├── .gitignore ├── LICENSE ├── README.md ├── assets └── images │ └── lab-banner.png └── coding-exercises ├── intro_coding.ipynb ├── mri_data.ipynb ├── python.ipynb └── working_with_spreadsheets.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Neuroinformatics and Brain Connectivity Lab 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # NBC Lab Onboarding 2 | Resources and activities for new NBC Lab members. Inspired by the [Whitaker lab's](https://github.com/WhitakerLab) great [Onboarding](https://github.com/WhitakerLab/Onboarding) repository. 3 | 4 | # Graduate Student Onboarding 5 | If you are a new graduate student with the NBC Lab, please complete the following steps: 6 | 7 | ## Getting Started on a Research Project 8 | 1. Read the [NBC Lab Handbook](https://docs.google.com/document/d/12QHTfVnjxmVAk9pki2hhspnFJB7QeDR3scstHV8KE_c/edit). 9 | 2. Complete the [NBC Lab Neuroimaging Training & Resources Plan](https://docs.google.com/document/d/16QDYc9xTYYMlgNUZJ4C0o-IuSS6xLpeR56r3b0ND49E/edit?usp=sharing). This comprehensive document provides resources for learning the basics of programming and data analytics. In addition, provides resources for learning basic Python modules for fMRI research. 10 | 3. Work your way through the [NBC Lab Boot Camp Videos](https://docs.google.com/spreadsheets/d/1gioc-fh-IEubmSVCfovSUdcbqCnWJ92kxk450epA60k/edit?gid=0#gid=0). Please add a row with your name to track your progress through these videos. 11 | 4. Complete the [Recommended Reading](https://drive.google.com/drive/folders/1cq5J_Nx4ZgcSYTDrgfYueWlYt34phNsl). 12 | 5. Once you have grasped the basics and are ready to work on your own, independent research project, trainees should complete the [NBC Lab Individual Development Plan for Predoctoral and Postdoctoral Scholars](https://docs.google.com/document/d/1ekRK--hkTqg_tBnRisoIZs_IVUrljK2tNyACyXpl8WY/edit?usp=sharing). Please create your own version of this file for editing: click on File → Make a copy and save to your Google Drive; share with others by clicking the blue Share link on the top right. 13 | 14 | ## Resource Access and Communication 15 | 1. Ask [Dr. Laird](mailto:alaird@fiu.edu) to give you access to the NBC Lab Outlook calendar. 16 | 2. Ask [Dr. Laird](mailto:alaird@fiu.edu) to give you access to the NBC Lab [Google Drive folder](https://drive.google.com/drive/u/0/folders/0B543K-QXbp21WERUMkc5SVhjODg). This is a private folder accessible only by lab members. All research results (e.g., figures, tables) and associated documentation (e.g., manuscripts, conference abstracts) should be saved to the appropriate project sub-folder so that they are accessible to everyone. This helps us avoid gdoc-related permissions issues. 17 | 3. Join the [NBC Lab](https://neuroinformaticslab.slack.com) and [FIU Neuroscience](https://fiuneuro.slack.com) Slack teams by asking [Dr. Laird](mailto:alaird@fiu.edu) to send you an invite. 18 | - We use the NBC Lab Slack workspace to share news and posts related to the lab. After you've joined, click on "Channels" and browse the list to see the available discussions, which are separated by lab project. Add yourself to any and all of the lab's channels so that you can stay up to date on all of our recent progress and discussions. 19 | - The FIU Neuroscience Slack workspace is great for inter-lab communication and for organizing journal clubs or other activities. The cognitive neuroscience grad student colloquium is also organized on this Slack workspace. 20 | - Please edit your profile in both workspaces to include your full name in your Display Name, not just your first name or username, as well as a photo. Add a helpful description of your role under “What I Do”, e.g., “Postdoctoral Fellow with Dr. Laird”, “Graduate Student with Dr. Sutherland”, “Research Assistant for ABCD”. We’d like this field to tell us about your educational level and who/which projects you’re working with/on. 21 | 4. [Sign up](https://github.com/join?source=header-home) for a GitHub account and join the [NBCLab](https://github.com/NBCLab) and [FIU-Neuro](https://github.com/FIU-Neuro) organizations by asking [Julio Peraza](mailto:jperaza@fiu.edu) to send you an invite. 22 | - We use GitHub to organize and share code related to our papers and ongoing projects. The NBC Lab organization is used for all lab-related projects, while the FIU-Neuro organization is used for tools that serve the whole program. 23 | - You can sign up for a free account. By default, this does not come with private personal repositories, but you can either host private repositories under the NBCLab organization or [request an education discount](https://help.github.com/articles/applying-for-an-academic-research-discount/). They generally respond within a day or so. 24 | - Set your [NBCLab organization visibility](https://github.com/orgs/NBCLab/people) to public (it's private by default). We want people to see that you're a member of the lab. For the FIU-Neuro organization, it's your choice. 25 | - Similar to your Slack profile, please edit your GitHub profile to include your full name in your Display Name, not just your first name or username, as well as a photo and a helpful description of your role (i.e., your educational level and your membership in the NBC Lab). 26 | 5. We perform our data analysis on the FIU's High Performance Cluster (HPC). [Julio Peraza](mailto:jperaza@fiu.edu) is your point of contact for the lab's use of the HPC. To obtain access, please send a request for an account to [hcpadmin@fiu.edu](mailto:hcpadmin@fiu.edu). Make sure you include your first and last name, Panther ID, and FIU username / email address. Ask to join the NBC Lab's group, led by Dr. Laird, and be sure to cc [Dr. Laird](mailto:alaird@fiu.edu) and [Julio Peraza](mailto:jperaza@fiu.edu) so that your request can be appropriately approved and processed. 27 | 6. We recommend that you use [Zotero](https://www.zotero.org) to help you collect, organize, cite, and share research. Download the application and browser plugin, then send your Zotero username to [Dr. Laird](mailto:alaird@fiu.edu) and she will add you to the NBC Lab Zotero group. 28 | 7. (Optional) There is also a great [BrainHack](https://mattermost.brainhack.org) Mattermost workspace with over 1500 members that anyone can join. That space has a lot of channels associated with interesting projects or initiatives. Mattermost is an open source Slack alternative. To join the BrainHack workspace, use [this invite link](https://mattermost.brainhack.org/signup_user_complete/?id=orpd9qqjb7gqpnwg5k1fdagrqa). 29 | 30 | ## Other Science Community Resources 31 | Sign up for the following science-related services: 32 | - [ORCiD](https://orcid.org) 33 | - [Google Scholar](https://scholar.google.com) 34 | - [Open Science Framework](https://osf.io) 35 | - [NeuroVault](https://neurovault.org) 36 | - [FigShare](https://figshare.com) 37 | 38 | ## NBC Lab Website 39 | 1. It's a great idea to have a place where folks can learn more about you and your research. Please create a page on the [NBC Lab website](https://nbclab.github.io) that's all about you. Follow the instructions on [this page](https://github.com/NBCLab/NBCLab.github.io/wiki/Adding-yourself-to-the-lab-website) to add your profile. Also see the [lab website's GitHub repository](https://github.com/NBCLab/NBCLab.github.io). 40 | 2. Provide links to your profiles on [ORCiD](https://orcid.org), [Google Scholar](https://scholar.google.com), [GitHub](https://github.com), [Open Science Framework](https://osf.io), [FigShare](https://figshare.com), [ResearchGate](https://www.researchgate.net), and [Twitter](https://twitter.com/home). 41 | 3. Update the website regularly as you [upload preprints, publish papers](https://nbclab.github.io/papers/), and [present posters at conferences and workshops](https://nbclab.github.io/posters/). 42 | 4. Win an award? Achieve a big milestone? Have an accomplishment you want the world to know about? Post a [news item](https://nbclab.github.io/news/) to the website! 43 | 44 | ## Regulatory Compliance and Training 45 | 1. Every member of our lab that will be involved in any part of our research must complete some online courses through a program called CITI. The courses provide historical information of the use of human subjects in research as well as guidelines for keeping information gathered from participants confidential and protected. These courses should be completed prior to your start in the lab. Here are the instructions for creating an account and accessing these courses: 46 | - Go to [CITI](https://www.citiprogram.org) 47 | - On the left side of the page select the "Register" option located under the heading "Create an account". 48 | - Enter "Florida International University" as your affiliated institution and click "Continue". 49 | - Enter your personal information and click "Continue". 50 | - Enter your preferred account information and click "Continue". 51 | - Enter demographic information and click "Continue". 52 | - Here it asks if you are "interested in the option of receiving Continuing Education Unit (CEU) credit for completed CITI Program courses?" select "No" and click "Continue". 53 | - In the section that asks for the "Institutional email address", enter your FIU email address. In the section that asks for your "Role in Research", select "Research Assistant". Towards the bottom, for the section that asks for the "Office phone" write 305-348-0464, and when it asks "Which course do you plan to take?" select "Basic Human Subjects - Social & Behavioral Focus". Click "Continue". 54 | - When it asks "Do you conduct research in any of the following settings?" select "Human Subjects Research" and "Health Information Privacy and Security (HIPS)". When it asks "Do you conduct or supervise studies that use human subjects?" select "Social & Behavioral Human Research Investigators Course". When it says "If you want to take Health Information Privacy and Security (HIPS), please make your selection below", select "Health Information Privacy and Security (HIPS) Course - Information for Investigators" 55 | - To finish creating your account click "Finalize Registration". Now you will be able to access the courses. 56 | - When you complete the courses you will get a certificate of completion. Please place a copy of the pdf certificate in the Google Drive under irb-resources --> citi-certs. 57 | - If you are a graduate student, postdoc, or new faculty member you also need to complete the ["Responsible Conduct in Research"](http://research.fiu.edu/rcr/training/) course. This completion certificate should be uploaded to our Google Drive: *nbclab --> irb-resources --> citi-certs*. Please create a folder with your last name as the folder name and upload your CITI certificate there. 58 | 2. Our projects are reviewed by [FIU's Institutional Review Board (IRB)](http://research.fiu.edu/irb/) to ensure the protection of human subjects in research. Please talk to [Dr. Laird](mailto:alaird@fiu.edu) and confirm that you have been added to the IRB-approved protocols for the project(s) you'll work on. You must complete this before working with any research participant or analyzing any data. 59 | 3. Learn more about the [FIU IRB Topaz Online Protocol Submissions](https://research.fiu.edu/irb/topaz/), including information on [Accessing the System](https://research.fiu.edu/irb/topaz/#accessingsystem). Submit a [Topaz Protocol Access Request Form](https://webforms.fiu.edu/view.php?id=2099084) so that you will show up in the system and can be added to our IRB protocols. 60 | 61 | ## MRI Safety Training 62 | Every member of our lab should complete MRI safety training so that you will be allowed to access the MRI facilities at FIU's [Center for Imaging Science](https://cismri.fiu.edu). Please click on [this link](https://fiu.qualtrics.com/jfe/form/SV_bkjyKMM2tDJwcxT) to begin this training. You will be provided an opportunity to watch two videos (the Siemens MRI safety video and the CIS MRI safety presentation) and complete the 25-question safety exam. To pass, you must complete the exam with an 80%. If you have any questions, please contact [Laura Ucros](mailto:lucros@fiu.edu). 63 | 64 | ## Physical and In-Person Access 65 | 1. Send a request to [Laura Ucros](mailto:lucros@fiu.edu) for keycard access to the lab (AHC4 380) as well as the east wing, which will allow you to swipe into the double doors by the elevator leading to our other offices. 66 | 2. Follow instructions [here](https://castic.fiu.edu/main/app/core/helpguides/Papercut-Mac.pdf) to obtain access to the printer queue. This will allow you to send your print jobs to a queue and release the jobs on the Toshiba printers in AHC-4 using your Panther ID card. 67 | 68 | ## Recommended Reading 69 | The NBC Lab's [Public Google Drive folder](https://drive.google.com/drive/folders/1cq5J_Nx4ZgcSYTDrgfYueWlYt34phNsl?usp=share_link) contains a folder called "recommended-reading". A list of recommended textbooks, journal articles, and youtube videos are provided, organized by topic. Topics range from an introduction to MRI data acquisition and analysis to recent papers published by lab members to papers related to newly launched projects. 70 | 71 | ## Graduate Student Emails 72 | Grad students have two email addresses- a student email (which has numbers, like `tsalo006@fiu.edu`) and an employee email (which generally doesn't, like `tsalo@fiu.edu`). The student email is accessible on Gmail, while the employee email is accessed through Outlook. Make sure to forward your employee email to your student email, or vice versa. Important emails may be sent to either or both, so it is very helpful to just forward one to the other. 73 | 74 | ## Necessary Software 75 | 1. Python 3: A large portion of the code used in the lab is written in Python. Install Python on your laptop using the [Anaconda distribution](https://docs.anaconda.com/anaconda/install/#installation). 76 | 2. [VSCode](https://code.visualstudio.com): Visual Studio Code (VSCode) is a text editor you can use to write code. There are a number of add-on packages you can install to add helpful features. 77 | 3. A [VPN Client](https://network.fiu.edu/vpn/): You will need a VPN client like Cisco AnyConnect in order to access the HPC from home. 78 | 4. An SCP Client: You will need an SCP client like [Cyberduck](https://cyberduck.io) to transfer files to and from the HPC. 79 | 5. [git](https://help.github.com/en/articles/set-up-git): git is necessary for version control and is a key component of GitHub. 80 | - You can also download [GitHub Desktop](https://desktop.github.com), which provides a simplified GUI for using git. 81 | 6. [Follow these instructions](https://help.github.com/en/articles/connecting-to-github-with-ssh) to set up git on the HPC. 82 | 83 | # Undergraduate Student Onboarding 84 | If you are a new undergraduate student with the NBC Lab, please complete the following steps: 85 | 86 | ## Getting Started 87 | 1. Review the contents of the NBC Lab [Undergraduate Google Drive folder](https://drive.google.com/drive/folders/1-LOsWMTxu-sRawUVtYR7J-QqyK90bwSQ?usp=sharing). This is a folder that contains our undergraduate training materials and information about ongoing projects. 88 | 2. In the Google Sheet, complete your cells in the worksheet tabs labeled "Student Preferences" and "Student Progress". 89 | 90 | ## Resource Access and Communication 91 | 1. Join the [NBC Lab](https://neuroinformaticslab.slack.com) Slack teams - Dr. Laird will send you an invite. If you are waiting for your invitation, please be patient and keep an eye on your junk/clutter folders. 92 | - We use the NBC Lab Slack workspace to discuss lab-related activities. After you've joined, click on "Channels" and browse the list to see the available discussions, which are separated by lab project (e.g., "casa", "embrace-raise", "littleriver"). Add yourself to any and all of the lab's channels so that you can stay up to date on all of our recent progress and discussions. 93 | - Please edit your profile to include your full name in your Display Name, not just your first name or username, as well as a photo. Add a helpful description of your role under “What I Do”, e.g., “Undergraduate Student”. 94 | 95 | ## Regulatory Compliance and Training 96 | 1. Every member of our lab that will be involved in any part of our research must complete some online courses through a program called CITI. The courses provide historical information of the use of human subjects in research, as well as guidelines for keeping information gathered from participants confidential and protected. These courses should be completed prior to your start in the lab. Here are the instructions for creating an account and accessing these courses: 97 | - Go to [CITI](https://www.citiprogram.org) 98 | - On the left side of the page select the "Register" option located under the heading "Create an account". 99 | - Enter "Florida International University" as your affiliated institution and click "Continue". 100 | - Enter your personal information and click "Continue". 101 | - Enter your preferred account information and click "Continue". 102 | - Enter demographic information and click "Continue". 103 | - Here it asks if you are "interested in the option of receiving Continuing Education Unit (CEU) credit for completed CITI Program courses?" select "No" and click "Continue". 104 | - In the section that asks for the "Institutional email address", enter your FIU email address. In the section that asks for your "Role in Research", select "Research Assistant". Towards the bottom, for the section that asks for the "Office phone" write 305-348-0464, and when it asks "Which course do you plan to take?" select "Basic Human Subjects - Social & Behavioral Focus". Click "Continue". 105 | - When it asks "Do you conduct research in any of the following settings?" select "Human Subjects Research" and "Health Information Privacy and Security (HIPS)". When it asks "Do you conduct or supervise studies that use human subjects?" select "Social & Behavioral Human Research Investigators Course". When it says "If you want to take Health Information Privacy and Security (HIPS), please make your selection below", select "Health Information Privacy and Security (HIPS) Course - Information for Investigators" 106 | - To finish creating your account click "Finalize Registration". Now you will be able to access the courses. 107 | 2. When you complete the courses you will get a certificate of completion. Please place a copy of the pdf certificate in the Google Drive in the folder "CITI-certs"; use the naming scheme "CITI-Xxxxx.pdf" where Xxxxx is your last name. 108 | 3. Our projects are reviewed by [FIU's Institutional Review Board (IRB)](http://research.fiu.edu/irb/) to ensure the protection of human subjects in research. Dr. Laird will add you to the IRB-approved protocols for the project you'll work on. But first, you need to register in FIU's [TOPAZ Online IRB Protocol System](https://research.fiu.edu/irb/topaz/). Submit a [Topaz Protocol Access Request Form](https://webforms.fiu.edu/view.php?id=2099084) so that you will show up in the system and can be added to our IRB protocols. For "Department", please list "Physics" since that is Dr. Laird's home department. 109 | 4. Please confirm that you have been added with Dr. Laird BEFORE interacting with any research participant or analyzing any data. 110 | 111 | ## MRI Safety Training 112 | Every member of our lab should complete MRI safety training so that you will be allowed to access the MRI facilities at FIU's [Center for Imaging Science](https://cismri.fiu.edu). Please click on [this link](https://fiu.qualtrics.com/jfe/form/SV_bkjyKMM2tDJwcxT) to begin this training. You will be provided an opportunity to watch two videos (the Siemens MRI safety video and the CIS MRI safety presentation) and complete the 25-question safety exam. To pass, you must complete the exam with an 80%. If you have any questions, please contact [Laura Ucros](mailto:lucros@fiu.edu). 113 | 114 | ## Extra Reading 115 | The NBC Lab's [Public Google Drive folder](https://drive.google.com/drive/folders/1cq5J_Nx4ZgcSYTDrgfYueWlYt34phNsl?usp=share_link) contains a folder called "recommended-reading". A list of recommended textbooks, journal articles, and youtube videos are provided, organized by topic. Topics range from an introduction to MRI data acquisition and analysis to recent papers published by lab members to papers related to newly launched projects. 116 | -------------------------------------------------------------------------------- /assets/images/lab-banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NBCLab/Onboarding/3a864c348801ad2772703f1d35c0efeed0767d95/assets/images/lab-banner.png -------------------------------------------------------------------------------- /coding-exercises/intro_coding.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Introduction to coding\n", 8 | "This is the most basic introduction we have to coding. We'll use bash as our example language, because the first programming you do will probably be in the terminal.\n", 9 | "\n", 10 | "## The terminal\n", 11 | "Here's a picture of the terminal. It's your main programmatic interface with the computer.\n", 12 | "\n", 13 | "\n", 14 | "The group of programming languages that are generally used in the terminal are called \"shell languages.\" Of those languages, bash is the most common, and that's the one we'll focus on here.\n", 15 | "\n", 16 | "Ignore the next cell. We're just setting something up to make navigating later cells easier." 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "%%bash\n", 26 | "orig=`pwd`" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "metadata": {}, 32 | "source": [ 33 | "## Comments\n", 34 | "Let's start with something simple. In all programming languages, there is a way to separate comments from code. In bash, you can use the # character." 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 2, 40 | "metadata": {}, 41 | "outputs": [ 42 | { 43 | "name": "stdout", 44 | "output_type": "stream", 45 | "text": [ 46 | "This is code\n" 47 | ] 48 | } 49 | ], 50 | "source": [ 51 | "%%bash\n", 52 | "\n", 53 | "echo This is code\n", 54 | "# This is not" 55 | ] 56 | }, 57 | { 58 | "cell_type": "markdown", 59 | "metadata": {}, 60 | "source": [ 61 | "You might have noticed the %%bash at the top of the last cell. That is just a Jupyter shortcut that lets us use bash in our notebooks, instead of Python or R.\n", 62 | "\n", 63 | "## Common commands\n", 64 | "### man\n", 65 | "The first command you should know is `man`, because it tells you what you need to know about other commands.\n", 66 | "`man` simply refers to the manual associated with a given command. If you know a command exists, but don't know what it does or how to use it, use `man`.\n", 67 | "\n", 68 | "NOTE: If a man page is too long for the terminal, it will allow you to scroll through it. If you want to quit the man page without scrolling through the whole thing, simply type `q` into the terminal." 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 3, 74 | "metadata": { 75 | "scrolled": false 76 | }, 77 | "outputs": [ 78 | { 79 | "name": "stdout", 80 | "output_type": "stream", 81 | "text": [ 82 | "\n", 83 | "ECHO(1) BSD General Commands Manual ECHO(1)\n", 84 | "\n", 85 | "N\bNA\bAM\bME\bE\n", 86 | " e\bec\bch\bho\bo -- write arguments to the standard output\n", 87 | "\n", 88 | "S\bSY\bYN\bNO\bOP\bPS\bSI\bIS\bS\n", 89 | " e\bec\bch\bho\bo [-\b-n\bn] [_\bs_\bt_\br_\bi_\bn_\bg _\b._\b._\b.]\n", 90 | "\n", 91 | "D\bDE\bES\bSC\bCR\bRI\bIP\bPT\bTI\bIO\bON\bN\n", 92 | " The e\bec\bch\bho\bo utility writes any specified operands, separated by single blank\n", 93 | " (` ') characters and followed by a newline (`\\n') character, to the stan-\n", 94 | " dard output.\n", 95 | "\n", 96 | " The following option is available:\n", 97 | "\n", 98 | " -\b-n\bn Do not print the trailing newline character. This may also be\n", 99 | " achieved by appending `\\c' to the end of the string, as is done by\n", 100 | " iBCS2 compatible systems. Note that this option as well as the\n", 101 | " effect of `\\c' are implementation-defined in IEEE Std 1003.1-2001\n", 102 | " (``POSIX.1'') as amended by Cor. 1-2002. Applications aiming for\n", 103 | " maximum portability are strongly encouraged to use printf(1) to\n", 104 | " suppress the newline character.\n", 105 | "\n", 106 | " Some shells may provide a builtin e\bec\bch\bho\bo command which is similar or iden-\n", 107 | " tical to this utility. Most notably, the builtin e\bec\bch\bho\bo in sh(1) does not\n", 108 | " accept the -\b-n\bn option. Consult the builtin(1) manual page.\n", 109 | "\n", 110 | "E\bEX\bXI\bIT\bT S\bST\bTA\bAT\bTU\bUS\bS\n", 111 | " The e\bec\bch\bho\bo utility exits 0 on success, and >0 if an error occurs.\n", 112 | "\n", 113 | "S\bSE\bEE\bE A\bAL\bLS\bSO\bO\n", 114 | " builtin(1), csh(1), printf(1), sh(1)\n", 115 | "\n", 116 | "S\bST\bTA\bAN\bND\bDA\bAR\bRD\bDS\bS\n", 117 | " The e\bec\bch\bho\bo utility conforms to IEEE Std 1003.1-2001 (``POSIX.1'') as\n", 118 | " amended by Cor. 1-2002.\n", 119 | "\n", 120 | "BSD April 12, 2003 BSD\n" 121 | ] 122 | } 123 | ], 124 | "source": [ 125 | "%%bash\n", 126 | "# For example, let's see what the command \"echo\" does\n", 127 | "man echo" 128 | ] 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "metadata": {}, 133 | "source": [ 134 | "### pwd\n", 135 | "`pwd` prints your current working directory (i.e., the folder you are in within the terminal)" 136 | ] 137 | }, 138 | { 139 | "cell_type": "code", 140 | "execution_count": 4, 141 | "metadata": {}, 142 | "outputs": [ 143 | { 144 | "name": "stdout", 145 | "output_type": "stream", 146 | "text": [ 147 | "/Users/tsalo/Documents/nbc/Onboarding/coding-exercises\n" 148 | ] 149 | } 150 | ], 151 | "source": [ 152 | "%%bash\n", 153 | "pwd" 154 | ] 155 | }, 156 | { 157 | "cell_type": "markdown", 158 | "metadata": {}, 159 | "source": [ 160 | "### cd\n", 161 | "`cd` changes your directory\n", 162 | "In bash, there are some helpful characters to know that you'll commonly use with `cd`:\n", 163 | "- `~`: your home directory\n", 164 | "- `.`: the directory you're in\n", 165 | "- `..`: the parent directory (one up from whatever you're referencing)\n", 166 | "\n", 167 | "Let's test those out." 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": 5, 173 | "metadata": {}, 174 | "outputs": [ 175 | { 176 | "name": "stdout", 177 | "output_type": "stream", 178 | "text": [ 179 | "/Users/tsalo/Documents/nbc/Onboarding/coding-exercises\n", 180 | "/Users/tsalo/Documents/nbc/Onboarding\n", 181 | "/Users/tsalo/Documents/nbc/Onboarding\n", 182 | "/Users/tsalo\n", 183 | "/Users/tsalo/Documents\n", 184 | "/Users/tsalo/Documents/notebooks\n", 185 | "/Users/tsalo/Documents/notebooks\n" 186 | ] 187 | }, 188 | { 189 | "name": "stderr", 190 | "output_type": "stream", 191 | "text": [ 192 | "bash: line 32: cd: notebooks/: No such file or directory\n" 193 | ] 194 | } 195 | ], 196 | "source": [ 197 | "%%bash\n", 198 | "pwd\n", 199 | "\n", 200 | "# back out one directory\n", 201 | "cd ..\n", 202 | "pwd\n", 203 | "\n", 204 | "# don't go anywhere\n", 205 | "cd .\n", 206 | "pwd\n", 207 | "\n", 208 | "# go to your home directory\n", 209 | "cd ~\n", 210 | "pwd\n", 211 | "\n", 212 | "# You should also know that there is a difference between\n", 213 | "# relative paths (paths to folders from where you are)\n", 214 | "# and absolute paths (paths to folders from the\n", 215 | "# computer's root directory)\n", 216 | "cd /Users/tsalo/Documents\n", 217 | "pwd\n", 218 | "\n", 219 | "# As long as you are in the above directory, these two\n", 220 | "# commands are equivalent\n", 221 | "cd notebooks/\n", 222 | "pwd\n", 223 | "\n", 224 | "cd /Users/tsalo/Documents/notebooks/\n", 225 | "pwd\n", 226 | "\n", 227 | "# But see what happens when you try to cd into a folder\n", 228 | "# you're already in\n", 229 | "cd notebooks/" 230 | ] 231 | }, 232 | { 233 | "cell_type": "markdown", 234 | "metadata": {}, 235 | "source": [ 236 | "### ls\n", 237 | "`ls` lists the contents of a folder. There are a lot of options associated with `ls`, although printing out the `man` page here would take up too much space, so we'll simply list a few of the more important ones.\n", 238 | "\n", 239 | "- `l`: list files/folders in long form (i.e., with extra information about the size of the files/folders and their owners\n", 240 | "- `a`: list _all_ files/folders. By default, `ls` does not show \"hidden\" files/folders, which are ones with names that start with a period (e.g., `.bashrc`)\n", 241 | "- `t`: list files/folders in order of when they were updated. By default, `ls` lists files/folders alphabetically.\n", 242 | "- `r`: reverse the order in while files/folders are listed. For example, in conjunction with `t`, you can list files/folders so that the most recent appear at the bottom, rather than the top." 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": 6, 248 | "metadata": {}, 249 | "outputs": [ 250 | { 251 | "name": "stdout", 252 | "output_type": "stream", 253 | "text": [ 254 | "Here is the standard ls:\n", 255 | "intro_coding.ipynb\n", 256 | "mri_data.ipynb\n", 257 | "python.ipynb\n", 258 | "working_with_spreadsheets.ipynb\n", 259 | "\n", 260 | "Here is ls -l:\n", 261 | "total 392\n", 262 | "-rw-r--r-- 1 tsalo AD\\Domain Users 10508 Apr 24 11:17 intro_coding.ipynb\n", 263 | "-rw-r--r-- 1 tsalo AD\\Domain Users 166771 Apr 23 08:31 mri_data.ipynb\n", 264 | "-rw-r--r-- 1 tsalo AD\\Domain Users 13346 Apr 23 09:28 python.ipynb\n", 265 | "-rw-r--r-- 1 tsalo AD\\Domain Users 1057 Apr 21 18:37 working_with_spreadsheets.ipynb\n", 266 | "\n", 267 | "Check out the more complicated ls -ltra:\n", 268 | "total 392\n", 269 | "-rw-r--r-- 1 tsalo AD\\Domain Users 1057 Apr 21 18:37 working_with_spreadsheets.ipynb\n", 270 | "drwxr-xr-x@ 7 tsalo AD\\Domain Users 238 Apr 21 18:51 ..\n", 271 | "-rw-r--r-- 1 tsalo AD\\Domain Users 166771 Apr 23 08:31 mri_data.ipynb\n", 272 | "-rw-r--r-- 1 tsalo AD\\Domain Users 13346 Apr 23 09:28 python.ipynb\n", 273 | "-rw-r--r-- 1 tsalo AD\\Domain Users 10508 Apr 24 11:17 intro_coding.ipynb\n", 274 | "drwxr-xr-x 6 tsalo AD\\Domain Users 204 Apr 24 11:17 .ipynb_checkpoints\n", 275 | "drwxr-xr-x 7 tsalo AD\\Domain Users 238 Apr 24 11:17 .\n" 276 | ] 277 | } 278 | ], 279 | "source": [ 280 | "%%bash\n", 281 | "echo Here is the standard ls:\n", 282 | "ls\n", 283 | "\n", 284 | "echo\n", 285 | "echo Here is ls -l:\n", 286 | "ls -l\n", 287 | "\n", 288 | "echo\n", 289 | "echo Check out the more complicated ls -ltra:\n", 290 | "ls -ltra" 291 | ] 292 | }, 293 | { 294 | "cell_type": "code", 295 | "execution_count": null, 296 | "metadata": {}, 297 | "outputs": [], 298 | "source": [] 299 | } 300 | ], 301 | "metadata": { 302 | "kernelspec": { 303 | "display_name": "Python [conda env:python3]", 304 | "language": "python", 305 | "name": "conda-env-python3-py" 306 | }, 307 | "language_info": { 308 | "codemirror_mode": { 309 | "name": "ipython", 310 | "version": 3 311 | }, 312 | "file_extension": ".py", 313 | "mimetype": "text/x-python", 314 | "name": "python", 315 | "nbconvert_exporter": "python", 316 | "pygments_lexer": "ipython3", 317 | "version": "3.6.4" 318 | } 319 | }, 320 | "nbformat": 4, 321 | "nbformat_minor": 2 322 | } 323 | -------------------------------------------------------------------------------- /coding-exercises/mri_data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Working with MRI data in Python\n", 8 | "There is an entire suite of Python libraries for reading, processing, and analyzing MRI data, primarily centered around the nipy ecosystem. We have more information about these libraries in the Onboarding wiki." 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "metadata": {}, 15 | "outputs": [], 16 | "source": [ 17 | "# This next line is a special Jupyter Notebook\n", 18 | "# command (also known as a \"magic\") that lets\n", 19 | "# us show figures *in* the notebook\n", 20 | "%matplotlib inline" 21 | ] 22 | }, 23 | { 24 | "cell_type": "markdown", 25 | "metadata": {}, 26 | "source": [ 27 | "## Loading data" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 2, 33 | "metadata": {}, 34 | "outputs": [ 35 | { 36 | "name": "stderr", 37 | "output_type": "stream", 38 | "text": [ 39 | "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", 40 | " from ._conv import register_converters as _register_converters\n" 41 | ] 42 | } 43 | ], 44 | "source": [ 45 | "# nibabel is used to read/write neuroimaging data\n", 46 | "import nibabel as nib\n", 47 | "\n", 48 | "# nilearn.datasets lets us download/use certain public datasets\n", 49 | "from nilearn import datasets" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 3, 55 | "metadata": {}, 56 | "outputs": [ 57 | { 58 | "name": "stderr", 59 | "output_type": "stream", 60 | "text": [ 61 | "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/nilearn/datasets/func.py:501: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n", 62 | " dtype=None)\n" 63 | ] 64 | } 65 | ], 66 | "source": [ 67 | "# Grab one subject's data from the ADHD200 dataset\n", 68 | "dset = datasets.fetch_adhd(n_subjects=1)\n", 69 | "func_file = dset.func[0]" 70 | ] 71 | }, 72 | { 73 | "cell_type": "code", 74 | "execution_count": 4, 75 | "metadata": {}, 76 | "outputs": [ 77 | { 78 | "name": "stdout", 79 | "output_type": "stream", 80 | "text": [ 81 | "\n", 82 | "[[ -3. -0. -0. 90.]\n", 83 | " [ -0. 3. -0. -126.]\n", 84 | " [ 0. 0. 3. -72.]\n", 85 | " [ 0. 0. 0. 1.]]\n", 86 | "(3.0, 3.0, 3.0, 2.0)\n" 87 | ] 88 | } 89 | ], 90 | "source": [ 91 | "# Load functional data from file\n", 92 | "img = nib.load(func_file)\n", 93 | "print(type(img))\n", 94 | "\n", 95 | "# We can load the actual 4D data as a\n", 96 | "# Nifti1Image with get_data\n", 97 | "data = img.get_data()\n", 98 | "\n", 99 | "# We can also access metadata like the\n", 100 | "# affine\n", 101 | "affine = img.affine\n", 102 | "print(affine)\n", 103 | "\n", 104 | "# Or the voxel sizes\n", 105 | "# *Note that the last one is the TR*\n", 106 | "voxel_sizes = img.header.get_zooms()\n", 107 | "print(voxel_sizes)" 108 | ] 109 | }, 110 | { 111 | "cell_type": "markdown", 112 | "metadata": {}, 113 | "source": [ 114 | "## Visualizing data" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": 5, 120 | "metadata": {}, 121 | "outputs": [ 122 | { 123 | "data": { 124 | "text/plain": [ 125 | "" 126 | ] 127 | }, 128 | "execution_count": 5, 129 | "metadata": {}, 130 | "output_type": "execute_result" 131 | }, 132 | { 133 | "data": { 134 | "image/png": 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\n", 135 | "text/plain": [ 136 | "" 137 | ] 138 | }, 139 | "metadata": {}, 140 | "output_type": "display_data" 141 | } 142 | ], 143 | "source": [ 144 | "from nilearn.plotting import plot_epi\n", 145 | "\n", 146 | "# First we grab the first volume from\n", 147 | "# the image and put that in its own\n", 148 | "# image object\n", 149 | "# To do this, we need the array of values\n", 150 | "# from get_data and the affine from the\n", 151 | "# image header\n", 152 | "img_3d = nib.Nifti1Image(img.get_data()[:, :, :, 0],\n", 153 | " img.affine)\n", 154 | "plot_epi(img_3d, draw_cross=False)" 155 | ] 156 | }, 157 | { 158 | "cell_type": "markdown", 159 | "metadata": {}, 160 | "source": [ 161 | "## Extracting ROIs\n", 162 | "It is very easy to extract mean values of masked regions (i.e., ROIs) from neuroimaging data in array format using pure `numpy`, as long as the mask image and the data image have the same dimensions, orientation, resolution, etc. However, since checking that explicitly every time can be tedious, it's generally easier to use `nilearn`, which has an entire module dedicated to masking and extracting data (`nilearn.masking`).\n", 163 | "\n", 164 | "`nilearn.masking` and `nilearn.input_data` include classes to build masks (even just from a list of coordinates!), extract data from ROIs, and even _unmask_ data (i.e., reshape values from ROIs into full brain maps). Below we go through a couple of these tools." 165 | ] 166 | }, 167 | { 168 | "cell_type": "code", 169 | "execution_count": 6, 170 | "metadata": {}, 171 | "outputs": [], 172 | "source": [ 173 | "from nilearn import input_data\n", 174 | "\n", 175 | "# The NiftiSpheresMasker builds separate masks for a\n", 176 | "# set of spheres centered on the seeds\n", 177 | "# You can then extract the mean value or timeseries\n", 178 | "# (in the case of 4D images) for each one of those spheres\n", 179 | "# The masker can perform a number of functions,\n", 180 | "# including bandpass filtering and smoothing, but we\n", 181 | "# have disabled those in this case, although we will\n", 182 | "# standardize the values for our plots in the next cell\n", 183 | "seeds = [[0, 0, 0], [20, 20, 20]]\n", 184 | "t_r = img.header.get_zooms()[-1]\n", 185 | "spheres_masker = input_data.NiftiSpheresMasker(\n", 186 | " seeds=seeds, radius=5., t_r=t_r,\n", 187 | " smoothing_fwhm=None, detrend=False, standardize=True,\n", 188 | " low_pass=None, high_pass=None)\n", 189 | "\n", 190 | "seed_timeseries = spheres_masker.fit_transform(img)" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 7, 196 | "metadata": {}, 197 | "outputs": [ 198 | { 199 | "name": "stderr", 200 | "output_type": "stream", 201 | "text": [ 202 | "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/matplotlib/figure.py:403: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", 203 | " \"matplotlib is currently using a non-GUI backend, \"\n" 204 | ] 205 | }, 206 | { 207 | "data": { 208 | "image/png": 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giZnzJgVLBEEQBEEQBKFakqZ484SMx65dhnjT9caNqVZmj8NP/0/1+WqK2XS6pHiTdgGQy3eD+jtvGU0nnspkq01KzpsgCIIgCJWJjsMPr4JUvNEjEYTGUkq8KbNNJWKO/uYH8Pi/Eh17vronlBRvXcajOG9Ark1A0G2ve8ESSwwGzZw3EW+CIAiCIFRm4BHYfzuMHWr0SAShsSSjxmNWvDVf0ZLp2RkA5qarFJyWePO05LZ528DuFPFmMmE26N7S6qq78xZRjAIoLVJtUhAEQRCEqlDNkKDkXGPHIQiNppTzBjD5bGPGsxhShgBNxWer27+U82a3g18adVuMhZPYbbAx5Kp7zlvWeTMLlqQzTRSiWwMi3gRBEAShXqQTxqMSbuw4BKHRJM3PgCXeWjaDy99UFSftpnjLLEW8gdmou3nCRZeT8YjChpDHCJuss/NmhWFmm3RnNPRmyrGsEhFvgiAIglAvVLOSWlLEm7DOme+82e3QeUpThU3a0/USb53ivJmMhZN0h7z4XXZiSbWu4iqS57x5nIbEWYvtAkS8CYIgCEK9UMV5EwQgG3KYFW+QqzjZJDjSMQD0RI3izdPCf+0fZtzM7zKcNxFvYDTo7mnx4HfZUTW9ro20Leetxaw2CazJvDcRb4IgCIJQL8R5EwSD5Lw+b2CIt9nBpqnG6lQN8Vb151mZA3eIE5E0V9/xFP+594SxXcIms0xEFDaEvPhdNqC+vd4i2bBJo2AJsCbz3kS8CYIgCEK9sHLerImrIKxXkhFwBcDuyG3bsAvQYfqFhg2rFlwZU2RW66Qnw+BtZe+AUaUykTKFSaDLcCKbRLQuF+mMxmQ0lXXeoL693qJJo9pk0GvkvIE4b4IgCIIgVMJy3iRsUljvJMOFIZOQV3GyOUIn3aZ4c9TivHlb+Z0p3hRLOFiNuuPr232bjBrfj1bOG1DXXm9RRcVmA7/LkXXeRLwJgiAIglAeK+dNwiaF9U4yWizeOnYCtqapOOnRTPGWqk28PXXcFG/pjLHdEm/rPO/NatDdHfLgdxsSJGK6ZfUgklQJup3Y7baceMtk6nb81YKIN0EQBEGoF2mzQIEifd6EdU4yAp5g4TaXF9q3N43z5jXFm0utVrzNkvG0cGjY2L9YvK1v5208bHw/9rQsj/MWUVRCXidAtmBJPQuirBZEvAmCIAhCvcg26RbnTVjnJCPFzhs0VcVJn2446S6zZcCCKHPMZHyomlEkQ0lbYZNdxuN6d94ipvO2XDlvikrQFG+eNVywxLlSJ0qn01x33XWcOHGCVCrFVVddxR/90R+t1OkFQRAEYfmxxJvkvAnrnWQEAjuKt3ftgmMPg6YZvd9WK5k0boyQPk+mygJEyhwjSQ8AG0IeCZucx0RYwW6DzoB7mQqWqAQ9hrRZywVLVky83XPPPbS1tfHFL36R2dlZ3vKWt4h4EwRBENYW4rwJgkE5523DaUZu6Mwx6Ny58uOqFrNirKrb8WaqcN40DZQwAzEnJ3X6afW5cgVL3AFw+dd92ORYOEln0IPTYcfvXoZWAUmVVp8LQAqW1IMLL7yQD33oQwDouo7D4VjgGYIgCILQZKTznDd97YXrCELVpCKFPd4ses8yHkcPrOx4asVsMj5GOz4tDtoChS9SEUDnSNjBedva8bgcOecNjNDJde68jUcUeloMZ9Jlt+Fy2OocNpnO5bxJwZKlEwgECAaDRKNRPvjBD/LhD394pU4tCIIgCCuDVW1SS+faBgjCekPXyztv3X1gd8LI6hZvasJwz0f0TmPDQm66WaRoJOnm3O3teF0OkgXibYOIt0iS7pAXAJvNRtDjrH/BEk9hwZKUuvYW0VYsbBJgZGSE97///Vx66aW86U1vKrlPf3//Sg5JyENRFLn+DUSuf+OQa99Y1tL1PzkWxmP+/NzBJ8l4Oxs6noVYS9e+GVmr19+mKuzWVMbDClMlXt+Olh2ozz/C4ObGvvaK13/kIH3AiN4BwPMHf0c6uKnssTwzRzgZCOsBOjIzpBMx5qLp7PG36F5cU0McW4Pvd7UMT8fYGtDp7+9HURQ8dp0T41N1+wyEEylSsTD9/f0MzqYAOHZ8kH7HTF2Ov1pYMfE2OTnJX/7lX/J3f/d3vOIVryi7X19f30oNSZhHf3+/XP8GIte/cci1byxr6vr/XAObA/QMu7b2QtcpjR5RRdbUtW9C1uz1j44D0L3lZLpLvb5nXwZHfk7f7t1gs63w4HJUuv4z6QEAhk3n7ZQtXbCxwnv14hQAKWeQC19xNj8/vo/h+Gzu+M/tgOePrs33uwrUjMZs8ii7tvXS17eL/v5+2kN+7B5fXa5JRtNR1KNs29RNX98uAlNxYIgNPRvp69uy9Bewwuzdu7fs71YsbPIb3/gG4XCYW265hcsvv5zLL78cRVFW6vSCIAiCsPyoyVxluaT0ehPWKWaxj5JhkwAbzzJCCCOjKzemGknFjTDJMcwy/wv1bjR/v7GnF4fdhtdlJ5nOK5ZhhU2u01zYyWgKXTcadFuEPE6idWrSbeXOWdUm13LBkhVz3q6//nquv/76lTqdUIGv3n+E49NxvvSOsxs9FEEQhLWFmoC2bRAdlXYBwvplIfFmFS0Z2Q8tG1dmTDWSNj+/UU83ZFjw85yMzeABTtpihFZ6XQ4UdV7Om6aCMgu+9mUa9eplPJJr0G0R9Dqz25eKJd5avIXVJtOZtSfeVnGDDWG5eGpwhqeOr634X0EQhFWBmoRAt/GztAsQ1isLireXALZVXXFSSxivQfH1GhsWcN6GR8YA2H2SEaLnLao2afV6W5/tAsbCZoPuPOetngVLIorh4FlNul0OIxx3LTpvIt7WIYlUhuQavJkFQRAaiq4bfd6CPcb/xXkT1isLiTdPyOjxNrJ/5cZUIxnFeA1ayHDStMRsxf3HJwzxdubOrQB4nXaUtIZuhUkGzPBLMx9wvVHOeatXqwBLBBaFTYrzJqwFlHRmTd7MgiAIDcVq0B0U501Y55g90nCXEW9ghE6u4nYBejKKqttxhIzPsxqvLN7mZiaJ46Ut6AfA4zL6GWcXy/2meItPLc+AVznj4SQ2G3QF3dltIY+zbk26I1bOm7ewVcBaNCtEvK1D4qlMYe8RQRAEYelY4i1bsCTSuLEIQpX8eN8JYnVslAzkFi7KOW9gFC2ZOw7x6fqeu14kI8Tw0hHyEdZ9FcWbruskItOknLnX67XEm1W0xG+2DVmv4i2i0Bnw4HTkpEfQ4ySpanXJS7OctxZTvNlsNtwOu4RNCmuDhDhvgiAI9Sdtije3H9xBCZsUVj2D03E+dOc+7jtY56qPC4VNAmw0i6aNPl3fc9eLVJQoPrqCHsIE0BPlc95enIrjUaPgbc1u85hhe9miJetdvIWTBfluAAEzxLEeiweRbNikK7vN7bRLwRJhbaCkjZw3fZ2WqxUEQVgWLOfN6QNPi7QKEFY9cwmjyENUqU+59izJiNHv0OUrv0+vKd5Wad6bLRUlpnvpDLiJ6P6KOW+/G5ihhRjuYEd2m+W8ZYuWuLzGos46FW9jEYXulkLxZoU41iN00mo5YB0TjKIllZw3TdPJaM03Fxbxtg6JpzJGXn0T3rCCIAirlqx484C3RZw3YdVjTZrj9U6lSEbBE6zcgDvQCS2bV23FSUc6RgwfHQE3YfwVq03+7vgMbfYEvlCuBYDXZTpv+b3e/B1NK94eeWGSi/75YZLq4u6V8XCSnpC3YFvIdN7qUbQkqqjYbOA3RTMYzlsl8Xbld57g+h+tUue3AiLe1hm6rpMwv6TXYhKnIAhCw0gnjEeXzwgXk4IlwirHCldLpOot3iKG+7wQq7hoiUONEcdLi89FWPdjq/B5fvrEHF3OBLa8sEmvc57zBkbRkiZtFbB/cI6DJ8LMxmt3aTOazmQ0WdZ5q4d4iyRVgh4ndntuwcDttFdMExqYivPg4Ykln3ulEfG2zjDCJY2f12ISpyAIQsNQjT5GOL3GxFWcN2GVE0uZzlvdxVu4cr6bxcazYeoIpGL1PX8dcKkxEnY/freDMH7sqfKf5+lYiqAeK8h5KwqbBCPvrUmdt6UI/aloEk2H7pZC580q61+PXm9RRc06eRZuR2XxlkhnGA0rjMwllnz+lUTE2zoj/0tksda3IAiCUALVnAA4vUbYpDhvwionGza5LM5bNeLtLNA1GDtU3/PXAVcmTtISb3oAZ6p89diYksKrzRdvJUrV+ztXb3XNBbDcMWURc8dSDboBQlbOW50KluTnuwG4Fqg2aUWiPXW8chuI1YaIt3VG/he0OG+CIAh1xHLeXOK8Cc2B5abEU3VuFZCKGsU5FqL3LONxFRYtcWfipBx+fG4nEXw40xHQiudNuq6jJaPY0RZ23gJdEG/OsMnoEpy3Ug26IVcZsi7Omxk2mY9ngZw367XsG1xl4u2JWyv+WsTbOiNR4LyJeBMEQagb6fnOm/R5E1Y30WSDnbfWLeBrX31FS3QdjxYn7fDjdxnOmx0t13w8DyWtEdDMsM8SzpuizitYko5DKr6sw18OLIGVWERxm/FIaectl/OWBl0HbfH3YSSpEvS6CrZVKliiaXp2HvzU8ZlFn3dZGN5X8dci3tYZCXHeBEEQloeCnLdWI4wyU+cS7IJQR5biplSkWvFmsxl5b6vNeVOTOMmgOgP4zJw3oGTFyYiSJmQzxVhBn7cyBUsAEs0XOmnlRybTtc8dx8KG89YVLBRvfpcDm80Uhk9+E/7pLFhkG6uoks6GYVpU6vNmCTeXw8aBobnV1Q9OqewEinhbZyQk500QBGF5mJ/zBhI6KaxqosoyhU1WW20SjNDJ8f7lXegYfgqOP1b9/qbDpjoDeJx2ogSM7SXyWCNJlRaKxZsVNpmcX7AEmrLiZGSJzltnwI3bWSg77HYbQbfTyHkbPQjhoUXnBEaTxQVLXBUKlliv45ytbSRVjcMjqyhSokJPQRDxtu7IX12TsElBEIQ6kjb7vLm8OddBGnULq5hlqTapmeGFnipy3sBw3jIpmDhcvzHM52fXwff+DCKj1e1vhjxrrgA2m42U03wtJZy3qKLSYqsQNpmeV7AEmrLi5FKqTY6HFTbMC5m0CHqdxiJCzCzZH63yPZpHRCnOeXNXKFhiibdXnGy8J/sGV1HopDhvQj75KyYSNikIglBHsk26fTnXQZw3YRWzFDelFIeG54hEzIlnNWGTYIg3WN7Qydnjhmv2i+ur29903jSXIdpSTvO1lAybLO28lQybDJhhk00o3pZSbXI8kiwqVmIR9DiNY1viLTJS8/Ezmk48lSmqNlkp580SoTu7g2wIeVZXxclE5UU/EW/rDHHeBEEQlomsePPkwialXYCwionVsWBJNKny1psf4e5fP2NsqFa8dewEV2D5mnVnVIgMG67X09+Ho79a+DlJszCJ+RrSLmsxpoTzlkzTks15a8tudzls2G3zxE4TO29LyY8cCytFxUosgl5TvEXHjQ2RsUWPLVSiYEm5ua4lqn0uB+dubeOp1VRxUpw3IR9x3gRBEJYJVTHy3Ww2cd6EpqCeBUsOnpgjldGYnDbzuaoVb3Y79L5k+SpORkaMXnKv+j/QfhL89BpQU5WfYzUNN0M/M27LeSuR86aotGCFTeby/Gw2G16XozBs0tsGNnvTiTdd17NCX6nRpU2pGpPRVEXnLaKouTzARThvWfFWolVAuUIk1nzY53ZwzrY2jk3GmIktcF+sBFpmwUU/EW/rDHHeBEEQlom0YrhuIM6b0BTEksacIJ5S0RdZ5c9iv+lcROfM3CF3leINoH0HzJ1Y0vnLEjaP23kKvPEmmHwOHv16xafoZs6b3RSgmqe88xZRVFpscXSXHxyFzo8h3vLEjt0Ovo6mK1iSSGfQzNtDqbLa5HQsxdcfOMIFX3iAjKbTt7F0AZuQ10lKiYHVBL3avMQ8IopR7KZkk+4y4q3QeWsHYN/QKnDfStxj83EuuIewphDnTRAEYZlQFSPfDYxWASDOm7CqsRwLTTcWdK0KiYvhwJAx6UxEzclntc4bGA5XiR4pNqG2AAAgAElEQVRqdWFuyHhs3QLdu2H3RfCrL8CZf1b2KWoijAtweA3nzePxoODBWyKcLZpU6SFekO9m4XXai8WOv7PpnDfrPoGF8yOPTkT5t4eO8sOnTpBUNS44tYvPX3wWrzltQ8n9gx4nbiXveiyiYIlVNbWmgiWmmeF1OejbGMBug6eOz/La07prPn9dSSxcOEXE2zqj0HmTVgGCIAglmTxilKze9rLqn6MqRqVJyHPeVlH5aUHIQ9d1okmVgNtBLJUhkcosSbztM523VGwWHNQm3tzLKd4GjcfWzcbjhZ+Hm38f7vsEnPO3JZ+SNsWb02u8Bp/LSdQWwFumz9tuRxxbKfHmchTPtQJdiy6H3ygscQQLi7f37dnL4Eyct52/hSteeRKn9lS+D4IeF97UNNgwQkoX47yZ4rKmgiXpnHgLeJyc1tuyOpp1L9AmACRsct2RSGew24yfxXkTBEEow4M3wD1X1/acdMLIeQMjfMrpk1YBwqolqWpkNJ1uMxcptoReb5PRJCdmE2xs9eLRrHyxGsVbJrVwLtpimBsycs2s8bRthVdfC8/eS2D4kZJPURPGoovLbwgyv9tBhECZgiUq7fZESefNMz/nDcDfAfHmCpu0wmth4Zy3mXiat567hRveeuaCwg0MwRVUTeet85TFFSwxxWVLCfGmajqaVhwSrOTlvIHR723f4GzJfVcUZWEBKeJtnZFIZ2j1GTHZkvMmCIJQBmW29nw1NZkTb2BMFiVsUlilWG0CrP5bSylacsDMFXrDGb0EMZvV1xo2Ccvjvs0NQevWwm0v/xvwtBIa/nXJp2SUMEndidtrfJ4N8eYv3aRbUWm1xUs2Jfe67MXOWxOGTUaSuQbqC4m3RErF767ewQ15nHRgXtfeM80CM7UJKCusM+gpzDl0OQyZUyrvzbrffabbfO62NiKKytHJWE3nrjvivAnzSZh9MOw2cd4EQRDKkoxCKl7bc9REoXjztkjBEmFJZDSdD97xFHsH6h9mZ1UPtEq4L6VdwL7BOew2eP3pPQQwW2a4q2zSDeAOGI/LIt5O5EImLZxuaNmII1n6umaUCDG8+M2Jvc/tYE7zly1Y0q1PQsvGot95nY5iseM3wya15pmDWc6b3VZZ5Ou6TjydyQqiagh6nXRhXtfeM0FL1xxWWq5gicdZQbyZjqg11vO2GW0eGh46uUCbABDxtu5IpDL4XU48zhJx2IIgCIJBKmr8q2UFWE3mct7AWIkX501YAuMRhXv2D/Pg4Ym6H9tyKzbUQbwdGJrl1O4QOzcECdoSqHaPIZCqxRJ6qWVwPeYGjWIl8wl240yUdsD0ZJSY7ss6SH63gxnNh15CvGUSc7Tpc0bFzHl4XWUKluiZqibpq4Wo6bx1Bj0Vq00mVQ1dz4UiVkPQ42SDbc5ox9B+knnC2vLeooqKzUZWbFu4LfFWwqywRLUl8E7uChLyOhvf702cN2E+iXQGr9tRMYkTMCYcXzwFXnhw5QYnCIKwWkhFjQlWpoYcnLQ4b0J9GZ0zXKyJSLLux45mnTfjnk2kF5fzpus6+wdnOXtrKxtCHlpsCZKOQG0HsUIsk3V23pIRQySVEm+BbpzJMi5LMkoUb1aE+N1O5vTSzltLwqxm2XFy0e+KWgWAUbAEmqpoSdR03rqCnooFS6wFgFrCJoMeJ522MGlvJ4RM97LGXm+RpErQ48RuFXUwcTsqizevy559jt1u45ytbTx1vMHiTZnNVS0ug4i3dUYilcHnslfsOg9AdAxiEzB2aOUGJwjrhMtufYybH3y+0cMQKmFNImtxAqwm3RbivAlLZCxsiLaJ6DKIN6U+ztvQTIKZeJqztrThsNvocqVIUHnyWcRyhU1avePm57wBBLtxKKUFlC0VJYYPv9sIw/O5zJw3JVzkxrcrlngrdt48TjtKUc5bh/HYREVLrHulK+iumPNmCbuaxJsZNpn0dEKwx9hYS9GS2BRXH7iY810DRb+ynLdSjboTJcI7z93axrOjYeJLKN6zZBKz4GuruIuIt3WGdbN6FnLerPLWVfSbEAShNg4MzfLMiEzqVzXWJDJdQ96bqoArb9IqzpuwRMbCy+e8WdUll5rzZrUIOGerMeFsdyaJ1CzelqlgSX6Pt/kEu3Go8ZILNPZ0lJjuLQibDOt+bFracNjz6FKHjR9Khk2WqjbZaTw2UdGSWFLFboN2/wLizbynfO7qO5EFPU66bHMkXB0Q6jU21uK8TT5LR3qUM53Hi37lquC8lWqNce62djQ917OwISizRnXUCoh4W2ck0hn8bqfhvJXpOg/kvsxEvAlCXdE0nUhSLeibI6wy1FQuXLKWoiVpBZye3P89rdLnTVgSlnibrJfztudi+O03gPpVm9w/OIvbaee0XiP0sc2hMJfxLvCseWSdtzrnvIVN8dayufh3AbMZc3S86FeOdKwgbNLndhDGHGNe6KSm6fRmRog723K9HfMoGTbpt8Imm0e8Rc2wRL/bUV3YZC0FS8ywyair3Vj88rbW1uvNfP86HImiX1nOW6lIs1LO2zlb27DZ4IHDxffEiiHOmzAfa6XB43SQrJB0KuJNEJaHaEpF13PVsYRVSP7qfy1OgJoszFXwhIzna1IcSlgco3nirS79pwZ+A0OPA/WrNnlgaI4zNrVkXY4QCaYzHvRaiv1kc97qvNgxN2Q0fg4VV4IkaIq3WHExGIcaI6b7spN7n8tw3oAC8RZPZ9jGOBF/ibBMwOOyF8+1LOct1kRhk6Z487ocFUW+dQ/VUrAk5IZ2okTs7eaGjWULlqRUjbfc/Bu+8t/P5Taa71+7XSna312h2qSR81Y4zvaAm/999ia+88iLDM8Wi8EVISHOmzCPRDqDz23kvJW6mbNYExYRb8Jq5CcfhR+9v9GjWBTWardVLECogonnDMeg1tL9iyVfsNUUNpkodN6slfg1GDr5bw+9wJ7fDjS+oe0aZ9zMeUtndOYSS1zwSSeM0F6zUEYsaVToa/e7sdlyIW+1oGY0nj4xx9lbcpNNPwnCmodwoobjLZfzNjcEoU3gKBHGFyzvvLnUGDG8WfHmdzsJUyzeoorKdvsYieC2kqf3Oh2kMlrh58TtB5d/RZy3Bw6P8akfH1zycWJJlaDXEG9KhZSbxCLEWyAzh92mM2s376FQb1nn7Uf7TrBvcJav/vIIDz5rvm/m+9diLxZbHnNBIV2yYIlWcpzX/Mlp6MBNv3i26tdQVxRx3oR5JFJG2KTHaSdZqdGiiDdhtRIdh999B158uNEjWRRhcwImYZOlUdIZrvqPvRzLb5Q6+Bi88Euj5PdKkF/xrlrBqGlGqGV+zpvVtHcNFi35518+z9/+6CDv/tbjjMw1aIV6HTAaVnCY1fCWXLTE+nueMMRbJKkSdBsV+nwux6Kct+cnoiTSmWy+G4BHixPVfQzXcl+4/IBteXLeSuW7QV7Y5LziGLqOK5NAsfuzlQh9bgcRy3nLW4yJxqJsYopky/aSp7CcnaKwvRVq1P1f+0f4j8eOL3mRJZpUCXic+FwOUqpGpszxFlNt0pUwHMhpW6uxIdhbsmBJRtP5xq9eYHdviN29Ia65ez/jESX7/rVSLPxdFfu8le5Ht7XDzxV/cBI/fOoEB080IPdNnDchH03TjVYBVsESyXkTmpF93wNNNb6wawnLWSVY4i0izltJBqfj3HdwlEdfyJvYWAUC1OKwmGUhf/U/XaUTYI1tHThvuq4TS6mcvaWV3x2f4Q1feYgf7ztR1XNH5hI8fqx5SqQvBm1uGPUfelCOPbrkY42FFU7tNop5LLloifX3PG48xswJORiT7dgixNt+s1jJWVtas9tcapQo/mybg6qw2YyiJXV33sr0eINcyf75YZPpOHY0Ug5/dpPf7SjpvCWnXsRu08m0nVTyFF6XMc0uznvrWBHxNjQTJ6Mt3bXNhU2WeT0m2WqTruoLlljO2YRu3kOhXqNgyby/7//9zChHJ2K8/7Wn8M+XnEsspfKxu/ejm88PULzQVqlVQKmCJRZ/85pTaPO5+Oy9/bWF/y6VjAqpCPjaK+4m4m0dYa38WNUmK+a8WSvPVTQLFIQVQ9Pgd981flaVkj13VjvhvLDJFf2j0CRYf/xnE3n91azQRbWGnmtLIZWXd1Ot85YVb2vfeVPSGpoOF75kIz/94AXs7A7yoTv3cfUdT1WsRAfwr786yhXffnxN3/sDRw7gzCg8u++RJR0nnlKJKCpnbDImtfUTb4ZoiJqhcGA4S4sJm9w3OEeL18lJnWbYo5rEnkkR0X2M1CLeADzB+ua8aRqEh6G1RLESAIcL1d1aHDZpzn/Seb3qjGqTVsGS3LxImzwGgL1EmwDIOW/F7QK6Vki8GQtf0/GlfXdGFUO8WWGGZcVbttpk9c6blfs3rpnfl6Fe0NIFffB0XeeW/3mB7Z1+3njmRk7tCfG3F53Ow0cmGR8xIjICerHwX6hJtyVG59Pqc/GhPzqVR49O5cIzVwJrTiNhk4JFfv+NqnPeknOSbC+sHl58GKaPwqlvMP5fS0WqVYLlvOn64gsErGWsazIXz1sptpy3TOXJ66+em+Cxo3WYEBWETdbovLnmNemGNee8WSXmAx4HJ3UF+P77XsEH/+hU/mv/MPcdrFzieyqWIpbKMB1bISHeAFJzxmRPDS9t0mc5V2duNu6juom3dAzUJNFkJuu8BdzORX0fHRia5eytbbnmyOZnJ27z1h5O6w7U13mLTRihzKV6vJmo3s7isElz/pNx5sSbr4zzZp81xJuz65SSx885VSXCJpe5YElK1bIFb2r+vKkpeP5+QwCTc2ktMVqu4uRiwiaJGZ+TUdUsWmO1C8grWvKb56c4MDTH+161MxtGfOnvb+PCM3pJhY33z6dVEG81hE1aXPqy7ezoCnDDTw+jVpovm9zx+HH+8SfPLG1hyloYkLBJwcL6sPmsapPzV4LyyY87b0J3Q1ij7L3N+FL7/fca/y9TkWo1k19lUoqWFGN9TxWE+WSdt8qT1xvvO8xnf9pf8zmfG4vwN9/by1X/sZcP3P47vvfwobxzVzmZtARmQZNuMwxojTlvVpXCgNnLyemwc+UfGs7DVLTyJNFavDjRqEpuK4AaNcPwYksTb1aD7lN7Qrid9vrlvAHEp4kqaUKePOdtAdd0Pko6w+HRSEHIpLVQYfe21O68uYP1zXmr1OPNJOPtKA6bNMeguvKdNydJ3GRsroLPs2vuRWK6B197b8nje51lnCp/Z4GztByMzinZyMOFPpcFzAzAty+E/3gbPP/fgJkfmSfeyjlvlngrF45Ykug4KVxMpNzG/63KoHm93m75n+fpDnl42/k5F9Vms/H5i1/CBpsxR/WqxfdOxbDJdKaiQ+h22rn2wt08Px7lricXzrf+v3uHuPXXx/jB76oLIS+JFe0mzptgYdnZXrcDt2OBJt35q1+S9yasBmKTcPgncPYl0H6Ssa1EUvNqJ5xXqCQiRUuKsKqVzcZLiLdM5QlIJJnm8Eik8ndbCe57epSfPj3KCxNRnhkJMz6VN6mqOmzSnFg714HzljTeo4AnN/EJeZzYbCyYW2P9/sRMk4q3w/fCM/dU3scUb65EcQn6WrB6vPW0eNkQ9DBZL+cNIDFNLJnJvod+d+0FSw4Nh8loekGlSUv4uP2tteW8Qf1z3qwCRxXEm+rtKBs2qbtyOW+WQ6M4QwUL2t7IcY7rPYR8rpLHLyt2Ap1GePYCC1JLYWgm9901U23Y5OF74V8vgPHDxv+njxk5rqZ4y16HMmk3iXQGj9OedceqIjZJxNFOxPxeIdhjPJqRNfsGZ3nkhSn+6oIdeJyFYqvNmcKL8do8mRLibYFWAZWcN4A3nNHD753Uzlf++7kFF1sHpozr/al7DjE4bV77ySMQqyEaxPqMivMmWCRSxs3rdzmM3iMi3oRmYv8dxuT9/PdAyPpyrxyitRoJJ8R5q0RWvBXkvFkFSypPdKKKSiqj8dxYbXkzYxGFzoCbX3zk1TzwsdfwkQuMld+07qi+VYBaynmzelc1j3jbNzjLp358sGLoT9xcCPS7c0UJ7HYbIY+zULxpWtECS9M7b7/+J3j4poq72MxcJm9yaSG8OfHmYUPIU3/nLakS9Biiw+eqPWzSKlaSX2nSylnzhtpqqzYJ9c95q8J5Uys4b7o7mN3ksNvwOO0ojmCBeAvEBxnQe7Iu9Hw8zgphk7CseW9DeQskC4ZNqin42XVw56XQvgOu+jU4PBAeIpHOoOkQ9ObEW/mwSbW2kEmA2DgxV3vu76EVNmmKt3/5n+dp9bm49GUlKnqawltv2YwrHSkqclLOedM0HSWtLegQ2mw2PvRHu5iMpvjtC+Xfq1hSZTKa5JLfN1pGfOz7+42KnN95Ezz4jxXPUYAizpswj2zYZDXOWzICdvPLSMSb0Gh0HfZ+B7a+DLr7jEmxO1icq9AEhPPDJsV5KyIXNpl3bapw3nRdzzqZT9dY3nlsTqG7JU90paJoOJglgKpUGcaVLpHz5vSC3dVUYZP37BvmO48OVAyhsyZZVr6URavflRNvY88YoVdfOR2mj2X3se7/oWZ13pTZrLNWDodiOLdBdWl/O0fDCgG3g5DXZYi3ejpv8SlTvOWct1oLluwfmqW3xVv42THFVzDUbobt1ZD/U++ct/AJcAUquhgZb4ch1vLPawlId6hgX7/bQdyeJ940jVZlmBF7b1mnyVOpYAkss3iLY7cZeXdlwyZTcdh3O9z6OvjtzfD774MrfwEdJ0PLJpg7UfB5t3L4yjXqTqS0gkWdqohNkHB35sSbywfeVoiM8vx4hJ8fGuM9r9hO0FPiuKZ4s3WeAnqm6P4pV7AkW8CvCqF5xiYjguLFqfL35nHTaXvlzk4+/b/P4PFj03z3f/YbC8x5338LslTnLZVKlf0nNCfWaqnXZRQsWdB5a9lk/NyE4u3Z0Qi/99n7GW7W1V2hkIFHYOoInP8XuW3BniYtWGI0xgWIJpfYdHcNksgWLKnNeUuqGqrZe6hm8RZR6G3JK/GfjKI6/cR1L+lElU5AqWqTNpsROtlEztugGWpVaWHBcmjywybBqNAWj0Xhl39vhF6NHjTaegw9ARgCe67ZnbfErOHUVBAl7qQh3lq1pf3tHA8n6Wk1hFHdxJtZAVU3nbf8VgG1Om9HJ2Kc1lsocCzh09rWTjyVKQgTX5C657yZbQJs5UP4VG+H8UN+6KQ1Bm+wYF+/20nc5s+Jt8gwTj3FmHNT2eNbYqeor67pvIWnRvjFodFlqb46NJNgY6uPDSEP07F5987IAbj3Y/Cl3fCjq4zv2HfsgTd+IdfupHULhE9kvwtCpXLeBp+AQz/MHjaRVmurNAkQnSDp6SCWX4E5tBGio/z7Q8fwuuy855UnlX6ulVfadarxOK9GgyXe0vPCJq3FKa9zYQ+rPeCmze/i6OTC4m17p5+3nbeZC8/o5ce/NKvNhocXPEeWKp23svL4wgsvxGazFd1QNpuNX/7yl9UPRFg1KHnVJj1OBxlNJ6PppVeMUjGjQtPs8aYUb789OsVEJMmxyRib2nwLP0FY3ey9zSj+cPpbcttCvU0p3iLJNN0hD2PhpOS8laBywZLyOTT51/LpoRrFWzjJSzblFV1Ixci4gsRTTtJKlKq+QUr1eQNjstxEzpvliEWTKt1l9onOK1hi8QrbIa4Y/ioMDhu5qX/8GfjqWTCyH856B0paI50x5hRNmfOm68bfQy1tPPo7Su7mSxviLYCCmojg9IVK7rcQo2GFnpAh3rqCHqbjKdIZDZdjkUFTiRnDURnZhxqbIqP1zGsVUJt4m0uk2bkhULjRFG9t7Z3AMCNzCVrL5IMVUfectwoNuk2y4i02AVa5fzPnzeEpfN98bgdRLQBJM1zfdFSmPWVaEZCf81Y6bPK3B4/w3qfSXP+/+virC05e8CXVwtBMgs3tPpLpDNNWDnFGhf/7F9D/X0ZY5OlvNhZFt7+yWOS2boFjD+fluJaoNvnwl4wq0LvfBA4j9LamsEldh9gEansX6YxOUjVDGUO96OFR7h8d48IzeukMeko/3xLdnaZ4S4aB3PvhNOe38523/Ei0ajipM8CLlcSbme+2vSOAzWbjhovP5MYv/RQ00MMnqDoDMDFrNKyf/3dkHmXF2wMPPFDtqWpi//793HTTTezZs2dZji+UJ7/aZL6VXPLmTUWh90zzic0n3o6MG39AJKdoDRCfhmd+DOe9G9y5BHKCPTCyr3HjWiThhMqmNh9j4eT6uz+f+4WxsnjWO8ruYq3+x1IZUqpmfFdlWwWUj/ywrmVvi5fDo2GSaqYoub0UakZjMpqcFzYZAXeARAwyyVpbBcyTek3kvOm6zpC5gmxN2EoRLxU2GZ3g2sn/nzFbF7z7x3Dya4ztPWfAsPE5tQS522lvTuctHTeEGxiT/TLizZ+eJaG78dlSzE6coGvb7kWdbiys8HsnGefYEPKg60buUk/+vWoxeQSevQ/+4IPlD5iYgZYtMPkcasQoUx/Md97SGXRdx1bBqcpnLpEuFmameOvs7MIQbwq7e1uqOh4e03nTNLDXIatnbgh6z6q4i+o1c8/yQ/BN583uK3TefC4HkWQg5+5MHzVO4ysvEC2xU1Td22wQrsengJP47E/72dLu58KXlK5auRiGZuK8fGcnM7GUkS+p63Df/zGE26uvhZf9f2XvYQBaNkNkhGjCcO0CHkdxn7fZAeN6jR6AzecRr9D4uiTKLGhpMr4NgPE97nU5INiLOv4wU7EUf3BKV/nnxyYAm7EoAUULZTabzYg0m++81VgVc0dXoGIbmoHpGK0+F61+4/PQEXDz3jOB/WBLRY1xeav4HCizC4ZMQhU5b7/85S+58sorefe7383ll1/Om970poVPXoZ///d/5/rrryeZXL7qOkJ5rEmRz+3IJtGWbReQihoxx57W5hRvY8aXr+QUrQEO/cDo73X+ewq3hzY2abXJdNYNXnfO2/98Dn7ykYqr6/lV2bLuWxVhk9Zn/ZU7O0lndJ4brS78aiKaRNeNohBZklFsniBx3YNerXhLN7/zNpdIEzGFWaRCSG+sVC+nyAhOVP7JdnlOuAFsPMdw3jQt+36e1hNiLpFuvsWLRK45c1GFQgtNI6iFOaIbq//hqRpCpvLQdZ3xcJJu877cYDoPZUMnf/dd+O+/rdzaJzELvnbwd5KJGhPRnHhzktH0yv1f89A0nYiSpqWMeOvpNERBTRUn3aaLV22RoEqkFWNiX6HHG5g5b1DwfmpKhITuxusu/Cz73A7m9LywyZljqDhQfBvLHt9brmCJtw2w4UxM0e53cfaWNj5811PsG5wtPsgisHq8bWn30xHwMB1NwWPfgCe/Ca/8ILz2usrCDYzm5nqG9JzhNIY8rsJqk7putBUAOP4oYIiimpw3K380aAi07Jwt1Is9NgbolcVbdMxwMa3XUuL+95So8aDkmRnVsKMrwPCcUtadHpiKs73TX7BtpyOXG5uaGarqPMZntA7i7Z/+6Z/4wAc+wMaNG3nrW9/Krl27qhtACbZt28Y///M/L/r5wtLIX2mo1HUeMMIGPEHjJmpC8fb8uCnemm1yIBQze9wI77CcYItQj9GDq57VyVaAcCJNh9+N12VfX/enloHxfmNhqP+/yu4WzyuakBNvCxcssfrnvXynsZJ+4ER1kyCrl1bvvIIldm+IBN7am3Q75ztvrU3jvOUXEankvMWSKk6z+l4W062YSLkK0y02nWM4mdNHs8VKTt9orEA3XU6ykndPlevhlpjBjsaLDqMyXnx6cRVxZ+JpUhkte19uCJnirVzFSdMFqtj4OTFjiDdfO5pZKMNyT61JbLzC+55PNKWi6ZR23twhult82G0wUst7bFV3rEfeW9jstdVaPqQRQPW0Gz/kVZzMKBGieItEiN/tIKz5jM+6moTpY4zaewj4yoe4lW0V4HCCrw1ncoZ2v5t/f/dL6Qp6+KvvPJErM78ERucUNB22tPvoDLo5K/4o+s8+CbsvMsKZq6HFcBR1s2pnwOPIFSxJZ4xiK1YfzAEjv6vmapPm58hutgfIrzjp0FXO6dQqp75EJyDYnc3lLPVd63bai3LelFrDJruMhYWB6dJ/D45Px9nWUSjemDmGZjc+H/sPHqzqPChzVTlvC5aE6e7u5txzz+XOO+/k4osv5oc//OFCTynLG97wBoaGKqvP/v7aG6wK1XF82BBhx48eYXrC+HI8dPg5pkLGzaUoinH9M2n6tDTjc3FCNh+ZiUEGm+h9mVUyTJllcY8ODtPf3xwThOz1FwroHR0k5PRxZN61aQln2Ay8sO83pFpKlBCugZW69rquE06kScXm8DlsDI5OsF7ecnd4gJ1mOf3YI//OcffZ2d/lX/+xydxi0f7+I6SnvJySiOACJseGmShzwfqPG39UfcoUQbedhw8OcF7LwpOgvebz4lMj9PcbuUo7wlOkApuI4UFPDld1b3ScGKAHePboAJo71yduo6IRiE7z/Cp9o/Ov/aMDuUnzc8cG2GIr3UR4aHQSr9PG4cOHs9uCw8+wFZjNeNl38Jms4+BRWjkZOLH3pxy0vRKAdrvxvjx64DkyW/zzD79q8Y/vw/qmGX3haWYcpxft4547xk5gzL0dkjA+cLji/VPuu+fotCHS0uFJ+vsVwhFD+B549kV6teLwrR0j/XiBFw89TmJDiUWOTJq+VJTxaBq/7iE1a+QLT48N02+fYW7KmPQeeOZZuoMLVwscixrjic5M0N+fE5Qbx4cIOLw8/9yztPsc9B8fpb+/OjevZXKOzcDz/ftJh2YYi6bxu+yEPDUWwAD8Y0+yHRiY1YhXuv4pFdXTRmTwMKPmfl0TJ4jpPuamJunvz11LVYkzkTYaST/39JNsHelnQOsmo8TKvscZs4jS4MhYwbEATna24ExM4nSpTA4d5W9f1cnH7hvm0n/9NV9+4yaCtRb+yGPfiNprTfAAACAASURBVPFdq4Un6Jh8hg/Zv0ai7TSOn/Ex9GefreoYntkkJwNzxw8BZzAyeIzEhDGmweFRjj31FDsA1dMGx37DkWeeIRJPkorbqvrOVBSFocF9bAEmYsY9cui5F3CEffhnVbYDv9cyV/FY2ycH0J1+TgyOswsYefEws855++sZxidnCo7z7LDxHTQ2PES/WmHBw8J0vH+97zn07YV5nhnNCDd/+SZ3wTl2jj1Hur2PwNQBnti3j+CWeQvQJdgxM0I6sJGhBa7fgp9Ql8vFE088gaqqPPzww8zMLK8L09fXt6zHX88EBw7jtM9y5hmnczR9Aphg60knc0q3sdrV399vXP+48Qe7e/MOiD0HqWhTvS9GXLJh5ftbO+jrW1y+wUqTvf5CIc84wddafG084/AY7OwOwI6lXbeVuvbxlEpGP8bJW3ppG0nj9IXWz3t+0PxjdNr/IvDsT+nbFMoWE8i//q7Ho4AhqNp7NtG3uwd+bEx6utpCdJW5Xs/Eh4Axzuo7lXMOJxmMpaq6tk/OvgiM8bKzd9NtFofg52m8XRtRhydx69Udhwlj5fe0M84uDJ08thVGfrNq3+f8a//wxAuAsRLe0tlDX1/pRRH3wf20+OZdF/UQAFG8bNy2k16zSiLqTvilm822cVo6e4ExXnfuLr7x+KPYQ11lz7E6eSH7U2/QTm+p9/RFQ1jZel4CxyFkT1Z878t994w+Ow6c4PzTT6FvezsnpTLwg0GcoQ76+k4p3FnX4T+N8MyTugJQ6nxmWGD3tl2gT2If+B0Ap596Mn1b23gueQIenWTz9pM4pXvhAiva8BwwyO6Tt9PXl5en9bQdAh309fWxtXMaxeas/t63HYXH4JQtPag9p/Gezz/Aa0/r5sY/e0l1z89HMV7f9rNemcuHKkF/fz/Olo20u9K0m+OMPwbjeDl522b6+nL5bD1Pp4hMG9dm19ZuiI8yoL+cLT1dFV+jy/EiobYSc5FHNtIyGqG73fg70AeEujfznm89zr/uS3Dre36v9tdt8nR0EBjhglPaaH/875klgH7xHezeVkNRlMRG+DlscBrfx+e+pA+/24nHOUCwrYMdbUbagvOcd8Fj36Cv20Vat7FxQ2dV73l/fz9bWg0xvO20c+DXh+no2UxfXw+Hw0aD9T852VP5WD+PwKbT2XXm+XAPbGzzs3He/gHvKP5g4d/aQW0UGGX3KSfTt7mVhdiipOEnJ0h524o+f8en4mT0Y5y3ayt9fUafN9QUJMZxn3cJ+q+fJjk3SrBnO1vnu3PzuU/Bu2ErfX197N27t+xuC4ZNfuYzn0FVVa666iruvvturrrqqgVfpLA6SeR1k7cS+UvmvFkhC+6gEWLRZGGTR8yQSZtNct7WBKloLiQiH6uRZxP1egubvctafC6CHidRZR21Chg7ZPSO/ONPATrsv7Pkbol0hi4zv2fWqpCWsqpNVsh5M8NtQl4XZ25p5bmxSHGoUglGwwoOu43OQJ7gSkXBHUR3+XFqVTr3ahKwgcNduN0qWKJV5z40ksHpRDYUMlYhpDeeUvHP77lk/t2I6r7CSqFOd7ZoiRU2uXNDALfD3nwVJ7Nhk7ayOW9a1FjFd7VvYkYPFTeArpKxuVyDbjDCu0IeZ+mct8horkl8ufNZf8fNnDen2c7AqjZpVQ6ttl1A7rts3n2QjBgpF8DGVm9tobFWzlsqxuMvTjMeSWZLsNeM1aC7pXLYJADBDQXXTU9GieIrGTY5qZqLEtPHIDnHC+oGQt7KPojX6Sj9XRToIpCZK+hf9sqdXXz4j3dxf/84B4YWn/9m9Xjr7f823tQ0V6Y+zqRtgRy3ooG3gjuIOzaC3ZYLrfW6HCipjFGsBOCsdxqPA4/UXm0yNgE2O75Wq2CJ8R3x6IQREXbGQtETMTNsMttTszjnze20F+VyZlsFVJnzFvK66Ap6SlactO7RbR15jtzscdA16NqF5t/ARts0dz0xuPCJlDrlvPX09LBr1y78fj+f/OQnOffccxc+ubAqUdKZbHyvp1LOm5Xj4Q6Y4q0+CbQrxfPjUQJuB5vbfOsrp2itkjcZKMCMkSeyuJySRmBNXkNepyHearw/h2cTPHi4TK7NamfsIHTtgg2nwbZXwv47SvbKSqQybDRdm9l4GjLpXIW/THnxZuW8BTwOztzcSjqj8+zowvmQY+Ek3SFPYcsUK+fX5cejKRV7emVRE8YEYn6lPk8LoNe3f9UyMTQT5+QNQey2yuItlswQmD9BM3NPY8wTb2AWLTmQ7d3X6nOxsc3bfBUnrb+FbdvKiqRU2FhMsge7mLW34UpUEZJVgtGwId6ybjBmr7dSOW9WvhtUKd46cKUj2NEKqk1C9eLNeo9L5ryZJfY3tvoYqaVRt9UUOxXlvqeNsM6xSA0FTwoGOGj8jVig5Dpg7Devz1tM9xblQ/ndDqbS5vthVjoe0HtKN4/Ow+NyFBcsAfB30KLNFYm/d79iOyGvk2/86oXi51TJ0EyC3hYvjsQ0qreLfn070/Ea+zTbbNCyGX9ilIDHma1C6nM5DPEzM2DcT5vOBX8X2sAjuVL/1RIbB38nQTNv0Fpwv3/IOJc/WeHzk4wa+dCBDWZPzdL5xW6HnZRaeA/WmvMGsKPLz4uTxWLSyoMrKFgyYzbmbt+Bo20zZwSj3P3kIGqlgkCZtFkssA7i7dOf/jRvf/vb+ehHP8pHPvIRPvrRjy540Eps2bKFu+++e0nHEBZHIlUs3ko26s6KtzznbRkaSC4XR8YjnNIdXNTkWFiF5E0GCvC2GsUhmqjXW9ic8LR4XQS9zpqrTd72yIu8d8+TaFrzfB6zjB0yHBiAcy6Bqedh6Mmi3eKpDN1mcYa5RDpXaRIqOm+RpIrbacfjNMQbVNeseyysFLYJyKQNkegOYvcEsKNV7C9XMDZXiRLu3vKJ9KuNwZkE2zp8BDyV781YXnPnLGZvrBjeYvG26RxIzmGfe5Ggx4nTYWdzm48TM3WoKriSKLNgs0PnzrKOfypsiAB3aAMxVwe+SpPPCoyFk3QG3NniYmD0eivpvNUq3nwd2NBpJZorWGLODart9WYtRLV454u3aJ548xJPZbIVTBfEdN40JcLPDhnf6xPhRVYnD5+oznUDCHQXiDd7KkoML/55fQx9bgeTGbN4xsh+AF7UexZ03jxOe3GTbgB/J616hNC8z1LI6+Lyl2/nvoOjHJ1Y3KLP0EyCLe1+43vHa7wf09EaxRtA62aCybGCMfrcphidHYC27YZw2vbybMXJmqtNBrqz1zCSVImnVB4fjJNwhCr/fbc+g0GzI6W3paTz5irlvKVqqzYJRsXJY1MlnLepOG6nvbDo1cyLxmPHDmjZzEnuOcYjSR6otPhqjd3XvuBYFhRvBw4c4P777+fOO+/krrvu4s47S4e6CKufeCoXNlmx2qRVvc+qNqlnmqqi35GxKKd0hwh5nRI2uRZIRnJVyPKx2YyKk00UNmlNiFt8LkKLWFyYiqZIZ3Rm50+OVzuJGWMlvMfMXTn9LYbw3n970a5KOkPA46TF66xJvEUVNTvB2NLuo83vqqpZ91hYoSc0L2QSTPFmOQFViIy06bzNxwr5XeXtAnRdZ2gmztZ2P0GPs7LzlsoUTWxJRtBcfjTsJZw3ozhN28wzWadmc5uvCZ23GWPRKNiTK3E+j0xkglk9QCjgJ+HuIKiWLvpi7JzGVmZhYCysFPVz2xDyMFlOvNmd0LGzgngzXUPTeQPosEfxm3MCf81hk6bz5i/lvBn3vJX3ODJbpXtmRlgMjIwzEUnSt7GFSFKtuXk4UFWD7izBDUbVRHPh2p6OEdV9RRN7v9tBWDfdFbN34aDeTWi+gJ2H12VHKZGiovk6cdkydLiK39Mr/mAHboedf3voaNHvqmFoJs6Wdh8kI9jNBaTp2GLE2xZa0mMFizUepz3nvLWbOavbX4l9doAepmsPmwxuwOO047TbiCoqjx+bJp3R0YI9EK0g3qx7PWCJt9aS37NGq4DC66+Yc98i8aZl4PZ3wQvFva5P6gowEUlmozwsBqbibG33Yc+P3pg+ZjTbDvZAyyaCyig9LR7uePx4+deT/YzWwXnbvn279GVbIyQKwiatnLcqwiahafLe5hJpxiNJTu0R523NkIqWdt4Agr3N5bxlV6udBL2135/WpHiqXLnw1cqYUcwiK968LdD3Jjj4n7n+aCZWzkSb322KtzzhtECTbmv11mazcebm1iqdt2ThJNl0kPAEcZlNevVqQh5VpbR4axLnbTKaQklrbGk3nLdYqrLzFpxfATCVW2QJzxdv3aeD3cWGaH+2L9jmdh/jkWT5djWrkYTZQDfw/9h7zzDLrvJM9N15n3yqToXurs5qJaRWJtkCTJhLsMcGjMCYAYfLxdjGHmDw2Nfjx9fjxwHGljwwGBsHHDCDbEwOGnsIEgghBEJZanUO1ZWrTp28874/1lo7rn3OqVZX1Wl5vufpp7tPnTq1a4e1vvd73+/9Jonci6NI8TorWPXLKOsK7Nwkqn5228GRT/xn5L7wf3O/ttg0QtMXGpmyyfopIuUs78oElUnZJADsVHpBwhnKJodbk5o9G4IAFDkgnt0Hu6oUvDWGBOmUeXv67Dw0WcRPPZfMaFvaqHTS9yl46z/jLQiW/FP2TbIJ85aU1OVUGU3QvqbmLKz8DphQB8om9QzZpKmS/GpKTK8NkyUNb7xlDz79g9mNzcoDYLtsxhsBb1KuDEUSAhfuDUV5NypuHRU1vNdzqgTTsklBrkrB294XAgCeJx5BLnlP9IvOElCYhCAIwZ5434lVqJIIfWxmAPNGWSzGvGl85k2V03PeWEEgNu4EAFaOAkfvAu77cOpzDtTouIDVeDHvzFoX+2pxB0rUTwFj+6n0dBcEs4m33DiOe44uZxetWE/txZBNzs/P46UvfSne9KY34U1vehN+6qd+auCHPuvDtYFO9qT1UQ3DHpJ5SxqWAJcMeGPz3S6fKqKoK/8HvD3T+MqvASfv2d5jyJJNAsS05FICbz3W88YMS5zh+0EANHpk8125EPnLdgYDbzsirnE3vJlstEfvir21Z7vQFQmVnIL1rjW8bNJwAvMFALhuCNMSw3bR6NnxJDmy/qkUvBndIZQHWeBNo05mI868naMSxj3jhHnrJ5vkGpaYbQgUqKaYN1kDpp+Dmd5RlOk1mqnm4PsZib3vAx95IfDoiLVYGHTIdXGKFBI4iaLQXcUaiPIDhSkUYMDs8q+9f/5BlKxFbjGGMG/xfq3JkoaW4aTv6bWTxFGxMNFfNimIJMHNMfAWSsAC2eQQJj8AucZlXYmzDZ5HQDxdr3dUiMRwaPBBe97OLCzhJVdM4gCdrbW4Uelkr06KPkMzb7R/ur0EeB5kt4cOz7BEkdCFBl+gQLdAwGFxkGGJwjcs6cgkSR8X+OvLO158EJ4PfOzbp4b7PWiEM97ygNmCoJUwlldRvyDmjUhP9yphESKnSMiZy+QZYMzbjuvgKgU8V3z6gmSTAII98d5jK7hpXxVSeWf//b2TAG9ZPW8c2aRhu9AVMX7/AoEcFifvTuX5bNbbqYhpie/7OLvaSc94WzsFjB0g/6bz8m67XIIP4J+yjEsuJvN2++2349Of/jTuuOMO3HHHHbj99tsHfuizPh74C+DDt1wS7mHRiMomw563IdwmgUsIvJFF8PKpEoqadEmAN9/3cf/J1Q0l8VsSZovc60/fNfi9mxWeSzbhfuDtEpJNNg3miEiYN8fz+ex3RjD3xdXOJca8LTwG5GthkgQAB14ClHYBD38y9tZewLwpRB4aBW/9mDfDiVXAD89U4Hg+nprPBk1LNCmciskmw55fPU/uu2ZzCOBlG/yeN3bvjjjzxgYDM/B2IYYlglpESZPT4A0Adt6AA/YxVBh4GyOJPddx0jGApSeB89lW2dsSPeoEx5gaDlCSeitY9Sso5xRIJfK+9eU57seNW+ehwcYDp+LSStv1sNK20rJJ6sIa63vzfZIojh8MGUHusVPJpygGzBuzgAc2bljSNByO02SDOOzRz58qaRAEYG5Y8CbJ8CQNvtnGaw7vxBQFrxtm3ho0Od6IbBIg5y5wTeUP6QYEuBRkNvIEvJUHgjeRu863JVLsqIIP3vaM5/Fj1+3EJ+4/g0Z3eKk8K8Qw5g1aCeMF9QKZNwLeZiIzH3VFQsWk93R1P/lbktGZvAnPFY8MbQIiOD0iV6Xnv6jJOLvWxZPzTdx6aCIszmblRu1lAAKQn6AHVuYWyVRJhJ0wLGFFwlTMP0KKHL4LPPX52Jf219LgbbVjoWO5cbMS3yc9b2P7yf/LuwAAO4U1vOjyyWzjkovJvH34wx+O/fnoRz+Kj3zkI2g0BstRnrWxchTorYWT5S+RiMom+zNvl65s8thiG7oiYmYsF1RxRj3uPb6Cn/qL+/HE0gW6am1WNM6Tv7ez35H9bF7PG0DAgNkM79kRj2bPhiaL0BUp6M/aiGlJKJu8BJm36WvjToyiBFz/JuD4VyEZpMJpux4cz0dOkVDOKXHZpCgPNCwpamHvyeHdZAN8vI90kjn6xWWTYc9vrkCStG57iP3OMUgfXzIuEdnkLAVRM9UcCpqEjslP4l3PR4/2JcaCypvLOSUtmwSAXTeg5LexX14Jfg4AzPIkRKzHsHNhZh+bFr06SaxYss8ZF6Caa1jzSyjrMrQqGWfSXD6fep9j9jDprUKDjftPxiv8SxSc8XreAMSlk91Vcm+NHySg0miQGVO8Y2f7OWXeJqVQ/qXLG3ebTDlN0hmxyNcAAIokYqqkYWFY2SSAnpBDSTDxsqunAqfNpY0yb2xMQGUDhiUAuZ50L+kgx5FNkv+7Knmm6xoBh9F1hxdZowKaAmHlS272+vLOl1yGjuXi4/efHvx70GDPMmPeoJVRK6pYu5CiHwXAO4XwHs0pEsYs6vLMmDcA9YmbcaUwi6I3nMmKbNC8skCep5Iu48Gz5LUfYuDNs8P7KhmdJVIokOhapFc3ZFjCNSuZexiYuQWoXQ48/pnYl3KqhJ0VPTYugEkoY8wbG90xzpg3At7QnMNPP28P5hsG7j3OWdsCafNFAG+maWJqagqvec1rMDMzg8XFRViWhV//9V8f+OHP2mCa8kvIxAMADJ5hCQ/9m20AAmm2vNTA21Ibl00WIYkCipqCnu32t2Ydgfgerbq2zRE7TrYBmttYqGEscD/mDbhkpJOkWk02eia1GZYd9v3QqGTlUup581xg6amw3y0a1/804LuonPlXAKFkK6dKqOYUUm1mzJte7TsqoG3aMde3XRUd4wUVj/YxLVnkgbeI8qBQIslVpz0E8HIMvi35JWJYMlvvolZQUdBkFLVsyTnriSrwep20EioMdCeDmpZc4RL7852VHAQhg3ljgL07YuDNSDJvCfDmedCsBlZRRklXkB8nSVu3nh5nsnzuKETBhyx4+N6J+Oew+3JHFniLMm/MaZLJJgH+eYuCN60EGzJqYphki6KAnCKhO+R61KSyyVh0aYJPwRtApJPzQzJvvu9j3dVwsOyjrCsYyytQJGHj4wJY4XHonrcJAAJhUunz3xN0qFI8RWb5k62Q/WhF3gngwmWT6yBrQ9HLXqOu3lnGS6+cxN98+/TQxi2z9R6Z8VZWAxnrWF5FfQPsXRCUeZvyw3tKVyRMOnTPjZzj5fGbIQo+JuoPDfXRkkFBWUQ26ftASZNx3Uwlsr9njANqL4XPIkDWWrsDuPF7mIwKSM95S5uVeMDCo8Qd99rXA6fvTeUW+2txx0mmWMgaEwAAKJH7BM053LxvPPZ98YO6iMzb2toa3vOe9+BFL3oR3vWud8G2bbz73e9Gq3VpAZeLGkymNeKbcTK6nDlvJm/2iNUhrJsohjfRJQLeji+1cfkUYWkKtKG+cyFOVVsYrNJkjZr9O5OebOd9HjGP4MYlNqi7aYQAg1Vrh2WHDdsLNqBLqudt7SSpQu7ggLfJK4DiNNQm2ewC+2Yqm2z0bPgskc9V+YwCjbbhxMDbMKYl3CQ5cs8VKXjrdYbseVM4zJtaAARp5Jm3c2s97KbV436Sc8bI5ZOGJdSoIgu82RNXw/Yl7LOPAyAFxKmSxm/eZ9d8lHrLfZ/KJsfCHpukOYixDhEummIZqiyiPEkSX6uRLi6tnTsS/PvM0lrMCZAN6J7i9LwB/cBbNiMYA2+CgKZQwlhCrpdXJXSH7HlrGv3AWzgMemdZHxq8PTrbQNNVsbfk08MUMFXSNz4uoHEOkNRQTjcoJIUcc3sxKMo7UiGYa8aCOXJaEtmPFuSdEASkJcSJ0BSRa1hSdxSYvoK83X+W7i/+yCGsdiz884NDDHkGKcTsKOtQPfpsaSXUCuoFGV35Sg51v4gJN7zXdUXElLtIDMMiUvGF8rWwfAnV5e8N9dlsUHwgm6T30/MP1iBLYgh6shwn20vhswgQWTCQWmtVOS1bNWzOPLq1kwS877weuOb1AHzgybh08sBkISabPLPahSBQljP4HAreGPOm6ORebM72z0uNdUKayCr/943EQPDWbrdx4gSplJ04cQKdTgf1eh3d7iU2n+ViBqu2XWLMW3TOW1/mzWqHMjVFJzfTJQDe2qaD8+s9XD5NqmKlDTIb2xGu5+Phs2ThNp0RBW/bmXQGErYy/+vFAZW5EYtotTqYa2MMVw2NJsSXlNvkwmPkbzbjLRlqEaJDkozo7J1KTiE9gcwsJDeWOW/N931iWJKQ8h2eqeDYUjvTtGSpZUKTxXjvToR5q1DwZg5jWGJnMG+CQJjjES/2nWPW4gBxmzT5ZjrMhTLlsBfIJvk9b01bwlF/N3Z1Q9BCZr31Y95GCLxZbdIHo1cJsySIaeaN9sD1FAJexiYJ8+Y108Wl7uLx4N86LDxwKvxds5i38QJJ6lLgTRCJ2yRLZHly0yh4A9BAEZUEeMup0tDsTl/ZZC4C3qr60IYlX3l8Hl3kMK2F989kSQtkpENHa54k/uLAFDcMNuuNPv+2nE+9heVPhkxyjFnsQDEyvDordEXi+gu0TBdrKEG3++dXz90/hh1lHQ+d6w/yWIQz3tj+WcJ4QUPTcGBvUIlk2B7m/RqqdngP5xQJO7zFmGQSADqujMf8g8gvDAneAuYt7HkDgB8+RJlb1iOdpazpJMEbUznEC3aaLKZ+byNCZgRBB69j5/XA1FXA1DUp6eSBWgHrXZuYaYEM6N5R1uNAsH6aPJNR5rcyAzTnkFMkCAL4DDcrDg0RA+/s3/7t38av/dqv4dZbb8Vv/MZv4Ld+67fwla98Be985zuH+gHPuvD9iGxytDfjaHjUGCGQTUqMecswLFEjtqe5sZDOHeE4QZ0mD1HmbaPMxnbE0cVWUIGx3FEDb0w2uY1FCmtAz1sgq7hUmLeIbJL1vA1ZXFjvhZX5C2o8365YfIIwT5NX8b+uFSHSZJ312+RVCdUcSVR7XQqm9GqmYYnpkF65pHzp8O4KXM/HkxmmJQsNMksrlnxFwFuxTMCb1ct+BgzbJSAnq+cNIEnFCO8Xrudjbr2HPbR63M9Mp8uYN65sMpt5axoOHvMOYKL1VGBAMDOW5zNvVkQ2OSpGTtF+FFEiAC7JcFHQZGkEvGiajnUUIXTTxib+WuggWFE83H8y7OtZaJpQJAFj+XgFXpFEjBfUuGx67STpS5K1UDbJc5xMgLc1v4iyH7+vC6q8gVEBHMMSjmxyZ0VH23QGFql838ddjy1AzZcgOyE5MF3WNm5Y0pwL+4yGjeIUOW+UefeU9J4TmLrIY0B+AstOPs0+coL0vHEMS0wHa34JitU/vxIEAeMFdWjTkvP1XmhWAhDwViT30kYdJ1umjTl/HGU7vNdzqoRdWIJfjYO3ruXie95VUBcfiRtNZYRkpnveABCzEmBwW0TEqRJARKIeB2+KJAwnm5x/GJC0cK+69nXAufvDXAhpx8mzq92002T9FH0mI89vmYA3QRBQUOVs5m0IySQwBHi77rrr8JnPfAb33nsvPvWpT+Hw4cN4y1vegle+8pVD/YBnXZgtIgECRnozTkbQS0JvVkEQCJXMZd46HPA2+szbsciYACDaUzS6A41/cDY8ryML3rZVNhluPtzIjRF5TL9BniMUrZ4dOJMFzPCQxQXmNDlRvDD5y7bF4uPAxBV8VgqgzBtJ1tg6pVPDEgAwexRM5aqZhiXM9KXEYd4AZA7r5tmxw2yTqqmSg0Tluo7Bb8Bv9Gzc8ntfxb8+uUhlkxy3SYCMCxhh5m2xacB2fewZJ+CTFRZ4qgX2WiEqm3RMAqxV0vPW5CTqjZ6Nx/0D0Kz1YG2ZqeYw3+jBS0rGGfPmWqOjcEn2oxSm0iCJ9prZWsg8rYtjUHppJkxvhcN6b57JxUxLlpoGpkp62sYcxHEyzrxFLMkLEdfEaHguSWgpePN9H6teEQU3fk/mVGkowxLL8dCzXQ7ztgqISmy9ZuMCBkknn5hr4uxaF9XqeFhAATBV0jc+KuBCwVt7MfjZnlJIvYWBt+/vezvwlk+hbdoDZ7wBdEg3p1DeMmzUUYLUG8wwMxn5oLBdD/ONJHgrY5wWAta6GwNvHdPFvF9D0Qj32LzkYSdW4SZ6CruWiwe8KyF4NjD7/YGfLRtrROpI94aXXDGJ1980ExTgoeTI13ngzerEnCoB9JVN8gxLdCUBgeYfIQoRid7X17ye/P3E54K3HEiANzLjrc+YABblXUCT9GLmVYnv5svcbMEZt5KITPD2q7/6qwCAW2+9NfXn33REF+tR2VSGiKgRAAtNFvk9b2ZiKPIlA95aUCUxqIKwIbLtDNe0UYgHz9SDZJ4rYd3OGAnZ5ICeN0G4pAZ185i3YWW9bDE/OFkc3PN28u7ROSeLT/D73VhEwZsVsjrVPDlP1hDMGzuHpUQVfGdFx0RRzex7W2qZKUc/ojwokXtL1uBChGvw3UxPrXTQNh2ykdsZc96AkWfegjEBlHljZiS8BINrT2UipgAAIABJREFUWBI8pwS8GbaXkok1ejYe92hCQ+VJM2M52K6flsVFB7OPimkJs/Fm7FVxMpN583Ih89SRx5Gz0sn5mDkLG+R+vXkmhyMLraDvbYFXVKCRGtTNZrwBRKEg59Kg0mgA8INjNx2PgDcn/lzkh5RNMnBe5oG3fC3mKsukuF96hD8ugcWd3zsLSRQwVRuPuQdPlTQ0enbfeY2x8P1QNrmRKEwRJofmdT5H7cHyp1VpApi5CW3TGWhWAgCaLMHx/JR5Wstw0BQrEIaQB1dySmBY1S/iM97omkNHBQDAWsbecXa1yz3HbcPBnD8B1Q5dnWvuCiTBh1XcG3tvz3LxoH8FfAjE7GNAyMZaWHAA8MOHJnDHG2+IKyFKO/ltEezZKwyWTaqSBNfz4UaKREZyVIDvE/BGjZXIL3oZsPMG4PFPBy/tHc9DFIDTKx10LQfLLZM/oHucA956dcDqEln6AObtL755Iv31SGSCtw996EMAgHvvvTf15990RI0RLiXwZsWZN4CAt+yetyjzVt0YeGst8BumNzmOL7ZxcLJAGl1xacgmHzq7jucdIBv9SPW8eS6pXgoSYRT6GEVsagzqeQOA0vToAJUBETMs2WBPJpPMXDZZRNvkDOqNxiffDHzzj5/ZwV6M6NVJESCr3w2IySajCgFW1bfNDqnmq/k+zBs5N8kquCAIuHamwmXefN+nzBsPvBXYB8AUNPgZoyhYv1bLsLOHdAMj3/MWWotT5i3ox0zfmyzpiDFvTN5MZZNAunLc7Nl4yt9LBhzPEfC2m44LOL+e6KG3Iv8fFdOS5ADdwhSn542AN6EYGmUYWg0lJ2517jkOpt0FrOWI7Oz6nQSosb63xaYRHxwficlShHnr1cnYIgbeBIHOeksA3kDyScBb23SwjhJ0pxGTpeaHZN7YteX2vEUkkwBww+4qfuKGXfjQ14/jz+5OJ6S+7+P9dx3BP9x/FrfdvBtaoRLLrZhpy/KwfW+9OnkWy0OOCWBRnCRMDs1dBI7aQ5VESKIQFDBaCZOkrGAMj5GQ7rUNBx2pOlRvZzWvBOqLfpGa8QYQwxIqm+RJ7i3Hw6s/+E385TdPpr7WNh3M+ZRJpi6eNZuAKaMYn6PXtVw4SgXC/luBe/8EOPovfY9VMutx8MWL8i5g/Uz6dZZj8gxLjDTzBiDW95aSTdZPE9C364b4z7n29cDcDwITElUmo6hOrXZxdo0zJsBokuuZYt7o/dicI8/ZAOZt0P0+UDZ533334Zvf/CbuuecevOIVr8AXv/jFQd/y7I4oKLmEwJvBZd6k/m6TLPRqWHUcJj77TuDzv3yhh3rBcWypHdLtGH3Z5FrHwqmVDm7ZPwZVFmGPkmyytQB4DjBxOfn/dt3rg3regEtmULdhu7AcL+iR0GQJqiQOPeeN9bxdNkmezcy+NzbYfOHRZ37QzzQWnyR/Tx/Ofo9aCA1LgnVKDJg3x+wS0yRJI6MCOD1QrEDDq4JfN1PBsaVWilFomQ66lsuXTUaYXlvMxZmgSMzSRKndM8k8okzwNuLMW504prHB2QwE85i3TiCb5DNvjI1Jznpr9GyYUOHUrowxb0AIHoMYZeaNySaLlKmJ3o/dFTRRQCEX9j7auUlUvfj+uTx3Eqrgolcl6+uhqoKcIgV9b4tNDiNMg4E3nw3nBkLwBpC+t2TxtBdnDduGg7pfhOg7sbU9p8rBM9gv2LXluk1GnCYBMoLg9tuux49fvwsf+F9H8Of3hADO9Xz85mcfw5/fcwI//fy9+P3XHSa5R5R5o+dh6L63JmX4yhtk3pg5BnXvFLW0bFIQBOSVEOC2OSZJvGAMT7Lg1jQcGAqdTeb2z1MqORXNns01EYpGasYbAKjFoH+yzpFNnl7toGO5QetJNNqmg3mfAvImkTuPWeQcd/NxgExmCcvAG/8emLoauPMtMclhMgjzNsARdPfziHoj6bvQ4YC3jJ43Bt6iPbwpw5L5R8jfUeYNAK55Hfn7ic8GLx2YKOLUShtnV/uNCdgf/5wAvJ2nzBsPvNWD9WWQS/pA8PYnf/In2L9/P/7+7/8en/zkJ3HnnXcO+pZnd0TlCCNcSU1Gl8O88XTAAOJuk8DGZZOt+ViD51ZEz3Jxrt7F5VNhtayoMvA2mrLJH5wh5/SmvWPQZRHmKIE3dv2mnkP+3q5Zb2aLJO39rHMvEdkkT2pU1OWhiwuNng1JFIIqX2bfG2sUX3iczK3Zzlh8nPzdj3lTSxHZJNnQcqocGJa4Zpf0PrB7gJPkMNMXXiJ17UwFng88OR+/h5d4M96A1PpnSzlIdgbzRs02jB79elbP28jLJnuYLunQ6KBmBsx4CQYDbzHDkkiSmMW8sf+Lu64PHEhnAuatD3gblUHdyQG6hUnS/x7pz/I7K1j1S3H5bmEKBcFAtxNe/+WzxHFTmCbrq+JbuHnfGO4/uYq26aBtOtngrajBdDxyz0fHBLAocnrxuMwbvcd7IStIgMngYhK7lpmyyUTIkog73ng9/v31u/D+u47gL755Aqbj4lc/+RA++cA5vOulh/D7r70WkiiQZ8+zA5Z9io5HGHpQNwNvpQ32vDEGaO0kutCRyxi8HXXkbG6UeUuAt5Zhw6Ez4wblk9W8Asv1BoLrYMZbRY8xb2O0GLbKkU0eWyT3MGPtotExHcyBXlPKvJWNOTi+iJY2HXtvz3JIX2B+HPiZLwAzNwP//HPAw5/kHqtkrMXBFy/23wr4HnD2O/HXebJJBt6SPW8SkWFGTUtSzNv8I0ThwXIeFtW9BEBGXCcP1PI4vZLBvCXHBLCIDOou8Bhu1ybML11fuD1xkRgI3nRdR61WgyzLmJycHGiJ+qyP9iJpZi9MjfRmnIzsnjee22QnDd4cYyj3IACUNl4b/L6LGCeW2/B94PLp8LiZrGdUZZM/OFuHLAq4bncFuiKNFvPG+t1ocrFthYoEC8KN0jSpig97f25TNHvkPixHNvuiJm/IsKSaUzBBkxneJgwgPA92J6wCblcsPk6SOeYaxguNjgrwvJi8W1dEqJII3+oSyaREGTLOoG52DnnOb9ftJpthUjrJTBBSSXLinnPlPCS3x614syq32aNJTz/mzWiOjnNiImYjYwKAiBMqTzZJi2Gx2VZWvOcN4MgmDRuqLEIqTQdgoqCR3sbUuIDoszwqzFtvHRDlcG8MZr2FLJfXWcGqX465MEpl8r76Ytjz1V04BgAo7KGMtGPgBQfHcWShhacXyFqbHBPAIjbrbY1T5S9MDJRNdqjLITmYULI3rGFJk94XFZ7bJAe8AQTA/ckbr8ePXrcTf/CVI3jNB7+FLz82j9/60avxvldeGeaW7PxS9m2qxJi3IcFbizFvGzUsob1XayfRQS7tREgjKi1tm3aqz5YXjHlLure2TQdeADj6F0jZczVIOhnMeJPFGHiTJaJmWOMoNo5Txi3FgNNjXPTHSR8bNdwoGnOY92sw3Dge6FpuYOoCvQK89TPAgRcDn3sn8L2/jn+wa0O2moNlk7ufS9b+ZA8dK1BEmTuJPp8ZsklGVnien57zNv8wYQt5xlrXvh5YfAxYJazx/okC2qYTeBZUo66wyQHdLALwdh55TU63SyQMkZ4xeCsWi3j729+OV7/61fjEJz6B8fHxQd/y7I72Ehm2l6teUrLJpNskkMG8+T6d15MAb8Dw7JvRIIv4FiYqxxNOkwDZLHKKNLKyyR+creM5u8rQFQmaMurM23aBt1a20ySL4qUxqJv1ZUUBRpG3iGfEes9GJa9gskiTtyzmzYlswGzG2nbFwuOEdetX9FMLEOADdjcYEJxXJQiCgEpeISyMkg+BEafvLeh541TBp8saJksaHj2fBG9ZzFu8eOUreei+wb1ODHSYxiDwViIzwjLm1G13zNZ72MOqx50V1M5/g/yTo1roWg40WQx6iwHEjBH69byVdYXI4lwrYFBnqrk082Z1iIusnBsd5o2ZCbB7mSWdEZbLby0R8BZ5xrUqWZ9aq6EaxV09CcuXMLabWpI7Bl5wkICeLzxMwEdyQDeLCfr8r7RMwryVdpHiBovCJDmm6P7LYd7qAXgL93VmWDJImsdl3jyX/JwM8AaQPfmDb7oBP3p4J06tdPBHb7gOb3/RwfibWO5B86taQYUkChuQTc4DEPoXjHjBZJO9NbR9PT0DjEZOldG1XNiuB8P2hpJNMkY7zbw54f42iHkbGrz1AjkyzCZ5hqh74nhB5YK3Y0vkXC+3zNQxtk0HNmRyv9O8oNA9j3P+ZIoF7CWliGoBePM/Ale8Cvjye4mM8vhXiSKEPdeDZJOKDux5HnD6W/HX20tknqCUAM96JVM2yZg3BqID8MYzK4nG7ueSvyPgDQDuPb7CMSs5TZ4BPdGnr+TI8TLmLbm2GvGe2kGKsYF33Qc/+EGcPXsWhw4dwtGjR3HbbbcN+pZnd7CJ7rJ+aTFvFp95S86+gN0jFHVyVABAFuZB1SzXIRV/gCy+yRt4k+LYUguyKIQPku8D576L29Rv46Zz3wDuEqgD1Q7g1R/YkmPqF47r4ZFzDbzpucRqV5el9LXYzmjMkkWQXe9t63mjzn/9grmKtRbTOvMRClatjlbli7o8dM9bkw7FDRrPM5m3SJKz8BhwzWsv7ICfaXgusPQUcMvP939fUGlvw7BcMtOabraVnALB6cVlkxzwxrWvpyEIAg5zTEsWKHhjsqwgrFYMvAlKAXlhCfVOvMru+37Q82Yx8KZkzHljCZrZyn7PNoXj+Zhv9LCHJXxf/I+oPv0V6Phrfs+b5cT73YCw500toixT8NZNyyYrOTncWywiEZqp5nB6NSFLtXsEsGulvmYOs/UuvvzoPN7x4oObrwqiZgK/84UnYLke/uAFlKmJ9pd1V7Hq74xJ6Qo1soZ210Jpt9Y8i0VxGntYX5Vj4LrdVeiKiC89SswgBjJvbZO62iXAT2GKyA6N9XThVWeJYYZsUiWuiJbrBYCDF9yet946AL8veAMIgPsfb74Ra91rAiAai+j9AdIzN1nUhh8X0DxPcrRkUj8o8hMABAA+2r4eMkjJt6kSerYTsP0bk00m3SZtCBn29smo5PlFkWScr/fwvAOUZEkUP8fzfPB2fKkNUQA8nzxThyLtJx3TgSiADpkmzJvePodz/jWYTIC3rsWZnabowBs/DtzzfuDBvwWOfAmo7gMuexn5+iDZJECkk3e/P2bogfYi/3u1corFVCVyTMywJCQzaAGqeZ6sM1ngjfW5UoB1kIK3luFg7zBjAljQWW/5IqfnLdGX+oyZN1VVcejQIQDAFVdcAVXt03vybyHYRHetdGkxb8yCWwkXGlUW00NYWaNwUjYJDMe8RRegIRyULlYcW2xj/0QhqLDg5N3Ax16J33U/hFcv/Dnw0D8Ap+4BvvvnIyGvO7LQQs92cdM+cm51RRqtOW+NWaCyN9IAPMLMW4lWTHl2wiMUvISntBHmjcom86qMnCJl97yNCvO2dpIcS79+NyAmk2KbP0vEqzkFEgNvfWSTLdOBKouZCefhmQpOLLdjG+JS00RJk/lAJKI8ELU8cjBT85EaPTtoKneZO2LWLLtAGjV6e8ZS2wmtxc//ADjyJQi+hwmhyR0g3zHdNEjmyCabiaJEs+eQryWS85mxHM7XE7JUu0PAW77Wl3n73EPn8Yd3HRleUvdMgpoJPHBqDfefWI0wbxS8eR4kYw1rKMcYqUqNGBVYjRC8lY1Z1PXd4VB3uwdVFnHLvvHAiKifYQnAZJMngfH98Tcw6/V2pO+tVyf3oBQ63NZ9eo9HWhxytI9x0LiAZs+GJotx2RlnQHdWiKLAB25AWKyLznoraxuQTV7AmACAnBtqttL2c+kh9DSYbLLdp882GYFsMgJ2fN9Hy3Ag5vkOiclgPcCNXrbzs+N6WGgaoQQ6Cd44zJvr+Ti50sGNe0kucm4tnh+1DFKsESozpOfN7kHpLeOcP5kCozHZZDRkFXj5bwPvfQp4w8dIH9mDf0O+VhyCId1/KwA/3vfWWeaDNw7zpiR63lJtRNT9Frtu5P98BhgpwJqp5iDTGYz7eAO6k/1uLMq7gOYsihphb2NrnnGRZZMjFz/4e2Dl+Pb9fDbR/VIDb2z4rRpeclXiMG88d7+NgLfoQ9Pbur6340vtmGQSi08AAN5d+e/4xT2fB35zliwewEjMrGPDuW/aSx5UMrZhlMDbOaCyO3Po5ZaF2Rrc83aJyCazDUuGd5tk2vpaUc12m2TFicLk9oI39rP7zXgDYjKpZBN5Na9Acg0qm2TMW/r3bhtOrJcwGYcD05LwPl5sGpjm2bEn3HYlvYgCDNQT55v1h0yXNTgmPefyIOZt9NQaix1y/+0ezwHf+P3g9d1KO9NtspBMbCOGJYokIq9KXMOSck4B2PBjakoyU82hY7nx99s9IgUsTPTteWNsTCYLfTGDMllrHQsLTQM+AykMJBnrEHwXawnZZHWSMG9ei6xPvudhpzOHXnFfCPYpm/yCgwQ8cIsK7PNyCmRRwPp6nax5KeaNytA6CfDGElCQa9hAkfQxJZg3AAP73pqGzTcrAVJukxuOANxHB3VrgcHQwLiQAd0sqHSyDT2z501XiLSUrefDMW9UNhmZfWg6HhzPh8zA24B8kjFv/WST8w0DrudngjfevnFurQvL8fDSKwnoT5qWdEzqqFneTYq662S4/Dl/MgXyDeY2mRWyBlz7k8DPfgl414M4//z/j5iaDIqZW9J9b+0lfr+cXs7seWNkBZOGBsWH+UfIWKSsQmMwgoAALDkyTzjmNOlY5BxlMW8VyrxpZO5cjDzpJWWTzybwZveAL/wK8P2Pbc/P931K1U5yb5CRjDP3AXe+BYZJHtj4nDcpNUg1ZN4yZJODIgretsi0xHRcnF7txMYEYPU4kBvDYuFqrNg06dPj1ZPtjB+cqWO6rAVua7oijVjPGwVvlwLzlq8RI4ERd5xscUw1NmJY0ujaAatRK2pYGeQ2uft5pHl/u3qGVmmRbeLK/u+LMG+9xODUck6B4hkDmbe22d+y+/BusvlGpZOLvEHIrkPYwohUV8mVkBPMVMWagberd5bhWgy8ZTFvEdnkiMVii84P7D1G+lGeQ2S2u5TWBmSTLdpbQ16v5BSuYUmceSPJ+W7euACLOozmJ/rOeWN9i6udrWDe1uHnqljtmOhaLlqOQHpYGPNGn7MVvxwz8pBVDesoQuyS960snUdBMCDUDoQSWsqWs763rH43IGStfN6YACBkI1LgbSz4b9tw4EEkiWJEITMseGv0bM6Mt+GZt74RFHOizJs+/Jy35hx6+jS+ffwC1j3KWnbQXzbZtdyIbHIYw5K0bJKBP7VAr8uAwk7Q89ZHNhkbEwCk9s+xvIp614IXGVbN/AJeeFkNmiymTEuCtbWymzDi82QEzSyn561rOchngN5UTBxCc/+rAXEIGMLre8ti3rTywJ631Nzj+UeAySuzJe0SLTpFPvcAlU7uHY/kyo1zpO2oH/PWXUVJIj8/9pxFmDfH9dKquERknjXLsvCBD3wAL3vZy3Drrbfila98JT74wQ/CcbbRuY9ZwG6XPMpsksShOE11taO3Eafi5N1EY0yrlynDkkzZ5AWCt22QTS41TXg+sGcsUgFZPQ7ULqfMBn1ANmq8sonx4Nk6bto7FsjDdGWEmDejSRapym7Cdmxnf2dybAUvRJE8kyMO3po9G7IoBBs5QDb+YXreXM9H03CChGmyqGIli21gphh7aJP1drFvRoNseFn2+SwiPW+9hOymmlOheox5YyxF+vduGQ7XrITFdFnHVEnDY+ej4M3EdIkzJgCIsb1qrog8zNR8JNbvdtWOMlSffm2YnrcRi4W2A0kEph74b+Q5eulvAgB2yu1M2WQqsbXasSSRB96ChD8Ab4x5I+t2zLTE7pJ7J1/rz7y1tpZ5s5Ry4Aq82DDorDcK3uhxrqGcSugb4hiUHtkPl888DQDITV8OiDJ8QQyYN9b3ljWgm8VkSYPaOE3+k2LeqGyyH3gzXRQ1GUJuPFZkzQ8tm+Qw3RcLvCVktQBh3lY71uC+cKsLGOv47qqGt/71dwMjo6GDgoGOn8s0LLkg2STHsISt+1qB9VP132PzqgRFEvr2vM03yDO0k90/ZisswILIJl3Pj+05x5fJmndoqoTdYzmcW4szb22TFmsqdE7ZmW8DAM75Uylzk67lZp63Zxz7X0SAY69OrrPVDu/1aOiVVL6iJYZ0p+Yezz8M7EwM505Grhor/DPTkhjzxnN/jQad9VbzyFoRK45FRpHwjKKSkQnePvCBD6BSqeCuu+7Cvffei09/+tPwfR8f+MA2mj3QZsltS9KYPILJJq3W9s9RGhT0hhC7y1CluEOYxut5iwxbDUItkPkXG2betga8Mde9yWi1cuUYUDtE3fzoYpeLN51uVyy3TJxb6+GmveFmqskSLGdEwBt7zqrETIVIhEeYeQNI0tkecfBGpUZRY4WSLsNyvTQDnvxeumGzwdW1gjZ4ztvu55G/twu8DXvtkrLJyOZfySnQYMKVdeI+CGSOCuAmUb4PfPW/AucfxHW7KwF48zwfSy0jGAAchBUab7BQc0XkYGEtcb7Pr/dQUCXsGc9BAwUPz5R5e/SfgOWj/d9zkWOx7eDfF49COHsf8KL3kX4UANMSn3nrWhmyyQjgLSfAm+f5cbdJINbzBiA+LsCmzFuhRv5txRNKFkxKl8lCX6zwPKC3jq4Y/o4LTSN0dgQC5m3NL6VGVnSUceQssh8258n1Hd99BSAI8EU1KLiosohfednleMPNu/sezmRJQ6FD5GspiVZuHIAwALzZpG8xP54hm+xfUOIyb+xznjF44/S80SLLwOtMC/tnLCKTTpoUDQwqw+snm8wpMgzbDQBQv6IRC43DvDHmrpjPEdZ6wKgAQRBQyal9ZZOsiMH6ImE2U7JJIM5UH1tsY6qkoZJTsGc8n5JNtqOySQA4+x34koZlVFLgLVl8u6jB+t7OfIc/oJuFTpm3SD+ZIiWYt6j7emuBKOqyzEqCz63Gcscfu24n3njL7rixUNaYABZUzjvuUvAWfc5666RgJSloDzFrMRO8PfHEE3jnO98JTSM3QbFYxLvf/W4cOXJk4IduWmw388Z6aoqTYTXDGr1KaixopUDprcQq/kAW88aSlwjzJgjDD+o2tp55Y3IKZqEOs0US+YlDcVnaiDBvQb/bvgh4GyXmjY0JqDDwtk0SYc8lidswAKC0k7hNjnDwqtUMcAySTjKpTCibJI3nUflLEIx5q8yQSh8blL3VMSx4iyTzSbeyal5BDhYsaKn+oGi0TAdF3lBdowHcewfwpffi2l1lnFhuE7OGrgXb9bEjKU/jKA8EtQBR8NHuxNf62XoPu8fyKOkKdNCEKrPnLW1Y8tDZerAWACDJxud+CbjzzVtqqrTYsvAu/5Pkeb/5ZwhoUkuYEJoZPW9uhslLnHlrRsBbxyKmKDzZ5FheQU6REsxbj7wvT/u3OOyb5/nB2p/JQl+sMJsAfLSECHjLYN6aYiW11xpaDSWXXGt35QQ8X8D0XiIn9iQt5hD7yy89hNfdOAC8FTWMmbMEPCYdnSUZyNfgt5fxm599DP/vZx5LgbcOZd6QYN5Y4aQ7YBB0Zs+bnIuPLbiQ4PS8MXnzQNMSmiMeNcg5eejcBgu1FAx0fX2AYYkTsHpD9bz1Yd5KujJ0G04lJ/c1LFnpmFBlMSxkpQxLyHmMSsCPL7eDlhPCvMXXnqDnjTFvy0eA6l6IohSTTVq0hy8L9D7jmLmZqIBO38sf0M1CrwCeE1tDk3PeGLOsKxKRTAKDwVuCebtx7xj+2xuuhyhGXG7XTpFnIGtMBWXeKjYprMQYNiN00hxkVgL0AW+KwtfxbuuQ7ijzth3DTgO0Pz3SMphYUKCiGKupxYj0vA3hNglsALzR6pGkblnPG9vAA8tv1mtDZZOdlGxye5m3H5ypQ5VEXDsTbrqavIluk0tHgI/80PC9T7QhGRWaQOjl7WHeIiYIA6M0Pfpuk5yEJwBvAxbrRpJ5K2pwPD/om4iFzZwPc8D0tdvIvDWHGxUSHRWQYN6qOQk5wUIPWsi8cUcF2HzDEnZPzD+MH5Eege8DT841+w/oBhLKA3J83Va8Mn6ezlMqaTI0gckm+8x5A2LP0W9//gn87hefDN9jd4nF++rxmHHIZscV7QdwyH4aeMl/DgFyYQI1NPhDui0n7TZptmJ9gknZZCNafFBock/vU0EQMDOWwxNzjdB9zepQ5o2Zb6TXrrWuBYcWLzJZ6IsVtOLe8ENQv9g0KOMf73mztfFUnuTmJjHu1eH7PpTGGSyLNcgaAfq+pG54/t9EScUOZw7+2EH+GwqTOH7qJP7nd8/i8w/Pwk+AtxZLyPO1hGySXNdh3CaT7CK6a8+cdQPIdRfEeM8bZd4WB5mWUPD2VJs8sw9fIHhro9+cNwmej8D4o8QrGiWCZ1gSzKbUZNqGM3iPreYHM28TBZXcf77PHRUAhODN932ciJi97RnLo9GzY/tKm7pNojhNessBCGP7kFOkGJPIG0d1UUPRyby1098Kn7msnjcgpgJTM5g3XZGo06QA7Djc/+dzXCxTsX6GKBeycBJl3komKTR3k8zbkE6TwADDEtu2YVlW7M+g4Y2bGg0K3pze4JO4GRFF+5cKeKObjmauph6q/sxbErxVN9bzVtmzpcybIBA9N4DQjZTKJgNZmloim8IIMG/XzJRjtua6IsJMDky/WLH4OLD0BDD7veHe35glizQbWrpd/Z0WJ5HOiuIOItvh9EONSrQMJ1WlZZKbQX1v67TfqkLtoieo/IXLOLAqvqKTDWn56fjst62KoZm3+KiAWM+bSjdbaOEAbN6ogKyeN6bWEGVcc+yjAHw8OrseJIFp2SSnYEDBRq/Tjr11tt7F7rEcSroMLWDeMsCbrBHpOX2OPM/H8aV2PBlle1phEvhW5DsjAAAgAElEQVTOnwKz3+d/1kUMo9PEL3j/iHV9D3D9m8MvFCYx5jfSs4hA3SaTzJvVSjFvPPBWzsmx683itpt34/6Ta/jTb9C1mw1mD5i39F4SPXeZzqsXK2jBb9Ul4E0UIrJJu0N+l84KumIBuVyaffULUygIBlrtJkq9c1hVQ2bNl7QNg7fJooY9wiLM8j7u15e8EtZX5rC/lodotSH4boJ5o89LUjZJRwn1Myzx/Xj/bRDdVSA/xv+mjYQgkHsk2vM2LPPWIs/7090QvG0oZ6VMTge5voYl7FikRA9zVrCeKzMCdlg/aUmXh2beqpxe0mistk3UmALJMQgDFQVvxTh4W2gaaJtOwLztoQ6KsxH2rW3SfUuUwhEM1X3QFTHGvLF/ZzGWFyX2v4gUI1eotDxrVAAQA8NJ5o1dh5xKmbeJywe7Widkk9zorvH78IIDKQB6FXmDgLc08zaWfj0jMu+68+fP41WvehVe/epXB39e9apXYW5ubuCHblo0Iz97O/re2kvETjQ/HlaURx28UaCSs9biM1nA7Om9uPSKJ5sENsa8qUWS+G8V89Y2USuoYT/f6nEAAjB+MC5LE8XhHsBNDNfz8ehsI9bvBtA5b5vV8+bSxGb56eHe35glFSKR3i9aaXtkk+zZGrSoAuGstxEeF8CrVpc2yLyxhInNSOL2gLA5b3KOgDffBZafeiaHfmExLHiTZCIdM1voWXG3yTGFnJeOp2Yalvi+n93zxvaMF/wSlPnv48eKx/D4+UaQ+KfcJs20YQmTgZm9cK1vGjaahkPBmwI96HnLAG+CEBsvM9fooWe7WG6Z4frLnrGX/RZQ2gV87pewuLaOw7/zL3j3nQ8Nb5XeL+YeBu77MPDp/wf48POg/fE+PEc8g2PXvCs+1Lg4hbK3nkoiLMeD7fooJBPbxGy8Sk5B13IDg4Bmjw2oT8smAeAdLz6I1984gz/+16P40qNz4ZDuPszbEmVPS7q8+cwb3ftWXALM9k8UsNAww+SxvQR0V9AUK2k5IQC5TNan+tJ5TNlz6BX3BF/zLgC8TeeBGWEVS0raEv++Eyt4YFnCbrWDj7zlZlQFCoISbpMFVSav2d2guJMLmLfs9ahjuXA9nwDxaHRXLw7zBlDwFj5vtYIKUQCWBzJv83DVEjrI4eZ9Y1humZhr8L/H83y85oPfwuefihABVBpY94uDwVvTQEmXh1KjiaIAVRYTzFvEfXjIvvJKXunPvHWsoK8t3D/TzBsrdjCnSTaUm5m+sb433/fRsSJzHansD9W90BUJRgTkMxZp03reAODAiwD4wJOfJ//PMiwBYjlLym0y2vO2dhKYuGLwz07IJrnRWxtcwCjPQO8R/BI3LAllk8OMD8oEb1//+tfxta99Lfbn61//Or74xS8O/NBNi+b5kBLdDolUZ4lsJqK0/Rbqwwa92fL2WjhNnkayGgGAbMKinG68z40BvSHYTqNJHp78+JYyb7GBn6vHCHWt6GlZ2rAM4ibFaseE6XjYX4v3BWiyCNcnQzYverDEYCPgrbI3/D/HvWlLIkikh5DesYrgCDtONo00eGNs0aCeNwbeat2TwIN/GzaeZzFvgkSScSYF2Q7pZMLprF94cj6UTUbAW0Um56XtKZmGJWxeEpd5Y/vEi98HlHbiV+TP4tHzjUA2OZVym+TIxulcMjsC3pi5xkw1PxzzBsTA24ll8nMczw+Hf7NnrLwb+PEPAitPw/zqH6BlOPjcw3N42e334K++dTIARBsOsw381cuBf/0vpG9k/CDOXvtLeKv1G/CuuS3+3sIESs5a6r4MEzSOYUnknDEJK+t7ixUfZI0oICImJIIg4A9/8jCeu38M7/2nh+FbHQKaGRjow7w9Z2c5zkDf/X7gM+8Y8qQMGbTgt2jpqOQU7B7Lk58fDOpeBjorWEclLScEoI+RHpjFU0+iJjThRwwNiGHJxsDnDUVyPLd/38YvfPz7ePAMKZQeX2rjnR9/ELY+gR1iE1fuKGFaoec5ZljCZJN0Jhtl31ji3enDvCULSUFcVPBWiDFvsiSiVhxiUHfzPAydXJMfu47sCQ+f5Sfcj8818OR8E988FWHUp67G12/+U3zNu6mPbJLc20stcyinSRa6LMaZNypNLGjS0H3lPBfXaKy2rVCBFIC3cA3OqRJyihQwb8cWGXgLe94ABI6Thu3B9fywn5j1vTHZpBMFb5ssmwTCvrf5h8n9LHEkqwF4GyybzCkSOU/se/qFXiEFBbfPXt1do4ZBfaK8C0qX5Ckx2aRxEWWTvHjb29620W+5eNE8H05A3y7mjS3WQwxd/b0vPYlvPL20BQeWEb4fAJWiU+f0vMUHFwIIB9Qmq0lDM2/rZLFIyDE2M5ZbZuiuBAROk0AkOQ7A29i29ryxZLtWjINjxjYMmu1xQcGYipWNgLdIw/x2GZawZ2uYnjcm8Rxhx8lmz0lVq4fteWPV1vJTnwS+/J9Qo43n3PlWdo/2jQjE9UotbhN4aw7HvAHw5BxXNlmRye/dcpVMw5JWv3lLzTmymeoV4If/I640HkFt5UGcWG6jVlCDAlYQPNkkZd4coxOwZOE8JSKb1AWLzM3iJRMsIvJjVvEGQgYpSDb0MnDoFcCN/wG7n/orHBZO4u9+/nl47v4x/N6Xn8KPfuhbuP/kBRTGWgtERvXj/wP4T08BP30nnrjiXfiWd10wADiIwiRyTgOO68Sk9Zn26MlRAfTzWKLJQFxZV7iyOID0/X70rbdgd0mG4LtYdxRy3USFa1jCEvmrd5ax2jGJPM7qEsnp0f+18fPTL+iecd7UUSuq2FHWiGyySCv/7SWgs4JVlLkGFvlxwpB1T3yH/K7Th4Kv+bK2YYOanSDX/wU33oDvnlrDT/7Zd/C6j3wbP/e3D0CVRbz05udAsFqQXBM31CizG5VNWkw2GQfHrHDSTzYZu5bRuJjgTSvGet4A0tM+sOetNY+GQnK0V1w9DVUW8fA5ft5yz9PENOLpFTOWLJ+o/BAcyNmGJfQcLTaNoWa8sdAVKWVYklclohgasq+8mlPRNh1uAcf3fay0I4Vs9nmJNXi8oKLOmLflNio5JZDhV/MKipocrG/h855k3vYFw8pZhLLJTQRvskbmvQF8sxIgYg4VAW8ZhiWaLJI1f5gcg80JzrpOvk+Zt8HgTW6TomKsSBJh3nhy9WRsGLxtW8+bbZDFgU1jb22DfLO9FMokOO5h0fB9H3/3ndP46pPbKOOy2kQyBaDkrnNlkwDifW9Wh38j58Zo1WHA3BSTMW81cr224H6JgTffB1ZPEA0zOG5++vYyb0zmNpEEbzwgfbGCMRXLTw++Hq5DiiRR8KaXt2csxkZ63pi704gyb7broWe7mcwbb55WNNa7NgqqBMlqAZ6DMY3kwNyeN6cXMkCiCExfAyxsseMkp1m+X3hyHj4bFRBZp4oi+f0ajhxh3uK/c+D6xquCt+aDJnHc9DOwtBp+Rf4s7jm6nO53AzJkk4R5y8EIGvnPU1nRzFgOBVWGDhuOqGU3qgMx5i0G3lo0IQ3AG60C/1+/j64yjj9SPorn7yngYz/7XPzl225B13Lx1r/+bvh9wwYrbFRDVj2wK0+eu8IURHiooh1LbFlSn48alrg2YfcTPW9ABLzR8xaARLVAesUSMV5Q8Vdvfg4A4M6HVshzka9xZZOLTQPjBRU7KjoM2yPH9tQXyR5kNFLJ/zMKyrzN9lRMFDTsKOtYaZuwc0zWSWSTy156TAAAVCbJelpZeRAAUJ0JB9d7F8C8scLom3/kRtz3Gy/D7/7ENVhtW1humfjLt92C6gRNsjvLeM4YuWaOSu4rJjMuMLdJIGhxEGkPVz/ZJJd5c21yzi+qbDIN3oZxm1wRxqFIAnZVc7hmVznTtOTuo8vQFaJ4+d7psNDcTQ5wTgQDJytti7/mZEQSvLWjPdBaZbieN/r8NDnsW8dyYToeainmLb4G14pqTDZ5+VQxkH4KghCb9RaAN3ac09cSJULtMuSUuNvkoPN20WL/i8jfvH43YCjmzbBdaLIIUUBK8p0ZbNRUVv5okr15IPNW2Q2hswRVcNBla6tjkfVQvwiyyazYNrdJBtYmLicXZ7uYt+JwzJtJewP6VbA2PRjDJEioePUUnc0MM2KyyawqxLBOjUaDJPv5GrmRN1lu5/s+ltsR8NaaJw8BY95Sssmxbe15C8GbGntdY25UAyyaLygY82a1Q8fWrGgvEMAfY97Y3J0t7u/kJdJZUZgkUqwRBW8hOxTf7JlT2TCyyWpeDTZk2e1hPK/ye31sIz4sesdhwrxtJfi2u4DvDQ/elDw8kyTzuUjFW3YJQFl3spm3vsNym3OhpFbNw3reL+HF0mM4YDyVHhMA0IRRCB0RgUA2mYcZyI1m6z3oikh6cUQBJdmGI6jpz4tGpK/lxFI7mA+UYt5YYTBXxed3vQdXieegn/rfEAQB/+4507j9tuthuz6emNvg2hqMugltrJsB8E0ybwSUTAiNWCLB/h0zLOEkiUnw1ujZEASgyK6tkk8xbywOVklacqYF/N19p8mxcGWTJqZKWpCsrrYt4KGPh28YtNYlwvd9PJLlTthbByQV8x0BtaKK6YoO3weWPXqt2ktAdxWLTjHdCwagUiP34CGLjFqa2heCN2JYssHREKyfPDeOvCrjbS/cj2+870fw3d98BW7cOxYb1H2oRK7BqQ45T4HMmCObBIgkdijmLQreWEK7ieBtuqz3B2+uA7QXMeePY2clB0kUcMOeKh4730gxVetdCw+dreOtL9gHWQS+E2Gyu7YDVRYhifxcl+VRbpZUOyN0RYy5M7ZMO1yz9DLJW/pJ8hCCt3UOeGN7QaDq4TnnghRI1iLgjUkmWewZzwfMGyvcBHMdD98GvOdxQK9AV5Nuk+S9myqbBOi8N/QBb+mWJlkiQI3dB8E8Uccg+c5GmLes/DGYcziYeQOAA2ozZN7YZ0ZGBWTcfkFkgrfbb78dd9xxR+zP7bffjsXFbWKSmNNkeRed6bTFPW++T3ve6KLILnYG88aStUHDLjc12II6th9Vv4G8zO95M6OAgckmkzHsjDQjwrwBm9731jSIrCeY8RaMCciSTW5zzxtlSiZKSdkkG+K5CeAt2iM0qO8tOeMN2L7+To5mPzNEibK9Q45D2OLgJjwg110ShXCQfEY0ehZJiNk5sXukgprFvCXBm9UiNsZbFQZfspMVjHkDEO/NpXby65YMSPSZSTBv7X7DcqPMG4Dirb+ABor4Ffmz6TEBQKg8iBYpqWwyJ5io0/608+s9zFRzQTGzILkwhwJvlHlbbuOFl5E1MmDQWKErMl7h2/515B/MXQ3AVTvI148ubLCYwuYgRpKeVGWdBd3nakIzBt661MAkNqSb41DMA29lXQlnIiV6mmJBe+H0fJEwlBnM21LLwHRZD1QMzfljxEb8speTN7C1bMi45+gyfuJPv42n5jnrXK8O6FWsdm0qmyT3zkLHI0ndyjHAc7DkFrnMm6ioaKCIktBDHWVohVDCSEYFXAjzJoRsAABJFEI2LABvK9iTI/fsIyvk3MeKHQnmDSDMSb9RAVzmje3zgxLXYSNDNrnSNrP7wjtLgO/htFUOerdu2FOFYXt4OvGs3Ht8BZ4PvOraHbhyQsd3ToR5yqBB01E55TAz3ljoiR4x4j5Mz2Gg5Oq/x7L9g2dashK0ZPRn3sbzBLyttkkxKgXexsigbt/3w0HkDGSKYnCNdVmM5Stb4jYJEPWdWoopCGKh5EnPd8KRXpHEGPNG+t02oO7hMHqxiBRU+gbdj/bJ9VDVwEiRoOeNM0szEZng7eDBgzhw4EDsz8GDB/He9763/4FtVlDXsC+eFtHVJre+wm40SNLAemtEkdxAmeCNPFzbyrwxND9xBVQ4GJPi1T2NZ1hidQCtCNfz8ftffhKzddbsPIAyDn5mg/a8MfC2uX1vwYBuBoZWjpG/k7LJGPPW2HoJII3ltglVElNyCzbEc3N63iLyqmHBWzUC3vThNpaLHrz+o36hD+EGtU3BGI5kYicIQnyQfEasd22SLLFE2e6iVtD4bpO2AVNQw8LRdpiWbAR4Iw7eYps/7QVatSSy5opyypkvZrkdDcciRhIR8AathG+M3YaXSw/hapnDzJitNNNLWbg8DKx1yHVkA7pZFEUbFoYDb2sdC2sdC9fsKqOSUwLzFBhN8vtFWL9zbQF1qQasngxeq+QV7CjrqYR0YLQXifQ04Tqoy0KaZaAAbwKNmGyyw3OU47jCsiSzGel5iyX7/cAbBeylUgVn1rqUeePLJqfLWpCs5p+4E4AAvPjXyBs2yLwxtuH0Cue4jHX4uTHUuxbGC1oA/BfZoO4l4ua64vN73gBgXSJJ3Yo6E3v9Qtwm0V2j/YAZIIP14nWWMCZ00IWGh+bjbEo28yb1Z96iLonB8TDwtjmGJQAwWSZsZ+ZYCDagu1vCTJWAtxv3kHs9KZ28++llVHIKbtgzhut36nj8fCNYo7uWG/S18SIm696QYUm65y24V4bcY6tBUSR9DhjzNlFI9rzF12DGvIVOk/H1bvdYDl3LxVrHCu8Vzj2dU/myyU3teQOIAuMddwO3vof/dUHgmqypshjkVz3bI9dxIzlGkAM/U+aNPP+/iH/E4aUvAM35FPMWGAr1iUzw9rrXvQ6vfe1rMTMzA0EQsGvXLrz2ta/F6173uv4HtlnRJEnlf/7XZZwwSlsP3nhDAfvM5mAVi2FcYzYtGNCiQGYM6ZsZiM8egdkG1CLOrXXxl986hbseo+d5GObN98OeN05FbzMiBd5Wj5Pkp0QSNm7Pm+9tj3sigJWWhYmimpIfa5vJvDlWeE2oaYnv+3A9Tv9b4xz5uxxJMAb0d27oODbSmG+2CNsip5PipZaBufXEZw1rqrMNEbNKT0RRkwf2vBHZZJR568Z6F6LhWV08uWTjv3+VFjKmnkMkpSMN3nJBsqZHN/8oeANIL5/DZ95S0j/W48Vkk+zly34MAHC5ezx9IFY7vZGroWySNfqfXycDulkURBsGBpgXUPDGkqbLpoq0jyfS86aVY6zfYtPAmraH2FlH4oodJRy5EPBWnI59fstwkOfNqaLMzYTQiN2bHZ5ElVO95jJvUTnhEOCtWinj7GqXzHrrxBUcrudjuWViqqSjVtQgwsOOU58GDr0c2H0LACFU6wwZrBDCtZbvrcNRy/B9InnfUaHMG3OcpMzoGsrcZxwAujLZEzv5PbHXfUnb+BzGXr1/khiRTQpGHT2phMfOE8aA5SYFjbpKq8X4oG5NRrfPPsQksDGQetHBG0c2Sff4QGacjCab8VYOns094zmMF9QYePM8H/ccXcaLLp+AJAq4fkcOng88cJKcg57l9pX+Rb+2EdmklpRNRt2Hh1S3VKnVP89xku0FA5m3ooqe7Qb3A082CQDn6j2+TJpGLtHDt+lDuqMxcai/Q6ReTjFkbDQWgHAkzUZaMwbJJrs09xjEvNUOAc//Rez2F/CWxT8C7rgK+NTP0u9lc944szQTkQneVlZW8IY3vAF33nknTp48iU984hN4/etfj6WlbXJPbM7B06voQUddGqfOWVvInnQ44K3PbA52049Cz5s7TiSENcRvOu6oAKsNqIVggT/PEuRhwJvdI31uejncWDZZNrncZpbfEfBWu4xU6UGqQIKQYN6AbUvyV9pmSjIJhMxbdHG/aOGaBARNXhUwbx/++nG89I/vThsQNWbJOYouZhdLNvm3rwH+YBfw4ecC//zzwLfuAI5/NVvn36eR+L989nG8+86H4y/mtneGX78Ieos4m31JH4J5S4I3q4uJIp9563bb6Hgy7j1G2QolR+bYbCl426BsUiHDhIFEwztN5JcN+pqkpkYFMJVDKpFqUml9lHkDsO+yq+D5AnZ6nL3MbKdl45IKX5SRFwysdS10LQdrHSuQZgFATnRg+IPAWxlwDJxcIGvPoclivI/HbMYkk47rYaVtolvcC6ydiH3UVTtKOL7c3thokfZiqk+kbTooqJw0QK/CFyTUhGaCeeMYlrB7Ug2vtSZL0BUxYljibJh5q1arWO1YMLVx4hwXAe2rHROeT+b01QoqbhUfI4Nvb/wPxPGzOD1YNmk0YgZO7FlaaHAKTMY6TJlcm1pBw3hehSIJhDUtTgIe+T3X/DJXNgkAhkaAjVvdH3v9QkYFoLcWY1BToRZIEbOzQoCnVsVT802Yjhtcz2Atyo3HwZsi9TUsafZIr5YYZWs3A7zZXcAL8ydmMJRp1ENbaRb8sYAVFwTS9xYFb0/ON7HcMvGSKwjAvWpSgyqLuI9KJ3u221f6F2WWsq41LzQO8xbreQMGFkirfWSTjHmLjQoQldTYJ9Yj+sCpNeRVCbsq8aHye8bDcQEsb+IZsyTdJrfMsGSY0NMGMKokwo7KJlWJK/nOjIvFvIkS8Or345enPo5fm/wz4BW/A4ztJwXz8YMAQGfrXSB4e//734/3ve99uOOOO/De974XH/rQh/Ce97wHf/iHf9j/wDYrmnNwi2QTXhVqZLHcIit6AGGzdyEJ3vrLJoex/Ny0oADFHCPgreLFbzpmWBJj3mjPBzv+UDY5BOiJuqVtUc9bwLwVaf9KZEwAEJGlRXvegG1L8lc7ZsppEggNS0xnk5g3WQMmrwCWj8C0HfzNfadxdq0bNC4HsX4ublYCXBzZpNkGZr8P7P0hoHY5cO57wNf+K/APPwn84G8zvifbrfDsaje9iY+wbJI9T7yqfEmXg2IJL3zfR6Nro5JTw2tgdzFRVNEynNQ9Y/Y6MKDiqYVmWKGdvnabmLfhe94EuwvAj8tuKPO2xMCbrGUalhS0RNLATK4SzNutV83AzE9jn8gBb1YnfcwCMTApihbqHSuY8RaVTeqw0POHkE0CmF1cQk6RMFPNEeYtKpuMVJOX2wSgONWDRP4ZSUaunC7BcjycXqXrs2MNdldsL8XMSgAiOeUyb6IILz+BGhLgLWlgAITSo8R5q+SUgHFuJGWTSiEAaamgPW+TY2TPWfVpYhXZS9g5myrr0BUJP618E12pDFz5GvrDZwK1Djd6deD2q4HHPhW8tNIiayGfeaujK5Hfr1YkJjVTJT0+6w3Aql/KlE26eWICo05eFnvdky/QsGRQhb8wSa55rw45Pw7b9XF0oZ1mU/JjG5NNJq8lEF6bQcc0bLCiXQTgswLtYibzdh6eqGINoWwSIH1vJ5bbQQHtnqNkRMBLriTgTZVE3LJvLDAt6VpOXwBywbJJRYy1RbTNqNvkxel5K2ly6CrOJOAJlc8YZe++d3oNl00W4yAc4aDu2QHMG3HPDH+fruVCkQQo0oZ9EC9+aGnmTYkyb7ZLfAY20vMm66R4OKjnTa/yv56IgibjaX8vkX/+3FeA9z4ZAL+O6YTjGTIi8ywvLCzghS98Yey1F7/4xZif34bh2ADQmIWZJxvPCiiQ2ErTkjZ54OPMW/ZsDqYLZw3e2xLGOiDK6OZJMl72spi3qGEJZd7oQ8v6AKBVAAj9wVtUY61XSNPoFoA3VRKJJMcxiSlD7fLYe2I9RdvNvLWs0Mo3Eqz/cFOYN8cgi87kVUCvjq89+GQA2s6sJRKoxmzcrASIMG9DDGnPioVHAfjAD/8q8Ob/CbznMeDXTxNGcP0s/3usdqyaH43FlhE8Y0GMMvPGZJOcxC5WXOCEYXuwXA8VXQ43G7sbuIolAbhtdmFAg+8jGN6LHYdJMrvJMuYgLgC8ib4DDXZ8pAlN8JcMgbDEkpoeFWASdzhWjAoig3kTBAG5yYMQefddhtuuoBYwJttY61jBmhhNEHXBRtcbQjYJYH5pCQcnCxBFAZNlIpv0fT+UTdJYoCBCYsl+hH27cgf5rKDv7au/A3zslf1/fmshxby1DJsP3gCgMIkJoRkrLHRNB4KQqK5nSI/KupIyLAlCLaRkcUFQwD5dI4nMgsPAW9j3xuZ9TZd1oLuGlwnfwwOlfxeyDOWZ/rLJlWPE3e/k3cFLbGbifFKODQC9BtoiOefMKXhHRSfXiPWXob9skvXLlyNjAgDKvLnWxpREw8yTKkwS0N+rI1chwPHR8+vp2V35WtywRO1vWNI0bM6Mtzp5bpQ+Q+o3Eoz9joA3VvTMZN6a8+hqk/AhxljxG/ZU4fvAo+fI/nXP08u4ZlcZU6XwWF94sIan5puod6yBskk2TgHYuGEJM4dzXDLaIjAsCcww+oM3SRRQ0uVM2WQt6mKdUfxk76l3bVw+lV7rCpqM8YKKc/UuOvR55/Wx5RQJlusF7Re9AaB3S4PX8xYxLOlZzLBkA7NkBYEAs35uk3oFkIa7J/J99v2O6Qw0fskEb6I4Aug5Gs059HSy+C0E4G0L+946SwSMRCtLfZg3Bha2nXnLjaErj8HzBZScOGDRkj1vnkuSJa0UyibrPZJYiCK5MYdi3qrkRs/XNocdnf0+meWGcMabIAhA/TTpZ5vggDf2kOgDqO9NDN/3CfOWlE36Psbn7oYAb3OYN9ciVaOJKwAA3/nufcGmc2Y1IV3igbchJR19Y+4h8vfOG8LXcmMkoWllONhmbD6G7WK9a6Nl2HHZp14lm982mdH0i6ZhQxQijMVnfgH4/t8AAIq60he8rdPm9JruBnMbYfeCIgBjDIKwe6iWy1AlEd89GQFvALC4RfPeNgreqElHAUaKefMgouNSNoDHvBkOFxSjeZ4UB3jysrH9ZL1IHXeGVFfJoyzZqHctzNLkfk8kQdRgoePJ/eeg0nOxsrIS9JlMl3TYro961w77hWkwhiG3gyb7kb63Q1NFiALw9AJNPs5+B1h6MluC7NqkkMYMt2i0DQd5nmwSgFCcxITQQCdSgOxQM4dYtT6QTcbPWyWnxIZ0x2WT+YBhSwWd/7Zzkuy150zKcHai4I2cm+myBjz2Kahw8C/qyyM/fDe5/lnXY+0U+Xv2+8FLzK1vPsm8eS5gNtDwyXHUqA8706kAACAASURBVCHEjnKcebPkIiwomeDtsutvRU+pYs8VN8Re99n8wo2YlnTrwzFvnRUC3soTGMsreGy2EQFv9Dhz47Ei6yDmLcWiAnRA90Vi3YCwaBcB+KpMRnNkjgtozqEhT0AUEPQkAsD1e8ie/8jsOho9Gw+erQeSSRbM+fX+k6vEsGRA3xZLrDfKvBkUPKRcXodk3gAyLoAH3tY6ZjgmAKD7Z7rneLwQvucyDngDEMx6a5sOiqrMHRGWdMgeJDfd0uDJJuWI26RDe96sDfS8AdStPKvnbQg2PBIFVcokd56RYcmuXbvwjW98I/ba3XffjZmZmYzv2MSwDaC7go5OmLc5j27GzS0c1N1eJIthFNT2lU2Sh9OwPb4xxFZEbx3Qq+i5AuooopAAb8meN5HOVCI9b2RxaJlOwBoMNIRgDwtL9vPjm8O8feYdwKffDoBIiyaSTpO1uCylqMsj0fPW6NmwXT8tmzz9Lez68tvwfPFIXMJ6scIxienH5FUAAH/pCH7xRy6DIABnViMJlNEgvSVJ2SSz3n0mssm5h0g1vBRPHlGaDiXJyeA5/yGUytquH2cqc1UAPvkdRixifSLtJeDRO4Fj/xsANSzpI5tkEpmaHElarE6wUa90wtdX2iZkz8R4tYzr91Tw3VMUvDFb5SygfLFjw8wbAUJ5wYhXb60uXEkHIJCERdI4Q7ozNrrWPFDeyR+cPbaPzoRMJMwWp+cNANQ8ypJJmbcuVEmMPceqb6HnK/3dYum56LbWcdkkua+nyhE2wUiCN3Js1RlSdIk6TuqKhP+fujePkuwu77s/t/a9qqt6nemZnk0z06N9QRIIs5jNgJ0TJy/GGyc2XuLYHDuEYzvvcWyfmBh439g4CcqLnTghJ0BsYRw7BhkECC0EJIGEJLT07EtP71t1177duu8fv/u7+62u6ukeTZ5zdHrUVV1169a9v9/zPN/v8/0eGk5yZrksiouVGdG4qvg0NKurgOa6/yrNDomwt6FQQEferA1Iz0H6ljf1SBZvjbYwD7YVNZGUmF1UPSwydOQtnc4wlAhzoaoXyZa9ZLnUQFF0NOb5z3IlchPPtyxNp8x+0Yj0W+dlIbx21mg6runrynKpYZ8l1B8vdpM2Of6xTIylUgNNFweph8Xe4tlIAIZufRfx37lCIGFvJnSlBUa/xZvaFgjxdsVSakQ0netFlMQQt07m+MGcqR5q0IwTeZfPW6+mc6necXvZ1dZ3b94NLMibHZ0dSUdZKfnNvC2wohQYz8Rs1L1sPMyRkSTPz27ynfNrqF2Nt5ywI9C3TeZIRII8pRdv24luyDVqIJ83y8yby/cz1j+7JRePsFnzUpt0sHqaJc/1N58wn+MUK5FxYEh4vVUaHd/PKM+RVJzsp+i9bhHLus5lxEKbbLQcVgF9K1q7X9eIftBwS/S6z8Q6u0Pa5G/91m/x53/+5/zKr/wKH/vYx/jVX/1VPv3pT/M7v/M7fR/croU+u7AVFovkUke/0K8n8lZZtdEjAM/qXoYsfgCbnOp1jcYmxIeot1XWtQzxlr2QciJvgY6eyFsESwCuWufeehZv0qtCT0AcdIxdi+oqLHwfFl8UyJvh8SaLtx7I22s48yY7u06DbikikqNi84HZtZCCJZl9NAIJTgQX+Ol7DzKRidmLN0kzchZviiI2gWsRLFl4Hvbd6f59arvizb35WGkz1vvstURVt4tSo2Mmr5eeFD912nc6Furp8ya7rENBS/HWrhvXkdXr7YXZTWK0yGez3Hs4z0vzerJm0HKu07lpbkEoLsQj+ohuSKAaKRfyVjMKu81aWzQhPGbePBOM0qKhOuuKoUOAZqqrypA+b86IpEgqLYq1NvNFoTRpRZ/CWoumFjbmajxD74KnlLqJvFmNup20yVKDcFChkMuJz+EhWnJmqSwQRDkz5SfSYRh024s3X7VJgNQoBQdt0nOQvlnS50Hs37Us3jw9Dj1ocUbI34UTHCwkOVPW10sL8rZSblJIRgivvARLL/HC8I8a66t4c73J7GcXYKCYGsx/n0ZbpdzssD8nlAdt6I6+562pcYYSEeN7H89GqbVUqhFRtFSDWRTFMQ/YR2hG8danaIncg3sJloA589ZpQHyI2/ZnObtcNtYL4zjjed0KyTRZ3s7nzRt528XiTTbtnF5vfkbdmgalReY6OdssqgwpWvL4mVXSsRB3HbTPJUVCAe45lOepC+s6grQd8iYeH0SwJKarM1r904xCPxQVe3QfDdJsPOxp0r1WaXkgb+79MxMPEdKvYS/aJMBkPs58sU654a96KOntBvLWR9F73SKaEQ0Oi+CNjTa5E8ES6E2bHBB5S0VD1FqqJ1vjmnze8vk8n//85/nQhz7EnXfeyS/+4i/y0EMPkc/vIjTeb+gIWzEkuiWltiLkg6/rzNuya+MjmhYUj657obMbm75G1Ml6EeI5aq0Oa1qWWMteSBlWARJ5kwlAJG07fpviZK/kz+krshfIm9o23+fZzxi0SUAoTabGbIpt4Jh5C8dFovEaIG9SzcyFvOmIYUqp751VQChCudnhrLqP+9Nr5BIRpgpJO21SJrJO2iSIc7pT5K2xJb6bfXe4H0uN+hdvXrLt2AfWbcnyazzP2CtsstByzkZvPqWiIRrtLm0f5UCJvGWDlo5zu2pcR+sWxcnnrxaJ0aKQy3Lv4QJqV+P7s8WBaDnbRber8YmvnObccg8abQ+xGc/XDEs5/obLKkDTizcDefOgTXojbwsCefOK3JT4WbQYl1to464IJ0gqTWPmzTrvBhDqNmkQ6YmgytdNYxZvhgjDVlUkG1bkbavBaDomioXCUYMqLuP4WJorGzWa8z8wf+lbvEm1ZHMP63Y1f7VJgOQwCRq06+b3LGYxHAlas+KTJIYp1dvGPWoXLNETbC/RknZdIP3BCFP5BKc3A8LqwjLztlIS54bTD4MSYG7/e9ioNk2Wi1zD/ObeNi6aFO75Z421+bZJcf4XrYqT+p633IrbGm+y8F7tinurFMyRdqow9hEm8tanaIlhBtxH8YZ+PmI5bp3M0ulqfO/yBsmIhfoq0QL9cybCQTpdzUh0neE987ZXyJu9uB+zCvxYo16ETp1LrazNwkPGnQdyrFWaPPzSIm88NkzIQ1Tj9UcKnFupsFlrbUv/k/fAoLTJriYYI4ZCrtXepIf1lDWyHrTJbldjo9q0N4Z91mBFURhKRogEAxzMuwtdEMhbS+1yaa3ad/F2wyFvYNvvIg7BEoG8lfUmY5/fYy/a5KDIWzSI2tVcbI1WR8y4p3Y68wbwyiuvkMvleMc73sF3v/tdPvWpT1Eq7Wzz73a7/N7v/R7vf//7+cAHPsCVK1e2/yMZevG2FhBDt7WWKhTEruvM26pdaRLMG8ODOunsVr4modMmG22VdTJEG07kTapNiuMLtK3IW9tA5gzRkm2RN4vaJOwN8ibfP5xAe+mvaFY3LQbd521KkzJcghDxodcEnfEt3taF31SK+h7RJhsQjPI3z89zRt3HIU0UaVOFBLNWwRKjeJt0v0Y0u/OZt8UXxU9P5G1cbPwdNw3Eb/NZttBmbKIlr7GSaK8wqEaaBhefEL+sLENXNRIAP09IaciaCViLtzqJiJBjt3q9/eDKGiGlSziW4O6pIYIBhe9e2hBCAsHotYnO6PHS/BZ/+sQF/vaFHoIQzbKridIrJLqWUupuq4CISDC26rpqqtMqoNkxB/9l6J14p1iJEUOHxM/iJfN3vbqwkQQxGmzV28xu1GyCCADBbosm4b6Kt0ygzqGCSE6laMLmpr6uWc7Zcrlh0CrJH/FE3jQNipdfNH/pRBJlyL3SUrwZhts9BEsAgpaiyZc26XHOsvEw5WbHMDa30Qnl872Qt3ZNp2orTBUSzG210eJD9pm3sjDoZu57MHqKZG6EroZJJ5M+lX6Kk8VLopk0fBzmnjNQu1v14m1h03Kv6XvFYitmE4QY14u3+Y74XjfJ+ouV9Ahz5q1f5K1PSfKkhSkUHzIK0xfntuxItUMZ2qDDeeQtbV1ow/U5B0Qdtg2PmTcQNOPVSpOucxRFb+Sfa6RdjRWAO3Sz7kqzw1tOjLgeB3iDPvfW1baXu5fnaCDapCx2OqqbNgk9raeskYuH2XKoTW7W23Q1HLRJ/wZaIRnh8HDSs4gFjPXtwmrF0yYAzHNUb4mcpdZW7WJTr2UYNFRL8aYjb92uGLcwZt76nXeDbZC34vYNFUtI5Nu575u05h0Wbx//+Mf5wz/8Qz784Q/zT//pP+XKlSt0Oh1+8zd/s++Ds8Y3vvENWq0WDz30EB/5yEf4xCc+0f8f693EFUXcXKJ4G79+yJum6TLLjpu+h3mxFRF4zYy66zptstVlTcsSchVvjpk3C22y1OiwfyhOIhK02wVsN/MWCAl0C8xB6F5D/IOGLAbv/jmUVoUfC3zHgryd8y7eYo7iLZZ7bZC3sizeHLRJWbwpjT2iTbbQQlE+9/QVqpmjhGsrUN/kYCHBWqVlnputOeEL40SY4dpok4ZYiVfxpjdEqqv23/dAQay0mVLdnzZZb6l84L88w0e//CrnV67RYPwaw+hWFy/B1iyMTAvxkeqakQD4Jf6yy5rC0plv1VAUhUIyalxXalfj7JyOsITipKIhbtmXMUVLetC8B4kndanti6s+Pl0wOPKm0yazgaZdarpdJxCx0ia9rALa7gSjXhRFnh9tMjUmEPhNSxPRmH/wmHkLJ4l2RUK/UW25EsSg2tCRt160SXE+DiQ6BushHgkK2mxRL0wcapOyQKBwVKyllqbTiXHx3PbCy6K4i2X7QN7MBqS83vyLN/HcoGXfqLVUki7kzXs2VSJtcv9w+byBt+Jkq2oU7AfzCdSuRjuad6hNNhlPR2DuOZi8x6CLGY2M1KjYi7yQt/qmOJf5I7D/bph/lnWdin3bfrGGeCFv842oIVYCpijGYkWDg29gJnxyIBqdDC2g7wftQZG3wYq38UyM4VQUtavZE0OZcOqvK1GnWtu9Hsn11vZddpr6DN7ez7yNpmOoXc3WsBIHJhr8C+qQq7ECcHIibeQ7bz4+6noc4OZ9GWMd2Y7+J8/RIGqTUQtSZfin2Yq3/pC3XELQJq10O8m+6Ic2CfAT9xzgA6+f8n0PadTd6Wq+s1dxSzEKQm3yhkPeLM1KKVgikS7DpLtfyqR8XYc/JCCaz63yQA0Mea6c4kCmoNAOi7fnn3+e//E//gef+9znuHjxIh//+Mf58Ic/TK3moxC1TTz33HP80A/9EAB33HEHL788gOpZaQFiOTY7YsGoG8XbdULe6kXhK+dFmwTPbkm50SGo0xJeE6NuXSHLSpsMtkq2xCeiJ0mSHmHQJqMpyg3RzZ4cihu+RgZi5afm19jSLQIkHaMgEtRd6PYbIbuON72TRn6anw4+ykgyIjae2rpLaRJM5M1Y7OJDu3tMfcZ6tUVAgZxlYFjYGwjJ8kygsWeCJesNOLtc4eipu8Xv1s4a3X+DOrk1J2ZFvJRmY5mdC4EsPC8EM5Iem3ta951yUid7CF5YkbdyD+Tt4lqFb51b47/870u8/ZNP8hN/+hR/+/z83lBTt4lSvS3QIYm63f5+8bO8aCQMfoqTm7U2oYBCTLUUSzpKPpyKsKYnMudXKnRb+r2qS3bfd6TAC1c3xWeOub1vdhLSJ+nS2h4UbyG3cmZQT+QEbdLDKsBrqF6KWfnRJgMBcU1aFSclCuR13JEkka6ZXE/mLQmi2kHRVBraNrTJSJIuCvsT9utvNB2lWpbIm11tUlLzpHmrVXHyYD5BLBwgUTwDo6cEVdCPJlhZEuuexbDX8HDqQZsEiDXN4s0TeWtWPFXtZIJ/daNu+3/AKM48FSfbdYNWOaWvUbVwDqriOKR5+fHwiliTJl9nIGKGaX0gKAp3r5k3ibbK4q26Sm1V/O7QcIJUNORA3sR3M1uL2JA3+d0slxrwwa/wlfA7BkrmZXQHnnnbGfKmKIqBvtmaHfJ16rJ4804qwWQ62ARLan0ezyDhN/OW9rEL0O/3JS3vSZsMBwPccSDHqYmMTYnSGqFggHsPi8+wXRESjwSJBD3sSXpEzKIxYNAmrddLn6MJ2XgYVac8y5DIsXF9qm1Bw/W4LwE++MbD/Oz9/sWbtTllo3ZaP4/e9JEI7Q2lNukxJhAJBWirXWP/j4cDgyNv8ZwQhnICNhKNG+AekOuoU7RE/v+OkbdoNGr8nJw0aVRekqH9RKVSIZUyT1IwGKTT6RORKi1AZr9xsbbULmpqXCgp+Ukj72ZIVGAA2mSl2TEWmtfELkAmafEhgzYpDsZEOAIBhXBQMToRJvImTLrT0RCTuuqQfK2ean7Nkn2x2AujbstGMXvk/dwSuMxU84w5D+JDm9Q0y2YUf42Qt0qTfDJqFPWAnoyJojKr1PcMebuw0SYTC/G61+nejatnDL77lfWaKPbnv2/SyZzRZ1fQM/zESsBEAvTibbnU4Oc+812TRubRFVspNdmnb8ClHoIlErF68Kfv5F+++yTL5Qb//KEX+OE/ery3sMQehBAsCcGlJwTl+9CbxAPlRWMD9y3e6m1yiTCK7EKHE0aXvpCKGl3X52eLRBW9sNFpiPceytNSu7xwdbO3Ulbfn6PN81c3iQQDXFqruulLMnxkqv1CWgXkgo4Etl0jEE0SDipiSN+BvGma5j3zJlkZfsgb6HYBFuRNmk370CZDqlm87c9ZZkX0ppegTfpfV52uRkWLMx6zP2csE6NZkeioOGeVZodKs2Mmmnnp9WYWb8GAws0jEYaaczB2s6A79xIs8RArge1pk3HLrHS11XELcvh44xnFm468udQmoTdtEkHtBthSsgbytlZpoWlwon1aPH/ydZb5T4doiVcxu2Ep3ibvEYezJNgBw6mo6d8mQ19PFppRGy0tFg6SS4RZ0ptJpXr7GmmTfapN9itYYvX0058rizc78qYnnPre2os2ueWFvMn9fTeRt7D3zNuoFPhxipbo9/sKQ56CJQD/7ifv4D//k3t6vq20DNiONpmKhNyKm9uEFXkzimArUtsv8hYX14t17k16FBojGQOq/TojFg4KWjL4mkVLimT9RhQs8VDvDOu0SXm88YhE3gY4RzGf0Yx+51AtYRRvDrsAlxqsT/hefc1mk8uXL9Ptdm3/bjQG8CKxRCqVolo1b8Rut0so5H77mZkZ1+8OrZxHjeVZWDGLgKtljUNal3MvfodO3JvDvFuRWHmOKeDKRoOa5fhi6xscBmbPv0K1mrX9TbFcZzgZYhE4d/EK4929Nat2Rrh8lWPAwkaNy9VF1jRxfJde/h6NvFlshgKwtLLGzMwMybq40M/NLrG+1SSRi5AMBpldrzAzM0O2WGcfcP6l79FOu0UtDqzNEyTKZf0cJddrHAQuvfp9GsMeM007iOzFV9gHnJvf4JudO5jUomRf+E8s7LubfcCFrQAtxzVUKYoF8YWXZ8gnQkw0FZLlVc57XGsA8dUXaCf30Ul40yt2GpcW10mHNds1np57HNkaSSl1llc3PO+Ba4mj9QoX621++FiCS2sNTgSjFM98h0bwLgC+N3OZ2xa+yOTGBeZO/Dxlj/cfq6tkapucG/DYAs0tThQvs3LgPax7/G2oVuEmYPHcC2x2D/Hk5QqPn1nlsdwsPw7MrZVcxzO7usV4MsTCFlyYXWAmozcdNI0TgTAb8xdYnZnhlSui2AmUV3jzaJQfeu8YXz1X5lNPrfH333mJ2yfM7mKj0diV855a+DbDr/wXZt/yKUOEQ3ZJ2+UNOpe/SWXfA6wu65/77POsZQX6+Oq5S6TqbvGWueV1YgGNlbmLjAKtaJ7GxhLzMzOEOnWWNmvMzMzw2EurFCJi4Z9b2aA8M0OmpaIAD3/3DLeoIQKby1y5hs/57StVIbV9NMmjFyo8+dxLjKXcCevRyjq1+EEW+3yvZkcUEEmtZvseDlc2aQWyJMMBriyssBmsk2xUjXu32enS6WrUS0Xb3+UuPMcEcG6lSqfqfQxjWobs+nc4q/9dYvlVsc4vrVPr2v9meKvGiNpAoYtGgMb6PDMN8V0FGxscBxpEOH9lnpmUNyI5t9XiFHES7U3bsUa1JvUtsT9cWtyg0Zzh6pZYL9XyOjMzLZROk5PA6pmnWQvdbPztHcHLBOgy186S6CbJFq8Yn8caUyuX0YIpZi2PvTov7puQ1vG89hVVvGestW48Xqq3aFS3bM8/Wl6nHh5jwfEaxRWRK5yeE0XX4pULrOqNq+jmEkeAuctnKav2veTA5ioBVeHKzAyaphENKszVIuxvLnNuZoazayJRza8+gxpOcXa1w2ZTFOGvXJjlWETsY/tIE197hQuO4yqceYZR4PRKEy0Q5kQwSmT+GeLhQ1w6f5ZMSOXiknk9jc5fJBeI0iRCq2xfn3NRhfPzYv/cKNeZTGoDryOKKs7J1UvnqLTGt33+yNVzFAIhTl+46m2DIaOrclIJoGhdTs+uoC1WGNLEd6616sZxBtpVTgDLV86wEZ9hfUk8Z+bcBYIlO4r1sn7NbCwvMKPpSN3y8+K+WavY8qNrjRPBKMXFK6xYXrNcEQXL909fsuVU47OvEAoO0SZEaekyM6v+cg4lx7SNde2fDAl2TGtrhZkZHxN54If3a5zK5gf6rteWxLowc+4CV+arhAJw8dwZAxCZaGgkK+u+eYmM8oZ4nedfOUu5IAqsV86La3594QozG0HC1QWR/61X2Nrhd1KIwXIJGpUtz8+5qK9RFy9fZSZQpNJo0yh7P9cvdmvfdUa4siY+/8XTbGmCtVArb1Frtnnl9FkANlaWqZdWUWMFrvZ5DOn1MpPAxVefpzlkrvPx1Rc4BMyuVaj2+Vqrcn08f5FEzWw4zOj32NrSPOkeoKBv8RaNRvnd3/1dz3/vJO666y4ee+wx3vOe9/DCCy9w/Phxz+dNT0+7f/nlDTjyegLrSUCcsOzB2+AFuGksBfs9/mY3o/MKAFOn7oFRy3utBuAbcHAkC47jrqtXODSW48zaMrmRcaanPRT8riU0Db7zH+DEe2HYjTYxJy6AfUdvJn21wLomOhGHR5Jw3DzWeGSOZCbL9PQ0y2fEwnjTqdtpPfIc+0eGODqS4ktnTrP/8DEyodvgu3BsPAsHPc75d7qQGzW/w3QNvgWHxzK297ymWH9EHONt9/L1yiJ/p76e9y8/hjKxDwIhjt79wy7J6rPNeXh6jfGDh4W/0pVDMP+497WmafA3b4c7fxbe/f8Mdmw9qJsAjW9uMDmcsL/v+lfFz9wU6a0msVTG+7iuIdr/s0NDC/MLb7uN6cksPHmcQneNwu03U/i7BZrBOJMX/gKGjzP5jl8VlCNnzE/BxSrTJ0/2ThicceGbAIze8S5Gj3h8rs5R+BJMpAJMTE/z9MYlYIVQRHyHk0dOwk32v9v6wlXePD3KD5ZmiaWHmJ4+aT4YH2I4EWR4epoXy7PACnfcfIJ9Og2ksL/Bp556lHY8z/T0IePPZmZmdue8v/hvYONVTlSehjd8SBxvrQ1c4u6hCqGzW+Tu+Afkbn0AvqQwkYJbTx6Dv19gaHSC6Wk3UtT9donRXFh0nQMhItkxIrEQmelpjl5ReOzSRU6ePMmlr67whokYLMHkoWNwQnyek08UuVQJkspPwMqr1/Q5//vMD0hHQ3zwrTfz6IVnCGQnmD7u0Tz7uwaR0Ulyfb7XzKuv0iHIUKRjP75HVGL5MQpbMZRoilx2FJa7xnOE599ljhzcx/S0hQa0/L8AuOmONwp7Aa8o3gnnv8j0oXHRMVUEqjV10ynY5zjujSl4BeK0aAXiPHDXLSaCvilEQpqEGcoWmJ723ttmX1miosXZlw7YzsvxS7BxtQJBOHzydigcpXh+DZjjzukjTB8V9EW+PslIoMyI5W/f9INHYBVS028ll2jB+b9m+sgBNxXokRLsu892bi+0F4AlhpIx32ui8TdJsu1Npqen0TSNRuciBydGmZ4+YT7pyy0iI/vIOl4jMFSGry6w0RA0tFtvPmU59wl4BCZHcq69k+8oEM8bxzQ1vMqGkifUKjF94jhz2howz1T7IsGD9zJ96mbUrkbgC1cIp4bMY5ubhrnHmD5xwk4FP1OB1DgnbxXNK56+g4Or5xnLxJmenuamV9p888yKeU7OBmjHclCDW45NMT1tFlhTT5XZqLaYnp6m3pllcmx44Pvrwpa47g5MjLjPhVecUyBRYPrUqe2f+/cFqG+Kz6ooFCYb/P6jS0wMD5nHqWnwN0HGMhHGpqepJYrw9SVGJiaZdvihnW+Ja+b2k8e4aUxHLFSRrE6dvAvGdnHviqYppKMUrPdKV2PkaytcqITs5/m5OnPhEUbSUW6/5WaPF/MP69o/DXzn1AnGMtGeLLOdfMrV4Co8tszE5EEia/Nk4jVOWb/Dywdh4cltr59SdB0eWyY/Psn0MbE2fOXqGQLKOvfefrNYl5YEmrPv8HH27XC9P/5ig1dXFpjaP8b0tDvHzGzWgTnyo2OcPHmApnqRyfER+9qwTezavuuM6ig8DPvySePzj57ros7WmThwCJjj6KEDxC90ID/e/zHEVuHbcGQiD4etfyOYXwdP3OHeO3xCy5bgKwsUxvYxPW3S+y93FoElbj5+lNrCOd+/9y3ePvvZz/Z1AP3GO97xDr797W/zkz/5k2iaxsc+9rH+/rDTFFS/zH4qCyatqBIdYQiuj2jJgLRJtatRa6kGJ35PrAJOPwxf/z1B6Xj777sfb0hqRY56W6UU1OFchzCE1fvCtArQaZOxsEFBmC/Wycj5JL9z3tiyF5LSjHQ3aZP1DaGaF06wWm7yZPCd/GT7cfj+fxc0KA9fKUmpMuwC4kPC4kGX0LdFbUPwoC3KZn3H45+A01+Gf/Gq58Pr1SaHCg5Kx9p5obiY2UeqVN6TeSxFFUp4kgbByAmY+y4ABwsJCnPfhOIr8OP/ybtwA3GtdzuCrhfxpqV4IoU1cwAAIABJREFUhiFWcrv346GIoO3otElpwL1ZXDff1xKNtspWvc1YJko65mFubaHEelF8xjJRkpEgF3qJbew0Ok3TBuCpB+HeX4JQ1KBoHi49Kx478mYhTZwaFbRJfaag4jMvtVlvCVVCqeoXThizQoVkhLaqMb9Z59xKhV+6JwpLCDEOPe47nOeh712le0+WwDXQJjVN48mzazxwbFg0zYCLqxXe5CzeNE3Qfwah7CgKDSVBRnEwO9p1CMd1b6MW5O0m3ZKm6BIsKS0I2p9f4QbCqBvE3Ft8yKRoedFodBpfkgaF3JCd+izpbqF4T9rk+ZUKw8Q5ahWeQRgPN7pVCGLMvEkqniFYAlBwK04e1a7Q0MK82ijwhoyO4ZfmxT0uwxDcsu9f8npL+M28AY1wnlxri47apa1qaBruuZZmb9rkYqnBWNoxZ7QdbdJCPzqYTzK7mAA0qBdZLjWEpUTxNNz6Y4CgkOaTEbvXW2ZSzKpXV+3m5BsXzRlCgP13c+DqnzM2Kta+iVyMtUqTVqcrhGUam7TCovlZcCgFj2divLpYErYLrc610SadhvF+MYiyY3IUUIyG22g6xh0HcpycsFzjimITI0v0oE3KtSyz17RJEA0Ix8xbMKDwIzeP88Xn5qi1Oua1WFpgmbynWMmg4TcTd60haYZi5s2D6h2zeJP57cMIqwAwLWQA1qot8smIuS5dI20STNGS7dUmVRrtLpqG3ebltYyYe+Yt6px5kz5vg6pNgps22e8cqiUkLdJJmzRnkUP0UhjpaRWwmxEIBPiDP/gD/vIv/5KHHnqIo0eP9veHxuD5fiqNjtH0r0b0buT1KN4qy0K5ysln9VGblJuiUbztdkLeVeGb/0b82+/zS1Wy+BCNlko1LIu3FdvTouGAMfOmdGoQiqEqQaotlXQsZCyGc8W6OT/iJxTTLAlJeRkDzrxpmrb9LFJN99JQFFbLTVZSp2D8NqEsV/BGvFJOQYhekvJSansnM3HFS+Lc+KhrrpVb3jYBhWPCBJg98HnTNILdFi1C5oY7ckKIpLSqHMon+NHNz4pk5pZ/7P86+mKo1rf4wveu9n+cC8+L1+7FBU+Pu4q3csl75k16/IxmYqRjHqbIFi/CzXqbcFCxDZ8risLR0RQXVv0pMTuO2afEZnDvL4v78gdfAMwicv/GM+IalfL1uuhS2ph58772DVNcOUcWTlgES8T19NjpFTQNTg7rG23YTGLuO5yn3lZZ7cSuSW3ywmqF+c06bzo+wkgqSjoa4qKXaEm7LoSKBkwc6kqMpKt4E/NPuUREnMdQxDYb5KvMVVoQs4W9wrAL0OfejITH26QbIB9pM5lzNC/04wmEYz0FSy6sVGgFk4TabuPhtNym9T3FKN6siWT+iMvrbbR+gXPafs6u1EyLD6ddQLMk5vLSdkretjNvQCNaoECJalO1nGtLgqZ2fIURZPGmabhNnQ3BEr+ZN/P6nSokuFjTz0N1jZVSg9sDF1G0Lky+znjesGX+UxyAj11A8ZKreIvQ4raImI/bl42jaRZhpPomtWBGfw97M2AsKwq9Yk3M4WV2IFhimnQPMPPWb5KYHHatvX/7aw/wy29y5F+WplcvwRLvmbc9ECwBcc95XB/vvnWcelvl8TOWRnR5gaudnKdNwI0SUuCj0VapNDpucZse6uXW8Jx5qzTJO20CrK+5gzigN+79hDNMtUlzjixxo1gFBMNin/RQm5THaqpNDrBPxe1z9Ub0qwBrCUPV1SlY0ufM23Ur3nYcRvG2j0qzY0j1lgJDwrjzeihOVlZFF9epwhdJAopLIUgmlCPpKAEFas1dTshf/mtYnREIlJeaFpjFRyxHraWiRJJiCNiBKFmRt2C7BpGkLSHabxRvNbE4B8LC/NYrpNqkjGhGFL19Fm9//9IS93/sUV/hBuNz6TfIarnJcCYG9/y8eMyLPgpuQQjDzNmjeJPncydeYaVFkbR6yF9Xmx3qbZXhtFfxdhSionhzGjZec3Q7KGioSsT0YJFd+bVzvFl5nhPaRdpv+HBvo0q9KP/Oq5f4rb/+AY+fWfF/rjUWXvAXK5FhMepe1ZOvellfdB2J9LKuMjaWiZGJeyBvsZxNsCQbD7voL8dGUlxY2YPi7dzXhRri235fII3f/vfQFZ4+YTrk156FI28xn5/eB+VFEpEgiuJvFbBZsxZvaZH46sWbVBf7+oz4Po4M6d+xBXl7na6gdqUSFIm2l6deH/HEWbF2vOn4MIqicHgk6a04ucOub404SSwJrKYZiXwuHhad5qCOvOkNkoqXXxKI4tnP401GzoK8gcXnzcMqQC82jmYDTE84EiIdMQlGtkHeVisEYhlXcjaWjpJWaqjBmIEULm81SMdCdpQrf1R0eC2NpfD6aS4FpjizXLYUb45ixcOgG4Q/HkA87E8P68QKDCtbVFodI8mwHZM8Zx4FbywcMBSNXcVbKCb2b8/izY7uTxUSLHb016+tsVxq8kBMFx3Zf7fxvEIqYpeQl15vVtGSVlVcG/lD5u900ZJbERSliZy4dxY2dYS0vkk1kNLfw428aRpGE2MnyFtXWgX0rTY5gJ/UA78Ob/nt7Z9n8T6NG8Wbl1VAR1dZtORCtXWxP3iwXq4pIilTRMgS9x0uUEhGePglvXHdrkO9yMVmxles5EYIp8+ba83yQItsceUp2LxKTiJvdfNaX6+0bDYWxmvsBvLmY38hr4F6S/VeG17rcAh0hYPCJF0WR/GQIhhYAyFvbgsCQGeERbz3Dp8w/F0dTRL5/ztWm7TG5cuXeeKJJ1haWrJ5S1yXkMl0Zj9Vq4JjRxOb0XWhTa7YZXdlKIooUBybcdlQEgqRjIR2V21SbcNjfwjjt8Lxd5nFrTNk8aHTJuORoOjCOWiTduStbhh0i+MPU0hGiIUDwi5AUfzN0dWO2MitxryKMpBR98XVCrWWStHp4WINibwhEv2RdBRufZ/owB57u+efuGiThiqhB7omE5+dIG+yqPUoCqUKms1Es14UCmqFYxBNk9DqhmH6roWBClgW9mG9eFs9w5uWPsPV7ghX9v9Y79fRv9dnZi4DGMa7fjFXrPHK2fMCBdi2eHMjb82qLN7sibLsho9loqSjYbvPG4jOmH7tb9W81d+OjqZY2Grsvv/i2Ufg0BvFZvDGDwvfwdMPU2q0uUM5T1CtC8qkDB15UxSFVNSjEEVQsMuNjtiwmzoV0aI2KZG3py+sc3QkSVLRz0fYTGKGU1GOjaY4s6Uv933IUXvFE2dXOTqSNBKkI8NJb683o3jLuh/rEVViJKzFm9oSsszhOJl4WEfeouZjmAWIp1XAdshbLCMaQdLrTVK0wt4+bwD/9h8e47d+xDHTodPNg9GEbwGuaRoXViqEE26z+9FMjAw1WiEzibDZBMgoOBQnq+solWU2M8c5vVQWn1cJeBRvugiOB20yFQ0R6DHboyaGKSglqs2OQe+xdYR7FOqKohj3n0uZT1F8kRVaVdv1ezCfYEOTaslrrJQb3B28INZNC9pTSDqRNwuNVIYs1C3IWzt9gDUtw9GWUK+cyIqG5aJUnGxsUiJJNBRwedyNZ8X1eHZZnIdrQ94G8Hnrt3g79vbejAoZNtqkRAS8kbeMsyFW39h91A1EIuxxfQQDCu+6ZZxvzqwIaqf0eOsOedoE3CgRC0m1yS6lRtstwb8d8vbQz8KXfp1YOEgkFLAZda9XWzYbi92gTb7u0BC/+6On+KGbhj0fDwQUoqEAjbZqUGxvGLVJ0HNzu1UAmIhlXO41g/i8RdJijfVSm4znB9IDiIUDKIp7rKrS7BDSz22v2LZ4+9znPsfv//7v8yd/8id89atf5aMf/WjfB7crYRRv+yg3O4zqczvX1evNQ2bZCI9OqolchUlEg7uLvD3/WbEB/fDvis2ptOhN06tvig0wFKXRVgXEnRwxu7B62GfeahBJGwlIOhZCURS7XUBmwrtgljdJzJGwJQp9I29FfTHq6YtXNzeu1XKTkVRULFC/+A07qmEJN21S3/g8aZOyeBsQees0zc/p8boSUbIhb5ICNXwTRNLEqdPYbZ83HWUJRiybWv6IQES/+5/IF3/Ap9V/wOzWNmiMvgnMXBbnp1jr/fx/941z/Ocv/E/xP/0gb+Vl0DRWy02R10lPMx/a5Fi6F/ImCr+tepucV/E2IhLxnibTg8bGRVGs3fQu8f/T/0Cc5//9J5RqLd4YfBlNCYjiTkZ6QjRTVGHL4YU420xxmzo/P5wwkhq5YbfULnceHDKNfsP2xP/ew3lelbfhDubeGm2VZy6u2+bbDg+nmN+su2djdtj1rWhREtZ5MB1dFLTJMOVGB9WBUhhrlTURajfEOiGRl14xNGVB3qrievPyOYzImbemiWDL0I8lHPWnTS5uNai2VGKpnLt405G3RsC81pdKDfu8G5h2Aet68bYihLS0kVOcXSqjSW8zZ/Em98iUkzbZ3tYItpscIU+Zcq3p7T9koJXeCVBWL9o8EalwQnS+nWHxeQPh9bau6ddSbY3lrQan1DOw3y77XkhF7FYBiYJA+Kznw2oTIH9Va/NC9yiTVTGrvE8ib1sm8lbsJikkIy4UXxbY55bFediRSXdwAORN0/amWLIUb5IO5+3z1nYX4rX13Z93A8+ZNxnvvXWCelvlibMrRj6yqOWZ/D+ENlludNyFviFv79Fca9dFo/fCY7A5azIR9FirNO0jGbtQvIWCAX7hjYfd650l4pEgjbZqXCs3jEk3uJA3yQIo1XWUUO41gyBvgYB4XWd+OAiVWQ9FUUhGQlQ8rAKS0dC2tmzbFm8PP/wwn/nMZ0in0/zcz/0cL7744kAHeM1RWoBYllYwQavTNZC3Wkv1R4F2Oyqrrq6lEdG0Kxkyhuh3EXlb2KyzVtyEJ/5fOHAf3PROQQtqV72TsfqmgTDVZfGWGnXRJqOhIE3dW0wUb0lL8SY2ov25OHObeiKVHndr7YIlYXNQiuL5vpG3Tb0g8KJrGFFbh0TeWABHnDREj3DTJvtA3ho9zMi9wlrQenwf0jx2xLrArp8XPwvHIJoirjVotXfZf0wV7xuyIm+hiEgE559FTe/ji+qbuLzWazQW43uNdMRmuuVEvByxWm5yoH4GDUXMJPaK1BioTdT6JuvVFifG0qSUhjCvdQhOLJcbRIIBcomwz8xbTvgQdlVzVswRx0bFYj3o3Fujrfr7mp37uvh50zvEz0AQ3vDrsPB9kotP8YbAy6hjt9s75oYA0BKpWMhTsGRTP88CedNpk+G4UaTlLYbvdx7M2cQzrHHf4TzLbb0Y2AEl+LuXNmh2urbi7YheBF9edyTgO0wcylqMeNdavMlCNG58jw1NT3h05K3iZXYr70U/g25rDB2yFG/ewhviGHrNaInjDEeTvpRvea2ls0O6KIG5tiSjIYYCDaqKifgtlxpu5G3oEKCYoiXLothIT91BtaWKBpuX15tBm3Qgb00P6pYjAskRAopGq7xmINU2atQ237X83rzuQ09kpauKNctSvO3PxdlS9NevrhMoz5HtFg26o4zhVJRys2PO4yqKKOCtyJtELYcOG79aLTd5sXuUbPUSNLZIREJk42EWNxuC6dIqs67GXZRJMAVlzq3oyNsOaJMoAUG5aveBvLWq4tofYLamr7DQzYN617/uwQIpea2pe1W8+SGziPUsn4zw8EtLJmtDy+2KYMlehdXnzfPek0wFL2aEwbDS4PnPk0uEjT24qdMwC66ZN8WbRbCLEQsFqVuKtxsKeYvZffNcyJvcawaZeQNxr/ghbwNGIhJ05buVpoeYjUdsW7xpmoaiKEYVGIn0UO/ai9iah8yksXGM6qpV9VZHL972mDbZ7frTJkFsWj60yXQsJJC3XkhSn/HPPv99Hvvcx8Xnfdvv6RuTPtPhRZ208OJrLStt0oG8hRxqk9GUIZwgE6LJobigTYJ/wSwLFhfylofaOpu1Fm21dzEk0RwvlStA7zqKmTdJr+uneIuGgkSCAY+ZN4/iTW70WteTb+8b1oLWA7WTxduws3hTgmL2Rk9+lPY2RdSgoXdzQ1HHpjYi5MwDD/wG4UiM2Y1t3lfvCo5FW4yko0ah7RebtRa3BS7Ryh21U2m9Qi9iSqtzqF2NOw8OkaROO+ieX1gpCaqsoihkYmFv5A2gscVmveWZNB7MJwkGFM4PMPemdjUe+MQ3+ezTV7yfcO5roiCW1DaA238KUmPcfu4/codygYCVMgkmra+8RMoHeZPnORePWGbekoJi1e0SCgYY0mcg7jiQ80Xebt6XpaTp53MHoiVPnF0lEgpw/+GCOI7/9qPc2noB8EAwd1q8daNENa/iLWEkzlstfcvSr2tPwRLDoLuP4i03JaT+u6o+vO6T7Miizuv+1I8lEkv4zrzJa21oSE9yHXOx+WCNEuIeVbsaK+WmQckzIhwTxZlE7FdegUSBqYOHADizVNaNqZ3F25IoDhxUu0qz46abOiKYFgVfp7Rk0CZt57rP4s0TkYok3Mm5PL+WmbdIKMBINk0tkEKtrnKorqv5WsRKwKSkb1hp906j7o2LIsmSDTwE5ewFTZ+Xnv8+ABPZGItbdWNfW2kn7LQ0PfLJCJFggLPXgLwBotnSD/K2A1W7viI+ZDS9wDupBN2I3PkZaxt7WLx578GhYIB33TzGozPLtMuiIb2hZW5s2qRVsMTr3uuFvMk1LZaDFz7PUCxozLzJ693WXGhVxD3pxSLYxYhHgtTbXeptOT97IxVv3sibLN5iXSkSNQDy5vG6gI6G92/QLSMZDbln3pqdbcVKoI/i7b3vfS8/8zM/w+zsLL/0S7/E29/uPVe0Z1GaN8RKAIM2WZXIW229/0HfnURjU0ik+9EmvWbeLHMYiXCo92zN2jn44ge3lQleX1vlneufhyNvNalXkhbkVbw1No0Nqt6StEkdebN0faMhc+Yt0HYib7J4S1CstcV3kJ4QC6qTly0XHGeiniig1Td42x8/wWe+fannZ9yWNtksie8ikTdoiP0UbyDmNMyZN73A9KJGbs0JSiEMNvdmFXHxQN4kncemCLV2TnTTQxEjOQx1dllIQyaWEUcX/8hbYOQkyt3/hKlCkitO9MQRjaA4vnvGgxSSERtlwyuKtTa3Bi6ylunDi0hHBLZWRZJ118EcKaVOI+Au3gQiIb7zdEwUPKoVDbMoiW7V2uQS7oQrEgowlU8MhLxVWx3Wqy2+edpDqKVVg0vfEjOo1gjH4P5fZX/5RcKKSuDoW+yPW6w3UrGwsW5YQ240GZvapJ6g6DMyhVSUeDjIibG0L/I2nIpQRhZvg9Mmnzy7yn2H86IJ9J0H4fK32L/5HACX1hzncQe0yU5Xo9yNEVUtxZElkb/zgPheLxX1605HlMvNDtFQwOiqAjaRq21j6JCQky8tiELCbyPvpY6ofw/ReIKSD23y8lqVdDREMqNv8I71MxOos9kV77FebaJ2NTfyBoLuZ0XeRk9xfFycZ0O0pDRvZw1UVsT+5aDhlLzkyh0RyYp9T62sGgwSGzVqW9pkL+TNA1lpSaqs/fqdKiTYVDK0tla4M3CeTiAGY3Y/L5m8rjvtAmwzbw6lSWBNR94AmBd2HhPZGAubDWOPWG7H7IIQeiiKwmgmajQTt0MyfSMU7W/mTe5J/c689RvGKIFYGxKRkO/Mmzfytoczbz46C++5dYJaS+XK1VkAlHjuxhLMcEQkKGacirU2aldzC4EYM28e67NsDr/+12DrKvfzkrEHG/P0tpm3Aa1adhixcFCfeRPrzQ11/n1m3iRbx280Y9uI59y54w6Rt2Q06Jp5qzbVbcVKoI/i7QMf+AAf/ehH+e3f/m0+8pGP8L73vW/gA7ymKC2IeTd9U5TIRU3OvIE5kL0X4UM5MSKadsHcVsGPbZG3mb8z1SN9otlR+UetL5GlTOet/8p8wEDePBQnLchbwxAsGRFqiJaixI681SCSMhKQtAV5A+H1ZkULbOGLvAnBko1qg0vbUPMkyuBLM7XIsRrImweVxStSMUsRHQgKioKzOFM7osMlBT0GmXuzIm8etLS1SpNsPGxPMtf1oXswFtpwZ5f9x1SJCjg6kq/7Rfi1ZyAcZ6qQ4Mp67+/m8csisbh1WCGXCG9bvIVqS4wrRS5HvA2LbaHP4tQ3xHV8eDjJULBJFXcXdaVsCjlIipKNbqgjb2q1SLnp4bs09yz8h7u4uTAYbVLOrX5/tmgvFgEuPSnOs6RMWuOeD1IPpGgShoP32x+z3EvpaMigAFpDFm+5WEBQpKXPGxiJ7rGRFG84WiAUDAi0KhB2KYdmYmFqir5JDVi8Legecm+6aURQyJ96EIBwY42JbKwH8ta/THWzo1ElTtiHNjk5FGciG+PMut6o60japAf9aBDkTXq9bV7RffR8Eh55znsgb7F4ilana9DQrTFXrDOZT6D4eIOmtBprbbGWLW/pc51exVvhqECPul1YmYGxm0nHwuzPxXXk7YCg1VmFqcpLnvtXpeGBojgimpV77KqRZCQHQN5MwZI+aZPGnKMdAZ0qJFhR03TKq9wZOEe5cItL3VAmr2tVh11AeVGs7eD2eEOszSWSqPljMCcaEhO5uLBr0NfyuUbUZRMgwzqbuOPiLRzrrwm9A0nyvsIxShCPBH183jr2mbdWTXxnezXz1u34npf7jxQYSoRZWJijEkgznt+5LP71CEURdFTfQl/eQ57Im96Qet0vQjzPD9cfMfYGk9XjoE1el+ItoM+8eTR2XuuIZVxWASD21GgoQEDO2w6MvDlok9cwh5rwGKvaNdrk29/+dtrtNu9+97s5efIkH/rQhwY+wB1HpyloftlJ4wOmYyHi4aBJmwTvGazdClkYDkibDAcVXZ1qm5m31TPi5+ZV36eslJr8WPApvqXewnzC4t6eGgcUH9qkOfNWk8hbSv8Mlo1dzLxZaJO6QTeYFBBJRZjfrJlzJE66qt/MW6KAoqmkqbFh3VQ7LfjWH9vOnUTefGmTFsqIXABH+0TeUlEHshH34C2XFwVdUnZ0B0LeFnV5dsWXNmlbXLtd0UF3Fm9qdVcVXbttn+LNElOFJFeLNXdRYomHX16hSowDiQ65eMQmU+yMjtrlcEvIbr/EEd/nGaEnlq1NcU2NpKPkw01Kmjt5XS41jO9cbn62uTe9YVEvrXn7S80+DRsXuDNd4vJajc42VF4ZEvkvNzqGspwR574mks2pB1x/p0XT/H/RD/I3yfe70AQSBYHylhd70CbFZxsK6edbqk2Ckej+u5+8gwd/+i79d3X3+yCUwQIJPUEbUG3yybNivXjziRF48t+K90gMQ2WVIyNJLjjtAoy1oP9NsdnpUtWihLpNM9G2CJYoisK9h/PMrMjiTSCMnma3pUVxjpyNJK8wvN4u6wmPH/KmFxMtj+JNLzLjiYRxTM6YK9ZFE8yneEt0K6y2o2ia5m3QLSN/VKxLi8+LYn5UINsnx9OcXipZFBYt1MnKikusBPpLEuJD4u+CtVWLhLVVbVJaBexg5s3iV2hE2xt5O5hPsqKmoLTAzcplOhN344zhpBfytl+s6ZUlkU9szUH+sO3v1ipNkcxN3i2QN01jXzbGRrVFS6fkrXfinrRJEF5vAMlIUDRQdhKhWH8+b3tJmwSb15uz6axpmht5M45nj2iT4Dv3Fg4GeOepcarFFYpa6ob2eJMRCweNYst174XjYj/wnHlbFI2lRB5u/yluKX+LQF0oUJlK1g7BkutQvIlcXDXmI2+smbesaGTprDZTsKQtjlOuXQPPvDlok82yaDLsBHmLBF0m3bVWh2QfCOa2K008Hudf/+t/zdNPPw1wfa0CjMHzfUZ3PRUNkYwGddrkuP15e3wMnuGhNilVvBRFGAT7FiMAq0Ke2GWsaomVUp0Dygoz2pTdVykUEYmvL/JmESyRyBvYireIlTZpESyRxSfgMOoeFHkTF3ReKVO0ysufewQe/QND6EHtmgbdvkhlTS+mEgVDldBGQ+wRKSttEmympEbI8zh+i/6ZBkDeyovi3Di6PTJcBt3lRZGoyBkpfaNKUqet7t49VquJZCjqnHmzxFQhQVvVTF8jRzTaKo/OLNMJpwm0ytsib1v1NrcFLqFqCt+r9aH4F8tCKEa3LBolw6ko2UCTYsdemNdaHcqNDqMSedObC/biTb/mS2Jjc6lN6vfz4bRKS+1ytdgHVQm7iM6zVyzXjaaJ4u3IW0wZe0s8d6XIp4r3w5s9vJYCAZFU9xAskd3VtCKVsdJmYqsnurFw0Nw0O3Wbx5s14skMXQIDI29PnF1lPBPjpvAaPPtf4a4PiHukusLh4SSXViv2faFZFn5sHufDLxo68gaYcy4W5A2EYuaq/LqkYInX7EhZtwnoR7Y5e0AIRhQl8uYz8xYMC0TTw8NRJt2JhPhbZ/GmaRpzxZqjeLMkaJ0mYa3Fhipol54G3TIkajTzZfFTbzTdNTXE2eUKGyEdYbPOvVWWPZG3cmP7mbdIcoi2FiTYWKeqS1hHrAWK3Pt2mzYZcSNv61qGdPUyUaVD5NB9rpeTxZWnXcDWPGzOikLOhbyJtVmZfJ3YG596kMmk2BM3N8ReuUmKvAdtEswi288Pq68IRbcdnQD2EHmze5965S21lora1exorVRY3tPizX/2/D23TZDpbrGipm5osRIZsVDQaDy7UG9pPeWHvMnG+V0fIKh1+BH1cZodlXW9Ke6yChiUDriDiIeDNDo3qNqkBBL0/S5sQd7i4aB5XQ1a5Dppk9fQUElE3eBOtamS2I2Zt6GhIf70T/+UBx98kK985SuEQteR0yoHjXWbANCRN7mw+BUSuxiV1csAlKJ+tMmMSKJU8wsQVB5xYyajPWbeul1YPSv+3QN5K67OE1PazGnDbnqbl2hLpymSOD2RtVkFgE20JBoK0Oqo0GkR6LYhkjL8f6RIzUgqSjQU0Is3vWB2on0Nf+QNIE/ZWGQAuPiE+KmMGgkSAAAgAElEQVTTUrfqbYPa7qVyJR6w0CYrTQrJSN+dTheyYTElNUImPGN68TYI8lbSTYFjWW/aZNUh5bsukCmGbxI/9QUkRYOGB+1qp1Gri+slHu9RvOlmnH6iJY+fWaXWUgklstAokUtE2Ky3fRs5xVqbo8oCs4xxptgHsqUokBolWF0hGQmSjIZIK3XW2xEbGmjYBBjFm4681d20yWZFXCuupFFfKw4mRVHUr1m39dp59rJFPXX1tGi8HH+n5999/plZ0tEQP3a7T/NHt95I6YPLTvRzsyYaQQadVgqWgDeFr91wiZXIGE7HqCqJgYu3VxZK3HNoCOWxj4nO8Jv/pZifraxwZFjQrG3myDvo+jY7Xaroxy0TevlTRxrvO5wX9FMwBUsaHbtNAJj3Yj8RDIu5qOJl0yrALyJJH9pkA5QgSf0ec4qWbNbaVFuq8MfzQt70tbNMnNVyg5VSg4Di8ISUIZs9M18SP0dOAvCWE2Jt/9aKvsbItUxtC4lxx8y22tWotdTtaX6BAEUlS0Qv3lwS1i1ZqHs30QzBEqe8POi0Scf954u8JdjAvKZSR1/verlEJEgsHDCQDfHG0qj7qqdNAOisiHQUbv5HcOB++Nq/4r2PvoPfDP0lzUUxzrClJX2Rt3GDxn0NuVEo3ifytsczbxavt1rbnrfY5m9lGMXbHsy8SRTcxy4A4A1HCwwHKze8WIkM6/Xpee/FMv7Im8x3R6dZzd7G+4OPs1VrsV5tEQkF3EJC12nmTZh0i5xFetndEOGwhDIFSzoiHzZYAzugTapNs7l4DQ2VZMRtJbarapPZbJY/+7M/44tf/CJnzpwZ+AB3HMbguak2mYwKEZBaSwhXEAjvKfK2Nn+Ropbi2XmfhdWjk1q2zGFI+oFnort5xRxS7oG8NVbEprPAqB15A10K2SykLq9VzaIkPkRb7dJWNVOwBGx2AYZgiYX/W260bV1ERVHYn9MVJ6NpATN7IW+RlGvWRi7qOaVi0CIBuCSLN4G2WH3DfK0CamaHY6XkKIa2iVQsbC+iYx7Im0x4xm8VPweZeZPd/ljOB3lz0CatNgFgLCBJ6qbU9S5ErSa+10TcLf4hY2pYFAN+c29//9IiQ4kwidQQNEvkEmFana5vkb1ZazGqFKlGRljYrBszlT0jNUaksWoI0MS1OqVuzDDlBjHvBhiCJTKJKHsgb229eMslvIu3iahevPU59yYX2AP5OM9etlw3Zx8RP4+5592K1RYPv7TIj9+1338AWfeqlOuFswtnKGZa6WkysfWi8HXsHlnWyCcjVEgMrDa5XmlyW+gqvPRXcP+viIIzNQrVVcMuwDb31ixvrzDqiIaqUZU0WflZHcjb0ZEUUUn/tQiW7Mig2xpDU2ItblZ6JzyRpA9tsgHhuPEdOpE36ZHpS5vU946ylmCl1GRpq8FIOurdmBo6JJDCdV3sSF83Tk1kGE1H+drFpqDwyrVMsizS9uLNU6XTJzaVLPHWOtWW6jKpNrwHfeIdp8b4zXed4Piox3mNJNzn00KVtcZUwTTqXmKYYNZdnCuKoht1O9QmQbAqpE2AB/I2kopAsgC/8Ah88Gu0Jh/gnwW/xIGX/yMAWyQNWqYzJG1yx0qToAuW9Fm8RdK+xfKOI+aeeXMyYCTDwdYQq+0lbVJSlf3nwMPBAOOhKhta+v8Y2qTMgzyRWl/kzd6Qmj/yPo4H5mleeob1SothpwehFLfa4xCCJV3qrQ6xcIBAoH+T6j0P2VDQGwyGYEm9LbzrWhWxlvrsl75hzIfq+eE1IG9JB/KmaZrRJNsuti3ePvKRj4g3SSb59Kc/zS/8wi8MfIA7DotBt5U2aYiAKMqee70FygssagV/QQdDIcjcjK3FWzIaotPVaHnN1sh5t3heUDp8Qi0KefJQ4ZDbUymzzzhPz10p8pY/epwnf6CjebGcnYscHxLS9BUH8qZ20Qz6S9J2/DL2D8WZK1q83soO5K255b1YSORNKVOstQSyUFqANf0Y9WOxSs87OcBG1DcABWJZVivNvpUmQVw3ZSfy5kTItuaEkElyRHST+0XeNE3v9k94Gjg2OyqlRseBvF0QSZZMMnXedUqp0/Qz6q5tDDzfWa+LxFFSurxiPBMjEgx4Kk422irfmFnmR24ZR9F9UyQV0Y86Way1GWUTLTVGV4N5HzqmLVJjJFtrxnca6VSpkOCqBQ2UhZy0CzFn3izfazgOwSiqTrF1I2/i/CW6FUbS0b7tAuQC++bjI8xv1k2K6bmvw9itZpJoib/+/hytTpefvu+g/wunJ6C8YBrJOxJ/w1fJquAoxRy8fKHa/rTJQioiFA0HQN7qLZVqS+VdS38mru0H/rl4IDkC7RpHM2KztilO7qDr2+hoVJzIm8UqAERyfnzfsPidLlhSbgiDcyO6XT3RGbB427hoCsL4hZ+pdKcBoaileLPfF/O6R6Zv8aZ/HyUSLJcb3gbdMkJRkwo4aqotKorCW06M8K1za2jZSbMZaBh024s3qxfpdlEO5oi3i9RaHRLOpGKb7zqXiPBrbz3mndRFUqIIVy3nq+1Nm0zHwjQjIjk6Hznp+37D6ShrVhQ4lhVr65ZevEXSrkLDZXB88D6CP/U53tr6Y34w8T4uTv5DOoT6QN6uoXgL94m81TZ2H3UDm0ovQCLsRgS2avZZeHE8e0mb1K+rXpY9mka6u0U5kGF64sYWLAHT6w3wpizHsm7kTa5ploZU9diPUdWixF7+POuVptuD8HrNvEWEYEm9rd5YSpNgXpN6g0GOAbXUrrBtaFbEGtQPvd4acjRI7qO1naPhSV3VVYI7zU6XTle7NuTtr/7qrwB49NFH+eQnP8knP/lJHnzwQba2BpeZ3nGU5sWJiqaMxDsZCdmHaTN76/UWqy2yoOV7FG/uzbjc7JDSqTySA+xcCAFz3u3Y23oWb6Et8Vhi5LD7ODL7xEXUrPDCVbHw/q+ndR+ceI6G1TwxENC93uwzb5oG7bop+exVvE0OJYwOskQLbNHY8hQI0PQLejhQQdN06oWkTEZSJvJmmYfznRGsbYhNJhBkrTxo8eYz82ZFREvzIjFSFG9BE7+oF0USkt7n+XeyEzxsPd61c4ICJRcOC23SS60OgK/+3/C5f9zfMenRbIjvLJn0L96CAYXJfNzzGpeUyffcOmHMd0o0y7d4qzYZVTaJ5UVBs50NAQCpMTLqhvhOuypBtU5Vi9lm0mTxZiBvMQ/kDSCeQ9MLaFvxpmnmWtEocXQk2Tfy1qps8pXIv+R3zv4EfxP5PZSHfga+/C9g9ilPlUlN0/j8M7PcMzXEyfEeSUV6HBpbZELiMzhFSzZrbdOgGzxn3mzhI1gCgoa32U3QHaB4W682uU+ZYWrj2/BD/8JM8vQZqn3hMpFgwI28Ddj1bXa61CTyZsy8uSl0JydF8bZREs9xzbzV1oX0f8ZdTPvG0CFzTfSbeQNvpAj04i1umcH0Q94SZkLqUbxJ5M3ToNsaEjkas9twvPXEKKVGh1JkzBw5MNSSvZG3fua0KqE8qU6RipeEdS+Fzu3Cy/jcxyoAIJwR3/186hbflxxORuwzb2DaJxQvCbESS7LW7WpsVFuuwiwWDlJJHOQvRn6Drxz9XcB/vtpqXbLjCEX793nbgZ/UthEMi+/RJljiaCTp13XWRZtUTORuN6MP5I1mmUC3zQff+ToO5AdEUF6DiFnUpj2vFy/krbYmBDEsyFs6O8SX1NczdOnL1Mqb9uu3271+tEmLSfcN5fEGluLNjryBng+3djgXGLM3OqzjPAMfYjSI2tUM3QmDYdjH7KBv8TY+Lmabjhw5wuHDh23/XbcoLRibcLXZIRkJEggoxMMWDxKvQmIXI9Vc1pE3nwXEkzbZNuZxpGqMp+Lk6hnRTRm7WVwITu80PWK1ebaUDBOjBa5uOBTyZJJSXmRmURzD5rqukBkfMs6TcWMlR1zFG0Cnrh9/JGUrPmVMDsVZr7bEgp7Z5y6YGyVPqtR6O0pLC3I8LQ0lm3DxcXFjHXy9MX8naZPhoEKt18xbPI+maawOXLyFqbctM0XxIbEgWjeGrTkTQYkP9Y+8GfReb9qkqQbloE1KyiRAKEpXCZJU6jT8kLfSPKy82nMGwBnNpkgcU8neG9uhQtKN6mJSJl9/pGD4pkjvND+j7mq5SEJpkh8TiNN2NgQApMfJamXGEwHjPqgocRvytlJuEgkFjOQh5UNTI5YjoC+stm54s2QWBM0tjo6kuLDan7rnqdP/nuPKVYJT91NX4gSKF+GV/ylmwG7+cdfzn7qwzqW1Kj9zfw/UDYxuakEr6p/FMS9V9yjeIna1SVt0Gj2Qtyhl4qi1/unA65UWHw5/kUZ8HO79ZfMBnYIdrK0yVUhw0UrnbgzuMdS0CpY4aZMWz7pbDorZrotLG2ia5rYKkIyAQWiTuUPmv3vNP3gJbMjjDMd60ibT0ZC4boMhUbRYu+v6v9vhFMulJsul5jbFmz73Nmov3h64aZhQQOFyZ8ikTUq1ZGfx1uifNlmLDJFVN6k12h60yR4KnduF1+ymgba6i+j2yM3MdA8yP/om35cspCJ22iSYxuUeNgGSDeJFwZ/ICaPutUqTdDQkqFYeMZbZDdpk3BtJd8YO/aT6CouIVzwSctHiS8bMm6NZEs+5xyV2I/qYeZOJeSC5B8jfHoT1Gkp5IVVeM28l95qWjYf5gvoWQp0ap8rftitNtquAdp2QN1G81VvqjSVWAuZ9ol8jYQsN3Zh528na5aRNGjNvO0PewCzaJOvsmmiTsViM733ve0xOTrr+uy6habD8MgyJYrFiUcaydYX2kjbZrJBQy6J48xFzMAoWH9qkVIHzVFBcPQ0jJ4TiGfiKlmQbixQjExwqJOl0NRMBA5vX2+mlEvcfyXNIF2PQLLRJ48ZyFG9RfcC0XbfSJs3iU4ZUclrYrJsFszXp9UHermzUKJLmUEJ0FTcqLTHvdvhNYg7DoE2KY57IxoUNhFfUhJdGqd6hpXb79ngDM9E3kA0Hxx/Qi7dJ8/F+Z94MX6l9nrRJw4dFFpudlpixsRZvikI7mCBF3R95qxcBDZZf6e+4gJZevKV7IG8gBAFmN2q2QubMUpmvvbrEj9wyLuZvomldsERH3ureyJu6Je7HobEDxMPBvoq3dlx01adiVUPEIBhNc7VoKd50g27J7Q8HAyQiQSOpMCKeI9jcIhYO2BMu6zrREMXbVr1tF9vwirnnODX3Bf67+k6C7/uv/McDf8TPxz8Fv30Z/tUyTNzm+pPPPzNLLhHm3bdsU0ToAkC5jthgnIn/Zk2nTVrNkHt5junzV16RT0YokUQbAHnb2ljiXuU0xZM/ZX9dw3ZkhSMjSS5aEcwdGMQ2Ol0P2mRNJLUBc5s6OiE25CsrmxaKiSVplrTifgVLwLQLgN4oki9tsgmhmFEIOQvwuWLNLqbgtJfRO+3RVJ7ZjRpb9ba30qQMKXI0ZkegMrEwd08N8WIpJZpi7YaleLMLbsnrbDu1SYBGJE+UJp1mxZ1UXEuH30sKXp5fj2s4P3qAd7c+QWjE3zuykIqyXm3aGzKZ/YLZUrziYROgsyK8irdsnMXNBusVNzJnjVg4yP919yRvPu5jJ9RPDIS87X3xlogEaasabUujWAqWuJC3vaBMwrZWAeL993Dmbg8iFhZrWSoa8qYSeyFvhuK5uZfk4hFe0I7RCiY43Dzt9niD6yZYomlin7rhirdIQuwfOjJmRd6MmbfdQt6i2R01MBKO+mCQWWTfZ/zFX/wFALOzs7TbbW699VZeffVVkskkn/3sZwc+yIFj9bRQAHvgNwC7AkvSanydHhfzVq1qb8rLTkKfJVvQCsxt1FG7GkHnDeeYedM0zUblkZ44LsVJTRPI210fgJzend+66qLCAAyrS1TT0xzShSUur1eNf8skRd2c5+xynp97wyHekY7CGXh2BYJJXQXIirwVLxmvLS9o1aK8U25UPWiTYjO9WqxzLD0hpLprG2LIG0TCZi1G9LiyXiOhpZkIiwW4uXRaLEaH3yw+b2UFul2KtRahgMJIOupvFVDfgNQ4qxVBnxuUNgniOsrGww4logOCrlPfMJHM+JBdbrtXGJ2xcbEBdup6QieOb7XiMBQvXhKS1Y7z1QkmSCkNf+RNFppLP4CDbqlsr2jrxVsvtUkQggC1liqG99NR1ipNPvjfvkcmFubX36Yni7EsdOrkIuIe8KNNanqRFEiPM1VQmN3Ynja5FcwzDOwLlYx7KZ4aYm7DSptsGvNuMtKxkBt5iw8RXr3gO+8GQKPEsVGxcJ9fqeDrCKZ24Mu/QSU8zIPq+/n5gMI9U3k+9c1zLmEfGSvlBo+8ssTPP3DIt1tvfgCxIac7a0DORpvUNE2feYvYN2SZ5PkKlnh/18OpCPNagsAAPm/hK08SUDQCx95mf0CKH1VWODJyE988vUJH7Yoiv0dCLwVsjPVLj2ZH86BNuj9LUFfSnF/dMAQUUteKvEmjbtieNrnpc85DMUJ6M8ELeZscsiDfruJNFNOx9BAvzYuEoCfydvtPiWTVo4h568lRXnwkDRHE/lVZFmuZw7bBUG+Ohuhsc3u2Y6KxEm1ukIw4VJdbFYj04eXoFREP2qRDpMYaBwviu+nl7VlIRmirGqVGx7z/s5MmtcmBvK0bBsdexVuMpy+uM5yObGtJ80fvu73n49tGKGaKl/WKenEPkTdTgdmaVGbjIkeQzTpjzSstCrXsPS/eesy81XTxteTw3hzD/8/em0dJctZXojf2zMg9s/aqruq9u9Tq1o7QhgzIAgmDByRhg8wM2AMe2wLP4IcxM3PeMCwGv2dsD3DmgVfwAAYLAR4wjC0LjHaEhHZVS71W115ZS+6ZkRnL++OLL/bcqjKrW7J/5+hIqsrKjMyM+OK7v3t/9/a46P2gqcSWMm+6bjetrP2FQzYZ4mEwLJbCB3GkcQq18wjeAGCjXEcqsg3muV8lZyyAL3qZt8IWmTcLvNGZt61LmSmmoco8+u9tMW90zi2dTuOee+7BJz7xCdx9990QxR67HDWrF79P/n3wFgBu8BYWeHsuqp9xAebmfYXJoK7pWMoHLK70AjG/SCrNowscHeL0AZL8POkyDh6ywVvA3FtFqWPUyKIem8Bu8+Z11ilRMsHb5vIs6qqOwyMxXGY2AD/30Io98+YEbyUn82aCN5N5M4QISorq25TSzYc76829GQ5k3tYJ85aCuSGff4D8Yu/PESmPoQHVDWyasz2yyFlhsP4PY5M4TZqug93KJgGHIUTYw7xRcxzKgnYz80bPPeo2Cbikk5R5s7q31GlywAPeeMK8NXWbdIK3DquhEKDL8K3BGz23ZtfLqDU0vO+vH8d6WcGf/7srMZow/9ZsVCQ58pzNgrq5stntj41gMi3jbAfM2xpDFr9hNm/JZCLxhIt5WynWrPkSWvGQ4M55A4BQEpJaQDLsWavo9xROEebNBG8t594e+yKw/Cy+O/YBQCTv/6rdaegG8OS54PPj7sfnoeoG3vGqNpJJwGLeYnVyTd43s2p1u6sNkkVHZJMF0kXkBMfMWzPDkmYzbxKKkME1SmRz0EElFx9Azoggtu9V7l/QzVI5iz0DETQ0UxFgGC3B2wf/9im8/2+e9P28qWGJ1wmMI99pvlS2GF2XYUlhiTiIeWSCLSsyaL9Oq5u50CwqQLGkqqSZYJ+PJOOt6s6g8oI3E0zH4kmsmHEYTQ1LALI2HXt74K9ee2gIizA304UFcs4HfBZ0Hexk5k01WfFwY6Nrw5KWFTTTVC+T7yLARODQMHmdqUxzgE1B2HpQXADgA29WYy3m39eMJsIo1lTMrlf8hhC9LiHUnnnTNQKu+mFYArhGBahi6LtPL+K//d1zeOOf3I/P3ncCGVkAN/8Y8M1fBf7kYiLj3/e6/hwPLxGDtZbMWx+jCvpQ1Eq/KXiT4gAMN8NfXCKfg4M9Z1kGibCAF5h9OMKcxUDY0SS0wFv/DVzovnKjUr/wZt4Acl40nXnb4rwu3ec63Sa32FCh6ymVS5aUHoA3WtmsvdHXNA0bGxstHt3DOv59YOxyiyp2sllkg68SaUSz3LEelGGCt8jgbgDAuaBNqId5K1o3RffMmw+8UafJwcOki82JgXEBa0tzkBgVSE5hICoiKvHuzbAQBsIpFLPEkXJ6NA6+nofCx3D/yU08epqcuJYTUHSQLAzmgkhPaN3cMFfZsAk+3SfPYFSCyLEkLsAL3gzDlE36F4vZ9TJqfAJindwUUiuPELCa3mPnzpVWka/WkZRFRES+uWzSvEhoyGWrDqy3fLJJTyipxbK5Zt46BW+LgDxA7JtDHj00yNyQLHL2d0DBG51dMUsTIoigag2vukpV7I3jUufgTa2bDmZc66bLZIZsXs+uV/Dhe57Bz87l8EdvvxTHJhyD6Ob3G9JKCAlsU+ZNqNomCVMZIsfU9dZzZSsaWRAzxqa1mU0kM1gu2AYuqx0zb0nIWrE58zZwCFAKGI2HEBY4nFptsjnIzwM//CRw4GY8FrreWlAvnUyCZTx5b2ZpuoGv/eQcrtufwd7BDrp6oSTAhxBrrOHXX7MX335yAXf+2U+wWqxZny9xm3RsklmOuKEGSfjM+augSkdFFAwZDIzgLCFvGQbG1x/GozgKOeS51jiB3LBKq9hH4wLWSuQ81RuBG/pKXcUTs5s47Q31BqBoOqqQYIBxzLxV/AyMCZIkqPjhcXKeuSQmxUVzPe1CwsIwQNJk31rmvMnBId2OzzwWElznY77aQElRW4O3WgGQ4hhK2KBkJLE1sHBwOIpGlGabzRNlQ2BAdwBr2aR02QRv9Q2/nKddvEKrCpRNNo+6ODqRwA9++wa8em/zjZIV1F0OiAsAAmMCgGDmbSxJvtP5zapbltaP4kNkXrXV/G0tD8DoH1BxxOdQUP9fv/Mc/vbxeQzGJHzyWg4PZj4G/OXNxGX3Ve8D3v8E8HO/15/jYRjSTOlg5u3lIpuUHLLJwKJ7KKd0srBEGjCsGxwlwgIeq00ixDSwS3M0/p3OxH2usEjez2a5fuG5TQJu8OabedvivC7HE9BnySY3t3xNRiyGm9wzqLFhT3Lebr/9drzpTW/C+9//frzlLW/Bu971ri0dZFdVXAEWHgcO3Wr9iAZHA8ShxTCIQ5nTsKPXpWzMQTcYTO0mDEng3JsQJl0RC7y5pQU0Kd2XXUadJgcPE3o8MRHIvBWWTwEAxMxuMAyDqYwcEBcwDjW3AJ5lsG8wClRzECJpxEM8/vKhswDsi8wO6iagnM686TWyQJZ0csPy3tRZlsFYMkTiAuI2eMtV6ji7Yjq8NZl508NpsNUNxCUW47nHCesG2B3h0go2yw2SJRaQLwOAzInVS4CcssDbYLRFd9pTlhW7D7yZbBYFb/R8CiWJXENzA5RirYF/fH4Z//ffPYff+MoThCWjMQGAw3LZzby5YwJOku8h7ABGAHSeyiYD3j8Fg1IcWJ3xHVez0hs1NMC75oaCaiIVBssAf3zvS/i7pxbxoTccIg6TznKY8yTDYlPDkrCSRZ0RgVACU5kI6qqOlWJrG+wFlSyiCW3D2iCn02kYBrCYq6GsqCgpqk9OFg8HM2+yUUEi5HnPxWXy+cVHgVoeLMtg31AEJ5sxbz/4MJG33vqHKDd0S0oUlXhcNBbHT8/6DW3+aWYFC7kq7rx6yve7wHLEnXzk1mn8j1++FM8s5PDmzz2If36RXKNJmvPmvNGIcjDz1sKwJCbxqDAmQOgEvK3OIN5Yw1PSFcG/jw6RmbcBclyns+WWXd/Hz26ioRko1zVseOYMaw0DIYEDI0ZtgBTknGk2IWROww9nCHhzNZqc12I3RefetpLz5mPe7LXe5TRJS4r7ZZNS3NWMGmrFvLUohmFw+CCx0tc254hsMjrie1xJUcEwxBK+7XOa94yEnnPPteha+3iFVmXNbjrBW6Vl7tL0aNydZ+UpatzgZt7MOWY+5Pss1koKeJYJNBuxFAeO5+1b8SGy1rRa17cRBtxRUebNMHDT9BA+/baj+NZvXotnPnoz/tevXY078X2Ec6eAN30G+OAM8MZP2aHx/SrnehBU5TWS9bsDLFMvypZNNmG8LTLAsT4XFwPXtGRYwI/L5NwerRy3f7GTsklz/6jqhsXWXlDllE06mDdpOzNvANnrOmWTW2XefIYlqvnz9p9lW3i3b98+fO1rX8O5c+cwNTWFdHoH6OmXfkD+fdgB3hzBdfSGU6lrCNHNdgur/a1WfWMOBSRw0a4BCD9dDDZeYBizk2oGrdbsWQLA6SbjZd6Okw08ReyJXYGGJUqWBItGRkjHcPdABM8veAwH4mMQZk9j/1CUnKC1HFg5hXcf2Y3P/pCwPPbMmyOoO7XbOqENU1de0MjxBi0uVlyACbpOnDqBX/77HyOmruOfGQQuoLPrFbCZASC7iWtCZxFSSmTeDXCAt1VsVkaxKy0T96Ig8OawY82uKRA51u161aZ8OVreodPCAgDGNjqwZuLy0MMZfPH+07hvZgVPzuWg6QYYhjRJP/D6MqaLi7Ye3coAsZk3At4cndu1k0DmgO8YDUFGFPPBM28UZO6+nkiK114iLqVtSm/UoDIC2omjJJ7DaCKMhVwVb7t8HL/5cwE3ZQfLnJSFpsxbtLGOIj+AjNlsAMh54NwMeWu1rGPdiCFZX7NuPgOZAQDzLsdJL9saCwn+69IExaOSR4ZUXCJMfShhdTb3DUbN0G3Pje7494Hj3wNu+iiQmkKlvuTqhl05lcY3fjqHhqZbLlbL+Rr+y7efxb7BCH7+oi5ke7FRy2jjFy8dx4GhGH79K4/jP3/7WQBAQhb88jQhwLaeZH403fwyDAMjlABUdJb1duqHAIAT0SbzlaYEOxURkZQF4jipmMxBwFrw0Kk167/nNqsuKZqiGeRGJjnBW9n/XlgWYAVMxji8uELOE1ejqbzaXUwALTr31mrmTeeBHrwAACAASURBVIiQSBBdc3fB1aoDvAnIO5oaNBuzrWwyFLcaE7LIuaWgXdZrpsex+mwSxsJJDJdWmjBvanPTBE9x5t9nULDuZwDs76mXsslGxZ6F20LRdXbN6ThJ1/TUHl8Ta62oIBMVAz+HUYdpTCvDkp4UbbioteYB3NsIA+6owinShG1UIIsR/LJX9l1cInPaV/37/rx+UHmijXxFDVO6zeo6T0WjAlrOvAF+5i0AJCdkEc/owygYYaRyz9m/2Enw5gAZF5xhCWCCN8K8cSwDjmWg6cb2mDfANPfpAfNmeWJ0b1jSlnn73Oc+h0wmg8suu2xngBsAvPgDIq1zWCGXFNW6odlzZCpZ6OWBzs0luigjN49FI42huIRdKbl5XEAojmayyebM24uEdaOV3BUom9Q3CShNjRH2b08mgrnNqssFCvExxOurdkhldRMIJ/Ge6/ZYF5RFadNZFdPlkc68QSlD42UUFN11/M4aT5LNfUFlUeISeOxp4nrI1c2FxsO8FWsNbJTrkOKDgKHjjexPyS8s8EZND1ZInlVYsCSxvnJo22lMQKsOrLfoBs8yjhEjACtAKa4TF638HDkeOtjvmIl7caWIP/g/x1GsqfgPN+7F37z31fjKr5EN7VpJcXf7m8gmM17mLWAxNoQoIkyTnDcK3uhnt/xsR+/bUBVobGcbj1fvzeC6/Rl86m1Hgz9bx40lERaauk0m1Q1UJNM9Mm3P0rWqbFHBJpMCV1q1ZDIjg+Q55jYrjow3D/MW4v1uk+Z3MCR4mKmCCd4k245532AUC7kqal6p6kN/QgD2NXcBAEqK5pr3uXJ3CtWGhhcWyfPUVR2/8dUnUK1r+OK7rnDZEret2IhLOXDRWBzfvet6vMZ0rxuJh/zZaYLsn79SFQBGU9kkADBh2lzogHk7dR9m2UkwiSbOjSbzBgB7B0zHyRYbh0dOrVub63MeFYOi6uSGKkbcUQFB5iu8hImYvVGIOd0mt5AxBwAYv4KcN63miYIMNgDT4dPBvCl+5m2Xz7DE8fmbTr20MTESD3W1tnnr2v0DWDIyUJeeJ2AgYOatWFM7BohhOYyCIWOAybtnMRwmV1uqwJm3AKlsF5UyjUVccQGiTDrjHqdJIEAV4aiRRMjCBH2feaP3nVZB3fQe0E/mzfk63iosdefi2ouKjbUeialsvGwkk4Ad0t165g0BzJv/c0+GBRhg8Zy+B+E1x35gB8Gbc87tgmTewmnSSNfImkylkxFOI6Z7W82oDJmeCFqDfFdbvCbpekrxQbmXM28Mw+C3fuu38Id/+IeWiUlfq14mOWCH3mR1U7wOjjYgMje5iYlA4LPd4kqLWDIyGIhKmMrILYK6g8CbKZsU3MgagO006QRviUkicWm4F2++MIeskUA8Roe1ZWi6QWbPzKqGRpBGAUcGzU16NQeEkkhFRPzKq6cg8azdFaGAyexmWVRyvQSDD1vH740KAEj3OFtU8MY/vh/nGkm8erCO777/esRhfi4e8EY/r2iKbB5eU78fZ7jdts24FCPmCqUVbFbqSEVEhEUetYbun5FySEayRcUdeN1BWTbedGPFMDDCSfz46RP4ja88QUJtaUwA4AJh1Kjm07cdxYfecBjX7MtYXdnNfIk4XlHmLexh9ODZINQrZMMbsJGAICOCJjlv9IY6cRXp0nY699YFePvM2y/BV37taktK6yvHjYUwb37ZZLWuYRAbqIfNEOdkCDzLtI0LyBYV5Pk0uQbMa2loYAACx2Buo+oL6KZFZ4ycM1SqeZyDvOc1i8umqUyCbJJUhciMASwUPACwuEQ29By5jitmziStK6fIYv34LPlePv69F/DkuRz+n9svwf6hLm8INO7E8R6Ssoi/evdV+KcP3khm53zMWzgAvPlz0bzFyxS8tWHeGlVg9mE8jGPNJWORIcv8aM9AFGfWyk3nLfKVBp5byOO2y8k1NucBbzXVlN04s9SasYiciLGofetyMW9bNdA4egfwO8dbA4dmEQ0OqWo8QDYZk3i3SoAyb/T7prJJ89xu6TTZQUUlHkpkFIPll8gPYkGyyUZH8270+bJGwgRvQcYIvWTeqoEZb52WwLFIygLWyx7W/Q2/D1z7Ad/j18v1puBN4FjLITjTxm1y20XPu1bgzboH9iEQ2/m8zcAbVS7sZMXbgbc12/H6ZVAdyybp+lyvkP8OcM+lM93H2f1gV54joyXAeXGbBABZuBBn3sxzw9yPCRzBFDHWvM62y7zRa2XLM2/UbdJk3uoqRI51STybVdtH3Hbbbbjpppuwb9++nQnpPvUjsoAdusX6kaLq0HTDlk16s9OSu3rPvBkGpMqSA7xFfDlYVjk6qSXFPQjOcywknnUzb8UlEm8weMj+GXWcpK6HZsmVeaxyw1Yndo9psX3GwWQs6KRjdixpbt6qm1YX7XffcAj/8B9fY19ksu0SB9jMG1MvQ+fDjvBW/+JCTS1CIoeJyT3YFypiLBnGvrgJNjwdb7phTw4Q8JbRsvgJjtoPYBggOgi1uApFJa56dIPsDQh1SkayRQWDXcpYfLJJADU+jnppHSdXS+T8ccqtHF3IpTy50F0zEOZNvbJhfl8W8+aWTWq6gY1y3T5emrsUZGUuyoiiBqURwDzSRSKSIXLJDhwnDcMAtDqMDsEbgNYd/5DN2qRkMVA2uVmpY5DJQZMJeOM5FhOpcPOcRLOyJQVVcYAwwvUiwEngBAnjyTDmNiu2SY1v5o1HXdNdJi8llty0MpzjNQ3D3HyMut4HjQuYy3neS8UthajUNVc3bCQRwkQqjMfPbuCeJ+bxvx6dxftesxdvOraFeavYCJEIOqV0IDIPenyoF936fDHA+ZA2flowb1LUfE/twNvsQ4Baw73KkeaSseggOa5GFXsHI1gpKKiW6Gyme+PwkzPr0A3g9dPDGIxJPvMnm3lzyiabsDC8hIGwfSN2SUyUYqBxUttimPaMTxDYAFzgjTQT7HNpfrOK8VTYfV1JMeKySzfqCnHqpee2t0GxlQoPTEGEeRwBsskgR+FmFZV4rCPeXDa51e41HyLOoN6ct20wbwABWr6g7kvfAUxd43vsWrE58wYAo0lyLDsmm2y0Yt52QDYJBBt1qYq7SblTFR8l77tZgHk/c+b6UDTnrSnrHfIwb1aGbMDMm5m3ei50kLBIqy/Yf0udiftcbuatC7XJThW9VizHSXK8EZjX2XZn3rYR0A2Q84FhSHMYIMybq0HWotp+2m9+85uxe/duTExMYHx8HILQ5xPixe+TD2bqWutH3jmysOCmGq15sVZOTd1WLQdBq2IJGaQjIibTMkqK6hu0B2CFF7uO1dHV9EkBnWYltJI0qHvW9dQJZQk50b5wraw3R1zAiRq54A+EzW5uLWd10XiOdWcqCSECsrzgrVGGzssOwxX/4nLLxaP47Dsuw9+//wbEB3dZC8sxEw8ank3TrJnvNThsL/g/qk+7nzQ6DK1ghjrLoh+Y03Iwb4VqA0m5u5spxzIIC5wFrgFgoSYhiRKypRqMwoIdEwC4GLTlfA2cmUFHKx7iIXIsGjkTvNGbGi+RhdPcHG+U69ANR0B3iTox+jdUhhgFyxjQlACJoSWZSQEjRwl4a3O+lxQVAhow2jhNdlwOw5KETGST3mZGrpBHnKm6OrSTmUiwU6ujskUF9fAgUFo23ffIa+1Ky5jfILLJkMD6GGG6AXWalhRAFuQk43jNygaZ54iNurqbUxkZLAPMFRzXtVonoMSxIJc8zBtAIgMePLmG//ztZ/HqvWn87hsOYUvVSdxJEPPmnXnrgHkLxcybWTvDkpM/hMFJeFg73Fwy5sh6o46Ty6vm+e0Bbw+fWkdIYHHprqQVCO8si3mTojaIbSab5ETweh2XTCQh8Y4uZaNGNjH96jgHgTfDcIM3iSgHqKx9frPinncDHNeR+T5Np96YxGM0EcKhke2bL4xMOmZqAwxLig4DsHYVkXic0UdxMXsGUc7R5LA6/FvcADGMP36hvr2ZN4A4R57KlvD1x87hv3/3efzKn/8Er/79+/C5+064HmcYBtZK9ZZOkmOmwmJHDEuA9swbw5JA4H5UK9mk1XTcaebNbKg2Y99ebuCto6gA2LJ2+r4DDEso87YaNceLFs0Ilu3Ed3RZbvB2ITJvbvBG97tRxrxXbnXtorLJbTZUGIZBRORRMpV5ZUXrSDIJdADe7rrrLnz+85/Hxz72MXz0ox/FN7/5zS0dZMf10v8BDtzs6hpYQ3ye4Gtqq4nEBOnYNaP7t1J5sikvisPgWNt4ITCzyiGbLFDmSnSCN94+VsAdE0CLAgenaYmuIaNlUQ7bjFAmQuICnDK054pkU5FS10g3VFdbdwIigz63SbZRgi7IgeCTlsizeMslY2STFR8jQERTcShJNvBzFTewP7dewUBUhJwgmzyN4fFg45DbkCQ6DKNIbgwpWbAWAJ9pieMiKSqdbzycFQ3ZF8nJ1SJmKxIG+SoSRglMo+K2lPYwb0MxyRXQzjAM6cY6A7pphRJW95LKd6ybf2nZet/e0nlz0xI0j1TdJK6mUhwYOUY2fG2kwvlqAxIavoDeLRcvEYt6022yruo+iWd1nVw3XNz+PKbSxCE1kLUG2URliwr0yBDZfBcWrEV1IiVjbrNqBXR7mUEK5gpVuzmyqZPrIQ6HS5nVwRyxmTclj5DAEYCYd2xKqeTVcQ1V6qpvUb1ydwrFmoqULOJz77icBFRvpei508oxN8iwxNuNtpi35uAtGidNiUa5zVp56j5UR69GDVLzza1Dgn3N3gGEBQ5PnzQVEB4W/pFT67hqdxoizwaCN0U1HMwblU1Wgg1EeAnQFNx2xQRuNOcCyZP0Od8oSDapNYhLoGPmDYAl5V3wBnQ7j49KJ82oAIZhcO8Hb8R7b9i+umVg3GGJH8S81dSuZJPf0a9DnKlidOmH9i96Ic8SI243wRaGO53WaCKE48tF/N63nsXXH5tDodaALHL48wfPuJx8CzUVdU1vybyNJYkLb0ruc9PaAm8tst6qG2RNauMcvOUKtZBNOrNMd7KCMmVpaappFvHyCOgGbJlhtBnrLUZM93Iv8xYw82Y2sNX4JPnuzgN4o0wi0Jlz7Y4XBfYex0nZoMzbFj+ncJKsW7QZv405VOKwTvYvpS72tm1Xgc3NTfzFX/wFjh07hm9961tQlDZBktutyrorIgBwDPGJHtkkXYgp8Onl3JspX1RksnjQYNBzGwGsiMM9rFhr+Fy8IlIA8xZO2+YhAAFDDOt6D0ZxCQJUNOL2LBbDMNg9IJP5ErN+uh62j5lKHkItdPGRQSIT1BrWycypFYt5Yxi45TFBFRsBYBCr8Bh5b48vuwHX2fUy+dzMC2gjeRRlhN3zCNEhcBXTEt3BvPlMSyobAB+GwYdMyU/34C0m8VYj4M/uP4MSE8FEWMEYY5qhuGbe7CDG5XwNIwm/FG0gKtmB1M6BYkfA91qR5ghR2aSdgeYtC7wFhZKaJjRgGALegLZzb/lqAyJUML0CbwCRddQKlmRj0zP3VtskYFZM2Z/HVIY0BZq5UxYVFYqqg6UgZv2kg3kLY6Ncx5m1cqCcjFp8O6VqOZ18jlHDGXTqDFJ3zxXsG4zinBO8VdzdNEXV0NAMH3h77aEhHB1P4H/+yuVdBcb7qh3zpip+RkmQ/TlvFMy1AG/pWBRlQ0Kt2CKvMz8PZI8jO3w9gBasgyOnMSELeOvl4zg1b242HMeaLSp4caWIa/eR9W5XWsZSvoq6Q+pacxqWtIoKAEgDQa3jHa+axJ/+2yvtn/c73yiIefOwnTHH+Vioqih6M96cx6cUCBA0NGu9iUr81psAjmLMtawBPrCRV1TUwLnmoIqGeDyqT2PeGMDAqXvsX1iyyS12rwHz+/bKJrcH3j5y6zT+8t1X4v4PvRbP//c34H/fdT0+9osXI19t4B9fWLEet2bGCQwEBHTTes91u/E/79xGY6bTsgxLmsgDAfMe0EfTuFbMWwsGqK/Vinmz5o1eRsyb0MZtkrqXd8G8ZWIhYOwyYOkp8oudBG8vB7dJwJf1FqY+DVtm3sz94eYZ83W2fl1GJd6aeSsr/iZxs2q7IoVCZNNarVYRCm3PBauzIxKA/Te5fkTZINoptNkZKps0N929nHszn0uLkk3ornQYDINg4wXnzFvNDyxkkXfLAKlZifOz5ATSXXEwb5VVEhPAJN2WvVOZiJX1pmo6nspqUFiZXOhOeV2zSk0Bcz8BPj2F9D2/hN/ivoNQLQudDxNWS+zAQppuOAtLGOAVqGDx2Lxb8nFuvYKptExu0MkprE8RUL5ZdmyUo8MQlA3wIAxGuJlssroJyBlU6hoMo8Xi16IiEo9SrYHVQg3ffnIBg0MjCKlFG7w5QDI4gXRlqptYylddttG0MlERoeoK2Uw6P+9Q0gIG9gaBMm8rBKQH3HB0c1CfqRd9vyNSWPM1ho+Q52jjOJmvNiAxDTAtZqC6LtOpkXaivYCMSmDltM1i0sZHs7k3Os8mJs1zanPW6ohRl74XlgqB2VfUCKLgmGXcrLOoGQJkzfE5BjFv5g1yMi1jteR4H1W3jp2y5t6b01gyjO++/3pcPrk1vbtV7Zg3JcCSPSjnzQISzb/vdEREETLq5RYB9Kd+BACYTb0aQIt5H4t5Iw2Jd1+7G2GjAo3hXWzvo6fJ9XXtPnLOT6Zl6AawmLOPn0QFcOQ91sukq67VgzfyJvPmq34P6gcxb5umzN0EmVEH8zYXFBPgPD6laM8ebmVOr1WZDc01JANt1EtdyCajEg8DLL6lXQ95/n57Q9kLplP0RF40qtuWTQ7HQ3jd4WFMZmTrPnbtvgwmUmF846d2pNCaue60Yt4mUjLeePEOABbLsKRFc7yyseXZmo7KdGB2mm1Zdb6YNwpagsCbw4H65VK06dky9D0UdzNvYixwTXM919hlwMoLRH1x3mSTFyB4C7tlkwJP1oOwbt57tjzzZpIjG6fdr7OFkiXOM/PWI/B288034/Of/zwOHz6Mt7/97RDFPg/u7rnBdyOjbAm1hPY5OFJwE5CTtuUqLEAFCy5BNlYSz2E0Hgqe3QnFTfe6OrFg9gCLiOQInjYMErI8GDAf44kLKNGA7sG9roftyUQwb8YFnF0vo67qUOQRwrxZkq8WzNubPgPc8SXg0neCq6ziQ8LfIqKsQhMTgccfWA45A6PkUWWjePycvejXGhqWCjVicsIwwG8/jfKlJB/GxbyZ3fs0ikjJgsX4+WSTlQ1ATjlyMLqXsUQlHmVFw5cePouGruOiPZPg6gVMMGaOjFM2CQDhJIwakU2OxP0MwEBUQrSeJTcY5wbJIZu0wFvEAd4ig+6cKLN0c3PINgLAm8OEBqJM8nbamJYUTOaNE3rIvJkscyJM1oFc1TMDat7kY4P2/KCd9RYcF0DBWyRtsnWGZnXEdqXJ32q6geGYH5TEgpi3Sh15RBBygTe6+Rjx2TEnwgIqDQMadTj1WHJTFrjTRbXrkqLkmJqCtwBGaYuyyUxURMGQoVVagbf7gNgYZtlJ628Cy2LeyPVzcDiGAwkDBUOG6nCLffjUGmIhHkfGyOc+aX6nTulkTdVJF5cyb5RVbGJYYjmrOavf4M3LvKl14H/fRc6Tw78AwG4qFWqN4IBu5/EpRbvD3muppzwAlRGxoid8a2lD01FtaB2voRLPgmMZ3KO9BoyhA898g/xiuzNvgNugRtfbhnRvtViWwS9duQsPnVy37uHrZaqK6PM8WydFmx3NjDkA0lTqJ1BhGDuo21vFRQLsdprlkmLk2ggEb2ZuZOTlI5u8fDKFr/37q1s3/KSEm3lrwnYmKfMWMcGb3gBWnyfNvh0KLRc41honkS/EmTdRJqoIsyFLmTdJ3+bMG91fb5wBOLF1PmibkkXe2mMQ2WSPDEvuvPNO3HXXXXjf+96Hj3/84/jiF7+45YPsqDySScCZfWBmlkkeR0I5Q76gVrLJwhLpTHRYRn4eK0YamZh9I5nMyBbj5Sp6odRLKCoNn4uXLPJ2vlg5SwCWc96NVnLSBUBr2bMAgOiQewZi90AEmm5gfrOKF5bIDZRNjHfOvEkx4MhbgTf9IfT/8BAuqf0p/u7I/0D2yK+hWPMff2A5tei1AnQxhpOrJWyaN8T5zQoMA9htsi5gGKTNm6RLamfKBweZnEs26cvFq5IUey8L201FQzxWijV85dFZvPHICJIZ8tpHhXmoDG8bMNAKJ6GWNlCpa02Zt6S6BsPbjXTKJkt1d6B4aTVQMgkAOk8+K84rhwPI9+qUwo4c60g2KaEBrpfMm0c2mfcwb1x5BQ2Dgxizb6h0s94sLoCCt/igg/mkskkHazHUQjbpnHnLV1XkjQjEusNRsbhINtm85GDeyO/phttyIvXIJmmTqK2UeDvlyXpzVRAoEWQCIpxzhB0wb5mIhAIiMJq5TeoaYd72vQ5rJkOebmYORD9Lk3kDgCMZoKCHXPK0h0+t4+o9GUt6FgTeFNUgTTnaCS2bG7MmhiXnhXnzgrd//hSw9DTwls8CMXJN2zJeNTig23l8StEG5q1k7lsplkU1MoFFI4OFnBsQ0HtRp+oFhmHInLUxAmPX1cBTXyPnXb1ENvTbkWXT8xiwzTr6AN4A4PYrJ8AywN1PkHus1Vi7IMBbJ8xbn2WTgGmB3mTmLTYayOL2veJjZP32lsW8vXxkkwzD4Nr9A21cnT3MWxO2cyoTwVsuGSN5oGOXkh8uPkn+doeYN8Bm38IX4swbYAZ1u2feJN2892wn5w0g4C2c3tZ1ERE5a49RqWsdg+Cmj/rIRz7S9I8+9alPdXl4XZQjIoBW0WNYIppo39rgM4yZ9dZCNvmP/wWY/ynwHzsLN9bNgG5nntjuTAT/NLPifzC9UGp5lGqqzwkxIjqYN8tpMoB5S+wCCt8kkiGOh7E5ixUjiaGUu4uym5qnrJVxfKkAnmUQHtgFnP5RZzNvjuI5FiU2hpfil+Bg2ECxlu/sph4ZJIO1xSWglgcnp4Ac8MTsJm66aNjaqNN4AYBItgBPgKoJZHYJRYg8a1HvvqiAygYwcrGDhd3azBs9rve9Zi+wSdzHLubOYZMZxKB3EDyUhGoaOwTNvA1GJQxhA2rkMATP3zllk5moaC/WpZWm4E0zNy1NwVvG4SA3chR47psmIxl8Qy9UVYhogJe2Z73tKikOlE87Zt7c4E2srmKdSWHEsZiFBA7DcakteMtkBgjwUGvWJj4dEc2BXi1w5s02iLCPI19toMREwSpO8LZs3wTFKADG6m7Gw7ZjZUIWfLJJ2hWTO+yIbaliI81n3gLBW5gwlFoD4M31xpp5a775zURFnDLCYJq5TS4+SRoP+1+H9ZMKUrLQet4nMmjPcQIYDTVwgovgSw+fxa1HRzG/WcHsegX/7prd1mOGYhJEnrWy3gzDIIYl1G0SsAyVmsom6cbNWTtpWDL7MPDgHwOXvQuYfrP1EKdhyfxmFVGJt2ZTrHIalvRLNglg9nWfx+/f/RI+mavakRPwjyF0UlGJR62hgbn0TuC7HwAWftYbeZYYseNxqBy1T+BtNBHGjQcHcffj8/jt1x/AWlEBsxNmJJ1URyHdfWbegObMWwsGqO8VG20jm3z5gLeOSorbe9nCElGjBZTIs/jsOy4j/2OY3gKLT+6obBIg9/eSol6YskkAkFO+qABRM/dY2515KywEEzHdHJ7Eo2zeC3tiWHLrrbfi1ltvRT6fx969e3H77bfj0KFDqNcD5Cq9LKdphFmlmls2yTAMZIFzB1+3C+pefg7InfPbazcpPTdvZbzRmszIWCvVLQBhleNmHCQ7lCXeBppBTpO0krvIhszsMgnFOcwZQ75ZHysuYL2MmSWSVcUlJggwoFKCLrTxIsda5gEdm4GwrL3hVAoIx1LgWcYKLaaunBbzBuIMyLOMh3kjbNekSDZelmGJ4p15I8xbaRvMG5W9vWp3GpdNpqzPaI9+DssIuAGEU9DNjk0g8xYRMMJsoCx5GLuQKXvQdXdAN9AR88arzQxLHN/pqGla0kI6ma82IDIN8GIvDUsSVs4b4JdNhpU15Dn/BmMqHQk2+wHJeBM4BglZtD8b8+bDMIw19xYkm5RFDhzLuKICctU6KlzcnVdUXLI3HyxLrllz4xz3xg1UNwmjYAJIOvO2FYfTjis22px5C8rTsoCEJ+AYaJnzJoscSkwEfL0JeDt5HwAG2PtarJfqzWMCaEWGbKAFgFFKiCVSeOzMBp5fzOORU+a82377+mJZBrtSYYt5U1QdBmCHdAM2IGzKvAXJJvskQaRFmbfiMvCtXwdSu4E3ftr1EKeMd36ziglvxhvgNiyh4K0Px5zeeznmjSEsbLqZN2/0TicVkThy/h/5N4QleuqrpjxrG5JJwO8uCmx75q1V/dJVk1gu1HD/iSyypTrSsth/M5JOql1UQKNGPp9+zrwBJnhrMvO20zEBtOLjBMR4q/wKBW+hOMkB1nXiTt3JnCHDEOnkAgVv27wuuyhqwnJBGpYAbubNzAYVtPL2VAPWWJKx7YZK1HSjNwyjNzlvN9xwA2644QbUajW8973vxRVXXIF3v/vd2Nho4VLWpyorKljGY0sqcW4tf6ugbrUObJD5MWyebf+Cug6utIRFI+MaLJ1Km46TXgbBIYMp1Pzhp05aFNnjRNMctBB64gLkyiKWmSHfpjETERGTeMK8LRdxeCRGpAWGTsAhy3elwZUE1go5LtbU5ja23qJSr1oeXDiBi8cTeGKWnB/n1suISbyrq8kwDFIR0Z2VZ4K3cQrevBl+AFnEqiQ0mTIsW40KAEzWDbBuhKJRxzkt4AIMJy32Joh5GxFrCDENFIRB398BBqAULObNeh+l1UDrbsA2LBE0D8jRNbLJc964qeNkC9OSfLWBEKOC4Xo/8xYSOEg865NNxhprKAr+m+lURg6O2QCwWlAwGJXIJtcD3gBiFgT4A7oB1US4dAAAIABJREFUck7FQry1IQXIrF+Nj7kH772bj1DC2uxbpidVh2xStqUQtFnT15sTbYQExSkEMW90g+uckaEbvxY5bwzDQBViENRS8AOWngYGDgJymoC3SJsZ56ibeYNSwECGxAZ8+eGzeOTUOjIREYeG3Z1gZ1wAXcfDLtlkG+YtaJPbb7dJliPmRI/9Kem4vu3PfJskN/MWkPEGmJEbokc22fvsruF4CDzLYCHnvu4s9UKn6zzIeitLHDnO6V8AnruHNAq3CzpFh2ySNla3GdLdql4/PYSBqIivPzbnb6ydzxLagLdOxiF6UaFkC/C2wwHdtOKjBMRoXgfqddLQ6qWb8oVQNHqqnCWxT/EOP/exy4DsDJl9Ow+yyQsbvFHmjWAIvlHZHsB1Ktu2eU3Kpht9raFDNzqfrW/bcqpUKnjkkUdQKpXwwAMP9D8qIKAolejsYMoib0cFAAT4lFbsoX1nbZwiFwFgu8O0qsoaWL3uY96aGi84OqklpeFjrsIij2pDI4YI2ReBocPBGllqvJKfAzQV8foKcpIf5DEMg6kBGU/N57GUr2F6NG5b6q68QE6sLjS4TuaNzLx1CIxio6QjVisAoQSunErh6fk8FFXD7EYFkxnZ13VOyx7wJoRRZmSMsGQTY8kmncBcyRNgGk7bEtotgLebLxrGu6/djdcdNsGT4wI800i5MoDIwaQg1PNgGANDAazPEAhQXWc9YMWaqcphKVfDaMLcjFQ3ycLahHkzOAk6WIiqB+TQ7rxzkYgMkJtpi7k3GhXQ05ub6TYJXUdSFnxuk0ltA1Vp0PdnUxkZ2aLin2UEYd4sq30KbB03H2r4EDTzBpANc6Hqlk02BAfzpmtkbXB2MEOtmDe3qxs95r4zb1o9WLLUzLAE8Dv1AS2ZNwDQxDjCWikYKOZmgTRpbqyVFZdsPLAiQ66ZNyhFCHICb7t8HN95ahE/fimLa/ZlfOvAZFrGufUKDMOw1nErKgBwgLfmUQG+Uorbn8FqV2KEfE83/i6w6yrfrwWORUhgUaw1sJALyHijReNl+iib5FgGI4mQj3krKWYDrBvZZMg2k8Kl7ySNkdM/3l5MAOCOCrBkk1sf/m9XAsfitssn8MPjq3hppdgyJmBHizJvQfsXYNthwB1XkGxSKQL14nlk3szGtHOdAcyA7peP02THZc6VW3N+nTp8jl1GPidgxwxLAHvPdsHKJsNpV1SAxLNg6qWtz7sB5B5Lr9ltnoMR042+1OXeti14++QnP4kvf/nLuO222/CNb3wDf/AHf7CtA91KFQPYrLDA2VEBgC23pPp5Z9E5M8Bm4FqVyeAtGWlXfhOd3/JZnpubdbWaR62h++QoEeccV/Z48Lyb8z3k5oDiIjhoqIbHAx+6OxPB03Nkc3p4NG5LwrLHu+4ESIIN3gqduk0CttSrlifgbXcKdVXHcwsFzK5XXJJJWmkv8wZgHUkMMmQTI/IsBI5xA3OHgUSpRYh4u7psMoWPvuWIHYPg+JyWjAxWCp4bZygJ3mhgPMJYHRtnZXSyIKwg5fs7AFCKG1gv1zFGWbuSOS/ZhHkDw0BhZUi6pznQrOs6eqwt8yahQTa7vapQHAAxK0iGRbcEVlWQQBH1sP/9TVo5iX72LVt0gDe6QXBsCm+/YgJ3vXZ/U5lXPCS4mLdcpQFNSpANh6YSIGDofuatZrtNArABYDXnMgYoW8xbnw1LgGDpZDPDEsBtW98B8wYAuhQHD9XvbGcYRJmQ2g2AzKYOtGXehsj1T40WzHmLd1+7G3VVx3q5buW7OWtXWkZRUZGvNmzmzTnzZskmg0K6WxiWSLH+mipEh4GJq4Ab/q+mD4mFBCzkqijWAjLeaFngrUBmh/s05zWeDFuul7SsmbcumhF3vXY/PnyLKfXfcyNpFuqN7cuzhAj5LjXVAd76x7wBwNuv2gVVNzC7XrlwmDdOIOdBM+aN3gP7bliSMtdNR1PufMUE0KKMn3furbL2ypNMAgR4GRqwbu5VO501HLvM8Rw7OPPGk9EF8UKQHweVnCH3KE21fRXqPZB80yb9Nq9JWeKg6Ya1L+7UGK3to/bt24cvfOEL2zq47VY5YIgvInln3hxB3Zl97idYPQ6AIRdFJ8ybCQAXjYxlsgGQTWI6IvqNF8wLRSnlAcQCZ94AoJpbQbScbT7gKIRJJzt/jsznAWjEdwU+dM+AvamZHo0BnNn10ButYwICSuSIbLKuGairusVEtK3YiC1Nk+K4YoqcxI+d2cD8ZgVvvNjfqUtHRBxfds/brBoJDBh2t48Ac8d367BuL6310Lbd8TktGBks5mpWJhn5PQFLB2LB4dKxBpkvXFA9n7f5vJsbhD0YTZqbEQu8BTNvAKBwMiQv80YZpHAKhmHYLMbIUeDEvU3DjPPVBgQ0es+8AYBCHCdzDsZLLyyDBaB5XTsBkvcH4jh5eMTdFcwWFVy6y1wIA2STF48TSW6zioV418xbvtqAPmB+J7W8I+PNcROU4kBh3vp7wN7UorIBpG2HVxqg2akWfUvldG8dPuL+nVICwLil0PT7doK3RoUAda/xjqdYyjgrBfd8UWWd3NRSU6irOvLVRgczbybLWs6S5pMJoA4Mx3D9/gE8eHLNyndzltNxkoGZvSNwdje0FfPGh5ozb/3etLzr26SBwTVff2IhHjOmC/B4sh14I42vfgHO8VTYmjukRc/zTkO6AeBVexwbFJYDjv0S8OAf9cawBCCzm7SZsA3b7U5q32AUr9qdxmNnNy4c8AbYZk1BtZPMG0DOS2rBf74CumnFm4G39Zb30pdt0WuK+iN0KleNjZLPo7Sys+BN5BAWuP5nQG+15AwAA6jlcP2BQUg8B+SL21cNhJLks+4B8wbAIg96Jpv8whe+gCuvvBLXX3+99U8/yyddA5FNejdOYZ9sskVQd/Y46SYPHuoMvOUJeKuERiB4uglkViNYNlk3nQm9M2OUedPnniA/GL64+WsndwG5czDM8Fc2PRX4MAoyMhERg1EzJJrSuN0ybzwHRdVRaRD2reOOrFOLHUpgMCZhKiPju08voqEZliums7zMm6YbWNYSiKv2LKUrWgFwM2+KipDA+r6XLRVnm1IsGRks5T1MhAnCdkeCTXq4EulInmt4JApmRyZngrexJGXeTDahxQ2nzkUQMoKZt7/6WQ6Xf/xe/NG9LxGb65FjpEO3GhyBUawo4KH1GLzZ851JWXDNvJU3TNY7QF5DWViv5Jh0nMjMG4BA8NauvMxbvtoAQ4F5LefOeKPlkE3S890tm7QBeUVRSQRSP62QrdD7AFe1IEbJ2vQ6wVutrWQSAPgIeW+Gd7aFzgOndluMatOMN1qURS6tEvZNU6zv7j/fOo3/dNNBS27uLKpiOLdRsZxlZZHvUDbZgnnrg/zQVfHRtuAiFhKsSJnmssm4PfPWx2OeSIaxUqihoenWz0oe9+Yt1aXvJP/uhWwSINJJKp/sM/MGEPYN6OD83slqNssJ7CDzZq57Tunk+WbemoK3jVcm80YZnexxwsY2U+p4i5qWADs888ZeuJJJwAZXlXW85ZIxfPzfXNwb5o1eK9u8JilYWzVdt3smm/z+97+PBx54AA8++KD1Tz9r0ZNJA5CoAC8gkr2yyfg4ACY4qDv7IjA0TWY51jth3ubRYARw0eDZnbNrHmaEDwGsALVKGCUf82Yia27uIbLxmPDPSliV2AXk5lDLnoFuMAhlgsHbngGyKTg8GiMdD4axF7kuM4NEnoWiaqjU9cDjb1rezTCAK6ZSeGGJfA6Taf8mJxURkas2rEDkQrWBrJFAxAXeODcwd1i3F2vqlgK6m5b5WS0aA1jKe26cJgjeFWoy51lYRI6JY8Vromg+ZylPOt5jCS/z1nwxbnAywrrnGjBvpM9tMKjUNXz2vhO47tM/xP/7jLnxaDL3Vq2a5ynXww0K3WjWCkiGRZfbZGWd3Fz5gA5tQhaQCAs447l21ssKdAMYpGYktAnTxU05FhIsyWOtoUFRdbCyYxNSCJgdcMgmeY5FWGBsw5KqO0+ppGiIiHx/O4vxcWI0tHHG/7sgRolucJ0zb2q1rWQSAEQTvNWKntkWB3ijGViZSAczbwABW5a8k2w+LhqL47dvOhD4uVEHUSd4C4ts51EBWt0/s6cUd3TWo1nFQ7x1aK1lkwVrXrhfNZ4KQzeAZcfaVqw1wLHM9poRAweAG38POHr79g7QCd46iLroVb3p6ChuvmgYN+z33+PPWwnh829YQp/fBd7o+nmeZt7kDLmHeUdiyq9g2SRA9q3RYcJ0d1rnAbyNxEP2aMiFWBZ4c5gtKqUeMG8J9/NvsSi5s1qkzFuPQronJiYQCu3cF+MNFAWIbNI77yJ7ZZO8SBYXL/OmNYD1k4R1y+wjUinvrIe38gtYYwcwEGBSMZWWsZSvWjNiAAhwkmLQmoA3+mWEFh4Bxq9obYVsumbW105jGSkMJoM3I5TJcEnQqGlJ18wbmXkrNyh461Q26dkMA7hyyj6RgzrumYgIwwByZmd/s1JH1khAUkvW9+JzErXyXNKdRxl0WuEUIMbAyQlf46DKkQVwVGoC3opLyHEDWC97fm9+FrUCOe4R58ybILdcWBtCFDKq0HXHxtS8kc7XQrjhwCDu+50b8bbLx/Fnz+koGGG89OyjvucxDAOKYm7se8q82QHXSVnAZqUBw9ypKjlyk5dSwTKPK6dS+Ifnl12mJTTjzWLe9r4WeNd33Pr9NhUP226TeRPECVHzPKyazBvDukPYqfGKeexRkSXMW71CNlCOBblSV/vvpMXxRB2wftL/u6DQVToL5lzLOmTepBhZH4p5T1YaBW/JKSuLcaAt82ZufkurXbk9RiQeA1ERcxsVqwkXFnj7fZXaMG+APy6glt/RTUuzoutTROSsPERfOWWTfQSclPlzzr2Van4DsC3Vaz8C7P257T2HC7xR5q3/4C0scvjTf3sljk70Dzh3XbzU2rCED/U1RgGAA7x5nHrF2Pm7thjGH6VSr5Bm1SsRvNEG6cap7qWqh24hyq7UnvaP7VH93i3T+OtfvXrHXq/roueIMxu0Xtr++RzqDfNGx6pWCz1m3hqNBt785jfjgx/8ID74wQ/id37nd7ZxmO3LO1wNkJuNF43KIucPck7sIvNizto4TebABqctFzWYksSmVVjAkjEQ6LQ2lYlAN4D5Tf/cm05Df70socgjgioi688BU9e1fu3EJKApEJafxLwxGBhMDACZqISP/+IRV/itxbx1O/PGk5m3ctfMm2eGCMCVu1PWc44EWLunzBnCTQu8NZCFebymrFAWeLcrYWWDbL6lBErduGF2UuEkkBjHaCLs6k4DwKpq5osJTcB+YRElaRBrJc8mUooBDIdGeQMDUREh2uGmMQEtNk0aH0EUVSu6AYAN3qoS0hEB+waj+NTbjuGhD78eBTaJ9aw/OL7a0MDSofNegjc6C7b6PJKyiLqqo2aCfj2/BM1gIKeCO7S/+dr92CjX8dVH7WvUAm/0WmNZYN9ru5oBioUEFBUVmm5Y7pdS1JzXqOXITT8y5J5TCiWIiYmZoRYROcLeeQK6ge6CM7dVmf3Bsu56QJfQmnlz0L5qtaONbzRObmblQgB4iw4Domw1JDrKeQM8zFtnN8VdZlyAzbxx5Dviw8RhFmgy80YDjT1Nkx0Op21WNI90IuV327WKgjelz8ybOXPnbIoWd+p87qRcssn+57xd0MW3YN4qm/2XTAL2htTFvC2dv3k3WvFxt2zylRrQDdjNHF3tXqo6egnwGw91vQfcToVFDokLIei+WQWBt14wb/Qz7hHzRvdCcodrc9tHvfe9793GYXVfPlAEunnyAyKf7XhiAlh6yv2z1Rny78FDZD4IIB2NoRap6PkFzGl7A7vOUw7Hyb2Dji8/FLc6z0HmKlewL4ExNGB3G/BmxgXIhdOYN67HVQEAiNa7nMANcIC37pm3dcfMW8fgKJQwbzhVawOyfzCKeIjHUDxkuzo6iuZGrZfq2D9EGLisYW5eSqtAagphkbOYOQC2dTvL9n4jfeOHAbWGsYdCWPSAtyUlhCkAGa5JsHtxCUroGqytezaRDAOEEtAqOTsmACDMW5sBa02IIsLUoKiarSOvbsKQ4siWNSscGyCAZ16UoSr+zC4SE2CCt166TUYGSA7Y7CNIHrgNAAnFDothoLSCdSSQigZLxa6YSuG6/Rl88f7TeNc1UwgJnLVgDbWzpG9R1HihZLoXAkDIBCiobgYHzDrkn5BiiAom8+Ywx6FVqWsk46rfldlP7Nd13W06EgRKgnLeGlV77rVFRRPks6kVA2bekkSmTZm3tjNBokxuglsAb5NpGT87t4mKM+cNINJJtUps/7mATQF3gYM383xsKpkEbPDGSX0Fb6PmvK0zLqDYjaNwv0twzG7uoGzygqxWM2/VjZ2xxQ+STRaWzp9kklZ8DFh4wv7/CjELe0WCN+cMbKcZb/9azYvey2lj1jCIo+q23SZ7xLyJdOaNXPvRDt0m2zJvBw8exOrqKhYXF7GwsIAnn3xyG4fZvryZNLpuoFxXfcPVssih1tCt2SkAdlC37mAtsi8CYMiGkzJvrUxLdA1GcQnnNHdMAC0rLmDNa1oSB2tuXnyySZHH1ewMdIYHdrWhl5O2u+ScMRh4DE0rtrWZN2JYotnMW6czZQxjd+TMBYdlGbzz6im86Whwx4iCDxfzZlDmjTBIsshZDn8ACPNmXiDFWo/B254bgAM/j9FkyGdYMl/hoRoskox3qA3E7a6chRodQbGm+o12QgmwSh6jTi14i4BuWroYRRRVi80CQMBbKIm6qlvMJS1GjIJtVKCo7tfPVxsQGbO50evcq6lrgXOPIhkiywdlu7jyClaMpAtgeusDrzuAtZKCv3mMsG9Zc7ZqO65vVk5btWGBt4gJUCzDEm8HM2TLPwFAFlkivXSY49AqK2rH9r3bqvReAlqKnsH8wJk3mvPmODcbtY7MHpLpjPmnG+5fbM5aMQFrpTpEjm0az+CqiBnUvQXwtpirWZJXq1lB2Zhmm3ieyiYvVPBGmbc24E2t9SboukVJPIehmOQK6i5dSODNYt5KhEXmpO5mfF5JxYf8DQlalY3+z7sB9rroNSw5XwHdtOKjhHmjw6SURYn4Y0he9uVcD86XScwrqUSZkAz0nGlUiepmu8zbnhuAg2/cdlMl6jEs6dnM21133YXHHnsMX//61/Gd73xn2+Dt3nvvbSm99MomKw0NhgFEA2STANzSycQuMgdBh90BkjifmiJfYDhFQEAr8FZcBmNovoBuWoNRCbLI+bPeMvuRLryAEBQf0AyLHK5mj2MtflF7G+SEDd42hBFbctdJbUM2Wde2wLwB9uLiAIy/d8th/KefPxj4cNrJXzcdJ3OVOtYo82aGcIZFb1SA3XUsKX4g34saTYSRqzRcr7tcqKEAGRG96P8D02mSMT9zb3YdwkkI9QLGnFbhpeX21saCCd6crHItB9WcNUt7gBEfikKGgnOe+Ip8xcm89dhRbfJaQMljVCHmGhSIS7VVZI1Uy/Pn6r0ZvGpPGl/48SnUGhqyRQUxid+WW1U8bFv9U8Y2EYuSBbtqyia9nWNH5AHgmHkLMAYo19XeRFO0q8x+8m+a70MryIiDE4mU2Mm8qZ0xb+lEEnWDg1ZxMG9ag8wDWxlvCjJRsbO5qKgZ1G2Bt87AyK60DE03cGqVMMcW80bjApoB0SDmzeN0eT7LZt5aMEj0M1JrfXfIHE+FXbLJHZMBd1KiownRqP7LlUwCZF61lWHJToA3jidzzTQCSNeD18+drvg4ub7p+mw12V6BzJsYBcz4lH9l3npUcsY+Z8xRiW3fK/a8BnjnN7bdbKKqntWCAolnwXfopN72UYZh4GMf+xj27NmDv/qrv0Iul2v3J03rE5/4BD7zmc9AdzJjnvIalpSsQFFPSLfZCXdJJ51Zb7SyL7pz1dJ7/ZsjZ1kZb2nbRMFRDMOQuABv1tvR2yFqFdwiPElyJBwVYeo4xpzCfLwDE4ZQ3Op+VeXggO6mNXkNcPAWYPzKrv5M4lkoDXvmrStwRBf1Di8EOsC/WbYNS3KsQzYJmjjvnHmz9f6lAPOaXhS18190sG9L+RqKTAx8Pe//gwIZnuaT5Dui7ny0VDGBiFGyYwJU88bTBrwZUgwcY6Bec5xf1U0oPPmMvAYIoUgMMmo4lXWzg1ZAN9AH5u0aAMBI7mfktUzmLaysIcelA+WyzvrA6w5gpaDg7ifm3QHdWyzKdBRqNvMWDwukiVHOEnajDfMWEVniNmnNvDlkk4rWf8MSwAHePKYlSoCtMcMQyZkvKqA98xaWeJQgQ6868hbzc6QbScFbud65jXpkkBiMdGFYAthZby+tFMExpIkEwMG8NXkv9Hx2GpZQ6bB0/g0oOpZN0uqjbBIgc29u2WTD59583op2v+sV8s+/VMkkQBovrQxLdkI2CQDhhA2SqhvEM+B8gwgrSsV0nCy/gmWTLGs3d/6VeetNySkbvHWpEOl3UVVPtaF11VRrC944joOiKKhWq2AYBprmz2HrtC6//HJ89KMfbfmY5ULN5eTYLJNGNru0FVdQN816M8GbpgJrJ9zgLbMv2I6blvm3zZg3gMy9+Zi3qeuRFwbxVv4h3+NDy09AZDSciXTooJcgc29qk4DuphXJAO/8uu0A12E5mbewwHWXoTZwiCwwQbMpASXxHGISbzFvm5UGouEwWYQdsslKAPNmGAZxSusD8zYSJxstp2nJcr6GGhdzS0homdI2OU3A27rHtKTCRpFA2Z55o2xwG9kkY27SG05WpLqJGk8WmrRHNhmOxCFDwZk1P3gT+wXekpNAYhcSqz8FABLUramIqJsoCe1vptftz+DyySS+8M+nsJirBhoDdVNUNlmsqShUG8ScTOIJG5w9Th7km3mj4M1m3oq1BvSy37CkXN8hpiI2SthCZ3PJMILdJgECbrwh3R1mZJXZKBjF0ZSwYgLozJvSPiaAFmXealsFbyVIvAPwU6DaTKUQZFjSJXDsZw2bc8qumWhvOY+zz/EG46kwFnM1y8G254692ymXbPJfwVsg82YYvviSvlY4Zd/zCuc5JoAWddI2m6aorBPlQZcjIi+bomz8+QbNr5SSM7ZskjJv25VN9qhCAmv5s3Wj8Gm7S7/zzjvx5S9/Gddddx1uvPFGTExMtH3Su+++G7/wC7/g+ueZZ57Brbfe2laGY3gyaSzw5pFNUl2oa5PvDeq2nCY9zFt+rrm23AzoXjIyGIgFd553ZyI4t1FxBZ+CZfHT6OtxrfEUUHa7uDGzD0EzGJwItQjndlZyEhpY8KkuwdsWy8m8dQ2Mrvtt4Nfv7+pPUhHRYt5ylTphk6LDFvMWFklouDXPWCHgTVF1qLrR25w3syzmLedm3upC3G2bTGv1OMCwiI4SeWjWw7wVmQjiTMWWTVoZb62ZN8bc1DWcrEh1EyWW/DzpkU2K4SgirIIza27TEtfMWy8NS2hNXoPQ4mMATIfHchYsDFSl9o0DhmHw/tcfwEKuip+dy/WAeTNDtqsN5KoNJMICYf/CSSD7EnmQ9yZIN8ymPCgistANQC2vkw2kw3K/rGjWUHFfi2XN5pIDvNXLAIxgUCLKHtlkrSPZJADUuCj4uuMcc2S8AWTmrXPmbYhco9VNklXXIYAcjocgciyqDQ0S77gV0ZtqO9mki3m7cMDbDQcG8PcfuB6HRloci4t56y94m0iGUdd0a40q1vqjXthS8SEAjCmb7Lz58IqsZuBNKRDnwR1j3hzg7XwHdNOKe5i3yjoBs2wXjeaXU/0r89bbcoI3S6VxYYA3hmEs9q0b8Nb2kWNjY3jDG94AALjlllvwwgsvtH3SO+64A3fccUfHB+Gth54+jvIoWcRfWCSd5fXlBcwY9oD92jL5+cyJU2Dy9obloBBB/szTWEnNIDb/I0wAOFMOoTZDXCfjVQnjMHDqiftQj/uzMIbPPosIG0YBMrJzZ5BbDHBMZEqoqzq+++DTmB6yX/sH+qtwE76OpR/+f8jtv836+eTMvTjL7MHZ9SpmzONoVUn5MGa0RTCNWkeP324VcptQVA3FWgMSo2/xNdfbP8SsMKvh3OomZmZmsJDNQWIMlBkZTPYsZmdmUNwkG+onn30BUbaOw2oVqyUNJ54l515pcw0zM/VWL9F11TUCFJ85OYeLI+Tint8ooRaRUS+cxinPZzLx0gMQYruRXSWAc+b0PGYiNoAqFg1cgjLKq3OYqSwjuvAEdgE4k62gpgd/vrVaDZViDYcALJ49AUmKAIaBw5UNLJXJebi2cBaNdbuRMVRUEEENz59bc31vp+c3LNnkmflF1Kq9PY+S0l6Mlv8W+9kVnJpL4oyQxR4ABSbW0fkzbBg4kJFwYl0B3yhv6zzP10gD58TsPM5lFYQ5AzMzM5hQOcRUAm5OZ6tQVPs1GE3BYQCrc6ewHpuBxJDn2Fg8i4wQw0nzeAzDQFlRUS1u7si1OC4MQVp6AafN1+KrWRwAsLRRRs7z+nt0DvX1ZSyYPz+olJEvVrHSwXE2mDC4esF6T4Mnf4Y0K+DF+RyMhQLWijUwSqmz9aqkYRQGCueegczLOHH8eMfvdzDCYaGgQ+JgvdZoVUUSQLlu4FzA68sry5gCMHv6JVRKBATJq8+Rn61sooL+f0/tigUwk19s+vvQ+jro3Wd2NY8K079j1kvkXvnQkzPYn5GgqDqqBft8rtV25j7TrA7yYeRW5hHKrYHRGcyex2M5H0U//5FSFbFaCSc8718snsM+AAv5Bgo7sQY1OEj5VZyemUHy1M8wCuDEShlq6Tx+L7qKwwyLtTPPYi0yg/GVM5D4qLVObqfO9/kfVFM6D0mI4KXTc+0f/DKunfrsh2sM4qUsTszMILo4Q/Zii2uoKRfG9y6xBkoAWK3e8efRFLw9/vjjOHnyJL70pS/hPe95DwCjWLS4AAAgAElEQVRA13V89atfxfe+972eHHCz4mKDmJ4mrNOstgxgGUcO7sf0mN2hLIU3gH9axtDYBKYPOLr9qSmk2QrS09PA6ncBAHuuutmWZ8QqwE+AfUkGODTtf/GnK1gVh5EwRBy7+KLA4xuerOP3f3wvFtQo3jZ9wPr57I82MMdPYdfqAxh9838lP2zUgG++gOeEWyDIMUxPB7ymp7ITH8avPnIFPrZ3HNPTu9s+frs1vngC+rM5VFQGmUSko2Pc1uv9pITVYg3T09NQ/iGLiYyMSHQvMPcTTE9PY09+FnhiA5N79mHIIKBwaOoQSrv2ADiHA7snMD3d5TxgBzUQXYAqRjE9PY1aQ0NBOQ1pbBhi7ln3Z2IYwPdeAg7cjMuOHYH8zTmwcsL1mPsfGYTEqLju2D5wUgSo/AQAsOfo1TZD7KmZmRkkJvcBzwGDCZk8n1IEDA2NyDAYBrjqkiPgnDNly7uAF+tYKamu1xdPPI+kSJjhPfsOAaM9/k4HWODxT+PnwidRDF+FPQMEuIqZ3R2fP7+LDN7714/j4OQopqf3b/lQGpoOfGMWciID5DcxmODIMRyfBMz9895LrvU7k31bxFBcwtD0NB44SySgEa4OIT5kvYdKXYWBM5gaH8H09L4tH2PHtXAp8PD9mD54gJgHrJElenTqAEa9n+uDKYRCPOL05/fUkR4aI2tfm3omnIJcPI299LHPFoHUFKaPXIyyokLRzpjfSwfv2TgGPAHE61lATna1fux/pIiFQhYhgbP/7uwEcAaIJAeCnytSAP4ZmBofAfabv2fPAgCmDhwBxvu7fvWk1njgn8h/Th24uK/HzKaKwH3L4JPDmNgzAOAM9u4axfQ0gY8zMzN9X/NbVihOWN4ygFDm/B7LeSjr858dBeY1//ufJc3M8QOXYHz/Dnw2JyeB9afJcaz8HQDgwKXX2S6v56t+MIRBUcHg9DTwaANIjffkXDnv539QPb0LYNUL77h6XDv22S/vB07eQ+6r6vMAgD2HjpIIsQugEpEVrFfLGEy5McITTzzR9G+acs7xeBxra2uo1+vIZrPIZrPY2NjAhz70od4etfeAGGDe44wF+LPTqIFAWfHM4DmDurPHSW6Rc3aChgw3My0pLGCNHQjMeLOeIiLiotE4Hj7lZpuKiobH4zcBc4/aMqSFxwFNwXHpKCqK6n+ygFopEOnEUKwzCdR2ixoF5GqalZnVz0pHRGyUqGyygZQskLmZ0ipgGNZ3W6lrLgOJZudCr2o0QWZDAPs7EKJpIpt0muwUFsgM2xiZYRyISlj3yCZX6uS746icy5SEWqHGTYoPkwaFQal9U76yoUWQCAtu4AZY7myVSskyDgGIbDIhmLLTDqV0XdXAQUDO4FXsceSqdeJIBoCJdz4bcdP0EP7bmy/CWy/bHhAXOBZhgUOx1kCuQmSTAGzXVVYInhcJJSzDkqhoLoXVnHvezVxfIjthWAIQ0xJdBXKz5P9byQGdM2+6TiRXHc4MGVIcsl6GQa23N886nCZpxlsXM28Akal3Ob9F595Czpm3toYl5trsmnnrzunyvNdOGpaYxikLm1XbAOxCMSwByBpG3Sb/Rc+8SW4ZNC06Lx3pbpZ9y0Vlk4ZB1vXI4PkHbgCRvjtn3nZKRno+6uc/Brz1C+f7KF45JWcAGGRM4gKbeQNsx8meyCYPHjyIgwcP4o477sDw8DAKhQJYlkU0ur03fPXVV+Pqq5tnnQ3HQ66g7lKNbEj9OW/UoSUgqHv+MfLfq8fd824AueBDyeZxAfkFLBmXtc2dunZfBn/96CxqDc2y8y/WGnhh8g1468ZfAM/eDbzmQ8DZhwAwOCNf4rZ/b1E0rG843odZpYCSHOBteqfAW8V2m0xFRDILplYBpegGbzU7d8veePTnGEcSIcyuE+OPJXPuMhQzL3qlYIOBRTMuwwRvmaiINY9hyaJi3uxqOaLXL60QANHmJijIZPOp082oCd6ymuyLCQBgbXQjqOHMehmXyuQY89UGJkUdaKA/N16GASavwbETT+AvKg2ohSXwAPh4mygE11MweM91funyVioW4lGoEsMSy+WPDrPHRoJnI6S4BY4oOGOrG8CAzYxS19MdiQoA3HEBmX2tnbHEiAWarVkZoTOgzoYTiKGMoqISw5fNs8D4FQCAtTIBRV25TQLk+u1y5oyCN4lzgjc689ZkIx8UFUBBbp/nx3pWOwjeohKPpCxgIVchcRjoXwNsSyVGiNNko/wvHLyFyYy+rrntxzs0u+pZhVOAoZFN7oUQ0E0rPmY33ctrwOSrz+/x9LMyO6Dy+JdUFOhXNi64mTfAxjOdBnT//+3de5QcZZk/8G91VfV9ei6ZZHIdcoUkQIQAEeQiBhFUXBEICdFwsupy8QIqi2FBEBfIAVbRXVhUOB51xVVBdPn5QwXE/EQQhE0CmJAguZDJdSZzn+6Zvtfvj7equnqmu6d7Mt1dXf39nMMZZjKTqbzT0/0+9Tzv8wAFMm/bt2/HpZdeipaWFjz77LO46KKLcPnll+OPf/zjsV9pAbObs9saG8OaRw+uy9rgWzXNERvekX6g553cadEpC3IHb8kYEOlCR6pl3A5471s4BfFkGlv2ZToRDsWSSDTMFi3733xC3Lna9yIw/STA1zj2WvPoHBSbEqNrWbkZmbeBaGmtSieqJeBGNJFGbySOWDKdaVgCAOGu7DEQlszbUJkzbzMbvWbQZjTNCTTppXbWjpOHtorGDG0nAhCZt9GjAjqGjeBN7+g3VMSMNwCqz5g/ZgRvomSmM+EbMyZAfIEI3nxSdtOSgZEEGlQ9W1iOhiUAcNz7MD11GHL4COL9h9GjNSAUHGeOYZmEfCqGYmJUwJjMW77Nhzc0JvMmx/qz7uga2d6KNCwBMi/axriAQsGb6hObXiATvCnFNXyQfU0ISDH0DETEYyzaPybz1lpKt0lDicHbHDPzZnkpMv6OkkYF2Kv987hUv+iWB1QkW2iMCzAez7bpNgmI57B4mHPezC6qo5qWGMFbpdriG8+bI336jDebdDwMzRTdL9NpvQO1Awd0U3mYwVuPLTNvxs1jf5EDuoECwdv999+Pe++9F6qq4jvf+Q4effRRPPnkk3jkkUeO/UoLmNXkyxrUPRRNwi27xsxOM4O3XGWTAPDui+LFfVqOetqW+UDvbtz7u534+asdmY/v3gQA+N94e84Zb1ZnzG2B7JLw0m4xbySd1hCOJUXZ4bIrge63gYObgf2vAcedA79bRqTEsslj7cJXLGNt01pmZlY5GRmkPUfFL1Gz350ZbxDpyg7Mh8dm3sq18ZjR5MNQNIlwLGkGcaFm/bqilo6Th7aKx5W+uWwdlXlLpzXsi+jraHSqDHcVdedUDYi78FI8u2zyUMw7ZkwAAHOzE5Ri2GOZ9TYwkkBI0YO3yR4VYDjufQCA+cNvIDV4GF1ak/hZVoGReesfSWSCXO94wVuj2d4+4HYB0OBODGSVTQ7nuXlUNv4p4rqMjpOF7hKqgUyZlfG2yMybOyj+jQP9PZkSTcuAbqCEzJs7mAkaJ5p5K6VsUs5TNulSylMiXA6SJNZK8VakJG1WkxjUXe7n0Alx6/MKOedNvB3dCTtyVDwnFTmO55gZz39m8GaTzFvDDCA2II4taGlnznij8jAeK8M94rVC9R/zcO3J5NcTEpMy5y2dTmPx4sXo7OzEyMgITjrpJASDQbjK3Jp1drMfRwajSOpt+MOxRM4yOb+ZnckTvO16TrzNlXlrmQ9t4AB+/vI7+Oazf8+0/H/jZ9D8rXgmemLBM2+ACHLeM7vRPPcWiSehGcHP0kvFOZvf/4soJZp7NvxupejM25sHBjA95C1t3toxcFvuelfiRd0IQoxgozkr89Y57pm3cgWYMxrFi+fh/hEcGRhByKvAGxqVedM0EbzNXG5+XWvQg95IzJyj1BOJozulbzyNoC/cWVTmzeNrQFqT4Ipnl00ejHpyB0b6RnduCNjTnR28BRVjVECZNodtJyPm8mNpYhukoU4c1ZrEz7IKQl4VhwdGkEprOTJvee4ce0JZQ7obMAKXlso6H2fccKlY2aQkidLJMZm3HNkZ1SdKzYCSM29ePXgb7O8ZMybAmMGY82ZBvms2br6UnHkT15sdvI1TNmlscq2Zt6g+C2+cUTS24gmVvWTSMEuvaBmK2bRsMsY5b+aNl1yZt3HOSk8qI3gLHxXf2y6zxoxZb53bxFsGb1Qs4zV9pFe8ptoo6wZkyiUnZc6booi/5M9//jPOOussAEAikUAkEsn3JZNiVrMPqbSGI3r2KRLLXconuyS4FReGc515A4B39FZerbmCtwWQtDRaEkfQHY7h+R1dYpP89u8QOf5SJKGMe+YNAN63oBVvHhjAUDSBIet5LH8LsOjCzNm79vchUGTmbfuhAfxxZxeuWtE+7udOFk9W8Fb+zXezvincrWfeGn3urLLJrPOMw336nX23GbyVKwtizGQ7NBDF4YGoGLBtZG+MDFrfu+KxMjMzcH1KwI20Js7vAcDhgREMaHr2IDogAr4iM28eVUYEXriMTbkevL077DHXLYteNjkvBOwdlXkLykbmrUzZCFlBZ+N7sBw74Yp0oQvNY+bQVUqDVzEz9mbwVkzmTT8rpbgkTFf1EsScDUsquNltWZA521GoYYl1zpvRuKTIOVn+RrHxiQz0jBnQ3R2OocGjmGd5i2JsLksM3hq8Kua3BjA9aHnemWjDklopmTR4GirWYGVWkw+ReAoHesXjpVznhifEHRA3ubQU57wBY5uWhI9WrlkJkHn+635bvLVL5s0IIo/8TbwNMHijIlkzb/Gwrc67ARNrWJI3eDvrrLOwZs0aPPTQQ1i3bh06Ojpw/fXX4yMf+cixX2kBRrMBYyM2FE3m/QcF3PLYssmG6aJ8ZvAA0Nie+4fUMh8AMFc6AkkCfvFaB7D9f4BUDIeOuxQAigzepiCV1vDau71jzxKcrM+5m7YUCEyB36NgJDF+5u2hP+5Cg0fB+rPnjvu5kyUr81aBO7JTzOBNz7wF9G6AkpyVeYtGo8C+l8zAbiiahFsZW0I7WaaHLJm3wSimN3qzS0iAMc1KAJjnI42MxaH+EQxBv4M80i82lsmRojJvkiQhAh9kS/CmKV4MJpU8mTfxfdobgL3dEaTTGqKJFOLJNPxySpyrkcv3M+1rPR2LXfvhjXaJsslAlTJvPhWxpAhWG336OjXo660HJWNYyiYBYKZH3zRZzrxF4uW9YZDTlIXAwH6xiYsNicxprtJX1S/u0qdTYiQJUPTmN9go/o3RoT4RvPmazSxQTykDus2/0AjeSs8kPX3DuVh9clPmA+aZtzznJ80h3aODtxppVmLwNFSswYrxurrziMjkhmzVbTIgGlAY/1+vCpVNjh5zUk7GTa9OfaavXQZFm8Hbm+ItM29ULLdfVKUM94gsv80yb8bN4WAJ+4y8u7prrrkGF1xwAYLBINra2tDR0YHVq1fjwgsvPPYrLWB2s9iMGk1LwrFE3oAiZymiSxa/5P0dwLTFOb/OaAowV+rEZafMxq+3HkAs8d/wTF2M/Z5FADaP27AEAJYf1wy34sJLu3rwkZPFi6GZuTrhwyIgWbASgAg0EykN8WQ6K1iyevvIEH637QhuWLkwkz2oAI9c2bLJ5kCOM28ulz4uoBM+PXhbtu0+4PDrwKofASj8WJgM0xu9kCTRafLwQBRLZ4QypXdG+eOhrWIzPS0zA3CK3tiheyiG49sacKg/iiQUaGoAUnQgMyagiOANAIYlH5Rk5sxb2tMEhIGWXIGR/iQ0K5DGSCKFzqEoZL10zOdKlq9ZiXGtM98LvPMgXEijU2tGk696mTeD+bvTPBf4zB+yAu0snpAoO0yJUrKZ7hEgidxlk5XMvBlNS3r3iruE+V5ojEAtMSJuDgBFZ1ndAXFTIh7uA4bfNUsmAaAnEit+TIAhMLGySQDwueXsERhm2eR4Z96sDUsGay/z9t7rxNmdCpjVJF5Xdx4ZhOKSsqotqs4dEFk3gJk3IPO7bIhUKfPWZbPgzbiOIyybpAnwt4hKrnjYdq8VZuathH1Gwc9csCDTrrS9vR3t7eUv5TPOHRmZt0gslbdxh88tjx0VAIhzb/0d+Qfw+ZoxIjdgQboL516wCK9t3QzPoVeBD96Jbj17Mt6ZNwDwqjJOP64Zf9ndg3MWiTtjZomn6gM+/1fzbrDf0kHRneeA+kObdiHglvHpcyanfXqxPGplyyZDXgWKS8K+XlHqZTaYCEwFwkfhV2Vc5noBJx58HHjfF4ETPwEACEeTZS33UWUXpgY92N87jO5wTGTeVJ94UbVm3tpOymoyMLVB/L/x2Dk8MCI2R75mEfSFO8UnFtnqeUTyQU1mMm8Jt8hm5CxJ1M+ITPeKzc/eoxHz98XnSpa/GcKs5YhpCjxSEn2uZjPwrjRrJiGrK+ecM/J/kXHeSD9XNk0ZWzZp3BwqpQvUMbN2nCxUDmicD0oMl5x5M56XUiP9QHwfMGOZ+Uc94bjZSKRowYmVTebUfBww91xg9um5/9zlEmeKrWeDYkNF3xyxjZMuq9i3Mma97e2OIORTIdnpbKA1w5ov21oPlBwjMJJx8RpSqTEBgHgOkT1iTi5gn+DN7RdZwb694n12m6RS+FsyDUvs8pjWZTJvk1A2WS1eVca0Bg8O9ouNVDhWuGxyzJBuINO0ZGqeye2ShCPyDCx2d6F9ih9fat2MNCSkT1pldg0spmwSEKWTOw4PYr8eiGQNuQ5OMw8hG2VXkTxNS3Z1hfF/3zyEq983t+LnhtxyZmNaicybJEloDriRSouB3GYZZLANCHdC6fobNqo/wLuh04AL7jS/LhxLlv2g/YwmH17f3w9Ny9xIgLcpM6j78BvArOVZX2PNvAHAof4oZjb5IHkbxdcZwVuRZwdGJD/UlB5IjPQjpoqNdu5uk2KzM00P3vZ0RzAwIjJJXilZ9u57jcEGvKGJYCPqqeDd4VFCuTJv4zFK1vSs6lRZD5j92Zk3VZbKVqqbU8vo4C1PaZ01eCsx82YErtpIr7jRZcm8dYfjE8i8TWLw5g4A6/9v7k7BBsUzdlSAze6m2kmzX4VPlfWOwjY67wZkl0rW86gAaybdMKyXk1aybFKSRMVJYljcJLFThstoWqL46vuxQqXzT7HtmTcjxpmUM2/VNKvZl3XmLd+G3eeWMZIrGDKalowe0G2xO9WGdqkT0DRclPp/eCl1Il466sHRodIO65+1QDypPveW2KDny1yZmbc8TUv+c9MueBUZn61w1g0YnXmrzAu7ce4t6xxXsE1sJH/xKfRLIfyi/RtZ57UKPRYmy8xGr9m1cXqj/mJqZNB6d4vyrFFleI0+FYpLQo8+3PjQwAhmNnnFC+AEyiZjLj88KT2QiPZj2CU2pTk7Oeobn6AUg0+VsefoqOCtzGWTTX4Vr6bF71ncV8G7w6OELAFb8cGbnnnTz71NMYI3b+b8VSSWrNyMN/O6QuKx0ru7cFBibF7iw5ZRAcVn3tKQMHVkrxgM3CTOBabTGnojsaIqD7JMsNvkhMnu2m9YUkGSJJnZt6DHRufdgOxNeF2XTebIvBkz3ipZNglkqg8apotMt12E9IyJnQJKqg3+KXq3SfudeVs8vQGzmnzm3NNi2Oi3MmN2sx8H+42yyWTegMLvVsZ2mwSAuWeLrFueO7exZApvxVoxJXEE2PsCAsMH8HvlfPz81f3oDseKOu9mWDa7EQG3jJf1kQH5rrVQ5u3d7gieev0gPnVme+l3vCeB23LmrVJdyIygLavELThN/HINHcFt6ldxVMvOOIQLPBYmy4xGn+X/9SyGT8+85WhWAgAul4SWgBvdQ3rZZL/RqbJRL5s8Iu5gWoKCQmJyAG4z89aHsBm85dhQu2RA9sCVHMbc1gD2dofN4M0tJcpeNtnkV/GT5IW4P7EaseCcsn6vQozHhSpLZsObcRkZLX1cQBPCotGM5YZBJJ4yB2hWlNFxMjaU/y6hmXkbKT14c7kQd/kxN653tdQzb/0jCaS1zM2Vos1eARx3NjD95NK+bqIUz6iGJTV45q3CZunddCvRlKok1o1UXZdN6r+71nJgM3ir8I0xM3izV3mZ2bSEnSapVL4WS+bNXq8VJ81qxEu3rCx+PA9sG7z5cKh/BPGkaMKQ7xCfL1e3SUA0Cfn8K3nT6vt7h/Fuug0upIE/3Q+oAQTf8wk8+9YRvNMZLumusyq78N75U5BMa3BJyLtx9Kn5M2//uWkXVNmFfzpvftHfdzJZM2+V6kLWEsyReTOemD98P971LsZwPHutKpF5MwM2iAYmAMQLmRG8Kb6c4ydagx70RGJIpNLoHIpiZqM3U25pjAko8g5mXPbDl86ceRtAEJJUIKPkDgDxCOa3BrDXUjbp1sqfefOpMvqUVjyc+jiaS83WTCLjcdtYynke88ybyLyFEEafFoSmaeanDMfzl22X1ZQFJZx5i1jmvBVfJhtTGjBP2y/eGTOgu8THTeMs4B9/W7nyLsWTaViSjIt/f611m6wwo+OkrcsmmXnLDt7CRvBW4fNd1sybnRgzO5l5o1L5p4jeBYlh22XeJsKWwdusJh8SKQ179fK1fNmggFsuevC11e6jEbyr6U9K+14ElnwMl515AhIpDW93DhV93s3wvgXiiSToUfJuHPNl3vb3DuNXWw9i7XvbMa2hvOeT8jEyb4oLFetCZtzZz8q8vecq4OqngNPW5+wkGo6Vt2EJAMxo0s8ouuXMHWpvk/ilP7RVNHbI0Xp/StCNo+E4Ogej0DR9ZpxZNtlZ0oHzhOyHVxsRTSgSw+jXAgh5VSj5hra7A0B8GPOnBrC/bwQ9+rlNRYuVPfMmSRKafEbgVL3gzShXDpXSpdU88yaCt4b0IHq1YNZIj3AsBX9VgreF4q770JEigrcJZN4ApNwhqFIKmiSbpebGmd+SRwVUmmzJvMX1zqw2u5tqN2bZpN2CN+tgbo4KyJN5q3DZpFElYpcB3YYQgzeaIOtjxmZn3ibClsFbZiaNvqkqOCpg/MHXo+2xBm8A8J41OGF6A05tF09YpQZvZ+nBW6FOjdZuk1a//dthpNIaPntudbJugBgMDQB+1VWxLmRGxi0r8+YJAvPPByRR+jYmeIsmy35ewyibFGMD9LXwNYtyzsNvADOX5/y6qUEPuodiODwgXnhnNOllk/EhYPBQSZ3w4koQClKi3BJAdzpQOJ2u+oFEBPNaA0ilNfzt4ACCHgWuVLzsDUuATACe80xehYR84verqZTgbVTZZCA1iAEtiMGRzO/ocCxZ0uyVSWN0nExExj/zlrCceSvh5x3QZ70dSE/B/9kmNonGuc1SnwMrTnFnMm+FBpmTySibLHf1QsmyyibrOPOmGkO6RwVvirfyj227Zt4YvNFEWRqRMfNWJqMHiuYrWxKjAkrPvO05GoYcbBWbt4aZwLzzAABrzhBndkrduCyZHkKzXy1YjmJk3kYHJFs7+tHe4jdfWKvByLz51co9HKaYZZP5GrxkN6OJJVOIp9JlL/mZqWferGffzM5bieG8M8OmBN3oicRwSD+raZZNAuLsUgmZt5Si333u7wAAHE34CwdGetnkvFbxdW8c6BfdF1PxzEysMjJmu+U8k1chDZayyaIZwZu++fcmB9GHIAajCfNTwtVoWAKIzJvBnS/zpj9G43q3ScUnOsUVyaPPeuv3zMQNP9uKbz37No7qHVNLPvNWadbMmz7qoVIDr2tVpmzSbg1LrGWTddxBMF/mLTC1pN/rSWEGb3bNvHFMAJXIGrw54EafzW7BCcZA0bf14K1Q2eR4g69z2dsdwbypQWDRdeKsh0sEVpcsm4kntxzEe+e3FP4LRnG5JKx9bzviyfwDV40NYMRy5k3TNGzp6DPLLqtFlSVIEhBwVy54yzQsyb1JFFnVYfP9cFSsW7mDt2kNXsguKXPeDcia+5UveGsNehBNpLGrS5RwmZk3QGwyS8i8pYxD+wMHAACH4z40hwpspo2yyVZxN6l/OIHp072ia5lxDWVkZN6aqph5C7hluAqdC8xFVsQdOD3z5o73o09bgsGRTPA2XK2GJc3zAEgAtAJlk/rjxJjzppaYZdWDnaVLl2F1cg4e/OMuhLwKXFL+30vbUDyZrnxG8OaAF+RyMl5X7XfmzdptksHbmG6TlT7vBogbloD9Mm/Nc0VAaZlLSVQUa7bWAZk3mz2LCz63jCkBdyZ4y5t5Ex8fiadKCt72dEdw0YltwMrbsj4e8Ch4/NqzJnTNN1+UfywBkGlkYs28HRqIomsohlPbm/N9WUVIkgS37Kpo8GaOCgjk3mz7RpVNhvWgt9wlP7JLwo0XLMLpcy0/EyOD5g5mZ0QsjAYPbx4YQMiriOv0WbpLlhC8pY0nln7RTOJwzFM4E6L6gfARNPpVTAm40ROJiyAmGatM5s0sm6zehl+SJMxu9pc+XNrbKM68pZNQE4MYQABDUUvZZLUalqheMa9yoKNA8GbMhbJk3kqhB/bylLm495yTccL0Btz99FtoCbghu2w0xDkX2Z0J2hi8FaUt5MFXLz4BHz3ZZh0Ejec7l1L2M7q2Jkkio5y0zHmLHAWCVQigWuaJn4dRvm0X7gBw045qXwXVIp8188bgrWxmN/vwxgFxRzzfht0MiBJJNKK4O+79w3H0RuJmlqJSVNkFt+JCxHLmbWtHHwBgeZWDN0A0KglUsGxyYVsQs5p8OHFm7szQ6DNvxoa6Euc1brhgUfYHjMzbjFPydow0OpT+7eCAaFYCZI8GKCF408zgTZRNdkQ9WFhE2SQAzGsNZIK3/liFzrwVDsQr5X8+f3bxYwIMnhAQ7YecEBnTPq1hTNlkVYI3QGycigreRiaYedN/95rnQpIkfPqceVg6M2SWTtqa4skMMI4aZ95YNlmIJEn43Pm5bz5VlZFtq+esm0HxZmfewkeBtgqN32mmZpgAAB/xSURBVLBacAHw5beAhuJft4hszWGZN1ueeQPErDdDvrJJY6MWyTUuII/dRzOb3EoLjBptsGVfPzyKC4tnVP+OsUeVK3rmbVqDFy/dshLHt+X+t/vdStaZNzPzVo2SHyODNit3ySSQOSfZG4lbgjdLYFpK8KbqTywDInjrSvjRXCjz5vaLc0/IPK5F5i2eaT9dRo026DYJAC0BN7xqicGbtxGIDUKOiRtFfVrQLJtMpTVEE+nSA8LJYmR5890ldMlisxePiACu1M2vEew0zTU/dOb8KfjYe2x2ziUXmQ1LHEP1AZAYvAHiBozRfEjTqlc2KUkM3MhZ3P5MdYoDXitsm3kz2hoDhTJvmbLJYhnjB+ZPrXzw5ncr2Zm3/X1YNrsRar4W8BV084dOgGLcybYBv1tGPJVGMpWGIrsyZ97K3G0yp6Z2Uc4y//y8n2JtcpM13NtQQsMS4yyS1t8BSDKG4ENLoZJENSC6EgKYP1Vs9Bt9qjhrV4GyyaUzQmgJuLNm5NUMbwgId0GOi+BtAEEM6o81ozNs1brzGSVLhTJKqk9s9pIjpWdZp58MhGYDrYvG/1y7UbxjG5Y44AW5LkmSuBNez50mDdaznNEBIJ2o/JgAIqfytwCDB5l5K6fZluAtX8OAzDmy4scF7DkahuKSMKfUszGTIODJZN5iyRS2Hxy0RckkAFx5xhwsmWafzXemJFasV1Uzb8FpwC37gIUfzPsp1lb+ucsmiw/eJOOJZeAgUp5GAFLhBhKjyiaBymbePrB4GrbcfmH1yguPhScERAcgx0X2JuIKmZk3I6NflW6TADDv/WIgfEuBcydqwNKwpMTN74IPAF/ZXptdGrNGBQwBkszMTS1z++t7xptB8WW6TUb0m6ml3PgjovyMjpMOOPNm2+DNaJ3vU+W8w4lzNQExaJqW82v2HI2gfYq/KtkunyXztv3QIOKptDlbjrL5jJ+tvoEeqlDDkrzG2Ri7FZdZPmhmoFQf4FJFq/cSNiYun9hMS+kE4qoovSw4583tB9JJIBk3M8qNflVsAiqQeatpo8omk94m88yb8bsaqMacNwBoWwp84VUgUKAbreqzNCyxz82Xshs9KsDTUPl26jR53AFm3gA982YEb13ibTXKJomcyD8FkFyOuNFn2+DNOPNWKNOSGXydHbz9ftthnH73H9AdHnvwfk93GPOrcN4N0M+86de6taMfAKreadKuRmdVKzUq4FgYs+vMzJskidLJEu+cyt7MXaGoIgK5lkLNQMyW8REsmhbE7ZcsxUdOmi42t/W0oZ8Ir5F5E8Fb2ttklk0aYz0C1cq8FcM47ziRzFstUzzZmTc2K6lt7oAjNlTHTLVm3o6KtyybJJocvhZRMumAG322Dd6MM2+FMi35yiaf39GFnkgcT/zvgayPp9Ia3u0ZNs8FVZqYXSaCty0dfZjV5ENbiJvrXEYH5uFYAopLgqeEkRCVZpx7m2kd8O1tLKlZCQB4VBURTfxdw7LYlI5bNgkA8QgkScJnzpmHVp++TvXcersY3kYgnYQy3AVIMlzeprFlk9XKvBVD9WcGyNdT8Ca7M5vc2CDPu9W6Mz8HnPGZal9F9SkecSMGsARvLJskmhTzzgMWXlDtq5gUtr2lHPQoaPKrBYM3X56yya37RVbrZ6924Nrz5sOlzyw61D+CeDJdvcybRzYDzdc7+nEKSybzMgLzEePMWzSJoFeBZOM7Jsa4gLZGyzmzk1dlt6gtgld1IQwfAohhSBKP1aZCw6fN4C0z1NwsKZPLf+atpukZG3fkEOBrRsjvxoAevFW9YUkxVD8Q7ReBTD1lWRWPaOaQTjN4c4JT1lb7CuxB8WZGX4T14K3E1w8iyuP0fxT/OYB90xgA2lv85jmiXAI5yiYHhhPY1RXG4ukN6Ogdxku7Mx0Udx8Vs5yqmXmLxFLoHIziYP+IbZqV2NHo84xDsaS9N9EAlkwPYfH0BngUS6bm/FuAFf9U0t/jUWWENZFFGUAQIa+S99wngEzwpnecBJDpWFaBhiU1TR/noIYPiuDNq2BID96MJjlVa1hSDKPbZL2VTRpnOVPxzJk3olqneLPLJn0tgGzj5x8iqgpbB28bP3EyvnbJkrx/7lVdkCRgxFI2+foBkXXbcPFitATc+O+/dph/tqeKM94A48xb0hzOzWYl+flUPTDXN9BDUfsHb19YuRBP33DuMf89HsWFCEQWpTcdKNysBMicFYnnCN7YsKQwPXhzRw4B/haEfKrZsMS4cWDrx53RabTeGpYYNyVSMQZv5Byjgzd2miSiHGwdvJ00qxGLp+c/iC5JEnyqnJV529rRB0kCTp/bjCtOm41n3+pE16B4MtzbHUGDVzHL2yrN7xFn3jbv64NbduHEmTxkn8/ozFs4mkTIW4UZbyWQJAmy69jLOr2WzFtPapwB3UDhssl62tBPhF426UqOAL4WhLwqBkeyG5bY+8ybD4iHRQaqnho+GOXAST3zVovjDohGU72ZG2+RbjYrIaKcbB28FUMMvs4Eb1s6+nFCWwMavCquWtGOVFrD4/+7H4DeaXJqsGrnpox5dS/t6sGJs0LZ5XWUxdgwW+e8VWXGWxV4FRkRiOCtM+FHc6FmJYAleAtnPmZ04mPDksL0zBsAUTbpUxBPpRFNpDINS1Qb/56qAWCkT///OgrUmXkjJ1K8ogwaEKMCOCaAiHJwQPAmm2WT6bSG1zv6zHLEea0BnL1wCn726n6k0hr2HI1UrVkJIDJvAPDW4UGcOofn3QoxzhkZP9twDZx5mywe1YWwXjZ5JO4bP3gzMi4JNiwpmTVj429Bg57dHYwmMBxPwqu6Cp83rDbVB2hp8f9KHZ15M4K3uN5pk6MCyAkUjyXzdpSdJokoJxvvSorjt8xO29MdwWA0mTU7be2K43CwfwS/33YEhwei1Q3eLHfwlx/H826F+PS1MrIfQ9E6yrxZyiYPRD1o9o9TLpqrbJINS4ozOvOmP8YGR5IIx5L2nvEGZDcpqafMm3GWc1hvSMXMGzmB4hPnV5MxIDrAskkiyslRwdsWvRHIcksjkAuXtqE16Mb9z+wEUL1Ok4AYFWDgcO7CZH2mmzkqIJZAQ51k3ryKyyyb7Er6SjjzZi2bZPBWFNUPSPrvpa8ZIZ8185ay93k3IPOzB+oz82bMwmLwRk5gPK4HD4q3LJskohwcELwp5jymrR39CHkVzG/NBGhuxYUrT5+DfT0iKzF/ahUzb/pd/GkNHsxsrKO75BPk17tzJlJpRBPpOiqblDGkZ976teD43SYVLwCJZZMTIUmZ0kl/i9kUZ3AkgUjNZd7qKHgzHtcRZt7IQYzf4YED4i27TRJRDjUfvPksmbetHX04pb3ZHMptuGpFO4weJXOnVC94MzJvy9ubbT1s2i5EYJ4yu/7VTdmk4sIz6dPxu6a16EXD+GWTkqS3jLeWTbJhSdGM0klfCxp9etlkNIlIPImA3W8YqJbns3oqmzQe18M94i3PvJETGJm3ftFkjWWTRJRLzQdvAT14C8eS+HvnEE6dM/Ys2ZwWP84/firmtQbgc1evDMrIvHG+W3FEM5oUhqJ68Gb3jfQkUWQX9kpz8D31UwCk8RuWAHrwZi2b1GcFMfM2PmPj72s2M29D0QQisVQNBG+WbFs9lU2OybwxeCMHMEa7DBjBG8smiWgsm+9MxufTszNv7u9HWssfGH179SlmEFAtC6cF8U/nzsMnls+q6nXUCr9bRkQPzAGgoU4yb4DIvh3uFy2jxy2bBMTZrayySSPzxuBtXEbmzdptciSJ4XgSM+xe3uy2zHary8wbyybJQYzgjZk3Iiqg5nfDxqiArfv7ASBvC/4mvxtNxWQwykiVXbjto0ureg21xKf/bI3gLeix95DuyeRVZRwNi3NrRT1ux5RNsmFJ0cyyyWZ4VRdUWcKgnnnz2/7Mmz/3/zudscnlmTdyEmvmTfEB7uo1WCMi+3JG2WQihS37+rBgagCN450PopphnHkLR+vrzBsAeBQXNE38f1Mxj+nRZZNG5o1lk+PzNiLtcgOqH5IkIeRVRcOSeBJBu3ebtAZsSh1l3uTRZ94YvJEDGNnzgQMi68az8USUQ80Hbz63Ak0DXt3by/b7DmOeeYvV15k3QGTeAFEqqhYzJHp02aSZeWPDknEtWInB9gvNjVLIp4qGJbEk/HZ/zGVl3urozFvWqAApe2QCUa1SrMEbz7sRUW4235mMz683IBmKJbGcwZujGDP8jMxbPZ158+jBW1Hn3QCxeQ13Zd5nw5LinXwFDisnwjgtG/Iq6AnHkEhpCFSxwVFR3PWaebM0LPGEmKEgZzB+h1MxjgkgorwckHmzDr5mF0cn8bsVROJJDEUTAOoseFPEr2ZRnSaB/GWTPPNWspBPxZFBEfzWVLfJusq86b8XWiozp4+o1llvwDDzRkR51HzwZgzRDbhlHN/Gcw9O4tPLJsOxJFwS4FNtngWZRF7VCN6KPMOZq2xSdjMjMQEhr4rD/XrwVisNSyRX5hxYPbBmlHnejZwiK3hjp0kiyq1iO5OhoSHcfPPNCIfDSCQSuOWWW3Dqqace899rlE2+Z04TZBc3qk7iV2Uk0xp6I3EEPUpdDTY3zrw1l1I2GR81KoAlkxPS4FUwkkgBqIHMm+wGJFls+uro9yMrUGXwRk5hHfcRYNkkEeVWsZ3JD3/4Q5x55plYv3499uzZg5tuugm//vWvj/nvNcomWTLpPEaziK6hmDl/q14YZZMtpZRNJiJAOg24XCLzxmYlExLyZR5rfrt3m5QkkX2rt5+1ywW4VCCdYPBGzsHMGxEVoWLB2/r16+F2iw1GKpWCxzM5WYGZjT64JOCchXyicxojq9o1FKurTpPABDJvRvlcckQEcskYM28TFLKcrbR92SQgmpa46uvmBgBxnjPO4I0cxHpGmWfeiCiPsuxMnnjiCfz4xz/O+tjGjRuxbNkyHD16FDfffDNuvfXWnF+7Y8eOkr/fz1cfh4Z4F3bs6Br/kymvaDQ6ofUvl76jogHHwZ4hTAsotrq2crCufzQyJN4O9mDHjsS4X9vcO4TpAP6+fStS3imY2dsFn+bCboev2WSxrv3wwID58a5DHdgR7azWZRVlgaZA01zYU8M/64k89yySFCgA+kZSOFLD//Zqs9vzfr3JWv90Ekv0j+/pCiMW58+l3Pj4rx6u/cSVJXhbtWoVVq1aNebjb7/9Nr7yla/gq1/9KlasWJHza5csWZLz41R+O3bssNX6708fAf7chf5oGifODtnq2srBuv5tf08B7wxh6fx2LFkyY/wvji0AtgDHHzcLaJkHvOkFRoKOX7PJYl37t6MHgb+K4c8nnbAIc1ttPkNsUxPgctX0z3pCzz2/9QOxfjS3taO5hv/t1Wa35/16M2b9fykDWgrzT34vxwVUAB//1cO1L2zz5s15/6xiNUG7du3CjTfeiO985ztYvHhxpb4t1TC/XrKWTGsI1tGYAOAYyiaNjpOpeH11H5xEIV/msWb7M2+AGBEg1Xzj4NIZ5/w8HBVADqL6gHgE8LVU+0qIyKYqtiP+1re+hXg8jnvuuQcAEAwG8d3vfrdS355qkHXj3FBvZ96MhiVFd5sMirfxiHibjHLG2wRZm+PUxFnLEy6u9hVUh3Gmk2feyEkUj2hcItfAcw8RVUXFnh0YqFGp/JYB7DWxiZ5EHj3z1lTsnDe3nnkzg7d4ducyKlpID96kWpkteN7N1b6C6jAzbwzeyEEUL7PJRFRQfe2Iqab41czDs97KJs9d1Iq93RG0BorMno0pm4wBKsdnTIRRNhlw19dswZrDzBs5keJlp0kiKqi+dsRUU3x1nHlbNrsJ31xVQvA1pmwyzrLJCTIyb9bML9mQwuCNHGjOCqDpuGpfBRHZWH3tiKmmWDfPoTob0l2y0WWTqRgblkyQ3y1DdkkI1NkNg5ojs2EJOdAnvlftKyAim6vDFmVUK6znjeqtbLJko8sm2bBkwiRJQsirIFALnSbrmXGm08vgjYiI6geDN7Itl0syA7h6K5ssmVufRcayyUnR4FXNURVkU2xYQkREdYjBG9maUTrJzNs4ZFWUkWWVTTJ4m6gZjV60hdit09bYsISIiOoQd8Rkaz63DETqb87bhLgDlrJJZt6OxX9+cjkUFztN2priBiABaqDaV0JERFQx3BGTrTHzVgI1kD2kmw1LJqw1yMDX9tSAOO/mYgEJERHVD+6IydaMc0c881YEt18Eb+kUoKU4pJuc7czrgUUfqvZVEBERVRR3xGRrRuYtwOYR4zPKJpMx8b7CzBs5WMs88R8REVEdYb0J2ZrfLSPoUeDi+aPxGWWTKT14Y8MSIiIiIkdh8Ea25ncrLJksllE2mYyL95l5IyIiInIU7orJ1q4+6zicf8LUal9GbXDrmbdkVLzPzBsRERGRozB4I1s7fW4LTp/bUu3LqA2qfuYtZWTe2LCEiIiIyElYNknkFGbZJBuWEBERETkRgzcip3CzYQkRERGRkzF4I3IKNQCkE0AsLN5n5o2IiIjIURi8ETmF2y/ejvSJtzzzRkREROQoDN6InMIdEG+N4I1lk0RERESOwuCNyCnUUcEbyyaJiIiIHIXBG5FTmJm3XvGWmTciIiIiR2HwRuQUxpm3YWbeiIiIiJyIwRuRU4wpm2TDEiIiIiInYfBG5BRjyiaZeSMiIiJyEgZvRE5hlk3qwZvCM29ERERETsLgjcgpRpdNsmEJERERkaMweCNyCmvZpCQDslLd6yEiIiKiScXgjcgpVB8ACUgnWTJJRERE5EAM3oicQpIAVT/3xmYlRERERI7D4I3ISYzSSWbeiIiIiByHwRuRkxgdJ9mshIiIiMhxGLwROYnKzBsRERGRUzF4I3ISlk0SERERORaDNyIncbNhCREREZFTMXgjchKWTRIRERE5FoM3IicxyiaZeSMiIiJyHAZvRE5ilE0q3upeBxERERFNOgZvRE7iDoq3CjNvRERERE7D4I3ISVTOeSMiIiJyKgZvRE5ilk0yeCMiIiJyGqVS32h4eBg33XQTBgcHoaoq7rvvPrS1tVXq2xPVB7NsksEbERERkdNULPP2+OOP48QTT8RPf/pT/MM//AMeffTRSn1rovrBskkiIiIix6pY5m39+vVIpVIAgEOHDiEUClXqWxPVD7Nskg1LiIiIiJymLMHbE088gR//+MdZH9u4cSOWLVuGq6++Gn//+9/xwx/+sBzfmqi+GWWTzLwREREROY6kaZpW6W+6e/duXHvttfjDH/6Q9fHNmzfD7/dX+nJIF41G4fVyPli1TMb6+7q2Yu6m69F18rXoWfqPk3RlzsfHfnVx/auHa19dXP/q4vpXD9e+sOHhYZx22mk5/6xiZZPf//730dbWhksvvRSBQACyLOf8vCVLllTqkmiUHTt2cP2raFLWvzEKbAKmzZiDafxZFo2P/eri+lcP1766uP7VxfWvHq59YZs3b877ZxUL3i6//HJs2LABTz75JFKpFDZu3Fipb01UP1g2SURERORYFQveWltb8YMf/KBS346oPvmniMCtgWM4iIiIiJymYsEbEVWAvwW48Q0gyOCNiIiIyGkYvBE5TWhGta+AiIiIiMqgYkO6iYiIiIiIaOIYvBEREREREdUABm9EREREREQ1gMEbERERERFRDWDwRkREREREVAMYvBEREREREdUABm9EREREREQ1gMEbERERERFRDWDwRkREREREVAMYvBEREREREdUABm9EREREREQ1gMEbERERERFRDZA0TdOqfRGGzZs3V/sSiIiIiIiIquq0007L+XFbBW9ERERERESUG8smiYiIiIiIagCDNyIiIiIiohpgi+AtnU7jjjvuwOrVq7Fu3Trs27ev2pfkeIlEAjfffDPWrl2LK664As8//zz27duHq666CmvXrsXXv/51pNPpal+mo/X09OD9738/du/ezbWvsO9///tYvXo1LrvsMjzxxBNc/wpKJBK46aabsGbNGqxdu5aP/wp54403sG7dOgDIu96PP/44LrvsMlx55ZXYtGlTNS/Xcazrv2PHDqxduxbr1q3DZz7zGXR3dwPg+peLde0Nv/nNb7B69Wrzfa59+VjXv6enB9dffz0++clPYs2aNejo6ADA9S+ZZgPPPPOMtmHDBk3TNG3r1q3addddV+Urcr5f/vKX2t13361pmqb19fVp73//+7Vrr71We+WVVzRN07Tbb79de/bZZ6t5iY4Wj8e1z33uc9qHPvQhbdeuXVz7CnrllVe0a6+9VkulUlo4HNb+4z/+g+tfQc8995x2ww03aJqmaS+++KL2hS98getfZo888oh2ySWXaKtWrdI0Tcu53l1dXdoll1yixWIxbXBw0Px/Onaj1/+Tn/yk9tZbb2mapmk/+9nPtI0bN3L9y2T02muapm3fvl27+uqrzY9x7ctn9Ppv2LBBe/rppzVN07SXX35Z27RpE9d/AmyRedu8eTPOPfdcAMApp5yCbdu2VfmKnO/iiy/GjTfeCADQNA2yLGP79u1YsWIFAOC8887DX/7yl2peoqPdd999WLNmDaZNmwYAXPsKevHFF3H88cfj85//PK677jqcf/75XP8KmjdvHlKpFNLpNMLhMBRF4fqXWXt7Ox588EHz/Vzr/eabb+LUU0+F2+1GQ0MD2tvbsXPnzmpdsqOMXv8HHngAS5YsAQCkUil4PB6uf5mMXvu+vj488MADuPXWW82Pce3LZ/T6b9myBZ2dnVi/fj1+85vfYMWKFVz/CbBF8BYOhxEMBs33ZVlGMpms4hU5XyAQQDAYRDgcxg033IAvfelL0DQNkiSZfz40NFTlq3SmX/3qV2hpaTFvWADg2ldQX18ftm3bhn//93/HN77xDfzzP/8z17+C/H4/Dh48iA9/+MO4/fbbsW7dOq5/mV100UVQFMV8P9d6h8NhNDQ0mJ8TCAQQDocrfq1ONHr9jZt2W7ZswWOPPYb169dz/cvEuvapVAq33XYb/uVf/gWBQMD8HK59+Yx+7B88eBChUAg/+tGPMGPGDDz66KNc/wmwRfAWDAYRiUTM99PpdNYPm8rj8OHDuPrqq/Hxj38cH/vYx+ByZR4OkUgEoVCoilfnXE8++ST+8pe/YN26ddixYwc2bNiA3t5e88+59uXV1NSEc845B263G/Pnz4fH48kKFrj+5fWjH/0I55xzDp555hk89dRTuOWWW5BIJMw/5/qXX67n+tGvw5FIJGtDRZPrt7/9Lb7+9a/jkUceQUtLC9e/ArZv3459+/bhzjvvxFe+8hXs2rUL99xzD9e+gpqamrBy5UoAwMqVK7Ft2zau/wTYInhbvnw5XnjhBQDA66+/juOPP77KV+R83d3d+PSnP42bb74ZV1xxBQBg6dKl+Otf/woAeOGFF3D66adX8xId66c//Skee+wx/OQnP8GSJUtw33334bzzzuPaV8hpp52GP//5z9A0DZ2dnRgZGcFZZ53F9a+QUChkvjA3NjYimUzyuafCcq33smXLsHnzZsRiMQwNDWH37t18LS6Tp556ynwNmDNnDgBw/Stg2bJlePrpp/GTn/wEDzzwABYuXIjbbruNa19Bp512Gv70pz8BAF577TUsXLiQ6z8BtkhvXXjhhXjppZewZs0aaJqGjRs3VvuSHO973/seBgcH8fDDD+Phhx8GANx22224++678cADD2D+/Pm46KKLqnyV9WPDhg24/fbbufYV8IEPfACvvfYarrjiCmiahjvuuAOzZ8/m+lfI+vXrceutt2Lt2rVIJBL48pe/jJNOOonrX0G5nm9kWca6deuwdu1aaJqGL3/5y/B4PNW+VMdJpVK45557MGPGDHzxi18EAJxxxhm44YYbuP5VMnXqVK59hWzYsAFf+9rX8POf/xzBYBDf+ta30NjYyPUvkaRpmlbtiyAiIiIiIqLCbFE2SURERERERIUxeCMiIiIiIqoBDN6IiIiIiIhqAIM3IiIiIiKiGsDgjYiIiIiIqAYweCMiIsf41Kc+hZdffjnrY3fffTeeeOKJMZ974MABXHnllZW6NCIiomPG4I2IiBxj1apVeOqpp8z34/E4Nm3ahI9+9KNVvCoiIqLJYYsh3URERJPh4osvxre//W2MjIzA5/Ph+eefx9lnn413330Xd911F2RZhsfjwV133ZX1dStXrsTvfvc7eDwefPOb38T8+fMxa9YsPPLII1BVFUeOHMGaNWvwyiuvYOfOnbj66quxdu1avPrqq/j2t78NWZYxZ84c/Ou//itUVa3Sv56IiJyOmTciInIMj8eDD37wg3juuecAAL/61a+wZs0afO1rX8Mdd9yBxx57DFdddRXuvffeov6+I0eO4MEHH8Sdd96J7373u7j//vvx6KOP4he/+AU0TcPtt9+Ohx56CI899hja2trw61//upz/PCIiqnMM3oiIyFGM0snOzk4MDg5i6dKl6OrqwpIlSwAAZ5xxBt555528X69pmvn/ixYtgqqqaGhoQHt7O9xuNxobGxGLxdDb24uuri586Utfwrp16/DSSy/h4MGDZf/3ERFR/WLZJBEROcoJJ5yASCSC//qv/8Lll18OAJg2bRp27tyJxYsX47XXXsPcuXOzvsbtdqOrqwuzZ8/Gzp07sWDBAgCAJEl5v09zczOmT5+Ohx9+GA0NDXj++efh9/vL9u8iIiJi8EZERI5z+eWX49/+7d+wadMmAKLj5F133QVN0yDLMjZu3Jj1+Z/97GdxzTXXYNasWQiFQkV9D5fLhdtuuw3XXHMNNE1DIBDA/fffP+n/FiIiIoOkWetDiIiIiIiIyJZ45o2IiIiIiKgGMHgjIiIiIiKqAQzeiIiIiIiIagCDNyIiIiIiohrA4I2IiIiIiKgGMHgjIiIiIiKqAQzeiIiIiIiIagCDNyIiIiIiohrw/wGjAsWizHRtIwAAAABJRU5ErkJggg==\n", 209 | "text/plain": [ 210 | "" 211 | ] 212 | }, 213 | "metadata": {}, 214 | "output_type": "display_data" 215 | } 216 | ], 217 | "source": [ 218 | "import matplotlib.pyplot as plt\n", 219 | "import seaborn as sns\n", 220 | "\n", 221 | "sns.set_style('whitegrid')\n", 222 | "\n", 223 | "fig, ax = plt.subplots(figsize=(15, 6))\n", 224 | "for i in range(len(seeds)):\n", 225 | " ax.plot(seed_timeseries[:, i], label=str(seeds[i]))\n", 226 | "legend = ax.legend(frameon=True)\n", 227 | "frame = legend.get_frame()\n", 228 | "frame.set_facecolor('white')\n", 229 | "frame.set_edgecolor('black')\n", 230 | "ax.set_xlim(0, seed_timeseries.shape[0]-1)\n", 231 | "ax.set_ylabel('Standardize BOLD signal')\n", 232 | "ax.set_xlabel('Volume')\n", 233 | "fig.show()" 234 | ] 235 | } 236 | ], 237 | "metadata": { 238 | "kernelspec": { 239 | "display_name": "Python [conda env:python3]", 240 | "language": "python", 241 | "name": "conda-env-python3-py" 242 | }, 243 | "language_info": { 244 | "codemirror_mode": { 245 | "name": "ipython", 246 | "version": 3 247 | }, 248 | "file_extension": ".py", 249 | "mimetype": "text/x-python", 250 | "name": "python", 251 | "nbconvert_exporter": "python", 252 | "pygments_lexer": "ipython3", 253 | "version": "3.6.4" 254 | } 255 | }, 256 | "nbformat": 4, 257 | "nbformat_minor": 2 258 | } 259 | -------------------------------------------------------------------------------- /coding-exercises/python.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# A basic introduction to Python (and, by extension, coding)\n", 8 | "This is an extremely basic introduction to Python. To become more familiar with this language, please complete the [Codecademy](https://www.codecademy.com) Python course.\n", 9 | "\n", 10 | "For the sake of this introduction, we will assume that you have already installed Python 3 on your laptop, and that you have also installed each of the libraries needed for this tutorial. If you see errors like `ModuleNotFoundError: No module named 'XXXX'` when running this notebook, try installing the missing library using `pip` (e.g., `pip install XXXX`) in a separate terminal (not in the notebook)." 11 | ] 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "metadata": {}, 16 | "source": [ 17 | "## Variables\n", 18 | "Variables come in classes, and each variable is an object of a given class. Each object has its own _methods_, which are functions specific to a given class.\n", 19 | "\n", 20 | "Variables can be named using any combination of letters, numbers, and underscores, as long as the name doesn't start with a number. You also don't want to name your variables the same as any of the Python keywords, which are variables that are reserved for built-in Python functions. These keywords will typically appear as a different color (e.g., green) in most IDEs or in Jupyter Notebooks.\n", 21 | "\n", 22 | "Some built-in variable types include strings, floats, integers, booleans, dictionaries, and lists." 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "metadata": {}, 28 | "source": [ 29 | "### Strings\n", 30 | "Strings are for text and are defined using single or double quotes." 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 1, 36 | "metadata": {}, 37 | "outputs": [ 38 | { 39 | "name": "stdout", 40 | "output_type": "stream", 41 | "text": [ 42 | "str methods: capitalize, casefold, center, count, encode, endswith, expandtabs, find, format, format_map, index, isalnum, isalpha, isdecimal, isdigit, isidentifier, islower, isnumeric, isprintable, isspace, istitle, isupper, join, ljust, lower, lstrip, maketrans, partition, replace, rfind, rindex, rjust, rpartition, rsplit, rstrip, split, splitlines, startswith, strip, swapcase, title, translate, upper, zfill\n", 43 | "\n", 44 | "What is the string?\n", 45 | "hello world\n", 46 | "\n", 47 | "What is the string in title case?\n", 48 | "Hello World\n", 49 | "\n", 50 | "What is the string in upper case?\n", 51 | "HELLO WORLD\n", 52 | "\n", 53 | "Is the string lower case?\n", 54 | "True\n", 55 | "\n", 56 | "Is the string composed entirely of numbers?\n", 57 | "False\n", 58 | "\n", 59 | "Does the string start with \"hello\"?\n", 60 | "True\n" 61 | ] 62 | } 63 | ], 64 | "source": [ 65 | "var1 = 'hello world'\n", 66 | "\n", 67 | "# We can see a list of the methods available for this new object with dir\n", 68 | "var1_methods = dir(var1)\n", 69 | "# I'm going to remove private methods (ones that start with _ or __) before printing them\n", 70 | "var1_methods = [method for method in var1_methods if not method.startswith('_')]\n", 71 | "print('str methods: {0}'.format(', '.join(var1_methods)))\n", 72 | "print()\n", 73 | "\n", 74 | "# As you can see, there are a lot of methods for strings\n", 75 | "# Let's check out a couple:\n", 76 | "# First we'll print out the original string\n", 77 | "print('What is the string?')\n", 78 | "print(var1)\n", 79 | "print()\n", 80 | "\n", 81 | "# Now let's look at the string in title case\n", 82 | "print('What is the string in title case?')\n", 83 | "print(var1.title())\n", 84 | "print()\n", 85 | "\n", 86 | "# Or capitalized\n", 87 | "print('What is the string in upper case?')\n", 88 | "print(var1.upper())\n", 89 | "print()\n", 90 | "\n", 91 | "# There are also methods for *inspecting* the object\n", 92 | "# Is the variable lower case?\n", 93 | "print('Is the string lower case?')\n", 94 | "print(var1.islower())\n", 95 | "print()\n", 96 | "\n", 97 | "# Is it composed of numbers?\n", 98 | "print('Is the string composed entirely of numbers?')\n", 99 | "print(var1.isdigit())\n", 100 | "print()\n", 101 | "\n", 102 | "# Does it start with the word \"hello\"?\n", 103 | "print('Does the string start with \"hello\"?')\n", 104 | "print(var1.startswith('hello'))" 105 | ] 106 | }, 107 | { 108 | "cell_type": "markdown", 109 | "metadata": {}, 110 | "source": [ 111 | "### Floats and integers\n", 112 | "Numbers can be stored in quite a few classes (especially with the `numpy` library), but the two main built-in classes for numbers are `float` and `int`. \n", 113 | "\n", 114 | "To create either, you can simply define the variable with a number. Floats include a decimal, while integers do not." 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": 2, 120 | "metadata": {}, 121 | "outputs": [ 122 | { 123 | "name": "stdout", 124 | "output_type": "stream", 125 | "text": [ 126 | "5 is a \n", 127 | "5.0 is a \n", 128 | "\n", 129 | "int methods: bit_length, conjugate, denominator, from_bytes, imag, numerator, real, to_bytes\n", 130 | "\n", 131 | "float methods: as_integer_ratio, conjugate, fromhex, hex, imag, is_integer, real\n", 132 | "\n", 133 | "Is the float number an integer?\n", 134 | "True\n", 135 | "\n", 136 | "Does the float start with \"hello\"?\n" 137 | ] 138 | }, 139 | { 140 | "ename": "AttributeError", 141 | "evalue": "'float' object has no attribute 'startswith'", 142 | "output_type": "error", 143 | "traceback": [ 144 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 145 | "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", 146 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;31m# above don't work with these variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Does the float start with \"hello\"?'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'hello'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 147 | "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'startswith'" 148 | ] 149 | } 150 | ], 151 | "source": [ 152 | "var2 = 5\n", 153 | "var3 = 5.\n", 154 | "print('{0} is a {1}'.format(var2, type(var2)))\n", 155 | "print('{0} is a {1}'.format(var3, type(var3)))\n", 156 | "print()\n", 157 | "\n", 158 | "# Let's see what methods each class has\n", 159 | "int_methods = dir(var2)\n", 160 | "int_methods = [method for method in int_methods if not method.startswith('_')]\n", 161 | "print('int methods: {0}'.format(', '.join(int_methods)))\n", 162 | "print()\n", 163 | "\n", 164 | "float_methods = dir(var3)\n", 165 | "float_methods = [method for method in float_methods if not method.startswith('_')]\n", 166 | "print('float methods: {0}'.format(', '.join(float_methods)))\n", 167 | "print()\n", 168 | "\n", 169 | "# Not as many methods as strings, but there are still some useful ones\n", 170 | "print('Is the float number an integer?')\n", 171 | "print(var3.is_integer())\n", 172 | "print()\n", 173 | "\n", 174 | "# You'll also notice that the string methods we explored\n", 175 | "# above don't work with these variables\n", 176 | "print('Does the float start with \"hello\"?')\n", 177 | "print(var3.startswith('hello'))" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "metadata": {}, 183 | "source": [ 184 | "### Booleans\n", 185 | "Booleans represent the true/false dichotomy. Booleans are defined using the keywords True and False." 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": 3, 191 | "metadata": {}, 192 | "outputs": [ 193 | { 194 | "name": "stdout", 195 | "output_type": "stream", 196 | "text": [ 197 | "bool methods: as_integer_ratio, conjugate, fromhex, hex, imag, is_integer, real\n", 198 | "\n" 199 | ] 200 | } 201 | ], 202 | "source": [ 203 | "var4 = True\n", 204 | "var5 = False\n", 205 | "\n", 206 | "bool_methods = dir(var3)\n", 207 | "bool_methods = [method for method in bool_methods if not method.startswith('_')]\n", 208 | "print('bool methods: {0}'.format(', '.join(bool_methods)))\n", 209 | "print()" 210 | ] 211 | }, 212 | { 213 | "cell_type": "markdown", 214 | "metadata": {}, 215 | "source": [ 216 | "### Lists\n", 217 | "Lists contain... well, lists. You can put anything in your list, and there are many list-specific methods that make it easy to look through or manipulate your lists." 218 | ] 219 | }, 220 | { 221 | "cell_type": "code", 222 | "execution_count": 4, 223 | "metadata": {}, 224 | "outputs": [ 225 | { 226 | "data": { 227 | "text/plain": [ 228 | "5" 229 | ] 230 | }, 231 | "execution_count": 4, 232 | "metadata": {}, 233 | "output_type": "execute_result" 234 | } 235 | ], 236 | "source": [ 237 | "some_lab_members = ['Angie', 'Matt', 'Cody', 'Mike', 'Veronica']\n", 238 | "len(some_lab_members)" 239 | ] 240 | }, 241 | { 242 | "cell_type": "markdown", 243 | "metadata": {}, 244 | "source": [ 245 | "### Dictionaries\n", 246 | "Dictionaries also contain collections of items, but they link each item to a \"key\". You can access the item by looking up the associated key." 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": 5, 252 | "metadata": {}, 253 | "outputs": [ 254 | { 255 | "name": "stdout", 256 | "output_type": "stream", 257 | "text": [ 258 | "Angie's full name is Angela Laird.\n", 259 | "Matt's full name is Matthew Sutherland.\n", 260 | "Cody's full name is Michael Riedel.\n", 261 | "Mike's full name is Michael Tobia.\n", 262 | "Veronica's full name is Veronica Del Prete.\n" 263 | ] 264 | } 265 | ], 266 | "source": [ 267 | "# Suppose, for example, that you know each lab member's nickname \n", 268 | "# or preferred name, but you want to know their full names. You\n", 269 | "# can have a dictionary that links each nickname to each full name.\n", 270 | "full_names = {'Angie': 'Angela Laird',\n", 271 | " 'Matt': 'Matthew Sutherland',\n", 272 | " 'Cody': 'Michael Riedel',\n", 273 | " 'Mike': 'Michael Tobia',\n", 274 | " 'Veronica': 'Veronica Del Prete'}\n", 275 | "\n", 276 | "# You can then look up those full names:\n", 277 | "for member in some_lab_members:\n", 278 | " print(\"{0}'s full name is {1}.\".format(member, full_names[member]))" 279 | ] 280 | }, 281 | { 282 | "cell_type": "markdown", 283 | "metadata": {}, 284 | "source": [ 285 | "## Modules\n", 286 | "We can access additional functions and classes by loading modules. Some modules are provided by Python (e.g., `os` and `glob`), while others are developed by third parties (e.g., `numpy`, `scipy`, and just about 90% of the other libraries you'll be using).\n", 287 | "\n", 288 | "To access a module, you need to have it installed on your computer. There are a number of package managers, include `pip`, `homebrew`, and `conda`. Most the time, trying to install a library with `pip` is a safe bet." 289 | ] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": 6, 294 | "metadata": {}, 295 | "outputs": [ 296 | { 297 | "name": "stdout", 298 | "output_type": "stream", 299 | "text": [ 300 | "[1 2 3] is a \n", 301 | "\n", 302 | "numpy.ndarray methods: T, all, any, argmax, argmin, argpartition, argsort, astype, base, byteswap, choose, clip, compress, conj, conjugate, copy, ctypes, cumprod, cumsum, data, diagonal, dot, dtype, dump, dumps, fill, flags, flat, flatten, getfield, imag, item, itemset, itemsize, max, mean, min, nbytes, ndim, newbyteorder, nonzero, partition, prod, ptp, put, ravel, real, repeat, reshape, resize, round, searchsorted, setfield, setflags, shape, size, sort, squeeze, std, strides, sum, swapaxes, take, tobytes, tofile, tolist, tostring, trace, transpose, var, view\n", 303 | "\n" 304 | ] 305 | } 306 | ], 307 | "source": [ 308 | "# We can import modules as is:\n", 309 | "import numpy\n", 310 | "\n", 311 | "# Or with aliases, to make them easier to type:\n", 312 | "import numpy as np\n", 313 | "\n", 314 | "# We can also import submodules or functions within modules:\n", 315 | "from numpy import random\n", 316 | "\n", 317 | "# We can even import everything from a module into the general namespace\n", 318 | "# But this is very much *NOT* recommended\n", 319 | "from numpy import *\n", 320 | "\n", 321 | "# For the rest of this tutorial (and all of the lab code),\n", 322 | "# we will assume you used one of the first three options,\n", 323 | "# but never the last one\n", 324 | "# To access the contents of the module, you can now use whatever you imported\n", 325 | "arr = numpy.array([1, 2, 3])\n", 326 | "print('{0} is a {1}'.format(arr, type(arr)))\n", 327 | "print()\n", 328 | "\n", 329 | "# Numpy arrays have their own methods:\n", 330 | "arr_methods = dir(arr)\n", 331 | "arr_methods = [method for method in arr_methods if not method.startswith('_')]\n", 332 | "print('numpy.ndarray methods: {0}'.format(', '.join(arr_methods)))\n", 333 | "print()" 334 | ] 335 | } 336 | ], 337 | "metadata": { 338 | "kernelspec": { 339 | "display_name": "Python [conda env:python3]", 340 | "language": "python", 341 | "name": "conda-env-python3-py" 342 | }, 343 | "language_info": { 344 | "codemirror_mode": { 345 | "name": "ipython", 346 | "version": 3 347 | }, 348 | "file_extension": ".py", 349 | "mimetype": "text/x-python", 350 | "name": "python", 351 | "nbconvert_exporter": "python", 352 | "pygments_lexer": "ipython3", 353 | "version": "3.6.4" 354 | } 355 | }, 356 | "nbformat": 4, 357 | "nbformat_minor": 2 358 | } 359 | -------------------------------------------------------------------------------- /coding-exercises/working_with_spreadsheets.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Working with spreadsheets in Python\n", 8 | "The [pandas](http://pandas.pydata.org) library is useful for reading, analyzing, and writing spreadsheet data.\n", 9 | "\n", 10 | "The standard for spreadsheet files is to use a comma-separated values (csv) or tab-delimited text file. While pandas can read Excel files, this is more difficult, often with no upside." 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 1, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "import pandas as pd" 20 | ] 21 | } 22 | ], 23 | "metadata": { 24 | "kernelspec": { 25 | "display_name": "Python [conda env:python3]", 26 | "language": "python", 27 | "name": "conda-env-python3-py" 28 | }, 29 | "language_info": { 30 | "codemirror_mode": { 31 | "name": "ipython", 32 | "version": 3 33 | }, 34 | "file_extension": ".py", 35 | "mimetype": "text/x-python", 36 | "name": "python", 37 | "nbconvert_exporter": "python", 38 | "pygments_lexer": "ipython3", 39 | "version": "3.6.4" 40 | } 41 | }, 42 | "nbformat": 4, 43 | "nbformat_minor": 2 44 | } 45 | --------------------------------------------------------------------------------