Unit 4 Challenge - Tier 3.ipynb

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    "# Springboard Data Science Career Track Unit 4 Challenge - Tier 3 Complete\n",
    "\n",
    "## Objectives\n",
    "Hey! Great job getting through those challenging DataCamp courses. You're learning a lot in a short span of time. \n",
    "\n",
    "In this notebook, you're going to apply the skills you've been learning, bridging the gap between the controlled environment of DataCamp and the *slightly* messier work that data scientists do with actual datasets!\n",
    "\n",
    "Here’s the mystery we’re going to solve: ***which boroughs of London have seen the greatest increase in housing prices, on average, over the last two decades?***\n",
    "\n",
    "\n",
    "A borough is just a fancy word for district. You may be familiar with the five boroughs of New York… well, there are 32 boroughs within Greater London [(here's some info for the curious)](https://en.wikipedia.org/wiki/London_boroughs). Some of them are more desirable areas to live in, and the data will reflect that with a greater rise in housing prices.\n",
    "\n",
    "***This is the Tier 3 notebook, which means it's not filled in at all: we'll just give you the skeleton of a project, the brief and the data. It's up to you to play around with it and see what you can find out! Good luck! If you struggle, feel free to look at easier tiers for help; but try to dip in and out of them, as the more independent work you do, the better it is for your learning!***\n",
    "\n",
    "This challenge will make use of only what you learned in the following DataCamp courses: \n",
    "- Prework courses (Introduction to Python for Data Science, Intermediate Python for Data Science)\n",
    "- Data Types for Data Science\n",
    "- Python Data Science Toolbox (Part One) \n",
    "- pandas Foundations\n",
    "- Manipulating DataFrames with pandas\n",
    "- Merging DataFrames with pandas\n",
    "\n",
    "Of the tools, techniques and concepts in the above DataCamp courses, this challenge should require the application of the following: \n",
    "- **pandas**\n",
    "    - **data ingestion and inspection** (pandas Foundations, Module One) \n",
    "    - **exploratory data analysis** (pandas Foundations, Module Two)\n",
    "    - **tidying and cleaning** (Manipulating DataFrames with pandas, Module Three) \n",
    "    - **transforming DataFrames** (Manipulating DataFrames with pandas, Module One)\n",
    "    - **subsetting DataFrames with lists** (Manipulating DataFrames with pandas, Module One) \n",
    "    - **filtering DataFrames** (Manipulating DataFrames with pandas, Module One) \n",
    "    - **grouping data** (Manipulating DataFrames with pandas, Module Four) \n",
    "    - **melting data** (Manipulating DataFrames with pandas, Module Three) \n",
    "    - **advanced indexing** (Manipulating DataFrames with pandas, Module Four) \n",
    "- **matplotlib** (Intermediate Python for Data Science, Module One)\n",
    "- **fundamental data types** (Data Types for Data Science, Module One) \n",
    "- **dictionaries** (Intermediate Python for Data Science, Module Two)\n",
    "- **handling dates and times** (Data Types for Data Science, Module Four)\n",
    "- **function definition** (Python Data Science Toolbox - Part One, Module One)\n",
    "- **default arguments, variable length, and scope** (Python Data Science Toolbox - Part One, Module Two) \n",
    "- **lambda functions and error handling** (Python Data Science Toolbox - Part One, Module Four) "
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    "## The Data Science Pipeline\n",
    "\n",
    "This is Tier Three, so we'll get you started. But after that, it's all in your hands! When you feel done with your investigations, look back over what you've accomplished, and prepare a quick presentation of your findings for the next mentor meeting. \n",
    "\n",
    "Data Science is magical. In this case study, you'll get to apply some complex machine learning algorithms. But as  [David Spiegelhalter](https://www.youtube.com/watch?v=oUs1uvsz0Ok) reminds us, there is no substitute for simply **taking a really, really good look at the data.** Sometimes, this is all we need to answer our question.\n",
    "\n",
    "Data Science projects generally adhere to the four stages of Data Science Pipeline:\n",
    "1. Sourcing and loading \n",
    "2. Cleaning, transforming, and visualizing \n",
    "3. Modeling \n",
    "4. Evaluating and concluding \n"
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    "### 1. Sourcing and Loading \n",
    "\n",
    "Any Data Science project kicks off by importing  ***pandas***. The documentation of this wonderful library can be found [here](https://pandas.pydata.org/). As you've seen, pandas is conveniently connected to the [Numpy](http://www.numpy.org/) and [Matplotlib](https://matplotlib.org/) libraries. \n",
    "\n",
    "***Hint:*** This part of the data science pipeline will test those skills you acquired in the pandas Foundations course, Module One. "
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   "source": [
    "#### 1.1. Importing Libraries"
   ]
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    "# Let's import the pandas, numpy libraries as pd, and np respectively. \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# Load the pyplot collection of functions from matplotlib, as plt \n",
    "import matplotlib.pyplot as plt"
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    "#### 1.2.  Loading the data\n",
    "Your data comes from the [London Datastore](https://data.london.gov.uk/): a free, open-source data-sharing portal for London-oriented datasets. "
   ]
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    "# First, make a variable called url_LondonHousePrices, and assign it the following link, enclosed in quotation-marks as a string:\n",
    "# https://data.london.gov.uk/download/uk-house-price-index/70ac0766-8902-4eb5-aab5-01951aaed773/UK%20House%20price%20index.xls\n",
    "\n",
    "url_LondonHousePrices = \"https://data.london.gov.uk/download/uk-house-price-index/70ac0766-8902-4eb5-aab5-01951aaed773/UK%20House%20price%20index.xls\"\n",
    "\n",
    "# The dataset we're interested in contains the Average prices of the houses, and is actually on a particular sheet of the Excel file. \n",
    "# As a result, we need to specify the sheet name in the read_excel() method.\n",
    "# Put this data into a variable called properties.  \n",
    "properties = pd.read_excel(url_LondonHousePrices, sheet_name='Average price', index_col= None)"
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    "### 2. Cleaning, transforming, and visualizing\n",
    "This second stage is arguably the most important part of any Data Science project. The first thing to do is take a proper look at the data. Cleaning forms the majority of this stage, and can be done both before or after Transformation.\n",
    "\n",
    "The end goal of data cleaning is to have tidy data. When data is tidy: \n",
    "\n",
    "1. Each variable has a column.\n",
    "2. Each observation forms a row.\n",
    "\n",
    "Keep the end goal in mind as you move through this process, every step will take you closer. \n",
    "\n",
    "\n",
    "\n",
    "***Hint:*** This part of the data science pipeline should test those skills you acquired in: \n",
    "- Intermediate Python for data science, all modules.\n",
    "- pandas Foundations, all modules. \n",
    "- Manipulating DataFrames with pandas, all modules.\n",
    "- Data Types for Data Science, Module Four.\n",
    "- Python Data Science Toolbox - Part One, all modules"
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    "**2.1. Exploring your data** \n",
    "\n",
    "Think about your pandas functions for checking out a dataframe. "
   ]
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    "properties.head()"
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    "**2.2. Cleaning the data**\n",
    "\n",
    "You might find you need to transpose your dataframe, check out what its row indexes are, and reset the index. You  also might find you need to assign the values of the first row to your column headings  . (Hint: recall the .columns feature of DataFrames, as well as the iloc[] method).\n",
    "\n",
    "Don't be afraid to use StackOverflow for help  with this."
   ]
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       "      <td>995816</td>\n",
       "      <td>973945</td>\n",
       "      <td>909574</td>\n",
       "      <td>886619</td>\n",
       "      <td>884062</td>\n",
       "      <td>839079</td>\n",
       "      <td>802168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barking &amp; Dagenham</th>\n",
       "      <td>E09000002</td>\n",
       "      <td>50460.2266</td>\n",
       "      <td>51085.77983</td>\n",
       "      <td>51268.96956</td>\n",
       "      <td>53133.50526</td>\n",
       "      <td>53042.24852</td>\n",
       "      <td>53700.34831</td>\n",
       "      <td>52113.12157</td>\n",
       "      <td>52232.19868</td>\n",
       "      <td>51471.61353</td>\n",
       "      <td>...</td>\n",
       "      <td>343471</td>\n",
       "      <td>346218</td>\n",
       "      <td>347481</td>\n",
       "      <td>343944</td>\n",
       "      <td>341264</td>\n",
       "      <td>341115</td>\n",
       "      <td>338301</td>\n",
       "      <td>338600</td>\n",
       "      <td>343037</td>\n",
       "      <td>340485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barnet</th>\n",
       "      <td>E09000003</td>\n",
       "      <td>93284.51832</td>\n",
       "      <td>93190.16963</td>\n",
       "      <td>92247.52435</td>\n",
       "      <td>90762.87492</td>\n",
       "      <td>90258.00033</td>\n",
       "      <td>90107.23471</td>\n",
       "      <td>91441.24768</td>\n",
       "      <td>92361.31512</td>\n",
       "      <td>93273.12245</td>\n",
       "      <td>...</td>\n",
       "      <td>597927</td>\n",
       "      <td>588354</td>\n",
       "      <td>572839</td>\n",
       "      <td>569078</td>\n",
       "      <td>574332</td>\n",
       "      <td>584285</td>\n",
       "      <td>587519</td>\n",
       "      <td>581031</td>\n",
       "      <td>582184</td>\n",
       "      <td>577548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bexley</th>\n",
       "      <td>E09000004</td>\n",
       "      <td>64958.09036</td>\n",
       "      <td>64787.92069</td>\n",
       "      <td>64367.49344</td>\n",
       "      <td>64277.66881</td>\n",
       "      <td>63997.13588</td>\n",
       "      <td>64252.32335</td>\n",
       "      <td>63722.70055</td>\n",
       "      <td>64432.60005</td>\n",
       "      <td>64509.54767</td>\n",
       "      <td>...</td>\n",
       "      <td>395215</td>\n",
       "      <td>394399</td>\n",
       "      <td>388948</td>\n",
       "      <td>387261</td>\n",
       "      <td>390037</td>\n",
       "      <td>388763</td>\n",
       "      <td>389314</td>\n",
       "      <td>383637</td>\n",
       "      <td>386701</td>\n",
       "      <td>377926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 349 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0                    1                    2    \\\n",
       "Unnamed: 0                NaT  1995-01-01 00:00:00  1995-02-01 00:00:00   \n",
       "City of London      E09000001          91448.98487          82202.77314   \n",
       "Barking & Dagenham  E09000002           50460.2266          51085.77983   \n",
       "Barnet              E09000003          93284.51832          93190.16963   \n",
       "Bexley              E09000004          64958.09036          64787.92069   \n",
       "\n",
       "                                    3                    4    \\\n",
       "Unnamed: 0          1995-03-01 00:00:00  1995-04-01 00:00:00   \n",
       "City of London              79120.70256          77101.20804   \n",
       "Barking & Dagenham          51268.96956          53133.50526   \n",
       "Barnet                      92247.52435          90762.87492   \n",
       "Bexley                      64367.49344          64277.66881   \n",
       "\n",
       "                                    5                    6    \\\n",
       "Unnamed: 0          1995-05-01 00:00:00  1995-06-01 00:00:00   \n",
       "City of London              84409.14932          94900.51244   \n",
       "Barking & Dagenham          53042.24852          53700.34831   \n",
       "Barnet                      90258.00033          90107.23471   \n",
       "Bexley                      63997.13588          64252.32335   \n",
       "\n",
       "                                    7                    8    \\\n",
       "Unnamed: 0          1995-07-01 00:00:00  1995-08-01 00:00:00   \n",
       "City of London              110128.0423          112329.4376   \n",
       "Barking & Dagenham          52113.12157          52232.19868   \n",
       "Barnet                      91441.24768          92361.31512   \n",
       "Bexley                      63722.70055          64432.60005   \n",
       "\n",
       "                                    9    ...                  339  \\\n",
       "Unnamed: 0          1995-09-01 00:00:00  ...  2023-03-01 00:00:00   \n",
       "City of London              104473.1096  ...               926023   \n",
       "Barking & Dagenham          51471.61353  ...               343471   \n",
       "Barnet                      93273.12245  ...               597927   \n",
       "Bexley                      64509.54767  ...               395215   \n",
       "\n",
       "                                    340                  341  \\\n",
       "Unnamed: 0          2023-04-01 00:00:00  2023-05-01 00:00:00   \n",
       "City of London                   900447               960485   \n",
       "Barking & Dagenham               346218               347481   \n",
       "Barnet                           588354               572839   \n",
       "Bexley                           394399               388948   \n",
       "\n",
       "                                    342                  343  \\\n",
       "Unnamed: 0          2023-06-01 00:00:00  2023-07-01 00:00:00   \n",
       "City of London                   995816               973945   \n",
       "Barking & Dagenham               343944               341264   \n",
       "Barnet                           569078               574332   \n",
       "Bexley                           387261               390037   \n",
       "\n",
       "                                    344                  345  \\\n",
       "Unnamed: 0          2023-08-01 00:00:00  2023-09-01 00:00:00   \n",
       "City of London                   909574               886619   \n",
       "Barking & Dagenham               341115               338301   \n",
       "Barnet                           584285               587519   \n",
       "Bexley                           388763               389314   \n",
       "\n",
       "                                    346                  347  \\\n",
       "Unnamed: 0          2023-10-01 00:00:00  2023-11-01 00:00:00   \n",
       "City of London                   884062               839079   \n",
       "Barking & Dagenham               338600               343037   \n",
       "Barnet                           581031               582184   \n",
       "Bexley                           383637               386701   \n",
       "\n",
       "                                    348  \n",
       "Unnamed: 0          2023-12-01 00:00:00  \n",
       "City of London                   802168  \n",
       "Barking & Dagenham               340485  \n",
       "Barnet                           577548  \n",
       "Bexley                           377926  \n",
       "\n",
       "[5 rows x 349 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties = properties.transpose()\n",
    "properties.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>City of London</td>\n",
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       "      <td>91448.98487</td>\n",
       "      <td>82202.77314</td>\n",
       "      <td>79120.70256</td>\n",
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       "      <td>E09000002</td>\n",
       "      <td>50460.2266</td>\n",
       "      <td>51085.77983</td>\n",
       "      <td>51268.96956</td>\n",
       "      <td>53133.50526</td>\n",
       "      <td>53042.24852</td>\n",
       "      <td>53700.34831</td>\n",
       "      <td>52113.12157</td>\n",
       "      <td>52232.19868</td>\n",
       "      <td>...</td>\n",
       "      <td>343471</td>\n",
       "      <td>346218</td>\n",
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       "      <th>3</th>\n",
       "      <td>Barnet</td>\n",
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       "      <td>93284.51832</td>\n",
       "      <td>93190.16963</td>\n",
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       "      <td>582184</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bexley</td>\n",
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       "<p>5 rows × 350 columns</p>\n",
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      "text/plain": [
       "                index          0                    1                    2  \\\n",
       "0          Unnamed: 0        NaT  1995-01-01 00:00:00  1995-02-01 00:00:00   \n",
       "1      City of London  E09000001          91448.98487          82202.77314   \n",
       "2  Barking & Dagenham  E09000002           50460.2266          51085.77983   \n",
       "3              Barnet  E09000003          93284.51832          93190.16963   \n",
       "4              Bexley  E09000004          64958.09036          64787.92069   \n",
       "\n",
       "                     3                    4                    5  \\\n",
       "0  1995-03-01 00:00:00  1995-04-01 00:00:00  1995-05-01 00:00:00   \n",
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       "\n",
       "                     6                    7                    8  ...  \\\n",
       "0  1995-06-01 00:00:00  1995-07-01 00:00:00  1995-08-01 00:00:00  ...   \n",
       "1          94900.51244          110128.0423          112329.4376  ...   \n",
       "2          53700.34831          52113.12157          52232.19868  ...   \n",
       "3          90107.23471          91441.24768          92361.31512  ...   \n",
       "4          64252.32335          63722.70055          64432.60005  ...   \n",
       "\n",
       "                   339                  340                  341  \\\n",
       "0  2023-03-01 00:00:00  2023-04-01 00:00:00  2023-05-01 00:00:00   \n",
       "1               926023               900447               960485   \n",
       "2               343471               346218               347481   \n",
       "3               597927               588354               572839   \n",
       "4               395215               394399               388948   \n",
       "\n",
       "                   342                  343                  344  \\\n",
       "0  2023-06-01 00:00:00  2023-07-01 00:00:00  2023-08-01 00:00:00   \n",
       "1               995816               973945               909574   \n",
       "2               343944               341264               341115   \n",
       "3               569078               574332               584285   \n",
       "4               387261               390037               388763   \n",
       "\n",
       "                   345                  346                  347  \\\n",
       "0  2023-09-01 00:00:00  2023-10-01 00:00:00  2023-11-01 00:00:00   \n",
       "1               886619               884062               839079   \n",
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       "3               587519               581031               582184   \n",
       "4               389314               383637               386701   \n",
       "\n",
       "                   348  \n",
       "0  2023-12-01 00:00:00  \n",
       "1               802168  \n",
       "2               340485  \n",
       "3               577548  \n",
       "4               377926  \n",
       "\n",
       "[5 rows x 350 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties = properties.reset_index()\n",
    "properties.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>346218</td>\n",
       "      <td>347481</td>\n",
       "      <td>343944</td>\n",
       "      <td>341264</td>\n",
       "      <td>341115</td>\n",
       "      <td>338301</td>\n",
       "      <td>338600</td>\n",
       "      <td>343037</td>\n",
       "      <td>340485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>93284.51832</td>\n",
       "      <td>93190.16963</td>\n",
       "      <td>92247.52435</td>\n",
       "      <td>90762.87492</td>\n",
       "      <td>90258.00033</td>\n",
       "      <td>90107.23471</td>\n",
       "      <td>91441.24768</td>\n",
       "      <td>92361.31512</td>\n",
       "      <td>...</td>\n",
       "      <td>597927</td>\n",
       "      <td>588354</td>\n",
       "      <td>572839</td>\n",
       "      <td>569078</td>\n",
       "      <td>574332</td>\n",
       "      <td>584285</td>\n",
       "      <td>587519</td>\n",
       "      <td>581031</td>\n",
       "      <td>582184</td>\n",
       "      <td>577548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>64958.09036</td>\n",
       "      <td>64787.92069</td>\n",
       "      <td>64367.49344</td>\n",
       "      <td>64277.66881</td>\n",
       "      <td>63997.13588</td>\n",
       "      <td>64252.32335</td>\n",
       "      <td>63722.70055</td>\n",
       "      <td>64432.60005</td>\n",
       "      <td>...</td>\n",
       "      <td>395215</td>\n",
       "      <td>394399</td>\n",
       "      <td>388948</td>\n",
       "      <td>387261</td>\n",
       "      <td>390037</td>\n",
       "      <td>388763</td>\n",
       "      <td>389314</td>\n",
       "      <td>383637</td>\n",
       "      <td>386701</td>\n",
       "      <td>377926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>71306.56698</td>\n",
       "      <td>72022.26197</td>\n",
       "      <td>72015.76274</td>\n",
       "      <td>72965.63094</td>\n",
       "      <td>73704.04743</td>\n",
       "      <td>74310.48167</td>\n",
       "      <td>74127.03788</td>\n",
       "      <td>73547.0411</td>\n",
       "      <td>...</td>\n",
       "      <td>529085</td>\n",
       "      <td>530652</td>\n",
       "      <td>528250</td>\n",
       "      <td>528734</td>\n",
       "      <td>523159</td>\n",
       "      <td>544681</td>\n",
       "      <td>546336</td>\n",
       "      <td>558495</td>\n",
       "      <td>546598</td>\n",
       "      <td>526198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 350 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0          Unnamed: 0        NaT 1995-01-01 00:00:00 1995-02-01 00:00:00  \\\n",
       "1      City of London  E09000001         91448.98487         82202.77314   \n",
       "2  Barking & Dagenham  E09000002          50460.2266         51085.77983   \n",
       "3              Barnet  E09000003         93284.51832         93190.16963   \n",
       "4              Bexley  E09000004         64958.09036         64787.92069   \n",
       "5               Brent  E09000005         71306.56698         72022.26197   \n",
       "\n",
       "0 1995-03-01 00:00:00 1995-04-01 00:00:00 1995-05-01 00:00:00  \\\n",
       "1         79120.70256         77101.20804         84409.14932   \n",
       "2         51268.96956         53133.50526         53042.24852   \n",
       "3         92247.52435         90762.87492         90258.00033   \n",
       "4         64367.49344         64277.66881         63997.13588   \n",
       "5         72015.76274         72965.63094         73704.04743   \n",
       "\n",
       "0 1995-06-01 00:00:00 1995-07-01 00:00:00 1995-08-01 00:00:00  ...  \\\n",
       "1         94900.51244         110128.0423         112329.4376  ...   \n",
       "2         53700.34831         52113.12157         52232.19868  ...   \n",
       "3         90107.23471         91441.24768         92361.31512  ...   \n",
       "4         64252.32335         63722.70055         64432.60005  ...   \n",
       "5         74310.48167         74127.03788          73547.0411  ...   \n",
       "\n",
       "0 2023-03-01 00:00:00 2023-04-01 00:00:00 2023-05-01 00:00:00  \\\n",
       "1              926023              900447              960485   \n",
       "2              343471              346218              347481   \n",
       "3              597927              588354              572839   \n",
       "4              395215              394399              388948   \n",
       "5              529085              530652              528250   \n",
       "\n",
       "0 2023-06-01 00:00:00 2023-07-01 00:00:00 2023-08-01 00:00:00  \\\n",
       "1              995816              973945              909574   \n",
       "2              343944              341264              341115   \n",
       "3              569078              574332              584285   \n",
       "4              387261              390037              388763   \n",
       "5              528734              523159              544681   \n",
       "\n",
       "0 2023-09-01 00:00:00 2023-10-01 00:00:00 2023-11-01 00:00:00  \\\n",
       "1              886619              884062              839079   \n",
       "2              338301              338600              343037   \n",
       "3              587519              581031              582184   \n",
       "4              389314              383637              386701   \n",
       "5              546336              558495              546598   \n",
       "\n",
       "0 2023-12-01 00:00:00  \n",
       "1              802168  \n",
       "2              340485  \n",
       "3              577548  \n",
       "4              377926  \n",
       "5              526198  \n",
       "\n",
       "[5 rows x 350 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "header = properties.iloc[0]\n",
    "properties = properties[1:]\n",
    "properties.columns = header\n",
    "properties.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o1uLbJAsoIjK"
   },
   "source": [
    "**2.3. Cleaning the data (part 2)**\n",
    "\n",
    "You might we have to **rename** a couple columns. How do you do this? The clue's pretty bold..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GKkmn1AnoVZS"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>ID</th>\n",
       "      <th>1995-01-01 00:00:00</th>\n",
       "      <th>1995-02-01 00:00:00</th>\n",
       "      <th>1995-03-01 00:00:00</th>\n",
       "      <th>1995-04-01 00:00:00</th>\n",
       "      <th>1995-05-01 00:00:00</th>\n",
       "      <th>1995-06-01 00:00:00</th>\n",
       "      <th>1995-07-01 00:00:00</th>\n",
       "      <th>1995-08-01 00:00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2023-03-01 00:00:00</th>\n",
       "      <th>2023-04-01 00:00:00</th>\n",
       "      <th>2023-05-01 00:00:00</th>\n",
       "      <th>2023-06-01 00:00:00</th>\n",
       "      <th>2023-07-01 00:00:00</th>\n",
       "      <th>2023-08-01 00:00:00</th>\n",
       "      <th>2023-09-01 00:00:00</th>\n",
       "      <th>2023-10-01 00:00:00</th>\n",
       "      <th>2023-11-01 00:00:00</th>\n",
       "      <th>2023-12-01 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>City of London</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>91448.98487</td>\n",
       "      <td>82202.77314</td>\n",
       "      <td>79120.70256</td>\n",
       "      <td>77101.20804</td>\n",
       "      <td>84409.14932</td>\n",
       "      <td>94900.51244</td>\n",
       "      <td>110128.0423</td>\n",
       "      <td>112329.4376</td>\n",
       "      <td>...</td>\n",
       "      <td>926023</td>\n",
       "      <td>900447</td>\n",
       "      <td>960485</td>\n",
       "      <td>995816</td>\n",
       "      <td>973945</td>\n",
       "      <td>909574</td>\n",
       "      <td>886619</td>\n",
       "      <td>884062</td>\n",
       "      <td>839079</td>\n",
       "      <td>802168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>E09000002</td>\n",
       "      <td>50460.2266</td>\n",
       "      <td>51085.77983</td>\n",
       "      <td>51268.96956</td>\n",
       "      <td>53133.50526</td>\n",
       "      <td>53042.24852</td>\n",
       "      <td>53700.34831</td>\n",
       "      <td>52113.12157</td>\n",
       "      <td>52232.19868</td>\n",
       "      <td>...</td>\n",
       "      <td>343471</td>\n",
       "      <td>346218</td>\n",
       "      <td>347481</td>\n",
       "      <td>343944</td>\n",
       "      <td>341264</td>\n",
       "      <td>341115</td>\n",
       "      <td>338301</td>\n",
       "      <td>338600</td>\n",
       "      <td>343037</td>\n",
       "      <td>340485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>93284.51832</td>\n",
       "      <td>93190.16963</td>\n",
       "      <td>92247.52435</td>\n",
       "      <td>90762.87492</td>\n",
       "      <td>90258.00033</td>\n",
       "      <td>90107.23471</td>\n",
       "      <td>91441.24768</td>\n",
       "      <td>92361.31512</td>\n",
       "      <td>...</td>\n",
       "      <td>597927</td>\n",
       "      <td>588354</td>\n",
       "      <td>572839</td>\n",
       "      <td>569078</td>\n",
       "      <td>574332</td>\n",
       "      <td>584285</td>\n",
       "      <td>587519</td>\n",
       "      <td>581031</td>\n",
       "      <td>582184</td>\n",
       "      <td>577548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>64958.09036</td>\n",
       "      <td>64787.92069</td>\n",
       "      <td>64367.49344</td>\n",
       "      <td>64277.66881</td>\n",
       "      <td>63997.13588</td>\n",
       "      <td>64252.32335</td>\n",
       "      <td>63722.70055</td>\n",
       "      <td>64432.60005</td>\n",
       "      <td>...</td>\n",
       "      <td>395215</td>\n",
       "      <td>394399</td>\n",
       "      <td>388948</td>\n",
       "      <td>387261</td>\n",
       "      <td>390037</td>\n",
       "      <td>388763</td>\n",
       "      <td>389314</td>\n",
       "      <td>383637</td>\n",
       "      <td>386701</td>\n",
       "      <td>377926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>71306.56698</td>\n",
       "      <td>72022.26197</td>\n",
       "      <td>72015.76274</td>\n",
       "      <td>72965.63094</td>\n",
       "      <td>73704.04743</td>\n",
       "      <td>74310.48167</td>\n",
       "      <td>74127.03788</td>\n",
       "      <td>73547.0411</td>\n",
       "      <td>...</td>\n",
       "      <td>529085</td>\n",
       "      <td>530652</td>\n",
       "      <td>528250</td>\n",
       "      <td>528734</td>\n",
       "      <td>523159</td>\n",
       "      <td>544681</td>\n",
       "      <td>546336</td>\n",
       "      <td>558495</td>\n",
       "      <td>546598</td>\n",
       "      <td>526198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 350 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0             Borough         ID 1995-01-01 00:00:00 1995-02-01 00:00:00  \\\n",
       "1      City of London  E09000001         91448.98487         82202.77314   \n",
       "2  Barking & Dagenham  E09000002          50460.2266         51085.77983   \n",
       "3              Barnet  E09000003         93284.51832         93190.16963   \n",
       "4              Bexley  E09000004         64958.09036         64787.92069   \n",
       "5               Brent  E09000005         71306.56698         72022.26197   \n",
       "\n",
       "0 1995-03-01 00:00:00 1995-04-01 00:00:00 1995-05-01 00:00:00  \\\n",
       "1         79120.70256         77101.20804         84409.14932   \n",
       "2         51268.96956         53133.50526         53042.24852   \n",
       "3         92247.52435         90762.87492         90258.00033   \n",
       "4         64367.49344         64277.66881         63997.13588   \n",
       "5         72015.76274         72965.63094         73704.04743   \n",
       "\n",
       "0 1995-06-01 00:00:00 1995-07-01 00:00:00 1995-08-01 00:00:00  ...  \\\n",
       "1         94900.51244         110128.0423         112329.4376  ...   \n",
       "2         53700.34831         52113.12157         52232.19868  ...   \n",
       "3         90107.23471         91441.24768         92361.31512  ...   \n",
       "4         64252.32335         63722.70055         64432.60005  ...   \n",
       "5         74310.48167         74127.03788          73547.0411  ...   \n",
       "\n",
       "0 2023-03-01 00:00:00 2023-04-01 00:00:00 2023-05-01 00:00:00  \\\n",
       "1              926023              900447              960485   \n",
       "2              343471              346218              347481   \n",
       "3              597927              588354              572839   \n",
       "4              395215              394399              388948   \n",
       "5              529085              530652              528250   \n",
       "\n",
       "0 2023-06-01 00:00:00 2023-07-01 00:00:00 2023-08-01 00:00:00  \\\n",
       "1              995816              973945              909574   \n",
       "2              343944              341264              341115   \n",
       "3              569078              574332              584285   \n",
       "4              387261              390037              388763   \n",
       "5              528734              523159              544681   \n",
       "\n",
       "0 2023-09-01 00:00:00 2023-10-01 00:00:00 2023-11-01 00:00:00  \\\n",
       "1              886619              884062              839079   \n",
       "2              338301              338600              343037   \n",
       "3              587519              581031              582184   \n",
       "4              389314              383637              386701   \n",
       "5              546336              558495              546598   \n",
       "\n",
       "0 2023-12-01 00:00:00  \n",
       "1              802168  \n",
       "2              340485  \n",
       "3              577548  \n",
       "4              377926  \n",
       "5              526198  \n",
       "\n",
       "[5 rows x 350 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties = properties.rename(columns = {'Unnamed: 0': 'Borough', pd.NaT: 'ID'})\n",
    "properties.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jy8BzXHmoWEw"
   },
   "source": [
    "**2.4.Transforming the data**\n",
    "\n",
    "Remember what Wes McKinney said about tidy data? \n",
    "\n",
    "You might need to **melt** your DataFrame here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S2wM0qLuo2Zt"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>ID</th>\n",
       "      <th>date</th>\n",
       "      <th>Average price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>City of London</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>1995-01-01 00:00:00</td>\n",
       "      <td>91448.98487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>E09000002</td>\n",
       "      <td>1995-01-01 00:00:00</td>\n",
       "      <td>50460.2266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>1995-01-01 00:00:00</td>\n",
       "      <td>93284.51832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>1995-01-01 00:00:00</td>\n",
       "      <td>64958.09036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>1995-01-01 00:00:00</td>\n",
       "      <td>71306.56698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Borough         ID                 date Average price\n",
       "0      City of London  E09000001  1995-01-01 00:00:00   91448.98487\n",
       "1  Barking & Dagenham  E09000002  1995-01-01 00:00:00    50460.2266\n",
       "2              Barnet  E09000003  1995-01-01 00:00:00   93284.51832\n",
       "3              Bexley  E09000004  1995-01-01 00:00:00   64958.09036\n",
       "4               Brent  E09000005  1995-01-01 00:00:00   71306.56698"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties_m = properties.melt(id_vars = ['Borough', 'ID'], var_name = ['date'], value_name = 'Average price')\n",
    "properties_m.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7kIsgAo7o3mf"
   },
   "source": [
    "Remember to make sure your column data types are all correct. Average prices, for example, should be floating point numbers... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZcR4IHbcpOaq"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16704 entries, 0 to 16703\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Borough        16704 non-null  object        \n",
      " 1   ID             15660 non-null  object        \n",
      " 2   date           16704 non-null  datetime64[ns]\n",
      " 3   Average price  15660 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(1), object(2)\n",
      "memory usage: 522.1+ KB\n"
     ]
    }
   ],
   "source": [
    "properties_m['date'] = pd.to_datetime(properties_m['date'], format = \"%Y-%m-%d %H:%M:%S\")\n",
    "properties_m['Average price'] = properties_m['Average price'].astype(float)\n",
    "properties_m.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "knLUXHLypOtw"
   },
   "source": [
    "**2.5. Cleaning the data (part 3)**\n",
    "\n",
    "Do we have an equal number of observations in the ID, Average Price, Month, and London Borough columns? Remember that there are only 32 London Boroughs. How many entries do you have in that column? \n",
    "\n",
    "Check out the contents of the London Borough column, and if you find null values, get rid of them however you see fit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BnvTW5a3p0fC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Borough          False\n",
       "ID                True\n",
       "date             False\n",
       "Average price     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties_m.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>ID</th>\n",
       "      <th>date</th>\n",
       "      <th>Average price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Unnamed: 34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Unnamed: 37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Unnamed: 47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Unnamed: 34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Unnamed: 37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Borough   ID       date  Average price\n",
       "33  Unnamed: 34  NaN 1995-01-01            NaN\n",
       "36  Unnamed: 37  NaN 1995-01-01            NaN\n",
       "46  Unnamed: 47  NaN 1995-01-01            NaN\n",
       "81  Unnamed: 34  NaN 1995-02-01            NaN\n",
       "84  Unnamed: 37  NaN 1995-02-01            NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties_m[properties_m.ID.isnull()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 15660 entries, 0 to 16703\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Borough        15660 non-null  object        \n",
      " 1   ID             15660 non-null  object        \n",
      " 2   date           15660 non-null  datetime64[ns]\n",
      " 3   Average price  15660 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(1), object(2)\n",
      "memory usage: 611.7+ KB\n"
     ]
    }
   ],
   "source": [
    "properties_2 = properties_m.dropna()\n",
    "properties_2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PGEx6mJsp6dG"
   },
   "source": [
    "**2.6. Visualizing the data**\n",
    "\n",
    "To visualize the data, why not subset on a particular London Borough? Maybe do a line plot of date against Average Price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nAg5pT9cqHAR"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub_city = properties_2[properties_2['Borough'] == 'City of London'].copy()\n",
    "plt.plot(sub_city.date, sub_city['Average price'])\n",
    "plt.title(\"Average price by date in City of London\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Average price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aWTPqSJeqHnC"
   },
   "source": [
    "To limit the number of data points you have, you might want to extract the year from every date value your *date* column. \n",
    "\n",
    "To this end, you *could* apply a ***lambda function***. Your logic could work as follows:\n",
    "1. look through the `Date` column\n",
    "2. extract the year from each individual value in that column \n",
    "3. store that corresponding year as separate column. \n",
    "\n",
    "Whether you go ahead with this is up to you. Just so long as you answer our initial brief: which boroughs of London have seen the greatest house price increase, on average, over the past two decades? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e0DF92cyqnu8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>ID</th>\n",
       "      <th>date</th>\n",
       "      <th>Average price</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>City of London</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>91448.98487</td>\n",
       "      <td>1</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>E09000002</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>50460.22660</td>\n",
       "      <td>1</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>93284.51832</td>\n",
       "      <td>1</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>64958.09036</td>\n",
       "      <td>1</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>71306.56698</td>\n",
       "      <td>1</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Borough         ID       date  Average price  month  year\n",
       "0      City of London  E09000001 1995-01-01    91448.98487      1  1995\n",
       "1  Barking & Dagenham  E09000002 1995-01-01    50460.22660      1  1995\n",
       "2              Barnet  E09000003 1995-01-01    93284.51832      1  1995\n",
       "3              Bexley  E09000004 1995-01-01    64958.09036      1  1995\n",
       "4               Brent  E09000005 1995-01-01    71306.56698      1  1995"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfg = properties_2.copy()\n",
    "dfg['month'] = dfg['date'].dt.month\n",
    "dfg['year'] = dfg['date'].apply(lambda x: x.year)\n",
    "dfg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset2 = dfg[dfg['Borough'] == 'City of London'].copy()\n",
    "subset3 = subset2.groupby('year')['Average price'].mean()\n",
    "subset3.plot()\n",
    "plt.title(\"Average price by year in City of London\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Average price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2knuTxAEqoJ4"
   },
   "source": [
    "**3. Modeling**\n",
    "\n",
    "Consider creating a function that will calculate a ratio of house prices, comparing the price of a house in 2018 to the price in 1998.\n",
    "\n",
    "Consider calling this function create_price_ratio.\n",
    "\n",
    "You'd want this function to:\n",
    "1. Take a filter of dfg, specifically where this filter constrains the London_Borough, as an argument. For example, one admissible argument should be: dfg[dfg['London_Borough']=='Camden'].\n",
    "2. Get the Average Price for that Borough, for the years 1998 and 2018.\n",
    "4. Calculate the ratio of the Average Price for 1998 divided by the Average Price for 2018.\n",
    "5. Return that ratio.\n",
    "\n",
    "Once you've written this function, you ultimately want to use it to iterate through all the unique London_Boroughs and work out the ratio capturing the difference of house prices between 1998 and 2018.\n",
    "\n",
    "Bear in mind: you don't have to write a function like this if you don't want to. If you can solve the brief otherwise, then great! \n",
    "\n",
    "***Hint***: This section should test the skills you acquired in:\n",
    "- Python Data Science Toolbox - Part One, all modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cKTyr437UgDa"
   },
   "outputs": [],
   "source": [
    "def create_price_ratio(f_df, actual_year = 2018):\n",
    "    subset = f_df.groupby('year')['Average price'].mean()\n",
    "    ratio = subset[actual_year - 20] / subset[actual_year]\n",
    "    return ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfg = dfg.rename(columns = {'Borough':'London_Borough'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20261973503252542"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "create_price_ratio(dfg[dfg['London_Borough']=='Camden'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'City of London': 0.18862157770244364, 'Barking & Dagenham': 0.20422256235393685, 'Barnet': 0.229452741207858, 'Bexley': 0.2353507654063011, 'Brent': 0.2043086864360114, 'Bromley': 0.24421308489837315, 'Camden': 0.20261973503252542, 'Croydon': 0.23803288028014052, 'Ealing': 0.23194048191708755, 'Enfield': 0.23455064269011858, 'Greenwich': 0.20995010893854213, 'Hackney': 0.16133493530705728, 'Hammersmith & Fulham': 0.24167443054605847, 'Haringey': 0.19475619095546956, 'Harrow': 0.24635417785626296, 'Havering': 0.2312015578701476, 'Hillingdon': 0.23807975835429931, 'Hounslow': 0.25148317824115635, 'Islington': 0.20643891170300288, 'Kensington & Chelsea': 0.19675491852791563, 'Kingston upon Thames': 0.2341619023428255, 'Lambeth': 0.20170435486140825, 'Lewisham': 0.18351246764721704, 'Merton': 0.21091380604361798, 'Newham': 0.18848754146121072, 'Redbridge': 0.2236545053715767, 'Richmond upon Thames': 0.24967779731157863, 'Southwark': 0.18127484171283456, 'Sutton': 0.24280551426824518, 'Tower Hamlets': 0.21613672276235532, 'Waltham Forest': 0.1713867782439487, 'Wandsworth': 0.21018518091593216, 'Westminster': 0.1867914047302468, 'Inner London': 0.19339152138506577, 'Outer London': 0.22629811224913096, 'NORTH EAST': 0.35359672319259133, 'NORTH WEST': 0.297316807594284, 'YORKS & THE HUMBER': 0.2979679995370457, 'EAST MIDLANDS': 0.2752747145789506, 'WEST MIDLANDS': 0.3019996429372807, 'EAST OF ENGLAND': 0.23998652920722377, 'LONDON': 0.2136854299558557, 'SOUTH EAST': 0.2612065640720062, 'SOUTH WEST': 0.26347009819935346, 'England': 0.26243599006976337}\n"
     ]
    }
   ],
   "source": [
    "boroughs = dfg['London_Borough'].unique()\n",
    "results = {}\n",
    "for borough in boroughs:\n",
    "    results[borough] = create_price_ratio(dfg[dfg['London_Borough']== borough])\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NORTH EAST</th>\n",
       "      <td>0.353597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEST MIDLANDS</th>\n",
       "      <td>0.302000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YORKS &amp; THE HUMBER</th>\n",
       "      <td>0.297968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NORTH WEST</th>\n",
       "      <td>0.297317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EAST MIDLANDS</th>\n",
       "      <td>0.275275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Ratio\n",
       "NORTH EAST          0.353597\n",
       "WEST MIDLANDS       0.302000\n",
       "YORKS & THE HUMBER  0.297968\n",
       "NORTH WEST          0.297317\n",
       "EAST MIDLANDS       0.275275"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results = pd.DataFrame(results.values(), results.keys(), columns = ['Ratio'])\n",
    "df_results = df_results.sort_values('Ratio', ascending = False)\n",
    "df_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_results.plot(kind='bar')\n",
    "ax.figure.set_size_inches(12, 6)\n",
    "plt.title('Growth ratio by borough')\n",
    "plt.xlabel('Borough')\n",
    "plt.ylabel('Ratio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_boroughs = ['City of London', 'Inner London',\n",
    "       'Outer London', 'NORTH EAST', 'NORTH WEST', 'YORKS & THE HUMBER',\n",
    "       'EAST MIDLANDS', 'WEST MIDLANDS', 'EAST OF ENGLAND', 'LONDON',\n",
    "       'SOUTH EAST', 'SOUTH WEST', 'England']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KkCGDQ3ni+3Pv+1K6dGkVKFDA6Zie9Py14nd87+9j0aJFiouLU9OmTRUXF2d/BAYGqmLFig4zz1+5ckUfffSRcufOrWTJkilZsmRKkSKFrl696jTE/XFw3v3f77106dKaPHmyPvvsM61bt85pBvW7ZciQQcePH3+s/X6Q9u3b6+LFi2ratKkOHjyokydPqlevXvYZ1xOHmKdJk0Zvvvmmpk6dqrFjx+rcuXPavn273nzzTfn6+jrUlaR33nlHc+bM0bBhw7RixQrNmjVL/v7+qlKlykPP+fj4eIfz8lFun0p07tw51alTR8YYzZo1y2mI/INm73/Qcw/Sv39/ffnll+rTp4+WLVumefPmKV++fKpevbrTrSoA/p5I5AH8raRLl05BQUFJXjRNnz5dGzduvO8SQ8HBwUqZMqVD2dmzZ5UpUyani50MGTIoWbJkOnv2rL2satWq9ovk3377TdWrV1fhwoWVMWNG/fbbb1q9erWuX7/+WIl8UvdBBgQEOM1KfD+ZM2d2KtuwYYNq1KghSfr222+1evVqbdy4UT169JCkR9723c6fPy9jTJLxEuciuPu9up9MmTIlWXbr1i1duXJF0p3jf/vttzV9+nRduHBBp0+f1vfff69WrVopICDgkfb3fnES9zHx3/sdz73H8rTnzqNIfI8zZszo9FxSZdKTnz+Jz997r+vjvi9PGt+K3/G9+3jy5ElJUqlSpRwa9Pz8/DRr1iydOXPGXrdx48b6+uuv1apVKy1atEgbNmzQxo0blT59+if6fCTivPu/927WrFlq1qyZxo8fb7/nvGnTpoqNjXV6bWBg4FO97/eqWrWqJk2apD/++EO5cuVSpkyZNHfuXH366aeS5HDv/OjRo9WoUSO1a9dOadOmVbFixZQ/f37VrVtXAQEB9mNduHChJkyYoLFjx6pTp06qUKGCXnvtNS1ZskTnzp1zmnPgXrly5XI4J/v16/dIx3L+/HlVr15dx48f15IlS5QzZ06H59OmTasbN27o2rVrTq89d+6c0qRJ80hx7rZr1y717t1bffv2Va9evVSpUiW9+OKLmj9/vlKlSqUuXbo89jYBuF8yT+8AANzN19dXVapU0eLFixUTE+NwQVywYEFJuu9Sb0n1TKRNm1br16+XMcbh+VOnTikuLk7p0qWzl1WtWlUTJkzQhg0btH79evXs2VPSnV7xJUuW6MiRI0qRIoWef/55Kw71kSR1TDNnzpSfn59+/fVXh0Tt7kmPHlfq1Knl4+OjmJgYp+cSe3fvfq/uJ6mL+NjYWPn7+zv09L/zzjsaNGiQJk6cqBs3biguLk5t27Z95P29X5zcuXNL+r9E5H7Hc++xPM25k/g7uHsiMsm54SN16tSy2Wz2hPRhx/M0Evft3LlzDuV3vy/ZsmVzeC6p9+VpPO3v+N7fSeK+zZ49+4HL6V28eFG//vqrPvnkE3Xr1s1efvPmTaf343Fx3v2fdOnSafjw4Ro+fLiio6P1888/q1u3bjp16pQWLlzoUPfcuXP2iTat0qxZM7355pvat2+f/Pz8lDt3bg0cOFA2m03ly5e310uePLmmTZumr776SkePHlWWLFmULl065c+fX2XLllWyZHcuhaOioiTdaSi6W6pUqZQ7d27t2LHjgfvzyy+/OPwu7p6M9X7Onz+vatWq6dChQ/r9999VpEgRpzqFCxeWJP3555967rnn7OWxsbE6c+aMChUq9NA499q2bZuMMU7H6ufnp6JFi2rFihWPvU0A7kePPIC/ne7duys+Pl5t27Z94HDNR1G1alVduXLFKcmdOnWq/fm769psNvXq1Us+Pj6qUKGCJKlatWpatmyZlixZogoVKthnN5Zk7120srfpYWw2m5IlS2YfGpoYf9q0aU51H7X3P3ny5Hruuec0d+5ch/oJCQn67rvvlC1bNuXNm/eh25k7d65u3Lhh//ny5cv65ZdfVL58eYf9zZw5s1599VWNGjVKY8aMUb169ZQ9e/aHbj/Rf//7X4ef16xZoyNHjqhSpUqS7sxKHRQUpO+++86h3rFjx7R06VKH3/v9POq5kzFjRgUGBmr79u0O9ebNm+fwc/LkyVWyZEn99NNPunXrlr38ypUrjzwT/aMqUKCAJDnNFF6lShVJcnpfNm7cqF27dj3S+/KonvZ3fK+aNWsqWbJkOnDggEqWLJnkQ7rz+TDGOPX8jx8/3n57x5PivEta9uzZ1b59e1WvXl1btmxxeC4uLk5Hjx61N8RaKVmyZCpQoIBy586tixcvaty4cXrppZeSbOhJnTq1ihQponTp0unnn3/Wnj171LFjR/vziYn3unXrHF539uxZ7d2716nh616FCxd2OBcflsgnJvEHDx7U4sWLVaxYsSTr1apVS4GBgU6rEySu7lC/fv0HxknK/Y715s2b2rJly0OPFcDfAz3yAP52ypUrp2+++UbvvfeeihcvrjZt2uiZZ56x9xgnLhl075DUpDRt2lTffPONmjVrpsOHD6tw4cJatWqVBgwYoDp16jgMk8+QIYMKFSqkxYsXq3LlygoODpZ0J5E/d+6czp07p6FDhzpsP7E3ZNy4cQoJCVFgYKBy5MiR5NBUq9StW1dDhw5V48aN1aZNG509e1ZffvllkkOWCxcurJkzZ2rWrFnKmTOnAgMD7T089xo4cKCqV6+uypUr6/3335e/v79GjRqlHTt2aMaMGY90L6avr6+qV6+uLl26KCEhQZ9//rkuXbqkvn37OtXt2LGjvYdp0qRJj/UebNq0Sa1atdKrr76qo0ePqkePHsqaNavatWsn6U4vWq9evfTxxx+radOmeuONN3T27Fn17dtXgYGB+uSTTx4a41HPHZvNprfeeksTJ05Urly5VLRoUW3YsEHTp0932ma/fv1Ut25d1axZUx07dlR8fLwGDx6sFClSPHVv8d2yZcumnDlzat26derQoYO9PF++fGrTpo1GjhwpHx8f1a5dW4cPH1avXr0UFhamzp07W7YP0tP9ju8VERGhfv36qUePHjp48KBq1aql1KlT6+TJk9qwYYOSJ0+uvn37KmXKlKpQoYIGDx6sdOnSKSIiQitWrNCECRPsS4k9Kc67Oy5evKjKlSurcePGyp8/v0JCQrRx40YtXLhQL7/8skPd7du369q1a6pcufJDt3vkyBH70oSJjVCzZ8+WdOf3n9hYc+rUKQ0ZMkTlypVTSEiIdu/erS+++EI+Pj4O8zJI0pw5c3TixAkVKFBAN27c0PLlyzVixAi1bdtWL730kr3eyy+/rN69e+udd97RsWPHVLx4ccXExGjw4MG6du2aQ9L/tK5fv25fNnH48OGKi4tzSKrTp0+vXLlySbpzn3/Pnj3Vq1cvpUmTRjVq1NDGjRvVp08ftWrVyqmBJPH9SlwWcdOmTfbRUInzLvznP/9RqVKl1KdPH127dk0VKlTQxYsXNXLkSB06dCjJRmEAf0Mem2YPAB4iKirKtGjRwuTIkcMEBASYwMBAkzt3btO0aVPz+++/O9Rt1qyZSZ48eZLbOXv2rGnbtq3JnDmzSZYsmQkPDzfdu3c3N27ccKrbuXNnI8n079/foTxPnjxGktm+fbvTa4YPH25y5MhhfH19HWYJrlixonnmmWec6jdr1syEh4c/9PglmXfffTfJ5yZOnGjy5ctnAgICTM6cOc3AgQPNhAkTjCSHGZYPHz5satSoYUJCQowke9z7zXi9cuVKU6VKFZM8eXITFBRknn/+efPLL788dF8Tt/f555+bvn37mmzZshl/f39TrFgxs2jRovu+LiIiwhQoUOCh20+UOHv44sWLTZMmTUyqVKnss4QntWTS+PHjTZEiRYy/v78JDQ01L730ksMyZcZYc+5cvHjRtGrVymTMmNEkT57c1KtXzxw+fNhp9nBjjPnxxx9N4cKFjb+/v8mePbsZNGiQ6dChg0mdOrVDvfv9/sPDw02zZs0e+l716tXLpE6d2mlf4+Pjzeeff27y5s1r/Pz8TLp06cxbb71ljh496lDvUc/fB82ebsyT/443btyY5PM//fSTqVy5skmZMqUJCAgw4eHhpmHDhua3336z1zl27Jh55ZVXTOrUqU1ISIipVauW2bFjh9N797iz1nPeNTPGGHPjxg3Ttm1bU6RIEZMyZUoTFBRk8uXLZz755BNz9epVh9f16tXLpEuXLsnv23slvs9JPe7+vZ09e9bUqFHDpE+f3vj5+Zns2bOb9957z2FpwbuP+9lnn7V/p5UsWdJMmDAhyVU4YmJiTPv27U3u3LlNYGCgyZIli6lbt65Zu3btQ/f9cSR+Zh7lWBONGDHC5M2b1/77++STT8ytW7ec6j1ou3e7cOGC6dGjhylQoIAJDg42GTJkMJUqVTILFiyw9FgBuI7NGGNc2lIAAEAStm/frqJFi+qbb76x92j+G92+fVvPPvussmbNqsWLF1u23RMnTihHjhyaOnXqI88UbzV+x39frjrv7hYfH6/cuXOrcePG6t+/v0tiAMC/FYk8AMCtDhw4oCNHjujjjz9WdHS09u/fb7+N4d8gMjJS1atXV+bMmRUbG6sxY8ZoxYoVWrx48WOtiPAoPvroI/3vf/9TVFSU05JWrvRv/x3/HbnzvEs0ZcoUvf/++9q3b99T39YAAHDEPfIAALf69NNPNW3aNBUoUEA//PDDvy7Bu3z5st5//32dPn1afn5+Kl68uBYsWOCSZKpnz54KDg7W8ePHFRYWZvn27+ff/jv+O3LneZcoISFB//3vf0niAcAF6JEHAAAAAMCLsPwcAAAAAABehEQeAAAAAAAvwj3ySUhISNCJEycUEhLySOsmAwAAAADwNIwxunz5srJkyfLQSWpJ5JNw4sQJt04KBAAAAACAJB09elTZsmV7YB0S+SSEhIRIuvMGpkyZ0sN7AwAAAAD4p7t06ZLCwsLs+eiDkMgnIXE4fcqUKUnkAQAAAABu8yi3dzPZHQAAAAAAXoREHgAAAAAAL0IiDwAAAACAF+EeeQAAAADAfcXHx+v27due3o1/BH9//4cuLfcoSOQBAAAAAE6MMYqNjdWFCxc8vSv/GD4+PsqRI4f8/f2fajsk8gAAAAAAJ4lJfIYMGRQcHPxIs6nj/hISEnTixAnFxMQoe/bsT/V+ksgDAAAAABzEx8fbk/i0adN6enf+MdKnT68TJ04oLi5Ofn5+T7wdJrsDAAAAADhIvCc+ODjYw3vyz5I4pD4+Pv6ptkMiDwAAAABIEsPprWXV+0kiDwAAAACAFyGRBwAAAADAizDZHQAAAADgkUV0m+/WeIcH1XVrvPs5fPiwcuTIoa1bt+rZZ5/16L7QIw8AAAAA+Mdo3ry5bDabbDabkiVLpuzZs+udd97R+fPnH2sb9evXdygLCwtTTEyMChUqZPEePz4SeQAAAADAP0qtWrUUExOjw4cPa/z48frll1/Url27p9qmr6+vMmXKpGTJPD+wnUQeAAAAAPCPEhAQoEyZMilbtmyqUaOGGjVqpMWLF0u6s/RbZGSkcuTIoaCgIOXLl08jRoywv7ZPnz6aMmWK5s2bZ+/ZX758uQ4fPiybzaaoqCh73RUrVqh06dIKCAhQ5syZ1a1bN8XFxbn8+DzflAAAAAAAgIscPHhQCxculJ+fnyQpISFB2bJl0/fff6906dJpzZo1atOmjTJnzqzXXntN77//vnbt2qVLly5p0qRJkqQ0adLoxIkTDts9fvy46tSpo+bNm2vq1KnavXu3WrdurcDAQPXp08elx0QiDwAAAAD4R/n111+VIkUKxcfH68aNG5KkoUOHSpL8/PzUt29fe90cOXJozZo1+v777/Xaa68pRYoUCgoK0s2bN5UpU6b7xhg1apTCwsL09ddfy2azKX/+/Dpx4oQ++ugj9e7dWz4+rhsATyIPAAAAAPhHqVy5skaPHq1r165p/Pjx2rt3r9577z3782PGjNH48eN15MgRXb9+Xbdu3Xrsmeh37dqlMmXKyGaz2cvKlSunK1eu6NixY8qePbtVh+PE4/fIjxo1Sjly5FBgYKBKlCihlStX3rfu3LlzVb16daVPn14pU6ZUmTJltGjRIoc6kydPtt/HcPcjsRUGAAAAAPDPljx5cuXOnVtFihTRV199pZs3b9p74b///nt17txZLVu21OLFixUVFaUWLVro1q1bjxXDGOOQxCeWSXIqt5pHe+RnzZqlTp06adSoUSpXrpzGjh2r2rVr66+//kqy9eKPP/5Q9erVNWDAAKVKlUqTJk1SvXr1tH79ehUrVsxeL2XKlNqzZ4/DawMDAy3d9yddO/HvsgYiAAAAAPxbfPLJJ6pdu7beeecdrVy5UmXLlnWYxf7AgQMO9f39/RUfH//AbRYsWFBz5sxxSOjXrFmjkJAQZc2a1fqDuItHe+SHDh2qyMhItWrVSgUKFNDw4cMVFham0aNHJ1l/+PDh+vDDD1WqVCnlyZNHAwYMUJ48efTLL7841LPZbMqUKZPDAwAAAADw71SpUiU988wzGjBggHLnzq1NmzZp0aJF2rt3r3r16qWNGzc61I+IiND27du1Z88enTlzRrdv33baZrt27XT06FG999572r17t+bNm6dPPvlEXbp0cen98ZIHe+Rv3bqlzZs3q1u3bg7lNWrU0Jo1ax5pGwkJCbp8+bLSpEnjUH7lyhWFh4crPj5ezz77rD799FOHHvt73bx5Uzdv3rT/fOnSpcc4EgAAAAD49/DWUcZdunRRixYttHfvXkVFRalRo0ay2Wx644031K5dO/3vf/+z123durWWL1+ukiVL6sqVK1q2bJkiIiIctpc1a1YtWLBAH3zwgYoWLao0adIoMjJSPXv2dPmxeCyRP3PmjOLj45UxY0aH8owZMyo2NvaRtjFkyBBdvXpVr732mr0sf/78mjx5sgoXLqxLly5pxIgRKleunLZt26Y8efIkuZ2BAwc6zFoIAAAAAPBOkydPTrK8cePGaty4sSRp0qRJ9qXlEg0cOND+//Tp09vXnb9b4j3wiSpWrKgNGzY85R4/Po/PWp/U5ACPMjHAjBkz1KdPH82bN08ZMmSwlz///PN6/vnn7T+XK1dOxYsX18iRI/XVV18lua3u3burS5cu9p8vXbqksLCwxz0Ul+O+fAAAAACAxxL5dOnSydfX16n3/dSpU0699PeaNWuWIiMj9cMPP6hatWoPrOvj46NSpUpp3759960TEBCggICAR995AAAAAAA8xGOT3fn7+6tEiRJasmSJQ/mSJUtUtmzZ+75uxowZat68uaZPn666dR/e02yMUVRUlDJnzvzU+wwAAAAAgKd5dGh9ly5d1KRJE5UsWVJlypTRuHHjFB0drbZt20q6M+T9+PHjmjp1qqQ7SXzTpk01YsQIPf/88/be/KCgIIWGhkqS+vbtq+eff1558uTRpUuX9NVXXykqKkrffPONZw4SAAAAAAALeTSRb9Sokc6ePat+/fopJiZGhQoV0oIFCxQeHi5JiomJUXR0tL3+2LFjFRcXp3fffVfvvvuuvbxZs2b2CQ0uXLigNm3aKDY2VqGhoSpWrJj++OMPlS5d2q3HBgAAAADe7t7J3fB0rHo/bYbfjJNLly4pNDRUFy9eVMqUKZOs44mJ5/4tMQEAAAB4Vnx8vPbu3asMGTIobdq0nt6df4yLFy/qxIkTyp07t/z8/Byee5Q8NJHHZ60HAAAAAPy9+Pr6KlWqVDp16pQkKTg4+JFWF8P9JSQk6PTp0woODlayZE+XipPIAwAAAACcZMqUSZLsyTyeno+Pj7Jnz/7UjSIk8gAAAAAAJzabTZkzZ1aGDBl0+/ZtT+/OP4K/v798fJ5+8TgSefztcF8+AAAA8Pfh6+srX19fT+8G7uKxdeQBAAAAAMDjI5EHAAAAAMCLMLQeEMP5AQAAAHgPeuQBAAAAAPAi9MgDHsIoAAAAAABPgh55AAAAAAC8CIk8AAAAAABehEQeAAAAAAAvQiIPAAAAAIAXIZEHAAAAAMCLMGs98C/iiZnynzTm08YFAAAA/qnokQcAAAAAwIuQyAMAAAAA4EVI5AEAAAAA8CIk8gAAAAAAeBEmuwPwj8MEewAAAPgnI5EHAAvQeAAAAAB3IZEHAC9F4wEAAMC/E/fIAwAAAADgRUjkAQAAAADwIiTyAAAAAAB4Ee6RBwA8Mu7LBwAA8DwSeQDA3xqNBwAAAI4YWg8AAAAAgBchkQcAAAAAwIswtB4AgHswnB8AAPyd0SMPAAAAAIAXIZEHAAAAAMCLkMgDAAAAAOBFSOQBAAAAAPAiJPIAAAAAAHgREnkAAAAAALwIiTwAAAAAAF6ERB4AAAAAAC9CIg8AAAAAgBdJ5ukdAAAAUkS3+U/82sOD6lq4JwAA4O+OHnkAAAAAALwIiTwAAAAAAF6ERB4AAAAAAC9CIg8AAAAAgBchkQcAAAAAwIuQyAMAAAAA4EVI5AEAAAAA8CIk8gAAAAAAeBESeQAAAAAAvAiJPAAAAAAAXoREHgAAAAAAL0IiDwAAAACAF0nm6R0AAACeEdFt/hO/9vCguhbuCQAAeBz0yAMAAAAA4EVI5AEAAAAA8CIk8gAAAAAAeBESeQAAAAAAvAiJPAAAAAAAXoREHgAAAAAAL0IiDwAAAACAFyGRBwAAAADAiyTz9A4AAIB/l4hu85/odYcH1bV4TwAA8E70yAMAAAAA4EXokQcAAP94nhgFwMgDAICr0CMPAAAAAIAXIZEHAAAAAMCLkMgDAAAAAOBFSOQBAAAAAPAiJPIAAAAAAHgRZq0HAAD4h2CmfAD4d6BHHgAAAAAAL0IiDwAAAACAFyGRBwAAAADAi3CPPAAAAJ6YJ+7LZy4AAP929MgDAAAAAOBFSOQBAAAAAPAiDK0HAAAAHoLh/AD+TjzeIz9q1CjlyJFDgYGBKlGihFauXHnfunPnzlX16tWVPn16pUyZUmXKlNGiRYuc6s2ZM0cFCxZUQECAChYsqB9//NGVhwAAAAAAgNt4tEd+1qxZ6tSpk0aNGqVy5cpp7Nixql27tv766y9lz57dqf4ff/yh6tWra8CAAUqVKpUmTZqkevXqaf369SpWrJgkae3atWrUqJE+/fRTNWjQQD/++KNee+01rVq1Ss8995y7DxEAAAB4Ik86CkBiJADwT+fRHvmhQ4cqMjJSrVq1UoECBTR8+HCFhYVp9OjRSdYfPny4PvzwQ5UqVUp58uTRgAEDlCdPHv3yyy8OdapXr67u3bsrf/786t69u6pWrarhw4e76agAAAAAAHAdjyXyt27d0ubNm1WjRg2H8ho1amjNmjWPtI2EhARdvnxZadKksZetXbvWaZs1a9Z84DZv3rypS5cuOTwAAAAAAPg78lgif+bMGcXHxytjxowO5RkzZlRsbOwjbWPIkCG6evWqXnvtNXtZbGzsY29z4MCBCg0NtT/CwsIe40gAAAAAAHAfj092Z7PZHH42xjiVJWXGjBnq06ePZs2apQwZMjzVNrt3766LFy/aH0ePHn2MIwAAAAAAwH08NtldunTp5Ovr69RTfurUKace9XvNmjVLkZGR+uGHH1StWjWH5zJlyvTY2wwICFBAQMBjHgEAAAAAAO7nsR55f39/lShRQkuWLHEoX7JkicqWLXvf182YMUPNmzfX9OnTVbeu82ycZcqUcdrm4sWLH7hNAAAAAAC8hUeXn+vSpYuaNGmikiVLqkyZMho3bpyio6PVtm1bSXeGvB8/flxTp06VdCeJb9q0qUaMGKHnn3/e3vMeFBSk0NBQSVLHjh1VoUIFff7553rppZc0b948/fbbb1q1apVnDhIAAAAAAAt5NJFv1KiRzp49q379+ikmJkaFChXSggULFB4eLkmKiYlRdHS0vf7YsWMVFxend999V++++669vFmzZpo8ebIkqWzZspo5c6Z69uypXr16KVeuXJo1axZryAMAAAAPwdr1gHfwaCIvSe3atVO7du2SfC4xOU+0fPnyR9pmw4YN1bBhw6fcMwAAAAAA/n48Pms9AAAAAAB4dCTyAAAAAAB4EY8PrQcAAADw78V9+cDjo0ceAAAAAAAvQiIPAAAAAIAXIZEHAAAAAMCLkMgDAAAAAOBFSOQBAAAAAPAiJPIAAAAAAHgREnkAAAAAALwIiTwAAAAAAF6ERB4AAAAAAC9CIg8AAAAAgBdJ5ukdAAAAAAB3iug2/4lfe3hQXQv3BHgy9MgDAAAAAOBFSOQBAAAAAPAiDK0HAAAAABdjOD+sRI88AAAAAABehEQeAAAAAAAvQiIPAAAAAIAXIZEHAAAAAMCLMNkdAAAAAPwDMcHePxc98gAAAAAAeBESeQAAAAAAvAiJPAAAAAAAXoR75AEAAAAAluC+fPegRx4AAAAAAC9CIg8AAAAAgBchkQcAAAAAwIuQyAMAAAAA4EVI5AEAAAAA8CIk8gAAAAAAeBESeQAAAAAAvAiJPAAAAAAAXoREHgAAAAAAL0IiDwAAAACAFyGRBwAAAADAi5DIAwAAAADgRUjkAQAAAADwIiTyAAAAAAB4ERJ5AAAAAAC8CIk8AAAAAABehEQeAAAAAAAvkszTOwAAAAAAwJOK6Db/iV97eFBdC/fEfeiRBwAAAADAi5DIAwAAAADgRUjkAQAAAADwIiTyAAAAAAB4ERJ5AAAAAAC8CIk8AAAAAABehEQeAAAAAAAvQiIPAAAAAIAXIZEHAAAAAMCLkMgDAAAAAOBFSOQBAAAAAPAiJPIAAAAAAHgREnkAAAAAALwIiTwAAAAAAF6ERB4AAAAAAC9CIg8AAAAAgBdJ5ukdAAAAAADAm0R0m//Erz08qO5Tx6dHHgAAAAAAL0IiDwAAAACAFyGRBwAAAADAi5DIAwAAAADgRUjkAQAAAADwIiTyAAAAAAB4ERJ5AAAAAAC8CIk8AAAAAABehEQeAAAAAAAvQiIPAAAAAIAXIZEHAAAAAMCLkMgDAAAAAOBFSOQBAAAAAPAiJPIAAAAAAHgREnkAAAAAALyIxxP5UaNGKUeOHAoMDFSJEiW0cuXK+9aNiYlR48aNlS9fPvn4+KhTp05OdSZPniybzeb0uHHjhguPAgAAAAAA9/BoIj9r1ix16tRJPXr00NatW1W+fHnVrl1b0dHRSda/efOm0qdPrx49eqho0aL33W7KlCkVExPj8AgMDHTVYQAAAAAA4DYeTeSHDh2qyMhItWrVSgUKFNDw4cMVFham0aNHJ1k/IiJCI0aMUNOmTRUaGnrf7dpsNmXKlMnhAQAAAADAP4HHEvlbt25p8+bNqlGjhkN5jRo1tGbNmqfa9pUrVxQeHq5s2bLphRde0NatWx9Y/+bNm7p06ZLDAwAAAACAvyOPJfJnzpxRfHy8MmbM6FCeMWNGxcbGPvF28+fPr8mTJ+vnn3/WjBkzFBgYqHLlymnfvn33fc3AgQMVGhpqf4SFhT1xfAAAAAAAXMnjk93ZbDaHn40xTmWP4/nnn9dbb72lokWLqnz58vr++++VN29ejRw58r6v6d69uy5evGh/HD169InjAwAAAADgSsk8FThdunTy9fV16n0/deqUUy/90/Dx8VGpUqUe2CMfEBCggIAAy2ICAAAAAOAqHuuR9/f3V4kSJbRkyRKH8iVLlqhs2bKWxTHGKCoqSpkzZ7ZsmwAAAAAAeIrHeuQlqUuXLmrSpIlKliypMmXKaNy4cYqOjlbbtm0l3Rnyfvz4cU2dOtX+mqioKEl3JrQ7ffq0oqKi5O/vr4IFC0qS+vbtq+eff1558uTRpUuX9NVXXykqKkrffPON248PAAAAAACreTSRb9Sokc6ePat+/fopJiZGhQoV0oIFCxQeHi5JiomJcVpTvlixYvb/b968WdOnT1d4eLgOHz4sSbpw4YLatGmj2NhYhYaGqlixYvrjjz9UunRptx0XAAAAAACu4tFEXpLatWundu3aJfnc5MmTncqMMQ/c3rBhwzRs2DArdg0AAAAAgL8dj89aDwAAAAAAHh2JPAAAAAAAXoREHgAAAAAAL0IiDwAAAACAFyGRBwAAAADAi5DIAwAAAADgRUjkAQAAAADwIiTyAAAAAAB4ERJ5AAAAAAC8CIk8AAAAAABe5KkT+WPHjun48eNW7AsAAAAAAHiIJ0rkExIS1K9fP4WGhio8PFzZs2dXqlSp9OmnnyohIcHqfQQAAAAAAP9fsid5UY8ePTRhwgQNGjRI5cqVkzFGq1evVp8+fXTjxg3179/f6v0EAAAAAAB6wkR+ypQpGj9+vF588UV7WdGiRZU1a1a1a9eORB4AAAAAABd5oqH1586dU/78+Z3K8+fPr3Pnzj31TgEAAAAAgKQ9USJftGhRff31107lX3/9tYoWLfrUOwUAAAAAAJL2REPrv/jiC9WtW1e//fabypQpI5vNpjVr1ujo0aNasGCB1fsIAAAAAAD+vyfqka9YsaL27t2rBg0a6MKFCzp37pxefvll7dmzR+XLl7d6HwEAAAAAwP/3RD3ykpQlSxYmtQMAAAAAwM0eOZHfvn27ChUqJB8fH23fvv2BdYsUKfLUOwYAAAAAAJw9ciL/7LPPKjY2VhkyZNCzzz4rm80mY4xTPZvNpvj4eEt3EgAAAAAA3PHIifyhQ4eUPn16+/8BAAAAAID7PXIiHx4ebv//kSNHVLZsWSVL5vjyuLg4rVmzxqEuAAAAAACwzhPNWl+5cmWdO3fOqfzixYuqXLnyU+8UAAAAAABI2hMl8sYY2Ww2p/KzZ88qefLkT71TAAAAAAAgaY+1/NzLL78s6c6Eds2bN1dAQID9ufj4eG3fvl1ly5a1dg8BAAAAAIDdYyXyoaGhku70yIeEhCgoKMj+nL+/v55//nm1bt3a2j0EAAAAAAB2j5XIT5o0SZIUERGh999/n2H0AAAAAAC42WMl8ok++eQTq/cDAAAAAAA8gidK5CVp9uzZ+v777xUdHa1bt245PLdly5an3jEAAAAAAODsiWat/+qrr9SiRQtlyJBBW7duVenSpZU2bVodPHhQtWvXtnofAQAAAADA//dEifyoUaM0btw4ff311/L399eHH36oJUuWqEOHDrp48aLV+wgAAAAAAP6/J0rko6Oj7cvMBQUF6fLly5KkJk2aaMaMGdbtHQAAAAAAcPBEiXymTJl09uxZSVJ4eLjWrVsnSTp06JCMMdbtHQAAAAAAcPBEiXyVKlX0yy+/SJIiIyPVuXNnVa9eXY0aNVKDBg0s3UEAAAAAAPB/nmjW+nHjxikhIUGS1LZtW6VJk0arVq1SvXr1SOQBAAAAAHChJ+qR9/HxUbJk/9cG8Nprr+njjz/Wvn37lDdvXst2DgAAAAAAOHqsRP7ChQt68803lT59emXJkkVfffWVEhIS1Lt3b+XKlUvr1q3TxIkTXbWvAAAAAAD86z3W0PqPP/5Yf/zxh5o1a6aFCxeqc+fOWrhwoW7cuKEFCxaoYsWKrtpPAAAAAACgx0zk58+fr0mTJqlatWpq166dcufOrbx582r48OEu2j0AAAAAAHC3xxpaf+LECRUsWFCSlDNnTgUGBqpVq1Yu2TEAAAAAAODssRL5hIQE+fn52X/29fVV8uTJLd8pAAAAAACQtMcaWm+MUfPmzRUQECBJunHjhtq2beuUzM+dO9e6PQQAAAAAAHaPlcg3a9bM4ee33nrL0p0BAAAAAAAP9liJ/KRJk1y1HwAAAAAA4BE81j3yAAAAAADAs0jkAQAAAADwIiTyAAAAAAB4ERJ5AAAAAAC8CIk8AAAAAABehEQeAAAAAAAvQiIPAAAAAIAXIZEHAAAAAMCLkMgDAAAAAOBFSOQBAAAAAPAiJPIAAAAAAHgREnkAAAAAALwIiTwAAAAAAF6ERB4AAAAAAC9CIg8AAAAAgBchkQcAAAAAwIuQyAMAAAAA4EVI5AEAAAAA8CIk8gAAAAAAeBESeQAAAAAAvAiJPAAAAAAAXoREHgAAAAAAL0IiDwAAAACAFyGRBwAAAADAi5DIAwAAAADgRUjkAQAAAADwIiTyAAAAAAB4ERJ5AAAAAAC8CIk8AAAAAABehEQeAAAAAAAvQiIPAAAAAIAXIZEHAAAAAMCLeDyRHzVqlHLkyKHAwECVKFFCK1euvG/dmJgYNW7cWPny5ZOPj486deqUZL05c+aoYMGCCggIUMGCBfXjjz+6aO8BAAAAAHAvjybys2bNUqdOndSjRw9t3bpV5cuXV+3atRUdHZ1k/Zs3byp9+vTq0aOHihYtmmSdtWvXqlGjRmrSpIm2bdumJk2a6LXXXtP69etdeSgAAAAAALiFRxP5oUOHKjIyUq1atVKBAgU0fPhwhYWFafTo0UnWj4iI0IgRI9S0aVOFhoYmWWf48OGqXr26unfvrvz586t79+6qWrWqhg8f7sIjAQAAAADAPTyWyN+6dUubN29WjRo1HMpr1KihNWvWPPF2165d67TNmjVrPnCbN2/e1KVLlxweAAAAAAD8HXkskT9z5ozi4+OVMWNGh/KMGTMqNjb2ibcbGxv72NscOHCgQkND7Y+wsLAnjg8AAAAAgCt5fLI7m83m8LMxxqnM1dvs3r27Ll68aH8cPXr0qeIDAAAAAOAqyTwVOF26dPL19XXqKT916pRTj/rjyJQp02NvMyAgQAEBAU8cEwAAAAAAd/FYj7y/v79KlCihJUuWOJQvWbJEZcuWfeLtlilTxmmbixcvfqptAgAAAADwd+GxHnlJ6tKli5o0aaKSJUuqTJkyGjdunKKjo9W2bVtJd4a8Hz9+XFOnTrW/JioqSpJ05coVnT59WlFRUfL391fBggUlSR07dlSFChX0+eef66WXXtK8efP022+/adWqVW4/PgAAAAAArObRRL5Ro0Y6e/as+vXrp5iYGBUqVEgLFixQeHi4JCkmJsZpTflixYrZ/79582ZNnz5d4eHhOnz4sCSpbNmymjlzpnr27KlevXopV65cmjVrlp577jm3HRcAAAAAAK7i0URektq1a6d27dol+dzkyZOdyowxD91mw4YN1bBhw6fdNQAAAAAA/nY8Pms9AAAAAAB4dCTyAAAAAAB4ERJ5AAAAAAC8CIk8AAAAAABehEQeAAAAAAAvQiIPAAAAAIAXIZEHAAAAAMCLkMgDAAAAAOBFSOQBAAAAAPAiJPIAAAAAAHgREnkAAAAAALwIiTwAAAAAAF6ERB4AAAAAAC9CIg8AAAAAgBchkQcAAAAAwIuQyAMAAAAA4EVI5AEAAAAA8CIk8gAAAAAAeBESeQAAAAAAvAiJPAAAAAAAXoREHgAAAAAAL0IiDwAAAACAFyGRBwAAAADAi5DIAwAAAADgRUjkAQAAAADwIiTyAAAAAAB4ERJ5AAAAAAC8CIk8AAAAAABehEQeAAAAAAAvQiIPAAAAAIAXIZEHAAAAAMCLkMgDAAAAAOBFSOQBAAAAAPAiJPIAAAAAAHgREnkAAAAAALwIiTwAAAAAAF6ERB4AAAAAAC9CIg8AAAAAgBchkQcAAAAAwIuQyAMAAAAA4EVI5AEAAAAA8CIk8gAAAAAAeBESeQAAAAAAvAiJPAAAAAAAXoREHgAAAAAAL0IiDwAAAACAFyGRBwAAAADAi5DIAwAAAADgRUjkAQAAAADwIiTyAAAAAAB4ERJ5AAAAAAC8CIk8AAAAAABehEQeAAAAAAAvQiIPAAAAAIAXIZEHAAAAAMCLkMgDAAAAAOBFSOQBAAAAAPAiJPIAAAAAAHgREnkAAAAAALwIiTwAAAAAAF6ERB4AAAAAAC9CIg8AAAAAgBchkQcAAAAAwIuQyAMAAAAA4EVI5AEAAAAA8CIk8gAAAAAAeBESeQAAAAAAvAiJPAAAAAAAXoREHgAAAAAAL0IiDwAAAACAFyGRBwAAAADAi5DIAwAAAADgRUjkAQAAAADwIiTyAAAAAAB4ERJ5AAAAAAC8CIk8AAAAAABexOOJ/KhRo5QjRw4FBgaqRIkSWrly5QPrr1ixQiVKlFBgYKBy5sypMWPGODw/efJk2Ww2p8eNGzdceRgAAAAAALiFRxP5WbNmqVOnTurRo4e2bt2q8uXLq3bt2oqOjk6y/qFDh1SnTh2VL19eW7du1ccff6wOHTpozpw5DvVSpkypmJgYh0dgYKA7DgkAAAAAAJdK5sngQ4cOVWRkpFq1aiVJGj58uBYtWqTRo0dr4MCBTvXHjBmj7Nmza/jw4ZKkAgUKaNOmTfryyy/1yiuv2OvZbDZlypTJLccAAAAAAIA7eaxH/tatW9q8ebNq1KjhUF6jRg2tWbMmydesXbvWqX7NmjW1adMm3b5921525coVhYeHK1u2bHrhhRe0devWB+7LzZs3denSJYcHAAAAAAB/Rx5L5M+cOaP4+HhlzJjRoTxjxoyKjY1N8jWxsbFJ1o+Li9OZM2ckSfnz59fkyZP1888/a8aMGQoMDFS5cuW0b9++++7LwIEDFRoaan+EhYU95dEBAAAAAOAaHp/szmazOfxsjHEqe1j9u8uff/55vfXWWypatKjKly+v77//Xnnz5tXIkSPvu83u3bvr4sWL9sfRo0ef9HAAAAAAAHApj90jny5dOvn6+jr1vp86dcqp1z1RpkyZkqyfLFkypU2bNsnX+Pj4qFSpUg/skQ8ICFBAQMBjHgEAAAAAAO7nsR55f39/lShRQkuWLHEoX7JkicqWLZvka8qUKeNUf/HixSpZsqT8/PySfI0xRlFRUcqcObM1Ow4AAAAAgAd5dGh9ly5dNH78eE2cOFG7du1S586dFR0drbZt20q6M+S9adOm9vpt27bVkSNH1KVLF+3atUsTJ07UhAkT9P7779vr9O3bV4sWLdLBgwcVFRWlyMhIRUVF2bcJAAAAAIA38+jyc40aNdLZs2fVr18/xcTEqFChQlqwYIHCw8MlSTExMQ5ryufIkUMLFixQ586d9c033yhLliz66quvHJaeu3Dhgtq0aaPY2FiFhoaqWLFi+uOPP1S6dGm3Hx8AAAAAAFbzaCIvSe3atVO7du2SfG7y5MlOZRUrVtSWLVvuu71hw4Zp2LBhVu0eAAAAAAB/Kx6ftR4AAAAAADw6EnkAAAAAALwIiTwAAAAAAF6ERB4AAAAAAC9CIg8AAAAAgBchkQcAAAAAwIuQyAMAAAAA4EVI5AEAAAAA8CIk8gAAAAAAeBESeQAAAAAAvAiJPAAAAAAAXoREHgAAAAAAL0IiDwAAAACAFyGRBwAAAADAi5DIAwAAAADgRUjkAQAAAADwIiTyAAAAAAB4ERJ5AAAAAAC8CIk8AAAAAABehEQeAAAAAAAvQiIPAAAAAIAXIZEHAAAAAMCLkMgDAAAAAOBFSOQBAAAAAPAiJPIAAAAAAHgREnkAAAAAALwIiTwAAAAAAF6ERB4AAAAAAC9CIg8AAAAAgBchkQcAAAAAwIuQyAMAAAAA4EVI5AEAAAAA8CIk8gAAAAAAeBESeQAAAAAAvAiJPAAAAAAAXoREHgAAAAAAL0IiDwAAAACAFyGRBwAAAADAi5DIAwAAAADgRUjkAQAAAADwIiTyAAAAAAB4ERJ5AAAAAAC8CIk8AAAAAABehEQeAAAAAAAvQiIPAAAAAIAXIZEHAAAAAMCLkMgDAAAAAOBFSOQBAAAAAPAiJPIAAAAAAHgREnkAAAAAALwIiTwAAAAAAF6ERB4AAAAAAC9CIg8AAAAAgBchkQcAAAAAwIuQyAMAAAAA4EVI5AEAAAAA8CIk8gAAAAAAeBESeQAAAAAAvAiJPAAAAAAAXoREHgAAAAAAL0IiDwAAAACAFyGRBwAAAADAi5DIAwAAAADgRUjkAQAAAADwIiTyAAAAAAB4ERJ5AAAAAAC8CIk8AAAAAABehEQeAAAAAAAvQiIPAAAAAIAXIZEHAAAAAMCLkMgDAAAAAOBFSOQBAAAAAPAiJPIAAAAAAHgREnkAAAAAALwIiTwAAAAAAF6ERB4AAAAAAC9CIg8AAAAAgBfxeCI/atQo5ciRQ4GBgSpRooRWrlz5wPorVqxQiRIlFBgYqJw5c2rMmDFOdebMmaOCBQsqICBABQsW1I8//uiq3QcAAAAAwK08msjPmjVLnTp1Uo8ePbR161aVL19etWvXVnR0dJL1Dx06pDp16qh8+fLaunWrPv74Y3Xo0EFz5syx11m7dq0aNWqkJk2aaNu2bWrSpIlee+01rV+/3l2HBQAAAACAyyTzZPChQ4cqMjJSrVq1kiQNHz5cixYt0ujRozVw4ECn+mPGjFH27Nk1fPhwSVKBAgW0adMmffnll3rllVfs26hevbq6d+8uSerevbtWrFih4cOHa8aMGUnux82bN3Xz5k37zxcvXpQkXbp06b77nnDz2uMf8EO2+TDEJOa/KebTxCUmMYn59435NHGJSUxiuj4uMYlJTM/FTCw3xjx8I8ZDbt68aXx9fc3cuXMdyjt06GAqVKiQ5GvKly9vOnTo4FA2d+5ckyxZMnPr1i1jjDFhYWFm6NChDnWGDh1qsmfPft99+eSTT4wkHjx48ODBgwcPHjx48ODBw6OPo0ePPjSf9liP/JkzZxQfH6+MGTM6lGfMmFGxsbFJviY2NjbJ+nFxcTpz5owyZ8583zr326Z0p9e+S5cu9p8TEhJ07tw5pU2bVjab7bGO69KlSwoLC9PRo0eVMmXKx3rtkyImMYlJzH9jTE/FJSYxiUlMYhLT22N6Ki4xH8wYo8uXLytLliwPrevRofWSnBJlY8wDk+ek6t9b/rjbDAgIUEBAgENZqlSpHrjfD5MyZUq3fhCJSUxiEvPfGtNTcYlJTGISk5jE9PaYnopLzPsLDQ19pHoem+wuXbp08vX1deopP3XqlFOPeqJMmTIlWT9ZsmRKmzbtA+vcb5sAAAAAAHgTjyXy/v7+KlGihJYsWeJQvmTJEpUtWzbJ15QpU8ap/uLFi1WyZEn5+fk9sM79tgkAAAAAgDfx6ND6Ll26qEmTJipZsqTKlCmjcePGKTo6Wm3btpV0597148ePa+rUqZKktm3b6uuvv1aXLl3UunVrrV27VhMmTHCYjb5jx46qUKGCPv/8c7300kuaN2+efvvtN61atcotxxQQEKBPPvnEaag+MYlJTGIS858Rl5jEJCYxiUlMb4/pqbjEtI7NmEeZ2951Ro0apS+++EIxMTEqVKiQhg0bpgoVKkiSmjdvrsOHD2v58uX2+itWrFDnzp21c+dOZcmSRR999JE98U80e/Zs9ezZUwcPHlSuXLnUv39/vfzyy+48LAAAAAAAXMLjiTwAAAAAAHh0HrtHHgAAAAAAPD4SeQAAAAAAvAiJPAAAAAAAXoREHgAAAAAAL0Ii74W2bNmiP//80/7zvHnzVL9+fX388ce6deuWB/cMT+vKlSu6dOmSwwN4mKNHj+rYsWP2nzds2KBOnTpp3LhxHtwreBvOIwDAP8HUqVN18+ZNp/Jbt27ZlzX/J2DWei9UqlQpdevWTa+88ooOHjyoZ555Rg0aNNDGjRtVt25dDR8+3NO7iMdw6NAhtW/fXsuXL9eNGzfs5cYY2Ww2xcfHe3Dv4A3Kly+vNm3aqEmTJoqNjVW+fPn0zDPPaO/everQoYN69+7t6V2EF+A8gre6ceOGtm/frlOnTikhIcHhuRdffNFDe/XP8ddffyk6Otqps4j39snFxcUpMDBQUVFRKlSokKd3x6W+++47vfXWW0k+98EHH2jw4MGWx/T19VVMTIwyZMjgUH727FllyJDhH3NtnczTO/BP0KNHD1WqVEnlypVTcHCwy+Pt3btXzz77rCTphx9+UIUKFTR9+nStXr1ar7/+ussS+X379ilPnjwu2fb9HDp0SDly5HBrTEn6/fff9fvvvyd5UTBx4kRLY7355pv27WbMmFE2m83S7f+dnDx5Uu+//779vb23HdFVX6yeOo/cZceOHSpdurQk6fvvv1ehQoW0evVqLV68WG3btiUBs0B0dLTCwsKcPp/GGB09elTZs2d3SdwLFy5o9uzZOnDggD744AOlSZNGW7ZsUcaMGZU1a1ZLY/1bziNPfQ9Jd/5+L1++PMm/La58f2/dupVkTFect/369dP777/vdD10/fp1DR482PLjXLhwoZo2baozZ844PefKxvCrV69q0KBB971WOHjwoEviutPBgwfVoEED/fnnn7LZbPbPSuL3oCs/K5MnT9Zrr73mlutqT0iWLJnCw8M9nlBeuXLF6dxNmTKlpTHat2+vVKlS6YUXXnAo79y5s2bOnOmSRD6xM+xex44dU2hoqOXxEjVv3lwtW7ZUhQoVXBbjbiTyFti8ebNGjhypmzdvqnjx4qpUqZIqVqyo//znP0qRIoXl8Ywx9g/db7/9Zv9ghIWFJfmHzCr58uVT5syZVbFiRVWsWFGVKlVSvnz5XBZPknLnzq0KFSooMjJSDRs2VGBgoEvjSVLfvn3Vr18/lSxZUpkzZ3Z5Yr19+3Zt3rzZ5e/l/bjzwrJ58+aKjo5Wr1693PLeJnLXefTzzz8/cl0rezJu376tgIAASXe+ExK3nT9/fsXExFgWJylfffVVkuU2m02BgYH2997X19eymA0aNEjy3Lk7ZuPGjS39TOXIkSPJ1v1z584pR44cLrkY2759u6pVq6bQ0FAdPnxYrVu3Vpo0afTjjz/qyJEjlg8P9NR55O6EyFPfQ99++63eeecdpUuXTpkyZXKIa7PZXJLI79u3Ty1bttSaNWscyl054qtv375q27atUwJ27do19e3b1/LjbN++vV599VX17t1bGTNmtHTbD9KqVSutWLFCTZo0cet5JN1p4NuwYUOSn5emTZtaFqdjx47KkSOHfvvtN+XMmVMbNmzQ2bNn1bVrV3355ZeWxUlK9+7d1aFDB7366quKjIxU2bJlXRpPutNb27t3by1btizJ9/bcuXOWxuvZs6e6d++u7777TmnSpLF02w/i7pGgM2fO1Ouvv66ff/7ZnuC+9957mjt3rpYtW2ZprGLFislms8lms6lq1apKluz/Ut34+HgdOnRItWrVsjTm3S5fvqwaNWooLCxMLVq0ULNmzSxvdL8bQ+stEh8frw0bNmjFihVavny51q5dq+vXr6t48eJat26dpbGqVKmisLAwVatWTZGRkfrrr7+UO3durVixQs2aNdPhw4ctjZfo5MmTWrp0qf0Y9+7dq4wZM9qT+rZt21oec8eOHZo4caL++9//6ubNm2rUqJEiIyPtvUaukDlzZn3xxRdq0qSJy2LcrXLlyurRo4eqVavmlnh3e9iF5ZYtWyyNFxISopUrV9pHlLiLu84jHx/HaUfu7sFI/DmRlX8on3vuOVWuXFl169ZVjRo1tG7dOhUtWlTr1q1Tw4YNHe57tlqOHDl0+vRpXbt2TalTp5YxRhcuXFBwcLBSpEihU6dOKWfOnFq2bJnCwsIsidm8eXP99NNPSpUqlUqUKCFjjLZu3aoLFy6oRo0a2rZtmw4fPqzff/9d5cqVsySmj4+PTp48qfTp0zuUHzlyRAULFtTVq1ctiXO3atWqqXjx4vriiy8UEhKibdu2KWfOnFqzZo0aN25s+Xe9p86jN95444EJUceOHS2N56nvofDwcLVr104fffSR22KWK1dOyZIlU7du3ZJ8b4sWLWp5zPt9VpYuXapGjRrp9OnTlsZLmTKltm7dqly5clm63YdJlSqV5s+fb9l3zKP65Zdf9Oabb+rq1asKCQlx+rttZbKZLl06LV26VEWKFFFoaKg2bNigfPnyaenSperatau2bt1qWax7xcfHa/78+Zo8ebLmz5+vHDly2BOjTJkyuSRm7dq1deDAAUVGRiY5QrJZs2aWxitWrJj279+v27dvKzw8XMmTJ3d43uprsESJjSIdO3ZM8jgrVqxoecyZM2eqXbt2Wrx4sSZOnKh58+Zp2bJlyps3r6Vx+vbta/+3a9euDh2q/v7+ioiI0CuvvCJ/f39L497t7Nmz+u677zR58mTt2LHDnq+99NJL8vPzszaYgaV2795txowZYxo2bGiSJUtm0qVLZ3mMbdu2mUKFCpmUKVOaPn362Mvbt29v3njjDcvj3c++fftMs2bNTLJkyYyPj49LY92+fdvMnTvXvPjii8bPz88ULFjQDBkyxJw6dcryWGnSpDH79++3fLv3s3//flOtWjUzefJks2nTJrNt2zaHhytlz57dDBo0yKUx7lagQAGzZcsWt8W7lzvPoyVLlpjixYubhQsXmosXL5pLly6ZhQsXmpIlS5rFixdbGmvZsmUmVapUxsfHx7Ro0cJe3r17d9OgQQNLY91r+vTpplKlSg6fmX379pkqVaqYmTNnmqNHj5py5cqZV155xbKYH330kXnnnXdMfHy8vSw+Pt60b9/edO/e3SQkJJg2bdqYcuXKPXWszp07m86dOxsfHx/z9ttv23/u3Lmz6dChg3nuuedM2bJlnzpOUlKmTGl/X1OkSGEOHDhgjDHm8OHDJiAgwPJ4njqPQkNDzapVq1y2/Xt56nsoJCTE/jt0l+DgYLNr1y63xEqVKpVJnTq18fHxsf8/8ZEyZUrj4+Nj2rVrZ3ncFi1amPHjx1u+3YeJiIgwf/31l9vj5smTx3Ts2NFcvXrV5bFSpUplP2dz5sxpli5daoy5c90SFBTk8viJTp48aYYMGWIKFy5s/Pz8TL169cxPP/3k8DfACilSpDBRUVGWbvNB+vTp88CHqyRPntzs3r3bZdu/n1GjRpmAgACTLVs2s2/fPpfGmjx5srlx44ZLYzyKLVu2mPbt25vAwECTLl0606lTJ7N3717Ltk+PvAVGjx6tFStWaMWKFYqPj1f58uXtvdRFihRx237cuHFDvr6+1rf2/H9XrlzRqlWrtHz5cq1YsUJRUVEqUKCA/VaCl156ySVx73bz5k2NGjVK3bt3161bt+Tn56dGjRrp888/V+bMmS2J8dFHHylFihTq1auXJdt7mHXr1jn1riX25Lp6sruUKVMqKipKOXPmdFmMuy1evFhDhgzR2LFjFRER4ZaYSXHHeVSoUCGNGTNG//nPfxzKV65cqTZt2mjXrl2WxEkUHx+vS5cuKXXq1Payw4cPKzg42Gk4uJVy5cqlOXPmOPVubt261T4h55o1a/TKK69YNjw7ffr0Wr16tVNL/t69e1W2bFmdOXNGf/75p8qXL68LFy48VazKlStLklasWKEyZco4tOIntu6///77Lpk/JGPGjFq4cKGKFSvm0CO/ePFiRUZG6ujRo5bHvN95lDx5cqceVqvkyJFDCxYsUIECBVyy/Xt56nsoMjJSpUqVcsnotfspVaqUhg0b5vQ95ApTpkyRMUYtW7bU8OHDHe5DTfyslClTxvK4165d06uvvqr06dOrcOHCTtdAHTp0sDymdGcCr3nz5mnKlCluvY87efLk+vPPP93yd7t8+fLq2rWr6tevr8aNG+v8+fPq2bOnxo0bp82bN2vHjh0u34dE69ev18SJEzVlyhRlzpxZFy5cUKpUqTRp0iRVqlTJkhilSpXSyJEj9fzzz1uyvb8rd4wE7dKlS5Lls2fPVrFixRxG0AwdOtTy+EePHpXNZlO2bNkk3VmFZfr06SpYsKDatGljebykxMTEaOrUqZo4caKOHz9uvw5atmyZvvjiC3Xu3PmpY5DIW8DHx0fp06dX165d1bZtW8sniUiKOydASuTn56c0adKoSZMmqly5sv7zn/+4dMKIu23atEkTJ07UzJkzlTx5cjVr1kyRkZE6ceKEevfurcuXL2vDhg2WxOrYsaOmTp2qIkWKqEiRIk4XBVZ/4RQsWFAFChTQhx9+mOQQp/DwcEvj3c3dF5apU6fWtWvXFBcXp+DgYKf31ur7z+7lzvMoKChIGzZsUOHChR3Kt2/frueee07Xr1+3JE6iuLg4LV++XAcOHFDjxo0VEhKiEydOKGXKlC6ZqyNRcHCw/vjjD5UsWdKhfOPGjapYsaKuXbumw4cPq1ChQrpy5YolMVOnTq0pU6Y4zTPw888/q1mzZjp//rz27dun0qVL6/z585bEbNGihUaMGOGW7/dEbdq00enTp/X9998rTZo02r59u3x9fVW/fn1VqFDB8olNq1Sporlz5ypVqlQO5ZcuXVL9+vW1dOlSS+MlckdClDp1aofv1qtXr7rle+juOSSuXr2qoUOHqm7dum5LOJcuXaqePXtqwIABScZ0xfm8YsUKlS1b1mWdCvcaP3682rZtq6CgIKVNm9ZpqLmrJp0rVqyYDhw4IGOMIiIinI7XVcOiX375Zb3++ut67bXXXLL9uy1atEhXr17Vyy+/rIMHD+qFF17Q7t27lTZtWs2aNUtVqlRxafyTJ09q2rRpmjRpkg4ePKj69esrMjJS1apV0/Xr19WzZ0/Nnj1bR44csSTexo0b1a1bN/Xu3VuFChVyy+fFE9fzBw4cUNu2bfXWW28leZxWdEImNoI/jM1mc8nflntXYcmbN68KFSrk8lVYbt++rZ9//lmTJk3S4sWLVaRIEbVq1UpvvvmmQkJCJN25zeCdd96x5PqERN4CP/30k/744w8tX75cf/31l4oWLapKlSqpUqVKKl++vOUX0du3b1fVqlWVKlUqHT58WHv27FHOnDnVq1cvl0yAlKh+/fpatWqVfH197cdXqVIll/aiDB06VJMmTdKePXtUp04dtWrVSnXq1HG4F3n//v3Knz+/4uLiLIn5oC8fV3zhJE+eXNu2bVPu3Lkt3e6jGDhwoFsvLKdMmfLA562+/yyRJ86jChUqyM/PT9999529lz82NlZNmjTRrVu3tGLFCkviSHfu065Vq5aio6N18+ZN7d27Vzlz5lSnTp1048YNjRkzxrJY96pbt65iY2M1fvx4FStWTNKd3vjWrVsrU6ZM+vXXX/XLL7/o448/1p9//mlJzA4dOmjGjBn6+OOPVapUKdlsNm3YsEEDBgxQ48aNNWLECI0fP16TJ0/WqlWrLImZaP/+/Tpw4IAqVKigoKCg+86Ma4VLly6pTp062rlzpy5fvqwsWbIoNjZWZcqU0YIFC5zup3xaPj4+io2NdRrBcerUKWXNmlW3b9+2NF4idyRED/vuuZuV30OPulqGqxLOxO+4pFZbcOWIr4SEBO3fvz/JCcOsns05U6ZM6tChg7p16+Y0T4krJd6Lez+ffPKJZbHunkj19OnT6tevn1q0aJHk321XLwl37tw5p4YxV6hXr54WLVqkvHnzqlWrVmratKnThHAnTpxQtmzZnM6xJ7Vv3z698cYbTvf+u+rzcu+Epu66nvfkSFB3SZ06tdatW6d8+fLpq6++0qxZsxxWYXFVA1+6dOmUkJCgN954Q61bt05yLpbz58+rePHiOnTo0FPHI5G32MWLF7Vy5UrNnj1b06dPl81m082bNy2N4e4JkO61fft2+60EK1eulM1mU6VKlTRz5kzLY+XJk0ctW7ZUixYt7ju5ya1btzRjxgyXJYGuVq9ePTVv3lyvvPKK22M/6CLTlT0Z7uaJ82j//v1q0KCB9uzZY1/iKTo6Wnnz5tVPP/1kacNN/fr1FRISogkTJiht2rT274QVK1aoVatW2rdvn2Wx7pXYOPH777/bLyjj4uJUtWpVTZs2TRkzZtSyZct0+/Zt1ahRw5KY8fHxGjRokL7++mudPHlS0p1h6O+9954++ugj+fr6Kjo6Wj4+PvZhdU/r3LlzevXVV7Vs2TLZbDbt27dPOXPmVGRkpFKlSqUhQ4ZYEicpS5cu1ZYtW5SQkKDixYtbPhxy+/btkqRnn31WS5cudbhYjo+P18KFCzV27FiX/W1xZ0L0b/OwBkNXTGqVmCQcOXLEaWk/VyQJadKk0caNG90+2Z07PWoDhauSMHc2YCaKjIxUq1atHng7hjFG0dHRlo1cLF26tJIlS+a2SeA8dT3vzpGgcXFxCgwMVFRUlAoVKmTZdh8mRYoU2rFjhyIiIvTiiy+qXLly+uijjxQdHa18+fJZPioy0bRp0/Tqq6+6ZZUtiUTeMufOnbPP5r58+XLt2LFDadOmVcWKFfXDDz9YGis0NFRbtmxRrly5HD74R44cUb58+RyWknCVrVu3atmyZVq2bJkWLlwom82mW7duuTyuux07dkw2m82lS0eMGzdOn332mVq2bOmR1nV3i4+P108//aRdu3bJZrOpYMGCevHFFy1dnuzvwhijJUuWaPfu3TLGqGDBgqpWrZrlF0Dp0qXT6tWrlS9fPofvhMOHD6tgwYK6du2apfGSsnv3bu3du1fGGOXPn99tyyleunRJkmuGPN6tadOmOnXqlMaPH68CBQo43K/euXNn7dy506XxXcnHx8d+TiZ1SRAUFKSRI0eqZcuW7t41l/D19U1yKcGzZ88qQ4YM/4jeKE969tlnlTdvXvXt2zfJmfKtviWvc+fOSp8+vT7++GNLt/soPDEs2p3Onj2r1157zSMNmHe7ceOGWxKj4OBgbd261W1/vzx1Pe/ukaC5cuXS3LlzXbJKxv14cjUfyX2NX6wjb4EiRYror7/+Upo0aVShQgW1bt1alSpVclnLU2BgoP3i9W579uxx2WREkjRs2DAtX75cK1eu1OXLl/Xss8+qYsWKevvtty0fKne3lStXauzYsTpw4IBmz56trFmzatq0acqRI4dLJvBJSEjQZ599piFDhtjv6Q0JCVHXrl3Vo0cPy4fuJd6f3q9fP6fn3DnEKfEC3pWt7Pv371edOnV0/Phx5cuXT8YY7d27V2FhYZo/f75Le1TcfR5Jd97LGjVqWNYTfT8JCQlJnifHjh2z35Plavnz51f+/PndEutu7rpnffHixVq0aJFTD3+ePHksuz/zXnffX303m82mwMBA5c6dWxUqVHjqRrBDhw7JGGNfJ/ruvyP+/v7KkCGDWxraNm/e7NDAl3irhtXu139x8+ZNly5J1LBhQ5UsWVLdunVzKB88eLA2bNhgeaN/ogsXLmjChAkO723Lli1dNsfNvn37NHv2bLclCfHx8friiy+0aNEit8xrk+jeYdGtW7dWmjRp9OOPP7p0WPTUqVPVqFEjBQQEOJTfunVLM2fOtHQd+c6dO8vPz0/R0dEOt1E2atRInTt3dmkin5CQoP79+2vMmDE6efKk/ZaxXr16KSIiQpGRkZbHLFmypI4ePeq2RN5T1/NVqlRxayLfs2dPde/eXd99953T7RGu8vnnn6tBgwYaPHiwmjVrZm9E+Pnnn126hPX9Gr9atWrlksYvEnkLtGnTxqWJ+71eeukl9evXT99//72kOxd10dHR6tatm0uHZ//3v/9VpUqV1Lp1a1WoUMEtF9Bz5sxRkyZN9Oabb2rr1q322xQuX76sAQMGaMGCBZbH7NGjhyZMmKBBgwapXLlyMsZo9erV6tOnj27cuKH+/ftbGs+qe7ue1NSpUzV48GD78Ou8efPqgw8+UJMmTSyP1aFDB+XKlUvr1q2zf5mfPXtWb731ljp06KD58+dbHlNy33l0v8QrKVbOP1C9enUNHz5c48aNk3TnO+HKlSv65JNPVKdOHcviJCU+Pl6TJ0/W77//nuT9sK6YxObkyZN6//337THvTcxc0fh19erVJCdiO3PmjNMFtVWGDRum06dP69q1a0qdOrWMMbpw4YKCg4OVIkUKnTp1Sjlz5tSyZcsUFhb2xHESh1F66rvo1KlTev3117V8+XKlSpVKxhhdvHhRlStX1syZMy27oE38fNpsNo0fP95h/pr4+Hj98ccfLm2MWrFiRZK3CdSqVUtffvmlS2Ju2rRJNWvWVFBQkEqXLi1jjIYOHar+/ftr8eLFKl68uOUxn3vuOe3fv99tScKff/5pb/S5dxZ1VzZMd+nSRc2bN7cPi05Uu3ZtNW7c2GVxW7RooVq1ajmNKLl8+bJatGhhaSLviQbMRJ999pmmTJmiL774Qq1bt7aXFy5cWMOGDXNJIv/ee++pY8eO+uCDD5IcIWn1SlSeup6vV6+eOnfurD///NMtI0G/+uor7d+/X1myZFF4eLjT/C6umBiyUqVKOnPmjNMqLG3atHHpKhPubvxiaL3F3NGr6e4JkDypWLFi6ty5s5o2beow7CgqKkq1atVSbGys5TGzZMmiMWPGOH2RzZs3T+3atdPx48ctj+kpQ4cOVa9evdS+fXuHRotvvvlGn332mSVLY9wtefLkWrdundNM7tu2bVO5cuUsm9X8Xu46jzw1sdWJEydUuXJl+fr6at++fSpZsqT27dundOnS6Y8//nDp8nPt27fX5MmTVbdu3SSH0Q4bNszymLVr11Z0dLTat2+fZExXLIVZt25dFS9eXJ9++qlCQkK0fft2hYeH6/XXX1dCQoJmz55tecwZM2Zo3LhxGj9+vH20yv79+/X222+rTZs2KleunF5//XVlypTJsvjTpk3TmDFjdOjQIa1du1bh4eEaNmyYcubM6bIlRhs1aqQDBw5o2rRp9gufv/76S82aNVPu3Lk1Y8YMS+Ikfj6PHDmibNmyOYwySFwerV+/fnruuecsiXevoKAgRUVFOfX27d69W8WKFXPJPZvly5dX7ty59e233ypZsjt9N3FxcWrVqpUOHjyoP/74w/KYP/74o3r27Om2ZMhTPDUs2sfHRydPnnRq4Nq2bZsqV65s6aoLISEh2rJli/LkyeNwjBs3blStWrV09uxZy2LdK3fu3Bo7dqyqVq3qEHv37t0qU6aMZSuS3C2pEZeunATOU9fzDxpZ6orj9NQ8KJ5YzSdTpkxatGiRihYt6nDeHjp0SIULF7b+OteyFen/5aZMmWIKFSpkAgICTEBAgClcuLCZOnWqS2P+/vvvZvDgwebzzz83S5YscWmsROfPnzdffvmliYyMNK1atTJDhgwxFy5ccFm8oKAgc+jQIWOMMSlSpDAHDhwwxhhz4MABExAQ4JKYAQEBZs+ePU7lu3fvNoGBgS6JeeXKFTN//nwzevRoM2LECIeHK0VERJgpU6Y4lU+ePNlERERYHi916tRm9erVTuWrVq0yqVOntjxeIk+cR+527do1M2HCBPPuu++ad955x3z77bfm2rVrLo+bNm1aM3/+fJfHuVuKFCnM1q1b3Rpz586dJn369KZWrVrG39/fNGzY0BQoUMBkzJjR7N+/3yUxc+bMmeRxbtmyxeTIkcMYY8zq1atNpkyZLIk3atQoky5dOvPZZ5+ZoKAg++dk0qRJplKlSpbESErKlCnNhg0bnMrXr19vQkNDLY9XqVIlc/78ecu3+zAlS5Y0ffv2dSr/5JNPTPHixV0SMzAw0OzatcupfOfOnSYoKMglMW02m9PDx8fH/u8/RYYMGcyWLVuMMY5/VxYtWmSyZctmebxnn33WFCtWzPj4+JjChQubYsWK2R9FihQxISEh5tVXX7U0Zp06dUzPnj2NMXeO8eDBgyY+Pt68+uqr5pVXXrE01r0CAwPN4cOH7bET39+dO3ea5MmTuyTm4cOHH/hwFU9cz//THT582OTPn98EBwcbX19f+/nTsWNH8/bbb7ssbooUKczevXvt/0+Mu2HDBpMmTRrL4zG03gL369Vs27atzpw5Y3mvZqIqVaq4fA3PuyU1RG/YsGEaMGCAy4boZc6cWfv371dERIRD+apVq5QzZ07L40lS0aJF9fXXXzsNk/76669dMlHH1q1bVadOHV27dk1Xr15VmjRpdObMGQUHBytDhgwuWVs4UUxMjMqWLetUXrZsWcXExFge74UXXlCbNm00YcIE+z1K69evV9u2bV06qZ8nzqNEt27d0qFDh5QrVy57j5grBAUFqWXLlm6fkMzf39/tSyeGhYXd9z5nVylYsKC2b9+u0aNHy9fX17628rvvvmtfXtBqMTExSS6HGBcXZx9FkiVLFl2+fNmSeCNHjtS3336r+vXra9CgQfbykiVL6v3337ckRlISEhKSXHPcz8/P8uH+t2/f1pEjR3TixAmlSpXK0m0/TK9evfTKK6/owIED9r/dv//+u2bMmOGy++NTpkyp6Ohop1sGjh496rL5M6xYUulxbdy4UT/88IOio6OdJt6dO3euS2K6e1h0/fr1JUlRUVGqWbOmQ49i4ogSq+MOHjxYlSpV0qZNm3Tr1i19+OGH2rlzp86dO6fVq1dbGutezzzzjFauXOk0g/oPP/zgsvkzrJyt/VEkzndw7/W8K+Y7+Lfp2LGjSpYsqW3btilt2rT28gYNGqhVq1Yui1uhQgVNnTpVn376qaQ73wsJCQkaPHjwA5e3fmKWNw38C7m7V9OYOz0Vn3/+uenatavp3Lmzw8NV/vOf/5jmzZub27dv28tu375tmjVrZsqXL++SmJ9//rkpWLCgWbdunQkJCTErV6403333nUmfPr0ZOXKkS2IuX77cJE+e3BQoUMC0bNnSREZGmgIFCpgUKVKYP/74w/J4FStWNK1btzZxcXH21rvo6GhToUIFM2fOHMvj3e2ZZ54x/fv3dyr/9NNPTaFChSyPd/78efPiiy8am81m/P39jb+/v/Hx8TH169d36cgOT5xHV69eNS1btjS+vr4OrcHvvfeeGThwoOXxjh07ZmbNmmVGjhzp1lEdX375pWnXrp1JSEhwaZy7LVq0yNSoUcM+yuKfqk6dOqZ48eL2Xj9j7vTGlyhRwtStW9cYY8zPP/9s2Wf1fj1ge/fuddloJGOMefHFF02FChXM8ePH7WXHjh0zFStWNPXr17c8XpYsWcxff/1l+XYfxa+//mrKli1rgoODTdq0aU3lypXN8uXLXRbvvffeM9myZTMzZ8400dHR5ujRo2bGjBkmW7ZspmPHji6L604zZswwfn5+pm7dusbf39+88MILJl++fCY0NNQ0b97cZXEvXrxoypUrZ1KlSmV8fX1NWFiY8fPzMxUqVDBXrlxxWdzJkyeb69evu2z794qJiTG9e/c2devWNbVr1zY9evQwJ06ccHncn3/+2YSGhppBgwaZ4OBgM3jwYNOqVSvj7+9vFi9e7NLYO3fuNP/73//MvHnzHB5W8/HxMSdPnnQqP3PmjMtHr7hzJGhcXJwZPHiwKVWqlMmYMaNJnTq1w8MV0qZNa3bv3m2Mcfx7dujQIZeNRjLG/aP3SOQtEBAQYPbt2+dUvnfvXpcM2+3fv7+x2Wwmf/78pmLFiqZSpUr2R+XKlS2Pl8gTQ/SMMebjjz82QUFB9iF6gYGB9qFernL8+HHz8ccfm5dfftk0aNDA9OjRw+Ei00qhoaH2L5vQ0FD7Bea6detMvnz5XBIz0ezZs42vr6+pWbOm6devn/n0009NzZo1TbJkyczcuXNdFnfv3r3m559/NvPmzUvys+MK7j6POnToYEqUKGFWrlxpkidPbv8jMm/ePPPss89aGmvixInG39/fpEiRwoSHh5uIiAj7I3EItqvUr1/fhIaGmhw5cpgXXnjBNGjQwOHhCqlSpbI3AqVIkcItFwXG3GmIWrRokZk2bZqZMmWKw8MVYmJiTLVq1ZwavqpXr25iY2ONMcYsXbrULFq0yJJ4BQoUMD/99JMxxvHCZ8SIES4b+m2MMdHR0aZYsWLGz8/P5MyZ0+TKlcv4+fmZ4sWLm6NHj1oeb+DAgaZZs2YOjdL/VDdv3jQdOnSwnzs+Pj4mICDAdOrUydy4ccNlcadOnWrKli1rMmfObG8cGjZsmP38slLhwoXN119/bYz5v/M2ISHBtG7d2vTu3dvyePfy1G2O3377renWrZs5e/asMcaYzZs3m2PHjrklvrssXLjQVKhQwSRPntwEBQWZcuXKWfZ9l5QDBw6YIkWKONwOkvh/VyTWNpvNnDp1yqk8KirKpX/LtmzZYjJlymRSpkxpfH19Tfr06Y3NZjPJkyd3yTVDr169TObMmc3gwYNNYGCg+fTTT01kZKRJmzatyzobUqdObXbu3GmMcfx7tnLlSpMhQwaXxEzkzsYvJruzQKFChdS4cWOnNUw/++wzzZo1S3/++ael8TJmzKjPP/9czZs3t3S7jxJ32rRpTktpLVq0SE2bNtXJkyddFvvatWv666+/lJCQoIIFC7pskgpJio6OVlhYWJITFkZHRyt79uyWxkufPr1Wr16tvHnzKl++fPrqq69Us2ZN7d69W8WLF3f5GuCbN2/WsGHDtGvXLvta5127dnXZ0DVPcud5FB4erlmzZun55593mPBk//79Kl68eJJLzjypsLAwtW3bVt27d7d8ecSHadGixQOfnzRpkuUxp0yZ8sDnmzVrZnnMX375RW+++aauXr2qkJAQh+8Hm81m6QRT99q9e7f27t0rY4zy58/vsqWRJk2apF69emnIkCGKjIzU+PHjdeDAAQ0cOFDjx4/X66+/7pK4iZYsWaLdu3fbv4eqVavmkjgNGjTQ77//rhQpUqhw4cJOE0q5aii2J127dk0HDhyQMUa5c+d26azNo0ePVu/evdWpUyf1799fO3bsUM6cOTV58mRNmTJFy5YtszRe8uTJtXPnTkVERChdunRatmyZChcurF27dqlKlSouuU3Mk+5d9m7Pnj32ZdmsXvZu4cKFSpEihX2J1m+++UbffvutChYsqG+++cZhNvB/gnr16snX11fffvutfTnOs2fPqmvXrvryyy9Vvnx5S+IUK1ZMNptN27Zt0zPPPONw2118fLwOHTqkWrVq2W/bsFqlSpWUN29ejR49WqlSpdK2bdvk5+ent956Sx07dtTLL79sabxcuXLpq6++Ut26dRUSEqKoqCh72bp16zR9+nRL40l3JlENDQ3VuHHj7BPUpk+fXi+99JKyZ8/ukmsTTyCRt8CcOXPUqFEjVatWTeXKlZPNZtOqVav0+++/6/vvv1eDBg0sjZc5c2b98ccfypMnj6XbfZgOHTroxx9/1JdffqmyZcvaj/ODDz7QK6+8ouHDh7t1f1zF19dXMTExTjN9nz17VhkyZLB8Ns8aNWqoefPmaty4sdq2bautW7eqQ4cOmjZtms6fP6/169dbGs/dunTp8sh1XbXerycEBwfbL2DvTuS3bdumChUq6OLFi5bFSps2rTZs2GCf2RzWy5s3r+rUqaMBAwa4NAnytG+//VafffaZjh49KknKmjWr+vTp45KlnhK5c11syb2NT2nSpNHevXuVLl06pU6d+oEr2riiMahly5YaMWKE0/3wV69e1XvvvaeJEydaHrNgwYIaMGCA6tev7/Ddt2PHDvuSUFYKCwvTggULVLhwYRUtWlTdunXTG2+8obVr16pWrVqWftd6aonRu1WtWlUlSpSwL3uX+P6uWbNGjRs31uHDhy2LVbhwYX3++eeqU6eO/vzzT5UsWVJdu3bV0qVLVaBAgX9MMpQoXbp0Wrp0qYoUKaLQ0FBt2LBB+fLl09KlS9W1a1dt3brVkjiJs7j37dtXXbt2ve98B/7+/pbEu1eqVKm0fv165cuXT6lSpdLatWtVoEABrV+/Xs2aNdPu3bstjZc8eXLt2rVL2bNnV+bMmTV//nwVL15cBw8eVLFixSz9jCY6fvy4qlSp4pHVfC5cuKANGzYkuSSv1X/PmOzOAq+88orWr1+vYcOG6aeffrL3JmzYsMElvZqdO3fWN9984/bE+csvv5TNZlPTpk3tEzD5+fnpnXfecZgY6Wk9TkugK3pOzP9fZuReV65cUWBgoOXxBgwYYJ+s6tNPP1WzZs30zjvvKHfu3C75I/k4PcEpU6Z86nj3/uHbvHmz4uPj7b2Ke/fula+vr0qUKPHUse7m6fOoVKlSmj9/vt577z1J/7ck5bfffqsyZcpYGisyMlI//PCDunXrZul2/04uXbpkPx8fdg5bcd7e6/jx4+rQoYPLk3hPN3y1bt1arVu31pkzZ5SQkODSi51E7lwXW3LNKJH7GTZsmD2JHjZsmEuXpk3KlClTNGjQIKdE/vr165o6dapLEvlDhw4lee0TEBCgq1evWh6vfPnyWrJkiQoXLqzXXntNHTt21NKlS7VkyRJVrVrV0lj3Lqd5+vRpXbt2zT5x4oULF1w+Ue2mTZs0btw4p/KsWbNaviTvoUOHVLBgQUl3Oq3q1aunAQMGaMuWLapTp46lse51v4Yvm82mwMBA5c6dW82bN39ow9zjiI+PtyfV6dKl04kTJ5QvXz6Fh4drz549lsVJXG4tIiJCr7/+ulMjpqv5+fnZ39uMGTPa1zwPDQ1VdHS05fGyZcummJgYZc+eXblz57ZPkL1x40aXHXvWrFkVFRWlmTNnavPmzUpISFBkZKTefPNNBQUFuSSm9PDReyTyf1MlSpTQd99955ZY77//vurWratcuXKpYMGCTrP9uiIpiY+P19q1a/XJJ59o4MCBLh2iFxoaaun2HlXiBbTNZlOvXr0cjis+Pl7r16/Xs88+a3nckiVL2v+fPn16LViwwPIYd0uVKtUjX0xaMfrg7mGUQ4cOVUhIiKZMmWIfknf+/Hm1aNHCsiFriTx1HiUaOHCgatWqpb/++ktxcXEaMWKEdu7cqbVr12rFihWWx3rhhRe0cOHCJNdttjrhK168uH7//XelTp3aPkTwfrZs2WJJzNSpU9tHytzvHDYuWutXkmrWrKlNmza5fJUDTzV83StdunQu3f7d7td4euzYMZd+jk+fPq09e/bIZrMpb968TutyW+Hu2zzceTvcpUuXZO7Mg6TLly87NELHx8drwYIFLmukyZEjh6KiopxmAP/f//5nTwqt9PXXX9vXbO/evbv8/Py0atUqvfzyy+rVq5else6ekX/69OkaNWqUJkyYYP987tmzR61bt9bbb79tady7BQYGJtmYuWfPHsvPYX9/f/vtfb/99ps9CUmTJo2lt4clpXfv3urfv79q165tXylp48aNWrhwod59910dOnRI77zzjuLi4tS6dWtLYhYqVEjbt29Xzpw59dxzz+mLL76Qv7+/xo0b55Lv/ipVquj06dPKli2bJGnDhg2aPn26ChYsqDZt2lgeL1GxYsW0adMm5c2bV5UrV1bv3r115swZTZs2TYULF7Y8XuLtTM8995w6duyoN954QxMmTFB0dLRLVva6ffu28uXLp19//VUtWrSwtLHnYbp27aqWLVu6bfQeifwTcnev5t3ee+89LVu2TJUrV1batGnd0sLv6+urmjVrateuXUqTJo1LPuiJPDVUK/EC2hijP//802FIk7+/v4oWLerSJZjc5e7E+vDhw+rWrZuaN29u7yVeu3atpkyZooEDB1oee8iQIVq8eLHDfXWpU6fWZ599pho1aqhr166WxfL0kL+yZctq9erV+vLLL5UrVy57C/TatWst//wMGDBAixYtsl9M3tsCbLWXXnrJ3oqeuCSSqy1dulRp0qSRJMvvsX0UdevW1QcffKC//vorycYSq5ZPdHfD18MaYu5mVaPMvbFtNpuqVq163/tErZY4rHzq1Kn2YY++vr5q2rSpRo4c6bKLrzfffFOVKlVSxYoVlTdvXpfESJTY2JXYSHEvm81mH95rtQ8++EDvvvuubty4IWOMNmzYoBkzZtjnWrBa4veCJPn4+OjDDz/Uhx9+aHmce/Xq1UuzZ892mLMiX758GjZsmBo2bKg333zTJXHduezdf/7zH3Xp0kXlypXThg0bNGvWLEl3GhQTk09XWbVqlT777DO1bdvWoXzs2LFavHix5syZoyJFiuirr76yLJHv2bOnfdTIZ599phdeeEHly5dX2rRp7cdupcaNG6tNmzZq0qSJYmNjVa1aNRUqVEjfffedYmNj1bt3b8tjSu4fCXr3qN2GDRsqLCxMq1evVu7cuV2y9LCfn59u3rzp9hFQkvtG7yXiHvkn5OPj89ATxFW9QyEhIZo5c6bq1q1r6XYfplSpUho0aJDlQ9X+blq0aKERI0a4ZHhuUk6ePKn3339fv//+u06dOuW0PrYrehcTVa1aVa1atdIbb7zhUD59+nSNGzdOy5cvtzReSEiI5s2b57BeqnQnSXvppZcsWw/7Xjt37tQzzzyT5HMLFy50SbLgTqlTp9awYcPcPgGmJ8TFxal///5q2bKlwsLC3Bb3QZMIumoUQNasWbV48WKnc3fHjh2qUaOGTpw48dQxHieZSxwOahVP3Sf69ttv67ffftPXX3+tcuXKSbqTNHTo0EHVq1fX6NGjLY13d9wVK1Zo7969ypQpkypWrKiKFSuqUqVKTuu8P60VK1bIGKMqVapozpw5Dsmuv7+/wsPDlSVLFktj3s3dcy0kJCRo//79Sd6TWqFCBZfEDA4O1vLly1W6dGmH8g0bNqhSpUoum6j20qVLqlOnjnbu3KnLly8rS5Ysio2NVZkyZbRgwQKnyRufRnR0tNq1a6ejR4+qQ4cO9t9f586dFR8f/1hzBjyuFClSKCoqSrlz53Yo379/v5599llduXJFBw4cUJEiRVxyy0aic+fOPXR+iyeVOnVqrVu3zj7R8axZs7R69WotXrxYbdu21cGDBy2P+W8xaNAg7d69W+PHj3doJHa1l19+Wa+//rpee+01t8QjkX9CjzM0tmLFipbGDg8P16JFiyz/w/8wixcv1kcffaRPP/1UJUqUcPpj4YrE9+zZs+rdu7eWLVuW5B/of8LkQLVr11Z0dLTat2+vzJkzO/2xeOmllyyNd7fg4GBt27bNaeLEvXv36tlnn7X8QqRp06ZasWKFhgwZoueff16StG7dOn3wwQeqUKHCQ2cjf1JBQUH64osv7PerS9LNmzfVtWtXTZgwQdevX7ckjqfu486UKZNWrlzp9gkwPSUkJER//vmnIiIiPL0rLuWphi93io+P17Rp01SzZk1lzpzZLTHTpUun2bNnq1KlSg7ly5Yt02uvvabTp0+7NH5sbKyWL1+u5cuX2xP7DBkyuGR29SNHjih79uxu65mKi4vTf//7X9WsWVOZMmVyy1wL69atU+PGjXXkyBGnhnBXNbJJd2Y4j46O1oQJE1SiRAnZbDZt2rRJrVu3VlhYmH7++WeXxE20dOlSbdmyRQkJCSpevLjLVnnwlOzZs6tz585OQ6+HDRumYcOGKTo6Wtu3b1eNGjUsnxvAXVKkSKEdO3YoIiJCL774osqVK6ePPvpI0dHRypcvn2XXJp6WJUsWVapUyT4iyVUrr9zNU6uTTJgwQf369VOLFi1cOnovEYm8F5o0aZIWLlyoSZMmuXX25Lt7pO6+KHDlfam1a9fWgQMHFBkZqYwZMzpdjLhiman7zVp/5swZZcqUyT7Rn1VCQkK0cuVKl9x//zD58uXTCy+8oCFDhjiUd+3aVb/++qulk7tId5ZAev/99zVx4kTdvn1bkpQsWTJFRkZq8ODBlvYk3G3u3Llq06aNSpUqpUmTJik2NlaNGzeWJH333XcqXry4JXHuPnfuN2rHFZ+XgQMHKiYmxqW9I3d7nN4JVzS21a9fX/Xr13fbCIS4uDgFBgYqKipKhQoVcktMyXMNX9KdyT3vbTh11SilwMBA7dq1Szly5HDJ9u8VHByszZs3q0CBAg7lO3fuVOnSpV3auyfdaRRetWqVPZnfsmWLChYsaNmM2Hfbvn17kuWJE4Zlz57d8smmgoODtWvXLqd75F3l2WefVd68edW3b98kG8NdNc/C6dOn1axZMy1cuNB+sR4XF6eaNWtq8uTJbpko0hU8eevo3b799lu98847qlOnjkqXLi2bzaYNGzZowYIFGjNmjCIjIzVkyBCHIf9P6+rVqxo0aJB9hOS934FW95A/99xzqly5surWrasaNWpo3bp1Klq0qNatW6eGDRvq2LFjlsZL5O6RoDNmzNCKFSu0fPly7d27VxkzZrSPRqpYsaLTd7EVPLE0ruT+0Xsk8hZw9zqbxYoVs082FxER4dTaY/V9jIkeNgrB6pEH0p0kd9WqVSpatKjl275X4uRAqVOn1r59+xwmjYmPj9cvv/yibt26WTKc9W4FCxbUf//7X4+s275gwQK98sorypUrl0OicODAAc2ZM8dls9JevXrVYcJEVyXwdztx4oSaNWumrVu36urVq2rRooWGDBli6eylK1asULly5ZQsWTK3fl4aNGigpUuXKm3atHrmmWdcPgHm4ySQrmhsGzt2rPr06aM333wzydFBrrjnLleuXJo7d65bvosSubvh69ChQ2rfvr2WL19unzxMcm1jreT+27aqVq2qtGnTaurUqfZJ4K5fv65mzZrp3Llz+u2331wS96OPPtKKFSu0bds2FSpUSBUqVFDFihVVoUIF+6znVnvYbYB+fn5q1KiRxo4da9mqLJUrV1bHjh3dNn9G8uTJtW3bNqch2O6yd+9e7d69W8YYFShQwOXzH0jS77//rmHDhmnXrl2y2WzKnz+/OnXqZEmvvCdvHb3X6tWr9fXXX2vPnj0yxih//vx67733VLZsWZfEe+ONN7RixQo1adIkyUahjh07Whpv+fLlatCggS5duqRmzZrZR3x+/PHH2r17t8t6jT05EvTkyZNatmyZfv31V82aNUsJCQkuP4/+yUjkLeDudTYfdk+j1fcxSndmgKxRo4bGjh3rlj9SiUqVKqWRI0fak0xXetgfr8TJgXr06GFp3MWLF2vIkCEaO3asR4YKHz16VKNHj7ZfiBQsWFBt27Z1yf3HkydPVqNGjVy69Mf9HD16VG+99Za2b9+uq1evqmfPnurZs+cDW08f18svv6zJkycrZcqU910b2xU81fLsKZ64X33SpEn64Ycf9N133zncb+wO7mr4Srw47tixY5IjoFzRWCu5/7atHTt2qFatWrpx44aKFi0qm82mqKgoBQYGatGiRfedT+Np+fj4KH369OrcubNeeukll/RC3WvevHn66KOP9MEHHzjM/D1kyBB98skniouLU7du3dSoUSN9+eWXlsRMXAqzc+fOSf4+ixQpYkmcRFWqVNGHH37o9XOdPKqvv/5anTt3VsOGDe2T1K5bt06zZ8/W0KFD1b59+6favidvHfW0VKlSaf78+fa5M9whPj5ely5dcuj0O3z4sH0ZQ1fwxEjQK1euaNWqVfae+a1bt6pgwYKqWLGi07KOVnLH6iR3O3r06H2vn9etW2d5PkMib4G773Hp06ePduzYodmzZ9vX2fTWe3fulT59eq1Zs8at9+Fu3LhR3bp1U+/evVWoUCGnnkYrL/DcOTnQvUOTr169qri4OAUHBzsdoyuGJntK5syZdfXqVb366quKjIx0Wav6vWbOnKl33nlH5cuX14QJExQVFaUWLVooPDxc06ZNs2xZGX9/fx05ckSZM2e+7y0a/2TXr1+39xwnctekka5WrFgx7d+/X7dv31Z4eLhTcuKqkVDulCJFCm3evNkt9y/ezRO3bV2/fl3fffedQwOmq9cX3rZtm/0iduXKlfL19bUPL61UqZJLEvvSpUvr008/Vc2aNR3KFy1apF69emnDhg366aef1LVrVx04cMCSmO5oaLv7loEDBw6oZ8+e+uCDD5K8J9XqhoNExhjNnj37vnP4uKo3NWvWrOrevbtTwv7NN9+of//+lo8a9CR3T2KYI0cOLViwwC2NbJ7k7pGgzz33nLZv365ChQqpUqVKqlChgsqXL++ykUiS51YnyZ8/v1avXq20adM6lK9evVp169bVhQsXLI3H8nMW8OQ6m+7UtGlTTZgwwWEZCVdLlSqVLl686DTZkysu8BJblg8dOuTyyYGGDx/usm0/DnffP3ns2DHNnz9fkydPVuXKlZUjRw61aNFCzZo1U6ZMmSyLc6/IyEh9+eWX+n/t3XdUFFnzN/DvACoZQRERyUFlJa6oqyvJhAnDs2bMu2ZBjBjAiFkxrgFUUAyLOUcEBBVFJSqCYMBVMCEGgiD0+4cv82MYTGv3NDPU5xzOkZ5xbqFDT1ffe6vGjRsHAOjYsSOSk5MxZswY2NrasvZ72rRpU8yaNQsuLi5gGAZhYWFfTGTLzxPSLj8/HzNnzkRYWBhev34t9jjXS+aKiopYWxL8NZJaJlxxVUefPn2++ly2EwUHBwc8efJE4ok8H+0ElZSUWGtZ9b1sbGxgY2MDT09PAJ8T+7Vr18LT05Oz5aXJyclV7lU3NDREcnIygM97zNkstFex1zpXbG1tIRAIRPb2jhw5Uvjn8se4XP7t5eWFbdu2wcXFpcoVLFx59+5dlasPOnXqhJkzZ7I+XnR0NLZu3YoHDx7gwIED0NPTw+7du2FsbCzcUsoFPooYLlq0CH5+fggJCZFIDSq+uhatXbsWPj4+ElsJev/+fSgrK8PExAQmJiYwMzPjNIkHgClTpiAqKgonTpwQ604ydepUzrqTtGvXDp06dUJkZKSwaPbly5fRo0cPzJ8/n/XxaEaeBe7u7iguLkbbtm2xaNEiPHz4UNg2aOLEiUhPT2d1vNLSUgQEBCAsLAxZWVkoLi4WeZyrGdzyO1tmZmZo0aKF2IzUmjVrWB+zZcuWUFBQkOhSz8uXL3/1ca5a2fCh4naC8lNBxX9jLvZPlnvx4gVCQ0MRHByMe/fuwc3NDaNGjUKPHj1YXe4OAGlpaV9MTnbv3o0hQ4awMs7Vq1cxZcoUZGZmIjc3F2pqalVe3AkEAtZ/Tw8ePPjFcwKXs8UTJkxAREQEFi5ciKFDh2LTpk14+vQptm7dimXLlnHSS7m0tBRLlizBli1b8Pz5c6Snp8PExAS+vr4wMjLirMWVJIwYMQLr16+HmpqaxLdMZGZmYuzYsfDw8KhyBRRXM5uSVrGCsrOzs0S3i8XHxwuL3EVHR+Pdu3ewtbWFi4sLVq5cyfp4dnZ2sLGxwbZt24Rt/EpKSvDXX38hMTER8fHxuHLlCjw8PFhLwF+/fi2cjXry5AkCAwNRWFgId3d3tGvXjpUxHj9+/N3P5aronpaWFkJDQzmrJfMlgwcPhq2tLaZPny5yfNWqVbh16xb27dvH2liHDh3CkCFDMHjwYOzevRt3796FiYkJ/v77b5w8eRKnT59mbazKJFXE0M7OTuS1MzIyJFaDSpJ71fleCZqUlCTs1BEdHQ05OTk4OTnBxcUFY8eOZX08vrqTMAyDvn374sWLFzh//jyuXbsGd3d3LF68mPUaCwAl8qyQdJ9NPz8/BAUFYcqUKfD19cWcOXPw6NEjHD16FH5+fsK7/WxzcXH54mMCgQCXLl1ifUxlZWXEx8dLdIaoqiSy4smPjTuk1aUqLB/7Jyu6fv06duzYgZCQEOjq6iIvLw9169bFzp07xU6+P+vTp0+IjIxEZmYmBg0aBDU1NTx79gzq6uoi/avZIicnh5ycHIksrV+/fj3mzJmDYcOGITAwECNGjEBmZibi4uIwYcIE+Pv7cza2gYEBdu3aBWdnZ6irq+P27dswMzPD7t27sW/fPk4u9BYuXIiQkBAsXLgQf/31F1JSUmBiYoKwsDAEBATg2rVrrI8paQzDICsrC9ra2hLrTlI+A/bo0SPhMUnMbAKSnfXjo4Iy8PlC+sOHD7CxsRHeRHB0dOT0HH/16lW4u7tDTk4O1tbWEAgESEpKQmlpKU6ePInWrVtj9+7dyMnJEUsMf1RycjJ69OiBJ0+ewNzcHPv374ebmxvy8/MhJyeH/Px8HDx4UGKrW7hmbGyMM2fOSKQVcMXryHfv3mHVqlVo27atyB75K1euYOrUqZg7dy5r49rZ2cHb2xtDhw6FmpoaEhMTYWJigoSEBLi5uXG6dVRSRQy/VXeqIrZrUElyrzrfRWorunXrFjZu3IjQ0FDOViPx2Z2kpKQE3bp1Q35+PpKSkrB06dKfrl3xRQyROiYmJszJkycZhmEYVVVVJiMjg2EYhlm3bh0zcOBAPkNjXbt27ZgLFy5IdMy8vDyRr5cvXzLnz59nWrVqxVy8eJGVMQQCASMnJ/fVr/LncMnBwYE5e/as2PGzZ88yDg4ODMMwzJEjRxgTExPWxszJyWFWrlzJWFpaMoqKisyAAQOE/8cFBQXMlClTGAMDA9bGYxiGefToEdO0aVNGWVmZkZeXZzIzMxmGYRgvLy9mzJgxrI5VccyysjJOXruyJk2aMHv37mUY5vM5ofzn8/X1ZSZMmMDp2CoqKsyjR48YhmEYPT095vr16wzDMMyDBw8YFRUVTsY0NTUV/i5W/HlTU1OZunXrcjLmp0+fmJUrVzIODg6Mjo4Oo6mpKfLFttLSUqZWrVpMeno666/9Jc2aNWP69OnDxMbGMg8fPmQePXok8sWVgwcPMkpKSsyff/7J1KlTR/j/uWnTJqZLly6cjcswn89H+/btYwYPHswoKChwes49ceIE8/btW85e/0vev3/PbN68mfH29mYmT57MbNmyhXn37h3r47i5uTHdu3dnoqOjmTFjxjB6enrMiBEjmNLSUqa0tJQZP34806pVK9bHZRiGSUtLY7Zu3cosWrSIWbBggcgXV4KDg5kBAwYwBQUFnI1RzsjI6Lu+jI2NWR1XSUmJefjwIcMwoufazMxMpk6dOqyOVZmLiwtz5swZTsfgW7NmzZjbt2/zHQbnbt++zaxZs4Zxd3dnNDU1GQUFBebXX39lpk6dKsxn2Obq6sr07duXKSwsFB4rKChg+vbty7Rv357VsRITE8W+YmJiGH19fWbs2LEix9lGiTxLSktLmbS0NCY6OpqJiooS+WKbsrIy8/jxY4ZhGKZhw4bMrVu3GIb5fGJVV1dnfTw+hYWFMZaWlszOnTuZmzdviv2iSFJUVBRjb2/PymtFRkZ+9xeXFBUVmdTUVLHjqampjKKiIsMwDPPw4UNGSUmJlfG6d+/O1KpVi/nll1+YgIAA5vXr12LPefr0KSMQCFgZr1zPnj0ZDw8P5uPHjyIXI5GRkYyZmRmrY1X05s0b5ty5c8zu3buZkJAQkS82KSkpCRMtbW1tJiEhgWEYhklPT2e0tLRYHasyKysr4fu0Y8eOzNSpUxmG+XxjUU9Pj5MxFRUVhT9vxf/PO3fucHbzwNfXl9HV1WVWrlzJKCoqMosWLWJGjRrF1KtXj1m3bh0nY1paWjLXrl3j5LWroqyszNy/f19i45WztbUV/k5U/P+Mj49ndHR0OBnz/fv3zJkzZxgfHx+mdevWTJ06dRg7Oztm8uTJnIxXE9SrV0/4ufz+/XtGIBAwcXFxwsdTU1MZDQ0N1sfdtm0bIy8vz+jo6DA2NjaMra2t8MvOzo718crl5+cznTt3ZlRVVZnmzZszdnZ2Il+ywMTERHijveLvZkhICNOsWTNOxz58+LDEr/9mz57NnD9/nsnPz+fk9Ss7d+4c06lTJ+HNEkm5desWk5SUJPz+6NGjTM+ePZlZs2YxHz9+ZH08eXl5pkWLFszUqVMldkMzOTmZ0dPTY+rVq8e4uroy7du3Z+rVq8fo6ekxKSkprI5VPvEmEAiEXxW/53JyjordsUDSBTkaN26M7OxsGBgYwMzMDOfPn4e9vT3i4uI4b3UVFxeHAwcOVLkPl4sKrf379wcg+SI2VdHW1kZaWhorr1VdWrY0bdoUy5YtE9s/uWzZMuFywadPn0JHR4eV8Ro0aICoqCjhcsCq6Orqsl4sKSYmBleuXBH+jOUMDQ3x9OlTVscqd+LECQwePBj5+flie+UFAgGrxe4aNmyI169fw9DQEIaGhoiNjYWNjQ0ePnwodk5i24gRI5CYmAgnJyfMmjUL3bp1w4YNG/Dp0yesXr2akzF/+eUXREdHi+19PXDgAGdVePfs2YPAwEB069YNCxYswMCBA2Fqagpra2vExsZysqVpxYoVmD59OjZv3ozmzZuz/vqVubq68tKPOy0trcraI+rq6qxX+AXEKyjPnj2b8wrKwOc9qcuWLRMWtqpchfvBgwecjJueno7IyMgqx/Tz82NtnNzcXGHRUlVVVaioqIh0f9HU1MT79+9ZG6/c4sWL4e/vz0mht68ZPnw4bt26BQ8PD4kWu5OkMWPGwMvLCzt27IBAIMCzZ89w7do1TJs2jdX3TlX+97//AZDs9d+tW7ewYcMGfPz4Efb29sLtNr///jsnW/D69++PgoICmJqaSrRr0ZgxY+Dj4wMrKys8ePAA/fv3R58+fXDgwAEUFBSwXpA5NzdX4h1smjdvjvv374t0JxkwYAAn3UkkUeDzSyiRZ8HYsWPRokULnDp1qspiFWzr3bs3wsPD0apVK3h5eWHgwIHYvn07srKy4O3tzdm4+/fvx9ChQ9GpUydcuHABnTp1wv3795GTk4PevXtzMiYfvxyVK7kzDIPs7GwsW7YMNjY2rI/HZ3G9TZs2wd3dHY0bN65y/yTw+eJy/PjxrIy3ffv2bz5HIBCwXpzoS3uw/v33X2FVUbZNnToVI0eOxJIlSzjf4+zq6ooTJ07A3t4eo0aNgre3Nw4ePIibN29+s/L5z6p4znFxccG9e/dw8+ZNmJqacvL7AnzepzhkyBA8ffoUZWVlOHz4MNLS0rBr1y7h+5ZtOTk5sLKyAvA5SXn79i0AoHv37vD19eVkTA8PDxQUFMDGxga1a9cWu/hg+yKvR48e8Pb2RnJycpVtvNzd3Vkdr5yuri4yMjLEqifHxMSw1hqyIj4qKAPAn3/+iaioKAwZMkQi1woAEBgYiHHjxqF+/fpo2LCh2A1FtpOxyj+TJH7GN2/eoG/fvpyPU9mpU6dw7tw5Tiu3V4WRYNu7GTNm4O3bt3BxcUFRUREcHR1Rp04dTJs2jbs9v/8fH9d/Z8+eRWlpKW7cuCGso/H333+jsLAQ9vb2iI2NZXU8vjoYpaenC/flHzhwAE5OTti7dy+uXLmCAQMGsB5XeRJfXFxc5XvWwMCAtbEuXboER0dHKCgoSKw7CVcFNb8HFbtjgaQKcnxJbGwsrl69CjMzM84utIDPFYvHjBmDCRMmCIueGBsbY8yYMdDV1f2hgiHVWXkl98q/Gq1bt8aOHTtYL2wjieJ6X/PhwweEhoYiPT0dDMOgadOmwmJwXIiKisKqVauQmpoKgUCAZs2aYfr06axVM65K//79oaGhgW3btkFNTQ1JSUnQ1tZGz549YWBgwHr1b+DzeSE5OZmTRKSysrIylJWVQUHh873ZsLAwxMTEwMzMDGPHjhVbicCGrl27Yt++fcLKwf7+/pgwYYIwIXr9+jXatWuHu3fvsj428LkP9pIlS3Dr1i2UlZXB3t4efn5+6NSpEyfjNWnSBLt27UKrVq3Qrl07dOvWDT4+Pvjnn38wadIkvHjxgvUxv1WciO2CRJLo/12VFStWICQkBDt27EDHjh1x+vRpPH78GN7e3vDz8+MkYZB0BWXgczvVU6dOCVshSYKhoSHGjx8vkdlqOTk5dOnSRbgy8MSJE3B1dRV2uPn48aMwUWLTqFGj4ODgwNn/25c0bdoUYWFhEu/m4Onp+dW2d1x8nhUUFODu3bsoKyuDpaUlJ7PT1U1aWhoiIyNx8eJFHD16FHXr1uWs0rmkqaur49atWzA3N0fHjh3RvXt3eHl5ISsrC02aNEFhYSGr46Wnp2PUqFG4evWqyHEuVlfIy8sjOztbWGi4devWOHToEPT09Fgb42uWLl0KHR0dkZUkALBjxw68fPmS9XMxJfIscHV1xYwZM6rs6ylLVFRUcOfOHRgZGaF+/fqIiIiAlZUVUlNT4erqymof2ooyMzOxdu1akcTPy8sLpqamnIxXua2NnJwctLW1OetVXT6zV66kpATx8fHw9fWFv78/2rdvz8m4fAgNDcWIESPQp08ftG3bFgzD4OrVqzhy5AiCg4MxaNAgTsZ99uwZXFxcIC8vj/v376NFixa4f/8+6tWrh+joaE4qy/fp0wcDBgxAv379WH/t6qDyh6W6ujoSEhKENy6eP3+ORo0aSXT7C5d8fHygrq6O2bNn4+DBgxg4cCCMjIyEK6GWLVvGd4hSbc6cOQgICEBRUREACGf9Fi1axPnYkqigDHyucn769GnOquJXpfLvJZe+1S6xHNuJ5tKlS7FmzRp069atypUkXHXyOXXqFDZs2IAtW7ZIpBd3Ob7a3knC8ePH0aVLF9SqVQvHjx//6nO5mLjavHkzoqKiEBUVhdLSUrRr107Y0YKtGzbv3r0TzlB/q4MRV8vRXV1doa+vjw4dOmDUqFG4e/cuzMzMEBUVhWHDhol0LmFD27ZtoaCgAB8fnypXI7G5eq9yx6CK3RYkwcjICHv37kWbNm1Ejl+/fh0DBgxgfaUJJfIsOHLkCObOnYvp06dX+SHCxd1aSe15q0hfXx+nT5+GlZUVbGxs4OPjg4EDB+LatWtwc3MTS0jZcO7cObi7u8PW1lYk8UtMTMSJEyfQsWNH1sa6fv06cnNz0aVLF+GxXbt2Yd68ecjPz0evXr2wYcMGzusQlLt8+TK8vb1x69YtTseR5HupWbNmGD16tNgWkDVr1iAwMBCpqamsjldRYWEh9u3bh9u3bwtncLnYK1Vu+/btWLhwIUaMGCGRZcp5eXm4ceNGlf+PbO7HL/etD0suE/kRI0bAw8MDrq6uvO1LvX79Oq5cucL5SqjS0lIcOXJE5EZmz549hasvZImkZv0q93J///69sCWci4sLunXrxsm4oaGhOHbsGEJCQiTWUpCv2WpJMjY2/uJjAoGAs9oDmpqaKCgokGgvbkCybe8kXdeh4ucKHyuEyidupk6dirFjx3KSSFe8CV6+ArQyrutAJSUlYfDgwcjKysKUKVOEbfUmTZqE169fY+/evayOp6Kiglu3bknkPct3Iq+oqIjU1FSx89KDBw9gaWkpvFnNFkrkWfClpdFc/SJ+a8/b7du3WR2v3KBBg9CiRQtMmTIF/v7+WLduHXr27IkLFy7A3t6ek2J3dnZ26Ny5s9hsl4+PD86fP8/qz9qlSxc4OzsLl70kJyfD3t4ew4cPR7NmzbBy5UqMGTMG8+fPZ23Mr0lNTYWDgwM+fPjA2RiSfi/VqVMHd+7cEduGkpGRgebNm7N+giv3+vVr1KtXDwCQlZWFoKAgFBYWwt3dnbMl/ZK8CPlWYT0uLij5TOTd3d1x/vx51KtXDwMGDICHhwdnRe7KVXwPPXnyBIGBgSgsLESPHj04q2ORkpKCnj17IicnB02aNAHw+cabtrY2jh8/Ltyzz6b8/HxERUVVWdCU7ZnNyksPv2THjh2sjqugoAA7OzvhTBvXvdzL2dnZITMzEwzDwMjISCzx4+Kzm6/Z6ppA0ltfKo579uxZ7Nixg7Mb0eUGDhz41boOXl5enI4vaUePHsXly5cRGRmJu3fvCm/wOTs7o127dqzcXIyKihLOUEdFRX31uZIujFxUVAR5eXmx88TPcnBwQEBAgETqScjLyyMnJwfa2toAPq9qKN8KLAnm5uaYN28ePDw8RI7v3r0b8+bNY/3mFyXyLKi8FLsytosgSHLPW0W5ubkoKipCo0aNUFZWhlWrVgn34fr6+kJTU5P1MRUVFZGcnAxzc3OR4+np6bC2tmY18dPV1cWJEyfQokULAJ+XekZFRSEmJgbA54Ig8+bNY33P79eK65WUlODKlSusjleRpN9LZmZmmD59OsaMGSNyfOvWrVi1ahXu37/P6njJycno0aMHnjx5AnNzc+zfvx9ubm7Iz8+HnJwc8vPzcfDgQfTq1YvVcSXNwsICXbt2lUhhvXKVPyzLaw+Uf1hyvbQ+Ly8PYWFh2Lt3L6Kjo9GkSRN4eHhg0KBBrC5z5fM91Lp1azRo0AAhISHC8+ubN28wfPhwvHjxAteuXWN1vPj4eHTt2hUFBQXIz8+HlpYWXr16BWVlZTRo0ICT2TdDQ0PY2dl9tbvCkSNHWB234tJWSfpWHZnyWTE28TVbzYfi4mI8fPgQpqamMrlipVxBQQH69OmDK1eucH5DiI+6DsDnLYadOnXC1q1bYWFhIdGxy719+xbR0dE4ePAg9u7dC4FAgI8fP/ISizSquG3g5s2bmDt3LpYsWVLlDUU2z8dycnJo3ry58ByQlJSEpk2bitUK4mrSc/ny5Vi5ciVWrlwJV1dXAEB4eDhmzJiBqVOnYtasWayOR4m8FJLknjfg23t4ynFxYaSvr481a9aIVaQNCwvDtGnTkJWVxdpYioqKuH//PvT19QEAv//+O9zc3DB37lwAwKNHj2BlZcV6+xxJF9erSNLvpc2bN2Py5MkYOXIk2rRpA4FAgJiYGAQHB2PdunViCf7P6tKlCxQUFDBz5kyEhobi5MmT6NSpE4KCggB8XkZ269YtVivR8lEETpKF9crxVdiqKv/++y/27duHHTt24P79+/j06RNrr83He6ickpISbt68iV9++UXkeEpKChwcHFgvSOTs7AwLCwts3rwZdevWRWJiImrVqgUPDw94eXmx3gFh/Pjx2L9/PwwMDDBy5Eh4eHiItCvjypMnTyAQCNC4cWMAwI0bN7B3715YWlpi9OjRnI9P2FVQUIBJkyYJZ8jT09NhYmICT09PNGrUCD4+PpzHUFhYiJKSEpFjXN0s6tevHyIiIvDHH39UWeyOzRtCfNR1KKetrY2rV6+KTeRwLTc3V1ixPjIyEikpKahXrx6cnJxw4MAB1scrKipCUlJSlVsX2Ny2paWlhfT0dNSvXx+amppf3ZbGxiq+ytsGylcpV8TFyuXvLbzNxY1T4PPP5OPjg/Xr1wtXtSkqKmLmzJmcbH2mRJ4Fu3bt+urjbO9PlfSety/t4amMiwv2hQsXIiAgAD4+PiKJ3/LlyzF16lRhks0GQ0ND7N69G46OjiguLkbdunVx4sQJYbG55ORkODk5sb5MWdLF9SriY//kkSNHsHr1auF++PKq9T179mR9rPr16+PSpUuwtrbGhw8foK6ujhs3bghXXdy7dw+tW7dmtVc1H0Xg+Cisx1dhq8pKSkpw6tQphIaG4tSpU9DS0sLTp09Ze30+3kPlbG1tsWbNGuFd/XKXLl2Cl5cXkpOTWR2vbt26uH79Opo0aYK6devi2rVraNasGa5fv45hw4bh3r17rI4HfL7hc/jwYezYsQNXr15Ft27dMGrUKHTq1Imz+gft2rXD6NGjMWTIEOG2hV9++QXp6enw9PTktD92Xl4eDh48iMzMTEyfPh1aWlq4ffs2dHR0JFZVWdZ4eXnhypUrWLt2Ldzc3JCUlAQTExMcP34c8+bNQ3x8PCfj5ufnY+bMmQgLC8Pr16/FHufqJqaKiorE2t7xUdeh3NSpU1GrVi2JFhK1trbG3bt3oaWlBUdHR+Gy+ubNm3My3tmzZzF06FC8evVK7DG2E9yQkBAMGDAAderUQXBw8FfPr2xsC/nWtoGKJL2FQBI+fPiA1NRUKCkpwdzcnLP6WrK79kiCKu8RKikpQUFBAWrXrg1lZWVWEvn169cL/1y+lD02NlYie94iIiKEf2YYBl27dkVQUJBELjp8fX2hpqaG1atXC5ejNGrUCPPnz2f953Rzc4OPjw+WL1+Oo0ePQllZWWT/dFJSEquV8ocOHYpNmzYJt14kJibC0tKS9b1JXyPJ99KnT5/g7++PkSNHCrcrcC03NxcNGzYE8Ln3t4qKisiMn6amJusrLCrfG+XqXmnFir7dunXD9OnTcffuXYn1/+Y6Qf+WiIgI7N27F4cOHUJpaSn69OkjXBXAJkm/hyqugFqyZAk8PT0xf/58tG7dGsDndqMLFy7E8uXLWRuzXK1atYQXdzo6OsjKykKzZs2goaHB6uqniurUqYOBAwdi4MCBePz4MYKDgzF+/HiUlJTg7t27nBS8S0lJQcuWLQF8Xt3VvHlzXLlyBefPn8fYsWM5S+STkpLQoUMHaGho4NGjR/jrr7+gpaWFI0eO4PHjx9+cFPheU6ZMwaJFi6CiooIpU6Z89blr1qxhZUw+HT16FP/88w9at24tkpxYWloiMzOTs3FnzJiBiIgI/P3338LP86dPn2Lr1q2cJp/6+vqcbg2xs7MT+XfMyMiAjo6OxOo6lCsuLkZQUBAuXLiAFi1aCFd7lePivTt69GhOE/fKJk6ciL59+8LPzw86OjqcjlUxOR8+fDinYwGiyXlWVhb09fWrnJF/8uQJ57HwQVVVFQ4ODpyPQ4k8C968eSN27P79+xg3bhymT5/OyhgBAQEi36uqqgpbZFQkEAhYT3Ar3ymTl5dH69atOV/G++nTJ+zZswcDBw6Et7e38GKZq/7mixcvRp8+feDk5ARVVVWEhISI7KnZsWMHqz2q9+zZg1WrVgl/nnbt2kl0mTsAbNu2TWLvJQUFBaxcuZKzAkBfUvmDg68q52yrak/2woULxY5xWfmWL40bN8br16/RuXNnbN26FT169OB0BYsk30N169YVW47Yr18/4bHyG0M9evRg/f/Vzs4ON2/ehIWFBVxcXODn54dXr15h9+7dnBTWq0wgEAi3GVVeYsqmkpIS4ezIxYsXhTe6mjZtylkbVeBzgj18+HCsWLFC5HOsS5curLbejI+PFy7z/tpstKycC1++fFllC9H8/HxOf8YTJ05g165dcHZ2xsiRI9GuXTuYmZnB0NAQe/bsweDBgzkZd/Xq1ZgxYwZnbe+qS82YlJQU2NvbA/i8XaIirv5fJ06cCEBy9RZevHiBKVOmcJ7EVzZ48GA4OzvDyclJIjUIjI2NRVYqlsvNzYWxsbHMXaPExcXhwIEDVRaNZbswOCXyHDE3N8eyZcvg4eHBynJEtvsOSgMFBQWMGzdOuASbqwS+nLa2NqKjo/H27VuoqqpCXl5e5PEDBw6wOjskqZnbr5H0+6pDhw6IjIyUyN3gcsOHDxdetBcVFWHs2LEi+7jZVp6MVD7GNi4TnerOz88Pffv25aTAZlUk+R6quAJK0pYsWSK8Ybpo0SIMGzYM48aNg5mZGWcrMCourY+JiUH37t2xceNGuLm5fbXzw8/45ZdfsGXLFnTr1g0XLlwQ9qp/9uyZsDMBF+Li4rB161ax43p6esjJyWFtnIrvIT7fT5Li4OCAU6dOYdKkSQD+73wbGBiI3377jbNxy5MQ4PMWqvJtd7///jvGjRvH2bgeHh4oKCiAqakpJ23vuNo7/KP4eO8WFhZi4sSJEqu38McffyAyMpLV1Z7fQ1VVFatXr8aYMWPQsGFDODk5Cbt4cFGXqar98cDn5eeS2EYqSfv378fQoUPRqVMnXLhwAZ06dcL9+/eRk5OD3r17sz4eJfIckpeXx7Nnz1h7PRMTE8TFxXF6oVHdtGrVCvHx8axX/v+a8iJllUmiCJOs69KlC2bNmoWUlBT8+uuvYkvl2F4CXnn2v3I7EID9GhYMw0gs8XN1dcXhw4eFhfRqCkkWJJP0e4jPvYLl+/6Bzzc2T58+zel4FYvdjRgxAvv375fI59vy5cvRu3dv4QohGxsbAJ+3q5QvueeCoqJilcVj09LShN0fyI9bunQp3NzccPfuXXz69Anr1q3DnTt3cO3atR/ap/ujTExM8OjRIxgaGsLS0hJhYWFo2bIlTpw4wek5ee3atZy9drk3b94gNDQUw4YNE1vG//btW+zatavKx6Sdj48PEhMTERkZCTc3N+HxDh06YN68eawn8hs3bkTfvn0RHR0t0faQ5TcUc3JyhEX91q1bhwkTJqBBgwasrUwq39ojEAjg6+srUmehtLQU169fh62tLStjVRdLlixBQEAAJkyYADU1Naxbtw7GxsYYM2YMdHV1WR+Pit2xoOJeVeD/2odt3LgR+vr6OHPmDCvjVO7bzJfKbaa4dODAAfj4+MDb27vKxM/a2przGLgiJyeHS5cuCW8QtGnTBmFhYcJKyuXY/hn53D8pyd7qfJFkEbjqck7ggySXrklS5XaUXyPN5z/g8/vXwMBAbE9uZVz8f5aWluLdu3ciqzoePXokbLXHhdGjR+Ply5cICwuDlpYWkpKSIC8vj169esHR0ZG1BO1HugtI8+9KRcnJyVi1ahVu3bqFsrIy2NvbY+bMmZxuCQkICIC8vDw8PT0RERGBbt26obS0FCUlJQgICJDqHuuLFi1CUlLSF6u09+vXDzY2NpgzZw6ncUj6PG9oaCist6CmpobExESYmJggIyMD9vb2393F6XsFBQVh7NixUFJSQr169UTOg5JoD5mfn4+YmBhhMn/79m1YWlqyViDSxcUFwOfCd7/99pvIdtXatWvDyMgI06ZNk3hnAi6pqKjgzp07MDIyQv369REREQErKyukpqbC1dWV9e1bNCPPgsr7iQQCAbS1teHq6orVq1fzExSLKl8UVJ5hLMfFSbV///4Aqr4rKQuJX/v27UWW1Hfv3h0AhPtEufgZ+dw/WROWg/NdBK4mkPTSNUmytbUV+f3/GrbODd9KpMuxXdhq6NChvO3TlpeXF9uawcV+44pWrVqFrl27okGDBigsLISTkxNycnLw22+/wd/fn7VxvrSqTJZZWVkJl0NLire3t/DPLi4uuHfvHm7evAkzMzOJ3WTjqu3doUOHvnr9OmbMGEybNo3TRJ6P87yk6y3MnTsXCxcuhI+PD2dbiaoyc+ZMREVFITExEc2bN4ejoyNmzZoFR0dHVleTlG+PGDFiBNatWyfxFRw3btxAZGRkla39uCr0qaWlJdyipqenh5SUFFhZWSEvLw8FBQWsj0eJPAskmZzcvXv3m3vp2P4AqXxRUNXSUq7Icm0Avn42PvZPXrp0CRMnTkRsbGyVy/TatGmDLVu2iHQJIN/n/fv339xjJmvLHyW9dE2SKp4X4uPjMW3aNEyfPl241/fatWtYvXo1VqxYwdqYFW9GMwyDpUuXYuzYsZxvJwoODub09Sv63psVAHeVuNXV1RETE4NLly7h9u3bwpnjDh06sDpOTbuZ+KVZUoFAgDp16ojMArLhS59nBgYG0NDQ4PzzTBJt7zIzM786S2pubs5pRwCAn/O8pOstFBcXo3///hJN4gFg5cqV0NbWxrx589CzZ080a9aM0/H4OCctWbIEc+fORZMmTaCjoyO22oEr7dq1w4ULF2BlZYV+/frBy8sLly5dwoULF4TtrNlES+tZVv7PycWbpLyfe1X/ZVzO4PLp9evXwj2TT548QWBgIAoLC+Hu7k5JnxRxd3eHi4uLyAxGRevXr0dERASOHDki4cikW/k54Utk8ZwASH7pGl9atmyJ+fPno2vXriLHT58+DV9fX9y6dYuTcSsuKZUVCxYsEP65qKgIf//9NywtLYUX57Gxsbhz5w7Gjx+PpUuXch5PUVER6tSpw/mKhMLCQjAMI9yb+vjxYxw5cgSWlpasdmHh07fOg40bN8bw4cMxb948VhImvj/PJkyYgIiICCxcuLDKtndsVMuvW7cuzp49K2x7WVlsbCzc3NyQl5f302N9CR/n+atXr8LNzQ2DBw9GcHAwxowZI1Jv4ddff2V1PG9vb2hra2P27Nmsvu63JCYmIioqCpGRkYiOjoa8vLyw2J2zszPrif232sJeunSJ1fGAzy1Uly9fLtHiysDnYpNFRUVo1KgRysrKsGrVKsTExAjbPbNdpJdm5Fmya9curFy5Evfv3wcAWFhYYPr06RgyZAir41y/fr1GFMVJTk5Gjx498OTJE5ibm2P//v1wc3NDfn4+5OTkEBAQgIMHD1abNinShI/9k4mJiV/te92pUyesWrWKlbFqmoMHD9a4QoySXrrGl+Tk5CprkRgbG+Pu3bs8RCS9Klbi/vPPP+Hp6SmsVl/xOVz2NC4rK4O/vz+2bNmC58+fCyti+/r6wsjICKNGjWJ9zJ49e6JPnz4YO3Ys8vLy0LJlS9SuXRuvXr3CmjVrOK2uLinBwcGYM2cOhg8fjpYtW4JhGMTFxSEkJARz587Fy5cvsWrVKtSpU4eVhInvzzNJtL2zs7PD0aNHv5jIHzlyBHZ2dj89ztfwcZ5v06YNrly5glWrVsHU1BTnz5+Hvb09rl27xkm9hdLSUqxYsQLnzp2DtbW1WLE7rpZ/29jYwMbGRrhtNTExEWvXroWnpyfKyspYv/lfXlC0XElJCRISEpCSksJZS2I5OTm0bduWk9euSvnKIAUFBaiqqgq/Hzt2LMaOHcvZuJTIs2DNmjXw9fXFxIkT0bZtWzAMgytXrmDs2LF49erVF+/a/hcGBgY1orDVjBkzYGVlhdDQUISGhqJ79+7o2rUrgoKCAACTJk3CsmXLKJH/D/jYP/n8+XOxD6iKFBQU8PLlSwlGJDvatm1bI84JFUl66RpfmjVrhsWLF2P79u3C7RMfP37E4sWLOV8KKcsOHDiAmzdvih338PBAixYtsGPHDk7GXbx4MUJCQrBixQr89ddfwuNWVlYICAjgJJG/ffs2AgICAHy+6dewYUPEx8fj0KFD8PPzk4lEPiQkBKtXr0a/fv2Ex9zd3WFlZYWtW7ciPDwcBgYG8Pf3ZyWR5/vzTBJt7yZOnIgBAwagcePGGDdunLAdb2lpKf7++28EBARg7969rIz1JXyd5yVZbyE5OVl4QyQlJUUiY5aLj48XFrmLjo7Gu3fvYGtrKyxQx6byc1Bl8+fPx4cPH1gfD/i82mHTpk0S6fIAfF7F8j0rrFhfIcmQn2ZkZMSEhISIHQ8ODmaMjIxYG0cgEDDPnz9n7fWqs3r16jGJiYkMwzDM+/fvGYFAwMTFxQkfT01NZTQ0NHiKjvwoExMT5vDhw198/NChQ4yxsbEEI5INNemcUNHr16+Zp0+fMgzDMKWlpczy5cuZHj16MN7e3kxubi7P0bHn+vXrTIMGDZj69esz7du3Z9q3b8/Ur1+f0dbWZq5fv87ZuKqqqkxmZiZnr883HR0dZseOHWLHd+zYwTRo0ICzcU1NTZmLFy8yDCP6b5yamsrUrVuXkzGVlJSYx48fMwzDMH379mXmz5/PMAzDZGVlMUpKSpyMKWlKSkpMenq62PH09HThz/jgwQPWfl6+P8+srKyYyMhIhmEYpmPHjszUqVMZhmGYdevWMY0aNWJtnNmzZzMCgYBRV1dnbG1tGTs7O0ZdXZ2Rk5NjZs6cydo4XyLJ87xAIGDk5OS++iUvL8/qmHyqW7cuo6CgwPz666/M1KlTmRMnTjBv376VeBz3799nNDU1OXnt0tJSxs3NjTExMWG6d+/O9O7dW+SLbZGRkcKviIgIRklJidmzZ4/I8fLfWzbRjDwLsrOz0aZNG7Hjbdq0YXUPj5OTE+tFW6qr3NxcNGzYEACgqqoKFRUVkeXDmpqawiVXpPrr2rUr/Pz80KVLF7HCbIWFhZg3b56wYj/5foaGhsKZkpqAr6VrfGnZsiUePnyI0NBQ3Lt3DwzDoH///hg0aJBY15CfsX79epHvP336hODgYNSvX1/kOFc9jSVt8uTJGDduHG7duiVcOhwbG4sdO3bAz8+Ps3GfPn0KMzMzseNlZWVilcfZYmZmhqNHj6J37944d+6ccIXgixcvZKYIZuPGjbF9+3YsW7ZM5Pj27duhr68P4HO9Hbb2pvL9eTZixAgkJibCyckJs2bNQrdu3bBhwwZ8+vSJ1aXY/v7+6NmzJ/bs2YOMjAwwDANHR0cMGjQILVu2ZG2cyvg4z3+tnsHVq1exYcOGKutT/ayRI0di3bp1UFNTEzmen5+PSZMmcbY6aPfu3XB0dOT9HHDt2rVvFuv9ryZNmoSIiAi4uLiItfbjgpOTk8j38vLyaN26Nee1ZqjYHQuaN2+OQYMGiS3ZWrx4Mf755x8kJyfzFJn0kpOTw/Pnz4X1ACr3rn/+/DkaNWokU0W84uPjYWVlBQWF/7u/duTIEdbbrPBRvfn58+ewt7eHvLw8Jk6ciCZNmkAgECA1NRWbNm1CaWkpbt++DR0dHVbGI7LpW0WtysnSeUESqtqHX5kkehpLUlhYGNatW4fU1FQAn7cxeHl5iSzPZluLFi0wefJkeHh4iBQUXLBgAS5evIjo6GjWxzx48CAGDRqE0tJSuLq64sKFCwCApUuX4vLlyzhz5gzrY0ra8ePH0bdvXzRt2hQODg4QCASIi4vDvXv3cPDgQXTv3h2bN2/G/fv3WUl0q9vnWVZWFm7evAltbW3s3LmTs+RPUqrLef7evXuYNWsWTpw4gcGDB2PRokUwMDBgdQx5eXlkZ2eLbY979eoVGjZsiE+fPrE6XmUZGRnIzMyEo6MjlJSUvqvt6X9RuTYTwzDIzs7GzZs34evrK1LDhC1qamrYv38/unXrxvprf+/4kigaSzPyLFiwYAH69++Py5cvo23bthAIBIiJiUF4eDjCwsL4Dk9qDR8+HHXq1AEg3rv+48ePfIbGiV9//RXq6urw8PCAr68vdu3ahdmzZ7M+U8NHXQEdHR1cvXoV48aNw6xZs0S6O3Tu3Bl///03JfHkmyq2S2QYRlg3Q09Pj8eouLV7925s3boVDx48wLVr12BoaIiAgACYmJigZ8+erIwhy20+v6Rfv36cJu1VmTdvHoYMGYKnT5+irKwMhw8fRlpaGnbt2oWTJ09yMuYff/yB33//HdnZ2bC1tRUeb9++/Q8VPq3O3N3dkZ6eji1btiAtLQ0Mw6BLly44evQojIyMAIDVWgDV7fPMwMAABgYGSExMREhIiNQn8nyf5589e4Z58+YhJCQEnTt3RkJCApo3b87qGO/evQPDMGAYRqyFbGlpKU6fPs1p7ZvXr1+jX79+iIiIgEAgwP3792FiYoI///wTdevWxerVq1kdr3JtJjk5OTRp0gQLFy7krHuGlpYWTE1NOXnt6oRm5Fly69YtBAQEIDU1FQzDwNLSElOnTuW8qqesGjFixHc9T5b65ebm5iIpKQlbt27FsWPHAHzuXcpGBdrq5M2bN8Jleubm5qy34iA1hyy2Sato8+bN8PPzw+TJk7F48WLcuXMHJiYmCA4ORkhIiMgFL/lxxcXFePHiBcrKykSOsz3rVtG5c+ewZMkS3Lp1S9hH3s/Pj/WL2e9N0tnqTFJTVafPs8TERNjb28vciiRJneffvn2LJUuWYMOGDbC1tcXy5cs5a3P8rVUHAoEACxYswJw5czgZf+jQoXjx4gWCgoLQrFkz4b/v+fPn4e3tjTt37nAyriTt3LkTZ8+exc6dO4XtNyWp8kpirtCM/E8o37cDAObm5vj777+rfA7fe1CkkSwl6F8SGxsLBQUFtGjRAsDnu4fOzs7Yv38/FBUVUVJSUuV+SmmnqakJBwcHvsMgpNrbsGEDAgMD0atXL5H9vy1atMC0adN4jEy63b9/HyNHjsTVq1dFjpcvK+UiEfr06RP8/f0xcuRIREVFsf76lfHRnYRvBQUFyMrKQnFxschxa2trzsakzzPZsGLFCixfvhwNGzbEvn37WFvt9CURERFgGAaurq44dOiQSA2o2rVrw9DQEI0aNeJs/PPnz+PcuXNo3LixyHFzc3M8fvyYs3Fv3bqF1NRUCAQCWFpacjrZuX79emRmZkJHRwdGRkZinSbY2jparvLN08oricuxffOUEvmfwFurASITJk+eDF9fX5Fjc+fOxbFjxxAZGYkTJ05g4cKFOHXqFKvjamlpIT09HfXr14empuZX38PlbW0IIZL38OHDKi906tSpg/z8fB4ikg3Dhw+HgoICTp48CV1dXc6LIAGfC3etXLmSs57JldWEm+HlXr58iREjRnxxvz9dg5Fv8fHxgZKSEszMzBASEvLF9nNsJWHlhdEePnwIAwMDiZyDKsrPz69ylvrVq1fCLa1sevHiBQYMGIDIyEjUrVsXDMPg7du3cHFxwf79+4X1sNgk6W2klW+eenh4SGRcSuR/Al/7eEpLSxEcHIzw8PAqlwVeunSJ0/EJO+7cuYNffvlF+P26deuwe/duXL58Gebm5lBQUMCKFStYHzcgIEBYITUgIEDiHyCEG+Hh4V88J0j7nsmvkeX3r7GxMRISEmBoaChy/MyZM9RH/ickJCTg1q1baNq0qUTH7dChAyIjIzF8+HCJjivrJk+ejDdv3iA2NhYuLi44cuQInj9/jsWLF7O+15dP39oukZeXx9nYkirG+yVcn+eHDh3Ky2dJamoqnjx5gt9//x0AsGnTJgQGBsLS0hKbNm3ibKuGo6Mjdu3ahUWLFgH4/O9bVlaGlStXwtnZmfXxJk2ahHfv3uHOnTvCz667d+9i2LBh8PT0xL59+1gfk4sCel/D181TSuR/Al+tBry8vBAcHIxu3bqhefPmMn0hK8uUlJSQkZEBIyMj7Ny5E5s3b0Z0dLRwf+bHjx85aTc4bNgwvHv3Dh8/fpSZYkc13YIFC7Bw4UK0aNFCYjOMfOBr6Rpfpk+fjgkTJqCoqAgMw+DGjRvYt28flixZgu3bt7M61qdPn7Bnzx507txZ2PpTVllaWuLVq1cSH7dLly6YNWsWUlJS8Ouvv4q9b93d3SUekyy4dOkSjh07BgcHB8jJycHQ0BAdO3aEuro6li5dylvVarZ9a7uEhoYGhg4dysnYkirGC/Bzng8ODmbttX7E9OnTsXz5cgBAcnIypkyZgqlTp+LSpUuYMmUKZ8lhecJ+8+ZNFBcXY8aMGbhz5w5yc3Nx5coV1sc7e/YsLl68KHIDuvxmBVfF7moKSuSl0P79+xEWFoauXbvyHQr5Ce7u7ujXrx8sLS0RGxsLHx8fYRLPMAyWLVvG2d472hYiW7Zs2YLg4GAMGTKE71A4xdfSNb6MGDECnz59wowZM1BQUIBBgwZBT08PGzZsYL0Ik4KCAsaNGydsxybLli9fjhkzZmDJkiWwsrIS2zvJVV2b8srpVbVA42pvfk2Qn58vrPCtpaWFly9fwsLCAlZWVqzvg+UTn9slXr16JSzGW168i6vktyad5x8+fAhLS0sAwKFDh9CjRw8sWbIEt2/f5vQa39LSEklJSdi8eTPk5eWRn5+PPn36YMyYMZg3bx7rq/jKysrEzrMAUKtWLbEVhGwpLS1FQEAAwsLCqqydIStbRymRl0K1a9eWySJoNc3GjRthYGAAeXl5bNq0CV26dMHFixdhZ2eHq1evIiMjQ6wYE1v4bu9C2FVcXIw2bdrwHQbnatK+33J//fUX/vrrL7x69QplZWUoLS3FkiVLMGHCBBQWFrI6VqtWrapcyi9rOnToAOBzC7aKuCx2B4CzC9aarkmTJkhLS4ORkRFsbW2xdetWGBkZYcuWLdDV1eU7PKnEZzHemnSer127NgoKCgAAFy9eFK6o0NLSEimozYWGDRtiwYIFIse4amHo6uoKLy8v7Nu3T1jE7+nTp/D29hY7D7NlwYIFCAoKwpQpU+Dr64s5c+bg0aNHOHr0KPz8/DgZkw/Ufo5Fkmo1sHr1ajx48AAbN26U2SW0NdGLFy+watUqJCcnQ09PD56enpxW261I1tt4ybqZM2dCVVVVrHgikU55eXmYMGECzp8/j1q1asHHxwcTJ07EggULsGrVKlhaWmLKlCkYOHAgq+MeOHAAPj4+8Pb2rnLpt6TOR1z7VtX4ytvmflZhYSHCw8PRvXt3AMCsWbPw8eNH4eMKCgpYuHChSC9p8v327NmDkpISDB8+HPHx8ejcuTNev36N2rVrIzg4GP379+c7RKnTunVr+Pr6imxLmDt3LrZv345z587hxIkTuHr1KuvFeGsad3d3FBcXo23btli0aBEePnwIPT09nD9/HhMnTkR6erpE4+GqheGTJ0/Qs2dPpKSkQF9fHwKBAI8fP4a1tTWOHTsmVj2fDaampli/fj26desGNTU1JCQkCI/FxsZi7969rI/JB0rkf0LlfTwnTpyAq6sr5/s1e/fujYiICGhpaeGXX34RW64iK/tDieRQIi/dvLy8sGvXLlhbW8Pa2lrsnFDVUl5SfY0fPx4nTpxA//79cfbsWaSmpqJz584oKirCvHnzWE80y8nJyYkdEwgEnM9Uy7qtW7fi5MmTOHHiBIDP59tffvkFSkpKAIB79+5h+vTpmDJlCp9hyoyCggLcu3cPBgYGqF+/Pt/hSCU1NTUkJyfDyMgIwOdivGvWrMHFixdhbm6Ou3fv4rfffsPbt2/5DVTKZWVlYfz48Xjy5Ak8PT0xatQoAIC3tzdKS0uxfv16icbDVSJf7sKFC7h37x4YhsEvv/zC2Ww8AKioqCA1NRUGBgbQ1dXFqVOnYG9vjwcPHsDOzk5m3ru0tP4n8LWPp27duhKrFEoIqf6SkpJga2sLAEhJSRF5jFbtSJ9Tp05h586d6NChA8aPHw8zMzNYWFhg7dq1nI778OFDTl+/upFU3/E9e/bA29tb5NjevXuFN05DQ0OxadMmSuRZoqysDHt7e77DkGp8FeOtaQwMDHDy5Emx4wEBATxEw77r168jNzcXXbp0AQB07NgRz549w7x581BQUIBevXphw4YNnLS8a9y4MbKzs2FgYAAzMzOcP38e9vb2iIuL42Q8vlAi/xP42sdTk/YPEcmhhE96Vax5QKTfs2fPhAWQTExMoKioiD///JPzcWV9b3w5SfcdT09Ph4WFhfB7RUVFkdUPLVu2xIQJE1gds6bIz8/H8uXLcfjwYTx69AgCgQDGxsb4448/MG3atCp7ZZNv47MYb02TmZmJnTt3IjMzE+vWrUODBg1w9uxZ6Ovri7QoZoOkWxjOnz8fzs7OwkQ+OTkZf/31F4YNG4ZmzZph5cqVaNSoEebPn8/quMDn1cvh4eFo1aoVvLy8MHDgQGzfvh1ZWVliN1alGSXyUuzly5dIS0uDQCCAhYUFtLW1+Q6JSIma1sarJvn3338hEAiocKEUq1zhV15eXux3kyu7d+/Gli1b8PDhQ1y7dg2GhoZYu3YtjI2N0bNnT4nEwDVJ9x1/+/atSP/tly9fijxeVlYmsmeefJ/i4mI4OTkhJSUFXbp0QY8ePcAwDFJTU+Hv748zZ87g8uXLVVbLJl/HZzHemiQqKgpdunRB27ZtcfnyZfj7+6NBgwZISkpCUFAQDh48yOp4km5hmJCQIOxVD3zuutWyZUsEBgYCAPT19TFv3jxOEvlly5YJ//zHH3+gcePGuHr1KszMzGSq1Scl8lIoPz8fkyZNwq5du4RVcOXl5TF06FBs2LCB7kCTb6pJ7V1qgrKyMmES8uHDBwCf9zhOnToVc+bMqXLvM6m+GIbB8OHDhcv/JHWjbfPmzfDz88PkyZPh7+8vnJmuW7cu1q5dKzOJvKT7jjdu3BgpKSlo0qRJlY8nJSVxUuxJ1m3evBn//vsvEhMTxf5t7927B2dnZ2zZsgWTJk3iKULppaioKFLZOyEhQViM18HBAbt375aZ4pd88vHxweLFizFlyhSoqakJj7u4uGDdunWsjyfpFb1v3ryBjo6O8PuoqCi4ubkJv3dwcMCTJ08kEkvr1q3RunVriYwlSZTIS6EpU6YgKioKJ06cQNu2bQEAMTEx8PT0xNSpU7F582aeIyTVHW3PkC1z5szB9u3bsWzZMrRt2xYMw+DKlSuYP38+ioqK4O/vz3eI5AcMGzZM5HtJ3WjbsGEDAgMD0atXL5HZjBYtWmDatGkSiUESJN13vGvXrvDz80O3bt3EKtMXFhZiwYIFrN88qAkOHz4MX1/fKm+QNG3aFHPmzMHBgwcpkWdBgwYNsGLFCr7DkDnJyclVVk/X1tbG69eveYiIXTo6Onj48CH09fVRXFyM27dvi7S8e//+PacrZtLT0xEZGYkXL16Itf+UlRZ0lMhLoUOHDuHgwYNwdnYWHuvatSuUlJTQr18/SuSlTH5+PpYtW4bw8PAqTzYPHjzgKTIiLUJCQhAUFCSyXMzGxgZ6enoYP348JfJShq8bbQ8fPoSdnZ3Y8Tp16iA/P5+HiLgh6b7js2fPRlhYGJo0aYKJEyfCwsICAoEA9+7dw8aNG/Hp0yfMnj2b9XFl3d27d0WugypzcXHBwoULJRcQIT+obt26yM7OFmtbHR8fLxPb49zc3ODj44Ply5fj6NGjUFZWRrt27YSPJyUlwdTUlJOxAwMDMW7cONSvXx8NGzYUqQMlEAgokSf8KSgoEFmqUq5BgwYoKCjgISLyM/78809ERUVhyJAh0NXVpaJz5Ifl5uaiadOmYsebNm2K3NxcHiIi0sjY2BgJCQliRe/OnDkjLL4nzTIyMmBmZobJkycjOzsbADBv3jx07twZe/bsEfYdZ5uOjg6uXr2KcePGwcfHB+VdfwUCATp27Ii///67ys908nV5eXmoV6/eFx+vV6+ezLSYIrJp0KBBmDlzJg4cOACBQICysjJcuXIF06ZNY3WvOl8WL16MPn36wMnJCaqqqggJCRHpdrBjxw506tSJs7H9/f0xc+ZMTl6/uqA+8lKoffv2qFevHnbt2iVcpldYWIhhw4YhNzcXFy9e5DlC8iPq1q2LU6dOCbdJEPKjWrVqhVatWon1nJ00aRLi4uIQGxvLU2REmuzcuRO+vr5YvXo1Ro0ahaCgIGRmZmLp0qUICgrCgAED+A7xp8jJyUFPTw8uLi7CLyMjI4n2Hc/NzUVGRgYAwMzMDFpaWpyOJ8vk5eWRk5PzxUK/z58/R6NGjTjriU3If1V+U7GkpAQjRozAvn37wDAMFBQUUFpaikGDBiE4OBjy8vJ8h8qKt2/fQlVVVeznyc3NhaqqKietDNXV1ZGQkCBs8ymrKJGXQikpKXBzc0NRURFsbGwgEAiQkJAARUVFnDt3jvV2FYRbxsbGOH36NJo1a8Z3KERKRUVFoVu3bjAwMMBvv/0GgUCAq1ev4smTJzh9+rTIUjZCviYwMBCLFy8WFiDS09PD/PnzMWrUKJ4j+3nR0dGIiopCZGQkrl27hqKiIhgYGMDV1VWY2MvCctaaQk5ODs2bNxfpCFDRp0+fcOfOHUrkSbVT+aaik5MTbt++jbKyMtjZ2cHc3JzvEKXeqFGj4ODggLFjx/IdCqcokZdShYWFCA0Nxb1798AwDCwtLTF48GAoKSnxHRr5QaGhoTh27BhCQkKo4wD5z549e4ZNmzaJnBPGjx+PRo0a8R0akUKvXr1CWVmZsCicrCkpKcG1a9cQGRmJyMhIxMbG4uPHjzAzM0NaWhrf4ZHvULFo1tfMmzeP40gI+TF0U5EbFVcl5ufnY82aNejWrRusrKzEiup5enpKOjxOUCJPCA/s7OxE9sJnZGSAYRgYGRmJnWy4qKJMCCGVBQYGwtnZuUbNBhUWFiImJgbnzp1DYGAgPnz4QDO4hPx/VIyXe3RTkT2ViwZ+iUAgkJn3LiXyUiotLQ0bNmxAamoqBAIBmjZtiokTJ1ZZ8IpUP987kwDQbAL5Pm/evMH27duF54RmzZphxIgRtAeXfLemTZsiPT0dDRs2hJOTE5ydneHk5CRTnytFRUW4evUqIiIiEBkZibi4OBgbG8PJyQmOjo5wcnKimTBC/r+BAwd+tRivl5cXT5HJHrqpSP4LSuSl0MGDBzFw4EC0aNECv/32GwAgNjYWcXFx2Lt3L/r27ctzhIQQSYqKikLPnj2hrq6OFi1aAABu3bqFvLw8HD9+HE5OTjxHSKRFTk4OIiIihMs+79+/D21tbTg7O2P//v18h/dTnJycEBcXB1NTU2HS7uTkRBXjCfkCKsbLHbqpyK2FCxdi2rRpYltWCwsLsXLlSplpP0eJvBQyMTGBh4eHWH/UefPmYffu3TKzXKSmMDExQVxcnFgbnby8PNjb29P/J/mm5s2bo02bNti8ebOwKmxpaSnGjx+PK1euICUlhecIibTJz89HTEwM9u/fj9DQUDAMg0+fPvEd1k+pVasWdHV10atXLzg7O8PR0ZHzKvWESDMqxssNuqnIPXl5eWRnZ4vVeXn9+jUaNGggM6sdKJGXQsrKykhKSoKZmZnI8fv378PGxoZ6yUsZOTk55OTkiJ1snj9/Dn19fRQXF/MUGZEWSkpKSEhIQJMmTUSOp6WlwdbWFoWFhTxFRqTJmTNnhDPxiYmJ+OWXX+Do6AhnZ2e0a9cOmpqafIf4U/Lz8xEdHY3IyEhEREQgISEBFhYWItsIvtTKjJCaiIrxcoNuKnJPTk4Oz58/FzunX7p0Cf3798fLly95ioxdVffsINWas7MzoqOjxRL5mJgYajMlRY4fPy7887lz56ChoSH8vrS0FOHh4d9duIPUbPb29khNTRVL5FNTU2Fra8tPUETqdOvWDdra2pg6darYOUkWqKiowM3NDW5ubgCA9+/fIyYmBhEREVixYgUGDx4Mc3NzWsEiQ3Jzc6lOyE9YvXo1MjMzoaOjQ8V4WZSXlye8qbh8+XIMHDiQbiqyRFNTEwKBAAKBABYWFiJ1HUpLS/HhwweZaklHM/JSaMuWLfDz80O/fv3QunVrAJ/3yB84cAALFiwQaTfl7u7OV5jkG+Tk5AB8rp5Z+dewVq1aMDIywurVq9G9e3c+wiNS5J9//sGMGTMwadIkkXPCpk2bsGzZMpFlkdbW1nyFSaq5tWvX4vLly4iOjoa8vLzwotLZ2Vkml9aWlZUhLi4OERERiIiIQExMDIqKimRmyWVNY21tjXbt2mHkyJH49ddfkZ6eju7duyM9PZ3v0KTWtwrzUjFedlS8qVi+IopuKv43ISEhYBgGI0eOxNq1a0VuSNeuXRtGRkbC+mKygBJ5KVSeAH6LQCCgCxIpYGxsjLi4OFpWRf6zb50Tym8W0TmBfK/k5GRERUUhIiICJ06cQL169ZCdnc13WD+lrKwMN2/eFC6tv3LlCvLz86Gnpyfs3ezi4gJDQ0O+QyX/werVq5GSkoJDhw6hffv2iI6OhoODA86cOcN3aIR8Fd1UZF9UVBTatGkjtopE1lAiTwghUu7x48ff/VxKUsi3xMfHC5Pd6OhovH//HnZ2doiLi+M7tJ+irq6O/Px86OrqClcauLi4wNTUlO/QyH9Q3tO88o3Mf/75BwMHDoSqqioePXpES+tZcOvWLWFrU0tLS9jZ2fEdklSjm4rcePfu3Xc/V11dncNIJIcSeUJ4sH79eowePRqKiopYv379V5/r6ekpoagIITWZu7s7YmJi8O7dO9ja2gqTXUdHR5m46Nm6dStcXFxgYWHBdyiEBf369YOLiwvGjRsnPHbjxg106NAB06dPx7Vr19CsWTOsXr2axyil24sXLzBgwABERkaibt26YBgGb9++hYuLC/bv30/7uP8juqnIDTk5OZE98VWRtdWJlMhLocpt5yqTld6IsszY2Bg3b95EvXr1vlrQTiAQUPs58k27du366uNDhw6VUCREmk2bNk2mEnci23R0dBAZGSms35CamgpHR0fMnTsXXl5eiI6OhoeHxw+tWCKi+vfvj8zMTOzevVv473z37l0MGzYMZmZm2LdvH88RSie6qciNqKio736uk5MTh5FIDiXyUqjykqaSkhI8fPgQCgoKMDU1pSqihNQwlduClZSUoKCgALVr14aysjJyc3N5iowQQrihqqqK27dvw8LCAo8fP4aTkxP8/PwwcuRIAEBGRgZsbGyQn5/Pc6TSS0NDAxcvXoSDg4PI8Rs3bqBTp07Iy8vjJzBCCABqPyeV4uPjxY69e/cOw4cPR+/evXmIiBDCpzdv3ogdu3//PsaNG4fp06fzEBGRVlFRUVi1apVwP2yzZs0wffp0am1Kqh1bW1tMnjwZffr0weLFizF+/HhhEg8AZ8+eFWvTS35MWVlZlcXCatWqJaxRQEh1VlBQgKysLBQXF4scl5UOPjQjL0NSUlLQvXt3PHr0iO9QyA9gGAYHDx5EREQEXrx4IfbhePjwYZ4iI9Lu5s2b8PDwwL179/gOhUiB0NBQjBgxAn369EHbtm3BMAyuXr2KI0eOIDg4GIMGDeI7REKEbt68if79+0NeXh7u7u4ICQnB4sWLYWtri8uXL2PBggVYs2YNRo8ezXeoUqtnz57Iy8vDvn37hK2Nnz59isGDB0NTUxNHjhzhOUJCqvby5UuMGDHii10rZGWPPM3Iy5C8vDy8ffuW7zDID/Ly8sK2bdvg4uICHR2dbxbqIOR7ycvL49mzZ3yHQaSEv78/VqxYAW9vb+ExLy8vrFmzBosWLaJEnlQrLVq0QGZmpvB7KysrzJo1Czk5OVBSUoKXlxcl8T9p48aN6NmzJ4yMjKCvrw+BQICsrCxYWVkhNDSU7/AI+aLJkyfjzZs3iI2NhYuLC44cOYLnz59j8eLFMlUAk2bkpVDlKucMwyA7Oxu7d++Go6MjFR+RMlpaWggNDUXXrl35DoVIqePHj4t8X35O2LhxI/T19amPMvkuderUwZ07d8SWI2dkZKB58+YoKiriKTJCvg/DMHjx4gU0NTVRu3ZtvsORGRcuXMC9e/fAMAwsLS3RoUMHvkMi5Kt0dXVx7NgxtGzZEurq6rh58yYsLCxw/PhxrFixAjExMXyHyAqakZdCAQEBIt/LyclBW1sbw4YNw6xZs3iKivxXGhoaMDEx4TsMIsV69eol8r1AIIC2tjZcXV1l6s4z4Za+vj7Cw8PFEvnw8HDo6+vzFBUh308gEEBHR4fvMGTGrl270L9/f3Ts2BEdO3YUHi8uLsb+/fupIwqptvLz89GgQQMAnyfMXr58CQsLC1hZWclUUXCakSeEZyEhITh79ix27NgBJSUlvsMhhNQwI0eOxLp16xAaGorJkydj5MiRaNOmDQQCAWJiYhAcHIx169ZhzJgxfIdKCJEgeXl5ZGdnCxOicq9fv0aDBg1kZp8xkT0ODg5YvHgxOnfujF69ekFdXR1Lly7F+vXrcfDgQZFtOdKMEnlCeFZQUIA+ffrgypUrMDIyEqsQK0t3Dgkh1U/Fi/UjR45g9erVSE1NBQBh1fqePXvyHCUhRNLk5OTw/PlzaGtrixxPTEyEi4sLtTYl1daePXtQUlKC4cOHIz4+Hp07d8br169Ru3ZtBAcHo3///nyHyApK5AnhWb9+/RAREYE//vijymJ38+bN4ykyQkhNICcnh5ycHLFZN0JIzWRnZweBQIDExET88ssvUFD4v524paWlePjwIdzc3BAWFsZjlISIy8jIqLLtZEFBAe7duwcDAwPUr1+fh8i4QXvkCeHZqVOncO7cOfz+++98h0IIqaGoWwYhpFx53ZWEhAR07twZqqqqwsdq164NIyMj/O9//+MpOkK+zMLCAnp6enBxcYGrqyucnZ1hZGQEZWVl2Nvb8x0e62hGnhCeNW3aFGFhYbC2tuY7FEJIDSQnJwcNDY1vJvO0jJZUR5W7dpQTCARQVFSEmZkZjI2NJRyVbAgJCUH//v2hqKjIdyiEfJfo6GhERUUhMjIS165dQ1FREQwMDODq6goXFxe4uLhAT0+P7zBZQ4k8ITw7deoUNmzYgC1btsDIyIjvcAghNYycnBzWrl0LDQ2Nrz5v2LBhEoqIkO8nJycHgUCAypez5ccEAgF+//13HD16FJqamjxFSQiRtJKSEly7dg2RkZGIjIxEbGwsPn78CDMzM6SlpfEdHisokZcSX7rjXBV3d3cOIyFs09TUREFBAT59+gRlZWWxYnc0C0a+R15eHm7cuIEXL16grKxM5DFqEUS+hvbIE2kWHh6OOXPmwN/fHy1btgQA3LhxA3PnzoWvry80NDQwZswYtGrVCtu3b+c5WumiqalZ5Uqdiqsdhg8fjhEjRvAQHSHfp7CwEDExMTh37hwCAwPx4cMHmem4QIm8lJCTkxP5vvLd54onWll5c9YUISEhX32cZsHIt5w4cQKDBw9Gfn4+1NTURM4HAoGAbgaRr/pSiylCpEHz5s2xbds2tGnTRuT4lStXMHr0aNy5cwcXL17EyJEjkZWVxVOU0ikgIAD+/v7o0qULWrZsCYZhEBcXh7Nnz8Lb2xsPHz7E7t27sWHDBvz11198h0sIAKCoqAhXr15FREQEIiMjERcXB2NjYzg5OcHR0RFOTk4ys7yeEnkpdPHiRcycORNLlizBb7/9BoFAgKtXr2Lu3LlYsmQJOnbsyHeIhBAJsrCwQNeuXbFkyRIoKyvzHQ6RMjQjT6SZkpIS4uLi0Lx5c5HjycnJaNmyJQoLC/H48WM0a9YMBQUFPEUpnf73v/+hY8eOGDt2rMjxrVu34vz58zh06BA2bNiAbdu2ITk5macoCfk/Tk5OiIuLg6mpqTBpd3Jygo6ODt+hcYISeSnUvHlzbNmyRazKeXR0NEaPHi3s/0ukw+3bt1GrVi1YWVkBAI4dO4adO3fC0tIS8+fPR+3atXmOkFR3KioqSE5OhomJCd+hEEKIRP3+++9QU1PDrl27hP3OX758iaFDhyI/Px+XL1/GxYsXMX78eKSnp/McrXRRVVVFQkKCWDuvjIwM2Nra4sOHD8jMzIS1tTXy8/N5ipKQ/1OrVi3o6uqiV69ecHZ2hqOjo0y1m6tM7ttPIdVNZmZmlUWJNDQ08OjRI8kHRH7KmDFjhBcXDx48QP/+/aGsrIwDBw5gxowZPEdHpEHnzp1x8+ZNvsMghBCJ2759Ox4+fIjGjRvDzMwM5ubmaNy4MR49eoSgoCAAwIcPH+Dr68tzpNJHS0sLJ06cEDt+4sQJaGlpAYBwSxch1UFeXh62bdsGZWVlLF++HHp6erCyssLEiRNx8OBBvHz5ku8QWUUz8lLI0dERtWrVQmhoKHR1dQEAOTk5GDJkCIqLixEVFcVzhORHaGho4Pbt2zA1NcXy5ctx6dIlnDt3DleuXMGAAQPw5MkTvkMk1dz27duxcOFCjBgxAlZWVmIFE6kAJiFEljEMg3PnziE9PR0Mw6Bp06bo2LGjWH0h8mMCAwMxbtw4dO3aFS1btoRAIMCNGzdw+vRpbNmyBaNGjcLq1atx48YN/PPPP3yHS4iY9+/fIyYmRrhfPjExEebm5khJSeE7NFZQIi+FMjIy0Lt3b6SlpcHAwAAAkJWVBQsLCxw9elRsCRSp3tTV1XHr1i2Ym5ujY8eO6N69O7y8vJCVlYUmTZqgsLCQ7xBJNfe1i1WBQEAFMAkhhPwnV65cwcaNG5GWlia8STJp0iSx4oKEVEdlZWWIi4tDREQEIiIiEBMTg6KiIpm5LqJEXkoxDIMLFy7g3r17YBgGlpaW6NChQ5VtQkj15urqCn19fXTo0AGjRo3C3bt3YWZmhqioKAwbNoy2SxBCCCFfER4ejvDw8Crbb+7YsYOnqAghklZWVoabN28iMjISERERuHLlCvLz86GnpwcXFxfhl6GhId+hsoISeUJ4lpSUhMGDByMrKwtTpkzBvHnzAACTJk3C69evsXfvXp4jJIQQQqqnBQsWYOHChWjRogV0dXXFJjSOHDnCU2SyoaysDBkZGVXeJHF0dOQpKkKqpq6ujvz8fOjq6sLZ2RnOzs5wcXGBqakp36FxghJ5KUV3n2VDaWkpYmJiYGVlJSwcU66oqAjy8vJi+50JqUpUVBRWrVqF1NRUCAQCNGvWDNOnT0e7du34Do0QQjijq6uLFStWYMiQIXyHInNiY2MxaNAgPH78GJXTBdq2RaqjrVu3wsXFBRYWFnyHIhFUBUQKLViwAJ06dUJ4eDhevXqFN2/eiHwR6SEvL4/OnTvj7du3Yo8pKipSEk++S2hoKDp06ABlZWV4enpi4sSJUFJSQvv27WlFByFEphUXF9N+bY6MHTsWLVq0QEpKCnJzc0WuNXNzc/kOjxAxY8aMqTFJPEAz8lKJ7j7LFgcHByxbtgzt27fnOxQipZo1a4bRo0fD29tb5PiaNWsQGBiI1NRUniIjhBBuzZw5E6qqqtRejgMqKipITEykIsqEVFMKfAdAfhzdfZYt/v7+mDZtGhYtWoRff/0VKioqIo+rq6vzFBmRFg8ePECPHj3Ejru7u2P27Nk8REQIIZJRVFSEbdu24eLFi7C2thZbybZmzRqeIpN+rVq1QkZGBiXyhFRTlMhLoT///BN79+6lu88yws3NDcDnpKtikR6GYWgPGvku+vr6CA8PF7vYCg8Ph76+Pk9REUII95KSkmBrawsAYr2hqZPPz5k0aRKmTp2KnJwcWFlZid0ksba25ikyQghAibxUorvPsiUiIoLvEIiUmzp1Kjw9PZGQkIA2bdpAIBAgJiYGwcHBWLduHd/hEUIIZ+gzlDv/+9//AAAjR44UHhMIBDTRQEg1QXvkpZCLi8sXHxMIBLh06ZIEoyGEVAdHjhzB6tWrhfvhy6vW9+zZk+fICCGESKPHjx9/9XFZ6cVNiLSiRJ6QaiA6Ohpbt27FgwcPcODAAejp6WH37t0wNjbG77//znd4hBBCSLXRp08fBAcHQ11dHX369Pnqcw8fPiyhqAghRLJoab2U+/fffyEQCKCnp8d3KOQ/OnToEIYMGYLBgwfj9u3b+PjxIwDg/fv3WLJkCU6fPs1zhKS6MzExQVxcHOrVqydyPC8vD/b29njw4AFPkRFCCPs0NDSE+981NDR4jka2HD9+HF26dEGtWrVw/Pjxrz7X3d1dQlERQqpCM/JSqKysDIsXL8bq1avx4cMHAICamhqmTp2KOXPmQE5OjucIyY+ws7ODt7c3hg4dCjU1NSQmJsLExAQJCQlwc3NDTk4O3yGSak5OTg45OTlo0KCByPHnz5/DwMBAeHOIEEII+ZqKnydfu56kPfKE8I9m5KXQnDlzsH37dixbtgxt27YFwzC4cuUK5s+fj6KiIvj7+/MdIvkBaWlpcHR0FDuurq6OvLw8yQdEpEbF2ZJz586JzEyVlpYiPDwcRkZGPERGCCGSERoaCg8Pjyofmz59OlauXCnhiKRbWVlZlX8mhFQ/lMhLoZCQEAQFBYksabKxsYGenh7Gjx9PibyU0dXVRUZGhljCFRMTAxMTE36CIlKhV69eAD7PjAwbNkzksVq1asHIyAirV6/mITJCCJGMiRMnom7duujevbvIcW9vb+zfv58SeZbl5eWhbt26fIdBCAFAa7ClUG5uLpo2bSp2vGnTpsjNzeUhIvIzxowZAy8vL1y/fh0CgQDPnj3Dnj17MG3aNIwfP57v8Eg1VlZWhrKyMhgYGODFixfC78vKyvDx40ekpaWJXdwSQogs2b9/Pzw8PHD58mXhsUmTJiEsLIxa0/2k5cuX459//hF+37dvX2hpaUFPTw+JiYk8RkYIAWiPvFRq1aoVWrVqhfXr14scnzRpEuLi4hAbG8tTZOS/mjNnDgICAlBUVAQAqFOnDqZNm4ZFixbxHBmRVjRrQgipKfbv34/x48fj/Pnz2LFjB44dO4aIiAhYWFjwHZpUMzExQWhoKNq0aYMLFy6gX79++OeffxAWFoasrCycP3+e7xAJqdEokZdCUVFR6NatGwwMDPDbb79BIBDg6tWrePLkCU6fPo127drxHSL5DwoKCnD37l2UlZXB0tISqqqqfIdEpMTy5cthZGSE/v37A/g8a3Lo0CHo6uri9OnTsLGx4TlCQgjh1ubNm+Ht7Q1tbW1ERETAzMyM75CknpKSEtLT06Gvrw8vLy8UFRVh69atSE9PR6tWrfDmzRu+QySkRqNEXko9e/YMmzZtwr1798AwDCwtLTF+/Hg0atSI79AIIRJGsyaEkJpkypQpVR4/ePAg7OzsYGpqKjy2Zs0aSYUlcxo1aoSDBw+iTZs2aNKkCRYvXoy+ffsiLS0NDg4OePfuHd8hElKjUbE7KdWoUSMqaicjioqKsGHDBkRERAj3OVd0+/ZtniIj0iI7Oxv6+voAgJMnT6Jfv37o1KkTjIyM0KpVK56jI4QQdsXHx1d53NTUFO/evRM+Xt5rnvw3ffr0waBBg2Bubo7Xr1+jS5cuAICEhARa8UBINUCJvJQqKipCUlJSlYlfxWr2pPobOXIkLly4gD/++AMtW7akCw/ywzQ1NfHkyRPo6+vj7NmzWLx4MQCAYRjq80sIkTlUxE4yAgICYGRkhCdPnmDFihXCLX/Z2dlUjJeQaoCW1kuhs2fPYujQoXj16pXYYwKBgC7cpYyGhgZOnz6Ntm3b8h0KkVITJ07EyZMnYW5ujvj4eDx69Aiqqqr4559/sHz5clrVQQiRSZ8+fYKioiISEhLQvHlzvsMhhBCJohl5KTRx4kT07dsXfn5+0NHR4Tsc8pP09PSgpqbGdxhEitGsCSGkJlJQUIChoSFNYHAoPT0dkZGRVa4A9fPz4ykqQghAM/JSSV1dHfHx8SLFXIj0OnPmDNavX48tW7bA0NCQ73AIIYQQqbFz504cOHAAoaGh0NLS4jscmRIYGIhx48ahfv36aNiwocjWP4FAQKu9COEZJfJSaOTIkWjbti1GjRrFdyiEBS9fvkS/fv1w+fJlKCsro1atWiKP5+bm8hQZkRbHjx+v8rhAIICioiLMzMxgbGws4agIIYR7dnZ2yMjIQElJCQwNDaGioiLyOCWb/52hoSHGjx+PmTNn8h0KIaQKtLReCm3cuBF9+/ZFdHQ0rKysxBI/T09PniIj/8XAgQPx9OlTLFmyBDo6OlTsjvywXr16QSAQoPJ92fJjAoEAv//+O44ePQpNTU2eoiSEEPb16tWL7xBk1ps3b9C3b1++wyCEfAHNyEuhoKAgjB07FkpKSqhXr57YUqcHDx7wGB35UcrKyrh27RpsbGz4DoVIqfDwcMyZMwf+/v5o2bIlAODGjRuYO3cufH19oaGhgTFjxqBVq1bYvn07z9ESQgiRBqNGjYKDgwPGjh3LdyiEkCrQjLwUmjt3LhYuXAgfHx/IycnxHQ75SU2bNkVhYSHfYRAp5uXlhW3btqFNmzbCY+3bt4eioiJGjx6NO3fuYO3atRg5ciSPURJCCJEmZmZm8PX1RWxsLK0AJaQaohl5KaSlpYW4uDgqdicjzp8/jwULFsDf37/KD0p1dXWeIiPSQklJCXFxcWLtl5KTk9GyZUsUFhbi8ePHaNasGQoKCniKkhBC2FdaWoqAgACEhYUhKysLxcXFIo9TnZn/7mu1VWgFKCH8o0ReCnl7e0NbWxuzZ8/mOxTCgvJVFZX3xpfvbaa2OuRbfv/9d6ipqWHXrl3Q1tYG8LmI4tChQ5Gfn4/Lly/j4sWLGD9+PNLT03mOlhBC2OPn54egoCBMmTIFvr6+mDNnDh49eoSjR4/Cz8+PZo0JITKLEnkp5OnpiV27dsHGxgbW1tZiM7hr1qzhKTLyX0RFRX31cScnJwlFQqRVWloaevbsiYcPH0JfXx8CgQBZWVkwMTHBsWPHYGFhgaNHj+L9+/cYMmQI3+ESQghrTE1NsX79enTr1g1qampISEgQHouNjcXevXv5DlHqFRcX4+HDhzA1NYWCAu3KJaS6oEReCrm4uHzxMYFAgEuXLkkwGkJIdcAwDM6dO4f09HQwDIOmTZuiY8eOVEeDECLTVFRUkJqaCgMDA+jq6uLUqVOwt7fHgwcPYGdnh7dv3/IdotQqKCjApEmTEBISAgBIT0+HiYkJPD090ahRI/j4+PAcISE1G91Wk0IRERF8h0BYVlRUhKSkJLx48QJlZWUij7m7u/MUFZEmAoEAbm5ucHNz4zsUQgiRmMaNGyM7OxsGBgYwMzPD+fPnYW9vj7i4ONSpU4fv8KTarFmzkJiYiMjISJHPlg4dOmDevHmUyBPCM0rkCeHZ2bNnMXToULx69UrsMdojT75XeHg4wsPDq7wZtGPHDp6iIoQQbvXu3Rvh4eFo1aoVvLy8MHDgQGzfvh1ZWVnw9vbmOzypdvToUfzzzz9o3bq1SB0fS0tLZGZm8hgZIQSgRF4q5efnY9myZV+8aKcqotJl4sSJ6Nu3L/z8/KCjo8N3OEQKLViwAAsXLkSLFi2gq6srVjiREEJk1bJly4R//uOPP9C4cWNcvXoVZmZmtKLtJ718+RINGjQQO56fn0+fM4RUA5TIS6E///wTUVFRGDJkCF20y4AXL15gypQplMST/2zLli0IDg6mQnaEkBqvdevWaN26Nd9hyAQHBwecOnUKkyZNAvB/3XUCAwPx22+/8RkaIQSUyEulM2fO4NSpU2jbti3foRAW/PHHH4iMjISpqSnfoRApVVxcjDZt2vAdBiGESNzr169Rr149AMCTJ08QGBiIwsJCuLu7o127djxHJ92WLl0KNzc33L17F58+fcK6detw584dXLt27Zsddwgh3KOq9VLI2NgYp0+fRrNmzfgOhbCgoKAAffv2hba2NqysrMTaCVIPXPItM2fOhKqqKnx9ffkOhRBCJCI5ORk9evTAkydPYG5ujv3798PNzQ35+fmQk5NDfn4+Dh48iF69evEdqlRLTk7GqlWrcOvWLZSVlcHe3h4zZ86EoaEh1NXV+Q6PkBqNEnkpFBoaimPHjiEkJATKysp8h0N+UlBQEMaOHQslJSXUq1dPZKuEQCCgmgfkm7y8vLBr1y5YW1vD2tpa7GbQmjVreIqMEEK40aVLFygoKGDmzJkIDQ3FyZMn0alTJwQFBQEAJk2ahFu3biE2NpbnSKXPqlWrMG3atC8+/u7dO3Tq1In+bQnhGSXyUsLOzk4kwcvIyADDMDAyMhK7aL99+7akwyM/oWHDhvD09ISPjw/1/Cb/iYuLyxcfEwgEuHTpkgSjIYQQ7tWvXx+XLl2CtbU1Pnz4AHV1ddy4cQMtWrQAANy7dw+tW7dGXl4ev4FKISUlJfz9998YMWKE2GMfPnxAx44d8fbtW9y9e5eH6Agh5WiPvJSgpWGyq7i4GP3796cknvxnERERfIdACCESlZubi4YNGwIAVFVVoaKiAi0tLeHjmpqaeP/+PV/hSbXdu3djyJAh0NTUFLn+/PDhAzp16oTc3FxcvnyZvwAJIQAokZca8+bN4zsEwpFhw4bhn3/+wezZs/kOhRBCCJEalbv2UBcfdvzxxx/Iy8vDoEGDcOrUKbi4uODDhw9wc3PDq1evEBUVRZ12CKkGKJGXQnFxcSgrK0OrVq1Ejl+/fh3y8vLCZWVEOpSWlmLFihU4d+4c7W8m361Pnz4IDg6Guro6+vTp89XnHj58WEJREUKI5AwfPhx16tQBABQVFWHs2LFQUVEBAHz8+JHP0KTen3/+idzcXPTq1QvHjh2Dr68vcnJyEBUVBV1dXb7DI4SAEnmpNGHCBMyYMUMskX/69CmWL1+O69ev8xQZ+S+Sk5NhZ2cHAEhJSRF5jGYXyJdoaGgI3x8aGho8R0MIIZI1bNgwke89PDzEnjN06FBJhSOTZsyYgTdv3qB9+/YwMjJCVFQU9PT0+A6LEPL/UbE7KaSqqoqkpCSYmJiIHH/48CGsra1pTxghROjdu3fUIogQQsh3q7zK6/Tp07CxsRFL4mm1FyH8oupaUqhOnTp4/vy52PHs7GwoKNAiC0JqilWrVn318fIWQYQQQsj30tDQEPkaOHAgLC0txY4TQvhFM/JSaMCAAcjJycGxY8eEJ9K8vDz06tULDRo0QFhYGM8Rkh8VFxeHAwcOICsrC8XFxSKP0R1v8iVfaxH0/v17dOrUiVoEEUIIIYTIIJqRl0KrV6/GkydPYGhoCBcXF7i4uMDY2Bg5OTlYvXo13+GRH7R//360bdsWd+/exZEjR1BSUoK7d+/i0qVLdMebfNXu3bsxfvx4HD16VOT4hw8f0LlzZ+Tm5lJrOkIIIYQQGUQz8lIqPz8fe/bsQWJiIpSUlGBtbY2BAweKVTwn1Z+1tTXGjBmDCRMmQE1NDYmJiTA2NsaYMWOgq6uLBQsW8B0iqcaCgoLg6ekp1iLoxYsXVF2YEEIIIURGUSJPCM9UVFRw584dGBkZoX79+oiIiICVlRVSU1Ph6uqK7OxsvkMk1dyKFSvg7+8vbBGUnZ1N1YUJIYQQQmQYVUaTEsePH0eXLl1Qq1YtHD9+/KvPdXd3l1BUhA1aWlrCTgN6enpISUmBlZUV8vLyUFBQwHN0RBpQiyBCCCGEkJqFEnkp0atXL+Tk5KBBgwbo1avXF58nEAhQWloqucDIT2vXrh0uXLgAKysr9OvXD15eXrh06RIuXLiA9u3b8x0eqcYqtwiqVasW6tevD09PT5HjVDCREEIIIUS20NJ6QniWm5uLoqIiNGrUCGVlZVi1ahViYmJgZmYGX19faGpq8h0iqaaqqlZflZ07d3IcCSGEEEIIkSRK5GXM06dPaUktIYQQQgghhMgwWlovI3JycuDv74+goCAUFhbyHQ75Du/evfuu56mrq3McCSGEEEIIIUSaUB95KZKXl4fBgwdDW1sbjRo1wvr161FWVgY/Pz+YmJggNjYWO3bs4DtM8p3q1q0LTU3NL36VP04IIYQQQgghFdGMvBSZPXs2Ll++jGHDhuHs2bPw9vbG2bNnUVRUhDNnzsDJyYnvEMkPiIiIEP6ZYRh07doVQUFBtDWCEEIIIYQQ8lW0R16KGBoaYvv27ejQoQMePHgAMzMzeHp6Yu3atXyHRligpqaGxMREmJiY8B0KIYQQQgghpBqjpfVS5NmzZ7C0tAQAmJiYQFFREX/++SfPURFCCCGEEEIIkSRK5KVIWVkZatWqJfxeXl4eKioqPEZECCGEEEIIIUTSaI+8FGEYBsOHD0edOnUAAEVFRRg7dqxYMn/48GE+wiMsEAgEfIdACCGEEEIIqeYokZciw4YNE/new8ODp0gIG/r06SPyPd2YIYQQQgghhHwPKnZHCE9GjBjxXc/buXMnx5EQQgghhBBCpAkl8oQQQgghhBBCiBShYneEEEIIIYQQQogUoUSeEEIIIYQQQgiRIpTIE0IIIYQQQgghUoQSeUIIIYQQQgghRIpQIk8IIYQQQgghhEgRSuQJIYQQUu0JBAIcPXqU7zAIIYSQaoESeUIIIUTGDR8+HAKBQPhVr149uLm5ISkpie/QCCGEEPIfUCJPCCGE1ABubm7Izs5GdnY2wsPDoaCggO7du//n1ysuLmYxOkIIIYT8CErkCSGEkBqgTp06aNiwIRo2bAhbW1vMnDkTT548wcuXLwEAycnJcHV1hZKSEurVq4fRo0fjw4cPwr8/fPhw9OrVC0uXLkWjRo1gYWHxXX/P2dkZkydPFomlV69eGD58uPD77OxsdOvWDUpKSjA2NsbevXthZGSEtWvXivy9V69eoXfv3lBWVoa5uTmOHz/O7j8SIYQQIiUokSeEEEJqmA8fPmDPnj0wMzNDvXr1UFBQADc3N2hqaiIuLg4HDhzAxYsXMXHiRJG/Fx4ejtTUVFy4cAEnT5787r/3LUOHDsWzZ88QGRmJQ4cOYdu2bXjx4oXY8xYsWIB+/fohKSkJXbt2xeDBg5Gbm/tT/xaEEEKINFLgOwBCCCGEcO/kyZNQVVUFAOTn50NXVxcnT56EnJwc9uzZg8LCQuzatQsqKioAgI0bN6JHjx5Yvnw5dHR0AAAqKioICgpC7dq1AQCBgYHf9fe+5t69e7h48SLi4uLQokULAEBQUBDMzc3Fnjt8+HAMHDgQALBkyRJs2LABN27cgJub20/+6xBCCCHShWbkCSGEkBrAxcUFCQkJSEhIwPXr19GpUyd06dIFjx8/RmpqKmxsbITJOAC0bdsWZWVlSEtLEx6zsrISJvEAvvvvfU1aWhoUFBRgb28vPGZmZgZNTU2x51pbWwv/rKKiAjU1tSpn7gkhhBBZRzPyhBBCSA2goqICMzMz4fe//vorNDQ0EBgYCIZhIBAIqvx7FY9XTNgBfNffk5OTA8MwIo+VlJSIvEZVqjpeq1YtsTHKysqq/PuEEEKILKMZeUIIIaQGEggEkJOTQ2FhISwtLZGQkID8/Hzh41euXIGcnJywqF1VvufvaWtrIzs7W/h4aWkpUlJShN83bdoUnz59Qnx8vPBYRkYG8vLy2PgxCSGEEJlEiTwhhBBSA3z8+BE5OTnIyclBamoqJk2ahA8fPqBHjx4YPHgwFBUVMWzYMKSkpCAiIgKTJk3CkCFDvrrP/Xv+nqurK06dOoVTp07h3r17GD9+vEiS3rRpU3To0AGjR4/GjRs3EB8fj9GjR0NJSemLs/2EEEJITUeJPCGEEFIDnD17Frq6utDV1UWrVq2EVeadnZ2hrKyMc+fOITc3Fw4ODvjjjz/Qvn17bNy48auv+T1/b+TIkRg2bBiGDh0KJycnGBsbw8XFReR1du3aBR0dHTg6OqJ3797466+/oKamBkVFRU7+LQghhBBpJ2C+tDmNEEIIIYQH//77L/T19XHx4kW0b9+e73AIIYSQaocSeUIIIYTw6tKlS/jw4QOsrKyQnZ2NGTNm4OnTp0hPTxcrcEcIIYQQqlpPCCGEEJ6VlJRg9uzZePDgAdTU1NCmTRvs2bOHknhCCCHkC2hGnhBCCCGEEEIIkSJU7I4QQgghhBBCCJEilMgTQgghhBBCCCFShBJ5QgghhBBCCCFEilAiTwghhBBCCCGESBFK5AkhhBBCCCGEEClCiTwhhBBCCCGEECJFKJEnhBBCCCGEEEKkCCXyhBBCCCGEEEKIFPl/hFJYvz8Z84wAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_results['no_borough'] = df_results.index.map(lambda x: x in no_boroughs)\n",
    "df_results2 = df_results.query('no_borough == False')\n",
    "ax = df_results2.plot(kind='bar')\n",
    "ax.figure.set_size_inches(12, 6)\n",
    "plt.title('Growth ratio by borough (only real boroughs) 1998 - 2018')\n",
    "plt.xlabel('Borough')\n",
    "plt.ylabel('Ratio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results2023 = {}\n",
    "for borough in boroughs:\n",
    "    results2023[borough] = create_price_ratio(dfg[dfg['London_Borough']== borough], 2023)\n",
    "df_results2023 = pd.DataFrame(results.values(), results.keys(), columns = ['Ratio'])\n",
    "df_results2023 = df_results.sort_values('Ratio', ascending = False)\n",
    "df_results2023['no_borough'] = df_results2023.index.map(lambda x: x in no_boroughs)\n",
    "df_results2023 = df_results2023.query('no_borough == False')\n",
    "\n",
    "ax = df_results2023.plot(kind='bar')\n",
    "ax.figure.set_size_inches(12, 6)\n",
    "plt.title('Growth ratio by borough (only real boroughs) 2003 - 2023')\n",
    "plt.xlabel('Borough')\n",
    "plt.ylabel('Ratio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Conclusion\n",
    "What can you conclude? Type out your conclusion below. \n",
    "\n",
    "Look back at your notebook. Think about how you might summarize what you have done, and prepare a quick presentation on it to your mentor at your next meeting. \n",
    "\n",
    "We hope you enjoyed this practical project. It should have consolidated your data hygiene and pandas skills by looking at a real-world problem involving just the kind of dataset you might encounter as a budding data scientist. Congratulations, and looking forward to seeing you at the next step in the course! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Conclusion***  \n",
    "The London Borough with the greatest rise in housing prices in the last two decades is **Hounslow**, with a ratio close to 0.25.  \n",
    "Followed by **Richmond upon Thames** and **Harrow**.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The London Borough with the lowest rise in housing prices in the last two decades is **Hackey**, with a ratio close to 0.16."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is remarkable that the **NORTH EAST** egion stands out with a significant growth ratio difference, exceeding 0.35."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Springboard Data Science Career Track Unit 4 Challenge - Tier 3 Complete .ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
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}