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predict the price of a potential listing for AirBnB in the city of Berlin, Germany. + +## Notebook + +The notebook for this repo 'airbnb_notebook.ipynb' is used to take 'Berlin.csv' which +is taken and cleaned up from the original at https://www.kaggle.com/brittabettendorf/berlin-airbnb-data and turn that into a predictive model for prices of airbnb listings in the Berlin area. This model is persisted to `berlin_model.gz` as a compressed joblib dump (instead of pickle). Next `app.py` reads this model and uses it to make predictions in the web interface, for now arranged as html under `templates/`. `features.json`contains a skeleton JSON of feature variables with default values set to '0', and is used to transform html form input into a format used to do the model prediction. `requirements.txt`, `runtime.txt`, and `Procfile` are all necessary to run on Heroku. \ No newline at end of file diff --git a/airbnb_notebook.ipynb b/airbnb_notebook.ipynb new file mode 100644 index 0000000..e2fd803 --- /dev/null +++ b/airbnb_notebook.ipynb @@ -0,0 +1,7128 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "op9sunrTOD-Y" + }, + "source": [ + "# Importing and Installing Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "colab_type": "code", + "id": "N_WDgKlZAFf3", + "outputId": "54932b23-e8f5-4dc7-fefc-011c826a2e6f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: eli5 in c:\\users\\james b\\anaconda3\\lib\\site-packages (0.10.1)\n", + "Requirement already satisfied: scipy in c:\\users\\james b\\anaconda3\\lib\\site-packages (from eli5) (1.4.1)\n", + "Requirement already satisfied: graphviz in c:\\users\\james b\\anaconda3\\lib\\site-packages (from eli5) (0.13.2)\n", + "Requirement already satisfied: numpy>=1.9.0 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from eli5) (1.16.5)\n", + "Requirement already satisfied: scikit-learn>=0.18 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from eli5) (0.21.3)\n", + "Requirement already satisfied: jinja2 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from eli5) (2.10.3)\n", + "Requirement already satisfied: tabulate>=0.7.7 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from eli5) (0.8.6)\n", + "Requirement already satisfied: attrs>16.0.0 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from eli5) (19.2.0)\n", + "Requirement already satisfied: six in c:\\users\\james b\\anaconda3\\lib\\site-packages (from eli5) (1.12.0)\n", + "Requirement already satisfied: joblib>=0.11 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from scikit-learn>=0.18->eli5) (0.13.2)\n", + "Requirement already satisfied: MarkupSafe>=0.23 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from jinja2->eli5) (1.1.1)\n", + "Requirement already satisfied: category_encoders in c:\\users\\james b\\anaconda3\\lib\\site-packages (2.1.0)\n", + "Requirement already satisfied: scikit-learn>=0.20.0 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from category_encoders) (0.21.3)\n", + "Requirement already satisfied: numpy>=1.11.3 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from category_encoders) (1.16.5)\n", + "Requirement already satisfied: statsmodels>=0.6.1 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from category_encoders) (0.10.1)\n", + "Requirement already satisfied: patsy>=0.4.1 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from category_encoders) (0.5.1)\n", + "Requirement already satisfied: pandas>=0.21.1 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from category_encoders) (0.25.3)\n", + "Requirement already satisfied: scipy>=0.19.0 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from category_encoders) (1.4.1)\n", + "Requirement already satisfied: joblib>=0.11 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20.0->category_encoders) (0.13.2)\n", + "Requirement already satisfied: six in c:\\users\\james b\\anaconda3\\lib\\site-packages (from patsy>=0.4.1->category_encoders) (1.12.0)\n", + "Requirement already satisfied: pytz>=2017.2 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from pandas>=0.21.1->category_encoders) (2019.3)\n", + "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\users\\james b\\anaconda3\\lib\\site-packages (from pandas>=0.21.1->category_encoders) (2.8.0)\n" + ] + } + ], + "source": [ + "!pip install eli5\n", + "!pip install category_encoders" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "CLoijWdh8qgN" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('seaborn')\n", + "import seaborn as sns\n", + "\n", + "import functools\n", + "\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.metrics import r2_score\n", + "\n", + "import category_encoders as ce\n", + "\n", + "import eli5\n", + "from eli5.sklearn import PermutationImportance" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "V9Hfxg3XBzi5" + }, + "source": [ + "# Loading and Reviewing Data Shape" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 700 + }, + "colab_type": "code", + "id": "8bhC2g0TxyWt", + "outputId": "849df18b-b610-426b-acc0-ec6ebd69e298" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(22552, 96)\n" + ] + }, + { + "data": { + "text/html": [ + "
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idlisting_urlscrape_idlast_scrapednamesummaryspacedescriptionexperiences_offeredneighborhood_overviewnotestransitaccessinteractionhouse_rulesthumbnail_urlmedium_urlpicture_urlxl_picture_urlhost_idhost_urlhost_namehost_sincehost_locationhost_abouthost_response_timehost_response_ratehost_acceptance_ratehost_is_superhosthost_thumbnail_urlhost_picture_urlhost_neighbourhoodhost_listings_counthost_total_listings_counthost_verificationshost_has_profile_pichost_identity_verifiedstreetneighbourhoodneighbourhood_cleansedneighbourhood_group_cleansedcitystatezipcodemarketsmart_locationcountry_codecountrylatitudelongitudeis_location_exactproperty_typeroom_typeaccommodatesbathroomsbedroomsbedsbed_typeamenitiessquare_feetpriceweekly_pricemonthly_pricesecurity_depositcleaning_feeguests_includedextra_peopleminimum_nightsmaximum_nightscalendar_updatedhas_availabilityavailability_30availability_60availability_90availability_365calendar_last_scrapednumber_of_reviewsfirst_reviewlast_reviewreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valuerequires_licenselicensejurisdiction_namesinstant_bookableis_business_travel_readycancellation_policyrequire_guest_profile_picturerequire_guest_phone_verificationcalculated_host_listings_countreviews_per_month
02015https://www.airbnb.com/rooms/2015201811071222462018-11-07Berlin-Mitte Value! Quiet courtyard/very centralGreat location! 30 of 75 sq meters. This wood...A+++ location! This „Einliegerwohnung“ is an e...Great location! 30 of 75 sq meters. This wood...noneIt is located in the former East Berlin area o...This is my home, not a hotel. I rent out occas...Close to U-Bahn U8 and U2 (metro), Trams M12, ...Simple kitchen/cooking, refrigerator, microwav...Always availableNo parties No events No pets No smoking, not e...NaNNaNhttps://a0.muscache.com/im/pictures/260fd609-7...NaN2217https://www.airbnb.com/users/show/2217Ian2008-08-18Key Biscayne, Florida, United StatesBelieve in sharing economy.within an hour96%NaNthttps://a0.muscache.com/im/pictures/21428a22-4...https://a0.muscache.com/im/pictures/21428a22-4...Mitte4.04.0['email', 'phone', 'reviews', 'jumio', 'offlin...ttBerlin, Berlin, GermanyMitteBrunnenstr. SüdMitteBerlinBerlin10119BerlinBerlin, GermanyDEGermany52.53453713.402557fGuesthouseEntire home/apt31.01.02.0Real Bed{TV,\"Cable TV\",Wifi,Kitchen,Gym,Heating,\"Famil...NaN$60.00NaNNaN$200.00$30.001$28.00411253 months agot021511412018-11-071182016-04-112018-10-2893.010.09.010.010.010.09.0tNaNNaNffstrict_14_with_grace_periodff43.76
12695https://www.airbnb.com/rooms/2695201811071222462018-11-07Prenzlauer Berg close to MauerparkNaNIn the summertime we are spending most of our ...In the summertime we are spending most of our ...noneNaNNaNWithin walking distance you'll find the S-Bahn...Außer deinem Zimmer kannst du noch die Küche u...NaNNaNNaNNaNhttps://a0.muscache.com/im/pictures/97d4f6e8-1...NaN2986https://www.airbnb.com/users/show/2986Michael2008-09-16Berlin, Berlin, GermanyLiving with my wife in Berlin. Like travellin...NaNNaNNaNfhttps://a0.muscache.com/im/pictures/50434955-1...https://a0.muscache.com/im/pictures/50434955-1...Prenzlauer Berg1.01.0['email', 'phone', 'reviews', 'jumio', 'govern...ttBerlin, Berlin, GermanyNaNPrenzlauer Berg NordwestPankowBerlinBerlin10437BerlinBerlin, GermanyDEGermany52.54851313.404553tApartmentPrivate room21.01.01.0Real Bed{Wifi,Kitchen,Elevator,Heating,Washer,Essentia...NaN$17.00NaNNaN$0.00$0.001$0.002407 weeks agot00002018-11-0762018-07-042018-10-01100.010.010.010.010.010.010.0tNaNNaNffflexibleff11.42
23176https://www.airbnb.com/rooms/3176201811071222462018-11-07Fabulous Flat in great LocationThis beautiful first floor apartment is situa...1st floor (68m2) apartment on Kollwitzplatz/ P...This beautiful first floor apartment is situa...noneThe neighbourhood is famous for its variety of...We welcome FAMILIES and cater especially for y...We are 5 min walk away from the tram M2, whic...The apartment will be entirely yours. We are c...Feel free to ask any questions prior to bookin...It’s a non smoking flat, which likes to be tre...NaNNaNhttps://a0.muscache.com/im/pictures/243355/84a...NaN3718https://www.airbnb.com/users/show/3718Britta2008-10-19Coledale, New South Wales, AustraliaWe love to travel ourselves a lot and prefer t...within a day100%NaNfhttps://a0.muscache.com/im/users/3718/profile_...https://a0.muscache.com/im/users/3718/profile_...Prenzlauer Berg1.01.0['email', 'phone', 'facebook', 'reviews', 'man...ttBerlin, Berlin, GermanyPrenzlauer BergPrenzlauer Berg SüdwestPankowBerlinBerlin10405BerlinBerlin, GermanyDEGermany52.53499613.417579tApartmentEntire home/apt41.01.02.0Real Bed{Internet,Wifi,Kitchen,\"Buzzer/wireless interc...720.0$90.00$520.00$1,900.00$200.00$50.002$20.00621125a week agot0002202018-11-071432009-06-202017-03-2092.09.09.09.09.010.09.0tNaNNaNtfstrict_14_with_grace_periodff11.25
33309https://www.airbnb.com/rooms/3309201811071222462018-11-07BerlinSpot Schöneberg near KaDeWeFirst of all: I prefer short-notice bookings. ...Your room is really big and has 26 sqm, is ver...First of all: I prefer short-notice bookings. ...noneMy flat is in the middle of West-Berlin, direc...The flat is a strictly non-smoking facility! A...The public transportation is excellent: Severa...I do have a strictly non-smoker-flat. Keep th...I'm working as a freelancing photographer. My ...House-Rules and Information ..............(deu...NaNNaNhttps://a0.muscache.com/im/pictures/29054294/b...NaN4108https://www.airbnb.com/users/show/4108Jana2008-11-07Berlin, Berlin, GermanyENJOY EVERY DAY AS IF IT'S YOUR LAST!!! \\r\\n\\r...NaNNaNNaNfhttps://a0.muscache.com/im/pictures/user/b5938...https://a0.muscache.com/im/pictures/user/b5938...Schöneberg1.01.0['email', 'phone', 'reviews', 'jumio', 'govern...ttBerlin, Berlin, GermanySchönebergSchöneberg-NordTempelhof - SchönebergBerlinBerlin10777BerlinBerlin, GermanyDEGermany52.49885513.349065tApartmentPrivate room21.01.01.0Pull-out Sofa{Internet,Wifi,\"Pets allowed\",\"Pets live on th...0.0$26.00$175.00$599.00$250.00$30.001$18.0053604 weeks agot00222972018-11-07252013-08-122018-08-1688.09.09.09.010.09.09.0tNaNNaNffstrict_14_with_grace_periodff10.39
47071https://www.airbnb.com/rooms/7071201811071222462018-11-07BrightRoom with sunny greenview!Cozy and large room in the beautiful district ...The BrightRoom is an approx. 20 sqm (215ft²), ...Cozy and large room in the beautiful district ...noneGreat neighborhood with plenty of Cafés, Baker...I hope you enjoy your stay to the fullest! Ple...Best access to other parts of the city via pub...The guests have access to the bathroom, a smal...I am glad if I can give you advice or help as ...Please take good care of everything during you...NaNNaNhttps://a0.muscache.com/im/pictures/21278/32a1...NaN17391https://www.airbnb.com/users/show/17391Bright2009-05-16Berlin, Berlin, GermanyI'm a creative person, adventurer, and travele...within an hour100%NaNthttps://a0.muscache.com/im/pictures/user/48c3d...https://a0.muscache.com/im/pictures/user/48c3d...Prenzlauer Berg1.01.0['email', 'phone', 'reviews', 'jumio', 'govern...ttBerlin, Berlin, GermanyPrenzlauer BergHelmholtzplatzPankowBerlinBerlin10437BerlinBerlin, GermanyDEGermany52.54315713.415091tApartmentPrivate room21.01.02.0Real Bed{Wifi,Heating,\"Family/kid friendly\",Essentials...NaN$42.00NaNNaN$0.00$0.001$24.002103 days agot152626262018-11-071972009-08-182018-11-0496.010.010.010.010.010.09.0tNaNNaNffmoderateff11.75
\n", + "
" + ], + "text/plain": [ + " id listing_url scrape_id last_scraped \\\n", + "0 2015 https://www.airbnb.com/rooms/2015 20181107122246 2018-11-07 \n", + "1 2695 https://www.airbnb.com/rooms/2695 20181107122246 2018-11-07 \n", + "2 3176 https://www.airbnb.com/rooms/3176 20181107122246 2018-11-07 \n", + "3 3309 https://www.airbnb.com/rooms/3309 20181107122246 2018-11-07 \n", + "4 7071 https://www.airbnb.com/rooms/7071 20181107122246 2018-11-07 \n", + "\n", + " name \\\n", + "0 Berlin-Mitte Value! Quiet courtyard/very central \n", + "1 Prenzlauer Berg close to Mauerpark \n", + "2 Fabulous Flat in great Location \n", + "3 BerlinSpot Schöneberg near KaDeWe \n", + "4 BrightRoom with sunny greenview! \n", + "\n", + " summary \\\n", + "0 Great location! 30 of 75 sq meters. This wood... \n", + "1 NaN \n", + "2 This beautiful first floor apartment is situa... \n", + "3 First of all: I prefer short-notice bookings. ... \n", + "4 Cozy and large room in the beautiful district ... \n", + "\n", + " space \\\n", + "0 A+++ location! This „Einliegerwohnung“ is an e... \n", + "1 In the summertime we are spending most of our ... \n", + "2 1st floor (68m2) apartment on Kollwitzplatz/ P... \n", + "3 Your room is really big and has 26 sqm, is ver... \n", + "4 The BrightRoom is an approx. 20 sqm (215ft²), ... \n", + "\n", + " description experiences_offered \\\n", + "0 Great location! 30 of 75 sq meters. This wood... none \n", + "1 In the summertime we are spending most of our ... none \n", + "2 This beautiful first floor apartment is situa... none \n", + "3 First of all: I prefer short-notice bookings. ... none \n", + "4 Cozy and large room in the beautiful district ... none \n", + "\n", + " neighborhood_overview \\\n", + "0 It is located in the former East Berlin area o... \n", + "1 NaN \n", + "2 The neighbourhood is famous for its variety of... \n", + "3 My flat is in the middle of West-Berlin, direc... \n", + "4 Great neighborhood with plenty of Cafés, Baker... \n", + "\n", + " notes \\\n", + "0 This is my home, not a hotel. I rent out occas... \n", + "1 NaN \n", + "2 We welcome FAMILIES and cater especially for y... \n", + "3 The flat is a strictly non-smoking facility! A... \n", + "4 I hope you enjoy your stay to the fullest! Ple... \n", + "\n", + " transit \\\n", + "0 Close to U-Bahn U8 and U2 (metro), Trams M12, ... \n", + "1 Within walking distance you'll find the S-Bahn... \n", + "2 We are 5 min walk away from the tram M2, whic... \n", + "3 The public transportation is excellent: Severa... \n", + "4 Best access to other parts of the city via pub... \n", + "\n", + " access \\\n", + "0 Simple kitchen/cooking, refrigerator, microwav... \n", + "1 Außer deinem Zimmer kannst du noch die Küche u... \n", + "2 The apartment will be entirely yours. We are c... \n", + "3 I do have a strictly non-smoker-flat. Keep th... \n", + "4 The guests have access to the bathroom, a smal... \n", + "\n", + " interaction \\\n", + "0 Always available \n", + "1 NaN \n", + "2 Feel free to ask any questions prior to bookin... \n", + "3 I'm working as a freelancing photographer. My ... \n", + "4 I am glad if I can give you advice or help as ... \n", + "\n", + " house_rules thumbnail_url \\\n", + "0 No parties No events No pets No smoking, not e... NaN \n", + "1 NaN NaN \n", + "2 It’s a non smoking flat, which likes to be tre... NaN \n", + "3 House-Rules and Information ..............(deu... NaN \n", + "4 Please take good care of everything during you... NaN \n", + "\n", + " medium_url picture_url \\\n", + "0 NaN https://a0.muscache.com/im/pictures/260fd609-7... \n", + "1 NaN https://a0.muscache.com/im/pictures/97d4f6e8-1... \n", + "2 NaN https://a0.muscache.com/im/pictures/243355/84a... \n", + "3 NaN https://a0.muscache.com/im/pictures/29054294/b... \n", + "4 NaN https://a0.muscache.com/im/pictures/21278/32a1... \n", + "\n", + " xl_picture_url host_id host_url host_name \\\n", + "0 NaN 2217 https://www.airbnb.com/users/show/2217 Ian \n", + "1 NaN 2986 https://www.airbnb.com/users/show/2986 Michael \n", + "2 NaN 3718 https://www.airbnb.com/users/show/3718 Britta \n", + "3 NaN 4108 https://www.airbnb.com/users/show/4108 Jana \n", + "4 NaN 17391 https://www.airbnb.com/users/show/17391 Bright \n", + "\n", + " host_since host_location \\\n", + "0 2008-08-18 Key Biscayne, Florida, United States \n", + "1 2008-09-16 Berlin, Berlin, Germany \n", + "2 2008-10-19 Coledale, New South Wales, Australia \n", + "3 2008-11-07 Berlin, Berlin, Germany \n", + "4 2009-05-16 Berlin, Berlin, Germany \n", + "\n", + " host_about host_response_time \\\n", + "0 Believe in sharing economy. within an hour \n", + "1 Living with my wife in Berlin. Like travellin... NaN \n", + "2 We love to travel ourselves a lot and prefer t... within a day \n", + "3 ENJOY EVERY DAY AS IF IT'S YOUR LAST!!! \\r\\n\\r... NaN \n", + "4 I'm a creative person, adventurer, and travele... within an hour \n", + "\n", + " host_response_rate host_acceptance_rate host_is_superhost \\\n", + "0 96% NaN t \n", + "1 NaN NaN f \n", + "2 100% NaN f \n", + "3 NaN NaN f \n", + "4 100% NaN t \n", + "\n", + " host_thumbnail_url \\\n", + "0 https://a0.muscache.com/im/pictures/21428a22-4... \n", + "1 https://a0.muscache.com/im/pictures/50434955-1... \n", + "2 https://a0.muscache.com/im/users/3718/profile_... \n", + "3 https://a0.muscache.com/im/pictures/user/b5938... \n", + "4 https://a0.muscache.com/im/pictures/user/48c3d... \n", + "\n", + " host_picture_url host_neighbourhood \\\n", + "0 https://a0.muscache.com/im/pictures/21428a22-4... Mitte \n", + "1 https://a0.muscache.com/im/pictures/50434955-1... Prenzlauer Berg \n", + "2 https://a0.muscache.com/im/users/3718/profile_... Prenzlauer Berg \n", + "3 https://a0.muscache.com/im/pictures/user/b5938... Schöneberg \n", + "4 https://a0.muscache.com/im/pictures/user/48c3d... Prenzlauer Berg \n", + "\n", + " host_listings_count host_total_listings_count \\\n", + "0 4.0 4.0 \n", + "1 1.0 1.0 \n", + "2 1.0 1.0 \n", + "3 1.0 1.0 \n", + "4 1.0 1.0 \n", + "\n", + " host_verifications host_has_profile_pic \\\n", + "0 ['email', 'phone', 'reviews', 'jumio', 'offlin... t \n", + "1 ['email', 'phone', 'reviews', 'jumio', 'govern... t \n", + "2 ['email', 'phone', 'facebook', 'reviews', 'man... t \n", + "3 ['email', 'phone', 'reviews', 'jumio', 'govern... t \n", + "4 ['email', 'phone', 'reviews', 'jumio', 'govern... t \n", + "\n", + " host_identity_verified street neighbourhood \\\n", + "0 t Berlin, Berlin, Germany Mitte \n", + "1 t Berlin, Berlin, Germany NaN \n", + "2 t Berlin, Berlin, Germany Prenzlauer Berg \n", + "3 t Berlin, Berlin, Germany Schöneberg \n", + "4 t Berlin, Berlin, Germany Prenzlauer Berg \n", + "\n", + " neighbourhood_cleansed neighbourhood_group_cleansed city state \\\n", + "0 Brunnenstr. Süd Mitte Berlin Berlin \n", + "1 Prenzlauer Berg Nordwest Pankow Berlin Berlin \n", + "2 Prenzlauer Berg Südwest Pankow Berlin Berlin \n", + "3 Schöneberg-Nord Tempelhof - Schöneberg Berlin Berlin \n", + "4 Helmholtzplatz Pankow Berlin Berlin \n", + "\n", + " zipcode market smart_location country_code country latitude \\\n", + "0 10119 Berlin Berlin, Germany DE Germany 52.534537 \n", + "1 10437 Berlin Berlin, Germany DE Germany 52.548513 \n", + "2 10405 Berlin Berlin, Germany DE Germany 52.534996 \n", + "3 10777 Berlin Berlin, Germany DE Germany 52.498855 \n", + "4 10437 Berlin Berlin, Germany DE Germany 52.543157 \n", + "\n", + " longitude is_location_exact property_type room_type accommodates \\\n", + "0 13.402557 f Guesthouse Entire home/apt 3 \n", + "1 13.404553 t Apartment Private room 2 \n", + "2 13.417579 t Apartment Entire home/apt 4 \n", + "3 13.349065 t Apartment Private room 2 \n", + "4 13.415091 t Apartment Private room 2 \n", + "\n", + " bathrooms bedrooms beds bed_type \\\n", + "0 1.0 1.0 2.0 Real Bed \n", + "1 1.0 1.0 1.0 Real Bed \n", + "2 1.0 1.0 2.0 Real Bed \n", + "3 1.0 1.0 1.0 Pull-out Sofa \n", + "4 1.0 1.0 2.0 Real Bed \n", + "\n", + " amenities square_feet price \\\n", + "0 {TV,\"Cable TV\",Wifi,Kitchen,Gym,Heating,\"Famil... NaN $60.00 \n", + "1 {Wifi,Kitchen,Elevator,Heating,Washer,Essentia... NaN $17.00 \n", + "2 {Internet,Wifi,Kitchen,\"Buzzer/wireless interc... 720.0 $90.00 \n", + "3 {Internet,Wifi,\"Pets allowed\",\"Pets live on th... 0.0 $26.00 \n", + "4 {Wifi,Heating,\"Family/kid friendly\",Essentials... NaN $42.00 \n", + "\n", + " weekly_price monthly_price security_deposit cleaning_fee guests_included \\\n", + "0 NaN NaN $200.00 $30.00 1 \n", + "1 NaN NaN $0.00 $0.00 1 \n", + "2 $520.00 $1,900.00 $200.00 $50.00 2 \n", + "3 $175.00 $599.00 $250.00 $30.00 1 \n", + "4 NaN NaN $0.00 $0.00 1 \n", + "\n", + " extra_people minimum_nights maximum_nights calendar_updated \\\n", + "0 $28.00 4 1125 3 months ago \n", + "1 $0.00 2 40 7 weeks ago \n", + "2 $20.00 62 1125 a week ago \n", + "3 $18.00 5 360 4 weeks ago \n", + "4 $24.00 2 10 3 days ago \n", + "\n", + " has_availability availability_30 availability_60 availability_90 \\\n", + "0 t 0 21 51 \n", + "1 t 0 0 0 \n", + "2 t 0 0 0 \n", + "3 t 0 0 22 \n", + "4 t 15 26 26 \n", + "\n", + " availability_365 calendar_last_scraped number_of_reviews first_review \\\n", + "0 141 2018-11-07 118 2016-04-11 \n", + "1 0 2018-11-07 6 2018-07-04 \n", + "2 220 2018-11-07 143 2009-06-20 \n", + "3 297 2018-11-07 25 2013-08-12 \n", + "4 26 2018-11-07 197 2009-08-18 \n", + "\n", + " last_review review_scores_rating review_scores_accuracy \\\n", + "0 2018-10-28 93.0 10.0 \n", + "1 2018-10-01 100.0 10.0 \n", + "2 2017-03-20 92.0 9.0 \n", + "3 2018-08-16 88.0 9.0 \n", + "4 2018-11-04 96.0 10.0 \n", + "\n", + " review_scores_cleanliness review_scores_checkin \\\n", + "0 9.0 10.0 \n", + "1 10.0 10.0 \n", + "2 9.0 9.0 \n", + "3 9.0 9.0 \n", + "4 10.0 10.0 \n", + "\n", + " review_scores_communication review_scores_location review_scores_value \\\n", + "0 10.0 10.0 9.0 \n", + "1 10.0 10.0 10.0 \n", + "2 9.0 10.0 9.0 \n", + "3 10.0 9.0 9.0 \n", + "4 10.0 10.0 9.0 \n", + "\n", + " requires_license license jurisdiction_names instant_bookable \\\n", + "0 t NaN NaN f \n", + "1 t NaN NaN f \n", + "2 t NaN NaN t \n", + "3 t NaN NaN f \n", + "4 t NaN NaN f \n", + "\n", + " is_business_travel_ready cancellation_policy \\\n", + "0 f strict_14_with_grace_period \n", + "1 f flexible \n", + "2 f strict_14_with_grace_period \n", + "3 f strict_14_with_grace_period \n", + "4 f moderate \n", + "\n", + " require_guest_profile_picture require_guest_phone_verification \\\n", + "0 f f \n", + "1 f f \n", + "2 f f \n", + "3 f f \n", + "4 f f \n", + "\n", + " calculated_host_listings_count reviews_per_month \n", + "0 4 3.76 \n", + "1 1 1.42 \n", + "2 1 1.25 \n", + "3 1 0.39 \n", + "4 1 1.75 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Loading up the original dataframe.\n", + "\n", + "berlin = pd.read_csv('https://raw.githubusercontent.com/BuildWeekAirbnbOptimal1/Data-Science/master/Berlin.csv')\n", + "print(berlin.shape)\n", + "berlin.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 510 + }, + "colab_type": "code", + "id": "yfzAdd9LyG5D", + "outputId": "21879ff0-76ba-4c2b-f3ad-507c6b58aa5f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['id', 'listing_url', 'scrape_id', 'last_scraped', 'name', 'summary',\n", + " 'space', 'description', 'experiences_offered', 'neighborhood_overview',\n", + " 'notes', 'transit', 'access', 'interaction', 'house_rules',\n", + " 'thumbnail_url', 'medium_url', 'picture_url', 'xl_picture_url',\n", + " 'host_id', 'host_url', 'host_name', 'host_since', 'host_location',\n", + " 'host_about', 'host_response_time', 'host_response_rate',\n", + " 'host_acceptance_rate', 'host_is_superhost', 'host_thumbnail_url',\n", + " 'host_picture_url', 'host_neighbourhood', 'host_listings_count',\n", + " 'host_total_listings_count', 'host_verifications',\n", + " 'host_has_profile_pic', 'host_identity_verified', 'street',\n", + " 'neighbourhood', 'neighbourhood_cleansed',\n", + " 'neighbourhood_group_cleansed', 'city', 'state', 'zipcode', 'market',\n", + " 'smart_location', 'country_code', 'country', 'latitude', 'longitude',\n", + " 'is_location_exact', 'property_type', 'room_type', 'accommodates',\n", + " 'bathrooms', 'bedrooms', 'beds', 'bed_type', 'amenities', 'square_feet',\n", + " 'price', 'weekly_price', 'monthly_price', 'security_deposit',\n", + " 'cleaning_fee', 'guests_included', 'extra_people', 'minimum_nights',\n", + " 'maximum_nights', 'calendar_updated', 'has_availability',\n", + " 'availability_30', 'availability_60', 'availability_90',\n", + " 'availability_365', 'calendar_last_scraped', 'number_of_reviews',\n", + " 'first_review', 'last_review', 'review_scores_rating',\n", + " 'review_scores_accuracy', 'review_scores_cleanliness',\n", + " 'review_scores_checkin', 'review_scores_communication',\n", + " 'review_scores_location', 'review_scores_value', 'requires_license',\n", + " 'license', 'jurisdiction_names', 'instant_bookable',\n", + " 'is_business_travel_ready', 'cancellation_policy',\n", + " 'require_guest_profile_picture', 'require_guest_phone_verification',\n", + " 'calculated_host_listings_count', 'reviews_per_month'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking the names of the columns in this dataframe.\n", + "\n", + "berlin.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Td7LcShBOMIq" + }, + "source": [ + "# Preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "BAjhX-dob-p0" + }, + "source": [ + "## Deciding what columns to keep" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "lUWZIKpg3w8E" + }, + "outputs": [], + "source": [ + "# Creating the first df with dropping the columns which I don't think we will need at all.\n", + "# Dropped columns are those with mainly high cardinality.\n", + "# Keeping that columns which a new renter can normally directly influence.\n", + "\n", + "columns_to_keep = ['id', 'space', 'description', 'host_response_time', \n", + " 'host_response_rate', 'host_has_profile_pic', \n", + " 'neighbourhood_group_cleansed', 'latitude', 'longitude', \n", + " 'property_type', 'room_type', 'accommodates', 'bathrooms', \n", + " 'bedrooms', 'bed_type', 'amenities', 'square_feet', 'price', \n", + " 'cleaning_fee', 'security_deposit', 'extra_people', \n", + " 'guests_included', 'minimum_nights', 'maximum_nights', 'instant_bookable', \n", + " 'is_business_travel_ready', 'cancellation_policy']\n", + "\n", + "berlin_preprocess = berlin[columns_to_keep].set_index('id')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 561 + }, + "colab_type": "code", + "id": "C7UsfY8R4VJs", + "outputId": "6f53d638-9234-4061-abff-90636921c69d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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spacedescriptionhost_response_timehost_response_ratehost_has_profile_picneighbourhood_group_cleansedlatitudelongitudeproperty_typeroom_typeaccommodatesbathroomsbedroomsbed_typeamenitiessquare_feetpricecleaning_feesecurity_depositextra_peopleguests_includedminimum_nightsmaximum_nightsinstant_bookableis_business_travel_readycancellation_policy
id
2015A+++ location! This „Einliegerwohnung“ is an e...Great location! 30 of 75 sq meters. This wood...within an hour96%tMitte52.53453713.402557GuesthouseEntire home/apt31.01.0Real Bed{TV,\"Cable TV\",Wifi,Kitchen,Gym,Heating,\"Famil...NaN$60.00$30.00$200.00$28.00141125ffstrict_14_with_grace_period
2695In the summertime we are spending most of our ...In the summertime we are spending most of our ...NaNNaNtPankow52.54851313.404553ApartmentPrivate room21.01.0Real Bed{Wifi,Kitchen,Elevator,Heating,Washer,Essentia...NaN$17.00$0.00$0.00$0.001240ffflexible
31761st floor (68m2) apartment on Kollwitzplatz/ P...This beautiful first floor apartment is situa...within a day100%tPankow52.53499613.417579ApartmentEntire home/apt41.01.0Real Bed{Internet,Wifi,Kitchen,\"Buzzer/wireless interc...720.0$90.00$50.00$200.00$20.002621125tfstrict_14_with_grace_period
3309Your room is really big and has 26 sqm, is ver...First of all: I prefer short-notice bookings. ...NaNNaNtTempelhof - Schöneberg52.49885513.349065ApartmentPrivate room21.01.0Pull-out Sofa{Internet,Wifi,\"Pets allowed\",\"Pets live on th...0.0$26.00$30.00$250.00$18.0015360ffstrict_14_with_grace_period
7071The BrightRoom is an approx. 20 sqm (215ft²), ...Cozy and large room in the beautiful district ...within an hour100%tPankow52.54315713.415091ApartmentPrivate room21.01.0Real Bed{Wifi,Heating,\"Family/kid friendly\",Essentials...NaN$42.00$0.00$0.00$24.001210ffmoderate
\n", + "
" + ], + "text/plain": [ + " space \\\n", + "id \n", + "2015 A+++ location! This „Einliegerwohnung“ is an e... \n", + "2695 In the summertime we are spending most of our ... \n", + "3176 1st floor (68m2) apartment on Kollwitzplatz/ P... \n", + "3309 Your room is really big and has 26 sqm, is ver... \n", + "7071 The BrightRoom is an approx. 20 sqm (215ft²), ... \n", + "\n", + " description host_response_time \\\n", + "id \n", + "2015 Great location! 30 of 75 sq meters. This wood... within an hour \n", + "2695 In the summertime we are spending most of our ... NaN \n", + "3176 This beautiful first floor apartment is situa... within a day \n", + "3309 First of all: I prefer short-notice bookings. ... NaN \n", + "7071 Cozy and large room in the beautiful district ... within an hour \n", + "\n", + " host_response_rate host_has_profile_pic neighbourhood_group_cleansed \\\n", + "id \n", + "2015 96% t Mitte \n", + "2695 NaN t Pankow \n", + "3176 100% t Pankow \n", + "3309 NaN t Tempelhof - Schöneberg \n", + "7071 100% t Pankow \n", + "\n", + " latitude longitude property_type room_type accommodates \\\n", + "id \n", + "2015 52.534537 13.402557 Guesthouse Entire home/apt 3 \n", + "2695 52.548513 13.404553 Apartment Private room 2 \n", + "3176 52.534996 13.417579 Apartment Entire home/apt 4 \n", + "3309 52.498855 13.349065 Apartment Private room 2 \n", + "7071 52.543157 13.415091 Apartment Private room 2 \n", + "\n", + " bathrooms bedrooms bed_type \\\n", + "id \n", + "2015 1.0 1.0 Real Bed \n", + "2695 1.0 1.0 Real Bed \n", + "3176 1.0 1.0 Real Bed \n", + "3309 1.0 1.0 Pull-out Sofa \n", + "7071 1.0 1.0 Real Bed \n", + "\n", + " amenities square_feet price \\\n", + "id \n", + "2015 {TV,\"Cable TV\",Wifi,Kitchen,Gym,Heating,\"Famil... NaN $60.00 \n", + "2695 {Wifi,Kitchen,Elevator,Heating,Washer,Essentia... NaN $17.00 \n", + "3176 {Internet,Wifi,Kitchen,\"Buzzer/wireless interc... 720.0 $90.00 \n", + "3309 {Internet,Wifi,\"Pets allowed\",\"Pets live on th... 0.0 $26.00 \n", + "7071 {Wifi,Heating,\"Family/kid friendly\",Essentials... NaN $42.00 \n", + "\n", + " cleaning_fee security_deposit extra_people guests_included \\\n", + "id \n", + "2015 $30.00 $200.00 $28.00 1 \n", + "2695 $0.00 $0.00 $0.00 1 \n", + "3176 $50.00 $200.00 $20.00 2 \n", + "3309 $30.00 $250.00 $18.00 1 \n", + "7071 $0.00 $0.00 $24.00 1 \n", + "\n", + " minimum_nights maximum_nights instant_bookable \\\n", + "id \n", + "2015 4 1125 f \n", + "2695 2 40 f \n", + "3176 62 1125 t \n", + "3309 5 360 f \n", + "7071 2 10 f \n", + "\n", + " is_business_travel_ready cancellation_policy \n", + "id \n", + "2015 f strict_14_with_grace_period \n", + "2695 f flexible \n", + "3176 f strict_14_with_grace_period \n", + "3309 f strict_14_with_grace_period \n", + "7071 f moderate " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "berlin_preprocess.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "Upbd5MaLOfOG", + "outputId": "281d8651-15fd-4157-c77f-50d8fc4accf5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There is is 22552 lines and 26 columns, in this set after dropping irrelevant columns.\n" + ] + } + ], + "source": [ + "print(f'There is is {berlin_preprocess.shape[0]} lines and {berlin_preprocess.shape[1]} columns,'\n", + " f' in this set after dropping irrelevant columns.')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vG8f4gq9ReKi" + }, + "source": [ + "## Cleaning the price columns" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "colab_type": "code", + "id": "zHYeRqBjRJj0", + "outputId": "2969b1d8-9e23-47bf-85db-aea1be0a5d58" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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pricecleaning_feeextra_peoplesecurity_deposit
id
2015$60.00$30.00$28.00$200.00
2695$17.00$0.00$0.00$0.00
3176$90.00$50.00$20.00$200.00
3309$26.00$30.00$18.00$250.00
7071$42.00$0.00$24.00$0.00
\n", + "
" + ], + "text/plain": [ + " price cleaning_fee extra_people security_deposit\n", + "id \n", + "2015 $60.00 $30.00 $28.00 $200.00\n", + "2695 $17.00 $0.00 $0.00 $0.00\n", + "3176 $90.00 $50.00 $20.00 $200.00\n", + "3309 $26.00 $30.00 $18.00 $250.00\n", + "7071 $42.00 $0.00 $24.00 $0.00" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Reviewing all the monetary columns from this dataframe.\n", + "\n", + "berlin_preprocess[['price', 'cleaning_fee', 'extra_people', 'security_deposit']].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "colab_type": "code", + "id": "bfBNDe8cR3St", + "outputId": "aab8ef20-2f03-4088-bdd6-5cabe2b5b004" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "7146\n", + "0\n", + "9361\n" + ] + } + ], + "source": [ + "# Checking how many missing values we have in each of these features.\n", + "\n", + "print(berlin_preprocess.price.isna().sum())\n", + "print(berlin_preprocess.cleaning_fee.isna().sum())\n", + "print(berlin_preprocess.extra_people.isna().sum())\n", + "print(berlin_preprocess.security_deposit.isna().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "vv8GQTd_Td9v" + }, + "outputs": [], + "source": [ + "# We are making an assumption that missing values mean that landlords don't charge\n", + "# for those particular services. Exchaning missing values with $0.\n", + "\n", + "berlin_preprocess['cleaning_fee'] = berlin_preprocess['cleaning_fee'].fillna('$0.00')\n", + "berlin_preprocess['security_deposit'] = berlin_preprocess['security_deposit'].fillna('$0.00')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "HM5qXQ9P7kl8" + }, + "outputs": [], + "source": [ + "# Wrangling strings in these features. Dropping $ signs and commas.\n", + "\n", + "berlin_preprocess['price'] = berlin_preprocess['price'].str.replace('$', '').str.replace(',', '').astype(float)\n", + "berlin_preprocess['cleaning_fee'] = berlin_preprocess['cleaning_fee'].str.replace('$', '').str.replace(',', '').astype(float)\n", + "berlin_preprocess['extra_people'] = berlin_preprocess['extra_people'].str.replace('$', '').str.replace(',', '').astype(float)\n", + "berlin_preprocess['security_deposit'] = berlin_preprocess['security_deposit'].str.replace('$', '').str.replace(',', '').astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 170 + }, + "colab_type": "code", + "id": "hDCHtgvN7nAB", + "outputId": "41f84ec2-f017-412d-b4ba-9d8f30cbc40f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 22552.000000\n", + "mean 67.143668\n", + "std 220.266210\n", + "min 0.000000\n", + "25% 30.000000\n", + "50% 45.000000\n", + "75% 70.000000\n", + "max 9000.000000\n", + "Name: price, dtype: float64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking the layout of the price distribution.\n", + "\n", + "berlin_preprocess['price'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 157 + }, + "colab_type": "code", + "id": "oG1fxY0-78N3", + "outputId": "dd4f25b1-89c3-4bef-998e-17460906413f" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Graphical presentation of the above listed distribution.\n", + "\n", + "square = dict(markerfacecolor='r', markeredgecolor='r', marker='.')\n", + "berlin_preprocess['price'].plot(kind='box', xlim=(0, 1000), vert=False, flierprops=square, figsize=(16, 2));" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "SXvykVDh8wZQ" + }, + "outputs": [], + "source": [ + "# Finishing initial preprocessing with dropping the values above $400 and equal $0.\n", + "\n", + "berlin = berlin_preprocess.drop(berlin_preprocess[ (berlin_preprocess.price > 400) | (berlin_preprocess.price == 0) ].index, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "kB-mUxbG9MHH", + "outputId": "758c1b9f-a2eb-43d6-df86-ba32e401846d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(22441, 26)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking the shape after initial preprocessing.\n", + "\n", + "berlin.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "fnfUMqyeX40Z" + }, + "source": [ + "## Dealing with missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + }, + "colab_type": "code", + "id": "dYLc8nVfX3tf", + "outputId": "7eaabe88-9008-4f1b-e073-8ef227788f1e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "space 8503\n", + "description 202\n", + "host_response_time 12845\n", + "host_response_rate 12846\n", + "host_has_profile_pic 25\n", + "neighbourhood_group_cleansed 0\n", + "latitude 0\n", + "longitude 0\n", + "property_type 0\n", + "room_type 0\n", + "accommodates 0\n", + "bathrooms 32\n", + "bedrooms 18\n", + "bed_type 0\n", + "amenities 0\n", + "square_feet 21996\n", + "price 0\n", + "cleaning_fee 0\n", + "security_deposit 0\n", + "extra_people 0\n", + "guests_included 0\n", + "minimum_nights 0\n", + "maximum_nights 0\n", + "instant_bookable 0\n", + "is_business_travel_ready 0\n", + "cancellation_policy 0\n", + "dtype: int64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Listing all the features with missing values.\n", + "\n", + "berlin.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "9WQz0U0NYVZF" + }, + "outputs": [], + "source": [ + "# Dropping the columns with large number of missing values\n", + "\n", + "berlin = berlin.drop(columns=['space', 'host_response_time', 'host_response_rate', 'square_feet'])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "3-YHMAGLZDxB", + "outputId": "3784b349-5e13-4475-dffe-1fa41dfbd734" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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descriptionhost_has_profile_picneighbourhood_group_cleansedlatitudelongitudeproperty_typeroom_typeaccommodatesbathroomsbedroomsbed_typeamenitiespricecleaning_feesecurity_depositextra_peopleguests_includedminimum_nightsmaximum_nightsinstant_bookableis_business_travel_readycancellation_policy
id
28711Newly renovated, quiet 1-room apartment (appro...tNeukölln52.48610613.434817ApartmentEntire home/apt3NaN1.0Real Bed{}60.030.00.010.022730ffflexible
55298The couch to sleep on is in the living room. T...tTempelhof - Schöneberg52.49837913.341816ApartmentShared room1NaN1.0Couch{}88.010.00.00.0123ffmoderate
138955quiet in the backyard with sunny balcony. slee...tFriedrichshain-Kreuzberg52.49097513.376910ApartmentEntire home/apt4NaN2.0Real Bed{Internet,Wifi,Kitchen,\"Buzzer/wireless interc...75.040.00.010.02260ffmoderate
189596Hello there, I am renting a very cosy 1,5 room...tMitte52.53046413.398893ApartmentEntire home/apt2NaN1.0Futon{Internet,Wifi,Kitchen,\"Buzzer/wireless interc...80.00.0100.0100.024365ffmoderate
245991The 45-square meter apartments are self contai...tReinickendorf52.58422313.231135ApartmentEntire home/apt2NaN1.0Real Bed{TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Pets all...23.00.0650.00.0230365tfstrict_14_with_grace_period
246007The 45-square meter apartments are self contai...tReinickendorf52.58202713.230722ApartmentEntire home/apt2NaN1.0Real Bed{TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Pets all...19.00.0550.00.0230365tfstrict_14_with_grace_period
260365Wonderful old apartment in Schöneberg between ...tTempelhof - Schöneberg52.49926413.339909ApartmentEntire home/apt2NaN1.0Real Bed{TV,\"Cable TV\",Internet,Kitchen,\"Washer / Drye...69.020.0300.00.01490ffflexible
274072Hi! Check out our beautiful studio apartment i...tPankow52.54947213.417323ApartmentEntire home/apt2NaN1.0Real Bed{TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Buzzer/w...60.030.00.00.013365tfstrict_14_with_grace_period
302167DETAILS: -47 m2 / 2 rooms/ max. of 4 persons ...tPankow52.53386013.427550ApartmentEntire home/apt4NaN1.0Real Bed{TV,\"Cable TV\",Internet,Wifi,Kitchen,Heating,\"...66.035.00.015.013365ffstrict_14_with_grace_period
323250Die Unterkünfte liegen im Bezirk Neukölln mitt...tNeukölln52.48250613.429294ApartmentPrivate room2NaN1.0Real Bed{Internet,Wifi,Kitchen,Heating,\"Family/kid fri...27.00.0100.014.01331ffstrict_14_with_grace_period
3239202 rooms (1 bedroom and 1 living room/studio) a...tNeukölln52.48649113.431478ApartmentPrivate room2NaN2.0Futon{Internet,Wifi,Kitchen,\"Washer / Dryer\",\"Pets ...30.00.00.00.01330ffmoderate
342637Herzlich Willkommen, ich biete eine 2 Zimmerwo...tPankow52.54155913.422995ApartmentEntire home/apt2NaN1.0Real Bed{TV,\"Cable TV\",Wifi,Kitchen,\"Buzzer/wireless i...65.040.00.00.01101125ffmoderate
507770Beautiful loft with sunny balcony. Only 20 min...tPankow52.55545913.410143ApartmentEntire home/apt2NaN1.0Real Bed{Internet,Wifi,Kitchen,Heating}37.00.00.05.0151125ffstrict_14_with_grace_period
510715Schöne große Wohnung in typischem, alten Berli...tTreptow - Köpenick52.49004013.446871ApartmentEntire home/apt4NaN2.0Real Bed{Wifi,Kitchen,\"Smoking allowed\",\"Pets live on ...50.00.00.00.01721ffstrict_14_with_grace_period
512476Die Wohnung liegt in einer ruhigen Seitenstraß...tTempelhof - Schöneberg52.45885113.387254ApartmentEntire home/apt4NaN1.0Real Bed{TV,Wifi,Kitchen,Heating,Washer,\"Smoke detecto...55.030.00.016.0231125tfmoderate
555352Caravan House Apartment Seerose We offer an ex...tTreptow - Köpenick52.34580313.643682Camper/RVEntire home/apt2NaN1.0Real Bed{TV,Wifi,\"Pets allowed\",\"Family/kid friendly\"}60.030.0150.015.0321125ffstrict_14_with_grace_period
563505Located in the heart of beautiful Charlottenbu...tCharlottenburg-Wilm.52.50373313.309323ApartmentEntire home/apt4NaN1.0Real Bed{TV,\"Cable TV\",Internet,Wifi,Kitchen,Heating,W...75.045.00.015.02329ffstrict_14_with_grace_period
577689Short version: Nice spacious apartment in a ce...tFriedrichshain-Kreuzberg52.51311913.469740ApartmentEntire home/apt2NaN1.0Real Bed{Wifi,Kitchen,\"Pets live on this property\",Ele...65.010.00.00.0121125ffmoderate
609459Very nice 4-room apartment in the heart of Ber...tTempelhof - Schöneberg52.48120313.359007ApartmentPrivate room2NaN1.0Real Bed{}70.00.00.020.01730ffflexible
628727the room looks out onto a tree-lined street, h...tPankow52.53172113.411291ApartmentPrivate room1NaN1.0Real Bed{Internet,Wifi,Kitchen,\"Smoking allowed\",\"Buzz...30.020.0200.015.01612ffstrict_14_with_grace_period
643058Nettes Zimmer im beliebten Prenzlauer Berg sup...tPankow52.52987713.423125Bed and breakfastPrivate room1NaN1.0Real Bed{Wifi,\"Pets allowed\",\"Buzzer/wireless intercom...25.00.00.00.0135ffmoderate
726236Bright, comfortable. fully equipped and right ...tNeukölln52.47064513.446697ApartmentEntire home/apt4NaN2.0Real Bed{TV,\"Cable TV\",Wifi,Kitchen,\"Buzzer/wireless i...47.045.00.010.02301125ffstrict_14_with_grace_period
958267My cute and clean apartment is based in a very...tMitte52.53025113.340921ApartmentPrivate room2NaN1.0Real Bed{Wifi,\"Free street parking\",\"Buzzer/wireless i...35.010.00.010.0131125ffstrict_14_with_grace_period
970871I live in shared flat on a lively street in Kr...tFriedrichshain-Kreuzberg52.50016013.438403ApartmentPrivate room2NaN1.0Real Bed{Wifi,\"Smoking allowed\",\"Free street parking\",...35.010.00.010.01615ffstrict_14_with_grace_period
1103355Die Wohnung liegt im Bezirk Berlin- Spandau, d...tSpandau52.52137313.190238ApartmentPrivate room2NaN1.0Real Bed{}77.00.00.00.0111125ffflexible
1230836Hallo, ich vermiete meine Einraumwohnung Berli...tPankow52.54452613.440108ApartmentPrivate room1NaN1.0Real Bed{}77.00.00.00.012323ffstrict_14_with_grace_period
1304376ein Gästezimmer in einer zwei- Zimmerwohnung m...tLichtenberg52.50918413.490027ApartmentPrivate room2NaN1.0Real Bed{}76.00.00.00.0111125ffflexible
1322019You want the best of both worlds? Staying righ...tNeukölln52.48365213.430076ApartmentEntire home/apt2NaN1.0Real Bed{Internet,Wifi,Kitchen,\"Buzzer/wireless interc...50.00.0100.025.0221125ffflexible
1339858A modern, cosy flat in Mitte, the most central...tMitte52.52315213.407555ApartmentEntire home/apt3NaN1.0Real Bed{TV,\"Cable TV\",Wifi,Kitchen,\"Buzzer/wireless i...90.015.00.015.0271125ffstrict_14_with_grace_period
1660112schöne wohnungtLichtenberg52.51436013.490989ApartmentPrivate room1NaN1.0Real Bed{}150.00.00.00.0111125ffflexible
3023254A quiet location in the heart of Friedrichshai...tFriedrichshain-Kreuzberg52.50861013.463191ApartmentPrivate room2NaN1.0Real Bed{Wifi,Kitchen,\"Pets allowed\",Heating,\"Family/k...25.010.00.020.01130ffmoderate
4809108Super zentrale Zimmer am Hackischer Markt in ...tMitte52.52941913.402798ApartmentPrivate room2NaN1.0Real Bed{TV,Internet,Wifi,Kitchen,Heating,\"Family/kid ...59.050.00.050.01320tfstrict_14_with_grace_period
\n", + "
" + ], + "text/plain": [ + " description \\\n", + "id \n", + "28711 Newly renovated, quiet 1-room apartment (appro... \n", + "55298 The couch to sleep on is in the living room. T... \n", + "138955 quiet in the backyard with sunny balcony. slee... \n", + "189596 Hello there, I am renting a very cosy 1,5 room... \n", + "245991 The 45-square meter apartments are self contai... \n", + "246007 The 45-square meter apartments are self contai... \n", + "260365 Wonderful old apartment in Schöneberg between ... \n", + "274072 Hi! Check out our beautiful studio apartment i... \n", + "302167 DETAILS: -47 m2 / 2 rooms/ max. of 4 persons ... \n", + "323250 Die Unterkünfte liegen im Bezirk Neukölln mitt... \n", + "323920 2 rooms (1 bedroom and 1 living room/studio) a... \n", + "342637 Herzlich Willkommen, ich biete eine 2 Zimmerwo... \n", + "507770 Beautiful loft with sunny balcony. Only 20 min... \n", + "510715 Schöne große Wohnung in typischem, alten Berli... \n", + "512476 Die Wohnung liegt in einer ruhigen Seitenstraß... \n", + "555352 Caravan House Apartment Seerose We offer an ex... \n", + "563505 Located in the heart of beautiful Charlottenbu... \n", + "577689 Short version: Nice spacious apartment in a ce... \n", + "609459 Very nice 4-room apartment in the heart of Ber... \n", + "628727 the room looks out onto a tree-lined street, h... \n", + "643058 Nettes Zimmer im beliebten Prenzlauer Berg sup... \n", + "726236 Bright, comfortable. fully equipped and right ... \n", + "958267 My cute and clean apartment is based in a very... \n", + "970871 I live in shared flat on a lively street in Kr... \n", + "1103355 Die Wohnung liegt im Bezirk Berlin- Spandau, d... \n", + "1230836 Hallo, ich vermiete meine Einraumwohnung Berli... \n", + "1304376 ein Gästezimmer in einer zwei- Zimmerwohnung m... \n", + "1322019 You want the best of both worlds? Staying righ... \n", + "1339858 A modern, cosy flat in Mitte, the most central... \n", + "1660112 schöne wohnung \n", + "3023254 A quiet location in the heart of Friedrichshai... \n", + "4809108 Super zentrale Zimmer am Hackischer Markt in ... \n", + "\n", + " host_has_profile_pic neighbourhood_group_cleansed latitude \\\n", + "id \n", + "28711 t Neukölln 52.486106 \n", + "55298 t Tempelhof - Schöneberg 52.498379 \n", + "138955 t Friedrichshain-Kreuzberg 52.490975 \n", + "189596 t Mitte 52.530464 \n", + "245991 t Reinickendorf 52.584223 \n", + "246007 t Reinickendorf 52.582027 \n", + "260365 t Tempelhof - Schöneberg 52.499264 \n", + "274072 t Pankow 52.549472 \n", + "302167 t Pankow 52.533860 \n", + "323250 t Neukölln 52.482506 \n", + "323920 t Neukölln 52.486491 \n", + "342637 t Pankow 52.541559 \n", + "507770 t Pankow 52.555459 \n", + "510715 t Treptow - Köpenick 52.490040 \n", + "512476 t Tempelhof - Schöneberg 52.458851 \n", + "555352 t Treptow - Köpenick 52.345803 \n", + "563505 t Charlottenburg-Wilm. 52.503733 \n", + "577689 t Friedrichshain-Kreuzberg 52.513119 \n", + "609459 t Tempelhof - Schöneberg 52.481203 \n", + "628727 t Pankow 52.531721 \n", + "643058 t Pankow 52.529877 \n", + "726236 t Neukölln 52.470645 \n", + "958267 t Mitte 52.530251 \n", + "970871 t Friedrichshain-Kreuzberg 52.500160 \n", + "1103355 t Spandau 52.521373 \n", + "1230836 t Pankow 52.544526 \n", + "1304376 t Lichtenberg 52.509184 \n", + "1322019 t Neukölln 52.483652 \n", + "1339858 t Mitte 52.523152 \n", + "1660112 t Lichtenberg 52.514360 \n", + "3023254 t Friedrichshain-Kreuzberg 52.508610 \n", + "4809108 t Mitte 52.529419 \n", + "\n", + " longitude property_type room_type accommodates \\\n", + "id \n", + "28711 13.434817 Apartment Entire home/apt 3 \n", + "55298 13.341816 Apartment Shared room 1 \n", + "138955 13.376910 Apartment Entire home/apt 4 \n", + "189596 13.398893 Apartment Entire home/apt 2 \n", + "245991 13.231135 Apartment Entire home/apt 2 \n", + "246007 13.230722 Apartment Entire home/apt 2 \n", + "260365 13.339909 Apartment Entire home/apt 2 \n", + "274072 13.417323 Apartment Entire home/apt 2 \n", + "302167 13.427550 Apartment Entire home/apt 4 \n", + "323250 13.429294 Apartment Private room 2 \n", + "323920 13.431478 Apartment Private room 2 \n", + "342637 13.422995 Apartment Entire home/apt 2 \n", + "507770 13.410143 Apartment Entire home/apt 2 \n", + "510715 13.446871 Apartment Entire home/apt 4 \n", + "512476 13.387254 Apartment Entire home/apt 4 \n", + "555352 13.643682 Camper/RV Entire home/apt 2 \n", + "563505 13.309323 Apartment Entire home/apt 4 \n", + "577689 13.469740 Apartment Entire home/apt 2 \n", + "609459 13.359007 Apartment Private room 2 \n", + "628727 13.411291 Apartment Private room 1 \n", + "643058 13.423125 Bed and breakfast Private room 1 \n", + "726236 13.446697 Apartment Entire home/apt 4 \n", + "958267 13.340921 Apartment Private room 2 \n", + "970871 13.438403 Apartment Private room 2 \n", + "1103355 13.190238 Apartment Private room 2 \n", + "1230836 13.440108 Apartment Private room 1 \n", + "1304376 13.490027 Apartment Private room 2 \n", + "1322019 13.430076 Apartment Entire home/apt 2 \n", + "1339858 13.407555 Apartment Entire home/apt 3 \n", + "1660112 13.490989 Apartment Private room 1 \n", + "3023254 13.463191 Apartment Private room 2 \n", + "4809108 13.402798 Apartment Private room 2 \n", + "\n", + " bathrooms bedrooms bed_type \\\n", + "id \n", + "28711 NaN 1.0 Real Bed \n", + "55298 NaN 1.0 Couch \n", + "138955 NaN 2.0 Real Bed \n", + "189596 NaN 1.0 Futon \n", + "245991 NaN 1.0 Real Bed \n", + "246007 NaN 1.0 Real Bed \n", + "260365 NaN 1.0 Real Bed \n", + "274072 NaN 1.0 Real Bed \n", + "302167 NaN 1.0 Real Bed \n", + "323250 NaN 1.0 Real Bed \n", + "323920 NaN 2.0 Futon \n", + "342637 NaN 1.0 Real Bed \n", + "507770 NaN 1.0 Real Bed \n", + "510715 NaN 2.0 Real Bed \n", + "512476 NaN 1.0 Real Bed \n", + "555352 NaN 1.0 Real Bed \n", + "563505 NaN 1.0 Real Bed \n", + "577689 NaN 1.0 Real Bed \n", + "609459 NaN 1.0 Real Bed \n", + "628727 NaN 1.0 Real Bed \n", + "643058 NaN 1.0 Real Bed \n", + "726236 NaN 2.0 Real Bed \n", + "958267 NaN 1.0 Real Bed \n", + "970871 NaN 1.0 Real Bed \n", + "1103355 NaN 1.0 Real Bed \n", + "1230836 NaN 1.0 Real Bed \n", + "1304376 NaN 1.0 Real Bed \n", + "1322019 NaN 1.0 Real Bed \n", + "1339858 NaN 1.0 Real Bed \n", + "1660112 NaN 1.0 Real Bed \n", + "3023254 NaN 1.0 Real Bed \n", + "4809108 NaN 1.0 Real Bed \n", + "\n", + " amenities price \\\n", + "id \n", + "28711 {} 60.0 \n", + "55298 {} 88.0 \n", + "138955 {Internet,Wifi,Kitchen,\"Buzzer/wireless interc... 75.0 \n", + "189596 {Internet,Wifi,Kitchen,\"Buzzer/wireless interc... 80.0 \n", + "245991 {TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Pets all... 23.0 \n", + "246007 {TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Pets all... 19.0 \n", + "260365 {TV,\"Cable TV\",Internet,Kitchen,\"Washer / Drye... 69.0 \n", + "274072 {TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Buzzer/w... 60.0 \n", + "302167 {TV,\"Cable TV\",Internet,Wifi,Kitchen,Heating,\"... 66.0 \n", + "323250 {Internet,Wifi,Kitchen,Heating,\"Family/kid fri... 27.0 \n", + "323920 {Internet,Wifi,Kitchen,\"Washer / Dryer\",\"Pets ... 30.0 \n", + "342637 {TV,\"Cable TV\",Wifi,Kitchen,\"Buzzer/wireless i... 65.0 \n", + "507770 {Internet,Wifi,Kitchen,Heating} 37.0 \n", + "510715 {Wifi,Kitchen,\"Smoking allowed\",\"Pets live on ... 50.0 \n", + "512476 {TV,Wifi,Kitchen,Heating,Washer,\"Smoke detecto... 55.0 \n", + "555352 {TV,Wifi,\"Pets allowed\",\"Family/kid friendly\"} 60.0 \n", + "563505 {TV,\"Cable TV\",Internet,Wifi,Kitchen,Heating,W... 75.0 \n", + "577689 {Wifi,Kitchen,\"Pets live on this property\",Ele... 65.0 \n", + "609459 {} 70.0 \n", + "628727 {Internet,Wifi,Kitchen,\"Smoking allowed\",\"Buzz... 30.0 \n", + "643058 {Wifi,\"Pets allowed\",\"Buzzer/wireless intercom... 25.0 \n", + "726236 {TV,\"Cable TV\",Wifi,Kitchen,\"Buzzer/wireless i... 47.0 \n", + "958267 {Wifi,\"Free street parking\",\"Buzzer/wireless i... 35.0 \n", + "970871 {Wifi,\"Smoking allowed\",\"Free street parking\",... 35.0 \n", + "1103355 {} 77.0 \n", + "1230836 {} 77.0 \n", + "1304376 {} 76.0 \n", + "1322019 {Internet,Wifi,Kitchen,\"Buzzer/wireless interc... 50.0 \n", + "1339858 {TV,\"Cable TV\",Wifi,Kitchen,\"Buzzer/wireless i... 90.0 \n", + "1660112 {} 150.0 \n", + "3023254 {Wifi,Kitchen,\"Pets allowed\",Heating,\"Family/k... 25.0 \n", + "4809108 {TV,Internet,Wifi,Kitchen,Heating,\"Family/kid ... 59.0 \n", + "\n", + " cleaning_fee security_deposit extra_people guests_included \\\n", + "id \n", + "28711 30.0 0.0 10.0 2 \n", + "55298 10.0 0.0 0.0 1 \n", + "138955 40.0 0.0 10.0 2 \n", + "189596 0.0 100.0 100.0 2 \n", + "245991 0.0 650.0 0.0 2 \n", + "246007 0.0 550.0 0.0 2 \n", + "260365 20.0 300.0 0.0 1 \n", + "274072 30.0 0.0 0.0 1 \n", + "302167 35.0 0.0 15.0 1 \n", + "323250 0.0 100.0 14.0 1 \n", + "323920 0.0 0.0 0.0 1 \n", + "342637 40.0 0.0 0.0 1 \n", + "507770 0.0 0.0 5.0 1 \n", + "510715 0.0 0.0 0.0 1 \n", + "512476 30.0 0.0 16.0 2 \n", + "555352 30.0 150.0 15.0 3 \n", + "563505 45.0 0.0 15.0 2 \n", + "577689 10.0 0.0 0.0 1 \n", + "609459 0.0 0.0 20.0 1 \n", + "628727 20.0 200.0 15.0 1 \n", + "643058 0.0 0.0 0.0 1 \n", + "726236 45.0 0.0 10.0 2 \n", + "958267 10.0 0.0 10.0 1 \n", + "970871 10.0 0.0 10.0 1 \n", + "1103355 0.0 0.0 0.0 1 \n", + "1230836 0.0 0.0 0.0 1 \n", + "1304376 0.0 0.0 0.0 1 \n", + "1322019 0.0 100.0 25.0 2 \n", + "1339858 15.0 0.0 15.0 2 \n", + "1660112 0.0 0.0 0.0 1 \n", + "3023254 10.0 0.0 20.0 1 \n", + "4809108 50.0 0.0 50.0 1 \n", + "\n", + " minimum_nights maximum_nights instant_bookable \\\n", + "id \n", + "28711 2 730 f \n", + "55298 2 3 f \n", + "138955 2 60 f \n", + "189596 4 365 f \n", + "245991 30 365 t \n", + "246007 30 365 t \n", + "260365 4 90 f \n", + "274072 3 365 t \n", + "302167 3 365 f \n", + "323250 3 31 f \n", + "323920 3 30 f \n", + "342637 10 1125 f \n", + "507770 5 1125 f \n", + "510715 7 21 f \n", + "512476 3 1125 t \n", + "555352 2 1125 f \n", + "563505 3 29 f \n", + "577689 2 1125 f \n", + "609459 7 30 f \n", + "628727 6 12 f \n", + "643058 3 5 f \n", + "726236 30 1125 f \n", + "958267 3 1125 f \n", + "970871 6 15 f \n", + "1103355 1 1125 f \n", + "1230836 23 23 f \n", + "1304376 1 1125 f \n", + "1322019 2 1125 f \n", + "1339858 7 1125 f \n", + "1660112 1 1125 f \n", + "3023254 1 30 f \n", + "4809108 3 20 t \n", + "\n", + " is_business_travel_ready cancellation_policy \n", + "id \n", + "28711 f flexible \n", + "55298 f moderate \n", + "138955 f moderate \n", + "189596 f moderate \n", + "245991 f strict_14_with_grace_period \n", + "246007 f strict_14_with_grace_period \n", + "260365 f flexible \n", + "274072 f strict_14_with_grace_period \n", + "302167 f strict_14_with_grace_period \n", + "323250 f strict_14_with_grace_period \n", + "323920 f moderate \n", + "342637 f moderate \n", + "507770 f strict_14_with_grace_period \n", + "510715 f strict_14_with_grace_period \n", + "512476 f moderate \n", + "555352 f strict_14_with_grace_period \n", + "563505 f strict_14_with_grace_period \n", + "577689 f moderate \n", + "609459 f flexible \n", + "628727 f strict_14_with_grace_period \n", + "643058 f moderate \n", + "726236 f strict_14_with_grace_period \n", + "958267 f strict_14_with_grace_period \n", + "970871 f strict_14_with_grace_period \n", + "1103355 f flexible \n", + "1230836 f strict_14_with_grace_period \n", + "1304376 f flexible \n", + "1322019 f flexible \n", + "1339858 f strict_14_with_grace_period \n", + "1660112 f flexible \n", + "3023254 f moderate \n", + "4809108 f strict_14_with_grace_period " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking the instances with missing bathroom values.\n", + "\n", + "berlin.loc[berlin['bathrooms'].isna()]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "DKbsIfW9Z5Lc", + "outputId": "3592b3a7-5550-4d1f-cb4e-bb07703401d1" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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descriptionhost_has_profile_picneighbourhood_group_cleansedlatitudelongitudeproperty_typeroom_typeaccommodatesbathroomsbedroomsbed_typeamenitiespricecleaning_feesecurity_depositextra_peopleguests_includedminimum_nightsmaximum_nightsinstant_bookableis_business_travel_readycancellation_policy
id
17904- beautiful studio apt in downtown Berlin - br...tNeukölln52.49547613.421821ApartmentEntire home/apt21.0NaNReal Bed{TV,Wifi,Kitchen,\"Free street parking\",\"Buzzer...49.050.00.015.01590ffstrict_14_with_grace_period
1757613Make yourself comfortable in a beautiful moder...tFriedrichshain-Kreuzberg52.51329413.459891ApartmentEntire home/apt21.0NaNReal Bed{TV,\"Cable TV\",Internet,Wifi,\"Wheelchair acces...60.030.0250.010.0111125tfstrict_14_with_grace_period
2094536The fine and inviting one-room-apartment is to...tFriedrichshain-Kreuzberg52.51378613.459694ApartmentEntire home/apt21.0NaNReal Bed{TV,\"Cable TV\",Wifi,Kitchen,\"Buzzer/wireless i...65.020.0200.00.01325ffstrict_14_with_grace_period
3429176This is an apartment which is directly adheren...tMitte52.50908613.402309ApartmentEntire home/apt21.0NaNReal Bed{TV,\"Cable TV\",Internet,Wifi,Kitchen,Heating,E...65.030.00.015.0111125tfflexible
7593011This flat is a quiet, spacious and very bright...tTempelhof - Schöneberg52.46529913.341703ApartmentEntire home/apt21.0NaNReal Bed{TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Buzzer/w...40.00.00.00.01141125ffflexible
7672145The apartment (47m²; kitchen, bathroom, living...tFriedrichshain-Kreuzberg52.52197913.432972ApartmentEntire home/apt21.0NaNReal Bed{Wifi,Kitchen,Washer}50.00.0100.00.0111125tfflexible
8919834Gemütlich und zentral. Direkt im friedrichshai...tFriedrichshain-Kreuzberg52.50992513.462696ApartmentEntire home/apt31.0NaNReal Bed{Internet,Wifi,Kitchen,Heating,\"Family/kid fri...25.00.0200.00.0111125ffflexible
12134242Die Wohung befindet sich in einer ruhigen Seit...tNeukölln52.48017713.433819ApartmentEntire home/apt41.0NaNReal Bed{TV,Wifi,Kitchen,\"Buzzer/wireless intercom\",He...29.040.0800.016.01251125ffmoderate
12196527An awesome home near Stegliz Business street i...tTempelhof - Schöneberg52.47199713.330821ApartmentEntire home/apt31.0NaNReal Bed{Kitchen,\"Hot tub\",Heating,\"Family/kid friendl...35.00.00.00.0111125tfflexible
12999963Das 1 Zimmer Apartment ( geeignet für 3 Erwach...tSteglitz - Zehlendorf52.43702713.223444CondominiumEntire home/apt31.0NaNReal Bed{TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Indoor f...95.058.0150.020.0431125ffstrict_14_with_grace_period
17728282Private room in a shared apartmenttFriedrichshain-Kreuzberg52.50072513.409222ApartmentPrivate room11.5NaNReal Bed{Wifi,Kitchen,Elevator,Heating,Washer,Essentia...39.036.00.013.01151125tfstrict_14_with_grace_period
28302220You are seeking a home away from home for a fe...tLichtenberg52.47962013.524906Serviced apartmentEntire home/apt11.0NaNReal Bed{TV,Wifi,Kitchen,Heating,Washer,Dryer,\"Smoke d...47.00.00.00.017180tfstrict_14_with_grace_period
28302855You are seeking a home away from home for a fe...tLichtenberg52.48051113.526313Serviced apartmentEntire home/apt21.0NaNReal Bed{TV,Wifi,Kitchen,Heating,Washer,Dryer,\"Smoke d...74.00.00.00.017180tfstrict_14_with_grace_period
28324659You are seeking a home away from home for a fe...tLichtenberg52.47997613.524899Serviced apartmentEntire home/apt21.0NaNReal Bed{TV,Wifi,Kitchen,Heating,Washer,Dryer,\"Smoke d...57.00.00.00.017180tfstrict_14_with_grace_period
28328056You are seeking a home away from home for a fe...tCharlottenburg-Wilm.52.50708513.327583Serviced apartmentEntire home/apt11.0NaNReal Bed{TV,Wifi,Kitchen,\"Paid parking off premises\",H...72.00.00.00.014180tfstrict_14_with_grace_period
28328620You are seeking a home away from home for a fe...tCharlottenburg-Wilm.52.50671713.329147Serviced apartmentEntire home/apt21.0NaNReal Bed{TV,Wifi,Kitchen,\"Paid parking off premises\",H...79.00.00.00.017180tfstrict_14_with_grace_period
28365580The flat is suitable for those, who look for a...tFriedrichshain-Kreuzberg52.52121913.434894ApartmentEntire home/apt31.0NaNReal Bed{Wifi,Kitchen,\"Smoking allowed\",\"Pets allowed\"...34.08.00.08.01330tfmoderate
28395262helles sehr schönes zimmer mit grißer schlafco...tPankow52.54513913.412284Bed and breakfastPrivate room21.0NaNReal Bed{TV,Wifi,Kitchen,Breakfast,\"Indoor fireplace\",...50.00.00.00.0111125tfflexible
\n", + "
" + ], + "text/plain": [ + " description \\\n", + "id \n", + "17904 - beautiful studio apt in downtown Berlin - br... \n", + "1757613 Make yourself comfortable in a beautiful moder... \n", + "2094536 The fine and inviting one-room-apartment is to... \n", + "3429176 This is an apartment which is directly adheren... \n", + "7593011 This flat is a quiet, spacious and very bright... \n", + "7672145 The apartment (47m²; kitchen, bathroom, living... \n", + "8919834 Gemütlich und zentral. Direkt im friedrichshai... \n", + "12134242 Die Wohung befindet sich in einer ruhigen Seit... \n", + "12196527 An awesome home near Stegliz Business street i... \n", + "12999963 Das 1 Zimmer Apartment ( geeignet für 3 Erwach... \n", + "17728282 Private room in a shared apartment \n", + "28302220 You are seeking a home away from home for a fe... \n", + "28302855 You are seeking a home away from home for a fe... \n", + "28324659 You are seeking a home away from home for a fe... \n", + "28328056 You are seeking a home away from home for a fe... \n", + "28328620 You are seeking a home away from home for a fe... \n", + "28365580 The flat is suitable for those, who look for a... \n", + "28395262 helles sehr schönes zimmer mit grißer schlafco... \n", + "\n", + " host_has_profile_pic neighbourhood_group_cleansed latitude \\\n", + "id \n", + "17904 t Neukölln 52.495476 \n", + "1757613 t Friedrichshain-Kreuzberg 52.513294 \n", + "2094536 t Friedrichshain-Kreuzberg 52.513786 \n", + "3429176 t Mitte 52.509086 \n", + "7593011 t Tempelhof - Schöneberg 52.465299 \n", + "7672145 t Friedrichshain-Kreuzberg 52.521979 \n", + "8919834 t Friedrichshain-Kreuzberg 52.509925 \n", + "12134242 t Neukölln 52.480177 \n", + "12196527 t Tempelhof - Schöneberg 52.471997 \n", + "12999963 t Steglitz - Zehlendorf 52.437027 \n", + "17728282 t Friedrichshain-Kreuzberg 52.500725 \n", + "28302220 t Lichtenberg 52.479620 \n", + "28302855 t Lichtenberg 52.480511 \n", + "28324659 t Lichtenberg 52.479976 \n", + "28328056 t Charlottenburg-Wilm. 52.507085 \n", + "28328620 t Charlottenburg-Wilm. 52.506717 \n", + "28365580 t Friedrichshain-Kreuzberg 52.521219 \n", + "28395262 t Pankow 52.545139 \n", + "\n", + " longitude property_type room_type accommodates \\\n", + "id \n", + "17904 13.421821 Apartment Entire home/apt 2 \n", + "1757613 13.459891 Apartment Entire home/apt 2 \n", + "2094536 13.459694 Apartment Entire home/apt 2 \n", + "3429176 13.402309 Apartment Entire home/apt 2 \n", + "7593011 13.341703 Apartment Entire home/apt 2 \n", + "7672145 13.432972 Apartment Entire home/apt 2 \n", + "8919834 13.462696 Apartment Entire home/apt 3 \n", + "12134242 13.433819 Apartment Entire home/apt 4 \n", + "12196527 13.330821 Apartment Entire home/apt 3 \n", + "12999963 13.223444 Condominium Entire home/apt 3 \n", + "17728282 13.409222 Apartment Private room 1 \n", + "28302220 13.524906 Serviced apartment Entire home/apt 1 \n", + "28302855 13.526313 Serviced apartment Entire home/apt 2 \n", + "28324659 13.524899 Serviced apartment Entire home/apt 2 \n", + "28328056 13.327583 Serviced apartment Entire home/apt 1 \n", + "28328620 13.329147 Serviced apartment Entire home/apt 2 \n", + "28365580 13.434894 Apartment Entire home/apt 3 \n", + "28395262 13.412284 Bed and breakfast Private room 2 \n", + "\n", + " bathrooms bedrooms bed_type \\\n", + "id \n", + "17904 1.0 NaN Real Bed \n", + "1757613 1.0 NaN Real Bed \n", + "2094536 1.0 NaN Real Bed \n", + "3429176 1.0 NaN Real Bed \n", + "7593011 1.0 NaN Real Bed \n", + "7672145 1.0 NaN Real Bed \n", + "8919834 1.0 NaN Real Bed \n", + "12134242 1.0 NaN Real Bed \n", + "12196527 1.0 NaN Real Bed \n", + "12999963 1.0 NaN Real Bed \n", + "17728282 1.5 NaN Real Bed \n", + "28302220 1.0 NaN Real Bed \n", + "28302855 1.0 NaN Real Bed \n", + "28324659 1.0 NaN Real Bed \n", + "28328056 1.0 NaN Real Bed \n", + "28328620 1.0 NaN Real Bed \n", + "28365580 1.0 NaN Real Bed \n", + "28395262 1.0 NaN Real Bed \n", + "\n", + " amenities price \\\n", + "id \n", + "17904 {TV,Wifi,Kitchen,\"Free street parking\",\"Buzzer... 49.0 \n", + "1757613 {TV,\"Cable TV\",Internet,Wifi,\"Wheelchair acces... 60.0 \n", + "2094536 {TV,\"Cable TV\",Wifi,Kitchen,\"Buzzer/wireless i... 65.0 \n", + "3429176 {TV,\"Cable TV\",Internet,Wifi,Kitchen,Heating,E... 65.0 \n", + "7593011 {TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Buzzer/w... 40.0 \n", + "7672145 {Wifi,Kitchen,Washer} 50.0 \n", + "8919834 {Internet,Wifi,Kitchen,Heating,\"Family/kid fri... 25.0 \n", + "12134242 {TV,Wifi,Kitchen,\"Buzzer/wireless intercom\",He... 29.0 \n", + "12196527 {Kitchen,\"Hot tub\",Heating,\"Family/kid friendl... 35.0 \n", + "12999963 {TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Indoor f... 95.0 \n", + "17728282 {Wifi,Kitchen,Elevator,Heating,Washer,Essentia... 39.0 \n", + "28302220 {TV,Wifi,Kitchen,Heating,Washer,Dryer,\"Smoke d... 47.0 \n", + "28302855 {TV,Wifi,Kitchen,Heating,Washer,Dryer,\"Smoke d... 74.0 \n", + "28324659 {TV,Wifi,Kitchen,Heating,Washer,Dryer,\"Smoke d... 57.0 \n", + "28328056 {TV,Wifi,Kitchen,\"Paid parking off premises\",H... 72.0 \n", + "28328620 {TV,Wifi,Kitchen,\"Paid parking off premises\",H... 79.0 \n", + "28365580 {Wifi,Kitchen,\"Smoking allowed\",\"Pets allowed\"... 34.0 \n", + "28395262 {TV,Wifi,Kitchen,Breakfast,\"Indoor fireplace\",... 50.0 \n", + "\n", + " cleaning_fee security_deposit extra_people guests_included \\\n", + "id \n", + "17904 50.0 0.0 15.0 1 \n", + "1757613 30.0 250.0 10.0 1 \n", + "2094536 20.0 200.0 0.0 1 \n", + "3429176 30.0 0.0 15.0 1 \n", + "7593011 0.0 0.0 0.0 1 \n", + "7672145 0.0 100.0 0.0 1 \n", + "8919834 0.0 200.0 0.0 1 \n", + "12134242 40.0 800.0 16.0 1 \n", + "12196527 0.0 0.0 0.0 1 \n", + "12999963 58.0 150.0 20.0 4 \n", + "17728282 36.0 0.0 13.0 1 \n", + "28302220 0.0 0.0 0.0 1 \n", + "28302855 0.0 0.0 0.0 1 \n", + "28324659 0.0 0.0 0.0 1 \n", + "28328056 0.0 0.0 0.0 1 \n", + "28328620 0.0 0.0 0.0 1 \n", + "28365580 8.0 0.0 8.0 1 \n", + "28395262 0.0 0.0 0.0 1 \n", + "\n", + " minimum_nights maximum_nights instant_bookable \\\n", + "id \n", + "17904 5 90 f \n", + "1757613 1 1125 t \n", + "2094536 3 25 f \n", + "3429176 1 1125 t \n", + "7593011 14 1125 f \n", + "7672145 1 1125 t \n", + "8919834 1 1125 f \n", + "12134242 25 1125 f \n", + "12196527 1 1125 t \n", + "12999963 3 1125 f \n", + "17728282 15 1125 t \n", + "28302220 7 180 t \n", + "28302855 7 180 t \n", + "28324659 7 180 t \n", + "28328056 4 180 t \n", + "28328620 7 180 t \n", + "28365580 3 30 t \n", + "28395262 1 1125 t \n", + "\n", + " is_business_travel_ready cancellation_policy \n", + "id \n", + "17904 f strict_14_with_grace_period \n", + "1757613 f strict_14_with_grace_period \n", + "2094536 f strict_14_with_grace_period \n", + "3429176 f flexible \n", + "7593011 f flexible \n", + "7672145 f flexible \n", + "8919834 f flexible \n", + "12134242 f moderate \n", + "12196527 f flexible \n", + "12999963 f strict_14_with_grace_period \n", + "17728282 f strict_14_with_grace_period \n", + "28302220 f strict_14_with_grace_period \n", + "28302855 f strict_14_with_grace_period \n", + "28324659 f strict_14_with_grace_period \n", + "28328056 f strict_14_with_grace_period \n", + "28328620 f strict_14_with_grace_period \n", + "28365580 f moderate \n", + "28395262 f flexible " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# And with missing bedroom values.\n", + "\n", + "berlin.loc[berlin['bedrooms'].isna()]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Lytt2EpKajGW" + }, + "outputs": [], + "source": [ + "# It seems that most common value for bathroom is 1 so we are filling that value with nans.\n", + "# Using 0 bedrooms for missing bedroom values - most likely studio apartments.\n", + "\n", + "berlin['bathrooms'] = berlin['bathrooms'].fillna(1.0)\n", + "berlin['bedrooms'] = berlin['bedrooms'].fillna(0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "rkEXDe7VbWbj" + }, + "outputs": [], + "source": [ + "# Finally filling out NaN on profile pic with f values.\n", + "\n", + "berlin['host_has_profile_pic'] = berlin['host_has_profile_pic'].fillna(value='f')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 408 + }, + "colab_type": "code", + "id": "ymqL9JEgbsD3", + "outputId": "5a20e820-dd26-49fa-c3d0-b3e4adeb07f3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "description 202\n", + "host_has_profile_pic 0\n", + "neighbourhood_group_cleansed 0\n", + "latitude 0\n", + "longitude 0\n", + "property_type 0\n", + "room_type 0\n", + "accommodates 0\n", + "bathrooms 0\n", + "bedrooms 0\n", + "bed_type 0\n", + "amenities 0\n", + "price 0\n", + "cleaning_fee 0\n", + "security_deposit 0\n", + "extra_people 0\n", + "guests_included 0\n", + "minimum_nights 0\n", + "maximum_nights 0\n", + "instant_bookable 0\n", + "is_business_travel_ready 0\n", + "cancellation_policy 0\n", + "dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking our data after this second step of cleaning.\n", + "\n", + "berlin.isna().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AW0FyiL7XNVv" + }, + "source": [ + "# Baseline and initial model" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "Oz2w0vRQXQat", + "outputId": "c1d76b6f-9a12-4d99-8d26-17c2144902ba" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "57.58437681030257" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Actual baseline mean of the price.\n", + "\n", + "berlin['price'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "77SAD8ZedhFS" + }, + "source": [ + "## Quick and dirty model using only numerical and categorical data" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "7X9VBEqf-ugM" + }, + "outputs": [], + "source": [ + "# Splitting our original dataframe into numerical and non numerical components.\n", + "\n", + "berlin_numerical = berlin.select_dtypes(include='number')\n", + "berlin_non_numerical = berlin.select_dtypes(exclude='number')" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "colab_type": "code", + "id": "GnyvTzGwJeYI", + "outputId": "cebd3491-1f1c-42b1-e17d-b7c7bab8328d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " latitude longitude accommodates bathrooms bedrooms price \\\n", + "id \n", + "2015 52.534537 13.402557 3 1.0 1.0 60.0 \n", + "2695 52.548513 13.404553 2 1.0 1.0 17.0 \n", + "3176 52.534996 13.417579 4 1.0 1.0 90.0 \n", + "3309 52.498855 13.349065 2 1.0 1.0 26.0 \n", + "7071 52.543157 13.415091 2 1.0 1.0 42.0 \n", + "\n", + " cleaning_fee security_deposit extra_people guests_included \\\n", + "id \n", + "2015 30.0 200.0 28.0 1 \n", + "2695 0.0 0.0 0.0 1 \n", + "3176 50.0 200.0 20.0 2 \n", + "3309 30.0 250.0 18.0 1 \n", + "7071 0.0 0.0 24.0 1 \n", + "\n", + " minimum_nights maximum_nights \n", + "id \n", + "2015 4 1125 \n", + "2695 2 40 \n", + "3176 62 1125 \n", + "3309 5 360 \n", + "7071 2 10 " + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "berlin_numerical.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "IiKahBGqP3AD" + }, + "source": [ + "### Engineering couple of features" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "7WiaMOnIJkOd" + }, + "outputs": [], + "source": [ + "# Adding two features using numerical data only.\n", + "# One will show what would be an average cost per day at full occupancy.\n", + "# Second feature shows what is the minimum expense out of pocket if you decide\n", + "# to rent that specific space. \n", + "\n", + "#berlin_numerical['price_per_person'] = berlin_numerical['price'] / berlin_numerical['accommodates']\n", + "#berlin_numerical['minimum_expenditure'] = berlin_numerical['price'] * berlin_numerical['minimum_nights']\n", + "\n", + "### Using these features created big leakeage into the data and produced r^2 scores of 0.98 - 1." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "colab_type": "code", + "id": "2c7uzpXdJ8Yc", + "outputId": "068570bc-0a55-4168-8993-d229f5e6041e" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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latitudelongitudeaccommodatesbathroomsbedroomspricecleaning_feesecurity_depositextra_peopleguests_includedminimum_nightsmaximum_nights
id
201552.53453713.40255731.01.060.030.0200.028.0141125
269552.54851313.40455321.01.017.00.00.00.01240
317652.53499613.41757941.01.090.050.0200.020.02621125
330952.49885513.34906521.01.026.030.0250.018.015360
707152.54315713.41509121.01.042.00.00.024.01210
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" + ], + "text/plain": [ + " latitude longitude accommodates bathrooms bedrooms price \\\n", + "id \n", + "2015 52.534537 13.402557 3 1.0 1.0 60.0 \n", + "2695 52.548513 13.404553 2 1.0 1.0 17.0 \n", + "3176 52.534996 13.417579 4 1.0 1.0 90.0 \n", + "3309 52.498855 13.349065 2 1.0 1.0 26.0 \n", + "7071 52.543157 13.415091 2 1.0 1.0 42.0 \n", + "\n", + " cleaning_fee security_deposit extra_people guests_included \\\n", + "id \n", + "2015 30.0 200.0 28.0 1 \n", + "2695 0.0 0.0 0.0 1 \n", + "3176 50.0 200.0 20.0 2 \n", + "3309 30.0 250.0 18.0 1 \n", + "7071 0.0 0.0 24.0 1 \n", + "\n", + " minimum_nights maximum_nights \n", + "id \n", + "2015 4 1125 \n", + "2695 2 40 \n", + "3176 62 1125 \n", + "3309 5 360 \n", + "7071 2 10 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Making sure that these engineered features are added properly.\n", + "\n", + "berlin_numerical.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "sbPGJss2Q-Wv" + }, + "source": [ + "### Running the model on numerical data only" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Y-0XK03b9rM9" + }, + "outputs": [], + "source": [ + "# Splitting the data into train, test groups.\n", + "\n", + "X_train_num, X_test_num = train_test_split(berlin_numerical, train_size=0.8, test_size=0.2, random_state=42)\n", + "\n", + "target = 'price'\n", + "\n", + "y_train_num = X_train_num[target]\n", + "X_train_num = X_train_num.drop(columns=target)\n", + "\n", + "y_test_num = X_test_num[target]\n", + "X_test_num = X_test_num.drop(columns=target)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "cuMls7Pt-QkR", + "outputId": "c34712fd-1995-4be4-8610-f213c00a2d17" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model based on numerical data only has a r^2 score of: 0.57\n" + ] + } + ], + "source": [ + "# Training the model and getting the initial score.\n", + "\n", + "berlin_num_pipeline = make_pipeline(\n", + " SimpleImputer(),\n", + " RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)\n", + ")\n", + "\n", + "berlin_num_pipeline.fit(X_train_num, y_train_num)\n", + "\n", + "y_pred_num = berlin_num_pipeline.predict(X_test_num)\n", + "score_num = r2_score(y_test_num, y_pred_num)\n", + "print(f'Model based on numerical data only has a r^2 score of: {round(score_num, 2)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 606 + }, + "colab_type": "code", + "id": "yoT2zahyAPG0", + "outputId": "98e10be5-4d6f-4e53-acfc-8f07f28a9314" + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Get feature importances from the initial numerical model.\n", + "\n", + "rf = berlin_num_pipeline.named_steps['randomforestregressor']\n", + "importances = pd.Series(rf.feature_importances_, X_train_num.columns)\n", + "\n", + "# Plot feature importances.\n", + "\n", + "n = 20\n", + "plt.figure(figsize=(10, 10))\n", + "plt.title(f'Top {n} features')\n", + "importances.sort_values()[-n:].plot.barh();\n", + "\n", + "# Based on this data I plan to drop all the data below 0.05 feature importance.\n", + "# In this case that would be bathrooms, maxium_nights and security_deposit." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "STYt0yccT8QH" + }, + "source": [ + "### Running the model on categorical data only" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 320 + }, + "colab_type": "code", + "id": "4grSm1KJhrsM", + "outputId": "faa3f31b-1977-4b12-d6f6-1a5db9d4e65d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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descriptionhost_has_profile_picneighbourhood_group_cleansedproperty_typeroom_typebed_typeamenitiesinstant_bookableis_business_travel_readycancellation_policy
id
2015Great location! 30 of 75 sq meters. This wood...tMitteGuesthouseEntire home/aptReal Bed{TV,\"Cable TV\",Wifi,Kitchen,Gym,Heating,\"Famil...ffstrict_14_with_grace_period
2695In the summertime we are spending most of our ...tPankowApartmentPrivate roomReal Bed{Wifi,Kitchen,Elevator,Heating,Washer,Essentia...ffflexible
3176This beautiful first floor apartment is situa...tPankowApartmentEntire home/aptReal Bed{Internet,Wifi,Kitchen,\"Buzzer/wireless interc...tfstrict_14_with_grace_period
3309First of all: I prefer short-notice bookings. ...tTempelhof - SchönebergApartmentPrivate roomPull-out Sofa{Internet,Wifi,\"Pets allowed\",\"Pets live on th...ffstrict_14_with_grace_period
7071Cozy and large room in the beautiful district ...tPankowApartmentPrivate roomReal Bed{Wifi,Heating,\"Family/kid friendly\",Essentials...ffmoderate
\n", + "
" + ], + "text/plain": [ + " description host_has_profile_pic \\\n", + "id \n", + "2015 Great location! 30 of 75 sq meters. This wood... t \n", + "2695 In the summertime we are spending most of our ... t \n", + "3176 This beautiful first floor apartment is situa... t \n", + "3309 First of all: I prefer short-notice bookings. ... t \n", + "7071 Cozy and large room in the beautiful district ... t \n", + "\n", + " neighbourhood_group_cleansed property_type room_type \\\n", + "id \n", + "2015 Mitte Guesthouse Entire home/apt \n", + "2695 Pankow Apartment Private room \n", + "3176 Pankow Apartment Entire home/apt \n", + "3309 Tempelhof - Schöneberg Apartment Private room \n", + "7071 Pankow Apartment Private room \n", + "\n", + " bed_type amenities \\\n", + "id \n", + "2015 Real Bed {TV,\"Cable TV\",Wifi,Kitchen,Gym,Heating,\"Famil... \n", + "2695 Real Bed {Wifi,Kitchen,Elevator,Heating,Washer,Essentia... \n", + "3176 Real Bed {Internet,Wifi,Kitchen,\"Buzzer/wireless interc... \n", + "3309 Pull-out Sofa {Internet,Wifi,\"Pets allowed\",\"Pets live on th... \n", + "7071 Real Bed {Wifi,Heating,\"Family/kid friendly\",Essentials... \n", + "\n", + " instant_bookable is_business_travel_ready cancellation_policy \n", + "id \n", + "2015 f f strict_14_with_grace_period \n", + "2695 f f flexible \n", + "3176 t f strict_14_with_grace_period \n", + "3309 f f strict_14_with_grace_period \n", + "7071 f f moderate " + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "berlin_non_numerical.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "876dFwIBLYAw" + }, + "outputs": [], + "source": [ + "# Dropping these text features as I'm unable to process them under the current deadline.\n", + "\n", + "berlin_non_numerical = berlin_non_numerical.drop(columns=['description', 'amenities'])" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "q-aA0KzAJM0j" + }, + "outputs": [], + "source": [ + "# Clean up property_type column\n", + "\n", + "berlin_non_numerical = berlin_non_numerical.replace(['Boutique hotel', 'Hotel',\n", + " 'Bungalow', 'Boat', 'Tiny house',\n", + " 'Houseboat', 'Camper/RV', 'Villa',\n", + " 'Aparthotel', 'Pension (South Korea)',\n", + " 'Cabin', 'Cottage', 'Resort',\n", + " 'Train', 'Casa particular (Cuba)',\n", + " 'Castle', 'Island', 'In-law', 'Chalet',\n", + " 'Tipi', 'Cave', 'Barn'], 'Other')" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KZgLuRMdKfiw" + }, + "outputs": [], + "source": [ + "# Clean up bed_type column\n", + "\n", + "berlin_non_numerical = berlin_non_numerical.replace(['Pull-out Sofa', 'Futon', 'Couch', 'Airbed'], 'Sofa\\Other')" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "wzMEvIrtG3Zs" + }, + "outputs": [], + "source": [ + "# Clean up cancellation_policy column\n", + "\n", + "berlin_non_numerical = berlin_non_numerical.replace(['strict_14_with_grace_period', 'super_strict_30', 'super_strict_60'], 'strict')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "colab_type": "code", + "id": "yau6jEfBym_7", + "outputId": "7704b803-a1da-4a0a-d21c-fda5f027b43a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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host_has_profile_picneighbourhood_group_cleansedproperty_typeroom_typebed_typeinstant_bookableis_business_travel_readycancellation_policy
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2015tMitteGuesthouseEntire home/aptReal Bedffstrict
2695tPankowApartmentPrivate roomReal Bedffflexible
3176tPankowApartmentEntire home/aptReal Bedtfstrict
3309tTempelhof - SchönebergApartmentPrivate roomSofa\\Otherffstrict
7071tPankowApartmentPrivate roomReal Bedffmoderate
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" + ], + "text/plain": [ + " host_has_profile_pic neighbourhood_group_cleansed property_type \\\n", + "id \n", + "2015 t Mitte Guesthouse \n", + "2695 t Pankow Apartment \n", + "3176 t Pankow Apartment \n", + "3309 t Tempelhof - Schöneberg Apartment \n", + "7071 t Pankow Apartment \n", + "\n", + " room_type bed_type instant_bookable is_business_travel_ready \\\n", + "id \n", + "2015 Entire home/apt Real Bed f f \n", + "2695 Private room Real Bed f f \n", + "3176 Entire home/apt Real Bed t f \n", + "3309 Private room Sofa\\Other f f \n", + "7071 Private room Real Bed f f \n", + "\n", + " cancellation_policy \n", + "id \n", + "2015 strict \n", + "2695 flexible \n", + "3176 strict \n", + "3309 strict \n", + "7071 moderate " + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking the cleaned non numerical dataframe.\n", + "\n", + "berlin_non_numerical.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 289 + }, + "colab_type": "code", + "id": "4LS07GM9Da-H", + "outputId": "284a3d39-0766-4658-db52-458da643cdfc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36 columns\n" + ] + }, + { + "data": { + "text/html": [ + "
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host_has_profile_pic_thost_has_profile_pic_fneighbourhood_group_cleansed_Mitteneighbourhood_group_cleansed_Pankowneighbourhood_group_cleansed_Tempelhof - Schönebergneighbourhood_group_cleansed_Friedrichshain-Kreuzbergneighbourhood_group_cleansed_Neuköllnneighbourhood_group_cleansed_Charlottenburg-Wilm.neighbourhood_group_cleansed_Treptow - Köpenickneighbourhood_group_cleansed_Steglitz - Zehlendorfneighbourhood_group_cleansed_Reinickendorfneighbourhood_group_cleansed_Lichtenbergneighbourhood_group_cleansed_Marzahn - Hellersdorfneighbourhood_group_cleansed_Spandauproperty_type_Guesthouseproperty_type_Apartmentproperty_type_Condominiumproperty_type_Loftproperty_type_Houseproperty_type_Serviced apartmentproperty_type_Townhouseproperty_type_Otherproperty_type_Bed and breakfastproperty_type_Guest suiteproperty_type_Hostelroom_type_Entire home/aptroom_type_Private roomroom_type_Shared roombed_type_Real Bedbed_type_Sofa\\Otherinstant_bookable_finstant_bookable_tis_business_travel_ready_fcancellation_policy_strictcancellation_policy_flexiblecancellation_policy_moderate
id
2015101000000000001000000000010010101100
2695100100000000000100000000001010101010
3176100100000000000100000000010010011100
3309100010000000000100000000001001101100
7071100100000000000100000000001010101001
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" + ], + "text/plain": [ + " host_has_profile_pic_t host_has_profile_pic_f \\\n", + "id \n", + "2015 1 0 \n", + "2695 1 0 \n", + "3176 1 0 \n", + "3309 1 0 \n", + "7071 1 0 \n", + "\n", + " neighbourhood_group_cleansed_Mitte neighbourhood_group_cleansed_Pankow \\\n", + "id \n", + "2015 1 0 \n", + "2695 0 1 \n", + "3176 0 1 \n", + "3309 0 0 \n", + "7071 0 1 \n", + "\n", + " neighbourhood_group_cleansed_Tempelhof - Schöneberg \\\n", + "id \n", + "2015 0 \n", + "2695 0 \n", + "3176 0 \n", + "3309 1 \n", + "7071 0 \n", + "\n", + " neighbourhood_group_cleansed_Friedrichshain-Kreuzberg \\\n", + "id \n", + "2015 0 \n", + "2695 0 \n", + "3176 0 \n", + "3309 0 \n", + "7071 0 \n", + "\n", + " neighbourhood_group_cleansed_Neukölln \\\n", + "id \n", + "2015 0 \n", + "2695 0 \n", + "3176 0 \n", + "3309 0 \n", + "7071 0 \n", + "\n", + " neighbourhood_group_cleansed_Charlottenburg-Wilm. \\\n", + "id \n", + "2015 0 \n", + "2695 0 \n", + "3176 0 \n", + "3309 0 \n", + "7071 0 \n", + "\n", + " neighbourhood_group_cleansed_Treptow - Köpenick \\\n", + "id \n", + "2015 0 \n", + "2695 0 \n", + "3176 0 \n", + "3309 0 \n", + "7071 0 \n", + "\n", + " neighbourhood_group_cleansed_Steglitz - Zehlendorf \\\n", + "id \n", + "2015 0 \n", + "2695 0 \n", + "3176 0 \n", + "3309 0 \n", + "7071 0 \n", + "\n", + " neighbourhood_group_cleansed_Reinickendorf \\\n", + "id \n", + "2015 0 \n", + "2695 0 \n", + "3176 0 \n", + "3309 0 \n", + "7071 0 \n", + "\n", + " neighbourhood_group_cleansed_Lichtenberg \\\n", + "id \n", + "2015 0 \n", + "2695 0 \n", + "3176 0 \n", + "3309 0 \n", + "7071 0 \n", + "\n", + " neighbourhood_group_cleansed_Marzahn - Hellersdorf \\\n", + "id \n", + "2015 0 \n", + "2695 0 \n", + "3176 0 \n", + "3309 0 \n", + "7071 0 \n", + "\n", + " neighbourhood_group_cleansed_Spandau property_type_Guesthouse \\\n", + "id \n", + "2015 0 1 \n", + "2695 0 0 \n", + "3176 0 0 \n", + "3309 0 0 \n", + "7071 0 0 \n", + "\n", + " property_type_Apartment property_type_Condominium property_type_Loft \\\n", + "id \n", + "2015 0 0 0 \n", + "2695 1 0 0 \n", + "3176 1 0 0 \n", + "3309 1 0 0 \n", + "7071 1 0 0 \n", + "\n", + " property_type_House property_type_Serviced apartment \\\n", + "id \n", + "2015 0 0 \n", + "2695 0 0 \n", + "3176 0 0 \n", + "3309 0 0 \n", + "7071 0 0 \n", + "\n", + " property_type_Townhouse property_type_Other \\\n", + "id \n", + "2015 0 0 \n", + "2695 0 0 \n", + "3176 0 0 \n", + "3309 0 0 \n", + "7071 0 0 \n", + "\n", + " property_type_Bed and breakfast property_type_Guest suite \\\n", + "id \n", + "2015 0 0 \n", + "2695 0 0 \n", + "3176 0 0 \n", + "3309 0 0 \n", + "7071 0 0 \n", + "\n", + " property_type_Hostel room_type_Entire home/apt room_type_Private room \\\n", + "id \n", + "2015 0 1 0 \n", + "2695 0 0 1 \n", + "3176 0 1 0 \n", + "3309 0 0 1 \n", + "7071 0 0 1 \n", + "\n", + " room_type_Shared room bed_type_Real Bed bed_type_Sofa\\Other \\\n", + "id \n", + "2015 0 1 0 \n", + "2695 0 1 0 \n", + "3176 0 1 0 \n", + "3309 0 0 1 \n", + "7071 0 1 0 \n", + "\n", + " instant_bookable_f instant_bookable_t is_business_travel_ready_f \\\n", + "id \n", + "2015 1 0 1 \n", + "2695 1 0 1 \n", + "3176 0 1 1 \n", + "3309 1 0 1 \n", + "7071 1 0 1 \n", + "\n", + " cancellation_policy_strict cancellation_policy_flexible \\\n", + "id \n", + "2015 1 0 \n", + "2695 0 1 \n", + "3176 1 0 \n", + "3309 1 0 \n", + "7071 0 0 \n", + "\n", + " cancellation_policy_moderate \n", + "id \n", + "2015 0 \n", + "2695 0 \n", + "3176 0 \n", + "3309 0 \n", + "7071 1 " + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Encoding the categorical values. \n", + "\n", + "encoder = ce.OneHotEncoder(use_cat_names=True)\n", + "\n", + "encoded = encoder.fit_transform(berlin_non_numerical)\n", + "print(f'{len(encoded.columns)} columns')\n", + "encoded.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "LjeID0A5He71" + }, + "outputs": [], + "source": [ + "# Splitting the data into train, test groups.\n", + "\n", + "X_train_encoded, X_test_encoded = train_test_split(encoded, train_size=0.8, test_size=0.2, random_state=42)\n", + "\n", + "y_train_encoded = y_train_num\n", + "y_test_encoded = y_test_num" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "gwMHTrvhHe7_", + "outputId": "101f12dd-543d-46eb-9ae1-1f4631210b7c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Encoded model based on categorical data has r^2 score: 0.28\n" + ] + } + ], + "source": [ + "# Training the model and getting the initial score.\n", + "\n", + "encoded_pipeline = make_pipeline(\n", + " SimpleImputer(),\n", + " RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)\n", + ")\n", + "\n", + "encoded_pipeline.fit(X_train_encoded, y_train_encoded)\n", + "\n", + "y_pred_encoded = encoded_pipeline.predict(X_test_encoded)\n", + "score_encoded = r2_score(y_test_encoded, y_pred_encoded)\n", + "print(f'Encoded model based on categorical data has r^2 score: {round(score_encoded, 2)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 606 + }, + "colab_type": "code", + "id": "wh2v8Ck7IuuS", + "outputId": "8eedd174-48d5-4f6f-a4e3-d929f12c3246" + }, + "outputs": [ + { + "data": { + "image/png": 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6zhHdYu0eM5TEqyeu7b63xnsu8KtezuvedkRERMSQlA88MVBWk3QNcDlwIKUi1OUkYFyt9vyQstzrhbrvCEkzgTWAM+t/G9RKzyzKMrEF/KPfAWMlHVbfn01Jwq7sI8ZZlHtwVrF9M6XK8t3G/vmUZOVmYJrtOyiJzDE1nvOAO2mhJkJ7Ahd1basJ3B2U+51ajfUy4NC67TDgBUmv6Nb0I8Dykr7Sx9io8V5DWSr4C8p9XQ/2csrfr0dfbUdEREQMZiM6O3v7ojmi/9UHN2xge/IinvdAPe/Zl9n/vwEb2T56Ec5ZBvgZsJPtJ+u9Pxfa3vLlxNJLfw/QD2MdCLsePm3Q/1GZOnncQIfQb4bDUonBJPPRXjIf7Sdz0l6Gw3x0dIwe0Wp77kmKYUXSSZQq0vj6fjfgsy0OPc32pfWYdYBLgTNtP7kYfV5Cebpc0xO2xy9qWxERERGx5KWSFBH9KpWk9jIcvgUcTDIf7SXz0X4yJ+1lOMxHKkkRsVRcdur4If8HNSIiIoa2PLghIiIiIiKiIUlSREREREREQ5KkiIiIiIiIhiRJERERERERDUmSIiIiIiIiGpIkRURERERENCRJioiIiIiIaEiSFBERERER0ZAkKSIiIiIioiFJUkREREREREOSpIiIiIiIiIYkSREREREREQ3LDXQAETG07Hr4tIEOYZFMnTxuoEOIiIiINpNKUkREREREREOSpIiIiIiIiIYkSREREREREQ25JyliMUhaBdjZ9gWLeN4ewM22/7xkIgNJmwAnAysAywPXAcfZni9pLLCy7RmSHgA2sP3skoolIiIiYjBKJSli8WwM7LYY5x0KrNTPsfydpNcD3wMOtb0dsDXwHPCNesi/AGOWVP8RERERQ0EqSTEkSJoAjKckIKsCxwPHAb+mJAmfBs6v+5cDjrJ9raR7gBuAtwKPAXsB84FvAetTvkg4yvZ0SXc12lsV2ETSRODzwOa2H5P0aWBF219rEeMHgE2B8yR9G1jf9n9IWha4HfhwjfEh4I3AFbaPlLQmcBYwCngWmGh7dg+X4mPAVNu/BrDdKekE4PeS1gAmAPMl3VaPnyJpnfp6D+CpvsZue69eJyMiIiJikEuSFEPJisAOQAdwC7AscILtX0o6BbjK9mk1WZgpaT3glcB36/KzrwIHAM8Aj9reV9JrgRmUJGrFRnvbAZNsn1Xb2xM4g5Kk7NEqONuXS7odmAQ8CNwmaTKwM2VJ3HPA2sBOwBM1xrcDRwCn275C0vaUpXT79HAN1gWu6tZvp6Q5wEjgXGCO7VskAZxje6akc+u1e21fY+91Bgahjo7RAx3CEjccxjiYZD7aS+aj/WRO2stwnY8kSTGUXG97AfCwpMeBDQHXfRsC3wWw/aCkJynJ1PO2Z9RjZgG7AC8C20raom5friYMNNprOge4SNIMSgLycF+B2p4n6XpKQvRJSuUL4A7bjwFIuhkQMBb4gqQjgBGUSldPHqQkWn9XK1WrA63iurX+nENJGMeyaGMf9ObOnTfQISxRHR2jh/wYB5PMR3vJfLSfzEl7GQ7z0VMSmHuSYijZDP5+X85KwCPAgrrvXmDbun8NYGXgL8DI+qADKPfv3A3cB3yv3tOzC/AD4PF6zILGz2UAbP8R+CtwJCVh6s3fzwPOBvYDXmf7zrptQ0mvrInNFsA9NZ4jajwHABf30v53gImS1q9jHQEcA/zY9jPd+gfo7Hb+wow9IiIiYkhLkhRDyWqSrgEuBw6kVIS6nASMq9WeH1Lu63mh7jtC0kxgDeDM+t8GtdIzC/hDrVA1/Q4YK+mw+v5sShJ2ZR8xzqLck7SK7ZuBN1MrXNV8SmJyMzDN9h3A54BjajznAXfSA9t/oiz5O6OO6WbKvUxdcd4KHCzpvT00sTBjj4iIiBjSRnR2dv8iOWLwqQ9u2MD25EU87wH64THYkv4N2Mj20YtwzjLAz4CdbD8paW3gQttbvpxYBtquh08bVH9Upk4eN9AhLFHDYanEYJL5aC+Zj/aTOWkvw2E+OjpGj2i1PfckRbxMkk6iVJHG1/e7AZ9tcehpti+tx6wDXAqcafvJxejzEmCVbpufsD1+UduKiIiIiH+USlJE9KtUktrLcPgWcDDJfLSXzEf7yZy0l+EwH6kkRcRScdmp44f8H9SIiIgY2vLghoiIiIiIiIYkSREREREREQ1JkiIiIiIiIhqSJEVERERERDQkSYqIiIiIiGhIkhQREREREdGQJCkiIiIiIqIhSVJERERERERDkqSIiIiIiIiGJEkRERERERENSZIiIiIiIiIakiRFREREREQ0JEmKiIiIiIhoWG6gA4iIoWXXw6cNdAj/ZOrkcQMdQkRERAwiqSRFREREREQ0JEmKiIiIiIhoSJIUQ5qkcyXtLGmCpJN7OW4VSXvX15Mlbb70ovx7DBMknSxpNUlnLMF+Jkoa2WL7hZKW7+GcN0nadUnFFBEREdFOck9SRLExsBtwge0ek6mlwfYc4MAl2MUXgPOA57v1u2cv54wDNgAuW4JxRURERLSFJEkxICStAPwvsBYwEjgcOAh4DbAqcLbtKZKmA7cDGwErAf9q+w+SjgJ2p/wOT7F9pqRDgL2BTuBC26f30PeXgXcAo4F7bX8SOBLYRNJEYCvgQuAaYCqwHrAs8HXbF/UUUw99HUtJLl4HrAwcYnumpH2Aw4DngN8AExvnrF3j31LSB4Fj6q5fAqcA59vevB57EXCK7Z+36LsDuIhSMR4JTAI2B1YDLpT0X8BXgPnAWcAJNdY1gW8DywNP12s6GXilpFm2f9RqrBERERFDRZKkGCiTgAds7ylpI2BHSmJwiaTVgeuBKfXYW2wfJulEYC9JPwF2AbYAXgF8WdJbgY8A21CSpKvrcf9A0krA47Z3kLQMcLekNYATgUm2z5K0VT38AOBR2x+TNBq4TdI1rWICeqs+PW17XI3xAknjgOOAt9meJ+kbta+nusW6HPBNYHPbj0g6GngWeEbSGGAOsE6rBKnaHHiCkuSMAVayfY6kLwJ7AlsCo2xvUfs7oZ53CvBl21dK+jdgkzq+DQZrgtTRMXqgQxhQw3387Sbz0V4yH+0nc9Jehut8JEmKgSLgCgDbd0l6DDhZ0oeAJymVjy6/rD9nU6ogoiQpL1IqHYfWD/NrUao/UKo2b27R7zPA6yR9j5KUrNitr6YNgatrjPMk3UOpKrWKqTfX1jbulrQasC5wt+15df8MSpJ4c7fzVqUkdI/U848HkHQ2MAH4I3B+L/1eAawPTKMsrftSi2PcYpuAG2uf3699TuhtgO1u7tx5fR80RHV0jB7W4283mY/2kvloP5mT9jIc5qOnJDAPboiBci/wTgBJ61IqJjfa/ijwA2BE49jObufeB7xd0jKSRkq6ivJh/27gvba3A84FftWi312ANW3vRbk3Z4Xa1wL++d/DvcC2NcbRwFjg/h5i6s1mtY2NgAdrG2Mkvarufw/w6xbnPQK8RtIq9fzT6wMlLqYkVXvQe5K0HfCQ7R0pCdJJdXtzrAtanNecm33qMsZW1yciIiJiSMqHnhgoZwLrSrqe8hCBGykVoZmUe3VekPSKVifavh24EvgZMBP4ru07KFWkmZJ+QamgPNji9FtqvzdRko3fA6sDvwPGSjqscexZwGtrTNOB47qqOovobXWZ3reB/W0/SrnP6Loax6q8tLSwOc4FlAc4XF5jGAH83PazlOrTI7Yf66XfO4D9Jd0IfA34ct1+A/Bj/jERbfoP4D/rvVf7AN+lJJzjJfX2cIeIiIiIIWFEZ+eifCEeEYuiPrhhju1v9XO7ZwAX2762P9vtD7sePq3t/qhMnTxuoEMYMMNhqcRgkvloL5mP9pM5aS/DYT46Oka3/NI49yRF9ANJlwCrdNv8BC/du9Sfff0U+HNXglQf6NAqC/ik7ftbbI+IiIiIXiRJiugHtj+0FPvasdv744Hjl1b/EREREUNdkqSI6FeXnTp+yJfmIyIiYmjLgxsiIiIiIiIakiRFREREREQ0JEmKiIiIiIhoSJIUERERERHRkCQpIiIiIiKiIUlSREREREREQ5KkiIiIiIiIhiRJERERERERDUmSIiIiIiIiGpIkRURERERENCRJioiIiIiIaEiSFBERERER0ZAkKSIiIiIiomG5gQ4gIoaWXQ+fNtAhADB18riBDiEiIiIGqVSSIiIiIiIiGpIkRURERERENCRJGoQkbSrp6F72T5B0covtD0ga1Y9xTJe0QT+2d6Gk7fqrvSWtp+u8lGOYJOnYXvZPkNQpaYvGtpGSHpV0rKTVJJ1Rt79b0sb19R6SVl/iA4iIiIhoQ7knaRCyfTtw+0DHEYPGfcBewM31/c7AEwC25wAH1u2fAi4E7gQOBSYBf16qkUZERES0gSRJbUTSBOD9wCuB9YCvALcCpwMjgL9QPsi+DZhke09J+wIHA48B84GLanNbSvop0AFMsX1W3X6mpLWBh4FPAC8AU2t/ywJft32RpOm1j/skTQJWA84FLqtx/Li2d4yk1wOvAvay/XtJpwLb1P0X2D6t9nkOMBLoBD5j+w5JBwH7AQ8Br+vj+mwO/A8wD3gEeBY4tltMVwH/DbxY9+9PqZheaHvL2s5NwJ7ABGCD2u/KwCG2Z/bQ9wrA/wJr1TEc0m3/IcDedWwX2j5d0kbA12v/r6ljniXpN8DPANV5+Jd6/c8FnqfMycdtPyjpy8C7axtft/0DSdsAp1Hm/EXgpt6uG3AFsJOkZWwvoCRM36txr01JjA6iJE9vr/O5KXBe7euA7mPro7+IiIiIQS3L7drPq21/ENgNmAycDRxkeztKEvD5rgMlrQocAWwN7EhJVLo8D+wE7AEc1tg+xfZ7gAcoCcQBwKO2twLeB3ypttuT1YAdbX+1vr/c9jjKB/EPS/ogsA6wJSVR2lvSWOAU4HTb76ZUKc6R9Or6ektgPLB8H9fmW8CE2t/veojpbODgOsYzKElKb56u7X2UkoD1ZBLwgO13UZKr5vK1McBH6ni3AXaXJOCtwOG231fj+GQ9ZV3gi7WtDuCdwA6UhPh9wInAypJ2AdaxvTXwXuBISa8BvkFJSHcA7u9jfFCS5xuB90gaDawE/Kl5gO1bgSuBz9v+DqVS+XHgzT2MLSIiImLISiWp/XQto5sNjAI2BM6on0tHAr9uHPtm4B7bTwNImtXYd5vtTklzKJUpgPm2u6oOsygfzAGuBrA9T9I9lKpG04jG6/ttz2+8v7X+nENJVjYEbrDdCTxfqzZj6vYZtZ/bJa1JqeLcbfu5Gv8tvV4ZWN323fX1DZRqUPeYVq/LEan9tbpnqDmea2tMd0tarZe+RUkEsX0XcFet/AFsRKkwXVPfr0yZmweBL0p6BhgNPFn3P2p7dn3dNc/nUBLeKylL4b4AjAU2q1U9KPO/FrCG7a7fg5/VvvpyAaWC9CbgEvpOSLv0NDYv5PkDpqNj9ECH0DZyLdpL5qO9ZD7aT+akvQzX+UiS1H46u703ZenVHyVtDbyhse+3wAZ1KdhzwOaU+09atQOwvKRNaxKxLXAXZYndtsCltcowllKdeLb2dR/wdsoHfoAFfcR7L6Vi8g1JI4GtgO/U7dsCP5K0KSWp+j0wpsY/n7KM8PyeLgwwW9IY2/dQqk9dmjH9WdLGtu8E3kNJKp8FXidpWUqysk7j+M2A8+vSuAfp2b2Uis80SesCXwJ+WvcZuBvYpSam/w78CvghsI/teyUdB6xdj281N+MpyeVxkvaiJEyXAtfZnihpGeCLlGs2R9KGtrtieryXuLtMB/4LWJ2ydG7vFscs4KXqctfrnsbW9ubOnTfQIbSFjo7RuRZtJPPRXjIf7Sdz0l6Gw3z0lARmuV37+zTl3pAbKFWRO7t22H6Uct/SDZQKxAqUZXY9eQ44RNIMyn043wbOAl4raSblg/Rxth+h3Af1P5J+QkmkFort/wPul3Qj5V6Zi23fBnyu0fcUYF/bc4GjKVWtK4C/9dH8gcBUSVdTEsJWY90f+Ga9XocC/14fTnAV8PM63t82jn+bpGvqtdi/l77PBNaVdD1wHo1lfLbvoFRaZkr6BbA+JeE6n5JU3QC8hZKg9OQXwIn12EmU+6ouA56q224FOm3Po3LwkeIAACAASURBVCwN/E6Ne61e2vy7ei/SVZTlhU/2cNjNwMmSNqTMyXmUSlersUVEREQMWSM6O1t9qR2DgaTlgCNsn1jfzwCOsj1jYCNbMupDHr5ve66kL1GWDx7/Mto7Fphj+1v9FWPArodPa4s/KlMnjxvoENrCcPgWcDDJfLSXzEf7yZy0l+EwHx0do0e02p7ldoOY7RckvUrSbZTlajdTqkqDlqQ3USoY3V1PWeb1U0lPUe7b+cQS6P8Myj1U3e1i+5n+7q+/SLoEWKXb5idsjx+IeCIiIiIGs1SSIqJfpZLUXobDt4CDSeajvWQ+2k/mpL0Mh/lIJSkilorLTh0/5P+gRkRExNCWBzdEREREREQ0JEmKiIiIiIhoSJIUERERERHRkCQpIiIiIiKiIUlSREREREREQ5KkiIiIiIiIhiRJERERERERDUmSIiIiIiIiGpIkRURERERENCRJioiIiIiIaEiSFBERERER0ZAkKSIiIiIioiFJUkRERERERMNyAx1ARAwtux4+ban2N3XyuKXaX0RERAx9qSRFREREREQ0JEmKiIiIiIhoSJIUERERERHRkCQphgVJO0uauAjHj5K032L2dXAf+6dL2mBx2m60MWchj5sg6eQW2x+QNGoR+3yNpBsl/XRRzouIiIgYbJIkxbBg+0rbZy3CKasBi5UkAUct5nntbiPgz7Z3HOhAIiIiIpakPN0uhgVJE4CdgbWA2cB6wC22Py1pa+BU4HngcWAf4EhgjKSjganAFGAU8FrgeNs/lHQncD2wMdAJjAcOBlaRdIbtA3sJ6XhJqwLPAR+3PVfSqcA2df8Ftk+TtDZwDjCy9vEZ23c0xnUS8Ora70HAh+qxT9TXAO+SdA2wEnCs7csb568JnFXH9iww0fbsFtdveeCbwOqSjrN9TC9ji4iIiBjUkiTFcPMWYEfgaeD3klYDdgcuAU4BdgNWBk4Exto+XtL7gFNtT5e0FXAc8ENK0vE924dI+i6wi+0TJR3SR4IEcIntCyUdCPynpGuBdYAtKf8uZ9ZtxwCn254maVNKwvQOAEmnAAtsHyRpGUoC9z7bCyT9BHhn7etvwAeADuBmSVc04jiltn+FpO2BkylJ4j+wPV/SYcCkdkuQOjpGD3QIbS/XqL1kPtpL5qP9ZE7ay3CdjyRJMdz81vY8AEkPUSooJ1EqR9cADwI3A69onPMQcJSkfSnVnJGNfb+sP2fXthbWjPpzFiWBeQi4wXYn8Lykm4AxwIZdx9q+vVZ+AF5PqWD9tu5bIGk+8D1JTwFvbMQ5s7b7iKQnKMlUl7HAFyQdAYwA5i/CGNrC3LnzBjqEttbRMTrXqI1kPtpL5qP9ZE7ay3CYj56SwNyTFMNNZ4tt+wDn2n4vcDcwEVjAS/8+TgDOs/0x4DpKMtFbeyNabOtu8/pzW+Au4F7qUjtJI4GtgN/U7dvW7ZsCXQ9seBjYCXhrfSjFxsDutj8CHFJj74rjnfX81YAVgUcbcdwHHGF7O+AA4OKFiD0iIiJiSEuSFAE/B74j6XpgHHAe8AiwvKSvAD8ATpd0A7ADsGof7d0j6fw+jtld0vTa3sm2/w+4X9KNwE3AxbZvAz4HHCJpBuW+qH27GqjVoU9R7hX6C/A3Sb8ArqJUplavh65Ql+79CDigntflc8AxdeznAXf2EXdERETEkDeis7PVF+EREYtn18OnLdU/KlMnj1ua3Q06w2GpxGCS+WgvmY/2kzlpL8NhPjo6RrdcAZR7kiKWAElvolRmuru+3R580J2kS4BVum1+wvb4gYgnIiIiYmlLJSki+lvnUP/WaTAZDt8CDiaZj/aS+Wg/mZP2Mhzmo6dKUu5JioiIiIiIaEiSFBERERER0ZAkKSIiIiIioiFJUkREREREREOSpIiIiIiIiIYkSREREREREQ1JkiIiIiIiIhqSJEVERERERDQkSYqIiIiIiGhIkhQREREREdGQJCkiIiIiIqIhSVJERERERERDkqSIiIiIiIiG5QY6gIgYWnY9fNpS6Wfq5HFLpZ+IiIgYflJJioiIiIiIaEiSFBERERER0ZAkKSIiIiIioiFJUgwLknaWNHERjh8lab/F7OvgPvZPl7TB4rTdaGPOQh43QdLJLbY/IGnUIva5iqS9F+WciIiIiMEoSVIMC7avtH3WIpyyGrBYSRJw1GKe1+42BnYb6CAiIiIilrQ83S6GBUkTgJ2BtYDZwHrALbY/LWlr4FTgeeBxYB/gSGCMpKOBqcAUYBTwWuB42z+UdCdwPSV56ATGAwcDq0g6w/aBvYR0vKRVgeeAj9ueK+lUYJu6/wLbp0laGzgHGFn7+IztOxrjOgl4de33IOBD9dgn6muAd0m6BlgJONb25Y3z1wTOqmN7Fphoe3YPMR8JbCJp4iImnBERERGDSpKkGG7eAuwIPA38XtJqwO7AJcAplErJysCJwFjbx0t6H3Cq7emStgKOA35ISTq+Z/sQSd8FdrF9oqRD+kiQAC6xfaGkA4H/lHQtsA6wJeXf5cy67RjgdNvTJG1KSZjeASDpFGCB7YMkLUNJ4N5ne4GknwDvrH39DfgA0AHcLOmKRhyn1PavkLQ9cDIlSWzlRGBSuyRIHR2jBzqEQSPXqr1kPtpL5qP9ZE7ay3CdjyRJMdz81vY8AEkPUSooJ1GqJNcADwI3A69onPMQcJSkfSnVnJGNfb+sP2fXthbWjPpzFiWBeQi4wXYn8Lykm4AxwIZdx9q+vVZ+AF5PqWD9tu5bIGk+8D1JTwFvbMQ5s7b7iKQnKMlUl7HAFyQdAYwA5i/CGAbU3LnzBjqEQaGjY3SuVRvJfLSXzEf7yZy0l+EwHz0lgbknKYabzhbb9gHOtf1e4G5gIrCAl/59nACcZ/tjwHWUZKK39ka02Nbd5vXntsBdwL3UpXaSRgJbAb+p27et2zcFuh7Y8DCwE/DW+lCKjYHdbX8EOKTG3hXHO+v5qwErAo824rgPOML2dsABwMW9xNy8JhERERFDVj7wRMDPge9Iuh4YB5wHPAIsL+krwA+A0yXdAOwArNpHe/dIOr+PY3aXNL22d7Lt/wPul3QjcBNwse3bgM8Bh0iaQbkvat+uBmp16FPAN4G/AH+T9AvgKkplavV66Ap16d6PgAPqeV0+BxxTx34ecGcvMf8OGCvpsD7GFhERETGojejsbPVFeETE4tn18GlL5Y/K1MnjlkY3g95wWCoxmGQ+2kvmo/1kTtrLcJiPjo7RLVcA5Z6kiCVA0psolZnurrd9zNKOZ1FIugRYpdvmJ2yPH4h4IiIiIpa2JEkRS4DtPwLbDXQci8P2h/o+KiIiImLoSpIUEf3qslPHD/nSfERERAxteXBDREREREREQ5KkiIiIiIiIhiRJERERERERDUmSIiIiIiIiGpIkRURERERENCRJioiIiIiIaEiSFBERERER0ZAkKSIiIiIioiFJUkREREREREOSpIiIiIiIiIYkSREREREREQ1JkiIiIiIiIhqWG+gAImJo2fXwaUu0/amTxy3R9iMiIiJSSYqIiIiIiGhIkhQREREREdGQJCkiIiIiIqKhrZIkSZtKOrqX/RMkndxi+wOSRvVjHNMlbdCP7V0oabv+am9J6+k6L+UYJkk6to9jtpF0VZ2vn0s6sG5f7Pj7+l2StIek1evriZJGLmL7a0u6aXFiW4i2XyFpjqRl6/t3SVog6R31/ShJD0paXdIZdVu//tuJiIiIGAraKkmyfbvt4wc6jmh/ktYF/hv4qO3tgHcDH5e08xLu+lBgpfr6C8CyS7i/hWb7OeB2YNO66f3AD4AP1PfvAmbY/rPtAwcgxIiIiIhBYak83U7SBMoHtlcC6wFfAW4FTgdGAH8BPgW8DZhke09J+wIHA48B84GLanNbSvop0AFMsX1W3X6mpLWBh4FPAC8AU2t/ywJft32RpOm1j/skTQJWA84FLqtx/Li2d4yk1wOvAvay/XtJpwLb1P0X2D6t9nkOMBLoBD5j+w5JBwH7AQ8Br+vj+mwO/A8wD3gEeBY4tltMV1GSghfr/v0pSe6Ftres7dwE7AlMADao/a4MHGJ7Zg99rwD8L7BWHcMh3fYfAuxdx3ah7dMlbQR8vfb/mjrmWZJ+A/wMUJ2Hf6nX/1zgecqcfNz2g5K+TElslqHMzQ8kbQOcRpnzF4HeKi4fA86z/TCA7Wck7QQ8Vff90++JpA8DB1F+5wA+DGxE+X2cD3T9LtFqXoE3UhKQ8ySdQ/nduRDYvYfxTKckLRtREqt/rc13SPoRZX4ut32CpHPr9b2yJnp72p4g6Q/AfcC9wDcb1/IPwNo1QWy6CtiW8u9re+CjwAXAccB2wJV1bH//vanj7Wp3LeAVdVy7Am8Cxtv+XS9zERERETGkLM1HgL/a9k6S1qd8+P8r8Cnb99SE6POUD3hIWhU4gvKB9DngukY7zwM7UT7M/ZiXPthOsX2TpK9SEogFwKO2PyZpNHCbpGt6iW81YDPb8yW9n/Lh9fy65OvDku4B1gG2pFy3mZKuBY4BTrc9TdKmwDmStqdUHMbWOG7t49p8C/iY7bslnQis0SKmXwD72b5d0nhKkvK5Xtp82vY4SW+lfEjepIfjJgEP1MR0I+B9lLlB0hjgI5TEsBO4WtJPgLcCh9v+laS9gU8Cs4B1gXG2Z0v6GfBOYLM6/s9SPryvLGljYB3bW9elXjdJugr4BiUh/bWkKX1cs9UpCcjf2X6ixg2tf0/eAnzA9tOSzqz7HwRG2d6inntCbe4Uus2r7XdIup2Xkuz/BPaUtEsP4wG4xfZhdV73oiQfK1ISuaeAG2rC1JM1gbfb/oukS4GTbP9Y0v7A2i2Ov4qS4H8P+FtN7kdI6qAkcVOAnpbXPWB7f0nfquN5v6TjKMnSf/US41LV0TF6oEMYdHLN2kvmo71kPtpP5qS9DNf5WJpJUtcH2tmUD2kbAmfUD7QjgV83jn0zcI/tpwEkzWrsu812p6Q5lMoUwHzbXVWHWcAO9fXVALbn1SRnvW4xjWi8vt/2/Mb7rsRmDiVZ2RC4wXYn8Hyt2oyp22fUfm6XtCalinN3Xf6EpFt6vTKwuu276+sbKNWg7jGtbrvrGs4AWt1z0xzPtTWmuyWt1kvfAq6ox94F3FUrf1AqIGsBXcnlypS5eRD4oqRngNHAk3X/o7Zn19dd83wOJeG9EniCskRtLLBZrbRAmf+1gDVsd/0e/Kz21ZM/UBKIlwYibdK4Bq1+Tx4BviPpKcoc3Vi3u0X7rea1Jz2NB+CX9edsyu8RwB2NhO4WSvLW1JzHR23/pRFT17+FG4B9avXtS3Xb1ygJ4frAzpRrDvATShVplO05tZLUym31518p1SuAx+k5qRoQc+fOG+gQBpWOjtG5Zm0k89FeMh/tJ3PSXobDfPSUBC7Ne5I6u703ZenVdpQq0uWNfb8FNpC0gqRlgM17aQdg+fptP5RqxV2U5UnbAtRK0ljgfspStTfUY9/eaGNBH/HeS11qV2/W3wr4Tbd+NqUkVb8HxtT4l6UsI+zN7Fq1gVKpahXTn2sFBuA9lKTyWeB1kpaV9BpKpavLZjWmjShJTU/upVR8kLSupAsa+wzcDby3ztO5wK8oyySPsf2J+r7rQ32ruRlPSS63p9wfcwTlA/h1tc1xwPcp12yOpA3ree/sJWYo1bH9aoUESSsCZ1IqTP8Ui6RXU5ac7UlZBvlMI+7ucw+t57Xr2GW6ve5pPP8UR7WhpBUlLQdsQbnGC/N7eRflviKovye2Z9rerv53eU3i76xjvKIeewWlsjm9RSxNrWKNiIiIGHYG8sENn6bc23EDpSpyZ9cO249S7hO5gfJt+AqU5VM9eQ44RNIMyn0e36Ysr3qtpJmUD4fH2X6E8gH/f+qysYW+6d72/wH3S7qRcq/MxbZvoyx56+p7CrCv7bnA0ZRv/a8A/tZH8wcCUyVdTUkIW411f+Cb9XodCvy77TmU5VU/r+P9beP4t9Xlhd+u5/bkTGBdSdcD51GW8XWN+Q5KFWlmXe63PiXhOh+YVmN5Cy8lJq38AjixHjuJcl/VZcBTddutQKfteZT7Z75T416rpwZrbA9QkutLagXneuA7tn/cwylPUqpTt1F+r57pI+5/mte6fRbl93aV2s6PexlPTx6j3GM3i/J7dA9lnv69/g6s0cN5RwCT6/XZjZ7/TVwFrFnbBbiFUoW6sofjF4qkn0pa/uW0ERERETEYjOjsbL8vj+s37EfYPrG+nwEcZXvGwEa2ZKg85OH7tudK+hJl+eBiP+Wv3kc1x/a3+ivGGHiS9gFutv1bSfsBW9n+1EDH1d2uh09bon9Upk4etySbH3KGw1KJwSTz0V4yH+0nc9JehsN8dHSMHtFq+9K8J2mh2X5B0qsk3UZ56tjNlG/tBy1Jb6JUarq7nrJk7af1XpknKE/n6+/+z6DcQ9XdLraf6e/++oukS4BVum1+wvb4gYhngM0GLpT0NOXpf/v2cXxERERELIa2rCRFxOCVSlJ7GQ7fAg4mmY/2kvloP5mT9jIc5mNQVZIiYvC67NTxQ/4PakRERAxtA/nghoiIiIiIiLaTJCkiIiIiIqIhSVJERERERERDkqSIiIiIiIiGJEkRERERERENSZIiIiIiIiIakiRFREREREQ0JEmKiIiIiIhoSJIUERERERHRkCQpIiIiIiKiIUlSREREREREQ5KkiIiIiIiIhuUGOoCIGFp2PXzaEml36uRxS6TdiIiIiO5SSYqIiIiIiGhIkhQREREREdGQJCkiIiIiIqIh9yTFoCXpXOBCYDVgA9uTezhuFWBn2xdImgxca/uWpRcpSJoAbAD8F3C07QP7oc3zgLcA5wJr9zT+Hs6dDFwLjKHFtZP0QN3+7MuNMyIiImKwSZIUw8HGwG7ABbZPHshAbM8BXnaCVO1k+/U1AVvUOE4GkDSmn2KJiIiIGDKSJEW/k7QC8L/AWsBI4HDgIOA1wKrA2banSJoO3A5sBKwE/KvtP0g6Ctid8vs5xfaZkg4B9gY6gQttn95D318G3gGMBu61/UngSGATSROBrSjVp2uAqcB6wLLA121f1FNMPfR1LKU69DpgZeAQ2zMl7QMcBjwH/AaY2Dhn7Rr/lpI+CBxTd/0SOAU43/bm9diLgFNs/7xF32cAK0uaBlza2P5P10nSD4CrgPOBmcC+wKH1OgC8S9I1dbzH2r680d6awFnAKOBZYKLt2a2uR0RERMRQkSQploRJwAO295S0EbAj5QP7JZJWB64HptRjb7F9mKQTgb0k/QTYBdgCeAXwZUlvBT4CbEP58H91Pe4fSFoJeNz2DpKWAe6WtAZwIjDJ9lmStqqHHwA8avtjkkYDt9VE4Z9iAnqrPj1te1yN8QJJ44DjgLfZnifpG7Wvp7rFuhzwTWBz24/8f/buPN6qsvz//wsFczrldAzxUypqb0EwjVSciRw+VoaV31IbxClJIzP8JpkDmn7SPg6JiTiRmhoOP43MIUcmURwIlelywuSHIZCmmAOo5/vHfe9cbvfeh+Ec2Oec9/Px8MHea61939e67wWPde3rXltJp5GSkLdzdWcesEWlBAkgIo6V9M2IGFCqJOXPVRqno0nJ0X7AZRHxN0nF5v4NfBVoBCZLuquw7zxgeETcJenLeSy+W2M8Wk1jY8Oq6LZd8NjVF89HffF81B/PSX3pqPPhJMlag4C7ACJimqRXgXMkfRN4g1RdKvlb/nMO6dkikZKU94G3gOMlfZtUlSolMesDW1Xo921gY0l/JCUl65b1VdQDuC/HuEjSDFJVqVJMtTyQ25guqSvQHZgeEYvy/vGkJHFy2ec2IiV08/PnzwSQdAUwEHiJVPlZFr2oME4REZKuA35G5QRnYkQ0AfMlvQ5sWNjXGzhZ0klAJ2DxMsbUYhYsWNT8QfYxjY0NHrs64vmoL56P+uM5qS8dYT6qJYH+dTtrDTOBHQEkdSdVTB6OiO8BN5Nutkuayj47C/iCpNUkdZF0LxDAdOBLEdGP9EMFT1fod3/gMxFxCHAysFbu6wM+fq3PBPbIMTaQkoHZVWKqpU9uoxcwN7fRU9I6ef9ewDMVPjcfWC//qASShkvaCbiFlFR9g2VPkiqOU56Dg4HhpMpQudJcdSUllgsL+2YBJ+X2jsnxmZmZmbVrTpKsNVwGdJc0DrgWeJhUEZpIelbnPUmfqPTBiJgK3A08RFoidn1EPEmqjkyU9DiwNSkhKfdo7vcR0s38C0A34Hmgt6SfFo69HNgwxzQWOKNU1VlGO+RlelcCR0fEQtJzRg/mODbiw6WFxfP8gPQDDnfkGDoBj+VfkxsPzI+IV5clkCrj9ApwPfAT0rLDHpIGlH10LUkPAH8GjslVpZITgdMLc/nUssRkZmZm1hZ1ampali/Nzawk/3DDvIgY2cLtjgBuiYgHWrLdleWAIWNa5R+VUUP7t0az7V5HWCrRlng+6ovno/54TupLR5iPxsaGTpW2+5kks2ZIuhXYoGzz63z47FJL9nUP8HIpQco/6FApOzg8ImZX2G5mZmZmK8hJklkzIuKbK7GvfcvenwmcubL6NzMzMzMnSWbWwm4/f0C7L82bmZlZ++YfbjAzMzMzMytwkmRmZmZmZlbgJMnMzMzMzKzASZKZmZmZmVmBkyQzMzMzM7MCJ0lmZmZmZmYFTpLMzMzMzMwKnCSZmZmZmZkVOEkyMzMzMzMrcJJkZmZmZmZW4CTJzMzMzMyswEmSmZmZmZlZgZMkMzMzMzOzgs6rOgAza18OGDJmhT4/amj/ForEzMzMbPm4kmRmZmZmZlbgJMnMzMzMzKzASZLVDUlXS/pvSQMlnVPjuA0kHZpfD5W008qL8j8xDJR0jqSukkas7P6rkTSvBdr4oaQuLRGPmZmZWVvkZ5KsLdoO+DpwQ0RUTaZWhoiYBxy7KmNoBScD1wJLVnUgZmZmZquCkyRrlqS1gN8DmwFdgCHAccB6wEbAFRFxqaSxwFSgF/BJ4P9ExN8lnQIcSLreLo2IyyQNBg4FmoDRETG8St+/Br4INAAzI+Jw4JfA5yX9ENgVGA3cD4wCtgRWBy6IiBurxVSlr2HANsDGwPrA4IiYKOm7wE+Bd4FngR8WPrN5jr+vpK8Bp+ddfwPOA66LiJ3ysTcC50XEYxX67gf8IvfxGWAk0B/4PHBRHt99gLOAd4B/AkcAi4DLgW2B54FP5PY+k7evmY//YR6X2/Nn7wQmF+JdG/gBsAfQNY/pgXn89yRVnS+IiJsrjZ2ZmZlZe+IkyZbGIODFiDhYUi9gX1JicKukbsA44NJ87KMR8VNJZwOHSPorsD+wM+kG/teStgW+A+xOSpLuy8d9hKRPAq9FxD6SVgOmS9oUOBsYFBGXS9o1H34MsDAivi+pAZgi6f5KMQG1qk9vRUT/HOMNkvoDZwA7RMQiSRfmvt4si7Uz8Dtgp4iYL+k0UnLytqSewDxgi0oJUsF/AdsDfYCbSQnfpsBtkkaSkp7dI2KupOOBU4CxwJo5SfsscFBu6zxgeETcJenL+Zx/SUqA+kTEYknHAt+LiJclnUxKIM+WdCpwsKT9c8y7SVoTeETSvRHxrxrnsMIaGxtas/kOyWNaXzwf9cXzUX88J/Wlo86HkyRbGgLuAoiIaZJeBc6R9E3gDVJ1qeRv+c85pBtykZKU94G3gOMlfZtUlSolMesDW1Xo921gY0l/JCUl65b1VdQDuC/HuEjSDFKSUSmmWh7IbUyX1BXoDkyPiEV5/3hSkji57HMbkRK6+fnzZwJIugIYCLwEXNdM39MiYomkfwHP50TmNVI1aCPgjYiYW4jjf4AFwKO5z5ckzcn7ewMnSzoJ6AQszttnR0Tp9VxguKQ3ScnYQ2Xx9Ab65GocpLHfDGjVJGnBgkXNH2RLrbGxwWNaRzwf9cXzUX88J/WlI8xHtSTQP9xgS2MmsCOApO6kisnDEfE9UsWjU+HYprLPzgK+IGk1SV0k3QsEMB34UkT0A64Gnq7Q7/7AZyLiENJzMmvlvj7g49fuTNJSMXIlqTcwu0pMtfTJbfQiJRGzgZ6S1sn79wKeqfC5+cB6kjbInx+ef1DiFlJS9Q2aT5JqxbkQ+KSkTcrimAXskvvsRkp2yNtPyuN7TI4D0tiVXAkcHhEDgZf5cB5L4zsLeDC30R+4CXihmXMwMzMza/OcJNnSuAzoLmkc6YH+h0kVoYmkZ3Xek/SJSh+MiKnA3aQqxUTg+oh4klRFmijpcWBrUkJS7tHc7yOkm/wXgG6kZ296S/pp4djLgQ1zTGOBM0pVnWW0Q16mdyVwdEQsJD2382COYyM+XFpYPM8PSD/gcEeOoRPwWES8Q6r6zI+IV5cjnlL7TcDRwK2SHgL2Bn4VEWOAOZImA78lJVMAJwKnF+bsqQrN/gGYnNtrII0twATSM0u3A29KmgA8ATQVKmpmZmZm7VanpqZl+ZLdrP3KP9wwLyJGtnC7I4BbIuKBlmy3Xh0wZMwK/aMyamj/lgrF6BhLJdoSz0d98XzUH89JfekI89HY2NCp0nY/k2QdjqRbgQ3KNr/Oh88utWRf9wAvlxKk/IMOlbKAwyNidoXtZmZmZraSOUmyDicivrkS+9q37P2ZwJkrq38zMzMzW3ZOksysRd1+/oB2X5o3MzOz9s0/3GBmZmZmZlbgJMnMzMzMzKzASZKZmZmZmVmBkyQzMzMzM7MCJ0lmZmZmZmYFTpLMzMzMzMwKnCSZmZmZmZkVOEkyMzMzMzMrcJJkZmZmZmZW4CTJzMzMzMyswEmSmZmZmZlZgZMkMzMzMzOzAidJZmZmZmZmBZ1XdQBm1r4cMGTMCn1+1ND+LRSJmZmZ2fJxJcnMzMzMzKzASZKZmZmZmVmBk6SVSNL2kk6rsX+gpHMqbH9R0potGMdYSdu0YHujJfVrqfZaW7VxXskxDJI0rMb+gXneP1nYtszjLGlzSY9U2L67hlKB+AAAIABJREFUpP8r6fuSDsrbhuW4+kkavSz9mJmZmbUnfiZpJYqIqcDUVR2HtRlrAxcCR7Z0wxExEZjY0u2amZmZtQdOklqApIHAV0g3tVsC5wJPAMOBTsA/gSOAHYBBEXGwpCOBHwOvAouBG3NzfSXdAzQCl0bE5Xn7ZZI2B14BDgPeA0bl/lYHLoiIGyWNzX3MkjQI6ApcDdye47gzt3e6pE8D6wCHRMQLks4Hds/7b4iIi3KfVwFdgCbgJxHxpKTjgKOAfwAbNzM+OwGXAIuA+cA7wLCymO4FLgbez/uPJlU6R0dE39zOI8DBwEBgm9zv+sDgfNNfqe+1gN8Dm+VzGFy2fzBwaD630RExXFIv4ILc/3r5nCdJehZ4CFCeh2/l8b8aWEKakx9ExFxJvwb2zG1cEBE3S9oduIg05+8DH6vwlLkG2E3S1yLiL2VxV2p/LJXnHkmr59fTIuJcSUPyWL4HjI+Ik6qM38fOOSLebyZuMzMzszbNy+1azqci4mvA14GhwBXAcRHRj5QE/Lx0oKSNgJOA3YB9SYlKyRJgP+AbwE8L2y+NiL2AF0kJxDHAwojYFdgbOCu3W01XYN+I+E1+f0dE9AfuAg6S9DVgC6AvKVE6VFJv4DxgeETsCRwPXCXpU/l1X2AAsEYzYzMSGJj7e75KTFcAP87nOIKUpNTyVm7ve6QErJpBwIsRsQspudq5tENST+A7+Xx3Bw6UJGBbYEhE7J3jODx/pDtwam6rEdgR2IeUEO8NnA2sL2l/YIuI2A34EvBLSeuRqkKHRMQ+wOxmzg9SInUY8FtJGxbirtZ+NZ2B64GHc4LUG/g2sGv+b+s8/5VUOmczMzOzds2VpJZTWkY3B1gT6AGMSPfcdAGeKRy7FTAjIt4CkDSpsG9KRDRJmkeqTAEsjohS1WES6cYc4D6AiFgkaQapqlHUqfB6dkQsLrx/Iv85j5Ss9AAmREQTsCRXbXrm7eNzP1MlfYZUxZkeEe/m+B+tOTLQLSKm59cTSBWM8pi65eWI5P4qPTNUPJ8HckzTJXWt0bdIiSARMQ2Ylit/AL1IFab78/v1SXMzFzhV0ttAA/BG3r8wIubk16V5voqU8N4NvA6cDPQG+uTKDqT53wzYNCJK18FDua+aIuJZSReREsemvLla+0XFsfp8Pod18/ttgEciYgmApAmkxLCSSufcqhobG1q7iw7HY1pfPB/1xfNRfzwn9aWjzoeTpJbTVPY+SEuvXpK0G7BJYd9zwDZ5Kdi7wE7ArCrtAKwhafucROwBTCMtsdsDuE1SA+nGeTZpqdomub0vkG74AT5oJt6ZpIrJhZK6kCoM1+TtewB/lrQ9Kal6AeiZ419MWkZ4XbWBAeZI6hkRM0jVp5JiTC9L2i4ingL2IiWV7wAb56ViDaRKV0kf4Lq8NG4u1c0kVT/GSOoOnAXck/cFMB3YPyemJwBPA38CvhsRMyWdAWyej680NwNIyeUZkg4hJUy3AQ9GxA8lrQacShqzeZJ6REQpptdqxF30u9zPdqSq3Kwq7Veb+yeArwKPSro77x8iqTOpWrUncC0pmSpX6Zxb1YIFi1Z2l+1aY2ODx7SOeD7qi+ej/nhO6ktHmI9qSaCX27WeHwHX5m/pzwGeKu2IiIWk55YmkCoQa5GW2VXzLjBY0njSczhXApcDG0qaCIwFzoiI+aTnoC6R9FdSIrVU8jMvsyU9THpW5paImAKcWOj7UuDIiFgAnEaqat0F/LuZ5o8FRkm6j5QQVjrXo4Hf5fE6HjghIuaRnlV6LJ/vc4Xjd5B0fx6Lo2v0fRnQXdI4UiLwn2V8EfEkqYo0UdLjwNakxOI6UlI1Afgc0K1G+48DZ+djB5Geq7odeDNvewJoiohFpKWB1+S4yys/VeXq3hHAJ/Kmau1XnfuIeDvHdy0pAb2JVM16lLSE809LG4+ZmZlZe9epqWmlf1Hc4eVv8E+KiLPz+/HAKRExftVG1jryjzzcFBELJJ1FWj545gq0NwyYFxEjWypGazkHDBmzQv+ojBrav6VCMTrGt4Btieejvng+6o/npL50hPlobGzoVGm7l9utAhHxnqR1JE0hLVebTKoqtVmSPkuqUpQbR1rCdo+kN0nP7RzWCv2PID1DVW7/XEWpS5JuBTYo2/x6RAxYFfGYmZmZmStJZtbCXEmqLx3hW8C2xPNRXzwf9cdzUl86wny4kmRmK8Xt5w9o9/+gmpmZWfvmH24wMzMzMzMrcJJkZmZmZmZW4CTJzMzMzMyswEmSmZmZmZlZgZMkMzMzMzOzAidJZmZmZmZmBU6SzMzMzMzMCpwkmZmZmZmZFThJMjMzMzMzK3CSZGZmZmZmVuAkyczMzMzMrMBJkpmZmZmZWYGTJDMzMzMzs4LOqzoAM2tfDhgyZoU+P2po/xaKxMzMzGz5uJJkZmZmZmZW4CTJzMzMzMysoFWW20naHvh6RJxZZf9AYJuIGFq2/cW8/Z0WimMsMCgiZrVQe6OBkRExtiXaa23VxnklxzAI6BoRw6rsvx7YFNgcWAy8DDwdEYNXQmxnAS/m/wZGxPeW8nPnAl8GToiICUtx/C+BLwEf5P9+ERF/q3LsxBzLc2XbvwOsDnwWGBcRDy9NrIXPnwW8GBFXLsvnzMzMzDqiVkmSImIqMLU12rb2JSK+CyBpGDAvIkau2oiWyneAHhHxdnMHStoO+G9gz4hokvRF4Eqgz7J0GBE3LlekZmZmZrbMljpJylWJrwBrA1sC50bE1ZJ6A8OBTsA/gSOAHUgVnIMlHQn8GHiVVCko3ez1lXQP0AhcGhGX5+2XSdoceAU4DHgPGJX7XB24ICJuLFaJStUK4Grg9hzHnbm90yV9GlgHOCQiXpB0PrB73n9DRFyU+7wK6AI0AT+JiCclHQccBfwD2LiZMdoJuARYBMwH3gGGlcV0L3Ax8H7efzRp2ePoiOib23kEOBgYCGyT+10fGBwRE6v0vRbwe2CzfA6Dy/YPBg7N5zY6IoZL6gVckPtfL5/zJEnPAg8ByvPwrTz+VwNLSHPyg4iYK+nXwJ65jQsi4mZJuwMXkeb8feCRWuNW5XzWAC4DupPm/RcRMUHSNGAC0AuYnvvYHXgb+CppvLcEPp3H7NiIeFjSwcDxOZ5xEfHLj3epu0nX458i4lc5obkon+/bpLk6CtgEuFvSPhGxuJlTeYVUJRso6a8R8bikXXOHuwAXkv7uzAFKlawzJXUl/V07OCJelPRbYJe8/w8R8TtJ1wFvAluQrv8f5Gu22rkeJOlQYC3StfR4pWNz1WknYF3SNXgocACwAGgATqp2HZqZmZm1B8v6TNKnIuJrwNeB0hKuK4DjIqIfKQn4eelgSRsBJwG7AfuSEpWSJcB+wDeAnxa2XxoRe5GWQB0NHAMsjIhdgb2Bs3K71XQF9o2I3+T3d0REf+Au0k3i10g3lX1JN9eH5kTvPGB4ROxJumm8StKn8uu+wABgjWbGZyRpqVR/4PkqMV0B/Dif4whSklLLW7m975ESsGoGkZZT7UK6sd25tENST1L1Y/f834GSBGwLDImIvXMch+ePdAdOzW01AjsC+wBPkObgbGB9SfsDW0TEbqTlZL+UtB7pxv+QiNgHmN3M+VVzDPByHqcDgUvz9vWAqyNijxzL2Dxn65ISSoBFecwOAy6R1AicAvSPiN2B7pK+VNbfJ0jX9Z7AT/K2K0iJ+F759f9GxOmkZOHLS5EgERGvkK7xPYHJkmYA++fdlwOHRcTOwDhSUgrw5xz/fcA3JR0IdCNdh3uQEq6e+dgXImI/0rV3dDPn+lxu9xhgRDPHPp3/zjWQlhbuBHwzx2FmZmbWri3rcrvSEro5wJr5dQ/SDRekCsYzheO3AmZExFsAkiYV9k3Jy4/mkb4xB1gcEaWqwyTSjTmkm0UiYlG+ydyyLK5Ohdezy25en8h/ziMlKz2ACRHRBCzJVZueefv43M9USZ8h3XRPj4h3c/yPVh2ZpFtETM+vJ5CqQeUxdcvLEcn9nVOhneL5PJBjmp6rC9WIlAgSEdOAabn6B6nqshlwf36/Pmlu5gKnSnqbdDP8Rt6/MCLm5Nelub6KlPDeDbwOnAz0Bvrkqh6k+d8M2DQiStfBQ7mvZdWbVG3cLb9fIydgAFPyn/8CZuTXr/HhNVkas6ckbQpsTarG3ZWv00/y8Wvo6TxHiyW9n7d9OiKezq/Hk6pUFUn6PSn5nhcRBxe2bw28GhGH5/c7AXfkMdsoIiLHenHeDx+9Ztfjo9fsYkmT8zaA0rNNc0hL+Gqda+n6fkrSfzVzbOQ/ewCTI+J94C1JpdhaTWNjQ2t30eF4TOuL56O+eD7qj+ekvnTU+VjWJKmpwrYgLfN5Kd/QblLY9xywTV4K9i7p2+jSjyhUamsNSdvnJGIPYBppqdUewG2SGkg3z7NJS9U2ye19gXTDD+nB+FoxzyRVTC6U1AXYFbgmb98D+HP+4Yl5wAtAzxz/YtIywusqDUw2R1LPiJhB+ta/pBjTy5K2i4ingL1ISeU7wMaSViclK1sUju8DXJeXxs2lupmkis8YSd2Bs4B78r4gLU3bPyemJwBPA38CvhsRMyWdQVoWBpXnZgDpRv0MSYeQEqbbgAcj4oeSVgNOJY3ZPEk9IqIU02s14q5mFqny8RtJa5OSstdrxFfUBxgt6fPA30lVvZeAfSJiiaQjgMcK51utzVckbZsT39JcVVRKgirYAThC0oH5B0meyefxHjBfUve8BPRk0vVeKZaZwCHAxXkZ4i6kpYiVjq11rjsBN0nagfR3qNaxpWt2GlCa3zWA7auNQUtZsGBRa3fRoTQ2NnhM64jno754PuqP56S+dIT5qJYEtsRPgP8IuFbSBFJV5KnSjohYCJxLqqrcTXoWYkmNtt4FBksaT/qG+0rSkqQN869+jQXOiIj5pOegLpH0V1IitVQi4i/AbEkPk56VuSUipgAnFvq+FDgyIhYAp5GqWncB/26m+WOBUZLuI92QVjrXo4Hf5fE6nvQLafNIzyo9ls+3+MtmO0i6P4/F0TX6voy0XGoccC2FZXwR8SSpijRR0uOkCsJcUsI3JsfyOWovpXocODsfO4j0XNXtwJt52xNAU0QsIi0NvCbHvVmNNmu5FNgun89DpGVlzSVHJV/MfY8EjslL3i4GxuUqzD58dIyrOQoYmc/vWGDIsp5ERNxEun4mS3qItCT1hIh4k7Ts7Zp8jr1If0cqGUNKricBD5Oeo3uq0oHNnOtWkh7I+wctzbjkLyzuz/3+f6QvC2r9HTYzMzNr8zo1NS3tfeeyk9SZ9JD32fn9eOCUiBjfap2uQvlHHm6KiAX54ffFUeVn0JeyvWG0nV98qwvyT123qLzE88CIGClpTVJVa/eIqFrVPGDImBX6R2XU0P4r8nEr0xG+BWxLPB/1xfNRfzwn9aUjzEdjY0OnSttb5SfASyLiPUnrSJpC+gZ6Mqmq1GZJ+iypUlNuHGkJ2z2S3iQtqTqsFfofQXqGqtz+sRQ/Sb2qSLoV2KBs8+sRMWBVxGNLbQGwS16K1wkYUStBMjMzM2sPWrWSZGYdjytJ9aUjfAvYlng+6ovno/54TupLR5iPapWklngmyczMzMzMrN1o1eV2Ztbx3H7+gHb/rZOZmZm1b64kmZmZmZmZFThJMjMzMzMzK3CSZGZmZmZmVuAkyczMzMzMrMBJkpmZmZmZWYGTJDMzMzMzswInSWZmZmZmZgVOkszMzMzMzAqcJJmZmZmZmRU4STIzMzMzMytwkmRmZmZmZlbgJMnMzMzMzKyg86oOwMzalwOGjFmhz48a2r+FIjEzMzNbPq4kmZmZmZmZFThJMjMzMzMzK3CStApJ2l7SaTX2D5R0ToXtL0paswXjGCtpmxZsb7Skfi3VXmurNs4rOYZBkobV2D9Q0kt5rh6U9LCkby9HP1dL+u8VCtbMzMysnfMzSatQREwFpq7qOKzNuCEihgJI2gB4StLNEdG0iuMyMzMza1ecJLUCSQOBrwBrA1sC5wJPAMOBTsA/gSOAHYBBEXGwpCOBHwOvAouBG3NzfSXdAzQCl0bE5Xn7ZZI2B14BDgPeA0bl/lYHLoiIGyWNzX3MkjQI6ApcDdye47gzt3e6pE8D6wCHRMQLks4Hds/7b4iIi3KfVwFdgCbgJxHxpKTjgKOAfwAbNzM+OwGXAIuA+cA7wLCymO4FLgbez/uPJlU+R0dE39zOI8DBwEBgm9zv+sDgiJhYpe+1gN8Dm+VzGFy2fzBwaD630RExXFIv4ILc/3r5nCdJehZ4CFCeh2/l8b8aWEKakx9ExFxJvwb2zG1cEBE3S9oduIg05+8Dj9QatzLrAW9HRJOkg4DjSNcWwEFAL+Ak0rW0BXBjRJxdOM+dSdfjQaTr5SNzCnwJ6BwR50m6DHgnIo6XdArwQkTcsAyxmpmZmbUpXm7Xej4VEV8Dvg4MBa4AjouIfqQk4OelAyVtRLqh3Q3Yl5SolCwB9gO+Afy0sP3SiNgLeJGUQBwDLIyIXYG9gbNyu9V0BfaNiN/k93dERH/gLuAgSV8j3Vz3JSVKh0rqDZwHDI+IPYHjgaskfSq/7gsMANZoZmxGAgNzf89XiekK4Mf5HEeQkpRa3srtfY+UgFUzCHgxInYhJVc7l3ZI6gl8J5/v7sCBkgRsCwyJiL1zHIfnj3QHTs1tNQI7AvuQEuK9gbOB9SXtD2wREbuRko9fSloPuJCUkO4DzG7m/CDNwVhJD5ASnO/n7Z8DvpqvrSBdL5ASwW8Bu1C43oBd83kcEBFzqDCnwK1AaVne50hzS277L0sRq5mZmVmb5UpS6ykto5sDrAn0AEake266AM8Ujt0KmBERbwFImlTYNyVXC+aRKlMAiyOiVHWYRLoxB7gPICIWSZpBqmoUdSq8nh0Riwvvn8h/ziMlKz2ACXkp15JctemZt4/P/UyV9BlSFWd6RLyb43+05shAt4iYnl9PIFWDymPqlpcjkvur9MxQ8XweyDFNl9S1Rt8iJYJExDRgWq78Qaq+bAbcn9+vT5qbucCpkt4GGoA38v6FOcmAD+f5KlLCezfwOnAy0Bvok6t6kOZ/M2DTiChdBw/lvmr5z3K7MvOBayS9SZqLh/P2pyPiPeC9HHvJvvk8luT3H5vTiHhJ0tq56jcT2EzSjsDrEfEGraixsaE1m++QPKb1xfNRXzwf9cdzUl866nw4SWo95c+JBGnp1UuSdgM2Kex7DtgmLwV7F9gJmFWlHYA1JG2fk4g9gGmkJVN7ALdJaiDdmM8mLVXbJLf3BdINP8AHzcQ7k1QxuVBSF1L14Zq8fQ/gz5K2JyVVLwA9c/yLScsIr6s2MMAcST0jYgYfVijKY3pZ0nYR8RSwFympfAfYWNLqpJv8LQrH9wGuy0vj5lLdTFLFZ4yk7sBZwD15XwDTgf1zYnoC8DTwJ+C7ETFT0hnA5vn4SnMzgJRcniHpEFLCdBvwYET8UNJqwKmkMZsnqUdElGJ6rUbcFeUq3hnAZ/Ome/kweaz2rNIw4L+AS0kJaqU5BbgD+A3w29z+xaQKX6tasGBRa3fRoTQ2NnhM64jno754PuqP56S+dIT5qJYEerndyvMj4FpJE0hVkadKOyJiIem5pQmkCsRafPgtfyXvAoMljSc9h3MlcDmwoaSJwFjgjIiYT1qWdYmkv5ISqaUSEX8BZkt6mPSszC0RMQU4sdD3pcCREbEAOI1U1boL+HczzR8LjJJ0HykhrHSuRwO/y+N1PHBCRMwjJQGP5fN9rnD8DpLuz2NxdI2+LwO6SxoHXEthGV9EPEmqIk2U9DiwNSnhuo6UVE0gLT3rVqP9x4Gz87GDSInF7cCbedsTQFNELCItDbwmx71ZjTZreYNUhZpCun7ebiY+ACLiSmA9SYdSYU7zYbeSloA+APwV+CKwYv+nWDMzM7M2oFNTk38Ya1WT1Bk4qfRgfb5ZPSUixq/ayFpH/pGHmyJigaSzSMsHz1yB9oYB8yJiZEvFaMvvgCFjVugflVFD+7dUKEbH+BawLfF81BfPR/3xnNSXjjAfjY0NnSpt93K7OhAR70laR9IU0nK1yaSqQJsl6bOkSk25caQlbPfkZ2heJ/06X0v3P4L0DFW5/SPi7Qrb64KkW4ENyja/HhEDVkU8ZmZmZh2RK0lm1qJcSaovHeFbwLbE81FfPB/1x3NSXzrCfFSrJPmZJDMzMzMzswIvtzOzFnX7+QPa/bdOZmZm1r65kmRmZmZmZlbgJMnMzMzMzKzASZKZmZmZmVmBkyQzMzMzM7MCJ0lmZmZmZmYFTpLMzMzMzMwKnCSZmZmZmZkVOEkyMzMzMzMrcJJkZmZmZmZW4CTJzMzMzMyswEmSmZmZmZlZgZMkMzMzMzOzgs6rOgAza18OGDJmuT43amj/Fo7EzMzMbPm4kmRmZmZmZlbgJMnMzMzMzKzASZKZmZmZmVlBiyVJkraXdFqN/QMlnVNh+4uS1mzBOMZK2qYF2xstqV9Ltdfaqo3zSo5hkKRhNfb3kzQ/z1Xpvx9WOO7WZehzXoVtzV2TH/tMleOWekyb67PC8f+5/iV1lfSUpO8t7eeXpx8zMzMzq63FfrghIqYCU1uqPWv3HoiIg2sdEBHfXJEOVsU1ubx9StoUuBM4LSL+1OKBmZmZmdlSazZJkjQQ+AqwNrAlcC7wBDAc6AT8EzgC2AEYFBEHSzoS+DHwKrAYuDE311fSPUAjcGlEXJ63XyZpc+AV4DDgPWBU7m914IKIuFHS2NzHLEmDgK7A1cDtOY47c3unS/o0sA5wSES8IOl8YPe8/4aIuCj3eRXQBWgCfhIRT0o6DjgK+AewcTPjsxNwCbAImA+8Awwri+le4GLg/bz/aFIVb3RE9M3tPAIcDAwEtsn9rg8MjoiJVfpeC/g9sFk+h8Fl+wcDh+ZzGx0RwyX1Ai7I/a+Xz3mSpGeBhwDlefhWHv+rgSWkOflBRMyV9Gtgz9zGBRFxs6TdgYtIc/4+8EitcatyPgNJ19JqwOnA9RHRVVJvPn69vQlcDmwLPA98IrdxNbBh/u9/ge8Urskfka6nMRExDPiEpBuAz+Z2DwJ2As7P5/wa8N0c3seuXUkHAcfluMif78WHfw8+NqYR8X6FU/8s8CfSXNxXZSw2AH5GGtuJETE0V+vmRcTIXD0dCewP3JXb7Qz0zf1D5b9nI4Gtcz+nRMRYSdOAZ4B3SdfUDXl8A+gfEVtVOAczMzOzdmNpK0mfioj9JG1Nuvn/F3BERMzIN58/JyUCSNoIOAnYnnST9WChnSXAfqSb+jtJN7mQbjofkfQbUgLxAbAwIr4vqQGYIun+GvF1BfpExGJJXwHuiIjr8k3kQZJmAFuQbhg7AxMlPUC6+RweEWMkbQ9cJenLwPFA7xzHE82MzUjg+xExXdLZwKYVYnocOCoipkoaQEpSTqzR5lsR0V/StqQb1M9XOW4Q8GK+Ie8F7E2aGyT1BL5DSgybgPsk/ZWUVAyJiKclHQocDkwCupNugOdIegjYEeiTz/9nwB7A+pK2A7aIiN3y8q1HJN0LXEhKSJ+RdGkzYwbQPye9JV/Of74WEQPyOZT2XcHHr7eHgTUjoq+kz5ISlJIHIuLC0jJJSRsDQ4HtSNfk+ZLWBdYFTo6IF3MsOwAHArcC5wFfJyWqUPna/Rzw1Yh4S9Jlef/cQhyVxrRS8ngL8DYfT8hfi4gBkjYAJgJfzH39QdI+FdohIt4G+knqBPwBuDYins9jWf73bAnp79mRkjYExpOuj3WBX0XE3yRdCPwpIkbkPvet1G9LaGxsaK2mOzyPbX3xfNQXz0f98ZzUl446H0ubJJWWD80B1gR6ACPyjVcX0rfOJVsBMyLiLQBJkwr7pkREU34WZO28bXFElG4cJwGlm7/7ACJiUU5ytiyLqVPh9eyIWFx4X0ps5pGSlR7AhIhoApbkqk3PvH187meqpM+QqjjTI+LdHP+jNUcGukXE9Px6AqkaVB5Tt7wMi9xfpedbiufzQI5puqSuNfoWuWoQEdOAabkCAamisRlQSi7XJ83NXOBUSW8DDcAbef/CiJiTX5fm+SpSwns38DpwMil57FNIcLrkfjaNiNJ18FDuq5aPLbfL11NUOLbS9bYt8Gg+95ckzSkcX95Gd2BaTiAATsj9vRoRL+ZtpWvyf4BfksZtLjA576907c4HrpH0Jum6ebis34+NqaSz+LCiWUoMjyBVdx6SNCUiZpWdx1akCtadeQwa8jkVdSp7fzEQhWptpb9nnYA9JO2ct3fOyVKx7x7ANfn1BFrRggWLWrP5DquxscFjW0c8H/XF81F/PCf1pSPMR7UkcGl/uKGp7H2Qll71I32rf0dh33PANpLWkrQaaflStXYA1shVHEjVimnAzPyaXEnqDcwmLVXbJB/7hUIbHzQT70zyjamkLsCuwLNl/WxPulF+AeiZ41+dVF2oZU6u2kCqVFWK6eVcgQHYi3ST/w6wsaTVJa1HqnSV9Mkx9eKjlYlyM0nVCSR1z0vHSgKYDnwpz9PVwNOkZWunR8Rh+X3p5rrS3AwgJZdfBm4mJUyzgAdzm/2Bm0hjNk9Sj/y5HWvE3JzyuSydS/n1NgvYBUBSNz6s4FVq43nSNVlakndLfgao0jl/F7g6Ir5EGr/SD0p85FhJnwLOICXFR5EqQeWJysfaj4hTIqJf/q+09G5aTqZ+Btycl1EWz2M2KcnaJ4/BxaTkreLfB0m/AjpFxK8KXVf6ezYL+GNuc3/SHL9W1vc08jjz0evbzMzMrN1a3l+3+xFwraQJpKrIU6UdEbGQ9NzSBFIFYi3Ssp5q3gUGSxpPWm50JWkp04aSJgJjgTMiYj7pBv+SvGxs9aUNNiL+AsyW9DBpudMtETGFtOSt1PelwJERsQA4jfRt+13Av5tp/lhglKT7SAlhpXM9GvhdHq/jgRMiYh5pieJj+XyfKxy/Q15eeGXnkYfmAAAgAElEQVT+bDWXAd0ljQOuJS3jK53zk6RqyMS83G9rUsJ1HTAmx/I5oFuN9h8Hzs7HDiLdnN8OvJm3PQE0RcQi4Hukqsr9pMpSS/rY9RYRY0gJ6mTgt8DCah/Oc3ouMC5fA1Miolry+RjpPMaRksBrqxz3BqliNoV0rb9N7bFsVkTcQkp+RlSI/4Ic/2RSQvMM6Vm/r0h6kJzMS9oR+EV6+Z9fDuxP5b9nl5GSx3Gk6/3vEVGeYJ4DfD33UVqiZ2ZmZtaudWpqqvRl+vKT1Bk4KSLOzu/Hkx4IH9+iHdUJpR95uCkiFuSlVIsj4swVaG8Y+WH8lorRbHnlZ/wWRMRjkvYmPcPVv9ZnDhgyZrn+URk1tGaztpw6wlKJtsTzUV88H/XHc1JfOsJ8NDY2lK8EAlrwJ8BLIuI9SetImkL6ZbvJtPKzDK0t/zBApYrCONKStXvycymvk341rKX7H0F6hqrc/oXnbOqO0v/naIOyza+XfpjB2oTZpErpe6Tq7U9WcTxmZmZmra7FK0lm1rG5klRfOsK3gG2J56O+eD7qj+ekvnSE+VhplSQz69huP39Au/8H1czMzNq35f3hBjMzMzMzs3bJSZKZmZmZmVmBkyQzMzMzM7MCJ0lmZmZmZmYFTpLMzMzMzMwKnCSZmZmZmZkVOEkyMzMzMzMrcJJkZmZmZmZW4CTJzMzMzMyswEmSmZmZmZlZgZMkMzMzMzOzAidJZmZmZmZmBU6SzMzMzMzMCjqv6gDMrH05YMiY5frcqKH9WzgSMzMzs+XjSpKZmZmZmVmBkyQzMzMzM7MCJ0lmS0HSBpIOXY7PfUNSt9aIKbc/UNI5ZdtGS+rXWn2amZmZtXdOksyWznbA15fjc8cDn2zhWMzMzMysFfmHG6xNkjQQGEBKQDYCzgTOAJ4B3gV+BFyX93cGTomIByTNACYA2wKvAocAi4GRwNakLw5OiYixkqYV2tsI+LykHwI/B3aKiFcl/QhYNyL+t0KMXwW2B66VdCWwdUT8X0mrA1OBg3KM/wD+C7grIn4p6TPA5cCawDvADyNiznKMURdgFLAlsDpwQUTcKGksMCgiZkkaBHQFzgFuAj4FrAX8PI/B/wF+BrwPTIyIocsah5mZmVlb4yTJ2rJ1gX2ARuBRUiLwq4j4m6TzgHsj4iJJmwITJW0JrA1cHxHjJf0GOAZ4G1gYEUdK2hAYT0qi1i2014+UWFye2zsYGAF8H/hGpeAi4g5JU4FBwFxgiqShwH8DD5KSr82B/YDXc4xfAE4ChkfEXZK+TEpgvltjHA6V1Lfwvicp6Tsmn9f3JTXk/u+v0saWpGRpb2Bj4HOSNiAlnl+MiLck/UHSPhFxb41YlltjY0NrNGt4bOuN56O+eD7qj+ekvnTU+XCSZG3ZuIj4AHhF0mtADyDyvh7A9QARMVfSG6RkaklEjM/HTAL2J1VJ9pC0c97eOSdLFNorugq4UdJ4YF5EvNJcoBGxSNI4UkJ0OKnyBfBkRLwKIGkyIKA3cLKkk4BOpEpXLTcUKzySRhfG4L5C/zNIyVBRp7x/uqRLgD8CXYDhwFakMbtTEkAD0L25c11eCxYsaq2mO7TGxgaPbR3xfNQXz0f98ZzUl44wH9WSQD+TZG1ZHwBJnyYtq5sPfJD3zQT2yPs3BdYH/gl0kfT5fMxuwHRgFvDHiOhHSppuBl7Lx3xQ+HM1gIh4CfgX8EtSwlTLfz4HXAEcBWwcEU/lbT0krZ2X4O0MzMjxnJTjOQa4ZalG4+OKY9BASr5mk5bwbZKP+ULe3xtoiIivAocBF+dj5wD75FguBiYvZyxmZmZmbYaTJGvLuublY3cAx5IqQiX/A/TP1Z4/kZ7reS/vO0nSRGBT4LL83za50jMJ+HuuUBU9D/SW9NP8/gpSAnJ3MzFOIj2TtEFETCZVZ64v7F9MSsomA2Mi4kngROD0HM+1wFMsn8uBDfO5jgXOiIj5pCrRJZL+SlqiCPAs0E/Sozme0yJiAXABMC5XufYnPaNlZmZm1q51ampqWtUxmC2z/MMN2yzrDwlIejF/7p0V7P/bQK+IOG0ZPrMa8BCwX0S8IWlzYHRE9K39ybblgCFjlusflVFD+7d0KEbHWCrRlng+6ovno/54TupLR5iPxsaGTpW2+5kks2Uk6X9IVaQB+f3XSb8AV+6iiLgtH7MFcBtwWUS8sRx93gpsULb59YgYsKxtmZmZmVltriSZWYtyJam+dIRvAdsSz0d98XzUH89JfekI8+FKkpmtFLefP6Dd/4NqZmZm7Zt/uMHMzMzMzKzASZKZmZmZmVmBkyQzMzMzM7MCJ0lmZmZmZmYFTpLMzMzMzMwKnCSZmZmZmZkVOEkyMzMzMzMrcJJkZmZmZmZW4CTJzMzMzMyswEmSmZmZmZlZgZMkMzMzMzOzAidJZmZmZmZmBU6SzMzMzMzMCjqv6gDMrH05YMiYZTp+1ND+rRSJmZmZ2fJxJcnMzMzMzKzASZKZmZmZmVmBk6TlJGl7SafV2D9Q0jkVtr8oac0WjGOspG1asL3Rkvq1VHutrdo4r+QYBkkaVmN/tWvh1hqfqTivkn68HPH1kzR6WT9nZmZm1lH5maTlFBFTgamrOg5ruyLim8vxsVOA37V0LGZmZmb2ISdJZSQNBL4CrA1sCZwLPAEMBzoB/wSOAHYABkXEwZKOBH4MvAosBm7MzfWVdA/QCFwaEZfn7ZdJ2hx4BTgMeA8YlftbHbggIm6UNDb3MUvSIKArcDVwe47jztze6ZI+DawDHBIRL0g6H9g9778hIi7KfV4FdAGagJ9ExJOSjgOOAv4BbNzM+OwEXAIsAuYD7wDDymK6F7gYeD/vP5pUtRwdEX1zO48ABwMDgW1yv+sDgyNiYpW+1wJ+D2yWz2Fw2f7BwKH53EZHxHBJvYALcv/r5XOeJOlZ4CFAeR6+lcf/amAJaU5+EBFzJf0a2DO3cUFE3Cxpd+Ai0py/DzxSa9yqnM+8iOgqaefcVidgLvDdfMhH5jX/t4GkEcDxwEhg6xzXKRExVtJTwDhguzwOA3JbW0v6K7Ah6Vq8SlJvKl/X55Ku48uB14Azgdfz66ciYtiynquZmZlZW+IkqbJPRcR+krYm3fz/CzgiImbkhOjnpEQASRsBJwHbA+8CDxbaWQLsR7qpv5N00wnpJvURSb8hJRAfAAsj4vuSGoApku6vEV9XoE9ELJb0FeCOiLguL/k6SNIMYAugL2mOJ0p6ADgdGB4RYyRtD1wl6cukG+7eOY4nmhmbkcD3I2K6pLOBTSvE9DhwVERMlTSAlKScWKPNtyKiv6RtgRuAz1c5bhDwYk5MewF7k+YGST2B75ASwybgvpwUbAsMiYinJR0KHA5MAroD/SNijqSHgB2BPvn8fwbsAawvaTtgi4jYLS+TfETSvcCFpIT0GUmXNjNmzbkcODgiZko6FuiRt39kXiPibEmDI+JYST8iXTNHStoQGJ/P9ZPAHyNisKTrgf2BeaSk8gBSEv6kpD8DV1D5ul4zInaWtDrwLLBLRLyS22txjY0NrdGsFXiM64vno754PuqP56S+dNT5cJJUWWkZ3RxgTdJN6whJkG42nykcuxUwIyLeApA0qbBvSkQ0SZpHqkwBLI6IUtVhErBPfn0fQEQsyknOlmUxdSq8nh0RiwvvS4nNPFKy0gOYEBFNwJJctemZt4/P/UyV9BlSFWd6RLyb43+05shAt4iYnl9PIFWDymPqlpcjkvur9MxQ8XweyDFNl9S1Rt8C7srHTgOm5cofQC9SMlpKLtcnzc1c4FRJbwMNwBt5/8KImJNfl+b5KlLCezepcnIyKXnsk6t6kOZ/M2DTiChdBw/lvpbXpyNiZj6vEQD5Wiuf16LewB65CgXQOSdLAH8rOy+AR0rzk6+vzal+XUf+sxF4IyJeye8nVIhjhS1YsKilm7SCxsYGj3Ed8XzUF89H/fGc1JeOMB/VkkD/cENlTWXvg7T0qh/p2/Y7CvueA7aRtJak1YCdarQDsEau4kCqVkwDZubX5EpSb2A2aanaJvnYLxTa+KCZeGeSl9pJ6gLsSqoIFPvZnnTz/QLQM8e/Omm5VS1zctUGUqWqUkwv5woMwF6km+93gI0lrS5pPVKlq6RPjqkXKampZiap4oOk7pJuKOwLYDrwpTxPVwNPk5aTnR4Rh+X3peSs0twMICWXXwZuJiVMs4AHc5v9gZtIYzZPUqnis2ONmJfGy7lqiaSTJH2jRoyl+GeRKkb9SNWim0nL4ap9bgdJnSWtQ0qOnqf6dV2ay/lAg6TG/L4432ZmZmbtlitJS+dHwLU5iQA4EugGEBELJZ1L+pb9VWAt0jK7LlXaehcYnG+K/w4MJd34XiFpYv78GRExX9Jw4BJJc6idPHxERPwl/6LZw8AawE0RMUXSibmfE3N8R0bEAqVf6ZsELAD+3UzzxwKjJL1Jem6lUlxHA7+T1In0bM+RETEvL1N7jJRYPlc4foe8vHCd/NlqLst9jyMtG/spqYJEfrbqftLSwk8Aj+bYrgPGSHoF+P+BjWq0/zhwnaT3SInCCaSqTD9JE4B1gdtyte97wDWSFpGez3qtWqPZYZL2LrzvV3h9TD6vD0jPhf2WtASykhmSriNdg1fksfgkMCIiPshVoUreIVXh1gOGRcSreclexesaILf3Y+BOSa+TvlR5tpnzNDMzM2vzOjU1VfrS2ZaWpM7ASRFxdn4/nvQQ/fhVG1nryD/ycFNOrs4iLR88cwXaGwbMi4iRLRWjtRxJvyD9WMW7OTm7JyKurfWZA4aMWaZ/VEYN7b8iIVozOsJSibbE81FfPB/1x3NSXzrCfDQ2NnSqtN2VpBUUEe9JWkfSFFJlZTKpqtRmSfosUOlGeBxpydo9uZL0OunX+Vq6///X3r3HbTbX+x9/DSPEdJA7YleI/Z7BOEaK0JRQP8aOQkUIoRzCj9ll51DEDv1M27mZZCOxt5rtuJPTYMKW8+CNMu2JxpmZyGHM/P74fu/d6t73fc/Bfbjmvt7Px2Mermut7/quz1rf675an+vzXaszKPdQdbWt7b/09f76isr/79FyXRa/ZHtsd+0XEbMoD6t4BZjGX5/cGBERETFkpZIUEX0qlaTW0g6/Ai5KMh6tJePRejImraUdxiOVpIgYEJefMnbIf6FGRETE0Jan20VERERERDQkSYqIiIiIiGhIkhQREREREdGQJCkiIiIiIqIhSVJERERERERDkqSIiIiIiIiGJEkRERERERENSZIiIiIiIiIakiRFREREREQ0JEmKiIiIiIhoSJIUERERERHRkCQpIiIiIiKiIUlSREREREREw/DBDiAihpbtDps0320njhvTj5FERERELJxUkiIiIiIiIhqSJEVERERERDRkul1EP5G0HLCN7YsWcLt/AG63/WQ/xbUHMNL2uPlouzhwFbAMMBbYekGPJyIiImJRk0pSRP9ZB9h+IbY7GHhHH8eysN4HLG97M2A0C3c8EREREYuUVJKibdQKylhKArI8cBxwLPAI8BqwP3BBXT8cOMr29ZIeBG4G1gKeB3YFXgfOAtag/NhwlO0bJT3Q6G95YF1J+wJHABvbfl7S/sCytn/QTYyfBdYDzpf0Y2AN2/+3VnTuAXaqMf4J+DvgatvflvR+4BxgKeBVYF/b0xfw/HwJOKTG/iiwb+1zDUlnA6t1Ho/tcxak74iIiIhFSSpJ0W6WBbYCPg2cCrwL+K7tXYGjgGttbw58HpggaTHg7cCFtZryMPA1YG/g2dp2LHB6o//O/o4Hrq8JxYXALrXNbsD53QVn+0pKMrQ78DNgh5ogbQPcQElgVgH2ADYCxkjaADgZGG/7E/X1iQtyUiS9h5IwjqnH+WI9zgOAB21/rcvxRERERAxZqSRFu7nJ9hzgKUkvAKMA13WjKMkMtp+QNBPoAN6wPbm2mQJsC7wJfFzSR+ry4TXRoNFf0wTg55ImAzNsPzWvQG3PknQTsDWwJ6XyBXCv7ecBJN0OiDIV7luSjgSGUSpdC2I1YKrtWfX9ZEoiecUC9rNAOjpG9Gf3UeU8t5aMR2vJeLSejElradfxSJIU7WZDAEkrUKbVPQ3MqeseAj4O3C1pZeDdwHPAEpLWtX0vsCkwtbb/o+0TJC0NfBt4oS6f0/jvYgC2/1vSi7XdhHnE+D/bAecCR1LuC7pP0irAKElvp1SVPgL8hFLhOtn2FEkjgS0W7LTwOLCmpGVsv1y3f6SXuPrEM8/MmnejeEs6OkbkPLeQjEdryXi0noxJa2mH8egpCcx0u2g3K0q6DriSMpXszca6EyjT1yYDv6Tc1zO7rjtS0i3AysDZ9d/IWumZAvyhVqiafgeMlnRIfX8uJQm7Zh4xTqHck7Sc7duB1akVrup14FLgdmBSTd4OB46u8ZwP3DePfXxF0p2d/yj3MR0N3CDpNsr9VGfO43giIiIihqRUkqLd3NTl0derdL6oU9h26GG7vWy/2mXZ7l0b2W729wRlCl+n4cBE22923a5LH0dR7o+i3hP1MuX+pE5P2f5sl21+T5mWN0+2zwPO62bVRfVf0zRgk7pd1+OJiIiIGJKSJEUMAEknUKpIY+v77YFDu2l6mu1f1DarAr8AzrY9cyH2eRmwXJfFL9keu6B9RURERLSTYXPnzh3sGCJiCNnusEnz/aUycdyY/gwlaI/55IuSjEdryXi0noxJa2mH8ejoGDGsu+WpJEVEn7r8lLFD/gs1IiIihrY8uCEiIiIiIqIhSVJERERERERDkqSIiIiIiIiGJEkRERERERENSZIiIiIiIiIakiRFREREREQ0JEmKiIiIiIhoSJIUERERERHRkCQpIiIiIiKiIUlSREREREREQ5KkiIiIiIiIhiRJERERERERDUmSIiIiIiIiGoYPdgARMbRsd9ik+W47cdyYfowkIiIiYuGkkhQREREREdGQJCkiIiIiIqIhSVK8ZZKWk/TFhdjuHySt1B8x1f4Xk3SqpGsl/aekyyWt9hb7vOwtbj/jrWw/n/sYLWnzfuh3KUl793W/EREREa0mSVL0hXWA7Rdiu4OBd/RxLE3bACvZ3sr21sAE4IdvpUPbn+uTyPrXjsCa/dDvikCSpIiIiBjy8uCGIUbSHsBYSvKxPHCc7X+X9ADwCPAasD9wQW0zHDjK9vWSHgRuBtYCngd2BV4HzgLWoCTVR9m+sUt/ywPrStoXOALY2PbzkvYHlrX9g27i/CywHnC+pB8Da9j+v5IWB+4Bdqox/gn4O+Bq29+W9H7gHGAp4FVgX9vTezgd04EPS9oZuA6YBFxV978FcDzwJvA74GvAl4C96nH+ANjB9p61/d3A1sB9tleU9BHgNGAY8ETddnVgfF32XO3rzzXetep+luzmXOwEfL1uRz32tYFvA3Moyck5tk+vcR9d270d2L2O0eV1nzcAewCvS7qLkhhOBkYDBp4CNqeM22dqHxOA99Q+D7J9v6RHgVsB1W12rPGsKek7to/r4ZxHRERELPJSSRqalgW2Aj4NnCppeF32Xdu7AkcB19reHPg8MEHSYpQL5gttbwY8TEkc9gaerW3HAqc39tHZ3/HA9bbPAS4EdqltdgPO7y5A21dSkqHdgZ8BO9QEaRvKhf5rwCqUC/6NgDGSNgBOBsbb/kR9fWJPJ8H2/cA+wA7AVOBO4KOShgHnAp+zvQUlydmjbvZCPf4rattlJG0E/M72043uzwH2tP0R4NfAqNrn121vSUnGjgC2BZayvQnwj/Ucd/X3wGfrdqYkYwArUyp0mwDflPReSrL1ZdtjgP+gjB+UROrTto8FzgNOtX0HMAK4qI7fx4Ep9fXbal/fAq6r53Nf4Mza32rAP9n+KNBRx+B44MEkSBERETHUpZI0NN1kew7wlKQXKBe5UC7AoVzQXwhg+wlJM2ubN2xPrm2mUC7w3wQ+XisnAMMlvadLf00TgJ9LmgzMsP3UvIK1PUvSTZTkYE+g8yL8XtvPA0i6nVLVGA18S9KRlMrL6z31K2md0r13rYnRVsAllOmB7wMukQSwNPArSqXHNaY3Jf0b8Dngo5QEqGkF2w/VtmfU/Y0Czqh9LkGptK0F3FHb/bek7qpeTwM/lfRnYCTwm7p8iu3Xat8PAB+iJHTja9uVKdUegMdt93Qu7qr/fRF4sL5+gVKNG01JQHeuy99d//tso0I3vbbtcx0dI/qj2+gi57m1ZDxaS8aj9WRMWku7jkeSpKFpQwBJK1Cm1HVWQObU/z5EqSrcLWllyoXxc8ASkta1fS+wKaX6AvBH2ydIWpoy5eqFLv3NoVYlayLwYm03YR5x/s92lCTkSGB52/dJWgUYJentlKrSR4CfUCpcJ9ueImkksEUv/X+KMg1wr5r0TAVeBp4B/giMtf2SpO0p0+I+0DgmavxnU6YTfqNL309KWsP2ozVhe4SSYO1ez8GmlETsDcq0xdPqQypWbnYi6Z3AsXXfANfy12l369Xq2pKUZOtRSvVotZpY/rTRthl387wCzO3lHD0MXGD7olqp2ruXbbr2+5Y988ysvuwuutHRMSLnuYVkPFpLxqP1ZExaSzuMR09JYKbbDU0rSroOuBI4wPabXdafQKkeTAZ+SbmvZ3Zdd6SkWygX82fXfyNrpWcK8IdapWr6HTBa0iH1/bmUJOyaecQ5hXJP0nK2b6fc03NhY/3rwKXA7cCkmrwdDhxd4zkfuK+X/sdTErq76zH9DNitxn8wcKWkKcABwANdN7b9eH35y26O+WvAxBrH+pTpdfvX47mZMg3wPtuTgOm1Evb/gGe79DOTUg26i3I/2F+Azif+LQFcXZd/z/azwL8Ct0u6lTKVrrunA/4W+IakT/RybjodD3xB0o2U8fpf56HhaeBtkk6aj34jIiIiFlnD5s7t7UfmWNTUBzeMtD1uIbadVrd99S3G8AVgbdvfWYBtFqMkC1vbnlkrSRfXe3najqQtgf1s7zKvtq1mu8MmzfeXysRxY/ozlKA9fgVclGQ8WkvGo/VkTFpLO4xHR8eIYd0tz3S76FOSTqBUkcbW99sDh3bT9DTbv6htVgV+AZxte+ZC7PMyYLkui1+yPXZB+4qIiIiISCUpIvpUKkmtpR1+BVyUZDxaS8aj9WRMWks7jEdPlaTckxQREREREdGQ6XYR0acuP2XskP/VKSIiIoa2VJIiIiIiIiIakiRFREREREQ0JEmKiIiIiIhoSJIUERERERHRkCQpIiIiIiKiIUlSREREREREQ5KkiIiIiIiIhiRJERERERERDUmSIiIiIiIiGpIkRURERERENCRJioiIiIiIaEiSFBERERER0TB8sAOIiKFlu8MmzVe7iePG9HMkEREREQsnlaSIiIiIiIiGJEkRERERERENSZIiIiIiIiIaFol7kiStB2xv+7ge1u8BjLQ9rsvyaXX5q30Ux43AfrYf7qP+LgbOsn1jX/TX33o6zwMcw37AiraP6WH9YsDJwGhgDvA6cLDt30vaHHjR9n0LuM9pwEjgEOB64D7gy7Z/vJCHgaSDgX9oLFobmGD7yB7az7C9YpdlxwAzbJ/1FuK4DdjF9rT5aPsu4Gpglu1PL+w+IyIiIlrdIpEk2b4HuGew44hFwjbASra3ApC0A/BDYCywF3AxJclZYLZPrH2uAuwNLHSSZPs04LTa3xbAucAPFra/AbI28KTtHQc7kIiIiIj+NGhJUq1KfAZ4O/Ah4CTb50kaDYwHhgHPUS5s16dUcHaR9FXgG8DzlCrBz2uXm0j6FdABnGn7nLr87HpR+xTwFWA2MLHuc3HgVNs/b1aJOqsVwHnA5TWOq2p/R0taAVgG2LVWKE4BNqvrL7J9Wt3nBGAJYC5wkO17JX2dcoH9J+C98zhHGwOnA7OAp4FXgWO6xHQt8CPgzbp+H8o0yottb1L7uQ3YBdiDUhF5L/Bu4EDbt/Sw76WBnwAfrMdwYJf1BwJfrMd2se3xktYGTq37f1c95imSHgVuBVTHYcd6/s8D3qCMye62n5D0fWDz2septi+VtBkloXi+HudtvZy26cCHJe0MXAdMAq6StCElgdpA0oPAR4BDa3+32B4naXngImBJwMAY26s3jvk8SpK1I7CmpO9QPkNb1CZrAz+yfWwv8f0NSR8AfgrsYPtZSe+kfG7eU5scZPt+YElJFwEfoIz9Tl366e683Uj5cWFt4B3A523/QdLx9VxMB5av278LuKC2Gw4cZft6SQ8AjwCvAaOAlSQda/vo+T3GiIiIiEXNYFeS3ml7a0lrUC78z6P8or6X7QdrQnQEJRGgXsQeCaxHuWi7odHXG8DWlIv6q4DOJOlM27dJ+mdKAjEHeNb2bpJGAHdJuq6XGFcENrT9uqTPAFfavqBOddqpXnCvCmxCOZ+3SLoeOBoYb3tSnS44QdIngYP561Sw387j/JwF7GZ7ar2wXbmbmO4E9rZ9j6SxlCTl8F76fMX2GElrURKCdXtotx8wrSamawOfAl4EkLQmsDMlMZwL/FrSfwJrAYfZvl/SF4E9gSnAapSEY7qkW4GNgA3r8R8KfBx4t6R1gFVtbyppKeA2SddSKkG72n5E0pm9nbC6732AfSnJ9h+BQ23fJOkaSpLzZ+BY4MO2X5H0r5K2oiTtv7R9Rn3f05Sy44HRzemfkj4LHASc2Ft8TfUYLwPG1WopwLeA62yfWf8ufkI5z8sC37I9rSY/6zf62ZbuzxvAHbYPqZ+fXSVdQUmmNqp9PlrbHQVcWxP8lSmf4w/VNt+1fbekLSk/JPRJgtTRMaIvuon5kHPdWjIerSXj0XoyJq2lXcdjsJOkzgvD6cBS9fUo4AxJUCoYjzTarw48aPsVAElTGuvusj1X0gxKdQrgddudVYcpwFb19a8BbM+qSc6HusQ1rPH6cduvN953JjYzKMnKKOBm23OBN2rVZs26fHLdzz2S3k+p4ky1/VqN/44ez0yxku2p9YZ9qCEAABBGSURBVPXNlGpQ15hWalxgT6b7i/Tm8VxfY5oqacVu2nYS5f4TbD8APFCrf1AqEx+kVGqgVKVWB54A/knSX4ARwMy6/lnb0+vrzrGeQEl4rwFeoiQHo4ENaxIAZfw/CKxsu/NzcGvdV/dBl0TLtneVNIwy5pd0OdbVKRXHq+rnbAQlkRtFqepAOd/zRdLHgW8D23SObV2+LHBFfXut7eO7bHo2JSG6uLFsNDCmVsKgnFuA5xv3DTU/453bdHfeAO6u/51O+byuBdxpew4wU9L9df0o4EKAWtGbSTlHUKpqfe6ZZ2b1R7fRRUfHiJzrFpLxaC0Zj9aTMWkt7TAePSWBg/10u7ndLDNl6tWWlCrSlY11jwEjJS1db9DfeB59va1WcaBUKx4AHqqvqZWk0cDjlKlq76ttN2j0MWceMT9EnWonaQngY5Rf55v7WY9yYft7yjStpSUtTqMa0IPptWoDpVLVXUxP1sQAyrSvR+qxvFfS4nUa1aqN9hvWmNamJDU9eYhSbUDSanWqVycDU4FP1HE6D7ifUrk52vZX6vvO5Ky7sRlLSS4/CVxKSZgeBm6ofY4BLqGcsxmSRtXtNuolZigVr+9LWrwmrlOBl+vrOZTP/OOUpGGruq8fAbdTPh8frf1s0rXjhs5+kLQ+ZSrg52zPbDay/WfbW9Z/f5MgSToIWAH4xy59Pwz8sMb1BWriQvfnsLlNd+etu+0MbCxpMUnLUBJ6+NvP68qU5Oy5xvFGREREtI3BriR1Z3/g/JpEAHwVWAmg3rNxEuVX/ueBpSnT7Jbooa/XgAPrtKU/AOMoF+7nSrqlbn+s7acljQdOlzSd3pOHv2H7CklbSvoN8DbgEtt3STq87ufwGt9XbT9T72OZAjwDvDyP7g8AJkr6M+X+q+7i2gf4l1o1mV33M6NOt/ovSmL5WKP9+nV64TJ1256cXfd9E+W+m0MoFSTqvVXXUaZkLQncUWO7AJgk6SnKNLfle+n/TuACSbMpF+HfpFQ9tpR0M2Wa1y9qte/LwE8lzaLcn/VCL/2Opzzd7u5aDZkD7FbX3U6ptO1MmZZ4U/2cTaMkFicC/yrpC8CTlM9Wd56mJOAnAf+Hct5/VsfgDttH9BJfp1OAe4HrazUL4BbKVL4Jkval3B90zHz0dTndn7f/1bBWNS+lfDaerMcCcAJlvHei/F3sa3t2d31EREREDHXD5s7t7Qfq1iJpOHBk56/ykiZTbjCfPLiR9Q+VhzxcUpOr71GmD3b7GPT57O8Y3uIjo4eyes/ZM7b/S9KnKPcAjRnsuBY12x02ab6+VCaOy6kdCO0wVWJRkvFoLRmP1pMxaS3tMB4dHSOGdbe8FStJPaq/bC8j6S5KZeV2FuDekVak8nSz87tZdRNlytqvaiXpJcrT+fp6/2fw1ylXTdva/ktf76+vSLoMWK7L4pdsj30L3T5OqabMplTPDnoLfUVERETEImqRqiRFROtLJam1tMOvgIuSjEdryXi0noxJa2mH8RgSlaSIaH2XnzJ2yH+hRkRExNA22E+3i4iIiIiIaClJkiIiIiIiIhqSJEVERERERDQkSYqIiIiIiGhIkhQREREREdGQJCkiIiIiIqIhSVJERERERERDkqSIiIiIiIiGJEkRERERERENSZIiIiIiIiIakiRFREREREQ0JEmKiIiIiIhoGD7YAUTE0LLdYZPmq93EcWP6OZKIiIiIhZNKUkREREREREOSpIiIiIiIiIYkSREREREREQ25J2mASFoP2N72cT2s3wMYaXtcl+XT6vJX+yiOG4H9bD/cR/1dDJxl+8a+6K+/9XSeBziG/YAVbR/Tw/rFgJOB0cAc4HXgYNu/74dYbgN2sT2tr/uOiIiIWFQlSRogtu8B7hnsOGKRsA2wku2tACTtAPwQGDuoUUVERES0iSRJb1GtTHwGeDvwIeAk4LfAeGAY8BywF7A+pYKzi6SvAt8AnqdUCX5eu9tE0q+ADuBM2+fU5WdLWgV4CvgKMBuYWPe3OHCq7Z83q0Sd1QrgPODyGsdVtb+jJa0ALAPsavv3kk4BNqvrL7J9Wt3nBGAJYC5wkO17JX0d2Bv4E/DeeZyfjYHTgVnA08CrwDFdYroW+BHwZl2/D2Uq6MW2N6n93AbsAuwBjKz7fTdwoO1betj30sBPgA/WYziwy/oDgS/WY7vY9nhJawOn1v2/qx7zFEmPArcCquOwYz3/5wFvUMZkd9tPSPo+sHnt41Tbl0raDDiNMuZvArf1ctqmAx+WtDNwHTCpnickPQjcDKxV+9qV8hn4cY13eeBc22fWz8M9wNrAO4DP2/6DpOMpidj02h5JfwecCSwFvAc4zvYvm5VMSScCD9s+r5fYIyIiIhZ5SZL6xjttby1pDcrF/4vAXrYfrAnREZREAEnLA0cC6wGvATc0+nkD2JpyUX8V0JkknWn7Nkn/TEkg5gDP2t5N0gjgLknX9RLfisCGtl+X9BngStsXSDoG2KleeK8KbEL5TNwi6XrgaGC87Ul1uuAESZ8EDuavU8F+O49zcxawm+2p9eJ85W5iuhPY2/Y9ksZSkpTDe+nzFdtjJK0FXASs20O7/YBpNTFdG/gUZWyQtCawMyUxnAv8WtJ/UpKPw2zfL+mLwJ7AFGA1YIzt6ZJuBTYCNqzHfyjwceDdktYBVrW9qaSlgNskXUupBO1q+xFJZ/Z2wuq+9wH2pSTbf6z7uImSjF9oe3L9PHwNuJGS5F0maaXarnMfd9g+pJ77XSVdQUngNgKWBR6t7UYCp9i+UdLHgGOBX/YW51vV0TGiP7uPhpzr1pLxaC0Zj9aTMWkt7ToeSZL6Ruc0uumUX+JHAWdIglLBeKTRdnXgQduvAEia0lh3l+25kmZQLoYBXrfdWXWYAmxVX/8awPasmuR8qEtMwxqvH7f9euN9Z2Izg5KsjAJutj0XeKNWbdasyyfX/dwj6f2Ui+mptl+r8d/R65kp08am1tc3U6pBXWNaqU5HpO7vxG76aR7P9TWmqZJW7GXfAq6ubR8AHqiVPyjVlQ9SKjVQqlKrA08A/yTpL8AIYGZd/6zt6fV15zhPoCS81wAvAd+iJI8b1ioOlPH/ILCy7c7Pwa11X90HXRIt295V0jDKmF9Sj/UN25Nr0ynAtsDFwCGSPlfjXaLR3d2NmFekJIF32p4DzJR0f13/J+ComtTP7dJHp2HdLFtozzwzqy+7ix50dIzIuW4hGY/WkvFoPRmT1tIO49FTEpin2/WNuV3emzL1aktKFenKxrrHgJGSlq436G/cSz8Ab6tVHCjVigeAh+praiVpNPA4Zara+2rbDRp9zJlHvA9Rp9pJWgL4GKXC0NzPepSk6vfAmjX+xSnTCHszvVZtoFSquovpyZoYAGxBSSpfBd4raXFJ76JUujptWGNam5LU9OQhSsUESatJuqixzsBU4BN1nM4D7qdUbo62/ZX6vjMx6G5sxlKSy08Cl1ISpoeBG2qfY4BLKOdshqRRdbuNeokZSsXr+5IWr4nrVODl+noJSZ2Vs03rusOB39j+co2jmcx099ncWNJikpahJMMA3wXOt70bpbrZ2cerwPtqsrYeEREREW0glaT+sT9wfk0iAL4KrARg+1lJJ1GqKs8DS1Om2XX3yz2UKXkH1ql8fwDGUS5gz5V0S93+WNtPSxoPnC5pOr0nD3/D9hWStpT0G+BtwCW275J0eN3P4TW+r9p+RtJ3KFWMZ4CX59H9AcBESX+m3H/VXVz7AP9SL8Rn1/3MqNPU/ouSWD7WaL9+nV64TN22J2fXfd9EuW/nEEoFiXpv1XWUqYVLAnfU2C4AJkl6ijLNbfle+r8TuEDSbErS901K5WZLSTdTprP9olb7vgz8VNIsyv1ZL/TS73jK0+3uljSz9r1bY/2Rkj4A/DdwFCWpPVPSlyj3ec2ux/S/1IrgpZTz+iTlPjEoydX4WsX8n3uVgH+mTP2cNo+YIyIiIoaMYXPndvcDefQXScOBI20fX99PBo5qTKEaUupDHi6pydX3KNMHu30M+nz2dwwww/ZZfRXjokR9/Ej4/rDdYZPm60tl4rgx/R1K0B5TJRYlGY/WkvFoPRmT1tIO49HRMaLb2wlSSRpgtmdLWkbSXZTKyu2UqtIiq1Y1zu9m1U2UKWu/qpWklyhP5+vr/Z/BX6eNNW1r+y99vb++IukyYLkui1+ynUd9R0RERAyiVJIiok+lktRa2uFXwEVJxqO1ZDxaT8aktbTDeKSSFBED4vJTxg75L9SIiIgY2vJ0u4iIiIiIiIYkSREREREREQ1JkiIiIiIiIhqSJEVERERERDQkSYqIiIiIiGhIkhQREREREdGQ/5+kiIiIiIiIhlSSIiIiIiIiGpIkRURERERENCRJioiIiIiIaEiSFBERERER0ZAkKSIiIiIioiFJUkRERERERMPwwQ4gIhY9khYDzgDWBV4D9rb9WGP9PsDXgNnA92xfMSiBtol5jUdt0wFMAUbbfnXgo2wv8/E38k1gl/r2KtvHDnyU7WM+xuPrwB7AXOC4fGf1r/n8zloMuBKYZPusgY+yfczH38d4YFNgVl001vZLAx7oAEslKSIWxg7AUrY/CowDTulcIWlF4CDKF+rWwPclLTkoUbaPHscDQNLWwK+AFQYhtnbV29/IasCXgI8BHwU+LWmdQYmyffQ2HssDB1DG45PAmZKGDUqU7aPX76zqe8ByAxpV+5rXeGwAbG17y/pvyCdIkCQpIhbOZsA1ALZvAz7cWLcxcKvt1+oX6WNALgD7V2/jATAH+BTw/ADH1c56G5PpwDa237Q9B1gCSHWvf/U4HrafBda1/QawIvCi7bmDEmX76PU7S9JOlO+tqwc+tLbU43jUKtMawDmSbpW01+CEOPCSJEXEwngH0Pwl6U1Jw3tYNwt450AF1qZ6Gw9sX2v7uYEPq631OCa237D9rKRhkk4G7rb9yKBE2T7m9TcyW9I3gNuAfxvo4NpQj+MhaW3gi8B3BiOwNtXb38cywI+ALwPbAAe0S+U7SVJELIyZwIjG+8Vsz+5h3QjgxYEKrE31Nh4xOHodE0lLARfWNgcMcGztaJ5/I7b/BXgfsLmkTwxkcG2ot/HYHVgZuJ5yn9ihkrYZ2PDaTm/j8Qpwmu1XbM+ijMu6Ax3gYMiDGyJiYdwKbAdcImkT4P7GujuA4+tF4JLAKOCBgQ+xrfQ2HjE4ehyTer/LJOB62ycNUnztprfxEPB9YEfgDcqN63MGI8g20uN42D6i87WkY4AZtq8Z8AjbS2//G/L3wMWSNqAUVzYDfjrwIQ68YXPnZtptRCyYxpNw1gGGAXsCnwEes/0f9el2+1K+UE+w/e+DFmwbmNd4NNpNA0bm6Xb9r7cxARYHfkaZ2tXpH23/ZqDjbBfz8Z11NLAt5el2V9s+btCCbQML8J11DCVJytPt+tF8/H0cAXye8iPC+e0yHkmSIiIiIiIiGnJPUkREREREREOSpIiIiIiIiIYkSREREREREQ1JkiIiIiIiIhqSJEVERERERDQkSYqIiIiIiGhIkhQREREREdGQJCkiIiIiIqLh/wPYwZpqqFBh6AAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Get feature importances for categorical data.\n", + "\n", + "rf = encoded_pipeline.named_steps['randomforestregressor']\n", + "importances = pd.Series(rf.feature_importances_, X_train_encoded.columns)\n", + "\n", + "# Plot feature importances.\n", + "\n", + "n = 20\n", + "plt.figure(figsize=(10, 10))\n", + "plt.title(f'Top {n} features')\n", + "importances.sort_values()[-n:].plot.barh();\n", + "\n", + "# It seems that these are very fragmented. From the top 20 we will keep\n", + "# property_type, cancellation_policy and instant_bookable. \n", + "# Neighbourhoud will be dropped since we are keeping the coordinates from the numerical set." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vlYj7z-3V7mn" + }, + "source": [ + "### Dropping the less important features and merging the final dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "H3kP_zfOEFD3" + }, + "outputs": [], + "source": [ + "# Dropping the unimoportant columns.\n", + "\n", + "berlin_numerical = berlin_numerical.drop(columns=['bathrooms', 'maximum_nights', 'security_deposit'])\n", + "berlin_non_numerical = berlin_non_numerical.drop(columns=['is_business_travel_ready'])" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 289 + }, + "colab_type": "code", + "id": "VHIcxbhOXbCS", + "outputId": "f42e44a0-dccd-4d4d-f64d-2e9b1e2c33a3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "35 columns\n" + ] + }, + { + "data": { + "text/html": [ + "
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host_has_profile_pic_1host_has_profile_pic_2neighbourhood_group_cleansed_1neighbourhood_group_cleansed_2neighbourhood_group_cleansed_3neighbourhood_group_cleansed_4neighbourhood_group_cleansed_5neighbourhood_group_cleansed_6neighbourhood_group_cleansed_7neighbourhood_group_cleansed_8neighbourhood_group_cleansed_9neighbourhood_group_cleansed_10neighbourhood_group_cleansed_11neighbourhood_group_cleansed_12property_type_1property_type_2property_type_3property_type_4property_type_5property_type_6property_type_7property_type_8property_type_9property_type_10property_type_11room_type_1room_type_2room_type_3bed_type_1bed_type_2instant_bookable_1instant_bookable_2cancellation_policy_1cancellation_policy_2cancellation_policy_3
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" + ], + "text/plain": [ + " host_has_profile_pic_1 host_has_profile_pic_2 \\\n", + "id \n", + "2015 1 0 \n", + "2695 1 0 \n", + "3176 1 0 \n", + "3309 1 0 \n", + "7071 1 0 \n", + "\n", + " neighbourhood_group_cleansed_1 neighbourhood_group_cleansed_2 \\\n", + "id \n", + "2015 1 0 \n", + "2695 0 1 \n", + "3176 0 1 \n", + "3309 0 0 \n", + "7071 0 1 \n", + "\n", + " neighbourhood_group_cleansed_3 neighbourhood_group_cleansed_4 \\\n", + "id \n", + "2015 0 0 \n", + "2695 0 0 \n", + "3176 0 0 \n", + "3309 1 0 \n", + "7071 0 0 \n", + "\n", + " neighbourhood_group_cleansed_5 neighbourhood_group_cleansed_6 \\\n", + "id \n", + "2015 0 0 \n", + "2695 0 0 \n", + "3176 0 0 \n", + "3309 0 0 \n", + "7071 0 0 \n", + "\n", + " neighbourhood_group_cleansed_7 neighbourhood_group_cleansed_8 \\\n", + "id \n", + "2015 0 0 \n", + "2695 0 0 \n", + "3176 0 0 \n", + "3309 0 0 \n", + "7071 0 0 \n", + "\n", + " neighbourhood_group_cleansed_9 neighbourhood_group_cleansed_10 \\\n", + "id \n", + "2015 0 0 \n", + "2695 0 0 \n", + "3176 0 0 \n", + "3309 0 0 \n", + "7071 0 0 \n", + "\n", + " neighbourhood_group_cleansed_11 neighbourhood_group_cleansed_12 \\\n", + "id \n", + "2015 0 0 \n", + "2695 0 0 \n", + "3176 0 0 \n", + "3309 0 0 \n", + "7071 0 0 \n", + "\n", + " property_type_1 property_type_2 property_type_3 property_type_4 \\\n", + "id \n", + "2015 1 0 0 0 \n", + "2695 0 1 0 0 \n", + "3176 0 1 0 0 \n", + "3309 0 1 0 0 \n", + "7071 0 1 0 0 \n", + "\n", + " property_type_5 property_type_6 property_type_7 property_type_8 \\\n", + "id \n", + "2015 0 0 0 0 \n", + "2695 0 0 0 0 \n", + "3176 0 0 0 0 \n", + "3309 0 0 0 0 \n", + "7071 0 0 0 0 \n", + "\n", + " property_type_9 property_type_10 property_type_11 room_type_1 \\\n", + "id \n", + "2015 0 0 0 1 \n", + "2695 0 0 0 0 \n", + "3176 0 0 0 1 \n", + "3309 0 0 0 0 \n", + "7071 0 0 0 0 \n", + "\n", + " room_type_2 room_type_3 bed_type_1 bed_type_2 instant_bookable_1 \\\n", + "id \n", + "2015 0 0 1 0 1 \n", + "2695 1 0 1 0 1 \n", + "3176 0 0 1 0 0 \n", + "3309 1 0 0 1 1 \n", + "7071 1 0 1 0 1 \n", + "\n", + " instant_bookable_2 cancellation_policy_1 cancellation_policy_2 \\\n", + "id \n", + "2015 0 1 0 \n", + "2695 0 0 1 \n", + "3176 1 1 0 \n", + "3309 0 1 0 \n", + "7071 0 0 0 \n", + "\n", + " cancellation_policy_3 \n", + "id \n", + "2015 0 \n", + "2695 0 \n", + "3176 0 \n", + "3309 0 \n", + "7071 1 " + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Encoding the categorical values. \n", + "\n", + "encoder = ce.OneHotEncoder()\n", + "\n", + "encoded = encoder.fit_transform(berlin_non_numerical)\n", + "print(f'{len(encoded.columns)} columns')\n", + "encoded.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Er9wWfmhXZ68" + }, + "outputs": [], + "source": [ + "# Creating the final dataframe.\n", + "\n", + "berlin_final = pd.merge(berlin_numerical, encoded, left_index=True, right_index=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PhuYoTcRXuBj" + }, + "source": [ + "# Final model" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 272 + }, + "colab_type": "code", + "id": "OQVJBOw_Eaqe", + "outputId": "0e58889a-f5b3-4d18-fdce-83c772f8637d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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22441 rows × 16 columns

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" + ], + "text/plain": [ + " latitude longitude accommodates bedrooms price cleaning_fee \\\n", + "id \n", + "2015 52.534537 13.402557 3 1.0 60.0 30.0 \n", + "2695 52.548513 13.404553 2 1.0 17.0 0.0 \n", + "3176 52.534996 13.417579 4 1.0 90.0 50.0 \n", + "3309 52.498855 13.349065 2 1.0 26.0 30.0 \n", + "7071 52.543157 13.415091 2 1.0 42.0 0.0 \n", + "... ... ... ... ... ... ... \n", + "29856708 52.533865 13.400731 2 1.0 60.0 0.0 \n", + "29857108 52.496211 13.341738 6 1.0 20.0 0.0 \n", + "29864272 52.531800 13.411999 2 0.0 85.0 0.0 \n", + "29866805 52.520802 13.378688 2 1.0 99.0 0.0 \n", + "29867352 52.473762 13.424447 2 1.0 45.0 0.0 \n", + "\n", + " extra_people guests_included minimum_nights host_has_profile_pic \\\n", + "id \n", + "2015 28.0 1 4 1 \n", + "2695 0.0 1 2 1 \n", + "3176 20.0 2 62 1 \n", + "3309 18.0 1 5 1 \n", + "7071 24.0 1 2 1 \n", + "... ... ... ... ... \n", + "29856708 0.0 1 2 1 \n", + "29857108 10.0 1 1 1 \n", + "29864272 0.0 1 3 1 \n", + "29866805 0.0 1 1 1 \n", + "29867352 0.0 1 5 1 \n", + "\n", + " neighbourhood_group_cleansed property_type room_type bed_type \\\n", + "id \n", + "2015 1 1 1 1 \n", + "2695 2 2 2 1 \n", + "3176 2 2 1 1 \n", + "3309 3 2 2 2 \n", + "7071 2 2 2 1 \n", + "... ... ... ... ... \n", + "29856708 1 2 1 1 \n", + "29857108 3 2 3 1 \n", + "29864272 2 4 1 1 \n", + "29866805 1 8 2 1 \n", + "29867352 5 2 2 1 \n", + "\n", + " instant_bookable cancellation_policy \n", + "id \n", + "2015 1 1 \n", + "2695 1 2 \n", + "3176 2 1 \n", + "3309 1 1 \n", + "7071 1 3 \n", + "... ... ... \n", + "29856708 1 2 \n", + "29857108 2 2 \n", + "29864272 1 2 \n", + "29866805 1 2 \n", + "29867352 1 2 \n", + "\n", + "[22441 rows x 16 columns]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "berlin_final" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['latitude', 'longitude', 'accommodates', 'bedrooms', 'price',\n", + " 'cleaning_fee', 'extra_people', 'guests_included', 'minimum_nights',\n", + " 'host_has_profile_pic_t', 'host_has_profile_pic_f',\n", + " 'neighbourhood_group_cleansed_Mitte',\n", + " 'neighbourhood_group_cleansed_Pankow',\n", + " 'neighbourhood_group_cleansed_Tempelhof - Schöneberg',\n", + " 'neighbourhood_group_cleansed_Friedrichshain-Kreuzberg',\n", + " 'neighbourhood_group_cleansed_Neukölln',\n", + " 'neighbourhood_group_cleansed_Charlottenburg-Wilm.',\n", + " 'neighbourhood_group_cleansed_Treptow - Köpenick',\n", + " 'neighbourhood_group_cleansed_Steglitz - Zehlendorf',\n", + " 'neighbourhood_group_cleansed_Reinickendorf',\n", + " 'neighbourhood_group_cleansed_Lichtenberg',\n", + " 'neighbourhood_group_cleansed_Marzahn - Hellersdorf',\n", + " 'neighbourhood_group_cleansed_Spandau', 'property_type_Guesthouse',\n", + " 'property_type_Apartment', 'property_type_Condominium',\n", + " 'property_type_Loft', 'property_type_House',\n", + " 'property_type_Serviced apartment', 'property_type_Townhouse',\n", + " 'property_type_Other', 'property_type_Bed and breakfast',\n", + " 'property_type_Guest suite', 'property_type_Hostel',\n", + " 'room_type_Entire home/apt', 'room_type_Private room',\n", + " 'room_type_Shared room', 'bed_type_Real Bed', 'bed_type_Sofa\\Other',\n", + " 'instant_bookable_f', 'instant_bookable_t',\n", + " 'cancellation_policy_strict', 'cancellation_policy_flexible',\n", + " 'cancellation_policy_moderate'],\n", + " dtype='object')" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "berlin_final.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [], + "source": [ + "# With OrdinalEncoder\n", + "berlin_ord = berlin_final[['accommodates', 'bedrooms', 'price',\n", + " 'cleaning_fee', 'extra_people', 'guests_included', 'minimum_nights',\n", + " 'neighbourhood_group_cleansed', 'property_type', 'room_type',\n", + " 'bed_type', 'instant_bookable', 'cancellation_policy']]" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"['cancellation_policy_strict', 'neighbourhood_group_cleansed_Tempelhof - Schöneberg', 'neighbourhood_group_cleansed_Treptow - Köpenick', 'neighbourhood_group_cleansed_Mitte', 'neighbourhood_group_cleansed_Spandau', 'property_type_House', 'property_type_Hostel', 'neighbourhood_group_cleansed_Steglitz - Zehlendorf', 'neighbourhood_group_cleansed_Reinickendorf', 'property_type_Bed and breakfast', 'room_type_Shared room', 'bed_type_Sofa\\\\\\\\Other', 'instant_bookable_f', 'property_type_Other', 'instant_bookable_t', 'property_type_Guest suite', 'neighbourhood_group_cleansed_Marzahn - Hellersdorf', 'neighbourhood_group_cleansed_Lichtenberg', 'cancellation_policy_flexible', 'room_type_Private room', 'bed_type_Real Bed', 'property_type_Apartment', 'property_type_Loft', 'neighbourhood_group_cleansed_Friedrichshain-Kreuzberg', 'property_type_Condominium', 'property_type_Serviced apartment', 'neighbourhood_group_cleansed_Pankow', 'property_type_Townhouse', 'cancellation_policy_moderate', 'neighbourhood_group_cleansed_Neukölln', 'room_type_Entire home/apt', 'neighbourhood_group_cleansed_Charlottenburg-Wilm.', 'property_type_Guesthouse'] not in index\"", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;34m'instant_bookable_f'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'instant_bookable_t'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;34m'cancellation_policy_strict'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'cancellation_policy_flexible'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m 'cancellation_policy_moderate']]\n\u001b[0m", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 2999\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3000\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3001\u001b[1;33m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3002\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3003\u001b[0m \u001b[1;31m# take() does not accept boolean indexers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[1;34m(self, obj, axis, is_setter, raise_missing)\u001b[0m\n\u001b[0;32m 1283\u001b[0m \u001b[1;31m# When setting, missing keys are not allowed, even with .loc:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1284\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"raise_missing\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mTrue\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_setter\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1285\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1286\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1287\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[0;32m 1090\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1091\u001b[0m self._validate_read_indexer(\n\u001b[1;32m-> 1092\u001b[1;33m \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1093\u001b[0m )\n\u001b[0;32m 1094\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[0;32m 1183\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"loc\"\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1184\u001b[0m \u001b[0mnot_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1185\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"{} not in index\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnot_found\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1186\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1187\u001b[0m \u001b[1;31m# we skip the warning on Categorical/Interval\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: \"['cancellation_policy_strict', 'neighbourhood_group_cleansed_Tempelhof - Schöneberg', 'neighbourhood_group_cleansed_Treptow - Köpenick', 'neighbourhood_group_cleansed_Mitte', 'neighbourhood_group_cleansed_Spandau', 'property_type_House', 'property_type_Hostel', 'neighbourhood_group_cleansed_Steglitz - Zehlendorf', 'neighbourhood_group_cleansed_Reinickendorf', 'property_type_Bed and breakfast', 'room_type_Shared room', 'bed_type_Sofa\\\\\\\\Other', 'instant_bookable_f', 'property_type_Other', 'instant_bookable_t', 'property_type_Guest suite', 'neighbourhood_group_cleansed_Marzahn - Hellersdorf', 'neighbourhood_group_cleansed_Lichtenberg', 'cancellation_policy_flexible', 'room_type_Private room', 'bed_type_Real Bed', 'property_type_Apartment', 'property_type_Loft', 'neighbourhood_group_cleansed_Friedrichshain-Kreuzberg', 'property_type_Condominium', 'property_type_Serviced apartment', 'neighbourhood_group_cleansed_Pankow', 'property_type_Townhouse', 'cancellation_policy_moderate', 'neighbourhood_group_cleansed_Neukölln', 'room_type_Entire home/apt', 'neighbourhood_group_cleansed_Charlottenburg-Wilm.', 'property_type_Guesthouse'] not in index\"" + ] + } + ], + "source": [ + "# With OneHotEncoder\n", + "berlin_rev = berlin_final[['accommodates', 'bedrooms', 'price',\n", + " 'cleaning_fee', 'extra_people', 'guests_included', 'minimum_nights',\n", + " 'neighbourhood_group_cleansed_Mitte',\n", + " 'neighbourhood_group_cleansed_Pankow',\n", + " 'neighbourhood_group_cleansed_Tempelhof - Schöneberg',\n", + " 'neighbourhood_group_cleansed_Friedrichshain-Kreuzberg',\n", + " 'neighbourhood_group_cleansed_Neukölln',\n", + " 'neighbourhood_group_cleansed_Charlottenburg-Wilm.',\n", + " 'neighbourhood_group_cleansed_Treptow - Köpenick',\n", + " 'neighbourhood_group_cleansed_Steglitz - Zehlendorf',\n", + " 'neighbourhood_group_cleansed_Reinickendorf',\n", + " 'neighbourhood_group_cleansed_Lichtenberg',\n", + " 'neighbourhood_group_cleansed_Marzahn - Hellersdorf',\n", + " 'neighbourhood_group_cleansed_Spandau', 'property_type_Guesthouse',\n", + " 'property_type_Apartment', 'property_type_Condominium',\n", + " 'property_type_Loft', 'property_type_House',\n", + " 'property_type_Serviced apartment', 'property_type_Townhouse',\n", + " 'property_type_Other', 'property_type_Bed and breakfast',\n", + " 'property_type_Guest suite', 'property_type_Hostel',\n", + " 'room_type_Entire home/apt', 'room_type_Private room',\n", + " 'room_type_Shared room', 'bed_type_Real Bed', 'bed_type_Sofa\\Other',\n", + " 'instant_bookable_f', 'instant_bookable_t',\n", + " 'cancellation_policy_strict', 'cancellation_policy_flexible',\n", + " 'cancellation_policy_moderate']]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "0F3OT_DrVemN", + "outputId": "e49b4d3f-cb0e-4027-cdce-e63eb4c3812a" + }, + "outputs": [ + { + "data": { + "image/png": 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zF9fpkz5HOwEwaJduvD9zPvNWFPGznbsSjUZoVpBHNBqhaF0J0+YtYVXxes649WFe+PALBuzUOYRRSds+Z1b/hOLxeGPgfqAp0BK4D5gG3Eb6NWAZcDLp8Pfb23oAtwMJoAw4m/SbCY8DS4DOwGPArkBf4MUgCK6Kx+NvAZ9Vby8BJgEHV9cwpHrbA0BXIAbcEgTB4/F4fJ/qGtZUf80PqsdwA7A70AiYGQTBcOBqYLd4PD4CeBm4F8iprnMEUAQ8ATQBcoGRQRC89RMc0tB98sxztOhUGHYZ25xoQQHJDRtqNyQT6UAqkSCjeXMKBu7NrCGHkiwtpfuEpyidOo3y+QsAyGjVik6338ay318bUvX113ePa7LmuEby8ih++CGKH/gHxGIUPjye8unTKQ8CyMyk6QknsuiYo8Mrvj7LzSdVHUIDpJJJiMbS5y1AMkHs4GFk/vIMql5+CoBIoybQdgcqbhxJtGcfMs+7koo/XBhG9fVTTi6UbfaJlGQyHZAkk+njWpo+j6OHn0Zq2QJYvYLokF+SmjktPcNa3yuSn0+ypPYakErUXlsjuXmUPjaOknEPQjRGy/vGUvHlF1TNmU35B++Sd/j2+/+/pLySgs1mjEWjEaoSSTJiUUrKK2iUXduWn53JhrIKSsoraVS9T35WJiXlFT/Yt1l+egblgtVr+fOrU7j9xIMA6Na6Ocfe8y/yMjM4sGdnGjfwWdXw3WOdn51FSVlFnT6pVIpIJFLdnsmGsvSbU4N7dv7O8/Vs15KJXy7kqP49mDhzIRsrDVnJyiFVXjv7nGQSItH0G4AAi4Pv7vNN/7xGRIeeRvKtZ7Z8nduYktJSGuXn1zzOz8ulpKS0Tp9YLMayFSsZfuGlFBQUsGOnjjRpVMC9D42npHQjlVWVfDL9C47/1vIh27OSTRUUbHbty8/JomRTWZ0+Kai9JuRks2FTObFolEgkwmHX3U3JpnKuPenQrVl2vVRSUUVBdu0ySNFohKpkkoxolJLyKhpttkRSflYGG8orKd5UzrqySu47ZhDPfrmIP789nRuHDqBbyyYcO+6N9OtT9/Y03k5nVQOUlJX/iHN089etLDaUldNjh7Y8+O/3OWnwAFauWc/cFUVsqqhg+ddraZyXywOXnMqdL77NP157lwsP9xOY0v+Vb1H+tLoBjwVBMAT4BXAp6WB3eBAEewL/Bnr+wLb7gF8HQbAfcCfwzaKSXYAzq5/vj9XPuWf1tm98GATBz4FsYGMQBAcBXwL7AecAq4MgGAgcCFwfj8dbAn8FTqzuuwBqwvbi6m0Dgb3i8XgHYAzwZhAE9wI3A38LgmD/6n/fSDoIbwscDpwE1H7WU9ulZEkJ0YLaH/jTs3jSwV9VcTEbP/ucqqIikhs3UvLBFHJ3Sa9nmdMjTrfHH2HFjX+i5IMpYZReryVLSojmb35cIzXHNbVpE8VjHyRVVkaqtJSNH7xPdo/0WtX5A3/Gxo8+IllS8n1Pq02lRHJrL1uRSKQ2qK6WePUZykYcRbRnH6K79CW1YT2J6jWqkzM/Jdp++15H8TvKNkF2bu3jSHVQ/Y2MTKInXwLZuSSfTn98PdJ/X6J7HEDsvGuhUVNiI0Zv5aLrv1RpKdG82mtAZLNra6psEyWPPJy+BmwspfzDD8jcqccPPdV2pSA7k9Ly2sA0laJmll5BdhalFbVro5eWV9I4J6t6n/T20op0cP1DfQGmLFjOhY++zo3DBrNjy6YEK7/mnTmLef2SE3j9NyewprSMV2bM3xrDDcVtr03h9Hv/xQUPvURJ+ebHqIJGOXVD+mgksll7JY1zfjjEH3noQCbOXMiIB54nEonQLM+lFagoI5K12XHYPKj+T1q2I3rsr0lOfh6Wzt1y9W1j/nr3/Zx63sWcP/IqSkprw+nSjZto1KjgO/07tGvLa089wolHH8GNt91B1x07c/Kxwzj7NyO56bY72W2XnWnWtMl39tve3Pb8W5x+68NccM8TlJTVflqqtKyCRrl1/x/XuSaUldM4N5tnp0ynZeMCXr32fF677gLueGkSq9au32r110cFWRmUVtS+YZdKQUb0m9eyDEo3ezOvtKKKxtlZNM3J5oCu7QAY3KUdX6wqJihaxzvzV/D6WUN5/ayhrNlYziuzl27dwdQDtz37JqffMpYL7nqMkk3fOkfz/sM5Wl5B49wcfrZzV/p378Twvz7E2Dc+YJfCdjTNz6NJQS4H9N4JgMG9d+KLRX4iSPpfGFb/tFYCR8Xj8XHAKCATaBMEwUyAIAjuDIJg2g9sax8EwafVz/MOsEv1v+cHQbAOWAusCoJgTRAEZaTfhP7GN3ehW0s6pAYoJj37uWf18xEEwYbq9q5AhyAIvlkP4JvPr28CWsfj8UeBe4CC6jFsrhfwzYzu0UDrIAhmAHcAj5IO2j2vtnMlH31M4wPS7yDn9etL2czaWT6bPp9OTjxOrFkziMXI79eXstlzyOnenR3vuZNFv76I9RPfCqny+m3TtKnkDx4MQE6fPpQHtUt6ZO24I4WPPp5+YyAjg9z+u1M2YwYAeQMHUvrOWyFUvG1IBNOJ9t0bgEj3nUkurg2UIu06kvXb6nW/E1Xp5SqSKZKzPifWd690n05dSa1etdXrrs9SC2YR6Vl9c5rC7qS+NVs6esbvSC1fSPKpe2pClsQNvyZx1+9J3PV72LCWxL3Xbe2y673yT6eRvc9+AGT22o3KubXXgIxOnWn1z/E114Csvv2pnPnlDz3VdqVvYVsmzUl/7PyzJavo3rpZTVuvDq2Yumgl5ZVVbCirYP7qtXRv3Yy+HdvwTvU+k+YsoX9h2x/sO2XBcm54+X3uOfUQdu3QCoCCnCyyMzLIzogRi0Zpnp/D+k0Nd3mbi4fsydgRRzHp6l+x+Ot1rN1YRkVVgo8XrKBPYZs6fXu2b8mH89M3sZ0ULKL/ju1+8Hnfm7uE83++O/eecTjRSISB3XxjMLV8PuxYfdPKdp1h9fL/2B+A5m2J/uIMki+NrV1CRAD85tyzePiu23j3pX+xeOky1q5bT0VlJR9/8hl9d92lTt9zL7uShYvToV5+Xh7RSIQ1xWspXruOR+/9O1dfeiErVn1F9y47hjGUeuXiwwcz9pJTmXTjJSwuKmZt6ab0NWHuYvp06VCnb88d2vDh7PTPCZO+nEf/boU0ycshLzuTWDRKfk4WWRkxNpZv3zdd7tu+JZMWpG9W/dnyr+nesnZd+l5tmzN16WrKqxJsKK9k/pr1dG/ZmH4dWvBO9T4fL11NtxaNKchOvzalX58iNM/LZv23PgGzPbj4yAMYe+npTPrTb+ueo3MW06fLDnX69uzYlg9nLwRg0oy59O9WyMJVX9OiUT7jLhvOmUMGEo1EaJyXQ/+uhbwzI/2G4NQ5i+nWrtXWHprUILgMyE/rMuD9IAjuisfj+wOHAcvj8Xj3IAjmxOPxK4DZ/2Fb7yAIPic9I/qb30BT3/eFvuU/9ZkJDAImxOPxRqTD5gXAyng83rM6NB9AOtweCnQMguD4eDzeCjia9FIlSWoD6FnAzUEQvBePx3sA+8Xj8V5AoyAIDovH4+2A94AXfkTdaqDWvfwKjffdh+7PPkMkEmHRby6j1YizKF+wkPWv/5vlN9xEt0ceBqD4+RcoC2az4wP3EcnOpsO1vwcgsWFDzU0ZlVby2mvk/2wfCh9/EiKw8ndX0Gz4GVQsWkTpm2+w4fnnKHzyaaiqYt2/nqFi7hwAsrp0Yf2/JoRcff2V/PAdYr13J+uPdxKJRKi48wYyDjue5MqlJKe+S3LRXLKvvxtIkfhkCsmZn8KcGUTP/m16eyRCxX1/CXsY9UrqiylEdupN7MIxQITE43cQ2fdw+HoFRKJEuuxMJJYBPfoCkHhpPCxyPfX/puzN18nZayAtH3yUSCRC8e+vpOCUX1G1ZBFlb09k40vP0+qhx0lVVbHxhX9RNd/ZkwAH9ujMe/OWctL9z5JKwZij9uPB9z6nsHkTDujRiVP23JVTH3ieZAou/vkAsjMzOHe/vlw14W2emjqLpnk5/PnY/cnLyvzevje+/D6ViQRXTUjfq6FziyZce8Qgfrl7D0594HkyY1E6NmvMUX12CvlIbHmZsRhXHPYzRlQfo2G796BNkwLmrlrDI+9PZ/RR+zHy0J8xesJbVFZ9QJfWzRiya9cffL7OLZsx6umJZGXE6Na6OaOOHLQVR1NPzfkcCuNET/gNAMlXxxPptz+ptUUw/4vv3SW6z+EQyyC6/zHpDeVldW/KKDIzMvjdxRdw5iWXkUqmOObwQ2nTuhVzFyxk3JPP8IeRlzLitJP53R9vIDMzg9ycHK6/aiTNmjZh6fLlHDN8BJkZmYy88DxisVjYw6k3MmMxrjjmQEb8/VGSqRTD9t6NNk0bM3dFEY+8/TGjTxjKyGEHMvqRl6h8LkGXNi0Y0jf9qaBp85dw0s0Pkkim+MWAXdmxTYv/8tUatgO7t+e9xas46dGJpIAxB/fnwamzKWxawAFd23NK326c+vjbJFMpLv7ZrmRnxBixRw9Gvz6VEx+dSEY0yo2H7E6Hxvn8sncXTn3srfTrU9N8jtqlc9jDC01mLMYVxx7EiNvHk0ymGDawT+05+tZHjD7xUEYeM4TR45+nsipBl7atGNKvJ1WJJJNnzOXpdz8hOzODUScMBeDyYw5i9LjneeydjynIyebPZwwLeYTaXCSyPa/Qvm2JpFI/JgvVj1EdUN8FrAa+Jr2O9Emkl8tIAiuA00mvWX3Lt7btTO061lWkl/lIkl5WZK94PJ4DzAqCoHP111oZBEHb6hnO5wZBMCsejz8G3B0EwVvxePxW0utQP0N6iZGupNeT/lsQBGPj8fjOwIPAhuo/nwJ3A8+TXsO6vLr/b4CFpJcruQd4rnqMOdXtFwOfAOOAQqACuCcIgof/07E6N9LYE28LOKd9s//eSf9neXm+r/dTK+zbPuwSGqTMjq3DLqFBWvXmjLBLaJDaXnFW2CU0TFnf/lCcfhILvWnelhAbflXYJTQ4iamvhV1CwzRvVtgVNEzdd/nvffR/FjvgZFPZ73F1VjNzqM2MqSiut+eJYbVCYVi9ZRhWbxmG1T89w+otw7B6yzCs3jIMq7cQw+otw7B6izCs/ukZVm8hhtVbhmH1FmFY/f0Mq+uqz2G1awtLkiRJkiRJkkJnWC1JkiRJkiRJCp2fbZckSZIkSZLUYDlbd9vh90qSJEmSJEmSFDrDakmSJEmSJElS6AyrJUmSJEmSJEmhM6yWJEmSJEmSJIXOsFqSJEmSJEmSFLqMsAuQJEmSJEmSpC0lGgm7Av1YzqyWJEmSJEmSJIXOsFqSJEmSJEmSFDrDakmSJEmSJElS6AyrJUmSJEmSJEmh8waLkiRJkiRJkhosZ+tuO/xeSZIkSZIkSZJCZ1gtSZIkSZIkSQqdYbUkSZIkSZIkKXSG1ZIkSZIkSZKk0HmDRUmSJEmSJEkNVjQSCbsE/UjOrJYkSZIkSZIkhc6wWpIkSZIkSZIUOsNqSZIkSZIkSVLoDKslSZIkSZIkSaEzrJYkSZIkSZIkhS4j7AIkSZIkSZIkaUtxtu62w++VJEmSJEmSJCl0htWSJEmSJEmSpNAZVkuSJEmSJEmSQmdYLUmSJEmSJEkKnTdYlCRJkiRJktRgRSNhV6Afy5nVkiRJkiRJkqTQGVZLkiRJkiRJkkJnWC1JkiRJkiRJCp1htSRJkiRJkiQpdN5gUZIkSZIkSVKD5WzdbYffK0mSJEmSJElS6AyrJUmSJEmSJEmhM6yWJEmSJEmSJIXOsFqSJEmSJEmSFDrDakmSJEmSJElS6DLCLkCSJEmSJEmStpQokbBL0I/kzGpJkiRJkiRJUugMqyVJkiRJkiRJoTOsliRJkiRJkiSFzrBakiRJkiRJkhQ6b7AoSZIkSZIkqcGKen/FbYYzqyVJkiRJkiRJoTOsliRJkiRJkiSFzmVAFIpz2jcLu4QG6Z7lxWGX0CCN2asw7BIanMyWjcMuoUGKtG0bdgkNUtPOK8MuQfrxKirDrqBhatk67AoapNTqJWGX0PCUl4VdQcPUwmvAFlHh+Srpu5xZLUmSJEmSJEkKnTOrJUmSJEmSJDVYztbddvi9kiRJkiRJkiSFzrHuHO4AACAASURBVLBakiRJkiRJkhQ6w2pJkiRJkiRJUugMqyVJkiRJkiRJoTOsliRJkiRJkiSFLiPsAiRJkiRJkiRpS4lGwq5AP5YzqyVJkiRJkiRJoTOsliRJkiRJkiSFzrBakiRJkiRJkhQ6w2pJkiRJkiRJUui8waIkSZIkSZKkBiuKd1jcVjizWpIkSZIkSZIUOsNqSZIkSZIkSVLoDKslSZIkSZIkSaEzrJYkSZIkSZIkhc4bLEqSJEmSJElqsKLeX3Gb4cxqSZIkSZIkSVLoDKslSZIkSZIkSaEzrJYkSZIkSZIkhc6wWpIkSZIkSZIUOm+wKEmSJEmSJKnBcrbutsPvlSRJkiRJkiQpdIbVkiRJkiRJkqTQGVZLkiRJkiRJkkJnWC1JkiRJkiRJCp1htSRJkiRJkiQpdBlhFyBJkiRJkiRJW0o0EnYF+rGcWS1JkiRJkiRJCp1htSRJkiRJkiQpdIbVkiRJkiRJkqTQGVZLkiRJkiRJkkLnDRYlSZIkSZIkNVhRvMPitsKZ1ZIkSZIkSZKk0BlWS5IkSZIkSZJCZ1gtSZIkSZIkSQqdYbUkSZIkSZIkKXTeYFGSJEmSJElSgxX1/orbDGdWS5IkSZIkSZJCZ1gtSZIkSZIkSQqdYbUkSZIkSZIkKXSG1ZIkSZIkSZKk0BlWS5IkSZIkSZJClxF2AZIkSZIkSZK0pUTCLkA/mjOrJUmSJEmSJEmhM6yWJEmSJEmSJIXOZUC0fYtE6HjD9eTuvDPJ8nIWX34FFQsX1TQ33n8wbS+9GICN02ew9KpRRBs1ovPttxItKCCalcXSa//IxqnTwhrBNqvzHrsz7KZruWX/w8IuZdsRiZB/+TXEuu0ElZWU3DCa5NIl3+nT6C93UjFpIuUTniDn1DPJ2mufdFNBI6ItWlL8i8Fbv/b6LBIhesL5RDrsCFWVJMb/DYpW1DYfcBTR/vsCkJzxEamXHoWcPKJnXkEkK4dUoorkgzfD+uKwRlAPRYgcdDyR1h2gqorkq4/A2qK6XXILiJ78W5L/HAOJqtrtzdsQPeVyknf8ru52QSRC1jmXEe3cDSorKL/jRlIrl9U0ZwwdRsb+hwIpKp/4J4mP34O8fLJ/ey2R7FyoqqT81utIrV0T3hhClkymuO7FyQQr15CVEeO6IwbRqUWTmvYnP57FE1NnEotGOXffPgyOd6K4tIzLn36TssoqWjfKZ8xR+5GblVHzfOeOf4UDenTihAE7s3ZjGVc88xal5RU0yc3huiMG0aIgN6zhblE/5bH8vr5Li9dz5YS3SaVStG/aiGsPH0RuVgb3T/qUF7+YR0F2Fmf+rDeD451CPApbRzKV4rqXpxB8VUxWLMZ1h+1Fp+aNa9qf/GQOT0ybnT5++/RicPcdKCrZxMhnJ1OZSNKqIJf/d/hAcjMzeHHGAh76cCaxSJSdWjdl9NA9iUb8YDTAm1OmcedjE4hFYxxz0H788pD967TPnL+I6+8eSzQaJSszk5suPZeWzdLnfDKZ5Jxrb+bne/bnhEN/Hkb59crEGfO467X3iUWjDNtjV47bu3ed9kVFxVz92CtAhO7tWnLNsJ8TjUa44B8TWFtaRkYsSnZmBveOOIYvl67i/Pv/RadWTQE4YeBuDO3bI4RRhW/izIXcNfHj9HHt34PjBuxcp33R1+u4+uk3AejepjnXHL4v0Wikpu3CcS/z3MUnALCxopLrnn2HpcXrqUwkufoX+9C7Y5utO6B6YuIXc7nrlfeIxaIM27MXxw3crU77oqJirh7/EkSqz9djDyIajTBhynQee/dTkskkB/TqznkHD6zZ5+O5Sxj58Au8ee15W3s4UoPgzOptSDwe/1U8Hr/x/7hPTjweX7hlKtr2NTnkYCLZ2cw+4miW33ATHUaPqmmL5ufTftRVzDvtDGYffjQVS5aQ0bw5rUecxYbJ7zL32ONZ9Jvf0nHMH0McwbZpyOUXc+r9t5ORkxN2KduUrH1/DllZrB9xChvv/Cv5F17+nT6551xEpHFtWFD28D9Yf8Fw1l8wnGTRKkr+eNXWLHmbENltb8jIInHzZST+9SDRYWfVNrZoS3TA4HTbzb8l2rMfdOhMZO8DYflCEn+9gtTUd4geOCy8AdRH3XtDRibJ8X8h+c6zRAd/6/h07kn0uF9DXqO627Ny0n2rDKm/T2zPfSEzi7LfnUPFw3eTNfzC2sZGTcg8ZBhlV55D2eiLyTrnMgAyDjiU5KL5lI26gKp33yDzqJNCqr5+eGPWQiqqEjx69pFceuAA/vTqlJq2og0bGTflC8afeQT3nTqUv/77IyqqEtz59jQO69WNcWceQc92LXji45k1+9z25ses21Re8/jeSZ/Sr7AN4848gpP33IVb3/hoq45va/qpjuUP9b35tQ85fveejDvzCPbo3I6x73/O7FVreHH6PB4760juP3Uot0+cyqaKhn+9eCNYQkUiwaO/GsqlB/TlT/+eWtNWVLKJcR/NYvzph3DfiT/nrxM/oaIqwf3vfcFRvbow7rSD6dqyCU9Mm01ZZRV/e+tTHjxlCI/86hBKyit5a87SEEdWf1RWVXHj/eP4xx9/x8M3juKJV9+kqHhtnT5j7n2YUeeezsM3juKggbtz31PP17Td+vCTrNtQurXLrpcqEwlu/Ndb3HfOsYy94Hie/OBzitbXPTZ/eu4tLhq6D+MuPIFUKsWbX8wFYNHqtYy78ATGXnA89444BoAvl67i9MH9GXvB8Yy94PjtNqiuTCS48aV3uW/44Yw960ie/OhLijZsrNPnTy+9y0UH7sG4EUeTSsGbMxcA8NwnAZc99jrFG8tq+j4w6VO6t2nOuBFHc93Rg1m4uu75vr2oTCS4ccKb3Hf+Lxl74Yk8+d5nFK0vqdPnT/96k4sOG8S4i09Kn6/T57B4dTGPvfspY399Ao9fehqVVQkqEwkAVhSv558TP6IykQxjSFKDYFit7VrBHgNYP/FtADZO+4S83rXv+ufv3p+yWbPo8PtRdH/mSapWr6ZqzRqK7vsHq8eNByASi5EsL//e59YPK5q3gHuGnRJ2GducjN36UvnBuwBUzficjJ671GnP2v8gSCapfH/yd/bN2u9AUuvXUznlva1S67Yk0nVnUl9W/+K/MCDSqVttY3ERib+PhlQSUimIxaCyApYthOzq2ZI5eVD9w6nSIjt0hQVfph+sWAhtC+t2SKVIPnE7lNX9JSsy5ESSk56DqoqtUue2JtazN4lPPgAgOXsG0a6b/cK+YR2bfnM6JBJEmjWH0vQvWslF84jk5qX75OaT2s5nq09bvJJ9unUEYLeObZixvHbG//RlRfQtbENWRoxGOVkUNm9MsGpN9T47ADCoe0fen5+ezf7qjPlEI+lt35hXtLbmcb/CNkxbvHJrDW2r+6mO5Q/1nVtUXHMs+xa2YdriVcwrKmZA53ZkZ2aQnZlBp+ZNmL3q660/+K1s2pKv2KdLewB269CKGStqxzx9+Wr67tCq9vg1a0TwVTG/O2h3Du/VhWQqxcr1pbTIzyUrI8b40w8hNzP9yYCqZJLsjFgoY6pv5i9ZTmG7NjQpyCcrM4P+O8eZOiOo0+eWkRfQs0t6Jn8ikSQ7KxOAVyZ/SDQaZVD/3t953u3R/FVr6NSyKU3ycsjKiNFvxw5MnV/3TZEZS75iQNfqa0HPHXl/ziJWbyhlw6Zyzv/Hvzjl9kd5a8a8dN+lX/HOl/M59e+PMeqxVykt2z5/RphftJZOLZrQJDc7fVw7tWPqwhV1+sxYtpoBO6avFYN2KuT9eenj3jg3m7FnH1mn77tzlpAZi3L2P1/grokf87PNXsu2J/NXfk2nls1qz9cuHZg679vn6yoGVL/eDdq5C+/PXsT7wSJ27diWK8e/xGm3P0rfLh3IjMUor6zi2ideY/RxB4UxHP0X0Yh/Nv9Tn7kMyLZn73g8/gbQGPgDUAKMARLAPOAcIBsYDzQD5n6zYzwefwsoqt5+GHA/0BWIAbcEQfB4PB7vC9xe/XxlwNmk39R4HFgCdAYeA3YF+gIvBkFwVTwePx84HUgCk4Mg+O6Uz3ooWlBAcsOG2g3JRDqMSiTIaN6cgoF7M2vIoSRLS+k+4SlKp06jfH76HeqMVq3odPttLPv9tSFVv+365JnnaNGp8L93VB2R/AJSJbXnayqRrDlfY126kTXkMEqu+g25Z3z342a5p53Fht9vE/8tt76cPNi02YyfZBKi0fTfyQSUrgcgOuxMUkvmw1fLSWVmE+3Zj9g1d0FeIxK3jAyp+HoqK4dU+abax6kkRKLpvwEWzfrOLpGBh8L8GVC07DttqpabDxs3P1cTEI2l/65+nDH0GLJOPJPKF55Mb9uwnlifPcj92zgoaEzZ1edv/brrkZLySgpysmoeR6MRqhJJMmJRSsoraJRd25afncmGsgpKyitpVL1PflYmJeUVzKme4XvrLw/kzrdrlwLr0bYFE2ctYud2LZkYLGJTZcN9c+CnOpY/1Ldn2xZMDBZxVJ+dmDhrERsrKtmpTXPum/QZpeUVVCaSfLpkFcft3vBnWZaUV1KQ/a1jnUySEY3WOaZQffzKK4lE0t+Po+9/gYqqBOcN6k00EqFl9bI04z6axcaKKgbu2G6rj6c+Ktm4iUb5eTWP83Nz2FBa9w3V1s2bATBt5mzGv/A6424axeyFS3jx7fe47cqLuOPRCVu15vqqpKycgtzNz8ksSsrqTu5JkSJSvfxMfnYWGzZVUFmV4FeDd+fUQf1Yt7GMk29/lF6F7ehV2JZj9+zFLh3bcPfrH3DHa+8x8ojBW3NI9UJJWUWda25+duZ/Oa7paynA4B6dv/N8xRvLWLepnPuG/4JnPwn488vvc+Nx298SNiVlFXXP15zvOV9T3z5fyyku3cTH85bwyCUnU1ZZxcm3jueJ357Gn5+dyPADBtCm6bc+PSjp/8SwettTSjpobgVMASqBfYIg+Coej/8R+BXpsPqLIAiujsfjewIHbLb/I0EQTIjH478GVgdBcGo8Hm8ETKsOwe8DzgqC4NN4PH4kcAtwGdAFGALkAguADsBGYBFwFTAcuDAIgg/i8fh58Xg8IwiCev8bWrKkhGhBfu2GaLRmhmRVcTEbP/ucqqL0TKGSD6aQu8vOlM9fQE6POJ3v/DvL/ziGkg+mfN9TSz+5VGkJkfzNz9dIzfmaPfQIoq1a0/jvDxBt1x4qK0muWEblB+8S69yFZMmG765vrbSyjZCz2Zqykeqg+hsZmURPvQTKNpF87E4AooedRPL1p0hNfgU6dCY24ioSY369lQuvxyrK0ut5f/M4EqkNqn9AZOc9YEMxkV57Q35josddSPKxv27xUrcpm0rTb658IxKtDaqrVb38NFWvP0vONX8hsWs/Mg87lsoJ46l67VkinbqSM3JMegb2dqogO5PS8tpZeakUZMSi1W1ZlFZU1rSVllfSOCerep9KcjIzKK1IB4PPfjaHVetLGT72RZat3UBmLEqHpo0YMWg3xrz0PmeMfZF9unWkXeOCrT7GreWnOpY/1HfkwXtx/Yvv8tL0eey5Y3ua5eXQtVUzTt5jZ84Z9wqFzRvTe4fWNMtr+EuKFWRn1jlGqRRkRKO1beXfOn7VwXZmLMoL5xzBewtWcOVz7/LQqQeTTKW4+Y1pLFqzntuO3a8mgNle3frwk0ydETB74RJ6x7vWbC/dVEajzX/mqvbSOx9w9xPPcs8fLqN5k8b84+kXWfV1Madf9f9Y9tVqMjMy6NCmJYP67/adfRu6216azLQFywiWr6Z3p7Y120vLK2iUW/f/6ebrpJeWV9A4N5uWjfM5fu/dyIhFadEoj54dWrOgaA0H9upG4+r9D+zVnTET3tw6A6onbnt9CtMWrSRY+TW9d2hds720vJJGudl1+tY9rpU0zqnbvrmmedkc0HNHIB1m3/f2Jz9x5fXbbS9OYtr8pQTLi+jdqfZNu9KyH3G+5mXTNC+HAd0Kyc/JJj8nm65tWzJ7eRFT5y9l8eq13PnKe6zbuInfPvgcf/nVEVttXFJD4TIg257JQRCkgiD4CtgEdASeqJ41PQQoBHYBPgQIguCbQPsb33yerSfwTnWfDcCXpGdZtw+C4NPqPu9UPxfA/CAI1gFrgVVBEKwJgqAMarKI4cC58Xj8baATsE385Fvy0cc0PiB985S8fn0pm1n7cb9Nn08nJx4n1qwZxGLk9+tL2ew55HTvzo733MmiX1/E+olvhVS5tkdVn39C5t6DAMjYpTeJeXNq2jbecQvrzzqJ9RcMp/zFZ9n02EM1S4ZkDtibyvcnhVLztiA170siuwxIP+gcJ7V8YZ326LnXkFq6gOSjf68NXDeWwKbqGVcb1tUNEEVq2XzoUv3y0a4zFC3/r/sk7/8DycdvI/n4bVC6nuSTt2/ZIrdBiZnTifXfG4DoTruQXDyvpi3SvpDsK/5f+kFVFanKSkglSZWsJ7Wxeu3FdcWQ993wZXvSt7Atk+ak37j7bMkqurduVtPWq0Mrpi5aSXllFRvKKpi/ei3dWzejb8c2vFO9z6Q5S+hf2JbLhuzJ4yOOYuzwX3BUn504fe9eDOrekY8XreTIPt154PTD2KFZI/oWNtybVf1Ux/KH+r43bynnD+7HvacOJRqJMLDrDqwp3UTxxjLGnXkEVw4dyIp1JXW+bkPVt2MrJs1Lf+rks2VFdK++0RxAr/YtmbrkK8qrEunj9/U6urduynUvT2HKwvQyNPlZGTVhyx9e+oCKqgS3Hze4ZjmQ7dklpx7HwzeOYvK4O1i8fBVrN5RQUVnFR1/Mom+PbnX6PjdxMuNfeI2Hbriajm3ToeHlZ5zIE7dcy8M3juLonw9i+FFDt8ugGuDiQ/dh7AXHM+m6c1m8ei1rSzdRUZXg4/lL6dOp7gz+nh1a8+Hc6mvBzAX079KB92cv5tKH0uuAl5ZXMGflarq2bs7Z9zzN54vSy118MGcRu2wW2G4PLj5oT8aedSSTrjydxWvWs3ZjWfq4LlxOn2/dELFnu5Z8WL1U1aTZi+nf+Yc/OdGvUzveCRYB8PGC5XRr0/CvpZu7+LBBjL3wRCZdfwGLi4prz9d5S+nTuX2dvj13aMOHcxYDMOnL+fTvsgP9uuzAR3MXU15ZxcbyCuatXE23di156eqzGXvhiYy98ESa5OUaVEv/I39C2fYMAIjH422BHGAhcGQQBOvi8fgRpJcF6QXsDTxbvaxH5mb7fzO1bSYwCJhQPbO6F+kZ08vj8XjvIAg+B/YDZlf3T/GfnQ2cGwRBWTwefxUYCLz9/2ukW8G6l1+h8b770P3ZZ4hEIiz6zWW0GnEW5QsWsv71f7P8hpvo9sjDABQ//wJlwWx2fOA+ItnZdLj29wAkNmxgwRlnhzkMbScq3n6DzD0G0vjecQCUjLmGnBNOI7F0MZWT3/rB/WKdOlPx4ftbqcptT+qz94n07EvsspsBSDx8K5EDjoKiFRCNEunei0hGJuzSP93+7FiSzz9M9OSLiO57GMRiJMf/Lcwh1D+zP4NOPYie9FuIQPLlcUR2P4BUcRHMmx52ddusxJS3ifUZQM4Nd0MkQvntY8g44nhSK5aR+GgyyQVzyLnxXiBFYtoHJGd8SuXypWRd8DsyDxkGGRmU33lT2MMI1YE9OvPevKWcdP+zpFIw5qj9ePC9zyls3oQDenTilD135dQHnieZgot/PoDszAzO3a8vV014m6emzqJpXg5/Pnb/H3z+zi2acOWEtwBo3Sif64/cdyuNbOv7qY5lXlbm9/bt3LIpo559h6xYjG6tmzHqsJ+REY2wpHgDv7xnApmxGJcP2ZNYtOHPvTkwXsh781dw0oOvkCLFmF8M5MEpX1LYrBEH7NSRUwb04NSHXiWZSnHx4D5kZ8Q4ZUAPrn15CndN/pxIJMI1h+zJlyu+5ulP59K/sDXDx70OwKkDenBgD5dmy8zI4IqzTuas0TeRTKY45qD9aNOyOXMXL2P8C68x6pzTGXPPw7Rr1YILx9wKwIBePbno5GNCrrz+yYzFuOLIwYy492mSqRTD9tiVNk0bMXfl1zwy+RNGH3sgI4/Yj9FPvE5lIkGXNs0ZsttOxKJR3p21kBNufYRoFC45dB+aFeTx+2MP5Ppn3iAzFqNlo3yu/eX2uRZwZizGFUMHMuLBF9LHtX9P2jQpYO5Xa3jk/S8YfeS+jDx0IKMnvEXla1Po0qoZQ3bt8oPPN2K/foye8BYn3v0MGbEoNx57wA/2bcgyYzGuOPoARtz1ZPq47tWr+nxdzSPvTGP0L4cw8qj9Gf3YK1S+kKRLmxYM6RMnFo1yzF69OfnW8aRIcd7BA2man/vfv6CkHyWSSv23DFL1RTwe/xVwApAFFABXkl5vejTpWfLrgdOANcA/gR2BWcCgIAji1bOvzw2CYFY8Hs8iveRHV9JLe/wtCIKx1eH2baRnRlcBZ5IOuB8LgmCveDyeA8wKgqBzdU0rgyBoG4/HzwIuJr0m9jLg7OqZ19/rkw6dPPG2gHuWF4ddQoM0Zi9/ifupNfn/2LvvOKmq+//jrzsz2ykiVYGlM4CgYEHFAhbsFUsMlthii9GoUbH+LLHGWGJLUWPXYMESY4sVBOygIl6lgxQBQdll2TIzvz9m3aIY9Rvhbnk9Hw8e7sw5M/O54517d9/3zDlDukVdQpMU9OgecQVN05qJU364k36y/F8cGHUJ0o9X8b2/1up/ENt296hLaHLSnzav6RzWm7LVP9xHP10L53ZeF+J7HNcovum+vt3buoM5VB1HffVFg91PDKsVCcPqdcOwet0wrP75GVavG0GP7hFX0DQZVq8bhtVqVAyr1wnD6p+fYfU6Yli9bhhWrxOG1WtnWF1fQw6rm/735iRJkiRJkiRJDZ5htSRJkiRJkiQpcobVkiRJkiRJkqTIGVZLkiRJkiRJkiKXiLoASZIkSZIkSVpXYg12OUF9myOrJUmSJEmSJEmRM6yWJEmSJEmSJEXOsFqSJEmSJEmSFDnDakmSJEmSJElS5FxgUZIkSZIkSVKT5WjdxsP/V5IkSZIkSZKkyBlWS5IkSZIkSZIiZ1gtSZIkSZIkSYqcYbUkSZIkSZIkKXIusChJkiRJkiSpyQqiLkA/miOrJUmSJEmSJEmRM6yWJEmSJEmSJEXOsFqSJEmSJEmSFDnDakmSJEmSJElS5AyrJUmSJEmSJEmRS0RdgCRJkiRJkiStK7EgiLoE/UiOrJYkSZIkSZIkRc6wWpIkSZIkSZIUOcNqSZIkSZIkSVLkDKslSZIkSZIkSZFzgUVJkiRJkiRJTZbLKzYejqyWJEmSJEmSJEXOsFqSJEmSJEmSFDnDakmSJEmSJElS5AyrJUmSJEmSJEmRc4FFSZIkSZIkSU2WCyw2Ho6sliRJkiRJkiRFzrBakiRJkiRJkhQ5w2pJkiRJkiRJUuQMqyVJkiRJkiRJkXOBRUmSJEmSJElNlgssNh6OrJYkSZIkSZIkRc6wWpIkSZIkSZIUOcNqSZIkSZIkSVLkDKslSZIkSZIkSZEzrJYkSZIkSZIkRS4RdQGSJEmSJEmStK4EQRB1CfqRHFktSZIkSZIkSYqcYbUkSZIkSZIkKXKG1ZIkSZIkSZKkyBlWS5IkSZIkSZIi5wKLkiRJkiRJkposl1dsPAyrFYnCQne9deGKbYqjLqFJumDyvKhLaHKua1MYdQlNUn67tlGX0CSlV5dHXULTVFkRdQVNU+duUVfQNH3wdtQVNElB575Rl9D0zPwg6gqaplVfRV1B09SuU9QVSGqAnAZEkiRJkiRJkhQ5w2pJkiRJkiRJUuQMqyVJkiRJkiRJkXPiYEmSJEmSJElNlqN1Gw//X0mSJEmSJEmSImdYLUmSJEmSJEmKnGG1JEmSJEmSJClyhtWSJEmSJEmSpMgZVkuSJEmSJEmSIpeIugBJkiRJkiRJWleCIOoK9GM5slqSJEmSJEmSFDnDakmSJEmSJElS5AyrJUmSJEmSJEmRM6yWJEmSJEmSJEXOBRYlSZIkSZIkNVkBrrDYWDiyWpIkSZIkSZIUOcNqSZIkSZIkSVLkDKslSZIkSZIkSZEzrJYkSZIkSZIkRc4FFiVJkiRJkiQ1WS6v2Hg4slqSJEmSJEmSFDnDakmSJEmSJElS5JwGRJIkSZIkSZIEQDKZjAG3AZsB5cDxYRjOqG4bDNxYp/s2wAHAW8CnwEfV948Lw/Cmn/rahtWSJEmSJEmSpG8cAOSHYbhtMpncBvgTsD9AGIZTgBEAyWTyEGBhGIbPJZPJXYGHwjD87f/ywobVkiRJkiRJkqRvbA88BxCG4eRkMrnltzskk8ki4FJgx+q7tgA2TyaTrwFfAKeFYbjop76wc1ZLkiRJkiRJarIC/9X79yO0Ar6qczuVTCa/Pej5OOCRMAyXVd/+BPh/YRgOB54Abv5xL1WfYbUkSZIkSZIk6RtfAy3r3I6FYVj1rT6HA3fUuf0y8Er1z+OAIf+XFzasliRJkiRJkiR94w1gL4DqOas/rNuYTCZbA3lhGM6vc/cdwEHVP+8CvPt/eWHnrJYkSZIkSZIkfWMcMDKZTE4kO3PIMclk8kxgRhiGTwF9gTnfeswY4K5kMnkKUAoc/395YcNqSZIkSZIkSRIAYRimgZO+dfcnddrfBg741mNmAzv9r69tWC1JkiRJkiSpyYr9yFUFFT3nrJYkSZIkSZIkRc6wWpIkSZIkSZIUOcNqSZIkSZIkSVLkDKslSZIkSZIkSZFzgUVJkiRJkiRJTVaAKyw2Fo6sliRJkiRJkiRFzrBakiRJkiRJkhQ5w2pJkiRJkiRJUuQMqyVJkiRJkiRJkTOsliRJkiRJkiRFLhF1AZIkSZIkSZK0rgRRF6AfzZHVkiRJkiRJkqTIGVZLkiRJkiRJkiJnWC1JkiRJkiRJipxhtSRJkiRJkiQpci6wKEmSJEmSJKnJClxhsdFwZLUkSZIkSZIkKXKOM4fsawAAIABJREFUrFbzFgR0vPQy8vr1I1NRweLzz6dy3tya5g4XXUzB5luQLi0B4POTTqTt6b8jv39/AOLt2pNe9TXzDjk4kvIbrCCg6OyLiPfuC5WVlFx1MekF87/Tp+WfbqNi/CuUjxtL/pHHkbvN9tmmFi2JtW3Hin1GrP/aG7HuQ7dk1DWXcv1Oe0ddSuMUBOT95lxiPfpAZQVrbrqCzKIF3+mTf+kNVE1+nap/Px5NnY1BEBDb51cEnYqhqpLUk3fCl1/U71PYkvivLyJ16wVQVQkFRcQOOokgr4DM6hLST90Jpauiqb+hCgLyfjuGeI8+ZCorWXPj5WQWfncfLbj8RqomvU7lM49BYREF514OhUUEiRzW/O0G0tM/jKb+BiSdyXDZs5MJl6wgNx7jsn2G0W3DVjXtj7z3KWPf/5R4LOCk7TdlRJ+uLPyqhAufnkgqnSYDXLr3tvRo25oXps/ljokfEgRwyJC+HDykb3Qb1gC88kHI7f8eTzwWMGrYEA7ZfvN67XO/+JIL7n0SgD4bd+Ciw/YiFgu4cuxzvD9zPoV5uZx54C5s1qNLzWP+9daHPPDqWzx0znHrdVsaonQmw2WvTyNcviq7744YSLfWRfX6fFlWzuhxk3ny0O3JS8Rr7v/PrMU8P3Mxfxw5eH2X3Si8/Np4bv3bHSTicQ46YD8OHXXAWvs9/exz3P/QWP55710A/OGa63hv6gcUFRYCcNsN19GyZYv1VndD9MpHM7j9uYnE4zFGbT2IQ4ZtVq997tIVXPDAvyEI6LNROy46eCSxWMC4Nz/k4TemkE6n2XlQH07efRgrS8vY64o76LNROwB2HdSHI0dsGcVmRSqdyXDZ828TfrGC3Hicy/bamm5tWta0PzJlBmOnzMiet4YNZETvziwtKeOcpydSmUrTvkUBV+69DQU5CZ76aDZ3vTmdFnk5HDioJwdt1ivCLWs4XpkacvszrxKPxRi13RAO2WHt+9nVY5+le8d2HDZ8q5r7vlxVyuhr7+DJi08hLydnfZUsNVmG1fpRksnk0cCXYRg+FXUtP6cWI0cS5OUx79BDyB88mPbnncfCk0+qac8bsAkLjj2a1IoVNfctveIP2R8SCYof/ieLLzh/fZfd4OXuuAvk5vL1CUeQ2GRTin57NqvOPa1en4ITTyNo1brm9pr77mTNfXcC0PK6W1l92/XrtebGbrezT2frIw+jvHR11KU0WvFth0NOLmVnHUcsOZC8409nzeVn1+uTe9RJBC1bfc8z6BtBvy0gkUPq75dBl17Edh9N+qEba9t7DyI28lAoqj0GxHbcl8y8T0m//jRBz02I7XoI6SfviqL8BisxbARBTi6rzziWWL+B5J1wBmsuOaten9yjTyZoWfu+5h50OFVT3qZy3EMEXbpRMOYKVp96xPouvcF5KZxHRVWKh47Zi6kLlnLtf97h1kN3BmBpSRn3vz2dR47bh/KqFEfc8yzDemzMza9OYfRW/dg1WcyEmZ9zw8vvccNBw7n+5Xd55Lh9KMxNsO9fnmSXZDFtCvMj3sJoVKZSXP3oC4w993gK8nI54rp/MGJQX9q3rg3urn30BU7bbyeG9u3OJQ8+w8sfhCTiMeYsWc4/zz2er1aXccLND/DIeb8GYPr8xTw28X0ymai2qmF5afYSKlJpHhq1LVMXr+DaiZ9w655b1LRPmLeU698MWb66vN7jrpzwMW/MX0a/dp7D1qaysoqr/nQDj95/NwUFBfzy6OPZacftad+uXb1+08NPefSJp6i7O077JOSOW//Mhm02WL9FN1CVqRRXj3uZsWcdRUFuDkfc+AAjBvaifas6x4EnXua0vXdgaJ9iLvnn87z84Wf07dyeh9+Ywj2nHkZuIsEtz06gMpXi4wVL2Gvz/lx48K4RblX0Xvp0Qfa8ddTuTP18Gde+9B63HjwcqD5vvRvyyK/2yJ63HniRYd07ccfkjzlgYA/2H9STW8Z/wNj3Z7DfwO7c9PpUHjtmT1rl53LcQy+zTbeOdN6geV9gqUyluPqR5xh73gkU5OVwxLV3MmLTJO1b114Q+HJVKWP+8ThzlyznmN1qjw0Tps3g+nEvsvzr0ihKl5okpwHRjxKG4d1NLagGKNhiS0pffx2ANVOmkD9wUG1jEJDbvTsd/3AFxQ+PpdXB9UdPtznyKEonTKDi00/XZ8mNQmKzIVROfgOAqmkfkOi/Sb323J1GQjpN5aQJ33ls7vBdyXz9NZVvTlwvtTYVS2fO5q+jDKD+F/FNBpN6dxIA6fAjYn3612/fbmfIZEi9MymK8hqVoFtfMp99kL2xYCZB5+71O2TSpO6+BspKau9r35nMp9nHZOZ9SlDcvEenrk18k8FUVe9/6U8+Iv6tfTSx/S6QzlD1du3xs+LxB6l8JvstgCAeh8r6AVZz9d78L9i+V2cANuvSnmmLltW0fbhwKUO6diA3Eadlfi7FG7Yk/GIF54zckuG9s6N9U+kMeYk48ViMf518AC3zc1lZVk4GKMxtviOqZi1aRrf2G9K6qIDcRJzNe3Xl3Rnz6vWZNm8RW/XpBsAOm/Rm0vRZzFy0lO0G9CIWC2jTopBYLGDpVyWsLFnNDU+8xHmH7B7F5jRI7y1awfZdsyHJZp3aMG3pV/XaY0HAXfsOpXV+br37h3Rqw8U71v99TLVmzp5NcdcutG7VitycHLYYshnvvD+lXp8VK1dy3Z9v4fzfn1lzXzqdZu68+Vz8hys57OjjefSJJvfn0k82a/FyurVrQ+vC/OxxoGdn3p1Z/1tA0+YvYaveXQHYYUBPJn06l0nhXAZ27cR5D/ybo25+iCE9O5MTj/Px/CVMX7CEo/78IL/7x5Ms/apkbS/b5L234Au277kRAJt1bse0xV/WtH24aDlDOrevPW9t0JJw6UrG7LI5+w7sQTqTYfGq1bQtymf+yhL6dWjDBgV5xIKAgRttyNSFy77vZZuNWYuW1jl/Jdi8d/F3zl+ryyv4zb47se829b8pEAsC7vrdr2hdVLA+S5aaNEdWC6gZOb0/0ApoB1wGXAp8CpQDIbAY+BvwZ2AokAv8vzAMn0wmk1cBO5K9AHJ9GIaPrO9t+L+ItWhBelWdr5mn0xCPQypFUFjIivvuZcVdd0I8TvF9D1D+4YeUhyHk5LDBYb9k7kEHRld8AxYUtSBTUvu+ZlK172u8Z29yd9ubkvPPoODYk7/z2IKjjmfV/zv7O/frv3v/8ado26046jIataCwiMzqOn8ApdMQi0M6RaxbT3JG7M6aK8eQO/r46IpsLPLyobys9nY6A7FY9j0FMjOnffcxi+cS9BtCZvFcgn6bQ07eeiq2ESksIlP6fftoLxI77c6aP5xL7uG/ru1T3T9o05b8cy6n/C9/Ws9FN0wl5ZW0yKsNlWNBjKp0mkQsRkl5JS3zaoO+otwcVq2pqBktPXv5V/zxP+9w86E7AZCIxXjxk7lc/tybDO/dhUSs+a7eU7KmnBYFtZ/dovxcSsrW1OuTIUNQvcJRUV4uq9aU069LJ+7+zyRGj9iKxV9+zYxFSymrqODSx17k3IN3I78ZXwD4tpKKKlrk1t13g5p9F2BY13ZrfdyevTfirc+Xr5caG6OS0lJatqgdWVpUWETJqtrjbSqV4oJL/8D5Z51BXn7tPr66rIwjDjuEY444nFQ6xVG/PpmBA/rTr2+f9Vp/Q1KypoIWBXWOofm5lKypf6E0k/nWcaCsnBWlZbwzcz4P/u5w1lRWcfiNDzD2rKPo0XFDBnTdjmHJ7jz9zjSueOw/3Hjs2qdoacpKyqtoUefcFIsF3zpv1R4XinITrCqvJAgCqlJpDrzr31RUpTh5u0G0yMthxrKvWFZaRlFuDpPnLqH7hn7j4rvnr7zvnL+6tGtDl3ZtGP/RZ/XuHzbAaVQai+b7G1rjY1ituloAI4H2wFtAHLg8DMP3k8nkJdV99gfahWE4NJlMdgJOTSaTFUCPMAy3SyaT+cDkZDL5YhiGKyPYhp8kXVJCrKjOPH+xAFIpADJlZay4524ya7InqdWTJ5HXrz/lYUjRsO1Y/fbbpEua55X9H5IpLSH4nvc1b8/9iLXvQKtb7iK20cZQWUl60edUTn6DePeepEtWfXd+a2k9yKwuJSj41n6bzu63iV32JmjXnoKrbiPouBFUVZFZspDUu5MjqraBK18DuXWmQQiCmqD6+6Rf/xexvY4g+NW5ZGZ8AF8bqnzH6lKC6jlRger3NbuP5uy6N7F2HSi45i/EOm4EVZWklywk9c4kYt17kX/+lZT/7SZSH74XUfENS4u8HEorqmpuZzKZmrAv21ZZ01ZaUUmr6lGqb85ZxOXPvsnV+29Pj7a1062M7NeNXZLFnP/UBJ78YCajBjevoOqmJ1/mvZnzCT9fwqbdO9fcX7qmgpbfmhIlFtT+qVhaXkGrgny2G9CLD+cu5Jgb7qVfl05sUrwRK0vLmPvFl1z20L8pr6xi5uKlXDX2ec47tHmPsm6Rm6C0cu37rn66G269nffen0r42Qw2HVg78rx0dSktW9Z+/X/a9E+YO28+l1x5DeUVFcyYNZsr/ng9Y848naNGH0ZBQXY/32bolnzy6WfNMqy+6ZnxvDdrAeHCpWzabaOa+0vXVNCy4AeOA4V5bFCYz1a9iynKz6MoP49endoxZ+kKtulTXHPBatdN+3LLv99YPxvUwLTIS9Q7N333vFV7XCitqKJVdXidE4/xr1/vw8Q5iznvX5O49/BdGbPL5vxu3AQ6tixgQMfsKOvm6qYnXuK9mfMIFyxh0x51z1/l39lvJa0/htWq67UwDNPAkmQyuQLoT3ZEdV1JYBJAGIaLgQuTyeQ5wBbJZPLV6j45QDegwYfVZe+9S4udd2HVs/8mf/BgysPaKT1ye/RgoxtuYu4B+0EsRsEWW/LV49mvUhcOG0bp669GVHXDV/XB++RsP4KKl54nscmmpGbWXn1efWvtXNQFx51C+stlNVOG5Gy1LZWTxq/3eiWA1MdTSQzdgarx/yGWHEh6zsyatoq7bq75OffwX5Nesdyg+r/IzPuUIDmEzLS3oEsvMl/88AWooHuSzNQ3yMyeTjBgSzLzPvvBxzQ3qY+nkth6B6pe/w+xfgNJz5lR01Z+559rfs494gQyK5Zng+riHuRfeA1rrjyP9Czf028M6dKBVz+bz54DujN1wVL6dGhT0zZo4/bc9Mr7lFelqKhKMWvZV/Tp0IY35yziqhfe5q+/3LVmbs+S8gpO+efL3DF6JLmJOAU5iXohTHNx+v7Z+b4rUyn2vfR2VpaWUZiXyzufzeOYkdvW69u/ayfe+nQOQ/t2Z/y0GWzdtztzliynbcsi7v/9MSz68ivOu+cJNu3emacvzn4D6/PlKznrjseafVAN2ek8Xp37BXv23oipi1fQZ8OWP/wgfa8zfpPdxyorq9j7oF+w8quvKCws5J33pnDcUbXTq206cBOeeeyfACxYuJAzx1zIBWefycxZszljzAWMe+g+0ukM770/lQP3bZ4LXZ++9w5A9XHgyjtrjwMzF3DMzkPr9e3fpSNvfTaPoX2KGf/xLLbuU0yvTu14aML7lFdWkUqnmbl4GcXtNuCih59j5GZJ9hzSj8mfzmVA145RbF7khnRuz6szPmfP/t2Y+vky+rSvnSN90EZtuen1qbXnreVf06f9Blz2/Nvs3q+Yrbt1pCg3QSyAqnSaqQuXc+/hu1KVTnPcQy/zu+Gb/ZdXbtpOP2AXoHq/veQWVpaurj5/zeWYkdtFXJ3UfBlWq64tAJLJZEey04F8AXx7KNx04JDqfq2BscCtwCthGJ6QTCZjwEXArPVV9P+i5IUXKNpue4r/+QgEsHjMubQ55lgq5s6l9OWXWPX0UxQ/8hhUVfHVE49TMSP7h35uz558/cS4iKtvuCpee4mcocNo9bf7ASi54iLyDzuK1IJ5VE549XsfF+/WnYq3nA9Y0UhNfJXEkK0puO4OCALW3HAZOQeOJr1wPqk3vYjyU2Smv0vQayDx4y+CICA17u8Ew/aA5UvIhO+v/THLFhEfdWL2569XkH7yjvVZcqNQ9cYrxDffmsIb7gQC1lx/KTmjDs/uo5NfX+tjco89lSAnl7yTfg9AZnXJdxZlbI527VfMxNkLGX33v8lk4Ip9t+PuydMo3rAlO/ct5oit+nPkPc+SzmQ4fcTm5CXiXP3C21SmUpz/VHa9he5tW3Pp3tuyz8CeHHnvc+TEYvTt2IZ9B/WMeOuikxOPc+7BIznh5gdIpzOMGjaYjhu0YsaipTz46ttc/Mu9OOeg3bj4gaeprErRs1N7dtu8P1WpNBOmzeCxN94nLyfBhYftGfWmNFi79uzIxAXLGP34JDLAFTsN4u6psyluVcjOPZpniPdzyMlJMOas33HcKaeRyWQ4aP996dihAzNmzuL+fz7CJeefu9bH9erZg3332oNDjzqWnESC/ffZiz69mveUADnxOOceuDMn3P4I6UyGUdsMouMGLZmxeBkPvv4eFx+6G+ccsBMXP/wclf9K07NjW3YbnCQei3HQNpty+I0PkCHDybsPY4OiAs7cdzgXPvgsD094n4LcHC4/bI+oNzESuya7MnHOYkbf9wKZTIYr9t6Gu9+aTnGbluzcpwtHbJHkyPtfzJ63dtyUvEScI7bsy6XPvc3tb3xIEARctNtWJGIxcuIxDv7Hc+QlYhw9tH+zXRS4ruz5aw9OuOm+7H47bAgd27RixsIvePDVt7h49D5Rlyg1K0HGpbVFzZzVJwKrgdbAhcBfgH5hGK6pngZkMfBXsnNWDyF7seNS4DngT8BWZKcSGReG4WX/7fXCPr3c8daBdu1c1GFduGDyvB/upJ/kuj37RV1Ck5S/pe/rulD25sdRl9AkFR4xKuoSmqbO3aKuoGn64O2oK2iS4idcGnUJTU7q9UejLqFpWrTgh/vop+uRjLqCJik+4rDm9zWvH+GF9hubQ9Wx29KFDXY/cWS16notDMMxdW53/+aHMAwvqXP/b9fy2DPXcp8kSZIkSZIUqZhLLDYarsYhSZIkSZIkSYqcI6sFQBiGd0ddgyRJkiRJkqTmy5HVkiRJkiRJkqTIGVZLkiRJkiRJkiJnWC1JkiRJkiRJipxzVkuSJEmSJElqsoKoC9CP5shqSZIkSZIkSVLkDKslSZIkSZIkSZEzrJYkSZIkSZIkRc6wWpIkSZIkSZIUORdYlCRJkiRJktRkBa6w2Gg4slqSJEmSJEmSFDnDakmSJEmSJElS5AyrJUmSJEmSJEmRM6yWJEmSJEmSJEXOBRYlSZIkSZIkNVmur9h4OLJakiRJkiRJkhQ5w2pJkiRJkiRJUuQMqyVJkiRJkiRJkTOsliRJkiRJkiRFzrBakiRJkiRJkhS5RNQFSJIkSZIkSdK6EhBEXYJ+JEdWS5IkSZIkSZIiZ1gtSZIkSZIkSYqcYbUkSZIkSZIkKXKG1ZIkSZIkSZKkyLnAoiRJkiRJkqQmK+b6io2GI6slSZIkSZIkSZEzrJYkSZIkSZIkRc6wWpIkSZIkSZIUOcNqSZIkSZIkSVLkXGBRkiRJkiRJUpPl+oqNhyOrJUmSJEmSJEmRM6yWJEmSJEmSJEXOsFqSJEmSJEmSFDnDakmSJEmSJElS5AyrJUmSJEmSJEmRS0RdgCRJkiRJkiStK0HUBehHc2S1JEmSJEmSJClyhtWSJEmSJEmSpMgZVkuSJEmSJEmSImdYLUmSJEmSJEmKnAssSpIkSZIkSWqyApdYbDQcWS1JkiRJkiRJipxhtSRJkiRJkiQpcobVkiRJkiRJkqTIGVZLkiRJkiRJkiLnAouKRPGQjaMuoUnKadcq6hKapOvaFEZdQpPz+2c/ibqEJumWPhtFXUKTlN+tXdQlNE3Lv4i6gqapXYeoK2iaOnWJuoKmqWxV1BU0PeXlUVfQNOUXRF1BkxQUtoi6BDUjgesrNhqOrJYkSZIkSZIkRc6wWpIkSZIkSZIUOcNqSZIkSZIkSVLkDKslSZIkSZIkSZEzrJYkSZIkSZIkRS4RdQGSJEmSJEmStK44Wrfx8P+VJEmSJEmSJClyhtWSJEmSJEmSpMgZVkuSJEmSJEmSImdYLUmSJEmSJEmKnAssSpIkSZIkSWqygqgL0I/myGpJkiRJkiRJUuQMqyVJkiRJkiRJkTOsliRJkiRJkiRFzrBakiRJkiRJkhQ5F1iUJEmSJEmS1GQFgUssNhaOrJYkSZIkSZIkRc6wWpIkSZIkSZIUOcNqSZIkSZIkSVLkDKslSZIkSZIkSZFzgUVJkiRJkiRJTZbLKzYejqyWJEmSJEmSJEXOsFqSJEmSJEmSFDnDakmSJEmSJElS5AyrJUmSJEmSJEmRM6yWJEmSJEmSJEUuEXUBkiRJkiRJkrSuBFEXoB/NkdWSJEmSJEmSpMgZVkuSJEmSJEmSImdYLUmSJEmSJEmKnGG1JEmSJEmSJClyLrAoSZIkSZIkqckKApdYbCwcWS1JkiRJkiRJipxhtSRJkiRJkiQpcobVkiRJkiRJkqTIGVZLkiRJkiRJkiLnAouSJEmSJEmSmqyY6ys2Go6sliRJkiRJkiRFzrBakiRJkiRJkhQ5w2pJkiRJkiRJUuQMqyVJkiRJkiRJkTOsliRJkiRJkiRFLhF1AZIkSZIkSZK0rgSxIOoS9CM5slqSJEmSJEmSFDnDakmSJEmSJElS5AyrJUmSJEmSJEmRM6yWJEmSJEmSJEXOBRbVvAUBOcefSaxbbzKVlVT+5RoySz6vaY7vfiCJ4XsCGSofvYf0exMhiJHzq1MJevYjyMmh8pF/ZO9XrSAgdtgpBJ17QFUlqQf+DEsX1TbvfACxLXYEID3tbTL/fgjyC4kddy5Bbj6ZVBXpu6+Dr1dEtQUNXxCQ95tzifXoA5UVrLnpCjKLFnynT/6lN1A1+XWq/v14NHU2Ad2Hbsmoay7l+p32jrqUxiMIiB1yEsHG3bPHgIdvgWWLa5tH7EdsyA4ApKe/Q+a5f0IQI3bgsQRde0Mih/RzD5GZ9k5EG9AIBAGx0acSdOmZfY/vvaHecTY2Yl+CYSMhkyH9rwfIfPhWdLU2cOlMhsten0a4fBW58RiXjRhIt9ZF9fp8WVbO6HGTefLQ7clLxGvu/8+sxTw/czF/HDl4fZfdKLzy0Qxuf34S8VjAqG0Gcci2m9Vrn7t0BRc8+CwE0KdTOy46eCSxWMC4Nz/i4TemkM6k2Xlgb07efVhEW7D+pdMZLntmAuHiL8lNxLlsvx3o1rZ1Tfsj73zC2HenE4/FOGnHwYxIdmNF6RrOfuxl1lRW0aFlEVccMJyC3MRa+y5cWcKFT75GKp0hk8lw6X470KPdBrzw8WzuGD+FIAg4ZIt+HLxFvwjfhWi8PGEit951P4l4jIP22ZND969/3p8xew4XXXMDmUyGfr17cdGZp/LpzNlcedNtNX2mTPuYW6++jB23Gbq+y29QXvl4Frf/503isRijttqEQ7YeWK997rKVXDD2BSCgT6e2XHTATsRiATc+N5FJn80jCALO3284mxZ3YuGKrxnz8AtkyNC6MJ8//nIPCnJzotmwBiB7jJhIuGQ5ufFvjhGtatofefcTxr4TEo8F1Z/74pq2eyd9xLKSMs4cuVUUpTd4r7w3jdueeJF4PMaoHYdy6E7brLXfVfc/SY+N2nPYLtlz0xX3juO9z+ZQlJ8HwK1nHEPLwoL1Vrd+vMD1FRsNw+oGJJlMHg30C8NwzM/wXHsAxWEY/u1/Liz7fPcCfYGjwzD85Od4zoYgttUOkJNH+YUnE/QZQM5Rv6Hij+dnG1u2JrHbgZSfcwzk5JJ3/X2UnzKR+I67QzxBxcWnQJt2xLfdKdqNaICCzbaFRC6p634P3ZPERh1P+q+XZxvbdiK21QhS154JZIifeS2pqZMI+m4KC+eQGvcPgu12J7brKNKP3xnpdjRk8W2HQ04uZWcdRyw5kLzjT2fN5WfX65N71EkELVt9zzPox9jt7NPZ+sjDKC9dHXUpjUowaGtI5JC68Vzo1pfYAceSvuPKbGPbjsS2GE7q+rOBDPHTriL1wWSCLr0gHid10xhovSHB4O0i3YaGLhg8jCAnl9Q1ZxD06Ef8kBNI3XZptrFFK2Ij9qHq8lMgkUvi0r9RNebISOttyF6avYSKVJqHRm3L1MUruHbiJ9y65xY17RPmLeX6N0OWry6v97grJ3zMG/OX0a+dx9m1qUyluPqJVxh75pEU5OZwxE0PMmKTXrRv1aKmz7VPvMJpe23P0D7FXDL2BV7+6DP6btyeh9+Ywj2n/oLcRIJbnnuDylSKnHj8v7xa0/HSJ3OoqErx0K/3Z+r8JVz7/JvcOno3AJauWs39b37EIyceSHlViiPufIphvbpw22vvsfeg3hw4pC9/Hz+Fse9MZ69Bvdba9+aX32H00E3YtX93JsyYzw3/eZsbDt2F6198i0dOPJDC3AT73vIou/TrTpui/IjfjfWnsqqKq266nUfvvI2Cgnx+eeLp7LT9trRvu2FNn+v/cidnnngcWw3ZlDF/uIaXJ0xi5PDtue/W6wF49uXX6NCubbMPqitTKa5++nXG/vaw7Gf/trGMGNCD9i1rLwJe+/TrnLb7MIb26sIlj73Eyx/PZOM2rZg6bxEPn/oLFq5Yxan3PM24Mw7n3vHvs+dmffjlsM248bmJPPb2NI7YrvleIHzpk7nZY8Tx+zF1/hdc+8Kb3PrLkcA3x4iPeeSE/bOf+7v+xbBenUlnMlz81AQ++Hwpu/XvHu0GNFCVVSmufuBJxl72Owrycjn8slvYacgA2m9Qe47/8usSxvz1IeYsXkqPjUbU3P/x3AXccc6vadOyxVqeWdL/hdOANFFhGD73cwXV1XYPw3CbphRUA8T7bUp6ypsAZD77mFivOqNIVn1F+dnHQCpFsEFbWF0CQGzwUDJfLiV3zLXknngO6XffiKL0Bi3FIUbzAAAgAElEQVToNYDMx+9mb8wJCbr1rm1csZTULRdDJg2ZDMTjUFkBn8+BvOor0PmFkEqt97obk/gmg0m9OwmAdPgRsT7967dvtzNkMqTemRRFeU3G0pmz+euoI6Iuo9EJeg4gM/397I25n2ZHS39jxTJSf7nkW8eASoJ+Q8isXE7shIuIHfYbMh+9HUntjUXQexPS1SPPM7M/IejWp7ax5GuqLjs5exxt3QbKSiKqsnF4b9EKtu/aDoDNOrVh2tKv6rXHgoC79h1K6/zcevcP6dSGi3fcZL3V2djMWrKcbu02oHVhPrmJOJv37My7sz6v12fagiVs1bsrADv078GkT+cyKZzLwOKOnPfgsxx1y0MM6dG52QTVAO/NW8z21e/JZl07Mm3h0pq2Dz9fypDijuQm4rTMz6V4w1aES76sfkwXAHbo05VJsz7/3r7n7L4Nw/tmR1qm0hnyEnHisRj/OvUQWubnsnJ1ORkyFOY2rzFNM+fMpbhLZ1q3akluTg5bbDaQd6Z+WK/PzVdewlZDNqWispKly1fQdsM2NW2ry8q4+Y67ueCMU9d36Q3OrC++pFvbOp/97hvz7uyF9fpM+/wLturZGYAd+nVn0mfzGdC5A38/7kCCIGDhiq9p26IQgH4bt+ersuzFwpI1FSRizTvCyH7es+/dZl07MG3hspq2Dz9fypCu3/3cl1el2H+z3py4w2bf97TN3qyFSyju2I7WRYXkJhJs3rcH74az6/VZvaac3xy4G/ttV3tBO51OM3fxMi6+61FGX3Yzj7325vouXWqSmtdvIQ1MMpksAP4BdANygMfqtP0WGA1kgIfDMPxzMpkcCFxP9iLDBsBpYRhOTCaTnwFvAElgCXAQcCTQD/gL8BAwH+gFvBWG4cnJZLId8CCQB4TAzmEY1kkT6tV5G9AmmUw+CRxc/Zx9quu4MAzDV5PJ5HDgCiAFzARODMOw8ud5p9ahgiIyq2v/iM+k0xCLQ7o6KE2niO8+ipxDj6Xq2UcBCFq2hk5dqLj6HGL9B5Nz8nlUXPLbKKpvuPILoay09nY6DbFY9r/pFJR+DUBs1HFk5s+CLxaSyckj1n9z4hfdDoUtSV1/TkTFNw5BYf19lzr7bqxbT3JG7M6aK8eQO/r46IpsAt5//Cnadiv+4Y6qL78Q1tQ5BmS+fQxYBUBs/6PJLJgFSxdCi1YE7Tcm/bfLodcmxEefRurm8yPagIYvyC8kU/Y97zFAOk1sp32J7Xsk6ZefjKbIRqKkoooWdb5SHgsCqtLpmkBkWHWQ/W179t6Itz5fvl5qbIxK1lTQovor0QBFebmUlNUfnZ7JZAiqv5NblJfLqrJyVpSW8c7MBTx4+uGsqazk8JseZOyZR9KqsHmM8i0pr6RFnQsjsVhAVSpNIh6jpLyClnm1bUV5OaxaU0FJeSUtqx9TlJtDSXnF9/b9ZrT07GUr+ePzb3Jz9YjMRDzGix/P5vJn3mB432IS8eYVCJaUrqZli9qRv0WFBZSU1L/QF4/H+XzREo45/WxatCiiR3GXmrZHn36WPXYezoYbtKa5y3726+57a/vsU/+zvybbnojHuPG5idw/YQoXHDACgI6tW3D9s2/wzJSQiqoUp47cev1sSAP1nWNEUPcYUUnL/NrzWVFu9nPfuiCP7Xp3Ydz7n0ZRcqNQUraGFnXOM0UFeawqK6vXp0uHtnTp0JbxH9SO3ysrr+Dwkdtz9J7DSafT/Oqq2xnYoyvJ4o3XW+1SU9S8fgtpeE4C5oRhuC1wNFAGkEwmBwC/ALav/ndAMplMApsAZ4VhuCvZ0PqY6ufpCVxU/TztgW9PQtUXOA4YCuyVTCY7ARcAT4RhOBx4hP9y4SIMw1OAL8Mw3B84HlgWhuGOwP7ArclkMgD+Doyqfr7Pq7en4SsrJSgorLkZBEFtUF0t9fzjrDnhAGL9BxPbZAiZVV+Tqp6jOj19CrGNu67XkhuFNashv848XUGdAAUgkUPsmLMhr4D0w9l5/mJ7jyb94qOkLj+Z1C0XEj/BkOq/yawuJSioM6dqrHbfTeyyN0G79hRcdRuJXfcm98DRxLdY+5xr0jqxZnXtNyUgO0Hct48BR52ZPQY88tfsfaWryEyrHk09cxp08Jf8/ybzQ+8xkH7laarOHk3QZyBBctP1XGHj0SI3QWllVc3tTCbT7Efu/S9uemY8v7r5YX5zxzhK1lTU3F9aXkHLgrx6fWN1Jo8sLa+gVUE+GxQVsFXvrhTl59K2ZRG9OrVjztLms4ZFi7wcSstr37dMhprguEVeLqUVtWNBSssraZWfW/2Y7P2lFdng+vv6Arw5eyG/fehFrh41gh7tNqjpM3JAD14963AqUymenPrZOt3OhuKGv97Fkb85k1POuYiS0toLgKWry2jZ4rtf6e+8UUdeGHsvvzxgH67+8+019z/9wkscsu9e66Xmhuqm5ybyq788ym/ufpqS8h/47Me+/dmvbf/dHsN47cLjueu1d5m3fCXXPTOBKw/djafPOpLz9xvOmH++sO43pgGr+3mH6nNWzTGifltpRe3nXmt34yPPctQVt/GbG+6itM5FldKyclr9iHmn8/NyOWr3HSjIy6WoIJ+t+/fmk3kLf/Bxkv47fxOPVhKYBBCG4UfAyur7B5Idbf0S8DLQFuhNNgS+KJlM3kN2hPM3l02XhWE4v/rn+cC3h57MCMNwVRiGKWBRdXt/4JtVAcf/hJoHkQ28XyU7EjwBdAA2AsZW378b0CiGIqbCD4kN2RaAoM8A0vNm1bQFG3Ul96w/VHesyk5Vkc6Q/uQD4kOywV/QrReZZUvWe90NXWbmxwSbVF8z6Z4ks3BOvfbYSReRWTCb9EO3ZEcDQnaalbLqeYFXfZUdmanvlfp4KvEts4t6xJIDSc+ZWdNWcdfNlJ1xLGVjTqbqP89QMe5BUu9OjqpUNUOZ2dMJBlR/RbJbXzIL59Zrjx1/AZnP55Aee3vNMSAz6+Pax2zcHVYsRd8vM3MasUHZeVGDHv3IfD6ntrFjF+InXZT9OVUFVZXZxEtrNaRTG8bPy+5vUxevoM+GLSOuqHE7fe8duOe3hzH+8lOYt2wFK0vLqKhK8c7MBQzuXv8iVP8uHXnrs3kAjJ8+my16dmHzHp15e8Z8yiurWF1ewczFyyiuE6g2dUOKOzH+s+yv9VPnL6FPh9qpJgZ1bs+7cxdTXlnFqjUVzFq2kj4d2jCka0der37M+M/ms0Vxp+/t++bshVz17CT+euQeDOzcHsiOhD3qrqepqEoRiwUU5OTUu5DQlJ1x4rHcd+v1vPHMo8xbsJCVX39NRWUl70z5gCGDBtTre9I5FzJnfnYx66LCQmJB9k/pVSUlVFRWslHHDuu9/obk9D2Gcc9JBzP+4l8zb9lKVq5ek/3sz17I4G4b1evbf+P2vDUz+16O/2QOW3TvzOQZ87l83CsA5ObEScRixIKAVoV5Nd8caN+qiK+/NUq7uRlS3JHxn2Xfu6nzv6BPx9p51Qd1bs+785bUfu6Xrqx3DNF3/e6QPbn3glMYf8ulzF2yjJUlq6moquKdcBaDe3f/wcfPWbSUwy+/hVQ6TWVVivc+nc2A7l1+8HGKRhD4r+6/hsxpQKI1newo6CeTyWRP4ErgXrLTckwD9gzDMJNMJs8APgSeAA4Pw3B6Mpm8FOhe/Tw/9Bfo2to/ArYFpgA/ZcjlJ8CCMAyvrJ7G5AJgKbAA2D8Mw6+SyeR+QKOYIDP91uvEN92S3MtvIwgCKm67isTevyC9eAHpd98gPXcGeX/4C5Ah9f6bpKdPgc+mEfv1Wdn7g4CKv/8p6s1ocDJTJxH0H0L899cBkLrvRoKdD4CliyAWI+gziCCRA5tkg6nUk/eQfvo+YoefRmzHvSEeJ/3An6PchAYvNfFVEkO2puC6OyAIWHPDZeQcOJr0wvmk3vwp15+kn1/mg8kEycHEf3cNAKkH/0wwYj9Ytjh7DOi9CUEiAf03z7b/6z4yE18gOPRk4mdcCwSkxt7+X15Bmfcnkum/OfFzrwcCUvf8idiuo8gsXUhm6mQyC2YRH3MDZCDz0dtkPv3wB5+zudq1Z0cmLljG6McnkQGu2GkQd0+dTXGrQnbu0THq8hqtnHiccw/YiRP+8ijpTIZRWw+k4wYtmbF4GQ+Of5+LDxnJOfuP4OJ/Pk/lv16nZ8e27Da4L/FYjIO2HsThNz1Ihgwn77YtGxT98Oi2pmLXft2ZOHMBo+94kkwGrjhgOHdP/IDiDVuzc79uHLH1QI6862nSGTh9l63Iy0lw0vAhnD/uNR599xM2KMznjwfvRGFuzlr7Xv3sJCpTKc4f9xoA3du25tL9dmCfTXtz5F1PkxOP0bfjhuy76VpnB2yychIJxpx2Esf9bgyZTJqD9tmDju3bM2P2HO5/9EkuOft0Tjjyl4z5w7Xk5CQoyMvnD+edBcDseQvo3MljxTdy4nHO3XdHTrhjHOkMjNpqAB1bt2DGkuU8OHEqFx+4M+fsswMXP/oSlakUPTtsyG7V+9vzH3zG4beOJZXJMHrYZnTZsDUX7D+CK554lVQ6Q4YMF1ZPD9JcZY8RCxl9x9NkyHDF/jty98QPKd6wVfUxYgBH/uMZ0pkMp++yBXk5Rj4/Rk4izpjR+/Hra/+WPWftuBUdN2zNjM8X88CLb/D/jj5orY/r1bkj+wzbnMMu+TOJeIz9t9+SPl06refqpaYnyDjSJjLJZDIfuAvoDMTJhtHtwjAck0wmzwYOIDun9FvAb4HTyU4dsoRsONwuDMORyWRycRiGnaqf82Gyc0p3p3bO6ofDMNymun0ycBjZMPk+sqOsFwJDwzCsszrTd2pdHIZhp2QymUd2yo9uQCvgtjAM/55MJncDLiY7Wv9r4KgwDL/4vucrO3QHd7x1IKddqx/upJ9szRxHeP7cfv9sk1qrtcG45bSdoi6hSco081Fc60psQL8f7qSfLjkw6gqapq9W/nAf/WTxkYdFXUKTk5rwVNQlNE2rG8VYrEYn6DXghzvpJ4sN3aeBj5uNxgfduptD1bHp3DkNdj8xrG6mksnkXsDSMAzfTiaTuwLnh2G48/p6fcPqdcOwet0wrP75GVavG4bV64Zh9bphWL2OGFavG4bV64Rh9c/PsHodMaxeJwyr1w3D6rUzrK6vIYfVfiek+ZoN3JVMJqvIjuo+LZlMXgysLbA+JgzD2eu1OkmSJEmSJEnNimF1MxWG4XSyc1bX9Q5wWQTlSJIkSZIkSWrmDKslSZIkSZIkNVlB0GBnvdC3xKIuQJIkSZIkSZIkw2pJkiRJkiRJUuQMqyVJkiRJkiRJkTOsliRJkiRJkiRFzgUWJUmSJEmSJDVZrq/YeDiyWpIkSZIkSZIUOcNqSZIkSZIkSVLkDKslSZIkSZIkSZEzrJYkSZIkSZIkRc4FFiVJkiRJkiQ1WYErLDYajqyWJEmSJEmSJEXOsFqSJEmSJEmSFDnDakmSJEmSJElS5AyrJUmSJEmSJEmRM6yWJEmSJEmSJEUuEXUBkiRJkiRJkrSuBEHUFejHcmS1JEmSJEmSJClyhtWSJEmSJEmSpMgZVkuSJEmSJEmSImdYLUmSJEmSJEmKnAssSpIkSZIkSWqyYq6w2Gg4slqSJEmSJEmSFDnDakmS/j979x0nRX3/cfw1u9foSFVBioAD2EU0YkNFYy9EYy+xoMYoxuSHxkKsiZqYRP35s6BJiIgtxt6IihEFFcGKOEiXIgLS4e72duf3x55XEKImwtwdr+fjwUP3vjO7n5mb/d7de777/UqSJEmSpMQZVkuSJEmSJEmSEmdYLUmSJEmSJElKnAssSpIkSZIkSWqwXF+x/nBktSRJkiRJkiQpcYbVkiRJkiRJkqTEGVZLkiRJkiRJkhJnWC1JkiRJkiRJSpxhtSRJkiRJkiQpcQVJFyBJkiRJkiRJG0oQBEmXoG/JkdWSJEmSJEmSpMQZVkuSJEmSJEmSEmdYLUmSJEmSJElKnGG1JEmSJEmSJClxLrAoSZIkSZIkqcEKHK5bb/itkiRJkiRJkiQlzrBakiRJkiRJkpQ4w2pJkiRJkiRJUuIMqyVJkiRJkiRJiXOBRUmSJEmSJEkNVhAESZegb8mR1ZIkSZIkSZKkxBlWS5IkSZIkSZIS5zQgSkThVu2SLqFBCjbfPOkSGqSSNq2TLqHB+d9de8KyZUmX0eD87LbRSZfQIN16at+kS2iQUh27JF1Cw7RmVdIVNEyrliddQYOUffuFpEtoeMpLk66gYaqoSLqCBileOC/pEiTVQY6sliRtfAbVkiRJkiRpLY6sliRJkiRJktRgub5i/eHIakmSJEmSJElS4gyrJUmSJEmSJEmJM6yWJEmSJEmSJCXOsFqSJEmSJEmSlDjDakmSJEmSJElS4gqSLkCSJEmSJEmSNpQgCJIuQd+SI6slSZIkSZIkSYkzrJYkSZIkSZIkJc6wWpIkSZIkSZKUOMNqSZIkSZIkSVLiXGBRkiRJkiRJUoPl+or1hyOrJUmSJEmSJEmJM6yWJEmSJEmSJCXOsFqSJEmSJEmSlDjDakmSJEmSJElS4lxgUZIkSZIkSVKDlXKFxXrDkdWSJEmSJEmSpMQZVkuSJEmSJEmSEmdYLUmSJEmSJElKnGG1JEmSJEmSJClxhtWSJEmSJEmSpMQVJF2AJEmSJEmSJG0oQZB0Bfq2HFktSZIkSZIkSUqcYbUkSZIkSZIkKXGG1ZIkSZIkSZKkxBlWS5IkSZIkSZIS5wKLkiRJkiRJkhqswBUW6w1HVkuSJEmSJEmSEmdYLUmSJEmSJElKnGG1JEmSJEmSJClxhtWSJEmSJEmSpMS5wKIkSZIkSZKkBsv1FesPR1ZLkiRJkiRJkhJnWC1JkiRJkiRJSpxhtSRJkiRJkiQpcYbVkiRJkiRJkqTEGVZLkiRJkiRJkhJXkHQBkiRJkiRJkrShBEHSFejbcmS1JEmSJEmSJClxhtWSJEmSJEmSpMQZVkuSJEmSJEmSEmdYLUmSJEmSJElKnAssSpIkSZIkSWqwgpQrLNYXjqyWJEmSJEmSJCXOsFqSJEmSJEmSlDjDakmSJEmSJElS4pyzWpu2ICA18ByCLbtARYbsI3fC4s+rm/c5nNROewKQ+2Qi8ahHq/dt14H0Rb8le/VZUJHZyIXXdQHBgccTtOsAFRXkXhwJSxfW3qRRU1In/4LcX26AbEX111u1J3XK/5C747LaX1f+ej38dILNO+Wv1yfvgy+/qL1N42akz7mK7B1X5K/LRk1I/eg8guJGxKtXknvqPli1Ipn666ogIHXcedX9wEP/C4tq9AP9jyS1894A5Ca/Q/zCwxCkSB1zJsFW3aGgkNwLDxJPeiehA6ifuuy2KwNvuoY/7HdY0qXUH0FAwemDCTp1g0w5mftugS/mVTWnDjiK9N4HQQzZJ+8n996bUFRCwU8vJ2jSHMrWkLn7RlixLMGDqFtGT57JnaPfIZ1KMbBPT47r27tW+6zFy7jisVcA6NG+FVcdsQ+pyvkOZy1exoUjnuepwScAMG/pCq78x2iyuZg4jrnm6H3p2nazjXtAdczoyTO58+Xx+fO7ay+O222t87toGVf8/WUIgvz5PTJ/fn/33FgmzpxPNpfjuN22/dp+m5pcHHPtqAlEC5dSlE5x7cF96bxZs6r2R9+fxiPvTSOdCjhvj23p331Llq4p49B7n6NHmxYADOjRkVN33QaAL1eXctKIl3nyzIMpLkgnckx10eiPpnLni+NIpwIG/mB7jttjx1rtsxYu4YqRz0MAPTZvw1XHHkgqFfD4Wx/x0BvvkYtz7L9dd87/Yb+EjiBZoz+ZyZ2jJ+bP3y49Oa5vr1rt+f701fz5a9+Kqw7fi1Qq4I5X3uG1aDbpVIrLDuvHDh3b8YuHX2LRitUAzF26gh23as8txw/gpufGMnHW5wRBwJBD9mCXzpsncKTJyMUx1z7/JtGCJfl+4PB+dG7VvKr90YlTeOTdKfl+YK8d6N9jK+YtW8mVT48lm8sRA9cctgddW7eo2ufXz46lRaNiLtm/TwJHVHeMnjSNO0eNy/+s2m07jttjh1rtsxYu4YqHXgACemzRhqsGHkAqFXDBfY+zdFUpBekUxYUF3DPoR0z9fDFXPzqKOIZwy7ZcMXB/0inHiKp+CsMwBfwfsCNQBpwdRdHUGu23AXsCXwUMRwGFwEigETAP+EkURau/62v7rqnjwjDcJwzDHb55y40vDMP+YRg+lHQd/41gu92gsIjs7ZeTfXYEqSNPr25s1Z7ULnuTvf0KsrdfTmqbnWCLzvm24kakjjgdKgxT16nHDvkA74FbyL32JKn+A2u3d+lF6rifQeNmtb9eVJLf1vO6TkHPPlBQSHbYtWT/+QipH55Uu7379qRPHwJNqn8JTe1zBPHsKWTvu574rX+SGnDcxi67zgu23z1/Xv90Kdmn/0bq6DOrG1u3J9Vn33zbn4aQCneGLTsT9O0P6TTZWy8je+8N0GaLxOqvjw76n8Gceu/tFJSUJF1KvZLqsycUFpG59kIqHrmXgpPOq25s2pz0AUeSue4iMjf9koLTBwOQ3u9Q4hmfkrnhYrJvvkr6yFMSqr7uyWSz3PjcGwz7yREMP/soHh3/MQtX1P5d+ubn3uCiAbsxYtAxxDG8MnkGAE+9G/HLh/7JktWlVdve/tLbnPSD7Rl+9lEM6r8Lfxz11kY9nromk81y4zOvM+ysIxg+6GgefXvSus/vQbsz4txjiOOYVybP4K1pc5m9eBkP/vRHjDhvIPf+ayLL1pSu51U2DS9/OpfybJYHTxnAJfvuwM2j36tqW7hyDSMmfMoDJx/AsB/vyx9f+4DyiiwfL1jCob06MfzE/Rl+4v5VQfXrM+Zz9iP/YvHqTfucri2TzXLjE6MZdv5xDL/wRB4d+wELl6+stc3NT4zmokP3YsRFJxEDr3z0KbMXLeGhN95j+M+O5+Gfn0ommyOTzSZzEAnK96fjGHbGYQw/60gefWfy19/vz4/jogF9GXHOUfn3+ycz+XjeQt6ZMZ+HzjuG3x9/ANc//ToAtxw/gOFnH8ltJ/+QZiXFXHboHnwyfzHvzl7AQ+cdw43H7s9vnn0jiUNNzMvRbMorsjz4k0O5ZP8+3PxS9SCJhSvXMGL8ZB44/RCGnXggf3xlIuUVWW5/9T1O6tuT4acdzKA9t+ePr0ys2ufhCRFTvliaxKHUKfn3/qsMO/dYhl9wPI+++QELl6+qtc3NT73KRYfsxYgLT8hfux/ls7pZi5Yy4sITGH7B8dwz6EcA/Om5MVx86N48cNGJlGYqGP3RtI1+TFq/IPBfzX/fwtFASRRFewCXAbes1b4L8MMoivpX/lsGDAVGRlG0N/AucO5/8r0yrK77zgS2TLqIhiro2ov4k3fzD2Z/SrBVt+rGpYvIDrse4hzEMaTTkCkHIHXceeSeewAyZQlUXfcFHbvBjI/zD+bPhM071d4gjsk9cjuU1v4lNjjoRHJjnoKK8o1SZ30TdN6G+NMP8g/mTCPo0KX2BnGO7F9vgjU1/rhq24F4Sn6fePYUgk7bbJxi65Fg697Ekyv7gVlT8qOlv7JkEdm7rl6rH8gQ9NyZeOliUoOuInXCBcQfjU+k9vpq4bQZ3D3Q0PS7CrbZntwH+WstnjaZVJewunHlcjJXngPZLEGLVsSr8/1A9sV/kH3qgfz+rdvB8iUbve66avrCpXRu3YIWjYopKkizS+ctmDBzfq1tJs1dRN+u+V/D9t6mE+OmzQGgeaNihp9zVK1thxzSj33D/M+7bDbe5EesTv9iSeX5Lcmf3y5bMGHmvFrbTJq7sPr8hp0ZN3UOO3Vqz/XH7le1TS6OKUht2udy4pyF7NU1f1N0xy3bMOnz6vfxh/O/ZOcObSgqSNOsuIhOmzUlWriUjxcsYfKCJZw28hUufvINFq5cA0AqCPjz8f1pUVKUyLHUVdMXLKZzm5a0aFx5vW7dgQnT59baZtKcBfTtvhUAe/fqyrgpsxgXzWK7Tu351cjnOe1/H2Tnrh0oTG9612u+P21eoz/dnAmz1u5PF9K38jr+qj+dMOtz+nXvSBAEbNmyGRW5HF+uWlO1z/++/A6n/GA72jZrQvvmjWlUWEB5RZZVZeUUbGKjVSd+9gV7desAwI4d2zJp/qKqtg/nLWTnrdrl+4GSIjq1akb0xRKGHLgr+3bvCEA2V/1z6b05X/D+3IX8eBf/Lpi+4Mva7/2uHZgwfU6tbSZ99gV9u+XP4969ujLu01ksWrGKFWvK+Ol9T3DK7Q/y6qR8KH3rGUeya7eOlFdkWbR8Fa2bNd7oxyR9j/YCXgCIouhNYNevGipHXfcA7gnD8I0wDM9cex/geWDAf/LCTgOSkDAMC4G7yH9zU8DNwI3A8UAWeAi4EDgY2CUMw4+BMcAnwGTgXuAPlfu2BC6Komjsel7rDPLD8ZsDbYBroyh6LAzDfYEbKl9vGtV3PP4MdAPSwB+iKHo4DMNXK1+7JxBU1lnzNY4DLql8rtejKLrsPz87G1FJo9qBaS4HqVT+v7ls1XQJqSNOI547AxbNJ3XQj4knT4T5sxIquh4oKiEuq/5FkzgHQSr/X4BZn3xtl6DfoTB9Eiyc+7U2VSougZrnNRdXX69APG3S1/f5fFY+WP18FkHPXaCweCMVW4+UNIbSGiMo4vX0A0edQTxnOiycB02bE7Tdktw910G3bUmfdBHZ2y9P6ADqn3f/8RStO3f65g1VS1DSmHhNzWs1W6sPIJcjNeAoCgaeQXbUP2psl6Pwst8TdOxK5uYhG7foOmxlaTlNawR2TYoLWVla+yZ0TExQOfSkSXEhK0rzN1P79+zytefbrEkjAGYsXMLvXhjL7ScfvIEqrx9WlmXWOr9FrCytfTM6jtc+v2UUFxZQXFhAJpvl8kdf5rjdetOkuHCj1l7XrCzL0LTGOUgFARW5HAWpFCvLMzSr0f1AhmMAACAASURBVNakqIAVZRm6tmpO7z03o1+XzXl60kxueGkifzp6T/p12XSmTfgu8v1B9e9ITYqLWLlmrf6g1vVaxIo1ZSxZtYZ3ps1h5OCTKc1kOPnWkTxyyak0b7xpfXJoZWk5TYtrvN+LCr/+fofq81eU709XlWZo2bi41n4rSstp1aQRi1eu4c3pc7ns0D0ASKdSBEHAYbc+zMrScq45et8Nf2B1yNf7gVR1P1CWodla539FaTmbVV6HMxYv43cvvcPtP96PhStWc8dr73PbcfvxwsczN/Zh1DkrS8to2mjtn1X/7neBIlasKSdTkeWM/rty6t67sGx1KSff/iDbd9qC1s0aM/fL5Zx116M0Kymma7tWG/V4pO9Zc6Dm/IHZMAwLoiiqAJoAt5PPJdPA6DAM31lrnxVAC/4Dm9btyLrlbGBRFEX7kA+SfwucAQwD/gKcFkXRv8jfkRgSRdFsYCvgpCiKLga2BX4RRdEA8hfHT77h9ZoCBwIHAX+oDMuHAQOjKNoXmFv5+udW1tWP/B2Q68MwbFP5HGOjKOoPPAxUpTJhGLYCrgEOiKJoL6BDGIYH/qcnZqMqXQPFjaofBzX+6AcoKCR18sVQ3IjcY8Pym/TZh9Ru+5M+/xpo1pL0oKEbueh6oLyUoKjGL+lBUB1Ur0fQezeC7fcgdfxgaNKc1HEXbuAi66GyUlj7vOb+/XnNvfYMQcs2pE6/FFq0guWLN3CR9VDp6rX6geDr/cBpl+T7gUfvzn9t1QriSZWjqadNgnZ+AEYbXly6On+T9Str/8wCci89SfmFx5EKdyDotVPV1zM3/pLMDRdTeOHVG6nauuvWf77F6fc+yQUjnq8Vpqwqy9CsUe0beqkan5FcVZahecm/v+H31vS5XPjAC9x47AGb7HzVt456i9PveYIL/vYcK8uq1/RYVVZOs5Jvd36XrSll0F+eoVu7Vgzqv2nPpQrQtLiQVeXV5zKO46pRpU2LaretKq+geXERP+jUjt07tQNgwDYdmfyFn6pYl1ufHcPptz/EBfc+vlZ/UP4N/UE5zRuV0LJJI/p234omJUW0btaEbpu3YebCTedc3/rPtzn93qe44IEXa7/fyzP//v1enn+/NylZ+/rN0LzyJteoSdM5bIfuVfP9PvneFNo0a8SLl5zIqF+cxB2vvMOCtaZraMjy/UD1VIm1+oHi9Z/Ht2bO58JHRnPjUXvRtXULXpg8iyWrSznvwZe4d+yHPPPRDB5/fyqbmlufe53T73iYC+57ch3v/do3m77+3i+mTfMmHL/HjhSkU7Ru1pheHdoxY+GXAHRo1ZwXLj+L4/vtwE1PvrpRjkfaQJYDNeduTVUG1QCrgVujKFodRdEK4BXyc1vX3KcZ8B/NN2RYnZztgUMrRyw/Rn6U+zTy38gFURS9t459FkVR9FXSNBe4KgzD4cCx5Ccx/3f+FUVRLoqiBcAS8lOLbAE8UlnDQUAnoBfwGkDlBfcx+VHWkL/4AMYCNT57THegLfBc5XP1Brb+hnrqhHjGJwS9dsk/6NSDeK3R0qkzLyOeN5Pc3++uCluzv/0Z2Tt/TfbOX8OKpWTvuXZjl13nxXOnw9bb5h9s0SU/EvUb5O69mtzDt5J7+FZYtZzco7dv2CLroXj2FIJtKhf76diN+IvPvnGfoEtI/P4b5IbfBEsWEs/+dMMWWQ/FMyYT9K4MQzpvQzxvrX7g7CuI584k98idVf1APP3j6n227AJL1lpAVNoA4ikfkdpxdwCCbr2IP5tR1RZs3pGCi67OP8hWEFdkIJcjffiJpPrlP30Xl5USf8ONw03B4AN3Z/jZRzHmV6cz+8vlLF1dSnlFlndmzmOnrdrX2rbXFm14u3IqgDFTZtOny/rnp39r+lx++8zr3H364WzXsd0GPYa6bPBBuzN80NGMueIMZi9eVn1+Z8xnp05rnd8ta5zfaBZ9um5BaaaCM+99ioF9enH+Abuu6yU2OTt3aMOY6fkpFd6ft4gebasHKW2/RSsmzFlEWUWWFWXlTF+8nB5tW3DVC+MZNSX/UfY3Zy2gd3tH963L4MP2ZviFJzDmup8ye9ESlq5ak79ep81hpy61b0T36tietz+dDcCYyTPos3VHdunagfFTP6MsU8HqsnKmfb6ITm1aJnEoiRh84G4MP/tIxlx2KrO/rPF+n7mO9/sWrXl7ev5vgnx/ujm7dNqc1z/9jFwuZt7SFcRxXPUplXHT5rDPNltV7d+ipJjGRYWkUymaFBdSVJBmddmms8j9zh3bMWZq/j39/pyF9GhXfUN0+y3bMmH2gnw/UFrO9EXL6NFuM96aOZ/fjhrP3ScOYLst8+PPTt2tF38/+wiGn3YwZ/fbnsO368oxO3Zf52s2ZIMP3YvhFxzPmGvPY/aipdXv/elz2Klz7Z/1vTq04+2p+b+78u/9DoybMptL/vY0kA+wP/18Ed3ateKC+x6vumHVpLioakS2VE+9ARwKEIbhD4APa7RtA7wehmG6cjDsXsDEmvsAh5CfIeI7cxqQ5HwCzImi6DdhGDYCrgAOAFYCqTAMj42i6O9AjuqbCjX/wrwNODmKoslhGF4DdPmG1+sDEIZhe/LD8udU/jsqiqJlYRgeWfnavYG9gcfDMGxGPlSfUeM55pBf7bPmfAMzgM+AA6MoylROO7KusL3OiT96i2CbHUhfeAMQkH34DoJ9joDF8yFIEWzdmyBdAD13BiD73AMwa0qyRdcHU96Hzj1JnfQLCCD3/AiCXfcnXrIQpn34zftrneLJEwi6bUf67KsgCMg+Poyg38GweAFx9O6691k0n/TA/Aw/8fIl5J68d2OWXC/EH7xJEO5E+uKbAMiOvI2g/5Gw6HNIpQi6b0tQUACVN7ayz9xPPHYUwY/PJ/3zm4GA7CN3JngE2lTkJrxOars+FF51GwQBFcNuJn3wscQL5pJ7dxzx7GkUDs3f6Mu9/zZx9AHZ+Z9ROGgI6X0PgVSaimE3J3wUdUdhOs2lh/Rj0F+fIRfHDOzTi/YtmjL1iy8ZOe4jhh61D0MO7cfQx18lM+ottm67GQdtt/578Tc++waZbI7LH8vf2+/SpuUm9zH1mgrTaS49bE8G/flpcjEM3LVn/vwu+JKR4z5k6NH7MuTQPfPnt+JNtm63GQdt140RYz9kzpfL+fv4j/n7+Pz6Fzccuz8dWzVP+IiSM2CbjoyduYCTRrxEDNxwyG78dXxEp5ZN2b9HB07p04NTR75MLobBe29PcUGaS/bdgSufH89D706lUWEB1x3cN+nDqNMK02kuPXo/Bt3193x/sPt2tG/ZjKmfL2LkmHcZetyBDDmqP0MffpHMM6+xdfvWHLTTNqRTKX60+/acfOtIYmLOP2gPWjZp9M0v2MDk+9M9GDT8ufz52yWkffMmTP1iCSPf/IihR+7NkEP2YOgTr5H5Zzbfn267NelUij6dt+DEe54gjmOuPHyvquecsWgZHTerft8ftmN3Js7+nJPufoJsHHP4jj3o2nbTuTEwoGcnxs6Yx0l/fY44hhuO2JO/vjmJTq2asf82nTilby9OHf48uThmcP9dKC5Ic+Oo8fkplZ7KL1zZpXULrjlsj4SPpG4pTKe59Kj+DLrnsfy1u9tX7/3FjHz9XYYeO4AhR+7L0Ef+SSabZev2rThox/x7/41PZnLCn0aSSsHFh+7FZk0bc/b+u3HFgy9QmE5TUlTAdccflPQhSv+Nx4EDwzAcS3464J+EYXgJMDWKoqfCMHwAeBPIAH+LomhSGIbXA8PDMDwHWASc9J+8cBDH8fdzCPpOwjAsJj8NR2fy4fETwAnkg+IU+bsPB5Gfs/oC8nNEj46iaPPK/S8BzgMWkA+Q20RRtM6pNyrD43PJD9NvAVwZRdELYRgeRH6lzhT5ofqnkR/ZPYz8aOpGwG1RFA2vHDG9BGgFrAJOJR9knxdF0QlhGJ4C/JT8XDUzgZ9EUVR79bwaKn7xIy+8DSDY3HkIN4R4xYqkS2h4li375m30nf3sttFJl9Ag3XqqIc+GUHDkEUmX0DClHMW1QSxxGq0NYkvXL/jerVyedAUNU2lp0hU0TK3afPM2+s7Shw3yl4F1WNC3tzlUDe3Hf1xnrxNHVickiqIy8uFwTdfU+P+vptm4u/IfQFUSGUXRH8jPVf1t/WvtRQ+jKBoFjFrHtqev5zl+FUVRzZXxXq38RxRFI4AR36EeSZIkSZIkSapiWN2AhGH4f+Sn8Vjbwxu7FkmSJEmSJEn6LgyrG5Aoin66AZ+7/4Z6bkmSJEmSJElKffMmkiRJkiRJkiRtWI6sliRJkiRJktRgBXV2OUGtzZHVkiRJkiRJkqTEGVZLkiRJkiRJkhJnWC1JkiRJkiRJSpxhtSRJkiRJkiQpcS6wKEmSJEmSJKnBClxhsd5wZLUkSZIkSZIkKXGG1ZIkSZIkSZKkxBlWS5IkSZIkSZISZ1gtSZIkSZIkSUqcCyxKkiRJkiRJarBcX7H+cGS1JEmSJEmSJClxhtWSJEmSJEmSpMQZVkuSJEmSJEmSEmdYLUmSJEmSJElKnGG1JEmSJEmSJClxBUkXIEmSJEmSJEkbShAESZegb8mR1ZIkSZIkSZKkxBlWS5IkSZIkSZISZ1gtSZIkSZIkSUqcYbUkSZIkSZIkKXEusChJkiRJkiSpwXJ9xfrDkdWSJEmSJEmSpMQZVkuSJEmSJEmSEmdYLUmSJEmSJElKnGG1JEmSJEmSJClxLrAoSZIkSZIkqcEKXGGx3nBktSRJkiRJkiQpcYbVkiRJkiRJkqTEGVZLkiRJkiRJkhJnWC1JkiRJkiRJSpxhtSRJkiRJkiQpcQVJFyBJkiRJkiRJG0rgcN16w2+VJEmSJEmSJClxhtWSJEmSJEmSpMQZVkuSJEmSJEmSEmdYLUmSJEmSJElKnAssSpIkSZIkSWqwgiBIugR9S46sliRJkiRJkiQlzrBakiRJkiRJkpQ4w2pJkiRJkiRJUuIMqyVJkiRJkiRJiXOBRUmSJEmSJEkNV8oFFusLR1ZLkiRJkiRJkhLnyGolYsErk5IuoUFq2eXzpEtokHKry5IuocEp6dwm6RIapFtP7Zt0CQ3S4PvHJ11Cg3THIT9MuoSGqbg46QoaplQ66QoapFTv3ZMuocHJvTcm6RIappUrkq6gYUo5flLS19kzSJIkSZIkSZISZ1gtSZIkSZIkSUqcYbUkSZIkSZIkKXHOWS1JkiRJkiSp4QqCpCvQt+TIakmSJEmSJElS4gyrJUmSJEmSJEmJM6yWJEmSJEmSJCXOsFqSJEmSJEmSlDgXWJQkSZIkSZLUYAUusFhvOLJakiRJkiRJkpQ4w2pJkiRJkiRJUuIMqyVJkiRJkiRJiTOsliRJkiRJkiQlzgUWJUmSJEmSJDVcKRdYrC8cWS1JkiRJkiRJSpxhtSRJkiRJkiQpcYbVkiRJkiRJkqTEGVZLkiRJkiRJkhJnWC1JkiRJkiRJSlxB0gVIkiRJkiRJ0gYTBElXoG/JkdWSJEmSJEmSpMQZVkuSJEmSJEmSEmdYLUmSJEmSJElKnGG1JEmSJEmSJClxLrAoSZIkSZIkqcEKUi6wWF84slqSJEmSJEmSlDjDakmSJEmSJElS4gyrJUmSJEmSJEmJM6yWJEmSJEmSJCXOBRYlSZIkSZIkNVyBCyzWF46sliRJkiRJkiQlzrBakiRJkiRJkpQ4w2pJkiRJkiRJUuIMqyVJkiRJkiRJiXOBRUmSJEmSJEkNVpBygcX6wpHVkiRJkiRJkqTEGVZLkiRJkiRJkhJnWC1JkiRJkiRJSpxhtSRJkiRJkiQpcYbVkiRJkiRJkqTEFSRdgCRJkiRJkiRtMEGQdAX6lhxZLUmSJEmSJElKnGG1JEmSJEmSJClxhtWSJEmSJEmSpMQZVkuSJEmSJEmSEucCi5IkSZIkSZIarpQLLNYXjqyWJEmSJEmSJCXOkdXatAUBLS//NYXb9CQuL2fJtVeS/Wx2VXOLIVdQtOMuxKtXAbD45z8lXrkSgCYnnUa6TRuW3/aHREqv04KAonN/SapLd8iUU3bHjcSfz61qLjhkIAX7HQrEZB75C9l3xkLjJhT/4hqC4kZQkaHsT9cSL/0yuWOoi4KA4gsvI921B3EmQ+mfriOeN+dr2zS67k9UjHuNzLOPQeMmNLr0OmjchKCgkNJ7/khu8ofJ1F/XBQGpk35G0HFrqMiQ/dsfYeH8quZU/yMI+h0IcUzumQeIP3w7uVrruiCg4PTBBJ26QaaczH23wBfzqppTBxxFeu+DIIbsk/eTe+9NKCqh4KeXEzRpDmVryNx9I6xYluBB1E9ddtuVgTddwx/2OyzpUuqNXC7m2mfHEi1YTFE6zbVH7k3n1s2r2h+d8AmPvBORTgWct89O9A87VbX9bdxHLFq5hksO7JtE6XXS6MkzufPl8aRTKQbu2ovjdutdq33WomVc8feXIQjo0b4VVx25D6nKkU6zFi3jwhHP89TFJwAw58vl/OrRl4mBLVs25Zpj+tOoqHBjH1LicnHMtS+OJ/piSf4aPXR3Om/WrKr90fem8sh7U/PXaL/t6N+9Q1XbO7O/YMjTY3nlgqMBePbjmfxtfP563qZtS4b+sC+pwJFmAK+Me4f/e+BR0uk0P/rhfvz40ANrtU+eNoPr77iPVCpFUWEhNw25kDabteS1tydyx4hHAejdfWuGXng2wSZ+Tkd/PJ07X3or3w/03Zbjdt+uVvusRUu54pFRQECPzVtz1dH7kUoF/OmFsYz7dDZBEHD5kfuyQ6fNmbdkOZc9NIqYmBaNS/jdiQfbD/yX/cBXfv38W7RoVMwl/XfaaMdRF43+aCp3vjiOdCpg4A+257g9dqzVPmvhEq4Y+TwE0GPzNlx17IGkUgGPv/URD73xHrk4x/7bdef8H/ar2udvr77DohWruOSIfTf24UgNgiOr65gwDFuFYXjSf7DfQ2EYFn3HfT7/Dtu+GYZhl+9QS//vUktSSvYbAEXFLDz9BJbddgstLrm0Vnthz94svuAsFp1zGovOOS0fVBcXs9n1N9P0+O/8bdpkpHffBwqLKL3sXMrvv4uin1xY3disBYUHD6T0V+dSOnQwRef+EoCC/Q8lN2s6pVdeQMUbL1N4tOd3bQX9+hMUFrH652dS9ufbKR70869tU3TG+QTNWlQ//tHJVLw3njX/cy5rbrmGkgsu/do+ygt26kdQWET2pp+T+8efSR83qLqxaXNS/Q8ne9PPyf7hMtInX7je5xGk+uwJhUVkrr2QikfupeCk86obmzYnfcCRZK67iMxNv6Tg9MEApPc7lHjGp2RuuJjsm6+SPvKUhKqvvw76n8Gceu/tFJSUJF1KvfLyJ7Mor8jy4NlHcsmAvtw86q2qtoUrVjPirY954KzDGXbqwfzx5Xcor8hSmqlgyGOvMnL85AQrr3sy2Sw3PvM6w846guGDjubRtyexcMXqWtvc/NwbXHTQ7ow49xjiOOaVyTMAeGpixC8fGsWSVWuqtv3982M5fvdtGXHuMey2dQeGv/7+Rj2euuLlKXPy1+hpP+SS/jtx88sTq9oWrlzDiAkRD5xyIMN+vB9//Nd7lFdkAZi/fBV/eXsymVwOgNJMBbe99gF/PekARp56ECvLMrw6de46X3NTk6mo4Ma7/8p9v72K+39/DY889xILv1xSa5sb/u8vXHnBWdz/+2s5cM/dGfbwE6xcvYbfDbufu677FQ/f9ls6bN6WJcuWJ3QUdUMmm+XGp19j2NnHMPy8Y3n0rQ9ZuGJVrW1ufvo1LvphP0b89Lh8P/DxND6e+wXvz57PQz87nltOOoRfP/YyAH8b8y6H7NiD+88/ju7tW/PY+ElJHFbivq9+4CsPv/spUxYu3ajHUBdlsllufGI0w84/juEXnsijYz9g4fKVtba5+YnRXHToXoy46CRi4JWPPmX2oiU89MZ7DP/Z8Tz881PJZHNksllKyzMMuf8ZRr7+bjIHJDUQhtV1zw7Akd91pyiKToiiqHwD1NOgFe/ch7KxYwDIfPg+Rb1r3PUPAgo6dablldfS5i8jaXzUwPyXi4pZ/cyTrLj37iRKrhfSvXYg++6bAOSmTCLVrWd144plrPn56ZDNEmzWClblfxnIzZpG0KhxfptGTYizFRu77Dovve1OVLwzDoDcJx+R7tGrVnvBXgdALqZi/Niqr5X/YySZZ/8BQJBOQ6Zs4xVczwTdtyU36R0A4hmfEHTuUd24cjkV154P2Sy02AzWrFzPswgg2GZ7ch+MByCeNplUl7C6ceVyMleek+8DWrQiXp0/l9kX/0H2qQfy+7duB8uXfO159e8tnDaDuwca8n9XE2d/zl6VI9B23Kodk+Ytqmr7cO5Cdt6qPUUFaZqVFNGpVXOiBV9SVpHlqB27c+7eO67vaTdJ079YQufWLWjRqISigjS7dNmCCTPn1dpm0tyF9O26JQB7h50ZNzX/CaHmjYoZPqj2qL+pXyxh78qR7Dt33pyJM+ezKZo45wv22noLAHbs0IZJn1d/8uzD+YvZuUPb6mu0ZTOihUspq8hyzYvjGfrD6lH/RQVpHjj1QBoV5j9cW5HLUVyQ3rgHU0dNnz2HTltuTotmTSkqLKTPtj2Z8FHtm1F/uPzn9OrWFYBsLktxUSHvfhzRo2snbrx7OCdfciWtW7akVcsW63qJTcb0L76kc+uWtGj8VT+wJRNmrN0PfEHfrfP97t49uzDu08/o3aEdw846hiAImLdkOa2b5v8u6LllW5atyf/+urK0nILUphlhfF/9AMB7cxfy/rzF/HinHmzqpi9YTOc2Na7XrTswYXrtm3iT5iygb/etANi7V1fGTZnFuGgW23Vqz69GPs9p//sgO3ftQGE6nf/9oO+2nHvgHkkcjtRgOA3IfyEMw0bA34Atgc+AfYApwHlRFH0ShuF5wOZRFF0dhuGFwElADDwURdFtYRgOBC4FMsBM4DTgCmDHMAwHAYvWbo+iqPYt0epaZgI9gbuAMqALsAVwRhRFE8MwPAs4H0gDT0ZRdHWNfV9dT803AAdXHlubym1bAPcBrSt3vyiKog/DMLwAOBuYD7T7T85nEoImTcitXFH1OM5mIZ3OhyiNGrPqoRGsHPFXSKVpM2w45R9/RMWnUyh78w0aH3FMcoXXdY2awOoaIyhyWUil8/+tfFxwyI8oOvEsMs/kPzbJiuWkd9qNRreNgKbNKb3ipxu/7rqucRPiVTVC0lyu6rymOnejYL8fUnr9pRSdfE71NpXbB5u1pmTIdZTddctGLrr+CEoaE6+pcd3GOUil8ucZIJcjtd8RpI44ldwrTyZTZD3x9XOZ/fq5HHAUBQPPIDvqHzW2y1F42e8JOnYlc/OQjVt0A/DuP56idedO37yhallZlqFpSfWH01JBQEU2R0E6xcqyDM1Kqj9u3qSokBWl5bRoVMye3Tvy+LtTkii5zlr7XDYpLmJlae2xFHEcV02R0KS4kBWl+RCqf68uX3u+Xlu0YfTHMzm6T09GT57J6symeSN7ZVkFTYtrXKOpgIpcjoJU5TVaXPMaLWBFWYbrR73DT3brRftmjav3CwLaNGkEwIh3IlZnKujXZfONdyB12MrVa2jWpPpcNWnciBWran8qoF3rzQCYOOkTHnjyBUbcci1vTHift9+fxON3/o7GjUo45ZKr2Kn3NnTtuOVGrb8uWVla/vV+YE3twRJxTI1+oKiqHyhIp/jTC2MZ8fp7XHF0fwDat2jKH55/g2ffiyivyPKzA3ffOAdSx3xf/cDClWu44/UPuW3gPrwweTabuvz1Wlz1eN3Xa1z7el1TxpJVa3hn2hxGDj6Z0kyGk28dySOXnEqLxiXs2bMrj7/10UY9Dn07m/oUTfWJYfV/ZxAwI4qi48Iw7AlMIh9W1xKGYW/geGAv8mH1S2EYvgicCPwxiqKHwjA8DWgO3EA+OL4nDMNH19H+bT6rMyuKonPDMDwHGBSG4VDgMvKjtsuAW8IwbPrvniAMw+3Ih+99gabAp5VNlwMvR1F0ZxiGPYC/hGF4GDAY2B7IARO+RY11QrxqFanGTaoeB6lUfuQkEJeuYeXI+4lLSwEoe/tNCrfpScWn/mH6jdasgpLqX4oIUtVBdaWK5x+j4p9PUnLVLWS324XCw44l8/gDVIx6kqBzN0qG3JAfga1qq1cRNK55XoOq81o44DBSbdrR6Ka7SLXfAioy5BbMI/vOOFJdulFy+W8ou+dWsh9OXM+TKy5dDcWNqr8QBNXhaqXc6KfJvfY86YuuIwh3II4+2MhV1g9x6WooqXkuU18/ly89SfnoZyn85W/J9dqJePJ7AGRu/CXBFltReMlvKP+fUzdm2dpENS0uZFVZpupxHMcUpFPrbFtVnqF5yXeadW2TcOuot5g4cz7R54vZYav2VV9fVVZOsxohAFBrfuRVZRmar9Ve05BD+3H9U2N47v1P2b17RzZrvGlOcdO0uIBV5Wtdo6ka12h5dYi/qryColSKCXO+YPaSFfzf6x+ybE05v3jydW45ai9ycczvR7/LrC9XcOsxe2/yf7j/6S8PMmHSZKbMmM0OYfeqr69avYZmTZp8bfvnXn2Dux58jLuvv5xWLVvQsnkzttumG21b5YPsXbfvzSfTZm6SYfWtL4xl4sx5RPMXsUOn6psgq8rKadZorX4gFdRqb16j/eKD+3FO/1054Y6H6dN1S37/7Ov85scHsVfYmX9NnsFlD4/irjOP2vAHVMd8X/3ATh3asmR1Gec98iqLVpWypqKCrq2ac8wOW2/0Y0rSrc+OYeL0uUTzF7JDpy2qvr7O6zVY+3otoWWTRvTtvhVNSopoUlJEt83bMHPhEnbovAWS/nub5mdovj+9gLEAURR9Aixcq/2rXm07oDPwMvAK+VHJ3YFLgH3CMPwX0I980FvTN7Wvz1cTJH0GlABbAx9FUbQmiqJcFEU/j6JofZ9h33vjTQAAIABJREFU/6rmbYF3KrdfDny1Itv2wJmVo7GHAZuRH9E9KYqisiiKMkC9WXWs7L2JFO+VX/SgcPsdyUytDqILOneh7V8eyI8GLCigaOc+ZCZ/nFSp9Up28oek++Q/+pTaZltys6dVtQVbdqL40t/kH1RUEGcyEOeIVy6vmg6AZUug8df/QNjUZT9+n4K+ewKQ6rkduZlTq9rK7ruN1YPPYM2Qc8n88xnK/zEyH1R36krJlTdReuOV+YUstV7xtEmktt8NgKBrT+K5M6sb23ckfd5V+f/PVkBFJj8sSOsUT/mI1I75kU9Bt17En82oags270jBRVfnH2QriCsykMuRPvxEUv0G5PcvKyWOv+2PPOm/s3On9oz5ND8VxfuffUGP9q2q2rbv0JYJsxdQlqlgRWk50xcupUe7zZIqtc4afNDuDB90NGOuOIPZi5exdHUp5RVZ3pkxn506ta+1ba8t2/B25Uesx0Sz6NN1/X/Yj536GT89YFfuOfMIUkFAv8qPYW9qdu7QljHT8tMovD93ET3atqxq236L1kyY8wVlFdn8Nbp4Odtv2ZrnBh3B8JMHMPzkAbRoVMQtR+0FwNUvvE15RZbbf7RP1XQgm7KLf3Ii9//+Wl5/+F5mz/ucpctXUJ7JMP7Dyezce5ta2z710ms88NTz/O1317DVFvnretseW/PpzM9Ysmw5Fdks70+eQrdOHZM4lMQNPrgfw887ljFDz2H2oqU1+oF57LRWgNdry7a8PS3f7475ZCZ9unTgzamfcd3jowEoKkxTkEqRCgKaNy6mWeVNwrbNm7B8zaY5pd331Q+cumvI339yCMNPHsDZP+jN4b27bHJBNcDgw/Zm+IUnMOa6nzJ70RKWrlqTv16nzWGnLrVvNvXq2J63P82PQh8zeQZ9tu7ILl07MH7qZ5RlKlhdVs60zxfRqU3Ldb2UpP+Av6H8dz4C9gCeCMOwG/mpMkrJT7/xCbALMBeIyI+6PiSKojgMw5+TD38HAVdHUfRFGIZ3A8cAM6i+ibCu9uHfoq61E5RpQM8wDIujKCoLw/Dv5EdCf2V9NV8UhmEKaAR8tZT7J8CIKIpGhmHYjvzUH9OB3pXTopQDOwMjvkWdiSt95Z+U/KAfbf76IEEQsOTXv6LpKWdQ8dksSv81mtXPPU3bvz1MXFHB6meeoGL61G9+UpF961+kd+pLyW/vgiCg7PYbKDjyeOL5c8mOf53cjE8pufEeICY78U1yk94jM28ORRdcRuHBA6GggLL/uynpw6hzKt4YTXqX3Wn8x/uAgNI/XEPhwJPJzfuM7JuvrXOfojN/RlBYRPF5+YUs49UrKb36Fxux6vojfncsca9dSF/6ByAgO/wWUgMGEi+cR/z+m8RzppO+7I8QQ/zReOIpH37jc26qchNeJ7VdHwqvug2CgIphN5M++FjiBXPJvTuOePY0Cofent/2/beJow/Izv+MwkFDSO97CKTSVAy7OeGj0KZiQM8ujJ02j5PufZqYmBuO2oe/jv2QTq2as3/Pzpyye29O/cuz5OKYwQf0odiAb70K02kuPWxPBv35aXIxDNy1J+1bNGXqgi8ZOe5Dhh69L0MO3ZOhj79KpuJNtm63GQdt1229z9elzWZc+dhoigrSdG/XiiuP2nsjHk3dMSDcirEzP+ek+0cRxzE3HPYD/vr2ZDpt1oz9e3TklD4hp474Z/4a3WeH9c5D/fHnX/LY+9Pos1U7fjIyv3jdqbuGDAg3zZsANRUWFHDpuWdw9uXXk8vF/Ojg/WjfpjVTZ33GA0++wJUXnMkNd/6ZLdq24cJrfwdA3x225aLTjueSM0/irF9dD8Ah++7BNl037emYCtNpLj1iHwbd+3i+H+jbu7IfWMzIse8z9Jj9GXL43gz9+8tkslm2bteKg3bIj2p/8YNPOfmOR8jGMSf125GOrVpwxVH9ueGJV8nmYmJirqycHmRT8331A6qtMJ3m0qP3Y9BdfycXxwzcfTvat2zG1M8XMXLMuww97kCGHNWfoQ+/SOaZ19i6fWsO2mkb0qkUP9p9e06+dSQxMecftActmzT65heU9K0EsSPD/mOV4exfyQe9s8iHyT8Gfk9+VPNcYHbl/M//AxwNFJMfeXwhcChwPbAYWAGcSX4k9EvA3eRD5lrtURQtXk8tM6mes/qhKIpeCMPwYOCEKIrOCMPwDOA88kH201EU/SYMw8+jKNo8DMND11PzxcCpwDxgq8r6V5Cfs7ol+WlJro6i6KkwDI8nP9XIwspjGBpF0avrO3dzd+7phbcBtOzS+ps30neWW71pjuDYkEo6t0m6hAYpV5r55o30nQ2+f3zSJTRId4y8MukSGqbi9U+rof/Csm8zE5++q9T+A5MuocHJvTcm6RIapi/X/hC1vhebd0i6ggYpfcjZm/YcT+ux6sg9zKFqaPLUuDp7nRhW/xfCMOwHNI2iaFTl/M0vRFG0/uEhqmJYvWEYVm8YhtXfP8PqDcOwesMwrN4wDKs3EMPqDcOweoMwrP7+GVZvIIbVG4Zh9QZhWL1uhtW11eWw2s8x/nemAw+GYfhroBC4YEO+WBiGuwHr+lz0w1EU3bkhX1uSJEmSJEmql1J1NpvVWgyr/wtRFH0O7LcRX+9toP/Gej1JkiRJkiRJ2lhS37yJJEmSJEmSJEkblmG1JEmSJEmSJClxhtWSJEmSJEmSpMQ5Z7UkSZIkSZKkhitwgcX6wpHVkiRJkiRJkqTEGVZLkiRJkiRJkhJnWC1JkiRJkiRJSpxhtSRJkiRJkiQpcS6wKEmSJEmSJKnBChyuW2/4rZIkSZIkSZIkJc6wWpIkSZIkSZKUOMNqSZIkSZIkSVLiDKslSZIkSZIkSYkzrJYkSZIkSZIkJa4g6QIkSZIkSZIkaYMJgqQr0LfkyGpJkiRJkiRJUuIMqyVJkiRJkiRJiTOsliRJkiRJkiQlzrBakiRJkiRJkpQ4F1iUJEmSJEmS1GAFKRdYrC8cWS1JkiRJkiRJSpxhtSRJkiRJkiQpcYbVkiRJkiRJkqTEGVZLkiRJkiRJkhLnAouSJEmSJEmSGq7ABRbrC0dWS5IkSZIkSZISZ1gtSZIkSZIkSUqcYbUkSZIkSZIkKXGG1ZIkSZIkSZKkxBlWS5L+n707D5OiOhs2fnfPBrLv+7AIFriCCC6AoCIGF0DiihI1GmJilMQv0SQaE018Y8xijEmM0fiKL25o4i5uiIqCIOCCKIXsyOawygCz9/dHNcMMAiGGoYaZ+3ddXDBd1dXPeaY4Vf306XMkSZIkSZJilxl3AJIkSZIkSZJUZZKJuCPQXnJktSRJkiRJkiQpdharJUmSJEmSJEmxs1gtSZIkSZIkSYqdxWpJkiRJkiRJUuxcYFGSJEmSJElSjZVIuMDigcKR1ZIkSZIkSZKk2FmsliRJkiRJkiTFzmK1JEmSJEmSJCl2FqslSZIkSZIkSbFzgUVJkiRJkiRJNVfSBRYPFI6sliRJkiRJkiTFzmK1JEmSJEmSJCl2FqslSZIkSZIkSbGzWC1JkiRJkiRJip0LLCoWra+/Iu4QpL1XXBR3BDXTus/jjqDGSbbvFHcINdJfhp4Wdwg10lWjfhV3CDXSXyb8Iu4Qaqb6DeKOoEYqC2fFHULNU1QQdwQ1U6PGcUdQM5WWxh2BapOECyweKBxZLUna/yxUS5IkSZKknVisliRJkiRJkiTFzmK1JEmSJEmSJCl2FqslSZIkSZIkSbGzWC1JkiRJkiRJil1m3AFIkiRJkiRJUlVJJBJxh6C95MhqSZIkSZIkSVLsLFZLkiRJkiRJkmJnsVqSJEmSJEmSFDuL1ZIkSZIkSZKk2LnAoiRJkiRJkqSaK+kCiwcKR1ZLkiRJkiRJkmJnsVqSJEmSJEmSFDuL1ZIkSZIkSZKk2FmsliRJkiRJkiTFzgUWJUmSJEmSJNVYiYQLLB4oHFktSZIkSZIkSYqdxWpJkiRJkiRJUuwsVkuSJEmSJEmSYmexWpIkSZIkSZIUO4vVkiRJkiRJkqTYZcYdgCRJkiRJkiRVmWQi7gi0lxxZLUmSJEmSJEmKncVqSZIkSZIkSVLsLFZLkiRJkiRJkmJnsVqSJEmSJEmSFDsXWJQkSZIkSZJUcyVcYPFA4chqSZIkSZIkSVLsLFZLkiRJkiRJkmJnsVqSJEmSJEmSFDuL1ZIkSZIkSZKk2LnAoiRJkiRJkqQaK5F0gcUDhSOrJUmSJEmSJEmxs1gtSZIkSZIkSYqdxWpJkiRJkiRJUuwsVkuSJEmSJEmSYmexWpIkSZIkSZIUu8y4A5AkSZIkSZKkKpNIxB2B9pIjqyVJkiRJkiRJsbNYLUmSJEmSJEmKncVqSZIkSZIkSVLsLFZLkiRJkiRJkmLnAouSJEmSJEmSaq6kCyweKBxZLUmSJEmSJEmKncVqSZIkSZIkSVLsLFZLkiRJkiRJkmJnsVqSJEmSJEmSFDsXWJQkSZIkSZJUYyUSLrB4oLBYrRqtrCzFLc+/Rbh6PdmZGdwybAAdmzUq3/74zHlMmPUJGckkV57Yk0FBRzZsKeBH/3yNguISWjaox60jBlI3O3OX+67cmM+NT79BaVmKVCrFzcMG0Ll5Y579cAEPTP2QjESCkb0CLuh7aIxZqDr7Mr/bj3flQy9ycveOXNDnUDZuLeD6f73OlsIiGtWtwy3DBtCsft24mhuLslSKWya+Q7hmA9kZSW458wQ6Nm1Yvv3x2fOZ8N58MpIJrux/JIO6dWDlpnxufHYqpWVlpICbzziezs0a8fInS7lv6hwSCTi31yGc0+uQ+BpWjZSlUtzy5lzCdZujHA86nI6N6lXaZ/22QkY9+Q5Pn9efnMyM8sdfXbSalxau5ren9tzfYVdbkz9Zwt2TZ5KRTDKyd3fO7VO5/1u6bhM3/PM1ALq1asrPzjqRZHpl7qXrNnH1+Ik8M/YCAFZu3MyN/5q8o48dMZDOLZrs3wZVI1GfO5VwzTqyM7b3uRX6g1nzmDAzjPqDE3syKMgt3/bgtI9Ym7+Na0/tE0foB7xOfY9h5G9u5g8nnRF3KAeUyfOWcPfk2WQkE4w8ujvn9ulRaXvUH7wOiXR/cGb/yv3BQy/xzDXnxRB59fFVc/iX12byZriMjGSSH59xAke2b8nHK/O4+ekpZGVm0L1NM356ej+SyQR/fGUG0xauIAH89Mx+HNm+ZSxtjcPkOZ9y94tvRdes447i3H6Vr+dL89Zzw/jnAejWtgU/O/c03p63iPteeQeAFClmL/yMp396BY3r1eWmRybyxdYCysrK+PXos8itRdesfXmu/r/HXmXt5q0ArNi4maM6tOL35w+u1ecqpO+xJr0bna/H9ODcnd5jLl27iRuemASJRJTjYdE91m9fmMrsJasoLSvj3L6HcW7fQ6N7rCcm73i/cHbtvsfabvLchdz9yjtRjvsexrnHHVlp+9K1G7jhkZeiHLduxs9GnkIymeCqfzzFxq0FZGYkycnK5O/fGhlTC6SawWlAqlAQBP/aw7bWQRD8dX/GsydBEPQMguCmPWy/NAiC23bx+IlBEBy5q+dUB5PmLaGopJRHvjWcawf34faXppdvy9u8lfHTP+Khy4dx7+ih3PHquxSVlPLXN2ZzxhFdGX/5MHq0acaEmZ/sdt+7XpvJqL6HMe6yMxlzYk/uePVdAH770jv84xunM/7yYfzvtDls2lYYVwqq1L7K73Z3vjazUq7+PuV9js5txfjLh3HRsYfxx0nv7tf2VQeTwmVRji87nWtP7s3tr84s35aXv43x737CQ5cM5d4LT+WO12ZH5+Xr7zOqT3fGfeNrjOl3BHe8NpvSsjL+8Nos/nHREB6+9HTunzaXDVsLYmxZ9TFp8RqKSst4ZOTxXHvsIdw+dV6l7W8ty+OK595l3dbK/4//562PuWP6fMr2Z7DVXHFpKbe98Db3XnYW464YzuPvfkxe+s3mdre/8DbXDO7L+DFnk0rBa58sBuCZ90J++Ogrlc7Lu16dwajjjmDcFcMZM+ho7nh5OrXZpHlLo/7gimFRn/vyzn3uxzx0+ZncO/pr3DFpJkUlpRQUl3DdP1/n4Xc/2cORtSdDfjSW0ffdRWadOnGHckCJ+oNp3HvpGYy7fBiPp++nKrp94jSuGdyH8d8aTiqV4rV5SwB45r35/PCxV2v9deqr5vDjlXnMXLyKR688m9+dfwq/evYtAH7+1Jv8+IwTGP+t4TTIyea5Dz/l45Vr+WD5Gh799gh+f/5gfv7UG3E0NRbFpaXc9q9XufeqCxg39mIen/oeeV/kV9rn9n9N4pozT2T8D0ZH16w58xlw6MGMG3sR48ZexMDDunL54OM4uHVzfv/UZM485jD+7/sXc82ZA1m8Zl1MLdv/9vW5+vvzBzPuimH86aLTaFAnhx+ffnytPlchnePn3uLey89i3JgRPD5j7q7vsYYcy/hvnx3l+JPFTF+4gmXrNvHId7/O+CtHct8bs9m0rYC7Xp7BqOMPZ9yYEdE91kvvxNSy6qO4tJTbnn6de8d8nXHfPY/H35lD3hdbKu1z+9NvcM3Qfoz/3vmkgNfmLgBg6bqNjP/e+Yz77nkWqqV9wGJ1FQrDcLe9VBiGq8Mw/O7+jGdPwjB8PwzDW77CU78JtN3X8ewrs5etpn/XDgAc1aEVc1fmlW+bsyKPXrmtyM7MoEGdbHKbNiRcsz79nPYADOjWgWmLVux23+tOO46Bh0Qj10rLUuUjLoNWTckvLKKopBRSKWrql032VX4BXpq7iGQiemy7hXkby38+OrcVs5et3l9NqzZmL/+c/ge3A+Co9i2Yu2pt+bY5K/Po1aFlhRw3IPx8A9edegwD0znefl5mJJM8950RNKiTzcZthaSAg7Kz4mhStTN71Qb6d2gOwFGtmzA3b1Ol7clEgvvP6kujOtmVHu/Vugk3nXjYfovzQLAobyMdmzWiUd0csjMzOLpjG2YtWVVpn7kr1tKnc3TZGHBILtMWfgZAw7o5jPvW8Er7Xjf0BAamRweXlqYqjWqvjaL+M90fdGjJ3JUV+oMVefTq8OU+t7CklOFHdeXbA46KK+wDXt7Cxdwz8uK4wzjgRP1Bwwr9QWtmLd25P8ijT+c2wC76gyuG7feYq5uvmsNZS1dzQtf2JBIJ2jZuQElZGeu3bGP1F1voldsagF4dWzN76WoObducey85g0QiwcqNm2lW76D93s64LFq9jo4tmtDooLpRfrt0YNbC5ZX2mbt8NX26RtehAYd2YVq4pHzb6g1f8Oy7H3HV0P4AzF78GWs2buabdz3CczPn0qdbLrXFvj5Xt/vzpJlcfNzhtGhQr1afqwCLPt+QvseqE+W4UxtmLVlZaZ8ox+l7rKAj0xZ8Rs/cVvzqnJPK9ylLpchMZnDdGScwsHtHAErLysjJ9Ev3i9asp2PzxjQ6KJ3jzu2YtXhFpX3mfraGPgen38t278S0+ctYu3kLm7cV8t1/PMXFdz3K6x8viiN8qUaxR/ovBEFwKXAWUBdoA9wJDAcOB34I3BOGYesgCF4H3k8/3hA4F0gAj4ZheFwQBHOAN4EjgBBYA5wIFAKnAzcAq8Mw/FsQBN2Bv4VhOOjfPS8Mw+LdxP0h8AZwJJBKx9wLuDIMwwuCILgc+B6wHigCHks/9bggCF4GWgB3A7OArwFHB0HwMXALcDBQB/hdGIaPEbP8wmLqVygwJZMJSkrLyMxIkl9YRIOcHdvq5WSxuaCI/MJiGqSfUy87i/zCot3u26ReNMpq8dqN/Pal6dx14akAdG3ZlHPueYqDsjIZ3KMTDevm7I/m7nf7Kr+frlnP83MW8sfzBvPXN2aXP6d762ZMnreUQ9s0Z3K4lG3FJfuvcdVEfmEx9XN2FJWTiSQlZWVkJpNRLivmODt9Xh6UPi/XbeK3r87krvOiG9TMZJJX5i3lly9OZ2DX9mQma+rHKP+Z/KIS6mdXzHGiPMcAJ6QL2Tsb2rUNM1bUnlFTeyO/oKhSn1AvJ4v8gsoj0lOkyueL294vAAzq3ulLx2tSL5r2Z3HeBn774lTuuuhrVRT5geFLfW6iYp9bTIM6O87j7f1Bo7o59Ovaniffmx9HyDXCe/96hmYda0/RaV/JLyii/k7XqPz0//ftUuyYP3L7OQswKF1Aqe2+ag63FBTT+KCcSs/bXFBEhyYNeXfxSvp0bsvr85ayrSi6r8rMSPLHV2YwftpH3HBmv6pvWDWRX1BI/QrfmKhXJ5v8nb4NmUpVyG9ONpsrbB83eQbfOKkv2VnRW+qV6zbR8KA63H/1hfx14lv849V3uPqME/dDS+K3r8/VpvXqsi5/G+8sWsGPTz++fHttPVfhy/cA9XKyv5zj1M73WIXkZGWSk5VJcWkpP318Euf2PZR6OVnUS7+/WJy3gd++MJW7Rg/df42ppqL72ArnY07Wl/sEduoTCgopLinj0oG9GT3gaDZtLeCiPz/KER1a06xB7fpARdqXHFn932sQhuHpwG+A7wAjgTHAZTvtNyMMw8HAK8CFOx8DeDgMwxOBAcDU9L+zgT0N2/uqz2sIPBKG4UBgBVB+ZQqCoDlwPdAPGAJUnLi1GDgNOBv4fhiGs4AXgeuADcBJ6fYPBarF8Lf6OVlsKdxxEU+lopucaFs2W4p21PO3FBbTsE52+jnR41uKosLq7vYFmL54JVc/8gq3jRxE5+aNCVev481Pl/HK9y/glR9cwPotBbw4t2Z+urqv8vv0B5+y5ostXDbueZ56fz7jps1hyqfLGTPgKFZszOeb455n1aYttGlYf/82sBqon5PFlqIdRfpUKlVeRI22VchxUYXzcskqrp4wmduG96dzhXnET+3ekdfHnktxaSlPf7hwP7WiequfncmW4l3nWHvnzlemc8l9T3PV+ImV3jhtKSymwU4f1iUrLGwS9Qt7/jBv+qIVXP3Qi9x2zim1fi7Fiv0npM/VjOQut1XsD6T96c5XZnDJfc9w1UMvkb/TOdmgzh76g6J/3x/UFv9tDuvV2fX9wa0jB3Hvm+9x5YMTaVqvbvmgC4Dvn9qXN66/mPvf+oBl6yp/w6imufO5N7jkzoe46u9PVPpAdUtBEQ3qVp7uJ5mseM0qKh+AUlaW4vWPFnD60TvmZW5Ury4nH9EVgEGHd+WjZZVHFtdEVXWuArw8dxFnHNmVjJ3uyWrTuQpw58vTueTvT3HVgy9UznFh0Z5zXOEea9O2Asb873Mc3LIpYwb1Lt9n+sIVXP1/E7ntvMG1+h7rzolvc8lfJ3DV/U+RX/if3MdGfULzhgdx/vFHkZmRpFmDg+jRriWL89bvt/ilmsh34/+999J/bwQ+CcMwRVS43Xliw+37Ld/FNoDtw0k3Ah+n/72r4+w8FHJvn7e7uHeOpyvwcRiGW8MwLAWmVnytdPtWA5U+JgzDcDPRaOy/E43ErhbvNnrltmbKp9HX+T5YvoZuLXdchI9o14JZS1dTWFzC5oIiFq3dSLeWTejVoRVvpp8z5dPl9M5tvdt9py9eya8nTuOe0V/j8HYtAKhfJ5uczMzyqRea1qvDFzV0zup9ld8fDjmWx8aMYNxlZzKi5yFccvwRDOjWgZlLVzO8Zzfuv+QM2jdpQK/cVrG0M0692rdkyoLoa9EffJZXOcdtWzBr2RoKS0rTOd4UnZdLVvHrl9/lngsHc3jbaFRwfmER33jwRYpKSkkmEtTNyqx0s1Wb9WrdhCnLoilsPli9gW5NG8Qc0YFn7KnHMu6K4Uz5ySUsW/8FG7cWUFRSyswlK+nZofL/2x5tmjMjPf3PlPnL6N2pzW6PO33RCn793Fvcc8mZHF7LFlHalV65rZjyabo/WP453Vo1Ld92RLt0f7C9z83bWKm/kPaXsaf2ZdwVw5jy49EsW7+pQn+wip65O/cHzZixKPoae9QftI4j5Grnv83h0bmteevT5ZSVpVi5cTOpVIom9eryxvxl/OrsQfztG0PZuK2A4w9uzzsLV/DLZ6cAkJ2ZQWYyWePvD8aeOZBxYy9iyv9cw7K1G9i4ZVuU34XL6dm5XaV9e7RvxYxPlwIw5eNF9D44mp7u01V5dGnVjDoVvpnV++D2vDk3Gggwa+FyurZpsZ9aFJ+qOlcBpi38jBMP2TE9YG08VwHGDjmWcWNGMOWGS1m2rkKOF+8ix20r3GOFS+nduQ0FxSV8875nGNm7B9855ZjyfacvXMGvn5vCPZedVevvscYO7ce4757HlJuvZNnajWzcmu4TFn1Gz53uU3u0a8mMBen3svOW0Ltze6bNX8a1//ccEBWwP129loNbNtvv7dBeSCb8U/FPNeY0IP+91D7ab0/bC4imGQE4+iu+/t6+3gKgexAEdYmmE+kLzNvDc8qAZBAEbYDeYRieHQRBHWB5EAT/F4ZhrPM2DO7eiakLP2PUfU+TSsGtIwbywNQPyW3aiJO7d+TiYw9n9P3PUpaCsaf0IScrkysH9uKnT77BE7Pm0figOvz2nJM4KDtrl/veNnFa9JWqJ6MFPjo1a8TNwwZw3jHdGX3/s2RlJOnQpCEjeh4SZxqqzL7K7+50ataInzz5OgAtG9TjV8Nrx1cpKxrcPZepi1cy6oEXohyf1Y8H3plLbtMGnHxILhf36cHocRMpS6UYO+hocjIzuO3ld6Pz8plokZpOzRpx8xnHc+bhXRj94ItkJZMc0qoJZx3RJebWVQ+Du7Ri6mdrGfWvaaSAW086ggc+WExuw4M4uXPt+4Dkv5GVkcH1Q09gzAPPUZZKMbJ3D1o1qs+Cz9fz8LSPuGn4iVx3+gnc9OTrFL88nS4tmjDk8N2fh7c9/zbFpWX89J+vAdCpeWNuHjFwfzWn2on63JWMuu9ZUqS4dfiJPDB1DrlNG6b73EMZ/b/PR/3BKb3JyfI2T/GJ+oPjGTPuhag/ODqgVcN6LPh8Aw+/8xE3DRvAdUOP56an3qT4ldKoPzjM61JFXzWHGckkvTuj7u1uAAAgAElEQVS24cK/P0UqleLGM6M5lTs2a8i3H5xI3axM+nZpy8Agl9KyMl6au4iL/v4UpWUpRh17GO2bNoy55ftHVkYG1599CmP++ihlKRh53JG0atyABavW8vCbM7np/K9x3dknc9MjEykueZ0urZszpFd3ABZ/vo72zRtXOt6Pzj6Fmx5+gUffeo/6dXL47aW1Z971fX2uAixeu4n2TXaci306t6m15yqkc3xGP8ak31uNPKZ7dI+1Zj0PT5vDTSMGct3p/aJ7rJJ36NKyCUMOP5jxU+fw2foveOLdj3ni3Whs263nnMxtz70V3WM9PgmATi0ac/PZg2JsYfyyMjK4fthAxvz9X9F53OdwWjVqwILV63j47fe56euncN2wgdw04WWKXyijS8umDDmqGxnJJG+HS7jgzodJJhJ8f2h/mtSvG3dzpANaIpX6qrVOpees7h6G4Y+DIPgacEEYhpcGQdATuA3oWWHO6ivDMJwXBMGVQGvgAXbMWb0kfZyCIAjeSR9nSRAET6WPswaYAOQTzRN9THrO6j0+LwzDXS7pu9PzbiMqRi9hx5zVlwFXEc1Z3QD4M5BVoa11gHlhGHYKguDb6X3PB8YCx6bjfC4Mw9/sLnelj/7OE08HjuKif7+P/jPrPo87gpqpfae4I6iZ7AOqxFWjfhV3CDXSXyb8Iu4QpL3XqOm/30f/mU1OP1AlMqrFLJc1T/a/+0K4voqMM79dvYfNxqTke2dah6og88/PVdvzxGK1KgmCIBO4PgzDW9M/vwncGIbhm/vydSxW64BioWrfs1hdNSxWVw37gCphsbpqWKzWAcVi9b5nsbpqWKyuGharq4TF6l2zWF1ZdS5W+/3QGioIgr7A7bvY9FgYhnfv7nlhGJYEQVAvCILZQBEwHZhSRWFKkiRJkiRJEmCxusYKw3AGMOgrPvenwE/3aUCSJEmSJElSHGrBoqw1RTLuACRJkiRJkiRJslgtSZIkSZIkSYqdxWpJkiRJkiRJUuwsVkuSJEmSJEmSYucCi5IkSZIkSZJqLhdYPGA4slqSJEmSJEmSFDuL1ZIkSZIkSZKk2FmsliRJkiRJkiTFzmK1JEmSJEmSJCl2LrAoSZIkSZIkqeZygcUDhiOrJUmSJEmSJEmxs1gtSZIkSZIkSYqdxWpJkiRJkiRJUuwsVkuSJEmSJEmSYmexWpIkSZIkSZIUu8y4A5AkSZIkSZKkKpN0vO5/IgiCJPBX4CigELgiDMMFFbb/ALgg/eMLYRjeHARBAvgM+DT9+LQwDH/yn762xWpJkiRJkiRJ0nYjgDphGB4fBMFxwO+B4QBBEHQBLgKOBVLAlCAIngS2ArPDMDzrv3lhP1aQJEmSJEmSJG3XH3gRIAzDd4BjKmxbDnwtDMPSMAzLgCygAOgNtAuCYHIQBC8EQRB8lRe2WC1JkiRJkiRJ2q4hsKnCz6VBEGQChGFYHIbh2iAIEkEQ/A54LwzD+cAq4NdhGJ4E/A8w/qu8sMVqSZIkSZIkSdJ2XwANKvycDMOwZPsPQRDUAR5K7/Pd9MMzgacBwjB8i2iUdeI/fWHnrJYkSZIkSZJUcyX+45ppbfc2cBYwIT1n9ZztG9IF6KeB18Iw/E2F5/wcWAfcHgTBUcCyMAxT/+kLW6yWJEmSJEmSJG33JHBqEARTgQRwWRAE1wILgAxgIJATBMHQ9P4/AW4DxgdBcAZQAlz6VV7YYrUkSZIkSZIkCYD0wolX7vTwvAr/rrObp57x3762c1ZLkiRJkiRJkmJnsVqSJEmSJEmSFDunAZEkSZIkSZJUc7nA4gHDkdWSJEmSJEmSpNhZrJYkSZIkSZIkxc5itSRJkiRJkiQpdharJUmSJEmSJEmxs1gtSZIkSZIkSYpdZtwBSJIkSZIkSVKVSSTijkB7yZHVkiRJkiRJkqTYWayWJEmSJEmSJMXOYrUkSZIkSZIkKXYWqyVJkiRJkiRJsXOBRUmSJEmSJEk1V9LxugcKf1OSJEmSJEmSpNhZrJYkSZIkSZIkxc5itSRJkiRJkiQpdharJUmSJEmSJEmxc4FFSZIkSZIkSTVXIhF3BNpLjqyWJEmSJEmSJMXOYrUkSZIkSZIkKXYWqyVJkiRJkiRJsXPOasUjOyvuCGqmouK4I6iZ2nWMO4Kap3nLuCOombZtiTuCmiknJ+4IaqS/TPhF3CHUSFed94u4Q6iR/vK378UdQs1UVBh3BNLeSTjOr0rkb447AknVkD2uJEmSJEmSJCl2jqyWJEmSJEmSVHMlEnFHoL3kyGpJkiRJkiRJUuwsVkuSJEmSJEmSYmexWpIkSZIkSZIUO4vVkiRJkiRJkqTYucCiJEmSJEmSpJrLBRYPGI6sliRJkiRJkiTFzmK1JEmSJEmSJCl2FqslSZIkSZIkSbGzWC1JkiRJkiRJip0LLEqSJEmSJEmquZKO1z1Q+JuSJEmSJEmSJMXOYrUkSZIkSZIkKXYWqyVJkiRJkiRJsbNYLUmSJEmSJEmKncVqSZIkSZIkSVLsMuMOQJIkSZIkSZKqTCIRdwTaS46sliRJkiRJkiTFzmK1JEmSJEmSJCl2FqslSZIkSZIkSbGzWC1JkiRJkiRJip0LLEqSJEmSJEmquVxg8YDhyGpJkiRJkiRJUuwsVkuSJEmSJEmSYmexWpIkSZIkSZIUO4vVkiRJkiRJkqTYucCiJEmSJEmSpJrLBRYPGI6sliRJkiRJkiTFzmK1JEmSJEmSJCl2FqslSZIkSZIkSbGzWC1JkiRJkiRJip0LLEqSJEmSJEmqsRJJx+seKPxNSZIkSZIkSZJiZ7FakiRJkiRJkhQ7i9WSJEmSJEmSpNhZrJYkSZIkSZIkxc5itSRJkiRJkiQpdplxByBJkiRJkiRJVSaRiDsC7SVHVkuSJEmSJEmSYmexWpIkSZIkSZIUO4vVkiRJkiRJkqTYWayWJEmSJEmSJMXOBRYlSZIkSZIk1VwusHjAcGS1JEmSJEmSJCl2FqslSZIkSZIkSbGzWC1JkiRJkiRJip1zVqvWmvzJEu6e9C4ZySQjj+nBuX0PrbR96dpN3PDEJEgk6NaqKT8bdiLJZKJ829XjJ/LM9y8A4LP1X/CTxyeRAto2rs/NZw+ibnbW/m7SflNWluKW598iXL2e7MwMbhk2gI7NGpVvf3zmPCbM+oSMZJIrT+zJoKAjG7YU8KN/vkZBcQktG9Tj1hEDqZuduct9P9vwBT958g1SqRRtGzfg5rMGUDc7k/umvM/zHy2kfk42l/c7kkFBxxizsP9M/jDk7hemkJFMMPKEXpzb/+hK25d+vp4bHnwagG5tW/KzC04nmUzwPxNe5L2FyzkoJ5trzz6Fozq3L3/OczPm8NDrM3jkusv3a1uqq8kfLeDul6ZFOT7uCM49/qhK25fmbeCGhydCArq1bs7PzjmVZDLBk9M/4tG336csVcbJh3flO6edEFMLqqev2s/+9oWpzF6yitKyMs7te9iXnlcbec2qWpPnLeHuybOjPuDo7pzbp0el7UvXbeKGf74e9QGtmvKzM/vvyO+6TVz90Es8c815MUR+YOvU9xhG/uZm/nDSGXGHcsAoS6W4ZdJ7hHmbyM5IcsupvenYpH759sc/XMyEOYvISCS58rjuDOrShl9P/oB5eRsBWLulgAY52Tw66qS4mlBtlKVS3PLCNMLVG8jOTHLLWf3o2LRh+fbHZ89nwqyQjGSCKwccxaBDOrByUz43PvM2pWVlpFJw85kn0Ll5dA+8rbiEK/7vZX457AS6NG8cV7NiZ16rRlkqxS3PTyVcs57sjAxuGda/cl5nhUyYNS/9nuooBh2SW77twXfmsjZ/K9cO7lPpmD9/9i0a1c350uO1RVkqxS0TpxN+viHK6RnHVc7pe58yYfb8KKf9j2BQt/bk5W/juqffori0jBb16/I/Z51A3ayorLatuIQrHn6VX55xPF2aN9rdy0raC/tsZHUQBD2DILhpD9svDYLgtl08viQIgjr7MI7XgyDovg+P92gQBIP21fGq2u7yvJ9juDIIgl/EGcO/U1xaym3PvcW9l5/FuDEjeHzGXPI2b620z+0vvM01Q45l/LfPJpVK8doniwF4ZnbIDx99mQ1btpXv+7uJUzn/2MMY/+2z6dulHePe+mC/tmd/mzRvCUUlpTzyreFcO7gPt780vXxb3uatjJ/+EQ9dPox7Rw/ljlffpaiklL++MZszjujK+MuH0aNNMybM/GS3+/7u5Rmcf0wPxl8+jL6d2jBu2ofMX7Oe5+cs5NErhnPf6KHcNXkW24pKYszC/lFcWsptT7zMvVdfxLhrL+Xxt2aTtym/0j63P/Ey1ww7ifE/vIwU8NqHIa/Pmc+SNet47Por+OOYc/nVoxPL9/9k+Wr+OfU9Uqn93Jhqqri0lNuemsy93zmXcVdfyONTPyTvi51y/NRkrjm9P+OvGRXl+KNPWbZ2A4++/T7jvnc+j/1gNMWlZRSXlsbTiGroq/az0xeuYNm6TTzy3a8z/sqR3PfGbDZtK4ipFdWD16yqVVxaym0vTOPeS89g3OXDeDx9faro9onTuGZwH8Z/a3iU33lLAHjmvfn88LFX2bC1dp+jX8WQH41l9H13kVlnn70NqBUmLVhJUUkZj1x4Etf2P5zb3/ywfFvelgLGv7eAh84fxL1f788dUz6iqKSUn5x0FOPOG8h9Xx9A/Zwsbjn16D28Qu0xad6y6H728jO49pTe3P7yu+Xb8vK3Mn7Gxzx02ence9EQ7nhtFkUlpdw1+T1G9enOuEuGMqb/kdzx2iwAPlq5lm88MJFlG76IqznVhnmtGpPmLU3n9SyuHXwMt788o3xbeV6/eSb3Xnwad0yK8lpQXMJ1/3qDh9/95EvHe2zmPOZ/vmF/NqHamRQup6i0lEcuHcq1J/fi9ldnlW/Ly9/G+Hfn8dAlX+PeC0/hjsnvUVRSyn1TP2LEEV0Y/43TOLh5IybMng/ARyvX8Y0HX2LZhs1xNUd7I5HwT8U/1dg+K1aHYfh+GIa37KvjSVVp0ecb6NisEY3q1iE7M4OjO7Vh1pKVlfaZuyKPPp3bAjAg6Mi0BZ8B0LBuDuPGjKi074LPNzAgiD697tWxNbOXrNoPrYjP7GWr6d+1AwBHdWjF3JV55dvmrMijV24rsjMzaFAnm9ymDQnXrE8/JxrZO6BbB6YtWrHbfRfkbWBAt+j4vXJbMXvZGhbmbaBPpzbkZGWSk5VJx6aNmL9m3f5v/H62aNVaOrZoSqN6daNz9eAOzFqwrNI+c5etok+3aJT5gMO6Mu2TRSxclUe/Qw8mmUzQpP5BJJMJ8jblszF/K3c8NYmfnHtaHM2plhatWUfH5o1pdFC6P+jSjlmLVlTaZ+5na+iTPucH9OjMtPlLmRYu5fDcVvzk4Yl848+P0KtzO7IyMuJoQrX0VfvZnrmt+NU5O0b8laVSZCZrd169ZlWtRXkb6disIY3q5kT57diaWUsr5yTKbxsABhySy7SFFfJ7xbD9HnNNkLdwMfeMvDjuMA44s1espX+nVgAc1bYZc1fvKDbNWb2eXu2aRfdVOVnkNq5PuHZT+faH3l9Av46tOKSFI/4AZi9bQ/+D2wFwVPuWzF21475yzoq19OrQcsc9apOGhGs2cN2QPgxM36OWlpWRkxldn4pKSvnTeSc7mhLzWlVmL1tT/l7qqPYtmbtybfm2L+W1aQPCNespLCll+FFd+faAyt8YfH/553ywIo/zeu+zMX4HpNnLP6d/l+je6ah2LSqfqyvX0qt9iwrnagPCzzfw41OP4awjulCWSrH6iy00q1cXgKLSUv507iC6NPNclfaFfzsNSBAElwKnAwcBBwO/AWYBfwISwDrgm0Av4MowDC8IguBy4HvAeqAIeCx9uOOCIHgZaAHcHYbh39OP3xMEQSdgDXAJUALcn369DOAPYRg+FgTB6+nXmBcEwZVAa+AB4Nl0HC+kj/fzIAhaAfWAC8MwXBQEwe+B/untD4dheGf6Nf8BZAEp4JowDD8IguAq4ApgFdDy3+SnL/AXYDPwOVAA/GKnmF4B7gJK09u/RfRBwaNhGB6XPs47wAXApUD39Os2Aa4Ow/Ct3bx2XeB/gY7pNly90/argVHptj0ahuGfgiA4HPhD+vUbp9s8NQiCT4G3gSD9e/h6Ov8PAMVEv5NvhGG4IgiCXwMnpo/xhzAMHw+CoD9wJ9HvvBR4Z095i1t+YTH162SX/1wvJ5v8gqJK+6RSKRLpT5vq5WSxuaAQgEE9On3peD3aNGfyx0sY0bs7kz9Zwtbimj3id+f8JZMJSkrLyMxIkl9YRIOcirnNYnNBEfmFxTRIP6dedhb5hUW73bdH62ZMDpcyouchTJ63lK1FxRzSqin3TvmALYVFFJeW8f7yNZx7TM2/wcovKKR+3Zzyn+vVySZ/p1GmKSqeq9lsLiike/vWPPDqNEYN6sPq9V+wYFUe24qKuPmfr3D9OUOoU8u/8l9RfkER9etUyHFONvnbCivtU7k/yGbztkI2bNnGzIWf8fDYiygoLuaiOx9mwrWjaXiQowThq/ez2z+QKi4t5aePT+LcvodSL6d2n69es6pWfkER9Stei7Kzvpxf2JHf7OhaBTCoe+2YjqoqvPevZ2jWMfff76hK8otKqF+hT0wmE5SUlZGZTJJfWEKDCtf3etmZbC4sBqCotIwJHy7msVEn7/eYq6v8ouJK//eTiYq5LP7yPWphEU3S1/jFazfx21dnctd5UT6Pzm21f4Ovxsxr1cgvLK78f79SXnd6T5Ud5bVR3Rz6HdyOJ9//tHxb3uat/OWN9/jT+afw4tzF+7UN1U2U053e01Y8V+vsfK4Wk0hE73vPvu85ikpK+c6AIwE4usMey0aS/kN7O2d1ozAMTwuCoBtREXYj8M0wDD9OF6avIyrIEgRBc+B6oCdQCEyucJxi4DSi4uoLwPZi9d1hGL4TBMHtRIXcMmBtGIajgyBoAMwOgmDSHuJrDfQOw7AoCILTgefDMByfnorinCAIPgY6A8el2/xWEASvAT8H/hSG4dNBEPQE/hEEwSnAWOCIdByzdvF6Ff0NGB2G4dwgCG4F2u0ippnAFWEYvh8EwXCiYvEP93DMrWEYnhwEwWHAw8BRu9nvSmBJ+gOCw4HBRL8bgiA4FDifqECfAl4NguAl4DDg/4VhOCcIglHAZcBUoAtwchiGy4MgeBvoA/ROt/9aYADQJAiCI4HOYRj2S0/f8k4QBK8AdxB9MDA/CIK7/03OYnPny9OZvWQV4ep1HNlhx83PlsIiGlQoVkF0A7BjezENd9pe0XWnn8CvnpnCCx98yrFd25ffcNVU9XOy2FK44418KgWZGcn0tmy2FBWXb4tyl51+TjF1sjLZUhRd/He373WnHcevnn+bF+Ys5NjObWlyUB0ObtGEi/oeyrfHv0hu04Yc2b5ljc7znU+/xuyFywlXrOHITu3KH99SUESDndpd+VwtomHdOvQ79GDmLF3JZXc8SPf2rTkstw0bt2xj6efrueWRFygsLmHh6jx+PeElfnJe7RxlfefzU5i9aAXhqjyOzG1T/viWwiIa1N1TfxDluHG9uvTp2oF6dbKpVyebg1s3Z0neBo7s2IbabF/0s5u2FfD9h16ib+d2jBnUe/8EXg15zapad74yg9lLVxOuWc+R7Xe8yYyuUXvIb9Ge8ytVpfrZmWypMA1aKgWZye33YJlsqfDh05aiEhqmCzHTlq7hmHbNaVDLP/yrqH52VqX70FQqVSGXWbu8RwWYvngVv5z4DreNGFA+r7J2MK9VY+fcVc7rTu+p9nCdevHjxWzYWsCVD73M2vxtbCsuoXPzxpzds1vVNqAa+nJOqXyuFu50rqb706yMJM99exhTF6/iJ8+8zYOja+d7Kakq7e00IO+n/14O1AF6AH9Nj3T+JtC2wr5dgY/DMNwahmEpUSF0u9lhGKaA1UQjtQGKwjDcPgp3KtHI3h7AmwBhGG4GPiYa5VtRxQlWFodhWHEIzPYC8/bX6QFMCcMwFYZhMdGo30N3ep33gQ5Eo5rnhmFYmN53BnvWNgzDuel/T9lNTG3Txyf9eoft4jgV2/NaOqa5REXv3QmAael9PwrD8I8Vth1O9KHApPTxmhH9blYAPwuCYBxwDtGIbIg+HFie/vf23/M/gLXAi0Qj5UuIivi907/7F9PP7wi0C8Nwfvr5b+8h5liNHXIs48aMYMoNl7Js3SY2bi2gqKSUmYtX0XOnT+57tG3OjPRUAFPCpfTuvPsC1NQFy/nuKcfw92+eRTKR4IT0dAE1Va/c1kz5NDpdPli+hm4tm5RvO6JdC2YtXU1hcQmbC4pYtHYj3Vo2oVeHVryZfs6UT5fTO7f1bveduvAzvjvoaP4+emiUz4Pbs37LNjZsLWD85cP4ydATWLUpv9Lr1jRjh5/MuGsvYcrt/49leRvYuGVbdK5+uoyeXdpX2rdHh9bMmL8EgClzF9C7ay5L1qyjWYN6jP/hZVw+5ASSiQRHdmrHszd9h3HXXsLvr/g6B7duUWsL1QBjzxjAuKsvYMovv8uytRVyvPAzenZqW2nfHu1bMePTaPqVKZ8spneX9hzduR3vLlhOYXEJWwuLWLh6Lbm1eOGf7f7bfraguIRv3vcMI3v34DunHBNHE6oNr1lVa+ypfRl3xTCm/Hg0y9ZXyO+SXeS3TTNmLIqmXpkyfxm9O+3p9kyqOr3aNmfK4tUAfLByHd2a71gM7IjWTZn12VoKS0rZXFjMovVflG9/Z9nnDOjseVtRr9yWTElPmfTBZ5/vdD/bnFnL1lBYUvEetTHTF6/i1y/N4J5Rp3J42+ZxhV6tmdeq0atDK6Z8WiGvrfaQ17xNdGu563vS0ccexhNjhjPu0tO5ov+RnHlEl1pZqAbo1aEFUxZG904frMijW4sdOTuibXNmLf886k8Lili0LsrpLROnM31J1AfXy86s9GG2pH1nb0dW77wMV0g0JcSyIAj6ARXfES0AuqenqCgE+gLzdnMcgOwgCHqmi7kDgI+Ipv4YADyZHll9BLCYaAqNNunjHU1UeIVoBPSe4v2EaATxHUEQZAEnAOPSjw8AnkmPrF4NLAIOTcdfRDS9yfjdJQZYHgTBoWEYfkw0cnu7ijGtDILgyDAMPwQGAvPTbWkZBEEG0IBo5Pd2vYHx6dHSlSdO/XK7+gBPB0HQBfgV8HJ6WwjMBYaGYZgKguAHwBzgKeCiMAw/CYLgZqBTev9d/W6GExX5bw6C4EKiEfNPApPDMBwTBEES+BlRzlYHQdAjDMPtMVXr1RqyMjK4/ox+jLn/WcpSMPKY7rRqVJ8Fa9bz8LQ53DRiINed3o+bnnyd4pJ36NKyCUMO3/nzkh06NW/Cjf+cTHZmBl1bNuXG4QP2Y2v2v8HdOzF14WeMuu9pUim4dcRAHpj6IblNG3Fy945cfOzhjE7nduwpfcjJyuTKgb346ZNv8MSseTQ+qA6/PeckDsrO2uW+nZo35san3yQ7I4OuLZtw4xn9yEwmWL5hM+fd8yRZGRn8aMixZCT32bT71VZWRgbXn3MqY+56iLKyFCNP6Emrxg1ZsCqPh19/l5suPJ3rvj6Emx56luKSUrq0bsGQo3tQUlrGW3MX8M+33yMnK5MbLxgad1OqrayMDK4fcRJj/vYEZakUI489nFaNG7Bg9VoenvIeN517KtcNH8RNj71E8XNv0qVVM4b0PISMZJKvH3sEF935MClSfGfI8TROz1unr97Pjp86h8/Wf8ET737ME+9+DMCt55xM+wqrs9c2XrOqVlZGBtcPPZ4x416I+oCjA1o1rMeCzzfw8DsfcdOwAVw39HhueupNil8ppUuLJgw5rEvcYauWGtytLVOXrWHUI5NJAbee1psHZs0nt3F9Tj64LRf36srox96gLJVibL/Dy+f+Xbwhn2GHOm1NRYO7d2TqopWMuv/56H52eD8emDaX3KYNODnI5eK+hzL6gYnRPerJR5OTmcltL8+Ipql6Ohqj1KlZI24+84SYW1K9mNeqMbhHR6YuWsGofzxHihS3Dh/AA9M+Irdpwx15/d/oOjb25N7kZO5tqaf2GhzkMnXRKkY98GKU0zNP4IHpH5PbpAEnH9KBi/t0Z/SDL0U5HdSTnMwMLu7TnZsnTufutz4kkUjws68dG3czpBopkUrtqka5Q3rO6u5hGP44Pe3DPKL5jH9PVFQGuJxodPX2OasvA64imr+4AfBnohG4lY4ThmGnIAhC4C2gG7CUaKR2AriXaDR1XaKpOsalp/j4HdHI3xXAMqI5lSvO/fw6O81rHYbhL4Ig+B3QD8gGJoRh+Jv0nNX3Ajnp+K4Ow3BmEATnAz8G8ohGGN8UhuHru8lPH6L5qPOJitsrgFt3iqkX0XzOCaLRyZen59G+h6iwuwBoTzS/9KXAIKJ5n+sBV4VhuMupSNJ5vJ9o6pEM4PtEI6q35/lHwIh0+2YQzWk9lmj6kDXAZ0DzMAxPDYJgdRiGrdPHfZRoepPlRIX6EqLi+w+A94h+932A+sCTYRjekp525AGiubs3A++HYfiLXcUNUPqvO/d84umrqfA1Ju1DLWv3tA5VonBb3BHUTNu2xB2BtPdKS+OOoEa66rxfxB1CjfSXv30v7hBqpvoN4o5A2juJmj9IJhYlvn+tChnfuNEh37tQeuu3rENVkHHDvdX2PPm3xer/VBAEmcD1YRjemv75TeDGMAzf3KcvVE2kF2OcEIZhXhAEvyKa1uSW/+J4vwBWh2H4t30VY3VksbqKWKyuGhar9z2L1VXDYrUOJBarq4TF6qphsbqKWKzWgcJiddWwWF0lLFbvmsXqyqpzsXqffzckDMOSIAjqBUEwm2ik8XQqz+V8wAmCIBd4cBeb3iCaWuPlIAjygU3AJVXw+n8lmmN7Z0PDMLTiI0mSJEmSJOmAVyUTGYVh+FPgp1Vx7DiEYbtmC08AACAASURBVLiMaGqO3XliH77WL3bx2Hf31fElSZIkSZIkqTryuyySJEmSJEmSpNi5RKwkSZIkSZKkmitRbado1k4cWS1JkiRJkiRJip3FakmSJEmSJElS7CxWS5IkSZIkSZJiZ7FakiRJkiRJkhQ7F1iUJEmSJEmSVHO5wOIBw5HVkiRJkiRJkqTYWayWJEmSJEmSJMXOYrUkSZIkSZIkKXYWqyVJkiRJkiRJsbNYLUmSJEmSJEmKXWbcAUiSJEmSJElSlUkk4o5Ae8mR1ZIkSZIkSZKk2FmsliRJkiRJkiTFzmK1JEmSJEmSJCl2FqslSZIkSZIkSbFzgUVJkiRJkiRJNVfS8boHCn9TkiRJkiRJkqTYWayWJEmSJEmSJMXOYrUkSZIkSZIkKXYWqyVJkiRJkiRJsXOBRUmSJEmSJEk1VyIRdwTaS46sliRJkiRJkiTFzmK1JEmSJEmSJCl2FqslSZIkSZIkSbGzWC1JkiRJkiRJip3FakmSJEmSJElS7DLjDkCSJEmSJEmSqkwiEXcE2kuOrJYkSZIkSZIkxc5itSRJkiRJkiQpdharJUmSJEmSJEmxs1gtSZIkSZIkSYqdCyxKkiRJkiRJqrmSjtc9UPibkiRJkiRJkiTFzmK1JEmSJEmSJCl2FqslSZIkSZIkSbGzWC1JkiRJkiRJip0LLEqSJEmSJEmquRKJuCPQXnJktSRJkiRJkiQpdharJUmSJEmSJEmxs1gtSZIkSZIkSYqdxWpJkiRJkiRJUuxcYFGSJEmSJElSzeUCiwcMR1ZLkiRJkiRJkmLnyGrFY8nCuCOomZq3jDuCmunDd+OOoGZq3T7uCGqeLV/EHUHNlMyIO4KaqX6DuCOokf7yt+/FHUKNdNWVf447hBrpL/dcHXcINU/jpnFHUDNtyY87gpqpaYu4I5BUDTmyWpK0/1moliRJkiRJO7FYLUmSJEmSJEn/n737jpOquhs//pnZxsLSe1vqMogCrogogiJiwd6jCBpjzc8YjU9ijIVHiSZE85hogiWojwVLwMQeO9IERUBXpVzpvSxVdoFtM78/Zll2EaPmEe7Cft6vly937jl35pw7h+/c+c655yp0JqslSZIkSZIkSaFzzWpJkiRJkiRJB65IJOwW6DtyZrUkSZIkSZIkKXQmqyVJkiRJkiRJoTNZLUmSJEmSJEkKnclqSZIkSZIkSVLovMGiJEmSJEmSpANX1Pm6+wvfKUmSJEmSJElS6ExWS5IkSZIkSZJCZ7JakiRJkiRJkhQ6k9WSJEmSJEmSpNB5g0VJkiRJkiRJB65IJOwW6DtyZrUkSZIkSZIkKXQmqyVJkiRJkiRJoTNZLUmSJEmSJEkKnclqSZIkSZIkSVLoTFZLkiRJkiRJkkKXGnYDJEmSJEmSJGmviUTCboG+I2dWS5IkSZIkSZJCZ7JakiRJkiRJkhQ6k9WSJEmSJEmSpNCZrJYkSZIkSZIkhc4bLEqSJEmSJEk6cEWcr7u/8J2SJEmSJEmSJIXOZLUkSZIkSZIkKXQmqyVJkiRJkiRJoTNZLUmSJEmSJEkKnTdYlCRJkiRJknTgikbCboG+I2dWS5IkSZIkSZJCZ7JakiRJkiRJkhQ6k9WSJEmSJEmSpNCZrJYkSZIkSZIkhc5ktSRJkiRJkiQpdKlhN0CSJEmSJEmS9pqI83X3F75TkiRJkiRJkqTQmayWJEmSJEmSJIXOZLUkSZIkSZIkKXQmqyVJkiRJkiRJofMGi5IkSZIkSZIOXJFI2C3Qd+TMakmSJEmSJElS6ExWS5IkSZIkSZJCZ7JakiRJkiRJkhQ6k9WSJEmSJEmSpNB5g0VJkiRJkiRJB66o83X3F75TkiRJkiRJkqTQmayWJEmSJEmSJIXOZUBUw0WIHH8+kaatoayU+DvPweb1VatkZhG98AbiT42EslJIr0V08CWQUQuiKcQnvgirl4TR+Golnkgw4o2PCNZtIj0lhRGnHkm7RvUqysd9Mp+xs74kJRrlmn7dGZDThvyC7dz08hRKyuI0zcrkd6f3JTMtlddnL+ap6XNJiUTp0qwBwwf3IRqJhNi76iGeSDBi0myCDVtJT4kyYsAhtKtfp0qdjduLGPLih7x8QT8yUlMqtr+7aA1vLVzDvSccuq+bHap4PMGI16cQrNlIemoKI87oT7vG9SvKx82Yx9iZc5Pj8phDGRBrx6bCHfzqH+PZUVJKs7p1uPusY8lMT91j3VWbC7jt5YmUxRMkEgnuPKM/HZo04O05i3l08qdEIhHO79WV83p1DfEo7DvxRIIRb88kyN+cHKMn96Zdw7oV5ePyFjL204WkRCNcc9TBDOjcis3bizjl0X+R0yT5vgzKacOww7sAsHHbDoaMeY+Xf3JylfFck8QTCUa89fGu2HpKn6rH9NMFjP10QfKY9j2EAZ1bV5TNWLaOm16dyvhrzwLg9TlLeOrjgJRohC5NGzD8pN41Mra+P28JD70/i5RohHMO68r5vQ+qUr50wxZu/ccEiEBO80bcflo/otEIo8bPYFKwjJRolJtP7UuPNs2YsyqfO1+eTFpqCl1bNuaWU44mGo3w53emM23hSiLALacdTY82zULpa5jiiQQj3vuEIH9LMh6c0It2DbMqysd9tpixny8iJRLlmiO7MqBjS37/fh7z8jcDsL5wB3Uz0nl+yHFhdWG/1P6IwznnD3dy33Gnht2Uai+eSDDi3U92fWadePhuY3QRYz9bnIyvfboyoFMrtpWUMuLdWazYUkhJWZxbB+bSsl5tfvnahxX7zcvfwi/6H8KFPTuF0a1q4f15S3lows44G+P8w/cQZ/85ASIRcpo1rBRnZzLpy/I4e8pR5XF2Pf9vzJsV528XHtGNwd1r3rH9Ic8H3p63jEc/nEMkAucf2pnzenbe5/2pTt6fs4iH3v2IlGiUc3ofzPl9DqlSvnT9Zm4d+zYQIadFY24/67jkZ/2bU5k2fxmRSIRbzjiWHtkt2LxtB6fc8yQ5LRoDMOiQTgzrlxtCr6T9m8nqA1gsFmsEnBwEwbPfc7+zgY+CIFi1d1pWjXTuDqlpxJ//E7RsT/SYs4m/MnpXebuuRPufAbV3JV0jvY4jsexLEp9MgIbNiJ5yKfFn7t33ba9m3guWU1xWxnM/HkzeynzueXcmoy5IfsHML9jOmI/nMe4np1BUWsbQp96ib4eWPDr1C87q3pEze3Tir5PyGDvrS350WBcemPApL111OplpqfzyxclMmL+CgV3ahtzD8L23eC3FZXGeO+co8tZs4p6p8xg1uFdF+ZRl+dz3UcCGbUVV9vvdlDl8sHw9XZvU2/0pD3jvzVtCcWkZz115JnnL13LPWx8xasiJAORv3caYj75g3NVnJ8flY6/Qt1MbHpw4i1O7d+bs3C6MnvwpY2fM5ZTunfZY9y/jZzDkiIMZdFB7pixYzp/e/Zg/XXA8970znXFXn03t9FRO/+sLHN+1PQ3r1Ar5aOx9781fmYwDQweRt2o997z/KaPO6Q+Ux4GZ8xl3yQkUlZUx9Jnx9G3fnDlrN3HKQdncNqhXleeasng19038jA3bdoTRlWrjvS9XJMfwJSeRt3I997w3i1HnHQvsPKYB4y49OTkun3mHvu1bkJ6awuqvCvnf6XMpiccB2FFSygOTPuOly09JxtaXP2DCgpUMzGkTZvf2uZKyMkb+axpjf3oOmWmpDB39MgO6tqNp3doVde55Yxo/H9SbIzq24o6XJzF+3hJaNchixuLVPH/N2azeUsANz73D2J+ew3+/NIlbTjua3OwW3P/OdF77bD6dmzUib/lanr/6LFZtLuBnz7zJiz87P8Reh+O9BasoLo3z3EXHkbdqA/dM+oxRZ/YFIL9wB2M+WcC4iwdSVBZn6PMT6JvdjN8c1xOAkrI4Q/8+gREnHBZmF/Y7J/7qevoMu5Ciwm1hN2W/8N6CVcnPrCEDk2N0Yh6jzjoaqDxGjy8fo+/Tt11zHv84IKdxfUYOPoIgfzNB/hZ6tGzEkz8aAMCnqzZw/5QvOL97xxB7Fq6Ssjgj35jG2GvOTsbZR19hQOwb4myHVtzxyuTyOFuXGUtW8/zVZ7F6SyE3PP8OY685mzmr1nPp0T247OgeIfYqfD/U+UBZPM59Ez9l3KUnJ89TH32d43Pa0LD2gX+euiclZWWMfHUSY6+7kMz0NIY+OJYB3TrQtO6uCUH3vDqJn5/UlyM6teGOf7zH+DkLadWwHnnLVvP8z37Eqk1b+dmTr/LiLy5mzsp1nHJojNvOGhBep6QDgMuAHNh6AGf8B/tdD9SIrFakdSdYMjf5YPUSaLFbQjSRIP7CX2FH4a5NsyaQ+PyD5INoNDnbWsxavo5+HVsB0LN1U2av3lBR9vmq9eS2aUp6agp1a6WT3bAuwbpN3HzC4ZzevSPxRII1XxXSuE4m6akpPHPpyWSmJX9LK43Ha+yMyt3NWr2Jfm2bANCzRUNm52+pUh6NRHj89COoXyu9yvbcFg0ZfszB+6yd1cmsZWvo1zn577pn2+bMXpVfUfb5ynxys5vvGpeN6hGs3Vi+TzKB1z+nLdMWrfzGujeddCTHdskGoCyeICM1hZRolNd+dj51a6WzeVsRCRLUTq8Zvw3PWpFPvw4tAejZqgmz12yqKPt89UZyWzdJHsOMdLIbZhHkb2bO2k3MXbuJS54dzw0vf0B+wXagfDz/aMDXxnNNM2vFOvp1LD+mrZswe83GirLPV28gt3Wl2NqgLkH+ZopKy7jzrY8ZflLvirrpqSk8M+yEGh9bF+Vvpl3jetTPzCA9NYXD2rVg5tLVVerMXplP7/Jx3L9LNtMWrmDm0jX07dyGSCRCqwZ1KY3H2Vi4nTVfFZKb3QKA3HYtmLV0Dd1aNWH0pacSiURYtXkrjevU/lo7aoJZK9fTr31zAHq2alw1HqzZSG7rxuXxII3sBlkE63d9pj3z6QKObtecLk3rf+159c3yFy7mkXOGht2M/UZyjCb//fZs1ZjZa3f7zGrV5Gtj9IMla0lLiXLlC5N56MO5HF0+xgESiQR3j/+U4YMOIyVa865a2WlR/ibaNaoUZ7ObM3Ppmip1Zq9aT+/25XE2py3TFq7cLc5mUVqWjLOzV+UzKVjGsEdf4bYXJ1JYVBxGt0L3Q50PpESjvHblacnz1O3FJBJQOz1tn/enuli0biPtGjegfu1ayfHavhUzF1edszd75Tp6d0zOVO/ftT3T5i+nW+tmjL787ORn/aavaJyV/Kyfs2Idc1eu45KHXuCGp18n/6vCr72mpG9XM749VxOxWOzHwJkkE8FNgBHAncCXQBHwU2BMeXkqcFsQBONjsdgcYDJwMLARuAgoBh4Gckj+6HBbEAQTYrHYF5WerwnQMxaLXQXcBBwRBMHGWCz2UyArCIKvTQeOxWKnAocCT8VisUeBnCAIfhWLxVKAT4Hzytu4GmgDvBEEwa2xWKwt8DegFrADuCoIguU/3NHbS9JrkSjavutxPA6RKCSSvzyzLPj6Pjvr165LdPAlxCf8c++3cz9QUFRCVsaupFI0GqE0Hic1GqWgqIS6lRJOdTLS2FpUQiQSobQsztmPvkZxaRk/7d+DaCRCk6xMAMZ8PI9txaX0LU8a1HQFxaVkVTqZjEZ2HWOAvuWJ7N0N7tyS6Ss37LHsQFdQVEJWrd3GZVmc1JQoBUXF1M3YbVzuKK4yXuukp1FQVPyNdXfOll68fjP3vvURf7noBABSU6K8M2cxv339A47tkk1qSs34bTgZB/Y8RguKS6hbqaxOeipbi0ro0Kge3Y5uSN/2LXh19hLufncWfz7raPqWJxBquoKi0n8fW/dwTO96ewaXHXEQzSvNYotGIjSpUx5bZwRsKymtkce4YEdxleNZJz2Ngh1VEx8JIFK+PEqd9OS/9cIdJTSonVFlv607imnbsB4fL15F7w6tmDBvKduLkz9gp6ZE+fM70xkz7QtuPe3ovd+xaqiguLRqPKgydkupm/71sQtQXBZn7GeL+fuQgfu8zfu7T/75Co3bZYfdjP1G8jNr19fhr39m7SrbOUY3bS9my45iRp/Xn5dnL+XeiZ8xcvARALy/cDWdG9ejQ6O6X3utmmT3c686Gen/Ps6Wn1MVFhXToNLs3p3bu7dpxnm9unJw66Y8PGEWo96fxU0nH7lP+lKd/FDnAwCp0SjvBMv57dsfc2yn1qTW4B9XCnYUf328bq96lWoiUXm8prN1R7I8NSXKn9+cypgpn3Jr+UzqDs0a0q3NkfTNyebVWfO4++UJ/HmYyzJVGzVw+bv9Vc349ly9ZAEnACcC9wENgN8GQXARcBvwThAExwDnA4/FYrEoUBt4JgiCfsA84GrgCmB9ed0zgVGVnn/n890NjA+C4G/AM8CF5XWGAU/tqXFBELxOMil9CfAccFZ5ovpk4H2SSfD2wI+B3sDAWCx2GPBH4IEgCI4r/3vk/+0w7SPFO4ikV7rkqXKi+t9p0pLoeT8jPuVVWLFg77VvP5KVkUZhcUnF40SCiiRqVkYahUW7ygqLSqhXfrKVlhLltavP4I5TjuQ3ryRnrMcTCe55dybTFq/m/vOOrTg5qOmy0lMpLNk1kz+RSFQcY+1Zcuzt+oKUSFCROM7KSK8yZguLSqhXK73KeC0sTiauv6kuwEeLV3Hdc+8w8pwBdGjSoKLOCd06MOG/LqakrIyX8+bv1X5WF1+PA7vGaFZ61bLC4lLqZaRzZHYz+mQn1/Md1KUNc9dtQrtkZaR+8zHNSKOweFdMKCwuJT0aZeaKdTw45XMufeZdtmwv5r9engKUx9bxs5i2ZA33n92/RsXW+9+ZzqWPvsK1z7xFQeXPo+IS6tbKqFK38jrehcUl1KuVQZ1au4/fZAy4+5wBjJ70Cdc89QaN6mRWWe7nhhOOYOKvh/L4lDyWbah6JUxNkJWeWmV8Vj0vqPp5tjMeAExbupbDWzepkniR9obdY2iVMZr+9fhaLyONBpnpDOycvJJwQKeWfFHpioFX5y7j/B4d9lHrq5/73/2YSx979etxtqi4yqQV2C3OFpVQLzOdOhnpVc7ZkudaGQw6qD0Ht24KwKBuHZi7erf7C9UQP+T5AMAJsbZM+NnZyfPULxbvu45UE/e/OZVLH36Ba594lYIq466Yupm7nRdEI1XK61Uqv+Hkvky87QoenziTZRs2c2SntvTplLxCc9AhnZi7Mh9J359Zjn1vYhAE8SAI1gKbgKbAzum7BwGTAIIgWAl8VV5eEgTBpPI6U4EY0B04JRaLTQD+AaTGYrHG5XX2MB2Yx4BhsVjsEGBN+ev/W0EQbAUmAicBlwGPlhflBUGwMQiCMuCjSu25pbw9w4H94k5CiVWLoEO35IOW7WH9d1imu1ELoqf9hPi/nty1hIjIbduUyQtXApC3Mp+cpruSdt1bNWHm8nUUlZaxdUcxizZsIadZA0a88REfLUleFlgnPbXixPWOf31IcWkZfzl/QMUl60ou5zF5WfKEJ2/NJnJq+Myd7yI3uwWT5ycv8shbvpacZg0ryrq3bsrMpWsoKilNjsv1m8lp1pDcts2ZVL7P5PnL6ZXd4hvrfrR4Fb9/YxqPDDuZQ8q/SBXsKOaSx1+luLSMaDRCZlpajbmJXW7rJkxelFxSIW/VenIqXcLfvWUjZq5Yn4wDRcUs2vAVOU3rc/ubH/P2lysA+HDpWro1bxRK26ur3NZNmbww+dmUt3J91djasjEzV1SOrV/RvVVj/nXV6Tx58SCevHgQ9TPT+Z8z+wFwx5vTk7H13GNqXGy9/oQjePKKM5h88zCWbdzC5m07KC4tY8aS1Rya3bxK3YNaNmb6ouQxn/zlMnq1b8Fh2S2YMn858XiCVZu3kkgkaFgnk4lfLuOuswfw8CWD2bx9B0d1asOHC1fy21cnA8nlV1Kj0RoTAyrLbdWEyYuTn/F5qzaQU+m+Cd1bVI4HJSza+FVF+YfL1tG/Q82b9a99L7dV428eoy0bMXNlpTG6YSs5TepzWKvGTCr/nJuxYj2dK+0zZ+0mcls1pqa6flBvnrz8dCb/ehjLNlSKs0vX7DnOli+1MHn+cnq1a8lh2c2ZsmBFeZwtKI+ztbjyqX/x2Yp1AHy4aCUHt9rzlYQHuh/qfKCgqIRLnnk3eZ4aiZBZ6TtYTXL9yX158przmDz8Spat37xrvC5exaHtql7Ve1CrpkxfmDxXnTxvCb3at+bDBcv57YvvA5Cetuuz/vYX3uXtz5OT2T5csJxuNfAGy9IPoWZ9U6keegHEYrHmJJf7WAfsnMo7F+gPfBKLxVoDDYENQFosFusZBEEecDQwu7z+iiAIfheLxTKBW0kmv6n0fHHKf5AIgmBZLBbbXF7vsW9pY8V+wGjg10CTIAg+i8Vi7YGDYrFYbZKzrPsA/0tyxvcfgyCYGovFugLHfr/DEpL5n0F2jOiFvwAg/tYzRA47jsTmfFj0xR53ifY7HVJSiR53bnJD0Y6qN2WsoQbFspm6aDVDnniTBAnuPq0vT3w0h+yGdRnYpS1De3dl2FNvEU8kuH7AoWSkpjC0d1fufOMjHpryGZFIhNtP7sOc1Rv4x6cL6JXdjMvGvAPAsN5dGdTVy1oHdWzO1BXrGfLPaSSAu4/rzhN5i8muV5uBHZp/6/410aCu7Zm6cAVDHn2ZRALuPutYnpj6GdmN6jOwazuG9jmEYY+/SjwB1x/fm4y0VK45NpdbXpzICzPn0aB2Le497zhqp6ftse7IN6ZRUlbGLS9OBKB94/rceUZ/TuvRmWGPv0paSpQuzRtxeo+acZf1QV3aMHXJWoaMeTc5RgcfwRMfB2Q3yGJgTmuG9sph2LPvJY9h/+5kpKZw47E9uO2Nj3n+kwVkpqXy25N7f+vr1CSDYm2ZumQNQ55+O7ke6qlH8sT0ucnYmtOGob1iDBvzTjK2HtPjG9ehnrNmI//IW0ivts247Nn3ABh2eIxBsZp189q0lBR+PfgornryX8QTCc45LEbzenVYsG4Tz374BcPP6M9Ng49i+EuTKHmnjI5NG3LiwR1JiUbp1a4lF/3tJRKJBLedlvwBoF3jelz91BtkpqVyRMdWHBvLpiwe563Zi7j4by9RFk8wpM/BtGlUI24FUsWgnFZMXbaWIc+9n4wHJ/XiiZlfJuNBp1YMze3MsL9PTI7dow+pGLuLNxVwRrd24TZeNcKgnNZMXbqOIc+OLx+jh/PEjPIx2rl8jD4/ITlG+x1MRmoKV/U5iOFvz+CiZ8eTGo0ycnDyM2vjtiJqp6fWqCtWvklaSjQZZ5964+tx9qPZDD+9HzedfCTDX55MSdl0OjZtwIkHdyiPsy24aPRLJBJw2+nJOPvfp/fnrtc+IC0lSpOsTO4885iQexiOH+p8ICsjjdMObs+wZ95Nnqc2bcDpB7fft52pRtJSUvj16cdw1aMvEk/AOb270bx+FgvWbuDZqXkMP3sgN53Wn+EvvEdJWRkdmzXixPLz+rc+m8/Fo8ZSlkgwpG9P2jSqz42nHM1tY9/l+WmfkZmexm/PGxRyD6X9UySRSITdhhqjfM3qq4FtQH2Sy348DHQNgmBHLBZrBDwONAIygduDIHgzFostITmjOhtYRnKWMyQTye1IJr0fDIJgdHndnc/XGngXeCQIgj/HYrFzgAeAduWzor+pnXeRXPbjxPI1rj8DRgVB8Eh5svp9YA7QHHghCIKRsVisI/AQyTWrM4HrgyCY9k2vUXbfzx14e0MTf7ndKzbWzMsN96oWbcJuwYGp8KuwW3Bgita8GxHuE1leHbJX+Jm1V1x7zV/DbsIBadQj14XdhANPA69Q2isKC8JuwYGpUdOwW3BASjnz//nL2R6UPTbcPFQlKZePqLbjxJnV+97EIAhurvS4/c4/giDYCJz1Dfv9JAiCHbttu2T3SkEQVH6+lSSXFtkpFXj83yWqy/e7jWQinfI1swtJrl+909ogCE7dbZ9FJJcLkSRJkiRJkqqPiCsh7y9MVtcQsVjsdySXGDmz/PEZwI17qHp/EAQvltfpALxIcma20/UkSZIkSZIk7TUmq/ehIAie+A/3a/8DvPYtuz1+BXjlW/ZZDBy627YlwJH/1/ZIkiRJkiRJUmXOgZckSZIkSZIkhc5ktSRJkiRJkiQpdC4DIkmSJEmSJOnAFYmE3QJ9R86sliRJkiRJkiSFzmS1JEmSJEmSJCl0JqslSZIkSZIkSaEzWS1JkiRJkiRJCp3JakmSJEmSJElS6FLDboAkSZIkSZIk7TVR5+vuL3ynJEmSJEmSJEmhM1ktSZIkSZIkSQqdyWpJkiRJkiRJUuhMVkuSJEmSJEmSQucNFiVJkiRJkiQduCKRsFug78iZ1ZIkSZIkSZKk0JmsliRJkiRJkiSFzmS1JEmSJEmSJCl0JqslSZIkSZIkSaHzBouSJEmSJEmSDlwR5+vuL3ynJEmSJEmSJEmhM1ktSZIkSZIkSQqdyWpJkiRJkiRJUuhMVkuSJEmSJEmSQucNFiVJkiRJkiQduKKRsFug78iZ1ZIkSZIkSZKk0JmsliRJkiRJkiSFzmS1JEmSJEmSJCl0JqslSZIkSZIkSaEzWS1JkiRJkiRJCl1q2A2QJEmSJEmSpL0m4nzd/YXvlCRJkiRJkiQpdCarJUmSJEmSJEmhM1ktSZIkSZIkSQqdyWpJkiRJkiRJUui8waIkSZIkSZKkA1ckEnYL9B05s1qSJEmSJEmSFDqT1ZIkSZIkSZKk0JmsliRJkiRJkiSFzmS1JEmSJEmSJCl03mBRkiRJkiRJ0oEr4nzd/YXJakmSJEmSJEkSALFYLAo8CPQEioArgiBYUKn8SuBqoBS4KwiC12KxWBPgWSATWAVcFgTBtu/72v6sIEmSJEmSJEna6SygVhAERwE3A/+zsyAWi7UAfg4cDZwE/D4Wi2UAw4FngyDoD3xCMpn9vZmsliRJkiRJkiTt1A94EyAIgg+B4GnZ5wAAIABJREFUwyuVHQF8EARBURAEW4AFQI/K+wBvAIP+kxc2WS1JkiRJkiRJ2qkesKXS47JYLJb6DWVbgfq7bd+57XszWS1JkiRJkiRJ2ukroG6lx9EgCEq/oawusHm37Tu3fW/eYFGhSLnslrCbcEBKrF8edhMOSJHWXcJuwoFn+9awW3BAKpv+5rdX0vcW7dYn7CYckOLBzLCbcGAqLgq7BQekUY9cF3YTDkjXXv2XsJtwwBk1+vqwm3BgqlP32+vo+1uxOOwWqCaJRsJuwf7mA+B0YGwsFjsS+LxS2XTg7lgsVgvIAA4Cvijf5xTgCWAwMPk/eWGT1ZIkSZIkSZKknV4ETojFYlOBCHBZLBa7EVgQBMErsVjsAZLJ6ChwaxAEO2Kx2F3Ak7FY7EpgPTDkP3lhk9WSJEmSJEmSJACCIIgD1+y2eV6l8tHA6N32WQuc/H99bdesliRJkiRJkiSFzmS1JEmSJEmSJCl0LgMiSZIkSZIk6cAV8QaL+wtnVkuSJEmSJEmSQmeyWpIkSZIkSZIUOpPVkiRJkiRJkqTQmayWJEmSJEmSJIXOGyxKkiRJkiRJOnBFnK+7v/CdkiRJkiRJkiSFzmS1JEmSJEmSJCl0JqslSZIkSZIkSaEzWS1JkiRJkiRJCp3JakmSJEmSJElS6FLDboAkSZIkSZIk7TXRSNgt0HfkzGpJkiRJkiRJUuhMVkuSJEmSJEmSQmeyWpIkSZIkSZIUOpPVkiRJkiRJkqTQeYNFSZIkSZIkSQeuiPN19xe+U5IkSZIkSZKk0JmsliRJkiRJkiSFzmS1JEmSJEmSJCl0JqslSZIkSZIkSaHzBouSJEmSJEmSDlyRSNgt0HfkzGpJkiRJkiRJUuhMVkuSJEmSJEmSQmeyWpIkSZIkSZIUOpPVkiRJkiRJkqTQmayWJEmSJEmSJIUuNewGSJIkSZIkSdJeE3G+7v7Cd0qSJEmSJEmSFDqT1ZIkSZIkSZKk0JmsliRJkiRJkiSFzmS1JEmSJEmSJCl03mBRkiRJkiRJ0oErGgm7BfqOnFktSZIkSZIkSQqdyWpJkiRJkiRJUuhMVkuSJEmSJEmSQmeyWpIkSZIkSZIUOm+wKEmSJEmSJOnAFXG+7v7Cd0qSJEmSJEmSFDqT1ZIkSZIkSZKk0JmsliRJkiRJkiSFzmS1JEmSJEmSJCl03mBRkiRJkiRJ0oErEgm7BfqOTFarxhs/+QNGPf4kqSkpnHvaKVxw1ulVyhcsXsLtv/8jiUSCrjmduP2/riclJYW/PfUsr7/zHll1anPF0Is4rl/fkHpQPY3/aBYPPv8iKdEUzj3hWC44+bgq5XMXLeWuh58kGo2SnpbGH268hiYN6wMQj8e5+s4/cnyfXlx4yvFhNL/aGj9xMqP+9mhyvJ51Bhecc9Ye6736xpuMeW4sf3/qcQDu+sMfmZX3GXVq1wbgwT/9kbp1s/ZZu6u78VOmMurxMaSmRDn3tMFccOapVcoXLF7C7X/4UzIOdO7E7Tf+jC8XLuZ39z9YUefT2XMYNXIExxx5xL5ufrX1/hcLeOitaaREI5xzZHfOP6pnlfKl+Zu49dk3IAI5LZpw+3knEI1GePGjL3j+g0+JJ+IMPKQzPz3J+LrT+GkzePCZcaSkpHDuScdxwSknVCmfu3Axd416bFdsvek6mjRswKTpsxg1ZhwA3Tp3ZPh1VxDxhJ33P5/PQ29OISUa5Zwje3L+0YdWKV+av5Fbx7wOQE6rptx+/kl8MG8Rj77zIQAJEsxauIKXb7mCBnUyGf7cG3y1bQfxeJzfDzud7KYN93mfwhRPJBjxr2kEazaRnhplxOlH065RvYrycbO+ZOzMgJRohGv692RAl7as2lLAba98QFk8TiIBd57Wlw5NkucD20tKueLpt/ntGX3p2KRBWN0KXTyRYMS7nxDkbyY9JcqIEw+nXcNdn+HjPlvE2M8WJ49rn64M6NSKbSWljHh3Fiu2FFJSFufWgbm0rFebX772YcV+8/K38Iv+h3Bhz05hdGu/0v6IwznnD3dy33GnfnvlGi6eSDDinZkE67Yk48BJh9OuYd2K8nF5Cxmbtyg5Xo/qlhyvxaWMeGdmcrzG49x6fC49WjYGyuPA2In89uTedGxc75te9oAXjycY8fpUgrUbSE9JYcQZ/WlX6XiMmzmPsTPK4+sxhzIgls2mwh386h/vs6O0jGZ1a3P3mceQmZ7Ko1PyeP3zRWRlpHH50T0YEMsOsWfhiScSjHj/M4L1XyVj6/E9adegUmz9Yiljv1hCSiTKNUfkMKBDC1Zt3cbNb31CggT1a6Vz70mHkZmWyhOzFvKPOctolJkOwB0De9Khod+1pP+UyWp9TSwW6w40DIJgUtht2dtKSkv5/f2jeOHxR8jMrMVFV13Lcf370rRx44o69z00mht/eiW9c3ty84jfM37yB2S3ac1rb7/LuMceAuDCK6/lyMMPI7NWrbC6Uq2UlJYy8tExjPvTb8nMyGDITXdyXJ9cmjbc9UXz7r89zW3XXMpBHdvx/BvvMfqFV/nNlUMB+PPT49iytTCs5ldbJSWl/P5//sQLY54gMzOTi358Bccd04+mTZpUqTc3+JIXXnqFRKVts+cFPDrqARo1rLlf9r9JMg48xAuPPZiMA1dfz3H9jqJp40YVde57+DFuvPpyeuf24Oa7/sD4KdM44dh+PD3qPgDeGD+RZk0am6iupKSsjJEvvc/YG4eRmZ7G0PufZcDBnWhab9eJ+z0vvc/PT+nHETnZ3DH2bcZ/MZ8urZry/Aef8uTPfkR6aip/ffMDSsrKSEtJCbE31UNJaSkjH3mCcX8ZSWatDIb84jaOO/JwmjbalRC9+8H/5bZrL+egTh14/rW3Gf33l7jukh9x7+ineeqPd9Kwfj0eHfsSm7Z8RaMG9UPsTfhKysoY+c93GfurH5OZns7QPz3FgO6dq47Rf77Hz087hiNy2nHH828y/vMvGdQzRv9uyeTeY+9+SG6HNnRq0YRbnn6N0w4/mMGHHcRHXy5l8doNNS5Z/d68ZRSXlvHc5aeSt2Id97z9MaMuTP7onF+wjTHT5zDuitMpKi1j6BP/om/HVvzl/U8Y0rsrg7q2Y8qClfxp/EweuGAgX6xaz52vT2PNV54PvLdgFcVlZTw3ZCB5qzZwz8Q8Rp11NAD5hTsY88kCxl18PEVlcYY+/z592zXn8Y8DchrXZ+TgIwjyNxPkb6FHy0Y8+aMBAHy6agP3T/mC87t3DLFn+4cTf3U9fYZdSFHhtrCbsl94b/5KikvjPDf0+OR4nZDHqLP7AZBfsJ0xsxYwbtggisrKGPrszvE6j5wm9Rl5ah+CdZsJ8jfTo2VjvlizkTvfnsmardtD7lX43pu3NBlfrziDvOXruOftjxh1UfIH6/yt2xjz0RzGXXVmMr4+/hp9O7XmwYmfcGr3Tpyd24XRk/MYO3MeR3VsxeufL+L5K5ITtIY89hp9OrQiM73mpYbeW7ia4rI4z13Qn7zVG7ln8hxGnZ48l88v3MGYvEWM+9Exydj6whT6tm3KU58sYnCXVlzUowN/njqXf8xZxtCeHZmTv5mRJ+ZycDO/a0k/BNes1p6cC3QLuxH7wsLFS8lu05r69eqSnpZGr549mPHpZ1Xq/OX3I+id25PikhLyN26kcaNGLFyylCMOO5SMjAwyMjJo17YNwYKFIfWi+lm0fBXZLZtTP6sO6Wmp9OoWY+bsoEqd+266loM6tgOgrCxORnoaAG9OmU40GqV/rx77vN3V3cLFi8lu24b69eolx2tuT2Z88mmVOps2b+aPD/yVW355Y8W2eDzO0mXLGX7X77jwx1fwwkuv7OumV2sLl+weBw5hRt7nVer85Xd30Du3RzIObNhE40rJwW3bt/OXR5/g1l/8bF83vVpbtHYD7Zo0oH7tWqSnpnBYx9bMXLSySp3ZK9bSu3NbAPof1IFpXy5lWrCUQ7Kb85tn3+CSvz5HbofWJqrLLVq2guxWLahfNys5Vg/uyswv5lapc98tv+CgTh0AKIuXkZGexidzAnI6ZDPykSe5+MbbaNygQY1PVAMsWrOBdk0bUr92ZvkYbcvMhcur1Jm9fA29OydnnPXv1pFpwZKKsjWbvuLVj7/g2sHJJMysxStYu3krP/nLc7w2Yza9c2reTLVZy9bSr1NrAHq2acbs1Rsqyj5fuZ7cts1IT02hbq10shvWI1i7iZtO7M2xOck4UBaPk5Ga/PdeXFrGAxcMpGMTx+qslevp174FAD1bNWb22k0VZZ+v3khuqybJ45qRRnaDLIL1W/hgyVrSUqJc+cJkHvpwLke3b16xTyKR4O7xnzJ80GGkRL3C4tvkL1zMI+cMDbsZ+41ZK9bTr0Ol8bqm0nhds5Hc1o3Lx2t6crzmb+GDxeXjddxEHpo2h6PLx3txaZwHzjqajo3r7vG1apJZy9bQr3N5fG3bjNmr1leUfb4yn9y2zXfF10b1CNZuTMbkzm0A6J/ThmmLVrIwfzO927cgIy2VjLRU2jWux5drN4bSp7DNWrWRfu2aAdCzZSNmr9tcUfb52s3ktmy0K7bWr0Ow4Su6Nq3HlqISAAqKS0mNJlNqc9ZtYfTH8xk6bgp/+3j+vu+MdICpeT+f7YdisdiPgZ+Q/HHhIeAGoAiYD1xVXu1xoBOQAtwXBMHfY7HYBCAPOAQoACYDJwENgBODINh15rDrtVoDPwaKY7HYLOCvQRAcUV72d+CPwJPlz3UwsBG4CCgGHgZyytt5WxAEE364o7B3FBQWUrdOnYrHdWpnUlBQdQZPSkoKK1ev4bLrbiQrK4sO7dpSv24Wf3vqGQoKt1FSWsInn3/Bj3ZbPqQmK9i2nbp1alc8rpNZi627zUZpVp7smzX3S5557R3G/OE2vlyynNcnTuX+3/ycUc+9uE/bvD8oKCykbtauGX91atehYGtBxeOysjJuvfMubvmvX5BRK6Ni+7bt2xl64flcNvRiyuJlXHLlTzmk20F07ZKzT9tfXRUUbqNu1u5xoKBKnWQcWMtl1/+KrKw6dMhuU1H2wqtvcPLAY03+7aZgRzFZlcZhnYx0CrYXVamTSCQqlqKok5HO1u1FbCrczoyFK3j2+ovZUVLCxfc/y9gbh1GvtleufC221s78emxtXB5bZ8/jmZffZMz/jOCDmXlMz5vNiw/dS+3MWgy98XYO7daFDm1a7dP2VzcFO4rIqnRFVJ1aexqjfG2M7vTk+9O55LgjSE9Lnk6v2rCFerVr8fh1F/HgG1N47N0Pue7UY/ZBT6qPguISsjLSKx5HIxFK43FSo1EKikqoW6msTkYaW4uKaVj+b3vx+i3c++4M/nLBQAAOy26OkgqKSsjK2PW1rcpxLS6hbqWyOumpbC0qYdP2YrbsKGb0ef15efZS7p34GSMHJ2cMvr9wNZ0b16NDIxOA38Un/3yFxu1q3o9P/6lkHEireFw1DpRSt1LZrvFalByv5x/Ly18s4d4JeYw8tQ+HtWmyp5eokQqKSsiqtVt8LYuTmlIeX2tVPq5pbN1RTEFRMXXL96mTnkbBjhK6NG/E6Cl5FBYVU1IW59Pl6zi/V+k+7091UFBcSlb6v4utu4/VUppnZXLfB3N5PVhJcVkZP+sTA2Bwl9YM6dGeOulp/Pz16UxYXJcB5T/aSPr+TFbvPzaRTFh/BOQGQbA1Fov9Cbi6vHx9EATDYrFYXWBWLBZ7r3z79CAIro/FYm8C24IgOCEWiz0JHAu8tPuLBEGwMhaLPQGsCYJgeiwW2x6LxboBa4AOQRB8HIvFagPPBEEwKRaL3VPehu3lbbg8Fos1BiaRTGZXS396+FFm5X1OsHAhPbodVLG9cNv2Pa7j27plC95+4VnGvfwaI+8fxR+G38LF553Dlb+4iXZtWtPz4G40NFHFn58ex8zZAV8uWU6P2K71Dwu376jyo8BO/5r0IQ+PfZlH7vgljerX47F/vM7aDZu49JbfsXLdetJSU2ndvAn9e/X82r41yZ9GPcSsT/II5i+gxyG7/lkVbiukbt1dXzRnz53H0mXLueN3f6CouJgFixZz9733cfON13PJkAvJzEwmBI484nDmfTm/xier//TI48z67AuCBYvocXDXiu2F27ZX+VFgp9Ytm/P22KcY98rrjHzgIf5w+80AvPr2ezxw93/vs3ZXd/e/PplZi1YSrM6nR3bLiu2FRcXUzcyoUjdaac3kwqJi6mXWokGdTHp3bkudWunUqZVOpxZNWJK/iR7tWlJT/fl/n2Pm7Ll8uXgZPWKdK7YXbtu+59g64QMefu4fPHLXLTRqUJ8G9epySJdOFcuFHN69G/MWLqmxyer7X5vIrIUrCFato0e7XcegcEcxdTOr/igSje4+RpNjOB5PMOGLBVx/2rEV5fXrZDKwe/L9GXBIZ+5/beLe7Ea1lJWeRmFxScXjRCJRMessK6NqWWFRCfXKkygfLV7Nb9/4kJFn9a9Yr1q7JI/drmRSIsGu45petaywuJR6GWk0yExnYOfk+B7QqSWjp8+rqPPq3GUMO2xXLJF+SLuPyapxIPXr47XWbuO1c6sq41VJWRlpFBbtFl9TonssKyxOxtesjHQKi0qolZZKYXEJdWul06lpAy4+ohtXj3mb7Eb16NG6KQ1rZ3zt9WqCrPTUbx6r3xBb/3t8Hr87IZd+7ZoxcfFabn5nFg+d3odLDu1Ykdw+tn1z5uZvMVkt/R+4DMj+IwA6ArODINhavm1nQvig8r8pL5tDcpY1wKzy/28u3w7JxPd3naI2muRM6yHAmPJtJZXWs54KxIDuwCnls7n/AaSWJ62rpV9ccwVPP3Q/H/zrJZatWMnmLV9RXFLCjE/yyD2kao79ml/+hiXLVgBQp3ZtopEIGzdtZtPmLTz3t79y643XsXrtOnI6dgijK9XKDcPO5+mRtzFlzCiWrVrL5q0FFJeU8vEX88jtWvVL0SvvT+GZ197mqd/fStsWycuvfvWTixh73508PfI2zj6+P5edNbjGJ6oBfnHtT3n60Yf54N03WbZ8BZu3bEmO11mfktuze0W9HocczOv/+DtPP/ow9428i84dO3Drr25kydJlDLnsSsrKyigpKWXWJ3kcfFAsxB5VD7+4+ic8Peo+Pnj9BZatWMXmr8rjwKefkdu96kpI19x0G0uWV44DyY/PrQUFFJeU0LJ5s33e/urq+lP78+R1FzL5t/+PZes3sblwO8WlZcxYuIJD21dNjh7UpjnT5y8DYPLcxfTq2IbDOrTm4wXLKSopZVtRMQvXrCe7Bt9YDeCGyy7i6T+OYMrfH2XZqjVs/morxSUlfPz5XHK7dalS95V3J/HMK2/w1L130rZlcmbqwTkdmb9kOZu2fEVpWRl5c7+kU6WrA2qa6087lievv5jJv/v5bmN0OYd2aF2lbnKMLgVg8pxF9OqUXK5i/up8OjZvTK30XbOuenVqw6TZySXBZi5cTueWTfdRj6qP3OxmTF6QjJV5K9aR02zXkkndWzdh5rK1FJWWsnVHMYvWbyanWQM+Wrya3781nUeGnMAhrZxFuSe5rRozefEaAPJWbSCnya6bqnVv2YiZK9dTVFrG1qISFm3YSk6T+hzWqjGTFq0GYMaK9XSutM+ctZvIbVVtT9O1n8tt3YTJ5WMvb9UGcpru+gGqe4tGzFyxc7wWs2jjV8nx2rrJrvG6PJ/ONfhGit8kN7s5k+eXx9fl68hpvuveKt1bN03G15Ly+Jq/mZxmDcnNbs6k+cnlrSbPX0Gvds3ZWLidTdt2MOby0/jN4CNZ/VVhlVhdk+S2asTkpesAyFu9sWpsbd6Amas27IqtmwrIaVyXehlp1C2fjd20Ti2+2lFCQXEpZz7zPoXFpSQSCT5avp5url1dPUWj/lf5v2rMmdX7jziwGOgWi8XqBEFQSHJ29JdACdAfeLF8ZnX38rpAlXusfZ/X2jlyXwB+CWwAzi/flhaLxXoGQZAHHA3MLt++IgiC38VisUzgVpJJ8WotLTWVm6+/lstv+CWJeIJzTz+F5s2asmDxEsaM+yd33HQjV11yMTf/9vekpaWSWasWd91yEw0b1GfFqlWce9lVpKWmcdN1PyXFNVUrpKWm8usrLuaK4X8gHk9w7gnH0rxJIxYsW8kzr73NbVdfyt2PPE3Lpo257u4/A9C7+0H8/OJzQ2559ZaWlsrN/3UDl/+/n5NIJDj3zNNp3qwZCxYuYszfx3HHLb/e436dOnbg9FNO5oJLfkJaaipnnnYKOZ067bFuTZSWmsrNP7+Gy2+4mUQizrmnnUzzpuVx4IWXueNX13PVsIu4+a57knEgoxZ3/ea/AFi8bAWtW3ip+p6kpaTw67OO46qHXyCeSHBOn0No3qAuC9as59nJnzD8/BO46cwBDP/7W5S8NomOzRtz4qFdSIlGObdPdy6+/1kSJPjpiUfRoE5m2N2pFtJSU/n11T/milvuSsbWk4+jeZPGLFi6nGdefpPbrv0Jdz/0OC2bNuG6EfcC0LvHwfz8kh9x40+GcPlv7gJg8LFH0aWDl7SnpaTw67OP56oHnyeegHOO7JEco6vX8+ykGQz/0cncdPZAhj/3BiWlE+jYogkn5iavwli8bgNtdvsR5VdnH8/wZ//F81M+IatWBvf++IwwuhWqQV3bMXXRKoY8/jqJBNx95tE8MW022Y3qMjCWzdAjujHsiTeIJ+D6gYeRkZrKyLenU1JWxi0vTwagfeP63Hla35B7Ur0MymnN1KXrGPLseBLA3ScdzhMzviS7QRYDO7diaG5nhj0/gXgiwfX9DiYjNYWr+hzE8LdncNGz40mNRhk5uDcAG7cVUTs9tWJ5G+mHNqhLa6YuXcOQZ95LxoHBvXni44DshlkM7NyaoYd1Zthz45NxoF/35Hg98iCGvzWDi8a8R2pKhJGn9Am7G9XOoK7tmbpwFUMefZUECe4+8xiemPo52Y3qMbBrO4b26caw/309GQeO70VGWirXHHMot7w4kRdmBTSoXYt7zx1AZloqyzdt5YK/vUxaSpRfndCblGqetNpbBnVqydRl+QwZOzkZWwcdyhOzFpLdoA4DO7ZgaM+ODHvhg+QxPaorGakp3Hpsd+6e+Dll8QQJEtw2oAd1M9K44aiDuOyfU0lPidKnbROObe/3A+n/IpJI/Ce5TO1L5WtWdw2C4OZYLDaE5JrVcWABcEX536NJzqbOBB4IguDJ8lnO1wRBMC8Wiz0PPBwEwYRYLPZn4MMgCJ7/htc7FbgXuDYIgvdjsdgDQNMgCC4qL19CckZ1NrAMuKx819FAO6Ae8GAQBKO/sVOb1jjw9oLE+uXfXknfW6R1l2+vpO9n+9Zvr6PvrWz6m2E34YAU7eaX5r0hHswMuwkHpvzVYbfgwFRY8O119L1de/Vfwm7CAWfU6OvDbsKBqY5rvO8VG/PDbsEBKeXae/1Fcg/K3njUPFQlKYOvqLbjxJnV+4EgCJ6o9PezwLN7qHbpHvYbUOnvCyv9fcO3vN7rwOuVNqWSTERX9pMgCHbstu2Sf/e8kiRJkiRJkvRNTFbXULFYLBt4ag9FE4Mg+O9K9d4GVgVBMH6fNU6SJEmSJElSjWOyuoYKgmAZMOA71DtxD9va74UmSZIkSZIkST8879ew36iZK+lLkiRJkiRJkqoVk9WSJEmSJEmSpNCZrJYkSZIkSZIkhc5ktSRJkiRJkiQpdN5gUZIkSZIkSdKBK+J83f2F75QkSZIkSZIkKXQmqyVJkiRJkiRJoTNZLUmSJEmSJEkKnclqSZIkSZIkSVLoTFZLkiRJkiRJkkKXGnYDJEmSJEmSJGmviUTCboG+I2dWS5IkSZIkSZJCZ7JakiRJkiRJkhQ6k9WSJEmSJEmSpNCZrJYkSZIkSZIkhc4bLEqSJEmSJEk6cEWdr7u/8J2SJEmSJEmSJIXOZLUkSZIkSZIkKXQmqyVJkiRJkiRJoTNZLUmSJEmSJEkKnTdYlCRJ/5+9+w6TqjwbP/6d2cbSO1Kk4wGULqgURUUjAhYssUDUqMSYor/kjZqoJGpMiKYZY0zEGPHFEuwNFBUQEAQBNQhykN5hqbKUrfP7YwbYBVTiK5xl9/u5Li+XPWdmnueZe5/nnHvuOUeSJEmSyq9YLOoW6BBZWS1JkiRJkiRJipzJakmSJEmSJElS5ExWS5IkSZIkSZIiZ7JakiRJkiRJkhQ5k9WSJEmSJEmSpMilR90ASZIkSZIkSTpsYtbrHi18pyRJkiRJkiRJkTNZLUmSJEmSJEmKnMlqSZIkSZIkSVLkTFZLkiRJkiRJkiLnDRYlSZIkSZIklV+xWNQt0CGyslqSJEmSJEmSFDmT1ZIkSZIkSZKkyJmsliRJkiRJkiRFzmS1JEmSJEmSJCly3mBRkiRJkiRJUvkVs173aOE7JUmSJEmSJEmKnMlqSZIkSZIkSVLkTFZLkiRJkiRJkiJnslqSJEmSJEmSFDmT1ZIkSZIkSZKkyKVH3QBJkiRJkiRJOmzisahboENkZbUkSZIkSZIkKXImqyVJkiRJkiRJkfMyIIpE0ezxUTehfMrbHXULyqfF/4m6BeVTXl7ULSh/8p0DDofij6ZE3YTyyXjV0aRm7ahbUC49NPKmqJtQ7vzg+geibkK59NCr90fdhPKpsDDqFkgqg6ysliQdeSaqJUmSJEnSfqysliRJkiRJklR+xazXPVr4TkmSJEmSJEmSImeyWpIkSZIkSZIUOZPVkiRJkiRJkqTImayWJEmSJEmSJEXOGyxKkiRJkiRJKr9isahboENkZbUkSZIkSZIkKXImqyVJkiRJkiRJkTNZLUmSJEmSJEmKnMlqSZIkSZIkSVLkvMGiJEmSJEmSpPIrZr3u0cJ3SpIkSZIkSZIUOZPVkiRJkiRJkqTImayWJEmSJEmSJEXOZLUkSZIkSZIkKXImqyVJkiRJkiRJkUuPugGSJEmSJEmSdLi/i1oOAAAgAElEQVTEYrGom6BDZGW1JEmSJEmSJClyJqslSZIkSZIkSZEzWS1JkiRJkiRJipzJakmSJEmSJElS5LzBoiRJkiRJkqTyK2a97tHCd0qSJEmSJEmSFDmT1ZIkSZIkSZKkyJmsliRJkiRJkiRFzmS1JEmSJEmSJCly3mBRkiRJkiRJUvnlDRaPGr5TkiRJkiRJkqTImayWJEmSJEmSJEXOZLUkSZIkSZIkKXImqyVJkiRJkiRJkTNZLUmSJEmSJEmKXHrUDZAkSZIkSZKkwyYei7oFOkRWVkuSJEmSJEmSImeyWpIkSZIkSZIUOZPVkiRJkiRJkqTImayWJEmSJEmSJEXOGyxKkiRJkiRJKr9i1useLXynJEmSJEmSJEmRM1ktSZIkSZIkSYqcyWpJkiRJkiRJUuRMVkuSJEmSJEmSIucNFiVJkiRJkiSVX7FY1C3QIbKyWpIkSZIkSZIUOZPVkiRJkiRJkqTImayWJEmSJEmSJEXOZLUkSZIkSZIkKXLeYFEV3sS5C3l47FTS0uIMPqUTl/TqUmr78g2buf1/X4VYjDYN63Hnt88hHo/xu+ffYs7ilcRiMW4Z3I+urY6NqAdlx8R5i3l4/HTS4nEG9ziBS07pWGr78pwt3P7MG0CMNg3rcufgM4nHY/zgny+ydcdu0tPiZGWk88iwi5i/aj03PvoSzerVBOCynp3o36VtBL2K3sRPFvHwG9OSMXpSBy7p2anU9uU5W7j9ybGpGK3LnRefRTwe48UZc3nmvY8oLi7mjA5t+P63erJ1xy7OvfdR2jSsC0C/Dm0Y2vfEKLoVuYnzl/Dw2zOS8dr9eC456YRS25dv3MrtY8YDMdocU4c7LzideDzGn9+YxvTPVhCLxfjFeafRsekxrNnyObc9M54ECWpUrsT9l59DdmZGNB2LwMQFy3h44hzS4jEGd23LJd3bldq+fNM2bn9+EsSgTYPa3DmwN/F4jIcmzGJyuIK0eJzbBvSkY5P6/PTfb7Nx+04AVm/dTqdjG/CHb/fjd2OnMWf5uuSc2/8UujY7JoKeRsd4/eYciXj981szmb54NTHgFwN70bFJ/SPf0QgUJxLcPXY64botZKbHuXtQL5rVrr53+7NzFjJmdkhaPMYNfTrR97hjWbMtlzteeY+i4mISCbhrYE9a1K0BwK6CQq773/Hcc15PWtatGVW3yoSJC5bz8KQ9cRtwyYkHidsXJiWPBerXKhG3s5m8MBW3555Cxyb1mb9mIzeOfoNmdZLjfFmP9vTv0CqCXkWrOJHg7rdmE27YlozXb51Is1rV9m5/9uPFjPl4STJeT2lP31aN2JlfyN1vzWbVth0UFBdz+5ld6NiwDpCK1zHvcs853WlZp/oXvaxKaN7jRAb/7i7+ePqAqJtyVJg4bzEPv/V+6lzreC45eb9zrY1buP3pN5PzwDF19p5rAezKL+CKB5/hJwN606dti+SxwFPjSAA1sitx/5BzK9SxAKTmgHHvE67fQmZanLsH9jxwzfpwYXIO6N2Rvm1Sa9ar05JrFnDXgFNoUacG4z9dzqPT5hKLwSVdjuPiLsdF1zGpHDBZfRQLguBqoG0Yhrf9F4+pBCwIw7D5l+wzDPhXGIYF/+dGlnEFRUWMeO5txtx6DdmZmQz5wyj6ntCGejWq7t3nvhfe5seD+tLjuGb86umxTPhPSJO6tfhwySqe+dk1LM/Zwv889iLP3XZthD2JXkFRESNemsSY/3cl2ZkZDHnwafoe34p61avs3ee+Vybx4/696dH6WH717FtM+GQR/Tq2YfnGrbx6y9XEStydd/6q9VzVtxvXVNBE6h4FRUWMeHECY376neS4/vlJ+p7QinrVS8ToSxP48YA+9GjTlF/9+00mzP2M4xrX45n3PmLUDy8jMz2dv46bSkFREfNXrefcru244+J+EfYqegVFRYx4dTJjfnRZclz/Noa+7VtQr1qJeH11Mj/+Vk96tGrCr55/hwnzF9OoVnU+XrGWZ374bdZs2c4PR73Ki//vSp6Y8iH9O7Xh8p6d+PMb03j+g3kM6dU5wh4eOQVFRYwYO50x3x9MdkY6Q0a+TN+2zahXrfLefe4bN50f9+tOj5aN+NXLk5mwYBmNalZl1tK1PHPDhazdlsvNT7/FmO8P5g/fTsbmtl15XP3PV7nt3FNYsHYTH65YzzM3XMjyTZ/zP2Pe5rkbL4qqy0ec8frNORLxOn/NRj5euZ5nvncBa7bm8sMn3+DFH14SVZePqHcWrCC/sIinrx3Ax6s2cN/4D3josjMByMndyeiZ83n2ukHkFRYx5PGx9GzZiAcnfsgV3dvSr20zpi5azZ8mzOYvl57BJ2s2ctfr01n3+Y6IexW9gqJiRoybzpgbLkzG7aOv0Df4grht0YhfvTIlFbfVmLVsLc987wLWbtvBzc+8xZgbLmT+mo1c1asj1/Tq+CWvWv6989lq8guLeXrImXy8ZhP3TfqYhy7sDUBO7i5Gz1nEs0P7kVdUxJCnJtKzWQMe+2ABberWYMSAkwg3bCXM2UrHhnX4ZN1m7ho/m3Xbd0Xcq6PH2T+7iZOGXkbejp1RN+WoUFBUxIiXJzHm5tS51l+foW/7/c61Xn6XH/fvlTzXeu5tJsxbRL8ObQC454UJxEo83xOT59C/c8DlvTrz57FTeX7GJwzp04WK5J0wtWZdcy4fr8rhvrdn8dClZwCpOeCDT3n22oHJNWvUOHq2aMSDkz5KrllBU6YuXs2fJszhTxedxh8nzObZawdSOTOdQX9/mTODptSqXCniHuoAMS8ucbTwndLB/AJIi7oRR8KSdRtpVq8WNSpnk5meRtdWxzJ78cpS+8xbsZbubZoC0Kd9K6aHy2hQsxrZmRnkFxaxY3ce6Wn+KS1Zv5lmdWtSo3Kl5Fi2aMzsJatK7TNv5Qa6t2oCQJ92LZj+2XI2bt/B9l153PjPlxjy4NNMmrc4ue+qDUyev4Shf32GO555kx278494n8qCJes20axurX3j2rIxsxfvP67r6d46Wdnfp31Lpi9czvRwOScceww/f3Is33nwabq0bExGWhrzV67n01Xr+c5fnuLmf71MzrbcKLoVuSUbNtOsTol4bd6I2UvXlNpn3uoNdG/ZGIA+bZsz/bOVtG9cn5HXXkgsFmPNls+pUzWZKGjbqB7bduUBkLs7n/R4xZkTluRspVmd6tTIzkqOZbNjmL18bal95q3OoXuLhgD0Oa4p0xevYvbydfRs3YRYLEajmtUoLC5m8459J/l/fWcWQ04+gXrVqtCgemWyM9KTc25exRpfMF6/SUciXts3qsvIqwYkx33rdupUqUxFMWfFenq3SsZhpyb1mbd2095tc1dvpMux9clMT6NapUya1qpOuH4Lt5zdndPaJNewouJistKTh6D5hUX85dIzaJmqsq7IluRsoVntEnHbtAGzl68rtc+8NRvp3jwVt22OZfri1fvFbVUKi5JxO29NDpPDFQx99BXuePFdduRVzGOsOas20rtF8ls6nRrVYd66LXu3zV23mS6N6yTjNSuTpjWrEuZs472l68lIi3P9s+/y8PT59GqefHx+YTF/uaAXLetUO+hr6UA5i5fyj8FDom7GUeOg51pLV5faZ96q9fvOtdo2Z/rCFQA8NnEWXZo3pG2jenv3PeBYoAKez85ZuaHEmlWPeWs37t02d01O6TWrdjXCDVu45awTOa11coyLihNkpaeRFo/z2vcvoFqlTLbuyiMBVK5gVerSN83K6qPfKUEQvANUB34F5AL3AkXAYuB7QBbwJFALWPRlTxYEwbXAMcAzQRDMB1aHYfhQEAS1gLeBnwK3A8Wp/R5Jbe8A/AWIAZuA74ZhuO0b7us3LndXPlWzs/b+u0qlTHJ37S61TwL2VvxWqZTF9l15pMXjxGIxBtz9d3J35XHXFeceyWaXSbm786ianbn331WyMsndnVdqnwSJfWOZlcn2XfkUFBZxdd8TGdqnK9t27ubKB5+mQ9OGdGh6DBef1IHjj23A3996n4fGT+OW8/oeyS6VCbm780uPa6WDjGti/3HNY8uOXcxavJKnbr6S3QWFXPnnJxnz0+/QokFt2h/bi55Bc16dNY97n3+bP3/3giPap7Igd3c+VSvtF6+79h9XSo9ratzT0+L8+Y1pjJ76Ebdf0BeABjWq8sdx7/H6RyH5hUX88KyTjkxHyoDc3flUzSoxlpkZ5O734VKpeTQzg+2789mxu4CalbNKPW777nxqV8lmU+4u3l+ymtvOPQVg35z7wL/J3Z3PXRecdvg7VoYYr9+cIxGvkBr3t2Yyevon3D6w1+HtVBmSm19QanzjsRiFxcWkx+Pk5hVQreTYZ2WwPS9/b+XZ0o3buP/tWTyYqmrr2rTBkW18GZabV3DgHPBlcZuVitu8fGqWqOzb8/sOTepzcbe2HN+4Hn+fNIeHJs7hlnNOPiJ9KUuS8bovoVQ6XgupVmJblcx0tucVsGVXHtt25zPyktN4+ZNl3D/pY0YMOImuTepG0YWj2ocvvEKdZk2jbsZRI3ksUGIdyso48FiAA48Fpi9cwfKNW7jrkrP4sMQH3Q1qVOOPr0/l9TkLyC8s5IffOoWKJjdv/zkg/sVrVmrd37tmbdqzZp0OQHo8zlsLlnPPGzM4rXUT0uMxJH19JquPfjuAAUA9YAZQAPQOw3BDEAT3AFeTTFZ/Eobh7UEQnASc8UVPFobhP4MguBO4DGgEPA08BFxBMuEN0BjoQrIyf24QBM8CI0kmqOenEt63kExql0kPvDqJOYtXEq7eQMfmjfb+fsfufKpll/66TrzEpSl27M6jenYWL8+YS93qVRn5w8vZsTufIX98gs4tG9OgZsW7Pt0DY6cyZ+lqwjUb6VjiGrI78r5iLPPyqZ6dRd3qVfj2KZ1IT4tTp1pl2jWuz9KczfTr0Jrqqcf369CGe1+ccGQ6VEY88PoU5ixZRbgmh47NGu79/VfGaF4+1StnUbNyJbq3bkqVSllUqZRFq2PqsixnCye3aUql1Cf9/Toex1/HvndkOlRGPPDGNOYsW0O4diMdm+4fr1ml9o3HD4zXPW4+pyfX9z2Ryx76N91aNOL3r0/lN5eeTe+gGe9+upTb/j2ev3/3/MPfoQg98NZM5ixfR7h+c6nr8e7IL6Bapf3GsmSM5hdQvVIWVSplsCO/YL/fJ08Kxs9bwoCOrUlLVfy+/NFC6lbLZuTV57Ijr4AhI1+mc9MGNCjx1dfyyHj95hzJeN3j5rN6cP2pnbnsHy/RrdkxNK1T/iuEq2aWHqdEIrG3cr9q1n5jmLdvDGcsXcs9495nxAV99l6vWvDA2x8cPG7z8qlWInkN+x8LFFA9O5MqWZmlqqaTY55Fv3bN984R/dq34N7XK9axwB7JeC3c++/S8ZpeatuO/EKqV8qgZnYmZ7ROnjv0bd2IkTMXHNlGq8J5YNx7qXOt/c4J8goOPBY4yLnWCzPnsmbLdq762xiWbNjM/NUbqFutCr9/bTK/uexb9G7bnHfnL+G2p9/g79ddeMT6VRYk16UvmgO+eN2fsWwt94ybwYjze9OixNp+VttmnBk05RevTOXl/yxmcOc2R6gnUvlT8b7rUf5MDcMwEYbhBmAXcCwwJgiCScDZQFPgeGAmQBiGexLaXykMwyXA9iAI2gNXAk+kNk0LwzAvDMNdwCdAK6Ad8LfU636XZKK7zLppUF9G3TyUKSNuZkXOFrbu2EV+YRGzFq2gc+pr1Hu0a9KAmQuXAzBl/mK6tW5KjcqVqJyVQVo8TpVKmWSmp7Ezr9xf4vugbjq3N6N+8G2m3H0DKzZu3TeWS1bRucQBFUC7xvWZuSh5mZUpny6lW8vGTF+4gp888SqQPKj6bN1GWtWvzfX/eJ7/pL6a/f5nyzm+gtycao+bBvRh1I8uZ8qvf1A6RhevonPz0n9e7Zo0YOZnya/5TZm/hG4tm9C1ZRM+WLSCvIJCdubls3jdRprWrcmdz7zB+I8XAvD+wuW0P7ZiVa7ddE5PRt1wMVOGX5+M1527k+O6dM2B8dqoHjNTl1yZsmAZ3Zo35v1FK7nnxYkAZGakkR6PE4/FqF45a2/ioF71Kny+X6VLeXTTWT0Ydd15TLltKCs2b9s3lsvW0nm/ish2Deswc0mymmfKwhV0a34MXZsew9TPVlJcnGDN1u0kEglqVckGYPriVZx63L6b1taolEXlzNScm5VRYeZc4/WbcyTj9f3Fq7nn1SkAZKbvG/eKoEvT+kxZlIzDj1dtoE39Wnu3dWhcl9kr1pNXWMj23fks2biVNvVrMmPpWn775kz+ccVZnNDI6tSSburXnVHXDmLKrUNZsalE3C5fd/C4TVVNTvlsJd2aNaRr0wZMXbQqFbe5qbitxPVPjOU/qzYA8P6S1RxfQce9S+O6TFmSPNb8eM0m2tTbl3TqcExtZq/aSF5hEdvz8lmy+XPa1K1B18Z1mZx6zKyVObT2Roo6zG7q34tRN17KlLtS51o7S5xrNf+Sc60Fy+jWogn3DxnAkz+6jFE3XkqfoDk/HdiHdo3rUz07i2rZe44FqvL5zt0HvHZ516VJyTUrp/Sa1aheas0qSq1Z22hTvxYzlq3lt+M/4B+X99u7ZuXm5fOdJ94gv7CIeCxGdkZ6hVn3pcPFyuqjX3eAIAiOASoBy4DzwzDcFgTBeSQvC9IBOAV4OQiCLsBXXUCpmH0fZIwE7gBWhWG4MQgCgM5BEKSRrNg+HvgMCIHvhGG4IgiCXkDDA5+27MlIS+PWi/ox7K9PU5xIMPiUTjSoWZ1Fa3N46t1ZDL+sP7cM7sfwp8ZS8EoRLRvU4ewubQGYs2QlV/z+cYqKEwzsfgItGtSJuDfRykhL49bz+zLskeeTY9njBBrUrMaidZt4auqHDL+4H7ecdxrDx7xFQVERLRvU5uxOx5EWj/PegmVc9ueniMfh5nN7U6tqZX55cT9+/cI7ZKSlUbdaFe669KyouxiJjLQ0br3wDIY9/GxyXE/ukBrXjTw1eQ7DLz2bWy44neHPvEHBa8XJGO0ckBaPc9HJHbnyz0+SIMH3v9WTmlWy+cmg07jjqXE8M/VDsjMzuOeyc6LuYiQy0tK4ddCpDHv0RYoTMLh7exrUqMqi9Zt4atrHDL/wDG4Z2Ifhz72TjNf6tTm7Y2sA3vzPZ1z50BiKEgmu6NmJJrVrcPv5fbn3pUkUFSdIkOCO1OUWKoKMtDRu7X8Kw0aNTcZo14AG1auwaMMWnnr/E4af14db+p/C8JcmU/BWES3r1eLs41uSFo/TrVlDLn/kJRKJBHcM7L33OZdu3EaTWvsSAAM6tWbOinVc8Y+XKEokGNipDS3q1Yyiu5EwXr85RyJeu7doyJvzlnDlIy9RVJzgipOOp0ntipHQ6te2GdOWrOGKx14nkYB7z+/F49Pn0bR2Nc4ImjKkR3uGPj6O4gTcdEZXstLTGTF+JgVFRfzi5WSCv3mdGtw1sGfEPSlbMtLiybh9YtyBcTtjHsMH9eaWc05m+MtTKCiaSct6NTn7+BapuD2Gy0e+RCIBdwxKxu0vB/Xh16+9R0ZanLpVs7nr/FMj7mE0+h3XmGnL13HFk+8k47V/dx7/IKRpraqc0boxQ7q2ZujTE5Lx2rsDWelpDDu5HcPfnMXlo98hPS3GiHMrzmWUFK2MtDRuPe80hj3yQnIe6H4CDWqkzrXe+4jhF52ZOtcaT8HY4uSxQKcvruy9/cIzuPfFCRQVF5MA7hh85pHrTBnRr21Tpi1dwxWPj03OAYN68fj7qTXruKYM6d6OoaOS8+5NfbuSlZ7GiPEfJNesV6YCqTVrwCkMPKElQ594g4x4nOMa1GJQh5YR904H5YcIR41YIpGIug36moIguJrk5ToygarAz0neGHE4yWTz58B3gM3Av4AWwAKgTxiGwZc87yigGXA6ycT2amBIGIZvBkHQF3gAWA/UAf4ShuGoIAi6AX9g340Zrw3DcOEXvUbR208YeIdDXsX7RPyISKsQ9xs9svLKfyVnJPKdAw6LTO/mflgYr4dHvvPrYZGR+dX76L/3+daoW1Du/OD6B6JuQrn00Kv3R92E8mnLpq/eR/+1tKG/MCt7EMVzxpuHKiHe9ewyGydWVh/FwjB8HHj8IJvGH+R3Q/+L571qz89BEKSTrNZ+q8Qun4ZheNl+j5kN9D3U15AkSZIkSZKkkkxWV1BBEAwjedPE/f08DMPpqX16Av8Abg/DsPhItk+SJEmSJElSxWKyuoIKw/AR4JGv2Gcayetdl/zdJGDSYWuYJEmSJEmSpArJZLUkSZIkSZKk8isWj7oFOkS+U5IkSZIkSZKkyJmsliRJkiRJkiRFzmS1JEmSJEmSJClyJqslSZIkSZIkSZEzWS1JkiRJkiRJilx61A2QJEmSJEmSpMMmHou6BTpEVlZLkiRJkiRJkiJnslqSJEmSJEmSFDmT1ZIkSZIkSZKkyJmsliRJkiRJkiRFzhssSpIkSZIkSSq/YtbrHi18pyRJkiRJkiRJkTNZLUmSJEmSJEmKnMlqSZIkSZIkSVLkTFZLkiRJkiRJkiLnDRYlSZIkSZIklV+xWNQt0CGyslqSJEmSJEmSFDmT1ZIkSZIkSZKkyJmsliRJkiRJkiRFzmS1JEmSJEmSJCly3mBRkiRJkiRJUvkVs173aOE7JUmSJEmSJEmKnMlqSZIkSZIkSVLkTFZLkiRJkiRJkiJnslqSJEmSJEmSFDmT1ZIkSZIkSZKkyKVH3QBJkiRJkiRJOmxisahboENkZbUkSZIkSZIkKXImqyVJkiRJkiRJkTNZLUmSJEmSJEmKnMlqSZIkSZIkSVLkvMGiJEmSJEmSpPIrZr3u0cJ3SpIkSZIkSZIUOZPVkiRJkiRJkqTImayWJEmSJEmSJEXOZLUkSZIkSZIkKXLeYFGSJEmSJElS+RW3Xvdo4TslSZIkSZIkSYqcyWpJkiRJkiRJUuRMVkuSJEmSJEmSImeyWpIkSZIkSZIUOZPVkiRJkiRJkqTIpUfdAEmSJEmSJEk6XGKxWNRN0CGyslqSJEmSJEmSFDkrqxWNxQuibkH5VKd+1C0on7Zvi7oF5U+l7KhbUD4VFkbdgvIpd3vULSifatSMugXlU8xalMNiR27ULSifqlSLugXlzkOv3h91E8qlHwz6WdRNKJce+vcvo26CpDLIo1lJkiRJkiRJUuRMVkuSJEmSJEmSIudlQCRJkiRJkiSVX14q7ajhOyVJkiRJkiRJipzJakmSJEmSJElS5ExWS5IkSZIkSZIiZ7JakiRJkiRJkhQ5b7AoSZIkSZIkqfyKxaJugQ6RldWSJEmSJEmSpMiZrJYkSZIkSZIkRc5ktSRJkiRJkiQpciarJUmSJEmSJEmRM1ktSZIkSZIkSYpcetQNkCRJkiRJkqTDJma97tHCd0qSJEmSJEmSFDmT1ZIkSZIkSZKkyJmsliRJkiRJkiRFzmS1JEmSJEmSJCly3mBRkiRJkiRJUvkVi0XdAh0iK6slSZIkSZIkSZGzslqSJEmSJEmS9IWCIMgGRgP1ge3AVWEY5uy3z/1Ab5I550fCMBwZBEFtYCHwSWq3F8MwfOCLXsdktSRJkiRJkiTpy3wfmBuG4a+CILgMuAO4ac/GIAhOB1qHYXhKEARZwLwgCJ4DugJPh2H4o0N5EZPVkiRJkiRJkqQv0xu4L/XzOODO/bZPBz5K/ZwA0oACoBvQNQiCd4ENwI/DMFz7RS9islqSJEmSJElS+RX3tn3/jSAIrgX+336/Xg9sS/28HahRcmMYhruB3UEQZACjSF4GJDcIggXA7DAM3w6C4ErgQeDiL3ptk9WSJEmSJEmSJADCMPwn8M+SvwuC4AWgWuqf1YCt+z8uCIJawHPApDAMf5v69QRgZ+rnF4G7v+y1/VhBkiRJkiRJkvRl3gPOTf3cH5hScmPqBozvAI+FYXhPiU2PAhelfj4TmP1lL2JltSRJkiRJkiTpyzwMjAqCYCqQD1wBEATBfSSrqXsBLYHrgyC4PvWYa4DbgMeCILgR2AFc92UvYrJakiRJkiRJkvSFwjDcCVxykN/fkvpxJvCnL3j46Yf6Ol4GRJIkSZIkSZIUOSurJUmSJEmSJJVfsVjULdAhsrJakiRJkiRJkhQ5k9WSJEmSJEmSpMiZrJYkSZIkSZIkRc5ktSRJkiRJkiQpct5gUZIkSZIkSVL5FbNe92jhOyVJkiRJkiRJipzJakmSJEmSJElS5ExWS5IkSZIkSZIiZ7JakiRJkiRJkhQ5b7AoSZIkSZIkqfyKxaJugQ6RldWSJEmSJEmSpMiZrJYkSZIkSZIkRc5ktSRJkiRJkiQpciarJUmSJEmSJEmR8waLkiRJkiRJksoxb7B4tLCyWpIkSZIkSZIUOZPVkiRJkiRJkqTIeRkQVTjFiQR3v/MhYc42MtPi3H1WN5rVqrp3+7P/WcqYuUtIi8W54eS29G3ZkJ0Fhdz99oes+nwHBUXF3H56Zzo2rM2rn67g8dmfkRaLMfiEZlzWqVWEPYvexE+X8fDEWaTF4wzu1pZLurcvtX35pm3c/vwEANo0qM2dg04lHo/t3faj0eN45abLANiZX8DdL09m1ZbPk2M+sDcdj21wZDtUBhQnEtz95geEG7aQmZbG3eeeRLNa1fZuf/ajRYz5aBFp8Rg39DyBvq0bk5O7i1tenUZBUTH1qmbzmwEnk52RziufLOWxGZ9SNSuDCzu05KIKHq97FBcnuPv1aYTrNyXH+Lw+NKtTfe/2Z2cvYMysMDnGp3amb9B077Ynpn/Cxtxd/OSs7lE0vcwpTiS4e9z7hOu3JOfXgT1pVrvEWM5ZyJgPFybHsndH+rY5ljXbcrnj1WkUFReTAO4acAot6tTY+5hfvj6NGtlZ/OSMbhH0KHpfZw7YY9aKDdzy6jQm/OCCUs/5y3EzkmPat/MR60dZNfHTZTz8zrdm81IAACAASURBVAfJdevEdlzSY791a+M2bn/uHYjFkuvWecl16/6x05izbC1FxcVc0uN4LunRnjVbt3PHcxP3xfKFp9GiXq1oOhaR4sSe+XRzaj7tXXoOmB0yZvYC0uJxbji1E32PKzGfvj+Pjbk7+Um/0vPpL1+dmozXfhV3nv0m54HxC1bw6PvzicXgks6tubhT6yPen7Li66z/W3bs5mfPT2R3YRH1q1Xm3vNPJTsznUenfszrc5dQNSuDa3t1LHWsUBFNnLeYh996Pzm39jieS07uWGr78o1buP3pN5Nz6zF1uHPwmXvPCXblF3DFg8/wkwG96dO2BWu2fM5tT40jAdTIrsT9Q84lOzMjgl4dPZr3OJHBv7uLP54+IOqmlFkTFyzn4UlzSIvHGNw14JIT25XavnzTNm5/YVIyRuvX4s6BvYnHYzw0YTaTF64gLR7ntnNPoWOT+sxfs5EbR79Bs9Tx62U92tO/Qyt+N246c5avIxaLccs5J9O12TER9FQ6OllZrQrnnUVryC8s5unLT+cnvU/gvsn/2bstZ8duRn+4iCe/3ZeRF/XmT1M+Ib+wiMc+WEibutUZ/e2+3H1WN5Zt2Q7A/ZPn8s+L+jD6sr78a9ZnbNudH1W3IldQVMSIse8x8ppBjLrufJ79YD4523eW2ue+se/x4349GD3sQhIJmPDpUgBe+TDkf555iy07d+/d97EpH9GmQW1GD7uQuy/sy7KNW49of8qKdxauIr+wiKe/8y1+0rcz970zZ++2nNxdjJ4d8uSQsxh56en86d2PyC8s4tH353PBCS0YPeQsWtWpzpgPF7Fl524emPwxj19xJk9c2Y/X5i1j9dbcCHtWdryzYHlyjK87j5/0685942fs3ZazfSejZ8znyWsHMnLoOfzpnVnkFxaxu6CQW56fxFMffBphy8ued8IVybG85lx+ckY37nt71t5tObm7GP3Bpzx5VX9GXn4Wf5owh/zCIh6c9BFXdG/LqO+cw7BeHfjThH0x/u/ZIQs3VMy//T2+zhwAsPbzHfxr5qcUFBeXer5/f/gZC3Mq9pjuUVBUxIjXpjLy2kGMGnYBz86cd/B16+yTGP29C0kkEkz4dCkzFq9mxaZtPH3jRYy+YTCPvjuHbbt28+D4mVxxygmMGnYBw/p25U9vvh9Rz6Kzdz69dhA/6Xci942fuXdbTu5ORs+cz5PfHcjIId/iT+/M3jefvvDuQefTf89awMINW45kF8qkb2oeKCou5o/vfsQ/LzuDp4aezWMzPi117FXRfJ31/2/vfsiADq0Y/d2BtDumDmNmL2Dh+s28PncJz1w3iEeHnsODE+ewK78wwp5Fq6CoiBEvT2LksIsYdeOlPPv+XHI+31Fqn/tefpcf9+/F6B9+mwQwYd6ivdvueWFCqavKPjF5Dv07B/zvD75N62Pq8PyMT45MR45SZ//sJoY++iDplSpF3ZQyq6ComBHjpjPyqnMZ9d1BPDtrwYHr/7jp/Lhfd0Zfd14yRhcsY/6ajcxatpZnvncBv7/0TH792nsAzF+zkat6dWTUtYMYde0g+ndoxYK1m/hwxXqe+d4FjLj4dH4zdloEPZWOXiar/0tBEJwTBMGw/2L/SkEQXPc1X+uHX7F9UhAEbb/Oc5d4jnWHuN/VQRCMOMjvlwVBcFSthHNWb6R382SFbqdGdZi3bt9J0Nx1m+nSuA6Z6WlUy8qgac2qhBu38d7y9WSkxbn++Sk8/P6n9Eo9Pqhbndz8gr0nBRX5cv1LcrbSrE4NamRnkZmeRtdmDZm9bG2pfeat3kj3Fo0A6HNcU6YvXgVA9ewsRl1/fql93/tsZXLM//UaD0+cRa82xx6ZjpQxc1ZtoHfLhgB0alyXees27902d+0mujSul4zXSpk0rVmNMGcrt53ZlUEntKA4kWDd9p3UqVKJlVtzaVu/FjWzs4jHYpzQsDYfr9kYVbfKlDkr1tE7VYXW6dj6zCsxLnNX59Dl2Ab7xrh2dcL1m8krLOL8Tq35Xp9OUTW7TJqzcgO9W6XGskk95q0tMZZrcuhybP0SY1mNcMMWbjnrRE5r3QSAouIEWelpAHy0agMfr87h0q7HHfmOlCFfZw7IKyzirjc/YPi3SleifrQ6h4/XbOLSzm2OaB/KqiUbtqTWrUrJdat5Q2YvW1Nqn3mrc/atW0Ezpi9aReemDfj1xafv3ac4kSA9nsYtA3pyWttmQDIpmJVe8b7AOGfFenqn/p47Ndl/Pt144BzwJfPpRytTc0C3/9OhbrnwTc0DafE4r10/kGqVMtm6K59EAipX4ArVr7P+l4zxPm2aMH3JahbnbKV782PIykgnKyOdZnWqs3D95oO+ZkWwZP1mmtWtSY3Kqbm1RWNmL11dap95q9bTvVVqHNs2Z/rCFQA8NnEWXZo3pG2jenv3bduoHtt25QGQuzuf9DRTGF8mZ/FS/jF4SNTNKNOW5GyhWe3q+85bmzZg9vLSaZF5azbSvXly3u3T5limL17N7OXr6Nm6CbFYjEY1q1JYVMzmHbuYtyaHyeEKhj76Cne8+C478vJpUL0K2Rnp5BcWsWN3Pulx41b6b/gX818Kw/CNMAwf+S8ecgzwtZLVwB1f83H6Ern5hVTN2ndgHo/HKExVnOTmFVKtxEF7lcx0tucVsGVXHtt2FzDyoj6c3qoh9787F4DWdWtw8eh3OG/UW5zW8hiqV8o8sp0pQ3J351O1RP+rZGWQuzuv1D4JEsRisb3bt6cq0fu2bX7AydKWnbvZtiuPkdcM5PS2zbl/3PTD3IOyKTevkKpZ+8a1dLwWUC3rwHiNxWIUFSc479HXmbl8PV2a1KNZ7eos2riNjTt2saugkPeXr2dXQdER709ZlJtXUCp247EYhUUlxrhSyTFOxm2N7Cx6pU5WtU9uXkHp+TUW3y9eS8wRqbGsVbkSGWlxlm7axv1vz+LGUzuRs30nD03+mDv7n3zE+1DWfJ054NfjZ3FNj3Y0qFZ577ac3F08NHUud5594pFrfBm3/99+laxMcvf7hlQisf+6lUdWRjo1sitRUFTEL559h0t6tKdKVga1qmSTkZbG0pwt3D92GjeeWfHG+sA5oGS85h84B+Sl5tNWjUs9T872nTz07ofcee4pR6bhZdw3NQ8ApMfjvBWu5MLHxnLisfVJj1fcUouvs/7n5uVTLfWYKpkZ5O4u4LgGtZm1fB078vLZunM3H63cwM6CiltZnTwnyNr77ypZGeTu2v+cgBJzaybbd+cxfeEKlm/ccsAlQxrUqMZTUz9i0H2jmLJgKed0qtgfYn+VD194haKCgqibUaYd0voPB5y37ijx91/y9x2a1Od/vnUS/3vdeTSpVY2HJiYvLxKLxRjwlzFc+/jrXNOrdFwrIrGY/5X8rwyreCUf/0dBEFwNnAM0A1YCrYCZYRh+PwiCXsAfgAJgC3AlcDvQPgiC4cBjwMNAJaAOcHcYhi8FQfAf4F2gI8l58Xzgh0DtIAj+FobhjV/SpLuDIKgL5AHfCcMwJwiCPwC9U9ufCsPwgSAImgP/BDJSr/HjMAw/LtGv3wA1Uq/7A2Bwat9tqZ8BTgmC4B2gOvCrMAxfL/H4Y4FHUn3bDQwLw3DlIQ7rEVU1M50dJb6al0iw95POqlnp7ChxcLkjv5DqWZnUrJTFGa2Sn6z2bdmQkTNDwpxtTF6ylreu60/ljHRuHTeTNxau4pzjKlYC64G3ZjBn+TrCdZvo2KT+3t/vyCugWnZWqX3jJSbEHXkFVK9UentJNStncUa7FkAymT3y3Q+/4ZYfHapmpbMjf98BZyKRKBGvGaViORmvyROrjLRk9dS0Zev4+WvTeeLKftx2ZldufnEqDapl075BsspaqXHM22+M0+IH3bYjv6BCfyj1VfaPyQPj9eBjOWPZWu4ZN4MR5/emRZ0a/O/M5FfTb3j67dQHLEW0qFODCyvgtVX/2zkgMx5n9qoNrNiynb9Nncu2Xfn89OWpdG5cjy0787hhzCQ27tjNrsJCWtSuzoUdWx7xPkXtgfEzmLNsbXLdKnEvhORJ6KGtW9t27ebmJ9+kR4vGDOu773rqMxav5p6X32XEpf0q3PWq4cC/89LxmnmQOeDg69Ab85cm54Anx7MxN/kha4u6Nbmwgn4r4JuaB/5wfvL04KzgWM48rgm/eG06L3+ylMEdK+Y9LL7O+l81K5MdeQVUyki+J9UqZdKqXk2u7NGe740eT9Pa1enYuB61Kle8Y6wHxr3HnKWrCdfk0LFZw72//+pzgnyqZ2fxwsy5rNmynav+NoYlGzYzf/UG6larwu9fm8xvLvsWvds25935S7jt6Tf4+3UXHrF+qfx44O0Pkuet6zfvd95aOgkNB1n/szOpkpXJjrz80r+vlEW/ds2pnorxfu1bcO/r7/HyR59Rt2o2I6/qz468AoY8+gqdmzagQfUqh7mXUvlgZfXXdxxwLdADODcIgmOAC4AXgNNIJqZrAfcC88MwvBtoC/whDMOz2JcUhmTy9+kwDE8DVgP9wzC8F9j8FYlqgBfCMDwDeBX4eRAEA4EWwMkkE9ZXBEHQAfg98JcwDE8FbiKZuAYgCILfA+lhGP6A5JUs6gD9wjDsQzJhvef7gzuAfsAA4K9BEJSMnz3Pf3rq5wMuGVJWdGlUlylLk1/z+XjNJtrU3XcjlQ7H1Gb2qo3kFRaxPa+AJZs/p03d6nRtXIfJqcfMWrWR1nWqUzUrnaz0NLLS00iLx6hdOYvPK+A1q2866yRGXXc+U35+FSs2f87WnbvJLyxi1rI1dN7vhojtGtZl5pLk1wCnLFxBt+YND/aUAHRt1pDJ4XIAZi1dQ+sGFe+kH6BL43pMWZz8WvrHqzfSpl7Nvds6NKzD7FUbkvG6O58lmz6nTb2a3P3mB8xYvh5IVljFY1BYXMzHazbxxJX9+O3AU1iy6XO6NqkbSZ/Kmi5NGzDls+QlaT5euYE2DWrv3dahcT1mr1hPXkFhcoxzttKmfsWMxUPRpUl9pixKjeWqnFJj1aFRaiz3xOvGbbSpX4sZy9by2/Ef8I/L+3FCo2RMDu3RjueuG8So75zDdT07MPCEFhUyUQ3//RzQoVEdxg4bxKgr+zHqyn7UyM7kD+f3ZuiJAc9d059RV/bjupPbM7B98wqZqAa46eyTGDXsAqbcfjUrNm3bt24tXUvnpvutW41KrFvhcrq1aMjugkK+++grDO7Wju+XqJ6esXg1v31tCv+4ZhAnlDgJrki6HFtiPl21gTYl1u4Ojeum5oA98+k22tSvedDnGXrS8Tw37HxGXX0u1/XuyMAOLStsohq+uXkgN6+A7zz5NvmFRcRjMbIz00slZCqar7P+d2nagMmfJetxpny2im7NGrB5xy627NzN6GsH8vP+J7P28x0V8ljhpv69GHXjpUy56wZWbNzK1p27knPrklV03u+Yv13j+sxclBrHBcvo1qIJ9w8ZwJM/uoxRN15Kn6A5Px3Yh3aN61M9O4tq2clEYr3qVfm8Al9nXf83N/XrzqhrBzHl1qGl1//l6w5c/xvWYebS5Lw75bOVdGvWkK5NGzB10SqKixOs2ZpLIpGgVpVKXP/EWP6zagMA7y9ZzfGN6lIjO5PKWRmkxeNUycogMz2NnXlWvEuHysrqr29RGIbbAYIgWEuyovg3JCup3yGZdJ4BlPwYeS1wRxAE15Ksbi553YM9ZaMrU891qCan/j+NZBJ5LTAlDMMEUBAEwftAe6Ddnn3DMPwoVQkN0IBkRfei1LbiIAjygaeDIMgFmpRo59TU824IgmAbyaT2Hh2AXwRBcCvJhHeZzdr2a9OIaSvWc8XTE0kA936rG4/PXkjTmlU5o1UjhnRpzdB/v0txIsFNvU4gKz2NYT3aMvyt2Vz+9ETS43FGnHMijatX4dKOLRn6zCQy0uIcW7MKFxzfPOruRSYjLY1b+/dk2OOvUZxIMLhbOxrUqMqiDZt5avonDD//VG45tyfDX5xEwfgZtKxXi7NP+OJEybDTujL8xUlc/vcXSE+LM+LiM45gb8qOfsGxTFu2jiv+dzyJRIJ7B5zM4zM/pWmtapzRpglDugUMHf1WMl5P7UhWehpDTjyOu974gIffm0ssFuPOs7uTHo+TkRbn4n+9QVZ6nKt7tKNW5aPqcvOHTb+2zZm2eA1XPPoqCRLce/6pPD5tLk1rV+eMts0YclJ7hv7r9eQYn9mNrAyXzi/Sr21Tpi1dwxWPjyWRgHsH9eLx9+fRtHY1zjiuKUO6t2PoqHHJsezblaz0NEaM/yB5OYVXpgLQvE4N7hrgV//3+DpzgA5NRloatw7oxbDHXqU4AYNPbJtct9Zv5qnpcxl+wWnccm6v5LpV+D4t69fi7BNaMXraXFZt/pznPpjPcx/MB+Dei89gxGtTKSgq5hfPvgNA83o1uevCvhH28Mjr164Z05as5op/vpaaT/vw+PRPkvNp0JQhPdoz9F9jk/F6RrcKeV3vr+ObmgeqZmUw8PjmDH3ybTLS4hxXryaDKvCx69dZ/284tTO/ePFdnpsTUrNyJe6/qC/ZGems3LKdSx95mYy0OD87qztpFfj6tBlpadx63mkMe+SF5DlB9xNoUKMai9Zt4qn3PmL4RWdyy3mnMXzMeArGFtOyfm3O7vTFH0bdfuEZ3PviBIqKi0kAdww+88h1RuVSRlqcW/ufwrAnksekg7sGNKhehUUbtvDUjHkMH9SbW845meEvT6GgaCYt69Xk7ONbkBaP063ZMVw+8iUSCbhjUPLbKr8c1Idfv/YeGWlx6lbN5q7zTyU7M505K9ZzxSMvU5QoZmDH1rSod/APaCUdKJZIJKJuw1GlxGVAmodheHLqd+8DlwEDgUlhGH4SBMHPgUzgX8BzYRj2CILgBWBkGIbjgiC4Brg6DMPTgiBYBrQNw3B36iaGC8IwfDwIgvVhGDY4oBH72jIJ+HPqUiI/IplYngJcE4bhRUEQZJBMYn8P+AXwRBiGrwRB0BkYFYZhp9QNFhsCk4DfAmtSbTwpCILKwGzgRpKXPTk/DMMLU1XkU4E2wFKSFeNPAb8Pw3Ba6qaPp4Vh+I8vanvRP35h4B0OdSpmJddht31b1C0ofyplR92C8qmw4l4j87AqtBLmsKjhSdthsduqw8Miz3E9LDwe+OZVqxF1C8qlHwz6WdRNKJce+vcvo25CuZR26U8r7tdmvkRi5XzzUCXEjm1fZuPEcoZv1gfAqFRFcj4wDNgAZAZB8DvgWeAvqQTxSuCrvoM/PwiC0WEYftntfC8IguBm4HPgqjAMtwRB0DcIgukkk+VjwjCcEwTB/wAjU//PIHkJEwDCMEwEQfBd4E2SlzDZEQTBLJLXwV4LNErtmh0EwQSgKvC91OP2PM3/AA8HQVAJyCZ5qRFJkiRJkiQpWhX40ldHGyurFQkrqw8TK6sPDyurv3lWUh0eVlYfHlZWHx5WVh8eVlYfHlZWHx4eD3zzrKw+LKysPjysrD48rKw+uMSqT81DlRBr0q7MxomV1WVcEARNgScOsundMAyd2SVJkiRJkiSVCyary7gwDFcAfaNuhyRJkiRJkiQdThX3NsWSJEmSJEmSpDLDympJkiRJkiRJ5ViZvUSz9mNltSRJkiRJkiQpciarJUmSJEmSJEmRM1ktSZIkSZIkSYqcyWpJkiRJkiRJUuRMVkuSJEmSJEmSIpcedQMkSZIkSZIk6bCJxaJugQ6RldWSJEmSJEmSpMiZrJYkSZIkSZIkRc5ktSRJkiRJkiQpciarJUmSJEmSJEmR8waLkiRJkiRJksov76941LCyWpIkSZIkSZIUOZPVkiRJkiRJkqTImayWJEmSJEmSJEXOZLUkSZIkSZIkKXLeYFGSJEmSJElSOeYdFo8WVlZLkiRJkiRJkiJnslqSJEmSJOn/s3fnYXbO9//HnzOTfRVJhIiQBG9b7PtSS1GUUq3aW1pUq0WXn/qWppYuqhu6oFUttTW0qlpbNZZYaotagjdJSJCQhSAh28z8/rhPkkmMLTPJnTl5Pq4r18zZzOu+r4/POed9fxZJUuksVkuSJEmSJEmSSmexWpIkSZIkSZJUOovVkiRJkiRJkqTStSs7gCRJkiRJkiQtNTU1ZSfQh+TIakmSJEmSJElS6SxWS5IkSZIkSZJKZ7FakiRJkiRJklQ6i9WSJEmSJEmSpNK5waIkSZIkSZKk6uUGi22GI6slSZIkSZIkSaWzWC1JkiRJkiRJKp3FakmSJEmSJElS6SxWS5IkSZIkSZJK5waLkiRJkiRJkqqYGyy2FY6sliRJkiRJkiSVzmK1JEmSJEmSJKl0FqslSZIkSZIkSaWzWC1JkiRJkiRJKp3FakmSJEmSJElS6dqVHUCSJEmSJEmSlpqamrIT6ENyZLUkSZIkSZIkqXQWqyVJkiRJkiRJpbNYLUmSJEmSJEkqncVqSZIkSZIkSVLp3GBRkiRJkiRJUhVzg8W2oqaxsbHsDFoB1Y+40oa3NMyZVXaC6tSpa9kJqk5Nl25lR6hKjVMmlh2hOtU6EW2pqK8vO0F1em1K2QmqU8+Vy05QnV56vuwE1adHr7ITVKeOHctOUJVOOPjMsiNUpYsa37Qq24zGV5+3DtVETb9By2078duXJEmSJEmSJKl0FqslSZIkSZIkSaWzWC1JkiRJkiRJKp0bLEqSJEmSJEmqXjXL7RLNWowjqyVJkiRJkiRJpbNYLUmSJEmSJEkqncVqSZIkSZIkSVLpLFZLkiRJkiRJkkrnBouSJEmSJEmSqpcbLLYZjqyWJEmSJEmSJJXOYrUkSZIkSZIkqXQWqyVJkiRJkiRJpbNYLUmSJEmSJEkqncVqSZIkSZIkSVLp2pUdQJIkSZIkSZKWnpqyA+hDcmS1JEmSJEmSJKl0FqslSZIkSZIkSaWzWC1JkiRJkiRJKp3FakmSJEmSJElS6dxgUZIkSZIkSVLVqqlxg8W2wpHVkiRJkiRJkqTSWayWJEmSJEmSJJXOYrUkSZIkSZIkqXQWqyVJkiRJkiRJpXODRUmSJEmSJEnVyw0W2wxHVkuSJEmSJEmSSmexWpIkSZIkSZJUOovVkiRJkiRJkqTSWayWJEmSJEmSJJXOYrUkSZIkSZIkqXTtyg4gSZIkSZIkSUtPTdkB9CE5slqSJEmSJEmSVDqL1ZIkSZIkSZKk0lmsliRJkiRJkiSVzmK1JEmSJEmSJKl0brAoSZIkSZIkqXrVuMFiW+HIakmSJEmSJElS6SxWS5IkSZIkSZJKZ7FakiRJkiRJklQ6i9WSJEmSJEmSpNK5waIkSZIkSZKk6uUGi22GI6slSZIkSZIkSaWzWC1JkiRJkiRJKp3FakmSJEmSJElS6SxWS5IkSZIkSZJKZ7FakiRJkiRJklS6dmUHkCRJkiRJkqSlp6bsAPqQLFZrhXXH48mFN42krraGA7ffjIN23HyRx8dPfo3TLr8BgHX6r8L3DtmH2toafjT8Fh4d+yJdOnbgm5/+OJsMGsC0N2cy7MobefPtWTQ0NPDjow5gYN+VyzisUt3x5BguvOU+6upqOXCboRy0/SaLPD5+yuucduVNUFPDOqv14Xuf3YPa2hquf+AJrrn3fzQ0NLDb0HX4yie2X/Cah8e8yCl//icjzvzKsj6c5dIdjyUX/utO6mprOXCHzThopy2bfd45w29mrX59OGTnrRbc99pbMzns3Eu4YdhX6di+/bKK3CbcMWo0v/37v4u2+7Gt+dyu2zb7vB9fcQODVuvLIR8v2ugPL7+eUc+9QNdOHQH4zTeOpnuXzsss9/LojtFjufC2+4s2uvVGHLTdxos8Pn7K65x2zS1ApR848OPU1tZwwh+uZ/rMWbSrq6Vj+3b87rjPMOaVaZxx7W00NkL078tpB+5GXe2KOSnsjifHcOGt9xfvWdsO5aDtmulfr7oZamCdVZv2r08W/WtjA7tttPYi/evldz7M1Ldm8s39dl7Wh7PcuWP0WC78938r7XZDDtp2sXY79XVOu/rW4v1r1d5N2u3fmf52k3Z77IElHcHyoaGxkbNufoCc/Dod6uo465PbsubKPRY8fu2jzzF81LPU1dZy/I5D2WWdAUyZ8Q6n3HAPc+sb6NutMz/ab3s6ty++orwzdx7HXHU7Z39yOwb36VnWYS0X7nhqHBfe/kDRRrfakIO22WiRx8dPnc5pw28DKm30gF2pra3hvFvu4/7nJlBTU8N3P7UzGw9clelvz2Kfcy9jnVV7A7D7RkM4csfNSjiqcjU0NnLWHY+TU9+kQ10tZ318E9ZcqduCx699cjzDn3yBuppajt96HXYZtCoT33qbU299lEYa6dmpAz/9xOZ0bt+OP40ay1+fmsDKnTsAcMZumzCoV7f3+tNVregH/ku++npxXvfdftF+YNSzDH/0Wepqazh+x43ZZZ01mPjGDE6/8T7qGxpoBM785HYM6t2T254ezyX3PUFNDRy02bp8drN1yzuwktzxzHguvHNU8f6/eXDQlusv8vj4aW9w2t/uLN6fVunF9/bdkdraGn4z4hHufnYCdbW1nLrPdmw8YBWemjiVr15xC2v2LvrTQ7begL2HDuEnN9/PqPGvUFNTwyl7bcvma65awpEu/9baeksO/MmZ/GLXT5YdRap6FqtLEBF/Aq4BVgXWy8xT3+N5KwN7ZeZVEXEqMCIzH1x2SSEijgLWA84DhmXmV5fl319a5tbXc851tzH8O8fQuWMHjvjZH9ll6Lr07bnwQ+W5193GiZ/ala3XXYszrvoXIx5P2tXV8sKr0/jLd47hjbff4bhfXcm1/3csP7/+dvbdeih7b7EhD+TzPP/K1BWuWD23vp5zrh/B8G99ns4d2nPEeVeyy0ZD6NujyTn9+whO/ORObL3OQM74y62MeOI51l29L9fc+z8u+9ohdGjXjl/ffA9z6+tpX1fHpNff5I93PMTc+oYSj2z5Mbe+nnOuvYXhC2CvjwAAIABJREFU/3ccnTu254hz/8AuGwd9e3Zf8JzX3prJqX/8G+NfncbRe/ZZcP89o8fwi+v/zbQ3Z5YRfbk2d14951x5A8PPOpnOHTtw+Fm/ZtfNNqDvSgu/WL325gxOvfhqXnhlCoNW22XB/U+Nf4lLTjmWXt1XzC+ki5tbX885f7+T4d84vOgHfnU1u2w4hL49ui54zrn/uJMT996RrddegzOu/TcjnhzD7huvw/ip07nxlKOoqVk44uG8m0Zy8j47seWQAXz36lu448mx7L7xOmUcWqmK83oHw795ZHFez7+qcl6b9q93cOI+Oxb96/DbGPHkc6zbf37/enDRv95yL3Pr66mvb2DYX27l8fGT2HOTFe+L/+Lm1tdzzg13MvzkSrv99TXsssFi7faGuzhx7x2Kdnvd7YwYPYbdh67D+GnTufH/fWGRdrsi+0++yJz6eq4+am8ee3kK597+CL/53K4ATJnxDlc89AzXfnEfZs+r54jLb2X7QatxyX1PcsDQwey/8RB+ffdjDB/1LF/YZgOenDiNM2/+L6+89XbJR1W+ufX1nHPj3Qz/+iFFG/3tcHbZYBB9uzdpozfezYmf2J6thwzgjL/+hxFPjaV/rx48NmES13ztYCa+/hZfu+xGrv/G4Tz18mT22TQ4/YBdyjuo5cB/xk5iTn0DV39uJx6b9BrnjnyK3+y3NQBTZs7iisfGce3BH2N2fQNHXHcP26/Rl8sfHcfe6/bn0I0Hcd59T/PXpyZwxCaDeWrKdM7ZczM2XGWlko+qfP/JCcyZV8/VR+/DYy9N4dzbH+Y3n9sNmN8PPM21X9q36Acuu5ntB/XnV3f+j8O2Wo/dYyD3jH2ZX44YxS8/szO/GPEI135pX7p0aMd+F93Ax2Mgvbp0KvkIl5259Q2cc/P9DD/+03Ru344jLvkHu8Sa9O3eZcFzzr35fk7cfSu2HtSfM/4xkhHPvED/lbrz8AuTuObLBzDpjZmcfM2/GX78p3lq4lS+sMPGHL3Dwguyz0yaxqMTXuWaLx/A+Nfe5NvD/8N1X1mxL7w2Z8//dxLbHHkIs2f6niQtCyvm8KS2Y2PgUwCZec6yLlQ3lZmvVEuhGmDcpKms2XdlenbtTId2dWw+ZA0eGTNhkeeMnjCJrdZZE4CdNlyb+58ex9hJU9hhgyHU1tbQq1sXamtrmPLGDEaNfZFXX3+TL573Z/754JNste5aJRxVuca9Mo01+/SiZ5dOxTkdvDqPjH1pkeeMfvFVtlp7DQB22mAw9z87nvtzPButsSr/d+VNfP5XV7PZ4NVpX1fH7LnzOHP4bQw7aI8yDme5NG7SlCbtth2brz3wXe327dlzOGG/Xdlv20VHXdbW1HDpyV+gZ9cVe9Rvc8ZNfJWB/frQs2uX4ryuO4hH8vlFnvP2rNmc8Ok9+dQOWyy4r6GhgfGvTGXYpddx2Fm/4q93PbCsoy93xr36Gmv2WWlhPzBodR4Zt3g/MJmthgwAYKf1B3H/c+OZ+tZM3npnNl/9w9854ldXc+fosQCcf9Sn2HLIAObMq2fqmzPp3eTL2Ypk3KvTFj2vg1fnkXEvL/Kc0S816V/XH7Swfx3Yj/+76mY+/+ur2WxQpX+dV8/+W23Il/fYrozDWe40226fb+b8zm+3663F/c9OWKzdXsOdT40rI/5yZdSLk9lxcH8ANlm9L6MnTVvw2BMTp7LZgL50aFdH904dGNirOzn5dU7dY0v2GzqYhsZGXnlzJr0r71Nz6uu54KBdGNx7xR5RDTBu8mus2btJG12rP488P3GR54x+eTJbDV4dqLTR515kg9VX4fdf+jQ1NTVMfP1Nencr+tCnXprM0y9P5vMXXsfJf/4XU1bQC9mjJr7GjmuuAsAmq63M6MnTFzz2xKvT2Wy1lYv22rE9A3t2Jae9yXp9e/DG7LkAzJgzj3aV2T5PTX6D3z/0HEdcew+/e+i5ZX8wy5FRL05mxyFFW9xkQF9GT5q64LEnJk5hszVWWdgPrFz0A6fssSU7r130sfUNjXRsV0ddbS3//MoBdO/UgenvzKYR6NJhxZoZOG7K66y5cg96du5Y/L8/sB+PjH9lkeeMnjiVrdZaDYCd1lmD+8e+zCPjX2H7tQdQU1ND/5W6Ma++gddmvsPoiVO4Oydw5CX/4PTr72Lm7Dn069GVzu3bMWdePTNnzVnQprWoKWOf5+IDjyg7hrTCcGR1ExHRGfgjsCbQHvgWcAKwEtAH+H1mXhgRdwL/AzYCegAHZeb4iDgdOIDivF6YmRdHxNeBw4BG4JrMvOA9/vaPgS2B7sDTmXk0cBqwSUQcB2xPMRr7P8ClwBCgDvhFZv7lvTK9x986g2K09CpAL+DrmXlPRBwOnAzMBp4DjmvymrUq+beNiH2B71ceehT4GXBFZm5dee5fgJ9l5kPvf8bLM2PWbLp17rjgdtdOHZjxzqxFntNI44KRUl07duCtWbNZb8Cq/On2+zlsl6145bU3GTNpCu/MmcPEadPp0aUzl558JL/911384bZ7+fp+uy7TYyrbjFlz6FaZ+giVczpr9iLPaWxc7Jy+M5vXZ77Dw2Nf5KqTD2fW3Hkcft6VDP/W5/npDXdw9G5b0W+l7qjw7nbb8V3tdkCfXgzo04uRTy76RWn7DYYsk4xt0Yx3ZtGtySidrp078tY77yzynAGr9GbAKr0Z+fgzC+57Z/YcDt9jR47ae2caGhr4wo8vZKNBaxAD+y+z7Muboo026Qc6NtMPLN63vjOHufPqOWqXLTlyp8154+1ZHP6rqxk6cDV6d+/Cy6+9yZcuupbunToyaJUVa8bKfDNmzaFbpyb/73fswIx3Pmz/+hJXnXQ4s+bO5fDzr2L4N4+kZ5dO7LDeIK5/4MllehzLq3ef3/bvPr/wrs8Ec+c1cNTOWyxst7++hqFrrLrCXlQBmDF7Lt06LuwDamtrmNfQQLvaWmbMnkv3Tk37h/a8NXsuNTU1zKtv4NOX/JM58+r5yk7FiL/N11hlmedfXhVtdLG+9V19wLvbKEC7ulrOu+U+rrjnf5xWGUk9aJVebDBgW7ZfZyA3jnqGH95wJ+cdueJNa58xZx7dOiz8Olxb06S9zplL944LC6NdO7Tjrdnz6NetM7+492n+lS8zp76er20TAOy97uoctvFadO3QnhP/9SB3Pt+dXQatmEspFP3AwnNXW1O7aD/QpI/o2qE9b82as2C09PPT3uCntz/MryozMtrV1vLvZ8Zz9i0PsPPaA2hXu2LNYpkxe+67/9+fNWeR5yz6/lScz5mz57BS08+2lfuHDliFz26xHhuu3peL7hzFb+4YxfE7b0ZNTQ2fvGA4M2bN4cz9P7ZMjq2tefRv/6D3mgPLjiGtMCxWL+p44IXMPCQiNgL2pCjQ/i0i+gN3ARdWnvtgZp4cET8EDo2IW4G9gW2AjsCPI2JD4GBgR4r3kdsrz1tERPQAXs/MPSKiFhgdEasDPwSOz8zfRcT8RSa/DEzNzCMjojswKiL+01wm4Jz3Oda3M3O3SsarImI34Exgs8x8KyJ+WflbMxbL2g74NbB1Zk6OiGHALOCdiNgAeAUYtLwWqs+/YQSjxr5IvvwqG6+1+oL7Z86aQ/fFppTVNpnSO3P2HHp07sQOGwzhifETOfqXl7PegFXZcOBqrNS1Cz27dWa3jYup1LtsvC7n33DHsjmg5cD5/xrJqHEvkROnsPGaqy24f+asOXTv/AHntEtHVurSia3WHkjXTh3p2qkjQ1btw7MTp/DIuJeYMHU6v73lPt54+x2+9ad/8POjPrXMjmt5cv7f/8OosRPIl15l40FN2+3sd51jfXjnXXszo559nmdfnMjGQ9ZccP/Md2bT40OsO92pYwc+/4md6Fz50rXN+mvzzISJK2Sx+vyb7mHU8y+TE6eycZN1DmfO/hD9QOeO9OnRlYO324R2dbX07t6F9VdfheenvEbv7l1YfeUe3PLdL3Hdfx/nJzfcyY8P23uZHVfZiv71ZXLSFDYe2KR/nT2H7k0uXEHz71krde3MVmuvQddOHejaqQNDVu3DC1NeX6SvXpGdf/O9lXa72PvX7Lkf4vx2pE+PLu/ZbldU3Tq2Z+acuQtuNzayYJRet47tmTl74WMzZ8+lR6X/bF9Xyz+//Cnue34S//ePe7n8yE8s2+DLqfNvuY9RL0wkJ01l44GL962LtdHad7fR+U7ea3uO3WVLDvnNX9hiUH+2HbIGnSpF2t03GsKvb/vvUj6S5VO3Du2YOWfegtuNjY0L22uH9os8NnPOPHp0bM/3RzzGj/bYjB3XXIW7nn+VU/89igv324bPbzp4QXF757X68fSUN1bYYnXRD7zHeV2sj5g5Zy49KsXYB16YxNk3P8A5++/IoCYzKvZYb00+HgP57j/u4YbHx3LgptW/HNj5tz/EqPGvkK++xsYDFl64mzl7ziIX/WDx96e59Ojcga4dOzBz9pxF7+/Ukd3XX2tB37D7BoP44b/u5Yb/PUefbp35/Rf2ZubsuRxxyT/YdGA/+jVZCkuqGi7b1mY4x2NRAdwPkJlPUoxkPiAirgBOpxhtPd+jlZ8vAp0qr30wM+sz8+3MPAnYkGKU9n+AEUBvYO1m/u47wCoRcTVwMdBtsb/V1PrA3ZWMbwFPUYyybi7T+xlR+W+Mplg7ezAwuvLfpPI3NmzmdX0oCuuTK68/KzMnAL8HjqIYRX7FB/zt0py0/25c9s0vMPLcbzFhyutMn/kOc+bV8/BzE9h08IBFnrv+Gqvy4LMvADBy9Bi2WHsgL7w6jd7du3LFt4/mS3tuT21NDT26dGKLIQO5e/QYAB55bgJrr9Z3WR9aaU765E5c9vVDGfmDExY9p2NfYtO1Fi3arT+gHw8+VyxbMfKpcWwxeACbDx7AQ2MmMHvuPN6ePYexr0xl7dX6cNNpx3LZ1w/lsq8fSs8unVfYQjXASQd8nMu+dTQjf/b/mDDlNabPfJs58+bx8HPj2XTwGmXHa7NOPmhvLj/tq4z89ZmMf3Uq02dUzmuOY9O11/rA178waQqHn/1r6hsamDuvnlHPPs8Gaw34wNdVo5P22ZHLTjiYkWcdz4Sp0xf2A+NeYtPFCqPrr74KD455EYCRTz/PFoNX5/5nJ/DNy28Eii9iz70ylSGrrMwJf7ieF6a8DhSjiVa0dYGL/vUQRp79VSZM/Qj969PPF/3roNV5aMyLi/SvA/u4nup8J+29A5d99XOMPLPSbt9u0m7Xep92+8wLbDFoQNFu//xPoGm77b3Mj2N5stkafRk5tlhC5bGXp7BO34XtbWj/Pjzy4mRmz6vnrVlzGDftDdZZZSXOuvkBHnihmNbetUO7RQovK7qT9tqey47/LCOHHVtpo7OKNvr8xHf3rf378mBl+bWRz7zAFmutzn/HvMjZ1xcDKDq0r6NdbS21NTV877rbue2J4nPrf8e8yAYDVsxR7Jv1X5mR4ycD8Nik11inz8K9Kob2W4lHJk4r2uvsuYx7fQbr9O5Oj47t6V4p9Pft2ok3Z81lxpx57H/lHcycM4/GxkYeeHEqG6zAa1dvNmAVRo4p2uJjL01hnVV6LXhsaP++PDLh1YX9wNQ3WGeVXjzwwiR+fNtDXHzo7mzUv9hzZcbsOXz+8luYM6+e2poaOrdfcfqHk3bfisu+tB8jv3MkE6a9sfD//fGvsOnAfos8d/3VevNgZVmgkc+9yBZrrsbmA/txz5iXaGhoZOL0GTQ2NtKrayeOvfwmHn+paPP/HfcyG/bvQ8/OHejSsT11tbV07dieDu3qeLvJhUVJKoMjqxf1NLAVcENEDKZY3uLflaU/dgWazo9rXOy1zwBfqYyMrgNuAr4NjAb2zszGiPgG8ARw0GKv3RtYIzMPjoi+wKeBGqCBd19QeBrYCbi+MrJ6KDB/cdXFM72fLYArKiPIX678NzaIiK6ZORPYGXi2mddNBlaKiJUz87WIuICiOH1d5XinNXN8y532dXV857N7cNyvrqShoZEDt9+Ufiv1YMykKVx150MMO3QfTvnMngy78kbmzqtn8Kp92XPz9ZlX38A9o8fw13sfpWP7dpx+SDHC7/99Zg+GXXEj19z9MN06deSnX1zxNqVoX1fHdz69G8ddeC0NjY0cuO1Q+q3UnTGvTOWqu0cx7HN7csoBuzLsmluY+88GBvfrzZ6bBnW1tXxm2405/LwraaSRr3xie1ZyXeVmFe12L447/8/FOd5+M/r16sGYiZO56s4HGXbYvmVHbJPat6vj1MM+xbHn/q44rx/bin4r92TMy69w5b/v5ftHfabZ1w1ZvR/7br85h5xxAe3qatl/xy1ZZ8CKOYpqvvZ1dXxn/1047nd/Lc7l1htV+oFpXHXPowz77O6c8qmdGTb838ytr2dwv5XZc5N1qaut5d5nXuCQ866ithZO3mdHenXrwjG7bc1pV99C+7o6OnVox9kH71n2IZaifV0d3zlgV4676LrivG6z0cL+deSjDDtoD07ZfxeG/eVW5v7z7kr/WpzXz2wzlMPPv6roX/fczv61Ge3r6vjOp3bmuN/9rTi/W21Ev56Vdnvv/xj2mY9X2u1tzL2pgcGrrMyem6xTtNt8gUPOv4ramhpO3ntHenVbsc/v7jGQ+8ZN4rA/3UIjjfxw3+350wNPMbBXd3Zbdw2O2Go9jrz8VhoaGzlpl03p2K6OI7ZajzNvfoAL73mcmpoavrfXNmUfxnKnfV0d39nvYxx3yfU0NMKBW21Av57dGPPqNK667zGGfXo3Ttl3J4Zd95+ib11lZfbcuBgfc+vjz3H4b4ZT39jIYdtvwoCVe/LNfXbg9OG3c839j9O5Q3vO/uzuJR9hOXYfshr3TZjCYcNH0gj8cPdN+dOosQxcqSu7DV6VIzYZzJHX3Vu01+3Wo2O7Ok7beSg/vOsJ6hsaaaSR03fZmO4d23Pydutz9N/uo0NdLdus0Yed1+r3gX+/Wu2+3kDue34ih/3pJhob4Yf77cCf/juagSt3Z7d1B3LEVutz5GU3V/qBzenYro5zbnuIufX1fPcf9wCwVu+enPnJ7dh3o8EcefkttK+tZd1+vdhv6OCSj27Zal9Xy3f23o7jLi/O14GbB/16dGXM5Ne56oHRDNtvR07Za1uG3TCSufUPMrjvSuy54SDqamvZYs1VOfT3f6exEU7fb0cAvr/fTvzgn/fSvq6WPt06c+b+H6Nzh3aMmvAqh/3uBuobG9h347UZ1HfFvdgiaflQ09j4Ueqb1S0iOlGsB706RcH5BuBLwFSKIuxGwAbArRTLczwTEccDq2bmGRHxfxQbItZSrFn9p4j4fxTrWHcEHgS+DvyBYtT2qhRrR58H3AjUU6wX3Rn4BvACcDvFaOtNK68ZQTGKeUjleRdk5mWVNavflek9jvMMYJfK3+sKnJCZj0TEYRRrVjcAY4BjgEMqGS9i4ZrVewPDKq9/FDixUoy/AOibmYd+0LmuH3GlDW9pmDPrg5+jj66T0+BaW02XbmVHqEqNUyZ+8JP00bnZ0NJRX192gur02pSyE1SnnivmmvlL3UvPf/Bz9NH06PXBz9FH17HjBz9HH9kJB59ZdoSqdFHjmyvGNISPavqr1qGaWqnfcttOLFavgCrF6lcy86JW/u/+FrguM0d80HMtVi8lFquXDovVrc5i9dJhsXopsVi9dFisXjosVi8dFquXDovVrc9i9dJhsXqpsFi9dFisfg8Wqxe1HBerXQakikXE34DFP1m/wcK1rVvzb90GTPwwhWpJkiRJkiRpmVluS7NanMXqKpaZy2zh5MxcMRcSlSRJkiRJktQqnNcqSZIkSZIkSSqdxWpJkiRJkiRJUuksVkuSJEmSJEmSSmexWpIkSZIkSZJUOjdYlCRJkiRJklTFasoOoA/JkdWSJEmSJEmSpNJZrJYkSZIkSZIklc5itSRJkiRJkiSpdBarJUmSJEmSJEmlc4NFSZIkSZIkSdWrxg0W2wpHVkuSJEmSJEmSSmexWpIkSZIkSZJUOovVkiRJkiRJkqTSWayWJEmSJEmSJJXODRYlSZIkSZIkVS83WGwzHFktSZIkSZIkSSqdxWpJkiRJkiRJUuksVkuSJEmSJEmSSmexWpIkSZIkSZJUOovVkiRJkiRJkqTStSs7gCRJkiRJkiQtPTVlB9CH5MhqSZIkSZIkSVLpLFZLkiRJkiRJkkpnsVqSJEmSJEmSVDqL1ZIkSZIkSZKk0rnBoiRJkiRJkqTqVeMGi22FI6slSZIkSZIkSaWzWC1JkiRJkiRJKp3FakmSJEmSJElS6SxWS5IkSZIkSZJK5waLkiRJkiRJkqqXGyy2GY6sliRJkiRJkiSVzmK1JEmSJEmSJKl0FqslSZIkSZIkSaWzWC1JkiRJkiRJKp0bLEqSJEmSJEmqYm6w2FY4slqSJEmSJEmSVDqL1ZIkSZIkSZKk0lmsliRJkiRJkiSVzmK1JEmSJEmSJKl0FqslSZIkSZIkSaVrV3YASZIkSZIkSVpqamrKTqAPyZHVkiRJkiRJkqTSWayWJEmSJEmSJJXOYrUkSZIkSZIkqXQWqyVJkiRJkiRJpatpbGwsO4MkSZIkSZIkaQXnyGpJkiRJkiRJUuksVkuSJEmSJEmSSmexWpIkSZIkSZJUOovVkiRJkiRJkqTSWayWJEmSJEmSJJXOYrUkSZIkSZIkqXQWqyVJkiRJkiRJpbNYLWmZiojaiKiLiJ0iokPZeapFRPSIiKER0bXsLJJUjSLCz83SCioiepWdQdKyFxFblJ1BWhHVNDY2lp1BWu5ExMeALhQXdH4FfC8zryo3VdsXET8BxgFrApsDr2bmF8pN1fZFxGeB04B2wHCgMTN/UG6qti8i6oCjgIHAHcCTmTm11FBVIiI+DgwGHgCezcxZJUdqkyrvVc3KzLuXZZZqFREHAXVAR+CnwLmZ+bNyU7VNETHsvR7LzLOWZZZqFBGrAz8B+gLXAY9n5gPlpmr7ImJn4DcU/cC1wPjM/EO5qdq+iOgOfAdYDfgXRXsdU26q6mBf0Loi4hpgLeAK4IrMnF5uImnF4AgRqXnnAs8BJwI7AMeXG6dq7JiZFwPbZeZewICyA1WJbwDbAlOBHwCfLjdO1biY4sLKnkB34PJy41SHiPgR8AXgOGAz4I/lJmrTvlL59zPgAuBI4BeAhb/W823g38ARwBrAfuXGadNerfzbDlgVGAusDGxaZqgq8jvgUqADcDdwfrlxqsbZwMeAV4AfAV8tN07VuJRiAMu6FOfWCwCtx76gFWXmIcDeQCNwbURcGRG7lJtKqn4Wq6XmvUPxhWpeZr5CMaJKLVcXEVsDL1SWAOlbdqAq0ZCZsylGVDcCM8sOVCWGZOYw4J3MvBHoWXagKrFjZn4emJGZlwGDyg7UVmXmoZl5KDAF2DIzjwW2ARyp3npmV36+Velnu5cZpi3LzIsrF6xrM/OrmXllZp6M57S1dMrMERSfBRL7gdbSkJmvUZzXWcBbZQeqEr0z81JgbmbeB9SUHaiK2Be0vn4UMy37UAwOOigiLi03klTdLFZLzXsTuB0YHhEnABNKzlMtLqdYVuVnFKPXvdLfOkZGxNXAgIi4CHio7EBVol1E9IEF01UbSs5TLdpFRCegsbLUSn3ZgarAak1+bwesUlaQKjQOeBi4NCK+T7F0jVqmd0QMAYiIAHqUnKdazI6IT1AMDNgWC1StZUxE/BjoExGnAuPLDlQtImK9ys8B+FmgNdkXtKKIeAC4EHgM2DYzT8rME4DVy00mVbd2ZQeQllOfoxhV+VREbARcUnagapCZvwV+W7l5cplZqklmfjci9gJGAU9n5j/LzlQlTgfupSgE/hfbbGv5JfAIxcyKByq31TJ/AEZHxJPABsD3S85TTU4HpmfmjIh4GGdYtIaTgasjoj/F9P8jS85TLY6jGAzQh2L5mq+UG6dqHA8cA4wEZgDHlhunapxIsQzY+hTrKru8SuuxL2hdRwCzMvPFiNiS4gI2mfmJcmNJ1c0NFqVmuDHF0hERPwC+RLHmVw3F9LT+5aZq+yJiP2CrzBwWEbcAv8jM28rOVS0iYhVgSmWJFbWCiOgFrA0876aVrSMiegDrAeM8py1XuVA9/7PAKZW764AfZ6ZrLLdQRPSk2BNgXGbOKDtPtYiIzYAARmfmE2XnqQYR0ZmiYB3Ak8DFmTm33FTVISL6AkMoNlp+rew8bV1licVmZeacZZmlmlRmrb6UmT+IiPMBMvOkkmNJVc+R1VLzfgf8HPgexcYUl1FsYKeW2RdYq7Lup1rPmcBeld8PBm4GLFa3UETsDPyGokB1bUSMz0w3AFpClaVq3lXwjwgy87ASIlWNiNgQuAhYCbgyIp50hkWL9QIOoVin8tDKfQ0snB2kJRQRn6EYsd6OYrm1xsz8Qcmx2rzKgIBdgQeBEyPi+sz8acmxqsHVwDMUn612oBgNfESpiapARHyVYpbFaGCDiDg7M68oOVZblzQZEFS5b/7vg8sKVQU2y8zjoShSR8TdZQeSVgQWq6XmdcrMERFxemZmRLjWV+t4FOjEwg2r1DrmZuZkgMx8IyJc9691nA18DPgr8COKJUEsVi+5ixa7Pf8LlVruAuBo4PcUbfRmwGJ1C2TmSIr9ADbPzFFl56ky36QYAHAL8AOKKdUWq1tuL2DrzGyo7AdwP2CxuuV6Z+apld9viIiRpaapHscCG2fmrIjoAtwFWKxugcxcsGF1RNRQtF1nWrVcTUT0zsxpEbES1tCkZcINFqXmuTHF0vEkMCkixkXE8xExruxAVeLBiLgqIr4eEX+muCiglmuoTEttzMxZwFtlB2rLMvOuzLwLeBo4EPg/YD/g8VKDVYnMHEPRVqdgW21NW0TE6Mr71jjft1pFQ2WGVWNleaWZZQeqEi8B3Su/twdeLTFLNRkdETsARMRQYHxEtH+/JRf0obwKzKv8/g4wrcRedTDpAAAgAElEQVQsVSUi9gbGALdFxLMRsUvJkdq6s4CHI2IUxZ4rZ5WcR1oheFVIat7iG1McX26cqnEwMAiYXnaQKnMisD/FeorDM/PGkvNUizER8WOgd0ScCowvO1CV+Evl36UUU6r/TLFEkJbcaxHxZaBrRByCfWxrOh7Yh2IjQLWOkRFxFTCgshboQ2UHqhL9gWcj4jGKjVbnRMR9AJm5fanJ2radgE9ExFyKiwAAz+LSCi1VC/yv0kY3A9pX+gWXBmu5M4BtM3NKRKwK/B2Xs1ximfnPiLiZoi4w2T1spGXDYrXUvL0y85D5NyLiRIpp1mqZ8cBM16xuHRGxb2Vd2vk7078OrBYRx2Xm70qM1qZFxLqZ+SxFkeoY4B6KkX/Hvu8L9aFl5vwlQR6LiM+VGqY6fAn4LjAV2LJyW61jamZ6oaoVZeZ3I2IvillAT7u+eqs5HHATtdb3hcx8uOwQVeiHTX6/srQU1emtyiwrMvOViHD2yhKIiF9n5tci4n6a7LlS2WvFC4DSUmaxWmoiIg4FPgXsGhG7Ve6uBYZisbo1rAGMbTKNutE3+xbpXfm5Wqkpqs+fgO2BazPz0yVnqUbPRMThwB3AFsC0iFgXoHKRQB9SRAxscrPpxn/dgNeWcZyqEhE/qvzaISJuBUZR+bKamd8tLVgViIjVgAnA88ApEfFSZv6v5FjV4K/ACOCSzHyy7DBV5NsRsRbFespXZKYzV1rHzynO6eWVJdfUQk3et9pFxD8pBltsjXsFLamzKz8Ped9nSVoqLFZLi7oFmERRBLy4cl8DMLa0RNXl4LIDVJPMvKzy67qZeXipYarLmIh4BegVERMr99VQXFzpX2KuarFe5d+XWLjB4sUUhcDd3utFatZfKj97U6xV+wSwIcVaoJuXFapK5GI/1Xoup9i09gTgOuCXwK6lJqoOm1Jssvj9iOhLUQi8JjNnlBurbcvMQyKiF3AYcG1ETAZ+n5l3lpuszdud4pzeGBEvUlxkub3kTG1dc+9bN5QRpEqcFBHv9ZgXraWlrKax0SV3pOZURv60pyim9M/M+0uO1OZFxACKL6UbUKz3943MfKHUUFUgIq6j2OzjWYqLK2SmU4FbKCJ+k5knNHP/mi4L0DIR0RsYAoxzp/qWi4jrgc9n5lsR0RW4OjM/VXauahARn1/srrnAi5l5Txl5qkFE3EFRqLo1M3ePiLsz82Nl56oGEVFDUbA+BlgbmAFc5tJgLRMR6wFHA3sCd1MM+OqcmV8sNVgViIj1gdOBPShmW5yVmf8qN1XbFhHtgK1Y9Hvs1eWmansi4gvv9ViTAUOSlhJHVkvNiIg/ANsBXYEuFCOr3Zii5X4PXEjxQX8X4A/Ax8sMVCUC+CfQF5gM1OOmPy3WXKG64o84AniJRcRBwA+Ap4GNIuKMzLyi5Fht3YDMfAsgM2dGhDMAWs8hFJ8D7qeYTt0JmBcRozLzG6Uma7s6AL8A7o6IXfH7SKuIiHMpNlu+C/hJZj4YEbXAI4DF6iUUEQ8Ab1N8hh02f9+VyvJAWkIR8VXg88CbFOf2KIri6n8Bi9Ut8zeKfnZ1oA6YCFis/ojmF6Qrxf8vs3Cw1YVl5pJWFH44lJq3PsVU6osppvlcV26cqtEpM/9R+f3vEfHNUtNUj+9TfPF/BugBfKXcOFWv5oOfovfxTWCLzJwREd0p1li1WN0yt0XEXcDDFAXVP5ecp5q0B3bLzIZK4e+mzNwrIu4rO1gbdhTFKMo/UBRXjyg1TfV4lkrfChARK2Xm9Ihw74WW+VpmPjT/RkTsnJl3ZeYnygxVBVYHDs3M55vcNzcivlxWoCrSMzN3johLgK8D/y47UBt3MTCd4jzuDFxCcaFF0lJksVpq3luZ2RgRXTNzakR0KDtQlWgXEUMz84mIGEqTnZXVIt8Dts7MyRHRD7gRuK3kTNXMdtsyDfOLKZVlK2aVHaity8zTImJDiousl2fmY2VnqiK9KQrWsys/V67c37G0RG3fGKAXsA3wCjAAGPe+r9B7iohVKS5UHwPcVVkKpJZibfCtXW5tyUTEThQjKb8REb+o3F1Hsdb6RqUFa+Mioo7iPK4PvFT5jjX/QuBuLrvYKuZWfnbNzHf8Htti6zRZqurvXqyWlg2L1VLzHomIbwMTI+Ia/H+ltZwIXFpZD3wicFzJearFtMycDJCZr0bEm2UHkt7H2Ij4OcVyQB/DDWxbLCKGNbm5XkTsn5lnlRaouvwGeDwiRlNsDHpuRHyXYkNmLZm/Uixb9VLldiNFf6Alsy1wEsWSYBdTzP5pAFymomVeB1aluDC1WuW+BuCU0hJVhy9SzFpdlWIjwBqK5evcB6D1XB8R3wMei4j/Uiy1oiXXKSK6ZObbEdGZ4mKLpKXMDRal91CZnv4OsDfwYGa+WnKkquDGaq0vIv5Gsb76XcAWFF+q7gTITHerbmURcUdm7lp2jraqydp/61GsW/37zJz7/q/S+2kybboG2ByozcxjSoxUVSrvW2sDYzJzWkTUZWZ92bnaqoi4LzO3LztHtYmIfTLzpmbu3z8zbygjUzWIiP6ZObGZ+7+fmWeWkakaRMQXM/PSZu7fJjMfKCNTNYmImsos4aHA1MycVHamtioiDgfOAJ6kmG3x/cy8ptRQ0grA0aJSE4uNTmtqM8BRai0UEZ8DzgaeAoa6sVqrafol9OXSUqw4RpQdoI1rpFhS4TWKvqAn4IWrFsjMi5vejoiby8pSLSLi9Mz8QURcTZOlfyKCzDysxGjV4Jn3KgBqyTVXqK44iUU/J+gjeJ92uvMyDVJlmitUV/wYN7FukYj4VWZ+vXKzP8VslnVLjNSmZeaVlc9Vg4DnM/O1sjNJKwKL1dKi5o+ePgB4HrgX2AoYWFqi6vIN3Fit1c3frVqtKyKeY9GpfnOBF3EKcEtdTLEM0B4UGwJeDuxTaqI2LiKafgldDd+zWsONlZ8XlZqiOu0ITIiIKZXbjZnZv8xAVc5NgZcOz+vS4XltuTcj4hygG8X66nuVnKdNi4jtgd9SLF3zUkQck5n/KzmWVPUsVktNzB+dFhEHZuZXK3dfGRHuotw63FhNbckI4FpgJLAdxeZVfwQuoCi2aMkMycxjImKnzLwxIk4tO1AVaDqyehbw7bKCVIsmm1SOolgOrFOJcapKZjrCb9lyzcelw/O6dHheW6iy6fJPgbUzc5ey81SBXwGHZeZTEbER8DvApaykpcxitdS83hExJDPHRkRQ7LKulnNjNbUl62bm7ZXf74yI72XmfyLi+6WmavvaRUQfoLEyw6Kh7EBtnWuoL1U3UMwEeLFy20JKC0XEtsDRQHuKUZT9M/MT5aaSpLYtIiax8D2qBug3/z5nr7TI9Mx8CiAzn4yIt8sOJK0ILFZLzTsZuDoi+gOvAEeWnKdaHAMcSzH9/2nAEZVans2JiOOB+yhGUMyOiC3wvbOlTqdYYmk14L8U/a2WwGJfTBfhF9NWU5uZR5QdospcAPwS+CzwBNCh3DhVz2UVlg7P69LheV1Cmbla2Rmq1OSIuIRixuUWQG1EHAeQmb8rNZlUxfzCLTUjM++JiL2BIcC4zHTzr9bxz8zcs+wQ0od0GHAasD9FQeVIYGvgi2WGausy866I2JBi058XM9ORqkuo6RfTiOiamTPduK7VPR4R2wD/o3JhIDPnlBupzZuemVdHxJ6ZeUZE3FV2oGoQEd8CLs/MKYs99Isy8lSLiOhJsZnigqWAMnM48PnSQlW3q8oO0NZFxFDgUmAAxaCrL2bmo+WmatOeqfxcB3gTuItiwIWfX6WlyGK11IyI+BxwNvAUMDQizshMNwJsuekR8SngWSpT/zPz2XIjSc3LzGmVDWrmf0Htmpk3l5mpGkTEgcDPgdeBHhHxlcx0X4AWiIhhQE/gW8D5EfFwZv6k5FjVYmdgP4ovpTWVn4NLTdT2NVYuWHWpLLW2atmBqsRM4O+VGRd/AG7JzMbMvPEDXqf3dxvFbMDXK7cbgeGZ+eJ7v0TvJSKeZ9Ei31yKJYFmZ+b6mfn7cpJVlQuAYzLzsYjYFPgNsEPJmdqszDyzufsj4vplnUVakVislpr3DWCLzJxRWVN1BGCxuuX6Upzb+RqB3UrKIr2viPgtxcZqk1hYpHJDlZb7HrBNZk6OiH7AjYDF6pbZPzO3AMjMgyLiXsBidSvIzE2auz8ivjx/U2Z9ZN8ENqQoqFwFXFRunOqQmRcBF1UuBJwGXBwRlwLnZeb0ctO1aW9k5lFlh6gi61F8pvoNcHFmPhgRmwFfff+X6SOonb9JcGb+LyLmlR2oSvUsO4BUzSxWS81ryMwZAJn5VkTMKjtQNXATMLUxWwNDMtMNAFvXtMycDJCZr0bEm2UHqgINEdEhM+dERHugtuxAK4CDAYvVSyAzR0fEHGBt4ADgpZIjVYWIWAk4hGJ5iunASRTf9W6gmCGgJXNrZf+Kp+bfkZl3l5inTcvM2QCVjewfrNz3aGWWhVrH3IjYFxhJsaH97JLzSNJHZrFaat7YiPg5cDfFm/zYkvO0ae8z5W9WZm5QTirpA42hWALEXb9b15sRcSvFmn9bUCwF8COAzPxuqcnarouAJyPiCYpRa+eWnGdF4CZgSygivgZ8GlgZ+BPFOqBfKzNTlXiIYhbgwU2XqKgsA6AltxPQkYUF/0aK7wdqmekRcTbwIMWstRfKjVNVvgT8DPgxxRI2x5YbR5I+OovVUvO+CHwZ2INiJMV3yo3T5jnlT23RQGB8RIyp3G7MTJcBabkbmvz+cpPfOy3+RH04mfmHiPgHxVrKY90UeJlwY6UldwhFAXBEZp4fEQ+VHahKnNt0vd+IODEzL8jM08oMVQW6ZebuZYeoQocDRwF7UWxgd3qpaapIZo6PiDOBDYBnM3N82Zkk6aOyWC01ryvwIjB/R/UDgeHlxWnbnPKnNurQsgNUo8y8rLn7I2IELquwRCojJ4+jUvCPCDLzi+Wmkt7T/GVq5hf8naLeAhFxKPApYNeImL/cWh2wEcW64GqZJyPiEOBRKm3WzcFbxSyK//enAk8AvSq/q4Ui4kSKz7APAN+OiOGZ+bOSY1Wj1z/4KZKWlMVqqXm3UYyonr8hTSMWq1uDU/603IuIYzLzEuB43j160mUqlh6XVVhyfwJ+TXGRVcuG7XXJXUWxjMKaEXET8PeS87R1t1BsBNybhRf8GnAJu9aySeXffG4O3jouBiZSzGJ9GLgc2KfURNXjUGCnzJxX2cfiPoplQbQEImINinO6YAZgZp6VmZ8pL5VU/SxWS817IzOPLjtEFXLKn9qC+QW/Z0pNseJxWYUl90rlAotaWUTUARuy6JfUB4FTSgvVxmXmryPiPxQjfzMzHy87U1uWma8Dd0bEsZl5V9l5qk1m7hoRvYEhwDiXWWo1QzLzmIjYKTNvjIhTyw5URWoycx5AZs6NiLllB2rjrgVuxwEB0jJlsVpqnjt/Lx1O+dNyLzNvrfx6ObAVrqWs5d8LlS/6Taep31ZupKpxE8XmavOn+zYCB2am6yx/RBHxY959UWqziDjEzVVbRceI2Bh4lmJkNZk5p9xIbV9EHAT8gGKjuo0i4ozMvKLkWNWgXUT0ARojojuVNqtWcU9EXAeMpNgf4N6S87R1b2WmA6ykZcxitdQ8d/5eOpzyp7bkr0Bf4KXKbfuBpctlFZZcRyAq/6BoqxarW0enzNz5g5+mD8HZKktXsOgGto0Um66qZb4JbJGZMypF1RGAxeqWOw24B1gDuB84udw41SMzvx0Rn6TY4P7SzLyp7ExtnOvWSyWwWC01z52/lw6n/KktWTUzty87xArkqQ9+ipqKiHaVqb5fLjtLFbs7Ij5BMaoSgMycUGKeNmv+5qoRsS2wdWZeEBFXAj8vN1l1yMyhABGxMvB6Zrq0UutoyMwZAJn5VkTMKjtQlegGdKDYv6YzUF9qmipSWbZmN4oLWH0i4t7MfKPkWG3ZppV/87luvbQMWKyWmucV1KXDKX9qS56JiP6ZObHsINWkUqg6GmhPMZq6f2Z+IjNPKDdZm3Q5cBiQLFxeoQZHVLamfsB5LLrhshexWuYCiv0rAL5HsUHox8oKUy0i4mPAb4E64NqIGJ+Zfyg5VjUYGxE/p5hZ9THcuLK1fB/YJjOnRMSqFButbltypmpxOXBj5edOwGXAAaUmasNct14qh8VqqXnu/L10OOVPbcmOwISImFK53ZiZ/csMVCUuAH4JfJZi7foO5cZpuzLzsMrPQc09HhFfzsyLl22qqhOZuX7ZIarMvMx8CiAzx0WEF65bxw8oiql/BX5EsU6txeqW+yL/v717D5asLO89/p1BxikMlDIiEEVNkPlxEW8kqBhuRuJRjGLMETAEw2VwUEQheA6ZQ7wSEo0mJgEFFBRPlBiMBWLhLVEBkXPEiAgMPISLhMNVMCiKIwOzzx+rd9iOeyTOnt1v9+rvp6qr11qzp+pXXfvS6+n3fZ5u98qL6HZYuCtww7ivqr4HUFV3JPlx60A9sriqTh0cX5HkVU3TjDn71kttWKyWZlFVe7fO0FNu+dPYqKqlrTP01L1VdXaS36mqtye5sHWgHtufblaA1t+Vg90AM3daObRubm5OchLdh9a7Arc2ztMXa6rq+0mmqmpVkvtaB+qJ6V0rG/HwjiCtp8HPPnS7LT9Lt4hlV7oB7JqDJNPvW+8eFFgvpnttb2qXqhfsWy81YLFamkWSE4HDmDG13hWVG4Rb/jQ21tWuom2qXphKshOwSZIAW7UO1GMWVeZuD2DfGee2WJm7Q4DldAOWr6FbsUaSR1eVBav1d32SP6frUXs8cHPrQD1xOl0boC/SDV7/MHBw00TjrdZ6hp8dDKr1N/PD6dcPHjDjflbrxb71UgMWq6XZvQx4qjdNG5xb/jRObFcxP44FdqJ7fT8BfLBtnF7zBnWOquoZrTP0TVWtousDvrbPYcu1uVgOHE63mvJHwLK2cXpju6qa7ql+bpKvN00z5qYHrWrDe6SdwUneVlXvGFaeHrFvvdSAxWppdpcDi3FL2gbhlj+NKdtVzI/DqurYwfEuTZNIjyDJV1ir6F9VFlTnhzsB5mZjuhZrGw3O/bBqw1icZJOquj/JzNdXGjd7tg4wpqb71u8DrMS+9dJQWKyWZncVcHuSO+hunqaqym2/688tfxpHtquYHzskeWxV3ds6yASw+Dd3ywfPC+g+XHnmL/hazY3F1bk5G7iWboX6C4CPAAc1TdQP76cbUncVsCPw9rZxpPXme4JfQpLfqKpv0u34+bfBA2BvurZAkuaRxWppdvsDv0bXo05z5JY/jam121Wc+ou/XP9FO9IN/7mbrjg15UyAuUuyGbAGeCXw2ar6D+B/tE01/qpq5oes1yY5tFkY6RdbUlXTK/7OS3Jx0zT9sZxuN+C2wE1VdU/jPNL68gPBX85vA98EDlzr+hQWq6V5Z7Famt3NwI/tWS1Nrqq6OskDwNOA/YD/1zhSX/xWVd0yfZJk+5Zh+iDJx+hunHYDFgK/B7yyqi5rGqwHkhwx43RrYNNWWSaAq/7m5uokL6iqS5LsDNycZGNgQVU90DrcGJsCzqTbHbgmCVW1onEmSfOsqt49OLykqj48fT3J0Y0iSRPFYrU0u23ohincODifqqrdWgaSNFxJjqJbpbo58FFgO+ColpnGWZKnA08E3p3kLXSFqYXAXwDPapmtB55aVX+f5LCq2jvJv7QO1CNbzzheBby6VZAJsLJ1gDG3O/DiJKvp+lcDXEdXbLWV3fo7s3UAaQPxA8FfQpIDgZcDeyeZnlWxENiZbtelpHlksVqa3f6tA0hq7gC6m/8vV9XfJHGV6tw8ju413RJ4zeDaGuADzRL1x6IkrwZWJnk8sKR1oB45ka4d0OLB+ROA7zZL0wNJdqP7ud8SuBVYVlWXV9Ub2iYbb1W1U5IFwBbA3VW1pnWmPrCVncZNkpOr6qgZ5x+rqoOBgxvGGkefB26ne0912uDaGuCGZomkCWKxWprdQ8Bf0/VWvQ44pm0cSQ0sHDxP9/izLdAcVNXFwMVJdh8cA5Bkm4ax+uLddD0VjwWOBv5X2zi9cgGwiIdnWEzRtVnR+vs74DVVtXKw4+J0uhY2moMke9GtAv4B8Lgky6rqS21TSRqWJG8ATgA2T/J7dCupFwBXA8xswaZHNpj98VXgq0m2ptuxsgB4CnBbw2jSRLBYLc3uQ8AHgYuAvYAz6IYsSJocn6D7HfCUJBcA5zbO0xcnJzmoqq5M8iq6las7tA415pZU1XR7irfaT3GDWlxVe7YO0TP3VtVKgKq6Ksn9rQP1xIl0MwFuS/JE4NOAxWppQlTVKcApSVZU1Umt8/RFkjOA5wOPATahW1n9vKahpAlgsVqa3eKq+szg+NwkxzZNI2noqurkQe/fp3en9Z3WmXriQOCMJHcCDwJ7NM4zttbRT3Ejuu9Z+yluGBcleTFwzfSFqvr3hnn64K4kHwa+DOwCLJweZFlVpzdNNt4eqqrbAKrq1iSrWgeS1MTfDVqDTbevoqo+1jDPuNuBrh3YacAK4FNt40iTwWK1NLtHJdl5sPJvZx5uAyCp55L8OT//M//sJAdU1YoWmXpmesDPo4EH6ArWWj/2U5x/WwLv52fbgNiyYm6uHTxvB/wQuJBukKXvtebmh0neSLcjaA/g+43zSGrjPLo2FdNtP/zdOjf3VdVUksdU1d1JFrUOJE0Ci9XS7I4Gzhz0p7oNOKJxHknDM11I2Rr4CV2R6iTgfc0S9cs/Aq+lK6i+CPga3YoV/ZKm+ykmuRDYlK5Q/UrgqqbB+iVVZZuaDesja19wtfoGcRBdv9o/A1YCh7aNI6mRhVV1UOsQPfKvSY4DbkvyD1hDk4bCHzRpdiuBI6rq8iT7MRhMIan/quosgCTfAP5oMATsYuCjwF+1zNYTx9O9lhsB5wD/0DRNP5wFfJFuxe9CugGAr2yaqD+uTPI84HIGq9Oq6oG2kcbeJ+ley4XArwH/BvxW00T98IGq+oPWISQ1950kzwW+jX+35qyqViT5FWAV8BLg/zaOJE0Ei9XS7D4O/DPdzelS4NXAa5omkjRsD84YAnZjkjWtA/XEW+i2qP8T3Yr1S4B3NU00/p5aVX+f5LCq2nvQa10bxh7AvjPOp4Bfb5SlF6rq+dPHSR7Lwy1sNDeLkzwDuI5ul4UFKmky7Qn87oxz/26th3W0BYRu2KJtAaV5ZrFamt0Tq+pUgKp6T5KvtA4kaehuTnIScCmwK3Br4zx9saaqvp9kqqpWJbmvdaAeWDQYprQyyePpelhrA6iqZ7TO0HM/ALZtHaInlgKfBbYA7gIewgKVNHGq6pmtM/TEtY/8JZLmi8VqaR2SLK2q65JsS7ddXdJkOQRYDrwUuAY4sW2c3rh+sFplSZLjgZtbB+qB9wD7A39MN3PhhLZxxl+Sk6vqqCSXstbKqqpywOIczHhNF9AVVr/UNlFvvI2uVdW1wGbAkW3jSGohycuBNwAb0/2eXeIHr+vl9tYBpElmsVqa3ZuBf0zyBLoBi8sb55E0ZFW1Cnh/6xw9tBw4nG6w4o+BZW3jjL+q+nSSq4CdgdNxF8CGMN2a5oCmKfpp5mu6qqrubJakX/4U2LWq7kqyJXA+XS97SZPlrcAb6d5vfQXYp22csXXgOq5P4e9Wad5ZrJZm923gkBkDFq9oHUiS+qCqHgRObZ2jT5IcRTdQcXO64ZXbAUe1zDTuZhRQ19DdsC6e8c/vHH6iXnkI+GtgR+C6JMdU1XfbRuqFe6rqLui+f5P8sHUgSU3cU1WXJlleVR9NckjrQOOoqv7zdUuylK5l1ZV0C9kkzTOL1dLsHLAoSRoXBwC7A1+uqr9JclnrQD1yDt37gVtaB+mRDwEfBC4C9gLOAH67ZaCe+GGSLwAXArsAmwzmLlBVDgOTJsdPk+wBbJzkxcDWrQONMxcESG1YrJZm54BFSdK4WDh4nu6t/NNWQXrovqqyB/iGtbiqPjM4PjfJMU3T9Md5M45tBSRNriOB7elmrbyLrkWQ1p8LAqQGLFZL6zBjwOLTcMCiJGl0fYJulepTklwAnNs4T59cleQAup1WUwBVdV3bSGPvUUl2rqork+zcOkxfVNVZrTNIGgmHVNX0UPBXDYZaf7JloDHnggCpAYvV0uzeBHxyMKDmNpyoLkkaUVV1cpJ/AZ7endZ3WmfqkWcNHtOmgBc2ytIXbwTOTLI13Xssh6xK0hwlOYxugPUOSV46uLwQWAT8SbNg488FAVIDC6amph75q6QJk2Q5cCywMbAAWF1V27VNJUnSz0vy1rWvVZVDADWSkhxXVe9tnUOS+iTJo+n6U68A/mxweQ1wV1W5GngOkuwI7ARcW1VXts4jTYKFj/wl0kQ6HNgTuAD4I+DqpmkkSVq3OwePu4AnAU9uG2f8JfnU4Pn2JLfNeNgLeO5emsT2apK0AVXVT6vqu8AxwEPAKrr72K0axhp7SZYBy6rqHOB9Sf6wdSZpEtgGRJrd3VV1e5JNq+qrSVyhJkkaSVV12szzJJ9rlaUvqur3B4evAw4BFjeM0zdbALcluYmurcpUVe3WOJMk9cXHgY8ArwJWAqcDL26aaLwdCUz/jdqXriXI/24XR5oMFqul2f0gyX7AVJLX0d1YSZI0cpIsnXH6q7iyekP6S+AI4N7WQXrkZa0DSFKPPQ74DPCmqjo4yX9rHWjMPVRVqwCqanUS++hKQ2CxWprd4cDTgOOB43DAoiRpdJ0643gV8MetgvTQ1VV1YesQPfO2tc5XJ7kFOKWq/qNFIEnqkUV07wP+ddBr+Vca5xl35yW5GPgG8By6DwIkzTMHLEqSJI2xQR/lLYHvAY8HfkLXw/r1VfWlltnGXZLXAsuBa6avVdWh7RKNvyRnAzcAFwPPA34TuBx4ZlW9vGU2SRp3SV4AvAI4CfgD4LKq+kbbVOMtybOA0A1YvKJ1HmkSOF4R06wAAAj4SURBVGBRkiRpvF0E7FRVWwPbA+cCLwHe1TRVPxwNvB/45IyH5maLqjqhqr5QVe8AFlXVnwKPbR1MksZdVV0CvBfYDDgfuKNtovGWZBvgd+iK1a9I8tbGkaSJYBsQSZKk8fakqiqAqrohyZOr6vokD7YO1gN3VJUF6g1rsyTbV9W1SbYHNk2yBLeqS9KcJfkA3QfWtwML6AbZOsR2/Z0D/DNwS+sg0iSxWC1JkjTebk/yF8DX6W5I70iyD/BA21i98JMkn6drUzEFUFUr2kYae28APp7kV4F/H5zvD5zYNJUk9cOuwLZVtaZ1kJ64r6pOaB1CmjQWqyVJksbbwcARdCuprgLeDjwbOLBhpr44v3WAvqmqy4Bd1rr8zSRvAz7dIJIk9ckNwGLg/tZBeuKqJAfwsx9aX9c2ktR/FqslSZLGWFWtAv52rcuXtsjSN1V1VusME2TP1gEkqQe2AW5Ocj0PF1dtA7L+njV4TJsCXtgoizQxLFZLkiRJam1B6wCSNK6SHF5VHwZuHjymTTWK1AtVtffM8ySLWmWRJonFakmSJEmtWVCRpPU3PQDw801T9EyS1wHHAhvTfai6GljaNJQ0ASxWS5IkSZIkjamq+sLg2fZVG9YyYC/gBOAc4M1N00gTYmHrAJIkSZImnm1AJEmj5u6quh3YtKq+CmzeOI80EVxZLUmSJGleJdljXf9WVRcBBw8xjiRJ/xU/SLIfMDVoCfL41oGkSWCxWpIkSdJ8O3LwvC2wCLgMeDbwI2CvqrplXf9RkqRGTgGeAxwPnAyc0TaONBlsAyJJkiRpXlXVgVV1IPA94DeqahnwXGBV22SSJK3Te4EvDlqBvAV4ReM80kSwWC1JkiRpWLaecfwo4AmtgkiS9AgerKqVAFV1I7CmcR5pItgGRJIkSdKwnAFcneQqYEfgbY3zSJK0LjcnOQm4FNgVuLVxHmkiLJiammqdQZIkSdKESLIZEODGqrqndR5JkmaTZDGwnO5v1jXAaVX107appP6zWC1JkiRpKJLsDJwJbAPcDhxaVZe3TSVJkqRRYc9qSZIkScPyt8DhVbUVcAhwcuM8kiRJGiEWqyVJkiQNy8KqugKgqr4NPNg4jyRJkkaIAxYlSZIkDcvqJC8DLgb2AOz9KUmSpP/kympJkiRJw3IY8Frga8AfAsvaxpEkSdIoccCiJEmSpKFJ8nRgR+C6QSsQSZIkCXBltSRJkqQhSXI08CFgN+D0JMc1jiRJkqQRYrFakiRJ0rAcCOxeVW8GXgDs3ziPJEmSRojFakmSJEnDsqCqHgSoqtXA6sZ5JEmSNEIe1TqAJEmSpInxtSSfAi4GdgcuaZxHkiRJI8QBi5IkSZKGJsm+wA7Ayqq6oHUeSZIkjQ6L1ZIkSZKGIslTgd8HNpm+VlXvbBZIkiRJI8We1ZIkSZKG5WzgMcCdMx6SJEkSYM9qSZIkScNzf1W9o3UISZIkjSbbgEiSJEmaV0mWDg7fDpwPfAuYAqiq6xrFkiRJ0ohxZbUkSZKk+XbajOMjZhxPAS8cchZJkiSNKFdWS5IkSRqKJG+pqr9snUOSJEmjyQGLkiRJkoblJUk2ah1CkiRJo8k2IJIkSZKGZQvgtiQ30bUAmaqq3RpnkiRJ0oiwWC1JkiRpWF7WOoAkSZJGl8VqSZIkScPy2lmuvXPoKSRJkjSSLFZLkiRJGpY7B88LgOfgDB1JkiTNsGBqaqp1BkmSJEkTKMnnquolrXNIkiRpNLiyWpIkSdJQJFk643Rr4MmtskiSJGn0WKyWJEmSNCynzTheBRzXKogkSZJGj21AJEmSJA1NkiXAtsCNVXV36zySJEkaHQ40kSRJkjQUSf478HVgBfB/khzUOJIkSZJGiMVqSZIkScNyLLBLVe0HPBt4U+M8kiRJGiEWqyVJkiQNy5qq+hFAVd1H17dakiRJAhywKEmSJGl4bkjyPuAiYA/ghsZ5JEmSNEJcWS1JkiRpWA4FbgReRFeoXtY2jiRJkkaJxWpJkiRJw7IR3T3IAuAhYE3bOJIkSRolFqslSZIkDctZwBOBLwHbAWe2jSNJkqRRYs9qSZIkScOyVVUdMDg+L8mFTdNIkiRppLiyWpIkSdK8SrIoySLgpiS/Obj2DOC6tskkSZI0SlxZLUmSJGm+FTBF16t6ryQPAIuAVU1TSZIkaaQsmJqaap1BkiRJkiRJkjThXFktSZIkaSiSHA68Gdhk+lpV/Xq7RJIkSRolFqslSZIkDcuRwL7AHa2DSJIkafRYrJYkSZI0LHdX1c2tQ0iSJGk02bNakiRJ0rxKctLg8PnAA8C36AYuUlUrWuWSJEnSaHFltSRJkqT5Vms9S5IkST/HldWSJEmShiLJwWtdWg3cUlVfa5FHkiRJo8VitSRJkqShSHIBsAlwKbArsBh4EPhWVR3TMpskSZLaW9g6gCRJkqSJsTHwwqr6E2Af4L6q2hN4bttYkiRJGgUWqyVJkiQNyxK6gjWD580Hx49uE0eSJEmjxAGLkiRJkoblFOA7Sa4Gtgfek2QF8Pm2sSRJkjQK7FktSZIkaWiSLAGeBlxfVfck2aiqHmqdS5IkSe1ZrJYkSZI0r5KcUFUnJjkb+JkbkKp6TaNYkiRJGjG2AZEkSZI0384fPJ/aNIUkSZJGmgMWJUmSJM2rqrpicPgtYB/gYLphi7c2CyVJkqSRY7FakiRJ0rCcCdwILAXuAM5oG0eSJEmjxGK1JEmSpGFZUlVnAqur6uvAgtaBJEmSNDosVkuSJEkamiTbD56fBDzUOI4kSZJGiAMWJUmSJA3L0XStQHYAzgFe3zaOJEmSRokrqyVJkiQNy3OAxwH3AlsB/9Q2jiRJkkaJK6slSZIkDcv/BH4XuKV1EEmSJI0ei9WSJEmShuXGqrq+dQhJkiSNJovVkiRJkobl/iSfA74NTAFU1Yq2kSRJkjQqLFZLkiRJGpYLWgeQJEnS6FowNTXVOoMkSZIkSZIkacItbB1AkiRJkiRJkiSL1ZIkSZIkSZKk5ixWS5IkSZIkSZKas1gtSZIkSZIkSWrOYrUkSZIkSZIkqbn/D/tvL3JD0PmMAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(25, 25))\n", + "cor = berlin_ord.corr()\n", + "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "HJqjyPI0Fjk2" + }, + "outputs": [], + "source": [ + "X_train_final, X_test_final = train_test_split(berlin_ord, train_size=0.8, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True])" + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test_final.columns==X_train_final.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "rN0ZexYHFugk" + }, + "outputs": [], + "source": [ + "target_final = 'price'\n", + "\n", + "y_train_final = X_train_final[target_final]\n", + "X_train_final = X_train_final.drop(columns=target_final)\n", + "\n", + "y_test_final = X_test_final[target_final]\n", + "X_test_final = X_test_final.drop(columns=target_final)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", + " max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n", + " oob_score=False, random_state=42, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# # Break into steps so we can just grab the model\n", + "\n", + "# imputer = SimpleImputer()\n", + "\n", + "# X_train_imputed = imputer.fit_transform(X_train_final)\n", + "# X_test_imputed = imputer.fit_transform(X_test_final)\n", + "\n", + "# model = RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)\n", + "\n", + "# model.fit(X_train_imputed, y_train_final)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# import pickle\n", + "# pickle.dump(model, open('model.pkl', 'wb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "CTnl7APRGHV5", + "outputId": "6ad40616-44a2-45cb-a893-4f6093ffb8e7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final model r^2 score: 0.55\n" + ] + } + ], + "source": [ + "berlin_pipeline_final = make_pipeline(\n", + " SimpleImputer(),\n", + " RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)\n", + ")\n", + "\n", + "berlin_pipeline_final.fit(X_train_final, y_train_final)\n", + "\n", + "y_pred_final = berlin_pipeline_final.predict(X_test_final)\n", + "score_final = r2_score(y_test_final, y_pred_final)\n", + "print(f'Final model r^2 score: {round(score_final, 2)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 606 + }, + "colab_type": "code", + "id": "iiRpABhtG4P_", + "outputId": "d9e30d41-36da-4862-b4b2-528873ea337e" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Get feature importances\n", + "\n", + "rf = berlin_pipeline_final.named_steps['randomforestregressor']\n", + "importances = pd.Series(rf.feature_importances_, X_train_final.columns)\n", + "\n", + "# Plot feature importances\n", + "\n", + "n = 50\n", + "plt.figure(figsize=(10, 10))\n", + "plt.title(f'Top {n} features')\n", + "importances.sort_values()[-n:].plot.barh();" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "# Getting a better accuracy\n", + "from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearchCV\n" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE for 3 folds: [18.79096622 18.61571982 18.14390912]\n", + "Mean score 18.516865053599975\n" + ] + } + ], + "source": [ + "pipeline = make_pipeline(\n", + " SimpleImputer(),\n", + " RandomForestRegressor(n_estimators=100, n_jobs=-1, random_state=42)\n", + ")\n", + "\n", + "k = 3\n", + "scores = cross_val_score(pipeline, X_train_final, y_train_final, cv=k,\n", + " scoring='neg_mean_absolute_error')\n", + "print(f'MAE for {k} folds:', -scores)\n", + "print('Mean score', -scores.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(memory=None,\n", + " steps=[('simpleimputer',\n", + " SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n", + " missing_values=nan, strategy='mean',\n", + " verbose=0)),\n", + " ('randomforestregressor',\n", + " RandomForestRegressor(bootstrap=True, criterion='mse',\n", + " max_depth=None, max_features='auto',\n", + " max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0,\n", + " min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=-1,\n", + " oob_score=False, random_state=42,\n", + " verbose=0, warm_start=False))],\n", + " verbose=False)" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline.fit(X_train_final, y_train_final)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "encoder = ce.OneHotEncoder(use_cat_names=True)\n", + "\n", + "X_train_encoded = encoder.fit_transform(X_train_final)\n", + "X_test_encoded = encoder.fit_transform(X_test_final)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final model r^2 score: 0.56\n" + ] + } + ], + "source": [ + "y_pred_final = pipeline.predict(X_test_final)\n", + "score_final = r2_score(y_test_final, y_pred_final)\n", + "print(f'Final model r^2 score: {round(score_final, 2)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['accommodates', 'bedrooms', 'cleaning_fee', 'extra_people',\n", + " 'guests_included', 'minimum_nights',\n", + " 'neighbourhood_group_cleansed_Mitte',\n", + " 'neighbourhood_group_cleansed_Pankow',\n", + " 'neighbourhood_group_cleansed_Tempelhof - Schöneberg',\n", + " 'neighbourhood_group_cleansed_Friedrichshain-Kreuzberg',\n", + " 'neighbourhood_group_cleansed_Neukölln',\n", + " 'neighbourhood_group_cleansed_Charlottenburg-Wilm.',\n", + " 'neighbourhood_group_cleansed_Treptow - Köpenick',\n", + " 'neighbourhood_group_cleansed_Steglitz - Zehlendorf',\n", + " 'neighbourhood_group_cleansed_Reinickendorf',\n", + " 'neighbourhood_group_cleansed_Lichtenberg',\n", + " 'neighbourhood_group_cleansed_Marzahn - Hellersdorf',\n", + " 'neighbourhood_group_cleansed_Spandau', 'property_type_Guesthouse',\n", + " 'property_type_Apartment', 'property_type_Condominium',\n", + " 'property_type_Loft', 'property_type_House',\n", + " 'property_type_Serviced apartment', 'property_type_Townhouse',\n", + " 'property_type_Other', 'property_type_Bed and breakfast',\n", + " 'property_type_Guest suite', 'property_type_Hostel',\n", + " 'room_type_Entire home/apt', 'room_type_Private room',\n", + " 'room_type_Shared room', 'bed_type_Real Bed', 'bed_type_Sofa\\Other',\n", + " 'instant_bookable_f', 'instant_bookable_t',\n", + " 'cancellation_policy_strict', 'cancellation_policy_flexible',\n", + " 'cancellation_policy_moderate'],\n", + " dtype='object')" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train_final.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import stats\n" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 5 tasks | elapsed: 5.8s\n", + "[Parallel(n_jobs=-1)]: Done 10 tasks | elapsed: 13.7s\n", + "[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 22.4s\n", + "[Parallel(n_jobs=-1)]: Done 24 tasks | elapsed: 26.8s\n", + "[Parallel(n_jobs=-1)]: Done 33 tasks | elapsed: 32.2s\n", + "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 44.6s\n", + "[Parallel(n_jobs=-1)]: Done 50 out of 50 | elapsed: 54.1s finished\n" + ] + } + ], + "source": [ + "param_distributions = {\n", + " 'simpleimputer__strategy': ['mean', 'median'],\n", + " 'randomforestregressor__n_estimators': stats.randint(50, 500),\n", + " 'randomforestregressor__max_depth': [5, 10, 15, 20, None],\n", + " 'randomforestregressor__max_features': stats.uniform(0,1)\n", + "}\n", + "\n", + "search = RandomizedSearchCV(\n", + " pipeline,\n", + " param_distributions=param_distributions,\n", + " n_iter=10,\n", + " cv=5,\n", + " scoring='neg_mean_absolute_error',\n", + " verbose=10,\n", + " return_train_score=True,\n", + " n_jobs=-1\n", + ")\n", + "\n", + "search.fit(X_train_final, y_train_final);" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best hyperparameters: {'randomforestregressor__max_depth': 15, 'randomforestregressor__max_features': 0.45249895164499965, 'randomforestregressor__n_estimators': 387, 'simpleimputer__strategy': 'mean'}\n", + "Cross-Validation MAE: 17.303924299839892\n" + ] + } + ], + "source": [ + "print('Best hyperparameters:', search.best_params_)\n", + "print('Cross-Validation MAE:', -search.best_score_)" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = search.best_estimator_" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_absolute_error" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train R^2 Score: 0.810881129705256\n", + "Train MAE: $12\n", + "Test R^2 Score: 0.5990658774386055\n", + "Test MAE: $17\n" + ] + } + ], + "source": [ + "y_pred = pipeline.predict(X_train_final)\n", + "mae = mean_absolute_error(y_train_final, y_pred)\n", + "print(f'Train R^2 Score: {pipeline.score(X_train_final, y_train_final)}')\n", + "print(f'Train MAE: ${mae:,.0f}')\n", + "\n", + "y_pred = pipeline.predict(X_test_final)\n", + "mae = mean_absolute_error(y_test_final, y_pred)\n", + "print(f'Test R^2 Score: {pipeline.score(X_test_final, y_test_final)}')\n", + "print(f'Test MAE: ${mae:,.0f}')" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=15,\n", + " max_features=0.45249895164499965, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=387, n_jobs=-1,\n", + " oob_score=False, random_state=42, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Break into steps so we can just grab the model\n", + "\n", + "imputer = SimpleImputer()\n", + "\n", + "X_train_imputed = imputer.fit_transform(X_train_final)\n", + "X_test_imputed = imputer.fit_transform(X_test_final)\n", + "\n", + "model = pipeline.named_steps['randomforestregressor']\n", + "\n", + "model.fit(X_train_imputed, y_train_final)" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "pickle.dump(model, open('berlin_model.pkl', 'wb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [], + "source": [ + "import joblib" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [], + "source": [ + "joblib.dump(model, open('berlin_model.gz', 'wb'), compress='gzip')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "Airbnb.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/app.py b/app.py new file mode 100644 index 0000000..4d5afff --- /dev/null +++ b/app.py @@ -0,0 +1,134 @@ +import pandas as pd +from pandas.io.json import json_normalize +from flask import Flask, jsonify, request, render_template +import joblib ## TO unpack +import pickle ## To use .pkl model +import json ## To load in 'features.json' +# from flask_sqlalchemy import SQLAlchemy ## Use with db +# from flask_migrate import Migrate ## Use with db +# import psycopg2 ## To connect to PostgreSQL Database +# from dotenv import load_dotenv ## To get .env +# import os ## To get .env + +# load_dotenv() + +# db = SQLAlchemy() + +# migrate = Migrate() + +# DB_NAME=os.getenv('DB_NAME', default='OOPS') +# DB_USER=os.getenv('DB_USER', default='OOPS') +# DB_PASS=os.getenv('DB_PASS', default='OOPS') +# DB_HOST=os.getenv('DB_HOST', default='OOPS') + +# conn = psycopg2.connect(dbname='DB_NAME', user='DB_USER', +# password='DB_PASS', host='DB_HOST') +# cur = conn.cursor() + +# Load the model from 'berlin_model.pkl' file or 'berlin_model.gz' +# model = pickle.load(open('berlin_model.pkl', 'rb')) +model = joblib.load(open('berlin_model.gz', 'rb')) + +# App +app = Flask(__name__) + +# Could Remove: vvvv +# Setting this to True makes the returned JSON look not-so-jumbled +app.config['JSONIFY_PRETTYPRINT_REGULAR'] = True + +### FOR DATABASE ### +# class AirBnB_features(db.Model): +# id = db.Column(db.BigInteger, primary_key=True) +# accommodates = db.Column(db.Integer) +# bedrooms = db.Column(db.Integer) +# cleaning_fee = db.Column(db.Integer) +# extra_people = db.Column(db.Integer) +# guests_included = db.Column(db.Integer) +# minimum_nights = db.Column(db.Integer) +# neighbourhood_group_cleansed_Mitte = db.Column(db.Integer) +# neighbourhood_group_cleansed_Pankow = db.Column(db.Integer) +# neighbourhood_group_cleansed_Tempelhof-Schoneberg = db.Column(db.Integer) +# neighbourhood_group_cleansed_Friedrichshain-Kreuzberg = db.Column(db.Integer) +# neighbourhood_group_cleansed_Neukolln = db.Column(db.Integer) +# neighbourhood_group_cleansed_Charlottenburg-Wilm. = db.Column(db.Integer) +# neighbourhood_group_cleansed_Treptow_Kopenick = db.Column(db.Integer) +# neighbourhood_group_cleansed_Steglitz-Zehlendorf = db.Column(db.Integer) +# neighbourhood_group_cleansed_Reinickendorf = db.Column(db.Integer) +# neighbourhood_group_cleansed_Lichtenberg = db.Column(db.Integer) +# neighbourhood_group_cleansed_Marzahn-Hellersdorf = db.Column(db.Integer) +# neighbourhood_group_cleansed_Spandau = db.Column(db.Integer) +# property_type_Guesthouse = db.Column(db.Integer) +# property_type_Apartment = db.Column(db.Integer) +# property_type_Condominium = db.Column(db.Integer) +# property_type_Loft = db.Column(db.Integer) +# property_type_House = db.Column(db.Integer) +# property_type_Serviced_apartment = db.Column(db.Integer) +# property_type_Townhouse = db.Column(db.Integer) +# property_type_Other = db.Column(db.Integer) +# property_type_Bed_and_breakfast = db.Column(db.Integer) +# property_type_Guest_suite = db.Column(db.Integer) +# property_type_Hostel = db.Column(db.Integer) +# room_type_Entire_home/apt = db.Column(db.Integer) +# room_type_Private_room = db.Column(db.Integer) +# room_type_Shared_room = db.Column(db.Integer) +# bed_type_Real_Bed = db.Column(db.Integer) +# bed_type_Sofa_Other = db.Column(db.Integer) +# instant_bookable_f = db.Column(db.Integer) +# instant_bookable_t = db.Column(db.Integer) +# cancellation_policy_strict = db.Column(db.Integer) +# cancellation_policy_flexible = db.Column(db.Integer) +# cancellation_policy_moderate = db.Column(db.Integer) +# feat_id = relationship('AirBnB_prices', uselist=False, back_populates='price_id') + +# class AirBnB_prices(db.Model): +# id = db.Column(db.BigInteger, primary_key=True) +# price = db.Column(db.Integer) +# price_id = relationship('AirBnB_features', uselist=False, back_populates='feat_id') + +# Routes +@app.route('/') +def index(): + return render_template('feature_input.html') + +@app.route('/price', methods=['POST']) +def predict_price(): + + # Takes data entered at index route via the 'feature_input.html' template + data = dict(request.form) + + # Parse the file 'features.json' containing a skeleton JSON of feature variables with + # default values set to '0'. This block of code compares the data from the request form + # to the default values from the JSON file and makes changes depending on what the input + # was. For features that have multiple permutations (neighbourhood_group_cleansed, etc), + # whichever was chosen in the drop-down list will be reassigned as an integer(1) while + # the others remain an integer(0) which denotes that the listing is not a particular + # feature permutation and thus, can be ran through the model. + with open('features.json', 'r') as f: + features_dict = json.load(f) + for key, value in data.items(): + if key in features_dict: + try: + features_dict[key] = float(value) + except: + features_dict[key] = 0 + elif value in features_dict: + features_dict[value] = 1 + # print(features_dict) + data = features_dict + + # This code will take the variable 'data' and convert it from JSON into a Pandas + # Dataframe which can be used with the next line of code + df = pd.json_normalize(data) + + # Prediction based on the Dataframe containing feature values. + # model.predict(df) returns the prediction as an n-dimensional array which is not able + # to be 'jsonified' so you must grab the index at [0] to take it out of the array and + # convert to JSON. The prediction is rounded to the nearest integer. + results = int(model.predict(df)[0]) + + # Return the features and predicted price as JSON + # return jsonify(features=data, price=results) + return jsonify(price=results) + +if __name__ == "__main__": + app.run(port=5000, debug=True) diff --git a/berlin_model.gz b/berlin_model.gz new file mode 100644 index 0000000..e00d63b Binary files /dev/null and b/berlin_model.gz differ diff --git a/features.json b/features.json new file mode 100644 index 0000000..4aa1b6e --- /dev/null +++ b/features.json @@ -0,0 +1,41 @@ +{ + "accommodates": 0, + "bed_type_Real_Bed": 0, + "bed_type_Sofa_Other": 0, + "bedrooms": 0, + "cancellation_policy_flexible": 0, + "cancellation_policy_moderate": 0, + "cancellation_policy_strict": 0, + "cleaning_fee": 0, + "extra_people": 0, + "guests_included": 0, + "instant_bookable_f": 0, + "instant_bookable_t": 0, + "minimum_nights": 0, + "neighbourhood_group_cleansed_Charlottenburg_Wilm": 0, + "neighbourhood_group_cleansed_Friedrichshain_Kreuzberg": 0, + "neighbourhood_group_cleansed_Lichtenberg": 0, + "neighbourhood_group_cleansed_Marzahn_Hellersdorf": 0, + "neighbourhood_group_cleansed_Mitte": 0, + "neighbourhood_group_cleansed_Neukolln": 0, + "neighbourhood_group_cleansed_Pankow": 0, + "neighbourhood_group_cleansed_Reinickendorf": 0, + "neighbourhood_group_cleansed_Spandau": 0, + "neighbourhood_group_cleansed_Steglitz_Zehlendorf": 0, + "neighbourhood_group_cleansed_Tempelhof_Schoneberg": 0, + "neighbourhood_group_cleansed_Treptow_Kopenick": 0, + "property_type_Apartment": 0, + "property_type_Bed_and_breakfast": 0, + "property_type_Condominium": 0, + "property_type_Guest_suite": 0, + "property_type_Guesthouse": 0, + "property_type_Hostel": 0, + "property_type_House": 0, + "property_type_Loft": 0, + "property_type_Other": 0, + "property_type_Serviced_apartment": 0, + "property_type_Townhouse": 0, + "room_type_Entire_home/apt": 0, + "room_type_Private_room": 0, + "room_type_Shared_room": 0 +} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..94a0989 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,29 @@ +alembic==1.4.1 +certifi==2019.11.28 +chardet==3.0.4 +Click==7.0 +Flask==1.1.1 +Flask-Migrate==2.5.2 +Flask-SQLAlchemy==2.4.1 +gunicorn==20.0.4 +idna==2.9 +itsdangerous==1.1.0 +Jinja2==2.11.1 +joblib==0.14.1 +Mako==1.1.2 +MarkupSafe==1.1.1 +numpy==1.18.1 +pandas==1.0.1 +psycopg2-binary==2.8.4 +python-dateutil==2.8.1 +python-dotenv==0.12.0 +python-editor==1.0.4 +pytz==2019.3 +requests==2.23.0 +scikit-learn==0.22.2.post1 +scipy==1.4.1 +six==1.14.0 +sklearn==0.0 +SQLAlchemy==1.3.13 +urllib3==1.25.8 +Werkzeug==1.0.0 diff --git a/runtime.txt b/runtime.txt new file mode 100644 index 0000000..c5d7ce3 --- /dev/null +++ b/runtime.txt @@ -0,0 +1 @@ +python-3.8.1 diff --git a/templates/feature_input.html b/templates/feature_input.html new file mode 100644 index 0000000..9e92b71 --- /dev/null +++ b/templates/feature_input.html @@ -0,0 +1,91 @@ + + + + + + Input Parameters Here + + + +

THIS IS THE PREDICTION PAGE

+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +