{ "cells": [ { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "# Analysis of Airbnb bookings across Seattle\n", "\n", "In this project, I will try to analyse and answer a few questions on the airbnb seattle dataset available on kaggle. This implementation is following CRISP-DM process i.e., the project is organized in the following steps,\n", "\n", "1. Business understanding\n", "2. Data Understanding\n", "3. Preperation of the data\n", "4. Modelling (If Necessary)\n", "5. Evaluation of the results\n", "6. Conclusion\n", "\n", "The questions that would be answered in this project are listed as follows,\n", "1. Predicting the suitable price based on the features of the room {property_type, room_type, no.of bedrooms, no. of bathrooms etc.}?\n", "2. What is the probable price for a room based on the neighbourhood?\n", "3. Which type of properties are common in each neighbourhood?\n", "4. What is the effect of selecting \"Strict\" cancellation policy on the frequency of bookings?\n", "5. What is the neighbourhood where the price is highest despite providing minimum amenities?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "pycharm": { "name": "#%%\n" }, "tags": [] }, "outputs": [], "source": [ "import pandas as pd\n", "import warnings\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "import plotly.io as pio\n", "from plotly.subplots import make_subplots\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import r2_score, mean_squared_error\n", "\n", "\n", "warnings.filterwarnings('ignore')\n", "\n", "pio.templates.default = \"plotly_white\"\n", "\n", "listings = pd.read_csv('./Data/listings.csv')\n", "calendar = pd.read_csv('./Data/calendar.csv')\n", "\n", "pd.set_option(\"display.max_columns\", None)\n", "pd.set_option(\"display.max_rows\", None)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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idscrape_idhost_idhost_listings_counthost_total_listings_countlatitudelongitudeaccommodatesbathroomsbedroomsbedssquare_feetguests_includedminimum_nightsmaximum_nightsavailability_30availability_60availability_90availability_365number_of_reviewsreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valuelicensecalculated_host_listings_countreviews_per_month
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std2.962660e+060.000000e+001.458382e+0728.62814928.6281490.0430520.0317451.9775990.5903690.8833951.139480671.4048931.31104016.3059021683.58900712.17363723.33754134.063845126.77252637.7308926.6060830.6980310.7972740.5954990.5682110.6290530.750259NaN5.8930291.822348
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max1.034016e+072.016010e+135.320861e+07502.000000502.00000047.733358-122.24060716.0000008.0000007.00000015.0000003000.00000015.0000001000.000000100000.00000030.00000060.00000090.000000365.000000474.000000100.00000010.00000010.00000010.00000010.00000010.00000010.000000NaN37.00000012.150000
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0241032https://www.airbnb.com/rooms/241032201601040024322016-01-04Stylish Queen Anne ApartmentNaNMake your self at home in this charming one-be...Make your self at home in this charming one-be...noneNaNNaNNaNNaNNaNhttps://a1.muscache.com/ac/pictures/67560560/c...NaN956883https://www.airbnb.com/users/show/956883Maija2011-08-11Seattle, Washington, United StatesI am an artist, interior designer, and run a s...within a few hours96%100%fhttps://a0.muscache.com/ac/users/956883/profil...https://a0.muscache.com/ac/users/956883/profil...Queen Anne3.03.0['email', 'phone', 'reviews', 'kba']ttGilman Dr W, Seattle, WA 98119, United StatesQueen AnneWest Queen AnneQueen AnneSeattleWA98119SeattleSeattle, WAUSUnited States47.636289-122.371025tApartmentEntire home/apt41.01.01.0Real Bed{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...NaN$85.00NaNNaNNaNNaN2$5.0013654 weeks agot1441713462016-01-042072011-11-012016-01-0295.010.010.010.010.09.010.0fNaNWASHINGTONfmoderateff24.07
1953595https://www.airbnb.com/rooms/953595201601040024322016-01-04Bright & Airy Queen Anne ApartmentChemically sensitive? We've removed the irrita...Beautiful, hypoallergenic apartment in an extr...Chemically sensitive? We've removed the irrita...noneQueen Anne is a wonderful, truly functional vi...What's up with the free pillows? Our home was...Convenient bus stops are just down the block, ...https://a0.muscache.com/ac/pictures/14409893/f...https://a0.muscache.com/im/pictures/14409893/f...https://a0.muscache.com/ac/pictures/14409893/f...https://a0.muscache.com/ac/pictures/14409893/f...5177328https://www.airbnb.com/users/show/5177328Andrea2013-02-21Seattle, Washington, United StatesLiving east coast/left coast/overseas. Time i...within an hour98%100%thttps://a0.muscache.com/ac/users/5177328/profi...https://a0.muscache.com/ac/users/5177328/profi...Queen Anne6.06.0['email', 'phone', 'facebook', 'linkedin', 're...tt7th Avenue West, Seattle, WA 98119, United StatesQueen AnneWest Queen AnneQueen AnneSeattleWA98119SeattleSeattle, WAUSUnited States47.639123-122.365666tApartmentEntire home/apt41.01.01.0Real Bed{TV,Internet,\"Wireless Internet\",Kitchen,\"Free...NaN$150.00$1,000.00$3,000.00$100.00$40.001$0.00290todayt1313162912016-01-04432013-08-192015-12-2996.010.010.010.010.010.010.0fNaNWASHINGTONfstricttt61.48
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" ], "text/plain": [ " id listing_url scrape_id last_scraped \\\n", "0 241032 https://www.airbnb.com/rooms/241032 20160104002432 2016-01-04 \n", "1 953595 https://www.airbnb.com/rooms/953595 20160104002432 2016-01-04 \n", "\n", " name \\\n", "0 Stylish Queen Anne Apartment \n", "1 Bright & Airy Queen Anne Apartment \n", "\n", " summary \\\n", "0 NaN \n", "1 Chemically sensitive? We've removed the irrita... \n", "\n", " space \\\n", "0 Make your self at home in this charming one-be... \n", "1 Beautiful, hypoallergenic apartment in an extr... \n", "\n", " description experiences_offered \\\n", "0 Make your self at home in this charming one-be... none \n", "1 Chemically sensitive? We've removed the irrita... none \n", "\n", " neighborhood_overview \\\n", "0 NaN \n", "1 Queen Anne is a wonderful, truly functional vi... \n", "\n", " notes \\\n", "0 NaN \n", "1 What's up with the free pillows? Our home was... \n", "\n", " transit \\\n", "0 NaN \n", "1 Convenient bus stops are just down the block, ... \n", "\n", " thumbnail_url \\\n", "0 NaN \n", "1 https://a0.muscache.com/ac/pictures/14409893/f... \n", "\n", " medium_url \\\n", "0 NaN \n", "1 https://a0.muscache.com/im/pictures/14409893/f... \n", "\n", " picture_url \\\n", "0 https://a1.muscache.com/ac/pictures/67560560/c... \n", "1 https://a0.muscache.com/ac/pictures/14409893/f... \n", "\n", " xl_picture_url host_id \\\n", "0 NaN 956883 \n", "1 https://a0.muscache.com/ac/pictures/14409893/f... 5177328 \n", "\n", " host_url host_name host_since \\\n", "0 https://www.airbnb.com/users/show/956883 Maija 2011-08-11 \n", "1 https://www.airbnb.com/users/show/5177328 Andrea 2013-02-21 \n", "\n", " host_location \\\n", "0 Seattle, Washington, United States \n", "1 Seattle, Washington, United States \n", "\n", " host_about host_response_time \\\n", "0 I am an artist, interior designer, and run a s... within a few hours \n", "1 Living east coast/left coast/overseas. Time i... within an hour \n", "\n", " host_response_rate host_acceptance_rate host_is_superhost \\\n", "0 96% 100% f \n", "1 98% 100% t \n", "\n", " host_thumbnail_url \\\n", "0 https://a0.muscache.com/ac/users/956883/profil... \n", "1 https://a0.muscache.com/ac/users/5177328/profi... \n", "\n", " host_picture_url host_neighbourhood \\\n", "0 https://a0.muscache.com/ac/users/956883/profil... Queen Anne \n", "1 https://a0.muscache.com/ac/users/5177328/profi... Queen Anne \n", "\n", " host_listings_count host_total_listings_count \\\n", "0 3.0 3.0 \n", "1 6.0 6.0 \n", "\n", " host_verifications host_has_profile_pic \\\n", "0 ['email', 'phone', 'reviews', 'kba'] t \n", "1 ['email', 'phone', 'facebook', 'linkedin', 're... t \n", "\n", " host_identity_verified street \\\n", "0 t Gilman Dr W, Seattle, WA 98119, United States \n", "1 t 7th Avenue West, Seattle, WA 98119, United States \n", "\n", " neighbourhood neighbourhood_cleansed neighbourhood_group_cleansed city \\\n", "0 Queen Anne West Queen Anne Queen Anne Seattle \n", "1 Queen Anne West Queen Anne Queen Anne Seattle \n", "\n", " state zipcode market smart_location country_code country \\\n", "0 WA 98119 Seattle Seattle, WA US United States \n", "1 WA 98119 Seattle Seattle, WA US United States \n", "\n", " latitude longitude is_location_exact property_type room_type \\\n", "0 47.636289 -122.371025 t Apartment Entire home/apt \n", "1 47.639123 -122.365666 t Apartment Entire home/apt \n", "\n", " accommodates bathrooms bedrooms beds bed_type \\\n", "0 4 1.0 1.0 1.0 Real Bed \n", "1 4 1.0 1.0 1.0 Real Bed \n", "\n", " amenities square_feet price \\\n", "0 {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A... NaN $85.00 \n", "1 {TV,Internet,\"Wireless Internet\",Kitchen,\"Free... NaN $150.00 \n", "\n", " weekly_price monthly_price security_deposit cleaning_fee guests_included \\\n", "0 NaN NaN NaN NaN 2 \n", "1 $1,000.00 $3,000.00 $100.00 $40.00 1 \n", "\n", " extra_people minimum_nights maximum_nights calendar_updated \\\n", "0 $5.00 1 365 4 weeks ago \n", "1 $0.00 2 90 today \n", "\n", " has_availability availability_30 availability_60 availability_90 \\\n", "0 t 14 41 71 \n", "1 t 13 13 16 \n", "\n", " availability_365 calendar_last_scraped number_of_reviews first_review \\\n", "0 346 2016-01-04 207 2011-11-01 \n", "1 291 2016-01-04 43 2013-08-19 \n", "\n", " last_review review_scores_rating review_scores_accuracy \\\n", "0 2016-01-02 95.0 10.0 \n", "1 2015-12-29 96.0 10.0 \n", "\n", " review_scores_cleanliness review_scores_checkin \\\n", "0 10.0 10.0 \n", "1 10.0 10.0 \n", "\n", " review_scores_communication review_scores_location review_scores_value \\\n", "0 10.0 9.0 10.0 \n", "1 10.0 10.0 10.0 \n", "\n", " requires_license license jurisdiction_names instant_bookable \\\n", "0 f NaN WASHINGTON f \n", "1 f NaN WASHINGTON f \n", "\n", " cancellation_policy require_guest_profile_picture \\\n", "0 moderate f \n", "1 strict t \n", "\n", " require_guest_phone_verification calculated_host_listings_count \\\n", "0 f 2 \n", "1 t 6 \n", "\n", " reviews_per_month \n", "0 4.07 \n", "1 1.48 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings.head(2)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Among the plethora of columns, we will first try and select the columns which are relavent for our question." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" }, "tags": [] }, "source": [ "# Processing the Data\n", "\n", "Since the data is presented in a straight forward way, we can directly skip to the step where we prepare the data to answer our questions. This step is said to take a relatively long time to finish, as there are a lot of aspects that needs to be focused on. \n", "\n", "For our project, we will generally be working with aggregations, hence the 2 aspects the important aspects that we need to make sure is to\n", "- make sure that there are no null values.\n", "- After that, we need to check for any outlier, and try to exclude them from the data. \n", "- Finally before sending it for the next step i.e., modelling, we need to process all the categorical variables into their respective types. \n", "\n", "Since most of the questions can be answered after procesing the outliers, the final step is done before answering the last question, i.e., the question which requires us to be able to predict from the data available." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "relavent_columns = ['id','neighbourhood_group_cleansed', 'property_type',\n", " 'room_type', 'accommodates','bathrooms',\n", " 'square_feet', 'bedrooms', 'beds',\n", " 'bed_type', 'amenities', 'cancellation_policy',\n", " 'minimum_nights', 'instant_bookable', 'review_scores_rating',\n", " 'review_scores_accuracy', 'review_scores_cleanliness','review_scores_checkin',\n", " 'review_scores_communication','review_scores_location', 'review_scores_value',\n", " 'price']\n", "\n", "listings_new = listings[relavent_columns]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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idneighbourhood_group_cleansedproperty_typeroom_typeaccommodatesbathroomssquare_feetbedroomsbedsbed_typeamenitiescancellation_policyminimum_nightsinstant_bookablereview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valueprice
0241032Queen AnneApartmentEntire home/apt41.0NaN1.01.0Real Bed{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...moderate1f95.010.010.010.010.09.010.0$85.00
1953595Queen AnneApartmentEntire home/apt41.0NaN1.01.0Real Bed{TV,Internet,\"Wireless Internet\",Kitchen,\"Free...strict2f96.010.010.010.010.010.010.0$150.00
23308979Queen AnneHouseEntire home/apt114.5NaN5.07.0Real Bed{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...strict4f97.010.010.010.010.010.010.0$975.00
37421966Queen AnneApartmentEntire home/apt31.0NaN0.02.0Real Bed{Internet,\"Wireless Internet\",Kitchen,\"Indoor ...flexible1fNaNNaNNaNNaNNaNNaNNaN$100.00
4278830Queen AnneHouseEntire home/apt62.0NaN3.03.0Real Bed{TV,\"Cable TV\",Internet,\"Wireless Internet\",Ki...strict1f92.09.09.010.010.09.09.0$450.00
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" ], "text/plain": [ " id neighbourhood_group_cleansed property_type room_type \\\n", "0 241032 Queen Anne Apartment Entire home/apt \n", "1 953595 Queen Anne Apartment Entire home/apt \n", "2 3308979 Queen Anne House Entire home/apt \n", "3 7421966 Queen Anne Apartment Entire home/apt \n", "4 278830 Queen Anne House Entire home/apt \n", "\n", " accommodates bathrooms square_feet bedrooms beds bed_type \\\n", "0 4 1.0 NaN 1.0 1.0 Real Bed \n", "1 4 1.0 NaN 1.0 1.0 Real Bed \n", "2 11 4.5 NaN 5.0 7.0 Real Bed \n", "3 3 1.0 NaN 0.0 2.0 Real Bed \n", "4 6 2.0 NaN 3.0 3.0 Real Bed \n", "\n", " amenities cancellation_policy \\\n", "0 {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A... moderate \n", "1 {TV,Internet,\"Wireless Internet\",Kitchen,\"Free... strict \n", "2 {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A... strict \n", "3 {Internet,\"Wireless Internet\",Kitchen,\"Indoor ... flexible \n", "4 {TV,\"Cable TV\",Internet,\"Wireless Internet\",Ki... strict \n", "\n", " minimum_nights instant_bookable review_scores_rating \\\n", "0 1 f 95.0 \n", "1 2 f 96.0 \n", "2 4 f 97.0 \n", "3 1 f NaN \n", "4 1 f 92.0 \n", "\n", " review_scores_accuracy review_scores_cleanliness review_scores_checkin \\\n", "0 10.0 10.0 10.0 \n", "1 10.0 10.0 10.0 \n", "2 10.0 10.0 10.0 \n", "3 NaN NaN NaN \n", "4 9.0 9.0 10.0 \n", "\n", " review_scores_communication review_scores_location review_scores_value \\\n", "0 10.0 9.0 10.0 \n", "1 10.0 10.0 10.0 \n", "2 10.0 10.0 10.0 \n", "3 NaN NaN NaN \n", "4 10.0 9.0 9.0 \n", "\n", " price \n", "0 $85.00 \n", "1 $150.00 \n", "2 $975.00 \n", "3 $100.00 \n", "4 $450.00 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_new.head()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now, it is time to reformat the price." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "listings_new.loc[:,\"price\"] = listings_new.price.str.replace(\"[$, ]\", \"\", regex=True).astype(\"float\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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idneighbourhood_group_cleansedproperty_typeroom_typeaccommodatesbathroomssquare_feetbedroomsbedsbed_typeamenitiescancellation_policyminimum_nightsinstant_bookablereview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valueprice
0241032Queen AnneApartmentEntire home/apt41.0NaN1.01.0Real Bed{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...moderate1f95.010.010.010.010.09.010.085.0
1953595Queen AnneApartmentEntire home/apt41.0NaN1.01.0Real Bed{TV,Internet,\"Wireless Internet\",Kitchen,\"Free...strict2f96.010.010.010.010.010.010.0150.0
23308979Queen AnneHouseEntire home/apt114.5NaN5.07.0Real Bed{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...strict4f97.010.010.010.010.010.010.0975.0
37421966Queen AnneApartmentEntire home/apt31.0NaN0.02.0Real Bed{Internet,\"Wireless Internet\",Kitchen,\"Indoor ...flexible1fNaNNaNNaNNaNNaNNaNNaN100.0
4278830Queen AnneHouseEntire home/apt62.0NaN3.03.0Real Bed{TV,\"Cable TV\",Internet,\"Wireless Internet\",Ki...strict1f92.09.09.010.010.09.09.0450.0
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" ], "text/plain": [ " id neighbourhood_group_cleansed property_type room_type \\\n", "0 241032 Queen Anne Apartment Entire home/apt \n", "1 953595 Queen Anne Apartment Entire home/apt \n", "2 3308979 Queen Anne House Entire home/apt \n", "3 7421966 Queen Anne Apartment Entire home/apt \n", "4 278830 Queen Anne House Entire home/apt \n", "\n", " accommodates bathrooms square_feet bedrooms beds bed_type \\\n", "0 4 1.0 NaN 1.0 1.0 Real Bed \n", "1 4 1.0 NaN 1.0 1.0 Real Bed \n", "2 11 4.5 NaN 5.0 7.0 Real Bed \n", "3 3 1.0 NaN 0.0 2.0 Real Bed \n", "4 6 2.0 NaN 3.0 3.0 Real Bed \n", "\n", " amenities cancellation_policy \\\n", "0 {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A... moderate \n", "1 {TV,Internet,\"Wireless Internet\",Kitchen,\"Free... strict \n", "2 {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A... strict \n", "3 {Internet,\"Wireless Internet\",Kitchen,\"Indoor ... flexible \n", "4 {TV,\"Cable TV\",Internet,\"Wireless Internet\",Ki... strict \n", "\n", " minimum_nights instant_bookable review_scores_rating \\\n", "0 1 f 95.0 \n", "1 2 f 96.0 \n", "2 4 f 97.0 \n", "3 1 f NaN \n", "4 1 f 92.0 \n", "\n", " review_scores_accuracy review_scores_cleanliness review_scores_checkin \\\n", "0 10.0 10.0 10.0 \n", "1 10.0 10.0 10.0 \n", "2 10.0 10.0 10.0 \n", "3 NaN NaN NaN \n", "4 9.0 9.0 10.0 \n", "\n", " review_scores_communication review_scores_location review_scores_value \\\n", "0 10.0 9.0 10.0 \n", "1 10.0 10.0 10.0 \n", "2 10.0 10.0 10.0 \n", "3 NaN NaN NaN \n", "4 10.0 9.0 9.0 \n", "\n", " price \n", "0 85.0 \n", "1 150.0 \n", "2 975.0 \n", "3 100.0 \n", "4 450.0 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_new.head()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" }, "tags": [] }, "source": [ "### Tackling the null values" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "square_feet 97.459403\n", "review_scores_checkin 17.234154\n", "review_scores_accuracy 17.234154\n", "review_scores_value 17.181771\n", "review_scores_location 17.155579\n", "review_scores_cleanliness 17.103195\n", "review_scores_communication 17.050812\n", "review_scores_rating 16.946045\n", "bathrooms 0.419068\n", "bedrooms 0.157150\n", "property_type 0.026192\n", "beds 0.026192\n", "id 0.000000\n", "cancellation_policy 0.000000\n", "instant_bookable 0.000000\n", "minimum_nights 0.000000\n", "neighbourhood_group_cleansed 0.000000\n", "amenities 0.000000\n", "bed_type 0.000000\n", "accommodates 0.000000\n", "room_type 0.000000\n", "price 0.000000\n", "dtype: float64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "100*listings_new.isnull().sum().sort_values(ascending=False)/listings_new.shape[0]" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The information of squarefeet is not provided in most of the listings, hence we can assume that this information has very little effect on the other parameters. Hence, we can drop them. However, the reviews cannot be dealt in the same way, As, we have the values for most of the listings, but for the entries which donot have any entries in the related field. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "id int64\n", "neighbourhood_group_cleansed object\n", "property_type object\n", "room_type object\n", "accommodates int64\n", "bathrooms float64\n", "bedrooms float64\n", "beds float64\n", "bed_type object\n", "amenities object\n", "cancellation_policy object\n", "minimum_nights int64\n", "instant_bookable object\n", "review_scores_rating float64\n", "review_scores_accuracy float64\n", "review_scores_cleanliness float64\n", "review_scores_checkin float64\n", "review_scores_communication float64\n", "review_scores_location float64\n", "review_scores_value float64\n", "price float64\n", "dtype: object" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_new.drop('square_feet', axis=1, inplace=True)\n", "listings_new.dtypes" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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review_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_value
count3171.0000003160.0000003165.0000003160.0000003167.0000003163.0000003162.000000
mean94.5392629.6363929.5563989.7867099.8095999.6089169.452245
std6.6060830.6980310.7972740.5954990.5682110.6290530.750259
min20.0000002.0000003.0000002.0000002.0000004.0000002.000000
25%93.0000009.0000009.00000010.00000010.0000009.0000009.000000
50%96.00000010.00000010.00000010.00000010.00000010.00000010.000000
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max100.00000010.00000010.00000010.00000010.00000010.00000010.000000
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" ], "text/plain": [ " review_scores_rating review_scores_accuracy \\\n", "count 3171.000000 3160.000000 \n", "mean 94.539262 9.636392 \n", "std 6.606083 0.698031 \n", "min 20.000000 2.000000 \n", "25% 93.000000 9.000000 \n", "50% 96.000000 10.000000 \n", "75% 99.000000 10.000000 \n", "max 100.000000 10.000000 \n", "\n", " review_scores_cleanliness review_scores_checkin \\\n", "count 3165.000000 3160.000000 \n", "mean 9.556398 9.786709 \n", "std 0.797274 0.595499 \n", "min 3.000000 2.000000 \n", "25% 9.000000 10.000000 \n", "50% 10.000000 10.000000 \n", "75% 10.000000 10.000000 \n", "max 10.000000 10.000000 \n", "\n", " review_scores_communication review_scores_location \\\n", "count 3167.000000 3163.000000 \n", "mean 9.809599 9.608916 \n", "std 0.568211 0.629053 \n", "min 2.000000 4.000000 \n", "25% 10.000000 9.000000 \n", "50% 10.000000 10.000000 \n", "75% 10.000000 10.000000 \n", "max 10.000000 10.000000 \n", "\n", " review_scores_value \n", "count 3162.000000 \n", "mean 9.452245 \n", "std 0.750259 \n", "min 2.000000 \n", "25% 9.000000 \n", "50% 10.000000 \n", "75% 10.000000 \n", "max 10.000000 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "review_cols = [x for x in listings_new.columns if 'review' in x]\n", "\n", "listings_new[review_cols].describe()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "It is assumed that the values at 50th percentile, could be considered a good value to be added in the place values for null values." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "percentile50 = listings_new[review_cols].quantile(0.5)\n", "listings_new.loc[:,review_cols]=listings_new[review_cols].fillna(percentile50)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "bathrooms 16\n", "bedrooms 6\n", "property_type 1\n", "beds 1\n", "id 0\n", "review_scores_rating 0\n", "review_scores_value 0\n", "review_scores_location 0\n", "review_scores_communication 0\n", "review_scores_checkin 0\n", "review_scores_cleanliness 0\n", "review_scores_accuracy 0\n", "cancellation_policy 0\n", "instant_bookable 0\n", "minimum_nights 0\n", "neighbourhood_group_cleansed 0\n", "amenities 0\n", "bed_type 0\n", "accommodates 0\n", "room_type 0\n", "price 0\n", "dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_new.isnull().sum().sort_values(ascending=False)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now it is time to deal with the null values which are quantitative. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "listings_new.loc[:,['bathrooms','bedrooms','beds']] = listings_new.loc[:,['bathrooms','bedrooms','beds']].fillna(listings_new.loc[:,['bathrooms','bedrooms','beds']].quantile(0.50))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "For the property type, let us check if there is a way we can infer the property_type from the room_type." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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idneighbourhood_group_cleansedproperty_typeroom_typeaccommodatesbathroomsbedroomsbedsbed_typeamenitiescancellation_policyminimum_nightsinstant_bookablereview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valueprice
21843335Rainier ValleyNaNEntire home/apt41.02.02.0Real Bed{\"Wireless Internet\",Kitchen,\"Free Parking on ...strict2f96.010.010.010.010.010.010.0120.0
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" ], "text/plain": [ " id neighbourhood_group_cleansed property_type room_type \\\n", "2184 3335 Rainier Valley NaN Entire home/apt \n", "\n", " accommodates bathrooms bedrooms beds bed_type \\\n", "2184 4 1.0 2.0 2.0 Real Bed \n", "\n", " amenities cancellation_policy \\\n", "2184 {\"Wireless Internet\",Kitchen,\"Free Parking on ... strict \n", "\n", " minimum_nights instant_bookable review_scores_rating \\\n", "2184 2 f 96.0 \n", "\n", " review_scores_accuracy review_scores_cleanliness \\\n", "2184 10.0 10.0 \n", "\n", " review_scores_checkin review_scores_communication \\\n", "2184 10.0 10.0 \n", "\n", " review_scores_location review_scores_value price \n", "2184 10.0 10.0 120.0 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_new[listings_new.property_type.isnull()]" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Based on the room_type, we can say that the property_type is apartment. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "listings_new.iloc[2184, 2] = \"Apartment\"" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "id 0\n", "minimum_nights 0\n", "review_scores_value 0\n", "review_scores_location 0\n", "review_scores_communication 0\n", "review_scores_checkin 0\n", "review_scores_cleanliness 0\n", "review_scores_accuracy 0\n", "review_scores_rating 0\n", "instant_bookable 0\n", "cancellation_policy 0\n", "neighbourhood_group_cleansed 0\n", "amenities 0\n", "bed_type 0\n", "beds 0\n", "bedrooms 0\n", "bathrooms 0\n", "accommodates 0\n", "room_type 0\n", "property_type 0\n", "price 0\n", "dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_new.isnull().sum().sort_values(ascending=False)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" }, "tags": [] }, "source": [ "### Checking for outliers." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "To check for the outliers we try to use the pre-defined `.describe()` method. or we can use graphs. In this case, I will be using the `describe()` method." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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idneighbourhood_group_cleansedproperty_typeroom_typeaccommodatesbathroomsbedroomsbedsbed_typeamenitiescancellation_policyminimum_nightsinstant_bookablereview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valueprice
0241032Queen AnneApartmentEntire home/apt41.01.01.0Real Bed{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...moderate1f95.010.010.010.010.09.010.085.0
1953595Queen AnneApartmentEntire home/apt41.01.01.0Real Bed{TV,Internet,\"Wireless Internet\",Kitchen,\"Free...strict2f96.010.010.010.010.010.010.0150.0
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" ], "text/plain": [ " id neighbourhood_group_cleansed property_type room_type \\\n", "0 241032 Queen Anne Apartment Entire home/apt \n", "1 953595 Queen Anne Apartment Entire home/apt \n", "\n", " accommodates bathrooms bedrooms beds bed_type \\\n", "0 4 1.0 1.0 1.0 Real Bed \n", "1 4 1.0 1.0 1.0 Real Bed \n", "\n", " amenities cancellation_policy \\\n", "0 {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A... moderate \n", "1 {TV,Internet,\"Wireless Internet\",Kitchen,\"Free... strict \n", "\n", " minimum_nights instant_bookable review_scores_rating \\\n", "0 1 f 95.0 \n", "1 2 f 96.0 \n", "\n", " review_scores_accuracy review_scores_cleanliness review_scores_checkin \\\n", "0 10.0 10.0 10.0 \n", "1 10.0 10.0 10.0 \n", "\n", " review_scores_communication review_scores_location review_scores_value \\\n", "0 10.0 9.0 10.0 \n", "1 10.0 10.0 10.0 \n", "\n", " price \n", "0 85.0 \n", "1 150.0 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#For Reference...\n", "listings_new.head(2)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "quant_features = listings_new[['accommodates','bathrooms', 'bedrooms',\n", " 'beds','minimum_nights','review_scores_rating',\n", " 'review_scores_accuracy',\t'review_scores_cleanliness','review_scores_checkin',\n", " 'review_scores_communication', 'review_scores_location','review_scores_value']]\n", "description = quant_features.describe()\n", "\n", "#computing higher quantiles for better information\n", "higher_quantiles = quant_features.quantile([0.9,0.99,0.999]).rename(index={0.9:'90%',0.99:'99%',0.999:'99.9%'})\n", "\n", "#Combining all the dataframes to get a complete description\n", "description = pd.concat([description, higher_quantiles])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "Index(['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max', '90%', '99%',\n", " '99.9%'],\n", " dtype='object')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "description.index" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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accommodatesbathroomsbedroomsbedsminimum_nightsreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_value
min1.0000000.0000000.0000001.0000001.00000020.0000002.0000003.0000002.0000002.0000004.0000002.000000
mean3.3493981.2583811.3072291.7352022.36930394.7867999.6990579.6322689.8234689.8420649.6760089.546359
max16.0000008.0000007.00000015.0000001000.000000100.00000010.00000010.00000010.00000010.00000010.00000010.000000
25%2.0000001.0000001.0000001.0000001.00000094.00000010.0000009.00000010.00000010.0000009.0000009.000000
50%3.0000001.0000001.0000001.0000002.00000096.00000010.00000010.00000010.00000010.00000010.00000010.000000
75%4.0000001.0000002.0000002.0000002.00000098.00000010.00000010.00000010.00000010.00000010.00000010.000000
90%6.0000002.0000003.0000003.0000003.000000100.00000010.00000010.00000010.00000010.00000010.00000010.000000
99%10.0000003.5000004.0000006.00000014.000000100.00000010.00000010.00000010.00000010.00000010.00000010.000000
99.9%15.1830004.5915006.0000009.00000030.000000100.00000010.00000010.00000010.00000010.00000010.00000010.000000
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" ], "text/plain": [ " accommodates bathrooms bedrooms beds minimum_nights \\\n", "min 1.000000 0.000000 0.000000 1.000000 1.000000 \n", "mean 3.349398 1.258381 1.307229 1.735202 2.369303 \n", "max 16.000000 8.000000 7.000000 15.000000 1000.000000 \n", "25% 2.000000 1.000000 1.000000 1.000000 1.000000 \n", "50% 3.000000 1.000000 1.000000 1.000000 2.000000 \n", "75% 4.000000 1.000000 2.000000 2.000000 2.000000 \n", "90% 6.000000 2.000000 3.000000 3.000000 3.000000 \n", "99% 10.000000 3.500000 4.000000 6.000000 14.000000 \n", "99.9% 15.183000 4.591500 6.000000 9.000000 30.000000 \n", "\n", " review_scores_rating review_scores_accuracy \\\n", "min 20.000000 2.000000 \n", "mean 94.786799 9.699057 \n", "max 100.000000 10.000000 \n", "25% 94.000000 10.000000 \n", "50% 96.000000 10.000000 \n", "75% 98.000000 10.000000 \n", "90% 100.000000 10.000000 \n", "99% 100.000000 10.000000 \n", "99.9% 100.000000 10.000000 \n", "\n", " review_scores_cleanliness review_scores_checkin \\\n", "min 3.000000 2.000000 \n", "mean 9.632268 9.823468 \n", "max 10.000000 10.000000 \n", "25% 9.000000 10.000000 \n", "50% 10.000000 10.000000 \n", "75% 10.000000 10.000000 \n", "90% 10.000000 10.000000 \n", "99% 10.000000 10.000000 \n", "99.9% 10.000000 10.000000 \n", "\n", " review_scores_communication review_scores_location \\\n", "min 2.000000 4.000000 \n", "mean 9.842064 9.676008 \n", "max 10.000000 10.000000 \n", "25% 10.000000 9.000000 \n", "50% 10.000000 10.000000 \n", "75% 10.000000 10.000000 \n", "90% 10.000000 10.000000 \n", "99% 10.000000 10.000000 \n", "99.9% 10.000000 10.000000 \n", "\n", " review_scores_value \n", "min 2.000000 \n", "mean 9.546359 \n", "max 10.000000 \n", "25% 9.000000 \n", "50% 10.000000 \n", "75% 10.000000 \n", "90% 10.000000 \n", "99% 10.000000 \n", "99.9% 10.000000 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "description.loc[['min','mean', 'max','25%','50%', '75%','90%', '99%','99.9%'],:]" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We can identify the outliers and remove them, by observing the difference between the mean and the min/max values. The other rows of the quantiles are just to get additional information.\n", "\n", "From the above description, the following can be stated to be outliers:\n", "* In the fields that contain the parameters regarding the room {accommodates, bathrooms, bedrooms, minimum nights} The outliers are those which have very high requirements.\n", "* In the fields that describe the reviews, the ones which have low rating/scores, can be identified as outliers, as even the 25th quantile are having very high difference with the minimum values.\n", "\n", "These can be figuratively represented using histograms, Boxplots, Scatterplots." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "variable=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#boxplots\n", "px.box(listings_new, y=['accommodates'])" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "So, any airbnb with accomodates more than 7 can be declared as outliers." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No. of excluded entries = 184\n" ] } ], "source": [ "original_entries = listings_new.shape[0]\n", "listings_new = listings_new[listings_new.accommodates<=7]\n", "print(f'No. of excluded entries = {original_entries-listings_new.shape[0]}')" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Since, the stays which require more than 7 accomodates are excluded, the other columns must also be automatically corrected. This has be done in the interest of maintaining the accommodates as similar to the general conditions as possible. Once we correct this parameter, the other parameters that describe the room would become subjective to the host, and would become necessary details, and cannot be further processed. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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accommodatesbathroomsbedroomsbedsminimum_nights
count3634.0000003634.0000003634.0000003634.0000003634.000000
mean3.0599891.1952391.1948271.5855812.365162
std1.4819700.4890000.7074110.86238416.705579
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25%2.0000001.0000001.0000001.0000001.000000
50%2.0000001.0000001.0000001.0000002.000000
75%4.0000001.0000001.0000002.0000002.000000
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99.9%7.0000004.1835004.0000006.00000030.000000
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" ], "text/plain": [ " accommodates bathrooms bedrooms beds minimum_nights\n", "count 3634.000000 3634.000000 3634.000000 3634.000000 3634.000000\n", "mean 3.059989 1.195239 1.194827 1.585581 2.365162\n", "std 1.481970 0.489000 0.707411 0.862384 16.705579\n", "min 1.000000 0.000000 0.000000 1.000000 1.000000\n", "25% 2.000000 1.000000 1.000000 1.000000 1.000000\n", "50% 2.000000 1.000000 1.000000 1.000000 2.000000\n", "75% 4.000000 1.000000 1.000000 2.000000 2.000000\n", "max 7.000000 8.000000 5.000000 7.000000 1000.000000\n", "90% 6.000000 2.000000 2.000000 3.000000 3.000000\n", "99% 7.000000 3.000000 3.000000 4.000000 14.000000\n", "99.9% 7.000000 4.183500 4.000000 6.000000 30.000000" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quant_features_rooms = listings_new[['accommodates','bathrooms', 'bedrooms',\n", " 'beds','minimum_nights']]\n", "higher_quantiles = quant_features_rooms.quantile([0.9,0.99,0.999]).rename(index={0.9:'90%',0.99:'99%',0.999:'99.9%'})\n", "\n", "\n", "pd.concat([quant_features_rooms.describe(), higher_quantiles])" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Though the assumption was correct to certain extent, Let us look at the boxplots once again after correcting the minimum nights." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "minimum_nights=%{y}", "legendgroup": "", "marker": { "color": "#636efa" }, "name": "", "notched": false, "offsetgroup": "", "orientation": "v", "showlegend": false, "type": "box", "x0": " ", "xaxis": "x", "y": [ 1, 2, 1, 1, 1, 3, 2, 3, 2, 3, 3, 3, 2, 3, 1, 2, 1, 1, 1, 1, 3, 1, 2, 2, 2, 2, 2, 4, 2, 1, 2, 1, 2, 3, 2, 3, 2, 2, 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "px.box(listings_new, y='minimum_nights')" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "By utilizing the advantages of plotly, we can zoom into the plot and find the upper fence, which is 3, but based on the context of the data, it is possible that a few of the hosts, require a minimum stay of a month, hence after taking that also into consideration the upper limit for minimum_nights is determined to be 31." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No. of excluded entries = 1\n" ] } ], "source": [ "original_entries = listings_new.shape[0]\n", "listings_new = listings_new[listings_new.minimum_nights<=31]\n", "print(f'No. of excluded entries = {original_entries-listings_new.shape[0]}')" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "So, there was only 1 entry that was excluded, and the remaining values seem logical, as not all would like to let people stay for 1 or 2 days, and would prefer people who stay for a month." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now, it is time to remove outliers from the **review** features." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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review_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_value
min20.02.003.02.02.04.02.0
1%72.07.326.08.08.08.07.0
10%89.09.009.09.09.09.09.0
25%94.010.009.010.010.09.09.0
\n", "
" ], "text/plain": [ " review_scores_rating review_scores_accuracy review_scores_cleanliness \\\n", "min 20.0 2.00 3.0 \n", "1% 72.0 7.32 6.0 \n", "10% 89.0 9.00 9.0 \n", "25% 94.0 10.00 9.0 \n", "\n", " review_scores_checkin review_scores_communication \\\n", "min 2.0 2.0 \n", "1% 8.0 8.0 \n", "10% 9.0 9.0 \n", "25% 10.0 10.0 \n", "\n", " review_scores_location review_scores_value \n", "min 4.0 2.0 \n", "1% 8.0 7.0 \n", "10% 9.0 9.0 \n", "25% 9.0 9.0 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quant_reviews = listings_new[['review_scores_rating','review_scores_accuracy',\t\n", " 'review_scores_cleanliness','review_scores_checkin',\n", " 'review_scores_communication', 'review_scores_location',\n", " 'review_scores_value']]\n", "\n", "#computing higher quantiles for better information\n", "lower_quantiles = quant_reviews.quantile([0.1,0.01]).rename(index={0.1:'10%',0.01:'1%'})\n", "\n", "#Combining all the dataframes to get a complete description\n", "pd.concat([quant_reviews.describe(), lower_quantiles]).loc[['min','1%','10%','25%'],:]" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "From the above table, we can set the limit to be values at 1% quantile." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No. of excluded entries = 98\n" ] } ], "source": [ "original_entries = listings_new.shape[0]\n", "listings_new = listings_new[(listings_new.review_scores_rating>=72) & (listings_new.review_scores_accuracy>=7.32) &\n", " (listings_new.review_scores_cleanliness>=6) & (listings_new.review_scores_checkin>=8) & \n", " (listings_new.review_scores_communication>=8) & (listings_new.review_scores_location>=8) &\n", " (listings_new.review_scores_value>=7)]\n", "print(f'No. of excluded entries = {original_entries-listings_new.shape[0]}')" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Hence from the initial state to the final stage, a total of 283 entries were excluded so as to get a more general structure of the data. Now all that is left is to \n", "* Reset the index to get continuous values,\n", "* Removing the excluded listings from the calendar dataframe \n", "* Answer the questions." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "(3535, 21)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Resetting the index\n", "listings_new = listings_new.reset_index().drop('index',axis=1)\n", "\n", "listings_new.shape" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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idneighbourhood_group_cleansedproperty_typeroom_typeaccommodatesbathroomsbedroomsbedsbed_typeamenitiescancellation_policyminimum_nightsinstant_bookablereview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valueprice
0241032Queen AnneApartmentEntire home/apt41.01.01.0Real Bed{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...moderate1f95.010.010.010.010.09.010.085.0
1953595Queen AnneApartmentEntire home/apt41.01.01.0Real Bed{TV,Internet,\"Wireless Internet\",Kitchen,\"Free...strict2f96.010.010.010.010.010.010.0150.0
27421966Queen AnneApartmentEntire home/apt31.00.02.0Real Bed{Internet,\"Wireless Internet\",Kitchen,\"Indoor ...flexible1f96.010.010.010.010.010.010.0100.0
3278830Queen AnneHouseEntire home/apt62.03.03.0Real Bed{TV,\"Cable TV\",Internet,\"Wireless Internet\",Ki...strict1f92.09.09.010.010.09.09.0450.0
45956968Queen AnneHousePrivate room21.01.01.0Real Bed{\"Wireless Internet\",\"Free Parking on Premises...strict1f95.010.010.010.010.010.010.0120.0
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" ], "text/plain": [ " id neighbourhood_group_cleansed property_type room_type \\\n", "0 241032 Queen Anne Apartment Entire home/apt \n", "1 953595 Queen Anne Apartment Entire home/apt \n", "2 7421966 Queen Anne Apartment Entire home/apt \n", "3 278830 Queen Anne House Entire home/apt \n", "4 5956968 Queen Anne House Private room \n", "\n", " accommodates bathrooms bedrooms beds bed_type \\\n", "0 4 1.0 1.0 1.0 Real Bed \n", "1 4 1.0 1.0 1.0 Real Bed \n", "2 3 1.0 0.0 2.0 Real Bed \n", "3 6 2.0 3.0 3.0 Real Bed \n", "4 2 1.0 1.0 1.0 Real Bed \n", "\n", " amenities cancellation_policy \\\n", "0 {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A... moderate \n", "1 {TV,Internet,\"Wireless Internet\",Kitchen,\"Free... strict \n", "2 {Internet,\"Wireless Internet\",Kitchen,\"Indoor ... flexible \n", "3 {TV,\"Cable TV\",Internet,\"Wireless Internet\",Ki... strict \n", "4 {\"Wireless Internet\",\"Free Parking on Premises... strict \n", "\n", " minimum_nights instant_bookable review_scores_rating \\\n", "0 1 f 95.0 \n", "1 2 f 96.0 \n", "2 1 f 96.0 \n", "3 1 f 92.0 \n", "4 1 f 95.0 \n", "\n", " review_scores_accuracy review_scores_cleanliness review_scores_checkin \\\n", "0 10.0 10.0 10.0 \n", "1 10.0 10.0 10.0 \n", "2 10.0 10.0 10.0 \n", "3 9.0 9.0 10.0 \n", "4 10.0 10.0 10.0 \n", "\n", " review_scores_communication review_scores_location review_scores_value \\\n", "0 10.0 9.0 10.0 \n", "1 10.0 10.0 10.0 \n", "2 10.0 10.0 10.0 \n", "3 10.0 9.0 9.0 \n", "4 10.0 10.0 10.0 \n", "\n", " price \n", "0 85.0 \n", "1 150.0 \n", "2 100.0 \n", "3 450.0 \n", "4 120.0 " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_new.head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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listing_iddateavailableprice
02410322016-01-04t$85.00
12410322016-01-05t$85.00
22410322016-01-06fNaN
32410322016-01-07fNaN
42410322016-01-08fNaN
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" ], "text/plain": [ " listing_id date available price\n", "0 241032 2016-01-04 t $85.00\n", "1 241032 2016-01-05 t $85.00\n", "2 241032 2016-01-06 f NaN\n", "3 241032 2016-01-07 f NaN\n", "4 241032 2016-01-08 f NaN" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Formatting the calendar dataframe.\n", "calendar.head()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "valid_listings = listings_new.id.to_list()\n", "calendar = calendar[calendar.listing_id.isin(valid_listings)]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "3535" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "calendar.listing_id.nunique()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now that the null values and the ouliers are dealt with, there is a small process that needs to be done before we can start answering the questions. Which is nothing but unpacking the amenities in the listings. Since we can also use this in the upcoming model, we are directly generating dummies for the amenities here itself." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "0 [TV, Cable TV, Internet, Wireless Internet, Ai...\n", "1 [TV, Internet, Wireless Internet, Kitchen, Fre...\n", "2 [Internet, Wireless Internet, Kitchen, Indoor ...\n", "3 [TV, Cable TV, Internet, Wireless Internet, Ki...\n", "4 [Wireless Internet, Free Parking on Premises, ...\n", "5 [Wireless Internet, Free Parking on Premises, ...\n", "6 [Wireless Internet, Pets live on this property...\n", "7 [TV, Cable TV, Internet, Wireless Internet, Ki...\n", "8 [TV, Internet, Wireless Internet, Kitchen, Fre...\n", "9 [TV, Cable TV, Internet, Wireless Internet, Ki...\n", "Name: amenities, dtype: object" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_new.amenities = listings_new.amenities.str.replace('\"', '', regex=False)\n", "listings_new.amenities = listings_new.amenities.str.replace('{', '', regex=False)\n", "listings_new.amenities = listings_new.amenities.str.replace('}', '', regex=False).str.split(\",\")\n", "new_df = listings_new.amenities\n", "new_df.head(10)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "temp_list = []\n", "for x in new_df:\n", " temp_list+=x\n", "\n", "net_contents = set(temp_list)\n", "net_contents = [x.replace('\"', '') for x in net_contents]\n", "net_contents=[x for x in net_contents if x != '']" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'\\n' in net_contents" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now that we got all the categories, it is now time to get the dummies." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "amenity_cols = ['amenity_'+x for x in net_contents]\n", "\n", "am_dummies = pd.DataFrame(columns = amenity_cols, index =new_df.index)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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amenity_Air Conditioningamenity_Smoking Allowedamenity_First Aid Kitamenity_Ironamenity_Kitchenamenity_Safety Cardamenity_Lock on Bedroom Dooramenity_Pets live on this propertyamenity_24-Hour Check-inamenity_Elevator in Buildingamenity_Cable TVamenity_Family/Kid Friendlyamenity_Shampooamenity_Poolamenity_TVamenity_Essentialsamenity_Smoke Detectoramenity_Hot Tubamenity_Suitable for Eventsamenity_Laptop Friendly Workspaceamenity_Washeramenity_Other pet(s)amenity_Hair Dryeramenity_Hangersamenity_Fire Extinguisheramenity_Indoor Fireplaceamenity_Free Parking on Premisesamenity_Doormanamenity_Dryeramenity_Buzzer/Wireless Intercomamenity_Breakfastamenity_Carbon Monoxide Detectoramenity_Heatingamenity_Pets Allowedamenity_Cat(s)amenity_Dog(s)amenity_Wheelchair Accessibleamenity_Internetamenity_Washer / Dryeramenity_Gymamenity_Wireless Internet
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" ], "text/plain": [ " amenity_Air Conditioning amenity_Smoking Allowed amenity_First Aid Kit \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "\n", " amenity_Iron amenity_Kitchen amenity_Safety Card \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "\n", " amenity_Lock on Bedroom Door amenity_Pets live on this property \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " amenity_24-Hour Check-in amenity_Elevator in Building amenity_Cable TV \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "\n", " amenity_Family/Kid Friendly amenity_Shampoo amenity_Pool amenity_TV \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "5 NaN NaN NaN NaN \n", "6 NaN NaN NaN NaN \n", "7 NaN NaN NaN NaN \n", "8 NaN NaN NaN NaN \n", "9 NaN NaN NaN NaN \n", "\n", " amenity_Essentials amenity_Smoke Detector amenity_Hot Tub \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "\n", " amenity_Suitable for Events amenity_Laptop Friendly Workspace \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " amenity_Washer amenity_Other pet(s) amenity_Hair Dryer amenity_Hangers \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "5 NaN NaN NaN NaN \n", "6 NaN NaN NaN NaN \n", "7 NaN NaN NaN NaN \n", "8 NaN NaN NaN NaN \n", "9 NaN NaN NaN NaN \n", "\n", " amenity_Fire Extinguisher amenity_Indoor Fireplace \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " amenity_Free Parking on Premises amenity_Doorman amenity_Dryer \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "\n", " amenity_Buzzer/Wireless Intercom amenity_Breakfast \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " amenity_Carbon Monoxide Detector amenity_Heating amenity_Pets Allowed \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "\n", " amenity_Cat(s) amenity_Dog(s) amenity_Wheelchair Accessible \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "\n", " amenity_Internet amenity_Washer / Dryer amenity_Gym \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "\n", " amenity_Wireless Internet \n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "7 NaN \n", "8 NaN \n", "9 NaN " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "am_dummies.head(10)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "#This is the straight-forward approach, Need to be updated with a better, efficient method.\n", "for i in range(len(new_df)):\n", " for j in range(len(net_contents)):\n", " if net_contents[j] in new_df[i]:\n", " am_dummies.iloc[i,j] = 1\n", " else: \n", " am_dummies.iloc[i,j] = 0\n", "\n", "# Adding the total amenities presented by the stay\n", "am_dummies['Total_amenities'] = am_dummies.sum(axis=1)\n", "\n", "#Removing the source column\n", "listings_new.drop('amenities', axis=1, inplace=True)\n", "\n", "# Adding the amenities dataframe to the listings_new dataframe\n", "listings_new = pd.concat([listings_new,am_dummies], axis=1)\n", "\n", "#Confirming that the datatypes are int\n", "am_cols = [col for col in listings_new.columns if 'amenity_' in col]\n", "listings_new.loc[:,am_cols] = listings_new.loc[:,am_cols].astype('int')" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "[10, 16, 14, 13, 6, 7, 7, 15, 16, 21]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[len(x) for x in new_df][:10]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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idneighbourhood_group_cleansedproperty_typeroom_typeaccommodatesbathroomsbedroomsbedsbed_typecancellation_policyminimum_nightsinstant_bookablereview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valuepriceamenity_Air Conditioningamenity_Smoking Allowedamenity_First Aid Kitamenity_Ironamenity_Kitchenamenity_Safety Cardamenity_Lock on Bedroom Dooramenity_Pets live on this propertyamenity_24-Hour Check-inamenity_Elevator in Buildingamenity_Cable TVamenity_Family/Kid Friendlyamenity_Shampooamenity_Poolamenity_TVamenity_Essentialsamenity_Smoke Detectoramenity_Hot Tubamenity_Suitable for Eventsamenity_Laptop Friendly Workspaceamenity_Washeramenity_Other pet(s)amenity_Hair Dryeramenity_Hangersamenity_Fire Extinguisheramenity_Indoor Fireplaceamenity_Free Parking on Premisesamenity_Doormanamenity_Dryeramenity_Buzzer/Wireless Intercomamenity_Breakfastamenity_Carbon Monoxide Detectoramenity_Heatingamenity_Pets Allowedamenity_Cat(s)amenity_Dog(s)amenity_Wheelchair Accessibleamenity_Internetamenity_Washer / Dryeramenity_Gymamenity_Wireless InternetTotal_amenities
0241032Queen AnneApartmentEntire home/apt41.01.01.0Real Bedmoderate1f95.010.010.010.010.09.010.085.01000100000110010000010000000100010000100110.0
1953595Queen AnneApartmentEntire home/apt41.01.01.0Real Bedstrict2f96.010.010.010.010.010.010.0150.00010110000010011100010001010110110000100116.0
27421966Queen AnneApartmentEntire home/apt31.00.02.0Real Bedflexible1f96.010.010.010.010.010.010.0100.00000110000011001100010001100100110000100114.0
3278830Queen AnneHouseEntire home/apt62.03.03.0Real Bedstrict1f92.09.09.010.010.09.09.0450.00010100000111011100000001000000110000100113.0
45956968Queen AnneHousePrivate room21.01.01.0Real Bedstrict1f95.010.010.010.010.010.010.0120.0000000000000100110000000001000001000000016.0
51909058Queen AnneHousePrivate room21.01.01.0Real Bedmoderate3f99.010.010.010.010.010.010.080.0001000000000100110000000001000001000000017.0
6856550Queen AnneCabinPrivate room21.01.01.0Real Bedstrict2f97.010.010.010.010.09.010.060.0000000010001100100000000000000001001000017.0
74948745Queen AnneApartmentPrivate room21.01.01.0Real Bedstrict3f97.010.09.010.09.010.010.090.00010100000101011100010001100101010000100115.0
82493658Queen AnneApartmentEntire home/apt41.01.01.0Real Bedstrict2f97.010.010.010.010.010.09.0150.00010110000010011100010001010110110000100116.0
9175576Queen AnneHouseEntire home/apt21.01.01.0Real Bedmoderate3f97.010.010.010.010.010.010.095.00011110010101011100110111010100110000100121.0
\n", "
" ], "text/plain": [ " id neighbourhood_group_cleansed property_type room_type \\\n", "0 241032 Queen Anne Apartment Entire home/apt \n", "1 953595 Queen Anne Apartment Entire home/apt \n", "2 7421966 Queen Anne Apartment Entire home/apt \n", "3 278830 Queen Anne House Entire home/apt \n", "4 5956968 Queen Anne House Private room \n", "5 1909058 Queen Anne House Private room \n", "6 856550 Queen Anne Cabin Private room \n", "7 4948745 Queen Anne Apartment Private room \n", "8 2493658 Queen Anne Apartment Entire home/apt \n", "9 175576 Queen Anne House Entire home/apt \n", "\n", " accommodates bathrooms bedrooms beds bed_type cancellation_policy \\\n", "0 4 1.0 1.0 1.0 Real Bed moderate \n", "1 4 1.0 1.0 1.0 Real Bed strict \n", "2 3 1.0 0.0 2.0 Real Bed flexible \n", "3 6 2.0 3.0 3.0 Real Bed strict \n", "4 2 1.0 1.0 1.0 Real Bed strict \n", "5 2 1.0 1.0 1.0 Real Bed moderate \n", "6 2 1.0 1.0 1.0 Real Bed strict \n", "7 2 1.0 1.0 1.0 Real Bed strict \n", "8 4 1.0 1.0 1.0 Real Bed strict \n", "9 2 1.0 1.0 1.0 Real Bed moderate \n", "\n", " minimum_nights instant_bookable review_scores_rating \\\n", "0 1 f 95.0 \n", "1 2 f 96.0 \n", "2 1 f 96.0 \n", "3 1 f 92.0 \n", "4 1 f 95.0 \n", "5 3 f 99.0 \n", "6 2 f 97.0 \n", "7 3 f 97.0 \n", "8 2 f 97.0 \n", "9 3 f 97.0 \n", "\n", " review_scores_accuracy review_scores_cleanliness review_scores_checkin \\\n", "0 10.0 10.0 10.0 \n", "1 10.0 10.0 10.0 \n", "2 10.0 10.0 10.0 \n", "3 9.0 9.0 10.0 \n", "4 10.0 10.0 10.0 \n", "5 10.0 10.0 10.0 \n", "6 10.0 10.0 10.0 \n", "7 10.0 9.0 10.0 \n", "8 10.0 10.0 10.0 \n", "9 10.0 10.0 10.0 \n", "\n", " review_scores_communication review_scores_location review_scores_value \\\n", "0 10.0 9.0 10.0 \n", "1 10.0 10.0 10.0 \n", "2 10.0 10.0 10.0 \n", "3 10.0 9.0 9.0 \n", "4 10.0 10.0 10.0 \n", "5 10.0 10.0 10.0 \n", "6 10.0 9.0 10.0 \n", "7 9.0 10.0 10.0 \n", "8 10.0 10.0 9.0 \n", "9 10.0 10.0 10.0 \n", "\n", " price amenity_Air Conditioning amenity_Smoking Allowed \\\n", "0 85.0 1 0 \n", "1 150.0 0 0 \n", "2 100.0 0 0 \n", "3 450.0 0 0 \n", "4 120.0 0 0 \n", "5 80.0 0 0 \n", "6 60.0 0 0 \n", "7 90.0 0 0 \n", "8 150.0 0 0 \n", "9 95.0 0 0 \n", "\n", " amenity_First Aid Kit amenity_Iron amenity_Kitchen amenity_Safety Card \\\n", "0 0 0 1 0 \n", "1 1 0 1 1 \n", "2 0 0 1 1 \n", "3 1 0 1 0 \n", "4 0 0 0 0 \n", "5 1 0 0 0 \n", "6 0 0 0 0 \n", "7 1 0 1 0 \n", "8 1 0 1 1 \n", "9 1 1 1 1 \n", "\n", " amenity_Lock on Bedroom Door amenity_Pets live on this property \\\n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "5 0 0 \n", "6 0 1 \n", "7 0 0 \n", "8 0 0 \n", "9 0 0 \n", "\n", " amenity_24-Hour Check-in amenity_Elevator in Building amenity_Cable TV \\\n", "0 0 0 1 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 1 \n", "4 0 0 0 \n", "5 0 0 0 \n", "6 0 0 0 \n", "7 0 0 1 \n", "8 0 0 0 \n", "9 1 0 1 \n", "\n", " amenity_Family/Kid Friendly amenity_Shampoo amenity_Pool amenity_TV \\\n", "0 1 0 0 1 \n", "1 1 0 0 1 \n", "2 1 1 0 0 \n", "3 1 1 0 1 \n", "4 0 1 0 0 \n", "5 0 1 0 0 \n", "6 1 1 0 0 \n", "7 0 1 0 1 \n", "8 1 0 0 1 \n", "9 0 1 0 1 \n", "\n", " amenity_Essentials amenity_Smoke Detector amenity_Hot Tub \\\n", "0 0 0 0 \n", "1 1 1 0 \n", "2 1 1 0 \n", "3 1 1 0 \n", "4 1 1 0 \n", "5 1 1 0 \n", "6 1 0 0 \n", "7 1 1 0 \n", "8 1 1 0 \n", "9 1 1 0 \n", "\n", " amenity_Suitable for Events amenity_Laptop Friendly Workspace \\\n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "5 0 0 \n", "6 0 0 \n", "7 0 0 \n", "8 0 0 \n", "9 0 1 \n", "\n", " amenity_Washer amenity_Other pet(s) amenity_Hair Dryer amenity_Hangers \\\n", "0 1 0 0 0 \n", "1 1 0 0 0 \n", "2 1 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "5 0 0 0 0 \n", "6 0 0 0 0 \n", "7 1 0 0 0 \n", "8 1 0 0 0 \n", "9 1 0 1 1 \n", "\n", " amenity_Fire Extinguisher amenity_Indoor Fireplace \\\n", "0 0 0 \n", "1 1 0 \n", "2 1 1 \n", "3 1 0 \n", "4 0 0 \n", "5 0 0 \n", "6 0 0 \n", "7 1 1 \n", "8 1 0 \n", "9 1 0 \n", "\n", " amenity_Free Parking on Premises amenity_Doorman amenity_Dryer \\\n", "0 0 0 1 \n", "1 1 0 1 \n", "2 0 0 1 \n", "3 0 0 0 \n", "4 1 0 0 \n", "5 1 0 0 \n", "6 0 0 0 \n", "7 0 0 1 \n", "8 1 0 1 \n", "9 1 0 1 \n", "\n", " amenity_Buzzer/Wireless Intercom amenity_Breakfast \\\n", "0 0 0 \n", "1 1 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "5 0 0 \n", "6 0 0 \n", "7 0 1 \n", "8 1 0 \n", "9 0 0 \n", "\n", " amenity_Carbon Monoxide Detector amenity_Heating amenity_Pets Allowed \\\n", "0 0 1 0 \n", "1 1 1 0 \n", "2 1 1 0 \n", "3 1 1 0 \n", "4 0 1 0 \n", "5 0 1 0 \n", "6 0 1 0 \n", "7 0 1 0 \n", "8 1 1 0 \n", "9 1 1 0 \n", "\n", " amenity_Cat(s) amenity_Dog(s) amenity_Wheelchair Accessible \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "5 0 0 0 \n", "6 0 1 0 \n", "7 0 0 0 \n", "8 0 0 0 \n", "9 0 0 0 \n", "\n", " amenity_Internet amenity_Washer / Dryer amenity_Gym \\\n", "0 1 0 0 \n", "1 1 0 0 \n", "2 1 0 0 \n", "3 1 0 0 \n", "4 0 0 0 \n", "5 0 0 0 \n", "6 0 0 0 \n", "7 1 0 0 \n", "8 1 0 0 \n", "9 1 0 0 \n", "\n", " amenity_Wireless Internet Total_amenities \n", "0 1 10.0 \n", "1 1 16.0 \n", "2 1 14.0 \n", "3 1 13.0 \n", "4 1 6.0 \n", "5 1 7.0 \n", "6 1 7.0 \n", "7 1 15.0 \n", "8 1 16.0 \n", "9 1 21.0 " ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_new.head(10)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" }, "tags": [] }, "source": [ "# Answering the Questions\n", "\n", "In the querstions that are mentioned in the above " ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" }, "tags": [] }, "source": [ "## What is the probable price for a room based on the neighbourhood?\n", "To answer this question, we need a new column, ratio of price and total amenities." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "mean_price_per_nbhood = listings_new.groupby(['neighbourhood_group_cleansed','property_type'])['price','Total_amenities'].mean().reset_index()\n", "mean_price_per_nbhood.Total_amenities = round(mean_price_per_nbhood.Total_amenities)\n", "priceVsnbhood = mean_price_per_nbhood.groupby('neighbourhood_group_cleansed').price.mean().reset_index()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "type": "box", "x": [ "Ballard", "Ballard", "Ballard", "Ballard", "Ballard", "Ballard", "Ballard", "Ballard", "Ballard", "Ballard", "Beacon Hill", "Beacon Hill", "Beacon Hill", "Beacon Hill", "Beacon Hill", "Beacon Hill", "Beacon Hill", "Beacon Hill", "Beacon Hill", "Beacon Hill", "Capitol Hill", "Capitol Hill", "Capitol Hill", "Capitol Hill", "Capitol Hill", "Capitol Hill", "Capitol Hill", "Capitol Hill", "Capitol Hill", "Capitol Hill", "Cascade", 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t/fCz5XLj74YkyFlOMxFITAHC78QcN1qNAAIIIJAAAsaL54z0dL8g8sjRo34PvAwVJutfH9DvZG12tjn8thtC795b7HfhbnV/5uPU9rj68AULnM1tCjdLXn9oaKhA2Pz1UG+irIbfwU69UG+UzA80DdVG4z6D2VoJqc2huZU3oVZD9QT4caOJCCCAAAII1KlAqAdeqkYZPwgPd81nvKtOf50efP4Z2gflkTwsXUcJdV1g/rrVawPzNY2V6w7jAIW6HjN/PZLwWw/uQ/W9pn3aPX6469Sxo4eLftee+Y7JOj1hOTgCSSRA+J1Eg01XEUAAAQTiK2C+eDbOCunSsbXfG5ia3jCoUFYtqkyI+QI71HbBLsRrCpr1WdDh2qGH56o9dsue6CVfjKNgpU2hAmPzm8dQ4XWwkiHB3nzYCb/NJVhUn4zlREK9EQo1Y8t8K7Han3GmfLBxCffmTG9DYfGBkA/ZCvWGt1lO07g9WCq+P5UcDQEEEEAAgdgIhCp7Yp45XLCnUEaPf1TMAa1qVbBrEeM1h5XybKF6Z/Wa0byeleur/n26i53wO9QHAKrtVictBOun1T5aDb/1ySs1hdbhrlP1CSz6tV5NZVtic2ayVwQQIPzmHEAAAQQQQCBGAuYLa+Nto3fdeIXcNfnvos8MDlUrUW+a/mbHPGPc6kV+qDdU5ltOa3rjEmwmu52a39EOv819UuVDzDPb1TrGIPri8wcEDe1D+ZhPjVA1OK2Up9Hf0BnL3+jexrDZyi3Ial/Gmd7nnd1X+zDFXMol2Hml11s37sP4BjxYsM8btRj9kmC3CCCAAAKuEQgVfptf/3Oym4QMv2u6O23RR1+F/DDbCqLVa0ar4bfVCRnB2lZT+G0+fjRmfpvbGu3wW13bhVr06y4rdcOtjCPrIICAfQHCb/tmbIEAAggggIAlgWAX1nqw+Njkm+WF198LCL+DzQIyHszqGw1mftf3sUVz5neomduRht/mGuCq0VbDb7WuPhvr0l+cKX+e8VLYN8X6Ldl6mG1lllakD9ay9EPCSggggAACCLhEIBrhNzO/3TPzO9xprV+Dndqja9AHuIfbnu8jgIB1AcJv61asiQACCCCAgC2BYOG3Hmxu37lHCvcdlJuvGarV8q5pBkxN4Xe4OoPGmtNWSozYqUNppV63MaCNdOa31T5arfkd6vZdK2VPQh2jNuG3eba6nfDbOEvbOKM71IlqHl8r4Xcof1s/DKyMAAIIIICAywXChd/6g6rzWjQLehdaTc/8UNeLvU/pos0YV/W/VSk8u0uo1/xwtbutXl9ZuaYwtjmeNb/tlFIJV1bO7F6b6yQ7s9rtjjfrI4DAcQHCb84GBBBAAAEEYiQQ6oLWWIrCWE5CDzLNJSbU13cU7A36wEvV9GBvtoLVFbQSfqv9WXlifbDj6hf/a9Zt8XuwU02hstU2mfsYLCCuKTTWP2RQ7TaWC1EfPOh9uX/aHL8628FOi2BvcHSvSGp+B3tDqY9duJrfqn2hyrDo/Xz1zf+TcWNGSGZG9Ux4Kw+1+uyr1X5vqq08pDNGP0LsFgEEEEAAgYQRCBV+66+9xtDayrVWsOuaUHegWUEKFk6by98Fu1awen0VbF81tSvY+sH6ZycgttpHq2VPsps29pXPa5fX3G+G9rOvLJRBP+8nndvnadfi6jryxccniKp/ri/q623zmmsPvJw+c55cPewCbX3jNRwPwbRy9rIOArUTIPyunR9bI4AAAgggEFKgpgvrUA+9MQbI+o6thKr6Rbe+jbmsivq61aDZ+MZn154ibZc9T+oU9AGIxgc1qnUenjhK7p0y21fOJdRxfe2cNT+gTneoN0/mWtShHvpkfnhksHrV5lrYap38HbuD1gw3D7B5WzXjukPblvL03AW+siN2HnhpHruZU++S/3zwP+2w+mx5qw8i1UNu45uqdz5c4tcF4xsz837DnX/8uCOAAAIIIIBAcAHz67lxLSvXIsZrLd+dggV7A66/Ii1HFur5Mua2hbrmiOT6yhwGm+WC1cEOFiDPX7jY0oO4rfbRTvgd6prKfB0a7NjGMQ12fli5a4+fNwQQqL0A4XftDdkDAggggAACjhPQQ8yxo4f7zUBxXENpUK0EanOrba0OzMYIIIAAAgggkFACdsuSJFTnfmpsMvQxEceFNiNQ1wKE33U9AhwfAQQQQACBWgqoGdHmUhVW6lfX8rBs7gABO7cCO6C5NAEBBBBAAAEEoiQQ7G6tULtWs6lzsptoNcPDPVw9Ss2rk90QftcJOwdFwPEChN+OHyIaiAACCCCAQM0C5nIgau1QJUGwdI+A3dqa7uk5PUEAAQQQQAABuwLJEAwnQx/tjjvrI4CACOE3ZwECCCCAAAIIIIAAAggggAACCCCAAAIIIICA6wQIv103pHQIAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAgPCbcwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAdQKE364bUjqEAAIIIIAAAggggAACCCCAAAIIIIAAAgggQPjNOYAAAggggAACCCCAAAIIIIAAAggggAACCCDgOgHCb9cNKR1CAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQIPzmHEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwnQDht+uGlA4hgAACCCCAAAIIxEvgy6VVUlhcfbSzTk+RZjnxOjLHQQABBBBAIDEE9lcelblFG2R/ZblkpdaXa3K7SVZqemI0nlYigEDCCxB+J/wQ0gEEEEAAAQQQQACBuhCY/rcK2bDJ6zt0ZqbIpPH1CMDrYjA4JgIIIICAYwX6rp0vK8uKfO1TAfjWnr8jAHfsiNEwBNwlQPjtrvGkNwgggAACCCCAAAJxENi+0yt/nlYRcKQhF6XI0ItT49ACDoEAAggggIDzBRaXFMh53y8IaOikVqfJ5Lz+zu9ALVu4YaNXNmw+/kG51d116+yRbl09VldnPQQQqEGA8JvTAwEEEEAAAQQQQAABmwLqzez0pwLD7xM7e2T87Wk298bqCCCAAAIIuFPg8b2r5a4dXyRt+L3g3UpZ+F6V7cHlw3TbZGyAQEgBwm9ODgQQQAABBBBAAAEEbAow89smGKsjgAACCCSlQKiZ3zPani13Nu/lehM9/M7JFsm18FyQomKR4n0ihN+uPzXoYBwFki78fmzWfFm6cr3MnHqXZDdtrFHvO1AiYybMkDXrtmj/f/HxCdK/T3ffMLy56FO5f9oc7f+XDBogD4wbKZkZ9eM4TBwKAQQQQAABBBBAwGkCc16tFPXAS33JzBCZdA81v502TrQHAQQQcKPAspXr5do7p/rlF2VHymXS9DnyzodLtC4/OH6kDBt8jq/7+jbqCz1P6uSXi8TSaOCGBfLJoQLfIXpn5sribkOToua3Hn537SzSvWtKWOb1G6tk4+bIw2+VXy1ZvtYRuVW48zEsBisgECWBpAq/VfD9/GuL/H7J6z+MA/qdrL0obN5WIPdNmS0PTRwlndvniXpxeHTWfN+LgtqHWu4ePTxKQ8BuEEAAAQQQQAABBBJVQM0AVyVQmuVU1+ZskJmoPaHdCCCAAAKJImAMsY2T94x5hT7Jb+zo4drkPnPWEe+QVM0AX1yyUwY2bi0DG+clCnWt25nM4XdN52OtYdkBAjYEkib8Vr/Y83fslp+f0csvzFYvANOfnidT7h2lzQQ3h+Hqh7VD25a+T0vNYbgNa1ZFAAEEEEAAAQQQQAABBBBAAAEEIhbQM4zxt1wl906ZLXq4rcLuiQ/PlnE3j9Am8qnFGD7qmYg+kc8chkfcIDasUSCe4bca09HjH5Vde4q0Numz+1+Y964cKj0ihw6VancFqDsCep/SxW/d668a7DfJ0/gBS6sWuTJr2ljtvApXOUHHCHc+ctogEE+BpAi/jZ9ofrt+i1/4HSzM1l8gxlxzmXbLkD4rXA2M+QXiWIX9BxfEc4A5FgIIIIAAAu4X8IqIx/3dpIcIIIAAAgg4VCAt1SMeD6/FsR4eYx6Rk9VYK98aama3aosxC5k5922teXr4bZ4ZXlnlFa+6pHLBos5HpyzxDL/NY66X61UZ16KPvvIF2Gq99xcvlS4d22iBth6aT5k4SrtLQOVkE6fM9q2vvl925Ki0btlMO+eGDxnoq5xgnExqNA/24Uq87zZwyjlAO+pewPXht/qhfWPhYl+9I3PYbf6+GhJz+H3FkIG+GuDmH+D9h8rrfhRpAQIIIIAAAkksoN7euOS9WhKPIl1HAAEEEEhkgcYN6klqinMCx0S2DNV280zaYGVNzEGkOfw23tVu3v5g6TGpqnLHFVVWI+c8o80p4bfxgw/zORasAkKw9c15mr6dMTPT922ushAqmHfjzyp9cp6A68Nv48Mqjfz67R+btu70mwkeLPyuaea384aUFiGAAAIIIIAAAggggAACCCCAgJsEzCUtjH1Tdb9zspv4Pb/MHDaGm/ntJisn9cWp4bf5YZTKTH9Aqrn8r+5pLIViPv/UjHHjwsxvJ52FtMX14bd5iM2fVFHzmx8CBBBAAAEEEEAAAQQQQAABBBBIJAHzzO1wNZap+V03o+vE8FsPvls2z9HK4NiZ+W2srFCTaLjzsW5Gg6Mmq0DSh9/mH3Lzp1PmsNz4wIhkPWnoNwIIIIAAAggggAACCCCAAAII1J2AOfxWLTHmFcHKotw3ZbY8NHGUVueZ+svxGbt4h981PddOr/duzsH0c0Wv5W2u+a3+r5YuHVv71fxWX9O/Z575He58jI8+R0GgWiDpw2+FEO5ptcbSKZcMGuCrH85JhAACCCCAAAIIIIAAAggggAACCMRbIFj4bS5loZex0NtmLFuhl4LNbto43k1PquPFO/w2ngP6GL8w713NXA+/9dD62junal9v1SJXmmU3keG/Ok97kKVajDmY+v6saWO1D03M+VlN51G48zGpTgQ6W6cCSRd+16k2B0cAAQQQQAABBBBAAAEEEEAAAQQQSAoBPfzOzRFplpsSts+FRVVSVCwy5KIUGXpxatj1WQEBBMILEH6HN2INBBBAAAEEEEAAAQQQQAABBBBAAAEEbAno4betjYTw264X6yNQkwDhN+eHawVKy0TmvVkpGzZVaX3s1iVFRgxLlQaZru0yHUMAAQQQQAABBBBAAAEEEEDAUQJv798qT+xdLYtLCqRPZq5Myusvl2V1dFQbY9WYDRu9sv6nTMLOMbp3SZFuXT12NmFdBBAIIUD4zanhWoHpf6uQDZu8fv274NzqAJwFAQQQQAABBBBAAAEEEEAAAQRiK5BfXiJ9186X/ZXlfgf6+MShMrBxXmwPzt4RQACBZHzgJaOePAI33HEsoLNtW3tk0vi05EGgpwgggAACCCCAAAIIIIAAAgjUkYCa9X355vcCjj6p1WkyOa9/HbWKwyKAQDIJMPM7mUY7yfoaLPzOzRZ5ZHK9JJOguwgggAACCCCAAAIIIIAAAgjEX+DFovVyXf7HhN/xp+eICCDwkwDhN6eCawWmPVkh32/2L3ty1ukpMvJqyp64dtDpGAIIIIAAAggggAACCCCAgGMEVNmTjmteCWjPW50vSpq6344ZDBqCQJIKEH4n6cAnQ7e37/TKvH9V+gLwEzt75NZRaTzwMhkGnz4igAACCCCAAAIIIIAAAgg4QkDN/p5csEy2lR+Spqn15c7mvSh54oiRoREIJIcA4XdyjDO9RAABBBBAAAEEEEAAAQQQQAABBBBAAAEEkkqA8DuphpvOIoAAAggggAACCCCAAAIIIIAAAggg4FyBzdsK5L4ps+WhiaOkc/u8qDR034ESGTNhhowdPVz69+kelX1GspPHZs3XNrt79PCwm9e0biyMamqQnXaH7VgNKyxbuV4enTVfZk69S7KbNq7NrnzbEn5HhZGdIIAAAggggAACCCCAAAIIIIAAAggggEBtBWIR7BJ+125UCL9r58fWCCCAAAIIIIAAAggggAACCCCAAAIIJIXAgg0LZOXuldIhq4Nz1X0SAAAgAElEQVQM7TZUsjKykqLfddlJp4TfdgyY+c3MbzvnC+sigAACCCCAAAIIIIAAAggggAACCCBQpwJ9Z/XVgm99UcH31ju2RiUAf3PRp/LfT77Wdv3ZV6u1v198fIL27+dfW+T7v7Hsh9rm/mlztO9dMmiAPDBupGRm1Bc9LF6zbkvA9/SZ2Zf+4iyZ8rdXte9ff9XgkKU81DGWLF8rjRo1kNcXfOTXDv04l15wprw4/z3te/fd+XuZNXeBX9kTYzt7ntTJVxZDlcm49s6p2nbGr5sHWT/O6X26+yz0/h45ejSgJEpNs8+t9D+UqznQVvsaPf5R2bWnyNdkNWZqjNS6h0qPyKFDpfLOh0ukVYtcmTVtrFYKRm/Dyd06+kzNY6C218fd+D19PPSxVvua/vQ8mXLvKK3UiPm4D44fKfk7dodsi2q4+XzR+6B3KlRb1PeNVuHGMZIfXsqeRKLGNggggAACCCCAAAIIIIAAAggggAACSS2wfNdyWbhhoWWD/P35MnfV3ID1L+x8oQxoM8Dyfk7LO00uPfHSgPVViPj03AW+gFQPFfUg0hx6qv/PX7jYFySrgLJD25YybPA5okJltagQVg82hw8ZqH1PD2wHn3+GFniHm1VtboexrrM6hqrF3S6vuS94NwfP5nZ+u2GrZGakS/G+gzJxymy//qqQNlg9bWP4rb5fdqRcJk2fIwP6naz1SR3DuG24WdcqsA7V/5pcjfs1u5od1bqLPvrKr3/qQwQVWhfsKdRC85uvGaq137ytcayVsepry+Y5mo2V8Nt4XLV9TW3R969b6ufHlImjtPOnpraoc8E4htT8tvxrgBURQAABBBBAAAEEEEAAAQQQQAABBBCIncCs5bPkpv/cFLsDhNjz6H6j5ZlLnwkafuvhqJq9bQ4Sjf/PSE/3C3/VztT331i42BdCGw9gDG3N4bQeJF8xZGDQh0maw1ZjUNulY+saZ13ntWgW0E69XcFmURtnMBvbHyygN7ZLzf6e+PBsGXfzCMnJauz7d7AHbtbU/x7dO9XoOnPu21qzVAhtHp9g4be+rnl8VPhtfiio7jHmmsuCtkF/kOTHX6zQZuLXNPPbeFz1b7O18VypTVtemPeuz0PvIw+8jPuvFA6IAAIIIIAAAggggAACCCCAAAIIIICAv4ATZ37bDb9VOQ3jYix9YixVodbRS2fUNvw2huVWw+9gwbq5faqNoUqfBAu/zWG/PvO9bV7zkB8CqGNYCb9DuZrDb+OHDbUNv/XZ63r4bTQzljaJRfht/tDBaltU+K3fbUD4zW9YBBBAAAEEEEAAAQQQQAABBBBAAAEEEligzzN9ZNWeVb4eNE1vKvl35ket5rfd8DvUbG0VBO/eW+ybHezUmd/G4LSm0yLczG81U14PtdV+xt50ZdBZ7FbD75pc1T6Y+T1fK7fDzO8E/mVG0xFAAAEEEEAAAQQQQAABBBBAAAEEEDALvL3+be2hlx2yOshl3S+LSvCtjmEuL1JT2RP1YENzbWo1I/tf73wiv77kXDHOUNZnaus1o2s789tcbkTV/B47ergvbA5X8/v9xUulS8c2ATW/lcGrb34ggwcN0B7caFzM4XewMFzv5/aCvb466MHO3nD9t+pqboP+8E7jAy/1oFz9XVOpkZrM9BI3+viZzwv1wcbSlev9ar8bj6v+XVPZE/V9Y03xYDW/9drydtsSjd8ePPAyGorsAwEEEEAAAQQQQAABBBBAAAEEEEAAgToUsBt+64H5/dPm+Fr94PiRfg+13LWnSFq1yJVm2U3k9L4naTOWw4W/ZgL9gZf6142lSYKF0Ob96+Hr868t0nZh3F4PjPV966VZzG3Qj7Nm3ZaAvhrXNT70M9RQWum/uc+6a7AQ+do7p2qHUiVnDh4q1eqOq1rj4epsqwdeqvHRFz001/9vLAtjdjF+b+JtV8vnS9fIlHtHaR8aBHvYZ01tUbPmzb6RtkW10xjER+PHifA7GorsAwEEEEAAAQQQQAABBBBAAAEEEEAAAQQCBMyhvFOJVIA75clXZeLtVwfMHI9Hm411uc0z1+NxfLceg/DbrSNLvxBAAAEEEEAAAQQQQAABBBBAAAEEEKhjgUQJv/WHNKrZ7fFajDOw1Qz7WdPGarO+WaInQPgdPUv2hAACCCCAAAIIIIAAAggggAACCCCAAAIIIOAQAcJvhwwEzUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIngDhd/Qs2RMCCCCAAAIIIIAAAggggAACCCCAAAIIIICAQwQIvx0yEDQDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIHoChN/Rs2RPCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAg4RIPx2yEDQDAQQQAABBBBAAAEEEEAAAQQQQAAB9wgsLimQTw4V2O7QuY3yZGDjPNvbsQECCAQKEH5zViCAAAIIIIAAAggggAACCCCAAAIIIBBlgckFy+SBXV/b3uukVqfJ5Lz+trdjAwQQIPzmHEAAAQQQQAABBBBAAAEEEEAAAQQQQCDmAnr43b5eY+mQ3ijs8fKPHpJtx0rEDeH3spXr5dFZ82Xm1Lsku2njsH1nBQRiJcDM71jJsl8EEEAAAQQQQAABBBBAAAEEEEAAgaQV0MPvcxu1koGNW4d1WFyyUz45tKvW4bcKnq+9c6o8OH6kDBt8TtjjxmIFwu9YqLLPSASSIvx+c9Gncv+0OT4f8w//vgMlMmbCDFmzbou2zouPT5D+fbr71jduf8mgAfLAuJGSmVE/Em+2QQABBBBAAAEEEEAAAQQQQAABBGwLbN5WIKPHPyq79hRp25rzibIj5TJp+hx558Ml2vfN2YceiKrv9TypEzNybY+A/Q3qKvx+bNZ8rbG79xaTYdkfNrZwmYDrw2/1y3/m3LfluhEXa7dZ6EH32NHDtYBbf3EY0O9k7dMw9WJy35TZ8tDEUdK5fZ6YP6nSf4HcPXq4y04FuoMAAggggAACCCCAAAIIIIAAAk4VUBPz2uY1903WM+cTxv+bsw9z1qH2tWT5WoLRGA92XYTfauynPPmq3HTNUJn299dk3M0jtHzLfE6orgc7L4yTR/UPSdS6atLopRecKS/Of0/7AMb44Yt+PjVq1EBeX/CRXH/VYPn5Gb38yp6YP5xR6+jZGpNOY3wiJvnuXR9+m8c3WNg9/el5MuXeUVo4bv6+evHo0Lal7zYRbttI8p8Yuo8AAggggAACCCCAAAIIRChQWCzy5dIqbetunT3Srasnwj2xGQIixgD7yNGjMvHh2b6gU/kYw3C1bv6O3b6w0Rx6xtpzbtEGyS8vkazU+nJNbjfJSk2P9SEdsf+6CL9VbvXZV6u1sTZnWubzwPh/td3EKbNl1rSxAZNB9fC7XV5z7QMTtai7DPSJpHp4baykYMzPMtLTtfVbNs/xnYPvL14q5wzoI+9+tETmL1zsuxPB3GZHDCSNSGiBpAu/9duEpkwcpX1aGizM1l8gxlxzmd8Psxpp8wvEsYrqCxcWBBBAAAEEEKgrAa+IEB7UlT7HRQABBBCwJrDqW5FnXqj0W3nwLz0y5MIUaztw8FppqR7xeHgtjucQ6RP39DAxWJhtDMfVHfFq0WfammcBV1Z5xasuqWKwnLb+DVl1pLpUi1pUAL7x5KtjFoCr89EpS12E3yrTUrOug2Ve6jzRJ4DqgfQVQwZq65rvJDDmZcpTzfzWqyio/xvPLxVgm+8kMG5fvL/Er8qCPj7mCajq62q7NxYu5q4Ep5zELmhH0oTfxrrexrpXwX6ozOG3/otAjbf5BWX/oXIXnAZ0AQEEEEAAgcQVUG9vYvReLXFRaDkCCCCAgOMEnp4tsiU/MJT760OJ/yrWuEE9SU1xTuDouMGPcoNUZvH8a4v8yk4YQ011V7s5nFTht/GudnP4fbD0mFRVRf9c/Lx0twzZ8U6AwMMtBsiYrFOiLFO9u6xGznlGW7zDb/N5EKr0r8q5crKb+IJwdc7UVPkgWPhtzNOshN/Gqgvm8FuvU69/neftxeRHI2l3mjTht/kHS781o7Yzv5P2zKHjCCCAAAIIIIAAAggggAAClgXumXxMivYFrv7cE/Us74MVETAKGGfeFuwpDJhZa2fmd6xkXyxaL9flfxyw+0mtTpPJef1jdVjH7Dfe4bexdrYRwVxfW5XAUR+GqEU9/04tdTnz2zjp1DGDR0NcI5B04bcaOWNNI/OnYtT8ds25TUcQQAABBBBAAAEEEEAAAccITHuyQr7f7D+zNjND5G+PEH47ZpASrCHGPEM13Yk1vxeXFMh53y8IkJ3R9my5s3mvBBO339x4ht/BSoioFgebDa7OFa/XK+NvuUqr760W8+RQFYYvXbleq8WtFmPZE/OM8mAPUK2p5rdq67/e+UR+fcm5ATW/jd/LzHDOLH77o88WThFwffitfiBfmPeuqPrd6odG/wEdPmSg9ulWsAdg3jdltjw0cVRAgX/9NhA1eHqdLKcMJO1AAAEEEEAAAQQQQAABBBBwrsCGjV6Z/lSFXwOvvDxVfjEw8Wt+O1fdXS179pWFMujn/XxhpQond+8t9tVGNs7cNYeT5hKuwcLKWGn1WTtfVpUdr/ndvn4jWXny8JjV/I5VPyLZbzzD71APMdVzL+PsavO5o/dNL6mj/q9mi+vnl3qgqgq/16zb4mMwlhQOF36rPM1Yjljfv56tmWesG/cdiTvbIGAUcH34rTpr/OFV/zf/EJl/AI1Pp1XrG38IqTvEDxACCCCAAAIIIIAAAggggEAkAoXFIhs2VklpmUi3rh5p15o62ZE4Jus2aibttXdO9XXfnE/oIadeP9mcfRi373lSJ21Gr14fPNamagb44pKd0qdBMxnYOC8pgm9lqoffHeo3lg7pTcIy5x89KPnlJeKEsjDGqgnmD1PCdoQVEHCQQFKE3w7ypikIIIAAAggggEBIARWGzHuzUnbsrL4tvlsXj/zq4lRpkAkaAggggAACCCCQmAIq+H5g1zLZX3FUstLS5Y7mveSyrI6J2RmbrdbDb5ub1Un4bZ4YavxwhfDb7giyvpMECL+dNBq0BQEEEEAAAQSSWuDvz1XIijX+9WAvODdFRgxLTWoXOo8AAggggAACiSmgZjH3XTtf9leW+3Xg4xOHajPA3b7oM97t9nNg49ZJ4WPXhfURiESA8DsSNbZBAAEEEEAAAQRiIHDDHccC9pqbI/LIJB6GFgNudokAAggggAACMRZ4e/9WuXzzewFHcUJZjxh3nd0jgIBDBAi/HTIQNAMBBBBAAAEEEAgafmeLPDKZ8JuzAwEEEEAAAQQST+DFovVyXf7HhN+JN3S0GAHXCBB+u2Yo6QgCCCCAAAIIJLrAtCcr5PvN/mVP+vT0yK03pCV612g/AggggAACCCShgCp70nHNKwE9f6vzRUlT9zsJh50uI+AoAcJvRw0HjUEAAQQQQACBZBbYvtMrc16plB8KqgPwEzt7ZOTv0qRZTjKr0HcEEEAAAQQQSGSBx/euFvXgxwM/1f2m5EkijyZtRyDxBAi/E2/MaDECCCCAAAIIIIAAAggggAACCCCAAAIIIIBAGAHCb04RBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAdcJEH67bkjpEAIIIIAAAggggAACCCCAAAIIIIAAAggggADhN+cAAggggAACCCCAAAIIIIAAAggggAACCCCAgOsECL9dN6R0CAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQIDwm3MAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwHUChN+uG1I6hAACCCCAAAIIIIAAAggggAACCCCAAAIIIED4zTmAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4DoBwm/XDSkdQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEECD85hxAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcJ0A4bfrhpQOIYAAAggggAACCCCAAAIIIIAAAggggAACCBB+cw4ggAACCCCAAAIIIIAAAggggAACCCCAAAIIuE6A8Nt1Q0qHEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBAi/OQcQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEXCdA+O26IaVDCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAoTfnAMIIIAAAggggAACCCCAAAIIIIAAAjET+ORQgSwuKZA+mbkyNKtjzI7DjhFAAAGzAOE35wQCCCCAAAIIIIAAAggggAACCCCAQEwEzvt+gRZ864sKwD/uNlSyUtNjcjx2igACCBgFCL85HxBAAAEEEEAAAQQQQAABBBCIssCGjV7ZsNlra6+52SJnn5FiaxtWRsDJAir0VuG3eZnR9my5s3kvJzedtiGAgEsECL9dMpB0AwEEEEAAAQQQQAABBBBAwDkCC96tlIXvVdlq0ImdPTL+9jRb27AyAk4WeHzvarlrxxcBTZzU6jSZnNffyU2nbQgg4BIBwm+XDCTdQAABBBBAAAEEEEAAAQQQcI6Amvm9flNg+K0H4kMuCpzh3SzHw8xv5wwhLYmCQKiZ34TfUcBlFwggYEmA8NsSEyshgAACCCCAAAIIIIAAAgggUHuBG+44pu3kuSfq1X5n7AGBBBDos3a+rCor8rW0aWp9ye/5O2p+J8DY0UQE3CBA+O2GUaQPCCCAAAIIIIAAAggggAACCSFA+J0Qw0Qjoyiwv/KovL1/q/bQy26Z2TKm2ckE31H0ZVcIIFCzAOE3ZwgCCCCAAAIIIIAAAggggAACcRIg/I4TNIdxjICq+a1qf+vLZVkd5a3OFzmmfTQEAQTcLUD47e7xpXcIIIAAAggggAACCNQosGGT17ZQ29YeaZBpezM2QAABESH85jRIJoGVZYXSd+0bAV1+ocN5cm1u92SioK8IIFBHAkkRfj82a748/9oiH/GD40fKsMHn+P6/70CJjJkwQ9as26J97cXHJ0j/Psd/Cb+56FO5f9oc7XuXDBogD4wbKZkZ9etoyDgsAggggAACCCCAAALRE9CDODt7HHdrmnTr6rGzCesigMBPAoTfnAqRCixbuV6uvXOqb3NzPlF2pFwmTZ8j73y4RFvHnH0Yt+95UieZOfUuyW7aONLmWNruxaL1cl3+xwHr8sBLS3yshAACURBwffitfvnPnPu2XDfiYu2X+uZtBTJ6/KMyZeIoLeDWXxwG9DtZC8TV9++bMlsemjhKOrfPE/Xi8Ois+b4XBRWkq+Xu0cOjwM8uEEAAAQQQQAABBBCoW4FpT1YENKCo2CtF+0Rys0VycwJD7hG/TpV2rQm/63bkOHqiChB+J+rI1X271cS8tnnN/bKMls1zfPmEMa/QJ/mNHT1cW9+cdah9LVm+NiaT+1Tgva38kAaWX14iLxauD8Ab2ChPBjZprX29ff1GzAKv+9OLFiDgWgHXh9/mkQsWdk9/ep5MuXeUFo6bv69ePDq0bembKW4Ow117ZtAxBBBAAAEEEEAAgaQVWPBupSx8r0qGXJQiQy9OTVoHOo5ALAQIv2Ohmpz7NAbYR44elYkPz5ZxN4/QJvKpxRiGq3Xzd+z2BeXmMDyaggM3LJBPDhXUvEv1+elPVbfObZQni7sNjWYT2BcCCCDgE0i68Nv86WewMFt/gRhzzWXaLUP6rHClZn6BOFZRxemEQEwF5r9dJTsL7NXiHNA/Rc7s7+zZWO/8116fFPKA/h5tBhoLAggg4C+gfp84+3ceI4ZAogksfL9KFv3XK4N/6ZEhF6YkWvNpLwKOFhgztlJr38xH3fPBUlqqRzweXovjfeIZw+1gYbYxHFd3xKtFv4vdnI1UVnnFa/8tWtAuv7Rvg+QfOej73v6qcpm/b5PsOVYmuWnpckmTDtpsb33pkNFE/pDdLWp86nxkQQABBHSBpAu/zWVLVPj9xsLFfrf6mMPvK4YM9NUAN7+g7D9UztmEQEwFnp4tsiXf3ov3L873yoWDYtqsWu/8j/fZ65M64JjrRTp3itIVWa17wA4QQMApAoaJQ05pEu1AIOEF3v9Q5IOPPJII1xQJj00Hkk5Avw7+60Puua5t3KCepKbYv75PusGPYofNE/lUVmG8q10dyhx+G+9qN4ffB0uPSVVV7M7JqYXfyCNFK+Se3L4yodmpUZQI3FVWI57RFlNgdo5AggkkVfitQu3de4v9gu7azvxOsPGmuQkosH2nV8pK/Ruuvvb6W5XSJs8jVw0LnDGSm+uRZjnO7qy6ndq8FBWLfLm0SpvdfdYZgbPMzj4j1fH9crY6rUMAAQQQQMCaAGVPrDmxFgKRCFD2JBI1tjEKqBxj4pTZMmvaWF+Jk9rO/I618OSCZfLArq+FB13GWpr9I4CAWSBpwu9gwbfCMH86Ss1vfkgSQWDDRq9Mf6pCTuzskfG3pyVCky210a39stR5VkIAAQQQQMBBAoTfDhoMmuI6AcJv1w1pXDsULPhWDVAzuZ1S8zsYCOF3XE8TDoYAAgaBpAi/zaVOjGdAsAdg3jdltjw0cZT2Cap5ZnhN++LMQiBeAm4Nid3ar3idFxwHAQQQQACBaAkQfkdLkv0gEChA+M1ZEalAsDvXjfsy5hXmsibmmeHGkiiZGbEvE0L4Hemosx0CCNRWwPXht/4Lf826LX5Wlwwa4Ct/Yl7nxccn+Gp8q43Ui8L90+Zo2xu3qy0+2yMQqYBbQ2K39ivScWY7BBBAAAEE6kqA8Luu5DluMggQfifDKMemjyrcfv61RX47b9Ui11f+RJ/c986HS7R1Hhw/UoYNPse3vgrPr71zqvb/nid1kplT75Lspo1j01jTXgm/48LMQRBAIIiA68NvRh0BNwq4NSR2a7/ceA7SJwQQQAABdwsQfrt7fOld3QoQftetP0evGwHC77px56gIICBC+M1ZgEACCrg1JHZrvxLwFKPJCCCAAAJJLkD4neQnAN2PqQDhd0x52blDBQi/HTowNAuBJBAg/E6CQaaL7hNwa0js1n657wykRwgggAACusD2nV4pK6v+X7cuHkfCfPFVlXy5tMpW2wqLvVJULJKbI9Isx16/2rb2yIhhqbaOx8oIJJMA4XcyjTZ91QUIvzkXEECgrgQIv+tKnuMiUAsBt4bEbu1XLYaaTRFAIMEFSstEduz0xrUXKnhskBnXQybtwfTZ0TqACoonja/nOH9zO2M9YCd29sj429NifRj2j0DCChB+J+zQ0fBaCBB+1wKPTRFAoFYChN+14mNjBOpGwK0hsVv7VTdnCUcNJ6Bma77+ZmW41QK+P+42Ah3baEm8gf57LZ4E425Nk25d7c3UjWf73HKswmKRCQ8cC+jOkItSZOjFzpr1rIffbVp7pF3r2J0bBw6KfLe+Sgi/3XKW049YCRB+x0qW/TpZgPDbyaND2xBwtwDht7vHl965VMCtIbFb++XS0zDhuxVpKPncE/USvu90IH4C+nmWlibSpLG14x6V/bLXs1pbubm3l6RLlqUND5aIVFSIEH5b4qr1SqF+hzgx+NXD766dRbp3Tal130PtQJVJUeVVnGgQs06zYwQiECD8jgCNTRJegPA74YeQDiCQsAKE3wk7dDQ8mQXcGhK7tV/JfK46ue9aOYofAstRvPZmpfxQ4JUrL08NOkOSGbVOHlXntU3/vZaTLXL2GeFDx6/2LZAnt1wvhyv3a51pmJolfznpQ+nYoHfYzqm6zsX7CL/DQkVpBXX3yJ+nVQTs7YJzUxxX75rwO0qDzm4QiJIA4XeUINlNQgkQfifUcNFYBFwlQPjtquGkM8ki4NaQ2K39Spbz0i39nPZkhXy/2cvsWbcMaB33w274feOqLrL3aL5fq0/P+pXce+KbYXtC+B2WKOor6L8vjDv+0/i0mJYWiaQThN+RqLENArETIPyOnS17dq4A4bdzx4aWIeB2AcJvt48w/XOlgFtDYrf2y5UnoYs7Rfjt4sGtg67ZDb8vWxpYU17N/n61X2HY1hN+hyWK+grqDhLlXlpWfRdJ314pjgu+VbsIv6M+9OwQgVoJEH7Xio+NE1SA8DtBB45mI+ACAcJvFwwiXUg+AbeGxG7tV/KdoYndY8LvxB4/p7XeTvj93t5Z8kz+LQFdSPPUlz+f9F85udHPauwe4bfTRt857SH8ds5Y0BIElADhN+dBMgoQfifjqNNnBJwhQPjtjHGgFQjYEnBrSOzWftkaXFaucwHC7zofAlc1wEr4vfHwMvn71hslv3RNjX3/xQnXy3XtpkmD1KZB1yP8dtWpE9XOEH5HlZOdIVBrAcLvWhOygwQUIPxOwEGjyQi4RIDw2yUDSTeSS8CtIbFb+5VcZ2fi95bwO/HH0Ek90H+vpaWJNG3i37IyT5F84r1XVssL2jeypJNcJM/IQdku2+UT7Wvt5Fw5JLvkS3lIKuSINJKWcoHnCTnRe3lANw8cFKmo4IGXThp/p7SF8NspI0E73CigB9nx6tu4W9OEh2/HS5vjRFOA8DuamuwLAQTsCBB+29FiXQRiIKC/IY3BrkPu8rkn6sXzcJaPRfhtmYoVYyhA+B1D3CTctf57zdz19Rmz5evM++Wop1jSvA2kz5F7pXfZ+JBCJSlb5dOG18vuep9r67Qtv1h+VjpLGlS1DNiGYCQJT7QwXSb85pxAIHYChN+xs2XP7hIg/HbXeNIbBBJJgPA7kUaLtrpSgPD7+LASfrvyFK/TTm3YVP0QOjvLvDcrZcdOr4wYliptW3vsbCq5OR5plmNrk6ivXFgsUlRsr9+ZmeLIh/RFHacOdqj/XtOM26TIzooV8s/DN2p/q6Vn/WEytOHj0iQlz1LrvjrynLxTeo8c8R6QDE8TGdxgqgzIuFHbdvsPVVJWxsxvS5BJthLhd5INON2Nq4Aefg+5KCWmx6W0VUx52XkcBAi/44DMIRBAIKgA4TcnBgJ1LBCvN6Sqmwvfq9J6y8zvOh50Dh83gYmrzjUAACAASURBVHjPxlJvfIdenBq3/gU7UCQfqJ3Y2SPjb0+r03a79eB6+F3u2S9LG9wrG9Kf07rapLKTnF36jOQdG2i762UpP8qXDW6R/Ppva9s2rzhDzj38gjSp7KL9n5nftkldv0G8rjWKikW+XFol/E5x/SlFBw0ChN+cDghYEyD8tubEWgggEH0Bwu/om7JHBGwJxOsNqWoU4betoWFlFwgkY/itZoapP+ZFzWYvOyLSJs8jDTL9v9uujUeb6c4SfYHtO71y92vPyrul90mpt0jqS0MZlHmvDMwIXeLEaivWVyySfx66SUq8u7RNfpk5WQZl3Ccjfp3KTH6riEmyXryuNQi/k+SEopt+AnbD748LX5Il+xbI4cr9MiD7Mjmv2e+lYWpWWFVmfoclYgWHCxB+O3yAaB4CLhYg/Hbx4NK1xBCI5A2puljOL12tdfCUxudY7mg8w+9ISi+ocE6VnFClJiIJ4rp1sVeiwjIcKyasgP6GNCc7tl1QpSZUsOyEmd+hekot89ieA8H2/s2ub2TUwlGi/lbLb07+jTxx0ROS19haiRMrLS4pL5E//veP8uzyZ7XVT2p2krx0+UtyWt5pVjZnnSQRiORaIxIawu9I1Ngm0QXshN+v/fCAvF7woF+Xz2v2B7mj05ywDITfYYlYweEChN8OHyCah4CLBQi/XTy4dC0xBPQ3pLk5Is1yw9cK/ProS7Lw8N1S5t2vdTA7pYPc1PRDyU5pH7bDGzbGr+xJJKUXwnYgzApOKOcSSY1pFfabZ+LW1oLtqwXsvCFV66vZWB8WztW2VbOwrm/3mDRPD/+ztX5jlWzcHL/w+98/lTCyM85ffFUpKpg66/QUaZZr74Oibp090q2rvW3stM1t6+4/sl8mfjhRnvn6Ga1rJ+aeKE9f8rQM6jgoZl39bPtncs1b18jW/Vu1Y9x2+m0y5YIp0rBew5gdkx0njoDd8Hvh7idl3s4/azNT1TKk5e3a78NwC+F3OCG+70YBO9ca9607X74r+TSA4e3TK8LSEH6HJWIFhwsQfjt8gGgeAi4WIPx28eDStcQQsBsSv5zdXFTtWOPSsfzXcs7h5yTNay3kiEdIrPcrM0NEPegtlkvxvuq9x6Nf4foRSZkN6vOGU438+3bekH5b8on8v3X+4WTz9A4yo8fXYW9Hjnf4Hcl5Frli/EL92rTRKds+v+J5ueeDe6SorEgy0zLlT+f+SSb8bELcmnffR/fJw589rB2vdePW8uyQZ2Vw18FxOz4HcqaAnfB779FtcuOqzgEdmdj1X3JG9tAaO0j47czxp1WxFbB6rbHiwPvy8Pe/lmPeIwENIvyO7Rixd2cIEH47YxxoBQLJKED4nYyjTp8dJaC/IVVlGdTsb3056j0oe6vWS1HlFimq2iQ/Vm6U3ZXfyu7KNTW2P9OTJemexpLuaSKZnmxJl4bavzM8TaT0QGNJlyz5/eVZ0iS9ifYnOyNbGtVv5Pu/+pr6f20XO2+0jcfaWrpKOjbobevw8SznEq5hqrSEeSkq9krRPpFcbYwDZ89SnzecauTft/qGVB3huW13yX/2/C3gYMPyxsvPcoZLpwZ9QjakrsLvroH5VORYQbZUQZb6cMnJ5Vyi2uFa7Gzl7pVy039ukq92fqXtRZU4efyix7UAOt7Lt3u/lesWXCdfF3ytHfqKk6+QpwY/Jc0bNo93U/yOp+qfq3NK3enCHS/xHYpXF/4ob3+8R5q1KZSsloVyoKJQDhzbKwfK98rBykI5eKxQ9h/bIwcrCmXfsd1BG3dR89FyU4e/19hwwu/4jitHc4ZAuGuNz4rnyb8KHpH80uDX8CfUby+z+2wO2xlmfoclYgWHCxB+O3yAaB4CLhYg/Hbx4NI15wuoWq2z/v2dLFq6SdKabZLyBptk99HNUnBko5RUFNVpB7Iyag7IVUjeNKOpX4jesH5DaZpe/bXPPmkoH3zQQFQ4171r+HIuHxW+JM9vu9t3i7WaXaZmmVlZnBR+B2uv/kEAAaKV0YzuOuHekKqjlVYelI+LXpZXtv8/KasqqbEB6oOZjg36SKeGfXx/Z6Y0lroKv9U5Fcsl3v2KZV9itW9V4uSe/7vHV3NblTiZeclMOb/j+bE6pOX9Pva/x0TNBD9ScUT7oHPGRTPkmt7XWN4+mit+sLhKXn+r0rdLdUfQuNvSeDBnhMjqzoK9h/dKYWmh9vePh3+s/rv0R+3f6ut7Du/R/q3+jtbSqWFfubD5jfLznOHSILVpwG4Jv6MlzX4SSUC/1lBlxfSl3Fsm/yt5Rd4pnipFFdt8X+/R4EL54ehq2V9Z/aDizJSmcn2LOdK3Yc13Vah1v11XJQdLRLhjMJHODne39YFd1R+yW10Wl+yUxSUFMrBxngy0OTlgUiueZWLVmfUQQCBQgPCbswKBGAscKj8k6wrXyebizbKxeKNsLNooW/ZtkQ1FG7Q3pzUtKmhrkd5RWmV0kbyMEyUvo6u8tGOCbDhUPbNQX4y3Ih+qKJbDVQelrPKgFuqVVhzQ/i6rLJGv1x7QSqb8/NzDcvDoQd+fA0cOHP/30QNaUBKtRc1Eb1SviWSmNpZGqdmSkdpQe8Os/t8gtYn27ypvhbxe8JeAQ6r6oqrOaLiF8DucUPJ+v6bwe1vZt/Kf3U/KJ0X/kPKq0Od8Tr3WUnxsZ0hE9TN6QlVvST/YRy7uc6qMHtxX2jRpE1N0K6F+NBpA+F2z4surX5a7379b+12uSpxMGjhJ7jn7nmjQR20f2w9slxv+fYN8sOUDbZ/ndThPnvvVc9Ipu1PUjmFlR8FK9fTp6ZFbb0izsrlj11EffsxdNVfUzP8OWR1kaLeh0qdl6LtEQnXEGFqrENsYZOvf07+m/o50qSeZ0rR+M2mSlitN6p0gTdJM/66XK1XeKvn7lhulrOqQ32HqedLlmPeo72s/z71SLjhhpPRucrxcFOF3pCPDdoksYPz9djSlSNamz5S1GU/LEU/1dX6qN1O6Hb1Weh0dJw0rq68PilJXSXnKAWl1zPqD63Ujwu9EPlvc1XbP8plx65C335i4HYsDIYCA+wQIv903pvSoDgQOHzss635cJ5v3bdbC7U3Fm7Q/3xd9r83Cqmlpl3myeA50lvZNukiPlt0kL7OrtErvLLn1g4dn6uFTH/34kqw5uFgapmXJgOyhYWtw6se3ExKrWWUqIFfBuJqhrgfkB44eD8r176u/9XX07+8vO1hjoGhlmLLrtZKBza6Wro1O02bZtkrvEnQzO/2yctxor8PM72iLWt9fsJD48+L5smj307L20Oe+HXVvdKZc2vI2+bRwnizd/2/t6+qDmRvaz5Dzm/1BmxG+5fAKUWV5th5eKSo433R4eciGNGvQTPq27KuFYPqfk0842XrDw6xJ+B01yoh2tGrPKq3EyZIflmjb12WJE6sdeHXNq3LHu3dotcjVMvWCqXEL6lW5kz9PCywJdWJnj4y/PbHD7/PmnieL8xf7DcOK0Sskr3Ge9vqvz8zWZ2cHm5kd7jqhpjFu0bCFqN83JzQ8QXIzc33/Vl9Tf1Spm5zMHFmxNEeWfNRGewaHlQcs75BPZZk8IQdlm1Yu7TS5XdrL+bJB/ikr5TnZJUt9zWoi7aSn/EF6ynWSeaytNjPVDWNr9WeL9RBQr8mHUnbIhqbT5FvPXKmQ6g/UG3hPkFM9t0hf702SIYbahhGSqZ+tigpmfkfIx2YxENDD73MbtYrB3qt3+cmh6rskCL9jRsyOEUgKAcLvpBjm8J38cmmVrN/olbIyr/TtlSLG2/bCb50ca5QeK/XN4FbBtprFrQfc4WZhndTsJOmc01nU32q2nbotvktOF2nXtJ1EWhs7EvV4hsR6v/I6FUqb9oerZ6Frf47PRFf/P1J5SLaXfStL9i0I26WM1EbSuUFf6dzgVOnYUJWe6CvtM3tIPPsVtpFBViD8jkQtOtvoIfHPBu2Rd/c8Ix/8+LxfPdsLml0nl7a6TTpk9vI7oPqQqWFqVthGqDB8W9kaWbFzjWwsWSEHMr7xle4xb5yRliG9W/SWU1udKr1a9NL+7tmipzZj2O5C+G1XLDrrqw/37v/ofvnb0ura8N1yu8nTlzztiBInVnqogu8737tTXln9ira6+mBGzQLv16qflc1rtU6wmd+JHpCq2d59Z/WtlYtxY1VuTAXWKtBWgbX270YttJI1esCth9rqb/V1q4vdh2uH2++B1A2yof4Lsin9FSlLOT4TPe/YedKtfKRc1GZEwn+wEc6A7yOgBL778TsZOuMh2Zz+mg9E3RE2rNU4rURQNBdqfkdTk31FQ0APv2NZkkQvrUL4HY0RYx8IJK8A4Xfyjr2v58HeEF1wboqMGJaa8DrqjemCDQtEvaFUtyKrW5JrWsoqynwzuLWA+6dZ3GoGd7iamSoEUQG3mt2pB9ydszuHPabbw+82eR5p1ybwIY/GcSit2i+Tt/fzq4movn9Z7mQpryqVbUdXSP7R5XK4sjhg+Op5MiT7WG/Jrewrd/+mnxbmnJbnrJpwhN9196vk0rEfydqMmbK1/j99jWiZ3kkGt7hZBp1wraWA20rrjeVBTvvZblEPHFQPG1xesFx7Y6x+h4Ra1O8MFYr3bdVXmy2u/lYzOGtaCL+tjEp011GlLcZ/MF4rR6E+sJg8cLKMP3t8dA8Sp739d/N/ZdTCUaJKoqhl3Fnj5IHzHojogxirTZ7zaqWoD9qNS6Leuq+uFRasX6B9CPLlji+DEjSo10ALrM1/1Exs4wxt9W81a1sF3rFc9NehNq09Ua+zvuLwAvns4BxZdfgdXxfUg7avP+13MrLvyLh8uBJLO/aNQDCBT7d9KtO+mCbvbDx+3qtJGb/Ju0fOyv5NTNAIv2PCyk5rIUD4XQs8NkUAgbgKEH7HlduZB3tgWoXs2OkNaNxzT9RzZoMtturxJY/LXe/f5bf2W1e+JRd2uVALovRg2ziDe/eh3TXuvWtOV+ma21W6N+uuBdwq8FZ/16Z2qtvDb4vDJYdStsmSBnfLUc8BbZMeR2+X9uW/8ttc3VJanLpKCtOWS2HaCilKXSGlKdW3wpkXNatWBeHqb/3fkcywtdr+mtYj/I6GovV9qGDqxZUvysxlM2XN3jW+DU/P+pVc3GK09G16ofWdWVwzXG1sVUd/xe4VsnrPaln741pZtnOZ9m9VMinY0rpxa9/527tlby0UN/6eIfy2ODBRWE2N1+j/jJbPt1eXyRnRY4Q8+stHtbIWibyoc0/NYp+xZIbWjY5ZHeXZIc/KBZ0uiFm3VHBTWFx9vaHuMmvXuuYPRmPWkAh3vGjjInnt29fkzXVvirobLNQy6dxJ2ocjTlrica2x/9hueWf7K/L+3mflYOoWX/fVnS7X9blOru1zrTYZgQWBRBaY/918+euXf5VlBct83WhbfrH0OHqXjB4U2wcdE34n8pnjzrYTfrtzXOkVAm4UIPx246ja7NNt9xyTsiDPekv08DvrkSytTrVxqZ9aX8ory2sUUuVI1B9VoqRjdkfRA281izsWSzzekOrtjmd5EL1fmRmi1RiN1VLq+VE2HfpGilJXSrtTV8g3u76R/P35QQ+nxlQF4f3y+mlhovq7cf3Gtpqm3ngU7bO1iWzYWCUbNnmlWxePdOuaYmvj3GyRs8+wt42tA7hoZfUg2aeWPiXPffOcVoNeLfWrmmq34N8+4FY5oX77mPU2XPgd6sDqwzd1h8qaPWu0c1fVkv7h4A9BV2+S3sQXiC//8BTJregj1/4ieiUXgh000n7FDDqOO1bPMvjTx3+SJ756QjuqKlc185KZCVPixCrV0p1L5Zq3r5H1heu1TVRA+diFj9kqqWH1WIm4nvrQ4/XvXpdXV78q+44c/+WvPgS/uufVWl3vJ7960te19k3by8qbVjou5I3XtYb+wMv6Hf4nx7q8LK+tec3vQz714dE1va+Ri7pcFPPTQdWaf/3NStvHGXdbYteht91hNrAkMGv5LHnk80dk6/6tvvX/0PsP2rMTHvtLV+1rQy6K7fUa4beloWKlOAoQfscRm0MhgECtBAi/a8Xnjo2feq5CVq7xn/mtArdHJifOzG8VdKkSAyo80ksNqFAp1KJmUOqhtgq69X+rv+O9+EJiiw+hqk371JtStcTjg414vdFW/TGH+qourzoPVuyqDsPVnw1FG4LSqXNBC8Rb9dPKTaiSKTWVnJj2ZIV8vznwTonajEtN2yZ6TdxYuRj3+/b6t2Xm1zNFlXLQlx7Ne8jtZ9wuX740QlK9GTF/QxrNkFidv6pcigrC1Tms/lazxEMtHRv01h4Iq263Vv/u1LCPZKb4f6jznz1/kyX73tZ20bx+B7m+/aOWSr5Es1/xOBeidYyXV78sd79/t/awQnXHyJ/P+7P88aw/Rmv3jtzPg58+qIX9alG/Ax+/6HH5Xa/fObKtsWxUYbHI0vy1smDLy/Lvra/I7tLjH0a1bNBGzmr6Wzmn2QjtZ01fVO3/wxX7tf/2aHJuQPPatvbU+JBJ9cForJd4vSbr4bf+2qVmyM/7dp7MWTFHvtjxha+b6s4JNRt8VL9Roj4wiMWyYaNXpj8V+KDVcMeKxzVSuDbwfWcIHCo/pH2oru4m1csfqteE0aeNlrFnjpU2TaofTh+vu7EIv51xXtCK4wKE35wNCCCQKAKE34kyUjFs5/dbvPLY3yu0p4erJS1V5NrfpsmA02L/ZiySbqmgW5UMUIGmmrGmSgmo+txWlq4N+8tfeyzxrdqts5qJW7f9jPZDqKw4xOONXbzeaKv+WpnRrm7xV0Hi8l3LfaG4sSSG0U29mdFnhqu/VTiuv8HRw29VNzUzI3YheNkRj/yw0yuE38HP6OKyYnl2+bOiZmIZZ/r/tudv5eb+N8vZbc+O6xvSeITE6neeCsFVPfF/fPa1FKeslqMpwW9DOKFeR2mb3kc6pJ8qJZWF8sH+6tnL+tIt81wZ3/rDsL8utv/glR8KvNqHB0MvTvznQITrsCpxMuadMaJquapFzVJVs6BbNWoVblNXfF99aHz9guvls+2faf25uMvFWikU/fefKzoZohM7Du6Ql1e9LLOXzJP80uPlktKrcqVT+RXSufxKaVFR/Xsl2oubXpPN4bfRSp1fz3/zvFaWyvgclfM6nKfdcXDFKVdEte58aZnIjh8CX6e/WFql1Z9XD3c/+/TAmbp1fV0Y7fMrUfYXyfVwrK6R1B0d07+YLs98/YzvTjL1ENo7zrhDbj39Vu2BtMaF8DtRzjLaGW0Bwu9oi7I/BBCIlQDhd6xkE2i/896slP/7RD2ESg+BvdK3p0duuaHub/tUt53rM7q/2vmVFlpu3rc5qG7L1FOkcempklt5qpxQ2U/2pH4pSxtM8Fv3gkP/9Ksj7YRAR80yKyqyF6Jqt/K+VSnqYZJXRfBg0ni8sXNa+B3qR1J9gKKHiqp+ozrfgi3qoWVqdnj5zr7iKewtv+zRT7o37xSzn/SaAoSYHTQBdqxKhKgyFCo80Rf1oDgVeKuZWOaHxsXrDWk8wu9gb7RVHfx9qd9JUZoq/bNKq4lvrLVb05BefvBryanoZWnUnfC70lJDI1xJze6btHiSPPa/x7Q9qBInz1z6jKhQLhkX9cHS2P+OFeWiHtz4l/P/IncN8H+Ghhtc1Ido/1jzD21msnFWcpq3oXSpvFx6pF0pHb3Rf0aAblf802dXyRJ+G8+Zhd8v1H6Pq/rp+tKofiPtbgMVhJ/R+oyYnWI8hyNmtLXasRPCb1U+bcrnU7TyafrSrmk7mfCzCTLmtDEh+xevaw1mftfqFGPjGAgQfscAlV0igEBMBJIq/H5z0aeSv2O33D16uB/mvgMlMmbCDFmzrvrhPC8+PkH69+nuW0dtd/+0Odr/Lxk0QB4YN1IyM+rHZEDqYqdOeeClHnSrmbnqln/1d6gZ3erhSSqIVDWb1d/qwYbLl9f3PUhLd1S3Ir+07C1J92bLyDMvk+bp/rfWdu+SUuczvyMZc/1W3ljNeImkTeZtEiX8DtZXNbtWL5ei12FWIZB5aZiaJZ1+KjfRueGpWtmJNhnHf3eo9b8t+UQW7n5SDldW3xI/ovWfpEfjwNvizfsm/PYXeWX1K/L3ZX+XJT8cv3NjYIeBckv/W+Q3J/+mzt+Q1lX4nZMd2PVjUip7ZZX86Fmt/b3O+7qUe6proJuXDMmRlt6+0sJzqrT09tP+burt4FutrEy0Z0K4OfxW4ecf//tH2XVol6jwTT2s0O0lTqz8jlceo/49St7Z+I62uioJ9dLlL2nPw0jkRd0F9Na6t7TQ+91N7/p15fLul0vXihGyd9nF0r1ThnS3+YwGuy5W7lqyu89Q68frNdnua5cqLfTSqpe0sijf/fidr/mqdNXIviPl971+L+rD52guhN/R1Iz9vvQ77sbdmhaza3Z1F+lfPv2L34cx6nfe+LPHyxUnXxG2k4TfYYlYIQYCm7cVyPSn58mUe0dJdtPj5e7KjpTLpOlz5J0Pq6+ZHxw/UoYNPsfXgmUr18u1d07V/t/zpE4yc+pdftvbaSrhtx0t1kUAgboUSIrw2/gL/vqrBvuF3/qLw4B+J2svCupF5L4ps+WhiaOkc/s8Uds+Omu+70XhsVnztfEyB+h1OYi1PfY9k48FfYBfLGciqZq2eukSNdtWBYxqtkWwpWfznlrIrUpPqAtRVYYiIy3Dcrf1C9JY9sdyY6K0YiKF32p2ers2sS0to25fVkssx1iVQ1Bvjp5d9I1sLFkuRWmrpNxTHWgbl1RvpuRW9pFmFX2ladWJ8nXm/XLMFDxedmCZ5FYerxdb02nh5A84onQ6h9xNQUmBFnjPXj5be6icWtQsVFUnVs30PvmEk8M2Qf/5V7e3x3KJd3kQO2+0n9gyUj4ufMn/PJU0SU9tKKWV/g8FViupD3W6NOwn6gOdjIN9pWJnP/ntLzu7ruyJeg7ATf+5SRbnL9Zskq3EidWfBzUzVznpP4P3/vxeeej8h6xu7pj1FmxYoM3wVs8IOFJx/CnfF3S6QK7qcZVWckM9ADleIbGCIfz2Pz3Uh5svrHxB+2DC+IGz+oBTPSTz0hMvjcr5RPgdFca47SSW4bf6vfDol4/6Sj2pTl3S9RK5+8y7bT3c2M5rcm3gmPldGz33bGucuBcsvDbmFfq6Y0cP1yb3mbMONclvyfK1EU/uI/x2z3lFTxBwu0BShN/6IAab+W3+xNQchqsXjw5tW/o+LTWH4W44QYI98FIFlpPviU7ZExV066UltBrdu1b4PSndaKjP6FYPHuyf118GtBng+7YqD6KHnHbc//1upbb6ryKoV6sCs2b+Zf3sHDpm6yZS+B0zhCA7jmX4rR/O+MDLkpStWghemLpcCrWyEyvlSEp1SFvT0uXo1XJ62SOSWdU83KpJWfP7w60fytPLnvabgaUeTKseYKluh1cBldVFf0Nqdf3arhevGdJ23miruw4e/n6YfFdSXce6QWpTuaPTHDkje6jsOrJZtpatkE2Hlsumw8tly+EVcqgysI54g9Qmcma7/toHkNoDYvP6SefszrXlqpPtVag2efFkefR/j2rHVyVOZl06S9SdBCzBBdTruJodr5cCUA+Hfu5Xz8k57Y/PJHOinfpgQwWpb6x9Q/YfOf5hpSqpcVXPq7QPPMylkiIpvVDbvsfjtSteob7dmd/B7MoqyuSfa/+pnW96/X21nnpIppoJfmO/G0U9rDrSJRHC73g/XDter12RjFkswu+5q+bKX7/8q/YMDX1RJXfGnTVO1HsRu4ud12S7+zauT/hdGz33bRts5rcKuyc+PFvG3TxCm8inFmMYbs5DzGG4XSXCb7tirI8AAnUlkPThd7AwW3+BGHPNZdotQ/qscDVI5heIYxXVM04TeSnaJzLz+UrZuau6FzlZIjddnyptq18vbS37juyTFbu+kW92Hy9dkn9ga9B99GreS07VSpecJqe27KeF3TUtGzeJPDazOsiO13L3mFTp2iVeR7N+HN2iayeRu29x5kPo/rfMK//7aUa21Z6ph1Op8zAzQ6RNBOdfPCwe+3ulbNwi2gNhc4J8MFJ8rEC2lK6QrYdXyFf7F8jW0pUhu980rbm0y+wh7Rv0kPaZPaWd+jujh9RLyZDiYpElX3vFyWNsdVytrKceZPvqmpe10Ht90TrfJkO6/kpu6X+rnN9xkJXdBKyjxsvu8kNBdamP1q1EGmTa2/rM01PkzP6xvdNBtWjM2Op+Df6l9WOpEPxw5QFpXt+//JO5h3vL82Xz4W9ky+FvZE3hctlxbKUc8RQGQGRnZEsf7YGw1eWnVCjeKauuAnH13ITwFvPXvi7jPlAlTgq0D1H+38/vl7sGjLU3yEm89uc7Ppfr/32dbN1ffafW9X1ukKkXTJOm6U0DVOa/XSU7C+w9z2JA/9r//Kzcs1Je+/YfMv+712VnyQ++dp1ywiky4pSr5Kqev5V2TUL/DCx8v0oW/ddeu2t7Ssx8NPav43q/unQSObFL+J+VSPsU7dcuda6p2eAvrZorBYd2+pr1s7Y/k2v7jJTfnHSFdkeQnUW3UL8/h1wY27uC7LTLuK5+rWF1e/XMh+31F0pJSr5291nXo7+T+t4sq5trryVOt6jtNbn6UGXOiudkxpIZsv3gNs2mYb2G2nn0xzP/KK0bt7HsZV5Rf00+I8av/2vXe6WkRKS2FhF3NMyGaake8Xhi9/slVu1O1P0GC7+DhdnG2d0z576tdVe/i908M7yyyiteGy+B9VY+o+3v3EYRvHGzCP/JoQJtzWN9brK4RfVq6nxkQQABBHQBwu+V6+WNhYv9bvUxh99XDBnoqwFufkHZf6jcVWeTCnxU8Ghl2X90n6zYvVxW7Vkhy3ctk9V7V/ouJs3b9zihl/Ru0Vf6tDhVC7pPbVlz0B3s+Ju3eGTm8yIZ6V5p1cpKCyNfZ9cukSNHPTLmepHOnWxcAUR+SFtb6hadJ4McwgAAIABJREFUOnjl5lG2NnX0yonQr6dni2zJ98hpfUWys2s+N7aVrZJ7NpweYH5C/fayv2KPHKs6fuu9caWW6Z2lRcrJklLcW7rnnCJ3X9VDumSfWGdjp4+L3Qb89aHwPzub9n0vz3zzlPzj25elrKJUO0RWerZc0+t6uaHvTdKmcVu7h631+voYO/XnX3Xwj/dVX9D/4vzwxrUB2bRFZGu+R/r8LF+yuq6Ulbu/0X7vr967QgrLAgPxxvWbSJ8WfaVvy9O0v3u3iE8grjRqklDn2dj/u00+3V5d4mRY9+Hy8MDp0qJhy9rwJO22f/l8kjz6VXW90OYNW8gj5z0ml3Xzr72v/xzZQVLn84URfM617WC+vP7dq/LGunmixlpf1O+PK066SoZ1v0LUdYiV5f0PRT74yCMdO3hFBcWxXNRx1GLld2Vt2xGvfu3b55GvV4jE4vrkg63vyavfviQLvv+Xj6NBWkP59UnD5Xc9rpXT847fLViTl24R6flW27Gwsn1trzU6ZPaWqd2Whj2U/js+ESwifU1W71ee/vpJeX7lM1J8pFgzyc7IkZtOvVVG9R2j/bu2i/6aXNv9WN0+Ugur+490vcYN6klqCoFjpH52twsVfpvrgJvDb+Nd7ebw+2DpMamqsn5tmb3hebvNjnj9fd2ut7VtViP3PKPNVsdZGQEEggoQfptqeislOzO/3XJeqRqYn2z7RLstWN36rWorGpfismJRtblVyRK9hMm2A9WzJsyLevikmgWoly45vXVg+BeJm17qo0ljkR4nxXamzrfrquRgiUgsH64TiYG+TSKUPQnXv+l/qwhYRc383rHTK5mZIu1aB148XzksNejXwx0rmt/Xb79VJXFyLbxfUg+7fH773b4mXNriNrmh/Qzt/wVHNsqOsrWSX7Zath5eLdtK18iuo5tCNlfdHXFK81O0W3LVw8BUPfyWjWIf3unnm13Hmm7ln//dfHnm62fk4/yPfbtVJTVuPf3WgN8/do9rdf1QpZS++KpS1K37Wtmj3MDz8FcXxfb3j5X2x+sW65oe5Lnj4A7tNeHrgq+1BxSrBxXvObwnoPlqhrUa2/6t+2vPbFAlU1TZjHgspcdKZdLiSdrt7WpRJU6eHfKsnNs+/ENn49G+RD7Gmr1rZOSCkdr4q2Vot6Ey89KZ0qpR9afT23d6paz68yzfor72+luVokqrXTUscLZzbq7HcqmxvYf3inoQ7uvfva5dl+hLbmauXN3rarnylCvlrLZn2SaOV3kQ1TBqftseHm0DdU2qxl69hqwrPH6nUPdm3bVnQqjyWM0bhi4rlkhlT9Q1b7161U7lclBKpUjKpFDKfvq79P+zdxVgVlVddE/AMMAMDN0p0ki3NIikhHRJCAgSUgIiICglISHdPy0NIiAp3d3dMUP31P+tPZ7nncebeMPEuW/2/T4/dV6ds865556z9tprO3nTxcA1dIt2vQdmSepPWZ0+p0SBmSk+JbcJNvZdKGwcXbYna/6yP2M2rGdySLPI590t+vvFCFpxdZbF5z9DogzUp1QfrhsSmRf2hvZeFy4HEYyo7WLv1ahezO+H7W2zvD/yEYgK5be9rVS2J2X/ffbb+/nwvH/Hi6D09MBCHcPzdnmPICAICAI2EYj15Ld4fhN7nw7eMTjYBMEhFqS1KkZ54+kNmxMoqohuWz8WUQLuQ+59Ib8/BL3QPxsRL2YdxsNe8luh8ODtdUrhFrrdhHovvJfPeJ+iA9dO0hvPk/TC7TTde3HPJqCwnkCg6ZOUnzAxDkIc5Lg9RWEjOsr2Egjer7xp6uGpTFjcevafHQHIik5FO3HQLDqviK4p0eHPGxYOOpDfttp4+/ltJsRBhjMpfucw3f330GJ8f0QIcYzX+X/JgrDwwes7fRbTvJu96eHb25zaPrj8YOpRQixOwoOdPe8Zs3cM9d/an8klTzdPGlZxWIjE0ocGbmGPtOTUEi5cidoA6koYNyGhKCIKV1bJWsWe5r/3XiG/Pwg+DhyiPkt0FWv+ecE+Wn9zLh19t5DeBD6zND5PnNpUKF5Lyh2n5nsd8n4UyO1EADtZEvuIx/RpnaiRjcBNRFDDcxDPRfUPisr6vPLh4rK7jj8g75c+9MbZm944PaRXzrb3AOH9XdfABOThn5E8AjKTR0Am8gjIQgkDMpEn/t8/M9X5zCNaihpHZO8X3j6q9z1xOUvH4g2ny26L/psPKfLQgDIDqEHuBvZ+XZS9X2Ghw54iyjopXxylCIjnd5TCK18uCAgCDoZArCe/rQtcWtuaWHuCGwtG6DoX7C0M2e1kQbr66niY3ckSPz9lTVCIsiYoQB8lLELZEhS2fCY6CkOqQ7OrKxGUMFF5QfXt5yfK76jEGONp75U+nZPdHsz2/kZY748o+R3W91q/bk0gwE//2L1jdPzecTr98DSdvH+SCzW99H1p86tRIDJX8lystGWVeMq8lD1p9hCbsXiFP6vu7bnCSyBc99tHe99MpiPvFlq+PkmcdNSrTCdqW7AtJYufzJ6fjbT3Yq2Eoszeq3YEiufa+xthvV9X8ttWu6HQBRFuVIiDJLe+QGCmDCxE6VwKUVrXgpTOpSAlc/lPIa7mW1jYPHO5Qrvjf0N34mzltxaM24SqJxhOHk7v+2XpkE0SVn/M8DoywdqtaUebr2zm5pbOUJpm1Z71nsI/ouT38rPLadHJRYR/Gy8QWShaWSdHnUiDScjvD4MyuslvY2HIy3EX03m3mXQ3zg5LJ9wDUhKKTOd41448/bPSO6cndD3uWnrhfJ2S+H9CGd+9T46HhkBIpD7WuZCI7GB/f/mQ3xfSszu033ZzSkAJXZJRQpek5OGSnP/bwyUp3X13jk692vTeR3PGr0Av/R6Rt981ehXwX8FXW7/h6ZqMcqTMQpkSZ+Jixvh3Zq/MlDlxZsJ+IrIu9ezKFgXlIS77baedb8bQed+/LM0tnKgajajdgypkrhBZXYi07xHyO9KgjLVfZIv8BhhGvsLa1sSa6zBaorjHs98mRApextrpJx0XBEyHQKwgv0Fgt+oW5E2prjnjvrf4eKuHwsmzQQWcjK/h//FQGDByFr9WvWLxYP7gOo64vWrG+YmT0zvnp+91JfubNpQsoCAl8ytEyfwKhtrV6FDkqn4l8SIqVSxqbQd0qqZui5QMyx4EwYioxkjHuR8dbYop8jukvl19cpWJcFgQgAzHf4Mct3W5u7pb1OEgw6ESx79TJkhJRgIhsnC84DaXzrpNJm/XI5avTOtbkXK+/YYqp69Fvbu4RtZPxbrvMRP5bWtwfF770MHbBy12KUfuHiFbVlpxAxJR0oD8lNS3ACUPwLOoEHn6BxExILH2xe9pIbpS+ZUit8BkdNptPL+e2D8nlXo5kVL5fRri/IiOZ5ejTk62MFkRPHh05N0CWvOyB70K9OFufxZ/EFWM198CQVjPLiNWF3230NG3i+iU7yp6E/jfHgXK7iZ5m1DdnHW5aGlkX4r8juzvDe37okP5GV2kfkyS3wrjFy436ELc2XTRbR69cL5pgT65XxFWUL80/C2p/yf0xdODwYYHe+I3Tj701smb3jj7sPIa/49/uyXyoTSZHzGBDYU2/g0blohcaT3ScvDX+p/kCZLTnp1J6PHdpJQnU3JK6ZmUvFzShvgTILZH3qpAN9+dsLynltcAqp10oOX/Xwc8pYd+V8n73VV66HeNvH2v0EPfa3T39VV6HHCd/J1eh9qFjIkyWshwRYpn8crCf1NWR+HBICqeXXsfr6Dld0bSpZdBFky48sdtRFkf9qXmlXNHi6I9PH23fo+Q3xFBTT4DBKy5C/ytTeNqliKWSty3fss+BmxI79ZUt1oZC3hGbiRvziw0eXh38koUseepkN8yJwUBQcAsCMQK8tssgxFZ7bSX/P47YT1WwRivhP4ZqOHTkL2HrdsaHQRCbCW/I0JKRpd3Y2TNWTN9jxoP+M5HZQYCsg/gPR/R1PGj947S6Qen6czDM4T/BjFutBoxYg7rlCR++cn9RV7KnSIvfZQoN6WPm4/iOIVe/RYHbhy2s8f7b0Pt7Xedtj+ZQv88m0UvAoIIMDfnhFTaoyVVTNyZ4r3ORqfPRbxfZporUdnWqCAQbLU3NM/vyO4fCKSmA/aRt+tReuhykHxcj7E60/qKE5iQkvkXYiLqscsZG697UMHXP1KeN13DbGJ0PLvCbIRJ3xDSXuONkzfti9/DYjmQ2D8HlXk5g5L7hV3/46HrAboSdyldjruEXjv/5x+f0rckZX3XiFYPahzlmSKOTn6jpkZ896ibdL6+xDVTIvrssrdlYe2RbsXZRBfjzqUrbstC/Oqkfp9QAPmyxchr5wf2NoHfnzx+cp6bIK7hO4//hu94Evck/N8pE6a0/Df+P6zATVj9stXIu3F28p85aOj/iV39KF3xPn1c4AYhoH718VW68vgKXXtyjf8dUo0f4w8gs0yR4VCLK4I8a5KslMgtkeWtkfns2vxwJq24O5Luvrkc1G/neFQ5WRv6Ik0P8rmeji5ejj4vc7vA/vfNQn5HBDX5jG4IKPK7VbKQs0s/tM1zvM/zV4jn94ciKZ8XBGI3AkJ+O+D420t++7gcpz89KlnU39g0l3k1kzK+qxVudKKDQDDaniTyDHfTIvTGp8/0sT2xVTQsrE7ZUzQsrO+S14MjEJED6YdgGJkEAvxyYZ0CIvzsw7P83/gHf7d1pY6XldLHy0WZE3xCWeIXoHTuOSltvI/5reOvtKGt3nMtHyvuVZv8A/3p4JN1lr+ld89J1VN2pnLJmlE85wT89+hWBX4I9jp/NjIJhND6GZ3kN9ph7QcLBaa3y2H+56HrYfJxPUrPna+GOjRNntwkWB2E54qOZ1d42mHG96hnskdCory53s/GOvVqI8190IEe+QUpb0t5tOCA2CO/WxTfOTHliF+OoE6953uB9j1bQPueL6KHfkEZeLjSxc1LRT0aUnGPxnT+SHr+W3QopMMaC7MWa45uUj8yn12hjYl6JufO4Uyh7Q0RrJ12rzmdfLUhrCEmd+dElNA5KSVyTUnxnb3IwzU5xfFLSi99klHGZMmoWc0UTHYnjR9EcoPsjuwLWX83btlnRfYhbUC2YGgZgyDBFTGuSHH1b1uFjo1tSRwvMRPjsFK5eCQTeQRmokoFslBKt8yUNl7ohNm6+xNo3+NV/HUp4mai2qm705Enf9Hqu2PpiV9QgAyWLdhrVEvZkTxcg8Yiup9dEcFeyO+IoCaf0Q0BRX5HR7uE/I4OlOU3BAHHRUDIbwccW3vJb0CA9HEf16B0SRTESRgQvsJ8Cr7oIBAi0q8PHd7o6NeHtlE+H70IRORA6oPiWo+JknqhwJZ9xbUypIu84lohIXXz2U0aNP0EnXxwnFxSnKYHAWfoyqtjIQKLA+v9tyETkCW96tPnKdtTXs/y732HkN+RM1+jo2iYsaXRlU0SHlL/lf8zuvzyMI242IBe+D9+D9BVRf3CBFknayvrxsIaBOvM+UsB/FL2j5y5wF5UqnXDBMzGG8KTjfUm4CUtuDWA1t4LsqKxvrxcU9Fjv/8K+WFtKZOkEZVJ1oQQPFPX2r+CsNCB/I7IvafDXgI1Dnx87CNTdx8I4OKVbKVW1D67Off4RBnS2ve8i8g8tMeKDPNw5o3v3vuZthnHUj7PCuTpmpQSx0llsxny7Ap5dF77vQ5SiD+5TpcfX7b8G+T45UeXQwywq29MSKkoMWUlT8pIiSkzJXLKxP++F3iEtlHvYD/sRM4USEHrQSLKREXpOypAHd5rHNbR169F+R2Re0o+IwjYg0C586vteTtde/eMrr97QRnjJqRMce1Ts23PXtuu35I3CwKCgCBgREDIbwecD+ypGQG1yKiJQYQBDmn2XtFRiNBR+2Uv1vJ+8yGgFHfRRSBGBCFFIMDKJU6coG94SCfJx+kcPQw8RQ/oBHnTaXpK10L8+sxUmT6nGZSQ3i8sqD4U3SnxEcHCDJ+JCAH3If2Krrmr+gWyLaxr5v3WtOf5vGBvgwVP73RBhS5Du2ApBGsGHUhJ63ZCWXz+UnCSEni0buoSVrei9fXwkN+qQcefbaWB56rYbJ9XnFRUKkl9Kp20IeVIWMLme3Qiv81arDkik8NMzy7cI0mThN7Ll/5PqP+ZCnTt9X/e2Jnc89G4vP/VpQjpG4T8jsgMCvoMLK2UanzUvMv0zPkKvXC+xlk8T10u2v3FXv45Kf/r/pTlXYMwPxtdz64wG2LjDaL8jghq8hmzIzDozkEafPcQDUxdmAalKWL27kj7BQFBwEQICPltosGKrKZGJPU1utJXI6uPxu+RzWVUoCrfaQ8CZiIQwuqXn9Mr2hu/O11wm/3eWyu9+CPcdklmXlPCwkjX11WAQ0fCV2FmD6mPjKXNCevTvX99blP5lqHir0bb5XWrIxa2MACpN2Lgv1EpTSaYPeT3g7fX6evjWd9reXiJR53Ib03gj5ZmmOnZFd46HLA/OfpyDRd+zBAvPxVIED4l4YfW4YiWATPBj9gKcD72v00+vtfpoe8VQt0Q/rfvNbr8eh/50dv3ejUo/WFK7xa6pzksA2/dDiQhv00wKaSJsQoBIb9j1XBLZwUBrRAQ8lur4Yiexgj5HT04y68IAgoBMxEIYfmmok84nPa5FpzIgkfqyEyX2cs3tAt++lLwMmbuDTOQ32ijvdeFy0EqaQRU7L0a1XOJFmsGe9plk/z2IhoxyLzkN/rf7lhWevgueAHTGim/JVhOhHUJ+R0WQlHzupmeXVGDwPvfKoHbD0M6PNZW6hcW3RpMS+4MCfaD8V0S0cJCQcW0Q7vE8zsshOR1QSBmEBDyO2Zwl18VBAQBIiG/ZRY4PAKi/Hb4Idaqg/YoV1XDdVCf2uObinafer6D1tz9jZBGnsItEzVOO5BSuIVdK0BSx6N+ukKRe/5fQtj4a7v3+3PBUdgDJEsanCiGH31oxc6ivtVh/wJ7Fj9637NYFSDs9e37ll3u7tHjOxx268P/DltFdXW2PQlvz+7G2Uk747emFy43+CMZ39WkMi9nUtzA0ANmxu/XwfM7vP11hPeZgfx2xDocjjB3QuqDPeQ39he/XKhLp5/v5K8D8d01yywq5hW2Wl/Ib0eeRdI3MyMg5LeZR0/aLgiYGwEhv809ftL6cCAg5Hc4QJK3RBoCZie/jZ7fkQaK4YvE8zsqUA3+nY6a3eOo/bKeEUjXX7zcn4yK9s7tXLUteBn1M/q/XxDyOzrRJjID+R0RRBy1XxHBIro/Yw/5/SFtE/L7Q9CTzwoCUYeAkN9Rh618syAgCISOgJDfMkMcBgFbarmwOqezF2BYbZfXBYHIRCAi98+H/L6kjn8IeqF/Fsrvc5cC7PqBZEmctFd+794fQPjHnitDOidqVFevQpH2tF/n99rj+f2h/RDbkw9FMGKfd1SS2FH7FbFRjt5PCfn9H94izoneuSe/pgcCQn7rMQ7SCkEgNiIg5HdsHHUH7XNEyDshvx10Mki37EYAatPXr2x/zPtRIO058D7piL/DRgPF+ECeWl+1qoZMOrrHN58dhd2gygcEAQdGQJHfbC2TzjlKe3r+YtD6I8rvKIX5vS93VJLYUfsVvbMjYr8m5LeQ3xGbOfIpR0FAyG9HGUnphyBgPgSE/DbfmEmLBQFBQBCIVgQUyWXvjwpRZS9i8n5BwDwIRHRd+JAeypryIeiF/lmzWnZFBBEhvyOCWuR8RshvIb8jZybJt5gVASG/zTpy0m5BwPwICPlt/jGUHggCgoAgEKUIoNAgiiXae9X+XOwm7MVM3i8ImAWBiKwLquAtCqyWLGa/WlzWlKibHUJ+Rx228s3vE77IGIvK69VroteviXTI8IwtxZqjcjzlux0HASG/HWcspSeCgNkQEPLbbCMm7RUEBAFBQBAQBAQBQcCECCi1uHj+m3DwHKjJovyOucGMSJDlQ1qrA/kdW4o1f8g4yWdjDwJCfseesZaeCgK6ISDkt24jIu0RBAQBQUAQEAQEAUHA5AhwHYHXwTtx83YgLV7hT+nT2i5EmjSJEyWLYkWoyWGV5kcCAkJ+RwKIEfwKBMDsvRat8KdbdwKpYR0XypD2/foioX1f0qQxv6ZIsWZ7R1ze78gICPntyKMrfRME9EZAyG+9x0daJwgIAoKAICAICAKCgOkQkCLUphsyh2sw7CZsFWtGAdXzlwIp+0dOlD3b+/Y7tarab8njcOBp1CG1lvTq7ErZs9lHfmvUDWmKICAIEJGQ3zINBAFBIKYQEPI7ppCX3xUEBAFBQBAQBAQBQcBBEYDC+8Yt+1SepYo5E/6RSxCIDAQiWpRVCqtGBvr2f0dI3thYS5A10qiuC2eNGC93d7JbDW5/y+QTgoAgEFkICPkdWUjK9wgCgoC9CAj5bS9i8n5BQBAQBAQBQUAQEAQEAUFAENAagYgUZUWHpLBqzAyreGPHDO7yq4JAdCIg5Hd0oi2/JQgIAkYEhPyW+SAICAKCgCAgCAgCgoAgIAgIAoKAIBBjCIg3doxBLz8sCEQbAkJ+RxvU8kOCgCBghYCQ3zIlBAFBQBAQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBKIMASG/owxa+WJBQBAIAwEhv2WKCAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCEQZAkJ+Rxm08sWCgCAg5LfMAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEBAEBIGYQkDI75hCXn5XEBAERPktc0AQEAQEAUFAEBAEBAFBQBCIIAI3bgfShUuBlDSJE2X/yIniu0fwi+RjgoAgIAgIAoKAAyMg5LcDD650TRDQHAEhvzUfIGmeICAICAKCgCAgCAgCgoCeCMxa4E97DgRYGufuTjSwdxxKlkTP9kqrBAFBQBAQBASBmEJAyO+YQl5+VxAQBIT8ljkgCAgCgoAgIAgIAoKAICAI2IkAFN8/jfR771M1qzpT7c9d7Pw2ebsgIAgIAoKAIOA4CMzxOUfX370I1qHtz2/T9ud3qJxHGirnkTbYaxnjJqRWSXM4DgDSE0FAENAKASG/tRoOaYwgIAgIAoKAICAICAKCgBkQOH8xkEZNfJ/8/jirE/Xu4mqGLkgbBQFBQBAQBASBKEGg3PnVtOPFnXB/d9mEaWh79trhfr+8URAQBAQBexAQ8tsetOS9goAgIAgIAoKAICAICAKCABGJ8lumgSAgCAgCgoAgYBsBKL+vvX0ebngyuXmI8jvcaMkbBQFBwF4EhPy2FzF5vyAgCAgCgoAgIAgIAoKAIEBEI8f70YXLgRYs3OMRDewjnt8yOQQBQUAQEAQEAUFAEBAEBAFdEBDyW5eRkHYIAoKAICAICAKCgCAgCJgOgd37A8j7URABXqqYixS7NN0ISoMFAUFAEBAEBAFBQBAQBBwZASG/HXl0pW+CgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAjEUgSE/I6lAy/dFgQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEBAEBAFBwJEREPLbkUdX+iYICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgkAsRUDI71g68NJtQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEBAEHBkBIb8deXSlb4KAICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCMRSBIT8jqUDL90WBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUHAkREQ8tuRR1f6JggIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCQCxFQMjvWDrw0m1BQBAQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQcGQEhvx15dKVvgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAiYCIGDx87R6KlLafLw7uSVyMNELZemCgKCgI4ICPmt46hImwQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQiGYEHj99Th2/H0sZ0qSgwb1ak3u8uNHcAiIhv6MdcvlBQcChERDy26GHN2Kde/fOlzZuP0glCuemZEkSRexLNPyUo/ZLQ6ilSbEQgXOXbpDP42dUolBucnZ2chgE9h05Qwniu1Oe7JnIyckx+vXq9VuaMm81VS5bxKH6pdb4T4vlo8SJEjrMHHTUZ9fTZy9px75jVOnTwhTf3c1hxks6Yi4EHPXZZa5RCF9rAwMDafXG3ZTjowz8j6NcIPgOHj9P7ZrWoDiuLo7SLemHyRHAvFy2djs9e/GKen3TiLJmTGPyHknzBQFBILYjIOR3bJ8BNvq/68BJGj5xIbm6uFCdap9Sw1rlKZ5b9Ed7I3toHLVft+950+adh+jFi9eUKX0qKlviE/JIGD+y4Yv273PEfgUEBNLRUxfpwLGzjGfhfNmpQN5sfK+Z+Xr1+g0N+nUOXbt1j5InTUw9OjSkLBlSm7lL3PYH3k+o3/Dp9PjJc8qdPRN1/qoupUiW2PT9QgfOXLhG3/YfT8UL5aIubepRyuRepu8X1vj5f2yiX/q2o6Renqbvj+oA+tV94CRq/EUF+q59A4foF0isFX/+w3uNmlVKUt9vmzoE6YN+Xbp2mxYs/5uu375HFUsXouqVips+XRsBmN2HTtGS1dvILW4cqle9DBUvmIvixo1j6vlo/ez6tnVdhyBVj5y8QD+NmUcXr96iYgVyUvf2DRwi0Hn5+h3qP2w6/dy3nUMRcdP+t5bc47lR8/pVTH0/SeMdC4ExU5cSxAT/7D/B58u61cqQUoP3aN+AiuTPwR22vi/x/+17j6a7930sgMwZ9z2/H9/54tUbevHiFa3fso9Sp0xKU0f24PtZfU+NyiVp2IQFlDdnFvq6aQ2atmCdxfbk9Zt3NHDULP4srjaNq1n2RSv+3EkDRs7iv1evWDzG1OqONQukN4KAYyEg5LdjjecH9wZKrN4bsN2kAAAgAElEQVRDp1CTOpX4IfW/5ZvoyMmLNKR3aya1zHo5Yr9wyN6w9QBNmLWc6nz+KeXNkYVOnrtCG7bup35dmlHhT7KbUqnqqP3CIXvc9OV04uxlalavMnkkiE+bdhxkRcWAbi1MTapu3H6A/tyyn4b0aUPXb92nXycvpoa1KtDnFYqacg5incM8nLFwPXk/eko9OzSkvYdP08TZK6lPpyZUKN/HZl0Kud1+/v406vfFnNlT6dNCTHzHd49n6j4Z13gEAB3lQr/6DptGmdKlIldXF4chv2/cfkADRs6kymUK0wPvxw7RL6wZazbtofEzllOrhlXpo0xpmQjHgfyHbi1Mu27g2TVswkK6dvMek3NQ6W/ZdYTXxkE9Wpk60GTr2dWxRW0OCpr1wp6ix6DfOVhWtkR+unH7Po2avJjy5MhCXzerYdpgOwIwIMTSpEpGbZtUN+3ewnpevXz1htfCVg2qUr5cWfll7KPuP3zM+3hHyqQz6z0VG9sNknvY+AXUt0tTunT1NivAlfUJnml4HqhgvPH/FTneoGY5m2Q5yO8/t+63EN747L7DZ/i779z3ZtK8WoVilu822p7Ec3Nj4jtViiSW17GGlymenzZs3UdL1263kOT4HUXYx8bxkz4LAoKAbQSE/JaZEQwB40MI3l44zM37YxMrH7u2rWfazaYj9uv4mcsc4UZg4pN/N8wYzFt3H9LAUbM5fdKMBzhH7JciUk+du0qDe35lsWTA37ftPkpL1myjoX3amDLABALk+1+mUdvG1S3z7e6DRzR49Bzq0qYu5fo4kylXWVsKM6TH/zplCQ3p3YZSp0hiyn6h0bjHQH6P+rGjqfthHADrNd60g2PVcPTr/OWbTIqAhPyyRjl+B4igXQdPUbbMaSl9mhSm6i6CL7/PWcWk6cdZ0rOqTB2iz168TlC6gwiKE8fVVP1CNgWeyT9/3zaYehjqW9xvQ/u0NV2QE8+ohSv/ZhEEnl0JE7jzmCjl/uVrt+m7Dg1MSajaenYhM2va/9bRiB/ak6dJM+hAPvUcPJl6dmxIObNl5PF68fI1DRk3j3J/nIlafPmZqe4r1VgQZDMWrafh/b4OZsmIAOEvE/7HCtGsmdKarm/Ya/w6eQmvG0kSexDuqe6DfqfM6VPRsH7tTB+YNt2ASIMZAZDO6tlsrfbGnMUzDfNTEdJf1izHojlrj27rz4KUxqWe+ca9Nv5undlh/L5HT57bzPxQanCceaFOV+03EvYyrIKAICAIAAEhv2UeWBCAEqv7wIn09PlLqlGpBKtTsfkH2QOF4NfNapoSLUfsF4iPn8bOo5zZMrBK39qLeMfe43T3gQ81ql3BVGPmqP3CHERGxY/dW7xHBoMImjhrJX1eoRhlz5reVONlVEfDD9Bo37Js3XZOeYSlhtkupTD7KHPaYPcX+ov1EONUq0ops3WL26vS/EsVzUu1Pwvqw9t3vuTi4mxKAgvtx/3Vb9h06telqWmDLbYmk1JH//hdSzp++hK/BQe7O/e8aeTvi+n85Rv0UeZ09Mv3bU1ldYXgC9Y8HJxPn79Gh0+c54MwDrCDfp1NFUoXoM/KFTXV/RXas8vX14+G/jafPi9fzHQB6dCeXQi0Q4n7U6/WplN/q2fXgaPnaNSADpaANPZO/1u+mUYP+sa05Df2FCDwz164RoN6fmUZm6s37nJAKV3q5Ka6t9DY5y9e0Q8jZlK1isXeWxvMHvjEnNt98CT17tSYbt5+wCr9LBnTUMpkXhYbFFjm+fr5seWQXIJAVCOgyGRFaOP3jKS18fUkXp4WItwrkQeT30bSOSzyG6/3/WU6e4rjCov8VqQ7fktd1lYo6u9ifRLVM0W+XxAwHwJCfptvzKKkxUqJhS9v26QGIY1o3PQ/+Lfq1yhLzetVMWUBMUftFw6dIHsG9mhl0/cQB7vAQDJduqSj9gsFZLf8czhE/zkcbFBL0WwFFUPz38SBbse+46b08g1JYYb1EJ7SOIS2blQtStbiqP5SleaPTAPUBjAS/TUqleS/mSnN2rjGf9Pqi/cIfPQPNgBQGZvp/lL9gg8sUvynL1hH2bKkowD/ICVuw9oVaPGqLdSxZW2L72ZUz53I+H5rgtuYLo01Y/3fe2lQz1amUzvi2fXjyFlMNmZIG1yJr8jvCqUKck0OFAYG2VW1XFHtPbPx7IL6D4Fba39v9HnouPmsCDdbzQD17CpWMBft3HecqlUszmvfguWbqX/X5qYLUljfm2/evqMp89bQgaNnWVGc2eQ1OP7+5zCt3bSHM+SMNW04ODNkMvXt0ixYBmRkrFXR8R3Yq/82Yzkr9JHdA2IP1jRrNu6mquWL8t+wP1y54R+u7QPVuyMVc44OjOU37EfAlmc3vgUe3JOHd+caFurZDWsRXEbF9eipSy3vC4v8jkzlt5Gst7/X8glBQBCIDQgI+R0bRjkcfUS67i/jF3ChMHVwg3UBNmLd2tXnvykv5nhucahkkTymKIIZnn4BHnju+QcEkEcCd1MQJMaUM2P02zjUIE8ePX7Oqn2zkFmO2i9rf7xw3JLav8XoG23tv4nXxkxZyoc0s2WMhKYwg2oaChUQJZ+VK6L9GFmvB0+evnjPogZE/7gZf1CJQrkJti4vX71mMstIlmA8dS3KamuNN/YbKuOfxsylRB4JbBZ907Vv6NeEWSvZ1gprOFRXuw+e4uJTsLQCAQ7bBuuMC90nJQjuP7fsY5IY1mpYG/HsBTEMZWeHFrVMSWKF9uzCXqr3kCn0Q7fmTBqs3byHfc6NY6frPAzt2bV11xFavXE3K/gRpDFLwN362XXs9CXORICf+cdZ0nEBNgTK9h89y1kWqItghiLiew6dYqK0dNG8vAygn1Bgbt5xiG1cVN0e/P3lyzfs3W4WayGsf7mzZw723FUBQvS5c+s69ObNO4ItQvo0yU2xj8cYITCLYuFf1ihLy9btoFYNPqN0aVLw2Qvrg1eihLTqr11M/BvHUPd1XtpnbgRsZVNYq8GVYhvcALIWULASlzXZDSV4q27DyVjwEu9TtidGRbktQU1ont9o0/L1O6he9bLveX4bX8NeQy5BQBAQBICAkN8yDzjlHZsvEFUgulVaHTaWIyct4oJU8PHCBvPMxWu06V8l0IDuLahg3v8Kv0Fh987Xz+IJGdPQhqdf2TKnYxuDE2evkLOTE2VMl5L6dGrMBXV0vrDB//7nqdSjQ8MQrTKgiBn46ywC4YVNRqkiebUnwR21X9i8LVq1hVVLtgoL4l7btf8kLV69lRLEj8fVy6EE0lmpij79OGoW+0bnyZ452O2ivG9HDujA99TRkxfp0Inz7DdavGAurdWOazbtpoUr/qZfB37zXno4iLtZi/+k0QO/4XEE+XPz7gMqmj8nFciTTdv7C2vhiEmL6NS5KzwGfb9tymOgiim2qP8ZKx2Vjy/sNHCYAeGNYMDAX+fwoVwV49JlbVRrfCLPBNS1bX0mU21d6NfFq7fp59/mk7GYHQ5HQ8bOpS+qfkpFC+TQpVv8TAZhX6Z4PkuKP+ZlmpTJuGjilRt3g6UGozDa+i17+VkNSwNd1w2s731/mUZQ6Ks6FThk43r95i3b14DwCfAPoH1HztCJM1coX64s2q8ZaP8D7yc08NfZfG8Zld/KXgMkKvoGshhqaRDGyLRAYFrne+yf/Sdp886D9EPX5sHWbVVgtm61T3mOYm3cvvcYfd+5ifbWDHh2Tf3fWot3tC37Fowbig5CbYtgDYK4UOKqe0tHG4pNOw7RghWbWemtrE1evX7LxRSrlC1CVcoW5nEaPWUJubu70evXb6l142pUs0pJbYObalEeP3M590mpS/F3Y+AThDcCgugfghgg4lQQQ5uF3UZDUOMAQb94bnF5n471/cSZy/TXtgPspQ/SW4hvnUfQ8dpmyz9b9dI6GAri+h5q/PRqHWz/pQhvfA7WIwjy4PmH+xKfmbnoTwtwRmuSsMhvBI8VuX7y7BX+DpyXjIU3UXdDXRAPGNcMxxst6ZEgIAjYi4CQ3/Yi5qDvh4IMPoeHjp9n5VWxAjnp6KlL9Mv4/7HaIGumNMGUIijAhQItSE9O+y9RjEMdyJXe3zTWRk0SWr9+7tuWUwv5cNAt6GB3+MQFjiJDoWWLpNRp+IE3UkEHdGsRahEtqM4wjhVKFaAvqpbWlhhR2Dpiv7CZBPEGNW3zepWDkQjKn/PK9TscfPLz86fJc1dzdoXyZNZp3qm24PC/9/BpbmuOjzJwjQCQkCCsRkxayOpUHLaHT1zEBA+IORxWsWGFh7FSoSFotmnnIVa0Qp0b0xfas/mfwzRjwTomdTAGWBt27D3G1hMDv2vF6yEOrAiSlSuRn0mf5KiL0LymtiQC+gUia87Sv+jH71oQAn84yGA8FBkO7BGkAZmKAsd4BoBMAQmulLoxPT7Wv6/WeNgKgbBXQT6QjUgnRw0L+Olj/oFUwFxDf+O4ujBRAgysU+l16COykdzc4ti0cYHPstGPHodBKPgRPEOB2W9b19H2+WXdL+B/6ept4kNv1+aULIknDZuwkJ48e0H1q5elE2cv83yEn7vOz2SQpVBoQn3bp1MTVutjjQRxOnvJBhrRvz1lypCKFZ0IcD57/oozzrDX2HXgBN+b6L9uCjVku4Csz5MjC9WvXobXQhRP/G3GH/Ti5Rsa0L05+fr6U99h00gF0XS4f0JrA8YFAQc8r3BhHAaNns1WVgg6q9fRV4wH9lDwov+2TV1LoPfIyQtsDTjihw7aFA1W2ZnTF6ylbu2+pFJF8/C91X/4DM7q9Pf3p54/TWZyHMFaWKOMmLiQKpQuRJ8WC1KL63qhHwguYQwQbL7v/ZgDgCDusa+9eechF9mGAjQgMIAD2KWL5WMLEZW1BLsh7EVCypaMib7jGQQRAdYHVaAe1mopkiUmf/9AWr1xFz97UyX34mL2W3YdIXe3uFSlXBGt+hET2MlvmgMB66wo64KX5uiFtFIQEAQcBQEhvx1lJCOpHzfvPGDCABusJIk8OFILpWa/4dNZlRUQEEAlC+ch+GrNWLjOcljA4aDfsGnUq2MjPnwjfR6kA0h0HS5b/YJPGVTfH2VKy5Hr7l9/SYk9E9JPY+cy8ZMhbUodmh5iG/hgvXUfp+uWKJybkib2pCIFclD2LOlp2v/W0tlL16li6UJUvVJx8nn0jKbMX0MDv2upffquo/YLhMGEWSvYgxNEb8IE7lSzSil69vwldR0wgdKkTGqxZYCyc+bC9UyGgCjR+QKpuvvQKVq4YgtduHKTCuXLTm2bBJEIm3Yc5KwSKJhqVwkikXGwQ5+UGkNXAhJBMfhjI238zn0fJrnbNKnG6rMZC9cHs5xAcGPouHnUsFZ57dTR1nPH18+f/eUf+jxlRSAyXUCE44J6FWv9t63rsjL39j1v6tR3HJMmRmJZx/mINR5FIJHB0/ubRjxGCDaBJNh76DQtX7+Tn18gSxp/UZF9l81os2HtR2+07IK3NNZ5rC1m8aTHswp7DpDbKN6MNePPLfstAQmjJ67uVkMIZEKlOXX+WvL0iM9ZV/HiuTHZmDdHZjIW+oQHPdYNkPz9h0+37J10vLfQj4mzV9KuAye5XyDhqlUoTp2/qsMZgwhgQBBhtHEBDsCjzuef6tilYG1SWY54dpUvmZ8LDuK5hLHLkiE1C0IgiihdNB97tuP9b9/6kqurC8WN46qdoABrIQpervt7r2UfX/HTgjR26jIKCAykazfvcWAa9l1bdx2lazfvcjYGCOakSTy1JVXRr9FTltK23UfJ2cWZxQJN61bisURQCRkXqgA8ziQDR83iQDue2f7+ATR/+SZ69eqNdkFqrNvKfgZrwshJCzl4hoxURXyv2bSHlq7ZRi0bVKV3vr7839+0/ML03vTaLw7SwAghYFR3IwNj6sgeFlsUIb8jBKl8SBAQBCIJASG/IwlIR/4aY8EjV1dXJrhAGoMcR6oTPMBBNOCC9y9IPpAKILdwUND1QloWFJs9OzRkIghkPkhH9AuKdhB10+avZXUMiosphbtu/UH7oN5EwAEKRxTGgRryq0af05nz11jRjwvqXJCQUGnpmOpvjau9/cIBfcPW/ewfC5WnruOFsUGGwdt37/jwiewD/A33y4IVfxMOePAYhYKw77fN6M59b1aYDerRiov2meVS1g2Ykw99nvDY4OB2974PWxwgNRmp8z+MmEEtvvzMNEX7bFnzWFtEbdtzlA/cilTWccxAxCHlGoQ9UvmVPQPGBkSIi7Mzr+v4/0a1K9K8PzZStQrFOLip64XAGZ5PiT096Kexc+i7rxuwQhrXyXNXadLslUyq4j5Ciryy2dDVz9waZ2s/etgcIKMERAkukCFY/w4dP2exrdF1rFS7sL9Yt3kPkzxQdkNhW6NyyWBKVGPADCpwkFjtmtSwKHd16yPILFhmYA+RJlVSi30Q9kXVKhazWNmoe27Woj8pfdoUlCtbJg6wpU8TvGCmDv3DWoHgbfvmtShTulRMgmPdwD2EIBpIRuX5ai2G0KH9YbVh2brtrMYv8kl2mjRnFY38oYOF6EfhSDyXxwzqxPcagqI7953gPiubwLC+P6ZfV6Rq07qVOQNh94FTvL4jsNkRJGrBnDT0t/nsva/zvh04oi+uLs4WwtgYVFJr4dFTFzkAgMxVz4Tx6d7DR3wfqtdjejxC+n0Eljr2GUOJPBPS8P5f8z4C91jn/r9R/txZqUubetwH3I/I0sIeUbdsEV2xlXYJAoKAICAICAJCfsscCBMBpONy6l2PVqwos1bMwcoASplf+n7N6Z84GOiawmvsLJRK8O+FlyWKGeEgeur8NVYHKj9S+JQdOnaO1XQ4NNSqUlI7lY+xT8oDF1YNKoVSHUR/6NaCD6dQNCF9FGmVIBKgnAH5CM9BXDp6WYbVL3jhgjSBHUWm9Klp6dptVPfzMvR5hf88OsOc6DH0Bnj5ggQGGYwLh2xYCoEkqfhpIVY0odAdDm5Q7MLnvVSRPFrPQ3VAhYIJ9w0ISCip/7d8Ey1du50m/dKNC4ut+PMfJivNVLQPqjn4Z//St61FIaf89Qf3bM12LpiL9WuUNQ2hj/FCaip8pof0bsMKuotXb7Hdgfp/vAdrJIIXN+8+5LVEVyIB7YTVxPY9x5gQBtmDe6pymUJMPGK8kCLfv1tzSp86OZ06f5Xf+3HW9FT500LaetIjywJEDywofP38qM/QqZzWDwsDEONYS5jw+TeFHsp9eBbj0jkgqIo9Yh1E5gH8OxWRqrylob7Nn/sjJuzWbt7LNl7I2qpQuiDFjetKG7bsp6IFclLK5F4xtJKH/rO26j7gHsNagnssedJErMKds2QD11HQLXgLyyD4tA7p04bJRHVhzmEvBRGBMYiG1yGGwLN7/5GzXC9G99oIeEYhaAFLl4E9WvF+VlmWwRYEawcC1Sh6jAKL2CdiPYwXL662amk1TrjHoPzGPaLIbfR3577jvJ/AfyN7CftE3EPAAYHP7FnTa73XgC0PssuqVyph6ZcaM9Qqwl5YBQ0hMMDfIDzQyfrEuHIoezLsmVAvBRfuMajyIYqA9RoyFAp9kp0WrfzbFNmBWi7I0ihBQBAQBASBWImAkN+xctjt6zQUuEhNRipyuZL52SIEm2NVmGrYxIV8KMUmE2nz8K/r0qau1gpBIKC8lq/fvMcHGWOhMHioXrhyiw+gIFZx4Bk8Zk6wQ7l9KEbfu6EGnDBzOXVtV58PZ/OWbWIFEw6iUK0aU/2h0gcRDnsUkK24dPSyRLtC69fzl6+ZCAKRX7poXlYaIzsBvpc4wOp8KSIOVjuqoCDIu8BAosMnztO8ZRtpaJ+2rHIEyTBk7Dwa3OsrJofwPij7Qfjr6NkJZRLGokPzWhZCUXn+3rnnY1EM4pA3Ze4aSps6WbA0edyj8G5OlzqFNsUklcUJip9hroEgGDJ2Pis3QURifUTAAuSJV6KEbBWAPqPgGPxGdb3g7e0ez43xh3IVvtIgWVHQDqQWSIZx05fT1Rt3KQ8sHE5forIl8/M9p6NyWqnAQe4g0wIBzp96fcXzEOOD+hYI5iIAWL5UASZTc2fPpLWvtHHuYDx+HDWbn7uYhxgj1Es4dvoS9e3chElyBDMa1CpPXok9aNHKLdSxZW2tA2e433+fs4pSJU9C9WuUY+9erAt3H/gwKXfr7gMmi7/v1IStk7D2pUuTnEkikEIqAK8CNVhDdbnnrEUEILlwj+XNiXusDA8t1gmoqFs1qKqdfZLaL+3af4IzQzDnFDGH2gCYc7i3jGIIqHOx34AFVuFPstOeg6foxavXXBRTV/929BNB2anz1lAZWJz4+bM1GTJGsF4gYwSZgtgzomBkv+EzOAhTsnBuzq7DOqPrdfnabV4zEJCuULqARfCg9n2wqunXpRnbbfT5eSqLPaA01vnC+QTzqkThPBYFtLWVGgQ5CCwhuwQWL8iwQz/ju7vp3DVL22ArhIyeL7EmBgTS7oMnuY4PRCso1oz9PvoEm6+eHYOLI/B+iCZ0LYJsigGQRgoCgoAgIAg4DAJCfjvMUEZ9R0D6XL1xh8ZMW0Y/dm/J6kDjJhMHA5Umr9LmN2w9wLYoKN6nlMVR39Lw/4Iq2gfLE9hm/DakC6dSdv9xAuX4KCMTPvD7bVynIm3afpC6tK1nUaSF/1ei950gRFVhHNieoP1I14WyzFaqP9J9oV7CIcfayxKqO5/HTylLxjQxvnkOqV9QsOOCFy4KLYIkKZTvYyYScLhBpgLUuvBwBwY6HgKg6oHXfIL47uyTDwISpI21fZBSRsJrNUUyL1arQhVkVOdG72wL/ddwMEXKOFKQUScA/qm4r0Bqwase4wGPTqTwftN3HJML+BuILyhw7z18zLYO8JtWSlAd+gdCHiQc1HHw7IW1iyoyiHm4dfcR9vCF5yqyYkBmfd2spg5ND1cbsH5A9T2451cWJS0O4CAYxw/twnYueB4Mn7iA6lUrox1RZ91JWEKBmMP8AykMz8n2zWvS0jXb2TcW9R/UhTlrFqUqrFxgYZXUKxETcLBKGtK7NaVNlZx6D53C95ZSeSIw/d2gSVSiUC62OdCFFLYeK+UvfeDYOXrz5i0HWlD7wCOB+3tkMT4LDLoPnMjrJt6nCC3sTWBrBqJVB2sKPFtRhO+P9Ttp5A/teb8BK6Fhfb+22LdgncR6CcsNBDsRdEdGBrIT4Jsa088uPINhDYeCeyj2iwt7pAmzVrIPMwjuU+euUosvq/AzDPtBkHJYR1DnAus+Ak9QsEKBq/OFACCy5vD8wX4WQgLr7BiQqhu3H+IAIUh/kMeYgyrrDngtWbONMqZNyXVZdLgwpxCU/mPdDi5SDVEE5hWshZJ4efLYrt+yjzKnT03ZsqQj+OwjgL3n0Gn6vHxRbbNiFLbYIxmLr1oLctTzGXsKHYO2tuYIRA/jZy7nva0ScyCjAvML82724g30c992hIAa9sCoy4QioCDMkfEEcRKC9XIJAoKAICAICAKxHQEhv2P7DIhA/7HpQpEf+CMaN5nGFF5s0EAsI9UVpPE/+0/QgO4tqGDejyPwi9H7EZCJOMCpzTFSJodPXMjEHNTuSA/9Y9127lOxArnY3kDX9H+QdA98nvDm15jqDxIIFzbLP42dxwdTHHKMXpbQqM5dtpGVrTj86XRQUP3CgRQe51D9YM4hNRuKGHhxwsJg/rJNBHIfyk6MKewBkD6vayAG/t4g7WEJgsJv1vZBIHRgQQQV2rt3ftRryBR69OQZWxKBJIppcsTWnaqsMrbvPc5F36B0NHp0gvAHwaDSykE4rN20m0lVEMuZM6TRkqhDv5BxAG9sY1FSZMmAiMT9hvaDjMueNQOTkAhyLP9zJxeU1Fl1ptLGocRv8kUlcnJ2ov7DZ1D1isXpr20H+N5q/EUFWrx6K2XJkIbXDlvp/8AIzwnlDxy9K/n7v6Z86KHCL1U0D42ctIg9bhVZBZLHbEpVPGfxjAKRinUNZBZIVKiisU4oha31+q+7XYOxkCfWNZBW1mQxRhhr4pI1W7kwN4JNKC6ZIqmXtrUEQIK/evWWFd4ovKrmntGaAXMTzy5Y1hQrmIvOX77BPttQG+uqmMaagWLOazfv4cLamJewsOnQojbtPXSKzly4Tm2bVuf9IGwbVOHIly+DgtSq4F9MrxHWv481I46rq4W4V0UVEZg1FnnH57buOkI79h1nKz30BxjgXkTmhdpz6dY/tAdjN2LiQi4iiUARMkSm/28dZwNibztg5CwO8NasXIL8AwIpjquLjt3gNt24fZ+27TnGGUnGuhVqD4sgLgIAKhiNQC+I/0a1K7ByWscLz1EE8sbPWM51YrCfV1lKWNd7D5nMWY5YS0D2Hz9zifcgsxZv4Ey0Yf3aaXtG0RFvaZMgIAgIAoKA4yIg5Lfjjm2U9wyHUaSUo4higH9AMFUWDnjGQw2UC1AgoJCkbl6W1kDhsDJh5gq2LUA6tbGgU2LPhJwOCmUxlE0nzlxhH2Mc9rJmCiqspuOF8YDiCopGFACCjyM21FBrQZkEpRk8ZJWXJZSd8C2G+u73Yd20tbBBH1b9tYsVj306NeFCdrA7gUoYdjXwKIU3M3yYgQFS6EHEIV1U5wt2NCDxocxSBQatvUYVGQQV54wF6/hwagZ1Dw7amFtIu4aHqnWKshqXkP6u87iBsBs0ejY9efqS+1f7s9I0cdYKi60BLIbgZ/xJ7qw0c+GfnBGDgICOF4J8CIYlSexJ+XJloWHjF1DfLk0JayBqI4ydupRgD4VsGdi7IP0fis5yJfJz6j+UxVBK/gzf8D5ttFn3FZmF9iHQCXIARIgqPmhWpSrWN9h8gJiCvzSIUxXAVeu/Iu6wvpjJrgHtRfDFSBbjnrFWeWJsMa5L12zTvpipcR6iL0ZrBlgKob9D+rSmEoVy87hOmbea9xgINOl64R7y8w/gOYi14eff/kd9v23Kz2U8v6BqP3XuCq8ZyDnrpTkAACAASURBVPYZPWUJubu7sX1I68bVqGaVkloF2Y04Y64tWPk3Na1TifcQxixHCAPQd/jtw2IC6wnuublLN7LHtq5rvOofCGBkXlUtV4SDZ2/e+fJ+sV3TGjzvoNKHkvjmnYecZYE9CSy+dCzMahwza6U+XkPWT+7smfk+gh0WfM7bNKlO1SoU1zLIbuwPAmT7j57lbAvs97CXgv8+RB+Dx8ylTq2+YKEHgoVPn7+k73+eRhcu3+SgGQLXyH6SSxAQBAQBQUAQiM0ICPkdm0c/EvtuVGXha6EIB2mM4pFIwYPlAaxFWjeqxv6PIFagUtMx9Rpqnb//OUyT567iQyc2mmg/DgVQOsJCw1igD56+sBZpULNcJCIauV+llLZQ9KCYE1JacZ08d4VTsGFrYvSyVMEMpE3CMgAkLBQnOqm/FULKAxGHGud/VbhDerWmWUs2sDJfeZkzWfL8Jb15846VZsvX72SlzMdZ0ms5D40zAAdro2UNyAJrSxS8H/chUpZBOOiqLDZ6dCLz4IcRMzgYYUyDtya1lLrV1dXForJGX7ftOcpksg62BsBfqbBafFmVi61C9Qzbk5YNPqOx05ZRi/qf8ZzD/di53zi2q0iVIgkXOwXJpeN6iH4pf2IUYFVev/gb+osis8hSWLJ6G2f2nL5wjfz9/dlfde7Sv9geC/YbOmYlgDRYtm4H/dCtOa8LYSlVI3dVjrpvUwp2KNwRkLh8/TZNnrvGogAMy64h6loW8W+2JovVmohsCqx3itjBnOzc/zfeexgtlHQmfoyBzVzZMtLQ3+ZzJs/FK7fI3z+AieHjpy+z57kiVvF3XdY9W6OqCixib6FsvFS2zK07D6jnT5NZpY9sLDwToDyuULqQlrUrrPtnneWI12EHgyK6EE0oeyVYe5lBdYtxgY95Ei8P3uOBDJ88dzWPC9Z7EN9q/ca4Hjx6jsZOX0bffd3AkrkQ8Ts76j6JDMEbdx5wVggulfmD+YigJ7ICB3RrwQW57z18RItXbeVaEJXLFmZ/bV33UOgL+oYx6P1NYxZ3YE4iQK0CzSqY1qdzE1q+fgdblaFmk1yCgCAgCAgCgkBsRkDI79g8+pHYdxCkuFDwZ+O/Nic/dm9Brq6udOHKTVbjJknkQYN7tWYiBeT4Vw0/1973ERYMKCYDgg3pxih6ifRIHNjUhYMDyDmo7eYs/Yv/rBOZ5esXpPoulC8bK21xoIESH+MABQw2+NYKGWMwA+m7KDiGA52OBJYaB4wBimmhP8g6GDBqJnVtW9+mVzSCLxivP9bv4JTm/l2bsTpN1wvzcOCoWfT9t015HJC6C9USPCDd48XlZkMVBDUnCD1YwJQqmpeVkrpa8qDNUHfvP3KGs0dUYAX3EwqOYd1AkOnR4+fsLQ3bGgQ3oGxqUb8KDRk3j0lVRcbqMna431DoDfcKAmWjpy6lFy9fUftmtZgoQDBt0K9zqHqlElSmeD5+D4qQgSAHUYkLfsC6eO0rXFHoctDoOYx3jUolyNMjAQciMDeN6f+4D2/fe8jEOIpoGj2NdRkj45qBACAsJeBHHJJSdfzQrqRqCxj7AHUrbBxAHIFoRXAgpu+3m3ceUMpkXvTsxSv29d198BRdunqLs66w/luPF/pjbdeg2zjZag+KN3//81Tq0aEhZzLhAjFnrCWAcRn5+yLK9XFGDrzreql7Br7evr7+HDCDbQMymDCeo6cs5UKmYwd34r2H0Z5MZwIc6xjwhz0UCrOCCEcwbMyUpRQQGMhrX5WyhTmwjoKEKEYIewqMo87kvjWpap1Z8eatb7DsJrwOBS6eDZirOtquGe8NFD1v1/NXDgqC+Ib/OWzLMD6w4YHlxtmLN2jesr9oUI+vgtl+6XqPoV1YE5FRgb0v9nsgjhMnSsiBi14/TebAEnzrkSG5Yes+DuDqui/EPmnNpj206q9/uF4PLF7GTf+DyW/saRHIhb0arAFDu6Aef/P2LSWM766t9ZDOc0raJggIAoKAIGAuBIT8Ntd4maK18CmGLzF8iOGTjYKDUKnCaxkbMRB3SH/FptPn0TMuJgkyS2dlFoBXlgZKvW4cjNUbd9OS1VuZrIMKAwVooNwyKmZicvBCq/hufXCDWtW6WBo22peu3WablEQeCahKuSLklcgjJrsU6m+jTzhgI00cxaiMpD02+zj8wJoBFzzDz126zgchXX1H0U7ltQ/iHn6xCLAYgzCYgyBI+nZuwsQCCGTYVcD/FoScrqpi64E02gyBxAdRXCBvNs6+QEYCCsbB1qBAno+CKT51nIwIIiFNfOzgzhaFnNHeQAUu4DkKb30cvjE3dfXax9yDnQaU3lgHC+bN9l76P8ZB+RejICjuRawVKDwI0kTXKzSlKlLLrS8OPo2A1UthKlYwJyvxUEiuTePqTObFxP2GNQL1KRAA7NSqDgfGYAuFtqIQH4pPh2XXoOv4WLcL3rbAH4FpBN1xGWsJqCAEAmwoQKt8mHXvn7LPQPvVswvPbxQbh80ECLzeQ6ZwFhPGO0XSxGxvozDQrX9oOzIPsBaWKf4JF2oeOWkhNa1bmTJlSEW7D5ziOYlsEVh4ofZDUGbCQer1TWNK5uWp9XPZ1rwzrvH3Hvjw/hfK23jx4nJwumfHhlrXv0EmAtZu7C+wjmHPvnnnYX4GHzt1kb3d4duOQG6/Ls24JoTuNl5qDw87OdQdqfv5p3zmUHtFzLeGtctTs3pVeN38bcZy7r8qGKzbfaXag+DYwpVbCMFpWNHADxx2fzivwO4kpGxNRZ4jYxJrDQJVYo+i6yhLuwQBQUAQEAQiCwEhvyMLSfkeCwJQFqPo259b9nOaHdILUyb3YmL4zj0fJu5QkBCqJmy44P0IX0T4eupuawCCEYc4KIVB7OPCIbzH4N/5QKNUFkgbhcf54J5fUSLPBFrPDqOXJdpqVO7jcKA2yfCVbtesBvcFisIfu7fkdFFceA8+h8MCxlqHC6n/KNSUIW1KKgVv5ZxZCOnW3QdO4sALSEYQyCDIF6/eEqqC6fY9b0qeJJEWARq2oJi4kA/T9auX4TbZUnNiDEAkD/x1Ft9nIIlKFckbjJQLLSgSE2OIg+jvc1aRezw3thnadeAUF5dF4T4VrEBfQf70/baJtl70CjuMFUgqBMRwWfu242+4/xBsQnAQhTHN4LWv+mcr/R+vGQMxCChBfQzbnuH927NHqa6XLaWqrUJ1uK+w5rdvXpMqli5oCa7h8z+Pn89WXzEV+MScg/XR1PlreW3AujW411eULnVyTo0fMWkRDendhrNDcBntGjKnT8Wqx9AymBAAASGmg3oVClWo9etVL8vWGsZaAuibKuiM8YD9lcp6wms6K3AhGECWRfrUyalBrfKUxMuTPP7dbxiDF1B7Hj11idKkSkqpkgeNp+6XCjJhz6eIRcwp1I/BcxqEPmqPZM6QipXSew+dpq5t63E9C10zzzbtOMTjU6Jw7mBrPCzVkLEFchj7JWTKgKT8fe4qGtC9pdZroZpHEAugD3WrlbEEcLHnRVAXQRfseRF06vbjBC4enD5tCqpcprA2NR7Cuh+QvYPMq14dG3GWBay6YBeFjCBkOCHwjloxKP6MNUb3S6nbv2lZmy0mQ7qw78AYftumLu/bcQ9OmrOS5y+sYNT5Rvf+SvsEAUFAEBAEBAF7EBDy2x605L12IYBNM9RKY6Yt440/DmhGYksdZLCRNoutAQ5u6//ex36IUEuAxL9734fVkLB5MfqOjpi00ELamUEVowYXgQs3t7isxscYgejpN2w69evS1EI2wjLkzy37WPUDcuvMhWucqv1L368tpIpdkyWK3ozxOn3+GsEXttKnBXkuKh9wECE7953gwpJd2tTlwx3IEqT3QqlWNH9OPhTc934czNYhippq19eCZIM6GL62UOvMWfIXEwVQ/thS+oAwRj8rlCpgIeWUvQjsNaA41uHCeO09dIby5MjEamGoknCYhv8mLryOomIoqAZyHJ78V2/e5QMbgmkxbTkRFobzlm2kJ89ecGq/KpJm9HI3k9c++mqd/o+/2QrEgMxDujnU35nSB5FayhomLMyi+3VrpSqKHhsvpcx99ORZsNoP6j1YC1EzAmP84OFjSpzII0a8Y3GvgNBAOrtSoYdl14A0+tAymNT9BwW5LjYiGC9Y1mAt37jtAFsVqIwKY9FcrBGYg7AWgWXP6fNXaVjfdtoWqsazaN+RM1ybAplWKFhnq4BfdN8fkfF7l6/dZsIR6m8UBlaBFFV0Flkwao1EwGb4hIX0S9922gTWQ8Ng6drtvC7iuXzm/DWasWg9VS1XjLNCWjWoysWOkZ3RrV19DkiBDE+ZPEmMrBHhHUu0HfcX1m9kHyxa9beloCzmKbKzKpUpzIXgETzbf/QMfd+5qeU+VL9jLMob3t+O6veBBIaFGshvCDdU8HD7nmP0U6/WbOGFYqAgg81QQ0Ct0RB/YF+XKrmXzaCRNfmt9lewjUqS2EP7ovBRPS/k+wUBQUAQEAQcEwEhvx1zXLXqlbJrgMJ7/MwVvKFU/qnK/9ZstgY4cOOAhvRyqH5OnL1MPTs05E2mKsR1+643exmDfDVTcTtVLA3pyVBcQZEF8tjoy4yxxIGo77fNyMXZiZXISLeEr6eul0pvBflWv0Y5JoRA4oOQHNqnLRMoA3+dzapjKJegXgWhCu9RT4/4TLbqqDwD2Tjq90WcIq7UnCDHEcQ4e+k6VSxdiKpXKs4WQ1Pmr6GB37VkFbXRXgT91PGyDsQYgyyJPOLTuOnL+d7DoRsZGSicWatKSS3HSR0uQdiDvMdlPQZm9Nq3JoahTLUOxBw9dZFg7QLFMbJ7UJgLRB7U7joX+rR1T0BZ12PQ7xxoMhZpVe/F+o8x9vXzo9FTlrD6TlfFoNEmBM+0sDKYrIOc1kUoY3INQfuBufLANhbN/ST3RzR03Dy2mlBWIqh7gaAuVOMNa5UnFxfnmGx+mL+t7MmgJra28rL+MGqwgLz7qtHn2hbFRDACewhkkaGgNp6vyEL4acxcXifgDY4L44T7CFkjIOV0v4wEL/YX/+w/wVlXilSdvmAd+0rDhsfnyXPOYurYshblyZGF1eM67jHQp137T3LBd2Q1Iug+8ZduXH/Elo0XxgiBNwSYoHb/OGt6Dvbivcjmgpe4Ll71KuCCDK3OX9WxBGLQZ/jv//zbfLbiUfUS1m7azbVUVCaajvMRc233oVP0+MlzfvbYmlPoNyyx5i7dSCN+aG+pmwAlPGqrwN5QLkFAEBAEBAFBwNEQEPLb0UZU4/6AJI4Tx4XKlyxgaSX8wc1sa4COgHxEunXHlrUpb44stOfQKS48M6J/e1a7h1XcTschA5EAxTOULlDrQ6EEZTQupXzEf4MAgqUB/KUH92yl9YEA7cXBDYdrZCXAM3rv4TN84EHhQZB2OJDDngcHNVU8DYdXHPQUsazjeNkiINEXkB9Qn/1v+WZ+C3xhEcTAhXFFMScUjdTxwI02qkAMVFeZ06cm2BzUrlqagywITuw+cJIL+aEYLWyTYIGAgzkyEnS3G7K2eLHltY/3bNp+iJat20ZJEntSm8bVmEzVdbywFvYc/DvbF3zdvCbfR8r/G7YTIBDUGoJ7cfzM5ZQxbSrq0LJWiN6kut1vIAdgy4B1IrSgEYg8WFEgQIogLzx0YWsTE17gIWFotGuASjq0DKYE7vE4yFm8YE4eRxDfQ8bO4/mI4oy6XfApPnn2CtcFABk8ac4qGvlDB8u6YG1BhHvN2clZq/ExYop5t2jlFlZLh7a2sS3WhAW0dfdR0xQ+Rj9tkfvqGQwCVdfgc2jzHsFNEPfw21eCD8w79AtFabHngDgic4bUdOj4eSb88V6sGepC8B2XLsEZBMxQqBRBZmT0YC2Eyt1YWPHkuau8J2bxQEAAP5s7tfqCxk5fxgrrXB/rVfsB9wx8s0Huw6oR+0FYJRkzRxTZrbI/l63dTh9lSkttmlRjNby6dAoI2pqbxvmEfT4yOGF3MvC7VhZbG93WcmmPICAICAKCgCAQWQgI+R1ZSMr3RAiBsGwNdCV5rDsLr8DRU5ZyQUioUHt0aMjF3cIqbhch0KL5Q1CjrvzrH4sPIP4fFiejfuxISRN7Ut9h06hF/c9C3DiDeIASGUXhdBhPkPfPX76mLf8c5uJTIEeePHtJ3/88lfp0bmJRwChvbd0V7dbTAYcvBFxAEBcvlItfVlYUP3RrwQdSW4XhonlahfvncNi8fvMe29GgoOcv37elQCLqM3QqB19U0U9rMlmHuRZWJ0GEIOUf5L61175KXwaBh3oCuMbPWM6qMzWuYX1/TLyu2n389CUmOfYfOUOli+Wjdk2r08Urt2jHvuPsV1+sQE4ex8Gj5zCxBWIY2RchFeiKib7Y+k0EzkDs1KhckhWBti6j9Uu2LOnp752HWIlrtOTQpT+qHZh/oWUwoTAhCGXlv4+gBttVODlZMhl06hPIYidy4voOUOBu33ssWHaW0TsbpPesxX/Sus17ua5F5U8LaVHfISJ47jpw0hKMhiXZ8vU7WG078of2WgenbZH74X1OKRJv5qL1nI1WtECOiEAX6Z/BXmPD1gO0YMVmDlwiAwE1D2xZxWG/8fDR0/e8shHwhUIXpLguamkFlLWNF/4OQhwZFVDzly4atD5CIIFaP1AhKzubSAc7Er4QQcrL1+5QxvSpKDAgkAvqIpvMmOFz6vxVFrcgwP702QuCTUi7pjX4max7QBAQQTQAgYDKRsIcxfiMnbaM9/RQ8+NvsHO8efehKezkImHo5SsEAUFAEBAEYgkCQn7HkoHWtZuh2RqYRWkbErZhFbdTpJ3153Uji0Fmrd20h2Yt+pNcXFzona8vEyBoPxTfF67ctOl9i34pW5tSRfNqZ4liTJOHBcOo3xfTsH7t2Gsal1H14+7uZkn7hQpNdwUuFJ0TZi6nru3qsyJr3rJNFuuWN29Bjs9mr1WlxNV1fTC2C+TAi1dvOPUdRAlU3ghcQL2Oy0iU4G9QFm/ZdYTc3eKyZ64aV137ar0WIt0fylp43SoLAARrlq3bwWnjcVxdwTtqEVCyxhSHZyidcYBOkdyLVfvwZu8/bDo1rlOJ3r59x0QqyCB43qJ2Au6rIWPn81qi+9oPMnXi7JUcEFRFfxUGRt/ids1qEgoFT5m3hiYP705lin+i6/QLNYMJBVuNRJCyFPmq4ec2rV9066Qq6tn96y95PbRlr4E245k9e/EGXl9wjyGjBJeystFJtW8LY6PViwqQ3X/4mO280B9kcJnlUkGmsJ5TeC7MX76ZA6Nx47hyscLPyhXRqpsomL36r110/fZ96v1NY7Y3gTofRbhVRl1IDcY6if2JstXTqWO2PLxR6B7Zj8YaOGF51et4f2H/h8Ct0e4P2OO5jAKgsMrD2g9LwF0HTzLZ//r122ABQeCjWzCXBQTj/0d9OjVhoU7QPv0tBycqlC5I5UvmZzs5PJfz5MhMCGKXLZmfs3t064tO94K0RRAQBAQBQcAcCAj5bY5xcthWhmZrYPZOh1XcztZGUmey2NfXjzfJCRLE401weDyjoXzHYUipBXUdU5AGIHdgqVGhVEG2alCK9sL5s3ORxSvX73CRKj8/fy54WrJIHu0IfSMBp8hf2J7cue9DYwZ14tTrkDw6bRGYOAAeOnGeC3XpVEwSxAi8OKEsrl+9DPkHBFoIfZAfKNy3dM02atmgKgdr8N/ftPxCa8W0tdf+hq37+X6Dul2p2OGdDY9cKPg3bN3HZJ0uxUrDuretSRykX4+YtIitlZTfLzKBfvm+nfaWNSBrDp+4wKp12H5kSJuSfW0rli5IV27c4X7BtxjFxpS1wdPnLziVvuWXn2mrKraVwZTr44xkLMqKtd9oKQK1I4oygmyFH7VuBPHLV2/YRuLMheus2EQBRYwf7itYPmGdxL2GooOw64FSHJ70CFSULREUrMA6COU4Cv7ppr413nfGcVHFt63JSKwZICPTp07OxT91zZCBCACe4H06NbYEIazXGNhpoHgkCpy2a1qTRk9dQk3rVKJ8ubKGtRzF6OtGdX5Y3tFYE1GnJCySPEY7ZPhx6/YqG57QiH7Mxx9HzuIaEKWK5NVuDVHdA5nt4uzMav45SzfwGv9xlnRc40HtGVVAEP8/8Nc51KrBZ9rNR2VLU/HTQlSnamk6cfYKzV78J40c0JEOHT9H8KUfP7QLZ2hhrzV84gKqV62Mdv3QZc5LOwQBQUAQEATMg4CQ3+YZK4dtqS1bg7AOBGYAw1oV40hkcXgsJnDIRjGntk2rM+ljPEAgMJAtc/oQbQNiYnzv3POmMdOWMdGBtND6NcqyChVpsF0HTKA0KZNS9/YNKE/2TKwcnLlwPaciQ7Gq84V+PfB5wgVJbWUj2Go7xheelipFFqQPisXpcIFsc3ZyIpBaKOAJ4gpFSrNlSceWKI+fvqDO/X+j/LmzUpc29Zi0NxZoBVGi62X02kemhVdiD/qyRjlurvLE5YKt1cuyeqtC6UJ8D+FzuluGqAKDGJNSRfPQhcs32T7pl75fs9IbqvYla7YxsY8xAmmyc/8JJpfTpkqm5ZBhTM5evEHHTl1kshT9MCo6jYpHEOF4Bri4OtORExepZuUS2pLgRrCtn1uwdIFKEKQkyBEEaKCE/2P9Ds5G6N+1mSUbI6YHDW1Dlkuxgrk4zR/EPIhHkPnD+n7NBTLhWYx7KImXJ61Yv5PSpUlO9x484loQsKwBKYR52K1tfa2DZxiXH4bPoH5dm1m86I32OwjOzF+2iX3dgcf5yzcoU7pU7NesFO4oVItsEjMoPBG0GDhqNgehm9erzFZmP46axdlAuivcQezjQg2O0C4Qj1AZf1mzXLAMC9jI/T53NVUtX0Sb57LqBwIWuH/g8Y0AjLGIsy2vekWO41mHuhd37ntzMdA0mq35D7yf0Nxlf3G/sN+AVQiKRSJbEHsMY+Dp1PlrbHWD+0hXqyusjRBCrNm0m+uJwLoFmPcfPoOqVyxOf207wAHdxl9UYC/0LBnScEaF7rURYvqZI78vCAgCgoAgoDcCQn7rPT6xqnVGWwNH67ijkcXoz95DZyhPjkw27SRU6v+CFX9zim+HlrUtPqpnLlyjX8YvsNg56FYgCIcCKAQb1i7PJAIONSCNobxCf6COrPRpIU7thb+ozmSq9X20dO12AhkOwiMkggPZB0h7hXIJ6kgEBHq0b8DqMx0uqEwPHDtr8aDHobTf8On0beu6bGmAwxxSkTFGUDAVypedCn2SnRat/NsUwQqFMe6T32YsZ2/RtKmT0coN/9CGLftpxA/tmeweOm4eE8VeiRKyupitHHq1DlYsTYfxMrbh8rXbNP+PzXTy3BUmgmHFAG96XAg6HT5xnr5r34AwplCbwb6ma9v67IluhstI9GBdABGO1HhjUVlrkl/3fiHT4J2vH5MgKF6HQrkgf6DWh4USntsqWI05eu7SdbZ2gK+xDheIQljUQPENsh5zbGCPVhwMBGkPYgcKTqjD8dxC4btLV28xiYo+zF6ygab/bx1Vr1SClak6ZcBY44tnKaw/oOY22u/AZ/nQsfNMbA3p05pKFMrNatUp81ZT1kxpmdTCmoL1Ej7GCPLGcXXRYfhstgH30A8jZ9HjJ8+oef0q1KxeFbp+6x4Twj9/35Z9tdGfN2/fab0ehgUw/LMxZgg+q+evIv2hpMZ8ZN99jS7sHybMWsn1bxJ7JqBHT56zrZXy/7ZuKtbMGYvW0/B+X/N6D6sy7LkQnNKpaDX2vFPmriGfJ8+YAEegCMGmAd1bkJtb3GABQdjcdOo7jvz9/al3p8Zaq9mN4wG7J2S99O3SlBJ7JiSQ+GOnLmUbqN+GdOGgkqPURtDolpGmCAKCgCAgCEQjAkJ+RyPY8lOxGwFjcTtrJMxMFtsaVSge4e/7c9925JEgPsHPGN7gUH2u3byHU0VBCIFUga8x1J3wFMSBVTcfY5CpsEXBIRsXyO9fJy+hahWLmcozG2235dFpHD+QjkPGzWPSHwdrHOJ0s6JQHvTIHij8SXY6ff4afVa+qMWTEgdnkHRQTGM+7T54klXSKFyFYpFmubAmqMMnLDbKlypAvb9pxOoskD9Q5eN++n3uKrZuADkCX1hcUNB5esTX1tIAh2mQqo1qV7AQpxg32BjABxyWNhirmlVKmkKFquaUUdGJMZo0ZxWN/KFDMBIHZM8/B04EK76IdVBnOw3VPwRklFr/ofdj6j5wEtuEgADH+ggidfHqLTSox1daZcTgXnro85Ru3X3ABLgi67He9Rj8O31RtTRVr1Scs0b6DZtGvTo2YkslpXqHt/vdBz5838FmSBdiP7S1zJh1gIyEob/NZw9fFJ2F5VDrxtXo+OnLjAn6hKAvlPEgznWdi6qA5OS5q1hRi33D/5Zv4pokqCuCjC2QjQjsogbEuOnLOECYJmUyUwWp1bgiiDtm2lILEazsKuAvXfuz0trag6D9UK1v3X2EDh0/T307N7GZ5YIA+w8jZgbbS+HZgGwMZN1hH6LTpfYT42euoFt3HnAbq1UqTtPmr7UEBNFeVUy3Ue2KNO+PjVSiUC7yTJiAcmfPrPWYKRU+7FBUsAJ/wzqYNVMay34ipNoICDT6PH5KWTL+916dxk/aIggIAoKAICAICPktc0AQ0ACBiJLFOhbUUfYMUMhBHahUaCDyfh43jzwSJqBff+zIhBAOP6xccnIiVxdnLqCJzbNOPsbY+MPbt2vbehbPQx0LNH3oNIYtRa8hU9hSZHCvryhfziy0/8hZJlkH9WylHSEChdnFq7cpeZJEwbxr0Y/xM5cz6aEKJ0IRiTHTTSUX0TGb/8cmLj57+vxV9plGyj+CRjUrl2TFHYpiwmdfFQON6O9E5+cQZNm57wSTplC7586eKTp/PtJ/a+P2g3Tg6FlW0RmzLEDyw7IHZDGIhRUb/qF1m/fyPacb2WMNytZdR8jJ2YlKF8nLSvkhqAAAIABJREFUZGqxArmYuILCFmOHIFOXNnVN40+M/t17+IgWr9rKmTw3bz+g5l9+xhkvuCbOWsnz0fgcM0shXcyth4+esmUQiEgELRBgxhxT3u4ofjd2cCfK/XEmzlIokDcb1aoSlImh6wVy1NfXn+tXqAtB2uETFlDRAjn5vlLFMmG7gfHbvucYNa5T0XRF+xAo27BtP/Xv0ox2HzpF4//NBELQF8GcEZMWMkmJwtXIXNDtwjPXzz8gxCwCrIVjpy2jwb1aU7kS+ZkYRp8RNBw98JtgY6xb31R7jAFB7DeQYTJ03HzOJlGFqmG7hqxBPNd0HCcjtih0OWj0HJ5XNSqVIE+PBHwPYc0IrTYC1pu5yzbS6zdvCZkmZrBO0nVOSbsEAUFAEBAEog4BIb+jDlv5ZkEgXAhElCz29fWlIWOhkKygVSEapJKjIOTw/u2DHV6gnu49dAorvlFIDP+PopKqQJC1tyxeh0JPhwJqUGD9NHYuJYjvTsUK5GT/WJ1ScsM10UJ4Ew6oKOY2ZspS6tO5CaeM/zplCRevS5rEkws8qWAEDqbrt+xjSwBdD3GqPyAKqlUszvYGIFKVr+2HYKXDZ5UP7KnzV/mAXTBvNrZHqVCqAKs3YdMAVSQKSSJNG5YpmK86X1Dgwq8XBfi6tqnHamKzXwjODJuwkB54P2bSI2O6lNwlkPywQilT7BMujIkL/tlm6jOeWVgv2H++Rjleo7HuIxtjaJ+2plwbcV8tWvU3Va9YglIm92Ll8IRZK7gAK4hWrCtmLKSL+YW2o2gzAtJ1q33KAWmoWK/euEPp06SgIycvskJVWU2guPWLV68pYXx3U6jc0V4EY1CsGnsL66LOWF9Q0Lr711+yWtws17J12+mh9xMeNwShB/f8itcOXBi/Bz6PWfUOH/dhfdtx5oUabwTfCuTJxnNZxwt+9L1+mkyNvqhAew+d5vvNK5EH3bh9n/p0Qo2RbPTn1n1UuUxhttvQtTCrCgiWL1mAg5kIIuXJkcVynwF7rI0YI5WVoON4GNuEfQTau2nHQWrdqBo/u0KrjfBR5nRcW+HAsXP0+7BunDUjlyAgCAgCgoAgoCMCQn7rOCrSpliFQETJYusDni5kMQ5lUHQbyWEcvqHqPnLygqWgnbFAkLOLM3vJQqkKchw+kUiH7fTVF5Qne2Yt5gP6hWJMUG3CtkXXw5i9YKFfSE2GTYYqwIVD3KYdh2jcjD/YAgCesBjTfsNncOo/FE5IM4c1ha4euGjv/qNnCSQkSHBHUSJhbP5Yv5MK5fuYsmdNz4T3bzP+YLIAgQn4LYMsgCINlg4IIPk8ekaVyhSiji1qa0WyYl2ApQtsTmBLYzabk7DuNfQPvr3wLEfWgSLpUqdIyuRCw1rlqW71Mqacm1BA/zRmLqtsC+T5iPYePsNFIkE+OsIFqy54myu7KwRnzVpIF+OBeQhFZ/rUyalBrfJc4BP1OGA1NPDXOUzWlSqShwOhQ8fO53UCzzpkPFUtXzTY806XvYaaZ2hPryGTmdxOnjQxE3Woa4EaELgU+Y3gLpTvCN7A+kVXexfVLwTKFq3aSsUL5uSAJvZHWFOu37pPD32ecIAa+ywUcYUqVxULtlYj65YhiPYgGAN/eViHIMCubImyZkzLfYLCH8F2ePKDTP3WUJhV1/UFayJU3whSGIMOqm4MRAQYP+y3YFcGkj+soqc69DWs2ghoI0h/2M3B+gu2PNhznTl/jfYcOk1tGlczRRBNB6ylDYKAICAICAJRi4CQ31GLr3y7IBAmAhEhi5+9eEUDRs7iAwEOBjjY6kYWGzsO4qDfsOnUr0tTbi9UPwNGzmTFIzxYofpBivmwfu34gLdw5d/sM4iDkaOQlmFOBM3eAOIDY9SqQVXOLIDyDGny8O8M+DeYAcLVDIc3zaCN1Obcf/iYSW4QObDOAPENQhKE8qfFglLiQZhPmb+G1YIgs3QJ3MAuY83G3eyFanabk/AMKu6p73+eStdvP+DCfHlz6BHYC0/bbb0HRA4sd7b8c5iDYsgIgdWEI1wgfFAoE4QqvLL/3LLP9IV0sQ7sO3KGUDS4SrkiVL1icS4uePLsFR6767fv0+DRc7mIHwJr8PCFXQqIcChA8WyG3y/I5a7t6mkTmOZg7c5DbMezfsteJlJVUWcVeD91LqiPmJ8QDkAZ3eubxpTMy1NbYg6BTczDYgVzWiy7UBgYdieoAXHq3FVe7/E6LMpAfrs4O9GwiQs54wnFhBH81S1DEMEKWJuAJFXWIMY1A+skiFQEZxAwxLMLAUTMQZ0v9dxFtlWTLypZBCCwYcM4wUZk/vLNvF6iOLVS8Rv7hPmqW82OsGojGAs9ox7CzbsP6M5db7b0qlGpeLDaOLoFznSeT9I2QUAQEAQEgchHQMjvyMdUvlEQ+GAEQiOLP8qU1lJQB956UM3oThajuN29B4+oQ8tajA1U3lDVwUv1zVtfTpmsULoAb5LRdxB3/bs2t3kw+mBw5QvChQBUTFAKDurRiv2ljYXg1Bcov1womSqXLcxFJnW1QwlXp034JqgAoRDs1q4+Zc6QmnsAggSkFny/VXG/P7fsp2OnL5om9dqEQxFqk0Eajp6ylN9jNpuTsMYCQRVfP3MU7QyrL+p1kFCXrt1mD/2GtSrQll2HHaKQrrH/CEKrdf3jrOnZyoaJuzqVLAEy2Dqs/GsX/fJ9WyYgsdcwksvhxTM63gfyEQUwP69QjMlGXNZ2aoq8z5whFfn6+bPlhi11e3S0197fQO2KQb/OYSU+MizUHP1u4CTq2bER/w0qcGTZDe7Zitd+6wxBe38zut+PjDPs/1RG2Tctv+D77tDxc/zscnZy5hoD1vsMXeqwIGABzJMk9uTxgMp95KRFlDdHFtq25ygHkLq2rc/3kq0L87XrgPFUKF92tpszettH91gYfy+k2ggvX74JZmeIz+B5MHPRelb39+7UiGpXKcVBJ91FOjGJr/y2ICAICAKCQPQgIOR39OAsvyII2IVAaGTxlRt3Od1cFdQxG1lsnZJrPJzFieNCo35fzKS38QBuF3jy5khBAIedJau3ccrqolVbmChAITilxMc8hGcnDmgowAXl2Yat+yzp2ZHSCPkSuxGAsgr+trARKZI/B38eFjDIDIE/M9TgckUvAggk9R4yhb6oWtq0NifRi5h+v+aIhXRPnrvKRXNhv4Mg9MhJC6lp3coWRaoiqzq0qMUWIijMbdx76DdKwVsE4tEYaMerMxauD1aUDwUKh09YSL/0baetP7bqlfJuf/biJXVqVYcJYOP+CQp41FFpUf8zKl4oF6v0jRmCuo8XMhr7DJ3K+wz4leO5tWbTbiZRR/Rvz31CliDGdFi/rylJYg9Ll3StRwIyHBl0ew6eYkEH7EBCqmOj6v+AIM+eNQNNmrOSn+PVKoT8megeU+vaCMig+Gf/CfqxewtL1o8KMH3VqCpnJ9x7+Jh6f9OIlq/fIRmd0T1g8nuCgCAgCAgCwRAQ8lsmhCCgOQJGsjhpYg/21subEwV1yrCqxGxksbFAkNokK39OHGymzFvDilUcAOSKeQSgDhz1+yJOEYfXNy5V8A4Hn4a1y1OzelXIPV5cLryIQ6uj+P7GPPr2twCq7/1HztB3HRpYAhVGawNHsaWwH5mY/YRuvrsxi4b5ft3RC+nCXmL4xIVsd4IgmZMTBcsow4hhr7H74CleV/B6jw4N2Sdc2SiBfH3n6xeiqjW6Rx3PrnHTl1GP9g1ZQWuLvEdQY/SUJVyg20imRndbw/t78Gifu/QvVnfDt/3R42c0ZnAnypczC//twpWbFh9ta6I/vL8RU++DRcuPo2azXUvponl5Xhnt1vwDAjlLEGOGC3MQNS5gS4TgLvbEOu09cD8sXLmF6zsYi5WGhC/2v7jHRv3Ykfdayqe/0qeFGBMdL9RGcHOLSy3qV+HxwjppKxPVbCIdHbGWNgkCgoAgIAh8OAJCfn84hvINgkCUImAki43eeigKZHayeOna7XTnnjf7c75+/VbLA0yUDq5JvxzFqHBIRTHMm3ce8GG8StkidPLcFfa1hOIYxSYxtji4KesNk3bX1M02WhvAb18uQUAQiDgCjlhIV1lGYF2H5cSFK7e4lsPVG3dp5IAOXCQS3tPzlm2koX3aspcxCMghY+dxnQG8jgtBttPnr9H3nZto56OthAIoEoniniDq8DfUGkkQPx5bsN33fkyLV23lgoRmsPFCoBPK/a+b16Q793xYYfzjdy15PKyJfliFYMziuLoQLG50raWCbIQhY+dSUq9EBIu/PYdO0ZDerblWjLWFC4orIpg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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = go.Figure()\n", "\n", "fig.add_trace(go.Scatter(x = mean_price_per_nbhood['neighbourhood_group_cleansed'],\n", " y = mean_price_per_nbhood['price'],\n", " marker_color = mean_price_per_nbhood['Total_amenities'],\n", " text = mean_price_per_nbhood.Total_amenities,\n", " mode='markers', name='Price while considering qty of amenities'))\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mean_price_per_nbhood = listings_new.groupby(['neighbourhood_group_cleansed','property_type'])['price','Total_amenities'].mean().reset_index()\n", "mean_price_per_nbhood.Total_amenities = round(mean_price_per_nbhood.Total_amenities)\n", "\n", "\n", "fig = px.scatter(mean_price_per_nbhood,\n", " x = 'property_type',\n", " y = 'price',\n", " color = 'Total_amenities',\n", " hover_data=['neighbourhood_group_cleansed'],\n", " template='plotly_dark')\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Results:\n", "So, from the above 2 graphs, we can decide on the probable price of a house type in each neighbourhood. The results can be listed as follows,\n", "\n", "- If you want to set the price solely based on the neighbourhood, then the prices observed is the highest for the Magnolia region, followed by West Seattle, Capital Hill, Queen Anne and Downtown regions, with the least price for listings available in Northgate, University District and Delridge.\n", "\n", "By adding more information like the property_type, we can even gather some more observations. These observations lead to the following results,\n", "- Listing a \"Boat\" in some of the other neighbourhoods could help us demand a high price. While that can be followed by listing a camper in Beacon Hill.\n", "- It is observed that, condos in West Seattle, Magnolia offer a high price in the listings which can't be considered as vehicles. While the least possible price listing is a \"Tent\" in some of the other neighbourhoods.\n", "\n", "Overall, one would be able to book a listing in seattle for a day, if his budget fits in the range of \\\\$ 84.00 to \\\\$132.00, in most of the neighbourhoods. \n", "\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" }, "tags": [] }, "source": [ "## What is the neighbourhood where the price is highest while providing minimum amenities?" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "If we observe correctly, there are some cases where there are no amenities providedm but the price of the listing is high, one such example is the listing of the camper at Beacon hill. Another listing with no amenities provided are **tent** and **tree houses**. Let us first take a look at them. " ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Apartment', 'House', 'Treehouse', 'Tent', 'Camper/RV', 'Bed & Breakfast']\n" ] } ], "source": [ "no_amenity_list = list(listings_new[listings_new.Total_amenities==0].property_type.unique())\n", "print(no_amenity_list)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "So, there are listings of apartments, houses with none of the amenities provided. As we can directly infer that the rentals of the tent can not be high, we are excluding them from further analysis. " ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "['Apartment', 'House', 'Treehouse', 'Camper/RV', 'Bed & Breakfast']" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "no_amenity_list.remove('Tent')\n", "no_amenity_list" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now our goal is to find the neighbourhoods vs price for this property types." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "(41, 4)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Exctracting the info needed.\n", "df = listings_new[(listings_new.property_type.isin(no_amenity_list)) & (listings_new.Total_amenities==0)].loc[:,['neighbourhood_group_cleansed', 'property_type' ,'room_type','price']]\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "customdata": [ [ "Private room" ], [ "Entire home/apt" ], [ "Entire home/apt" ], [ "Private room" ], [ "Entire home/apt" ], [ "Entire home/apt" ], [ "Entire home/apt" ], [ "Shared room" ], [ "Entire home/apt" ], [ "Entire home/apt" ], [ "Entire home/apt" ], [ "Entire home/apt" ], [ "Entire home/apt" ], [ "Entire home/apt" ], [ "Entire home/apt" ], [ "Entire home/apt" ], [ "Entire home/apt" ], [ "Entire home/apt" ], [ "Entire home/apt" ] ], "hovertemplate": "property_type=Apartment
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"text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(df,\n", " x = 'neighbourhood_group_cleansed',\n", " y = 'price',\n", " color='property_type',\n", " hover_data=['room_type'],)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "So, by using the advantage of **plotly**, we can find the highest price of each property type in the neighbourhood. The results are obtained in the following table.\n", "\n", "| Neighbourhood | Property Type | Price |\n", "|---------------|-----------------|-------|\n", "| Downtown | Apartment | 300 |\n", "| West Seattle | House | 350 |\n", "| West Seattle | Treehouse | 55 |\n", "| Beacon Hill | Camper\\RV | 375 |\n", "| Capitol Hill | Bed & Breakfast | 165 |\n", "\n", "But one thing that is to be noted that there might be the influence of some excluded factors like Square_feet of the houses or areas that might play a big role in this parameters. However, to limit the scope of the analysis, they are not being considered." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Which type of properties are common in each neighbourhood?" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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neighbourhood_group_cleansedproperty_typecount
0BallardHouse132
1BallardApartment60
2BallardTownhouse10
3BallardCondominium3
4BallardLoft2
\n", "
" ], "text/plain": [ " neighbourhood_group_cleansed property_type count\n", "0 Ballard House 132\n", "1 Ballard Apartment 60\n", "2 Ballard Townhouse 10\n", "3 Ballard Condominium 3\n", "4 Ballard Loft 2" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nbhood_listings = listings_new.loc[:,['neighbourhood_group_cleansed', 'property_type']]\n", "nbhood_listings = nbhood_listings.groupby('neighbourhood_group_cleansed').value_counts()\n", "nbhood_listings = nbhood_listings.reset_index().rename(columns={0:'count'})\n", "nbhood_listings.head()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "total_listings = listings_new.loc[:,['neighbourhood_group_cleansed', 'property_type']].groupby('neighbourhood_group_cleansed').count().reset_index()\n", "total_listings = total_listings.rename(columns={'property_type':'tot_count'})" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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neighbourhood_group_cleansedtot_count
0Ballard215
1Beacon Hill111
2Capitol Hill528
3Cascade81
4Central Area339
5Delridge78
6Downtown505
7Interbay10
8Lake City61
9Magnolia50
10Northgate73
11Other neighborhoods733
12Queen Anne277
13Rainier Valley148
14Seward Park40
15University District103
16West Seattle183
\n", "
" ], "text/plain": [ " neighbourhood_group_cleansed tot_count\n", "0 Ballard 215\n", "1 Beacon Hill 111\n", "2 Capitol Hill 528\n", "3 Cascade 81\n", "4 Central Area 339\n", "5 Delridge 78\n", "6 Downtown 505\n", "7 Interbay 10\n", "8 Lake City 61\n", "9 Magnolia 50\n", "10 Northgate 73\n", "11 Other neighborhoods 733\n", "12 Queen Anne 277\n", "13 Rainier Valley 148\n", "14 Seward Park 40\n", "15 University District 103\n", "16 West Seattle 183" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "total_listings" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "temp = pd.Series(index = range(nbhood_listings.shape[0]))\n", "temp.fillna(0)\n", "for neighbourhood in total_listings.neighbourhood_group_cleansed.to_list():\n", " bool1 = nbhood_listings.neighbourhood_group_cleansed == neighbourhood\n", " bool2 = total_listings.neighbourhood_group_cleansed == neighbourhood\n", " temp[bool1] = nbhood_listings[bool1]['count']/int(total_listings[bool2]['tot_count'])\n", " \n", "nbhood_listings['percentage'] = round(100*temp,3)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
neighbourhood_group_cleansedproperty_typecountpercentage
0BallardHouse13261.395
1BallardApartment6027.907
2BallardTownhouse104.651
3BallardCondominium31.395
4BallardLoft20.930
\n", "
" ], "text/plain": [ " neighbourhood_group_cleansed property_type count percentage\n", "0 Ballard House 132 61.395\n", "1 Ballard Apartment 60 27.907\n", "2 Ballard Townhouse 10 4.651\n", "3 Ballard Condominium 3 1.395\n", "4 Ballard Loft 2 0.930" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nbhood_listings.head()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Since, we have to find the common properties of each neighbourhood, we can exclude those which contribute less than 5% of the count." ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "nbhood_listings = nbhood_listings[nbhood_listings.percentage>5]" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "bingroup": "x", "histfunc": "avg", "hovertemplate": "property_type=House
neighbourhood_group_cleansed=%{x}
avg of percentage=%{y}", "legendgroup": "House", "marker": { "color": "#636efa", "pattern": { "shape": "" } }, "name": "House", "offsetgroup": "House", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ "Ballard", "Beacon Hill", "Capitol Hill", "Cascade", "Central Area", "Delridge", "Interbay", "Lake City", "Magnolia", "Northgate", "Other neighborhoods", "Queen Anne", "Rainier Valley", "Seward Park", "University District", "West Seattle" ], "xaxis": "x", "y": [ 61.395, 63.063, 27.462, 9.877, 56.932, 70.513, 40, 78.689, 64, 54.795, 60.709, 28.52, 70.946, 77.5, 34.951, 67.213 ], "yaxis": "y" }, { "alignmentgroup": "True", "bingroup": "x", "histfunc": "avg", "hovertemplate": "property_type=Apartment
neighbourhood_group_cleansed=%{x}
avg of percentage=%{y}", "legendgroup": "Apartment", "marker": { "color": "#EF553B", "pattern": { "shape": "" } }, "name": "Apartment", "offsetgroup": "Apartment", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ "Ballard", "Beacon Hill", "Capitol Hill", "Cascade", "Central Area", "Delridge", "Downtown", "Interbay", "Lake City", "Magnolia", "Northgate", "Other neighborhoods", "Queen Anne", "Rainier Valley", "Seward Park", "University District", "West Seattle" ], "xaxis": "x", "y": [ 27.907, 19.82, 64.773, 77.778, 29.204, 19.231, 88.911, 40, 18.033, 20, 34.247, 31.105, 59.928, 19.595, 22.5, 58.252, 22.951 ], "yaxis": "y" }, { "alignmentgroup": "True", "bingroup": "x", "histfunc": "avg", "hovertemplate": "property_type=Townhouse
neighbourhood_group_cleansed=%{x}
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EIAABCEAAAhBohADy2zw1Gjr5bd7r/h7cxHVQ92isHzdhb3JvrzIsffv0liHF/UxuEdq2yO/QLg0DgwAEIAABCEAAAhCAAAQgAAEIQAACEMg0Ashv8xXNRPndmKB21utujJzzlHdDMY1J9cZqfpuvUHh6QH6HZy0YCQQgAAEIQAACEIAABCAAAQhAAAIQgECGE0B+my9wJspvJaiXr1zj+0GXXjRja5o745HfXvR4HQIQgAAEIAABCEAAAhCAAAQgAAEIQAACENAmgPzWRkUgBIwJcPJbRJyF5hXR2F8VcP56QWzxfOMVoAMIQAACEIAABCAAAQhAAAIQgAAEIACBrCGA/M6apWaiISCQ9fI7tp5O7N9jf41CPdlUXerJrVwQgAAEIAABCEAAAhCAAAQgAAEIQAACEPBDAPnthxaxEDAjkPXyW536nlq+SGaWjZbuXTpKbK0bJbu7dupQ98TTRNYUMltKWkMAAhCAAAQgAAEIQAACEIAABCAAAQiEnQDyO+wrxPgyiUDWy2+1mEpwr3riOVk4u9Ra2znzl0n5lNHSrGlTmTFnsfTt07tOfsfK8kxKBuYCAQhAAAIQgAAEIAABCEAAAhCAAAQgkFgCyO/E8qV3CDgJIL9FxDrNfdu98vGnu+X9HTvran7bp8CHDuovJxzX0+IWK78/+3wfGQUBCEAgrQnkRCJSG42m9RwYPAQgkN0EcnNzpKamNrshMHsIQCCtCeTk5EhtLe9jab2IDB4Cfgise1rk1hn6LabNFzl8v5cK89WqRX4oh/fxJyI7d+r/zNumTUTaFoVyKgwqDgJZL7+VzLZPerduWSh2ze+SQf1lwOl9PU9+V9fob5441ocmEIAABBJPICIivJUlnjN3gAAEEkcgEhHhQ7zE8aVnCEAg8QT4fizxjLkDBEJEoPr5J2XvTdO0R9T8+tslp0cv7fhUBeblqjez8F0PPVIjK1frf8A46JwcGTwgN3wTYURxEch6+a1Ofd+3co1cN3GkFDTb/wmV86GW1PyOK69oBAEIQAACEIAABCAAAQhAAAIQgAAEINAAAcqeJDctkim/G3pWoH3QtnRMSV1lieQSyO67Zb38Vie/x0yqlPKy0VYCOk9+Dynut78kysLlsqDiGlEnw51iPLtTh9lDAAIQgAAEspPA+g1RWf14jfbkL/1FnrRupR1OIAQgAAEIQAACEIBAhhNAfid3gZHfyeUdtrulVH7bNbUffnytHNq+jfXAyY7t2x5QaiTR0JTgHnF1Rd1trp80su4Bl+ofV6x6SqbPXmy9PvCMvvVOiSd6bPQPAQhAAAIQgEC4CDyztlaWLNWX3zOnNpH27cI1B0YDAQhAAAIQgAAEIJA6Asjv5LIPo/xuzDXGPmsw9nmE9qHd9Rs3H+ApY19bMm8yJ81FJKXy2y4pomprz1mwTC4acqZ079LROm0dW4okuduCu0EAAplEIPrZTtl12WDtKRUMv0qaDizRjicQAhDILgLI7+xab2YLAQhAIJMIvPGW/oNeeOBbJq08cwkbAeR3clckbPK7oSoTH3z4iXXYdvuOj2Vq+SKZWTbacqSx8ju2PPPdKx6V4jP6WkDHTr5J1DMMVSWL2GccJpd4uO6WMvmtPo0om7VIJo4bZp32dspvFihcScJoIJDuBJDf6b6CjB8C4SKA/A7XejAaCEAAAhDQI1BVJTJ2QpVesIjwwDdtVARCwDcB5LdvZEYNki2/ndUlnAO3T2LHllR2nvZW8Y3J76N6Hm5Vy+jQrkjGj6l/YC9WqMdKcyOAad44lPKbk99pnlUMHwIhI4D8DtmCMBwIpDkB5HeaLyDDhwAEIJClBJDfWbrwTDuUBJDfyV2WZMtv57MD1UxjH3gZe3rbeUDYTX47n1Volz2xSzfHlnS2CVP6JMVlT1R9m7XrNkjZVRfJzYsfsMqeFLUqrHdMP7nbgbtBAAKZSAD5nYmrypwgkDoCyO/UsefOEIAABCAQPwHkd/zsaAmBoAkgv4Mm6t5fGOW3GrF9elv35LeS387L2e6TT3dTQrqRNEjZyW97PA19MsGnEsl9E+BuEMh0AsjvTF9h5geB5BJAfieXN3eDAAQgAIFgCCC/g+FILxAIggDyOwiK+n2ETX4rF1pWvkgWzi616nqrk+B2ze+9X39tHQouHVNiPazS9qbKlaqyJ39++En56cAfSkGzfKuut10ipaHDxKqtumKluT65zIhMufzODIzMAgIQCDMB5HeYV4exQSD9CCC/02/NGDEEIAABCIggv8kCCISHAPI7uWsRNvmtZq+qYUyfvdgCMfCMvtbDLpXQVpfzoPD5g0+Xzz//UoYO6m9JbCXK71i6qg6g8wCxXV7FLolydK/DZUHFNdK6ZWFygYfsbsjvkC0Iw4EABIIngPwOnik9QiCbCSC/s3n1mTsEIACB9CWA/E7ftWPkmUcA+Z3cNU2m/E7uzLibDoGUye/YTyMaG6xduF1nMsRAAAIQaIgA8pu8gAAEgiSA/A6SJn1BAAIQgECyCCC/k0Wa+0DAmwDy25tRkBHPPlcr/3y+VrvLH5yYIyeflKMdT2C4CaRMfisssU83Vf/21d59MmPOYus4v6plo/7ct09vGVLcL9wkGR0EIBBaAsjv0C4NA4NAWhJAfqflsjFoCEAAAllPAPmd9SkAgBARQH6HaDEYSsYTSJn8Vie/y2YtkonjhlnF3Z2Xqnvz3rYPrKeeqjo3961cU6/2TcavChOEAAQCJYD8DhQnnUEg6wkgv7M+BQAAAQhAIC0JIL/TctkYdIYSQH5n6MIyrVASCKX8dgrv7Ts+ljnzl0n5lNFZX6A9lBnEoCCQBgSQ32mwSAwRAmlEAPmdRovFUCEAAQhAoI4A8ptkgEB4CCC/w7MWjCTzCaRMfjvLm6inlTovp/x+bdNmqVy4nKeTZn4uMkMIJIwA8jthaOkYAllJAPmdlcvOpCEAAQikPQHkd9ovIRPIIALI7wxaTKYSegIpk9+KjJLcZeWLZOHs0rrSJ/aDMEvHlIiS4qoEytp1Gyh7EvpUYoAQCC8B5Hd414aRQSAdCSC/03HVGDMEIAABCCC/yQEIhIcA8js8a8FIMp9ASuW3wvvOlu0yZlKlvL9jZx3tJfMmW+KbCwIQgEAQBJDfQVCkDwhAwCaA/CYXIAABCISTwNdfi6x+okZ7cEf3ypHDu0a049M9EPmd7ivI+DOJAPI7k1aTuYSdQMrld9gBMT4IQCD9CSC/038Ng57Bs8/Vyj+fr9XuduKVedqxBGY+AeR35q8xM4QABNKTwK7dIqXTq7QHf/55uXJW/xzt+HQPRH6n+woy/kwigPxO7mrWfvi+1H70vvZNcw45VHLaHaodT2C4CSC/w70+jA4CEAiAAPI7AIgZ1sVDj9TIytX68vu2yiaSh//OsCyIfzrI7/jZ0RICEIBAIgkgv93pIr8TmX30DQF/BJDf/niZRu9dfofsvf9O7W6a/ewSaVYySjuewHATSKn8bqjkiY3r6F6H85DLcOcOo4NA2hBAfqfNUiVtoMjvpKHOyBshvzNyWZkUBCCQAQSQ38jvDEhjppAlBJDfyV3oVMhv9ZzDEVdXyPWTRsqQ4n4Jm3Aqn5WovO7U8kUys2x03bMcEzZRg45TJr+/2rtPZsxZLH379JZjv9ND7l7xmEwcO0wKmuXLjQuXy6knHUPdb4OFpSkEIPA/AshvsiGWAPKbnDAhgPw2oUdbCEAAAokjgPxGficuu+gZAsESQH4Hy9Ort1TIb+U21fXBh5/IdRNHWr4zERfy25tqyuT3p7v2SNmsRTJx3DBrlHPmL5PyKaOldctCUZ+O3LdyTUKTwxsNERCAQKYQQH5nykoGNw/kd3Ass7En5Hc2rjpzhgAE0oEA8hv5nQ55yhghoAggv5ObB8mW38p5lv/+brl8+GCZfetSy31279LRmrR9WvrHZ/1Aym++2/q3URcUy/gxJXWvj5lUKe/v2HnAa7bobtGiudz70BMypPhU+de6DXWxdhWNO5c9Ykn3rds/lPUbN8uh7dvIvN/8Uv64/G/y8ONrrb8vnF1aNyb7gLJ6TV32aXU1j7GTb5Ifn/l9WbJ8tXWfgWf0tXytutShZruN+vuSeZNDeZA5FPK7qFWhlRRlV11kyW+VCE4Zntwtwd0gAIFMI4D8zrQVNZ8P8tucYTb3gPzO5tVn7hCAQJgJIL+R32HOT8YGAScB5Hdy8yHZ8lsd6n36uVctoa1OgHft1KGu9IldArr49JOs123BXDqmxBLHf1vzvPTo9i1LTNux5WWjrdeU/J4+e3E9ydzQyW91z+df3lRXTlr9fdUTz9UJb/tUurq/Lb47tCuqG499WFn5WiW/O3dsV094qyoeqpQLZU888thZ9kQBcyZDKo/sJ3f7cTcIQCAZBJDfyaCcXvdAfqfXeoVttMjvsK0I44EABCCwnwDyG/nNXkgsgbffjUpNtd49WraMSId2erHZGIX8Tu6qJ1t+O8s5KxFeuXB5nYhuSBg7ZbSTTKw7bciXNia/VT/2afLYGOfft+/4+IC63bajPe3k71ry2xbzqk+nv0V++8xj+5MO+zi+8/i9z64IhwAEIFCPAPKbhIglgPwmJ0wIIL9N6NEWAhCAQOIIIL+R34nLLnq2RNq0Ktm9R4/FKSflyIgLc/WCszAK+Z3cRU+m/I6tZhF7srshYaxk9HvbPrBkdWwJEkXKLkOSKPntLLNir4y6J/I7uXnK3SAAAQjETQD5HTe6jG2I/M7YpU3KxJDfScHMTSAAAQj4JoD8Rn77Thoa+CKA/PaFyzUY+R0cS52ekim/7dIkseOy63q7nfweO/wnVh1tuwRJsk5+N1Z6Olbcqzlx8lsn476JcT7w0i76bjfngZc+QBIKAQh4EkB+eyLKugDkd9YteaATRn4HipPOIAABCARGAPmN/A4smeioQQLI7+ASA/kdHEudnpIlv2NltT0252nwTz7bU6/MiFOGd2zf1pLfdk1tWz6XDOpv1dhu6OR3bFkVW1Cr/+uUPVFxTuGu/q7G9Pa7/5ETv9vLtexJQ3JcZz2SHROKB17Gym8eeJnsNOB+EMhsAsjvzF7feGaH/I6HGm1sAshvcgECEIBAOAkgv5Hf4czMzBkV8ju4tUR+B8dSp6dkye/GamDbUnzooP5S1PpgiS0zsmTeZOuBlupSMnvE1RXWnw9t30batj5YSs49rVH57SyTcnSvw63a4ncue8RqryO/C5rlH1BqRd1XlaO2H3jZWM1vdQ/nSXfnPHTWJVkxoZTfPPAyWcvPfTKGwNd7pfrtDdrTyT2si0RatdGOT/dA5He6r2Dw40d+B880m3pEfmfTajNXCEAgnQggv5Hf6ZSv6ThW5Hdwq4b8Do6lTk/Jkt86Y0mXh0TqzCVdYpIuv9UiN1RI3QnM/oQh9kR4ukBlnBBINoHa7Vtl99UXat+2+dgyyT9toHZ8ugciv9N9BYMfP/I7eKbZ1CPyO5tWm7lCAALpRAD5jfxOp3xNx7Eiv4NbNeR3cCx1etq3ZpXsW7P/NLTOld9/gOT3L9YJ9R2D/PaNzLhB0uW3PWK3mt/Gs6IDCGQZAeS3+4Ijv7NsQ2hMF/mtAYmQRgkgv92T4813ovLQqhrtDPp5SZ4c2l47nEAIQAACjRJAfrsnR1WVyNgJVdoZNOicHBk8IFc7nsDMJ4D8Dm6Nkd/BsaQnCHgRSJn89hoYr0MAAvoEkN/Ib/1sIVIRQH6TByYEkN/u9F58JSrzF1drI55amifdOke04wmEAAQg0BgB5Dfym92RWALI7+D4Ir+DY0lPEPAikFL5bT8VdP3GzQeM0y7S3rplodcceD1NCcy9pVo2vRXVGr36oVj9cMzVMAHkN/KbveGPAPLbHy+i6xNAfiO/2RMQgEA4CSC/kd/hzMzMGRXyO7i1RH4Hx5KeIOBFIKXy+8aFy63x2U8f9RpsIl93Pk01Vrw7n1w68Iy+ct3EkaKehsplRgD5bcbP2Rr5jfwOLpuIguztAAAgAElEQVSyoyfkd3asc6JmifxGficqt+gXAhAwI4D8Rn6bZRCtvQggv70I6b+O/NZnRSQETAmkTH6Hqea3Et+VC5fLgoprJPakeexrYRL2pouf6vbI7+BWAPmN/A4um7KjJ+R3dqxzomaJ/EZ+Jyq36BcCEDAjgPxGfptlEK29CCC/vQjpv4781mdFJARMCWS9/PaS8Ep2d+3UQYYU97NYu4ny2MX4f/fWyAcf6pX16NwpIuf/JLseJoL8Nt2+/2uP/EZ+B5dN2dET8js71jlRs0R+I78TlVv0CwEImBFAfiO/zTKI1l4EkN9ehPRfR37rsyISAqYEUia/1cBjxbLpZOJp/86W7TJmUqW8v2NnXfNRFxRbpVi+2rtPZsxZLH379K6T3yp+avkimVk2Wrp36eh6yxsqq+W9rXryu9cRESm9IrtqWiO/48nYhtsgv5HfwWVTdvSE/M6OdU7ULJHfyO9E5Rb9iuz9WmSL5vfPitdhHSPS4iDIQWA/AeQ38pu9kFgCyO/g+CK/g2Op09N7+/bIe1/v0Qm1Yro2LZSu+TyDUBtYyANTKr+VSL57xWMyceywlNXQVie571u5pq6Ot/0QzpJB/WXA6X0t+T10UH854bie1lLGyu+vvq5pdInn3ByVrdv05PeRPUR+eVlOyNMl2OHdvLBW3nxHr88unSIy4cqIXnAWRkXf3yr7Jv1Ce+Z5o38luf2KtePTPTD62U7Zd+UQ7WnkXfRLyT1nqHZ8ugdGciISrdV7r0r3udrjX/VoVB55VH/ON83Kkbzs+nwyU5Y6IfP41wtRuec+/fyZPjFH2h2SkKGEstNX1ov84f/Vao9NfX1XX+dNrpyciNRm2fuYCa8wt1Xie+4t+vvr0otz5NijwjwjxpZMAkp+T7tB//1nyKCInHaq2ftPUPNLxvdjVVUi46fq8xlwVkSKzwoHn6A4048ZganX18puTX/4/e9F5MIS8qcx4rUvPCVVv5+uvSD5194mke69tONTFVjQNJwVDa7d/oJc9/6/tbHMOPR7cm3HE7TjwxJIqeaGVyJl8tuWzOs3bm5wZLEPnUxUIsXKb3Uf9YDLtes2yOQrfy4VN//J9eT3F3urGx1a5S1KfuuN/IgeIleMzq4vDLfcHpW3tOW3yPhfZhcfvczZH6Xkd/WvLtZuknvpJMnJIvktn+2Uqqt+qs/nwl9Kzjk/045P98CcSERqo/qiId3nq8avxPfqx/RnUjkzgvzWx5XxkWtfiMrS+/WnOXVCJOvk9+I/6b+nqK/vXTrp82woMjcSkZosex8zIxbe1lu2itx4q37+jPxFBPntspzRv94jNa8+r7fgBxVK3v9drxcb0qjdu0Wmz9TPn/N+HJH+p4ZjMsn4fkzJ7wnT9Pmcc6aIEuBcELAJTLshKns05Xff70Xkguw5T+Q7SZT8rrn519rt8mYo+b3/UGaYr4OahfPEULLkt/KJ02cvbnCJkuE5kd8N746Uye+wbFZ1knvO/GVSPmV03cMuVbK+t+0Dq/SJSc1vyp64rzJlT4LbBZQ9cWepTn7vumywNvCC4VdJ04El2vEEph8Byp6k35qFacSUPXFfjRdficr8xY0fDohtPbU0T7p1Rq6EKcdTOZZ3t0Rl5o36+TNuVJ4cfwz509iafTl/luxbs0prSSOtiqTl7X/Rig1rEGVP3FdGye+xE6q0l2/QOTkyeEA4T3FqT4LAQAlQ9iQ4nJQ9CY6lTk/Jkt/OsdgHa6+bODJp1S6Q38jvBgnYdb07tCuyZLd9Ir10TIlV6iT2AZd+Egn5jfzWeRMOIgb5jfwOIo+yqQ/kdzatdvBzRX4jv4PPKnq0CSC/g80F5Lc7z/PPy5Wz+mdP6Unkd7D7Kxt7Q34Ht+rI7+BY6vQUFvntPBk+8Iy+dSWYY6tSOA/iKk9ZNmuRTBw3zJqqeg7hj8/6gZTffLf1d/u5herPqt3nX+6Vzz//Uh5+fK0c2r6NLJxdWvfMwtgqHEvmTa4r8xzrOxsa0x1L93+g7uzX9qrqfuq6ftLIuucm6qxNMmKy/uS3ghy7+LEL1Vhyei0Q8hv57ZUjQb2O/EZ+B5VL2dIP8jtbVjox80R+I78Tk1n0qgggv4PNA+Q38ttJAPkd7P6qefN1qXp5v+zRuZqde6FIswKd0NDGIL+DWxrkd3AsdXoKg/xu6HDtBx9+Ygnw7Ts+rqtKoeYzdvJN0rljuwNe++SzPTJmUqUUn35Sgwd4lcBe9cRzdcLbefpc9auea9i3T29LTqtKGKqv8rLRlgB3k9+vbdpc73mJai7qOqrn4VafzgPFtqjv3qWjztIkJSal8tv56YD9qUHH9m3rLUZSKCToJshvd7CUPQku8ZDf7iwpexJcrmVKT8jvTFnJ1MwD+e3OnbInqcnLTLkr8jvYlUR+u/Pk5Lc7H8qeuPP5+q/3yld/vFl706qyQqq8UDpfyO/gVg/5HRxLnZ7CIL9j5bKSz+oU98yy0WK70KGD+lvTeeQf+5/XcdGQM+WTT3fL08+9asluZxsll22vqtp5CWwl2O372WLaOSY3+f3IE2tl+co1sqDimrqS0Wp8seNR/xZbPlpnfRIdk1L5bQMZcHpfmbNgmbWoagEaeghlokEkon/kN/I7EXnVUJ/Ib+R3snItU+6D/M6UlUzNPJDfyO/UZF523BX5Hew6I7+R304CnPwOdn8hv915nnJSjoy4kJrxjVFCfge7H716C4v87tqpQ11JEGc5E+VCbUeq5tKpYzvZtv3DetOyT2s7BbZf+e31zEN1QyXZ1eVW9sQu2aKEujo9/v6OnfXGGrbSJymT385FVp9wOOV3Qw+h9ErkML6O/EZ+Jysvkd/I71gCy1bUyGNP1mqlYF6eyG2VTbRiMyUI+Z0pK5maeSC/kd+pybzsuCvyO9h1Rn4jv5Hfwe4pZ2/Ib+S3SXYhv03o+W8bFvntlMuxp6aVbFYnvls0byaXDBsgqsTJwj/ufxD1mIvPtQ4Lp+rkd0Gz/DrotnBX5VOO/U6PunItrVsW+l+YJLUIpfzm5HeSVj/Ft6HsSXALgPxGfiO//e0n5Lc/XkTXJ4D8Rn6zJxJHAPkdLFvkN/Ib+R3snkJ+V8nuPXpMOfntzgn5rZdHQUWFQX4r11lWvqiuHrc66W3X/FZy2a7BbdfztiXz1u0f1pUbMZHfiqWzPndsze+G6oOrNqom+VNrX5Ye3b5lCXin/FaVPJx9qnjV79vv/kfO7n9iUMtn3E/K5LcauQ227KqL5ObFD1hlT4paFVqF3UsG9Q/d00H90ubktzsx5LffjGo8HvmN/EZ++9tPyG9/vIhGfvvJAWp++6FFbCwB5HewOYH8Rn4jv4PdU8hv5HdQGYX8DoqkXj9hkN+2B50+e7E1aLt0iH2qOraEiR3/3rYP6kqRmMhvdR9VhUM51/UbN1tjWDJvslUrXF2xz2UcUXKOvLrhHUt+qwdejri6og72qAuK68bkbKcC7Gc68sBLR26qTz6cAGPh66VxOKOQ3+7rgvwOLm+R3+4ss/GBl5Q9cc8J5Hdw7z/Z2BMnv91XHfmdjbsiuDkjv4NjqXpCfiO/kd/B7inkN/I7qIxCfgdFUq+fVMhvvZERlQwCKT35nYwJpvIeyG/kd7LyD/mN/I4lgPxGfifr/Scb74P8Rn6b5P2WbVFZ/mCNdhfnn5crnb8V0Y5P90Dkd7AriPxGfiO/g91TyG/kd1AZhfwOiqReP0t2bpK7dr6hFywiw9scKSPa7D8RzZX+BFIqv2Pr2yicztox6kmm6Xwhv5Hfycpf5DfyG/ntb7dx8tsfL6LrE0B+I79N9sTGN6NSeWu1dhelV+RJryOQ340BGzcqT44/Jnv4aCfON4HIb+Q38tvvrtGP54GX7qyo+e3OB/mtv9eIhIApgZTJ74Zq2diT4YGXpsuaHu0pexLcOiG/kd/Ib3/7CfntjxfRyG8/OUDZE3dayG93Ppz89rPbvGOR38hv5Lf3Pok3AvmN/I43d1Q75LcJPdpCwB+BlMlvVWS9bNYimThumPW0UOelCrjPmb9MyqeMltYtC/3NKETRnPx2Xwzkd3DJivxGfiO//e0n5Lc/XkQjv/3kAPIb+e0nX2Jjkd8m9A5si/xGfiO/g91Tzt6Q38hvk+xCfpvQoy0E/BFImfzm5Hf9hVK/zqp+rTWbLuR3cKuN/EZ+I7/97Sfktz9eRCO//eQA8hv57SdfkN8mtLzbIr+R38hv730SbwTyG/kdb+6odshvE3q0hYA/AimT32qYqrxJWfkiWTi7tO70tzr1PWZSpYwbPlio+e1vMdMtGvkd3Iohv5HfyG9/+wn57Y8X0chvPzmA/EZ++8kX5LcJLe+2yG/kN/Lbe5/EG4H8Rn7HmzvIbxNytIWAfwIpld9quLbsfn/HzrrRL5k3WU44Lv2fqkrZE/eERH7737CNtUB+I7+R3/72E/LbHy+ikd9+cgD5jfz2ky/IbxNa3m2R38hv5Lf3Pok3AvmN/I43d5DfJuRoCwH/BFIuv/0POX1aIL+R38nKVuQ38hv57W+3Ib/98SIa+e0nB5DfyG8/+YL8NqHl3Rb5jfxGfnvvk3gjkN/I73hzB/ltQi6+tl9+LPLFzqh244PaRKR5W+1wAkNOIGXy2+2BlyFnpj085DfyWztZDAOR38hv5Le/TYT89seLaOS3nxxAfiO//eQL8tuElndb5DfyG/ntvU/ijUB+I7/jzR3ktwm5+NpueKhGNqys1W7ce1CO9B6cqx2fqkDlVsdOvklKx5QEWkHDfk5j3z69PUtSq4oeU8sXycyy0XUlrVPFo7H7Ir8TuCLIb+R3AtOrXtfIb+Q38tvfbkN+++NFNPLbTw4gv5HffvIF+W1Cy7st8hv5jfz23ifxRiC/kd/x5g7y24RcfG1TIb9vXLhc7li6qm7AA8/oK9dNHCkFzfLjm0QDrZDfeihTJr/V8FQinHrSMYF+OqE37eREIb+R38nJNBHkN/Ib+e1vtyG//fEiGvntJweQ38hvP/mC/Dah5d0W+Y38Rn5775N4I5DfyO94cwf5bUIuvrbJlN/2qWk1UqfsXrHqKenUsV2gDjRR8js+yuFtlVL5rY7G373iMZk4dlign3yEBTfyG/mdrFxEfiO/kd/+dhvy2x8vopHffnIA+Y389pMvyG8TWt5tkd/Ib+S39z6JNwL5jfyON3eQ3ybk4mubTPn9wsubpHLhcllQcY20blnY4IBtab1+42br9SXzJtdJcXVQ+PMv98rnn38pDz++Vg5t30YWzi6tKymiXOqYSZXy/o6ddX3HtrdPnI+6oFjGjymx4pR8//uT/7b+/PRzr9bdV/3Zjrf7iS17otquXbdBWrRoLvc+9ES9MceWPVHjV5d9X8XjvpVrrA8C9n79tVWm5cTjetbdU52Iv7jkbLn617dYc0rECfmUye/YhY7NhqN7He6aKPGle3JbIb/dec+9pVo2vaX3wIFunSMytTQvuQuYRndDfrsvVvSznbLrssHaK1ow/CppOnD/F4h0vZatqJHHntSraZaXJ3JbZZN0nWpc40Z+x4WNRt8QeGZtrSxZWqPNY+bUJtK+nXZ42gciv92XcOObUam8tVp7nUuvyJNeR0S049M98N0tUZl5oz6fcaPy5PhjsoeP3/VFfrsTO/+8XDmrf45frGkbX1UlMnZClfb4B52TI4MHhL/mrfaEAg5EfrsDPeWkHBlxIfnTGKWq556ULyqnamdl4axFktujl3Y8gfUJJFN+x8rf2LWIFcu2zC4vG20JcNV+1RPP1QlvWzw75XHJoP5WLe7Yk9/OWHXfGXMWS4d2RZaIVq/Nv+uhev1On724Trw31Nau+a1ec8Y6Bf8nn+2pV/NbV36rMdnj79yxXT05HnQN85TJ72zYiMhv91VGfge3C5Df7iyR3+58kN/ee1F9OKA4cUFAEUB+u+cB8hv5bfJOgfw2oXdgW+Q38ttJAPkd7P5CfiO/TTIK+W1Cz3/bZMvvrp06NPqgyIYeEOkUxm7y+LVNm+udKnfK76N6Hm7JbudDKp2S+h/PvmSd3rZLscSeUHf+vVnTpvX6copxVbNczWHO/GVSPmW0xCO/bbkd+0GAnwdt+skC5LcfWj5jkd/Ib58pE3c48hv5HUuAk9/uOcHJ77jfbmiI/PbMAeQ38tszSVwCkN8m9JDfu3aLlE7XP9nMyW/3fOPktzsf5Dfy2+QdG/ltQs9/22TLbzVCu+xH7Gid4tgui6Lk8nvbPrDaeMlvu4SIktANye+hg/rXlVBx3gv57T9vAmlhG31nDZuO7dse8ElFIDdLQSfIb+R3stIO+Y38Rn77223Ib3+8iK5PgJPf7hmB/EZ+m7xnIL9N6CG/kd/u+cPJ72D3F/Ib+W2SUchvE3r+2yZTfnvV/Obk902SVSe/1acZ6lcBBpzeV+YsWCYXDTnTKuDuLIauPslI1wv5jfxOVu4iv5HfyG9/uw357Y8X0chvPzmA/EZ++8mX2Fjktwk95DfyG/kd7A5y7w35jfw2yTfktwk9/22TKb/tg75qlHaJEfVndbq7U8d2YpcnsWtxN1TzW8W7PTDSlsfKn464uqJe3e7lK9dYz1C0S5c4a34no+xJQ7XDbRb2Ay+zRn6ro/llsxbJxHHDRJ32dsrvhn4FwH9qp74F8hv5nawsRH4jv5Hf/nYb8tsfL6KR335yAPmN/PaTL8hvE1reban57c6IsifufCh7gvyOJTB+WpXs3uP93qMieOClOyfkt14eBRWVTPltj1kd+L1j6aq6KQw8o2+dDLfLlazfuNl6fcm8yXWlStzKnqgDwrbwVu1Un1u3f1h3klr9m/O+oy4orpPosXW7E1XzO7bKx4iSc+TVDe80+EDLjK/57Sa/Ofkd1PYOdz888DK49UF+I7+R3/72E/LbHy+ikd9+cgD5jfz2ky/IbxNa3m2R38hvJwHKnnjvGT8RnPx2p4X8Rn772U+Jjk2F/E70nOhfn0BKH3hpf+pQdtVFcvPiB6yyJ0WtCmXs5JukZFD/Rp+Mqj+91EZy8tudP/I7uPxEfiO/kd/+9hPy2x8vopHffnIA+Y389pMvyG8TWt5tkd/Ib+S39z6JNwL5jfyON3dUO05+m9Dz3/a9Z2tlyz9rtRt2+UGOdD05RzuewHATSKn8Vmicx/VtVM7j/uHG5z465DfyO1n5i/xGfiO//e025Lc/XkQjv/3kAPIb+e0nX5DfJrS82yK/kd/Ib+99Em8E8hv5HW/uIL9NyNEWAv4JpFx++x9y+rRAfiO/k5WtyG/kN/Lb325DfvvjRTTy208OIL+R337yBfltQsu7LfIb+Y389t4n8UYgv5Hf8eYO8tuEHG0h4J8A8ts/M+0WyG/kt3ayGAYiv5HfyG9/mwj57Y8X0chvPzmA/EZ++8kX5LcJLe+2yG/kN/Lbe5/EG4H8Rn7HmzvIbxNytIWAfwIpl9+q7vf02YvrRn5o+zaycHapdO/S0f9sQtYC+Y38TlZKIr+R38hvf7sN+e2PF9HIbz85gPxGfvvJF+S3CS3vtshv5Dfy23ufxBuB/EZ+x5s7yG8TcrSFgH8CKZXfSnwvX7lGFlRcI61bFlqjf2fLdhkzqVLKy0bLCcf19D+jELVAfiO/k5WOyG/kN/Lb325DfvvjRTTy208OIL+R337yBfltQsu7LfIb+Y389t4n8UYgv5Hf8eYO8tuEHG0h4J9AyuT3p7v2yNjJN0npmJIDJLd6COZ9K9fIdRNHSkGzfP+zirPFV3v3yYw5+0+hO+/tPJ0+8Iy+2uNCfiO/40xF382Q38hv5Le/bYP89seLaOS3nxxAfiO//eQL8tuElndb5DfyG/ntvU/ijUB+I7/jzR3ktwk52kLAP4GUyu+yWYtk4rhhB5Q4Uae/58xfJuVTRtedCPc/NX8tbPH98ONrxSm4lYivXLi87nT6jQuXWx2PH1PieQPkN/LbM0kCCkB+I7+R3/42E/LbHy+ikd9+cgD5jfz2ky/IbxNa3m2R38hv5Lf3Pok3AvmN/I43d5DfJuTibPv5RyLqP92rxSEi6j+ujCCQMvlty+ahg/ofcPI7FfJbSe2unTpYi7p23Ya60932vw8p7me9FivD3bIA+e2+R+beUi2b3opqbaRunSMytTRPKzYbg5Df7qse/Wyn7LpssHZqFAy/SpoO9P6AS7vDFAQuW1Ejjz1Zq3XnvDyR2yqbaMVmShDyO1NWMjXzeGZtrSxZWqN985lTm0j7dtrhaR+I/HZfwo1vRqXy1mrtdS69Ik96HRHRjk/3wHe3RGXmjfp8xo3Kk+OPyR4+ftcX+e1O7PzzcuWs/jl+saZtfFWVyNgJVdrjH3ROjgwekKsdn22ByG/3FT/lpBwZcSH50xilqueelC8qp2pvm8JZiyS3Ry/teAJjCLzyZ5FXVuhjOXaIyLE/1Y8PKFI5yDuWrpKjex1er0R0QN1nbTcpk9+KeGPlTVSZkfe2faB1ujqIlXOe5lb3tuW36luVQenbp7fY8luJ+anli2Rm2WjrxHpNTePytnxejby3VU/u9vx2RK4Zm11fGG6cXyNvvK3HR8nvyVdnzzemfvNaye8vSn+u3azZmMnSpH+xdny6Byr5/fnY87Sn0fQXV0p+8VDPeL3s9ewmIQHLH6yVJ57SG6GS37fOya4Pl1aurpW//k3vwwG1QIqP4sQFAUXg2edq5Y/L9PPnN2V5WSW/X3olKrct0f9wYPI1uaK+zptcEYlIVPTe80zuE0TbTW9G5aYF+nzU94c9s0x+V8zT53P5JbnyXeR3o6m5d0G5VD25Sit1I62KpMVtD2nFhjVo126RSTP0PzwZ+pMcOfOH4fkZIxpN7PuYkt9X/kp/fw08OyKDzg4Pn3jyzuyri/sd9z28XL7+0y3aw2qx4EFR+yydr4kzamT3Hr0ZnHxiRC4elt75ozfT+KKqn39Svrppunbj5tffnhbyOzc3kbtOG9eBgUmU334OzToHGtsu1j8azD7rm6ZMfts1v9dv3Oy5CIn8xCNWtDckv52n02OT75M9Xzc6/t/fliP/+Y/n9KyAHt2jctklif1mR28kyYu6fXFE3t6s98bY6TCRK8dmFx9fK/HBNsmZOly7SfSSiRI9ZYB2fNoH7topOeO9ZbY9z+iwKyR6lvenvHrZmxp6f3lY5Ol/6Y1QSd3ya7Nrf/39CZFHn9Djo1ZQ8ckm+f3q6xH551r9nLg093fSpOpzvWQ/8jiJ9huoFxvSqOfXidz3gH7+TLpa5JC2+jxDOm3tYa1/PSJ/XKodLldeHpXO39KPbyiySV6OVFXrfyBhdjez1m+9I3L7nfr5o74//HZ3s3umU+ut20RuXqjP5+ILRY7unT37y+9aRhbPFnl2tV6zlkUSvfF+vdiQRikpd/1v9fNnULFIvx+EI39yIiK1CR6Kkt9TrtPnc9bpUfnR6SFdbM1hJRJp5O/3S+Te+ZojEalV+6tlesvv6ysiskfzW74TjhcZOiSRK6CNPpSBkXVPS2T+DO2x1U69VeTwntrxqQosKmyaqlu73zcN5LfTR6rnHyK/g0ullMnv4KZg1pP9KwWxvai635Ov/LlU3Pwn15Pfbnen7In72lD2xCx3na0pe+LOkrIn7nwoe+K9F1VZmGyS34/+o1bufVD/ZNjcL4aI7P7UG6SI5PcvlubjpmjFhjWIsifuK0PZE3c+lD1x50PZk2Df+Sh74s6TsifufCh74s6HsifufCh74s6HsifBfr3z7C1E8tvpIUddUGxVvVDie/rsxXXTuHjo2bLzk12inktoX0vmTT6gbLTnvAmwCGS9/I7Ng9hPWqj5nbidgvwOji3yG/kdS4Ca3+45Qc1vdz7Ib3c+yG/kt8lXcOQ38tskf/y2RX4jv50EqPntdwchv2MJjJ9WpV32BPmN/A52xxn2FhL53VC1iQ7tiuoEuPMZhJz8NlxzR3PkdwzLWPkdW3PHWR/caxk4+e1OCPntlUH6ryO/kd/Ib/39oiKR38hvfxlTPxr5jfw2yR/kN/LbJH/8tkV+I7+R3353jX48J7/dWSG/kd/6uykJkSGQ31/t3XfAcwWdzvEfz75U9wxCyp4EmxPIbw/5rV52/vqBKody3cSRohLR60J+I7+9ciSo15HfyG/kt7/dhPxGfvvLGOS3H16UPXGnhfxGfvvZT6axyG/kN/LbdBc13h75jfw2yS7KnpjQi6NtiOR37HMF58xfJuVTRgvyO4511WyC/NYEFU8Y8hv5HU/exNMG+Y38Rn772znIb+S3v4xBfvvhhfxGfvvJl9hYan6b0DuwLfIb+Y38DnZPOXtDfiO/TbIL+W1CL462IZLfffv0liHF/axJcPI7jrWMo0lS5fenu/bI2Mk3SemYEunR7TApm7VIJo4bJt27dIxj6OFvgvxGficrS5HfyG/kt7/dhvxGfvvLGOS3H17Ib+S3n3xBfpvQ8m6L/EZ+I7+990m8Echv5He8uaPaIb9N6MXRNgTyW41aVZZYvnKNLKi4Rpo1bWqVQWms5rfToZ5wXM84Jk0Tm0DS5bctvItaFSK/HXnY64iIlF6Rl1WZSc3v4JYb+Y38Rn7720/Ib+S3v4xBfvvhhfxGfvvJF+S3CS3vtshv5Dfy23ufxBuB/EZ+x5s7yG8TcnG2TbL8HnF1Rb2BHtq+jSycXWod/lXPErxj6Srr9VEXFFsPu7TFuPOBl/a/TZ+92Hp9ybzJggSPb/2TKr+dxd1PO/m7yG/HmiG/3RO4W+eITC3Nrg8H/Gxp5Lc7rehnO2XXZYO1kRYMv0qaDtz/BShdr2UrauSxJ2u1hp+XJ3JbZROt2EwJQn67r+Sj/6iVex+s0V7uuV8MEdn9qVZ8fv9iaT5uilZsWIN44KX7yiC/3flQ89udD2VPgn3nQ3678zz/vFw5q39OsNBD3FtVlcjYCVXaIxx0To4MHpCrHZ9tgchv9ybQggUAACAASURBVBXngZfufDj5neR3jCTK7yTPjNtpEEiq/FbjsY/tr9+42XV4R/c63Po1gNYtCzWmEc4Qyp64rwsnv4PLW+S3O0vktzsf5Lf3XlQfDihO2XIhv91XGvmN/DZ5L0B+I79N8sdvW+Q38ttJAPntdwe5xyO/kd8mGYX8NqEXR9t3nhJ5+yn9hj36iXTfX5ebK/0JJF1+28iUBKfm9/8SiJPf7puJk9/ufJDfyO9YApz8ds8JTn6780F+I79NvsXl5Lc7PeQ38ttkf/lti/xGfiO//e2aW/9QLdWav/x2VtX90vGpW7Rv0PL2v0ikVZF2fBgDx0+rkt179EbGyW93TshvvTwiCgJBEEiZ/A5i8GHvg5Pf7ivEye/gMhj5jfxGfvvbT8hv5Le/jKkfzclvd3rI72Dld1nxa9K55WdaKRtpcbDkHdVHKzasQZQ9CXZlkN/Ib+S3vz11eWmVVFfrtbm0w/3S86Vb9YJFBPmtjSorApHfWbHMTDIkBFIuv194eZPEFoLPlCLuyG/kd7L2OfIb+Y389rfbkN/Ib38Zg/z2wwv5Haz8nlkwQZpuXqe1BLk9eknhrEVasWENQn4HuzLIb+Q38tvfnkJ+u/Pi5Le/fHKLRn4Hx5KeIOBFIKXyW4nvyoXL69X2fmfLdhkzqVLGDR8sQ4rTu74O8hv57bUBg3od+Y38Rn77203Ib+S3v4xBfvvhhfxGfvvJl9hY5LcJvQPbIr+R38hvf3sK+Y389pcx8Ucjv+NnR0sI+CWQMvn91d59MmPOYhk6qL+ccFzPeuNWUvy+lWvkuokjpaBZvt85hSYe+Y38TlYyIr+R38hvf7sN+Y389pcxyG8/vJDfyG8/+YL8NqHl3Rb5jfxGfnvvE2cE8hv57S9j4o9GfsfPjpYQ8EsgZfLb7YGX6vT3nPnLpHzKaGndstDvnEITj/xGficrGZHfyG/kt7/dhvxGfvvLGOS3H17Ib+S3n3xBfpvQ8m6L/EZ+I7+99wnyW58RZU/0WXlFIr+9CPE6BIIjkDL5zcnv+ovY64iIlF6RF9zKpkFPPPAyuEVCfiO/kd/+9hPyG/ntL2OQ3354Ib+R337yBfltQsu7LfIb+Y389t4nyG99RshvfVZekchvL0K8DoHgCKRMfqsprFj1lCxfuYaa3yKC/HZP6m6dIzK1NLs+HPCzzZHfyG/kt58dI4L8Rn77yxjktx9eyG/kt598MZXfV5V8Jkd1+0rvlnlNJKfdoXqxGRKF/EZ+I7/9bWbKnrjzQn77yye3aOR3cCzpCQJeBFIqv9XgVH3vEVdX1BvnknmTD6gD7jWRML5O2RP3VeHkd3BZi/xGfiO//e0n5Dfy21/GIL/98EJ+I7/95Iup/L7u4GvloDee1LplTsfOcvC8e7RiMyUI+Y38Rn77283Ib+S3v4yJPxr5HT87WkLAL4GUy2+/A06neOQ38jtZ+Yr8Rn4jv/3tNuQ38ttfxiC//fBCfiO//eQL8tuElndb5DfyG/ntvU+cEchv5Le/jIk/GvkdPztaQsAvAeS3X2I+4pHfyG8f6WIUivxGfiO//W0h5Dfy21/GIL/98EJ+I7/95Avy24SWd1vkN/Ib+e29T5Df+owoe6LPyisS+e1FiNchEBwB5HdwLA/oCfmN/E5getXrGvmN/DaV37876y/a6ZrXo7fk9uilHR/GQOQ38tskL59ZWytLltZodzFzahNp3047PO0Dkd/Ib5MkfndLVGbeWK3dBWVP3FEhv5HfyG/ttxMrkJPf7ryQ3/7yyS0a+R0cS3qCgBcB5LcXIYPXkd/Ib4P08dUU+Y38NpXfFdtO0865ZkNHivovnS/kN/LbJH+R3+70kN/Ib5P9hfz2pvfFl94xdkRkySzZt2aVVoNIqyJpebv+h+FanSY5aNdukdLpVdp3Pf+8XDmrf452fLoHVlWJjJ2gz2fQOTkyeEBuuk/b1/iR3+64kN++0sk1GPkdHEt6goAXAeS3FyGD15Hf7vB44KVBcsU0RX67s4x+tlN2XTZYG3jB8Kuk6cAS7fgwBi5bUSOPPVmrNbS8PBHktzuq2yqbiOKULdej/6iVex/UP9k894shIrs/1cKT379Ymo+bohUb1iDkt/vKIL/d+Wx8MyqVt+qfbJ5ZMEGabl6ntR3Ub+UUzlqkFRvWIOS3+8rs+FBk6kx9eTmtaLa0ev0RreVGfmthSusg5Lf38iG/3Rkhv71zSDcC+a1LijgImBNAfpszbLQHP/K7f4cNcu5/Z2qP5qCrfi25RxylHR/GQOR3cKuC/HZnifx254P89t6LyG93Rshvdz6UPXHnM7U0T7p1jnhvxAyJQH67LyTyG/ltstU5+e1OD/ntnV3Ib+S3d5YEE4H8DoYjvUBAhwDyW4dSnDF+5Pfph7wkxa+O175Ti1//TvKO6qMdH8ZA5Lf7qixYXC3rXolqLd13Wm6VSzYN14pVQc3Hlkn+aQO149M9EPmN/I4lQNkT95zg5Lc7H05+u/Ph5Lc7H+Q38tvk+ypOfrvTQ34jv032l2qL/EZ+m+aQbnvkty4p4iBgTiBl8vvTXXtk7OSbZP3Gza6zuH7SSBlS3M98pinoAfntDh357c5n/h3V8uKrevK718FbZdQbyO/GiCK/kd/Ib39fBJHfyG9/GVM/GvmN/DbJH05+u9NDfiO/TfYXJ7+96SG/kd/eWRJMBPI7GI70AgEdAimT32pwNy5cLl07dagnt7/au09mzFksQwf1l6N6Hm79uW+f3mkpwJHfyG+dTdhYDPLbhF79tshv5Dfy299+Qn4jv/1lDPLbDy9OfrvTQn4jv/3sp9hYTn6700N+e2cX8hv57Z0lwUQgv4PhSC8Q0CGQMvmtTn6XzVokE8cNk+5dOtYb64pVT8l72z6Q8WNK5IWXN8l9K9fIdRNHSkGzfJ05hSYG+Y38NklG5LcJPeQ3D7x0zx/KnrjzQX4jv03egTn57U4P+Y38NtlfnPx2p4f8Rn6b7C/VFvmN/DbNId32yG9dUsRBwJxAKOW3U3hv3/GxzJm/TMqnjJbWLQvNZ5zEHpDfyG+TdEN+m9BDfiO/kd8mOwj5jfw2yR/kN/LbJH84+e1OD/mN/DbZX5z89qaH/EZ+e2dJMBHI72A40gsEdAikTH47y5uccFzPemN1yu/XNm2WyoXLZUHFNchvByUeeKmT3ukdg/wObv0oe+LOMi9PpGLbadrAmw0dKeq/dL44+e2+eshv5LfJ/kZ+I79N8gf5jfw2yR9OfrvTQ357ZxfyG/ntnSXBRCC/g+FILxDQIZAy+a0GpyR3WfkiWTi7tK70if0gzNIxJaKkuCqBsnbdBsqexKwm8lsnvdM7Bvkd3Pohv5HfsQSQ38hvk3eYZ9bWypKlNdpdzJzaRNq30w5P+0DkN/LbJImR38hvk/xBfiO/TfJHtUV+I79Nc0i3PfJblxRxEDAnkFL5rYb/zpbtMmZSpby/Y2fdbJbMm2yJ73S/KHvivoJzb6mWTW9FtZa5W+eITC3N04rNlCDkd3ArifxGfiO//e0nTn6780J+u/NBfiO//b3j1I9GfiO/TfIH+Y38Nskf5Lc3vfHTqmT3Hu84FXHKSTky4sJcveAsjEJ+Z+GiM+WUEUi5/E7ZzJNwY+Q38tskzZDfJvTqt0V+I7+R3/72E/Ib+e0vY+pHI7+R3yb5g/xGfpvkD/Ib+W2SP8hvb3p+5Hf/Pl/LhcVfeHf6TURO2/basZkQiPzOhFVkDulCAPmdwJVCfiO/TdIL+W1CD/nNAy/d84eyJ+58kN/Ib5N3YOQ38tskf5DfyG+T/EF+Byu/f3bqR3Lmdz7SXpK8Xsdqx4Y1kLIn7ivjR35feOjf5PgXK7SX+uB590hOx87a8ekeiPxO9xVk/OlEIGXy267tfeJxPWX8mJKUMbMfvPnw42vrxhBbdkXVHZ8+e7H1+sAz+mrXH0d+I79NEhv5bUIP+Y38Rn6b7CDkN/LbJH+Q38hvk/xBfiO/TfIH+R2s/P5l+7uk68tLtJek1fJntGPDGoj8Rn4nKzeR38kizX0gIJIy+a3gqwdejrj6f58E+hHLQS2ekvB3LntExg7/iRQ0yz/gIZxqjJULl8uCimukdctCuXHhcuvWOsIe+Y38NslT5LcJPeQ38hv5bbKDkN/Ib5P8QX4jv03yB/mN/DbJH+Q38tskf1Rb5Dfy2zSHdNsjv3VJEQcBcwIpld+xw3eesD661+F1wtl8mvo92CfSS8eUWA/dVLK7a6cOMqS4n9VJrAx36xn5jfzWz7wDI5HfJvSQ38hv5LfJDkJ+I79N8gf5jfw2yR/kN/LbJH+Q38hvk/xBfnvTo+yJNyPdCOS3LiniIGBOIFTyW4nmO5ausmaVKvn9zpbtMrV8kcwsGy0d27eVGXMWS98+vevkt/P17l06uq4A8hv5bbJFkd8m9JDfyG/kt8kOQn4jv03yB/mN/DbJH+Q38tskf5DfyG+T/EF+e9NDfnsz0o1AfuuSIg4C5gRSKr+dJ73VVFJR9sSJ0K7/bctu++9DB/W3ToGrK1Z+f7pnX6Or8PvbIrLtP3qL9MM2L8qg10r1gkUkOmGuSK/jtePDGLhwcUTe3qw3ss6HiVw5NqoXnCFRf7xHZP2GiNZsjizcKqPfHK4Vq4Kil0wUOWWAdnzaB+7aKZHxQ7WnER12hchZP9WOD2PgXx4WefpfevmTlydSse007WlEzx0uMlg/37Q7TmLg358QefQJPT5qWOXXRkVxypbrqWcjsvIR/dnO+XyIRPZ8qtfg5HMkOnKSXmxIo55fJ3LfA/r5M+lqkUPaZs/XsPWvR+SPS/UXb+I5L0n75nu0GkQLW4t8+6gDYpvk5UhVda1WH6kOeusdkdvv1M+f3+SXSvMtL+oNu1tPiU6brxcb0qit20RuXqjPZ/pBM6Tl20/pzaZDJ4nOvEsvNqRRH30ckdnz9Af3q5a/lUM2rdZr0LJIojferxcb0qjde0Su/61+/gwqFun3g3C8P+dERGoTPJSqKpEp1+nzubztEumxXn/PRO94IqSZoT+ssmsjUl2tFz+i7X1y1Hr991xrf7Us0us8pFG/qYjIns/1BlfSbrWc+Mpv9YLVz6jq/blDJ+34tA9c97RE5s/QnkZ06nyRw/d7qTBfrQvzwzw8xpalBFImv8PywEt73W3R3aFdUV0971gZrmJj5Xd1TeM/aJXPq5UtW/W+gzn9kJek+NXx2mlYMO0myf1OH+34MAbeNL9W3nhbj8+Jbd6Ukjeu1J5GwVXXSu73TtGOD2Pgwjtr5aX1enx6HbxVRr2hLyObjpksTfoXh3HaCRlT9LOd8sXY87T7bvqLK6VJsb4s1+44iYHLH6yVJ57Syx+/8jv/Z5dI05+NTOJsgr/VytW18te/6YuyW+fkZZX8fmxNrdz3kD6fuV8MEdmtJ7+b/LBYmo0tC35Rk9jjs8/Vyh+X6fP5TVmetG+XxAGm+FYvvRKV25bUaI+iIm+c5G3bqBWfe1QfaT7tQPMXkYhERe89T+tGCQza9GZUblqgz2dmwQRpunmd1ohye/SS5jfcrhUb1iB18rtinj6faw++Vlq88aTWdHI6dpaDbrxbKzasQTs+FPl1uaaZE5GpRbOl9et6n2ZGWhVJi9seCuvUtcalTn5PmqHP57LvPi9HNX1Nq2/JyZWmQ0fpxcYZFY0m9n1Mye8rf6W/v65of5d08/HAyxZLNT+IipNPMpr9clKNtvwe1f5+6fXyrdrDOmjBg6L2WTpfk2bUiPqQSee6oMNq6fOSvvxuXvknUe/T2XJVP/+k7L1puvZ0m1+/UHJ69NKOT1VgXm5Oqm7NfSHQKIGUye8wrUlD4tseHzW/E7dSc2+plk1v6X2Dd1LRGzL09cu1B3PQhJnS5MQfaseHMZCyJ8GtipLfuy4brN1hwfCrpOnAEu34MAZS9sR9VR56pEaUANe9bmx+hURqqrTC80/9kTQddIFWbFiDKHvivjLPrK2VJUv15cHMqU2ySn77LXtS0WSc5G3Vk995R/WRFr/+XVi3jta4Nr4Zlcpb9eWcX/ldOGuR1jjCGkTZE/eVUfJ76ky9r0eqp2lFs6WVD/nd8va/hDU1tMblt+zJhHbzpcMr92n1LU3ypdXd6X2yWcnvsRP08+eX7e+Srj7kd6vlz+ixDHEUD7x0XxzKngSXvJQ9CY4lPUHAi0DWy++GTnc7ocU+4FLJcHWNH+Mtxqj57Z5+yG93Pshvr7cv/deR3+6s/J78bjZ0pKj/0vnyK7/nfny2SFXjZa6cLNQHJ+oDlHS+kN/Ib5P8RX6700N+u/NBfiO/Td5/kN/u9JDf3tmF/EZ+e2dJMBHI72A40gsEdAhkvfxWZUzGTKqU93fsrMdr1AXFdYLbWZvcT11y5DfyW2cTNhaD/DahV78t8hv5HUsA+e2eE8jvYOV3xRl/l8Icvd8RzunwLWmS5mW7kN/Ib5Ov4Mhv5LdJ/iC/kd8m+aPaIr+R36Y5pNse+a1LijgImBNIqfxuTDyraR3d63BZUHGNtG5ZaD7LFPWA/EZ+m6Qe8tuEHvKbsifu+YP8Rn6bvMP4LXsyp3a4RHZs1bqlKtmlSnel84X8Rn6b5C/yG/ltkj/Ib+S3Sf4gv73pUfbEm5FuBPJblxRxEDAnkDL57Sw3cux3esjdKx6TiWOHSUGzfFGlRU496Rg54bjwP8nWbQmQ38hvky2K/Dahh/xGfiO/TXYQJ7/d6SG/3fkgv5HfJu8/yG/kt0n+IL+R3yb5g/z2pof89makG4H81iVFHATMCaRMfn+6a4+UzVokE8cNs2YxZ/4yKZ8y2jrpreps37dyjVw3caQlw9P1Qn4jv01yF/ltQg/5jfxGfpvsIOQ38tskf5DfyG+T/EF+I79N8gf5jfw2yR/ktzc95Lc3I90I5LcuKeIgYE4gFPK7qFWhlP/+bim76iJLfqtyKE4Zbj7N1PSA/EZ+m2Qe8tuEHvIb+Y38NtlByG/kt0n+IL+R3yb5g/xGfpvkD/Ib+W2SP8hvb3rIb29GuhHIb11SxEHAnEDK5Lez7MmQ4n5WqZOunTqI+rN6wOTadRs4+e2yvi1+/TvJO6qPeQaksIe5t1TLpreiWiM4qegNGfr65VqxKkjVS1V1U9P5Qn4Ht3o88NKdZV6eSMW207SBNxs6UtR/6XxR89t99ZDfyG+T/Y38Rn6b5A/yG/ltkj/Ib+S3Sf4gv73pIb+9GelGIL91SREHAXMCKZPfsUNXZVDGTr5J1m/cLIe2byMLZ5dK9y4dzWeYwh44+e0OH/ntzgf5HdzmRX4jv2MJIL+R3ybvMNT8dqeH/EZ+m+wv5Dfy2yR/kN/Ib5P8QX5700N+ezPSjUB+65IiDgLmBEIjv82nEr4ekN/Ib5OsRH6b0KvfFvmN/EZ++9tPnPx254X8Rn7721H1oze+GZXKW6u1u5hZMEGabl6nFZ/bo5cUzlqkFRvWIOQ38tskN5HfyG+T/EF+e9NDfnsz0o1AfuuSIg4C5gSQ3+YMG+0B+Y38Nkkv5LcJPeQ3Nb/d84eT3+58kN/Ib5N3YE5+u9NDfrvzQX4jv03ef5DfyG+T/EF+e9NDfnsz0o1AfuuSIg4C5gSQ3+YMkd9xMqTsiTs45HecidVAM05+u7Ok5rd3rs39+GyRqn3egSLSdGCJFAy/Sis2rEHIb+S3SW4iv5HfJvmD/EZ+m+QP8hv5bZI/yG9veshvb0a6EchvXVLEQcCcAPLbnCHyO06GyG/kd5yp47sZ8hv5HUuAk9/uOYH8Rn77fqN1NEB+I79N8gf5jfw2yR/kN/LbJH+Q3970kN/ejHQjkN+6pIiDgDkB5Lc5Q+R3nAyR38jvOFPHdzPkN/Ib+e1v2yC/kd/+MqZ+NPIb+W2SP8hv5LdJ/iC/kd8m+YP89qaH/PZmpBuB/NYlRRwEzAkgv80ZIr/jZIj8Rn7HmTq+myG/kd/Ib3/bBvmN/PaXMchvP7yo+e1OC/mN/Pazn2Jjkd/Ib5P8QX5700N+ezPSjUB+65IiDgLmBJDf5gyR33EyRH4jv+NMHd/NkN/Ib+S3v22D/EZ++8sY5LcfXshv5LeffImN3fGhyNSZVdpdTCuaLa1ef0QrPtKqSFre/het2LAGIb+R36a5eXlplVRX6/VyaYf7pedLt+oFi1j7S+2zdL6Q38GtHvI7OJb0BAEvAshvL0IGr99QWS3vbY1q9XD6IS9J8avjtWJVUItf/07yjuqjHR/GQOS3+6rwwMvgshb57c6SB1565xoPvHRnNPeLISK7P/UGKSL5/Yul+bgpWrFhDXpmba0sWVqjPbw5tcMlsmOrVnyTE38oB02YqRUb1iDKnrivDPLbnQ8nv935IL/d+SC/3flUVYmMnaD/4ckv298lXV9eov3lptXyZ7RjwxqI/HZfGeR3cJmL/A6OJT1BwIsA8tuLkMHryG8PWXJLtWx6S+/DgZOK3pChr1+uvRpKHCiBkM4X8ju41UN+I79jCfDAS/ec4OS3Ox/ktzsf5Dfy2+QrOPIb+W2SP8hv5LdJ/qi2yG/kt2kO6bZHfuuSIg4C5gSQ3+YMG+0B+Y38Nkkv5Le3PHh/h96HJ82rdkrP287TXo6C4VdJ04El2vFhDFy2okYee7JWa2ic/PbGxMlvj/fzBJ78fmVZjXy1y3uNVETHb70rnQv1fr3f6vG7JSKF7fU6d0Qhv5HfvpPG0YCT3+70kN/ufDj57c4H+e3Oh5Pf3u/eyG/kt3eWBBOB/A6GI71AQIcA8luHUpwxyG/kd5ypYzVDfrvTu/OeGnn2OT2527Fgp4zf/DPt5UB+u6NqNnSkqP/S+eLkt/vqhenk9+qpVfL5Dr1sO/qEdXJkwc16wSqq+Dcibbvrx38TifxGfvtOGuS3NjLkN/JbO1kaCER+u9NDfntnF/LbnRFlT7xzSDcC+a1LijgImBNAfpszbLQH5Lc7XGp+u/NBfiO/Td6eOPntTg/5jfy2CCC/Td5mGm1L2RN3rJz8dueD/EZ+m7wxIb+R3yb5o9oiv5Hfpjmk2x75rUuKOAiYE0B+mzNEfsfJEPmN/I4zdaxmnPx2p5dI+f2nUcNlUaeW2sv3Su/wlZBBfiO/kd/aW9h3IPIb+e07aRwNkN/Ib5P8QX4jv03yB/ntTY+T396MdCOQ37qkiIOAOQHktzlD5HecDJHfyO84Uwf5rQEukfK78rLhckPrao1R7A+J9hmrHZuswETK76pzz5evSi7Rnkrn/BbasckKpOyJO2nKnrjzSaT83nDyD+Ta4lO1t8Lsb31fjitoqx2fjEBOfrtTRn6786Hmtzsf5Lc7H8qeeL/Lc/LbnRHy2zuHdCOQ37qkiIOAOQHktznDRnug7Ik73ETK708nXCtfHnO81uo2jeRKj6b6p1i1Og0giLIn7hA5+e3OB/ntzieR8nvR8GEyqUOu9rvAjmNHSLu8Au34ZAQiv5HfJnmWSPn97A9PlR/37aw9vMePOFdOLzxMOz4Zgchv5LdJniG/kd8m+YP89qaH/EZ+e2dJMBGJlN97PxN5/1W952Op2Rx6TI40axXMvOgFAmEkgPxO4Kogv1Mnv0dOulweiOzRWt0jm7aSTUddoBWbzCDkN/LbJN+Q38hvk/xBfiO/TfIH+e1OD/mN/DbZX8hv5LdJ/iC/vekhv5Hf3lkSTEQi5feHG6PyVKX+b+r2K82Tdr0iwUyMXiAQQgLI7wQuCvIb+W2SXsjv9JHfX3yov9L5B4k0OUg/Pt5I5DfyO97cUe2Q38hvk/xBfiO/TfKHsifu9JDfyG+T/YX89qaH/EZ+e2dJMBHI72A40gsEdAggv3UoxRmD/EZ+x5k6VjPkd/rI7/svrdJe6t7n5krvc3O04+MNRH4jv+PNHeS3NzlqfrszQn4jv713UeMRyG/kt0n+UPPbnR7y2zu7kN/Ib+8sCSYC+R0MR3qBgA4B5LcOpThjkN/I7zhTB/mtAS5MNb+R3+4Llm0PvKTmt3s+5Pcvlubjpmjs8v0hq6dWyec79MKPPmGdHFlws16wiir+jUjb7vrx30Qiv5HfvpPG0YCyJ+70kN/Ib5P9hfxGfpvkj2qL/HYnyAMvTTPsf+2R38GxpCcIeBFAfnsRMngd+e0OL5EPvKTmtzv75mPLJP+0gQbZnfqmyG/3NeDktzsfHnjpzoeyJ+58kN/Ib5Ovgshv5LdJ/lD2xJ0e8hv5bbK/kN/e9JDf3ox0I5DfuqSIg4A5AeS3OcNGe0B+I79N0ouyJ+70kN/Ib5P9hfxGflsEOPltso0abUvZE3esyG/kt8nGQ34jv03yh7In3vQ4+e3OCPntnUO6EchvXVLEQcCcAPLbnCHyO06GnPx2B4f8Rn7HubWsZpz8dqeH/EZ+I79N3mHc2yK/kd8m2UXZE3d6yG/kt8n+Qn5700N+I7+9sySYCOR3MBzpBQI6BJDfOpTijOHktzs45DfyO86tZTXj5Lc7PeQ38ttkf1H2xJ0eZU+Q3yb7i5Pf7vSQ38hvk/1F2RN3eshv7+xCfiO/vbMkmAjkdzAc6QUCOgSQ3zqU4oxBfiO/40wdqxknv93pIb+R3yb7i5Pf7vSQ38hvk/3FyW93eshv5LfJ/uLktzs95Dfy22R/qbbIb+S3aQ7ptkd+65IiDgLmBJDfGgxXrHpKps9ebEUOPKOvXDdxpBQ0y/dsifxGfnsmiUsA8hv5bZI/nPx2p4f8Rn5bBKj5bfI202hb5Dfy2ySxOPntTg/5jfw22V+c/Pamh/xGfntnSTARyO9gONILBHQIIL89KL3w8iapXLhcFlRcI61bFsqNC5dbLcaPKfHki/xGfnsmCfI7bkSc/HZHh/xGfse9uUSEk9/u9Ch74s4H+Y38Nnn/QX4jv03yh5Pf7vSQ397ZhfxGfntnSTARyO9gONILBHQIIL89KCnZ3bVTBxlS3M+KjJXhbs2R38hvnU3YZG4FJQAAIABJREFUWAwnv93pIb+R3yb7i5Pf7vSQ38hvk/2F/EZ+m+QP8hv5bZI/yG/kt0n+qLbIb+S3aQ7ptkd+65IiDgLmBJDfLgy/2rtPZsxZLH379K6T3+9s2S5TyxfJzLLR0r1LR9cVQH4jv022KPIb+W2SP5z8dqeH/EZ+WwQoe2LyNtNoW+Q38tsksZDfyG+T/EF+I79N8gf57U1v/LQq2b3HO05FXHjo3+T4Fyv0gkXk4Hn3SE7Hztrx6R6I/E73FWT86UQA+a0hv4cO6i8nHNfTioyV3x/v+rrRHm5ZmCP/+a9eOvywzYsy6LVSvWARqSmdK9Ge39WOD2Pgojsj8s67Ea2hndBqk5y/caxWrAr6xfjL5K9NvtCK79Gkpfyry0+1YpMZ9P+Wiry+MUfrlkcUbpXL3hyuFauCakZMlOjJ52jHhzHwvgcisu4lvfzp0OxjmfDuUO1p1J4/TmrP1M+JNaV666QG0PVska4/qtUeS7yBK1dF5Nm1enzy8kQqtp2mfauKURfLb9vWaMd/1GOkdmyyAh/9R0Qe/4ceHzWmOR+dLZHqfVrDm39RiUz9VhOtWBW0sduF0ja3mXZ8MgKf+WdE/rpan8/sPedJzuefaQ2t9gdnS+0lk7RiVdBzFRH56iO9sfQ6/t/ynRa3aPf9Wb/pUt26m3a8HfjvFyNy/4N6Y1JtKqovlryPtmndp/a7p0jtuOu0YsMa9NqGiPxpmT6fG3Iul2b/fUNrOk+deooM/kEXrVgVtOKwAXJqwaHa8ckIfHtzRP6wRJ/Pdfnj5aAtL2kNLdrtSKmZMl8rNqxB2/4jcuvt+l9Xpx30a2n19tNa04l26CQ11y/Rig1r0McfR2Tu7/Xz51ctfyuHbFqtNZ1oyyKpmXufVmxYg5SUmzVHP3/+r+gW6fT6n7WmE22SLzXzH9GKDWuQKnsy/Xp9Ppe3XSI91t+lPZ3qRY9rx4Y1cOpvcqSmWm90w9sul6PXL9ALFpFqtb9aFmnHhzFw5uwc2fO53siGHrJaTnr1t3rB6mfU65eIep/Olivy4tOSu+Ba7enWTLlVot32eymv69O3IvLKbfpfK469PCqtvx316lbr9bYtm2rFEQSBZBJAfrvQ1jn57Sa/k7mQ3AsCEIAABCAAAQhAAAIQgAAEIAABCEAAAqkigPxOFXnu60YA+e2RHyY1v0k9CEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQSA0B5LcH99gHXCoZrq7xY0pSs2LcFQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABDwJIL89EYmsWPWUTJ+92IoceEZfuW7iSClolq/RkhAIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhBIBQHkdyqoc08IQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhBIKAHkd0Lxpqbzffuq5G9rXpDvf+870raoZWoGEeK7wifEi8PQIJAFBNa+uEEOal4gRx3ZVSIR/aewZwEaqa2Nyh/u+av0+nYXOfmEoyUnBz6x677p7a3y2a7P5aTje5E/DWwKxWfnp7vl+32+Q/7AJxveNpljiAjs2v2FPLn2ZTnz1O9J84KmIRoZQ0kHAv95/yN55vn1MmTAqZKf3yQdhpzUMapytC+88oaMvujH0iQvN6n3Toeb4TjSYZUYYyoJIL9TST9B91ZfNCtuuUfycnPlvOJT5fxzT5NmTSnTYuOGj3viqS+cSs69umGz5OfnSb++x8oRh3dCInyDDT7kj8lb94cffyZTKhbJp5/tke8c2VV+eckQade2lUmXGddWMRo7+UZp17a1jB8zVL7d7VsZN8d4J/TlV3vl2rlL5OQTj5bBZ58cbzcZ207x+fWcO+Xl19+WhbNLpXuXjhk713gmZufP48+8KLOnXS5nnHp8PN1kbBv19f3Zf78m9z70D2ma30R+OrCf9D2+NxLqmxWPRqPy9nv/lbv//Jhs+e8HcsYpfWTgmX2ldcvCjM0JPxNTfFaselqW3PuIFBQ0lRElA+RH/b9n/TzGJaLef25d8qDc88Dj0iy/iVwybID8/Kc/4kOCb5KjuqZG5i950PrbuBE/IW8a2DS3/2mlFDRrKr/42Y/YUg0QUI5j7oJ7RZ2rGTroNPnZwH58/SJTIOAggPzOsHRQJw4m3XCbXHjemXLCcT3lT3/+u7y4/i25ftJIOaQNggU+7gmvTstdO/dOqa6ptX7oU9efH35KvnVoW/m/S38mLQ4qyLAd42868CF//GVM/Wj1g/Ef7nlYPv5kl0y4/Hz517rX5ZY7H5BfXXGh9DnmCJOuM6atYnTPA4/J2+/+V0acP8B6z2nT+uCMmZ/pRP625nlZ9fhzcsOvRklhi+am3WVce8XnwdXPSuFBBTLm4nOR3zErrPg88cxLctBBBTLgtBOt7xO59hNQYq785nvkvW0fWGJFndpVHxKo9+trS0dk/fuQem/+y9//Kb//w59lxPnnSI+uh1kiXD0XadrVF/M1TES2/vdDmVK+SKZcdZH14a0SvW1aHSyXDz8XkSli/VbyXx/9p9ww+VLr1O4jTzwv9/7lCSkvGy3dux6W9W9Fr2x4R8p//yeZPX2sdD6sXdbziAXwxZd7ZfrsO2REyTlyTO/u1stb/rNDdnz0qXzv2COz/pBWrOO4677VsuHNLXz9YidBAPmduTmgvgldu25D3UM51Terf7z/79Ypw/+79KdZ/yvS8Gk899WJp/Kb75ZmzZpauWL/toA6iXDfyjWyfuNmmXb1L6R5QbPM3UAuM4OP+7LDx3tbvLNlu0wtXyQz1Q9635xIVSUa5t52r1w/aZQc2q7Iu5MMj3DKg95HdM3w2fqbnpJwk2fdLpdeMFD69untr3EWRCs+6uHkw4eeLQ8/vlauHv2zOmG5bfuH8ta7/5VTTjgqa09B2XyuHHmerP7H83LqScdY8lt9n6g+5G5/SJGcetLRWZApB07R/tBNHRa5bsIldR/02yd533nvvzL+8pKsFpgb3nzP2l8zJ18qPXt0roP41rv/kTnzl8kNv7o0q3+LqaFTu7aMumLET+pkXVZusG8mrX4GU9/zTLriAmsvqf317AuvyYK7HpK5M8Zl9fdAX+3dJzPmLLa+tg8p3n/4yL7Uhwbq+8dsL/WhGKhTzeo9qKhVoaj35WuunS/dOnWQ8imjs/bnUztPYh2HKiN4/1/XWLkzcdywrP76lc3vu8y9PgFOfmdQRihpcM2MW2TXni/kx2d+X37+07Pk4BbNLbGian9f9vNBGTRb/1OBjzuzl157y/oBpvLaKw74BnTP51/KzYtXyKUX/jhrf7iBD/nj/13nfy3sDwd6dDvM+s0cu9a3+uFPvUcf2b2TnPuj7C5joeTBLYsfsOTkZT//sfWNuvq3mppaqwRBNl/O3xrgh5iG5aX6rYqv/n97Zx1nVbXF8UUJ0t2NdHcj3d3djVJKDC01dCPd3dKSEpKCdEmLtIAICki9z2/hvu/M5U6AxMw5v/PPew535p7zPfvss/dav/Vbj5/o8zVk/ALxaltH1fGrN+6Wucs2ybUbv8ug7i3k89wZHTeUzPgBn5b1yuucU6xANg1+Y2Pcd8QsTcA5VW2I9SGqJnt1qC/uSTd48EIY0LdTY8eqv/H+6jtytqROntDH+wsP0tOnz6T/6DlSqlBORyfloNr96ptvZWjPlpI5XXKdY67fuitf9RkvXb+ozeC3iFz49bp06jtBmtQqI6UK59B1EN7xB4/+opUDTraH2b7niFa9wY4qUsRwrncUk97/f12D0a6fjmny5MrVWzJ0wkJJmiiuxIoexWWDgoDv02fPHLdm9E04gorl7oOm6hyUOEFsx619eMEk4E6AwW+bjAmr4gABSpS2jpqyVK+uatnPpV6V4hI5UnibXO2bXwb5+M8MPmp/P3ria4UAGDp5YUo+fo8h8vGbDypypi5YK4O6NX+tEfGcpRt1sd64Zmn/H1QbfwLBAyTghvZq5UrA4V0G1VOn1rUkepSIEipUSBsT8P3SPFUNWD+NDU64sGEc29/DGsBFk1TMRwiwTJqzSuLHiSFhw4aRsxd+U3uGT8M4rweKlQ8s8KAwrFauoGROn1yfOfT1qFw6v2OrAzHH7Nx3VIPf7k3mEPzuP2qOKsJjxYgiv169KafPXZHC+TI7Zk0EBr2GTJc+Xzd6LUFigt+F82ZxZGIJ87BR7UaNElHOnP9V4seJqQ13V6zfKRnTJKPtieVldez0Rek5eJr2rKhXrbhjniG/Fi4QGHXuP0lqVijs4xkySUskbr2+rOPYqiWwA4vRU5dpM3RYnuC9BZHEqg27pGShHPozBL7xzG3acUDX2k6Je/jlFf/KKma6NK5VStKlTOLI9TMvmgSsBBj8tsl4QDniwDHzZKBXM9fCFIoDvBxQ+gs1D14c8FcLEzqU5MmezlGb5IDwMQoWBIDDhQvjuAXZiEmLNSvsXm5nfUT+fPi3BBNxpNcs+fg9WQaEj02m2ze+DGxsegyeJqWL5JQSBXP4+H34zHoNnCKli+SSEgWzv/HftsMvYMPy+Mk/2m/AWvILxRPYJEkYW54+ey57DpzQ5Bw2OkY574SkHK4R73JUcDWtXea1AOWTf57K6ClLNUlQo0Kh1xqI4d3/8qXY1g/T8EH1BN5fJlEQMmRILfVFMz7jw+tEKx13PgjUDRk/X+pULib37j+Q2Us2qGWFVW1oh3nlTa4B5eLw+u7Yovprv7b1x59l5YZdMtCrqdy7/1BmLf5e10rWCh67z0N4pjAHwVrAvbkl9hqd+01UW7wUSePbeq7xbUxZVbv4jJf3ZEEyIE2KRJIiWQLdT8CXeO2WPVpxgYSceYe9yTgNip/FGgcNLmuUL+TaOyCZgnc7Em4VS+ZzsUCg7vmLF9qzwSl8cE/xfI2f8Z30+bqhVmybw7zL+nVpoo2/r9+8I2HCfOLIBrPYf6LZd7Wyn8uSNdulYfUSEj9uTJ2XXr3nw8t33/+olV6De7RwVJ8zTzEOM4bOnL8i3wyfKUN6ttR5hwcJOJ0Ag982GAHY+OKFgAwnAt2mPByLcZT+mtJWBBhOnr0kG/9VuPTsUF+ypP9/kzWUNf7z9JntmhoGhA/UT+jQPn3BOokYIayWcX7VsobkzZ7OMQswbP7QZM43X0tYD6Akb+ai76VogazqPYdgjFMO8vH7TvvHBwtXeM+h2VrOLKmlVsUijhk/qzbukvnLN6unpfviE5vm6QvXyfDerXXu3ffzKZ2nc2RKraXTULHa/Zgyb40qdRBUMSW/VpuG1g0ravAA3rKDxs7XJC8UmEgq9B42UzdBpvmRHVn9dPi09Bo6XdmkS5XE13cSEgioIrj463Uf/RlQdQAPbCjH0MTPbgf4zFi0Xry9mmsA98btu/L91v1a9YZAwbczv9NLxjgKETy440QA7nwQ/Ea5OJSXWPMgYQArGCT+Eei9cv2Wo+YfjI2d+47Jph0/SY929XyoK41nM4J0hfJk1orK1Zt2y9CerfQ9hgCdE+ahW7//Ib2HzdA5xGqN465M3XPwpGzbc1hL7J1iVXX3jwfSpf8kaVKrtCZvfbOBQaIJc/HClVvVWgfe+6aHDjhi/2VHZniGegyeqqIrVJuYClJTDTeiTxt58fyFWjEdPXVBggcLJonix5IubWpJ3NjR7fa68ng9SLxhTob1FLyscbgnvR/+9Ui6DZqqjWbzZEurvvtOanp96uxlFZGgHxWSlLDJOXryvPavwL4VQW8nBr59i3FYxxCqArH+QQIOa20ciA3Fc8jz5YhJhBcZYAIMfgcYVeD+IBRy8LQ8cOSMtKxfXnJmTi2Hjp+TgWPmagY0WeK48tdfj3Xji7JxZALRNAJZZjP5Qdly/PQF6dy6lu1Ky/3jc+PWXRk2YaGM9+6gC3uUkIMdfNKhJnPCgWvGwqJMkVyqQvUt6IYF2bote2Xt5r3Sv0sTx2TXycfvp8AvPvi3LgMmSaE8maRkoZza8AhBl+7t60nShHFs/3ghsbhp50GZOm+NKr8RdEJp/fY9hwWB394dG+ocjecPJZ3oWr/7p+Py8O9HGkQwG2SUvoJdgdwZbVWZgsQs3l1IAkCNiqZ7nnyI8d4aPnGR+jZjszhv+SblgFJ8O1tZgM+egydk/IwVGjTBOx6JR8zFmIfRkDh96qSqosP7HBYNtSsX1RJXU3VgApx2fNjA56+/H3kMBLhb6fgnAnACHxP8Dh82jDz465F4fVFbe8Vg/kGwqWDuTBrAjIFeMfXK2Wqu8e3+Qp2K5yZdqqRStUwBnZ8RbBo9dak8/Oux9OxQT3sPgFGOzKll/6FTgv4N8E/fuutnDZ53b1fPtvMQgrNQVe4+cFy6tKmt8w+eJawFkXga3P2V0hKK5/pVSzjO+xvJfeyvTGAXSUgc9aoW1/+1KpoRHJ84Z5Umu43VGax0oJ7Hu8zaTNQu8xHWgH2Gz1RvZuyrIkcMJ+NnfqeJgrbNqsjISUs0+daz/avkEzzAl63d7iOJaxcWnq4D7/KJs1bJ/QcP5YtGlfVdBvuOtZv3uFTMryzgDuh7HkpfBHox55jm1/gbWCPCcseOogkIRSACwFxjrhnPWczokeX585eycsOP+vzEjhFFUFmw5cef5dPQn0jxgtltr5Q3MQ5Yd7VqUEGFM+jvAbHahcvXdAydv3RNBoyeI9XLF9Lm1otX/8Dm6XaeVHhtvhJg8Ntmg+PKtVvqiYVJP2qkCFoKlCtLGuk2aIqgGcKLFy8kT7ZX2fep89fowgvBFpQtdvOeLJ1a1dTNNQIs2AwhiG6nwxMfBFqgHoQiEwsulJXnzZFOps5bKwnixdRgsFMOqHsGjZsnt+/c1wAcysjKl8grx09flGkL1kq82DGkUqn8kintZzJi8mLJki6FFMmfxSl4hHz8vtWe+FQomU83yBNnr1JbD2x8sHGGUhwbQrM5dMIgwuYOGxgEK6/dvKNBpia1S+vcg2Z985ZvVl/Z/DkzyIuXL7Scs2j+rNqUzqiAkJyzltvbiRuuEfYcwYLJaz7EphkmFCyYo8Gvjdcoef78uTY/yps9vS03fNb7iyQKFNxIHLWoX17n41FTlugG+NmzZ7Lgu62aFAgVMoQmCLApRFL78IlzGuB09zK209jxdC3Gh9dY6WAM+ScCsDsTXB8CdR37jNdKr2+92+saEPMPNtCmmSrY9R81W60K7FxVYb3ff9x/KONmrJAf9x/TCkA0VCuYJ7MqUFFZibkblUsIsEChi0oLrJOta2c7jx88Pwi4TZqzWvmAF4IoSNCmTZlY3+lIUFob8uLz+D2sG510IFCHpAgqCX7YfViDTqZSsnWDimrtceDIaX134Xjy5KmEDBlCQoUMadv3mBEBTJm7RquTin2eTSsJMO9A9QxFM4RIHZpXk8gRw0vfkbPU5gwJhZcitlepmvc7mn6jegmV2X07NZIkCeO8tkfHmEGVzva9R3SMQdSGPT7GGcZUskRxbfm4IVli+r4Y+y48V6gYMIHvVRt3y+JVP0iD6iXln6dP9f/jmTMBc1uC+feirt74XfcX2HNhrVyxVH6pVragJpnQ0Bl7B9Pw2zerHTvz4bWRAAgw+O2AcWBt5AMPzF8uXNHyMgTHv+nUWD3AsfHBgaAC1C5Qt9hZKWa97ShFHDB6ri7C8LLASwMBg2fPnkuvjg3UwxCKl5+PnZX61YrrAs3OXnRQ+MBXbt+hUxI1ckT1tkS2/YtGlVTZgw0OVJpIpGD8QBlv97J663h5Uz7w6YNKDMwypEmqySg7B6Hc+SBwa9QGCMpNnrtGfTCx+YMKCPOMk8aPp1cOyoKRoGxZv4LsOXBcTv5yWZrWKaMWVVkzpNTFKhSsSCCg2iJalIi2fnNhTl6wYouqwI0PMa4fm0J4zuL68c6CsqVmhSIye+kGKV04pyZunXAgWAClKoLaCDZ93bKGvpMQZPIeM08DUXiu3JPaTmBjvUZPPrx+iQCcwgdBg7HTlknECOF0nvnzwd/SdcAktXozlW7utnkQRGzbfVga1SxlS2sG671HcgCB7zixoqkAAM8WnqVOfSdoYBcNDHEYyw8koyCUSJM8sSYzE8SNaduh9Kp52jSt4EK1SYxokTQ4iecKP8ea2QTenDz/ILE0ae4qadesmgwYNUcb80GNiUoc2KBhHWQUrHi3wZrJu1tzl+WFbQeQhwuDLRMqTfAeg/AGwqwLv17XPSqqk1HV/GmY0I4RSqCiAmsbVBJg7vFkAYefYQxBKABBCQLnEHclSRjXtskT96EDdXerLiMkUsTwMqh7cxWRYB76ovtoyZQ2mbRtUkUTTrBLWfDdFvH6sq5tK3P8my/wjIEB9g+mkhQWKIPHzxdY4GLM7frpmJQsmMPW+1P/OPHfnUGAwW8H3GeUY2o50FcNtczO2BOgdBqLeJRPIes+0Ku5xIkZVdUtdi/htN52U+5at0oxV/AEwYWDR89IkfxZdWGPzeKxUxd0UZYpXXJdyJryRrsPoZ+P/aIvzW++bqyLMSy6Zi/dKPf/fKgeYo8ePdFkyeXfbmrwBb69RglkNoh29TLE9fnH58DhMzJ57mrtag8FPTZF2CDaPYBpngv40Q0aO09L7aAwNIsslOP1/qqhBhes4ydmtMgahEEAzymHNQGHcYH5B4Fu2FCN6d9OwocLo30dyhTN7VJtOIUNrhPzL5phFs6XWW1j4P0NiwL4Y1r9Z8ENapYEcWJo4MrOSUpwweYPCl6oUyuVzCenzv2qvvrwvoYyEwkCNArF++ruvQe6uQEfqHntHKAzzwaCAVDnIgHpnwggRPBgsvfnk3L05AXHJClNE1RUCwwev0AbOppmhggi9B42Xd/7KCP3HjtPfrnwm+TOmtbFB5YpTtkwe+ppgXkI3DAPIQgMVfjMRetlaK9WtlWpIjHQfdBUDbilT5XE9RpCQNeaiDMBO3wAohokuw8dOysHjp6RtCkS214EgOuG2hJ2TP1Hz1Wrs3w50us7yVqN8/zFSx/vNoiP4CGeIK5zGmJi3MDyDApm2H1g7Bw/c0kFNhAb9Rk+w1WljOrCqzduS8Y0nzkmyGudZ7BHx4HkAJoYYg0N60AkejFHIbiJ/RaEXPDGtvOBgD9iFbCfgkc8DsxD2GOhYhKWghCPZM2YUhas2KwVcuDixMNwsTZ0Xrdln9pY9epQX8U1S9dst72FoBPvPa/5dQIMfjtgVGDzh+AbJrqCeTJpWRmahUHBgiYj3uPmq40FFmdYWEDl27ZJZceo6DAEoDwdO325qpuzZ07lCmzDpuDEmYuqxkiUILZm17XbdLmCakXghMM0q4keLbI0qFZC/nzwlwyftNgVeLIu5FGOhpJhKDVM4zq7exn6xwdBFzQYQfki1CuoIsAGx0l2H0iobd55ULq3rafl4zgQBIdqBSWKxpYB42fXT8fV1gHNIfHc4ZlDKXq54nk0iGXHA0kjeF4mTRRXy8Ph14gNIIJLEcOH9VFuj+vvN3KWVCyZX3Jk/v8chHkeic24Ngz6QnWKBBzsB5B0RCAO3rzwvjSK5zlLNupzljNLGjlz/ldJHD+2fGFpKGbHcYNrgpIQG98Tv1zS60eSG0EWJLXb9xonKT9LKCfPXNKxBdshlP7GjRXdMYEDc9/9EgGkSJpAvMfOlXv3H+rzd+Dwafnjz4fS9cs6+vzZ/TAWJyUL5dCxA9Vhv5FzVM2MxAnWRwiswBYPyUysJ/PnzCjXbv4ut36/58Pmwszrdks8Yf7B2rlNw4qqjMN7CfPQK6/9AjpEjCq6YfWStraKwXhANReuG70FYAGD4Ar6DBl7JauoJlKEsOI9dr7OOWiyhn+DmAQiAFQT2v04dvqivrOjRYmkwVwEnPp1bqx7LMzd8HWGwhnrQzSVx8+wPoSC3glJXKx/oGK+fOWGvrOh4DXzBxJz2JshyHvn7n3xGjhZ1c3gZcfmoJ6eBfh4/3rtliaMcLhb4KFyx+q1Dx9sNHqG3YXTDrynkGjDtRuhDfp31a9WQvB+w3sN+xFYurqLbMDVrqI2iB5GTFosPdrXV5EjEgTowwShGmIZeKdDJY85HXZW8APHWsgJ87PTnhFeL21PHDUGsMG5+Os1GTF5ifTq0EAVc9ZsMVThppTcKHqXrd2hm2VsDu3YQMM6AKASw/UjgNC7YwP1RociFT58KA8+fBy2JyVUHVa1zOeOCX6DEQJr+34+pSpn8GlSq4wGnm7cvvea3yUCcFAG4aUaP24MH16Gj588UYV4ymQJbTWefOODBTwqC7Dxw8amfbMqunGGpQ7G1S/nr8iLly8lZdIEtmsya322tDHW1r1qW5E7W1rJnjGVBuJu3fnjtfEDxSG8ZqGmg1/flp0/y/7Dp1TNaiww7DhxY8E55NsFGvBGIhLBSih63BOSmLOh9oVyN3bMqLpJhhrz2GlUpqzVuct4i9qRE9TOUH3DGx1JXJO8xJzTr0tjZQFF68TZKyVZ4ng6zpxwYA766fAZyflvQgSBufhxY6qS7satO1oCbA5s8pymwvRLBLBl50EVB0A1Z5SHy9ft1EovNMF2QlNeBFigYsb7GYH/UoVzypeNK8nTp89fa2KI8QNfUTyHsCEqWzSPK2GH4B2CwFAG2+nAexwN+lC9BT99BOpgt2R9Lx06flYrdkzi386VKKhW+u3675IuZWINVILP2Okr5Iddh7RfDDjBJhDBFASbIBrp2LK6VCj+qtkzAnRQYZrEgZ3GiqdrwRoIiUqMGShyoYbHWtlr4BQN+KIKF2sfqHmrlPlce37MX75Z8uXM4KPS9Ifdh+Thw0dSqkhOWwXqTFNnzLlYC43u19a1R0XyG/vQSXNWaQAYcznmF4y573/YrxW6kSKEs/sQcl2fuwUc3lWwM4WgLUTw4GprmiNTakdWCaKKYMy0Za4gL6AhYQsxycbtP8mRE+fFq20duXr9d+k7YpY0q1NWxxaeTfT+ali9hC0Tl7iIxfAFAAAgAElEQVR+CIvQMD548OD6jH3dqoYmBBAbQoKgTuWiEhl2uMNnak8d9ANBIooHCdiNAJXfdrujAbgevAg+CRVSfR6t2WL30iqonuETtXTtdm3C0r1dXcdYNQAjAr0zFn0vg7o110U6Flzoag8/3oFdm8qnn4aWH/cdk4Urt2qwqXalImrrYDfFk3VIYdOLIHiG1EnVQsdaVm8y5tgAqp9hjxba1MZ4GSKosHnHAfUNRWOST8PYryTPygcqwqMnz0uZorl08QX1MspgsUCFnyiCdVD1ILGC6oKeHRr4KCMOwKMc5D6C6z9z4Yp6feOZcR8/WKAheIKFKTbKCMigqWGsmFE1qGt3qwZsAM9fvqpKywK5Murmz5qQfFVlME1gWYVn8OzFq7Ln4AmpVCqf2lqg9NPO8w8GPBbqUB3GixNdalcsqlZM/UfPkXSpksjZC7/J8+cvpHGt0rrJ+e36Ld0ko4cBlFDG0sE8OOCJecluid1X/o5b5ZuvG6o1ChqpdmtbV1mZZJxTVZjuIgAkULAJhocxml/jMJYNxjIGFXJXrt+WJAni2G6sWF8iptoEARRTHg4lHVS66IlielUY5SE+g3Ukgt092tfTYB42zt3b17NtwgBj4vHjV1ZvtSoWcTVRQ/DE9MpBw3SnVqLg+UKPk9Wbdus7G2MGzxeSKbfv/CHrt+7TRnSYk1FhgOStp3EX5BY3ATxh09Q5ZIjgutZBwBtrQhxocG1taA3P9N5Dp6tCHkEocDp36ao+b6jWtfuB6x09dZm+sxCs6/JFbe3JlDBeLMmeKaUMGjdfYseMpskBHMGDBbf1/Gzu98btByRCuE9VSOLute/eqBgMIZhAcBw9VOxu+4HrhX/8mKnLpHSRXPqcwEIRPtdIGmAugtc+rFKgfobFEIRI85Zv0mSSXfemVuGDtek3fo41IpTfUMtPmb9GyhTJrSI/ePCHDh1KZixcL+lSJtHxxoME7ECAwW873MW3vAYEWHbsPaJBJrU/sZRw4gWKsk4EBnCsWL9TTp+7LJ1b17K1QtWK0iwq0HkcqgxrsiBbppQa4MWmENnTJ0/+kQmzVkrRAtmkfPE8tg9AgRNKV9v2HCsVS+TTxSc2OfcfvArOlS2aSwrkyuTyMiycL6sqMaGKmjCogwb27H5gowcWsMgpnDeLXLh8TZW9WFyNnrJUN80VS+bTsYJFGP4NDUSNp5/d+WBjN/TbBdKpdS3XNaM0D4tT4+WMwAsWrOWK5dEkSvtmVW2pyvDtXlt5wAPUulk2ySaTMDCbaLuWbVoZITELNSEa8ubInFp7VkC1gjJxVPAMn7hYrXRGftNGy8y7DZqqHpAFc2dyBbuhyoTqrl2zKrqwt9OBJMrTZ8+0LBwB/t7DZuh7CgEUqjBf3WkjAnj8BPYVczWQieS1ebdZ+6AgeIDkJd5zqMiwexDcPAuYo3sMmird2tV1NTHEv7krD7FeDBUqhAybsMhH8M5Oz5T7tWD8QBRiEmd4ruD5jff7kRPnNLnt1EoUvJOePX8haHCNsTFk/HwNvCHghLl77rKNsnj1Nhk/sL0G41BBAEsZVMHlyJRK7QeNPZpdxxDGD+wBUZmEedn6TJnKLauQBMHONZv2qOgG7zInHPCXR5Vb7BhRtZIU77ORk5dI+RJ5ZfOOg4qgZYPyGvSGZzj4NKtbVorlz+qIpn1IpGBdjKpSVBJgL4GKjF5DZ6hFHCoDkYRDtQCqvfH8OeVAMnLfoVOaOCleMLs8f/ZCk/+oTEEFDxJzGf5tXHz1X5HN8+fPpXObWpI3e3pHJFHMWMD6BlUV0aNF0hjP3Xt/ytZdh9Tia9Kc1YLnEFXL2GM4pem1U54Tp14ng99OvfNu141AuCnhvPzbDenQe7wuPhEAV++5xPFk4cot0uerRrpYxQIWpXt2U8xZsWABj8XnyMlLdUGBI3vGlKqAunjlhgwaN8/VJBT/BnuCWUu+1xeG6aZs1+Fl/C4RLIC/2uqNu7XU9cCRM64yzV37j/vwMoSC9cSZS3L/wUO1lIF/uFGS2ZWTaVy4//BpfZZgm5M2RSL5dtZKV2m0uXY0sIHq5+DRX1TxjA2OST7ZlY/1utx9DD1Z6uAzE2etUtUv/Ojsfli9Hk1JtLuq0l35Y8qroWYxKh/M79gAodmN3XwyMU8jEfnKr/CVBzgYwOILlQK47kUrf5As6VOoLzY2OEiubN55wKW6s3PCANUWKGmFZ37alEn8VWHa/Zlyvz6MH/RhgJ0XPEDhcYnSYCS8Yclgejrkz5VRexDACg5jzevL2rZ/z1uTBKaixCicSxfJqc1nzYHg3RfdRqmvsdWOyc5rRHPtJpEG+4o0yRP5W4mC9xgqVOw2F/s2d8AvHWKAlvXKu9Z8UC/D1gyBOlQxwUoPPUBgYwHLlA4tqtnK1sOveRUBOveG1qbCCX68sEFzf887ZZ5GNYlp3ogg3JDxCzSpgnm6eb1yPsYInkOoVLFfQyWK3fdhmEf2HDgp6VIldlW1wSMdPZk6t64ps5ds0GHSrmlVZQhPfiic8ewZuzwnzM/Hz1xUmy4kBDAm0Jdq0cqtMtCrmauCGVUoNSsU0ThI6cI5HdXzDKpvCPmQuI0WJaL2b4B1IBrOIgaCxJPptYOgOARaeXOkV8GAnS0WnTLHOvE6Gfx24l33cM3wVMMB5RxKyHNmTiPY3MDqY8feo7p5RhNMsxmEXUqjGqUc43uNBoUosWtUs5QyWrJmm9oMQAlmtRmA2u7gkV9k5uLvladdFxhQfU+cs0r6dWoiESOE1RJWeIXCxgPWC+5ehlYFa+wYUXQhnzhhbMdsbrCRwUICC9C1W/bKmXO/aiDSUyAGHBGIQXknLBvgyWZ3KwtwwJiCuhKBSXh7Q9ESMmRIH36X+Exrr1G6YI0RLZJULVvQMSofbFqCBQsutSoWdo0HbH7gow4WtSsVldt37mtSDsED+PoVyptZ6lctLv1GzdZ/RzM7Ox6Yb/oMnykJ4sSQ6uULSdQoEbUs2D2BgqD41Ru35e/HT2Tg6LmuCgM7MrFeE4ICUPFCBe+XCjNlsgSvoUDSDiWwR09ekE8+CalVO3bpAYKgEwIqMaNFUasGBOmQ2IYt07BerfTd5l5tgc/3HDxNmtQq7agqFDMwXlnqbFF/dBNcsgbvCuTKIAjAIKjZoHoJR6hUzbyC9Q880v2qRMmcLrlWX2BdDTsLJwTAsY9A1R8CKaiEg4d+qs8SqQctni88b5hbmtYuq3uOUVOWqjIzXLgwjkgS4Jp3/3RccmdL57IDtPZjgtrbqvDFs4g1N6yYsB9xSgAK80rLLiOkbNHcqvhGQA6VXrDSiRUjquTNnk5FW95j5ul76vPc9q8wdX9hw2oRe1XsGZA0qVymgHJauWGXLF2zXXp1rC/hw36qVQcQ1zStU8b2+zDTUBX7rrZNq+hedcW6HRr8vnH7rlaemGpT39aCxprp5YuXtrPKw/sLPQaMCARzzblL1yR8uDAa+DYJA3jLw0ovVKiQsmztdvlx/3EZ0qOFq0/KqbOXJXGCOKqw50ECgZkAg9+B+e58hHPDS2LExMWSOEFsDSwhK4zNDjLI/bs01aAUrAiwkMUi487dP6VWpSIaYLG7itd6O9CsxwS3rT/HAgMZZXj4QZmgXlmpkrjsLT7CLX1vX+lbZ2x3GwZjqYOSOwTgTCAXpWYI8uIoViCbxIsd/b2da2D6w2jIgtLNvp0aS8zokV2nZhS7eI7gh45gVZ9hM+TLJpVtZ8vg6X5g3Pzz9JVVw83b96TnkGnatAZqeBxGCQVfXigOwWf1xl1SpXQBeSmiigUnJAms7Kyl0lhwQj2WOX1yDTDguVu6docsXvWDZE73mQ/P3sD0PLyrczFBWihzUeYK9Y7VL90s7E2FAQIv+BnUh/DDxsbZCePHNxUmvB3dFfCaVBg2Qy0MqpQpoLcKfOPHia5qMiShgvKBvh4IFOD+Q+mNJAGsqlBCDsWlJ7UlfgYmfTs3dmQzqJ37jsnKDT9Kn68auu6/1fLD9PLAGglqOrs1vvRvvPtXiYLkSed+E9UiDtYXMaNF1lL7VJ8l9O9PB+l/BxcEbLftOaK9TdKnTqp7CVQOooE8niusi6BKTZE0viq/0Yh2w7af1BotepSIjrFcRLWJtR+T+3t+1JRl2k8HewtY7HyeJ5Paftm5ggmDHxXHuN6sGVLoftNUXGBNiIT3cryb4sZQCx1Y58DWAsIuJ1k1oHcXgv/tYBGYOqmuaVCV3L7XWJ2PIbqBrQeaGqN3CmwuTOPwID3B+HPymH8QnJ2/YotyaNOoksSPHV3tXtOlSuqqGvT0Z0yflH0/n9Rxh94xmLNNs187ccMzhr3XJ6FCSc8Or6on3OcjXC/2aLDTQ4VFnJjRNAmF/0ZCFwk5HiQQmAkw+B2Y785HOjc0P4DvLoJNCJrsOXhSFxLIortvBtGEBC8U+K5CHe2UUntYVKCxEzbNRi2HBcZX33yr3qrYSOMAS/hgfvN1I00cOOFw9zKEEgEea0N6tHQxgA0DFl5QaUKxsXj1D9K0VhlX8yhwguUDFq4Fcme01aIei7BVG3drGR48v1MlT6hKKGz8oPYJEya0/jeaGiK7ni9HBl8VLHjmoBqym/LHWA1UKJlP/dKRhLMqoax2MKaJTaQI4aRDi+qvLUh9S9IE9WcR8zOCcIXzZdZkAIJSS9dsU0Wm4YMEAQItsGhIkyJxUL/kNzp/9wbO5pfdk7lIRg0eN98xwUzfVJju5c/G2grzUbumVdTmDAeeJ8xdKJO1Q2k5Nm1Dvl2owaRHj55Ii3rlVC2Hw1pVgSCCUZDh31D19fDhI5mzbKPs2n/MYxkw5nr0CoGC3C6JFTCYv3yzJtag+lILmIFTBJYfZt2D+btz/4n6DoPSGVYpeJej8ilFsgS2ep97mpR8q0TBZ60JOTQWhRVB3NjR1NfYaQfeWbt+OqZrZgRuIQBABQYqmSJFDK/jKknC2IImo3sOnNB5yAmVcL9evSk/7D6sAW1UEljf80gYTJm3Rsb0byvJk8TXfRqqvSACMB7GThlHeJcvXLlVVbtI/r8S3myRcxd/02Q/DgQ3f7nwm+TOmlYypEkqubKkcZRQCwzAac3mPdKxeXXt33Xw6BkpWyyPVqBAYALP8H4jZ0nFkvklR+ZUThk+uj+H6hv7c78SAEiSw6pyeK/WuoeFchxJcuzfYL9oN+sYq9UQBoOnptfrtuyT3QeOS68O9eXA0TPyVZ9vpVzxvGotEyJEcMeMIV5o0CTA4HfQvG/v/axNiQ+UFwheYiERPETw15pr4EQQ9HViqT38q+ENeuvOH9KweklVekPJjJeBUcEjWTB4/HxXQGra/HWSJ3s6Vb445YByBxsar7Z1dINjNsZQgZuyRNiijJ/xnfT5uqFEDB9Wgwzune/txguqFahPEejG82YSBAiUYFGG6goEZkb0aaMBBlPeiSAvlK2RI4b32ADRLpyQ/Jgwe5UGs0sWyik9Bk9Vz3QoMt0P8Dt78aomVFrVr+BKoryam+Zro0yjILcLH/dS6RGTFkuCeDG1Y7sJUsILG/6YULLCw+/ileuqlnJCqbTVL93cc+NXDPsuM/dA6dJ72HT1L4QvOOYeNNCy24bGOu49qTDdnwtYFGAOHt6nzWtNeMFx7PTlalOAjQ5YQQ0VlA8oTmEVZJTLnvxUYbtkGmF+EiqkdBkwSVWIUK7CVxSBhZ7t6+taAAeS5FDh4b1mt8om85ygeSFUhK0bVtT3u3vl15ET53XuhpWesZnp17mxi1FQHjN+nbt7JUqZIrnEvaGzXa89oNeF8YD3c4RwYdVS0CTxMYbcq3aQzBw0dr5aFThBqWoYIuiPeQVzCCpw0EgVYwne6EgkwQYNAeCkCeNq80yo68OE+STIz8cBGUNGcIQgZJmiueTe/YfSzXuydGpVU5P98HZG4A7/DVEOvI3LF8/rQ2Rjvsc0QLZLktLKD8HbVxUUNXWONr2IYNPUuGYptWBCY1H0SHFSvwZTTYoeQrUrFvVVoOYe/DZxDzyLHZpXtbWwxFPTa/zM+pxBgAQvfqxx8BxBFOikOTogcxU/E7gIMPgduO5HoDsbKDHgYw0rAmz8xkxb7sOuwXg9OrXUHjcMDBDs3rLzZzl66rx83bKGqrzMJvDq9d+lY8vq2uzRiQ2hTMnYrd/v6UsR/+vuGYoSKiQJenaor4t2vEwR/IWKFXYWdj+MFUrvrxpqoMnd4gMbnfEzVkidKsUEDexWb9otDauXkkWrXqle7BbYdb/fUH2j5BDPkSntRXBz9NRlWj5eqnBOtc5Bom7jjgOarAsZIrhH2wu7jiVs7EKH/kQtqDD/WAN1kSKEFZRKY35CsA6VF0gklC+exzaK1IDcVyidoDbs3q6eK8iJBGX/UbP1OULQcvrCdbJm0x5pVresY/zkPbHDeEJiAGpLTwEBBD+DSTBVPv/99+PXmo8F5H4Epc8YJTzsGpA8wTvsl/NXfNgJgRmCBwiwuCdwkXwya6mgdN3+nSuuE9eGJBsOa3UgErTdvKdo7xP0G8CaCAlfVMO1aVRR52ynHGY8wC/fNOb17dqdZNWA52rTzoMyc9H3mtCH1/7v9+5r9al1bYM1EsQmg7q30MazTjmsezA0ckQyDUISsDp+5pKMnLRYGzyO7tdWokQKL90GTVWf/YK5M9nOm9jTPYdn88LvtqoK9crVW1KvWgmtyvnrr8c+rGPM72IOgnL35u/3XCIAU2mIBAwS4HY7zPVBpYw+MGZ/inlbm4MOnqZVOrBJgYhkz8ETjrDRebV/f6IK+KiRI/paYYs9bL+RczTRDw9sY/cG604Eee1uN2RNDLknJq2VKcU/z64irciRIqiIjQcJBFYCDH4H1jsTCM9r4/YDEipUCCmUJ7Pr7Fhq//8bZTyaWzWoIOlTJdXFGBr3DO7eQstaTTd3JzaEwgsTpVRYNGzc/pOcu3jVR8NHawkVNomGlVMa1uCa0Uxk0uxVavPy7Nlz+fPBXxr8f6VmmSLd2tZxKQyggmrVdaQ0qlHSh496IJw23sspmUBUkoRxVAUPdRS8iNFUtFzxPKrihcoOJa/YQCOhYPfj9p0/dBODZyxJgjgCtQpsY9D0CL0IYM0A9Rg8/GCXM2D0XOWFhJQTLJlwzb2GTtfrx7OE/zUBKSgOoY43QV4oo9CvQTeG7V/5HjrtQCUBen8g0OvbgcADNn5GsQmlMzY9drNhwvVjLKBhWM0KhVUhDl9MBHVh64HDeD3DsgHMkMCdOm+Nq1cKnkddD/Roadv5CM+TtTEf1ow79x31UQ3nrn6GpQUSlXZUXFqfG4yfBSu2SJ3Kxfycb827beuuQzpO8uZIr+8zOz5TnuYVT0kC09gZtjnWedppc7IZG0XyZ3U1r8bPkHBKljiurq0XrfxBA7gnfrkkz58/1/nHvbeMtbmdXRhCMLLgu81SpkhuDUh6smowll/b9xyWnFnS6Jqoca3Suh5CtaVRRtuFifU6TIU2mqLnzppG14ZxY0XzWD1qhCW37tx7ram8tTePHTlZr8n6bsIYGT11qVy6clMGejW1feWSb/fW/f2NxAH271a7Rdjh+mUHZ/dxw+sL/AQY/A789yhQn6F/pfZ239C43xxkPYdPXCw/7DqkCsuvWtaQtCkTa2Z564+HpM/XjVyKQ6c2hMLLE+MGXnPY3CEQjvJxZNRhaeGJlZUzFvso4cufM4M2XbXTAVU3kiiw70AZOTxUoTCEosWqenaaMt79HpsGLPAwRCNVHMdOX1R1PBZhCMAh8G1UmnYaI35dC4IEl6/c0KZih0+ck4Fdm2oz0C79J6kaygTq3INUTpmnrX77mGuOn76oSl14oiM5t37rPu1BgOaXmFugsiuQK6MqgvBsosu9Uw4EDzRJaam2sF671UIGczGCewgI169awmNZuZ24oWlhryHTtReKmX9MKXDXL+poUAGJKGOvg+ftyZOn2hMFdil2ft6QOII/PJJwpqGhsWJyV89DUYZnDGsCKA/hy2tnu6GAPAOvrBp2yjdfN9T5Bn0/ftx/XP3Vrb0uAvK3guJnPCUJrA0f/UoCQCW9bstembZgrXh9WdeW/sXoTdBn+EwNfpctmlsiRginXvruVgRgcfXGbYG1hVWZeunKDVfDdbsmvT1ZNeBZgAgAyTjTrNeIAI6cPCdjB7SzfaM+rH8Q/EZQGw12r924o72rurevp/aLOLDOgRVR8qTxpUi+LLJ07Xa10sH+DCIA+NGjjwz2snZu0otkCvow1ahQyDUu8P6aOGeVNnt85ZX+iSa9wefK9duOsBMEg9t376vFCdZB7nsLrKP9s4MLiu8lnrO9CDD4ba/7+cGvxq9SeyeoLQMC3HQk96shlKe/g9JXZFBzZkltq82yKX1GCSsUdLhGNDxCIyMszKDObNuksq8+atgcInEA70e7WqIgKBAqZEgNBLgrMN19i03Z54EjZ6TY59nU8xnNf+x8YAzNWLRetu0+LK0bVJTECWNrKX2xAlm1+SNsPWYv3SDeXs0doWp2v9dYoD78+7GWhyOYAJU3rGDM82INJiDIe+jYWW1akzZFYkc0hMLm+NKv13VswDcVcxCaq+XPmV6iRokoy9fukPhxY8iNW3c1wIlGYlDt4n8L5/1/5ZOdnzFsYhDAhb9s6SK5XgtKWpOUV6/fltZeI7V578CuzVxlwXblYyzNrt28I20aVtKAwcCxcyVBnJjSskF5Qa8Uq70OWMFXtVfHBmoh55QDjdZgX4brThQ/lsd5GSyhuhwzbZkkihdb+ZlgHQJ4wYKJrdY/ft17k9S1JpAQaOk9bIZWoMSPE8MpQ8d1ne6NnX0DgHfenGWbNOmLBBNK8OGBbccDVg3oLwSld+OapSVL+uQBtnhzbxJpRz64JncPb/cmvPhMQEQAdp6D5i3fJMGCBVffeJOQBZMRExdroBtzMY45SzZqRSrmcJPExf4EczOsU+yYsASHecs3C/ZVSJaYtTMEAdijDureXKtxYCeIhFS6VEnkyIlz8nmeTI6xjMF7G/ZU33Rq7KpmC4gdnN1tYuw6p9rpuhj8ttPd/AjX4lep/Uc4nUD5lbOXbPCzIZSnF4HxUkfJK2wL7HhgcQFfPgRqoXDy1OTI/bo9Ncu0IxvrNSFwsnnnQenSprYutqxBp+u37kinvhOked1yki1jStn38ylZv3WvqjLsmhgwbLAp+eXCFdmx94gcPPqLquL6dmok8KDz8p4cIAUqymDhNYoyWQT5TKNaO40p4x+PBlBVyxSQ5y9eSp9hM6RwvsxaPeE9dr5uXuC/C5/wY6cuaLDKNO2zEwvfrsU9IPAquLlFzl38TZMGhhlUQHb0BPWNi7VUGvMLPGVRLo0NsDVJaXyesTlG9RPmKjurwsALwba1W/bKuOkrtO8Hyudhg+LedM09+b3v0ClBWTl8ee2s5IWqEopUeHyjNwPWiniu+nZqLLB+O3X2smzfe0SSJ4kvOTOn1goVqBBha5E6eSINTCGhmSntZ5oYd8LhyarBagmHdZKTVIa45xCBIKDSpU0tX+2nMNagVoUSs1mdcjJ80iKpU6moJiudcMAGb/D4BQGyeMMYg/q7Y4vqTkDjukZUl6CBs3e3Zq5moP5VFGAOmjhrle7fUJlid+shE+i/+8cDrS5I/VlCnY9D/hvoPnrqgtpaeXdrrsIK9OLZtuewqsLtmNRVO8q1O1Rs1a5ZVQ1w4/+jxwnWhWs275Ep89bImP5t9T2GtfagcfOkSukCjph7TCUl7N2+bFJFKyb9soOD5zzWAlgbOuWd7qhJNghdLIPfQehmBdZT9VRqb+dN3ZveB78aQiVLFNfjn/Pko/Wm3xvUPu/uJebp/LFwx2YaPs5WdTO8ZtH0Bs3ZsOG204Hxs3bzXpk0Z5Uq43F933q3V8UqFBqwgEFQrm6V4rr5w+IC9hZO8Us3G2QErlG66SmA4Gk8QO07YMwc+fPB3xqUGdqzle1U4lA/BQ8WTP76+7GWa8LW49MwobWkFZYo6EsAf31YW1QonleD/1jcY4z55fVsp+cL14Ig71fffCsVS+aTMkVzvRbARHknOKFJH+ZsjBc0G0MAFM+aHZVP5h6bsl4EbdEUCur4aQvWyaPHT9SaCYfV5/nG7Xu6Odx/+JQqxiNFCGe34eLr9bgncEMED66eqpi3UfmF/0azNag2Yc+ApCU2gXazQUHAH+XQaCCHDTEOWHkcP31BgwYoEe/uPUVqVSoqT578o3N2xrSfqYIOiTf0vMAYQ+UFglV2DzqBjyerBquVBfz3/VMZwqMWakwnKeugPuw9dIbkyZ5O6lUpJg/+7e2AceYUpTySab9eu6VVW/4dnno5GBFAkoSxpViB7LZ8n+Ed32PwVA36I/gWkIoCBMcRMK9QMq/MX75Zm4Sjp4xdn6/Fq7epZRcsdZBQ6jdqtiYmsRa08iqQK5PMWLhOduw7Ku2bVrW9zRkaWy5ZvU32HjyhPRjqVSkuwUMEl+6Dpqpo5vsf9ut+DAr6hSu3StKEcbXqxCn9LFCNjH1qiBAh/LSDgwUTnkFUy6VMlkCeP3+h0xUaifIggQ9JgMHvD0nb5t9lLbW3+aW+9eUFpMwOG0d4qjWtU0byZHu1ccSB34WKPHmSBBqAsNvh3wIe6sIOvcepRUGurGlUiZEgbszXmtahDO/ps2e2VCKgseOx0xdUZYgFR6+hM6RTq5qqtpy1+Hst9cW/wwsSnsZOO/DsYEEKGx0oMXw7EGSBpQOqKhIliC2rNuyypSUBmoAiCNmzfX21osAGsNugKfJl48q6AUTJYqnCOVWVicB4g+olVVmIwCZUrE46jH0QEgLw2K9XrYT6pGOjC+uBwePnS88O9SXcp2FUFY4A5jdfN3J5PjuFlXuS0gQIhvZq5Sp9RUAKSkRUYkSJFMEpaATzD8ZJxxY1lIWVFQJxmLORYEKS0jTI/rJJZX27DswAACAASURBVEmXMontGOF9jjHwz9NnEit6FIE6FYHshPFiabAbbL5uWUMD/9gE47NoFgqlIZJNXQdMll/OX9GkgV2rctxvutWqwT2RgneUXypDqDbx7/f/fCgdWlSXUCFD2G5MuV/Q0ZPnpceQ6XLvjz/1fQUBwOXfbsi3s1bKgK5NtfcHDtiEwIfezknKgNxscOg9bLo2UDUVTFhP49nDAXW93froWLmg6TASJZiDMeekSBrf1QTcnR+Cvb2HTte9BoK/4NR35CwNasJaz44HLD1gsdSiXjnJmz29zF2+SeeTtk2qqMp77eY9ygsCCtgOTpm7RsoUze0IVbz7/YadIPpVeLWtI5EjhlcxxMhJi9VmcHS/tlpN6rR+Fv7ZwZ298JtMX7hO+nzVSAUkqNbBfIPeTU6fm+04nwTma2LwOzDfHZ6bLQlYG0K5X6DZ8MBrLEK4T6VlgwpSLH9WXazBkmDgmHnqdZ0wXkzNKjtF5WOSBuDVol559Q4dNmGhqjBQivbtzJVigi9Y4MKfd3CPlhI7RhTbetLBAgbKDAS/sdAyZfjwwUZpuV2bGfk3Kbh7PVo/j+cLHn4Dx8yVtk2rSMHcmWTF+p3qOYsFvt0OPDerN+7WpBlsK06cuSQlCuVQT0LYwwwZP1/qVC6mAVxsjOcu2yhQ/4wf2F6VGU48sOld8N1mKVMktz5XOBComzZ/rc498DAOE+YTadekiq0DBb7de2uSEl7X/UfPkZyZ00jpIjldv4JN9JxlG9X+xFTo+PVc2nGc4dmDahDv6qplPpdhExdpACFMmNBaoYLkLZoZ5suRwbZVOgjIXrv5uzaTxXyCACSOM+evSDfvKTrn5s2RToPcQycslIFezTVpYKy9unxRWxkhkVkwTyY7DhNfr8maOEGAwD+VIeZvBBCQTLCjBYH7e3z91v0yYdZ3avEGmxy8u/CugzLzxYsX0rlNLU1cYp3UdeBkfQazZ06l62q7VVoE9MHAc4hxBJsKVBIY1XzWDCk06WRH2zd3NpiTbt6+K97j5kmTWmW0qbynA3M1qnaG9GjpWks7wTIG+9Op89fKyu9/1IbxSPDDbgk9UUzfKmNzBlEFrBeRhEKlaZTIEbURohMO07y5SP6sqpTHgZ+BTbLEcXWO8aufBaqVkZyzW1WTb3ZwePejShnJySplPtc9GMQApk8V1kt4h5mEpRPGEK/x4xFg8Pvjsec3k8BrBLDhQTnwAK9mEiFcWEFDUfgaY5O4etNuVSrUrlRUA7pWlQ/Kqe3cGMo98A9weFHOWvK9TJ23VgZ6NVU1Bl6gpiELmh7BIgXB4E6ta9puQ2gSJfcf/KVN+UxgAQzsWpb5X6YMbHpWbvhR+o6cLaUK5dDyV/iiIyiFwEz54vb01n/1rDyWsxevSoyokQSlh2bzDy9VqL5b1ivv2vjCIiV06FAcQ5bBBk9wL+8p2kitWZ2yti59fpNnDApdBFNg35E+1f/Vy+AFZTwCUMGDBZcfdh2ScTOWS/d29TQJ44QDQbd5Kzar9zA2fONnfqeBlIgRwmrQCUkUVOyM6NPGdhvggNzf85euypylm7RSCQEDNHREJY61OsUEpvBOc1pTXgQRbt+9r8Ek/1SGSPJ7j50nmdMnt/V7zDquwAQJ3JjRI7t+DHuCQWPnSY7MqV2VSwhYYu2cKW1yTXyjugDzkN17onh6BpGUHDdjhfTt3Fjtl/qNnKW9UWBP9eLlC5k8Z7XAAqVGhcK2D2JiTsG7yZPiFAHgjn3Gq20O1okIUGIN1W/kHMmQJqkq551yGDUv5mjso3CgzwWs8ZAwMWtJ2OVBFFCtbEGnoFGbLnijI/iNatuIEcJpst+vfhYpkyXU+Rze2NbGxnaHZhqpIlG5aecBaVqrjDYBz5M9rfqjw1YG4hzMzaiM40EC75MAg9/vky7/Ngm8AQGjFMNCyywqjLfsgFGzJUL4cDKs1ytfYqvKBwEZ+GneuXdfgxB2O6AYhN8ugiaVS+f3odpx90bHf+OFCt/Qx4//8dH4EJtJlGDD/sEuB64JFgzwmYMyDo3ErFY5drnO/3od2NAh2IRFafd2dbVB5vQF63STh0qBhtVL6gLMNDiKFye6oDmL3Q/D5dDxs1KtXEFVpKb6LBFLEC03Hs/YrCUbZMvOg9KzQwNJmzKx3YfFG10fqnAGjJ6j/s0YQ7BbQMAJAV68j/DcoYnqN50aOcaD1x0glM4jJy+R3l81VGWzaUIL+zK7ltAHdBAhELB0zXapWaGwvpuhOkQCDipDJHERdHJ6U17/VIZodD176Qbx9mqu60OML/xOuHBhHJXENJUohfNmUSWqUahiPYheDXi/I3CHwLfTbL3wPGKuXrd1r/rwY17u1raurqtNABPJzAOHT2t/EFSElS+ex/VvqPhB89ECuTPaekyZYO/JXy4rG6wTI0eKoPsrjCHM4fsPnZYwoUOpz7wRnQR0vgtqn8P+a+6yTWr3gmQAbM7GTl8uA7s285F4gpd81gwpbVvF5Nt9w14cif6N23+SxjVLazWgX/0ssC9t7TVSUiVPqAzttB/1a2yjiWrbHmNUDd+/SxOJFSOqYMzAR//g0TP6TGFvBt9wJOUqlswvOTI7z7ozqM0PQfV8GfwOqneO5207AlDLTZi1UgZ1b+FjUQEFWef+E1XxjQW92QgZlY/7Ah+fR8NRO3loofkKgvzWskyjEGtRt5z6W0OtYS3NQwAGQQeoFbCJXrlhlzbc6ty6lpbx2elAgOD8pWvqX82ysdfvLBaoO/cdUXsG42mJ8TJr8QbZvveINhDFwh4VBq29RumCFAqxqmULumyH7DRerNdiGhpu23NE1bsodeXxigDmmEHj5kmkiOEda3MSkLGAJAoaxxoVJiqW8B5CsATjCRYfdg8S+MUJQTckqCfNXqXBIzR0/PPBX7oJZHPw/5MzlQStG1RQKwscSGizKa94VBlCfYnS8d7DZqowAP7Ey9fu0CoDiCiwVkTSyfg7gyfmezyrqECwm/0H5pxO/SZIh+bVtKmztREvrtWoD2HzBR9n/De8n+1uE2OeMKyJB4yeq5Vu8ERPkjCO/hMSUL9c+E3V3nFjRde19DcjZqrq2SQNjIWTqTy1a6Up7Ci6DZysApo0KRILEipnLlyRT0N/outrqMVPnr0kG7f9JDv3HdU+INbnKyDvy6D8Gbzb4fltTR4h2dZ/1GypUb6Qikgw78xZtkl+vXpTvmpZw1F7Ev/6WVy5dltV34nix9LKL1jDof8OrGN2HzghTWqVtt3+FOMdFcor1u1U20UIAGDBhPkZ42X/oVNq34m9GeyG4DuPteSdu39KzYqFdaw5ef0YlOeLwHruDH4H1jvD83IcAdgyYCNj9WrGRgWbZqhTe7Svr+VAUG8YlQ+UPXiBQMmCRSmyq2jk16ZRRVs20TKDwlh+YJGO4DZsX+DPd/vOfVWMXbtxRxcYRvGD5mLdvCfrCxYLWh4kAAJQgpvmWKFChlQFq1FjYsys3rhLS1wZoHLeeMEcA0Ul5tTiBbPZWu32Lu8uAkpDxi9Qf0c0x4Kvvt2CbG/LC4EUzCuYZ1o3rOir3+zb/v2g/nsYO5PnrlErJlQNRIkUXvrBpopNefXWuqsM4dWMYAsqK7y+rCP7D5/WhC5sLRBgQPUF+OXOmlaqly8oYT8No/0LBoyaI/26NLGdtQWCbht3HJD8OTLI1Ru3Zcy05dr/xCTl0HS1P669cxP14kdiBfNUp9a1JHqUiLYMOlnnBPBB1RsSS0YEgPmoQ6+xWvEFEcW1m3ekVqUiGtxFXxQEv6H2hVIcybqokSPYutJ0886D2vS7bpVir723MD/99ddjtbaAgAbimmETFul7zile1xBpwToHCSb0WzK++mikjmQJ7Dqh3I0dM6q0a1rVo7LZzv7OfvWzgKDGmpC7cfse0pFy5twVWbZuh5QtmstVCWbnJKXZe3XqN1HSp0rqarSLgDjiFxBLwPIMojf0tcqZObUmK3mQwLsiwOD3uyLJv0MC74EAVN1oDNWtbR0N2lpVPvAZw6IUZZze3ZppABwBYOPNZmffZwS9ew2drqoCLM6tzaHixo7mY4GB24JSahxWj7r3cLv4J4MYAWx8dx84Lj3a1VPVBQIJVjUmFqCHjp+Tecs3aSk+FAgIJNipqiKI3TKebiAmgODcxNkr1bvRyTYn/t0ilJIj2cZ55HVSCNDtOnBc7v3xQEoWyilDv13Apry+DChrUh8NDBE4qFAin8t6AO+v2Us3CiwrjF8vFLwpkiZ4zULOvzEb1P594/YDEipUCCmUJ7OeuqmYRCUKAimmUjBJwtjaPH7PgRPSrmkVKVkoh6OSdas2oiLyoqtBKPYYg8bN16Alxgy4ofKiTNHcHq1kgtq4eNvzRVWBl/dk3V+hoSrsBWHzNXX+GrW7QEIB83qIEMFfS5TbqQ8P5pRzl65qH48a5QurWOvm7XvawBBNr5EkQSNR+Mj79n7DvhUWGGi8bkdxiW/9LHDdmH+H9mqlyUkcEL1NW7BWk76d29SUCsXzaoUzKhC8x8yzbWIFgr6F320VNLVG7MLdWx7xC/wMXJBscqJN1dvOVfw9/wkw+O0/I36CBD4aAXhh3rh1V1o2KK8LKqvK5/mLl9Jn2AwpnC+zZouxKIOiDA0joGpxymG80nHNUL8fPXXBlRBA6S+sLIZOWCgDvZq7FhxOYcPr9JvAj/uPya3f70mRfFmlx+Cp6j8HCx1zwCoHz2CvjvUlfNhP1fsQKvCmdcpQCczBRQIWAggOICGJUnqn25xwYLw7AmzK6zvLY6cvyokzF9Vu4I8/H6qlBRTgppkjAihQecOnGOsja9WgtcLw3d2twPuXrNcOyxcIIh49fqIVGFhbQxU+aOx8GejVTGLFiBJ4L+Qdnxn6fYydtlzHCKwY3G0UoY7f+uMh6fN1I00mWCtNnVTRA6EEbE56dagvIUOGlF8uXNFm6VEjRZBvOjWWEMGDeWw6C0sQu3sYo5Fqi87D1Sqwf5emAlsh3w4kV5CkK5I/i1oOYX6HFSWqWOw2nqz9LGDb0X/0HLVeRJLAHCYJ16hmSU1CQQ3+VcvqMmbqMp2zsae1GxdPYwPzzjfDZ0r39vW09xAOk9zt+kUdtWriQQLvigCD3++KJP8OCbxnAu7WHe6LUqsnnxNelgY3gi7zVmyWOpWKasbcmhBQtc+4+ZIp7WdSoUTe93yH+OeDKgH4zO37+aR0bFndFdSG33P7XmN1g4xyzrzZ0/8bYJiji3UnbZCD6n3leX9YAlAx2dUL9sOS5LcZAmzKG7CxgKASqgSrly8kqAp8/uKFqgxNAAUl5Ag6Xf7tpnpdx4wWWSvnrA187dgUHPSg7B47bZnkyJxa2VgrBY1QBHYFwycu0p47sPZwyoE5G1YfE2Z9Jy9fwlrnsSqaUSUJG0EkNNs2qayVp9ZKUwhLnHTs3HdMVm74Ufp81VBV8e4WDUiugBXGDrgV+zybtGpQQQ4fP/daRaGduJm+KEi4+WZzYr1e7FvXbdnnqrC8+Ot16TN8pnzRqJIP4YmdGOFaTD8L2Hmhtw4OT2pn/BzP2dR5azSR4JQkJaprgwULLrUqFtZgP95R7j0b7DYmeD0fjwCD3x+PPb+ZBN6IgFXlg4WXtbmj1ZPPqH7e6I/b5MMoXV2xfqeWisHfEkFNdysLm1wqL+M9E4C34ZrNe6Rj8+o6ptCRvGyxPOoTCv/9UCFDyLK1O7TJGMrIaWHwnm8I/zwJkIAjCbApr9+33SSdLl25oc3CoDZFUBfWMUN6ttTml6hiOnzinHh9UVv9inf9dFymL1gnw3q3dgV7sVaCZVHXfz9jx8FmKgWt1i/4GewD0UAUQd+bv9/TkvwDR85oELNaWfilh7YjDh/XBIENkgRI9mMfYVXHP3363IewBMx+OX9FEwtQZdq9IR2ScGj2iMBtwTyZtCIXAghYw8AHHJYo9auW0PUgPvvT4TPyWeK40nfkbJeHsd0G0N6fT2plSYt65f20OTHXjUA5vMFb1C3nI9CNxMHStdulb6dGum+z64HrRHV2rUpFNcGEhqC9h87QHg1G7WyU8bBm+jx3Rrui8Pe6WLHtLyJ+4D8QYPD7P8Djr5LAxyKwePU29XFEc8dHj56oosfpL0vcC2wCnz57JqE/CSXGo88sSD/WveL3Bk0C8KR71QyrpqrBUZ4IP8M4saJJ45qlNMCAADkW7fDv7d6urqvcPGheMc+aBEiABEggqBFYu2WvXLl6S+240PwbpfN9R8ySmhUKawDFU8NvlJn3HzVbmz8iiOeUpuCwIliwYov6yBtVpVXRDIVmp74TtOFqtowptenx+q171fbDScISd3W8tdL0xq07uudIniS+hAnziRw9eV6+blVDsqRPEdQenTc+X6z7Lv56TUZMXiK9OjTQJJPVjhLVpzh8U/W+8RcG4l9ApcijJ/9IpAjh/D1L8EAyBetos6Y2vwTPcMxFEJVgLkLgc9Kc1WrhaZrV+vsFQeQDSIz8+eBvva45SzdqE+Pmdcu6rE3wnKHKANcOm6FDx87KgaNnJG2KxJIrSxqtbrb7gYQaqpZQlcSKbbvf7Y9zfQx+fxzu/FYS+E8EoLpAiSLUp9ZF6adhPvlPf9dOv4ws+469R3xYWdjp+ngt75cAkifwAa9UKr8UyptZF6dYwOO5Q2MjLPzh/40DyvDT5y6rHQpUdTxIgARIgARI4EMQgKXJ2OnL5bfrt6V88bzaxPmTUCGlU6ua+j5C0AkBBQRZTCMxNEe/ev13XR/hcGqJOYKZxiqvSP6sMmLiYk1616hQSOpWKS5YU4+eukwyp0vuKCUmxDW/XrulATfjSwyxDRTzvYdOl2DBg2nwF2p5WFd8O+s76dmhgUT8d030Icb9x/wOrAHxjCHR1HPINOnSppYmA8xh9TBOECeGHD9zUbbtPiwpkiWQYvmzOiKIab0/SKZ0954iA7yaSbJEcX3culNnL8uwCYtkWO9WEjlieHn27Lkm4+LGjmbr3jrYu8NyqEub2lqd497A2HvsfK0oLVYgmyYEjp26IL06NpAY0SJ/zKH/Qb4b7ytY6OF9xYME3jUBBr/fNVH+PRL4gASQNR4yfoFUL19QPfl4kAAJvDsCxs8QHo65s6aRCiXzyZ2796VD7/ESOVJ4DYCjC3myxPFk4cot0uerRroZ5EECJEACJEACH4oAErNocImmfCihNypBBFSGfrtAOrWu5Wr47a7qRWDlq2++1WQu3nlF82dVD1684+x+nD73qzbdQ/ASAc1eQ2do0uDKtVsya/H3Uvzz7HLs9AUpWzS3rT2J/brP1krTk2cuydQFa6VkwZzKrWH1kpIxbTIZNG6+tG9WVQUC+w+dlnLFcjsiwIuqAQRv0XTW9Foy1jqwzYE/OJTNEFAUzptZ1ax2tvbwNI6099LYeRI3dnS1FbL2pDLezo8f/6OJOCidYTeIgLDdrYZw7Ru3HZD1P+yTHu3qyaqNu1xNeLfsPCh9hs1UJhWK59VnCUpx7C9Q5c2DBEjg7Qkw+P327PibJEACJEACNieAzRyC37fu3FPFCjZ5pmM7Shh37D0qA8fM1aZQXJTafDDw8kiABEggCBMwgTnTCPPxk6fqQ5s/Z3opUTCHJnQnzlmlHs6w/nDSgWqvfqNma/Ab9gtgAUsZKHb7dmrsmOZz7vfcWmkKqzckWOALbvhMmbdGLWIQwEMyYdGqH9TCwqmVqAiIj5i0WFrUKyeLV23TpEDiBLGd9Cj5uFask2cv2SCrN+6W9s2q6VyDYC5sKrfu+lkmzFopQ3u1khhRIznWwhPVFYPHz5eOLWpoDwbYVpUqnFNu3/lD1m/dJw2ql5TrN+9ocByCGx4kQAJvT4DB77dnx98kARIgARJwEAFsAlEWjY1M1bIFtSQRm0Es7J3Umd1Bt5yXSgIkQAK2IQCV97gZK9TrGx7WnpqCwzIODTLROBz9U5xyGF/i+w/+UuW7aeKI9z7L71+NAth5DJ+4yIcfMwJ3YBQ7RlTB2Nm5/6h83bKGyyrun6ev+vA44UD1AAKXqBjImyOdVuYWzptFG2E6/cA4QaJk846DEiNaJHn492OJEimCNtiFIt7qd33vjz/VbgiiE6y1nWQVAyumIePna1+Cz5LEU1/wucs2Ciowxg9srw1meZAACbw9AQa/354df5MESIAESMBhBOCris0NFqiZ030mew6e1I2ykzuzO2wI8HJJgARIIEgSQIDXBCNNX4v61Uq4LD3w75PnrlHLgbpVimnDx5mLv9drheIwd9a0mvS16wE18/wVW2Thyq1SME8mKZArg+TJls6ul/vG14XxsX7rfpm3fJM0qVVaMqb9TH2+Ta8TNH/86+/HOlbAcvn6nbJm0x75plOj17ye3/jLg8gvIACOJuh4TmBlcePWXcdVUfh1q7B2vnTlukSJHEFiRouinKx+1/BOR4Vl8qTxpUi+LNpUHqpnBMlhGYMEDCx3sO62q80grg+q75b1yrvsg/BchQ4diom4IDIP8DQDLwEGvwPvveGZkQAJkAAJBEIC2AA++OuRwJfv6Mnz4vVlHUf4WwbCW8FTIgESIAESeAsCUH3D8gSWHkaZCmX4wDHzZKBXMzl0/KwsWrlVOrWuqU3WZixcL+lSJZGKJfP58O19i68O9L/y96PHcv7SNUmUILZjmji+yU25euN3Wfn9j3L56k1t9I0qAhyw+4BatUDOjDJ4/AL9GfzUneAf74kf1odL1myXHu3rOUb9/ibjCJ81FRew9GjdsOKrcTRxsQa6WzYor/89Z8lGyZ8rg/xx/6EmFBLEjfmaf/ibfm9g/jysYibOXqVzcLVyBbWPQ6rPEtk68RiY7wfPzV4EGPy21/3k1ZAACZAACXwgAvAsfPrMOSW9Hwgrv4YESIAESOADEIC6sOeQ6WpZAR/ePQeOq6VF0kRxtQnm161qSMY0yfRMUPU0bMIi+ebrRo71v/4AtyTIfsXTp8+k/+g5EidmNNm044A2gaxcpoCjlapYI6JhauL4sfnM+DKyrX7XsaJHkWDBRO7+8UD6DJ8pqT9LqEHukCFDIkyu46pjn2+ldYMK0qhmaVs3xURSAIr3bXuOSPpUSSR96qRBdm7giZNAYCLA4Hdguhs8FxIgARIgARIgARIgARIgARL4AARgT3Hq3K8CtSGaOkePGkm9wBFo6tWhvquqCXYDaMrWv0sTiRA+rEybv07yZE+ngRlzwPsZTQ9TJInPaqgPcO8C01fAlqHrgEly+eotGdC1qY9xEZjOk+cSeAnA1zpurGiSL0d6efjXI21AmzNzam0mD7uUPsNmqJf68+cvZOuuQzrOYLvDgwRIgAQCSoDB74CS4udIgARIgARIgARIgARIgARIwMYENmz7SY6eOu+jceH8FZvl6vXfpWPL6nLizCVp32usNvNLEC+mFCuQTeLFji5HTp6XcdNXiHe3ZhpE5+EMAlDvDp+4WC/WyTYnzrjb7+8qz128Kr2HzZAW9cpJ3uzpZe7yTXL/z4fStkkVj815b96+J3OWbZRmtcv6UNY/ffZcFeRsVPv+7hX/MgkEVQIMfgfVO8fzJgESIAESIAESIAESIAESIIF3SAAN6KCybNWggqRPlVR2Hzguo6YslcHdW0jc2NGkz7CZUrRANsmaIYU2xdx36KQGqAaPmy9liuZmA+h3eC8C+5+CHU7nfhPVC97pNieB/V4FhfNDImXq/LXqKQ+rD9gsoaGq18Ap0rxuWR/NefG51Zv2SOG8meWzxPGkcL4sEib0JzJl3hoNmndoUV1ChQwRFC6b50gCJPCBCDD4/YFA82tIgARIgARIgARIgARIgARIILATuHLtlqp5f9h1SIPcX7WsIWlTJpYN2/bL1h8PSZ+vG8mnYT5xXQZ+vnbLXvH6oo6qvhGw4uEMArC7ocrWGff6Y1ylJxumsxd/06aqXdvUlnBhw8jcZZu02WqJgjm0MWSwYMHYZPRj3Cx+JwkEcgIMfgfyG8TTIwESIAESIAESIAESIAESIIGPSQCqTCgwv2hcydUIE+djfp4kYWyB5cCeAyekXdMqUrJQDg1C8SABEiCBtyUAO6UxU5dJr44NJFH8WII+Bd5j56kyHH7g5jA/z5w+uZQvnvdtv46/RwIkYGMCDH7b+Oby0kiABEiABEiABEiABEiABEjgvxKYvWSD/PHnQ2ndsKJL6fvy5Uu1KXj0+Inr51BlDho7XwZ6NZNYMaL816/l75MACTiYAOYY2CvNWrJB2jerKvf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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.histogram(nbhood_listings,\n", " x = 'neighbourhood_group_cleansed',\n", " y = 'percentage',\n", " color = 'property_type',\n", " barmode='group',\n", " histfunc='avg')\n", "\n", "fig.show()\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "**Observations**:\n", "- In all the neighbourhoods, the most common types of listings are House, Apartment, except for Downtown, where most of the listings are apartments.\n", "- Following the House and apartment listings, the next more common kind are the Townhouse listings, which can be found in Beacon Hill, Delridge, Central Area, Magnolia.\n", "- In Interbay, the Loft listings can be said as more frequent than the remaining neighbourhoods.\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" }, "tags": [] }, "source": [ "## What is the effect of selecting \"Strict\" cancellation policy on the frequency of bookings?" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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listing_iddateavailable
02410322016-01-041
12410322016-01-051
22410322016-01-060
32410322016-01-070
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" ], "text/plain": [ " listing_id date available\n", "0 241032 2016-01-04 1\n", "1 241032 2016-01-05 1\n", "2 241032 2016-01-06 0\n", "3 241032 2016-01-07 0\n", "4 241032 2016-01-08 0" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "calendar.available = pd.get_dummies(calendar.available, drop_first=True)\n", "\n", "calendar.head()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "booked_data = calendar.groupby('listing_id').available.sum()\n", "listings_new = listings_new.set_index('id')\n", "\n", "# Combining the dataframe and the series along the listing ids\n", "listings_new = pd.concat([listings_new,booked_data], axis=1).reset_index().rename(columns={'index':'id'})" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "cancellation_policy\n", "flexible 240.0\n", "moderate 240.0\n", "strict 253.0\n", "Name: available, dtype: float64" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "round(listings_new.groupby('cancellation_policy').available.mean())" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" }, "tags": [] }, "source": [ "**Generalization**\n", "\n", "From the above result, we can see that, though the cancellation policy is shown to have very less effects, we can get some generalizations like,\n", "- The places with **strict** cancellation policy are slightly free i.e., slightly booked less than the places with remaining cancellation policy.\n", "- There is close to no effect of the flexible and moderate cancellation policies on the frequency to book the listings.\n", "\n", "Now that we have reached this conclusion for the entire Seattle, now is the time to try and look a bit deeper into the differences in each neighbourhood, and try to correlate our earlier answers to this answer." ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "bingroup": "x", "histfunc": "avg", "hovertemplate": "cancellation_policy=flexible
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbhood_cancel_df = round(listings_new.groupby(['neighbourhood_group_cleansed', 'cancellation_policy']).available.mean()).reset_index()\n", "nbhood_cancel_df['booked_days'] = round(100*(365 - nbhood_cancel_df.available)/365,3)\n", "\n", "fig = px.histogram(nbhood_cancel_df,\n", " x = 'neighbourhood_group_cleansed',\n", " y='booked_days',\n", " color = 'cancellation_policy',\n", " barmode='group',\n", " histfunc='avg')\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "**OBSERVATIONS**:\n", "Based on the graph, most of the places follow our intuitive understanding **except** for *Interbay, Delridge, Rainer Valley, Seward Park*. There might be many possible factors based on which the bookings are high, hence an easy conclusion cannot be drawn for these cases." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Predicting the suitable price based on the features of the room {property_type, room_type, no.of bedrooms, no. of bathrooms etc.}?\n", "\n", "This question is answered by building a multi variate linear regression model, where the predicting value is y, and the features are all the remaining values." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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idneighbourhood_group_cleansedproperty_typeroom_typeaccommodatesbathroomsbedroomsbedsbed_typecancellation_policyminimum_nightsinstant_bookablereview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valuepriceamenity_Air Conditioningamenity_Smoking Allowedamenity_First Aid Kitamenity_Ironamenity_Kitchenamenity_Safety Cardamenity_Lock on Bedroom Dooramenity_Pets live on this propertyamenity_24-Hour Check-inamenity_Elevator in Buildingamenity_Cable TVamenity_Family/Kid Friendlyamenity_Shampooamenity_Poolamenity_TVamenity_Essentialsamenity_Smoke Detectoramenity_Hot Tubamenity_Suitable for Eventsamenity_Laptop Friendly Workspaceamenity_Washeramenity_Other pet(s)amenity_Hair Dryeramenity_Hangersamenity_Fire Extinguisheramenity_Indoor Fireplaceamenity_Free Parking on Premisesamenity_Doormanamenity_Dryeramenity_Buzzer/Wireless Intercomamenity_Breakfastamenity_Carbon Monoxide Detectoramenity_Heatingamenity_Pets Allowedamenity_Cat(s)amenity_Dog(s)amenity_Wheelchair Accessibleamenity_Internetamenity_Washer / Dryeramenity_Gymamenity_Wireless InternetTotal_amenitiesavailable
0241032Queen AnneApartmentEntire home/apt41.01.01.0Real Bedmoderate1f95.010.010.010.010.09.010.085.01000100000110010000010000000100010000100110.0346.0
1953595Queen AnneApartmentEntire home/apt41.01.01.0Real Bedstrict2f96.010.010.010.010.010.010.0150.00010110000010011100010001010110110000100116.0291.0
27421966Queen AnneApartmentEntire home/apt31.00.02.0Real Bedflexible1f96.010.010.010.010.010.010.0100.00000110000011001100010001100100110000100114.0143.0
3278830Queen AnneHouseEntire home/apt62.03.03.0Real Bedstrict1f92.09.09.010.010.09.09.0450.00010100000111011100000001000000110000100113.0365.0
45956968Queen AnneHousePrivate room21.01.01.0Real Bedstrict1f95.010.010.010.010.010.010.0120.0000000000000100110000000001000001000000016.0302.0
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" ], "text/plain": [ " id neighbourhood_group_cleansed property_type room_type \\\n", "0 241032 Queen Anne Apartment Entire home/apt \n", "1 953595 Queen Anne Apartment Entire home/apt \n", "2 7421966 Queen Anne Apartment Entire home/apt \n", "3 278830 Queen Anne House Entire home/apt \n", "4 5956968 Queen Anne House Private room \n", "\n", " accommodates bathrooms bedrooms beds bed_type cancellation_policy \\\n", "0 4 1.0 1.0 1.0 Real Bed moderate \n", "1 4 1.0 1.0 1.0 Real Bed strict \n", "2 3 1.0 0.0 2.0 Real Bed flexible \n", "3 6 2.0 3.0 3.0 Real Bed strict \n", "4 2 1.0 1.0 1.0 Real Bed strict \n", "\n", " minimum_nights instant_bookable review_scores_rating \\\n", "0 1 f 95.0 \n", "1 2 f 96.0 \n", "2 1 f 96.0 \n", "3 1 f 92.0 \n", "4 1 f 95.0 \n", "\n", " review_scores_accuracy review_scores_cleanliness review_scores_checkin \\\n", "0 10.0 10.0 10.0 \n", "1 10.0 10.0 10.0 \n", "2 10.0 10.0 10.0 \n", "3 9.0 9.0 10.0 \n", "4 10.0 10.0 10.0 \n", "\n", " review_scores_communication review_scores_location review_scores_value \\\n", "0 10.0 9.0 10.0 \n", "1 10.0 10.0 10.0 \n", "2 10.0 10.0 10.0 \n", "3 10.0 9.0 9.0 \n", "4 10.0 10.0 10.0 \n", "\n", " price amenity_Air Conditioning amenity_Smoking Allowed \\\n", "0 85.0 1 0 \n", "1 150.0 0 0 \n", "2 100.0 0 0 \n", "3 450.0 0 0 \n", "4 120.0 0 0 \n", "\n", " amenity_First Aid Kit amenity_Iron amenity_Kitchen amenity_Safety Card \\\n", "0 0 0 1 0 \n", "1 1 0 1 1 \n", "2 0 0 1 1 \n", "3 1 0 1 0 \n", "4 0 0 0 0 \n", "\n", " amenity_Lock on Bedroom Door amenity_Pets live on this property \\\n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", " amenity_24-Hour Check-in amenity_Elevator in Building amenity_Cable TV \\\n", "0 0 0 1 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 1 \n", "4 0 0 0 \n", "\n", " amenity_Family/Kid Friendly amenity_Shampoo amenity_Pool amenity_TV \\\n", "0 1 0 0 1 \n", "1 1 0 0 1 \n", "2 1 1 0 0 \n", "3 1 1 0 1 \n", "4 0 1 0 0 \n", "\n", " amenity_Essentials amenity_Smoke Detector amenity_Hot Tub \\\n", "0 0 0 0 \n", "1 1 1 0 \n", "2 1 1 0 \n", "3 1 1 0 \n", "4 1 1 0 \n", "\n", " amenity_Suitable for Events amenity_Laptop Friendly Workspace \\\n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", " amenity_Washer amenity_Other pet(s) amenity_Hair Dryer amenity_Hangers \\\n", "0 1 0 0 0 \n", "1 1 0 0 0 \n", "2 1 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " amenity_Fire Extinguisher amenity_Indoor Fireplace \\\n", "0 0 0 \n", "1 1 0 \n", "2 1 1 \n", "3 1 0 \n", "4 0 0 \n", "\n", " amenity_Free Parking on Premises amenity_Doorman amenity_Dryer \\\n", "0 0 0 1 \n", "1 1 0 1 \n", "2 0 0 1 \n", "3 0 0 0 \n", "4 1 0 0 \n", "\n", " amenity_Buzzer/Wireless Intercom amenity_Breakfast \\\n", "0 0 0 \n", "1 1 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", " amenity_Carbon Monoxide Detector amenity_Heating amenity_Pets Allowed \\\n", "0 0 1 0 \n", "1 1 1 0 \n", "2 1 1 0 \n", "3 1 1 0 \n", "4 0 1 0 \n", "\n", " amenity_Cat(s) amenity_Dog(s) amenity_Wheelchair Accessible \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " amenity_Internet amenity_Washer / Dryer amenity_Gym \\\n", "0 1 0 0 \n", "1 1 0 0 \n", "2 1 0 0 \n", "3 1 0 0 \n", "4 0 0 0 \n", "\n", " amenity_Wireless Internet Total_amenities available \n", "0 1 10.0 346.0 \n", "1 1 16.0 291.0 \n", "2 1 14.0 143.0 \n", "3 1 13.0 365.0 \n", "4 1 6.0 302.0 " ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_new.head()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "id int64\n", "neighbourhood_group_cleansed object\n", "property_type object\n", "room_type object\n", "accommodates int64\n", "bathrooms float64\n", "bedrooms float64\n", "beds float64\n", "bed_type object\n", "cancellation_policy object\n", "minimum_nights int64\n", "instant_bookable object\n", "review_scores_rating float64\n", "review_scores_accuracy float64\n", "review_scores_cleanliness float64\n", "review_scores_checkin float64\n", "review_scores_communication float64\n", "review_scores_location float64\n", "review_scores_value float64\n", "price float64\n", "amenity_Air Conditioning int32\n", "amenity_Smoking Allowed int32\n", "amenity_First Aid Kit int32\n", "amenity_Iron int32\n", "amenity_Kitchen int32\n", "amenity_Safety Card int32\n", "amenity_Lock on Bedroom Door int32\n", "amenity_Pets live on this property int32\n", "amenity_24-Hour Check-in int32\n", "amenity_Elevator in Building int32\n", "amenity_Cable TV int32\n", "amenity_Family/Kid Friendly int32\n", "amenity_Shampoo int32\n", "amenity_Pool int32\n", "amenity_TV int32\n", "amenity_Essentials int32\n", "amenity_Smoke Detector int32\n", "amenity_Hot Tub int32\n", "amenity_Suitable for Events int32\n", "amenity_Laptop Friendly Workspace int32\n", "amenity_Washer int32\n", "amenity_Other pet(s) int32\n", "amenity_Hair Dryer int32\n", "amenity_Hangers int32\n", "amenity_Fire Extinguisher int32\n", "amenity_Indoor Fireplace int32\n", "amenity_Free Parking on Premises int32\n", "amenity_Doorman int32\n", "amenity_Dryer int32\n", "amenity_Buzzer/Wireless Intercom int32\n", "amenity_Breakfast int32\n", "amenity_Carbon Monoxide Detector int32\n", "amenity_Heating int32\n", "amenity_Pets Allowed int32\n", "amenity_Cat(s) int32\n", "amenity_Dog(s) int32\n", "amenity_Wheelchair Accessible int32\n", "amenity_Internet int32\n", "amenity_Washer / Dryer int32\n", "amenity_Gym int32\n", "amenity_Wireless Internet int32\n", "Total_amenities float64\n", "available float64\n", "dtype: object" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_new.dtypes" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "Index(['neighbourhood_group_cleansed', 'property_type', 'room_type',\n", " 'bed_type', 'cancellation_policy', 'instant_bookable'],\n", " dtype='object')" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_new.drop('available', inplace=True, axis=1)\n", "categorical_cols = listings_new.select_dtypes(include=['object']).columns\n", "categorical_cols" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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idaccommodatesbathroomsbedroomsbedsminimum_nightsreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valuepriceamenity_Air Conditioningamenity_Smoking Allowedamenity_First Aid Kitamenity_Ironamenity_Kitchenamenity_Safety Cardamenity_Lock on Bedroom Dooramenity_Pets live on this propertyamenity_24-Hour Check-inamenity_Elevator in Buildingamenity_Cable TVamenity_Family/Kid Friendlyamenity_Shampooamenity_Poolamenity_TVamenity_Essentialsamenity_Smoke Detectoramenity_Hot Tubamenity_Suitable for Eventsamenity_Laptop Friendly Workspaceamenity_Washeramenity_Other pet(s)amenity_Hair Dryeramenity_Hangersamenity_Fire Extinguisheramenity_Indoor Fireplaceamenity_Free Parking on Premisesamenity_Doormanamenity_Dryeramenity_Buzzer/Wireless Intercomamenity_Breakfastamenity_Carbon Monoxide Detectoramenity_Heatingamenity_Pets Allowedamenity_Cat(s)amenity_Dog(s)amenity_Wheelchair Accessibleamenity_Internetamenity_Washer / Dryeramenity_Gymamenity_Wireless InternetTotal_amenitiesneighbourhood_group_cleansed_Beacon Hillneighbourhood_group_cleansed_Capitol Hillneighbourhood_group_cleansed_Cascadeneighbourhood_group_cleansed_Central Areaneighbourhood_group_cleansed_Delridgeneighbourhood_group_cleansed_Downtownneighbourhood_group_cleansed_Interbayneighbourhood_group_cleansed_Lake Cityneighbourhood_group_cleansed_Magnolianeighbourhood_group_cleansed_Northgateneighbourhood_group_cleansed_Other neighborhoodsneighbourhood_group_cleansed_Queen Anneneighbourhood_group_cleansed_Rainier Valleyneighbourhood_group_cleansed_Seward Parkneighbourhood_group_cleansed_University Districtneighbourhood_group_cleansed_West Seattleproperty_type_Bed & Breakfastproperty_type_Boatproperty_type_Bungalowproperty_type_Cabinproperty_type_Camper/RVproperty_type_Chaletproperty_type_Condominiumproperty_type_Houseproperty_type_Loftproperty_type_Otherproperty_type_Tentproperty_type_Townhouseproperty_type_Treehouseproperty_type_Yurtroom_type_Private roomroom_type_Shared roombed_type_Couchbed_type_Futonbed_type_Pull-out Sofabed_type_Real Bedcancellation_policy_moderatecancellation_policy_strictinstant_bookable_t
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327883062.03.03.0192.09.09.010.010.09.09.0450.00010100000111011100000001000000110000100113.0000000000001000000000001000000000001010
4595696821.01.01.0195.010.010.010.010.010.010.0120.0000000000000100110000000001000001000000016.0000000000001000000000001000000100001010
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" ], "text/plain": [ " id accommodates bathrooms bedrooms beds minimum_nights \\\n", "0 241032 4 1.0 1.0 1.0 1 \n", "1 953595 4 1.0 1.0 1.0 2 \n", "2 7421966 3 1.0 0.0 2.0 1 \n", "3 278830 6 2.0 3.0 3.0 1 \n", "4 5956968 2 1.0 1.0 1.0 1 \n", "\n", " review_scores_rating review_scores_accuracy review_scores_cleanliness \\\n", "0 95.0 10.0 10.0 \n", "1 96.0 10.0 10.0 \n", "2 96.0 10.0 10.0 \n", "3 92.0 9.0 9.0 \n", "4 95.0 10.0 10.0 \n", "\n", " review_scores_checkin review_scores_communication review_scores_location \\\n", "0 10.0 10.0 9.0 \n", "1 10.0 10.0 10.0 \n", "2 10.0 10.0 10.0 \n", "3 10.0 10.0 9.0 \n", "4 10.0 10.0 10.0 \n", "\n", " review_scores_value price amenity_Air Conditioning \\\n", "0 10.0 85.0 1 \n", "1 10.0 150.0 0 \n", "2 10.0 100.0 0 \n", "3 9.0 450.0 0 \n", "4 10.0 120.0 0 \n", "\n", " amenity_Smoking Allowed amenity_First Aid Kit amenity_Iron \\\n", "0 0 0 0 \n", "1 0 1 0 \n", "2 0 0 0 \n", "3 0 1 0 \n", "4 0 0 0 \n", "\n", " amenity_Kitchen amenity_Safety Card amenity_Lock on Bedroom Door \\\n", "0 1 0 0 \n", "1 1 1 0 \n", "2 1 1 0 \n", "3 1 0 0 \n", "4 0 0 0 \n", "\n", " amenity_Pets live on this property amenity_24-Hour Check-in \\\n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", " amenity_Elevator in Building amenity_Cable TV \\\n", "0 0 1 \n", "1 0 0 \n", "2 0 0 \n", "3 0 1 \n", "4 0 0 \n", "\n", " amenity_Family/Kid Friendly amenity_Shampoo amenity_Pool amenity_TV \\\n", "0 1 0 0 1 \n", "1 1 0 0 1 \n", "2 1 1 0 0 \n", "3 1 1 0 1 \n", "4 0 1 0 0 \n", "\n", " amenity_Essentials amenity_Smoke Detector amenity_Hot Tub \\\n", "0 0 0 0 \n", "1 1 1 0 \n", "2 1 1 0 \n", "3 1 1 0 \n", "4 1 1 0 \n", "\n", " amenity_Suitable for Events amenity_Laptop Friendly Workspace \\\n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", " amenity_Washer amenity_Other pet(s) amenity_Hair Dryer amenity_Hangers \\\n", "0 1 0 0 0 \n", "1 1 0 0 0 \n", "2 1 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " amenity_Fire Extinguisher amenity_Indoor Fireplace \\\n", "0 0 0 \n", "1 1 0 \n", "2 1 1 \n", "3 1 0 \n", "4 0 0 \n", "\n", " amenity_Free Parking on Premises amenity_Doorman amenity_Dryer \\\n", "0 0 0 1 \n", "1 1 0 1 \n", "2 0 0 1 \n", "3 0 0 0 \n", "4 1 0 0 \n", "\n", " amenity_Buzzer/Wireless Intercom amenity_Breakfast \\\n", "0 0 0 \n", "1 1 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", " amenity_Carbon Monoxide Detector amenity_Heating amenity_Pets Allowed \\\n", "0 0 1 0 \n", "1 1 1 0 \n", "2 1 1 0 \n", "3 1 1 0 \n", "4 0 1 0 \n", "\n", " amenity_Cat(s) amenity_Dog(s) amenity_Wheelchair Accessible \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " amenity_Internet amenity_Washer / Dryer amenity_Gym \\\n", "0 1 0 0 \n", "1 1 0 0 \n", "2 1 0 0 \n", "3 1 0 0 \n", "4 0 0 0 \n", "\n", " amenity_Wireless Internet Total_amenities \\\n", "0 1 10.0 \n", "1 1 16.0 \n", "2 1 14.0 \n", "3 1 13.0 \n", "4 1 6.0 \n", "\n", " neighbourhood_group_cleansed_Beacon Hill 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neighbourhood_group_cleansed_Other neighborhoods \\\n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "\n", " neighbourhood_group_cleansed_Queen Anne \\\n", "0 1 \n", "1 1 \n", "2 1 \n", "3 1 \n", "4 1 \n", "\n", " neighbourhood_group_cleansed_Rainier Valley \\\n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "\n", " neighbourhood_group_cleansed_Seward Park \\\n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "\n", " neighbourhood_group_cleansed_University District \\\n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "\n", " neighbourhood_group_cleansed_West Seattle property_type_Bed & Breakfast \\\n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", " property_type_Boat property_type_Bungalow property_type_Cabin \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " property_type_Camper/RV property_type_Chalet property_type_Condominium \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " property_type_House property_type_Loft property_type_Other \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 1 0 0 \n", "4 1 0 0 \n", "\n", " property_type_Tent property_type_Townhouse property_type_Treehouse \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " property_type_Yurt room_type_Private room room_type_Shared room \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 1 0 \n", "\n", " bed_type_Couch bed_type_Futon bed_type_Pull-out Sofa bed_type_Real Bed \\\n", "0 0 0 0 1 \n", "1 0 0 0 1 \n", "2 0 0 0 1 \n", "3 0 0 0 1 \n", "4 0 0 0 1 \n", "\n", " cancellation_policy_moderate cancellation_policy_strict \\\n", "0 1 0 \n", "1 0 1 \n", "2 0 0 \n", "3 0 1 \n", "4 0 1 \n", "\n", " instant_bookable_t \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 " ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for col in categorical_cols:\n", " listings_new = pd.concat([listings_new.drop(col, axis=1), pd.get_dummies(listings_new[col], prefix=col, drop_first=True)], axis=1)\n", "\n", "listings_new.head()" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "x = listings_new.drop('price', axis=1)\n", "y = listings_new['price']" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Since we seprated the inputs and outputs, it is now time to split the datasets. To do that, we can simply use the train_test_split() method. Then we can directly initialize the model." ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
LinearRegression(normalize=True)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "LinearRegression(normalize=True)" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=35)\n", "\n", "#initializing the model and training it.\n", "model = LinearRegression(normalize=True)\n", "model.fit(x_train,y_train)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Evaluation of the model\n", "\n", "To evaluate the model, the following metrics are selected.\n", "1. R2 Score - Measure of he proportion of the variance in the dependent variable(price) that is predictable from the independent variable(s)(the features)\n", "2. Root Mean Squared Error - Difference in the predicted values and the true values." ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Score on train data :\n", " R2-Score:0.49 \n", " RMSE: 2896.15\n", "Score on test data :\n", " R2-Score:0.54 \n", " RMSE: 2126.72\n" ] } ], "source": [ "#Predict and score the model\n", "y_train_preds = model.predict(x_train)\n", "print(f\"Score on train data :\\n R2-Score:{r2_score(y_train, y_train_preds):.2f} \\n RMSE: {mean_squared_error(y_train, y_train_preds):.2f}\")\n", "\n", "y_test_preds = model.predict(x_test)\n", "print(f\"Score on test data :\\n R2-Score:{r2_score(y_test, y_test_preds):.2f} \\n RMSE: {mean_squared_error(y_test, y_test_preds):.2f}\")\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The model has ended up doing slightly worse on the train data than the test data. which is better, but we can see that there is a scope of improving the R2 score. This might be improved probably by adding some more features, which are relevant to the price. Let us try to get more information regarding the predictions on the test set." ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "mode": "markers", "type": "scatter", "x": [ 60, 95, 155, 149, 125, 125, 99, 200, 204, 85, 275, 143, 209, 200, 180, 29, 90, 55, 95, 55, 39, 89, 69, 75, 85, 115, 70, 150, 85, 80, 120, 232, 103, 76, 89, 38, 60, 250, 130, 80, 148, 170, 125, 65, 190, 35, 110, 85, 75, 40, 260, 450, 135, 295, 115, 105, 75, 65, 100, 75, 350, 225, 100, 65, 85, 124, 115, 45, 115, 200, 195, 74, 200, 149, 60, 125, 110, 100, 85, 39, 63, 89, 175, 150, 160, 81, 225, 119, 63, 35, 60, 79, 200, 69, 90, 155, 95, 64, 125, 50, 69, 40, 250, 38, 55, 90, 50, 60, 135, 95, 55, 185, 85, 138, 75, 80, 275, 129, 125, 88, 100, 129, 40, 100, 99, 99, 69, 65, 109, 85, 100, 85, 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783+vHs4Xv88cM7otBzjid+zuAQYEAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIxJBA7MXv7GKV2f6z836vWL0+JxLcSfy284Hr77LF7x679Uo32btHpxhODYZUrQSamlOybXuT9NiVoqvV6qO49uv9Lf/h8y6uzq3icW35uEm6dqqXDg11VdxLuhY3Atv+0yySSknXzg1xGxrjqWICrPGq2Dkx7xprvJg7uEqHxxqvSh0ToW69/rrIqlUiq1eLrFkjsmKF9X/92UcfuQ8klYrQIOlqKARiL37nU8qP/DbJ+d0slviI+B3KXKQRjwTYGHkExWmhE2BjFDpSGvRAgI2RB0icEjoBxO/QkdKgBwKs8TxA4pSSEGCNVxKsNFqEAGs8pkgxAh9+KLJkicjKldaXitv67/LlInV1Ihs2uLfQt6/I8OEiQ4ZYX4MHiwwaJDJsmMhuuxW7Mr+PG4GaF7/zC1zmpzXJF8vtlCiXTTxLdJGA+B23W6L6x8PGqPp9FNcesjGKq2ere1xsjKrbP3HtHeJ3XD1b3eNijVfd/olz71jjxdm71Ts21njV65ty9mzrVpGlSy1RWyO2d+4U+de/RP72N5GNG917ss8+1u+GDrW+VOAeMcISuPX7zp3LOQquVe0Eal78Vgdt3vKhXDj1eln8+qq0v+6+YWpbjm/9Pjt1yknHjGrLH474Xe3TO579Y2MUT79GYVRsjKLgpfj1kY1R/HwahREhfkfBS/HrI2u8+Pk0KiNijRcVT8Wrn6zx4uVPL6PRlCT/+IfIo49aUdz6tWlT+5YqaGvUdrawbQveXq7JORBQAjUnfofpdsTvMGnSllcCbIy8kuK8sAmwMQqbKO15IcDGyAslzgmbAOJ32ERpzwsB1nheKHFOKQiwxisFVdosRoA1XjFC0fz9smWZFCUqdttpS/TneqiQrRHe2YcK2ypoa5oSjdy2v7eju6NJgl5XEwHEbwNvIH4bwMM0MAE2RoHRYWhIgI2RIUDMAxFgYxQIG0aGBBC/DQFiHogAa7xA2DAKgQBrvBAg0oRvAqzxfCOrGgMVsu3IbVvg3r7dKjr5xhuF3ezWLZOe5JOftHJua+5tTVUycGDVDIuOxJgA4reBcxG/DeBhGpgAG6PA6DA0JMDGyBAg5oEIsDEKhA0jQwKI34YAMQ9EgDVeIGwYhUCANV4IEGnCNwHWeL6Rlc1AhWyN2NYIbbvQ5Ecfifz975bA7XYcdpgVta2itkZy2wL3nnuWretcCAKOBBC/DSYG4rcBPEwDE2BjFBgdhoYE2BgZAsQ8EAE2RoGwYWRIAPHbECDmgQiwxguEDaMQCLDGCwEiTfgmwBrPN7JQDWyBe8sWkffeE3nssUw099tvF15qwACRtWutnx98sMjIkSKf+ITI/vuLHHCAyH77hdo9GoNAqAQQvw1wIn4bwMM0MAE2RoHRYWhIgI2RIUDMAxFgYxQIG0aGBBC/DQFiHogAa7xA2DAKgQBrvBAg0oRvAqzxfCPzbfD++yLr1llR3HaaEo3kXr5c5J132m/Ojt62/1Whu39/S/TmgEDUCCB+G3gM8dsAHqaBCbAxCowOQ0MCbIwMAWIeiAAbo0DYMDIkgPhtCBDzQARY4wXChlEIBFjjhQCRJnwTYI3nG5mjwVtvWcK2itq2sG0L3RrVrYK1CuBOhy1sa4oSu8ik/l+/OCAQJwKI3wbeRPw2gIdpYAJsjAKjw9CQABsjQ4CYByLAxigQNowMCSB+GwLEPBAB1niBsGEUAgHWeCFApAnfBFjjeUemAne+sK2R25q65Pnn3dvp0kVk+HArLckee+Tm4Vbhm6O2CVx3xwNpAJdNPKviIDZv+VAunHq9LH59Vbovd98wVT51yIjQ+oX4bYAS8dsAHqaBCbAxCowOQ0MCbIwMAWIeiAAbo0DYMDIkgPhtCBDzQARY4wXChlEIBFjjhQCRJnwTYI2Xi0yLS9oR3Pp/TU2ydatVZHLHDne83bpZoraK2Zp3e/DgTKHJfv18uwWDKiOwbbvIS4tb5P1NIgP6JeTQAxOh9bBaxO/tO3bK9HmLZNTh+8tpJ35BVr6xQa6ctVCunTZBhuzTN5TxIn4bYET8NoCHaWACbIwCo8PQkAAbI0OAmAciwMYoEDaMDAkgfhsCxDwQAdZ4gbBhFAIB1nghQKQJ3wRqcY2noramIGlpEXn0UZHXXrMEbxW6nQ67yOSuu1ppSQYNygjcdnqSvuFog779h0HpCajgPXNuUzrC3z5U/L5ofIPxxf/50hL59qWz29o56ZhRcukFZ8plM26Rrxz7abn7gSfSv7tj7vfk36+ukKvmLmo7Nz8q+6HH/tz2+wNHDpbbZn9Xdu/RTbKvkf3z/M6r2D3v1vtk1hUT0nb5YrjxYEUE8duAIuK3ATxMAxNgYxQYHYaGBNgYGQLEPBCBWtwYBQKFUagEEL9DxUljHgmwxvMIitNCJ8AaL3SkNOiBQFzXeK++ahWTXLw4E8mt4raK3O0de+5pRWxr5LYtbKvgrTm79XcctUfgd483yyNPtBQM/OrGBhnYzzwCPD/y2049MrDvnjJzyjjp0rljWoh+8PfPyuknHZX+XoXuBx55pk3gzv/+laWrpUvnTrJp81aZNmthWjzX6G09b83atx1TrKhIPv+OB9ra1AGHHZWO+G1w/yB+G8DDNDABNkaB0WFoSICNkSFAzAMRiOvGKBAMjMpGAPG7bKi5UBYB1nhMh0oRYI1XKfK1fd2orvG2bRNZtswSs+30JHaBSbuwpKYbWb++0L8qYmcL2/b/NV3JLrvU9nxg9IUE5i5IyrKVqYJfTLm4QYYPK534/b2JZ7nm285OSdK3T++cdCXZHc0Xr/Oju7PPVfH7V4880ya4I35X2d2A+F1lDqmR7rAxqhFHV+Ew2RhVoVNqoEtR3RjVgGtiPUTE71i7t2oHxxqval0T+46xxou9i6tygNW8xlOBe8kSS9y2he01a0SSSZF//Uvkww/dke6zj8gRR4h0754RujWaW6O4u3atSlfQqSolUKnI73zxW4XriY3z5a13NqZJ7d2nVzqi2xa/zxwzukAsV/H7rl8+lkPWLfUJkd9VOgHtbiF+V7mDYto9NkYxdWwEhsXGKAJOimEXq3ljFEPcDKmVAOI3U6ESBFjjVYI611QCrPGYB5UgUOk13nvvZYRtO4L7o4+sApPvvts+EbvApP5rC9v2v5VgyTXjSSCd83tOk2zPKnh6yIEJuTiEnN9KzC3tSbb4bQvfs6ZNSAvcfiK/9x2wV7qAZbGDnN/FCFX494jfFXZAjV6ejVGNOr4Khs3GqAqcUINdqPTGqAaRM2QRQfxmGlSCAGu8SlDnmojfzIFKESjHGk9FbBW2d+wQeeUVkb/8JSN4b91aOHK7wKT+RoVtzcE9ZEgmglt/Nnx4pYhx3VoksG27yIsvt8j7m1IysF+dHHqQeboTm6Pm4f7HC6+1pRuxc37ni99Xzloo106bkM7drVHa+bm8s3OAP/nM8zJ0UP+CnN96zXseekpOPGZUuqhl9pFf4DJbYNdrhnGQ89uAIuK3ATxMAxNgYxQYHYaGBBC/DQFiHohAOTZGgTqGUawJIH7H2r1VOzjWeFXrmth3jDVe7F1clQMMa4335ptWfu3XX8+N5F66VEQjud2Ozp2tVCSDBmUE7v33F+nb1/qeAwJxJ2CL3YtfXyUnHTNKLr3gTLlsxi2Sn/YkO4XJgSMGpbHYYrj+P+f3Iwe3Fa5Uofzbl85uw3j+1050LHipJ2T3Rb+/+4aprnnHg/gF8TsItVYbxG8DeJgGJsDGKDA6DA0JsDEyBIh5IAJhbYwCXRyjmiWA+F2zrq/owFnjVRR/TV+cNV5Nu79ig/ezxlu5MiNsr16dKTipgrcebgUmNce2naJEI7Y1ktsuMqkiNwcEIFAbBBC/DfyM+G0AD9PABNgYBUaHoSEBNkaGADEPRMDPxijQBTCCgAMBxG+mRSUIsMarBHWuqQRY4zEPKkEgf42nhSXt3Nt2kUktLKlR3fq929Gzp8ioUSK77poRtu10JXvvXYmRcU0IQKDaCCB+G3gE8dsAHqaBCbAxCowOQ0MCbIwMAWIeiADidyBsGBkSQPw2BIh5IAKs8QJhwygEAqzxQoBIE74J/OVvSbn9lnp57rmEaGR3e0efPpkIblvY1tQkmrake3ffl8YAAhCoMQKI3wYOR/w2gIdpYAJsjAKjw9CQABsjQ4CYByKA+B0IG0aGBBC/DQFiHogAa7xA2DAKgQBrvBAg0kQbgXXrRF59VWTDBpHzznMH8+jjzTJxfH36PD322iuTe3u//UQGD7YEb/0/AjcTDAIQMCGA+G1AD/HbAB6mgQmwMQqMDkNDAmyMDAFiHogA4ncgbBgZEkD8NgSIeSACrPECYcMoBAKs8UKAWINNqGitIrf9pfm3X35ZRFOV2Ecq5Q5m9dom+dc/G2TwoEQ6grtLlxqEyJAhAIGyEED8NsCM+G0AD9PABNgYBUaHoSEBNkaGADEPRADxOxA2jAwJIH4bAsQ8EAHWeIGwYRQCAdZ4IUCswSYSCedBDxqUieCeP19Ei046HazxanDSMGQIVIgA4rcBeMRvA3iYBibAxigwOgwNCbAxMgSIeSACbIwCYcPIkADityFAzAMRYI0XCBtGIRBgjRcCxAg38cYbhYUmf/YzkW7d2h/UBReIdO5spSax83BrihKvB2s8r6Q4DwIQMCWA+G1AEPHbAB6mgQmwMQqMDkNDAmyMDAFiHogAG6NA2DAyJID4bQgQ80AEWOMFwoZRCARY44UAMUJN/PjHIn//u8iKFSJLljh3/F//Ejn00NIOijVeafnSOgQgkCGA+G0wGxC/DeBhGpgAG6PA6DA0JMDGyBAg5oEIsDEKhA0jQwKI34YAMQ9EgDVeIGwYhUCANV4IEKugCRWy+/YtXhzy6KNFnnkm0+GePa2iknaBSY3kPukkEf15KQ/WeKWkS9sQgEA2AcRvg/mA+G0AD9PABNgYBUaHoSEBNkaGADEPRICNUSBsGBkSQPw2BIh5IAKs8QJhwygEAqzxQoBYhiZ27LCitZ2+NHWJHg8/LDJmTPud0XO2bbPSlajo3b17GTrvcAnWeJXhzlUhUIsEEL8NvI74bQAP08AE2BgFRoehIQE2RoYAMQ9EgI1RIGwYGRJA/DYEiHkgAqzxAmHDKAQCrPFCgFiGJr7xDZFf/ML9Qv36idx4o8jpp5ehMyFcgjVeCBBpAgIGBK6744G09WUTzzJoJVxT7dO+A/aS0078QqgNI34b4ET8NoCHaWACbIwCo8PQkAAbI0OAmAciwMYoEDaMDAkgfhsCxDwQAdZ4gbBhFAIB1nghQPTRxJYtIkuXiqxcaX0tXy7y1a+KnHpq+41cc43IT39qRWzr15AhIsOHW+lKDjjARweq5FTWeFXiCLpRswSqSfx+6LE/y1VzF6V98cPGcYjf1TQrEb+ryRu10xc2RrXj62obKRujavNIbfSHjVFt+LnaRon4XW0eqY3+sMarDT9X4yhZ45XWK08/LbJoUSZdyebNhde77DKR+fNL249qa501XrV5hP7UEoF/vrREvn3p7LYhn3TMKLn0gjPlshm3yFeO/bTc/cAT6d/dMfd78u9XV7QJ0/qzu2+YKp86ZESbbbZwfeDIwXLb7O/K7j26SfY1sn/eHmciv6twFiJ+V6FTaqBLbIxqwMlVOkQ2RlXqmJh3i41RzB1cpcND/K5Sx8S8W6zxYu7gKh4ea7xgzlm/XqSpSWTffdu3v+sukfHjc88ZOTITwa1R3J/7nMhBBwXrR1StWONF1XP0OyiBu+8WsfPzu7XxrW8V/0wJq538yO/NWz6UC6deLwP77ikzp4yTLp07yvYdO+XB3z8rp590VPp7FbofeOSZNoE7//tXlq6WLp07yabNW2XarIVp8XzIPn3TdmvWvl00xQrid9DZVUI7xO8SwqVpVwJsjJgclSLAxqhS5Gv7umyMatv/lRpCFLMrAAAgAElEQVQ94nelyNf2dVnj1bb/Kzl61nju9NessVKTaJHJVatEli2z/q8/275d5KyzRO6/v33vaVqTZ57JpCoZOLCS3q6ea7PGqx5f0JPyEBg9WuTZZ9u/1p/+JKLntXeE1Y6b+P29iWflRHZn92XlGxvkylkL5dppE6Rvn94yfd4iGXX4/gVpSvLbVrt5t94ns66YkI4KdzsQv8szF31dBfHbFy5ODokAG6OQQNKMbwJsjHwjwyAEAmyMQoBIE74JIH77RoZBCARY44UAkSYCEWCN54ztySdFjj/eHWm3blZxSc3DzeGfAGs8/8ywiDYBjdjWB2rtHd/+trfI7zDa8Sp+q3A9sXG+vPXOxnTX9+7TKx3RbYvfZ44ZXSCWa9t3/fKxnKF6SX2C+F2FcxzxuwqdUgNdYmNUA06u0iGyMapSx8S8W2yMYu7gKh0e4neVOibm3WKNF3MHV/HwamGN9/rrmQhujdzu0EHk+uvbd4pGbH/605n0JMOGWUUm7YKTvXtXsVMj0DXWeBFwEl2MNQEv4rctfM+aNiEtcPuJ/N53wF6+C1ciflfhlEP8rkKn1ECX2BjVgJOrdIi1sDGqUvQ13S02RjXt/ooNHvG7Yuhr+sKs8Wra/RUdfNzWeJs2iUyblikw+eabhXj33FPknXcqit3o4tu2i6xdn0q30aWLyMB+CaP2KmHMGq8S1LkmBDIENA/3P154rS2/t53zOzvtSbbYrbm7tYhlfi7v7BzgTz7zvAwd1L8g57de9Z6HnpITjxlF2pOoTULE76h5LB79ZWMUDz9GcRRx2xhF0Qe12Gc2RrXo9cqPGfG78j6oxR6wxqtFr1fHmKOyxvv4Y0vQPvjg9rlpLu6uXXPPGTAgk3PbjtzWlCVRPN7fJDLvpibZuCnT+zHH18kpJ9RHajis8SLlLjobQwK22L349VVy0jGj5NILzpTLZtwi+Tm/s1OYHDhiUJqE5vxWMVyPnN+PHNxWDFOF8m9fOruN3PlfO9G14KUK8VfNXdR2rp1axb6GKf5EKpWyHhdy+CaA+O0bGQYhEGBjFAJEmghEICobo0CDw6hqCbAxqlrXxLpjiN+xdm/VDo41XtW6JvYdq6Y13saNmYhtu9CkCt769d57liu8KBi33irSv78leO+/f7xc+LvHm+WRJ1oKBjV7egfp3TM6Y2WNFx1f0VMIRJ0A4reBBxG/DeBhGpgAG6PA6DA0JFBNGyPDoWAeIQJsjCLkrBh1FfE7Rs6M0FBY40XIWTHrajWt8Xr1EtG0JW7HfvuJ/OlPIn2tgMOaPOYuSMqylYUxjFMubpDhw6KT/oQ1Xk1OXwYNgYoQqAnxOz98/oeN43KSrmeH+qsX7r5hak6l0mx7fRVg5pRx0qVzR0H8rsicrfmLsjGq+SlQMQDVtDGqGAQuXHYCbIzKjpwLigjiN9OgEgRY41WCOtdUAqVY461bl4ngXrVKRItH3nCDSL9+7TMfPdqK8NaI7eHDRQYNErELTer/OUTue6hZnn62MPL76saGSOX+Zo3HbIYABMpFIPbi9/YdO+W2n/1Wzht7Qjqpen4Cd/399HmLZNTh+6cFcadk7vPveKAtZ012NVTE73JNU66TTYCNEfOhUgRKsTGq1Fi4bnQIsDGKjq/i1FPE7zh5szrHosXqVLxaurxF9P8D+iXk9FPqRKRJeuzasTo7Ta9iSyCMNd6CBSJ//KMleGu6kh07CnE9/bTIMcfEFmPZBvbm+pRcMzeZc739hiSkcXJD2foQxoVY44VBkTYgAAEvBGIvfudDcBK75916n8y6YkJaHM//vYrd+w7Yqy1SXBO222J4s1gL0949OnlhzTkQCIUA4ncoGGkkAIEwNkYBLotJjRNgY1TjE6BCw0f8rhD4GrqsU85eFa8mjmtB/K6heVAtQw1jjXfGGSIPPpgZ0W67WdHb+qWR24MHi5xwgkifPtUy6mj3QwXwF1+2or+7dknIcaP14Vm0DtZ40fIXvYVAlAnUnPitkd0TG+fLrGkT0qlNssVsFb/1sKO7L/zWV3OiwvV32ZHhPXbrlT4f8TvKt0D0+o74HT2fxaXHYWyM4sKCcZSPABuj8rHmShkCiN/MhlITmDEnKes2FObsnX9tCvG71PBpv4CArvG6NHSSpUutqG27wKT97/z5ImPHtg/uqadE3n03I3hr7m4OCLRHgDUe8wMCECgXgZoRv7Pzemfn/Fbx+1ePPNOWx9tJ/D5zzOi2HOBO4nfXztF6vahck4vrlIZAS0qkqalZOnWsL80FaBUCLgS27WyRrh2jF1WCQ6NN4D9NLdKhPiF1ddEp4BRt4vReCeiDZkmlpEMDn3nMiNIQmH19Sja8VSh+z/tRijVeaZDTqguB66+rk+uvS8jG951P6N5DZM7clHzzW4U5poEKARMCrPFM6GFrQqBrJ7QUE35RtK0Z8dt2Tn5akzAiv7lxojj1o9vn5paUNCVbpDPid3SdGNGeayQkn3cRdV6Eu71jZ3NagKxH/I6wF6PXdf07qwfid/R8F5Ue/+IBkedfyBW/e+4mMvV7rPGi4sNq7adGX69amZBVqxIyeHBKRn268CFLdt+vm18nV/2gTrp3F9l//5SMHJmS/Q+Q9L8j90/JXntV60jpV9QJsMaLugej238CWKPru6A9rznxW0E99NifZc3at+WyiWel05iQ8zvo9MGuEgRIe1IJ6lxTCZD2hHlQCQK8ElsJ6lyTtCfMgVITeH+TyKJfJGXZSkuY7LW7yMRx9dKrZ5K0J6WGH5P233hDRAtI2qlJVq8WWbJE5OOPMwO88EKRW29tf8DvvCPy7uadcuCI8hZa1UKvDz/eLC8utoq+jhiakLNPa5DePWPiIIZRlABrvKKIOAECEAiJQOzFb0138tP7HhfN392lc0ex05+cNWZ0uoilUwHMK2ctlGunTZAh+/QtyAlu5wNX4VyFID3I+R3SbKQZTwQQvz1h4qQSEED8LgFUmixKgI1RUUScUAICiN+5UJcuT8nTzzaLCrZdu4h89sg6+cwRpIQJe+qxxgubaOXb03tHEiJduogM7Bdu+q577hH5+tcLx7jLLiJDhli5t7/yFZHzzivOoRJrPKeirwP6JWR6IylFi3ssHmewxouHHxkFBKJAIPbitzpBBeu7fvlYmz+yc37rD7Pzgev3d98wtS3Ht36vkeJXzV2Utj/pmFFt+cERv6MwxePXRzZG8fNpVEZUiY1RVNjQz9IRYGNUOra07E4A8TvDRiMyL5/ZJNu35/KacnGDDB8WrphX63OSNV58ZoDeN/NuSsra9ZmUI8ceVSdjT8vNM7t8uciqVSL6b3aRyU98QuTXv26fx+LFItdemykwqWK3fgVJU1KJNd7cBZk3H7JHeueNHeIzERhJuwRY4zFBIACBchGoCfG7VDARv0tFlnbbI8DGiPlRKQKV2BhVaqxct3oIsDGqHl/UUk8QvzPe1sjVeTcnC9w/5vg6OeUECkaFeV+wxguTZmXbeuqZFrn/N80Fnbi6sUG2bk7IKadYorfbMXKkyGuvlW8MlVjjuYnfC2Z3SL9hwhF/Aqzx4u9jRgiBaiGA+G3gCcRvA3iYBibAxigwOgwNCVRiY2TYZcxjQICNUQycGMEhIH4XF7+dolgj6Oqq6jJrvKpyh+fO6FsRduT2li1WmpH7HmqWp5+1CudmH/rGxK5dE9K/v/XTvffOpCgZNizz//32E+nWzXMXjE+sxBrPiZHmvp8zg8hvY4dGpAHWeBFxVEy6adcZ0DdypkwivVJM3Op5GIjfnlEVnoj4bQAP08AE2BgFRoehIYFKbIwMu4x5DAiwMYqBEyM4BMTvjNM0z/fUmU0FXjzvnPp07m+O8AiwxguPZalaeuCBTHqSlSst0futtzJXU8F661YRp3zWepadLujll0VU7NZc4NVwVGKNp0KUCuB/e956SLDfkISMPb0+9Nzo1cCXPjgTYI3HzCgngUX3ZD5vSK9UTvLVcS3EbwM/IH4bwMM0MAE2RoHRYWhIoBIbI8MuYx4DAmyMYuDECA4h7uL3iy+nZO0GKxfx8CGJorm7//pcS1qk2r7DciZR36WZ1KzxSsM1zFYTLmnuBw/ORG3fequki8PmPzSq5qhm1nhhzhLa8kqANZ5XUpwXBoFJlze1rWMQv8MgGq02EL8N/IX4bQAP08AE2BgFRoehIQE2RoYAMQ9EgI1RIGwYGRKIs/jtFJF69qn1ctxoorgNp42xOWs8Y4RFG3j7bSvX9rJlIhq5rUUm9fslS0TWrhXp3r39JsaPF9llF6uwpKYmUdFbI7idjjfXp+TFl1vSRS+HD61LvylRrbmsWeMVnTqcUAICrPFKAJUmXQmMvyTzFhvid+1NFMRvA58jfhvAwzQwATZGgdFhaEiAjZEhQMwDEWBjFAgbRoYE4ix+Z0c+2ZgG9EvI9EbyXxpOG2Nz1njGCF0bGDNG5I9/FNm2zf0aL7wgcthhpetDNbfMGq+avRPfvrHGi69vq3FkN9+ZlJcWW2+9IX5Xo4dK2yfEbwO+iN8G8DANTICNUWB0GBoSYGNkCBDzQATYGAXChpEhgTiL39mRT9mY2AgaTpoQzFnjeYe4enUm9/Ypp4j07du+7dFHizzzjMiuu1pR20OGZCK39Xv9sotQeu9FfM5kjRcfX0ZpJKzxouSt6PdV38ZZ9ItmWbchhfgdfXf6HgHit29kGQPEbwN4mAYmwMYoMDoMDQmwMTIEiHkgAmyMAmHDyJBAnMXvy2c0ycbNuYC00FzjZCK/DaeNkbnmVdev5paU9NmjTsaeVl+1KTKMBurT+LnnRJ5/PiN0a5oSTVmSffzudyInn9x+w2qnxSj79PHZgRo5nTVejTi6yobJGq/KHEJ3IBBjAojfBs5F/DaAh2lgAojfgdFhaEiAjZEhQMwDEWBjFAhbRY200Nvfnm+RpctbZPiwOvnMEXXSu2dFu+T74nEWv7XY5S13JduYdOksMu7cBjn0IJdKfr7pYeCXQL5P1J5UNBbFb3xD5Be/KCS6xx6ZCO6LLhIZNcov9ficr9GM27db4xk+NNh9zBovPvMhSiNhjRclb9FXCESbAOK3gf8Qvw3gYRqYAOJ3YHQYGhJgY2QIEPNABNgYBcJWMaNt20Uun9nUJsRoR7p0EZkzvUOkoljjLH6rT/QBxdp1Vt7LAf0TkXs4UbEJXqILZ+chzb7E1Y0NMrBfMDGzRF313ayKslpMUotKavS1ftmFJq+4QuQ732m/ybvvFvnzny2hW7+0wOSIEVb6Eg6R/AK2vXqKTG/0/3nLGo/ZVAkCrPEqQZ1rQqA2CSB+G/gd8dsAHqaBCSB+B0aHoSEBNkaGADEPRICNUSBsFTPStA0/vbe54Ppnn1ovx42uq1i//F447uK3Xx6cX1oCcxckZdlK62FE9jHl4gYZPiya4vcdd4jMmCHy9tvu7C67TGT+fHO2+tDt6Wdb0g117SJy7FHR+awxGb1GfF8zN/MWh91Wsc9b++2cbdtS0rtXIv12zrad/5HePTqZdAdbCPgmwBrPNzIMIACBgAQQvwOCUzPEbwN4mAYmgPgdGB2GhgQQvw0BYh6IABujQNgqZpQfhWh3ZMzxdXLKCfUV65ffC9eC+K3CmR5Rjyz269tqPP+pZ1rk/t/kPjTSdDQ3zelQVd19/XWRV18V2X13kWOOab9rd90lMn68dY4Wl7SLTNoR3PrvyJHmw1Mhd+bc3LdNaiVlzNLlKZl3c6H4fciBCbl4vHMOf+U1dWZTDnjlddHEZsRv8+lICz4JsMbzCYzTIQCBwAQQvwOjQ/w2QIepAQHEbwN4mBoRQPw2wodxQAJsjAKCq5CZmxhz0fnRyikdZ/FbRe9b7kzKxk3WJNE0CReNj356jQpN+VAuq5HLNy/MRH+r8K0FLz97ZGUimJcutUTu114TeeUVK23Jv/+dGepZZ4ncf3/7Q9+4UWTzZkv0LuXh9sAtylHzXnm5fd6297DRjdcF56XkiEM6er0050EgFAKs8ULBSCMQgIAHAojfHiC5nULktwE8TAMTQPwOjA5DQwKI34YAMQ9EgI1RIGwVNcrPX7zfkIQ0TnaOQqxoR9u5eJzF7xlzkrJuQ26KjfYiRb36SAXc+x5qThc71UML7519Wj2R5V4BisiWj1Ky/q0m2X9Y5UTIhx8WOeWUwk5r7n47gvvYY0W0yGQ1HLUsfit/p/u5vVzxi+7J3KPZ/kP8robZXFt90EK/y9ckpUe3Ojn84HpqT9SW+xktBMpOAPHbADnitwE8TAMTQPwOjA5DQwKI34YAMQ9EAPE7ELaKG2l08fZtIl26RjOtRpzF7/GX5KY80MmiwuZNs81SbKjwbeddtiegRpVrsVMObwTCXuNp5Pbq1SLLllmFJrVI5Jw57fdFz29szBSYHDbMEr379fM2hnKf5ZQyRvsQh2KhXljqQyettbBxUyp9Hx96UF27D5zceE36rxY5eDg5v70w5xxzAvkPYXTuTpnEG0jmZGkBAhBwI4D4bTA3EL8N4GEamEDYG6PAHcGw5giUS/xW0eyn9zTL2tZ8tBo9eN65DUSE1NyMswaM+F2jjq/wsGtN/O61u8icGWYitRZtXLoiJW3lGRMiGl9+141m7VZ4KrR7+WwBR4X+ceeYFag0WeO9+67ID35gidz6tXZtYdcHDhR5441qJuq/byr+zpzTJBs3Z2zDeJPBf0+iYeHESwtenvyVJDm/o+HCyPfSKe+8Dkrn4bhzo1MbJPKOYAAQqDECiN8GDkf8NoCHaWACJhujwBfFEAKtRX579yh9VFB+ygSFz0a2dqcg4nf5fa8PoB55vFmWrEhJ756JdCThycdXJvdw+UcvouP/54tW8cFPHRq/tB1OEdphFCSddk1S3tuYm05F1e87F8RT/HYsUmkQQa/pOzRljOZiP/TAhIw5wZp7H30ksmqVyEEHtX83bNkisttuueeo2K2R24MHW5Hc++8v8pWvVOKuKu01VdBduy6VvneV2fBhbY9gSnvhCLeu+cL1sN/OKVeAQ4SR0fWQCLjlqo9iirSQkNAMBCBQBgKI3waQEb8N4GEamADid2B0GBoSKNfGyOmVfO36nTGOHjR0TazNEb/L614VkS6f2STbt+de97xzKld8r5wENH3AT++1hG/7iOPYVbh98WUrN7c+3DhutPVwQyPy7JzdvXtakXhej8tnJGXj5jzxO8af3Rrpvmxl4XhV8B/QPyEnH18vhx7kTYR97MkWuX1Ri2z9ICEfbk3I1g9Etn2UkJ07EvL++5YHUg6XyvfNTTeJ7LuvJXSPHOnVc5xnSkBzF69tzaM/oK8+MPTmd9PrhmlfrjVemH2OelsqAj/8hPX3pmsXkWOPqq+JBzdukd/HHlWXLvTLAQEIQKAUBBC/DagifhvAwzQwAcTvwOgwNCRQro3RpMubZPuO3M526Sxy05x4Rg8auiX25ojf5XWxW0RWrbyOfPmM3PQJSr9W8larIDFzbu6DD007pXlYvRxTZybl/U21I347vaWUFqlF2lK/zJ7ewVPKrk6dRXb+x53y8OEi//u/Ir17e/FE8HP0/o9qnv7gozazdHoDIIw3Kcx65d+6XGs8/z2Lp4WTAKx5r6c3evvMiDqV/DeQdJ0//fLaGHvUfUf/IRBVAojfBp5D/DaAh2lgAojfgdFhaEigXBuj/CI42u1aEd4MXRRLc8Tv8rq11sXvWn7zRNNuPPKEFQ2efXgtHHjJFU3y8cd5xjFOe+L0lsDHHyZk6xb9kvTXHrvVyT2/SEifPu3fx/0HpmTbxyLdd0tJj91T0q27/l/k+5Pr5IujSx9FrJHLi+5Ntr3xoQ98pkxCiPLy6TtjTlLWtUZ92+eHUUDWy7XDPKdca7ww+xzlttw+by86vyGSbw4E8cW/X0vJ3/+ZlJ49EvKVLzeko985IAABCJSKAOK3AVnEbwN4mAYmgPgdGB2GhgTKtTHStAsaEfLmOiuCcMSwhJx8Qj2LYkP/RdUc8bu8nkunPZlR+PZFHFN/OJF1ErL6903IjMu9RT+X11vhXs0tjceUi70VcfzR/KSseSOVCXtuDQKPa85vpT9uQkqe+0dK3npLU5UkpDk3Y07aQX/+s8jnP9++r75/dZN8sCXvnJTI1Zc3pHNYl/pweuOKWhveqMflgVm51njeqMb/LDfx++xT69vSUMWZQv4bEwP6WW8ZIYDH2euMDQKVJYD4bcAf8dsAHqaBCSB+B0aHoQ8C2dHXGsE07pwGGbDPTilHwUsf3YzsqSowPv1siyxdbkVZDh9WWwUF/TgO8dsPrXDO1SjQ+x7S/M1We7WUh1PHfstdyRyQtRKJZxr5bacBSaf9aM39EdWUVcuWiey5Z2EByfw7bMwYkUcfzfy0Uycrertbj5R075GS8d+ul7POFNljj/bvzbao+VZu6fzeCZFvja2XL3zae971oJ8ATgJuraT7CcrMtnN6aNRrd5E5M6KVqg3x23Qm+LN3+lujLXh908bf1arrbF0DT57aVNCpWlprVJdH6A0EaoMA4reBnxG/DeBhGpgA4ndgdBh6JOD0KrcK4JMuTMl++3T02AqntUfAKbVLFHOElsPLiN/loMw1sgloLtbn/pVMJ28+8vAGTzmb40AwnfN7Tm7Uv5+UU2r/o/9uko9aU590aBD51tcaZNQnSx+5HIT/q6+KrFghsnKl9a/9tXq11dq994p87Wvtt/z44yKbN1sFJje81yJ//2dz+qGRvi1wygneC15OuyYp720szJfuNeo+yPizbZzE7/2GJKRxcvzfeDBll04Zc0+yrVaJPvAZd270UlcgfpvOBP/2+XmvayXq2y29Gp85/ucQFhCAgHcCiN/eWRWcifhtAA/TwAQQvwOjw9AjASdhVk0vOC8lRxyC+O0RY7unxSnKTjf+Dz/RLGvXp9KFAT97ZL2cfHx4kYphit9vrk9J754JXqsNYxLHuI1lq0SWr7Kiv4cNbpD9Bsd4sHlD04g8FSa0cOXwYQlfKTfaXmNv1XBTCZERPgpmlpPyhReK3H67+xX79hWZPVvkG98oT69+/1SL/ObR3JwpnTqK3DKvPNHDTsU7a0WIC8PD+uBnY+vDi169EpF7YKZ/G99+b6fstUdHX/d8GOxoQ0Q/d2sp3YdTsU+dB34etjJvIAABCPglgPjtl1jW+YjfBvAwDUwA8TswOgw9EsiPRLHNEL89AvRwmpP4HcX0AG4bmDDTRIQhfuvbDPf9prmtmNvwoQm5aDy5JT1M1Zo75cGHW+TxP+SKkCccUy+nnxzeA524QnXLGX7njaUTcD/8UGTpUitqe5U+tFgu8qUvFY/Ynj9f5KabRIYMsaK29d8RI0QGDRIZNkykc+fyeknFL40efmmx9eSg3NHDen39nHzx5Rbp2lXk0APr5LNHMufLOwsqc7X8+1b/PmruZQ4IlJKA09+Lcr3pUspx0TYEIFC9BBC/DXyD+G0AD9PABBC/A6PD0CMBpzyEuhFvvKxFBvTp5LEVTmuPgFNRvSgWF3PLWRlmChdT8TtdwHFmU5vwbfulFqIa9eGERpPVUkSZ6SfPd76XlGQyN/1Ehw4Jue2/EYOKsS2H+P2Xv4jccUcmRcnGjYW9uuAC65wgh36mrd2QEi2+piJgue8d1nhBvIZNUAJuf8PDfIAdtG/YxZuA/cDt1aXNsmevhBx+cH36bSMOCEAAAqUigPhtQBbx2wAepoEJsDEKjA5DHwT09XWNANPFae9eIiefUC9dd6XgpQ+E7Z6qrxjfsjBTUFCLY100oSFyrxtHQfyuxdySOuZF9yZl4yZrGqqId965tZO72uQ+Tb+VkRLRlB162IUbSxm9bNLfarJ1Spnl9Y2Wt96S9AOqwUVSzNx/v8jYsbmj1ohtO3pb//3MZ0QOO8w/mXzxXtM4TW/sUFYBnDWef7/FwUKj7u0Cw8OHJMomAroVuQ3zAXYc/MMYSkfANMChdD2jZQhAIG4EEL8NPIr4bQAP08AE2BgFRoehIQGKIRkCdDCPemSuU4E8HWaYr66aboz0QcM1c638zdlHnHNLXj6jqU1Isccc5/GGeWeq+J2O+7aDvxMimvxhYQlTd4TZ/0q2pZ8HNy9MyroNmdQdF49vaBPy3nwzE7Ftpyixi0xu2yZy4okiv/99+yNYs0bkySctsVu/9tknnBG7PSQr9xsiry5tkXc3JuXAER0jlzc6HE8Eb0V9KAkRLdA9sF90IkidHhqVK/LaqcC5euC8c+pJexN8KmLpg4DpGs/HpTgVAhCocQKI3wYTAPHbAB6mgQkgfgdGh6EhAcRvQ4AxNdfob40eU8FLI9iPHV0vx40OL1dsGBsjJzG4lOKCvjGhufP/9nxL2usaQao5xsslyDjllN9vSEIaJ9d26g71i/rELi6mDwTy01pMuKRJNON3WjprjQCvT4gsvKF0eavj9tGgIvi27amc+f7Xv4p87nPuI91lF0v8fuCBytBwEwHLFQGrzKbPapL/7LTGr48PPj+qTr79tfrKAInQVfV+vuXOpCxdkUlXdOxRdTL2tGiwq+TndTot2Iwm2b4j43B9W2POjPK+8RCh6UZXQybw2oom6denXnp0C2/dGHIXaQ4CEIgJAcRvA0cifhvAwzQwAcTvwOgwNCSA+G0IEPNABMIQv1VYevjxZnl/oyWOaCG3UhZz07RB9/8mt2iiRiPeNLs8AmolxZRATi6DkYo8M+c2taWC0UuqT+ZMzxV5xk1qkkRdq/jdKkKmUiKLFpTHd2VAYXwJu8CkHbWdTIrcdlv7zWpak098IlNgUotKapFJ+9899zTullEDbpHf5RK/5yxIyvKVubnm9Tv9zCh33nEjkBUwdvq81W5c3Vi+B45Bh62fS5OnNhWYl/Nhpf59/OtzzbLhnRbp20f/Ntbz1kFQh2LnmQCFyD2j4kQIQCAkAojfBiARvw3gYRqYACrGLa8AACAASURBVOJ3YHQYGhJA/DYEiHkgAmGI34EubGDkVvivXGKM0/XLJeIZYAvV1E6BoIULVTzUjfaie5vTIbUa1Z2WGRMi4/Je7z9/cpMk8jIm6Ll31Wjak48+EpkyJZOuRNOO5B/6EEHTlkT9yC9EXM4I2EmX50bf2izHfrVejj2aiMj25pZb3uow02+Vcm47+b4SBbBZ45XSy7SdT8Bp3pc7zRRegQAEaosA4reBvxG/DeBhGpgA4ndgdBgaEmBjZAgQc08ENEe3Fr6z87ZWWvzW/vhNV1Jp8dtOu2JHug8flpBTTvCeAkAjAe9/KJlOD6LHsUfVy6EHRSOHrvZ93k2ZCG+dR2NPrZe33knJ439osdKZtB4qap9wTJ2ccXKGjVPUvJ5eywUv8x8G9OuXybltF5o880xPt3fVn6QPSd7flJLePRNy6EGFaXFKNYDGGU2yaXNh65MmNsjB+0fj3isVm2LtRl38zo9c14cuUyaXP2qdNV6xmcbvwyIQ9ULkujb66T1JeXFxa32LLiIXn5+pbxEWJ9qBAATCJYD4bcAT8dsAHqaBCSB+B0aHoSEBNkaGADEvSkDzZD/9rJUnW4/hQxNywbiUdO1ULx0ayhv9ePOdSXmpdWOjffETkeSUP1jzoWse1UofKhTZea9HDE3ImBPqC8R9pxzpUYmizPeb8lYB/NOfrJM//iUzt2w/aPobLe5mH07it4q/Uc75rSlH7BQl+YUm9XfKp71j4UKRPfawBG9NXcIRPoEHH25OP5zJPjo0iNw2v/KfGeGPNtwW9YHX1JlZhWoTIr2r5PPW60h1DGvXpdJvqQzob72tUu6DNV65idfu9dwKkVfijYcgXnB64FbO1HZB+owNBCAggvhtMAsQvw3gYRqYAOJ3YHQYGhDQherb7+2Uvfbo6DsK1uCymNYQAbfN0CkniRz/xfKK327F7/ykLfn5/RoVJLJ9e0r67iXy7XPKF8ln5zhfu94SU4YPqxMtAPfUM83yyBO5ApsW49S81/bhFpEVlQJyblH3xxxVJ3/IerBijzc/HcycG5OyfFVu7uWRwxPyvf+KXrHQL31J5H//V+eg8wfJPvuI/OlPIoMG1dAHjYeh6j1QCQFSBfC//CMlTcmU7NErIed/o0EG9PXQ4Ro/RaMwr7slKWvWZu7b0Z+tk6+f5f1tlxpHmB4+4jezoJwE8tNM6bVLWYg8zLFV+u2+MMdCWxCoJQKI3wbeRvw2gIdpYAKI34HRYRiQQP4iT6Nxp0yKnhAUcPiiQuLGTdam2s4fHLQt3aSrIKmHcuTIEHATnL90jMipJ5ZX/HZ7jd7rxsxJQD70wIRcNN7bfWOnLdG5sm17SoYPrZOxp9V7jgZ0y/mt/VqWV1RPPTB7eoe2AmdOfdcZO3RQQk77Sr3xPVDqOe+2KdXI9Xk3Jwsun+/TK69tkrffsfKBW4nBRfbqI3LtlZWPwNWobTuC+4wzRIoVifz0p0X+8Q+R/v2tiO0DDxQZOVLkgAOsr112kXRqGy0G++LilvT/9W2As09rqMmCd/n3vZ971nReOxVk9fO2ien1o2wf5YKX1cQd8buavBH/vtjrnHfea5G6REK+dHR00qu5rTOy11Lx9yAjhED0CCB+G/gM8dsAHqaBCSB+B0aHYQACL76cklvuKi4YBWg6Eib5m2qNkp0yKSMU+hlEvrhr0paf65b6XB3Xfb9pbosuPfmEejn5eP8pStwijj/3GZFzT68O8dtr6g+n1BvqhwWzO3gSsBfdY6UmyT78PHQ6/5ImSahw2/p8JZWyxOv6eikqftspBOxrq212zmd9tVcffvnNg17qeWi37/QQxX6VOp/rZ46ok3Hn5kaHjp/c1MatjUGZC17+/e8izz8vsnKl9bV8ufWVfTz5pIhGdrd3qM3ee4vsuqv7WU4PevQh3/RGbw9qyuXXUl/H7c0TTYmjqXFKfWjKJ/17Yz8StQuy3uTxM6PU/avm9qOe87ta2CJ+V4snaqsfla7rEoS20zqjf9+EzLi8tv5uBmGHDQQqSaAmxO/r7nhA7vrlY22cf9g4Tk478Qtt32/e8qFcOPV6Wfz6qvTP7r5hqnzqkBFtv3/osT/LVXMXpb8/6ZhRMnPKOOnSuWP69TA9evfoVEkfcu0aI4D4XWMOr/Bw3TaV+akCKtzNkl3eKf+vk2DmpQNOle2DtuXleuU4J18ota/pNUI6u4/pyMc5TbKxteibiq6qBNlikIq/GjldjlyoTkKYFiHTnN1ert9e9LEWnyx2OOXcVhuvRRfPn9yUI1ir7a67iBz9+brCtCcOuXHTDzQeapbtO1qjn/O6XO15OfWhnRXJbEXNq3hp+y39JsfGlPTqlXCMbq6GgpdaPPLXvy6cJRrprXm39WvyZJHDDy82k4r/3m2uep1rxa8QjTPc3jwp19+6q3+clA3v5Kbb0e8aL6aIWrEZhPhdjJC33yN+e+PEWeESiKL4rQT0YeWLL1tBCl27ioyt0Temwp0NtAaB0hKIvfi9fcdOue1nv5Xzxp4gu/foJivf2CATG+fLrGkT0gK3/n76vEUy6vD904K4/v7KWQvl2mkTZMg+feWfLy2R+Xc8ILfN/m7aXoV0PS6beBbid2nnJq27EED8ro2poUKgXZROR6wiae+e5R+7myBQrmi48o84c8Uwq9GH2VYpmajoe8udSdm4ybqKCs7nneueAsHtzYCggpHOe81LrbxWv6G5b3NHW840ANoHFVWCbGycIre1Ha+Coqn47STgdu+WkB9d2ZAWte2oco1UGvf1woKXNvWlK1Myb0Hhmx/7DUlI4+ToRTip8K1jX7q8JZ0H3elz1enBgT6IuWuB97Qn27aJLFkiYqcpyY7gnjlTZNy49u9iLTCp6UpsoXvwYJERI6w0JWEfpRK/9V728qAo7PEEbc/tMzroZ5nffnzv6ibZsqXQatLEBjl4/+IPzPxeL07nOz2ErZYCw1HijPgdJW/Fp69RFb/j4wFGAoHaIRB78TvflU5i97xb75NZV0xIi9v5v1exe98Be7VFimeL4c3SMd08kd+1c8NUw0gRv6vBC6Xvg5Mg4afYXlg9VAFDhbh0BGjr4ScCNqx+VKIdHfvkqU0Flw4S9RpmW6Vk4VSAqL3xhi1+22OLysMCN1+oGKNR7Nn3jR8RzUk89yM4O4nfg/ZJyJWX+Resndry05dSzlcvbdsss7LAZD7LuljFPrNF2vyUMZr3O5UQuevG4uL3zTeL/OhHIu9oznCXY9o0kR//OPeXGkH28BNW6iBNKzP21PKk2tBe6MOQp/MKgQYVDnXe68Mzu66Bvm1w4XnRiFx2+lunfMr1d/fSK5Ly0ce5kd96/UkTGuTgTyB+F7vX9cHt355rkTfXpUTfrjlutPcaCcXajvvv7WCL9zcnpffuDemHglF6cBV3/8R9fIjfcfcw44NA9RCoOfHbTnHyvYlnpSO/8yO71TV2dPeF3/pqTlS4/i47MrzHbr3SnkT8rp4JXQs9QfyOv5crHYGWT1gFjb8+1ywb3mmRvn00hUB9RaLQK+F5p9zNXvM+5/fX6YFGkPQgpeTgJHRqbnIVCJ2OHJG3VSRUmSYoI/sabvl3gzx4KCWv9tpOFzddl5L3N6VkQP+ErxzZanvzwmRbfm4Vm8ee7h6hnd+Pid9tkubclOGyd5+E/PAK/+K3kxBf6jc/1P8vLU7J2nWZCG0/YoxG7C9bkZL3NrYWq20tXpmdu9xmlj+WtnsgJbJju8jWrQnZ+kFCZkyrl9Gj259Nt9wicvHF1jl21Lb+O2RI5nuN4M4+3OZ6uURXu+iY/TaA37mWPZbZNyZlxapcAbdTJ5Fb5hZ/cFCp+zT7uvp5psU/39+YSr/GftxR9WkhtRzHtGuS8t7GPPE7ZeXXL1cfyjFOrlFdBJwKrerf/OmN3lJ8Vddo6E0UCSB+R9Fr9BkC0SRQc+J3dtoSdZmK37965Jm2PN76s3zx+8wxo9tygDuJ3107+99MRnO60OtqINCSEmlqapZOHXOLdFVD3+hDOASWrxRZcEeLVbDOPhIiBx4gMuFbpS+85TaKbTtbpGvHyl0/HLr+W/nTX1KyebNI5y4iwwYnZNgQ/22ohW4yn/u/VFoc6tIlIUceHrytYD0objW5MU8xFZHu3RPyox+4C0C/uD8lz7+QmawD+olMucR8nsyY1SKbWvN/2z0/96yEHPnJ8ohRxWlV7xmTWv3Y9hmSEBm0b0K++1/B2Om8Xb+h8B5YtyEli1+1OGi08BEh+EbbnHtDrgjYc3eRGdO8zanfPZaSp/+kVTpb88XrQxntYFb++GzPHX9cQg45ICFPP1UnK1ek5KHfimzdkpAtmxPSlPXix9fOScmdiwrvD/ve1nvgpZdS0qNHnRx3TEo0pYyX47GnUvLEU4URv6eOScjRn/fWhpfrlOOcxqtbZEfWW0L2NWfPJJK0GP+7703JCy+mMrn6W+ftjCvq0vdWGEfUUtGEMWbaaJ+Afrbf80Dh5w9/a5k5pSagn0f6d3PZ8pToA5eDPlEXeH1d6r7SfjwJdO2ElhJPz7qPqqbEbxW13353U47QHUbkNzdOrd02lR1vc4vmwW2RzojflXVECa++YnVKbrw9U+jPvtQRh4l8/ezKiSHb/tMslfi80wXy+rcknRKgX19LZOMoDYFJl6faCkzaV9itu8g1VzrPu7RQeWNhX849U4xFai18+fhTko7C1IjdUZ80b7M01Kqv1cmXF4oZQweJTP5OeJ8f+pDupp/kXuegA0TGf9PsGmkx+OlCppMu8PawaPqslGz+INc+3UvVwx26Nv4bCXlzTZ2cfWahuN7QINKtR0oGDkyl83SPH+8sfs++QWTDW7ksGi8RTwK423hP/YqK39U3t9rrUePVKdlh1YLPOWbPSJBGoYgr7fvJnkU6VcO6Zx96JCXP/K/VAU2rc/wx0Ztb0boTotNbt8+f448VOfE4s8/y6FCgp5UgsOD2lKxYnXtlr3/nK9Ffrhk/AgSwxs+nxUZUM+K3k/CtcDSSm5zfxaYJv68mAqQ9qSZvlKYvbmlPRn+2Tr5+VuWeUleiGJKmBJh3UzItfNtHOYselsbD7beanYNXo2HGntoghx5Unk1oOt+x6oRZCkyXTiI3u6QtcCuIaue31t8vXZFKC1/Dh9b5Gof6/pHHm2XNupTs0TMhI/ark5OP9xb9Wwm/5V9TU29o6g5NezJiaELGnOA9bYlp/9tS7GSlovGTc9zL9Z1SAqmd16KebtdwKxZaLJWOFpXUApPXzmuR999LyYdbEukI7l27peTLpzSLytY6t7PvJDuNzooVIldckUlN8u6mFnl7Y4t03SUlX/hMvRz/Rfd5Z5qmyqlYn7KZPb1D5NJLXXltk7zzbq5n6xIiP7khGmlPvMz7Up6jn3kv/LtF3n2vRQbvUy/HjTb/vHP7jI7i/Col+1pt261uR7WlZKtV/8R13G5/9zTf/LhzK7fPiStvxgUBCFgEakL8zk91ku18pwKYV85aKNdOmyBD9ulbkBM8uy0VgvQg5ze3UzkJIH6Xk3ZlruUmplR6UVgJ8dtJYNPItZtmexdTtJjcshVWxOahB9WlizlV66F9vf83zTnd0/HmF+UrVf8nXZ5bpFGv015xw/aEP31gkV9Iz8+Di+9f1SQfbM0daalzTYfF1Ulwai93eljXtdt59q8puffBpDS3TiWdQ42TO8iAvuFdySmHvbZeTKQu1gOne0BtnHJgq9j95S+LqHjtduyya0rGjkvKkH0T0q2byKcOr5Pddk1Il67imIfdzoH9xjrrCdA+/RMy9jT34nlu98CxR9Wl7bwcKkA99UxzOse73m9arK9cD7y89M/rOSre/vi6ZFu6GI20P+eMevni56v3M9fr2Mp1XthrPH0I98gThW8sIG6Wy6PVf538QteasmnG5aT0rH7PRbeHUS9qHl3y9BwCtU0g9uK3XeBy8eurcjx90jGj2tKf5J9z9w1T23J8q9FDj/1Zrpq7KG2fbYf4Xds3T6VGH/bGqFLj4LruBNwiIvwIh6XgWwnx201gWzDbWzGm7Chqm0nYEbBhsnaLpjUVFL32MT8KrHMnkVNOrJfevRIyoF+iIBJVhcLLZxQK5ipUXjM3WXDZ9oT07JOziwCqBGlH6/bbOyEzp1b/pnzmnKSs3VCYeqRcRQydHmKEXSy0VJHf//eCyH8vaJH161My/ABLtHO7Zz/6SNKCth59+liR2w2dUvLR9pR072F99dg9Jb17icz/kbcHZk6R5+09eHQrWFnpz2uv93zY59mFXrXdXr0KPzPCvp5pe/r31i72qW+oqK/9FFc1vX62vbJ7dVmLvP1Osxx8QIOvIrlu/XATv8v1NyVMPrRVOgIqRv7r1SY57IAOFFgtHWZabiWgn3WTp2YV1Wj9uZ+HxsCEAAQg4JdA7MVvv0D8nI/47YcW54ZFAPE7LJLV3Y5Gjqpwu721eFjYwpXf0asounRVkwzs21DWyGk38dtragUnEbCcEbjKWcWxdeu1oE9Chg9tP31J0JQPfv3Z3vkqBm3cmJK161Lyuyebc1LOOEVe6/kPP96czs3dtavIcUfVpzfP4y8p3NiomO6WQiW7T0//qUV+qRHw2UUKUyKdO3uzzx+fbrS0j2vXWylYPnNEaSNrL/tBUrZ+WCh+N05uSEf2lvpwYj90UEKmXhregwP1+8w5uQ8+/Ai+v/qVFbGtXxrBvXy5yPr1uWSWLEu5RmjbZ774osjw4ZKee3rYvn7x5RbRvPH62Xmyj5QzTp8Zxd42yRcYNXJSfV0pEbXU8ysu7acf3s1syvmM078P0xu9PVwNk0P6fpqb25cwhCCnCMsunUXmzCj/GMPkRVvhE6hEgEP4o6DFqBC4/rakvLoks06qqxO5akq4b6hFhQX9hAAEykMA8duAM+K3ATxMAxNA/A6MLpKGuiHu3bNyXVdxQHNuq2hoHxoBPL0xPBGtvdE55aP0Iwg4iYB6Pa/iuSn5/OjYYsKKY7qM3S2hotyHacoZJ/bdu4tc98PiY0lH085JFlR97d4tIdf9yP/cy3+tW1mW8rX/adck5b2NeeJ3SmTKpIayRNU5sg/Izm3e6WfDwp8nZfGr1ji77yYy8Rvex7fLLiLbthW2vu++mdzbc+dmIrvLNf+DfmbonN3eOh59+BPlQwVTffCkov/AftEeS3t+qIaoaP3M18jzd99LyaYtVm+ziXt9y6nY31FNq6NH9gPKKM9R+h4+AcTv8JnSojMBO/I7/WZfVo0ZP+t72EIAAhDwSwDx2y+xrPMRvw3gYRqYAOJ3YHQYBiDgViyrlMJhfjdViFnSmrO7d8+EfPZI7/ljnUTPckXR2+kQ0uv61twdKmoUi45VQUbHrA8cVETzE7UawMUFJum0BetTcvcvm+W99wujl70+OBg/uTXy21ZyUiIdO4vc6lI8M7sjbvkg9+ydkB9f5U381jaefrZZNn2QkjfWFpIpZQ79n/ysWZ7/V1ae3dbCk5qrvhzRwOMuaZK61jlnz78ePbw9ePA6h37+y2b59W9TsvWDhGz5QNL/7thmfb38ski/fu23dNFFIhrppalK9ttPZPW6Fnlvc4ts255KC65nn9ZQkQd/F3y3SVryUiTX1Yv85LriD228sqvG8/R+2bFT5Be/SsrmzZkeVnOaKFOObuJ3sc9o0+va9o7Xz87zFEIO/bD6SjvxJ4D4HczHft7uC3aF+FmR8zt+PmVEEIgCAcRvAy8hfhvAwzQwAcTvwOgwDEDglw81yx+eLSyWdfyxdXLGGG/F3AJcNjQT3ZTMW5BsSx/Ta3eRiyaEk0u1WCd1cT/3pqRo0Tf7SKVETlB2J1cnO420X3RvMp0GQPua3Xd7DJ7F70uaLM2/VT9PJUSGD7HSQWQf2QKQRpqOPbVeBvRPOOYMH9g/IVdPKS5+56QzyBOT7Gt7zT9ezM9Ov3dKCVLOiKbzJzcV+K5Dg8ht880F3LPPFvn970U+/tidzLPPinzhC97JOYmA5XzDJLun52u6nqy5r/eBhuLedaM5O+9Eynfm2g0is65rkp2tz6rshyXZ9/7s6R0q8iCi1BTciquWKx+2W1qv7HHHlX2pfUv7/gkgfvtn5vftPv9XiKeFW22jUgYlxJMko4IABPwQQPz2QyvvXMRvA3iYBiaA+B0YXeQMVbhdtiKVLjSoX+WIGM2HdNPCZvn3K4Xi96c/VSfnf706BVwnR6df4xcpS8oJ+/qPPdUiDz1qvWqeffTdKyHXTCsu4FZiwmrxSs2TrIeT+J3OuT3HmwjoVGw0P6LSKa2NXnvSxAa58fZk+vV//bLjzwf2t/LxFjuy220T4POyN5Q6ulMFeJ1372+yIvj9po9Q26UrrZEP6JuQQw/ynn7CKXVHQ73I7e1ELy9dahWN3G239umefLLII4+IdOigqU5S0q17Kv1v9x5W6pNrp9fLIQcV81Du700L2/q7mvvZ+pn703ub5c03rbQf1o0gcuAnEnLpxOq8Z03H3jgjKZs2577hkf+8yK8YbKdNqdTfLT9M8t8OKtebQdrH7M9bpz6XWwhSQcquixAF3/nxM+cWJ4D4XZxR9hkUO/bHK/9sp7/7fv/WmPUAawhAoNYIIH4beBzx2wAepoEJIH4HRhcpw/zCh8VyRZdqcD+8Lilr1qQKopeHDE7IFSEWz2uv/7oh15yoemjkcFTy6f729y3y6P8rFL977i4ytwI5vL3MkWzRNC1+q/aXJQJqNKjXyG+9nqbNWbrCKjI5fGhdgYDrlnbgqyfUy28fswpeZh9eo5fz0/XYY7Hb06jviydUb0FCp4hUP+kn3IqNfuebHdIFJleutApN6r9aaHLNGovyAw+InHlm+zNFi1NqhP7zLzXLI0/kPhjTQo8zLvcvEruJ3+WKelWx9ua73N946Ld3QmZO9T8uL/dcpc9xKvCpfcoWwK9u9Pa2jIpBt9yZlI2brFHZb3L4SVVVCR72w9EuXcub49yxrkJnkc9/JiX99mrwleLLlFv+Z6b67uLzvefwN70+9pUngPjtzwduqTv8/K32d8X4na1rnc1bm6VDfUIOP6Ted5BA/IgwIghAoJQEEL8N6CJ+G8DDNDABxO/A6CJj6BZNUmxBrULi08+2pFNWqFg+9tSGtNiYncpCIfiJJrt6VlLWv51Kp65Ii6Ct6QAGDhC5+vvFI3D1enp9zb2sR6+eVg5rr0U8nTYX5UwfYTJpbvlJs7z4amHUfOcuIjfP9sbO5PpBbAvEb4dgYz/id7E+uInfI/dLyOvLCvONa3teru92D51wTJ0c9Tnv869Y/0v1e6dc9SpGac5wL8fkqcl07uzs4+Xn6+Wf/3DOlz9ypMgBB4hMmmSlK1F+jzzeLC8uTsneeyXkU4fWycnHF9ra+ek3bkqlU9WMDZin2+ktAU1RVK5CryoAb9uRecXAJN2PF//kn6NvCegDPv1XHxTpZ3S53vRxE7/tPvpJD2RaJDcIuyjb6IPdeQsyb9t06SzynXH10r9fUnrs2rGsQ3OaB+WMgjcdbPY9pOsLvYc4/BFA/PbHC/HbHy+3s7d83CRdO9VLhwbu2XCI0goEIOBGAPHbYG4gfhvAwzQwAcTvwOgiY+i2oG5vI+pUmFLFskkXdJCbftKUFsSzj/POqfcUVfbA75rl//2xUMD90hfr5KxTiqc9cRqLnyh2t4hQLwJopR3+q9+2yBN/tER/O3WH/qvpIa77oTcRs9xjyBYhnVJl+xHCvPTdaX6oADRscJ28/FrhvNM2vfpeI4oefry5Ld97tT80UcH5/oea05Hyeti5prOfP+jYt24V0RQl2ZHbY8aInHFGhvjVP26S9e+0Bs7rgysR2f5Bg7zwXCItch94oIgK3vp18MG5nlIR6ftXN8nOnbk/P/eMejn686XZnOo1F92TlJcWW2MvZ25+FSA1/YTepFkvObTdtzYFr/POy7zPPicnP33rL/x8Rvq9Xv75ToK1prTRBx6a+kKjtr0K8VH+vDblaGKvc9Au9FqpNZ7T2yJB3+QwYRHENl1jYW7uOqdSNQOC9L9abBC//Xsi/0G1rl+mXx7PGgn+6XizQPz2xomzIAABcwKI3wYMEb8N4GEamEClNkaBO4yhbwJBokmcIif1wmO/Wi/3/bYw9YbX6G+7KI2dN1mjv1Ug8pqOwK1fXvP6/eDapLz9bmEEsNfr+4YfosFjT6fkwUesvNXZR589Ra69sjrFb+2nPkh5cXGLdOyQkJ1NKdm2TYUZES02Ofa0es9CmLal80ejgvVwyyGrbwY89UxzOtesnnPKCfWSqBOZe2OyIO1JsbzVTu779yspGTakMjnz/Uwnp2hvtd+wrk6WvZqQbR8l5D/bE7KpNaVEdtuTJ4vceGPmJ+MnW2Ju9lFfL3JHOzm/7XOffyElP/l5sqDr+w5MyA++F8/UH068sh/+6IOIuxaU5p51enCp8C8633pzp9SH3tv/c3+zvPJ6SpLJlOy5R0LGf7NBBvT1f2XEb//M8i0qtcZzEr/DfthpTse5Bbc3iLyuM0rVr6i1i/jt32P6+amf4fabVp89svrfLPM/ytJaIH6Xli+tQwACGQKI3wazAfHbAB6mgQlUamMUuMMxM7RfSy/1sPJFhM6dRMYcX58WJoYPLRTy/IrffqJgVYzXzaXOPc3Lp+Kk17zbmrv8r8+35EQ+Kzuvm9JLrmiSjz8upF2qKMww/fr0n1ocHzzsNzQhjZPcRUQVjF9abKVAiPLr2/npdvRNhCmTvOUO1jk396bWBwet+p8KkIP3TciVl3kTYPPviUMPTMhF473ZZs8D9YfXND1+5s+GDSL/+Y/IoEGWlZP4pGNesaRO/vxU7lsWGrE9dKjIkCHWv5/7XG4Et1NbdQmRn9xQXMB1m7c9d0/I3Bn++flhEsa5et9o3mk7gl6jqMed4567OB35PbPJelbQGiXftqJDfQAAIABJREFU9qZG69zru5fINdOKswvSfzfhrliaqyDXcrLR8Ssvffikh0muZych3+uD1rDGE/V2KrXGc1pDlOsBjKnPEL9NCVr2iN/hcKQVfwQQv/3x4mwIQCA4AcTv4OzSiwQ9evfoZNAKphDwR6BSGyN/vayes1UIsUXEoL3KF1NMxAE/fdC0DRpNsmpNSl5ZkmoL5HS6vgqNt9yVG61pv345c05TW+oH+/p+NrUqRP72983y/uaU9No9Iaee5F38/s1jLfLok825Qagpkdkzcl8LVca6+bajhA89qE5UoJ8xOynrNrRWXmztvEo0mvvY66v4fpiHea5bBK2mE5j4beeUMU6vb+vDDhWNy33YPmkrNjo0IWef5r0g0cWNrfMuK5fEgQck5NKJ3sZi56DNjsA9+9R6OW508dQbmkJk5lzrfmjLVy8qgnpL96N22WKQ3nMnH+/t2tl+euMNKz2JfmmhyGXLrCKT+qUR9aedJvLgg5aFk2A9oG9CPvPJelm8OCGHHGQJ3QMHFp8JTm2pGxbeWFzA1Uj5m35SGHU/dHBCpl7izXfplDNPNLelW9I8/045w4uPxP8Z+cWCtQUVwOdMdx/7+ZObcor6qk067YyIDNonIZf9V+mKo1Y68tuJl5/88vke0r9F+uaI/u3SIrde7lf/Xo6nhc4F/WpuSUmfPep81ccIg4he+/3WN3VGDK3z/JA7jGubtOFUIFjb81qo1eTacbJF/I6TN6MzFsTv6PiKnkIg6gQQvw08iPhtAA/TwAQQv72hcxKsx57qXfjKvopTVJGJOOBtBJmznIQsp/zf2k8VHlQs1teV7QhtFQI19/H7G61iaiosexUk0mlPrmmSVEtW3uo6kdlXe8tp6DUiy+l1eRU6l67QFByt4ZiKJCHStbPITXOKi3g2wX+/lpIXXrTyRx93dH2g1/nttlQcsCNKRwxLtFtUy63oYnsCrlsEfyU28U6imJ88qk6CYkODyO3z3X2nfO/7TatomrLma7oQoc9CrX/8S4vc8+vchy6qZeoDla+dVjxXvZsg6Sfdzh/+IHLsse53+667inz1qyL/8z/WOU73gJ83NLKv5PSZoQ/DvNw36Xk7u1X8zkpW/5lP1cm4rxdn5zbvvb7t4ffzMf98t9Qb7fnOideee4j84PsJ6drZm+Bv0u/8lDflzLUcdqoSfVi6dKWmS0qlUyVReNDbzHB6gO3n89bbVeJ5lq739CH/xs2Z8UWpWGe1eAXxu1o8UVv9QPyuLX8zWghUkgDitwF9xG8DeJgGJoD47Q2dk4gYVLB2EwfKIUjaObfzR12uXJy3/bRZ/u8lK22JfaiIOOrwOpnwzeJCmFMxNW0nWwjTjevkqU0FjtUxjvt6g8y5sUk2tW5qO3cWOed07w8x7v11s/zhL5n+a99PPaFevnJ88ejh/A45CfntpSZwKmSn0aTTL3dP/eE21zQCuGtXK/rYa8oZb3eK+1nfuyopW7ZaDx7SQbCt+d69ppxxEhQ7dUzILfOcxUS3uX7euSJHHFYvHRqK+0xFa41UX/VGSprsgo1Zk7dYyhmbRr6vt2xOyNYPEnLQyHrZ/rGI5s/OzrHtRHH1apHDDhMZuI/IsKFWkcnBg0WGDbPSleyxR66Vjv++h5Ki4qGK/sOH1fnOsW63qOztgplp17WI7DcsIY2Tiwu56ZQzN1spZ9qKbqZE0uw82LtFYZYrjYfbPbTA5W0Rt3lXVyeiDww+MdLbZ53p/abpZj74MCU9uic8P5w0vabahyl+k/YkuEfc/laWY50RvNfVY6l/b/Wz6831LRKlqPXqIUjak2ryRS31BfG7lrzNWCFQWQKI3wb8Eb8N4GEamADitzd0YQrW7YkpuuF6urVYX6+eCTl2dJ0M7BdukbJKFqKa9sOkvPd+YcFJr0Ubr78tKa8uKbSfNLFBDt7f4tSewD/29HqZd1OyLX2Cnn+ej9QVF1zaJC15l+/UUeSWee7Rx7qBfvrZZlmyIpX2pZ2CxU7DkT0D24vMswuX5hcLbU8EdIuUt1N/6AOc6Y2ZqHu9Rq9eCdec1BpNqGPRQ+enpp8olr/ajm7/+/+1SEtrxL/a2+O402PhP6d5u0sXkRtnO7N3inzU66pg/OWj62TksPaLbToVirVdb9+RX/hsnXzzrPYf2nzwgcjZ57TIK69IWvD+6MPC+7lnT5GNG90/i3ROz7upSTZmFaf0mrJFW7VTzmj6CD1GDE3Ieed6T72hczUdMZ8FQHOeX+wh57lb5LZX8dotat6rvbdPePeznO6hYg8LNUXPDiuTXdvRdt+KiBb7vOr7xR8cBO17/t+YcqY6cnpYUYyX2zjDFNKDsoyq3bXXJWX1G4V/K8v1xkRUudHv8AgQ+R0eS1ryTgDx2zsrzoQABMwIIH4b8EP8NoCHqWcC9ivEaqDRn584QIWRJumxa0fPbcThxOx0CCoAHje6/Ryy+ZvwbBFMBUuNoD30IG8itZOYo6+laxSkFkrbvj1DOF+cDIO9UxS73w2ximla0Ez750ecv/b6pKxeU7ghH6SFB79bXAz6yc+a5fkXshTUViD5/XcSlvV1ec0b+5KmPck6/ETwOwmw2pRb9LJy+v7VTbJzZybqVc9vvLhB5t2cm1Pd7lJ+WzpfNN9xWhx1mGLtiYBrN4j8cF5TWnR2O1T816MtPYhIugiqFnPMzoOeHcGr5yvF7ruKXH+tu/CfPdeyc23bfdGf3eUhb7Se7ySEtVf8zkn81utp9LFi1Ejcqxs7uKatKRA9NWK9NVpd22luEhl1WIN8Z3zx+z6Rd8ouu6Zkt54pOe6LdTJihBW5fcYZ7j5yShnT3rzLb8nJ3o8g+j/3N8uzf8udRBec1yBHHFJ87E6+s+sHFHtworZuD7P8pIwx/dzUuaDzX4/evYo/9JkyvUk269slWfnp7bnjdp+b9tG2d3vo46cug2lf7FzT2o6+WaJ/X4PUVPAbdW/a7zjZz1mQlOUr8/7WpkQWzKn++hZx8kMtjwXxu5a9X7mxI35Xjj1XhkCtEUD8NvA44rcBPEw9EXDaFJ/05Tr54heaa0r8dhNT2hOAcwRrBxXPr0it0XEvvmyJSbaYsnR5i/z0XiuqNvvwE+HpaSKIpItg2YWoNBLZj4CdLwo6CaVu/Vj482Z5TsXrvOPoL9TJuacXT3uiYupTz7a0FR3U4oMqMOX7ToUqfe17e2t+Z408vHhCg9y8MCnL8gUBEXFLYZDfzwma/iHvhw0NCbl9vrNwr8X+FvwkWVD8TsX+TRtTsuXD3MbyIySzo4+dxGO1bm9+6DxTUTtdpDHrUnbqZWV35in18vsnmwuKmObnh/7R/KSseTN39PqdPrQZPsRZBM15WOAyAK9pTzSC+L4Hm9v8Z/vUTVRLF/vMKs7alnKjVbjX7vTfKyHXTHP2nc7zBx9uETtFyYdbErLlA5GtWxLy4Qci27dbY9ZCk3r/t3fcfrvI9p0i695ulj57payUMyd4L/bpJgJ6fWh1+Yzc/LV2X72yd3ro072byHU/8p4rP/sz57NHFn9jIJun3gfqD/tQMdXrw0avn4lhnWenXcqO9M5/+OHnoY/ffrm97WESKa/3nj6U9fuw02/f8893St3hNde86bWjbj/r+qSsWJ3K/O1p/RvQOKmhbKmuos6Q/psRQPw244d1MAKI38G4YQUBCPgngPjtn1mbBeK3ATxMPRFwE1DmX5uqKfE76Gv0djSbisZ2zuhs8F6FqLRg1ho5vXFTSvr3S6TF51KIFl4mhoqEy1ZYgvR+Q+vaTWPh9uCgmECvY3v62Za0gJKvger3Jx9fly6oWexwSkXRa3fNe+0czab9TRc53G6N8dEnW+Td91pDf7OyOHiNPr5qVlLeeitjr4MZODAhV7ukMLj3gWb5418LxX5bGEvUtUaEi5UPeMrk3Pzd+XOigF1KZM4M92KhKh5pgU+VaTVdS12eRq3tHXlYnTz/r8I+5gvxl1zRJB9/nPGQPQYt/Hrs0c75s8+/xMq9bud7zhcB9XcqAquPVGwP+7BF05VrUtKc91zJZukmAOvDws9+WmT7Nvfo5r37puS5fyRkwICwe57bnlv+Xq8PbUohfqsvF97gXfwuLaHqad2t5kB+D70+ePA7Mre/b37SO2VfM3/doOlu9K2Qchwqus9bkHmIqdcMOo5y9LearpH9llTbI8sify+qqf/0JfoEEL+j78MojgDxO4peo88QiCYBxG8DvyF+G8DD1BMBNwEE8dvC5zUyzk2k9ip+pyNS5+amN1HxWF8Pv2ZuYSoMp9fV7UJ82m87h/T/Z+87wKco7vff3Tt6BwsKiFSxoKKxRI0YW8Suv2jsUUSJEbEDxgJYQrOCRgElav7WGDWaiImo2BWjotgFG8VGR+qV/T+f2Z272dnZu927+7b7fuZ5eIC7qe/M7u2+8877ibQItEym0wCFjsebyGeqclfN/5cIoPseyuCd911SVVXd0v/pU6naJmoxThAu6vNzs10mkxS0Jx2fDBD21D4FKiR1/YZNwNffKAo4DYNmzYA7JkYj8c69OGghUkj5/eS/s/jXf/Osa468VpTYFADv8INtdOtqBawBTGtNEBkEoO2SyoXW7V9muHMgld4mGveXe9h44+0g+a3P6fAr0li3Lqj8HnpGEnvubiaIh16cQsarWtiNFHDJiGPDEWetE4E2+a4sPv3EEZ7bq1cBq1Za2Gu/LFq1cXBPAc/xnXd1sHAh0Ladg3YdHLRpB7RrD7RpS/92BPZ0/d1xdzrnx03jID9t3dKD1uTceXTiAiCrJCIRoybTdVfMR1n6fNMG23c/uKcM1BaLlVf7ZlJ+00bKtBjkt1QPU72EUTUn9be2XLufuDiJwLhjUr6THLSxRptkca1Hwoj0qL91cfuu56drZcLkFFZ4XvfNWgBDTk3WW9V/ueOtZPkH/p7Bi6/67+tk9TTtlmi/dZXsC9fVOBFg8rtxzntdj5rJ77qeAW6fEWg8CDD5XcZcM/ldBnhcNBICJuU3vRRff3XjUn6XYnuiAmwijON42IapOEkJqPtxmzyNTYSoblERaUEAGDMhjUVL/IRmp47AhNHmF+Qw8lsnYO/0SFe1HyrxS58LD1wH2HlHG8OHFld9Rx0T5VPXuko+hRGwURW0JhKwkIXB+x87mDI1v6Fhan/rLS1c+yezklLHW7VSkHgccoCNk44346fOQ9jYj/xNQqjidTsYfRNk2MgUNng2MupcnHRsuPI7gJcDNG0KtG4NQRbrZHihTRBJ5n7mnVIY0N/Oqcb1tXHHHcBzzwFvvuVg6VILGYO9+m+OyaDLNtmC5Lest9A9Y8YDaSwjf2cl6detICQ1P/9CwU1Na53WAgWspLqo7L572QXJTNP9Xl4LtLExOEbAS2Ow0ZbAbeOiEWn6PYvuMRRoNS4ZG+ceoOZVNwupzaNiWM6U0qZq0ZOhExe02efx/XQddu0CjB0ZDbtS2qc1IjcIqXypntuVOo1E/aETU3HstajfRtuTFsCUkCC3pWBVrWXKtUqqVlx4XLWHAJPftYc1t5RHgMlvXg2MACNQWwgw+V0G0kx+lwEeF42EgO6DTIXOOMnGLv0bh+c3EZH/fjaDFSsdtGljCTLim4UOSAEZ10OWXso/+sRBKg00b27hsF/bOPKwaLYNxV5KhSXKIgedOllGCxKTgj9O0EZ1scQN4EhlRTC3la5nCBHYlCiAIynXZbrwihTWrgsuS0+wHPiiksHrCtkOhBHAUS0IjHg5wN0F1MM33p7Bp19kc3YvUgUvQWjZHJgyIZwIEwEvZ2aw1CNYdc3sDn1tXHK+mfymtfbZAtf2RFffy/aJcN6soyXa+HaR60e9755BT+VLrk5j9Wrd8RyIRX4D2GpLC21aw+i9XkhRSmN5+12gWVMHzZq7XiqHDDQT/yefDDz8cH6ZNWkKodxu29ZB2/b0B+iyjYOWrZzQYKX6IqVNr4cfd4lustrZZy/Xqse0JnRiu9zTIpFu8FomU796dLcw9MygKr1Y/Xc/kBYbCfJ6p/V0bgHFv1pf2MZB1JM2xfpW7HvThh3dL2mDrzbId9r0oI0eNRXaYCw2ntr8nmIGPPJEMA5F1OCZdC+mUxGfzXfvG4Q72STRxk2UFPZbGfV+HaWNas0Ttske55RVtWLD46odBJj8rh2cuRU/Akx+84pgBBiB2kKAye8ykGbyuwzwuGhkBIiIIGJ13XoH2/Wx0a4d/TtVZ57f0pO5pkmIz78EJt7m+g/LREeAb/1zfALEFITQRKbQ2IhQJNsBSoQ3eVuLl9IPnLz3teX6YEf1nS6FsNYXCPWNgmE9+e8MflrmkqMyEbFHR+TD0jXj01hCvtdK0l+oLxjpP3Yvs5p8p+m7445M4IhDohEixRY7qS599jGK9NtkQdB1awtjRkbzsC0V+/++mMWn87P44MMgedypg4UJY4q3f9dfM/jf3KA9CRHBd4SQ5w897nqt+whzBYQ4JwauGZfCku819B1gfAHPcbI9SWfydhtEnm7WwbXqoX7piTZBfvoBmD/f/2fBAoD+UNrvoAy229HdTNi8o4Xxo/3Y0fV5021ZLF4MJJo4aNPeQXMvIKWOg50o3wbAtCZ0S5Ew8rsm/YuNpxQ86xkiX8m3OaoS13RCJOraiWqVVOy6LvX7uth4UPta6j2j1PFSOWn7RH/TbyudRCjlN7ZcC5Uw8jzqZieT36WvAtMJtTi/daW3XB0l1WuILKzoGuIUDwEmv+PhxbkrgwCT35XBkWthBBiB4ggw+V0co9AcTH6XAR4XLRmBVKZuyG8iRGY8mPfJJaUkkTG6T27JA9MKTpySxuee+kz9qpBqNaxtUxBCYuJGXOBXP5te3EntmLAsPDnTr6Zr3col4qOk625MC8W6mjq2tzBxbHEClcroXqBUE/2Rr3aFFJlRbU+uvCGFH370j0Yoj5Xj/+q3ur90FBz0PEToP/J4WhA/XyxwkBXG4m4u2bbbAVIPW2jTyhE+2ybP8LD2B1+YElUKv3LPuoX+OeO28LmT68BEvFMdzZsDtxdQfsu+TLg5gy++CRLGiQQw9WZz+399MANSjuvpjJMS+MWu8QgxqSKnxSKU5N6mzUhN9a+2RQEvpd+4Bz3IY33sqCaYNDnlswuhdffqCwnceGP47Ldo6WC3vbPot5M7Jj3oYmDjw1vbNP/kUa2r7mkshTy/o6zDsOtcDeAaRgLWpHdyWIwHOaY46uMoBH8YVmH3jKjkuayX6qFTDOQXHsczXLeSkvXVJPYqFkOGp/xm6+LGUfi0SJR1F5bHZLFTjs0M3VdfeyuDhYsdz24nEfl3Okx9HBV703VTid+KcvBtSGXp3v/qW1lkMw623MK1iaqpZ6yGhEuxvpquoZqKSVGsLw35eya/G/LsNdy+M/ndcOeOe84INDQEmPwuY8aY/C4DPC5aMgJ1RX6biJmafKkNI7+PHZSIbFciQb59ehpzDQre009MYuC+eW1pGGFE9ej+yvRZVN/p80emsHF9ntglIqVJM+DOSYXJc2GnstjBpClBA2TyLD/4ABv9ets++xJ9YUVVcb7/oYMp09Muq+4RpbIuU6i7/fexcMbvopH3N9ycxpffuBUnbODo3yRw4EBbBBHV7QWkNYve5mEH2/jtUfF9xgcPTwnCVdYnemEB94QE/lPxClO96wRu2MU89d4M3n7PVTxLXKlsoYCdxSx24tw4wkjEMBXn8uXAaedksHK5hRXLgM23dNBnhyySSWDqTU3EJgWdQqHUoiWECnnqVOCGG4Devf1/evQAJt+dQsKwRFQLhDCVLwXdpLWipzgnLsKwkl7kS5fJEx6ujRIpbSVh+/2PDj78OIu1dN16l26cgJOyDQriSimKcps2Au6YnvcjN9neRLWPKIf8pv6a1mEc+4Wc8tzbQYrjlx4WtDGq+jjONWLKe85FKf/mm2HTptw21PJh441qVVLJvlRi44HGI7zu17knRop53Vey/9VQV1094zVk7Or6tEhDxk7tO5Pf1TKTDWscTH43rPni3jICDRkBJr/LmD0mv8sAj4uWjEC5L0ZEytCfuGqiKD65JQ/KUPD2uzOYOy+ogD3nzAT2GhDvOGuYmk0lv8N8p4nwomQiv6OSQVJJKLXfkogtRGQ99nQGz85Sxu/5dcuyhdTDKpwmZS19T0ETjz3cjyMpBt/7IAt6kVQDJcqgjVISTAric05PYu9fmGhx/2Te+1AGr7yZt/GQRPCZJydx38NBUp+OKr/7fhYbNvrrIQKWvKf32M3GAftGV0Cf7ZHfam2FCFSfRY5nORFYnhZwdwh5ruYl1evE29K+IJHU9q/2sXHm78xEfthL/EEHJLBb/3gKWprPsRP8djZSvbtwIfD008BHHwEffwx8+CGwdKl/pL37ZTHw0IxQYE+LMF4dJ9M9I5kA7lJU74XIb2pXD7DpZIF7pkQ7cRH3fhS2USTr+d1xCVCw0ijJpICN6vNP9yIin/XAttRuVPL7yuvT+OEn/2kTum5OOzHaBhL1gUhMsoCifhOJGdVyxWTfQET+4FOje0fPeCADuXFAG30UIDaq73SU+SmUR9wztAzinlEgTkA5bVYqSGU5fZBlTddAMVutSrTLdeQRKPcZrzFiyeR3ZWadye/K4Mi1xEOAye94eHFuRoARKB0BJr9Lxw5MfpcBHhctGYFSX4yICKNAVqQkphRFiah20kh+b21hdETv5bgDJvLlsmtS2LQpX5LIz+v+FE1trLYX9cXIpG6XvpGSiJH1EiFTKOih2r5JSViIyAoLOOcL/hjxGD6R32Mnpn1kDtUziJTUR5uJsGEj0tiwMeh1jSzgEPeXhSDRfv2r4kTgRVem8fPPwbp+ubuNN94Jbm4Q3jrWKpZUU/cuwDUjohGgcf17feSPyfeEVPtNgTtvdNsnfNevh9HWIc7GAxGNtNmzcjUE6Zny293nIIijoKVCdB3RmJYup5gBVo7AfPJJ4Ljj/Ffllp0dNG3uiECTbdpa2HzLLDbv7M5dVNJVrfHiq1JYs8bfxrbbWLjq0vw1HKZ6HbCLuwniC/xJG0A1SEKqm2RhgVajbniFKfijljfds+jaIAI5SqIAtj+v9W8e7LaLjT8OjlY+Shthee5/JI2XXw9e8/vsYWPwafHalx7Y5fQnbllSrS9c4viskrrFiDMQt736pPymvtPmxXOzXZuvzTpZbL0Rd0LLzF/qM16ZzTbo4lGf8Rr0IGuh80x+1wLI3EQAASa/eVEwAoxAbSHA5HcZSDP5XQZ4XLRkBEp9MTKpn+N4yJ57ccr1ZFbSllsAN1xpJiErRVq8/EYWmzYAS1c6QolIRCMpEcmmgIJRRkk5Sw8lM1kq3KIFzxS+5g/kbQdI9T3snKQgECfcmsKKVW4FTZsAp54QXYk46toUli7z97R9e+DGsWbsZr2YxcNP+j3GqbTOxUYhJCWZK+y0vfmzbKCQZQ0FSlz8vecT7TVMJGTWyvuMb7+dhUv/6N+IoPn5zPNp79fHEgGnzrssZSRyB/S3xdF4PZG6lurRVa8qGUk4kD9zlKBWRv/eImTuuFvSWPC1G+A0Zz1BKnCPfO23nYWzTkli0pS8bQutycGnJDFg57xmlAJEko2AnnQCWFUJqxYpxPxK1b2qgJb+u2qQya++Aj7/3A062bcv8M9/Fr4yKLjkU0+5ViXPv5rB0pXBuZA1EAalqF6J/J8yLY3lK92aum1lYcSFyUAgP91qgdbAFltYmDI1eDKAbFTIgqUmkkpYh/m9R7WiEHXNDwbJHT0ietBKInTo+qVEGxfSmiXK2E2bPk2bAn8pYrUUpe5ieaTdj56vJm2yivUpzvdE/tLvwPoNbina6Bx8qv/ajlNflLx6gFIOdBgFtfqXhzauafN24aIsunW1QSdt4gYuLfUZr/6hUXs9omc0OuW0bEW+zYZyv6k9lIq3xOR3cYw4R+URYPK78phyjYwAI2BGgMnvMlYGk99lgMdFS0ag1BejsGBqUQhU6mwuEJ7i3WFSw6lEFhGCRx+WtwogMmzRYgedOromzOQfTC+GfXvbBW1YwpTQsYJwPZ6Rtss5A+gwIkon7iWBLIlIsv04ZKAtjuJHSdeMS2Pxd07OwoHIxA4dSiO/qT0RvJGsNwoEbZT9ev9jB5OnaspvANv3sXDZsCSIeJX2MmRtQC/rDz2RxazZGf/Rf69NSULrCmST8ooCIpJ1SyrIYWKPXW307GHhqZmZHMkkX1bJYuam29Mg32eRNMsX7yPRv2JBrc69KAXy7lZTkyZ55bZp/lQiKhd4U/FBOPwgG9/96GDuPH/Fuq3Fg49m8MJrQVK5bTvgTxc1ya15H+kaYrUiydhVKyy8OiuB778Lt5zp08clwqMkeW1J0l23GZFY3xNhrVFeNYgp/V+uqSh9ofuDtNagDavJ0/yWMVRH65YWbh0X//RHlPbVe1cY+R31nvPgYxm88Ip/7sm6564aIu718ZnIb1ox0yPOYxS8wvK8+EoWDzwW3PQ57sgEjjgk2oZlOe1Xoiyt48XfZ8TuV5etai6ws9pX+p2ha2DjBuCdD7LilBZtUO+7V/SN3kqMvT7XQb/NdIolqgVP1LEQ9qT2p9+1OMFZ1fqFzdTElNigl6mUwKWlPuNFHWu15pOnnL5dnC0aC6VaMSh3XEx+l4sgly8FASa/S0GNyzACjEApCDD5XQpqXhkmv8sAj4uWjECpL0a6skx2IAqBSnmjBFArFKiMyFRSwsqk+18XIpXC6iVylRTAxVK5R2Jvm5rGvI+Dx/jLwY76bCpPY6XgkLNfzQb8jlXpd1Qii5Tz9z6cAVFOUsVMI6GNCyLA1TmhPhH5Tarnr7wAlSq2khCkv0m5PWxIHnvT5gq9+JNHs1T+qpN//JEJHH6oS4SppCf9n04pvDfPtR0QfbbzAStNc13ISoI2bfK7Hl5pCngZQgL6PL/z2X3Ndt3KQsu++y1zAAAgAElEQVSWZh94dU6f+W8Wj/8rk+u8HsCQSJazT0vi+okZzJsHQXr12THvj+5r1AN/w3rggemu8rlz53yQSSK8e/Z0/28nHSz63l2vxdTxNF4KLitJ79wqV5Y7nZKI6vltWgdnnRL9lASt/4efyGD9OmXaZMBSB2jTxsItN9QM+U3k1aTJrnqQ7lQBmtYBzv19EnvuXtzrPizOQG0FbTR53Yfdc4rdP+N+b/Kapzpqa+xx+xuWf91Gl/xu2bxm1pupXROBSvminjio1NjrWz1EbJJtmzxZRBuNJx0X/b4SOsfrIQJKSzs4yjegv4Xzh8Sf83KfM2QfS33Gq29zxv1peAgw+d3w5qwaeszkdzXMIo+BEWgYCDD5XcY8MfldBnhctGQESn0xMr2YFTsWSqTk0zMz+HS+g6QNrFnr77b+Qh728keBJf/2aFD+q6oryWJkxHDzC6cgvx/IgBTXwgOYuuEARw+qHfL7wivSWLsuSH4XU4ESsUhBD194NYs1axyfkrpje2CiZnviUxxLqD2QpNpbfkyE5NRbits/kIXKQ09kAkR66zZAsyR8x4Q9WEUTRnpPmbAO7YFJSv/DvLV32N7CR+rGgWflMaaA/YNal8l7WVflFpoHImOJUMqNxwIKWQqo5LeRAAXQqhXQpbNlDIKqkt9z3nFw1/1p2J5dytdf2Fi92sLqlcDqVe7f69bmkaYgpmeebzb7ljhs3dnCcYcl0a+fa/+jJ1OwQRnkMuym47OGMUiet9gcuOrSJkWP7+se5/oGF5H9RCqF2QCoJzyETQ+p4L1Oy26172DhxrHxiak4N1xaAw89nhGnVHJXvdeR4wYlcGQEu6Uwz+9i94w4/SyUV2z6eBgK7Kj/EQO1VqIPtBboVMe6dRAbRYcMTAjrloaU6oL8Dgu6Wuwabki4ltLXsACyE0YXvy8Vas9UL+Uv5ToNe/6JEyiX2i71Ga8UXLkMI6AiwOQ3r4e6QIDJ77pAndtkBBonAkx+lzHvTH6XAR4XLRmBcl6MVA/Zbbq6gawK+VHqKk4i4MjTmI4G9+ttB8iMsJe/k45NmD2sFYuHQuT3518CE28LkoJU78G/Ln6M3qREjOKpSuXIEuSRJzLCOkOnbi4fnsR2vcyEDlko0Iu1P9KkS0BRid49LIy6KE/i5UhLz+KDWDcirIgkX7rCq0ZRwLZqZeG2CPYP9z2YwStvBa032rRBIBihXJRhwf5UXlS3cDCdLCCMW7cGPv08uHFwxkkJ7P9L89wFyG/B4uU3PSSGsr+FlN9/vjktlPRqKrTpIwjcCWl3o0W1W/EIbJq7tm0tETCU1kWheoV1x3TXcoZ6MGOyebNim+6AnciiRStgr/0ySOjZFODbtwOuvSKc8AkjXUmFT1YBRw1K+CwDdLJNEP5Ke7YNYT9DYygWJFevy7SO9tzdxrlnmE9r+Ih7ibd2eW3e0cK40TVLftOckhqePLv1NOggG/8XEihWzav7mNN3cYLklvwD4RWkDQ2VuKe13KIlMGV88Q2zctuuRHn6LSHv5GXLXWsjss+qbfKcye9KzGRl6qipzaRKEdY0yjAiPWqQW4lUOc94lUGba2msCDD53Vhnvm7HzeR33eLPrTMCjQkBJr/LmG0mv8sAj4uWjEBtvRiFKdAKEYemMkT4XHBuEvTyWii1bWNhp+0tyECJat5nX8jisX8GPWQPGmjj5Ii+20Rkv/aWW0fLFhb23atwICqTglbaVsi+7beXLTYCKLCjvolgVEJ7pKIkpVTrDXoJf2qm3+pE5ssRkl7D9Hm/3uFKeRW7J/+dxb/+G8SO5oXIJN23msboOY34iHvdsqNZU+AOJXgezT1ZPahB4oYNSWLq/WmsXh2c+UIbF6plhCRQqU+iX5oymdTSt9wQTgZfMDKV65PsRaFAr7RO/nR9ClkPMtXze/06C2tWAdlNNnbrb+ONtxwRYPLHHyz87aEsBh3iXwek/J52v7vuqduvPZ9AIumgbXsL7do7aNPOwR67I3figYh3svugI/6vzcng20XA8hVK0ERJbJ6exN6/MG+6hNkbhY09zE9/px3c+ud9pJxYsFyv+kljwglUE97q7DdrBtwx0V+exv36W1l88rmDRd/lCWfTCDt2sDBxTM2T3/9+LosnyLJGSyqRRdiRrYwpBTzLHaDzlsD1IQGCZR1EoH0+P4sWLSzs0NcWeFOi+0ycwHkmr/tCAYpL/kGqgYKmey+dcihX5Ru3q3VBfodZxjR225MZD7ibIYWux7jzS/krZVUi29ZJeno2GHxqcWs2te+19YxXCl5cproRYPK7uue3vo6Oye/6OjPcL0ag+hBg8ruMOWXyuwzwuGhJCKhqOPKl1FWcJVUaUqgU8puqIuLiudkeydySrElcpalJueXjMZX/6Ee8730og1ffDL749iUC+IKaIcJ8QQiVIJM0RtFVpb968EciL4eP8ivVcySqtCBwgLsVJfA/nspi5vNBsk2W0wMRtmsL3HRdOAkpX+rDrDvIioACXt4xPZ23PlFU5zm5sqeAVv9PAHTsCEzUSFAaNwUxFWRdV5esu+XOND76NJ7ymwigiZNTWL4ir/b2eVJ7g5JzUOiIepgdS5hfu8nz+/l/JbD4WxvpAvs3b70F7LmnezFR/4mo+eTTLL74Kj92kxK6kB92mPp4jwE2hp5pJlSMXtPehoY8hTBiWNKnojVdm4TpXfeljacDCnnd0/U/44H8Joh+e6HNiqk359etiXwXiJHa3HAwoEkSuLOWgkaOvzWN+cr8yftSzpfcC2xnsnOR8yDvFdKyafL48I0aleDTLWOI/B18SlKcvImSTOuefP5Hj6yZ+2WUPkXNY1LNU9lSrCiitqnno3U55720CF44cJ/aCXgp+0DXEN2/Fy1x0KkDsM9e0ey9Sh1rQyhn2hAhbCYU2IiLMi76zaITbnLTlspEORVWqG76DaFEJy1KCczJ5HeUmeM8NYEAk981gSrXWQwBJr+LIcTfMwKMQKUQYPK7DCSZ/C4DPC4aGwFT0MdCCtbYDRgKmFSccf0r1WpJ1bhuvYOZs7JIETcs/SCknYeSWSXY7n0kg1dfD5LfO25n4+I/RlNVSXUpBbbqua0t/JKJzCSCVlVuUz6yOyACplDSBMg+YkYnv/W8VC99piu/n35WG6MDZC038J5OotlWeBBC3Xs5MA4HELYlHglJ+WnD4vW3HNcb2NRhGTCTKrNc3KJYKHy+wAmo/knlf/P14SScjwRUrHHkOAphr4/1oj+l8fNaP/neoqmFY36TFKrtI48EunbNlzJ5fs98IoElC200aQKh1m7b3sHJJ9giwGSvXm6QyW7d3DrUgHV6P+X/O29hoW0biNMH9CcshW0c/Ho/G6eeYF73ugJfrDUFQ/o3bRipFhJCaf5WFu99kMVmnSzsu6draRSm4o4a6NUUdLF1K+DWP+fJ7zDlJRHfFCxVT02bAn9RThxU4j5XqA5S769e46BbN0tYHAmybGxKkKJq0u+Lpdg0+Ox+DL77ce73JvK7XFKvprGW9dc1+W36rW3syuvamvtC7agb63Sfoo31sJMXcfpL13Ruwz7CqbA4dZeSl8nvUlDjMpVAgMnvSqDIdcRFgMnvuIhxfkaAESgVASa/S0WOSI5VG0Xpzdo1K6MWLsoIREMgjEyJ6ycZrTU3F71sPvx4XhlcyhFeU3s+YiaEaFUJtgcey+DFlz0mzCNnqdjuu9r44+Di5HeYulRqKOWReiI9pI+zThjqymsal9p1XZV43qUppKRS2DBGnfzWlW0m4pTalNw0kd/TbzUrvwOEIpHonmWIoIFtIKkpcP/foxnMfs3F2Oj5rfpfwyPPIypwiZB9+Y0slq1w0K+PjUN/XdhyRl0fOa91xe+cVLQSC7JvIfWfyRLi7beBi0dmsXK54wWYtLBqpV85+49/AMcfn1+lqu2AxIGCUxLx3aKlS6LT5/eE+Hfrvq+iDtlZAIU8r/VrhdYjbQSoa496MLpAsFCqQyrwp0zPYP1Gf6BV+v7cM5LYc/fiCuLhI1NYtyF4BUclv6+6Po3vfpKyc3fxbt/HxmXD8tdsGPlN3trPzNJsgBxg804WxteC5zdhOGlKGrRZJhOdtjl4YAKTbg8eAdDtoO6ckcE77wfZ+0LYxb0vFrqXmzYuigU4jvPbUJN5TeQzXeejRzapCNlZrO96rAvKr5/uKVYHf88IlIoAk9+lIsflykWAye9yEeTypSDA5HcpqHEZRoARKAUBJr9LQc0rw+R3GeBx0dgI1AX5HbuTEQsUVTh6x5mlWnvOu1msXhOsPKrtiYkMljYEkmQm5aYvgKHCPocFgFQtRS4YmsQunk8y9TQs+JU6CpUII7Jt7ISUsCAJ2Q/wfU6EaBj5rbat2yfI9vWAlWHrS+SXwQfpb9slc4kMvvPGmgmeFwh4qfG0ckykxD1mUHggvLPOAu69N7humrcAdt3FVWyfdx6wzz7+PNJ2YOGSIHHsweFT7aul9bWmepbTMOJ6F4t58QIv0polVXZUD1nfBozSySMPTeDYI4oHih09Po3Figc3VaF7vRe65IeNSGGDu0ecS/13sHDh0Lzq30R0Umba1Lt2Yjpw4mHX/jaGDSm+4RXxVhSaLez6pUCt9z8cPBWibwz+ZUYG7871B7yltTBlQjR/+rB7TtSNBx1XIo8pQG8pNgzlYllKebqOZs3OCjsKUqzTdR7V8qWU9tQyca2Sym2PyzMCKgJMfvN6qCsEmPyuK+Qbd7tMfjfu+efRMwK1iQCT32WgzeR3GeBx0aIIEBmqqllNZAwRGkSmNLSkHmuXdh7Sj5jGNPjUpPCMHjXW9c3W88jxRg14qRKSYcTyPnvYeP1tv1JTkKwe06orv3PBL70KJT9LR7GPPswlFom8Jw9sXb0rqjWoh6X9xIefOPjo06BqVCXE2rezcOO1ZusQn9I9ZMDNmwG3K4EHfR7nBquR3Dx44Ns2MO2Wmll70i85kwZWrrDw8xoLq1ZYWL0SWLMa4t/XjrVwySWFV/7f/gZcPz6LNu0grEratnPQroMjiPuoJGLAusNTwKuWNWovfKSj4qGuuskcdoiN3x4VncCVgTDjesgOG5nCBoNymwjc/X9ZnPy+dlIK3y5U/De8a0H1qi80AyYSkch/3S5H33ghb+0B/W2Q57mqmqf136WzhWv/VPO+1WGKdLK/UE/DyPHrthg0ps8WOG5sALrevZMLhXyrVb9002Wrx0Iodt+n+8CyZW4HpAd/sTL8vesBTZuQamooljE8fw0fASa/G/4cNtQRMPndUGeuYfebye+GPX/ce0agISHQqMjvx595GV8v/B6XDD3RN0crVq3BeaNuwbxPvhSf33vrKOyxa79cHip39cQZ4v9HHLQ3xl4+GC2aN2Xbk4a00ivUVz3QmUp0VqgJQZjecXcay5a7NVIwtbNOdQNuEWlMfZBquMGnucEkK5mIgJ31Uhbr1jnCPuGTz7NYsdJtoVK2J1QXjWPpcpeY2babDSJjKUmSxqjWVgZKoz7r1ERBz2SZ3aeEDiF2yRJg7jy/NzSVVwNVyvqICBefU13kTSwyuiQh9UsnuAYPTwnfbknwSyW1bp1RLEilSohtTSTgFeEkIFmNPPdSBt98Cyxf5Uq31ZXSspWFyePy5fUNApFfKZBTMHv1WAlgeg2R33dNBUaOBFavCl/ZfzgPuPMvxVf+kOEp/8CL2JaoNc6Z62DaX/PqYzHN3hwXIs8lmSssWwx+9j23Bf50ceGNAxE0zXKV4qVe47femcGH2iYKzSPZxETxyh0zIS2C7umbT1E3Dkzkd99eFkYMD65bSfB36mSJvr3/sYPJU9OCPBY2N97i79TBwsQxdUd+07VNfXxqZgZLPWLZ5N0ufOvfyuZPa9A9Ay7xb7LoketODRpL98fPPNU//Q4U8ocvfiVwjqgIsOd3VKQ4X00gwOR3TaDKdUZBgMnvKChxnkojwOR3pRHl+hgBRiAMgUZBfr8991OcedF4gcHZJx/uI7/Xb9iE0ZNmYO/dd8Dxh++PBd8swZXjpuOGK85Br+5bg8reNPVR3Dn+YnRo1wY3T31U1EMEOiu/G9eFZfKNJgQqHQjLpDpTvVpr8sVIWG9MTOWId5P/RjkBLwkvIvUkwVVoBYUpL2Wf2rQCbri6MJGkEkqEK20ahNkJHHawjXTKJf5zySPc6BMi4XJksFRz0ue6JYcDHD3IFsf0ZSISMEcghhCoepDKgPJT89w2KWhNeL7/oYMp09KCSFUtUNq1Af7v6AT69rYF2UhzT4QeBT38eS2wfpNXWwiB27w5cLvh1AGNgzZL3pmbxetzXCw7b24hYdv4eZWFLbcARlxaeMPmvvuAM8902xcBJkWQSaCtp+Bu09bBuGuT6Nuz+D3o7AtTPtKfSphU90R4PfVsRqx9Cix40nFJLFvl4OHHghYX1PvLLkiiU0eXqDUlwuHpf2fx3kdBBX/HjsDE0Wby2xRQcf99bZxxYnSluOzPQ49n8PzsbI48Fps2ACaMjkZ+SwW+Or44p01MgQuj3j9E4NEp7rpVEwUKvfn66CcOBJm82Ck4V6b5U73f5fedPDum4qsO+NezWTw507924vY9Sjucp2YQINX+G29nsHGTg8MPboJuW9dMO1wrI6AjUJPPeIw2I1AIASa/eX3UBQJMftcF6twmI9A4EWgU5LecWpPym8juSX95GOP+dI4gt3UynMjubbt1FsQ4JZUMz6Cp+IwDXtbPi0cqJ6l3pJorN+kBCWV9Rx3mJzrLbcekliQyjggrSjX5YiQIJxnMLcQuo9Sgabqajubk/CHJUBVkmOeuSl6rRBqRjS1bmMlIIjWfmukFc1QE2OpcycChhMGn87N4838OflzqeT5TwEhNwUtkOJGxq1cHZ3zAzjbOPztPVgasM6iIA6j2EQHltUe4S9WrUBzLOXFcS5woljc0HrKPkKp00bRC5BOJPuzsJLbrk79Gnn0hi7//M+PnHdX14AA9eti48uL8GAn/ibdmMO99155k9UoLa1ZbwrYkJYl0AN17ZnH2eRlcfVk4gfnSK3T6ISOsSnLNev+QBH6Y7Yg+G4S9qES6fGTdYJ1TFdW6vvEg6xg/uomwQDBtcNBnhN3gU5KhXsRP/juLf/03SJ533tLC9SHWHSbCmfrTo7uF9m2BowZFP+0R5uNeyHpDxY9wmTQ5LTaNKElLojjey3TdE/nsKtjtyL7NpPyeMjUYWDIO+a4GcaX+77idhYv/GF01TgT4a29lsH49bYi4yutCqm0Vu7B5pDUVRXVf7u8Ily8dAbpn3n5PWsy7WPctgMsvaDh+6aWPnEvWBwRq8hmvPoyP+1B/EWDyu/7OTTX3jMnvap5dHhsjUL8QaPTkt67spumR6u7zfn+sTxVO36nK8HbtO4nZZPK79ha1bgnSrYtLoOpkgjyuL3s2oL+br5SUIyVDyOBKk99CqSqP+lOHvWCDtuWS+P93jI1OHdNo19rdfKlk8hHOFSS/SX05fJTr360mkwpUKjUp36NPZvDNQpfulNYi0gGBmFny1u7X2/YRFaY1oa+HXABHKzyY2jkXpSBsKzz8RSc0AnzHfrbRm7tPLwsjFWsHE/lN4xmhqId1r+iAXFkBjsqSb/VdEQJOvvKGg/seDpKINDZaU5T0DY0bbk7jq2+CFjACN68MWXGMHpG/pgjjOW9ZmPlEUKGcSNBGAam3HWzVFdhxlwwuODeJXXYyb0oFTj9ocxB17DS2a8ZlsPi7bB5OC+jV3cYVl+T7GXbK4LgjE/jH0xmX/FbgIAwklx5m40Ft0ykCUj/riax9Rl9uvh9dclUaq9cY7HeyLvakuL9xbLQTDybltcBkRHQiT7XhiHJio9z7ERGPtFn15dcOUsFlK+yRVK/6sPY+X+CAyH89/faYBA47sLjfebnjKHfjodz2uXzpCAR+L8iOS7vflV579ZekTaO587LiNBH9tpRq21T9SJlHyOR3Y535uh83k991PweNsQdMfjfGWecxMwJ1gwCT33M/xd+fnp3z8aZp0MnvE446IOcBbiK/WzYvjVStmylv2K1OvsvB/C/9xFD/HYFzfp8nM976n4MHHg2SRxcMtdGnV7zxf/AhcPf9imLYQAiXUm+hXlwxJou16/w5FMGvUCDu90sHtmXho4/dcZIybeB+8cen94P8fSfeomGn8ZOnnmhhr1/EU9J/sQCYMpXYu7xnMrXSp6eF4X/I10VBxibc4nqaS77xwP2BVi1sPP0fhcD0On7oQRb+966D5Vpwsj1/YeG0E/P1jh6XxQotD1Xxq71tnHC8eTYuuNyd91wthiH36A589U2wfL++Fv44JF9guFeXJI5zCm6v6HFHWdh5Jwtjx3kWGSEbD2pLRCjfMq44iTflLgdfaNcM1aOqv3sr8/D11xauHedg4bek3ibfbVJyW4J0PeL/8jYU3bcBLh2Wb3/4iCzW/WzhjZftnE1Ju/YOWrcDWrcOXo8H7m/h2CPN64jqkinMpmbyxOJjpzouvyqLjYrynD5r3Rr48zX58s885+DZ54J93HsPC2/O0czSNeyovrC+zH4Z+Me//OuWxtO7F3DhH8z9v3Y8eeEH15SqgD/9ZBt77Fb8Xiau51v941LnungNtZuDrv+Jt2aF4jZnE6QtkS02t3DV5cXvP08942DW7OCc9uoZjn0lR/v4Uw5mv+pvn1TrE66Ntm4r2ReuKx4C6v1HLRn1nhOvterKnfutV4Z12CEWDj+k+DVbXUiUPhramE6lMmjWNL7VVemtcklGAFi3KYuWTfk3itdC7SKwMZVFk4QFWypyard5bq0RI9CyGf/ONrbpZ/Jb8/SmBRBX+c0XTs1fNkTizPvYwsznFBsEr1kifieMyb9YCSJrVrBPhx2M2C9ggbo8BWrTpkD3rsAB+xFpWdnxDx9pUH0qTYS9QhIOo0dakY/lm3pN5NN1kxxkPbGq7EnzphA+3Tvv6MTGkNoRJNxt/hapblL+Djk9//ndfwPIo1of45DTLdz9tyAuBx0APD87OJLePeAj1W+c4uDbRVo+Bzj+aHcOTemCEU7A7kLP17enBVKY+jrsAMccARw00K1XeDiPLl7X5AkWiDigNf7zOuD7H4K9ygXfJAVwU2DSdcUJhVvvAL781rymZOktO1l45G8JLJgfXl/TZsCpQ93AnVRb/+2Bc8/M56cxCtLS+6hYzw78FULJ79F/drDCC3aZC7TpwSHrHT3KAnkwF0vDRxjIawo8OCHfQ9P6pHoPHAi88JJ5HqQVytZbWRh1kbkX78wF7nsoHzBSBsscsDMFazUjJEhbrU19A2D7PhbOG1Js5O73dE3ThiClFs0t/PpX0crVRS6x2ehd5znymzqiQLV9XwvnnV28d39/AnjlzRDye2ix1Vm8/mI56LqfPBVY8l2+D6eegNgbh8Xa4e8rj8CYcQ6We0GeZe0d2wNjrqj5dVP50dRujUKg8FWwTfp94xQNgUyWTr1k0ZzJ72iAca6KIbBuYwb8TlsxOLmiiAhs2JRBk6SNBJPfERHjbJVCgAWslUKy4dTT6Mlv9vyu/4tVP7ovqQT5KtV1awtjRubV92Fe0VF9blVEwuwQyg1yScrOMN9X1fNbVz/qY6e+qiLhcvtVU77mYcFCd9/FxnmD87uuYZYPW3e2fCSSnKODDrDxvBqg0vtCt6IQliIPZCD1xJK4LBT4bwh5RUd4X5f2IZKgJD/ni/6QxLJlDmY8mBYBFMW8qRysF3hQrV61opA+3eJ7L5M6z/RvekacfmvhwH8//wzccnsW3yzJBMai2p7QtUFqdUqbbw5stTWwbmPWCzLp2pVQ4MlmzT1FPqmXe1q44qL8dTdhchpf0EaAsiabNoGwrsiR9srF9ftTkvjVXmaAx9+axvyv3LpEP3UcHGDyhGjWHyYPfaru7tv82NEaoXsHEeF0T6GApa1aW5h4W9CuJ8x3Xr+bhnmJH/mbBI49PFxdNe3+NN56x7vavYlXkSJ/dpqz+p5o/HTdkec3pWI+/777T8jph0I2MyoeFLBw0m3BgJlH/CaB4wpgX2lMCYP16wCyuonqF17pPnB98RAw/e5X2t4sXo8aTu4wux/9fttwRlS7PaVNs6++zeK7H9Lo26sJW8bULvyNvjW2PWn0S6BOAGDbkzqBnRtlBBolAo2e/NYDXKq2Jr26b+0LcEkBMaUq/JKhJ4IeEiix53fNXjsm8krlRQ4eaOOk4/MEqlDajknlgrRR70ghOnpkNLLMR6CoASC9L+joOtUVNWgZkR9z57nkz6dfZPH5/LwS0OR5bfIbVfsU4ISUD846JSGCspkS4XLH3Wl85rVPHqZnneoPnqcHpZT1lPvi7wukqXSOSG3yVSZSjIihC69IBSxfKLvw3qZ/eJ7bEsEd+lrIZOCqr5Vk6u+IMSmfPcqu/W0MGxJ+3Olcz/M70up2gKMPt0Vgx9mvu7YNInlzI8nvnJeLMhZZv0oOqHjpGyAyP31OQR+XLQPmz3f/LFgAfPEF8OWX7t8//eTmPuO8FJLE9UqYHKBdB2DgL23su1dCrOX33wd69wZatQL+fHMaC75x8n7n0utcWWubb25h3FV5Ajbg003XXUegezcb787N+sh3Io/J71wNsqniLK55xV9cp8jl2KPMTZjfuh4wk64P8qmljZpUCnhtTgarV3lTqHWA+n/0INdvPmwM1DexiXF72n8wwCtL5HqxFBY08cRjEzj01/X/aLLpXrbPnjYGn2oeu7pJFsJ9Q7/fF8Lwz7eQd3g2Z/HTvq2FG6+r/5sGxdYFf1/zCNBv4Uefu1ZdO25H98n6f73VPCrFW2DyuzhGYTno/jd2Yir//ADEut+V3jKXZARcBJj85pVQFwgw+V0XqHObjEDjRKBRkN8U1PLMi8b7ZvjeW0flfLxXrFqD80bdgnmffCnyqN/R/x9/5mVcPXGG+O6Ig/bO+XzZgHUAACAASURBVIMz+V3zF00YaUoENJG3RDwdckAioKgjIuu52a53R8sWlnhxVVV3hZTX+qhIjUh1LVvuCL9tIq0KEV5qeR+JSQSuQeiqB58jsvzhf2RcQjeMAVIaEcSwR06OHx1Oys94IIPX5+S9lKkKPYhXmFI1iqKcMCWMiMjWU5jyWw6PFMXDhyTxxDMZn6e7/F5VKat1k0J3xPCkmB/CmhLNtUpUUNv/eT6DF1/1j53yFgr8N+n2dK7OXJuSkDXM5blnJPG3v6d9L65UzmfboUj3VZT0gJNUjtTP5NUtgyvqS4HqvWdyE7RvD6zyLEJUbMjXeocdgNZtHWzVMy08u9VEJPdtf/arnwnD9+Zl8fIbDjZt8m8oiLJKJ5q3AG4fny8fprDec3fLVTHLst7fI4YFyW+5hkSgQrnREXLdTB4fbTOLApc6NPUScFKSJ4FpNzcRljS0GfTdDw5eeyuDH37M7w/I7FTUVgNuymCjNxVW3RNc8vqXGxjS9iTqZhL17+IrU2KDRyYK+DgxYsDLStyh1QC0dP+LuulHbZvWBG2I0ImLsDTtvgzmvONuluhrnjYxLx8ebeNR3nNym0fUoAWhmI96/64EflxHw0WALADoBs7HYqPPoemZLer9Lnor1ZkzLEBx1N+66kSFR1WbCDD5XZtoc1sSASa/eS0wAoxAbSHQKMjvmgKTye+aQtZfr4lAMZGFUXpDRDZZUUhlbrFj+Hqd9GI366WMIM0oHTwwgQE7h3tjqIRzGI9dSK195fVp/PCTgYT0OqZwqdh2GwtXXRquarzoTyms+TlPAlLQRSLjdAXsA3/P4OXXs8h4XPEuO9q44NxwlaquKCfv8ZOOCyrQz7885Q88qAS/pOH07mHhuCMSINKZkrDKUAhLSYaqc7JZJ2D8NXkijfoy66UsPvsiKwhDIuNz3tGiUv8GxAXnJIXvuCnR5sMtf0kL2w63QwBxkKT/M21inHRsAg8/qbCUyhxZJCC0teE4wFGDbGzW0d2cWbQor+Am5fYjj2Wx1/5ZtGzlekZLEo96K3y1PfJ74EBg3Tpgxx0BK+ngu6VZtO/ooHt3Bycdl8Trb2fx3gdB4j+RsDD15vx6US0nwoJMqlPStg1w8/V57E0qX9qc2GpLC2+/F2xfx163K8qt+hDyO+ox+htuTOOrhQp7bQH9+tg485REQGUnxk0NqyJPw4VLnv9/mVSc/BabSRO89UxkvleX6cRH2P2LSNzZr2XE+uixLYybfVHufaXkof5PmpK/X9K1PfiUZMF7ntqO6d6t21Sp+U2bbwTZQb+i+AA2duxXXC0v66spC6dScOQyDRMBJr9Lmze6Z9Fvzrr1DgbsbLN1R0QYw1TzvGEXEUDOVjYCTH6XDSFXUAICTH6XABoXYQQYgZIQYPK7JNjcQkx+lwFejKK6GoZU35cPT5b0QnXBSL8dCnUjqipJ2KmM9R9JpfKFXkzUl5kw8ruQqpraI89oNeVIYUkKKhUXUjKfc3Ea2YzrHyLoXo8PJPWwTPTSes34FDZt8Ld5zchwvE1qJSLJpiiq4JwaTFFO62S2JBSpD6TCJZU6/dtHTWsgdmhvYdLYPIEbppySo9HngAjrg0PsI6ZMz+D9D7M5pxCxWaDy8RJHAORDTCcCJHGvL28TmfzR3AS2aG8LqxL6s0HDnOo4/PgMtuqat23wrYMscM+UJpC2Ot8uInsdP8lM89C9i41P5wfJZyLwVc9w/cVb77P6f/r3ySckcPCv8iyxiWyktU32IdL2R+2/ft2Yrk11c0ctm0wCd0VQXlMZqnftBo949jYOunexQBtoTz8bxIVU4pY3rLBrNmEDU28pTn7TPeOiK1LI0uaHQn4X2nSJcWus8awm2xX92i7UCROZU8i2JMx2qUN7YPNOFnbbxRY2AFESk99RUOI8hRBg8pvXR20iYDqdR+0XOtFXm/3jtqofASa/q3+O6+MImfyuj7PCfWIEqhMBJr/LmFcmv8sAL2ZRIk+JwCMLE1ISxTl6L5sKs/SIHEDN4P9NdRciz1UyVtgnBFhR4IJzw9XH512awiYSjnosIBGWAfsLhZAlRSmpiIk4JjK0XVtL/L16jYOFi9SIi15HHOBuhfx+820H0/+f36OYch400MbJxycEGf3uB1mQd+5O27s+3YXUSp06WaIv/3s3i8XfK8S7Yf5pU2PKhDyheN2NaXxDil0DZpIR11Wkqu90qHrZg4FqHl4A+6EXp5CW3KjE3yNPiQgnK5ZjDs8rt6mb1P5XX1lo1sJBs2Zux7fe0sKSH4LjmPWvBL75Mr8i2rYF+vUDevYEEkkHC3/Iomt3By1ahij/HeDyC5KhhLuErfe2lhs8UhO4U8DMaUrATHUeVZeQHOlP5D+p17NAk6bAnQbymdYHqe4pbdfHvU7DAtCqGzWFrk2qS/rU07/lNUCbK1ECCIbZsdB1r3vFU/2C/CasDNeaxLRZU+COCMrv9z90MHl68HraZw8bg0+LrmKOebusWHaTjztVHlV1T+Q/3QPpHkSpXx8LRw8K2lTJDofZXKn3vEInZdSBh1ktRbFwqhiAXFGDRoDJ7wY9fQ2u86b7X9Tn0wY3WO5wvUSAye96OS1V3ykmv6t+inmAjEC9QYDJ7zKmgsnvMsCrg6JhZEiP7hauvKR4ELRSlITU5qTJKSxbkbetyGlNPeUwWYQccoBZzVgs6GKOFFIsREgVm5ZWHRJnqVo2OHyoRNb9D2fw8htBNSxh1K+3hZnPK99ZwLmnJzHnPbOyl4KCTpzsV8qr9h268puCX157RX4epI2GoM28gaqKdfq4T08Loy7Kl1HVw8WsO6jKMSOboNvW5sU4ZHgqRxjrda1cbmHVSgu7bJ/Aup/zwSbJroTS707LYrc9smjfzgLNx+tv+QM+Up7F39g47YSECDJJfzp1yvfj/Y8dTJnqTqJx0wQAqY979TATuOqIfv0rGy+84s6bJLLp32RbcssNTYQv9WcLHMx5N4vvvQ2KAHYe/66SkEPOSGLvX4Rb/sg+EAFKxPqiJXkS37RhZCKp6cWfkomkjkrAmghcqpd8n43Kbwk6XZ+evYy+cdC3tyUCdhZLs17MGq1wot5zitVf09/XdvA6U7Bi5fIXw41jeUX37IcfT4v7L22uHXyALU5ocKp+BOR9bd06B9t0tUCBVuMmJr/jIsb5y0WAnhlfeTODVCqDLp2THGi1XEC5fCwEmPyOBRdnrhACTH5XCEiuhhFgBIoiwOR3UYjCMzD5XQZ4dVT07OGpvHKalJ0O0FcjUMO6Jsjz0S4hqpLOZ50W9LdW6xAB4xY5+N/cLF54Nevj0ah9ItHCArD94dKUkcg+7XcOHn3SwqZNmhWHogLXxxFGBqsk4tR7M0Z/ZiKmlxAxqqWWLYGzTk7ijnv8bDuRixSIVP9cJbJ0SwvdBuOSq1JYtdrvry2JcKn+HbiPjTN+lyezVJuGgG2HEnxSen9TkEoKyGhKFCiR1N0iKb7Tb8y28fEH4QTaVlsB118PoFk+wKgJe6pa91tX+zF2YhoLF4eovukUge36pOeIYYNHBxF+Q05PYsp0fTfEVfNv08XCXx/0+5RLWx3Nbj0A0fbbWbj0j8UJYFlQBiRt0RJGyyKTxcbpJyax5Icsnn/JvyETRw0XZsfSrauFsRP8Nkidt7Cwx24WtuniEmXfLs7iq6+Bjz7N299QoNCrL4sWdHHOOw6m3R/EPg6BW0e3StGsSbVPJOLgU2uOQKZTAE/NzGDZMsLfce+XyiUaZ+7rEjtuu+4QMF3zA/pbOH9I9PsV9Z7J77qbw8bccirjYN36FNq1btqYYaiTsdPzOv0p5XRpnXS4wo0y+V1hQLm6SAgw+R0JJs7ECDACFUCAye8yQGTyuwzw6qAokW8Tp6R9wQqJL+zYHpg0Nty/lwismc9nsHIV2Ye43r2qhqyQb7Q6zBdfyeKBx4IBEQv5dF84KoW16/LkjyBRAfTqCXTewkab1sBX35AthEcOhpgUhxHf3btZuPqyPCHw6D8z+O8Lbl1qVbvuZGHuh27gRT8pauHu25IgsoG8nSnwIqnsyNqA/HsfeSIkAKQHDPl8U5BLk5XNBaOC/uqiXwoRvWM/Cxefl+8/bVDcPt1VGVNfkwkgncmXUWnu1Eag77YJ9O7h+m5/8QVw0EHAaae5nSPlt/Bp1tr86P0EPnzXQtt2Dvbbz8Zuu7p2JT16AH36AOSJLMpfmMpNv8BNAU8SzIXIbypMa+9/72fxztysP8gmYWBTEEDL56ct6gWEHUi3Lq4POW2smBS85CMqTyWo65TsVzq2t/D1t25lYWtHV+pX4pImspWClJLVz3tzs1iz1q01p/73bE+kDU/UNqUdC9kmEektX2ylvzzVIwOPhtVJeeO+EIedNolq3RF1fDWZj65tur8QIUABgslWqbaSyQc+TrDQ2uont1O/EKjUiQUmv+vXvDaW3jD5XfszbQrcHie4c+33uGZaZPK7ZnDlWgsjwOQ3rxBGgBGoLQSY/C4DaSa/ywCvDop+/iUw8bY8ISm7QAraaVrwun/OzGDWS1msXe+SnyppKq07JAW0TTfgsvOL+w8/9HgmoGClPpz62wR69bTw9MwMPp3vCBKOyOCjD7PhU8MaiG0iOI8+LK+wDuG+cwSitA2R6nVdOU32FxNv0zYIHOCEYxN49MlMQIVJ9YXZTwR8nBVrFkFoOhCWIFNvNm88/PHylFC2B5LiA1JIffzY0xk8O8sj8h3gx+8tfPKBjdWrLKxeaWGjIcDk4MHAPfe4LartG3F1gLBAoPQiNXxUfq0JD23VSJuU2wlgeoSgiQuXQCiUcxpwrzNJG7jysiQmTU5jvTIWEzlI/Xnx1Sy+/9H1zSdVPhG5YX7YI4YnBWEu5inkNMHOO9oYfm7NKICvGZcOnDTQ+xHV9qSUWw2R1rNmZ4TyvlNHC/vuaYeezihWP5HHMx7Iz1HUALvF6m0M3xN20/+WxsaN7mj772iBTmtE8XpvDPjwGM0IhJHfhTaaTTUx+c0rrC4QYPK79lE3BRuNE9y59ntcMy0y+V0zuHKthRFg8ptXCCPACNQWAkx+l4E0k9+lgSdsQBbTsU5XnRpFTUlkFClCKZH6sJT04KMZvPBa0M+aLDSmK4H/SLEsrSBCyeSsqwBXfaupX3SsOoyYIdW5DNyXsxJ2APL8fu6ljPClVROpQ0mlesd017NWqodzffLsV4YPTeb8oam83mcKbJiRQR69LyWC+tj/+2JWkNx6IuX3u/McoT4WCmZK3vjvVrBTy6nkt25xouYLIzGJPKY1oidViUz9+u1RSaxdC/Tq5c+pEiBU5psFNp5/xk/Wbr6Fg113seDYWbRuA/Tt5+CkE2wM2NkKKLflkEUrDpBIAFMLkNc+/3ETgWwBYdjJkch1TwS37jvdpImFm65LYvr9acz7yFW6t2sPXDQ0GbAVofVM61qmDu2AsVeQJ7vfi5u+l0FEyft95qwslq1wkA0uCRx2sI3fHlUz5HcYKa/O8OSIAS/j3itozY2dmMKy5f6ScYmzuO1y/iACtAmp+rJ36khBXqNZzjCeLgJ0H6YNryi/s9WCmclCSQ+oHGWsTH5HQYnzVBoBJr8rjWjx+iq1YVa8pfqdg8nv+j0/1do7Jr+rdWZ5XIxA/UOAye8y5oTJ7/jgkfXI7feksV4hNYsdY1fJaGqxVAIkzIKgeTMLt0/MW2f4fKNDVK/SfkQnJAupOv9yTwbvfhAk3/fe08abc4Kfq962hNt9D2Xw4zLNA9ohMigpSGIiisjug8jLXfrb2KGPS3FLP/EwQlEln6fdlxGBD/VEAf6IuNe3HUy+1VI1T3Ms7SoIL1LYS7sPtX7V+sM9eprBp19kRaDHhNfg2p9dtfbqlRA+4GtWWELBve5nCxs2AIceCvznP/5eqy8z5N1NeRd9baFtewjLktZtHfTobmP1aiJ4/WV339nGF1+RzU0QbwmCIL9DVOtUm2/dGnZRTNipvZDEn+gBxctUjiAQjs2aA/vvbYsTCmqi62PC6LyaXirH9TndczcbewywA77s55/tbuBMut1TfmsbJlQPfTRgJwvDzonnoRv1jqEGG82V0TCsKeU3XWty7Gp/WbEddfYqk08/PSFrPXigjZOOr5lNl8r0vH7Uov/W0kYzbc42BhKcfut1P/9izxmmWWPyu36s5cbWCya/a3/Gw8jvmtpkr/0RRmuRye9oOHGuyiLA5Hdl8eTaGAFGIBwBJr/LWB1MfscHTxDLHziuatrzbi52tNBE2pYadO2ya1JYuUrpNymvT0jg4F/lfWwLBU2UJR0iIw3WtzphTUQpkQ5EJt5yZxoffaqRqQBoLK8XIb+p3etvSud8mFXkybs5CqFhJBQ92xIimohEXfCVI4L76SnM95k+v2dynmg1BRqjurIeeSsIXD/8OPvUBPr2tsUY7qANgvddf2u5wfDTDxaeejScZKUAhL/5DfCPf/jrVlWjwilFI0+p/q5b2lj8Y3C8VJMxSKXiN055SA1MNjU0f1KlTuScVP8L5fYyM5lK5cMIXH2jRvZfXDeicwApGclfPRfwUhm+uiZmvZjFwwY1/+abWRh3dRLSD5uKb9fHnQd1rYadfiDifOiZNUNCDhuRwgbP6sJ0l6lJApTJ7/j39ZooETYPHPAyGtomv/SGEmg12ggL55KBpilXp06lKd+Z/K7ETHAdcRFg8jsuYuXn10U2VGNj/K1h8rv8tcQ1xEeAye/4mHEJRoARKA0BJr9Lw02UYvI7PnhXXp/CDz8Fy4WpKypBgNBLMJGTS5c5+O5HYN5H+eCBrdsCV1/iJ4912xNJ0steF7LwIHKBgj3ecXc6Z5tA5D4Fzpn3aRYvG2xXhpyexAN/T2Md+TarlXtEq7RTIULyq2+C5Pnlw5KR/IjPHp7yB030SNQLhiZx54w00um8tYou8Q61f3GAuxXyW7cpkJhlskDCBlYJ9bar2l6zykEqZeFXB2WEKpysNWSASa9rohsb1gOP3d8Ebdo7QrHdjv4m9XZ7B4ceZGFYAd9p6g8R8guXeLjJgXjY9ulp44svDWS/1wGxR+MRzmT3ovaPshB2D/49P9f0Gc03Ka9V+xsT9vrGgXpVqOtequf1jYNmzYDuIeS3ej2Fkd89ulu48hLzpsIV16Xx01INM+2yJYU42cPURKLAlw8/rnitWMBO/Sz03NZCv96l+29H6WvAq94rVIpylIrS+pv1UkacnKCNMOnnH6UvjTlP2EmdUjc+GxuWpk3jxkjmlDPvTH6Xgx6XLRUBJr9LRa68cvTc8Z53OnOzTpY4YdTY4ksw+V3eGuLSpSHA5HdpuHEpRoARiI8Ak9/xMcuVYPI7PngmNRrVEqaADTv6HkfBNnJMymdroZPXJjKFSNPX38oKVWxOhewAWRuQgm+TMph8ut+bl8XceX6SmgjR/tvbRkuR009M4pvFfmLc1MfvfnCM5HfUY5lC+U1J4SulDYmPwvRI3natLaxa41md5EzK/XOuW3eo5DeR6W+9khBWJUR2/7zaTJQSMSwJXtE9L1uo2lwZQrFTA7K3YyaksGhJcL3+5sAE/vNC0NA6rG29Bpq7vz3q2oOoSSdKz5FjVJTbgvye4g/2SeuNNmpIgf+hosA3IZdMAIMOsX2eyNQH6dkt+0PX0IWjlICZ3heDDrLxf0ebldsTJqfxxYL8GtbxiHP9xb9LuBY+qtcz1VGbtiN6+6WShsJ+YWLKZ/NEY6nJjYNS8K6vZUxH0aNu9tXXMdVWv5j8Lh9pJr/Lx5BriI8Ak9/xMeMSlUGAye/K4Mi1xEOAye94eHFuRoARKB0BJr9Lx46V3yVgd824NJZ8H1QvF/LvLYcACVOO5zy7vSPR++5lY7teVkBB/dhTGTw7izw7gjYYUkTctCmw5WYW9t/Hxq9/ZUMn2yVMwjf7C3fsklAn1veQA2zMetlsvSHLko/zxhSwZrUCukdIDz83iV12Kq7AJTJEkN0yecErdTWz/HpAf1sQ+TKRdYkInpkBVq6w0GkzB4mk3/dax/veO5qI/DK1bOWpttu5f7dp56Bnn6xLflNT1CdJftNnms2IrkAn65npBYJOynZnPJDBa3OyPkE7YUHY/bzWESrj9aS89+xOREatbffLPPNO/xy4j4WXXw+uZ52oldgr3LdoQlXNE0k9cqyfKKWa6Y/BYUeo5afd0kQQxaQuXr/eEQFSTzo+6Ov78ltZPPRoBptSFKjTwjZdgasuDbeSef9DB5OnpX0nBWidn3yCg+5dmgQCapZwKyhYxLRJpnuZV7pNvT5pm1CqZQLVF3b/qclgoTWNS23XT2q8FaszaJKwsPuuiRpfe7U9vppqzxT0sdTTCzXVx/peL5Pf9X2GqrN/TH5X57w2hFEx+d0QZqn6+sjkd/XNKY+IEaivCDD5XcbMsPI7PnimF3LyLp4ywa+AVWsmEoqsSBYudsQRxNYtLcGTUjIR1mrZMA/qMOsSUm4TES4TEYtPPeu2FkZCSlWsDMQ5eaqZ4O+yFYT6WFppCBsNADtub+Fjgxe4qrYlNe/3PzpIpX3CbUFm//7kBPb/pYke9c/PuRenhPe2miQZrltqpDYBA3ZI4oWXsliyxA006dqVAOvXuRTuKeek0LIlMP1W/9yRavhzCo5pAZ99aKN5C6BtWwdtOjhIqnyrZiNi6gt5qzs0NOkZTg3TRgT95fleT5kYvnbkWO9/JC1IallOktsXKBsHRFQu/t7Bg495bL0w2vbjRYEzBVYOxFocNiRpDI6oryNBfnt9FmuJSH7txIPJc5LyduxgYflyatjfF1J+31Ug4KbMbSLV6TuT+pj6MHee61/eooWFZSscOBmgWQsHzZtYWL8xi3ZtbBw1qGZJyCjBWePffWq/xMtvZHH/w8GTBTv2s3DxeTUTLLT2R1nzLfKLUXyM6Rp+bnYmt+FKv2vqb1v8GhtWCbqff+adXqE4BnTCK25i8jsuYpy/Eggw+V0JFLmOUhBg8rsU1LhMuQjwM165CHJ5RoARiIoAk99RkTLkY/I7PnjkpztpcjqnsqUadKKQPiO7gFmzM4Lw7tTRwr57uj6/JjuEQhYCYf69YbYWur2BrtxUy0kyUwbvJHKyYztgzVoI/2w9dWhvYcVKv0qY/rddLxufLyis/KYAfxSQ0pSojn69LRw8MFHQg/m8S1OCPNeTSghv3ADM+ncS3y8urCQn7+1Dj84I32014KXPL11RTkseOedfrRDAsj8SW8rTrCmQTQNpb8hh87VjXxsXn1886KLcxBC8taLeHmHwS5dBKm/+S9ptX5L0mhJ8v71tnHlyAvrJBN12hMb3h0vSSGf8c08q+mnKxkGYXzqpyP/5bFaQ/eomxeadgHHXFCf+w9THetBIE/lOfvNnnZoMWHeQ3czoEdECrca/SwBjJqSxSPq0exWUaj1SSvuVKjPnHQfT7tN2rADsvION4UOLr9tK9aOh18MvRg19Bmu3/6ZN71K84pn8rt1549YgTqHJZz0SVJw/JMmnXXhh1BoCTH7XGtTckIIAP+PxcmAEGIHaQoDJ7zKQZvK7NPCknQCVNlkK0Pfkk7tsub/+a0YkA8Q55ShGipHlBfkoqySrsNcwdN+kQqcXaVLQfb7AQe8eFrbcwsLKlcBH5MmsVZIjcFV/Do+07NABWLEi2Og2XWx8sygfhFPmSCYtdN4C6NfHDaJJVhC6Qlvk9doKIyQ/+QSYPx+4bkIWy5cCA/bMol2HoFVHwnLJ5hmeD/VmmwF9+wI9ewJ9+gAvvOaS3UR8k5pbNE2+1SEBL32Ete5XEoI9bXRsI6w7EgJz6fscRn5HtY948ZUsHpCKbqXt8aPDCVxVfWzqfrPmwB3eiQUimJcud7BZx6B1DjV34Z9SWLvWP2gi+O+YlCevw1TCgw5KiM2R+V86iuE8BEZkmVMshZHfOhlksheiuskq4ZEngurlmrRQ0DfJ6Lq8fHjDIwEE9lO8HSfvxABtlh19mI1jBjH5XWztyu/5xSgqUpyPEAi7lxWyVzMhx+Q3r6faRIAsnvTf2qhxTWqzn9xW9SLA5Hf1zm19Hhk/49Xn2eG+MQLVhQCT32XMJ5PfZYBXoGgYWUcKWD0Inqym2EstkWnr18ElonTjZaUvYUS6IOwXOyAlLKX3P3YwZWpQRh1Qhnt1E3nXpq2FH38Mks677GTj/Q+zPkUyFSO1++BTE0IFT1YUD/0jYyS/JTG78CsbzZvY2LAO2LDOwtdfA99+GwT6oMMz2La3lFQD++xlo1sXK0ekfvCBS3i3bu0vKwJmGnYMVOxV9bLG//v9tv1xN0VDuoLfV5fJfztGEMRp92X8wUY9y5nhf0hilx3MKvcLr0hh7ToXgzDynTZktukSzW9dKsipPlJxEyA6dmSxo9ZG7R49yMaaNcDs1/zK/6iB/0TQxQkp32kL6oN+4iKMMDpooI3nDacOajoApbpJRl7mZDPTEJOuYqd7weiRNaeab4gYFeszvxgVQ4i/VxEIu5dFvV/Lupj85nVVmwiErduov/W12VduqzoRYPK7Oue1vo+Kn/Hq+wxx/xiB6kGAye8y5pLJ7zLAK1C0EPk9a3Y2QOIVU37LpqQFSo5+1gjVpk2A/jvYaNYMQn1MthCU1GOo9H/6nAjjaycWJr/V4Iikkp33iYOPSS2upUMPTODrb7JCWa4meuEhAvCOe9x2RMBJTeirkrJ/vy+J1auCRGyr1hRckhTbwBadHXTp7oA+o6QrtwvN6OALU7DVgI+OGzxx2i157+LA3JFY2QbIK1sGGZV+1/T/Nq0hgoTuu1cC5MtKiUjvufMc/PCTg00bXX9sSiaKOepL4cQprg+5GLNS10nHJnDwr83q6f++mMWjT7qK5zDym0jMblsXvw5MNh6dOgATxuSV39J2RLXToTGHKa+jrnvqHZ1emPFA3m5ItzyhPPoJCfpMKq5Naz0uHTyBzgAAIABJREFUkVQcperMIWMWrFvvrj91rVfniCs/Kn4xqjym1VyjKbYIjbfYJrmOCZPf1bxK6t/YwtYt/9bWv7mq1h4x+V2tM1u/x8XPePV7frh3jEA1IcDkdxmzyeR3GeAVKBrm000kIKk///pg3oKByLnBpyYLel3LpkgBO2psytcyUdFbdHRJ7xdf9RPTO21vo0M74OU3Xb9l6e1N1iNhFiyq7Qn1rVUr4BcDbJxwdALXjEuLgIo5EtdTHx/1mwQOPdDGzOeymDsXWLkCsB0by5cBr7zm4Pvv3BK/+GUG2/XPK7bpM9UG5X+v29i40UK79hC2JkcdbmHux3mspNe1UB17ntv09z23FfeNpnxnDw/ariRsC1MV8pvySZsY+jcFxCTLlpYtLIwcmwqov4mEPfn4vP2DyXda4J4FWrUksh1Y87NLyh58QHTriFLIb+o/rZlX38zi+ZczWL/ev2gJTyKvJWlf6GowedDqymsiSUeMTmHDxnxNzZsBw//QBBNv869byhGH/I5ypdJYb5+e99omjMlahYLkmTaA6DtOjEBtIMAvRrWBcvW0YYotUopNE5Pf1bMmGsJITM8/+iZ5QxgH97HhIsDkd8Odu4bcc37Ga8izx31nBBoWAkx+lzFfTH6XDh4RbaSEDLOM0IP/bbuNhROOccm2li0tfLswKwhVskOIQj5SOSIXh48KkohbbG6hfVsElNdydAHVr+OqlYkcf/CxNJZ5Pt6kcM54ARJ1b27yV174nYNvFyrkNzXgAJ/PS+D9d22sXl0Yz/67ZbDnfop3uWKfIeTMlBR5tG4TE6ZejqqGUz2w1Z5GKU/zTeQ39TPXRQvYvo+NS4flSVT92K/e51KtNkTgwfvzSn2p/p48vkkkOw0RMHNmHnua3w7tgUljo20cEF6EwXsfuHUM2NkOrFtan5eNTrlqd+9UQtNmwLWjmgQ2baiOXftbGDYkr7ov/Wr0l5QBPynArJ7mzN2EPXdtWqmmGkU9hOdTMzNYttwRa40C05qwbRRglDhIfjEqEbhGXEzaJtHfcZ4TVMiY/G7EC6iOhk7PGu9+4GD5ckf8TpB4IIq1Wh11l5utMgSY/K6yCW0gw+FnvAYyUdxNRqAKEGDyu4xJZPI7PnhEBN1xd1r4Z1OiYD7Dzk4aySD58vrJFw7+9Z+8gpnsSTZ69tNEKpMCNYofsE6Ayt6TwrVJEwur1wT9uMMIY+mdMaC/hfOHJLFwCTD+1jRSmxykUsCK5RZWLLOwaaOFDp0cbN0tKwJl/mDw/P78/QReeSlvvdGrF9C7t/vn2++y2Jhy0Kadg3YdNeLcG8A5ZyYw/V5zQMJZszM5cl4ov1W1uKdmj6r8NpHfUW1TRDDHhzIB75IWzS1MmZAUVidkS/LtIkdsUgiGPMTnu5QjwCZFFWFRKOClvrpJ/UzkNW12EPFc6ZfSsD6SYpGSGgiL1iwR37VNovKLUbx7XqHgvUxoRMeSX4yiY8U5K4cAk9+Vw5Jrio5AKkPPQSm0a80bzdFR45yVQICf8SqBItcRFwF+xouLGOdnBBiBUhFg8rtU5EjJucr1J9isXbMyamlcRU2+wp06AhNGBxW0Ikjlei9IZQGYiACnwJDF0mcLHEyaHPTpbtvGQibrYO1arQaPHDZ5TUtSfP06C727JvDUv7NYvswlvNdovts9+2bx68MyoH6+Pifo+b3Hrgn062mjSxeX8FaTapdhJOId4JqRSaEsJZ9smbpubWHMyCTIg5t8JNdv8Htd5zI6wN2Tw9XLqmr10y+cQMBNUkBPv7W4+vnJf2fxr/8GCfpkEhh0sDmQqerNrWIS1edbLaOfJJDf6UE2i62hmvw+rI9S7U7XA83nZh3jnXioZJ/5xSgemoXiFxwzqPg9K15r1ZubX4yqd27r88iY/K7Ps1O9fWPyu3rntr6PjJ/x6vsMVWf/+BmvOueVR8UI1EcEmPwuY1aY/I4PXlg0e906I+cxHMKAqkRwGHlu6t0fLkkhrXKwDrD/vhaI2P3xR8U2RPHFNpHfpEp++3UbH7xjJrASSaBtOwoy6aDLNg522zMrPKqffjZIfhcjcz//EnjuxQzmfphFVu2717Fzz0xgrwE2xGbBOnfUuiKYvrtuUlrYaeipkG3JyDEpv3LcgvA/F8lyPb0njytOfs96KSt8o/VEwUU372Rh0RKD6r7MIJdqW2HEcjHs46/w0kuEed3Xpz7W1YuROAWy2D0V0K1LdKuj0mejMiWZ/K4MjvxiVBkcuZZ4CDD5HQ8vzl0ZBJj8rgyOXEt8BOrqGS9+T7lENSHAz3jVNJs8FkagfiPA5HcZ88Pkd3zwopDfKgkYpv5VP48TEIiU1NPvTwvbFCKwe3S3cNygJO66J4N33gXWrARWr7LEn5atHBx2XMYle1UG3FOEL/qayG8bW2zhwEo4aNteEt5Ay9aOdEYRwRkpKCcR0kQmkwpbpmJ9NwVK1BXgF5yTxC47GSl63wSNGpvG0uVBkjmM/A4jY9VKt9zcwg1XFfedfuUNB/c9lA7YnlBAR1KPq5jI+sne5NqJfqU+4TV6ZDSfbrWfJhKS5oUCVkaxzIm/0ksrUZ8DSxKGXy/ehG27NsV2vYuvt9IQCJYSweumpH0BR0sJXlep/sSpp1Dw3kMOyFsdxamzMeblF6PGOOt1P2Ymv+t+DhpjD5j8boyzXj/GzOR3/ZiHxtYLfsZrbDPO42UE6g4BJr/LwJ7J7/jgmRS4fXtZGDE8T6DqhG+Y3YckpKPansjevvMOMGMG8N//AvPnh4+ByO+TznbV0kTQkkcveVJTuyr113Nb4Muvg/UQzTximN+XmZSrz812FdAUsHPfveyC5Ktps8DHxTvA5RdE8342Wc7o2BcjjPVRFiqv5n3/QweTp6Vd3DzwCNeOHSx07wafZQtlkZsCRLi+NieLpcscbNPVwsEHJCIHONX7Susqh31LVNyzO/7VYC4hve4pSFt9IebHTEj71Pmkvh49ovimRyUwIdse1dKH6qRYAVPGFz9xUIn2y63judlZn197TQUqLbef9bk8vxjV59mp3r4x+V29c1ufR8bkd32eneruG5Pf1T2/9XV0/IxXX2eG+8UIVB8CTH6XMadMfpcGHgX2oz+USA19yAH+gJUmlS4Rpb22tdC3j4X5Cxws/s5VMA/YOR/w8qOPgC+/BBYtAs47L7xvzzwDHHGE//u+fQEKNLlNd2DVz1msXu+g0+ZZNFXiDR16QAL/mZ1xYzHKYIwORJ8oWKOebNvCtFvKIwhV25FAA16T48c0iUQIE6kqgzZK7E86PhlalvLrSnW9D1EVuOQdPmqMG6VUJe936mfj+KNtzPh/mRy5SsT3+eckxWYDp7pHwHT6gHpVW37pYadFJo+vX6r9YjNF97X6tKFRrL/16Xt+MapPs9F4+sLkd+OZ6/o0Uia/69NsNK6+MPnduOa7voyWn/Hqy0xwPxiB6keAye8y5pjJ7zLAK1LURPqOH+2SvI895iq26c+CBcAXXwCLF/srNHlbyxzffQc8+SRAhHfPnkCPHv6yRNSOneC3Jzl4oI3t+9pCvawqv4nIpe/e/F82EDCTLFWuvKQ88nvsxLTwOlaTHBup0enfo0dGJ4qJyPxsflZ4J/frY4kgnIUSbVIQYS5tScifu8e2FpIJYEB/GwN2jkZQh3kf77S9jYv+4PqmU5/oD80xp/qDQLFAnDXd0yhWSTXdB66/bhHgF6O6xb+xts7kd2Od+bodN5PfdYt/Y26dye/GPPt1N3Z+xqs77LllRqCxIcDkdxkzzuR3GeAVKSrtQYj4JYsFUnivXGZhl13CC1LwRfp+552BCROAdu1K75+0niDPXlKnkwpZqJfHklm4P/3uuKQggW+7K42flrpEdefOFoYNCVdVR+3ZpVensGq1m1sophXZtGjJAUZEtD3R7ReoOBH3Jx1vDtqp9pFwKEeJPecdB9Pu9/t3U/2bbwaMu7ph2FdEnbNqy0cbIH99MBis9KxTEsK2p6aTSXkedd3WdN+4/tpBgF+MagdnbsWPAJPfvCLqAgEmv+sCdW6TEGDym9dBXSDAz3h1gTq3yQg0TgSY/C5j3pn8LgM8uApuUm1/9pmr4v70U+Ddd4FttjHXu3YtsPXWQO/eQMdODnr0BHbub2G3Aa6Cu3Pn8vpTrPS/ns3iyZlBErBfXwuXnV+ewjus7aEXp5AmhxhP/E1qbz2FBazU842/NY35X0UPeFkMjzjfk+f3FE81r5YjJfm4a2oGuzj947zhCJjsb0oNPFoqznRy4NP5rlXSZh1dr3xOjQcBfjFqPHNdn0bK5Hd9mo3G0xcmvxvPXNe3kTL5Xd9mpHH0h5/xGsc88ygZgfqAAJPfZcwCk99lgAdgq62A77/31/H888CBB4bXq6tQKfDd4FNc5XXURApoUpRv1snCdr0soeyOkqbfm8Fb77kEnJpqUr183qUppDzBtDHwJ4DLtaCaYWP5wyUppIPcPQqR50R8PjUzI/DqRKTjnnZkvNR+kGqerGx8AS8BHLCvjdNPLK48jzI/nKc0BGhuPveI5b69baPtDK0DuvaWrkxjs/bJooFaS+sJl2IEzAjwixGvjLpAgMnvukCd22Tym9dAXSHA5HddId+42+VnvMY9/zx6RqA2EWDyuwy0mfwuAzwAl1wCrFvnqrYp2OTuuwPbblu4zgtG+r24KXffXhZGDI+mHjb5B0clj//2SAYvvZYVnt9q6rq1hTEjo7UfF7Hrb0rj629dtbYaKFKt55oR0Ty/h41MYcMGrQcOcPfkcNuRMRPSuUCUsmRUvPSxznggg9fnZHPjaNGc/MqjBeuMixvnj4aAyVKkUCBLfjGKhivnqiwC/GJUWTy5tmgIMPkdDSfOVVkEmPyuLJ5cW3QE+BkvOlacs3II8DNe5bDkmhgBRqAwAkx+l7FCmPwuA7wSiw65MOi5TVVFsf4g9erwUcHyvXtaGHVhcfJaBG2cks6Rt5KMPuwQG789qmbUy6TKnTw1jSXfO0b2Ow7xfsnVKaxeRcbhHvieA0oY+U0+39dODPp0U5DMwaeWNl5pX0HWFeTj3rJFiQuBi1UEAdPmRqeOwITR5g0RfjGqCOxcSUwE+MUoJmCcvSIIMPldERi5kpgIMPkdEzDOXjEE+BmvYlByRTEQ4Ge8GGBxVkaAESgLASa/y4CPye8ywCuxaDnKb+E7PT1I5rZqCdw2rnjQRSLPL74yhYxmHULq5W5blzigmMVIqfvqWxn8/LODHt1tHD0oEZlAJuX1a3OybtBMUpJbQLcCqnVB9t8exIvKjK4hpXtMODh7mQjE3UziF6MyAefiJSHAL0YlwcaFykSAye8yAeTiJSHA5HdJsHGhCiDAz3gVAJGriI0AP+PFhowLMAKMQIkIMPldInBUjMnvMsArseg/Z2bw9LN+3+1CNg1qM2Fkbts2Fm6+PqLy+/Y0yHtbqKe9v48+zMYxg0pTQpcCQ6kvRkTe3z49jc8XuOw3qcYHn5bANl3MnuefLXAwaXKQ/N52GwtXXVocr1LGxmVqFwHyYV+2wt8mBbOcMIaV37U7E9xaIQT4xYjXR10gwOR3XaDObZb6jMfIMQLlIsDkd7kIcvlSEOBnvFJQ4zKMACNQCgJMfpeCmleGye8ywCujKKmfv13sEuD9escLwHj28BQslet1gN12tfHHwcXJa5M/MvXhqAZCfseFnDYLJk5J+/Ai4r93DwtXXMzkd1w862N+PYAs9fF3xyVwyAG2sbv8YlQfZ7H6+8QvRtU/x/VxhEx+18dZqf4+Mfld/XNcX0fIz3j1dWaqu1/8jFfd88ujYwTqEwJMfpcxG0x+lwFeHRV9+PEMnnspb/3RvDkwZlS0oIthHtiFyMKaGGZtvRhJj3QhcCfLccsVvNc22V8TGHKdeQSkD3uUzSR+MeKVUxcI8ItRXaDObTL5zWugLhCorWe8uhgbt1m/EeBnvPo9P9XaO37Gq9aZ5XExAvUPASa/I8zJ48+8jKsnzhA5jzhob4y9fDBaNG/KticRsKuPWYjEXrjIAQVd7NbViuyZTWMh3+zX5+RtV/r2sjBieO2qoGvzxei52Vk88kTe5JysUmi8HKiyPq7smu8TvxjVPMbcQhABfjHiVVEXCDD5XReoc5u1+YzHaDMCKgL8jMfroS4Q4Ge8ukCd22QEGicCTH4Xmfe3536Km6Y+ijvHX4wO7drg5qmPihKXDD2Rye/Gec2AyPP169zBb9fH7Jddk9DU9ovR0uXAsmWuT3hdjLcmseS64yHAL0bx8OLclUGAX4wqgyPXEg8BJr/j4cW5K4NAbT/jVabXXEs1IMDPeNUwiw1vDPyM1/DmjHvMCDRUBJj8LjJzRHZv260zjj98f5FTJcMzaCo+26xds4Y6/9zvBogAvxg1wEmrki7zi1GVTGQDGwa/GDWwCauS7jL5XSUT2cCGwc94DWzCqqi7/IxXRZPZgIbCz3gNaLK4q4xAA0eAye8CE7h+wyaMnjQDe+++Q478XvDNElw5bjpuuOIctGvficnvBn4BNMTu84tRQ5y16ugzvxhVxzw2tFHwi1FDm7Hq6C+T39Uxjw1tFPyM19BmrHr6y8941TOXDWkk/IzXkGaL+8oINGwEmPyOQH6fcNQB2GPXfiKnifxu2EuAe88IMAKMACPACDACjAAjwAgwAowAI8AIMAKMACPACFQ/AuzeUP1zrI+Qye8I5HeY8rtX961zvt+Nb+nwiBkBRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQYgYaDAJPfDWeuKtVTJr+LIFnI85sCYHJiBBgBRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQYAUaAEah/CDD5XWRO1ACXRHYTGU7pkqEn1r/Z5B4xAowAI8AIMAKMACPACDACjAAjwAgwAowAI8AIMAKMACPACAgEmPyOsBAef+ZlXD1xhsh5xEF7Y+zlg9GiedMIJTkLI8AIMAKMACPACDACjAAjwAgwAowAI8AIMAKMACPACDACjEBdIMDkd4moMyFeInBcLBYCdNLgnoee8ZW5bsRgHH/4/uIzXoex4OTMRRCg9fT1wu8DJ1tWrFqD80bdgnmffClquPfWUbkgwLwOeVmViwAFkp70l4cx7k/nQLUTU+9vso2zTz48tz7pZNaZF40XX/XfvifuHH+xr3y5/eLy1YkArbehI27Cdz8sC107hX5bi90PqxM1HlW5CKzfsAmjJ83Av59/M1eV+luqryvKtNWWnTB14qWgGEN6efVZsNy+cfnqRkD/LdXXTrF7Gr9rVPf6qKnR6b+1uoCQn/FqCnmuVyIg1+Aff39Mjjsp9t5a7H7I6DZsBJj8LmH+2AqlBNC4SEkIFLLZ4XVYEqRcyICASiKq5CJllS/cMvAvPUhcOW46brjiHPFCzuuQl1SpCKgPmCbyml6M3nznY+NpK30dFspbav+4XHUiQPeshUt+zL0I0e/s9z8uz62zQve0YvfD6kSMR1UJBOh+99eHZ+K83x8rTo/SOrti3PQcuS3vh5cOPdG3uSzbVp8Hi+WtRH+5jupAgO5Zd973JM46aZDYHNbXTrF7Gj/jVcc6qItR0HNZt623yN3P9Hdafsari1lpPG2qmy/qhh8/4zWeNWAaKZPfJcw/B8EsATQuUhIChchvXoclQcqFCiBgUn7rqlz9RYnXIS+pchEopPwOI7/1taqT4eX2ics3HgRML0LbduucI8fV75evXOM7paDfDxsPajzSchHQSchChDZ9d8Wfp+PyP54kNp0pcQyicmegcZY3kd3qySt+xmuc66I2Rq2T3YXIb37Gq40Zqd425G/msMHH4f5H/wMp4JK/nfyMV71zX2xkTH4XQ0j73vSiwy/dMUHk7JER0G1P5M4lr8PIEHLGGAiYyG+dGFJfuknBRse41YcKvh/GAJyzCgSi2p6opxJ04oeVkLyYSkVAfQGnOgrd05avWI2bpj7qs9hhErJU5Bt3Of23Uj9qrVqemH5X+bRL414/pY5eqiHHXXGOUOTyM16pSHK5OAjI99bOW3TMWdfptif8jBcHUc4bhoD6PrBTv56+Z7pi/Ak/41X/umLyO+Ycy4vmhKMOyB3jYbInJoicvSQE1AdWeTPndVgSlFwoBIEw8vvvT8/2WU9IskeS37wOeUmVg0AY+a3WKR9mTzzqAKHI1U8cMPldzgw03rL681uxZzx6MQq7H14y9MTGCySPPBYCUU4M0O/xo0/PFhst+okDaozJ71iQN/rM6uaKbgHAz3iNfnnUKABSyKV7fvMzXo3C3igr15/h9N9afsZrlMvCN2gmv2OugWI7RvI4YsxqOTsjEAkBSfgMOnBvVtxGQowzxUGAld9x0OK8lUIgCvktyR4ZkJWV35VCv/HWoysgCYliz3isCmq866VSIzcpIE11q1Yn9L0aa4PJ70rNRuOrR7/HsfK78a2BuhpxsQ079R2En/HqapYadrumwNFyRLTpV4w/4We8hj3/UXrP5HcUlLQ87HFbAmhcpCIIqGuP12FFIOVKFATY85uXQ10gUAr5zX6QdTFT1dOmifiWoyv028qe39WzBupiJFGJb+qbSn53bN+GPb/rYsKqtE3195PjulTpJNfDYRV71lPXJT/j1cMJbIBdMgka+BmvAU5kBbvM5HcJYHLk6xJA4yKxEfj/7d2tbhRtAAXgVQRuA4dAoQmCoHCQYDA4giPB1OAJBjQJP4IEDIq0qApc74SfBK6BzCazmU62nbBtzTlPJf224TznzbB7vu7s8MLn4PBo9fDenfVj52/Pdg7/m9QDFgS2jd/bPhxp+htozqFjdVaBbS+IhnP3Zf/76v7dW6srly+th6Ane69Wzx4/WN9ybH49XPqNorP+HT0+R2DpVnWnXdOWroc5SpKct8DSrU6Gczd8Dde34Wt+TZv+JqTbPJ13O7k/bzgr7z9/Ww23qZv+WzreQmzpmuY5Xu7ZuOhkbz5+Xd2+eePYh/T+/P13fRvF4ctzvItuwM/f9u+u53jd58L4vWP/0w9pOO0eVjv+eA8jsHn79f7h0Ubjw+u9zQuj8cXR85fv1t93Dh2aXQWGJwKPnr449vDpWZu/jcw53FXa46YC296eOP/Ao7efDjYPmd6ndPjD6bm9fu3qsQ8hJE3gJIH5h2yN/930unbac7yl6yF5AtsExncb/Pj159i3x2ve/Pvza9r4In58Tji/HlIncJLAeM/l8fvzs7N0TfOa19naRWD+2mL+OnXpXHqOt4u6x0wFTvqfzp7j9Z4T43dv95ITIECAAAECBAgQIECAAAECBAgQIEAgVsD4HVu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"text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = go.Figure()\n", "\n", "fig.add_trace(go.Scatter(x = y_test,y = y_test_preds,\n", " mode='markers'))\n", "fig.add_trace(go.Scatter(x = y_test, y=y_test,\n", " line=dict(color='blue', dash='dash')))\n", "\n", "fig.update_xaxes(range=[0,400])\n", "fig.update_yaxes(range=[0,400])\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "So, according to the model, we can infer that, there is a huge difference in predictions and actual prices as the actual price increases." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "# Conclusions\n", "\n", "The main takeaways from the analysis are listed as follows,\n", "- In most of the cases, moderate strict policy is shown to be beneficial, except in the cases of a few regions.\n", "- Most of the listings are priced between \\\\$84 and \\\\$133, while there are some outliers in all the cases.\n", "- Houses and apartments are common listings, while the other listings are completely region specific. For example, 'Townhouses' are found easily in the central area rather than in the other less common areas.While it is better to note that **Downtown** is an exception in the case of house listings, as less than 5% of the listings are houses.\n", "- Irrespective of the property type, the highest prices are more often observed in **Mognolia** while the least prices are observed in **University District**.\n", "- The linear regression model can be considered a good example as, from our analysis of the prices, we got to know that most of the prices are in the range of 84 to 133, which fall in the region where the predictions can be said as good approximations.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.4" } }, "nbformat": 4, "nbformat_minor": 4 }