U+7d\nuzesra0hFotVvvr06QMrKyuVMiMjwcsyExHphY7tnDFnyqswNn70/5tCAUTtjsHFy01zFi3SvFrP\nlL/55pvGjIOISOd4d2yL2RNfwZqtByGXV42Jlcnk+GT7Icx7+3V4d2yr5QjJ0PAUl4ioDj2e88SM\n8cPwzyE2FRUyrP7iAK5ez6j9jUQNwKRMRPQEfXp4YXLIYJWysvIKRG76P9zIuK2lqMgQMSkTEQkw\noE83jB/5kkpZcUk5lkftR+btO1qKigwNkzIRkUCBfj3x2pC+KmWF90uwbMOXuFd4X0tRkSFhUiYi\nqoeRg1/E0AGqi9/cLbiPT3ccgUwm01JUZCiYlImI6kEkEmFskD9eerGbSvkfqbew/+sftRQVGQrB\n6yk/lJeXh7KysmrlLi4uagmIiEjXiUQivPn6QOTcuYfLf95Qlh/77md0bOuMHs95ai840muCzpT/\n+usvTJs2DY6OjujYsSO6dOlS7YuIqCkxMjLCzDeGw9ZaddbDjXtikJtfoKWoSN8JOlOeM2cOYmJi\nMHbsWHh5ecHU1FTTcRER6TzrFpaY/eYrWLJut3JykeKScqzdehBL3xsPUxMTLUdI+kZQUv7hhx+w\ndOlSTJ48WdPxEBHplY7tnDEmyB+7D/2gLLt+KwfRB7/HpMeebSZ6EsEDvbhmJxFRzQL9eqJnF9X7\nyN+dS0Dcf3/TUkSkrwQl5VdeeQUnTpzQdCxERHpJJBLh7TFD0Vpiq1K+5cvjyODEIlQPgi5f+/v7\nY/78+SgqKkJAQABsbGyqtenXr5/agyMi0heWzcwx+80RWLhmFyoqqp5XLn9QiU+2HsTHc0Nhbsax\nOPRkgpLy6NGjAQDp6enYt2+fslwkEkGhUEAkEuHu3buaiZCISE+4O7fGxNcGYvO+R+vLZ9zOxxf7\njmPmhOEQ/XNVC6IaCErKMTExmo6DiMgg+PXqgj/TbuHHXy4ry85fvIJOHVwQ0NdHi5GRPhCUlF98\n8UVNx0FEZBBEIhHeDB6E67du42ZWnrJ818Hv0N7VEe3dnLQYHek6TrNJRKRmZqYmmP3mKzA3e/Sc\ncmWlHJ9sP4yiklItRka6TlBSfvnll2v9Gj58OMaNG4c1a9YgNzf3ids6f/48QkJC8Mwzz8DGxgZ7\n9+6tte2sWbNgY2OD9evXq5SXl5dj7ty5aNeuHZycnBASEoLMzEwhH4WIqFE4tbLH22OGqpTl5Rci\nKjoGCoVCS1GRrhOUlBUKBVJTU3Hu3DncunUL5eXluHXrFs6dO4erV68iPT0dq1atQq9evfDnn3/W\nua3i4mJ07twZkZGRsLCwqLXd0aNHcfHiRTg6Olarmz9/PmJiYrBt2zYcP34c9+/fR3BwMFdoISKd\n0qv7Mxjcv4dK2f9+S8Wx7+K1FBHpOkFJefr06TAzM0NsbCwSExNx6tQpJCYmIjY2FmZmZpg3bx4u\nXrwIBwcHfPTRR3VuKyAgAIsWLcLw4cNhZFTz7m/evInw8HBs3boVxsaqt70LCwuxe/duLF26FH5+\nfujatSs2b96M33//HWfOnBH2qYmIGsnYoAHwcFe9j7z/6x9xJSVdSxGRLhOUlJctW4bw8HB07dpV\npbxr166YN28ePv74Y7Rp0wYzZ87ETz/99FQBVVZWYtKkSZgzZw46duxYrT4xMREVFRXw9/dXljk7\nO6Njx4745ZdfnmrfRETqZmwsxqyJI9DC8tGVQblcgU93HEHBX0VajIx0kaDR12lpaXBwcKixzsHB\nAdeuXQMAtG3bFiUlJU8VUEREBOzs7PDmm2/WWJ+bmwuxWAx7e3uVcolE8sR72ikpKU8VW1PFfqs/\n9lnDGHK/DfPrii/+73vl6+LiYny4dgfeCgmAWNzwMbeG3Gd1MdSpnwUlZVdXV+zatQsvvfRStbqd\nO3fC1dUVAJCfnw87O7sGBxMXF4d9+/YhLi6uwduoi6H+EDUpJSWF/VZP7LOGMfR+8/DwQGmlEQ6d\nPK8sy7pzH4kp2Qh5uX+DtmnofdYUCUrKYWFhmDJlCnr37o1hw4ZBIpEgLy8Px44dwx9//IGtW7cC\nAH788Uf4+DT84fhz587h9u3bKpetZTIZFi9ejE2bNuHKlSto2bIlZDIZ8vPzVc7e8/Ly0KtXrwbv\nm4hI014b0hdXr2Xgt6uP7icf/vYneLZtg+7PMrmSwKT86quvwt7eHhEREVi7di0qKipgYmKCbt26\n4fDhw+jfvz8A4OOPP4ZYLG5wMJMmTcLw4cNVykaOHImRI0fijTfeAFB1H9vExASxsbF47bXXAACZ\nmZlITk6Gr69vg/dNRKRpRkZGmDkhCOErtuFe4aP7yVHRMYicNxES++rrClDTIigpA4Cfnx/8/Pwg\nl8uRn58Pe3v7aqOnzc3Nn7idoqIi5T1ouVyOjIwMJCUlwdbWFi4uLpBIJKoBGhujVatWyks01tbW\nGDduHBYvXgyJRAJbW1ssWLAAXl5eyj8OiIh0lY2VJd4NDcLSz/ZCLq96XrmopAyfbD+MD2eNg4mJ\n4P+WyQDVe3SBkZERJBJJrY8zPUlCQgKkUimkUilKS0sREREBqVSK5cuXC95GREQEAgMDERoaikGD\nBsHS0hL79+9/qrN0IqLG8kwHV4we7qdSlpaejd2Hf9BSRKQrav2TbMWKFRg/fjwcHR2xYsWKOjci\nEokQFhYmaId9+/ZFQUGB4AAvX75crczMzAyrVq3CqlWrBG+HiEiXDPX3xZ9pt/Br0qPR09+evYiO\n7ZzRp4eXFiMjbao1KUdGRuKll16Co6MjIiMj69xIfZIyERFV/b85bezLCF+xDbn5hcryL748Djfn\nVnBuXfNjqGTYar0Gfe/ePeVI6nv37tX5xbWUiYjqz7KZOd6bNBImJo9uvZWVV+CTrQdRVv5Ai5GR\ntnCVKCIiLWrr0hqhrwaolGXczseW/Se4cEUTJCgpp6am4uLFi8rXpaWl+PDDDxEcHIwvvvhCY8ER\nETUF/r27QurrrVJ27r+/44fzCVqKiLRFUFKeO3cujh49qnz90UcfYcOGDbh9+zY++OADbNmy9YAN\naQAAE6xJREFURWMBEhEZOpFIhDdfHwgXR9X7yDsOnMK1m9laioq0QVBS/u2335QTc8jlcuzfvx9L\nlizBjz/+iDlz5mDnzp2ajJGIyOCZm5nivUkjYW5moiyrrJRj7bZDKCop1WJk1JgEJeW//vpLOad1\nUlISCgoKlDNvvfjii0hP5xJkRERPy6mVPd4aHahSlpdfiI27Y3h/uYkQlJQlEolyFq7Tp0+jbdu2\ncHZ2BlC10gkn7SAiUo/ePp0xuH8PlbKLl1Nx7PuftRQRNSZB87kNHjwYS5cuxR9//IF9+/YhNDRU\nWXflyhW4u7trKj4ioiZnbNAApN7IQsqNLGXZ/pgz8HB3QmcPNy1GRpom6Ex5yZIlGDhwIE6fPo3B\ngwfjvffeU9adOHEC/v7+GguQiKipMTYWY9bEEWhhaaEsk8sV+HTHERT8VVTHO0nfCTpTtrS0xGef\nfVZj3alTp9QaEBERAQ521pjxxjBEbPw/ZVnBX8X4bMcRLJgxircNDVS9Jg/Jz8/HyZMnsW/fPty7\ndw8AUFZWBrlcrpHgiIiasq6d2+OVQX1Uyn5PuYn/fHNWSxGRpglKygqFAv/+97/RuXNnjBo1CjNm\nzFCOuB49ejRWr16t0SCJiJqq14b0xbOeqveRj5yKx/9+S6nlHaTPBCXltWvXYsuWLQgLC8MPP/yg\nMjR/0KBB+PbbbzUWIBFRU2ZkZIR3QoNga91cpTwqOgZ3C3l/2dAISsrR0dEICwvD+++/jy5duqjU\ntWvXDtevX9dIcEREBFi3sMSsiSNgZCRSlhWVlGHXoVhUVFRqMTJSN0FJOTs7Gz169KixzsTEBCUl\nJWoNioiIVHVq74LRw/1Uym5m5yNq9zH8dvUGSsvKtRQZqZOg0deOjo74448/IJVKq9X99ttvcHPj\nc3NERJo21N8XyWkZ+G/SVWVZ/P/+RPz//oRIBDi3dkAH9zbo4OaIDu5t4OLowFHaekZQUg4KCsLK\nlSvRpUsXPP/88wCqJlBPTU1FVFQU3njjDY0GSUREVf/vTh07FDdXbkfOnQKVOoUCuJV9B7ey7yA2\n/hIAwNzMBO1cHdHezREe7m3Qwc0J9rZW9d6vQqFASWk5SkrLUFxahqKSMpSVPYBIJIJYbAQjIyOI\njYwgFj/618jICMbGYhj93cZYLIaRkaiqXCxGMwsziESiJ++8iRGUlMPDw3HhwgUMGTIELi4uAIAJ\nEyYgMzMTPXv2xOzZszUaJBERVbFsZo73Jr2CDz/dg+Li4jrblpVX4ErKTVxJuakss7VuDg93J3Rw\nc4KDnTVKSstR/HeyLS75x1ep6vfqnnp707KZsLNpod6NGgBBSdnCwgJff/01vvrqK5w+fRrt2rWD\nnZ0d5s6di9dffx3GxoI2Q0REauDu3BprF76FoyfOoKQCSL2RhcycfEHvvVdYhAuXruLCpatPbqxB\nRkb1miajyRCcTcViMUJCQhASEqLJeIiISABb6xbo49MJHh4eAIDikjKk3cxG6o1MpNzIQlp6Fgrv\n6+4gXLGYSbkmgpJyWVkZEhISkJOTA5FIhFatWqFr164wNzfXdHxERCSAZTNzPNepLZ7r1BZA1X3g\nvLuFSLmeibT0qsUtrmfcRkWFrEHbNzczgWUzc1g2M0fzZhawMDeFQgHI5XLI5HLIZH9/yWV//yuH\n/O9/K2XyqnZ/t5Er5BDzTLlGdSbl8vJyLFq0CNHR0SgvL1dOGiISiWBubo6JEydi0aJFMDU1FbzD\n8+fPY/369bh06RKys7MRFRWFMWPGKOuXLVuGo0ePIjMzEyYmJujSpQsWLFgAX19flbgWLlyIgwcP\noqysDFKpFGvWrEGbNm3q+/mJiAySSCRCS3sbtLS3QZ8eXgCAykoZbmblIjU9C6k3slD+oAKWzczR\nzMIMzZtZwNLCXPX1399bWpjD2JijuBtDnUk5ODgYZ8+exZAhQxAQEABnZ2coFApkZmbi5MmT2Lhx\nI5KTk/HVV18J3mFxcbFyus633367Wr2HhwdWr14NNzc3lJaWYuPGjXj11Vdx8eJFtGzZEgAwf/58\nHD9+HNu2bYOtrS0WLFiA4OBg/Pjjjxz+T0RUC2NjMdq5OqKdqyMC+vpoOxyqQa1J+ciRI4iLi8Ou\nXbvw8ssvV6sfP348jh49iokTJ+LYsWMYNmyYoB0GBAQgICAAADBt2rRq9cHBwSqvP/74Y+zevRuX\nL1/GgAEDUFhYiN27dyMqKgp+flUP0m/evBne3t44c+YMBgwYICgOIiIiXVPrRf0DBw5gxIgRNSbk\nh4YPH46goKB6nSnXx4MHD7Br1y5YWVnB29sbAJCYmIiKigqVNZydnZ3RsWNH/PLLLxqJg4iIqDHU\neqaclJSEhQsXPnEDAwcOxLJly9Qa1MmTJ/Hmm2+ipKQErVu3xuHDh5WXrnNzcyEWi2Fvb6/yHolE\ngtzc3Dq3m5LCVVUagv1Wf+yzhmG/1V9T7bOHo84NTa1JOT8/H87Ozk/cgLOzM+7cuaPWoPr27Yu4\nuDjk5+dj165dmDBhAr777ju0bt36qbZrqD9ETUpJSWG/1RP7rGHYb/XHPjM8tV6+LikpgZmZ2RM3\nYGpqirKyMrUGZWlpiXbt2uH555/Hhg0bYGJigujoaABAy5YtIZPJkJ+v+qB8Xl6e8myaiIhIH9U5\n+jorK6vaZeLHZWZmqjWgmsjlcjx48AAA0LVrV5iYmCA2NhavvfaaMobk5GSVx6aIiIj0TZ1JWchC\nEwqFol6TihcVFeHatWsAqpJtRkYGkpKSYGtrC2tra3z22WcYNGgQWrVqhfz8fGzZsgVZWVkICgoC\nAFhbW2PcuHFYvHgxJBKJ8pEoLy8v9O/fv9b98hJPw7Df6o991jDst/pjnxkeUUFBQY3TjO/bt69e\nGxo9erSgdnFxcTWO6B41ahTWrFmDyZMn4+LFi7h79y7s7OzQrVs3vP/++yrrOT+cPOTAgQMqk4cI\nuQdORESkq2pNykRERNS4OPkoERGRjmBSJiIi0hFMykRERDpCb5Ly+fPnERISgmeeeQY2NjbYu3ev\nSn1ubi6mTp2KTp06wdHRESNHjkRaWppKm+vXr2PMmDFo3749XFxcMGHChGqzgHl7e8PGxkbla8mS\nJZr+eBqxdu1a+Pn5wcXFBe3bt0dwcDCuXLmi0kahUCAiIgKdOnVC69atERgYiD/++EOlTXl5OebO\nnYt27drByckJISEh1R6FKygowJQpU+Dq6gpXV1dMmTIFBQUFGv+M6taYfcZjrXq/7dy5E0OHDoWr\nqytsbGyQnp5ebV+GcqwBjdtvhnS8GTK9ScoPV5eKjIyEhYWFSp1CocCYMWNw7do17N27F2fPnoWL\niwuGDx+O4uJi5ftHjBgBhUKBY8eO4eTJk3jw4AFCQkIgl8tVthcWFobk5GTl15w5cxrtc6rTuXPn\n8Oabb+Lbb7/FsWPHYGxsjKCgINy7d0/Z5tNPP0VUVBRWrFiB06dPQyKRYMSIEbh//76yzfz58xET\nE4Nt27bh+PHjuH//PoKDgyGTPVqXddKkSUhKSsKBAwdw4MABJCUl4a233mrUz6sOjdlnAI+1x/ut\npKQE/v7+CA8Pr3VfhnKsAY3bb4DhHG+GTC9HX7dp0wYrV65UrsOcmpqKHj16IC4uTrlwhVwuh6en\nJxYtWoTx48fj9OnTGDlyJK5fvw4bGxsAQGFhIdzd3XH48GHlM87e3t6YMmUKZs6cqZXPpklFRUVw\ndXXF3r17MXjwYCgUCnTq1AmTJ09W/nKWlpbCw8MDH330EUJDQ1FYWIgOHTogKioKr7/+OgAgIyMD\n3t7eOHDgAAYMGKCcuOXkyZN44YUXAADx8fEYPHgw/vvf/+r1s5Sa6jOAx9rj/fZPCQkJ8PPzw6VL\nl+Dm5qYsN+RjDdBcvwGGfbwZEr05U65LeXk5AMDc3FxZZmRkBDMzM8THxyvbiEQilalDzc3NYWRk\npGzz0Pr169G2bVu8+OKLWL16tXI2MX1XVFQEuVyu/KMkPT0dOTk5KituWVhYoHfv3soVt4SsynXh\nwgU0b95cZUa1F154AZaWlnq/cpem+uwhHmu963WMGPKxBmiu3x4y1OPNkNQ5o5e+8PT0hLOzM5Yu\nXYrPPvsMlpaW2LhxIzIzM5GTkwMAeP7559G8eXMsWrRIeR9lyZIlkMlkyjYA8NZbb+G5556DnZ0d\n/ve//2HJkiVIT0/H+vXrtfHR1Co8PBze3t7o2bMnACg/t0QiUWknkUiQnZ0NQNiqXLm5ubC3t1eZ\n2U0kEsHBweGJK3fpOk31GcBj7eHrh/0mhCEfa4Dm+g0w7OPNkBhEUjYxMcGePXswY8YMtG3bFmKx\nGP3798e//vUvKBRVV+cdHBywc+dOvPfee9i6dSuMjIwwcuRIdOnSBUZGjy4YzJgxQ/n9s88+Cysr\nK0yYMAEffvgh7OzsGv2zqcsHH3yAn3/+GSdPnoRYLNZ2OHpB033GY43+iccbAQZy+RqoWqji3Llz\nSE9PR3JyMg4ePIi7d+/C3d1d2cbf3x+JiYlITU1FWloavvjiC2RnZ6u0eVz37t0BQDlftz6aP38+\nDh48iGPHjql81latWgGoWmHrn/654paQVblatmyJ/Px85R9AQNXguzt37ujtyl2a7rOaNPVjTQhD\nPNYAzfdbTQzheDNEBpOUH7K2toaDgwPS0tKQkJCAIUOGVGtjb28PGxsb/Pjjj8jLy8PgwYNr3d7l\ny5cBPPrl0Dfz5s1T/rJ7enqq1Lm5uaFVq1aIjY1VlpWVlSE+Pl55z+6fq3I99PiqXD179kRRUREu\nXLigbHPhwgUUFxfr5cpdjdFnNWnqx5oQhnasAY3TbzXR9+PNUInDw8OXaDsIIYqKivDnn38iJycH\nu3fvRufOnWFlZYUHDx7A2toaR44cQW5uLhQKBc6fP49JkyZBKpXi/fffV25jz549KCsrw4MHD3Dq\n1CnMmDEDEydOVC4BeeHCBRw5cgTm5uYoLS1FbGws5s2bh759+2LixIna+ugNNmfOHOzfvx87d+6E\ns7MziouLlY+ImZqaQiQSQSaTYd26dWjfvj1kMhkWLFiAnJwcrFu3DmZmZjA3N8ft27exdetWeHl5\nobCwELNnz4aVlRU+/PBDGBkZwcHBAb/++isOHDgAb29vZGZmYvbs2ejevbveParSWH3GY616vwFV\n91CvXbuGlJQUxMTEwN/fH8XFxTA1NYWFhYVBHWtA4/WboR1vhkxvHomqa3WpTZs24fPPP8f69euR\nm5uLVq1aISQkBGFhYTA1NVW2XbJkCfbt24d79+7B1dUVoaGhmD59unLQSGJiIubMmYOrV6/iwYMH\ncHFxwSuvvIJ3330XzZo1a7TPqi4PR3A+bt68eZg/fz6Aqkt/kZGR2LlzJwoKCuDj44PVq1ejc+fO\nyvZCVuUqKChAWFgYTpw4AQAYPHgwVq5cWWsMuqqx+ozHWs39FhERgRUrVlTbTlRUlPIRSEM51oDG\n6zdDO94Mmd4kZSIiIkNncPeUiYiI9BWTMhERkY5gUiYiItIRTMpEREQ6gkmZiIhIRzApExER6Qgm\nZSIdMH78eLi7u9e4qEJcXBxsbW2xadMmLURGRI2JzykT6YDc3Fz4+vpCKpVi165dyvLS0lL06dMH\nEokEJ06cUFk8hYgMD3/DiXRAy5YtERkZiaNHj+Lrr79WlkdGRiIrKwsbNmxotIQsk8lQWVnZKPsi\nIlVMykQ6Ijg4GAMHDsTcuXNRWFiIxMREbNiwAeHh4fDw8FBpe+jQIfj7+8PR0RFubm6YOHEisrKy\nVNp8+eWXGDJkCNq3bw9nZ2f069cPX331lUqbsrIy2NjYYMWKFVi5ciWeffZZSCQSpKWlafzzElF1\nvHxNpEOysrLwwgsvYOjQobh8+TKMjY3x/fffq6yvu3HjRixYsABvvPEGAgMDUVhYiOXLl0MkEiEu\nLk45l3FERATs7e3Rvn17GBkZIS4uDuvWrcP69euV80iXlZWhdevWcHR0RIcOHTBlyhSYm5vDx8eH\na+wSaQGTMpGOiY6OxjvvvAMTExOcOXMGXl5eyrqCggJ07twZo0aNwpo1a5Tlqamp8PX1xapVq2pc\n9Ucul0Mul2PatGlIS0vDDz/8AOBRUnZxccHFixdVFnAhosbHy9dEOmb8+PFo3bo1AgMDVRIyAMTH\nx6OkpASvvfYaKisrlV/u7u5wd3fHTz/9pGx79epVTJgwAZ06dYK9vT0cHBzwn//8BykpKdX2GRAQ\nwIRMpAOMtR0AEVVnYmJSY5LMy8sDAAwaNKjG97m5uQGoOqMOCgqCjY0Nli5dCjc3N5iammLTpk04\nevRotfdxoXsi3cCkTKRHHt7n3bp1K9q3b1+tvkWLFgCqzqizsrKwd+9edOvWTVlfUVFR43YfrilO\nRNrFpEykR/r06QMLCwvcuHEDr776aq3tSktLAQDGxo9+xfPy8vDdd99pPEYiajgmZSI9Ymtri8WL\nF2PhwoW4ffs2/P390bx5c2RnZyMuLg7/+te/MHz4cPTq1QuWlpaYPXs2wsLCcP/+faxcuRISiaTa\no1NEpDuYlIn0zNtvvw0XFxdERUVh//79kMlkcHR0RJ8+fdC5c2cAgKOjI6Kjo7Fo0SKMHz8eTk5O\nmD59Om7dusXpOol0GB+JIiIi0hF8JIqIiEhHMCkTERHpCCZlIiIiHcGkTEREpCOYlImIiHQEkzIR\nEZGOYFImIiLSEUzKREREOoJJmYiISEf8P8RMC25tbfeEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110330668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_and_design_and_percentages_table.plot(\"year\", \"Design Percent\")\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Design Percent Occupied (%)\")\n",
    "plt.title(\"Overcrowding according to Designed Capacity\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "**What is the trend of the line?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*double click this cell to type your response*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Staffed Capcacity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have seen how prisons exceed the number of people allowed per designated space. However, a key aspect to these institutions are the hundreds of people employed to maintain this system. The number of people needed to maintain this system differs from both the number of people within these institutions and the number of people these institutions are designed to hold. We will observe the differences in the following section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the sections above, we will use the `select()` function to look at the following variables: `year`, `total_population`, `staffed_capacity`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population</th> <th>staffed_capacity</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>790             </td> <td>760             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>2859            </td> <td>3224            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>794             </td> <td>842             </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (9498 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_pop_and_staff = prisons_updated.select(\"year\", \"total_population\", \"staffed_capacity\")\n",
    "total_pop_and_staff.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see what the `staffed_capacity` and `total_population` for **all** the prisons in a given year, we will group by **year** using the function `group` and summing over all the prisons in that year, so that we can see how the population changed over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population sum</th> <th>staffed_capacity sum</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>1623806             </td> <td>1603264             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>1739423             </td> <td>1713086             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>1788142             </td> <td>1788475             </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop_and_staff_sum = total_pop_and_staff.group(\"year\", sum)\n",
    "pop_and_staff_sum.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Likewise, to the previous example, we want to calculate a proportion in this question as well. This time we want to calculate the proportion of people within a single prison given the amount of staff employed to maintain the prison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 101.28126123,  101.53740092,   99.98138079,   99.77159239,\n",
       "         99.58683247,   99.48019624,   99.67990711,   99.20419093,\n",
       "         99.2917914 ,   98.58947776,  100.15123527,   98.60342226,\n",
       "         98.65553117,   97.77068179,   98.05314295,   99.07368789,\n",
       "         98.33165891,  100.11090436,   95.30052336,   90.83579994,\n",
       "         91.49697673,   96.43139026,   96.93737037])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_column = pop_and_staff_sum.column(\"total_population sum\")\n",
    "staff_capacity_column = pop_and_staff_sum.column(\"staffed_capacity sum\")\n",
    "\n",
    "staff_capacity_percentage = (population_column / staff_capacity_column)* 100\n",
    "staff_capacity_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using these calculated percentages, we will add them to our table. Recall the previous example, to add another column to a table, we use the function called `with_column()`. \n",
    "- *data_table*`.with_column(`*column_name, column_values*`)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population sum</th> <th>staffed_capacity sum</th> <th>Staff Percent</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>1623806             </td> <td>1603264             </td> <td>101.281      </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>1739423             </td> <td>1713086             </td> <td>101.537      </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>1788142             </td> <td>1788475             </td> <td>99.9814      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "staff_and_total_percent_table = pop_and_staff_sum.with_column(\"Staff Percent\", staff_capacity_percentage)\n",
    "staff_and_total_percent_table.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate the change over time, we will create a line plot to reflect the **overcrowding of California's state prisons over time according to the staffing capacity of prisons.**\n",
    "\n",
    "The format for plotting is: \n",
    "* **To draw plot**:\n",
    "*data_table*.`plot(`*x_variable*, *y_variable*`)`. Where the x and y variables stand for the names of the columns in our data table\n",
    "* **To label plot**: We use `plt.xlabel`(*x_axis name*), `plt.ylabel`(*y_axis name*), `plot.title`(*plot_title*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11443d630>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3h6KiotBlc3JycO/ePWhra2PJkiWMcr169cKcOXNQW1uLf/75h0tXNTU1/Pzzz1xyPz8/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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1108b48d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "staff_and_total_percent_table.plot(\"year\", \"Staff Percent\")\n",
    "\n",
    "plt.xlabel(\"Year\") \n",
    "plt.ylabel(\"Staff Percent Occupied (%)\")\n",
    "plt.title(\"Overcrowding according to Staffed Capacity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "\n",
    "**What trend do you notice on the line plot?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*double click this cell to type your response*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Designed Capacity versus Staff Capacity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through the last two examples, we saw how crowded prisons have been overtime by looking at their designed capacity and staffed capacity. However, it is still unclear what the difference between the two percentages is. Therefore, to compare the two capacity rates and how they have changed over time we will create a new plot.\n",
    "\n",
    "To do this, we will create a new table with three columns, namely \n",
    "* Year\n",
    "* Design Percentage\n",
    "* Staff Percentage\n",
    "\n",
    "\n",
    "*Please refer to the intro to Python notebook if you do NOT recall how to make a table*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the **years** in our table we will use a function called `np.arange` since we know the range for the years we want to use. We will illustrate with an example below. Notice that the upper bound is **NOT** inclusive. Thus, we will have to add 1 to the year in which we want to end. \n",
    "\n",
    "The other two columns can be obtained by calling the variables we had defined in the sections before. Namely, **capacity_percentage** for Design Percentage and **staff_capacity_percentage** for Staff Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,\n",
       "       2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,\n",
       "       2018])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_bound = 1996 \n",
    "upper_bound = 2018 + 1\n",
    "years = np.arange(lower_bound,upper_bound)\n",
    "years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>Design Percentage</th> <th>Staff Percentage</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>185.859          </td> <td>101.281         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>190.978          </td> <td>101.537         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>186.899          </td> <td>99.9814         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1999</td> <td>189.598          </td> <td>99.7716         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2000</td> <td>188.514          </td> <td>99.5868         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (18 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_table_design_and_staff = Table().with_columns([\"year\", years,\n",
    "                                    \"Design Percentage\", capacity_percentage,\n",
    "                                    \"Staff Percentage\", staff_capacity_percentage])\n",
    "\n",
    "compare_table_design_and_staff.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11096b438>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AnpUrVzJ58mTkcjlOTk6YmZlRqlQpZDIZ+/fv57///lO6TkxMDPB+tTb5ZWVlhbOzM35+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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114461b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_table_design_and_staff.plot(\"year\")\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Percent Occupied\")\n",
    "plt.title(\"Designed Capacity versus Staff Capacity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "**Consider the large gap between the two lines. Why is staff capacity much closer to 100% than designed capacity?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*double click this cell to type your response*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Interactive Widget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have created an interactive widget for you in this section. In this example, you will be able to scroll through all of the prisons in Calfornia, and you can see how the designed capacity versus population has changed over time.\n",
    "Run the following cell to produce the widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31d5019c04be401882d5f39cbce4851a"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.HBox([widgets.VBox([inp]), out])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "**Select any institution from the widget that has 3 points on its graph and analyze how that institution's designed capacity changed over time. Predict how would the design capacity compare with the staff capacity of that specific institution.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*double click this cell to type your response*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. GENDER DIVISION <a id='subsection4'></a>\n",
    "---\n",
    "**Context:** The system reinforces the idea that the average inmate holds certain characteristics. Since we have limited data sets we cannot investigate many aspects of this claim. However, in order to get a better understanding of the populations that the prison industrial complex was made to hold, we will investigate a few variables such as gender divisions, design capacity, and staff capacity. \n",
    "\n",
    "\n",
    "Let's begin by looking at all the **unique** institutions on our dataset. To do this, we will use the `group` function. The count of the table below represents the number of months for which we have data for a given institution. The maximum counts that any of the institutions *should* have is 276 since we are only looking at 23 years (12 months/year X 23 years)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>institution_name</th> <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>ASP (AVENAL SP)                      </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CAL (CALIPATRIA SP)                  </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CCC (CA CORRECTIONAL CENTER)         </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CCI (CA CORRECTIONAL INSTITUTION)    </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CCWF (CENTRAL CA WOMEN'S FAC)        </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CEN (CENTINELA SP)                   </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CHCF (CA HEALTH CARE FAC - STOCKTON) </td> <td>66   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CIM (CA INSTITUTION FOR MEN)         </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CIW (CA INSTITUTION FOR WOMEN)       </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CMC (CA MEN'S COLONY)                </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CMF (CA MEDICAL FAC)                 </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>COR (CA SP, CORCORAN)                </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CRC (CAL REHAB CTR, MEN)             </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CRC (CAL REHAB CTR, WOMEN)           </td> <td>138  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CTF (CORRECTIONAL TRAINING FAC)      </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CVSP (CHUCKAWALLA VALLEY SP)         </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>DVI (DEUEL VOCATIONAL INSTITUTION)   </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>FOL (FOLSOM SP) (FEMALE)             </td> <td>72   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>FOL (FOLSOM SP) (MALE)               </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>FRCC (FRCCC BAKERSFIELD)             </td> <td>17   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>HDSP (HIGH DESERT SP)                </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ISP (IRONWOOD SP)                    </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>KVSP (KERN VALLEY SP)                </td> <td>163  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>LOS ANGELES COUNTY (CAMP)            </td> <td>7    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>LOS ANGELES COUNTY (STATE PRISON)    </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MCSP (MULE CREEK SP)                 </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NCWF (NO CAL WOMEN'S FACIL)          </td> <td>85   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NKSP (NORTH KERN SP)                 </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>PBSP (PELICAN BAY SP)                </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>PVSP (PLEASANT VALLEY SP)            </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>RJD (RJ DONOVAN CORRECTIONAL FAC)    </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SAC (CA SP, SACRAMENTO)              </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SATF (CA SUBSTANCE ABUSE TREAT FAC)  </td> <td>257  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SBRN (SAN BRUNO CO. JAIL)            </td> <td>20   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SIERRA CONSERVATION CENTER (FIRECAMP)</td> <td>116  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SIERRA CONSERVATION CENTER (PRISON)  </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SOL (CA SP, SOLANO)                  </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SQ (SAN QUENTIN SP)                  </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SRTA (SANTA RITA CO. JAIL-RC)        </td> <td>5    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SVSP (SALINAS VALLEY SP)             </td> <td>272  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>VALLEY SP (FEMALE)                   </td> <td>204  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>VALLEY SP (MALE)                     </td> <td>75   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>WSP (WASCO SP)                       </td> <td>276  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prisons_updated.group(\"institution_name\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the data is made up of both male and female prisons. We have to sort these prisons into separate groups before we can analyze them separately. However, the institution names in our table are not always easy to decipher as male or female institutions. We had to double check whether an institution was male or female using sources outside of this notebook. One of the institutions that we found to be particularly interesting when looking at these outside sources was **VSP (VALLEY SP)**.\n",
    "The California Department of Corrections and Rehabilitation began converting Valley State Prison into a facility for low-risk male inmates in October 2012, and it completed by January 2013. \n",
    "[Press here to learn more](https://web.archive.org/web/20120111104018/http://cdcrtoday.blogspot.com/2011/12/cdcr-announces-plan-to-convert-female.html) and [Here](https://www.cdcr.ca.gov/Facilities_Locator/VSP.html) From these links you will learn that this shift came as a result of the policy of realignment. We can also notice that monthly count for this institution was above of 276, which could indicate monthly double counts for male and female during this period. Though we handled this issue before we read the data at the beginning of the notebook, running these sanity checks is important to not get misleading results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Unique Female Prisions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will find all the data corresponding to female institutions. In the cell below, we look only at the prisons which we know are strictly female."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall the first section where we separated the data for VSP into male and female sections, so that we could use them now. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "female_institutions = make_array(\"CCWF (CENTRAL CA WOMEN'S FAC)\", 'CIW (CA INSTITUTION FOR WOMEN)',\n",
    "       'CRC (CAL REHAB CTR, WOMEN)', 'FOL (FOLSOM SP) (FEMALE)',\n",
    "       'FRCC (FRCCC BAKERSFIELD)', \"NCWF (NO CAL WOMEN'S FACIL)\", \"VALLEY SP (FEMALE)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filtering**: Often times we want to look at a portion of the data that meets certain conditions so that we can view a pattern among smaller portions of the data. In this case, we want to observe the difference between female and male institutions. To do that we are filtering data into two groups. The first contains **only female institutions** and the second contains only male institutions. \n",
    "\n",
    "Filtering works by looking at a single column of values and checking the filtering condition to see if each value in the column is true. All of the true values are put into a new table. \n",
    "\n",
    "The format for filtering is: \n",
    "* **To filter table**:\n",
    "*data_table*.`where(`*column_name*, *filter*`)`. \n",
    "    - the `column_name` is one of the columns in the data_table\n",
    "    - the `filter` has two parts:\n",
    "        - there must be some condition to the values we are looking at: `are.equal_to()`, `are.contained_in()`, `are.not_contained_in()`\n",
    "        - the second part is the input to the first part. (can be a number or a word or list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Unnamed: 0</th> <th>year</th> <th>month</th> <th>institution_name</th> <th>population_felons</th> <th>civil_addict</th> <th>total_population</th> <th>designed_capacity</th> <th>percent_occupied</th> <th>staffed_capacity</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2         </td> <td>1996</td> <td>1    </td> <td>NCWF (NO CAL WOMEN'S FACIL)  </td> <td>786              </td> <td>4           </td> <td>790             </td> <td>400              </td> <td>197.5           </td> <td>760             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3         </td> <td>1996</td> <td>1    </td> <td>CCWF (CENTRAL CA WOMEN'S FAC)</td> <td>2846             </td> <td>13          </td> <td>2859            </td> <td>2004             </td> <td>142.7           </td> <td>3224            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4         </td> <td>1996</td> <td>1    </td> <td>CRC (CAL REHAB CTR, WOMEN)   </td> <td>91               </td> <td>703         </td> <td>794             </td> <td>500              </td> <td>158.8           </td> <td>842             </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (1065 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "female_data = prisons_updated.where(\"institution_name\", are.contained_in(female_institutions))\n",
    "female_data.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will group together each institution to learn about the number of occurrences for each institution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>institution_name</th> <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>CCWF (CENTRAL CA WOMEN'S FAC) </td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CIW (CA INSTITUTION FOR WOMEN)</td> <td>276  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CRC (CAL REHAB CTR, WOMEN)    </td> <td>138  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>FOL (FOLSOM SP) (FEMALE)      </td> <td>72   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>FRCC (FRCCC BAKERSFIELD)      </td> <td>17   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NCWF (NO CAL WOMEN'S FACIL)   </td> <td>85   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>VALLEY SP (FEMALE)            </td> <td>204  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "institution_name               | count\n",
       "CCWF (CENTRAL CA WOMEN'S FAC)  | 276\n",
       "CIW (CA INSTITUTION FOR WOMEN) | 276\n",
       "CRC (CAL REHAB CTR, WOMEN)     | 138\n",
       "FOL (FOLSOM SP) (FEMALE)       | 72\n",
       "FRCC (FRCCC BAKERSFIELD)       | 17\n",
       "NCWF (NO CAL WOMEN'S FACIL)    | 85\n",
       "VALLEY SP (FEMALE)             | 204"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_prisons = female_data.group(\"institution_name\")\n",
    "female_prisons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.1 Design Capacity versus Staff Capacity for Female Institutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the last section, we will use the `select()` function to get relevant data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population</th> <th>designed_capacity</th> <th>staffed_capacity</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>790             </td> <td>400              </td> <td>760             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>2859            </td> <td>2004             </td> <td>3224            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>794             </td> <td>500              </td> <td>842             </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (1065 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fem_year_pop_capacities = female_data.select(\"year\", \"total_population\", \"designed_capacity\", \"staffed_capacity\")\n",
    "fem_year_pop_capacities.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to combine all the values for the same year, so we will use the `group()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population sum</th> <th>designed_capacity sum</th> <th>staffed_capacity sum</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>108007              </td> <td>72120                </td> <td>104654              </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>120200              </td> <td>70920                </td> <td>115384              </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>125888              </td> <td>70920                </td> <td>123282              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fem_year_groups = fem_year_pop_capacities.group(\"year\", sum)\n",
    "fem_year_groups.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the same technique as the other example, we will calculate the percent occupied for female prisons. First, we will do this for designed capacity and directly after we will calculate for staffed capacity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin by getting the female total population. We will do this by selecting the column we are interested in, and then add it to a new variable called `female_total_population`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fem_total_population = fem_year_groups.column(\"total_population sum\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will calculate the design capacity percentages and the staff capacity percentages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "169.4867456288776"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fem_design_capacity = fem_year_groups.column(\"designed_capacity sum\")\n",
    "\n",
    "fem_design_percentage = (fem_total_population / fem_design_capacity) *100\n",
    "fem_design_percentage[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 103.20389092,  104.17388893,  102.11385279,   98.5043845 ,\n",
       "        100.29576714,   99.26380683,  100.40932828,  101.93347193,\n",
       "        100.57430312,  104.14789159,  102.9611944 ,   98.83839444,\n",
       "         98.81032945,   97.20246599,   96.3765347 ,   97.98165807,\n",
       "         96.07339952,  101.42329767,  100.96428124,   88.56918109,\n",
       "         86.04898387,   95.87570793,   96.5775201 ])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fem_staff_capacity = fem_year_groups.column(\"staffed_capacity sum\")\n",
    "\n",
    "fem_staff_percentage =(fem_total_population / fem_staff_capacity)*100\n",
    "fem_staff_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the percentages we just calculated we want to add them to the table, so we will use the `with_column()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population sum</th> <th>designed_capacity sum</th> <th>staffed_capacity sum</th> <th>Design Percent Occupied</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>108007              </td> <td>72120                </td> <td>104654              </td> <td>149.76                 </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>120200              </td> <td>70920                </td> <td>115384              </td> <td>169.487                </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>125888              </td> <td>70920                </td> <td>123282              </td> <td>177.507                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fem_year_design = fem_year_groups.with_column(\"Design Percent Occupied\", fem_design_percentage)\n",
    "fem_year_design.show(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population sum</th> <th>designed_capacity sum</th> <th>staffed_capacity sum</th> <th>Design Percent Occupied</th> <th>Staff Percent Occupied</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>108007              </td> <td>72120                </td> <td>104654              </td> <td>149.76                 </td> <td>103.204               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>120200              </td> <td>70920                </td> <td>115384              </td> <td>169.487                </td> <td>104.174               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>125888              </td> <td>70920                </td> <td>123282              </td> <td>177.507                </td> <td>102.114               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fem_year_design_and_staff = fem_year_design.with_column(\"Staff Percent Occupied\", fem_staff_percentage)\n",
    "fem_year_design_and_staff.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, we have calculated the percentages of design and staff capacity for female institutions, and have all the information we need to plot them. However, we have information we no longer need in the table, so we will select only the columns that we need using the `select()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>Design Percent Occupied</th> <th>Staff Percent Occupied</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>149.76                 </td> <td>103.204               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>169.487                </td> <td>104.174               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>177.507                </td> <td>102.114               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fem_plot_table = fem_year_design_and_staff.select(\"year\", \"Design Percent Occupied\", \"Staff Percent Occupied\")\n",
    "fem_plot_table.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot both the Design Percentage and the Staff Percentage, we will use the `plot()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1111c10b8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CSElJyVP9ss7n7QF0QUFBjB07FplMRuPGjbG0tERPTw81NTV27NjBpUuXFI7z7Nkz4MNa\nxXLL1tYWd3d39u/fT1RUFOXLl2f9+vUkJSVJ/Q5zw8zMjCZNmrBv3z4uXryIk5MTCQkJ7Nq1Cxsb\nG9zc3KS8We//8+fPqxwElOXly5dKadldb2traw4cOMCMGTPYv3+/1CpmbGxM//79GT58eI5PJ95n\n2bJlzJ07l+DgYGbOnAlkjpb38PBg6tSpeZrP1NjYGG9vbyngj4+Px9fXl5UrVzJq1Cg8PDxy/b7/\n0PdtfhH3XN7l1z0nCELR8EUGt1FRUdy/fx9NTU2qVKkCID1q9/DwYP369bkuq3nz5jRv3pxXr15x\n9uxZ9u/fz+LFi+nVqxfbtm1TCCDelfVoLjY2VuX2x48f57oe2ck6LwcHB44dO5anffKjXlmtNefP\nnychISHHvoxvO3fuHNu3b6dRo0YEBwcrPPLOyMhg7ty52e6bXf2yzuftAYTTp0/HxMSE8PBwqaUo\ny6lTp5TKyPpR8eDBg1yfy4fo27cv+/btY/ny5UyZMoXly5ejqalJ9+7d81SOt7c3+/btY+3atfj5\n+bF582aSk5Pp1q2b9NQC3lyT/v37S4Fibr1dzrvs7e1ZsmQJ6enp/Pfff/zzzz8sXryYqVOnkp6e\nLg2Sygoe0tPTVZbz7NkzpR90enp6jB49mtGjRxMTE8Px48fZtGkT27Zt4+rVqxw7duyDF2sxNDRk\nzpw5HDhwgOjoaGkmgPf5mPdtfhH33IfJr3tOEITC74v8uZrVgtmqVSvpEaa9vT0GBgacPXtW5ZRf\n76Onp0e9evXw9fVlypQpyOVypf5b78oahHb8+HGlbXfv3uX+/ft5rse7SpQogYODA9evX5f6F79P\n1iChrOlz3vbixQupO0Vu2Nra0qhRI5KTk/H3939v/qwWm5s3bwKZj7bf7ct55swZXr16lW0ZJ06c\nUGi9zpJ1PlnXPS4ujmfPnuHq6qr0Jfvy5Uupe8bbsvog79+//73nkp23Bypmp3nz5lhbW7N27VrC\nw8O5cuUKrVq1Uqrn+7Rq1QqZTEZwcDBpaWmsW7cONTU1vv32W4V8NWrUQF1dXeV7MT9oaGjg7OzM\n4MGDCQ4OBpCmC4M3A0Gjo6OV9o2KinrvXKxmZmZ06NCBdevWSf08r169+lF1VldXp3jx4gAK06Dl\n9Pf7mPdtfhH3nLLPec8JglD4fVHBbUJCAiNGjGDjxo0YGhoyceJEaZumpiYDBgzg8ePHjBw5kqSk\nJKX94+LiFAK7I0eOKPUVgzcDOooVK5ZjfTp37oympiaLFy9W6H4gl8uZPHlytq1YeTVo0CBSU1MZ\nOHCgyrkqX7x4IfVrgzcB0ebNmxXSAWbOnJnnSd+zpjny9/fH399f5TWLiYnBx8dH+gLLGmD3bt/e\n2NhYRo4cmePxoqKiWLJkiUJaaGgoJ0+exM7OTpqWyMjIiGLFinH+/HmFx+2pqamMGTNG5Y+Bvn37\noqmpyR9//KFysFFufpBkDTZ8e57Xd6mrq9O7d2/i4uKk6aH69Onz3rLfpaOjQ6dOnYiNjSUoKIhT\np05Rt25dbG1tFfKVKVOGrl27cvHiRfz8/FT+je7fv5+neY6zWg7fper+qFq1Kurq6mzcuFHhb5GY\nmKg0cBLgyZMnXLp0SSk9JSVFeoz9vvsPYPr06dI8su8KDQ3l2rVraGpq4urqKqXn9Pf7mPdtfhL3\nnKLPec8JglD4FdluCVmDYjIyMqTld48fP86rV6+oWLEiCxcupFy5cgr7jBo1isuXL7Ny5Ur27t1L\ngwYNsLCw4MmTJ9y6dYsTJ07Qr18/qRVizJgx3L9/n9q1a2NtbY2uri7//fcfYWFhlCpV6r3zIpYt\nW5Zx48YxefJk6tevT4cOHaR5buPj46lcuTL//fffR1+L7777jgsXLrBo0SKqVKmCu7s71tbWPHv2\njLt373Ls2DEaN24sTYZeokQJ/P396d27N61bt6Z9+/aYmZlx4sQJLl++TN26dXPdxQGgYsWKhISE\n0LNnT3x9fVm0aBENGjRQWAr02LFjpKen8/333wNQrVo1ateuzbZt22jevDm1a9fm8ePH7N+/Hzs7\nO2m2C1Xc3d2lAUKOjo7SnJt6enoEBARIj9HV1dX58ccfmT17NnXr1qVVq1akpqZy+PBh4uPjqV+/\nvtKiG5UqVeLPP/9k+PDhNGrUSJpzMz4+noiICFJSUlQu1PG2xo0bs2XLFoYOHSrNWGBgYED//v0V\n8nXv3p3p06fz4MEDypcvT8OGDXN9zd/m7e3N4sWLmTx5svRalZkzZ3Lz5k1mzJjBhg0bqFu3LiYm\nJjx69IgbN25w6tQppk2blusVoNavX8+yZcuoVasW5cqVo1SpUty7d4+dO3eioaHBTz/9JOU1NTWl\nW7durF27lvr169O8eXOSk5MJCwvD2tpa6e/94MEDGjRogIODA46OjlhYWJCYmMiBAweIioqibdu2\nlC9f/r11nD9/PtOnT8fZ2ZmqVatSpkwZnj9/zoULF6SBmZMnT1Y4fk5/v4953+Yncc8p+tz3nCAI\nhVuRDW6zuh5oaWlRokQJzM3N8fLyonXr1nh4eKjsi6epqcnKlSsJCQlhzZo17Nu3TxpkZWVlxc8/\n/0zXrl2l/CNGjGDHjh2cO3dO+nA1NzfHx8eHgQMH5mqKmp9//hlzc3MCAwNZu3YtJUqUwN3dnUmT\nJtGhQweVU+Z8iJkzZ9K8eXOWLFnCkSNHiI+Px8DAAHNzc/r160enTp0U8nt5eRESEsKMGTMIDQ1F\nW1ubunXrsm/fPmbPnp2n4BagZs2a0kTpu3btYt++fSQkJKCnp4etrS0//vgjPXv2lJZD1tDQYN26\ndUydOpW9e/eycOFCzMzM6NGjByNHjlSYFP5dNWrUYNSoUUybNo1FixYBmV9uEyZMkPpYZxk/fjyl\nS5dm1apVLF++nJIlS9KoUSN+/fVXlbMGAPTs2RMHBwcCAgI4ceIEu3btolSpUlSsWJF+/fq991p0\n796d6OhogoODmT9/PqmpqVhZWSl90ZYpUwYPDw9CQ0Pp1atXjn1bc1KtWjW++eYbrly5QvHixbMd\n6Kivr8/27dtZtWoVmzZtYvv27SQnJ2NkZISNjQ2+vr60b98+18ft1KkTqamp/Pvvv1y8eJGkpCRM\nTU3x8PBg4MCBSosmzJkzBxMTEzZt2sTSpUsxMTGhc+fO/PLLLwotp5DZyjhu3DgOHz7M0aNHefLk\nCQYGBpQrV46hQ4fmOKn/2zZs2EBYWBhHjx4lLCyM2NhY1NTUMDMzo2vXrvTt21fp2Dn9/T7mfZvf\nxD33xue+5wRBKNyK3CIOX4rnz59jb2+Pk5MT+/btK+jqFAlZE8qPHj2asWPHFnR1PppcLsfV1ZW7\nd+9y5cqVXM2dLAifk7jnBEEoir6oPreFUVxcnMKE5ZA5ovjXX38lOTkZT0/PAqqZUNB27NjB9evX\n6dixo/iSFYTPQNxzgvB1KLLdEoqKHTt2MHnyZBo1aoSFhQXx8fEcO3aMGzdu4OTkpPTYTPjyzZ49\nm/j4eFauXImuru5nHYgkCF8jcc8JwtdFBLefWNWqValTpw7Hjh2TJtK3sbFh5MiRDB06VOXSpcKX\nbdKkSWhqamJvb8/EiROVBj4KgpC/xD0nCF8X0edWEARBEARB+GKIPreCIAiCIAjCF0MEt4IgCIIg\nCMIXQwS3giAIgiAIwhfjqwxur1+/XtBVKJLEdcs7cc0+jLhueSeumSAIQqavMrgVBEEQBEEQvkwi\nuBUEQRAEQRC+GCK4FQRBEARBEL4YIrgVBEEQBEEQvhhihTJBEARByIO0tDQSExMLuhqC8NUqXrw4\nmprZh7AFFtz+73//Y9u2bdy4cQNtbW1q1KiBr68vDg4OUh65XM706dNZsWIFCQkJVK9enT/++INv\nvvlGypOSksKvv/5KSEgIycnJNGjQgD///BMLC4uCOC1BKPISk5K5diuaMqUMsDIzKujqCEKhkpaW\nxosXL5DJZKipqRV0dQThqyOXy0lISEBfXz/bALfAuiUcOXKEvn37smfPHrZu3Yqmpibt2rUjPj5e\nyuPv709gYCAzZszgwIEDGBkZ0b59e168eCHlGTt2LNu2bWPJkiXs3LmTFy9e0LVrV9LT0wvitASh\nSJLL5dy4/YCg1dsZMH4u04M2MnLaX6wJPYBcLlboFoQsiYmJIrAVhAKkpqaGTCbL8elJgbXcbt68\nWeH1woULsba25sSJE7Rs2RK5XE5QUBDDhg3Dy8sLgKCgIOzs7AgODqZ37948e/aMVatWERgYSOPG\njaVynJycOHToEO7u7p/9vAShKHmVnMLR0/+x78g5bkc/Utq+dd8JHsXGM6hHW3S0tQqghoJQ+IjA\nVhAK1vvuwUIzoOzly5dkZGQgk8kAuHPnDo8ePaJJkyZSHj09PerWrcu///4LwPnz50lNTVXIY2lp\nScWKFaU8giAoux39iMXrdzFg/Fz+Wr9bZWCb5d/zkUzyX038sxfZ5hEEQRCEwqLQDCgbM2YMTk5O\nuLq6AvDoUeaXrZGRYp8/IyMjYmJiAHj8+DEaGhqULl1aKc/jx49zPJ5YzefDiOuWd4XlmqWmpnHu\nym2OnbvKnftPcsyrpgZv90aIuHyDIb/N5YcuTTE3LvWJa5qpsFy3ouRrvWZ2dnYFXQVBEAqRQhHc\njhs3jhMnTrB79240NDQ+yzHFh2HeXb9+XVy3PCoM1yz64RPCjpwj/GQEiUkpQOZI03epqUFVxwo0\nc6uKpZkRsxZt4u79WGl7ajos3fwPw/q0p6pjhU9a58Jw3Yoacc2EwqJ169Y4ODgwa9asgq6KUADW\nrFnDL7/8wv379z+qnDp16tC2bVvGjh2b530LvFvC2LFjCQkJYevWrdja2krpJiYmAMTGxirkj42N\nxdjYGABjY2PS09OJi4vLNo8gfI1SU9M4evo/JvmvZsTURew8dEoKbN8lK1mcDh71CJg4kNEDulCt\nsh3GpWVMGtaDKg7lFPImp6QyY8FGdoef/hynIQhCPvHx8UEmkyGTyShTpgwVKlTA09OTv/76i9TU\n1Hw91urVq/ntt9/ytUxV/Pz8pHMqVaoUjo6ODBkyhCdPcn4yVVj4+PjQtWvXXOVNSEjg119/xcXF\nBSMjIypUqECfPn24du3aJ65l3nXo0IHz588XaB0KNLgdPXq0FNja29srbLOxscHExISDBw9KacnJ\nyRw/fpxatWoBUKVKFbS0tBTy3L9/n8jISCmPIHxtHjyKY+TvfzF3eSiXr9/NNp9TJVt+7teBwMmD\n6erZEKPSMoXtxfR0+OXHzrRoUF0hXS6HZZv2sjx4LxkZGZ/kHARByH+NGjUiMjKSiIgINm/ejIeH\nB35+frRs2TJf5+01NDREX18/38rLiZ2dHZGRkVy6dIk///yT3bt34+Pj88HlpaWlFboZYhISEmje\nvDl79+5l+vTpnD17lnXr1pGYmEiTJk04e/ZsQVdRgZ6enlKX0s+twILbkSNHsnbtWv766y9kMhmP\nHj3i0aNHvHz5EsgcCefj44O/vz9bt27l8uXLDBw4kOLFi9OpUycADAwM6N69O76+vhw6dIgLFy7w\n448/4ujoSKNGjQrq1AShwKSnpzN76WYexsar3K5fXI82TWvh7zuAXwd7U6tKJTQ1s+8KpKGhQZ8u\nLejVqRnvDk7ddeg0sxYF8ypZdYuwIAiFi46ODiYmJpibm+Ps7MzgwYPZvn07Fy5cwN/fX8r3+vVr\nad55MzMzGjduTFhYmLQ9NTWVX375hUqVKmFsbIyjoyMTJ06Utrdu3ZpRo0ZJrx8/fky3bt0wNTXF\nycmJdevWUadOHfz8/KQ8MpmM5cuX07NnT8zNzXFxcWHDhg3vPSdNTU3pnDw8PBgwYABhYWG8evUK\ngAcPHtCnTx9sbGywsbGhS5cuREVFSfv7+flRp04d1qxZQ5UqVTA2NiYxMRG5XE5AQADVqlXD2NgY\nBwcHJk2aJO2X23JDQkKoUqUKlpaWeHt7S0+a/fz8WLduHXv27JFanw8fPqzyHKdMmUJ0dDShoaG0\nbNkSKysratasybp16yhXrhyDBg1SCMjXrl1L3bp1MTY2xs7OjgEDBkjbnj17xs8//0zFihUxMTHB\n1dVVmr0GCF3QAAAgAElEQVRqzZo1SmsEHD58GJlMJtU7K8+uXbuoXr06JiYmeHp6cvv2bWkfVeXs\n2rWLhg0bYmJigrOzM1OmTOH169fS9tjYWL799ltMTU2pXLkyq1atyuGv/n4F1ud28eLFANI0X1lG\njx4t9a8YOnQor169YtSoUdIiDps3b1b4Rejn54eGhga9e/eWFnFYsGDBZ+u7KwiFSdix8wr9ZLM4\n2FnjXq8qtVwqoqWV99u+ZaOamJSR4b9sC8kpbx5hnr10A985qxj9YxdKG5b8qLoLQlHWdfDvn/V4\nG+aNy5dyHBwccHd3Z9u2bYwbl1nmoEGDuHXrFn/99RcWFhbs3buXbt26ceDAAZycnFiwYAE7duxg\nyZIlWFtb8+DBgxwHM/r4+PDw4UO2bt2Krq4uv/76K/fu3VPKN3PmTHx9ffH19WXVqlUMHjyYunXr\nYmVllevz0dHRISMjg9TUVORyOW3atMHV1ZUdO3agra1NQEAAXl5enDx5kmLFigGZszMFBwezfPly\ntLW10dXVZfLkySxZsoRp06bh5ubG06dPpUftSUlJuSr37t27bN68mdWrV5OUlESfPn2YMmUKc+bM\nYciQIVy7do34+HgWLlwIZLZ4vysjI4OQkBA6d+6MmZmZwjZ1dXUGDx5M//79uXTpEk5OTixbtowx\nY8YwYcIEPDw8SExM5J9//gEy5zPv0qULCQkJBAYGUqFCBW7evElSUlKury9kLp41Y8YMAgMD0dPT\nY8yYMXz//fccPnxY5RRdYWFh9O/fHz8/P+rVq8e9e/f4+eefSUlJYerUqQAMHDiQe/fusWXLFvT0\n9Bg3bhx372b/5PF9Ciy4TUhIeG8eNTU1xo4dm2NnYh0dHWbNmiU6rgtfvReJr9iwLVwhzamSLb06\nNcfStMxHl1+tsh2Thvdg5sKNxMW/mRbsTvRjxv+xnF9+7Ew5a7McShAEoTCqVKkS4eGZnx23bt0i\nODiYiIgIKajs378/hw4dYvny5fz555/cu3eP8uXLU7duXdTU1LCyssq2K+D169cJCwtj37591KxZ\nE4D58+fj7OyslLdr165SH9Tx48ezYMECjh07lut+qdeuXWPp0qVUr16dkiVLsmrVKuRyOfPnz5eC\nrjlz5lChQgX27NlD+/btgcyW6oULF0pjdV6+fMn8+fPx8/Oje/fuAJQtW5bq1TO7aIWEhOSq3LS0\nNObPn4+BgQEAvXr1Ys2aNQCUKFECXV1dqTU9O0+ePCEhIUGp62aWihUrAnDjxg2cnJyYNWsWPj4+\nDB48WMrj4uICwKFDhzh58iQnTpyQ9nt7rFNupaWlMX36dGrXrg1kri9QpUoVwsPDVT41/+OPPxgy\nZAjff/89kHktJ06cyI8//siUKVOIiopi37597N69WyozKCiIKlWq5LluWQrFbAmCIHy8jdvDeZmU\nLL3W1dFiUPc2GBrkX983W0sTpo3sxYwFG7l1783cuPHPXuI7exU/9fKipkvFfDueIAifnlwul4K0\nCxcuIJfLpSAjS0pKCg0aNADA29ub9u3bU716dZo0aUKzZs1o1qwZ6urKPR2vXbuGuro6VatWldIs\nLS2VWiEBHB0dpX9rampSunRppUHl74qMjMTCwoL09HRSUlJwc3OTulhcuHCBO3fuYGlpqbBPUlIS\nt27dkl6bm5srDEKPjIwkJSWFhg0bqjxmbsu1srKSAlsAU1PTTzrYLTY2lgcPHmRb74iICExNTaXA\n9kOpq6tLgT6AtbU1ZmZmXL16VWVwe+HCBc6ePavQ9SUjI4NXr17x6NEjIiMjsy3zQ4ngVhC+ALej\nH7LviOKggg4t6uVrYJvF0ECficO6E7AilNMRbx5Fvk5N48/FIXzXrgmeTWqJVZwEoYi4evWq1IKX\nkZGBmpoaBw4cQEtLcVVCXV1dIHMwd0REBAcOHCA8PBwfHx8qV67Mli1bVAa4ufXu8dTU1N47uKts\n2bJs2rQJdXV1zMzM0NHRkbZlZGTg5OTE0qVLlfZ7uwuAqqkRc5LbclWdT14H4ZYpUwYDAwMiIyNV\nbs9KL1++fJ7KVUVdXV3peqelpanMm5fP94yMDEaPHk27du2UtpUp8+apYn5+Z4jgVhCKOLlczvLg\nfQqLLpgaGdKqsesnO6aujjYj+nVkTehBtoe9WQ1QLofVfx/gYWw8vTs1z3GwmiB8SfKrD+zndvny\nZcLCwhg5ciQAzs7OyOVyHj16JLXUqqKvr4+XlxdeXl54e3vTtGlTbt68SYUKinNg29vbk5GRwfnz\n56lRowaQOatR1mJMH0tbW5ty5cqp3Obi4kJwcDClSpWSVj/NDXt7e3R0dAgPD1cZNH5ouarqnp6e\nnmMedXV1OnbsyPr16xk7dqxCa2ZGRgbz5s3jm2++wcnJCTU1NczNzQkPD6dx48ZKZTk7O/Pw4UMi\nIyNVtt6WKVOGpKQknj9/TsmSmWMoLl68qJQvIyODM2fOSF1R7t27R0xMTLYtwi4uLly7di3bv1PW\ne0RVmR+qwOe5FQTh4xw/e4UrNxQHZ/Ts2PSDBo7lhbq6Ot3bu/NDNw/U1RV/ce8/co4/F4eIqcIE\noRBJSUnh0aNHxMTEcPHiRebNm4enpydVqlRhyJAhAFSoUIEuXbowcOBAQkNDuX37NufOnSMgIICt\nW7cCMG/ePIKDg4mMjOTmzZts2rSJkiVLYm5urnRMOzs73N3dGT58OKdOnSIiIoJBgwahp6f3yZ/u\ndO7cGWNjY7y9vTly5Ai3b9/m6NGjjB8/XmFmg3fp6+szYMAAJk2axOrVq7l16xZnzpxhyZIlH1Xu\nu6ytrbly5QrXr18nLi4u2/mGJ0yYgJmZGe3atWPXrl1ER0dz6tQpvL29uXnzJoGBgdK1HDFiBEFB\nQQQGBnLjxg0iIiIICAgAoGHDhtSoUYMePXoQFhbG7du3OXjwINu3bwegRo0aFC9enMmTJ3Pz5k1C\nQ0Olwf9v09TUZOzYsZw8eZKIiAh8fHyoVKlStrNU/fLLLwQHBzNt2jQuX77MtWvXCA0NleZCtrOz\no2nTpgwfPlwqc+DAgejp6eX6Wr5LBLeCUIQlp7xm9ZYwhbQqDuU++Qpib2vqVo2xA7tRTE9bIf3s\npRuE/xvx2eohCELODh06RMWKFalcuTJeXl7s2rWLMWPGsHPnToVH84GBgXz33Xf89ttv1KxZk65d\nu3L06FGsra2BzOBv7ty5uLu707BhQy5evMimTZukWQLeNX/+fMzNzfH09MTb25tOnTphZGQkdXP4\nVIoVK8bOnTuxtbWlV69euLq64uPjQ0JCwntbXH19fRk2bBizZs3C1dWVHj168ODBg48u9209e/bE\n3t6exo0bU758eU6cOKEyn6GhIfv378fd3Z3Ro0dTtWpVunXrhp6eHmFhYVSrVk3K27dvX2bNmsXK\nlSupU6cOnTp14urVq0Bmg8SmTZuoVasW/fv3p1atWowZM0YKqg0NDVm0aBEHDx6kbt26rFixgvHj\nxyvVR0dHhxEjRjBgwACaNm1KRkYGq1evzvbHiru7Oxs3buTIkSO4u7vj7u7O7NmzFfosz58/H2tr\na9q2bcu3335L586dpffbh1BLSEgoXLMVfwZimcoPI65b3n3qa7Zhezibdx+VXmtqqjNr7A+Ym5T+\nZMfMTvTDJ0wP2kBs3DMprZRMH3/fAWi/0/fsfcR7Le/ENfs8nj17pjBISMi7uLg4KlWqxOLFi5Wm\nAxUKt/xaWjc/5HQvipZbQSiiHj2JZ1uY4i/9lo1qFkhgC2BpWgbfn75DS+tNP9unCS/YdfBUgdRH\nEITCITw8nB07dnD79m1OnTpF7969KV26NE2bNi3oqglfKBHcCkIRtWpzGKmpbwYjyEoWp0MLtwKs\nERiVltGiQQ2FtND9x3mR+KqAaiQIQkFLS0tj2rRp1K1bV3qc/m5XCEHITyK4FYQiKOLqLU5FXFNI\n8/ZqQjE9nWz2+HzaNa9L8WJv6pGYlMKWvccKsEaCIBQkd3d3jh07xoMHD4iKimLDhg3ZjpwXCrfv\nvvuuUHRJeB8R3ApCEZOWls6K4L0KaXa25jRwrVxANVKkX1wPr6Z1FNL2/HOaJ0+fZbOHIAiCIOQf\nEdwKQhGz5/AZoh/GKaT17ty8UC2a0LJxTUrJ3iwgkZqazsYd/xRgjQRBEISvhQhuBaEIefYikeCd\nikFi4zoulLdRnl+yIGlradGldX2FtH9OXuTO/ccFVCNBEAThayGCW0EoQtZtPUTSq9fS62J62nRr\n06jgKpSDBq5OWJq+mblBLod1Ww8WYI0EQRCEr4EIbgWhiIi684BDJy4opHVq1QBZycI54lhDQwNv\nryYKaef+i+K/a3cKqEaCIAjC10AEt4JQBMjlcpYH70P+1pIrlqalaVG/esFVKheqVa5ApfKWCmlr\nQg8gl391a8cIgiAIn4kIbgWhCDh86hLXbilOv9KjYzM0NTWy2aNwUFNT47t2iq23UXdi+Pf81QKq\nkSAI+S0jI4Nhw4ZRtmxZZDIZhw8fVpkmFB0ymYzQ0NCPKiMgIAAnJ6d8qlHeiOBWEAq5V8kprNly\nQCGthrMdLt8UjXki7cta4upir5C2ftsh0tLSs9lDEIT89uTJE0aMGIGTkxPGxsbY2dnRtm1bDh58\n0w/eycmJgICAPJe9d+9e1qxZw/r164mMjKRWrVoq0951584dZDKZ9J+NjQ0tW7bkyJEjH3Wun8vh\nw4eRyWTExcW9PzOwadMmmjVrhoWFBebm5ri7u7Nhw4ZPXMsPExkZiYeHR0FX44OJ4FYQCrnNe46S\n8DxReq2lpUGPDkVr2cpv2zZGXf3NVGUxj+M5cPx8AdZIEL4u3bt358yZM8ybN4/Tp0+zYcMGmjVr\nxtOnTz+67Js3b2JiYkKtWrUwMTFBW1tbZVp2QkJCiIyMZMeOHZQsWZIuXbpw+/btD65PamrqB+/7\nqfj6+jJo0CBatWrFoUOH+Oeff2jTpg1Dhgxh4sSJBV09JSYmJujoFPyiQB9KBLeCUIjFPH7KzoMn\nFdI8m9TCpIxhAdXow5iblKZJ3SoKacG7jpCc8jqbPQRByC8JCQkcP36ciRMn0rBhQ6ytralWrRpD\nhgyhY8eOALRu3Zp79+4xYcIEqSUV4OnTp/Tt2xcHBwdMTU2pXbs2q1evlsr28fFh3LhxREdHI5PJ\ncHJyUpmWk1KlSmFiYkLlypWZM2cOSUlJHDp0CMgcb+Dv70+VKlUwNTWlbt26Cq2dWa2/wcHBtGnT\nBlNTU5YtWwbAqVOnaNOmDebm5lhbW9OmTRtiYmLyVG5oaCjt2rXDzMyMWrVqSS3dd+7coU2bNgCU\nL18emUyGj4+PyvM7c+YM/v7+TJw4keHDh2NnZ0eFChUYNmwYEydOZM6cOZw5c0bKf+3aNbp164a1\ntTUWFhY0a9aM//77T9q+du1a6tatK7XADxgwQNqmqjvBuy3yMpmMRYsW0aVLF8zMzKhcubJSC/K7\n5Tx48IA+ffpgY2ODjY0NXbp0ISoqSmEff39/7O3tsbCw4Mcff+Tly5cqr8fnoFlgRxYE4b1Wbt5P\nWlqG9LqUTJ92zesWYI0+XKeWbvzz70Vep6YB8Ox5IjsOnKRjS7cCrpkgfLxi9375rMdLspqZ67wl\nSpSgRIkS7Ny5k9q1a6Orq6uUZ/Xq1bi5ufHdd9/Rt29fKT05ORkXFxeGDh1KyZIlOXToEMOHD8fK\nyoqGDRsyffp0rKysWLNmDQcOHEBDQwMtLS2ltNzKqltKSgoAU6dOJTQ0lD/++IMKFSpw6tQphg4d\nikwmo0WLFtJ+kyZNYurUqQQEBKClpcXFixdp06YNXbt2Zdq0aejq6nL8+HHS0tLyVO7UqVOZPHky\nf/75J7NmzaJPnz5cvHgRS0tLVq5cSY8ePThx4gSGhoYqryvAxo0bKVGiBP369VPa1rdvX6ZNm0Zw\ncDDVq1cnJiYGDw8PatWqxd9//42hoSFnz54lPT2zG9eyZcsYM2YMEyZMwMPDg8TERP75J+8L5Pj5\n+fHbb7/x+++/s2XLFgYMGIC9vT1Vq1ZVypuUlESbNm1wdXVlx44daGtrExAQgJeXFydPnqRYsWL8\n/fffTJ06lZkzZ1K/fn22bNmCv7+/9CPpcxPBrSAUUmcvXefspRsKad+3a4KuTvaP9wozQwN9Wjdx\n5e89x6S0rfuP09StKgb6hXM6M0H4EmhqahIYGMjQoUNZsWIFzs7O1KpVi3bt2lGjRg0ADA0NUVdX\nR19fHxMTE2lfc3NzfvrpJ+l1r169+OeffwgODqZhw4YYGBigr6+Purq6wn6q0t4nMTGRyZMno6Gh\ngZubG4mJiQQGBrJ582bq1s38UW9ra8uZM2dYvHixQhDav39/vLy8pNcTJ07EyckJf39/Kc3e3l46\nTm7LHThwIC1btgTgt99+Y/369Vy8eJE6depgaJj5BM3IyIjSpd/M6f2uqKgobGxsVHbN0NHRwdbW\nlhs3Mj/rFy9eTLFixVixYoWUv1y5N+MrZs2ahY+PD4MHD5bSXFxccryuqrRp04bevXsDMHLkSA4f\nPkxQUBCLFi1SyhsSEoJcLmf+/PnSSphz5syhQoUK7Nmzh/bt2xMUFMS3336rVObNmzfzXLf8IIJb\nQfhEkl6lEP0wjmL6huhoa6Gjo42OtiZamprvXSo3NTWNlZv3K6R9U8GKutUdPmWVP7k27rXZf+Qc\nLxJfAZCcksrm3Ufo3bnFe/YUBOFjeHl50aJFC44fP87JkycJCwtj3rx5TJgwgREjRmS7X3p6OrNn\nz2bz5s3ExMTw+vVrXr9+jZtb/j1xadWqFerq6iQlJWFqasr8+fNxdHTk7NmzJCcn06lTJ4XPzNTU\nVKytrRXKeLfFMSIiAk9PT5XHi4yMzHW5jo6O0r/NzMwAiI2N/bATzYWIiAjq1KmjMhCOjY3lwYMH\nNGzY8KOPU7NmTaXXe/fuVZn3woUL3LlzB0tLxWkdk5KSuHXrFpB5Tbt3765UpghuBeEL8ehJPH/v\nOcY/Jy/y/PkLihdXbJVUV1fLDHa1tdDV0UZbWxNdbW10dP4/TVuLF4nJxDyOl/ZRU4NenZq9Nygu\n7IoX06WDRz1WhLwJ3PcdOUfLRjUxNSpVgDUThC+frq4ujRs3pnHjxowePZohQ4Ywffp0hgwZku2A\nr4CAAObNm8f06dNxcHCgRIkSTJ48OV8DvL/++gtHR0cMDAwoVerN50BGRmaXrHXr1mFlZaWwj6am\nYvjy7udsTvJSrpaWlvTvrM/fvM7TXb58eY4fP05KSorSIK2UlBRu375N/fr1s9k7b9TU1JTql9UV\n40NlZGTg5OTE0qVLlbZltV4XNiK4FYR88jD2qRTUZmRk/+GXkSHnVfJrXiW/BhKzzfe2pvWqYmtp\nmk81LVjN3Kqx89ApYuOeAZCensGGbeEM7dO+gGsmCB8uL31gC4uKFSuSlpZGcnIy2traaGtrS307\nsxw/fhwPDw+6desGZAZ2N27cwMDAIN/qYWFhQdmyZVXWT0dHh3v37uW5tdLZ2TnbvqgfU+7bsn4Q\nvHvN3tWpUycWLlzIkiVLGDhwoMK2xYsXk5iYSOfOnaV6b9iwgdevXyv94DAyMsLc3Jzw8HAaN26s\n8lhlypTh4cOH0uvHjx8rvM5y+vRphZbW06dPU7FiRZVluri4EBwcTKlSpbLtQ1uxYkWVZRYUEdwK\nwkeKefyUv/cc5fCpSzkGtR+qRDFdunh+/GOowkJLS5NubRoRsPzNSNxjZ6/g6V6L8jbmBVgzQfgy\nPX36lJ49e/L999/j6OhIiRIlOH/+PHPnzqVhw4aULFkSAGtra44fP06XLl3Q0dGhdOnSVKhQgb//\n/pvjx49TunRpFi1axN27dz/L5Pz6+voMGTKECRMmIJfLqVevHi9fvuT06dOoq6vTq1evbPcdMmQI\nzZo1Y+jQofTr108aUNa4cWOsrKw+uNy3WVlZoaamxp49e2jZsiW6urqUKFFCKV/NmjUZPHgwEydO\n5PXr13h6eqKmpsb27duZNm0aw4YNo3r1zNUm+/bty9KlS+nVqxcjR45EJpNx9uxZ7O3tcXZ2ZsSI\nEYwbNw4jIyNatGhBUlIS4eHhDBkyBIAGDRqwePFiatWqhbq6OlOmTFE50G3btm1Uq1YNNzc3QkND\nCQ8PJywsTOV5du7cmYCAALy9vRk3bhyWlpbcv3+fnTt30qdPH8qXL8+AAQMYMGCAQplnzpwpsAFl\nYiowQfhADx7FMW/lVoZPWUD4v6pbaw309TAzNsTQoATFi+mgqZn3W65np2aULFEsP6pcaNSr7oCt\npeJAk7WhB8WyvILwCRQvXpyaNWuyYMECWrduTZ06dZg8eTKdOnWSps0CpOm7qlatSvny5QEYNWoU\n1apVo3PnzrRq1YpixYpJrYyfw/jx4xkzZgzz5s2jdu3atG/fnq1bt2JjY5Pjfs7OzmzZsoVr167R\nrFkz3N3dCQkJkboZfGi5bzM3N2fs2LFMnToVOzs7Ro0alW3eqVOnMnfuXLZv3079+vVxc3Nj69at\nzJ07V2GeW3Nzc3bu3Elqaipt2rShQYMGLFq0SOou0bdvX2bNmsXKlSupU6cOnTp14urVqwrHsbW1\nxdPTk549e9K9e3fKlCmjVJ8xY8awdetW6tWrx9KlSwkMDKRatWoq616sWDF27tyJra0tvXr1wtXV\nFR8fHxISEqTgtUOHDowZM4YpU6bQoEEDLl++rNRK/TmpJSQkFNi3ydGjRwkICODChQvExMQQGBjI\nd999J21/+fIlkyZNYseOHTx9+hRLS0t69+7NoEGDpDwpKSn8+uuvhISEkJycTIMGDfjzzz+xsLDI\n9rjXr1/Hzs7uk57bl0hct0z3Hz5h856jHD39H9nFYuYmpWjfoh7GJbWpVEnxUU9aWjopr1NJeZ1K\ncsprXqemkpyS+e+s9JSUVNLS06lgY45d2ezfy0VZxNVbTJu3TiFt7MCuVHEoL95rH0Bcs8/j2bNn\n+fpIXhA+N5lMxooVKxRmlyiKcroXs+2W4OzsnOfBK2pqapw/n/tVhxITE3FwcODbb79VmIQ4y/jx\n4zl06BALFizAxsaGY8eOMXToUEqXLi31/xk7diw7d+5kyZIlGBoaMn78eLp27Up4eHie5tYThPeJ\nfviEzbuOcOzs5RyD2g4ebtSr7oC6ujrXr19XyqOpqYGmpgbFi6meE/Fr4VypLE6VbLl49baUtnbr\nwSKzrLAgCIJQOGUb3NarV08puD1//jxXrlzhm2++kR5ZREVFSWlVqlRRVVS2mjdvTvPmzQFUNl+f\nPHmSrl270qBBAwBsbGxYtWoVZ86coVu3bjx79oxVq1YRGBgoda5euHAhTk5OHDp0CHd39zzVRxBU\nuRcTy+ZdRzh+7kq2Qa2laWk6eLhRp9o3qKuL3j659Z1XE8ZcfTMC9070Yw6fuoSZ4dcd+AuCIAgf\nLtvgNigoSOH19u3b2blzJ1u3blWasiI8PJyePXsyfvz4fK1c7dq12b17Nz169MDS0pJ///2XS5cu\nSRNKnz9/ntTUVJo0aSLtY2lpScWKFfn3339FcPv/5HI5cfHPKVFcr8guAFAQHsY+Zf22Qxw/ezXb\nPFZmZejg4UbtqpVEUPsBylqZUq+6A0fPXJbSNu74h0HdxL0rCILwKSQkJBR0FT65XM+W8Pvvv9O/\nf3+Vc7E1bNiQH374gWnTptG6det8q9yMGTMYNmwYlStXljpTz5w5Ew8PDyBzigsNDQ2llUGMjIx4\n/PhxjmWrelz8JUhPzyD26TOiHz4l+lEc0Q/juP/oKckpqQBUtreiWV0XrM2VO5jnxpd63d51/9FT\n5q3eJV23d5kZyWju5oJLJVvU1NSU1th+29dyzT5UTUdrDhw9Q1p65tyTiYmJHD17FU1N0a0or77W\n95roaywIwttyHdzevHkzxykdZDKZtFJFflm4cCEnT56UJlo+duwYEyZMwNramqZNm35U2V/Ch2Fq\nahrRD59w695D6b879x/zOlVxwmYNTW2Ka2a22N66/5RFmw5S1bE8HT3c8jRY6WsZsPI04QX/W7Fb\n4bplsbE0pqOHG64uFXPVJ/1ruWYfww5od/cJuw69mRNx39EIvm3v8dX3S84L8V4TBEHIlOvgtmzZ\nsqxZs4YePXoozeP24sUL1qxZg62tbb5V7NWrV0yePJnly5dL6zpXrlyZixcvEhAQQNOmTTE2NiY9\nPZ24uDiFqS5iY2OpU6dOvtWlMMjIyODGnQf/H8Q+4ta9h0Q/jCUtLeODyjv3XxTn/ovC+ZuydGpZ\nn4rlLN+/01cgOeU1MxZs5GnCC4V0W0sTOrV0o4azfZFfJawwat+iHodORPz/whaQ+CqFkF2H6dGx\nWQHXTBCUyeVy8TkgCAXofdNG5jq4HT9+PD179qRmzZp069ZNWk3k5s2bbNiwgdjYWJYvX/5RlX1b\namoqqampSjMeaGhoSEvnValSBS0tLQ4ePCjNu3f//n0iIyOpVatWvtWloN1/+ITZSzZzL+bJB5eh\nraWp1KILEHHlFhFXblHZ3oaOLd1wsMv9HH9fmoyMDPyXbeF29COF9JaNatCzY9Ff+rYwM9AvTtum\ntdmw/c2KQjsOnsLBzoYazvYFWDNBUFS8eHFpfk/xmSAIn59cLichIQF9ff1s8+Q6uPX09GTTpk34\n+voyZ84chW1OTk7MmzcvzwO4Xr58yc2bN4HMwCI6OpqIiAgMDQ2xsrKiXr16TJo0ieLFi2NlZcXR\no0dZv349kyZNAsDAwIDu3bvj6+uLkZGRNBWYo6MjjRo1ylNdCqvb0Y/4PXAdz14k5XofA/1ilLUy\nVfjPqJQBF67cJGTXEa7duq+0z6Vrd7h07Q4OdtZ0bOmGo53NV/fBvXLzfs5euqGQVt2pAj06NP3q\nrkVBaNXYlf1HzxEX/6bVPHDVVn4f1Qcz41I57CkIn4+mpib6+vo8f/68oKsiCF8tfX19aSyWKh+0\niFCrhQEAACAASURBVMOjR4+4d+8ekLn8nImJyXv2UO3w4cO0adNGKf3bb78lKCiIR48eMWnSJA4e\nPEh8fDxWVlb06NGDwYMHS8FG1iIOwcHBCos4WFpm/5i9qPRNu37rPn5B60lMSsk2T5lSJSlrmRXE\nmlDWyhRZyRLZBmNyuZxL126zefdRLl+/m225FctZ0rGlG86VykplFZXr9iF2HTrF8uB9CmllrUyY\nOKz7R80w8SVfs08h8mY0k/xX8/z5C4oXLw6AtYURU37uKWb6eA/xXhMEQchUoCuUFZSi8CVw+fod\nZizYqDRa3/mbslS2t5VaZPWL633UMUJ2HeHStTvZ5rGzNadjSzeqOJTnxo0bhf66fYjTEdf4469g\nhTlsSxvqM21kLwwNsn/skRtF4b1W2Ow8eJL5K0Ol4BagvmtlBnVvI1rQcyDea4IgCJnyNDHnjRs3\n6N+/P9988w1GRkaEh4cDEBcXx6BBgzh9+vR7ShBy4/zlKPzmb1AKbFs3rsm4gd3walYH50plPyqw\nBXCws2HCT98xaXh3nL8pqzLP9dsPmB60kXGzlnHt9oOPOl5hdPNuDHOXb1EIbHV1tBg9oOtHB7bC\nh2nZqCbVHBTfj4dPXmLv4TMFVCNBEAShKMl1cHvx4kWaNGnCwYMHqVmzJunp6dK20qVLc+XKFZYs\nWfJJKvk1OXkhklmLNikN/urYsh7dP1Hfz0rlrRg/6FumjuhJVcfyKvPcvPuQBev28u/57Bc0KGri\n4p8zc+EmUl6/udbq6moM79sBGwvjAqzZ101NTY0urepiaao4f/XKzfu5rqK/uCAIgiC8LdfB7aRJ\nkzAxMeH06dPMnj1baRoGd3d3/v3333yv4Nfk8KlLzF6yWWl6L2+vxnRp3fCTP5K1K2vBGJ+u/D6q\nFzWclR9vyuWwcO0OHscV/dVNkl6lMH3BBuKfvVRI7925OVUcVAf4wuejo63FiB86oaf7pp9tWloG\n/1uymWcvEguwZoIgCEJhl+vg9sSJE/Ts2RMDAwOVQZaVlRUPHz7M18p9TfYfOUvgyq1kZCj+aOjT\npTlezT7vnL3lbcwZ1b8zM8b0oVaVigrbEpNS8F/2N2lp6dnsXfilp6czZ+nf3L0fq5Du6V6L5vWr\nF1CthHeZm5TG53tPhbSnCS+Yu2yLwpMjQRAEQXhbnvrc6ujoZLvt8ePHOW4Xsrfj4En+Wr9bod+n\nmhoM/N6TFg1qFFi9bC1N+blfR3p0VFwN7sbtGNZuPVhAtfo4crmcpZv2cuH/2Lvv+Cjq/H/gr9ma\n3guQSiAEQpVeAwQFlH6IgBTlKyDWO1EU1N+Bd3pY0DvlsLcTsdxRlABiA6Q3JXRCEkggAZKQXrfN\n/P7YsGGySdiE3WzK6/l48ID5zOzMO8MS3vnse96fsxdk4/17dsLsyfFOiopqM6BXZ0y4U96z+tT5\ndFk/XCIiopvZnNz27NkTP/74Y437DAYDNmzYgH79+tktsNZAkiRs3L4XX2z4RTauVCrw53lTMHxg\nDydFJnfPiH5WZQpbdxzG7yeb3zr2W3Ycwi97j8nGOkS0xeMPTOKT+E3UzAkj0DU6XDb2/c8HcPh4\nkpMiIiKipszm5Pbpp5/Gjh078OSTT+LkyZMAgGvXruGXX37BxIkTkZKSgsWLFzss0JZGkiR8vXmX\n1QyUWq3EMwumYlDvLk6KzJogCFg0azx8vd1l4+99uQW5+c2nkfmhxHNY990O2VigvzeefXgatBq1\nk6KiW1EqlXhy3hT4esuX/X53bQKuZOU6KSoiImqqbE5u4+Pj8cEHHyAhIQFTpkwBADzyyCOYNm0a\nzp49iw8//BADBw50WKAtiSRJ+Ox/P+H7nw/IxrUaFZ5bdB96d2t6vSo93V0xd9JwKBRVs5vFpeV4\n+7PvmkX9bUraFfz7P5tlpR9urho8t+g++Hh51P5CahJ8vNzx1EN/glJZ9S2rvEKPf36yERU6vRMj\nIyKipsbm5XcBYNq0aRg3bhx27tyJ1NRUiKKI9u3bIz4+vs41fqmKKIp4f91W/HbopGzczVWDpY/M\nQExU7SurOVtkaBBmTBiBr76vqrdNupCB/23bjZkTRzoxsrrl5Bbg9Q/+K2uvplQq8NRDUxHWNtCJ\nkVF9xESFYs6UUbKV5C5dycFHX29jWQkREVnUK7kFADc3N4wbN84RsbR4RqMJ//7iexz4Q94r1tPd\nFS88PhPtw9o4KTLbTbxzIM4kpyPxTNUDWd/9dABdOoY3yRZapWUVePW9b1FYXCYbnz99LHp0rnnh\nCmq6xg7vi+SLmdj3+xnL2N6jZ9AxMgR3j2DNPxER1bNbAjWc3mDAmx9vsEpsfbzcsfwvs5tFYguY\n628fnTOhxvrH/MJiJ0VVM6PRhH9+uhEZ1+R1mZPuGoT4wb2cFBXdDkEQsPD+exDeTj7jvnbTr0i6\nkOGkqIiIqCmpNbn19fWFv78/9Hq9ZdvPz6/OX/7+/rWdrtV7/8ut+ONUimwswM8LLz01p9l9NO7t\n6Y4nH5yEmz8FLiwuw+rPv4coirW/sJF9tXknTp5Lk40N6t0ZMyeOcEo8ZB8uWg2eeuhPcHOtWuDB\nZBLxz082oqCICzwQEbV2tZYlPPvssxAEASqVSrZN9ZeWkSX7GBUA2gT64v89cT8C/LydFNXtiY2O\nwH3j4mTdHk4nX8KGH/Zi2rg4J0ZmdiUrFz/sOiIb69Q+BI/OmcD3cQtgXuBhAt78aINlLL+wBO98\ntgkvPD4TSqXSidEREZEz1ZrcLlu2rM5tst32347KttsF++GvT86Cr3fzfghv8ujBOJN8CSeT0ixj\nG7bvRZfocHTrFOm0uADgq+93ylZ7C/DzwjML74VGzZZfLUX/njGYeNdAbP75oGXsdPIlfJ2wC7Mn\nj3JiZERE5EysuXWw4tJy7Dt6WjY2c+LIZp/YAoBCocDjD0yEt1dV/1tJAlZ//r1TPx4+m3IJR06c\nl43NnjwK3p7utbyCmqsZ44ejW6cI2VjCL4dwKPFcLa8gIqKWrl7JbUFBAV555RXExcUhMjISkZGR\niIuLwyuvvIKCggJHxdis7dh3TNaCKsDPC326dXRiRPbl4+WBx+dOlNXfFhSV4t9ffA/p5qayjUSS\nJKuFGqIj22HgHZ0bPRZyPPMCD5Ph5yP/YfG9L7eguLTcSVEREZEz2ZzcXrhwAUOHDsWqVatgNBox\nbNgwDBs2DEajEatWrcKQIUOQmprqyFibHZPJhJ/2/iEbGz2sT4urB+zRuT2mjBkiGzt5Lg3f/bS/\n0WM5eOwcktOuyMbm/OlO1tm2YN6e7njqoSlQqeQLPBzm7C0RUatkc3K7ZMkSFBUV4fvvv8f+/fux\ndu1arF27Fvv378d3332H4uJiPPfcc46Mtdn5/VQKrudVLU+rUatabAuqe+8eitjocNnYt1t+w9mU\nS40Wg8FgxNebd8rG+vfs1KQXxiD76NQ+FONGDpCNpWVkOSkaIiJyJpuT2wMHDmDRokWIi7N+En74\n8OF4+OGHsX9/48/UNWU/VnuQbEjfrvB0d3VSNI6lVCrxxAOT4OVR9fVJEvDO59+jqKSsjlfaz097\n/0DW9aryGKVS0aRXTiP7qv5DDJNbIqLWyebk1tvbGz4+PrXu9/Hxgbd382xr5QiXrmTj1Pl02djY\n4X2dFE3j8PPxxGNzJ8rG8gqK8d6XWxxef1tSVo6NP+yVjd055A60C2bv5dYiMjRYtn3pSnaT6rtM\nRESNw+bkds6cOfjyyy9RXGy9ClVhYSG+/PJLzJ07167BNWc/7v5dtt2lY5jVf74tUa/YDph410DZ\n2B+nUrBlxyGHXnfTj/tRUlZh2XZ10WDq3UMdek1qWvx8PGWfjFToDLiWk+/EiIiIyBlq7XNbXXR0\nNARBQN++fTFz5kxERUUBAFJTU/HNN98gMDAQ0dHR2LRpk+x1U6ZMsW/EzUBJWTn2HD4pGxsT17Jn\nbW82fdxwnEu5jPMXMy1jX2/ehc5RYYhuH2L36+XkFmD7b/IFGybdNYitv1oZQRAQGRYsW5UuLSOL\ns/dERK2MzcntwoULLX9+++23rfZnZ2dj4cKFso+fBUFolcntzgPHodNXtf/y8/FEvx6dnBhR41Kp\nlHjywUlY+tqnltlUk0nEW59sxMtPPwB/Xy+7Xu+bhF0wGqs+fvbz8cQ9I/vb9RrUPESEWCe3g/vE\nOi8gIiJqdDYntwkJCY6Mo8UQRRE/VStJGD2sN1SqltX+61YC/X3wyOzxeOPD9ZaxvIJi/GPN13hp\n8Vx4uNnnwbrU9CvYe1S+tPGM8cOh1XAlstaofVgb2TYfKiMian1sTm6HDmX9oi2OnU5Fdm6hZVut\nVrbY9l+30rdHJ4wfNQBbfq2qt824lotVH67H84/NuO2lcGtasCE8JBDD+ne7rfNS81W9rj0tk8kt\nEVFrw+V37eyHarWfg3vHturaz1mTRmJArxjZ2NmUy/j3fzbf9pPsx06n4HSyvI/u7MmjoFDwbd1a\ntQ30hUZd9TN7YVEp8gutH4IlIqKWy+aZ2wkTJtzyGEEQsHnzZpsvvm/fPqxevRrHjx/H1atXsWbN\nGsyaNUt2TEpKClasWIHdu3fDYDAgOjoaH330EWJizAmTTqfDiy++iA0bNqCiogJxcXF48803ERJi\n/weXbiXz2nVZvR/Q8tt/3YpCocDjD0xEUck3OJty2TJ+KDEJ/9nwMx68d3SDVg8zmUxWs7Y9urRH\nzy5Rtx0zNV9KpRLhIYFISbtqGUvPzIavt2cdryIiopbE5ikuURQhSZLsl9FoxMWLF7F3715cuXKl\n3jNxpaWliI2NxauvvgpXV+sazLS0NIwZMwYRERHYvHkzDhw4gBdffBHu7lUzocuWLUNCQgI++eQT\nbNu2DcXFxZg+fTpMJlO9YrGH6u2/OrUPQVR420aPo6nRqNV4ZuG9CG0jf2p9+2+/Y/MvBxt0zp0H\njiPjWq5lWxDMs7ZEkaHyutuLl685KRIiInIGm2dut27dWuu+7du34y9/+QteeeWVel189OjRGD16\nNADg0Ucftdr/8ssvIz4+XnbeyMhIy58LCwuxdu1arFmzBiNHmlei+uCDD9C9e3fs2rULo0Y1XrJT\nVq7Db4dOyMZa+6ztzTzcXPH8YzPx4pv/QV5B1cfEX32/E77eHojr393mc5VX6PDfbXtkY3H9uyMi\nJMhu8VLzFRkir7tNZ90tEVGrYpfixLFjx+K+++7DsmXL7HE6AOaZ4u3btyMmJgZTp05Fhw4dMHLk\nSGzcuNFyTGJiIgwGA+Lj4y1joaGhiImJwaFDjl00oLrfDp1Ahc5g2fbxcseAXp0bNYamzt/XC0sf\nmQ53N61s/P11W3Hi3EWbz5Pw60EUFpVatjVqFaaPH263OKl5s3qojB0TiIhaFZtnbm+lffv2+Oij\nj+x1OuTk5KCkpARvvfUWnn/+eSxfvhy7d+/GggUL4O7ujjFjxiA7OxtKpRL+/vKPuwMDA5GdnV3n\n+ZOTk+0WqyRJ+HbzrygtrUq4hvWOxsWLF+x2jabCHvdt2uh++OCbn2E0VZWxvPTPz/H47LutSheq\nKywuwzff/wq9oarsZPDgHsi7noW8600zibHne601aeh90xuMKCsrxY2W2ykXS3Hy1Bm4aJtee7gK\nnR4XLmcjJf0q0q/kQKtRY+KofmgTUPtS53Vpre+16OhoZ4dARE2IXZJbo9GITZs2WSWZt+NG/e49\n99yDxx9/HADQo0cPJCYm4qOPPsKYMWNu6/z2/GaYeCYV5XrRUgusUikw+9674ePlYbdrNAXJycl2\nuW/R0dHw8QvE259twk1rfuDrHw7h5acfQJB/7f+xf/DVVqg1LlBrzNvenm5YMHsy3Fy1tb7Gmex1\nz1qb271v0e3DkZlVVZOtcfNCdFSoPUK7LRU6Pc6lXsaZ5Es4nZyGC5euQRQl2THf/foH3nh+Qb27\nfvC9RkRkZnNy+9hjj9U4XlhYiKNHjyIrK6veNbd18ff3h0qlsnRFuKFTp06W0oSgoCCYTCbk5uYi\nICDAckxOTg4GDRpkt1hu5Ydd8vZfA+/o0uISW3sb1LsL8guL8Z8Nv1jGCotKsfLdb/DSU3Ph5eFm\n9ZrLV3Ow88Bx2di99wxrsoktOU9kaLAsub14+RpinJDc6vQGJF3IwJnkdJxJvoSU9Cswmep+8Dbj\nWi6OnkxG/54xdR5HREQ1szm53b17t1XLJkEQ4OPjg4EDB2Lu3Lmy2tfbpdFo0Lt3b6uP2VJSUhAW\nFgYA6NWrF9RqNXbu3Ilp06YBADIzM5GUlIQBAwbYLZa6XM3OQ+IZefnB2Dg+SGaLe0b2R25BsWyR\nhytZeXj9/f/i/z05y2qVsXXf7ZDN9LYL9kP8oNa5QAbVLSI0GPt+r1q5rrHqbvUGA5IvXsHp5DSc\nSb6E5LRM2dLQttr88wH069GpQW3yiIhaO5uT25MnT9r94iUlJbhwwZwYiqKIjIwMnDhxAr6+vggL\nC8OTTz6JefPmYfDgwYiLi8OePXuwceNGrFu3DgDg7e2NOXPmYPny5QgMDISvry9eeOEFdO3aFSNG\njLB7vDWpvtRuh4i2iG7f+D12m6vZk+NRUFgsW0I3Oe0K3v5sE56ePxVKpXnZ4lPn03DsdKrstfdP\nHNnqljUm21R/qMzRHRMMBiM+/d+P2HPkFAyG+rUhDG3jjw4R7fDboarvsclpV5B0IQOdO4TZO1Qi\nohbPbg+UNcSxY8dki0OsXLkSK1euxMyZM/Hee+9h/Pjx+Ne//oW33noLS5cuRVRUFN5//31Zve3K\nlSuhVCoxb948yyIO77//viUpcqQKnR67Dsk/Jmf7r/oRBAGPzJ6AwuIynExKs4z/fjIFn/z3RyyY\ncTcAYO3GX2Wv69whFH17dGrMUKkZqZ7cXrqSDaPR5LAfhn7Zdww79h+/9YEA2gb5omunSMR2DEfX\nThGWEqbC4lLZp0AJvx5kcktE1AA2J7dffPEFfv75Z6xdu7bG/XPnzsXYsWNx//3323zxYcOGoaCg\noM5jZs2aZbVq2c20Wi3eeOMNvPHGGzZf1152Hz6JsnK9Zdvb0w2D7ujS6HE0dyqVEovnT8WKt9ci\nPaOqy8Wv+xLh7+OFQH9vq4+VZ08exY9sqVbenu7w9fZAfmEJAMBoFJGZleuwXshHT5yvdV9wgA9i\noyPQtVMEukZHwM+n5tXSJt45SJbcHj2RjMxr1xHSJqDG44mIqGY2J7effvop+vatfVayTZs2+Pjj\nj+uV3DZnkiRh+66jsrFRQ+6AWu3UyfBmy81Vi2WPTMf/e+sL5OQWWsb/u3U33Fw1smMH9e7M0g+6\npcjQYEtyC5jrbh2R3BoMRpy/mCkbG9a/G7p3ikTXThEI8PO26Tyx0eHoENEWqelVSwcn/HoIi2aN\ns2u8REQtnc29ZlJTU9G1a9da93fp0gUpKSl2Cao5OJmUJnsaW6lU4K6hvZ0YUfPn6+2JZY9Mh6e7\nfCnmm2fHVSoFZk4c2dihUTNkVXeb4ZhleFMvXYXeYLRs+/l44rE5EzB8YA+bE1vAXKIzYdRA2die\nIyeRX1hcyyuIiKgmNie3giAgLy+v1v15eXmW3rStwfbf5O2/BvSMqfXjRrJdSJsALHl4GjS1zICP\nGdYHwQG+jRwVNUeNtVLZ2ZRLsu3Y6PAGl8z079kJwTct4GA0itj+29E6XkFERNXZnNz27NkTGzZs\ngE6ns9pXUVGB9evXo0ePHnYNrqnKup6PP07JZ6nHjujnpGhanpioUDz54CRUzw/c3bSYMnaoc4Ki\nZscquc3MgiRJtRzdcKeT02XbsR3DG3wupVKJcfH9ZWM/7/0D5RXW33eJiKhmNie3ixcvxrlz53DP\nPfcgISEBKSkpSElJwebNm3HPPffg/PnzWLx4sSNjbTJ+2vOHrN9qZGgwOrEG1K769YzBQ/eNlY3d\ne/cwq5IFotoEB/jC1aWqXru0TIfreYV1vKL+jEYTzl/IkI3FRkfc1jlHDOwpe5+Xlumw44BtnRiI\niKgeD5SNHDkS7777Lp599lk88MADlnFJkuDp6YnVq1fjzjvvdEiQTUmFTo+dBxJlY3eP6Msn9x3g\nrmG94e/rib1HTqNzxzDWNFO9CIKAyNBgnE25bBlLy8hCYB3LO9dX6qWr0Omr6m19vT3QJvD2yma0\nGjXGxPXB+h/2Wsa27jiEMcP6sK8zEZEN6vVo/4wZMzBu3Djs2LEDaWlpAIDIyEjEx8fD07N11Jvu\nPXIKpWVVHxF6urticJ9YJ0bUsvXuFo3e3aKdHQY1UzUlt/3suKztmRpKEuzxg+7ouD7Y/MtBy4Nq\nufnF2P/HGcT1737b5yYiaunq3bfK09MTkyZNckQsTZ4kSVYPd8QP7gWNWl3LK4jImaq3/kqz80pl\nZ2p4mMwevD3dMWJgD/y05w/LWMKvBzGsXzd+SkREdAs219xu27YNS5YsqXX/kiVLsH37drsE1VSd\nTk7H5avXLduCAIwexo/KiZqq9mFtZNv27JjgiHrbm42L7y97qPJSZg5OnLtot/MTEbVUNie3q1ev\nRllZWa37Kyoq8Pbbb9slqKbqx2qztv17xtSrjyURNa6Q4AAolVXf5q7nFaG4tNwu5754+RoqdAbL\nto+XO9oG+dnl3ADQJtAPA3p1lo0l/HrQbucnImqpbE5uz5w5g169etW6v2fPnjh37pxdgmqKcnIL\ncKTaEptjh9e+YhsROZ9arUJoW/nytel2Kk1wVL3tzaov6nDyXBouXnbMYhRERC2Fzcmt0WhERUVF\nrfvLy8tr7IHbUphEEQPv6AyFwvyfV3hIILrcRj9LImockSGOWczBut7WfiUJN3SMbGdVx5vwC2dv\niYjqYnNyGxsbiy1bttTYBF0URSQkJKBz5841vLJlaBPoh7/835/w75cew5QxgzH5rsF8sIOoGXDE\nSmVGownnUi/LxrrY6WGy6iaMGiDbPnDsLHJyCxxyLSKilsDm5HbRokU4fPgw5syZg+PHj0On00Gn\n0yExMRGzZ8/G0aNH8fDDDzsy1ibB39cLMyaMwJC+XZ0dChHZoHpym26H5LZ6va23pxtCgv1v+7w1\nuaNrR4S2qTq3KErYuvOwQ65FRNQS2NwKbOrUqbhw4QJeffVVbNu2TbZPEAQ899xzmD59ut0DJCK6\nHRHVyhIyrl2H3mC4rRZ+NbUAc9QnOYIgYMKdA/Hel1stYzv2J2IqV+wjIqpRvfrcLlmyBNOmTUNC\nQoJsEYcJEyYgMjLSAeEREd0edzcXBPl7IzvXvPSuKEq4fCUHHSLaNficVg+TOaDe9mZD+nTFNwm/\nIb+wBACg0xvxy94/MGXMEIdel4ioOar3Ig6RkZF44oknHBELEZFDRIYGW5JbAEjPzG5wcmsymZDk\nwP62NVGrVbh7RD989f1Oy9gPu45gXHx/LiJDRFSNzcmtTqfDwYMHcf78eRQXF8PDwwOdO3fGgAED\noNVqHRkjEdFtiQxtg8PHq1r53U47rbSMLJRX6C3bjqy3vdmdQ+7Aph/3Wa5dWFyG3YdO4s6hXEiG\niOhmt0xuJUnC6tWr8a9//QsFBQWybgmCIMDHxwd/+ctf8MQTT7B7ABE1SVYPlWVmN/hc1ettuzig\nv21N3N1cMGrIHdjy6yHL2NadhxE/uBcUCpufDSYiavFumdwuWLAAGzZsQIcOHbBw4UJ069YNnp6e\nKC4uxqlTp7B+/XqsWLECJ0+exEcffdQYMRMR1Yt1cpsFURQblBSeSbZObhvLPSP64YddR2AyiQCA\nK1l5OHoyGf17xjRaDERETV2d39m/+eYbbNiwAU888QQOHTqEpUuXYvz48Rg+fDjGjx+PpUuX4vDh\nw3jyySexYcMGfPvtt40VNxGRzfx8PGWdBSp0BlzLya/3eURRxLlU604JjcXf18uqDSEXdSAikqsz\nuf3iiy8wZMgQ/O1vf4NSqaz5BAoFVqxYgcGDB+Pzzz93RIxERLdFEAS7LOaQlpGFsvKqeltPd1eE\ntQ287fjqY3y8fFGH8xczrR5wIyJqzepMbk+dOoUJEybYdKIJEybg9OnTdgmKiMjeIuyQ3J62agHW\nOPW2N4sICUKv2CjZ2OZfDjRqDERETVmdya3RaISrq21Nwl1cXGA0Gu0SFBGRvdVUd1tfZ2t4mMwZ\nJtw5ULZ99EQysrgkLxERgFskt5GRkdi/f79NJ9q/fz8XciCiJqt9WBvZ9sV6ztya620vy8Yc3d+2\nNl2jIxAVLv96dh3iJ2dERMAtktvJkydj/fr1+PHHH+s8yU8//YQNGzZg8uTJdg2OiMhe2gb6QqOu\nahBTWFSKgqISm19/6Uo2Sst0lm1Pd1eEt2vcetsbBEHAxDsHycaOnkyt19dDRNRS1ZncPv7444iO\njsasWbPw5z//Gfv370dRUREkSUJRUREOHDiAP//5z7j//vsRHR2Nxx9/vF4X37dvH2bMmIEuXbrA\nx8cH69atq/XYv/zlL/Dx8cHq1atl4zqdDkuWLEFUVBTatWuHGTNmIDMzs15xEFHLp1QqER4iT0br\nU3d7uloLsM4dwpza27t/z04I8ve2bBtNIn7YdcRp8RARNRV1Jrdubm7YvHkzhg8fji+++ALjx49H\nZGQk/P39ERkZiXHjxuGLL77AsGHD8N1338HNza1eFy8tLUVsbCxeffXVOmt7v//+e/z+++9o27at\n1b5ly5YhISEBn3zyCbZt24bi4mJMnz4dJpOpXrEQUcsXGSr/KL8+ye2ZGh4mcyalUolx1Ton/Lz3\nD6SmX3FSRERETcMtF3EIDAzEhg0bcPToUWzfvh3nzp1DSUkJPDw8EBMTgzFjxqB///4Nuvjo0aMx\nevRoAMCjjz5a4zGXLl3C0qVL8d133+Hee++V7SssLMTatWuxZs0ajBw5EgDwwQcfoHv37ti1axdG\njRrVoLiIqGWKCAmSbadl2LYMryRJOJdSvd7WucktAIwY2APrt+1BcWk5AKC0TIfn3/gcPbq0DSPN\nHgAAIABJREFUx5TRg51WE0xE5Ey3TG5v6Nu3L/r27evIWKwYjUbMnz8fzzzzDGJirFfgSUxMhMFg\nQHx8vGUsNDQUMTExOHToEJNbIpJp38CZ2/TMLJSUVVi2PdxcEBESXMcrGoeLVoPRcb2x4Yd9svET\nZy/ixNmL6NwhFFPGDEHPLlFcHp2IWg2bk1tnWLlyJfz8/PDQQw/VuD87OxtKpRL+/v6y8cDAQGRn\n1712fHJyst3ibE143+qP96xhHHHfdHoDyspKIUnm7dS0Upw8dQYuWnWdr/vtyBmUlpZattuH+CEl\nJcXu8TVEbGQgDgR4Irm0VBYjAPx+Igm/n0hCWFt/3DWkB7o5oS9vY4iOjnZ2CETUhDTZ5HbPnj34\n6quvsGfPHoecn98M6y85OZn3rZ54zxrGkfctun04MrNyLdsaNy9ER4XW+Zrvdx6Hu7u7ZXvYwN5N\n6u/1rdgu2PrTb/j9XAbOVHvwDQDyiirw7Q+HsT/xAqaMGYJBvbtAoajzkQubSZIEnd4ArUbdIhNn\nImp+mmxyu3fvXly7dk1WjmAymbB8+XK89957OHPmDIKCgmAymZCbm4uAgADLcTk5ORg0aFBNpyWi\nVi4yNFiW3KZlXENMHcmtJEnW/W2dtHhDbQRBQExUCMaPGYGzKZew6cf9OH72gtVxl69exzuff4//\nbt2NyaMHYVi/7lCpal5avTpJklBQVILLV68j42oOMq5dx+UrOcjMuo7SMh18vNzRrVMEusVEolun\nSAT6+9j7yyQiskmTTW7nz5+PSZMmycamTp2KqVOn4oEHHgAA9OrVC2q1Gjt37sS0adMAAJmZmUhK\nSsKAAQOszklEFBEajH2/n7FsX7xcd93tpSs5lge2AMDdTWv1YFpT0qVjOLp0DEdq+hVs+nE/jpw4\nb3XMtZx8vL9uG/63bQ8m3jkI8YN7QqOuKs0oLC7F5as5yKiWyN5cd1xdQVEp9h49g71Hzfe2TaCv\nJdHt2ikCXh7166ZDRNRQTk1uS0pKcOGCeXZBFEVkZGTgxIkT8PX1RVhYGAID5T0pVSoVgoODLR8H\nent7Y86cOVi+fDkCAwPh6+uLF154AV27dsWIESMa+8shomagvsvwVm8B1rlDmN0+0nekDhHt8MzC\ne3HpSja++3E/9v9xxlJrfENufjE++99P2PjjPvTu2hHZ1/ORce06CovLbvv613LycS0nH7/sPQbA\nfN+7xUSie0wkOncIg4tWc9vXICKqic3J7WuvvYYJEyYgNja2xv1nz57F5s2b8dxzz9l88WPHjmHC\nhAmW7ZUrV2LlypWYOXMm3nvvPZvOsXLlSiiVSsybNw8VFRWIi4vD+++/D6XSto/aiKh1qZ7cXr6a\nA6PRVOvH82dT5DWsXZpYScKthLcLwpPzJmPauDh8//MB7D58EiaTKDumsKgUOw8cb/A1BAFWiXN1\naRlZSMvIwpZfD0GlUiA6MgTdOkWiW0wkOka0s7k8gojoVoSCgoJbfEsy8/X1xYcffmj5+L+6jRs3\nYv78+cjLy7NrgI7Ah3wahvet/njPGsbR923RC+8gv7BqqdrXl82vsdRAkiQsWPovWVnCymfnISrc\nekEZZ7P1nl3PK0TCrwfx6/5EGAz1W+xGq1EhtG0gQtsEILRtIMLaBiCsbSB8vDyQeukqTiWl4dT5\nNJy/mAGjUbz1CSvdP3EEJo0eXK9YiIhqY7eyhJKSEqjVdbfTISJqCiJDg2XJbXpmVo3J7eWr8npb\nN1eN1cxvcxPg541508Zgypgh2LLjEH7e8wcqdAbZMWq1EiHBAQhrF4iwykQ2tG0Agvx9au2IEBMV\nipioUEy9eygqdHqcS72Mk0lpOJWUhvTMrDpndrt2irTjV0hErV2dye2pU6dw8uRJy/aBAwdgNBqt\njisoKMCnn37KGSoiahYiQ4Nx7HSqZTvt8jXE9e9udVz1tlqdO4Q3i3pbW/h4eWD25FGYdNdgHD2R\nhKLiMrQL9kdo20AEB/jc1tfpotWgV2wH9IrtAAAoLi3H6fNplpndq9n5lmPd3bSICm9T26mIiOqt\nzuR2y5YteO211wCYW8189tln+Oyzz2o81sfHBx9++KH9IyQisjPrh8pqXvSludfb2sLT3RUjB/Vy\n+DUG3tEFA+/oAsBcGnHqfBpOJqXBRatpMT8wEFHTUGdy++CDD2Ls2LGQJAnx8fF4/vnncdddd1kd\n5+7ujvbt20OlarKdxYiILKont2kZWZAkSfaRuyRJVp0Suka3vOTWGQL8vDFiYE+MGNjT2aEQUQtU\nZzbapk0btGlj/rgoISEBMTExVu25iIiam+AAX7i6aFBeoQcAlJRV4HpeoWzhgYxr11FUUlVv6+rS\n/OttiYhaA5s/Cxo6dCgTWyJqEQRBsHqArHppQvWShJioULYYJCJqBupVR/Drr79i7dq1SEtLQ0FB\nAaRqj78KgoDExES7BkhE5Ajtw9rgXGqGZfvi5Wvo26OTZbt6SUJsdESjxUZERA1nc3L7zjvvYMWK\nFQgKCkLv3r1rXcyBiKg5qD5zm3bTSmXmelv5zG1sC3yYjIioJbI5uX3//fcRFxeH//3vf+xnS0TN\nXvswefuptIyq5DYzK1e2BK2LVm11PBERNU0219wWFBRg0qRJTGyJqEUICQ6AUln1LfB6XpFlwYaz\nydXrbcO4PCwRUTNhc3Lbp08fJCcnOzIWIqJGo1arENo2QDaWXlmawBZgRETNl83J7apVq7Blyxb8\n97//dWQ8RESNJjKk5n63p6sv3sDkloio2bC55nbu3LnQ6/VYtGgRnnrqKbRt29aqLY4gCDh48KDd\ngyQicoSI0GDgUNUS42kZWbianYfColLLmFajQlRYW2eER0REDWBzchsQEIDAwEB07NjRkfEQETWa\n9tWX4c3IsipJ6BQVynpbIqJmxObkduvWrY6Mg4io0UVUK0vIuHYdx89ekI11ZX9bIqJmxeaaWyKi\nlsbdzQVB/t6WbVGUcPSk/MFZLt5ARNS81Cu5zcvLw8svv4wxY8agd+/eOHz4sGX8tddeQ1JSkkOC\nJCJylMhqpQmiWLXyokatQodw1tsSETUnNie36enpGDp0KP7973/DYDAgLS0N5eXmnpB+fn7YuHEj\nPv74Y4cFSkTkCJGhtS/OENOB9bZERM2NzTW3y5cvhyRJOHjwIDw9Pa0eLLvnnntYl0tEzU71mdub\nccldIqLmx+aZ2127dmHBggWIjIyEIAhW+yMiInDlyhW7BkdE5Gh1JrestyUianZsTm51Oh18fHxq\n3V9YWAiFgs+nEVHz4ufjCU93V6tx1tsSETVPNmejXbp0wb59+2rdv3XrVvTo0cMuQRERNRZBEGqc\nve3UPgRqtc2VW0RE1ETYnNw+8sgj2LRpE1atWoX8/HwAgCiKOH/+PObPn4+jR4/isccec1igRESO\nElFDcsuSBCKi5snmaYlp06YhIyMD//jHP/CPf/wDADB16lQAgEKhwEsvvYS7777bMVESETlQTTO3\nXTqGOSESIiK6XfX6zO2pp57CtGnTsHnzZly4cAGiKKJ9+/aYMGECIiMjHRQiEZFjtQ+TtwPTqFXo\nGNnOSdEQEdHtqHdBWWhoKB599FFHxEJE5BQhwf6IjmyH5DRzx5fhA7pDo1Y7OSoiImoIm2tuDx48\niLfeeqvW/f/85z8tK5bZat++fZgxYwa6dOkCHx8frFu3zrLPYDBg+fLlGDx4MNq1a4eYmBjMnz8f\nly9flp1Dp9NhyZIliIqKQrt27TBjxgxkZmbWKw4iat0EQcDi+VMx9e4hePDeuzB7yihnh0RERA1k\nc3L72muv4eTJk7XuP3XqFF577bV6Xby0tBSxsbF49dVX4eoqb8VTVlaG48eP45lnnsFvv/2Gr776\nCpmZmbj33nthNBotxy1btgwJCQn45JNPsG3bNhQXF2P69OkwmUz1ioWIWjc/H0/cN2447h7RDy5a\njbPDISKiBrK5LOHEiRN45plnat3fr18/rFq1ql4XHz16NEaPHg0AVqUO3t7e+O6772Rj//znPzFw\n4EAkJSWha9euKCwsxNq1a7FmzRqMHDkSAPDBBx+ge/fu2LVrF0aN4uwLERERUWti88xtWVlZjSuT\n3aykpOS2A6pLcXExAFgWk0hMTITBYEB8fLzlmNDQUMTExODQoUMOjYWIiIiImh6bZ247duyIHTt2\nYNGiRTXu/+WXXxAVFWW3wKrT6/V48cUXMXbsWISEhAAAsrOzoVQq4e/vLzs2MDAQ2dnZdZ4vOTnZ\nYbG2ZLxv9cd71jC8b/XXWu9ZdHS0s0MgoibE5uR27ty5ePbZZ/Hss89i2bJl8PX1BQDk5eVh5cqV\n2LFjB1555RWHBGk0GrFw4UIUFhbi66+/tss5+c2w/pKTk3nf6on3rGF43+qP94yIyMzm5HbBggU4\nefIkPvroI3z88ccICgoCYJ49lSQJ999/Px555BG7B2g0GvHQQw/hzJkz2LJlC/z8/Cz7goKCYDKZ\nkJubi4CAAMt4Tk4OBg0aZPdYiIiIiKhpq1ef23feeceyiENaWhoAIDIyEpMmTcLQoUPtHpzBYMD/\n/d//4ezZs9iyZQuCg+WrCPXq1QtqtRo7d+7EtGnTAACZmZlISkrCgAED7B4PERERETVtNiW3er0e\nR44cQZs2bTBs2DAMGzbMLhcvKSnBhQsXAACiKCIjIwMnTpyAr68v2rZtiwceeADHjh3D119/DUEQ\nkJWVBQDw8vKCq6srvL29MWfOHCxfvhyBgYHw9fXFCy+8gK5du2LEiBF2iZGIiIiImg+buiWoVCpM\nnjwZO3bssOvFjx07hri4OMTFxaG8vBwrV65EXFwc/vGPfyAzMxPbtm3D1atXMWLECMTExFh+bdy4\n0XKOlStXYty4cZg3bx7Gjh0Ld3d3fPPNN1AqlXaNlYiIiIiaPptmbhUKBcLDw+3e6mvYsGEoKCio\ndX9d+27QarV444038MYbb9gzNCIiIiJqhmzuc7to0SJ8/vnnyMnJcWQ8REREREQNZvMDZWVlZXBz\nc0Pv3r0xbtw4REZGWi2ZKwgCnnzySbsHSURERERkC5uT2xUrVlj+/O2339Z4DJNbIiIiInImm5Pb\n48ePOzIOIiIiIqLbZnNyGx4e7sg4iIiIiIhuW70WcQCA1NRU7N27Fzk5OZg2bRoiIiKg1+uRlZWF\n4OBgaDQaR8RJRERERHRLNie3oijiqaeewtq1ayFJEgRBQL9+/SzJ7ZAhQ7BkyRI88cQTjoyXiIiI\niKhWNrcCe/PNN/Hll1/ihRdewM8//wxJkiz7PDw8MGHCBGzZssUhQRIRERER2cLm5HbdunWYPXs2\nnn76aURFRVntj42NRWpqql2DIyIiIiKqD5uT2ytXrqBPnz617nd1dbX7CmZERERERPVhc3IbFBSE\nS5cu1bo/MTERYWFhdgmKiIiIiKghbE5uJ06ciE8//VRWeiAIAgDg559/xjfffIPJkyfbP0IiIiIi\nIhvZnNwuXboUoaGhiIuLw4IFCyAIAt566y3ceeedmD59Orp164bFixc7MlYiIiIiojrZnNx6eXnh\np59+wuLFi5GdnQ0XFxccPHgQpaWlWLp0KbZt2wZXV1dHxkpEREREVKd6LeLg4uKCp59+Gk8//bSj\n4iEiIiIiarBbJrcVFRXYtm0b0tPT4efnhzFjxqBNmzaNERsRERERUb3UmdxevXoV99xzD9LT0y2L\nNri5ueGbb77BsGHDGiVAIiIiIiJb1Vlz+/LLL+PSpUt49NFH8e2332LlypVwcXHBc88911jxERER\nERHZrM6Z2127dmHmzJl4+eWXLWNBQUGYP38+MjMzERIS4vAAiYiIiIhsVefMbVZWFgYMGCAbGzhw\nICRJQkZGhkMDIyIiIiKqrzpnbk0mE1xcXGRjN7YrKiocF1VzJ+ogmAogmAqhMBZU/rkAgrEQUGhh\n0kRAdImCqG4HCEpnR0sOIOivQFX6O9qIKVDnd4Sk8oeo8oek8oek8uXfOxERkYPcsltCWloafv/9\nd8t2UVERACA5ORkeHh5Wx/fp08eO4TVBkhGCqbgyWTUnrQrLnwshmPIhiOV1nkJZfgooBCBoYNK2\nh0nbHqI2CqImFBDq1Z2NmhLJCGX5aahK9kOpuwgA8JBKoS65Wu1AJUSVb2XCG1CZ8AZUJr++fA8Q\nERHdBqGgoECqbaevr69lid2bSZJkNX5jLC8vz/5R2llycjKio6Mb9FqXrHeh0KfZN6AbBDVMmnCI\n2g4waaMgasMAQe2YazXA7dy3Fs1UAnXpIahKDkIwFcp2lZaWwt3dvR4nEyApfSGqzUmvSdseJtdu\nrS7h5Xut/njPiIjM6vwfc82aNY0VR7Mhqryh0Dvo5JIBSl0qlLpUmFNaJUzacIjaKHOyqwkHFFoH\nXZzqS6G7BFXJfqjKjgMw2emsEgRTHpQm8w+JqpIDkJS+MHjFw+jep9UluURERPVV5/+U999/f2PF\n0WxISh8bjlJCUnqZP3pWekNS+kBS+UBSekMw5kGpuwiF7iIEseQW5zFBqbsIpe4i1PgVgBIml04w\neA6BqI0GaphVJwCSAQpDNgTDVSgM1yBIBoiqAIiqQEjqYPPfYUPvnWSEsuwE1CX7oNBfrvNQk7YD\nsssCEertDcGYC4XhOgRjLgSxuF6XFEz50ORvgLroVxi8RsLo3rdJzegTERE1JZwGqidJ6QNJ4VmZ\nrPpAVHpbEldJ6Wv+s8IDEGpvRGH0HApIEgRjNpS6C1DoLkKpuwDBVHSLq5ugrDgLZcVZiKogGD2H\nwOjWu/XO5kqS+aE9w7XKRPYqFPorUBivAxBrf52ggagOhqgKhKgOgqQOhqgKgqTyq/VBL8FYCFXp\nQahKDtX9Q4mggdGtNwyegyGp26CoMBkGr2ofFYs6CMbrUBhzzUmv8XrV73W8BwRTATT5m8xJrucI\nGN37AwpNHTeIGo1khKr0KBS6CwAUgEILSXCBpNACggskhUvlmBZQuFTtU7gAUPEHVSIiO3Jqcrtv\n3z6sXr0ax48fx9WrV7FmzRrMmjXLsl+SJLz66qv4z3/+g4KCAvTp0werVq1Cly5dLMfodDq8+OKL\n2LBhAyoqKhAXF4c333zTYT14jR6DYfQccvsnEgRI6mAY1cGAx6DKZDe3clY3FUrdRQim/FpfrjBm\nmxOdgh9gdO8Ho8cgSOqA24+rqZIMUBiuQaG/BsFwxfxnw1UIYlkDzqWHQn+5hplXJUR1oDnRVQdB\nVAdDErRQlR6Fqvw06io9kFT+MHgMNs+qKlzrvr5CC0kTApOmhveoqINgzIPCmAtlRRJUpUetriuY\niqAp2Ax10Q4YPIfD6DGw9f6A0wQodJegyd8AhaH6g4O2UlYmwVqI6iDofSZAUgfZNUYiotbEqclt\naWkpYmNjMXPmTCxatMhq/9tvv401a9ZgzZo1iI6Oxuuvv44pU6bgyJEj8PT0BAAsW7YM27Ztwyef\nfAJfX1+88MILmD59On777TcolQ5ot+SoGRZBgKQOgFEdAHj0Mw8Z86HQXYCy8pdgzLV+mVQBdcke\nqEv2wuTSGQbPoRC1He0Tp2SAQp9pTgSNufATi6AqKYCo8rHMUtu1BlQyQjDmV85i5lqSPMGYUzkb\nW+uzj3ZiqkyarwF1N7yoeoVLDIweQ2ByibHPPVdoIWnawqRpC5NbNxi8RkFV/BvUpYcAySg7VBBL\noCncCnXxThg842D0GFw5E0iNQtRBXbgd6pL9uL33pqnyh7QyKE35cMnJQnmbxfy7JCJqoDq7JTSm\nkJAQvP7665aZW0mS0LlzZyxYsADPPPMMAKC8vBzR0dH4+9//jnnz5qGwsBAdO3bEmjVrcN999wEA\nMjIy0L17d6xfvx6jRo2q8VrN8qliSYJCfwmqkn1QlZ1EXbOIDSpZqCyTUOgvQ6m/DIXukjnJu+k6\n1k/+C5CUnuan+1W+loRXVPpCqty2+thcLIfCmAfBeP2m5LUymTUVwl4JrKT0hahpC1HdDpKghcKY\nDYUhC4Ix55at2m55bsEFRve+MHoMvuVsub3ea4KpCKri3VCXHAAkQ81xKVxh9BgGg+eQW88eN3FN\n/d+osvwsNPmbIJgKHHJ+o3s/6P2m1es1Tf2eERE1liZbc5ueno6srCzEx8dbxlxdXTF48GAcOnQI\n8+bNQ2JiIgwGg+yY0NBQxMTE4NChQ7Umt82SIEDURkCvjYDBu+76T0vJQuF2GN361lyyYCqqSmL1\nl6HQZ0CQ6rswhwTBVATBVASFPr3mIxQe5t6tQOXDVA0oI6iLoIGobgNR3Raipl3ln9vUntxJEgSx\nGIIhCwpDNhTGbAiGbPMDaLd40MuZdc6S0gsGn/EweI6Auni3ebZQkrftEMRyqIt+gqp4N4yeg2Hw\niAOUbo0aZ4tnKoImfzNU5Sdq3G106w2TSzQEscL870nUV/5eAUHUAVJF5T6duQRFqrCakQcAVekR\nmFy7weTapYarEBFRXZpscpuVlQUACAwMlI0HBgbi6lVzbVt2djaUSiX8/f2tjsnOzq7z/MnJyXaM\n1hmiIEjh8JDOw0dKhBY1fb2lQPF2IOtHlArtUYG20CIbLtI1qFAM6/9Sb620tLS+rwCQ1YArWTPA\nC3ohEDoEQCcEQI9AGOBdrRzACMDWpaGDKn+ZKaRyaJAPjZQHDXKhkfKhRAkMgi8Khe4oRxhQIgC4\nVK+47f9ei4ZCCoWPdAw+UiIUqN6brhQoSYCEbRBR/YEzARIE2TZQrWd15bYJHtAJgdAhCDohEHr4\nQWrEVmRN6t+oJMFLOoMAaTd00EFXbbcBXshW3InyivDKES+bTy1IRiigQ4i4ARpU9Qk3ln6GS4rZ\nEAXbZ+Gb1D1rRJyxJqKbNdnk1tFazjfDLoA0EQp9emXJwinUVLLgjizIk0zbFhaQlH4wacMgqtsh\n+2o62gRoKpcUzoNgKoa962AlpTdElV/lql2VS9Yq/SCqg6BRuEADwHpdPMdyAeDZwNc69qPiHoA4\nDerifVCV7G1guYWE2v8OCyt/pVRuK81dJjTtIKrbWX53RG1og+6bZIBCfxWCWGrugqH0s0sdtGDI\ngSZ/A5S6CzB/y7z526YCBs9hELzuQuhtdq5Q6Pzgkv0ubu700cXtFPT+trVkZFkCEZFZk01ug4OD\nAQA5OTkICwuzjOfk5CAoyDzbFhQUBJPJhNzcXAQEBMiOGTRoUOMG7EyCAFEbCb028pYlC3WRFK4Q\nNeEQNWEQNWEwacIAZVUqmZuVDD//m/7zlIyVyw7nVya8+eYHwkz5UBjzK2toq7fkUlYmr35Vyavl\ndz/2b60vhRsM3nfB4DkM6pJ9UBXvsX/ph4UJCsMVKAxXZKOSyh8mdchNSW8IoGzojwM2EsvMbd/0\nmeYWcIZMKAw5uPn9Jik8IWojYNJEQNRGmOOqz/tLMkJd/BvURb/WWDogqkOg87sXUk1dLxpA1IbD\n4DXSfL1KqrJEmFy7w+TW3S7XICJqDZpschsREYHg4GDs3LkTvXv3BgBUVFTgwIED+Nvf/gYA6NWr\nF9RqNXbu3Ilp08wPX2RmZiIpKQkDBgxwWuzOJKm8YfAeA4NXPJRlx6Eu3muVjJgpIWpCYKpMZEVN\nOCSVf/1mugQVJHUAJHVAzV1lJZO5JteYXxmbeVGLunoAUwMpXGDwGgWDx1CoSg5CXbLHhr7J9iEY\nc6Ey5gI31aFaekErPCsfOvSEpPSq/OUJSeEFSelRa1/hqhPd6GV8xZzMGjKh0F+ps02eJS6xGMry\nU1CWn6ocUULUhMKkjYCoiYBJGwEoay4fMLf3Wl/5UGX1E2ug97rL3K/6VvHXk8FrFJTlZ2X/ZjX5\nG1GubS/7QZOIiGrn1OS2pKQEFy5cAACIooiMjAycOHECvr6+CAsLwyOPPIK33noL0dHR6NixI1at\nWgV3d3fce++9AABvb2/MmTMHy5cvR2BgoKUVWNeuXTFixAgnfmVNgKCGyb0vTG59oNCnQ1l2EoJU\nYZ5Z04ZDVLd1/FKugtKc0FY+UEaNQKGF0Ws4jJ5xgFiGqrIDeaUtpJvLEaqXJUiAZDJ3mNBnWhLL\n+nQGEMRiCPpbrcQmQFK4VyW8lsTXA/7ieWizd0FhuAJBrG+dd21MUOjTZQ8/Sip/y8yuSRMBSeUL\ndeFPtbb3Mrl0gt73T+ZPGRxBUEHnNx2uWe/gRnmRIJZCm78BOv+5zXOxB1EPQSyFIJZAMJUClb8L\nYon571bUQx8w29lRElEL4tTk9tixY5gwYYJle+XKlVi5ciVmzpyJ9957D3/+859RXl6OJUuWWBZx\n2Lhxo6XH7Y3XKJVKzJs3z7KIw/vvv++YHrfNUWXJgqiNdHYk1JgEAVDK66rrWx1tUgfC5Nr1poEy\ny8yp5XdjTgPOXBWROcEpAap1N/OVSqHU2VYXLjujyh+SwtM88ylVf9DOmmXWueyPus+rcIfeZwJM\nbnc4PMGUNG1h8L4T6sIfLWPK8tNQlh2Dyb23Q6/dEILhOpQVZ82f0oilVYmrqTJ5teHvQS8ZHf/D\nNhG1Gk2mz21j4oMXDcP7Vn8t/p6JOigMWTclvVfMK3XVUKNaH9Y9latTQlQH3fRgWwhETduqFnCS\nyVyLq0uDQn8JSl1ag3vSGt36QO8z3uqHBYeSTHDJfle2ip6kcEVF8GJIKu8aX+KM95pClw6XnI9s\nSmDrUt7uRUi1lIgQEdUXf1QmooZTaM1lLtrwqjHJCMFUCMFUbOmDLJiKzaUKN4/ZWm4gaG7qY3zj\nwbXguh8OE8z1taImtGrIWGAuS9BdglKfBoX+CupcUlnpB53fVIguTvjhRFBWlie8bVm0QxDLoclf\nD13A/zWJ8gTBmAvt9f/cdmILADCV1Fr/TERUX0xuici+BJWllVudJKP5o2tTUWXiW5kEm4qRX1YE\nlf8dENUhlQ863v5DiJLKByaVD0xuPc1VEJLBvICJLh1KfToUuvTKhFsJg+dQGLzusl5Is52fAAAQ\nuUlEQVRhrxFJ6iDovcdCU5BgGVNWJEFVehhGDyc/MCuWQ5vzmY0dWZSQlO7mBV0U7pY/48aY0p11\n+URkV0xuicg5BJW5o4LKx2pXbm4y/NwcPGMqqCFqoyBqo8wLmkgSBFMBJEHbZFZ2M3oMNdfb6i5Y\nxjQFCTC5dHJeQigZob2+FgqjfOEYo1tviNr2NyWy5sQVgkuTmGkmotaDPZmIiABAEMwJYxNJbAEA\nggC93zRAuGkGWdJDk/ffyo4XjUySoMnfBKUuRTZsdOsJvd90GD0GwOTaDaI20rzkt8KViS0RNTom\nt0RETZik8jc/0HYTpS4VqpJ9jR6LqngXVKVHZGOiJgJ6v/uYxBJRk8HkloioiTO6D4DJpZNsTFP4\nAwRDTqPFoCw7AU3hD7IxSeWPioAHuLIgETUpTG6JiJo6QYDe915IN1qdAYBkgDbvW0CqcX1Au1Lo\nLpmvdRNJ4YqKgHlcOY2Imhwmt0REzYCk8oHeZ6JsTKG/BFXxbw69rmDMh/b655aWZGZK6PznQFIH\nOfTaREQNweSWiKiZMLn1hsk1VjamKfwJgv6qYy4olkOb84lVyy+d358gunR0zDWJiG4Tk1siouZC\nEKDznQpJcXNHB1NleULtC1I0iGSE9vqXVi2/DJ4jYXLvZ99rERHZEZNbIqLmROkJve8U2ZDCcAV+\n0mH7XUOSoMn/HkpdsmzY6NoDBu+x9rsOEZEDMLklImpmTG49YXTrJRvzlQ5Doc+wy/lVxbuhKj0k\nGxM14dD7T2fLLyJq8pjcEhE1Q3qfyZCUnpZtARK019dCVbwbguF6g8+rLDsJTeE22Zik9EVFwINs\n+UVEzQKX3yUiao6UbtD7TjV3MqgkmPKhKdgCYAtEVSBMrl1gcomFqI0ABOUtT6nQX4Y27xsAVauf\nSQpXVAT+H1t+EVGzweSWiKiZMrnGwujeF6rSo1b7FMYcKIpzoC7eDUnhCpNLDEyusebFIBTWSwwL\nxnxocz6voeXXbEjqYMd9EUREdsbkloioGdP7TAIkI1Ba+3K8glgOVVkiVGWJABQwaSNhcukCk2ss\nJHWgueXX9c8giMXyc/v+CaJLtIO/AiIi+2JyS0TUnCm00Pvfj/TcaHT00UFZfgZK3UUAtbUGE6HU\nXYBSdwEo3ApRFQAIGigM12RHGTxHwujBll9E1PwwuSUiagEMgg+MntEweg4FxHIoK85DWX4Wyopz\nEMSyWl+nMFo/fGZy7c6WX0TUbDG5JSJqaRSuMLn1hMmtJyCJUOgvmRPd8tNWizJUJ2rCoPOfwZZf\nRNRsMbklImrJBAVEbSREbSQMPndDMOZWJrpnzaUJN5UvsOUXEbUETG6JiFoRSeUPo+fQyvKFCnP5\ngi4VElQweA0HbuqdS0TUHDG5JSJqrRQuMLn1gMmth7MjISKyG65QRkREREQtBpNbIiIiImoxmNwS\nERERUYvB5JaIiIiIWgwmt0RERETUYjC5JSIiIqIWQygoKJCcHQQRERERkT1w5paIiIiIWgwmt0RE\nRETUYjC5JSIiIqIWg8ktEREREbUYTG6JiIiIqMVolsntvn37MGPGDHTp0gU+Pj5Yt26dbH92djYe\neeQRdO7cGW3btsXUqVORmpoqO+bixYuYNWsWOnTogLCwMDz44IPIzs6WHdO9e3f4+PjIfq1YscLR\nX55DvPXWWxg5ciTCwsLQoUMHTJ8+HWfOnJEdI0kSVq5cic6dO6NNmzYYN24czp49KztGp9NhyZIl\niIqKQrt27TBjxgxkZmbKjikoKMDChQsRHh6O8PBwLFy4EAUFBQ7/Gu2tMe8Z32vW9+3zzz/H+PHj\nER4eDh8fH6Snp1tdq6W814DGvW8t6f1GRFRds0xuS0tLERsbi1dffRWurq6yfZIkYdasWbhw4QLW\nrVuH3bt3IywsDJMmTUJpaanl9VOmTIEkSdi8eTO2b98OvV6PGTNmQBRF2fmeffZZJCUlWX4988wz\njfZ12tPevXvx0EMP4ccff8TmzZuhUqkwefJk5OfnW455++23sWbNGrz22mvYsWMHAgMDMWXKFBQX\nF1uOWbZsGRISEvDJJ59g27ZtKC4uxvTp02EymSzHzJ8/HydOnMD69euxfv16nDhxAg8//HCjfr32\n0Jj3DOB7rfp9KysrQ3x8PJYuXVrrtVrKew1o3PsGtJz3GxFRdc2+z21ISAhef/11zJo1CwCQkpKC\nvn37Ys+ePejevTsAQBRFdOrUCX/9618xd+5c7NixA1OnTsXFixfh4+MDACgsLERkZCQ2bdqEESNG\nADDPbixcuBBPPPGEU742RyopKUF4eDjWrVuHu+++G5IkoXPnzliwYIHlP7ny8nJER0fj73//O+bN\nm4fCwkJ07NgRa9aswX333QcAyMjIQPfu3bF+/XqMGjUKSUlJGDBgALZv346BAwcCAA4cOIC7774b\nR44cQXR0tNO+5tvlqHsG8L1W/b7d7NixYxg5ciSOHz+OiIgIy3hLfq8BjrtvQMt+vxERNcuZ27ro\ndDoAgIuLi2VMoVBAq9XiwIEDlmMEQYBWq7Uc4+LiAoVCYTnmhtWrV6N9+/YYOnQoVq1aBb1e3whf\nheOVlJRAFEVLcp+eno6srCzEx8dbjnF1dcXgwYNx6NAhAEBiYiIMBoPsmNDQUMTExFiOOXz4MDw8\nPDBgwADLMQMHDoS7u7vlmObKUffsBr7XBtfrPdKS32uA4+7bDS31/UZEpHJ2APbWqVMnhIaG4m9/\n+xveeecduLu7491330VmZiaysrIAAP369YOHhwf++te/WurMVqxYAZPJZDkGAB5++GH06NEDfn5+\n+OOPP7BixQqkp6dj9erVzvjS7Grp0qXo3r07+vfvDwCWrzswMFB2XGBgIK5evQrAXMusVCrh7+9v\ndcyNeuXs7Gz4+/tDEATLfkEQEBAQYFXT3Nw46p4BfK/d2L5x32zRkt9rgOPuG9Cy329ERC0uuVWr\n1fjyyy/x+OOPo3379lAqlRgxYgTuuusuSJK5AiMgIACff/45Fi9ejI8//hgKhQJTp05Fz549oVBU\nTWY//vjjlj9369YNXl5eePDBB/HSSy/Bz8+v0b82e3n++edx8OBBbN++HUql0tnhNAuOvmd8r9HN\n+H4jImq4FleWAAC9evXC3r17kZ6ejqSkJGzYsAF5eXmIjIy0HBMfH4/ExESkpKQgNTUVH374If5/\nO/cb0mTXxwH8mzVTyVkoTtKcOlmgRaXQG1GWVKghhBiUvUnZu6ShjU37R0F/rdZYiElCo8IkTIYV\nggUyRIQRWUQhib6zcm46aculzT0vpOtpzft57ud+dG5X3w/4wnP9vHbO4cD1Vc+5Pn/+HFDzu7y8\nPADA2NjYCo9g5TQ2NuLJkyfo7u4OGKtMJgMATE5OBtRPTk4iOTkZAJCcnAyfzwen0/kfa5xOp/CL\nBLB4yM/hcAg1kWal52wpf/pa+zvEuNaAlZ+3pYhhvRER/STKcPtTQkICkpKSMDo6iqGhIZSVlQXV\nJCYmYuPGjbBarZicnERpaelf3u/du3cA/v2QiTR6vV54aCqVyoBrcrkcMpkMfX19QpvX68Xg4KCw\np3Hnzp2QSCQBNePj48LBHgDYvXs33G43bDabUGOz2eDxeAL2RkaKUMzZUv70tfZ3iG2tAaGZt6VE\n+nojIvrV2oaGhvOr3Yn/ldvtxvDwMCYmJvDgwQPk5ORAKpVibm4OCQkJsFgssNvt8Pv9GBgYgFqt\nRlFREU6ePCnc4+HDh/B6vZibm0Nvby9qa2tRU1ODQ4cOAVh8SFosFsTExGB2dhZ9fX3Q6/UoLCxE\nTU3Nag39H9Nqtejo6IDZbEZaWho8Ho/warTo6GisWbMGPp8PRqMRCoUCPp8Pp0+fxsTEBIxGI9av\nX4+YmBh8+fIFbW1tyM3NxczMDOrq6iCVSnHhwgVERUUhKSkJr169QmdnJ7Zv347x8XHU1dUhLy8v\n4l7RFKo541oLnjdgcY/p2NgYRkZG8PTpUxQXF8Pj8SA6OhqxsbGiWmtA6OZNbOuNiOh3EfkqsP7+\nfpSXlwe1HzlyBC0tLbhz5w5u374Nu90OmUyGw4cPQ6fTITo6Wqg9f/482tvbMT09jfT0dFRXV+P4\n8ePC4ZQ3b95Aq9Xi48ePmJubw5YtW1BRUQGNRoO4uLiQjXW5/Dxx/Tu9Xo/GxkYAi//SvXr1Ksxm\nM1wuF/Lz83Hjxg3k5OQI9d+/f8eZM2fQ2dkJr9eLoqIi3Lx5E2lpaUKNy+WCTqdDT08PAKC0tBRN\nTU1/2YdwFao541pbet6uXLmCa9euBd2nublZePWfWNYaELp5E9t6IyL6XUSGWyIiIiKipYh6zy0R\nERER/VkYbomIiIhINBhuiYiIiEg0GG6JiIiISDQYbomIiIhINBhuiYiIiEg0GG6JiIiISDQYbonC\nTHl5OTIzM+FwOIKuud1ubNu2DQUFBfjx48cq9I6IiCi8MdwShRmj0YjZ2VmcOnUq6Nrly5fx6dMn\nmEwmrFu3bhV6R0REFN4YbonCjEKhgFarxePHj9HX1ye0v337Fq2trVCr1cjPzw9Zf759+xayzyIi\nIvp/MdwShSGNRoOcnBzU1dVhdnYWCwsLqK+vR0pKCs6ePSvUuVwu6HQ65ObmIjk5Gbt27cKtW7ew\nsLAQcD+DwYD9+/cjKysLMpkMBQUFaG9vD/pcpVKJqqoqvHz5EiqVCjKZDK2trSs+XiIiouWyxuVy\n+Ve7E0QUzGazoaSkBBqNBqmpqdBqtWhvb0dZWRmAxf23+/btg91uR3V1NVJTU2Gz2dDR0QG1Wo3r\n168L91IoFDh48CC2bt0Kn8+HZ8+eYWBgAM3NzTh69KhQp1QqER8fD4fDgZqaGsjlcmRkZEClUoV6\n+ERERP8Iwy1RGNNqtTCbzYiNjcWePXtw//594dqlS5fQ0tKC/v5+ZGZmCu0XL16EwWDA0NAQ5HI5\ngMWtBXFxcUKN3+/HgQMHMD09jcHBQaFdqVTCbrejq6sLxcXFIRghERHR8uK2BKIwdu7cOSQmJsLv\n96OpqSngmsViQUFBAaRSKZxOp/ClUqmwsLCAgYEBofZnsJ2fn8f09DSmpqZQWFiI4eFheL3egPtm\nZWUx2BIRUcTicWuiMCaVSpGdnQ273Y6UlBSh3e/3Y3R0FCMjI1AoFEv+7K+vErNYLDAYDHj//j18\nPl9A3devXxETEyN8n5GRsbyDICIiCiGGW6II5Pcv7ibau3cvamtrl6zJysoCAFitVhw7dgyFhYUw\nGo1ISUmBRCLB8+fPcffu3aDDZ78GXSIiokjDcEsUgaKiopCeng6Px/NfD3tZLBZIpVJ0dXVBIpEI\n7S9evFjhXhIREYUe99wSRaiKigoMDg7CarUGXZuZmcH8/DwAYO3atQAQsB3B4XCgo6MjNB0lIiIK\nIf7llihC1dfXo7e3F5WVlaiqqsKOHTvg8Xjw4cMHdHd34/Xr15DJZCgpKUFbWxsqKipQWVmJqakp\n3Lt3D5s3b4bT6VztYRARES0rhluiCLVhwwb09PTAYDDAYrHg0aNHiI+PR3Z2NhoaGrBp0yYAi/ty\nTSYTTCYTGhsbkZaWhhMnTkAikaC+vn6VR0FERLS8+J5bIiIiIhIN7rklIiIiItFguCUiIiIi0WC4\nJSIiIiLRYLglIiIiItFguCUiIiIi0WC4JSIiIiLRYLglIiIiItFguCUiIiIi0WC4JSIiIiLRYLgl\nIiIiItH4FxDk0xZcHi95AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110674e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fem_plot_table.plot(\"year\")\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Percent Occupied\")\n",
    "plt.title(\"Designed Capacity versus Staff Capacity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 3.2 Unique Male Prisoners"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to observe the changes in male prisons separate from female prisons. In order to do that, we will filter the table to have only male institutions. To do this, we will use the `where()` function which allows us to select the rows that contain male institutions. Another way to think about it is that we are excluding rows with female institutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Unnamed: 0</th> <th>year</th> <th>month</th> <th>institution_name</th> <th>population_felons</th> <th>civil_addict</th> <th>total_population</th> <th>designed_capacity</th> <th>percent_occupied</th> <th>staffed_capacity</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>6         </td> <td>1996</td> <td>1    </td> <td>WSP (WASCO SP)                   </td> <td>4475             </td> <td>62          </td> <td>4537            </td> <td>2484             </td> <td>182.6           </td> <td>4484            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>8         </td> <td>1996</td> <td>1    </td> <td>SRTA (SANTA RITA CO. JAIL-RC)    </td> <td>811              </td> <td>0           </td> <td>811             </td> <td>395              </td> <td>205.3           </td> <td>750             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>9         </td> <td>1996</td> <td>1    </td> <td>RJD (RJ DONOVAN CORRECTIONAL FAC)</td> <td>4577             </td> <td>0           </td> <td>4577            </td> <td>2200             </td> <td>208             </td> <td>4566            </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (8430 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_data = prisons_updated.where(\"institution_name\", are.not_contained_in(female_institutions))\n",
    "male_data.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.1 Design Capacity versus Staff Capacity for Male Institutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to analyze the design and staff capacities over time, so we will use the `select()` function to get relevant data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population</th> <th>designed_capacity</th> <th>staffed_capacity</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>4537            </td> <td>2484             </td> <td>4484            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>811             </td> <td>395              </td> <td>750             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>4577            </td> <td>2200             </td> <td>4566            </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (8430 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_year_design_staff = male_data.select(\"year\", \"total_population\", \"designed_capacity\", \"staffed_capacity\")\n",
    "male_year_design_staff.show(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population sum</th> <th>designed_capacity sum</th> <th>staffed_capacity sum</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>1515799             </td> <td>801556               </td> <td>1498610             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>1619223             </td> <td>839877               </td> <td>1597702             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>1662254             </td> <td>885820               </td> <td>1665193             </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_grouped_year = male_year_design_staff.group(\"year\", sum)\n",
    "male_grouped_year.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin by getting the male total population. We will do this by selecting the column we are interested in, and then add it to a new variable called `male_total_population`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "male_total_population = male_grouped_year.column(\"total_population sum\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will calculate the design capacity percentages and the staff capacity percentages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 189.10706176,  192.79287324,  187.6514416 ,  190.43302577,\n",
       "        189.58483506,  188.26986429,  188.7134137 ,  190.28339268,\n",
       "        193.61065069,  190.57552881,  188.89850953,  195.38616189,\n",
       "        189.57973217,  182.65700591,  178.77529575,  173.6337556 ,\n",
       "        151.67108148,  144.40785033,  137.91593335,  132.36983713,\n",
       "        131.50935704,  131.76706454,  131.78131568])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_design_capacity =  male_grouped_year.column(\"designed_capacity sum\")\n",
    "\n",
    "male_design_percentage = (male_total_population / male_design_capacity)* 100\n",
    "male_design_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 101.14699622,  101.34699712,   99.82350394,   99.86811985,\n",
       "         99.53535885,   99.49516184,   99.63285895,   99.02889624,\n",
       "         99.20607427,   98.22028826,   99.95281601,   98.58657586,\n",
       "         98.64438976,   97.81217575,   98.17054621,   99.14607073,\n",
       "         98.45594391,  100.0464141 ,   95.01929712,   90.94850361,\n",
       "         91.76682659,   96.45729307,   96.95383898])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_staff_capacity =  male_grouped_year.column(\"staffed_capacity sum\")\n",
    "\n",
    "male_staff_percentage = (male_total_population / male_staff_capacity) *100\n",
    "male_staff_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as in the female institution example, now we are adding both of these percentages to the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population sum</th> <th>designed_capacity sum</th> <th>staffed_capacity sum</th> <th>Design Percent Occupied</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>1515799             </td> <td>801556               </td> <td>1498610             </td> <td>189.107                </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>1619223             </td> <td>839877               </td> <td>1597702             </td> <td>192.793                </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>1662254             </td> <td>885820               </td> <td>1665193             </td> <td>187.651                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_year_design = male_grouped_year.with_column(\"Design Percent Occupied\", male_design_percentage)\n",
    "male_year_design.show(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>total_population sum</th> <th>designed_capacity sum</th> <th>staffed_capacity sum</th> <th>Design Percent Occupied</th> <th>Staff Percent Occupied</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>1515799             </td> <td>801556               </td> <td>1498610             </td> <td>189.107                </td> <td>101.147               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>1619223             </td> <td>839877               </td> <td>1597702             </td> <td>192.793                </td> <td>101.347               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>1662254             </td> <td>885820               </td> <td>1665193             </td> <td>187.651                </td> <td>99.8235               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_design_and_staff = male_year_design.with_column(\"Staff Percent Occupied\", male_staff_percentage)\n",
    "male_design_and_staff.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the plot we only need 3 columns, so we will use the `select()` function to get those columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>Design Percent Occupied</th> <th>Staff Percent Occupied</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>189.107                </td> <td>101.147               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>192.793                </td> <td>101.347               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>187.651                </td> <td>99.8235               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_plot_table = male_design_and_staff.select(\"year\", \"Design Percent Occupied\", \"Staff Percent Occupied\")\n",
    "male_plot_table.show(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11099a9e8>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cCsJnIC7hBZPnreH03zdU1mVkZLFs435+X7Gdl6lpxdA6QRAEQSg6Ii1BENRI\nSnnJX6fCuHLjPrZWpjSq5YmzvXVxN0utB5FPmbloI7FxL/Isd/Lide5HPmFYn4442YmHnAiCIAif\nJ9FzW4QyMjJJT88o7mYIeYh8/IxlG/9k0E+BrNl2mLDrd9l35DxjZixn/OwVHDp+8aPq/bz4z20m\n/fqHSmDraGtB364t0NfTUVoe9eQ5P81ZyZHTYUXZTEEQ/kNat27N6NGji7sZQjFZu3YtdnZ2711P\n7dq1CQgIeKdtRc9tEXj0+Bnb9p/g9N/X0dXRpl3T2rT1qinm4v1IKBSK/w9iz3Hp2t1cy4U/iCb8\nQTSrt4VQp6obTetWprSjTbHk3ykUCvYdOceqrYd4M5W2kltphvbuQAl9XSqUdea35Vt5GPnvY6xf\npWewcM0ert1+SN+u3ujqaBdx6wVBKGoDBw5k/fr1AGhpaSGXyylfvjw+Pj706tULbe3C+xxYs2ZN\nntM0FZaAgABmzpwJgIaGBjY2NjRp0gQ/Pz/Mzc0/+P7f18CBA3n+/DkbN258a9n4+HjmzJnDrl27\niIqKwtjYmAYNGjB27FiVJ7wWt44dO9K8efNibYMIbj+gexGP2bb/BGcu3ZSWZWSksX7nEc6G3WRQ\nj7bYW3/8F+DnKjXtFUfPXuHPI+eJfBJbgO3SOXwyjMMnw3C2t8KrbiXqVnWnZAm9D9jaf2VmZrIy\n+CAHjl1UWefdsCrfdGwq/XCytTJj+qherNh8gMMnlXtrQ89c4V7EY4b37YitlVmRtF0QhOLTqFEj\nFi9eTGZmJs+ePePo0aMEBASwceNGduzYQcmSJQtlPyYmJoVST364urqye/duMjMzuXz5MkOGDOHx\n48ds3rz5nerLyMhAU1Pzoxo0GB8fLwWLM2bMoEKFCjx+/Jg5c+bQpEkTdu7cSZUqVYq5lf/S19dH\nX1+/WNsg0hI+gJt3HxGwYANjZy5XCmxfF/4gmrEzl7Hz0GmysrKKuIX/bTGx8azZHsKgiYEs27g/\n18DWxNiADi3qUMmtNLl9zt1/9IRlG/czYMLvLFi9i1v3Hn3QWQmSU1KZsWiTSmArk0Hvzs3p3bmF\nyh0BHW1t+vu2ZlD3NuhoK/+efRgVw7hZyzlx/uoHa7MgCB8HXV1drKyssLW1xdPTk++//57du3cT\nFhbGvHnzpHKvXr3Cz88PNzc3bGxsaNy4MSEhIdL69PR0fvzxR8qXL4+lpSXu7u74+/tL699MS3j6\n9CndunVo0iqoAAAgAElEQVTD2toaDw8P1q9fr3LLWS6Xs3LlSnr27ImtrS0VK1bMV4+mlpaWdEze\n3t4MGDCAkJAQXr58CUBUVBR9+vTByckJJycnunTpQnh4uLR9QEAAtWvXZu3atVSqVAlLS0uSk5NR\nKBQEBgZSpUoVLC0tcXNzY/LkydJ2+a13y5YtVKpUCXt7e3x9fYmNjZXWr1+/nv379yOXy5HL5Rw7\ndkztMU6dOpVHjx6xY8cOWrZsiYODA9WrV2f9+vWULl2awYMHK33vrFu3jjp16mBpaYmrqysDBgyQ\n1iUkJDBixAjKlSuHlZUVNWrUYOvWrYD6dIJjx44hl8uldueU2bdvH1WrVsXKyoo2bdpw//59aRt1\n9ezbt4+GDRtiZWWFp6cnU6dO5dWrV9L6mJgYvvrqK6ytralQoQKrV6/O41V/O9FzW0gUCgVXbt5n\n+/4TXL39MF/bpKdnsnb7Yc6F3WRg9zai9+wDUigU3Lz7iL1/neVs2E2VW/mvc3W2pWWj6tSsVF56\nAEJMbDyHT4Xx16kw4hKSVLZ5lZ5B6JkrhJ65goONOV51K1OveoVCPYansfHMXLiRR4+Vg3F9PR2G\n9elAJbcyeW7fsJYnpRytmbtsm1JAn5qWzu8rd3AjPIJvOjZFW1t8LAhCQXX9/uci3d/G+eMLpR43\nNze8vLzYtWsX48dn1zl48GDu3bvH//73P+zs7Dhw4ADdunXj8OHDeHh4sGjRIvbs2cOyZctwdHQk\nKiqK27dv57qPgQMH8vjxY3bu3Imenh4//fQTERERKuVmzZqFn58ffn5+rF69mu+//546derg4OCQ\n7+PR1dUlKyuL9PR0FAoFbdu2pUaNGuzZswcdHR0CAwPx8fHh7NmzlChRAoAHDx4QHBzMypUr0dHR\nQU9PjylTprBs2TKmT59OvXr1eP78OZcuXQIgJSUlX/U+fPiQrVu3smbNGlJSUujTpw9Tp05l7ty5\nDBkyhFu3bhEXF8fixYsB9T3eWVlZbNmyhc6dO2NjY6O0TkNDg++//55+/frxzz//4OHhwYoVKxg7\ndiwTJ07E29ub5ORkjh49CmR/D3bp0oX4+HiCgoJwcXHh7t27pKSk5Pv8AqSlpTFz5kyCgoLQ19dn\n7NixdO/enWPHjqnt8Q4JCaFfv34EBARQt25dIiIiGDFiBGlpaUybNg2AQYMGERERwfbt29HX12f8\n+PE8fJi/WEod8S32nhQKBRf/ucPW/ce5cz8613KlHKxo41WLM39f52zYLaV1t+5FMmbGMrq1a0Sr\nRtUL/XZIVlYWdx8+JiU1DVO5IabGhpTQ1y3UfXys0tMzOHnxOvuOnOVexJNcy2lqalCrUnlaNqqO\naynVRHgLMzld2zSkU8t6/H01nJCTl/j76h21QXJE9DNWBh9k7Y7DlLYzw6veS1xL2WFjafrOr+2t\ne4+YsySYhBfKH0LmpkaMGdAFR9v8zX7gaGvJzz/25n/r93L8/DWldQeOXeTOgyiG9emAlXnR3VYU\nBKF4lS9fntDQUADu3btHcHAwly9floLKfv36ceTIEVauXMkvv/xCREQEZcqUoU6dOshkMhwcHKhZ\ns6baum/fvk1ISAgHDx6kevXqACxYsABPT0+Vsl27dqVr164ATJgwgUWLFnHy5Elp2dvcunWL5cuX\nU7VqVYyMjFi9ejUKhYIFCxZIn71z587FxcWF/fv306FDByC7p3rx4sVYWmZ/jiYlJbFgwQICAgLo\n0aMHAKVKlaJq1aoAbNmyJV/1ZmRksGDBAulBA7169WLt2rUAGBgYoKenJ/Wm5+bZs2fEx8fnmldb\nrlw5AO7cuYOHhwezZ89m4MCBfP/991KZihUrAnDkyBHOnj3L6dOnpe2cnZ3zdW5fl5GRwYwZM6hV\nqxYAixcvplKlSoSGhtKoUSOV8nPmzGHIkCF0794dyD6X/v7+9O/fn6lTpxIeHs7Bgwf5888/pToX\nLlxIpUqVCty2HCK4fUdZWVmc/vsG2w6cUBqs86Zype3p6F2Xil+URiaTUbeqGycvXGP5pv0kpaRK\n5V6lZ7BqyyHOhd1kwNetsbYwfe82Poh8ytGzVzhx/qpKb6O+ng5mckNMTYwwkxthJjfEzMQI05x/\njQ2LLIe0sCgUCp7HvyD8YTThD6IIfxjN3YfRJKfkPruBYUl9mtarTPP6VTGVG751H5qamlTzLEs1\nz7LExiVy5PRlQk7+rXYarvT0TC5evcfN+0+lfZVxsqFsKXtcnG1xcbLN1zk+cf4qC9fuJj09U2m5\nq7Mto/p1Qm5k8NY6Xqenq8P3PX34wsWRFcEHyMj4Ny3m7sPHjJu1nEHd21LN8+MapCAIwoehUCik\nIC0sLAyFQiEFGTnS0tJo0KABAL6+vnTo0IGqVavSpEkTmjVrRrNmzdDQUM10vHXrFhoaGlSuXFla\nZm9vr9ILCeDu7i79v5aWFmZmZsTE5P79CnDz5k3s7OzIzMwkLS2NevXqSSkWYWFhPHjwAHt7e6Vt\nUlJSuHfvnvS3ra2tFNjm1JmWlkbDhg3V7jO/9To4OCg9Qcva2ppnz57leTzvIyYmhqioqFzbffny\nZaytraXA9l1paGhIgT6Ao6MjNjY23LhxQ21wGxYWxsWLF5VSX7Kysnj58iVPnjzh5s2budb5rkRw\nW0AZGZmcOH+V7QdPEvXkea7lPMo707FFXb5wcVTqrZPJZNSt5o6bqyP/27CPC1fuKG13/U4EPwYs\nxdenCS0aVC1wT19cwguOn7/K0bNX8gy6X6a+4tHjWJVb3K/LCYBN5IaYmxgjy0wj6ZUMBxsLzEyM\nij3hPuFFMuEPorj78HF2QPswmoTE5Hxt62BjTqvGNahX3R2ddxwlbGZixJct69GhRR3Crt8l5MQl\nLvxzm6ws9TkPL5JfcunaXWlGBpkM7KzMcC1lj6uzLS7OdjjYmEtfEAqFgi37jrN5r2oeVu0q5RnU\no+07t10mk9G0XhXKONny27KtPHkWL61LTklj9pJg2njV5Ku2jaTUDEEQPk83btyQevCysrKQyWQc\nPnxYZQYFPb3sH+OVKlXi8uXLHD58mNDQUAYOHEiFChXYvn272gA3v97cn0wme+sYhlKlSrF582Zp\ntgRd3X/vSmZlZeHh4cHy5ctVtns9BaCgA+nyW6+64ynoGBtzc3OMjY25eVP9+J2c5WXK5J2Wlh8a\nGhoq5zsjQ/30pQX5/s/KymLMmDG0b99eZd3rs1oUZkwhgtsCCH8QxW/LtxETm5BrmWqernRoXhcX\nZ9s86zIxNmR0v84cO/cPK4MPKPUupr3KYMXmA5y9dIMB3dtgaSbPs67UtFecDbvJsXP/cOXGvTzz\nSQvizQA4OTmZw2ezn4Clr6eDg40FDrYWSv8aGxbOaNs3JaekEv4wmnv/H8TeeRD11ocWvEkmgyoV\nXGjZqDoVyjoX2oWkoaFBZXcXKru7EJfw4v97cy/l+T4BUCiQzu9fp7JnMtDT1cbFyRbXUnY8jnnO\nqYuqTxzr6F2XLq0bFEr7SzlYE/BjHxat3a2SLrM75AyRj58xrE8H9HR1cqlBEAQovBzYonbt2jVC\nQkIYNWoUAJ6enigUCp48eSL11KpjaGiIj48PPj4++Pr60rRpU+7evYuLi4tSubJly5KVlcWlS5eo\nVq0aAJGRkURH557GVxA6OjqULl1a7bqKFSsSHByMqakpcnne36NvtllXV5fQ0FC1QeO71quu7ZmZ\nmXmW0dDQ4Msvv2TDhg2MGzdOqTczKyuL+fPn88UXX+Dh4YFMJsPW1pbQ0FAaN26sUpenpyePHz/m\n5s2bantvzc3NSUlJITExESMjIwCuXLmiUi4rK4sLFy5IqSgRERFER0fn2iNcsWJFbt26levrlPMe\nUVfnuxLBbQFYmpvwIkk18Vomg9qVv6B9i7oFevKTTCajQQ0PKpR1Zsn6vfx9NVxp/dXbDxn98//o\n3t6LpvUqKwUzWVlZ/HPrAUfPXuFc2E1S09Lz3JeerjZOdlYkvEgmNj5R5RZ3Qb1MfcWte5Hcuhep\ntNzYsIQU7Npbm+Nga4m9tTklS+iRkZHJy7RXpLxM5WVqGskpadn/vkwj5WUqqWmvSH6ZysvUV6Sk\npJKSmkbKyzQSXiQr9SwWVMkSujSo4YF3w2qFku6RFxNjQzq0qEv75nW4evsBh4+dJfFlBnfuR/Ey\n9dVbt09NS+efWw/459YDlXVaWhr0921NgxoehdrmkiX0GPHtl+w9co612w+Tmflvz8LfV8OZFriO\nMQO7YliyeKd2EQTh/aSlpfHkyROysrJ49uwZoaGh/Prrr1SqVIkhQ4YA4OLiQpcuXRg0aBDTp0+n\nYsWKxMXFcfz4cZycnGjXrh3z58+XZj7Q1tZm8+bNGBkZYWur2qnj6uqKl5cXw4cP59dff0VXV5dJ\nkyahr6//we/+de7cmcDAQHx9fRk/fjz29vZERkayd+9e+vTpk2tvp6GhIQMGDGDy5Mno6OhQt25d\naUBZ375937neNzk6OnLo0CFu376NqakpRkZGaucbnjhxIqGhobRv3x5/f388PDyIjo7ml19+4e7d\nu+zcuVM6lyNHjmT8+PFYWFjQokULUlJSCA0NZciQITRs2JBq1arxzTff8PPPP1OmTBnu3btHcnIy\nbdq0oVq1apQsWZIpU6YwaNAgrly5wtKlS1Xao6Wlxbhx45gxYwZ6enqMHz+e8uXLq01JAPjxxx/p\n2rUrDg4OdOjQAS0tLa5fv86FCxeYMmUKrq6uNG3alOHDhzN37lz09PSYMGHCe00nJoLbAjAsqU/z\nBlXZefA0kD0IqV41d9o3r/NeMx2Yyg0ZM6ALoWcu88eWg6S8/DcISk1LZ+nGPzkTdoP+X7UiJfVV\nrnm0b5LJwLN8aerXqEB1z7JS75tCoSAxKYXn8S+IjUvkecILnj3P/vd5XCKx8S/eOQBOeJFCwgvV\n4ExHW4tXRfB0Nh1tLUo5WFPa0ZrSjja4ONm+10CudyWTyahQ1hldWTqurq5kZWUR+SSWO/ezfxDc\nuR9FRHRMvnvZDUvqM/K7L/nCxfGDtbd14xqUdbbjt+VblXrFb9+Pwu/XVYwf3A1zU+M8ahEE4WN2\n5MgRypUrh6amJsbGxnzxxReMHTuWXr16oaPz792ZoKAg5syZw6RJk4iKisLExIQqVapQv359IDv4\n+/3337l79y4ymQwPDw82b94szRLwpgULFvDDDz/Qpk0bLCwsGDt2LPfv35fSHD6UEiVKsHfvXvz9\n/enVqxeJiYlYW1tTv379t/a4+vn5IZfLmT17NsOHD8fS0pJu3bq9d72v69mzJ8ePH6dx48YkJSWx\na9cu6Ry/zsTEhEOHDjF79mzGjBlDdHQ0RkZGNGjQgJCQEKUe0759+6KtrU1QUBD+/v6YmJjQrFkz\nILsXePPmzUyaNIl+/fqRlJSEs7MzY8eOlfazZMkSJk2axJo1a6hTpw4TJkygf//+Su3R1dVl5MiR\nDBgwgEePHlGtWjXWrFmT6/esl5cXmzZtYvbs2cyfPx8tLS3KlCmDr6+vVCbnPdKuXTvMzMwYM2bM\ne+Uny+Lj4z/cpJwfqdu3b+Pq6vpO28YnJjNi2iLqVnWnXdNaWLwlZaCgnj1PYNG6PVy5cV9lnaam\nhlKvWm6c7C2pX70C9aq5Y2L89kFS6uQEwHEJL4iNe8GTZ3H8ffk6qRkQER2Tr17ID01LSwNHW0vK\nONpQxsmG0o622FubfVRPfsvrvZbyMo3wh1HcuR/1/wFvJIlJL1XK2VmZ8eOAzh+81zlHfGISAQs2\ncv+R8uwSpnJDxg3qmu+ZGd7H+1yj/1XinBWNhIQEpUFCQsHFxsZSvnx5li5dio+PT3E3RyiAtWvX\n8uOPPxIZGfn2wh9YXtei6LktILlRSRZMHfLBchDNTY2ZMPgrQk78zeptIUrpBnkFtibGBtSr7k79\n6h4FSo3IjUwmw9iwJMaGJXG2twbA1d4EV1dXFAoFsXGJRETH8DAqhojoGCKiYoh88uy90x1ybw84\n2FhQ+v8D2TKONjjaWn7Sc7KW0NfFo1wpPMqVArJ/UDx5Fsft+1HcvhdJ5ONn2NuY07l1AwxKFF1K\ngNzIAL+h3fnlf8FKPfDP41/gP3c1P/bvQvky+Z93UhCE/7bQ0FCSkpJwd3cnJiaGqVOnYmZmRtOm\nTYu7acJn6tONDIrRhx5ckzOSveIXpVm0do/a/MvsdmhTo1J56levQIWyTu81SrWg7TM3Ncbc1JjK\n7v8OHsjKyuJxTBwR0TE8in5GRPRTIqJiiHr6nKwsBTJZdkCnr6dLCX1dSujpUkJfjxJ6Ov+/TC97\neU4ZPV309XQooa+HtYXJZz+oSSaTYW1hirWFKfUL+QEQBVVCX5exA7syf9VOTv/976C25JQ0ps9f\nz9De7cVUYYIg5EtGRgbTp0/n/v376OvrU61aNfbu3Vtoj/sVhDeJtISPnEKh4ODxi6zdfpjUtPRc\n82iLwruet4yMTNIzMtDT1Sn26cOK2qf0XlMnKyuLlcEH2X/0gtJymQz6fdWKJnXefZLtvHzq5604\niHNWNERagiB8HERawidMJpPRvH5Valdx4+7DaBxtLd45j7a4aGlpirlSP1EaGhr07twcuVFJNu4+\nKi1XKGDxur3EJybRoUXd/9yPFkEQBOHjVTT3sYX3ZlhSn4pflP7kAlvh0yeTyejoXY/+vq14M4bd\nuPsoKzYfKPDE5IIgCILwoYjgVhCEfGlSpxKjvuuEzhuD+PYfvcDvK7eTXgRTvQmCIAjC24jgVhCE\nfKvmWZYJ339FyRK6SstPXbzBjIUbSXmZlsuWgiAIglA0RHArCEKBlC/jgP+wHpgYGygt/+fWAybP\nW0N8Yt4PFxEEQRCED0kEt4IgFJijrSVTR3yDrZXygyXuP3rCxF/+4HHM82JqmSAIgvBfJ4JbQRDe\niYWZnMnDv8HVWfl58k9jE5j06ypu3yv+J9gIgvDhZWVlMWzYMEqVKoVcLufYsWNqlwmfDrlczo4d\nO96rjsDAQDw8PAqpRQUjgltBEN6ZkUEJfhriS2X3MkrLE16k8NMvfzB3+VYePX7354MLglA4nj17\nxsiRI/Hw8MDS0hJXV1fatWvHX3/9JZXx8PAgMDCwwHUfOHCAtWvXsmHDBm7evEnNmjXVLnvTgwcP\nkMvl0n9OTk60bNmS48ePv9exFpVjx44hl8uJjY3NV/nNmzfTrFkz7OzssLW1xcvLi40bN37gVr6b\nmzdv4u3tXdzNeGciuBUE4b3o6eow6rtONKyp+gv91MUbjJq+hMCV24l6kr8vAEEQCl+PHj24cOEC\n8+fP5/z582zcuJFmzZrx/Pn7pxDdvXsXKysratasiZWVFTo6OmqX5WbLli3cvHmTPXv2YGRkRJcu\nXbh///47tyc9Pf3thYqYn58fgwcPplWrVhw5coSjR4/Stm1bhgwZgr+/f3E3T4WVlRW6urpvL/iR\nEsGtIAjvTUtLk4Hd29CuWS2VdQoFHD9/jRHTFhO0epfIxxWEIhYfH8+pU6fw9/enYcOGODo6UqVK\nFYYMGcKXX34JQOvWrYmIiGDixIlSTyrA8+fP6du3L25ublhbW1OrVi3WrFkj1T1w4EDGjx/Po0eP\nkMvleHh4qF2WF1NTU6ysrKhQoQJz584lJSWFI0eOANlP6Zw3bx6VKlXC2tqaOnXqKPV25vT+BgcH\n07ZtW6ytrVmxYgUA586do23bttja2uLo6Ejbtm2Jjo4uUL07duygffv22NjYULNmTamn+8GDB7Rt\n2xaAMmXKIJfLGThwoNrju3DhAvPmzcPf35/hw4fj6uqKi4sLw4YNw9/fn7lz53Lhwr9Pgbx16xbd\nunXD0dEROzs7mjVrxtWrV6X169ato06dOlIP/IABA6R16tIJ3uyRl8vlLFmyhC5dumBjY0OFChVU\nepDfrCcqKoo+ffrg5OSEk5MTXbp0ITw8XGmbefPmUbZsWezs7Ojfvz9JScU3uLhYn1B24sQJAgMD\nCQsLIzo6mqCgIL7++mtpfVJSEpMnT2bPnj08f/4ce3t7evfuzeDBg6UyaWlp/PTTT2zZsoXU1FQa\nNGjAL7/8gp2dXXEckiD8Z8lkMr72aYKbiyMbdoVy/9ETpfUKBRw9c4Xj5/6hYU1POnrXxdJMXkyt\nFYTCVSLixyLdX4rDrHyXNTAwwMDAgL1791KrVi309PRUyqxZs4Z69erx9ddf07dvX2l5amoqFStW\nZOjQoRgZGXHkyBGGDx+Og4MDDRs2ZMaMGTg4OLB27VoOHz6MpqYm2traKsvyK6dtaWnZ0wpOmzaN\nHTt2MGfOHFxcXDh37hxDhw5FLpfTokULabvJkyczbdo0AgMD0dbW5sqVK7Rt25auXbsyffp09PT0\nOHXqFBkZGQWqd9q0aUyZMoVffvmF2bNn06dPH65cuYK9vT2rVq3im2++4fTp05iYmKg9rwCbNm3C\nwMCAb7/9VmVd3759mT59OsHBwVStWpXo6Gi8vb2pWbMm27Ztw8TEhIsXL5KZmQnAihUrGDt2LBMn\nTsTb25vk5GSOHj2qUu/bBAQEMGnSJH7++We2b9/OgAEDKFu2LJUrV1Ypm5KSQtu2balRowZ79uxB\nR0eHwMBAfHx8OHv2LCVKlGDbtm1MmzaNWbNmUb9+fbZv3868efOkH0lFrViD2+TkZNzc3Pjqq6+U\nfnnkmDBhAkeOHGHRokU4OTlx8uRJhg4dipmZGd26dQNg3Lhx7N27l2XLlmFiYsKECRPo2rUroaGh\nBbqgBEEoHJXdXajkVoZzl2+xee9RHkbGKK3PylLw16kwjp69QuPaFenQvA7mpuqfDy4IwvvT0tIi\nKCiIoUOH8scff+Dp6UnNmjVp37491apVA8DExAQNDQ0MDQ2xsrKStrW1teWHH36Q/u7VqxdHjx4l\nODiYhg0bYmxsjKGhIRoaGkrbqVv2NsnJyUyZMgVNTU3q1atHcnIyQUFBbN26lTp16gDg7OzMhQsX\nWLp0qVIQ2q9fP3x8fKS//f398fDwYN68edKysmXLSvvJb72DBg2iZcuWAEyaNIkNGzZw5coVateu\njYmJCQAWFhaYmZnlelzh4eE4OTmpTc3Q1dXF2dmZO3fuALB06VJKlCjBH3/8IZUvXbq0VH727NkM\nHDiQ77//XlpWsWLFPM+rOm3btqV3794AjBo1imPHjrFw4UKWLFmiUnbLli0oFAoWLFggPWp97ty5\nuLi4sH//fjp06MDChQv56quvVOq8e/dugdtWGIo1uG3evDnNmzcHst9Abzp79ixdu3alQYMGADg5\nObF69WouXLhAt27dSEhIYPXq1QQFBdG4cWMAFi9ejIeHB0eOHMHLy6voDkYQBIlMJqNGxXJU9yzL\n6b9vELz3KI8eK+fcZmZmcej43xw5HUaT2pVo37wOZiZGxdRiQfi8+fj40KJFC06dOsXZs2cJCQlh\n/vz5TJw4kZEjR+a6XWZmJr/99htbt24lOjqaV69e8erVK+rVq1dobWvVqhUaGhqkpKRgbW3NggUL\ncHd35+LFi6SmptKpUycpqILsnFpHR0elOt7scbx8+TJt2rRRu7+bN2/mu153d3fp/21sbACIiVH+\nwV6YLl++TO3atdUGwjExMURFRdGwYcP33k/16tVV/j5w4IDasmFhYTx48AB7e3ul5SkpKdy7dw/I\nPqc9evRQqfM/Gdy+Ta1atfjzzz/55ptvsLe358yZM/zzzz/Sr8hLly6Rnp5OkyZNpG3s7e0pV64c\nZ86cEcGtIBQzmUxG7SpfULNSOU5dvE7wvmNEPVHOuc3IyOLAsYv8dTqMpnUr49OsdjG1VhA+b3p6\nejRu3JjGjRszZswYhgwZwowZMxgyZEiuA74CAwOZP38+M2bMwM3NDQMDA6ZMmVKoAd7//vc/3N3d\nMTY2xtT037mzs7KyAFi/fj0ODg5K22hpKYcvJUuWzPf+ClKvtra29P85gbBCocj3viA7J/fUqVOk\npaWpDNJKS0vj/v371K9fv0B15kYmk6m0LycV411lZWXh4eHB8uXLVdbl9F5/bD7q4HbmzJkMGzaM\nChUqSG+4WbNmSdNTPH36FE1NTZXbARYWFjx9+jTPum/fvv1hGv2ZE+et4MQ5y2ZprEP/zo35+9o9\n9h+/xLO4FyplgveEsmP/cepWKUfz1Ffo6+U+wlpQ9V99r7m6uhZ3EwqUA/uxKFeuHBkZGaSmpqKj\no4OOjo6U25nj1KlTeHt7S6mACoWCO3fuYGxceKlEdnZ2lCpVSm37dHV1iYiIKHBvpaenZ665qO9T\n7+tyfhC8ec7e1KlTJxYvXsyyZctU7lIvXbqU5ORkOnfuLLV748aNvHr1SuUHh4WFBba2toSGhkp3\nq99kbm7O48ePpb+fPn2q9HeO8+fPK/W0nj9/nnLlyqmts2LFigQHB2NqapprDm25cuXU1llcPurg\ndvHixZw9e1b6dXXy5EkmTpyIo6MjTZs2fa+6P4YPw0/N7du3xXkrIHHOVJUvX44uPs04du4fgvcd\nJyY2QaXMkbPXuBv1nAFft8GzvOqXnqBKvNeE3Dx//pyePXvSvXt33N3dMTAw4NKlS/z+++80bNgQ\nI6PsdCBHR0dOnTpFly5d0NXVxczMDBcXF7Zt28apU6cwMzNjyZIlPHz4sEgm5zc0NGTIkCFMnDgR\nhUJB3bp1SUpK4vz582hoaNCrV69ctx0yZAjNmjVj6NChfPvtt9KAssaNG+Pg4PDO9b7OwcEBmUzG\n/v37admyJXp6ehgYGKiUq169Ot9//z3+/v68evWKNm3aIJPJ2L17N9OnT2fYsGFUrVoVyB5gtnz5\ncnr16sWoUaOQy+VcvHiRsmXL4unpyciRIxk/fjwWFha0aNGClJQUQkNDGTJkCAANGjRg6dKl1KxZ\nEw0NDaZOnap2oNuuXbuoUqUK9erVY8eOHYSGhhISEqL2ODt37kxgYCC+vr6MHz8ee3t7IiMj2bt3\nL7dDhW0AACAASURBVH369KFMmTIMGDCAAQMGKNV54cKF/+aAsry8fPmSKVOmsHLlSimZu0KFCly5\ncoXAwECaNm2KpaUlmZmZxMbGYm5uLm0bExND7dri1qYgfKw0NTVpVKsi9apVIPTMZbbuP8Gz54lK\nZWLjXjB9/nqa1qtM9/ZN0Nf7dOdcFITiVLJkSapXr86iRYu4e/cur169wsbGhk6dOjF69Gip3Pjx\n4xk2bBiVK1cmLS2N+Ph4Ro8ezYMHD+jcuTN6enr4+vrSuXNnbty4USRtnzBhAhYWFsyfP5+RI0di\naGiIh4cHQ4cOzXM7T09Ptm/fzpQpU2jWrBk6OjpUrlxZGufzrvW+ztbWlnHjxjFt2jR++OEHunXr\nxsKFC9WWnTZtGhUqVGDp0qXMnDkTADc3N37//XepVzynzr179zJp0iTatm2LTCbDzc2NuXPnAtnB\nr7a2NkFBQfj7+2NiYkKzZs2U9jNkyBDatGmDhYUFkydP5ubNmyrtGTt2LDt37mTMmDGYm5sTFBRE\nlSpV1La9RIkS7N27F39/f3r16kViYiLW1tbUr19fCl47duzI/fv3mTp1Ki9fvqRly5YMGjSIdevW\n5ft8FiZZfHx8wZJHPhA7OztmzZolTQWWmJiIo6MjGzZsUHpKxrBhwwgPD2fXrl0kJCTg4uLCggUL\npC79yMhIKlSoQHBwcK45t6KH492I81Zw4pzlT3p6Bn+dDmPb/pM8j39BcnKyUg6dhZkxA75uTYWy\nzsXXyI+ceK8VjYSEhEK9JS8IRU0ul/PHH38ozS7xKcrrWsy159bT01NpFGF+yGQyLl26lO/ySUlJ\n0ki6rKwsHj16xOXLlzExMcHBwYG6desyefJkSv5fe3ceF1XV/wH8c2dj2EFWFRRURHHNJXdS7HHN\n3HNLy1JLs0xzzZ4wnxItM9OHzNTyyTT75ZaYWT6PS4pblqi5IuSGCggMO8x2f38MTo4szsgMA+Pn\n/XrNi7nnHO793uMFv9w59xxXVwQHByM+Ph6bN2/Ge++9BwDw9PTE2LFjER0dDT8/P+NUYM2aNUP3\n7t0tip2I7Ecul6FXt7bo3rElvt99CJt/MP14LD0jG/9asQm9I9ti9MAeUDpxLC4REZWt3OS2S5cu\npZLbhIQEXLhwAU2bNkXDhoa15JOSkoxlrVu3tujgp06dMq7wARgmFY6JicGoUaOwatUqfPnll3jv\nvfcwadIkZGVlITg4GPPnz8ekSZNMvkcqlWL8+PHGRRw+//xzznFLVAMp5HKMGRgFfw8n7D58ttTM\nCj//+jsSzifh1TH9ERFW305REhFRdWb2sIRdu3Zh6tSp2LBhQ6kpKw4ePIgXXngBsbGx6N+/v00C\ntSZ+fPdo2G+WY589msTERNQPCcHmuIPYvf8EHpx5RxCAvk+1x8hnu8NJIS97J48ZXmtVg8MSiKqH\nin4WJebuZNGiRZg0aVKZc7E99dRTmDhxIj744INHj5KI6D4KuRzjhjyN6GnPI9DPdC5FUQR2H/gN\ncxavxaXkm3aKkIiIqiOzk9vk5OQKp3Tw8vIyrlRBRGQtTRvVw5K5L6Nv93al6m6nZSH6k6+xYfv/\noNZo7BAdERFVN2Ynt6Ghodi4cSPy8vJK1eXm5mLjxo0ICQmxZmxERAAApZMCLw7rhXffGAM/H9OP\noUQR2PW/45izeB0S/0qxU4T0OLF0hSoisq6H/QyaPc/t/Pnz8cILL6B9+/YYOXKkcTWR5ORkfPfd\nd0hPT8f69esrFSwRUUWaNa6PpW9PxMYd+/DLoT9M6m6lZuKfy/6DZ5/uhGH9ukIh51hcsj5XV1eo\nVCp4eXlZPKMQEVWeKIpQqVRwd3cvt43Zye0zzzyD77//HtHR0cbJhO9p0aIF/v3vf5c7rywRkbUo\nnRR4eUQftG8VjtWbfjRZ/EEUgR/2HsWVqymYP3UUZ00hq5PJZHB3d0dOTs7DGxORTbi7u0MmKz+F\ntWiFsqioKERFRSE1NRU3btwAYFh+LiAgoHJREhFZqGWTUHw0byI2bP8v9h05bVJ3LvE6Dv32J7p3\nbGWn6MiRyWQyzphAVI090vK7AQEBTGiJyO5cnJ3wyuj+6NC6CVZv2o1MVa6x7vDJc0xuiYgeQ2Y/\nUAYAV65cwaRJk9C0aVP4+fnh4MGDAICMjAy89tprOHnypE2CJCKqSOuIhpg/dZRJ2Z+XrkKVU/oB\nWCIicmxmJ7dnz55FVFQU9u/fj/bt20On0xnrfHx8cOHCBaxbt84mQRIRPUxQoC/qB/kbt0UROPrH\nBTtGRERE9mB2cvvee+8hICAAJ0+exCeffFJqGoaePXvi+PHjVg+QiMhcXdo2M9mOP3nOTpEQEZG9\nmJ3cHjt2DC+88AI8PT3LnP4kODgYd+7csWpwRESW6NI2wmQ78eotpGWo7BQNERHZg0Vjbp2cnMqt\nS0tLq7CeiMjWfGt5IrxBkEnZkd/P2ykaIiKyB7OT21atWuHnn38us06j0WDr1q1o37691QIjInoU\nXdqZ3r2N/51DE4iIHidmJ7dvvfUW9u3bhzfeeANnz54FANy5cwf//e9/8eyzz+LKlSuYMWOGzQIl\nIjJHxyeaQiL5e+jU9ZR0XL+VZseIiIioKpmd3EZFRWH16tWIi4vD4MGDAQCTJ0/G8OHDceHCBXzx\nxRfo2LGjzQIlIjKHp7srmoeHmJRxaAIR0ePDokUchg8fjv79+2P//v1ISkqCXq9HaGgooqKiKlzj\nl4ioKnVt2wxnLvxl3D7y+3mMeOapMh+GJSIix2LxCmUuLi7o37+/LWIhIrKKdi0bQy6XQqMxzMed\neleFK1dvISy0rp0jIyIiW7NotgQioprA1UWJNs0amZRxaAIR0eOh3Du33t7ekEgkuH37NhQKBby9\nvR/6kZ4gCMjIyLB6kERElurcNgLHEy4Zt4/8cR5jh/SERMK/6YmIHFm5ye3s2bMhCAJkMpnJNhFR\nTdCmeSM4KxUoLFIDAFQ5+Th/5TqaNw6xb2BERGRT5Sa38+bNq3CbiKg6U8jlaNeyMQ6d+NNYdvi3\nc0xuiYgcHD+fIyKH1bVdM5PtE6cvQqPR2ikaIiKqChYltyqVCh988AEiIyMREhKCkJAQREZG4oMP\nPoBKxfXbiah6ad44BO6uzsbt/IJinL6QbMeIiIjI1sxObpOTk9G1a1csXboUWq0W3bp1Q7du3aDV\narF06VJ06dIFSUlJtoyViMgiMpkUHZ9oYlLGWROIiByb2fPczpo1Czk5Ofjhhx8QGRlpUnfw4EGM\nHTsWc+bMwZYtW6weJBHRo+rSrhn2Hj5l3D559jKKitVQOinsGBUREdmK2Xdujx49ildffbVUYgsA\nTz31FF555RUcOXLEqsEREVVWk4bB8PH+ewXFYrUWv59NtGNERERkS2Ynt56envDy8iq33svLC56e\nnlYJiojIWgRBQKc2ESZl8b+fs1M0RERka2Ynt2PHjsU333yD3NzcUnXZ2dn45ptvMG7cOKsGR0Rk\nDQ/OmnD6QjJy8wvtFA0REdmS2WNuw8LCIAgC2rVrh1GjRqFBgwYAgKSkJGzevBl+fn4ICwvD9u3b\nTb5v8ODB1o2YiMhCIUEBqBNQC7dSMwEAWq0eJxIuomeXJ+wcGRERWZvZye2kSZOM7z/99NNS9Wlp\naZg0aRJEUTSWCYJQYXIbHx+PlStX4vTp07h9+zZiY2MxZswYkzZXrlzBggUL8Ouvv0Kj0SAsLAxr\n1qxBeHg4AKC4uBjvvPMOtm7diqKiIkRGRuLjjz9G3bp1zT01InJwgiCgc5sIbPnpsLHsyO/nmdwS\nETkgs5PbuLg4qx88Pz8fERERGDVqFF599dVS9VevXkXv3r0xcuRI7Ny5E15eXrh8+TJcXV2NbebN\nm4fdu3dj3bp18Pb2xvz58zFixAgcPHgQUqnU6jETUc3UpV0zk+T2XOI1ZGXnwtvTvYLvIiKimsbs\n5LZr165WP3ivXr3Qq1cvAMCUKVNK1b///vuIiorCBx98YCwLCQkxvs/OzsaGDRsQGxuLHj16AABW\nr16NFi1a4MCBA+jZs6fVYyaimqlOgA9CggJw9WYqAEAUgSN/XED/Hk/aOTIiIrKmarv8rl6vx549\nexAeHo6hQ4eiYcOG6NGjB7Zt22Zsk5CQAI1Gg6ioKGNZUFAQwsPDcfz4cXuETUTVWNf2pg+WHeGs\nCUREDsfsO7cDBgx4aBtBELBz585KBXRPeno68vLysGzZMrz99tuIjo7Gr7/+iokTJ8LV1RW9e/dG\nWloapFIpfHx8TL7Xz88PaWlpFe4/MZHzXD4K9pvl2GePxhb9FuilRH5+vnH79LkrOHrid/h6e1j9\nWPbwuF5rYWFh9g6BiKoRs5NbvV4PQRBMynQ6HW7cuIGUlBQ0aNAAtWvXtlpger0eANCvXz9MnToV\nANCyZUskJCRgzZo16N27d6X2z1+GlktMTGS/WYh99mhs2W/tWp3FhSs3jNu3MwvR6cm2NjlWVeK1\nRkRkYHZy++OPP5Zbt2fPHrz55psmY2Mry8fHBzKZzDgrwj2NGzc2Dk3w9/eHTqdDRkYGfH19jW3S\n09PRqVMnq8VCRI6jc9sIk+Q2/uQ5DO7dpdQf70REVDNZZcxtnz598Nxzz2HevHnW2B0AQKFQoE2b\nNqU+Zrty5QqCg4MBAK1bt4ZcLsf+/fuN9SkpKbh06RI6dOhgtViIyHF0aN0EEsnfiezNOxm4fivd\njhEREZE1mX3n9mFCQ0OxZs0ai74nLy8PycnJAAzDEG7evIkzZ87A29sbwcHBeOONNzB+/Hh07twZ\nkZGROHToELZt24aNGzcCMCwJPHbsWERHR8PPz884FVizZs3QvXt3a50aETkQT3dXtGwSioTzycay\n+JPnUL+uvx2jIiIia7HKnVutVovt27eXerDrYU6dOoXIyEhERkaisLAQMTExiIyMxKJFiwAAzzzz\nDJYvX46VK1eic+fOWL16NT7//HOT8bYxMTHo378/xo8fjz59+sDV1RWbN2/mHLdEVK7ObR+YNeGP\n8yYL0BARUc1l9p3b1157rczy7OxsnDx5EqmpqRaPue3WrRtUKlWFbcaMGVNq1bL7OTk54aOPPsJH\nH31k0bGJ6PHVvmVjKOQyqDVaAEB6RjYSr6agcWiQnSMjIqLKMju5/fXXX0s9cCEIAry8vNCxY0eM\nGzfOZL5ZIqLqysXZCW2aN8KxUxeNZUd+P8/klojIAZid3J49e9aWcRARVanObSNKJbdjB/fkkCYi\nohqu2q5QRkRkS080awgXZ4VxOzu3AOcSr9sxIiIisgazk9uvv/4aY8eOLbd+3Lhx2LRpk1WCIiKy\nNYVcjvYtTefRjj/J5XiJiGo6s5PbL7/8EgEBAeXWBwYGYu3atVYJioioKnRuG2Gy/duZS9CUPGRG\nREQ1k9nJbVJSEpo1a1ZufdOmTXHlyhWrBEVEVBVahIfA093FuJ1fUIyE80l2jIiIiCrL7ORWEARk\nZmaWW5+ZmQm9Xm+VoIiIqoJUKkWH1k1MyuJ/59AEIqKazOzktlWrVti6dSuKi4tL1RUVFWHLli1o\n2bKlVYMjIrK1ru2bm2z/fvYKCotK/54jIqKawezkdsaMGbh48SL69euHuLg4XLlyBVeuXMHOnTvR\nr18/XL58GTNmzLBlrEREVtc4tC58a3kYt9UaLX47c9mOERERUWWYPc9tjx498Nlnn2H27Nl44YUX\njOWiKMLd3R0rV67E008/bZMgiYhsRRAEdG4bgZ17jxnL4v53DE80awR3V2c7RkZERI/C7OQWAEaO\nHIn+/ftj3759uHr1KgAgJCQEUVFRcHd3t0V8REQ21+WB5PZ6SjoWfvoN5k8dBS8PNztGRkRElrIo\nuQUAd3d3DBw40BaxEBHZRf26AWjTvBH++PPvGV+u30pH9Ccb8M/XR8O3lqcdoyMiIkuYPeZ29+7d\nmDVrVrn1s2bNwp49e6wSFBFRVRIEAW+8OBDNwuqZlN9Jz0L08g24nVb+TDFERFS9mJ3crly5EgUF\nBeXWFxUV4dNPP7VKUEREVc1Z6YS5U0bgiWYNTcrvZuYgevkGXEtJs1NkRERkCbOT2/Pnz6N169bl\n1rdq1QoXL160SlBERPagkMsxc+IwdGpjOvdtdk4+/rViI5Ku3bJTZEREZC6zk1utVouioqJy6wsL\nC8ucA5eIqCaRyaR4/YWBeKpDC5Py3PxC/GvlJly4ct1OkRERkTnMTm4jIiKwa9cuiKJYqk6v1yMu\nLg5NmjQp4zuJiGoWqVSKyc8/gz5PtTUpLyxSY1HsZi7RS0RUjZmd3L766qs4ceIExo4di9OnT6O4\nuBjFxcVISEjA888/j5MnT+KVV16xZaxERFVGEAS8OKwXBvXqZFKu1mjx0Rff48TpS3aKjIiIKmL2\nVGBDhw5FcnIyFi9ejN27d5vUCYKAOXPmYMSIEVYPkIjIXgRBwKhne8BZ6YRvdx4wlmu1enyybhsm\nP/8MIp9sUf4OiIioylk0z+2sWbMwfPhwxMXFmSziMGDAAISEhNggPCIi+xvUqzOcFHKs37LXWKbX\ni/hsQxzUag2e7trGjtEREdH9LF7EISQkBK+//rotYiEiqrb6dm8PpZMcqzftxr1HD0QRWLN5DwqL\n1RjQs6N9AyQiIgAWJLfFxcU4duwYLl++jNzcXLi5uaFJkybo0KEDnJycbBkjEVG10KNTayidnLDy\nPz9Ap9Mby7/Zvg9FRWoM69cNgiDYMUIiInpociuKIlauXInly5dDpVKZzJYgCAK8vLzw5ptv4vXX\nX+cvdSJyeJ3aNIWTQoZl67ZBo9EZy7f8dBiFxWqMHdyTvwuJiOzoocntxIkTsXXrVjRs2BCTJk1C\n8+bN4e7ujtzcXPz555/YsmULFixYgLNnz2LNmjVVETMRkV21aR6GuZNH4KPV36OoWGMs/3HfCRQV\nq/Hyc70hlUrtGCER0eOrwuR28+bN2Lp1K15//XVER0eX+mX9zDPPYPbs2Vi4cCFWrFiBp59+mjMm\nENFjoXnjEMyfOgqLV32H/IK/F7D5X3wCMrJyMG38YLg4c8gWEVFVq3Ce26+//hpdunTBwoULy70L\nIZFIsGDBAnTu3Bnr16+3RYxERNVS49AgvPvGGHi6u5iUJ5xPRvQnXyM9Q2WnyIiIHl8VJrd//vkn\nBgwYYNaOBgwYgHPnzlklKCKimiIkKBDR056Hj7e7Sfn1W+mYv3Q9Ev9KsVNkRESPpwqTW61WC2dn\nZ7N2pFQqodVqrRIUEVFNUjfQFx/MfBEN69c2Kc/OLcDCFRtx9I8LdoqMiOjxU2FyGxISgiNHjpi1\noyNHjnAhByJ6bHl7uiN62vPo0DrcpFyt0WL5l9ux/ed4k9lmiIjINipMbgcNGoQtW7bg559/rnAn\nv/zyC7Zu3YpBgwZZNTgioprESSHH9JeHYFCvTqXqNscdxGff7IJGw0+4iIhsqcLkdurUqQgLC8OY\nMWMwbdo0HDlyBDk5ORBFETk5OTh69CimTZuG0aNHIywsDFOnTrXo4PHx8Rg5ciSaNm0KLy8vbNy4\nsdy2b775Jry8vLBy5UqT8uLiYsyaNQsNGjRAnTp1MHLkSKSkcIwbEdmHIAgY9WwPTH6+P2Qy01+x\nvx4/iw9iv0VOXoGdoiMicnwVJrcuLi7YuXMnnnrqKXz99dd45plnEBISAh8fH4SEhKB///74+uuv\n0a1bN+zYsQMuLi4V7a6U/Px8REREYPHixRWO7f3hhx/w+++/o3bt2qXq5s2bh7i4OKxbtw67d+9G\nbm4uRowYAZ1OV8aeiIiqRveOrTD/tVFwc1GalF+4cgP//Pg/uJWaYafIiIgc20MXcfDz88PWrVtx\n8uRJ7NmzBxcvXkReXh7c3NwQHh6O3r1748knn3ykg/fq1Qu9evUCAEyZMqXMNtevX8fcuXOxY8cO\nDBs2zKQuOzsbGzZsQGxsLHr06AEAWL16NVq0aIEDBw6gZ8+ejxQXEZE1RITVx7/eegEfrv4/3E7L\nMpbfSc/COx+vx4wJQ9G8cYj9AiQickAPTW7vadeuHdq1a2fLWErRarWYMGECZs6cifDw8FL1CQkJ\n0Gg0iIqKMpYFBQUhPDwcx48fZ3JLRHZXJ8AH/3rrRSxbuxXnE68by/MLirEodjMmjOiDqM6t7Rgh\nEZFjMTu5tYeYmBjUqlULL7/8cpn1aWlpkEql8PHxMSn38/NDWlpahftOTEy0WpyPE/ab5dhnj8bR\n+u253u3wvV6DE2eumJQvW/M9Tp09jwE92kEQhEodw9H6zFxhYWH2DoGIqpFqm9weOnQImzZtwqFD\nh2yyf/4ytFxiYiL7zULss0fjqP32z/Bw7Nx7FJt2HjApP3H2KkRBgakvPAulk+KR9u2ofUZEZKkK\nHyizp8OHD+POnTsIDw+Hj48PfHx8cOPGDURHRyMiIgIA4O/vD51Oh4wM0wcz0tPT4e/vb4+wiYjK\nJQgCBvbqjBkThkAhN7238NuZy1iwfAMysnLsFB0RkWOotsnthAkTEB8fj0OHDhlftWvXxpQpU/DD\nDz8AAFq3bg25XI79+/cbvy8lJQWXLl1Chw4d7BU6EVGFOrRuggVvPg8vD1eT8r9upGLO4nU4eeay\nnSIjIqr57DosIS8vD8nJyQAAvV6Pmzdv4syZM/D29kZwcDD8/PxM2stkMgQEBBg/evP09MTYsWMR\nHR0NPz8/eHt7Y/78+WjWrBm6d+9e1adDRGS2hvXrYNGs8Viy+v9w7ebfzwjk5hfioy+2oFe3Nhg7\npCcUcrkdoyQiqnnMvnO7ZMkSnD9/vtz6CxcuYMmSJRYd/NSpU4iMjERkZCQKCwsRExODyMhILFq0\nyOx9xMTEoH///hg/fjz69OkDV1dXbN68GVKp1KJYiIiqmo+3B957cyzaNG9Uqu6XQ39g/kfrceN2\nuh0iIyKquQSVSmXWYufe3t744osvMHz48DLrt23bhgkTJiAzM9OqAdoCH7x4NOw3y7HPHs3j1m+i\nKGLn3qP47sdfodPpTeoUchnGDXkaT3d9osLZFB63PiMiKo/Vxtzm5eVBzo/PiIgsdu9Bs4XTxyHA\n18ukTq3RYu13e7Bs7Vbk5hfaKUIiopqjwjG3f/75J86ePWvcPnr0KLRabal2KpUKX375Je8aEBFV\nQqOQOlg852V8+f3POHTiT5O6E6cvI+n6bUwd9ywiwurbKUIiouqvwuR2165dxnG0giDgq6++wldf\nfVVmWy8vL3zxxRfWj5CI6DHi4uyEqeOeRcsmoVj33R4UFWuMdRlZuVi4YiOG9OmCoX268tkCIqIy\nVJjcvvjii+jTpw9EUURUVBTefvtt/OMf/yjVztXVFaGhoZDJqu2aEERENUrkky3QOLQuVqz/AUnX\nbhvLRRHY+lM8/rx0Da+/8Cz8fLwq2AsR0eOnwmw0MDAQgYGBAIC4uDiEh4eXmp6LiIhsI9CvFhZO\nH4fvfjyInXuPmdRdSr6JOUvWYeLIfujUpqmdIiQiqn7MvtXatWtXW8ZBRERlkMmkGDMwCs0bhyB2\nQxyyc/KNdfkFxVj+5XacuZiMLq1C7RglEVH1YdE4gv/973/YsGEDrl69CpVKBVE0nUVMEAQkJCRY\nNUAiIgJaNW2Aj+ZNwGcb4pBwPtmkbt+R0zjxxzlMGC3A38cTPt4e8HR3rXDqMCIiR2V2crtixQos\nWLAA/v7+aNOmDSIiImwZFxERPcDT3RVzJ4/A7gO/YdMP+6DV/j0nbmpGNpZ/ud24LZNJUMvTHb61\nPFHL0w0+3p7w8XaHj5cHfGt5oJaXB9xdnS1KgEVRhFarg1qjhUarhUajRbFGC61WC1GE8YbHgzc+\nDGUoty40OBAyGR+OIyLrMDu5/fzzzxEZGYnvv/+e89kSEdmJIAjo3+NJNAurh0+/2oFbqWUvnKPV\n6pGWkY20jOxy96WQy1DLyx2+3h5QKhWGpFWjNfn693sN1BotyshNK23N4jfh4eZi/R0T0WPJ7ORW\npVJh4MCBTGyJiKqBkKBAxMx+Ceu37MX+o6cfaR9qjRZ30rNwJz3LytFZpqy7uUREj8rs5LZt27ZI\nTEy0ZSxERGQBpZMCr47pjy7tIrDz518hkTkhU5WLDFUO8guK7R0eEZFdmJ3cLl26FMOHD0fr1q3x\n3HPP2TImIiKyQIvwUCglWpNVIguLipGhykVGVg4yVTm4m5mDDFUu7mZlI0uVi7tZOSYLRJhLKpVA\nIZdBIZdBXvJVJpNCIhhWc79/CO+98bwPjuu9tylAMO6TiMhazE5ux40bB7VajVdffRXTp09H7dq1\nS62OIwgCjh07Vs4eiIioqjgrnRAU6ISgQN8y60VRREFhMe6WJL9qjRZOCjnkMtPE1Ukhh0wmLamT\nclU0Iqr2zE5ufX194efnh0aNGtkyHiIiqgKCIMDVRQlXFyXq1/W3dzhERFZjdnL7448/2jIOIiIi\nIqJK40AnIiIiInIYFiW3mZmZeP/999G7d2+0adMGJ06cMJYvWbIEly5dskmQRERERETmMHtYwrVr\n19C3b19kZmYiIiICV69eRWFhIQCgVq1a2LZtG+7evYuPPvrIZsESEREREVXE7OQ2Ojoaoiji2LFj\ncHd3L/VgWb9+/Tgul4iIiIjsyuxhCQcOHMDEiRMREhJS5lrk9evXx61bt6waHBERERGRJcxObouL\ni+Hl5VVufXZ2NiQSPp9GRERERPZjdjbatGlTxMfHl1v/448/omXLllYJioiIiIjoUZid3E6ePBnb\nt2/H0qVLkZWVBQDQ6/W4fPkyJkyYgJMnT+K1116zWaBERERERA9j9gNlw4cPx82bN7Fo0SIsWrQI\nADB06FAAgEQiwXvvvYe+ffvaJkoiIiIiIjOYndwCwPTp0zF8+HDs3LkTycnJ0Ov1CA0NxYABAxAS\nEmKjEImIiIiIzGNRcgsAQUFBmDJlii1iISIiIiKqFLPH3B47dgzLli0rt/6TTz4xrlhGREREtVbn\n0gAAIABJREFURGQPZt+5XbJkSYVTgf355584fPgwtm7dapXAiIiIiIgsZfad2zNnzuDJJ58st759\n+/Y4ffq0VYIiIiIiInoUZie3BQUFZa5Mdr+8vDyLDh4fH4+RI0eiadOm8PLywsaNG411Go0G0dHR\n6Ny5M+rUqYPw8HBMmDABN27cMNlHcXExZs2ahQYNGqBOnToYOXIkUlJSLIqDiIiIiByD2clto0aN\nsG/fvnLr//vf/6JBgwYWHTw/Px8RERFYvHgxnJ2dTeoKCgpw+vRpzJw5EwcPHsSmTZuQkpKCYcOG\nQavVGtvNmzcPcXFxWLduHXbv3o3c3FyMGDECOp3OoliIiIiIqOYzO7kdN24c9u7di9mzZxsXcQCA\nzMxMzJo1C/v27cPYsWMtOnivXr3w7rvvYuDAgaWW7vX09MSOHTswZMgQhIWFoW3btvjkk09w6dIl\nXLp0CYBhyd8NGzZg4cKF6NGjB1q3bo3Vq1fj3LlzOHDggEWxEBEREVHNZ/YDZRMnTsTZs2exZs0a\nrF27Fv7+/gCAtLQ0iKKI0aNHY/LkyTYLFAByc3MBwPhgW0JCAjQaDaKiooxtgoKCEB4ejuPHj6Nn\nz542jYeIiIiIqheL5rldsWKFcRGHq1evAgBCQkIwcOBAdO3a1RbxGanVarzzzjvo06cP6tatC8CQ\nWEulUvj4+Ji09fPzQ1pamk3jISIiIqLqx6zkVq1W47fffkNgYCC6deuGbt262TouE1qtFpMmTUJ2\ndja+/fZbq+wzMTHRKvt53LDfLMc+ezTsN8s9rn0WFhZm7xCIqBoxK7mVyWQYNGgQFi1ahIYNG9o6\nJhNarRYvv/wyzp8/j127dqFWrVrGOn9/f+h0OmRkZMDX19dYnp6ejk6dOlW4X/4ytFxiYiL7zULs\ns0fDfrMc+4yIyMCsB8okEgnq1atn8VRflaXRaDB+/HicO3cOcXFxCAgIMKlv3bo15HI59u/fbyxL\nSUnBpUuX0KFDhyqNlYiIiIjsz+wxt6+++ir+/e9/4/nnn4efn59VDp6Xl4fk5GQAgF6vx82bN3Hm\nzBl4e3ujdu3aeOGFF3Dq1Cl8++23EAQBqampAAAPDw84OzvD09MTY8eORXR0NPz8/ODt7Y358+ej\nWbNm6N69u1ViJCIiIqKaw+zktqCgAC4uLmjTpg369++PkJCQUnPTCoKAN954w+yDnzp1CgMGDDBu\nx8TEICYmBqNGjcLcuXOxe/duACiVqMbGxmLMmDHG75FKpRg/fjyKiooQGRmJzz//HFKp1Ow4iIiI\niMgxCCqVSjSnobe398N3JgjIzMysdFC2xrFpj4b9Zjn22aNhv1mOfUZEZGD2ndvTp0/bMg4iIiIi\nokozO7mtV6+eLeMgIiIiIqo0ixZxAICkpCQcPnwY6enpGD58OOrXrw+1Wo3U1FQEBARAoVDYIk4i\nIiIioocyO7nV6/WYPn06NmzYAFEUIQgC2rdvb0xuu3TpglmzZuH111+3ZbxEREREROUya55bAPj4\n44/xzTffYP78+di7dy9E8e/n0Nzc3DBgwADs2rXLJkESEREREZnD7OR248aNeP755/HWW2+hQYMG\npeojIiKQlJRk1eCIiIiIiCxhdnJ769YttG3bttx6Z2fnKl/BjIiIiIjofmYnt/7+/rh+/Xq59QkJ\nCQgODrZKUEREREREj8Ls5PbZZ5/Fl19+aTL0QBAEAMDevXuxefNmDBo0yPoREhERERGZyezkdu7c\nuQgKCkJkZCQmTpwIQRCwbNkyPP300xgxYgSaN2+OGTNm2DJWIiIiIqIKmZ3cenh44JdffsGMGTOQ\nlpYGpVKJY8eOIT8/H3PnzsXu3bvh7Oxsy1iJiIiIiCpk0SIOSqUSb731Ft566y1bxUNERERE9Mge\nmtwWFRVh9+7duHbtGmrVqoXevXsjMDCwKmIjIiIiIrJIhcnt7du30a9fP1y7ds24aIOLiws2b96M\nbt26VUmARERERETmqnDM7fvvv4/r169jypQp+O677xATEwOlUok5c+ZUVXxERERERGar8M7tgQMH\nMGrUKLz//vvGMn9/f0yYMAEpKSmoW7euzQMkIiIiIjJXhXduU1NT0aFDB5Oyjh07QhRF3Lx506aB\nERERERFZqsLkVqfTQalUmpTd2y4qKrJdVEREREREj+ChsyVcvXoVv//+u3E7JycHAJCYmAg3N7dS\n7du2bWvF8IiIiIiIzPfQ5DYmJgYxMTGlymfPnm2yLYoiBEFAZmam9aIjIiIiIrJAhcltbGxsVcVB\nRERERFRpFSa3o0ePrqo4iIiIiIgqrcIHyoiIiIiIahImt0RERETkMJjcEhEREZHDYHJLRERERA6D\nyS0REREROQwmt0RERETkMJjcEhEREZHDeOgKZfQAUQeIWkDUQYD2vm1tGdv3tRV1ALQQJS7Qy+tA\nlPkCgmDvsyEiIiJyKHZNbuPj47Fy5UqcPn0at2/fRmxsLMaMGWOsF0URixcvxn/+8x+oVCq0bdsW\nS5cuRdOmTY1tiouL8c4772Dr1q0oKipCZGQkPv74Y9StW9cmMSsyv4es4I9K70cUlNAr6kCvCIJe\nXhd6RV2IMj8mvERERESVYNdhCfn5+YiIiMDixYvh7Oxcqv7TTz9FbGwslixZgn379sHPzw+DBw9G\nbm6usc28efMQFxeHdevWYffu3cjNzcWIESOg0+lsE7Qgtc5uxCJIi5Mhz/0VTpnfwvnOUrik/BNO\naasgz4qDNP8PCJpUQNRb5XhEREREjwO73rnt1asXevXqBQCYMmWKSZ0oili1ahXefPNNDBw4EACw\natUqhIWFYcuWLRg/fjyys7OxYcMGxMbGokePHgCA1atXo0WLFjhw4AB69uxp/aAFG3aZqIa0+C9I\ni/+673gK6BV1oJMHQa+oAwhyAGJJe/Hv9zAkwYJ4/7Zo2laQQBQUgEQBUXACBLlhW1BAlCiAkjII\nHIptN6IIQZsBifoaJJo7gEQJvTzQMJRF6sU7+9WNqIFEfRsS9Q1I1Dch6LKhc46A1q2T1f4QJiIi\ny1TbMbfXrl1DamoqoqKijGXOzs7o3Lkzjh8/jvHjxyMhIQEajcakTVBQEMLDw3H8+HGbJLeiIC9J\nCmUAZIb/wARpybYUEGSG9ybbUuO2oM2ERHMTgr7QzAOqISm+CknxVaufS7kEecl5Ohm+Sgxf6+pU\ncErzKWkjALiXaAl/v4y5l+S++nt0hjvRog6AvmRMsh6AvqRcayi/VwZdSVsREJQQpa4QJa4QJS4Q\nJa6AcbvkVbINiXPlEgtRvO/YOkBwtl1SKWogUd+EpPgapOqrkBRfh6DPK7up4ARRXtuQ7CpKvsoD\nDedLtifqIWhSIVXfNCSzmpuQqG8DMP2USFp8BbKCP1DsPRyiorZ9YiUieoxV2+Q2NTUVAODn52dS\n7ufnh9u3bwMA0tLSIJVK4ePjU6pNWlpahftPTEx8xMjCS16VIIqQIQdKpMFJTIWTmAYnpEGKosrt\n18acARRl3rB3GGbRQQk9lNDBBXrIIEAPoSRhFqAr2b73VVtSpzeW30+EFFq4QwNPaAQPaOEBjeAJ\nDTyggSf0UFaY/N5/rUnFPDjjNpTiLSjF23BCOkTooAOgeehZ5QPIBHDOpFQDD6gFXxTDt+SrDzTw\nrvF34Mv9GRVFSFEAGXIgRw7kYrbhvWjYlqDI+G+vFVygg+GlhQt0Jtuuhj/eyjmGHNlwQiqUYqrh\n5xRpkEADswYK5V+CmBWDLKE9MoX2VXYX99F/r9VsYWFh9g6BiKqRapvc2lq1+2UoihB0KsNdPPVN\nSDQpho859QX2jswoPz8frq6u9g7DAhoA2Wa0k+Dhw881AO6WvB4gKKCX+UCUeUMvrQVRVsv4/trV\nZDSoDUjU1yAtvgZBl/XANyvNiO9hdABSS173YpJBL61lSLqNQ1XEkiQeJXfMHxjCIqKkrGRb4gK9\n1AOi1LOcl7ttEmhRRFLiWYSF1IKgzYSgzYJEl3nf+yxArOhPASn+/rd/yL+/oIAocYMoLXlJ3Et+\nDm+U8bOnKHmZzw1nUVd+F+paw6FXBFn0vZZKTEysfr/XiIjsoNomtwEBAQCA9PR0BAcHG8vT09Ph\n7+8PAPD394dOp0NGRgZ8fX1N2nTq1KlqA64sQYAo84ZO5g2dSwtD2QMJr6DNgAARIoQKhgWUUWbc\n1gGiBoJeDYjFEEQNIKpLttUQRMNXspCohkRzG9DcxoP354L1+VCoLPyDQFBApwiGXlEPglgEieY2\nBPUdCKIFd/ZFLSTaij+9eCh9IaTajAoaSCBKPUpentBLvQzvZZ4QJa73XV/Fpl/FYkB/73orvu96\nVAP6YghiMRroVVCmVsEfUqIagi4Tgi7z0Xch9S759wqGRH0DssIzJvUSzW0oU/8NjftT0Hg+XTJu\nnoiIbKXaJrf169dHQEAA9u/fjzZt2gAAioqKcPToUSxcuBAA0Lp1a8jlcuzfvx/Dhw8HAKSkpODS\npUvo0KGD3WK3mrISXlsTRUOCK2qMye695Del8Brq+dWD4S6f4WE1Q+os3vdCmQ+6Gc5HCkAKEZKS\n9yVfBcl9ZdKSu4GGbbHkrqqgLyh55QP6fAi6/JLtPAj6fAi6AkO5Pt9Kd7ulf3+UbOOEX5TWgs6p\nPvROIdAp6kGUB5b+GPveHzqaO5BobkHQ3DE8yKS9C5j3QbkN6CHoVBB0KgAoldg7IlHiBr0i2DCF\nnyIYOkUQIHUzaaMrOAtF1g4I+tz7SvWQ5+6HtPCc4S6uU/2qDZyI6DFi1+Q2Ly8PycnJAAC9Xo+b\nN2/izJkz8Pb2RnBwMCZPnoxly5YhLCwMjRo1wtKlS+Hq6ophw4YBADw9PTF27FhER0fDz88P3t7e\nmD9/Ppo1a4bu3bvb8cxqMEEwzJoAJ/z9YbZBoSBCr7TPx56i1MUYx8Mb6wB9YUnSm29ITgXZ38n1\nfQ/4GR4GlOL+hwMN5feNodUXGR4E1Bk+Fhe0mZBoDXf7JNosC5NfKfSKIEMyq6gPvVN9iFKPh3/b\n/X/oOP89zzNEDQRNGiSa24bEV234appYOR5RUJYM//CCXmoYEmIcFiJ1M/zho8sz/PGjyzW89Hml\nyh58GMz0GE7GJPbeV3NmrNC5tEChU0MoVDtLzYkt0aZBmfYZNG5dofHsDUgsG+ZQY4k6w8+RvhCC\nWFjyvsDwYK2+GFqPp+wdIRE5EEGlUpmdM1jboUOHMGDAgFLlo0aNwqpVq4yLOKxfv95kEYeIiAhj\n23uLOGzZssVkEYegoPLHt3Fs2qNhv5VBFAF9vjHZNSS+WcZkODtbBXffptCVJLJ6Rd2q+VhalwdB\nlwOYDGG5bwYL4f7ZLISSu+cwaWNIAnMg6LIh0WVD0KqM24Iu23AX3Uby8ovh4hlcksDWgl7qbfLe\nMCNGJWewEEVALCpJdPMg6A1fRYlzyaIq/pU+hrTwAhRZ2yDoSo/9FWU+KPYeDr2yQaWOcY+9fj4F\nzR1ICy8Y+lAsNHxyci+R1Rca/tAUiyvcR0HQIttOs0hEjxW7Jrf2wiTt0bDfLOfQfSZqIGizSxJe\nVUnSmwOJLhvQF943f3LJvMrG+ZUVECUlXwVFyVRzhnmW7823nHjlGsIaN7b3GVqHvhAK1Y+Q5Z8o\ns1rr1glqz36AxKlSh6nya01fCEX2HsjyjgHmf65SpoI675Ya3kFE9Kj4pzIRPRpBDlHuC1Hu+/C2\nFu/bgRarkDhDXWsYtC6t4JS5pdSMGbK8o5AWXkBxrWHQK2tAQi+KkBacgkK1q9w5mS0l6IsgMrkl\nIithcktEVAX0yjAUBs6AIvsnyPKOmNQJOhWU6WuhdW1vuIsrrZ5T7gmaNCiytkNanGTpd0KUKAHB\nGaLE8ILk3nvl4zP2mIiqBJNbIqKqInGC2nsQtM4t4ZT1PYQHplqT5f8GacFZaD2egsata6WHKliN\nXg157j7Icw6irIfwRJkPNK4dAIkrRInyvgTWpSSprXihEyIia2JyS0RUxfTKBigMmA55zi+Q5x7C\n/WNWBbEI8uyfIcuNh8ajJ7RuHez6sJXhobgdZSxAAgBSaDx6QOPRg/P3ElG1weSWiMgeJApovJ6B\nzrklFJn/V2rRDUGfB4XqB8hzD0Ht2Qs6lyeq9O6noFVBoYqDtPBsmfU6pzCovQfbZsw1EVElMLkl\nIrIjvVM9FAVOgyz3COS5+0stQiLoMuGUuRn63IPQePaBTtnEtkmuqIUsNx6KnL1lzuEsSj2g9hoA\nnXNLDjUgomqJyS0Rkb0Jcmg9noLW7UnIcw9Cnnu4VGIp0dyG092voHMKhcazL/ROIVYPQ1J8FYqs\n7YblpEsHCY1b55LFJ5RWPzYRkbUwuSUiqi4kztB49oHGrTMUOfsgyzuOBx/gkhb/BWnaZ9ApI6D2\n7A1RUbvyx9UVQJH9I2T5v5VZrVcEQ+09xLAICRFRNcfkloioupF6QO09CBq3rpDn/AJZQULpJkXn\n4Vx0AVqXNtB49ip/X6JoWCWsZGU5yf2rzN1beEObUfYQhJJkW+vaoWRVOyKi6o/JLRFRNSXKfaH2\nGQ2N+1NQZP8MadHFB1tAVvA7ZAUJ8NU3gCw31WSJZElJ8gpRY/GxtS5toPbqD0jdrXMyRERVhMkt\nEVE1JyrqotjvJUiKkqHI/gkS9bUHWujgJSZAoUqs9LH0Mn+ovQdDr2xY6X0REdkDk1siohpCr2yA\nIqcpkBadhzx7DySaVKvtW5S4QuPeDVr3SLvOq0tEVFn8DUZEVJMIAnTOzaBTNoW04BQU2b+Us8DC\n/d+jgF7qCVHqAVHqWfIqeS8r2Za4cVwtETkEJrdERDWRIIHOtS0KXVpBln8SuQUn4eRa25i86qUe\nxsSVy98S0eOEyS0RUU0myKB164hUiQ88aoXZOxoiIrvjZ1BERERE5DCY3BIRERGRw2ByS0REREQO\ng8ktERERETkMJrdERERE5DCY3BIRERGRwxBUKpVo7yCIiIiIiKyBd26JiIiIyGEwuSUiIiIih8Hk\nloiIiIgcBpNbIiIiInIYTG6JiIiIyGHUyOQ2Pj4eI0eORNOmTeHl5YWNGzea1KelpWHy5Mlo0qQJ\nateujaFDhyIpKcmkzV9//YUxY8agYcOGCA4Oxosvvoi0tDSTNi1atICXl5fJa8GCBbY+PZtYtmwZ\nevTogeDgYDRs2BAjRozA+fPnTdqIooiYmBg0adIEgYGB6N+/Py5cuGDSpri4GLNmzUKDBg1Qp04d\njBw5EikpKSZtVCoVJk2ahHr16qFevXqYNGkSVCqVzc/R2qqyz3itle639evX45lnnkG9evXg5eWF\na9eulTqWo1xrQNX2myNdb0RED6qRyW1+fj4iIiKwePFiODs7m9SJoogxY8YgOTkZGzduxK+//org\n4GAMHDgQ+fn5xu8fPHgwRFHEzp07sWfPHqjVaowcORJ6vd5kf7Nnz8alS5eMr5kzZ1bZeVrT4cOH\n8fLLL+Pnn3/Gzp07IZPJMGjQIGRlZRnbfPrpp4iNjcWSJUuwb98++Pn5YfDgwcjNzTW2mTdvHuLi\n4rBu3Trs3r0bubm5GDFiBHQ6nbHNhAkTcObMGWzZsgVbtmzBmTNn8Morr1Tp+VpDVfYZwGvtwX4r\nKChAVFQU5s6dW+6xHOVaA6q23wDHud6IiB5U4+e5rVu3Lj788EOMGTMGAHDlyhW0a9cOhw4dQosW\nLQAAer0ejRs3xrvvvotx48Zh3759GDp0KP766y94eXkBALKzsxESEoLt27eje/fuAAx3NyZNmoTX\nX3/dLudmS3l5eahXrx42btyIvn37QhRFNGnSBBMnTjT+J1dYWIiwsDD861//wvjx45GdnY1GjRoh\nNjYWzz33HADg5s2baNGiBbZs2YKePXvi0qVL6NChA/bs2YOOHTsCAI4ePYq+ffvit99+Q1hYmN3O\nubJs1WcAr7UH++1+p06dQo8ePXD69GnUr1/fWO7I1xpgu34DHPt6IyKqkXduK1JcXAwAUCqVxjKJ\nRAInJyccPXrU2EYQBDg5ORnbKJVKSCQSY5t7Vq5cidDQUHTt2hVLly6FWq2ugrOwvby8POj1emNy\nf+3aNaSmpiIqKsrYxtnZGZ07d8bx48cBAAkJCdBoNCZtgoKCEB4ebmxz4sQJuLm5oUOHDsY2HTt2\nhKurq7FNTWWrPruH11pni64RR77WANv12z2Oer0REcnsHYC1NW7cGEFBQVi4cCFWrFgBV1dXfPbZ\nZ0hJSUFqaioAoH379nBzc8O7775rHGe2YMEC6HQ6YxsAeOWVV9CyZUvUqlULf/zxBxYsWIBr165h\n5cqV9jg1q5o7dy5atGiBJ598EgCM5+3n52fSzs/PD7dv3wZgGMsslUrh4+NTqs298cppaWnw8fGB\nIAjGekEQ4OvrW2pMc01jqz4DeK3d277Xb+Zw5GsNsF2/AY59vREROVxyK5fL8c0332Dq1KkIDQ2F\nVCpF9+7d8Y9//AOiaBiB4evri/Xr12PGjBlYu3YtJBIJhg4dilatWkEi+ftm9tSpU43vmzdvDg8P\nD7z44ot47733UKtWrSo/N2t5++23cezYMezZswdSqdTe4dQItu4zXmt0P15vRESPzuGGJQBA69at\ncfjwYVy7dg2XLl3C1q1bkZmZiZCQEGObqKgoJCQk4MqVK0hKSsIXX3yB27dvm7R5UJs2bQAAycnJ\nNj4D25k3bx62bt2KnTt3mpxrQEAAACA9Pd2kfXp6Ovz9/QEA/v7+0Ol0yMjIqLBNRkaG8Q8JwPCQ\n3927d41tahpb91lZHvdrzRyOeK0Btu+3sjjC9UZEdI9DJrf3eHp6wtfXF0lJSTh16hT69etXqo2P\njw+8vLxw8OBBpKeno2/fvuXu7+zZswD+/k+mppkzZ47xP83GjRub1NWvXx8BAQHYv3+/sayoqAhH\njx41jmls3bo15HK5SZuUlBTjgz0A8OSTTyIvLw8nTpwwtjlx4gTy8/NNxkbWFFXRZ2V53K81czja\ntQZUTb+VpaZfb0RE95POnTt3gb2DsFReXh4uXryI1NRUbNiwAREREfDw8IBarYanpyd27NiBtLQ0\niKKI+Ph4TJgwAZGRkXjrrbeM+/jmm29QVFQEtVqNX375BVOnTsVLL72E4cOHAzD8J7ljxw4olUoU\nFhZi//79mDNnDrp164aXXnrJXqf+yGbOnInNmzdj/fr1CAoKQn5+vnFqNIVCAUEQoNPpsHz5cjRs\n2BA6nQ7z589Hamoqli9fDicnJyiVSty5cwdr165Fs2bNkJ2djenTp8PDwwPvvfceJBIJfH19cfLk\nSWzZsgUtWrRASkoKpk+fjjZt2tS4KZqqqs94rZXuN8AwxjQ5ORmJiYmIi4tDVFQU8vPzoVAo4Ozs\n7FDXGlB1/eZo1xsR0YNq5FRghw4dwoABA0qVjxo1CqtWrcLnn3+OlStXIi0tDQEBARg5ciRmz54N\nhUJhbLtgwQJs2rQJWVlZqFevHsaPH4/XXnvN+HBKQkICZs6cicuXL0OtViM4OBhDhgzBtGnT4OLi\nUmXnai33nrh+0Jw5czBv3jwAho90Fy9ejPXr10OlUqFt27ZYunQpIiIijO2Li4vxzjvvYMuWLSgq\nKkJkZCQ+/vhjBAUFGduoVCrMnj0bP/30EwCgb9+++PDDD8uNobqqqj7jtVZ2v8XExGDJkiWl9hMb\nG2uc+s9RrjWg6vrN0a43IqIH1cjkloiIiIioLA495paIiIiIHi9MbomIiIjIYTC5JSIiIiKHweSW\niIiIiBwGk1siIiIichhMbomIiIjIYTC5JSIiIiKHweSWqJoZMGAAQkNDcffu3VJ1eXl5aN68Obp0\n6QKtVmuH6IiIiKo3JrdE1czy5ctRWFiIt99+u1TdokWLcOvWLaxYsQIymcwO0REREVVvTG6JqpmG\nDRti5syZ+L//+z/s37/fWH769GmsXr0aEyZMQNu2bassnoKCgio7FhERUWUxuSWqhqZNm4aIiAhM\nnz4dhYWF0Ov1mDFjBgIDA/HPf/7T2E6lUmH27Nlo1qwZ/P398cQTT+CTTz6BXq832d+yZcvQq1cv\nNGjQAAEBAejSpQs2bdpU6riNGzfG6NGj8d///hfdu3dHQEAAVq9ebfPzJSIishZBpVKJ9g6CiEo7\nceIE+vTpg2nTpqFu3bqYOXMmNm3ahH79+gEwjL/9xz/+gbS0NIwfPx5169bFiRMnsHnzZkyYMAEf\nffSRcV8NGzbEoEGDEB4eDp1Oh127diE+Ph6xsbEYM2aMsV3jxo3h7u6Ou3fv4qWXXkL9+vUREhKC\n7t27V/XpExERPRImt0TV2MyZM7F+/Xo4OzujR48e+Prrr411H3zwAVatWoVDhw4hNDTUWP7+++9j\n2bJlOHXqFOrXrw/AMLTAxcXF2EYURfTv3x9ZWVk4evSosbxx48ZIS0vDtm3bEBUVVQVnSEREZF0c\nlkBUjb377rvw8fGBKIr48MMPTep27NiBLl26wMPDAxkZGcZX9+7dodfrER8fb2x7L7HVaDTIyspC\nZmYmunXrhosXL6KoqMhkvw0aNGBiS0RENRYftyaqxjw8PNCoUSOkpaUhMDDQWC6KIpKSkpCYmIiG\nDRuW+b33TyW2Y8cOLFu2DOfOnYNOpzNpl5ubC6VSadwOCQmx7kkQERFVISa3RDWQKBpGEz399NOY\nOnVqmW0aNGgAADh48CBefPFFdOvWDcuXL0dgYCDkcjl+/PFHrFmzptTDZ/cnukRERDUNk1uiGkgi\nkaBevXrIz89/6MNeO3bsgIeHB7Zt2wa5XG4s37t3r42jJCIiqnocc0tUQw0ZMgRHjx7FwYMHS9Vl\nZ2dDo9EAAKRSKQCYDEe4e/cuNm/eXDWBEhERVSHeuSWqoWbMmIFffvkFw4YNw+jRo9GqVSvk5+fj\n/Pnz2LlzJ/744w8EBASgT58+WLt2LYYMGYJhw4YhMzMTX331FerUqYOMjAx7nwYREZEHvXxVAAAA\nrUlEQVRVMbklqqHc3Nzw008/YdmyZdixYwe+/fZbuLu7o1GjRpg7dy68vb0BGMblrlixAitWrMC8\nefMQFBSEN954A3K5HDNmzLDzWRAREVkX57klIiIiIofBMbdERERE5DCY3BIRERGRw2ByS0REREQO\ng8ktERERETkMJrdERERE5DCY3BIRERGRw2ByS0REREQOg8ktERERETkMJrdERERE5DCY3BIRERGR\nw/h/sBc6N3Po0nIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fcd2c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_plot_table.plot(\"year\")\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Percent Occupied\")\n",
    "plt.title(\"Designed Capacity versus Staff Capacity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Female vs Male prisons: Designed and Staffed Capacities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although we can compare the two plots side by side to contrast male and female institutions, it would be better to look at them on the same graph, so we will set up the table to plot them together. Here are the two tables we used to draw the plots above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>Design Percent Occupied</th> <th>Staff Percent Occupied</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>149.76                 </td> <td>103.204               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>169.487                </td> <td>104.174               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>177.507                </td> <td>102.114               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fem_plot_table.show(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>Design Percent Occupied</th> <th>Staff Percent Occupied</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>189.107                </td> <td>101.147               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>192.793                </td> <td>101.347               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>187.651                </td> <td>99.8235               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_plot_table.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we merge the two tables, we want to differentiate between the column in `Design Percent Occupied` in the female table versus in the male table. To do this, we will relabel the column names. The function that will relabel to the column names is called `relabel()`. \n",
    "\n",
    "The format for the function is: \n",
    "- *data_table*.`relabel(`*original_name*, *new_name*`)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We relabeled the design column and the staff column for both male and female institutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>Male Designed Percentage</th> <th>Staff Percent Occupied</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>189.107                 </td> <td>101.147               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>192.793                 </td> <td>101.347               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>187.651                 </td> <td>99.8235               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_one_relabeled = male_plot_table.relabeled(\"Design Percent Occupied\", \"Male Designed Percentage\")\n",
    "male_one_relabeled.show(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>Male Designed Percentage</th> <th>Male Staffed Percentage</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>189.107                 </td> <td>101.147                </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>192.793                 </td> <td>101.347                </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>187.651                 </td> <td>99.8235                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_all_relabeled = male_one_relabeled.relabeled(\"Staff Percent Occupied\", \"Male Staffed Percentage\")\n",
    "male_all_relabeled.show(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>Female Designed Percentage</th> <th>Staff Percent Occupied</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>149.76                    </td> <td>103.204               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>169.487                   </td> <td>104.174               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>177.507                   </td> <td>102.114               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fem_one_relabeled = fem_plot_table.relabeled(\"Design Percent Occupied\", \"Female Designed Percentage\")\n",
    "fem_one_relabeled.show(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>Female Designed Percentage</th> <th>Female Staffed Percentage</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>149.76                    </td> <td>103.204                  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>169.487                   </td> <td>104.174                  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>177.507                   </td> <td>102.114                  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fem_all_relabeled = fem_one_relabeled.relabeled(\"Staff Percent Occupied\", \"Female Staffed Percentage\")\n",
    "fem_all_relabeled.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have distinct design and staff capacities for male and female groups, we can match the values by year. They contain information over the same years. To combine tables, we use the function called `join()`. The format for the function is:\n",
    "- *data_table_one*`.join(` *same_column*, *data_table_two*`)` where same_column is the name of the column that appears in both tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>Male Designed Percentage</th> <th>Male Staffed Percentage</th> <th>Female Designed Percentage</th> <th>Female Staffed Percentage</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>189.107                 </td> <td>101.147                </td> <td>149.76                    </td> <td>103.204                  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>192.793                 </td> <td>101.347                </td> <td>169.487                   </td> <td>104.174                  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>187.651                 </td> <td>99.8235                </td> <td>177.507                   </td> <td>102.114                  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_and_female_table = male_all_relabeled.join(\"year\", fem_all_relabeled)\n",
    "male_and_female_table.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3.1 Designed Capacity: Male versus Female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115824860>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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SJbX64uLilK8vX768yr7q1atrbEPbtm1ZvHgxNWvWpHXr1nh5eVGrVi2VgOF9ffvttyxc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1159b8be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "select_design = male_and_female_table.select(\"year\", \"Male Designed Percentage\", \"Female Designed Percentage\")\n",
    "\n",
    "select_design.plot(\"year\")\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Percent Occupied\")\n",
    "plt.title(\"Male versus Female Designed Capacity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3.2 Staffed Capacity: Male versus Female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115e067f0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jatWqeHh4MHnyZL0R66zOnDmjUy9AmzZtcn3blyAIgiAIgiBk9tZONsx4G5eV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MAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157e5550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "select_staff = male_and_female_table.select(\"year\", \"Male Staffed Percentage\", \"Female Staffed Percentage\")\n",
    "\n",
    "select_staff.plot(\"year\")\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Percent Occupied\")\n",
    "plt.title(\"Male versus Female Staffed Capacity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3.3 Male Prisons versus Female Prisons in both Staffed and Designed Capacity\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115f98cf8>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uzOzZsxk7diwHDhygbdu2NGrUCBcXlyK7rQiCIHxoREBdQoMHD2bcuHEEBwcTEBBA7dq1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\nnST1wdy84P07MjKSnJwcBg0aJHkfVKlUeHl5Sc7RpEkT3b/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gYCanpVPtKdtm/G8ps49O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QUXdGY9hBCiyyhKcHKWH+p16cir9DYI1KcOlzckQo+2lbMKdobRIzJ47pEBvPnBF6zduL3BumOF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f59160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_and_female_table.plot(\"year\")\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Percent Occupied\")\n",
    "plt.title(\"Male Prisons versus Female Prisons\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does the combination of male and female institutions compare with the individual institutions? To answer this question, let's look back to the beginning where we defined the table called `compare_table_design_and_staff`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>Design Percentage</th> <th>Staff Percentage</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>185.859          </td> <td>101.281         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>190.978          </td> <td>101.537         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>186.899          </td> <td>99.9814         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_table_design_and_staff.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to combine the total percentages table with the table that contains the percentages for male and female prisons separately. To combine the two tables, we will use the `join()` function from the last section. Here is the format:\n",
    "- *data_table_one*`.join(` *same_column*, *data_table_two*`)` where same_column is the name of the column that appears in both tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year</th> <th>Design Percentage</th> <th>Staff Percentage</th> <th>Male Designed Percentage</th> <th>Male Staffed Percentage</th> <th>Female Designed Percentage</th> <th>Female Staffed Percentage</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>185.859          </td> <td>101.281         </td> <td>189.107                 </td> <td>101.147                </td> <td>149.76                    </td> <td>103.204                  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>190.978          </td> <td>101.537         </td> <td>192.793                 </td> <td>101.347                </td> <td>169.487                   </td> <td>104.174                  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>186.899          </td> <td>99.9814         </td> <td>187.651                 </td> <td>99.8235                </td> <td>177.507                   </td> <td>102.114                  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (20 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_joined = compare_table_design_and_staff.join(\"year\", male_and_female_table)\n",
    "all_joined.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To view all of the design percentages, we must select those columns from the table using the `select()` function. With those columns, we can use the `plot()` function to display the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1100033c8>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a1/bPz6/Bgwejp6fHxIkTtZ7TmZmZajdxDg4ONGrUiNDQUH777Te1tPv27cv3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kX/PnQaJruzl3/rKfegJzdOA5GzmVILPPP0Sxvl6fdNT0Fho+o7tfVGyDqQvrOr0RMpT1EqEdel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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1160cdc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "design_all = all_joined.select(\"year\", \"Design Percentage\", \"Male Designed Percentage\", \"Female Designed Percentage\")\n",
    "design_all.plot(\"year\")\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Percent Occupied\")\n",
    "plt.title(\"Male Prisons versus Female Prisons Designed Capacity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To view all of the staff percentages, we must select those columns from the table using the `select()` function. With those columns, we can use the `plot()` function to display the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11611b8d0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KEXP8/Pw09vPQtOxNRYsWRSaT5dkURyqV0rFjR8LDw5k6dSoSiYQffvjhrWkL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w83IfPosRrtIqpNuCxqaLW+RQcCHZ6TRbPKu0LT+n0MfvJDJN3dG38/58SUPUU1cukBQADQXa/G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\nERGRj/w/7nUIZDp7oBIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115df36a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "staff_all = all_joined.select(\"year\", \"Staff Percentage\", \"Male Staffed Percentage\", \"Female Staffed Percentage\")\n",
    "staff_all.plot(\"year\")\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Percent Occupied\")\n",
    "plt.title(\"Male Prisons versus Female Prisons Staffed Capacity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are all the plots together on the same plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11647aa20>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AgoODkcvlJtvExcVx5coV4zaC8G+jTknj8pKVRIeFPzSYtqvsS6U3uhH40VDc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd14860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_joined.plot(\"year\")\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Percent Occupied\")\n",
    "plt.title(\"Male Prisons versus Female Prisons\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "**Comment on the plot above. What are some similarities we about th designed capacity of prisons in California? What are the differences between designed and staffed capacity? What are some patterns unique to female institutions? Why does the total population look almost identical to the male prisons percentages?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*double click this cell to type your response*"
   ]
  }
 ],
 "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
}