Data driven - Bloghttps://nekrasovp.github.io/2023-10-14T15:00:00+03:00Nekrasov Pavel personal pageПримеры технического долга2023-10-14T15:00:00+03:002023-10-14T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2023-10-14:/technical-debt-examples.html<p>В этом посте я хочу более подробно рассмотреть примеры технического долга в существующем коде.</p><h1>Примеры технического долга</h1>
<h2>Сожержание</h2>
<ul>
<li><a href="#примеры-технического-долга">Примеры технического долга</a></li>
<li><a href="#сожержание">Сожержание</a></li>
<li><a href="#аннотация">Аннотация</a></li>
<li><a href="#технический-долг-в-самом-коде">Технический долг в самом коде</a></li>
<li><a href="#технический-долг-в-документации">Технический долг в документации</a></li>
<li><a href="#другие-источники-возникновения-технического-долга">Другие источники возникновения технического долга</a></li>
<li><a href="#что-дальше">Что дальше?</a></li>
</ul>
<h2>Аннотация</h2>
<p>В прошлой статье мы дали <a href="https://nekrasovp.github.io/what-is-technical-debt.html">определение технического долга</a> и рассмотрели некоторые примеры технического долга в реальной жизни. По <a href="https://thenewstack.io/to-reduce-tech-debt-eliminate-dependencies-and-refactoring/">данным Appian</a>, 58% организаций согласились с тем, что управление техническим долгом мешает среднему разработчику программного обеспечения разрабатывать приложения, которые нужны их пользователям.</p>
<p>В этом посте я хочу более подробно рассмотреть примеры технического долга в существующем коде. В идеале хорошее понимание различных типов технического долга и того, как он выглядит, может помочь вам избежать его неконтролируемое разрастание.</p>
<p>Ниже приведены примеры технического долга, классифицированные по местам его возникновения.</p>
<h2>Технический долг в самом коде</h2>
<p><img alt="distracted-guy-tech-debt" src="images/distracted-guy-tech-debt.jpeg"></p>
<p>Существует множество способов создания технического долга и типов технического долга.</p>
<p>Краткое напоминание: технический долг — это долговая метафора, популяризированная Уордом Каннингемом, который <a href="https://www.productplan.com/glossary/technical-debt/#:~:text=Years%20later%2C%20Cunningham%20described%20how,you'll%20be%20paying%20interest.">определяет</a> технический долг следующим образом: «С заемными деньгами вы можете сделать что-то раньше, чем в противном случае, но потом, пока вы не вернете эти деньги, вы будете платить проценты. Я думал, что занять деньги — это хорошая идея, я думал, что выбросить программное обеспечение за дверь, чтобы получить некоторый опыт работы с ним, — это хорошая идея, но, конечно, в конечном итоге вы вернетесь назад и, узнав что-то об этом программном обеспечении, вы отплатите за это. кредит путем рефакторинга программы, чтобы отразить ваш опыт, полученный вами». Позже Стив МакКоннелл развил <a href="https://www.castsoftware.com/blog/steve-mcconnell-on-categorizing-managing-technical-debt">метафору</a>, разделив технический долг на два типа: преднамеренный и непреднамеренный.</p>
<p>Одним из источников технического долга является нарушение правил «чистого кода». Эти правила включают, помимо прочего, правило «один и только один раз». Иногда вы пишете плохой код, который не является модульным, код, модули которого не связаны между собой, код, модули которого называются странно и не соответствуют тому, что они должны делать, или код, который не читается. Иногда вы используете необычные методы кодирования, которые малоизвестны. Иногда вы называете свои переменные, методы и классы не в зависимости от того, что они должны делать.</p>
<p>Иногда вы пишете «уродливый» код или код с «длинными методами, множеством временных переменных, списками параметров, которые передаются в несколько функций, и методами, которые знают о модели данных больше, чем им нужно». <a href="https://medium.com/beamdental/refactoring-for-readability-5776cd2d2c22">Пишет</a> старший Full Stack инженер Кертис Отери.</p>
<p>Эвальдас, руководитель технической группы Euromonitor International, <a href="https://dev.to/jess/whats-your-worst-technical-debt-story-4ef0">написал</a>, что его текущая кодовая база является примером технического долга. Базы данных содержат «огромный реляционный монолит» десятилетней давности.</p>
<p>Другой <a href="https://insights.sei.cmu.edu/sei_blog/2019/05/managing-the-consequences-of-technical-debt-5-stories-from-the-field.html">пример технического долга</a> в коде привел Ипек Озкая, старший сотрудник технического персонала Института разработки программного обеспечения Карнеги-Меллон. Одной успешной компании по производству морского оборудования удалось накопить 3 миллиона строк кода за 16 лет, создав ошеломляющий объем технического долга. С годами на рынок выходили новые компании, сотрудники менялись, и компания выпускала все больше и больше продуктов, на которые в конечном итоге распространяется гарантия или контракты на техническое обслуживание.</p>
<p>Технический долг в системе программного обеспечения значительно замедлил работу команды и потребовал огромного объема работы. Было трудно вносить небольшие изменения или дополнения в продукты, и это часто нарушало код, создавая краткосрочные и долгосрочные трудности. Когда они запускали новые продукты, каждый выпуск требовал от разработчиков нескольких дней ручного и трудоемкого регрессионного тестирования существующих продуктов.</p>
<h2>Технический долг в документации</h2>
<p>Одним из крупнейших источников технического долга является недостаточная или устаревшая документация. В идеале разработчики документируют, почему они сделали свой выбор и что они намеревались сделать, как они сгруппировали свои объекты в модули (и почему) и т. д. Также рекомендуется обновлять документацию по мере рефакторинга и добавления в код. .</p>
<p>Обычно техническая задолженность в документации возникает из-за того, что программисты просто действуют слишком быстро и используют ярлыки. Но есть определенные крайние случаи, на которые следует обратить внимание. Например, программист и профессор компьютерных наук Челси Трой отметила один «вопиющий случай» перфекционизма, который привел к технической задолженности в документации. В данном случае Трой взяла на себя управление проектом у разработчика, который объединил все свои пул-реквесты перед тем, как покинуть организацию. Никто ничего не знал о ее проекте, и Трою пришлось восстанавливать контекст проекта с нуля, что обошлось примерно в 30 000 долларов.</p>
<p>Чтобы этого не повторилось, директор попросил разработчика, который, как он подозревал, мог проверять код ушедшего программиста, проверить код Троя. «Этот разработчик уничижительно отзывался о работе и навыках моего предшественника, что дало мне представление об их рабочих отношениях», — <a href="https://chelseatroy.com/2020/02/21/intercultural-vision-2-passing-as-perfect/">написал</a> Трой. «Когда разработчик начал проверять мою работу, его комментарии касались исключительно вариантов, с которыми он не согласен. Ух ты, неудивительно, что первоначальный разработчик объединил все свои PR».</p>
<p>Неудивительно, что в документации компании по производству морского оборудования также фигурировала техническая задолженность. Новые сотрудники обычно понятия не имели, почему код может сломаться, поскольку документация по проектированию и выбору программ была скудной и устаревшей.</p>
<h2>Другие источники возникновения технического долга</h2>
<p>Роберт Лефковиц — бывший главный архитектор Warby Parker. В 2019 году он выступил с <a href="https://thenewstack.io/to-reduce-tech-debt-eliminate-dependencies-and-refactoring/">докладом</a> , в котором описал, как решения по управлению проектами могут повлиять на технологический долг. Например, <strong>долг проектирования</strong> часто возникает через сторонние библиотеки, а не только внутри самого кода. «Если вы не хотите технологического долга, избегайте использования библиотек или фреймворков», — сказал Лефковиц. Его примером был React.js. Библиотека, разработанная Facebook, отлично подходит для сложного веб-интерфейса и мобильного интерфейса, которым пользуются миллиарды человек. Но большинство компаний не работают в таких масштабах, поэтому использование React.js замедлит разработку.</p>
<p>Чтобы продемонстрировать, какой технический долг могут добавить внешние библиотеки, Лефковиц удалил все зависимости из примера приложения iOS, включая внешние библиотеки, такие как Alamofire. Это увеличило размер кода с 80 000 до 35 000 строк. Разработчики могут заменить эту функциональность, перейдя на iOS, поскольку она уже понимает HTTP, или переписать ее с нуля.</p>
<p>В результате этого шага потребовалось поддерживать на 45 000 строк кода меньше. Даже если вы импортировали код, когда владелец обновит его, вам придется обновить его в программах, которые его используют. Для дальнейшей количественной оценки выигрыша Лефковиц отметил, что если приложение опирается на 300 зависимостей, а разработчики обновляют каждую из них раз в год, то разработчикам придется пересобирать приложение почти каждый день. Принимая это во внимание, возможно, имеет смысл заново изобрести велосипед, если только вы не создаете проекты, содержащие как минимум 5 миллионов строк кода.</p>
<p>Технический менеджер Ноа Торп выразил это так: «Мы платим арендную плату за каждую строку кода, каждый день». И разработчики также платят арендную плату за свои зависимости. Они могут включать библиотеки и платформы, а также все службы, связанные с вашими приложениями, к которым обычно также подключена библиотека. «Когда вы добавляете библиотеку в свой проект, вы платите арендную плату за всю эту библиотеку и за ее использование», — <a href="https://hackernoon.com/managing-technical-debt-1806424e7d40">написал</a> тренер Nerd Карл Ташиан. «Итак, вам нужно обосновать каждую библиотеку, каждый плагин. Даже крошечные. Они быстро накапливаются. А если вы примените сверхлегкий и дисциплинированный подход, вы будете поражены тем, насколько быстро вы сможете двигаться».</p>
<p>Если предположить, что вам нужны функции Alamofire и теперь вам нужно создать их самостоятельно, удаление Alamofire, например, вероятно, приведет к увеличению технологического долга. В Ruform мы используем React.js, потому что мы достаточно велики и получаем выгоду от совместного использования кода в модулях/компонентах, которые мы можем повторно использовать в нашей кодовой базе, что обычно помогает нам избежать технического долга.</p>
<p>Итог: вообще избегать использования библиотек или фреймворков — не очень хороший совет, но разумно обосновать каждую добавляемую зависимость.</p>
<p>Другой <a href="https://medium.com/redbubble/depth-first-decision-making-fa347999441b">пример</a> технологического долга за пределами самого кода привел технический директор Redbubble Том Соммер. Они создали свой кластер Kubernetes по индивидуальному заказу еще до того, как появились ныне популярные более управляемые решения, такие как EKS и GKE. Поэтому, прежде чем они могли перейти, им нужно было подумать, как это может повлиять на их приложения и команду.</p>
<h2>Что дальше?</h2>
<p>Технический долг живет в самом коде, документации, а также в ваших библиотеках и фреймворках. Надеемся, эти примеры технического долга помогут вам лучше понять, что это такое, как он работает и как его минимизировать в будущем.</p>Что такое технический долг?2023-09-16T15:00:00+03:002023-09-16T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2023-09-16:/what-is-technical-debt.html<p>Что такое технический долг? Примеры, профилактика и лучшие практики</p><h1>Что такое технический долг? Примеры, профилактика и лучшие практики</h1>
<h2>Сожержание</h2>
<ul>
<li><a href="#что-такое-технический-долг-примеры-профилактика-и-лучшие-практики">Что такое технический долг? Примеры, профилактика и лучшие практики</a></li>
<li><a href="#сожержание">Сожержание</a></li>
<li><a href="#аннотация">Аннотация</a></li>
<li><a href="#что-такое-технический-долг">Что такое технический долг?</a></li>
<li><a href="#примеры-технического-долга">Примеры технического долга</a><ul>
<li><a href="#каковы-недостатки-технического-долга">Каковы недостатки технического долга?</a></li>
<li><a href="#какие-виды-технического-долга-существуют">Какие виды технического долга существуют?</a></li>
<li><a href="#как-возникает-технический-долг">Как возникает технический долг?</a></li>
<li><a href="#намеренный-технический-долг">Намеренный технический долг</a></li>
<li><a href="#непреднамеренный-технический-долг">Непреднамеренный технический долг</a></li>
<li><a href="#задолженность-по-документации">Задолженность по документации</a></li>
<li><a href="#инфраструктурный-долг">Инфраструктурный долг</a></li>
<li><a href="#каковы-признаки-технического-долга">Каковы признаки технического долга?</a></li>
<li><a href="#как-управлять-и-предотвращать-технический-долг">Как управлять и предотвращать технический долг</a></li>
</ul>
</li>
<li><a href="#почему-все-это-важно">Почему все это важно?</a></li>
<li><a href="#каковы-лучшие-практики">Каковы лучшие практики?</a></li>
<li><a href="#какие-инструменты-могут-использовать-компании-для-предотвращения-технического-долга">Какие инструменты могут использовать компании для предотвращения технического долга?</a></li>
</ul>
<h2>Аннотация</h2>
<p>Большинству разработчиков программного обеспечения знакома проблема технического долга, но это менее верно для тех, кто не занимается программированием.</p>
<p>Однако эту концепцию важно понимать, поскольку она применима не только к программированию, но и к широкому спектру сценариев, в которых краткосрочные решения могут иметь долгосрочные последствия. Ниже приведены примеры технического долга и лучшие практики, которые вам необходимо знать.</p>
<h2>Что такое технический долг?</h2>
<p>Технический долг возникает, когда команды разработчиков программного обеспечения ставят скорость доставки выше оптимального качества кода.</p>
<p>Часто это делается намеренно. Пользователю может срочно понадобиться функциональность, поэтому разработчики решают развернуть «достаточно хороший» код с намерением исправить его позже.</p>
<p>Метафора долга уместна. Когда кто-то берет на себя долг, это часто происходит потому, что финансовые ресурсы необходимы на краткосрочной основе. Однако это создает дополнительные затраты в будущем, когда этот долг придется погасить.</p>
<p>Технический долг возникает, когда команды разработчиков ставят скорость доставки выше оптимального качества кода.</p>
<p>Технический долг работает аналогичным образом, обменивая выгоду на дополнительную работу в будущем.</p>
<p>Если код в конечном итоге не будет пересмотрен и исправлен, он может стать проблемой, так же как могут начисляться проценты и штрафы, если платежи по кредиту не производятся.</p>
<p>Технический долг не обязательно является проблемой, но он может стать таковой, если продукт плохо оптимизирован или если допущено распространение нефункционального кода.</p>
<h2>Примеры технического долга</h2>
<p>Классическим примером технического долга является проблема 2000 года (Y2K).</p>
<p>Многие разработчики программного обеспечения в 1960-х и 1970-х годах предпочитали экономить драгоценную память, сохраняя даты в виде двух цифр: «73» вместо «1973».</p>
<p>Такая практика продолжалась в течение многих лет, даже несмотря на то, что цены на память падали. Многие из этих программ были внедрены в операционный бизнес и использовались гораздо дольше, чем кто-либо ожидал.</p>
<p>По мере приближения 2000 года тысячи предприятий и правительственных учреждений осознали, что расчеты дат в массовом масштабе окажутся неудачными. Это привело к лихорадочным усилиям по очистке, стоимость которых оценивается в 100 миллиардов долларов .</p>
<p>Технический долг не ограничивается программным обеспечением. Например, лучшая практика в области кибербезопасности — предоставлять права доступа к файлам ролям внутри организации, а не отдельным лицам.</p>
<p>Предположим, помощник по административным вопросам получает разрешение от начальника на получение временного доступа к конфиденциальным документам, которые ему обычно не разрешают просматривать. Если ИТ-организация предоставляет исключение и не может его отозвать позже, это означает, что она предоставила постоянный доступ к конфиденциальным документам. В конечном итоге учетная запись может быть скомпрометирована и стать уязвимой.</p>
<h3>Каковы недостатки технического долга?</h3>
<p>Если краткосрочные исправления быстро рефакторингуются и разработчики знают, как справиться с техническим долгом, недостатков мало. Это может даже принести пользу, поскольку это может позволить бизнесу быстро реагировать на возможности или проблемы.</p>
<p>Риск возрастает, когда долги наслаиваются друг на друга.</p>
<p>Быстрые исправления могут быть плохо документированы или не документированы вообще. А когда люди, которые выполняли быстрые исправления, уходят, в компании может не остаться никого, кто знает, как должен работать код или даже о том, что быстрые исправления существуют.</p>
<p>Улучшения или изменения могут создавать непреднамеренные конфликты, которые приводят к сбоям или медленной работе программ. Инновации замедляются, поскольку организации не могут внести улучшения из-за страха, что изменения сломают приложение.</p>
<h3>Какие виды технического долга существуют?</h3>
<p>Двумя основными категориями технического долга являются <strong>преднамеренный</strong> и <strong>непреднамеренный</strong>.</p>
<p>Намеренный технический это тот долг который берется на себя сознательно и стратегически.</p>
<p>Непреднамеренный технический возникает как «нестратегический результат плохой работы».</p>
<p>В 2014 году группа ученых разработала <a href="https://www.researchgate.net/publication/286010286_Towards_an_Ontology_of_Terms_on_Technical_Debt">таксономию технического долга</a> , которая включает 13 различных типов, в том числе:</p>
<ul>
<li>Архитектурный долг(Architecture debt)</li>
<li>Долга кода(Code debt)</li>
<li>Долг ошибок(Defect debt)</li>
<li>Долг проектировки(Design debt)</li>
<li>Долг процессов(Process debt)</li>
<li>Долг тестирорвания(Test debt)</li>
</ul>
<p>Эта классификация полезна, поскольку она охватывает все области, в которых краткосрочное мышление может создать долгосрочные проблемы.</p>
<h3>Как возникает технический долг?</h3>
<h4>Намеренный технический долг</h4>
<p>Намеренный технический долг — это сознательное решение, которое должно быть задокументировано и запланировано для рефакторинга.</p>
<p>Например: у регионального менеджера по продажам есть крайний срок для создания отчета, который его платформа не может предоставить. Поэтому он внедрил программу создания отчетов с открытым исходным кодом, чтобы уложиться в срок. Это технический долг.</p>
<p>Однако если команда разработчиков выделяет ресурсы для удаления средства создания отчетов с открытым исходным кодом и изменения основной системы отчетов, это является намеренным техническим долгом.</p>
<h4>Непреднамеренный технический долг</h4>
<p>Непреднамеренный технический долг возникает, когда изменения или дополнения вносятся из соображений целесообразности и без преднамеренных планов по рефакторингу кода.</p>
<p>Это также может быть результатом плохих проектных решений, вызванных недостатком знаний или несоблюдением стандартов разработки. Непреднамеренный технический долг в области тестирования возникает, когда наборы тестов неполны или тестирование сокращено или пропущено ради удобства.</p>
<h4>Задолженность по документации</h4>
<p>Задолженность по документации является обычным явлением и возникает, когда разработчики слишком спешат тщательно документировать свой код.</p>
<p>В долгосрочной перспективе это может стать большой проблемой, если человек уйдет из компании и не сможет оставить след, который другие смогут использовать для понимания его кода. Задолженность по документации стала основной причиной проблемы 2000 года.</p>
<h4>Инфраструктурный долг</h4>
<p>Долг инфраструктуры возникает, когда приложения создаются с учетом определенных компонентов инфраструктуры, таких как базы данных и файловые системы. Если такие зависимости не задокументированы и компания переходит на новую инфраструктуру, приложение может не работать.</p>
<h3>Каковы признаки технического долга?</h3>
<p>Предупреждающими признаками технического долга являются:</p>
<ul>
<li>Проекты застревают из-за того, что разработчики не имеют представления о кодовой базе.</li>
<li>Выскакивают ошибки, которые сложно исправить из-за сложности или отсутствия документации.</li>
<li>Исправления ошибок, которые создают новые ошибки или постоянное снижение производительности.</li>
</ul>
<h3>Как управлять и предотвращать технический долг</h3>
<p>Знание того, как справиться с техническим долгом, начинается с разумных методов разработки. В среде DevOps это включает тестирование как со сдвигом влево, так и сдвигом вправо.</p>
<ul>
<li><strong>Сдвиг-влево при тестировании</strong> перемещает процесс тестирования на более ранние стадии цикла разработки и на протяжении всего цикла, так что проблемы можно предвидеть и решить до того, как они будут внедрены в производство.</li>
<li><strong>Тестирование со сдвигом вправо</strong> направлено на получение обратной связи после того, как приложения будут запущены в производство, чтобы можно было обнаружить ошибки на ранней стадии и исправить их до того, как программное обеспечение получит широкое распространение.
Они создают ограждения, которые предотвращают чрезмерный рост проблем.</li>
</ul>
<p>Обходные пути, которые создают технический долг, неизбежны и часто необходимы. Однако важно, чтобы разработчики документировали их, включая причины, по которым были применены хаки, и инструкции по их устранению.</p>
<p>Регулярные проверки существующего кода также могут позволить членам команды проверять работу друг друга и выявлять пробелы или нарушения в документации.</p>
<h2>Почему все это важно?</h2>
<p>Говорят, что каждая компания теперь является компанией-разработчиком программного обеспечения. Даже предприятия тяжелой промышленности стремятся собирать данные, чтобы их клиенты могли получать больше пользы от покупаемых ими продуктов.</p>
<p>Ежегодно создается больше программного обеспечения, чем было создано за всю историю вычислений, и эта тенденция ускоряется.</p>
<p>В то же время организации-разработчики испытывают большее, чем когда-либо, давление, требующее быстрого запуска проектов в производство. Для спешащих разработчиков естественно срезать углы, а менеджеры проектов должны понимать и укреплять свои команды, что тестирование и документация — это не одноразовые вещи.</p>
<h2>Каковы лучшие практики?</h2>
<p>По мере того, как организации внедряют методы DevOps, им следует разъяснять, что такое технический долг, и применять гибкую тактику для управления им. Это может включать в себя реализацию:</p>
<ul>
<li>Тестирование сдвига вправо и сдвига влево</li>
<li>Методы A/B и канареечного тестирования для выявления проблем до того, как они выйдут из-под контроля.</li>
</ul>
<p>Коллегиальные проверки кода позволяют по-новому взглянуть на работу разработчиков. Разработчики должны работать с последовательным и ограниченным набором инструментов и языков и иметь контрольный список задач, которые необходимо выполнить на каждом этапе.</p>
<p>Эффективная <a href="https://www.mendix.com/blog/why-you-should-consider-devops-for-your-organization/">организация DevOps</a> дает разработчикам большую свободу выбора того, как им создавать свои творения, но также обеспечивает ограждения, гарантирующие, что они не выйдут из-под контроля.</p>
<h2>Какие инструменты могут использовать компании для предотвращения технического долга?</h2>
<p>Практики, изложенные выше, являются хорошим началом. Дополнительные преимущества могут быть реализованы за счет:</p>
<ul>
<li>Использование автоматического тестирования для запуска нескольких циклов отладки при каждом изменении кода при каждом изменении модуля.</li>
<li>Создание надежных процедур структуры кода, включая обязательную документацию, для защиты от обходных путей.</li>
<li>Использование инструментов управления проектами, позволяющих командам видеть состояние работы каждого.</li>
</ul>
<p>Программисты работают в командах по два человека, чтобы они могли понимать решения друг друга.
Все больший объем программного обеспечения пишется с использованием инструментов <em>low-code</em> и <em>no-code</em>. Они в значительной степени самодокументируются, поскольку блок-схемы и методы перетаскивания позволяют представить визуальное представление логики и желаемых результатов.</p>
<p>Применяя эти инструменты для профессиональной разработки программного обеспечения, организации могут извлечь выгоду из этих функций самодокументирования. Менеджеры по разработке должны поощрять свои команды рассматривать <em>low-code/no-code</em> как повышение производительности, а не замену их элегантных творений.</p>Update Django user password2023-09-02T15:00:00+03:002023-09-02T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2023-09-02:/update-django-user-password.html<p>Update Django user password from shell.</p><h2>Update Django user password from shell</h2>
<p>Enter Django Shell</p>
<div class="highlight"><pre><span></span><code>python manage.py shell
</code></pre></div>
<p>Update password</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">django.contrib.auth.models</span> <span class="kn">import</span> <span class="n">User</span>
<span class="n">users</span> <span class="o">=</span> <span class="n">User</span><span class="o">.</span><span class="n">objects</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">users</span><span class="p">)</span>
<span class="n">user</span> <span class="o">=</span> <span class="n">users</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">user</span><span class="o">.</span><span class="n">set_password</span><span class="p">(</span><span class="s1">'123'</span><span class="p">)</span>
<span class="n">user</span><span class="o">.</span><span class="n">save</span><span class="p">()</span>
</code></pre></div>
<p>If you are using custom user model</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">your_app.auth.models</span> <span class="kn">import</span> <span class="n">User</span>
<span class="n">users</span> <span class="o">=</span> <span class="n">User</span><span class="o">.</span><span class="n">objects</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">users</span><span class="p">)</span>
<span class="n">user</span> <span class="o">=</span> <span class="n">users</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">user</span><span class="o">.</span><span class="n">set_password</span><span class="p">(</span><span class="s1">'123'</span><span class="p">)</span>
<span class="n">user</span><span class="o">.</span><span class="n">save</span><span class="p">()</span>
</code></pre></div>MySQL server has gone away errors2023-07-11T13:00:00+03:002023-07-11T13:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2023-07-11:/mysql-server-errors.html<p>This post is about errors after "The MySQL server has gone away", which means that the MySQL server (mysqld) timed out and closed the connection.</p><h2>Abstract</h2>
<p>MySQL server has gone away, can be a frustrating error to solve. This is partly because, to solve this error, sometimes the solution involves multiple layers, application, or service config changes. This article includes solutions I’ve seen for this MySQL server general error.</p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#mysql-server-has-gone-away-error-log-examples">MySQL server has gone away error log examples</a></li>
<li><a href="#mysql-wait_timeout">MySQL wait_timeout</a></li>
<li><a href="#mysql-max_allowed_packet">MySQL max_allowed_packet</a></li>
<li><a href="#mysql-innodb_log_file_size">MySQL innodb_log_file_size</a></li>
<li><a href="#other-causes-of-mysql-server-has-gone-away">Other causes of MySQL server has gone away</a></li>
<li><a href="#mysql-database-charset-and-collation">MySQL database charset and collation</a></li>
<li><a href="#exceeding-mysql-max_connections-setting">Exceeding MySQL max_connections setting</a></li>
<li><a href="#still-unresolved-see-mysqls-help-page">Still unresolved? See MySQL’s help page</a></li>
</ul>
<h2>MySQL server has gone away error log examples</h2>
<p>Keep in mind that this error can be logged in a few ways, as listed below. In addition, at times, the error is only an indication of a deeper underlying issue. This means the error could be due to a problem or bug in your connecting application or remote service. In this case, you need to check ALL related error logs with the same timestamp to determine whether another issue may be to blame. Here are error log examples of the MySQL server has gone away error:</p>
<p><code>General error: 2006 MySQL server has gone away</code></p>
<p><code>Error Code: 2013. Lost connection to MySQL server during query</code></p>
<p><code>Warning: Error while sending QUERY packet</code></p>
<p><code>PDOException: SQLSTATE[HY000]: General error: 2006 MySQL server has gone away</code></p>
<h2>MySQL wait_timeout</h2>
<p>The reason for MySQL server has gone away error is often because MySQL’s <em>wait_timeout</em> was exceeded. MySQL <em>wait_timeout</em> is the number of seconds the server waits for activity on a non-interactive connection before closing it. You should make sure the <em>wait_timeout</em> is not set too low. The default for MySQL <em>wait_timeout</em> is 28800 seconds. Often, it gets lowered arbitrarily. That said, the lower you can set <em>wait_timeout</em> without affecting database connections can be a good sign of MySQL database efficiency. </p>
<p>Also, check the variables: <em>net_read_timeout</em>, <em>net_write_timeout</em> and <em>interactive_timeout</em>. Adjust or add the following lines in my.cnf to meet your requirements:</p>
<p>```wait_timeout=90
net_read_timeout=90
net_write_timeout=90
interactive_timeout=300
connect_timeout=90</p>
<div class="highlight"><pre><span></span><code><span class="o">##</span> <span class="n">MySQL</span> <span class="n">max_allowed_packet</span>
<span class="o">*</span><span class="n">max_allowed_packet</span><span class="o">*</span> <span class="k">is</span> <span class="n">the</span> <span class="n">maximum</span> <span class="k">size</span> <span class="k">of</span> <span class="n">one</span> <span class="n">packet</span><span class="p">.</span> <span class="n">The</span> <span class="k">default</span> <span class="k">size</span> <span class="k">of</span> <span class="mi">4</span><span class="n">MB</span> <span class="n">helps</span> <span class="n">the</span> <span class="n">MySQL</span> <span class="n">server</span> <span class="n">catch</span> <span class="k">large</span> <span class="p">(</span><span class="n">possibly</span> <span class="n">incorrect</span><span class="p">)</span> <span class="n">packets</span><span class="p">.</span> <span class="k">As</span> <span class="k">of</span> <span class="n">MySQL</span> <span class="mi">8</span><span class="p">,</span> <span class="n">the</span> <span class="k">default</span> <span class="n">has</span> <span class="n">been</span> <span class="n">increased</span> <span class="k">to</span> <span class="mi">16</span><span class="n">MB</span><span class="p">.</span> <span class="k">If</span> <span class="n">mysqld</span> <span class="n">receives</span> <span class="n">a</span> <span class="n">packet</span> <span class="n">that</span> <span class="k">is</span> <span class="n">too</span> <span class="k">large</span><span class="p">,</span> <span class="n">it</span> <span class="n">assumes</span> <span class="n">that</span> <span class="n">something</span> <span class="k">is</span> <span class="n">wrong</span> <span class="k">and</span> <span class="n">closes</span> <span class="n">the</span> <span class="k">connection</span><span class="p">.</span> <span class="k">To</span> <span class="n">fix</span> <span class="n">this</span><span class="p">,</span> <span class="n">you</span> <span class="n">should</span> <span class="n">increase</span> <span class="n">the</span> <span class="o">*</span><span class="n">max_allowed_packet</span><span class="o">*</span> <span class="k">in</span> <span class="n">my</span><span class="p">.</span><span class="n">cnf</span><span class="p">,</span> <span class="k">then</span> <span class="k">restart</span> <span class="n">MySQL</span><span class="p">.</span> <span class="n">The</span> <span class="k">max</span> <span class="k">for</span> <span class="n">this</span> <span class="n">setting</span> <span class="k">is</span> <span class="mi">1</span><span class="n">GB</span><span class="p">.</span> <span class="k">For</span> <span class="n">example</span><span class="p">:</span>
</code></pre></div>
<p>max_allowed_packet = 512M
```</p>
<h2>MySQL innodb_log_file_size</h2>
<p>You may need to increase the <em>innodb_log_file_size</em> MySQL variable in your my.cnf configuration. MySQL’s <em>innodb_log_file_size</em> should be 25% of <em>innodb_buffer_pool_size</em> (if possible, no less than 20%). Remember that the larger this value, the longer it will take to recover from a database crash.</p>
<p>This means, for example, if your buffer pool size is set to <em>innodb_buffer_pool_size</em>=16G and your <em>innodb_log_files_in_group</em> setting is still set to the recommended default of 2 files (<em>innodb_log_files_in_group</em>=2), then your <em>innodb_log_file_size</em> should be set to 2G. This will create two (2) log files at 2GB each, which equals 25% of <em>innodb_buffer_pool_size</em>=16G.</p>
<p><strong>WARNING</strong>: You must stop MySQL server in order to change <em>innodb_log_file_size</em> or <em>innodb_log_files_in_group</em>. If you don’t, you risk catastrophe! (Read: <a href="https://dev.mysql.com/doc/refman/8.0/en/innodb-redo-log.html">MySQL Log Redo instructions</a>.)</p>
<h2>Other causes of MySQL server has gone away</h2>
<h3>MySQL database charset and collation</h3>
<p>Changing the default database charset to latin1 and default collation to latin1_general_ci seemed to have solved MySQL server has gone away for sometimes.</p>
<h3>Exceeding MySQL max_connections setting</h3>
<p><em>Max_connections</em> set the maximum permitted number of simultaneous client connections. Be careful with this setting!! Exhaustion of memory and other resources can occur <a href="https://linuxblog.io/my-cnf-tuning-avoid-this-common-pitfall/">when set too large</a> and scheduling overhead also increases. As a guide, set <em>max_connections</em> to approximately double the previous number of maximum simultaneous client connections. E.g., if after a month of uptime, the maximum simultaneous client connections were 114, then set to <em>max_connections</em>=250. Before you go crazy with this setting, please read: <a href="https://dev.mysql.com/doc/refman/8.0/en/client-connections.html">How MySQL Handles Client Connections</a>.</p>
<h2>Still unresolved? See MySQL’s help page</h2>
<p>Oracle has put together a <a href="https://dev.mysql.com/doc/refman/8.0/en/gone-away.html">nice self-help page for MySQL server has gone away</a> errors. On that page, they also suggest that you make sure MySQL didn’t stop/restart during the query. Excerpt:</p>
<p>“You can check whether the MySQL server died and restarted by executing <a href="https://dev.mysql.com/doc/refman/8.0/en/mysqladmin.html">mysqladmin version</a> and examining the server’s uptime. If the client connection was broken because <a href="https://dev.mysql.com/doc/refman/8.0/en/mysqld.html">mysqld</a> crashed and restarted, you should concentrate on finding the reason for the crash.”</p>Python app with poetry & docker2023-02-20T12:00:00+03:002023-02-20T12:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2023-02-20:/python-poetry-docker.html<p>This post is about building multi-stage docker image with python app and poetry.</p><h2>Abstract</h2>
<p>This post is about building multi-stage docker image with python app and poetry.</p>
<p>Lets start from the end? here is the final Dockerfile:</p>
<div class="highlight"><pre><span></span><code>FROM python:3.11-slim as os-base
ENV <span class="nv">PYTHONUNBUFFERED</span><span class="o">=</span><span class="m">1</span>
ENV <span class="nv">PYTHONDONTWRITEBYTECODE</span><span class="o">=</span><span class="m">1</span>
RUN apt-get update
RUN apt-get install -y curl
FROM os-base as poetry-base
RUN curl -sSL https://install.python-poetry.org <span class="p">|</span> python3 -
ENV <span class="nv">PATH</span><span class="o">=</span><span class="s2">"/root/.poetry/bin:</span><span class="nv">$PATH</span><span class="s2">"</span>
RUN poetry config virtualenvs.create <span class="nb">false</span>
RUN apt-get remove -y curl
FROM poetry-base as app-base
ARG <span class="nv">APPDIR</span><span class="o">=</span>/app
WORKDIR <span class="nv">$APPDIR</span>/
COPY pyproject.toml ./pyproject.toml
RUN poetry install --no-dev
COPY your_project ./your_project
FROM app-base as main
CMD tail -f /dev/null
</code></pre></div>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#build-stage-1-os-base">Build Stage 1: os-base</a></li>
<li><a href="#build-stage-2-poetry-base">Build Stage 2: poetry-base</a></li>
<li><a href="#build-stage-3-app-base">Build Stage 3: app-base</a></li>
<li><a href="#build-stage-4-main">Build Stage 4: main</a></li>
</ul>
<h2>Build Stage 1: os-base</h2>
<div class="highlight"><pre><span></span><code>FROM python:3.11-slim as os-base
ENV <span class="nv">PYTHONUNBUFFERED</span><span class="o">=</span><span class="m">1</span>
ENV <span class="nv">PYTHONDONTWRITEBYTECODE</span><span class="o">=</span><span class="m">1</span>
RUN apt-get update
RUN apt-get install -y curl
</code></pre></div>
<ol>
<li>Named stage.</li>
<li>PYTHONUNBUFFERED forces the stdout and stderr streams to go straight to terminal.</li>
<li>PYTHONDONTWRITEBYTECODE prevents Python from writing .pyc files to disk. This is useful inside a docker container as a marginal performance gain if your process runs only once and needs not be saved to byte code for subsequent invocations.</li>
<li>"... used to resynchronise the package index files from their sources", therefore does not hurt to run.</li>
<li>We need curl for installing poetry in the next step.</li>
</ol>
<h2>Build Stage 2: poetry-base</h2>
<div class="highlight"><pre><span></span><code>FROM os-base as poetry-base
RUN curl -sSL https://install.python-poetry.org <span class="p">|</span> python -
ENV <span class="nv">PATH</span><span class="o">=</span><span class="s2">"/root/.poetry/bin:</span><span class="nv">$PATH</span><span class="s2">"</span>
RUN poetry config virtualenvs.create <span class="nb">false</span>
RUN apt-get remove -y curl
</code></pre></div>
<ol>
<li>Named stage.</li>
<li>Installing poetry as per the install instructions.</li>
<li>Docker variables declared with ENV can be used across all docker stages. Here we're modifying our PATH environment variable so that we can execute poetry here and in subsequent stages.</li>
<li>While we are inside a docker container there is no immediate reason to use a virtual environment.</li>
<li>Since we have no use for curl anymore, it doesn't hurt to remove it. In case you're wondering what the "-y" flag does, it's answering "yes" if the uninstaller asks you a question.</li>
</ol>
<h2>Build Stage 3: app-base</h2>
<p>Here's where you'll copy your app specific files and directories. I left it quite open so you can replace it with your own.</p>
<div class="highlight"><pre><span></span><code>FROM poetry-base as app-base
ARG <span class="nv">APPDIR</span><span class="o">=</span>/app
WORKDIR <span class="nv">$APPDIR</span>/
COPY pyproject.toml ./pyproject.toml
RUN poetry install --no-dev
COPY your_project ./your_project
</code></pre></div>
<ol>
<li>Named stage.</li>
<li>The ARG instruction defines a variable that users can pass at build-time to the builder with the docker build command using the --build-arg <varname>=<value> flag.</li>
<li>WORKDIR does what it says on the tin. It's also magically created if it doesn't exist.</li>
<li>Notice that we are only copying pyproject.toml and NOT the poetry.lock.</li>
<li>Copying project files</li>
</ol>
<h2>Build Stage 4: main</h2>
<p>This is where you launch your app. We're not launching an app in this tutorial but using the space to show you a little trick instead.</p>
<div class="highlight"><pre><span></span><code>FROM app-base as main
CMD tail -f /dev/null
</code></pre></div>
<p>Your container will now remain running. You can either ssh to it to e.g check manually the dependencies have been installed.</p>How to List Users in Linux2022-08-07T15:00:00+03:002022-08-07T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2022-08-07:/linux-list-users.html<p>This article will show you how to list users in Linux systems.</p><h2>Abstract</h2>
<p>Have you ever wanted to list all users in your Linux system or to count the number of users in the system? There are commands to create a user, delete a user, list logged in users, but what is the command to list all users in Linux?</p>
<p>This article will show you how to list users in Linux systems.</p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#get-a-list-of-all-users-using-the-etcpasswd-file">Get a List of All Users using the /etc/passwd File</a></li>
<li><a href="#get-a-list-of-all-users-using-the-getent-command">Get a List of all Users using the getent Command</a></li>
<li><a href="#check-whether-a-user-exists-in-the-linux-system">Check whether a user exists in the Linux system</a></li>
<li><a href="#system-and-normal-users">System and Normal Users</a></li>
<li><a href="#conclusion">Conclusion</a></li>
</ul>
<h2>Get a List of All Users using the /etc/passwd File</h2>
<p>Local user information is stored in the <em>/etc/passwd</em> file.
Each line in this file represents login information for one user.
To open the file you can either use <strong>cat</strong> or <strong>less</strong> :+</p>
<div class="highlight"><pre><span></span><code>less /etc/passwd
</code></pre></div>
<p>Each line in the file has seven fields delimited by colons that contain the following information:</p>
<ul>
<li>User name.</li>
<li>Encrypted password (x means that the password is stored in the /etc/shadow file).</li>
<li>User ID number (UID).</li>
<li>User’s group ID number (GID).</li>
<li>Full name of the user (GECOS).</li>
<li>User home directory.</li>
<li>Login shell (defaults to <strong>/bin/bash</strong>).</li>
</ul>
<p>If you want to display only the username you can use either <strong>awk</strong> or <strong>cut</strong> commands to print only the first field containing the username:</p>
<div class="highlight"><pre><span></span><code>awk -F: <span class="s1">'{ print $1}'</span> /etc/passwd
</code></pre></div>
<div class="highlight"><pre><span></span><code>cut -d: -f1 /etc/passwd
</code></pre></div>
<h2>Get a List of all Users using the getent Command</h2>
<p>The <strong>getent</strong> command displays entries from databases configured in <strong>/etc/nsswitch.conf</strong> file, including the passwd database, which can be used to query a list of all users.</p>
<p>To get a list of all Linux userr, enter the following command:</p>
<div class="highlight"><pre><span></span><code>getent passwd
</code></pre></div>
<p>As you can see, the output is the same as when displaying the content of the <strong>/etc/passwd</strong> file. If you are using LDAP for user authentication, the <strong>getent</strong> will display all Linux users from both <strong>/etc/passwd</strong> file and LDAP database.</p>
<p>You can also use <strong>awk</strong> or <strong>cut</strong> to print only the first field containing the username:</p>
<div class="highlight"><pre><span></span><code>getent passwd <span class="p">|</span> awk -F: <span class="s1">'{ print $1}'</span>
</code></pre></div>
<div class="highlight"><pre><span></span><code>getent passwd <span class="p">|</span> cut -d: -f1
</code></pre></div>
<h3>Check whether a user exists in the Linux system</h3>
<p>Now that we know how to list all users, to check whether a user exists in our Linux box we, can simply filter the users’ list by piping the list to the grep command.</p>
<p>For example, to find out if a user with name jack exists in our Linux system we can use the following command:</p>
<div class="highlight"><pre><span></span><code>getent passwd <span class="p">|</span> grep jack
</code></pre></div>
<p>If the user exists, the command above will print the user’s login information. No output that means the user doesn’t exist.</p>
<p>We can also check whether a user exists without using the <strong>grep</strong> command as shown below:</p>
<div class="highlight"><pre><span></span><code>getent passwd jack
</code></pre></div>
<p>Same as before, if the user exists, the command will display the user’s login information.</p>
<p>If you want to find out how many users accounts you have on your system, pipe the getent passwd output to the wc command:</p>
<div class="highlight"><pre><span></span><code>getent passwd <span class="p">|</span> wc -l
</code></pre></div>
<h3>System and Normal Users</h3>
<p>There is no real technical difference between the system and regular (normal) users. Typically system users are created when installing the OS and new packages. In some cases, you can create a system user that will be used by some applications.</p>
<p>Normal users are the users created by the root or another user with sudo privileges. Usually, a normal user has a real login shell and a home directory.</p>
<p>Each user has a numeric user ID called UID. If not specified when creating a new user with the <strong>useradd</strong> command, the UID will be automatically selected from the /etc/login.defs file depending on the <strong>UID_MIN</strong> and <strong>UID_MAX</strong> values.</p>
<p>To check the <strong>UID_MIN</strong> and <strong>UID_MAX</strong> values on your system, you can use the following command:</p>
<div class="highlight"><pre><span></span><code>grep -E <span class="s1">'^UID_MIN|^UID_MAX'</span> /etc/login.defs
</code></pre></div>
<p>From the output above, we can see that all normal users should have a UID between 1000 and 60000. Knowing the minimal and maximal value allow us to query a list of all normal users in our system.</p>
<p>The command below will list all normal users in our Linux system:</p>
<div class="highlight"><pre><span></span><code>getent passwd <span class="o">{</span><span class="m">1000</span>..60000<span class="o">}</span>
</code></pre></div>
<p>Your system UID_MIN and UID_MIN values may be different so the more generic version of the command above would be:</p>
<div class="highlight"><pre><span></span><code><span class="nb">eval</span> getent passwd <span class="o">{</span><span class="k">$(</span>awk <span class="s1">'/^UID_MIN/ {print $2}'</span> /etc/login.defs<span class="k">)</span>..<span class="k">$(</span>awk <span class="s1">'/^UID_MAX/ {print $2}'</span> /etc/login.defs<span class="k">)</span><span class="o">}</span>
</code></pre></div>
<p>If you want to print only the usernames just pipe the output to the cut command:</p>
<div class="highlight"><pre><span></span><code><span class="nb">eval</span> getent passwd <span class="o">{</span><span class="k">$(</span>awk <span class="s1">'/^UID_MIN/ {print $2}'</span> /etc/login.defs<span class="k">)</span>..<span class="k">$(</span>awk <span class="s1">'/^UID_MAX/ {print $2}'</span> /etc/login.defs<span class="k">)</span><span class="o">}</span> <span class="p">|</span> cut -d: -f1
</code></pre></div>
<h2>Conclusion</h2>
<p>In this tutorial, you learned how to list and filter users in your Linux system and what are the main differences between system and normal Linux users.</p>
<p>The same commands apply for any Linux distribution, including Ubuntu, CentOS, RHEL, Debian, and Linux Mint.</p>
<p>Feel free to leave a comment if you have any questions.</p>Change swap size in Ubuntu2022-08-06T15:00:00+03:002022-08-06T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2022-08-06:/linux-change-swap-size.html<p>If you want to change the size of your swap file just follow the following steps.</p><h2>Change swap size in Ubuntu</h2>
<p>In a nutshell swap is a piece of storage (used from your harddisk) which can be used as additional RAM. If you want to change the size of your swap file just follow the following steps:
1. Turn off all running swap processes
<code>bash
swapoff -a</code>
2. Resize swap (change <strong>XG</strong> to the gigabyte size you want it to be)
<code>bash
fallocate -l XG /swapfile</code>
3. CHMOD swap
<code>bash
chmod 600 /swapfile</code>
4. Make file usable as swap
<code>bash
mkswap /swapfile
````
5. Active the swap file</code>bash
swapon /swapfile
```</p>
<p>Some commands may take some time to be executed, just wait patiently for the commands to finish.</p>
<p>To verify your swap size run the following command and you will see the swap size</p>
<div class="highlight"><pre><span></span><code>free -m
</code></pre></div>Python object reference2022-07-20T12:00:00+03:002022-07-20T12:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2022-07-20:/python-object-reference.html<p>This post is about how python treats object assignment and some of the hidden gotcha’s that can cause unintended errors along the way.</p><h2>Abstract</h2>
<p>This post is about how python treats object assignment and some of the hidden gotcha’s that can cause unintended errors along the way. Instead of “boxes” it is better to think of variables as “labels” that we attach to objects. And, as everything in python is an object its important to remember that all objects have three things; identity, type and values. Values are the only things that change once an object is created, and it values that we often care about, and hence label.</p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#labels">Labels</a></li>
<li><a href="#equality-and-identity">Equality and Identity</a></li>
<li><a href="#alias-issues">Alias Issues</a></li>
<li><a href="#copies">Copies</a></li>
<li><a href="#summary">Summary</a></li>
</ul>
<h2>Labels</h2>
<p>Lets look at the assignment of variables.</p>
<div class="highlight"><pre><span></span><code><span class="n">a</span> <span class="o">=</span> <span class="mi">2</span> <span class="c1"># we label the integer 2 as 'a'</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">a</span> <span class="c1"># 'a' is now labelled as 'b'</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">b</span> <span class="c1"># and 'b' is now labelled as 'c'</span>
</code></pre></div>
<p>Above we can see that the object <strong>2</strong> is assigned to the variable <strong>a</strong>. Each subsequent assignment thereafter is simply a reference to the same object. When viewed through this lense you can start to see how objects have labels. It is not feasible that the <strong>2</strong> can exist in three different boxes rather we visualise <strong>2</strong> having three sticky notes attached to it. If we changed <strong>a</strong> like this <code>a = 20</code> then it is just a matter of peeling off the sticky note with a written on it from <strong>2</strong> and attaching it to <strong>20</strong>. To further aid in this thinking, always read assignments from right to left. The right side is where the object is created or retrieved and the left is what we bind to it.</p>
<p>When an object like <strong>2</strong> has many labels we called this <strong>aliasing</strong>. <strong>Aliasing</strong> is an important concept to grasp, and to illustrate why we will examine the identity of <strong>a</strong>, <strong>b</strong>, and <strong>c</strong>.</p>
<div class="highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="nb">id</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="si">=}</span><span class="s1">'</span><span class="p">)</span> <span class="c1"># id(a)=11171976</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="nb">id</span><span class="p">(</span><span class="n">a</span><span class="p">)</span><span class="si">=}</span><span class="s1">'</span><span class="p">)</span> <span class="c1"># id(a)=11171976</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="nb">id</span><span class="p">(</span><span class="n">b</span><span class="p">)</span><span class="si">=}</span><span class="s1">'</span><span class="p">)</span> <span class="c1"># id(b)=11171976</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="nb">id</span><span class="p">(</span><span class="n">c</span><span class="p">)</span><span class="si">=}</span><span class="s1">'</span><span class="p">)</span> <span class="c1"># id(c)=11171976</span>
</code></pre></div>
<p>All aliases of a have the same identity which in python is unique integer representing its C memory address. If any change were to be made the identity integer would also change to reflect that.</p>
<h2>Equality and Identity</h2>
<p>Let’s check out Equality and Identity (and aliases, too)</p>
<p>An object’s identity never changes once it has been created. However its values might, and generally this is what we care about more. Python gives us the option to check either like so:</p>
<div class="highlight"><pre><span></span><code><span class="k">assert</span> <span class="n">a</span> <span class="o">==</span> <span class="n">b</span> <span class="c1"># compares the values</span>
<span class="k">assert</span> <span class="n">a</span> <span class="ow">is</span> <span class="n">b</span> <span class="c1"># compares the identities</span>
</code></pre></div>
<p>Lets extend this using a more complex example using some dictionaries.</p>
<div class="highlight"><pre><span></span><code><span class="n">orig_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'name'</span><span class="p">:</span> <span class="s1">'original'</span><span class="p">,</span> <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'dict'</span><span class="p">}</span>
<span class="n">my_dict</span> <span class="o">=</span> <span class="n">orig_dict</span>
<span class="k">assert</span> <span class="n">orig_dict</span> <span class="o">==</span> <span class="n">my_dict</span>
<span class="k">assert</span> <span class="n">orig_dict</span> <span class="ow">is</span> <span class="n">my_dict</span>
</code></pre></div>
<p>Both orig_dict and my_dict are equal in identity, and their values.
Lets create the same dict with other name.</p>
<div class="highlight"><pre><span></span><code><span class="n">new_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'name'</span><span class="p">:</span> <span class="s1">'original'</span><span class="p">,</span> <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'dict'</span><span class="p">}</span>
<span class="k">assert</span> <span class="n">orig_dict</span> <span class="o">==</span> <span class="n">new_dict</span>
<span class="k">assert</span> <span class="n">orig_dict</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">new_dict</span>
</code></pre></div>
<p>In this case, both new_dict and orig_dict share equal values but not the same identity. new_dict is not an alias of my_dict or orig_dict, and thus has his own unique identity. This is because we created an entirely new identity albeit with the same values as orig_dict.</p>
<p>Much of the time we care mostly about the values an object holds not its identity but you will see is in a lot during conditionals such as:</p>
<div class="highlight"><pre><span></span><code><span class="k">if</span> <span class="n">x</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="o">...</span>
<span class="k">if</span> <span class="n">x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="o">...</span>
</code></pre></div>
<h2>Alias Issues</h2>
<p>Something I didn’t realise until it came back to haunt me much later is that aliases can have unintended side effects with mutable types. Let’s say we have two lists, the original and its alias. The alias will have items added to it but we want the original untouched for whatever reason.</p>
<div class="highlight"><pre><span></span><code><span class="n">orig_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="s1">'a'</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]</span>
<span class="n">my_list</span> <span class="o">=</span> <span class="n">orig_list</span>
</code></pre></div>
<p>Looks good, we can now make changes to my_list.</p>
<div class="highlight"><pre><span></span><code><span class="n">my_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'smth'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">orig</span><span class="p">)</span> <span class="c1"># [1, 'a', True, [1, 2], 'smth']</span>
<span class="nb">print</span><span class="p">(</span><span class="n">new</span><span class="p">)</span> <span class="c1"># [1, 'a', True, [1, 2], 'smth']</span>
</code></pre></div>
<p>After appending to my_list it becomes apparent that this change has affected both lists. This happens because the alias works two way with mutable types. I think this is really important to know - aliases are not copies!</p>
<h2>Copies</h2>
<p>If aliases aren’t copies then how do we copy?</p>
<div class="highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="nb">id</span><span class="p">(</span><span class="n">orig_list</span><span class="p">)</span><span class="si">=}</span><span class="s1">'</span><span class="p">)</span> <span class="c1"># id(orig_list)=140152571215488</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="nb">id</span><span class="p">(</span><span class="n">my_list</span><span class="p">)</span><span class="si">=}</span><span class="s1">'</span><span class="p">)</span> <span class="c1"># id(my_list)=140152571215488</span>
</code></pre></div>
<p>By using the list() class we successfully create two new objects. Now if we append or remove items from either list it does not propagate through. Except, it does sometimes.</p>
<p>In this case we are only making a new copy of the overall object but not any mutable nested types within the copy. So while any changes made within the first layer of the object are contained within the copy, any mutable objects nested more deeply will be aliases.</p>
<p>Confused, an example.</p>
<div class="highlight"><pre><span></span><code><span class="n">orig_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="s1">'a'</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]</span>
<span class="n">new_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">orig_list</span><span class="p">)</span> <span class="c1"># or with slices new_list = orig_list[::]</span>
<span class="n">new_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'smth'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">orig_list</span><span class="p">)</span> <span class="c1"># [1, 'a', True, [1, 2]]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">new_list</span><span class="p">)</span> <span class="c1"># [1, 'a', True, [1, 2], 'smth']</span>
<span class="c1"># first layer is not affected as it is a copy, not an alias</span>
<span class="n">orig_list</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'smth_new'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">orig_list</span><span class="p">)</span> <span class="c1"># [1, 'a', True, [1, 2, 'smth_new']]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">new_list</span><span class="p">)</span> <span class="c1"># [1, 'a', True, [1, 2, 'smth_new'], 'smth']</span>
</code></pre></div>
<p>While the orig_list and new_list are independent of each other when making changes to the first layer of abstraction, any mutable types within that are simply aliases of the copies source.</p>
<p>Another example to check this out.</p>
<div class="highlight"><pre><span></span><code><span class="c1"># before we started making alterations to the lists</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">orig_list</span><span class="p">))</span> <span class="c1"># 139683504118592</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">new_list</span><span class="p">))</span> <span class="c1"># 139683504118592</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">orig_list</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span> <span class="c1"># 139683504118592</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">new_list</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span> <span class="c1"># 139683504118592</span>
</code></pre></div>
<p>Inspecting the identities reveals that only the overall object's were initialised as new objects but the nested types within were bound to the original nested type - an alias!</p>
<p>This is something to take into consideration when passing variables around that have nested types. To circumvent this immutable types such as tuples can be used in place.</p>
<p>Python can do deep copies which will take care of this issue, but it has its own drawbacks. Of which we not be discussed here as this post is already quite long.</p>
<h2>Summary</h2>
<p>In python all objects have a type, identity and values. Only the values can change after it is created and knowing a little bit more about how this works can help us prevent unintended bugs.</p>
<p>Notes:</p>
<ul>
<li>assignment does not create copies</li>
<li>nested mutable types within shallow copies are aliases</li>
<li>equality has two different checks; identity, and values</li>
</ul>Authentication plugin 'caching_sha2_password' cannot be loaded2022-06-15T12:00:00+03:002022-06-15T12:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2022-06-15:/fixing-caching-sha2-password.html<p>Fixing "Authentication plugin 'caching_sha2_password' cannot be loaded. The specific module can not be found" errors</p><h2>Contents</h2>
<ul>
<li><a href="#contents">Contents</a></li>
<li><a href="#summary">Summary</a></li>
<li><a href="#reason">Reason</a></li>
<li><a href="#resolution">Resolution</a></li>
<li><a href="#references">References</a></li>
</ul>
<h2>Summary</h2>
<p>You have installed MySQL 8 and are unable to connect your database using your MySQL client. Every attempt to connect using your MySQL client results in the following error:</p>
<div class="highlight"><pre><span></span><code><span class="err">Authentication plugin 'caching_sha2_password' cannot be loaded: dlopen(/usr/local/mysql/lib/plugin/caching_sha2_password.so, 2): image not found</span>
</code></pre></div>
<p>or</p>
<div class="highlight"><pre><span></span><code><span class="err">Authentication plugin 'caching_sha2_password' cannot be loaded. The specific module can not be found</span>
</code></pre></div>
<h2>Reason</h2>
<p>As of MySQL 8.0, <strong>caching_sha2_password</strong> is now the default authentication plugin rather than <strong>mysql_native_password</strong> which was the default in previous versions. This means that clients that rely on the <strong>mysql_native_password</strong> won't be able to connect because of this change.</p>
<h2>Resolution</h2>
<p>At a server level, revert to the mysql_native_password mechanism by adding the following to your MySQL configuration files:</p>
<div class="highlight"><pre><span></span><code><span class="k">[mysqld]</span>
<span class="na">default_authentication_plugin</span><span class="o">=</span><span class="s">mysql_native_password</span>
</code></pre></div>
<p>At a user level, revert to the mysql_native_password mechanism via the following process.</p>
<p>Open a terminal window and connect to your MySQL instance via the command line:</p>
<div class="highlight"><pre><span></span><code>mysql -u <span class="o">[</span>USERNAME<span class="o">]</span> -p
</code></pre></div>
<p>Enter your MySQL password and press enter and you should be logged into your MySQL instance.</p>
<p>Now run the following SQL command, replacing <code>[USERNAME]</code>, <code>[PASSWORD]</code> and <code>[HOST]</code> as appropriate.</p>
<p>Note: <code>[HOST]</code> can be the IP address of your computer which would allow access from your computer only or, in the case of a local development environment, you can use % to allow from any host.</p>
<div class="highlight"><pre><span></span><code><span class="k">ALTER</span> <span class="k">USER</span> <span class="s1">'[USERNAME]'</span><span class="o">@</span><span class="s1">'[HOST]'</span> <span class="err">\</span>
<span class="n">IDENTIFIED</span> <span class="k">WITH</span> <span class="n">mysql_native_password</span> <span class="err">\</span>
<span class="k">BY</span> <span class="s1">'[PASSWORD]'</span><span class="p">;</span>
</code></pre></div>
<p>or</p>
<div class="highlight"><pre><span></span><code><span class="k">ALTER</span> <span class="k">USER</span> <span class="s1">'[USERNAME]'</span><span class="o">@</span><span class="s1">'%'</span> <span class="err">\</span>
<span class="n">IDENTIFIED</span> <span class="k">WITH</span> <span class="n">mysql_native_password</span> <span class="err">\</span>
<span class="k">BY</span> <span class="s1">'[PASSWORD]'</span><span class="p">;</span>
</code></pre></div>
<p>Now you should be able to go back to your MySQL client and connect as normal.</p>
<h2>References</h2>
<p>2.11.4 Changes in MySQL 8.0 - https://dev.mysql.com/doc/refman/8.0/en/upgrading-from-previous-series.html</p>
<p>6.4.1.2 Caching SHA-2 Pluggable Authentication - https://dev.mysql.com/doc/refman/8.0/en/caching-sha2-pluggable-authentication.html</p>Use Black and organize imports on save in VSCode2022-05-10T12:00:00+03:002022-05-10T12:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2022-05-10:/python-black-isort.html<p>How to use Black to format python code, and organise imports on save.</p><p>How to use Black to format Python code, and organise imports on save in VSCode.</p>
<p>Here’s the relevant bit of VSCode’s <strong>settings.json</strong>:</p>
<div class="highlight"><pre><span></span><code><span class="p">{</span>
<span class="nt">"python.formatting.provider"</span><span class="p">:</span> <span class="s2">"black"</span><span class="p">,</span>
<span class="nt">"editor.formatOnSave"</span><span class="p">:</span> <span class="kc">true</span><span class="p">,</span>
<span class="nt">"editor.codeActionsOnSave"</span><span class="p">:</span> <span class="p">{</span>
<span class="nt">"source.organizeImports"</span><span class="p">:</span> <span class="kc">true</span>
<span class="p">}</span>
<span class="p">}</span>
</code></pre></div>
<p>It turns out that VS Code uses the <strong>isort</strong> library to sort imports, and <strong>isort</strong> has a <strong>Black</strong> compatability profile.</p>
<p>If you use <strong>Poetry</strong>, add these lines to the <strong>pyproject.toml</strong> file at the root of the project:</p>
<div class="highlight"><pre><span></span><code><span class="k">[tool.isort]</span>
<span class="na">profile</span> <span class="o">=</span> <span class="s">"black"</span>
</code></pre></div>
<p>Alternatively, create a file called <strong>.isort.cfg</strong> at the root of the project, with this content:</p>
<div class="highlight"><pre><span></span><code><span class="k">[settings]</span>
<span class="na">profile</span><span class="o">=</span><span class="s">black</span>
</code></pre></div>How to rebase git branch2022-04-21T12:00:00+03:002022-04-21T12:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2022-04-21:/git-rebase.html<p>Choosing between git rebase and git merge remains one of the most discussed topics in the community.</p><h2>Abstract</h2>
<p>Choosing between <strong>git rebase</strong> and <strong>git merge</strong> remains one of the most discussed topics in the community. Some may say that you should always use merging, some may say that rebasing is a more correct way to do things. There is no right or wrong way of using these two commands. It mainly depends on the user and the workflow. In the scope of this topic we will discuss how to rebase your branch.</p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#steps-to-rebasing-branch">Steps to rebasing branch</a></li>
<li><a href="#fetching-changes">Fetching changes</a></li>
<li><a href="#integrating-changes">Integrating changes</a></li>
<li><a href="#pushing-changes">Pushing changes</a></li>
<li><a href="#rebasing-vs-merging">Rebasing vs Merging</a></li>
<li><a href="#fetching">Fetching</a></li>
</ul>
<h2>Steps to rebasing branch</h2>
<p>Here are the steps to follow while rebasing a branch:</p>
<h3>Fetching changes</h3>
<p>You should receive the latest changes from a remote git repository. Thus the first step is running git fetch:</p>
<div class="highlight"><pre><span></span><code>git fetch
</code></pre></div>
<h3>Integrating changes</h3>
<p>The second step is running <strong>git rebase</strong>. Rebase is a Git command which is used to integrate changes from one branch into another. The following command rebase the current branch from master (or choose any other branch like <em>develop</em>, suppose, the name of remote is origin, which is by default):</p>
<div class="highlight"><pre><span></span><code>git rebase origin/master
</code></pre></div>
<p>After <strong>git rebase</strong>, conflicts may occur. You should resolve them and add your changes by running git add command:</p>
<div class="highlight"><pre><span></span><code>git add .
</code></pre></div>
<p>After resolving the conflicts and adding the changes to the staging area, you must run git rebase with the --continue option:</p>
<div class="highlight"><pre><span></span><code>git rebase --continue
</code></pre></div>
<p>If you want to cancel the rebasing rather than resolving the conflicts, you can run the following:</p>
<div class="highlight"><pre><span></span><code>git rebase --abort
</code></pre></div>
<h3>Pushing changes</h3>
<p>The final step is <strong>git push</strong> (forced). This command uploads local repository content to a remote repository. To do that, run the command below:</p>
<div class="highlight"><pre><span></span><code>git push origin HEAD -f
</code></pre></div>
<p><strong>--force</strong> that is the same as <strong>-f</strong> overwrites the remote branch on the basis of your local branch. It destroys all the pushed changes made by other developers. It refers to the changes that you don't have in your local branch.</p>
<p>Here is an alternative and safer way to push your changes:</p>
<div class="highlight"><pre><span></span><code>git push --force-with-lease origin HEAD
</code></pre></div>
<p><strong>--force-with-lease</strong> is considered a safer option that will not overwrite the work done on the remote branch in case more commits were attached to it (for instance, by another developer). Moreover, it helps you to avoid overwriting another developer's work by force pushing.</p>
<h2>Rebasing vs Merging</h2>
<p>Rebasing and merging are both used to integrate changes from one branch into another differently. They are both used in different scenarios. If you need to update a feature branch, always choose to <strong>git rebase</strong> for maintaining the branch history clean. It keeps the commit history out of the branch, contrary to the <strong>git merge</strong> command, which is its alternative. While, for individuals, rebasing is not recommended in the case of feature branch because when you share it with other developers, the process may create conflicting repositories. Once you need to put the branch changes into master, use merging. If you use merging too liberally, it will mess up <strong>git log</strong> and make difficult to understand the history. Merging preserves history whereas rebasing rewrites it. If you need to see the history absolutely the same as it happened, then use merge.</p>
<h2>Fetching</h2>
<p>The git fetch command downloads commits, files, and refs from a remote repository into the local repository. It updates your remote-tracking branches. The git fetch command allows you to see the progress of the central history, not forcing you to merge the changes into your repository. It does not affect your local work process.</p>Capturing shell output in python2022-03-26T12:00:00+03:002022-03-26T12:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2022-03-26:/shell-capture.html<p>In this article we will use python subprocess to run shell comand and capture its output.</p><h2>Abstract</h2>
<p>Sometimes we spend significant time in Python trying to do something which is trivial is <em>bash</em>. This is especially useful when we are working with very large files that will take a long time to read in. Why read in an entire file to get the last line, when we could just use <code>tail -n 1</code>? Or if we want the line count, why read it in when <code>wc -l</code> will get the job done faster?</p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#use-python-subprocess-check_output">Use python subprocess check_output</a></li>
<li><a href="#use-python-subprocess-run">Use python subprocess run</a></li>
</ul>
<h2>Use python subprocess check_output</h2>
<p>When we use Python, capturing shell output is easy. You can use the subprocess module to get the output in bytes, then decode and parse it.</p>
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">subprocess</span>
<span class="n">fname</span> <span class="o">=</span> <span class="s1">'path/to/file'</span>
<span class="n">last_line</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">check_output</span><span class="p">(</span><span class="s2">"tail -n 1 "</span> <span class="o">+</span> <span class="n">fname</span><span class="p">,</span> <span class="n">shell</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">last_line</span> <span class="o">=</span> <span class="n">last_line</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">'UTF-8'</span><span class="p">)</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
</code></pre></div>
<h2>Use python subprocess run</h2>
<p>If you're using Python 3.5+, and do not need backwards compatibility, the new run function is recommended by the official documentation for most tasks. It provides a very general, high-level API for the subprocess module. To capture the output of a program, pass the subprocess.PIPE flag to the stdout keyword argument. Then access the stdout attribute of the returned CompletedProcess object:</p>
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">subprocess</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="s1">'ls'</span><span class="p">,</span> <span class="s1">'-l'</span><span class="p">],</span> <span class="n">stdout</span><span class="o">=</span><span class="n">subprocess</span><span class="o">.</span><span class="n">PIPE</span><span class="p">)</span>
<span class="n">result</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">'utf-8'</span><span class="p">)</span>
</code></pre></div>Why you need Python Global Interpreter Lock and how it works2022-03-20T15:00:00+03:002022-03-20T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2022-03-20:/python-gil.html<p>In this article we will discuss how the GIL impacts application performance and how that impact can be mitigated.</p><h2>Abstract</h2>
<p>The <strong>Python Global Interpreter Lock (GIL)</strong> is a kind of lock that allows only one thread to control the Python interpreter. This means that only one particular thread will be running at any given time.</p>
<p>The operation of the <strong>GIL</strong> may seem irrelevant to developers creating single-threaded programs. But in multi-threaded programs, the absence of the <strong>GIL</strong> can negatively affect the performance of processor-dependent programs.</p>
<p>Because the <strong>GIL</strong> only allows one thread to run, even in a multi-threaded application, it has earned a reputation as an "infamous" feature.</p>
<p>This article will talk about how the <strong>GIL</strong> affects the performance of applications, and how this very impact can be mitigated.</p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#what-problem-does-the-gil-solve-in-python">What problem does the GIL solve in Python?</a></li>
<li><a href="#why-was-gil-chosen-to-solve-the-problem">Why was GIL chosen to solve the problem?</a></li>
<li><a href="#the-impact-of-the-gil-on-multithreaded-applications">The impact of the GIL on multithreaded applications</a></li>
<li><a href="#how-to-deal-with-gil">How to deal with GIL?</a></li>
</ul>
<h2>What problem does the GIL solve in Python?</h2>
<p>Python counts the number of references for correct memory management. This means that objects created in Python have a reference count variable that stores the number of all references to that object. As soon as this variable becomes equal to zero, the memory allocated for this object is freed.</p>
<p>Here is a small code example demonstrating how reference counting variables work:</p>
<div class="highlight"><pre><span></span><code><span class="o">>>></span> <span class="kn">import</span> <span class="nn">sys</span>
<span class="o">>>></span> <span class="n">a</span> <span class="o">=</span> <span class="p">[]</span>
<span class="o">>>></span> <span class="n">b</span> <span class="o">=</span> <span class="n">a</span>
<span class="o">>>></span> <span class="n">sys</span><span class="o">.</span><span class="n">getrefcount</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="mi">3</span>
</code></pre></div>
<p>In this example, the number of references to an empty array is 3. This array is referenced by variable <em>a</em>, variable <em>b</em>, and the argument passed to <em>sys.getrefcount()</em>.</p>
<p>The problem that the <strong>GIL</strong> solves is that in a multi-threaded application, multiple threads can increment or decrement this reference count at the same time. This can lead to the fact that the memory is cleaned up incorrectly and the object to which the reference still exists is deleted.</p>
<p>The reference count can be secured by adding blockers on all data structures that are propagated across multiple threads. In this case, the counter will change exclusively sequentially.</p>
<p>But adding a lock to multiple objects can lead to another problem - deadlocks, which only happen if there is a lock on more than one object. In addition, this problem would also reduce performance due to the repeated installation of blockers.</p>
<p><strong>GIL</strong> is a single blocker of the Python interpreter itself. It adds the rule that any execution of bytecode in Python requires an interpreter lock. In this case, the deadlock can be avoided because the <strong>GIL</strong> will be the only lock in the application. In addition, its impact on processor performance is not critical at all. However, it is worth remembering that the <strong>GIL</strong> confidently makes any program single-threaded.</p>
<p>Although the <strong>GIL</strong> is used in other interpreters, such as Ruby, it is not the only solution to this problem. Some languages solve the problem of thread-safe memory deallocation using garbage collection.</p>
<p>On the other hand, this means that such languages often have to compensate for the loss of the single-threaded benefits of the <strong>GIL</strong> by adding some additional performance enhancement features, such as JIT compilers.</p>
<h2>Why was GIL chosen to solve the problem?</h2>
<p>So why is this solution being used in Python? How critical is this decision for developers?</p>
<p>According to <a href="https://www.youtube.com/watch?v=KVKufdTphKs&feature=youtu.be&t=12m11s">Larry Hastings</a>, the <strong>GIL</strong> architecture is one of the things that made Python popular.</p>
<p>Python has been around since the days when operating systems didn't have the concept of threads. This language was designed to be easy to use and speed up the development process. More and more developers switched to Python.</p>
<p>Many of the extensions that Python needed were written for existing C libraries. To prevent inconsistent changes, the C language required thread-safe memory management, which the <strong>GIL</strong> was able to provide.</p>
<p>The <strong>GIL</strong> could be easily implemented and integrated into Python. It increased the performance of single-threaded applications because only one blocker was in control.</p>
<p>Those C libraries that were not thread-safe became easier to integrate. These C extensions are one of the reasons why the Python community has grown.</p>
<p>As you can see, the <strong>GIL</strong> is the actual solution to the problem that the CPython developers faced early in Python's life.</p>
<h2>The impact of the GIL on multithreaded applications</h2>
<p>Looking at a typical program (not necessarily written in Python) it makes a difference whether that program is limited by CPU performance or I/O.</p>
<p>CPU-bound operations are all computational operations: matrix multiplication, search, image processing, etc.</p>
<p>I/O performance-bound operations (I/O-bound) are those operations that are often waiting for something from I/O sources (user, file, database, network). Such programs and operations can sometimes wait a long time until they get what they need from the source. This is because the source may be doing its own (internal) operations before it is ready to produce a result. For example, the user can think about what exactly to enter in the search string or what query to send to the database.</p>
<p>Below is a simple CPU-bound program that simply counts down:</p>
<div class="highlight"><pre><span></span><code><span class="o">>>></span> <span class="kn">import</span> <span class="nn">time</span>
<span class="o">>>></span>
<span class="o">>>></span> <span class="k">def</span> <span class="nf">countdown</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="o">>>></span> <span class="k">while</span> <span class="n">n</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="o">>>></span> <span class="n">n</span> <span class="o">-=</span> <span class="mi">1</span>
<span class="o">>>></span>
<span class="o">>>></span> <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="o">>>></span> <span class="n">countdown</span><span class="p">(</span><span class="mi">50000000</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="o">>>></span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Average execution time: </span><span class="si">{</span><span class="n">end</span> <span class="o">-</span> <span class="n">start</span><span class="si">}</span><span class="s2"> seconds"</span><span class="p">)</span>
<span class="n">Average</span> <span class="n">execution</span> <span class="n">time</span><span class="p">:</span> <span class="mf">1.7815375328063965</span> <span class="n">seconds</span>
</code></pre></div>
<p>Now the countdown is carried out in two parallel streams:</p>
<div class="highlight"><pre><span></span><code><span class="o">>>></span> <span class="kn">import</span> <span class="nn">time</span>
<span class="o">>>></span> <span class="kn">from</span> <span class="nn">threading</span> <span class="kn">import</span> <span class="n">Thread</span>
<span class="o">>>></span>
<span class="o">>>></span> <span class="k">def</span> <span class="nf">countdown</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="o">>>></span> <span class="k">while</span> <span class="n">n</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="o">>>></span> <span class="n">n</span> <span class="o">-=</span> <span class="mi">1</span>
<span class="o">>>></span>
<span class="o">>>></span> <span class="n">t1</span> <span class="o">=</span> <span class="n">Thread</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">countdown</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="mi">50000000</span><span class="o">//</span><span class="mi">2</span><span class="p">,))</span>
<span class="o">>>></span> <span class="n">t2</span> <span class="o">=</span> <span class="n">Thread</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">countdown</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="mi">50000000</span><span class="o">//</span><span class="mi">2</span><span class="p">,))</span>
<span class="o">>>></span>
<span class="o">>>></span> <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="o">>>></span> <span class="n">t1</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
<span class="o">>>></span> <span class="n">t2</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
<span class="o">>>></span> <span class="n">t1</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
<span class="o">>>></span> <span class="n">t2</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
<span class="o">>>></span> <span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="o">>>></span>
<span class="o">>>></span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Average execution time: </span><span class="si">{</span><span class="n">end</span> <span class="o">-</span> <span class="n">start</span><span class="si">}</span><span class="s2"> seconds"</span><span class="p">)</span>
<span class="n">Average</span> <span class="n">execution</span> <span class="n">time</span><span class="p">:</span> <span class="mf">3.138119697570801</span> <span class="n">seconds</span>
</code></pre></div>
<p>In the multithreaded version, the <strong>GIL</strong> prevented threads from running in parallel.</p>
<p>The <strong>GIL</strong> does not greatly affect the performance of I/O operations in multi-threaded programs, since the lock is propagated through the threads while waiting for I/O.</p>
<p>However, a program whose threads will work exclusively with the processor will not only become single-threaded due to blocking, but it will also take more time to execute it than if it were originally strictly single-threaded.</p>
<p>This increase in time is the result of the appearance and implementation of blocking.</p>
<h2>How to deal with GIL?</h2>
<p>If the <strong>GIL</strong> is causing you problems, here are some solutions you can try:</p>
<p>Multiprocessing versus multithreading. A fairly popular solution, since each Python process has its own interpreter with memory allocated for it, so there will be no problems with the <strong>GIL</strong>. Python already has a multiprocessing module that makes it easy to create processes like this:</p>
<div class="highlight"><pre><span></span><code><span class="o">>>></span> <span class="kn">import</span> <span class="nn">time</span>
<span class="o">>>></span> <span class="kn">from</span> <span class="nn">multiprocessing</span> <span class="kn">import</span> <span class="n">Pool</span>
<span class="o">>>></span>
<span class="o">>>></span> <span class="k">def</span> <span class="nf">countdown</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="o">>>></span> <span class="k">while</span> <span class="n">n</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="o">>>></span> <span class="n">n</span> <span class="o">-=</span> <span class="mi">1</span>
<span class="o">>>></span>
<span class="o">>>></span> <span class="n">pool</span> <span class="o">=</span> <span class="n">Pool</span><span class="p">(</span><span class="n">processes</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="o">>>></span> <span class="n">r1</span> <span class="o">=</span> <span class="n">pool</span><span class="o">.</span><span class="n">apply_async</span><span class="p">(</span><span class="n">countdown</span><span class="p">,</span> <span class="p">[</span><span class="mi">50000000</span><span class="o">//</span><span class="mi">2</span><span class="p">])</span>
<span class="o">>>></span> <span class="n">r2</span> <span class="o">=</span> <span class="n">pool</span><span class="o">.</span><span class="n">apply_async</span><span class="p">(</span><span class="n">countdown</span><span class="p">,</span> <span class="p">[</span><span class="mi">50000000</span><span class="o">//</span><span class="mi">2</span><span class="p">])</span>
<span class="o">>>></span> <span class="n">pool</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="o">>>></span> <span class="n">pool</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
<span class="o">>>></span> <span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="o">>>></span>
<span class="o">>>></span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Average execution time: </span><span class="si">{</span><span class="n">end</span> <span class="o">-</span> <span class="n">start</span><span class="si">}</span><span class="s2"> seconds"</span><span class="p">)</span>
<span class="n">Average</span> <span class="n">execution</span> <span class="n">time</span><span class="p">:</span> <span class="mf">1.0929028987884521</span> <span class="n">seconds</span>
</code></pre></div>
<p>You can notice a decent performance improvement compared to the multi-threaded version. However, the time indicator did not drop to half. This is due to the fact that process management itself affects performance. Multiple processes are more complex than multiple threads, so they need to be handled with care.</p>
<p>Often, the <strong>GIL</strong> is seen as something complicated and incomprehensible. But keep in mind that as a python developer, you will only encounter the <strong>GIL</strong> if you write C extensions or multi-threaded CPU programs.</p>
<p>At this point, you should understand all the aspects required when working with the <strong>GIL</strong>. If you are interested in the low-level structure of the <strong>GIL</strong>, check out <a href="https://youtu.be/Obt-vMVdM8s">Understanding the Python GIL</a> by David Beazley.</p>Ways to find out which process is listening on a specific port2022-03-16T15:00:00+03:002022-03-16T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2022-03-16:/linux-port.html<p>In this article, we will show three different ways to find the process listening on a specific port in Linux.</p><h2>Abstract</h2>
<p>There are at least three ways to find out which <em>process</em> is listening on a particular <em>port</em>. </p>
<p>A <em>port</em> is a logical object that represents a communication endpoint and is associated with a process or service in the operating system.</p>
<p>In this article, we will show three different ways to find the <em>process</em> listening on a specific <em>port</em> in Linux.</p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#using-the-netstat-command">Using the netstat command</a></li>
<li><a href="#using-the-lsof-command">Using the lsof command</a></li>
<li><a href="#using-the-fuser-command">Using the fuser command</a></li>
</ul>
<h2>Using the netstat command</h2>
<p>The <strong>netstat</strong> (network statistics) command is used to display information about network connections, routing tables, interface statistics, and beyond. It is available on all Unix-like operating systems, including Linux, as well as on Windows.</p>
<p>If <strong>netstat</strong> is not installed by default, use the following command to install it:</p>
<div class="highlight"><pre><span></span><code>sudo yum install net-tools <span class="c1">#RHEL/CentOS</span>
sudo apt install net-tools <span class="c1">#Debian/Ubuntu</span>
sudo dnf install net-tools <span class="c1">#Fedora 22+</span>
</code></pre></div>
<p>Once installed, you can use <strong>netstat</strong> along with <strong>grep</strong> to find the <em>process</em> listening on a specific <em>port</em> on Linux like so:</p>
<div class="highlight"><pre><span></span><code>sudo netstat -ltnp <span class="p">|</span> grep -w <span class="s1">':80'</span>
</code></pre></div>
<p>The above command uses the following options:</p>
<ul>
<li><strong>l</strong> instructs <strong>netstat</strong> to show only listening sockets.</li>
<li><strong>t</strong> - indicates the display of tcp connections.</li>
<li><strong>n</strong> - indicates that it is necessary to show ip-addresses.</li>
<li><strong>p</strong> - allows you to show the process ID and process name.</li>
<li><strong>grep -w</strong> - matches exact string (':80').</li>
</ul>
<h2>Using the lsof command</h2>
<p>The <strong>lsof</strong> (LiSt Open Files) command is used to list all open files on a Linux system. To install it on your system, enter the command below.</p>
<div class="highlight"><pre><span></span><code>sudo yum install lsof <span class="c1">#RHEL/CentOS</span>
sudo apt install lsof <span class="c1">#Debian/Ubuntu</span>
sudo dnf install lsof <span class="c1">#Fedora 22+</span>
</code></pre></div>
<p>To find the process listening on a specific port, type:</p>
<div class="highlight"><pre><span></span><code>sudo lsof -i :80
</code></pre></div>
<p>The above command uses the following options:</p>
<ul>
<li><strong>i</strong> lists IP sockets.</li>
</ul>
<h2>Using the fuser command</h2>
<p><strong>fuser</strong> shows the PID of processes using specified files or filesystems on Linux.</p>
<p>You can install it like this:</p>
<div class="highlight"><pre><span></span><code>sudo yum install psmisc <span class="c1">#RHEL/CentOS</span>
sudo apt install psmisc <span class="c1">#Debian/Ubuntu</span>
sudo dnf install psmisc <span class="c1">#Fedora 22+</span>
</code></pre></div>
<p>You can find the process listening on a specific port by running the command below:</p>
<div class="highlight"><pre><span></span><code>sudo fuser <span class="m">22</span>/tcp
</code></pre></div>
<p>Then find the process name using the <strong>PID</strong> number, with the <strong>ps</strong> command:</p>
<div class="highlight"><pre><span></span><code>sudo ps -p <span class="m">177</span>
</code></pre></div>
<p>The above command uses the following options:</p>
<ul>
<li><strong>p</strong> specific process PID</li>
</ul>Custom logger handlers2022-02-25T18:45:00+03:002022-02-25T18:45:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2022-02-25:/custom-logger.html<p>The Python logging module may be used to build your own custom logger featuring special functionality according to your use case.</p><h2>Contents</h2>
<ul>
<li><a href="#contents">Contents</a></li>
<li><a href="#logging">Logging</a></li>
<li><a href="#custom-logger-with-stdout-and-file-handlers">Custom logger with stdout and file handlers</a></li>
<li><a href="#check-whether-the-logger-has-a-stream-handler-andor-a-file-handler">Check whether the logger has a stream handler and/or a file handler</a></li>
<li><a href="#disable-logger-console-output">Disable logger console output</a></li>
<li><a href="#disable-logger-file-output">Disable logger file output</a></li>
<li><a href="#verbosity-setting-for-the-custom-logger">Verbosity setting for the custom logger</a></li>
<li><a href="#custom-logging-level-that-overrides-the-verbosity-setting">Custom logging level that overrides the verbosity setting</a></li>
<li><a href="#putting-the-custom-logger-to-use">Putting the custom logger to use</a></li>
<li><a href="#quiet-mode">Quiet mode</a></li>
<li><a href="#verbose-mode">Verbose mode</a></li>
</ul>
<h2>Logging</h2>
<p>The Python <a href="https://docs.python.org/3/library/logging.html">logging</a> module may be used to build your own custom logger featuring special functionality according to your use case, such as:</p>
<ul>
<li>Adding both console and file handlers (i.e. logging to both stdout and to a file)</li>
<li>Temporarily disabling console logging (e.g. if a global verbose flag is False)</li>
<li>Temporarily disabling file logging (e.g. if we want to print with color to stdout using ANSI codes, but without color to the log file)</li>
<li>Adding an extra logging level that logs to both console and log file (e.g. even if a global verbose flag is False)</li>
</ul>
<p>This srticle will show how to build such a custom logger for the above use cases, that we will be going through incrementally.</p>
<h2>Custom logger with stdout and file handlers</h2>
<p>We will be creating a <em>CustomLogger</em> class based on <em>logging.getLoggerClass()</em> (the logger used in Python’s <em>logging</em> module) with a default stdout handler. If the user of the class specifies a log directory, then a file handler will also be added. The format of the logs is also different:</p>
<ul>
<li>we use the bare message to log for the stdout handler</li>
<li>we show the date, time, logging level, file and line number for the file handler</li>
</ul>
<div class="highlight"><pre><span></span><code><span class="k">class</span> <span class="nc">CustomLogger</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">getLoggerClass</span><span class="p">()):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">log_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="c1"># Create custom logger logging all five levels</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>
<span class="c1"># Create stream handler for logging to stdout (log all five levels)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stdout_handler</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">StreamHandler</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stdout_handler</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stdout_handler</span><span class="o">.</span><span class="n">setFormatter</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">Formatter</span><span class="p">(</span><span class="s1">'</span><span class="si">%(message)s</span><span class="s1">'</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">enable_console_output</span><span class="p">()</span>
<span class="c1"># Add file handler only if the log directory was specified</span>
<span class="bp">self</span><span class="o">.</span><span class="n">file_handler</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">log_dir</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">add_file_handler</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">log_dir</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">add_file_handler</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">log_dir</span><span class="p">):</span>
<span class="sd">"""Add a file handler for this logger with the specified `name` (and</span>
<span class="sd"> store the log file under `log_dir`)."""</span>
<span class="c1"># Format for file log</span>
<span class="n">fmt</span> <span class="o">=</span> <span class="s1">'</span><span class="si">%(asctime)s</span><span class="s1"> | </span><span class="si">%(levelname)8s</span><span class="s1"> | </span><span class="si">%(filename)s</span><span class="s1">:</span><span class="si">%(lineno)d</span><span class="s1"> | </span><span class="si">%(message)s</span><span class="s1">'</span>
<span class="n">formatter</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">Formatter</span><span class="p">(</span><span class="n">fmt</span><span class="p">)</span>
<span class="c1"># Determine log path/file name; create log_dir if necessary</span>
<span class="n">now</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s1">'%Y%m</span><span class="si">%d</span><span class="s1">_%H%M%S'</span><span class="p">)</span>
<span class="n">log_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">name</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">" "</span><span class="p">,</span> <span class="s2">"_"</span><span class="p">)</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">now</span><span class="si">}</span><span class="s1">'</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">log_dir</span><span class="p">):</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">log_dir</span><span class="p">)</span>
<span class="k">except</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="si">{}</span><span class="s1">: Cannot create directory </span><span class="si">{}</span><span class="s1">. '</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">log_dir</span><span class="p">),</span>
<span class="n">end</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">sys</span><span class="o">.</span><span class="n">stderr</span><span class="p">)</span>
<span class="n">log_dir</span> <span class="o">=</span> <span class="s1">'/tmp'</span> <span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">platform</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'linux'</span><span class="p">)</span> <span class="k">else</span> <span class="s1">'.'</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Defaulting to </span><span class="si">{</span><span class="n">log_dir</span><span class="si">}</span><span class="s1">.'</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">sys</span><span class="o">.</span><span class="n">stderr</span><span class="p">)</span>
<span class="n">log_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">log_dir</span><span class="p">,</span> <span class="n">log_name</span><span class="p">)</span> <span class="o">+</span> <span class="s1">'.log'</span>
<span class="c1"># Create file handler for logging to a file (log all five levels)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">file_handler</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">FileHandler</span><span class="p">(</span><span class="n">log_file</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">file_handler</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">file_handler</span><span class="o">.</span><span class="n">setFormatter</span><span class="p">(</span><span class="n">formatter</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">addHandler</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_handler</span><span class="p">)</span>
</code></pre></div>
<p>Notice that we store both the stdout handler and the file handler as members of the <em>CustomLogger</em>, through the instance variables <em>self.stdout_handler</em> and <em>self.file_handler</em>. This is to facilitate the forthcoming tasks, namely to check whether the logger has such handlers and to be able to enable and disable them individually.</p>
<h2>Check whether the logger has a stream handler and/or a file handler</h2>
<p>The Python logging documentation explains that <em>logging.StreamHandler</em> is the base class for <em>logging.FileHandler</em>. We add methods to our <em>CustomLogger</em> class to find out whether the logger instance has a console logger and/or a file logger:</p>
<div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">has_console_handler</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">([</span><span class="n">h</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">handlers</span> <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">h</span><span class="p">)</span> <span class="o">==</span> <span class="n">logging</span><span class="o">.</span><span class="n">StreamHandler</span><span class="p">])</span> <span class="o">></span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">has_file_handler</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">([</span><span class="n">h</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">handlers</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">logging</span><span class="o">.</span><span class="n">FileHandler</span><span class="p">)])</span> <span class="o">></span> <span class="mi">0</span>
</code></pre></div>
<h2>Disable logger console output</h2>
<p>Using the <em>has_console_handler()</em> check to ensure internal API consistency, we can now write methods for our <em>CustomLogger</em> class to temporarily enable/disable console output. Since the logger retains the instance variable <em>self.stdout_handler</em>, we can add it and remove it as we please:</p>
<div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">disable_console_output</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_console_handler</span><span class="p">():</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">removeHandler</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stdout_handler</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">enable_console_output</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_console_handler</span><span class="p">():</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">addHandler</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stdout_handler</span><span class="p">)</span>
</code></pre></div>
<h2>Disable logger file output</h2>
<p>Similarly to what we’ve seen above, via the <em>has_file_handler()</em> check in order to ensure internal API consistency, we can now write methods for our <em>CustomLogger</em> class to temporarily enable/disable file output. Since the logger retains the instance variable <em>self.file_handler</em>, we can add it and remove it as we please:</p>
<div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">disable_file_output</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_file_handler</span><span class="p">():</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">removeHandler</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_handler</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">enable_file_output</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_file_handler</span><span class="p">():</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">addHandler</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_handler</span><span class="p">)</span>
</code></pre></div>
<h2>Verbosity setting for the custom logger</h2>
<p>In certain situations, you way need to create a more quiet or a more verbose logger. If the logger needs to be quiet, you may want to skip logging altogether. This means we need to override the <em>info()</em>, <em>debug()</em>, <em>warning()</em>, <em>error()</em> and <em>critical()</em> methods of the base class and make them behave differently according to the verbosity setting. (Your use case might be different and you may want to keep logging enabled for <strong>ERROR</strong> and <strong>CRITICAL</strong> levels for example.)</p>
<p>First, we introduce a verbose boolean flag to the <em><strong>init</strong>()</em> method of our <em>CustomLogger</em> class and store its value in the logger instance. Then, we override the logging methods:</p>
<div class="highlight"><pre><span></span><code><span class="k">class</span> <span class="nc">CustomLogger</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">getLoggerClass</span><span class="p">()):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">verbose</span><span class="p">,</span> <span class="n">log_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="o">...</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
<span class="k">def</span> <span class="nf">debug</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_custom_log</span><span class="p">(</span><span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">debug</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_custom_log</span><span class="p">(</span><span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">info</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">warning</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_custom_log</span><span class="p">(</span><span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">warning</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">error</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_custom_log</span><span class="p">(</span><span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">critical</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_custom_log</span><span class="p">(</span><span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">critical</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_custom_log</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Helper method for logging DEBUG through CRITICAL messages by</span>
<span class="sd"> calling the appropriate `func()` from the base class."""</span>
<span class="c1"># Log normally if verbosity is on</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
<span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="c1"># If verbosity is off and there is no file handler, there is</span>
<span class="c1"># nothing left to do</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_file_handler</span><span class="p">():</span>
<span class="k">return</span>
<span class="c1"># If verbosity is off and a file handler is present, then disable</span>
<span class="c1"># stdout logging, log, and finally reenable stdout logging</span>
<span class="bp">self</span><span class="o">.</span><span class="n">disable_console_output</span><span class="p">()</span>
<span class="n">func</span><span class="p">(</span><span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">enable_console_output</span><span class="p">()</span>
</code></pre></div>
<h2>Custom logging level that overrides the verbosity setting</h2>
<p>Well all this is fine, but what if you do want to display certain messages after all, even if verbosity is off? Think of them as “system” or “framework” messages that should not be silenced.</p>
<p>The solution to this is to add a new logging level to the <em>CustomLogger</em> class. Let’s call it <strong>FRAMEWORK</strong> and have it log at <strong>INFO</strong> priority (for the purpose of illustration). We just need to add the level and to write the corresponding <em>framework()</em> method:</p>
<div class="highlight"><pre><span></span><code><span class="k">class</span> <span class="nc">CustomLogger</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">getLoggerClass</span><span class="p">()):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">verbose</span><span class="p">,</span> <span class="n">log_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="o">...</span>
<span class="n">logging</span><span class="o">.</span><span class="n">addLevelName</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">,</span> <span class="s1">'FRAMEWORK'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">framework</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Logging method for the FRAMEWORK level. The `msg` gets</span>
<span class="sd"> logged both to stdout and to file (if a file handler is</span>
<span class="sd"> present), irrespective of verbosity settings."""</span>
<span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">msg</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</code></pre></div>
<p>For neat display in the file logs, we should also think of changing the formatter to accommodate 9 characters instead of 8:</p>
<div class="highlight"><pre><span></span><code><span class="n">fmt</span> <span class="o">=</span> <span class="s1">'</span><span class="si">%(asctime)s</span><span class="s1"> | </span><span class="si">%(levelname)9s</span><span class="s1"> | </span><span class="si">%(filename)s</span><span class="s1">:</span><span class="si">%(lineno)d</span><span class="s1"> | </span><span class="si">%(message)s</span><span class="s1">'</span>
</code></pre></div>
<h2>Putting the custom logger to use</h2>
<p>We can now test the logger in both quiet and verbose mode.</p>
<h3>Quiet mode</h3>
<p>In quiet mode this is really straightforward:</p>
<div class="highlight"><pre><span></span><code><span class="n">quiet_log</span> <span class="o">=</span> <span class="n">CustomLogger</span><span class="p">(</span><span class="s1">'quiet'</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">log_dir</span><span class="o">=</span><span class="s1">'logs'</span><span class="p">)</span>
<span class="n">quiet_log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s1">'We now log only to a file log'</span><span class="p">)</span>
<span class="n">quiet_log</span><span class="o">.</span><span class="n">framework</span><span class="p">(</span><span class="s1">'We now log everywhere irrespective of verbosity'</span><span class="p">)</span>
</code></pre></div>
<h3>Verbose mode</h3>
<div class="highlight"><pre><span></span><code><span class="n">verbose_log</span> <span class="o">=</span> <span class="n">CustomLogger</span><span class="p">(</span><span class="s1">'verbose'</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">log_dir</span><span class="o">=</span><span class="s1">'logs'</span><span class="p">)</span>
<span class="n">verbose_log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s1">'We now log to both stdout and a file log'</span><span class="p">)</span>
<span class="n">verbose_log</span><span class="o">.</span><span class="n">disable_file_output</span><span class="p">()</span>
<span class="n">verbose_log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">msg</span> <span class="o">+</span> <span class="s1">', but not here'</span><span class="p">)</span>
<span class="n">verbose_log</span><span class="o">.</span><span class="n">enable_file_output</span><span class="p">()</span>
<span class="n">verbose_log</span><span class="o">.</span><span class="n">framework</span><span class="p">(</span><span class="s1">'We now log everywhere irrespective of verbosity'</span><span class="p">)</span>
</code></pre></div>Custom color formatter with Python logging2022-02-24T21:47:00+03:002022-02-24T21:47:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2022-02-24:/color-formatter.html<p>This article explains how to get up and running with custom color fomatter in your logger.</p><h2>Contents</h2>
<ul>
<li><a href="#contents">Contents</a></li>
<li><a href="#logging">Logging</a></li>
<li><a href="#the-scenario">The scenario</a></li>
<li><a href="#the-logger">The logger</a></li>
<li><a href="#a-custom-color-formatter">A custom color formatter</a></li>
<li><a href="#run-it">Run it</a></li>
</ul>
<h2>Logging</h2>
<p>There comes a time in the life of a Python package when proper logs beat <em>print()</em> ing to standard output. The standard Python library offers the versatile <a href="https://docs.python.org/3/library/logging.html">logging</a> module.</p>
<p>This article explains how to get up and running with logging. You basically have the choice between the basic logging configuration and creating your own custom logger, more easily adapted to meet your specific requirements.</p>
<h2>The scenario</h2>
<p>Let us suppose you need to log both to standard output and to a file. Furthermore, you want your console output to be colored. If your intention is to avoid bringing new dependencies to your project (otherwise you’d use <em>loguru</em>, or at the very least <em>colorama</em>), you can do this with a bare-bones custom <a href="https://docs.python.org/3/library/logging.html#logging.Formatter">logging.Formatter</a> class and with <a href="https://www.lihaoyi.com/post/BuildyourownCommandLinewithANSIescapecodes.html">ANSI escape codes</a>.</p>
<h2>The logger</h2>
<p>Here is what your logger might look like:</p>
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">datetime</span>
<span class="c1"># Create custom logger logging all five levels</span>
<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
<span class="n">logger</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>
<span class="c1"># Define format for logs</span>
<span class="n">fmt</span> <span class="o">=</span> <span class="s1">'</span><span class="si">%(asctime)s</span><span class="s1"> | </span><span class="si">%(levelname)8s</span><span class="s1"> | </span><span class="si">%(message)s</span><span class="s1">'</span>
<span class="c1"># Create stdout handler for logging to the console (logs all five levels)</span>
<span class="n">stdout_handler</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">StreamHandler</span><span class="p">()</span>
<span class="n">stdout_handler</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>
<span class="n">stdout_handler</span><span class="o">.</span><span class="n">setFormatter</span><span class="p">(</span><span class="n">CustomFormatter</span><span class="p">(</span><span class="n">fmt</span><span class="p">))</span>
<span class="c1"># Create file handler for logging to a file (logs all five levels)</span>
<span class="n">today</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">date</span><span class="o">.</span><span class="n">today</span><span class="p">()</span>
<span class="n">file_handler</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">FileHandler</span><span class="p">(</span><span class="s1">'my_app_</span><span class="si">{}</span><span class="s1">.log'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">today</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s1">'%Y_%m_</span><span class="si">%d</span><span class="s1">'</span><span class="p">)))</span>
<span class="n">file_handler</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>
<span class="n">file_handler</span><span class="o">.</span><span class="n">setFormatter</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">Formatter</span><span class="p">(</span><span class="n">fmt</span><span class="p">))</span>
<span class="c1"># Add both handlers to the logger</span>
<span class="n">logger</span><span class="o">.</span><span class="n">addHandler</span><span class="p">(</span><span class="n">stdout_handler</span><span class="p">)</span>
<span class="n">logger</span><span class="o">.</span><span class="n">addHandler</span><span class="p">(</span><span class="n">file_handler</span><span class="p">)</span>
</code></pre></div>
<h2>A custom color formatter</h2>
<p>For building our own custom formatter, we will extend the <em>logging.Formatter</em> class, give it the log format we want, and instruct it to print out each message level in a distinct color. ANSI escape codes for 8-color, 16-color and 256-color terminals may be found <a href="https://www.lihaoyi.com/post/BuildyourownCommandLinewithANSIescapecodes.html">here</a>.</p>
<div class="highlight"><pre><span></span><code><span class="k">class</span> <span class="nc">CustomFormatter</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">Formatter</span><span class="p">):</span>
<span class="sd">"""Logging colored formatter, adapted from https://stackoverflow.com/a/56944256/3638629"""</span>
<span class="n">grey</span> <span class="o">=</span> <span class="s1">'</span><span class="se">\x1b</span><span class="s1">[38;21m'</span>
<span class="n">blue</span> <span class="o">=</span> <span class="s1">'</span><span class="se">\x1b</span><span class="s1">[38;5;39m'</span>
<span class="n">yellow</span> <span class="o">=</span> <span class="s1">'</span><span class="se">\x1b</span><span class="s1">[38;5;226m'</span>
<span class="n">red</span> <span class="o">=</span> <span class="s1">'</span><span class="se">\x1b</span><span class="s1">[38;5;196m'</span>
<span class="n">bold_red</span> <span class="o">=</span> <span class="s1">'</span><span class="se">\x1b</span><span class="s1">[31;1m'</span>
<span class="n">reset</span> <span class="o">=</span> <span class="s1">'</span><span class="se">\x1b</span><span class="s1">[0m'</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fmt</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">=</span> <span class="n">fmt</span>
<span class="bp">self</span><span class="o">.</span><span class="n">FORMATS</span> <span class="o">=</span> <span class="p">{</span>
<span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">grey</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">,</span>
<span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">blue</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">,</span>
<span class="n">logging</span><span class="o">.</span><span class="n">WARNING</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">yellow</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">,</span>
<span class="n">logging</span><span class="o">.</span><span class="n">ERROR</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">red</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">,</span>
<span class="n">logging</span><span class="o">.</span><span class="n">CRITICAL</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">bold_red</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">reset</span>
<span class="p">}</span>
<span class="k">def</span> <span class="nf">format</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">record</span><span class="p">):</span>
<span class="n">log_fmt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">FORMATS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">record</span><span class="o">.</span><span class="n">levelno</span><span class="p">)</span>
<span class="n">formatter</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">Formatter</span><span class="p">(</span><span class="n">log_fmt</span><span class="p">)</span>
<span class="k">return</span> <span class="n">formatter</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">record</span><span class="p">)</span>
</code></pre></div>
<h2>Run it</h2>
<div class="highlight"><pre><span></span><code><span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s1">'This is a debug-level message'</span><span class="p">)</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'This is an info-level message'</span><span class="p">)</span>
<span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s1">'This is a warning-level message'</span><span class="p">)</span>
<span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s1">'This is an error-level message'</span><span class="p">)</span>
<span class="n">logger</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="s1">'This is a critical-level message'</span><span class="p">)</span>
</code></pre></div>
<p><img alt="python-logging" src="images/python-logging.png"></p>
<p>The ANSI escape codes can obviously be substituted with <a href="https://pypi.org/project/colorama/">colorama</a>, if extra dependencies are not an issue.</p>Analytics checklist2022-02-10T15:00:00+03:002022-02-10T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2022-02-10:/analytics-checklist.html<p>Five ways to help users become more data-driven in order to guarantee the success of your analytics project</p><h2>Abstract</h2>
<p>When it comes to analytics, most people focus on the technology side of the house. Yet
soft skills have emerged as the unsung heroes of successful business analysis projects.
And one of the top soft skills in demand is the ability for technical teams to help
business users become data driven.
A key enabler of data-driven users is modern analytics technology that
enables them to foster their data curiosity and engage in an ongoing conversation with
data. But, even with a modern analytics platform, IT still needs to educate users—who,
whether from frustration or ignorance, aren’t delving into data on their own—how to
identify, manipulate, and leverage key data in order to make better business decisions.</p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#analytics-user-experience-not-the-requirements">Analytics user experience, not the requirements</a></li>
<li><a href="#think-generically-not-specifically">Think generically, not specifically</a></li>
<li><a href="#understand-that-tables-and-charts-go-together">Understand that tables and charts go together</a></li>
<li><a href="#ensure-your-analytics-solution-lays-a-fopundation-for-future-artificial-intelligence">Ensure your analytics solution lays a fopundation for future artificial intelligence</a></li>
<li><a href="#track-your-analytics-usage-to-make-smarter-future-investments">Track your analytics usage to make smarter future investments</a></li>
</ul>
<h2>Analytics user experience, not the requirements</h2>
<p>The status quo right now in analytics is for a user to
request a report and IT to deliver it. But that approach
rarely yields true business insight. That’s because
business users often can’t articulate what they want
because they simply don’t know what’s possible.
Or—even worse—they’ve been conditioned not to ask
for anything exemplary because it’s too hard, if not
downright impossible, to deliver it.</p>
<p>Instead of focusing on what users think they need,
users need to first ask themselves how what they
need plays into the analytics user experience. And,
as IT, you need to stop the mindless task of building
only the reports requested by users. With this new
approach, you’ll be able to foresee what the user’s
next request might be, prevent problems about
how to slice and dice the data, and create improved
analytics processes that eliminate the need for users
to manually manipulate the data you deliver—such as
by joining together multiple, disparate reports in Excel.</p>
<p>Find out:</p>
<ul>
<li>What business problem is the user trying to solve? What’sthe business context for the information?</li>
<li>What are users doing right now to try to get the information they need? What isn’t working with that approach?</li>
<li>How will the information be used?</li>
<li>How familiar will users be with the report?</li>
<li>Do users need visual indicators, such as labels, on the report?</li>
<li>Do users need training materials within the report?</li>
</ul>
<h2>Think generically, not specifically</h2>
<p>It may seem counterintuitive, but you need to
think generically, not specifically, when it comes to
modern analytics. Legacy analytics tools fostered an
environment where users were conditioned to request
and then stitch together their own separate reports.
This process is neither accurate nor scalable, and it
creates redundant work resulting in mismatched data.</p>
<p>Instead, help users understand what they need the
end result to be, so you can connect data for them
in such a way that multiple variations of the same
analysis don’t have to be built.</p>
<p>For instance, ask them:</p>
<ul>
<li>Can we create a larger data view of this
information? Is there a way we can set up this
analysis to provide the most data and have
access to the right things?</li>
<li>Can we implement security to make one generic
model—a semantic description of the data set—
to be used for multiple things?</li>
<li>Can we develop one master dashboard that
uses filtering capabilities to let people slice and
dice the data however they want, for self-service
analytics?</li>
<li>What’s the next question or questions the
information is likely to elicit?</li>
</ul>
<h2>Understand that tables and charts go together</h2>
<p>Two core types of visual analysis are used today:
tables and charts. Tables display actual numbers,
but the eye has a hard time spotting trends in tables
and correlating those numbers with large data sets.
Charts, on the other hand, focus on visual storytelling
but often don’t give the level of detail required
because they don’t let users dig into the numbers
behind the graphics.</p>
<p>What’s really needed is a happy merger of the two—
much like peanut butter and jelly: pictures that help
users understand aggregated data, coupled with the
ability to drill into the row level transactions that make
up the chart.</p>
<p>With this approach, you:</p>
<ul>
<li>Help your audience quickly and clearly
understand the data and insights;</li>
<li>Let users quickly identify and discount outliers
from their analysis, or dig deeper to understand
and learn from the outliers; and</li>
<li>Tell a more powerful story because you deliver
at-a-glance understanding of high-level data via
visualization capabilities while enabling users
to validate and gain additional context into
specific data.</li>
</ul>
<h2>Ensure your analytics solution lays a fopundation for future artificial intelligence</h2>
<p>Even if your organization doesn’t yet know what it
might do with AI, it’s important to plan for it. That
means investing only in modern analytics technology
that can support future AI needs, rather than relying
upon old tech stack capabilities. Lay the foundation
now, so you can do what you need to do in the future.</p>
<h2>Track your analytics usage to make smarter future investments</h2>
<p>It’s the norm in analytics to build and deliver reports
yet have no insight into if they’re actually valuable or
used regularly. Looked at another way—do the people
who use analytics outperform others?</p>
<p>Unfortunately, reaching this level of analysis is
a luxury most organizations can’t afford. That’s
because most can’t even answer the basic question
of which reports are run or used, and they’re stuck in
an endless cycle of unmet requirements and delivery
expectations.</p>
<p>The questions they really need to answer surround
analytics usage and the resulting benefits, so they can
hold business users accountable to the report usage.
With this approach, you can force business users to
prioritize the reports they want delivered and their
timelines, as well as the reports they should retire to
keep the environment clean and productive</p>
<p>Ask questions like:</p>
<ul>
<li>Does the report people reference
five times per day actually make a
difference?</li>
<li>Can you say people who use
dashboards outperform those who
don’t by X percent?</li>
<li>How do you justify the dollar amount
and time invested in analytics
technology?</li>
<li>How do you demonstrate the massive
impact you bring to the company to
convince leadership to invest more
money into analytics?</li>
</ul>
<p>Implementing this new approach will help
your budgeting and planning exercise
for the next fiscal year, so you can make
smarter future investments. It also will
require both leadership and business users
to assume accountability for their report
requests. As a result, both business and IT
leadership will thank you for it and together
will unlock growth opportunities within the
organization.</p>Install nvm, node and yarn for your Node.js application2022-01-11T15:00:00+03:002022-01-11T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2022-01-11:/install-nvm-node-yarn.html<p>The official package manager for node is npm which comes pre-installed with node. We use npm to install yarn</p><h2>Abstract</h2>
<p>The official package manager for node is npm which comes pre-installed with node. You use npm to install yarn — so you do things in this order nvm -> node/npm -> yarn.</p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#install-nvm">Install nvm</a></li>
<li><a href="#use-nvm-to-install-nodenpm">Use nvm to install node/npm</a></li>
<li><a href="#use-npm-to-install-yarn">Use npm to Install yarn</a></li>
<li><a href="#summary">Summary</a></li>
</ul>
<h2>Install nvm</h2>
<p>Open a terminal on your system or connect a remote system using SSH. Use the following commands to install curl on your system, then run the nvm installer script.</p>
<div class="highlight"><pre><span></span><code>sudo apt install curl
curl https://raw.githubusercontent.com/creationix/nvm/master/install.sh <span class="p">|</span> bash
</code></pre></div>
<p>The nvm installer script creates environment entry to login script of the current user. You can either logout and login again to load the environment or execute the below command to do the same.</p>
<div class="highlight"><pre><span></span><code><span class="nb">source</span> ~/.profile
</code></pre></div>
<p>Now check nvm installation with next command</p>
<div class="highlight"><pre><span></span><code>nvm --version
</code></pre></div>
<h2>Use nvm to install node/npm</h2>
<p>Run next command to find the available node.js version for the installation.</p>
<div class="highlight"><pre><span></span><code>nvm ls-remote
</code></pre></div>
<p>We will use v12.22.10 at this time, simply run</p>
<div class="highlight"><pre><span></span><code>nvm install v12.22.10
</code></pre></div>
<p>Check locally installed node versions run</p>
<div class="highlight"><pre><span></span><code>nvm list
</code></pre></div>
<p>You can also select a different version for the current session. The selected version will be the currently active version for the current shell only.</p>
<div class="highlight"><pre><span></span><code>nvm use v12.22.10
</code></pre></div>
<p>To confirm it worked, run <em>node --version</em> — if you see a version number, you’re in business!
Note that <em>npm --version</em> should also now succeed.</p>
<h2>Use npm to Install yarn</h2>
<p>To install yarn, simply run</p>
<div class="highlight"><pre><span></span><code>npm install --global yarn
</code></pre></div>
<p>To confirm it worked, run <em>yarn --version</em> — if that works, you’re good to go!</p>
<h2>Summary</h2>
<p>Congratulations, you just installed nvm, node+npm, and yarn, and you’re now ready to tun your node.js application.</p>Setting Up Nginx, Gunicorn for Flask app2021-11-03T15:00:00+03:002021-11-03T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2021-11-03:/setting-up-nginx-gunicorn-flask.html<p>This article describes how to configure a Nginx and gunicorn to run Flask app.</p><h2>Abstract</h2>
<p>In this article we will to go through the process of deploying a flask app on a Linux server. We will use gunicorn as a WSGI server to communicate with our flask app, and Nginx as a proxy server between the gunicorn server and the client.</p>
<p>We need gunicorn between flask and nginx because the flask development server although good for debugging is weak and will not stand in production, so we need gunicorn as a wsgi server to communicate with flask.</p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#install-pre-requisite-packages">Install pre-requisite packages</a></li>
<li><a href="#create-python-virtual-environment">Create Python Virtual Environment</a></li>
<li><a href="#install-flask-and-gunicorn-packages">Install flask and gunicorn packages</a></li>
<li><a href="#setup-flask-web-application">Setup Flask Web Application</a></li>
<li><a href="#configure-gunicorn">Configure Gunicorn</a></li>
<li><a href="#create-wsgi-entry-point">Create WSGi Entry Point</a></li>
<li><a href="#access-flask-web-app-using-gunicorn">Access Flask web app using Gunicorn</a></li>
<li><a href="#configuring-nginx">Configuring Nginx</a></li>
<li><a href="#summary">Summary</a></li>
</ul>
<h2>Install pre-requisite packages</h2>
<p>Firstly, we need to make sure that python3 is installed on the Linux, run the following command to test if python exists:</p>
<div class="highlight"><pre><span></span><code>$ python3.9 --version
Python <span class="m">3</span>.9.9
</code></pre></div>
<p>However, if python is not installed, you can install it using the following commands:</p>
<p>Update the repository</p>
<div class="highlight"><pre><span></span><code>$ sudo apt update
</code></pre></div>
<p>Install python</p>
<div class="highlight"><pre><span></span><code>$ sudo apt install python3.9
</code></pre></div>
<p>Now we need to make sure that pip (python's package manager) is installed. Run the following command:</p>
<div class="highlight"><pre><span></span><code>$ python3.9 -m pip --version
pip <span class="m">20</span>.0.2 from /usr/lib/python3/dist-packages/pip <span class="o">(</span>python <span class="m">3</span>.9<span class="o">)</span>
</code></pre></div>
<p>If pip is not installed then you can install the same using this command:</p>
<div class="highlight"><pre><span></span><code>$ sudo apt install python3.9-pip
</code></pre></div>
<h2>Create Python Virtual Environment</h2>
<p>Before installing any of the packages that we are going to use, we need to create a python virtual environment to keep different versions of packages separated.</p>
<p>We will create a separate directory to store our project files:</p>
<div class="highlight"><pre><span></span><code>$ mkdir flask-gunicorn
</code></pre></div>
<p>Move to the directory that contains your project and create a virtual environment named <em>venv</em> (or any name):</p>
<div class="highlight"><pre><span></span><code>$ <span class="nb">cd</span> flask-gunicorn
$ python3.9 -m venv venv
</code></pre></div>
<p>The above command will create a directory named <em>venv/</em> that contains everything related to the virtual environment including the binary file that activates the virtual environment, to activate <em>venv</em> virtual environment run the following command:</p>
<div class="highlight"><pre><span></span><code>$ <span class="nb">source</span> venv/bin/activate
</code></pre></div>
<p>Once we install python packages, they will be stored in <em>venv/lib/python-version/site-packages</em></p>
<h2>Install flask and gunicorn packages</h2>
<p>Once the virtual environment is activated, we are ready to install the packages that we need:</p>
<div class="highlight"><pre><span></span><code><span class="o">(</span>venv<span class="o">)</span> $ python -m pip install flask gunicorn
</code></pre></div>
<h2>Setup Flask Web Application</h2>
<p>Now since Flask is successfully installed, we will create python web application using flask. We will start by creating a python file and naming it "app.py":</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">flask</span> <span class="kn">import</span> <span class="n">Flask</span>
<span class="n">app</span> <span class="o">=</span> <span class="n">Flask</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
<span class="nd">@app</span><span class="o">.</span><span class="n">route</span><span class="p">(</span><span class="s1">'/'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">index</span><span class="p">():</span>
<span class="k">return</span> <span class="s1">'Hello world!'</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
<span class="n">app</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
</code></pre></div>
<p>To start the python web application, just execute app.py using python binary as shown below:</p>
<div class="highlight"><pre><span></span><code>$ python app.py
* Serving Flask app <span class="s1">'app'</span> <span class="o">(</span>lazy loading<span class="o">)</span>
* Environment: production
WARNING: This is a development server. Do not use it in a production deployment.
Use a production WSGI server instead.
* Debug mode: off
</code></pre></div>
<p>Access 127.0.0.1:5000 on your browser of the same Linux server and you should be able to get "Hello World!" message.</p>
<p>Hit <em>Ctrl+C</em> on your terminal to exit the web application once you are finished.</p>
<h2>Configure Gunicorn</h2>
<p>Now that our basic web app is up and running, we will continue to configure Gunicorn. Using our Flask application from earlier, we can get it up and running using just a few steps</p>
<h3>Create WSGi Entry Point</h3>
<p>First, we need to create a python file that will be used as an entry point to our application by gunicorn, we will name it wsgi.py:</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">app</span> <span class="kn">import</span> <span class="n">app</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span>
<span class="n">app</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
</code></pre></div>
<h3>Access Flask web app using Gunicorn</h3>
<p>Gunicorn offers a lot of command line options (flags) which can be used to tune the performance of the server to match your needs, the most commonly used options are <code>-w</code> to specify the number of workers that the server will use and <code>--bind</code> which specify the interface and port to which the server should bind (<code>0.0.0.0</code> will be your server's public IP address)</p>
<p>Now we can test the gunicorn server and see whether it can run the flask app, use the following command to start the gunicorn server with 4 workers <code>-w</code> (You can increase or decrease the number of workers depending on your server's specs), we also need to specify the interface and the port to which the server should bind using the <code>--bind</code> command line option:</p>
<div class="highlight"><pre><span></span><code><span class="o">(</span>venv<span class="o">)</span> $ python -m gunicorn -w <span class="m">4</span> --bind <span class="m">127</span>.0.0.1:8000 wsgi:app
<span class="o">[</span><span class="m">2021</span>-11-03 <span class="m">14</span>:27:01 +0300<span class="o">]</span> <span class="o">[</span><span class="m">15406</span><span class="o">]</span> <span class="o">[</span>INFO<span class="o">]</span> Starting gunicorn <span class="m">20</span>.1.0
<span class="o">[</span><span class="m">2021</span>-11-03 <span class="m">14</span>:27:01 +0300<span class="o">]</span> <span class="o">[</span><span class="m">15406</span><span class="o">]</span> <span class="o">[</span>INFO<span class="o">]</span> Listening at: http://127.0.0.1:8000 <span class="o">(</span><span class="m">15406</span><span class="o">)</span>
<span class="o">[</span><span class="m">2021</span>-11-03 <span class="m">14</span>:27:01 +0300<span class="o">]</span> <span class="o">[</span><span class="m">15406</span><span class="o">]</span> <span class="o">[</span>INFO<span class="o">]</span> Using worker: sync
<span class="o">[</span><span class="m">2021</span>-11-03 <span class="m">14</span>:27:01 +0300<span class="o">]</span> <span class="o">[</span><span class="m">15407</span><span class="o">]</span> <span class="o">[</span>INFO<span class="o">]</span> Booting worker with pid: <span class="m">15407</span>
<span class="o">[</span><span class="m">2021</span>-11-03 <span class="m">14</span>:27:01 +0300<span class="o">]</span> <span class="o">[</span><span class="m">15408</span><span class="o">]</span> <span class="o">[</span>INFO<span class="o">]</span> Booting worker with pid: <span class="m">15408</span>
<span class="o">[</span><span class="m">2021</span>-11-03 <span class="m">14</span>:27:02 +0300<span class="o">]</span> <span class="o">[</span><span class="m">15409</span><span class="o">]</span> <span class="o">[</span>INFO<span class="o">]</span> Booting worker with pid: <span class="m">15409</span>
<span class="o">[</span><span class="m">2021</span>-11-03 <span class="m">14</span>:27:02 +0300<span class="o">]</span> <span class="o">[</span><span class="m">15410</span><span class="o">]</span> <span class="o">[</span>INFO<span class="o">]</span> Booting worker with pid: <span class="m">15410</span>
</code></pre></div>
<p>As seen in the above output, the gunicorn server is listening on port 8000 of the localhost, and 4 workers have started each with a different process Id (PID). If you visit <code>127.0.0.1:8000/</code>, you will see the root directory of your website (same as we say with python web app on port 5000 earlier:</p>
<h2>Configuring Nginx</h2>
<p>Let's start by installing nginx:</p>
<div class="highlight"><pre><span></span><code>$ sudo apt install nginx
</code></pre></div>
<p>Then navigate to the nginx directory:</p>
<div class="highlight"><pre><span></span><code>$ <span class="nb">cd</span> /etc/nginx/
</code></pre></div>
<p>This directory contains all the files related to nginx, we need to create a configuration file that will make nginx act as a proxy for our flask app.</p>
<p>The main configuration file is the one named <code>nginx.conf</code>, by convention, this file is not touched by developers or sys-admins, new configuration files are created in the <code>sites-available/</code> directory and then sym-linked to the <code>/sites-enabled/</code>directory.</p>
<p>Let's create a new file called <code>my-server</code> in the <code>sites-available/</code> directory:</p>
<div class="highlight"><pre><span></span><code><span class="nt">server</span> <span class="p">{</span>
<span class="err">listen</span> <span class="err">80</span><span class="p">;</span>
<span class="err">location</span> <span class="err">/</span> <span class="err">{</span>
<span class="err">include</span> <span class="err">proxy_params</span><span class="p">;</span>
<span class="err">proxy_pass</span> <span class="n">http</span><span class="p">:</span><span class="o">//</span><span class="n">unix</span><span class="o">:/</span><span class="n">tmp</span><span class="o">/</span><span class="n">my-server</span><span class="o">/</span><span class="n">ipc</span><span class="o">.</span><span class="n">sock</span><span class="p">;</span>
<span class="p">}</span>
<span class="err">}</span>
</code></pre></div>
<p>Next run the <code>sudo nginx -t</code> to make sure that the syntax of the configuration file is ok</p>
<div class="highlight"><pre><span></span><code>$ sudo nginx -t
nginx: the configuration file /etc/nginx/nginx.conf syntax is ok
nginx: configuration file /etc/nginx/nginx.conf <span class="nb">test</span> is successful
</code></pre></div>
<p>Once the syntax check passes, create a symbolic link of this file into sites-enabled directory:</p>
<div class="highlight"><pre><span></span><code>$ sudo ln -s /etc/nginx/sites-available/my-server /etc/nginx/sites-enabled/
$ sudo ls -l /etc/nginx/sites-enabled/
total <span class="m">0</span>
lrwxrwxrwx <span class="m">1</span> root root <span class="m">34</span> Nov <span class="m">03</span> <span class="m">10</span>:56 default -> /etc/nginx/sites-available/default
lrwxrwxrwx <span class="m">1</span> root root <span class="m">36</span> Nov <span class="m">03</span> <span class="m">11</span>:05 my-server -> /etc/nginx/sites-available/my-server
</code></pre></div>
<p>If everything is fine run <code>sudo nginx -s reload</code> for the configuration file to take place:</p>
<div class="highlight"><pre><span></span><code>$ sudo nginx -s reload
</code></pre></div>
<p>The above configurations instructs nginx to listen on port 80 and proxy all the connections to the socket that we created earlier, so that gunicorn can read from the socket and allows our flask app to respond, then gunicorn takes the response from the flask app and writes it to the socket so that nginx can read from the socket and return the response to the user.</p>
<h2>Summary</h2>
<p>In this article, we went through the process of deploying a flask app using gunicorn and nginx. This setup allows us to utilize the concurrency of gunicorn and nginx and also facilitates the scaling process in case of an expansion.</p>Building job queue2021-10-01T15:00:00+03:002021-10-01T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2021-10-01:/building-job-queue.html<p>In this article, we will build simple job queue on top of rabbitmq-server with aio-pika package.</p><h2>Abstract</h2>
<p>Whether your application is in the cloud or in your own data center, RabbitMQ is a lightweight and extremely powerful tool for creating distributed software architectures that range from the very simple to the incredibly complex.</p>
<p>In this article, we will build simple job queue on top of rabbitmq-server with aio-pika package.</p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#install-pre-requisite-packages">Install pre-requisite packages</a></li>
<li><a href="#run-rabitmq-community-docker-image">Run RabitMQ community docker image</a></li>
<li><a href="#activate-virtual-environment-and-install-dependencies">Activate virtual environment and install dependencies</a></li>
<li><a href="#prepare-shared-function">Prepare shared function</a></li>
<li><a href="#create-asyncronious-worker">Create asyncronious worker</a></li>
<li><a href="#create-job-queue-and-run-shared-function">Create job queue and run shared function</a></li>
<li><a href="#run-it">Run it</a></li>
</ul>
<h2>Install pre-requisite packages</h2>
<h3>Run RabitMQ community docker image</h3>
<div class="highlight"><pre><span></span><code>docker run -it --rm --name rabbitmq -p <span class="m">5672</span>:5672 -p <span class="m">15672</span>:15672 rabbitmq:3.9-management
</code></pre></div>
<h3>Activate virtual environment and install dependencies</h3>
<div class="highlight"><pre><span></span><code>python -m venv venv
<span class="nb">source</span> venv/bin/activate
python -m pip install aio-pika
</code></pre></div>
<h2>Prepare shared function</h2>
<p>In this example we will use simple shared function:</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">sleep</span>
<span class="k">def</span> <span class="nf">simple_func</span><span class="p">(</span><span class="n">text</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Good morning </span><span class="si">{</span><span class="n">text</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
</code></pre></div>
<h2>Create asyncronious worker</h2>
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">asyncio</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">from</span> <span class="nn">aio_pika</span> <span class="kn">import</span> <span class="n">connect</span><span class="p">,</span> <span class="n">IncomingMessage</span>
<span class="n">loop</span> <span class="o">=</span> <span class="n">asyncio</span><span class="o">.</span><span class="n">get_event_loop</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">wrapper</span><span class="p">(</span><span class="n">pickled_fn</span><span class="p">):</span>
<span class="p">[</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">]</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">pickled_fn</span><span class="p">)</span>
<span class="n">func</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
<span class="k">async</span> <span class="k">def</span> <span class="nf">on_message</span><span class="p">(</span><span class="n">message</span><span class="p">:</span> <span class="n">IncomingMessage</span><span class="p">):</span>
<span class="n">wrapper</span><span class="p">(</span><span class="n">message</span><span class="o">.</span><span class="n">body</span><span class="p">)</span>
<span class="n">message</span><span class="o">.</span><span class="n">ack</span><span class="p">()</span>
<span class="k">async</span> <span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="c1"># Perform connection</span>
<span class="n">connection</span> <span class="o">=</span> <span class="k">await</span> <span class="n">connect</span><span class="p">(</span><span class="s2">"amqp://guest:guest@localhost/"</span><span class="p">,</span> <span class="n">loop</span><span class="o">=</span><span class="n">loop</span><span class="p">)</span>
<span class="c1"># Creating a channel</span>
<span class="n">channel</span> <span class="o">=</span> <span class="k">await</span> <span class="n">connection</span><span class="o">.</span><span class="n">channel</span><span class="p">()</span>
<span class="k">await</span> <span class="n">channel</span><span class="o">.</span><span class="n">set_qos</span><span class="p">()</span>
<span class="c1"># Declaring queue</span>
<span class="n">queue</span> <span class="o">=</span> <span class="k">await</span> <span class="n">channel</span><span class="o">.</span><span class="n">declare_queue</span><span class="p">(</span>
<span class="s2">"task_queue"</span><span class="p">,</span>
<span class="n">durable</span><span class="o">=</span><span class="kc">True</span>
<span class="p">)</span>
<span class="c1"># Start listening the queue with name 'task_queue'</span>
<span class="k">await</span> <span class="n">queue</span><span class="o">.</span><span class="n">consume</span><span class="p">(</span><span class="n">on_message</span><span class="p">)</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span>
<span class="n">loop</span> <span class="o">=</span> <span class="n">asyncio</span><span class="o">.</span><span class="n">get_event_loop</span><span class="p">()</span>
<span class="n">loop</span><span class="o">.</span><span class="n">create_task</span><span class="p">(</span><span class="n">main</span><span class="p">())</span>
<span class="c1"># we enter a never-ending loop that waits for data and runs</span>
<span class="c1"># callbacks whenever necessary.</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" Waiting for messages. To exit press CTRL+C"</span><span class="p">)</span>
<span class="n">loop</span><span class="o">.</span><span class="n">run_forever</span><span class="p">()</span>
</code></pre></div>
<h2>Create job queue and run shared function</h2>
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">asyncio</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">from</span> <span class="nn">aio_pika</span> <span class="kn">import</span> <span class="n">connect</span><span class="p">,</span> <span class="n">Message</span><span class="p">,</span> <span class="n">DeliveryMode</span>
<span class="kn">from</span> <span class="nn">shared</span> <span class="kn">import</span> <span class="n">simple_func</span>
<span class="k">async</span> <span class="k">def</span> <span class="nf">submit_helper</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">loop</span><span class="p">):</span>
<span class="c1"># Perform connection</span>
<span class="n">connection</span> <span class="o">=</span> <span class="k">await</span> <span class="n">connect</span><span class="p">(</span><span class="s2">"amqp://guest:guest@localhost/"</span><span class="p">,</span> <span class="n">loop</span><span class="o">=</span><span class="n">loop</span><span class="p">)</span>
<span class="c1"># Creating a channel</span>
<span class="n">channel</span> <span class="o">=</span> <span class="k">await</span> <span class="n">connection</span><span class="o">.</span><span class="n">channel</span><span class="p">()</span>
<span class="n">message_body</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">dumps</span><span class="p">([</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">])</span>
<span class="n">message</span> <span class="o">=</span> <span class="n">Message</span><span class="p">(</span>
<span class="n">message_body</span><span class="p">,</span>
<span class="n">delivery_mode</span><span class="o">=</span><span class="n">DeliveryMode</span><span class="o">.</span><span class="n">PERSISTENT</span>
<span class="p">)</span>
<span class="c1"># Sending the message</span>
<span class="k">await</span> <span class="n">channel</span><span class="o">.</span><span class="n">default_exchange</span><span class="o">.</span><span class="n">publish</span><span class="p">(</span>
<span class="n">message</span><span class="p">,</span> <span class="n">routing_key</span><span class="o">=</span><span class="s2">"task_queue"</span>
<span class="p">)</span>
<span class="k">await</span> <span class="n">connection</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">submit_job</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">loop</span> <span class="o">=</span> <span class="n">asyncio</span><span class="o">.</span><span class="n">new_event_loop</span><span class="p">()</span>
<span class="n">asyncio</span><span class="o">.</span><span class="n">set_event_loop</span><span class="p">(</span><span class="n">loop</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">loop</span><span class="o">.</span><span class="n">run_until_complete</span><span class="p">(</span><span class="n">submit_helper</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">loop</span><span class="p">))</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
<span class="n">submit_job</span><span class="p">(</span><span class="n">simple_func</span><span class="p">,</span> <span class="s1">'world'</span><span class="p">)</span>
</code></pre></div>
<h2>Run it</h2>
<p>We will need to open 3 terminals to notice all executions</p>
<ol>
<li>
<p>Run docker container with RabitMQ</p>
</li>
<li>
<p>Run async worker</p>
</li>
</ol>
<div class="highlight"><pre><span></span><code>python worker_async.py
</code></pre></div>
<ol>
<li>Run job queue and call our shared function</li>
</ol>
<div class="highlight"><pre><span></span><code>python job_queue.py
</code></pre></div>
<p>Finally you have to get something like this in your terminals</p>
<p><img alt="sio-pika-example" src="images/aio-pika-example.png"></p>Setting Up GitLab CI for a Python Application2021-07-10T15:00:00+03:002021-07-10T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2021-07-10:/setting-up-gitlab-ci.html<p>This article describes how to configure a Continuous Integration (CI) process on GitLab for a python application. This article utilizes one of my python applications to show how to setup the CI process.</p><h2>Contents</h2>
<ul>
<li><a href="#contents">Contents</a></li>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#what-is-ci">What is CI?</a></li>
<li><a href="#getting-started-with-gitlab-ci">Getting Started with GitLab CI</a></li>
<li><a href="#creating-a-single-job-in-gitlab-ci">Creating a Single Job in GitLab CI</a></li>
<li><a href="#running-tests-with-pytest-on-gitlab-ci">Running Tests with pytest on GitLab CI</a></li>
<li><a href="#final-gitlab-ci-configuration">Final GitLab CI Configuration</a></li>
</ul>
<h2>Abstract</h2>
<p>This article describes how to configure a Continuous Integration (CI) process on GitLab for a python application. This article utilizes one of my python applications to show how to setup the CI process.</p>
<p><img alt="gitlab-ci-example" src="images/GitLab_CI_Final_Pipeline_Condensed_Screenshot2.png"></p>
<p>In this article, I’ll show how I setup a GitLab CI process to run the following jobs on a python application:</p>
<ul>
<li>Unit and functional testing using pytest</li>
<li>Linting using flake8</li>
<li>Static analysis using pylint</li>
<li>Type checking using mypy</li>
</ul>
<h2>What is CI?</h2>
<p>To me, Continuous Integration (CI) means frequently testing your application in an integrated state. However, the term ‘testing’ should be interpreted loosely as this can mean:</p>
<ul>
<li>Integration testing</li>
<li>Unit testing</li>
<li>Functional testing</li>
<li>Static analysis</li>
<li>Style checking (linting)</li>
<li>Dynamic analysis</li>
</ul>
<p>To facilitate running these tests, it’s best to have these tests run automatically as part of your configuration management (git) process. This is where GitLab CI is awesome!</p>
<h2>Getting Started with GitLab CI</h2>
<p>Before jumping into GitLab CI, here are a few definitions:</p>
<ul>
<li><strong>pipeline</strong>: a set of tests to run against a single git commit.</li>
<li><strong>runner</strong>: GitLab uses runners on different servers to actually execute the tests in a pipeline; GitLab provides runners to use, but you can also spin up your own servers as runners.</li>
<li><strong>job</strong>: a single test being run in a pipeline.</li>
<li><strong>stage</strong>: a group of related tests being run in a pipeline.</li>
</ul>
<p><img alt="gitlab-ci-pipeline-example" src="images/GitLab_CI_Pipeline_Screenshot.png"></p>
<p>GitLab utilizes the ‘.gitlab-ci.yml’ file to run the CI pipeline for each project. The ‘.gitlab-ci.yml’ file should be found in the top-level directory of your project.</p>
<p>While there are different methods of running a test in GitLab CI, I prefer to utilize a Docker container to run each test. I’ve found the overhead in spinning up a Docker container to be trivial (in terms of execution time) when doing CI testing.</p>
<h2>Creating a Single Job in GitLab CI</h2>
<p>The first job that I want to add to GitLab CI for my project is to run a linter (flake8). In my local development environment, I would run this command:</p>
<div class="highlight"><pre><span></span><code>flake8 --ignore<span class="o">=</span>E501 project
</code></pre></div>
<p>You can find an accessible description of how to configure flake8 in the official documentation at <a href="https://flake8.pycqa.org/en/latest/user/configuration.html">Configuring Flake8</a></p>
<p>This command can be transformed into a job on GitLab CI in the ‘.gitlab-ci.yml’ file:</p>
<div class="highlight"><pre><span></span><code><span class="nt">image</span><span class="p">:</span> <span class="s">"python:3.9-slim"</span>
<span class="nt">cache</span><span class="p">:</span>
<span class="nt">paths</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">deps_cache</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">venv/</span>
<span class="nt">before_script</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">python --version</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">python -m venv venv</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">source venv/bin/activate</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">pip install -r test-requirements.txt --cache-dir deps_cache</span>
<span class="nt">stages</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">Static Analysis</span>
<span class="nt">flake8</span><span class="p">:</span>
<span class="nt">stage</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">Static Analysis</span>
<span class="nt">only</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">master</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">merge_requests</span>
<span class="nt">script</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">flake8 --ignore=E501 project</span>
</code></pre></div>
<p>This YAML file tells GitLab CI what to run on merge request and commits pushed up to master. Let’s break down each section…</p>
<p>The first part (image: “python: 3.9-slim”) instructs GitLab CI to utilize Docker for performing ALL of the tests for this project, specifically to use the ‘python:3.9-slim‘ image that is found on DockerHub.</p>
<p>The second part (cache) ised to specify a list of files and directories to cache between jobs. You can only use paths that are in the local working copy.
Caching is shared between pipelines and jobs. Caches are restored before artifacts.</p>
<p>The third section (before_script) is the set of commands to run in the Docker container before starting each job. This is really beneficial for getting the Docker container in the correct state by installing all the python packages needed by the application.</p>
<p>The fourth section (stages) defines the different stages in the pipeline. There is only a single stage (Static Analysis) at this point, but later a second stage (Test) will be added. I like to think of stages as a way to group together related jobs.</p>
<p>The fourth section (flake8) defines the job; it specifies the stage (Static Analysis) that the job should be part of and the commands to run in the Docker container for this job. For this job, the flake8 linter is run against the project directory.</p>
<h2>Running Tests with pytest on GitLab CI</h2>
<p>When I run my unit and functional tests with pytest in my development environment, I run the following command in my top-level directory:</p>
<div class="highlight"><pre><span></span><code>py.test -v tests
</code></pre></div>
<p>This command transformed into a job on GitLab CI in the ‘.gitlab-ci.yml’ file like this:</p>
<div class="highlight"><pre><span></span><code><span class="nt">pytest</span><span class="p">:</span>
<span class="nt">stage</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">Test</span>
<span class="nt">only</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">master</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">merge_requests</span>
<span class="nt">script</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">py.test -v tests --doctest-modules --cov project --cov-report term --cov-report xml</span>
<span class="nt">artifacts</span><span class="p">:</span>
<span class="nt">paths</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">coverage.xml</span>
<span class="nt">reports</span><span class="p">:</span>
<span class="nt">cobertura</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">coverage.xml</span>
</code></pre></div>
<p>In this part of the script we also create coverage report and store it in coverage.xml file</p>
<h2>Final GitLab CI Configuration</h2>
<p>Final script which run pylint, mypy, flake8, pytest, coverage and allow failure of Static Analysis stages looks like this:</p>
<div class="highlight"><pre><span></span><code><span class="nt">image</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">python:3.9-slim</span>
<span class="nt">cache</span><span class="p">:</span>
<span class="nt">paths</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">deps_cache</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">venv/</span>
<span class="nt">before_script</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">python --version</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">python -m venv venv</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">source venv/bin/activate</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">pip install -r test-requirements.txt --cache-dir deps_cache</span>
<span class="nt">stages</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">Static Analysis</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">Test</span>
<span class="nt">flake8</span><span class="p">:</span>
<span class="nt">stage</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">Static Analysis</span>
<span class="nt">only</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">master</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">merge_requests</span>
<span class="nt">allow_failure</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="nt">script</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">flake8 --ignore=E501 project</span>
<span class="nt">pylint</span><span class="p">:</span>
<span class="nt">stage</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">Static Analysis</span>
<span class="nt">only</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">master</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">merge_requests</span>
<span class="nt">allow_failure</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="nt">script</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">pylint --fail-under=8 project</span>
<span class="nt">mypy</span><span class="p">:</span>
<span class="nt">stage</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">Static Analysis</span>
<span class="nt">only</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">master</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">merge_requests</span>
<span class="nt">allow_failure</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="nt">script</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">mypy --ignore-missing-imports project</span>
<span class="nt">pytest</span><span class="p">:</span>
<span class="nt">stage</span><span class="p">:</span> <span class="l l-Scalar l-Scalar-Plain">Test</span>
<span class="nt">only</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">master</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">merge_requests</span>
<span class="nt">script</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">py.test -v tests --doctest-modules --cov project --cov-report term --cov-report xml</span>
<span class="nt">artifacts</span><span class="p">:</span>
<span class="nt">paths</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">coverage.xml</span>
<span class="nt">reports</span><span class="p">:</span>
<span class="nt">cobertura</span><span class="p">:</span>
<span class="p p-Indicator">-</span> <span class="l l-Scalar l-Scalar-Plain">coverage.xml</span>
</code></pre></div>
<p>To configure pylint we will use next <em>.pylintrc</em> file:</p>
<div class="highlight"><pre><span></span><code><span class="k">[MASTER]</span>
<span class="na">init-hook</span><span class="o">=</span><span class="s">"from pylint.config import find_pylintrc; import os, sys; sys.path.append(os.path.dirname(find_pylintrc()))"</span>
<span class="k">[MESSAGES CONTROL]</span>
<span class="c1"># Disable the message, report, category or checker with the given id(s). You</span>
<span class="c1"># can either give multiple identifier separated by comma (,) or put this option</span>
<span class="c1"># multiple time (only on the command line, not in the configuration file where</span>
<span class="c1"># it should appear only once).</span>
<span class="na">disable</span><span class="o">=</span><span class="s">C0103</span>
<span class="k">[DESIGN]</span>
<span class="c1"># Maximum number of arguments for function / method</span>
<span class="na">max-args</span><span class="o">=</span><span class="s">5</span>
<span class="c1"># Maximum number of attributes for a class (see R0902).</span>
<span class="na">max-attributes</span><span class="o">=</span><span class="s">10</span>
<span class="c1"># Maximum number of boolean expressions in a if statement</span>
<span class="na">max-bool-expr</span><span class="o">=</span><span class="s">5</span>
<span class="c1"># Maximum number of branch for function / method body</span>
<span class="na">max-branches</span><span class="o">=</span><span class="s">12</span>
<span class="c1"># Maximum number of locals for function / method body</span>
<span class="na">max-locals</span><span class="o">=</span><span class="s">15</span>
<span class="c1"># Maximum number of parents for a class (see R0901).</span>
<span class="na">max-parents</span><span class="o">=</span><span class="s">7</span>
<span class="c1"># Maximum number of public methods for a class (see R0904).</span>
<span class="na">max-public-methods</span><span class="o">=</span><span class="s">10</span>
<span class="c1"># Maximum number of return / yield for function / method body</span>
<span class="na">max-returns</span><span class="o">=</span><span class="s">10</span>
<span class="c1"># Maximum number of statements in function / method body</span>
<span class="na">max-statements</span><span class="o">=</span><span class="s">50</span>
<span class="c1"># Minimum number of public methods for a class (see R0903).</span>
<span class="na">min-public-methods</span><span class="o">=</span><span class="s">0</span>
</code></pre></div>
<p>Check this resource to find more about pylint messages and they meaning: <a href="http://pylint-messages.wikidot.com/">PyLint Messages</a></p>
<p>In this article we didnot cover some intersting techincs like testing against different python version with tox, configuring static analysis tools from 1 source of thuth pyproject.toml file and some other cool features, i will try to cover this in next articles.</p>Obstacles to successful devops2021-04-09T10:00:00+03:002021-04-09T10:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2021-04-09:/devops-questions.html<p>In today’s enterprises, software is your company’s everyday face, whether through the desktop, the cloud, or a mobile device, to all parts of the globe.</p><p>Software updates serve customer’s demands. Each one you deliver is your opportunity to renew -- or, if
botched, destroy -- their trust. How can you make every update top-notch at top speed?</p>
<p>In today’s enterprises, software is your company’s everyday face, whether through the desktop, the cloud, or a mobile device,
to all parts of the globe.
Cars are computers on wheels. Thermostats are data terminals. Banks live in your phone.
In this new world, software updates serve customer’s demands. Each one you deliver is your opportunity to renew -- or, if
botched, destroy -- their trust. How can you make every update top-notch at top speed?</p>
<p>That’s why DevOps is important for your organization. When you speed
the delivery of quality software, customers get what they clamor for, and
you can respond rapidly to shifts in market demand.</p>
<p>DevOps speeds the delivery of quality software by reducing friction as it
moves between stages and stakeholders for testing, evaluation, and
release. Identifying and resolving these pain points forges successful
DevOps.</p>
<p>Table of Contents:</p>
<ul>
<li><a href="#track-builds">Track builds</a></li>
<li><a href="#knowing-about-your-builds">Knowing About Your Builds</a></li>
<li><a href="#a-common-system-of-record">A Common System of Record</a></li>
<li><a href="#automate-builds">Automate builds</a></li>
<li><a href="#manual-processes">Manual Processes</a></li>
<li><a href="#automation-and-process-management">Automation and Process Management</a></li>
<li><a href="#dependecy-management">Dependecy Management</a></li>
<li><a href="#wild-dependencies">Wild Dependencies</a></li>
<li><a href="#dependency-management">Dependency Management</a></li>
<li><a href="#repository-chain">Repository chain</a></li>
<li><a href="#moving-builds-through-your-pipeline">Moving Builds Through Your Pipeline</a></li>
<li><a href="#build-promotion">Build Promotion</a></li>
<li><a href="#cloud-solution">Cloud solution</a></li>
<li><a href="#meeting-growing-demands">Meeting Growing Demands</a></li>
<li><a href="#enterprise-readiness">Enterprise Readiness</a></li>
<li><a href="#universal-solution">Universal Solution</a></li>
<li><a href="#cost-of-adapting-to-change">Cost of Adapting to Change</a></li>
<li><a href="#a-universal-solution">A Universal Solution</a></li>
<li><a href="#conclusion">Conclusion</a></li>
</ul>
<h2>Track builds</h2>
<h3>Knowing About Your Builds</h3>
<p>Your developer teams can produce many builds every day. How
will you keep track of them all?</p>
<p>Without a versatile solution, you may know which build is most
current, but not which build is best. Nor can you reliably trace
through their history, or where their many parts came from.</p>
<p>When your builds fail, can you identify and roll back the
problem parts? You’ll want to pinpoint which builds are having
problems and where they occur in the build process so you or your
developers can quickly provide a fix.</p>
<h3>A Common System of Record</h3>
<p>A central home for your builds is a single source of truth for
all artifacts moving through your pipeline. Organizing and versioning
your build outcomes into repositories means you can readily find the
best-functioning, most current build.</p>
<p>A repository manager that tracks where artifacts are used
and their prior versions provides a rich set of data that helps you
trace everything to its source or ancestor. You can quickly see the
differences from one version to the next, gain visibility into how each
was made, and find insights that help you fix your builds when they
go wrong.</p>
<h2>Automate builds</h2>
<h3>Manual Processes</h3>
<p>Every place in your pipeline that requires a human to step in opens
a risk. Individual signoffs add delays. Redundant rebuilds for
release introduce uncertainty. Scripts for tool management or
build deployment that have to be manually changed, maintained,
and executed cost time and are prone to mistakes.</p>
<p>Any of these costly blocks slow getting the right code to the end
user.</p>
<h3>Automation and Process Management</h3>
<p>A repository that holds your builds and artifacts offers
convenience. But one that collects intelligence about them as well
gives you power. The more you know about your binaries, the better
you can automate, and enable your build production tools to make
smart decisions that can unify and speed your delivery pipeline all
the way to deployment.</p>
<p>Your repositories solution will need to provide your build tools with a
rich, versatile interface for queries and commands, so they can do
their work without your intervention. If it uses a standard, platform-independent interchange technology, such as REST APIs, you’ll
be free to choose whichever CI server fits you best.</p>
<p>Once you can automate your pipeline, you can be better assured that
every build released into production adhered to the same process,
and conforms to a common standard.</p>
<h2>Dependecy Management</h2>
<h3>Wild Dependencies</h3>
<p>Developers draw dependencies for many languages and
technologies, each with its own requirements and interface. How
will you manage them all?</p>
<p>These external resources can change at any time, and quality
control ranges from fierce oversight to none at all. How can you be
certain of what’s in every build? How can you reliably reproduce
one? What harmful changes might sneak in?</p>
<p>What’s more, your build processes can’t run any faster than your
link to those remote resources. A heavy load slows builds down; an
outage forces your otherwise sound builds to fail.</p>
<h3>Dependency Management</h3>
<p>Bring your dependencies under control with local repositories
that proxy the remote resources where dependencies are
stored. With a locally-held cache of those dependencies, the
version you need is always available to complete your builds,
and to do so at top speed.</p>
<p>Even better, once your binary management system governs
your dependencies, it can maintain the same kind of information about them as other artifacts. Track their history and
usage, and always know which version of a dependency is
employed in every build.</p>
<h2>Repository chain</h2>
<h3>Moving Builds Through Your Pipeline</h3>
<p>Many pipelines require a fresh complete or partial build of the code
at each staging transition of testing, validation, and release.
Each new build takes additional time, and might require each
stakeholder to manually evaluate and launch. Even worse,
as developers continue to change shared code, each rebuild
introduces uncertainties that require repeating the same quality
checks in each stage.</p>
<p>Once a build has passed through checkpoints, how do you
physically advance it to the next stage? A manual process to
promote a build to the next staging location is prone to errors.
And you'll need a way to communicate the status of that build to
the entire team as it transitions through your pipeline.</p>
<h3>Build Promotion</h3>
<p>Speed your pipeline by creating separate repositories for each
evaluation stage, and promote immutable builds from one
repository to the next. As the identical build ascends through
successively more rigorous approvals, it accumulates more
information that follows the build through the promotion chain.</p>
<p>This helps your pipeline automation make even smarter decisions
and make use of ready mechanism.</p>
<h2>Cloud solution</h2>
<h3>Meeting Growing Demands</h3>
<p>You need to operate big today, and bigger tomorrow. A heavy load
from many line-of-business teams can slow down your entire
development pipeline, while any single point of failure in your
infrastructure can be catastrophic.</p>
<p>Geographically distributed teams need to be able to always reach
the same pipeline resources at the same speed. And any
interruption of service to update or to upgrade capacity wastes
vital production time.</p>
<h3>Enterprise Readiness</h3>
<p>Enterprise-ready solutions provide the flexibility and muscle that
can adapt as you grow.</p>
<p>A repository manager that can work in the cloud can help infinitely
scale costs of storage and computing with your needs. The more
cloud providers your binary management tool can work with, the
greater control you’ll have. And a SaaS subscription option ensures
your resources are always available and up-to-date.</p>
<p>A high availability, active/active clustering configuration can assure
repository responsiveness under load. That redundancy also
provides failover for disaster recovery, and enables expansion and
maintenance with zero downtime.</p>
<p>A binary manager that supports multi-site replication can provide
the regional proximity distributed teams need to share the
resources of their pipelines speedily across the globe.</p>
<h2>Universal Solution</h2>
<h3>Cost of Adapting to Change</h3>
<p>Reaching all your customers means developing in many languages,
across many runtimes. One department may write code for the cloud
in Go, another for mobile devices in Java. Yet each technology has its
own requirements and supporting tools.</p>
<p>What kind of infrastructure will you use for DevOps? Today it might
make the most sense to run securely in your own data center.
Tomorrow you may need the flexibility of the cloud, or to combine
them for the benefits of each. You’ll want to be free to choose the
vendors that fit your requirements best, and to change nimbly when
your needs shift.</p>
<h3>A Universal Solution</h3>
<p>A universal binary repository manager automates your delivery
pipeline no matter what language used or platform run on. Control
through REST APIs enable working with the tools you already use.</p>
<p>As the core of your DevOps system, your binary repository manager
must function equally well in the cloud as on your own servers
on-premises. A solution that can easily promote builds across those
environments enables DevOps in a powerful hybrid cloud. And integrated support for all major providers, such as AWS, Google Cloud
and Azure, empowers a multicloud strategy that prevents vendor
lock-in.</p>
<p>You should be able to choose how to pay for it, too. Whether you
need the fixed licensing costs or a flexible SaaS subscription,
a solution available as both will free you to build the systems
you use now and in the future. </p>
<h2>Conclusion</h2>
<p>A fully featured binary repository manager will help you automate your software delivery pipeline and guide you to a new way
of work.
It can provide you control over and insight into your processes so you can resolve problems as they arise and
continuously improve your methods.
When robustly designed, your repository manager can flexibly adapt to the special needs
of your organization.</p>Визуализации картографических данных в Jupyter notebook2021-04-04T15:00:00+03:002021-04-04T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2021-04-04:/mkrf-spb-geo-data.html<p>В этой статье, мы углубимся в изучение Jupyter Interactive Widgets и Ipyleaflet package, для визуализации картографических данных в Jupyter notebook.</p><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>В этой статье, мы углубимся в изучение Jupyter Interactive Widgets и Ipyleaflet package, для визуализации картографических данных в Jupyter notebook.</p>
<p>Мы будем использовать интересные данные из <a href="https://opendata.mkrf.ru/opendata/7705851331-egrkn/">Единый государственный реестр объектов культурного наследия(памятников истории и культуры) народов РФ</a></p>
</div>
</div>
</div>
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<div class="input">
<div class="prompt input_prompt">In [1]:</div>
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<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">zipfile</span><span class="o">,</span> <span class="nn">json</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">ipyleaflet</span> <span class="kn">import</span> <span class="n">AwesomeIcon</span><span class="p">,</span> <span class="n">Map</span><span class="p">,</span> <span class="n">Marker</span><span class="p">,</span> <span class="n">MarkerCluster</span><span class="p">,</span> <span class="n">Heatmap</span><span class="p">,</span> <span class="n">WidgetControl</span><span class="p">,</span> <span class="n">basemaps</span><span class="p">,</span> <span class="n">basemap_to_tiles</span>
<span class="kn">from</span> <span class="nn">ipywidgets</span> <span class="kn">import</span> <span class="n">Dropdown</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Загружаем данные в виде zip архива <a href="https://opendata.mkrf.ru/opendata/7705851331-egrkn/">ссылка</a></p>
</div>
</div>
</div>
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<div class="prompt input_prompt">In [ ]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mkrf_path</span> <span class="o">=</span> <span class="s1">'./data/data-49-structure-6.csv.zip'</span>
<span class="n">zf</span> <span class="o">=</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">mkrf_path</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">zf</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">'data-49-structure-6.csv'</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
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<div class="cell border-box-sizing code_cell rendered">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fdf</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">'Объект'</span><span class="p">,</span> <span class="s1">'Регион'</span><span class="p">,</span> <span class="s1">'Вид объекта'</span><span class="p">,</span> <span class="s1">'Категория историко-культурного значения'</span><span class="p">,</span> <span class="s1">'На карте'</span><span class="p">]]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">fdf</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fdf</span> <span class="o">=</span> <span class="n">fdf</span><span class="p">[</span><span class="n">fdf</span><span class="p">[</span><span class="s1">'Регион'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'г. Санкт-Петербург'</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fdf</span><span class="o">.</span><span class="n">to_pickle</span><span class="p">(</span><span class="s2">"./data/spb-mkrf.pkl"</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">spbdf</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_pickle</span><span class="p">(</span><span class="s2">"./data/spb-mkrf.pkl"</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rtypes</span> <span class="o">=</span> <span class="n">spbdf</span><span class="p">[</span><span class="s1">'Вид объекта'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
<span class="n">rtypes</span>
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<pre>array(['Памятник', 'Ансамбль', 'Достопримечательное место'], dtype=object)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">spbdf</span><span class="p">[</span><span class="s1">'Категория историко-культурного значения'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
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<pre>array(['Федерального значения', 'Регионального значения'], dtype=object)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">icon_monument</span> <span class="o">=</span> <span class="n">AwesomeIcon</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'monument'</span><span class="p">,</span><span class="n">marker_color</span><span class="o">=</span><span class="s1">'orange'</span><span class="p">)</span>
<span class="n">icon_group</span> <span class="o">=</span> <span class="n">AwesomeIcon</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'object-group'</span><span class="p">,</span><span class="n">marker_color</span><span class="o">=</span><span class="s1">'green'</span><span class="p">)</span>
<span class="n">icon_landmark</span> <span class="o">=</span> <span class="n">AwesomeIcon</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'landmark'</span><span class="p">,</span><span class="n">marker_color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">)</span>
<span class="n">markers</span> <span class="o">=</span> <span class="p">{</span><span class="n">e</span><span class="p">:[]</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">rtypes</span><span class="p">)}</span>
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">spbdf</span><span class="o">.</span><span class="n">itertuples</span><span class="p">():</span>
<span class="n">lng</span><span class="p">,</span> <span class="n">lat</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="mi">5</span><span class="p">])[</span><span class="s1">'coordinates'</span><span class="p">]</span>
<span class="n">rtype</span> <span class="o">=</span> <span class="n">r</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="k">if</span> <span class="n">rtype</span> <span class="o">==</span> <span class="s1">'Памятник'</span><span class="p">:</span>
<span class="n">icon</span> <span class="o">=</span> <span class="n">icon_monument</span>
<span class="k">elif</span> <span class="n">rtype</span> <span class="o">==</span> <span class="s1">'Ансамбль'</span><span class="p">:</span>
<span class="n">icon</span> <span class="o">=</span> <span class="n">icon_group</span>
<span class="k">elif</span> <span class="n">rtype</span> <span class="o">==</span> <span class="s1">'Достопримечательное место'</span><span class="p">:</span>
<span class="n">icon</span> <span class="o">=</span> <span class="n">icon_landmark</span>
<span class="n">markers</span><span class="p">[</span><span class="n">rtype</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Marker</span><span class="p">(</span><span class="n">location</span><span class="o">=</span><span class="p">[</span><span class="n">lat</span><span class="p">,</span> <span class="n">lng</span><span class="p">],</span> <span class="n">icon</span><span class="o">=</span><span class="n">icon</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>
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<p>Эта карта использует Leaflet Marker Cluster plugin для отображения памятников на карте Санкт-Петербурга от Esri.</p>
<p>Виджет выпадающего списка Ipywidgets Dropdown для просмотра объектов разных видов, заменяющий слои карты.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">center</span> <span class="o">=</span> <span class="p">[</span><span class="mf">59.928871952236804</span><span class="p">,</span> <span class="mf">30.322255639340106</span><span class="p">]</span>
<span class="n">zoom</span> <span class="o">=</span> <span class="mi">12</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">Map</span><span class="p">(</span><span class="n">center</span><span class="o">=</span><span class="n">center</span><span class="p">,</span> <span class="n">zoom</span><span class="o">=</span><span class="n">zoom</span><span class="p">,</span> <span class="n">basemap</span><span class="o">=</span><span class="n">basemaps</span><span class="o">.</span><span class="n">Esri</span><span class="o">.</span><span class="n">WorldStreetMap</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">marker_cluster</span> <span class="o">=</span> <span class="n">MarkerCluster</span><span class="p">(</span><span class="n">markers</span><span class="o">=</span><span class="n">markers</span><span class="p">[</span><span class="s1">'Памятник'</span><span class="p">])</span>
<span class="n">m</span><span class="o">.</span><span class="n">add_layer</span><span class="p">(</span><span class="n">marker_cluster</span><span class="p">)</span>
<span class="c1"># Adds a Dropdown widget</span>
<span class="n">dropdown</span> <span class="o">=</span> <span class="n">Dropdown</span><span class="p">(</span>
<span class="n">options</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">markers</span><span class="o">.</span><span class="n">keys</span><span class="p">()),</span>
<span class="n">value</span><span class="o">=</span><span class="s1">'Памятник'</span><span class="p">,</span>
<span class="n">description</span><span class="o">=</span><span class="s1">'Категории'</span>
<span class="p">)</span>
<span class="c1"># Handles Dropdown control event</span>
<span class="k">def</span> <span class="nf">on_click</span><span class="p">(</span><span class="n">change</span><span class="p">):</span>
<span class="n">type_name</span> <span class="o">=</span> <span class="n">change</span><span class="p">[</span><span class="s1">'new'</span><span class="p">]</span>
<span class="k">global</span> <span class="n">marker_cluster</span>
<span class="n">m</span><span class="o">.</span><span class="n">remove_layer</span><span class="p">(</span><span class="n">marker_cluster</span><span class="p">)</span>
<span class="n">marker_cluster</span> <span class="o">=</span> <span class="n">MarkerCluster</span><span class="p">(</span><span class="n">markers</span><span class="o">=</span><span class="n">markers</span><span class="p">[</span><span class="n">type_name</span><span class="p">])</span>
<span class="n">m</span><span class="o">.</span><span class="n">add_layer</span><span class="p">(</span><span class="n">marker_cluster</span><span class="p">)</span>
<span class="n">dropdown</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">on_click</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">)</span>
<span class="c1"># Adds control to the map</span>
<span class="n">basemap_control</span> <span class="o">=</span> <span class="n">WidgetControl</span><span class="p">(</span><span class="n">widget</span><span class="o">=</span><span class="n">dropdown</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="s1">'topright'</span><span class="p">)</span>
<span class="n">m</span><span class="o">.</span><span class="n">add_control</span><span class="p">(</span><span class="n">basemap_control</span><span class="p">)</span>
<span class="n">m</span>
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<p>Эта карта использует Leaflet heatmap plugin для отображения зон города Санкт-Петербурга максимально насыщенных объектами культурного наследия. На этот раз используем основу Esri.WorldTopoMap</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">center</span> <span class="o">=</span> <span class="p">[</span><span class="mf">59.928871952236804</span><span class="p">,</span> <span class="mf">30.322255639340106</span><span class="p">]</span>
<span class="n">zoom</span> <span class="o">=</span> <span class="mi">12</span>
<span class="n">mh</span> <span class="o">=</span> <span class="n">Map</span><span class="p">(</span><span class="n">center</span><span class="o">=</span><span class="n">center</span><span class="p">,</span> <span class="n">zoom</span><span class="o">=</span><span class="n">zoom</span><span class="p">,</span> <span class="n">basemap</span><span class="o">=</span><span class="n">basemaps</span><span class="o">.</span><span class="n">Esri</span><span class="o">.</span><span class="n">WorldTopoMap</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">create_heatmap_data</span><span class="p">():</span>
<span class="s2">"Return a list of some random lat/lng/value triples."</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">spbdf</span><span class="o">.</span><span class="n">itertuples</span><span class="p">():</span>
<span class="n">lng</span><span class="p">,</span> <span class="n">lat</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="mi">5</span><span class="p">])[</span><span class="s1">'coordinates'</span><span class="p">]</span>
<span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lat</span><span class="p">,</span> <span class="n">lng</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="k">return</span> <span class="n">data</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">heat</span> <span class="o">=</span> <span class="n">Heatmap</span><span class="p">(</span><span class="n">locations</span><span class="o">=</span><span class="n">create_heatmap_data</span><span class="p">(),</span>
<span class="n">radius</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">blur</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">gradient</span> <span class="o">=</span> <span class="p">{</span><span class="mf">0.4</span><span class="p">:</span> <span class="s1">'red'</span><span class="p">,</span>
<span class="mf">0.6</span><span class="p">:</span> <span class="s1">'yellow'</span><span class="p">,</span>
<span class="mf">0.7</span><span class="p">:</span> <span class="s1">'lime'</span><span class="p">,</span>
<span class="mf">0.8</span><span class="p">:</span> <span class="s1">'cyan'</span><span class="p">,</span>
<span class="mf">1.0</span><span class="p">:</span> <span class="s1">'blue'</span><span class="p">})</span>
<span class="n">mh</span><span class="o">.</span><span class="n">add_layer</span><span class="p">(</span><span class="n">heat</span><span class="p">)</span>
<span class="n">mh</span>
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<div class=" highlight hl-ipython3"><pre><span></span>
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Шаблон ретроспективы2021-02-11T11:00:00+03:002021-02-11T11:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2021-02-11:/retrospective-template.html<p>Шаблон для проведения ретроспективы на русским языке</p><h1>Оглавление</h1>
<ol>
<li>Сбор данных</li>
<li>Генерация идей</li>
<li>Выбор решений</li>
<li>Закрытие</li>
</ol>
<p>Форма не важна, важна сомвестная заинтересованность участников в достижении общей цели.</p>
<p>Фокус на обстоятельствах и задачах лежащих внутри проекта(по времени и возможностям).</p>
<p>Не более 3 часов.</p>
<p>https://app.funretrospectives.com/</p>
<h2>Сбор данных</h2>
<ol>
<li>Сбор информации(фактов, проблем, позитива, негатива)</li>
<li>ранжирование информации и определение приоритетов</li>
</ol>
<p>Метафоры.
- Что помогает/мещает двигаться к цели.
- Что мешает быть более эффективным</p>
<p>Worked well/Kinda worked/Didnot work.</p>
<p>KALM(Keep/Add/More/Less)</p>
<p>Матрицы Запланированно/Сделанно.</p>
<table>
<thead>
<tr>
<th>-</th>
<th>Не сделанно</th>
<th>Сделанно</th>
</tr>
</thead>
<tbody>
<tr>
<td>Запланированно</td>
<td>Проблемы</td>
<td>Повод для городости</td>
</tr>
<tr>
<td>Не запланированно</td>
<td>Ограничения проекта</td>
<td>Возможные причины проблем</td>
</tr>
</tbody>
</table>
<h2>Генерация идей(поиск решений)</h2>
<p>"Пять почему" ака ДАО Тойота.</p>
<p>Brainstorm(в режиме Free-for-all).</p>
<ul>
<li>Безопасность на этапе генерации идей</li>
<li>Безопасность высказываний</li>
<li>Обсуждаем только саму идею</li>
<li>Разговариваем с молчунами</li>
<li>Придумываем фильтры(по времени, деньгам)</li>
<li>Компонуем идеи</li>
</ul>
<h2>Выработка решений и разработка плана реализации</h2>
<p>SMART(Specific, Measurable, Achievable, Relevant, Time bound):</p>
<ul>
<li>Точные и конкретные</li>
<li>Измеримые</li>
<li>Достижимые</li>
<li>Релевантные</li>
<li>Цели со сроком</li>
</ul>
<p>План Кто-Что-Когда(Assignee to tasks).</p>
<h2>Завершение</h2>
<p>Анализ ретроспективы.</p>
<table>
<thead>
<tr>
<th>+(things done well)</th>
<th>d(things to change)</th>
</tr>
</thead>
<tbody>
<tr>
<td>-</td>
<td>-</td>
</tr>
<tr>
<td>-</td>
<td>-</td>
</tr>
</tbody>
</table>
<p>Благодарность участникам.</p>
<p>Что было полезным для каждого из участников.</p>
<p>Оформленные решения и таски(бэклог), основной документ до следующий ретроспективы(важность вышеобсужденного понятна всем участникам, затратившим силы).</p>Understand GraphQL Schema2020-11-29T14:00:00+03:002020-11-29T14:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2020-11-29:/graphql-schema.html<p>In this note, we will discuss what is a GraphQL schema and different ways to retrieve the GraphQL schema from a GraphQL server</p><h1>Understand GraphQL Schema</h1>
<ul>
<li><a href="#understand-graphql-schema">Understand GraphQL Schema</a></li>
<li><a href="#goal-of-the-note">Goal of the note</a></li>
<li><a href="#glossary">Glossary</a></li>
<li><a href="#definition-of-a-graphql-schema">Definition of a GraphQL Schema</a></li>
<li><a href="#the-importance-of-the-graphql-schema">The Importance of the GraphQL Schema</a></li>
<li><a href="#how-to-get-the-schema--graphql-explorer">How To Get The Schema — GraphQL Explorer</a></li>
<li><a href="#how-to-get-the-schema--introspection-queries">How To Get The Schema — Introspection Queries</a></li>
<li><a href="#hiding-the-graphql-schema">Hiding the GraphQL Schema</a></li>
<li><a href="#conclusion">Conclusion</a></li>
</ul>
<h2>Goal of the note</h2>
<ul>
<li>Describe what is GraphQL schema</li>
<li>Understand how do we retrieve schema from the GraphQL server</li>
<li>Hiding the GraphQL Schema</li>
</ul>
<h2>Glossary</h2>
<ul>
<li><strong>GraphQL schema</strong>: A document describing how a GraphQL server is structured.</li>
<li><strong>GraphQL query</strong>: A type of request sent to the GraphQL server used to retrieve records.</li>
<li><strong>GraphQL mutation</strong>: A type of request sent to the GraphQL server used to create, update, or delete records.</li>
<li><strong>GraphQL types</strong>: This is an object that we interact with in the GraphQL schema. An object can be a User, a Message, a Post, etc.</li>
</ul>
<p>Understand GraphQL types, queries, and mutations. This is an <a href="https://www.freecodecamp.org/news/a-beginners-guide-to-graphql-86f849ce1bec/">example</a> if you’re just getting started with GraphQL.</p>
<h2>Definition of a GraphQL Schema</h2>
<p>A GraphQL schema is a document describing how a specific GraphQL server is structured. A GraphQL schema is like a SQL database design. Every GraphQL server has a schema.</p>
<p>Below is an example of a GraphQL schema:</p>
<div class="highlight"><pre><span></span><code><span class="nx">type</span> <span class="nx">Query</span> <span class="p">{</span>
<span class="nx">users</span><span class="o">:</span> <span class="p">[</span><span class="nx">User</span><span class="p">]</span>
<span class="p">}</span>
<span class="nx">type</span> <span class="nx">User</span> <span class="p">{</span>
<span class="nx">id</span><span class="o">:</span> <span class="nx">ID</span><span class="o">!</span>
<span class="nx">email</span><span class="o">:</span> <span class="nb">String</span>
<span class="p">}</span>
</code></pre></div>
<p>In the schema above there is a GraphQL query called users which returns an array of users and a User type with an id and email field.</p>
<p>Every GraphQL server has a schema.</p>
<h2>The Importance of the GraphQL Schema</h2>
<p>The GraphQL schema is important for end users because it can provide the following info:</p>
<ul>
<li>GraphQL queries we can make to the GraphQL server.</li>
<li>GraphQL mutations we can make to the GraphQL server.</li>
<li>A list of GraphQL types.</li>
<li>What GraphQL types are available to query.</li>
<li>What GraphQL types are available to mutate (aka create, update, or delete).</li>
<li>Fields within a GraphQL type. For example, a User type will have a name, email, and bio field.</li>
<li>Relationships between GraphQL types. For example, a User can have many Friends. A Post can belong to a User.</li>
<li>Business logic on how the application potentially works. For example, if the Post type has a field called “hidden,” we can implicitly assume that Posts can be hidden from other users. Following this rabbit hole will allow us to understand how features work in the application.</li>
</ul>
<p>GraphQL schema is important because it provides us structure of the GraphQL server. The next question we must ask then is how do we retrieve this schema from the GraphQL server?</p>
<p>Below are two such ways: the GraphQL Explorer and Introspection Queries.</p>
<h2>How To Get The Schema — GraphQL Explorer</h2>
<p>Some GraphQL servers provide a GraphQL Explorer (typically called GraphiQL). The GraphQL Explorer is a page where you can manually input GraphQL queries into the page (see the left pane in example below) and see how the GraphQL server responds (see the right pane in the example below).</p>
<p><img alt="GitHub GraphQL Explorer" src="images/graphiql1.png"></p>
<p>You can typically find the link to the application’s GraphQL Explorer in the API documentation.</p>
<h2>How To Get The Schema — Introspection Queries</h2>
<p>Some GraphQL servers don’t provide a convenient GraphQL API explorer. Instead, to get the schema we need to send a HTTP request to the GraphQL server endpoint asking for the GraphQL schema. This type of HTTP request is called a <strong>GraphQL introspection query</strong>.</p>
<p>The GraphQL introspection query’s body is made up of GraphQL syntax-text that represents what we are asking the GraphQL server to return.</p>
<p>For example, the following GraphQL syntax-text is asking the GraphQL server to return a list of all queries available to us from the schema:</p>
<div class="highlight"><pre><span></span><code><span class="p">{</span>
<span class="err">__schema</span> <span class="err">{</span>
<span class="err">queryType</span> <span class="err">{</span>
<span class="err">fields</span> <span class="err">{</span>
<span class="err">name</span>
<span class="p">}</span>
<span class="err">}</span>
<span class="err">}</span>
<span class="err">}</span>
</code></pre></div>
<p>Below are two more GraphQL syntax-text requests you can make to a GraphQL server:</p>
<p>Returns all mutations available to us from the schema:</p>
<div class="highlight"><pre><span></span><code><span class="p">{</span>
<span class="err">__schema</span> <span class="err">{</span>
<span class="err">mutationType</span> <span class="err">{</span>
<span class="err">fields</span> <span class="err">{</span>
<span class="err">name</span>
<span class="p">}</span>
<span class="err">}</span>
<span class="err">}</span>
<span class="err">}</span>
</code></pre></div>
<p>Returns the entire schema: queries, mutations, fields, etc.:</p>
<p><a href="https://gist.github.com/nekrasovp/ef015aceed36065a732b4cebf2e5e8c1">GraphQL Endpoint Introspection Query gist</a></p>
<div class="highlight"><pre><span></span><code><span class="err">query</span> <span class="err">IntrospectionQuery</span> <span class="p">{</span>
<span class="err">__schema</span> <span class="err">{</span>
<span class="err">queryType</span> <span class="err">{</span> <span class="err">name</span> <span class="p">}</span>
<span class="err">mutationType</span> <span class="p">{</span> <span class="err">name</span> <span class="p">}</span>
<span class="err">subscriptionType</span> <span class="p">{</span> <span class="err">name</span> <span class="p">}</span>
<span class="err">types</span> <span class="p">{</span>
<span class="err">...FullType</span>
<span class="p">}</span>
<span class="err">directives</span> <span class="p">{</span>
<span class="err">name</span>
<span class="err">description</span>
<span class="err">args</span> <span class="err">{</span>
<span class="err">...InputValue</span>
<span class="p">}</span>
<span class="err">locations</span>
<span class="err">}</span>
<span class="err">}</span>
<span class="err">}</span>
<span class="err">fragment</span> <span class="err">FullType</span> <span class="err">on</span> <span class="err">__Type</span> <span class="p">{</span>
<span class="err">kind</span>
<span class="err">name</span>
<span class="err">description</span>
<span class="err">fields(includeDeprecated:</span> <span class="err">true)</span> <span class="err">{</span>
<span class="err">name</span>
<span class="err">description</span>
<span class="err">args</span> <span class="err">{</span>
<span class="err">...InputValue</span>
<span class="p">}</span>
<span class="err">type</span> <span class="p">{</span>
<span class="err">...TypeRef</span>
<span class="p">}</span>
<span class="err">isDeprecated</span>
<span class="err">deprecationReason</span>
<span class="err">}</span>
<span class="err">inputFields</span> <span class="p">{</span>
<span class="err">...InputValue</span>
<span class="p">}</span>
<span class="err">interfaces</span> <span class="p">{</span>
<span class="err">...TypeRef</span>
<span class="p">}</span>
<span class="err">enumValues(includeDeprecated:</span> <span class="kc">true</span><span class="err">)</span> <span class="p">{</span>
<span class="err">name</span>
<span class="err">description</span>
<span class="err">isDeprecated</span>
<span class="err">deprecationReason</span>
<span class="p">}</span>
<span class="err">possibleTypes</span> <span class="p">{</span>
<span class="err">...TypeRef</span>
<span class="p">}</span>
<span class="err">}</span>
<span class="err">fragment</span> <span class="err">InputValue</span> <span class="err">on</span> <span class="err">__InputValue</span> <span class="p">{</span>
<span class="err">name</span>
<span class="err">description</span>
<span class="err">type</span> <span class="err">{</span> <span class="err">...TypeRef</span> <span class="p">}</span>
<span class="err">defaultValue</span>
<span class="err">}</span>
<span class="err">fragment</span> <span class="err">TypeRef</span> <span class="err">on</span> <span class="err">__Type</span> <span class="p">{</span>
<span class="err">kind</span>
<span class="err">name</span>
<span class="err">ofType</span> <span class="err">{</span>
<span class="err">kind</span>
<span class="err">name</span>
<span class="err">ofType</span> <span class="err">{</span>
<span class="err">kind</span>
<span class="err">name</span>
<span class="err">ofType</span> <span class="err">{</span>
<span class="err">kind</span>
<span class="err">name</span>
<span class="p">}</span>
<span class="err">}</span>
<span class="err">}</span>
<span class="err">}</span>
</code></pre></div>
<p>Below is an example of a CURL request where we ask the GraphQL server to return us every query from the GraphQL schema:</p>
<div class="highlight"><pre><span></span><code>curl <span class="s1">'https://beta.motu.world/graphql'</span> -H <span class="s1">'Content-Type: application/json'</span> -H <span class="s1">'Accept: application/json'</span> --compressed --data-binary <span class="s1">'{"query":"{\n\t__schema{\n queryType {\n fields{\n name\n }\n }\n }\n}"}'</span>
</code></pre></div>
<p>By running the above CURL request, we get the following results:</p>
<div class="highlight"><pre><span></span><code><span class="p">{</span><span class="nt">"data"</span><span class="p">:{</span>
<span class="nt">"__schema"</span><span class="p">:{</span>
<span class="nt">"queryType"</span><span class="p">:{</span>
<span class="nt">"fields"</span><span class="p">:[</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"activity"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"activities"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"category"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"categories"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"currency"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"currencies"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"itinerary"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"itineraries"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"leg"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"legs"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"like"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"likes"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"provider"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"providers"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"providersProperty"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"providersProperties"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"questionary"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"questionaries"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"review"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"reviews"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"files"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"role"</span><span class="p">},</span>
<span class="p">{</span><span class="nt">"name"</span><span class="p">:</span><span class="s2">"user"</span><span class="p">}</span>
<span class="p">]</span>
<span class="p">}</span>
<span class="p">}</span>
<span class="p">}</span>
<span class="p">}</span>
</code></pre></div>
<h2>Hiding the GraphQL Schema</h2>
<p>The GraphQL schema by default exposes valuable information such as connections between types, accessible fields, etc. Therefore, hiding the GraphQL schema(whether that’s part of the schema or the entire schema) from certain users can be an advantage.</p>
<h2>Conclusion</h2>
<p>A GraphQL schema is a document describing how a specific GraphQL server is structured. A GraphQL schema is important because it helps end-users understand how a GraphQL server works.</p>
<p>There are two ways to get the GraphQL schema: accessing an application’s GraphQL Explorer and Introspection Queries. Once we have the schema, we can begin work on further investigating the GraphQL server.</p>Just another COVID-19 data visualisation2020-10-27T15:00:00+03:002020-10-27T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2020-10-27:/covid-dashboard.html<p>The goal of this notebook is to explore COVID-19 and World Bank API data</p><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<h1 id="From-epidemic-to-pandemic">From epidemic to pandemic<a class="anchor-link" href="#From-epidemic-to-pandemic">¶</a></h1><p><img style="float: left; margin:5px 20px 5px 1px; width:40%" src="https://www.nps.gov/aboutus/news/images/CDC-coronavirus-image-23311-for-web.jpg?maxwidth=650&autorotate=false"></p>
<p>In December 2019, COVID-19 coronavirus was first identified in the Wuhan region of China. By March 11, 2020, the World Health Organization (WHO) categorized the COVID-19 outbreak as a pandemic. A lot has happened in the months in between with major outbreaks in Iran, South Korea, and Italy. </p>
<p>We know that COVID-19 spreads through respiratory droplets, such as through coughing, sneezing, or speaking. But, how quickly did the virus spread across the globe? And, can we see any effect from country-wide policies, like shutdowns and quarantines? </p>
<p>Fortunately, organizations around the world have been collecting data so that governments can monitor and learn from this pandemic. Notably, the Johns Hopkins University Center for Systems Science and Engineering created a <a href="https://github.com/RamiKrispin/coronavirus">publicly available data repository</a> to consolidate this data from sources like the WHO, the Centers for Disease Control and Prevention (CDC), and the Ministry of Health from multiple countries.</p>
<p>In this notebook, you will visualize COVID-19 data from the first several weeks of the outbreak to see at what point this virus became a global pandemic.</p>
<p><em>Please note that information and data regarding COVID-19 is frequently being updated. The data used in this project was pulled on 27-10-2020, and should not be considered to be the most up to date data available.</em></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="n">matplotlib</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">'fivethirtyeight'</span><span class="p">)</span>
<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">201910</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">requests</span>
<span class="kn">import</span> <span class="nn">io</span>
<span class="n">url</span><span class="o">=</span><span class="s2">"https://github.com/RamiKrispin/coronavirus/raw/master/csv/coronavirus.csv"</span>
<span class="n">s</span><span class="o">=</span><span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">)</span><span class="o">.</span><span class="n">content</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">'utf8'</span><span class="p">)</span>
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<span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DatetimeIndex</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'date'</span><span class="p">]),</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">'date'</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
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<th>date</th>
<th>province</th>
<th>country</th>
<th>lat</th>
<th>long</th>
<th>type</th>
<th>cases</th>
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<th>date</th>
<th></th>
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<th>2020-01-22</th>
<td>2020-01-22</td>
<td>NaN</td>
<td>Afghanistan</td>
<td>33.93911</td>
<td>67.709953</td>
<td>confirmed</td>
<td>0</td>
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<tr>
<th>2020-01-23</th>
<td>2020-01-23</td>
<td>NaN</td>
<td>Afghanistan</td>
<td>33.93911</td>
<td>67.709953</td>
<td>confirmed</td>
<td>0</td>
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<th>2020-01-24</th>
<td>2020-01-24</td>
<td>NaN</td>
<td>Afghanistan</td>
<td>33.93911</td>
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<h2 id="Confirmed-cases-throughout-the-world">Confirmed cases throughout the world<a class="anchor-link" href="#Confirmed-cases-throughout-the-world">¶</a></h2><p>The table above shows the cumulative confirmed cases of COVID-19 worldwide by date. Just reading numbers in a data frame makes it hard to get a sense of the scale and growth of the outbreak. Let's draw a line plot to visualize the confirmed cases worldwide</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">wdf</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="s1">'W'</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">wdf</span><span class="p">[</span><span class="s1">'cases'</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Worldwide confirmed cases'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Confirmed cases'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="prompt input_prompt">In [6]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">wdf</span><span class="p">[</span><span class="s1">'cases'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">kind</span><span class="o">=</span><span class="s1">'bar'</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">'date'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'cases'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Worldwide daily mean'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Confirmed cases'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">c_c_wdf</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">'cases'</span><span class="p">,</span><span class="s1">'country'</span><span class="p">]]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">c_c_wdf</span><span class="p">[</span><span class="s1">'cases'</span><span class="p">]</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="s1">'W'</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Worldwide confirmed cases'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Confirmed cases'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rdf</span> <span class="o">=</span> <span class="n">c_c_wdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'date'</span><span class="p">,</span> <span class="s1">'country'</span><span class="p">])[</span><span class="s1">'cases'</span><span class="p">]</span><span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="s1">'first'</span><span class="p">)</span><span class="o">.</span><span class="n">unstack</span><span class="p">()</span>
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<div class="prompt input_prompt">In [10]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rdf</span> <span class="o">=</span> <span class="n">rdf</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="s1">'W'</span><span class="p">)</span>
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<div class="prompt input_prompt">In [11]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">rdf</span><span class="p">[</span><span class="s1">'Russia'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'line'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">)</span>
<span class="n">rdf</span><span class="p">[</span><span class="s1">'Germany'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'line'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">)</span>
<span class="n">rdf</span><span class="p">[</span><span class="s1">'Ukraine'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'line'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Date'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Confirmed cases'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'European countries'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="prompt input_prompt">In [12]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">rdf</span><span class="p">[</span><span class="s1">'China'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'line'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Date'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Confirmed cases'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Chinese statistic, all clear'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<p>We will get some usefull country statistics from <a href="http://wbdata.readthedocs.org/">World Bank API</a></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">wbdata</span>
<span class="n">wbdata</span><span class="o">.</span><span class="n">get_topic</span><span class="p">()</span>
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<pre> id value
---- -------------------------------
1 Agriculture & Rural Development
2 Aid Effectiveness
3 Economy & Growth
4 Education
5 Energy & Mining
6 Environment
7 Financial Sector
8 Health
9 Infrastructure
10 Social Protection & Labor
11 Poverty
12 Private Sector
13 Public Sector
14 Science & Technology
15 Social Development
16 Urban Development
17 Gender
18 Millenium development goals
19 Climate Change
20 External Debt
21 Trade</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">wbdata</span><span class="o">.</span><span class="n">get_indicator</span><span class="p">(</span><span class="n">topic</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
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<pre> Suicide mortality rate (per 100,000 population)
SH.STA.TRAF.P5 Mortality caused by road traffic injury (per 100,000 people)
SH.STA.WASH.P5 Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (per 100,000 population)
SH.STA.WAST.FE.ZS Prevalence of wasting, weight for height, female (% of children under 5)
SH.STA.WAST.MA.ZS Prevalence of wasting, weight for height, male (% of children under 5)
SH.STA.WAST.Q1.ZS Malnourished children (wasting, -2SD) (% of children under 5): Q1 (lowest)
SH.STA.WAST.Q2.ZS Malnourished children (wasting, -2SD) (% of children under 5): Q2
SH.STA.WAST.Q3.ZS Malnourished children (wasting, -2SD) (% of children under 5): Q3
SH.STA.WAST.Q4.ZS Malnourished children (wasting, -2SD) (% of children under 5): Q4
SH.STA.WAST.Q5.ZS Malnourished children (wasting, -2SD) (% of children under 5): Q5 (highest)
SH.STA.WAST.ZS Prevalence of wasting, weight for height (% of children under 5)
SH.STA.WST3.Q1.ZS Malnourished children (wasting, -3SD) (% of children under 5): Q1 (lowest)
SH.STA.WST3.Q2.ZS Malnourished children (wasting, -3SD) (% of children under 5): Q2
SH.STA.WST3.Q3.ZS Malnourished children (wasting, -3SD) (% of children under 5): Q3
SH.STA.WST3.Q4.ZS Malnourished children (wasting, -3SD) (% of children under 5): Q4
SH.STA.WST3.Q5.ZS Malnourished children (wasting, -3SD) (% of children under 5): Q5 (highest)
SH.SVR.WAST.FE.ZS Prevalence of severe wasting, weight for height, female (% of children under 5)
SH.SVR.WAST.MA.ZS Prevalence of severe wasting, weight for height, male (% of children under 5)
SH.SVR.WAST.ZS Prevalence of severe wasting, weight for height (% of children under 5)
SH.TBS.CURE.ZS Tuberculosis treatment success rate (% of new cases)
SH.TBS.DTEC.ZS Tuberculosis case detection rate (%, all forms)
SH.TBS.INCD Incidence of tuberculosis (per 100,000 people)
SH.UHC.CONS.TO Number of people pushed below the 50% median consumption poverty line by out-of-pocket health care expenditure
SH.UHC.CONS.ZS Proportion of population pushed below the 50% median consumption poverty line by out-of-pocket health care expenditure (%)
SH.UHC.NOP1.CG Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)
SH.UHC.NOP1.TO Number of people pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure
SH.UHC.NOP1.ZG Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)
SH.UHC.NOP1.ZS Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)
SH.UHC.NOP2.CG Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)
SH.UHC.NOP2.TO Number of people pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure
SH.UHC.NOP2.ZG Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)
SH.UHC.NOP2.ZS Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)
SH.UHC.OOPC.10.TO Number of people spending more than 10% of household consumption or income on out-of-pocket health care expenditure
SH.UHC.OOPC.10.ZS Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)
SH.UHC.OOPC.25.TO Number of people spending more than 25% of household consumption or income on out-of-pocket health care expenditure
SH.UHC.OOPC.25.ZS Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%)
SH.UHC.SRVS.CV.XD UHC service coverage index
SH.VAC.TTNS.Q1.ZS Tetanus toxoid vaccination (% of live births): Q1 (lowest)
SH.VAC.TTNS.Q2.ZS Tetanus toxoid vaccination (% of live births): Q2
SH.VAC.TTNS.Q3.ZS Tetanus toxoid vaccination (% of live births): Q3
SH.VAC.TTNS.Q4.ZS Tetanus toxoid vaccination (% of live births): Q4
SH.VAC.TTNS.Q5.ZS Tetanus toxoid vaccination (% of live births): Q5 (highest)
SH.VAC.TTNS.ZS Newborns protected against tetanus (%)
SH.VST.OUTP Outpatient visits per capita
SH.XPD.CHEX.GD.ZS Current health expenditure (% of GDP)
SH.XPD.CHEX.PC.CD Current health expenditure per capita (current US$)
SH.XPD.CHEX.PP.CD Current health expenditure per capita, PPP (current international $)
SH.XPD.EHEX.CH.ZS External health expenditure (% of current health expenditure)
SH.XPD.EHEX.PC.CD External health expenditure per capita (current US$)
SH.XPD.EHEX.PP.CD External health expenditure per capita, PPP (current international $)
SH.XPD.EXTR.ZS External resources for health (% of total expenditure on health)
SH.XPD.GHED.CH.ZS Domestic general government health expenditure (% of current health expenditure)
SH.XPD.GHED.GD.ZS Domestic general government health expenditure (% of GDP)
SH.XPD.GHED.GE.ZS Domestic general government health expenditure (% of general government expenditure)
SH.XPD.GHED.PC.CD Domestic general government health expenditure per capita (current US$)
SH.XPD.GHED.PP.CD Domestic general government health expenditure per capita, PPP (current international $)
SH.XPD.OOPC.CH.ZS Out-of-pocket expenditure (% of current health expenditure)
SH.XPD.OOPC.PC.CD Out-of-pocket expenditure per capita (current US$)
SH.XPD.OOPC.PP.CD Out-of-pocket expenditure per capita, PPP (current international $)
SH.XPD.OOPC.TO.ZS Out-of-pocket health expenditure (% of total expenditure on health)
SH.XPD.OOPC.ZS Out-of-pocket health expenditure (% of private expenditure on health)
SH.XPD.PCAP Health expenditure per capita (current US$)
SH.XPD.PCAP.PP.KD Health expenditure per capita, PPP (constant 2011 international $)
SH.XPD.PRIV Health expenditure, private (% of total health expenditure)
SH.XPD.PRIV.ZS Health expenditure, private (% of GDP)
SH.XPD.PUBL Health expenditure, public (% of total health expenditure)
SH.XPD.PUBL.GX.ZS Health expenditure, public (% of government expenditure)
SH.XPD.PUBL.ZS Health expenditure, public (% of GDP)
SH.XPD.PVTD.CH.ZS Domestic private health expenditure (% of current health expenditure)
SH.XPD.PVTD.PC.CD Domestic private health expenditure per capita (current US$)
SH.XPD.PVTD.PP.CD Domestic private health expenditure per capita, PPP (current international $)
SH.XPD.TOTL.ZS Health expenditure, total (% of GDP)
SM.EMI.TERT.ZS Emigration rate of tertiary educated (% of total tertiary educated population)
SM.POP.NETM Net migration
SM.POP.REFG Refugee population by country or territory of asylum
SM.POP.REFG.OR Refugee population by country or territory of origin
SM.POP.TOTL International migrant stock, total
SM.POP.TOTL.ZS International migrant stock (% of population)
SN.ITK.DEFC.ZS Prevalence of undernourishment (% of population)
SN.ITK.DFCT Depth of the food deficit (kilocalories per person per day)
SN.ITK.DPTH Depth of hunger (kilocalories per person per day)
SN.ITK.MSFI.ZS Prevalence of moderate or severe food insecurity in the population (%)
SN.ITK.SALT.ZS Consumption of iodized salt (% of households)
SN.ITK.SVFI.ZS Prevalence of severe food insecurity in the population (%)
SN.ITK.VAPP.Q1.ZS Vitamin A supplements for postpartum women (% of women with a birth): Q1 (lowest)
SN.ITK.VAPP.Q2.ZS Vitamin A supplements for postpartum women (% of women with a birth): Q2
SN.ITK.VAPP.Q3.ZS Vitamin A supplements for postpartum women (% of women with a birth): Q3
SN.ITK.VAPP.Q4.ZS Vitamin A supplements for postpartum women (% of women with a birth): Q4
SN.ITK.VAPP.Q5.ZS Vitamin A supplements for postpartum women (% of women with a birth): Q5 (highest)
SN.ITK.VITA.Q1.ZS Vitamin A supplements for children (% of children ages 6-59 months): Q1 (lowest)
SN.ITK.VITA.Q2.ZS Vitamin A supplements for children (% of children ages 6-59 months): Q2
SN.ITK.VITA.Q3.ZS Vitamin A supplements for children (% of children ages 6-59 months): Q3
SN.ITK.VITA.Q4.ZS Vitamin A supplements for children (% of children ages 6-59 months): Q4
SN.ITK.VITA.Q5.ZS Vitamin A supplements for children (% of children ages 6-59 months): Q5 (highest)
SN.ITK.VITA.ZS Vitamin A supplementation coverage rate (% of children ages 6-59 months)
SP.ADO.TFRT Adolescent fertility rate (births per 1,000 women ages 15-19)
SP.DTH.INFR.ZS Completeness of infant death reporting (% of reported infant deaths to estimated infant deaths)
SP.DTH.REPT.ZS Completeness of total death reporting (% of reported total deaths to estimated total deaths)
SP.DYN.AMRT.FE Mortality rate, adult, female (per 1,000 female adults)
SP.DYN.AMRT.MA Mortality rate, adult, male (per 1,000 male adults)
SP.DYN.CBRT.IN Birth rate, crude (per 1,000 people)
SP.DYN.CDRT.IN Death rate, crude (per 1,000 people)
SP.DYN.CEBN.Q1 Mean number of children ever born to women aged 40-49: Q1 (lowest)
SP.DYN.CEBN.Q2 Mean number of children ever born to women aged 40-49: Q2
SP.DYN.CEBN.Q3 Mean number of children ever born to women aged 40-49: Q3
SP.DYN.CEBN.Q4 Mean number of children ever born to women aged 40-49: Q4
SP.DYN.CEBN.Q5 Mean number of children ever born to women aged 40-49: Q5 (highest)
SP.DYN.CONM.Q1.ZS Current use of contraception (modern method) (% of married women): Q1 (lowest)
SP.DYN.CONM.Q2.ZS Current use of contraception (modern method) (% of married women): Q2
SP.DYN.CONM.Q3.ZS Current use of contraception (modern method) (% of married women): Q3
SP.DYN.CONM.Q4.ZS Current use of contraception (modern method) (% of married women): Q4
SP.DYN.CONM.Q5.ZS Current use of contraception (modern method) (% of married women): Q5 (highest)
SP.DYN.CONM.ZS Contraceptive prevalence, modern methods (% of women ages 15-49)
SP.DYN.CONU.Q1.ZS Current use of contraception (any method) (% of married women): Q1 (lowest)
SP.DYN.CONU.Q2.ZS Current use of contraception (any method) (% of married women): Q2
SP.DYN.CONU.Q3.ZS Current use of contraception (any method) (% of married women): Q3
SP.DYN.CONU.Q4.ZS Current use of contraception (any method) (% of married women): Q4
SP.DYN.CONU.Q5.ZS Current use of contraception (any method) (% of married women): Q5 (highest)
SP.DYN.CONU.ZS Contraceptive prevalence, any methods (% of women ages 15-49)
SP.DYN.IMRT.FE.IN Mortality rate, infant, female (per 1,000 live births)
SP.DYN.IMRT.IN Mortality rate, infant (per 1,000 live births)
SP.DYN.IMRT.MA.IN Mortality rate, infant, male (per 1,000 live births)
SP.DYN.IMRT.Q1 Infant mortality rate (per 1,000 live births): Q1 (lowest)
SP.DYN.IMRT.Q2 Infant mortality rate (per 1,000 live births): Q2
SP.DYN.IMRT.Q3 Infant mortality rate (per 1,000 live births): Q3
SP.DYN.IMRT.Q4 Infant mortality rate (per 1,000 live births): Q4
SP.DYN.IMRT.Q5 Infant mortality rate (per 1,000 live births): Q5 (highest)
SP.DYN.LE00.FE.IN Life expectancy at birth, female (years)
SP.DYN.LE00.IN Life expectancy at birth, total (years)
SP.DYN.LE00.MA.IN Life expectancy at birth, male (years)
SP.DYN.TFRT.IN Fertility rate, total (births per woman)
SP.DYN.TFRT.Q1 Total fertility rate (TFR) (births per woman): Q1 (lowest)
SP.DYN.TFRT.Q2 Total fertility rate (TFR) (births per woman): Q2
SP.DYN.TFRT.Q3 Total fertility rate (TFR) (births per woman): Q3
SP.DYN.TFRT.Q4 Total fertility rate (TFR) (births per woman): Q4
SP.DYN.TFRT.Q5 Total fertility rate (TFR) (births per woman): Q5 (highest)
SP.DYN.TO65.FE.ZS Survival to age 65, female (% of cohort)
SP.DYN.TO65.MA.ZS Survival to age 65, male (% of cohort)
SP.DYN.WFRT Wanted fertility rate (births per woman)
SP.DYN.WFRT.Q1 Total wanted fertility rate (births per woman): Q1 (lowest)
SP.DYN.WFRT.Q2 Total wanted fertility rate (births per woman): Q2
SP.DYN.WFRT.Q3 Total wanted fertility rate (births per woman): Q3
SP.DYN.WFRT.Q4 Total wanted fertility rate (births per woman): Q4
SP.DYN.WFRT.Q5 Total wanted fertility rate (births per woman): Q5 (highest)
SP.HOU.FEMA.ZS Female headed households (% of households with a female head)
SP.M15.2024.FE.ZS Women who were first married by age 15 (% of women ages 20-24)
SP.M18.2024.FE.ZS Women who were first married by age 18 (% of women ages 20-24)
SP.MTR.1519.Q1.ZS Teenage pregnancy and motherhood (% of women ages 15-19 who have had children or are currently pregnant): Q1 (lowest)
SP.MTR.1519.Q2.ZS Teenage pregnancy and motherhood (% of women ages 15-19 who have had children or are currently pregnant): Q2
SP.MTR.1519.Q3.ZS Teenage pregnancy and motherhood (% of women ages 15-19 who have had children or are currently pregnant): Q3
SP.MTR.1519.Q4.ZS Teenage pregnancy and motherhood (% of women ages 15-19 who have had children or are currently pregnant): Q4
SP.MTR.1519.Q5.ZS Teenage pregnancy and motherhood (% of women ages 15-19 who have had children or are currently pregnant): Q5 (highest)
SP.MTR.1519.ZS Teenage mothers (% of women ages 15-19 who have had children or are currently pregnant)
SP.POP.0004.FE.5Y Population ages 00-04, female (% of female population)
SP.POP.0004.MA.5Y Population ages 00-04, male (% of male population)
SP.POP.0014.FE.IN Population ages 0-14, female
SP.POP.0014.FE.ZS Population ages 0-14, female (% of female population)
SP.POP.0014.MA.IN Population ages 0-14, male
SP.POP.0014.MA.ZS Population ages 0-14, male (% of male population)
SP.POP.0014.TO Population ages 0-14, total
SP.POP.0014.TO.ZS Population ages 0-14 (% of total population)
SP.POP.0509.FE.5Y Population ages 05-09, female (% of female population)
SP.POP.0509.MA.5Y Population ages 05-09, male (% of male population)
SP.POP.1014.FE.5Y Population ages 10-14, female (% of female population)
SP.POP.1014.MA.5Y Population ages 10-14, male (% of male population)
SP.POP.1519.FE.5Y Population ages 15-19, female (% of female population)
SP.POP.1519.MA.5Y Population ages 15-19, male (% of male population)
SP.POP.1564.FE.IN Population ages 15-64, female
SP.POP.1564.FE.ZS Population ages 15-64, female (% of female population)
SP.POP.1564.MA.IN Population ages 15-64, male
SP.POP.1564.MA.ZS Population ages 15-64, male (% of male population)
SP.POP.1564.TO Population ages 15-64, total
SP.POP.1564.TO.ZS Population ages 15-64 (% of total population)
SP.POP.2024.FE.5Y Population ages 20-24, female (% of female population)
SP.POP.2024.MA.5Y Population ages 20-24, male (% of male population)
SP.POP.2529.FE.5Y Population ages 25-29, female (% of female population)
SP.POP.2529.MA.5Y Population ages 25-29, male (% of male population)
SP.POP.3034.FE.5Y Population ages 30-34, female (% of female population)
SP.POP.3034.MA.5Y Population ages 30-34, male (% of male population)
SP.POP.3539.FE.5Y Population ages 35-39, female (% of female population)
SP.POP.3539.MA.5Y Population ages 35-39, male (% of male population)
SP.POP.4044.FE.5Y Population ages 40-44, female (% of female population)
SP.POP.4044.MA.5Y Population ages 40-44, male (% of male population)
SP.POP.4549.FE.5Y Population ages 45-49, female (% of female population)
SP.POP.4549.MA.5Y Population ages 45-49, male (% of male population)
SP.POP.5054.FE.5Y Population ages 50-54, female (% of female population)
SP.POP.5054.MA.5Y Population ages 50-54, male (% of male population)
SP.POP.5559.FE.5Y Population ages 55-59, female (% of female population)
SP.POP.5559.MA.5Y Population ages 55-59, male (% of male population)
SP.POP.6064.FE.5Y Population ages 60-64, female (% of female population)
SP.POP.6064.MA.5Y Population ages 60-64, male (% of male population)
SP.POP.6569.FE.5Y Population ages 65-69, female (% of female population)
SP.POP.6569.MA.5Y Population ages 65-69, male (% of male population)
SP.POP.65UP.FE.IN Population ages 65 and above, female
SP.POP.65UP.FE.ZS Population ages 65 and above, female (% of female population)
SP.POP.65UP.MA.IN Population ages 65 and above, male
SP.POP.65UP.MA.ZS Population ages 65 and above, male (% of male population)
SP.POP.65UP.TO Population ages 65 and above, total
SP.POP.65UP.TO.ZS Population ages 65 and above (% of total population)
SP.POP.7074.FE.5Y Population ages 70-74, female (% of female population)
SP.POP.7074.MA.5Y Population ages 70-74, male (% of male population)
SP.POP.7579.FE.5Y Population ages 75-79, female (% of female population)
SP.POP.7579.MA.5Y Population ages 75-79, male (% of male population)
SP.POP.80UP.FE Population ages 80 and above, female
SP.POP.80UP.FE.5Y Population ages 80 and above, female (% of female population)
SP.POP.80UP.MA.5Y Population ages 80 and above, male (% of male population)
SP.POP.BRTH.MF Sex ratio at birth (male births per female births)
SP.POP.DPND Age dependency ratio (% of working-age population)
SP.POP.DPND.OL Age dependency ratio, old (% of working-age population)
SP.POP.DPND.YG Age dependency ratio, young (% of working-age population)
SP.POP.GROW Population growth (annual %)
SP.POP.TOTL Population, total
SP.POP.TOTL.FE.IN Population, female
SP.POP.TOTL.FE.ZS Population, female (% of total population)
SP.POP.TOTL.MA.IN Population, male
SP.POP.TOTL.MA.ZS Population, male (% of total population)
SP.REG.BRTH.FE.ZS Completeness of birth registration, female (%)
SP.REG.BRTH.MA.ZS Completeness of birth registration, male (%)
SP.REG.BRTH.RU.ZS Completeness of birth registration, rural (%)
SP.REG.BRTH.UR.ZS Completeness of birth registration, urban (%)
SP.REG.BRTH.ZS Completeness of birth registration (%)
SP.REG.DTHS.ZS Completeness of death registration with cause-of-death information (%)
SP.UWT.LMTG.Q1.ZS Unmet need for family planning (for limiting) (% of married women): Q1 (lowest)
SP.UWT.LMTG.Q2.ZS Unmet need for family planning (for limiting) (% of married women): Q2
SP.UWT.LMTG.Q3.ZS Unmet need for family planning (for limiting) (% of married women): Q3
SP.UWT.LMTG.Q4.ZS Unmet need for family planning (for limiting) (% of married women): Q4
SP.UWT.LMTG.Q5.ZS Unmet need for family planning (for limiting) (% of married women): Q5 (highest)
SP.UWT.SPCG.Q1.ZS Unmet need for family planning (for spacing) (% of married women): Q1 (lowest)
SP.UWT.SPCG.Q2.ZS Unmet need for family planning (for spacing) (% of married women): Q2
SP.UWT.SPCG.Q3.ZS Unmet need for family planning (for spacing) (% of married women): Q3
SP.UWT.SPCG.Q4.ZS Unmet need for family planning (for spacing) (% of married women): Q4
SP.UWT.SPCG.Q5.ZS Unmet need for family planning (for spacing) (% of married women): Q5 (highest)
SP.UWT.TFRT Unmet need for contraception (% of married women ages 15-49)
SP.UWT.TFRT.Q1.ZS Unmet need for family planning (total) (% of married women): Q1 (lowest)
SP.UWT.TFRT.Q2.ZS Unmet need for family planning (total) (% of married women): Q2
SP.UWT.TFRT.Q3.ZS Unmet need for family planning (total) (% of married women): Q3
SP.UWT.TFRT.Q4.ZS Unmet need for family planning (total) (% of married women): Q4
SP.UWT.TFRT.Q5.ZS Unmet need for family planning (total) (% of married women): Q5 (highest)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">indicators</span><span class="o">=</span><span class="p">{</span><span class="s2">"SP.URB.TOTL"</span><span class="p">:</span><span class="s2">"popUrban"</span><span class="p">,</span>
<span class="s2">"EN.URB.LCTY.UR.ZS"</span><span class="p">:</span><span class="s2">"popLgstCity"</span><span class="p">,</span>
<span class="s2">"EN.POP.EL5M.UR.ZS"</span><span class="p">:</span><span class="s2">"urbBelow5m"</span><span class="p">,</span>
<span class="s2">"EN.POP.SLUM.UR.ZS"</span><span class="p">:</span><span class="s2">"popSlums"</span><span class="p">,</span>
<span class="s2">"SP.POP.TOTL"</span><span class="p">:</span><span class="s2">"popTotal"</span><span class="p">,</span>
<span class="s2">"EN.POP.DNST"</span><span class="p">:</span><span class="s2">"popDens"</span><span class="p">,</span>
<span class="s2">"SH.XPD.CHEX.GD.ZS"</span><span class="p">:</span><span class="s2">"healthExp"</span>
<span class="p">}</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">wbdata</span><span class="o">.</span><span class="n">get_country</span><span class="p">()</span>
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<pre>id name
---- --------------------------------------------------------------------------------
ABW Aruba
AFG Afghanistan
AFR Africa
AGO Angola
ALB Albania
AND Andorra
ANR Andean Region
ARB Arab World
ARE United Arab Emirates
ARG Argentina
ARM Armenia
ASM American Samoa
ATG Antigua and Barbuda
AUS Australia
AUT Austria
AZE Azerbaijan
BDI Burundi
BEA East Asia & Pacific (IBRD-only countries)
BEC Europe & Central Asia (IBRD-only countries)
BEL Belgium
BEN Benin
BFA Burkina Faso
BGD Bangladesh
BGR Bulgaria
BHI IBRD countries classified as high income
BHR Bahrain
BHS Bahamas, The
BIH Bosnia and Herzegovina
BLA Latin America & the Caribbean (IBRD-only countries)
BLR Belarus
BLZ Belize
BMN Middle East & North Africa (IBRD-only countries)
BMU Bermuda
BOL Bolivia
BRA Brazil
BRB Barbados
BRN Brunei Darussalam
BSS Sub-Saharan Africa (IBRD-only countries)
BTN Bhutan
BWA Botswana
CAA Sub-Saharan Africa (IFC classification)
CAF Central African Republic
CAN Canada
CEA East Asia and the Pacific (IFC classification)
CEB Central Europe and the Baltics
CEU Europe and Central Asia (IFC classification)
CHE Switzerland
CHI Channel Islands
CHL Chile
CHN China
CIV Cote d'Ivoire
CLA Latin America and the Caribbean (IFC classification)
CME Middle East and North Africa (IFC classification)
CMR Cameroon
COD Congo, Dem. Rep.
COG Congo, Rep.
COL Colombia
COM Comoros
CPV Cabo Verde
CRI Costa Rica
CSA South Asia (IFC classification)
CSS Caribbean small states
CUB Cuba
CUW Curacao
CYM Cayman Islands
CYP Cyprus
CZE Czech Republic
DEA East Asia & Pacific (IDA-eligible countries)
DEC Europe & Central Asia (IDA-eligible countries)
DEU Germany
DFS IDA countries classified as Fragile Situations
DJI Djibouti
DLA Latin America & the Caribbean (IDA-eligible countries)
DMA Dominica
DMN Middle East & North Africa (IDA-eligible countries)
DNF IDA countries not classified as Fragile Situations
DNK Denmark
DNS IDA countries in Sub-Saharan Africa not classified as fragile situations
DOM Dominican Republic
DSA South Asia (IDA-eligible countries)
DSF IDA countries in Sub-Saharan Africa classified as fragile situations
DSS Sub-Saharan Africa (IDA-eligible countries)
DXS IDA total, excluding Sub-Saharan Africa
DZA Algeria
EAP East Asia & Pacific (excluding high income)
EAR Early-demographic dividend
EAS East Asia & Pacific
ECA Europe & Central Asia (excluding high income)
ECS Europe & Central Asia
ECU Ecuador
EGY Egypt, Arab Rep.
EMU Euro area
ERI Eritrea
ESP Spain
EST Estonia
ETH Ethiopia
EUU European Union
FCS Fragile and conflict affected situations
FIN Finland
FJI Fiji
FRA France
FRO Faroe Islands
FSM Micronesia, Fed. Sts.
FXS IDA countries classified as fragile situations, excluding Sub-Saharan Africa
GAB Gabon
GBR United Kingdom
GEO Georgia
GHA Ghana
GIB Gibraltar
GIN Guinea
GMB Gambia, The
GNB Guinea-Bissau
GNQ Equatorial Guinea
GRC Greece
GRD Grenada
GRL Greenland
GTM Guatemala
GUM Guam
GUY Guyana
HIC High income
HKG Hong Kong SAR, China
HND Honduras
HPC Heavily indebted poor countries (HIPC)
HRV Croatia
HTI Haiti
HUN Hungary
IBB IBRD, including blend
IBD IBRD only
IBT IDA & IBRD total
IDA IDA total
IDB IDA blend
IDN Indonesia
IDX IDA only
IMN Isle of Man
IND India
INX Not classified
IRL Ireland
IRN Iran, Islamic Rep.
IRQ Iraq
ISL Iceland
ISR Israel
ITA Italy
JAM Jamaica
JOR Jordan
JPN Japan
KAZ Kazakhstan
KEN Kenya
KGZ Kyrgyz Republic
KHM Cambodia
KIR Kiribati
KNA St. Kitts and Nevis
KOR Korea, Rep.
KWT Kuwait
LAC Latin America & Caribbean (excluding high income)
LAO Lao PDR
LBN Lebanon
LBR Liberia
LBY Libya
LCA St. Lucia
LCN Latin America & Caribbean
LCR Latin America and the Caribbean
LDC Least developed countries: UN classification
LIC Low income
LIE Liechtenstein
LKA Sri Lanka
LMC Lower middle income
LMY Low & middle income
LSO Lesotho
LTE Late-demographic dividend
LTU Lithuania
LUX Luxembourg
LVA Latvia
MAC Macao SAR, China
MAF St. Martin (French part)
MAR Morocco
MCA Central America
MCO Monaco
MDA Moldova
MDE Middle East (developing only)
MDG Madagascar
MDV Maldives
MEA Middle East & North Africa
MEX Mexico
MHL Marshall Islands
MIC Middle income
MKD North Macedonia
MLI Mali
MLT Malta
MMR Myanmar
MNA Middle East & North Africa (excluding high income)
MNE Montenegro
MNG Mongolia
MNP Northern Mariana Islands
MOZ Mozambique
MRT Mauritania
MUS Mauritius
MWI Malawi
MYS Malaysia
NAC North America
NAF North Africa
NAM Namibia
NCL New Caledonia
NER Niger
NGA Nigeria
NIC Nicaragua
NLD Netherlands
NLS Non-resource rich Sub-Saharan Africa countries, of which landlocked
NOR Norway
NPL Nepal
NRS Non-resource rich Sub-Saharan Africa countries
NRU Nauru
NXS IDA countries not classified as fragile situations, excluding Sub-Saharan Africa
NZL New Zealand
OED OECD members
OMN Oman
OSS Other small states
PAK Pakistan
PAN Panama
PER Peru
PHL Philippines
PLW Palau
PNG Papua New Guinea
POL Poland
PRE Pre-demographic dividend
PRI Puerto Rico
PRK Korea, Dem. People’s Rep.
PRT Portugal
PRY Paraguay
PSE West Bank and Gaza
PSS Pacific island small states
PST Post-demographic dividend
PYF French Polynesia
QAT Qatar
ROU Romania
RRS Resource rich Sub-Saharan Africa countries
RSO Resource rich Sub-Saharan Africa countries, of which oil exporters
RUS Russian Federation
RWA Rwanda
SAS South Asia
SAU Saudi Arabia
SCE Southern Cone
SDN Sudan
SEN Senegal
SGP Singapore
SLB Solomon Islands
SLE Sierra Leone
SLV El Salvador
SMR San Marino
SOM Somalia
SRB Serbia
SSA Sub-Saharan Africa (excluding high income)
SSD South Sudan
SSF Sub-Saharan Africa
SST Small states
STP Sao Tome and Principe
SUR Suriname
SVK Slovak Republic
SVN Slovenia
SWE Sweden
SWZ Eswatini
SXM Sint Maarten (Dutch part)
SXZ Sub-Saharan Africa excluding South Africa
SYC Seychelles
SYR Syrian Arab Republic
TCA Turks and Caicos Islands
TCD Chad
TEA East Asia & Pacific (IDA & IBRD countries)
TEC Europe & Central Asia (IDA & IBRD countries)
TGO Togo
THA Thailand
TJK Tajikistan
TKM Turkmenistan
TLA Latin America & the Caribbean (IDA & IBRD countries)
TLS Timor-Leste
TMN Middle East & North Africa (IDA & IBRD countries)
TON Tonga
TSA South Asia (IDA & IBRD)
TSS Sub-Saharan Africa (IDA & IBRD countries)
TTO Trinidad and Tobago
TUN Tunisia
TUR Turkey
TUV Tuvalu
TWN Taiwan, China
TZA Tanzania
UGA Uganda
UKR Ukraine
UMC Upper middle income
URY Uruguay
USA United States
UZB Uzbekistan
VCT St. Vincent and the Grenadines
VEN Venezuela, RB
VGB British Virgin Islands
VIR Virgin Islands (U.S.)
VNM Vietnam
VUT Vanuatu
WLD World
WSM Samoa
XKX Kosovo
XZN Sub-Saharan Africa excluding South Africa and Nigeria
YEM Yemen, Rep.
ZAF South Africa
ZMB Zambia
ZWE Zimbabwe</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">countries</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'RUS'</span><span class="p">,</span> <span class="s1">'DEU'</span><span class="p">,</span> <span class="s1">'UKR'</span><span class="p">,</span> <span class="s1">'FRA'</span><span class="p">]</span>
<span class="n">wddf</span><span class="o">=</span><span class="n">wbdata</span><span class="o">.</span><span class="n">get_dataframe</span><span class="p">(</span><span class="n">indicators</span><span class="p">,</span><span class="n">country</span><span class="o">=</span><span class="n">countries</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">wddf</span>
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<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>popUrban</th>
<th>popLgstCity</th>
<th>urbBelow5m</th>
<th>popSlums</th>
<th>popTotal</th>
<th>popDens</th>
<th>healthExp</th>
</tr>
<tr>
<th>country</th>
<th>date</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="5" valign="top">Germany</th>
<th>2020</th>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>2019</th>
<td>64324835.0</td>
<td>5.529423</td>
<td>NaN</td>
<td>NaN</td>
<td>83132799.0</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>2018</th>
<td>64096118.0</td>
<td>5.541869</td>
<td>NaN</td>
<td>0.01</td>
<td>82905782.0</td>
<td>237.307597</td>
<td>NaN</td>
</tr>
<tr>
<th>2017</th>
<td>63861626.0</td>
<td>5.542036</td>
<td>NaN</td>
<td>NaN</td>
<td>82657002.0</td>
<td>236.595495</td>
<td>11.246835</td>
</tr>
<tr>
<th>2016</th>
<td>63592936.0</td>
<td>5.545256</td>
<td>NaN</td>
<td>0.01</td>
<td>82348669.0</td>
<td>235.712929</td>
<td>11.130659</td>
</tr>
<tr>
<th>...</th>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th rowspan="5" valign="top">Ukraine</th>
<th>1964</th>
<td>22308888.0</td>
<td>6.008955</td>
<td>NaN</td>
<td>NaN</td>
<td>44796964.0</td>
<td>77.322800</td>
<td>NaN</td>
</tr>
<tr>
<th>1963</th>
<td>21721791.0</td>
<td>5.955798</td>
<td>NaN</td>
<td>NaN</td>
<td>44288608.0</td>
<td>76.445340</td>
<td>NaN</td>
</tr>
<tr>
<th>1962</th>
<td>21129702.0</td>
<td>5.909113</td>
<td>NaN</td>
<td>NaN</td>
<td>43752230.0</td>
<td>75.519513</td>
<td>NaN</td>
</tr>
<tr>
<th>1961</th>
<td>20541161.0</td>
<td>5.866387</td>
<td>NaN</td>
<td>NaN</td>
<td>43206345.0</td>
<td>74.577276</td>
<td>NaN</td>
</tr>
<tr>
<th>1960</th>
<td>19963644.0</td>
<td>5.825820</td>
<td>NaN</td>
<td>NaN</td>
<td>42664652.0</td>
<td>NaN</td>
<td>NaN</td>
</tr>
</tbody>
</table>
<p>244 rows × 7 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dateindex</span> <span class="o">=</span> <span class="n">wddf</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">get_level_values</span><span class="p">(</span><span class="s1">'date'</span><span class="p">)</span>
<span class="n">dateindex</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DatetimeIndex</span><span class="p">(</span><span class="n">dateindex</span><span class="p">)</span>
<span class="n">popTotal2019</span> <span class="o">=</span> <span class="n">wddf</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">dateindex</span><span class="o">.</span><span class="n">year</span> <span class="o">==</span> <span class="mi">2019</span><span class="p">][</span><span class="s1">'popTotal'</span><span class="p">]</span>
<span class="n">popTotal2019</span>
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<pre>country date
Germany 2019 83132799.0
France 2019 67059887.0
Russian Federation 2019 144373535.0
Ukraine 2019 44385155.0
Name: popTotal, dtype: float64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">rdf</span><span class="p">[</span><span class="s1">'Russia'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">other</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="n">popTotal2019</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'line'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">)</span>
<span class="n">rdf</span><span class="p">[</span><span class="s1">'Germany'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">other</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="n">popTotal2019</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'line'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">)</span>
<span class="n">rdf</span><span class="p">[</span><span class="s1">'Ukraine'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">other</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="n">popTotal2019</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'line'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Date'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Confirmed cases'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Confirmed cases per Total population'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
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<p>We will update this notebook from time to time to get more clear understanding of situation from provided statistic point of view.</p>
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How to share data with a statistician2020-09-29T14:00:00+03:002020-09-29T14:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2020-09-29:/share-data.html<p>This is a guide for anyone who needs to share data with a statistician or data scientist.</p><p>Initial article was writen by <a href="http://biostat.jhsph.edu/~jleek/">Jeff Leek</a></p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#what-you-should-deliver-to-the-statistician">What you should deliver to the statistician</a><ul>
<li><a href="#the-raw-data">The raw data</a></li>
<li><a href="#the-tidy-data-set">The tidy data set</a></li>
<li><a href="#the-code-book">The code book</a></li>
<li><a href="#how-to-code-variables">How to code variables</a></li>
<li><a href="#the-instruction-list/script">The instruction list/script</a></li>
</ul>
</li>
<li><a href="#what-you-should-expect-from-the-analyst">What you should expect from the analyst</a></li>
</ul>
<h2>Abstract</h2>
<p>This is a guide for anyone who needs to share data with a statistician or data scientist.
The target audiences I have in mind are:</p>
<ul>
<li>Collaborators who need statisticians or data scientists to analyze data for them</li>
<li>Students or postdocs in various disciplines looking for consulting advice</li>
<li>Junior statistics students whose job it is to collate/clean/wrangle data sets</li>
</ul>
<p>The goals of this guide are to provide some instruction on the best way to share data to avoid the most common pitfalls
and sources of delay in the transition from data collection to data analysis. The <a href="http://biostat.jhsph.edu/~jleek/">Leek group</a> works with a large
number of collaborators and the number one source of variation in the speed to results is the status of the data
when they arrive at the Leek group. Based on my conversations with other statisticians this is true nearly universally.</p>
<p>My strong feeling is that statisticians should be able to handle the data in whatever state they arrive. It is important
to see the raw data, understand the steps in the processing pipeline, and be able to incorporate hidden sources of
variability in one's data analysis. On the other hand, for many data types, the processing steps are well documented
and standardized. So the work of converting the data from raw form to directly analyzable form can be performed
before calling on a statistician. This can dramatically speed the turnaround time, since the statistician doesn't
have to work through all the pre-processing steps first. </p>
<h2>What you should deliver to the statistician</h2>
<p>To facilitate the most efficient and timely analysis this is the information you should pass to a statistician:</p>
<ol>
<li>The raw data.</li>
<li>A <a href="http://vita.had.co.nz/papers/tidy-data.pdf">tidy data set</a> </li>
<li>A code book describing each variable and its values in the tidy data set. </li>
<li>An explicit and exact recipe you used to go from 1 -> 2,3 </li>
</ol>
<p>Let's look at each part of the data package you will transfer. </p>
<h3>The raw data</h3>
<p>It is critical that you include the rawest form of the data that you have access to. This ensures
that data provenance can be maintained throughout the workflow. Here are some examples of the
raw form of data:</p>
<ul>
<li>The strange <a href="http://en.wikipedia.org/wiki/Binary_file">binary file</a> your measurement machine spits out</li>
<li>The un formatted Excel file with 10 worksheets the company you contracted with sent you</li>
<li>The complicated <a href="http://en.wikipedia.org/wiki/JSON">JSON</a> data you got from scraping the <a href="https://twitter.com/twitterapi">Twitter API</a></li>
<li>The hand-entered numbers you collected looking through a microscope</li>
</ul>
<p>You know the raw data are in the right format if you: </p>
<ol>
<li>Ran no software on the data</li>
<li>Did not modify any of the data values</li>
<li>You did not remove any data from the data set</li>
<li>You did not summarize the data in any way</li>
</ol>
<p>If you made any modifications of the raw data it is not the raw form of the data. Reporting modified data
as raw data is a very common way to slow down the analysis process, since the analyst will often have to do a
forensic study of your data to figure out why the raw data looks weird. (Also imagine what would happen if new data arrived?)</p>
<h3>The tidy data set</h3>
<p>The general principles of tidy data are laid out by <a href="http://had.co.nz/">Hadley Wickham</a> in <a href="http://vita.had.co.nz/papers/tidy-data.pdf">this paper</a>
and <a href="http://vimeo.com/33727555">this video</a>. While both the paper and the video describe tidy data using <a href="http://www.r-project.org/">R</a>, the principles
are more generally applicable:</p>
<ol>
<li>Each variable you measure should be in one column</li>
<li>Each different observation of that variable should be in a different row</li>
<li>There should be one table for each "kind" of variable</li>
<li>If you have multiple tables, they should include a column in the table that allows them to be joined or merged</li>
</ol>
<p>While these are the hard and fast rules, there are a number of other things that will make your data set much easier
to handle. First is to include a row at the top of each data table/spreadsheet that contains full row names.
So if you measured age at diagnosis for patients, you would head that column with the name <code>AgeAtDiagnosis</code> instead
of something like <code>ADx</code> or another abbreviation that may be hard for another person to understand. </p>
<p>Here is an example of how this would work from genomics. Suppose that for 20 people you have collected gene expression measurements with
<a href="http://en.wikipedia.org/wiki/RNA-Seq">RNA-sequencing</a>. You have also collected demographic and clinical information
about the patients including their age, treatment, and diagnosis. You would have one table/spreadsheet that contains the clinical/demographic
information. It would have four columns (patient id, age, treatment, diagnosis) and 21 rows (a row with variable names, then one row
for every patient). You would also have one spreadsheet for the summarized genomic data. Usually this type of data
is summarized at the level of the number of counts per exon. Suppose you have 100,000 exons, then you would have a
table/spreadsheet that had 21 rows (a row for gene names, and one row for each patient) and 100,001 columns (one row for patient
ids and one row for each data type). </p>
<p>If you are sharing your data with the collaborator in Excel, the tidy data should be in one Excel file per table. They
should not have multiple worksheets, no macros should be applied to the data, and no columns/cells should be highlighted.
Alternatively share the data in a <a href="http://en.wikipedia.org/wiki/Comma-separated_values">CSV</a> or <a href="http://en.wikipedia.org/wiki/Tab-separated_values">TAB-delimited</a> text file. (Beware however that reading CSV files into Excel can sometimes lead to non-reproducible handling of date and time variables.)</p>
<h3>The code book</h3>
<p>For almost any data set, the measurements you calculate will need to be described in more detail than you can or should sneak
into the spreadsheet. The code book contains this information. At minimum it should contain:</p>
<ol>
<li>Information about the variables (including units!) in the data set not contained in the tidy data </li>
<li>Information about the summary choices you made</li>
<li>Information about the experimental study design you used</li>
</ol>
<p>In our genomics example, the analyst would want to know what the unit of measurement for each
clinical/demographic variable is (age in years, treatment by name/dose, level of diagnosis and how heterogeneous). They
would also want to know how you picked the exons you used for summarizing the genomic data (UCSC/Ensembl, etc.). They
would also want to know any other information about how you did the data collection/study design. For example,
are these the first 20 patients that walked into the clinic? Are they 20 highly selected patients by some characteristic
like age? Are they randomized to treatments? </p>
<p>A common format for this document is a Word file. There should be a section called "Study design" that has a thorough
description of how you collected the data. There is a section called "Code book" that describes each variable and its
units. </p>
<h3>How to code variables</h3>
<p>When you put variables into a spreadsheet there are several main categories you will run into depending on their <a href="http://en.wikipedia.org/wiki/Statistical_data_type">data type</a>:</p>
<ol>
<li>Continuous</li>
<li>Ordinal</li>
<li>Categorical</li>
<li>Missing </li>
<li>Censored</li>
</ol>
<p>Continuous variables are anything measured on a quantitative scale that could be any fractional number. An example
would be something like weight measured in kg. <a href="http://en.wikipedia.org/wiki/Ordinal_data">Ordinal data</a> are data that have a fixed, small (< 100) number of levels but are ordered.
This could be for example survey responses where the choices are: poor, fair, good. <a href="http://en.wikipedia.org/wiki/Categorical_variable">Categorical data</a> are data where there
are multiple categories, but they aren't ordered. One example would be sex: male or female. This coding is attractive because it is self-documenting. <a href="http://en.wikipedia.org/wiki/Missing_data">Missing data</a> are data
that are unobserved and you don't know the mechanism. You should code missing values as <code>NA</code>. <a href="http://en.wikipedia.org/wiki/Censoring_(statistics)">Censored data</a> are data
where you know the missingness mechanism on some level. Common examples are a measurement being below a detection limit
or a patient being lost to follow-up. They should also be coded as <code>NA</code> when you don't have the data. But you should
also add a new column to your tidy data called, "VariableNameCensored" which should have values of <code>TRUE</code> if censored
and <code>FALSE</code> if not. In the code book you should explain why those values are missing. It is absolutely critical to report
to the analyst if there is a reason you know about that some of the data are missing. You should also not <a href="http://en.wikipedia.org/wiki/Imputation_(statistics)">impute</a>/make up/
throw away missing observations.</p>
<p>In general, try to avoid coding categorical or ordinal variables as numbers. When you enter the value for sex in the tidy
data, it should be "male" or "female". The ordinal values in the data set should be "poor", "fair", and "good" not 1, 2 ,3.
This will avoid potential mixups about which direction effects go and will help identify coding errors. </p>
<p>Always encode every piece of information about your observations using text. For example, if you are storing data in Excel and use a form of colored text or cell background formatting to indicate information about an observation ("red variable entries were observed in experiment 1.") then this information will not be exported (and will be lost!) when the data is exported as raw text. Every piece of data should be encoded as actual text that can be exported. </p>
<h3>The instruction list/script</h3>
<p>You may have heard this before, but <a href="http://www.sciencemag.org/content/334/6060/1226">reproducibility is a big deal in computational science</a>.
That means, when you submit your paper, the reviewers and the rest of the world should be able to exactly replicate
the analyses from raw data all the way to final results. If you are trying to be efficient, you will likely perform
some summarization/data analysis steps before the data can be considered tidy. </p>
<p>The ideal thing for you to do when performing summarization is to create a computer script (in <code>R</code>, <code>Python</code>, or something else)
that takes the raw data as input and produces the tidy data you are sharing as output. You can try running your script
a couple of times and see if the code produces the same output. </p>
<p>In many cases, the person who collected the data has incentive to make it tidy for a statistician to speed the process
of collaboration. They may not know how to code in a scripting language. In that case, what you should provide the statistician
is something called <a href="http://en.wikipedia.org/wiki/Pseudocode">pseudocode</a>. It should look something like:</p>
<ol>
<li>Step 1 - take the raw file, run version 3.1.2 of summarize software with parameters a=1, b=2, c=3</li>
<li>Step 2 - run the software separately for each sample</li>
<li>Step 3 - take column three of outputfile.out for each sample and that is the corresponding
row in the output data set</li>
</ol>
<p>You should also include information about which system (Mac/Windows/Linux) you used the software on and whether you
tried it more than once to confirm it gave the same results. Ideally, you will run this by a fellow student/labmate
to confirm that they can obtain the same output file you did. </p>
<h2>What you should expect from the analyst</h2>
<p>When you turn over a properly tidied data set it dramatically decreases the workload on the statistician. So hopefully
they will get back to you much sooner. But most careful statisticians will check your recipe, ask questions about
steps you performed, and try to confirm that they can obtain the same tidy data that you did with, at minimum, spot
checks.</p>
<p>You should then expect from the statistician:</p>
<ol>
<li>An analysis script that performs each of the analyses (not just instructions)</li>
<li>The exact computer code they used to run the analysis</li>
<li>All output files/figures they generated. </li>
</ol>
<p>This is the information you will use in the supplement to establish reproducibility and precision of your results. Each
of the steps in the analysis should be clearly explained and you should ask questions when you don't understand
what the analyst did. It is the responsibility of both the statistician and the scientist to understand the statistical
analysis. You may not be able to perform the exact analyses without the statistician's code, but you should be able
to explain why the statistician performed each step to a labmate/your principal investigator. </p>Publishing articles workflow2020-09-09T15:00:00+03:002020-09-09T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2020-09-09:/publishing-articles-workflow.html<p>In this article, I would like to document my blogging experience. After writing and distributing articles for a long time, I kind of set up a framework when it comes to distributing articles.</p><h2>Abstract</h2>
<p>In this article, I would like to document my blogging experience.
After writing and distributing articles for a long time,
I kind of set up a framework when it comes to distributing articles.
From idea to pushing the spread button. Of course, this structure is constantly evolving,
and over time I have tried to change it to suit my needs.</p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#idea">Idea</a></li>
<li><a href="#article-writing">Article writing</a></li>
<li><a href="#article-checking">Article checking</a></li>
<li><a href="#article-publishing">Article publishing</a></li>
</ul>
<h2>Idea</h2>
<p>It's simple, the idea comes from a self-learning process. Whenever I find myself in a situation
where I don't understand certain concepts, I first try to fully understand the topic.
And when I reach a certain level of understanding, I try to describe the concept in my own words.
So that I can understand when I look at this next time.</p>
<p><em>This means that I primarily write articles for myself only.
And if my articles are useful to someone, it will be an additional bonus!</em></p>
<h2>Article writing</h2>
<p>I use <a href="https://code.visualstudio.com">VS Code</a> to write articles because i prefer to write articles with Markdown,
Jupyter notebooks and Latex, in my opinion, VS Code offers a few very useful plugins.
To get started on a new article, all I have to do is create a blank <em>.md</em> or
<em>.ipynb</em> file and start writing.</p>
<p>Latest vscode supposrt <em>.ipynb</em> files out of the box.</p>
<p>I use a VS Code extension called <a href="https://marketplace.visualstudio.com/items?itemName=yzhang.markdown-all-in-one">Markdown All in One</a>,
which adds some important things like keyboard shortcuts, table of contents, auto preview, linter,
and more when it comes to Markdown.</p>
<h2>Article checking</h2>
<p>When the article is written, I go to <a href="https://app.grammarly.com">grammarly.com</a> and paste the entire article to
check spelling and grammar errors.
On top of that, it also offers alternative words and unnecessary word removal, which can make
reading enjoyable.</p>
<p><em>In the past, I didn't care much about my articles being grammatically correct.
But over time, I realized that this is important for the reader. So, this important step has entered my workflow.</em></p>
<h2>Article publishing</h2>
<p>At the moment, everything is in place and the article is ready for publication.
A simple git push is enough to post the article in my case,
since I have hosted my <a href="https://nekrasovp.github.io">blog</a> on GitHub pages and it starts a new build every time I push to
Git.</p>
<p>I described this automation in next <a href="https://nekrasovp.github.io/pelican-github-script-automation.html">article</a>.</p>
<p>After the code is published, it will take around 20 seconds for the article to appear on the
blog and for the atom feed to update.</p>
<p>For a full-fledged repost on social networks in the future,
I plan to add automatic banner creation to the workflow.</p>How to get well-developed IT solution2020-08-29T14:00:00+03:002020-08-29T14:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2020-08-29:/business-it-solutions.html<p>In this note we try to understand how to: clarify requirements and determine the importance and urgency</p><h1>How to get well-developed IT solution</h1>
<p>You can download <a href="https://github.com/nekrasovp/nekrasovp.github.io/blob/master/content/business-it-solutions.md">raw Markdown file in blog repository on github</a>.</p>
<h2>Goal of the note</h2>
<ul>
<li>Develope better it solution</li>
<li>Сlarify requirements</li>
<li>Determine the importance and urgency</li>
</ul>
<h2>Client-developer relationship</h2>
<p>The importance of a culture of client-developer relationship</p>
<p><img alt="client-developer relationship culture" src="images/business-it-solutions-1.png"></p>
<h3>Client goals</h3>
<ul>
<li><strong>Good</strong>: <em>You need to ensure a maximum lead time of 30 minutes</em></li>
<li><strong>Good</strong>: <em>Inform the client about significant stages of the order</em></li>
<li><strong>Bad</strong>: <em>Add the field to the table</em></li>
</ul>
<h3>Developer goals</h3>
<ul>
<li><strong>Good</strong>: <em>We need to use outsource for this task</em></li>
<li><strong>Good</strong>: <em>This employee will be underutilized</em></li>
<li><strong>Bad</strong>: <em>It's not worth attracting customers this way, no one does like this</em></li>
</ul>
<h2>Developer role</h2>
<div class="highlight"><pre><span></span><code>+-----------------------------------------------+
|Company strategy, business goals, markets, etc.|
+------------------------+----------------------+
|
v
+------------------------+----------------------+
| Tasks, problems, clients |
+------------------------+----------------------+
|
v
+------------------------+----------------------+
| IT developer |
+------------------------+----------------------+
|
v
+------------------------+----------------------+
| Classes, functions, queue, dicts, etc. |
+------------------------+----------------------+
|
v
+------------------------+----------------------+
| Server, code base, ci/cd pipeline |
+-----------------------------------------------+
</code></pre></div>
<p><a href="https://en.wikipedia.org/wiki/Domain-driven_design">Domain-driven design</a></p>
<p><a href="https://github.com/heynickc/awesome-ddd">Awesome Domain-Driven Design</a></p>
<p><a href="https://books.google.ru/books?id=ccRsBgAAQBAJ&lpg=PR1&dq=eric%20evans%202015&hl=ru&pg=PP1#v=onepage&q=eric%20evans%202015&f=false">Domain-Driven Design Reference: Definitions and Pattern Summaries<br>
Eric Evans</a></p>
<p><a href="https://books.google.ru/books?id=_i6bDeoCQzsC&lpg=PP1&dq=inauthor%3A%22Robert%20C.%20Martin%22&hl=ru&pg=PP1#v=onepage&q&f=false">Clean Code: A Handbook of Agile Software Craftsmanship <br>
Robert C. Martin</a></p>
<h2>Specification example</h2>
<h3>Original assignment</h3>
<p>Briefly but succinctly state the prerequisites for creating a product.</p>
<h3>Business opportunities</h3>
<p>How solving these problems will help business.</p>
<h3>Business goals</h3>
<p>Define countable business goals.</p>
<h3>Success criteria</h3>
<p>In which the project will be considered successful</p>
<h3>Vision of the solution</h3>
<p>How it will differ from those existing on the market.</p>
<h3>Business risks</h3>
<p>Describe the potential problems and risks that are currently understood.</p>
<h3>Assumptions and dependencies</h3>
<p>Indicate what important assumptions are accepted as true.</p>
<h3>Main functions</h3>
<p>Indicate main system functions without roles.</p>
<h3>MVP</h3>
<p>Describe the minimum product, less than which there is no point in making it.</p>
<h3>Versions</h3>
<p>Describe at least 2 next versions you intend to release within the product.</p>
<h3>Limitations and exclusions</h3>
<p>Describe the deliberate limitations we are aware of.</p>
<h3>Roles</h3>
<p>Define all kind of roles involved in product development.</p>
<p>Template for roles definition:</p>
<ul>
<li>Value:</li>
<li>Attitude:</li>
<li>Interest:</li>
<li>Restrictions</li>
</ul>
<h3>Project priority</h3>
<p>Describe the project in terms of functions, quality, timing, costs and personnel.</p>
<h4>Functions</h4>
<p>All MVP functionality have to be performed.</p>
<h4>Quality</h4>
<p>All safety tests must be performed. 80% of use cases should be covered by autotests.</p>
<h4>Timing</h4>
<p>The mvp version should be released on <em>exact date</em>, maximum delay period is 3 weeks.</p>
<h4>Costs</h4>
<p>Not need to purchase of third-party software or services.</p>
<h4>Personnel</h4>
<p>Estimated team composition: 1 developer, 1 designer, 0.5 tester</p>
<h3>Use cases</h3>
<p>Describe product use cases for each involved roles.</p>
<p>One of the most difficult part, add more references!</p>
<h2>DDD Building blocks</h2>
<ol>
<li>Entity<br>
An object that is not defined by its attributes, but rather by a thread of continuity and its identity.<br>
Example: Most airlines distinguish each seat uniquely on every flight. Each seat is an entity in this context. However, Southwest Airlines, EasyJet and Ryanair do not distinguish between every seat; all seats are the same. In this context, a seat is actually a value object.</li>
<li>Value object<br>
An object that contains attributes but has no conceptual identity. They should be treated as immutable.<br>
Example: When people exchange business cards, they generally do not distinguish between each unique card; they are only concerned about the information printed on the card. In this context, business cards are value objects.</li>
<li>Aggregate<br>
A collection of objects that are bound together by a root entity, otherwise known as an aggregate root. The aggregate root guarantees the consistency of changes being made within the aggregate by forbidding external objects from holding references to its members.<br>
Example: When you drive a car, you do not have to worry about moving the wheels forward, making the engine combust with spark and fuel, etc.; you are simply driving the car. In this context, the car is an aggregate of several other objects and serves as the aggregate root to all of the other systems.</li>
<li>Domain Event<br>
A domain object that defines an event (something that happens). A domain event is an event that domain experts care about.</li>
<li>Service<br>
When an operation does not conceptually belong to any object. Following the natural contours of the problem, you can implement these operations in services. See also Service (systems architecture).</li>
<li>Repository<br>
Methods for retrieving domain objects should delegate to a specialized Repository object such that alternative storage implementations may be easily interchanged.</li>
<li>Factory<br>
Methods for creating domain objects should delegate to a specialized Factory object such that alternative implementations may be easily interchanged.</li>
</ol>
<h2>Conclusion</h2>
<p>Generated on previous steps artifacts and other software quality attributes define stack and implementation. </p>
<p><a href="https://en.wikipedia.org/wiki/FURPS">FURPS: Functionality, Usability, Reliability, Performance, Supportability</a></p>
<div class="highlight"><pre><span></span><code>+---------+ +---------------------------+
|Artifacts+---+ +-->+Stack selection |
+---------+ | +-------------------+ | +---------------------------+
+-->+System architecture+---+
+---------+ | +-------------------+ | +---------------------------+
|FURPS +---+ +-->+Classes, methods, services,|
+---------+ |graphs, integrations |
+---------------------------+
</code></pre></div>Move blog to github and automate deploying with git hooks2020-08-22T20:00:00+03:002020-08-22T20:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2020-08-22:/pelican-github-script-automation.html<p>In this post we will describe how to move your blog to github and automate git hooks tho update gh-pages branch on commits</p><p>This blog is powered by <a href="http://docs.getpelican.com/en/stable">Pelican</a> and hosted through
GitHub using <a href="https://pages.github.com/">GitHub Pages</a>. </p>
<p>In this post we will describe how to move blog to github and automate git hooks to update gh
-pages branch on commits.</p>
<p>For those of you not familiar with these technologies,
Pelican is a static site generator - meaning you can write your content in a format such as
Markdown or Jupyter notebook(ipynb) and Pelican will automatically generate the HTML files for you;
and GitHub Pages is a service provided by GitHub for hosting a
website under the <your-username>.github.io URL.</p>
<p>Previously when blog was hosted on bitbucket we solve it using two separate repositories: one for
the
website "source" files, and one for the output which was be served using bitbucket.io.</p>
<p>But i decide to move to one repository with different branches. This is how to achieve this:</p>
<p>The first step is to create two <em>branches</em>:</p>
<ul>
<li><strong>master</strong> will contain the blog's "source" files, namely - all the files such as the content
folder
which contains the actual posts, and pelicanconf.py file.</li>
<li><strong>gh-pages</strong> will contain only the contents of output.</li>
</ul>
<p>Github will use <strong>gh-pages</strong> branch for serving <a href="https://nekrasovp.github.io/">Blog pages</a></p>
<p>Then update your .gitignore file with output\ folder.</p>
<p>To automate lets create <em>pre-push</em> git hook in <code>.git/hooks/pre-push</code>:</p>
<div class="highlight"><pre><span></span><code><span class="ch">#!/bin/sh</span>
<span class="k">while</span> <span class="nb">read</span> local_ref local_sha remote_ref remote_sha
<span class="k">do</span>
<span class="k">if</span> <span class="o">[</span> <span class="s2">"</span><span class="nv">$remote_ref</span><span class="s2">"</span> <span class="o">=</span> <span class="s2">"refs/heads/master"</span> <span class="o">]</span>
<span class="k">then</span>
pelican content -o output -s publishconf.py
ghp-import -m <span class="s2">"Update with latest content"</span> output
git push --no-verify origin gh-pages
<span class="k">fi</span>
<span class="k">done</span>
<span class="nb">exit</span> <span class="m">0</span>
</code></pre></div>
<ol>
<li>The first thing the script does is iterating over the commits that are about to be pushed.
Specifically, only commits that are pushed to the <strong>master</strong> branch are of interest to us.</li>
<li>If commits are pushed to <strong>master</strong>, it executes pelican command using publishconf.py.
This will generate the production version of the blog into output.</li>
<li>The <a href="https://pypi.org/project/ghp-import/">GitHub Pages Import</a> tool is used for copying the contents of output to a branch named
<strong>gh-pages</strong> with provided commit message.</li>
<li><strong>gh-pages</strong> is pushed to the remote <strong>gh-pages</strong> branch. --no-verify skips the pre-push hook
so this script won't run again.</li>
</ol>
<p>Now, whenever I push to source to <strong>master</strong> branch i get fresh version of my static site on github
pages at the same time.</p>
<p>We can use different ways to achieve the same goal,
check this post <a href="https://musteresel.github.io/posts/2018/01/git-worktree-for-deploying.html">Using git worktree for deploying to GitHub Pages</a> about git worktree automation.</p>
<p>We can even build pages on remote servers,
check this <a href="https://github.com/nelsonjchen/gh-pages-pelican-action">GitHub Pages Pelican Build Action</a>
and <a href="https://github.com/nelsonjchen/pelican-action-demo">GitHub Pages Pelican Build Action Demo</a> for example.</p>Essay(simple paper) template2020-08-21T15:00:00+03:002020-08-21T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2020-08-21:/essay-paper-template.html<p>In this article template for essay(simple paper) is provided. There are useful markdown examples and source references.</p><h1>Essay(simple paper) template</h1>
<p>You can download <a href="https://github.com/nekrasovp/nekrasovp.github.io/blob/master/content/essay-paper-template.md">raw Markdown file in blog repository on github</a>.</p>
<h2>Contents</h2>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#example-chapter">Example Chapter</a></li>
<li><a href="#language-and-writing-style">Language and Writing Style</a><ul>
<li><a href="#some-basic-rules-of-english">Some Basic Rules of English</a></li>
<li><a href="Clear Writing">Clear Writing</a></li>
<li><a href="Titles and Headings in Initial Caps">Titles and Headings in Initial Caps</a></li>
<li><a href="Use a Spelling Checker">Use a Spelling Checker</a></li>
<li><a href="Use a Dictionary">Use a Dictionary</a></li>
<li><a href="Use a Thesaurus">Use a Thesaurus</a></li>
</ul>
</li>
<li><a href="#list-of-figures">List of Figures</a></li>
<li><a href="#bibliography">Bibliography</a></li>
</ul>
<h2>Abstract</h2>
<p>This is a placeholder for the abstract. It summarizes the whole paper to give a very
short overview. Usually, this the abstract is written when the whole paper text is finished. </p>
<h2>Example Chapter</h2>
<p>This is my text with an example <a href="Figure 1.1">Figure 1.1</a> is an example of
Command-line depiction of the <a href="https://en.wikipedia.org/wiki/Mandelbrot_set">Mandelbrot set</a>, just like the picture Brooks and
Matelski included in their article of <a href="https://math.williams.edu/files/2016/02/pme100_BOOK_mandelbrot.pdf">1978</a> on Kleinian groups.</p>
<p>Now you are able to write your own document.
Always keep in mind: it’s the content that matters, not the form.
But good typography is able to deliver the content much better than information
set with bad typography. This template allows you to focus on writing good content
while the form is done by the template definitions.</p>
<p><img alt="Mpl example Rosenbrock function" src="Figure 1.1"><br>
Figure 1.1: Example figure.</p>
<h2>Language and Writing Style</h2>
<p>This chapter is an adopted version of a single chapter of Andrews thesis
template <a href="http://ftp.iicm.edu/pub/keith/thesis">Andrews, 2011</a> in its version from 2011-12-11.</p>
<p>Using this chapter here is meant as a teaser. If you do like this chapter,
please go and download the full template to read its content: <a href="http://ftp.iicm.edu/pub/keith/thesis">Andrews,
2011</a>.</p>
<p>The classic reference for English writing style and grammar is <a href="http://www.jlakes.org/ch/web/The-elements-of-style.pdf">Strunk and
White (1999)</a>. The original text is now available for free online <a href="https://www.bartleby.com/141/">Strunk (1918)</a>,
so there is no excuse at all for writing poor English. Readers should consult
it first, then continue reading this chapter. Another good free guide is
<a href="http://stipo.larc.nasa.gov/sp7084/">McCaskill (1998)</a>.</p>
<h3>Some Basic Rules of English</h3>
<p>There are a few basic rules of English for academic writing, which are broken
regularly by my students, particularly if they are non-native speakers of
English. Here are some classic and often encountered examples:</p>
<ul>
<li>Never use I, we, or you._<br>
Write in the passive voice (third person).<br>
Bad: You can do this in two ways.<br>
Good: **There are two ways this can be done.</li>
<li>Never use he or she, his or her.<br>
Write in the passive voice (third person).<br>
Bad: The user speaks his thoughts out loud.<br>
Good: **The thoughts of the user are spoken out loud</li>
<li>Do not use slang abbreviations such as “it’s”, “doesn’t”, or “don’t”.<br>
Write the words out in full: “it is”, “does not”, and “do not”.<br>
Bad: It’s very simple to . . .<br>
Good: It is very simple to . . .</li>
<li>Do not use abbreviations such as “e. g.” or “i. e.”.<br>
Write the words out in full: “for example” and “that is”.<br>
Bad: . . . in a tree, e. g.the items. . .<br>
Good: . . . in a tree, for example the items. . .</li>
<li>Do not use slang such as “a lot of”.<br>
Bad: There are a lot of features. . .<br>
Good: There are many features. . .</li>
<li>Do not use slang such as “OK” or “big”.<br>
Bad: . . . are represented by big areas.<br>
Good: . . . are represented by large areas</li>
<li>Do not use slang such as “gets” or “got”.<br>
Use “becomes” or “obtains”, or use the passive voice (third person).<br>
Bad: The radius gets increased. . .<br>
Good: The radius is increased. . .<br>
Bad: The user gets disoriented. . .<br>
Good: The user becomes disoriented. . .</li>
<li>Never start a sentence with “But”.
Use “However,” or “Nevertheless,”. Or consider joining the sentence
to the previous sentence with a comma.<br>
Bad: But there are numerous possibilities. . .<br>
Good: However, there are numerous possibilities. . .</li>
<li>Never start a sentence with “Because”.<br>
Use “Since”, “Owing to”, or “Due to”. Or turn the two halves of the
sentence around</li>
<li>Never start a sentence with “Also”. Also should be placed in the middle
of the sentence.<br>
Bad: Also the target users are considered.<br>
Good: The target users are also considered.</li>
<li>Do not use “that” as a connecting word.
Use “which”.<br>
Bad: . . . a good solution that can be computed easily.<br>
Good: . . . a good solution which can be computed easily</li>
<li>Do not write single-sentence paragraphs.<br>
Avoid writing two-sentence paragraphs. A paragraph should contain
at least three, if not more, sentences.</li>
</ul>
<h3>Clear Writing</h3>
<p>The written and spoken language of your paper is English as appropriate
for presentation to an international audience. Please take special care to
ensure that your work is adapted to such an audience. In particular:</p>
<ul>
<li>Write in a straight-forward style, using simple sentence structure.</li>
<li>Use common and basic vocabulary. For example, use “unusual” for
“arcane”, and “specialised” for “erudite”.</li>
<li>Briefly define or explain all technical vocabulary the first time it is
mentioned, to ensure that the reader understands it.</li>
<li>Explain all acronyms and abbreviations. For example, the first time
an acronym is used, write it out in full and place the acronym in
parentheses.<br>
Bad: . . . When using the gui version, the use may. . .<br>
Good: . . . When using the Graphical User Interface (gui) version, the use
may. . .</li>
<li>Avoid local references. For example, not everyone knows the names of
all the provincial capitals of India. If local context is important to
the material, describe it fully.</li>
<li>Do not “play on words”. For example, do not use “puns”, particularly
in the title of a piece. Phrases such as “red herring” require cultural as
well as technical knowledge of English.</li>
<li>Use unambiguous formats to represent culturally localised things such
as times, dates, personal names, currencies, and even numbers. 9/11
is the 9th of November in most of the world.</li>
<li>Be careful with humour. In particular, irony and sarcasm can be hard
to detect if you are not a native speaker.</li>
<li>If you find yourself repeating the same word or phrase too often, look
in a thesaurus such as Roget (2004) and Roget (1995) for an alternative
word with the same meaning.</li>
</ul>
<p>Clear writing experts recognise that part of writing understandable documents is understanding and responding to the needs of the intended
audience. It is the writer’s job to maintain the audience’s willingness to go
on reading the document. Readers who are continually stumped by long
words or offended by a pompous tone are likely to stop reading and miss
the intended message.</p>
<ol>
<li>Capitalize the first word of the title or heading. </li>
<li>Capitalize the last word of the title or heading. </li>
<li>All other words are capitalized unless they are conjunctions (and, or, but, nor, yet, so, for),
articles (a, an, the), or prepositions (in, to, of, at, by, up, for, off, on). <ul>
<li>According to the Chicago Manual of Style and the Modern Language Association (MLA) Handbook,
no prepositions (regardless of length) are capitalized unless they are the first or last word
of the title or heading.</li>
<li>The Gregg Reference Manual: is a bit more specific: Capitalize all words of four or more letters.</li>
<li>Be sure to check to see which style manual the company is following, assuming they do follow one.
If they are using Chicago or MLA, you would not capitalize prepositions like "through" and "after."
If they use Gregg, both of those prepositions would be capitalized. There are other style manuals
that may have different rules, so be sure to find out what the preferred style is for your business.</li>
</ul>
</li>
</ol>
<p>Author: <a href="https://www.webucator.com/how-to/how-capitalize-headings-titles.cfm">Janie Sullivan</a></p>
<h3>Use a Spelling Checker</h3>
<p>In these days of high technology, spelling mistakes and typos are inexcusable.
It is very irritating for your supervisor to have to read through and correct
spelling mistake after spelling mistake which could have been caught by an
automated spelling checker. Believe me, irritating your supervisor is not a
good idea.</p>
<p>So, use a spelling checker before you hand in any version, whether it is a draft
or a final version. Since this is apparently often forgotten, and sometimes
even wilfully ignored, let me make it absolutely clear:</p>
<h3>Use a Dictionary</h3>
<p>If you are not quite sure of the meaning of a word, then use a <a href="7">dictionary</a>.</p>
<h3>Use a Thesaurus</h3>
<p>If a word has been used several times already, and using another equivalent
word might improve the readability of the text, then consult any English <a href="8">thesauri</a>.</p>
<h2>List of Figures</h2>
<h2>Bibliography</h2>Pascal's Triangle2020-08-01T15:00:00+03:002020-08-01T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2020-08-01:/pascals-triangle.html<p>In this notebook we will build and visualise Pascal's Triangle with networkx and pydot</p><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<h1 id="Pascal's-Triangle">Pascal's Triangle<a class="anchor-link" href="#Pascal's-Triangle">¶</a></h1><p>Check out this link for other implementations and thoughts:</p>
<ul>
<li><a href="https://en.wikipedia.org/wiki/Pascal%27s_triangle">Wikipdeia</a></li>
<li><a href="http://oeis.org/wiki/Pascal_triangle#Pascal.27s_triangle_and_binomial_coefficients">OEIS</a></li>
<li><a href="https://nekrasovp.github.io/number-sequences.html#Pascal'sTriangle">Number sequences</a></li>
</ul>
<p>Pascal's Triangle connects to the Binomial Theorem (originally proved by Sir Isaac Newton) and to numerous topics in probability theory. The triangular and tetrahedral number sequences may be discovered lurking in its columns.</p>
<p>We will create a functions which will help to build successive rows of Pascal's Triangle. By prepending and appending a zero element and adding vertically, a next row is obtained. For example, from [1] we get [0, 1] + [1, 0] = [1, 1]. From [1, 1] we get [0, 1, 1] + [1, 1, 0] = [1, 2, 1] and so on.</p>
<p><img src="https://upload.wikimedia.org/wikipedia/commons/0/0d/PascalTriangleAnimated2.gif" alt=""></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># simple Pascal's triangle realisation</span>
<span class="c1"># populated with Pascal's triangle rows give an infinite sequence of finite sequences</span>
<span class="c1"># this implementation support few printing styles</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="k">class</span> <span class="nc">PascalsTriangle</span><span class="p">():</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rowcount</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rowcount</span> <span class="o">=</span> <span class="n">rowcount</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_create</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Create an empty list and then append lists of 0s, each list one longer than the previous"""</span>
<span class="k">return</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">r</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">rowcount</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
<span class="k">def</span> <span class="nf">populate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Populate an uninitialized list with actual values"""</span>
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pt</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pt</span><span class="p">[</span><span class="n">r</span><span class="p">])):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pt</span><span class="p">[</span><span class="n">r</span><span class="p">][</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">factorial</span><span class="p">(</span><span class="n">r</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">factorial</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">factorial</span><span class="p">(</span><span class="n">r</span> <span class="o">-</span> <span class="n">c</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">print_left</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Prints the triangle in a left-aligned format to demonstrate data structure"""</span>
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pt</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pt</span><span class="p">[</span><span class="n">r</span><span class="p">])):</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="si">{:>4}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pt</span><span class="p">[</span><span class="n">r</span><span class="p">][</span><span class="n">c</span><span class="p">])),</span> <span class="n">end</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">print_centre</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Prints the triangle in a conventional centred format"""</span>
<span class="n">inset</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(((((</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pt</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="mi">3</span><span class="p">))</span>
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pt</span><span class="p">)):</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" "</span> <span class="o">*</span> <span class="n">inset</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span>
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pt</span><span class="p">[</span><span class="n">r</span><span class="p">])):</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="si">{:>3}</span><span class="s1"> '</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pt</span><span class="p">[</span><span class="n">r</span><span class="p">][</span><span class="n">c</span><span class="p">])),</span> <span class="n">end</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="n">inset</span> <span class="o">-=</span> <span class="mi">3</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">triangle</span> <span class="o">=</span> <span class="n">PascalsTriangle</span><span class="p">(</span><span class="mi">13</span><span class="p">)</span>
<span class="n">triangle</span><span class="o">.</span><span class="n">print_centre</span><span class="p">()</span>
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<pre> 0
0 0
0 0 0
0 0 0 0
0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">triangle</span><span class="o">.</span><span class="n">populate</span><span class="p">()</span>
<span class="n">triangle</span><span class="o">.</span><span class="n">print_centre</span><span class="p">()</span>
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<pre> 1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
1 5 10 10 5 1
1 6 15 20 15 6 1
1 7 21 35 35 21 7 1
1 8 28 56 70 56 28 8 1
1 9 36 84 126 126 84 36 9 1
1 10 45 120 210 252 210 120 45 10 1
1 11 55 165 330 462 462 330 165 55 11 1
1 12 66 220 495 792 924 792 495 220 66 12 1
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<p>Notice the triangular numbers (1, 3, 6, 10...) and tetrahedral number sequences (1, 4, 10, 20...) appear in the slanted columns. <a href="http://www.4dsolutions.net/ocn/numeracy0.html">learn more...</a></p>
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<p>Each number in Pascal's Triangle may be understood as the number of unique pathways to that position, were falling balls introduced through the top and allowed to fall left or right to the next row down. This apparatus is sometimes called a Galton Board.</p>
<p>For example, a ball could reach the 6 in the middle of the 5th row going 1,1,2,3,6 in four ways (counting left and right mirrors), or 1,1,1,3,6 in two ways. The likely pattern when many balls fall through this maze will be a bell curve.</p>
<p><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f6/Pascal%27s_Triangle_4_paths.svg/685px-Pascal%27s_Triangle_4_paths.svg.png" alt="Pascal Triangle 4 paths"></p>
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<p>Pascal's Triangle can be represented as oriented weighted graph. We will use networkx DiGraph library to build a tree structure from our triangle. Change plot size by matplotlib fig_size and plot graph using networkx builtin plotting methods in next order: draw_networkx_nodes, draw_networkx_edges, draw_networkx_labels.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># networkx Pascal's triangle realisation</span>
<span class="c1"># populated with Pascal's triangle rows give an infinite sequence of finite sequences</span>
<span class="c1"># fillin graph objects from populated triangle</span>
<span class="c1"># include plotting method with colorisation support </span>
<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="k">class</span> <span class="nc">GraphTriangle</span><span class="p">(</span><span class="n">PascalsTriangle</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rowcount</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">GraphTriangle</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">rowcount</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">populate</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_create_root_node</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_fill_graph</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_create_root_node</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="mi">0</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_fill_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Fill graph enumerating throw previously generated list respecting to weight attribute"""</span>
<span class="k">for</span> <span class="n">y</span><span class="p">,</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">:]):</span>
<span class="n">row_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>
<span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">y</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">w</span><span class="p">)</span>
<span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">y</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">y</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">x</span> <span class="o">==</span> <span class="n">row_len</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">y</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">x</span><span class="o">-</span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">y</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">y</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">y</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">y</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">x</span><span class="o">-</span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">y</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">plot_digraph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">color_node</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">"""PLot graph with pydot, set fig_size and draw nodes, edges and labels"""</span>
<span class="n">labels</span> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span><span class="nb">int</span><span class="p">(</span><span class="n">w</span><span class="p">[</span><span class="s1">'weight'</span><span class="p">])</span> <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">)}</span>
<span class="n">node_colors</span><span class="o">=</span><span class="s1">'#eecccc'</span>
<span class="k">if</span> <span class="n">color_node</span><span class="p">:</span>
<span class="n">node_colors</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">digraph</span><span class="o">.</span><span class="n">pt</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">r</span><span class="p">]</span>
<span class="n">pos</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">drawing</span><span class="o">.</span><span class="n">nx_pydot</span><span class="o">.</span><span class="n">pydot_layout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s1">'dot'</span><span class="p">)</span>
<span class="n">box_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pt</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">box_size</span> <span class="o">=</span> <span class="mi">9</span> <span class="k">if</span> <span class="n">box_size</span> <span class="o"><</span> <span class="mi">9</span> <span class="k">else</span> <span class="n">box_size</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="n">box_size</span><span class="p">,</span><span class="n">box_size</span><span class="p">))</span>
<span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_nodes</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">node_size</span><span class="o">=</span><span class="mi">2000</span><span class="p">,</span>
<span class="n">edgecolors</span><span class="o">=</span><span class="s1">'#000000'</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">node_color</span><span class="o">=</span><span class="n">node_colors</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="nb">min</span><span class="p">(</span><span class="n">node_colors</span><span class="p">),</span> <span class="n">vmax</span><span class="o">=</span><span class="nb">max</span><span class="p">(</span><span class="n">node_colors</span><span class="p">),</span>
<span class="n">cmap</span><span class="o">=</span><span class="s1">'Set3'</span><span class="p">)</span>
<span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_edges</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">edgelist</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">(),</span> <span class="n">arrows</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_labels</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">font_size</span><span class="o">=</span><span class="mi">18</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">digraph</span> <span class="o">=</span> <span class="n">GraphTriangle</span><span class="p">(</span><span class="mi">9</span><span class="p">)</span>
<span class="nb">type</span><span class="p">(</span><span class="n">digraph</span><span class="o">.</span><span class="n">G</span><span class="p">)</span>
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<pre>networkx.classes.digraph.DiGraph</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">digraph</span><span class="o">.</span><span class="n">plot_digraph</span><span class="p">()</span>
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anMzExs27oVqyMjJT8r8lcWFhZYHRWFrVu24Pjx46LXk3N2IhIehyEiM6TRaBA6aRKWhIdLdp3Mw7Ru0QKJs2YhZOJEaDQa0erIOTsRiYPDEJEZioqMhIerq6i3kDfECF9fuLu4IDoqSrQacs5OROLgrfVEZqasrAwOjz8uyIaKYrhWWooBY8ag+MoVwR9fIefsRFR/vLWeqJG6+4gNUxwGAKBd69YY7O2N5ORkwdeWc3YiEg+HISIzotPp8NHatQgdNcqgdVYkJWFSRAQ8g4PRyssL7s89J1CHvwl94QWsXbMGOp1OsDWFyg6Im1+M7EQkLg5DRGYkLS0NNpaW6Nerl0HrxK1Zg8zsbHTt2BEtmjcXqLs/9Hd3h7WFBdLT0wVbU6jsgLj5xchOROLiMERkRg4dPIggX1+D99U5u2cPitLSsHvtWrRv00ag7v6gUCgQ7OeHQ4cOCbamUNkBcfOLkZ2IxMVhiMiMnMnOhkf37gav07VTJwG6eTB3NzecOX1asPWEyg6In1/o7EQkLg5DRGZCp9Ph7Llz8HBzM3YrdeLh5oaz586hPnes1kbO2YlIfByGiMxEUVERHmnWDG3M5Jbttq1aoZm9PYqKigxeS87ZiUh8HIaIzIRKpYKLg4Ox26gXFwcHqFQqg9eRc3YiEh+HISIzUVFRATtbW2O3US92NjaoqKgweB05Zyci8XEYIjITVVVVsLK0NHYb9WLVpAm0Wq3B68g5OxGJj8MQkZmwsrJCVU2Nsduol6rqalhbWxu8jpyzE5H4OAwRmQlbW1tozOxrF01lJWwF+HpLztmJSHzmdd6ZSMacnZ1xubhYkLW27d+P/169CgAoLS9HVXU1lm7cCADo3KEDxg4fLkidy8XFcHZ2NngdIbMD0uQXKjsRiY/DEJGZcHR0xC21GjfKygy+xXxTaipOnD37p5+9v24dAMC7d29BhoHrN2/itloNR0dHg9cSMjsgfn4hsxOR+DgMEZkJpVIJTw8P5OTlYfBTTxm01t716wXqqnY5eXno7ekpyOMzhMwOiJ9fyOxEJD5eM0RkRvp4eSHn0iVjt1EnuXl56NO3r2DryTk7EYmLwxCRGQkcOhRfZWSY/GMe9Ho9UtPTERgYKNiacs5OROLiMERkRvz9/VFZU4NT588bu5UHOpmbiyqdDn5+foKtKefsRCQuDkNEZkSpVGJKWBg+3bnT2K080Ke7dmFKWBiUSuH+ipFzdiISF/+0EpmZkJAQHDlxAtdKS43dyn1dKy3FP0+cwKRJkwRfW87ZiUg8HIaIzEzLli0REhKCqFWrjN3KfUWuXInQ0FC0FOEJ83LOTkTi4TBEZIbiExKQk5+P/UePGruVP9mXkYFclQpx8fGi1ZBzdiISB4chIjNkZ2eHpJQUzEpMxM3ycmO3A+C3nZzDlyxBckoK7OzsRKsj5+xEJA4OQ0RmysfHB2PHjcPUhATcuXPHqL3U1NRganw8xr30Ery9vUWvJ+fsRCQ8DkNEZmxxYiK0CgVmLFxotP139Ho93l20CFVKJRYtXixZXTlnJyJhcRgiMmPW1tbYk5qKopISTDPCWZK7Z0WKSkqwJzUV1tbWktWWc3YiEhaHISIzZ29vj8NHjuCnX37BhIgIya6jKS0vx8TZs1Fy+zYOHzkCe3t7SereS87ZiUg4HIaIGgF7e3sc+PpruLi7w2f8eOzLyBC13r6MDDw9fjxc3N2x/8ABow4Dcs5ORMJQ1Oe7di8vL312draI7RCRoY4fP46QiRPRw8EBi2fNQrvWrQVb+1ppKSJXrkTu5ctISkmBj4+PYGsL4W52d2dnJMyYIavsRPR3CoXijF6v93rY63hmiKiR8fHxQeaJE8g4dQr9Ro/GGzExyMrJafBFxnq9Hlk5OZgcFQWPoCB0cHBA7oULJjkM+Pj4IPfCBXx37Rr6jBwpWPY3YmLgGRwMjV5vstmJqOEsjd0AEQlv/fr1CA4OxqpVq5CcnIypCxfC2sICwX5+cHdzg4ebG9q2alXr71+/eRM5eXnIzctDano6tHfuIOztt6Fs1gztHn3UpPfSuX79Oq589x3O5eRg3759gmUPefttvP3227C05F+bRI0NvyYjamRu3rwJZ2dnZGVlwcnJCQCg0+mQlpaGw4cPI/vUKZzLyUEze3u4ODjAzsYGVk2aoKq6GprKSlwuLsZttRq9PT3Rp29fBAYGws/PD0qlEoWFhRgwYAAKCgpM9pETkydPRrt27ZCQkABAuOwAEBgYiBdeeAFvvvmmMSMSUR3V9WsyDkNEjUxkZCR+/vlnbNy4sdbX6HQ6FBcXQ6VSoaKiAlqtFtbW1rC1tYWzszMcHR2hUCju+7uvvfYaOnTogHgTfOzE3WFNpVKhVS1nfwzJfurUKYwaNQoFBQWwsbERMwoRCYDDEJEMXb9+Ha6urjh79iy6dOkiSo3vvvsOffr0QX5+Ptq2bStKjYaaMGECnJycEBMTI1qNoKAgDB48GFOnThWtBhEJg8MQkQy99957qKiowJo1a0StExYWhqZNm2LJkiWi1qmPvLw8PPPMMygsLETz5s1Fq5OTk4Nhw4ahsLDQpK+dIiIOQ0Syc/XqVfTo0QMXL17EY489JmqtH3/8ET179sS3336LDh06iFqrrsaOHYvevXsjIiJC9Fovvvgi+vXrh1mzZolei4gajsMQkcy88847aNKkCZYvXy5JvRkzZuDOnTtYvXq1JPUe5Pz58wgMDERhYSGaNm0qer1vv/0Wfn5+KCwsRLNmzUSvR0QNw2GISEb+85//wMPDA/n5+WjXrp0kNX/++We4ubkhNzcXnTt3lqRmbUaOHImBAwdixowZktV8+eWX0b17d0RGRkpWk4jqh8MQkYy8+eabaNWqFRYuXChp3dmzZ6O8vBzr1q2TtO69srOz8fzzz6OgoAC2traS1VWpVPD29kZBQQFatGghWV0iqjsOQ0QyUVxcjL59+0KlUqG1gI+fqIvS0lI4Ozvj9OnTcHBwkLT2XcOGDcOIESMQFhYmee3Q0FB06tQJsbGxktcmoofj4ziIZCI2NhbvvPOO5IMQALRu3Rr/+Mc/EBcXJ3ltAPj3v/+NS5cu4bXXXjNK/ZiYGKxZswY3btwwSn0iEgbPDBGZsfz8fDz99NMoLCzEI488YpQeysvL0a1bNxw/fhwuLi6S1vb398f48eONNgwBwJQpU9C8eXMsXrzYaD0Q0f3xzBCRDCxYsAAzZsww2iAEAC1atMD06dOxYMECSetmZGTg+++/x4QJEySt+1eRkZHYsGEDSkpKjNoHETUchyEiM3XhwgVkZGSYxE7IU6dORVpaGi5evChJPb1ej+joaMyfPx9NmjSRpGZtOnXqhAkTJmDRokVG7YOIGo7DEJGZmjdvHmbNmgV7e3tjt4JmzZph1qxZmDdvniT1Dh8+jJs3b+Kll16SpN7DzJ49G59//jl++OEHY7dCRA3AYYjIDJ05cwYnT57ElClTjN3K78LCwvDNN9/g7NmzotbR6/WIiorCggULYGFhIWqtumrfvj1ef/11JCQkGLsVImoADkNEZigmJgZz5swxqWdj2dnZYc6cOaI+JBUA9u7di6qqKowaNUrUOvU1a9YsbN++HVeuXDF2K0RUTxyGiMzMN998gwsXLmDy5MnGbuVvJk+ejPPnzyMrK0uU9XU6HaKjoxEbGwul0rT++mrTpg3CwsKMts0AETWcpbEbIJIjnU6HoqIiqFQqVFRUoKqqClZWVrC1tYWzszMcHR1r/bCPiYlBdHQ0rK2tJe764WxsbBAVFYWYmBgcPny41tc1NP/OnTthZWWFoKAgMWM02MyZM9GtWzcUFBSgW7du932NIceeiMTBfYaIJKDT6ZCWloZDBw/iTHY2zuXkoLm9PVwcHGBnawsrS0tU1dRAU1GBy8XFuKVWw9PDA328vBA4dCj8/f2hVCrxr3/9C6GhocjPzzf6XVS1qa6uhouLC5KTkzFw4EAAwuTX6/Xo2bMnli9fjqFDhxo5Ze3i4+ORn5+PTZs2ARDu2BNR/fFxHEQmoKysDElJSfho7VrYWFoiyNcXHt27w8PNDW1atqz1926UlSEnLw85ly7hq4wMVNbUYEpYGL788ku89dZbmDhxooQp6i85ORmffvop9uzZg+TkZEHy9+vfH4WFhcjKyoJCoZAwTf3cvn0bjo6O2LNnD7KysgQ79iEhIWj5gN8jor/jMERkRBqNBlGRkUhKSkKAtzdCR41Cv169GvQhrtfrcTI3Fxt37MCBjAy89dZbSHj/fZO6ePqvbt26BScnJ1RWVGDowIGC5F/7xRc4dvo0Xnv9dcQnJJhsfo1Gg+HDhuHUqVMY7usrSPakXbtw5MQJhISEmHR2IlPDYYjISDIzMxE6aRI8XF0RP20a2gn4zLBrpaWIWrUKOfn5SEpJgY+Pj2BrC+Vu/p5OTlg4c6as8v9+7F1cED99uqyyE5kiDkNEEtNqtYgID8e2rVuxJDwcwwcNEq3W/qNHMSsxEWPHjcPixESTuJhazvnlnJ3IlHEYIpKQWq1GcFAQbACsjoxEqxYtRK95s7wc78THo0qpxJ7UVKPuRC3n/HLOTmTq+KBWIomo1WoMCQhAxxYtkLJokSQfhgDQ6n/1OjRvjiEBAVCr1ZLU/Ss555dzdqLGhMMQkQG0Wi2Cg4Lg1KEDVs6dK/njISwtLbEqMhKO7dvj+eBgaLVaSevLOb+csxM1NhyGiAwQER4OGwDLZ8822u3eCoUCK+bMgZVOh9kREZLWlnN+OWcnamx4zRBRA2VmZmLM6NE4vnmzZF+PPEhpeTmeHj8eX+7cKcmdRnLOL+fsROaE1wwRiUij0SB00iQsCQ83iQ9DAGjdogUSZ81CyMSJ0Gg0otaSc345ZydqrDgMETVAVGQkPFxdRb2FuiFG+PrC3cUF0VFRotaRc345ZydqrPg1GVE9lZWVweHxx5G1fbugm+oJ5VppKQaMGYPiK1dEeXyDnPPLOTuROeLXZEQiufuIDVP8MASAdq1bY7C3N5KTk0VZX8755ZydqDHjmSGietDpdHBxdsYHc+eiv7u7QWutSEpCbn4+cvPz8f2PP6Jzhw7I3btXkD6zcnIwdeFCXFapBH3iuRD5C7//Htu//hoZWVn47ocfoK2qQtdOnRDs74+3xo9HU1tbg/sUI79Qx17M4w6Id+yJzBHPDBGJIC0tDTaWlujXq5fBa8WtWYPM7Gx07dgRLZo3F6C7P/R3d4e1hQXS09MFXVeI/F989RXWbd6Mxzt1wqzXX8eCqVPh1KULEj76CENDQ1FRWWlwn2LkF+rYi3ncAfGOPVFjZmnsBojMyaGDBxHk6yvIvjJn9+xB106dAABPjRmDXysqDF7zLoVCgWA/Pxw6dAiDBw8WbF0h8gf5+2NGSAia3/MIiZDRo+HYuTOWffopNqWmYvLYsQb1KUZ+oY69mMcdEO/YEzVmPDNEVA9nsrPh0b27IGvd/UAUi7ubG86cPi3omkLk9+ze/U+D0F0jhwwBAOQVFRm0/l1C5xfq2It93AFxjj1RY8ZhiKiOdDodzp47Bw83N2O3Uicebm44e+4c6nNd4IOInf/Hn38GALQV6OJkIfPL/dgTNXYchojqqKioCI80a4Y2ZnLLcttWrdDM3h5FAp1pETP/nTt3sGTjRlhaWGB0YKAgawqZX+7Hnqix4zBEVEcqlQouDg7GbqNeXBwcoFKpBFlLzPxzly1D9oULmPPWW+jWtatg6wqVX+7Hnqix4zBEVEcVFRWwE+C2bynZ2digQqALdMXKn/DRR9iwfTsmjhyJGSEhgq4tVH65H3uixo7DEFEdVVVVwcrSvG7AtGrSBFqtVpC1xMi/6OOPseyTTzD+ueewfO5cQdcGhMsv92NP1NhxGCKqIysrK1TV1Bi7jXqpqq6GtbW1IGsJnX/x+vVI3LAB44YPx+roaEG2K/grofLL/dgTNXYchojqyNbWFhoz+9pBU1kJW4G+3hEyf+KGDVi8fj3GDhuGD+fNE22nZKHyy/3YEzV25nXel8iInJ2dcbm4WLD1tu3fj/9evQoAKC0vR1V1NZZu3AgA6NyhA8YOH25wjcvFxXB2djZ4HUC4/Bu3b8eijz9Gp/bt8Uy/fthx8OCf/nvbVq3gO2CAwXUA4fICbXZFAAAgAElEQVQLeeylOO6AsMeeqLHjMERUR46OjrilVuNGWZkgt1hvSk3FibNn//Sz99etAwB49+5t8Ifi9Zs3cVuthqOjo0Hr3CVU/nOXLgEAfigpQdj8+X/77969ewsyDAmZX8hjL/ZxB4Q/9kSNHYchojpSKpXw9PBATl4eBj/1lMHr7V2/XoCuapeTl4fenp6CXYsjVP418+djzX2GIKEJmV/IYy/2cQeEP/ZEjR2vGSKqhz5eXsj535kNU5ebl4c+ffsKuqac88s5O1Fjx2GIqB4Chw7FVxkZJv+YA71ej9T0dAQKtJvzXXLOL+fsRI0dhyGievD390dlTQ1OnT9v7FYe6GRuLqp0Ovj5+Qm6rpzzyzk7UWPHYYioHpRKJaaEheHTnTuN3coDfbprF6aEhQl+y7qc88s5O1Fjp6jPKV8vLy99dna2iO0Qmb6ysjI4PP44srZvRzuBnrAupGulpRgwZgyKr1xBSxEeLCrn/HLOTmSOFArFGb1e7/Ww1/GfDkT11LJlS4SEhCBq1Spjt3JfkStXIjQ0VLQPQznnl3N2osaMwxBRA8QnJCAnPx/7jx41dit/si8jA7kqFeLi40WtI+f8cs5O1FhxGCJqADs7OySlpGBWYiJulpcbux0Av+1mHL5kCZJTUmBnZydqLTnnl3N2osaKwxBRA/n4+GDsuHGYmpCAO3fuGLWXmpoaTI2Px7iXXoK3t7ckNeWcX87ZiRojDkNEBlicmAitQoEZCxcabf8ZvV6PdxctQpVSiUWLF0taW8755ZydqLHhMERkAGtra+xJTUVRSQmmGeEswd2zAkUlJdiTmgpra2tJ68s5v5yzEzU2HIaIDGRvb4/DR47gp19+wYSICMmuIyktL8fE2bNRcvs2Dh85Ant7e0nq/pWc88s5O1FjwmGISAD29vY48PXXcHF3h8/48diXkSFqvX0ZGXh6/Hi4uLtj/4EDRv8wlHN+OWcnaiy46SKRwI4fP46QiRPR08kJC2fOFHRzvmulpYhcuRK5ly8jKSUFPj4+gq0tlOPHj2PShAlw7dIFy+bMkVX+u8e+h4MDFs+aJavsRKaorpsuchgiEsGvv/4KF2dn3Lp1C4EDByL0hRfQ390dCoWi3mvp9XqczM3Fp7t2YX96Op4eOBC7d+826VuoP/jgA6xauRKlpaUY7O0tSP4N27fj4LFjmDJlCuLi4002/48//ojubm5QAAh4+mlhjv3OndiXno7Ro0fj4/XrTTY7kamp6zBkKUUzRHJz5MgRtG3XDrnnz+Ozzz7D1IULYW1hgWA/P7i7ucHDzQ1tW7Wq9fev37yJnLw85OblITU9Hdo7dxD29tuYPG0aXn/9dZN+7lRlZSUSExOxY8cOODs7Izk5WZD8U8LCoPrxRwx85hmTHgbWrVuH0S++iKVLlwqWPeztt9Hfzw/p6ekmnZ3IXPHMEJHAdDod3N3dsXDhQowYMeL3n6WlpeHw4cPIPnUK53Jy0MzeHi4ODrCzsYFVkyaoqq6GprISl4uLcVutRm9PT/Tp2xeBgYHw8/P7fQAaMWIEAgMD8c477xgzZq1Wr16NI0eOYO/evb//TKj8e/fuRWRkJHJyckxyILxx4wZcXFxw5swZdO3aFYBw2bVaLZydnbFlyxY89dRTxg1KZCb4NRmRkWzbtg0rVqzAN998U+tXIzqdDsXFxVCpVKioqIBWq4W1tTVsbW3h7OwMR0fHWn/37NmzGDFiBAoLC03uLIFGo4GTkxP2798PT0/PWl/X0Px6vR4DBgzAzJkzMWbMGDGjNEh4eDjUajXWrl1b62sMOfaffPIJNm/ejLS0NLEiEDUqHIaIjKCmpgZPPPEEVq9ejSFDhohWZ9SoUXjyySfx3nvviVajIZYsWYKTJ09ix44dotU4dOgQpk+fjosXL8LCwkK0OvVVUlKC7t2748KFC+jYsaMoNaqrq+Hm5oYNGzbA19dXlBpEjQmfWk9kBJs3b0a7du0QEBAgap0FCxZgyZIluH37tqh16uP27dtYunQpFixYIGqdIUOGoE2bNti8ebOodepr4cKFmDhxomiDEAA0adIE8+bNQ3R0tNF2vSZqjDgMEQmkuroaCxYsQFxcXIPuHKqPJ554Av7+/vjggw9ErVMfq1evxuDBg9GjRw9R6ygUCsTFxWHBggWorq4WtVZd/fe//8WmTZswe/Zs0WuNHz8epaWlOHz4sOi1iOSCwxCRQJKTk+Hg4IBnnnlGknrz5s3DihUrUG4CT04vLy/HihUrMG/ePEnqDRo0CF27dkVKSook9R4mISEBkydPxqOPPip6LQsLCyxYsABRUVE8O0QkEA5DRALQarWIi4tDXFycZDVdXFwwfPhwrFixQrKatVm+fDmee+45ODs7S1bz7v/fWq1Wspr3c+XKFezYsQOzZs2SrObo0aNRVVX1pzv2iKjhOAwRCWDDhg3o2bMnBgwYIGndmJgYfPjhhygtLZW07r1u3LiBNWvWICYmRtK6Tz75JHr06IGNGzdKWvevYmNjERYWhtYC7jb9MEqlErGxsYiOjoZOp5OsLlFjxbvJiAxUUVEBJycnfPXVV+jTp4/k9d988020bNkSixYtkrw2AEREROCXX37BunXrJK995swZBAUFobCwELa2tpLXV6lU8Pb2RkFBAVq0aCFpbb1ej379+iE8PBwvvviipLWJzAXvJiOSyEcffYT+/fsbZRACgKioKGzYsAE///yz5LVLSkqwceNGREVFSV4bAPr06YN+/foZZRADfrurb/r06ZIPQsAfF5LPmzcPd+7ckbw+UWPCM0NEBlCr1XBycsKRI0fQs2dPo/UxdepUWFhYSH790PTp06HX67Fq1SpJ697rwoULCAgIQGFhoaRPcL948SL8/f1RWFiIZs2aSVb3Xnq9Hj4+PpgyZQpeeeUVo/RAZMq46SKRBBYuXIjz589jy5YtRu3j6tWr6NGjh6gb/v3VDz/8gF69euHSpUto3769JDVrM27cOHh4eEhya/tdo0ePxoABA4y+8WV6ejrefPNN5OXlwdKSj5skuheHISKR/fLLL3BycsLx48fh4uJi7HYwa9YsaDQarFmzRpJ6YWFhsLe3R2JioiT1HiQ/Px8DBw5EQUEBHnnkEdHrnTt3DsOHDzeZR6L4+fnhlVdeQWhoqLFbITIpHIaIRDZ//nx89913SE5ONnYrAIDr16/D1dUVZ8+eRZcuXUSt9d1336FPnz64fPky2rRpI2qtupo4cSIcHBwk2evoueeew5AhQ0zmYbknTpzAyy+/DJVKBSsrK2O3Q2QyOAwRiai0tBQuLi44deoUHBwcjN3O76Kion6/qFlMr732Gh577DFJ91V6mKKiIvTv3x8qlQqtWrUSrc7Jkyfx4osvQqVSwcbGRrQ69fXss88iKCgIU6ZMMXYrRCaDwxCRiObMmYObN2/i448/NnYrf1JWVoZu3bohKysLTk5OotQoKCjAk08+iYKCArRs2VKUGg31xhtvoE2bNnj//fdFqzFkyBCMHj0ab7zxhmg1GuL06dMYOXIkCgoKjLLNAJEp4jBEJJJr167Bzc0NOTk56Ny5s7Hb+ZvY2FgUFBTg888/F2X9V155BS4uLoiOjhZlfUP85z//gaenJ/Lz89G2bVvB18/MzMTEiRNx+fJlNGnSRPD1DRUcHAxfX19Mnz7d2K0QmQQOQ0QPodPpUFRUBJVKhYqKClRVVcHKygq2trZwdnaGo6MjlMq/b8U1c+ZMVFVVmdRDUu9169YtODk54ejRo+jevft9X9PQ7JcuXcKgQYNQWFiI5s2bix2lQf7xj3/AxsYGS5cuve9/b2h2vV6PQYMGISQkBJMmTRI5RcPk5uZi6NChKCwsRNOmTe/7mobmJzJHHIaI/kKn0yEtLQ2HDh7EmexsnMvJQXN7e7g4OMDO1hZWlpaoqqmBpqICl4uLcUuthqeHB/p4eSFw6FD4+/ujpKQETzzxBL799lt06NDB2JFqtXjxYpw5cwbbt28HIEx2pVKJF198EX379kV4eLiRE9bup59+whNPPIGLFy/iscceEyz7P//5T7z99tv49ttvTfoW9jFjxqBPnz6IiIgAINyxJzJHHIaI/qesrAxJSUn4aO1a2FhaIsjXFx7du8PDzQ1tHnDNy42yMuTk5SHn0iV8lZGBypoatO/QAe7u7vjwww8lTFB/v/76KxwdHfHll1/i9OnTgmQPDg7G55s2obi4uNazDqZi5syZuHXrFtzc3ATJPiUsDFu2bMG7776Ll156ScIk9Xf37N3p06exc+dOwfKHhISY3DViRA/DYYhkT6PRICoyEklJSQjw9kboqFHo16sXFApFvdfS6/U4mZuLNV98geNnziA0NBTxCQkmscfM/Wg0Gjw3YgSysrIw3NdXkOzrtmxBelYWJk+ebPLZZ777LpKTkzHCzw+vjR5tcPaNO3bg66NH8eZbbyHBhLMDv+Xv368fvrtyBc8OGiTIsU/atQtHTpxASEiISR97or/iMESylpmZidBJk+Dh6or4adPQTsAnil8rLUXUqlXIyc9HUkoKfHx8BFtbCL9nd3FB/PTp8swuw+MO/JHf3dkZCTNmyC4/0V9xGCJZ0mq1iAgPx7atW7EkPBzDBw0Srdb+o0cxKzERY8eNw+LERFhbW4tWqy6YXZ7ZAeYnqg2HIZIdtVqN4KAg2ABYHRmJVhI8SfxmeTneiY9HlVKJPampkj4o9F7MLs/sAPMTPUhdhyHeIkCNglqtxpCAAHRs0QIpixZJ8oEAAK3+V69D8+YYEhAAtVotSd17Mbs8swPMTyQUDkNk9rRaLYKDguDUoQNWzp0LCwsLSetbWlpiVWQkHNu3x/PBwdBqtZLVZnZ5ZgeYn0hIHIbI7EWEh8MGwPLZsxt0x4wQFAoFVsyZAyudDrP/t7+LFJhdntkB5icSEq8ZIrOWmZmJMaNH4/jmzZJ9RfAgpeXleHr8eHy5c6fod9swuzyzA8xPVFe8ZogaPY1Gg9BJk7AkPNwkPhAAoHWLFkicNQshEydCo9GIVofZ5ZkdYH4iMXAYIrMVFRkJD1dXUW8jbogRvr5wd3FBdFSUaDWYXZ7ZAeYnEgO/JiOzVFZWBofHH0fW9u2CbiwnlGulpRgwZgyKr1wR/BEGzC7P7ADzE9UXvyajRu3uIzZM8QMBANq1bo3B3t5ITk4WfG1ml2d2gPmJxMIzQ2R2dDodXJyd8cHcuejv7t7gdVYkJSE3Px+5+fn4/scf0blDB+Tu3StYn1k5OZi6cCEuq1SCPfVbqOwF332HJRs3Ijc/HyXXr6Ompgad2rfHYG9vvDNhAtq3aWNQn8wubHZA3u97ooaq65khSymaIRJSWloabCwt0a9XL4PWiVuzBi0feQS9XFzwy+3bAnX3h/7u7rC2sEB6ejoGDx4syJpCZf/p2jX8fOMGRvj64rF27WBhYYG8wkJ8tns3dh8+jH9t3oy2rVo1eH1mFzY7IO/3PZHYOAyR2Tl08CCCfH0N3lvl7J496NqpEwDgqTFj8GtFhRDt/U6hUCDYzw+HDh0S7ENBqOzP9OuHZ/r1+9vPn+zdG6GzZ2PL3r2YOnFig9dndmGzA/J+3xOJjecwyeycyc6GR/fuBq9z9wNBTO5ubjhz+rRg6wmVvTad27cHAJQLcMaA2YXLDsj7fU8kNg5DZFZ0Oh3OnjsHDzc3Y7dSJx5ubjh77hzqc21ebcTIXqnVorS8HD/+/DPSs7Lw7vvvAwACvL0NXpvZhckOyPt9TyQFfk1GZqWoqAiPNGuGNmZy227bVq3QzN4eRUVFcHJyMmgtMbJ/vmcPIpYs+f1//99jj+HjuDg86elp8NrMLkx2QN7veyIpcBgis6JSqeDi4GDsNurFxcEBKpXK4A8FMbIPGzQI3bp2xa8VFTh/+TIO/utfuFFWJtj6zG54dkDe73siKXAYIrNSUVEBO1tbY7dRL3Y2NqgQ4CJVMbJ3fPRRdHz0UQDA8EGDEOTnB/8JE1Cp1WJGSIjB6zO7MBcny/l9TyQFXjNEZqWqqgpWluY1w1s1aQKtVmvwOlJk79GtG3q6uOCTHTsEWY/ZDc8OyPt9TyQFDkNkVqysrFBVU2PsNuqlqroa1tbWBq8jVfZKrRblv/wiyFrMbnh2QN7veyIpcBgis2JrawuNmZ1611RWwlaArziEzP7zjRv3/Xlmdjbyiorg1bOnIHWYXZivtuT8vieSgnmddyXZc3Z2xuXiYkHW2rZ/P/579SoAoLS8HFXV1Vi6cSMAoHOHDhg7fLggdS4XF8PZ2dngdYTM/t6iRSi5cQMD+/ZF5/btUVlVhdy8POw6fBj2dnaImz5dkDrMbnh2QN7veyIpcBgis+Lo6IhbajVulJUZfJvxptRUnDh79k8/e3/dOgCAd+/egnwoXL95E7fVajg6Ohq8lpDZXwgMxNZ9+7DtwAGUlpVBoVCgc/v2mPTCC3hnwgR0+t8GhIZgdmGyA/J+3xNJgcMQmRWlUglPDw/k5OVh8FNPGbTW3vXrBeqqdjl5eejt6WnwIxQAYbOPDAjAyIAAg3t6EGYXJjsg7/c9kRR4zRCZnT5eXsi5dMnYbdRJbl4e+vTtK9h6zC7P7ADzE4mJwxCZncChQ/FVRobJb/Wv1+uRmp6OwMBAwdZkdnlmB5ifSEwchsjs+Pv7o7KmBqfOnzd2Kw90MjcXVTod/Pz8BFuT2eWZHWB+IjFxGCKzo1QqMSUsDJ/u3GnsVh7o0127MCUsDEqlcH/MmF2e2QHmJxKToj6nXL28vPTZ2dkitkNUN2VlZXB4/HFkbd+Odq1bG7udv7lWWooBY8ag+MoVtBT44ZrMLs/sAPMT1ZdCoTij1+u9HvY6ju5kllq2bImQkBBErVpl7FbuK3LlSoSGhorygcDs8swOMD+RWDgMkdmKT0hATn4+9h89auxW/mRfRgZyVSrExceLVoPZ5ZkdYH4iMXAYIrNlZ2eHpJQUzEpMxM3ycmO3A+C3HX3DlyxBckoK7OzsRKvD7PLMDjA/kRg4DJFZ8/Hxwdhx4zA1IQF37twxai81NTWYGh+PcS+9BG9vb9HrMbs8swPMTyQ0XkBNZk+r1WL4sGHo0Lw5VkVGGmXXW71ej2kJCbh66xb2Hzgg2dO6mV2e2QHmJ6oLXkBNsmFtbY09qakoKinBNCP8S/nuv4yLSkqwJzVV0g8EZpdndoD5iYTEYYgaBXt7exw+cgQ//fILJkRESHYtRWl5OSbOno2S27dx+MgR2NvbS1L3Xswuz+wA8xMJhcMQNRr29vY48PXXcHF3h8/48diXkSFqvX0ZGXh6/Hi4uLtj/4EDRv1AYHZ5ZgeYn0gIvGaIGqXjx4/j1VdeQY/HH8eyOXME3aDuWmkpIleuRO7ly0hKSYGPj49gawvh+PHjCJk4EW5du2JJRIQss/d0csLCmTMFzz53+XKcLygwyezAb/knTZgA1y5dZPe+J7qful4zxGGIGq1XX30VV4qL8e2332KwtzdCX3gB/d3dG3ShqV6vx8ncXGzYvh0Hjx3DW1OmID4+3mRvI87IyMDo0aOhq6nBYB8fQbJ/umsX9qWl4YUXXsCGjRtNNrtarYZzt25Qq9UIHDhQsOxfHz0KJycn/Pubb0w2OwAsX74c6z76CNevXxfsff/Jzp3Yn56O0NBQLFm61KTzE92rrsOQpRTNEEnt8uXLOHjwIAoKCqDX65GcnIypCxfC2sICwX5+cHdzg4ebG9q2alXrGtdv3kROXh5y8/KQmp4O7Z07CHv7bVy5dg1PPvmkSX8gJCQkYPHixRg1apSg2b0DArB//36Tzn7gwAF06twZBw8eREpKimDZl6xZg759++KHH36As7OzhInqTqPRYNmyZfjqq6/g4OAg6LFv9uijaNa8uUkfe6KG4pkhapTGjx+PJ554AnPnzv39ZzqdDmlpaTh8+DCyT53CuZwcNLO3h4uDA+xsbGDVpAmqqquhqazE5eJi3Far0dvTE3369kVgYCD8/PygVCpx8OBBvPvuu7hw4QIsLCyMmPL+jh49itdffx15eXlo0qQJAOGyV1dXw9XVFZ9++imeeeYZIyf9uzt37qBnz55YsWIFAgMDAQiXHfhtyLx06RK++OILY8as1bJly3DixAns2rXr958Jlf+///0v3N3dkZeXh0cffdSIKYnqjl+TkWxdvHgRgwcPRmFh4QMv7tTpdCguLoZKpUJFRQW0Wi2sra1ha2sLZ2dnODo63verBb1ej6effhpTpkzByy+/LGaUetPr9Rg4cCDeeOMNvPrqq7W+rqHZAeCzzz7Dxo0b8a9//csoe9s8yKZNm/Dxxx/j2LFjtfZmSPbbt2/DyckJ6enp6NGjh5hR6k2tVsPJyQlHjhxBz549a32dIfmnTZsGpVKJFStWiBWDSFAchki2Ro0ahaeeegozZ84UrUZGRgbeeOMN5OXlwdLSdL5tPnz4MKZNm4aLFy+Kdtbqzp076NGjBz744AMEBASIUqMhqqur4ebmho0bN2LQoEGi1Vm6dCmysrKwY8cO0Wo0xMKFC3H+/Hls2bJFtBolJSXo0aMHzp8/j44dO4pWh0go3HSRZOns2bP45ptvMGXKFFHr+Pr6onPnzvjss89ErVMfer0eUVFRmD9/vqhf31lYWGD+/PmIiopCff4xJbbPPvsMXbp0EXUQAoCwsDD8+9//xrlz50StUx+//PILli9fjvnz54tap3379ggNDUVCQoKodYikxmGIGpWYmBjMnTtXkos84+LiEBsbi6qqKtFr1cW+fftQWVmJF198UfRaY8aMgUajwf79+0WvVRdarRaxsbGIi4sTvZadnR3mzJmDmJgY0WvV1YoVKzB8+HC4uLiIXis8PBzbtm3Dd999J3otIqlwGKJGIysrC+fPn8fkyZMlqeft7Q03Nzd88sknktR7EJ1Oh+joaMTGxv5+sa+YlEolYmNjER0dDZ1OJ3q9h/nkk0/Qo0cPPPXUU5LUmzx5MnJzc3Hy5ElJ6j1IaWkpPvjgA8mGs7Zt22LKlCmSDJ5EUuEwRI1GTEwMoqKiJH1GUmxsLBISElBRUSFZzfvZtWsXLC0tERwcLFnN559/HkqlErt375as5v1UVFTg/fffR2xsrGQ1bWxsEBkZaRJnh5YuXYpRo0bBwcFBspozZ85EamoqCgsLJatJJCYOQ9QoHDt2DEVFRQgJCZG0bt++feHl5YWPP/5Y0rr3unPnDubNm4e4uDhJ7+5SKBSIi4vDvHnzJH9I6L3WrVv3+3GQUkhICAoKCpCZmSlp3Xtdu3YN69evR1RUlKR1W7ZsiWnTpmHBggWS1iUSC4chMnt3LxyOiYn5fV8dKcXGxmLx4sX49ddfJa8NAFu3bsUjjzyCoUOHSl772WefRbNmzbBt2zbJawO/3U6+ePFio3woW1lZ/X420lgXki9atAjjx4/H//3f/0lee9q0aTh06BAuXbokeW0ioXEYIrP3z3/+E9euXTPanj+9evXCwIED8eGHH0peu6amBgsWLEB8fLxR9vxRKBSIj4/H/PnzUVNTI3n9Dz/8EIMGDUKvXr0krw0Ar7zyCkpKSpCWliZ57Z9++gnJycl/2lhUSs2bN8d7770n+h1sRFLgMERmTa/XIzo6GvPnzzfqfj/z58/HsmXLcOvWLUnrfv755+jYsSP8/PwkrXsvPz8/PPbYY9i0aZOkdW/duiXJ7eQPYmlpifnz5yM6Olrys0Pvv/8+QkND0aFDB0nr3uvtt99GZmYmcnNzjdYDkRA4DJFZO3DgAH799VeMGTPGqH24ubkhMDAQq1atkqxmVVWVZLeTP8jda4cWLFgg6TYDK1euxNChQ+Hq6ipZzfsZO3Ysbt++ja+//lqymt9//z22bNmCiIgIyWreT9OmTREREWESF5ITGYLDEJmtu2eFpLqd/GHmzZuHVatWoaysTJJ6n376KVxcXODj4yNJvQd5+umn4ezsjKSkJEnq3bx5E6tXr8a8efMkqfcg924zINXZofj4eLz11lto27atJPUe5K233sKZM2dw+vRpY7dC1GDG/wQhaqDdu3dDoVDg+eefN3YrAAAnJyc8//zzWLZsmei1KisrER8fL+nt5A8TFxeH+Ph4VFZWil5r2bJlGDlyJBwdHUWvVRcjR46EXq/Hnj17RK9VWFiI3bt3i/q4mfq4u81AdHS0sVshajA+m4zM0p07d+Du7o7ExEQMGzbM2O387vvvv0fv3r2Rn58v6r/aV61ahfT0dKSmpopWoyGCgoIwePBgTJ06VbQa169fh6urK86dO2eUu6hqs3//fsyePRu5ubminqmcMGECnJycTOqrqaqqKri4uGDTpk3w9vY2djtEv+OzyahR2759O5o1a4Znn33W2K38SZcuXTBu3DgkJiaKVuPXX3/FokWLTOqs0F2xsbFYtGgRNBqNaDUWL16Ml156yaQGIQAYNmwYmjZtiu3bt4tWIy8vDwcPHsT06dNFq9EQVlZWiI6O5tkhMls8M0Rmp6amBj169MCaNWswePBgY7fzNz/++CN69uyJb7/9VpQ7fRITE3H69Gl8+eWXgq8thNGjR6N///6YNWuW4GtfvXoVPXr0wMWLF/HYY48Jvr6hjhw5gnfeeQcXL14U5e7GsWPHwtPTE7NnzxZ8bUPV1NTAzc0NH3/8sVHvbiS6V13PDHEYIqPR6XQoKiqCSqVCRUUFqqqqYGVlBVtbWzg7O8PR0fG+XzckJycjOTkZGRkZRtlbpy7effdd1NTUYPXq1ff97w3NfuvWLTg5OeHo0aPo3r272DEa5Ntvv4Wfnx8KCwvRrFmz+76mofnfeecdWFlZSXJdVkPo9XoMGjQIoaGhmDhx4n1f09Ds58+fx5AhQ1BUVISmTZuKHaVBvvjiC6xduxbHjx+v9c9mQ/MTNQSHITI5Op0OaWlpOHTwIM5kZ+NcTg6a24Ea8psAACAASURBVNvDxcEBdra2sLK0RFVNDTQVFbhcXIxbajU8PTzQx8sLgUOHwt/fHzU1NXB1dUVKSgqefvppY0eq1bVr1+Dm5oacnBx07txZkOxKpRJxcXG4fPmy5Hv61NfLL78MNze33x8TIUT+H374AZ6ensjLy0O7du2MnLB2x44dw6RJk3D58mU0adJEsGP//PPP45lnnsGMGTOMHbFWd+7cQa9evbB06dLfv8IWKj9RQ3AYIpNRVlaGpKQkfLR2LWwsLRHk6wuP7t3h4eaGNi1b1vp7N8rKkJOXh5xLl/BVRgYqa2rg6emJG6WlSE9PlzBBw8yZMwdXr15Fr169BMk+KSQEy5Ytw8mTJ9GtWzcJk9SfSqWCt7c3Tp8+jV27dgmSv03bthgwYABWrFghYZKGGTJkCJ599lno9XpBsj8XFIStW7eiqKgItra2Eiapvx07dmDx4sU4dOgQkpOTBck/JSwMISEhaPmA3yO6Hw5DZHQajQZRkZFISkpCgLc3QkeNQr9evRr01ZZer8fJ3Fys/eILHMvOxmuvvYb4hATY2dmJ0LnhNBoN3ps5E0lJSRjh54fXRo82OPv6bdtw+PhxvPnmmyadHfgt/1NPPonCwkIMGzRIkGO/5osvcPzMGYSGhpp0fo1Gg8mvv45du3bhOX9/QbKv27IF6VlZmDx5sklnB357XpyzszPUt29j6MCBguRP2rULR06cQEhIiMnnJ9PCYYiMKjMzE6GTJsHD1RXx06ahXevWgq19rbQUUatWISc/H0kpKSax6eC95Jwd+CO/u7MzEmbMkFV+Hvvf8vfs1g0L331XdvnJ9HAYIqPQarWICA/Htq1bsSQ8HMMHDRKt1v6jRzErMRFjx43D4sREWFtbi1arLuScHZB3fjlnB5ifTBeHIZKcWq1GcFAQbACsjoxEqxYtRK95s7wc78THo0qpxJ7UVNjb24te837knB2Qd345ZweYn0wbN10kSanVagwJCEDHFi2QsmiRJH8hAkCr/9Xr0Lw5hgQEQK1WS1L3XnLODsg7v5yzA8xPjQfPDJHBtFothj37LDq2aIGVc+caZe8fvV6PaQkJuHrrFvYfOCDZqXM5ZwfknV/O2QHmJ/PAM0MkmYjwcNgAWD57ttE2QVQoFFgxZw6sdDrMjoiQrK6cswPyzi/n7ADzU+PCM0NkkMzMTIwZPRrHN2+W7BT5g5SWl+Pp8ePx5c6dot9tIufsgLzzyzk7wPxkPnhmiESn0WgQOmkSloSHm8RfiADQukULJM6ahZCJE0V9WKicswPyzi/n7ADzU+PEYYgaLCoyEh6urqLeRtsQI3x94e7iguj/PQpCDHLODsg7v5yzA8xPjRO/JqMGKSsrg8PjjyNr+3ZBN1YTyrXSUgwYMwbFV64IvoW/nLMD8s4v5+wA85P54ddkJKq7j9gwxb8QAaBd69YY7O2N5ORkwdeWc3ZA3vnlnB1gfmq8OAxRvel0Ony0di1CR40yaJ0VSUmYFBEBz+BgtPLygvtzzwnU4W9CX3gBa9esgU6nE2xNobL/laayEh5BQWjl5YXwxYsNXk+M7ICw+ct++QXRK1eiz/PPo8NTT6Hb4MEIevNNfHPunMFr89ib7rE3xz/31PhxGKJ6S0tLg42lJfr16mXQOnFr1iAzOxtdO3ZEi+bNBeruD/3d3WFtYSHoE+6Fyv5XC9etw83ycsHWEyM7IFz+/169Ct9XX8XWffsQ5O+PJRERmBESgs6PPYafrl0zuE8ee9M99ub4554aPw5DVG+HDh5EkK+vwXuLnN2zB0Vpadi9di3at2kjUHd/UCgUCPbzw6FDh/6fvTOP6yn7//irEPVt/PSprAmVkq1S2cpWiOzGYBhLtmSLr1GIYUyJ7Ix9ScyMNPYl21QGGbTbWlQMRoZSkpZP9Tm/P8h3hspnucunznk+HvPHfLqd13l633s6937uuZezNrly/ycJSUnYcegQvKdN46xNPtwB7vzdly5FSWkprgYHY9ns2fhmyBDMGDsWW5ctw5cuLir3k9VefWtfFY97RvWHTYYYChMTHQ3r1q1Vbqe5kREHvakcK0tLxERFcdYeV+5llJaWYq6vL5y7dMEgJyfO2gW4dwe48b8eG4sb8fGYM24cGhoYoLikBPmFhRz18H+w2qtf7YGqedwzqj9sMsRQCJlMhti4OFhbWordFbmwtrREbFwcFFk1WRF8uG/75Rc8ePQIAV5enLVZBpfuAHf+lyIjAQBGDRvi63nz0NjBAUaOjrAfPhwhoaFcdBUAq7061l4ouPZnVH/YZIihEGlpafi/L76AQRVZtmookeALXV2kpaWp3BbX7n/+9RdW79yJBVOmwLhxY07a/CdcugPc+af++ScAYK6fH7Jfv8a25cuxeelS1KpZE9O/+w4/nzrFRXdZ7dWw9kLBtT+j+sMmQwyFSElJgYWJidjdUAgLExOkpKSo3A7X7vNXrYJx48aY8c03nLX5MVy5A9z5571/QrCujg5O7dyJr/r3xzdDhiB0zx783xdfwJfDlUCs9upVeyHh0p9R/WGTIYZCFBQUQEdbW+xuKIROnTooKChQuR0u3UNCQxFx4wbWL16MWjVrctJmeXDlDnDnX+f9m8W/dHGBVq1aHz6vV7cu+nfvjr+zsvDg/dUjVWG1V6/aCwmX/ozqD39HIqNaIpVKocXjAM4HWrVqoaioSOV2uHIvkkqxZMOGDw+vS3/yBACQ8X5JeW5eHtKfPIF+vXr4vy++UCmLK3eAO//G9esDQLkP7mvwfnVRTm6uyjkAq7261V5IuPRnVH+q1t7NEB0tLS1IS0rE7oZCSIuLUfv91QhV4Mq9sKgImdnZuHjtGi5eu/bJz0POnUPIuXP43tMTs8eNUymLK3eAO/8Obdog8OjRcp8nVPaZoUSicg7Aaq9utRcSLv0Z1R82GWIohLa2NvKr2KXn/MJCaHNwiZ8rdx1tbQSuWvXJ51k5Ofh21So4d+2KbwYPRpuWLVXO4sod4M5/QM+eWLRuHULOncP8yZOhq6MDAHiemYnQy5dhamwMk6ZNVc4BWO3VrfZCwqU/o/rDJkMMhTA3N0dyejonbR0+exZPMjIAvPtjIC0uxto9ewAATRs1wqgBAzjJSU5Ph7m5ucrtcOVeq2ZNDOnd+5PPHz97BgBo0aRJuT9XBq7cAe7869WtixWenvjvypXoO3Eixg4ejOKSEuw7cgTS4mKs5nCpOau9etUeqHrHPYMO2GSIoRCmpqbIzctDZna2ystsfzp5EpGxsf/6bOWOHQAAhw4dOBkUX756hTd5eTA1NVW5LS7dhYBLd4Bb/4nDh0O/Xj1sPnAA/jt2QFNTE3bt2mGXry86W1tz0l9We/WsfVU77hl0wCZDDIXQ1NSEjbU14hMT0btrV5XaOr1rF0e9qpj4xER0sLHh5BUKXLqXh3HjxngVHc1Ze1y6A9z7D3Jy4vzJy/+E1V49a1/VjnsGHbCl9QyFsbWzQ/z9+2J3Qy4SEhNha2/PWXs0uwN0+9PsDjB/RvWGTYYYCuPSrx9ORUSo/aPuCSE4GR4OFw5e/FkGze4A3f40uwPMn1G9YZMhhsI4OzujsKQEt27fFrsrlXIzIQFSmQxOHH4VQ7M7QLc/ze4A82dUb9hkiKEwmpqa8JgxA/uOHhW7K5Wy79gxeMyYAU1N7nZzmt0Buv1pdgeYP6N6o6HIJU87OzsSzeFNfoyqS3Z2NkxatMCNkJBynyQsNi+ystB55EikP3wIPY5X/9DsDtDtT7M7wPwZVQ8NDY0YQojd57ZjU2eGUujp6cHNzQ1LNm0Suyvl4rNxIyZNmsTLgEizO0C3P83uAPNnVF/YZIihNL5+fohPSsLZy5fF7sq/OBMRgYSUFPzg68tbBs3uAN3+NLsDzJ9RPWGTIYbS6OjoIDAoCAsCAvAqJ0fs7gB490RbrzVrsD8oCDrvX/PABzS7A3T70+wOMH9G9YRNhhgq4ejoiFGjR2OOnx9KS0tF7UtJSQnm+Ppi9Ndfw8HBgfc8mt0Buv1pdgeYP6P6wW6gZqhMUVERBri6olHdutjk4yPKU18JIfD080NGbi7OhoYK9rZqmt0Buv1pdgeYP6NqwG6gZghG7dq1ceLkSaQ9fw5PEc4Uy84M054/x4mTJwUdEGl2B+j2p9kdYP6M6gWbDDE4QVdXFxcvXcKz168x3ttbsHsJsnJyMGHhQjx/8wYXL12Crq6uILn/hGZ3gG5/mt0B5s+oPrDJEIMzdHV1EXruHCysrOA4ZgzORETwmncmIgLdxoyBhZUVzoaGijog0uwO0O1PszvA/BnVA3bPEIMXrl27honjx6NVs2ZYt2gRpw9oe5GVBZ+NG5GQnIzAoCA4Ojpy1jYXXLt2DW4TJqC1iQkCFizg3H3x+vW4/eCBWroD7/zHjR2LNiYm1Na+VfPmWOvtzb37hg1ISElRS3fgnf/okSNh06oV1i5cSFXtGeoJu2eIISqOjo6YMm0a0jIy0HnkSEz77jvciI9X+iWPhBDciI/HtO++g/XgwahVty4S7txRywHR0dERF3/7DRE3bqDTV19x6m47bBhSnj5VW3fgnX/Hzp2RW1rKae2nLl0K68GDUb9ZM7X1d3R0xNbt23EjIYHz/b7D0KHILipSW3cA6NKlC3Tr1kVtPT1O/Sf7+MB68GAYW1iotT+j6lJT7A4wqid5eXnYuHEjLl26BCMjI+zfvx9z/P1Ru0YNDHFygpWlJawtLWEokVTYxstXrxCfmIiExEScDA9HUWkpZsyciZ6urggJCYG2traARoqxZcsWTHRzw/fff8+p+9pt22Bvb49Hjx6hdevWAhrJz71793D58mWkpqaipKSEU3+9Jk2graOjts+SIYTA19cXa9etw+DBgzl191y0CCNGjIBMJhPQSDGCg4Ohr6+PX48cQU5ODqf+T1+9gp29vdrWnlG1YV+TMXjB398fCQkJCA4O/vCZTCZDWFgYLl68iOhbtxAXH48vdHVhYWICnTp1oFWrFqTFxcgvLERyejre5OWhg40NbO3t4eLiAicnJ2hqaqKkpARt2rTB1q1b0bt3bxEty+evv/5Cu3btcO/ePTRq1AgAd+4AEBAQgKioKPz6669ialbIiBEj0KlTJyxYsODDZ1z5Z2RkoE2bNrh79y4aN24somX5XLp0CbNnz8bdu3dRs+a7c00uaz9q1CjY2Nhg4cKFYmqWS0lJCSwtLbFz585/vTGeK/+wsDB4eHjg/v37H/5tGYzPIe/XZGwyxOCc169fo2XLlrhy5QpatWpV4XYymQzp6elISUlBQUEBioqKULt2bWhra8Pc3BympqYVPrvk0KFD2Lx5M65fvy7K800qY+bMmdDR0cGaNWsq3EYV97dv38LMzAznzp2DtbU1XxpKER8fD1dXV6SmplZ6Bq+K/7fffovCwkL8+OOPfGkoBSEEXbp0wdy5czF69OgKt1PFPTExET169EBqairq1q3Ll4pS7Nu3Dz/99BPCw8Mr3U5Zf0IInJycMH78eLi5ufGlwahmyDsZAiFE7v9sbW0Jg/E5li9fTsaPH89rRmlpKWnTpg05e/YsrzmK8ujRIyKRSMiLFy94zdm4cSMZPHgwrxnKMGjQILJp0yZeM168eEEkEgl59OgRrzmKcubMGdK2bVtSWlrKa864cePI999/z2uGohQVFZFmzZqRa9eu8Zpz9epV0rx5c1JUVMRrDqP6ACCayDG/YZMhBqdkZWURfX19kpqaynvW0aNHSYcOHYhMJuM9S14mT55MfHx8eM8pKCggRkZG5ObNm7xnycuNGzeIkZERKSgo4D1r8eLFZMqUKbznyItMJiM2Njbk2LFjvGc9ePCA6Ovrk6ysLN6z5GXbtm3ExcVFkKy+ffuS7du3C5LFqPqwyRBDFBYtWkSmTp0qSJaQf4DkoeyP1KtXrwTJ2759u2B/gOShb9++ZMeOHYJklU26Hzx4IEje5xB6Yj5lyhSyePFiQbI+R35+PmnSpAm5deuWIHk3b94UbNLNqPqwyRBDcP7++28ikUjIn3/+KVimUF9NyMM333xDVqxYIVheUVERad68Obl69apgmRVx5coVwb+++P7778m4ceMEy6uIkpISwb+yFerrWHnYsGGD4F/ZDho0iGzcuFHQTEbVhE2GGILz3//+l8yaNUvQTJlMRjp16kQOHTokaO7H3L9/nxgaGpLXr18Lmrtv3z7Ss2dPQTM/RiaTkR49epDAwEBBc1+/fk0MDQ1JYmKioLkf88svv5DOnTsL/nXtzJkzyfz58wXN/Ji8vDzSsGFDEh8fL2huXFwcadiwIXn79q2guYyqB5sMMQTlr7/+Inp6euTZs2eCZ1+8eJFYWFiQ4uJiwbPLGDlyJFm1apXgucXFxaRly5YkLCxM8OwyfvvtN2Jubi7Kv7+/vz8ZNWqU4LllFBcXE3Nzc3Lp0iXBs8U85spYvXo1+eqrr0TJHjFiBAkICBAlm1F1YJMhhqDMnDmT/Pe//xUlWyaTke7du5P9+/eLkh8fH08aNmxI8vLyRMn/6aefSJcuXUS5kVwmk5HOnTuTn3/+WfBsQt5dmWjQoAFJSEgQJT8wMJD06NFDtJv4582bJ/jV2DLKrszdu3dPlPy7d+8SQ0NDkpubK0o+o2rAJkMMwSi7f+Hvv/8WrQ+///47adGiBZFKpYJnDxkyhGzYsEHw3DJKSkpI69atSWhoqODZZ8+eJW3atCElJSWCZ5exfv16MnToUMFzi4qKSIsWLciVK1cEzy5DjPv0ylixYgX55ptvBM/9J2PHjiU//PCDqH1gqDdsMsQQjClTppBFixaJ3Q3Sp08fsnPnTkEzb926RZo0aSL6ypZff/2V2NraCnqFQiaTkQ4dOpAjR44IllkeZauZoqKiBM3dsWMH6du3r6CZ5bFw4ULBVnCW8erVK7VYzZeSkiLoCk5G1YNNhhiCkJqaqjbPPLlx4wZp2rQpKSwsFCyzX79+ZNu2bYLlVURpaSmxsrIix48fFyzz2LFjxNraWi1W8m3dupX0799fsDx1es5TZmYmkUgkJC0tTbBMHx8fMmnSJMHyKsPNzY0sWbJE7G4w1BQ2GWIIwvjx48ny5cvF7sYHBgwYQLZs2SJI1rVr10izZs3U5mm4p06dIu3atRNkclJaWkratm1LTp8+zXuWPBQWFhJjY2MSGRkpSN7mzZvJwIEDBcmSh2XLlpEJEyYIklX2BPCHDx8Kkvc5Hj58SCQSCXn58qXYXWGoIWwyxOCdxMREYmBgQHJycsTuygdiYmJIo0aNBFly26tXL7J3717ec+RFJpORjh07kuDgYN6zDh06RDp16qRWT//es2cPcXJy4j3n7du3pFGjRiQ2Npb3LHnJyckhBgYGgjxm4NtvvyUeHh685yjC9OnTyYIFC8TuBkMNYZMhBu+MGjWK+Pv7i92NTxg+fDhZu3Ytrxnh4eHEzMxM1OX85XHhwgXSqlUrXm9oLi4uJhYWFuTixYu8ZSiDVColpqamJCIigtecNWvWkC+//JLXDGVYuXIlGT16NK8ZGRkZRCKRkKdPn/KaoyhPnjwhEomEZGRkiN0VhprBJkMMXklISCANGjQgb968Ebsrn3Dnzh1e+yaTyYiDgwP56aefeGlfFWQyGXF0dCQHDhzgLSMoKIh069ZNra4KlXHw4EHi6OjIW99yc3NJ/fr1yd27d3lpXxXevHlDGjRoQG7fvs1bxpw5c8jcuXN5a18VPD09iaenp9jdYKgZ8k6GNN5tKx92dnYkOjpa7u0Z6o1MJkNaWhpSUlJQUFAAqVQKLS0taGtrw9zcHKamptDU1Cz3d4cNG4Zu3brhv//9r8C9lo8xY8agbdu2WLx4cYXbKOt//vx5zJ8/H7dv30aNGjX41FCKy5cvY/LkyUhKSkKtWrXK3UZZ9+LiYrRq1Qr79u1Djx49+FZRmNLSUrRr1w4bNmyAi4tLuduost/7+fnh/v37+Pnnn/nUUJp169YhMjISx44dq3AbZf2fPHkCKysrJCYmokGDBnxqKMXz58/RunVr3L59G0ZGRuVuo0rtGVUTDQ2NGEKI3We3Y5MhepDJZAgLC8OF8+cREx2NuPh41NXVhYWJCXS0taFVsyakJSXILyhAcno6cvPyYGNtDVs7O7j06wdnZ2doamoiJiYGgwcPRmpqKrS1tcXWKpfk5GQ4OjriwYMHqFevHgBu/DU0NNCxY0d4eXnhq6++EtmyYnr37o3Ro0djypQpALir/e7duxESEoJLly6JbFgxISEhWLt2LW7evAkNDQ3O3HNyctCyZUtERkbC3NxcbM1yyc/PR8uWLXHq1CnY2toC4K7206dPR7169bBq1SqRLSvG29sbubm52L59OwDu3BlVFzYZYnwgOzsbgYGB2L5tG+rUrInBvXrBunVrWFtawkBPr8Lfy8zORnxiIuLv38epiAgUlpTAY8YMnDt3DkOHDsXMmTMFtFAcNzc3GBsbY+7cuZz5d+/RAzdv3sTt27fVepD8448/MHr0aNy8eRO//PILJ+5Tp03D5s2bceTIEXTu3FlAG8WQyWSwsbGBt7c3nj9/ztl+n5GRgaysLOzbt09AG8X58ccfce7cOfz000+c7fejRo3C1m3bkJqaCn19fQFtFCMzMxMWFhYICwtDeHg4Z7V3c3ODXiW/x1Bf2GSIgfz8fCzx8UFgYCD6ODhg0pdfomP79tDQ0FC4LUIIbiYkYO+RIzgbEYFp06bBf9Uq6Ojo8NBzbrh//z46d+oETU1N9HV05MR/+y+/4PKtW5gyZQp8/fzU1j8/Px/WVlbIyMhA/x49OHHfHRKCC1evwt3dXe3dx44ZgwsXLmCgkxMn7vuOHcOZsDCMHTsWm7dsUVt34N3Jj5mpKUpKSuDSrRsn/juDg/Hb9euYNm2a2te+T+/eiI+Px4BevThxDzx2DJciI+Hm5qbW7ozyYZMhyrl69SomTZwI61at4Ovpifocns29yMrCkk2bEJ+UhMCgIDg6OnLWNleU+bcxMcHqBQuo8i9zb29ujpXz5lHpTvt+39bMDKvmz6fK/0PtLSzgO3cuVe6MimGTIUopKiqCt5cXDgcHY42XFwb07Mlb1tnLl7EgIACjRo/G6oAA1K5dm7cseaHZn7nT6Q7Q7U+zO+PzsMkQheTl5WHI4MGoA2Czjw8k728c5pNXOTmY7esLqaYmTpw8CV1dXd4zK4Jmf+ZOpztAtz/N7gz5kHcypL53gDIUIi8vD3379EGTevUQtGqVIIMCAEje5zWqWxd9+/RBXl6eILkfQ7M/c6fTHaDbn2Z3BvewK0PVgKKiIrj2748m9eph4+LFSt0sqCqEEHj6+SEjNxdnQ0MFvXxMsz9zp9MdoNufZneGYrArQxTh7eWFOgDWL1woyqAAABoaGtiwaBG0ZDIs9PYWNJtmf+ZOpztAtz/N7gx+YFeGqjhXr17FyBEjcO2XXwS7TFwZWTk56DZmDH49elSQFRc0+zN3Ot0Buv1pdmcoDrsyRAH5+fmYNHEi1nh5qcWgAAD69eohYMECuE2YgPz8fF6zaPZn7nS6A3T70+zO4Bc2GarCLPHxgXWrVrwuJVWGgb16wcrCAkuXLOE1h2Z/5k6nO0C3P83uDH5hX5NVUbKzs2HSogVuhIRw+nAxrniRlYXOI0ci/eFDXh5jT7M/c6fTHaDbn2Z3hvKwr8mqOWWv2FDHQQEA6uvro7eDA/bv389L+zT7M3c63QG6/Wl2Z/APuzJUBZHJZLAwN8eWxYvRycpKpbY2BAYiISkJCUlJ+POvv9C0USMknD7NST9vxMdjjr8/klNSOH2pKVf+ErvyTxb+o62NJ1evKt1uGXz4K+OuaI2j796F37ZtiLl7F9DQQMf27bFs1iy0s7CQu5/Mne33Yte+IqrimMdQHnmvDNUUojMMbgkLC0OdmjXRsX17ldv6YetW6P3f/6G9hQVev3nDQe/+RycrK9SuUQPh4eHo3bs3Z+1y6d/FxgYThg3712c1a3JzWPDhr4y7IjWOunMHg93d0cjQEAvd3QEAe0JCMGDqVJzftw+tzczkymTubL8Xu/YVURXHPAb/sMlQFeTC+fMY3KsXJ8/XiD1xAs2NjAAAXUeOxNuCApXbLENDQwNDnJxw4cIFTgcGLv2bN2mCka6uHPTqU/jwV8ZdkRovWrMGtWrVwpndu9G4fn0AwNA+fdD5q6+wZMMGHNu6Va5M5s72e7FrXxFVccxj8A+7jlcFiYmOhnXr1py0VTYo8IWVpSVioqI4bZNLfwCQFhcjj6clsVz7K+Mub43TnzxB7P37GOLs/GEyAACN69fHEGdn/H7rFv7OzJQ7l7mz/Z4raB/zGPzDJkNVDJlMhti4OFhbWordFbmwtrREbFwcFLk3rTK49j8VFoYmjo4w7t4d5n36wDsgALkcvmuIS3++ax937x4AwL6cryLs2rUDIQQJSUlyt8fc2X5fFWrPNVzXniEM7GuyKkZaWhr+74svYFBFlm4aSiT4QlcXaWlpMJPznovK4NK/Q5s2GNK7N0yaNsWbvDxciozE7pAQRMbG4vy+fdDV0VE5g0t/vmuf8f7KRyNDw09+1vj9Z89evJC7PebO9vuqUHuu4br2DGFgk6EqRkpKCixMTMTuhkJYmJggJSWFk4GBS//fgoL+9f+jBw5Em5Yt4bttG3YeOoT5kydzksOVP9+1LygsBADU1tL65GdlL6Es20ZemDvb79W99nzAZe0ZwsC+JqtiFBQUQEdbW+xuKIROnToo4OgmRb79Z48fD61atXAxMpKzNrny59tdu04dAECRVPrJz4qKiv61jbwwd7bfqwrtYx5DGNhkqIohlUqhxdESWKHQqlXrwx8UVeHbv1bNmmhoaIhXOTmctcmVP9/ujQwMAAAZL19+8rNn7z/7583F8sDc2X6vKrSPeQxhYJOhKoaWlhakJSViw4EPOwAAIABJREFUd0MhpMXFH75qUBW+/QuLivDs779hKJFw1iZX/ny727RpAwCIun37k59F37kDDQ0NWLVqpVCbzJ3t96pC+5jHEAY2GapiaGtrI7+KXX7NLyyENkeXubnyr+gMeOWOHSgpLUW/bt1UziiDK3++a2/StClsWrfGybCwf10hyXj5EifDwtDd3h4N3l9BkRfmzvZ7VaF9zGMIQ9W69siAubk5ktPTOWvv8NmzeJKRAQDIysmBtLgYa/fsAQA0bdQIowYMUDkjOT0d5ubmKrcDcOe/du9eRN+9i252djBq0AB5BQX4LTISV6OjYdu2LaaOGsVBb9/Blb+y7orUeOX8+RgyfToGTJny4d9g9+HDIDIZfpg7V+Fs5s72e1WhfcxjCAN7N1kVQyaTQaKnh6ijRzlZajpo2jRExsaW+zOHDh1wetculdp/+eoVOo4YgeycHE6eHsuVf+jly9h75AiS0tLw6vVr1NDUhImxMYb27o0ZY8eiDkeXuLn0V9Zd0Rrfun0bK7dvR8zdu9DQ0IB9+/b4btYshb8mYu5svxez9hVR1cY8hmqwd5NVUzQ1NWFjbY34xET07tpV5fZUPfA/R3xiIjrY2HA2KHDl79qzJ1x79uSkT5XBpb+y7orWuGP79jixfbui3fsE5s72ezFrXxFVbcxjCAO7Z6gKYmtnh/j798XuhlwkJCbC1t6e0zZp9mfudLoDdPvT7M4QBjYZqoK49OuHUxERav+4d0IIToaHw8XFhdN2afZn7nS6A3T70+zOEAY2GaqCODs7o7CkBLfKWQasTtxMSIBUJoOTkxOn7dLsz9zpdAfo9qfZnSEMbDJUBdHU1ITHjBnYd/So2F2plH3HjsFjxgxoanK7m9Hsz9zpdAfo9qfZnSEMbDVZFSU7OxsmLVrgRkgI6uvri92dT3iRlYXOI0ci/eFD6PHwgkWa/Zk7ne4A3f40uzOUR97VZGz6WkXR09ODm5sblmzaJHZXysVn40ZMmjSJt0GBZn/mTqc7QLc/ze4M/mGToSqMr58f4pOScPbyZbG78i/OREQgISUFP/j68ppDsz9zp9MdoNufZncGv7DJUBVGR0cHgUFBWBAQwOkLFlUhKycHXmvWYH9QEHR0dHjNotmfudPpDtDtT7M7g1/YZKiK4+joiFGjR2OOnx9KS0tF7UtJSQnm+Ppi9Ndfw8HBQZBMmv2ZO53uAN3+NLsz+IPdQF0NKCoqwgBXVzSqWxebfHxEefIpIQSefn7IyM3F2dBQQd/YTLM/c6fTHaDbn2Z3hmKwG6gponbt2jhx8iTSnj+HpwhnS2VnR2nPn+PEyZOCDwo0+zN3Ot0Buv1pdmfwA5sMVRN0dXVx8dIlPHv9GuO9vQX7Pj0rJwcTFi7E8zdvcPHSJejq6gqS+zE0+zN3Ot0Buv1pdmdwD5sMVSN0dXUReu4cLKys4DhmDM5ERPCadyYiAt3GjIGFlRXOhoaKPijQ7M/c6XQH6Pan2Z3BLeyeoWrKtWvX4DZhAtqZmcF//nxOH1L2IisLPhs3IiE5GYFBQXB0dOSsba64du0aJk6YgFbNmmHdwoVU+ZfVvo2pKVZ/+y2V7lbm5vCbN48qd+B//pbNm2ONtzdV/rSPeYzykfeeITYZqsa8ffsWbdu0QVZmJlx69MCk4cPRycpKqZsNCSG4mZCAfUeP4kx4OL7++mv8uHWrWi8lDQwMxPJly5Cbm4veDg6c+O8IDkbY9etwd3fHD76+auv/8uVLtDQzAyEEfbt148R9z5EjCI2IwNSpU7Fq9Wq1dc/Pz0evnj1x79499O/Zk5v9/tgxnA0Ph7OzMw6HhKitOwDExMTAyckJGgD6ODpy4r8rJAQXrlyBh4eHWu/3tI95jE+RdzJUU4jOMMThjz/+gFbt2kh7+BA//fQT5vj7o3aNGhji5AQrS0tYW1rCUCKp8PdfvnqF+MREJCQm4mR4OIpKSzFj5ky0trdHUlKSWg8KJSUl8Pf3x77AQHTo0AH79+/nxP+bcePwe1QUvl2wQK399+3bh74uLti5cydn7jNmzkSJlhaMmjZVa/ecnBw8SE1FVHQ0zp8/z5n7mClTsHDhQmhpaQloozirV6/G4sWLMW3aNM5qP93DA7dTU+E6YIBa157mMY+hGuzKUDWFEIKuXbtizpw5+PrrrwEAMpkMYWFhuHjxIqJv3UJcfDy+0NWFhYkJdOrUgVatWpAWFyO/sBDJ6el4k5eHDjY2sLW3h4uLC5ycnKCpqYm3b9/C1NQUFy9eRPv27UU2LZ/AwEAcOHAA4eHhH84KufKfP38+pFIptmzZIrJl+eTm5sLMzAy///47LC0tAXDnfv/+ffTs2ROpqamoW7euyKblM2vWLNSpUwdr164FwJ07IQS9evXCxIkTMXHiRHElKyAhIQH9+vVDamoq/vOf/wDgzv+XX37Bjz/+iMjISFGWsn8Ovsc8MzMzXLhwQW3HPEb5sK/JKOfs2bPw9vbG7du3K3yDskwmQ3p6OlJSUlBQUICioiLUrl0b2traMDc3h6mpaYWD3oYNG/D777/jxIkTfGoohVQqhYWFBQ4ePFjpd/vK+r948QKWlpaIi4uDsbExnypKsWLFCjx48AAHDx6scBtVav/NN9/AwsICS5cu5UtBaR4/fgwbGxskJSXB0NCw3G1Ucb969SomTJiA5ORk1KpVi08VpRgyZAh69eqFuXPnVriNsv6lpaVo37491qxZA1dXVz41lILmMY9RMfJOhkAIkfs/W1tbwlB/ZDIZ6dChAzly5AhvGfn5+aRx48YkKiqKtwxl2bFjB+nbty+vGQsXLiTTpk3jNUMZXr16RfT19cmDBw94y0hJSSH6+vrk1atXvGUoy9SpU8miRYt4zejTpw/ZuXMnrxnKcOvWLdKkSRNSUFDAW8avv/5KbG1tiUwm4y1DGYQa85o0aaKWYx6jYgBEEznmN2wyVA05duwYsbGxIaWlpbzmbN26lfTv35/XDEUpKCggRkZG5ObNm7zmZGVlEX19fZKWlsZrjqL4+PiQyZMn854zadIksmTJEt5zFCE1NZXo6+uTrKwsXnNu3LhBjIyMeJ10KEO/fv3Itm3beM0oLS0l1tbW5Pjx47zmKArNYx6jcthkiFJKS0tJ27ZtyenTp3nPKiwsJMbGxiQyMpL3LHnZtGkTGTRokCBZy5YtIxMmTBAkSx5evHhBJBIJefToEe9ZDx8+JBKJhLx8+ZL3LHkZP348Wb58uSBZAwcOJJs3bxYkSx6uXbtGmjVrRoqKinjPOnXqFGnbti3vEw95EXrMa9asmVqNeYzKYZMhSjl06BDp1KmTYJex9+zZQ5ycnATJ+hxv374lDRs2JHFxcYLk5eTkEAMDA5KYmChI3uf49ttvyYwZMwTL8/DwIAsWLBAsrzISExOJoaEhycnJESQvNjaWNGrUiLx9+1aQvM/Rq1cvsnfvXkGyZDIZ6dixIwkODhYk73PQPOYxPg+bDFFIcXExMTc3JxcvXhQsUyqVElNTUxIeHi5YZkUEBASQESNGCJq5cuVKMnr0aEEzy+PZs2dET0+PPH36VLDMp0+fEj09PZKRkSFYZkWMGjWK+Pv7C5r55ZdfkjVr1giaWR5hYWHEzMyMFBcXC5Z54cIFYmFhIWhmedA+5jE+D5sMUcj+/ftJt27dBL+58cCBA8TBwUHUmypzc3NJ/fr1yd27dwXNffPmDWnQoAG5ffu2oLkfM3v2bDJ37lzBcz09PcmcOXMEz/0nCQkJpEGDBuTNmzeC5t69e5fUr1+f5ObmCpr7T2QyGenatSs5ePCg4LmOjo4kKChI0NyPEWvMO3jwoOhjHkM+2GSIMqRSKWnRogX5/fffBc8uKSkhrVq1IufPnxc8u4wffviBjB07VpTsdevWkWHDhomSTQghjx8/JhKJhDx//lzw7IyMDCKRSMjjx48Fzy5j6NChZP369aJkjxkzhvj6+oqSTQgh586dI5aWlqSkpETw7MuXLxMTExMilUoFzyZE/DHP0tJS1DGPIR9sMkQZO3fuJL179xYt//Dhw8TOzk6UM6VXr14RAwMDkpKSIng2If97zEB0dLQo+dOmTSPe3t6iZBNCiJeXF3F3dxclOzo6mjRp0oTk5+eLkp+cnEwMDAxIdna24NkymYzY2dmRkJAQwbPLcHZ2Jrt27RIlW+wxLyQkRLQxjyE/bDJEEQUFBaRp06bkjz/+EK0PpaWlpH379uTkyZOCZy9ZsoS4ubkJnvtPtmzZQlxdXQXPTUtLIxKJhGRmZgqeXcbLly+JRCIh6enpgmf379+f/Pjjj4Ln/pOJEyeSpUuXCp574sQJ0r59e1FXdV2/fp00bdqUFBYWCppL+5jHkB82GaKIzZs3kwEDBojdDVEG57I/xA8fPhQsszzKHjNw/fp1QXMnTJhAvvvuO0Ezy2Pp0qVk4sSJgmZev36dGBsbC/6H+GPS09MFn5CWlpaSdu3aqcUfYldXV7JlyxZBM2ke8xiKwSZDlPD27VvSqFEjEhMTI3ZXRLls7+XlRaZPny5YXmXs2rVL0Mv2SUlJon1F8zHZ2dnEwMCAJCcnC5bp7OxMdu/eLVheZbi7uwv6VeXhw4eJvb29WnxFEx0dTRo3bizYV5W0j3kMxWCTIUpYu3YtGT58uNjd+ICQN3SW3bz75MkT3rPkQSqVEhMTE3L58mVB8r7++mvi5+cnSJY8+Pr6kjFjxgiSFRERQUxNTUW7efdjnjx5IthN7OqwYOFjhg0bRtatWydIFs1jHkNx2GSIAsqWk9+5c0fsrnxAyKW+np6exNPTk/ccRQgKChJkqe+dO3dI/fr1BV9OXhlC7Y9ly7oPHDjAa46izJkzR5DHG6jDoyw+5vbt24Lsj+o65jk4OAj+eAOGfLDJEAX4+fmpxQP/PkaIh8A9efJEbR74909KSkqIhYUFuXDhAq85w4cPV4sH/n1MQEAA+fLLL3nNOH/+PGnVqpXanYlnZGQQPT09Xq9UqvMD/0aNGkVWrlzJa4a6jnnh4eGCP/iSIR9sMlTNKbtHIykpSeyulAvfrweYPn068fLy4q19VQgODiYdO3bk7cw9JiZGrV4F8U/K7ueIjY3lpX2ZTEbs7e3J4cOHeWlfVRYsWEA8PDx4a1+dXwWRmJhIDAwMeHslCu1jHkM55J0MabzbVj7s7OxIdHS03NszKkcmkyEtLQ0pKSkoKCiAVCqFlpYWtLW1YW5uDlNTU2hqapb7u8uWLcOff/6J/fv3C9tpOYmMjMTYsWORkpICLS2tcrdR1v/hw4ews7NDcnIyDAwM+FZRGJlMBmtra6xcuRIDBw6scBtlaz9w4EC4uLhg9uzZfGoozebNm3Hp0iWcPn26wm2U9T99+jSWLFmCuLi4Cv99xCQzMxMWFhaIiYlB8+bNy91GWfeioiKYm5vj0KFD6Nq1K88myjFhwgS0aNECy5cvr3AbZf1pHvMYyqOhoRFDCLH77HZsMiQcMpkMYWFhuHD+PGKioxEXH4+6urqwMDGBjrY2tGrWhLSkBPkFBUhOT0duXh5srK1ha2cHl3794OzsDE1NTWRlZcHCwgK3bt2CiYmJ2FoV0r9/fwwePBgeHh4AuPOfNGkSjIyMsGLFCpENK+bEiRP4/vvvERMTA01NTc7cb9y4gZEjR+LBgweoXbu22JrlUlhYiJYtW+LIkSPo1KkTAG5qDwAdOnTA999/jyFDhoipWClLly7Fs2fPsHfvXgDc7ffbtm3DmTNnEBoaKrJhxaSlpaFTp05ITk6Gvr4+AG78s7OzqR7zGMrDJkNqRHZ2NgIDA7F92zbUqVkTg3v1gnXr1rC2tISBnl6Fv5eZnY34xETE37+PUxERKCwpgceMGXj8+DEKCgqwc+dOAS0UJyoqCsOGDcOtW7cQHBzMif9XX32Fnbt2IS0tDfXq1RPQRjEIIbC3t8fs2bORlZXFWe1PnTqFMWPGYNq0aQLaKM7OnTtx9OhRHD58mLN938HREQkJCYiNjYWGhoaANoqRnZ2Nli1b4vz587hy5Qon7lOmTsXGjRtx+vRp2Nl9dlwXlWnTpkFfXx9eXl6c1d64WTM0bdpUba8KlREdHY2hQ4dyOuZ5zJgBNzc36FXye4yKYZMhNSA/Px9LfHwQGBiIPg4OmPTll+jYvr1SAzkhBDcTErDv2DGcCQvDuHHjsHHTJujo6PDQc27Iz8+HjY0Nnv31F/r36MGJ/47gYIRfv46p06bB189Pbf3z8/Mx7ptvcC40FAOdnTlx33vkCM5GRMDd3R0r/f3V1h0AcnJyYGZqiuLiYrh068aJ/7aff8bvUVGYMmWK2tfexcUFsTExGNCrFyfuu0NCcOHqVbi7u6u1OwAkJyfD3s4Ompqa6OvoyIn/1p9/xrWYGEyaNEmt/fPz89HBxgZ/cTjmBR47hkuRkXBzc1Nrd3WFTYZE5urVq5g0cSKsW7WCr6cn6r+/ZMwFL7KysGTTJsQnJSEwKAiOjo6ctc0VZf7tzc2xct48qvxZ7d/5tzMzg//8+VT5f6i9hQV8586lyh34n38bExOsXrCAKn+axzx1hk2GRKKoqAjeXl44HByMNV5eGNCzJ29ZZy9fxoKAAIwaPRqrAwLU4h4Smv1pdgfo9qfZHaDbn2b3qgCbDIlAXl4ehgwejDoANvv4QCLAPS2vcnIw29cXUk1NnDh5Erq6urxnVgTN/jS7A3T70+wO0O1Ps3tVQd7JELtNnSPy8vLQt08fNKlXD0GrVglyUACA5H1eo7p10bdPH+Tl5QmS+zE0+9PsDtDtT7M7QLc/ze7VEXZliAOKiorg2r8/mtSrh42LF4uy0oUQAk8/P2Tk5uJsaKigl09p9qfZHaDbn2Z3gG5/mt2rGuzKkIB4e3mhDoD1CxeKtuRXQ0MDGxYtgpZMhoXe3oJm0+xPsztAtz/N7gDd/jS7V1fYlSEVuXr1KkaOGIFrv/wi2GXSysjKyUG3MWPw69Gjgqw4oNmfZneAbn+a3QG6/Wl2r4qwK0MCkJ+fj0kTJ2KNl5daHBQAoF+vHgIWLIDbhAnIz8/nNYtmf5rdAbr9aXYH6Pan2b26wyZDKrDExwfWrVrxupRSGQb26gUrCwssXbKE1xya/Wl2B+j2p9kdoNufZvfqDvuaTEmys7Nh0qIFboSEcPpwLa54kZWFziNHIv3hQ14e406zP83uAN3+NLsDdPvT7F6VYV+T8UzZKzbU8aAAgPr6+ujt4MDbu3xo9qfZHaDbn2Z3gG5/mt1pgF0ZUgKZTAYLc3NsWbwYnaysVGprQ2AgEpKSkJCUhD//+gtNGzVCwunTnPTzRnw85vj7IzklhdM3H3Ppn/36NdYHBiL08mU8e/ECujo6sDQ1xaLp09HFxkaltvnwV8ZdkRrPXL4ch86cKfdngatWYUjv3nJlqkvtU//8EyHnziHixg08evoURVIpmhsZYYizM6aPGYP/aGv/a/sHjx7h+y1bEBkbi+LiYrRv1QoL3d3R3d5eoX6y2otf+8qg4bgvDz7He4C/2ldl5L0yVFOIzlQ3wsLCUKdmTXRs317ltn7YuhV6//d/aG9hgddv3nDQu//RycoKtWvUQHh4OHrLOZDKA1f+TzIyMMjdHW/z8/HNkCEwNTZGbl4e7qWm4tmLFyr3kw9/ZdyVqfGOFSs++cy2bVu5M9Wl9j+fOoW9v/6Kft2746t+/VCrZk1cjYmB3/btOPHbb7gYGAjtOnUAAA+fPkW/yZNRs0YNzBk/HnV1dXHg+HGMmDULIZs3o2enTnL3k9Ve/NpXBC3HfXnwOd4D/NWeBthkSAkunD+Pwb16cfJ8idgTJ9DcyAgA0HXkSLwtKFC5zTI0NDQwxMkJFy5c4PTA4MrffelSlJSW4mpwMBoaGHDUu//Bh78y7srUeKSrq9J9BNSn9oOdnTHPzQ11//HKALcRI2DatCnW7duHn06exNRRowAAK378Ea/fvEHEwYNoZ2EBABg1YAC6jhwJr9WrcfPoUblzWe3Fr31F0HLclwef4z3AX+1pgF1HU4KY6GhYt27NSVtlBwZfWFlaIiYqitM2ufC/Hhv77pLuuHFoaGCA4pIS5BcWctTD/8G1vzLuytSYEILcvDzIZDKFf7cMdai9TevW/5oIlTGsb18AQGJaGgDgbUEBzl+5Akdb2w8TIQDQ1dHBuKFDkfr4MWLv3VOor6z27LjnCq7GfL7He4Cf2tMAmwwpiEwmQ2xcHKwtLcXuilxYW1oiNi4OitwbVhlc+V+KjAQAGDVsiK/nzUNjBwcYOTrCfvhwhISGctFVANz6C1n7Zj17onnPnmjs4IBhM2Yg+u5dhdtQ19oDwF9//w0AMHx/M+q9Bw9QJJXCvl27T7a1e/8VUdz9+wplsNqrX+3Zcc8/XNeeFthkSEHS0tLwf198AYMqsnTRUCLBF7q6SHt/Bq4qXPmn/vknAGCunx+yX7/GtuXLsXnpUtSqWRPTv/sOP586xUV3OfUXovb19fXhMWYM1i1ciINr12KemxviExMxYMoUXL55U6G21LX2paWlWLNnD2rWqIERLi4AgOcvXwIAGtWv/8n2ZZ9lvN9GXljt1a/27LjnH65rTwvsniEFSUlJgYWJidjdUAgLExOkpKTAzMxM5ba48s97/6RUXR0dnNq5E1q1agF49/AwmyFD4Lt1K74eOJCTFRFc+QtR+2WzZ//r/wf07IkR/fqhx5gx+HbVKkQfP65Qe+pY+8Xr1iH6zh0snTkTLZs3BwAUvP+qpGw/+Cd1tLQAQKmvU1jt1av27LgXBi5rTwvsypCCFBQUQOej5cDqjk6dOijg6EY9rvzrvH/D8pcuLv/6A1ivbl30794df2dl4cH7s0hV4cpfrNqbGhtjSJ8+SH/y5MOZtbyoW+39tm/H7pAQTBg2DPPc3D58XraiTFpc/MnvFEqlAN65KAqrvfrUHmDHvVBwWXtaYJMhBZFKpdCqWbUuqGnVqoWioiJO2uLKv/H7rz7Ke4BZg/crTHJyc1XOAbjzF7P2xo0aAXj3UkZFUKfar9q5E+v27sWYQYOwfvHif/2soaEhACCjnKXVZZ81er+NIrDaq0fty2DHvTBwWXtaYJMhBdHS0oK0pETsbiiEtLgYtd+fkakKV/4d2rQBgHKfK1L2maFEonIOwJ2/mLVPf/IEQPl/RCpDXWq/etcuBOzejdEDBmDz0qWfLFFubWaG2lpaiLpz55PfLbuBWJnVPKz24tf+n7DjXhi4rD0tsMmQgmhrayO/il1+zC8shDZHl3m58h/Qsyd0//MfhJw79+E+AgB4npmJ0MuXYWpsDJOmTVXOAbjz57v2bwsKUFjO2dztpCSc/O03mLdogRYKLs1Vh9oH7N6N1bt2YZSrK35ctqzc+0F0dXTg0q0brsXE4G5KyofP8/LzcfDECZgaG8P2/R9SRWC1Z8e9qtA+5tNC1br2pwaYm5sjOT2ds/YOnz2LJxkZAN5dBpcWF2Ptnj0AgKaNGmHUgAEqZySnp8Pc3FzldgDu/OvVrYsVnp7478qV6DtxIsYOHozikhLsO3IE0uJirPby4qC37+DKX1l3eWuc/vgxRs6ZA9eePWHStCn+o62Nuw8e4OdTp1BDUxMbfXwUzha79ntCQrBq504YNWyIHh074sj58//6uaFEgl6dOwMAvps1C1eiovDlrFnwGDMGX/znPzhw/DgyXr5E8MaNSj3wjtWeHfeqwuWYL8R4D3Bbe1pg7yZTEJlMBomeHqKOHuVkqeWgadMQGRtb7s8cOnTA6V27VGr/5atX6DhiBLJzcjh5YjbX/qfDw7H5wAEkpqZCU1MTdu3awWvqVHS2tla5bYBbf2Xd5a3x35mZWLZpE+Lu30dGZiYKCwvRwMAA3ezsMNfNDebvV17JizrUvrL3bQGf7uPJDx9ixUfvJvOeNk2hV3GUwWrPjnsxa18efI/3APe1r+qwd5PxhKamJmysrRGfmIjeXbuq3B4XO39lxCcmooONDWcHBdf+g5ycMMjJiYOelQ+X/sq6y1vjBgYG2PHDD8p27xPUofZbly/H1uXL5c6waNECP69fr2QP/w2rPTvuxax9efA93gPc154W2D1DSmBrZ4d4BZ+GKxYJiYmwVfCN35+DZn+a3QG6/Wl2B+j2p9mdFthkSAlc+vXDqYgItX/cOSEEJ8PD4fL+Kb9cQbM/ze4A3f40uwN0+9PsTgtsMqQEzs7OKCwpwa3bt8XuSqXcTEiAVCaDE8eXo2n2p9kdoNufZneAbn+a3WmBTYaUQFNTEx4zZmDf0aNid6VS9h07Bo8ZMzh5tP0/odmfZneAbn+a3QG6/Wl2pwW2mkxJsrOzYdKiBW6EhCj8MDQheJGVhc4jRyL94UPo8fCCQZr9aXYH6Pan2R2g259m96qMvKvJ2PRRSfT09ODm5oYlmzaJ3ZVy8dm4EZMmTeLtoKDZn2Z3gG5/mt0Buv1pdqcBNhlSAV8/P8QnJeHs5ctid+VfnImIQEJKCn7w9eU1h2Z/mt0Buv1pdgfo9qfZvbrDJkMqoKOjg8CgICwICMArBV+iyBdZOTnwWrMG+4OCoKOjw2sWzf40uwN0+9PsDtDtT7N7dYdNhlTE0dERo0aPxhw/P5SWloral5KSEszx9cXor7+Gg4ODIJk0+9PsDtDtT7M7QLc/ze7VGXYDNQcUFRVhgKsrGtWti00+PqI8+ZMQAk8/P2Tk5uJsaKigbyym2Z9md4Buf5rdAbr9aXavarAbqAWkdu3aOHHyJNKeP4enCGcLZWcHac+f48TJk4IfFDT70+wO0O1PsztAtz/N7tUVNhniCF1dXVy8dAnPXr/GeG9vwb5PzsrJwYSFC/H8zRtcvHQJurq6guR+DM3+NLsDdPvT7A7Q7U+ze3WETYY4RFdXF6HnzsHCygqOY8bgTEQEr3lnIiLQbcwYWFhZ4WxoqOgHBc3+NLsDdPvT7A5hsfIYAAAgAElEQVTQ7U+ze3WD3TPEE9euXYPbhAmwMjeH37x5nD6k60VWFnw2bEBCSgoCg4Lg6OjIWdtcUeZv2bw51nh7c++/cSMSkpPV0r/MvZ2ZGfznz6fKHXjnP3HCBLQyNsa6RYuo8i+rfRsTE6xesIAqd+Cd//hvvoFlixZYT2ntaRzz1Bl57xlikyEeyc/PR/fu3ZGUmIj+PXti0vDh6GRlpdTNdoQQ3ExIwL5jx3Du8mVYWlri9ytX1Hop5Y0bN9Dv/QsD+zg6cuK/58gRhEZEYMrUqVi9erXa+r99+xatLS2RnZ0Nl+7dOav92fBw9OrVC78eOaK27gCwc+dOrPL3R05ODno7OHDivzskBOevXIGHhwd+8PVVW/+///4b5ubmACHo260bN7U/ehRnwsMxctQobN++XW3dAWD69OmIi41FSkoKZ7XfGRyM365fh7u7u1rX/ubNm3Dp2xcAfWOeuiLvZKimEJ2hlczMTDx8+BCxcXE4e/Ys5vj7o3aNGhji5AQrS0tYW1rCUCKp8PdfvnqF+MREJCQm4mR4OIpKSzFj5kz4rl+PLl26ICsrS60PjFWrVmHZ8uWYOHEi9u/fz5m/pq4uGjZsqNbuV65cge4XXyAmNhYHDx7kzH2Chwc8PT1Rq1YtAW0Uo7i4GKtXr0bQgQNo164dZ7Wf7uGBe48eoU/fvmpd+927d2PQoEHYsmULp/u9VdeuiImJUWv3x48fIyQkBElJSahVqxZn/uPHj8flqCjMnTdPrf1pHvOqOuzKEI+4u7tDIpHA398fACCTyRAWFoaLFy8i+tYtxMXH4wtdXViYmECnTh1o1aoFaXEx8gsLkZyejjd5eehgYwNbe3u4uLjAycnpwwv4Fi5ciJycHOzYsUNMxQqJjo7G0KFD8eDBA2hrawPgzj8lJQUODg548OAB6tWrJ7LppxBCYG9vj4ULF2LEiBEAuK19nz59MHLkSEydOlVMzQrZtWsXjhw5gosXL374jCv/X3/9FQEBAbh165Yoy5k/R3Z2NszNzXH9+nW0bNkSAHfuBQUFMDMzw6lTp2Brayuyafl8POYB3PmzMU99xzx1hn1NJjLp6emwt7dHSkoK9Cv47lgmkyE9PR0pKSkoKChAUVERateuDW1tbZibm8PU1LTCAT8rKwvm5uaIioqCiYkJnypK0b9/fwwaNAgzZsyocBtV/N3c3NC0aVOsWLGCLwWlOXHiBJYvX47Y2NgK3x6tivuNGzcwcuRIPHjwQO2W1BYWFqJly5Y4cuQIOnXqVOF2yvrLZDLY2NhgxYoVGDJkCJ8qSrF06VL89ddf2LdvX4XbqFL7rVu34uzZswgNDeVLQWnkGfMA5f3VfcxzdXXFwIEDeRvzJk2aBCMjI7Uc89QZeSdDIITI/Z+trS1hyMeECRPIsmXLeM1YunQpmThxIq8ZyhAZGUmMjY1JYWEhbxnp6elEIpGQzMxM3jKUobS0lLRr146cOnWK1xxXV1eyZcsWXjOUYfPmzWTAgAG8Zpw8eZK0b9+elJaW8pqjKC9fviQSiYQ8fPiQt4zCwkJibGxMrl+/zluGskyYMIF89913vGawMU9CXr58yVtGdQRANJFjfsMmQzyQlJREDAwMSE5ODq852dnZxMDAgCQnJ/OaoyhOTk5kz549vOe4u7sTb29v3nMU4fDhw6Rjx45EJpPxmhMdHU0aN25M3r59y2uOIrx9+5Y0atSIxMTE8Jojk8mIvb09CQkJ4TVHUby8vMj06dN5z9m9ezdxdnbmPUcRysa87OxsXnNoH/OmT59OvLy8eM+pTrDJkIiMHj2arFy5UpAsX19f8vXXXwuSJQ/h4eHE1NSUSKVS3rOePHlCJBIJef78Oe9Z8lBSUkJatWpFLly4IEjesGHDyNq1awXJkoc1a9aQ4cOHC5J1/vx5YmlpSUpKSgTJ+xwZGRlEIpGQJ0+e8J4llUqJqakpiYiI4D1LXkaPHk38/PwEyWJjnoRkZGTwnlVdYJMhkbh9+zZp0KABefPmjSB5ubm5pH79+uTOnTuC5FWGTCYjDg4O5ODBg4Jlzpkzh8ydO1ewvMo4cOAAcXR05P2qUBlC72uVIfR+KMa+Vhmenp7E09NTsDyh97XKYGOesPuh0PtaVYdNhkRCjLN1Ic/IK+PcuXOCn61nZGQQPT09Qc7IK0MqlRITExPBz9aFPCOvDDHO1oU8I6+MJ0+eED09PUHP1suuQp4/f16wzIqgecwT4wqlkFchqwNsMiQCMTExotzHUXavRmxsrKC5/0QmkxE7Ozty+PBhwbMXLFhAPDw8BM/9J2LdxyHU/WmVUXYfR1JSkuDZQt2rURli3ccRHBxM7O3tRb06FBMTQxo1asTGPIFZsGCBIPenVQfYZEgEBgwYINoKn82bN5OBAweKkk2IuCt8hFjFUxlir/ARYuViZXz33XeirfCJjIwkzZo143UVT2WIucJHqJWLlcHGPDrHvKoEmwwJzPXr13lfWlkZBQUFpGnTpuSPP/4QPLu0tJS0b9+enDhxQvDsMpYsWUImTZokSvaPP/5IXF1dRckmhJC0tDTRHjOQmZlJJBIJSU9PFzy7jP79+5OtW7eKku3m5kaWLl0qSjYhhBw/fpxYWVmJ8gf5+vXrpGnTptSOeVZWVtSOeVUJNhkSmN69e5Ndu3aJ2oedO3eSPn36CJ4bEhJC7OzsRL1c/+rVK6Kvr09SUlIEzc3PzyeNGzcm0dHRguZ+zLRp08jChQsFz/X29ibu7u6C5/6TqKgo0qRJE5Kfny9obnJysiDLyStDJpMRW1tb8uuvvwqezcY8cce8sq+nhR7zqhpsMiQgly9fJiYmJqLfyCmVSkmLFi3I77//LlhmSUkJsbS0JOfOnRMssyJ++OEHMnbsWEEz161bR4YNGyZoZnk8fvyY6OnpCfqYgefPnxM9PT3y+PFjwTIrYujQoWT9+vWCZo4ZM4b4+voKmlkeoaGhgt/Ey8Y8ese8qgabDAmETCYj3bp1I0FBQWJ3hRBCSGBgIOnevbtgZywHDx4kXbt2VYslvmVLbu/duydI3ps3b0iDBg3I7du3Bcn7HLNmzSLz5s0TLG/u3Llk9uzZguVVRkJCAmnYsCHJy8sTJO/u3bukfv36JDc3V5C8ypDJZKRLly7kp59+EiyvW7duZP/+/YLkfQ7axzxDQ0Ny9+5dsbuitrDJkEBcvHiRWFhYqM3D34qLi4m5uTm5dOkS71lSqZSYmZmR8PBw3rPkZfXq1eSrr74SJMvf35+MGjVKkCx5ePbsGdHT0yNPnz7lPevp06dET0+PPHv2jPcseRk5ciRZtWqVIFkjRowgAQEBgmTJQ1hYGDEzMyPFxcW8Z5WNeUJkyYOQY15xcbHajXkBAQFkxIgRYndDbWGTIQGQyWSkY8eOJDg4WOyu/ItffvmFdOrUifczlz179pBevXrxmqEoeXl5pGHDhiQuLo7XnJycHGJoaEgSExN5zVGU+fPnkxkzZvCe4+HhQb799lvecxTh/v37xNDQkLx+/ZrXnLi4OFGWk3+Onj17kr179/KaUTbmHTp0iNccRRFqzNu7d6/ajXlljxnge8yrqrDJkACcPn2atGvXTu1eGFlaWkratm1Lzpw5w1tGYWEhadasGbl27RpvGcqyceNGMnjwYF4zli9fTsaPH89rhjK8ePGCSCQS8ujRI94yHj58SCQSCXnx4gVvGcoybtw48v333/OaMWjQILJp0yZeM5Th6tWrpFmzZqSoqIi3jNOnT5O2bdtSOeYVFRWp7Zi3adMmMmjQILG7oZawyRDPlJaWEmtra3L8+HGxu1IuR48eJTY2NrydKW3dupX069ePl7ZVpaCggBgZGZGbN2/y0n5WVhbR19cnqampvLSvKosXLyaTJ0/mrf1JkyYRHx8f3tpXhdTUVKKvr0+ysrJ4af/GjRvEyMiIFBQU8NK+qri4uJBt27bx0nbZmHfs2DFe2lcVvse8bdu2UTvmVWXknQxpvNtWPuzs7Eh0dLTc21cFZDIZ0tLSkJKSgoKCAkilUmhpaUFbWxvm5uYwNTWFpqbmJ7939OhR+Pv7IyoqChoaGiL0vHLIu8krlixZguHDh5e7jbLuBQUFaNmyJY4fPw57e3u+VZRix44dOHHiBM6fP1/hNsr6L168GJmZmdi1axefCkrz6tUrmJub48aNGzAzMyt3G2XdU1NT0blzZzx48AB6enp8qyjF1KlTUb9+ffj5+ZX7c2XdAcDFxQXDhw+Hu7s7nwpKc+vWLQwfPhypqamoU6dOuduoMuatXLkS0dHRbMxTQ3bs2IHjx4/jwoULFW6jyr5fVdHQ0IghhNh9djvaJkMymQxhYWG4cP48YqKjERcfj7q6urAwMYGOtja0ataEtKQE+QUFSE5PR25eHmysrWFrZweXfv3g7OwMQgisrKwQEBAAV1dXsZUq5OzZs/D29kZCQgJq1KjBibumpiY2btyIy5cv48SJE2IrVohUKoWFhQUOHjwIR0dHANzUPjMzE5aWloiLi4OxsbHIlhWzYsUKpKam4sCBAwC4cdfU1MS4ceNgbm6OpUuXimxYMX/++Sc6dOiApKQkGBoacuZ+9epVjB8/HsnJydDS0hJbs0KGDBkCJycneHp6AmBjHhvzVPevyrDJ0EdkZ2cjMDAQ27dtQ52aNTG4Vy9Yt24Na0tLGFRyhpuZnY34xETE37+PUxERKCwpQecuXZCcnIybN2+q5RlSGYQQdOnSBZMnT8abN284cZ88ZQrWr1+P3377De3btxfQRnECAwNx4MABHDt2jLPaGzVtCjMzM+zevVtAE8XJzc2FmZkZTp06hevXr3PiPvzLL7F3716kp6ejbt26AtoozqxZswAAJiYmnLh7zJiBo0ePYurUqZg4caJAFsqRkJCAfv36ISoqCiEhIZyNeUlJSbh16xaVY96GDRtw6dKlKjHmBQUF4dixY9i/fz9n+76bm5vaXgn+HGwy9J78/Hws8fFBYGAg+jg4YNKXX6Jj+/ZKHdCEENxMSMC2n3/GlagoTJ4yBb5+ftDR0eGh56qTn5+PCePH48yZMxjk7MyJ++6QEFy4ehXu7u5q7Q68nxCYmqKosBAu3btz4r/1559xLSYGkyZNUmv//Px8uPbvj6ioKAzo1YsT9x3BwQj/4w9MnTpV7d3nzZ2LAwcOYKCTEyaPGKGy+94jR3A2IgLTp0+H38qVausOvPO3s7XFkydP0L9HD87GvN+jojCFjXlq6w7wM+YFHjuGS5GRcHNzU3v/8mCTIQBXr17FpIkTYd2qFXw9PVFfX5+ztl9kZWHJpk2IT0pCYFDQh8uS6gLN7sD//NuZmcF//nyq/D/U3sICvnPn0ulO+X5vZW4Ov3nzqPJntad3zKsMqidDRUVF8PbywuHgYKzx8sKAnj15yzp7+TIWBARg1OjRWB0QgNq1a/OWJQ80uwN0+zN3Ot0Buv1pdgeY/+egdjKUl5eHIYMHow6AzT4+kNSrx3vmq5wczPb1hVRT8//bO8+wKK62Ad+IDcQCVqLGCqhobBFNsIH1tZsgKokFNRo1thRrmlGsr0YTezfW2CL2ikbltSvYUFTUxK4ICoIg7Pl+KHwWUHZ3ZnbxzH1d/nAYzjM3z+wzZ8+cOcP6wEAcHBxUj5kWMruD3P66u5zuILe/zO6g+2eEjHaGMvc08VeIjY2lcaNGFM2Xj8XjxmlyYgA4PY/nnCcPjRs1IjY2VpO4LyKzO8jtr7vL6Q5y+8vsDrq/0rwzI0MJCQk0+89/KJovH1OGD7fIEw9CCAYEBHDr0SM2b9mi2RCizO4gt7/uLqc7yO0vszvo/sYg3cjQkMGDyQlMHjrUYo9+2tjY8OuwYWQ3GBg6ZIhmcWV2B7n9dXc53UFuf5ndQfdXg3diZGj//v34+vhwYPlyzYYK30RkdDR1/PxYvXat6rPuZXYHuf11dzndQW5/md1B9zcWaUaG4uLi6Na1KxMHD7aKEwMgf758TPjuO/y7dCEuLk61ODK7g9z+uruc7iC3v8zuoPurSabvDH0/YgRVypVT9XFCU2jh5UVlNzd++P571WLI7A5y++vucrqD3P4yu4PuryaZ+jZZVFQUpUuV4tCqVYouMKUUdyMjqeXrS8SVK4ovZS6zO8jtr7vL6Q5y+8vsDrq/qUhxmyzlFRvWeGIAFMqfn4aenixatEjxtmV2B7n9dXc53UFuf5ndQfdXm0w7MmQwGHBzdeX34cOpWbmyye38unAhoefPE3r+PNdu3KC4szOhGzcqdpyHQkLoP3YsF8LDFXv7r1Lu42bPZsIbXjia1daWu4cPm9y+Gu5gvP+la9dYtXUrew4d4ur16yQkJlKyWDFaN2jAl35+5LKzS933+JkzrN66lZCwMM5evMjj+Him/fQTfi1bGn2c1pD7i1evMnHePELPn+f2vXskJSVRrEgRGnp60q9zZ4oUKJC675vOh5EDBtCvU6cMH6furp/3es2zXO7TIzNe78wloyNDWbU4GDXYvXs3ObNmxcPMtwiPmj4dx7x5+cDNjYcxMQod3f9Ts3JlctjaEhQURMOGDRVpUyn3lt7elC5e/LXtZy9e5PclS2hat65Z7avhDsb7L9uwgfmrV9O0bl3aNW1KtqxZ2X/8OAEzZ7J+1y52LFyIXc6cAOwMDmbe6tW4lCyJu4sLR06dMvk4rSH3N+/e5c79+7Tw8uK9QoWwtbUl7NIl/vjrL/7asYO/ly+noJPTS78T8PXX5H9lcmbl8uWNOk7dXT/v9ZpnudynR2a83mlFpu0Mbd+2jVZeXmavsXBi/XpKFisGwMe+vjyOj1fi8FKxsbGhtbc327dvV+zkUMrd3cUFdxeX17YPCggA4PPWrc1qXw13MN6/VYMGDPL3J88Ly8b7+/hQpnhxJi1YwNLAQL5o3x6Abj4+9OvcmVx2dgTu2mXWRcEacl/Pw4N6Hh6vbf+oWjW6DR3Kio0b6d+ly0s/a16/Pu+/955Zx6m76+e9XvMsl/v0yIzXO62wjnEsEzh+7BhVKlQwu52UE0NNKpcvz/GjRxVrTyn3tIh78oR1O3bgXKgQDT76yOz2lHYH4/2rVqjw0gUhhbaNGwMQdvly6rZC+fO/dPvAXKw198WLFAEgOp1vh49iY0lKSjIrhu6un/dKodc8ea93WpEpO0MGg4ETJ09Sxcjha0tRpXx5Tpw8iTHzs9JDbff1O3cS8/gxfi1bYmtra3Z7SrqDsv437twBoKCKExKtJfdPEhKIjI7mxp07BB06xNdjxgDQyNPztX1rd+xIyfr1cfb0pEm3buwMDjbpeHV3/by3dO4zgkw1TwuU9teKTNkZunz5Mnlz56aAFT2+9yYKOjmR28GByy98EzMVtd2XBgZiY2PD561aKdKeku6gnH9ycjIT580jq60tPk2aKHJsaWEtuV+yfj0uDRtSqXlzfL76ioexscweNYqPqlZN3Sdv7tx0aduW8d99x7JJk/ihb1+u37pFh4EDWW7CJEvdXT/vLZ37jCBLzdMKpf21IlPOGQoPD8etdGlLH4ZRuJUuTXh4OGXLljWrHTXdL169yqGQEOp5eFCiaFHF2lXKHZTzHz5pEsdOn+aHvn1xKVnS7PbehDXkvln9+riULMnj+HhOXbjAtr//5n5U1Ev79Pbze+n//wE+b9UKz/btGTF5Mq0aNMDB3t6ouLq7ft7rNc88ZL7eaUmm7AzFx8djr+D9bS2wz5mTeAUmq6npvjQwEDB/EuGrKOUOyvgHzJzJ3FWr6NK2LYP8/RU5rjdhDbkvWrgwRQsXBp5NEm7l7U2Dzp15kpDwxr+BU758dP30U8bPmcORU6fwrlXLqLi6u37em4te8+S93mlJprxNlpiYSPasmasflz1bNhISEsxuRy33pKQk/tyyBce8eWnh5aVo20q5g/n+42bPZtL8+fi1bMnk4cMVOaa3YY25d3dxoZKbG/PXrHnrvilPVz2IjjY6ju6un/fmotc8ea93WpIpO0PZs2cn0cynPbQm8elTcuTIYXY7arlv27+fu5GR+DZrRo7s2RVtWyl3MM9//Jw5TJg7lw7Nm/PbDz+Y/ZhqRrHW3D9JSCD64cO37hfxzz8Ar63JkxF0d/28Nxe95sl7vdOSTNkZsrOzIy6TDcHFPXmCnQJDnWq5pwwXd1J4uBiUcwfT/SfMncv4OXNo36wZ0376SdPVUS2Z+zv376e5ff+xY4RdvsyHlSoBz74lP4qNfW2/67dvs2DtWpzy5jVpwTfdXT/vzUWvefJe77Qkc429PcfV1ZULERGKtPXn5s38e+sWAJHR0SQ+fcp/580DoLizM+2bN1ckzoWICFxdXc1uR0n3FG7du8fugwep5u5OBRUmvCnlDqb5z1u1inGzZ1OsSBHqeXiwZtu2l35e0MkJr+fzQf69dYs/N28G4PzzONv27ePm88eR2zdvTnFnZ6PiWzL3344bx+3796lbowbFixThSWIioWFhrNuxAwd7e0YNHAjA4/h4qrRqRbP69XErWZK8efJw6do1lqxfz+P4eOYGBKSuVmwMurt+3puLXvPkvd5pSabsDJUpU4ZHsbHcj4oy+3HDpYGBBJ848dK2MbNmAeBZrZoiJ8e9Bw+IiY2lTJkyZrelpHsKKzZuJDk5mU5t2ijS3oso6Q6m+Z88dw549k2/z88/v/Zzz2rVUi8K127cSM1/Cpv27GHTnj0A1KpSxaiLgqVz/0mTJqzctIk/t2whMioKGxsbihcpQtdPPqFf584Ue74AYc4cOWjp7c3xM2fYsncvj+PiyJ8vH/U8POjfuTPVK1Y0+nh1d/2812ue+ch8vdOSTPuiVq969ejj40PDjz+29KG8lZ3Bwcxat46gvXsVaU9md5DbX3eX0x3k9pfZHXR/c8joi1oz5ZwhgOoffkjI828+1k5oWBjVa9RQrD2Z3UFuf91dTneQ219md9D9tSDTdoaaNG3Khj17rH7JbyEEgUFBNFFwtVeZ3UFuf91dTneQ219md9D9tSDTdoYaNGjAk6Qks96urAWHQ0NJNBjw9vZWrE2Z3UFuf91dTneQ219md9D9tSDTdoayZMlC7z59WLB2raUP5Y0sWLeO3n36KPpIq8zuILe/7i6nO8jtL7M76P5akGknUANERUVRulQpDq1aRSEV38BsKncjI6nl60vElSs4KvySPZndQW5/3V1Od5DbX2Z30P1N5Z2fQA3g6OiIv78/30+daulDSZMRU6bQrVs3VU4Mmd1Bbn/dXU53kNtfZnfQ/dUmU3eGAEYHBBBy/jybreQxvhQ27dlDaHg4o0aPVi2GzO4gt7/uLqc7yO0vszvo/mqS6TtD9vb2LFy8mO8mTDDpZYpqEBkdzeCJE1m0eDH29vaqxZHZHeT2193ldAe5/WV2B91fTTJ9Zwigdu3atO/Qgf4BASQnJ1v0WJKSkug/ejQdOnbE09NT9Xgyu4Pc/rq7nO4gt7/M7qD7q0WmnkD9IgkJCTRv1gznPHmYOmKEZm9mfhEhBAMCArj16BGbt2zR7K29MruD3P66u5zuILe/zO6g+xuDFBOoXyRHjhysDwzk8u3bDLBAjzmlh3z59m3WBwZqemLI7A5y++vucrqD3P4yu4PurwbvTGcIwMHBgR07d3Lz4UM6Dxmi2T3VyOhougwdyu2YGHbs3ImDg4MmcV9EZneQ2193l9Md5PaX2R10f6V5pzpD8OwE2bJ1K26VK1Pbzy/1rctqsWnPHur4+eFWuTKbt2yx6IkhszvI7a+7y+kOcvvL7A66v5K8M3OG0uLAgQN07dyZciVKMGnYMEUXqrobGcmIKVMIvXCBhYsXU7t2bcXaVoIDBw7g36UL7qVLM/6776Ryh2f+nT7/HPdSpaTNffmSJZk4ZIiU7pXKlmXsN98o7j781185FR5ule6g1zy95slZ895ERucMvdOdIYBp06bx6+TJPHjwgIaennT75BNqVq5s0oQzIQSHQ0OZv2YNm/fsoUePHoyfMMFqHye8efMm5cuVwwZoVKeOIu4L1q1jc1AQdevWZd1ff1mtO0D37t05d/Ys58+fVyz3c1etYtu+ffTu3ZtRo0dbrX9wcDAtW7RAGAw0rF1bmdyvXcumoCDatWvHrNmzrdb98ePHlHNz4+HDhzSpW1ex837r3r24ubmx/8ABq3UHdWte9+7dmTBxotX66zVP3pqXHhntDGXV4mAsRUJCAhMmTGDVqlW4ubmxaNEi+o8dSw5bW1p7e1O5fHmqlC9PQSendNu49+ABIWFhhIaFERgUREJyMn369iVbvnzkL1DAqk+MWbNm8cmnnzJ58mRF3bv360evXr2wtbXV0MY4rly5QmBgIBcuXCBLliyK+ffu04cL169T38vLqnM/ZswYfhk1is8++0zR3H9Yrx779++3avfdu3fjlD8/IaGh/PHHH4q5j506lZo1a3Lnzh1KlSqloVHGSUhIYOLEiaxcuZJy5crpNU+ymrd+/XrCw8NVqXn16te36tybyzs9MjRt2jS2bdvGpk2bUrcZDAZ2797Njh07OHbkCCdDQsjt4IBb6dLY58xJ9mzZSHz6lLgnT7gQEUFMbCzVqlaleo0aNGnSBG9vb7JkyUJERAQeHh5cuHCB/Fb4npj79+/j5ubGsWPHUgu3Uu4AzZo1o0WLFvTp08eSmuni7+/P+++/z8iRI1O3KeUfGBjITz/9xIkTJ6zyhYQHDx6kQ4cOhIeHpz7loZR7QkICLi4urFq1ilq1alnY9HUMBgNVq1Zl1KhRtGrVKnWbUuf9jz/+yPXr11mwYIElNdNl+vTpbNmyhc2bN6du02ueHDWvW7duFCtWjF9++SV1myw1701If5ssLi4OFxcXNm7cSLVq1dLdz2AwEBERQXh4OPHx8SQkJJAjRw7s7OxwdXWlTJky6Q4x9urVCycnJ8aOHauWhskMGTKEhw8fMmvWrHT3MbykOn8AACAASURBVMf92LFjtG7dmkuXLmFnZ6eWhkmEh4fj6enJxYsXyZcvX7r7meovhMDDw4MhQ4bg4+OjpopJNGzYkPbt2/PFF1+ku485uZ8zZw6rV69m586daimYzOrVq5k4cSKHDx9O9/jNcY+OjsbFxYXg4GBcXV3VVDGa+Ph4ypYty4YNG6hevXq6+5lb8xwdHRk3bpxaGiajds07fvw4rVq1krrmDR48mHbt2qmpojgZ7QwhhMjwv+rVq4vMwn//+1/xySefqBrjn3/+EU5OTuL27duqxjGWW7duCScnJ/Hvv/+qGqdNmzZi8uTJqsYwhY4dO4qAgABVY2zdulWUL19eJCUlqRrHWPbs2SNKly4tEhMTVYuRmJgoSpcuLfbu3ataDFNISkoS5cuXF9u2bVM1zujRo4Wfn5+qMUxh0qRJom3btqrG0GueddY8Pz8/MXr0aFVjWGvNexvAMZGB/s072RmKiYkRhQsXFqdPn1Y9Vr9+/cSgQYNUj2MMAwYMEP3791c9TmhoqChcuLCIiYlRPVZGOX36tChUqJDqx2QwGMTHH38sli5dqmocYzAYDKJ27dpi8eLFqsdatGiRqFOnjjAYDKrHyihLliwRnp6eqh/To0ePRKFChcSZM2dUjWMMMTExolChQuLUqVOqx+rXr58YOHCg6nGMQa95hcSjR49UjZNS85YsWaJqHKWRujM0ZswY0aFDB01i3bx5Uzg5OYnr169rEu9t/Pvvv8LR0VHcunVLk3i+vr5i7NixmsTKCJ9++qmYOHGiJrF2794typYtK54+fapJvLexfft24ebmpsk3t6dPnwpXV1exY8cO1WNlhMTERFG2bFkRFBSkSbwJEyYIHx8fTWJlhDFjxoj27dtrEuvmzZvC0dFR2prXvn17veZZSc3LCNJ2hqKjo0WBAgXE+fPnNYv57bffij59+mgW70307t1bfPfdd5rFO3funChYsKCIjo7WLGZ6nDhxQjg7O4vHjx9rFtPLy0vMnz9fs3jpYTAYhIeHh1i5cqVmMVesWCFq1qxpFaND8+bNE97e3prFe/z4sXB2dhYnT57ULGZ6pNS8sLAwzWLKXPPCwsL0mmcFNS+jSNsZ+umnn0SXLl00jXn37l3h5OQkrl69qmncV7ly5YpwcnIS9+7d0zRup06dxM8//6xpzLRo0aKF+O233zSNeeDAAVGiRAmRkJCgadxX2bBhg6hUqZJITk7WLGZycrKoWLGi2Lhxo2Yx0+LJkyeiRIkSIjg4WNO4U6dOFS1bttQ0Zlr89NNPonPnzprGlL3mde7cWa95Fq55GUXKztD9+/eFk5OTuHz5suaxhw8fLrp376553Bfp1q2bGDFihOZxL168KPLnzy8iIyM1j53CwYMHRfHixUV8fLzmsZs0aSJmzJihedwUkpOTRZUqVcS6des0j7127VpRtWpVi44OTZ8+XTRt2lTzuPHx8aJYsWLi0KFDmsdOITIyUuTPn19cunRJ89gy17xLly5ZvOYdOnRI2ppnDFJ2hoYOHSp69uxpkdgPHjwQ+fPnFxcvXrRI/PDwcJE/f37x4MEDi8Tv0aOHGD58uEViCyFEo0aNxOzZsy0S+8iRI6Jo0aIWKUpCCLFmzRpRrVo1i3RIDAaDqFq1qli7dq3msYUQIi4uThQtWlQcPXrUIvFnzZolGjdubJHYQggxbNgw8cUXX1gktl7zLFvzGjduLGbNmmWR2Ck1Ly4uziLxjUG6ztCdO3eEk5OT+Oeffyx2DCNHjhSdOnWySOzPPvtM/PLLLxaJLYQQV69eFU5OTuLu3buax/77779FqVKlVH2c/G20atVKTJkyRfO4SUlJokKFCmLz5s2ax05h06ZNwt3d3SKP3P7666+idevWmsdNISEhQZQsWVLs27dP89gpNe/atWuax05h5MiR4vPPP7dIbEvXvGvXrlms5u3bt0+UKlXKoreqWrVqJX799VeLxc8o0nWGBg0aJPr162fRY3j48KEoWLCgOHfunKZxz549KwoWLCgePnyoadxX6du3r/jmm280jWkwGETdunXFokWLNI37KiEhIaJIkSIiNjZW07jLli0TH330kUVvUxkMBlGrVi2xfPlyTePGxsaKIkWKiNDQUE3jvsrChQtFvXr1NM/B119/Lb766itNY75KSs07e/aspnH1mldXLFy4UNO4r2KpmmcsUnWGrl+/LpycnMTNmzctfShi3LhxwtfXV9OYPj4+Yvz48ZrGTIsbN24IR0dHcePGDc1i7ty5U7i6ulrFo57t2rXTNA9Pnz4VLi4uYteuXZrFTI+dO3cKFxcXTfNgic9aWqQsM6BlHlI+a9ZS89q1a6dpTL3mWU/NGzdunKUP441I1Rnq06eP5r3z9IiNjRWFCxcWISEhmsQ7efKkVfXOBw0apNm3VYPBIGrWrKn5iER6aP1tdcGCBRYZkUgLrb+tWmoUNj2WLVsmatWqpVku+vbtK77++mtNYr0N2WueliN0lhqFTQ9rGaF7E9J0hiw5VyU9tJzHYKm5Kumh5TyGTZs2iYoVK2r6OPnb+PzzzzWZx2DJuSrpkTJ3S4t5DJacn5cWSUlJwt3dXZO5Wyk1786dO6rHyih6zdOm5m3evFm4u7tLWfNMRZrOkKVn9KdFfHy8Jk+4HD58WBQrVsxiTzGlx9ChQ1V/wsXSTzGlR8oyA2o/4TJz5kyLPsWUHo0aNVL9CZeUx8kt9RRTemj1VF+PHj3EsGHDVI1hLCk178iRI6rGsdaap8VTfQaDQVSrVk3ammcqUnSGrDkJM2bMUH3tkyZNmoiZM2eqGsMUtFj7xBrWt0mP7t27q7r2Scr6NocPH1YthqkcOnRI9YvV8OHDRY8ePVRr31SSk5NF1apVVV3vyRrW9EoPveapW/PWrVsnbc0zByk6Q506dRIjR4609GGkSUJCgihRooQ4cOCAKu3v379flCxZ0mpXAVVzVdyUWxKbNm1SpX1zUfvW7ZQpU6xi5eP0aNGihZg6daoqbVvLysfpsXHjRlVv3VrLau9pIXvN+/nnn1WreSmrvcta88who50hm2f7ZowPP/xQHDt2LMP7ZwSDwcDly5cJDw8nPj6exMREsmfPjp2dHa6urpQpU4YsWbK89nthYWHUq1ePS5cukSdPHkWPSSnmz5/PsmXLCAoKSvPnproDeHl50alTJ7p166amgsk8fPiQsmXLsn//fsqVK5fmPqb6r1ixgqlTp3Lw4EFsbGzUVjGJPn36kCtXLiZOnJjmz011j4uLo0yZMmzdupUqVaqorWESJ0+epHnz5ly6dAl7e/s09zHV/9tvvyU+Pp7p06errWESQgg++ugjBg4cSIcOHdLcx1T38+fPU6dOHS5dukTevHnVVjGJt9U8MN0/M9Q8FxcX9u3bp3jNW7lyJVOmTJGy5pmLjY3NcSHEh2/dT+vOkMFgYPfu3Wzfto3jx45xMiSEPA4OuJUujb2dHdmzZiUxKYm4+HguRETwKDaWqlWqUP3DD2nStCkNGjQgS5YstG/fnmrVqjFkyBCzjkdNnj59SoUKFZgzZw5eXl6KuQcFBdGrVy/CwsLImjWrpTXTZezYsZw6dYoVK1YAyuTeYDDg7u7O9OnTadiwoYUN0+fGjRt88MEHnD17liJFiiiW+4kTJ3LkyBFWr15tacU34uPjQ61atfj2228BZXJ/584dKlasyOnTp3nvvfcsbJg+O3fupF+/fpw5c4asWbMqlvsOHTpQpUoVhg4damnFdEmpebNnz8bb2xtQJvd79+6VuuZVrFiR33//nUaNGlnYMH1u3LhBpUqVOHv2LM7Ozoqd9+ZidZ2hqKgoFi5cyMwZM8iZNSutvLyoUqECVcqXp4CjY7q/dz8qipCwMELOnWPDnj08SUqiTZs2/LFkCREREeTKlcuk49GKpUuXMm3aNHx9fRVx792nDytXrmTAgAF89tlnGpoYT2xsLGXLlmXt2rUcPnxYEf8aHh5cvXqV4OBgq/2GlMKgQYOIi4vDzc1NEfdu3bszadIk/v77b9zd3TU0MZ6zZ8/i7e3N0aNHWbNmjSL+zu+9R8WKFZkxY4aGJsYjhKBevXp07NiR+Ph4xWre4j/+ICIiAgcHBw1tjGfp0qXMnDmTjRs3smjRIkX8k5KTGTZsGD179tTQxHj0mjeIx48fU65cOcWud/7+/ji+4ffehtV0huLi4vh+xAgWLlxII09Pun36KR4ffGBSUoUQHA4NZdaKFQQdOsQXX3zB6ICAdIfiLU1cXBwjhg9n1qxZNPPy4ot27cx2n7dmDVv37KHXl18SMGaM1brDM//mzZtz5PBhmnt5KZL7GcuWse/YMbp37271uf/6669ZvGgRLby96e7jY7b7nD//ZMeBA/Tq1cuq3eGZv0eNGly7epX/1K+vSO6nL1vGgePH6datm1X7x8XF4e/vz4bAQFo2aCBVzQOIiYnBpWxZ4uLiaFq3riL+M5cvZ//x4/j7+1u1f1xcHC2aN+ewXvMUqXkL161jZ3CwWXm3is7Q/v376da1K1XKlWP0gAEUyp8/w7/7Nu5GRvL91KmEnD/PwsWLqV27tmJtK4HM7vCCv5sbowcOlMpfz/0z/8qurgQMGiSVv577Z/6VypZl7DffSOWv1zzrPO8t2hlKSEhgyODB/LlyJRMHD6Z5/foZjmEsm/fu5bsJE2jfoQPjJ0wgR44cqsXKCDK7g9z+MruD3P4yu4Pc/rq7dbtbrDMUGxtL61atyAn8NmIETvnyZbh9U3kQHU2/0aNJzJKF9YGBFrunLrM7yO0vszvI7S+zO8jtr7tbv3tGO0OKPscWGxtL40aNKJovH4vHjdPkjwPg9Dyec548NG7UiNjYWE3ivojM7iC3v8zuILe/zO4gt7/u/m65KzYylJCQQLP//Iei+fIxZfhwi8x6F0IwICCAW48esXnLFs2GEGV2B7n9ZXYHuf1ldge5/XX3zOOu+cjQkMGDyQlMHjrUYo//2djY8OuwYWQ3GBiq4fpDMruD3P4yu4Pc/jK7g9z+uvu7567IyND+/fvx9fHhwPLlmg2XvYnI6Gjq+Pmxeu1a1Wfdy+wOcvvL7A5y+8vsDnL76+6Zy12zkaG4uDi6de3KxMGDreKPA5A/Xz4mfPcd/l26EBcXp1ocmd1Bbn+Z3UFuf5ndQW5/3f3ddTe7M/T9iBFUKVdO1UfqTKGFlxeV3dz44fvvVYshszvI7S+zO8jtL7M7yO2vu7+77mbdJouKiqJ0qVIcWrVK0UWWlOJuZCS1fH2JuHLFrOW800Jmd5DbX2Z3kNtfZneQ2193z5zumtwmS3nFhjX+cQAK5c9PQ09PFi1apHjbMruD3P4yu4Pc/jK7g9z+uvu77W7yyJDBYMDN1ZXfhw+nZuXKJh/ArwsXEnr+PKHnz3Ptxg2KOzsTunGjye29yqGQEPqPHcuF8HBF3oALyrkDxMbFMWflStZu386/t26RPVs2ypQoQZe2benYooVZs/XVcAfj/S9du8aqrVvZc+gQV69fJyExkZLFitG6QQO+9PMjl53dS/sfOXWKKQsXEnrhAlEPH1KkQAHq1KjBoK5dKVmsWIaP01py7/Rh2l9KctnZ8e/+/a9t33HgADOXLyckLIzExETeK1wYr5o1mWDEUxPWkvtxs2czYe7cdH+e1daWu4cPp/7/4tWrjPz9d4JPnODp06d8UK4cQ3v1om6NGkYdp557y+beUp95sJ7cp4Wa9R6s2x3Uvd6n557RkaGspgbevXs3ObNmxeODD0xtAoBR06fjmDcvH7i58TAmxqy20qJm5crksLUlKCiIhg0bKtKmUu4GgwHf/v05cuoUHZo354v27Yl/8oS127fz1ciRhF+5ws/9+5vcvhruYLz/sg0bmL96NU3r1qVd06Zky5qV/cePEzBzJut37WLHwoXY5cwJwK7//Y8OAwdSqlgxvvD1xSlfPs5fvswff/3FxqAgDqxcyXuFCmUorjXl/qOqVenStu1L27Jmff3jN37OHMbPmYP3Rx8xtFcv7HLm5Prt25y7eNGoeNaS+5be3pQuXvy17WcvXuT3JUtoWrdu6rYr16/TtHt3stra0r9zZ/I4OPDHX3/h89VXrPrtN+rXrJnh49Rzb9ncW+ozD9aV+xdRu96D9bqnoOb13lx3kztD27dto5WXl9k92RPr16f2/D/29eVxfLxZ7b2KjY0Nrb292b59u2Inh1Lux86c4VBICF927MiYb75J3d69XTtq+viwaN06sz4cariD8f6tGjRgkL8/eV5YOt3fx4cyxYszacEClgYG8kX79gDMXL4c2yxZ2LZgAflfeGKhXJkyDBw9msBdu+jt55ehuNaU+5JFi+LbrNkb99l7+DDj58xh2Jdf8l2PHuYcptXk3t3FBXcXl9e2DwoIAODz1q1Tt/0ybRoPY2LYs2QJldzcAGjfvDkf+/oyePx4Dq9dm+G4eu4tm3tLfebBunL/ImrXe7Be9xTUvN6b627yONrxY8eoUqGCqb+eirFDoKZQuXx5jh89qlh7SrnHPH4MQJGCBV/anj1bNvLnzfvaULIpKO0OxvtXrVDhpaKYQtvGjQEIu3w5dVvM48fkyJGDfLlzv7Svc4ECANgb+TexptwnPn1K7Bse//x14UIKOjkxqGtX4NmQusFgMCkWWEfu0yLuyRPW7diBc6FCNPjoIwAex8ezbd8+alevntoRAnCwt6dTmzZc+ucfTpw9a1QcPfeWy70lP/NgXblPQYt6D9bpnoLa13tz3E3qDBkMBk6cPEmV8uVNCqo1VcqX58TJkxgzPyo9lHSv7u5O3ty5+f2PP1i/axfXb9/m4tWr/DJtGiHnzzO4Z0+zYyjpDsr637hzB4CCL0zK865Vi9jHj+nz88+cCQ/n5t277D54kO+nTMG1VCk+eV5MM4q15H7D7t0UrV2b9+vWxbVRI4ZMmMCjF96r8zg+nv+dPEl1d3eWBgbi/p//8H7duhSvU4fuw4ZxNzLS6JjWmvv1O3cS8/gxfi1bYmtrCzy7bZaQmEiNSpVe2//DihUBOHnunFFx9NxbX+61+MyD9eT+RbSo92Cd7lphjrtJt8kuX75M3ty5KaDCo5tqUNDJidwODly+fJmyZcua1ZaS7vny5GH55MkMGDWKbkOHpm53yJWLxRMmKLKeg5LuoJx/cnIyE+fNI6utLT5NmqRuH+Tvz/2oKJZt2MDqrVtTtzfy9GRuQAC5c+UyKo415L6auzutGzakdPHixMTGsjM4mLmrVhF84gTbFizAwd6eK//+S3JyMsfOnGHP4cMM6NKFiq6uHDx5ktkrV3L20iWClizB/vk8i4xgrblfGhiIjY0Nn7dqlbrt9r17ADinMTckZdut5/tkFD331pV7rT7zYB25fxUt6j1Yp7tWmONuUmcoPDwct9KlTflVi+FWujTh4eFmnxxKu+eys6NcmTI0rVcPjw8+IOrhQ+avXk3PESNYOmkSXrVqmR1DKXdQzn/4pEkcO32aH/r2xaVkydTttlmy4FywIPU8PGhevz6OefNyODSUuX/+SY/hw1k2eTLZ0ph8+iYsnftdixe/9P8OLVrg7uLC6BkzmL1iBd907556C+V+VBRTvv+ezm3aAM8WFMudKxcT5s5l5aZNdPPxMSq2teX+4tWrHAoJoZ6HByWKFk3dHv/kCfDslsGr5MyeHXh2e81Y9NxbT+61/MyD5XOfFlrUe7BOd60w1d2k22Tx8fEm3ce1JPY5cxKvwGQtJd3PXbpE0+7d8apZk18GDKCFlxed2rRhy/z5FCpQgIEBASQnJ5sdRyl3UMY/YOZM5q5aRZe2bRnk7//Sz/qOHMnSDRtYMG4cndq0oYWXF6MGDmTsN9+w63//Y8WmTUbHs8bc9+vcmezZsrEjOBiAnM/fupwlSxbavzLZtmOLFgAcOH7c6DjWlvulgYHAyxOngdQnixKfPn3td54kJgIYNTKSgp5768i91p95sL7ca1XvwfrctcRUd5M6Q4mJiWQ3oaduSbJny0ZCQoLZ7SjpPmPZMp4kJND6lZnv9jlz0tjTk39v3eKfW7fMjqOUO5jvP272bCbNn49fy5ZMHj78pZ9dv32b1Vu30tjT87ULX8rf6H8mXBSsMffZsmalSMGCPIiOBkh9dDhf7tzkeD4SkkLh5xNJox89MjqONeU+KSmJP7dswTFvXlp4eb30s5RJpbfu3n3t91K2Ob8y8TQj6Lm3fO4t8ZkH68u9VvUerM9dS0x1N6kzlD17dhKTkkz5VYuR+PQpOZ5/AzMHJd1T5kAkp/HUSNLzbwhJCsRSyh3M8x8/Zw4T5s6lQ/Pm/PbDD689qnnz+UXvjX8PE745WWPunyQkcPPOHQo6OQHPVlAtVqQIUY8evXY7KOXvkrKvMVhL7gG27d/P3chIfJs1e+2iX6FsWXJkz87R06df+71jZ84AmPREi557y+beUp95sL7ca1XvwfrctcRUd5M6Q3Z2dsQpvB6Q2sQ9eYKdAsN9Srq7lSoFwIpXVuB8GBPD1r//Jl+ePJRS4FFEpdzBdP8Jc+cyfs4c2jdrxrSffkpzdVSXEiWwtbVly969ry3IlfI3qmrCBdGSuU/59v8qY2bNIik5maZ16qRua9+sGUIIFq1d+9K+C9asAZ5NKDUWa8h9Cim3yDq9cosMnj1C36ROHQ4cP86Z8PDU7bFxcSxZv54y779PdXd3o2Pqubdc7i35mQfrq/la1XuwPnctMdXdpPEvV1dXLkREmPKrr/Hn5s38+3xoMDI6msSnT/nvvHkAFHd2pn3z5orEuRARgaurq9ntKOne28+PP7dsYeS0aZy7dImalSsT9egRf6xfz+3795k4ZEiaK9Uai1LuYJr/vFWrGDd7NsWKFKGehwdrtm176ecFnZzwqlULx7x5+bJjR6YvXUq9zz6jc5s2OObJw+HQUFZv20apYsXo9HxyqTFYMvf/nT+fY2fOUOfDDylWuDCx8fHsCg5m/7FjVK9YMXXhOXg2l2RDUBA/Tp3K5X/+oaKLC4dCQ1m9dSt1a9SgbaNGRh+zpXOfwq1799h98CDV3N2pkM7Exh+/+op9R4/y6Vdf0dvPj9y5cvHHX39x6949Vk6ZYtKib3ruLZN7S3/mwfpqvlb1HqzPPQUtrvemupv0bjKDwYCToyNH1641+5G7lj17EnziRJo/86xWjY1z5pjVPsC9Bw/w8PEhKjra7FU0lXSHZ68gmDh3Ln8fPcq9yEhy5sxJJVdXvuzYkZbe3ma3r6Q7mObf9+ef3zgJ8sU8CyH4Y/16lqxfz4WICBISE3EuVIjGtWszpGdPo//mls79lr17mb9mDecvX+bBw4fYZslC6fffp03DhvT57LPUybMpREZHM2bmTLb+/TeR0dG8V7gwnzZuzLc9ery279uwhtynMHnBAkbPmMGvI0a89mqKF7lw5Qq/vPJusiE9exr1Ko4U9NxbLveW/MyD5XOfHmrXe7Bed1D/ep+We0bfTWbyi1q96tWjj48PDT/+2MTD1o6dwcHMWreOoL17FWlPZneQ219md5DbX2Z3kNtfd8+87hntDJn8Oo7qH35IiJErwlqK0LAwqhv51us3IbM7yO0vszvI7S+zO8jtr7u/++4md4aaNG3Khj17FFvuXS2EEAQGBdHkhRVPzUVmd5DbX2Z3kNtfZneQ2193f/fdTe4MNWjQgCdJSRw5dcrUJjThcGgoiQYD3grdjwW53UFuf5ndQW5/md1Bbn/d/d13N7kzlCVLFnr36cOCVx4FtTYWrFtH7z590nys01Rkdge5/WV2B7n9ZXYHuf1193ff3eQJ1ABRUVGULlWKQ6tWUeiFtxBbC3cjI6nl60vElSs4KvyiOZndQW5/md1Bbn+Z3UFuf909c7qrPoEawNHREX9/f76fOtWcZlRjxJQpdOvWTZWiILM7yO0vszvI7S+zO8jtr7u/2+5mj6WNDggg5Px5Niv4CKcSbNqzh9DwcEaNHq1aDJndQW5/md1Bbn+Z3UFuf9393XU3uzNkb2/PwsWL+W7ChHSXn9eayOhoBk+cyKLFi7G3t1ctjszuILe/zO4gt7/M7iC3v+7+7rorMsuqdu3atO/Qgf4BASSb+FI9pUhKSqL/6NF06NgRTxPe52MsMruD3P4yu4Pc/jK7g9z+uvu76W7WBOoXSUhIoHmzZjjnycPUESMUWQLeWIQQDAgI4NajR2zeskWxNza/DZndQW5/md1Bbn+Z3UFuf90987hrMoH6RXLkyMH6wEAu377NAAv0GlN6iZdv32Z9YKCmRUFmd5DbX2Z3kNtfZneQ2193f/fcFV2IwsHBgR07d3Lz4UM6Dxmi2X3FyOhougwdyu2YGHbs3ImDg4MmcV9EZneQ219md5DbX2Z3kNtfd3+33JVdlYtnf6QtW7fiVrkytf382LRnj9IhXmLTnj3U8fPDrXJlNm/ZYrGiAHK7g9z+MruD3P4yu4Pc/rr7u+Ou2JyhtDhw4AD+XbpQ2c2NgIEDFV2s6W5kJCOmTCH0wgUWLl5M7dq1FWtbCWR2B7n9ZXYHuf1ldge5/XV363TXfM5QWtSuXZvQ06cpUa4ctXx96fnjjxwKCTH5hW9CCA6FhNDzxx+p5etLiXLlCD192upODJDbHeT2l9kd5PaX2R3k9tfdM7e7qiNDLxIVFcWiRYuYMX06OWxtae3tTeXy5alSvjwFnZzS/b17Dx4QEhZGaFgYgUFBJCQn06dvX7p27araKqtKI7M7yO0vszvI7S+zO8jtr7tbj3tGR4Y06wylYDAY2L17Nzt27ODYkSOcDAkht4MDbqVLY58zJ9mzZSPx6VPinjzhQkQEMbGxVKtaleo1atCkSRO8vb0VfwGhVsjsDnL7y+wOcvvL7A5y++vulne32s7QqxgMBiIiIggPDyc+Pp6EhARy5MiBnZ0drq6ulClTxiLrGGiBzO4gt7/M7iC3JeLx0gAAAJ9JREFUv8zuILe/7q69uyqdIRsbm3vANXMOTEdHR0dHR0dHI0oIIQq+bSejOkM6Ojo6Ojo6Ou8amfNmpI6Ojo6Ojo6OQuidIR0dHR0dHR2p0TtDOjo6Ojo6OlKjd4Z0dHR0dHR0pEbvDOno6Ojo6OhIjd4Z0tHR0dHR0ZEavTOko6Ojo6OjIzV6Z0hHR0dHR0dHavTOkI6Ojo6Ojo7U/B/FfKkArxJedwAAAABJRU5ErkJggg==
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Simulating a Galton board with Markov chains2020-01-25T15:00:00+03:002020-01-25T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2020-01-25:/markov-chain-bean-machine.html<p>We will use the stationary distribution of an absorbing markov chain to simulate a Galton board.</p><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<h1 id="Simulating-a-Galton-board-with-Markov-chains">Simulating a Galton board with Markov chains<a class="anchor-link" href="#Simulating-a-Galton-board-with-Markov-chains">¶</a></h1>
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<p>We will use the stationary distribution of an absorbing markov chain to simulate a Galton board.</p>
<p>Usefull links:</p>
<ul>
<li><a href="https://www.codedrome.com/galton-board-in-python/">Galton Board simulator in Python</a></li>
<li><a href="https://vknight.org/blog/posts/galton-board/">Simulating a Galton board with Markov chains, eigenvalues and Python</a></li>
</ul>
<p>The <a href="https://en.wikipedia.org/wiki/Bean_machine">galton board</a> (also sometimes called a "Bean machine") is a lovely way of visualising the normal distribution.</p>
<p>This can be modelled mathematically using <a href="https://en.wikipedia.org/wiki/Markov_chain">Markov chains</a>.</p>
<p>We first need to set up the state space we're going to use. Here is a small diagram showing how it will be done:</p>
<p><img src="https://vknight.org/blog/src/2018-11-26-galton-board/diagram/main.png" alt=""></p>
<p>There are a few things there that don't look immediately like the usual galton board:</p>
<ul>
<li>It is essentially rotated: this is purely to make the mathematical formulation simpler.</li>
<li>There is a probability $p$: this is to play with later, it is taken as the probability of a given bead to fall to the left when hitting an obstacle. In the usual case: $p=1/2$.</li>
<li>We limit our number of rows of obstacles to $N$.</li>
</ul>
<p>Using this, our state space can be written as:</p>
$$
S = \{(i, j) \in \mathbb{R}^2| 0 \leq i \leq N, 0 \leq j \leq i\}
$$<p>A straight forward combinatorial argument gives:</p>
$$
|S| = \binom{N + 1}{2} = \frac{(N + 1)(N + 2)}{2}
$$
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="k">def</span> <span class="nf">all_states</span><span class="p">(</span><span class="n">number_of_rows</span><span class="p">):</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">number_of_rows</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="k">yield</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">get_number_of_states</span><span class="p">(</span><span class="n">number_of_rows</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">((</span><span class="n">number_of_rows</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">number_of_rows</span> <span class="o">+</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bm</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">all_states</span><span class="p">(</span><span class="n">number_of_rows</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">matrix</span><span class="p">(</span><span class="n">bm</span><span class="p">))</span>
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<pre>[[0 0]
[1 0]
[1 1]
[2 0]
[2 1]
[2 2]
[3 0]
[3 1]
[3 2]
[3 3]
[4 0]
[4 1]
[4 2]
[4 3]
[4 4]
[5 0]
[5 1]
[5 2]
[5 3]
[5 4]
[5 5]]
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<p>We can test that we have the correct count:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">number_of_rows</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">all_states</span><span class="p">(</span><span class="n">number_of_rows</span><span class="o">=</span><span class="n">number_of_rows</span><span class="p">)))</span> <span class="o">==</span> <span class="n">get_number_of_states</span><span class="p">(</span><span class="n">number_of_rows</span><span class="o">=</span><span class="n">number_of_rows</span><span class="p">)</span>
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<p>Now that we have our state space, to fully define our Markov chain we need to define our transitions. Given two elements $s^{(1)}, s^{(2)}\in S$ we have:</p>
$$
P_{s^{(1)}, s^{(2)}} =
\begin{cases}
p & \text{ if }s^{(2)} - s^{(1)} = (1, 0)\text{ and }{s_1^{(2)}} < N \\
1 - p & \text{ if }s^{(2)} - s^{(1)} = (1, 1)\text{ and }{s_1^{(2)}} < N \\
1 & \text{ if }s^{(2)} = N\\
0 & \text{otherwise} \\
\end{cases}
$$<p>The first two values ensure the beads bounce to the left and right accordingly and the third line ensures the final row is absorbing (the beads stay put once they've hit the bottom).</p>
<p>Here's some Python code that replicates this:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">transition_probability</span><span class="p">(</span><span class="n">in_state</span><span class="p">,</span> <span class="n">out_state</span><span class="p">,</span> <span class="n">number_of_rows</span><span class="p">,</span> <span class="n">probability_of_falling_left</span><span class="p">):</span>
<span class="k">if</span> <span class="n">in_state</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">number_of_rows</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">in_state</span><span class="p">,</span> <span class="n">out_state</span><span class="p">):</span>
<span class="k">return</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">out_state</span> <span class="o">-</span> <span class="n">in_state</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])):</span>
<span class="k">return</span> <span class="n">probability_of_falling_left</span>
<span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">out_state</span> <span class="o">-</span> <span class="n">in_state</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])):</span>
<span class="k">return</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">probability_of_falling_left</span>
<span class="k">return</span> <span class="mi">0</span>
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<p>We can use the above and the code to get all states to create the transition matrix:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_transition_matrix</span><span class="p">(</span><span class="n">number_of_rows</span><span class="p">,</span> <span class="n">probability_of_falling_left</span><span class="p">):</span>
<span class="n">number_of_states</span> <span class="o">=</span> <span class="n">get_number_of_states</span><span class="p">(</span><span class="n">number_of_rows</span><span class="o">=</span><span class="n">number_of_rows</span><span class="p">)</span>
<span class="n">P</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">number_of_states</span><span class="p">,</span> <span class="n">number_of_states</span><span class="p">))</span>
<span class="k">for</span> <span class="n">row</span><span class="p">,</span> <span class="n">in_state</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">all_states</span><span class="p">(</span><span class="n">number_of_rows</span><span class="o">=</span><span class="n">number_of_rows</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">col</span><span class="p">,</span> <span class="n">out_state</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">all_states</span><span class="p">(</span><span class="n">number_of_rows</span><span class="o">=</span><span class="n">number_of_rows</span><span class="p">)):</span>
<span class="n">P</span><span class="p">[</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">transition_probability</span><span class="p">(</span><span class="n">in_state</span><span class="p">,</span> <span class="n">out_state</span><span class="p">,</span> <span class="n">number_of_rows</span><span class="p">,</span> <span class="n">probability_of_falling_left</span><span class="p">)</span>
<span class="k">return</span> <span class="n">P</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">P</span> <span class="o">=</span> <span class="n">get_transition_matrix</span><span class="p">(</span><span class="n">number_of_rows</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">probability_of_falling_left</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">P</span><span class="p">)</span>
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<pre>[[0. 0.5 0.5 0. 0. 0. 0. 0. 0. 0. ]
[0. 0. 0. 0.5 0.5 0. 0. 0. 0. 0. ]
[0. 0. 0. 0. 0.5 0.5 0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0. 0.5 0.5 0. 0. ]
[0. 0. 0. 0. 0. 0. 0. 0.5 0.5 0. ]
[0. 0. 0. 0. 0. 0. 0. 0. 0.5 0.5]
[0. 0. 0. 0. 0. 0. 1. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0. 0. 1. 0. 0. ]
[0. 0. 0. 0. 0. 0. 0. 0. 1. 0. ]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 1. ]]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">matplotlib.patches</span> <span class="k">as</span> <span class="nn">mpatches</span>
<span class="k">def</span> <span class="nf">print_matrix</span><span class="p">(</span><span class="n">P</span><span class="p">):</span>
<span class="c1"># get the unique values from transition matrix</span>
<span class="n">values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">P</span><span class="o">.</span><span class="n">ravel</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
<span class="n">im</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s1">'none'</span><span class="p">)</span>
<span class="c1"># get the colors of the values, according to the colormap used by imshow</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="n">im</span><span class="o">.</span><span class="n">cmap</span><span class="p">(</span><span class="n">im</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">value</span><span class="p">))</span> <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">values</span><span class="p">]</span>
<span class="c1"># create a patch for every color </span>
<span class="n">patches</span> <span class="o">=</span> <span class="p">[</span><span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
<span class="n">label</span><span class="o">=</span><span class="s2">"Falling probability: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">values</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">values</span><span class="p">))]</span>
<span class="c1"># put those patched as legend-handles into the legend</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="n">patches</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.05</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="c1"># draw gridlines and hide ticks</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">which</span><span class="o">=</span><span class="s1">'major'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'both'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">ticks</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">+.</span><span class="mi">5</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">[</span><span class="mi">0</span><span class="p">]))]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">ticks</span><span class="o">=</span><span class="n">ticks</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">ticks</span><span class="o">=</span><span class="n">ticks</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">which</span><span class="o">=</span><span class="s1">'both'</span><span class="p">,</span> <span class="n">labelbottom</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">labelleft</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">print_matrix</span><span class="p">(</span><span class="n">P</span><span class="p">)</span>
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<p>Once we have done this we are more of less there. With $\pi=(1,...0)$, the product: $\pi P$ gives the distribution of our beads after they drop past the first row. Thus the following gives us the final distribution over all the states after the beads get to the last row:</p>
$$
\pi P ^ N
$$
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<div class="prompt input_prompt">In [179]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">expected_bean_drop</span><span class="p">(</span><span class="n">number_of_rows</span><span class="p">,</span> <span class="n">probability_of_falling_left</span><span class="p">):</span>
<span class="n">number_of_states</span> <span class="o">=</span> <span class="n">get_number_of_states</span><span class="p">(</span><span class="n">number_of_rows</span><span class="p">)</span>
<span class="n">pi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">number_of_states</span><span class="p">)</span>
<span class="n">pi</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">P</span> <span class="o">=</span> <span class="n">get_transition_matrix</span><span class="p">(</span><span class="n">number_of_rows</span><span class="o">=</span><span class="n">number_of_rows</span><span class="p">,</span> <span class="n">probability_of_falling_left</span><span class="o">=</span><span class="n">probability_of_falling_left</span><span class="p">)</span>
<span class="n">last_row_prob</span> <span class="o">=</span> <span class="p">(</span><span class="n">pi</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">matrix_power</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">number_of_rows</span><span class="p">))[</span><span class="o">-</span><span class="p">(</span><span class="n">number_of_rows</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):]</span>
<span class="k">return</span> <span class="n">last_row_prob</span>
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<p>Let us use this to plot over $N=30$ rows and also overlay with the normal pdf:</p>
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<div class="prompt input_prompt">In [202]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">scipy.stats</span>
<span class="n">number_of_rows</span> <span class="o">=</span> <span class="mi">30</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">number_of_rows</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">number_of_rows</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'coral'</span><span class="p">)</span>
<span class="n">exp_b</span> <span class="o">=</span> <span class="n">expected_bean_drop</span><span class="p">(</span><span class="n">number_of_rows</span><span class="o">=</span><span class="n">number_of_rows</span><span class="p">,</span> <span class="n">probability_of_falling_left</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">exp_b</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Probability"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Bin position"</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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
"
>
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<div class="prompt input_prompt">In [203]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plot_bean_drop</span><span class="p">(</span><span class="n">number_of_rows</span><span class="p">,</span> <span class="n">probability_of_falling_left</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">expected_bean_drop</span><span class="p">(</span><span class="n">number_of_rows</span><span class="o">=</span><span class="n">number_of_rows</span><span class="p">,</span>
<span class="n">probability_of_falling_left</span><span class="o">=</span><span class="n">probability_of_falling_left</span><span class="p">),</span>
<span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
<span class="k">return</span> <span class="n">plt</span>
</pre></div>
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<p>We can also plot this for a changing value of $p$:</p>
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<div class="prompt input_prompt">In [205]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">cm</span>
<span class="n">number_of_rows</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">number_of_rows</span><span class="p">):</span>
<span class="n">plot_bean_drop</span><span class="p">(</span>
<span class="n">number_of_rows</span><span class="o">=</span><span class="n">number_of_rows</span><span class="p">,</span>
<span class="n">probability_of_falling_left</span><span class="o">=</span><span class="n">p</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">"p=</span><span class="si">{</span><span class="n">p</span><span class="si">:</span><span class="s2">0.02f</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">viridis</span><span class="p">(</span><span class="n">p</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Probability"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Bin position"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
</pre></div>
</div>
</div>
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<p>We see that as the probability of falling left moves from 0 to 1 the distribution slow shifts across the bins.</p>
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Use Cython to speed up your Python code2019-09-02T15:00:00+03:002019-09-02T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2019-09-02:/use-cython-to-speedup-your-python-code.html<p>In this notebook will try to convert pure Python code to Cython and test speed up with magic functions.</p><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>If we have a code on pure Python usually we can speed it up with some tiny changes using Cython.</p>
<p>In its core Cython is an intermediate between Python and C/C++. It allows you to write your Python code with minor changes and then translate it into C code.</p>
<p>As usual im referencing to <a href="https://cython.readthedocs.io/en/latest/">official Cython documentation</a> with all details.</p>
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<p>The first thing we will do we will write our functions.</p>
<p>Lets use <a href="https://nekrasovp.github.io/number-sequences.html">number calculations</a> from other atricle in this blog.</p>
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<p>We will start with recursive calculation.</p>
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<div class="highlight"><pre><span></span><span class="c1"># stern_python.py</span>
<span class="k">def</span> <span class="nf">stern_n</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span>
<span class="k">return</span> <span class="n">n</span>
<span class="k">elif</span> <span class="n">n</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="n">stern_n</span><span class="p">(</span><span class="n">n</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">stern_n</span><span class="p">((</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">stern_n</span><span class="p">((</span><span class="n">n</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">run_stern</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="n">stern_array</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="n">stern_array</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">stern_n</span><span class="p">(</span><span class="n">i</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">stern_array</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">stern_python</span> <span class="k">as</span> <span class="nn">python_s</span>
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<p>Our Cython equivalent of the same functions look very similar.
For that we will create new file.</p>
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<div class="highlight"><pre><span></span><span class="c1"># stern_cython.pyx</span>
<span class="n">cpdef</span> <span class="nb">int</span> <span class="n">stern_n</span><span class="p">(</span><span class="nb">int</span> <span class="n">n</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span>
<span class="k">return</span> <span class="n">n</span>
<span class="k">elif</span> <span class="n">n</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="n">stern_n</span><span class="p">(</span><span class="n">n</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">stern_n</span><span class="p">((</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">stern_n</span><span class="p">((</span><span class="n">n</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
<span class="n">cpdef</span> <span class="n">run_stern</span><span class="p">(</span><span class="nb">int</span> <span class="n">n</span><span class="p">):</span>
<span class="n">stern_array</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="n">stern_array</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">stern_n</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
<span class="k">return</span> <span class="n">stern_array</span>
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<p>Next we will take list manupulation function with lazy calculation.</p>
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<div class="highlight"><pre><span></span><span class="c1"># hofsconwc_python.py</span>
<span class="k">def</span> <span class="nf">hofsconwc</span><span class="p">(</span><span class="n">i</span><span class="p">):</span>
<span class="n">hf</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n</span> <span class="o"><=</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">hf</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">hf</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">hf</span><span class="p">[</span><span class="n">hf</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span> <span class="o">+</span> <span class="n">hf</span><span class="p">[</span><span class="n">n</span> <span class="o">-</span> <span class="n">hf</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="k">return</span> <span class="n">hf</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">hofsconwc_python</span> <span class="k">as</span> <span class="nn">python_h</span>
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<p>and our Cython variant.</p>
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<div class="highlight"><pre><span></span><span class="c1"># hofsconwc_python.pyx</span>
<span class="n">cpdef</span> <span class="n">hofsconwc</span><span class="p">(</span><span class="nb">int</span> <span class="n">i</span><span class="p">):</span>
<span class="n">hf</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n</span> <span class="o"><=</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">hf</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">hf</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">hf</span><span class="p">[</span><span class="n">hf</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span> <span class="o">+</span> <span class="n">hf</span><span class="p">[</span><span class="n">n</span> <span class="o">-</span> <span class="n">hf</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="k">return</span> <span class="n">hf</span>
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<p>Next we will create <em>setup.py</em> file which will compile the Cython into C code.</p>
<p>We will use *.pyx as filename because we wanna try multiple files compilation.</p>
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<div class="highlight"><pre><span></span><span class="c1"># setup.py</span>
<span class="kn">from</span> <span class="nn">distutils.core</span> <span class="kn">import</span> <span class="n">setup</span>
<span class="kn">from</span> <span class="nn">Cython.Build</span> <span class="kn">import</span> <span class="n">cythonize</span>
<span class="n">setup</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'Testing calculation'</span><span class="p">,</span>
<span class="n">ext_modules</span><span class="o">=</span><span class="n">cythonize</span><span class="p">(</span><span class="s1">'*.pyx'</span><span class="p">),</span>
<span class="n">requires</span><span class="o">=</span><span class="p">[</span><span class="s1">'Cython'</span><span class="p">],</span> <span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>ls -ll
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<pre>total 224
-rw-rw-r-- 1 pavel pavel 187 ноя 7 13:21 hofsconwc_cython.pyx
-rw-rw-r-- 1 pavel pavel 180 ноя 7 13:20 hofsconwc_python.py
drwxrwxr-x 2 pavel pavel 4096 ноя 7 13:31 __pycache__
-rw-r--r-- 1 pavel pavel 39 окт 22 18:46 README.md
-rw-rw-r-- 1 pavel pavel 1006 ноя 7 12:20 run_test.py
-rw-rw-r-- 1 pavel pavel 170 ноя 7 12:14 setup.py
-rw-rw-r-- 1 pavel pavel 305 окт 22 20:41 stern_cython.pyx
-rw-rw-r-- 1 pavel pavel 295 окт 22 20:01 stern_python.py
-rw-r--r-- 1 pavel pavel 189442 ноя 7 13:48 'Use Cython to speedup your Python code.ipynb'
drwxrwxr-x 7 pavel pavel 4096 ноя 7 12:32 venv
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<p>All we need to do is just perform</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>python setup.py build_ext --inplace
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<pre>running build_ext
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>ls -ll
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<pre>total 680
drwxrwxr-x 3 pavel pavel 4096 ноя 7 13:48 build
-rw-rw-r-- 1 pavel pavel 132506 ноя 7 13:48 hofsconwc_cython.c
-rwxrwxr-x 1 pavel pavel 119808 ноя 7 13:48 hofsconwc_cython.cpython-37m-x86_64-linux-gnu.so
-rw-rw-r-- 1 pavel pavel 187 ноя 7 13:21 hofsconwc_cython.pyx
-rw-rw-r-- 1 pavel pavel 180 ноя 7 13:20 hofsconwc_python.py
drwxrwxr-x 2 pavel pavel 4096 ноя 7 13:31 __pycache__
-rw-r--r-- 1 pavel pavel 39 окт 22 18:46 README.md
-rw-rw-r-- 1 pavel pavel 1006 ноя 7 12:20 run_test.py
-rw-rw-r-- 1 pavel pavel 170 ноя 7 12:14 setup.py
-rw-rw-r-- 1 pavel pavel 109538 ноя 7 13:48 stern_cython.c
-rwxrwxr-x 1 pavel pavel 93632 ноя 7 13:48 stern_cython.cpython-37m-x86_64-linux-gnu.so
-rw-rw-r-- 1 pavel pavel 305 окт 22 20:41 stern_cython.pyx
-rw-rw-r-- 1 pavel pavel 295 окт 22 20:01 stern_python.py
-rw-r--r-- 1 pavel pavel 189442 ноя 7 13:48 'Use Cython to speedup your Python code.ipynb'
drwxrwxr-x 7 pavel pavel 4096 ноя 7 12:32 venv
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<p>It works! Now we can import Cython functions.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">stern_cython</span> <span class="k">as</span> <span class="nn">cython_s</span>
<span class="kn">import</span> <span class="nn">hofsconwc_cython</span> <span class="k">as</span> <span class="nn">cython_h</span>
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<p>So we are ready to test our calculations with some magic functions.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">timeit</span> _ = python_s.run_stern(10000)
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<pre>757 ms ± 39.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">timeit</span> _ = cython_s.run_stern(10000)
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<pre>7.26 ms ± 80.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
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<p>Yes we have <strong>x100</strong> time speed up with just type definition and precompiling our functions.</p>
<p>But what if we will try more real functions like hofsconwc without useless recursion.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">timeit</span> _ = python_h.hofsconwc(10000)
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<pre>2.75 ms ± 375 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">timeit</span> _ = cython_h.hofsconwc(10000)
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<pre>1.4 ms ± 26.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
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<p>Yes so easy we get <strong>x2</strong> speed up for our calculations.</p>
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Number sequences visualisation with python2019-08-29T15:00:00+03:002019-08-29T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2019-08-29:/number-sequences.html<p>In this notebook we will compute and visualise some famous and nice looking number sequences.</p><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<h1 id="Number-sequences">Number sequences<a class="anchor-link" href="#Number-sequences">¶</a></h1><p>Visit <a href="http://oeis.org/">The On-Line Encyclopedia of Integer Sequences® (OEIS®) oeis.org</a></p>
<ul>
<li><a href="#Binary-Representation">Binary Representation </a></li>
</ul>
<ul>
<li><a href="#Positive-integers">Positive integers</a></li>
<li><a href="#Integer-squares">Integer squares</a></li>
<li><a href="#Integer-square-root">Integer square root</a></li>
<li><a href="#Powers-of-2">Powers of 2</a></li>
<li><a href="#n*(n+1">n*(n+1)/2 sequence</a>/2-sequence)</li>
<li><a href="#Prime-numbers">Prime numbers</a></li>
<li><a href="#Fibonacci-numbers">Fibonacci numbers</a></li>
<li><a href="#Pascal's-triangle">Pascal's triangle</a></li>
</ul>
<ul>
<li><a href="#Stern's-diatomic-series">Stern's diatomic series</a></li>
<li><a href="#Fly-straight,-dammit-series">Fly straight, dammit series</a></li>
<li><a href="#Hofstadter-Q-sequence">Hofstadter Q-sequence</a></li>
<li><a href="#Hofstadter-Conway-sequence">Hofstadter-Conway sequence</a></li>
<li><p><a href="#A-chaotic-cousin-of-the-Hofstadter-Conway-sequence">A chaotic cousin of the Hofstadter-Conway sequence</a></p>
</li>
<li><p><a href="#Langton's-ant">Langton's ant</a></p>
</li>
</ul>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">reload_ext</span> watermark
<span class="o">%</span><span class="k">watermark</span> -v -m --iversions
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<pre>pandas 0.25.3
seaborn 0.10.0
matplotlib 3.1.1
numpy 1.17.2
CPython 3.7.3
IPython 7.8.0
compiler : GCC 8.0.1 20180414 (experimental) [trunk revision 259383
system : Linux
release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
CPU cores : 4
interpreter: 64bit
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<h2 id="Helpers">Helpers<a class="anchor-link" href="#Helpers">¶</a></h2><p>Define functions to draw the plots</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">show_scatter</span><span class="p">(</span><span class="n">a</span><span class="p">):</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="p">)),</span> <span class="n">a</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"."</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">show_bitmatrix</span><span class="p">(</span><span class="n">a</span><span class="p">):</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'Greys'</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s1">'nearest'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">([])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">([])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h2 id="Binary-Representation">Binary Representation<a class="anchor-link" href="#Binary-Representation">¶</a></h2><p><a href="http://mathworld.wolfram.com/BinaryPlot.html">Binary plot on mathworld.wolfram.com</a></p>
<p>A binary plot of an integer sequence is a plot of the binary representations of successive terms where each term is represented as a sequence of bits with 1s colored black and 0s colored white. Then each representation is stacked to form a table where each entry represents a bit in the sequence.
To make a binary plot of a sequence we need to convert each term of the sequence into its binary representation. Then we have to put this representation in a form which make us able to build the plot easily. The following function converts the sequence in a matrix where the element i-j represents is the bit j of the element i of the sequence:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">seq_to_bitmatrix</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
<span class="c1"># maximum number of bits used in this sequence</span>
<span class="n">maxbit</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">bin</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">s</span><span class="p">)))</span>
<span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">),</span><span class="n">maxbit</span><span class="p">),</span><span class="n">dtype</span><span class="o">=</span><span class="s1">'uint8'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">e</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
<span class="n">bits</span> <span class="o">=</span> <span class="nb">bin</span><span class="p">(</span><span class="n">e</span><span class="p">)[</span><span class="mi">2</span><span class="p">:]</span> <span class="c1"># bin() -> 0b100, we drop 2 chars</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span><span class="n">b</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bits</span><span class="p">):</span>
<span class="n">M</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">maxbit</span><span class="o">-</span><span class="nb">len</span><span class="p">(</span><span class="n">bits</span><span class="p">)</span><span class="o">+</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> <span class="c1"># fill the matrix</span>
<span class="k">return</span> <span class="n">M</span>
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<h2 id="Positive-integers">Positive integers<a class="anchor-link" href="#Positive-integers">¶</a></h2><p>0, 1, 2, 3...</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">posint_a</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_scatter</span><span class="p">(</span><span class="n">posint_a</span><span class="p">)</span>
<span class="n">show_bitmatrix</span><span class="p">(</span><span class="n">seq_to_bitmatrix</span><span class="p">(</span><span class="n">posint_a</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
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<h2 id="Integer-squares">Integer squares<a class="anchor-link" href="#Integer-squares">¶</a></h2><p>0, 1, 4, 9, 16, 25...</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sqint_a</span> <span class="o">=</span> <span class="p">[</span><span class="nb">pow</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">)]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_scatter</span><span class="p">(</span><span class="n">sqint_a</span><span class="p">)</span>
<span class="n">show_bitmatrix</span><span class="p">(</span><span class="n">seq_to_bitmatrix</span><span class="p">(</span><span class="n">sqint_a</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
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<h2 id="Integer-square-root">Integer square root<a class="anchor-link" href="#Integer-square-root">¶</a></h2><p>0, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3,</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sqrtint_a</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">n</span><span class="p">))</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">500</span><span class="p">)]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_scatter</span><span class="p">(</span><span class="n">sqrtint_a</span><span class="p">[:</span><span class="mi">50</span><span class="p">])</span>
<span class="n">show_scatter</span><span class="p">(</span><span class="n">sqrtint_a</span><span class="p">)</span>
<span class="n">show_bitmatrix</span><span class="p">(</span><span class="n">seq_to_bitmatrix</span><span class="p">(</span><span class="n">sqrtint_a</span><span class="p">[:</span><span class="mi">50</span><span class="p">])</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
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<h2 id="Powers-of-2">Powers of 2<a class="anchor-link" href="#Powers-of-2">¶</a></h2>
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<div class="prompt input_prompt">In [277]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">n</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">)]</span>
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<div class="prompt input_prompt">In [278]:</div>
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vLKK/dku9sJ7RNJrtv0+trFtL+hu+/u7uXuXl5aWtqp8eVNb3rTjq0LgKPpec97Xu6666492faW33VcVceSfDTJ92YjsB9M8qPd/fDX+zu+6xiAo+Qbfdfxlu/RdvdTVfXGJA8kuSzJPd8osgDAV23nZqh0928l+a3hsQDAoeOboQBgkNACwCChBYBBQgsAg4QWAAYJLQAMEloAGCS0ADBIaAFgkNACwCChBYBBQgsAg4QWAAYJLQAMEloAGFTdvfMrrTqb5I93cJXPTfKZHVzfUWQfXjr7cGfYj5fOPrx0O70P/153L51vxkhod1pVrXf38l6P4yCzDy+dfbgz7MdLZx9eut3chy4dA8AgoQWAQQcltHfv9QAOAfvw0tmHO8N+vHT24aXbtX14IN6jBYCD6qCc0QLAgbSvQ1tVN1fVY1V1pqrevNfj2a+q6rqqem9VPVJVD1fV7Yvpz6mqd1fVxxZ/Pnsxvarq3y3264er6rv29ifYP6rqsqr6g6q6f/H6hVX14GJf/deqesZi+hWL12cW84/v5bj3k6q6qqp+vao+UlWPVtXLHIsXpqr+9eLf8kNVdW9VPdOxuLWquqeqnqyqhzZNu+Bjr6pet1j+Y1X1uksd174NbVVdluTtSX4gyQ1Jbq2qG/Z2VPvWU0l+srtvSPLSJG9Y7Ks3J3lPd1+f5D2L18nGPr1+8bgtyTt2f8j71u1JHt30+t8k+dnu/vtJPpvk9Yvpr0/y2cX0n10sx4a7kryru78zyYls7E/H4jZV1TVJ/lWS5e5+UZLLkrw2jsXt+KUkNz9t2gUde1X1nCRvTfKSJDcmeeu5OF+07t6XjyQvS/LAptd3JLljr8d1EB5J/nuSVyR5LMnVi2lXJ3ls8fznk9y6afmvLHeUH0muXfxDvCnJ/UkqGx9oP7aY/5VjMskDSV62eH5ssVzt9c+w148k35LkE0/fF47FC9qH1yT5VJLnLI6t+5N8v2Nx2/vveJKHNr2+oGMvya1Jfn7T9L+x3MU89u0Zbb56sJ3z+GIa38DistGLkzyY5Pnd/enFrD9N8vzFc/v2/H4uyU8l+evF67+b5HPd/dTi9eb99JV9uJj/54vlj7oXJjmb5D8sLsH/QlVdGcfitnX3E0n+bZI/SfLpbBxbp+JYvFgXeuzt+DG5n0PLBaqqZyX5jSQ/0d2f3zyvN/5r5hbzr6OqfijJk919aq/HcsAdS/JdSd7R3S9O8v/y1Ut1SRyLW1lcpnx1Nv7T8q1JrszXXg7lIuzVsbefQ/tEkus2vb52MY3zqKrLsxHZX+7udy4m/5+qunox/+okTy6m27df6+VJbqmqTya5LxuXj+9KclVVHVsss3k/fWUfLuZ/S5L/u5sD3qceT/J4dz+4eP3r2QivY3H7vi/JJ7r7bHd/Kck7s3F8OhYvzoUeezt+TO7n0H4wyfWLO+2ekY2bAdb2eEz7UlVVkl9M8mh337lp1lqSc3fMvS4b792em/7PFnfdvTTJn2+6tHIkdfcd3X1tdx/PxrH2O939Y0nem+SHF4s9fR+e27c/vFj+yJ+ldfefJvlUVX3HYtL3JnkkjsUL8SdJXlpVf3vxb/vcPnQsXpwLPfYeSPLKqnr24urCKxfTLt5ev3G9xZvar0ry0SR/lOQtez2e/fpI8t3ZuBzy4SQfWjxelY33ad6T5GNJfjvJcxbLVzbu6P6jJH+Yjbsb9/zn2C+PJN+T5P7F829L8oEkZ5L8WpIrFtOfuXh9ZjH/2/Z63PvlkeRkkvXF8fjfkjzbsXjB+/BnknwkyUNJ/nOSKxyL29pv92bjfe0vZePqyusv5thL8i8W+/NMkn9+qePyzVAAMGg/XzoGgANPaAFgkNACwCChBYBBQgsAg4QWAAYJLQAMEloAGPT/AcUtovzM+cxHAAAAAElFTkSuQmCC
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<h2 id="n*(n+1)/2-sequence">n*(n+1)/2 sequence<a class="anchor-link" href="#n*(n+1)/2-sequence">¶</a></h2><p>n = n*(n+1)/2</p>
<p>0, 1, 3, 6, 10, 15, 21...</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">n</span><span class="o">*</span><span class="p">(</span><span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">)]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_scatter</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="n">show_bitmatrix</span><span class="p">(</span><span class="n">seq_to_bitmatrix</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
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<h2 id="Prime-numbers">Prime numbers<a class="anchor-link" href="#Prime-numbers">¶</a></h2><p><a href="http://oeis.org/A000040">A000040</a></p>
<p>The prime numbers.</p>
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<div class="prompt input_prompt">In [281]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">gen_primes</span><span class="p">():</span>
<span class="n">D</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">q</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
<span class="k">if</span> <span class="n">q</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">D</span><span class="p">:</span>
<span class="k">yield</span> <span class="n">q</span>
<span class="n">D</span><span class="p">[</span><span class="n">q</span> <span class="o">*</span> <span class="n">q</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">q</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">D</span><span class="p">[</span><span class="n">q</span><span class="p">]:</span>
<span class="n">D</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">p</span> <span class="o">+</span> <span class="n">q</span><span class="p">,</span> <span class="p">[])</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
<span class="k">del</span> <span class="n">D</span><span class="p">[</span><span class="n">q</span><span class="p">]</span>
<span class="n">q</span> <span class="o">+=</span> <span class="mi">1</span>
</pre></div>
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<div class="prompt input_prompt">In [282]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">f</span> <span class="o">=</span> <span class="n">gen_primes</span><span class="p">()</span>
<span class="n">pa</span> <span class="o">=</span> <span class="p">[</span><span class="nb">next</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">)]</span>
</pre></div>
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [283]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_scatter</span><span class="p">(</span><span class="n">pa</span><span class="p">)</span>
<span class="n">show_bitmatrix</span><span class="p">(</span><span class="n">seq_to_bitmatrix</span><span class="p">(</span><span class="n">pa</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
</pre></div>
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<h2 id="Fibonacci-numbers">Fibonacci numbers<a class="anchor-link" href="#Fibonacci-numbers">¶</a></h2><p><a href="http://oeis.org/A000045">A000045</a></p>
<p>Generates the Fibonacci numbers, starting with 0</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">fib</span><span class="p">():</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span>
<span class="k">while</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">yield</span> <span class="n">x</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="o">+</span><span class="n">y</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">f</span> <span class="o">=</span> <span class="n">fib</span><span class="p">()</span>
<span class="n">fib_a</span> <span class="o">=</span> <span class="p">[</span><span class="nb">next</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">200</span><span class="p">)]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_scatter</span><span class="p">(</span><span class="n">fib_a</span><span class="p">)</span>
<span class="n">show_bitmatrix</span><span class="p">(</span><span class="n">seq_to_bitmatrix</span><span class="p">(</span><span class="n">fib_a</span><span class="p">))</span>
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<h2 id="Pascal's-triangle">Pascal's triangle<a class="anchor-link" href="#Pascal's-triangle">¶</a></h2><p><a href="https://oeis.org/A007318">A007318</a></p>
<p>read by rows: C(n,k) = binomial(n,k) = n!/(k!*(n-k)!), 0 <= k <= n.</p>
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<div class="prompt input_prompt">In [287]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">prod</span><span class="p">;</span>
<span class="k">def</span> <span class="nf">C</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="k">return</span> <span class="n">prod</span><span class="p">([(</span><span class="n">n</span><span class="o">-</span><span class="n">j</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">j</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">k</span><span class="p">)])</span>
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<div class="prompt input_prompt">In [288]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">res</span><span class="o">=</span><span class="mi">1</span>
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">15</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="s2">"</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">'</span><span class="si">{:>5}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">C</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">i</span><span class="p">))))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">res</span><span class="p">)])</span>
<span class="n">res</span><span class="o">+=</span><span class="mi">1</span>
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<pre> 1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
1 5 10 10 5 1
1 6 15 20 15 6 1
1 7 21 35 35 21 7 1
1 8 28 56 70 56 28 8 1
1 9 36 84 126 126 84 36 9 1
1 10 45 120 210 252 210 120 45 10 1
1 11 55 165 330 461 461 329 164 54 10 0
1 12 66 220 495 792 924 792 495 220 66 12 1
1 13 78 286 715 1287 1716 1716 1287 715 286 78 13 1
1 14 91 364 1001 2002 3003 3432 3003 2002 1001 364 91 14 1
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">islice</span>
<span class="k">def</span> <span class="nf">pascal</span><span class="p">():</span>
<span class="n">row</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
<span class="k">yield</span> <span class="n">row</span>
<span class="n">row</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="n">j</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="n">row</span><span class="p">,</span> <span class="n">row</span><span class="o">+</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">islice</span><span class="p">(</span><span class="n">pascal</span><span class="p">(),</span><span class="mi">0</span><span class="p">,</span><span class="mi">11</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="si">{:^60}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">""</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">n</span><span class="p">:</span> <span class="s2">"</span><span class="si">{:>5}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n</span><span class="p">),</span> <span class="n">r</span><span class="p">))))</span>
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<pre> 1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
1 5 10 10 5 1
1 6 15 20 15 6 1
1 7 21 35 35 21 7 1
1 8 28 56 70 56 28 8 1
1 9 36 84 126 126 84 36 9 1
1 10 45 120 210 252 210 120 45 10 1
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">C</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="k">if</span> <span class="n">k</span><span class="o"><</span><span class="mi">0</span> <span class="ow">or</span> <span class="n">k</span><span class="o">></span><span class="n">n</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span>
<span class="n">res</span><span class="o">=</span><span class="mi">1</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">k</span><span class="p">):</span>
<span class="n">res</span><span class="o">=</span><span class="n">res</span><span class="o">*</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="n">i</span><span class="p">)</span><span class="o">//</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">res</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">res</span><span class="o">=</span><span class="mi">1</span>
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">11</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="s2">"</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">'</span><span class="si">{:>5}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">C</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">i</span><span class="p">))))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">res</span><span class="p">)])</span>
<span class="n">res</span><span class="o">+=</span><span class="mi">1</span>
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<pre> 1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
1 5 10 10 5 1
1 6 15 20 15 6 1
1 7 21 35 35 21 7 1
1 8 28 56 70 56 28 8 1
1 9 36 84 126 126 84 36 9 1
1 10 45 120 210 252 210 120 45 10 1
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<p>Concatenate lists to look at it as number sequence</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">C</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span> <span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">C</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">show_scatter</span><span class="p">(</span><span class="n">_</span><span class="p">)</span>
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<h2 id="Stern's-diatomic-series">Stern's diatomic series<a class="anchor-link" href="#Stern's-diatomic-series">¶</a></h2><p><a href="https://oeis.org/A002487">A002487</a></p>
<p>Stern's diatomic series (or Stern-Brocot sequence): a(0) = 0, a(1) = 1; for n > 0: a(2<em>n) = a(n), a(2</em>n+1) = a(n) + a(n+1).</p>
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<div class="prompt input_prompt">In [293]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">stern_n</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="k">return</span> <span class="n">n</span> <span class="k">if</span> <span class="n">n</span><span class="o"><</span><span class="mi">2</span> <span class="k">else</span> <span class="n">stern_n</span><span class="p">(</span><span class="n">n</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span> <span class="k">if</span> <span class="n">n</span><span class="o">%</span><span class="k">2</span>==0 else stern_n((n - 1)/2) + stern_n((n + 1)/2)
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<div class="prompt input_prompt">In [294]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stern_a</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">stern_n</span><span class="p">(</span><span class="n">i</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5000</span><span class="p">)]</span>
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<div class="prompt input_prompt">In [295]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_scatter</span><span class="p">(</span><span class="n">stern_a</span><span class="p">)</span>
</pre></div>
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<h2 id="Fly-straight,-dammit-series">Fly straight, dammit series<a class="anchor-link" href="#Fly-straight,-dammit-series">¶</a></h2><p><a href="https://oeis.org/A133058">A133058</a></p>
<p>1, 1, 4, 8, 2, 8, 4, 12, 3, 1, 12, 24, 2, 16, 8, 24, 3, 21, 7, 27, 48, 16...</p>
<p>a(0)=a(1)=1; for n>1, a(n) = a(n-1) + n + 1 if a(n-1) and n are coprime, otherwise a(n) = a(n-1)/gcd(a(n-1),n).</p>
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<div class="prompt input_prompt">In [296]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">gcd</span>
<span class="n">ra</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n</span><span class="o"><=</span><span class="mi">1</span><span class="p">:</span>
<span class="n">ra</span><span class="p">[</span><span class="n">n</span><span class="p">]</span><span class="o">=</span><span class="mi">1</span>
<span class="k">elif</span> <span class="n">gcd</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">ra</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">n</span><span class="p">)</span><span class="o">==</span><span class="mi">1</span><span class="p">:</span>
<span class="n">ra</span><span class="p">[</span><span class="n">n</span><span class="p">]</span><span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">ra</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">ra</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">ra</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span><span class="n">gcd</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">ra</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">n</span><span class="p">))</span>
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<div class="prompt input_prompt">In [297]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_scatter</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">ra</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
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<h2 id="Hofstadter-Q-sequence">Hofstadter Q-sequence<a class="anchor-link" href="#Hofstadter-Q-sequence">¶</a></h2><p><a href="https://oeis.org/A005185">A005185</a></p>
<p>1, 1, 2, 3, 3, 4, 5, 5, 6, 6, 6, 8, 8, 8, 10, 9, 10, 11...</p>
<p>Hofstadter Q-sequence: a(1) = a(2) = 1; a(n) = a(n-a(n-1)) + a(n-a(n-2)) for n > 2.</p>
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<div class="prompt input_prompt">In [298]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hofs</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10000</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n</span><span class="o"><=</span><span class="mi">2</span><span class="p">:</span>
<span class="n">hofs</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">hofs</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">hofs</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="n">hofs</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">+</span><span class="n">hofs</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="n">hofs</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">2</span><span class="p">]]</span>
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<div class="prompt input_prompt">In [299]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_scatter</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">hofs</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
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<h2 id="Hofstadter-Conway-sequence">Hofstadter-Conway sequence<a class="anchor-link" href="#Hofstadter-Conway-sequence">¶</a></h2><p><a href="https://oeis.org/A004001">A004001</a></p>
<p>1, 1, 2, 2, 3, 4, 4, 4, 5, 6, 7, 7, 8, 8, 8, 8, 9, 10, 11, 12, 12, 13, 14, 14...</p>
<p>Hofstadter-Conway $10000 sequence: a(n) = a(a(n-1)) + a(n-a(n-1)) with a(1) = a(2) = 1.</p>
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<div class="prompt input_prompt">In [300]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hofsconw</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5000</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n</span><span class="o"><=</span><span class="mi">2</span><span class="p">:</span>
<span class="n">hofsconw</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">hofsconw</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">hofsconw</span><span class="p">[</span><span class="n">hofsconw</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">+</span><span class="n">hofsconw</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="n">hofsconw</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
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<div class="prompt input_prompt">In [301]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_scatter</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">hofsconw</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
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<h2 id="A-chaotic-cousin-of-the-Hofstadter-Conway-sequence">A chaotic cousin of the Hofstadter-Conway sequence<a class="anchor-link" href="#A-chaotic-cousin-of-the-Hofstadter-Conway-sequence">¶</a></h2><p><a href="https://oeis.org/A055748">A055748</a></p>
<p>1, 1, 2, 2, 2, 3, 4, 4, 4, 4, 5, 6, 7, 8, 8, 8, 8, 8, 8, 9, 10...</p>
<p>a(1) = 1, a(2) = 1, a(n) = a(a(n-1)) + a(n - a(n-2) - 1) for n >= 3</p>
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<div class="prompt input_prompt">In [302]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hofsconwc</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n</span><span class="o"><=</span><span class="mi">2</span><span class="p">:</span>
<span class="n">hofsconwc</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">hofsconwc</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">hofsconwc</span><span class="p">[</span><span class="n">hofsconwc</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">+</span><span class="n">hofsconwc</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="n">hofsconwc</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
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<div class="prompt input_prompt">In [303]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_scatter</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">hofsconwc</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
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<h2 id="Langton's-ant">Langton's ant<a class="anchor-link" href="#Langton's-ant">¶</a></h2><p>Wikipedia, <a href="https://en.wikipedia.org/wiki/Langton%27s_ant">Langton's ant.</a></p>
<p><a href="https://oeis.org/A274369">A274369</a></p>
<p>Let the starting square of Langton's ant have coordinates (0, 0), with ant looking in negative x-direction. a(n) is the x-coordinate of the ant after n moves.</p>
<p>0, 0, 1, 1, 0, 0, -1, -1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1...</p>
<p><a href="https://oeis.org/A274370">A274370</a></p>
<p>Let the starting square of Langton's ant have coordinates (0, 0), with the ant looking in negative x-direction. a(n) is the y-coordinate of the ant after n moves.</p>
<p>0, 1, 1, 0, 0, -1, -1, 0, 0, -1, -1, -2, -2, -1, -1, 0, 0, -1, -1, -2...</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">ant</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="n">steps</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)]</span>
<span class="n">black</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">y</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">position</span> <span class="o">=</span> <span class="p">[(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)]</span>
<span class="n">direction</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="k">if</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="n">black</span><span class="p">:</span>
<span class="n">black</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">))</span>
<span class="n">direction</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">black</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">))</span>
<span class="n">direction</span> <span class="o">-=</span> <span class="mi">1</span>
<span class="p">(</span><span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="p">)</span> <span class="o">=</span> <span class="n">steps</span><span class="p">[</span><span class="n">direction</span><span class="o">%</span><span class="k">4</span>]
<span class="n">x</span> <span class="o">+=</span> <span class="n">dx</span>
<span class="n">y</span> <span class="o">+=</span> <span class="n">dy</span>
<span class="n">position</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">))</span>
<span class="k">return</span> <span class="n">position</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lang_x</span><span class="p">,</span> <span class="n">lang_y</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">ant_p</span> <span class="k">for</span> <span class="n">ant_p</span> <span class="ow">in</span> <span class="n">ant</span><span class="p">(</span><span class="mi">15000</span><span class="p">)])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_scatter</span><span class="p">(</span><span class="n">lang_x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_scatter</span><span class="p">(</span><span class="n">lang_y</span><span class="p">)</span>
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<div class="prompt input_prompt">In [308]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">lang_x</span><span class="p">,</span> <span class="n">lang_y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"p"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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Stock market portfolio optimisation2019-07-12T15:00:00+03:002019-07-12T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2019-07-12:/stock-market-portfolio-optimisation.html<p>The goal of this notebook is to provide an introduction to stock market portfolio optimisation using Python.</p><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<h1 id="Stock-market-portfolio-optimisation.">Stock market portfolio optimisation.<a class="anchor-link" href="#Stock-market-portfolio-optimisation.">¶</a></h1><p>The goal of this notebook is to provide an introduction to stock market portfolio optimisation using Python.
We will use pandas_datareader module to get data and PyPortfolioOpt for optimisation.
We will walk through a simple Python script to retrieve, analyze, and visualize data for predefined portfolio of stocks.</p>
<p>To read and use this notebook clone it from remote repository to your local computer and run in preferred environment.</p>
<p>Permanent link to repository: <a href="https://bitbucket.org/Nekrasovp/stock-market-portfolio-optimisation">bitbucket.org/Nekrasovp/stock-market-portfolio-optimisation</a></p>
<p>You can view this notebook on Jupyter Notebook <em>nbviewer</em> website: <a href="https://nbviewer.jupyter.org/urls/bitbucket.org/Nekrasovp/stock-market-portfolio-optimisation/raw/521575bd0a5fcdc80621e57f572313b98c312619/Stock%20market%20portfolio%20optimisation.ipynb">nbviewer.jupyter.org/urls/bitbucket.org/Nekrasovp/stock-market-portfolio-optimisation</a></p>
<h3 id="Table-of-content:">Table of content:<a class="anchor-link" href="#Table-of-content:">¶</a></h3><ul>
<li><a href="#project-setup">Project setup</a></li>
<li><a href="#Pulling-data">Pulling data</a></li>
<li><a href="#Preparing-data">Preparing data</a></li>
<li><a href="#Analyze-portfolio">Analyze portfolio</a><ul>
<li>Calculate the 'Markowitz portfolio', minimising volatility for a given target return.</li>
<li>Calculate the Sharpe-maximising portfolio for a given volatility (i.e max return for a target risk).</li>
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</li>
<li><a href="#Visualise-portfolio">Visualise portfolio</a><ul>
<li>Widget that allows you to interactively change the allocation.</li>
<li>Plot with individual stock's annual return and annual risk, efficient frontier curve and best allocation cases.</li>
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<h1 id="Project-setup">Project setup<a class="anchor-link" href="#Project-setup">¶</a></h1>
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<p>First thing we'll do is import the required dependencies.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">ipywidgets</span> <span class="kn">import</span> <span class="n">interactive</span><span class="p">,</span> <span class="n">FloatSlider</span><span class="p">,</span> <span class="n">IntText</span><span class="p">,</span> <span class="n">HBox</span><span class="p">,</span> <span class="n">Layout</span><span class="p">,</span> <span class="n">VBox</span><span class="p">,</span> <span class="n">interact</span><span class="p">,</span> <span class="n">Dropdown</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">'ignore'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">'fivethirtyeight'</span><span class="p">)</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = 'retina'
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<p>We will use <a href="https://pydata.github.io/pandas-datareader/stable/index.html">pandas_datareader</a> to get data from the market. And <a href="https://pyportfolioopt.readthedocs.io/en/latest/">pypfopt</a> for optimisation goals.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">pypfopt.efficient_frontier</span> <span class="kn">import</span> <span class="n">EfficientFrontier</span>
<span class="kn">from</span> <span class="nn">pypfopt</span> <span class="kn">import</span> <span class="n">risk_models</span><span class="p">,</span> <span class="n">expected_returns</span><span class="p">,</span> <span class="n">discrete_allocation</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas_datareader</span> <span class="k">as</span> <span class="nn">pdr</span>
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<p>Making the same request repeatedly can use a lot of bandwidth, slow down your code and may result in your IP being banned.</p>
<p>pandas_datareader allows you to cache queries using requests_cache</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">requests_cache</span>
<span class="n">expire_after</span> <span class="o">=</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">session</span> <span class="o">=</span> <span class="n">requests_cache</span><span class="o">.</span><span class="n">CachedSession</span><span class="p">(</span><span class="n">cache_name</span><span class="o">=</span><span class="s1">'cache'</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">'sqlite'</span><span class="p">,</span> <span class="n">expire_after</span><span class="o">=</span><span class="n">expire_after</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">reload_ext</span> watermark
<span class="o">%</span><span class="k">watermark</span> -v -m --iversions
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<pre>matplotlib 3.1.1
pandas 0.24.2
requests_cache 0.5.0
pandas_datareader 0.7.0
numpy 1.16.4
CPython 3.7.3
IPython 7.6.1
compiler : GCC 8.0.1 20180414 (experimental) [trunk revision 259383
system : Linux
release : 4.15.0-54-generic
machine : x86_64
processor : x86_64
CPU cores : 4
interpreter: 64bit
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<h1 id="Pulling-data">Pulling data<a class="anchor-link" href="#Pulling-data">¶</a></h1>
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<p>Define tickers you would like to analyze</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">securities</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'PRSP'</span><span class="p">,</span>
<span class="s1">'GOOGL'</span><span class="p">,</span>
<span class="s1">'IBM'</span><span class="p">,</span>
<span class="s1">'BTI'</span><span class="p">,</span>
<span class="s1">'BA'</span><span class="p">,</span>
<span class="s1">'BIDU'</span><span class="p">,</span>
<span class="s1">'CMA'</span><span class="p">,</span>
<span class="s1">'WDC'</span><span class="p">,</span>
<span class="s1">'SLB'</span><span class="p">,</span>
<span class="s1">'SKM'</span><span class="p">,</span>
<span class="s1">'F'</span><span class="p">,</span>
<span class="s1">'EIX'</span><span class="p">,</span>
<span class="s1">'FAST'</span><span class="p">,</span>
<span class="s1">'NOK'</span><span class="p">,</span>
<span class="s1">'DLR'</span><span class="p">,</span>
<span class="s1">'INTC'</span><span class="p">,</span>
<span class="s1">'CBPO'</span><span class="p">,</span>
<span class="s1">'NVDA'</span><span class="p">,</span>
<span class="s1">'SHI'</span><span class="p">,</span>
<span class="s1">'CAT'</span><span class="p">,</span> <span class="p">]</span>
<span class="n">sec_data</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">security</span> <span class="ow">in</span> <span class="n">securities</span><span class="p">:</span>
<span class="n">sec_df</span> <span class="o">=</span> <span class="n">pdr</span><span class="o">.</span><span class="n">DataReader</span><span class="p">(</span><span class="n">security</span><span class="p">,</span>
<span class="s1">'yahoo'</span><span class="p">,</span>
<span class="n">start</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2018</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
<span class="n">end</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2019</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
<span class="n">session</span><span class="o">=</span><span class="n">session</span><span class="p">)</span>
<span class="n">sec_data</span><span class="p">[</span><span class="n">security</span><span class="p">]</span> <span class="o">=</span> <span class="n">sec_df</span>
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<p>By looking at keys of resulted dict we can explore which securities we fetched successfully.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sec_data</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
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<pre>dict_keys(['PRSP', 'GOOGL', 'IBM', 'BTI', 'BA', 'BIDU', 'CMA', 'WDC', 'SLB', 'SKM', 'F', 'EIX', 'FAST', 'NOK', 'DLR', 'INTC', 'CBPO', 'NVDA', 'SHI', 'CAT'])</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sec_data</span><span class="p">[</span><span class="s1">'GOOGL'</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
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<th></th>
<th>High</th>
<th>Low</th>
<th>Open</th>
<th>Close</th>
<th>Volume</th>
<th>Adj Close</th>
</tr>
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<th>Date</th>
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<th>2018-07-10</th>
<td>1173.500000</td>
<td>1162.560059</td>
<td>1169.989990</td>
<td>1167.140015</td>
<td>1066700</td>
<td>1167.140015</td>
</tr>
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<th>2018-07-11</th>
<td>1180.439941</td>
<td>1155.369995</td>
<td>1155.619995</td>
<td>1171.459961</td>
<td>1662600</td>
<td>1171.459961</td>
</tr>
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<th>2018-07-12</th>
<td>1201.989990</td>
<td>1173.099976</td>
<td>1174.859985</td>
<td>1201.260010</td>
<td>2207400</td>
<td>1201.260010</td>
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<th>2018-07-13</th>
<td>1210.439941</td>
<td>1195.290039</td>
<td>1202.800049</td>
<td>1204.420044</td>
<td>1630600</td>
<td>1204.420044</td>
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<th>2018-07-16</th>
<td>1208.709961</td>
<td>1193.400024</td>
<td>1203.810059</td>
<td>1196.510010</td>
<td>1339200</td>
<td>1196.510010</td>
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<p>Let's look at how the price of each stock has evolved within give time frame.
While price of each stock is different there is no meaning to look at prices
themselves so we will plot daily returns (percent change compared to the day before).
By plotting daily returns instead of actual prices, we can see the stocks' volatility.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
<span class="k">for</span> <span class="n">security</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">sec_data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'PctChange'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">Close</span><span class="o">.</span><span class="n">pct_change</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">PctChange</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="n">security</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'daily returns'</span><span class="p">)</span>
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<pre>Text(0, 0.5, 'daily returns')</pre>
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I8BpwLvAe6kzAv3wIDtpUliVsCVlO832wMbA7sDn6zbfTUiDm54fKnNzIoeEfEOSsftssy8peGxtQ54Z2uLRMQplA/t9wF/OKY2vBVYQpmQ+gDgbkogfAA4m9KJsSAzn6rbL+blgbEkM++exmadTLmF/0uZ+dw01iu1hvkBmfnjiPg48Ang5oi4HHgYeBvwTsrwXr8N/HpwLVK7mRWSmhh3VmTmLyPiI8AFwBURcRXlyvDdKXfB3w3sSdd7ekSc1aeqCzJzxbpur9RWZsHL2rNPPcZrKd8x/hG4MyKO6/nBWJooZsVL7Ti/Z9Vy4G8j4qfAZygdr/2GSJUmglkxUGdEzy9MY50agZ2tLRERHwY+TRlK7119JnbuXAmxOf111o88pne9jf8SymTUe9cJqwGeAc6JiG2AxcBHgbPqc4tZMzRnxwpKOK11m2ub/rg+NHCkPsyPNTLz7Ii4F/gI5YrSDagT3APvpXS2rpza2Unt0NKskDTNZkJWAGTmhRHxE8rd7wsp7+P3AicCv0n5IaT7Pf1jfaq5gZIX66XNUpuYBUPb9BjwrYi4nfKD8UURsVNmPjv8bKT2MSsa+VfgfGDPiNg0M59uuJ/UGmZFfxGxJXA08Cxw0SufgdYlO1tboN6xcT6wjBI2/ToClgN7Ucb4vrNn/zmUscZfYPAEz03sDrwe+K+uHz+7XU/5wfPtnRWZufOQ+n4AvAjsGhFz+sy7+Ia6HDSuOZTOktcB387M5cObL00e86PvXA6X02fI8YjozKV0+5DjSq3U4qyQNI1mUFYAkJnX02ee9Yi4sP7x9q5tY0hVne8Rg+ZLavK9RJoYZkGzLMjMJyPiVuAoyjxydzTZT2oLs6JxVqyKiKcpQ5O+GrCzVRPFrBiaFScAG1FG9PTCzzFzztZZLiJOp4TN3cA7B4QNwHV12W98/wOBTYBb1nKY3Y3qcqsBz29dl883qSwzV1HmY9uEMkxgr0Pq8ro+z3V4G700gPkBDM+Pl0TEbsB+wNLMXNZkH6kt2pwVkqbPDMuKgSJiHuWCzP8FvtVwtx8ADwFvjIhd+jw/pc8VUpuZBcDUsmC7uuy9OFRqNbMCaP57xHxKR+vTwM8btkFqBbMCGJ4VJ9elfR8zgJ2ts1hE/B1lUug7KVd1DHvDvYzyhvz+iNirq465lPnNAD63lk1aRrmtfceIOKmnrfOA0+rDa6dQZ6dNZ9e2dupbABxPCbCX3YFWt9kJeA/w2KBtpEllfvTPj4jYrLeSOqfSv1HeM0+fwvGlWW9CskLSWpqBWUFEbNpn3SbAlyjzOP990x9bMjOBz9eHn4qIl75HR8SRlAu7vg98e23bLc1mZsHLsyAidqzTHPRr258CC4CfAEsbnZDUAmZF36zYpQ4H2tuGrYEv1ocX9xm1S2ots2L4d4yIOAB4E7AsM2+Zynlo3Yjymmq2iYgTKJMxv0iZJP2pPputyMwLuvY5ihI8q4CLgceBI4D5df1x2fMPIiLOZc3dI/sDuwHfBB6p65Zk5pKedn0RCMoPnXdRrr46gnK3yXeBhU1DJyKCMjfbMZS5TK4GXkvpKJkLHJ2ZVw7Y9xPAmcB5mXlqk+NJk8D8GJwftc0HA7dS5ljYrh5/HnBaZp7X5NhSG0xQVmwFnNu16hjK8FwXAp22npOZ93Xtsz/Q6ex9DWWOlJXA1zrbZOaJTY4vzXYzOCs+DJxKmRPpEcpngM4UI5/OzMVTPM+NKFeV70sZ6vNaYEfgWMrd9Isy87aefcwKTQyzoH8W1HO8lPL94kHgZ7UN+wB7UOadPywzvVhDE8GsGJgVJ1I6XW6iDHP6eN3+vZR5G+8ADnKYUE0Ks2Lwd4yufS8CPgCckpmfmcpxtY5kpmUWFuAsyg+Aw8oNffbbD7gGeIIycfJS4KPABgOOs+IVjnFWn30OBK6gBM5qypeHO4EzgLkjnOuc2saltc1P1HPYd8g+GwAP1zbOH/frZbHMpGJ+DM4P4FDKh5yVlA81j1J+HNln3K+bxbK+y6RkBbBzg/Nc2LPPia+0z7hfP4tlfZWZmhWUjoxrak48T7nS/RrgkLU4102AfwAeAJ6jjJJxKfDmAdubFZaJKWZB/yyg/GB6LnAbpaN1NWUo0O/V9TuM+7WzWNZnMSsGZsUelI6lpZQR+lZTOopuBP4C+I1xv3YWy/osZsXg7xh1ny3q+f0KmDfu18tSine2SpIkSZIkSZIkSdIInLNVkiRJkiRJkiRJkkZgZ6skSZIkSZIkSZIkjcDOVkmSJEmSJEmSJEkagZ2tkiRJkiRJkiRJkjQCO1slSZIkSZIkSZIkaQR2tkqSJEmSJEmSJEnSCOxslSRJkiRJkiRJkqQR2NkqSZIkSZIkSZIkSSOws1WSJEmSJEmSJEmSRmBnqyRJkiRJkiRJkiSNwM5WSZIkSZIkSZIkSRqBna2SJEmSJEmSJEmSNAI7WyVJkiRJkiRJkiRpBHa2SpIkSZIkSZIkSdII7GyVJEmSJEmSJEmSpBHY2SpJkiRJkiRJkiRJI7CzVZIkSZIkSZIkSZJG8H977J4r0TRnOwAAAABJRU5ErkJggg==
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<p>It seems that EIX has experienced a real "roller coaster" in the study period.</p>
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<div class="prompt input_prompt">In [50]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sec_data</span><span class="p">[</span><span class="s1">'EIX'</span><span class="p">][</span><span class="s1">'Close'</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'price in $'</span><span class="p">)</span>
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<div class="prompt output_prompt">Out[50]:</div>
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<pre>Text(0, 0.5, 'price in $')</pre>
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<p>Now we have a dictionary of dataframes, each containing the historical ohlcv prices data.</p>
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<h1 id="Preparing-data">Preparing data<a class="anchor-link" href="#Preparing-data">¶</a></h1>
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<p>For analyzing we will use only Close prices from each ohlcv tuple.</p>
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<div class=" highlight hl-ipython3"><pre><span></span> <span class="k">def</span> <span class="nf">build_pricing_data_from_ohlcv</span><span class="p">(</span><span class="n">ohlcv_data</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Build new pricing dict from ohlcv_data 'Close' column</span>
<span class="sd"> Add base asset column with price equal to '1'.</span>
<span class="sd"> :param ohlcv_data: Pricing data</span>
<span class="sd"> :return: Formated pricing data dict</span>
<span class="sd"> """</span>
<span class="n">data_c</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
<span class="k">for</span> <span class="n">m</span><span class="p">,</span> <span class="n">ohlcv</span> <span class="ow">in</span> <span class="n">ohlcv_data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">data_c</span><span class="p">[</span><span class="n">m</span><span class="p">]</span> <span class="o">=</span> <span class="n">ohlcv</span><span class="p">[</span><span class="s1">'Close'</span><span class="p">]</span>
<span class="k">return</span> <span class="n">data_c</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">prices_df</span> <span class="o">=</span> <span class="n">build_pricing_data_from_ohlcv</span><span class="p">(</span><span class="n">sec_data</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">'display.max_columns'</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">prices_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>PRSP</th>
<th>GOOGL</th>
<th>IBM</th>
<th>BTI</th>
<th>BA</th>
<th>...</th>
<th>INTC</th>
<th>CBPO</th>
<th>NVDA</th>
<th>SHI</th>
<th>CAT</th>
</tr>
<tr>
<th>Date</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2018-07-10</th>
<td>22.250000</td>
<td>1167.140015</td>
<td>144.710007</td>
<td>52.250000</td>
<td>347.160004</td>
<td>...</td>
<td>52.160000</td>
<td>106.720001</td>
<td>253.250000</td>
<td>60.139999</td>
<td>141.250000</td>
</tr>
<tr>
<th>2018-07-11</th>
<td>22.559999</td>
<td>1171.459961</td>
<td>144.940002</td>
<td>51.380001</td>
<td>340.600006</td>
<td>...</td>
<td>51.200001</td>
<td>105.809998</td>
<td>247.529999</td>
<td>59.200001</td>
<td>136.759995</td>
</tr>
<tr>
<th>2018-07-12</th>
<td>22.809999</td>
<td>1201.260010</td>
<td>146.449997</td>
<td>51.740002</td>
<td>346.029999</td>
<td>...</td>
<td>52.349998</td>
<td>100.459999</td>
<td>251.229996</td>
<td>60.500000</td>
<td>139.419998</td>
</tr>
<tr>
<th>2018-07-13</th>
<td>22.870001</td>
<td>1204.420044</td>
<td>145.899994</td>
<td>51.799999</td>
<td>350.790009</td>
<td>...</td>
<td>52.220001</td>
<td>100.889999</td>
<td>249.320007</td>
<td>61.150002</td>
<td>140.750000</td>
</tr>
<tr>
<th>2018-07-16</th>
<td>22.770000</td>
<td>1196.510010</td>
<td>145.460007</td>
<td>51.090000</td>
<td>356.100006</td>
<td>...</td>
<td>52.009998</td>
<td>101.400002</td>
<td>248.199997</td>
<td>62.650002</td>
<td>138.080002</td>
</tr>
</tbody>
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<p>5 rows × 20 columns</p>
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<h1 id="Analyze-portfolio">Analyze portfolio<a class="anchor-link" href="#Analyze-portfolio">¶</a></h1>
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<p>Methods of "portfolio optimization" as a solution to a particular optimization problem is a widely studied area of human knowledge.</p>
<p>Look more here:</p>
<ul>
<li><p><a href='https://colab.research.google.com/github/cvxgrp/cvx_short_course/blob/master/applications/portfolio_optimization.ipynb#scrollTo=QQDQCwAeWw96'>Stanford University Convex Optimization Group - Portfolio optimization</a></p>
</li>
<li><p><a href='https://evgenypogorelov.com/portfolio-rebalancing-python.html#portfolio-rebalancing-python'>Portfolio Rebalancing Using Python</a></p>
</li>
<li><p><a href='https://github.com/tthustla/efficient_frontier/blob/master/Efficient%20_Frontier_implementation.ipynb'>
The Efficinet Frontier (Markowitz Portfolio Optimisation)</a></p>
</li>
</ul>
<p>As mentioned above we will use <em>PyPortfolioOpt</em> package because its provide
clear and uncomplicated access to pandas and scipy optimisation methods used in our case.</p>
<ul>
<li><p><a href='https://pyportfolioopt.readthedocs.io/'>Financial portfolio optimisation in python, including classical efficient frontier and advanced methods. </a></p>
</li>
<li><p><a href='https://reasonabledeviations.science/2018/09/27/lessons-portfolio-opt/'>Portfolio optimisation: lessons learnt</a></p>
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<p><em>Expected returns</em> models:</p>
<ul>
<li>mean historical return</li>
<li>exponentially weighted mean historical return</li>
</ul>
<p><em>Risk</em> models:</p>
<ul>
<li>sample covariance</li>
<li>semicovariance</li>
<li>exponentially weighted covariance</li>
<li>mininum covariance determinant</li>
<li><p>shrunk covariance matrices:</p>
<ul>
<li>manual shrinkage</li>
<li>Ledoit Wolf shrinkage</li>
<li>Oracle Approximating shrinkage</li>
</ul>
</li>
</ul>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">generate_analysis_model</span><span class="p">(</span><span class="n">pricing_data</span><span class="p">,</span> <span class="n">weight_bounds</span><span class="p">):</span>
<span class="sd">"""Generate efficient frontier model with simple and intuitive estimators</span>
<span class="sd"> :param pricing_data: Adjusted closing prices of the asset, each row is a date</span>
<span class="sd"> and each column is a ticker/id.</span>
<span class="sd"> :param weight_bounds: Minimum and maximum weight of an asset</span>
<span class="sd"> :return: Efficient Frontier model</span>
<span class="sd"> """</span>
<span class="c1"># Annualised mean (daily) historical return from input (daily) asset prices</span>
<span class="n">mu</span> <span class="o">=</span> <span class="n">expected_returns</span><span class="o">.</span><span class="n">mean_historical_return</span><span class="p">(</span><span class="n">pricing_data</span><span class="p">,</span> <span class="n">frequency</span><span class="o">=</span><span class="mi">252</span><span class="p">)</span>
<span class="c1"># Annualised sample covariance matrix of (daily) asset returns</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">risk_models</span><span class="o">.</span><span class="n">sample_cov</span><span class="p">(</span><span class="n">pricing_data</span><span class="p">,</span> <span class="n">frequency</span><span class="o">=</span><span class="mi">252</span><span class="p">)</span>
<span class="k">return</span> <span class="n">EfficientFrontier</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">weight_bounds</span><span class="o">=</span><span class="n">weight_bounds</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">make_discrete_allocation</span><span class="p">(</span><span class="n">prices_df</span><span class="p">,</span> <span class="n">cleaned_weights</span><span class="p">,</span> <span class="n">total_portfolio_value</span><span class="p">):</span>
<span class="n">latest_prices</span> <span class="o">=</span> <span class="n">discrete_allocation</span><span class="o">.</span><span class="n">get_latest_prices</span><span class="p">(</span><span class="n">prices_df</span><span class="p">)</span>
<span class="n">da</span> <span class="o">=</span> <span class="n">discrete_allocation</span><span class="o">.</span><span class="n">DiscreteAllocation</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="n">cleaned_weights</span><span class="p">,</span>
<span class="n">latest_prices</span><span class="o">=</span><span class="n">latest_prices</span><span class="p">,</span>
<span class="n">total_portfolio_value</span><span class="o">=</span><span class="n">total_portfolio_value</span>
<span class="p">)</span>
<span class="n">allocation</span><span class="p">,</span> <span class="n">leftover</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">lp_portfolio</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">allocation</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Funds remaining: $</span><span class="si">{:.2f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">leftover</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">draw_cleaned_weights_with_perfomance</span><span class="p">(</span><span class="n">cleaned_weights</span><span class="p">,</span> <span class="n">p_perfomance</span><span class="p">):</span>
<span class="c1"># Filter non-zero values</span>
<span class="n">non_zero_weights</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">w</span><span class="p">:</span> <span class="n">w</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">></span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">cleaned_weights</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span>
<span class="c1"># Plot horizontal bar chart with resulted weights</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
<span class="n">_</span> <span class="o">=</span> <span class="n">non_zero_weights</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">_</span><span class="o">.</span><span class="n">keys</span><span class="p">())),</span>
<span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">k</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">_</span><span class="o">.</span><span class="n">values</span><span class="p">()],</span>
<span class="n">align</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span>
<span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s1">'#f3ba2f'</span><span class="p">,</span>
<span class="n">edgecolor</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span>
<span class="n">tick_label</span><span class="o">=</span><span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">_</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Ticker allocation, %'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Portfolio tickers'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Expected annual return: </span><span class="si">{:.2%}</span><span class="s1"> Annual volatility: </span><span class="si">{:.2%}</span><span class="s1"> Sharpe Ratio: </span><span class="si">{:.2f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">p_perfomance</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">p_perfomance</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">p_perfomance</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
<span class="k">for</span> <span class="n">rect</span> <span class="ow">in</span> <span class="n">ax</span><span class="o">.</span><span class="n">patches</span><span class="p">:</span>
<span class="c1"># Get X and Y placement of label from rect.</span>
<span class="n">x_value</span> <span class="o">=</span> <span class="n">rect</span><span class="o">.</span><span class="n">get_width</span><span class="p">()</span>
<span class="n">y_value</span> <span class="o">=</span> <span class="n">rect</span><span class="o">.</span><span class="n">get_y</span><span class="p">()</span> <span class="o">+</span> <span class="n">rect</span><span class="o">.</span><span class="n">get_height</span><span class="p">()</span> <span class="o">/</span> <span class="mi">2</span>
<span class="c1"># Number of points between bar and label. Change to your liking.</span>
<span class="n">space</span> <span class="o">=</span> <span class="mi">5</span>
<span class="c1"># Vertical alignment for positive values</span>
<span class="n">ha</span> <span class="o">=</span> <span class="s1">'left'</span>
<span class="c1"># If value of bar is negative: Place label left of bar</span>
<span class="k">if</span> <span class="n">x_value</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="c1"># Invert space to place label to the left</span>
<span class="n">space</span> <span class="o">*=</span> <span class="o">-</span><span class="mi">1</span>
<span class="c1"># Horizontally align label at left</span>
<span class="n">ha</span> <span class="o">=</span> <span class="s1">'left'</span>
<span class="c1"># Use X value as label and format number with one decimal place</span>
<span class="n">label</span> <span class="o">=</span> <span class="s2">"</span><span class="si">{:.1f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x_value</span><span class="p">)</span>
<span class="c1"># Create annotation</span>
<span class="n">plt</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span>
<span class="n">label</span><span class="p">,</span> <span class="c1"># Use `label` as label</span>
<span class="p">(</span><span class="n">x_value</span><span class="p">,</span> <span class="n">y_value</span><span class="p">),</span> <span class="c1"># Place label at end of the bar</span>
<span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="n">space</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="c1"># Horizontally shift label by `space`</span>
<span class="n">textcoords</span><span class="o">=</span><span class="s2">"offset points"</span><span class="p">,</span> <span class="c1"># Interpret `xytext` as offset in points</span>
<span class="n">va</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="c1"># Vertically center label</span>
<span class="n">ha</span><span class="o">=</span><span class="n">ha</span><span class="p">)</span> <span class="c1"># Horizontally align label</span>
<span class="c1"># plt.savefig("image.png")</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">EFmodel</span> <span class="o">=</span> <span class="n">generate_analysis_model</span><span class="p">(</span><span class="n">prices_df</span><span class="p">,</span> <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">))</span>
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<h3 id="Calculate-the-'Markowitz-portfolio',-minimising-volatility-for-a-given-target-return.">Calculate the 'Markowitz portfolio', minimising volatility for a given target return.<a class="anchor-link" href="#Calculate-the-'Markowitz-portfolio',-minimising-volatility-for-a-given-target-return.">¶</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">EFmodel</span><span class="o">.</span><span class="n">efficient_return</span><span class="p">(</span><span class="n">target_return</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
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<pre>{'PRSP': 0.07457978484549457,
'GOOGL': 0.0,
'IBM': 0.0,
'BTI': 0.0,
'BA': 0.0,
'BIDU': 3.754050022231059e-18,
'CMA': 2.5343225781848666e-18,
'WDC': 7.632783294297951e-17,
'SLB': 5.0550926292136644e-18,
'SKM': 0.09059050741712786,
'F': 0.0,
'EIX': 0.10282214987821588,
'FAST': 0.6087972153096218,
'NOK': 2.0477868532819965e-17,
'DLR': 0.12321034254954018,
'INTC': 0.0,
'CBPO': 0.0,
'NVDA': 0.0,
'SHI': 1.6506978076091805e-17,
'CAT': 0.0}</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cleaned_weights</span> <span class="o">=</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">clean_weights</span><span class="p">(</span><span class="n">cutoff</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">rounding</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">p_perfomance</span> <span class="o">=</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">portfolio_performance</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">draw_cleaned_weights_with_perfomance</span><span class="p">(</span><span class="n">cleaned_weights</span><span class="p">,</span> <span class="n">p_perfomance</span><span class="p">)</span>
<span class="n">make_discrete_allocation</span><span class="p">(</span><span class="n">prices_df</span><span class="p">,</span> <span class="n">cleaned_weights</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span>
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<pre>15 out of 20 tickers were removed
{'PRSP': 31, 'SKM': 37, 'EIX': 15, 'FAST': 195, 'DLR': 10}
Funds remaining: $7.08
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<h3 id="You-can-do-better-than-1/N">You can do better than 1/N<a class="anchor-link" href="#You-can-do-better-than-1/N">¶</a></h3><p>Here is a quote from <a href='https://reasonabledeviations.science/2018/09/27/lessons-portfolio-opt/'>Portfolio optimisation: lessons learnt</a>:</p>
<p>"A significant body of research suggests that, in light of the aforementioned failures of MVO, we should instead use 1/N portfolios(<a href='https://www.semanticscholar.org/paper/Optimal-Versus-Naive-Diversification%3A-How-is-the-DeMiguel-Garlappi/5056fa2683e15c4c84d13c3dbfb949dc133850a3'>Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?</a>) – it is noted that they often beat efficient frontier optimisation significantly in out-of-sample testing.</p>
<p>However, an interesting paper by Kritzman et al. (2010) finds that it is not the case that there is anything special about 1/N diversification: it is just that the expected returns are an incredibly poor estimator and any optimisation scheme which relies on them will likely go astray.</p>
<p>The easiest way to avoid this problem is to not provide the expected returns to the optimiser, and just optimise on the sample covariance matrix instead. Effectively we are saying that although previous returns won’t predict future returns, previous risks might predict future risks. This is intuitively a lot more reasonable – the sample covariance matrix really seems like it should contain a lot of information. Empirical results support this, showing that minimum variance portfolios outperform both standard MVO and 1/N diversification.</p>
<p>In my own work I have found that the standard minimum variance portfolio is a very good starting point, from which you can try a lot of new things:</p>
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<li>Shrinkage estimators on the covariance matrix</li>
<li>Exponential weighting</li>
<li>Different historical windows</li>
<li>Additional cost terms in the objective function (e.g small-weights penalty)</li>
<li>..."</li>
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<h3 id="Calculate-the-Sharpe-maximising-portfolio-for-a-given-volatility-(i.e-max-return-for-a-target-risk).">Calculate the Sharpe-maximising portfolio for a given volatility (i.e max return for a target risk).<a class="anchor-link" href="#Calculate-the-Sharpe-maximising-portfolio-for-a-given-volatility-(i.e-max-return-for-a-target-risk).">¶</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">EFmodel</span><span class="o">.</span><span class="n">efficient_risk</span><span class="p">(</span><span class="n">target_risk</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
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<pre>{'PRSP': 0.05055098383161007,
'GOOGL': 4.934204086981531e-16,
'IBM': 1.4046584533543294e-15,
'BTI': 0.0,
'BA': 0.0,
'BIDU': 0.0,
'CMA': 2.7773193900931803e-16,
'WDC': 0.0,
'SLB': 0.0,
'SKM': 4.137525554375604e-15,
'F': 0.0,
'EIX': 0.11517889383646501,
'FAST': 0.7954449526321907,
'NOK': 5.570156423780059e-16,
'DLR': 0.0388251696997469,
'INTC': 7.154311323052942e-16,
'CBPO': 2.3506926114837123e-15,
'NVDA': 0.0,
'SHI': 0.0,
'CAT': 0.0}</pre>
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<div class="prompt input_prompt">In [61]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cleaned_weights</span> <span class="o">=</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">clean_weights</span><span class="p">(</span><span class="n">cutoff</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">rounding</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">p_perfomance</span> <span class="o">=</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">portfolio_performance</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">draw_cleaned_weights_with_perfomance</span><span class="p">(</span><span class="n">cleaned_weights</span><span class="p">,</span> <span class="n">p_perfomance</span><span class="p">)</span>
<span class="n">make_discrete_allocation</span><span class="p">(</span><span class="n">prices_df</span><span class="p">,</span> <span class="n">cleaned_weights</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span>
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<pre>16 out of 20 tickers were removed
{'PRSP': 21, 'EIX': 17, 'FAST': 254, 'DLR': 3}
Funds remaining: $7.97
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<h1 id="Visualise-portfolio">Visualise portfolio<a class="anchor-link" href="#Visualise-portfolio">¶</a></h1>
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<p>One of the goal of this notebook is visualising Efficient portfolio. To achive this we will provide:</p>
<ul>
<li>Portfolio securities list.</li>
<li>Maximum target return and minimum volatility cases information.</li>
<li>Widget where you can choose different portfolio allocations along the Efficient frontier curve.</li>
<li>Final allocations for presented portfolio value.</li>
</ul>
<p>Next will plot:</p>
<ul>
<li>Individual stock's annual return and annual risk.</li>
<li>Maximum target return and minimum volatility allocation.</li>
<li>Efficient frontier curve.</li>
<li>Target return portfolio perfomance</li>
</ul>
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<div class="prompt input_prompt">In [63]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">generate_portfolio</span><span class="p">(</span><span class="n">prices_df</span><span class="p">,</span> <span class="n">target_return</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">target_risk</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> :return: Expected annual return(mu), Annual volatility(sigma), Sharpe Ratio</span>
<span class="sd"> """</span>
<span class="n">weight_bounds</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
<span class="n">EFmodel</span> <span class="o">=</span> <span class="n">generate_analysis_model</span><span class="p">(</span><span class="n">prices_df</span><span class="p">,</span> <span class="n">weight_bounds</span><span class="p">)</span>
<span class="k">if</span> <span class="n">target_return</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">EFmodel</span><span class="o">.</span><span class="n">efficient_return</span><span class="p">(</span><span class="n">target_return</span><span class="o">=</span><span class="n">target_return</span><span class="p">)</span>
<span class="k">return</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">portfolio_performance</span><span class="p">()</span>
<span class="k">if</span> <span class="n">target_risk</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">EFmodel</span><span class="o">.</span><span class="n">efficient_risk</span><span class="p">(</span><span class="n">target_risk</span><span class="o">=</span><span class="n">target_risk</span><span class="p">)</span>
<span class="k">return</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">portfolio_performance</span><span class="p">()</span>
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<div class="prompt input_prompt">In [64]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">draw_portfolio</span><span class="p">(</span><span class="n">prices_df</span><span class="p">,</span> <span class="n">opt_obj</span><span class="p">,</span> <span class="n">total_v</span><span class="p">,</span> <span class="n">target_return</span><span class="p">,</span> <span class="n">target_risk</span><span class="p">):</span>
<span class="n">weight_bounds</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
<span class="n">EFmodel</span> <span class="o">=</span> <span class="n">generate_analysis_model</span><span class="p">(</span><span class="n">prices_df</span><span class="p">,</span> <span class="n">weight_bounds</span><span class="p">)</span>
<span class="k">if</span> <span class="n">opt_obj</span> <span class="o">==</span> <span class="s1">'target_return'</span><span class="p">:</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">efficient_return</span><span class="p">(</span><span class="n">target_return</span><span class="o">=</span><span class="n">target_return</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">opt_obj</span> <span class="o">==</span> <span class="s1">'target_risk'</span><span class="p">:</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">efficient_risk</span><span class="p">(</span><span class="n">target_risk</span><span class="o">=</span><span class="n">target_risk</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">opt_obj</span> <span class="o">==</span> <span class="s1">'max_sharpe'</span><span class="p">:</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">max_sharpe</span><span class="p">()</span>
<span class="k">elif</span> <span class="n">opt_obj</span> <span class="o">==</span> <span class="s1">'min_volatility'</span><span class="p">:</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">min_volatility</span><span class="p">()</span>
<span class="n">cleaned_weights</span> <span class="o">=</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">clean_weights</span><span class="p">(</span><span class="n">cutoff</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">rounding</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">p_perfomance</span> <span class="o">=</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">portfolio_performance</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">draw_cleaned_weights_with_perfomance</span><span class="p">(</span><span class="n">cleaned_weights</span><span class="p">,</span> <span class="n">p_perfomance</span><span class="p">)</span>
<span class="n">make_discrete_allocation</span><span class="p">(</span><span class="n">prices_df</span><span class="p">,</span> <span class="n">cleaned_weights</span><span class="p">,</span> <span class="n">total_v</span><span class="p">)</span>
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<div class="prompt input_prompt">In [67]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">prices_df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="c1"># Calculate the 'Markowitz portfolio', minimising volatility for a given target_return</span>
<span class="n">max_annual_return_portfolio</span> <span class="o">=</span> <span class="n">generate_portfolio</span><span class="p">(</span><span class="n">prices_df</span><span class="o">=</span><span class="n">p</span><span class="p">,</span> <span class="n">target_return</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
<span class="c1"># Calculate the Sharpe-maximising portfolio for a given volatility(max return for a target_risk).</span>
<span class="n">min_annual_volatility_portfolio</span> <span class="o">=</span> <span class="n">generate_portfolio</span><span class="p">(</span><span class="n">prices_df</span><span class="o">=</span><span class="n">p</span><span class="p">,</span> <span class="n">target_risk</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Portfolio tickers:</span><span class="si">{</span><span class="n">securities</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Minimising volatility for maximum target_return portfolio allocation:</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Annualised Return:</span><span class="si">{</span><span class="n">max_annual_return_portfolio</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.1%</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Annualised Volatility:</span><span class="si">{</span><span class="n">max_annual_return_portfolio</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">.1%</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Sharpe Ratio:</span><span class="si">{</span><span class="n">max_annual_return_portfolio</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Sharpe-maximising portfolio for a minimum volatility(max return for a target_risk) allocation:</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Annualised Return:</span><span class="si">{</span><span class="n">min_annual_volatility_portfolio</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.1%</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Annualised Volatility:</span><span class="si">{</span><span class="n">min_annual_volatility_portfolio</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">.1%</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Sharpe Ratio:</span><span class="si">{</span><span class="n">min_annual_volatility_portfolio</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">opt_obj</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tr</span><span class="p">,</span> <span class="n">tri</span><span class="p">):</span>
<span class="n">draw_portfolio</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">opt_obj</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tr</span><span class="p">,</span> <span class="n">tri</span><span class="p">)</span>
<span class="n">opt_obj_widget</span> <span class="o">=</span> <span class="n">Dropdown</span><span class="p">(</span><span class="n">options</span><span class="o">=</span><span class="p">[</span><span class="s1">'target_return'</span><span class="p">,</span> <span class="s1">'target_risk'</span><span class="p">,</span> <span class="s1">'max_sharpe'</span><span class="p">,</span> <span class="s1">'min_volatility'</span><span class="p">],</span>
<span class="n">value</span><span class="o">=</span><span class="s1">'target_return'</span><span class="p">,</span>
<span class="n">description</span><span class="o">=</span><span class="s1">'Otimisation objective:'</span><span class="p">,</span>
<span class="n">style</span><span class="o">=</span><span class="p">{</span><span class="s1">'description_width'</span><span class="p">:</span> <span class="s1">'initial'</span><span class="p">},</span>
<span class="n">disabled</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="p">)</span>
<span class="k">def</span> <span class="nf">update_opt_obj</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">):</span>
<span class="k">if</span> <span class="n">opt_obj_widget</span><span class="o">.</span><span class="n">value</span> <span class="o">==</span> <span class="s1">'target_return'</span><span class="p">:</span>
<span class="n">risk_slider_widget</span><span class="o">.</span><span class="n">disabled</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">return_slider_widget</span><span class="o">.</span><span class="n">disabled</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">elif</span> <span class="n">opt_obj_widget</span><span class="o">.</span><span class="n">value</span> <span class="o">==</span> <span class="s1">'target_risk'</span><span class="p">:</span>
<span class="n">risk_slider_widget</span><span class="o">.</span><span class="n">disabled</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">return_slider_widget</span><span class="o">.</span><span class="n">disabled</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">elif</span> <span class="n">opt_obj_widget</span><span class="o">.</span><span class="n">value</span> <span class="o">==</span> <span class="s1">'max_sharpe'</span><span class="p">:</span>
<span class="n">risk_slider_widget</span><span class="o">.</span><span class="n">disabled</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">return_slider_widget</span><span class="o">.</span><span class="n">disabled</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">elif</span> <span class="n">opt_obj_widget</span><span class="o">.</span><span class="n">value</span> <span class="o">==</span> <span class="s1">'min_volatility'</span><span class="p">:</span>
<span class="n">risk_slider_widget</span><span class="o">.</span><span class="n">disabled</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">return_slider_widget</span><span class="o">.</span><span class="n">disabled</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">opt_obj_widget</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="n">update_opt_obj</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">)</span>
<span class="n">total_v_widget</span> <span class="o">=</span> <span class="n">IntText</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s1">'Total value:'</span><span class="p">,</span> <span class="n">disabled</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">risk_slider_widget</span> <span class="o">=</span> <span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="nb">min</span><span class="o">=</span><span class="n">min_annual_volatility_portfolio</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="nb">max</span><span class="o">=</span><span class="n">max_annual_return_portfolio</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">step</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span>
<span class="n">description</span><span class="o">=</span><span class="s1">'Target risk:'</span><span class="p">,</span>
<span class="n">disabled</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">continuous_update</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">layout</span><span class="o">=</span><span class="n">Layout</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="s1">'50%'</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="s1">'40px'</span><span class="p">),</span>
<span class="n">readout</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">readout_format</span><span class="o">=</span><span class="s1">'.1%'</span><span class="p">,)</span>
<span class="n">return_slider_widget</span> <span class="o">=</span> <span class="n">FloatSlider</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="n">max_annual_return_portfolio</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="mf">0.9</span><span class="p">,</span>
<span class="nb">min</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="nb">max</span><span class="o">=</span><span class="n">max_annual_return_portfolio</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">step</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span>
<span class="n">description</span><span class="o">=</span><span class="s1">'Target return:'</span><span class="p">,</span>
<span class="n">disabled</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">continuous_update</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">orientation</span><span class="o">=</span><span class="s1">'horizontal'</span><span class="p">,</span>
<span class="n">style</span><span class="o">=</span><span class="p">{</span><span class="s1">'description_width'</span><span class="p">:</span> <span class="s1">'initial'</span><span class="p">},</span>
<span class="n">layout</span><span class="o">=</span><span class="n">Layout</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="s1">'50%'</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="s1">'40px'</span><span class="p">),</span>
<span class="n">readout</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">readout_format</span><span class="o">=</span><span class="s1">'.1%'</span><span class="p">,)</span>
<span class="n">interactive_plot</span> <span class="o">=</span> <span class="n">interactive</span><span class="p">(</span><span class="n">f</span><span class="p">,</span>
<span class="n">opt_obj</span><span class="o">=</span><span class="n">opt_obj_widget</span><span class="p">,</span>
<span class="n">x</span><span class="o">=</span><span class="n">total_v_widget</span><span class="p">,</span>
<span class="n">tr</span><span class="o">=</span><span class="n">return_slider_widget</span><span class="p">,</span>
<span class="n">tri</span><span class="o">=</span><span class="n">risk_slider_widget</span><span class="p">,)</span>
<span class="n">interactive_plot</span>
</pre></div>
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<pre>Portfolio tickers:['PRSP', 'GOOGL', 'IBM', 'BTI', 'BA', 'BIDU', 'CMA', 'WDC', 'SLB', 'SKM', 'F', 'EIX', 'FAST', 'NOK', 'DLR', 'INTC', 'CBPO', 'NVDA', 'SHI', 'CAT']
Minimising volatility for maximum target_return portfolio allocation:
Annualised Return:26.9%
Annualised Volatility:26.6%
Sharpe Ratio:0.27
Sharpe-maximising portfolio for a minimum volatility(max return for a target_risk) allocation:
Annualised Return:-3.8%
Annualised Volatility:12.7%
Sharpe Ratio:0.13
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<p>Next we can plot each individual stocks on the plot with the corresponding values of each stock's annual return and annual risk. This way we can see and compare how diversification is lowering the risk by optimising the allocation.
For example we will take recommended in previous state portfolio by filtering presented dataframe.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">individual_assets</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'PRSP'</span><span class="p">,</span> <span class="s1">'GOOGL'</span><span class="p">,</span> <span class="s1">'IBM'</span><span class="p">,</span> <span class="s1">'BTI'</span><span class="p">,</span> <span class="s1">'BA'</span><span class="p">,</span> <span class="s1">'BIDU'</span><span class="p">,</span> <span class="s1">'CMA'</span><span class="p">,</span> <span class="s1">'WDC'</span><span class="p">,</span> <span class="s1">'SLB'</span><span class="p">,</span> <span class="s1">'SKM'</span><span class="p">,</span> <span class="s1">'F'</span><span class="p">,</span> <span class="s1">'EIX'</span><span class="p">,</span> <span class="s1">'FAST'</span><span class="p">,</span> <span class="s1">'NOK'</span><span class="p">,</span> <span class="s1">'DLR'</span><span class="p">,</span> <span class="s1">'INTC'</span><span class="p">,</span> <span class="s1">'CBPO'</span><span class="p">,</span> <span class="s1">'NVDA'</span><span class="p">,</span> <span class="s1">'SHI'</span><span class="p">,</span> <span class="s1">'CAT'</span><span class="p">]</span>
<span class="c1"># individual_assets = ['PRSP', 'SKM', 'EIX', 'FAST', 'DLR']</span>
<span class="n">t_return_opt</span> <span class="o">=</span> <span class="mf">0.16</span>
<span class="n">i_stocks_assets_data</span> <span class="o">=</span> <span class="n">prices_df</span><span class="p">[</span><span class="n">individual_assets</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">max_annual_return_portfolio</span> <span class="o">=</span> <span class="n">generate_portfolio</span><span class="p">(</span><span class="n">prices_df</span><span class="o">=</span><span class="n">i_stocks_assets_data</span><span class="p">,</span> <span class="n">target_return</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">min_annual_volatility_portfolio</span> <span class="o">=</span> <span class="n">generate_portfolio</span><span class="p">(</span><span class="n">prices_df</span><span class="o">=</span><span class="n">i_stocks_assets_data</span><span class="p">,</span> <span class="n">target_risk</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
<span class="n">max_annual_volatility_portfolio</span> <span class="o">=</span> <span class="n">generate_portfolio</span><span class="p">(</span><span class="n">prices_df</span><span class="o">=</span><span class="n">i_stocks_assets_data</span><span class="p">,</span> <span class="n">target_risk</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
<span class="n">opt_portfolio</span> <span class="o">=</span> <span class="n">generate_portfolio</span><span class="p">(</span><span class="n">prices_df</span><span class="o">=</span><span class="n">i_stocks_assets_data</span><span class="p">,</span> <span class="n">target_return</span><span class="o">=</span><span class="n">t_return_opt</span><span class="p">)</span>
<span class="n">i_stocks_perfomance</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">individual_assets</span><span class="p">:</span>
<span class="n">EFmodel</span> <span class="o">=</span> <span class="n">generate_analysis_model</span><span class="p">(</span><span class="n">i_stocks_assets_data</span><span class="p">[</span><span class="n">a</span><span class="p">],</span> <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">))</span>
<span class="n">EFmodel</span><span class="o">.</span><span class="n">efficient_return</span><span class="p">(</span><span class="n">target_return</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
<span class="n">i_stocks_perfomance</span><span class="p">[</span><span class="n">a</span><span class="p">]</span> <span class="o">=</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">portfolio_performance</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">([</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">i_stocks_perfomance</span><span class="o">.</span><span class="n">values</span><span class="p">()],</span>
<span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">i_stocks_perfomance</span><span class="o">.</span><span class="n">values</span><span class="p">()],</span>
<span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span><span class="n">s</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
<span class="k">for</span> <span class="n">a</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">i_stocks_perfomance</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">'offset points'</span><span class="p">)</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">min_annual_volatility_portfolio</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">max_annual_return_portfolio</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">ef_curve_portfolios_perfomance</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">target</span><span class="p">:</span>
<span class="n">EFmodel</span> <span class="o">=</span> <span class="n">generate_analysis_model</span><span class="p">(</span><span class="n">i_stocks_assets_data</span><span class="p">,</span> <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">))</span>
<span class="n">EFmodel</span><span class="o">.</span><span class="n">efficient_risk</span><span class="p">(</span><span class="n">target_risk</span><span class="o">=</span><span class="n">t</span><span class="p">)</span>
<span class="n">ef_curve_portfolios_perfomance</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">portfolio_performance</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">target_ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">max_annual_volatility_portfolio</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">max_annual_return_portfolio</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">30</span><span class="p">)</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">target_ret</span><span class="p">:</span>
<span class="n">EFmodel</span> <span class="o">=</span> <span class="n">generate_analysis_model</span><span class="p">(</span><span class="n">i_stocks_assets_data</span><span class="p">,</span> <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">))</span>
<span class="n">EFmodel</span><span class="o">.</span><span class="n">efficient_return</span><span class="p">(</span><span class="n">target_return</span><span class="o">=</span><span class="n">t</span><span class="p">)</span>
<span class="n">ef_curve_portfolios_perfomance</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">EFmodel</span><span class="o">.</span><span class="n">portfolio_performance</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">ef_curve_portfolios_perfomance</span><span class="o">.</span><span class="n">values</span><span class="p">()],</span>
<span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">ef_curve_portfolios_perfomance</span><span class="o">.</span><span class="n">values</span><span class="p">()],</span>
<span class="n">linestyle</span><span class="o">=</span><span class="s1">'-.'</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s1">'efficient frontier'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">opt_portfolio</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">opt_portfolio</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">marker</span><span class="o">=</span><span class="s1">'p'</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span><span class="n">s</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Minimum Volatility for target return'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">max_annual_return_portfolio</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">max_annual_return_portfolio</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">marker</span><span class="o">=</span><span class="s1">'*'</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">'r'</span><span class="p">,</span><span class="n">s</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Minimum volatility for maximum return'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">min_annual_volatility_portfolio</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="n">min_annual_volatility_portfolio</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">marker</span><span class="o">=</span><span class="s1">'*'</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">'g'</span><span class="p">,</span><span class="n">s</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Sharpe-maximising portfolio for minimum volatility'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Portfolio Optimization with Individual Stocks'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'annualised volatility'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'annualised returns'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">labelspacing</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">'best'</span><span class="p">)</span>
<span class="c1"># ax.savefig("image.png")</span>
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czl+ArY7H5t547bkBNRPn/nZqwS33Viskn7bqAF1tpdwBL3a7PcDtibCxvcinR/ZdqGU6kLTssEv12gWWtP49wnAPXyq2DGmAeBp9yvy3C6IQtlvbLGmDrGmEbGmKvdikDf6+K6/CpjAG1IH1PpvwEqA70m+fz9h4CpnPO0Kcjyb93PqqEOHO2Ht1KyldvdlJc3SPKztXYj8HOm+d7uqbxdjAWt3CwA04Is+9bn7+y6UssvuSqPO7i2dyyP/7nB4kACjs0UCjd4Otv92tatZM/ArcC/zf36sc352FgdfP4OWF43QPhxDvPON+4g5vWNMVf5PC+8QcuKxhh/44HkC2vtWJwuz2aQHtzyisDpsuo5YLsx5jljTF7rRfLr2dTN/bQ4XZLmiXutrQRuxfm3xxBr7fNBVtnv8/f9ufjdFxERueApmCIiIiKFyTuWRg9rbR8/QQ7vG9pbbPaDyH7jfhqyvtnt68ecFzNfFMS+5Ijbh7+3P/d1IQSVvOWIAerkcHO+wZDcBDB818nSasBHArAxm7zWup9h+B+3oiBsyWa5d5+2WWttkHTeayU3/f5nYYxpB7zuft0K9LTWJgVJX8MY85IxZgfOOd2NU+m/0Z3W+yTPrgVRXvneC6uzSfuNz9/XBEmX3XnyfbM/t+fAO86Jh4yDp3uDJt5g37JM88GpEPZuNyfjpeSHYMcmP45LTuW2PL7nfy3BfUd6y8DcmuJ+huGMdZFZf9L/Hz7Fz/Ls5GR/sluer4wxfzDGfGqMOUF6F4abSH9ePOuTvECfF9baH6y1/XC62LwVeAInILfLJ1lJnFYkORrE3Y/8ejbd4H5usdbmpkWnr0buthrhBO+7W2vfy2adr3G6+ASne8HNxpix7nnNzbhdIiIiFxwFU0REROR8WE/GgVsb4gzCGm2t7WCtDdTNifc/76FUKvimCdaa40QIeRWEgtiX3JbhfJTjmM/flwRMFZjvOsG6EzmWzVvAAL5daOV3S59A4rNZ7i1zqOnCg6YKgTGmEc6b6hE456eLtTbg/WCMaY9TgT0CJwiX3VvKuRq4Owd8z11212+o166/bth8+V5buT0HvkGQNuB0dUh6i5OlmT5b+7Rg8Q2snM9gSkqwLt7In+OSE3kpj+/599edXhprbSLpAczcWkr6G/73+Vnunbcbp/I6p7z7kxTs/nUF3d/8YhxvAAtxBiz3hLBaQT8vALDWnrPWrrLW/stae5+19lKcoMWXPsmGGWNuysNm8uvZ5A0w/ZaHsnj1AWq6fz9trZ2f3Qpu93ddSA+SXwH8Gee8HjPG/GSM+Zsxpmo+lE9ERKRYUjBFREREzocz1tpNPtMWa+1vIVSCF4TC6OLrYvQT6RWc12Xq3igUN/j8/UP+FOniZYypAszDaWV0DrjL7VIuUPpKwCycFkJJwL+B23GCoKWstcZaa0jvpg+yD7ZclNyu5na7X73BkZtxjm0K8JU7bznOPRON0yLFN/0Oa+2+gi6r5J37u+btkuxqY0xa93fueCcN3a9Ts2mVVpwMAf7o/v0rzmDvTYDyQAmf58X/+axTaM8Ld8yyzjgtkbzuKaTiFJQvgNPu3+ONMS2DJfay1u7AeT61AybitOZNJb2l7F+BncaYu/O9xCIiIsWAgikiIiJSlHm7bgmlZUOoLRkKS1HYF9+ucAq0HG5XZt4u1coC7XOyPs7bzV5fBUwFlULo7973LdqieG0UKGNMKeBzoK47a4i1dmU2q92DUxEK8Edr7Qhr7XI3COrbQqC8n3ULiu+5y+76LWrPA2+rkluNMZGkB0m+d8e2wG1lsMGd38YYUwJokWl9yTnf8x/0jXr3mIfSqiI7vt13+Q5Ef1+ANDnh3Z9Id3DxYLJrQeD7ckGw52h24wV5gyS/A7dYa1+31v5krT2ZqRvB8/m8CMotl++YM5fnIbv8ejZ5W3RmGWsnF74BOgJxOIHbL4wxt4WyonUssdY+Yq29Fue83QHMxAmslAWmG2Pq50M5RUREihUFU0RERKQo+8n9vNIYk10FV3P30/qsl1sF8bZwYe1LGmttPLDD/XpTCEGIZu7nSWBPLjbpOzD8iFBXMsY0ALq6X08BHwZJXprsx5Vp6n6m4vTf7+tCeTPcL3cA4SmkH4O/WWtnhLCq7zH9IEi6G4Msg/w9vr73wi3ZpG3u83dhjZPkyxsMKYNT9jaZ5mdO19ZNVzpAulBd0Nd3iHyvm6YBUzluIH0g8Vyz1m4mvaukPsaYCLdrtz7uvNVuC4DcyMn+ZLfcd2yrYIGOhkGWQfpYVEustUeDpMvueXG++Q64nvleycm9k1/PJu81c6UxJjfdY2ZgrV2FM8h9LE4AZL47blZO84mz1s611vYlfdybSKBnXssoIiJS3CiYIiIiIkXZIvczAhgcKJExph7pLR++s9b+HihtiBJ8/i4ZMFXOFNa+BCpHVaB7kHK0xBm4FuDLXHZH8y5w0P27vTHm/uxWcN8Mf4f0Cs1XvW/uBzEkSH71cCqmwanAPJ0pSUGc66Lk/wHe7limW2ufC3E93wrlMkHS/SmbfPLz+C4lfXDwB7IJBvp2J7QoYKrzxzcY0pn0QOWSAOmaA53cvy3pg9PniLU2BaebNrgwr+9sWWsPAD+7X+9wu7wL5IF83PT77mdVoANOK4HK7rzctkoBZ/wKr4DlNcbEAL2yyetXn7+DjRnSP5t8It3PgM8KY0xNnNYNBcoNIIfKN7iTodvDHN47+fVs+sz9NMAT2WwzJNbatThddp3ACc7+zxjTMQ9Z+l5/lQOmEhERuUApmCIiIiJF2Tuk9/n9N2NMk8wJjDGlcSqmvJW//8mH7foO/pqXrj98Fda+ZPYq6V27vGKMqeWnHJWAt31m5aocbkuYfqSPnfKmMWZooPTGmArAJ6QPzP09EErl/zBjzO1+8isJvEf6gNQT/axbEOe6SDDGDAGecb+uJEjQyY9tPn8PCpD/CJzAQDDHSK+QzNPxtdYeBD5yv14L/CVAuR4mveXHl9ban/2lO5/csm9xvw4DSgGJOOfF1wqc41XaTQfws7X2SB42773GL6jrO4dedT/LAO+4rUQyMMZ0JsC1nkszSb/2B5De3dc5nPGIcsVaux5Y637taYy5N3MaN6DwBlAhm+xWkf578Ij7zMycV0fSx0MJxPu8aOUGsDPnEYXTwu18BPRWGWPuc7s3DMgdcN63xeQnfpKFdO/k47NpKumtQEcYY/oG2qbb2imk1ivuNdMG53lcCphjjOmaOZ0xprUxJrvnhG8g5teAqURERC5QeW7CLCIiIlJQrLW/u5UP7+EMyrzKGPNvYDEQD1wDPAVc6a4y11o7PR827VvBOd6tePuV9Eqno9banI4hUlj7krkcm40xY3EGka0JfG+MeRGnEjcFZ+DZkTgDjQO85nYVktvtLXMr9d/GeXv5TbeFylRgI043M1VxKnoGAZXcVX8G7sw0Poc/h3EqJxcYYyYCc908r8Z5s9fbXdUCa62/Csx8P9dFgTGmETDJ/XoCp2uWy7N5aXufTyugD4B/4FR+jjPGXIrz1vRRoB4wEOct85XArYEytNYmGmPWumnuNMY84q4T7yZJtNbmpELucZxrpSrwV2PM9TgtoPbijEXQn/SulOLI+BZ4YVuKc3/HuN/XWGt9W+5grT1tjPkWp2VKjM96ebECqAPcbIz5GzCP9O6drLV2ax7zLw7exhmvpDlOF4Jr3OfvZpwxUu7ECV7twunuKrsgRLastceNMfNxWgB281n0P3d8nLz4I7AG5/6cboxpg1OZfxxoADyMM97OOpxneqAyHjbGzAL64jwrlxljXgB2A1Xcsj/obqtFoHyAycA/gShghTFmAk4w3Pub8jjOmE1Bnxf5pBHOSwkTjTH/wxk7ZAvOc7AEcClOELgP6fUhU6213/jJKyf3Tp6fTe7zsjfOOGHec9sHJzC3A+fFhJo4x7A3MIH0QGFQ1toN7nXyJc65/cQY09taO8cnWXtgtDFmDTAfZwynwzgtZWrg3CfecX+OkoegoIiISLFlrdWkSZMmTZo0acr3CafixLrT8jzmNRznLW4bZJoNlA6w/gM+6W4NcZtTg2zr2Uxpp7nzk8/DvrTzSdffz/L6gcrpk8bgdP+Umk05XgPC8+l6uA0nQBJsexan8m0yEJNNfivd9Dtwumo5HCTPr4GovJ5rnIo37/z/+skn6PJMafe76b4MdT9zkk+m6yTUqX+mPAbidF0TKP16oFp2+4zTZ39KgDx2ZEqb7b2EE5DYkc2+7AduCLB+Ts7TWJ+0NfN4D/TMVMa/Bkj3j0zpugfJM9t9wakkjw9wnJIzpQ3pWUYIz5kQjodv2QNd3/lWHqCie80GumYO4AS1g96b2S3PlLaHn+3cEcJ6Qe97N00XnNaOgfbnU5xWBH7vb598KgO/BMlnHU6AINizLxKn8j1QHqnudR30tzi75SFeVxuDlCPzlILTYjEir/eOmz5PzyaffJr5XGfBpodycR80xOl+0+L8W6SXz7KxIWzT4rTYuTk350eTJk2aNGkq7pO6+RIREZEiz1r7Gk4lxX9wKuRP4bRG2IczOHkna20Pm+kt7zwaBDyE82bqcdL7Q8+TQtqXzGWw1trRwHXAW8B2nAqjBJxWGe8Dzay1w63Tb3x+bHM5TkVlT5xgyVacge2TgEM4bz6PBRpbawfZ7MdJ8c17vbsvL7n5xuO8+fsNzhvct1lrTwXOoWDOdXFnrX0fp1LvI5xzlIzzNvJKnDffm7nfs8tnEc6b1LNwurA5m8dybQGuwjlnS90yJOGcu5U4LbyusNZ+l5ftFIBlOBWRXoFanPjOT8F5Sz3XrLUbcQKO7+Hc6wX2bCnKrNPCrBnwCE6A4BRwBqd1ygTgOmvtT4FzyJW5gO+4V0eBL/IjY2vtPJz74A2cFjXncLpxWo7zTOtBCM8y6wwYfwtOsOMXnOsjDifwNALn3g3aOs9am4TTUm0YzrPc+7u2B6dVxW3W2jE528PcsdY2xulq6wngY5zf2ViceykBJ5CwDGd/G1lrH7bW+j1OOb138uvZZK1djdPC6CGccVUOufkk4Izt8iFOC5F3guUTIO/NQGuc4GEkMNNt/QIwHrgLJ8C0EuffA2fcbR/GGePpceBya+26nG5bRETkQmCstdmnEhEREREpQowxK3G6ndlpra1f2OURERERERGRC5tapoiIiIiIiIiIiIiIiAShYIqIiIiIiIiIiIiIiEgQCqaIiIiIiIiIiIiIiIgEoWCKiIiIiIiIiIiIiIhIEAqmiIiIiIiIiIiIiIiIBGGstYVdBhERERERERERERERkSJLLVNERERERERERERERESCUDBFREREREREREREREQkiIjCLoCki42NVZ9rIiIiIiIiIiIiIiLnQUxMjAk1rVqmiIiIiIiIiIiIiIiIBKFgioiIiIiIiIiIiIiISBAKpoiIiIiIiIiIiIiIiAShYIqIiIiIiIiIiIiIiEgQCqaIiIiIiIiIiIiIiIgEoWCKiAS1fft2tm/fXtjFEMmWrlUpLnStSnGi61WKC12rUpzoepXiQteqFCe6XuV8UDBFREREREREREREREQkCAVTREREREREREREREREglAwRUREREREREREREREJAgFU0RERERERERERERERIJQMEVERERERERERERERCQIBVNERERERERERERERESCiCjsAoiIiIiIiIhIziQlJXHq1CkSEhJITU0t7OJIDoSHhwOwb9++Qi6JSHC6VqU40fV68QoLC6N06dJERUURGRlZoNtSMEVERERERESkGElKSuLIkSNERUVRtWpVwsPDMcYUdrEkRGfPngWgVKlShVwSkeB0rUpxouv14mStJSUlhfj4eI4cOUKVKlUKNKCibr5EREREREREipFTp04RFRVFdHQ0ERERCqSIiIjIRckYQ0REBNHR0URFRXHq1KkC3Z6CKSIiIiIiIiLFSEJCAmXKlCnsYoiIiIgUGWXKlCEhIaFAt6FgioiIiIiIiEgxkpqamtY3vIiIiIg44+YU9DhyCqaIiIiIiIiIFDPq2ktEREQk3fn4t5GCKSIiIiIiIiIiIiIiIkEomCIiIiIiIiIiIiIiIhKEgikiIiIiIiIiclH44YcfuPPOO7n00kvxeDzceuutaeTa2bgAACAASURBVMt27txJv379uPzyy/F4PNSuXRuAcePG4fF4WLFiRa636/F46NKlS57Lf75NmjSJpk2bcskll+DxeHj99dcLu0gh69KlCx6Pp7CLISIiF5CIwi6AiIiIiIiIiEhBi4uL45577uHcuXP07t2bChUqULVqVQBSUlLo168fv/76K71796Z69eqUKlWqkEucd9OnT2f48OG89tpr9OvXL0frfvLJJzzzzDNcc801DBs2jBIlSnDTTTcVUElzbtiwYcycOZMff/yROnXqFHZxRETkIqBgioiIiIiIiIhc8L7//nuOHj3KmDFjeOKJJzIs27NnD1u2bGHgwIG8/PLLGZYNHTqUnj17UrNmzVxve926dZQuXTrX6xeGhQsXAjBr1iyqVatWyKXJuUmTJpGQkFDYxRARkQuIgikiIiIiIiIicsE7ePAgAJdcckmOllWsWJGKFSvmaduXX355ntYvDN5jUhwDKQC1atUq7CKIiMgFRmOmiIiIiIiIiFxAPB5PkZ7y0/r16xkwYACXX345lStX5qqrruKxxx5LCwSA0+rE4/EwbNgwAIYPH55WlunTp2cYz2TChAlpy8aNGwcEHzNl27ZtDB8+nMaNG1OlShXq169Pp06deOedd7KcE39jpiQnJ/Pf//6Xdu3aUatWLapVq0bLli156623SE1NzZDWdz/27NnDkCFDuPTSS6latSq33XYbCxYsyJC+S5cuDB8+PMs+ezwe9uzZE/CYZt5ff+fOuz+HDx/m4YcfpmHDhlSoUIHp06enpTl06BBPPvkkjRs3pnLlylx22WX079+fDRs2ZNmm9zxMnz6dr7/+mi5dulCzZk1q1arFPffcw9atW7Mcz5kzZwLQpEmTtPI1btw4w/4Hut6WLFnC3XffzaWXXkqVKlW49tprGTNmDCdPnsyStnHjxjRu3Ji4uDhGjx5N48aNqVSpUtr1ISIiFw+1TBERERERERGRYmfq1Kk89thjlCxZkk6dOlGjRg127tzJlClTWLBgAYsXL6ZWrVrExMQwcuRINm7cyPz58+ncuXNapXvjxo0ZOXIke/fuZebMmbRo0SJtUHrfwen9WbhwIYMGDeLcuXO0a9eOXr16ERsby6ZNm3j55Ze5//77g66flJTEvffey5IlS2jQoAG9evWiZMmSrFixgqeffpr169fz1ltvZVlv3759tG3blrp169K7d29OnDjBp59+St++fZkzZw6tWrUCoG/fvsTExGTZZ4CYmJiA5fLu94wZM9i3bx8jR470m+7EiRO0a9eOcuXK0bVrV8LCwqhSpQoAu3fvplOnThw8eJBWrVrRq1cvDhw4wJw5c1i0aBFTpkyhY8eOfo/p/PnzadeuHYMHD2br1q0sWrSI77//nrVr16a1EBo5ciTz5s1j06ZN/PGPf0zbn2D75TV+/HjGjx9P+fLl6dChA5UrV+bnn39m4sSJLF68mEWLFhEdHZ1hnaSkJLp168aJEydo06YNUVFRGqdFROQipGCKiIiIiIiIiBQrO3bsYMSIEdSuXZt58+ZRvXr1tGVfffUVd911F88880xai4dRo0Yxffp05s+fT5cuXTIMxn7NNdewYsUKZs6cya233sqoUaOy3f7x48d58MEHSU5O5vPPP88SeDlw4EC2ebz44ossWbKEBx98kPHjxxMeHg5ASkoKjz76KNOmTaN79+5ZWrSsXLmSZ555hmeeeSZt3t13303Pnj2ZOHFiWjDFu4/+9jmYli1b0rJlS1auXMm+ffsCHo9ffvmF3r1789prrxERkbF6acSIERw8eJBnn32WJ598Mm3+/fffT+fOnRk2bBgbN26kXLlyGdabN28es2fPpnXr1mnznnvuOf79738zbdo0Hn30UQBGjRrF3r172bRpE8OGDQs5sPH1118zfvx4br75Zj788MMMLVemT5/O8OHDGTduXJZWJ4cOHeKKK65g3rx5lC1bNqRtiYjIhUfdfImIiIiIiIhIsfLOO++QlJTE+PHjMwRSAFq3bk2nTp1YsGABp06dKpDtz5gxg7i4OIYMGeK3BUuNGjWCrp+amspbb71F1apVGTduXFogBSA8PJyxY8dijOGjjz7Ksm6tWrV46qmnMsxr27YtNWvW5LvvvsvlHuVciRIlGDt2bJZAyoEDB1i6dCk1a9ZMC354NW3alJ49e3LixAn+97//ZcmzZ8+eGQIpAAMHDgTIl3178803AXj55ZezdAHWr18/Gjdu7PeYA4wdO1aBFBGRi5xapoiIiIiIiIhIsfLtt98CsGrVKr7//vssy48dO0ZKSgo7d+7k2muvzfftr1+/HoD27dvnav2dO3dy4sQJLrvsMl544QW/aUqXLs22bduyzG/cuHGG4ItXzZo1WbduXa7Kkxu1a9emcuXKWeb/9NNPADRv3pzIyMgsy1u1asWHH37ITz/9RJ8+fTIs83euatasCeB3PJOc+vbbb4mMjGTOnDnMmTMny/KkpCSOHTvG77//ToUKFdLmlypViquvvjrP2xcRkeJNwRQRERERERERKVZ+//13AF555ZWg6U6fPl0g24+NjQWgWrVquVr/xIkTgBNUmTBhQsB0/sofaFyQ8PDwLIPWFyTv+CiZxcXFAVC1alW/yy+55BIg/Rj68rdv3pYvKSkpuSqnr99//53k5OSgxxyc4+4bTKlUqRLGmDxvX0REijcFU0RERERERESkWPEOEL53794sg4WfD95K/4MHD3LVVVfleP2oqCgAunbtyrRp0/K1bOdLoOCC93wcPnzY7/JDhw5lSHc+RUdHk5qayu7du3O0ngIpIiICGjNFRERERERERIqZm266CYDVq1cXyvZvvPFGABYvXpyr9Rs0aEBMTAzr168nKSkpP4uWge+g9ufLNddcA8CaNWtITk7OsnzFihUANGnSJE/b8e5bTlrj3HTTTZw8eZLNmzfnadsiInJxUjBFRERERERE5AJy8uTJIj3lhwcffJDIyEhGjx7Njh07sixPTEzkm2++yZdt+dO3b1+io6N59913WbVqVZblBw4cCLp+REQEQ4cO5dChQ4wcOZKEhIQsaQ4dOsSWLVvyVE5vV1X79+/PUz45UaNGDW6//Xb27t3LG2+8kWHZ+vXr+fjjj/F4PHTt2jVP2/Hu2759+0Je509/+hMAjz76KAcPHsyy/MyZM2nj8YiIiGSmbr5EREREREREpFi5/PLLefXVV3nooYe45ZZbaNu2LfXr1ycpKYn9+/ezevVqKlWqVGAV4xUrVuTtt99m4MCB3HHHHbRv356rrrqKU6dOsWnTJg4cOJA2EHsgTz/9NJs2beLdd99lwYIFtGzZkurVq3P06FF27tzJ2rVrGTNmDFdeeWWuy3nzzTdTpkwZJk2axIkTJ9LGORk6dGjAsVfyw7///W86dOjAmDFjWLp0Kddddx379+/ns88+IywsjNdeey2tq7Pcat26Na+88gqPPvoo3bp1o1y5csTExDB06NCg6/ztb3/jueee44YbbqB9+/bUqVOHM2fOsG/fPlatWsUtt9zCJ598kqeyiYjIhUnBFBEREREREREpdnr37s3VV1/Nq6++yooVK1i2bBllypShWrVqdO/enbvuuqtAt9+hQweWLVvGf/7zH77++muWLl2Kx+OhQYMGjBgxItv1IyMjmTFjBrNmzWLGjBksXLiQM2fOUKlSJerUqcOf//xn7r777jyV0ePxMGXKFCZMmMCMGTM4c+YMAPfcc0+BBlPq1q3LsmXLePHFF1m0aBErV64kKiqKtm3b8uSTT3L99dfneRtt27Zl7NixTJkyhTfeeIPExERq1aoVNJgC8Nhjj9G0aVPefPNN1qxZw/z584mOjqZatWoMHDgwz8dcREQuXMZaW9hlEFdsbKxOhhQ527dvB5w+fUWKMl2rUlxcDNdq2M6dpF52WWEXQ/LBxXC9yoXhYrtW9+3bR61atQq7GJJLZ8+eBaBUqVKFXBKR4HStSnGi61Ugd/9GiomJMaGm1ZgpIiIiIpJvwnbtIuqGGzC7dxd2UURERERERETyjYIpIiIiIpJvIubMwQKRc+YUdlFERERERERE8o2CKSIiIiKSL5JSLWHTZ2CAsOkzSEpVD6YiIiIiIiJyYdAA9CIiIiKSK9Za1hxJZPr2eNYcPkfKvv38vHuvs2zXbm6a+D0RNWtwS9WS9GtQhluqlMCYkLujFRERERERESkyFEwRERERkRyx1jJrZzzjfjjF0bOpJCRbLPDIhsVYN1aSYsK444fFTIy+j51x8czelUDlUmGMui6K3peVyVVQxePxBF3+2muv0a9fvyzzT58+TcOGDTl16hS9e/fmzTffDJrP7NmzmTZtGj/++COxsbFER0dTuXJlmjRpQps2bbj33nv59ddfuf7663NU/i+++IJmzZrlaB0REREREREpGhRMEREREZGQHTiTwtCvfueH40nEJ2fsxmvwujmUSToHQNmkswxeN4eJre/DAvHJlj2nUxixOpap2+J5q3UFapQNz1UZRo4c6Xd+48aN/c7/5JNPOHXqFMYYPv/8cyZMmBAwMDN8+HCmT59OmTJl6NChA7Vr1yYlJYXt27ezYMECVq9ezb333kv58uWzlCM1NZUXXngBYwxPP/10lrxr1qyZwz0VERERERGRokLBFBEREREJyerD57h78XHOJlsyxVGoGneUKw//mmFew8M7qRp3lMPRldPmxSdb1h5JpOmnh/m4fUVuqVoyx+UYNWpUjtJPnjyZ8PBwhg8fziuvvMIHH3zAH//4xyzpVqxYwfTp06lVqxaLFi2iWrVqGZYnJiayatUqAMqXL5+lHMnJybzwwguEhYXluIwiIiIiIiJStGkAehERERHJ1urD5+i56Dink7IGUgDu2riE5PCM7+kkh0dw58alWdImWzidZOmx6DhrDp8rqCID8NNPP/HDDz/Qpk0bHnnkESIjI5kyZYrftOvWrQOge/fuWQIpACVKlOD2228v0PKKiIiIiIhI0aRgioiIiIgEdeBMCncvPp6lWy9fg9Z+SrnEhAzzyiUmMGjdpwHXiU+29Fp8nANnUvKtrJm9//77APTt25dKlSrRvn17fvnll7TAia8KFSoAsGPHjgIrj4iIiIiIiBRP6uZLRERERAKy1vLgV79zNkggpcKZkzT5bavfZdce2EKFMyf5vaz/MUrOJluGfvU7cztVCnlQ+nHjxmWZV7t27SyDz8fHx/PRRx/h8Xjo3Lkz4ARV5s+fz+TJk7n55pszpG/Xrh1RUVEsWLCAvn370qNHD66//nrq1asXctlERERERETkwqRgioiIiIgENGtnPBuOJ/nt2sur+6alJIVHUColKcuypPBIum1axuSmd/ldN9nCD8eT+HBnPL3rlw2pTBMmTMgyr0WLFlmCKbNnzyYuLo4HHniAkiWdsVk6dOhApUqVmDNnDuPGjSMmJiYtfa1atZg6dSqPPvoo8+fPZ/78+QBER0dz880307t3b3r27ElYmBp3i8jFw9pUEhJ+onTpazBGzz8RERG5eCmYIiIiIiJpzPHjRCx1xjmx1vLzuli6n00Nus4jX08jKlMXX15RifE8+vVUzkVEBs3j5x/DiLg5Jq0FSHKbNtiKFf2mPXnyZHa7AaR38eUbZImMjKRXr15MmjSJjz76iAceeCDDOrfddhs//PADq1ev5ptvvuHHH39k7dq1fPnll3z55Zd88MEHzJw5kxIlSoRUBhGR4iwp6Si7dg3izJm1lCvXlLp1JxMZWbmwiyUiIiJSKBRMEREREZE0YTt3Unr4cEhOJqV0aZ5PtgRplAJAeGrwYEuDo3uY9NFzQdMYoORMQ3hCAkREcGbuXFICBFNC8fPPP/Ptt9/SqFEjrrvuugzL+vbty6RJk3j//fezBFMAwsLCaNGiBS1atACcoNKSJUsYNmwYS5YsYfLkyQwdOjTXZRMRKQ7i4paxe/cgUlLigWROn17H5s03UrfuZKKjby/s4omIiIicd2qjKyIiIiJpUm6+mdOrVpFarx4pSSlEnYsnOsBU7lw8Z87FszHpLLOAfwKPQZbgS9mkswHz8E5R5+JJSU4htV49Tq9aRUqm8UxyavLkyQD88ssveDyeDFOrVq0A2LhxI9999122eRljaNeuHaNHjwbg66+/zlPZRESKstTURPbvH8Wvv/YlJSUW8HbhmERKSiy//tqX/ftHYW3Wrh1FRERELmRqmSIiIiIiGaQ2aMDpb77h87sf5sZvPuNo8jl2A3sg7XMPsBdI9LP+X4AKOdzmmchSzGl2F11n/Qfc8U1yKyEhgQ8//JDw8HD69u3rN82BAwdYunQp77//PjfccENI+ZYrVw5wWqqIiFyIzp3bxc6d95KYuBdr/XffaG0Cx45NJi5uKZdd9gElS9Y7z6UUERERKRwKpoiIiIhcpFJSUvjtt9/Yu3cve/bsYe/evRmn/QcgNSXH+e4l9GBKYngE8ZGl6Nf/nyxu3JrfIksQfHSV7M2ZM4fY2Fg6duzIxIkT/aY5efIkDRs2ZPbs2Tz//PNERUWxaNEiEhMT6dixIxERGf+ZfOrUKSZNmgRA8+bN81hCEZGi5/jxWezf/zipqWeB4N03WpvAuXPb2bKlBTVr/puKFXufn0KKiIiIFCIFU0REREQuUNZajh8/zu7du9m9ezd79uzJMO3fv5/k5OR83+4e4NoQ0p0uUZqN1S6nx+CXORRTmdLGcCQhlRplw/O0fe/A8wMGDAiYxuPxcMcdd/Dhhx/y8ccfM3jwYLZu3cqYMWMoX748zZo149JLLyUiIoIDBw6wcOFC4uLiaNq0Kffff3+eyiciUpSkpJxi796HiYtbRGpqfA7WTCU1NZ79+x8nLu4LateeSHh4VIGVU0RERKSwKZgiIiIiUoydO3eOvXv3snv3bnbt2pUWOPEGT86cOXPey7QnhDTxkaX4f+0eZHzbB7FhzjB+YQaSUvPWhdbWrVtZs2YNl1xyCR06dAiaduDAgXz44YdMnjyZwYMH06dPH2JiYli+fDk///wzK1euJD4+Ho/Hw7XXXsudd97JfffdR2RkXtvOiIgUDfHxP7BzZx9SUk5g7blc5ZGaGk9s7AJ++eUmLrvsA8qUCSWcLiIiIlL8KJgiIiIiUsTFxcWlBUp+/fVXdu3alTYdOHCgyI3hsTfIsoSIEpwsHc2d909kXZ1rMixLtRAZZgKue/LkyWy3fcUVV4SUDqBFixYZ0laqVIkBAwYEbdESTERERMjbFhEpTNamcvjwKxw6NB5rz+ZDfmdJTj7Etm2duOSSkVSt+gjGhOVDSUVERESKDv3rRkRERKQIu+OOO6hduzatW7dm4MCBPPfcc0yZMoUVK1awf//+IhdIgcAtUxIiSrCq3nVcMXpelkAKQLK1VCmtf56KiBSkpKQjbN/ehcOH/5kvgRRf1iZw6NA/2b69K0lJR/I174LSpUsXPB5PnvMZN24cHo+HFStW5EOpJJCOHTtSsWLFAt1GcnIyHo+H7t27Z5g/duxYPB4Pq1evzjbt+RQbG8tTTz1F48aNqVixIh6Ph19++aXQyiMiciHT/1ZFREREirDy5csXdhFyLFAwpWRyIifKxHCqVDm/y+uWiwjaMkVERPImLm4JmzffyJkz63M4PkrorI3nzJlv2bz5JuLilhbINrw8Hg8ej4fy5cuza9eugOm6du2alnb69OkFWqaLzY4dO/B4PDRs2JCUlJSgadeuXYvH46FFixbnqXSBNWrUiOuuu67A8vcXeCkozz77LG+//TZXX301I0aMYOTIkVSuXLnAt+tr+fLleDweXnjhhfO63YJ2Ps9jcSqLyMVM3XyJiIiI5CNrLUePHmXbtm1s376dbdu2sWPHDrZv306fPn3o0aNHjvK79NJLC6ik+aM0UMdnqg00DJA2DOj8ywoikxNJiiiRYZkBmlYt4Xc9ERHJm9TURA4cGMPx41OwNuE8bDGJlJRYfv21HxUrDqBGjX8QFlYwz/iIiAiSk5OZOnUqf/nLX7Is37lzJytXrkxLl9mkSZNISMj7MRk6dCg9e/akZs2aec6rOKlfvz4tWrRg1apVLFy4kM6dOwdM+/777wMwaNCg81S63ImIiGDdunWUKVMmX9MWlIULF3LllVcyc+bMQiuDiMjFQsEUERERkXzywQcfMHLkSGJjY/0u37p1a47zLPRgSqmyUKEGlK+e9tkkPpb/fDWFq5LOUgknEBKq5LAwbt+xjkVX3pphfpkIQ/8GhVcRISJyoUpMPMCOHXeRmLjvPAVS0lmbwPHjUzh1ajn1639KiRLV830bVapUoWrVqkyfPp3Ro0cTEZGxmmPKlCmA0z3U3Llzs6xfq1atfClHxYoVC7z7qaJq0KBBrFq1iilTpgQMpsTFxfHZZ59RpkwZ7rnnnvNcwpy7/PLLCyRtfktJSeHIkSM0bBjoVRYREclP6uZLREREJJ/ExMQEDKQAbN++Pcd5FnQwpWzZsjRq1IiOHTsydOhQxo4dy5QpU/jqq6/YtWsXtV9ZD099Cg+8Bj1Gw+2DePLEAVolnaMyOQukAESdi6ff+qyVWZVLh9G0ilqmiIjkt9OnVxVKIMXL2gQSE/dy+vTKAtvGwIEDOXz4MAsWLMgwPykpiRkzZtC0aVOuvPJKv+v6GzNlxYoVeDwexo0bx08//cQ999xD7dq1qVatGp07d2bt2rVZ8gk0ZorH46FLly4cOXKE4cOH06BBA+rVq0fXrl355ptvADhz5gxjxozh6quvpkqVKtxyyy3MmTMn5G0A7NmzB4/Hw7BhwzLMHzZsGB6Ph927d/PWW2/RtGlTqlatSuPGjXnppZfSxl6bM2cObdq0oXr16tSvX5+nnnoq5BY73bp1o0KFCixevJiDBw/6TfPxxx9z5swZ7rzzTmJiYjIsW7p0KT169KBOnTpUrVqVG2+8kb///e/ExcWFtP1z587x5ptv0qtXr7RjWLduXe68806WLFmSIa23O6rffvuNXbt2pXX/5vF4ePjhh4GcjYPiL22jRo148cUXAejUqVNa/t5g28CBA/F4PKxZs8ZvnrNnz8bj8fDMM88E3bbv+DFfffVV2nZ8y5Kamsp///tfbrvtNqpXr0716tVp06YN7733XpZx93z35eDBgwwfPpwmTZpQvXp1Zs2aFbAcQ4cO5c477wTg+eefz3BMvV1SnTx5kpdffpmuXbvSsGFDKleuTP369enbty/r168Pely9ZbnyyiupUKFChrJs27aN/v37U6dOHWrUqEGHDh1YvHgxU6ZMwePx+C33/v37eeKJJ2jSpAlVqlShXr169OnThw0bNmRIl915DMa327Nvv/2Wu+++m7p16+LxeDhw4ECBlCXYeEKBjoe3u7vY2FieeeYZrr76aipVqpTWXZtv12KzZ8/m9ttvp1q1atSrV48HHniAQ4cOZXssRC40apkiIiIiApw+fZrt27ezZcsWtm7dypEjR3j99ddzlMcVV1wRdPnOnTtJTU0lLCz091nyGkyJiIigdu3a1KlTh7p161KnTp0MU4UKFTAmcEhk9PVnGLE6lvhk5z/c4SnJdNu0jDD8D3x/ukRpksIjiExJplxi1kqYMCx3blpCeEoyKeHOP0XLRBhGXRsVtBwiIpI70dFtgeBjWRS8FKKj2xVY7j179uTPf/4zU6dOpWvXrmnz58+fz9GjR/nb3/4WdEyVQDZs2MArr7zCTTfdxIABA9i/fz+ff/453bt3Z8WKFTRo0CCkfGJjY+nQoQPlypWjZ8+eHDt2jM8++4yePXuyaNEiHn/8cU6cOEHHjh1JSkrik08+YfDgwdSoUYObbropx+X2Z8yYMaxcuZKOHTty++2388UXX/CPf/yDxMREypcvz3PPPUeXLl1o1qwZy5Yt4+233yYlJYV//etf2eZdsmRJevfuzRtvvMH06dN58skns6TxthAaOHBghvlvv/02Tz/9NOXKlaN79+5UqlSJFStW8K9//YsFCxbwxRdfZAm+ZHbs2DFGjRpF06ZNuf3226lUqRKHDh3iiy++oFevXrz66qv069cPgLp16zJy5Ehef/11wsPD+b//+7+0fJo0aZLtvobiT3/6E/PmzWP16tX069cvres377//hgwZwmeffcbkyZO55ZZbsqz/3nvvATB48OCg2+nXrx8tW7bkhRdeoE6dOtx7771p+whO17P3338/n376KbVq1WLAgAEAzJ07l8cff5w1a9bw5ptvZsn3+PHjtG/fnujoaLp06YIxJugYLF27dsUYw6xZs2jZsiXNmzdPW+bd982bNzN27FhatGhBx44diYmJYd++fXzxxRcsXryYjz76iNtuuy1oWe64444MZdmyZQsdOnQgLi6ODh060KhRI3bt2kWfPn1o376937L+8MMP9OjRg5MnT9KuXTu6devGsWPHmDdvHn/4wx/44IMPaNOmDZD9eQzFmjVr+Oc//0nz5s257777OHr0KJGRkYVSlkDOnTtH165dOXXqFO3ataNcuXLUrl07Q5o333yTBQsW0KlTJ2699Va+/fZbPv74YzZt2sTXX39NiRJ6IUouHgqmiIiIyEXl9OnTbNu2jc2bN6cFTjZv3sy+ffuypJ0wYQJRUVEh5127dm1KlChBYmKi3+Xx8fEcOXKESy65JOQ8L7nkEkqXLh307dDy5ctTr1496tatS7169ahTp07a9+rVqxMeHh7y9jLrfVkZpm6LZ+2RRJIttPp1PTZAe5T4yJI83/7/eOH2wTy99F2eXTyJMknnsqSzGFr++h3LGzQlwsB1FSO55zJ18SUiUhAiIipSqtRVJCT8UGhlKF36aiIiKhRY/lFRUfTo0YMZM2Zw4MABatSoATgV+NHR0dx1110hBQUyW7hwIa+99lpaRTw4Fd2PP/44kyZN4qWXXgopn02bNjF48GBeeuklwsLCOHv2LK1bt+bhhx/mjjvuUyKQrQAAIABJREFUoGnTpsydO5dSpUoB0Lt3bzp37sx//vMfpk+fnuNy+7NhwwZWrVpF9epOV2vPPPMM119/PRMnTqR06dIsX7487aWQc+fO0apVK6ZNm8aoUaNCGsx80KBBvPHGG0ydOpUnnngiwwsSP/30Exs2bKBRo0Y0bdo0bf7u3bsZPXo0UVFRLF26lPr166cte/TRR3n//ff5+9//nu1xrlixIps2bUrbN6+TJ0/yhz/8gb/85S/06tWLkiVLUrduXUaNGsXUqVMpWbIko0aNyv7g5dBDDz3EyZMnWb16Nf3796dZs2YZlrdu3ZrLL7+cOXPmMH78+Awto7xj/DRv3jzbl3Tuu+8+kpOTeeGFF9L2y9esWbP49NNPue6665g7dy5ly5YFnAHrO3fuzKxZs+jYsSN33XVXhvU2bdpE3759mThxIklJSQBp16Y/3bp1Izo6mlmzZtGqVSueeuqpLGkaNmzI1q1bqVAh43Ng7969tGvXjtGjR6e11ApUlsz/nh0xYgSxsbG8/PLLGYJ0X3zxBX369MmSV1JSEoMGDSIhIYH58+dnOC8HDhygTZs2PPTQQ2zYsIESJUpkex5DsWTJEl555ZW0QFZhliWQ3377jYYNG7Jw4cKAY/8sXbqUZcuWpXUnZ61l8ODBzJkzhwULFtCtW7d8K49IUaduvkREROSClJCQwIYNG/jggw/461//Su/evbnmmmuoWbMmbdq0Yfjw4UycOJFFixb5DaRAzrvlioiIyFAR4M+ePXtylGdYWBj16tWjevXqNG/enP79+zNmzBjee+89li9fzu7du9m1axdLly7l3XffZcyYMQwYMICWLVtSq1atPAVSAIwxvNW6AqUinEqRvt/Ny9LiJCGiBL9FV+K24ZMZ3+5BUsIjGNd+KLcNn8zBqEokZBpsvmxiAn2/mwdAqQjD262Dt44REZG8qVixH8YUTtA6LKwMFSr0yz5hHg0cOJCUlBSmTZsGOJW0y5Yt4+6778714OC33HJLhkAKQP/+/YmIiOC7774LOZ8yZcrw97//PcNb5D169CAiIoKTJ08yYcKEDJXVzZs3p3bt2mzcuDFX5fbnqaeeyhBs8Hg8dOrUifj4eIYMGZKh4r5kyZLcddddJCYmhjze2xVXXEGzZs3Ys+f/s3fn8TFd7wPHPzfJZLNNkkpsISkiErElJPatltqKqLUo/VXRjapSXSxddNNFF8pXtSSCqlLUWmsikigRsScktSslQRLZ7u+PdKZGJqtJJuF5v155hXvuPee5d86McZ97zklk9+7dBmW6USn331BesWIFGRkZvPDCC7m+P82YMYMKFSro98mPra1trkSK7hyHDx/O9evXc02bZG6jR48mLS0t16LxP/74o/5G9YPSvRdmzpypT6QAVKxYkRkzZgD/vTb3srW15f3333/g75D30mq1uRIpkPMgUp8+fTh27JjRKeLyiiUhIYF9+/ZRv379XP1KN3rifr///juJiYmMHz8+VzKiZs2avPTSS1y8eJHQUNNNSdisWbNc8Zkrlvy8//77+X5Ojh8/3mBdHkVR9AmsgwcPlnh8QpQlMjJFCCGEEOVaeno6cXFxHD9+3ODn7NmzueaCLqoTJ07QvHnzIh3j4eHBsWPH8ixPTEw0eCqzMHbv3q2fEsAcalawZHVXJwI3/03g4W1Yqtn6sjvWtmxu0JbRwz7glm1Fg+Oi6jSmwfSN/Lh8Ot1PhlEhPQ0AKzWbgTFbmTRsFqu7PkaNCqb7z7oQQojctNreXLgw3Sxtq2oWWm3vgnd8QH5+fnh5eREUFMSUKVNYunQp2dnZRm9kFlbTpk1zbdNoNDg7O3Pz5s1C11O3bt1cI10tLS1xdnbmzp07+mmZ7lWjRg2ja0kUV7NmzXJtq169OmD8PHXJiYsXLxa6jVGjRhEeHs5PP/2kn7IpNTWVVatWYWtrq5+GSufw4cMAtG/fPlddjo6ONGrUiIiICOLi4gpcYP3o0aPMmzeP/fv3c/nyZe7eNRwZm9daLuYybNgw3nvvPX766Sf9Ojfp6emEhITg5ORkkif9Y2JisLS0pE2bNrnK2rZti6IoxMTE5Cpzc3Mzmvh4UPv27eP777/nwIED/P3337lGcl+6dEnfJwuKRZdobNmypdEHcgICAnIlIqKiooCc7+Jz5szJdUxcXBwAJ0+e1E+v9aB8fX2NbjdHLHmpUKFCge8vY58fuhGARfksFOJhIMkUIYQQQpQL2dnZJCYmcuzYMY4fP67/ffr0aTIzM0ukzcI+jXmvguZPL85/5s2ZSNEJcLHhj6pnsfo3kZKlWJCqsWFC4Dssa5n3Aq23bCsSOPorRkat47tf3sc24y6WajZW2Vn84ZyAp0ut0joFIYR4ZGk01bCxqU9a2tFSb9vGpj4ajUuptDVq1CimTp3Ktm3bWL58OU2bNn2gdTDyWqvD0tKSrKzCr0NTuXLlPOvJr8yU32+MtaN72j+/soJGhdzrqaeeYtq0aWzcuJHr16/j5OTE2rVrSU5OZtCgQQbTWQH6Bebzmv7UxSWn3yQlJeXb7v79++nXrx/Z2dl06NCBnj17UrFiRSwsLDh8+DCbN2/OlVwxtypVqjBw4ECWLl3Kvn37aN26NRs2bODatWu8/PLL2NjYPFD9qqqSnJyMi4sLVla5b/3Z2Njg4OBg9No6Ozs/UNvGrF27ljFjxmBnZ0fHjh1xc3PD3t4eCwsL9uzZQ3h4uNHXKK9YdH0nr3Jj2//55x8A1qxZk2+sd+7cybe8KPKKzxyx5KUw0/gZ+yzU9auifBYK8TCQZIoQQgghyhRVVbl69SrHjx/n6NGj+sTJiRMnSElJKdVYTpw4UeRjGjRogKWlJe7u7tSvXx8PDw/q1atH/fr1qV+/vv4/T+VR0x3rsM5IJdXalkSH6vR57lviqtYp+EBFYWnLfoS7NWX94peoffMSFTPSaLpjHWlP5p6GQQghhOk5Og7j0qX3UdW81+AyNUWxw8mp5Kf40hk8eDAzZ87ktdde4+LFi7zxxhul1nZp0E0TZuzmZUEJh9JgZ2fHoEGDWLhwISEhIbz00kv6aaSeffbZXPvrkjhXrlwx+jDKlStXDPbLy6effkpaWhqbNm3KNWXSJ598wubNm4tzOiVuzJgxLF26lB9//JHWrVuzZMkSFEUxeq2KSlEUKleuzPXr18nKyso1TVZ6ejo3btzAwcHB6LGm9sEHH2Bra8uuXbtyvdYXLlwgPDzc6HF5xaIb6XX16lWj5ca26/rRqlWr6NatW6FjfxB5xV8SsSiKgqqqZGdn51qYPr/PB5lqV4iikTVThBBCCGE2t27dIioqip9++ok33niD3r17U69ePRo0aEC/fv146623CAoK4uDBg6WeSIHijUzp06cPly5d4sCBA4SEhDBr1ixGjBhBQEAATk5OJRBlKVFVNP8+Pac8O5LQtTvIdH8ceyslj+Xo/6MAFawUMh6vy951O1BGjfivvgecik0IIUThODj0BbIL3M+0stFq+5Raa1qtlr59+3LhwgUqVKhAYGBgqbVdGnQjO86fP5+rrKysCaJLBAQFBXHq1CnCw8Px8PCgdevWufZt3LgxgNF1IW7cuMHRo0ext7cvcNTvmTNnqFq1qtFFucPCwoweY2FhUaJP1OeX+NJp2rQpfn5+rFu3jqioKEJDQ2nXrh1169Y1SQw+Pj5kZmYaTVSEhoaiquoDjdy6V0Hne/bsWRo2bJjrtczKyiIiIqLI7en6TmRkpNFpfffv359rW4sWLQDyTNwYU5jXsThKIhatVkt2drbRqfkOHTpUjCiFEMZIMkUIIYQQJS4zM5OTJ0/y66+/8v777zN06FCaNGmCq6srXbt25dVXX2XhwoWEhoZy/fp1c4erl5iYSGpq0Z7gtbGxwdrauuAdyxnlxg2wtyfl55+5+8knPN3QgUMDXVjTzYnh9e2pX9kKjQXYWSpUsFKws1TQWED9ylYMr2/PL92cODTQhacbOnD3009J+flnsLPLqVcIIUSJs7Z2xdq6dim3WQdr69KdzvHtt98mKCiIX375Jdc6JeWdbv2F4OBggynAzp8/zyeffGKusAx4eXnRokULTpw4wauvvgrkXnheZ8iQIVhZWbFgwQISEhIMyt577z1u377NkCFDCpzutHbt2ly7do3jx48bbF+yZAm7d+82eoyjoyPXrl0rsem/dOt8GEt83WvMmDHcvXuXUaNGoaoqY8aMMVkMzzzzDACzZs0y+D57584dZs+eDcCIESNM0lZB5+vq6srp06f1o40gZzT6hx9+yOnTp4vcnpubG61ateL06dP60U86mzdvNpqg6927N7Vr1+b777/njz/+MFpvREQEaWlphT6v4iqJWHSfDz/99JPB9h07drB27VpThC2EQKb5EkIIIYSJXb9+nSNHjhAbG8vRo0c5evQoJ0+eLHNzVRvj7OyMh4cHDRs2xNPTkwYNGhidZ/pRpDo6cis21mCboigEuNgQ4JIzr3dGtsrV1GwyslU0FgrOdhZoLIyPW8l84olc9QkhhChZjo5DuXz5Y1S15P9NVhQbHB2Hlng793N1dcXV1bXU2y0Nfn5+tG7dmn379tG5c2fat2/P1atX2bx5M126dDH5Dd/iGjVqFFFRUYSHh2NjY8OwYcOM7ufu7s7777/PtGnTaN++Pf369cPJyYm9e/dy4MABPD09effddwtsb8KECezevZvu3bvTr18/KlWqxMGDB4mMjKRv37789ttvuY7p0KEDMTExBAYG0qpVK6ytrWncuDHdu3d/4PMHaN++PYqiMHPmTGJjY6lSpQoWFhZMnjzZYL8BAwbw9ttvc/HiRZydnenVq5dJ2oecZNWmTZv47bffCAgIoFevXqiqyoYNGzh37hxPP/00AwYMMElbnp6eVKtWjZ9//hkLCwtq1aqFoigMHTqUWrVqMWHCBKZMmUK7du3o27cvlpaWhIeHEx8fT/fu3dmyZUuR25w7dy49evRg4sSJbNq0CW9vb86cOcOGDRt48skn2bRpk8F0VzY2NixbtoyBAwcSGBhIQEAAPj4+2Nracv78eQ4ePEhiYiLx8fHY2toChX8di6okYhkxYgTffPMNn376KTExMTRo0IBTp07xxx9/0Lt3b6PvAyFE0cndASGEEEKY1EcffcSiRYvMHUa+nJ2d9ckSXeLE09NT/8SXKB6NhULNCpYF7yiEEMIsHBz6cflyaY1gUHBw6FdKbT06li9fzrvvvsvvv//OwoULqVu3LrNnz6ZTp078+uuv5g4PyEkQTJ8+neTkZPr06ZPv96tx48ZRr149vvnmG9auXUtaWhq1atVi4sSJTJo0yejC1/fr3r07y5cvZ+7cuaxZswZLS0t8fX3ZsGEDp0+fNnoTeerUqdy6dYstW7YQHh5OVlYWI0aMMFkyxcvLi2+//ZZvv/2W//3vf6SlpWFpaZnrJrytrS2BgYEsWrSI4cOHFzgKpygURWHJkiUsXryY4OBglixZAuSs7zdx4kRGjx5tsrasrKwICgpi5syZ/Prrr9y6dQuAtm3bUqtWLZ5//nlsbW1ZsGABwcHB2NnZ0bp1a77//nt++eWXYiVTvLy82Lp1K7NnzyY0NJQ9e/bQqFEjli9fTmxsLJs2bco1Oq1JkyaEhYXx7bffsmXLFoKCgrCwsMDFxYWmTZvy1ltv6afT07VRmNexOEwdi4uLCxs3bmTGjBmEhYURGhpKs2bNWLduHXFxcZJMEcJEFGNzCwrzSEpKkhdDlDm6IbcFzVMrhLlJXy07fvzxRyZOnGjuMICc4fANGzbU/3h6etKwYUOzJk2kr4ryRPqrKC8etb567ty5Yo++OHq0CenpiSaOKDdraze8vcvGOh5ljW7qHt0T5+LR1qNHDyIiIjh06BBubm7mDsdAee2ro0eP5tdff+XQoUO4u7ubOxxRSsprfxWmVZzvSFWqVCloGVA9GZkihBBCCL309HROnTpFbGysfpquRYsW8dhjjxW6Dm9v7xKM0LhKlSoZJEu8vLxo2LAhVatWRVEK/b1ICCGEeOg5Og7lypXPUdX0EmtDUaxxdBxSYvUL8bCIiIhg//79dO/evcwlUsq6rKwsrl+/jrOzs8H2HTt2sG7dOry8vCSRIoQwOUmmCCGEEI+4ZcuWERYWRmxsLCdPniQjI8OgPDY2lo4dOxa6Pk9PTxNH+B9ra2s8PDzw8vLSJ0waNmyIq6urJE2EEEKIQtBq+3Hlylcl3IolDg79S7gNIcqv//3vf1y6dIng4GAsLS158803zR1SuZOamoqXlxft27enfv36WFpacvz4cXbu3ImtrS2fffaZuUMUQjyEJJkihBBCPOI2bNiQ7zzFRU2mVKpUCTc3NxISEoodk6IouLu76xMm3t7eNGzYkLp168qC8EIIIcQDsLPzxMrKgYyM1BJrw8rKEVvbBiVWvxDl3eeff86VK1d4/PHH+fDDD2natKm5Qyp3bGxsGD16NHv27OHAgQOkpKTg5OTEgAEDmDRpEj4+PuYOUQjxEJK7EUIIIcRDRFXVIo/Q8PHxKTCZUlTe3t6FTqY4OzsbjDTx9vamQYMGVKhQocjtCiGEEKJgDg6DuHr1WyCjwH2LToODw6ASqFeIh8exY8fMHUK5p9Fo+PTTT80dhhDiESPJFCGEEKKcunnzJkeOHDH48fHxYf78+UWqp6CntoqbTNm4caPBNjs7Ozw9PfHy8sLb21v/U5T1WIQQQgjx4BwcBnDt2kKys02fTLGw0ODgMMDk9QohhBBCmJskU4QQQohy4PLly8TExBj8GBv5cf96J4VRUDLl5MmTpKenY21tXeg6AwIC6N27tz5x0qhRI9zc3LC0tCxyfEIIIYQwLTs7HywsKpCdnWLyui0sKmJn18jk9QohhBBCmJskU4QQQogyRFVVEhISciVOrly5UqjjT58+TUpKCvb29oVu083NjUqVKnHr1i2j5RkZGZw+fRpvb+9C19m5c2c6d+5c6P2FEEIIUXoURcHBYSB//70IyDRhzVY4OAws8pSjQgghhBDlgSRThBBCCDPJzMzk1KlTHD58WJ80OXLkCMnJycWuMzs7m+PHj+Pr61voYywsLGjUqBHh4eF57hMbG1ukZIoQQgghyjZHx6Fcu/Y/VNV0iQ9FUXB0HGKy+oQQQgghyhJJpgghhBCl4O7duxw/fpyYmBgOHz7M4cOHiY2NJS0tzeRtHTlypEjJFMAgmVKhQgX91FyNGjXS/1kIIYQQDw97+8Y0aXLZ5PUqikzpKYQQQoiHkyRThBBCCBNLTU3l6NGj+qTJ4cOHOXbsWLHWMymOI0eOFPmY4cOH065dO3x8fKhTpw4WFhYlEJkQQgghyhJJfAghhBBCFJ4kU4QQQogHcOvWLWJjYzl8+DDR0dHExMRw8uRJsrKyzBZTcZIpTZs2pWnTpiUQjRBCCCHKO4v4eLLr1jV3GEIIIYQQZiXJFCGEEKKQbt26RUxMDNHR0Rw+fJhDhw4RFxeHqqrmDs1AYmIiqqrK4q9CCCGEeGAWZ89SydeX5OhoVDc3c4cjhBBCCGE2kkwRQgghjLg/cRIdHc3p06fLXOLExcWFJk2a4OPjQ+PGjfHx8cHNzU0SKUIIIYQwCau1a1EBzdq1pE+caO5whBBCCCHMRpIpQgghBBAdHU1YWFiZTpy4u7vTuHFjgx8XFxdzhyWEEEKIh5h1SAjKv78lmSKEEEKIR5kkU4QQQghg8eLFLFu2zNxhAGBpaUmDBg0MkiY+Pj5UqVLF3KEJIYQQ4hGinD+PRWIiABYJCSgXLqDWrGnmqIQQQgghzEOSKUIIIQTQrFkzsyRTbG1t8fb2pkmTJjRu3JgmTZrQsGFDbG1tSz0WIYQQQoh7adavBwuLnL9YWKBZv570cePMG5QQQgghhJlIMkUIIcRDITMzk4SEBCIiIkhJSWHs2LFFOr5p06YlFNl/KlSooB9p0qRJE5o2bYqHhwdWVvLPsRBCCCHKHuvgYJTUVACU1FSsg4MlmSKEEEKIR5bcvRFCCFHuqKrKmTNn+PPPPzl06BCHDh0iOjqatLQ0ABwcHHj++eeLtAi7l5cXVlZWZGZmmiTGypUr06RJE/1P06ZNefzxx7G0tDRJ/UIIIYQQJUm5cgWLU6cMtlmcPIly5QqqrNkmhBBCiEeQJFOEEEKUO0lJSfj6+uZZfuPGDRITE3Fzcyt0nba2tjRs2JAjR44UOR5HR0eaNm2qT5o0adKEOnXqFCmZI4QQQghRlmg2bAArK0hP/2+jlRWajRtJHzPGfIEJIYQQQpiJhbkDEEIIIYpKq9VSv379fPc5dOhQkett1qxZgfs4OTnRpUsXJk+ezNKlS4mJiSE+Pp41a9YwY8YMnnrqKdzc3CSRIoQQQohyTRMcjJKSYrBNSUlBExxspohKRq9evdBqtQ9cz5w5c9Bqtezdu9cEUYn7ldb1NdYf9u7di1arZc6cOQXuW9oWLFiAv78/1apVQ6vV8t1335k1nrIuMTERrVbL+PHjzR2KEKKckmSKEEIIs8nMzCQmJoZz584V+djmzZvnW16cZMr966Y4OjrmSpzExcXxyy+/8M4779C3b19q164tiRMhhBBCPFSUf/7BMjbWaJnlkSMo//xTyhEZp9Vq0Wq1ODg4cPbs2Tz36927t37f4IcsGSSKZvz48Wi1WhITE0uk/rwSLyXhl19+Ydq0adjY2DB+/HimTp1KixYtSrxdIe4liWTxqJFpvu6hKEotYDbQA3ACLgFrgVmqqt4oxPEVgH5AL6A54ApkAyeBEOBrVVXT865BCCEebhcuXODAgQNERUVx4MABDh8+TGpqKtOmTWPatGlFqsvX15eVK1fmWX7w4MEix9e2bVsmT56sn67L1dVVEiVCCCGEeORYbdwIGo3hFF86Gg1Wv/9OxjPPlH5gRujWvFu2bBnvvvturvL4+HhCQ0PzXBtvwYIFpKamPnAcY8eOJTAwkFq1aj1wXcJ8itIfTNV3imvLli0ArFy5kurVq5stjvKkRo0aREZGUrlyZXOHIoQopySZ8i9FUeoC+wBnYB1wAmgJvAr0UBSljaqq1wuoph0QBPwD7CQnEeMA9AU+AwYoitJFVdW0kjkLIYQoO1JSUoiOjjZInly6dMnovsVJfOS3ZgrA4cOHyc7OxsKi8IMwPTw8eOedd4ocixBCCCFEWaVcv47Vjh1FOsbm++9R7twxXt+dO9gsWAA2NkWqM7NzZ1QnpyIdUxjOzs64uLgQHBzM9OnTsbIyvM2xdOlSAHr06MGGDRtyHe/q6mqSOJycnHAqgfMTpaso/cFUfae4dP+3kkRK4Wk0Gjw8PMwdhhCiHJNpvv7zHTmJlFdUVe2nquo0VVU7A18ADYAPClHHZeAZoLqqqgP/reMFwAM4CLQGXiyZ8IUQwnxUVSUuLo6QkBAmT55M+/btcXV1pWfPnrz77rusX78+z0QKwJ9//omqqkVqs1GjRmg0mjzLb926RVxcXJHqFEIIIYR42FjEx2P34ovYvfACtpMmFerHIj6+wDoLW5fdCy9g9+KLBdb5IEaNGsWVK1fYvHmzwfaMjAyWL1+Ov78/np6eRo8taI2MmJgYBg0aRO3atalevTo9e/YkIiIiVz15TXWj1Wrp1asXV69e5cUXX6R+/fq4u7vTu3dv9u3bB8CdO3d45513aNSoEc7OzgQEBLB27dpCtwF5rwWhm9YqISGBhQsX4u/vj4uLCz4+PsydO1f/HXzt2rV07tyZGjVqUK9ePaZMmVLoUReBgYFotVqOHDlitHzNmjVotVrefvttg+3x8fG88MILNGzYkKpVq+Lp6ckLL7xAfBH6yoYNGxg7diy+vr7UqFGDGjVq0KFDBxYsWEB2drbBvlqtlpCQEACaNGmin/rNx8dHv09R1kG5f9/x48fTp08fAD7++GN9/brXbMmSJWi1Wj766COj9V25coXHHnuM1q1b59vu/f3g3nbutXv3bgIDA3Fzc8PZ2RlfX19mzpxJUlJSnueSnp7Oxx9/jJ+fH87OzrzyyisFXgdjfbxGjRp069atyH08KSmJefPm0adPH7y8vKhatSp169ZlyJAhREZG5tp/6tSpaLVapk+fnqts6dKlaLVa+vXrp+8LpfE+KWhdloI+cw4dOkRgYCC1a9emTp06jBgxgvPnzwOQkJDAmDFjqFu3LtWqVaN37955vu+MubedP//8k0GDBuHm5pZr6rsLFy4wZcoUmjRpgrOzM+7u7gwZMiTXQ5A+Pj58/PHHAPTp08doX8zvPRUcHGx0+kUfHx98fHxITk5m+vTp+Pj48Nhjj+mnz7v3PbBu3To6d+5M9erV9Z8hFy9eLPQ1EaKoZGQK+lEp3YAE4Nv7imcAY4ERiqJMVlXV+OM5gKqq0UC0ke23FEWZCwQDHYG5polcCCHM4+bNm/z5559ERUXx559/cuDAAW7cKHA2xDxdv36dxMRE3NzcCn2MjY0NPj4+Bl/oNBoNPj4+NG/enKZNm1K1atVixySEEEII8TDIatmS22Fh2A8ZgsXFiygmmJZISU2lMBOhqnZ2ZLu7k7JiBdn16z9wu3kJDAzkrbfeYtmyZfTu3Vu//ffff+fvv/9m5syZ+a6pkpfo6GjmzZtHixYtGDlyJOfPn+e3337jqaeeYu/evdQv5DklJSXRvXt3KlasSGBgINeuXWPdunUEBgaydetWJk2axI0bN+jRowcZGRn88ssvjB49mpo1a5psDYx33nmH0NBQevToQadOndi0aRPvvfce6enpODg4MGvWLHr16kWrVq3YuXMnixYtIisri88//7zAuocOHcoff/zBihUrDBITOroExrBhw/TbDh48SL9+/bh16xZPPvkknp6enDp1ilWrVrFp0ybWrl1b4BqJALNmzcLCwkKfTElOTmbPnj1MmzaNgwcPsnDhQv2+U6dOZePGjcTz77F+AAAgAElEQVTGxjJu3DiqVKkCoP/9oHr16qU/3zZt2tC2bVt9We3atWnWrBkzZswgKCiIKVOmYGlpaXB8UFAQmZmZPPvss/m2o6t3+fLlnDt3jqlTp+baZ8mSJbz22mtUqFCBp556iqpVqxIaGsqXX37J5s2b2bx5s9Eb3CNGjODQoUM88cQTRUos3d/Hb9y4wZo1a4rcx0+dOsV7771H69at6datG1qtlvPnz7Np0ya2b9/OihUreOKJJ/T7v/fee+zfv5/58+fToUMHunfvDsDx48eZNm0aLi4uLFy4sNCzFZTk+6QwDh06xFdffUWbNm0YOXIkx44dY/369Rw/fpzly5fTo0cPPDw8GDJkCOfOnWP9+vX079+f6OhoKlasWOh2oqKi+OKLLwgICOCZZ57h+vXrWFtbAzmfewMGDODGjRt06dKFPn36cP36dTZu3EiPHj0ICgqiW7duQE4SauPGjYSFhTF06FBq165tkusAOcnwvn37cuPGDTp37kylSpWoU6eOwT6LFy9m06ZNPPnkk7Rp04aoqCjWrVvH8ePH2bt3LzZFHEEpRGFIMiVHp39/b1VV1eDRhX8TIWHkJFsCgD+K2UbGv79zT9IqhBBlmKqqxMfHExERQWRkJJGRkRw/ftzk7Rw8eLBIyRSAvn374uXlha+vL05OTtSrVw8vLy+TxyaEEEIIUZ5l16/P7X37sJ02DesVK0ySUCmIamdH+tChpM2ZU+QpwYqqUqVKDBgwgOXLl3PhwgVq1qwJ5DyZXrlyZfr371+sm51btmzh22+/Zfjw4fptS5YsYdKkSSxYsIC5cwv3nGRsbCyjR49m7ty5WFhYkJaWRocOHXj55Zfp06cP/v7+bNiwAVtbWwAGDx5Mz549+fLLL3M9sV1c0dHRhIWFUaNGDQCmTZtG8+bN+frrr7Gzs2PXrl00aNAAgLt379K+fXuCgoJ48803C3xAqXfv3lSuXJmff/6ZWbNmGUy1duXKFXbs2EGTJk3039NVVWXcuHEkJyezcOFCBg0apN9/zZo1jBkzhhdeeIGIiIgCb4KvWrUKd3d3g23Z2dlMmDCBFStWMHbsWPz8/AB48803+euvv4iNjWX8+PG5bsw+qN69e1OlShVCQkJo27Ytb775Zq59Bg8ezKJFi9i2bRs9evTQb1dVlaVLl2Jvb8/gwYPzbaddu3a0a9eO0NBQzp07l6udv/76i6lTp1KxYkX++OMPg2mtJk+ezOLFi5kxYwZfffVVrrrPnTtHeHi4fsq6tLTCzVJ/fx8H6NSpE+PGjStSH/fw8ODEiRO5psy7cOECXbp0Yfr06QbJFGtra5YsWUKHDh2YMGGCfuTFmDFjSEtLIyQkBGdn50KdA5Ts+6Qwtm7dmus98dJLL+kTGC+99BKvv/66vuyTTz7hww8/ZNmyZXmOhDFmx44dfPHFF4wePdpge2ZmJqNHj+bOnTusX7/eICF46dIlOnfuzMsvv0xMTAw2NjZMmDCBpKQkwsLCGDZsGO3atXuAszd0+fJlGjRowMaNG6lQoYLRff744w927NiBt7c3kNNfx48fz6+//srvv/9O//79TRaPEDqSTMnR4N/fp/IoP01OMsWD4idTxvz7e3O+exlr/PTpYjYphOlIP3x0pKWlcezYMWJiYvQ/xoaCm9r27duNPsmWH93TX/eSvirKC+mrojyR/irKi0elr1paWhb6Jue90ubMwbpzZ6pMmJAzusTIguwPSrWyQrWzI+m770jv2hVUFYoRa6HbU1XS0tIYOnQoy5Yt48cff2Ty5MmcO3eOnTt3MmLECCwsLPSLz2dkZBhcO930P/duS09PB6Bly5YEBgYalA0cOJApU6YQFRVlsF1Xf3p6eq7Xxs7OjrfeektfL8CAAQOYNGkSN2/eZPbs2QYxNG/eHFdXV2JiYgrdxt27dwHIysoyKMvKygJg0qRJODo66stsbW3p1q0bK1as4IUXXqBOnToGx/Xp04fPPvuMI0eOFDjtFOQ85BQUFMSmTZvo2rWrfntwcDBZWVk8/fTT+vojIyM5deoUfn5+9O3b16Ddnj174u/vT0REBLt376ZVq1b5nnv16tWNvhfGjBnDihUr2LJlC40aNcp1Pe7evWv0uPz6Q2ZmZqH7zv376gwfPpxFixaxePFiOnbsqN++c+dOEhMTGTJkCDY2NoV6fxtrH3JGrKSnpzNu3Dhq165tUD5lyhRWrlzJypUrmT17tv7JfV1db7zxBhUqVMhVZ0HxGOvjffr04aWXXipSH7exsTF6/k5OTvTq1YvFixcTFxdHrVq19GU1atTg008/Zdy4cYwZMwY3NzeOHz/OxIkT8ff3N6irNN4nebWhk1+/8ff3z/WeCAwMJCgoiEqVKjF+/HiDsv79+/Phhx8SHR1dqD6ja6dRo0YMHTo01zGbN2/m7NmzjB8/Hj8/P4NyBwcHJkyYwDvvvMO2bdv0Sa38PpfyOl+djIwM/e97y3XTqr377rtG/73Ttfncc89Rt25dg/Lhw4fz66+/EhERwZNPPlngNREPn4yMjDy/DxZ2RGl+JJmSQzemM6+7hbrthRvfeB9FUV4CepAzBdgPxalDCCFKypUrVwwSJydPntR/mSxNx44dK/U2hRBCCCEeNeldu3I9NJQqY8Zgdfw4FikpJqs7296ezIYNSfrhB7JdXExWb2E0b96chg0bEhISwqRJk1i+fDnZ2dk888wzxa6zSZMmubZpNBqqVq1apIeN6tatm2sKHktLS6pWrUpKSorRERLVq1fPtT7BgzB2LtWqVcuzTLeoeX7rHt5r8ODBBAUFsWrVKoNkyqpVq9BoNAZPiMfExAAYPPV+rzZt2hAREUFsbKw+mZKXf/75h++++44//viDxMREUu7rz5cvXy5U/KXF09OTgIAAduzYYTCKKigoCICRI0c+cBu6NTSMXV+tVkujRo3Yv38/cXFx+if6dZo1a1asNk3ZxyMjI1m0aBF//vkn165dM0jQQM5rem8yBaBfv37s3buX4OBg9u/fj7+/P1OmTCnyeZT0++RB2m/UqFGuqeGK235er/OBAwcAOH/+PJ9++mmuct10iadPnzYYIVQSbG1tC5x1wtj10r2nSuOBUPFokmRKCVMUZQDwJTmL0weqqppRwCG5mCJrJkRx6bK50g8fDpmZmcTGxrJ//379lF26xezMxcbGhsaNG9OmTZsH6mfSV0V5IX1VlCfSX0V58aj11XPnzumnyymWOnVI3bYNm7lzsfn8c5NM+6Xa2XH3tddIf+01rAu5PoEpKIqivxbPPvssU6dOZe/evaxcuZKmTZvq12PQTT2l0WgMrp1uSqJ7t+nWDnB0dDR6na2srMjOzjYo09VvbW2d65gqVaoYbNM9RW1lZUXlypWNtqHRaMjMzCx0G7oRBpaWlgZlupuvjz32WJ7HODk55Sq79++F6Wvt2rWjXr16bN26lbS0NLRaLdHR0Zw4cYJevXrpb3AC+gW7a9asabRu3Y3yO3fu6MuNnfvNmzd58sknSUxMxNfXl6FDh+Lg4IClpSVJSUksWLAg1zXUXQ8bGxujbefXH6ysrArdd+7f915jx45l//79rFy5kunTp3PlyhW2bNmCj49PoUYB5dc+wO3btwFwdXU1GoNuCqvU1FR9ua6u2rVroyj/rYh07wiN/Nzfx3WK2sfXr1/PqFGjsLW1pWPHjri7u2Nvb4+FhQWhoaGEhYWhqqrR+gYMGKCfMmzcuHFGp4YqjfdJXm3o5NdvHBwcch1jb28P5CTC8nodsrKyCvU+1bVTvXp1o/snJycDOa9Dfu7evZvve/NeefVTyOkDut/3liuKwmOPPYadnZ3R9nVt3v96paWlGSScHujfSVFuaTQaHn/88RKrX5IpOXTpyrxWHdNtv1mUShVF6QesAK4CnVRVPVO88IQQonhSU1M5cOAA+/fvJzw8nKioKG7dumXWmNzd3WnRogV+fn60aNECb29v/Zc6IYQQQghRSiwsuDtlCpmdOmE/bBhKUhLKv9PTFIVqY4NapQopy5eT9e/aFOYyePBgZs6cyWuvvcbFixd54403zBqPqeluShobRV4WnsIeMmQI77//vn7dE93C80OHDjXYr3LlykDOCHljdKNJdPvlZdmyZSQmJjJ16tRc64ZERkayYMGCYp1HSevTpw/Ozs4EBQUxdepU/cLz969fUVy663b16lUaNmyYqzy/63tvIsUcPvzwQ6ytrdm5c6d+bRKdiRMnEhYWZvS469ev8/LLL+sTD9OnT6ddu3Y89thjJR7z/fJ7n0LZeK/m9Trr+sTy5cvp2bOnSdrSXY/MzEyD9ZQg/2th7r4oRF4kmZLj5L+/PfIo1z3elNeaKrkoivI0sJycESmdVVV9NCbvFUKY1Y0bN/SJk/DwcKKjo/XzkJpDpUqV8PX1xc/PT/9jji+0QgghhBDCuCw/P25FRWE/YgRWERFFSqioNjZk+vuTEhQEBdz4Lg1arZa+ffuycuVKKlSoQGBgoLlDMimtNmfmcWMjy6Ojo0s7nFyGDBnChx9+SEhICCNGjOCXX37BycmJ7t27G+zXuHFjgDxvjO/duxcwPoXPvc6cyXletW/fvrnK8qpb99S6bh0HU9PVn9+0yRqNhpEjR/LZZ5+xadMmli5dSsWKFXn66adNEkPjxo1Zv349e/fupUOHDgZlN2/eJDY2Fltb21zJirLgzJkzeHp65ootOzub/fv3Gz1GVVXGjx/PxYsXmTdvHgCvvPIK48aN4+effy71m/L5vU+Tk5OJj48v1XiKQjeSLzw8vNDJlILeU/deDzc3N4OysvC5JURRld7Y27Jt57+/uymKYnBNFEWpBLQBUgDjn9z3URRlOBACXAQ6SCJFCFFSLly4wKpVq3jttddo1aoV7u7uDB06lHnz5hEVFVWqiRRFUfDy8mLkyJHMmzeP8PBwEhISWLt2LW+//TY9evSQRIoQQgghRFlUpQqqVgtFHZly9y6qg0OZSKTovP322wQFBfHLL79QqVIlc4djUr6+vkDOou66BZgh5yblJ598Yq6w9GrVqkX79u2JiopiwYIFXLt2jYEDB+qn8tEJCAigfv36hIeHs27dOoOydevWER4eTr169QpcL6V27doAhIaGGmw/fPgwX3zxhdFjHB0dgZyp8kqCrv6CplIeNWoUlpaWvPHGGyQmJjJw4ECT9ddBgwah0WhYuHChPuGk88EHH5CcnMygQYP001GVJbVr1+bMmTMGa4CoqsqcOXM4ceKE0WO++eYbtm7dyoABAxg5ciQjR45kwIABbN++XZ9cKU2VKlXCw8ODiIgIg5izsrJ466239NPclUU9e/bE3d2d//3vf2zdutXoPpGRkQZrExX0ntJ9bi1dutRg++7du1m9erUpwhaiVMnIFEBV1XhFUbYC3YAXga/vKZ4FVAC+V1X1jm6joiie/x5r8GmuKMoochaZTyRnaq/EEg5fCPEImzt3Lj/88INZ2nZwcKBly5a0aNGCFi1a0KxZswKH4gshhBBCiDIoPR3Ntm0U9fltBdBs20ZqejqUkWlbXV1dcXV1NXcYJcLPz4/WrVuzb98+OnfuTPv27bl69SqbN2+mS5cuZl8LEXKm9Nq1axezZ8/W//1+iqIwf/58+vfvz+jRo+nZsyceHh6cPn2ajRs3UqlSJRYsWKCfHigvQ4YMYd68ebz55pvs3buXunXrEh8fz5YtW+jTpw9r1qzJdUyHDh2YN28er776Kn379qVixYpUqVKFsWPHmuT869evT40aNVizZg0ajQZXV1cURWHw4MH65A/k9NNu3bqxadMmIGe9H1OpU6cOc+bM4fXXX6dDhw7069ePxx57jLCwMCIjI/Hw8GDWrFkma8+UJkyYwKRJk2jfvj19+/bFysqKiIgITp48SY8ePdi8ebPB/gcPHmT27NnUqVPHIIH25ZdfcvDgQd577z1at26tH3FRWl5++WVefvllunfvTr9+/bCxsSE0NJSMjAwaNWpEbGxsqcZTWBqNhmXLlhEYGMigQYPw9/fHx8cHOzs7Lly4wMGDB0lISODkyZP6KdXatWuHhYUFs2fP5vjx4/qRKFOmTAFg+PDhzJs3j88//5zY2FgaNGhAXFwc27dvp3fv3vz2229mO18hikNGpvxnAjlrm8xTFGWtoihzFEXZAUwiZ3qvt+7b//i/P3qKonQiJ5FiQc5ol9GKosy872diiZ+JEOKRUdDTWqbk6enJyJEj+eabb4iKiuLMmTOsXLlS/yVdEilCCCGEEOWT1Z49YFXMZy0tLbH6d1omUfKWL1/OyJEjuXjxIgsXLuTIkSPMnj2bmTNnmjs0IGc9kMqVK5ORkYGXlxdNmzY1up+fnx87duxg4MCBREVFMW/ePCIjIxk4cCA7duzArxDr71SvXp1NmzbRrVs39u/fz6JFizh37hxz585lxowZRo/p0qUL77//PhqNhvnz5/PBBx/w9ddfG923OCwtLQkKCiIgIIB169YxZ84cPvjgAxITcz9n+8wzzwDQrFmzPK9Tcf3f//0fa9aswc/Pj99++41vv/2Wv//+m1deeYWtW7fi4OBg0vZMZfTo0Xz77be4uLgQEhLCzz//TM2aNdm+fXuuad+SkpL068z88MMPVKny3zLIlStXZsmSJVhYWPDcc89x82aRlkB+YCNGjGDevHlUr16dkJAQ1q5dS8uWLdmyZYtBnGVRo0aNCA0NZeLEiSQnJxMcHMwPP/xAdHQ0jRs35vvvv8fJyUm/f4MGDZg/fz7Ozs4sXryYDz74gA8++EBfXrVqVTZu3EjXrl3Zt28fP/zwA8nJyfz666+5pgAUojxQVFU1dwxlhqIorsBsoAfgBFwCfgVmqap64759VQBVVZV7tj0LLCmgmURVVd2MFSQlJcmLIcqc06dzZqmrX79+AXuan6pmk5oag51dY+6bse+hde7cOXx8fExeb4UKFfD19aVly5b4+/vTokUL/RMmZVV56qvi0SZ9VZQn0l9FefGo9dVz586ZdPSF3fPPo1m9GqUY9wdURSFj0CBSv//eZPE87NLS0gCwtbU1cyTCnObMmcPHH3/MvHnzGDlypLnDMUr6qihPpL8KKN53pCpVqhR6cK5M83UPVVXPAaMLuW+ui6yq6o/Aj6aNSghRGBkZf3P27LPcuRNBxYr+uLn9iEZT1dxh5UtVVU6dOkVoaCihoaGcOHGCsLCwAoez38vV1ZVatWo98JD+OnXq4O/vT8uWLWnZsiVeXl5YFffpRCGEEEIIUX5kZqLZtCnPRIpqb4+q0aBkZKDcM0++jqKqaDZuJDUzs/ijW4R4xNy6dYslS5bg4ODAwIEDzR2OEEKIQpJvOkKIci85eScJCc+SlZUCZHL7diTHj/vh5vYjlSt3Mnd4eqqqEh8fz+7du/UJlL///ttgnxMnTuDl5VWkelu1asXPP/9c6P0tLS1p3LgxrVq1wt/fH39/f6pVq1akNoUQQgghxMPBMiwMVVGMrpei2tqS9vrrpL/yCtZffYXtp5+i/PvkrwFFwXLfPrLaty/xeIUoz7Zs2cLhw4fZvHkzV69e5b333tOvPSGEEKLsk2SKEKLcys5O5+LFGVy79iOqmnpPSQZZWUmcOTOMxx57lpo1Z6MoGrPFea9+/frlO4okNDTU5MkUe3t7/Pz8CAgIoHXr1vj5+VGxYsUitSGEEEIIIR5O1j//jHLnjsE21cYGVaslZflysnx9AUifPJmsjh2xHzoUJSkJ5e7d/w64cwfN6tWSTBGiAGvXriUkJARnZ2dee+01XnzxRXOHJIQQoggkmSKEKJfu3j1LfPwQ0tP/ui+R8h9VTeXatR9JTt5B3borsLFxL+UoDSmKQseOHQkKCspzn9DQUMaOHVukegMCAgz+7uTkREBAgD550rhxYzSaspFMEkIIIYQQZUh2Nla//YaSna3fpNrbk9GlC6nffguVKxvsnuXry60DB7CfMAGrHTv0034p2dlo1q0j7csvoQhT1grxqJk/fz7z5883dxhCCCGKSZIpQohy5/r1lZw/P4ns7DQgO999VTWVu3dPc+JEG2rV+gInp8GlE2QeCkqmhIWFkZ2dXaR1Uzw9PRk1ahTNmzcnICAADw8PFKXQa2cJIYQQQohHlGVkpD6RolpYgK0tqXPnkjF0aN4HVa5MyrJlaEJCsHv9dUhLQ8nORsnKwjIqiix//1KKXgghhBCidEkyRQhRbmRl3eKvv14mOXkr2dm5F7/MWzbZ2SmcPz+J5ORN1K79NZaWlYoVg6qqnDx5kl27dpGamsqkSZOKdHz7AqY+uH79epHXTbGwsOCrr74qUhxCCCGEEEJoVq+GlBRUOzuyXV1JCQkhu27dgg9UFDKGDSOrZUvshw7F4vx5SEnJmepLkilCCCGEeEjJ+FshRLmQknKIY8dakJS0qYiJlP9kZ6eQlLSZY8dakJISXejjkpOTWb9+Pa+88gre3t4EBAQwbdo0vvzyS7KysooUg7OzM97e3vnuExoaWqQ6hRBCCCGEKDJVRbNmDQDpI0dyOzS0cImUe2TXq8ftsDDSR4z4rz5VLYlohRBCCCHMTkamCCHKNFXN5sqVeVy+/BGqmmaC+tLIzLzMqVNPUq3aVFxcXkFRLO7bR+XEiRNs376drVu3Eh4eTmZmZq66kpKSOHz4MM2bNy9SDJ06deLo0aN5lsfFxRWpPiGEEEIIIYpKuXED7O1JWbSIzC5dil+RtTVpn3xCZrdu2E2ciHLjBqqjo+kCFUIIIYQoIySZIoQoszIyrnL27ChSUw+bJJFyL1VN5fLlT0hO3oq7+4/cvWvPnj179AmU8+fPF6qeXbt2FTmZ0rFjR7755hv93z09PWnbti1t2rShTZs2ODs7F6k+IYQQQgghikp1dORWbKzJ6st84gmT1ieEEEIIUdZIMkUIUSYlJ/9BQsJosrJSgQyT16+q8NdfKURE7Ccy0ouYGEhPzz36pCC7du3itddeK9IxrVq1YvTo0bRv316SJ0IIIYQQQgghhBBClAOSTBFClCnZ2elcuPAO168vRVVTTVp3ejocPAgRETk/ly4BZP/7Uzz79+8nJSUFe3v7Qh9ToUIFvvjii2K3KYQQQgghRGmKvxFPXYeiracihBBCCPGwkWSKEKLMSE+/QFxcf9LTz5kskZKSAvv3w969OQmUVNPmZ0hPTyciIoJOnTqZtmIhhBBCCCHKgLM3z+L7ky/Ro6Nxq+Jm7nCEEEIIIcxGkilCiDLj9u0wkyRSkpIgLCwngfLnn5Bh+lnCDOzatUuSKUIIIYQQ4qG09vTanN+n1jKxxUQzRyOEEEIIYT6STBFClBmVK3cBsop17N9/5yRPQkPh8GHILv7MXYXi5ubGE088QYcOHWjXrl3JNiaEEEIIIYSZhBwLyfl9PESSKUIIIYR4pEkyRQhRZlhZOWFr601q6qFC7X/uXE7yZM8eOHGiZGOzsbGhTZs2dO3alW7dulG3rswZLYQQQgghHm7nb50nMTkRgISkBC7cukDNSjXNHJUQQgghhHlIMkUIUaY4OQ3nwoWTqGpKrjJVhfj4nOTJ3r2QkFCysbi6utKtWze6du1Ku3btqFChQsk2KIQQQgghRBmy/vR6LBQLACwUC9bHrWdcs3FmjkoIIYQQwjwkmSKEKFO02t5cuDDdYNv587B9O2zbBhcvllzbVlbQunUrunXrRdeuXfHw8EBRlJJrUAghhBBCiDIs+FgwqZk56xmmZqYSfCxYkilCCCGEeGRJMkUIUaZoNNWwsanP5ctH2bkzJ4Fy/HjJtefsDC1bgr8/tGrVED+/TSXXmBBCCCGEEOXElTtXOHXjlMG2k/+c5MqdK7hUcDFTVEIIIYQQ5iPJFCFEmZGSksKmTZtYtiyTPXtKbhH5hg2hbVto1Qrc3EBRQFHsqFFjZMk0KIQQQgghRDmzIW4DVooV6aTrt1kpVmyM38iYxmPMGJkQQgghhHlYmDsAIcSjLSsri927dzNhwgQaNGjAc889x65dJ02aSLGwgGbN4JVXYNUq+O47GDYM3N1zEik5stFq+5iuUSGEEEIIIcqx4GPBpGQarmOYkplC8NFgM0X04IKDg9FqtQQHl99zeBSNHz8erVZLYmJiibUxZ84ctFote/fuLdbxpRHjw2z16tW0a9eOWrVqodVqmTZtWrHqMfY6JCYmotVqGT9+vKnCNcpU51BW7N27F61Wy5w5cx64Lq1WS69evUwQlTCmNK5vXv2hV69eaLXaQu37sJCRKUIIs0hISGDx4sWsXr2aS5cumbx+jQb8/KBdO2jdGqpUyX9/a+s6WFvXMnkcQgghhBCi/MrIVrmamk1GtorGQsHZzgKNxcO/pt4/qf8Qey3WaNmRa0f4J/UfHO0cSzmq3LKysggKCmLlypUcO3aM27dvo9VqcXZ2xtfXlyeffJKePXuaO0whHmk+Pj4AHDlyxGh5ZGQkzz//PG5ubowZMwY7OztatGhRmiE+sIfhHMSjLTExkSZNmjB06FDmz59fIm306tWLsLAwbt68WSL1lxZJpgghzCIxMZGvv/7apHXa2UFAQE4Cxd8f7O0Ld5yi2ODoONSksQghhBBCiPJHVVX2X00n+HQK+6/cJfF2FlaKgoUC2Spkqip1KloS4GLD8Pr2BDhboyjFT67c/zSnMevXr6ddu3ZAzsiKF198kV27duHq6qqPOS4ujtu3b1OnTh0cHXMnOe7evcuJEydQFAVPT0+sra3zbXPjmY1oLDSkZ6XnKtNYaPj9zO884/1MYU6xxGRlZTF48GC2b99OlSpV6N69OzVq1CA9PZ0TJ06wevVqTp8+LcmUh8CMGTOYNGkSNWrUKLE2xo4dS2BgILVqFe8Bu9KI8WG1detWVFVlwYIF+Pv7m7z+GjVqEBkZSeXKlU1et05Jn4M5+Pr6EhkZiZOT0wPXFRkZiZ2dnbWge+4AACAASURBVAmiEuZSlP5gyr5TFkkyRQhhFm3btqV69eoPPCqlcmVo0yYngeLrCwX8vzAPCg4O/R4oDiGEEEIIUX6pqsrK+BTmHLrF32nZpGaqqP+WZej/lCMuOYv45BTWnE2lqq0FbzarxOC69g+UVJk6dWqeZbVr1873WEVRqF27NidPnuT8+fNUrFjRIFlyLeUayyOWc/fuXapWrcrJMycLjOf7Q99zJ+OO0bI7GXdYcGgBNpY2BdZzr851OuNkZ7obK6tXr2b79u00atSIjRs3UuW+oegpKSkcOHDAZO0J86lWrRrVqlUr0TacnJwe6MZfacT4sNLdEyip66fRaPDw8CiRunVK+hzMwd7e3mTXraSvvyh5RekPpuw7ZZGsmSKEMAtLS0sGDhxYrGMdHKBfP/j8c1izBt54I2cx+eIlUkCjqYaNjXvxDhZCCCGEEOXahTtZ9N50jdfCk0i8nUXKPYmUvKhASqZK4u0sXgtPovema1y4k1XsGN588808f+rUqVPg8TY2NtSsWZOsrCz++usvVPW/M4g6E8V7Me8x58Qc3t7/NpP+mFTgT/zN+Hzbi78ZX6h6Jv0xiRe2vMCL214k/kb+dRZVZGQkAMOGDcuVSIGcmznt27fP8/g9e/bQq1cvatWqhaurK4MGDeLkydyJpri4OGbOnEnHjh2pW7cuzs7ONGrUiFdffZULFy7k2v/eueL//PNPBg0ahJubm8E6Dn5+fvj5+ZGUlMSUKVNo2LAhLi4u+Pv7s2DBAoPX714HDhxg5MiReHh4ULVqVby9vZk4cWKRH1C7d32Q1atX06FDB6pXr46npyfTp0/n7t27AOzevZtevXrh6upKnTp1GDt2LP/884/Ra/nqq6/i7++Pq6sr1apVo1WrVnz00UekpaUZ7JuQkEDt2rVxc3Pjr7/+Mii7c+cOLVu2xNHR0WDtkoLWwTh79iwjR47E3d2dWrVq0b9/f44dOwbAtWvXePXVV2nQoAEuLi506tSJPXv25HtN7rVv3z4GDx6Ml5cXzs7OeHh48MQTT/DRRx8Z7FdQjImJiYwZM4bHH38cFxcXOnbsyObNm42+PklJSUybNg0vLy9cXFxo0aIF33zzDQkJCUVa++PevhgZGclTTz1F7dq1qVWrFgMGDODQoUN5tj9r1iz8/PyoU6cODRo0YMCAAezatSvfNu7v77o1is6dO8e5c+fQarX6n/Hjx+daw6hJkyb68nuvY3R0NCNGjKBevXr699/kyZO5fPlyoa5DfmumXL58mddffx0fHx+qVq1K3bp1eeaZZ4iOji5U3SVxDrq+lJCQwPfff0/r1q2pVq1aodbE8PHxwcfHh9u3b/Pmm2/i7e1NtWrVaNu2LRs2bAAgMzOTzz77jObNm+Pi4kLTpk1ZuHBhrroKWiMjMzOTuXPn0rx5c5ydnfH29mbGjBmkp+ce0WhsTQ9Tfw5ptVr69+9v9LqUxmeIMVFRUWi1WoYPH57nPi1btsTZ2ZkbN27ot2VnZ/PDDz/QqVMnatasSY0aNejUqROLFy8mu5ALDF+6dImPP/6Y7t276//N8PT05P/+7/84ceKEwb5z5syhSZMmAISEhBi8V3V9uyjroNy/r+5ah4WFARjU36tXL7KysvD29sbV1ZXbt28brXPKlClotVrWrVtXqPMvSTIyRQhhNoMHDy70VF+2ttC2LXTtmjMCxdLSNDEoijWOjkNMU5kQQgghhChXwq/c5elt10nLVMksKIOSh5RMlYir6fj/eoXVXZ0IcCnaiA1TcXJyIikpiaSkJP7++2+cnZ1JSUmhemZ1fmr1E28ffZtLdy6Rmpn6wG0Vtg47Kzvcq7izou8K6jvWf+B27+Xg4ABAfHzRkzRbtmzh999/54knnmD06NGcPHmSrVu3cvDgQSIiIgxGKKxfv54ffviBdu3a0bJlS6ytrTlx4gRLly5l8+bN7Ny50+jUTlFRUXzxxRcEBATwzDPPcP36dYMRQxkZGfTr14+kpCQGDBhAeno669evZ9q0acTFxfHZZ58Z1Lds2TImTpyIjY0NTz75JDVr1iQ+Pl4fx7Zt2/RTvxXWwoUL2b59O7169aJt27bs3LmT7777jhs3btCzZ0+ee+45unXrxqhRo4iMjGTVqlX8888/rF692qCer776ilOnTuHv70+3bt1IS0sjIiKCjz76iNDQUNatW4flv/+Bc3Nz4+uvv2bUqFE8//zzbNy4ESurnFtTkydP5tSpU0ybNk0/tV1B/vrrL7p06UKDBg0YNmwYf/31Fxs2bKB3795s27aNwMBAKlWqRP/+/blx4wZr1qzh6aef5sCBAwVer+3btzNo0CAqVar0/+zdeVxU5f4H8M8Zhm0AQdBBBZFCyl9XFLUbuBLigporIirmUqnXtLq5oWWZlam5kJWZ+4J7QKbgnopLgGma2s1EU1Q0MGQfgVnO7w8ucx1ngAFmRtTP+/XypZ4553meM5w5wPM9z/eLXr16oUmTJsjJycEff/yBNWvWGF1g/ObNmwgJCYG3tzciIiKQk5OD77//HsOHD8fOnTt1gn7FxcXo168ffv31V7Rq1Qrh4eHIz8/H4sWLkZycbFR/Dztz5gyio6Px8ssv44033sCff/6J3bt346effkJ8fDw6dOig3Tc3NxehoaG4dOkS2rZti7Fjx+LevXvYvXs3Bg4ciCVLlmDMmDF6fRi63ps3b46oqCht/YUHgxl+fn7w8vJCVFQUEhMTcfHiRfzrX//SBkbL/963bx9GjhwJURTRv39/NG3aFOfOncOaNWuwZ88e7N27F97e3jV6X65fv45evXrhzp076NKlCwYPHoyMjAzs3LkTBw4cwMaNGxEaGlppG35+fmY7h6ioKCQnJ6Nnz57o0aOH9jNUFZVKpb3ee/fujdLSUsTFxWHkyJH4/vvvsXr1apw5cwbdunWDra0tdu7cienTp6NBgwYYNGiQ0e/fG2+8geTkZHTr1g1OTk44ePAgli5dirt37+Kbb74xuh1T3Ydqypz3kH/+85/w9fXFwYMHce/ePb00nGfOnMHly5fRr18/7fc0ABg/fjy+++47eHp64tVXX4UgCEhISMCUKVOQkpKCVatWVXleP/30E7744gt07twZ/fr1g4ODA65evYoffvgBe/fuxb59+7T1jDp16oS8vDx8++23aNmypU7gq3yf2nB2dkZUVBS2bNmCmzdv6qzG9fLygpWVFUaOHIl58+YhLi4Oo0aN0jn+/v372LFjB9zd3etE6k4GU4io1q5fv47MzMxq5wZt2bIlXnjhBW3E/2ESiQTt2gHdu2vQqVNZTRTTs0L9+oafXiAiIiKiJ1dyZgnCDmRDUdMoygNUIlCoFDHoQDbiezy6gIqXlxd+//133LlzB46Ojrh+/TpEUUTn/+uM5LbJmJE0A9t+32aSgEpV7KX2GPZ/wzAvaB5spaZ/P/r27YulS5di7dq1KCgowCuvvAJ/f/8q06IBQGJiIuLj4xEUFKTdNmfOHERHR2PTpk145513tNsjIiLw5ptvwtZW9xwOHz6MwYMHY9GiRViyZIleH4cPH0Z0dLTBiWcAyMzMxDPPPIPk5GRt2++99x6Cg4OxevVqDBw4EB07dgRQtjpm8uTJ8PLyQmJiok7wJikpCQMHDsSMGTO0TxAbKykpCUePHsXzzz8PoKy2TlBQELZv3459+/YhPj4enTp1AlD2pPSgQYNw6NAhnD9/Hq1atdK2s3jxYjRr1kwv1d2nn36KRYsW4YcfftCZpO3fvz9ef/11rFmzBnPnzsXs2bOxZcsWbNu2DZ07d8b06dONPoeTJ09i1qxZmDp1qnbb559/js8++wwhISEYOHAgFi9eDImkLDFLcHAw/vWvf+Gbb76p8gnrDRs2QKPRICEhQW9CMTs72+gxnjhxAjNmzNAJvoSHhyMsLAxfffWVTjDlyy+/xK+//oqwsDCsXr1a+55OmTJF53qtjkOHDuHzzz/HuHHjtNsSExMRGRmJSZMm4fTp09r356OPPsKlS5cwevRoREdHa1cHTJkyBcHBwYiKikLXrl31VsxVdL0HBARgy5YtAMpW4D2sVatWuHHjBi5evIgJEybotFtYWIgJEyZApVIhISFBJ+jzxRdf4KOPPsK7776L77//vkbvy+TJk3Hnzh296+f1119H7969MWHCBFy4cAGOjo4VttGqVSuzncP58+dx7NixageL7ty5g1atWiEhIUF7b4mIiEDv3r0xatQoPPPMM/jpp5+0NbsmTpyIf/7zn4iOjq5WMOXatWtISUnRBgE++OADdOrUCdu2bcPs2bPh7u5uVDumug/VlDnvIQAwbNgwfPzxx4iNjdX5DAJlq0DK9ykXGxuL7777Dq1atcKePXu019+sWbPQp08ffPfdd+jRowfCw8Mr7bdLly64fPkynJycdLZfuHABoaGhmDNnjjYg1blzZ3h5eeHbb7+Fn5+fwc9qbbi4uGDmzJk4ceIEbt68abD9UaNGYeHChVi3bp1eMCU+Ph55eXl44403YG1tbdKx1QTTfBFRjSiVSuzatQuDBg1CmzZt8M4771S4HL0yERERettat26NuXPn4j//+Q+ioxuhe3dzBVIAqdQVdnbPm6dxIiIiIqqTMorUCD9omkDKgxQqEYMPZlc75de8efMM/omOjq5WO1KpFF5eXtBoNLh8+TJKSkogl8vh5OQEW6ktokOisb7PetSzqQdriXkmJKwl1qhnUw/r+6zHkpAlZgmkAGW/M6xcuRJyuRw7duzAyJEj0apVKzzzzDOIjIzE3r17Kzw2LCxMb2K6fPLmzJkzOtubNGmiF0gBgK5du6JFixb48ccfDfbh5+dXYSCl3IcffqjTdv369TFt2jQA0AmMrFmzBkqlEvPnz9dbBRMUFIRevXph3759KCgoqLS/h40bN047gQmUpYsbOHAgNBoNevTooZ3ABMoedBsyZAgA4OLFizrteHt7G6wZNHHiRAAw+B7NnTsXLVu2xBdffIGVK1di2rRpaNCgAVatWqWdtDSGl5cX3n33XZ1t5ROTpaWl+Pjjj3XaCw8Ph1QqxYULF4zuw1Dh7OrUV2natKn261ouJCQEnp6eetfb1q1bIZFI8OGHH+q8p56enkan93rYs88+izfeeENnW58+fdCxY0f8+eef+OmnnwCUvV87duyAo6MjZs+erdO/j48Pxo0bh9LSUmzbtk2vD2Ou9+ras2cPcnJyMGjQIJ0gBABMmjQJXl5eOHLkCG7evFnttjMyMnD48GF4enrqBE+BsgBQWFgYcnJysHv37kd2Dm+//XaNV93Mnz9f597SoUMHNGvWDLm5ufjoo4+0gRSg7PMbEBCA33//HWq18d+75syZo7OawsHBAeHh4dBoNBWmkDPEVPehmjL3PSQiIgISiUQbOClXvmKoYcOG6N69u3b7pk2bAJQFNh8M5Dk4OGDOnDkAgI0bN1bZb8OGDfUCKUDZZ7Vz5844fvw4lEqlUedgCeWp7M6dO6eXZm/9+vWQSCQYOXLkIxqdLq5MIaJquX79OjZu3IhNmzYhKytLu/3SpUtISUlB+/btq9VeWFgYPvroI3h4eCAiIgLh4eFo0aKF9nW1egiyspYBMMdN3hr16w8xQ7tEREREVFeJooixSfdQbOJASrlilYhxSfeQ0KuB0UXpFyxYYHB7vXr19CZ5quLs7AxHR0cUFhbCxsYGjRs31nm95zM9cWrUKby6+1X89vdvUKgU1Wq/MjKpDP9o8A/E9I1BIwfzF2IeOHAgXnnlFRw/fhzJyck4f/48UlJSkJiYiMTERAwdOhTLly/X+zr4+/vrteXp6QmgLM3Rg0RRxI4dO7BlyxZcvHgRubm5OhOONhUUbmzXrl2lY5dKpQZX9pdPHJ4/f1677eeffwZQ9gT1L7/8onfM33//DbVajatXr8Lf3x8JCQl6E31+fn545ZVXdLa1adNGr63y68XQe1QeyLl9+7bO9qKiInz77bdISEjA1atXUVBQoPOgnaGaLnZ2dli3bh2Cg4Mxffp0CIKADRs2VLuAt5+fn176o/Jz8PHx0ZtMtLKyglwu1zsHQ8LDw7F7926EhIRg0KBB6Ny5MwICAuDh4VHrMQJl11x57R8AyM/Px7Vr1+Dp6WmwVlJgYGC1+i3Xvn17gwGqTp064eTJkzh//jw6deqEtLQ0KBQKBAYG6kySl+vSpQsWLVqkc22Wq+p6r4lff/1V2+/DpFIpOnTogBs3buD8+fPVTnFXfg4dOnQw+KR7ly5dsGPHDpw/f15n1UB11eYcavqeOjs745ln9GvCNm7cGOnp6RV+tlUqFTIzMw2mLTSkOvfRypjqPlRT5ryHAICHhweCgoJw5MgRXLp0STvftXfvXuTk5ODNN9/UpjoEyq4ZiUSiE0Qq17FjR1hZWRn8DBqyf/9+rF27FufOnUN2djZUKpXO69nZ2dW+55rT66+/jh9++AHr1q3D0qVLAQC//fYbfv75Z3Tv3t2oGnKWwGAKERlt9OjR2LlzZ4Wvr1+/vtrBFE9PTxw7dgz/+Mc/DP6AV7/+IPz990poNKYPpkgk1qhf3/hlrERERET0+Nt+VYFz2coa10ipikoEzmYrseOqAhHNHYw6pjoTT1UpKCjQFnAtLS2FQqHQS1PTyKER9kfsx+JTi7Hk5yUmSftlL7XH5H9OxuSXJkMiWC4JhrW1Nbp27YquXbsCANRqNXbt2oVJkyZh27ZteOWVV/SCCIYK1pdPZj38ZPZ7772H5cuXo1GjRggJCUHjxo1hZ2cHANr874bI5fJKx+3q6mpwgr08NU5+fr52W3mx5S+//LLSNsu/7omJiXpPQQ8bNkzvfahXr55eG+Vjquy1B59mViqV6NevH86cOYMXXngBAwcORIMGDbTv54IFC7Spoh7WvHlz/OMf/0BqaipatGih/RpWh6Fxlvdt6LXy8zDmiex+/fph+/bt+Prrr7Fp0yasW7cOQNkE7+zZsxEcHGzUGA1db+XjeLCYdPnKooYNGxrcv6prqiIVHffwtVb+d0XpmconXfPy8kw2tsrUZjyPsm1T9VPT97Sy6x4wfD0a+mxX5cHVLQ+3U50VLqa4D9WGOe8h5YYPH44jR45g69at2tUlhlJ8AWXXTP369Q0G6qVSKdzc3HD37t0q+1y+fDlmzpwJFxcXBAcHw9PTE/b29hAEQVvjp6J786PSpUsXPP/884iLi8Onn34KJycnrF+/HkDZfGRdwWAKERmtqqXMO3fuxPz58w0+xVKZygpa2dv7QSJxgEZjuifmykkkjrC3b2nydomIiExNqRGRdV8DpUaEtUSA3F4Ca4lxT7wT0f+Iooh5ZwtMnt7rYQpVWT9DfGRGr04xBZVKhRs3bkAQBHh6euLWrVtIT09HixYt9CbuJYIE0wKmIdgrGMN3D0deSR5K1NWfWLG1soWzrTO29N2CFxu/aKpTqTErKysMHDgQv/32GxYtWoRjx47pBRGMdffuXaxYsQIvvPAC9u/fr/eEclxcXIXHVvV1v3fvHtRqtd7XJTMzE4DuJF75v2/cuFHh5N6Dli9fri36bW579uzBmTNnMHz4cL2i03/99VeFq64AIDo6GqmpqXBzc8Pvv/+OJUuW6NQtqAt69uyJnj17oqioCKdPn9Y+6R0REYFjx47pZFWorfLrq6KJ0gczQ1RHRcc9fK2V/13R/n/99ZfOfg8yx32uvJ/ycVZnPI+ybVP1Y8nvHU8CQRAqDOLUNihWW6+88grq1auHHTt24MMPP8S9e/dw6NAhtGzZUm8+rF69esjJyYFSqdRbNaVSqZCdnW0wfdfD+82fPx/u7u5ISkrSW31SvtqxLhozZgxmzJiB7777DsOGDcOOHTvQpEkThIaGPuqhabFmChEZrapIcElJicH8qbUhCALq1x8M08d+pahffzB/QCEiojpJFEUkZ5Zg0okcvBj3F5rE3MaLcZnouDMLL8Zl/vf/f2HSiRwkZ5bUqG4Z0dMoJasUfxdrqt7RBO4Wa5CaVWqRvsrdunULpaWlaNSoERo0aAC5XI7S0lJkZGRUeMyLjV/Ez6N+RkCTANhaVa++ia2VLQKaBOD0qNN1IpDyoPLJptrcH69fvw6NRoPg4GC9yauMjAxcv369xm2rVCqkpqbqbT9x4gQA6BRW/uc//wkASE5OrnF/5vLnn38CAPr27av32smTJys8LjU1FZ999hl8fX2RnJwMX19fzJs3r06eI1BWryAoKAifffYZJk+ejNLSUhw8eNCkfdSrVw/e3t64ffs20tPT9V5PSUmpUbspKSk6K2DKPXyt+fr6QiaTadPZPez48eMAyuoVVcfDK3CMVT6u8nE+SKVSaa+V6o7nwbZTUlL0Uh8BNT/XivoxxzmQLhcXF4Pf69RqdbVqJJmDvb09BgwYgDt37uDo0aP47rvvoFKpDKaQa9WqFTQajbaW0YNOnjwJtVpd5fWSnZ2NvLw8vPTSS3qBlMLCQm36uQfVZFVRdRnTx7BhwyCTybB+/Xpt4fkRI0YYXMn5qDCYQkRG8/PzqzJv54YNG0w+oePqOuy/QQ/T/REEAa6uQ006TiIiotoSRRHbrhTBPzYTYQeysTlNgSv5aig1wH21iCKViPtqEUoNcCVfjc1pCoQdyIZ/bCa2XSmq8fdgFxcXnT+urq549tlnMWHCBOzbt8+o/b29vdGnTx9s3ry5wnEUFxfjq6++QkhICLy8vNCwYUM8//zzCAoKwrRp0/QmGzZv3qzXl1wuR6tWrTBhwgRcunSpRudLT6/NaQqzr0opp1CJ2JRm+tXVFcnJyUFOTg4cHBy0KWUaNWoEe3t77cRKRZxtneFi61LtlSkl6hLUt6uPera1e3q7JmJjY3HkyBGDk7SZmZnYsGEDgLIc8zXl5eUFoGzC9cHJn8LCQrzzzjsGJ2Gr4+OPP9ZJs5KTk4NFixYBACIjI7Xbx44dC2tra7z33nu4cuWKXjulpaUGJ94sofw9evj+ff36dcyePdvgMbm5uXj99ddhZWWFNWvWQC6XY+3atZBKpRg7dixycnLMPm5jnDx50uDXuHzliEwmM3mfQ4cOhUajwccff6zzvfTWrVs1Xm109epVrF69WmdbYmIiTp48iWeffVZbGN3Gxgbh4eEoKCjA3Llzdfa/du0aVqxYAWtra0RERFSrf1dXV/z999+4f7966QT79OmD+vXrIy4uTu9J+uXLlyM9PR0vv/xyteulAGV1LIKDg3Hjxg299/X06dOIjY2Fi4tLjVe1WeIcSFe7du2QkZGBo0eP6mxfuHBhhekYLWn48OEAgG3btmHbtm2QSqUYMkS/hu+IESMAAHPmzIFC8b+fIRQKhTZF2KuvvlppXw0bNoRMJsO5c+e06R+BstRoM2bMQHZ2tt4xLi4uEAQBt27dqv7JGcnV1RUAKv16ODs7Y/DgwTh//jzmzp0LKysrjBo1ymxjqgmm+SKiahk9ejTOnDlT4euXLl3CqVOnDBZTrCmZrBVat/7LZO2VE4S6E9kmIiLKKFJjXNI9nM1WGj3ZK6Jswja9UI3JyXmIuazAyiBXeDjU7HtcVFQUgLKnJS9fvow9e/bg9OnTuHPnDj777LNK9//zzz+RkJCAkydP4ty5c1i4cKHOvoWFhejTpw9+/fVXuLu7o2/fvnB3d0dhYSEuXryI9evXIy8vz2DBzZYtW6JPnz4AynJJnzhxAlu3bsXOnTuxa9cu7VPbRFVJySyBpdZxiQBSMy2zMqW0tBQ3b96ERCJBs2bNtKuvy///xx9/4ObNm3BwcNApdKs9Xl2Kg9dr9pT9wWsHUaouhY2V4ULs5nL69Gl8++23cHd3R2BgoLYwbXp6Og4cOID79++jd+/e6N+/f437cHd3R1hYGOLi4tC5c2cEBwcjPz8fR44cgZ2dHfz8/Gr8xLO7uztKSkrQvn179OrVC0qlErt27cJff/2FN954QycI9Nxzz+Hrr7/GpEmTEBgYiJCQEDRv3hxKpRK3bt1CcnIyGjRo8EhSt4SGhuLZZ5/FsmXL8J///AetWrXCrVu3sH//fvTo0cPgxNzEiRNx69YtLFiwQPvkvp+fHz799FNMmzYNEyZMMHnGg5qIiorCnTt3EBAQAC8vL9jY2ODcuXM4duwYmjZtirCwMJP3+c477yAxMRFxcXFIS0tD165dkZ+fj++//x4dOnRAYmKiwVqjlenWrRtmzZqlTSv0559/Yvfu3bCzs8PXX3+t095HH32E5ORkrFq1CmfPnkX79u1x79497N69GwUFBVi4cCG8vb2r1X9QUBB++eUXhIWFoUOHDrC1tUXLli3Rq1evSo9zdHTE119/jdGjR6NPnz4YMGAAPD09ce7cORw+fBju7u744osvqjWWB0VHR6Nnz5744IMPcPjwYbRp0wa3bt3CDz/8AIlEgmXLllWZTqkq5j4H+p+33noLP/74I0aPHo2BAweifv36OHXqFNLT09GpUyeDq4MsKTAwEM8++yx27twJpVKJ0NBQg/WRwsPDsWfPHnz//fcIDAxEnz59tHVO0tPTMWjQIINBmAdJJBKMHz8e0dHR6NChA3r37g2lUonjx48jJycHnTt31q6+Kufo6IgXX3wRycnJGDt2LHx8fGBlZYVevXqhZUvTpMcPCgrCzp078eqrr6JHjx6ws7ND06ZNMXSo7oPOr7/+OjZu3Ijbt28jNDQUHh4eJunfVBhMIXqKGcrRW5VBgwbh/fff1ymICJTdrHv06IExY8bgxRdNv8SfgQ8iInqSJWeWIPxgNopVYo2LYitUIlKzShHwfSZiu7sh0L166XoAYObMmTr/37x5MyZNmoTly5dj/Pjx2snKivZPSUlB7969sXr1akycOFFnwmX58uX49ddf0bVrV2zbtk2vsGZubi7++OMPg+Py8/PT6UsURbz55pvaQp4JCQnVPld6+ig1ZYFHDFaXFgAAIABJREFUS7peqNLWOqrMvHnzKnytT58+OimfHiaKIm7cuAG1Wo2mTZvC1lb3s29vb4/GjRvj9u3buHnzJp555hm9No7dPAappGbTA1aCFY7fPI4Q75AaHV9TkyZNgo+PD44ePYrffvsNhw8fRnFxMVxdXdGpUycMHjwY4eHhtU7r+9VXX8Hb2xvx8fFYvXo1GjRogF69euG9996r8ungylhbW2Pnzp345JNPEB8fj+zsbHh7e+Pf//43xo8fr7d/REQEWrZsia+//hrHjx/HkSNHIJPJ0LhxY/Tv3x8DBw6szWnWmIODA3bt2oU5c+bgxIkTSE5Ohre3N6ZNm4aJEyciPj5eZ/8VK1YgMTERvXr10jvPsWPHIikpCQkJCVi2bBkmTpxoyVPRM2XKFCQkJODs2bNISkqCRCKBp6cnpkyZggkTJhgswF1b9vb22L17Nz777DPs2rUL33zzDZo1a4bJkydrgynVneBv164dpk+fjrlz52LVqlUQRRFdunTBBx98gLZt2+rsW79+fRw4cADR0dHYvXs3VqxYATs7O7Rt2xZvv/02unbtWu1zmjp1KvLy8rBv3z6kpqZCrVZj2LBhVQZTgLL73/79+7F48WL8+OOPyM/Ph7u7O1577TVMmzYNjRs3rvZ4ynl7e+PIkSNYtGgRDhw4gBMnTsDJyQkhISGYOnWq3ntTU+Y8B/qfoKAgrF+/HosXL0Z8fDxkMhmCg4Oxdu3aSr/HWtKwYcO0q74Mpfgqt2bNGnTs2BGbNm3SFmB/7rnnMGnSJLz++utG9fX+++/Dzc0NMTExWL9+PerVq4eXX34Zs2bNqvD9WLFiBWbOnIlDhw4hNjYWoiiiSZMmJgumjBw5Ejdv3kRcXByWLl0KlUqFjh076gVTWrdurX1YYcyYMSbp25QE5leuO/Ly8vjFILPTaDTYu3cvvvzyS/Tp0wdvv/12pfunpaUBKMufWm7q1KnaZcKenp549dVXMWLEiDoXLaani6Frlagu4rVKD0vOLEHYgWyTph6SSQXE9zA+oFI+IfRwjvS0tDQMGTIE165dw/r16zFgwIBK9wfKnry7dOkSNmzYoPNEeHh4OA4ePIhNmzYZnTZj8+bNmDhxIoYNG6aXhuPMmTMICQmBTCbD7du3jWqPnlzG3FszitR4MS4T99WW+7XL3krA6TD3CleLGTMZu2zZMm3Kp/LPxNGjR+Hv7w+grFh0RkYGnJ2d8eyzzxpsQxRFpKWloaioCM2aNdOm2ig3du9YxP4RC7EG63YECBjSYghWhK6o9rFPq/KJqYsXLz7ikdDjZMOGDXjnnXcQHR1t1ATj8ePH0bdvX0RFRek9/GCs4uJiAICdnV2NjieyJF6vT4aCggL83//9H1xcXHD+/Plqr8a7efNmtVPnOTs7G/3kBVemED0lSkpKsGPHDnz11Ve4fPkygLIl8OPHj9d7eq0qo0aNQkZGBkaPHo1u3brVqUJQREREj5OMIjXCD5o2kAKUrVIZfDAbqQMrnsQ1VvnDV9V9uvvhVEL169cHUJa73RRqOi56eik1IqpYIGJyEqGs34oYCkhWJjIyEpGRkTr5xuVyOeRyeaXHCYKA5557zuBrKo0Ke//cW2EgRSaVwdrKGkq1EgqVfg0YESISryZCpVHVeHULEf3PnTt39FYr3Lx5EwsXLoRUKkVoaOgjGhkRkfmtXbsWhYWFmDp1arUDKZbAn3SInnBKpRJr167FF198gTt37ui89tdff2HHjh3VXp7u5+eHrVu3mnKYRERETx1RFDE26R6KzVQMu1glYlzSPST0alDjgENqairS09MhCALatGlT5f4nT57E5cuXYWNjg3bt2um8NmjQIOzYsQNz587FjRs30KNHD7Ru3RqNGjWq9rhEUdSukn24H6KKWEsEVBLXMAuNiCpTfD1qJzNOVniPsJPaYepLU/H2i29j6emlWHhqIYpVxXr7CRDwU8ZP6NK0i7mHS/TEGzlyJJRKJfz9/eHs7IwbN25g//79UCgUmD17NtNCEdETJy8vD2vXrsXt27exceNGNGrUyOiUZpbGYArRE+zo0aOIioqqMP84AHz55ZeIjIysk9FeIiKiJ9n2qwqcy1bWuEZKVVQicDZbiR1XFYho7mDUMeU5lFUqFdLS0pCYmKitT+Ll5VXp/uUF6EVRxCeffKIXJAkNDcX8+fPx2WefYc2aNVizZg2AsiLMXbp0wejRo3UKLj/owoUL2r7KC9BfuHAB9vb2+OCDD4x7Q+ipJ7eXQGXhNNcqUYTcvm7/nP3dpe9QpCzS2WZrZQsXWxds6bcF7RqVBSynvDQFL3u9jGG7hiGvJA8l6hLt/kWqIsReimUwhcgEIiIisH37duzatQv5+flwcHBAu3btMHbsWPTr1+9RD4+IyORyc3MxZ84c2Nrawt/fHwsWLKh2fShLYc2UOoQ1U8hUrl+/jlmzZhldjHXz5s3o06ePwdeY258eF7xW6XHBa5WAspUV/rGZFimG7e1ohbOD3StdnfJw3QZBEODs7AwfHx/069cP77zzTqX7P3jcV199hREjRlTYl0KhwJEjR3Dq1CmcP38ep06dQlFR2UTutGnT8P7772v3La8P8SBra2u4u7ujY8eOePfdd9GiRYsK+6Knh7H31hfj/sKVfMsVofetJ8XPYe4mb7cm+cAN0YgaeC/3Rn5pvnabTCpDSLMQLOuxDPVs6+kdk1+SjzcPvInD6Yd10n452zrj2r+uQSLU7eBRXcC8/vS44LVKjxNerwSwZgoRVUNRURG++OILfPnllygpKan6gP9aunQpevfuzZzjREREFpKSVYq/izUW6etusQapWaVGFaM3VIDemP2Liorw888/Y9KkSZg8eTKaNm2KoKAgg8fIZDL06dNH+yBHaWkpNmzYgBkzZmDhwoXo27cvWrVqpXOMoQL0RDUR6G6Lq/mKGpRZrz4BQIC7jQV6qrlTd05BI5bdiySCBHZWdljcdTGGvTCswmPq2dZDzCsx2Pr7Vkw9PBXF6mJoRA3UGjV+vvMzApoEWGr4RERERBbFR0aIngCiKCI+Ph4vvfQSFi5cWK1ACgCcOnUKKSkpZhodERERPWxzmsLkRecrolCJ2JSmXzTalBwcHPDyyy9j27ZtUKvVePPNN6FQGNenjY0Nxo4di7CwMADAsWPHzDlUespF+spgL7XMA0QyqYARvjKL9FVTsZdioVApYC+1R3OX5jgeebzSQEo5QRAw/IXhOBZ5DD4uPrCX2kOhUiD2j1gLjJqIiIjo0WAwhegxd+HCBfTp0wevvfYaMjIyqn28RCLBoEGD4ObmZobRERERkSEpmSUWeTIeAEQAqZmlFumrZcuWGDVqFDIyMvDNN99U69jyvMhMQ0zmFCi3QUM7y/wa3NBeggB53V2ZIooi4i/HAwBG/mMkTow4AZ/6PtVqo3n95jg54iRe/cerZe39Ec/PMBERET2xGEwhekzdu3cPU6dORVBQEH766adqH29nZ4c33ngDv/zyC9auXYvnnnvODKMkIiKihyk1okVqpTzoeqEKSo1lJjinTp0KW1tbfPXVVzppw9auXYuff/7Z4DGXL1/Gzp07AaDCIvREpiAIAma2cYLMzKtTZFIBM/2dzJpGt7ZBi5ziHMisZfiu/3dYELwANlY1C/zYWNng8+DP8d2A72BvbY+c4pxajYuIiIioJizxQAdrphA9ZlQqFdavX4+5c+ciJ6f6v6jY2dnhzTffxIQJE9CwYUMzjJCIiIgqk3VfA6kgQGmxtSmAVBCQdV8DDwcrs/fVpEkTjBkzBt9++y2WLl2K2bNnAwAOHTqEyZMnw8vLC4GBgfDw8EBJSQmuXr2Kw4cPQ6lUYvz48Wjbtq3Zx0hPtwgfGWIuK5CaVQpzZNuTCkAbN2sM8TFfii+JRAK1Wg2ptOa/0rvau+Li6xdNNqZu3t1M2h4RERFRdajVakgk5l07wmAK0WPkxIkTiIqKwm+//Vaj4/v3749PPvkEXl5eJh4ZERERGUupESGxTMkGLYkAi61MAYDJkydj48aNWLFiBSZMmAC5XI6PP/4Y7du3R1JSEn7++WckJCRApVJBLpejZ8+eGDFiBEJDQy02Rnp6CYKAlUGuCPg+E4VK038u7KQCVgW5mnVVir29PRQKBerVq2e2PoiIiIgeJwqFAvb29mbtg8EUosfArVu38OGHHyI+Pr5Gx7/wwguYP38+unTpYuKRERERUXVZSwRYMK4BANCIZf1W5MF0XMaoan+5XI7bt2/rbGvevDneeustvPXWW0b3ExkZicjIyGqNjcgYHg5WiO3uhkEHsqEw4fIUmVRAbHc3NDHzKjAnJydkZWWV9SmTwcrKyqzBGyIiIqK6SBRFqNVqKBQKFBQUQC6Xm7U/BlOI6jBRFLFu3TrMmjULCoWi2se7uLjgvffew2uvvVarFABERERkOnJ7CVQWLtCsEkXI7VkukehBge62iO/hhsEHs1GsEmuV8ksqlK1Iie3uhkB3W9MNsgLW1taQy+UoKChAZmYmNBqN2fsk01EqlQDKvo5EdRmvVXqc8Hp9ekkkEtjb20Mul5v968/ZVaI6SqlUIioqCmvXrq32sYIgYMyYMXj//ffh5uZmhtERERFRTVlLBDRztMKVfMsVofd2lFa6MoXoaRXobovUge4Yl3QPZ7OVNVqlIpMKaONmjZVBrhapS1TO2toarq6uFuuPTCctLQ0A8Oyzzz7ikRBVjtcqPU54vZIlMJhCVAfl5ORg1KhROHbsWLWPbd++PebPn4/WrVubYWRERERkCoHutriar7BICXoBQIC7jQV6Ino8eThYIaFXA+y4qsC8swXIKtbgvkqs9PMpoCyI0tBOgpltnDDER8Y0W0RERERPOAZTiOqYtLQ0RERE4M8//6zWcU2aNMHHH3+MsLAw/iJHRERUx0X6yhB/7b5JazVURCYVMMJXZvZ+iB5ngiAgorkDhvjIkJpVik1pCqRmluJ6oQpSQYBEKKs9pBJFeDtKEeBugxG+MgTIbfizNxEREdFTgsEUojrkyJEjGDVqFPLz840+xsbGBm+//Tb+/e9/w9HR0YyjIyIiIlMJlNugoZ0E6YXmT/XV0F6CADlXphAZQxAEBLrbauueKDUisu5roNSIsJYIkNtLmDKPiIiI6CnFKpREdcSqVaswePDgagVSevfujdTUVMyaNYuBFCIioseIIAiY2cYJMql5J2VlUgEz/Z345DxRDVlLBHg4WMHbSQoPBysGUoiIiIieYgymED1iSqUSU6dOxbRp06BWG/d0qq+vL+Li4rBlyxY888wzZh4hERERmUOEjwxt3KxhrniKVADauFljiA9TfBEREREREdUWgylEj1Bubi4GDx6M1atXG33MoEGDcOzYMYSEhJhxZERERGRugiBgZZAr7MwUTbGTClgV5MpVKURERERERCbAYArRI3LlyhV069YNSUlJRh8zY8YMrFmzBvb29mYcGREREVmKh4MVYru7mTzdl0wqILa7G5o4WJm0XSIiIiIioqcVgylEj0BSUhJCQkJw5coVo/a3t7fH+vXrMWPGDD5dSkSPjFIjIqNIjesFKmQUqaHUiI96SERPhEB3W8T3cIOjtVDrlF9SAXC0FhDfw01bQJuIiIiIiIhqT/qoB0D0tFmzZg2mT59udH2Uxo0bY8uWLWjTpo2ZR0ZEpEsURaRklWJzmgIpmSVIL1RDKgiQCIBGBFSiiGaOVgh0t0WkrwyBcptaBXxdXFx0/m9jYwMnJyd4eHigdevW6NevH7p27QorK/0n7SdMmICtW7di2bJliIyMrLSfefPmYcGCBTrbbG1t0aRJE3Tp0gWTJ09Gs2bNanweRDUR6G6L1IHuGJd0D2ezlVCoqh+slEkFtHGzxsogV3hwRQoREREREZFJMZhCZCEqlQozZ87EqlWrjD7G398fW7ZsQZMmTcw4MiIiXaIoYvtVBeadLcDdYg3uq0SUT+sqoTvBeyVfjav5CsRfu4+GdhLMbOOECB9ZrYIqUVFRAAC1Wo28vDxcunQJ27dvR0xMDNq0aYNVq1ahefPmNW6/XMeOHdGpUycAwL1793D8+HFs2LABP/zwA3788Uf4+PjUug+i6vBwsEJCrwbY8d/PX9ZDnz9DBJQFUco/f0Nq+fkjIiIiIiIiwxhMIbKA3NxcvPbaazh8+LDRxwwYMADffPMNZDKZGUdGRKQro0hd7SfjRQAKlYj0QjUmJ+ch5rKiVk/Gz5w5U29bVlYWpk+fjp07d2LAgAE4cuQIGjZsWKP2y3Xq1EmnL41Gg6FDh+LAgQNYvHgxvvnmm1q1T1QTgiAgorkDhvjIkJpVik1pCqRmluJ6oUpvZZi3oxQB7jYY4StDQC1XhhEREREREVHlGEwhMrOrV69i6NChSEtLM/qYqKgoREVFQSJhWSMispzkzBKEH8xGsUpEDTIMASgLqqRmlSLg+0zEdjddzQa5XI61a9fi77//xokTJ7B48WLMnz/fJG2Xk0gkGD58OA4cOICzZ8+atG2i6hIEAYHuttrPkFIjIuu+BkqNCGuJALm9BNYSBk+IiIiIiIgshTO1RGZ07tw5hISEGB1IsbOzw5o1azBz5kwGUojIopIzSxB2IBuFypoHUsqpRKBQKWLQgWykZJaYZoAoC3ZMmzYNABAXFwdRrOVAKyGV8nkTqlusJQI8HKzg7SSFh4MVAylEREREREQWxtlaIjPJyspCREQEcnNzjdq/UaNGSExMRFhYmJlHRkSkK6NIjfCD2TUqeF0ZhUrE4IPZyChSm6zNwMBASKVS3L17F+np6SZrFyir0RITE6Pth4iIiIiIiIioHB+7JDIDtVqNcePGITMz06j9W7dujS1btsDDw8PMIyMi0iWKIsYm3UOxiQMp5YpVIsYl3UNCrwYmqedga2sLV1dXZGVlITs7G97e3jVu68SJE5g3bx4AICcnB0ePHsXly5fRokULTJ8+vdZjJSIiIiIiIqInB4MpRGawePFiHD161Kh9+/Xrh+XLl8PBwcG8gyIiMmD7VQXOZStrndqrIioROJutxI6rCkQ0N819rjy9V22DMydPnsTJkyd1tvn5+SEhIQHOzs61apuIiIiIiIiInixM80VkYqdOnTK6KPLUqVOxfv16BlKI6JEQRRHzzhaYPL3XwxSqsn5MUeOkuLgYOTk5AAA3N7datRUVFYXc3Fzcu3cPFy9exPjx43HhwgWMGTMGGo2m1mMlIiIiIiIioicHgylEJta6dWuMGzeu0n1sbW2xatUqzJo1i4XmieiRSckqxd/Flgka3C3WIDWrtNbtpKSkQKVSQS6Xo1mzZiYYWVlhe09PTyxYsAD9+/fH4cOHsXLlSpO0TURERERERERPBs7iEpmYra0t5s+fj5iYGNSrV0/vdUEQsHnzZoSHhz+C0RER/c/mNIXZV6WUU6hEbEpT1KoNjUaDRYsWAQAGDx5simHp+fTTT2Fra4vPP/8c+fn5ZumD6Gmj1IjIKFLjeoEKGUVqKDWWue8QERERERGZEoMpRGbSt29fHDt2DG3bttXZPmXKFHTr1u0RjYqI6H9SMktgqSlNEUBqZs1Xpty9exevvfYaTpw4AU9PT0yZMsV0g3tA06ZNMXLkSNy7dw9ff/21WfogetKJoojkzBJMOpGDF+P+QpOY23gxLhMdd2bhxbjM//7/L0w6kYPkzBKTpAAkIiIiIiIyNxagJzIjb29v7Nu3D7Nnz8by5cvRsWNHzJgx41EPi4gISo2I9EK1Rfu8XqiCUiPCWlJ54fh58+YBKFuJkpeXh0uXLiElJQWlpaVo164dVq1aVWG9lJiYGJw4ccLga+Hh4ejatWuV45wyZQo2bdqE5cuXY/z48bWuzUL0tBBFEduvKjDvbAHuFmtwXyVqA7bKh0K3V/LVuJqvQPy1+2hoJ8HMNk6I8JFBECq/Pxji4uKit83Gxgbu7u7o2LEj3n33XTz//POVtjFgwAAcPXoUHh4eOH/+PKysrKo9DiIiIiIierIxmEJkZjY2Npg3bx46deqEtm3bQirlx46IHr2s+xpIBUFvgtOcpIKArPsaeDhUPkm5YMECAGX3T0dHRzRt2hRDhw5Fv3790LVr10prTaWkpCAlJcXga35+fkYFUxo1aoTXXnsNy5Ytw5IlSzB37twqjyF62mUUqTEu6R7OZiuNTh8ooiwFYHqhGpOT8xBzWYGVQa5V3iMqEhUVpf13fn4+fvnlF2zbtg27d+/G3r170apVK4PHXb9+HUlJSRAEARkZGTh48CBCQ0NrNAYiIiIiInpyCVxWX3fk5eXxi0F1TlpaGgDA19f3EY+EqHK8VqvneoEKHXdmochCNVMAwEEq4OQAObydnu6gMq9VepwYc70mZ5Yg/GA2ilUianNLkQqAnVRAbHc3BLrbGn1c+cqU3NxcvdemTZuGVatWYdiwYVi+fLnB4+fMmYPo6Gi8++67iI6ORmhoKLZt21azk6BHhvdWepzweqXHBa9VepzweqWacnZ2Nnp5PGumEBERPYWsJQIsXQNaI6LKFF9E9HhJzixB2IFsFCprF0gBAJUIFCpFDDqQjZTMEpOMr3w1WnZ2tuE+VSps2bIF9erVw/Tp0+Hv74+DBw/i9u3bJumfiIiIiIieHAymEBERPYXk9hKoLLw6VSWKkNvzRw+iJ0VGkRrhB7ONTutlLIVKxOCD2cgoqn1dp6NHjwIA/P39Db6+Z88eZGZmYuDAgbC3t8fw4cOhVquxadOmWvdNRERERERPlqc7zwYREdFTyloioJmjFa7kW64IvbejlCtTiJ4QoihibNI9FJspVWCxSsS4pHtI6NXA6KL08+bN0/67oKAAZ8+eRUpKCnr27Im33nrL4DEbNmwAAERGRgIAwsPDMWvWLMTExGDq1KmV1mgiIiIiIqKnC4MpRERET6lAd1tczVdYpAS9ACDA3cYCPRGRJWy/qsC5bGWtU3tVRCUCZ7OV2HFVgYjmDkYds2DBAr1tLVq0wODBg+Hk5KT32o0bN3DkyBH4+vripZdeAgDUr18foaGh2LVrF3788Ud07969didCRERERERPDD5qRVRN0dHRWLZsGUQLp8chIjK1SF8Z7KWWWSkikwoY4SuzSF9EZF6iKGLe2QKTp/d6mEJV1o+xP3Pl5uZq/2RkZODHH39Ew4YNMXbsWHzyySd6+2/cuBEajQbDhw/X2V7+//JVK0RERERERACDKUTVcvz4cXzyySd4//33MXz4cOTk5DzqIRGRkZQaERlFalwvUCGjSA2lpauv10GBchs0tLPMjwIN7SUIkHNlCtGTICWrFH8XayzS191iDVKzSqt9nIODA9q1a4eYmBg4ODhg6dKluHXrlvZ1tVqNzZs3QyKRICIiQufYbt26wd3dHfv27UNmZmatz4GIiIiIiJ4MTPNFZKSsrCy88cYb0GjKJg/27t2LLl26YN26dXjxxRcf8eiI6GGiKCIlqxSb0xRIySxBeqEaUkGARAA0Ylkx9GaOVgh0t0WkrwyBchuj8/I/KQRBwMw2TpicnGfWJ8xlUgEz/Z2euveX6Em1OU1h9lUp5RQqEZvSFAh0t63R8S4uLmjevDl+/fVX/Prrr/D09AQA7Nu3D3fu3AEAvPDCCxUev2nTJkyZMqVGfRMRERER0ZOFK1OIjKBWqzFu3Di9pxNv3ryJ0NBQpv0iqkNEUcS2K0Xwj81E2IFsbE5T4Eq+GkoNcF8tokgl4r5ahFIDXMlXY3OaAmEHsuEfm4ltV4pq/FlWq9XYsGEDevfuDW9vbzRo0ADNmzdHhw4d8NZbb2HPnj3afY8fPw4XFxf06dPHYFv79+9HkyZN0KhRIyQmJgIA0tPT4eLiAhcXF3h4eKCgoKDC8/f399fue/z48UrHHeEjQxs3a5gr25dUANq4WWOID1N8ET0pUjJLLFJrCQBEAKmZ1V+Z8qDc3FwA0D4QA5Sl+AKAnj174tVXX9X7U57qKyYmhj/jERERERERAK5MITLKkiVLcPToUYOvqVQqvP/++zhx4gRWrlxpsMApEVlGZomAf+/9G2ezlUY/NS2i7Mnn9EI1JifnIeayAiuDXOHhYGV0v2q1GhERETh06BCcnZ3Rs2dPNGnSBKWlpbh06RJiY2ORlpaG3r17V9nWpk2b8O9//xuOjo7Ytm0bAgMDdV6XSqUoKipCXFwcRo8erXd8UlISrl+/DqlUCpVKVWV/giBgZZArAr7PRKHS9BOGdlIBq4JcuSqF6Amh1JTdLy3peqEKSo0Ia0n17yMJCQlIT0+HtbU1AgICAAC3bt3CoUOH4OLigg0bNsDOzs7gsdeuXUNycjKOHj2K4ODgWp0DERERERE9/hhMIarC8ePHMW/evCr3y83Nhb29vQVGRESGnMuT4J3/2KJUU4qaZp9RqESkZpUi4PtMxHZ3MzqtTGxsLA4dOoSWLVsiMTERzs7Ouu0qFDh9+nSV7SxZsgQff/wxPDw8EBsbi//7v//T28ff3x83b97Ehg0bDAZTNmzYAFtbW3Tp0gUHDx40avweDlaI7e6GQQeyTZq6RyYVENvdDU2qEZgiorot674GUkGA0mJrUwCpICDrvqbKIPeDP68pFAr88ccf2vvghx9+CLlcDqBstYlarcaQIUMqDKQAwKuvvork5GSsX7+ewRQiIiIiImIwhagq33zzjU5aCEPc3NywZs0aSKX8SBE9CsmZJXjrN1sUa2q/+kElAoVKEYMOZCO+h3EBlVOnTgEAhg8frhdIAQCZTIYuXbpUeLwoipgxYwZWrFiBFi1aIDY2VpvX/2FSqRSRkZFYsmQJLly4AD8/P+1r2dnZSExMRP/+/au9EiTQ3RbxPdww+GA2ilVijQNSQFlqL7v/BlJqWueAiOompUZEDRaI1IpEKOu3KgsWLND+28rKCg0aNEBoaCjGjRunDYZoNBps3rwZADDJLmPuAAAgAElEQVRy5MhK2xswYABmzJiBPXv24O7du2jYsGEtzoKIiIiIiB53rJlCVIUNGzbgX//6V6X7rFy5Ek2aNLHQiIjoQRlFaoQfzDZJIOVBCpWIwQezkVFUdTqb+vXrAwCuXr1a7X5KS0vx+uuvY8WKFQgICMDevXsrDKSUGzlyJARB0Ob8L7dlyxaUlpZWOUFYkUB3W6QOdEeA3AayGhZRkUkFBMhtkDrQnYEUoieQtUSAEXENk9KIqDTFV25urt6f7Oxs/PHHH9i6davOqhKJRIKLFy8iNzcXLVu2rLRfmUyGGzduMJBCREREREQAGEwhqpKNjQ3mz5+PmJgY1KtXT+/1KVOmICQk5BGMjIhEUcTYpHsoNmFqqgcVq0SMS7pXZfHhvn37wtraGmvXrsW4ceOwa9cu3Lhxo8r2i4qKMGTIEMTHxyM0NBQ7d+7UBmYq4+3tjS5dumDHjh24f/++dntMTAx8fHzQuXPnqk+uAh4OVkjo1QDR7Z3h7WgFmVRAVWEVAYCDVIC3oxWi2zsjoVeDatWcIaLHh9xeApWFC7KrRBFye/7aQkREREREjxZ/KyEyUt++fXHs2DG0bdtWu619+/aYOXPmIxwV0dNt+1UFzmUra5WSqjIqETibrcSOq4pK92vdujVWrlwJuVyOHTt2YOTIkWjVqhWeeeYZREZGYu/evQaPO3fuHI4ePQpfX19s2rSpWnWXRo0ahby8POzcuRMA8NNPP+Hy5cs1XpXyIEEQENHcAWcHuyO+hxsifWXwrSeFtQSwtxLgIBVgbyXAWgL41pMi0leGuB5uODvYHRHNHVhsnugJZi0R0MzRssFSb0dpjYrPExERERERmRILPBBVg7e3N/bt24cPP/wQsbGxrJNC9AiJooh5ZwtMWjDdEIWqrJ8hPrJKgwQDBw7EK6+8guPHjyM5ORnnz59HSkoKEhMTkZiYiKFDh2L58uU6bfj6+qKkpARpaWmIiorCokWLjA5EvPLKK3Bzc8PGjRsxbNgwrF+/HtbW1hg+fHitz7mcIAgIdLfVputSakRk3ddAqRFhLREgt5dwgpPoKRTobour+QqLlKAXAAS421igJyIiIiIiospxZQpRNZWn/UpNTWWdFKJHKCWrFH8XayzS191iDVKzSqvcz9raGl27dsX777+P7du3488//8S6devg4OCAbdu2ITExUWd/uVyOPXv2wMfHB2vWrMHEiROh0Rh3TjY2Nhg6dCiSk5Nx6tQp7Nq1C7169TJrXn9riQAPByt4O0nh4WDFQArRUyrSVwb7GtZVqi6ZVMAIX5lF+iIiIiIiIqoMgylENeTm5vaoh0D0VNucpjD7qpRyCpWITWmVp/oyxMrKCgMHDsSECRMAAMeOHdPbx9PTE3v27EGLFi2wZcsWjB07FiqVyqj2R40aBQAYM2YMiouLMXr06GqPkYiougLlNmhoZ5lfIxraSxAg58oUIiIiIiJ69BhMISKix1JKZolFUswAgAggNbPqlSkVcXJyKmungqLN7u7uSExMhJ+fH+Li4jBq1CiUllbd33PPPYf27dsjIyMDXl5eCA4OrvEYiYiMJQgCZrZxgszMq1NkUgEz/Z1Yh4mIiIiIiOoEBlOIiOixo9SISC9UW7TP64UqKDWGgyGxsbE4cuSIwRRdmZmZ2LBhAwCgY8eOFbbv5uaG3bt3o127dkhMTERkZCSKi4urHNfSpUuxadMmxMTEcMKRiCwmwkeGNm7WMFc8RSoAbdysMcSHKb6IiIiIiKhuYOVsIiJ67GTd10AqCFBabG0KIBUEZN3XwMPBSu+106dP49tvv4W7uzsCAwPRrFkzAEB6ejoOHDiA+/fvo3fv3ujfv3+lfbi4uGDnzp0YMmQIDh48iCFDhmDr1q1wcHCo8JjnnnsOzz33XO1OjoiomgRBwMogVwR8n4lCpenvxXZSAauCXBkkJiIiIiKiOoPBFCIieuwoNSIsXftcIqDClSmTJk2Cj48Pjh49it9++w2HDx9GcXExXF1d0alTJwwePBjh4eFGTQo6OTkhLi4Ow4cPx9GjRxEWFoYdO3aY+nSIiGrNw8EKsd3dMOhAtklrWMmkAmK7u6GJgeA1ERERERHRoyJUlL+dLC8vL49fDKpz0tLSAAC+vr6PeCRE/5NRpMaLcZm4r7bcbdPeSsDpMHeDK1OIqoP3VXqcGHO9pmSWYPDBbBSrRNQmpiIVylakxHZ3Q6C7bc0boqcS7630OOH1So8LXqv0OOH1SjXl7Oxs9OO6rJlCRESPHbm9BCoLPwygEkXI7fltk4joYYHutkgd6I4AuU2Ni9LLpAIC5DZIHejOQAoREREREdVJnBUiekBBQcGjHgIRGcFaIqCZo2VXiHg7SmFt6dxiRESPCQ8HKyT0aoDo9s7wdrSCTCqgqjumAMBBKsDb0QrR7Z2R0KsBV/8REREREVGdxZopRA/o2bMnSktLERwcjK5du6Jz585wdHR81MMiIgMC3W1xNV9hkRL0AoAAdxsL9ERE9PgSBAERzR0wxEeG1KxSbEpTIDWzFNcLVZAKAiQCoBHLVvp5O0oR4G6DEb4yBMhtWGieiIiIiIjqPAZTiP7r9u3b+M9//gMAuHLlClatWgVra2u89NJLCAkJQdeuXdG6dWv+sk9UR0T6yhB/7b5Jix5XRCYVMMJXZvZ+iIieBIIgINDdVpuuS6kRkXVfA6VGhLVEgNxewpV+RERERET02GEwhei/jhw5ordNqVTi5MmTOHnyJJYtW4a0tDQGU4jqiEC5DRraSZBeqDZ7Xw3tJQiQc2UKkblx0v3JZC0RmL6LiIiIiIgeewymEP2XoWDKg4KDgyGRsMwQUV0hCAJmtnHC5OQ8s65OkUkFzPR3YiCVyAxEUURKVik2pymQklmC9EK1XjqoZo5WCHS3RaSvDIEmSgd15coVrF27FidOnMCNGzdQWFgIR0dH+Pj4oH379hg8eDD8/f31jlMoFFi3bh0SEhLwxx9/oKCgAK6urvD398ewYcPQv3//Ssd3+/ZtfPvtt/jxxx9x48YNKJVKuLu7o0OHDhg7dizatm1r8nHPmzcPCxYsQFRUFGbOnFmzN4yIiIiIiIgYTCECAI1GY1QwhYjqlggfGWIuK5CaVQpzxFOkAtDGzRpDfJjii8iURFHE9qsKzDtbgLvFGtxXidr6R8qHKiFdyVfjar4C8dfuo6GdBDPbOCHCR1ajoIooiliwYAE+//xzaDQatG7dGoMGDUL9+vVRUFCA3377DStXrsTXX3+NhQsXYuzYsdpjf//9dwwdOhTp6elo2rQp+vXrB1dXV9y6dQv79+/H/v37ERwcjHXr1sHFxUWv7x9++AETJkyAQqFA27ZtMWLECNjY2ODSpUuIjY3F1q1b8c477+Cjjz7SO7fajJuIiIiIiIhMg8EUIgDnz59HdnZ2pft07drVQqMhImMJgoCVQa4I+D4ThUrTR1PspAJWBblyVQqRCWUUqTEu6R7OZiuNXlUmAlCoRKQXqjE5OQ8xlxVYGeRa7dRRCxYswPz58+Hp6YnVq/+fvXsPj7K61///XpOZhEyCBAIJEg5RiEItCoIkKBSQirirRQVBBcVD0b0ttVelSOlWPFaKYtn+6qFKa0VAOSsColAUBCWxKGrtFzCgBERIIBAgmRxmMuv3Rw4Fck6eDAncr+vKFfI86zQwQDL3rPX5KykpKRXaHDx4kJdeeoljx46VX8vMzOSGG27gwIEDPPDAA/z+97/H7f7Pt9FHjhzhF7/4BevWrePOO+9k6dKlJ+1m3bBhA3fddRcej4c5c+YwYsSIk+bctm0bY8aM4bnnniMqKooHH3zQkXWLiIiIiIiIc3RmkQiwbt26au//6Ec/4txzzw3RakSkLhKiwlhyVSwtXM6GKV63YclVsXTQOf8ijtmcWUjyW5mkZRXV+3g+X8CSllVE8luZpGYW1rrf7t27mTlzJuHh4SxevLjSQAKgXbt2TJs2jV//+tfl15588kkOHDjAyJEjmTZt2klBCkDr1q15/fXXSUxM5MMPP2TJkiXl94LBIJMmTaK4uJjp06dXCFIAevTowZtvvonH4+Hpp59mz549jqxbREREREREnKMwRQT44IMPqr2vI75EmraU+Aie/3Eh3jCLu4GbSNwGoj2GZcNiSYmPcGaBIsLmzEJGrskm128bfCxfwEKu33LjmuxaByrz5s0jEAgwYsQIevToUWP7ssAkPz+fRYsWAVTYMXKiqKgoJk6cCMBrr71Wfn3Tpk3s3LmTc889l9tvv73K/hdddBE/+9nPCAQCzJs3r8HrFhEREREREWcpTJGz3vHjx0lLS6u2zdChQ0O0GhGpr0vOCbLo0gKS48Lx1jNR8boNyXHhpN0QryBFxEH78oq5aW12vXejVMUXsIxam82+vOIa25b9Xz9w4MA6zbF161YKCws599xzufDCC6ttW/bmiy1btlBcXLKm1NRUAAYMGEBYWPU73cr6n/h9SX3XLSIiIiIiIs7SW9fkrLdp0yYCgUCV91u0aEH//v1DuCIRqa/4CMvKa9qyqLSwddYpha0rYygJUcoKW4+uZ2FrEamctZYJGw5T4HCQUqYgYLlnw2FWXtO22r+7WVlZAHTo0KHCvYyMDN54442TrrVq1Yr77ruPzMxMABISEmpcS1mboqIiDh8+TLt27erV/8CBAw1et4iIiIiIiDhLYYqc9Wo64uvyyy8nMjIyRKsRkYYyxjCmWxSju3pJyypiXrqPtMwiducGcBuDy0DQQsBaEqPdJMeHMy7JS3JcuEIUkUawcJePL7L9DT7aqyoBC1uz/Sza5WNMt6h6jbFnzx5mzJhx0rVOnTo1+VCiua5bRERERESkOVKYImc91UsROTMZY0iJjyg/rssftGTlB/EHLR6XIS7Shcel8ESkMVlrmb71uOPHe53KFyiZp7qdZXFxcezYsYP9+/dXuDdw4EBycnIACAQCtG3b9qR+APv27atxHWVtwsPDadOmTb37t2/fvsHrFhEREREREWepZoqc1Xbv3s2uXbuqbaN6KSJnBo/LkBAVRmJLNwlRYQpSzhD+oGVfXjG7jwfYl1eMP9i4L9pL3aRmFXGoIBiSuQ4WBEnLKqryfnJyMgAfffRRnca99NJLiYiIYP/+/ezYsaPatuvXrwegb9++5fVRUlJSgJJjRcvqqNTUv2ytDVm3iIiIiIiIOEthipzVPvzww2rvt2/fnh49eoRoNSIiUhNrLZszC5m46Qh9lx6gw9wf6Ls0kyvezqLv0szSrw8wcdMRNmcWYq3CldNpfrqv0XellPEFLPPSfVXeHzt2LG63m+XLl9cYipwoMjKSUaNGATBz5swq2+Xn5/PCCy8AcMcdd5RfHzBgAOeffz779+9n3rx5Vfbftm0bK1euxO12M27cuAavW0RERERERJylMEXOarU54ks1FERETj9rLQt25tFrSSYj12QzP93HzmPF+IOQX2zJC1jyiy3+IOw8Vsz8dB8j12TTa0kmC3bm1TtUiYmJISYmpsrrJ37ExcXRs2dP/vu//7vSF703btxITEwMl112GSNGjKhyTbm5uXTq1Kl83IyMjHqtvSlIzSwkVHGWBdIyq96Zct555/Hb3/6WoqIiRo0aRVpaWqXtjh49WuHaQw89RFxcHIsXL+bJJ58kEAicdD8nJ4fx48fz7bffMnjw4PLwBSAsLIxnn30Wl8vF7373O1asWFFh/B07dnDLLbfg9/uZPHkynTt3dmTdIiIiIiIi4hzVTJGzViAQYMOGDdW20RFfIiKn3768Yu7ZcJit2f5a73KwlOxUyMgt5oHNR5n7jY9XBrUhISrM0bVNmTKl/NfHjh3j888/Z8GCBaxYsYLVq1dz8cUXV+gTFhbGDz/8wIcffsiVV15Z4f6yZcs4fvw4bre7wov2zYk/WPL7H0q7cwPldZEqM2XKFKy1PPPMM1x99dX06tWLPn360Lp1a44ePcqePXvKj9q6/PLLy/ude+65LFu2jFtuuYWZM2eyaNEifvrTn9K6dWv27t3LmjVryMnJYfDgwbz22mu4XCe/X2nIkCHMnj2biRMnctttt9GnTx+Sk5MJDw9n27ZtfPDBB/j9fu6//34efPBBx9Z9olWrVrFnz55K71155ZXcdNNNNf32ioiIiIiInNUUpshZ67PPPuPYsWNV3jfGMHjw4NAtSEREKticWchNa7MpCFjqe1qUL2BJyyoi+a1MllwVS0p8hGPrmzp1aoVrkydPZvbs2bz00ku89NJLFe7369ePzz77jDlz5lQapsyZM4f27dvTsWNHtmzZ4thaQy0rP4jbGPwh25sCbmPIyg9WGZoZY5g6dSqjRo3i1VdfZePGjSxevBifz0d0dDTnnXced911F2PGjKFXr14n9f3xj39Mamoqf//731m5ciXLli0jLy+P1q1bk5yczM0338z1119f5Y7WkSNHkpKSwl/+8hfWrVvH66+/jt/vJy4ujpEjRzJhwgT69Onj+LrLfP3113z99deV3mvVqpXCFBERERERkRooTJGzVk1HfF1yySW0bds2RKsREZFTbc4sZOSabEdqbgQs5PotN67JZtkwZwOVU1155ZXMnj2b7OzsSu+3atWKIUOG8O6773Lo0KGT/q/5+uuv+eyzz5g0aRIff/xxo60xFPxBSxUbRBqNy5TMW5OkpCSmT59e5/GjoqKYOHEiEydOrM/ySEhI4IknnuCJJ56oV//6rHvq1KmVhn4iIiIiIiJSN6qZImetmsKUyt4tLCIiobEvr5ib1joTpJzIF7CMWpvNvrzGO36q7LilqnYIAFx//fX4/X7efPPNk67PmTMHYwy33XZbo60vVDwuQy1yDUcFLVUe8SUiIiIiIiLSENqZImelnJwcPvvss2rbKEwRETk9rLVM2HCYAoeDlDIFAcs9Gw6z8pq2VR7JVFsn7hI4fvw4W7duJTU1lauvvppf/epXVfbr06cP559/Pq+//np5u/z8fBYtWsSgQYNITExs0LqagrhIFwEb2jQlYC1xkXqvkNTMH7Rk5QfLa+zERboUxImIiIiISLUUppQyxnQEHgeGA7HAfuBt4DFr7ZE6jNMGmAZcD5wLZAPvAdOstd87vW6pnw0bNhAMBqu8HxUVRb9+/UK4IhERKbNwl48vsv31rpFSk4CFrdl+Fu3yMaZbVIPGmjFjRoVr3bt3Z9SoUbRs2bLKfsYYbr/9dh599FE+/vhjrrjiCpYvX87Ro0cZP358g9bUVHhchi7RYew8Froi9InRbr0gLpWy1pKaVcT8dB+pmYVk5BbjNgaXKdnRFLCWLtFhpMRHMDbJS0pceIPDVhERERERObPorXuAMaYr8BlwJ/ApMAv4Fvg1sNkYE1vLcWKBzaX9dpWO82npuJ8ZY853fvVSHzUd8TVgwADCw8NDtBoRESljrWX61uOOH+91Kl+gZB7bwJ0TOTk55R/79u1j3bp1tGvXjgkTJtRYF+PWW2/F4/EwZ84coOSIr9jYWH72s581aE1NSUp8BKF6OdoAyfH6v1tOZq1lwc48ei3JZOSabOan+9h5rBh/EPKLLXkBS36xxR+EnceKmZ/uY+SabHotyWTBzrwG/xvxzTffMHnyZPr370/nzp1p164d3bt3Z/To0bz++usUFhZW2m/mzJnExMQQExNDenp6hfuvv/56+f3afMTG1urHGRERERERqYZ2ppR4EYgD7rfW/rnsojHmT8BvgD8A/12LcZ4CLgD+ZK2ddMI49wPPlc4z3MF1Sz1Ya2sMU4YOHRqi1YiIyIlSs4o4VFD1zkEnHSwIkpZV5Fgx+qioKPr06cPcuXO56KKLeO6557jzzjvp2LFjpe3j4uIYPnw4K1as4NNPP2Xz5s1MnDjxjArzxyZ5WfZdfqOHYwBet2FckrfR55HmY19eMfdsOMzWbH+tn4OWkrA1I7eYBzYfZe43Pl4Z1IaEqLA6zz9jxgxmzJhBMBikX79+3HzzzURHR5OVlcWmTZu4//77efXVV8vrLJWvwVpef/11jDFYa5kzZw5PPvnkSW0uvvhipkyZctK13bt3s3DhQrp06cLNN9980j2XS++hExERERFpqLM+TCndlTIM2A28cMrtR4B7gNuMMZOstXnVjBMN3AbkAY+ecvt54AHgamPM+dbab51ZvdTHzp072bt3b7VtVC9FROT0mJ/uC8kL71Dygum8dJ9jYUqZmJgYunXrxpdffsmXX35ZZZgCcMcdd7BixQruvPNOgDPmiK8yKXHhtGvhIiO38Y/6ahfpIjnuzAmipGE2ZxZy09psCgK23kcG+gKWtKwikt/KZMlVsXX6t+LZZ59l+vTpdOzYkddee42+fftWaPPee+/x/PPPV7j+wQcfsGfPHm699VbWrVvHm2++ybRp004KWnv16kWvXr1O6rd+/XoWLlxIYmIiU6dOrcMjFRERERGR2tBblGBI6ec11tqT3gprrT0OfAx4gZQaxkkBIoGPS/udOE4QeP+U+eQ0qWlXSufOnenatWuIViMiIidKzSwkVCXLLZCWWdQoY+fk5ABUW58LYMiQIXTq1Il9+/Zx+eWXk5SU1CjrOV2MMUzt3RKvu3EP+/K6DVN7tVSNCwFKgpSRa7LJ9dc/SCkTsJDrt9y4JpvUzMqP5DpVRkYGf/zjH/F4PCxatKjSIAVg+PDhLF26tML1sqP/xo8fz0033UR2djYrV66s/4MQERERERFHnPU7U4ALSz9/U8X9dEp2rlwArGvgOJSOUyeVnZMs9bdixYpq7/fp04edO3eGaDXNh56H0lzoudp8BYKw+3gkhKzKBnx33M+2Hem4a3h7SVXPq8qur1+/noyMDNxuN+3atStvs2/fvkr7PvXUUxw4cIDExMSTxsvPzwdKju4pKmqc0CcULrVwoTeCr467KLbO/9mGGUt3bzG9gz+gv/6Np7n825pZaBj9eQt8xc4+13wBy43vH2TRpQXER1Sf0Lz88sv4/X6GDRuGx+Op0+9ddnY27777Lp07d6ZNmzZcccUVPP/887z44ov07Nmz2r7ff/99yVp9vmbz59UYzubHLs2Pnq/SXOi5Ks2Jnq9SFSfevKgwBVqVfj5axf2y6zEhGkcaUVFREVu2bKm2TUpKTZuQRESkMWT7DW5Dg99JXhduUzJvTS+OVuWVV14p/3V+fj7fffcdn3zyCQD33XdfrYo+d+/ene7du9dr/ubAGHjiwqLSF7idHz/CVTK+NqWItfDwjnAKG6nsUmGwZPyXexZW+3z74osvALjsssvqPMeKFSsIBAJce+21AHTr1o0ePXrw2WefsXfvXjp16lSvtYuIiIiISMMpTGkGzrQjP06njRs3UlBQUOV9l8vFmDFjiIlR5lWmLNHX81CaOj1Xmz/P8QBhW7MgGLo0JczlomOXRBJbVv8tUVXPq9mzZ/9nrLAw2rZty/Dhw7nnnnsYMuTkkz0PHDhQqzHLREZGApCYmEiXLl2qbdvUJQHL2hVy45psR2vieN2GZcPaOl73Rv6jOf3bumBnHjt8Rym2jfNvSLE17PC52eqKZUy3qCrbHT9ecuJv79696/T7Zq3l3XffxeVyMXHiRDp06ADAnXfeyYMPPsiGDRt47LHHquxftvvN6/U2iz8vpzWn56qInq/SXOi5Ks2Jnq8SCgpT/rNjpFUV98uu54RoHGlENdVL6du3r4IUEZHTxOMyocxRgJLcxuOq+i3mZbVPanu9OgMHDiQnJ6fW287fe++9Os/RlKXER7BsWCyjGlgUHEp2FLVwmzoXBZczl7WW6VuPOxrWVcYXKJlndFev4zV6PvroI7777juGDh1aHqQA3HTTTTz00EO88cYbPPTQQ3g8HkfnFRERERGR2lEBethR+rmqWiZlcWZVtVCcHkcaUU1hypVXXhmilYiIyKniIl0EGukd5VUJWEtcpL4dCpWU+AjSbognOS683kXpvW5Dclw4aTfEK0iRcqlZRRwqaKTzvU5xsCBIWlbVdYzi4+MB+OGHH+o07muvvQbArbfeetL11q1bM3z4cA4ePMi7775bt8WKiIiIiIhj9OoBfFj6eZgx5qTfD2NMS+AKwAek1jBOKpAPXFHa78RxXJQUsT9xPgmxgwcP8uWXX1bbRmGKiMjp43EZukSHhXTOxGh3tTtTxHkJUWGsvKYts/q3IjE6DK/bUNOfgAGi3IbE6DBm9W/FymvakhAV2ueKnD7+oGVfXjG7jwfYl1eMv5ItbPPTfY2+K6WML2CZl+6r8n5Z/b2PPvqo1mMeOnSIVatWAXD33XcTExNz0sc777wD/CdwERERERGR0Dvrj/my1u4yxqyhJOz4JfDnE24/BkQBL1tr88ouGmO6l/bdfsI4ucaYucA9wKPApBPGmQgkAu9ba79tnEciNVm/fn2191u1asWll14amsWIiEilUuIj2HXMRyheEjVAcnx4CGaSUxljGNMtitFdvaRlFTEv3UdaZhG7cwO4jcFlSo5gC1hLYrSb5PhwxiV5SY4Ld/xoJWl6rLWkZhUxP91HamYhGbnFFZ4XXaLDSImPYGySl5S4cFIzC0Py7waABdIyq96ZMnbsWGbNmsU777zD9u3b6d69e5VtCwsLiYiI4M0336SoqIhevXrRs2fPStuuXr2a9evXs3v3bhITExv4KEREREREpK7O+jCl1H3AJ8D/Z4wZCmwDkoEhlBzL9b+ntN9W+vnUn+Z/DwwGHjDG9AI+BXoAI4AsSsIaOU3eXv12tfcHDRqE262/EiIip9PYJC/LvssPyTvMvW7DuCRvo88jVTPGkBIfUX5clz9oycoP4g9aPC5DXKRLO4fOItZaFu7yMX3rcQ4WBMkP2PKAxH9KVLLzWDG7jvlY9l0+bSMM+3wNOOLrNxeVfJ7178qvtz4Xpq4Cz3+OldudG8AftFx6ycXs3buXQ4cO4Xa7K9TeK9ulUpULLriATzrD+3IAACAASURBVD/9lDlz5gDwxBNP8OWXX/Lee++xfft2cnJyygvKd+vWjUOHDjF37lwefvjh+j9eERERERGpF71yTPnulL7A48Bw4L+A/cBzwGPW2iO1HCfbGNMfeAS4HhgIZAN/B6ZZa79vjPVLzb498i2r1qyqts3QoUNDtBoREalKSlw47Vq4yMgtbvS52kW6SI7TzpSmxOMyOr7rLLUvr5h7Nhxma7a/1mGqpeTIrT2NHb4e2Q8bXoefTii/5DaGrPyKAc6UKVPKf/3xxx/z8ccfY0trQf34xz+mZcuW5OXlsXfvXo4cOYLL5WLjxo3s3LmT8847jwkTJnDgwAE6duzIsGHDaN++PXl5eXz11VekpaUBJUd9TZ06VW8CEhEREREJMX0HXspauxe4s5Ztq3yLpLX2MPDr0g9pIl5Z+wrkVt9m8ODBIVmLiIhUzRjD1N4teWDz0UbdneJ1G6b2aqkjo0SagM2Zhdy0NpuCgCVEZU9qz3sOYGDd3yBlFES3BsBlqLR2y9SpU0/6eseOHQwdOpTc3Fy+++47ioqKaNOmDX369OHnP/85Y8aMYeLEiQDs27ePQCDAE088wf/8z/9UCEt2797NT3/6Uw4dOsTq1au57rrrGucxi4iIiIhIpRSmyFlhyaol1d7v1q0bXbp0CdFqRESkOmO6epn7jY+0rKJGeWHVbaB3rIfRXXXEl8jptjmzkJFrskNWPL7OPJEw5A54ewa8/yKMLDn9N2ip1RF0F154Ia1btyY3N5dPPvmk0u83Z8+ezf79+9m0aROTJ0/mV7/6VaVjJSYmsnPnzvI6K6caPHgwOTk5dXt8IiIiIiJSa67TvQCRxvb98e/J/jq72jZXXnlliFYjIiI1McbwyqA2tHA3zq6RFm7D7EFttCtF5DTbl1fMTWubcJBSZsAt0LYTbF4EBzMACFhLXKQzP0rt2rWLTZs24fV6qwxSTlRZkCIiIiIiIo1PYYqc8Zb+ayk2o/of0hWmiIg0LQlRYSy5Khavw4GK121YclUsHVSXQ+S0stYyYcNhCpp6kAIQ5oGf/QaKA7DiTwAkRrtrtTOlNjZv3gxA7969OeeccxwZU0REREREnKcwRc54r658FQJV3/d4PAwYMCB0CxIRkVpJiY9g2bBYoj2GhmYqbgPRHsOyYbGkxOtd3SKn28JdPr7I9je9GilV6XU1JPaCf/0Dvv2M5Phwx4bOzMwEICEhwbExRURERETEeQpT5IyWmZfJ3i/2VtsmOTmZ6OjoEK1IRETqIiU+grQb4kmOC6/3LhWv25AcF07aDfEKUkSaAGst07ceb/rHe51qxGQAXCtmMi5JNZdERERERM42ClPkjLZy50rszup/UB86dGiIViMiIvWREBXGymvaMqt/KxKjw/C6DTXFKgaIchsSo8OY1b8VK69pS4KO9hJpElKzijhUEDzdy6i7xF5wyTCCu79i36aVjg0bHx8PwA8//ODYmCIiIiIi4jz36V6ASGP6+yd/x2ZVH6YMGTIkRKsREZH6MsYwplsUo7t6ScsqYl66j7TMInbnBnAbg8tA0JYUhU6MdpMcH864JC/JceEqNC/SxMxP9zW/XSmlWoz4Df5/f8jjjz/Odddd58iY/fv3B+Dzzz/n+PHjtGzZ0pFxRURERETEWQpT5Ix1OP8w2z7dVm2btm3bcvHFF4doRSIi0lDGGFLiI8qP6/IHLVn5QfxBi8dliIt0OVYUWkQaR2pmIc0xSnEb6NO9Gz1/cTd/+ctfePnllx0Zt2vXrgwYMIBNmzbx/PPPM3Xq1GrbFxYWEhGhIwtFREREREJNx3zJGWvVt6tgV/VthgwZgsulvwYiIs2Vx2VIiAojsaWbhKgwBSkiTZw/aMnILT7dy6iXFm7D7EFtmDJlCq1ateLZZ58lLy/PkbGfeeYZoqOjmTlzJi+++CLFxRV/j/bs2cP48eP5/PPPHZlTRERERETqRjtTpFnIzs/mg4wP6tTnL5/9heL06n9Yj+4ezeLti2s95pVdriQ2MrZO6xARERGREln5QdzG4G9me1O8bsOSq2LpEBUGUa2ZNGkS06ZNc2z8Hj16sHTpUsaPH8/vf/97XnzxRQYNGkT79u3Jy8vjX//6F2lpabhcLiZPnuzYvCIiIiIiUnsKU6RZ2HVkF79c+0sCwQBet7dWffzf+yG/+jYLixayeF3NYYov4MPtcrNy5EqFKSIiIiL15A9amtsGsmhPSZBSdrwgwL333stf//pX9uzZ49g8ycnJ/POf/2TOnDmsXr2a9957j6NHj+L1ejn//PO5//77ueOOO+jcubNjc4qIiIiISO0pTJFmoV+Hfnw89mNufudmfsj9gfxADSkJwI4a7seBL9IH/uqbRbojOa/VeSz4+QKS2iTVes0iIiIicjKPyxBsaptSZv270sveP/8/esd6eGVQGxKiwk66FxERwVdffVWr4f/1r3/VeiktW7Zk4sSJTJw4sdZ9REREREQkNFQsQpqNpDZJfDLuE27ucTOR7siaO9RQL4VuNQ8R6Y7klh638Mm4TxSkiIiIiDRQXKSLgA1tmuICukSH4XUbatoUY4AotyExOoxZ/Vux8pq2FYIUERERERE5O2lnijQrEe4IZg2dxfDzhzNh9QTyA/n4g5VsLSkE9tYwWNeqb3lcHiLdkcy+ZjZXn3d1Q5YsIiIiIqU8LkOX6DB2HgtdEfqu57j59MY40rKKmJfuIy2ziN25AdzG4DIQtBCwlsRoN8nx4YxL8pIcF44xzew8MhERERERaVQKU6RZuvq8q/l0/KfctuI2/n3o3/gCvoqN/ouS3SnfUhKunMgNVHHctNft5aK2FzH3urm0j2rv6LpFREREznYp8RHsOuYLSQl6AyTHlwQjKfER5XVP/EFLVn4Qf9DicRniIl14mlsxFxERERERCSkd8yXNVvuo9rw/5n1+c9lvKh77FQH0BcYADwJ3AYOAjpT8VN0F8FQcM9IdyQOXPcD7Y95XkCIiIiLSCMYmeYl0hya48LoN45K8Fa57XIaEqDASW7pJiApTkCIiIiIiIjXSzhRp1lzGxeTkyQzpPIRbV9zK0cKjFBafsg0ljJJdKJ2BIYAPOKV+fURYBK0iWvHGdW/Q99y+IVm7iIiISHPh5E6OlLhw2rVwkZHb+Ed9tYt0kRwX3ujziIiIiIjImU9hipwR+p7bl3+O/ye3rbyNtB/SKgYqJ/KWfpSKCIsguUMy866dxzkR5zT6WkVERESaOmstqVlFvPSNhy+Oh7H/kx8q1BjpEh1GSnwEY5O8pNShxogxhqm9W/LA5qP4Ao132JfXbZjaq6VqnzhAx6KJiIiIiChMkTNIq4hWxETEVB+kVKKwuJDWLVorSBEREZGznrWWhbt8TN96nIMFQfIDbiwlL5r7T6lysvNYMbuO+Vj2XT7tWriY2rslY7p6axVejOnqZe43PtKyighY4DcXVd/hlieh3w21fhxuA71jPYzuWvGIL6lZWZg2P91HamYhGbnFjoVpIiIiIiLNlcIUOWMUFRexdvfaevVd+91aioqLCA/TMRAiIiJydtqXV8w9Gw6zNdt/wo6R6l8gt4AvYMnILeaBzUeZ+42PVwa1ISEqrNp+xhheGdSG5LcyyfWfENJcfV/lHRK61/6BAC3chtmD2ugF/jqqGKbZ8gjNyTCtKt988w2zZ89m06ZN7Nu3j/z8fGJjY7n44ou59tprGTNmDBEREQDExMRU6O/xeGjfvj1XXHEFv/nNb7jwwgtPur9x40auu+66Cn3atWvHZZddxi9/+Uv69etX6e/LO++8w4IFC9i6dSvZ2dlER0dz4YUXcu2113LXXXfh9Sq4ExERETnTKUyRM8ZHez/C7arfUzrMhLFx70aGJg51eFUiIiIiTd/mzEJuWptNQcBS35O3fAFLWlYRyW9lsuSqWFLiI6ptnxAVxpKrYrlxTTa+sovDf1m/yU/gdRuWXBVLhxoCHTlZ5WFa9eobplVmxowZzJgxg2AwSL9+/bj55puJjo4mKyuLTZs2cf/99/Pqq6+yfv36k/pNmTIFgMOHD5Obm8u3337LggULWLFiBatXr+biiy+uMFenTp249dZbAfD5fGzZsoXly5ezYsUK5syZc1LgkpOTw5133smHH37IOeecw7Bhw+jcuTNHjhxh3bp1PPTQQ7zyyissXLiQHj161Plxi4iIiEjzoTBFzhgLty0ktyi3Xn1z/bks2r5IYYqIiIicdTZnFjJyTbYj9UsCFnL9lhvXZLNsWM2BSkp8BMuGxTK8wTOXHO3VojRIqWleOdnpCNNO9OyzzzJ9+nQ6duzIa6+9Rt++fSu0ee+993j++ecrXJ86dSoA6enpACQlJTF58mRmz57NSy+9xEsvvVShT+fOncv7lXnqqad4+umneeihh8rDlGAwyB133MH69esZOnQos2fPpk2bNuV9AoEATz31FH/605+48cYb2bBhA3FxcbV+3CIiIiLSvLhO9wJEnBAIBlj97Woslf/053V7aRXRCq+78u33FsuqXasIBAONuUwRERGRJmVfXjE3rXUmSDmRL2AZtTabfXnFNbY98UV3r7t+R0R53YbkuHDSbohXkFJHZWFarr/+QUqZE8O01Mza1THMyMjgj3/8Ix6Ph0WLFlUapAAMHz6cpUuX1mrMK6+8EoDs7OzaLRyYMGFC+XoOHToEwOLFi1m/fj3nnXcec+fOPSlIAXC73UybNo0bb7yR/fv38+STT9Z6PhERERFpfhSmyBnh430fV3k+cwt3C37b77fsuncXk/pNooW7RaXtDIZP9n3SmMsUERERaTKstUzYcJgCh4OUMgUByz0bDmNt7ce/8qu/0uqDF3GveQHeK/349K1K2xogym1IjA5jVv9WrLymbb2OlzqbNYUwbf78+fj9fn7+85/zox/9qNq2ZfVSalJ2FFivXr1q1R446Xla9nPFnDlzAJg4cWK1NVEefPBBABYuXEhBQUGt5xQRERGR5kXHfMkZYfH2xeT58066FhEWQUxEDG/8/A36tO8DwKR+kxjceTC3vHMLRwuPUlj8n3fM5QXyWLJ9CT/p9JOQrl1ERETkdFi4y8cX2f4G70aoSsDC1mw/i3b5GNMtqlZ9Vs7+U4VrpttltOh/Iy4DQQsBa0mMdpMcH864JC/JceEqNF8PoQrTVl7Ttto/n9TUVAAGDRpUr3mmT58OlNRMycvL47vvviM1NZWrr76aX/3qV7UeZ/bs2QAkJiYSGxtLIBBgy5YtAAwePLjavt27d+fcc89l//79bN26lf79+9frsYiIiIhI06YwRZq9oA3yTvo7BG2w/JrX7WVol6G8MOwFzok456T2fdr3Ycv4Ldy35j4+yPgAX8BXPs7yncv5v5/+Hy6jTVsiIiJy5rLWMn3rccd3JJzKFyiZZ3RXb60Cj5ycnArX/EFLVn4Qf9DicRniIl14XApPGqqphGmZmZkAdOjQoV7zzJgxo8K17t27M2rUKFq2bFlpnz179pSHMGUF6Ddv3ozL5eKJJ54A4MiRIxQVFQGQkJBQ4zoSEhLYv38/Bw4cqNfjEBEREZGmT2GKNHuf7v+0PEhxGRctwlrw7JXPcsuPbqmyzzkR5zD32rm8ue1NfvvBbykoLiBogxQHi/nn/n+S3CE5VMsXERERCbnUrCIOFQRrbuiAgwVB0rKK6l3LxOMyOr7LYU01TKuPsgAuPT2d/Px8/H4/jz76KBMmTGD79u08/PDDFfrs3bu3PIRxu920bduW6667jokTJ5KcrJ8DRERERKRyevu9NHtLti/BF/AR6Y6kW0w3No7dWG2QUsYYw60/upWPxn5E15iuRLoj8QV8LNmxJASrFhERETl95qf7Gv2F9DK+gGVeui8kc0ntnI4wrSrx8fEA/PDDDw2eKzIykj59+jB37lyioqJ47rnn+P777yu0u+KKK8jJySEnJ4dDhw6xfft25s6de1KQ0rp1a8LDwwHYt29fjXOXtWnfvn2DH4eIiIiINE0KU6RZs9ay7JtlANx+0e1sGreJrq271mmMbq278fG4j7ntottKxtuxrE6FUkVERESam9TMQkL13Y4F0jKrfjFdQq8phWkpKSkAfPTRR47NGRMTQ7du3QgEAnz55Zf1GsPtdtOnT0ndxbKC9lXZsWMH+/fvJyIigt69e9drPhERERFp+hSmSLN2pOAIXo+XxSMWM2PIDMLDwus1TnhYOE8PeZrF1y8m0hPJkYIjDq9UREREpGnwBy0ZucUhnXN3bgB/UG9WaSqaUpg2duxYPB4P77zzDtu3b692rMLCwlrPW3b8VzBY/x04t99+OwAvvPAC+fn5VbabOXMmAGPGjKFFixb1nk9EREREmjaFKdKstYlsw9d3f83QxKGOjPfTxJ/y9d1f0yayjSPjiYiIiDQ1WflB3I1Uv6IqbmPIyg/NsVJSvaYWpnXp0oXf/e53FBUVMXr0aLZu3Vppu3/84x+MGjWqVvOtXLmSjIwMPB5Pg2qgjB49moEDB/Ltt98yfvz48oCmTHFxMX/4wx9YvHgx7du353//93/rPZeIiIiINH0qQC8iIiIichbxBy2u0GYpuAzamdJElIVp/pDtTflPmJYQFVbp/UmTJhEIBJgxYwZDhgwhOTmZXr16ER0dTVZWFp988gm7du2q9Ait6dOnA3D48GHy8/PJyspi7dq1AEybNo24uLh6rzssLIy5c+dy++23s2bNGi655BKuvvpqOnXqxJEjR1i3bh0ZGRl07tyZBQsWlNd/EREREZEzk8IUEREREZGziMdlCHWuEbQl81bl1Hf8S+NpqmHalClTuP766/nrX//Kpk2beOONNygoKKBNmzb07NmTX//614wZM6ZCvxkzZpT/OiwsjLZt2zJ8+HDuuecehgwZ0uC1x8TEsHz5ct5++20WLFjAhg0bOHz4MFFRUVx44YX84he/4O6778br9TZ4LhERERFp2hSmiIiIiIicReIiXQRsaNOUgLXEReqE4aagKYZpZS688EKeeeaZWo15agCXnp4OQFJSUpV9Bg4cWK/gzhjDDTfcwA033FDnviIiIiJy5tBPNCIiIiIiZxGPy9AluvLjlhpLYrS7Vi+mS+NTmCYiIiIiUj/6jlZERERE5CyTEh9BqKINAyTHh4doNqmJwjQRERERkfpxLEwxxrQwxlxgjOlcyT2vMWaGMSbVGLPVGDPdGBPt1NwiIiIiIlJ7Y5O8RLpD8+K2120Yl6R6Ek2JwjQRERERkbpzcmfKvcA24PcnXjTGhAEfAb8F+gGXAA8C/zDGqGaLiIiIiEiIpcSF065FaDapt4t0kRynF9ObEoVpIiIiIiJ15+RPUMNLP8895frNwKVAAfBH4BHgGHAZMMHB+UVEREREpBaMMUzt3RJvI7+g7nUbpvZqiTE64qkpUZgmIiIiIlJ3Tn4HfUHp5y9PuX4LYIGHrbW/t9Y+AfyCkh3fNzs4v4iIiIiI1NKYrl56x3porDzFbaB3rIfRXbUroalRmCYiIiIiUndOhintgGPW2tyyC8YYF/CT0i/nndB2ORAELnJwfhERERERqSVjDK8MakOLRnpBvYXbMHtQG72Q3kQpTBMRERERqRsnw5RwIOKUaz2BaGCbtTar7KK1NgDkAC0dnF9EREREROogISqMJVfFOr5Dwes2LLkqlg5RYY6OK85RmCYiIiIiUjdOhin7gQhjTNcTrl1b+nljJe2jgGwH5xcRERERkTpKiY9g2bBYoj2mwbsU3AaiPYZlw2JJiT/1fVbS1ChMExERERGpPSfDlLLA5FljTFtjzMXAryipl7L6xIbGmCRKdrH84OD8IiIiIiJSDynxEaTdEE9yXHi9X1j3ug3JceGk3RCvIKUZUZgmIiIiIlI7ToYpM4EAcB2QCWwF4oBtwMpT2v5X6ec0B+cXEREREZF6SogKY+U1bZnVvxWJ0WF43QaDrbaPAaLchsToMGb1b8XKa9qSoN0IzY7CNBERERGRmrmdGsha+5Ux5gbgeSCRkgLz64FfWGuDpzSfUPr5H07NLyIiIiIiDWOMYUy3KEZ39ZKWVcSLW/bz5fEwfih04TYGl4GghYC1JEa7SY4PZ1ySl+S4cNXGaObKwrRFu3xM33qcrIIg+QFbbZxmKAlR2rVwMbV3S0Z39ep5ICIiIiJnLMfCFABr7bvA+caYdkCutTb/1DbGGDcwtvTLbU7OLyIiIiIiDWeMISU+gtgL/ICfxK7dyMoP4g9aPC5DXKQLj0svmp9pTg3T5qX7SMssYnduQGGaiIiIiJz1HA1TylhrD1ZzLwB82RjzioiIiIiI8zwuo+O7ziJlYVrZcV3+oFWYJiIiIiJnvUYJU0REREREROTMoDBNRERERKQRwhRjjAu4HPgx0BrwVNfeWvu402sQERERERERERERERFxiqNhSmkB+j8D59amOWABhSkiIiIiIiIiIiIiItJkORamGGN+CiwGXEAR8CmwDyhwag4REREREWkaVEdDRERERETOJk7uTPk9JUHKBuBWa+1+B8cWEREREZHTyFpLalYR89N9pGYWkpFbjNsYXAaCFgLW0iU6jJT4CMYmeUmJC8cYhSsiIiIiInJmcDk4Vh9Kju26Q0GKiIiIiMiZwVpYsDOPXksyGbkmm/npPnYeK8YfhPxiS17Akl9s8Qdh57Fi5qf7GLkmm15LMlmwMw9rbb3mjYmJISYmhh//+McUFFS+2b1nz57ExMQQCAQqvb9161buu+8+LrnkEtq3b0+nTp24/PLLefjhh/nhhx8q7bNx40ZiYmL42c9+Vun9999/nw4dOtC+fXtWrVpVr8cmIiIiIiLNj5NhigGOWWszHBxTREREREROk8xCw73/iuCBzUfJyC3GF7DUFI1YwBewZOQW88Dmo1y7+hD78orrvYbvv/+el156qU59rLU88sgjDBkyhEWLFnHBBRdw7733Mm7cOCIjI/nzn/9M3759Wb58eZ3GnTdvHmPHjiU8PJy33367ysBFRERERETOPE4e87UN6G2MaWGtVZ0UEREREZFmbHNmIaM/b0FhEIrrubvEF7CkZRWR/FYmS66KJSU+ok79Y2JiMMYwa9Ysbr/9dmJjY2vV7+mnn+a5556jc+fOLFy4kB49epx0f/ny5dx7773cddddvPXWW/zkJz+pccw//elPPP744yQkJLBkyZIKY4qIiIiIyJnNyZ0pL1ISztzm4JgiIiIiIhJimzMLGbkmG1+xodg2rO5JwEKu33LjmmxSMwvr1Nfr9TJ58mSOHTvGH//4x1r1ycjI4JlnnsHj8fDmm29WGnqMGDGCp556iuLiYiZNmkQwGKxyPGstU6ZM4fHHH6d79+68//77ClJERERERM5CjoUp1to5wN+A/zPG3OzUuCIiIiIiEjr78oq5aW02vkD9dqNUxRewjFqbXecjvyZMmMB5553Ha6+9xq5du2psP3/+fAKBANdeey0XXXRRle1uv/122rdvT3p6Ops2baq0TVFREXfffTcvv/wyycnJrF69mo4dO9Zp/SIiIiIicmZwLEwxxrwKhAGFwHxjzHfGmMXGmFer+fibU/OLiIiIiEjDWGuZsOEwBQ4HKWUKApZ7NhyuU1F6j8fDo48+it/v55FHHqmxfWpqKgCDBw+utp3b7WbgwIEApKWlVbifl5fH6NGjWbZsGcOHD+ftt9+mdevWtV63iIiIiIicWZysmXIHJfUmy84B6FL6UR0L3O3gGkREREREpJ4W7vLxRbafRspSCFjYmu1n0S4fY7pF1brfiBEj6NevHytXrmTz5s3079+/yraZmZkAJCQk1DhuWZsDBw5UuPfFF18AkJSUxLx583C7nfzRSUREREREmhsnfyJ4nJJwREREREREmhlrLdO3Hnf8eK9T+QIl84zu6sWY2tdjefLJJxk2bBgPP/ww//jHPxpxhSWSkpIoLCwkPT2dKVOmMHPmzDqtV0REREREziyOhSnW2kedGktEREREREIrNauIQwVVF2J30sGCIGlZRaTER9S6T79+/RgxYgTLly9n2bJl3HjjjZW2i4uLY8eOHezbt6/GMcvatG/fvtJxXn75ZUaMGMHf/vY38vPzef7553G5HDspWUREREREmhEna6b8vPSjrVNjioiIiIhIaMxP9zX6rpQyvoBlXrqvzv0eeeQRPB4Pjz32GEVFRZW2SUlJAWD9+vXVjlVcXFxeeD45ObnSNh07duTdd9+le/fuvPHGG0yYMIFAIFDndYuIiIiISPPn5Nuq3gaWAAUOjikiIiIiIiGQmlkYsjN7LZCWWXkYUp3zzz+fu+++m4yMDF5++eVK24wdO5awsDBWrlzJtm3bqhxr3rx57N+/n6SkJAYMGFBlu/j4eFatWkXPnj1ZunQp48ePrzLIERERERGRM5eTYcph4Ji1NtfBMUVEREREpJH5g5aM3OKQzrk7N4A/WPf4ZsqUKbRq1Ypnn32WvLy8CvcTExN54IEH8Pv93HLLLWzfvr1Cm5UrV/K73/2OsLAwnn322RqP7oqNjWXFihX06dOHVatWMXbsWAoK9B4yEREREZGziZMF6P8NXG6MOcdae8zBcUVEREREpBFl5QdxG4M/ZHtTwG0MWflBEqLC6tSvdevWTJo0iWnTplXZZurUqfh8Pl544QUGDBjA0KFD6d69O36/n08//ZQtW7YQGRnJ3/72N37yk5/Uat6YmBjefvttRo8ezdq1axk9ejRvvvkmUVFRdVq/iIiIiIg0T07uTHkFCAN+5eCYIiIiIiLSyPxBi8uEdk6XoV47UwDuvfdeOnfuXPXYLhd/+MMfWLduHaNGjWLbtm28/PLLzJkzh7y8PCZOnMiWLVu4/vrr6zRvy5YtWbp0KYMHD+ajjz5i5MiRHDum95GJiIiIiJwNHNuZYq2db4zpBzxmjGkBzLLWHnZqfBERERERaRwel6GeuUa9BW3Jx4DpaAAAIABJREFUvFXJycmp8l5ERARfffVVjXP06dOHPn361GldAwcOrHZur9fL22+/XacxRURERESk+XMsTDHGfFD6Sx/we2CKMWYncBCo6gBma60d6tQaRERERESk7uIiXQRsaNOUgLXERTq5UV5ERERERKTxOFkzZXAlY3cv/ahKiN//JiIiIiIip/K4DF2iw9h5LHRF6BOj3dXuTBEREREREWlKnAxT7nRwLBERERERCaGU+Ah2HfOF5N1OBkiODw/BTCIiIiIiIs5wsmbKHKfGEhERERGR0Bqb5GXZd/n4Ao0fp3jdhnFJ3kafR0RERERExCk6pFhEREREREiJC6ddi9D8eNAu0kVynHamiIiIiIhI86EwRUREREREMMYwtXdLvO7GrWPidRum9mqJMaqXIiIiIiIizYdjx3wZY26vTz9r7etOrUFEREREROpvTFcvc7/xkZZVRGOc9uU20DvWw+iuOuJLRERERESaFycL0L8G9apXqTBFRERERKQJMMbwyqA2JL+VSa7f+TSlhdswe1Ab7UoREREREZFmx8kw5SOqD1NaAT2ACCAH+NLBuUVERERExAEJUWEsuSqWG9dkO1qM3us2LLkqlg5RYY6NKSIiIiIiEiqOhSnW2sE1tTHGeIEHgEeAddbaJ52aX0REREREnJESH8GyYbHc+P5BCoNQbOu/k8RtSnakLLkqlpT4CAdXKSIiIiIiEjohLUBvrfWVBiiPAo8ZY64L5fwiIiIiIlI7KfERLLq0gItbButdlN7rNiTHhZN2Q7yCFBERERERadZCGqac4M+UHAn2wGmaX0REREREahAfYXm5ZyGz+rciMToMr9tQU6xigCi3ITE6jFn9W7HymrYk6GgvERERERFp5pysmVJr1tpjxphjQK/TMb+IiIiIiNSOMTCmWxSju3pJyypiXrqPtMwiducGcBuDy0DQQsBaEqPdJMeHMy7JS3JcuArNi4iIiIjIGeO0hCnGmHZADJB3OuYXEREREZG6McaQEh9RflyXP2jJyg/iD1o8LkNcpAuPS+GJiIiIiIicmUIephhjwoHnS7/8KtTzi4iIiIhIw3lcRsd3iYiIiIjIWcOxMMUYM62GJi2AjsAwoB0lNVNmOTW/iIiIiIiIiIiIiIhIY3ByZ8qjlAQk1Snb958P/M5au8TB+UVERERERERERERERBznZJjyOtWHKQEgB/gXsMJae8TBuUVERERERERERERERBqFY2GKtfYOp8YSERERERERERERERFpKlynewEiIiIiIiIiIiIiIiJNmWNhijHmA2PM4jq0f9MYs86p+UVERERERERERERERBqDkzVTBgMH6tA+Bejs4PwiIiIiIiIiIiIiIiKOO53HfIVRfcF6ERERERERERERERGR0+60hCnGmAggDjh2OuYXERERERERERERERGprXof82WM6QwknnI53BgzEDBVdQNigFuAcOCT+s4vIiIiIiIiIiIiIiISCg2pmXInMO2Ua62B9bXoWxa2/F8D5hcREREREREREREREWl0DQlTcoA9J3zdBQgC31fTJ0jJ0V7/Bv5mrf2wAfOLiIiIiIiIiIiIiIg0unqHKdba54Dnyr42xgSBg9ba85xYmIiIiIiIiIiIiIiISFPQkJ0pp3oMyHVwPBERERERERERERERkdPOsTDFWvuYU2OJiIiIiIiIiIiIiIg0FU7uTClnjLkUuAroBERaa+8+4V440B6w1tq9jTG/iIiIiIiIiIiIiIiIUxwNU4wx7YDXgWFllwAL3H1CMxeQCsQZY/paa79wcg0iIiIiIiIiIiIiIiJOcjk1kDHGC/wDuBo4APwdyDu1nbW2APhL6dw3OTW/iIiIiIiIiIiIiIhIY3AsTAEmAj2BfwIXWWt/QdUF6ZeVfv6Jg/OLiIiIiIiIiIiIiIg4zskwZTQlR3rdb63NqaHt/wP8wIUOzi8iIiIiIiIiIiIiIuI4J8OUCygJSD6tqaG1NggcA2IcnF9ERERERERERERERMRxToYpYYDfWmtramiMMUA0ldRUERERERERERERERERaUqcDFP2Al5jzLm1aHs5EAHsdHB+ERERERERERERERERxzkZpqwt/fzf1TUyxoQBT1FSX+VdB+cXERERERERERERERFxnJNhykygEPidMeYXxpgKYxtjLgP+AQwEjgJ/dnB+ERERERERERERERERxzkWplhrM4BxpV++DBwE2gAYYz43xhwEUoFBlIQut1hrDzk1v4iIiIiIiIiIiIiISGNwcmcK1tplwABgM9Aa8AAG6AXElv46FRhorX3fyblFREREREREREREREQag9vpAa21/wQGGGPOp6TQ/LmUhDaZwGZr7Q6n5xQREREREREREREREWksjoUpxphzSn+ZZ60tttZ+C3zr1PgiIiIiIiIiIiIiIiKng5PHfOUAh4EODo4pIiIiIiIiIiIiIiJyWjl5zFcuELDW7nVwTBER+f/Zu/Pwqss7///POyQBEpBgIGkRBUWsWrVYbZOOImpHRetSFNDi2qk43WfaDqN8x+7+hjqtw9h2OlY7rbK4IKUtg7UFFxZbwWpBazsqoqDCmEiQNYTk5Ny/P05CWbLzyUkCz8d15Trh/tyf+75Pe9rrnLzOfb8lSZIkSZIkdakkd6a8DhSEEBKvw5INIYS/CSH8OoSwKYSwM4TwQgjhH0MIvdoxxhEhhC+EEB4NIawNIewKIVSFEBaFEC7vzPVLkiRJkiRJkqTOkWSYMgfIAz6e4JhZEUK4DFgKnAX8AvghkA9MBx5sx1BfAL4PvA94Evh34LfAaODnIYR/T3DZkiRJkiRJkiQpC5LcRfJd4FLgxyGEd2OMjyc4dqcJIRwG3APUA2fHGJ9taP8q8AQwPoRwVYyxLaHKMw1jLNlnjhOA5cCXQgizY4zPJfokJEmSJEmSJElSp0kyTLmFTPhwArAwhPAC8DTwDpmgokkxxm8luIaOGA8MBmY0BikAMcaaEMKtwOPAZ2jDDpUY47xm2v83hPAQMBk4GzBMkSRJkiRJkiSphwgxxmQGCiENRCDs0dzS4AGIMcY21yTpDCGEWcDVwKQY4wP7XMsFtpA58qtfjHHXAczzn8BngX+MMd7ZVJ8tW7Y0+Z/X6tWrOzqtJEmSJEmSJEmHtJEjRzbZPmDAgNDkhSYkuTNlBi2HJ93V+xoeX9n3QowxFUJ4HXg/cAzwvx2ZoOEosSvI/OezsIPrlCRJkiRJkiRJXSCxMCXGeENSY2XZgIbHLc1cb2wv6sjgIYQA/AQoBX4UY2x3INNcaiZlQ+POKF+H6u58raqn8LWqnsTXq3oKX6vqSXy9qqfwtaqexNersiGnqxeQhBDC2hBCbMfPrCwu7w5gArAM+HIW55UkSZIkSZIkSQlI8pivrrQGqGlH/w17/N6482RAUx33aN/c3kWFEP4N+BKwFPjYgdRckSRJ3VtdOlK5M01dOpKXEyjpm0NeTpuPXpUkSZIkSd3YQRGmxBg/egC3vwycDhwHPLfnhYYC9EcDKeC19gwaQpgO/CPwJHBxjLH6ANYoSZK6mRgjyytrmb26muUVu1i3vZ7cEMgJkI6QipFh/XpRXtqbq0cWUF6ST+b0T0mSJEmS1NMcFMd8HaAnGh7HNnHtLKAA+H1bd5WEjP8kE6QsIrMjxSBFkqSDRIyRB1/dwai5FVyxsIrZq6t5dWs9dWnYWR/ZkYrsrI/UpeHVrfXMXl3NFQurGDW3ggdf3UGMBzb/K6+8wpQpU/jIRz7CUUcdxeDBgzn++OOZOHEiM2bMYNeuv75lKSoqoqioiIEDB/L66683O+bFF1+8u+/s2bOb7ffWW29x+OGHU1RUxLe+9a0DeyKSJEmSJPUghikwF9gIXBVCOL2xMYTQB7it4Z//tecNIYSCEMLxIYSj9mkPwN3AZ4FHgUtjjDs7c/GSJCl71u+o5+JHN/Llp7ewbns91alIa9lIBKpTkXXb6/ny01v4+z/1pmJXx3ao3H777ZSXl3PPPffQv39/rrrqKr7whS/wt3/7t7zyyit88Ytf5IILLtjrntzcXGKMzJw5s8kx16xZw1NPPUVubusblmfMmEE6nSaEwOzZs0mlUh16HpIkSZIk9TQHxTFfByLGuDWEMJlMqLI4hPAgsAm4FHhfQ/tD+9z2YTLHdy0Bzt6j/WvAjcBOYBVwSxPHeayKMf4y4achSZI62dMVu5iwqIqaVCTVwd0l1anIC9tymPjHPswbvIvy0t5tvveOO+5g2rRpDB06lHvvvZfTTz99vz6/+c1v+OEPf7hXW0lJCaWlpcyePZv/9//+336hyYwZMwAYO3YsCxYsaHb++vp6Zs+ezWGHHcbEiRP5yU9+wq9//WsuvfTSNj8HSZIkSZJ6KnemAA3hxhgyheKvAL4A1AFfBq6Ksc0Hchzd8NgXmAp8vYmfjye3ckmSlA1PV+ziioVVbK/reJDSqD4GqusDly+sYnlFm04RZd26dXznO98hLy+POXPmNBmkQCYQ+fnPf75f+/XXX09FRQW/+c1v9mqvq6vj/vvvp6ysjOOPP77FNSxatIj169czbtw4/u7v/g74axAjSZIkSdLBzjClQYzxdzHGi2KMA2OMfWOMJ8cYp8cY65vouzjGGGKMZ+/TfkNDe0s/N2TrOUmSpAO3fkc9ExZVUX2gKco+qlOR8YuqWL9jv7ca+5k9ezZ1dXVceumlnHjiiS327d17/90uV1xxBYWFhfsd9fXrX/+ad955h+uuu67VNdx7770ATJo0iRNPPJFRo0bxxBNP8MYbb7R6ryRJkiRJPZ1hiiRJUjNijExesomahIOURjWpyE1LNtHaJtjly5cDMGbMmA7N079/fy6//HIee+wx1q9fv7t9xowZHHbYYYwbN67F+zds2MCiRYs49thjKSsrAzKhSjqddneKJEmSJOmQYJgiSZLUjIfWVLOqqu6Aj/ZqTirCyqo65qypbrFfRUUFAEOGDOnwXNdffz319fXMmjULgDfeeIMnn3ySCRMmUFBQ0OK9s2bNor6+nkmTJu1umzBhAvn5+cyePZv6+tZ310iSJEmS1JN1qAB9COFrSS0gxvitpMaSJElKSoyRaSu3JX68176qU5l5Jo4oIITQafOcfvrpnHjiicyaNYspU6YwY8YM0ul0q0d8pdNpZs6cSU5ODlddddXu9oEDBzJ27Fjmz5/Pb3/7Wy666KJOW7skSZIkSV2tQ2EK8A2gqb8shGbam9LY1zBFkiR1O8sra9lYk87KXO/UpFlRWUt56f71TgBKS0t5+eWX2bBhwwHNc/3113PzzTezaNEi7r//fkaNGsUHPvCBFu95/PHHefPNN/noRz+6386YSZMmMX/+fO677z7DFEmSJEnSQa2jYcoMmg9NPg4MAKqB54DGg7mHAKcDBcBmYH4LY0iSJHWp2aurO31XSqPqVGTW6upmw5Ty8nKWLl3K0qVL21QsvjlXXnkl3/jGN/jyl7/Mhg0b+Od//udW72ksPP/4449TVFTUZJ/HHnuMt956i6FDh3Z4bZIkSZIkdWcdClNijDfs2xYy51LMAfoBtwJ3xhh37NOnAPgHMrtRCmKMEzsyvyRJUmdbXrEra9/6iMCKitpmr1999dVMnz6d+fPn89JLL3H88cc323fXrl307t10KFNUVMSll17KQw89RGFhIVdccUWL66qoqOC3v/0thx12GJdddlmTfVavXs3y5cuZNWsWt9xyS4vjSZIkSZLUU3V0Z0pTvgBcDnwlxvgfTXWIMVYD00IIO4E7QgifjzH+MME1SJIkHbC6dGTd9uwWVV+7PUVdOpKXs3/dlGHDhnHLLbfw7W9/m4kTJ3Lfffdx6qmn7tfvscce48477+R//ud/mp3n1ltv5ZJLLmHQoEH079+/xTXNmjWLVCrFhAkTuOOOO5rs89prr3Haaacxa9Ys/vmf/5mcnJxWnqkkSZIkST1PkmHKJ4EUcFcb+t4F/BvwKcAwRZIkdSuVO9PkhkBdFk8kzQ2Byp1pjijs1eT1r3zlK6RSKW6//XbOOeccysrKGDVqFP369aOyspLf//73rFmzpsmQZU9HHnkkRx55ZKvriTEyY8YMAK699tpm+x1zzDGcccYZPPXUUyxatIgLLrig1bElSZIkSeppkvzq4LHA9hhjTWsdG/psb7hHkiSpW6lLR5rYINKpckJm3pbcfPPNPP3000yePJmtW7dy//338/3vf5+FCxdy9NFH8/3vf5/f/OY3iaxn8eLFrFu3jlNOOYVRo0a12Pf6668H/lpfRZIkSZKkg02SO1NqgaIQwrAY47qWOoYQhgNFwLsJzi9JkpSIvJxAK7lG4tKRJo/42tf73vc+vvvd77ZpzM2bN7d5/ltvvZVbb71197/POeecNt8/YcIEJkyY0Oa5JEmSJEnqaZLcmfL7hsf/CiHkN9cphJAH/IhMrdXfJTi/JElSIkr65pCK2U1TUjFS0td6I5IkSZIkdUdJfmK/DUgDFwCrQgg3hhCOCyH0a/g5LoRwI7CyoU898O0E55ckSUpEXk5gWL+ma5d0luH9ctu0M0WSJEmSJGVfYsd8xRhXhBCuBX4KHA/8uJmuAagBPhlj/ENS80uSJCWpvLQ3a7ZWZ6UEfQDKSpvd2CtJkiRJkrpYomdJxBgfBE4CfgZsIfO3gT1/tgD/DZwUY3woybklSZKSdPXIAvrmZmenSEFu4JqRBVmZS5IkSZIktV+SBegBiDG+BnwK+FQI4RhgcMOldxquSZIkdXvlJfkM7pPDuu31nT7X4L45lJW4M0WSJEmSpO6qU6ucxhhfizGuaPgxSJEkST1GCIGpp/anoJN3pxTkBqaO6k8I1kuRJEmSJKm76rQwJWQMCiEc1VlzSJIkdaYrRxRwanEenZWn5AY4tTiPiSM84kuSJEmSpO4s8TAlhPCREMJ8YCtQAby2z/WiEMJ/hxB+EkLwLweSJKnbCiFw95jD6dNJaUqf3MA9Yw53V4okSZIkSd1comFKCOFzwFLgYqCQvxae3y3GuBkYBHwSuCLJ+SVJkpJ2RGEv5p5XnPhxXwW5gbnnFTOksFei40qSJEmSpOQlFqaEED4M3AmkgVuAo8jsTGnKz8iELBclNb8kSVJnKS/tzbzzi+mXFw74yK9eIVLQKzLv/GLKS3sns0BJkiRJktSpktyZ8mUyAck3Yoz/FmN8q4W+SxoeP5jg/JIkSZ2mvLQ3K8aVUlaS3+FdKgW5gQ/0TzPngzUGKZIkSZIk9SC5CY41uuHxR611jDG+G0LYBgxNcH5JkqROdURhLxZcOIg5a6qZtnIblTVpdqYisYV7ApkQZXCfHKae2p9T0xuwRIokSZIkST1LkmHKIGBrjHFLG/vXA/kJzi9JktTpQghceWwhE0cUsKKyllmrq1lRUcva7SlyQyAnQDpCKkaG98ulrDSfa0YWUFaSTwiB1au7+hlIkiRJUnbUpSOVO9PUpSN5OYGSvjnk5fjtMvVMSYYpW4DDQwj5McbaljqGEAYBA4D1Cc4vSZKUNSEEykt77z6uyw8JkiRJkg51MUaWV9Yye3U1yyt2sW57/X5fOhvWrxflpb25emQB5Q1fOuuIVatWcfbZZ3Paaadx11137Xd97ty53Hjjjbv7Dh8+fK/rO3fuZPjw4eTk5LB27VqeeeYZLrnkkr369O3bl8MOO4xjjjmG008/nfHjx/OBD3ygxXW99dZb/OQnP+HJJ59k7dq17Nixg6KiIt7//vczduxYJk2axIABAzr0nNW1kgxTngfOJXPc1+Ot9L2BzKkXKxKcX5Ikqcvk5QSOKOzV1cuQJEmSpKyLMfJQw3HI7+xzHHLdPgcjv7q1njVbq5n3+s7dxyFfOaKg3aHKKaecQlFREatWrWL79u3069dvr+tLliwhhECMkaVLl+4XpqxYsYJdu3Zxzjnn0Lv3X2taHnnkkUyaNCmz9ro6Nm7cyPPPP88PfvADfvCDHzBhwgSmT5++33wAM2bMYMqUKezatYuTTjqJ8ePHU1RUxKZNm1i+fDlTp07lu9/9Lq+99lq7nqu6hyTDlBnAR4FpIYRzY4zbm+oUQjgf+BYQgZ8mOL8kSZIkSZIkKYvW76jnpiWbWFlVR3WqpYqSfxWB6lRk3fZ6vvz0Fma+Us3dYw5v1xfUcnJyOPPMM1mwYAF//OMfOeuss/a6vnTpUs4880z+/Oc/s2zZMq677rr9rgOMGTNmr/ajjjqKqVOn7jffCy+8wKc//Wkefvhh3n33XebOnbvX9Tlz5vDFL36RoqIiZsyYwQUXXLDfGMuXL+ef/umf2vwc1b3kJDjWLDI7Uk4HVoQQvgT0BgghXBJC+HwI4RHg10Af4JcxxkcTnF+SJEmSJEmSlCVPV+yi7BcVrKisbXOQsq/qVGRFZS1lv6hgecWudt3bGIQ8++yze7WvW7eOdevWMWbMGM444wyWLVu2373NhSnNOeWUU/jVr37FoEGDeOyxx1iwYMHua9u2bePmm28G4Kc//WmTQQpAeXk5jz/e2qFO6q4SC1NijBEYB/wKOAH4Hpm6KAC/BO4ELmyYcx5wbVJzS5IkSZIkSZKy5+mKXVyxsIrtdZEO5ii7pSJsr4tcvrCqXYFK426UP/zhD3u1L1myZPf10aNH8/bbb/PSSy/tvr5161ZWrlxJUVFRqzVQ9jR48GBuuOEGAB5++OHd7b/61a949913+dCHPsS5557b4hh7HimmniXJnSnEGLfHGMcB5wH3A68DNUAt8CbwEHBhjHF8jLE6ybklSZIkSZIkSZ1v/Y56Jiyq6vBulOZUpyLjF1Wxfkd9m/q/733v4z3veQ9r1qzh3Xff3d2+bNky+vXrxwc/+EFGjx4N/HUnCsDvfvc76uvrOfPMM8nJad+fyBvHe+6553a3LV++HGj7Lhf1TImGKY1ijI/HGK+NMR4bYyyMMfaNMQ6PMX4ixvjbzphTkiRJkiRJktS5YoxMXrKJmoSDlEY1qchNSzaROQipdWeddRYxxr2O+lq2bBkf+chHyM3N5YQTTmDw4MF7hSntPeJrT+9973sBqKqq2t1WUVEBwJAhQ9o9nnqOTglTJEmSJEmSJEkHn4fWVLOqqu6Aj/ZqTirCyqo65qxp28FGjUd9NYYpL7/8Mm+//fbuHSQAZ555Jk899RTpdBo4sDClMeQJIbT7XvVsWQtTQggnhRA+HUL4hxDCidmaV5IkSZIkSZJ04GKMTFu5LfHjvfZVncrM05bdKY2BSGPdlD3rpTQ688wz2bx5My+88AIbN27kL3/5C0OGDOG4445r99refvttAIqLi3e3lZaWArBhw4Z2j6eeI7EwJYRwQQjh9yGEf2vi2i3ASuA/gX8HXggh3JzU3JIkSZIkSZKkzrW8spaNNemszPVOTZoVlbWt9jvyyCMZOnQob775Jm+99RZLly5lwIABnHLKKbv77Fk3ZdmyZcQY9wpb2mPZsmUAnH766bvbysvLd4+vg1eSO1MmAmXAn/ZsDCGMAv4/oBewHljbMO+/hhDOSHB+SZIkSZIkSVInmb26utN3pTSqTkVmrW7bUV8f+tCHAFi8eDFPPfUUZ5xxxl6F5Y877jhKS0tZunTpAR3x9c477/Czn/0MgAkTJuxuv+yyyxg4cCDPPPMMixcvbnGMXbt2tXtedQ9JhillDY8L92m/CQjAPGB4jHEE8MOGts8mOL8kSZIkSZIkqZMsr9hFdqIUiMCKitZ3psBfd4n86Ec/YvPmzXvVS2k0evRonn76aZ544gmg/WHKn/70Jz7+8Y9TVVXF+eefz0UXXbT7Wv/+/bn99tsB+OQnP8njjz/e5Bh/+MMfOO+889o1r7qP3ATHKgFqY4wV+7SPJfPanxZjbNwDdhvwecCdKZIkSZIkSZLUzdWlI+u212d1zrXbU9SlI3k5LRd7/9CHPkQIgb/85S8ATR7hNXr0aObOncuOHTsYOXIkQ4YMaXKsN954g2nTpgGQSqWoqqpi1apVrFq1CoCJEycyffr0/e6bOHEiNTU1TJkyhSuuuIKTTz6ZsrIyioqK2LRpE8888wwvvvjiXrVW1LMkGaYUAdv3bAghvBcYDmyMMT7X2B5jrAwhbANKE5xfkiRJkiRJktQJKnemyQ2BuqztTYHcEKjcmeaIwl4t9hs4cCDHHnssq1evpri4mBNPPHG/PnvuVmlpV8qbb765e5dJnz59GDBgAMcccwxf+MIXmDBhwl61WPZ13XXXce6553LPPffw5JNPMmfOHKqrqxkwYAAnnHAC//qv/8o111zT2tNWN5VkmLIVGBhCKIwx7mhoO7fh8akm+kfAA+IkSZIkSZIkqZurS0da2SCSuJyQmbct7r//fkaOHNns9WOOOYbNmzc3e3306NEtXm+roUOH8s1vfpNvfvObBzyWupcka6a80PD4dwAhhECmXkoEntyzYwhhIHAY8H8Jzi9JkiRJkiRJ6gR5OYE25hqJSUdaPeJLypYkw5QZZIrK3xFCeAR4BhgN7AQe3Kdv46F1/5vg/JIkSZIkSZKkTlDSN4dUzG6akoqRkr5J/glb6rgkX4n3AQ+QOTrsQuA0oBb4fIzxnX36Nh4M93iC80uSJEmSJEmSOkFeTmBYv5ZrlyRteL9cd6ao20isZkqMMQJXhxDuAsrJ1FB5PMb46p79Qgh5wFrgTmB+UvNLkiRJkiRJkjpPeWlv1mytzkoJ+gCUleZnYSapbZIsQA9AjHEZsKyF63XAlKTnlSRJkiRJkiR1nqtHFjDv9Z1Upzo/TinIDVwzsqDT55HaygPnJEmSJEmSJEmtKi/JZ3Cf7PxJeXDfHMpK3Jmi7sMwRZIkSeqm6tKR9TvqWbstxfod9dSls1vwU5IkSdpTCIGpp/anILdz65gU5AamjupPCNZLUffRoWO+QgivNfz6aozx/H3a2iPGGEd0ZA2SJEnSwSbGyPLKWma51zlGAAAgAElEQVSvrmZ5xS7Wba8nNwRyAqQjpGJkWL9elJf25uqRBZSX5B/QB8yioqL92vLz8yktLeWMM87g8ssv5+ijjwZg2bJlXHLJJe0a//nnn2fYsGGcfPLJvPnmm7v/LUmSpJ7ryhEFzHylmhWVtXTGaV+5AU4tzmPiCI/4UvfS0Zopwxsea5poaw+/WidJkqRDXoyRh9ZUM23lNt6pSbMzFXe/Ua7b5y3zq1vrWbO1mnmv72RwnxymntqfK0cUHFCocvPNN+/+fevWrfzxj3/kwQcf5Fe/+hX33HMPI0eO5KijjtqrH8CWLVu46667OOyww/jMZz6z37gDBgzo8JokSZLUPYUQuHvM4ZT9ooLtdcn/ebdPbuCeMYe7K0XdTkfDlHMaHqubaJMkSZLURut31HPTkk2srKprcyHPCFSnIuu21/Plp7cw85Vq7h5zOEcU9urQGqZOnbpf25QpU7jnnnt44IEHuPjiixk2bNh+/datW8ddd93FgAEDmhxDkiRJB6cjCnsx97xiLl9YlWgx+oLcwNzzihnSwfe1UmfqUJgSY1zSljZJkiRJzXu6YhcTFlVRk4odPiKhOhVZUVlL2S8qmHteMeWlvRNZ27nnnss999zD5s2bExlPkiRJB5fy0t7MO7+Y8Qf4fhYyR3v1aQhSkno/KyXNAvSSJElSF3i6YhdXLKxie92BffAESEXYXhe5fGEVyyt2JbK+xYsXA3DCCSckMp4kSZIOPuWlvVkxrpSykvwOF6UvyA2UleSzYlypQYq6tY4e8yVJkiSpg9bvqGfComSPRIDMLpXxi6pYMa60XUd+TZs2bffv27ZtY+XKlSxfvpwzzzyTa665JtE1SpIk6eByRGEvFlw4iDkNNQAr96kB2JRAJkRprAE48QBrAErZ0KEwJYRwVlILiDEuTWosSZIkqbuLMTJ5ySZqEg5SGtWkIjct2cSCCwe1+QPp7bffvl/b8ccfzwUXXEBhYWHSS5QkSdJBJoTAlccWMnFEASsqa5m1upoVFbWs3Z4iNwRyAqQjpGJkeL9cykrzuWZkAWUl+YYo6jE6ujNlMbQYLrZVPIA1SJIkST3OQ2uqWVVVd8BHezUnFWFlVR1z1lRz5bFtC0L2rIuyY8cOXnrpJb7xjW/w1a9+lddee43p06d3zmIlSZJ0UAkhUF7ae/dxXXXpSOXONHXpSF5OoKRvDnk5hifqmToaZLxBMmGKJEmSdMiIMTJt5bbEj/faV3UqM09HjksoLCzktNNOY+bMmZxwwgnMmDGDr3zlKwwdOrSTVitJkqSDVV5OaNfxs1J31qEwJcY4POF1SJIkSQe95ZW1bKxJZ2Wud2rSrKis7XARz6KiIoYNG8ZLL73E888/b5giSZIk6ZCW09ULkCRJkg4Vs1dXd/qulEbVqcis1dUHNMbWrVsBSKezEwBJkiRJUndlvRJJkiQpS5ZX7MraWbkRWFFR2+H7FyxYwIYNG8jNzaWsrCy5hUmSJEmHCGvGHFwMUyRJkqQsqEtH1m2vz+qca7endn9wa8m0adN2/15dXc3LL7/MokWLAPjsZz9LSUlJp65TkiRJOhjEGFleWcvs1dUsr9jFuu315IZAToB0hFSMDOvXi/LS3lw9soDykvx21zhU10k8TAkh9AXGA2cAQ4BCoLlXRIwxfjTpNUiSJEndTeXONLkhUJe1vSmQGwKVO9OtFv28/fbbd//eq1cvBg0axNixY/nYxz7mrhRJkiSpFTFGHlpTzbSV23inJs3OVNz9rn/f9/+vbq1nzdZq5r2+k8F9cph6an+uHFHQoVClqKgIgKFDh/Lss8/Sp0+f/fqcfPLJvPnmm2zcuJHc3FwmT57Mww8/zPe+9z1uvPHGFscfN24cTz75JDNnzuSSSy5h9uzZfO5zn9urT2FhIQMGDGDkyJF8+MMf5qqrrmLEiBGtrv173/set912GwB/+MMfGDlyZFufdpdJNEwJIZwL3A8MJhOgNL5S9nwl7NmWvU+SkiRJUheqS0eyvaM/J2Tmbc7mzZtbvH/16tUtXh82bFirYwD86U9/arWPJEmS1BOt31HPTUs2sbKqrs31ESOZGofrttfz5ae3MPOVau4ec3irX4JqzltvvcV//dd/8aUvfanVvtdddx0PP/wwM2bMaDFMWbduHYsXL+Y973kPF1544V7XTjrpJD72sY8BUFNTwzvvvMNzzz3Hd7/7Xe644w5uuukmbrvtNnJzm44fYozMmDGDEAIxRu67777dwUp3lliYEkI4FvgVmZ0ojwGPANOBLcBXgFLgb4FzgI3AN4HtSc0vSZIkdWd5OYEWco1OkY54JrMkSZLUSZ6u2MWERVXUpCJtzFH2U52KrKispewXFcw9r5jy0t7tur+oqIgQAtOnT+e6666juLi4xf6jR4/m2GOP5YUXXmDVqlWMGjWqyX4zZ84kxsjVV1+9Xyhy8sknM3Xq1P3uWbp0KZ/97Ge566672LVrF9OnT29y7CeeeII33niDSZMm8fjjj/PAAw/wta99jfz8/DY+666Rk+BYU8gEKbNijOfHGO9saN8ZY/xpjHFaw5FeY4E+wCeBBxOcX5IkSeq2SvrmkIrZTVNSMVLSN8m3/JIkSZIgE6RcsbCK7XUdD1IapSJsr4tcvrCK5RW72nVvQUEBU6ZMYevWrXznO99p0z3XX389ADNmzGjyen19Pffffz8hBK699to2r+Wss85i3rx55Ofnc++99/L888832e++++7bvY4JEyZQVVXFggUL2jxPV0nyk9W5ZHYotbgfJ8a4EPhH4IPAPyU4vyRJktRt5eUEhvXr2Lb9jhreL9edKZIkSVLC1u+oZ8KiqjYf69VW1anI+EVVrN9R3677Jk+ezNFHH829997LmjVrWu3/iU98gvz8fObOnUt1dfV+1xctWsSGDRs4++yzGT58eLvWctxxx/Hxj3+cGCNz587d73plZSWPPvooxx57LGVlZUyaNAmAe++9t13zdIUkw5QjgNoY4yt7tKXJ7ELZ1/1ACpiY4PySJElSt1Ze2ptsRRsBKCvt3tvkJUmSpJ4mxsjkJZuoSThIaVSTity0ZBOxHbva8/Ly+MY3vkFdXR1f//rXW+0/aNAgPvaxj7F161Z+8Ytf7He9cefIDTfc0OY17OnMM88E4Lnnntvv2uzZs6mrq9sdopx44omMGjWKZcuW8dprr3VovmxJMkzZxf41ULYBA0IIe32KizHWADuAoxOcX5IkSerWrh5ZQN/c7MQpBbmBa0YWZGUuSZIk6VDx0JpqVlXVHfDRXs1JRVhZVcecNfvvGGnJZZddxoc//GEWLFjA008/3Wr/xqBk5syZe7W//fbbLFq0iMGDB3PRRRe1aw2NhgwZAkBVVdVe7Y2F53Nycrjqqqt2t0+aNGl3IfruLMkw5S0ywcme1Wga9xSdvmfHEMJ7gAGQtS/mSZIkSV2uvCSfwX2yU8NkcN8cykrcmSJJkiQlJcbItJXbEj/ea1/Vqcw87dmdAnDbbZkKHF/96ldb7XvWWWdx9NFHs3z5cl5++eXd7bNnzyaVSjFp0iTy8vLat/AGjesOYe8//y9dupTXX3+dc845Z3fgAjBhwgTy8/O5//77qaur69Cc2ZDkJ7m/AL2AD+zR9jiZwORrIYQ+AA27VBqL069McH5JkiSpWwshMPXU/hR08u6UgtzA1FH99/vwIkmSJKnjllfWsrEmnZW53qlJs6Kytl33fPjDH+ayyy7j2WefZd68eS32DSFw3XXXAX8tRB9jZObMmYQQdhep74j/+7//A6C4uHiv9sa6KI1HfDUaOHAgY8eO5Z133uHXv/51h+ftbEmGKY+SCU4u26Pt+2SO/joPeDOE8DsyO1jGkylWf0eC80uSJEnd3pUjCji1OI/OylNyA5xanMfEER7xJUmSJCVp9urqTt+V0qg6FZm1un1HfQF8/etfJy8vj29+85vU1rYcxlx99dXk5eXx4IMPUltby9KlS1m7di2jR4/mmGOO6ejSeeqppwA4/fS/Hli1ceNGHnnkEQA+9alPUVRUtNfP/Pnzge5diD639S5tNhcoBDY2NsQY14cQLgFmkSlQ/5GGS9XA1BjjrxKcX5IkSer2QgjcPeZwyn5Rwfa65D+I9ckN3DPmcHelSJIkSQlbXrGL7EQpmZ0IKyratzMF4JhjjuFTn/oUd911Fz/+8Y9b7FtSUsKFF17I/PnzeeSRR1iwYAHQ8cLzAK+88gq//OUvCSEwfvz43e0PPPAAtbW1jBo1ipNPPrnJex999FEWL17M2rVrGT58eIfX0FkSC1NijNuB/2yifUkI4WgyQcpQYAvwuxjjlqTmliRJknqSIwp7Mfe8Yi5fWJXoN9sKcgNzzytmSGGvxMaUJEmSBHXpyLrt9Vmdc+32FHXpSF5O+74odfPNN/PAAw9wxx13kJPT8uFU119/PfPnz+eHP/whL774IsXFxVx88cUdWu9TTz3FZz7zGWpra7nxxhv3Ck0ai8vfcccdnHbaaU3ef9ttt/G9732PmTNntqnuS7YluTOlWTHGFLAsG3NJkiRJPUF5aW/mnV/M+EVV1KQiB5Kp5IbMjpS55xVTXto7uUVKkiRJAqByZ5rcEKjL2t4UyA2Byp1pjmjnl6UGDhzIV77yFb72ta+12vfcc8/lqKOO4rnnngNg8uTJ5Ofnt3jPn/70J6ZNmwZAbW0tlZWVPPfcc7z00kvk5OTwuc99jm9961u7+y9btoxXX32VE088sdkgBeDaa6/ljjvuYPbs2UydOpXc3KzEF23WvVYjSZIkHULKS3uzYlwpNy3ZxMqqug7tUinIDZxanMfdYw5v94csSZIkSW1Tl460c4PIAcsJmXk74u///u/5yU9+whtvvNFiv8ZC9LfddhtAmwrPv/jii7z44osAFBQUUFRUxMiRI7n00ku56qqr9qu30ljgvrHgfXOGDRvG2WefzZNPPsmjjz7KJZdc0upasinEmL0kTS3bsmWL/2Wo21m9ejUAI0eO7OKVSC3ztaqewteqmhJjZM6aaqat3EZlTZqdqdji990CmRBlcJ8cpp7an4kjCjqlRoqvV/UUvlbVk/h6VU/ha1U9STZer+t31HP6zyvYWZ+9P+H27RV49opSvzTViQYMGNDmD1KJ7UwJIXTkwLgYY3R3jCRJkg5pIQSuPLaQiSMKWFFZy6zV1ayoqGXt9hS5IZATIB0hFSPD++VSVprPNSMLKCvJt9C8JEmSlAUlfXNIZXljQipGSvq2XPNE2ZNkkNGRT3F+8pMkSZIahBAoL+29u+5JXTpSuTO9u+hkSd+cdheflCRJknTg8nICw/r14tWt2StCP7xfru//u5Ekw5SjW7k+APgQ8I/Ae4FPAi8kOL8kSZJ0UMnLCW7plyRJkrqJ8tLerNlanZUS9AEoK225ELyyK7EwJca4rg3dXgghzAQeBf4bOC2p+SVJkiRJkiRJ6ixXjyxg3us7qU51fpxSkBu4ZmRBp8+jtsv6gWsxxlrgi8Ag4OvZnl+SJEmSJEmSpPYqL8lncJ/s/El9cN8cykrcmdKddEn1mhjjn4GtwNiumF+SJEmSJEmSpPYIITD11P4U5HZuHZOC3MDUUf0JwXop3UmXhCkhhHygACjuivklSZIkSZIkSWqvK0cUcGpxHp2Vp+QGOLU4j4kjPOKru+mSMAWYRKZey4Yuml+SJEmSJEmSpHYJIXD3mMPp00lpSp/cwD1jDndXSjeUWAH6EMJRrXTpAwwFLgMmAxF4OKn5JUmSJEmSJEnqbEcU9mLuecVcvrAq0WL0BbmBuecVM6SwV2JjKjmJhSnA6+3oG4AVwLcTnF+SJEmSJEmSpE5XXtqbeecXM35RFTWpyIFkKrkhsyNl7nnFlJf2Tm6RSlSSx3yFVn7SwCZgCfBZYHSMcUeC80uSJEmSJEmSlBXlpb1ZMa6UspL8DhelL8gNlJXks2JcqUFKN5fYzpQYY1fVX5EkSZIkSZIkKeuOKOzFggsHMWdNNdNWbqOyJs3OVKSljSqBTIgyuE8OU0/tz8QRBdZI6QGSPOZLkiRJkiRJkqRDSgiBK48tZOKIAlZU1jJrdTUrKmpZuz1FbgjkBEhHSMXI8H65lJXmc83IAspK8g1RehDDFEmSJEmSJEmSDlAIgfLS3ruP66pLRyp3pqlLR/JyAiV9c8jLMTzpqQxTJEmSJEmSJElKWF5O4IjCXl29DCUk0TAlhNALmAyMB04CBrYyR4wxGuhIkiRJkiRJkqRuK7EgI4TQH3gMOJ1MDZ023ZbU/JIkSZIkSZIkSZ0hyV0hXwM+BOwC7gF+CawHahKcQ5IkSZIkSZIkKauSDFOuACLwmRjjvQmOK0mSJEmSJEmS1GVyEhxrCJACZic4piRJkiRJkiRJUpdKMkx5B9gZY6xLcExJkiRJkiRJkqQulWSY8hugfwjhhATHlCRJkiRJkiRJ6lJJhinfAt4F7gwh5CU4riRJkiRJkiRJUpdJsgB9AP4OuBd4NoTw78CzwLaWbooxvpHgGiRJkiRJkiRJkhKVZJjy+h6/DwB+2oZ7YsJrkCRJkiRJkiRJSlTSO1OycY8kSZIkSZIkSVLWJBamxBiTrL8iSZIkSZIkSZLULRiASJIkSZIkSZIktcAwRZIkSZIkSZIkqQWGKZIkSZIkSZIkSS1IsgA9ACGEscB44CRgIJDXQvcYYxyR9BokSZIkSZIkSZKSkliYEkLIAx4CLmtsasNtMan5JUmSJEmSJEmSOkOSO1NuBj5OJiB5BPglsB6oSXAOSZIkSZIkSZKkrEoyTLmaTJAyNcb4bwmOK0mSJEmSJEmS1GWSLEA/HEgDP0hwTEmSJEmSJEmSpC6V5M6UzUDvGOPOBMeUJEmSJEmSJEnqUknuTFkCDAghHJngmJIkSZIkSZIkSV0qyTDlNjLF5m9PcExJkiRJkiRJkqQulViYEmN8Efg4MDaE8GgI4ewQQmFS40uSJEmSJEmSJHWFxGqmhBDq9/jn+Q0/hBBaui3GGJOs2yJJkiRJkiRJkpSoJIOMFlOTBO+RJEmSJEmSJEnKmiTDlKMTHCvrQgh/A9wKlAN9gdXAT4EfxBjrW7q3lXFvBb7d8M/zYoyPHehaJUmSJEmSJElS9iQWpsQY1yU1VraFEC4Dfg7UAA8Bm4BLgOnAGcCEDo77QeBrwHagXyKLlSRJkiRJkiRJWZVYAfqeKoRwGHAPUA+cHWP8VIxxCjAKeBoYH0K4qgPj9gFmAn8AfpHgkiVJkiRJkiRJUhYd8mEKMB4YDDwYY3y2sTHGWEPm2C+Az3Rg3Glkjj67AUgf4BolSZKkLlOXjqzfUc/abSnW76inLh27ekmSJEmSlFUhxuQ/CIUQRpM5HmsIUEjzheZjjPFTiS+gHUIIs4CrgUkxxgf2uZYLbAHygX4xxl1tHPNc4DHgSzHGO0MI9wLX00rNlC1btjT5X8bq1avbMq0kSZKUiBjh+a05/E9FL1Zt68WGmkBuyLypj0AqwpA+kVH967mktJ4PHJYmNPeOX5IkSZK62MiRI5tsHzBgQJs/ySRZgJ4QwknA/cD7973U8Bj3aYtAl4YpwPsaHl/Z90KMMRVCeJ3M8zkG+N/WBgshDADuBZYB309umZIkSVLnihEerezFj9/I4926QE0aYsNb+dQ+X/t5Y2fgzZ2BRRtzGZgX+fuj6riwpL7DoUp9fT3z58/n0UcfZc2aNezYsYPDDjuM4uJi3v/+9zN69GjGjBkDwHPPPcenP/1pPvjBD/LjH/+4yfE2bNjAZZddxnvf+17mz5/farskSZIktSSxMCWE8F7gcTJHZv0FWAT8A5ni6/8BlALnAiOAjcCPgVRS8x+AAQ2PW5q53the1MbxfgAcTqb+SiLbfppLzaRsaNwZ5etQ3Z2vVfUUvlbVXa3fUc9NSzaxsqqO6n2Tk2ZEAjvTsHNX4PbX+7Bwax53jzmcIwp7tWvu+vp6rrzySh577DEGDBjA2LFjGTJkCLW1tbz00kssXLiQiooKbrzxRgDefvttAPr27dvs/5by8/MByM3N3atPc+3q2fz/VvUkvl7VU/haVU/i61XZkOTOlH8iE6T8BrgsxlgXQvgHYHuM8WuNnUIINwE/BD4IXJzExCGEtcCwdtwyO8Z4TRJz77OOK4Brgc/FGF9LenxJkiSpMzxdsYsJi6qoScX9dqC0VXUqsqKylrJfVDD3vGLKS3u3+d65c+fy2GOPcdJJJ/HII48wYMCAva5XV1fz7LPPNnO3JEmSJHW+JMOUsWSO7fqXGGNdc51ijHc3HIX1HeBzZIKVA7UGqGlH/w17/N6482RAUx33aN/c0oAhhMOBu8jszvmvdqxFkiRJ6jJPV+ziioVVbd6N0pJUhO11kcsXVjHv/LYHKs888wwAkyZN2i9IASgoKOCss8464PVJkiRJUkclGaYMA+qBVXu0RaCpT1B3AdOA60ggTIkxfvQAbn8ZOB04DnhuzwsNBeiPJnMcWWs7TY4CBgEfBdKh6cOiFzW0fynG+B8HsGZJkiTpgK3fUc+ERckEKXuqTkXGL6pixbjSNh35NXDgQADWrFmT6DokSZIkKSlJhilpYMs+dUK2A4eFEHrFGOsbG2OM20IIW8kEGF3tCeBqMjtrHtjn2llAAbA0xrirlXGqgP9u5tpZwEjgUTK7Yl7s8GolSZKkBMQYmbxkEzUJBymNalKRm5ZsYsGFg2jmi0a7XXLJJdx555389Kc/Zdu2bVx88cWMGjWKo446qsX73njjDaZNm9bktS1bmiuJKEmSJEntl2SYsh44JoSQE2NMN7StBU4CTgFWNnZsOOariPYdzdVZ5gK3A1eFEH4QY3wWIITQB7itoc9ex3aFEArI7ESpjjG+ARBjfBO4sakJQgj3kglT/j3G+FhnPAlJkiSpPR5aU82qqroO10hpTSrCyqo65qyp5spjC1vs+4EPfIC7776bW265hTlz5jBnzhwgs2Plb/7mb7jmmmu48MIL97vvzTff5Pbbb++U9UuSJEnSnnISHOtlMuHMCXu0LQMCmeL0e/p2w+NfEpy/Q2KMW4HJQC9gcQjhJyGEfyNzXNlHyIQtD+1z24eB/wVmZHOtkiRJUhJijExbuS3x4732VZ3KzLP35vWmjRs3jhdffJF58+YxZcoULrjgAtLpNI888gif+MQn+PSnP73fOGeccQabN29u8uf555/vrKclSZIk6RCUZJiykExwcvEebT8A6sjs+vhTCGF2COF5MoXnI92kUHuM8ZfAGGApcAXwBTLr/jJwVWzLpz9JkiSph1heWcvGmnTrHRPwTk2aFZW1beqbl5fHueeey7/8y7/w0EMP8dprr/Gzn/2MwsJCHnzwQR555JFOXq0kSZIkNS3JMOUh4A5gR2NDjPFl4PqGtvcDnwBObrg8PcbYXI2RrIsx/i7GeFGMcWCMsW+M8eQY4/Q9a73s0XdxjDHEGM9u49g3NPT3iC9JkiR1udmrqzt9V0qj6lRk1urqDt3bq1cvxo0bx2c+8xkAli5dmuTSJEmSJKnNEquZEmOsAqY00f5gCOEx4EJgKLAFeCzG+EpSc0uSJElqu+UVu8jW1usIrKho286U5vTv3z8zlhvGJUmSJHWRJAvQNyvGuBGYmY25JEmSJDWvLh1Zt32/zdedau32FHXpSF5OaPL63LlzKS4uZsyYMeTk7L15vqKigvvuuw/I1EiRJEmSpK6QlTBFkiRJUvdQuTNNbgjUZW1vCuSGQOXONEcU9mry+rPPPstdd91FaWkp5eXlDBs2DIB169axcOFCdu7cyUUXXcRll12WtTVLkiRJ0p4MUyRJkqRDSF060swGkU6TEzLzNufzn/88I0aMYPHixfz5z3/miSeeoKamhsMPP5wzzzyT8ePHM2HCBELI8sIlSZIkqYFhiiRJknQIycsJtJBrdIp0pNkjvgCGDh3K5MmTmTx5cpvGGz16NJs3b26xz7Bhw5rs01y7JEmSJLUkp/UukiRJkg4WJX1zSGW5kHsqRkr6+tFDkiRJUs/lJxpJkiTpEJKXExjWr+naJZ1leL/cFnemSJIkSVJ3Z5giSZIkHWLKS3uTrWgjAGWl+VmaTZIkSZI6h2GKJEmSdIi5emQBfXOzE6cU5AauGVmQlbkkSZIkqbMYpkiSJEmHmPKSfAb3yc5HgcF9cygrcWeKJEmSpJ7NMEWSJEk6xIQQmHpqfwo6eXdKQW5g6qj+hGC9FEmSJEk9m2GKJEmSdAi6ckQBpxbn0Vl5Sm6AU4vzmDjCI74kSZIk9XyGKZIkSdIhKITA3WMOp08npSl9cgP3jDncXSmSJEmSDgqGKZIkSdIh6ojCXsw9rzjx474KcgNzzytmSGGvRMeVJEmSpK5imCJJkiQdwspLezPv/GL65YUDPvIrN0C/vMC884spL+2dzAIlSZIkqRswTJEkSZIOceWlvVkxrpSykvwO71IpyA2UleSzYlypQYokSZKkg45hiiRJkiSOKOzFggsHMf0jAxjerxcFuYFAbPGeABTmBob368X0jwxgwYWDOMKjvSRJkiQdhHK7egGSJEmSuocQAlceW8jEEQWsqKzlR8/+H89v68WGXTnkhkBOgHSEVIwM75dLWWk+14wsoKwk30LzkiRJkg5qhimSJEmS9hJCoLy0N8XH1QF1DB9xLJU709SlI3k5gZK+OeTlGJ5IkiRJOnQYpkiSJElqUV5O8PguSZIkSYc0a6ZIkiRJkiRJkiS1wDBFkiRJkiRJkiSpBYYpkiRJkiRJkiRJLTBMkSRJkiRJkiRJaoFhiiRJkiRJkiRJUgsMUyRJkiRJkiRJklpgmCJJkiRJkiRJktQCwxRJkiRJkiRJkqQWGKZIkiRJkiRJkiS1wDBFkiRJkiRJkiSpBYYpkiRJkiRJkiRJLTBMkSRJkiRJkiRJaoFhiiRJkiRJkiRJUgsMUyRJkiT9/1+xiCAAACAASURBVOzde5hfVX0v/vcnFwgJEOSStMhNkepRBBQUFBFFReEnKhW8gD3S/orVVlur9TlSba2n9qH8nla09vzsEX+iFStgRD2CtoqI4gVUxHvFgIKV1gSDCYQQyGX9/vh+JwzDzM7M5DszyfB6Pc9+9nz3Za21Z1Z2vvN9z9oLAACADsIUAAAAAACADsIUAAAAAACADsIUAAAAAACADsIUAAAAAACADsIUAAAAAACADvNmugEAALCj27C5ZeU9m7Nhc8v8OZUlu8zJ/Dk1080CAABgQIQpAAAwQa21XLvyvnxk+bpcu+Le3Lp2U+ZVZU4lm1uysbUcuOvcHLN055x5yMIcs2SnVE0+XNm0aVMuuuiiXHLJJfnRj36UtWvXZo899siSJUty5JFH5qSTTsrJJ5+cJLnmmmtyyimn5Nhjj80VV1zRWe6tt96aww8//EHbFyxYkH333TdPfOIT88pXvjKHHHLIpNsOAAAwGwhTAABgnFprueTmdTn3hrty+/rNuWdjS+vv27Dlq56b7tyUm+9cl8t+dk/2WTAn5zxht7z04IUTDlU2bdqUl770pbnyyiuzePHiPPe5z82+++6b++67Lz/+8Y+zbNmyLF++fEuYMhm77757XvOa12x5fccdd+SrX/1qli1blquuuipf+cpXsu+++066fAAAgB2dMAUAAMbhtrs35VVfuiM3rNqQdRvb1k9I0pKs29hy69pNecPX1+TDP1mX9x2/Zx6+aO646122bFmuvPLKHHroobniiiuyePHiB+xft25dvvWtb03kUh5k8eLFOeeccx60/ZRTTsk111yTD33oQ6PuBwAAeKgwAT0AAGzF11fcm6M/sSLXrbxv3EHKSOs2tly38r4c/YkVuXbFveM+7xvf+EaS5IwzznhQkJIkCxcuzNOf/vRJtWlrjj766CTJqlWrpqR8AACAHYUwBQAAOnx9xb158edWZe2GlknmKFtsbMnaDS2//blV4w5UHvawhyVJbr755m2rfBKGgpwjjjhi2usGAADYnnjMFwAAjOG2uzfl9M+vmvRolLGs29hy2udX5bpTl271kV+nnHJK3v3ud+cDH/hA7rrrrjz/+c/PEUcckQMOOGBg7VmzZk3OPffcLa9Xr16dr33ta/nRj36U5z//+XnZy142sLoAAAB2RMIUAAAYRWstZ3/pjqwfcJAyZP3Glld96Y5cftLenZPSH3744Xnf+96XN7/5zbn00ktz6aWXJumNWHnqU5+aV7ziFTnppJO2qS133nlnzjvvvAdtf/zjH5/nPOc5mTfPrw0AAMBDm8d8AQDAKC65eV2+s2rDNj/aaywbW3LDqg259OZ1Wz321FNPzQ9+8INcdtlledOb3pTnPve52bx5c6644oq8/OUvz6tf/eq0NvmG7r///lm9evWW5dZbb82nP/3prF+/Pq9//evzwQ9+cNJlAwAAzAbCFAAAGKG1lnNvuGvgj/caad3GXj3jCULmz5+fE044IW95y1tyySWX5Kc//WkuvPDCLFq0KBdffHGuuOKKgbVr8eLFOe6443LeeeeltZa3ve1tueeeewZWPgAAwI5GmAIAACNcu/K+/Gr95mmp6/b1m3PdyvsmfN7cuXNz6qmn5jWveU2S5Mtf/vKgm5b9998/ixcvzpo1a3LTTTcNvHwAAIAdhTAFAABG+MjydVM+KmXIuo0tFy3f+qO+xrLbbrslyTY95mssGzduzN13350k2bx5esIlAACA7ZEwBQAARrh2xb2ZniglaUmuWzH2yJRly5bli1/84qhhxooVK/KhD30oSXLssccOvG2XXnppNm7cmD333DOPfexjB14+AADAjmLeTDcAAAC2Jxs2t9y6dtO01nnL2o3ZsLll/px60L5vfetb+ad/+qcsXbo0xxxzTA488MAkya233prPfe5zueeee3LyySfnhS984QPOW758+ZZHgI2033775S1vecuW12vWrMm555675fVdd92V7373u/nqV7+aOXPm5O///u8zf/78QVwqAADADkmYAgAAw6y8Z3PmVWXDtI1NSeZVZeU9m/PwRXMftO+1r31tDj744Fx99dX54Q9/mKuuuirr16/Pnnvumac97Wk57bTTcvrpp6fqgUHMypUr89GPfnTU+g499NAHhCl33nlnzjvvvC2v58+fnyVLluTEE0/MGWeckRe96EUDulIAAIAdkzAFAACG2bC5ZZQBIlNqTvXqHc1+++2Xs88+O2efffa4yjruuOOyevXqcR174IEHdh67fPnycZUDAAAw25kzBQAAhpk/pzJGrjFlNreM+ogvAAAAtg/CFAAAGGbJLnOysU1vmrKxtSzZxVtzAACA7ZXf2AAAYJj5cyoH7vrguUum0kG7zjMyBQAAYDsmTAEAgBGOWbpzpivaqCRHL91pmmoDAABgMoQpAAAwwpmHLMwu86YnTlk4r/KKQxZOS10AAABMjjAFAABGOGbJTtlnwfS8Vd5nlzk5eomRKQAAANszYQoAAIxQVTnnCbtl4RSPTlk4r3LOEbulynwpAAAA2zNhCgAAjOKlBy/ME/aan6nKU+ZV8oS95uclB3vEFwAAwPZOmAIAAKOoqrzv+D2zYIrSlAXzKhccv6dRKQAAADsAYQoAAIzh4YvmZtlz9hr4474Wzqsse85e2XfR3IGWCwAAwNQQpgAAQIdjlu6cy07cK7vOr21+5Ne8SnadX7nsxL1yzNKdB9NAAAAAppwwBQAAtuKYpTvnulOX5uglO016lMrCeZWjl+yU605dKkgBAADYwQhTAABgHB6+aG4uP2nvnP+UxTlo17lZOK+ytVilkiyaVzlo17k5/ymLc/lJe+fhHu0FAACww5k30w0AAIAdRVXlpY9alJccvDDXrbwvFy1fl+tW3Jdb1m7MvKrMqWRzSza2loN2nZejl+6UVxyyMEcv2clE8wAAADswYQoAAExQVeWYpTtveVzXhs0tK+/ZnA2bW+bPqSzZZU7mzxGeAAAAzBbCFAAA2Ebz55THdwEAAMxi5kwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEwBAAAAAADoIEzpq6qnVtVnquqOqrqnqr5XVa+vqrmTLO+0qvq3qvpVVa2vqp9X1aeq6phBtx0AAAAAAJg682a6AduDqnphko8nWZ/kkiR3JDklyflJjk1y+gTKmpfkQ0nOSLK8X96aJL+R5ClJjkxy7QCbDwAAAAAATKGHfJhSVbsnuSDJpiTPaK19q7/9L5JcleS0qnpZa+3icRb59vSClL9J8pettc0j6ps/sMYDAAAAAABTzmO+ktOS7JPk4qEgJUlaa+uTvLX/8jXjKaiqfiPJnyW5trX21pFBSr/cDdveZAAAAAAAYLpUa22m2zCjquqiJGcmOaO19tER++al94iunZLs2lq7dytlvTbJe5K8Psn7kvxfSR6V5K4kX2mtfbfr/DVr1oz6w1i+fPn4LgYAAAAAAHiAQw45ZNTtixcvrvGW8ZB/zFeSR/fXPxm5o7W2sap+luRxSR6Z5N+3UtaT+uuFSX6c5IDhO6vq40n+e2tt3Ta1GAAAAAAAmDbClGRxf71mjP1D2/cYR1lL+uu/TvLVJC9KL6Q5NMk/JnlxkrVJzppIA8dKzWA6DI2M0g/Z3umr7Cj0VXYk+is7Cn2VHYn+yo5CX2VHor8yHWbFnClVdUtVtQksF01RU4a+n3ckOaW1dkNr7e7W2nVJXpBekPI7VfXwKaofAAAAAAAYsNkyMuXmJOsncPx/Dvt6aOTJ4tEOHLZ99TjKHTrmC621O4fvaK39V1Vdl+RZSY5Kcts42woAAAAAAMygWRGmtNaetQ2n35heuPFbSa4fvqM/Af0jkmxM8tNxlpWMHbz8ur/eZeLNBAAAAAAAZsKseMzXNrqqv37eKPuent5k8l9rrd07jrKu7K8PHWP/4/rrn42/eQAAAAAAwEwSpiTLkvwqycuq6qihjVW1IMk7+i/fO/yEqlpYVY+pqgNGlHVNku8keVpVnTrinLOT/LckNyX51mAvAQAAAAAAmCqz4jFf26K1dmc/6FiW5Oqquji9CeRfkOTR/e2XjDjtyUm+mORLSZ4xrKxWVa/sb/94VX06yU/SG5FyUpK7k7yytbZpSi8KAAAAAAAYGCNTkrTWPpnk+CRfTvLiJK9LsiHJG5K8rLXWJlDW95I8Mck/J3lSktcneUKSjyQ5srX2tcG2HgAAAAAAmEoP+ZEpQ1prX01y8jiPvTpJdez/WZKzBtIwAAAAAABgRhmZAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYAgAAAAAA0EGYA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<p>As you can see from the above plot, the stock with the least risk is SKM at around 0.20. But with portfolio optimisation, we can achieve even lower risk at 0.14, and still with a higher return than SKM.
Best risk/return allocation are in FAST in our case and its matched with portfolio optimised for minimum volatility for maximum return.</p>
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ntUh7bi7t9iVpVlP5uWP+cZOkNQref3ob7+1TFzXvNyXlGqUY37gqzxckfbXN37BLWrXLbf7GXB6/a5F261zaE7pY11q591/aIu3YXNqbS9Jk38/z6fmr0+dZVdLIgfz+ebT3oGUrXrHc/TxJx6en26hpbJV0F+90xYDjLygO8PMqstxBMZHJHEn7KS52tlAEQZ5RnKh9z8w+XvRmi7H4rlJ0J1qouGjcTXFitKliDJe7FGMtz7CYzboon50UQamVFAfQ0xV3vDZKZdpNMX7Wo7m3/VfRxSTfIukb6bX8o89kZHWVWbHd9k3/P6zoHpzl8RVFi6kTJdXRQm8JRWD0dMWJ2Zap/NsqZu7OxvXaRY3xjqqsn/J6TDFpz6aK7fwVxYWrJB1sudaQJZZRfG+rKU4At1K0kvuE4mJGihaXRd2O6zZSMfD+NxTjT26s+Fy7KcZnXKSYPOO3ZrbiIJRHipP4HRXjF01WzMT5Hkk/bUr3v4ruPiMU42/up/h9b6i4SD03pdtO0tlmNjL33kMU3+e16fn96v8bWF8RGOqlXRT1xm2KfXQTxSynX1LceGj2KUVw9krFeJ8bKYIbxytOskZJmmlm43pc7nZtmfv/1mGQT13Wy/3fqjz55es2Lbs+/X2fmS3btGy3pjSyGFMvG5rhQO8/9EDd9lJjcotTc69nQwkspZjIq12/UdTtJyh+l+9QTChzTVq+nuIGSiuHp7JdkN6/oaJez1oLjpF0Wmpt1ktLSLpP8fv7hOK3u6GiLj1MMfahSfqOme1WlslAuPtfFfWHJH08tcwplFqLZV0Qz3T355uWf1xxM3SUItgxXVG/bKyoe7Jj5s6KOrXbiU8Gctw5QVE3n5d7rajuPleDY6+0vqzl4rUl5cm6+f4k/R2t2GeqZGOAPqemrumDzWIyv7XT0znu/txQlqeHNlXcDLhXcT72qEXPkZ+ZWXP93Syrq/u04DOzsYqgUz6NLHqifF5xnnpgDWVvdovifF2SNrJoMV5HPOCzivPeyxT1/zsVx+j8vn1qRQvRJRStpL8vaVfFNcBGipuzx6kxEeMpZrZxG+U5VnHud0HK753p+c9L0p+smPjtLEUDig0VwxxlrQdfJ+mS1PKwHzObmvJYVjGp3FclvS/ls72ipfJCRaDrEothf3otv2/eXZJmpKJxwizFtdd2ijK/W9FT4E8p3fqSzik4fn4pLcvGFL5bxXVdp63wT1ajLrxH0mcU58KbK8ZOfTKVfbqZfb7DvDtV17ldO57O/V+4r5Usf2OLc5tRZna/4hr7NkkPSHrMojfpdl2UE+0a6mgvDx6dPtS3NcLfFHcjWz363Y1LeS2lxp3MhZIm5pYdn1vPwW2UxRUHkzEF6d6mRsvHuZKWK0hzUVr+H5Xc9VQcxP+S0j3WnI8iwDovLX9W0rYV23FpSSs1vTYp91mmtPFd1FHmCYpgtisOqK8tyGNNxUXJS9t6APvPqkXbP7d8hBqtrl6UtFZJuvz3/ncVtEZRnKxkac4pyWd2Ls08SW8tSLOS4gDp6W+/u5Gqt2XrOi3y2EaNlqOHlqSZllvP+C6/qylN2/l7LdLvlku7X0W6A3PpPl7xncxpo4xtf85cupktvhdPZahqWT2pKf3JkkYUpPtWLs0BbeQ1u5vvqoPv9HVq1IXPSlqly3xGKgIYWblLW+4OoKwv/TbbTP+rXHne2SLtRrm0vyzYFxakZVcqbjK8WxHMzN7zkVz6I9JrF/byu0vrGqFGa74rC5bfmZZd2yKfmbnPslAxEVhzmqUVs+962h4rFaRpriOmlawv3+J1p4Ll+d/AlDb3izkly9dSRYsRxQXSTSmPu0p+t+NbfaY2vquv5/KYVJEu38p6i6ZlY9U4p3i6aL9O39PlVduvne2reo47L+1XbWyflts4t3xmyfLstzC7m+W5dCMUQQqXdFNFujVy26CsTIPWslV9eyR9p8P3Lk4tW6seCyV9sSKPT+bSnqhohLGTGj3CnlVqcZf2g2y7FB6va9oeP276DP9RBAM/oQgQtdXiTX1bKHrKt6iX4JG5NJ8uyavV7//1kh5Kefy+JM3UpvIc1iLP6U3py671/l8uzYyS/Str+f8zSUuW5LOFoueHSzq+hn25tGVr2seydDdUpFta0utarHP/XF79WminNNl++68O95t+LVsV5zwvlV2pR1fBdng0pZmv8uv8Olq25q8ZWvUstVQnuKR/d7EuU6Nn6nxJ4yrSTmnaf1cvSNNuy+PTVNCrhcfAH0NeAB48On2o/S7v+ce5Ffm9UY1uH3crWr/khxi4RAUnDyVl2ahiPV/LpZvatGzj3LIdW3z+t+TS7tW07LDcss93sW0n5d4/pUXausqc73K5S0Ue+UCa93gfG6dGAPigkjT57/1tFXn9NaV5tGT57Fw+B1bkk++etX7B8pntbBvVcBGf8jkn5VHYfVv1B1vvkLREi/S3prRntpF31m35sorvZE4b+bT9OXPpZrb4XkqD/Ln0+d/qAyo5SVLMZJwF7s5uI6/Z3e4TbWyrkYr6NFvX4QPIKx9E7vcd1lTel36bbabPD3PRqrvhm3Np+3VLU7QSKjue/bIpn/mK3hOV+19N22SbXDn2LVj+zdzyfjeOculm5tIdW5Fuv1y6fgH1pjriepUfq/Pb++iC5fnfwJQ294s5A9iO+Qvhtxcsz9cH07pcxxpqdA89uSLdbSnNv5u3n6KXQFaO0u6a6nuDoN+FfSfbt8VnanXceWm/aiOvlts4t3xmyfI5qqg3Wy1vSpsPjk8sSTMtl2azkjSDEmxVBEyy/eshFQREWrx/cQi2XqdGT6OVFa0vV1AEzU5V3+7XhUEYRfDkvFy6/GOR+g5t8Zn0+t9UcBOmxu2xtKLFZ9kx5ilFY4p9JC3bYh/I3nOPpFEl6VZU4ybBaQMo91dSHi9IelXB8nyw9R+ttqH6BluvVfnxY0k1hvh6qnndatzMu0slgdZc2qwhz9Ot0raxL89qWra0ohXmEYqW764I7m5Vwz6TnV+fUbK8zmBrfviHDSry2TuXbnpJmjqCrfkbklPaSJ/doHyky/WdlFvfj0vSjFHj5nb2WLcg3c8UPf4+oGi4NErRCGoDRW+g/BB4tQ9bwoNhBAB5zOj3mfR0vKLL4cnp+cOSPuGpxmrhFq+elOR/FZWZFF0b87KBt+ep74DXReW9RdE6VoruiXk7pb9Zt/teqqvMWfeFhyX9tiKbsxTduGqVBsRf3czebGZvSYP4v0aN8raanOsWdy+cpCnJ9okVrfWkUlVdAvP7VulEL3VLEwysYmZvzLZP2kbZMBTrmdmSg1CU0939hYpyTlAEVKRGt+Eql6W/mzQNJTAcXOklkyuVOMubuv5mPLqUZ8NQFO437j7b3S09JnVW1I4co8ZM9X9VXBB0zMw+qMbszI+rdbfbwbJ07v+i4R7y5pe8T5Lk7scrWnfMVpwMP6/oOfE5xVARmax79+HuPkeSzGycmR2TJpSYnyYGOTl1+R2obCiZ+YrxBpv9Qo3j3F4Fy4v8X8WyTuq908qO1e7+TzW65w1a/SlJZra8mb3eYtK+rP7M12W1TgCZcff/qDER24fNrN9+ZjFxZzYZxy8Ktl92rrJIcQFYtq45im6okrSBma3cZbGzcg2X485gOlmN7t37NC9MXb2zoZ5udfcrijJx901y9fnVRWkGKh1vT1cEEl1xnlz7+dkw8E53P9jd/+juD7v7C+7+hLtf7u57KBoBLEppv5eG1uoj/aY+pOhu/Q9F3fmkpEslbePuMyXJzFZTzC7+ouJG1qL0+voWE18+ajEp3b/M7Jtp+JiueAz38L5Urj/lPkPmVYrf/k8l3WVmu7SR7a+9YNb0tL65anRjb6v+TcextZvqzWfS4pGSWk0y+MtsG7bp5xXHj4VqDJnzKsWwElk5RyiGfpLiXGyhqs1Of7OA10Bsl5+EStFK+hZFcHC04pxhe3f/Y1UmeWY20sxeY2Zvaqp7syFPenK8yq1/KcXNDEn6q7tXDT9wmuL8SOp/XS1JcvdVc/Xhg10Wq5NzO6lxftfvmNum76gx4ehUM/ulmb3dzEal84mdFL2e1m4qzzIFee3j7nu7+2/c/R53X+Duz7j7je7+LUXDqey4+qE2f+voQK/HrQJ67bI6ggPufoqZbSPpY4qxV6U4gdyzg8r5mqqF7v6wxezFr1f/k4Rs/KExkhZ1MOTZatk/aayWLN+/eu/HzqqjzEtJelN6em3ViZG7LzSzv6vvOI1dSWMuHagY1+/NihO3Miu1yO5fLZY/lvt/eTXGcW32qLs/WrKsKJ+eMrMPK8aG20xxcllmpKKr6cM9LlKr8Z7yY3id08H+OErRkvmRbgrVI52ObdXuPtjz/aaMmR2sGMNZitZeH2rjoqQon0mKE2xTnNB+OAWVhoN8wHtUi7T5mX0L62p3v1AVN7LM7JOKi5JbFIFspXF5r1IE0BYoWi2upQh8bm9mm3UYyM+vbwVF6whJOq8ouOLuc8zsMkVLxo+b2Vfa+J6r9t9O6r1Wv4PHFXXZYNSfb5R0kCJg3irI3eoYMxCnKo6Zyym+u+YbUXs0pW22fvp7u7s/VrA870rFWKtSnItc2llRh+VxZ9C4+4Nmdp7ivGSymX3e3fPj922nxr5UGvjuNTNbQ9HqMbt5/CV3v3ioytNLrRpauPsZZralYs6BZRS/p+kF6V5QjDtdNfb0cYptepy7Xy9JZra54ibGMorGBncqWjYeLmlLM9u26iZ0i7K7oqX4OWn82E0V45q+Q9K7FOdFkrRKSrOru59dkWU75yFrq6L+tZix/nOKHhStxjRtVW92eh5VeQ2nuEGceaui9aUkvUFRF0nSV8ysz/wfLazWOknX5kv6mbv/qVXCdP24t+LG9YaKQG2ZXh6vpDh3yc6PrqpK6O7Pm9n1imGW1jcza7NxVKc6ObeTGuXv6jo8nUd9UDFZ6hjFTZ2i8d1/rQja75ieP9WcoI067J9m9jnFMFhStA4frLHNXxFo2Qo07K++A1P/2N1nlSUu8FAHaZond+i2FUj+LtaKavym7y9IW7c6yjxWjYlWOtl+XTOztyom9JimGN6gVavGVncmn2mxPB9ArlpXXfkMSLpzeo5iQpLtVH3Bm+n27m0nHm+xfCAtqYruBg+lVp+1Wbv7zpC04DWzAyR9Nz29T9Gl7b8VbynLZ1NJv1NcCCxQBGzbbrExCPInuq1+N/nl/U6QWzGzlRQTibiiFVQW0JyuuFi5TdLa7v5WSa9VTDy0qmIioW7trsZFWFFgLpNNlLWSGsG3Uu5etf92Uu8Ni9+BmX1MEQCfqtaBVqm39edZamyXfGBVqWXo5PT0Sne/q+D92blKOzed82k6mjhxGB93BlvWI+lV6n9xnbV2na/q31/PpJabf1AMUSFJ33L3Y4aiLMPIz3P/b1GaqoKZba+YxOk+xVAsSj1uTlGcn/xK0sru/hZFMGyu4ibKZ7svdoO7P+7uF7j7Ye6+s+J8ahc1WqOapB8VtY7PGVD9a2bfUtyw2V2tA61S699/p+dRra4v8svz9dtQnnteob6TUG2lGGrhPkXAb4aZfbsqg3SD9kpFL5nNVB1olXpf7+a3bSfHnSXUuxupbZ/bWbT0yL7Xjs/tMu7+B0VQ/yfqf1PxVkmfVky6l59EtdUN0TJnq1HWzasSonO0bAUaPq2+lei7zWzpQWghKjV+i/9VY3bSdrQ6uemlxa7M6eLyTEXQQYoT2V8pgq8PS5qf3QU0s/8oLpS7nVl5cfU1NVqv3aQYU/dqxff8jLu/KElmdrjSRYEGZxu92GJ5/ni2l2L8rXYNxs2JTrT6rIsNixl6j0tPH1AEWu+ueEtZPhtL+r2ijn5BMc7XBbUVtB75Frarq3ofzAfhummZe4ziouSn7n6lFAErNYYY+Lq73ydJ7j7PzA5M5dnOzF7bTbBbfYcFOL/N1uOfVLSeekUws3UU3cGXVBzrjlG0TLtT0jx3n5/SraUY30/qYf3p7k+b2W8U+8XWZrZqrrfOjmpc2J5SmMHgGa7HncH2B8XYkOsoWvieJElmtoqi27cUY293e1HdtVSGPyha8kkxIVZXQ8G8zNye+3/V0lQlzGwZNW6CHeDuWdA2TNSaAAAgAElEQVRjS0VPuOclfS67oebuN5rZsZK+rahff9Btwcuk39t5ZnaLYoKiVylauG6haNVcK4vZ0A9LTx9UTEg1W9EL5qlseAIz20Ex1qzU+vc/WOdR+XPPY9UYhq4d9w5w3U97DNOW9ycz+7miVegbJH3dzP6UgndFfqSYsFOKISV+rJj09wFJz+WGs/i14obAy7HebaX53K7KamrsEwPqdZV6be0nab9U/45R9IJ8qf5PvWikGB+2qx56qefo3Yrg7jJmtrzHEGSoAcFWQJKZvVMxW6YU46Qsrxhk/FhF65R2rNJBmrlNrz+i6E4/RtI/uuwGMVdx53iEYszRXqujzI8rWmaZOtt+3dpSjfHpvuvuX69IO7Zi2XD00omlmY2oGJJh2ZLXM/umv3cpJtcou9kw3LZP/iTjmYIT0F7Jn9CX9hYxs1bb/WXHzD6lxkXkw4pA6x1d5LOh4gJvecX23t3df1NbQevzj9z/60qqKuO6uf9v7WQlZraVorvfw5IOzi16kxqtTvqM5+ju15nZ84pWK29XY/y1dtf5FkUX006918xWc/cHunjvYGrrd5xU/Zb3UqOb4QcrulcPZv15qiLYOlIxVFLWlTkb6/h5RXfEInMV5xPtBJHyaZrPcVpZXI87tXJ3N7OfSjpK0sZmtr6736yYCC4bo/ang10uM3u1pD8qurBL0vfd/RuDXY5hKt9Ss5su/dMU80X8rum4lo2N+a805mleVr+/xcxGlY2VOlDufpeZXaQY21WK8+fag61q/P4XStrc3e8sSdfL3/8qqg6O5a8/8t9H/tzTB/Hcs5S7P2pmn1C0WDVJx5nZBtlNq0xq1bprevpHSVtXXMsNVt2b37adHHdeUGOc07o1n9tV6frcroq7P6Sm1tdm9lo1rvcHOj73QOsxlGAYAbzimdlyitaNSyrGV9lMjbF49k3jprRj46qFFhNGjE9Pb2pafF36+yrFeEkdS+M2ZWMUTWzR3ac0mw7S1lHm+YrurpL0zjTQfKHUKnWgA7Ovn/v/9Ip1vUntdWMcTvLdVapOit5ctsDMVlRjDKnftmjV3U3gpZeuy/3fVVe+nE5+BwPe7i9HZjZFERQwxcXIVu7eaky3onzeJukSxQzQLyomYjmzxqLW6fLc/5NapM2PPf3ndleQxrn+cXr6BXfPd5XMT8BXNDb0vIJ07do79//n1RhDrOyRBYFHqqn7+jDV1u84HaPeWLZcjWPMYy3GsRzM+vMPagTX95CkNEZj1lLyd0Xj7ybZucob04V5lU1z/zef45Sq+bjTi/H6BqKb8sxUY4KVbOiA7Pd3u7tf1u8dPZSGLPmDGkGEY9z9q4NZhmEuP8lRpzex1leM7fyM+g8JkNXTRXV59ns1xXjMvXRf7v9e/b6yevPaikCr1Nt6s/IaTtLE3P/5+u12NYagG+i5Z208JsfLbqKtp+LjcH7OijPKAq1pSItW11917Ru3q1H/bVKVMJ0PZdefN/VovNasTFmPkM2senLG/Lnd5aWp6vGR3P+/Kk3VQmpdn/VYeMLdnx1QqdAHwVYgLlyzSuagdFdyDzXuIP0sTQjQylvMbKOK5Xur0f2i+SIs383yy22sq8x56e/yimEROpW/yFmqNFWoq8zZXfKVJe1Uke7DioDLQORb81eNlbT/ANczFPKT3lTthx+vWNbW9jGzd6jFSdAQuFGNbbCHDWw27Ox30Oo3INWz3V9WzOzjkv5XUd89Kuk97v6P6ncV5rO+YpKdsYpW+3u6e9cnlL2Whke4Pj3dKnUV7ye9Pik9va7DYRW+rgj2XerupzUty1+U9+nqZjFz9asL0rWULiyy/fdOdz/O3U+veiha5mWtWad0sr4hcrcaF4tVv+OdVD0uXFaHji67eZhe37doWS+kXg7/l56+1cw2UIzVmrXArRr/MztXGaG+Afc+zGxNNYYTusHdO5m4qs7jzkvnMOlCfKh1ciyRFK3SFGPoSTHJ3HsVwwpIgzwxVgqwX6JGMOw4d//iYJZhMZAPkpZ11e4nje34U8X+P837T/SY1dNF3ZazaxJXh+NCWpvjv+TkA5xF4zrXIasDqn7/y6u351F7lW2bdAzMgpVPK1qMSoou2Iqx5KVoNDKph2Xs1GFqjJX7jTQRVl6710QfUetxuDuu64qkRjjZDaVN0jwbZXZX42ZDzybpy00mJ8X5aGEjrLSf5HuM9GyoKzMbIym76XW/GseMbuyjxvfWdh2G9hBsxSuame2p6FYnxThYJ0ovNdffU3EiM1bSaenOXisnpQqweT1vU1wkS9F1vs9FsrtfrujCIcUstJXds9JkEnunMVzyjlfjjvd3zWybijyWTi0W8vJjV1a13qmzzD9RowvncWkChub3rqEYw2mg8mNrTSkp5wdU06QDg2x27v8vFl3op9aGO1fk8Yga+8/7i1oype/v/5pfH2rpZGhaerq8YryxykkWzGzjNAZYs+x3sHLR77nJlYqub5L0uRTUal7PNpI+0yKfIWNmk8zM02P2APOarGiZNULRHWzr1A2203zWVZz0rai4WNirILjYTj7jc59tTqfv70I2E/UIST9pbgGRnp+oxvlXv5mry6QW9wcrTuL3K0hym6SsRcInmpZ9LLfOv7e7zuT9asxAXNbdvI8U4DsrPZ1gMbnZsOXu8xRjE0rSzrlx0F5iZqtL+mGLrLJjzDLq2+ok7ygNvJdGp/IB1T3UCBw8rBhTtszP1eia+c0U8Owj1XmnqNHNvdMxJOs87rR9DjNIsvK8ocMgVzZR1lg1xoBcoDbG1jWzq3N1Xtc3RdOx72JJb0svHe/un+82vzqY2fTcZzu49TsGtK73pxZfZcvNYlKnXdJLj0r6RQermKq4eXCjin8z2Y27tQrqz6x+v6WLIQTeYWazzOzdrfZJM/usorefFJPvtJzZvktZvbmexbBuzeUYpajDmq9Z6rShGsGrZkeq0Shnprs/3bT822qcB/6yRYAwOy+Z0m1B2+Xu/1TjmL2W+rduzQ/rtHtRi00zW0+tj3tSo657bdXvpk3H5f6fmQLtzeV6o6TvpacL1Ojx05zuwVyd0fGYyjnHqPEdH1XSoOPbatwc+VFu/OV8eUbnyvN82coshggoW7aCpN+qMbTFfkX1gJm9q+iauinNdup7HnpsVXp0jjFbsbhb1mIsuXbcl+8qZzGRxfHp6X8UkxG8xN0vMrOjJX1J0rskfUvSoRX5/03RIuZ6M/u+4kRpKUlbpzyycd4OKKqAFXds/6qoqI8ws10UQYsbFHdSl1NcPGyqOLkbp2jt8NIYLu4+NwWQf6MYu2+WmZ2huCM3R9HSbLyiVdVHFHezzs29/74UkBgvaW8z+4eie3bWpePZprvvdZT5X2Y2XdIhirv115vZ9xSDu0ux7b+qCKDdoMaJfzcuUnQFWVUxRMRYxcnx/YpujLsqTmLvVFzktDMj6rDg7jeZ2eWKbkxbS7rAzGYoWpe9VtGS6WOKsb42K8ljkZmdKukAxThAV6V9+RbF8WJzRZe3lRRBxmEVQHH3X5jZForf8iaS/mVmP1MEoh9UtOR6jeKkeidFa53vSLqwKas/K1pxjZB0spn9UBGUyFq+/SfrZpPGxvqV4iR2XUmz0za7W7H/7Kz4nf1VJdv95cLMdlIEREYqTkoPkvRiizr68ebJmixafv5Bjd/fsYp6oSqfBe5+e8XyVmV/g6KuyVs1t3xK07IH3b1fkMrdzzSzCyXtIGkbSZeb2TGK/eH1kr6gRuu8C9z9rOY8Kpyo2Ie/VdTV0t0XmNlpiv3t62a2QHFD7O2Svp+SzWre3m34ZO7/toKtubSfS//vpVxroGFqhiKwNVoxychhiknFllHUqwcp6oDbVR7IO1XxmU1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\n", 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\n", 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"text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 688}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "14 out of 20 tickers were removed\n{'PRSP': 44, 'SKM': 126, 'EIX': 10, 'FAST': 76, 'DLR': 17, 'CBPO': 8}\nFunds remaining: $13.72\n"}]}}, "0a0db4469e534e3d9ef6a9311d1e08f6": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntTextModel", "state": {"description": "Total value:", "layout": "IPY_MODEL_89dcf071664d41d4a5c65f53d4e1b0f0", "step": 1, "style": "IPY_MODEL_8bba9628f9ce4c44991f9ebbb8d782c2", "value": 10000}}, "0a2cdead17704e339c7c0979ca0d64b3": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {"height": "350px"}}, "0a32d752e6014f01970daa7d00516cea": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", 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uW7SxlPKmJOcmGZXkiSQPd9c9r5Syf4PrAwAAAAD0lCYD2P/TfZ+yRPupSUqSbybZrtb6m0k+2237vw2uDwAAAADQU5oMYDdN8mKt9akl2sclqUn+X611frftY913O2ABAAAAgBGryQC2P8kLizaUUrZIsl2S/6m13rmgvdb6dJLnkmzW4PoAAAAAAD2lyQB2VpK+Usp6i7S9uft+8wDja5I5Da4PAAAAANBTmgxg7+2+/1GSlFJKOue/1iQ3LDqwlLJhkg2S/KLB9QEAAAAAekqTAexl6TxY64JSyqQktyc5MJ1jCb62xNiDuu9TG1wfAAAAAKCnNBnATkxyeZLRSd6WZM8kLyb581rrM0uMfXf3/XsNrg8AAAAA0FNGNzVRrbUmOaGUckmSfdI5E/Z7tdafLzqulLJmkoeTXJjkqqbWBwAAAADoNY0FsKWUDbpf3lpr/c+ljau1zk1yRlPrAgAAAAD0qiaPIJiR5NkkWzY4JwAAAADAKquxHbBJfpXkpVrrYw3OCQAAAACwympyB+xDSdYtpTQZ6gIAAAAArLKaDGCvSLJmkt9vcE4AAAAAgFVWkwHs+Ul+lOTzpZRDG5wXAAAAAGCV1ORxAR9Mcn2S30oypZRyb5LbkjyTZN7SLqq1frTBGgAAAAAAekaTAezZSWqS0v3+jUl2W8b40h0vgAUAAAAARqQmA9jL0glUAQAAAABIgwFsrfU9Tc0FAAAAADASNPkQLgAAAAAAFtHkEQQMg/7+vuEuAYbRbw93Aa+KGTNmDncJAAAAwCA1vgO2lPLaUsqEUsrUUsqvSikvLdHfX0o5s5Tyd6WUNZteHwAAAACgVzS6A7aUcnQ6D+NaN0npNi/2YK5a64xSypuTHJjkp0n+vckaAAAAAAB6RWM7YEspr0vylSTrJflCkoOSTF/K8C+mE9Ae3tT6AAAAAAC9pskdsGckWSfJP9RaP5AkpZR5Sxl7Xfd97wbXBwAAAADoKU2eAXtoOscNfHJ5A2utTyX5dZJtGlwfAAAAAKCnNBnAbp7kuW64uiLmJFmrwfUBAAAAAHpKkwHsr5OsV0oZtbyBpZQxSfqTPNvg+gAAAAAAPaXJAPb+7nx7rsDYd3bH3tng+gAAAAAAPaXJAPaKJCXJOaWUpc5bStk1ycfTOS/2Kw2uDwAAAADQU5oMYD+f5N4kb0nyvVLK0UlGJ53QtZRyeCnlc0l+kOQ1SW5J8vUG1wcAAAAA6Cmjm5qo1jq3lDIuyVVJDk5y0CLd9yzydUknhB1fa61NrQ8AAAAA0Gua3AGbWusvk+yX5NQktyaZm07gWpLMT3J7ktOSHFRrnd7k2gAAAAAAvaaxHbAL1FpfSvJPSf6plDIqneMG1kjyP90+AAAAAIDVQmM7YAd68FatdV6t9Zla61MDha+llK2bWh8AAAAAoNc0eQTBpSszuJSyXZLvN7g+AEu46aabcsIJJ2SnnXbKpptumte97nUZP358pkyZskLX77rrrunv7x/wtdNOO7VcPQAAAKz6mjyC4N2llJm11r9Y3sBSym8muSHJVg2uD8AizjzzzEyYMCFbbbVV3va2t2WjjTbK9OnTc8899+Tmm2/OYYcdtkLzbLDBBjnttNNe0b7++us3XTIAAACMOE0GsM8k+b+llFm11o8sbVApZeck30uyZZKbG1y/FaWUugLDfqfWemN3/HuS/EuSibXW93Tb1kgncD4oybtqrZcPsM72SX6czoPLdqu1Pt5E/cDqaeLEiZkwYUKOP/74XHjhhVlrrbUW6587d+4Kz9XX15cPfehDTZcIAAAAq4UmA9jDktyY5IOllBm11vOXHFBK2SXJdUk26449osH12/b3y+h7eFkX1lrnl1JOSidg/Vwp5fu11icW9HcfVvavSdZPcpzwFRiKOXPm5JxzzsnWW289YPiaJGuuueYwVAYAAACrn8YC2Frrj0spb09ybZKPd48j+MKC/lLKG7t9G6cTwh5Va32hqfXbVms9e4jXP1xKeX86u2MvLaUcVmtdsLv2Q0n2TfLVWuvXh1YpsLq74YYbMn369Jx22mlZY401cs0112Tq1KlZe+21s+eee2bvvfdeqflefPHFfP3rX8/jjz+eddddN7vsskv233//jBo1qqVPAAAAACNHkztgU2u9tZRydJKrklxUSnmu1np5KWWvJN9NsmGSyUnG11rnNLn2qqDWemkp5cgkRyd5f5LPlFJ+O8mZSR5L8mfDWR8wMtx1111JknXWWScHHXRQfvrTny7Wv99+++Wyyy7LxhtvvELzPfXUU/nTP/3Txdq23XbbfO5zn8sBBxzQTNEAAAAwQq3R9IS11ilJ3pWkprPT8yNJpqQTvl6V5OjVMXxdxKlJnkry/7rh67+mE4SfVGudMayVASPC9OnTkyQTJkxIkkyePDmPP/54brnllrz5zW/OrbfempNOOmmF5jrhhBNy5ZVX5sEHH8yTTz6ZW2+9NSeffHIeffTRvOMd78h9993X2ucAAACAkaAs/C34hicu5eQk/7Tg2yTfTOd805daWbAlizyEa2lnwM6utX58kfHvyRIP4RpgzsOTXJ1kTpK1k3y61vqBZdUxc+bMAf+i+vv7lnUZMALcccePVmr8eeedl29961tZa6218o1vfCNbbrnly32zZ8/OMccck6effjpf+tKXsttuuw2qps985jP5yle+kkMOOSTnn/+KI78BAABgRNhxxx0HbO/r6ysrOkfjO2AXqLX+S5LT0wlfL09y7KoWvi7hrKW8PriyE9Vav5PkpnTC10eTfLi5MoHV3ZgxY5IkO++882Lha9I5lmDfffdNktx///2DXuOYY45Jktx9992DngMAAABWB4M6A7aUMm8lhtckxyU5rpRXBMO11troObRtqbWucKq9PKWUNyc5qPvt1kn+T5LvNzU/MLIs7adtS7PXXnvlsssuy2abbTbgtWPHjk3SCWpXdu4FNt100ySdHbWDnYPmTJs2LcnK/1uBkcj9SsjXpgAAIABJREFUAAu5H2Ah9wN0uBeGx2B3wJaGXq3twO1VpZT+JJcmeSnJaekE1BNLKWOGsy5g5Dj44INTSskDDzyQ+fPnv6J/6tSpSToP0hqsH/2ocyzCdtttN+g5AAAAYHUw2AD0tQ2+VjcXJdkmyVm11kuSfCLJdkn+YTiLAkaOsWPHZty4cXn88cdz8cUXL9Z3/fXX53vf+176+vpy6KGHJknmzp2bBx98MA899NBiY3/2s5/l17/+9Svmf+SRR3LGGWckSY499tiWPgUAAACMDIP69f9a6yNNF7I6KKW8M8nxSW5JJ3hNkrOT/F6SU0op3+6eDwswJJ/61Kdy33335SMf+UimTJmS3XbbLY888kgmTZqUUaNGZcKECenr6zzE78knn8zee++dbbbZJvfdd9/Lc3zzm9/M5z73uey3337ZZpttsv766+ehhx7KlClTMnv27Bx22GH5i7/4i+H6iAAAALBKWCXOXx0JSilbJbk4ya+SnFhrnZ8ktda5pZQ/TPKjJF8spexaa50+jKUCI8BWW22VG2+8MZ/4xCcyefLk3HrrrRkzZkzGjRuX008/PXvuuedy5zjwwAPz85//PPfee29+8IMf5Pnnn09fX1/22WefvPOd78xxxx2XAc72BgAAABbRWABbSvmNJHsleaHWesdyxu6V5DeS3F5rnd1UDW0qpZy9jO5v11rvWca1JZ1zXzdM8se11v9etL/W+pNSyt8l+WQ6Ie07hlwwsNrbeOONc/755+f8889f5rhtt902M2bMeEX7AQcckAMOOKCt8gAAAGC10OQO2HcnuSTJZ5IsM4BN8idJTum+Lm2whjadtYy+h5MsNYBN8r4kb0lyZa31S0sZc0GSI5L8QSnl3bXWfx1UlQAAAABAz2gygP2D7vuXV2DsF5L8cZJj0+MBbK11pX6/ttZ6aZb4TLXWC5NcuJzr5ic5aCXLAwAAAAB62BoNzrVzkheT/HgFxt7VHfu6BtcHAAAAAOgpTQawmyf5Va21Lm9gd7fnc91rAAAAAABGpCYD2FlJ+rsP41qm7pj+JC80uD4AAAAAQE9pMoC9tzvf+BUYe0ySUUl+0uD6AAAAAAA9pckA9ookJcmnSym7LG1QKeUNST6dpHavAQAAAAAYkUY3ONc/JzktyRuT3FFK+eckk5M82u3fNsnvJXlPknWS3JfkCw2uDwAAAADQUxoLYGutL5VS3p7kO0nelE4Ye9oAQ0uSe5IcWWud29T6AAAAAAC9pskjCFJrfTLJPkn+PMntSealE7iW7te3J/mzJPvUWh9vcm0AAAAAgF7T5BEESZJa64tJLkpyUSlldJLXdLuerbW+1PR6AAAAAAC9qvEAdlHdwPXpNtcAAAAAAOhVjR5BAAAAAADAQoPaAVtKOaj75fO11h8t0bZSaq3fH8x1AAAAAAC9brBHENyYpCb5WZLXL9G2MuoQagAAAAAA6GmDDT8fTSc8fXKANgAAAAAAMsgAtta63Yq00b4ZM2YOdwkwbKZNm5Yk2XHHHYe5EgAAAICBeQgXAAAAAEBLGgtgSykHlVL2WYnxew/2wV0AAAAAAKuCJh+AdWOSXyTZagXHfz3JNg3XAAAAAADQM5o+gqC0PB4AAAAAYJUxnGfAjkny4jCuDwAAAADQqmEJYEspeyd5TZInhmN9AAAAAIBXw6DPXy2lnJTkpCWaX1NKuX5ZlyXpT/L6JDXJ5MGuDwAAAADQ64byAKztkhyyRNtaA7QtzfeTnDmE9QEAAAAAetpQAthvJ3m4+3VJ8s9JZib5y2VcMz/JrCT311p/PoS1AQAAAAB63qAD2Frrj5P8eMH3pZR/TvJCrXViE4UBAAAAAKzqhrIDdjG11mF5oBcAAAAAQK8SmgIAAAAAtEQACwAAAADQEgEsAAAAAEBLBLAAAAAAAC0RwAIAAAAAtEQACwAAAADQEgEsAAAAAEBLBLAAAAAAAC0Z3dbEpZRNk+yRZJNu0zNJ7qq1Pt3WmgAAAAAAvaTxALaUckCSjyU5cCn930/yt7XWW5peGwAAAACglzR6BEEp5b1JbkgnfC1J5id5uvua1207OMmNpZQ/bXJtAAAAAIBe01gAW0rZPclnk4xKckuStyZZv9a6Ra11iyRjkozr9o1K8tnuNQAAAAAAI1KTO2A/0J3viiSH1FqvrbXOWdBZa51Ta52Szg7Yf0snhD29wfUBAAAAAHpKkwHswUlqkr+qtc5f2qBu3192xx7S4PoAAAAAAD2lyQB2kyQzaq2/WN7AWuuTSWZ0rwEAAAAAGJGaDGBnJRlTSllveQO7YzboXgMAAAAAMCI1GcDelc65ru9bgbHv7469s8H1AQAAAAB6SpMB7BeSlCTnlFI+VkrpW3JAKWWLUsqnk3w0nTNgv9Dg+gAAAAAAPWV0UxPVWr9ZSvlykj9M8qEkHyil/DjJE0nWSTI2yY5J1kwnqJ1Ya/1WU+sDAAAAAPSaxgLYrvckmZrkg+mc8br3AGNmJTkvyacaXhsAAAAAoKc0GsDWWmuSj5dS/jHJ7ybZI8km3e5n0jkndkqt9fkm1wUAAAAA6EVN74BNktRaf53k290XAAAAAMBqqcmHcAEAAAAAsAgBLAAAAABASwZ1BEEp5frul4/UWk9eom1l1FrroYOpAQAAAACg1w32DNhDuu8PDNC2Muog1wcAAAAA6HmDDWBP7r7PHKANAAAAAIAMMoCttU5ckTYAAAAAgNWZh3ABAAAAALREAAsAAAAA0JJBHUFQShnbVAG11kebmgsAAAAAoJcM9iFcDzW0fh1CDQAAAAAAPW2w4WdpaP2m5gEAAAAA6DmDCmBrrc6OBQAAAABYDr/+v4rr7+8b7hJeVTNmzBzuEgAAAABghdnJCgAAAADQktZ2wJZSNk2yR5JNuk3PJLmr1vp0W2sCAAAAAPSSxgPYUsoBST6W5MCl9H8/yd/WWm9pem0AAAAAgF7S6BEEpZT3JrkhnfC1JJmf5Onua1637eAkN5ZS/rTJtQEAAAAAek1jAWwpZfckn00yKsktSd6aZP1a6xa11i2SjEkyrts3Kslnu9cAAAAAAIxITe6A/UB3viuSHFJrvbbWOmdBZ611Tq11Sjo7YP8tnRD29AbXBwAAAADoKU0GsAcnqUn+qtY6f2mDun1/2R17SIPrAwAAAAD0lCYD2E2SzKi1/mJ5A2utTyaZ0b0GAAAAAGBEajKAnZVkTCllveUN7I7ZoHsNAAAAAMCI1GQAe1c657q+bwXGvr879s4G1wcAAAAA6ClNBrBfSFKSnFNK+VgppW/JAaWULUopn07y0XTOgP1Cg+sDAAAAAPSU0U1NVGv9Zinly0n+MMmHknyglPLjJE8kWSfJ2CQ7JlkznaB2Yq31W02tDwAAAADQaxoLYLvek2Rqkg+mc8br3gOMmZXkvCSfanhtAAAAAICe0mgAW2utST5eSvnHJL+bZI8km3S7n0nnnNgptdbnm1wXAAAAAKAXDSqALaWcmeRXtdZPD9Rfa/11km93XwAAAAAAq6XBPoTr7CR/vWhDKeW/Syk/GHJF0LArr7wyZ5xxRt72trdlm222SX9/f0499dRBz3fTTTflhBNOyE477ZRNN900r3vd6zJ+/PhMmTKlwaoBAAAAGAkGewRBzSvD2+3SedgW9JTzzz8/P/nJT7L++utnyy23zHPPPTfouc4888xMmDAhW221Vd72trdlo402yvTp03PPPffk5ptvzmGHHdZg5QAAAACs6gYbwD6bZKNSypha6+DTrNVQKaUuZ8jJtdZLX41aVhfnnXdettpqq2y//fa5+eabc8QRRwxqnokTJ2bChAk5/vjjc+GFF2attdZarH/u3LlNlAsAAADACDLYAPYHSX4vyVWllG8k+VW3/TdKKSeuzES11ssGWcOq7u+X0n7Pq1rFauCggw4a8hxz5szJOeeck6233nrA8DVJ1lxzzSGvAwAAAMDIMtgA9qNJfifJwUkWTbc2SPIvKznXahnA1lrPHu4aWHE33HBDpk+fntNOOy1rrLFGrrnmmkydOjVrr7129txzz+y9997DXSIAAAAAPWhQAWyt9Y5SypuSnJpklyS/keSQJHOT3NZYddAj7rrrriTJOuusk4MOOig//elPF+vfb7/9ctlll2XjjTcejvIAAAAA6FGD3QGbWuvPk/zNgu9LKfOTPFtr/Z0mCoOBTJs2bUjXP/HEE0mSWbNmrdRc//Vf/5UkmTBhQl772tfmi1/8Ynbaaac8+eSTufDCC3Prrbfm2GOPzec///kh1cfgDPXfBYwU7gVYyP0AC7kfYCH3A3S4F1bcjjvuOOQ5Bh3ADuDRJE81ON+IVko5e4Dmhz2AqzfNnz8/STJq1KhccMEF2XLLLZMkO+ywQ84///wcc8wxueuuu3Lvvfdmt912G85SAQAAAOghjQWwtdbtmpprNXHWAG03Jbn0Va5jlTLUnzr88pe/TJJssMEGKzXX2LFjkyRvfOMbc/DBB7+i/61vfWu+/OUv5+mnn27kJyOsmAU/sfNnzurOvQALuR9gIfcDLOR+gA73wvBoLIAtpcxL8sta61YrOP6hJNvUWpvchbvKqLWW4a6BFbfDDjskSfr6+gbs7+/vT5LMnj37VasJAAAAgN63RoNzle5rZa+BnnfwwQenlJIHHnjg5eMIFjV16tQkybbbbvtqlwYAAABAD2sygF1ZayeZN4zrwyvMnTs3Dz74YB566KHF2seOHZtx48bl8ccfz8UXX7xY3/XXX5/vfe976evry6GHHvpqlgsAAABAjxuWX/8vpWyeZNMkTw/H+qxevvOd72TSpElJkqef7vyTu/3223PaaaclSTbaaKN87GMfS5I8+eST2XvvvbPNNtvkvvvuW2yeT33qU7nvvvvykY98JFOmTMluu+2WRx55JJMmTcqoUaMyYcKEpR5RAAAAAMDqadABbCnloCSHLNG8finlzGVdlqQ/ybju17cMdn1YUffdd18uv/zyxdoefvjhPPzww0mSbbbZ5uUAdlm22mqr3HjjjfnEJz6RyZMn59Zbb82YMWMybty4nH766dlzzz3bKB8AAACAVViptQ7uwlLOSnJWkgUTlEW+Xual3fdnkxxSa/3JoApYRZVSarLyD+GaOXPmgH+2/f2r147LGTNmDncJ9BBPb4QO9wIs5H6AhdwPsJD7ATrcC83p6+tb4WxvKEcQ3JNk4iLfn5RkdpIrlnHN/CSzktyf5Fu11v8ZwvoAAAAAAD1t0AFsrfXKJFcu+L6UclKSmbXWk5sobKRa2Z2vAAAAAMCqq8mHcB2d5KlSyqha67wG5wUAAAAAWCU1GcB+K50jBl6b5LEG5wUAAAAAWCU1GcD+KslLtVbhKwAAAABAkjUanOuhJOuWUpoMdQEAAAAAVllNBrBXJFkzye83OCcAAAAAwCqryQD2/CQ/SvL5UsqhDc4LAAAAALBKavK4gA8muT7JbyWZUkq5N8ltSZ5JMm9pF9VaP9pgDQAAAAAAPaPJAPbsJDVJ6X7/xiS7LWN86Y4XwAIAAAAAI1KTAexl6QSqAAAAAACkwQC21vqepuYCAAAAABgJmnwIFwAAAAAAixDAAgAAAAC0pMkzYF9WSjkkybFJ9kiySbf5mSR3Jbmi1npjG+sCAAAAAPSSRgPYUsrGSb6S5C0Lmhbpfm2SvZL8aSnl2iTvrrVOb3L91dGMGTOHuwQAAAAAYCkaC2BLKWsluTbJbukEr7cluT7J490hWyd5c5J9k/xukimllH1qrS82VQMAAAAAQC9pcgfsnyd5Y5Jnkxxfa712gDF/V0o5LMnl3bF/luQfGqwBAAAAAKBnNPkQrncmqUlOXUr4miSptU5Jcmo6u2SPa3B9AAAAAICe0mQAu3OS2Um+tQJjv9Ud+7oG1wcAAAAA6ClNBrBrJplba63LG1hrnZ9kbhp+CBgAAAAAQC9pMoB9NMmYUsoeyxtYStkzyZjuNQAAAAAAI1KTAex/pHOu65dKKZssbVApZbMkX0rnvNhJDa4PAAAAANBTmjwC4BNJTkqyW5IHSilfTHJjkieSrJNkbJLfSfKeJOsmeTbJJxtcHwAAAACgpzQWwNZany6l/F6SbyfZPMkZ3deSSpJfJPn9WuvTTa0PAAAAANBrmjyCILXW25O8PslZSe5L55iB0n3VbtuZSXaptd7R5NoAAAAAAL2mySMIkiS11hlJzklyTillzSSv6XY9W2ud2/R6AAAAAAC9qvEAdlHdwPWpNtcAAAAAAOhVQw5gSylrJ/n9JHsm2SDJjCQ/THJ1rfWloc4PAAAAALCqGlIAW0rZL8k30nno1pIeLqX8fq31vqGsAQAAAACwqhr0Q7hKKVsl+U464euCh2w9s6A7yWuT/EcppW+oRQIAAAAArIoGHcAmeX+S/nSOHDgxybq11s2TrJfkfUleSLJlklOGWiQAAAAAwKpoKAHs76az6/V9tdZ/rbW+mCS11tm11s8mOSudnbCHDb1MAAAAAIBVz1AC2O3TCWD/fSn931hkHAAAAADAamcoAeyYJM/UWmcP1FlrfaT75XpDWAMAAAAAYJU1lAA26eyAXZ4yxDUAAAAAAFZJQw1gAQAAAABYitFDvP41pZTrhzCm1loPHWINAAAAAAA9aagB7FpJDhnCmBU5wgAAAAAAYJU0lAB2YmNVAAAAAACMQIMOYGutJzdZCAAAAADASOMhXAAAAAAALRHAAgAAAAC0RAALAAAAANASASwAAAAAQEsEsAAAAAAALRHAAgAAAAC0RAALAAAAANASASwAAAAAQEsEsAAAAAAALRHAAgAAAAC0RAALAAAAANASASwAAAAAQEsEsAAAAAAALRHAAgAAAAC0RAALAAAAANASASwAAAAAQEsEsAAAAAAALRHAAgAAAAC0RAALAAAAANASASwAAAAAQEtKrXW4a2AFzJw5c8C/qP7+vle7FAAAAADIjBkzh7uEYdPX11dWdKwdsAAAAAAALRHAAgAAAAC0RAALAAAAANASASwAAAAAQEsEsAAAAAAALRHAAgAAAAC0RAALAAAAANASASwAAAAAQEsEsAAAAAAALRHAAgAAAAC0RAALAAAAANASASwAAAAAQEsEsAAAAAAALRHAAgAAAAC0RAALAAAAANASASwAAAAA8Ko466yzcuSRR2aXXXbJ5ptvnu222y4HHnhgPv7xj+fZZ58d8Jof/vCHecc73pHtttsum2++efbbb79cdNFFmTdv3kqt3d/fv9TXW97yliY+3oBKrbW1yWnOzJkzB/yL6u/ve7VLAQAAAIDMmDFzpa/ZZJNN8sY3vjE777xzNtlkk/z617/Oj370o9x9993ZYostcu2112brrbd+efykSZNy4oknZp111snRRx+dDTfcMN/97nczbdq0HHXUUZk4ceIKr93f359tttkm73rXu17Rt9VWW+XEE09c4bn6+vrKio5dLQPYUsqoJH+U5N1Jdk0yJsn/JvllktuTXFVrvao79pAkNyS5qdZ6yABzvT3JFensJj6u1nplKWW7JA91h/w6yRa11ucGuLYk+XmS7btNv1NrvXGgmgWwAAAAAPSSwQSws2fPzjrrrPOK9nPOOScXXHBBTjnllFxwwQVJklmzZmWPPfbIrFmzcs0112T33Xf//9u79zg9yvp++J+vhDNCIAoUxCBKsSKeqohGBBHwxFnAahHwQX3QWtFfbfujPK1WBbGWIlq1WqwgKMVWBSSKiqgcpKJWiyhCtATxgMghAQxnr+ePuZcsy26ym2R2k933+/W6X7P3zDUz32vvHYZ89tprHjzGfvvtlyuuuCIf//jH8/KXv3xc5549e3bmzZuX+fPnT7jukSYSwM64KQgG4ev5ST6W5ClJvpjkpCRnJvl1klcl+atxHus1Sc5Jck+SPVtr545ocn+SDZO8coxDvDBd+Hr/xHoBAAAAAGue0cLXJDnggAOSJD/72c8eXHfuuefm5ptvzkEHHfRg+Dp0jOOOOy5J8vGPf7zHaleNWVNdwBR4ZZIXJ/mfJLu11h4S1VfVBkmevbyDVNWxSU5I8oskL26t/WiUZt9LMjfJ69IFviO9Ll14e1GSl0ygDwAAAAAwbVxwwQVJkh133PHBdZdcckmSjDo/67x587LBBhvkiiuuyD333JN11113XOdZvHhxzjjjjNx0003ZeOON87SnPS3PetazVkEPxjYTA9jnDpanjQxfk6S1tiTdlAOjGkwb8P4kb07y43Th6w1jNL8/ySeSHFtVT22t/c+w4zwqyQFJ/jPJzJsHAgAAAIAZ64Mf/GDuvPPO3H777fnBD36Qyy+/PDvuuGPe+ta3PthmwYIFSZInPOEJD9t/1qxZmTt3bq6++uosXLgwO+yww7jOe9VVV+XP//zPH7LuyU9+cj760Y8+JPxdlWZiAHvLYPmHE92xqtZJ8skkr0jyrST7ttZGfzzbUqcm+b/pRru+adj6I5Ksk+Rfk7x2orUAAAAAwFQaCkhXxMknn5xbb10aqz3nOc/J29/+9tx222257bbbkiS33NLFeLfeeuuo51p77bWTdKHqIx6x/JlWX/WqV2WPPfbI3Llzs84662ThwoX55Cc/ma997Wt52ctelk996lPZfPPNH7LP9ttvv8J9HDLj5oBN8rkk9yU5uqrOqKqDqmruOPbbKMn8dOHrF9LN+bq88DWttf9NN8XAn1bV+sM2vTbJgrEeugUAAAAA09WXv/zlfOc738kFF1yQf/iHf8gvf/nLHHbYYfnJT37S2znf+ta35qlPfWpmz56dDTbYIE960pNy4oknZo899siiRYty5pln9nLeGTcCtrX2/ao6LMkpSQ4bvFJVtya5OMm/tda+MMqufzxYXpPkoNbaRB6c9a/pHrh1SJJPVtWuSZ6Y5K9XrBcAAAAAMLVWxejQ7bffPrvsskte/OIX55nPfGZOOOGEXH755UmSOXPm5Oc//3k222yzUc913333JemmEFiZWt785jfnoosuyo9//ONV0qeRZuII2LTWPpPksUlelORdSc5P9704IMl5VXX6YK7X4a5JsjDJDkk+MMr2Zfl8kpvTTUOQJK9PNwr3tBXsAgAAAABMG4997GOzww475Oqrr35w6oGhMPSnP/3pw9rff//9uf766zNr1qxsu+22K3XuOXPmJEmWLFmyUscZy4wMYJOktXZfa+0rrbW/a63tm+RR6aYX+F2Sw5PsP2KXG5M8P8mCJG9I8m9VNa7vX2vt3nRzxz6vqp6T5OAk57XWblo1vQEAAACANduNN96YJFlrrbWSJLvuumuS5MILL3xY28suuyxLlizJzjvvnHXXXXelzvvd7343SVY6yB3LjA1gR2qtPTAYGXvyYNUeo7S5IV0I++MkRyb5VFWNdxqHfx0sP5NkvSQfW6mCAQAAAGAN8tOf/jSLFy9+2Prf//73ede73pXf/va3efazn53Zs2cnSfbff//MmTMnn/vc5/L973//wfZ33313jj/++CTJUUcd9ZBjLVmyJNdee21uuOGGh6y/6qqrHpyyYOT6d73rXUmSQw89dOU6OIYZNwfsONwxWI46xUBr7caq2i3JV5P8SZL1quoVg1GuY2qt/aSqLkmya7qpDL666koGAAAAgNXbV77ylbzzne/MLrvskrlz52azzTbLTTfdlMsuuywLFy7MFltskVNOOeXB9htvvHFOOeWUHHHEEdlnn31y0EEHZdNNN82XvvSlLFiwIPvvv38OOuigh5zje9/7Xvbdd9/Mmzcv8+fPf3D9hz70oVxwwQV5znOek6233jrrrrtuFixYkAsvvDAPPPBAjjjiiBx88MG99HvGBbBV9cp087F+rbX2+xHbtszSeVovHusYrbWbq2qPJBekmzf2nKo6qLV293JO//p0D9+6vrXWVrQPAAAAALCm2X333XPdddfl8ssvz5VXXpnFixdnww03zOMf//i84hWvyNFHH51NN930Ifvss88+mT9/fk466aScd955ueeee7Lddtvl+OOPz9FHH53xPqbpZS97We6444786Ec/yiWXXJK77747m222Wfbcc88cccQReelLX9pHl5MkNdNywKp6f5Jj0s3pemmS6wabHpfkZUnWT3JukgNba62qdk/y9STfbK3tPuJYj0wyP92o1ouS7Nda+11VbTs47mWtteeNo6Yzk/xpkhe01r4xWpvFixeP+kHNnr3J8g4PAAAAAKvcokUPn05gpthkk03Gl/xmBo6ATXJSugdp7ZnkKUlelG5O1luSfCPJp5N8ejwjVFtrd1TVi9MFtnsm+XJV9ReXAwAAAABrlBk3AnZNZQQsAAAAAKsTI2DH5xF9FgIAAAAAMJMJYAEAAAAAeiKABQAAAADoiQAWAAAAAKAnAlgAAAAAgJ4IYAEAAAAAeiKABQAAAADoiQAWAAAAAKAnAlgAAAAAgJ4IYAEAAAAAeiKABQAAAADoiQAWAAAAAKAnAlgAAAAAgJ4IYAEAAAAAejJrqgtg5SxatHiqS4Aps2DBgiTJ9ttvP8WVwNRyLcBSrgdYyvUAS7keoONamBpGwAIAAAAA9EQACwAAAADQEwEsAAAAAEBPBLAAAAAAAD0RwAIAAAAA9EQACwAAAADQEwEsAAAAAEBPBLAAAAAAAD0RwAIAAAAA9EQACwAAAADQEwEsAAAAAEBPBLAAAAAAAD0RwAIAAAAA9EQACwAAAADQEwEsAAAAAEBPBLAAAAAAAD0RwAIAAAAA9EQACwAAAADQEwEsAAAAAEBPBLAAAAAAAD0RwAIAAAAA9EQACwAAAADQEwEsAAAAAEBPBLAAAAAAAD0RwAIAAAAA9EQACwAAAADQEwEsAAAAAEBPBLAAAAAAAD0RwAIAAAAA9EQACwAAAADQEwEsAAAAAEBPBLAAAAAAAD0RwAIAAAAA9EQACwAAAADQEwEsAAAAAEBPBLAAAAAAAD0RwAIAAAC2Am/AAAAgAElEQVQA9EQACwAAAADQEwEsAAAAAEBPBLAAAAAAAD0RwAIAAAAA9EQACwAAAADQEwEsAAAAAEBPBLAAAAAAAD0RwAIAAAAA9GTWVBfAypk9e5OpLmHKLVq0eKpLAAAAAIBRGQELAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE9mTXUBMNnOPffcXHrppbnqqqty1VVX5Y477sihhx6aj33sY+M+xqc+9an82Z/92TLbPOIRj8itt966suUCAAAAsAYTwDLjvO9978tVV12VjTbaKFtttVXuuOOOCR9jp512yl//9V+Puu3yyy/PxRdfnL322mtlSwUAAABgDSeAXQFV1UZZfW+SXyf5ZpITW2tXL+cYX02yZ5JfJNm2tfbAKi+UUZ1wwgnZeuuts9122+XSSy/NvvvuO+FjPOUpT8lTnvKUUbcNBa9HHHHEStUJAAAAwJpPALty/n7Y15sk2TnJ4UleXlXPa639YLSdqmq7JC9M0pI8JslLkpzfc60MPP/5z+/t2D/60Y/yne98J1tttVVe9KIX9XYeAAAAANYMAtiV0Fp7x8h1VfXBJG9K8pYkR46x6+uSVJITk/zfJK+PAHZaOO2005Ikhx12WNZaa62pLQYAAACAKfeIqS5gGvrKYPno0TZW1ax0weztSd6Z5HtJXlpVW09KdfTmrrvuymc+85mstdZaOfzww6e6HAAAAABWAwLYVW/PwfK7Y2zfL8mWSc5urd2V5LQkayX5f/ovjT59/vOfz+LFi7PnnnvmMY95zFSXAwAAAMBqwBQEK6Gq3jHs7cZJnpVkXrrpBP5xjN1eP1h+YrD8dJKTkhxVVce31n7fQ6nT2oIFC1Z431/+8pdJkttvv32ljpMk//Iv/5Ik2XvvvVf6WEyM7zd0XAuwlOsBlnI9wFKuB+i4FsZv++23X+ljCGBXzttHWffjJGe11u4YuaGq5ibZK8k1rbXLk6S1dmtVfSHJy5O8KMmXeqyXnvzsZz/LlVdemc033zzz5s2b6nIAAAAAWE0IYFdCa62Gvq6qDZPsmO7BWp+qqh1ba8eN2OW16aZ9OG3E+tPSBbCviwB2wlbmNxE33nhjkmTjjTdeqeOceuqpSZLXvOY1eeITn7jCx2Fihn5jtyp+GwVrMtcCLOV6gKVcD7CU6wE6roWpYQ7YVaS19rvW2hVJDkryuyR/VVXbDG2vqqF5Xn+f5IwRu1+Q5MYk+1bVlpNUMqvI3XffnbPPPjtrrbVWXv3qV091OQAAAACsRgSwq1hrbVGSa9KNLn7GsE37JNkq3ff8F1XVhl5J7kv3YK5Z8TCu1cp9992Xa6+9Ntddd92Ybc4555wsWrTIw7cAAAAAeBhTEPRj08FyeMD9usHy/CS/GWWftZIcme5hXO9prbX+ypvZzj///MyfPz9JctNNNyVJrrjiirzhDW9IksyZMyfvfve7kyS/+tWvsvPOO2ebbbbJD3/4w1GPd/rppydJjjzyyJ4rBwAAAGBNI4BdxarqgCSPSzeq9VuDddskeXGS25Ic0lq7e4x9n5DkeUn2TPLVSSl4BvrhD3+Ys8466yHrFi5cmIULFyZJttlmmwcD2OW55pprcvnll2frrbfO3nvvvapLBQAAAGANJ4BdCVX1jmFvN0zypCQvGbz/m9ba0EjXo9KNcD1zrPB14NR0AezrI4DtzbHHHptjjz12XG3nzp2bRYsWjbl9hx12WOZ2AAAAAGY2AezKefuwrx9I8tskX0jyz621ryZJVT0iS+d1PXU5x/uPJKck2b+qNm+t3bSK6wUAAAAAJpEAdgW01moCbX+f5LHjbLskyewVrQsAAAAAWL08YvlNAAAAAABYEQJYAAAAAICeCGABAAAAAHoigAUAAAAA6IkAFgAAAACgJwJYAAAAAICeCGABAAAAAHoigAUAAAAA6IkAFgAAAACgJwJYAAAAAICeCGABAAAAAHoigAUAAAAA6IkAFgAAAACgJwJYAAAAAICezJrqAlg5ixYtnuoSAAAAAIAxGAELAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANCTWVNdACtn9uxNproEmELPnOoCYDUxvmth0aLFPdcBAADASEbAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAMKpzzz03f/mXf5mXvOQl2WabbTJ79uy8/vWvX+Y+3/72t3PIIYdk2223zZZbbpnnPve5+fCHP5wHHnhg3Of91a9+lY9+9KM5+OCDs9NOO2XzzTfP4x73uBxwwAE577zzVrZbAAAAk2rWVBcAAKye3ve+9+Wqq67KRhttlK222ip33HHHMtvPnz8/hx9+eNZbb70ceOCB2XTTTXPBBRfkb/7mb/Ltb387p59++rjO+7GPfSzvf//7M3fu3Oy6667ZYostcsMNN+QLX/hCvvGNb+SNb3xjTjjhhFXRRQAAgN5NywC2qtqIVb9PcluSK5Oc2lr79DjaLx60Py3J6a21kW1SVesleVOSQ5I8Mcn6SW5J8qsklyf5j9baN4e1PzLJJ0Yc5t5B+28m+YfW2o/H208A6NMJJ5yQrbfeOtttt10uvfTS7LvvvmO2vf3223PMMcdkrbXWyvnnn5+nP/3pSZLjjjsu++23X84999x89rOfzctf/vLlnvcZz3hGzj///Dzvec97yPprrrkme+21Vz784Q/n0EMPzdOe9rSV6yAAAMAkmJYB7DB/P1iunS4g3T/JC6rqma21/7Oc9k9IcmCS3ZI8M13Q+qCq2ihdaPqMJDcm+exguVGSpyZ5fZLZgzYj/U+ScwZfb5Jk9yRHJDm0qvZorf3XRDsKAKva85///HG3Pffcc3PzzTfnT/7kTx4MX5NkvfXWy3HHHZf9998/H//4x8cVwO63336jrt9hhx1y4IEH5vTTT88ll1wigAUAANYI0zqAba29Y/j7qnphkq8meUtVfaC1tnA57ecluTjJG6vqpNbadcM2vyVd+PqVJPu21u4dse+mSf5ojNJ+MPxcVVXpRsYekeQ9SV4wvh4CwOrhkksuSZLsueeeD9s2b968bLDBBrniiityzz33ZN11113h86y99tpJklmzpvX/wgAAANPIjHoIV2vta0l+kqSSPGsc7S8b1v6PR2x+7mD5kZHh62Df21pr3xpnXS3Jhwdvdx7PPgCwOlmwYEGS5AlPeMLDts2aNStz587N/fffn4ULF67wOW6//facd955qarsscceK3wcAACAyTSjAtiBGiwfNqfrctw34v0tg+Ufrlw5D1rRugBgyt1+++1Jko033njU7UPrFy9evELHb63lzW9+c2666aYcddRR2WGHHVasUAAAgEk2o/5+r6r2TLJDupDzO+No//x0c8fem+SKEZvPTnJYkndV1bZJ5if579bar1egrkryxsHbb090fwAYj6FRqivil7/8ZZIuaB3tOPfd1/2ecuHChXnggQcetv2uu+5Kktxwww3ZdNNNJ3z+k08+Oeecc06e/vSn5zWvec1K9YWZwc8ILOV6gKVcD9BxLYzf9ttvv9LHmNYBbFW9Y/Dl2umC1wPSjTQ9ubV2/XLaDz2Eq5K8bWSw2lo7v6qOSfLOJG8YvFJVNya5KMlHW2sXj1Ha04ada+ghXE9LcleS4ybYTQCYchtuuGGS5M477xx1+9D6Rz7ykRM+9gc+8IF8+tOfztOf/vS8//3vzzrrrLPihQIAAEyyaR3AJnn7YNmSLEpySZKPt9bOXE77IS3JUa21T4zWuLX2gao6Ncle6eaEffpg+aokr6qqd7XW/m6UXZ86eCXd1Aa/TnJGkhNbaz8eV88AYIJW5je3N954Y5JuKoHRjrPTTjvl6quvzr333vuw7ffff39uvPHGzJo1K7vtttuEHsJ17LHH5owzzsiuu+6as88+OxtssMEK94GZYWg0x6oYqQBrOtcDLOV6gI5rYWpM6zlgW2s1eD2itbZZa+0FywhfH2yfZKN0oeoNSf6lqsZ80kdrbUlr7dzW2l+31vZOslmSNyV5IMnfVtXTRtnt9GG1rdNam9taO1z4CsCaatddd02SXHjhhQ/bdtlll2XJkiXZeeedxx2+ttbytre9LR/5yEfyghe8IJ/5zGeErwAAwBppWgewK6q19rvW2oVJ9k2yVpLTq2pc/+prrd3bWvtQkrMGqzymGYBpb//998+cOXPyuc99Lt///vcfXH/33Xfn+OOPT5IcddRRD9lnyZIlufbaa3PDDTc8ZH1rLcccc0xOPfXU7LXXXjnrrLOy/vrr998JAACAHkz3KQhWSmvtyqr61yRHJ3lrkuMnsPsdg2Wt8sIAYBKcf/75mT9/fpLkpptuSpJcccUVecMb3pAkmTNnTt797ncn6aYmOOWUU3LEEUdkn332yUEHHZRNN900X/rSl7JgwYLsv//+Oeiggx5y/O9973vZd999M2/evAfPkyTvfe9788lPfjLrr79+dtppp5x88skPq22nnXbKPvvs00u/AQAAViUB7PK9O8lrkrytqj7cWrstSarq6CQ/aK3918gdquqJSQ4ZvB3rQVwAsFr74Q9/mLPOOush6xYuXJiFCxcmSbbZZpsHA9gk2WeffTJ//vycdNJJOe+883LPPfdku+22y/HHH5+jjz46VeP7neT113fPybzrrrvyT//0T6O2eeUrXymABQAA1gjVWpvqGla5qmpJN6frqmhfVe9Pcky6h2QdO1h3TpL9kyxMclm6+WLXTbJ9khclWTvJB1prxww7zpFJPpFuDtgjJ9KnxYsXj/pBzZ69yUQOA8AMtmjR4qkuAXrnwRKwlOsBlnI9QMe1sOpssskm4/6rd3PAjs97kixJ8uaq2mKw7q+SvC3JT5LskuTNSf4syVOTnJ9k3+HhKwAAAAAw80zLKQjGO/J1vO1ba79JsuGIddcmOWnwGu95Tkty2kRqAwAAAADWXEbAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANATASwAAAAAQE8EsAAAAAAAPRHAAgAAAAD0RAALAAAAANCTWVNdACtn0aLFU10CTJkFCxYkSbbffvsprgSmlmsBAABg9WUELAAAAABATwSwAAAAAAA9EcACAAAAAPREAAsAAAAA0BMBLAAAAABATwSwAAAAAAA9EcACAAAAAPREAAsAAAAA0BMBLAAAAABATwSwAAAAAAA9EcACAAAAAPREAAsAAAAA0BMBLAAAAABATwSwAAAAAAA9EcACAAAAAPREAAsAAAAA0BMBLAAAAABATwSwAAAAAAA9EcACAAAAAPREAAsAAAAA0BMBLAAAAABATwSwAAA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\n", 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SzGY2mGgG/GXiwukSYE+iP8bhwCHECJz9iMGE9q2Sz45EAGxJ4oR5DdHsbf2kTHsSzXnfzsw2mRgoYv/MZ8cmn2Vf7ZrgNqvMxHo7KHk/hRiIIs3jSKLW3oWUBrjoiH5EEPYa4qLvq0n5v05ctD6fpNuZYrXihiV5vUMMCDKcWM9HEqPeA/zcMrU8q1iI+N2WI07UWxJN/PYmBlaCqEl6VIEydVRfIsB1LLAD0V/dcOK3vY7YrgYDN5nZoGqZNNnpxIARtxE1B75C1Li5qCzdxUSTyj5EbYaDif17PeBbRHABIlB8vbWveXgM8XumtSBeY959YBjRr1Vn2pk4bvyL2EY3Ivqb/SmlGgBZ3ycCwf8kRrtdn7iJ+R1xITc/MMbMlujkcneYmS1kZiub2Q+JC/a0RtVF7j6zkTzdfQoRZAMYbmZbVVn2N4jtHCB3sAszW4vodxYimPheHUV6PPk71OZtybFn8vd9Svs9wK+BJYh+IIvWDG7UCCLADHEzkPorceyE9ueKWk4gzh23EDVX1iOOt1cl0xcDrjSzWgMHf50Ynf5FognihsR+cRKxXxhwjpl1Wm3ARLPPIXVz91mUmnlvYjm1nhPpdcdL7v7P7AQzWx24k9IALFcRAyqlx8xxyeerAfd04Jjfj/jtfkUcwzcmjuO7ENv3e8Sx6mKLAamyHiGOvcdmPtufeY/NXTWA0HnJ8rIDLFY6V6Tnm8uI4ArE8TrPSPh0EO0/NKGsPcVXM++f68TlZI9NeQM9ZmuH1ipPdvoaZdPS4/nu2Q+T49m3y9JgZssRQeU5wEFevcZfQzxq/6Y1gJcDflOhlnEjdiCCbM8S63gDos/OU4nr/z7AeWb2hSrz9yMegv6WeEg4nDjG7Egcw99N8viVme1aoDxHEv07309cJ6fXPfM0jU+cQQxQehcxMOt6xH3blcn0gcCt1cpv0QrjZqJbp7eJ89rOlK5FTyOOYcsC47rgPATtt8VJOen6EAMbHk1cR69PnDu/S5zXnRho8G82b2u8s4ljWXoemE3lY93f6yz7icRvaMT6/FlSpo2JB2FpzetDiPNDT5E9bjxfNVXIO27Uxcy+TNwDQBw36h1ocePk70R3/8Ci1c8jxH3qi8T6fjOpPb1CR8oqFXR3ZFkvvXrqi/Y1eB4hBmWo9ao4yAxR6+WJJK+PgQ0z035H7SeO2bI4MXjOYhXSrUupRudUKtQ8IwJW6VPT1aosb2FKTwDfKc+HCOZOT6Z/AHw9Zz0uCCxZ9tmIzHcZWeC3aEaZVycC505c9C1fIY8ViODzp+u6A9vPspXWf2Z6H0q1dOYAK1ZJl/3dnwAWr5Bmi0yaG6rkMz6TZjqVn5gvSQS8Pfk7Tw0MmltjN3dwJCJQmtaIPa5KmtGZ5Qxt8LcaWbaeT6+Rfs9M2rzaC4dm0u2V85u0FShj4e+ZSTemxu/iSRnyavuNKEt/CRVqNQC/zKQ5pEBe4xv5rTryonbtt7F5+2zBZSxJqTbubCIwsyNxU7Yj8eAorYl0M5kaDRXy6kuce5xowlvtu4ysMr8RtR+deHiwN3HRfQyl2qjnZdJ/LZN2nvNLJ/welyfL+7B8ecSDEydqVg7KyaN83x1dJV22Ju+OBfaLcVRotUDc4Kdpfl1lWVX3v5zfcGiF6c06h7R1ZJ+jfQ3Iius3SbdpJt0vK0y/NzO9Yo1n2g/QUnH9FZhe67wyhGjy7sDdBbarEQXWUe46psEau0Wnl6X9E6VrzWVy0r2YpPs3lVuStVyNXSJo9npmn1i92eVOlrMCpRqAkyqtv0zaJzLrcZEa+e5dbR8iHsKn024gjtdfpzT4ZLvB4oiH41WPU01aDz/LlMmJ68aLgQOAL1FwYCTa17x04KZK8xItKdI0p1TJayXya14OIgJlTgTEKm3725SV59Iav/GosvTzDJCXpPthJs3fqpQtbT10K7BwlXzWIO7xHLi5wd+u6OBpX6J07/QG0D8nba1j7/aZ/WaemudJmsKDp1Gjxi4RCE7vIV4FhlRIswzwUiafirVx6foau2lLmSKtun6RKds+HVjmfER8wYnuH7LTatbYJWIdaZp7aV/Tu9JrOjnxA70a+A27uwB66dVTXxTvNiD7ujEnv1WJp6zpheBitO+m4Y5qFw4VyrJ+znKOyqQbVTYtO4LvdjW+/1qZtPuVTcs2t/xxA+t2RGb+kTXSNqvM2RPMzjl5ZIN23snb2BKULpgOq5Im+7tXHeEZeCjvIoD2gd1Dc/I5PZOu0sjMY4qsGwoEdguuo3TU5aeqTB+dWc7QBpcxMpPHRKBfjfTPJWnnab5aIW0amLsn5zdpK5BP4e+ZSTemxu9SNRiUSZ/dV1+nykU90cwqDRZeXyCv8Y1uEx3YlrLrMPuaRNxwNKV5GFEb8ECiFUGl5T1PBAhzm8YStUycqGWyVM53GZmTx4ZU777lOUpdbvSnFOjZrQt+iwHEQ0EnBgMqn75ZppwVHxQk6bL77uPVfkNiYLs03Vk19osPqRIQI4KpaeDosSppqu5/Ob/h0AbXY5FzSFtH9zlKXfe8nJPmwiTN3PLvQ9Q0Tr/r33Py6Efp+DqbCs1Ki67fGt/nkExZl6ixXY0okF/uOqZrA7vZQHy14MmITJpjqqRpxcButvnyRc0uc2Y5x2WWc1yNtNmgUe45hqhVnqY9s8L0czPTy1+/zKTbLvnsFWoEkzu4HvoAf8wp04fAPUTNyLwHdNkA3XvV0ibHh7SLgvs7UO7dMsv7YoXp2cDu21QJsGbSZwO7/yU/+HlP5tizYtm09AH5dCpU4ihL+9PMMgc3sA6qBnaJa5jViFZp0zLlnaeCQgPLvTXJ76Eq05sZ2L0oM32nnHy+lkl3TZU0XR3YTSsAtBVIm+2C5387sMx0+3ujfPujWGB32bJ934kHEAcRAfT5ifv07IP2GdR4IKBX8Ze6YhDpIu7+ItHfFcRN5FWUmm9NAfb25MhYwwR3z2uindZ0gnian5U2O5pOnFzzyjuBOCBDNFvJ2jH5m3Zd0JmaVeZvJH+nELUBqvkLcSHTVBYDugwxsy+a2VpJE+vBlMpba+C2CZ7fgX26TQyq0MSp3JU507LbVq1mt01jYRkzWzVdP8k6SrvyWNM6OKhOQde4+yc55VydCBJBqYl3nnuSvxtZjYGgusE/3f3fdaT/i7t/VGmCu8+g1KS/4nbj7uPd3ZLXiPqK2hRps+ZhxAOjPYhm5isQx7GDm7SczYkm6dWaiq5KBHbXr5ZB0rwyHWX8J+7+ViMFcfeHiODutcS+9DERhDqL6AM1PdYdTfR/Ns7dr03K0NdiYMynksFfppnZrRajJXfUHkRrDmjfDUPqPqImIZR1zZPjymrnUHd/nggYQO3j2p3u/maVfOYSfb0XyaepmnAO6YjLk78rmtmmFcq2ABEsgejns60sSfZapOo1Q3Ls/WPy73xEALJDzGxxM1vRYqDLdL2lzUuNaOnUa3h0gfFM8u8BVZL9IPn7CVW6EXD3n2eO16c1uZhNZ2ZHUepi5l9EUKozlmOUBrObSwTd86THuY8LXOPPqjDfp9z9R8Rv+iixDb9PdI20m7ufkJRvIUrdv/3Ik+57zGwFM7vUzF63GIxskpn9ypKBUhvh7nPd/ftEYOxm4vyS1Z84H/4a+LeZHURtf3f3qZUmJMeH9Dq40PHXzBYzsy+U7f/ZctY6bv7V3d8vsqzE1dWukxLp8c2ILhuy0vudce7+bo3ljM+8H14tUUGrZQcoI7bDF4jrhMWIh9S7u/sVRTNMrumXrXBNPyVJsm6yL3Wm9LzzOvC3aonc/S7i4SXAVpXK5e4bZY6HDza/qCXJvU7aTU6RwcdyjxsFl7kWpS6GDi2w/VWySOZ9/6RcW7r7he7+prvPdvcJ7r4/sW1BDDp8YiNllnnV6mdMRMI9zQhEuPtlZrY10e/TN9OPiUE83qg+Zzu5/R66+xQzayOCCmuXTU5H/lwMmFvHOXW59E3Sn1ea70Nef/879WpGmRcgnj4DPOo5fY25+8dm9gTt+2lrSHLBfCjR/9kXKY1wWsmSNbJ7ocb0dzLvB1Dqd7fc2+7+dpVplfLpVGb2baIvwE1of1FQri/RD+qUnDTN8FSN6dnRc2+oY3ucn6hd11CArpPU+q7lim6DnbbdJPvU53KSTKp2E+bRB252+3kE+LOZXQ5cD/zezFZ390M6UL7/Ifr260vUzB0N3E00q1uGqAl0AnEjvKmZfc/dry/Lw4gHdP2JIGOlwGdh7v4cZf0yli1vdaJJ7YeUHj5CjPL+HeIc9SKx/W4LbG1m3/boP75Raf+UrxOtVcrL7Gb2J2L9rWtmX3L3J2rkWWv7fJc4xtTaPrt9O081+RzSEZcTv4URDyXuK5u+I9EMHqIrgHLDMu8fqLGsbN+8a1Pq47cwM/sKUSv365T69K2mM9dbd7mQCO6tYmYj3H18OsHMFqcUQLrFo6/UlmZm+wEnJ/++RbTKqqc/8np8ldJDu3+4+39qpE+DfPOZmdUI7i6QeV/x2trdLyG/T9/RROWRsekxOjnG30ts6+8Tge/ViHEmtjGz4cnD2Ya4+z+Af5jZwkR3P+sTtfQ3oXQtPgC4wMwWdve8vkw7fPw1szWIsSi+CSxfI79a+3+910m1+qZ/KPP+03s0M+tP6Ti5m5ntRnHL1U7SMCeuBW6olTC5dtmdOL9vTP41/fxEUK/h7a5GWQYQD+0h7lVr9TH9T+Lh9iBim/lvJ5RpNeKBZSUz02NJch86hzjfF+m3uuZxo0a5+hLHlPmJc8Kf680jUf5A4w/uXm3/+QWxnSwO7GJmC3ZBPKHXU41dka73P5RqDgGc7+7jqiWuoGJNoippygcfWbqO5WQtlHk/iNKx47UG86tHM8q8OHFDCvWtv4aZ2dqUAjtrkX9DDrWfstaqMZC9aMlbVrPy6RAzm9/MbiD6gfsG+ReAqYaeRNep1lPqRrdHaL9N9gT1PpEvuu10Zs3knYnaaNVeVWvBVuPuNxNNXAF+ZGYNPdRJajxkg7obuvu17v6Wu3/i7pPd/WJikLq3iIvxMWZWvk39kOiv9ENKgz12pguIC/oT0xrcZvZdIqj7AdEUfXUiQPZbolLAJclNfN2Sm+50MLcr3X1OlaRpX6FQrNZus7bPovl06jV0J5xDGubuk4jAEMB3kiBE1t7J3w+JY3q59FpkLrUfzmUfctc9gJqZ/YwIruxN7aAudM15patdQWk7/kHZtL2Jh0Yw76CgLcfM9iQGfzPinLa1u9cKDnZE9liUF2BNZQfjrHXMzF4H1T2IZ3LMOIy4x/hRZtJFRADzbmA5d1+baDnyMjFI0wn1LqsSd3/f3e9091Pd/TtEgGxLSrVsAU62GNitmg4dx81sfyIY+wNqB3Wh9v5f73VSrfuH7PTs8W1JGj+ndPTachLtByjbgnjI+y9ivzqWGjXTk3PC34gg8NZ0/zV9dhDfIhWnOnTeKegeql+7lrdkSeMERdZjh44bROuG9ZNldqTlWvmyb6mWMAni3p38Oz+9rOVMd1GNXZGudyDtD8JbdOGTqnSfn0zUHCuqnmZIzdZyZU6a0VxH9DcE0dT4auImfQowK621YWavEIO5dHaTpJ7mKKI/OYCniT6QHyR+5/fTYI+ZnUA82YWuWUfVgkyp7HlzP6JJZFFd8SCkHrW+62fJDUTtJYhuAu7OSVvN/pRuNk9294oX2O7+qpn9lmh+tkiyvN9mkhyb/L0H2MDMNmBea2Xeb2hmaU2Je919ctECJzfBWxAjkP+q7LsAXOzu/5eU25Og2Z7AUkQf8UW6IymXDYz81Mx+WjVlyXfN7KfuXqRZYsvroeeQPxHNqgcSI9dflyx/KUrn5hs7UvOvo8xsC6IvQIiHJ78C/kEELmam24+ZbUmMWg+98Nzr7tPN7Bqi2f6uZrZ4pmnt95O/r1Iagb4lJS1+/kQcd9OBeOqtYVnP8gYA30r+fRf4a4HZXgHWSd4PIX+E+yFl89VTtj5EcKgf0d/uq8nnKxN9lgP8MD0vuft/zOw44iHASDM7vECtxrokx6i7zexrxHXe8sRDhXQg0aYyszUprYOZRHPv24kA9nR3n5WkW4M450Ht/b+rrpOy15bXUl/z9KItPquZ7dHVLPvPAAAgAElEQVSVXdb/mdkYokXNcGAvM7s7qTFeyXFE384Q/d2fQzxge4321/RnAEck6XrdsbeJXiGC7IMKxAgaPm4k0mvOu4FNqrRE/GLm/caZNOPTFsfJeWcapdY7tcqSnd6RSjOSUGBXpAslzQNPSf6dQTQlWhM4m+h8v4giNVDSNOX9VL1FNL9aDHi2QH9flUwlnpj3Ifr362zNKHM6uqhR3/pr1FeJ2hAAp7r70TlpF+/gsrrapxe5ZtYn50agVs2UtCbiy8RABNUuWnra+sl2pfB+hYvhzpK9uahaq6PRWpStwt3HULtPw0ZkuycZ2mAea2TeP1Y11bzTv1g2LW1Wtw3FHmaNonT+2IV4OFKTmS0JnEEcG0e5e6V+B+/PzuPuH5nZ40Qt+y9TZ2A36cpn75oJ5zWICCRXqg3a06Tnx1q1r/L21Z54DrmOqNm+INEdQ/pb7EGpeWmlbhigdC3Sh7iBy6vVtmzmfcW+NnOk55U5wBYefStX0tPOK53hAiKw25/Y535rZhtSau59cbMDeV3JzHYmjj9pEO8b7l7Pg9ZGZPsGr9WXaupZ4kEIxDkiL7CbPYc8V2fZRhGtQZ6g/YPC9Fg+LemWJys9vi9G9Fn7Up3LLMTd30keNKQPT1fNS98BB1CKbWyfPpSsoDP3/1r3D9np2eNb9hqkXxdeW1bl7h+Y2feIbXFB4DQzu678oXXyUCFtGfAC0X//LCrrqmNvdt0uWzVV5TT1nncKcfci5Ug9S+lYvQb515QdOW5A6ZpzB0rHqjyHJC+I7rmyDwifpTTGTa1WRtnpVcc2keLUFYNIFzGzRYkaN/MRzRU3IZ4kAxxkZrtWm7dMpdpb2eUsTSkw8XTZ5PTEsAhxU163ZPCCtEbEhmbWSFOaeoKzzSjzLKJJEcBXkouQipKaUh0dhCbbn+A1OctajWLNbHqS7AVd3gVaebDqU2Y2iFKfYDfVeBL9lTrK1hWyF1ebdzCvevaDDq93yZVtstlIUzZof2Faa6C/7PTyAWe6yllEwPRidy/vMzUdgLFSX93TytLUYztKN7YXEbV/a73S1hf70xrS7afWDWzevtrjziFJTdy0X+Vtkpq6EEFeqNJfciJ7LbJRjUVlBwKqt/Zlut6ezgnqQu3zSiMPkDtT3eVJgpyPJ/9+v+zvHIp1I9Ajmdn2wJ+J4+h7wDYeA0V2tuwxqOj6uyfzfkS1REn/pFsk/86gffcFucxsWaLSyFzgoLLubYocy7PpOku2z9LO2r/S/f/1nKAudO51Ze49GqVuiCBzXEz6hE7vU4ZbDxls12MgzPRBwVJApRY2y1HqvuCvOUFd6KJjbxJ8bkv+3aDAQG3poLBTKfhwvJMVPW4sTGmd/isZS6I7ZVu7rVIj7cqZ9z1hnbc8BXZFus75lA5ihyVPY/ehVHPlj2b2+QL5rGVmef1IHkCpecvtZdOynd8fQePSm7sBRNcS9coG8haomio0q8y3JX+XJpqBVfNtSs1IGpVtDZHX99X/dHA53eHfmfd52+FeOdMKrR8z+zK1gwBd7SlK62CfCv2j1iPdD2rtA9Cc9S7V7Zl5X/5ArKjsb7RF1VRhRJX5cPeBXhp9ueILOD4zy36ZaTcWKWjSj/A+RA30n1VIkgYBhlSY9vmyNPVIAyMOnOTu19R6Ueqn7etmVqTPxO6W/p7rVbuZTAIx5SOiZ/XUc0g6kF8/onuM1SndVF6V019y9lqk6jVDEsw4IPl3Nu1Hfi8iXW9555WFgX1r5FPPNUpX+LQ8FoPBFnVB8ndY0hx+j+T/cWlT/VZjZtsAfyH6ZXwf2Nbd/5k/V1OW+0VKQbmn3b1Wq4zUPygFUL9rZtW2zW9QGhj0b3V2O3MOEZg9390fKZuWHqeXS1pMZH2+QrrOkg3ovdxJy0i/X9UKJ8k6KO93upn2rNAHeVZ6fHPgzrJp6f3OsjTWsqWznEmpz9dDkwEYs4pe029M7b5U02PdfAWCsbWk553BwPY55cq2kLmjwZahzXYjpZZ6++dUSNqb0jnqL40syN37F7jmPD0zy56ZaeXd+WRbVVUdADBpMZaOZ/EOjV93S4YCuyJdwMz2Bb6X/Hu9u18I4O5vEjcYTtTuubLgU9o/mNk8T9fNbF0gbbL5LnBldnryBPsfyb+7m9mx5EgGuDrAzMqbFv2O0oXqqWa2dU4eCyYH8KxsX6O5TbKaWOYLKJ0kzzGzebqRSALrvyr/vAEvZt6PrFLOXYhBklrN+Mz7n1S62DCzkUSz6WreorT97GBmS5QnSH6/KxovZudILvhGJ/8OAMZmaq5VZGYbmNk3K0xK94OlK+3PZf5JqWbnjyrdOCT74f/WyKfbmNkIM/PkNb4Llrekme1R6+YgOT5ng0lXVknXlin/0ApJxmbeH2tmX6iQBos+c9OuE+YCN+eVr9mSwFAa8Dnc3d+pkCyt6dfu5tLMVqEU3HicOiT7dLofPFBHYCkdobkPtQNyPcH45O9yVLg5T9b/GPIDhj31HHIHUTMX4sHAPplpl82bPLj7E5QGX/ummX2/StKTKTUrvcrd36qSrpp0va1iZsPLJyZBnUuoPYp84WuULtJoea6mVIP8ckq1u/9Qa0YzOy1zvPt5HcvsNGa2FdGv7QLEwI7bVWhtUCSfbTLfrWg/w43U1iUJ0P46+XcJoqVEeXkGEsFZiHPCGUXzTwLduxH7ZaUuW9Lj9PzMG2hJj0/TKHvAWGC5S5rZA2a2Q637FjPbidLD04+JQbY6Q7r/D0y66igvhxHrec1OWj5E65+zK00wsx9Seuh7iyeDlWb8mlKA/VyrMZCrmS1tZp3+cM/dpxL3fRAPEA4rS/IapZY1O1e5P12e6l31lOcFcb5fOS9hAedSGmzvd5UeDCeVM7L9PZ9TniZJ92DmmNHpFU6SfmvTc+oawJEVyjSEUl/M71P6jcrTddmx3N2fBm5K/t3NzObp2iE5XlxIaRDP81u5W6CeRH3sihSzsMWI50X8190/bd6U3AinB9tXKDWFA8DdbzOzs4jmLZsCvyQ6oa/mEaLG3uMWndA/TlzkbpXkkfbbd0h5P0iJvYCHiJpYJyYXP2OIZl/vAYsSNw7DiRHolyCaU3zaJ567T02CIX8lnoyPM7M/E0+b24gaw0OJWmm7EU/Hb8zM/18za0vSHGBmzxJN3NPmOx+4e7ZT9WaU+QUzOw04hqil8LiZnQ48kCTZlKi5NiDJtyMjdN5GDGawLNHNxuLETdVrxA3ld4gL6peIgH5uYLAncfenzez/iG4ItgJuMbNziZuK5YHdiYcY91PqZ6k8j7lm9ieij6bBwAPJtjyBOC9tRlw4LkkENOe5Qe9O7n65mW1O7MsbAS+Y2R+JgM4bxA3UYGA9onb4MCJgcWtZVvcSAcU+wCUWA2pNodQU7RV3/yBZ5ttmdjURSFkDGJ+ss0mUBrL6AbGfVFzvn0GLEIGN083semIgj1eIC+C0f/PdKNUaADjK3RuqTeTud5nZrUTwchngsWTfuJu4cV6a6I9sFKUL2vPcvVP6NcxxNHG8vMvdqz08uZTYvzczsyuIQFDaJ28forni2CrzVrM3pevOa+uY71biOL8IEeQ8JTd197uQCLjOB1xkZisBfyceLK4DHErsww9Qav5ZrkeeQ9x9jpldRfSV+WWiX06AJ939mRqz/4A4zy9MPJzegtg/3wBWSKZvm6SdTOUmv7WMIY65fYhz06+A+4gg4NrAj4jf4D7inF/NE8RxYmHgSDObQvRdmD5cm+7ur1ebuRPcm3n/OzM7mWjant4Mv+7u89S4dPf3kv33YErB7Nfo+odJqzHvtp5us32Th8FZk929XbceZrYpETDoT5wjfw5MrXFtPtPd/9NwwUvLzvYNPpv6HzqfRZxr1gJGJQGmC4lr1GHEd0kD9r9JgiNFyrUQcF7y76FeYeBCd3/ZzO4lrqsuSIJuTwFfBw5Pko1pMLCyEfGbvGlmNxHHtH8T57uFiPExdqb9g/6T3P2/5Rk1yWWUWgRcaWa/IQZJnAGsTuwHw6m9/3fEw8RvvDLxAHUSce7/HqUWVTMp9VH6qeQ6bw8i8L0IcKeZ3UDca71MHH8GEdvR14jf8BVK20Bn+hVxXlsEOMTMzvZkQEZ3/9jMriTW/QrENf2ZxDFzPiKY/WPiXPUg+S3xsse685P7ttcoHesmV7m3nYe7TzCzUynd9z2RlOu+JL+NiPu+9Nh4jrs/WCTvLnIsMc7CYKIS1WpExYMZRJcfRxPXZRDXrh0dRK9Zfkwc75cCrjezC4gYwDTiOHcIpfPBBEoDnkpHubteeulV4UUEHb2B18hMHvMTNzJO9L+4SZVlzUdcDDhx87d5TllGEyepuVWWPwc4ssZ3G0wEG4p8n4+AIVXy2Y64wa+Vx84V5h2Zk358Z5SZuNm7MGe+WUTgbEz6WQe2ny2JYES1Zb1MXGi2VfvOST5p+jE1ljc6k3Zohenjk2ltNfIZkclnZJU0KxE33tW+2zii/8hPt9kKeSxKaZuv9JpN1D6t9b1ypxf8rbLb4oiC8xjxEGZ2wW3yiAp5LEhc+FabZ0RZ+kHERVC19I8SF1JVtxnKjiUFvmfN7aHoNlaWV8XtvZkv6juGvwscUCO/tlrbGnHTM7bgMi8iBklp5Ltlt/vc36VsvtWI49xHwKo10l5XpdyzgR0aKPOzyfxzgcF1zntVZvmbZT4fWW1/yfn95tn26tkvKHB+IAII1c7Rs4lBvrK/4TzbE807h+ROb+B3XLtCWQ4rOO9GRCA3b794AVgpJ4+qx7dk+vk18r88Wbe5+w/xgL1aHmPK0tb6DcaTf2zM3aaI880/cspT8Tsk865Tlvakgr/VaZl5ft7BbWZUjd+k/DWuRnkazifJa5taacrS75hJf12D6+BzRKWBvPJeBPSpI8/Tk/lurZFudaKlVKVlPg0s2sD3GUDtfTn7+pAIPlUrX6Ftjehz3IGPGthvnejaZ9285ZVtH3vUuX1vQXQBUG357wKb1shvc+DVguv1sQa3x/6ZPF4oOE92HzyxbNpA4oFYtXLOIipDZPNYtsIy+hAVQ6rls0cmbc3thjh2nk7183H6+i05+x4RkE7TbtTIOm/wd1qbeDhQrdxzgOPq+N0aOpaX5VFkn/hyjXJ7sk6X66p1+Vl4qSsGkc51OqUBv4539/srJfIYjXxP4iluH+JJ8zzN0zPpTyae1t5APMmcTdSYvJYYjTS3GZe7v+buXwW2JpqUvUA8AZxDNAN6hngquB9x4q3YZNbdbyFq7BxJdPT+FhHA/oAYBOBy4FuU+kjMzjuGuHgaSwQJc/sTa0aZ3X2uux9EBKRvIUahnUU88f4TsKG7F2kqVJO7/4P47S9J8v+Y6EfoMeIp7Jfc/YVmLKuredRo/DLwG6LG2CziYvV+4sJtW9r3UVgpj5lEDZIjiYvBD5J5/g1cTPwWv++kr9BhHk4gtv8Tie8+hdj+PwT+Q9S6OxYY5u5nVsgjHUTxVGIdzKBUK6HSMqcST7lPIALCHxLHjMeIGnTDvf6my73ZK8T6PY7Y35+ldIx6j9jWbiT6KV3R3S/u6ALd/T1334mo7TqGGAF9JqXj1FNE4GkDdz/QYzDKrnQB8cDxVHd/sUbaPYgaXROIfXw6UfN0c3evqymtmW1IqYn9/e7+Wl76Cv6ced/jB1Fz9/OJmuB/I84zHxPnuSuBjT3pjqlGHj3yHOJRmzA7sNMnROC9yLwPEq1pfk60xphKfK8pRH+TBwNre4O15pNlHEzUjkz7Nk3X/U3ALu6+NznH2Uw+xwPfJYI0b9J9gxzi7k60BDiKaJXxLqWupWrN+xRxfoG4mf5jZ5Sxl9sv876hQec8aqmuTzywvpc4Lswmal5fC2ydnBMK1Zw1s2HE8flDanTBlBwn1ieuc9Nt+T9ELczNvGANyLI8ZxAVLoYTlU3+RnSFkJ7v3ieOW+OIWpEru/up9S6ngXIdT1yD3krp+PI6sR/vTfS1+lEnFmEWcW9zEHFdODX57CXimvmLXqP7EI8u6FYmWnTdSKzHD4nv8hYREDuXuJfZsEo2neFXlPraPcQyfe16tFQdTtQifTIp7wfEA8iLgPXdveaxJ9n+tybOcY8Qx/CGm+kn1+o/I1rQ/QGYSPv7jUuJ+41Diu57XSk53w4j9qFHiGP/R0TQ9FIiyHx89Ry6h7s/TtQs/ylxrk+vg94grsf3Iu5ZurLlS69nca0gIj1Z0p/jpOTf4919dLcVRkRERER6PDNbkLiZHgDc7u7f6OYiifQqZjaKeGAL8eCuJzXnF5HPCNXYFRERERER6X12J4K60H6QIBEREeklFNgVERERERHpRZJBv45I/v0vpdHKRUREpBfpVzuJiIiIiIiI9GRmtjRRQ3cp4DBKfVuf2g19eouIiEgXUGBXRERERESk9Z0B7Fv22b2oGwYREZFeS10xiIiIiIiI9B4fAy8BpwLbufucbi6PiIiIdBJz9+4ug4iIiIiIiIiIiIjUQTV2RURERERERERERFqMArsiIiIiIiIiIiIiLUaDp33GTZ8+XX1xiIiIiIiIiIiI9ACLLbaYFU2rGrsiIiIiIiIiIiIiLUaBXREREREREREREZEWo8CuiIiIiIiIiIiISItRYFdERERERERERESkxSiwKyIiIiIiIiIiItJiFNgVEeliEydOZOLEid1dDJHPBO1vIl1D+5pI19H+JtJ1tL9JT6fAroiIiIiIiIiIiEiLUWBXREREREREREREpMUosCsiIiIiIiIiIiLSYhTYFREREREREREREWkxCuyKiIiIiIiIiIiItBgFdkVERERERERERERajAK7IiIiIiIiIiIiIi1GgV0RERERERERERGRFqPAroiIiIiIiIiIiEiLUWBXREREREREREREpMUosCsiIiIiIiIiIiLSYhTYFREREREREREREWkxCuyKiIiIiIiIiIiItBgFdkVERERERERERERajAK7IiIiIiIiIiIiIi1GgV0RERERERERERGRFqPAroiIiIiIiIiIiEiLUWBXREREREREREREpMUosCsiIiIiIiIiIiLSYhTYFREREREREREREWkxCuyKiIiIiIiIiIiItBgFdkVERERERERERERajAK7IiIiIiIiIiIiIi1GgV0RERERERERERGRFqPAroiIiIiIiIiIiEiLUWBXREREREREREREpMUosCsiIiIiIiIiIiLSYhTYFREREREREREREWkxCuyKiIiIiIiIiIiItBgFdkVERERERERERERaTL/uLoD0TAMHDuzuIoiIiLS0pQcP5sXnnuvuYoiIiIiISC+lwK5UNnp0d5dARESkpU3RuVRERERERDqRumIQERERERERERERaTEK7IqIiIiIiIiIiIi0GAV2RURERERERERERFpMrwzsWtjWzH5vZk+Z2ZtmNtvM3jWz583scjPb08wWrDDvGDPzKq85SR6PmtmZZrZyjXKMzMnrfTObZGbXm9nuZta34HdbyswOM7M7zOw/ZvahmU0zs3+Z2WVmtquZ9crfVUREREREREREREKvCwCa2XDgMeBW4H+AtYGlgfmAgcDqwF7AVcB/zezwOgKhfZI81gN+CjxrZqMaLOpCwFBgV+Aa4H4zWyZvBjP7KfAS8GtgK+DzQH9gMWBVYB/geuBxM9uwwXKJiIhINxk7dixHHHEE2267LUOGDGHgwIEceOCBufM89NBDfOc732Ho0KEsu+yyDB8+nPPOO485c+YUXu6pp57KwIEDc1/rrrtuR7+eiIiIiIg0Ub/uLkAzmdnewB+B+ZOPHiUJdAJvA4sSwdRtgB2BJYCzgEuAaRWy/AbwWub/fsBywHbAgclyzjezie5+V43iHQuMzfy/KLAhcDgwJHl/o5kNd3cv+159gYuBfZOPZgFXEMHrV4ng7heB7wGbA+sAd5vZ7u7+txrlEhERkR7izDPPZMKECSyyyCIMHjyYmTNn5qa/5ZZb2Geffejfvz+77LILiy++OOPGjePoo4/moYce4rLLLiu03E033bTqtHHjxvHUU0+x9dZb1/VdRERERESkc/WawK6ZjQAuBfoCHwDfd/erKyS9B7gsqR07Gsircfuiu7eVffYk8HczexL4Q/LZEUCtwO5kd59Q9tkDZnYF8DDwBWAjImh8c1m64ykFdV8AdnL3F8vS3AtcZGb7EEHgBYE/m9kGFZYrIiIiPdApp5zC8ssvz4orrsh9993HDjvsUDXtjBkzOPTQQ+nbty8333wzX/rSlwA45phj2HHHHRk7dizXX3893/rWt2oud7PNNmOzzTab5/M5c+ZwxRVXALDvvvvOM11ERERERLpPr+iKIekr9yoiqDsX2KFKUPdT7v6mux8MfAf4uN5luvsfganJvxvUO38mn7eBUzIfbZudbmZfAY5K/p0CbFkhqJvN70/A95N/FwQuNzNrtHwiIiLSdTbffHNWWmklipy6x44dy9tvv82uu+76aVAXoH///hxzzDEAXHzxxR0qz+23387kyZNZf/31WWuttTqUl4iIiIiINFevCOwC+xNdJACc7+7/KDqju//F3d9vcLmTkr8LNDh/6sHM+xXKpv2M0u90uLu/Xiszd78MuCP5d13KgsUiIiLS+u69914Attpqq3mmbbLJJiy00EI8/PDDzJo1q+FljBkzBlBtXRERERGRnqi3BHb3S/468JsuXO7Q5O8rHcwnW2O4b/rGzAYCuyT/vkYMslbU2Zn3+zdeNBEREemJJk6cCMDKK688z7R+/fqxwgor8Mknn9DW1tZQ/pMnT+bOO+9kwIAB7Lrrrh0pqoiIiIiIdIKWD+ya2QCiVipEn7gvddFy9wOWTP4dm5e2gLUz77ODtW1CKdB7i7sXH946aux+mLyvPiKKiIiItKQZM2YAMGDAgIrT08+nT5/eUP6XX345c+bMYffdd2ehhRZqrJAiIiIiItJpesPgacMoBT8fa3Leq5rZIpn/+xJdPnwTOCj57FngzEYXYGbzE4Ovpe7OvF8n876u7+bun5jZ08CGwDJmtlyRbhxERESkedJatY2YPHkyEAHcSvl8/HE0+Glra2POnHmf/X74YTzfffXVV1l88cXrWvbcuXO59NJLAdhyyy079D3ks0XbikjX0f4m0nW0v0lnWGWVVTqcR28I7A7KvH+zyXnfljNtGnA88Ht3r7sqTBIw3jDJY/3k4zbgukyyJTPv36h3GWXzLAkosCsiItJLLLzwwgC89957Faenny+66KJ15/3Pf/6TN998k2HDhlXs6kFERERERLpfbwjsZu9WGh0ErREDgQOAqcCFBdJfamaX5kx/A9jJ3bMjnGS/W+W7tnzZeSq30xQREZFO05Gn8G+8Ec9nBwwYUDGfYcOG8fzzzzN79ux5pn/yySe88cYb9OvXjy222IIFFqhvnNdf/vKXAIwaNaopNQmk90trMml7Eel82t9Euo72N+npWr6PXWBm5v3CTc77C+5u6YtYX4sDI4C/AisCF5jZWR1YxkTgVGAtd3+6bFr2uy1C/bLzzGhgfhEREemhNttsMwDuvPPOeabdf//9fPDBB2ywwQZ1B3Vff/11br/9dg2aJiIiIiLSw/WGwO7UzPtlOnNBHqa5+z3uvitwVTLpcDPbqsbsxxL9AQ8D1gJWAga4+6rufrS7T60wz9uZ98s2UOTsPG9XTSUiIiItZ6eddmLQoEHccMMNPPHEE59+/tFHH3HyyScDcMABB7Sb54MPPuDFF1/k1VdfrZpvdtC0BRdcsHMKLyIiIiIiHdYbumJ4BphDDGy2Xhcv+0zgu8n7/YF5q8yUTHb3CXXm/1TmfV3fzcz6Amsn/07RwGkiIiI9380338wtt9wCwJQpUwB4+OGHOfjggwEYNGgQJ510EhBdNJxzzjnsu+++bL/99uy6664svvji/P3vf2fixInstNNO89S4feyxx9hhhx3YZJNNPl1O1ty5c7n88ssBGDlyZGd9TRERERERaYKWD+y6+wwze5IIfK5qZqu4e1cNV/hC5v2wTsj/fkpB6+3MrK+7zzvsdWVfB9JqNv/XCWUTERGRJnvmmWe4+uqr233W1tZGW1sbAEOGDPk0sAuw/fbbc8stt3DWWWdx0003MWvWLFZccUVOPvlkRo0ahZnVtfy77rqLV199lfXXX58111yzw99HREREREQ6T8sHdhOXEoFdAw4FfthFy+1X5X1TuPs0M7sR+BYwGNgNuDp/rk8dmnmfN2ibiIiI9BBHHXUURx11VF3zbLTRRlx33XWF0m622WZMmzat6vStt946d7qIiIiIiPQcvaGPXYBLgLSrgYPNbMuiM5rZt8ys0UHXvpJ5X72zuo45HZibvP+1mdXsa9fM9gK+kfz7JPD3TiqbiIiIiIiIiIiIdINeEdh19w+B7xHdFvQB/mZmu+fNY2ZLmdnvgb8A89W7TDNbADgx89HN9eZRhLs/ApyR/LsscJeZrZxTru8RgW6Aj4C93d07o2wiIiIiIiIiIiLSPXpLVwy4+91mtj/wB2Ah4Boz+wkRuH0CmAosAqxA1GbdKfk/z6pmlk1jwKLAl4GDgTWSz18ELmrSV6nkF8AQIni9BjDBzC4HbgH+C/QHvphM3yKZ5yNg9wYGbBMREREREREREZEertcEdgHc/U9m9jJwLvAlYP3kVc3bwPHAjCrTbyuw2MeBXdz9o3rKWg93/8TM9gaeAY4hgsvfT16VPA0c5O4PdlaZREREREREREREpPv0qsAugLvfb2brAdsC2wObAMsBiwPvA68BjxK1XW9091l1LuJ94E0ioHsdcL27z2lS8atKulM43cwuBfYmvt9qwFLAbOAN4CHgRuCv7j63Wl4iIiIiIiIiIiLS2npdYBc+DYLemrzqnXckMLJJ5RgDjGlGXpk8pwBnJS8RERERERERERH5DOoVg6eJiIiIiIiIiIiIfJb0yhq70t3lvLcAACAASURBVASjR3d3CURERFra0oMHd3cRRERERESkF1NgVyqaNm1adxdBpNeaOHEiAKussko3l0Sk99P+JiIiIiIivZW6YhARERERERERERFpMQrsioiIiIiIiIiIiLQYBXZFREREREREREREWowCuyIiIiIiIiIiIiItRoFdERERERERERERkRajwK6IiIiIiIiIiIhIi1FgV0RERERERERERKTFKLArIiIiIiIiIiIi0mIU2BURERERERERERFpMQrsioiIiIiIiIiIiLQYBXZFREREREREREREWowCuyIiIiIiIiIiIiItRoFdERERERERERERkRajwK6IiIiIiIiIiIhIi1FgV0RERERERERERKTFKLArIiIiIiIiIiIi0mIU2BURERERERERERFpMQrsioiIiIiIiIiIiLQYBXZFREREREREREREWowCuyIiIiIiIiIiIiItRoFdERERERERERERkRajwK6IiIiIiIiIiIhIi1FgV0RERERERERERKTFKLArIiIiIiIiIiIi0mIU2BURERERERERERFpMQrsioiIiIiIiIiIiLSYft1dAOmZBg4c2N1F6DRLDx7Mi889193FEBERERERERERaZgCu1LZ6NHdXYJOM6UXfzcREREREREREflsUFcMIiIiIiIiIiIiIi1GgV0RERERERERERGRFvOZDOya2Qgz8+Q1usJ0z7xeN7OF6shvVJV8Gn2NyFnu5mZ2lpk9mpRzlplNN7OJZvZnMzvAzAZ0ZF2JiIiIiIiIiIhIz/OZDOzWaVngh91diCwzW8vM7gHuAQ4H1iPKOT8wAFgZ2A34I/CamZ1gZv27q7wiIiIiIiIiIiLSXBo8rZgjzex8d59Z53zDcqZdCnylQLpJ2X/MbBvgWmDR5KPngOuAh4ApwILAEODrwM7AQOAXyTwT6iu+AIwdO5b77ruPCRMmMGHCBGbOnMluu+3GRRddVFc+xx13HE888QQvv/wyU6dOpX///gwZMoTtttuOAw88kCWWWKKTvoGIiIiIiIiIiPQ2CuzmextYEhgEHAacUM/M7l41kGpm7xdJVzbPWsD1wELAJ0mZznP3uRWSX21mPwaOBH5WT7mlvTPPPJMJEyawyCKLMHjwYGbOrDe+H8477zzWWWcdRowYwVJLLcX777/Po48+ymmnncZll13GHXfcwec+97kml15ERERERERERHojBXbz3QWsRNSsPdzMznX3d7ujIGZmwFVEUBdgP3e/Im8ed58OHGNmdwIzOrmIvdYpp5zC8ssvz4orrsh9993HDjvs0FA+r776Kv37z9sjxoknnshZZ53F2WefzVlnndXR4oqIiIiIiIiIyGeA+tit7RfJ38WAI7qxHDtS6rJhbK2gbpa73+3ur3ROsXq/zTffnJVWWomIrTeuUlAXYOeddwbg5Zdf7lD+IiIiIiIiIiLy2aHAbg3uPg64L/n3EDNbqpuKsl/m/dndVAbpBOPGjQNgzTXX7OaSiIiIiIiIiIhIq1BXDMX8ArgbWBg4Cji8KxeedMOwWfLvDODerly+NNe5557Le++9x4wZM3jyySd54IEHWHPNNTnssMO6u2giIiIiIiIiItIiFNgtwN3Hm9ldwNeAg83sV+7+WhcW4fPAEsn7J6sMliZ1mDhxYkPzTZ48GYAZM2Y0nMfZZ5/NO++88+n/G2+8Mccddxzvvvsu777bLV04SzdpdBsSkfppfxPpGtrXRLqO9jeRrqP9TTrDKqus0uE81BVDcccmf/tn3neVQZn3b3bxsqXJbrvtNh555BHGjRvHGWecweTJk9lrr7144YUXurtoIiIiIiIiIiLSIlRjtyB3f9DMbga2Bw4wszPcva2LFr9o5v37XbTMXq3RpyJvvPEGAAMGDOjwk5VVVlmFjTbaiG222YavfOUrnHLKKTzwwAMdylNaQ/q0txlP50Qkn/Y3ka6hfU2k62h/E+k62t+kp1ON3fr8AnBgfuC4LlzuzMz7hbtwudIFPv/5z7Paaqvx/PPPM3Xq1O4ujoiIiIiIiIiItAAFduvg7k8C1yf/7m1mq3bRorPRvmW6aJnShdKawH379u3mkoiIiIiIiIiISCtQYLd+vwTmAn2B47toma8A6Whb65qZfrce6uOPP+bFF19k0qRJ7T5/6aWXmD59+jzp586dy4knnshbb73FhhtuyMCBA7uqqCIiIiIiIiIi0sLUx26d3P15M7sK2AvYzcxO7oJlupndC+wEDAA2B8Z39nIl3Hzzzdxyyy0ATJkyBYCHH36Ygw8+GIBBgwZx0kknAfDaa6+xwQYbMGTIEJ555plP87j99ts54YQT2GijjVhhhRVYYoklmDJlCvfffz9tbW0ss8wynHPOOV38zUREREREREREpFUpsNuY0cAexPo7EeiKiNylRGAX4McosNtlnnnmGa6++up2n7W1tdHW1gbAkCFDPg3sVjNixAgmTZrEAw88wNNPP8306dNZeOGFWWmlldh9990ZNWoUiy++eGd9BRERERERERER6WUU2G2Au79sZmOA7wM7A/d1wWJvAp4BhgE7mdle7n5FkRnNbATwb3d/pRPL12sdddRRHHXUUYXSrrDCCkybNm2ez9dYYw3OPPPMZhdNREREREREREQ+o9RXa+NOBGYn73/W2Qtzdwe+C3yQfHSpmf1vXn+7ZjbAzE4C7iC6cBAREREREREREZFeQDV2G+Tur5jZRcAPgaW6aJkTzOxbwLXAosDvgP8xsz8DDwNvAf2BIcDXgF2BJbqibCIiIiIiIiIiItJ1FNjtmJOBA4AFu2qB7j7OzIYDvycGUVsDOD5nlpnAGcCLXVA8ERERERERERER6QIK7HaAu79hZr8Djuji5U7g/9m78zCpyjP//+9bQAG3BlQIsoqo0fBzGYNRghBF1EQkkhhHMS5ZFDXRJA5GzIy2MSIuKAYFYswCiYrOJNFGf3EPbnE3ER00ogPuioY0uKLA8/2jCmyhl+rq011d3e/XddVVdc55lrsazqV++vE5MCIiRpB7oNoIYFtyq3M/BN4EngBuBf47pfRuS9YnSZIkSZIkqXm1y2A3pTQfiHqu13mtlrZnAGcUUcPIxvapZYx7gHuaOo4kSZIkSZKk8uLD0yRJkiRJkiSpzLTLFbsqQGVlqStoNtv07l3qEiRJkiRJkqQmMdhVraqrq0tdgiRJkiRJkqQ6uBWDJEmSJEmSJJUZg11JkiRJkiRJKjMGu5IkSZIkSZJUZgx2JUmSJEmSJKnMGOxKkiRJkiRJUpkx2JUkSZIkSZKkMmOwK0mSJEmSJEllxmBXkiRJkiRJksqMwa4kSZIkSZIklRmDXUmSJEmSJEkqMwa7kiRJkiRJklRmDHYlSZIkSZIkqcwY7EqSJEmSJElSmTHYlSRJkiRJkqQyY7ArSZIkSZIkSWXGYFeSJEmSJEmSyozBriRJkiRJkiSVGYNdSZIkSZIkSSozBruSJEmSJEmSVGYMdiVJkiRJkiSpzBjsSpIkSZIkSVKZMdiVJEmSJEmSpDJjsCtJkiRJkiRJZcZgV5IkSZIkSZLKjMGuJEmSJEmSJJUZg11JkiRJkiRJKjMdS12AWqeKiopSlyBJkiSpjG3TuzfPLVxY6jIkSWqzDHZVu8rKUlcgSZIkqYwt9b8pJElqVm7FIEmSJEmSJEllxmBXkiRJkiRJksqMwa4kSZIkSZIklRmD3XpExMiISLW8VkfEvyJiSUQ8EBE/j4gjI6JLAWPOXztOEfUMqKOeFBErI+KNiLg7In4cEVsV960lSZIkSZIktXYGu8XZCKgA+gP7AN8HrgVei4gphQS8zWBjoCfwJWAK8ExEjCpBHZIkSZJUlJtuuomJEydy8MEH07dvXyoqKjjhhBPqbL9y5Up++ctfst9++7Hddtux7bbbMnToUM444wxeeumlRs//8MMPc/jhhzNgwAB69erFPvvsw4wZM1i9enVTvpYkSc2iY6kLKCMzgRk1jjclF+5+DhgBfDl//GNgTEQcklJa3Iz13AT8Z43jLYAdgFOAPYGtgD9FxJCU0pJmrEOSJEmSMnHxxRfz9NNPs9lmm9G7d2/eeeedOtuuWrWKsWPH8tBDD7HDDjvwta99jU022YQnnniCq666irlz53L77bez0047FTT3LbfcwjHHHEPnzp057LDD6NatG7feeitnnXUWDz/8MLNnz87qa0qSlAmD3cItTSk9Xcv524CpETEQ+BW5FbM7AzdHxN4ppRXNVE91LfX8NSJmA78FjgE2A04nt6JYkiRJklq1yZMns+2227Lddttx//33M2bMmDrb3nzzzTz00EOMGDGCP/3pT2y00UafGueiiy5i+vTpXHnllQ3Ou2LFCk477TQ6dOjAzTffzO677w7AT37yEw499FBuuukm/vCHP/C1r32t6V9SkqSMuBVDRvKrcw8Abs2f2hk4pwR1JD69kveAlq5BkiRJkoqx7777MmjQICKiwbZLliwBYPTo0Z8KdQG+/OUvA/D2228XNO9NN93E22+/zbhx49aFugCdO3fmJz/5CQC/+tWvChpLkqSWYrCboZTSauBY4P38qRMjokcJ6ngZWPtvMH1ben5JkiRJam5rt1i48847WbNmzaeu3XbbbQCMHDmyoLHuu+8+AEaN2vAxJcOGDaNr16488sgjrFy5sgkVS5KULbdiyFhKaWlEXAt8h9w+vKOB60pQytrd/T8uwdySJEmS1KwOPPBAxowZw7x589hnn30YMWIEG2+8MX//+9956KGHOOGEE/jud79b0FiLFi0CYPvtt9/gWseOHenfvz/PPPMMS5YsYccdd8z0e0iSVCyD3eZxO7lgF2A4LRzsRsRWwDb5wyUtObckSZIktYSIYM6cOUyZMoVLLrmEZ599dt21ESNGcPjhh9OxY2H/ybtiRe7RKFtssUWt19eeX758eROrliQpOwa7zeOJGp93KMH8PwLWbkp1QwnmlyRJkqR1K2GL8eqrrwK50LW2cVauXEllZSV//etfOeOMM9h3333p3LkzTz75JFOnTuXggw9mypQpjBgxosG5Pv449z86LlmyhNWrV29w/YMPPgDg5Zdfplu3bkV/pyw15WcrqXG839QcBg8e3OQx3GO3efyzxufuLTFhRGwREXtGxO+ASfnTzwIzWmJ+SZIkSWpJs2fP5s477+Skk05i3LhxbLXVVmy22WYMGzaMKVOmsGrVKqZOnVrQWJtuuikA7777bq3X157ffPPNsylekqQMuGK3edT8t4Hm+if/sRFxbB3X1gA3AqeklKqbaX5JkiRJqldTViO98cYbQG4bhNrGefTRRwEYN27cBtcHDx5MRUUFr7/+Oj169KB79/rX2wwZMoRnnnmGjz76aIOxVq1axRtvvEHHjh0ZMWIEm2yySdHfKQtrVw5msdJLUv2839TauWK3edQMc1eUYP5XgGkppTdKMLckSZIkNbuPPvoIgLfffnuDaytXrly3yrZTp04NjjV8+HAA7rzzzg2uPfDAA7z//vsMHTq05KGuJEk1Gew2j61qfF7WTHPcBAzJv3YFDgIuIrdauB9we0QMb6a5JUmSJKmk9t57bwAuvfRSVq5c+alra7di2GOPPT61fcLy5ct57rnn1q0GXmvs2LH06NGDP/7xj/ztb39bd/7DDz/k/PPPB+Db3/52c30VSZKK4lYMzWOPGp//0UxzVKeUnq5xvAC4LSJuAO4FugLXRMTnUkqlWDUsSZIkSY1y8803c8sttwCwdOlSAB555BFOOukkAHr06MHPfvYzAE4//XRuvfVW7rnnHj7/+c8zatQoOnfuzMMPP8zjjz9Oly5dmDJlygbjn3LKKRx55JHMnDlz3fktttiCyy+/nGOPPZZDDjmEcePG0a1bN/785z+zaNEixo4dy7hx41riRyBJUsEMdpvH6Bqf72vJiVNKj0dEJbnVu32BicB/tWQNkiRJklSMp556iuuuu+5T55YsWcKSJUsA6Nu377pgt3fv3txzzz1MmzaN22+/nWuuuYY1a9bQs2dPjjrqKH7wgx+www47FDz3IYccwi233MLUqVOpqqpi5cqVbLfddpx//vlMmDCBiMjse0qSlIVIKZW6hlYrIkYCf8kfnptSqiygzzbAEqALuW0R+qeUltW4Ph8YAZBSatS/GUTEAGBx/nB2Sum4OtptAjwP9AHeAwaklDbceApYvnx5rX8BKqZNa0xpkiRJkvRplZVUV/ss56z5MCep5Xi/qRS23HLLgvNC99jNUER0AGaTC3UBflEz1G0pKaWV5FbsAmwK/LCla5AkSZIkSZLUfAx2MxIRA4E7yD3EDGAh8NPSVcQvgbVPBPheRFSUsBZJkiRJkiRJGXKP3cJtExGfq3HcFegG7AKMBL4MdMhfWwgc0tBDyyLiuALm/SildG1ji00pfRgRU4GLgS2A04BzGzuOJEmSJEmSpNbHYLdwJ+Vf9akGrgIqU0ofFDDmbwposxxodLCbNxP4MbAVcFpEXJpSeqfIsSRJkiRJkiS1Em7FUJw15ALXl4EHgSuAo4DeKaUfFxjqNruU0nvAZfnDbsD3SliOJEmSJEmSpIy4YrceKaX5QMFPoitwzJFN6LuERtaTUpoMTC52TkmSJEmSJEmtjyt2JUmSJEmSJKnMuGJXtausLHUFkiRJksrYNr17l7oESZLaNINd1aq6urrUJUht1qJFiwAYPHhwiSuR2j7vN6lleK9JkiS1PLdikCRJkiRJkqQyY7ArSZIkSZIkSWXGYFeSJEmSJEmSyozBriRJkiRJkiSVGYNdSZIkSZIkSSozBruSJEmSJEmSVGYMdiVJkiRJkiSpzBjsSpIkSZIkSVKZMdiVJEmSJEmSpDJjsCtJkiRJkiRJZcZgV5IkSZIkSZLKjMGuJEmSJEmSJJUZg11JkiRJkiRJKjMGu5IkSZIkSZJUZgx2JUmSJEmSJKnMGOxKkiRJkiRJUpkx2JUkSZIkSZKkMmOwK0mSJEmSJEllxmBXkiRJkiRJksqMwa4kSZIkSZIklRmDXUmSJEmSJEkqMwa7kiRJkiRJklRmDHYlSZIkSZIkqcwY7EqSJEmSJElSmTHYlSRJkiRJkqQy07HUBah1qqioKHUJqsU2vXvz3MKFpS5DkiRJkiRJJWawq9pVVpa6AtViqX8ukiRJkiRJwq0YJEmSJEmSJKnsGOxKkiRJkiRJUpkx2JUkSZIkSZKkMmOwmxcRIyMi1XhVFdCnskb7nRpou1lEnBgRVRGxOCLei4h3IuL5iLghIo6JiI0bWWdlA227RcQjNdr/oZA5JEmSJEmSJLVuBrt1GxMRQ7MYKCKOAZ4HZgFjgAFAV2AzYBBwODAbeCYiDs5ozq2Au4HP509dBxyRUvooi/HV+i1btow5c+Ywfvx4dt99d3r16kW/fv046KCDmDNnDmvWrCl67Ouvv56KigoqKiqYM2dOhlVLkiRJkiSpEAa79ftZUweIiJ+RC217AmuAG4AjgS8Aw4BjgZvzzbcDbo6IE5s4Zy9gPrBb/tRvgKNTSquaMq7Ky4033sipp57K448/zp577slJJ53EmDFjWLhwIaeeeirHHXccKaVGj/vKK68wceJENttss2aoWpIkSZIkSYXoWOoCWqm3ga2AAyJi35TSvcUMEhHfAX6SP3wDGJtSemS9Zn8F5kTEgcB/A5sDMyPihZTSnUXMuS25lbo75E/NAk5OxSR4KmuDBg3iuuuu48ADD2SjjT75Hc7ZZ5/N/vvvT1VVFVVVVYwdO7bgMVNKnHLKKXTv3p0xY8Ywffr05ihdkiRJkiRJDXDFbu2uAN7Pfz6vmAEiog9wef7wA2BULaHuOiml24CxQAKCXNjbtZFz9gfu5ZNQd1pK6SRD3fZpxIgRHHzwwZ8KdQF69uzJ8ccfD8D999/fqDFnzZrFvffey5VXXknXro366ylJkiRJkqQMGezW7k1g7VLEfSNidBFjnEZuH12A81NK/9tQh5TSX4Cr84efAY4rdLKIGEQu1N0uf+qClNIPC65W7UqnTp0A6Nix8EX7//jHPzj33HOZMGECw4YNa67SJEmSJEmSVACD3bpdBKzIf27Uqt2ICD4JZT8EZjSi+7Qan79V4Hw7kgt1++VPnZNSOqsRc6odWbVqFXPnzgVg1KhRBfc58cQT6dOnD2effXZzlidJkiRJkqQCGOzWIaW0DLgsfzg0Ig5tRPedye3RC3BfSulfjZh3IbAof7hbRGxeX/uI+BxwD9A7f+qMlNJPG1Gr2pnKykoWLlzI6NGj2X///Qvqc+GFF7JgwQJmzJhBly5dmrlCSZIkSZIkNcSHp9XvUuD7QHfgpxExr8D9anet8fnxIuZ9AhgMdAA+BzxYzzynkAuRE3BqSumKIuZTGVm0aFHDjeowd+5crrjiCgYMGMAZZ5xR0FhPP/00l156KePHj6dbt27r+ixbtgyAN998s0k1tWf+3KSW4/0mtQzvNanleL9JLcf7Tc1h8ODBTR7DFbv1SCmtAC7OH+4KHF5g161qfH6jiKlr9tmqzlbw1RrXTzPUVX1uuOEGpk6dysCBA5k5cyZbbrllg31WrVrFOeecQ79+/ZgwYUILVClJkiRJkqRCuGK3YT8HfgD0BM6NiD+klFY30Kfm9gnvFjFnzT5b1NMuAZH/PCYirkoprSxiPpWRYn6jM2PGDC6++GJ23nlnbrrpJrbeeuuC+lVXV/PSSy8B1PnAtPPPP5/zzz+fCRMmMGXKlEbX1h6t/W1vFr+dk1Q/7zepZXivSS3H+01qOd5vau0MdhuQUno/Ii4g91CznYCjgdkNdHunxufNipi2Zp8VdbbKPZRtJLALcADwPxExLqX0cRFzqo2aNm0alZWVDBkyhBtvvJEePXoU3HeTTTbhm9/8Zq3XnnzySRYsWMDee+/N9ttvz9ChQ7MqWZIkSZIkSQ0w2C3MLOA/gD7A2RFxbQPh6ds1PvcqYr6afd6us1Xu2ihyD0/bATgEmBsRR6SUVhUxr9qYiy66iMmTJ7Pbbrvxpz/9iW7dutXZ9uOPP2bx4sV06tSJgQMHAtClSxemT59ea/sLLriABQsWcOSRR3LMMcc0S/2SJEmSJEmqncFuAVJKKyPiZ+QC3u2AbwG/qKfLkzU+/1sRU+6Rf18NPN1AbW9ExH7AvfnaxgFzIuLolNKaIuZWG3HttdcyefJkOnTowN57782sWbM2aNOvXz/Gjx8PwGuvvcbQoUPp27cvTz31VEuXK0mSJEmSpEYw2C3cr4EzyIWn/xkRv62n7ULgn0APYHhEdEsp/auQSSLis8DazVv+llJ6p772ACmlV2uEu/2AI4GPIuL4lFIqZF61PS+++CIAq1evZubMmbW2GTZs2LpgV5IkSZIkSeVjo1IXUC7yWy/8NH/YB5hQT9sE/DZ/2Lm+trU4rcbn3zSivheB/YDX8qeOBWZFRNTdS23ZpEmTqK6urvd1yy23rGvfv39/qqurC16tu3Z8t2GQJEmSJElqeQa7jfN74Nn850lA13raXg58kP/8n/mVuPWKiH2B7+YPX+eTcLggKaUXgP2BpflTJ+TrkCRJkiRJktSGGOw2QkppNVCZP+wJHFdP25eBH+YPuwJ3RcSedbWPiFHAPHJ/Jgk4NqX0fhE1Pksu3P1n/tT3I+Kixo4jSZIkSZIkqfUy2G28G4AF+c9b19cwpfQL4ML84WeAhyPiuog4IiKGRsTeEfHNiKgC7gC2ANYAJ6WU7ii2wJTS08ABQHX+1MSIOK/Y8SRJkiRJkiS1Lj48rZFSSikizgZuLLD9mRHxD3IB79bAv+dftVkMfC+l9P9nUOffIuIgcoHx5uS2g/ggpTS5qWNLkiRJkiRJKi1X7BYhpXQT8Ggj2v8GGAScDNwCvEhu/933gP8D/kBuW4fPZhHq1pj3YeDL+XkAzo+I07MaX5IkSZIkSVJpuGI3L6U0H4hGtB/ayPHfAWbmX0Uros77gc2aMqckSZIkSZKk1sUVu5IkSZIkSZJUZlyxq9pVVpa6AtVim969S12CJEmSJEmSWgGDXdWqurq61CVIkiRJkiRJqoNbMUiSJEmSJElSmTHYlSRJkiRJkqQyY7ArSZIkSZIkSWXGYFeSJEmSJEmSyozBriRJkiRJkiSVGYNdSZIkSZIkSSozBruSJEmSJEmSVGYMdiVJkiRJkiSpzBjsSpIkSZIkSVKZMdiVJEmSJEmSpDJjsCtJkiRJkiRJZcZgV5IkSZIkSZLKjMGuJEmSJEmSJJUZg11JkiRJkiRJKjMGu5IkSZIkSZJUZgx2JUmSJEmSJKnMGOxKkiRJkiRJUpkx2JUkSZIkSZKkMmOwK0mSJEmSJEllxmBXkiRJkiRJksqMwa4kSZIkSZIklRmDXUmSJEmSJEkqMwa7kiRJkiRJklRmDHYlSZIkSZIkqcwY7EqSJEmSJElSmTHYlSRJkiRJkqQy07HUBah1qqioKHUJbdI2vXvz3MKFpS5DkiRJkiRJZc5gV7WrrCx1BW3SUn+ukiRJkiRJyoBbMUiSJEmSJElSmTHYlSRJkiRJkqQy0y6C3YgYGRGpEa/KBsbrFREf12h/SiPriYg4NCKui4hFEfFORKyKiOURsTAi/hQRkyJiaERslO+zUyO/Q12vXk34UUqSJEmSJElqBdxjtzjj+fTP7hjgykI6RkRv4AZgWC2Xt8i/Pgt8NX/uS8D8YguVJEmSJEmS1Pa0x2B3JjCjgTZLG7h+bP79XWAzYGhE7JRSera+ThHRGbiTXHAL8ATwW+BJYAWwObAj8EXgEKBHje7/BwypZ/j5+faLgUPrafd2fTWq9Vi2bBk333wzt912GwsXLuT1119n4403Zuedd+aoo47i6KOPZqONCl90/+qrrzJ58mTuuusuli1bRs+ePfnKV77CmWeeSUVFRTN+E0mSJEmSJGWtPQa7S1NKTxfbOSJ255OA9cfAQr1uTQAAIABJREFUJUAXcmHvpAa6n8wnoe5VwISUUlqvzX3A1RHREfga8BpASukjoM66I2JV/uNHTfl+aj1uvPFGfvSjH9GrVy+GDx9Onz59WLp0KfPmzePUU0/lzjvvZPbs2UREg2MtXryY0aNH89Zbb/HlL3+ZHXbYgccff5xZs2Zx1113cdttt9G9e/cW+FaSJEmSJEnKQnsMdptq7Wrd94DZwL7AEcDREfGTlNKaevqu3V7hQ+D0WkLddVJKq4DrM6hXZWrQoEFcd911HHjggZ9amXv22Wez//77U1VVRVVVFWPHjm1wrNNPP5233nqLCy+8kBNPPHHd+bPOOosZM2Zw3nnncdlllzXL95AkSZIkSVL22sXD07ISEZ2Ao/KHf0wpvQf8Ln/cB9i/gSH65d/fTCm92wwlqg0ZMWIEBx988AbbLfTs2ZPjjz8egPvvv7/BcRYvXszdd99Nv379+O53v/upa5MmTWLTTTfl+uuv57333suueEmSJEmSJDUrg93GORjYOv/59/n324C38p+P3aDHp63Mv28bEZtnXJvakU6dOgHQsWPDi+7vu+8+APbbb78NQuLNN9+cvfbai/fff5/HHnss+0IlSZIkSZLULAx2G2dtcPs6cBes2zJhbv78YQ0Etk/k3zsCv4qITZulSrVpq1atYu7c3F+5UaNGNdh+0aJFQG5rh9qsPf/8889nVKEkSZIkSZKaW3vcY3ebiPhcPdf/lVJ6df2TEdEdOCR/eG1KaXWNy78Dvg90Bb4O/KaOsa8gtx9vAIcDB0TEPHIPTHsUeDofFKsNWxu0FmvatGksXLiQYcOG0a9fvwbHe/nllwH48MMPa227alXur9wLL7zQ5NrUOP68pZbj/Sa1DO81qeV4v0ktx/tNzWHw4MFNHqM9rtg9CXiqntf5dfQ7Etg4//n3NS+klB4F/pE/rHM7hpTSA8DJwMf5UxXAN4GrgL8ByyPirog4JSK2aNzXUnswd+5crrnmGgYMGMC5555b6nIkSZIkSZJUIu1xxW6x1ga2T6eU/l7L9d8D5wH7RsSAlNKS2gZJKc2KiPnARHKre2sGuF2B/fKvyog4OaX03xnVr1ai2N/IXHXVVUydOpWddtqJm266iZ49exbUr2/fvgB07ty51rnX7tM7aNCgTH5bpIat/W2vP2+p+Xm/SS3De01qOd5vUsvxflNr1x5X7J6bUop6Xset3yEiPgt8Pn/4uzrG/T2QyG2zcEx9BaSUnk0pfRvYCtgb+GF+3CU1mm0F3BARRzTiu6mNmjFjBmeccQY777wz8+bNKzjUhU/+AfTCCy/Uen3t+e23377phUqSJEmSJKlFtMdgtxhrV+uuAa6trUF+he79+cN6g90afT5OKT2UUpqWUjompTQQ2Ad4uEazn0dEl+LKVlswbdo0zjrrLIYMGcK8efPYeuutG9V/+PDhANx9992sWbPmU9feeecdHn74Ybp27cqee+6ZWc2SJEmSJElqXga7DYiIjYCj84cbAS9HRKrtBQzPtxsUEV8sZr6U0oPAaOCl/KltgBFN+AoqYxdddBGVlZXstttuVFVV0aNHjzrbfvzxxzz33HMsXrz4U+cHDhzIfvvtx0svvcQvf/nLT1274IILeO+99zjiiCPYdNNNm+U7SJIkSZIkKXvusduwUcC2RfQ7lk9W8DZKSmlFRFxPbh9egMHArcWMpfJ17bXXMnnyZDp06MDee+/NrFmzNmjTr18/xo8fD8Brr73G0KFD6du3L0899dSn2k2dOpXRo0fz4x//mHvuuYcdd9yRxx57jPvuu4/tt9+e//qv/2qR7yRJkiRJkqRsGOw2bO02DCuBb5HbjqE+JwBfAg6PiO+nlD4sct7XanxORY6hMvbiiy8CsHr1ambOnFlrm2HDhq0LduszcOBA/vKXvzB58mTuuusu7rjjDnr27MmECRM488wzqaioyLR2SZIkSZIkNS+D3XpExBbAYfnD21NKte6vu16fRC7Y3RL4KjC3xrVIKRUa0tbc8PT/CuyjNmTSpElMmjSp4Pb9+/enurq6zut9+vRhxowZWZQmSZIkSZKkEnOP3fodDqx9cNn/FNjnFmDtKt1j17s2LyImRETX+gaIiDHAkfnDauAvBc4tSZIkSZIkqR1wxW791gazHwFVhXRIKb0bEbeSW617QER8JqX0ev7yAGAmcHFEzCO3B+8zwL+ATcjtpXtY/hX5PmeklD7I4LtIkiRJkiRJaiMMdusQEdsBX8wf3pVSqvv/cd/Q/5ALdjsARwMX58+/DOwCbEZuRe6RtfbOeQ+YmFL6ZWPqliRJkiRJktT2GezW7Rg+WTVb6DYMa80j97C1Tcit+r0YIKV0cETsBBwIDAN2BvqQC3pXAv8E/he4E7gmpfRGE7+DJEmSJEmSpDaoXQS7KaX5fBLSFtqnEqgscr4VQOc6rj0LPAtcXszY9czZK8vxJEmSJEmSJLVePjxNkiRJkiRJkspMu1ixqyJUVpa6gjZpm969S12CJEmSJEmS2gCDXdWquroxz4qTJEmSJEmS1JLcikGSJEmSJEmSyozBriRJkiRJkiSVGYNdSZIkSZIkSSozBruSJEmSJEmSVGZa7OFpEfE54IvAJsAdKaWFLTW3JEmSJEmSJLUlma3YjYgDI+KvEXFRLdfOBP4GXAlcCiyIiB9nNbckSZIkSZIktSdZbsXwDWAv4KmaJyNiN+B8oAPwKrAkP+/kiBiW4fySJEmSJEmS1C5kGezulX+/fb3zJwAB/BEYkFIaBFyRP3dyhvNLkiRJkiRJUruQZbC7DfBRSunN9c4fBCTggpTSmvy5n+XfXbErSZIkSZIkSY2UZbBbAXxQ80REfAYYAPwzpfT42vMppaXAO0DPDOeXJEmSJEmSpHYhy2B3BbBlRGxa49x++ff7a2mfgJUZzi9JkiRJkiRJ7UKWwe6C/Pu3ACIiyO2vm4C/1GwYEd2ALYDXM5xfkiRJkiRJktqFLIPdOeQeiDY1Im4BHgGGk9ueYe56bffNvz+T4fySJEmSJEmS1C5kGezOBq4DOgIHA/8GfAR8L6X01nptj86/35Xh/JIkSZIkSZLULnTMaqCUUgLGR8Qs4Avk9ty9K6X0fM12EdEJWAJcDlRlNb8kSZIkSZIktReZBbsRsUX+419TSvfV1S6l9DEwMat5JUmSJEmSJKm9yXIrhmpgGdA7wzElSZIkSZIkSevJbMUu8C6wKqX0coZjSpIkSZIkSZLWk+WK3cVA14jIMiyWJEmSJEmSJK0ny2D3BqAT8NUMx5QkSZIkSZIkrSfLYPdi4DHgFxGxf4bjSpIkSZIkSZJqyHLbhDOBu4HPArdHxALgQeAtYHVdnVJKP82wBkmSJEmSJElq87IMdiuBBET+eFfg/6unfeTbG+xKkiRJkiRJUiNkGezOIRfUSpIkSZIkSZKaUWbBbkrpuKzGkiRJkiRJkiTVLcuHp0mSJEmSJEmSWkCWWzGoDamoqCh1Ccrbpndvnlu4sNRlSJIkSZIkqRXJPNiNiIHAD4EDgL5A55RSxxrXK4BTye3HOyWl9HHWNSgDlZWlrkB5S/2zkCRJkiRJ0noyDXYj4jByD1HrCkT+9KceqJZSqo6I/YDhwELgD1nWIEmSJEmSJEltXWZ77EbETsA1wKbAVcC+wNt1NP8lueD3kKzmlyRJkiRJkqT2IssVuxOBzsBlKaXTASJidR1t78y/D81wfkmSJEmSJElqFzJbsQvsT27bhYsaaphSehN4j9wevG1CRIyMiNTI143rjfHbGtcGrHftMxHxr/y1FyNiswJquqTGeN/L9htLkiRJkiRJKpUsg91ewDv50LYQK4GNM5y/TUspvQ78R/6wHzClvvYRMRT4Qf7wfuDK5qtOrcmyZcuYM2cO48ePZ/fdd6dXr17069ePgw46iDlz5rBmzZqCxzrnnHM49NBD2WWXXejVqxcDBgxg+PDhTJkyhWXLljXjt5AkSZIkSVJ9styK4T1gi4jokFKqawsGACJic6ACWJrh/K3JTGBGAe1WNGbQlNKvIuJIcqujT46I61JKD6zfLiI2Bn4NdAA+BL6dUkrrt1PbdOONN/KjH/2IXr16MXz4cPr06cPSpUuZN28ep556KnfeeSezZ88mIhoca8aMGey6666MHDmSrbfemvfee4/HHnuMKVOmMHv2bO644w769OnTAt9KkiRJkiRJNWUZ7P4vMAz4N+CRBtoeQW618OMZzt+aLE0pPd1MY38XeBroCvwqInZNKa1cr81/ArvkP5+bUnqumWpRKzRo0CCuu+46DjzwQDba6JNF+WeffTb7778/VVVVVFVVMXbs2AbHevnll+ncufMG58877zymTp3KZZddxtSpUzOtX5IkSZIkSQ3LciuGG4AAzouIOseNiCHkthFIwDUZzt8upJQWkwtuAXYEzql5PSJ2Bc7MHz4BXNJy1ak1GDFiBAcffPCnQl2Anj17cvzxxwNw//33FzRWbaEuwFe/+lUAXnjhhSZUKkmSJEmSpGJlGez+AlgAjALuiojDyK8IjoghEXFIRFwJPAR0Bx4Ars9w/vbkcnI/R4CJEbEbQER0JLcFQydgFfCtlNKq0pSo1qhTp04AdOzYtMX6t956KwC77LJLAy0lSZIkSZLUHDLbiiGl9HFEHARUASOAfWtc/nuNz0EulBznvq/FSSmtiYhvA38j9wC6X0XEXuQerrZHvtmFKaUnS1WjWp9Vq1Yxd+5cAEaNGtWovtOnT+fdd99lxYoV/P3vf+fBBx9kl1124Yc//GFzlCpJkiRJkqQGZLnHLimlNyJiH+A44Fjg8+SCR4DVwGPAb4FfuZK0aVJKCyPifOBccmHulcAx+cvPAOeVqja1TpWVlSxcuJDRo0ez//77N6rv9OnTWbr0k2cdjho1ihkzZrDVVltlXaYkSZIkSZIKEM25aDYiOpDbdmEj4J9tOcyNiJHAX/KHM4EZBXRbnFJ6r8YYvyUXiAMMTCktaWDOTuQeQDekxuk1wBdTSg8WUvfy5ctr/QtQMW1aId3VEiorefTRR5s0xNy5c5k6dSoDBgzg6quvZssttyxqnH/+858sWLCAK664gvfff5/LLruMnXbaqUm1SZIkSZIktTeDBw+u9fyWW24ZhY6R2R67tT0wLaW0OqX0VkrpzdpC3Yjok9X8rcxJwFMFvD7flElSSh8DJ6x3enqhoa7ahxtuuIGpU6cycOBAZs6cWXSoC9CjRw++9KUvccUVV7B8+XLOOeechjtJkiRJkiQpc1luxfBbPtkKoEERMQC4G9guwxrao+HrHb9WkirUrOr6LU5DZsyYwcUXX8zOO+/MTTfdxNZbb51ZPTvttBNPPfUU3bt3p0ePHpmM214sWrQIKP7PVVLhvN+kluG9JrUc7zep5Xi/qbXLbMUucHRETC+kYUQMAu4F+mc4f2tybkopCnjNb8okETGY3B67NVVGhGG5mDZtGmeddRZDhgxh3rx5mYW6a73xxhsAdOjQIdNxJUmSJEmS1LAsg923gJPzD/SqU0TsCNwD9AEeyHD+diUiArga6ELuwXT/kb/UBbiqVHWpdbjooouorKxkt912o6qqqt4VtR9//DHPPfccixcv/tT5559/nuXLl2/Qfs2aNZx33nm89dZb7LXXXlRUVGRevyRJkiRJkuqX5VYMo4H5wJkRUZ1Sunj9BhGxC3An0DPfdkyG87c3JwH75j9PTSlNjYidgO8A+0fE8Sml35SuPJXKtddey+TJk+nQoQN77703s2bN2qBNv379GD9+PACvvfYaQ4cOpW/fvjz11FPr2tx+++389Kc/5Qtf+AL9+/ene/fuLF26lAceeIAlS5bQs2dPLr/88hb7XpIkSZIkSfpEZsFuSunJiPgKcAcwJSKWp5TWrRyNiF3z17YiF+6OTSl9kNX87UlE9AWm5A8XAZX5zxOBrwCfAS6JiFtSSktbvkKV0osvvgjA6tWrmTlzZq1thg0bti7YrcvIkSNZvHgxDz74IAsWLGD58uVsuummDBo0iCOOOIIJEybQrVu3zOuXJEmSJElSw7JcsUtK6a8RcRhQBcyIiHdSStdFxOeBW4FuwJ+BcSmllVnO3c78AtgcSMB31wbkKaXqiPge8AegOzAdOKJkVaokJk2axKRJkwpu379/f6qrqzc4v/POO3PxxRssvJckSZIkSVIrkOUeuwCklG4HjiIXOv42In4C3E4u1K0CDjPULV5EHAMcnD+8KqV0T83rKaU/An/MH34jItzuQpIkSZIkSWpjMg92YV24eAK5FcE/BbYkFzZ+PaX0UXPM2R5ERE/gsvzhq8AZdTT9HrB2CeaMiNi8uWuTJEmSJEmS1HIy3YqhppTSbyJiC3JB5HXAN1NKa5prvlZmm4j4XAHtPkopPdeIca8gt8UCwEkppRW1NUopvR4RE4FfAn3I7cd7SiPmkSRJkiRJktSKFRXsRsTqRjRPwL8D/x4RG1xLKTVbuFxCJ+VfDXkRGFDIgBExDvh6/vD6lNK8+tqnlK6OiKOALwEnRcQ1KaW/FjKXJEmSJEmSpNat2K0YIqNXs2wF0dZERDfgyvzhP4HvF9j1BOADcj/rqyNi42YoT5IkSZIkSVILK3a17MBMq2gDUkrzyQWoTRnjOOC4Ws7/C/hMEeM9D3RtSk2SJEmSJEmSWp+igt2U0otZFyJJkiRJkiRJKkxb3N9WWaisLHUFytumd+9SlyBJkiRJkqRWJrNgNyK6AJ8HPkgpPdpA288DXYBHUkofZlWDslNdXV3qEiRJkiRJkiTVIcuHlx0N/AX49wLafrcRbSVJkiRJkiRJNWQZ7H49//67AtpeRe5BY9/IcH5JkiRJkiRJaheyDHZ3BD4Cniyg7RP5tjtlOL8kSZIkSZIktQtZBru9gHdTSqmhhimlNcA7+T6SJEmSJEmSpEbIMthdAVTkH6JWr3ybCuCDDOeXJEmSJEmSpHYhy2B3QX68cQW0/RrQAXg6w/klSZIkSZIkqV3IMti9gdwD0S6NiF3qahQRnwMuBVK+jyRJkiRJkiSpETpmONavgZOAXYFHI+LXwJ+Bl/LX+wNfBo4DOgNPAVdlOL8kSZIkSZIktQuZBbsppVUR8RXgZmA3ciHvSbU0DeDvwKEppY+zml+SJEmSJEmS2osst2IgpfQa8AXge8AjwGpyQW7kPz8CnAJ8IaX0SpZzS5IkSZIkSVJ7keVWDACklD4CZgAzIqIj0D1/aVlKaVXW80mSJEmSJElSe5N5sFtTPshd2pxzSJIkSZIkSVJ7k+lWDJIkSZIkSZKk5lfUit2I2Df/8f2U0mPrnWuUlNK9xfSTJEmSJEmSpPaq2K0Y5gMJ+Aew83rnGiM1oQZJkiRJkiRJapeKDVVfIhfKvlbLOUmSJEmSJElSMyoq2E0pDSjknCRJkiRJkiQpez48TZIkSZIkSZLKTGbBbkTsGxFfaET7ocU+cE2SJEmSJEmS2rMsH1w2H3gd2LbA9tcDfTOuQZIkSZIkSZLavKy3Yohmbi9JkiRJkiRJ7V4p99jdHPiohPNLkiRJkiRJUlkqSbAbEUOB7sCrpZhfkiRJkiRJkspZ0fvbRsSxwLHrne4eEXfX1w2oAHYGEvDnYueXJEmSJEmSpPaqKQ8uGwCMXO/cxrWcq8u9wNlNmF+SJEmSJEmS2qWmBLs3AkvynwP4NbAc+EE9fdYAK4D/TSk934S51cwqKipKXYKUuW169+a5hQtLXYYkSZIkSVKTFR3sppSeBJ5cexwRvwY+SCnNzqIwlVhlZakrkDK31L/XkiRJkiSpjWjKit1PSSmV5EFskiRJkiRJktTeGMZKkiRJkiRJUpkx2JUkSZIkSZKkMmOwW8YiYmREpEa8KktdsyRJkiRJkqSmM9iVpEa4/vrrqaiooKKigjlz5hTU57XXXuMXv/gFX//61xkyZAj77LMPo0aN4qtf/SpVVVXNXLEkSZIkSWqLMnt4mkpuJjCjgTZLW6IQqa165ZVXmDhxIpttthnvvvtuwf2uuuoqpk2bRv/+/Rk+fDidOnXijTfeYP78+cyfP5+TTz6ZyZMnN2PlkiRJkiSprTHYbTuWppSeLnURUluVUuKUU06he/fujBkzhunTpxfcd4899uDmm2/mi1/8IgCLFi0CYM2aNRxwwAHMmDGDb3zjG+y2227NUrskSZIkSWp73IpBkgowa9Ys7r33Xq688kq6du3aqL6HHnroulC3ph133JHDDjsMgPvuuy+TOiVJkiRJUvtgsCtJDfjHP/7Bueeey4QJExg2bFimY3fq1AmAjh39HygkSZIkSVLhmi1JiIhtgD2ArfOn3gKeSCm5z6uksrFq1SpOPPFE+vTpw9lnn53p2CtWrKCqqoqIYL/99st0bEmSJEmS1LZlHuxGxBeBnwHD67h+L/CfKaUHsp5bkrJ24YUXsmDBAm699Va6dOmS2bgpJU499VSWLl3Kd77zHXbcccfMxpYkSZIkSW1fpsFuREwAppPb4iGA1cDb+cs98vONAOZHxPdSSr/Icv52bpuI+Fw91/+VUnq1xaqRWqm1Dy4rxNNPP82ll17K+PHj6dat27q+y5YtA+DNN99s1Hg1TZs2jRtvvJHdd9+d448/vuhxJBXGe0xqGd5rUsvxfpNajvebmsPgwYObPEZme+xGxO7AFUAH4AHgQGCzlNJnUkqfATYHDspf6wBcke+jbJwEPFXP6/zSlSaVn1WrVnHOOefQr18/JkyYkOnYP//5z7n22mvZfffdmTZtGhtvvHGm40uSJEmSpLYvUkrZDBTxe+Ao4AbgqJTSmjrabQTMBb4OXJNS+mYmBbRDETES+EuBzWenlI5b/+Ty5ctr/QtQMW1a8YVJrVVlJdXV1QU1ra6uZsCAAQW1nTBhAlOmTCmo7aRJk5g5cyZ77rknVVVVdO3ataB+koqzdnVFFr8Nl1Q37zWp5Xi/SS3H+02lsOWWW0ahbbPcimEEkIAf1hXqAqSU1kTED4CvASMznL+9OzelVFnqIqS2YpNNNuGb36z9905PPvkkCxYsYO+992b77bdn6NChDY6XUmLixIlcffXV7LXXXlxyySWGupIkSZIkqWhZBrtbA9UppdcbaphSei0iqvN9JKnV6dKlC9OnT6/12gUXXMCCBQs48sgjOeaYY9adf//993nllVfo0qULffv2XXc+pcRpp53GnDlzOOCAAzjnnHPYZJNNmv07SJIkSZKktivLYHcFUBERm6aU3quvYURsCmwB/CvD+SWppB5//HHGjBnDsGHDuOWWW9adv/DCC5kzZw5dunRhyJAhzJ49G4Du3buvazNkyBAOOeSQFq9ZkiRJkiSVpyyD3SeAA4BTgQsaaHsauQeoPZ7h/JLUKr344osAfPDBB1x66aW1tjnyyCMNdiVJkiRJUsGyDHavAkYD5+VX5F6cUlpes0FEfAaYSC78Tfk+klRWJk2axKRJkzY4P3z48FofzjZz5kxmzpy57tgN+CVJkiRJUlNlFuymlP4YEb8DvglMAk6PiCeBV4HOQD9gMNAJCGB2SulPWc0vSZIkSZIkSe1Flit2AY4DngHOJLeHbm2Pil8BTAYuyXhuSZIkSZIkSWoXMg12U0oJmBIR08ntt7sHsHX+8lvk9uG9PaX0fpbzSpIkSZIkSVJ7kvWKXQBSSu8BN+ZfkiRJkiRJkqQMbVTqAiRJkiRJkiRJjdMsK3bVMlJK88k9iE6SJEmSJElSO1JUsBsRd+c/vphSOn69c42RUkr7F1ODJEmSJEmSJLVXxa7YHZl/f7aWc42Ripxfza2ystQVSJnbpnfvUpcgSZIkSZKUiWKD3ePz78trOac2oLq6utQlSJIkSZIkSapDUcFuSml2IeckSZIkSZIkSf+PvfsOs7q88z7+/opUpUsRpTwqLhFjiUGMimLXsNiwN8iTLCrhISFlk2g2TGJZE9cVRdCETXbBWBA1oargGnR1UWwRjAViQFFCEQVEpMn9/HHO4ADT58ycOcz7dV2/a37lLt8DnitXPnNz/3Jvj3wXIEmSJEmSJEmqGoNdSZIkSZIkSSow1dqKISK65aqAlNJ7uRpLkiRJkiRJkhqC6r48bXGO5k81qEGSJEmSJEmSGqTqhqqRo/lzNY4kSZIkSZIkNRjVCnZTSu7NK0mSJEmSJEl5YkArSZIkSZIkSQXGYFeSJEmSJEmSCkytvbgsIjoCXwE6ZG+tAl5JKa2srTklSZIkSZIkqSHIebAbEccDNwL9ynj+DPDTlNJzuZ5bkiRJkiRJkhqCnG7FEBHXAH8iE+oGsA1YmT0+z947EZgTEVfncm5JkiRJkiRJaihyFuxGxJHAXUAj4DngDGDvlNK+KaV9gZbAmdlnjYC7sn0kSZIkSZIkSVWQyxW738+O9xDQP6U0O6W0qfhhSmlTSmkWmRW7D5MJd7+Xw/klSZIkSZIkqUHIZbB7IpCAkSmlbWU1yj77brZt/xzOL0mSJEmSJEkNQi6D3Q7AmpTS3ytqmFJaBqzJ9pEkSZIkSZIkVUEug911QMuI2Kuihtk2rbJ9JEmSJEmSJElVkMtg9xUy++aOqETb72TbvpzD+SVJkiRJkiSpQchlsPsbIIAbIuLGiGi9c4OI2Dci/h34BZk9dn+Tw/klSZIkSZIkqUHYM1cDpZQejYh7gSuBnwDfj4jXgA+AZkA3oCfQmEwAPCGl9IdczS9JkiRJkiRJDUXOgt2sIcCbwI/J7KF7dClt1gE3A/+W47klSZIkSZIkqUHIabCbUkrALRExBjgN+ArQIft4FZl9eGellDbkcl5JkiRJkiRJakiqFexGxM+A9Smlfy/teUrpU+CP2UOSJEmSJEmSlEPVfXlaEfCDkjci4m8R8XyNK5IkSZIkSZIklau6WzEkdg2Fe5B5SZokSZIkSZIkqRZVd8XuR0D7iGiZy2IkSZIkSZIkSRWr7ord54FBPh0PAAAgAElEQVSvA1MjYjKwPnu/eURcVZWBUkoTq1mDalGbNm3yXYIkSZIkSZIaoI5durDwjTfyXUa9FymlqneK6AP8CWhBZlsGgChxXmkppUZVLkA5s3bt2lL/ztqMHl3XpUiSJEmSJElQVMSaNWvyXUVetG7dOirbtlordlNKL0bEEcBQoDfQHOgPbAHmVmdMSZIkSZIkSVLlVHcrBlJKfwX+ufg6IrYBH6WUTspFYZIkSZIkSZKk0lX35WmleQ9YmsPx8iYiWkTEtyJiekQsjYjPImJzRHwYEa9ExL0RMTwiDi6lb1FEpOzRv4J5joiIVSXa/6DEsx4l7hcfu8xXxrizd+p3S5X/ECRJkiRJkiTVW9VesbuzlFKPXI2VTxHRF3gQ6FHK4/bZ40jgimz75imljdWYpw/wBNCWzN7E/y+lNLaCblcC/1LBuPsDJ1e1HkmSJEmSJEmFI2crdiPi84j4oArtF0fE1lzNnwsR0ROYxReh7gxgCPA1MmHuKcBIYCrwWQ3mOQ54kkyouw0YWkGoWxwcXxERFW2gfAWZv9dq1ydJkiRJkiQVio8++oiJEydy+eWXc+SRR9K5c2e6devGmWeeycSJE9m2bdsO7a+99lratGlT7nH22WdXau533nmH0aNHM3DgQHr37k2HDh3o2bMnl156Kc8880xtfNztcrZiF4jsUdU+9clNQKvs+bdSSr8tpc1TwOiIaEUm9P28KhNkt2eYDuyV7TskpfT7CrpNBS4kEzj3A8r7r+Kq7M8pwCVVqU2SJEmSJEkqNH/84x/53ve+R+fOnenXrx/7778/K1euZNq0aYwYMYInn3ySCRMmULxecsCAAXTr1q3UsSZNmsSSJUs47bTTKjX3TTfdxKOPPkqvXr047bTTaNu2LYsWLeKxxx7jscce45ZbbuGaa67J2WctKVJKuRko8/K05SmlLpVsvwzYJ6XUJCcF1FBENAI+AZoDL6WU+lRznCJgVPbypJTSnBLPTgf+mJ1jC3BZSunhMsbpASzOXv4SOA44HvhtSulbZfTpA8zLXg4gs+IY4JcppR+X1mft2rWl/gfQZvTo0m5LkiRJkiRJtauoiDVr1lS6+dNPP82GDRs444wz2GOPLzYoWLFiBaeccgrvv/8+EyZM4Jxzzil3nDVr1vClL32Jzz//nDfffJP27dtXOPd9993HoYceyuGHH77D/WeffZbzzjuPiGD+/Pl07ty5Up+ldevWlV4Im8uXp1VaRHQGOgIf5mP+MnQgE7gCvJPrwSPiH8msvG0ObAIuKCvULcPE7M8LIqJZGW2KV+u+DLxRrUIlSZIkSZKkAnLiiSdy1lln7RDqAnTq1IlvfOMbQCZorcikSZP47LPPGDhwYKVCXYDLL798l1AX4Pjjj+f4449n8+bNvPDCC5Uaq6qqvRVDRJwA9N/p9t4R8bPyugFtgDOz589Vd/5asKnE+ZdyOXBEnE/mhWyNyex9e15K6YkqDvMQcCfQGjgHmLTTHI35YuuFiUiSJEmSJEkNXOPGjQHYc8+KY9AJEyYAMHjw4DqfuzpqMupJZLYcKPlP+ffii20IylK8nPgj4Oc1mD+nUkofR8QSMvvYHhYR1wG3pJS2lduxAhFxKZmgdU9gPTCw5PYMVahvbURMBS4CrmSnYJfM1gv7kNni4QEyfxeSJEmSJElSg7R161YefPBBAE499dRy286bN4833niDgw46iBNOOKHGc7/33ns8/fTTtGjRguOOO67G45WmJsHun4EJJa4HAxvJrCwtyzZgHfAX4A8ppdU1mL823AHcnj2/Cbg6G6b+L/Ai8E6q2qbEV5J5wdoeZD73WSml/61BfRPJBLtnRETHlNLKEs+Kt2F4PKW0KiIMdiVJkiRJklSQFi1aVOMxRo8ezRtvvMFxxx1Ht27dyh3zzjvvBODrX/96jefevHkzw4YNY9OmTYwYMYJVq1axatWqHdr07NmzRnNADYLdlNIUYErxdUQMBtamlL5R46ry5w4y2zAMzV53A4ZnD4DVEfE0mdWyj6aUtlYw3v/N/twEnJJSeqmG9T0BrCSzP/Gl2XqJiHZkVuyC2zBIkiRJkiSpgXvwwQe577776NGjBz//efmbBqxfv54nn3ySxo0bM3DgwBrN+/nnnzNq1Chee+01TjvtNK644ooajVeeXG7wcB6wIiIapZQ+z+G4dSa7GvfqiJgEjATOILMvbrH2wPnZ462IuCyl9Gp5Q5LZeqIpcDZQo2A3pbQ1Ih4AvkNmNfAd2UeXAE2ANcC0mswhSZIkSZIk5VtNVrT+5je/4bbbbqNXr15MmTKFTp06ldt+/PjxbNy4kUGDBtGnT59qz/v5558zdOhQnnzySc477zzGjx9fa/vrQmaLgFz5A/As0CWHY+ZFSumplNJAoB1wOnA98ChQcuuIXsDTEdG7nKF+AnySPf+XiPhJDsorXpF7VEQckj0v3tH5oZTSplL6SJIkSZIkSbu9cePG8c///M8ccsghTJs2rcJQF754adqQIUOqPe+WLVv45je/ySOPPMKFF17If/zHf9RqqAu5DXbXA+tSSktzOGZepZTWp5Rmp5RuTikNAjoDg4D3s01aAqPLGeIF4OvAp9nrmyNiZA1reoXMHsUAV0bEPwBHZ6/dhkGSJEmSJEkN0ujRo7nuuuv48pe/zLRp0+jQoUOFfV566SVef/11DjroIPr161eteTdv3szgwYP54x//yCWXXMKvf/1rGjVqVK2xqiKXwe5ioEVE1G4UnUcppa0ppUfJhLWbs7dPzu5xW1afZ8lsw/BZ9ta/R8SwGpZSHOBeTublbJB5sdtzNRxXkiRJkiRJKji/+tWvKCoq4ogjjmDq1Km0b9++Uv3+67/+C4DBgweX227t2rUsXLiQ5cuX73B/06ZNXHHFFcycOZMrr7yScePGscceuYxcy5bLEPYh4BfAucDDORy33kkpLYiIF4B+ZMLxA4GPymn/VEScT+Zlc02AuyJiU0rpt9Us4ffAvwJdge9m791bzbEkSZIkSZKkgnX//fdz880306hRI772ta9xzz337NKmW7duXH755TvcW7duHX/4wx9o2rQpl112WblzTJ8+nW9/+9tceuml3H333dvvjxw5klmzZtG+fXv23XdffvnLX+7S9/jjj6/2auDy5DLYvZXMytRfR8THKaX/zuHY9dGyEueposYppccj4kIyoXdj4DfZcPf3VZ04pbQsIv4bOA1olp3fYFeSJEmSJEkNzrvvvgtkXl5WMnQt6bjjjtsl2J08eTKffvopgwYNqvQK37LmXr16Nb/61a/KbFcbwW6kVGEmWbmBIn4GNAe+DewFzAfmAquAz8vql1L6RU4KqEMREcBC4CAyoWrHlNKH2WdFwKhs05NSSnN26nsB8CDQiMyfy2UppYdKmaMHme0tAH6ZUvrxTs8vA36XvfzflNLJVelfbO3ataX+B9BmdHlbB0uSJEmSJEm1pKiINWvW5LuKvGjdunVUtm0uV+wWkQk5iyc/HDisnPaRbV8vgt2I2Bt4CrgRmJFSKjOMBn5OJtQF+J/iULcyUkoPR8RgMvvkNgLuy67cnVKVelNK9wP3V6WPJEmSJEmSpN1DLoPdiVRiS4J6rg+ZfXD/HhFTyKw4XgysA1qSCaqvAL6Wbb8J+H5VJ0kp3RcRTYH/IPN38FBEnJtSeqzmH0GSJEmSJEnS7i5nwW5KaUiuxsqTrcByoDOwL3BN9ijL+8DglNJL1ZkspfS7iGgGjCXzQrVHI+IfG8DexJIkSZIkSZJqKJcrdgtaSmljRHQBjgFOyf78BzIhbzNgA5ngdz4wHXgopbShhnOOi4gmwO3ZOaZGxJkppf+pybiSJEmSJEmSdm85e3maCpMvT5MkSZIkSVK94svTKqVWVuxGRH/gIuArQIfs7VXAK2RWus6pjXklSZIkSZIkqSHI6YrdiNgHuA84tfjWTk2KJ5sNXJFS+jBnk6taylyx26ZNXZciSZIkSZIk0bFLFxa+8Ua+y8iLqqzYzVmwm90r9gXgMDKB7lzgKTIvGQPYHzgZ+BqZgPc14JiU0uacFKBqKSvYlVR7Fi1aBEDPnj3zXIm0+/P7JtUNv2tS3fH7JtUdv2/Kh3xtxTAcOBz4CLg0pTS7lDb/EhGnAw9k236bzIvDJEmSJEmSJEmVtEcOx7qYzErcoWWEugCklGYBQ8ms6r0kh/NLkiRJkiRJUoOQy2D3H4CNwB8q0fYP2ba9cji/JEmSJEmSJDUIuQx2GwNbUiU27U0pbQO2kNutICRJkiRJkiSpQchlsPse0DIivlJRw4g4CmiZ7SNJkiRJkiRJqoJcBrszyeyb+9uI6FBWo4joBPyWzH68M3I4vyRJkiRJkiQ1CLncCuGXwGDgMOCtiBgPzAE+AJoB3YCTgCFAC+Aj4Fc5nF+SJEmSJEmSGoScBbsppZUR8XXgj0Bn4IfZY2cB/B04N6W0MlfzS5IkSZIkSVJDkcutGEgpzQMOAUYBC8hstxDZI2Xv/QzonVJ6MZdzS5IkSZIkSVJDkcutGABIKa0BbgBuiIjGQLvso49SSltyPZ8kSZIkSZIkNTQ5D3ZLyga5K2pzDkmSJEmSJElqaGoc7EZEU+Bc4CigFbAGeAGYllLaWtPxJUmSJEmSJEk7qlGwGxHHApPJvCxtZ0si4tyU0oKazCFJkiRJkiRJ2lG1X54WEfsB08mEusUvR1tV/Bj4P8DMiGhd0yIlSZIkSZIkSV+odrALfAdoQ2brhauAFimlzsBewAjgM6AL8M2aFilJkiRJkiRJ+kJNgt3TyKzSHZFS+n1KaTNASmljSukuYBSZlbun17xMSZIkSZIkSVKxmgS7B5AJdh8p4/nkEu0kSZIkSZIkSTlSk2C3JbAqpbSxtIcppXezp3vVYA5JkiRJkiRJ0k5qEuxCZsVuRaKGc0iSJEmSJEmSSqhpsCtJkiRJkiRJqmN71rB/u4h4qgZtUkrplBrWIEmSJEmSJEkNSk2D3SZA/xq0qcxWDpIkSZIkSZKkEmoS7E7IWRWSJEmSJEmSpEqrdrCbUvpGLguRJEmSJEmSJFWOL0+TJEmSJEmSpAJjsCtJkiRJkiRJBaamL0/TbqpNmzb5LkHarmOXLix84418lyFJkiRJklRvGOyqdEVF+a5A2m6l/z1KkiRJkiTtwK0YJEmSJEmSJKnAGOxKkiRJkiRJUoEx2JUkSZIkSZKkAmOwWw0R0SIivhUR0yNiaUR8FhGbI+LDiHglIu6NiOERcXApfYsiImWP/tWYe0mJ/jsfmyNiRUT8KSJ+EBG+AU2SJEmSJEnaDfnytCqKiL7Ag0CPUh63zx5HAldk2zdPKW2so/IaAx2zR3/gBxFxXkppbh3NL9Ubo0aN4tVXX+Wdd95h9erVNGvWjK5duzJgwACGDh1Ku3btKj3WE088wT333MPbb7/NRx99RKdOnTjiiCP49re/zdFHH12Ln0KSJEmSJKl0BrtVEBE9gVlAq+ytGcBk4G1gI9AOOAw4CTgNaF6L5SwDztjpXjPgAOAqYADQCZgeEf+QUvqwFmuR6p1x48Zx+OGH079/fzp06MCnn37KSy+9xC233MKECROYPXs2+++/f4XjjBo1ijvuuIN27doxYMAA2rdvz9/+9jdmzpzJ1KlTueeee7j44ovr4BNJkiRJkiR9wWC3am7ii1D3Wyml35bS5ilgdES0AoYAn9dSLVtSSq+Xcv8l4KGI+D1wOZmw+VvALbVUh1QvLV26lGbNmu1y/4YbbuC2227j9ttv57bbbit3jBUrVjBmzBg6duzIc889R4cOHbY/e+aZZzj77LO5+eabDXYlSZIkSVKdc4/dSoqIRsA/Zi9fKiPU3S6ltC6ldGdKaUvtV1eqkkGu/1ZcDU5poS7AueeeC8A777xT4RhLly5l27ZtHHXUUTuEugAnnHACLVu2ZPXq1TUvVpIkSZIkqYoMdiuvA19srVBxIpR/i0ucN81bFVI98/jjjwPQu3fvCtseeOCBNGnShFdeeWWXAPe5557jk08+4cQTT6yVOiVJkiRJksrjVgyVt6nE+ZfyVkXl9Shx/l6+ipDybcyYMaxfv55169bx5z//mblz59K7d29GjhxZYd+2bdtSVFTE9ddfT9++fRkwYADt2rVj8eLFPPbYY5x00kmMHj26Dj6FJEmSJEnSjgx2Kyml9HFELCETmB4WEdcBt6SUtuW1sLL9c4nzKXmrQsqzMWPGsHLlyu3Xp556KuPGjWOfffapVP9hw4bRrVs3hg8fzoQJE7bfP+CAA7jssst22aJBkiRJkiSpLkRKKd81FIyI+C5we4lb7wFTgf8FXgTeSRX8gUZEETAqe3lSSmlOFWtYAnQHlgFn7PS4KXAAcAVwdvbegymlS8sab+3ataXW28ZViKpPiop48cUXazTE6tWrmT9/PnfddRcbNmzg9ttvp1evXhX2mzhxIuPGjePiiy/mwgsvZJ999mHJkiWMHTuW559/niuvvJIRI0bUqDZJkiRJktSw9OzZs9T7rVu3jsqO4R67VXMH8JsS192A4cD9wCJgVUQ8EhEXRURtr4buAizY6XgJeIhMqPsW8A3gslquQyoI7du356STTuKuu+5i7dq1jBo1qsI+L7/8MmPGjKFfv36MHDmS/fffn2bNmtGrVy9uvfVWOnbsyH333cf7779fB59AkiRJkiTpC27FUAXZ1bhXR8QkYCSZFbONSzRpD5yfPd6KiMtSSq/WfaUA9AKGAn8DnslTDVLOlPWbrOqM06tXLxYsWEC7du1o3759mW2Lt14466yzSp3/6KOPZvr06axbt65K9S1atGh7LZJql983qW74XZPqjt83qe74fVN954rdakgpPZVSGgi0A04HrgceBVaXaNYLeDoietdSGe+mlKLkQSao3xe4EHgN+BowOyIuqKUapIK0fPlyABo1alRuu02bMu9M/PDDD0t9vnp15ivfpEmTHFYnSZIkSZJUMYPdGkgprU8pzU4p3ZxSGgR0BgYBxf8uuyVQZ5vVppQ+TyktTyk9DBwPLASaAL+LiHZ1VYeUb3/9619Zu3btLve3bdvGDTfcwKpVq+jbty9t2rQBYMuWLSxcuJDFixfv0P7YY48FMit3ly1btsOz2bNn8/zzz9OsWTP69u1bS59EkiRJkiSpdG7FkEMppa3AoxGxiMx+t02AkyOiXUrpozquZX1E3E3mZW8tgQvYcX9gabc1a9YsfvGLX3DMMcfQvXt32rVrx8qVK3nuuedYsmQJnTp14o477tjeftmyZRx99NF07dqVBQsWbL9/zjnn0L9/f+bMmUPfvn0ZMGAAnTp14u233+aJJ54gpcSoUaNo187fm0iSJEmSpLplsFsLUkoLIuIFoB+ZVdEHAnUa7Ga9VeL8y3mYX8qL/v37s3jxYubOncv8+fNZu3Yte+21FwceeCAXX3wx11xzDW3btq1wnD322IPJkyczfvx4Hn30UWbMmMGGDRto27Ytp59+OldffTUnn3xyHXwiSZIkSZKkHRns1p6S/2475amGPcs4l3ZrhxxyCLfeemul23fv3p01a9aU+qxx48YMGzaMYcOG5ao8SZIkSZKkGnOP3VoQEQEclb1MwJI8lfLVEudL81SDJEmSJEmSpBwz2K2kiNg7IuZFxNkR0aiC5j8HDsqe/09K6cNaLm8XEdEd+HaJWzPqugZJkiRJkiRJtcN/nl81fYApwN8jYgowF1gMrCPzgrLDgCuAr2XbbwK+X854Z0ZEj0rMO7WUl681johDd7q3B9CBzN6+w4H22fv3ppReq8Q8kiRJkiRJkgqAwW7lbQWWA52BfYFrskdZ3gcGp5ReKqfNjyo595Hs+vK1LsCCSvS9H/inSs4jSZIkSZIkqQAY7FZSSmljRHQBjgFOyf78BzIhbzNgA5ngdz4wHXgopbShrssEPgHeI7OaeEJK6bk6rkGSJEmSJElSLTPYrYKUUiITmM6twRhFQFEN+veobl9JkiRJkiRJuwdfniZJkiRJkiRJBcYVuypdUVG+K5C269ilS75LkCRJkiRJqlcMdlWqNWvW5LsESZIkSZIkSWVwKwZJkiRJkiRJKjAGu5IkSZIkSZJUYAx2JUmSJEmSJKnAGOxKkiRJkiRJUoEx2JUkSZIkSZKkAmOwK0mSJEmSJEkFxmBXkiRJkiRJkgqMwa4kSZIkSZIkFRiDXUmSJEmSJEkqMAa7kiRJkiRJklRgDHYlSZIkSZIkqcAY7EqSJEmSJElSgTHYlSRJkiRJkqQCY7ArSZIkSZIkSQXGYFeSJEmSJEmSCozBriRJkiRJkiQVGINdSZIkSZIkSSowBruSJEmSJEmSVGAMdiVJkiRJkiSpwBjsSpIkSZIkSVKBMdiVJEmSJEmSpAJjsCtJkiRJkiRJBcZgV5IkSZIkSZIKjMGuJEmSJEmSJBUYg11JkiRJkiRJKjB75rsA1U9t2rTJdwmSpAasY5cuLHzjjXyXIUmSJEn1lsGuSldUlO8KJEkN2Er/d0iSJEmSyuVWDJIkSZIkSZJUYAx2JUmSJEmSJKnAGOxKkiRJkiRJUoEx2N1JZJwVEWMj4rWIWBERmyPi44h4MyLujYhLI6J5KX3/KyJSieP+Ss553E79UkQ0q2Tf4SX6bImITlX9zJIkSZIkSZIKi8FuCRFxLPAyMBMYBhwGdAQaA22AXsAVwP3A+xHxvYgo78/w3IhoWYmpB9eg7JJ99wQuq8FYkiQVpJQSEyZM4JRTTmG//fajS5cu9O/fn0ceeYRt27ZVebwpU6Zw/vnnc8ABB9CpUycOPfRQLr30Ul588cVaqF6SJEmSqm7PfBdQX0TElcB/AE2yt14CHgFeAT4EWgI9gDOBs4F2wG3A74A1pQy5EWgOXAD8ZznzNgMuzF5+lu1T2ZoPAb6avVwP7E0m6L29smNIkrQ7GDp0KJMnT6ZDhw4MGjSI5s2bM2fOHG655Rbmz5/P/fdX6h/RsHXrVq699lomT57MgQceyPnnn0+rVq1YsWIFL774In/+85/p06dPLX8aSZIkSaqYwS4QEf3JhK+NgA3At1JKD5TS9GlgQna7gyLgmnKGnQJcDFxJOcEumZC4DZkg+Ang3CqUXrxa9zPgR8BY4PCIODyl9FoVxpEkqWBNmzaNyZMn0717d5566inat28PwObNmzn//POZOXMmU6dO5eyzz65wrH/9139l8uTJ/OAHP+C6665jjz12/Ic5W7ZsqZXPIEmSJElV1eC3YsjulXs/mVB3GzCwjFB3u5TSipTStWRW2pb1//Duzf7sHxFdyxmuOJydBqytQt2NyGwLAZkQeQLw6U5jSpK025s+fToAw4cP3x7qAjRp0oRrrsn8Dnb8+PEVjrNixQrGjBlDnz59+OlPf7pLqAvQuHHjHFUtSZIkSTXT4INd4P8C+2bP704pPVXZjimlh1NKn5bx+E/AUiD4IoDdQXbl7+nZy4mVnTfrVKBL9vzebB1/yF5fHhGuxpYkNQgrV64EoEePHrs822+//QCYO3cumzdvLnecKVOmbF/l+9lnnzFlyhRuv/12xo8fz4IFC3JetyRJkiTVhOEffCP7MwGjczjuNuA+4MdktmP411LaXEbm72Al8DiZ/Xgrq3hV7kpgVvb892RC5I5k9gKeXuWqJUkqMMWrdN99991dnn3wwQdAZu/cJUuWcPDBB5c5ziuvvALAZ599Rp8+fXj//fd3eH722Wdzzz330KJFi1yVLkmSJEnV1qBX7EZEK+CI7OXClNJfczxF8SrcL0XEV0t5XhzOPpBS2lrZQbN1F+/F+2CJvk8Cy3caW5Kk3drpp2f+8cvYsWP5+OOPt9/fsmULv/nNb7Zfr1lT2rtOv/Dhhx8CcNNNN9GtWzeefvppPvjgA5588kmOPPJIpk6dyve///1a+ASSJEmSVHUNfcXul8nsrQvwcq4HTym9GREvAV8FrgJeKn4WEV8GDs9eVnUbhouA5tnz4r18SSl9HhH3A98DBkZE25TSx6UNIElSfbdo0aJKtTvssMM45phjeP755znqqKM48cQTadKkCfPmzWP16tV07tyZ5cuX88EHH5Q75vr16wFo1aoVN910Ey1atGDZsmW0bt2am2++mUGDBjFp0iSuuOIKOnbsmJPPKO1uKvu9lVRzft+kuuP3TbWhZ8+eNR6jQa/YBdqXOF9RS3MUh7aX7LTvbfGK2r+klF6p4pjFfd9KKb2007PfZ382BS6u4riSJBWcRo0acfvttzN8+HDatm3LjBkzmDFjBt26deO3v/3t9q0T2rZtW+44e++9NwB9+vTZfl5sn332oXfv3mzbto0333yzdj6IJEmSJFVBQ1+x27LEeVkvQaupB4HbgA5k972NiEZk9teFEituKyMiDgSOz17+fufnKaVXI+IvQG8yAfA91axbkqS8qupvsG+88UZuvPHGHe69/vrrLF26lPbt29O/f/9y+x955JE8+eST7L///qXO3aVL5p2lbdu2zclv16XdSfFKJr8bUu3z+ybVHb9vqu8a+ordT0qc71UbE6SUVpF5MRpktmMAOB3Yl8wL1nYJZytQPEYqp29xWHxMRJT9lhhJknZzs2bNYsuWLQwaNKjCtsXBb1krct966y0AunfvnrP6JEmSJKm6Gnqwu7rEeadanKd4O4aBEdGaL8LZp1JKH1R2kIiIEn2fTSnt+vrvjPvIhMbgS9QkSQ3AunXrdrk3f/587rzzTlq1asXIkSO339+wYQMLFy5k6dKlO7Q/9thj+fKXv8zcuXOZNm3aDs8mTJjA22+/zQEHHMCRRx5ZOx9CkiRJkqqgoW/FsAD4nMwL1I6qxXmmAR8DbYFvAedk71f1pWknAD2y5/0iIlWiz5UR8S8ppW0VN5UkqTCdd955NGvWjEMOOYS9996bt99+m1mzZtG0aVNuu+029t133+1tX375ZQYOHB9aoAwAACAASURBVMhxxx3HjBkztt+PCO6++24GDBjAVVddxZlnnslBBx3EW2+9xezZs9lrr724++67adSoUWklSJIkSVKdatArdlNK64A/Zy8Pjoha2TQlpbQJeCh7eSPQHFgPPFrFoaqz+rYrcFI1+kmSVDDOOecc1q9fz6RJkxg7dix/+ctfGDJkCJMmTeKooyr/u9tDDz2Up59+mksuuYRXX32Vu+++m/nz53PRRRcxZ84c+vbtW4ufQpIkSZIqr6Gv2AX4TzKrdQP4DjC8luaZCFwNNMteP5pSqvQL2yKiBXBB9vIZ4O6KugC/JRMiXwX8d5WqlSSpgIwYMYIRI0bscr/4hRcl9evXjzVr1pQ5Vo8ePbj77or+Z1aSJEmS8stgF34HXE/mZWbXRsSjKaWnKtMxIgYBj1cmoE0p/W9EvAEcmL01oYp1ng+0zJ7/OqX0YCXquwg4FxgUEd9OKa2v4pySJEmSJEmS6qEGvRUDQErpM+ByMnvt7gFMi4iLy+sTER0iYizwMNC4CnP1Tik1yx6VCo9LKN6GYROZPXsr4+Hsz72Ail8HLkmSJEmSJKkguGIXSCn9KSL+LzAeaAE8GBHfJxOMvgqsBvYGugNnkHn52d51VV9E7A+cnL2clVL6pJJdp5EJgpuSCYarukpYkiRJkiRJUj1ksJuVUpoYEe8AY4AjgT7ZoywfAj8H1tVBeVfyxerqh8trWFJKaV1EzAb+EegfEd1TSu/WRoGSJEmSJEmS6k6D34qhpJTSc2RepDaAzMvJ5gOrgK3AWuBN4F7gEmD/lNJdKaVtdVBa8TYMm4GpVew7OfszyATEkiRJkiRJkgqcK3Z3klJKwMzsUdW+Q4AhNZi71P4ppV41GHMiMLG6/SVJkiRJkiTVP67YlSRJkiRJkqQC44pdla6oKN8VSJIasI5duuS7BEmSJEmq1wx2Vao1a9bkuwRpt7Vo0SIAevbsmedKJEmSJElSoXIrBkmSJEmSJEkqMAa7kiRJkiRJklRgDHYlSZIkSZIkqcAY7EqSJEmSJElSgTHYlSRJkiRJkqQCY7ArSZIkSZIkSQXGYFeSJEmSJEmSCozBriRJkiRJkiQVGINdSZIkSZIkSSowBruSJEmSJEmSVGAMdiVJkiRJkiSpwBjsSpIkSZIkSVKBMdiVJEmSJEmSpAJjsCtJkiRJkiRJBcZgV5IkSZIkSZIKjMGuJEmSJEmSJBUYg11JkiRJkiRJKjAGu5IkSZIkSZJUYAx2JUmSJEmSJKnAGOxKkiRJkiRJUoEx2JUkSZIkSZKkAmOwK0mSJEmSJEkFxmBXkiRJkiRJkgqMwa4kSZIkSZIkFRiDXUmSJEmSJEkqMAa7kiRJkiRJklRg9sx3Aaqf2rRpk+8SJEm7oY5durDwjTfyXYYkSZIkFTyDXZWuqCjfFUiSdkMr/d8XSZIkScoJt2KQJEmSJEmSpAJjsCtJkiRJkiRJBcZgN4cion9EpHKOTyPinYiYFBEDS+m/pIL+lTmGlBiv+N6cuvxzkCRJkiRJklS7DHbrVgvgAOAiYGpEzIyIFnmuSZIkSZIkSVKB8eVpteduYFyJ6wBaA18FvgP0AM4C7gGuyrY5HWhSxng3Audkz88AlpXR7v1qVyxJUj2TUmLixIlMnDiRt956i5QSBx98MFdddRVDhgxhjz0q/h31+vXr+fGPf8xrr73G4sWL+fjjj2nZsiXdunXjggsuYPDgwey111518GkkSZIkKXcMdmvPypTS66XcfzYiJgHzgX2AKyLixymlZSmlhWUNFhFrSlwuTCktyW25kiTVP0OHDmXy5Ml06NCBQYMG0bx5c+bMmcP3vvc9XnjhBX79619XOMa6deuYMGECX/nKVzj99NPZZ599WLduHc888wzXXXcdEydOZNasWbRq1aoOPpEkSZIk5YbBbh6klP4eEROA75NZyftVYGp+q5IkqX6ZNm0akydPpnv37jz11FO0b98egM2bN3PllVcyadIkBgwYwNlnn13uOJ06deK9996jcePGuzwbOnQoDz30EP/5n//Jd77znVr5HJIkSZJUG9xjN38WlzhvmrcqJEmqp6ZPnw7A8OHDt4e6AE2aNOH6668HYPz48RWO06hRo1JDXYBzzsnscvTOO+/UtFxJkiRJqlMGu/nTo8T5e/kqQpKk+mrlypUA9OjRY5dnxffmzp3L5s2bqz3H448/DkDv3r2rPYYkSZIk5YNbMeRBRHTiixemLQVeyWM5kiTVS8WrdN99991dni1ZsgSArVu3smTJEg4++OAKx9u6dSu33norAB9//DFz585lwYIF9OvXj8GDB+eucEmSJEmqAwa7tadjRBy6073WwFHAd4COwGbg/6WUttR1cZIk5cuiRYsq1e6www7j4YcfZvTo0RxxxBG0bt0ayAS0P/3pT7e3W7BgARFR4ZybNm3il7/85Q73v/71r/OjH/2IpUuXVvFTSCpNZb/fkmrO75tUd/y+qTb07NmzxmMY7Naea7NHWR4A/i2l5GpdSZJKcfrppzNz5kyef/55LrroIk488USaNGnCvHnzWL16NZ07d2b58uXssUfldpZq2rQpL774IiklVq1axbx58xg7dixXXXUVd955J126dKnlTyRJkiRJuWOwmz/nAJsiYmRKaU2+i5Ekqa5U5TfT06ZNY+zYsTz00EPMnDmTpk2bcvzxx1NUVMTgwYNZvnw5Rx55ZKn78MIXqyt2nvPggw/muOOO4/jjj+e0005j3LhxTJo0qdqfSWroyvquSco9v29S3fH7pvrOYLf2/DylVFTyRkQ0I/PStEHAT4AhwNERcXJKaUVdFyhJUn3XuHFjvvvd7/Ld7353h/sbN27knXfeoX379mWGupXRp08fWrduzbPPPlvDSiVJkiSpblXu3y4qJ1JKG1NKb6WUbgLOz94+BLgtj2VJklRwHnnkETZv3sygQYNqNM4nn3zCJ598QqNGjXJUmSRJkiTVDYPdPEkpzQJey15eFBF75bMeSZLqo3Xr1u1yb/78+fzsZz+jTZs2jBw5cvv9DRs2sHDhwl1ehPbXv/6VjRs37jLO5s2b+eEPf8i2bds444wzcl+8JEmSJNUit2LIr7eAw4HGQC/g5fyWI0lS/XLeeefRrFkzDjnkEPbee2/efvttZs2aRfPmzXnggQfYd999t7d9+eWXGThwIMcddxwzZszYfn/KlClcffXV9O3bl65du9K6dWv+/ve/86c//YkVK1bQs2dPbrjhhnx8PEmSJEmqNoPd/NqzjHNJkgScc845PPLII0yaNImNGzey7777MmTIEEaOHMl+++1XqTFOPfVUGjduzIsvvsi8efNYv349LVu2pFevXgwfPpxvfvObtGjRopY/iSRJkiTllmFinkREAEeVuLW0rLaSJDVUI0aMYMSIEZVq269fP9asWbPL/cMPP5wLLrgg16VJkiRJUl65x27+DAN6ZM9fTSkty2MtkiRJkiRJkgqIK3ZrT8eIOHSne02B/wMMAi7J3tsG/KguC5MkSZIkSZJU2Ax2a8+12aM8nwBXp5Rm10E9kiRJkiRJknYTBrt1awvwMfAmMAv4XUppeX5LkiRJkiRJklRoDHZzKKU0B4haGnsIMKSKfWqlFkmSJEmSJEn55cvTJEmSJEmSJKnAuGJXpSsqyncFkqTdUMcuXfJdgiRJkiTtFgx2Vao1a9bkuwRpt7Vo0SIAevbsmedKJEmSJElSoXIrBkmSJEmSJEkqMAa7kiRJkiRJklRgDHYlSZIkSZIkqcAY7EqSJEmSJElSgTHYlSRJkiRJkqQCY7ArSZIkSZIkSQXGYFeSJEmSJEmSCozBriRJkiRJkiQVGINdSZIkSZIkSSowBruSJEmSJEmSVGAMdiVJkiRJkiSpwBjsSpIkSZIkSVKBMdiVJEmSJEmSpAJjsCtJkiRJkiRJBcZgV5IkSZIkSZIKjMGuJEmSJEmSJBUYg11JkiRJkiRJKjAGu5IkSZIkSZJUYAx2JUmSJEmSJKnAGOxKkiRJkiRJUoEx2JUkSZIkSZKkAmOwK0mSJEmSJEkFxmBXkiRJkiRJkgqMwa4kSZIkSZIkFRiDXUmSJEmSJEkqMAa7kiRJkiRJklRg9sx3Aaqf2rRpk+8SClbHLl1Y+MYb+S5DkiRJkiRJuzGDXZWuqCjfFRSslf7ZSZIkSZIkqZa5FYMkSZIkSZIkFRiDXUmSJEmSJEkqMAa7kiRJkiRJklRgDHarICL6R0TKHkWlPE/lHBsjYllEPB4R10REswrmmrNT/5srWePlO/VbXs2PK0mSJEmSJKmeMtitO02BfYEzgLuBBRHRswr9L4+IqES7wdUpTvkzZcoUfvjDH3LWWWfRtWtX2rRpw9ChQ6s11hNPPMF5553HIYccQufOnTn88MMZPHgw8+bNy3HVkiRJkiRJyqc9813Abuol4Bs73WsB9AKGAscBBwEzI+LQlNKmcsbaBDQBugH9gT+V1TAiugCnZC8/A5pXp3jVrVtvvZXXX3+dvffemy5duvDJJ59Ua5xRo0Zxxx130K5dOwYMGED79u3529/+xsyZM5k6dSr33HMPF198cY6rlyRJkiRJUj4Y7NaOT1NKr5dyf15E/J5MOHsCmXD3PODBcsZaA7wFnAhcSTnBLnAFmVXYHwB/A/pVvXTVtZtvvpn99tuPAw44gGeffZaBAwdWeYwVK1YwZswYOnbsyHPPPUeHDh22P3vmmWc4++yzufnmmw12JUmSJEmSdhNuxVDHUkrbgFtL3Dq6Et3uzf68ICLKW4V7Vfbn/cC2apSnPDjhhBM48MADqdxOG6VbunQp27Zt46ijjtoh1C0ev2XLlqxevbqmpUqSJEmSJKmeMNjNj8UlzptWov1kMlsrtATOLa1BRBwF9M5eTqxRdSo4Bx54IE2aNOGVV17ZJcB97rnn+OSTTzjxxBPzVJ0kSZIkSZJyza0Y8qNHifP3KmqcUloXEVOAS8isyn2glGbFq3VfTSm9XpPVnyo8bdu2paioiOuvv56+ffsyYMAA2rVrx+LFi3nsscc46aSTGD16dL7LlCRJkiRJUo4Y7NaxiNgD+H72MgHTKtl1Iplg97SI6JxSWl5izMbApSXaqQEaNmwY3bp1Y/jw4UyYMGH7/QMOOIDLLrtsly0aJEmSJEmSVLgMdmvHXhFx6E73mgO9gG+ReXEawK9SSm9UcsxZwHKgM3AZ8O8lnp0FdAC2ktlfV3m2aNGiavX74IMPAFi3bl2Vx5g4cSLjxo3j4osv5sILL2SfffZhyZIljB07ln/6p3/imWeeYcSIEdWqS7Wjuv+dSKo6v29S3fC7JtUdv29S3fH7ptrQs2fPGo/hHru146vAgp2OeWRW054AvACcn1L6cWUHTCl9zheh7ZU7PS7ehuGJlNLKGtStAvXyyy8zZswY+vXrx8iRI9l///1p1qwZvXr14tZbb6Vjx47cd999vP/++/kuVZIkSZIkSTngit386ANcGxGLUkqvV6HfvcD3gCMi4tDsXrptgX8s8Vz1QHV/67J8eWaHjVatWlVpjOKtF84666xS+x199NFMnz6ddevW5eQ3QqqZ4t/2+nch1T6/b1Ld8Lsm1R2/b1Ld8fum+s4Vu7Xj6ZRSlDyAxkBXYDCZF6adBjwbEcdXdtCU0p/JrP6FL1bpXgI0BdYCU3L1AVRYNm3aBMCHH35Y6vPVq1cD0KRJkzqrSZIkSZIkSbXHYLeOpJS2ppTeTylNBI4FPgJaA7+PiKqsnC5+Odpl2RexFQe8k1NKG3NXseqjLVu2sHDhQhYvXrzD/WOPPRbIrNxdtmzZDs9mz57N888/T7Nmzejbt2+d1SpJkiRJkqTa41YMeZBS+ntE3At8B+gOnEzm5WiVcR9wC7AfcC1wTPb+xDJ7qF6bPn06M2bMAGDlyswWyfPmzePaa68FoH379tx4440ALFu2jKOPPpquXbuyYMGC7WOcc8459O/fnzlz5tC3b18GDBhAp06dePvtt3niiSdIKTFq1CjatWtXx59OkiRJkiRJtcFgN3/eKnH+ZSoZ7GZD4SeBM4B/y95eDDyb2/JUVxYsWMADDzyww70lS5awZMkSALp27bo92C3LHnvsweTJkxk/fjyPPvooM2bMYMOGDbRt25bTTz+dq6++mpNPPrm2PoIkSZIkSZLqmMFu/uxZxnllTCQT7DbLXt+bUko5qUp17ic/+Qk/+clPKtW2e/furFmzptRnjRs3ZtiwYQwbNiyX5UmSJEmSJKkeMtjNn6+WOF9axb5/AD4EWgIJuDdXRUmSJEmSJEmq/wx28yAivgJckr3cDMyuSv+U0mdAh1zXJUmSJEmSJKkwGOzWjr0i4tCd7u0JdAJO/f/t3XmcXGWd7/HPz4BEgiSAAQyEBBFBIuAoE4ioRLaoqICCjLLlXtHLqKNy3e6oDBF0xAURozDO9WJyAQVGB1BE1oBhiQMoGECWAIlssiWkQzbW3/xxTpOyrep0VVd35XR/3q9Xv07VWZ7ze1J9cpJvP/0c4OPABuX6b2bmE4NZnCRJkiRJkqRqM9gdGLsBt61lnxeBU4ETBr4cSZIkSZIkSUOJwe7geRFYBtwHXAecmZnzO1uSJEmSJEmSpCoy2G1CZl4DRC/bG25r4VxTO3m8JEmSJEmSpHXXyzpdgCRJkiRJkiSpOY7YVX0zZnS6gsrafNy4TpcgSZIkSZKkIc5gV3UtXbq00yVIkiRJkiRJasCpGCRJkiRJkiSpYgx2JUmSJEmSJKliDHYlSZIkSZIkqWIMdiVJkiRJkiSpYgx2JUmSJEmSJKliDHYlSZIkSZIkqWIMdiVJkiRJkiSpYgx2JUmSJEmSJKliDHYlSZIkSZIkqWIMdiVJkiRJkiSpYgx2JUmSJEmSJKliDHYlSZIkSZIkqWIMdiVJkiRJkiSpYgx2JUmSJEmSJKliDHYlSZIkSZIkqWIMdiVJkiRJkiSpYgx2JUmSJEmSJKliDHYlSZIkSZIkqWIMdiVJkiRJkiSpYgx2JUmSJEmSJKliDHYlSZIkSZIkqWIMdiVJkiRJkiSpYgx2JUmSJEmSJKliDHYlSZIkSZIkqWIMdiVJkiRJkiSpYtbrdAFaN40ZM6bTJUjSoNt83Dju+dOfOl2GJEmSJElrZbCr+mbM6HQFkjToHvfvPkmSJElSRTgVgyRJkiRJkiRVjMGuJEmSJEmSJFWMwa4kSZIkSZIkVcyQD3YjYmpEZIOvVRHxYERcHBHHRMTIOsdP7+X4FRGxKCIuioijIuLlfawpIuKAiDg7Iu6JiKcj4vmI6IqIOyPiwoj4ckTsERF/8xn1t0+SJEmSJEmSqm3IB7trMRLYGjgA+L/ArRGxQxPHbwhMAN4HzAZuiohtejsgIjYHrgYuBg4Htgc2AkYAGwM7AgcCXwPmAfs3UQ/0v0+SJEmSJEmS1nHrdbqAQXYGcHrN+w2BNwKfAV4P7AD8JiImZeaqOsd/Bbio5v2WwBuALwCvBnYBfhkRb87MF3oeXI7ovRzYtVz1R+AnwC3AMoqAdwfgLcB7gbGD0CdJUgvOOeccPvGJT/S6z8te9jKWLFmy1rZOOOEEbrnlFu677z4WL17MyJEjGT9+PAcccAAf+9jH2HTTTdtVtiRJkiRpiBhuwe7jmXl7j3U3RsRZwDXAZGBb4CPAD+oc/3CP428HroyIMylG1+5EEdoeBPyizvHHsCbUnQ38z8x8scc+1wH/LyJGlO08OMB9kiS1YOedd+aLX/xi3W3z5s1j7ty57Lfffn1q6/TTT2fXXXdl6tSpjB07lhUrVnDzzTdz8sknM3v2bK644gq23nrrdpYvSZIkSaq44Rbs1pWZqyLiy8AV5ap30UQImpnLIuJk4P+Xq/ajfrB7ULl8HvhMnVC3ts0XGrTR15r61SdJUu922WUXdtlll7rbugPdo48+uk9tPfjgg4wc+bdTop900kmccsopnHrqqZxyyimtFytJkiRJGnKG+xy7tX5X83pCC8f/oeb1+Ab7dM+/uzgzl7Zwjmb1t0+SpCbdcccd3HTTTYwbN45p06b16Zh6oS7AQQcVPw+877772lafJEmSJGloMNhd47ma1yNaOL52Tt3nGuzzTLncPCIGY8LE/vZJktSkWbNmAXDEEUcwYkT//uq99NJLAZg0aVJ/y5IkSZIkDTFOxbBG7e/TPtLC8TvVvF7UYJ8/lOcJ4MyIODIzn27hXH3V3z5JkpqwatUqzj//fEaMGMFRRx3V9PEzZ85k+fLlLFu2jFtvvZV58+YxadIkjjvuuAGoVpIkSZJUZQa7a3yp5vXVzRxYPuis9n/d5zfY9YfAkRSjZw8EHoqIXwHXAjcCt2dmo9G+rWi5T5I0XC1YsKDlYy+++GK6urp461vfyqpVq5pu69RTT2XJkiUvvZ8yZQonnHACTz31FE899VTLdal/n6ukvvNakwaP15s0eLzeNBC23377frcxrKdiiIhXRMSUiPglax5stgz4UR+P3yIi9gOuA95arj43M2+ot39m3gx8FHi2XLUxcDjwbxSjebsi4uqI+FREjOlEnyRJrbvgggsAOPjgg1s6/rLLLuOmm27i0ksv5Vvf+hYPP/wwRxxxBHfddVc7y5QkSZIkDQGRmZ2uYUBFxFT6Plp1GfCBzLyy5vjpwE/6cOwK4HTgS5n5/Fpq2h74PHAo0CjAXQJ8KjPPqXP8VPrRp1pdXV11vwHGfO97fWxekoaQGTNYurS1Z1veeeedTJkyha222or58+f3e35dgAceeIDddtuN7bbbjnnz5vW7veGoe3RFO34aLqkxrzVp8Hi9SYPH602dMHr06OjrvsN6xG6NB4GZwM6NAtA++D0wc22hLkBmLsjMjwFjgd2BTwOzgftrdtsUODsimp+ksdCOPkmS+qidD03rts0227DDDjtw5513snjx4ra0KUmSJEkaGobbHLtnUIyq7bYaWJyZfZ248CvAReXr9YFtgMOAfwDeDsyNiMmZ+URfGitD4BvLLwAiYjJwCmumdvheRPxnZi5v0Ex/+yRJ6qfVq1dz3nnnMWLECI488si2tv3oo48CtC0sliRJkiQNDcMt2H08M2/vx/EP9zj+FuCiiLiWIlydCPyY4sFoLcnMGyPincCtwGuBTYB9gQsbHNLfPkmS+unCCy9k6dKlTJs2ja233rruPs899xwLFy5k/fXXZ9ttt31p/b333svYsWMZPXr0X+3/4osv8vWvf50nnniC3XffnTFjWpp6XZIkSZI0RA23YHdAZOYZEfEe4N3A+yJi78yc04/2VkTEz4Djy1VO5iJJ67DZs2cDMH369Ib7PPLII0yePJnx48dz2223vbT+8ssv58QTT2SPPfZgwoQJbLrppjz++ONcf/31LFq0iC222ILTTjttoLsgSZIkSaoYg932+SLwLiCAfwX26Gd7j9S8HtpPuJOkCrv77ruZN28eW221Ffvvv3/Tx0+dOpWFCxcyb9485s+fT1dXF6NGjWK77bbjsMMO49hjj2WTTTYZgMolSZIkSVVmsNsmmXl7RFwAvB/YPSL2y8wraveJiMjMvoa0u9W8vr/hXpKkjtphhx1YunTpWvebMGFC3f122mknvv3tbw9EaZIkSZKkIexlnS5giPlazevj62z/z4j4ZERs1FsjETENOLp8uxy4sk31SZIkSZIkSRoCDHbbKDNvAX5dvn1bROzVY5fxwEzg0Yg4twx5946IN0bE5Ig4PCLOB37DmtHUX8rMZYPTA0mSJEmSJElV4FQM7XcScED5+njgtzXbHgLeDIwCDiu/GllFEerOHIgiJUmSJEmSJFWXwW6bZeZ/RcQVwH7APhExJTPnldsOiojXAdOAPYFJwNbAK4FngCXAn4CrgLMz85F655AkSZIkSZI0vA35YDczrwGiH8fPAmY1eUzDx6Jn5j3APRRTMrRa0zX0o0+SJEmSJEmSqs05diVJkiRJkiSpYob8iF21aMaMTlcgSYNu83HjOl2CJEmSJEl9YrCrupYuXdrpEqQha8GCBQBsv/32Ha5EkiRJkiRVlVMxSJIkSZIkSVLFGOxKkiRJkiRJUsUY7EqSJEmSJElSxRjsSpIkSZIkSVLFGOxKkiRJkiRJUsUY7EqSJEmSJElSxRjsSpIkSZIkSVLFGOxKkiRJkiRJUsUY7EqSJEmSJElSxRjsSpIkSZIkSVLFGOxKkiRJkiRJUsUY7EqSJEmSJElSxRjsSpIkSZIkSVLFGOxKkiRJkiRJUsUY7EqSJEmSJElSxRjsSpIkSZIkSVLFGOxKkiRJkiRJUsUY7EqSJEmSJElSxRjsSpIkSZIkSVLFGOxKkiRJkiRJUsUY7EqSJEmSJElSxRjsSpIkSZIkSVLFGOxKkiRJkiRJUsUY7EqSJEmSJElSxRjsSpIkSZIkSVLFGOxKkiRJkiRJUsUY7EqSJEmSJElSxRjsSpIkSZIkSVLFGOxKkiRJkiRJUsUY7EqSJEmSJElSxURmdroGdVBXV5ffAJIkSZIkSdI6YPTo0dHXfR2xK0mSJEmSJEkVY7ArSZIkSZIkSRVjsCtJkiRJkiRJFWOwK0mSJEmSJEkVY7ArSZIkSZIkSRUTmdnpGiRJkiRJkiRJTXDEriRJkiRJkiRVjMGuJEmSJEmSJFWMwa4kSZIkSZIkVYzBriRJkiRJkiRVjMHuMBcRW0fEmRHxSEQ8ExGLIuJ7EbFJp2uThpLy2soGX492uj6paiLikIiYGRHXRsSy8lo6ey3HvCUiLomIJRGxKiLmR8RnImLEYNUtVVEz11tETOzlfpcRce5g1y9VRURsFhHHRMQFEXFvea/qiojrIuIjEVH3/+/e36TmNHuteW/Tumy9ThegzomI7YAbgM2Bi4C7gMnAp4F3RsSembm4gyVKQ00X8L0665cPdiHSEPAVYFeK6+chYMfedo6IA4FfAKuB84AlwHuBU4E9gUMHF0prRgAAEZFJREFUslip4pq63kp/BC6ss/72NtYlDTWHAmcAfwGuBh4AtgDeD/wYeFdEHJqZ2X2A9zepJU1fayXvbVrnxN9+n2q4iIjLgP2BT2XmzJr13wWOA36Umcd2qj5pKImIRQCZObGzlUhDQ0S8gyJguhfYi+If5edk5hF19t243G80sGdm3lyuHwnMAaYAH8pMR1tIdTR5vU0EFgKzM3P64FUpVV9E7A2MAn6dmS/WrN8SuBEYDxySmb8o13t/k1rQwrU2Ee9tWkc5FcMwVY7W3R9YBPywx+YTgBXAkRExapBLkyRprTLz6sxcUGckRT2HAGOBc7v/01u2sZpiJCLAPw5AmdKQ0OT1JqlFmTknM39VGzSV6x8F/q18O7Vmk/c3qQUtXGvSOsupGIavd5TLy+v8ZfZ0RFxPEfzuAVw12MVJQ9QGEXEEsA3FD0/mA3Mz84XOliUNeXuXy0vrbJsLrATeEhEbZOYzg1eWNKSNi4j/BWwGLAbmZeb8DtckVdlz5fL5mnXe36T2q3etdfPepnWOwe7wtUO5vKfB9gUUwe7rMNiV2mVL4Kwe6xZGxP/IzN92oiBpmGh4z8vM5yNiITAJeA1w52AWJg1h+5VfL4mIa4CjM/OBjlQkVVRErAccVb6tDXG9v0lt1Mu11s17m9Y5TsUwfI0ul10NtnevHzMItUjDwU+AfSjC3VHAzsCPgInAbyJi186VJg153vOkwbMSOAl4M7BJ+dU9L+9U4Cqn+pKadjLwBuCSzLysZr33N6m9Gl1r3tu0zjLYlaRBkJlfLedyeiwzV2bm7eXDCb8LvAKY0dkKJUnqv8x8PDP/JTP/kJlLy6+5FL8J9l/Aa4FjOlulVB0R8Sngs8BdwJEdLkcasnq71ry3aV1msDt8df/0dnSD7d3rlw5CLdJw1j05/9s7WoU0tHnPkzosM58Hfly+9Z4n9UFEfBI4DfgT8I7MXNJjF+9vUhv04Vqry3ub1gUGu8PX3eXydQ22b18uG83BK6k9niiX/uqONHAa3vPKudS2pXhAxv2DWZQ0DHnPk/ooIj4DzARupwiaHq2zm/c3qZ/6eK31xnubOspgd/i6ulzuHxF/9X0QEa8E9qSYR+Z3g12YNMzsUS79B7c0cOaUy3fW2fZ2YEPgBp8YLg0473lSH0TEF4FTgVspgqbHG+zq/U3qhyautd54b1NHGewOU5l5H3A5xYObPtFj81cpftp0VmauGOTSpCEnIl5fbzL9iJgI/KB8e/Zg1iQNMz8HngT+ISJ2614ZESOBr5Vvz+hEYdJQExFv6jlooFy/D3Bc+dZ7ntRARBxP8QCn3wP7ZOaTvezu/U1qUTPXmvc2rcsiMztdgzokIrYDbgA2By4C7gR2B95BMQXDWzJzcecqlIaGiJhBMRH/XODPwNPAdsABwEjgEuDgzHy2UzVKVRMRBwEHlW+3BKZRjJS4tlz3ZGZ+rsf+PwdWA+cCS4D3ATuU6z+Y/qNIqquZ6y0irqGY0usG4KFy+y7A3uXr4zOzO3CSVCMijgZmAS9Q/Gp4V53dFmXmrJpjvL9JTWr2WvPepnWZwe4wFxHjgRMpfn1nM+AvwAXAVzPzqU7WJg0VEbEXcCzwdxT/IR5F8RCLW4GzKEbH+5ex1ITyByYn9LLLnzNzYo9j9gS+DEyh+KHKvcCZwPcz84WBqVSqvmaut4j4CHAw8AbgVcD6wGPAPOAHmXlto0ak4a4P1xrAbzNzao/jvL9JTWj2WvPepnWZwa4kSZIkSZIkVYxz7EqSJEmSJElSxRjsSpIkSZIkSVLFGOxKkiRJkiRJUsUY7EqSJEmSJElSxRjsSpIkSZIkSVLFGOxKkiRJkiRJUsUY7EqSJEmSJElSxRjsSpIkSZIkSVLFGOxKkiRJkiRJUsUY7EqSJEmSJElSxRjsSpIkSZIkSVLFGOxKkiSpaRGR5dfENrQ1tWxrUb8Lq4iImF72+Zo6264pt00f/MoGV0QsKvs6tdO1SJIkVY3BriRJ0jBTE8o2+3VNp2tXNUTEGyNixnAIp2tFxM4R8euIWBYRKyLi6ojYcy3H/DQiXoiI3QarTkmSNDSs1+kCJEmSNOgea7B+U2B9YDXQVWf7kprXd5fL59pYl4aONwInAL8FZvWy330U328rB6GmARURrwOuB14JPA+8AEwF5kTEPpl5XZ1j9gY+BJyRmTcPYrmSJGkIMNiVJEkaZjJzy3rryxG5ewHnZeb0tbSxY/sr03CTmft0uoY2mkER6s4CPk4R7n4N+AJwMvDW2p0j4uXAD4EngC8PYp2SJGmIcCoGSZIkSeq/fShG6X46M1dl5nMUge1jwJSI2LDH/p8DdgS+kJlPDW6pkiRpKDDYlSRJUtPW9vC0iBgVEZ+LiBsiYklErI6I+yPilxFxeESs38S59o2I5eX5Tq6zfaOI+FJE3BQRXeW5FkTE9yNifIM2X3pAWUSMiYhvRsRdEbEyIpY2UdubIuLkiLguIh6IiGciYnHZ/jERMaKvbTVxzo3L+Wv/WP65LI+I+RHx1YgYvZZjm/pcWulfRCTwk/LtXnXmap5as2+vD0+LiC0i4pSaz6YrIm6MiM9GxAYNjplVtjkjIkZExGfKP6uVZZ8vHqD5bDcDnszMZd0rMvN54M8U/+/apKbGiRSh73XA7AGoRZIkDQNOxSBJkqS2ioidgF8DE8tVzwPLgPHAtsB7KeYiXdSHtg4GfgZsAPxzZp7cY/vrgd8AE2rO9QzwWuCfgCMi4r2ZeX2DU4wFfg+8pjzu2b70scblFIEeFPPErqSYq3iv8uvgiDiwDPj6LSJeC1zJmv52z027c/k1PSL2zcwFdY5t5XNppX+PAa8ANqaYg7l2bmbo459xREym+Gw3LVc9Dbwc+Pvy68iI2D8zH2/QxHplf6eVdTxDEa4eAOwTEXtn5ry+1NJHi4FXRcTG3eFuGXxPAF4Eakflfr/sy8czM9tYgyRJGkYcsStJkqS2iYhNgUspwsOFwEHAqMzcDNiQYp7Rn1CEimtr6yjgP1gTgPUMdUcDl1AEZ/8B7AqMzMyNgO2An1IEeb+IiDENTvMvFA+MexewYWZuDDQzmvNyiodfvTozR2XmJsBGwJHAo8C7geOaaK+hck7WX1D090Fg//JcGwH7Ag8A2wAX9BzN2o/Ppen+lXM4f7p8e0Nmbtnj64Y+9HUT4EKKUPc2YHL52WwEHEoRku4KnNNLM5+gCIAPAzbKzFeWx9wOjAROW1sdTZoDjABOi4iREbEexRy7WwC/y8yVZd/eRxGiz8zM29pcgyRJGkYcsStJkqR2+j8UI0CfBN6WmQ93byjnHL2+/OpVRPwTRfD2AnBUZp5dZ7fPUwSVP8vMD9duyMz7gcPLQPOdwDHAd+q0sQHw7sy8vebYe9dWX82+H66zbgVwdkT8GZhL8SCtb/e1zV4cBuxCMfr0r2oGroqIdwO3AJOAw4Eza7a39LkMcv9qfRJ4NbAU2D8zHy3P/QLw84hYBlwG7FuOvJ1Tp40xFH29rqb2+RExHbgZ+PuI2CYzH2hTzScC7wGmA0dQfO9uQPF5/TNARLyC4vv6EeCENp1XkiQNU47YlSRJUjsdVS6/UxseNiMijqf4VfVngUMahLoAR5fLU3pp7qflcr8G23/TIyBtm8y8liKYnBgR49rQ5CHl8qJ6NWfmHcDPy7cf7LG5359LnfO1u3+1uvv64+5Qt8e5Lwe6p1Ho2ddu19aGujXH/h54qHz7hv4WWtPuncDbKEZGr6aYfmEusG9mzi13O57ihxH/OzOfjojNI2J2OW/xyoiYExFvbldNkiRpaHPEriRJktqifCDUFuXbS1prIr5L8av9K4ADM/OqBjuOB7buPlf5wK56Xl4u6z5EjTXhYMsi4lCKEbJvopizd2Sd3cZRjNLsjzeVy6t72WcOxdQJ3fv2+3MZxP51n+/lrAlc19bXKdT0tYebejn2YYrvn0162adpmXkrxbQefyMidgQ+C1yZmedFxEiKPkwCrqIYTX0wcE1ETC6DYkmSpIYMdiVJktQuW9S8buXX27dhzXyt/9go1C29uub15n1oe8MG65/oS2H1lHOonk8RxnV7hiKge6F8P5bit+RGtXqeGmPLZW8jbrtHom4WEVE+mKulz6UD/eu2KWt+s7AvfR3bYPvTvRy7ulyu30Rd/fXDcvnJcvl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\n", 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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 685}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "15 out of 20 tickers were removed\n{'PRSP': 26, 'SKM': 7, 'EIX': 16, 'FAST': 235, 'DLR': 6}\nFunds remaining: $16.69\n"}]}}, "1ccfe0eb957f4419bfb0e0299cd2b71e": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_30867f71b275460d9c43d8d345f77fb3", "outputs": [{"data": {"image/png": 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\n", 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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 695}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "16 out of 20 tickers were removed\n{'PRSP': 20, 'EIX': 16, 'FAST': 261, 'DLR': 2}\nFunds remaining: $4.99\n"}]}}, "26101610dfc8429ebb26b785e3db819a": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "2613e824fe174c879e8967569d767449": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "262011a7e3ea4a95995d7aa7a218eb59": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "SliderStyleModel", "state": {"description_width": ""}}, "263bd636f05e4384a4b6bfc545ff72cd": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": 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\n", 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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 695}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "16 out of 20 tickers were removed\n{'PRSP': 20, 'EIX': 16, 'FAST': 261, 'DLR': 2}\nFunds remaining: $4.99\n"}]}}, "283a989a20c84f168e2da6857186555f": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "FloatSliderModel", "state": {"description": "tr", "layout": "IPY_MODEL_6cc5c889dbb149d590fb97a95f5802a7", "max": 0.2687043293906299, "step": 0.01, "style": "IPY_MODEL_c28efc935e324e6198db862103e4162d", "value": 0.13}}, "28420dba0aaa46699469db15593b8530": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {"height": "40px", "width": "50%"}}, "2843ee43fd794fb39964f98ddae65fa4": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_2fe2d674795e40719a410dc4d971740f", "outputs": [{"data": {"image/png": 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1r389e+21V8aMGZO999476623Xn7+85/njjvuyFvf+tYcccQRnf0FAEAvCGAXopSyVpIzkuyc5MEkJ3SiTq31wVLKN9PaAfvJJMd1og4AAEBfPPDAA0mSmTNn5qyzzupxzPbbbz8ngE2Sd77znRk/fnxOOumkXHrppXnhhRey4YYb5otf/GI+9KEPLdGO1Te/+c257rrr8qUvfSnXXXddnnnmmay//vr593//9xx11FGLdZQBALzcSq19vVNq6VZKmf0L+Fz7dYUkQ5NsltbRA4OS3JLkkFrrffM8e26SMUluTvKSvrl8v9Z6dSllbFrB6udqrWN7WMe6Sf6UZEaSDWutT83dP3Xq1B7/oIYOHbLwNwgAALAQv/nNrW61hrm47R3m53MxvyFDhiz2/0G0A/ZvZu86fTHJ00keSHJekh8lubrWOmshz27f/urJnUmuXlTxWuujpZSzknwiyWfS2gkLAAAAACzFlvsAttba6xPaa62HJTlsMceOTTJ2EWM+GcErAAAAACwzVujvBQAAAAAALKsEsAAAAAAAHSKABQAAAADoEAEsAAAAAECHCGABAAAAADpEAAsAAAAA0CECWAAAAACADhHAAgAAAAB0iAAWAAAAAKBDBLAAAAAAAB0igAUAAAAA6BABLAAAAABAhwhgAQAAAAA6RAALAAAAANAhAlgAAAAAgA4RwAIAAAAAdIgAFgAAAACgQwb29wLomylTpvb3EqDfTZgwIUmy0UYb9fNKoHv4XMD8fC5gfrM/FwBA59gBCwAAAADQIQJYAAAAAIAOEcACAAAAAHSIABYAAAAAoEMEsAAAAAAAHSKABQAAAADoEAEsAAAAAECHCGABAAAAADpEAAsAAAAA0CECWAAAAACADhHAAgAAAAB0iAAWAAAAAKBDBLAAAAAAAB0igAUAAAAA6BABLAAAAABAhwhgAQAAAAA6RAALAAAAANAhAlgAAAAAgA4RwAIAAAAAdIgAFgAAAACgQwSwAAAAAAAdIoAFAAAAAOgQASwAAAAAQIcIYAEAAAAAOkQACwAAAADQIQJYAAAAAIAOEcACAAAAAHSIABYAAAAAoEMEsAAAAAAAHSKABQAAAADoEAEsAAAAAECHCGABAAAAADpEAAsAAAAA0CECWAAAAACADhHAAgAAAAB0iAAWAAAAAKBDBLAAAAAAAB0igAUAAAAA6BABLAAAAABAhwhgAQAAAAA6RAALAAAAANAhAlgAAAAAgA4RwAIAAAAAdIgAFgAAAACgQwSwAAAAAAAdIoAFAAAAAOgQASwAAAAAQIcIYAEAAAAAOkQACwAAAADQIQJYAAAAAIAOEcACAAAAAHSIABYAAAAAoEMG9vcC6JuhQ4f09xKgC7y5vxcAXcjnAubncwHz87mgGVOmTO3vJQB0LTtgAQAAAAA6RAALAAAAANAhAlgAAAAAgA4RwAIAAAAAdIgAFgAAAACgQwSwAAAAAAAdIoAFAAAAAOgQASwAAAAAQIcIYAEAAAAAOkQACwAAAADQIQJYAAAAAIAOEcACAAAAAHSIABYAAAAAoEMEsAAAAAAAHSKABQAAAADoEAEsAAAAAECHCGABAAAAADpEAAsAAAAA0CED+3sBAAAAwPJj8uTJufzyy3PVVVflnnvuyaOPPppBgwZl0003zcEHH5xDDz00K6zwt/1iDz/8cE4++eTceeedeeihhzJlypQMGzYsr371q3PIIYfkwAMPzIorrrhYtT/84Q/nBz/4wULHvPnNb85ZZ53Vp/cIMDcBLAAAAPCy+clPfpJPfOITWWeddTJq1Kist956eeKJJ3LZZZflox/9aK699tqMGzcupZQkyf3335+LLrooW221VUaPHp3VV189kydPzrXXXpsjjzwyF1xwQS6++OIMHLjoiGP06NEZMWJEj30XXHBBJk6cmO22267R9wtQaq39vYaOKKUs6o29v9Z6bg/PvSLJI0kGJ/lerfV9i6hzYJIPJHlTkqFJpiZ5IsltSa6utX6vlPKaJBOW8C2MqrXeNPuHqVOn9vh+hg4dsoTTAgAAQLOmTJm62GN//vOf57nnnss73vGOl+x0ffzxx7Prrrvm4Ycfzrhx47L33nsnSV588cUMHDjwJWOTZPr06dl3331z00035bvf/W723XffPqx/Sl73utdl5syZGT9+fIYOHZqNNtqo1/PBsmbChFas5XPxN0OGDCmLO3Z52AH7uQW037mA9oPSCl9rkneXUj5Wa/2/ngaWUr6T5P1JnktyeZKJSQYk2TjJXklGJfleksk9rGOFJJ9t1/l8D9M/uID1AQAAwFJrp5126rF97bXXzvvf//4cf/zxuemmm+YEsIMGDepx/IorrpjRo0fnpptuyp/+9Kc+remCCy7IX//61+y///4ZOnRon+YCmNcyH8DWWscu4SOHJ5mZ5GtJPpXkvUlOm3dQKWXntMLXB5JsV2t9ZJ7+QUl2aq9hcpKx8/QPTCuAndWLNQIAAMAyZ/ZZrotznMDMmTNzzTXXJEk222yzPtUdN25ckmTMmDF9mgegJ8t8ALskSilbJnlzkiuTnJjk40n+OT0EsElmHwrz3/OGr0lSa30xyTUdWioAAAAsU2bMmJEf/vCHSZLddtttvv5JkyblnHPOSa01kyZNyvXXX58///nPOeCAA7Lnnnv2uu4tt9ySe+65J695zWuy4447zvmn1gBNEcC+1OHt13NrrU+WUq5IsncpZdta6y/nGTup/fral295AAAAsGwaO3Zs7rnnnuy+++7Zdddd5+ufNGlSvvKVr8z5uZSSf/3Xf82xxx7bp7rnnntuErtfgc5ZHi7h6ukM2InzXsBVSlklyaNpHT+wbq31hVLKPkkuTiuQff8840ckuTut82IvSXJBkt8k+VNdjF9q+wiC6Ulm1loXGYS7hAsAAIBu9Zvf3Nqn53/4wx/mpJNOysiRI/Otb30rQ4Ys+L91Z86cmSeffDLXX399zj777Gy44YY5+eSTF/rMgjzzzDPZc889M3PmzFxxxRXOfwXms6CLx5bkEq4VFj1kqXdcD1+H9TDuwCSrJflBrfWFdtv4JE8m+cdSykv+Jq+1PphkvyT3J9k7yfeTTEgypZRyZSnl4FLK8vD7BQAAgF678MILc9JJJ+XVr351zjrrrEUGqQMGDMg666yTgw46KEcffXTuuuuunH322b2qfcUVV+T555/PLrvsInwFOmaZ3wFba12sNLqU8sskb03yllrrrXO1n5LkY0k+Ums9s4fnVkiyQ5Idk7wpyfZJ1mp3X5Vkr/Z5sPM+ZwcsAAAAy4QpU6b26rkzzzwzRx99dDbddNNccsklWXPNNZfo+alTp2aDDTbI6173uvzyl/OeHLhoO+ywQ+6+++5cdtllGTVqVJLMOQN2QbveYHnkczE/O2CXUCll87TC17vnDl/bzm2//nNPz9ZaZ9Vab6y1fqHWul+SdZLsmeSJJO/I386VBQAAANpOOeWUHH300dl8881z2WWXLXH4miSPPvpoktau2CV166235u67785rXvOaOeErQCcIYFtmh6SvL6XUub+S3NHu27KUsvWiJqotP00y+xTwt3VgvQAAALDU+upXv5qxY8dmyy23zKWXXprhw4cvcOydd96ZmTNnztf+zDPP5NOf/nSS5B3veMdL+qZOnZo//vGPeeyxxxY4r8u3gJfLIv/p+7KulPJ3SQ5N6/KtcxcwbP0ku6e1C/aWxZz66dkl+rI+AAAAWJZ8//vfzwknnJABAwZk2223zTe+8Y35xowYMSKHHHJIklZY++tf/zpbb7111ltvvayyyir5y1/+kmuuuSZTp07NNttsk6OOOuolz19++eX5yEc+koMOOihnnXXWfPNPmzYtF198cVZaaaUcfPDBnXmjAG3LfQCb5IAkQ5NcVmv9p54GlFKGJnkkyXtKKZ+otT5dSvmHJIOSXF5rnTHP+MFpnRubJDd2bukAAACwdHnggQeSJDNnzuwxHE2S7bfffk4AO2bMmLziFa/IbbfdlptvvjnPPfdchg4dmi233DL77rtvDj300AwcuGTxxkUXXZRnn302+++//0J33wI0Ybm/hKuUclNaF2ftXWu9dCHjvpfWTtkP1VrPLqX8W5ITk0xO8j9J7ksyI63dsqOTDEnyiyS71lqf72E+l3ABAACwTOjtJVzdyGVDMD+fi/ktySVcy/UO2FLK69IKXx9NMn4Rw7+ZVgB7eJKzk4xL8n9J3p5kiyQ7J1m13XZbkouSfLvWOr0TawcAAAAAut8yG8Auaudre8y9WcwzWmutN849ttb6ZJJvt796s74Zi1sbAAAAAFg6rdDfCwAAAAAAWFYJYAEAAAAAOkQACwAAAADQIQJYAAAAAIAOEcACAAAAAHSIABYAAAAAoEMGvlyFSimvT7JDkpWSXFNrveflqg0AAAAA0B8a2wFbSnlHKeUXpZSv9tD36SR3JPl6kq8l+V0p5f81VRsAAAAAoBs1eQTBPybZJsldczeWUrZM8sUkA5L8JcnEdt0TSinbN1gfAAAAAKCrNBnAbtN+vXqe9sOTlCQ/TjKy1vr3Sc5otx3RYH0AAAAAgK7SZAC7VpIXa62Pz9O+R5Ka5Eu11lntti+0X+2ABQAAAACWWU0GsEOT/HXuhlLKuklGJplUa71tdnut9YkkTydZu8H6AAAAAABdpckAdlqSIaWUVedqe1v79aYextckLzRYHwAAAACgqzQZwP6u/fqBJCmllLTOf61Jrp97YCll9SSrJXm0wfoAAAAAAF2lyQD2vLQu1jqplDI+yS1JRqV1LMEP5xm7Y/v13gbrAwAAAAB0lSYD2HFJfpBkYJI9k2yV5MUkR9Zan5xn7KHt1581WB8AAAAAoKsMbGqiWmtNckgp5RtJ3prWmbA/q7XeN/e4UsqKSSYmOTXJpU3VBwAAAADoNo0FsKWU1drf/qLW+j8LGldrnZ7kU03VXd5NmTK1v5cA/W7ChAlJko022qifVwLdw+cC5udzAfPzuQCAzmvyCIIpSSYneWWDcwIAAAAALLUa2wGb5JkkM2qtDzU4JwAAAADAUqvJHbD3J1mllNJkqAsAAAAAsNRqMoC9MMmKSfZpcE4AAAAAgKVWkwHsiUluTXJ2KWXXBucFAAAAAFgqNXlcwKeTXJfkdUmuLqX8LskvkzyZZOaCHqq1fr7BNQAAAAAAdI0mA9ixSWqS0v75DUm2WMj40h4vgAUAAAAAlklNBrDnpRWoAgAAAACQBgPYWuthTc0FAAAAALAsaPISLgAAAAAA5iKABQAAAADokMYD2FLKq0spp5VS7i2lPFNKmTFP/9BSyrGllM+WUlZsuj4AAAAAQLdo8hKulFL2TesyrlWSlHbzSy7mqrVOKaW8LcmoJPck+VGTawAAAAAA6BaN7YAtpWyS5PwkqyY5J8mOSZ5awPBvphXQvrOp+gAAAAAA3abJHbCfSrJykpNrrZ9MklLKzAWMvbb9unWD9QEAAAAAukqTZ8DumtZxA19d1MBa6+NJnk2yfoP1AQAAAAC6SpMB7DpJnm6Hq4vjhSSDGqwPAAAAANBVmgxgn02yaillwKIGllIGJxmaZHKD9QEAAAAAukqTAezv2/NttRhjD2yPva3B+gAAAAAAXaXJAPbCJCXJ8aWUBc5bStk8yZfTOi/2/AbrAwAAAAB0lSYD2LOT/C7Jbkl+VkrZN8nApBW6llLeWUr5epJfJRmW5OYkFzRYHwAAAACgqwxsaqJa6/RSyh5JLk2yU5Id5+q+c67vS1oh7H611tpUfQAAAACAbtPkDtjUWh9Lsl2Sw5P8Isn0tALXkmRWkluSfDjJjrXWp5qsDQAAAADQbRrbATtbrXVGkm8l+VYpZUBaxw2skGRSuw8AAAAAYLnQ2A7Yni7eqrXOrLU+WWt9vKfwtZSyXlP1AQAAAAC6TZNHEJy7JINLKSOT3NhgfQAAAACArtJkAHtoKeX0xRlYSvn7tMLXDRqsDwAAAADQVZoMYJ9MckQp5YsLG1RK2TjJz5Osl+TmBusDAAAAAHSVJgPY3ZNMS/LpUsqnehpQStksyQ1JXtl+3bPB+gAAAAAAXaWxALbW+tsko5M8n+TLpZTD5+4vpbwhyfVJ1k5ybZLRtdZnm6oPAAAAANBtmtwBm1rrL5Lsm2R6kjNLKQclSSnlLUmuS7JGkiuTvKvW+tcmawMAAAAAdJtGA9gkqbVeneTgJDXJuaWUY5JcnWT1JJcm2bfW+kLTdQEAAAAAuk3jAWyS1Fp/nOTwJAOTfD7JkCQ/TvLuWuuLnagJAAAAANBtOhLAJkmt9btJPpGkJPlBkn+stc7oVD0AAAAAgG4zsDcPlVJmLsHwmuQ9Sd5TSpmvr9baqzUAAAAAAHS73oaf8yWp/TwPAAAAAEDX6W0A++pGVwEAAAAAsAzqVQBba32g6YUAAAAAACxrOnYJFwAAAADA8q6xC7BKKX+X5C1J/lpr/c0ixr4lyd8luaXW+nxTawAAAAAA6CZN7oA9NMn1Sd6zGGP/eQnGAgAAAAAslZoMYN/dfv3eYow9J0lJ8o8N1gcAAAAA6CpNBrAbJ3kxyW8XY+zt7bGbNFgfAAAAAKCrNBnArpPkmVprXdTAWuusJE+3nwEAAAAAWCY1GcBOSzK0fRnXQrXHDE3y1wbrAwAAAAB0lSYD2N+159tvMcbun2RAkrsbrA8AAAAA0FWaDGAvTOtira+VUjZb0KBSyuuTfC1JbT8DAAAAALBMGtjgXN9J8uEkb0jym1LKd5JcmeTBdv8GSf4hyWFJVk5yV5JzGqwPAAAAANBVGgtga60zSimjk1yeZMu0wtgP9zC0JLkzyV611ulN1QcAAAAA6DZNHkGQWusjSd6a5MgktySZmVbgWtrf35LkI0neWmt9uMnaAAAAAADdpskjCJIktdYXk5yZ5MxSysAkw9pdk2utM5quBwAAAADQrRoPYOfWDlyf6GQNAAAAAIBu1dEAls4bOnRIfy8BusCb+3sBXW/KlKn9vQQAAABYLvUqgC2l7Nj+9rla663ztC2RWuuNvXkOAAAAAKDb9XYH7A1JapI/JNl0nrYlUfuwBgAAAACArtbb8PPBtMLTR3poAwAAAAAgvQxga60jF6cNAAAAAGB5tkJ/LwAAAAAAYFnVWABbStmxlPLWJRi/dW8v7gIAAAAAWBo0eQHWDUkeTfKqxRx/QZL1G14DAAAAAEDXaPoIgtLh8QAAAAAAS43+PAN2cJIX+7E+AAAAAEBH9UsAW0rZOsmwJH/pj/oAAAAAAC+HXp+/WkoZk2TMPM3DSinXLeyxJEOTbJqkJrmyt/UBAAAAALpdXy7AGplk53naBvXQtiA3Jjm2D/UBAAAAALpaXwLYnySZ2P6+JPlOkqlJPr6QZ2YlmZbk97XW+/pQGwAAAACg6/U6gK21/jbJb2f/XEr5TpK/1lrHNbEwAAAAAIClXV92wL5ErbVfLvQCAAAAAOhWQlMAAAAAgA4RwAIAAAAAdIgAFgAAAACgQwSwACzUz3/+8xxyyCF57Wtfm7XWWiubbLJJ9ttvv1x99dWLPcdVV12VfffdN5tuumnWWWedvOENb8iYMWNyyy23dHDlAAAA0P8au4QLgGXPsccem9NOOy2vetWrsueee2b48OF56qmncuedd+amm27K7rvvvsg5jjvuuJx66qkZNmxYRo8eneHDh+fPf/5zrrjiilx66aX5xje+kQMPPPBleDcAAADw8hPALkIppS7GsF1qrTe0xx+W5LtJxtVaD2u3rZDk+iQ7Jjm41vqDHupsmOS3SaYn2aLW+nAT6wforXHjxuW0007LQQcdlFNPPTWDBg16Sf/06dMXOcfjjz+e008/PWuttVZuvvnmrLnmmnP6brzxxuy111454YQTBLAAAAAsswSwi+9zC+mbuLAHa62zSilj0gpYv15KubHW+pfZ/aWUAUn+K8krkrxH+Ar0txdeeCHHH3981ltvvR7D1yRZccUVFznPQw89lFmzZmWrrbZ6kR7eDQAAIABJREFUSfiaJDvuuGMGDx6cSZMmNbZuAAAA6DYC2MVUax3bx+cnllI+ltbu2HNLKbvXWmfvrv1Mkm2TfL/WekHfVgrQd9dff32eeuqpfPjDH84KK6yQq666Kvfee29WWmmlbLXVVtl6660Xa56///u/z6BBg3L77bdn0qRJGT58+Jy+m2++OU8//XRGjx7dqbcBAAAA/a5jAWwpZa0kb0oye8vTk0lur7U+0ama3a7Wem4pZa8k+yb5WJJTSilvTnJskoeSfKQ/1wcw2+23354kWXnllbPjjjvmnnvueUn/dtttl/POOy9rrLHGQudZffXVM3bs2BxzzDHZZpttMnr06AwbNiz3339/rrzyyuyyyy455ZRTOvY+AAAAoL81HsCWUnZI8oUkoxbQf2OS/6i13tx07aXE4Um2S/KlUspNaR09MDDJmFrrlH5dGUDbU089lSQ57bTTsvHGG+fKK6/M5ptvngceeCCf/exnc91112XMmDEZP378Iuc64ogjMmLEiBx55JEZN27cnPYNN9wwBx988HxHEwAAAMCypPztX8E3MFkpH0pyepIVkpQkM5M81e4enr8FvjOTHFlrPbux4h0y1yVcCzoD9vla65fnGn9Y5rmEq4c535nksiQvJFkpyddqrZ9c2DqmTp3a4x/U0KFDFvYYQJLkN7+5dYnGn3DCCbn44oszaNCgXHTRRXnlK185p+/555/P/vvvnyeeeCLf/va3s8UWWyx0rvPOOy9nnnlmDjzwwBxwwAFZY401MnHixHz961/Pr371q7z3ve/NRz/60V69LwAAAOikjTbaqMf2IUOGlMWdY4WmFlNKeWOSM5IMSHJzknckeUWtdd1a67pJBifZo903IMkZ7WeWFsct4OvTSzpRrfXyJD9PK3x9MMnRzS0ToO8GDx6cJNl4441fEr4mrWMJtt122yTJ73//+4XOc9ttt+X000/PqFGjctRRR2W99dbLyiuvnE022SQnnnhi1lprrZx//vl5+GF3DwIAALBsavIIgk+mFehemOTgWuusuTtrrS8kubqUcm2SHyZ5d5JPJHlvg2vomFrrYqfai1JKeVuSHds/rpdkmyQ3NjU/wLwW9H/sFuQtb3lLzjvvvKy99to9PjtixIgkraB2YXPPPnJgzz337HHc1ltvncsvvzzTpk1b4jWycBMmTEiy5H/2sCzzuYD5+VzA/HwuYH4+F33T2A7YJDslqUmOmjd8nVu77+PtsTs3WH+pUEoZmuTcJDOSfDit38O4Usrg/lwXwNx22mmnlFLyv//7v5k1a/6/0u+9994kyQYbbLDQeV544YUkfztTdl6TJk1KkgwaNKgvywUAAICu1WQAu2aSKbXWRxc1sNb6SJIp7WeWN2cmWT/JcbXWbyT5SpKRSU7uz0UBzG3EiBHZY4898vDDD+ess856Sd91112Xn/3sZxkyZEh23XXXJMn06dPzxz/+Mffff/9Lxm633XZJWjthH3nkkZf0XXPNNfnVr36VlVdeOdtss00H3w0AAAD0nyaPIJiWZGgpZdVa67MLG1hKWTXJakn+r8H6Xa+UcmCSg9I6B/cr7eaxSf4hyQdLKT9pnw8L0O/+8z//M3fddVeOOeaYXH311dliiy3ywAMPZPz48RkwYEBOO+20DBnSugjwkUceydZbb531118/d91115w59t577+y888654YYbss0222T06NFZe+2184c//CFXXXVVaq057rjjMmzYsP56mwAAANBRTQawtyd5e5KPJvnSIsZ+LK2LuG5rsH5XK6W8KslZSZ5J8r7ZxzTUWqeXUt6b5NYk3yylbF5r7fnf6gK8jF71qlflhhtuyFe+8pVceeWV+cUvfpHBgwdnjz32yCc+8YlstdVWi5xjhRVWyEUXXZRvfvOb+fGPf5zx48fnueeey+qrr57dd989//Iv/5K3ve1tL8O7AQAAgP7RZAB7TpLdkxzf3uF6Yq116twDSinrJvlUWiFtbT+zVCiljF1I909qrXcu5NmS1rmvqyf5p1rrn+fur7XeXUr5bJKvphXSHtDnBQM0YI011siJJ56YE088caHjNthgg0yZMqXHvhVXXDFHHHFEjjjiiE4sEQAAALpaYwFsrfXHpZTvJXlvks8k+WQp5bdJ/pJk5SQjkmyUZMUkJcm4WuvFTdV/GRy3kL6JSRYYwKYVOO+W5JJa67cXMOakJO9K8u5SyqG11v/q1SoBAAAAgK7R5A7YJDksyb1JPp3WGa9b9zBmWpITkvxnw7U7otZalnD8uWntdp277dQkpy7iuVlJdlzC5QEAAAAAXazRALbWWpN8uZRyelrnwb4pyZrt7ifTOif26lrrc03WBQAAAADoRk3vgE2S1FqfTfKT9hcAAAAAwHJphf5eAAAAAADAskoACwAAAADQIb06gqCUcl372wdqre+fp21J1Frrrr1ZAwAAAABAt+vtGbA7t1//t4e2JVF7WR8AAAAAoOv1NoB9f/t1ag9tAAAAAACklwFsrXXc4rQBAAAAACzPXMIFAAAAANAhAlgAAAAAgA7p1REEpZQRTS2g1vpgU3MBAAAAAHST3l7CdX9D9Wsf1gAAAAAA0NV6G36Whuo3NQ8AAAAAQNfpVQBba3V2LAAAAADAIghSAQAAAAA6RAALAAAAANAhHbsAq5SyVpI3JVmz3fRkkttrrU90qiYAAAAAQDdpPIAtpeyQ5AtJRi2g/8Yk/1Frvbnp2gAAAAAA3aTRALaU8qEkp6d1tEFJMjPJU+3u4e16OyW5oZRyZK317CbrL4+mTJna30uAfjdhwoQkyUYbbdTPKwEAAAB4qcbOgC2lvDHJGUkGJLk5yTuSvKLWum6tdd0kg5Ps0e4bkOSM9jMAAAAAAMukJi/h+mR7vguT7FxrvabW+sLszlrrC7XWq9PaAfvfaYWwn2iwPgAAAABAV2kygN0pSU1yVK111oIGtfs+3h67c4P1AQAAAAC6SpMB7JpJptRaH13UwFrrI0mmtJ8BAAAAAFgmNRnATksyuJSy6qIGtses1n4GAAAAAGCZ1GQAe3ta57p+dDHGfqw99rYG6wMAAAAAdJUmA9hzkpQkx5dSvlBKGTLvgFLKuqWUryX5fFpnwJ7TYH0AAAAAgK4ysKmJaq0/LqV8L8l7k3wmySdLKb9N8pckKycZkWSjJCumFdSOq7Ve3FR9AAAAAIBu01gA23ZYknuTfDqtM1637mHMtCQnJPnPhmsDAAAAAHSVRgPYWmtN8uVSyulJ3p7kTUnWbHc/mdY5sVfXWp9rsi4AAAAAQDfqVQBbSjk2yTO11q/11F9rfTbJT9pfAAAAAADLpd5ewjU2yb/N3VBK+XMp5Vd9XhEAAAAAwDKit0cQ1Mwf3o5M67ItAAAAAADS+x2wk5MML6UMbnIxAAAAAADLkt7ugP1Vkn9Icmkp5aIkz7Tb/66U8r4lmajWel4v1wAAAAAA0NV6G8B+PskuSXZKsuNc7asl+e4SziWABQAAAACWSb0KYGutvymlbJnk8CSbJfm7JDsnmZ7kl42tDgAAAABgKdbbHbCptd6X5N9n/1xKmZVkcq11lyYWBgAAAACwtOt1ANuDB5M83uB8AAAAAABLtcYC2FrryKbmAgAAAABYFqzQ1ESllJmllL8swfj7SykzmqoPAAAAANBtGgtgk5T215I+AwAAAACwTGoygF1SKyWZ2Y/1AQAAAAA6ql8C2FLKOknWSvJUf9QHAAAAAHg59PoSrlLKjkl2nqf5FaWUYxf2WJKhSfZof39zb+sDAAAAAHS7XgewSXZJclySOlfbqu22hZl97uvkJJ/rQ30AAAAAgK7WlwD2ziTj5vp5TJLnk1y4kGdmJZmW5PdJLq61TupDfQAAAACArtbrALbWekmSS2b/XEoZk2RqrfX9TSwMAAAAAGBp15cdsPPaN8njpZQBtdaZDc4LAAAAALBUajKAvTitIwZeneShBucFAAAAAFgqNRnAPpNkRq1V+AoAAAAAkGSFBue6P8kqpZQmQ10AAAAAgKVWkwHshUlWTLJPg3MCAAAAACy1mgxgT0xya5KzSym7NjgvAAAAAMBSqcnjAj6d5Lokr0tydSnld0l+meTJJDMX9FCt9fMNrgEAAAAAoGs0GcCOTVKTlPbPb0iyxULGl/Z4ASwAAAAAsExqMoA9L61AFQAAAACANBjA1loPa2ouAAAAAIBlQZOXcAEAAAAAMBcBLAAAAABAhzR5BuwcpZSdk/xjkjclWbPd/GSS25NcWGu9oRN1AQAAAAC6SaMBbClljSTnJ9ltdtNc3a9O8pYk/1JKuSbJobXWp5qsDwAAAADQTRoLYEspg5Jck2SLtILXXya5LsnD7SHrJXlbkm2TvD3J1aWUt9ZaX2xqDQAAAAAA3aTJHbBHJnlDkslJDqq1XtPDmM+WUnZP8oP22I8kObnBNQAAAAAAdI0mL+E6MElNcvgCwtckSa316iSHp7VL9j0N1gcAAAAA6CpNBrAbJ3k+ycWLMfbi9thNGqwPAAAAANBVmgxgV0wyvdZaFzWw1joryfQ0fAkYAAAAAEA3aTKAfTDJ4FLKmxY1sJSyVZLB7WcAAAAAAJZJTQawV6R1ruu3SylrLmhQKWXtJN9O67zY8Q3WBwAAAADoKk0eAfCVJGOSbJHkf0sp30xyQ5K/JFk5yYgkuyQ5LMkqSSYn+WqD9QEAAAAAukpjAWyt9YlSyj8k+UmSdZJ8qv01r5Lk0ST71FqfaKo+AAAAAEC3afIIgtRab0myaZLjktyV1jEDpf1V223HJtms1vqbJmsDAAAAAHSbUmvt3OSlrJhkWPvHybXW6R0rtoybOnVqj39QQ4cOebmXAgAAAACZMmVqfy+h3wwZMqQs7tgmz4CdTztwfbyTNQAAAAAAulWfA9hSykpJ9kmyVZLVkkxJ8uskl9VaZ/R1fgAAAACApVWfAthSynZJLkrr0q15TSyl7FNrvasvNQAAAAAAlla9voSrlPKqJJenFb7OvmTrydndSV6d5IpSikNKAQAAAIDlUq8D2CQfSzI0rSMH3pdklVrrOklWTfLRJH9N8sokH+zrIgEAAAAAlkZ9CWDfntau14/WWv+r1vpiktRan6+1npHkuLR2wu7e92UCAAAAACx9+hLAbphWAPujBfRfNNc4AAAAAIDlTl8C2MFJnqy1Pt9TZ631gfa3q/ahBgAAAADAUqsvAWzS2gG7KKWPNQAAAAAAlkp9DWABAAAAAFiAgX18flgp5bo+jKm11l37uAYAAAAAgK7U1wB2UJKd+zBmcY4wAAAAAABYKvUlgB3X2CoAAAAAAJZBvQ5ga63vb3IhAAAAAADLGpdwAQAAAAB0iAAWAAAAAKBDBLAAAAAAAB0igAUAAAAA6BABLAAAAADwsjjuuOOy1157ZbPNNss666yTkSNHZtSoUfnyl7+cyZMn9/jMr3/96xxwwAEZOXJk1llnnWy33XY588wzM3PmzCWqfd555+XjH/94dt1116y77roZOnRovvCFLzTxthaq1Fo7XoS+mzp1ao9/UEOHDnm5lwIAAAAAmTJl6hI/s+aaa+YNb3hDNt5446y55pp59tlnc+utt+aOO+7Iuuuum2uuuSbrrbfenPHjx4/P+973vqy88srZd999s/rqq+enP/1pJkyYkL333jvjxo1b7NojRozItGnTMnTo0Ky++uq5//7782//9m/5j//4jyV+H0OGDCmLO3bgEs++DCilDEjygSSHJtk8yeAk/5fksSS3JLm01nppe+zOSa5P8vNa6849zDU6yYVp7SZ+T631klLKyCT3t4c8m2TdWuvTPTxbktyXZMN20y611huaeI8AAAAA0G0eeuihrLzyyvO1H3/88TnppJNy8skn56STTkqSTJs2LR/72McyYMCAXH755XnjG9+YJDnmmGOy11575ZJLLsmPfvSj7L///otV+zvf+U5e+9rXZsSIETn//PPzkY98pLk3thDL3REE7fD18iTnJNkiyRVJTkryX0keTXJwkn9fzLnen+QnSV5Islut9ZJ5hsxIsmqSgxYwxa5pha8zluxdAAAAAMDSp6fwNUn22WefJMmf/vSnOW2XXHJJnnrqqey3335zwtfZcxxzzDFJkm9/+9uLXXu33XbLiBEjerPsPlked8AelGSPJL9NslOt9SV7pUspqyTZZlGTlFI+k+SEJA8n2aPW+vseht2WZIMk/5xW4Duvf04rvL0uyZ5L8B4AAAAAYJnx05/+NEmy2WabzWn7n//5nySt4HRe22+/fVZZZZXccssteeGFF7LSSiu9PAvtheUxgN2u/XruvOFrktRan0vryIEetY8NOCXJR5Pck1b4+tAChs9I8t0knymlvKHW+tu55lkjyT5J/juJg3gBAAAAWG6cfvrpeeaZZzJt2rTceeed+eUvf5nNNtssRx111JwxEyZMSJK85jWvme/5gQMHZoMNNsi9996biRMnZuONN37Z1r6klscAdlL79bVL+mApZVCS85IcmOQXSd5Va+35era/+VaST6e12/XIudrHJBmU5JtJ/mlJ1wIAAAAAS6vTTz89TzzxxJyfd9ttt5x55plZY4015rRNmzYtSbLaaqv1OMfs9qlTl/wysJfT8hjA/jjJ/0vyoVLK4CQXJ7mt1vrAIp57RZLxSXZLclmSA2utf11UsVrrn0sp1yU5pJTyqbme+ackE2qtN5RSBLAAAAAALFVm71DtjfHjxydJJk2alN/97nc544wzst122+Xkk0/OJptskiSZPn16kmTixImZOXPmfHP89a+tmO2hhx7K6quvvkT1H3/88STJ5MmTF/o+NtpooyWatyfL3SVctdY7khya5PH264+STCylTCqlXFxKedcCHt0qrfD1D0n2W5zwdS7fTDI0yQFJUkoZlWSTtHbHAgAAAMByafjw4dlll11yxhlnZOrUqTnuuOPm9K266qpJkmeeeabHZ2e3Dx48uPML7YPlcQdsaq0XllIuTrJLkh2SvLH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"text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 688}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "14 out of 20 tickers were removed\n{'PRSP': 44, 'SKM': 126, 'EIX': 10, 'FAST': 76, 'DLR': 17, 'CBPO': 8}\nFunds remaining: $13.72\n"}]}}, "28711de6b91a43d3b3e0f7c559ee80c3": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "DropdownModel", "state": {"_options_labels": ["target_return", "target_risk", "max_sharpe", "min_volatility"], "description": "Otimisation objective:", "index": 0, "layout": "IPY_MODEL_9cf31efd4eb547d69b59f6af8aca667a", "style": "IPY_MODEL_ff01f4c84e7844b58402a380a3388f56"}}, "287af1ae533b4c1abc70b2dc8fb50ae1": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "28b8491deca846c9ae2d2f414e92c171": {"model_module": "@jupyter-widgets/base", 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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 685}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "16 out of 20 tickers were removed\n{'PRSP': 20, 'EIX': 16, 'FAST': 261, 'DLR': 2}\nFunds remaining: $4.99\n"}]}}, "37b0d8767a774622b5ddaba963456ccd": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {"height": "40px", "width": "50%"}}, "37ddb61c479e42bd9012f594de609937": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_69abe5efaaf0465a9ede9b72e3dc3528", "outputs": [{"data": {"image/png": 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\n", "text/plain": "<Figure size 432x288 with 1 Axes>"}, "metadata": {"image/png": {"height": 277, "width": 573}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "14 out of 20 tickers were removed\n{'PRSP': 42, 'SKM': 113, 'EIX': 11, 'FAST': 98, 'DLR': 16, 'CBPO': 5}\nFunds remaining: $22.24\n"}]}}, "3a643f1ea1fa4b73af68f42551cad074": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntTextModel", "state": {"description": "Total value:", "layout": "IPY_MODEL_26de3e51bb7349fdad9416cda7d622c6", "step": 1, "style": "IPY_MODEL_04490634a3a342309b08cbf4dba693f0", "value": 50000}}, "3a6da3b1b1e849e1a6fcd4328df199b2": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_9636e7a5188649bb9103394f47e79a7f", "outputs": [{"data": {"image/png": 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DAAAAAIySzbocrLX2r1V1UJIvJjmjqu5urZ1bVc9L8vUk2yb5WpJXttbu77I3G276AddO9BQAAAAAmIR22nHWRE/hMaFaa90PWvXKJJ9NsjzJu5K8LcnWGYS6r26tPdB5U9Zo8eLF3f+PzKR14403Jkl22WWXCZ4Jj3V+S3TB74gu+B3RBb8juuB3RFf8luiC39HGs/XWW9f61na55+1DWmvnJzkyg5W978oguD0/ye8JbgEAAAAA1m2jhLdJ0lr7hyR/nqSSnJvBitsHN1Y/AAAAAIBRMqY9b6tq2QaUtyS/n+T3q35hRXBrrXW67y4AAAAAwCgYa3C63vsyPErjAAAAAACMlLGGt0/pdBYAAAAAADzCmMLb1tp/dz0RAAAAAAAettEeWAYAAAAAwNh19rCwqnpckucl+Xlr7bvrqH1ekscl+U5r7b6u5gAAAAAAMCq6XHl7WJJLkvz+etT+0QbUAgAAAABMOl2Gt783fP/UetSemaSSvLrD/gAAAAAAI6PL8PaXkzyQ5Nr1qL16WPuMDvsDAAAAAIyMLsPb7ZPc01pr6ypsrS1PcvfwHgAAAAAAVtFleLskyTbDB5et1bBmmyQ/77A/AAAAAMDI6DK8vW443ivXo/Z3k2yaZH6H/QEAAAAARkaX4e3nMngI2Qeq6llrKqqqX03ygSRteA8AAAAAAKvYrMOx/j7JUUmeneS7VfX3Sb6W5EfD609O8jtJXpNk8yTzkpzZYX8AAAAAgJHRWXjbWnuwqmYn+XKS3TIIco9aTWkluSbJy1prS7vqDwAAAAAwSrrcNiGttduS/EaSNyX5TpJlGYS1Nfz8nSRHJ/mN1totXfYGAAAAABglXW6bkCRprT2Q5IwkZ1TVZklmDC8tbK092HU/AAAAAIBR1Hl4u7JhWHvnxuwBAAAAADCKOt02AQAAAACAboxp5W1V7T38+LPW2r+tcm6DtNYuHct9AAAAAACjbKzbJsxJ0pJcn+SZq5zbEG0ccwAAAAAAGFljDU5/lEHwettqzgEAAAAAME5jCm9bazuvzzkAAAAAAMbGA8sAAAAAAHqos/C2qvauqt/YgPo9xvqQMwAAAACAUdflw8LmJLk9yRPXs/6zSXbqeA4AAAAAACOh620TaiPXAwAAAABMChO55+30JA9MYH8AAAAAgN6akPC2qvZIMiPJrRPRHwAAAACg78a832xVHZHkiFVOz6iqb63ttiTbJHlmkpbka2PtDwAAAAAwysbzsLCdk+yzyrmpqzm3Jpcmefs4+gMAAAAAjKzxhLf/kuTm4edK8vdJFif5s7XcszzJkiT/0Vr7/jh6AwAAAACMtDGHt621a5Ncu+K4qv4+yc9ba2d3MTEAAAAAgMlsPCtvH6G1NiEPPwMAAAAAGEUCVwAAAACAHhLeAgAAAAD0kPAWAAAAAKCHhLcAAAAAAD0kvAUAAAAA6CHhLQAAAABADwlvAQAAAAB6SHgLAAAAANBDm22sgatqZpLdk2w3PHVXkqtba3durJ4AAAAAAKOi8/C2qn4zyXuS7LWG65cm+avW2tyuewMAAAAAjIpOt02oqj9OckkGwW0lWZ7kzuFr2fDcC5PMqao3dNkbAAAAAGCUdBbeVtVzknwoyaZJ5iZ5cZItW2s7tNZ2SDI9yW8Pr22a5EPDewAAAAAAWEWXK2/fOhzvc0n2aa1d1Fq7f8XF1tr9rbULM1h5+/kMAtw/77A/AAAAAMDI6DK8fWGSluQtrbXlayoaXvuzYe0+HfYHAAAAABgZXYa32yVZ1Fq7fV2FrbXbkiwa3gMAAAAAwCq6DG+XJJleVVusq3BYs9XwHgAAAAAAVtFleHt1BvvY/sl61P7psPaqDvsDAAAAAIyMLsPbM5NUkndX1XuqautVC6pqh6r6QJJ3ZbDn7Zkd9gcAAAAAGBmbdTVQa+38qvpUkj9MclySt1bVtUluTbJ5kicl2SXJlAxC3rNbaxd01R8AAAAAYJR0Ft4OvSbJfyY5NoM9bfdYTc2SJCcmeX/HvQEAAAAARkan4W1rrSU5qapOT7J/kt2TbDe8fFcG++Je2Fr7WZd9AQAAAABGTdcrb5MkrbV7k/zL8AUAAAAAwAbq8oFlAAAAAAB0RHgLAAAAANBDY9o2oaq+Nfz43621165ybkO01tp+Y5kDAAAAAMAoG+uet/sM3/9rNec2RBtjfwAAAACAkTbW8Pa1w/fFqzkHAAAAAMA4jSm8ba2dvT7nAAAAAAAYGw8sAwAAAADoIeEtAAAAAEAPjWnbhKp6UlcTaK39qKuxAAAAAABGxVgfWHZTR/3bOOYAAAAAADCyxhqcVkf9uxoHAAAAAGCkjCm8ba3ZKxcAAAAAYCMSwgIAAAAA9JDwFgAAAACghzbaw8KqamaS3ZNsNzx1V5KrW2t3bqyeAAAAAACjovPwtqp+M8l7kuy1huuXJvmr1trcrnsDAAAAAIyKTrdNqKo/TnJJBsFtJVme5M7ha9nw3AuTzKmqN3TZGwAAAABglHQW3lbVc5J8KMmmSeYmeXGSLVtrO7TWdkgyPclvD69tmuRDw3sAAAAAAFhFlytv3zoc73NJ9mmtXdRau3/Fxdba/a21CzNYefv5DALcP++wPwAAAADAyOgyvH1hkpbkLa215WsqGl77s2HtPh32BwAAAAAYGV2Gt9slWdRau31dha2125IsGt4DAAAAAMAqugxvlySZXlVbrKtwWLPV8B4AAAAAAFbRZXh7dQb72P7JetT+6bD2qg77AwAAAACMjC7D2zOTVJJ3V9V7qmrrVQuqaoeq+kCSd2Ww5+2ZHfYHAAAAABgZm3U1UGvt/Kr6VJI/THJckrdW1bVJbk2yeZInJdklyZQMQt6zW2sXdNUfAAAAAGCUdBbeDr0myX8mOTaDPW33WE3NkiQnJnl/x70BAAAAAEZGp+Fta60lOamqTk+yf5Ldk2w3vHxXBvviXtha+1mXfQEAAAAARs2YwtuqenuSe1prH1jd9dbavUn+dUO4cAAAIABJREFUZfgCAAAAAGADjfWBZSckedvKJ6rqh1X17XHPCAAAAACAMW+b0PKLwe/OGTyYDAAAAACAcRrrytuFSR5fVdO7nAwAAAAAAANjXXn77SS/k+SLVXVeknuG5x9XVYdvyECttU+OcQ4AAAAAACNrrOHtu5Lsm+SFSfZe6fxWSf5hA8cS3gIAAAAArGJM4W1r7btVtVuSI5M8K8njkuyTZGmSKzqbHQAAAADAJDXWlbdprX0/yV+sOK6q5UkWttb27WJiAAAAAACT2ZjD29X4UZI7OhwPAAAAAGDS6iy8ba3t3NVYAAAAAACT3SZdDVRVy6rq1g2ov6mqHuyqPwAAAADAKOly24Qavjb0HibINttsM9FTgCTJTjvOyrz510/0NAAAAAB6pcvwdkP9ryTLJrD/pHf3hc+e6ClAkmT6AddO9BQAAAAAeqezbRM2RFVtn2Rmkp9ORH8AAAAAgL4b88rbqto7yT6rnN6yqt6+ttuSbJPkt4ef5461PwAAAADAKBvPtgn7JnlHkrbSuS2G59ZmxT63C5O8cxz9AQAAAABG1njC22uSnL3S8RFJ7kvyubXcszzJkiT/keSC1tqCcfQHAAAAABhZYw5vW2tfSPKFFcdVdUSSxa2113YxMQAAAACAyWw8K29XdVCSO6pq09basg7HBQAAAACYdLoMby/IYFuEpyT5cYfjAgAAAABMOl2Gt/ckebC1JrgFAAAAABinTToc66Yk06qqy0AYAAAAAGBS6jK8/VySKUle0eGYAAAAAACTUpfh7SlJ/i3Jx6pqvw7HBQAAAACYdLrc4uDYJN9K8itJLqyq65JckeSuJMvWdFNr7V0dzgEAAAAAYCR0Gd6ekKQlqeHxs5P82lrqa1gvvAUAAAAAWEWX4e0nMwhjAQAAAAAYp87C29baa7oaCwAAAABgsuvygWUAAAAAAHREeAsAAAAA0ENd7nn7kKraJ8mrk+yeZLvh6buSXJ3kc621ORujLwAAAADAqOg0vK2qX0pyTpLfWnFqpctPSfK8JG+oqouSHNZa+2mX/QEAAAAARkVn4W1VTU1yUZJfyyC0vSLJt5LcMizZMcmLkjw/yf5JLqyq32itPdDVHAAAAAAARkWXK2/flOTZSRYmOaS1dtFqav66qg5Icu6w9ugkp3Y4BwAAAACAkdDlA8sOTtKSHLmG4DZJ0lq7MMmRGazO/f0O+wMAAAAAjIwuw9tfTnJfkgvWo/aCYe0zOuwPAAAAADAyugxvpyRZ2lpr6ypsrS1PsjQdPzANAAAAAGBUdBne/ijJ9KrafV2FVfXcJNOH9wAAAAAAsIouw9uvZrCP7Seqars1FVXVrCSfyGB/3K902B8AAAAAYGR0uW3ByUmOSPJrSf6rqj6eZE6SW5NsnuRJSfZN8pok05IsTPK+DvsDAAAAAIyMzsLb1tqdVfU7Sf4lyfZJjhm+VlVJbk/yitbanV31BwAAAAAYJV1um5DW2neSPDPJO5LMy2BrhBq+2vDc25M8q7X23S57AwAAAACMki63TUiStNYWJXl3kndX1ZQkM4aXFrbWlnbdDwAAAABgFHUe3q5sGNbesTF7AAAAAACMonGHt1X1v5K8Islzk2yVZFGSK5N8qbX24HjHBwAAAACYjMYV3lbVC5Kcl8EDylZ1c1W9orU2bzw9AAAAAAAmozE/sKyqnpjkyxkEtyseSHbXistJnpLkq1W19XgnCQAAAAAw2Yw5vE3yp0m2yWCbhMOTTGutbZ9kiyR/kuTnSZ6Q5PXjnSQAAAAAwGQznvB2/wxW2/5Ja+3TrbUHkqS1dl9r7UNJ3pHBCtwDxj9NAAAAAIDJZTzh7VMzCG//eQ3Xz1upDgAAAACADTCe8HZ6krtaa/et7mJr7b+HH7cYRw8AAAAAgElpPOFtMlh5uy41zh4AAAAAAJPOeMPbSaWq9qmqtoGvf1lljH9c6drOq1zboar+Z3jtv6tqy/WY0/tXGu9N3X5jAAAAAGCibDbO+2dU1bfGUdNaa/uNcw4jo7V2e1W9LclZSZ6U5KQkawxkq2qPJH82PLw8yYc3+iShB77whS/k8ssvz/z58zN//vzcfffdefWrX50zzzzzF2qXLl2as846K/Pmzct1112X66+/PkuXLs1pp52Www8/fIP63nLLLTn11FNzzTXX5Mc//nEWLVqUGTNm5ClPeUoOPfTQHHzwwZkyZUpXXxMAAACY5MYb3k5Nss84atZn24W++kiSM9ajbsmGDNpa+0RVHZJkvyRvrKpzW2tzV62rqqlJ/j7JpknuS/L61tpj+e8J6+2UU07J/Pnzs+WWW+YJT3hC7r777jXW3nvvvTnuuOOSJDNnzsysWbNyyy23jKnvTTfdlPPOOy/Pfe5zM3v27Gy77bZZuHBhLr744rzpTW/KZz/72VxwwQXZbLPx/qcVAAAAYHzh7dmdzeKx6c7W2vyNNPYfJZmfZFqST1TVs1tr969S81dJnjX8/M7W2g0baS7QOyeeeGKe+MQn5qlPfWouv/zyvPSlL11j7bRp03Leeedl1113zfbbb5/3vve9Ofnkk8fUd88998zNN9+cTTZ55I4zS5cuzUEHHZTLLrssX/rSl3LQQQeNaXwAAACAlY05vG2tvbbLifCw1tpNVfVXST6Q5JeTvCPJX664XlXPTnLs8PDqJO9/1CcJE2jvvfde79qpU6dm//3376Tv1KlTV3t+ypQpmT17di6//PL84Ac/6KQXAAAAgAeW9dffJfn28PMxVbVbklTVZhlslzAlyYNJXtdae3BipggkybJly3LRRRclSZ71rGetoxoAAABg/diYsadaa8ur6vVJ/j2DfYM/UVV7Jnlbkt2HZSe31q6dqDnCZLVgwYKceeaZaa1lwYIFueSSS/LDH/4wr3rVq3LggQdO9PQAAACAESG87bHW2veq6m+SvDODwPbDSQ4fXv7PJO+eqLnBZLZgwYJH7JtbVXnzm9+ct7/97RM4KwAAAGDUCG/HbmZV/ep61N3UWrt3HH3em+T3kuya5MjhueVJXr+ah5jBY9aNN944pvtuvfXWJMmSJUvWa4yFCxcmSe64444x96yqfPe7382yZcty11135ZJLLsnHPvaxzJkzJ6eeemq23nrrMY07WY31fwdYmd8RXfA7ogt+R3TB74iu+C3RBb+jsdtll13GPYY9b8fuqCTz1uP1vPE0aa0tzcOh7Qqnt9auGM+4wPhtuumm2X777XPIIYfkL//yLzNv3rx87GMfm+hpAQAAACPCytvHhr1WOb5tQmYBG9FY/zXqJz/5SZJkq622Wq8xZsyYkSSZNWtWJ/8CtsLMmTNz/PHHZ/78+Z2OO8pW/Outvxfj4XdEF/yO6ILfEV3wO6Irfkt0we+oH6y8Hbt3ttZqPV5zxtOkqnbJYM/blZ1QVU8dz7hAt26//fYkg9W4AAAAAF0Q3vZYVVWSs5I8LsmyJG8bXnpckjMnal4w6hYvXpwbbrjhoVW9K1xzzTVZtmzZL9Tfc889OfbYY5MkL37xix+VOQIAAACjz7YJ/XZUkr2Hn/+2tfa3VfWMJP9/kv2q6rWttX+YuOnBxPjyl7+cr3zlK0mSO++8M0nyne98J0cddVSS5PGPf3ze8573PFR/6qmn5oYbbkiSzJs3L0lyzjnn5IorBltHP//5z8/hhx/+iPGPPvroHHLIIfnIRz7y0Pn3ve99ufLKK7PHHntkxx13zLRp03LrrbfmoosuyuLFi7PnnnvmLW95y0b85gAAAMBkIrztqaraKclJw8Mbk5ww/HxMktlJdkjy/qr6Smvtzkd/hjBx5s2bl3PPPfcR526++ebcfPPNSZKddtrpEeHtxRdfnLlz5z6i/sorr8yVV1750PHK4e2aHHHEEdlyyy1z1VVXZe7cufnZz36WbbbZJrvttlsOOuigHHbYYdlsM/9ZBQAAALohZeivjyWZnqQl+aPW2s+TpLW2qKrelOSfk8xIcnqSgydsljABjjvuuBx33HHrXb9ile76OvTQQ3PooYf+wvkXv/jFtkUAAAAAHjX2vO2hqjo8yYHDwzNba/935euttfOTnD88fHVVvfTRnB8AAAAAsPEJb3umqmYlOXV4eGuSv1hD6ZuSLBp+PqOqpm/suQEAAAAAjx7bJozdzKr61fWoe6C1dsMGjPuhDLZDSJKjWmtLVlfUWru9qo5J8vEkO2awP+7RG9AHAAAAAOgx4e3YHTV8rct/J9l5fQasqlcm+b3h4Wdba19aW31r7ayq+oMk+yY5qqrOaa396/r0AgAAAAD6zbYJPVFV2yb58PBwQZI3r+etRyb5eZJKclZVTd0I0wMAAAAAHmVW3m6A1tqcDELS8YzxmiSvWc35/0mywxjG+36SaeOZEwAAAADQP1beAgAAAAD0kPAWAAAAAKCHhLcAAAAAAD0kvAUAAAAA6CHhLQAAAABADwlvAQAAAAB6SHgLAAAAANBDwlsAAAAAgB4S3gIAAAAA9JDwFgAAAACgh4S3AAAAAAA9JLwFAAAAAOgh4S0AAAAAQA8JbwEAAAAAekh4CwAAAADQQ8JbAAAAAIAeEt4CAAAAAPSQ8BYAAAAAoIeEtwAAAAAAPSS8BQAAAADoIeEtAAAAAEAPCW8BAAAAAHpIeAsAAAAA0EPCWwAAAACAHhLeAgAAAAD0kPAWAAAAAKCHhLcAAAAAAD0kvAUAAAAA6CHhLQAAAABADwlvAQAAAAB6SHgLAAAAANBDwlsAAAAAgB4S3gIAAAAA9JDwFgAAAACgh4S3AAAAAAA9JLwFAAAAAOihzSZ6Akyc6QdcO9FTgCTJTjvOmugpAAAAAPSO8HYSW7Ro0URPgcegG2+8MUmyyy67TPBMAAAAAEabbRMAAAAAAHpIeAsAAAAA0EPCWwAAAACAHhLeAgAAAAD0kPAWAAAAAKCHhLcAAAAAAD0kvAUAAAAA6CHhLQAAAABADwlvAQAAAAB6SHgLAAAAANBDwlsAAAAAgB4S3gIAAAAA9JDwFgAAAACgh4S3AAAAAAA9JLwFAAAAAOgh4S0AAAAAQA8JbwEAAAAAekh4CwAAAADQQ8JbAAAAAIAeEt4CAAAAAPSQ8BYAAAAAoIeEtwAAAAAAPSS8BQAAAADoIeEtAAAAAEAPCW8BAAAAAHpIeAsAAAAA0EPCWwAAAACAHhLeAgAAAAD0kPAWAAAAAKCHhLcAAAAAAD0kvAUAAAAA6CHhLQAAAABADwlvAQAAAAB6SHgLAAAAANBDwlsAAAAAgB4S3gIAAAAA9JDwFgAAAACgh4S3AAAAAAA9JLwFAAAAAOgh4S0AAAAAQA8JbwEAAAAAekh4CwAAAADQQ8JbAAAAAIAeEt4CAAAAAPSQ8BYAAAAAoIeEtwAAAAAAPSS8BQAAAADoIeEtAAAAAEAPCW8BAAAAAHpIeAsAAAAA0EPCWwAAAACAHhLeAgAAAAD0kPAWAAAAAKCHhLcAAAAAAD0kvAUAAAAA6CHhLQAAAABADwlvAQAAAAB6SHgLAAAAANBDwlsAAAAAgB4S3gIAAAAA9JDwFgAAAACgh4S3AAAAAAA9JLwFAAAAAOgh4S0AAAAAQA8JbwEAAAAAekh4CwAAAADQQ8JbAAAAAIAeEt4CAAAAAPSQ8BYAAAAAoIeEtwAAAAAAPSS8BQAAAADoIeEtAAAAAEAPCW8BAAAAAHpIeAsAAAAA0EPCW+D/tXfncZJV9d34P98Aso0sLqCGTXAHVFQ2kYAbxgVEXAlKeJ644JJoHtH8NOKSiODj8qioYxKjqCQR0SgaN0QEVExERZGIiEFUQFBA9s3B8/vj3paip7qnu6dn+k7P+/161etW3Xvuved0namp+tSpcwEAAAAYIOEtAAAAAMAACW8BAAAAAAZIeAsAAAAAMEDCWwAAAACAARLeAgAAAAAMkPAWAAAAAGCAhLcAAAAAAAMkvAUAAAAAGCDhLQAAAADAAAlvAQAAAAAGSHgLAAAAADBAwlsAAAAAgAES3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABgg4S0AAAAAwAAJbwEAAAAABkh4CwAAAAAwQMJbAAAAAIABEt4CAAAAAAyQ8BYAAAAAYIDWXegKsHA222yzha7Cgtt6qy3zw3PPX+hqAAAAAMByhLdrsetOfshCV2HB3Xm/Hyx0FQAAAABgLNMmAAAAAAAMkPAWAAAAAGCAhLcAAAAAAAMkvAUAAAAAGCDhLQAAAADAAAlvAQAAAAAGSHgLAAAAADBAwlsAAAAAgAES3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABgg4S0AAAAAwAAJbwEAAAAABkh4CwAAAAAwQMJbAAAAAIABEt4CAAAAAAyQ8BYAAAAAYICEtwAAAAAAAyS8BQAAAAAYIOEtAAAAAMAACW8BAAAAAAZIeAsAAAAAMEDCWwAAAACAARLeAgAAAAAMkPAWAAAAAGCAhLcAAAAAAAMkvAUAAAAAGCDhLQAAAADAAAlvAQAAAAAGSHgLAAAAADBAwlsAAAAAgAES3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABgg4S0AAAAAwAAJbwEAAAAABmhRhLdVtW9VtSluN1XVL6vqP6rq+VW1wZj9D5tm/xuq6qKqOqmqDq2qO82wTlVVT66q46vqJ1V1XVUtq6prquq8qvpMVf1tVe1RVcs9DyvbJgAAAABgzbYowtsV2CDJVkmenOSfkny/qu4/i/03SrJtkgOSfCTJWVW1zXQ7VNUWSb6W5D+SHJLkvkmWJFknySZJHpDkqUnenORbSfabRX2SlW8TAAAAADBwizG8XZpk55Hb7klelOS8fvv9k3yxqjacYv/XTdr/8Un+Osmv+u0PTvLZqlpn3M79yNyTk+zTr/pBklf0j3dJsneS5yf5UJLfrKY2sQqdcMIJ2WyzzbLZZpvlox/96Kz3P+mkk3LQQQdl++23z5ZbbpmddtopBx98cM4666xVUFsAAAAA1hTrLnQFVoFft9bOnbTu21X1sSSnJdktyb2T/EWS947Z/5JJ+5+b5JSq+lC6UbIPSvKQJAcm+dSY/Z/fb0+6kbr/u7X2+0llvpHkn/sA+MAkv1zFbWIVufjii/OqV70qS5YsyfXXXz+rfZctW5YXv/jFOfHEE7PDDjvkoIMOyiabbJLLL788Z511Vr7//e9n1113XUU1BwAAAGDoFmN4O1Zr7aaq+tskX+lXPTGzCDpba9dW1TFJJoZWPj7jw9sD++WyJK8YE9yOHvO2KY4x0zqtVJtYOa21vPSlL81d7nKX7L///jn22GNntf/RRx+dE088MUcccURe+9rX5o/+6I4D4X/3u9/NZ3UBAAAAWMMsxmkTpvOfI/e3ncP+3xu5v/UUZSbmw72ytXb1HM4xWyvbJuboAx/4QM4444y8733vy0YbbTSrfS+//PIce+yx2XXXXfO6171uueA2SdZbb735qioAAAAAa6C1ZuRtb3Qo49g5a1fgtimONeqWfrlFVd2ltXbVHM4zGyvbJubg/PPPz5ve9KYcfvjh2WuvvXLGGWfMav+TTjopt956aw466KDcdNNNOfnkk3PhhRdmyZIl2WOPPbLzzjuvopoDAAAAsKZY28LbB4/cv3QO+z9o5P5FU5T5Xn+eSvKhqnpea+26OZxrpla2TczSsmXL8qIXvShbbbVVXv/618/pGN/7XjeI+6abbsquu+6aiy+++A7bDzjggHzgAx+Y9YheAAAAABaPtW3ahNeO3P/abHbsLy721yOrPjFF0ffl9hG6T01ycVUdX1Uvqqpdqmq+fws/5zYxN29961tzzjnn5P3vf3823HDDOR3jiiuuSJIcddRR2WabbXL66afnkksuySmnnJJddtkln/3sZ/PKV75yPqsNAAAAwBpm0Y+8raoNkzw0yWuS7N+vvjbJP8xw/y3TjW79uyR79Ks/3lo7c1z51tp3quoFST6Q5E5JNklySH9Lkpuq6r+SfDrJR+cyL+7Ktok7uuCCC2Zc9txzz8073/nOHHLIIdl8883/sO9VV3WzY1x++eUzOt7111+fJNlkk01y1FFHZaONNsqll16aTTfdNG95y1vy9Kc/PSeccEKe+9znZosttphDq1a92fzdYDr6EvNBP2I+6EfMB/2I+aAfMV/0JeaDfjR3973vfVf6GItx5O0bqqpN3JLcmOTM3DHkfHpr7TdT7P/hSftfluTkdMHtDUneluR501WgtfbhJDsl+ackk8PZDZPsm+TdSf6nqg7Jiq1sm5gHy5Ytyxve8IZss802Ofzww1fqWEuWLEmS7Lrrrn+4P+Fud7tbdtxxx/z+97/Peeedt1LnAQAAAGDNtehH3o74ZZLPJHl7a+0XczzGd5Mc21pbtqKCrbULkrywql6S5GHpwt+HJdk7yfZ9sbskOb6q1mmtfXQO9ZmPNq31ZvotyNVXX51f/KL7M++1115jyxx11FE56qijcvjhh+eYY46Z8li77LJLTjnllGy11VZjz3+ve90rSbL55pvPy7c082niG7eh1Ys1j77EfNCPmA/6EfNBP2I+6EfMF32J+aAfDcNiDG+XJnn/yOObk1zZWvvtDPd/XZKT+vvrJdkmybOTPCfJnyQ5o6p2m+ko1z7o/XZ/S5JU1W5J3pHkUf2qd1XVv7fWrp/iMCvbJubB+uuvn+c9b/yg6x/84Ac555xzsueee+Y+97lPdtttt2mPte++++Ztb3vblCNrf/zjHydJtt1225WrNAAAAABrrMUY3v66tXbuSux/yaT9z05yUlV9PV2Aul2SD6a7GNmctNa+XVV/muT7Se6TZPMkj0s3inaclW0T82DDDTfMscceO3bb0UcfnXPOOScHH3xwDj300D+sv/HGG3PxxRdnww03zNZbb/2H9Y985COz884751vf+lY+97nPZf/99//Dto985CM5//zzs/3222eXXXZZdQ0CAAAAYNAW45y3q0RrbWmSL/QPD6iqx6zk8W5I8m8jq4xBX4S++93vZrfddltujtyqytKlS7Ppppvm0EMPzcEHH5wjjzwyz3zmM/Pyl788G2+8cZYuXZp11llngWoOAAAAwEIT3s7O3yRp/f23zMPxLh2536YsxaK000475fTTT89znvOcnH322Vm6dGnOOeecPOtZz8ppp52W3XfffaGrCAAAAMACWozTJqwyrbVzq+rTSQ5KsntVPb619pXRMlVVrbWZBrGPGLl/4XzVk9XvNa95TV7zmtcst37vvffO1VdfPeV+2223XZYuXboqqwYAAADAGsrI29l788j9I8ds//eqellVLZnuIFX1hCR/3j+8Pskp81Q/AAAAAGAREN7OUmvt7CSf7x/uXVX7TCqydZJjk1xWVR/vg9zHVNVDq2q3qjqkqj6R5Iu5feTza1tr166eFgAAAAAAawLTJszN3yd5cn//yCSnj2y7OMnDk2yc5Nn9bSo3pQtuj10VlQQAAAAA1lzC2zlorf1XVX0lyeOTPLaq9mytfavfdmBV3S/JE5LslWTHJFsluXOSW5JcleRHSb6a5PjW2qXjzgEAAAAArN0WRXjbWjstSa3E/sclOW6W++w3zbafJPlJuukT5lqn07ISbQIAAAAA1mzmvAUAAAAAGCDhLQAAAADAAAlvAQAAAAAGSHgLAAAAADBAwlsAAAAAgAES3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABgg4S0AAAAAwAAJbwEAAAAABkh4CwAAAAAwQMJbAAAAAIABEt4CAAAAAAyQ8BYAAAAAYICEtwAAAAAAAyS8BQAAAAAYIOEtAAAAAMAACW8BAAAAAAZIeAsAAAAAMEDCWwAAAACAARLeAgAAAAAMkPAWAAAAAGCAhLcAAAAAAAMkvAUAAAAAGCDhLQAAAADAAAlvAQAAAAAGSHgLAAAAADBAwlsAAAAAgAES3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABgg4S0AAAAAwAAJbwEAAAAABkh4CwAAAAAwQMJbAAAAAIABWnehK8DCufN+P1joKiy4rbfacqGrAAAAAABjCW/XYldfffVCVwEAAAAAmIJpEwAAAAAABkh4CwAAAAAwQMJbAAAAAIABEt4CAAAAAAyQ8BYAAAAAYICEtwAAAAAAAyS8BQAAAAAYIOEtAAAAAMAACW8BAAAAAAZIeAsAAAAAMEDCWwAAAACAARLeAgAAAAAMkPAWAAAAAGCAhLcAAAAAAAMkvAUAAAAAGCDhLQAAAADAAAlvAQAAAAAGSHgLAAAAADBAwlsAAAAAgAES3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABgg4S0AAAAAwAAJbwEAAAAABkh4CwAAAAAwQMJbAAAAAIABEt4CAAAAAAyQ8BYAAAAAYICEtwAAAAAAA1SttYWuA6vYNddc40kGAAAAgAHYdNNNa6ZljbwFAAAAABgg4S0AAAAAwAAJbwEAAAAABkh4CwAAAAAwQMJbAAAAAIABqtbaQtcBAAAAAIBJjLwFAAAAABgg4S0AAAAAwAAJbwEAAAAABkh4CwAAAAAwQMLbtUBVbVVVH6qqS6vqlqq6qKreVVWbL3TdGJaqekZVHVtVX6+qa6uqVdXxK9jnkVX1haq6qqpuqqpzquoVVbXO6qo3w1FVd62q51fVp6vqp32fuKaqvlFVf1FVY//f0Y8Yp6reWlVfrapf9v3iqqo6u6reUFV3nWIffYlpVdVz+//fWlU9f4oyT6mq0/rXr+ur6r+q6s9Xd10Zjv79c5vidtkU+3g9Yqyqemz/Xumy/vPZpVX15ap60piy+hF/UFWHTfNaNHG7bcx++hHLqaonV9XJVXVx3y8urKoTq2rPKcrrRwukWmsLXQdWoaraIcmZSbZIclKSHyfZLcmjk5yfZK/W2pULV0MJ5+yZAAAW20lEQVSGpKq+n+QhSa5PcnGSByT5l9bac6co/9Qkn0pyc5ITklyVZP8k90/yydbaM1dHvRmOqjo8ydIkv0rytSS/SLJlkoOSbJquvzyzjfznox8xlaq6Ncn3kvwoya+TbJxkjySPSHJpkj1aa78cKa8vMa2q2jrJD5Osk2RJkhe01j44qczLkhyb5Mp0/ejWJM9IslWSd7TWjlitlWYQquqiJJsledeYzde31t4+qbzXI8aqqv+b5FXp3mt/MckVSe6e5OFJTmmtvXqkrH7EHVTVQ5McOMXmvZM8JsnnW2tPGdlHP2I5VfXWJK9O937nM+lei+6T5IAk6yY5tLV2/Eh5/WgBCW8Xuar6cpL9kvxVa+3YkfXvTPLXSf6htXb4QtWPYamqR6d7I/nTJPukC9/GhrdVtUlfbtN0XwJ8p1+/QZJTk+yZ5ODW2sdXU/UZgKp6TLqA7fOttd+PrL9Hkm8n2TrJM1prn+rX60dMqao2aK3dPGb9UUlem2Rpa+0l/Tp9iWlVVSX5SpJ7J/n3JEdkUnhbVdul+6L7hiQPb61d1K/fPMlZSXZI8sjW2rdWZ91ZeH14m9badjMo6/WIsarqBUn+MclHkrywtXbrpO3rtdZ+19/Xj5iVqvpWui+5n9pa+2y/Tj9iOf1ns0uS/CbJg1trvx7Z9uh0feNnrbXt+3X60QIzbcIi1o+63S/JRUneN2nzG9J9MHleVW28mqvGQLXWvtZau2B0VOQ0npFulMDHJ168+2PcnOR1/cMXr4JqMmCttVNba58bDW779Zcl+UD/cN+RTfoRUxoX3PY+0S/vO7JOX2JF/irdiKT/le490Dj/O8n6Sd47EdwmSWvtt0ne0j/0pTcr4vWI5VTV+kmOSverpOWC2ySZCG57+hEzVlU7pwtuL0ny+ZFN+hHjbJsuD/yv0eA26TKBJNel6zcT9KMFJrxd3B7dL08eE6Rcl+SbSTZK9yIPs/WYfvmlMdvOSHJjkkf2b1QhSSY+kCwbWacfMRf798tzRtbpS0ypqh6Y5Jgk726tnTFN0en60RcnlWHts34/Z/Jrq+rlVfXoKeb583rEOI9PF378e5Lf93NN/k3fl8bNL6kfMRsv7Jf/3FobnfNWP2KcC9JNC7VbVd1tdENV/UmSOyc5ZWS1frTA1l3oCrBK3b9f/mSK7RekG5l7vyRfXS01YjGZsn+11pZV1c+S7Jhk+yTnrc6KMTxVtW6SQ/uHo//p60esUFUdkW5+0k3TzXf7qHTB7TEjxfQlxupffz6WbrTba1dQfLp+9KuquiHJVlW1UWvtxvmtKWuAe6TrS6N+VlX/q7V2+sg6r0eMs2u/vDnJ2Ul2Gt1YVWekm1rqN/0q/YgZqaoNkzw3yW1JPjhps37EclprV1XV3yR5Z5IfVdVn0s19u0O6OW+/kuRFI7voRwtMeLu4bdovr5li+8T6zVZDXVh89C9m45h0H1K+0Fr78sh6/YiZOCLdhe8mfCnJYSMfcBN9iam9PskuSR7VWrtpBWVn0o827ssJb9cuH07y9ST/ne7npNsneVm60W5frKo9W2s/6Mt6PWKcLfrlq9JdiHPvJN9PNw/329MNqjkxt08vpR8xU89K1w8+P3oh155+xFittXf187l/KMkLRjb9NMlxk6ZT0I8WmGkTAFilquqvkrwy3UWAnrfA1WEN1Fq7R2ut0o16OyhdaHJ2VT1sYWvG0FXV7ulG277DRcZYGa21N/Xzul/eWruxtXZuf9HfdybZMMkbF7aGrAEmPnsvS3JAa+0brbXrW2s/TPK0dBcN3meKKRRgOhNTJvzDgtaCNUpVvTrJJ5Mcl27E7cZJHp7kwiT/UlX/d+Fqx2TC28Vt4tuPTafYPrH+6tVQFxYf/YsVqqqXJXl3uhEmj26tXTWpiH7EjPWhyafTjU66a5KPjmzWl7iDfrqEj6b7id+RM9xtpv1oqpEnrH0mLsb5JyPrvB4xzsTzffboBRGTpJ+GZeKXSbv1S/2IFaqqHZM8Ml34/4UxRfQjllNV+yZ5a5LPttb+T2vtwv6Lye+l+zLpkiSvrKrt+130owUmvF3czu+X95ti+8RVuqeaExemM2X/6j8w3zvdyIILV2elGI6qekWSY5Ocmy64vWxMMf2IWWut/TzdFwI7jlxkQV9isiXp+sMDk9xcVW3iluQNfZl/6te9q388XT+6Z7pRKReb75YRE9O3bDyyzusR40z0i6nCjd/2yw0nldePmM5UFyqboB8xzlP65dcmb+jf43w7XV64S79aP1pgwtvFbeIf4n5VdYfnuqrunGSvdPO1/efqrhiLwqn98k/HbPuTJBslObO1dsvqqxJD0U+A///SzeX26ElzJo3Sj5ire/XLiQ8q+hKT3ZLkn6e4nd2X+Ub/eGJKhen60RMnlYEk2aNfjn5g9XrEOF9N0pI8aPJns97EBcx+1i/1I6ZVVRukm5LstnT/l42jHzHO+v3y7lNsn1h/a7/UjxaY8HYRa639T5KTk2yX5KWTNr8p3QiBj7XWbljNVWNx+GSSK5I8p6oeMbGyfxPx5v7h0oWoGAurqo5Md4Gy7yZ5bGvtimmK60eMVVX3q6rlfppVVX9UVUelu/DLma21iZFK+hJ30Fq7qbX2/HG3JJ/ti32kX3dC//jD6ULfl1XVdhPHqqrN082dm9z+M3nWElX1wKraeMz67ZK8t394/Mgmr0csp//VyOeSbJPk5aPbqmq/JE9INyr3S/1q/YgVeWaSzZN8ccyFyiboR4zz9X75wqr649ENVfXEdAP9bk5yZr9aP1pg1Vpb6DqwClXVDun+wW2R5KQk5yXZPcmj002X8MjW2pULV0OGpKoOTHJg//Ae6d5EXpjbX9yvaK0dMan8J9O9sH88yVVJDkhy/379s5oXmbVKVf15uknvb0s3ZcK4eSEvaq0dN7KPfsRy+mk3jk43MvJnSa5MsmWSfdJdsOyydF8O/GhkH32JGamqN6abOuEFrbUPTtr2l0nek67PnZBu1MkzkmyV7sJnR4S1St9fXpnkjCQ/T3Jduou7PDnJBunmmXxaa+3WkX28HrGcqtoq3WezrdONxD073c+ND0w3Kvc5rbVPjZTXj5hSVX09yaPSXQDvc9OU04+4g370/5eTPC7d/2mfTvfe+oHpplSoJK9orb17ZB/9aAEJb9cCVbV1kr9LN8T9rkl+le4f55tGRizB6IfZqfy8tbbdpH32SvK3SfZM9wHmp0k+lOQ9U8y7xCI2gz6UJKe31vadtJ9+xB1U1U5JDk/3oWSrJJsluSHdF4+fT9c3Jl8AT19iRqYLb/vt+yc5IsnD0v1S7UdJ3tta+8jqrCfDUFX7pHs92iXdl9sbpxsh+f0kH0v3S7blPlR5PWKcqrp7ktenCz3umeTadAMljm6tfXtMef2I5VTVA9P933Rxku1W1Bf0IyarqvXS/UL7OUkelG7qg6vSzXf7ntbayWP20Y8WiPAWAAAAAGCAzHkLAAAAADBAwlsAAAAAgAES3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABgg4S0AAAAAwAAJbwEAAAAABkh4CwAAAAAwQMJbAAAAAIABEt4CAAAAAAyQ8BYAgLGqqvW37ebhWPv2x7popSu2hqiqw/o2nzZm22n9tsNWf81Wr6q6qG/rvgtdFwCANY3wFgBgERoJXmd7O22h686aoaoeWlVvXBsC6FFVtXNVfb6qrq2qG6rqa1W11wr2+dequq2qHrG66gkALA7rLnQFAABYJS6fYv1dkqyX5OYk14zZftXI/fP75e/msV4sHg9N8oYkpyc5bppy/5Ouv924Guq0SlXV/ZJ8M8mdkyxLcluSfZOcWlWPba19Y8w+j0lycJKlrbXvrMbqAgCLgPAWAGARaq3dY9z6fmTtPklOaK0dtoJjPGD+a8baprX22IWuwzx6Y7rg9rgkL0kX4L45yauTHJPkUaOFq+pOSd6X5DdJ/nY11hMAWCRMmwAAADAzj0032vblrbWbWmu/SxfKXp5kz6raaFL5I5I8IMmrW2u/Xb1VBQAWA+EtAABjreiCZVW1cVUdUVVnVtVVVXVzVV1YVZ+tqkOqar1ZnOtxVXV9f75jxmxfUlWvraqzquqa/lwXVNV7qmrrKY75h4uCVdVmVfXWqvpxVd1YVVfPom4Pq6pjquobVfWLqrqlqq7sj//8qlpnpseaxTk36eeT/UH/d7m+qs6pqjdV1aYr2HdWz8tc2ldVLcmH+4f7jJk7ed+RstNesKyqtqyqd4w8N9dU1ber6pVVtf4U+xzXH/ONVbVOVb2i/1vd2Lf5P1bR/LJ3TXJFa+3aiRWttWVJfp7us9XmI3XcLl2w+40kH1kFdQEA1gKmTQAAYNaq6kFJPp9ku37VsiTXJtk6yb2T7J9ubtCLZnCspyX5tyTrJ3lNa+2YSdsfmOSLSbYdOdctSe6T5C+TPLeq9m+tfXOKU9w9yXeTbN/vd+tM2jji5HShXdLN23pjurmD9+lvT6uqp/Yh3kqrqvskOSW3t3dirtid+9thVfW41toFY/ady/Myl/ZdnmTDJJukmxN5dK7kZIZ/46raLd1ze5d+1XVJ7pRk1/72vKrar7X26ykOsW7f3if09bglXYD65CSPrarHtNa+NZO6zNCVSe5WVZtMBLh9uL1tkt8nGR1d+56+LS9prbV5rAMAsBYx8hYAgFmpqrsk+VK6gPBnSQ5MsnFr7a5JNko37+eH0wWHKzrWoUlOzO0h1+TgdtMkX0gXjp2Y5CFJNmitLUmyQ5J/TRfWfaqqNpviNK9Pd5G2JybZqLW2SZLZjMo8Od0Fp+7ZWtu4tbZ5kiVJnpfksiRPSvLXszjelPo5Uj+Vrr2/TLJff64lSR6X5BdJtkny6cmjUlfieZl1+/o5lV/ePzyztXaPSbczZ9DWzZN8Jl1w+8Mku/XPzZIkz0wXhD4kyb9Mc5iXpgt5n51kSWvtzv0+5ybZIMm7V1SPWTo1yTpJ3l1VG1TVuunmvN0yyX+21m7s23ZAuqD82NbaD+e5DgDAWsTIWwAAZuv/SzeS84oke7fWLpnY0M8B+s3+Nq2q+st04dptSQ5trR0/ptir0oWR/9Za+7PRDa21C5Mc0oeWf5rk+UnePuYY6yd5Umvt3JF9f7qi+o2U/bMx625IcnxV/TzJGekuXvW2mR5zGs9O8uB0o0jvUOckX62qJyU5O8mOSQ5J8qGR7XN6XlZz+0a9LMk9k1ydZL/W2mX9uW9L8smqujbJl5M8rh9Be+qYY2yWrq3fGKn7OVV1WJLvJNm1qrZprf1inur8d0mekuSwJM9N13fXT/d8vSZJqmrDdP360iRvmKfzAgBrKSNvAQCYrUP75dtHA8LZqKoj0/2s/NYkz5giuE2SP++X75jmcP/aLx8/xfYvTgpB501r7evpwsftqupe83DIZ/TLk8bVubX230k+2T981qTNK/28jDnffLdv1ERbPzgR3E4698lJJqY8mNzWCV8fDW5H9v1ukov7hzutbEVHjntekr3TjXC+Od1UCWckeVxr7Yy+2JHpvnD4P62166pqi6r6SD+P8I1VdWpVPXy+6gQALG5G3gIAMGP9RZi27B9+YW6HqHem+xn+DUme2lr76hQFt06y1cS5+otkjXOnfjn2wmW5PQCcs6p6ZrqRrg9LN4fuBmOK3SvdaMuV8bB++bVpypyabpqDibIr/bysxvZNnO9OuT1UXVFb98xIWyc5a5p9L0nXfzafpsystda+n24KjuVU1QOSvDLJKa21E6pqg3Rt2DHJV9ONin5aktOqarc+DAYAmJLwFgCA2dhy5P5cfoq+TW6fP/XFUwW3vXuO3N9iBsfeaIr1v5lJxcbp5zT9RLrAbcIt6UK42/rHd0/3i7aN53qeEXfvl9ONnJ0YUXrXqqr+Ylhzel4WoH0T7pLbfwU4k7befYrt102z7839cr1Z1Gtlva9fvqxfviBdcLu0tfaSJKmq5yb5WLq5cp++GusGAKyBTJsAAMDqdFmS0/v7R1fVDtOUHX2vunlrrVZw226K49w2xfqZeEG6YPPGJH+VZOvW2gattbtPXJwrt49GrZU4z2TjRr6uCgvVvlGrq62rVFX9WZLHpJu24vx+9VP65XtHiv5rui8UnlBV66zGKgIAayDhLQAAs3H5yP1t57D/LekCrW8m+eMkp1bVVMcZPdc2czjXfHhmv/z71tqxrbWLRzf24dvd5vF8E6OEp2vvxFQSV/ajbpO5Py+ru30Trko3X2wys7bOefT06lBVm6Sbl/miJEeNbJp4Ln42saK19vv+8cZZNX9bAGAREd4CADBjrbWL0o2eTZInzfEY1/f7fjtdcHdqVW01ptzPcnsoOXaO0dVgol5nT7F9r8zvyNHv9ctHT1PmMZPKrszzsjLtmwhfZz0it7V2a5KJC7LNqq0D9eYk90jy8tbajWO2T/4bbrjqqwQALAbCWwAAZutj/fKVVfXHczlAa+3aJE9IF8ptny7AveeYosf1yyOmO1d1NptLXVbgmn6585hzrpsutJtPn+yXT6yqXcacc8ckz+gffmLS5rk8LyvTvmv75Vz/7hNtPWzcc19V+6W7WFmyfFsHo3+eXpLkP1prn520+ef98uEj5TdLcp90F+y7YrVUEgBYYwlvAQCYrbemu8jU3ZJ8vaoOqKo7JUlVrVdV+1TVx8eNph3VWrs6yeOTnJPkvkm+WlWTL0x1TJIL+3OdWVXPqqo/jFqsqm2q6oXpQuAD56l9o77SL4+sqqdOzFFaVQ9I8rkku6UL4ebLCen+Hknymap6XFVVf87HJvlCugtw/XeSf5m071yel5Vp33/3ywdV1e5zaOt7k/wq3SjUL1XVI/pzr1NVT0/y8b7cKa21U+dw/ClV1UVV1arquJU8TiVZmuTWdHMGT/aFfnl0VW1RVesneXu6Nn+5tbYy8zEDAGsB4S0AALPSWrsy3TQGFye5d5KTklxfVVeku/DVaUmenWTdGRzrqiSPS/KjJA9MckpV3XVk+9XpRuiel26KhROSXFdVV1TVjelGNv5DkocmaZl/b0/yP0k2SfKZJDdV1TV9fR6f5PDM4+jJfjqBp6dr1zbpwtXrq+qGJKf0636R5KDW2i2T9p3L8zLn9rXWLkhyRn+8/6yqK/tQ9KKq2mMGbf1tusD9t0kenOSsqro2yfXpRuVuni7IPmRFx1pAL0iye5K39NN8TPaPSX6c5BHpprW4JslfpGvj61ZXJQGANZfwFgCAWWut/TDJjukCqO8kuSndBZh+kS4EPDhdiDiTY/0myWOTnJ8uxPvK6BQIrbWfJpn4afrX0oV9myZZli7c+8ckT05y/Dw0bXLdrkqyR7rRlRPtuSldG/dprR23Cs750yQPSfJ3uX1e2PT3/z7Jg1trP5li31k9L/PQvoOSvD/dBbiWpLtA17aZ4TzArbVvJ3lQkv+X5CfpRhUv6+v+qiS7t9Z+PZNjzVQ/HcTEhcLOWonj3C3J0UkuSPK2cWVaazelm9P3+CRXp5sn+GtJ9m2tnTfXcwMAa4+6/QK1AAAAi1s/Kvhb6aaY2GHyCGYAgCEx8hYAAFib7NMv3yq4BQCGzshbAABgrVFVn083Dcf2rbWbF7o+AADTEd4CAAAAAAyQaRMAAAAAAAZIeAsAAAAAMEDCWwAAAACAARLeAgAAAAAMkPAWAAAAAGCAhLcAAAAAAAMkvAUAAAAAGCDhLQAAAADAAAlvAQAAAAAGSHgLAAAAADBAwlsAAAAAgAES3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABig/x9jKVLUVBuKnAAAAABJRU5ErkJggg==\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 695}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "16 out of 20 tickers were removed\n{'PRSP': 20, 'EIX': 16, 'FAST': 261, 'DLR': 2}\nFunds remaining: $4.99\n"}]}}, "484ab70b92734fb795b12f201601b6c3": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "48624c7597f743d2859aef44b8e8b931": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "486f17034d324598a63cba2d0a4ef075": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_841582b9fa594f65936a68ac6fb6d834", "outputs": [{"data": {"image/png": 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\n", "text/plain": "<Figure size 432x288 with 1 Axes>"}, "metadata": {"image/png": {"height": 277, "width": 573}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "14 out of 20 tickers were removed\n{'PRSP': 42, 'SKM': 113, 'EIX': 11, 'FAST': 98, 'DLR': 16, 'CBPO': 5}\nFunds remaining: $22.24\n"}]}}, "487ad77fe4484c8aa42a05f29d571eec": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_ac3f8a78ed1b48b5a0d963c4f1142a17", "outputs": [{"data": {"image/png": 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prices not in a dataframe"]}]}}, "49ebce5d2f88484bb133d301c5ae4e37": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntTextModel", "state": {"description": "Total value:", "layout": "IPY_MODEL_1f07f6026d904d538333341a62304555", "step": 1, "style": "IPY_MODEL_4b0167f41d8649ed861aa4ad3088dd21", "value": 10000}}, "4a20949684e14130b10cbff31beebd9b": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_d2092a544c5542daba6502c056bb2eb7"}}, "4a4d7b17f85b491ba42d7d3d2e4dae38": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "FloatSliderModel", "state": {"continuous_update": false, "description": "Target risk:", "disabled": true, "layout": "IPY_MODEL_9b66754611714b41813670e709a737f2", "max": 1, "readout_format": ".1%", "step": 0.001, "style": "IPY_MODEL_2bde2e2b337840f0beb6db7cdd89a05d", "value": 0.26633182662046506}}, 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ErtYLaW7xex1w7xvgeN8EbkJz++GbkjyzquoGOO6KNYi/AkyKmb90+Z1NHud+sjn/k8u5n2zOv8bhmS4Aquo/gROBvZM8eoGml7TbO/Qx7Eybi+YZ5/7ANPB04F0LLbghSZIkaTKNW0h4Mc2qhK9uF7noZebWw30XGijJTsD69u3ne7VpF824P82qhYcB75/vh5glSZIkTaaxCl1V9W3grcCdgWfP02wzcB3N8197LjDck2me5fomcNoCx7yEZnXDc4HHACcsEPgkSZIkTZixCl2to4GtwEtpVhj8FVX1XZrf1Lox8Ikkd5/bJsmBwBtpwtnTq+r6hQ7Y/ijzA4GzgAOBjyW56TLPQ5IkSdIYGLtb4arq8iSvAv5ugWYbgd2AFwBfTvIp4Gs0Qey+wN7Az4FDq2ruUvHzHfcnSfalWUb+IcDJSQ6oqit3+GQkSZIkjbyxC12tNwHPANb2+rC9cvXCJB8CnknzXNaDaK5sbQGOAd5QVT9YykGr6ook+wMfp3lm7FNJHlpVP12s79YNa5ZyKEmSJEkjYmRDV1Vlgc+upnmua7ExzqK5JXApxz0VWOjYVwH7LWVMSZIkSeNrHJ/pkiRJkqQVw9AlSZIkSR0ydEmSJElShwxdkiRJktQhQ5ckSZIkdcjQJUmSJEkdMnRJkiRJUocMXZIkSZLUIUOXJEmSJHXI0CVJkiRJHTJ0SZIkSVKHDF2SJEmS1CFDlyRJkiR1aOdhF6DG6k0XDrsE3aB2bTZnLH3et25YM+BaJEmS1KUVf6UrSS3yOmyefjdP8tO2zXv6OM6jk3wqyY+SbE/y4yTnJXlvkie0be7SRz1zX+sG/JVIkiRJGiGjdKXrqHn2nzvP/kOBWwAFHJzkuVX1k14Nk7wL2ABcBZwEbAF2An4POAD4U+C9wOU96rgR8PL2OEf3GP7789QnSZIkaQKMTOiqqo1L7PI04Drg9cBfA08A3jS3UZL1NIHrfOC+VXXRnM93AfZpa7gc2Djn851pQtf1O1CjJEmSpDG34m8v3BFJ/gjYC/g08PfAduDweZrft92eODdwAVTVNVV1SieFSpIkSRp7Yxm6aK5yAWyuqh8B/wL8fpL79Gh7Wbu96w1SmSRJkqSJkqoadg0LSjJTYK9nurZU1eY57XcFLqa5tfC2VXV1kgOBj9KEsA1z2t8R+CrN818fBz4EnA18p/r4ctrbC7cD11XVordrbtu2reeYrl6ofp297qphlyBJkjQxpqameu5ftWpV+h1jZJ7pAo7sse80YPOcfY8Gbgm8taqubvedDPwI+L9JnldV22YaV9X3kxwEvB14RPsC+GmSM2kW0PhgVV0/sDORJEmSNDFGJnRVVb9JcubWwk2z+m5PchzwXOBxwFvnjP2ZJHcB1gH3B+4B3A/Yv309MckBVXXN8s5CWr75/tqi0TA9PQ04j5PK+Z9czv1kc/41Vs90Jfk/wL2Br1bVOXM+3txuey6oUVXXV9XpVfXKqjoIuA3wEOCHwIP5ZZiTJEmSpL6NVejil8Ho9+f+SDHw3+1nf5TkXosNVI1/A45odz2wg3olSZIkjbmRub1wMUluBjyeZgGNzfM0uwPwZzRXu87qc+grZg6xnPokSZIkTaaxCV3AIcBq4JNV9dReDZKsBi4CHpPkBVV1RZKHArsAJ1XVtXPa34LmOTCA07srXZIkSdK4GqfQNXNr4bHzNaiqrUk+THNF7LHAPwN3p/kB5cuT/AfwbeBamqtiDwNWAWcCb+uudEmSJEnjaixCV5K70aw2eDHN8vALeQdN6HoaTeh6N/ATYD/gD4D1wG7tvi8CJwDvrKrtXdQ+Y+uGNV0OrxXGVYwkSZImx4oPXf0sFV9VX6fPZ66q6vTZbavqR8A729eO1Hdtv8eWJEmSNHnGbfVCSZIkSVpRDF2SJEmS1CFDlyRJkiR1yNAlSZIkSR0ydEmSJElShwxdkiRJktQhQ5ckSZIkdcjQJUmSJEkdMnRJkiRJUocMXZIkSZLUIUOXJEmSJHXI0CVJkiRJHTJ0SZIkSVKHdh52AWqs3nThsEsYWVs3rBl2CZIkSdK8JuZKV5Lq47V+VvvD2n2bZ+27UZLT2v2HznOc30lyRZLLk9y++zOTJEmStJJN4pWuoxb4bMtCHavq+iRPAr4MvCXJ6VX1v5eokuwEvA+4OfCYqvrBAOqVJEmSNMImLnRV1cZl9t+S5LnAJmBzkj+rqmo/fjFwH+C4qvrQ8iqVJEmSNA4m5vbCQaqqzcBHgX2B5wIk2Qs4ArgAeObQipMkSZK0ohi6dtzTgEuBV7eB6300Vw6fVFVbh1qZJEmSpBVj4m4vTLJxno9+UVWv6XecqvpxkqcCnwTOAG4CvL6qPrf8KrUU09PTwy5hh41y7Voe536yOf+Ty7mfbM7/aJqamlr2GBMXuoAj59m/Deg7dAFU1UlJTgP2Ab4PvGSZtUmSJEkaMxMXuqoqgxoryQOB+7dvbw/sDZw+qPHVn0H89eGGNvOXrlGsXcvj3E82539yOfeTzfmXz3TtoCSrgc3AtcDTgQLeneQWw6xLkiRJ0spi6NpxbwXuABxZVf8EvBZYC/zDMIuSJEmStLIYunZAkkcDhwKfpwlbABuBc4GnJPnzIZUmSZIkaYUxdC1RkjXA24CfAU+squsBqmo78ATgauAdSX5zeFVKkiRJWikmbiGNBZaMB/hYVZ27QN/QPMe1O/DUqvru7M+r6qtJXg78HU0wO2TZBUuSJEkaaRMXuph/yXiALTS3CM7nOcC+wMer6p3ztDkGeDhwcJLHV9X7dqhKSZIkSWNhYkLXUpeKr6rNNFe1Zu97I/DGRfpdzy+Xke/b1g1rltpFkiRJ0gjwmS5JkiRJ6pChS5IkSZI6ZOiSJEmSpA4ZuiRJkiSpQ4YuSZIkSeqQoUuSJEmSOmTokiRJkqQOGbokSZIkqUOGLkmSJEnqkKFLkiRJkjpk6JIkSZKkDhm6JEmSJKlDhi5JkiRJ6tDOwy5AjdWbLhx2CRqgrRvWDLsESZIkrRBe6ZIkSZKkDhm6liFJLfI6bNg1SpIkSRouby8cjKPm2X/uDVqFJEmSpBXH0DUAVbVx2DVIkiRJWpm8vVCSJEmSOmTokiRJkqQOpaqGXcPISjLz5fV6pmtLVW2eu3Pbtm09v3CXjB8vZ6+7atglSJIkaQCmpqZ67l+1alX6HcNnugbjyB77TgM238B1SJIkSVphDF0DUFV9p1xNhvn+IjJjenq6r3YaP879ZHP+J5dzP9mcf/lMlyRJkiR1yNAlSZIkSR0ydEmSJElShwxdkiRJktQhQ5ckSZIkdcjVC1eIrRvWDLsESZIkSR0wdC2DS8VLkiRJWoy3F0qSJElShwxdkiRJktQhQ5ckSZIkdcjQJUmSJEkdMnRJkiRJUocMXZIkSZLUIUOXJEmSJHXI0CVJkiRJHTJ0SZIkSVKHDF2SJEmS1CFDlyRJkiR1yNAlSZIkSR1KVQ27homybdu2nl/46k0X3tClSJIkSSva1g1rhl3CvFatWpV+23qlS5IkSZI6NNKhK8lOSQ5PclqSy5NsT/LDJF9JcmySA2a1XZ+kkpw6z1gPS3Jlkp8neUS7b23bp5L8LMkt5umbJN+Z1XZ9F+crSZIkafTsPOwCdlSSnYCTgP2BrcDJwA+AXYA9gccCewCf6GOsDcDbgSuAh1fV5+c0uRbYDTi0bTfXg4DfaduN7HcqSZIkafBGOSAcShO4vgzsU1XbZn+YZFdg78UGSfJi4FU0gW3/qvpaj2ZfBO4EHE7v0HU4cDXwWeAhSzgHSZIkSWNulG8vvG+73Tw3cAFU1VVV9bn5Ore3BL6RJnCdB9x3nsAFzRWsTcBeSf5wzji/CRwIfBi4fOmnIUmSJGmcjXLouqzd3nWpHZPsAnwAeA5wJvCnVXXBIt2OBYrmqtZsT6K5pfEdS61DkiRJ0vgb5dsLPwL8P+Av2wUuPgp8sarOX6TfzWme/9oX+CTw6Kr6+WIHq6rvJvks8Lgkfz2rz1OB6ao6NclTd/RkJEmSJP2q6enpYZfA1NTUsscY2StdVfXfwOOBS9vth4EtSS5L8tEkD5+n65/QBK5vAgf1E7hmeQewGjgEIMmf0izWceyOnYUkSZKkcTfKV7qoquOTfBR4ALAO+ON2eyBwYJL3AIfVr/4C9DeBmwC/B7wpyTOr/1+I/ijwY5pbDN8DPA3YDmwewOlIkiRJmmUQV5lWgpG90jWjqrZX1aer6oiqejjwm8CjgSuBJwKPmNPlEuD+wDTwdOBdSfr6HqrqGpqwtS7JfYCDgU9U1Q8HczaSJEmSxs3Ih665quq6qjoe+Id21wN7tLmAJnidBxwGvD9Jv1f9ZhbMOB64Kb2XkJckSZIkYAxD1yxXtNv0+rCqLgH2Ac4FHgOc0K5quKCq+gbwH8DtgS3AKYMoVpIkSdJ4GtnQleTQJPv1ujUwyW345dLup883RlX9mOZK2Fk0z4F9LMlN+zj804BH0izE0e/zYJIkSZIm0CgvpLE38FzgkiRnAN9r998ZeBhwM+DjwIkLDVJVP0myL80y8g8BTk5yQFVduUCfbwDfWP4pSJIkSRp3oxy6jqFZDGNf4A+AB9M8Y3UZcCpwHHBcP1eiquqKJPvThLR9gU8leWhHdfe0dcOaG/JwGrKZ35wYlxV51D/nfrI5/5PLuZ9szr9GNnS1i2G8pX310/5U5nm+q/38KmC/Obt/ulCfHmM8nuY3wyRJkiQJGOFnuiRJkiRpFBi6JEmSJKlDhi5JkiRJ6pChS5IkSZI6ZOiSJEmSpA4ZuiRJkiSpQ4YuSZIkSeqQoUuSJEmSOmTokiRJkqQOGbokSZIkqUOGLkmSJEnqkKFLkiRJkjpk6JIkSZKkDhm6VojVmy4cdgmSJEmSOjCWoSvJTkkOT3JaksuTbE/ywyRfSXJskgNmtV2fpJKc2se4a9u2c18/TzKd5C1J7tDpyUmSJEkaKTsPu4BBS7ITcBKwP7AVOBn4AbALsCfwWGAP4BPLOMw24A2z3t8K2Ad4BnBwkntUlZeuJEmSJI1f6AIOpQlcXwb2qaptsz9Msiuw9zKPsbWqNs7dmeQTwMOBw4Ff+1ySJEnS5BnH2wvv2243zw1cAFV1VVV9rqNjn9Juf6uj8SVJkiSNmHEMXZe127sO4dj7tttzhnBsSZIkSStQqmrYNQxUkj8GvkBz6+T7gY8CX6yq8+dpvx74HHBaVa1fZOy1wPf49We6dgfuD/wf4L3A4VV1ba8xtm3b1vMLX73pQs5ed9VCh5ckSZJ0A5uamuq5f9WqVel3jLF7pquq/jvJ44E3Ao9vXyS5HDgdeFdVfXKZh1kFHNlj/38BH5ovcEmSJEmaPGMXugCq6vgkHwUeAKwD/rjdHggcmOQ9wGG145f5zq+qtTNvkqxqj/EG4F+S/GVVvX2pg86XojV+pqenAed8Ejn3k835n1zO/WRz/jWOz3QBUFXbq+rTVXVEVT0c+E3g0cCVwBOBRwzwWNuq6lTgYCDAa5PcbFDjS5IkSRpdYxu65qqq66rqeOAf2l0P7OAY3wYuB1YznIU8JEmSJK0wExO6Zrmi3fb94Fu/kuwM3KJ9O4nfrSRJkqQ5xi4YJDk0yX5Jfu3cktyG5oeLoVlUY9CeBdyYZtn6r3YwviRJkqQRM44LaewNPBe4JMkZNEu8A9wZeBhwM+DjwIlz+u2RZPM8Y36/qo6Y9X51ko2z3t8SuAewD3A98Iyq2r6ck5AkSZI0HsYxdB0DTNP8UPEfAA8Gbkpz9elU4DjguB4rF94aeNI8Y34ZmB265i4Zvx24FPgg8PqqOnupRW/dsGapXSRJkiSNgLELXVV1AfCW9tVP+1Pp8/muqtrSb1tJkiRJgjF8pkuSJEmSVhJDlyRJkiR1yNAlSZIkSR0ydEmSJElShwxdkiRJktQhQ5ckSZIkdcjQJUmSJEkdMnRJkiRJUocMXZIkSZLUIUOXJEmSJHXI0CVJkiRJHTJ0SZIkSVKHDF2SJEmS1KGdh12AGqs3XTjsEnSD2rXZnOG892vrhjXDLkGSJGmHeKVLkiRJkjo00qEryR5J3pzkq0m2JbkmyUVJTk7ylCQ3mdW22tf1SX53gTE/N6vtYQu0u0OuU5IiAAAZs0lEQVSS69p2rxrwqUmSJEkaEyMbupIcAXwNeBbwU+DdwOuAfwX2AI4FPj+n27VAgKfMM+YUsL5tt5in0nx/BWxI4q2akiRJkn7NSIauJC8BjgIuBO5dVfetqudU1Uuq6ilV9bvAw4Er5nS9FDiH+UPSU9vtJxc5/k7Ak2nC3tuA2wAH7PAJSZIkSRpbIxe6kqwFNgLbgYdW1Rd6tauqk4D9e3z0DpqQ9Odzxr0xcBhwJnDeImU8BLg98CGa0AVweB/lS5IkSZowo3hL3AbgxsAHq+qrCzWsqqt77P4A8Hqaq1ofm7X/AOC3gf8H3GWRGp7WbjdX1VeTfBH4syR3qqrz+zgHSUs0PT097BIGatzOR0vj/E8u536yOf+jaWpqatljjNyVLmBdu/33HelcVVcAHwT2T3L7WR8dTnO74PEL9U+yBngo8K2qOrPdvZnmu3zqfP0kSZIkTaZRvNJ123b7g2WM8Q6axTSeDByd5E7AfsA/V9VVSRbq+2RgJ5qgNeM44BjgyUk2VtV1y6hNUg+D+CvTSjDzV85xOR8tjfM/uZz7yeb8axSvdC1b+xzY/9CEpJkrVDeiCWPzats+BbgeeM+s8S6nWXzjdsDDOipbkiRJ0ggaxdB1cbtds8xx3gHciWZRjA3AF6vqvxfp8+C2zylVdeGczza326chSZIkSa1RDF1ntNsHLXOc9wI/B/6JJsC9vY8+M4HqwbN+QLmSFL9cZn7/JHdYZm2SJEmSxsQoPtO1CXgx8Kgkd6+qeZd3T3KTeVYwpKq2JjkReAJwJc2qhvNKMrPM/E+BE+ZptgdwP5rnvo5a7EQkSZIkjb+RC11VtSXJRuBvgZOTHFJV58xtl2R/4EXAAxcY7mXAR4AftasaLuTJNN/X+6vqGb0aJLkL8C3gKUleUVXXL3pCkiRJksbayIUugKp6VZKdgSOBs5OcCZwD/Ay4NXB/YKrdt9A43we+v9jx0ixnOLMc/LELjPftJKcB62meFTt50ZORJEmSNNZGMnQBVNXRSU4AngE8gGYxjJsClwHnAq8F3jegw+0L3Bn476r60iJt30ETup7GEkLX1g3LXRdEo8SlYyVJkibHyIYugKr6OvDsPtsu+ONbc9q+jObWw5n3pwB99a+q42h+t0uSJEmSRnL1QkmSJEkaGYYuSZIkSeqQoUuSJEmSOmTokiRJkqQOGbokSZIkqUOGLkmSJEnqkKFLkiRJkjpk6JIkSZKkDhm6JEmSJKlDhi5JkiRJ6pChS5IkSZI6ZOiSJEmSpA7tPOwC1Fi96cJhl6Ab1K7N5gznvR9bN6wZdgmSJEk7zCtdkiRJktShiQldSarH6+okW5K8O8ndZrVdP0/7hV5r275bZr+XJEmSNNkm8fbCo2b9exVwL+CJwKOSrKuqc4Etc9oBrAaeC2wD3tBj3K2DL1WSJEnSqJu40FVVG+fuS/Jm4FnA84DDqmoLsHFOm7U0oWtrrzEkSZIkqZeJub1wEZ9ut7811CokSZIkjR1DV2PfdnvOUKuQJEmSNHYm7vbCJBtnvb0lcE/gfsBJwOuGUZOkhU1PTw+7hIEbx3NS/5z/yeXcTzbnfzRNTU0te4yJC13AkT32nQd8oKquuKGLkSRJkjTeJi50VVVm/p1kN2BP4DXA+5PsWVUvHVpxknoaxF+YVoqZv3KO0zmpf87/5HLuJ5vzr4l+pquqrqyqs4CDgCuBFyW5w5DLkiRJkjRGJjp0zaiqrcA3aa783WPI5UiSJEkaI4auX9q93fqdSJIkSRoYAwaQ5EDgzsB24MwhlyNJkiRpjEzcQhpzlozfDbg78JD2/Uuq6tIbvChJkiRJY2viQhe/umT8dcCPgE8C/1hVpwynJNi6Yc2wDq0hcBUjSZKkyTExoWv2UvE72H8LsOgYVbV2OceRJEmSNF58pkuSJEmSOmTokiRJkqQOGbokSZIkqUOGLkmSJEnqkKFLkiRJkjpk6JIkSZKkDhm6JEmSJKlDhi5JkiRJ6pChS5IkSZI6ZOiSJEmSpA4ZuiRJkiSpQ4YuSZIkSeqQoUuSJEmSOrTzsAtQY/WmC4ddwsBt3bBm2CVIkiRJQzeWV7qSVJKab/+c19VJtiR5d5K79eizflbb7yXJPMe8eZKfzmq7dvBnJkmSJGnUTOqVrqNm/XsVcC/gicCjkqyrqnN79LkWWAvsB3y6x+ePAW7RtpvU71WSJEnSHBMZDqpq49x9Sd4MPAt4HnBYj26fAR4AHE7v0HU4cDHwfWDvAZUqSZIkacSN5e2FO2gmSP3WPJ9fBnwEeESSX2mT5A9orpZtornSJUmSJEmAoWu2fdvtOQu0eQdwY+BJc/YfDhTwzg7qkiRJkjTCUvVr602MvJlFNKoqvfbzq8903RK4J3A/4GTgsVV1xaw+64HPAe8HngB8C7iuqvZoP78ZcBFwTlXtl+SMdqw7V9WWubVt27at5xc+jqsXnr3uqmGXIEmSJC3L1NRUz/2rVq3qucBeLxP5TBdwZI995wEfmB245qqqSnIs8Jok96+q04GDgdU0V8EkSZIk6VdMZOiafQUsyW7AnsBrgPcn2bOqXrpA983AK2huKTwdeBrwY+BjnRU8oub7q4Bgenoa8DuaRM79ZHP+J5dzP9mcf038M11VdWVVnQUcBFwJvCjJHRZofynwSZrl5e8DrAPeXVXX3CAFS5IkSRopEx+6ZlTVVuCbNFf/7rFI87cDNwOOb997a6EkSZKkngxdv2r3drvY93IKcD5we+D0qvpmp1VJkiRJGlkT+UxXL0kOBO4MbAfOXKhtVV2f5CDgjsDXb4DyJEmSJI2oiQxdSTbOersbcHfgIe37l7TPbS2oqr4EfGnw1UmSJEkaJxMZuvjVJeOvA35EszjGP1bVKcMoaOuGNcM4rCRJkqSOjWXomvujyIvtX2SsU4G++1XVuqUeQ5IkSdL4ciENSZIkSeqQoUuSJEmSOmTokiRJkqQOGbokSZIkqUOGLkmSJEnqkKFLkiRJkjpk6JIkSZKkDhm6JEmSJKlDhi5JkiRJ6pChS5IkSZI6ZOiSJEmSpA4ZuiRJkiSpQ4YuSZIkSerQzsMuQI3Vmy78339v3bBmiJVIkiRJGqSBXulKctckr0/ypSSXJ9nebr+Q5HVJ/mSefrsmeX6S05P8OMk1SS5O8skkhyTJIsddk+Tvknw5yU+T/DzJd5NsTnLPLupOsjFJJdnY9xckSZIkaeIMJHSlcSTwdeD5QAEfAv4OeB/wc+DZwDlJnjmn757AV4HXA3cEPgy8Dvh3YB1wPPCpJKvnOfbBwLeAvwauBt4JvAk4D3gscFaS1/YKbsupW5IkSZL6MajbC48ANgIXAIdW1efnNkjy28DzgFWz9t0GOAW4LfBq4IiqunbW578BHAc8GDg+yf5Vdf2szx8IfBDYDhxSVSfOOeaewEnAi4CfAa8YRN2SJEmS1K9lh64kvwO8DLgGeEhVfa1Xu6r6IfCSJLOP+UqawPXBqnpJjz6XJ3kU8BVgP+BQ4P3tcW8EvA3YCXjm3MDV9v9akgOALwJHJHlPVZ0/gLolSZIkqS+DuL1wA014O3G+4DLbzJWsJDcDHt/uPnqB9lcCx7Rvnzbro32AuwIXAccu0P9/gI+1NT55uXVLkiRJ0lIM4urN/drtZ5fYby/gJsBFVfX1Rdqe0m7vnWSnqrqO5nkvgFPb94v1P2RWrbDjdXduenp62CXoBuJcTy7nfrI5/5PLuZ9szv9ompqaWvYYgwhdt2m3F879IMla4LA5u7dW1RtobiuE5nmqxcy02QW4FfDDHex/u1n7drRuSZIkSepb188prQWOnLPvfGClh5e1DLHuQaRprWwzf+lyriePcz/ZnP/J5dxPNudfg3im65J2e7u5H1TVqVWVqgpw43n63aGPY8y0uQa4bBn9L+px/KXWLUmSJEl9G0Tomllm/UFL7Hc2ze9q3S7J3RZpu2+7/a9Zz2+d0W7XJ9mpz/6zl4Tf0bolSZIkqW+DCF2bgWuBg/sIT/+rqn5O8xtc0Czd3lO7yuEL2rdvn/XRacC3aa5UPXluv1n99wQe2db4ruXWLUmSJElLsezQVVXfofm9rV2Af01y33maru6x72XApcBjk7xi7m9hJdkdOBG4C/AZ4AOzjnsd8AzgeuCNSR45d/A2TH2C5hbBV8z8RtcA6pYkSZKkvgxqIY2jgQAvBz6f5IvAWcDlNKFlLb+8xe/0mU5VdVGSP6MJRi8DHp/k39p+dwQeBuxOE7gOqarrZx+0qk5J8jiaK1gfSXIWzW2D1wB7Ag+mCVx/D7xiUHXPcWC72mEvn66q4+b5TJIkSdIEGEjoqqoCNib5APCXwAOAxwK7AVcA3wHeBry3qr40p+9X2lsA/wI4CHg0cHOa4HMm8B7ghPYYvY79wSRnAM8F9gcOpwlal9BcGXtLVZ016Lpn+cP21ctWfnkLpSRJkqQJlHmyjDqybds2v3C5dOwEc+4nm/M/uZz7yeb8j6dVq1al37aDWEhDkiRJkjQPQ5ckSZIkdcjQJUmSJEkdMnRJkiRJUocMXZIkSZLUIUOXJEmSJHXI0CVJkiRJHTJ0SZIkSVKHDF2SJEmS1CFDlyRJkiR1yNAlSZIkSR0ydEmSJElShwxdkiRJktShnYddgBqrN1047BJ0g9q12ZzhvC9m64Y1wy5BkiRpWbzSJUmSJEkdWtGhK0nNeV2X5MdJPpvksX22vzzJqUkOS5J5jnPTJH+V5AtJtiW5JsnFSb6Y5B+T7DOn/WE9jnV1ku8l2Zzk7l19J5IkSZJGy6jcXnhUu70xsAfwCOABSfaqqhcs0v4uwCOBfYC9gGfNbpjk5sBpwD2AS4APt9ubA38IPA1Y3baZ68vAx9p/rwLWA08C/m+SB1bVfy31RCVJkiSNl5EIXVW1cfb7JA8CTgGel+RNVbVlkfb3A04HnpHkmKr63qyPn0cTuD4NPLyqrpnTd3fgbvOUdu7sY7VX0jbRBK9XAw/o7wwlSZIkjasVfXvhfKrq34FvAAHu2Uf7z89q/ydzPr5vu33b3MDV9v1JVZ3ZZ10FvLV9e69++kiSJEkabyMZulozz2fVEvttn/P+snZ71+WV8792tC5JkiRJY2gkbi+cK8m+wO/RBJuz+2h/f5pnwa4Bzprz8YeAxwOvSLIWOBn4UlVdvAN1BXhG+/YLS+0v6ddNT08Pu4ROjOt5qT/O/+Ry7ieb8z+apqamlj3GSISuJBvbf96YJmwdSHNF6R+q6vxF2s8spBHgr+aGqao6KclzgaOBp7cvklwCfBb456o6fZ7S/mjWsWYW0vgj4OfAS5d4mpIkSZLGUJrHkFamJHOLK2ArzaqB76yq9y3Sfna/p1TVpgWOtSuwH80zXn/cbndrP35FVR0xq+1hNAtmzLYduJhmlcPXVNV5vY6zbdu2njX648hSb+P248gzf+UcxF/NNHqc/8nl3E825388rVq1qufPUfUyEle6qqrvE5rdPsluwH2AdwL/lOT8qvrsPH2uAj7evkiyC3A48Ebg5Uk+UlXnzun27qo6bCm1SZIkSZoso7yQxqKq6sqq+gzwcGAn4N3tFa1++l5TVW8BPtDuemBHZUqSJEkaY2MdumZU1VeAdwC3B56/xO5XtNslXW2TJEmSJJiQ0NV6JXA18FftDx4DkOQvk9y7V4ckewCHtG/nW0xDkiRJkuY1Es90DUJVXZjkn4DnAi8CXtx+tD/wtiRbgM8DFwA3AaaAB9OsgPimqlp0aXpJkiRJmmtiQlfr1TSLYzwnyRuq6lKaAPYfwL7AvWmWl98ZuBQ4CXhXVZ3UdWHjtkKbFuYqRpIkSZNjRYeuHV21cIHPL+WXy8DP7PsWcEz76vc4m4HNS6lNkiRJ0mSapGe6JEmSJOkGZ+iSJEmSpA4ZuiRJkiSpQ4YuSZIkSeqQoUuSJEmSOmTokiRJkqQOGbokSZIkqUOGLkmSJEnqkKFLkiRJkjpk6JIkSZKkDhm6JEmSJKlDhi5JkiRJ6pChS5IkSZI6ZOiSJEmSpA4ZuiRJkiSpQ6mqYdcwUbZt2+YXLkmSJI24VatWpd+2XumSJEmSpA4ZuiRJkiSpQ4YuSZIkSeqQoUuSJEmSOmTokiRJkqQOuXqhJEmSJHXIK12SJEmS1CFDlyRJkiR1yNAlSZIkSR0ydEmSJElShwxdS5Tk9kneleSiJFcn2ZLkDUl2X+I4v9H229KOc1E77u27PrZ23LDmv21X87wuGczZaSGDmPsk+yU5Jsm/J7msnb8z+uh39yTHJ/lhkl8k+WaSo5LcbHlnpX4Na/4X+P++kvzX8s9Mi1nu3CfZLcnjkhyX5BtJrkxyRZJzkrwwyS4L9PX//SEb1vz7//74cfXCJUjyu8CZwG8DHwe+AdwLeADwTeB+VXVZH+Pcqh3nrsBngbOBPYBHAD8E7lNV3+3i2NpxQ57/LcBq4A09hvxZVb1ux85K/Rjg3H+MZp5/AXwb+H3g81W1boE+e9P8d3Jj4ETgAuCBwF7A54EHVdXVO3xyWtSQ57+A84HNPT7+QVUdu6ST0ZIMYu6T7A/8K3A58Dmaud8dOAC4TTv+g6rqF3P6+f/+kA15/v1/f9xUla8+X8CngAKePWf/69v9/9TnOP/ctj9mzv7ntPv/ratj+xrZ+d8CbBn2dzCprwHO/X2A/9/e/YVMVtYBHP/+drHaVtH+SVHRprStiW0XheLGrqsgQrhZ4k1GiQQGCl1sF9GVXnTRTRh4IURbZFEQBVFkWm1pZRIFXkTlrpooaKKBtma4G/66eJ6JYXzf2ZnZ87zP7LzfDxwO73nOPM/M/OZ33vmdOX8uBLYCO+pjfztl/a3AX+p6B8aWb6F8CUvgC73fn1WfesW/PiaBX/d+DzbrNETsgQ8A1wOvmVh+FvCn2s/BiTZzfwmmXvGv7eb+ik3+0jWjurfjEcqX3/Mz85WxtrOAp4EAzs3Mf0/p50zKrxmvAG/LzGNjbVuAx4B31TEeG3JsLa5n/Gvb4wCZuWOwF6WZtMq/iNgB/J0pv3RExOXAL4H7M3PfRNt5wKOUPaHvTjfmTfSMf10vgfsy87IFnr5OwUb8742ITwDfAX6SmVePLTf3O+sZ/9pm7q8Yz+ma3f46v3c88QDqF+ffAa8HLjlJP5cA2yj/aI+NN9R+75kYb8ixtbie8R95bUR8MiK+GBGfi4j9EbF13heiufXMv8vr/GeTDbUoP0Ip0s9rMLaKZdj+nhMRN9bcvzki3NZvjI2I/Yk6/+/EcnO/v57xHzH3V4hF1+zeW+dH1mk/Wuc7G/Qz1NhaXM/4j7wVuAv4EuXcrsPA0YjYt8a6Gk7P/DP3+1uGGOwGvk7J/TuA30fEQxFxUcMxtTGxv7HOJ4urZfjcbXY94z9i7q8Qi67ZnV3nL6zTPlp+ToN+hhpbi+sZf4BvAFdQCq/twEWUc8N2AHdHxO6TjKvF9cw/c7+/3jH4CrAHeAvlHJAPUc7p2Q0cjoi3NxpXjWMfEbcAVwEPAYc2cmzNpGf8wdxfORZd0mkgM2/LzMOZ+UxmvpSZf87Mz1I2ytuAW/s+Q0ktZObBzHwgM5/LzBcz84+ZeR3wA+DNwOc7P0UtICI+Tjli4R/AtZl54iQP0QqZJf7m/uqx6JrdaI/G2eu0j5Y/36CfocbW4nrGf5o763zvjOtrfj3zz9zvb1ljYO631yT2EXEN8D3KRZUuy4lbhLQcW3PpGf9pzP3TlEXX7B6u8/WO3X1Pna937O+p9DPU2Fpcz/hP82ydb59xfc2vZ/6Z+/0tawzM/fYGj31EXAd8H3gG2JeZD6+z6rJ+7jaTnvGfxtw/TVl0ze5XdX5lvbT3/9VLh+4BXgJOdpfwB4H/AHvq48b72QJcOTHekGNrcT3jP83oSkbz7inT7Hrm3+E6v2qyoV42eiflstHGv51l3f6a++0NGvuIuB74LvAU5Qv30Smrm/v99Yz/NOb+acqia0aZ+ShwL+XCBTdPNN9G2eNw1/i9GiJiV0TsmujnRcoV6Lbz6vNwbqn93zP+c/MiY2tYPeMfERdExKv2aNX7/NxR//z2nC9JMxoq9gu6D/grsDciDoz1vwX4cv3zTu/T007P+EfE+yPijLWWU65mBuZ+M0PGPiI+DXwLeALYO8MhZeZ+Zz3jb+6vJm+OPId6o7wHgHOBH1E2iBdT7uVwBLg0M/85tn65pXhmTPTzptrPTsrerD8AFwAfpRzje2lN9oXH1vB6xT8ibgUOAvdT9mweA84HPgK8Dvgp8LHMPD70a1YxYOw/DHym/nkmcC0l5neP1snMGyYeczHlc3IG5cpVT1CuZPlByn1irsjMl4d5pVpLr/hHxDeBq4HfAE8CLwO7KL9+bAW+BtzkF+92hoh9ROwHfkHZ0X2IEstJz2fm7RNjm/ud9Yq/ub+iMtNpjgl4J+Xy3U8Dxylfgm8H3rDGulne4jX7eSPw1fr447W/Q8A7hhjbaXXiD+yjHJLwN8oJuycox3T/HPgUdeeJ0/LHHrhh1LbetM7Y76OcB/Ac5Z/vEcqe1m2935fNMvWIP3AN8EPgEeBfY9uKHwMHer8nm2U61djPEnfg8XXGNvc3YfzN/dWc/KVLkiRJkhrynC5JkiRJasiiS5IkSZIasuiSJEmSpIYsuiRJkiSpIYsuSZIkSWrIokuSJEmSGrLokiRJkqSGLLokSZIkqSGLLkmSJElqyKJLkiRJkhqy6JIkSZKkhiy6JEmSJKkhiy5JkiRJasiiS5IkSZIasuiSJEmSpIYsuiRJkiSpIYsuSZIkSWrof4Ba3hL4lnSoAAAAAElFTkSuQmCC\n", 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\n", "text/plain": "<Figure size 432x288 with 1 Axes>"}, "metadata": {"image/png": {"height": 244, "width": 408}, "needs_background": "light"}, "output_type": "display_data"}]}}, "4e0bb3fc954048f1a3930478a4e44709": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_0d728097ddac43bdb02a63f66d9c082a", "outputs": [{"data": {"image/png": 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\n", "text/plain": "<Figure size 432x288 with 1 Axes>"}, "metadata": {"image/png": {"height": 277, "width": 573}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "14 out of 20 tickers were removed\n{'PRSP': 42, 'SKM': 113, 'EIX': 11, 'FAST': 98, 'DLR': 16, 'CBPO': 5}\nFunds remaining: $22.24\n"}]}}, "4e724a0e82534861a38d5b03026d8e97": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "4e79a3fc6fcf4b4f8a42042423ade5db": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "DescriptionStyleModel", "state": {"description_width": ""}}, "4ec05930d9ec43f69610bc06a9dcfadf": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "4ee9f58017534b0aa8faa0f481f5e20e": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", 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\n", 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ftzdVfxl5W5KR7c8Xtd9/lGRa+/OBizzxYs+23zcqpazV8NwAAAAAgC5++MMfJkm23nrrBW3f/va3kyT77rtvZs6cmcsuuyxnnHFGzj///Pz+978flHn2RZNbMRyTZLUkZ9ZaP5IkpZS5i+n7k/b7jg3WXxbmB7d/TnJ90toyoZRyaZIPJdm7lLJWrfWvi3n+9iSvSOvP/WullIPbq34BAAAAgH764he/mCeffDKzZs3Kr371q0yePDlbb711jjrqqAV9br/99iSt7Rte85rX5PHHH19wr5SS97///Tn99NMzbNiwZT7/3mgy2H1TWvvmfmZpHWutj5VSnkprD95lbVQp5VVLuP9ErfXhhRtLKevlhRXG36q1dg2tv5lWsDs8ybuTfGMxY5+d1n68Jck/Jtm9lPK9tA5Muy3JXe29dQEAAABgyLr//vv79NyZZ575oqD2da97XT75yU/miSeeyBNPPJEkeeSRR5Ikxx9/fHbdddccdthhGTVqVO66666cdtppOe+881JKySGHHNLr+vNrP/bYY0v8DuPGjev12Atrco/dDZL8tdb6WA/7P5tk1Qbr99RhSe5cwuvTi3lu/7ww34u63qi13pbk3vblYrdjqLXelOTwJPN3Xx6R5L1pbV3xf0lmllKuL6UcUUpZu3dfCwAAAACGth/96Ee57bbb8sMf/jCf+cxn8vDDD+eAAw7IPffcs6DPvHnzkiSbbrppTjnllGy22WYZPnx4dtxxx5x22mlZaaWV8q1vfavHB6gNliZX7D6VZO1SyrCFVrMuor2/7IgkUxusP9DmB7Z31Vp/1c39i5KclGSXUspmtdYp3Q1Sa/2vUsqktLaueHeSrgHu8LQOlntjkhNKKYfXWq9oaP4AAAAA0BH6u6J13Lhx2WmnnbLHHnvk7/7u73LKKadk8uTJSZKRI0fmoYceyjvf+c5sueWWizy36aab5sEHH8zcuXOz1VZb9arueuutlyQZPXp0I6tyl6TJFbu/aY+3fQ/67tfu+8sG6/fUp2qtZQmvgxZ+oJTyyiQ7tC+/uZhxL0prK4qSZMKSJlBrvafW+v4kL03yuiRHtced0qXbS5NcXkrZrxffDQAAAABo22STTbLFFlvkt7/9baZPn54k2XzzzZMk66yzTrfPjBgxIkkye/bsZTPJPmoy2L08rVDzpFLKYsctpWyT5LS0QtCLG6w/kOav1p2X5FvddWiv0P15+3KJwW6XZ+bUWm+utX6+1jqh1vqyJH+f5JYu3c4qpazet2kDAAAAwND26KOPJsmCw9DGjx+fJLn77rsX6fvss8/md7/7XZJWKLw8azLY/UqSO5K8Ocn1pZS9097qoZSyTSnl7aWULyW5Ocl6SW5KclmD9QdEO6Q+oH25UpI/llJqd68kO7f7vbyU8oa+1Ku1Tk7yliQPtZtGJdm1H18BAAAAAFZYDzzwQGbOnLlI+7x583LSSSdl2rRpee1rX7tgJe473vGOjBkzJldddVV++csXbygwceLEzJo1KzvvvHNGjx69oH3mzJm57777FoTEy4PG9tittc4ppeyR5L/TCiJ36XK76560Ja1wd59aa22q/gB6c5KN+vDcgXlhBW+v1FpnlVIuS2sf3iQZl+SHfRkLAAAAAFZk1113XU488cTstNNO2XTTTbPeeutl6tSpuemmmzJlypSMHj06X/jCFxb0X2ONNXLOOedkv/32y9ve9rbstddeGTNmTH75y19m8uTJGTlyZD7/+c+/qMY111yTI444Ivvvv3++/OUvv+jemWeemfvuuy9JcueddyZJLr744gV7+r7uda/LhAk9+gf+vdLk4WmptT5aSvn7JAelFWzukGTV9u25SX6R5PwkX6u1Pt9k7QE0fxuGZ5O8L63tGJbkkCS7JfnHUsqHaq193YzjkS6fOyEABwAAAIBlbvz48XnwwQczefLk3HHHHZk5c2bWWGONvPzlL89+++2XQw89NOuuu+6Lntltt91y/fXXZ+LEiZk0aVJmzZqV0aNH533ve1+OOeaYjBkzpsf1f/KTn+Smm256Udstt9ySW255YbfVgQh2y0Aumi2lDEtr24WVkkwfrDC3lDI+yU/bl5+qtZ7Qw+fWTvJoktWTfK/W+o4ePLNfkkvbl/vXWi/tcq/0dJVyKeWiJP/Svtyz1vr9pfR/NMnoJPfWWrdcUt+uZs6c2e18Riz0txIAAAAAsEyccEJmzJgx2LMYFOuss07pad/G9tjt7sC0WuvcWuu0Wutj3YW6pZSNm6o/QP4xrVA3Sb7dw2euTTJ/le6BC937Xinl0FLK8CUNUErZK8n+7csZeSGUBgAAAABo9PC083vTuZSyWZIbG6w/EOYHs8+ltXfwUtVan8wL++HuXkrpum57syRfTvJYKeVbpZTDSym7lVL+tpTy2lLKAaWUK5NcnRf+s/lorfWZfn8TAAAAAGCF0eQeuweUUmbWWj+0tI6llJentQq1L4eSLROllL9J8ob25fW11t6s//52knclGZbkgCQT2+1/TLJ1kjXTWpG7f7dPtzyV5Jha61d7M28AAAAAYMXXZLA7LcnhpZRZtdbjF9eplLJFkuuTbJjk5w3Wb9qEJPP3tOjpNgzzfS+tw9Zektaq34lJUmt9WyllyyRvTfL6JFsl2TitoPfZJNOT/CbJT5JcXGt9tJ/fAQAAAABYATUZ7L4lyaQkHyulzKi1Tly4Qyll67RCy9Htvns1WH+xaq2T8kJI29NnTkhyQh/rzUqy2mLu3ZPkniRf6MvYS6i5QZPjAQAAAADLr8b22K21/jrJnmkdHHZaKeWQrvdLKa9Oa/uF0WmFu3vWWp9qqj4AAAAAwFDR5OFpqbX+b5K9k8xJck4pZf8kKaXskOSGJC9N8oMkezkQDAAAAACgbxoNdpOk1npdkvckqUnOL6Ucn+S6JOsm+e8ke9dan226LgAAAADAUNF4sJsktdbvJDkkrT18T0yyTpLvJHl3rfW5gagJAAAAADBUO4BRyAAAIABJREFUDEiwmyS11m8kOTqtQ8suSfJPtdbnB6oeAAAAAMBQsXJfHiqlzO1F95rkn5P8cyllkXu11j7NAQAAAABgqOprqLpIQjvI4wAAAAAADBl9DXZf1ugsAAAAAADosT4Fu7XWPzQ9EQAAAAAAembADk8DAAAAAGBgNHZwWSll9SQ7JHmm1nrbUvrukGT1JLfWWmc3NQcAAAAAgKGgyRW7ByT5aZJ/7kHfD/SiLwAAAAAAXTQZ7L67/f7NHvQ9N0lJ8k8N1gcAAAAAGBKaDHa3SPJckl/3oO/t7b5bNlgfAAAAAGBIaDLY3SDJk7XWurSOtdZ5Sf7afgYAAAAAgF5oMtidlWRE+xC1JWr3GZHkmQbrAwAAAAAMCU0Gu3e0x9unB333TTIsyV0N1gcAAAAAGBKaDHYvT+tAtDNKKVsvrlMp5VVJzkhS288AAAAAANALKzc41teTHJbk1UluK6V8PckPkjzUvr9pkn9IclCS1ZLcmeTcBusDAAAAAAwJpQdnnfV8sFI2THJNkr9Na0Vut92S/CrJO2qtf2qsOH0yc+bMbv9zGjFixLKeCgAAAABk1IYb5r677x7saQyKddZZp/S0b6PBbpKUUlZN8q9J3ptk+7ywKvj5JL9McmGSr9Van2u0MH2yuGAXBtv999+fJBk3btwgzwRe4HfJ8sZvkuWN3yTLG79Jljd+kyxv/CaXP70JdpvciiFJ0g5sz0lyTill5STrtW89Xmt9vul6AAAAAABDTePBblftIHfqQNYAAAAAABhqVhrsCQAAAAAA0Dt9WrFbStml/fHpWusvFmrrlVrrjX15DgAAAABgqOrrVgyTktQk9ybZaqG23qj9mAMAAAAAwJDU11D1obRC2Ue6aQMAAAAAYAD1KdittW7WkzYAAAAAAJrn8DQAAAAAgA7TWLBbStmllLJTL/rv2NcD1wAAAAAAhrImDy6blOTPSTbqYf/LkoxteA4AAAAAACu8prdiKAPcHwAAAABgyBvMPXbXSvLcINYHAAAAAOhIgxLsllJ2TLJekocHoz4AAAAAQCfr8/62pZQDkxy4UPN6pZQblvRYkhFJtkpSk/ygr/UBAAAAAIaq/hxctlmS8Qu1rdpN2+LcmOQT/agPAAAAADAk9SfY/W6SKe3PJcnXk8xM8u9LeGZekllJflNrfaAftQEAAAAAhqw+B7u11l8n+fX861LK15M8U2u9oImJAQAAAADQvf6s2H2RWuugHMQGAAAAADDUCGMBAAAAADqMYBcAAAAAoMMIdgEAAAAAOoxgFwAAAACgwwh2AQAAAAA6jGAXAAAAAKDDCHYBAAAAADqMYBcAAAAAoMOsPFADl1JGJdkuych207Qkt9dapw5UTQAAAACAoaDxYLeU8oYkJyfZeTH3b0zyn7XWm5quDQAAAAAwFDS6FUMp5dAkP00r1C1J5iWZ2n7NbbftmmRSKeXfmqwNAAAAADBUNBbsllJek+TsJMOS3JTkrUnWrLWOqbWOSbJWkj3a94YlObv9DAAAAAAAvdDkit2PtMe7PMn4WuuPa63Pzr9Za3221npdWit2v51WuHt0g/UBAAAAAIaEJoPdXZPUJEfVWuctrlP73r+3+45vsD4AAAAAwJDQZLA7MsmMWuufl9ax1vpIkhntZwAAAAAA6IUmg91ZSdYqpayxtI7tPmu3nwEAAAAAoBeaDHZvT2vf3CN70PfD7b6/bLA+AAAAAMCQ0GSwe26SkuSkUsrJpZR1Fu5QShlTSjkjyYlp7bF7boP1AQAAAACGhJWbGqjW+p1SyjeTvDfJcUk+Ukr5dZKHk6yWZJMk45KsklYAfEGt9aqm6gMAAAAADBWNBbttByX5bZKPpbWH7o7d9JmV5JQkn224NgAAAADAkNBosFtrrUlOK6V8McnuSbZLMrJ9e1pa+/BeV2t9usm6AAAAAABDSdMrdpMktdankny3/QIAAAAAoEFNHp4GAAAAAMAyINgFAAAAAOgwfdqKoZRyQ/vjH2qtBy/U1hu11vqmvswBAAAAAGCo6useu+Pb7/d009YbtY/1AQAAAACGrL4Guwe332d20wYAAAAAwADqU7Bba72gJ20AAAAAADTP4WkAAAAAAB1GsAsAAAAA0GH6tBVDKWWTpiZQa32oqbEAAAAAAIaCvh6e9mBD9Ws/5gAAAAAAMCT1NVQtDdVvahwAAAAAgCGjT8FurdXevAAAAAAAg0RACwAAAADQYQS7AAAAAAAdZsAOLiuljEqyXZKR7aZpSW6vtU4dqJoAAAAAAENB48FuKeUNSU5OsvNi7t+Y5D9rrTc1XRsAAAAAYChodCuGUsqhSX6aVqhbksxLMrX9mttu2zXJpFLKvzVZGwAAAABgqGgs2C2lvCbJ2UmGJbkpyVuTrFlrHVNrHZNkrSR7tO8NS3J2+xkAAAAAAHqhyRW7H2mPd3mS8bXWH9dan51/s9b6bK31urRW7H47rXD36AbrAwAAAAAMCU0Gu7smqUmOqrXOW1yn9r1/b/cd32B9AAAAAIAhoclgd2SSGbXWPy+tY631kSQz2s8AAAAAANALKzc41qwkI0opa9Ran1pSx1LKGknWTvJEg/Vp0IgRIwZ7CsAgG7Xhhrnv7rsHexoAAABAN5oMdm9PsnuSI5OcupS+H05rj91fNlifJp1wwmDPABhkU/33AAAAACy3mtyK4dwkJclJpZSTSynrLNyhlDKmlHJGkhPT2mP33AbrAwAAAAAMCY2t2K21fqeU8s0k701yXJKPlFJ+neThJKsl2STJuCSrpBUAX1Brvaqp+gAAAAAAQ0WTWzEkyUFJfpvkY2ntobtjN31mJTklyWcbrg0AAAAAMCQ0GuzWWmuS00opX0xrv93tkoxs356W1j6819Van26yLgAAAADAUNKnYLeU8okkT9Zaz+jufq31qSTfbb8AAAAAAGhQXw9POyHJf3RtKKX8vpRyc79nBAAAAADAEvV1K4aaRUPhzdI6JA0AAAAAgAHU1xW7jydZv5SyVpOTAQAAAABg6fq6YvfmJP+Q5L9LKVckebLdvnopZUJvBqq1XtjHOQAAAAAADEl9DXZPTLJbkl2T7NKlfe0k3+jlWIJdAAAAAIBe6FOwW2u9rZTyt0kOSbJ1ktWTjE8yJ8nkxmYHAAAAAMAi+rpiN7XWB5J8dP51KWVeksdrrbs1MTEAAAAAALrX52C3Gw8leazB8QAAAAAA6EZjwW6tdbOmxgIAAAAAYPFWamqgUsrcUsrDvej/YCnl+abqAwAAAAAMFY0Fu0lK+9XbZwAAAAAA6IUmg93eekmSuYNYHwAAAACgIw1KsFtK2SDJqCR/GYz6AAAAAACdrM+Hp5VSdkkyfqHmNUspn1jSY0lGJNmj/fmmvtYHAAAAABiq+hzsJtktySeT1C5ta7TblmT+vrqPJ/lUP+oDAAAAAAxJ/Ql2f5Xkgi7XByaZneTyJTwzL8msJL9JclWtdXo/6i9XSinjk/y0l49dXWt9V5cxzk/rzzFJXlZrndLl3pgkd6e14vmhJFvXWp9cypw+m+Qj7csP1VrP7uX8AAAAAIDlUJ+D3Vrr1Umunn9dSjkwycxa68FNTIwXq7X+uZTyH0nOS7JJktOSfHBx/UspOyb59/blz5N8acAnCQxpF198cY444ogl9llppZXy+OOPL3WsWmsuvPDCXHjhhbnnnntSa80rXvGKTJgwIQcddFBWWmkwz/4EAACAwdefFbsL2zvJY6WUYbXWuQ2O24m+nOScHvSb1ZtBa61fK6Xsn+RNSQ4vpVxSa11kn+JSyqpJvp5kWFqrqN9fa60L9wNo0jbbbJNjjz2223uTJ0/OjTfemN13371HYx1yyCG54oorMnLkyOy7775ZffXVM2nSpBx99NG55ZZb8pWvfKXJqQMAAEDHaTLYvSqtrRZeluSPDY7biabWWu8aoLE/kOSuJMOTfK2U8upa67ML9fnPJFu3P3+q1nrfAM0FYIFtt9022267bbf35ge6Bx54YLf3u/re976XK664IptuumluuOGGrL/++kmS5557Lu9973tz2WWXZc8998w73vGO5iYPAAAAHabJf8v6ZJJZtdahHuoOqFrrg2kFt0myRRY6rK6U8uokH2tf3p7ks8tudgCL+s1vfpPbbrstG264Yd761rcutf8111yTJPngBz+4INRNklVXXTXHH398kuSrX/3qwEwWAAAAOkSTwe6DSYaXUppcBUz3vpDk5vbnY0opf5sk7T/7rydZJcnzSd5Xa31+cKYI0HL++ecnSQ444IAMGzZsqf2nTp2aJNlss80WuTe/bfLkyXnuueeamiIAAAB0nCaD3cvTChTf1eCYdKPWOi/J+5M8l9Z2Gl9rh7r/kWS7drfTa62/HqQpAiRJnnnmmVx++eUZNmxYJkyY0KNn5q/S/cMf/rDIvSlTpiRJnn/++QWfAQAAYChqMtidmOQXSb5SSnlTg+PSjVrr3Uk+3b7cLsmX8sK2DL9NctJgzAugq6uuuiozZ87Mm9/85my88cY9euYtb3lLkuRLX/pSnnjiiQXtc+bMyamnnrrgesaMGc1OFgAAADpIk9smfCzJDUlemeS6UsodSSYnmZZk7uIeqrWe2OAclhejSimv6kG/B2utT/WjzqlJ3p1kmySHtNvmJXl/NweqAfTa/fff36/n/+u//itJK6zt6Vjbbrttdtppp9x8883Zfvvts+uuu2bVVVfNrbfemunTp2eDDTbIo48+mocffrjf8+urwaoLi+M3yfLGb5Lljd8kyxu/SZY3fpPL3rhx4/o9RpPB7glJapLSvn51ku6PR28p7f4rYrB7WPu1NLslmdTXIrXWOaWUQ9IK0Of7Yq118uKeAVhWfve73+WOO+7IqFGj8vrXv77Hzw0bNixnnnlmLr744vzgBz/Itddem1VXXTXbb799PvOZz+TYY49Nkqy77roDNXUAAABY7jUZ7F6YVlDLsrXzQtePDMosgBVSf/4G8bzzzkuSHHzwwdlyyy17/fzJJ5+ck08++UVts2fPzp/+9Kesv/76GT9+fJ/n1lfz/xa7ib9ZhSb4TbK88ZtkeeM3yfLGb5Lljd9kZ2ss2K21HtTUWCuAT9VaTxjoIqWUcUk+tVDzCaWUb9dafz/Q9QEWZ/bs2bnssssybNiwvPe9721s3CuvvDLPPfdc9t1338bGBAAAgE7U5OFpLEOllJLkvCSrp7WH8X+0b62e5NzBmhdAknz3u9/NjBkzlnho2pw5c3LfffflwQcfXOTerFmzFmm744478olPfCIjRozIUUcd1ficAQAAoJM0uRUDy9ZhSXZpf/5crfVzpZQtk/xrkjeVUg6utX5j8KYHDGUXXHBBkuSggw5abJ9HHnkkO+64Y8aOHZs777zzRff23nvvrLbaatlqq62y5ppr5t577811112X1VdfPZdccknGjBkzkNMHAACA5d6ABLullPFJ/inJdklGtpunJbk9yeW11kkDUXeoKKWMTXJa+/L+tA6uS5JjkuyZZEySz5ZSrq21Tl32MwSGsnvvvTeTJ0/ORhttlLe85S19GuOd73xnrrzyylx22WWZPXt2xowZk4MOOihHHXVUNtpoo4ZnDAAAAJ2n0WC3lPLSJBcnefP8pi63X5ZkhyT/Vkr5cZIDaq1/abL+EPKVJGuldVjdB2qtzyRJrXVGKeWDSa5Msl6SLybZb9BmCQxJW2yxRWbMmLHUfptuuuli+x155JE58sgjm54aAAAArDAaC3ZLKasm+XGSbdMKdCcnuSHJn9pdNk7yxiSvS7J7kutKKTvVWp9rag5DQSllQpK3tS/PrbX+T9f7tdbvlFK+k2SfJP9USrmo1vq9ZT1PAAAAAGDgNLli94NJXp3k8ST711p/3E2fj5dS3pLkknbfI5Kc2eAcVmillNF54c/r4SQfXUzXD6YVoo9Ick4pZVKt9a/LYIoAAAAAwDKwUoNj7ZfW1gCHLCbUTZLUWq9Lckhaq3r/ucH6y5NRpZRX9eD1il6Oe3ZaWywkyWG11kWPjU9Sa/1zWvvtJq2V0qd11w8AAAAA6ExNrtjdIsnsJFf1oO9V7b5bNlh/eXJY+7U0f0iyWU8GLKXsk+Td7cvLlra9Qq31vFLKe5LsluSwUsrFtdb/7UktAAAAAGD51uSK3VWSzKm11qV1rLXOSzInDR/etqIqpayb5Evty+lJPtTDRw9J8kxaq6PPa++DDAAAAAB0uCaD1YeSvKKUsl2t9fYldSylbJ9krST3Nlh/UNVaJ6UVoPZnjIOSHNRN+xNJxvRhvAeSDO/PnAAAAACA5U+TK3a/n1aw+bVSysjFdWofAPa1tPbjvbbB+gAAAAAAQ0KTK3ZPT3Jgkm2T3FNK+WqSSUkeTrJakk3S2u/1oLRWkT6e5DMN1gcAAAAAGBIaC3ZrrVNLKf+Q5LtJNkhyTPu1sJLkz0neVWud2lR9AAAAAIChosmtGFJrvTXJVkk+meTOtLZbKO1Xbbd9IsnWtdbbmqwNAAAAADBUNLkVQ5Kk1jojyUlJTiqlrJJkvfatx2utc5quBwAAAAAw1DQe7HbVDnIfG8gaAAAAAABDTb+D3VLKS5K8K8n2SdZOMiPJLUm+V2t9vr/jAwAAAADwYv0Kdkspf5/kirQOS1vYlFLKu2qtd/anBgAAAAAAL9bnw9NKKRsluSatUHf+4WjT5t9O8rIk3y+lrNPfSQIAAAAA8II+B7tJPpxkRFpbL0xIMrzWukGSNZIcmeSZJBsmeX9/JwkAAAAAwAv6E+zuntYq3SNrrRfVWp9Lklrr7Frr2Uk+mdbK3bf0f5oAAAAAAMzXn2D3b9IKdq9czP0ruvQDAAAAAKAh/Ql210oyrdY6u7ubtdY/tD+u0Y8aAAAAAAAspD/BbtJasbs0pZ81AAAAAADoor/BLgAAAAAAy9jK/Xx+vVLKDf3oU2utb+rnHAAAAAAAhpT+BrurJhnfjz492coBAAAAAIAu+hPsXtDYLAAAAAAA6LE+B7u11oObnAjLmRNOGOwZAINs1IYbDvYUAAAAgMXo71YMrKBmzJgx2FNgiLv//vuTJOPGjRvkmQAAAAAsf1Ya7AkAAAAAANA7gl0AAAAAgA4j2AUAAAAA6DCCXQAAAACADiPYBQAAAADoMIJdAAAAAIAOI9gFAAAAAOgwgl0AAAAAgA4j2AUAAAAA6DCCXQAAAACADiPYBQAAAADoMIJdAAAAAIAOI9gFAAAAAOgwgl0AAAAAgA4j2AUAAAAA6DCCXQAAAACADiPYBQAAAADoMIJdAAAAAIAOI9gFAAAAAOgwgl0AAAAAgA4j2AUAAAAA6DCCXQAAAACADiPYBQAAAADoMIJdAAAAAIAOI9gFAAAAAOgwgl0AAAAAgA4j2AUAAAAA6DCCXQAAAACADiPYBQAAAADoMIJdAAAAAIAOI9gFAAAA+P/t3XmYJlV5N+DfI4vAsAwiqzKOItFoJCAG5SN8gAioiCiiiUEUvphoEiKuSYhLSExc4gZxIRFjEFDAJexqEBAUXIhbjMEgooOissu+w/n+qGpohu6Z3qZ7avq+r+u96n1rOfWcecuS/vXpUwADI9gFAAAAABgYwS4AAAAAwMAIdgEAAAAABkawCwAAAAAwMIJdAAAAAICBEewCAAAAAAyMYBcAAAAAYGAEuwAAAAAAAyPYBQAAAAAYGMEuAAAAAMDACHYBAAAAAAZGsAsAAAAAMDCCXQAAAACAgRHsAgAAAAAMjGAXAAAAAGBgBLsAAAAAAAMj2AUAAAAAGBjBLgAAAADAwAh2AQAAAAAGRrALAAAAADAwgl0AAAAAgIER7AIAAAAADIxgFwAAAABgYAS7AAAAAAADI9gFAAAAABgYwS4AAAAAwMAIdgEAAAAABkawCwAAAAAwMIJdAAAAAICBEewCAAAAAAzM6nNdACunhQsXznUJAAAAU7bJFlvkzFNPnesyAGCFEewytsMPn+sKAAAApuxqP9MAsIozFQMAAAAAwMAIdgEAAAAABkawCwAAAAAwMIJdAAAAAICBEewCAAAAAAyMYBcAAAAAYGAEuwAAAAAAAyPYBQAAAAAYGMEuAAAAAMDACHYBAAAAAAZGsAsAAAAAMDCCXQAAAACAgRHsAgAAAAAMjGAXAAAAAGBgBLsAAAAAAAMj2AUAAAAAGBjBLgAAAADAwAh2AQAAAAAGRrALAAAAADAwgl0AAAAAgIER7AIAAAAADIxgdwxVtU5VvbKqzqiqn1fV7VV1V1VdW1XfqarjquqQqvqNMY49vKpa/9p1OefZtqquGbX/G0dtWzxq/cjrIecbp90vLXXcuyb9jwAAAAAArLQEu0upqqcn+Z8kRyfZO8mjk6yVZI0kGyXZLsnLknwwySVVtdYUz/M7Sc5N8sgkLckhrbX3LuewAyfQ7qOTPHMqNQEAAMxH119/fY499tgccMAB2W677bLZZptl0aJFefazn51jjz02991333Lb+PM///MsXLgwCxcuzE9+8pNJnf/qq6/Om970pmyzzTbZZJNNstVWW+WAAw7I9773val2CYB5YPW5LmBlUlVbJzkryfr9qjOTfCbJJUnuSPKIJNsk2S3JHknWnuJ5dkry+f489yV5VWvtY8s45I504fLLquptrbW2jH1fli6wv32q9QEAAMwnp5xySl7/+tdns802y84775xHP/rRufrqq3P66afnNa95Tc4+++x84hOfSFWNefwXvvCFHHfccVl33XVzyy23TOrcl19+efbaa69ceeWV2X777bPPPvvk2muvzRlnnJGzzjorJ554YnbfffeZ6CYAqxjB7oP9Qx4IdV/ZWvvXMfY5N8kRVbV+koOS3DuZE/TTM5yRZEF/7EGtteOXc9hpSV6cZHGSnZN8ZRn7vrxfnprk9ydTGwAAwHy01VZb5YQTTshee+2Vhz3sgT9sfdvb3pbdd989p512Wk477bTsu+++Dzn22muvzaGHHpr99tsvV111VS688MJJnfuv/uqvcuWVV+ZVr3pV3vWud90fHv/4xz/Obrvtlj/7sz/Lt7/97SxYsGB6nQRglWMqhl5VrZbkef3Hb40T6t6vtXZTa+2fWmt3T+Ice6Ybqbsgyd1Jfn8CoW6S/DTJyH8dvHy8nfrpHX6z/3jcROsCAACYz3bZZZc85znPeVComySbbrppDj744CTJBRdcMOaxhx56aJLkve9d3sx6D3XHHXfk7LPPzsMe9rC85S1vedCI4Mc//vE54IADcuWVV+a0006bdNsArPoEuw/YOA9MXXDZTDdeVc9LN/J27SR3Jtm/tfbZSTRxbL/cfxnz+o6Evt9OcvGUCgUAAOB+a6yxRpJk9dUf+gevn/zkJ3PmmWfmAx/4QB7xiEdMuu1f//rXufvuu7PRRhtlvfXWe8j2xYsXJ0nOP//8SbcNwKpPsPuAO0e9/81x95qCqtovyb8neXi6uW/3ba1N9leun0431+4GSR7y9z9VtUYemHrh2KW3AwAAMDn33HNPTjzxxCTJs571rAdt+9nPfpbDDjssL3nJS7L33ntPqf2FCxdmtdVWy3XXXTfm3LxLlixJ0k3LAABLE+z2Wmu/TrKk/7hNVf11VU3736eqXprkpCRrJLklyXNba/8xhfpuTDfiN0kOHGOXvZM8Mt0UDydMrVoAAABGHH744bn44ouz5557PugBZvfdd1/+5E/+JAsWLMg//uM/Trn9tddeOzvvvHPuu+++vOMd73jQtp/85Cf55Cc/mSS54YYbpnwOAFZdHp72YEcm+UD//h+SvKqqTkvytST/meSy1lqbRHsHpnvA2sOS3JTkOa21r02jvmOTvCTJXlW1SWvt6lHbRqZh+GJr7ZqqMrM+AABAkksvvXTSx5x44on50Ic+lMWLF+cv/uIvHtTG8ccfnwsvvDBHHHFErrnmmlxzzTVJkttvvz1JN9L23nsn9pztV7/61fnWt76Vj3zkI/nqV7+abbbZJjfccEPOPffcbLnllrn55ptzzz33TKkPrLx8n6xsXJOzb+utt552G0bsPtiRST466vOiJIck+VSSS5NcU1Wfq6qXVNVEQvH/l+7f+M4ku08z1E2S/0hydbpA/qUjK6vqEelG7CamYQAAAJiWT3/603nf+96Xxz72sTnqqKOywQYb3L/t8ssvz1FHHZV99tknO+2007TPtdVWW+W4447L3nvvnV/96lc56aST8p3vfCcvfelL86Y3vSlJsuGGG077PACseozYHaUfjfuqqjopyeuS7JVuCoURGyXZr3/9b1X9QWvtu8tqMkmlm1v3+Um+Nc367qmqE5Icmm408JH9pt9PsmaSG5KcPp1zAAAArGomMyrqIx/5SN7znvfkSU96Uk499dRsvPHGD9p+ySWX5K677srpp5+e008f+8ev/fbbL0k3svd5z3vehOrbbbfdHrL+uOOOS5LstNNOMzKyi7k3MirS98nKwjU5bILdMbTWzk1yblWtm2THJL+TZPsku6QLd5PkiUnOr6odW2v/M05ThyV5c5L1kry1qm5vrb1zmuUdmy7Y3b6qntRauzjJK/ptn26t3Tn+oQAAAIzniCOOyOGHH56nPOUpOeWUU7LRRhs9ZJ9FixblwAPHeuxJctZZZ+Wqq67KC17wgqy33npZtGjRtOo56aSTkiT777//tNoBYNUk2F2G1totSb7Uv9JPv/D8dCNlH50usD0iyR7jNPHNJM9N8sUkC5K8o6ruaK19YJz9J1LTd6rqf5I8OcmBVXVMkh36zaZhAAAAmIJ//Md/zDve8Y5su+22Ofnkk8ed/mCbbbbJBz/4wTG37b333rnqqqvytre9LY973OMetO26667Lddddl4022uhBgfGdd3Zjcx7+8Iffv661lve973254IILst9++2XbbbedbvcAWAUJdiehtXZPkn+vqkvTTauwZpJnVtUjWmvXj3PMBVX1/CRnJFnObBFXAAAgAElEQVQ7yfur6s7W2kemUcqxSd6d5IBR6y5rrV04jTYBAADmpU996lN5xzvekdVWWy077rhj/vmf//kh+yxatCgHHHDAGEdPzEc/+tG8+93vzl/+5V/msMMOu3/9ZZddluc85znZbbfdsmjRotx99905//zzc/HFF2fHHXfMEUccMeVzArBqE+xOQWvtv6vqm0l2TvdwtK2SjBns9vufW1X7JTk1XRj8oT7c/dcplnB8kncm2TLJa/t1x02xLQAAgHnt8ssvT5Lce++9Oeqoo8bcZ6eddppWsDueTTbZJHvuuWcuuuiifPGLX8waa6yRJzzhCXnPe96Tgw8+OKuv7sd2AMbm/yGm7pej3rfl7dxa+2JVvTjJZ9M9kO2jfbh7/GRP3Fr7ZVWdk24KiLX68wt2AQAApuCwww570CjaqTrzzDMnfY5HPvKROfroo6d9bgDmn4fNdQFDVFWV7mFqSReqLpnIca2105L8QZJ70/3bH1NVL5liGcckubN/ndda+8kU2wEAAAAABkaw26uqdavqoqp6flWttpzd/zbJ4/v3X22tXTvR87TWPpvkFUnuS7Jakk9W1b6Trbe19qnW2lr965mTPR4AAAAAGC5TMTzY76SbB/dXVXVqkq8n+WmSm5Ksl2SbJC9LsmO//51J3jDZk7TWPllVD0/ysXTfwaer6gWttS9MvwsAAAAAwKpOsPuAe5JcmWSzJJsneXX/Gs8VSV7RWvvWVE7WWvt4Va2V5MPpHqj271X1vNbaOVNpDwAAAACYPwS7vdbaHVW1RZJnJNm9Xz4hXci7VpLb0gW/309yRpJPt9Zum+Y5P1JVayb5QH+O06rq2a21r06nXQAAAABg1SbYHaW11tJNv/D1abRxeJLDJ7H/EUmOGGP9kiQ1jTqmdTwAAAAAsPLy8DQAAAAAgIER7AIAAAAADIxgFwAAAABgYAS7AAAAAAADI9gFAAAAABgYwS4AAAAAwMAIdgEAAAAABkawCwAAAAAwMIJdAAAAAICBEewCAAAAAAyMYBcAAAAAYGAEuwAAAAAAAyPYBQAAAAAYGMEuAAAAAMDACHYBAAAAAAZGsAsAAAAAMDCCXQAAAACAgRHsAgAAAAAMzOpzXQArqcMPn+sKAAAApmyTLbaY6xIAYIUS7DKmG264Ya5LYJ679NJLkyRbb731HFcCD3BdsrJxTbKycU2yshm5JgFgVWQqBgAAAACAgRHsAgAAAAAMjGAXAAAAAGBgBLsAAAAAAAMj2AUAAAAAGBjBLgAAAADAwAh2AQAAAAAGRrALAAAAADAwgl0AAAAAgIER7AIAAAAADIxgFwAAAABgYAS7AAAAAAADI9gFAAAAABgYwS4AAAAAwMAIdgEAAAAABkawCwAAAAAwMIJdAAAAAICBEewCAAAAAAyMYBcAAAAAYGAEuwAAAAAAAyPYBQAAAAAYGMEuAAAAAMDACHYBAAAAAAZGsAsAAAAAMDCCXQAAAACAgRHsAgAAAAAMjGAXAAAAAGBgBLsAAAAAAAMj2AUAAAAAGBjBLgAAAADAwAh2AQAAAAAGRrALAAAAADAwgl0AAAAAgIER7AIAAAAADIxgFwAAAABgYAS7AAAAAAADI9gFAAAAABgYwS4AAAAAwMAIdgEAAAAABkawCwAAAAAwMIJdAAAAAICBEewCAAAAAAyMYBcAAAAAYGAEuwAAAAAAAyPYBQAAAAAYGMEuAAAAAMDACHYBAAAAAAZGsAsAAAAAMDCCXQAAAACAgRHsAgAAAAAMjGAXAAAAAGBgBLsAAAAAAAMj2AUAAAAAGBjBLgAAAADAwAh2AQAAAAAGRrALAAAAADAwgl0AAAAAgIER7AIAAAAADIxgFwAAAABgYAS7AAAAAAADI9gFAAAAABiY1ee6AFZOCxcunOsSAObcJltskR9dfPFclwEAAAAPIdhlbIcfPtcVAMy5q90LAQAAWEmZigEAAAAAYGAEuwAAAAAAAyPYBQAAAAAYGMEuAAAAAMDACHYBAAAAAAZGsAsAAAAAMDCCXQAAAACAgRHsAgAAAAAMjGAXAAAAAGBgBLsAAAAAAAMj2AUAAAAAGBjBLgAAAADAwAh2AQAAAAAGRrALAAAAADAwgl0AAAAAgIER7AIAAAAADIxgFwAAAABgYAS7AAAAAAADI9gFAAAAABgYwS4AAAAAwMCs8sFuVe1aVW2c1+1V9fOqOqOqXllVa41x/EHLOP7WqlpSVadW1curas0J1lRVtXdVHV9VP6qqm6vqnqq6sap+WFWnVNWbq+oZVfWQ72i6fQIAAAAAhm2VD3aXY60kj06yd5Kjk3yvqp4wiePXSfKYJM9P8okk/1lVi5Z1QFVtkuTLSc5IckCSrZOsm2S1JOsneWKSfZP8fZKvJ9lzEvUk0+8TAAAAALCSm2/B7lFJnjLq9fQkr0ryw377E5J8oarWHuf4tyx1/B5JXpfkV/32bZKcVlWrjXVwP6L3rCS79Kv+K8lr+8/bJdk5ySuTfDzJNbPUJwBmSGstn/jEJ7L77rvnUY96VLbYYovsuuuu+fjHP5777rtvwu3cddddOfLII7PTTjtl8803z5ZbbplnP/vZOfnkk1dg9QAAAAzJ6nNdwCy7urX2g6XWXVRVxyU5L8kOSR6b5A+TfGiM43+x1PE/SHJ2VX083ejaJyX57SQvSPK5MY5/Zb896Ub4/r/W2tI/6V+Q5F/7cPgFSX6+gvsEwAz54z/+43zmM5/JxhtvnBe96EVZe+21c9555+X1r399vvnNb+Zf/uVfltvGXXfdlf322y8XXHBBFi1alAMOOCD33XdfzjrrrBx88MG5+OKL8+Y3v3kWegMAAMDKbL4Fu2Nqrd1eVW9O8qV+1XMyiRC0tXZTVb0rybH9qj0ydrD7gn55T5LXjhHqjm7z3nHamGhN0+oTAJNz+umn5zOf+Uwe85jH5Nxzz81GG22UpAtqDzzwwJx00knZe++98/znP3+Z7Rx99NG54IILssMOO+Tkk0/OggULkiS33HJLnve85+W9731vnvvc52a77bZb4X0CAABg5TXfpmJYlm+Mev+YKRz/nVHvtxxnn5H5d69rrd0whXNM1nT7BMAEnXHGGUmSQw455P5QN0nWXHPN+0fYHn300RNu5w1veMP9oW6SrLvuunnjG9+Y1lo+9rGPzWTpAAAADJBg9wF3j3o/5hy5y3HvOG2Ndme/3KSqHjGFc0zWdPsEwARdffXVSZLFixc/ZNvIuq9//eu56667pt3O+eefP+U6AQAAWDUIdh+wzaj3v5zC8U8a9X7JOPuMjOqtJB+vqvWmcJ7JmG6fAJigkVG6l19++UO2LVmyJElyzz333P9+Ou1cccUVuf3226deLAAAAIMn2H3AX496/+XJHNg/6Ox1o1Z9epxdP5wHRvbum+SKqjq+ql5VVdtV1RqTOe8ETLlPAEzOnnvumST58Ic/nF//+tf3r7/77rvzzne+8/7PN9yw7Jl4Rtp53/ve96Dw9tZbb8373//++z/feOONM1I3AAAAwzSvH55WVWsn2TbJYUn26VfflGT5jy3vjt803ajYv0vyjH71ia21r421f2vtW1X1R0n+OcmaSdZPckD/SpLbq+qbSU5OcuxU5uGdbp8AeLBLL710Quu22WabPOMZz8g3vvGNbL/99tlll12y5ppr5qKLLsp1112XzTbbLFdeeWV+8YtfjHn8iD322CMnnXRSvvnNb2b77bfPTjvtlNZaLrzwwlRV1l133dxyyy1ZsmRJbr755hntK8O1rGsK5oJrkpWNa5KVjWuSlY1rcvZtvfXW025jvo3Y/ZuqaiOvJLcl+VoeHIC+qLV2zTjH/9tSx1+Z5Kx0oe6tSd6T5MBlFdBa+7ckv5Xk6CRLB7drJ9k1yZFJLquqA7J80+0TADNgtdVWywc+8IEccsgh2XDDDXPmmWfmzDPPzKJFi/Kv//qvWWeddZIkG2644TLbWWeddfKxj30sBx10UFZfffWccsop+dKXvpTtttsuRx99dO67776sttpq2WCDDWajWwAAAKykqrU21zWsUFW1a5Y/DcHPk5yS5L2ttZ8tdfxBSf5tAqf6SpKXtdZ+PonaVk/y1HTB8FOT7JzkcUvt9orW2rFLHbdrptGn0W688cYxL4CFRxyxnOYB5oHDD3/Q1Akjv8We7G9W77jjjixatCjrrbdeLrvssimXs2TJkmy77bbZdtttc9555025HVYdU70mYUVxTbKycU2ysnFNsrJxTa58Nthgg5rovvNtKoajknxk1Oc7klzXWvv1OPsv7S1JTu3fr5FkUZLfS/L7Sf5vkq9U1Q4THR3bWrsnyUX9K0lSVTskeV+S3+1XHVFV/95au2WcZqbbJwBWsM997nO566678qIXvWha7ZxwwglJkv33338mygIAAGDA5luwe3Vr7QfTOP4XSx3/3SSnVtVX04Wri5N8LN2D0aaktXZRVT07yfeSPD7JhkmelW707Vim2ycAZshNN92U9ddf/0Hrvv/97+dtb3tbFi5cmNe97oHnbN5222254oorsvbaa2fLLbdcbjtf/vKXc+SRR+axj31sDj744BXXCQAAAAZhvgW7K0Rr7aiqel6S5yZ5flU9s7V27jTau7WqTkjy1n6V8fAAA/DCF74wa621Vp70pCdl3XXXzSWXXJKzzjora6+9dk444YRsvvnm9+/77W9/O/vss0922mmnnHnmmQ9qZ4cddsiTn/zkbL311llrrbXyX//1XznvvPOy6aab5lOf+lQWLFgw210DAABgJSPYnTl/meQ5SSrJO9LNmzsdvxz1ftWeCBlgFbHvvvvmc5/7XE466aTccccd2XzzzXPQQQflda97XR71qEdNuJ0Xv/jFOeecc3LRRRfl7rvvzpZbbplDDz00hx566HIfvgYAAMD8INidIa21H1TVyUn2S/L0qtqjtfal0ftUVbWJP63uaaPe/2Sm6gRgxXnNa16T17zmNRPad+edd37Qg9lGe/vb3563v/3tM1kaAAAAq5iHzXUBq5i/H/X+rWNs//eqOqSq1l1WI1W1V5JX9B9vSXL2DNUHAAAAAKwCBLszqLX23SQjEyXuXFW7LLXLlkk+mOTKqjqxD3mfWVXbVtUOVXVAVX06yRfywGjqv26t3TQ7PQAAAAAAhsBUDDPv7Un27t+/Ncn5o7ZdkWT7JAuS/F7/Gs/t6ULdD66IIgEAAACA4RLszrDW2jer6ktJ9kiye1Xt2Fr7er/tBVX1G0n2SrJTkicneXSS9ZLcmeT6JBcnOSfJ8a21X451DgAAAABgflvlg93W2nlJahrHH5PkmEkes+cytv0oyY/STckw1ZrOyzT6BAAAAAAMmzl2AQAAAAAGRrALAAAAADAwgl0AAAAAgIER7AIAAAAADIxgFwAAAABgYAS7AAAAAAADI9gFAAAAABgYwS4AAAAAwMAIdgEAAAAABkawCwAAAAAwMIJdAAAAAICBEewCAAAAAAyMYBcAAAAAYGAEuwAAAAAAAyPYBQAAAAAYGMEuAAAAAMDACHYBAAAAAAZGsAsAAAAAMDCCXQAAAACAgVl9rgtgJXX44XNdAcCc22SLLea6BAAAABiTYJcx3XDDDXNdAvPcpZdemiTZeuut57gSAAAAgJWPqRgAAAAAAAZGsAsAAAAAMDCCXQAAAACAgRHsAgAAAAAMjGAXAAAAAGBgBLsAAAAAAAMj2AUAAAAAGBjBLgAAAADAwAh2AQAAAAAGRrALAAAAADAwgl0AAAAAgIER7AIAAAAADIxgFwAAAABgYAS7AAAAAAADI9gFAAAAABgYwS4AAAAAwMAIdgEAAAAABkawCwAAAAAwMIJdAAAAAICBEewCAAAAAAyMYBcAAAAAYGAEuwAAAAAAAyPYBQAAAAAYGMEuAAAAAMDACHYBAAAAAAZGsAsAAAAAMDCCXQAAAACAgRHsAgAAAAAMjGAXAAAAAGBgBLsAAAAAAANTrbW5roE5dOONN7oAAAAAAGAlsMEGG9RE9zViFwAAAABgYAS7AAAAAAADI9gFAAAAABgYwS4AAAAAwMAIdgEAAAAABqZaa3NdAwAAAAAAk2DELgAAAADAwAh2AQAAAAAGRrALAAAAADAwgl0AAAAAgIER7M5zVfXoqvp4Vf2yqu6sqiVVdURVbTjXtTE/9ddgG+d15VzXx6qpqvavqg9W1Ver6qb+ejt+Ocf8n6r6fFVdX1W3V9X3q+q1VbXabNXNqmsy12RVLV7GfbNV1YmzXT+rnqraqKpeWVUnV9WP+/vejVV1QVX9YVWN+XOFeyUrymSvSfdKZkNVvbuqzqmqn/fX5PVV9d2q+puq2micY9wnWWEmc026Tw5TtdbmugbmSFVtleRrSTZJcmqS/02yQ5LdklySZKfW2nVzVyHzUVUtSbIwyRFjbL6ltfbe2a2I+aCqvpfkt5PckuSKJE9M8snW2svG2X/fJJ9LckeSk5Jcn2SfJE9I8tnW2otno25WXZO5JqtqcZKfJvmvJKeM0dwPWmufXWHFMi9U1auTHJXkV0m+nORnSTZNsl+SDdLdE1/cRv1w4V7JijTZa9K9ktlQVXcl+U6Si5NcnWRBkmckeVqSXyZ5Rmvt56P2d59khZrMNek+OUyC3Xmsqv4jyZ5JXtNa++Co9e9P8rok/9Jae/Vc1cf81Ae7aa0tnttKmE+qard04dmPk+yS7gfE8UK09fv9Nkj3C7Bv9evXSnJukh2TvLS15jfaTNkkr8nF6f4j/BOttYNmr0rmk6p6ZrofBs9srd03av1mSS5KsmWS/Vtrn+vXu1eyQk3hmlwc90pWsKpaq7V2xxjr/yHJXyc5qrX2p/0690lWuElek4vjPjk4pmKYp/rRunsmWZLkw0tt/psktyY5sKoWzHJpALOutfbl1tqlbWK/7dw/ycZJThz5D/C+jTuSvKX/+CcroEzmkUlek7DCtdbOba2dPjpA69dfmeSf+4+7jtrkXskKNYVrEla4sQK03qf75daj1rlPssJN8ppkgFaf6wKYM7v1y7PG+I+hm6vqwnTB7zOSnDPbxTHvPbyqXpZkUbpfMnw/yVdaa/fObVmQJHlmv/ziGNu+kuS2JP+nqh7eWrtz9sqCbFFVr0qyUZLrkny9tfb9Oa6J+eHufnnPqHXulcylsa7JEe6VzIV9+uXoa819krk01jU5wn1yQAS789cT+uWPxtl+abpg9zci2GX2bZbkuKXW/bSqDm6tnT8XBcEo494/W2v3VNVPkzw5yeOS/HA2C2Pe26N/3a+qzkvyitbaz+akIlZ5VbV6kpf3H0eHE+6VzIllXJMj3CtZ4arqjUnWTTfNwtOS/G66AO1do3Zzn2TWTPCaHOE+OSCmYpi/NuiXN46zfWT9wlmoBUb7tyS7pwt3FyR5SpJ/SbI4yReq6rfnrjRI4v7Jyue2JG9Psn2SDfvXyLy8uyY5x9RKrEDvSvJbST7fWvuPUevdK5kr412T7pXMpjemm+LwtekCtC8m2bO1ds2ofdwnmU0TuSbdJwdIsAusVFprf9vPmXZVa+221toP+of4vT/J2kkOn9sKAVYurbWrW2tva619p7V2Q//6Srq/vPlmkscneeXcVsmqqKpek+QNSf43yYFzXA4s85p0r2Q2tdY2a61VusEq+6Ubdfvdqnrq3FbGfDWRa9J9cpgEu/PXyG//Nhhn+8j6G2ahFpiIkYdg/N85rQLcPxmI1to9ST7Wf3TvZEZV1SFJjkxycZLdWmvXL7WLeyWzagLX5JjcK1mR+sEqJ6cLxjZKcuyoze6TzLrlXJPjHeM+uRIT7M5fl/TL3xhn+8iTEcebgxdm28ifiPjTD+bauPfPfl6/x6Z7WMtPZrMoGId7JzOuql6b5INJfpAuQLtyjN3cK5k1E7wml8W9khWqtXZ5ul86PLmqHtmvdp9kzoxzTS6L++RKSrA7f325X+5ZVQ+6DqpqvSQ7pZtf5RuzXRiM4xn90n/YMNfO7ZfPHmPb/02yTpKveXoxKwn3TmZUVf1lkg8k+V66AO3qcXZ1r2RWTOKaXBb3SmbDFv3y3n7pPslcW/qaXBb3yZWUYHeeaq1dluSsdA+k+rOlNv9tut/CHNdau3WWS2Meq6rfHGsy9qpanORD/cfjZ7MmGMNnk1yb5Per6mkjK6tqrSR/3388ai4KY36qqqcu/Uvafv3uSV7Xf3TvZNqq6q3pHkz17SS7t9auXcbu7pWscJO5Jt0rWdGq6jeq6iHTKlTVw6rqH5Jski6o/XW/yX2SFWqy16T75DBVa22ua2COVNVWSb6W7n/Mpyb5YZKnJ9kt3RQM/6e1dt3cVch8U1WHp3vgxVeSXJ7k5iRbJdk7yVpJPp/kha21u+aqRlZNVfWCJC/oP26WZK90v43+ar/u2tbaG5fa/7NJ7khyYpLrkzw/yRP69S9p/g+WaZjMNVlV56WbQulrSa7ot2+T5Jn9+7e21kZ+QIQpqapXJDkm3aieD2bsp7gvaa0dM+oY90pWmMlek+6VrGj9lCDvTHJBkp8muS7Jpkl2SfegqivT/QLi4lHHuE+ywkz2mnSfHCbB7jxXVVsm+bt0f/6xUZJfJTk5yd+O+k0izIqq2iXJq5Nsly7IWJDuYQHfS3JculHkblrMuP6XCn+zjF0ub60tXuqYnZK8OcmO6X7x8OMkH0/yT621ifw5E4xrMtdkVf1hkhcm+a0kj0yyRpKrknw9yYdaa18drxGYqAlck0lyfmtt16WOc69khZjsNeleyYpWVb+V7meZ303y6CQLk9yabtDUmenuew95qJ/7JCvKZK9J98lhEuwCAAAAAAyMOXYBAAAAAAZGsAsAAAAAMDCCXQAAAACAgRHsAgAAAAAMjGAXAAAAAGBgBLsAAAAAAAMj2AUAAAAAGBjBLgAAAADAwAh2AQAAAAAGRrALAAAAADAwgl0AAAAAgIER7AIAMGlV1frX4hloa9e+rSXTLmwgquqgvs/njbHtvH7bQbNf2eyqqiV9X3ed61oAAIZGsAsAMM+MCmUn+zpvrmtnGKpq26o6fD6E06NV1VOq6syquqmqbq2qL1fVTss55lNVdW9VPW226gQAVg2rz3UBAADMuqvGWf+IJGskuSPJjWNsv37U+0v65d0zWBerjm2T/E2S85Mcs4z9Lkt3vd02CzWtUFX1G0kuTLJeknuS3Jtk1yTnVtXurbULxjjmmUlemuSo1tq3ZrFcAGAVINgFAJhnWmubjbW+H5G7S5KTWmsHLaeNJ858Zcw3rbXd57qGGXR4ulD3mCR/mi7c/fskf5HkXUl+d/TOVbVmkg8nuSbJm2exTgBgFWEqBgAAgOnbPd0o3UNba7e31u5OF9helWTHqlpnqf3fmOSJSf6itfbr2S0VAFgVCHYBAJi05T08raoWVNUbq+prVXV9Vd1RVT+pqtOq6oCqWmMS53pWVd3Sn+9dY2xft6r+uqr+s6pu7M91aVX9U1VtOU6b9z+grKoWVtW7q+p/q+q2qrphErU9tareVVUXVNXPqurOqrqub/+VVbXaRNuaxDnX7+ev/a/+3+WWqvp+Vf1tVW2wnGMn9b1MpX9V1ZL8W/9xlzHmat511L7LfHhaVW1aVe8b9d3cWFUXVdUbqurh4xxzTN/m4VW1WlW9tv+3uq3v8xkraD7bjZJc21q7aWRFa+2eJJen+7lrw1E1Lk4X+l6Q5BMroBYAYB4wFQMAADOqqp6U5Mwki/tV9yS5KcmWSR6bZJ90c5EumUBbL0xyQpKHJzmstfaupbb/ZpIvJHnMqHPdmeTxSf48ycuqap/W2oXjnGLjJN9O8rj+uLsm0sdRzkoX6CXdPLG3pZureJf+9cKq2rcP+Katqh6f5Ow80N+RuWmf0r8OqqpntdYuHePYqXwvU+nfVUnWTrJ+ujmYR8/NnEzw37iqdkj33T6iX3VzkjWT/E7/OrCq9mytXT1OE6v3/d2rr+POdOHq3kl2r6pntta+PpFaJui6JI+sqvVHwt0++H5MkvuSjB6V+099X/60tdZmsAYAYB4xYhcAgBlTVY9I8sV04eFPk7wgyYLW2kZJ1kk3z+i/pQsVl9fWy5N8Jg8EYEuHuhsk+Xy64OwzSX47yVqttXWTbJXkU+mCvM9V1cJxTvO2dA+Me06SdVpr6yeZzGjOs9I9/Grz1tqC1tqGSdZNcmCSK5M8N8nrJtHeuPo5WT+Xrr8/T7Jnf651kzwryc+SLEpy8tKjWafxvUy6f/0czof2H7/WWttsqdfXJtDXDZOcki7U/e8kO/TfzbpJXpwuJP3tJJ9cRjN/li4A/r0k67bW1uuP+UGStZIcubw6JuncJKslObKq1qqq1dPNsbtpkm+01m7r+/b8dCH6B1tr/z3DNQAA84gRuwAAzKS/SjcC9NokO7fWfjGyoZ9z9ML+tUxV9efpgrd7k7y8tXb8GLu9KV1QeUJr7Q9Gb2it/STJAX2g+ewkr0zy3jHaeHiS57bWfjDq2B8vr75R+/7BGOtuTXJ8VV2e5CvpHqT1nom2uQy/l2SbdKNPH1RzknOq6rlJvpvkyUkOSPLxUdun9L3Mcv9GOyTJ5kluSLJna+3K/tz3JvlsVd2U5D+SPKsfeXvuGG0sTNfXC0bV/v2qOijJt5L8TlUtaq39bIZq/rskz0tyUJKXpbt2H57u+zosSapq7XTX9S+T/M0MnRcAmKeM2AUAYCa9vF++d3R4OBlV9dZ0f6p+V5L9xwl1k+QV/fJ9y2juU/1yj3G2f2GpgHTGtNa+mi6YXFxVW8xAk/v3y1PHqrm19j9JPtt/fMlSm6f9vYxxvpnu32gjff3YSKi71LnPSjIyjcLSfR3x1dGh7qhjv53kiv7jb0230FHt/jDJzulGRt+RbvqFryR5VmvtK/1ub033y4jXt9ZurqpNquoT/bzFt1XVuVW1/UzVBACs2ozYBQBgRvQPhNq0/6JD7fMAAAZ4SURBVPj5qTVR70/3p/23Jtm3tXbOODtumeTRI+fqH9g1ljX75ZgPUcsD4eCUVdWL042QfWq6OXvXGmO3LdKN0pyOp/bLLy9jn3PTTZ0wsu+0v5dZ7N/I+dbMA4Hr8vq6Y0b1dSn/uYxjf5Hu+tlwGftMWmvte+mm9XiIqnpikjckObu1dlJVrZWuD09Ock660dQvTHJeVe3QB8UAAOMS7AIAMFM2HfV+Kn/evigPzNf6J+OFur3NR73fZAJtrzPO+msmUthY+jlUP50ujBtxZ7qA7t7+88bp/kpuwVTPM8rG/XJZI25HRqJuVFXVP5hrSt/LHPRvxCPywF8WTqSvG4+z/eZlHHtHv1xjEnVN14f75SH98o/ShbpHtdb+NEmq6mVJjks3N++LZrE2AGCATMUAAMDK4sok5/fv31lVWy1j39H/Hbtha62W81o8Tjv3jrN+Iv4oXeh5W5LXJNmytbZWa23jkQeF5YFRrDWN8yxtrBGzK8Jc9W+02errClVVf5DkmemmwrikX/28fvmhUbt+Kt0vG/aqqtVmsUQAYIAEuwAAzJSrRr1/zBSOvzNd2HVhkkclObeqxmtn9LkWTeFcM+HF/fLtrbUPttauGL2xD+YeOYPnGxldvKz+jkxPcV0/WjeZ+vcy2/0bcX26+WmTifV1yqOuZ0NVrZ9uHuglSf5h1KaR7+KnIytaa/f1nxdkxfzbAgCrEMEuAAAzorW2JN2o2yR57hTbuKU/9qJ0od65VfXoMfb7aR4ILMec03QWjNT13XG275SZHXH6nX652zL2eeZS+07ne5lO/0aC2UmP5G2t3ZVk5OFwk+rrSurvk2yW5NDW2m1jbF/633DtFV8SALAqEOwCADCTjuuXb6iqR02lgdbaTUn2ShfYPS5duLv5GLse0y/fuKxzVWfhVGpZjhv75VPGOOfq6QK9mfTZfvmcqtpujHM+Ocn+/cdPL7V5Kt/LdPp3U7+c6r/7SF8PGuu7r6o90z04LXloX1ca/ff0p0nOaK2dttTmy/vl9qP2X5jk8ekeHnjtrBQJAAyWYBcAgJn07nQPvHpkkq9W1fOras0kqao1qmqXqjpxrFG4o7XWbkiyR5LvJ9k6yTlVtfRDst6V5Cf9ub5WVS+pqvtHO1bVoqr643QB8QtmqH+jfalfvrWq9h2ZE7Wqnpjk9CQ7pAvoZspJ6f49kuSUqnpWVVV/zt2TfD7dw8D+J8knlzp2Kt/LdPr3P/3ySVX19Cn09UNJfpVu9OoXq+pp/blXq6oXJTmx3+/s1tq5U2h/XFW1pKpaVR0zzXYqyVFJ7ko3R/HSPt8v31lVm1TVw5O8N12f/6O1Np35nwGAeUCwCwDAjGmtXZduaoQrkjw2yalJbqmqa9M9hOu8JL+XZPUJtHV9kmcluTjJbyY5u6o2GrX9hnQje3+YbtqGk5LcXFXXVtVt6UZE/kuSbZO0zLz3JrksyfpJTklye1Xd2NezR5JXZwZHXfZTFLwoXb8WpQteb6mqW5Oc3a/7WZL9Wmt3LnXsVL6XKfevtXZpkq/07X2jqq7rA9MlVfWMCfT11+nC+F8n2SbJf1bVTUluSTead8N0IfcBy2trDv1RkqcneUc/dcjSPprkf5M8Ld1UGTcm+cN0fXzLbBUJAAyXYBcAgBnVWvvvJE9OF059K8nt6R4G9bN0AeFL0wWME2nrmiS7J7kkXcD3pdHTKrTWfpxk5M/dv5wuCNwgyT3pgr+PJtk7yfEz0LWla7s+yTPSjcoc6c/t6fq4S2vtmBVwzh8n+e0kf5cH5qFN//7tSbZprf1onGMn9b3MQP/2S/KRdA8DWzfdw8IekwnOO9xauyjJk5J8IMmP0o1Gvqev/U1Jnt5au3oibU1UP8XEyEPL/nMa7TwyyTuTXJrkPWPt01q7Pd0cwscnuSHdvMRfTrJra+2HUz03ADB/1AMPywUAAJi/+tHEX083bcVWS498BgBYmRixCwAA0NmlX75bqAsArOyM2AUAAEhSVWemm9rjca21O+a6HgCAZRHsAgAAAAAMjKkYAAAAAAAGRrALAAAAADAwgl0AAAAAgIER7AIAAAAADIxgFwAAAABgYAS7AAAAAAADI9gFAAAAABgYwS4AAAAAwMAIdgEAAAAABkawCwAAAAAwMIJdAAAAAICBEewCAAAAAAyMYBcAAAAAYGAEuwAAAAAAAyPYBQAAAAAYmP8PlJ5f2R4Qc+sAAAAASUVORK5CYII=\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 699}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "14 out of 20 tickers were removed\n{'PRSP': 41, 'SKM': 104, 'EIX': 11, 'FAST': 115, 'DLR': 16, 'CBPO': 2}\nFunds remaining: $14.40\n"}]}}, "56321db46a6e4728a9213e26fd1e8be9": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntTextModel", "state": {"description": "Total value:", "layout": "IPY_MODEL_0c1c6f656646486ba90bb22666e3c59e", "step": 1, "style": "IPY_MODEL_c1e06b30f00542c6816a7dffdf1c8f0a", "value": 10000}}, "56325a16978e4805a1a107427615b606": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "5634c6bc0d204f8982d0a5b017d52a57": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_8d40d8a203f3489e811fcc4acce80722", "outputs": [{"data": {"image/png": 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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 695}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "16 out of 20 tickers were removed\n{'PRSP': 20, 'EIX': 16, 'FAST': 261, 'DLR': 2}\nFunds remaining: $4.99\n"}]}}, "564e7bf8e16146bfac59a0caef5cdad9": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "DescriptionStyleModel", "state": {"description_width": ""}}, "569a9f06621b40d3b5aa3eb8d4a18515": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "56bec44b4515431bb6e681b73dbfb201": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_0e8b200116674410b72eb1374da6173c", "outputs": [{"data": {"image/png": 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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 688}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "12 out of 20 tickers were removed\n{'PRSP': 42, 'BA': 1, 'SKM': 125, 'F': 14, 'EIX': 9, 'FAST': 66, 'DLR': 16, 'CBPO': 9}\nFunds remaining: $1.80\n"}]}}, "56ce3e86586c4b078a83802b36eb26cb": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_e5f1039874f84790abd2bccf5cf1a03f", "outputs": [{"data": {"image/png": 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\n", 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"text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 688}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "14 out of 20 tickers were removed\n{'PRSP': 42, 'SKM': 112, 'EIX': 11, 'FAST': 102, 'DLR': 16, 'CBPO': 4}\nFunds remaining: $15.66\n"}]}}, "5ed7cc95748a49a1ace1e9b13f6d1b1f": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": {"_model_module_version": "1.5.0", "_view_module_version": "1.2.0", "description_width": "initial"}}, "5f54d588b34042c2ae021f5d24c37cf7": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "5fad7470c8de44208e334a98b3d5a4c6": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "5fc3e100d1de4f17bbd1bce624f1690b": {"model_module": 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 252\u001b[0m \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 253\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m<ipython-input-353-cda349d2a19a>\u001b[0m in \u001b[0;36mf\u001b[0;34m(tr, tri, 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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 689}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "14 out of 20 tickers were removed\n{'PRSP': 42, 'SKM': 108, 'EIX': 11, 'FAST': 108, 'DLR': 16, 'CBPO': 3}\nFunds remaining: $18.64\n"}]}}, "73bab33c71e049c99f34b0d6ddfbbaad": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "VBoxModel", "state": {"_dom_classes": ["widget-interact"], "children": ["IPY_MODEL_f880569ea65a4416b89bddc19e991d68", "IPY_MODEL_d56dd3838b754e998f3559b025587e3d", "IPY_MODEL_feed6c75552e47f98ac254197c01122a", "IPY_MODEL_c1303972c69a47d4bd0f0c03ba73fa26"], "layout": "IPY_MODEL_366e17ba4e6941b0acfe85485752148f"}}, "73e8bd5acb2b4b09ad9c12e051f84943": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "DescriptionStyleModel", "state": {"description_width": 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\n", "text/plain": "<Figure size 432x288 with 1 Axes>"}, "metadata": {"image/png": {"height": 277, "width": 573}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "15 out of 20 tickers were removed\n{'PRSP': 41, 'SKM': 101, 'EIX': 12, 'FAST': 121, 'DLR': 16}\nFunds remaining: $18.14\n"}]}}, "741cc07abb5f41fabb5f1cc20b37d2eb": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {"display": "flex", "flex_flow": "columnn", "width": "70%"}}, "74239995ce634bcdbffef041bd9b2036": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "74289afe65784fbc884f8c574c39a79e": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_bd838e920f73488c88f7e1eb9bbd0e85", "outputs": [{"data": {"image/png": 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\n", 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\n", 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"outputs": [{"data": {"image/png": 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\n", "text/plain": "<Figure size 432x288 with 1 Axes>"}, "metadata": {"image/png": {"height": 244, "width": 429}, "needs_background": "light"}, "output_type": "display_data"}]}}, "78474521a8bc44b19e9518695e7596b1": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_4a9ca94247c54ef4a1b020d4024d1488", "outputs": [{"data": {"image/png": 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\n", 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\n", 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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 695}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "15 out of 20 tickers were removed\n{'PRSP': 29, 'SKM': 22, 'EIX': 15, 'FAST': 216, 'DLR': 8}\nFunds remaining: $3.43\n"}]}}, "7a42ef6b89a7440d9e6d0aa621451165": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntTextModel", "state": {"description": "Total value:", "layout": "IPY_MODEL_6d5ef6328ae546f9bedfbce895830bc4", "step": 1, "style": "IPY_MODEL_6dff94a547964a6eb637371db45a2550", "value": 10000}}, "7a68058b841641d6a5fbe65dd6966e42": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "7a99c8c7c54340d58c1b12ce057c32ac": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "FloatSliderModel", "state": 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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 688}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "14 out of 20 tickers were removed\n{'PRSP': 413, 'SKM': 1070, 'EIX': 110, 'FAST': 1099, 'DLR': 163, 'CBPO': 26}\nFunds remaining: $12.30\n"}]}}, "7fcbcd0e05714d4ca24740d7282f0ad6": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "SliderStyleModel", "state": {"description_width": ""}}, "7ff5fcfca3624fddb8a935e31019fbb0": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "VBoxModel", "state": {"_dom_classes": ["widget-interact"], "children": ["IPY_MODEL_9e82f7bbcdcd4c31aae3a35b6427e068", "IPY_MODEL_202aca45cfb945539629e9ce3727ce25", "IPY_MODEL_a61fb3cab9eb4520b3eaf9f658466c2c", "IPY_MODEL_41dae3cc2633489a9460b63402355d42", "IPY_MODEL_bafb3aa2c3c14e59bb53e8c9cc28ed9f"], "layout": 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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 695}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "16 out of 20 tickers were removed\n{'PRSP': 20, 'EIX': 16, 'FAST': 261, 'DLR': 2}\nFunds remaining: $4.99\n"}]}}, "826bb4f902644a0eaa0b1763235d3323": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "82a6c5db33234f0fa469e339411581b9": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "TextModel", "state": {"description": "x", "layout": "IPY_MODEL_48081022651d4b68aedcfb5bde984582", "style": "IPY_MODEL_711f389f290d4f59a2ad727f9e456963", "value": "50000"}}, "82c759e578564a38b28e94b6aadb5b6a": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {"height": "350px"}}, 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\n", 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1svyYpFMUF/xHStpa0STkZEUQd5Cks8zss7VmNrO1FM1ft1Bc4PxU0qcU/QWOlfRlxYiLgxWD0hySk8+eikDVqooT2y8Vzb22TMr0KUUz1hcys81UDDhwWOazE5LPsq9uTU87VWbFejsieT9LMaBBmscxilpwP1JloIR2DFYES3+puDj7YFL+DysuLh9J0u2tYrXMNkvyekkxsMRYxXo+RjHKuSQda5lakzlWVPxuaypOqDsrmrYdpBigR4qamd8sUKZ2LaMIRJ0gaQ9Ff2pjFb/t1Yrtai1JvzOzUXmZdNhZioEHblA8if+AogbLxVXpLlE0JRykqB1wpGL/fr+kTyiCAFIEdK+x7jX5jlf8nmmtgqfVcx/YTNHvUpn2Vhw3/qnYRrdR9If6dVWeqGf9lyJge4didNMtFTcb31dccC0naZKZrVJyudtmZiua2QZm9kXFhXVaQ+lid5/XSp7uPksRDJOksWa2S86yP6LYziWp7qAJZrapol9UKYJ+rzRRpL8nf8dYz5YRn0r+vqrKfi9J35G0iqKfwqI1bVs1ThEIluKiPfUbxbFT6n6uaORkxbljsqImyPsVx9tfJNOHS7rCzBoNFPthxWjkjyma3m2t2C9OVewXJul8Myutdl2i0+eQprn7fFWaN29ndWoRJ9Lrjsfd/Y7sBDPbWNJNqgzk8QvFwDzpMXNK8vlGkm5p45g/WPHbnaM4hm+rOI7vo9i+X1Ecqy6xGNgo6x7FsfeEzGeHqeexubcGorkwWV52oL5a54r0fHOpIggixfG6nvHSm4Mm/7gDZV1afDDz/h8lLid7bKo3YGC2tmWj8mSnv7tqWno8PyD7YXI8+2RVGpnZmorg72JJR3h+DbqWeNSmTWvUrinpuzVq7bZiD0Uw7GHFOt5K0afkGYrr/0GSLjSzdXPmH6x4WPk9xcO8sYpjzJ6KY/jLSR7nmNm+BcpzjKL/4dsV18npdU+PJuGJbysGuvyzYoDP9yvu265Ipo+Q9Ie88lu0arhe0Z3RC4rz2t6qXIueqTiGrSFpSi+ch6Tu2+KMOukGKQbIO05xHb2l4tz5acV53RUD1v3eerZuO09xLEvPAwtU+1j3xybLforiNzTF+vxGUqZtFQ+s0prMX1acH5YW2ePGI7mpQr3jRlPMbAvFPYAUx41mB+zbNvk73d1fs2hFc4/iPvUxxfp+LqmNvE47ZUUNfR0B5sWr3Ze614i5R9G5f6NXzcFKFLVI7kvyWihp68y076vxE7xsWVwxCMvwGuneq0oNyRdVoyaXIrCUPoXcKGd5Q1V5ovZSdT6KoOucZPprkj5cZz2uIGnVqs/GZb7L+AK/RSfKvLEiwO2Ki7O1a+SxjiJI/Oa6bmP7WaPW+s9MH6RKrZfFktbLSZf93e+TNLJGmp0yaa7NyWdqJs0c1X4CvaoiMO3J3x41GtTZGrB1B9lRBDTTGqYn5qSZmFnOmBZ/q/FV6/msBuk/lUlbrzbAUZl0n63zm3QVKGPh75lJN6nB7+JJGerVnhtXlf6nqlFLQNK3Mmm+XCCvqa38Vu281Lg22XX19tmCy1hVldqtCxQBlD0VN097Kh7wpDV7rlemhkCNvJZRnHtc0XQ177uMz5nfFLUJXRHkP0hxcXy8KrU7L8yk/1AmbY/zSwm/x2XJ8l6vXp7iAYcraiqOqpNH9b47MSddtmbsngX2iymq0QpAcSOepvlOzrJy9786v+GYGtM7dQ7pamefU/cahTXXb5Ju+0y6b9WYfmtmes0axOo+0EfN9VdgeqPzymhFU2+XdHOB7WpcgXVUdx2rxRqwRadXpf25Kteaq9dJ91iS7l+q3TKr39WAVQS3nsnsExt3utzJctZRpUbdjFrrL5P2vsx6XKlBvgfl7UOKh+XptGsVx+sPqzKIYbdBxxQPsXOPUx1aD9/IlMkV142XSPqcpPep4AA76l6T0SX9rta8ipYJaZrTc/JaX/VrMo5SBLRcEbiqte3vVlWenzX4jSdUpe8x0FqS7ouZNL/PKVvaGucPkobm5PNuxT2eS7q+xd+u6CBc71Pl3ulZSUPqpG107N09s9/0qMmdpCk8CJca1IBVBGzTe4inJI2ukWZ1SY9n8qlZu1W9XwM2bXlSpJXU/2bKdnAby1xWEV9wRbcH2WkNa8AqYh1pmlvVveZ0rdcc1Ykf8GrhN+zrAvDi1e5LxZvLZ1+/rZPfOxVPLdMLtuHq3j3Bn/JO8DXKsmWd5Xwzk25C1bTsiK0fb/D9N82kPbRqWraZ4VdaWLfjMvOPb5C2U2XOngj2rpNHNrjmJW9jq6hyYfPVnDTZ3z13RF9Jd9U7Wat7APaoOvmclUlXayTeSUXWjQoEYAuuo3SU3Qdypk/MLGdMi8sYn8ljumqMCl+V/h9J2h7NNmukTQNot9T5TboK5FP4e2bSTWrwu+QGbTLps/vqM8q5+FY0L0qDetcUyGtqq9tEG9tSdh1mXzMUNwYdaRalqF13uKJWfq3lPaII5NVtEqqoteGKWhur1fku4+vksbXyuy35hypdTQxRJSCzfy/8FsMUD+9cMahM9fQdMuWsGdBP0mX33b/n/YaKAdLSdOc22C9eV07gShH0TAM8f8tJk7v/1fkNx7S4HoucQ7ra3edU6bLmiTppfpSkWVL9fRQ1d9Pv+sc6eQxW5fi6QDWaUxZdvw2+z5czZV2lwXY1rkB+ddexejcAmw2Y5wU5xmXSHJ+Tpj8GYLPNdi/udJkzyzkxs5wTG6TNBnfqnmMUtbTTtGfXmH5BZnr161uZdB9PPntSDYK+ba6HQeo+Unv163VJtyhqGtZ7kJYNpL2SlzY5PqRN829vo9z7Z5b3rhrTswHYF5QTCM2kzwZg/6P6QcpbMsee9aqmpQ+y56hGZYuqtF/PLHOtFtZBbgBWcQ2zkaKV1+xMeXtUJGhhuX9I8rsrZ3onA7AXZ6bvVSefD2XS/TInTW8HYNMH9V0F0ma7nvlCG8tMt79nq7c/FQvArlG177viQcERikD3cor79OwD8blqELjnVfxFFwRAFXd/TNEfkxQ3e79QpdnSLEkHeXIEa2Cau9drmpzWHJLi6XhW2txmjuIkWK+80xQHTimaa2TtmfxNm+yXqVNl/kjyd5bi6XqeXysuODrKYmCQ0Wb2LjPbNGlavJYq5W00ANg0r98RerpNjKrRtKfaFXWmZbetRs1NO8bC6mb2znT9JOso7cJiE2tzcJaCfunui+qUc2NFMEeqNG2u55bk7zbWYEChPnCHu/+rifS/dvc3ak1w97mqNGWvud24+1R3t+Q1rrmidkTanHczxYOdAxXNq9dRHMeO7NBydlQ0xc5rIvlORQB2y7wMkmaF6ajSX3P351spiLvfpQjCXqXYlxYqgkXnKvroTI91xyn655ri7lclZVjGYoDFB5JBRGab2R8sRsdt14GK1hFS9+4HUrcpauZJVV3S1HFF3jnU3R9R3NhLjY9rN7n7czn5LFH0RV4kn47qwDmkHZclf9czs+1rlG15VUacv9Xdu6qSZK9Fcq8ZkmPvT5J/l1UECttiZiPNbD2LARPT9ZY2qzRFy6EBw6Prh4eSfz+Xk+zzyd9Fymk+7+7HZo7XZ3a4mB1nZt9UpWuVfyqCR2Usx1QZFG2JIjheT3qcW1jgGn9+jfne5O5fUvym9yq24VcVXQLt7+4nJ+VbUZVuz77kSbc1ZraOmf3MzJ6xGNRqhpmdY8mAm61w9yXu/l+KANb1ivNL1hDF+fA7kv5lZkeosT+6+4u1JiTHh/Q6uNDx18yGm9m6Vft/tpyNjpu/cfdXiywrcWXedVIiPb6ZoquCrPR+Z4q7v9xgOVMz78fmJSpoo+xAV4rt8FHFdcJwxcPkA9z98qIZJtf0a9S4pp+VJHlvsi+VKT3vPCPp93mJ3P3PioeMkrRLrXK5+zaZ4+FfO1/UiuReJ+0epsggVnWPGwWXuakqXescVWD7q2WlzPshSbl2dvcfuftz7r7A3ae5+2GKbUuKwWtPaaXM6KlR/1pAf3NLJwIG7n6pme2q6JfoY+nHisEgns2fs5u6/fK5+yyLke7XlfSeqsnpSI/DJS1p4ty3Zvom6W8qzfcub75/mGZ1oszLK57mStK9XqcvLHdfaGb3qXs/Yi1JLmyPUvTP9S5VRrSsZdUG2T3aYPpLmffDVOkXttoL7v5CzrRa+ZTKzD6p6KtuO3U/eVdbRtFP56w6aTrhgQbTs6OlXtvE9ricorZaS4G0kjT6rtWKboOlbTfJPvX2Oklm5N0sefTRmt1+7pH0KzO7TNI1kn5gZhu7+5fbKN9/K/qeW0ZR03WipJsVzclWV9SsOVlxw7q9mX3G3a+pysMUD9KGKIKBtQKUhbn7P1TVb2DV8jZWNCV9XZWHhFKM6r2f4hz1mGL7/aikXc3skx79m7cq7T/xGUXrj+oyu5n9XLH+3mtm73P3+xrk2Wj7fFlxjGm0ffb5dp7q8DmkHZcpfgtTPDy4rWr6nqqMwP7zGvNvlnl/Z4NlZfuOfY8qfdAWZmYfUNRy/bAqfc7mKXO99ZUfKYJwG5rZOHefmk4ws5GqBHome/Tl2a+Z2aGSTkv+fV7RyqmZ/rKb8UFVHq79xd3/3SB9Goxb1sysQRB2+cz7mtfW7v5T1e9zdqKiksd16TE6OcbfqtjWX1UEqDdSjIOwm5mNTR6itsTd/yLpL2Y2VNHNzZaKWu/bqXItPkzSRWY21N3r9bXZ9vHXzN6tGCvhY5LWbpBfo/2/2eukRn2n35V5/+Y9mpkNUeU4ub+Z7a/i1mycpGWuuBa4tlHC5NrlAMX5fVvVv6ZfThF8a3m7a1CWYYqH61LcqzbqA/kOxUPoUYpt5j8llGkjxYPFWualx5LkPnSx4nxfpF/lhseNBuVaRnFMWU5xTvhVs3kkqh88/Njd8/af/1VsJyMl7WNmK/RCPGHAowYskO+/VamJI0k/dPcpeYlrqFkzJydN9SAWb2tiOVkrZt6PUmUff7rF/JrRiTKPVNw4Ss2tv5aZ2XtUCcBsqvo3zlLjp5aNnsBnLy7qLatT+bTFzJYzs2sV/ZR9RPUv1FItPdltUqOnvq1uj1L3bXJp0OwT7qLbTpk1ffdW1O7Ke+XWKs3j7tcrmnZK0pfMrKWHL0kNgmzwdWt3v8rdn3f3Re4+090vUQx29rzionmSmVVvU19U9Kf5uiqDBpbpIsWF9ylpjWgz+7Qi+Pqaogn2xopA1vcUD9l/mtxsNy25OU4HBbvC3RfnJE37spSK1YLt1PZZNJ9Sr3VLOIe0zN1nKAI4krRfEizIOij5+7rimF4tvRZZosYP0bIPo5seiMvMvqEIghykxsFXqXfOK73tclW2489XTTtI8XBH6jm4ZL9jZp9SDCJminParu7eKIjXjuyxqF4gNJUd1LHRMTN7HdT0YJDJMeOrinuML2UmXawINN4saU13f4+iJcYTisF+Tm52WbW4+6vufpO7n+Hu+ykCWTurUmtVkk6zGCAsT1vHcTM7TBE0/bwaB1+lxvt/s9dJje4fstOzx7dV1fo5pd1ryxnqPtDVToqHsf9U7FcnqEFN7+Sc8HtFsHZX9f01fXYw2CIVnNo67xR0i/KvXatbhqRxgiLrsa3jhqK1wJbJMttpCVa97Ml5CZNg683Jv8tpgLVE6SvUgAXyHa7uB8udevHJT7pvzlTUxCqqmeY3ndbvypw0H7la0R+OFE1sr1TcTM+SND+tBWFmTyoGBSm7Kc7S5puK/s4k6UFFH71/VfzOr6ZBGTM7WfGkVOqddZQXDEplz2+HKpoCFtUbDyya0ei7vpVcq6gNJEXz+JvrpM1zmCo3hae5e80LYXd/ysy+p2h2tVKyvO9lkqQjsN8iaSsz20o9bZp5v7WZpTUPbnX3mUULnNys7qQYcfqcqu8iSZe4+/8l5fYkuPUpSasp+jAv0g1HtWwA4+tm9vXclBWfNrOvu3uR5nj93lJ6Dvm5ojnxCMVI5Vcny19NlXPzb9upSdcuM9tJ0VedFA85zpH0F0WAYV66/ZjZzopRyqUBeO519zlm9ktFc/V9zWxkpknpfyV/n1JlxPF+KWlB83PFcTcd0KXZGovNLG+YpE8k/76sGNm9kSclbZ68H636I5qPrppk1uB8AAAgAElEQVSvmbINUgRxBiv6g30q+XwDRZ/akvTF9Lzk7v82sxMVwfrxZvY/BWoJNiU5Rt1sZh9SXOetrQj+pwNSdpSZbaLKOpinaOZ8oyLQPMfd5yfp3q0450mN9//euk7KXltepeaaZRdtQZlngUcXbln/Z2aTFC1Uxkr6rJndnNTAruVERd/DUvTHfr7iQdjT6n5N/21JRyfpBtyxt4OeVATDRxWIEbR83Eik15w3S9oup2XfuzLvt82kmZq24E3OO7NVaQ3TqCzZ6e1UbkGCACxQQ9Is7vTk37mKJjSbSDpP0Yl7EUVqdKRpqvtRel7R7Gi4pIcL9EdVy4uKJ9CDFP3Pla0TZU5HkzQ1t/5a9UFF7QJJOsPdj6uTdmSby+ptb16MmtmgOhfsjWp6pDX7nlB0aJ93cbG0rZ9sFwKv1rhoLUv2JiC3lkSrtRL7C3efpMZ97rUi2y3HmBbzeHfm/d9yU/Wc/q6qaWlzst1U7KHTBFXOH/soHmI0ZGarSvq24tg4wd1r9Yt3e3Yed3/DzP6uqLW+hZoMwCZd2BzUMGFPoxQB31q1K5c26fmxUW2mevvq0ngOuVpRU3wFRTcE6W9xoCrNKmt1PyBVrkUGKW606tUSWyPzvmZfkHWk55XFknby6Pu3lqXtvFKGixQB2CGKfe57Zra1Ks2cL+l0wK03mdneiuNPGmz7iLs380C0Fdm+qxv19Zl6WPHAQopzRL0AbPYc8o8myzZB0briPnV/oJcey2cn3dFkpcf34Yo+VR9vcpmFuPtLyQOB9CHnO+ulb8PnVIlB7J4+PKyhzP2/0f1Ddnr2+Ja9Bhnci9eWudz9NTP7jGJbXEHSmWZ2dfXD5ST4n9a0f1TRv/x81dZbx97sul0jN1XtNM2edwpx9yLlSD2syrH63ap/TdnOcUOqXHPuocqxqp4vJy8puqXKPsh7WJUxWBq12slOzx17A8XRBQFQxcxWVtRgWVbRTG87xZNZSTrCzPbNm7dKrdpQ2eW8TZUAwoNVk9MD+EqKm+emJZ3gpzUMtjazVpqQNBNE7USZ5yua0kjSB5KLhZqSmkftDmaS7e/ul3WWtZGKNS9ZmmQvvOpdSFUHld5kZqNU6bPqdw2e7H6gibL1huxF0I5t5tXMftD2ekdd2aaKrTThkrpfQDYaMC47vXrgkt5yriKweYm7V/fpmQ7kV6sv6dlVaZrxcVVuQC9W1KZt9EpbMxym/iHdfhrdaNbbV5e6c0hSszXt93e3pOarFMFYKac/30T2WmSbBovKDijTbG3GdL09WCf4KjU+r7TyoLdMTZcnCUb+Pfn3v6r+Llax5vNLJTPbXdKvFMfRVyTt5jHgYNmyx6Ci6++WzPtxeYmS/jN3Sv6dq+7N9usyszUUlTuWSDqiqluXIsfybLqyZPvULGv/Svf/Z+oEX6Vyryvr3qOp0v2OlDkuJn0Wp/cpY20pGbTVY0DFNKC/mqRaLVbWVKXZ/m/qBF+lXjr2JkHiruTfrQoM+JUOLvqiCj7ELlnR48ZQVdbpP5OxDvpStvXYhg3SbpB5vzSs836PACzQ0w9VOdh8NXm6ebAqNUF+YmbvKJDPpmZWr5/Dz6nSrOPGqmnZTtSPVuvSm7Bhii4VmpUNuC2fmyp0qsw3JH/fpmj+lOeTqjSfaFW2FUC9vpn+u83l9IV/Zd7X2w4/W2daofVjZluo8c16b3tAlXVwcI3+O5uR7geN9gGpM+sd+T6VeV/94Kqo7G+0U26qMC5nPrn7CK+MtlvzJemkzCyHZqb9tkhBk35uD1bU6P5GjSTpzfroGtPeUZWmGWkAwyWd6u6/bPRSpR+xD5tZkT79+lr6e74/76YvCZhUj4CdtbSeQ9IB4QYruoXYWJWbv1/U6c83ey2Se82QBB0+l/y7QN1H+i4iXW/1zitDVRnFPk8z1yi94c3yWAwqWtRFyd/NkmbgByb/T0mbqPc3ZrabpF8r+g18VdJH3f2O+nN1ZLnvUiV49qC7N2rlkPqLKoHOT5tZ3rb5EVUGmPx9k92tnK8IoP7Q3e+pmpYep9dMWiBkvaNGurJkA29PlLSM9PvlVgxJ1kF1v8id9KkafWRnpcc3l3RT1bT0fmcNtdZSpCxnq9In6VHJQH5ZRa/pt1Xjvj7TY92yBYKmjaTnnbUk7V6nXNkWJ39qsaVlp/1WlZZvh9WpOHSQKueoX7eyIHcfUuCa86zMLJ/KTKvuxibbSil3ILmkBVY63sJLav26GxkEYIEMMztE0meSf69x9x9Jkrs/p7gRcEVtmSsKPvX8sZn1eFptZu+VlDZVfFnSFdnpyRPhvyT/HmBmJ6iOZKCkz5lZdZOa76tyQXmGme1aJ48VkgNtVrYvzLpNkTpY5otUOZmdb2Y9uk9IAuDnVH/egscy78fnlHMfxWA7/c3UzPuv1booMLPxiubCeZ5XZfvZw8xWqU6Q/H6Xt17MciQXZhOTf4dJui5TE6wmM9vKzD5WY1K6H7yt1v5c5Q5Vakp+qdYFfrIffqH686WFmY0zM09eU3theaua2YGNLuKT43M26HNFTrquTPnH1EhyXeb9CWa2bo00sujTNe0yYImk6+uVr9OSAE4amPkfd3+pRrK05ly3m0Az21CVIMTf1YRkn073gzubCAClI/IOUuPA2dJgavJ3TdW4iU7W/yTVD+wtreeQPylqukoRwD84M+3SnsmDu9+nyiBeHzOz/8pJepoqzSl/4e7P56TLk663Dc1sbPXEJPjyUzUeNbzwNUovabU8V6pSI/syVWpL/7jRjGZ2ZuZ4d2wTyyyNme2i6Hd1ecUAgR+vUXu/SD67Zb5b0X5wW6n9qiSQ+p3k31UULQ+qyzNCEUSV4pzw7aL5JwHp/RX7Za2uStLj9HLqGRBJj0+zVfUgsMByVzWzO81sj0b3LWa2lyoPORcqBmsqQ7r/j0i6qKguhynW8yYlLV+K1jTn1ZpgZl9U5eHsZE8Gvcz4jiqB8AuswYCgZvY2Myv9IZy7v6i475Mi0P/VqiRPq9JSZe+c+9O1ld9FTXVeUpzvN6iXsIALVBm07fu1HuAmlSiy/RGfX50mSffXzDGj9IohSb+q6Tn13ZKOqVGm0ar0FfyqKr9RdbpeO5a7+4OSfpf8u7+Z9ejSIDle/EiVwSB/2J+7w1ma0AcsBpqhFiNcF/Efd3+zWU9yw5oeFJ9UpQmYJMndbzCzcxXNOraX9C1FZ+Z57lHUgPu7RWfmf1dcjO6S5JH2K/fl6n56Ep+VdJeiZtMpyUXKJEVzp1ckray4wB+rGHF8FUUzgjf7bHP3F5OgxW8UT5qnmNmvFE9vuxQ1cMcoanntr3ja/NvM/P8xs64kzefM7GFF0+602cpr7p7tnLsTZX7UzM6UdLziqf/fzewsSXcmSbZX1AQbluTbzoiMNyg6xV9D0b3ESMXNz9OKG7/9FBe+jysC73UDeEsTd3/QzP5P0fx+F0mTzewCxcX/2pIOUDxsuF2VfoCq81hiZj9X9CG0lqQ7k215muL8sYPiAm9VReCxx410X3L3y8xsR8W+vI2kR83sJ4rAy7OKG521JL1fUdt6M0Vg4Q9VWd2qCPwNUowq/z3FADvp0/cn3f21ZJkvmNmVioDHuyVNTdbZDFUGRPq8Yj+pud7fglZSBCDOMrNrFANCPKm4UE37395flafwkvRNd2+pdo67/9nM/qAIMq4u6W/JvnGz4gb3bYr+siaocuF5obuX0u9eHccpjpd/dve8hxw/U+zfO5jZ5YqATdpn7CBFM73rcubNc5Aq14dXNTHfHxTH+ZUUwcjT66buez9SBEaXlXSxma0v6Y+KB4CbSzpKsQ/fqUqzx2pL5TnE3Reb2S8UfTluoeg3UpLud/eHGsz+ecV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tSf6Q5O8WtiulbJjkhCT/XWv9Yyll2b3pcjNmzFjpvk888USSZO7cuSs8zg9/+MOMGTMmxx9/fI477rhsvvnmmTVrVi677LJ86lOfyu23354zzjhjpWujGZ357wXoOM8arDqeN1h1PG+wanjWaMqgQYM6PYY9YFfM6MW+zkqyX5L7k/yo1vrisjrVWmuS7yd5Vyll4duc/ipJn7SujmUdcPfdd+fSSy/N/vvvn7POOivbb799evXqlcGDB+eCCy7IlltumSuvvDKPP/54d5cKAAAAQBexAnYF1FoXLVMtpWycZEiS85NcWUoZUmv94nK6X5Hk3LRuO3B7klOSPJfkJ40VzDJ15rcXTz31VJJks802W6FxFm45cPjhhy+131577ZWJEydm7ty5XfLbFTpv4W9Q/TygWZ41WHU8b7DqeN5g1fCssSawAnYl1VpfrrXeleTYJC8n+VwpZcBy2j+d5IYkI0op+6R19ey4Wuu8VVIw3e71119Pkjz33HNLvT579uwkSc+ePVdZTQAAAAA0SwDbSbXWliQPpnU18fvaaf69JBsmubrts+0H1kLz58/PQw89lJkzZ77p/Ac+8IEkrSthn3zyyTddmzJlSu6888706tUrQ4cOXWW1AgAAANAsWxB0jb5tx/YC7SlJHkkyMMnttdYHG62KLjNx4sRMmjQpSfLMM88kSe666658+tOfTpL0798/X/va15IkTz75ZPbaa68MGDAg06dPXzTGUUcdlWHDhuXWW2/N0KFDM3z48Gy11VZ58MEHc/PNN6fWmtGjR6dfv36r+O4AAAAAaIoAtpNKKUcneXuS+Ul+uby2tdYFpZRjk+yQ1pd3sYaYPn16fvSjH73p3KxZszJr1qwkyYABAxYFsMuy3nrrZcKECRk7dmx+/OMfZ9KkSXnllVfSt2/fHHroofn7v//7HHjggU3dAgAAAADdQAC7Akop5yz2ceMkuyQ5vO3z2W37vC5XrfWeJPd0fXU06Qtf+EK+8IUvdKjtwIED09LSstRrG2ywQUaNGpVRo0Z1ZXkAAAAArKYEsCtm9GLf/znJs2l9sda3a61TuqckAAAAAGB1JYBdTK21rMj5dsa6NUmH+9Va91vROQAAAACA1Vt7L40CAAAAAGAlCWABAAAAABoigAUAAAAAaIgAFgAAAACgIQJYAAAAAICGrN/dBdA5LS1zursEAAAAAGAZrIAFAAAAAGiIABYAAAAAoCECWAAAAACAhghgAQAAAAAaIoAFAAAAAGiIABYAAAAAoCECWAAAAACAhghgAQAAAAAaIoAFAAAAAGiIABYAAAAAoCECWAAAAACAhghgAQAAAAAaIoAFAAAAAGiIABYAAAAAoCECWAAAAACAhghgAQAAAAAaIoAFAAAAAGiIABYAAAAAoCECWAAAAACAhghgAQAAAAAaIoAFAAAAAGiIABYAAAAAoCECWAAAAACAhghgAQAAAAAaIoAFAAAAAGiIABYAAAAAoCHrd3cBdE6fPr27uwRYi72/uwtgKVpa5nR3CQAAANBhVsACAAAAADREAAsAAAAA0BABLAAAAABAQwSwAAAAAAANEcACAAAAADREAAsAAAAA0JD1u7sAAGjSlVdemdNOO225bdZbb708//zz7Y41evTo/PrXv87DDz+c2bNnp1evXhkwYECGDx+eU045Jf369euqsgEAAFhLCGABWKvtuuuu+fznP7/Ua1OnTs3tt9+eQw45pENjjRkzJrvvvnuGDRuWLbbYIi+//HJ+9atf5fzzz8+4ceMyZcqUbL/99l1ZPgAAAGu4tTKALaXUJU4tSPJCkvuSfL/WelUH2s9pa39FknG11iXbpJTSK8npSY5LMjjJhklmJ3kyydQkE2qtty3W/uQk/7bEMPPa2t+W5Ju11t939D4BaN9uu+2W3XbbbanXFgavI0eO7NBYjz32WHr16vWW8+eee24uvPDCXHTRRbnwwgtXvlgAAADWOmtlALuYr7QdN0hrQHpUkg+VUt5fa/2Hdtq/I8kxSQ5I8v60Bq2LlFI2SWto+r4kTyW5pu24SZLdk5ySpE9bmyX9JslP2r7vnWRYkpFJPlpKObDWeueK3igAK+Z3v/tdpk2blm233TaHHXZYh/osLXxNkqOPPjoXXnhhHn744a4sEQAAgLXAWh3A1lrPWfxzKeWgJFOSfKaUckmtdVY77fdNcnuSUaWUC2utMxe7/Jm0hq+TkxxRa523RN++Sf6fZZR27+JzlVJKWlfGjkzyz0k+1LE7BGBlXXHFFUmSE044IT169OjUWDfddFOSZMiQIZ0tCwAAgLXMWh3ALqnW+t+llAfSGozumWRWO+3vaGu/S5I9kiwewH6g7Xj5kuFrW98Xkvyyg3XVUsqYtAawe3WkDwAr79VXX83VV1+dHj165KSTTlrh/pdeemleeumlzJ07N/fee2+mTp2aIUOG5KyzzmqgWgAAANZk61QA26a0Hd+yp2s75i/xeXbb8Z2dK2eRla0LYJ0yY8aMTo8xceLEzJkzJ/vtt19effXVFR7zoosuyvPPP7/o8z777JPRo0fnhRdeyAsvvNDp+tZVXfGzBTrG8warjucNVg3PGk0ZNGhQp8dYrwvqWGOUUg5O8q60hpzTOtD+g2ndO3ZekruWuDy+7XhuKWVMKWV4KWWblayrJBnV9vF/VmYMADru2muvTZIcc8wxK9X/5ptvzrRp03LTTTflm9/8Zp544omccMIJeeCBB7qyTAAAANYCpda1b8FlKWXhTS3+Uq13JTk6SY8kFy3+Eq5ltF/4Eq71k5xZa710KfOckeSraX2R1kJPJflZku/WWm9fov3Jad3rdWkv4XpPkleTLPUlXHPmzFnqD6pPn95LOw2w1mppmdOp/vfff3/22WefbLfddrnvvvs6vf9rkjz66KN5//vfn5133jlTp07t9HjrmoWrFbriN8vA8nneYNXxvMGq4Vmju/Tu3bu036rV2r4Fwei2Y03SkuT/JvlBrfU/2mm/UE3yyVrrvy2tca31klLK95McktY9Yd/bdvx4ko+XUs6ttX55KV13b/tKWrc2+FOSf09yfq319x26MwBWSle+fGuhHXbYIe9617syffr0zJ49O/379++ScQEAAFjzrdUBbK21w0n04u1LKRsn2SfJD5J8p5TySK31Z8vo80qS69q+UkrpmeRTSb6V5P+UUn5ca713iW7jaq0nr0htAHTea6+9lvHjx6dHjx458cQTu3Tsp556Kkm6LNQFAABg7bBO7QHbUbXWl2uttyQ5Iq1bFowrpWzUwb7zaq2XJflR26kDGyoTgBX0k5/8JC0tLTn44IOz/fbbL7XN/Pnz89BDD2XmzJlvOv+HP/whc+a8dfuDBQsW5Nxzz82zzz6boUOHpk+fPo3UDgAAwJpprV4B21m11vtKKWOTnJrkrCRfX4HuL7YdV2gVLgDNGTduXJLk5JNPXmabJ598MnvttVcGDBiQ6dOnLzo/efLkfPWrX83ee++dgQMHpl+/fnnmmWdyxx13ZNasWdlqq63yrW99q+lbAAAAYA0jgG3f15J8Isk/llLG1FpfSJJSyqlJ7l3aC7NKKYOTHNf28fYlrwOw6j344IOZOnVqtttuuxx66KEr3H/YsGGZOXNmpk6dmvvuuy9z5szJxhtvnJ133jnHH398Tj311PTt27eBygEAAFiTCWDbUWt9opTynSRnJvlcki+0XfpwkstLKbOS3JHksSRvSzIoyWFJNkhySa112iovGoC3eNe73pWWlpZ22w0cOHCp7XbZZZdccMEFTZQGAADAWswesB3zz0leSXJGKWWrtnOfS/KPSR5IsneSM5KclmT3JBOTHFFrPbMbagUAAAAAVhNr5QrYWusK7bvaXvta69NJNl7i3ENJLmz76ug8VyS5YkVqAwAAAADWXFbAAgAAAAA0RAALAAAAANAQASwAAAAAQEMEsAAAAAAADVkrX8K1LmlpmdPdJcBaa8aMGUmSQYMGdXMlAAAAwJrKClgAAAAAgIYIYAEAAAAAGiKABQAAAABoiAAWAAAAAKAhAlgAAAAAgIYIYAEAAAAAGiKABQAAAABoiAAWAAAAAKAhAlgAAAAAgIYIYAEAAAAAGiKABQAAAABoiAAWAAAAAKAhAlgAAAAAgIYIYAEAAAAAGiKABQAAAABoiAAWAAAAAKAhAlgAAAAAgIYIYAEAAAAAGiKABQAAAABoiAAWAAAAAKAhAlgAAAAAgIYIYAEAAAAAGiKABQAAAABoiAAWAAAAAKAhAlgAAAAAgIYIYAEAAAAAGiKABQAAAABoiAAWAAAAAKAhAlgAAAAAgIYIYAEAAAAAGiKABQAAAABoSKm1dncNdMCcOXP8oAAAAABgNdC7d+/S0bZWwAIAAAAANEQACwAAAADQEAEsAADA/9/enUdbWpV3Hv/+FiAlhQwyagRLMUZFxajBgU4YCnGKAwoaFZVearcxxqE16U4nRBy6xRWjUbRps1yKCzSQ4FKTiEqgQCaNQ2IQgwpKQZMICiXFUAyCT/+x97FOrufeutO54/ez1ln7nvfd736ffd/71r713H32K0mSNCYmYCVJkiRJkiRpTEzASpIkSZIkSdKYpKoWOwZJkiRJkiRJWpGcAStJkiRJkiRJY2ICVpIkSZIkSZLGxASsJEmSJEmSJI2JCVhJkiRJkiRJGhMTsMtEkgcl+ViSf09yV5KNSf4iye6LHZu0kvR7qyZ5Xb/Y8UnLTZJjkpyc5KIkt/R76fRtHPPUJGcn2ZTkjiSXJXlTku0WKm5puZnJvZZk3RRjXSU5Y6Hjl5aLJHskeXWSzyS5qo9Tm5NcnORVSUb+H9uxTZq5md5vjm9ayrZf7AC0bUkOAC4F9gY+B3wXOBh4I/CMJIdU1U2LGKK00mwG/mLE9tsWOhBpBfgT4CDa/XMd8IipKid5HvBp4E7gTGAT8Bzg/cAhwLHjDFZaxmZ0r3X/Anx2xPbL5zEuaaU5FjgF+BFwPnAtsA/wAuCjwDOTHFtVNTjAsU2atRnfb53jm5ac/PLPqZaaJF8CjgLeUFUnD21/H/Bm4CNV9drFik9aSZJsBKiqdYsbibQyJDmclgy6CjiU9svzJ6vquBF1d+n1dgUOqapv9O1rgA3AU4CXVJWzF6QJZnivrQOuBj5RVccvXJTS8pfkCGAt8Pmq+vnQ9n2BrwH7AcdU1af7dsc2aZZmcb+tw/FNS5RLECxxffbrUcBG4MMTdr8NuB14eZK1CxyaJEnbVFXnV9WVI2YmjHIMsBdwxuA/qL2NO2mz+wB+dwxhSsveDO81SbNUVRuq6u+Gk0F9+/XA/+1vDxva5dgmzdIs7jdpyXIJgqXv8F6eM+IfnVuTXEJL0D4ZOG+hg5NWqB2THAfsT/sjx2XAhVV17+KGJa14R/TyiyP2XQhsAZ6aZMequmvhwpJWrAcm+a/AHsBNwFeq6rJFjklazn7Wy3uGtjm2SeMx6n4bcHzTkmMCdun7tV5+f5L9V9ISsA/HBKw0X/YFTpuw7eok/7mqvrwYAUmrxKRjXlXdk+Rq4EDgocAVCxmYtEI9rb9+IckFwCur6tpFiUhappJsD7yivx1Otjq2SfNsivttwPFNS45LECx9u/Zy8yT7B9t3W4BYpNXg48B6WhJ2LfAY4CPAOuALSQ5avNCkFc8xT1oYW4B3Ak8Adu+vwbqxhwHnubyVNGMnAY8Gzq6qLw1td2yT5t9k95vjm5YsE7CSNKSq3t7XGrqhqrZU1eX9IXfvA+4LnLi4EUqSNDdV9eOq+tOq+qequrm/LqR9quofgYcBr17cKKXlI8kbgLcA3wVevsjhSCvaVPeb45uWMhOwS9/gL6K7TrJ/sP3mBYhFWs0Gi7z/1qJGIa1sjnnSIqqqe4CP9reOd9I0JHk98AHgX4HDq2rThCqObdI8mcb9NpLjm5YCE7BL3/d6+fBJ9v9qLydbI1bS/PhJL/3IijQ+k455fa2vh9AetPDDhQxKWmUc76RpSvIm4GTgcloy6PoR1RzbpHkwzfttKo5vWlQmYJe+83t5VJL/cL2S3A84hLbOyVcXOjBplXlyL/3lWBqfDb18xoh9vwXsBFzqU6KlsXK8k6YhyX8H3g98i5YM+vEkVR3bpDmawf02Fcc3LSoTsEtcVf0AOIf2AKDfm7D77bS/3pxWVbcvcGjSipPkkaMWZU+yDvhQf3v6QsYkrTJnATcCv5PkiYONSdYA7+pvT1mMwKSVJMnjJ/5hv29fD7y5v3W8kyaR5ATaQ4C+CayvqhunqO7YJs3BTO43xzctZamqxY5B25DkAOBSYG/gc8AVwJOAw2lLDzy1qm5avAillSHJibQF3S8ErgFuBQ4Ang2sAc4Gjq6quxcrRmm5SfJ84Pn97b7A02kzDy7q226sqrdOqH8WcCdwBrAJeC7wa337i8pfXqRfMpN7LckFtGWsLgWu6/sfCxzRvz6hqgaJIUlDkrwSOBW4l/Zx6M0jqm2sqlOHjnFsk2Zhpveb45uWMhOwy0SS/YB30D66sgfwI+AzwNur6qeLGZu0UiQ5FHgt8Ou0/7yupT0Q4VvAabTZ5v6jKc1A/8PG26aock1VrZtwzCHAHwNPof3x4yrgY8AHq+re8UQqLW8zudeSvAo4Gng0sCewA3AD8BXgQ1V10WSNSKvdNO41gC9X1WETjnNsk2Zopveb45uWMhOwkiRJkiRJkjQmrgErSZIkSZIkSWNiAlaSJEmSJEmSxsQErCRJkiRJkiSNiQlYSZIkSZIkSRoTE7CSJEmSJEmSNCYmYCVJkiRJkiRpTEzASpIkSZIkSdKYmICVJEmSJEmSpDExAStJkiRJkiRJY2ICVpIkSZIkSZLGxASsJEmSJEmSJI2JCVhJkqQVLEn117p5aOuw3tbGOQe2TCQ5vvf5ghH7Luj7jl/4yBZWko29r4ctdiySJEnLjQlYSZKkJWooeTrT1wWLHbuWhySPS3LiakgiD0vymCSfT3JLktuTnJ/kkG0c86kk9yZ54kLFKUmSVobtFzsASZIkTeqGSbbfH9gBuBPYPGL/pqGvv9fLn81jXFo5Hge8DfgycOoU9X5A+3nbsgAxjVWShwOXAPcD7gHuBQ4DNiRZX1UXjzjmCOAlwClV9Y0FDFeSJK0AJmAlSZKWqKrad9T2PsP1UODMqjp+G208Yv4j02pTVesXO4Z5dCIt+Xoq8DpaEvZdwB8CJwH/abhykvsAHwZ+AvzxAsYpSZJWCJcgkCRJkrSarKfNen1jVd1RVT+jJVZvAJ6SZKcJ9d8KPAL4w6r66cKGKkmSVgITsJIkSSvYth7ClWRtkrcmuTTJpiR3Jvlhkr9N8rIkO8zgXEcmua2f76QR+3dO8j+TfD3J5n6uK5N8MMl+k7T5iwddJdktyXuSfDfJliQ3zyC2xyc5KcnFSa5NcleSm3r7r06y3XTbmsE5d+nrq/5L/77cluSyJG9Psus2jp3RdZlN/5IU8PH+9tARawkfNlR3yodwJdknyZ8PXZvNSb6W5C1JdpzkmFN7mycm2S7Jm/r3akvv89+Pab3VPYAbq+qWwYaquge4hvb/o92HYlxHS85eDHxiDLFIkqRVwCUIJEmSVqkkjwI+D6zrm+4BbgH2Ax4CPIe2VubGabR1NPBXwI7AH1XVSRP2PxL4AvDgoXPdBTwM+H3guCTPqapLJjnFXsA3gYf24+6eTh+HnENLvEFbx3QLbS3dQ/vr6CTP64m4OUvyMOBctvZ3sHbqY/rr+CRHVtWVI46dzXWZTf9uAO4L7EJbI3h47WCY5vc4ycG0a3v/vulW4D7Ab/TXy5McVVU/nqSJ7Xt/n97juIuWBH02sD7JEVX1lenEMk03AXsm2WWQhO0J6gcDPweGZ7l+sPfldVVV8xiDJElaRZwBK0mStAoluT/wRVqS72rg+cDaqtoD2Im2DubHacm/bbX1CuBv2Jqomph83RU4m5bg+hvgIGBNVe0MHAB8ipZw+3SS3SY5zZ/SHjz2TGCnqtoFmMnsyHNoD1F6QFWtrardgZ2BlwPXA88C3jyD9ibV1wz9NK2//w84qp9rZ+BI4Fpgf+AzE2eHzuG6zLh/fY3hN/a3l1bVvhNel06jr7sDn6UlX78NHNyvzc7AsbRk5kHAJ6do5vdoidoXAztX1f36MZcDa4APbCuOGdoAbAd8IMmaJNvT1oDdB/hqVW3pfXsuLdl9clV9e55jkCRJq4gzYCVJklan/0GbUXkj8JtV9W+DHX1NzEv6a0pJfp+WILsXeEVVnT6i2h/QEop/VVUvHd5RVT8EXtYTj88AXg28d0QbOwLPqqrLh469alvxDdV96YhttwOnJ7kGuJD2QKY/m26bU3gx8FjabM7/EDNwXpJnAf8MHAi8DPjY0P5ZXZcF7t+w1wMPAG4Gjqqq6/u57wXOSnIL8CXgyD6TdcOINnaj9fXiodgvS3I88A3gN5LsX1XXzlPM7wB+GzgeOI72s7sj7Xr9EUCS+9J+rv8deNs8nVeSJK1SzoCVJElanV7Ry/cOJ/lmIskJtI9o3w0cM0nyFeCVvfzzKZr7VC+fNsn+L0xIZM6bqrqIlkBcl+SB89DkMb383KiYq+o7wFn97Ysm7J7zdRlxvvnu37BBXz86SL5OOPc5wGD5gIl9HbhoOPk6dOw3gev620fPNdChdq8AfpM20/hO2rIDFwJHVtWFvdoJtD8a/LequjXJ3kk+0dfV3ZJkQ5InzFdMkiRpZXMGrCRJ0irTHyy0T3979uyayPtoH2m/HXheVZ03ScX9gAcNztUf/DTKfXo58mFcbE3izVqSY2kzTh9PW1N2zYhqD6TNepyLx/fy/CnqbKAtGTCoO+frsoD9G5zvPmxNjG6rr09hqK8TfH2KY/+N9vOz+xR1ZqyqvkVbzuKXJHkE8Bbg3Ko6M8kaWh8OBM6jzU4+GrggycE9oStJkjQpE7CSJEmrzz5DX8/mY937s3U90d+dLPnaPWDo672n0fZOk2z/yXQCG6Wv8fnXtKTZwF20RNq9/f1etE+HrZ3teYbs1cupZrAOZnbukST9AU+zui6L0L+B+7P1E3XT6etek+y/dYpj7+zlDjOIa64+3MvX9/I1tOTrKVX1OoAkxwGn0daOfeECxiZJkpYhlyCQJEnSTF0PfLl//e4kB0xRd/j3zd2rKtt4rZuknXsn2T4dr6ElJ7cAbwD2q6o1VbXX4IFTbJ0VmjmcZ6JRM1DHYbH6N2yh+jpWSV4KHEFbAuJ7ffNv9/JDQ1U/RfujwNOTbLeAIUqSpGXIBKwkSdLqc8PQ1w+exfF30ZJSlwC/AmxIMlk7w+fafxbnmg/H9vKdVXVyVV03vLMn0Pacx/MNZutO1d/Bsgw39dmvMPvrstD9G9hEWz8VptfXWc9iXghJdqGtU7wR+F9DuwbX4urBhqr6eX+/lvF8byVJ0gpiAlaSJGmVqaqNtFmsAM+aZRu39WO/Rku+bUjyoBH1rmZrYnHkmpsLYBDXP0+y/xDmdwbnP/Xy8CnqHDGh7lyuy1z6N0igznhmbFXdDQweMjajvi5R7wL2Bd5YVVtG7J/4Pbzv+EOSJEkrgQlYSZKk1em0Xr4lya/MpoGqugV4Oi2x9lBaEvYBI6qe2su3TnWuNLvNJpZt2NzLx4w45/a0xNt8OquXz0zy6yPOeSBwTH/71xN2z+a6zKV/t/Rytt/3QV+PH3XtkxxFewAX/HJfl4x+nV4H/H1V/e2E3df08glD9XcDHkZ7CN2NCxKkJElatkzASpIkrU7voT04aU/goiTP7U+1J8kOSQ5NcsaoWa3Dqupm4GnAZcCvAuclmfiwpZOAH/ZzXZrkRUl+MXswyf5J/gstkfv8eerfsH/o5QlJnjdYs7M/7f7vgINpibT5cibt+wHw2SRHJkk/53rgbNpDpb4DfHLCsbO5LnPp33d6+agkT5pFXz8E/Ig2G/SLSZ7Yz71dkhcCZ/R651bVhlm0P6kkG5NUklPn2E6AU4C7aWvoTnR2L9+dZO8kOwLvpfX5S1U1l/WJJUnSKmACVpIkaRWqqptoSwJcBzwE+BxwW5IbaQ9zugB4MbD9NNraBBwJ/CvwSODcJHsM7b+ZNlP2CtpyBWcCtya5MckW2gzDjwCPA4r5917gB8AuwGeBO5Js7vE8DXgt8ziLsX80/4W0fu1PS5DeluR24Ny+7VrgBVV114RjZ3NdZt2/qroSuLC399UkN/XE5sYkT55GX39KS5r/FHgs8PUktwC30WbH7k5LRr9sW20totcATwL+d18yY6K/BL4LPJG2RMRm4FW0Pv7JQgUpSZKWLxOwkiRJq1RVfRs4kJZE+gZwB+2hQtfSEnkvoSUCp9PWT4D1wPdoibh/GF5OoKquAgYf8z6flrDbFbiHlqD7S+DZwOnz0LWJsW0Cnkyb5Tjozx20Ph5aVaeO4ZxXAQcB72DrOqn0r98JPLaqvj/JsTO6LvPQvxcA/4f2UKmdaQ+dejDTXBe3qr4GPAp4P/B92uzee3rsfwA8qap+PJ22pqsvrTB4+NXX59DOnsC7gSuBPxtVp6ruoK1xezpwM23d3POBw6rqitmeW5IkrR7Z+tBVSZIkSVr6+uzcr9CWazhg4kxiSZKkpcQZsJIkSZKWm0N7+R6Tr5IkaalzBqwkSZKkZSXJ52lLWjy0qu5c7HgkSZKmYgJWkiRJkiRJksbEJQgkSZIkSZIkaUxMwEqSJEmSJEnSmJiAlSRJkiRJkqQxMQErSZIkSZIkSWNiAlaSJEmSJEmSxsQErCRJkiRJkiSNiQlYSZIkSZIkSRoTE7CSJEmSJEmSNCYmYCVJkiRJkiRpTEzASpIkTcYhqQAAAERJREFUSZIkSdKYmICVJEmSJEmSpDExAStJkiRJkiRJY2ICVpIkSZIkSZLGxASsJEmSJEmSJI2JCVhJkiRJkiRJGpP/D3Hl8IXZyvSNAAAAAElFTkSuQmCC\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 688}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "7 out of 20 tickers were removed\n{'PRSP': 31, 'IBM': 1, 'BTI': 25, 'CMA': 14, 'SLB': 9, 'SKM': 113, 'F': 42, 'EIX': 3, 'FAST': 7, 'NOK': 48, 'DLR': 15, 'INTC': 3, 'CBPO': 11}\nFunds remaining: $0.60\n"}]}}, "8afea60e96d241a79eaf01c7f009a166": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "8b1b0e53776b46e8be2cbf9aa692b153": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntTextModel", "state": {"description": "Total value:", "layout": "IPY_MODEL_3a2f0473cc274a3cbb0f11f0743db986", "step": 1, "style": "IPY_MODEL_7618d479914f44cca501429bf0d2c8fb", "value": 10000}}, "8b1c9c0842e64723b1268f0ac741887a": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_1ee7fd335e0e42fc9def0dd9bfce936e", "outputs": [{"data": {"image/png": 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hSdZIcneS+eNsv3nU1z/rl79fhnWx6tgmybuTXJjkuEW0+690f97uXA41DaqqtkpyaZKHJFmY5N4kOyY5v6p2aa1dMs4+OyfZP8mxrbX/WI7lAgCrAAEsAMAKqrW26Xjr+xGuc5J8sbV20GKO8YRlXxkzTWttl+muYRk6PF34elySN6QLYd+T5LAkRyR59ujGVbVmkk8k+d8k71yOdQIAqwhTEAAAADPJLulGvR7SWrurtfb7dMHqb5M8s6rWGdP+0CRPSHJYa+13y7dUAGBVIIAFAFiFLe4hXFW1blUdWlWXVdXNVXV3Vf13VZ1eVQdU1RpTONeuVXV7f74jxtm+XlW9o6q+U1Xz+3PNraqPVtVmExzzvgddVdXsqvpAVV1dVXdW1S1TqG27qjqiqi6pql9W1YKquqk//qurarXJHmsK53xoP7/qD/vvy+1V9aOq+oeqWn8x+07pc1mS/lVVS/LZ/u2cceYS3nFU20U+hKuqNqmqI0d9NvOr6sqqemtVrTXBPsf1xzy8qlarqjf336s7+z6fOdB8qxsmubG1duvIitbawiTXpvv/0Qajatw8XTh7SZLjB6gFAJgBTEEAADBDVdUTk5yVZPN+1cIktybZLMljkuyVbq7MeZM41j5JTkqyVpK3t9aOGLP9T5N8LcmjR51rQZLHJfm/SV5eVXu11i6d4BQPT/LdJFv0+90zmT6Ocm664C3p5jG9M91cunP61z5VtXcfxC21qnpckvNyf39H5k7dun8dVFW7ttbmjrPvknwuS9K/3yZZO8lD080RPHru4GSS3+Oqenq6z/Zh/arbkqyZ5Gn966+ravfW2g0THGL1vr/P7etYkC4E/csku1TVzq21b0+mlkm6KclGVfXQkRC2D6gfneQPSUaPcv1o35c3tNbaMqwBAJhBjIAFAJiBquphSc5JF/L9IskLk6zbWtswyTrp5sH8bLrwb3HHOjDJl3J/UDU2fF0/ydnpAq4vJXlKklmttfWSPDbJiekCt69U1ewJTvP36R48tkeSdVprD00yldGR56Z7iNIft9bWba1tkGS9JH+d5Pokz0/yN1M43oT6OUO/kq6/1yXZvT/Xekl2TfLLJI9K8tWxo0OX4nOZcv/6OYYP6d9e1lrbdMzrskn0dYMkp6YLX3+c5On9Z7Nekv3ShZlPSfKFRRzmjemC2pcmWa+19pB+n58kmZXkI4urY4rOT7Jako9U1ayqWj3dHLCbJLm8tXZn37cXpAu7P9Za+/EyrgEAmEGMgAUAmJn+Lt2IyhuT7NBa+5+RDf2cmJf2r0Wqqv+bLiC7N8mBrbXPj9PsbekCxZNaay8bvaG19t9JDuiDx+cleXWSD41zjLWSPL+19pNR+/58cfWNavuycdbdkeTzVXVtkovSPZDpg5M95iK8NMmT043mfEDNSb5ZVc9P8v0kT0pyQJJ/G7V9iT6X5dy/0d6U5I+T3JJk99ba9f25703y5aq6NcnXk+zaj2Q9f5xjzE7X10tG1f6jqjooyX8keVpVPaq19stlVPM/JtkzyUFJXp7uz+5a6T6vtydJVa2d7s/1r5O8exmdFwCYoYyABQCYmQ7slx8aHfJNRVW9K92vaN+T5MUThK9J8op+eeQiDndiv9xtgu1fGxNkLjOttYvTBYibV9UjlsEhX9wvTxuv5tbafyb5cv/2JWM2L/XnMs75lnX/Rhvp62dGwtcx5z43ycj0AWP7OuLi0eHrqH2/m+RX/ds/W9pCRx33p0l2SDfS+O500w5clGTX1tpFfbN3pfuhwVtaa7dV1cZVdXw/r+6dVXV+Vf35sqoJAFi1GQELADDD9A8W2qR/e/aSHaI+nO5X2u9Isndr7ZsTNNwsySNHztU/+Gk8a/bLcR/GlftDvCVWVfulG3G6Xbo5ZWeN0+wR6UY9Lo3t+uW3FtHm/HRTBoy0XerPZTn2b+R8a+b+YHRxfX1mRvV1jO8sYt//SffnZ4NFtJmy1toP0k1n8SBV9YQkb01yXmvti1U1K10fnpTkm+lGJ++T5IKqenof6AIATEgACwAw82wy6usl+bXuR+X++URfP1H42vvjUV9vPIljrzPB+v+dTGHj6ef4PCVdaDZiQbog7d7+/cPT/XbYukt6nlEe3i8XNYJ1ZGTnhlVV/QOeluhzmYb+jXhY7v+Nusn09eETbL9tEfve3S/XmEJdS+sT/fJN/fI16cLXY1trb0iSqnp5ks+lmzv2RcuxNgBgJWQKAgAApur6JBf2X7+/qh67iLaj/725QWutFvPafILj3DvB+sl4Tbpw8s4kByfZrLU2q7X28JEHTuX+UaG1FOcZa7wRqEOYrv6Ntrz6OqiqelmSndNNAfGzfvWe/fLjo5qemO6HAs+tqtWWY4kAwEpIAAsAMPP8dtTXj16C/RekC6UuTfInSc6vqomOM/pcj1qCcy0L+/XLf2qtfay19qvRG/sAbaNleL6R0bqL6u/ItAw39aNfkyX/XJZ3/0bcnG7+1GRyfV3iUczLQ1U9NN08xfOSvHfUppHP4hcjK1prf+jfr5thvrcAwCpEAAsAMMO01ualG8WaJM9fwmPc3u97Zbrw7fyqeuQ47X6R+4PFcefcXA5G6vr+BNu3z7Idwfm9frnTItrsPKbt0nwuS9O/kQB1yiNjW2v3JBl5yNiU+rqCek+STZMc0lq7c5ztY7+Haw9fEgCwKhDAAgDMTJ/rl2+tqj9ZkgO01m5N8tx0wdoW6ULYPx6n6XH98tBFnas6s5eklsWY3y+3Huecq6cL3palL/fLPapq23HO+aQkL+7fnjJm85J8LkvTv1v75ZJ+30f6etB4n31V7Z7uAVzJg/u6wug/pzckObO1dvqYzdf2yz8f1X52kselewjdjculSABgpSWABQCYmT6Q7sFJGyW5uKpe0D/VPlW1RlXNqaqTxxvVOlpr7ZYkuyX5UZItk3yzqsY+bOmIJP/dn+uyqnpJVd03erCqHlVVr00X5L5wGfVvtG/0y3dV1d4jc3b2T7s/I8nT0wVpy8oX030/kuTUqtq1qqo/5y5Jzk73UKn/TPKFMfsuyeeyNP37z375xKr6iyXo68eT/CbdaNBzquqp/blXq6oXJTm5b3dea+38JTj+hKpqXlW1qjpuKY9TSY5Nck+6OXTHOrtfvr+qNq6qtZJ8KF2fv95aW5r5iQGAGUAACwAwA7XWbko3JcCvkjwmyWlJbq+qG9M9zOmCJC9NsvokjnVzkl2TXJXkT5OcV1Ubjtp+S7qRsj9NN13BF5PcVlU3VtWd6UYYfjLJNklalr0PJfmvJA9NcmqSu6pqfl/Pbklel2U4irH/1fwXpevXo9IFpLdX1R1JzuvX/TLJvq21BWP2XZLPZYn711qbm+Si/niXV9VNfbA5r6qeMYm+/i5daP67JE9O8p2qujXJ7elGx26QLow+YHHHmkavSfIXSd7XT5kx1qeSXJ3kqemmiJif5FXp+vj/lleRAMDKSwALADBDtdZ+nORJ6UKk/0hyV7qHCv0yXZC3f7ogcDLH+t8kuyT5Wbog7hujpxNorf08yciveX8rXWC3fpKF6QK6TyX5yySfXwZdG1vbzUmekW6U40h/7krXxzmtteMGOOfPkzwlyT/m/nlS03/9T0me3Fq7ZoJ9p/S5LIP+7ZvkmHQPlVov3UOnHp1JzovbWrsyyROTHJXkmnSjexf2tb8tyV+01m6YzLEmq59aYeThV99ZiuNslOT9SeYm+eB4bVprd6Wb4/bzSW5JN2/ut5Ls2Fr76ZKeGwCYOer+h64CAACs+PrRud9ON13DY8eOJAYAWJEYAQsAAKxs5vTLDwhfAYAVnRGwAADASqWqzko3pcUWrbW7p7seAIBFEcACAAAAAAzEFAQAAAAAAAMRwAIAAAAADEQACwAAAAAwEAEsAAAAAMBABLAAAAAAAAMRwAIAAAAADEQACwAAAAAwEAEsAAAAAMBABLAAAAAAAAMRwAIAAAAADEQACwAAAAAwEAEsAAAAAMBABLAAAAAAAAMRwAIAAAAADEQACwAAAAAwkP8PJYnsDkH4/7IAAAAASUVORK5CYII=\n", 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\n", 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lQZp3qHzCp+z1T0nLdfh83QmY1i3abwrKNZdifOOyPN+Q9J2KbZCf5G7pHrf5yrk8Lq9Iu2ku7Qk9rGvF3OevqUi7SC7t7QVpst9ncvp7ifR9lpY0fDDHP6+av+lQF4AXr25faijYmvI6NpfXd9qWza1W4//1/ImioCw3pn//ozjhryNpA8UYfvmT784FZVlGcZfbFcHFUxWNl3UVjaO9FJOlZPl8qSCfrdQKNE2TdK6kz0n6cCrTDorA5tNqBVvnVDQ0xuTyPyi9l3+t0Kcy/yiX5knF4xpZHvtLekZxofxWQ2gQv/l2iovGcxWzho9SPFL1CcXENXflyvLLknyyNH9X9O56UDF5zUfTdt5f0wctRxXkMyEtf0rRCHtZMej6RopHy3ZO72f5jC3IZ1ydbaN6F70vKSaVOEjSpxUTSXw0/bYX5PavR1Vw0aHmg63ZsThecQf3Q4qJgr5WtB0Uk3HsoWj8rKUI8l2cW36Nco0NSe9U7Oc35b5f+zGwuqT5e/meuXTjKn6X7Lv+K+2jH1FMRPItSfOl9KNy6a/L/buT4ljfUFG/ZY3oZyUtWlCufF6DqlNr/q75enf3irSbq1UHv7/gd+54bDVQzgnZOrr4zJG5cu1akOb9kianNGd0WL5TWjZF0rJty/43Lbsn954pJp9xxeNt/f798ueJT+TeXy63vw0IArXlkT9urkufO0vSZxT18ac0/cV3x/2yqeNAzQZbv604v56attX66TttoTgvPJRb14El6ymsL2r+Tlelz7+iXGC+Q7r51AqeD7i4S9swC9y+oQgSb6qog3fU9LPW36YONwTrbF9JdyjO8T9U3BRdJ722k3RyOh48lfU9bZ9dWFE3n5Bbz2YaWHcv2c02Vu/B1q7OJYpJVbKy/Lzidz0xl3blDstnWLA17TvZDaZXVXKDusNnhyku+Ecp2mNZmV+W9M4+l3tZtdoxD5SkywI1L2rgzYZHJZ2i6uD4n1P6i9veXyS37fbNvf8BxfnuxX5sB8UTGFNz3+M7KrnpU5FXPmiW1b8TJH1BUT+Mattfn1TbzcVcXtco2jw/VRzz6yrq8M8qxh7NgtZTFeNldsojH2zN2lFXpPw+pKi3ds+lzwdbs2u4XyvOQ2sp6qHf5tI8rHTDu2LdDyrG6/xUymcLScfltvtDKrgxVWObdxNszd9Q6BgoVtxAeF1xw3M/Rb25lmJytC8qnkTK8rhZ0hwdPr+6pH+kNP9R57pu3txn6gRbz8qlmSjpa4q28PqKzgiTcsu/WeMYdvUebP1sLo+fVqRdJpd2Qg/rWjK/vSvSrpBLO6X9t0lpsvPBm2pdr2evSYqJ8gpvgvAa/GvIC8CLV7cvTd9ov6mgUm9/Dbgbl/KaW60A3uuSPpJbdlxuPd+tURZPJ5sRHdKtqdbFzLOSFuyQJrswekgFdz0VPYqyBs1z7fkoJo/JN3w3K9mO80pavO29UbnvMrrGb9FEmVdR60LuQXVoXCp6zz6a39aD2H+W7rT9c8uHqXUxPU3SigXp8r/7rZIW6ZBmw1yaiwrymdB24nt/hzSLK4Kxnv4dcDdSzQZb31ORxyfUulD5QUGasbn1jOzxtxrdtp2rGjlfyKXdsyTdPrl0A25+qOACerDfM5duXMXv4qkMZT2rR7WlP00dLpgUA/pnafaukdeEXn6rLn7Td6lVF74qaamStAsq6gSXdFjR/q6ZK9g6r1q9Sd6UdI4iyP9hxRMHP0vf2xUTuQy44FJMmPJsSnO3Yty+9RQT0GRBwwNy6XdV6/wzoLHdh+2SBQ8ead/nJP0+LXuirCxtx03H842iLr4ml6ZT3djIcaBmg63LqeAJhLR8bsUkcVmd3/F8lCvPuB5/px3Ltm8u3c65dF9sWzZc8VRPdj78VMHvdL5Ktl+d7avq88771bpx/asa+9XIGtuodBurx2Br3eVtaf+U0j4jaa6CNPngZlGZZmSw9bu5dZ1dI/08bcdr++t5zYALf03/JNWPS9I9UVFeV1w7fKskjy/n0v5SEcTaSq0nwl5VCgKlYym7edHxfN3Q9z+x7Ts8pBi2YBdF4L9WjzdNHzTzlG+npwQPy6X5akFeVcf/CopgrSsmP+uUZo+28hxSkefhbemLrvX+J5fm2A7LV1HrZtApkuYsyOfjat1oPa7H365WsDXtY1m620rSzasOTzO0pflaLq8BPbRTmmy/vbvL/WZAHaUIUr9VdnUIcKft8ExKM0XF1/lNBFvz1wxVT5ZaqhNc0n96WJep9WTqFBV0kkhpR7ftv8t2SFO35/HZkubpZfvwqvhNh7oAvHh1+1L9R97zr0tK8ltZrQb8A4px8PJDDPxOHRoPBWVZu2Q9B+bS7dG2bJ3csgEXM21pV8+lHdO27JDcssI7fSV5j8p9fnRF2qbKfFRu2TYleeQDad7nfWxRtQLA+xakyf/ua5bk9beU5pmC5RNy+exTkk/+8aw1OiwfV2fbqIugQkU+F6U8/lGwfGxuPSN7XMfoXB73qSKQpFav5F/XyDvrcfTHkt9kYo18an/PXLpxFb9LYZA/lz5/rD6ugkaSYibjrDfFhTXymtDrPlFjWw1XK8jkqnhUVK1eave2fz/NpMHW9DlT1FdFjyQ+ouhFX9iwVfSoeb3g8xOULuYUj4Q9qwjCDnj6og/b5D25cgwYHkVxsZ4t36okn/xxc3FJui1z6fbq13GgBoOtNbfj+3Pr+2xBmsL6ouY65lWrN961Jemym6YvKddrPy3LD/lxYkkeI9S6QHxabcGGbrZvxXfKHjV/Xp2DOvn9amSN/Eq3sWZssDUfHC8KaIzOpdmpIM0MCbYqOhFkN45eVcFQH22fKQq2vinpFyq5+dZgubdQ66bVkyroqZjS/l0RkNtY0eNsDkUv6o8rHnHOP37dMQijOB9cWvK9R+fSfj29f5N67G1acxvMq+l7bLa/Xkr1wlfa64QO2zL7zIMqvkmwmFo36CuD8iXrOyDl8YY69NbX9MHWO6u2oaYPtt6s4mu9OdUa4uul9nUrbvK5pPtVEGjNpc068rxclbbg84XB1vS7rqZ4OuC1lGaypI0b2Gey9vX5BcubDLbmh3/4QEk+u+bSHV6Qpolg6/dzeYyukT67IfZ0j+s7Obe+juddxTk3/+Soq0Mve0Xw/1RF79zlFcMzzK/oQX+oph8Cr/FhS3g5E2QBHjP6fT39OVLRE+m09PdTknbxVGNVuMPdbypZfqqiMpPiMY28bODtSZp+wOtO5b1Drdlf12tbvFX690XFHfR+aqrMm6d/n5J0WUk2v1E8lt+oNCD+smb2PjNbPQ3iv4xa5a2anOsOd+84SVOS7ROLWfWkUmeXLMvvWytW5NOYNMHAUma2crZ90jZ6JiVZzczmnAFFOc/d3ygp5yqKsR+lOIar/DH9u66ZDR9s4Rp2vbv/p4v0v3H3yZ0WuPuLak1c1HG/cfcJ7m7pNaq7onbl54rH+KS4CfHDooRmtqHiwkmKnjAdv99M6oOKoGPR7L/vVPQm3LgoA3e/RHFBf6Wi3puq6OV6sKQtvDVD+JGKm0O/dPcbpLcmGvmBxWSIU9IkHReY2aoNfLcxuf+f3mH5RYqLyPa0Zc4qWdZNvTeo46BfzGx+M1vezFbN1Z/59nejE0BmPCYv+k36c5SZLduhbO+QtEn68yJ3f6UtSb6tUtimcPdJikfBpXgSY9DfyWKixndbTHaYbbdJafHCip5us5ML1TqvfqUgTfb+c2r9ttNx9x1y9fl5DZdRUkwCpAggzpve2sfd76/x0SmKenENxcX+JopHgh9VDDt1cton+yK1E85TBEBd0b4va1d+2N2/6+7XuvtT7v6Gu7/g7n9y9y8qbqq9mdL+1MyWac8gXT9sqxhe5E7FNnhR0Wv/E+4+LpXtHYrZxacpHnV/M72/hsWEf9nEgXeb2X+bWc+TiKW64dOpXH/IfYfMAopj/38l3W9m29TI9gLvMGt6Wt+zis4sUs3618wWtZh4Nn/8Z/XTcMUNqzLnZNuwpl8VXeul8+0Z6c8FFMM6ZeUcprg5KsU56HWVm5D+zQJeg7F5fhIqxU2POxTBwXkUT7ts6e7XlmWSZ2bDzWwZM3tvW5v/0ZSkL+er3PrnVrR9JOlv7v6PkuRnK4KF0sDrakmSuy+dqw+f6LFY8+b+33EfbzOlw+e68WO1Jgzcw8zOMbMPmtlcFhNJbqUYI32ltvLM1yGvr7j7ru5+sbs/6O5T3f0Vd/+Hux+s6DiVnXe2rXmsowtzDHUBgEH6YxPBAXc/3cw+oRjn7ZPZ24oxRutWzjdWrOMpixlrV9DARsI66d8Rkt60+pP6vtUotZj1NMv3b6kx1U9NlHluSe9Nf95c1jBy99fN7FbFWKaDkmZ73EcxjtP7FA23IotXZHd3xfLncv9fSK2LxXbPuPszBcs65dNXZradpN0UwfGymXqHK8Yee6rPRSprcEmt/VGSLupif5xLEax6updC9UnVd21Xdx/s+35TxGKG873TnxMlbVt0UWJm8yruxpukU919wowoYxNSQ/UcRSP7UUVwdLzi+FhEUX8dqhh77HIz29fdj+mUl7v/VSWz2ZrZxoqg7pOKJydkMTP57xTH7TTFvvFOxbjdW5rZpu7+tx6/23DFGG5STKwyYL9z91fM7DeKHnifMrMl3b2qbijbf7up92aa4yAFXfZVXISvpNiXi1SdYwbjDEXQ2xT7ymFty3dW6/x3hgbKbhi8opggpcz1ioCZFG2R0jZRJ2a2qaQ9FcfJIhXJF1eMDzhbcPcpZjZOEZTb2MxWzN90M7P3qRXkOdPdp3TIpu9S+2m8WrObH+vuJ9f5bApm3dH29rVmdowiePsZSR82s4+7+7+bKrMkmdlyit6a2U3vb7v71TXKW7b8fDPbSDG2+nyK+vHwDuneUNwYO7Iku6NT2Y5291tSmTdQbOv5FDfd/q3o2XiopI3MbLOym9AVZXfFzbGLzGwRxb71YcV4nesr2kWStFRK8zl3v7Akyzr170oqqX/TjPV7KYaqWqIiv6p6s9t2VFV9lT9vvl/R+1KS3q1WXXWAmR3QxTr7dmNBEfA7xd3/UJUwXT/uqjhHfEgRqC3Sz/OVFE+bzp3+/9eyhO4+2cxuUQzZtoaZWc3OUd3K38Sdq0b6rPw9XYe7+0Qz+y/FDbgRips6X+iQ9AJF0D5rJ77UnqBGHfYvM9tLrZuleyiGwkJD6NkKtHxNrR45UnTdH9/F55/sIs1ibe8v2cV68vJ3sRZT65h+rMf8utFEmRdR6yK0m+3XMzN7v2IQ/rGK4Q2qejVW3Zls7wnULh9ALltXU/kMSrpzepFikoDNVR5ozfR697Ybz1cs73V/lDrfDR5KVd+1Xd19Z0h68JrZ3opJ36R4hH5jd3+05CM/UlzAPKEIPMwSzGwJSWcqjoenFGOAn+buj6UeUU+7+wWKQOs9ivr652ZW1AO2bF1zK8YdlGKok6x31rcVgdYnFY/fra64WL5QcSyPsy7uRLTZXBG4lToH5jJZj9c5FcG8KoX7b9sNuKr9d6Y4DlIQ/G7Fb/FulQdapf7Wn39UPN4rxYV0u+y9hxU93NplbZWna/QSy9+Ybm/jlEpPUBynuFHwX6oOtEoz5rwzo2UT4Jki+JH3lbZ0M5yZLaiYSGfN9NapihvXg5J6nu+s6KX1DsX4oY1JNz9+r1aA+GB3/3lD2f8q9/+PF6YqYWZbKm6IPaLo6Zvd3Dpd0T45VzG52+qKYNizihsS3+i92C3u/ry7/9bdD3H3rRXtqW3U6o1qko5PN0KLDKr+NbODFTdsdlR1oFWqPv67bUdVXV/kl+frt6Fse/5FrZ7iayieljlAsR/NLelYM/tRWQZmtqhiu5+kaDtU9Zjud72b37Z1OjtlaeZQ/26k5oOYpddEqX2V/a4Dgp91ufvvFUH9kzSwM8tdkr6qmLR4/tz7z6k3F6pV1g16zAMF6NkKtHxV01eiG5rZvDOgh6jUOhYfVYxlU1dV46afZrkyp8fdf62YJEuKhuy5iuDrU5KmZHcBzewhxYy1vQYmZlUHKsb2kaIn01GKsZgelfSKu0+TJDM7VOmiQDNmG02rWJ4/n41RjL9V14y4OdGNqu86yzCzPRQ9dqQYU3O6bp/CAAAgAElEQVRjd3+gJP0Ckr6Z/vydpC0KYoP5R4g3NrPsmB5f8VhoP31BrXPIMUUBZXeflC6AzlRceI5RzALcjYMU46de7e7n5t7/cvr3Z+5+Z1rf1NRzYStFr6iPKi6uupUfFuC4FByr85mmghozvXTheoFicrc3FEGjSxRDGDyb9UZMj55mx3nf6k93dzM7U/FY6Spmtran4Y7MbE21eq6e1eUjt037olrDOT2g6AF4nSJQ/ErWC97MvqwI8Emz4bnZ3e8zs2sVj9ePNrOD3X1a6rGeBcavd/e7ZnTZUt18pWKWeCnqr6821ZPM3R81s78pLvY/YWaLunuvwYO3pCEPfq+48SHFhFiFQ9j04N7c/5cuTFXAzOZTjE8uxaRYWdBjI8V5brJivOrXJcnd/2Fmv1DclPyyoo3WqNTOu9TM7lBMULSA4qbdxxW9gxtlZpsr5pyQInj2P4rH7SdKeikbnsDMPqkYa1aqPv5nVDsq3/b8hVrD0NXx8CDX/bLHMG15fzCzXyl6hb5b0vfM7A8peNfJ8ZLWzj6rmOTsVkV77bXccBYXKG4IzHb1bg0P5f4/YDieNu9Qa594qCxhFXd/SPGkx56pHhuheAryrXrRzFZO/33a3Xt6Qi89OfqAIrg7n5ktlG6AoQEEWwFJZvZhtR6ve1Fxd2w1xYlzj6LPtVmqizTPtr3/tOJx+hGS7uyx8ZpNkjJMMeZovzVR5ufV6sXRzfbr1UaKR1SkmNzleyVp6/SsmZm81bA0s2ElF87zF7yf2T39e79iwp2imw0z2/bJNzJe6dAA7Zd8g77waREzq9rusx0z202ti8inFIHW+yo+Noda23EXde6N1+6/c///oOLicCjkx0T9e0Xa/PL3FabqwGLcwe8oLsK/lnt/QcVjmlL0eHmLuz9uZv9R1Nlrqctgq5ktptaY4N1YPR/gm4nVOo6TsmN5O7V65nzd3Yt6IM7I+vMMRbBViqBm9lvs0pamk6ytskTFeUWaPtDU3sapkp13XpD0UXcv6mU2s513+uEkRbB1GcXjoZcpboBmj+/O8F6tKSB4heLxciluUo/pQ4A+G0rJFJO5DCrYmp42uFZxk0mKm1DfL/lIL/I9NXt5pH+sYr6Iy9394tz72diYd3uMeZqX1e+rm9lcXjBW6mC5+/1mdpVibFcp2s+NB1vVOv5fl7RByRAS/Tz+l1J5cCx//ZH/PfJtT5+Bbc9C7v6Mme2iOM+bpKPN7ANZZ4lMujn4ufTntZI2LbmWm1F1b37b1rl5kaV5Q61xTpt2Z+7/VWPf55c3dlMsnROnOy+a2TvVut6/YZCrGGw9hgIMI4C3vXSBeq7ikcfXFI9RZGPx7J7GTaljnbKFZrakokElDRz7LLvwXkBxIdy1NG5TNkbRRyoe9ynMpou0TZR5iuJxWinG6SoLVs2pwQ/Mnn9ct3DiCDN7r+o9Pj8zyT+uUtYoKgzspIBKNobUZRW9uj/cRdlmhHzwqqdH+XK6OQ4Gvd1nR2Y2WhEUMMXFyMadxviczeQbqFWTxuWXV02o0e4kxbhhP/LpJ6XJT8DXaWzoFzqkq2tntcYq+7FaY4gVvXbLffbLmvnVPY6l8mO51jlGM7D+TDc4sguxHcxszvSI8o7pvZtKjs2srTK/iid8y3ws9/9ux0vM8v5DSaBVqt5u/RivbzB6Kc+laj0au1vbv5MUPadnmNSWvFwxLqIUTwft0h64acg7c//v+RFcKSZYU/RozYIfP3f37wwmzwL5SY7KhscZIA0hs6/iia/2IQGyerqsLjdFL/p+eiT3/34dX9nxf3PFWL39rDdLr+EUw/9k8tdw96o1BN1g256N8ZgwM6srVlNrvPW8/JwV5xcFWtP5our6q6l94161JphatyxhGk4pu/78Z5/Ga83KlNXJ61n5pMD5eUX+1KfyZD6f+/+5hakqpJtpWc//F9z91UGVCtMh2ArEIxNZJbNvuiv5RbXuIJ2SBtavsrqZrV2yfFe1Hr9oH5T/otz/96+xriKXpn8XUgyL0K18cG3uwlShqTJnd8mXVHnPqe0UMxAPRr43f9lYSV8rWTazyk8UUrYflo2hWGv7mNlaqmgEDYF/qLUNvphubvQqOw6qjgGpme0+WzGznRWP+pqip9Im2SPtVTxmeraql1rjgkrSRrllQ9WrVZp+X9iwMFUYVfC5UmY2JuX9L0lHtC3OX5R3etRt2Q7p6sqGEHhZEeQ9r+J1qloTjuxgg5g5e0ZIQ09kvegKj+P06P1qJVnNrOeY7HhZXDEJ6CfU6hFUNv5uvq1S2KYws4XUmsDjacVjqN3ItlvZeWcZtWb9LtJNG2ZG6OZcIumtmc+zR5E/aWbrKXq6SjHcw4wY2kqSlI7bSxRjQUoxtt+O/Qi0mtl71AqmTVJrrOFe8lpUMQxNFsQ72t2/NbgSFsoHSYse1R4gje34v4p9f2x6ZDgvq6c71eXZNYmry6B0D2N25wOc9xemGpw6x/9C6m87akzRtknBtSxY+bJyT4ak4/Xy9OeHzWxUH8vYrUPUGiv3+2kirLy656vPq3oc7q7ruk5SJ5w/pj/XTfNsFNlRrZsNpZPdDbJM2WRyUtyM7dgJK+0n2RMjk9Ua8qJxZjZC8YSTFEOhlU1eV+Urav1utesw1EOwFW9rZvYlSTulPy90919Kb3XX/5KiIbOIpLPTnb0qJ6cKsH09a0rKHll/XtLZ+eXu/ifFIxyStL2ZlT7mZDGJ0a5pDJe849S64/0TM/tESR7zpjv/efmxK1dWiQbLfJJaj3AenS6o2j+7nGIMp8HKj601uqCcn1VDkw7MYBNy//9Wp17Cqbfh1iV5PK3W/vOZdMHSnsdSks7qvZj9kRpDY9OfCynGGyudZMHM1kljgLXLjoMlOx3Pba5Xq2fiXp2CSuk4/Hr7+zMLMxtlZp5eEwaZ1/aSxinaF88qHku7ffCl7Lk8I3PfbWKfV3e5Whc2e6bhaTqVaSXFmKuZSzul6/C5xRUBVpe0e/ujo2msv6xX0C5tn80eS5akW+qsL/fZtdTqvXWZu08uS59zfvp3YRVcnMxksgu8j5jZgB5KFjN2/6r9/TZ1zjF7q7chGQbjfLV6C31RrcDBVJX3iLlMrUlydjezAeOzp3PNSWo95n68dz9Derbd1jezd7cvTOOFnqfqyVtqt2FmkG7OJXknK+qS4YqepJZ7v5SZnZer83boqrTT5zOX4gJ+s/TWJZK+0O1va2bbdWrXtaVZSvH7Zu2Ws7IxStvS7ZH7bie1L09pRigCL9kkXse5+zc7pa0o02dSj6+i5WYxqVN2A+AZxTi2de2huGn9D3UedzWrp1c0s4+1Lcvq9zt6GEJgLTMbb2YbVgVezewbiqf9pLgZVTmzfY+y43+1TufNtC+eoVYd0w8fUit41e4wtTrljHP3l9uW/0itduA5FQHCrF0yuteC1uXu/1Krd+uKGti7NT+s046demya2WqSjqmxuqyue2fZcVPT0bn/j0uB9vZyrSzpp+nPqYqOUwOY2RO5OqPrMZVzfq7Wb3xEQYeOH6l1c+T43PjL+fLMkytPYVvKYoiAomULK87N2fX0np3qATNbv0bdu7mkw3Nv/aIsPbrHmK2Y1c1vZqvXTPuI5yZOSXfRswk+HtL0jz3K3a8ysyMVMwqvL+lgST8oyf8mRY+YW8zsZ4qG0tySNk15ZOO87d2pAlbcsf2boqL+oZltowha3Ka4k7qg4uLhY4rG3aKKCVLeeuTO3Z9NAeSLFTNGjjez8xV35CYqGuwjFb2qPq+4m3VJ7vOPpIDESEm7mtmdisezs4u0V9vuvjdR5rvN7HBF8GG5tP1+qhjcXYpt/x1FAO02tRrQvbhK8SjI0ooLx0UUjeP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\n", 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PJlS2NwHHEy4WPg58ktA/+ulUWX5akE+SZiJhp3yJMHjqdnE9n0rHoMnwnHzGxumvE/povgNcSLio24JwUZjuuzkqJ5/RZdYN5U66ZwEPxW1gX0Lf3+3ib3tLavt6FVguJ49RqeUM6eJvNSKVR7Iv/p4wGv6WhAHKTspbD4QnTZxA6G++BaHp8h2p6X8idaJJGKxsEypPqHiVzvvAJqROvur5nql0o2v8Lsl3fSZuo8OA7YFTiIEOOl7kPpT6ewRhX9+FUL8lJzrTgWVzypXOq6E6tcS2kLkf1CjTVDo+9Wl+XEcjacL4MMDNqbw/XiPtN1JpT6iadmJq2tdr5LNFKu2YqmlHxM/nkBorI067Ok57LvWZEQbfc+DUZv9+GWVPHyc+mfp8jdT21ukitGCbeCjOdwOwH6E+/hRhXy/cLpu1H9DcYM83CcfX6+K62jF+p70Ix4V/p5Z1esFycuuLkr/TH+L8synog08IpCbBuz9lTN8ltf/NIwSpdifUwYfT8elHfyfjhkSZ9Ut4otHjwHmEmzLbxNfBwDVULlBnAetWzTuYUDdfmVrOHnSuu1esZx3T9WBPXccSwiDwSVm+X+N3/UkqbacnQNG7gz0LqJwzJq+ZwG/ICOJ1x4tw3E+W/YkuzG+EYNW28bdI9o35wBYZ6c+L0/9L1cUY8GCcdmfqs4Gp7a6hoEDBd/hLah3cRhfHd6PjRftfCS2lXiScS29LOHc5jUr9sgDYLiev/yWc/19NOPdL6s29Yx6vppaVeXylY7AnOY96gvBEom2AHYBvURlPMB3sSa7hJhDOu7cEPgFckfqNM68FY15bUGkcMIvwAIXPEI5DOxPOp1+ncr3UacyXLuyDtYI9yflCp/OVqnST4vc+h9AQYpv4232WME5gEryYQdV5LmFA4k3oeCNsFzrXdcvnbDeZwR46PkXqHcJ14s6E65EvEloSJ9PvKPhuDdeH8TsmedQciydVtrnJttaFZf44tcy8MXu2TP02hU/1y9l31yK0xH6qzLqsK/+GM+h40vDXjA0q67VGTl6LUwkgfAAMS027IrWc00qUxYF/UNVaIKb7GJXKbjoZFSuVE7N/A+vnLG9JKie0b1bnAyxPOHA6IQCxR8F6HJDe+eJnw1PfZUSJ36IZZd6ASkX6ErBqRh5r0rGi9wa2n49mrf/U9H5UTubnkxPhrPrdHwc+kpFml1Sa23PyGZtKMxPYLCPN8lQOEq+TcSeM5gZ71q2RxyepBHzOzkkzKrWcIV38rUZUrefcOxMx/WGptCcWpPtqKl2n4Ct1DKpZz/dMpRtd43fxWIailmXDq9L/jIy7DXRstfKVEnmN7cpvVcc6Gl5ynurvl/d6Cdi6wfKlT3yXr5H2s6m0F1RNuzA1rdYdwBVSaR+umrYU4TjhhDtunyGcpH6PStDiW6n0x1A5/tQcjK8Jv+f4uLxXqrc5wpMpnHAyWvR4+PQ2kXm8IdTF6QuzrLqxKfsBzQ32rEFOC8w4fXHCE36SOj/zeJQqz+gu/k7pFgyd1m8qXbqV2ZFV0xYhtGpOjoefyvmd0gHTTuuvzPql9nFnMyo3zn5eYrsaUmIdFa5jevDR61Qu/N8AFstJM5DKOV5emXpzsKfW61d08e50yXKsSaUOnUxOC5Ea3zvr9T75NzPXI1xfePyN9ybczLsxNf/+qfSXUHDO2KT1cEhV+WfG8pxEuMgv9ahuOl60O/AoGTdgCIHXJM2NBb9N5nYfpw+gclNjOhktfOkY7HFCr42iunivqvR3k3HcItxET7abf2ZMX5TKje0J5LdwXJnQIMAJT0Arvf3lbItFAzSvlqor3qdg0GRq171bUmlp0qmFWkzTtEevE86PksDZTGDzjDRL0vFGQ2ZdR3OCPZun8ri1RPr7Uukz4w8l8liKSqvA+cD1hMHytyLcDLuU0NrMCdfYmTdzq/Ks1Qrrwbxtt+7yN5xB+S5H6df/FeS3HpUTiMmEcRDSXbz+mLdDZpQl96IDOD2VrvqOcPrJAJ1OpqrSbpJKe3TVtHNS077WhXU7PDX/iBppm1XmH6SmHVCQR/pC3puxMRYsa1kqAai8Owjp3/1jBXk9GtO8kTN9bCqfrxbkc3Eq3aYZ00eXWTfUcVFTI5/bYx7/yJk+KrWcIV1cxohUHpOo/WSMpFXWr0vkndxxHVfwm0wpkU/p75lKN7rG75IbZEylT++rne4YptItQyXyf1uJvMZ2dZsouY6Gl5xnF8IB7CuEYPnShBOqVQkXsY+n8nyTjDvcdZTvyVRehRcahBP1JO2PqqalbxDsVSOfAam0T2RMP4DKBUL1ayyVO5MrEE58F5Bzx7HJv+W6qXJ06p4KfD41ff+CfNLbRNFdufT6/nJ37Qc0MdhTcj1ullregTlpcuuLkssYQKXr1f0F6ZKbNrOo6jJCaA2ZlCPzJD+mG0Sl6/g0qu5m1rN+a3yn5ClCb5Hd9T69XQ0pkV/hOqZngz3p4NwhOWlGpNLk3e3tjcGeawkt3Q4kXtQTLtY2J3S9THeDr3kx1UDZz04t5+wufu/q1w1UtXrPmPeUnHkd+Fkq3eaEev9tMm58NnldjCK/y+sHhHPX/yWjW2cqj/RF+wIKuu8SbkY48GoDZU5fd+ydMX2Dqu+QW/aYPh3seRdYqSBtuovX8KppR1I5d6sVODk4lU9mK6c6tsX3qz7vT+jufgwdW9Cd0YTtJWk1OTVnejODPWekphddF61H5cbzIzlpmhHs2TGVx+gS6e9Mpd+4gXXej3Az5olUfunXS4QATm5Asyq/vGDPa4QAcNNuFvaKAZrT3P154Evx7RBCdPtn8f3rwOc9rqUanvQw+nme5PGM0HlU9GRk/JnAb2uU90nCyT2Eu71p+8e/bxMehdudmlXmPePf14G7CrK5ldCEsKniQGGrm9mGZraJmW1C6J6WlLfW4NBPunvmIMFRsk0sZ7UHNc4ccb0qH4C1a+TTNBasFAfd2yS1jt6ISTY2s0V7oChjvGBUeTPbgDD2B4R9uJZk8NxtLTyNqTd52N3/VUf6W939/awJHgYDTgbOzdxu3H2su1t8Da+vqN3mQXff0d0vd/e/u/ssd//A3V919xsJJ31jYtqPAJc3sKwBqf/n1kg7J2e+ZuaDu/8focnybwn13lxCK5+zCIGk5ElnlxKC0z9190cA4oCvZ1sYjH+OhYHVbzGzjWqUqYz0U2x+kTH9dsLNk+q0RW4omFZPvdfQftBdzGxJM1vTzDZK1Z/pc6GmPoAg4e7vEY6bAMPNbPXqNGa2MqGrAoSWBLOrkqTPVXLPKdx9JpWBbpenCd/JwoMC1rEw2H6y3mbGyYMJTdDbyW1UjqvH5aRJPn+Tym/bgbsfmqrPx2SlaYHj3P0YD0/2esnd57r7bHf/h7ufRajPk+/+GTM7oNkFMDMDjopvFxCCtvXYBdiUEKjdhRDAeY4QpBsdz0EyufulhAD+eEL9+D7hhsWJhAvzZODknxIu2L/t7q/Gz5e38LCJl6wy4PzVFp7W1WXuPorQJWYMIdCR1p/wm5wHvGBmZ8X1V2Siuz9TMD2py1ex+HCYIjn1ZlqtOmasu79Sazkp97j7awXT04Py5l3DTXD3SbXKlfq/+nqoXounB0EmBLheIJR1ZcINkJPd/YJ6MjWzFcxs3apz/uT6a6VGt70SkvU7l9CNLFO8fr8vvt0m6xqrSfVhPed2UOP8rg5bEW6g5Z27rRGn71wyv5sJddimMe+DCet3WcLNypEl9vNS+jcjk5Rxzbg4cfdfmNknCf3890k+JjTLnFoym8dqLON1C09sWItwsEjbJv4dBCyoY12vnPxjZv1T+T4aT/S6UzPKvDiwfnw7wYsf//iBmT1O9qMt62LhiUZfJWzoGxKaqudZvkZ2z9aY/mbq/2WonKxWe8Pd38iZlpVPtzKzgwl9ZncgNCfMswjhYvv1bi5SradRbJP6//Y6tsfFCBXdtK4UqpvU++SNsttgt283zVIrwB7rg2MIJ90rA3ua2RB3n9KFxaUDBItVva+WPkmtrmOr8ylSlA8A7v4XQnPdTGa2G+FA/xqh5SgWnszzR8J+O5+wbaxK6H62t5nt7u6P1ihb3vIWIdzBBHjM3Tttd+4+28xuJbRA+JSZrejuteqGou23nnqv1+wH8WT464QLvKGEcT7y1DrGNOJ6QtDNCNtK9Un/56gc/67PmH/T+Hc2odtBkYcJ4/RBOBcpPCfKYma7Ey6AdyUcV4osT+gS0RbcfY6Fp4J9E9jNzNZOB/3NbEPC2G0Av3T3ORnZ9Eol6vNnzOzLVAKGJwD/1+Ri7EolQHi/u9d6yk0H7v5c1UcPmtkVhKD3ocCjZrZXrLez5r+TcMc/zwmELlR/I1x4YeHJTX8hBKjnEIJLQwlBv33MbHt3/3c936OqTBOBw+K5+NZx+R8nHD+GxGSLE3oNLE9oaZun3nPhTudcMSD9dULvirVorN6s9zyqVn01gRAk7Ef+Ndw2VuOJuFVWrp2kIXdTECxJM7O9CNvgLmQ/HS5tebKfLtcsyXHnac95OmrKw4ShJSzO1+mR6E1Qz7kdlDi/q8XM/odwTF6cMFTKWYRuidOA5Qhjlp5LOCb8zsy+5O6FjTzc/S1Cq9jEROA2M7uW0ML3fGAzMzu0ZCOXXL2uZU/KSVTuSEJosvz7OuYvighXp1mu6vOuPoov/ejf5ais3+7cCRPNKPNHqFTm9ay/LjOzzQgD344idC+r1aqjVlS2+k5otXQAq2hZzcqnIWa2mJndTng09J4UB3oSjUSuy3qrxvRGHmc5sHaSHlXru1Yru+30thZMDXH3dwl3KhJl725USz96ttb2np5e/cjaZuVTUzw5Tx5Z+nV3T+66fZNwov4aoZ/7JoTHu98Wlzm6gTs3exICR5AdGEgkLX4WJQQTasndfqtuANTafnvFfhCDcM8Sfot1KL5gge6tP8dReXTr5zOmJ5+9DDyQMT05V5lWdDMmSt8Yqz7HKRRbkF5BCFQeRO1AD/TMcaenJQOqGrHFR8pxVenazW1U6sKduiH/dEvDn+WmqoO7zyXcFHudEMD4WVfq19jC7gJCHXW8u8+Pk75HCPQ8SeiWuBnhsemPEeriRlq0fsjd57j7Q+5+qbt/zt3XIgR/0nXCl81si4JsGjqHjcGGZwjBnrVpvN6s9zyq8Poi3kBPAg/V9VvmY8lLaPTccy6VlhqbEo79X6DyyPXjgHtiY4BMFh55fw3hiZifpnagB7qx7o0t3JL6v0xjiy4fd+pQz7lddZqunN+tSmh5uDjhen4bd/+Fu//X3ee5+2vufhMhOPsvwv50hZmtn5tpAXd/mBDQhfAAnEO7kk9abw72fJGOP9AuZtZTJxPJjvgqHXfcWq8v9FD5svS5MsfuRr8mDNIM4aJkL0If8gGEgT3N3Y1w8gu1Dzjt5nRCv3oId3K/QGhCOIjQnzNZP+el5umJdTS/xvT0wexo6tsmeyI4Wo9a31Uqnk/9/9HcVMXSd0Y7dXWpkp5efUe1WfmUcSZh/Jx740E/kdSv33X3p+DDC5IvE5p4b0B4sl5XpC+Wrkg3H69qSv5Azjxtz8yWJTyxcGnCuG8/JLQoWJUwnlBSf6Yvdrqt/ox3534Z325gZlunyvoxKndQbygRzOlOR1LpTj+Z8DSfjxFO+hdLrbd0AKTtjs2xC8j98e2IpItxbLGXBOYedvenW1G+7hS7pk6ObweaWdNa4MW8PhPfvkV4KmdTxK6Pf4hvNyBst/X6IeEc6wp3nwBgZgMJ42hAeFrw1Li8NwlPhQTY38y6pWVgLMdehKdaJT7bHcuKLZjGEMZxmkvonjyczvXmkunZamTbI+dRMTiRXNs+RH3nnudV51cnd/cnU6+H3f3nhCehfdiFl9AqJM9xhIAlhC5gJxJaLlXXvV9KzdN2dW8N9ZzbpdN8QOhKV68jqATUvp/XvTDWBRfGt/2pdFPtittT/zcc7Gl2N66mMLOtqDRvfpsQod8YuIzQrK2MlepIM73q82mE7kyDgKe62HwqGaSzH2HMme7WjDK/ReUuVj3rr6t2JQzoBWFw0TMK0pa5s9ibfHhgM7N+BSfuS+Z8njg+/n2RMOBrXhPE3rZ+0k2CZ3sYJ6onpE/IbL1ZAAAgAElEQVQocoPZZlZrvUvXpC+ac8d0quEpKiexGxHGUsiT7jtdfcH1VE66evMpFMeGGEloWnxS6vOlCU38Af6cnsfd/2tm/yLU2VsQmj7Xs8zlqIwJV49NzGxrLx7PrjcotR9HRfvywVTuLn7J3fNaYPRk/Xk9YaBVCEGV5Lf4fFWaLMm5ygo1jivQMdhafY5TS3LcmUEYtDTvLntvO+50h6sI4yitQujGeRfhBkxyUd+OrXoSzajPsxxK5eLpJs8Z26sB6S74Qyg+hnRgZnsTjj+vUtlPIVyDJF1G/lw12yOEOmsRQnDpT/UVtxx3n2tm1wE/ih+tV5S+AYcSriUgjO+UVx915/5feH0Rb/4nAcgP6zd3X2BmbxL2z4E9eO6Zy93nm9mxhIGFPwp8y8yuy+m6mNS9bxDO+fPq7h6pe+P6fIswvEKZG3iNHHfKlulNM5sal7V+0bEwbidD4tsXvDK+Yj3S54cTa6RNT98wN1Vt1XVYQ3pdy554gnwTocn5e4RmcPfGyceb2UF581bZpmhijFwPiW+r+74nP9ZShBPxunkYvDbpozqsi62S6gnYNKPMSR9kgK1ihDxTbJXT6KCPm6b+zx2oKzaFK9NUrzdJNxUsqpRzK4N4QZf0Ib6rxrhPW9VRtp6QrvC62p0nUc9+0PB6l4Zsnvr/1S7mMS71//AaaZPp71JpKp34K5X+2bXySY899mCNtNWuIlwEnO/uL6Y+Tw9OmDU22IyMdGV9jsqFx3cIT0cseh2bmreVLVDLKrsfQ/G+XOoYQw/Wn7G1yCPx7aFmtmhsMXJ4/OyvWeMvRcm5ypJ0/G5Ztk/9X+94GUneDxQEeqD2emtonIFu0JXy3Emla8KxVX9nElqOtZ3YkmWd+HZG7KbbLOk6qClduKqsmvq/dLeN+J2vjG+/6u7peZN6ekF1nrGb1ztV6bpLepDj7tq/kv3f6dg1u1p31puF13Bx2ck1St413GZZgwS3godB85OuOYsTxnfJkgx8/ceCQA/0bN2brN+N4jV6keS4s4Dw1KrukpwnDqL4WnRHQjwB6j+3S6QD3bUegJOe3pXAUqJLdVieXhfsAX5C5QDz9RiVPZJK/81rzWyNEvlskm4ineEYKk3f7q2alm4+dWqJZeVJBn9bhtAtrV7pi/tao+U3q8xJ89cVKb5zfDDl+pIWSbcsK+ore1LBtN4qPVBl0XZYNIZGqfUT+21vW7JcPeUfVNbBkTG42lXJflDziRE0Z71LF5jZCoT+xRAO9FljjpQxnkrLsP/J6z5gZsOptJz5XfXFSHz/u/h2HTPbJSefZQj1GYSxHsaXLaiZHU0YQPEZ4JKqyekAT1ZT49Uz0pWVdMd6hxBkGlPjdR2VAS8PNbMlurDMHhPHPEoGEM3dj2PXp40Lsuqtx5hkHKXlCQ+h+CSVO6JF4y+lz1VyzyniNn1YfDuNOlo2RMl6KzrurEIY8LpIPecwPaGeYwnwYXemJCCxj5ntQOWJaTfUuAnTlx1HZT3dV5SwHnFg62Hx7T/joMRNY2YfofJU2QWE8XXKOptwE/ged7+talpST/ej44VYsr8NqkpXWp3jCqUv8l/MTdWYZP83iseD6c5681NmVtS6J30DI+8arj9hzKHe4joqXZCOMLN10xPjDfakNV1R3bsmBQ+LiJpZ9ybrdzHCwx4ymdk6hIGKITw0oivnNmWl989jc1N17Gqc+cTEEtLXFZnnkSnDc+ar12Gp/2s9jKGmXhXsMbOjCH3jAG5LRrKOd5aOIkQqPwL8yso9nvmarKhuPEFMugy9RdUjtt39QSr9tA8xs3RTzqxyL2Zmx2RUTFdQuXt7oYUnjOXlMSCjr2967JLC5ppNLPNVVJrQ/9AyHukXg23fK8q/pPT4HiNyynkgYbyAvmZs6v9TslpJmdkIwgBseaZR2X72i+NPVOexEsWPSm6J2I1wVHy7DHBnDAbkMrNtzGyfjEnJfrBiibs0D1OJpn8566I27odfqv68tzCz4akxV8b2gvJ8pKjuimmWI4y7kARmbvScpz6Z2ZTU9xtSPT3eJU0CJ8sQ6qEOJ8Px7lLSlN2Bi3OKlv78R9V3pWK+l6fKfUlqMM5Csb6+JC7/+DgWT/p7zCL0uYeqwXjNLOkWAuFpL6XF4G7SguquOrpAJHdoB1N5NG1vlty5G2ZmnVoHxou6Wk82KXOM+Qpd6xLXiJupPA72SCpPVZtL5QlIWe6iMo7K8RYGUe0gHmuuotLN6MexpXE9kvW2YzyBr17GUoSWUrWChqXPYXpIPceStGsIgYNFCOMMWurzQmY2JlXfNTz2QqPMbMes87qqNHsCF6U+uiwn3V6p71b2ASpdatUTl1W4DcX6/UYqgZffeckn+JrZpoSxd94l+/zgKSqPea4eXD15v4COY+qU9YiZHWE1HoFuZtsCX0t9VB2QapYy9ea36PzI82YaQLi532m4ETPbj8o6f8Ldx1YlGU2lnvzfWvudmS1jZqc0VtzaYuA4GaJkEeDbVdMXUDln2DUGdTqI9dYYarcwaWbdey2VAb/PtfDo9+pyDSTcqEiuzX+QlVET68M7qGynx5nZjhnL2ofKDcgJ5AStzeyRVJmybpzfRaWl1FfMbPOMNEkvlJGpj+6smr6omR2btU1XpdsX+FZ864TtuTHu3tCLEAX3+PoroQlamdfgqnzWJTRVcsLTKgZnLOuS1LLOqVGWx+LfFwl9ILcmNOcaRbgbmqT7XM73WpkQgU3STSAEHXYk9MndiRAxvI7QL9GBdTLy2Z8QPPH490bCXeStYpk+C/yYcGF/QMb8k+O8MwgHyc0Jg85tAKzRTWU+P5XHVEJkfNv4+mYs6xzC3UInXtt3YdsZSBgsK1nWzcC+hG5onyJs4PMJXctej2nG5uSV5DG6xjJHpdIOyZg+Nk6bUiOf4al8RuSkGZdK8zvCHdyPx+/4S8KJwUOpNKMy8vhhavpz8fcbRujeeBqhxdt8Qh/you9V+L1L/l4jUnkMLznPNal5phMuvveO62EYYeyD8wmRaye0UqjO46hUHrcRIusbUtkPBlal/0Uq/SOEC9uPE05Mfkw4YUuv907bDB3rkk6/S1e2h7LbWFVemdt7Hb/ZUvF3S7/+L5X/RdXTC9bFM4TB5z5DqAs+Ftfp+VT2TyecrKxYUKYptbZFwkXkxFS63xL2m60IF8ZPp6b9uMY6+HEq7dNx/q2A/WK+ybSJhMEny67bZDu7riDNGan8LyPUxUem1tfTgNX5m16RyvPTdcy3OqHOcUIT8S7VDzX2m2buB7ul8poBnEI4Zm5LeOTwFMKF2YfbSUYeqxDucDqhKfZVhEFOtyDUC8m+ML7oe9X63l3cN2+N+c2J38OBO0rMt0v8Lk4IbF8B7A5sSRhr4+FUWf8OLF6wT+cdd76emv5qXN87xHX/ZcJ5VfV6G56znGSbe4JwPrQxlbp7+XrWMZW6Y2wXt6m6jiVV86brCgceLfk7j0nNc2iD28yWdK7PZ8a8/5sxbbuMPC6K29zdwFcJrcq2IHSbOSxul8lv5oQAeF559kql+32J8vcnnFMm2/1ydXz3iwjnOvcTLoT2ItTjWxLOrc4nXOAm5ZkKrFUybyM8Ut2BUwvSXR/TzCWcf20X12FSx9Tcf3PyTa5J3orLOIHQtfhjqd/ll4T9Pfl+P8vIZ4nU9KtKrM8k7Ucz9ts5dKxj9ozr+uC47VTv/52WR9inkumnlVgP6e3p0fh3AqERwBaEY8KPUuvhA8K4Nll5fYzKNaUTei18If5mH4/r9yRCN8zZwLwG9stkm36/RNrFCNe4yTFp3arpp6fK/G9C4HH71LaWzJte953WQdW6f5xw/pSue5etZ7shtJ5J0swidEPbiXBMPpZwfZJMz90PaG59uFtqW5hNaJm3QyzXBalt+D3CE7Ty8nmkaF3GNNel0rwDfJdw3N08bkvfIYwxnKT5dcH+OZVwXvp5wjnh5rHcxwC/SeXhhId7dHkdfbjshjPoeNJQz2tE1cafnLDNA3bIWdaiVII484GdC8oyivCElAU5y58PfKvGd1uF0BWhzPd5H1g9J59PUQmuFL2ygj0jCtKP7Y4yE1p8/bRgvjmEC5bRyWcN7qzvFCzrRULFNCXvO8d8kvSjayxvVCrtkIzpY+O0KTXyGZ7KZ0ROmqGEE+W87/Z7wonmh9tsRh5LU9nms15zCQeDWt+rcHrJ3yq9LQ4vOY8Rnjwwt+Q22elEi3CH5+mCeYZXpV+O0HQ7L/0EwqM5c7cZ2ifYk/4epV4N5vEAsFqNMk0psy0SgtcTaixvNLBIjeUtQscAYNbrr1Sd8NbIc9c43zQKLlgIx7aHcpb5NrB1nb/n4oTuTU64yOt0IV9j/iQQMJ/UzQJ6YbAnpklfkFS/ZhGCB6Pztt2Yx+foeJFU/fobYTDQ3O9V63t3cd/cP6MsB5ac99N0PLHMej0MrJAz/5BUulEZ0/vT+aQz/VpACPyOSH02PGdZPy/IZ1RV2lq/wRQK6sVa2xR1Hksy1nk67bElf6tmXtwU7Q9Zr6wL8LJ5zCEENHKD0dQf7Elv850uhpr43f9M1UV0jbxPjPP9k/CU07x0K1IJdFa/XqbGsa8g32fq+G7zCK0mOh33aFKwJ07/ApWgctYredx80bbWSLDnMFJ1e8brPeDgGvltQmiRVWa9Tm9gvywd7InpT0gt95dV0xYjBKbyyrmAcLxO55EXoLipIJ/TUulKbTeEoH+tc/lfU3DTjCbWhzG/Qym+hnwL2LdGHmWCPYvW2B6r18GAGvtnrdds4JuNrp/k1Vu6cV1MZVDhc9y9eqR7ADw0gTuMcJLXj9Cdq1PXllT67xD6Vt9OiPjPJdz9uAXY3t2/W1Qod/+Pu+9KuPPxM+BZwgnWfMKJ9hOELmBHEyrLl3PyuQdYm3A3YhzhAmEe4W7ec4SI/WeAezLmHU2oAO8kBA7mVqdpdpndfYG7H08IUt1DGBV8DiHKfD0wzPNH56+Lu99P+O1/FvP/gHAxM5HwJISPe/5glb2ah8FatyAcmF8grMO3CCcixxJauBT29/fQFWQnwrbzOGGbeY/QF/Q6wm/x4276Cg3z4FzC9n8e4bu/Ttj+3yPcpfgD4bfe1N2rxz3Bw5gIOxAuLh4nbM+5T6HxMKjddoQ7D0/H5cwibFOnEPb9aXnzSyf/IdRP3yUEc14gbMfzCC0uniQ0893N3Xd191fyMqqHu/+X0JLgeMJF3DRC/fcK4a783u4+wmt0u3L3+e5+FGF/uy3OPzfmNzbmv52Xb+6/OKGFCISDce4gih66du1B2BYnxeVOJ5wMDPP6n4p1AJUBi+/yMKh+PZKuXP3oA49hd/fTCC1w7iNsc3MIF/xXA1u4+10l8riBUB/cTDj+f0D47f9MuFO6rRcPQtxdfkfHpxZOJ+McIIu730kY2/B8Qr02g8pjZe8m3Anfsav1nIduX58m7Bt/IdSfyTnAGMK+fnrJ7I4l3EF/kPAdm/lUp7rUeyypcjeVJ6TMori7XW/2U8LvMZqwDl4hHCPnEFoK30/oXrKWu1/k8SqlSdJ1Tr0DM19EuLD7CWHf/RfhIu8DwnY1kfDddids+5PKZGpmHyVsD07ojpu7fXromrwtoaXLK1T2uWsILQe6dOxz9w0JrU2+SThGPUU4X59P+G3+Q/hdzgE2cvev1TruNcrdf0bYV24lbBfzqIxp96U4rVuetlQpgo8gPO7+j3HZcwl10LXAZu5eOAaLhzFfNyV05RlD2GZmE75Lcp1xDaGHxWrd8i2yJdc7AIeluyfGc4Z9COv4UcI2PodwrvwrQiOHUSWX83lCgOYhwm/V0Dbj7pcTnkz1Q8K5dfq4cDOwp7t/1pv/dL2iMo0h/MbfJ1zvvhNfTxFiC5u4+91NWM4HcXvcjlDPPEFlH32bsD5+Rrhh8FnPGMstrpetCC2+74x5JPvWbMJvfA+hO+na7t6M4VKAGLFvF3H8h8nx7Tl17BAiIiIiIp2Y2eqEQGM/4Op4Q0xEmiSOQZY8VOGweCEvIg3qLS17RERERER6o2OonDP/tJUFERERKUvBHhERERGRDPHR2sljph9x97qeoCciItIqhY//EhERERFZmJjZqoQBnVcjPGRghTjpvJYVSkREpE4K9oiIiIiIVPyK8Gj2tJvc/betKIyIiEhXKNgjIiIiItLZ+4Sn+IwmPIVGRESkz2irp3GJiIiIiIiIiCzsNECziIiIiIiIiEgbUbBHRERERERERKSNaMwekWjmzJnq0ygiIiIi0scNGjTIWl0GkVZTyx4RERERERERkTaiYI+IiIiIiIiISBtRsEdEREREREREpI0o2CMiIiIiIiIi0kYU7BERERERERERaSMK9ohIrzFp0iQmTZrU6mIsdLTeW0PrvXW07ltD6711tO5bQ+tdRFpJwR4RERERERERkTaiYI+IiIiIiIiISBtRsEdEREREREREpI0o2CMiIiIiIiIi0kYU7BERERERERERaSMK9oiIiIiIiIiItBEFe0RERERERERE2oiCPSIiIiIiIiIibUTBHhERERERERGRNqJgj4iIiIiIiIhIG1GwR0RERERERESkjSjYIyIiIiIiIiLSRhTsERERERERERFpIwr2iIiIiIiIiIi0EQV7RERERERERETaSP9WF0Cktxs8eFCpdDNmzOzmkoiIiIiIiIjUppY9IiIiIiIiIiJtRMEeEREREREREZE2omCPiIiIiIiIiEgbUbBHRERERERERKSNKNgjIiIiIiIiItJGFOwREREREREREWkjCvaIiIiIiIiIiLQRBXtERERERERERNqIgj0iIiIiIiIiIm1EwR4RERERERERkTaiYI9Ii7366qt86UtfYoMNNmDFFVdk00035bTTTmPGjBml5h8/fjyDBw+u+XrllVe6+ZuIiIiIiIhIb9C/1QUQWZhNnjyZPfbYg2nTprHPPvuw3nrrMXHiRK666iruu+8+/vCHP7DssssW5rHGGmswcuTIzGlPP/00v/nNb9hoo41YbbXVuuMriIiIiIiISC+jYI/Uxcw2AL4E7AqsDgwA3gAeB24HbnD3OTGtZ2QxF/gvMA64yN2fqcp/OPBA1TwfAK8DfwG+7+5/ySiXAZ8BPg9sDSwPzAKeieW6yt3frf8bd69TTjmFadOmcfHFF3P88cd/+PkZZ5zBlVdeyXnnncdll11WmMeaa67J6aefnjntmGOOAeDII49sXqFFRERERESkV1M3LinNzM4CngJOBt4GfgF8D/gdsAFwLfDnjFnPSb2uBF4FjgT+amYfy1ncS6l5fghMBg4GHjKzA6vKNRj4A/BrYBdgbCzXr4FVgUuBp8xs4y587W4zefJk7r//ftZYYw2OO+64DtNOP/10llxySW6++WZmz57dpfynT5/O3XffzYABAzj00EObUWQRERERERHpA9SyR0oxszMIgZeXgc+6+6MZafYFTqn+3N1HZaT9ESFo9DVgRMYip1TPZ2bnAGcRgjd3xM/6EYI6uxMCPke4+/TUPP2Bc4HTgXvNbAt3f63mF+4B48ePB2C33XajX7+Ocdell16aYcOGcf/99zNhwgR22WWXuvO/8cYbmTNnDoceeiiDBw9uSplFRERERESk91PLHqnJzIYAowjdqfbJCvQAuPvdwF4ls703/l2hjqL8OP5dy8yS+Q4nBHpeBA5KB3pimea5+xnAzcAqwPl1LK9bTZo0CYChQ4dmTk8+f+GFF7qU//XXXw/A0Ucf3aX5RUREREREpG9Syx4p42hgUWCMuz9ZlDAZr6eE3ePfCXWUw9KLin+T/k+X1hiT51zgEODzZvZld3+/juWWkgRvynr55ZcBeP/99zPnnTdvHgAvvvhi3XlPnDiRSZMmMXToUJZddtm652+1vlbedqH13hpa762jdd8aWu+to3XfGlrv3WfddddtdRFEei0Fe6SMHePf+7oys5mNSr1dhjCA8g7A3YSxdcr6Uvz7L3d/I3bR2jZ+9qeiGd39aTP7D6F1z1bAQ3Ust8+54447ADjwwANrpBQREREREZF2o2CPlLFy/PtKF+c/O+Ozp4Gb3H1WzjxDUkGiJYFhwE7AAuCb8fNlgcXi/y+XKMfLhGDPKiXS1q3eOwurr746AEsssUTmvP37h91z6NChdeX91ltvMXbsWAYMGMDJJ5/cp8brSe586S5Nz9J6bw2t99bRum8NrffW0bpvDa13EWklBXuk27n7h92vzGxJYGPgIuBXZraxu5+ZMduaVIJE84BphEeoX+ruD3dzkXtEcuB/8cUXM6cnn6+zzjp15ZsMzHzYYYf1qUCPiIiIiIiINIcGaJYy/hv/rtpoRu4+290fAw4CZgPfMrPVM5KOc3eLr0XdfRV3/0xVoOdNYG78PyuPakma/3T5CzTRTjvtBMD999/PggULOkybNWsWjz76KAMHDmSrrbaqK99kYOYRI0Y0pZwiIiIiIiLStyjYI2Uk49t8olkZuvsM4DlC67ItupjHPCB5MtjuRWnNbENC96051DcodLdZa6212G233fj3v//NNddc02HahRdeyOzZsznkkENYcsklP/z8+eef5/nnn8/N8+GHH+a5555jo402YtiwYd1WdhEREREREem91I1Lyvg5cDrwGTPbyN2fzktoZovX8USuj8S/jQQdryWM5fMNMxvt7u/lpPvf+PeX3fEkrq669NJL2WOPPRg5ciTjxo1j/fXXZ8KECYwfP5511lmHb3/72x3Sb7PNNgDMmDEjM7/Ro0cDcNRRR3VruUVERERERKT3UsseqcndpwCjCIMh32Nmmf2KzGwv4Hdl8jSzA4C1gA+ARsbg+RXwALAOcKuZfSQ90cwWMbNzgcMJ3dG+3TmL1llrrbV44IEHOPzww5k4cSJXXHEFkydP5oQTTuBPf/oTyy67bOm8ZsyYwV133cWAAQM45JBDurHUIiIiIiIi0pupZY+U4u4XxEednw381cweJnSHegdYCdgZWJeMLlJVj15fEtgI2Du+P8PdX2ugXPPN7DPArcA+wL/M7B7gJcLTuvYkBJWmAPu5+9SuLqu7rLbaalx55ZWl0ua16AEYPHgwU6f2uq8nIiIiIiIiPUzBHinN3c81s18DJwG7AkcDSwDTgb8DFwM3ZMyafvT6fMKTtX4DXOHuf2xCud4ys92BzwKfJ4wttBwhEPUM8GPgJ+7+bqPLEhEREREREentFOyRurj7M8CXS6a12qk6zTMW6Mp8DtwSXyIiIiIiIiILLY3ZIyIiIiIiIiLSRhTsERERERERERFpIwr2iIiIiIiIiIi0EQV7RERERERERETaiII9IiIiIiIiIiJtRMEeEREREREREZE2omCPiIiIiIiIiEgbUbBHRERERERERKSN9G91AUR6uxkzZra6CCIiIiIiIiKlqWWPiIiIiIiIiEgbUbBHRERERERERKSNKNgjIiIiIiIiItJGFOwREREREREREWkjCvaIiIiIiIiIiLQRBXtERERERERERNqIgj0iIiIiIiIiIm1EwR4RERERERERkTaiYI+IiIiIiIiISBtRsEdEREREREREpI0o2CMiIiIiIiIi0kYU7BERERERERERaSMK9oiIiIiIiIiItBEFe0RERERERERE2oiCPSIiIiIiIiIibUTBHhERERERERGRNqJgj4iIiIiIiIhIG1GwR0RERERERESkjSjYIyIiIiIiIiLSRhTsERERERERERFpIwr2iIiIiIiIiIi0EQV7RERERERERETaiII9IiIiIiIiIiJtRMEeEREREREREZE2omCPiIiIiIiIiEgbUbBHRERERERERKSNKNgjIiIiIiIiItJGFOwREREREREREWkjCvaIiIiIiIiIiLQRBXtERERERERERNqIgj0iIiIiIiIiIm1EwR4RERERERERkTaiYI+IiIiIiIiISBtRsEdEREREREREpI0o2CMiIiIiIiIi0kb6t7oAIr3d4MGDWl2EhchWrS7AQkrrvTW03rvDjBkzW10EERERkZZTyx4RERERERERkTaiYI+IiIiIiIiISBtRsEdEREREREREpI0o2CMiIiIiIiIi0kYU7BERERERERERaSMK9oiIiIiIiIiItBEFe0RERERERERE2oiCPSIiIiIiIiIibUTBHhERERERERGRNqJgj4iIiIiIiIhIG1GwR0RERBZad955J6eeeip77703q6++OoMHD+aLX/xibvpZs2Zx3nnnsfXWW7PSSiux5pprctBBBzFu3Li6ljt+/HgGDx6c+xo1alSD30xEREQWZv1bXQARERGRVrnkkkt48sknWWqppVhllVWYNWtWbtoZM2aw11578eyzz7Lhhhty9NFHM3v2bH7729/y6U9/mssvv5wjjzyyruXvsMMO7Ljjjp0+32677er+LiIiIiIJBXuk25mZV300F3gbeBn4G3AbcK+7z8+YdzRwFHC0u4+usZxRwNlVH88BXgHuBy5w9yl1fwEREWlbF1xwAauuuiprr702Dz30EPvtt19u2gsvvJBnn32W/fbbj5///Of07x9Oo8466yx23XVXRo4cySc+8QlWXXXV0svfcccdOf300xv+HiIiIiJp6sYlPemc+PouMAaYAXwe+C3wiJmt16TljEst6xpCwOc44G9mtm6TliEiIm1g5513ZujQoZhZzbT33HMPAGecccaHgR6AFVZYgZNOOon33nuPG264odvKKiIiIlKWWvZIj3H3UdWfmdlKwI+AzwJ/MrOt3P31Bhc1Nr0sM+sH/AbYBzgDOLrB/EVEZCH02muvATBkyJBO05LPxo0bx8iRI0vnOXnyZK6++mpmzZrFiiuuyPbbb8/QoUObUVwRERFZiCnYIy3l7q+Z2aHACsBwQjDma01exoLYHWwfYOtm5i0iIguP5ZZbjqlTp/LSSy+xwQYbdJg2ZcoUAF544YW68tmGp58AACAASURBVLzlllu45ZZbOny2//77c/nllzN48OCGyisiIiILLwV7pOViMOZ8QrDnMDP7urtXj/PTLB90U74iItILTJo0qctpX331VQDefvvtzHyGDRvGnXfeyZlnnsl3vvMdFllkEQDeeustLr/88g//L1OG2bNnc/LJJ7PDDjuwyiqrMHfuXJ5++mmuvPJK7rrrLl566SWuvvpq+vVrvx739fxG0lxa962h9d591l1XIzSI5FGwR3qLh4B5wIrAEGByszI2s0WAY1LLERERqdsJJ5zAI488wn333ceUKVPYeuutee+993jwwQdZYYUVmDp1aungzNChQzt01xo4cCDbb789m222GUcccQT/+Mc/GD9+PLvsskt3fR0RERFpYwr2SK/g7nPMbDqwEqFLVyPBnuHxyVwAywKfBDYAngbOa6ScIiLSu5W5y5vcZa9OO3XqVACWWWaZzHzWXXddxo8fzyWXXMLvf/97brvtNpZbbjkOPvhgTjzxRLbYYgtWWGGFhu80H3744Xzve99j8uTJHHvssQ3l1ZvkrXfpflr3raH1LiKtpGCP9CbJo1Aa7cK1S3yl/R0Y7u4zG8xbREQWYiuuuCKXXHIJl1xySYfPx40bB8AWW2zR8DKWW245AN59992G8xIREZGFU/t1BJc+ycyWILTCAZjWYHbnuLsBiwBrAJcDHwNuiU/mEhERaaoxY8YAcPDBBzec14QJE4Dsp36JiIiIlKELX+ktdiS0NHvN3ac0I0N3X+DuL7v7V4FbgT2Ak5uRt4iILHwWLFjAO++80+nzMWPGMGbMGIYNG8a+++7bYdr06dN5/vnnmT59eofPH3/88cxl3Hzzzdx+++0stthiHHDAAc0rvIiIiCxU1I1LWi62tjkzvr2xmxZzCrAfcJaZjXb3t7tpOSIi0ofcfffd3HPPPQC8/vrrADz22GOceOKJQOhSdf755wOhW9V6663H8OHDWWuttejXrx+PPvoojz32GOuvvz6jR4/uNEDz1VdfzcUXX8zIkSM5/fTTP/z8yCOPpH///nz84x9nlVVWYc6cOfztb39j4sSJ9O/fn8suu4w111yzJ1aBiIiItCEFe6SlzGxF4ArCY9f/DVzQHctx93+b2TWElj2nAGd3x3JERKRveeKJJ7jppps6fDZlyhSmTJkCwOqrr/5hsGfxxRfnoIMO4pFHHmHs2LEArL322nz729/mxBNPZODAgaWXe8wxxzB27FgeffRRpk+fjruz8sorc/jhh3PiiSey6aabNuX7iYiIyMLJ3BsdC1ekmJklG9k58W8/YDCwMaH71mLAY8AR7v5C1byjgaOAPwMdpqXc6O73xidwnU0Ys2dURjlWBl4kPOJ9bXd/Iz195syZmTvD4MGDir+giIj0GjNm1B6HX0/IaQ2t99bRum8NrffWGTRokNVOJdLe1LJHelLSmmYuMAt4CbgeuA24190XFMy7Q3xl+Ttwb62Fu/t/zewnwDeA0wktfERERERERETaioI90u3ik7G6Ou8IYETJtKOAUTXSnIKCPCIiIiIiItLG9DQuEREREREREZE2omCPiIiIiIiIiEgbUbBHRERERERERKSNKNgjIiIiIiIiItJGFOwREREREREREWkjCvaIiIiIiIiIiLQRBXtERERERERERNpI/1YXQJrLzDYBdgQWB/7o7k+3uEgiIiIiIiIi0oMU7OljzGxP4GzgIXf/VtW004DzqLTYcjM7090v7uFitpUZM2a2uggLjUmTJgGw7rrrtrgkCxet99bQehcRERGR7qJuXH3P/wDDgCfSH5rZx4DvAIsArwJTCL/vBWa2Qw+XUURERERERERaRMGevmdY/Htv1edfBAy4HRji7kOBK+JnJ/Vc8URERERERESklRTs6XtWBOa6+2tVn+8FOHChuy+In50f/6plj4iIiIiIiMhCQsGevmcw8F76AzNbGRgCTHf3icnn7v46MAtYqScLKCIiIiIiIiKto2BP3/M2MMjMlkx9tlv8+1BGegfmdHupRERERERERKRXULCn7/ln/PsFADMzwng9DjyQTmhmHwGWAf7bkwUUERERERERkdZRsKfvuZ4w6PKlZnYP8BiwE6Fr15iqtDvHv8/0XPFEREREREREpJUU7Ol7fgHcBPQH9ga2BOYCJ7v7tKq0n4t/7+u54omIiIiIiIhIK/VvdQGkPu7uwBFmdhWwLWEMn/vc/YV0OjNbFJgC/BC4q6fLKSIiIiIiIiKtoWBPH2Nmy8R/H3b38Xnp3P0D4NSeKZWIiIiIiIiI9BbqxtX3zADeBFZpdUFEREREREREpPdRy56+5x1gnru/3OqCiIiIiIiIiEjvo5Y9fc9kYKCZKVAnIiIiIiIiIp0o2NP33AIsChzQ6oKIiIiIiIiISO+jYE/fcwkwAfipmX2i1YURERERERERkd5FXYH6ntOA+4ENgXvN7J/AX4BpwPy8mdz93J4pnoiIiIiIiIi0koI9fc8owAGL7zcHNitIbzG9gj0iIiIiIiIiCwEFe/qe6wnBGxERERERERGRThTs6WPcfUSryyAiIiIiIiIivZcGaBYRERERERERaSMK9oiIiIiIiIiItBEFe/ooM1vLzC43s2fM7B0zm1c1fbCZnWVm3zazRVtVThERERERERHpWRqzpw8yswMJAzUPpPJUrg6DNrv7DDPbDdgJeBq4rUcLKSIiIiIiIiItoZY9fYyZbQD8ClgSuBrYGXgjJ/k1hGDQvj1TOhERERERERFpNbXs6XtOBZYALnP3UwDMbH5O2j/Fv9v0RMFEREREREREpPXUsqfv+QShy9Z3ayV099eA2cDq3V0oEREREREREekdFOzpez4KzIqBnDLmAIt1Y3lEREREREREpBdRsKfvmQ0saWaL1EpoZksDg4E3u71UIiIiIiIiItIrKNjT9zxF+N22LJH2kJh2YreWSERERERERER6DQV7+p5bCE/YOs/Mcn8/M9sUuIgwvs+veqhsIiIiIiIiItJiCvb0PT8F/gnsDtxnZgcSn6pmZpua2b5m9mPgEWBZ4M/Aza0qrIiIiIiIiIj0LD16vY9x9w/MbC/gLmAXYOfU5L+n/jdCwOcgd/ceLKKIiIiIiIiItJBa9vRB7j4V2B74IvAw8AEhuGPAAuAx4ERgZ3d/o1XlFBEREREREZGep5Y9fZS7zwOuBa6NT+ZalhC8mx6niYiIiIiIiMhCSC17+pisQZndfb67T3P317ICPWa2Ws+UTkRERERERERazTScS99iZte7+5F1pB8C3O/ua3dbodrEzJkzM3eGwYMH9XRRRERERER6tRkzZra6CLkGDRpkrS6DSKupZU/f8zkz+1GZhGY2FHgQWLN7iyQiIiIiIiIivYWCPX3PNOAkM/tOUSIzWx8YB/w/e/cdZlV19n38e4sSFQgoohgVUYM1UZ9o1FixBCW89m6sryXGgiXFxzeJNY/GJER9sCQxMWKNLahYosYeo7G3qBEV1FgAQQYLiMD9/rHP6DDMMDMwM6fM93Nd5zqcvdfe5z5r9gDzm7XXWpFi+XVJkiRJktQFGPZUnyHANOC/I+JHTTWIiHWA+4GvlJ6HdlZxkiRJkiSpvAx7qkxmPgsMA2YAv4iIIxruj4j1gPuA5YC/AcMy8+NOL1SSJEmSJJWFYU8Vysx/ALsCnwEXRcS+ABHxTeBeYBngDmDHzJxetkIlSZIkSVKnM+ypUpl5F7AfkMBlEfET4C5gKeAWYNfM/LSMJUqSJEmSpDIw7KlimfkX4AhgUeAMoDfwF2CPzJxZztokSZIkSVJ5GPZUucz8E3AiEMA1wF6ZOau8VUmSJEmSpHJZtNwFqHkRMbsNzRPYB9gnIubZl5l+rSVJkiRJ6gIc2VPZop0efp0lSZIkldWpp57KTjvtxDrrrEP//v0ZOHAgW2yxBb/4xS+YMmXKXG2///3v06dPn/k+dtppp1a/d0T0ioj/iYiXI2JGRHwQEXdGxLbt/TmlShCZWe4a1IyIWLm9zpWZb7TXuWpVXV1dk98Mffr07uxSJEmSpIo2dWpdm4/p168f6623HmussQb9+vXj448/5oknnuDpp59m+eWX5+6772bFFVcE4NZbb+X5559v8jzXXnst48eP58wzz+TYY4+dZ3/v3r3nutUhIpYC/g6sDfwL+BvQE9iZYiXjwzLzj23+QFIFM+zRAouIli6eQzLzsiaO6wm8A/QCrsjMA1t4n72B/wt8A+gD1AETgSeBuzLzioj4KjC2jR9hi8z8e/0Lwx5JkiSpdRYk7JkxYwaLL774PNvPPPNMRowYwaGHHsqIESNaeN+prLXWWsyePZuXXnqJvn37ztOmibDnfGA4xWI2e9fPcRoRywJPUAQ+q2fmf9r8oaQK5Twuag+nN7P9mWa270sR9CSwR0Qcl5kfNNUwIi4FDgE+AW4FxgPdgDWAnYAtgCuAKU3UsQjws9L7nNHE6d9spj5JkiRJ7aypoAdgl112YcSIEbz22mstnuPaa69l+vTp7L777k0GPc3YtfR8SsPFbDJzYkT8BjiX4pfLTf3MIFUlw54qExFLAN8Epmfm4y20/SawBPBYZs7oqJoy87Q2HnIEMBv4DfAj4ADgfxs3iojBFEHPG8CmmflOo/3dga1KNUwBTmu0f1GKsGfOAtQoSZIkqRP89a9/BWCdddZpse2oUaMAOOigg9ryFv1Lz683sa9+27YY9qiGGPZUn/2B3wLnAfMNe4DDgUNLj8s6tqzWiYj1gQ2BO4BfAcdT1DlP2ANsWnq+oXHQA5CZM4G7O6hUSZIkSR1g5MiRfPTRR0ybNo1nnnmGRx55hHXWWYcTTjhhvsc99thjvPjii3z1q19lyy23bMtbvg8sD6wCvNho36ql5zXackKp0hn2VJ89Ss9XtKLt74HDgL2okLCHYlQPwGWZOSkibgd2johvZeYjjdpOLj2v3nnlSZIkSepII0eOZOLEiZ+/3m677bjoootYZpll5nvcZZddBrR5VA/AbRQ/F50eEftk5myAiOgH1CdMS7X1pFIlc4LmKhMR44HlgCWzhS9eRCxCMdfNO5m56vzaLmAt9e/f1Jw94xtPzhwRSwLvUtzCtXxmfhoRuwCjKcKfQxq1HwC8QDG/z83AtRSjmV5r6bOXjl8U+AyYnZktBptO0CxJkiS1zuOPP7HQ55g8eTLPPfccF1xwAZ988gnnnnsua665ZpNtP/roI4YOHcrs2bO5/fbb6dOnD4MGDWqybRMTNC8P/BNYieLni3uAHhSrcb0NrA/MyMwlFvpDSRVikXIXoDbrD3zUmrAjM+cAH/LFPaod5dQmHgc30W5v4MvANZn5aWnbbcAkYK+ImCtVycw3gd2AcRR/EV9NseLW1Ii4IyL2KwVakiRJkqpM37592Xrrrbnggguoq6vj1FNPbbbt7bffzowZM9h6663p06dPm94nM9+lmPf0QopfJB8FDKP4ZfKepWYTmz5aqk6O7KkyETGRYojhlzNzegttlwCmAdMys9VT1behlgTIzGipban9I8AmwDcz84kG288DjgOOzsyLmjhuEWBzYEuK5dc3A5Yt7b4T2Kk0f0/j4xzZI0mSJHWABVl6fX622GILnn/+eV577bUmV9nafPPNeeGFFxgzZgxbbLHFfM/VeGTP/ETENhQjfW7MzD1aai9VC0dFVJ/nKL5uu7Wi7e4Uy5S/0KEVtUJEfJ0i6HmhYdBTclnp+fCmjs3MOZn5YGb+PDN3oxipNJQifd+eL+YBkiRJklSF3nvvPQC6des2z74nnniCF154ga9+9astBj0L4MDS89XtfWKpnAx7qs91QAC/iYhm1yaMiK9RLG2epWPKrT6Q+VpEZMMH8HRp3/oRsVFLJ8rCX4FTSpu26YB6JUmSJLWTV199lbq6eUcDzZkzhzPPPJNJkyax8cYbN3mLVmsnZq6rq+OVV16pn6PncxGxSET0bNw+Ig6gCHv+AdzU+k8jVT5X46o+lwLfB9YDHo+ISymWMX+ztH9l4DsUc+YsDjxPsSpX2ZRuJ9ufYmLmy5ppthIwhGJ0z2OtPPWH9W+xMPVJkiRJ6lh33XUXZ5xxBptssgkrr7wySy+9NBMnTuThhx9m/PjxLLfccpx//vnzHDdt2jRGjx7Nl770Jfbbb7/5vsett97K0UcfDXA2c88huiQwISLuBl4D5lBMDfEt4CVgz9J8p1LNMOypMpk5KyKGAbdSzBr//dKjsQCeoZjP5rNOLLEpewJ9gDGZeVhTDSKiD/AOsE9EnJiZH0bEd4DuwK2ZOatR+14U8/wAPNhxpUuSJElaWIMHD2bcuHE88sgjPPfcc9TV1dGjRw9WW2019t57b4488kiWWmre1c+vv/56Pv74Y3bfffcm5/JppU+BP1PMA/rt0raxwE+A8zLzkwU9sVSpnKC5SkVEd+Aw4ABgA74I7mYBTwKXA39sauLidqyhVRM0R8TfKZLznTPzlvm0u4JiBNCRmfm7iPgh8CtgCvAQ8CrF51uJYvb83hRDLrfNzBlNnM8JmiVJkqQO0N4TNLentkzQLNUqw54aUAo1li69nNJ4FEwHvm+LYU9ErAW8CLwLrJSZs+fTdkvgAeCpzNwgIvoBO1Gk7+sCXwF6AB9Q3J52PUWg1eTIJcMeSZIkqWMY9kiVzbBHKjHskSRJklrHsEeqbK7GJUmSJEmSVEOcoLmClW5rAvgkM59otK1NMtNJjCVJkiRJ6gIMeyrb/UAC/wbWbrStLRK/1pIkSZIkdQkGAJXtTYqg5p0mtkmSJEmSJM3DsKeCZebA1myTJEmSJEmq5wTNkiRJkiRJNcSwp8pExJYRsUkb2m+0oJM6S5IkSZKk6uNtXNXnfuBdYIVWtr8WWAm/1pIkSZIkdQmO7KlO0cHtJUmSJElSlXK0R+3rBcwsdxHVbOrUunKX0GWMHTsWgEGDBpW5kq7Ffi8P+7187PvysN/Lx74vD/tdUjk5sqeGRcRGwNLA2+WuRZIkSZIkdQ5H9lS4iDgIOKjR5qUj4t75HQb0AdYGErijg8qTJEmSJEkVxrCn8g0EBjfa1r2Jbc15EDil/cqRJEmSJEmVzLCn8t0EjC/9OYBLgTrg+PkcMweYBvwrM1/t0OokSZIkSVJFMeypcJn5LPBs/euIuBSYnpmjyleVJEmSJEmqVIY9VSYznVRbkiRJkiQ1y+BAkiRJkiSphhj2SJIkSZIk1RDDHkmSJEmSpBpi2CNJkiRJklRDDHskSZIkSZJqiGGPJEmSJElSDTHskSRJkiRJqiGGPZIkSZIkSTVk0XIXoIUTEcsC3wD6lTZNAp7KzInlq0qSJEmSJJWLYU+ViojNgZ8DWzSz/0Hgp5n5cKcWJkmSJEmSysrbuKpQRBwJ3EcR9AQwB5hYeswubdsKuD8ivleuOiVJkiRJUucz7KkyEfFfwAVAN+BhYHugZ2Yun5nLA72AHUr7ugEXlI6RJEmSJEldgGFP9fkBxdftOmBwZt6dmZ/W78zMTzPzLoqRPTdQBD4nlqVSSZIkSZLU6Qx7qs9WQAInZOac5hqV9h1faju4c0qTJEmSJEnlZthTffoBUzPz3ZYaZuY7wFS+WKlLkiRJkiTVOMOe6jMN6BURPVpqWGrz5dIxkiRJkiSpCzDsqT5PUczDM7wVbY8rtX2yQyuSJEmSJEkVw7Cn+vyeYmn1MyPi5xHRu3GDiFg+In4DnEExZ8/vO7lGSZIkSZJUJouWuwC1TWb+JSKuAA4ATgZ+EBHPAm8DiwMDgEHAYhSh0KjMHF2ueiVJkiRJUucy7KlOBwMvAf9NMSfPRk20mQacBfy688qSJEmSJEnlZthThTIzgV9ExEjg28A3+GLFrUkU8/rclZmflKlESZIkSZJUJoY9VSwzPwZuKj0kSZIkSZKcoFmSJEmSJKmWGPZIkiRJkiTVEG/jqmARcW/pj29k5iGNtrVFZua27VeZJEmSJEmqVIY9lW1w6fnlJra1RS50JZIkSZIkqSoY9lS2Q0rPdU1skyRJkiRJmodhTwXLzFGt2SZJkiRJklTPCZolSZIkSZJqiCN7pBb06dO73CV0IRuWu4BOMXVqXcuNJEmSJGkBGfZUsIgY0F7nysw32+tckiRJkiSpchn2VLZx7XSexK+1JEmSJEldggFAZYsKO48kSZIkSapwhj0VLDOdQFuSJEmSJLWJYYIkSZIkSVINMeyRJEmSJEmqId7GVeUiYlngG0C/0qZJwFOZObF8VUmSJEmSpHIx7KlSEbE58HNgi2b2Pwj8NDMf7tTCJEmSJElSWXkbVxWKiCOB+yiCngDmABNLj9mlbVsB90fE98pVpyRJkiRJ6nyGPVUmIv4LuADoBjwMbA/0zMzlM3N5oBewQ2lfN+CC0jGSqtidd97Jrrvuytprr03//v1Zb731OOigg3jsscfadJ63336bo48+mjXXXJNll12Wr3/964wYMYJp06Z1UOWSJEmSOpu3cVWfH1CEdNcB+2XmnIY7M/NT4K6I+BvwZ2AP4ETggM4uVFL7OPXUUzn//PNZeumlGTZsGH379uX111/n9ttv55ZbbuG3v/0te++9d4vnGTduHEOGDGHSpEl85zvfYfXVV+fJJ5/kz3/+M4888gj33XcfSy+9dCd8IkmSJEkdyZE91WcrIIETGgc9DZX2HV9qO7hzSlswEZGteAxu0P7g0rbLGmxbJCIeKG3ft5n3WTUiPoyIKRGxYsd/MmnhTZgwgZEjR7Lsssvyz3/+k5EjR3Laaadx+eWXc+ONN5KZnHXWWa061w9+8AMmTZrEOeecw9VXX81pp53GmDFj2G+//XjjjTc488wzO/jTSJIkSeoMjuypPv2AqZn5bksNM/OdiJjKFyt1VbrT57Nv/PwOzMw5EXEQ8CxwYUQ8mJlv1++PiG7AlUBPYJ/M/E871Ct1uLfeeos5c+awwQYb0K/f3N/KW265Jb169WLy5MktnmfcuHHce++9DBgwgMMPP3yufUcccQSjR4/m2muv5ec//zk9evRo188gSZIkqXMZ9lSfaUCfiOiRmR/Pr2FE9AC+DHzQKZUtpMw8bSGPHx8RxwF/Ai6LiCGZmaXdJwPfAq7OzGsXrlKp86y22mp0796dp556ismTJ9O3b9/P9z388MN8+OGHDBs2rMXzPPTQQwBss802LLLI3IM6e/TowXrrrcejjz7KE088wVZbbdW+H0KSJElSp/I2rurzFMXEy8Nb0fa4UtsnO7SiCpKZlwGjge0oPj8RsSFwCvAWcHTZipMWwFJLLcVpp53GxIkT2XjjjTnuuOM4/fTTOfjgg9ltt93YeuutOe+881o8z9ixY4EiPGrKSiutBMCrr77afsVLkiRJKgtH9lSf3wNDgDNLI3d+lZl1DRtExPLAjygCoSwd05UcAWwKnB0Rf6e4fWtR4KDMnFrWyqQFcNRRRzFgwACOOeYYRo0a9fn2VVddlf3222+e27uaUr/aVu/evZvc37NnTwDq6uqa3C9JkiSpehj2VJnM/EtEXEGxutbJwA8i4lngbWBxYAAwCFgMCGBUZo4uV71tERGnNbNrRmb+orXnycz3I+IwYAzwd+BLwG8y876Fr1JaePWjbFrr8ssv56KLLmLvvfdmzz33ZJlllmH8+PFceOGFHH744Tz44IMMHz7/wX71Ic6ECRPm+/7vv/9+m+vTwrG/y8e+Lw/7vXzs+/Kw3zvOoEGDyl2CVLEMe6rTwcBLwH9TzMmzURNtpgFnAb/uvLIW2qnNbK8DWh32AGTmrRHxAMXqZW8C/28ha5PK4sknn2TkyJEMHjyYE0444fPta665Jr/61a/Yfffdueqqq9htt91YccXmF5mrH7nz0UcfNbm/fnuvXr3asXpJkiRJ5WDYU4VKkw7/IiJGAt8GvsEXK25NopjX567M/KRMJS6QzIz2OldEbANsWXq5IrAx8GB7nV9aGG35LVT9bVtDhw5t8riNNtqIW2+9lWnTps33vBtttBFXXnkldXV187QbO3Ysb731FgCbbrqpvyXrJPW/6bW/O599Xx72e/nY9+Vhv0sqJ8OeChYRpwAfZeZvmtpfWo3rptJDJRHRB7gMmEUxb9EFwKiIWDczPyxnbVJbffrpp0Bxe1VT6pdd7969+3zPs8UWWwBw7733MmfOnLlW5Pr444959tlnWXLJJdlwww3bo2xJkiRJZeRqXJXtNOCHDTdExOsR8Wh5yqkaFwErAadm5m+Bc4CBwLnlLEpaEJtuuilQjPB555135tp399138+ijj7L44ouz8cYbA/DZZ5/xyiuvMG7cuLnarrLKKmyzzTa8+eabXHLJJXPt+/3vf8/06dPZe++96dGjRwd+GkmSJEmdwZE9lS2ZN5AbSDERs5oQEXsD+wIPU4Q8UIRm3wEOjYibMvPWMpUntdnOO+/M4MGDuf/++9l4440ZNmwYyy23HP/+97+58847yUxOPfVUll56aQDeeecdNtpoI1ZaaSWef/75uc41YsQIhgwZwkknncQDDzzAGmuswRNPPMFDDz3EgAED+NnPflaOjyhJkiSpnRn2VLYpQN+I6OXtRy2LiBWAi4GPgAMzcw5AZn4WEQcATwCXRMTXM7Ppe2KkCrPIIotw/fXXc8kll/CXv/yF2267jU8++YSlllqKIUOG8L3vfY9tttmmVedaZZVVuO+++zjrrLO45557uPvuu1luueXYZ599OPzwwz8PjCRJkiRVN8OeyvYoxYiUWyLieooQA2CJiDiwLSfKzMvbu7j2Np+l1wFuysxn5nNsUMzTsxRwWGa+3nB/Zr4QET8DfkkRCO250AVLnWSxxRbjqKOO4qijjmqx7corr8zUqVOb3b/iiity0UUXzbXNJWElSZKk2mLYU9nOALamWD58ywbbvwz8qY3nqviwh+aXXgcYDzQb9lBMxLwdcHNm/rGZNiOAHYE9ImL/zLxygaqUwxyR+gAAIABJREFUJEmSJKmCGfZUsMx8PCLWB44A1gGWAAYDnwGPlLG0dtXWJdcz8zKKUTwNt50PnN/CcXOYOzSTJEmSJKnmGPZUuMx8Ffhx/euImANMycyty1eVJEmSJEmqVIY91edNYEK5i5AkSZIkSZXJsKfKZObActcgSZIkSZIq1yLlLkBtExGzI+LtNrQfFxGzOrImSZIkSZJUOQx7qk+UHm09RpIkSZIkdQGGPbXvS8DschchSZIkSZI6h2FPDYuI/sCywPvlrkWSJEmSJHUOJ2iucBGxJTC40eaeEXHK/A4D+gA7lP78cMdUJ0mSJEmSKo1hT+XbGjgVyAbbepS2zU/9PD1TgNM7oC5JkiRJklSBDHsq3zPAqAavDwJmANfN55g5wDTgX8DozJzcceXVvqlT68pdQpcxduxYAAYNGlTmSiRJkiSpehn2VLjMvBm4uf51RBwE1GXmIeWrSpIkSZIkVSrDnuqzKzAhIrplpqtsSZIkSZKkuRj2VJ/RFLdprQK8VeZaJEmSJElShTHsqT4fAbMy06BHkiRJkiTNY5FyF6A2GwcsGREGdZIkSZIkaR6GPdXnOmAxYJdyFyJJkiRJkiqPYU/1+RXwBPC7iNi23MVIkiRJkqTK4q1A1ee/gXuBtYC7IuI54BFgEtDs6lyZeUbnlCdJkiRJksrJsKf6nAYkEKXX6wHrzqd9lNob9kiSJEmS1AUY9lSfyynCG0mSJEmSpHkY9lSZzDy43DVIkiRJkqTK5QTNkiRJkiRJNcSwR5IkSZIkqYZ4G1cVi4jBwF7AN4B+pc2TgKeA6zLz/vJUJkmSJEmSysWwpwpFxDLAVcB29Zsa7F4F+CbwvYi4G9g/M9/v5BIlSZIkSVKZGPZUmYjoDtxNsdx6AI8A9wL/KTVZEdgG+BbwbeCuiNgkM2eWoVxJkiRJktTJDHuqzzHAesAUYN/MvLuJNj+LiCHANaW2RwPndl6JkiRJkiSpXJygufrsDSRwRDNBDwCZeRdwBMXon306qTZJkiRJklRmhj3VZw1gBjC6FW1Hl9qu2aEVSZIkSZKkimHYU30WAz7LzGypYWbOAT7D2/UkSZIkSeoyDHuqz5tAr4j4RksNI2IDoFfpGEmSJEmS1AUY9lSf2ynm4fljRPRrrlFELAf8kWJ+n9s6qTZJkiRJklRm3t5Tfc4BDqJYev3liLgEuB94G1gcGABsDRwMLEmxatcvy1GoJEmSJEnqfIY9VSYzJ0bEd4CbgP7Aj0qPxgJ4F9glMyd2YomSJEmSJKmMvI2rCmXmY8DawKnA8xS3akXpkaVtpwDrZObj5apTkiRJkiR1Pkf2VKnMnAqcCZwZEYsBS5d2TcnMz8pXmSRJkiRJKifDnhpQCncmlLsOSZIkSZJUfoY9VSIivgTsAmwAfBmYCvwTGJOZs8pZmyRJkiRJqhyGPVUgIjYFrqeYkLmx8RGxS2Y+38llSZIkSZKkCuQEzRUuIlYAbqUIeuonYJ5UvxtYBbg9InqXp0JJkiRJklRJDHsq33FAH4rbtg4ElszM/kAPYDgwHfgKcGjZKpQkSZIkSRXDsKfyfZtiNM/wzLwyM2cCZOaMzLyAYvn1AIaUsUZJkiRJklQhDHsq36oUYc+Nzey/vkE7SZIkSZLUxRn2VL5ewKTMnNHUzsx8o/THHp1XkiRJkiRJqlSGPdUhW9EmOrwKSZIkSZJU8Vx6XWpBnz4udNZ5Nix3AV2U/V4e9nv52PcLY+rUunKXIEmSWmDYUx2Wjoh7F6JNZua27V2UJEmSJEmqPIY91aE7MHgh2rTmNjBJkiRJklQDDHsq36hyFyBJkiRJkqqHYU+Fy8xDyl2DJEmSJEmqHq7GJUmSJEmSVEMMeyRJkiRJkmqIYY8kSZIkSVINMeyRJEmSJEmqIYY9kiRJkiRJNcSwR5IkSR1iypQpXH755Xz3u99l1113ZfPNN2fAgAHssMMOXH755cyZM6fFcxx77LH06dOHPn368Prrr7f6vZ977jnOPvtstt9+e9ZYYw369evHWmutxaGHHsozzzyzMB9LkqSK59LrkiRJ6hA33XQTJ554Iv3792e99dajf//+zJo1izFjxjB8+HD+9re/MWrUKCKiyePvuOMOrrjiCnr27MlHH33Upvc+8cQTeeKJJ1h//fXZcccd6dGjB88//zw33ngjN998M5deeik77bRTe3xMSZIqTmRmuWtQDYqIbsD/BfYHvg70Aj4A3gMeA27JzFtKbQcD9wEPZObgJs41DLiOYiTaPpl5c0QMBMaVmnwMLJ+ZHzZxbACvAquWNm2dmfc3VXNdXV2T3wx9+vRu6eNKktRlTJ1a1+q2DzzwAJ988gnbb789r732GgCDBg1iwoQJbLvttvznP/9h1KhR7LzzzvMc+/7777Ppppuy+eabM2HCBB5++GGeeuopVl111XnaNuV3v/sd3/72t+dpf91113HEEUew9NJL8/LLL9O9e/dWf55qNXbsWKDoe3Ue+718evfu3XSCLHUh3saldlcKem4Ffg+sC9wOjACuBN4F9gN+3MpzHQLcBHwKbJeZNzdqMgvoAezbzCm2pQh6ZrXtU0iSpIW11VZbMXToUBZZZO7/ci633HIccsghAPz9739v8tjjjjsOgF//+tcL9N7f+973mgyG9tprL1ZbbTWmTJnCiy++uEDnliSp0nkblzrCvsAOwLPAVpk5168AI2JJYOOWThIRJwNnAf8BdsjMfzXR7ElgZeBwinCpscMpgqJ7gaFt+AySJKkDLbbYYgAsuui8/x296qqruO2227jqqqtYeumlO+y9u3Xr1u7nliSpEjiyRx1h09LzZY2DHoDM/CQz72vu4CicTxH0vAhs2kzQA8WInT8BG0bEeo3OswywC3AjMKXtH0OSJHWEWbNm8ec//xmA7bbbbq59b775JieffDJ77bUXw4YNa/f3fvzxx3n55Zf5yle+wtprr93u55ckqRIY9qgjTC49r97WAyOiO3ANMBz4B7BFZr7VwmF/AJJiFE9DBwHdgUvaWockSeo4p512Gi+++CJDhgxh2223/Xz7nDlz+P73v0+PHj345S9/2e7v+8EHH3DkkUcCcNZZZzmyR5JUs7yNSx3hL8BJwJER0QsYDTyZmW+0cFxP4DZgO2AMsHdmTm/pzTLz9Yi4F/huRPyowTGHAWMz8/6IOGxBP4wkSfpC/aSzC+rMM8/kggsuYODAgfz4xz+e63xXXnklDz/8MOeddx6TJk1i0qRJAEyfXvzTPn78eGbPnr1A7zt9+nSGDx/Oa6+9xoEHHsg666yz0J+l2nS1z1sp7PeO4+TXUvMc2aN2l5lPU6zCNaH0fCMwPiImR8ToiNixmUM3oAh6/g3s1pqgp4FLgD7AngARsQWwJsWoH0mSVAGuu+46RowYwSqrrMLFF19M795frHj5xhtvcPHFF7Pjjjuy2Wabtev7Tp8+neOPP55nnnmG/fbbj2OPPbZdzy9JUqVx6XV1mIhYDNga2Bz4r9Jzn9Luy4GDMzMbLL3+b+BLwEDgYuDobOYCbbD0+sOZuXnp9q+3gZczc4uIuALYG1gxMydGxJXAd3HpdUmSFkpbll5v6PTTT+fcc89l7bXX5uabb6Zfv35z7b/11lvZf//9W3WuK6+8kv/zf/5Pq9p++OGH7LXXXjzyyCMcd9xxnH766W2uvdq5BHh52O/l49LrkrdxqQNl5mfAXaVH/ZLsuwOXAgdS3N51U4ND3gMOAO4Bvg8sERGHZuacVrzXzIi4HDgxIr4F7AHckpkT2/EjSZKkBXDeeedx7rnnsvrqqzNmzBj69u07T5sBAwZwwAEHNHn8XXfdxYQJE9hll13o1asXAwYMaNX71tXVsccee/D444/zwx/+kJ/+9KcL9TkkSaoWhj3qNJk5G7guIr4O/BTYhrnDHjLzrYjYkiLwORhYPCIOyMxZrXiLS4ATgeuAxWl6KXZJktSJfvnLX3LWWWex1lprMXLkyCaDHoB1112XkSNHNrlv2LBhTJgwgVNOOYVVV111rn2TJ09m8uTJ9O3bd65zT506lV133ZWnn36ak08+mZNOOqn9PpQkSRXOsEfl8GHpucnhlZn5XkRsBdwN7EMR+OydmTPnd9LMfDkiHgK2AMaXjpckSWVy9dVXf77q1frrr8+1117L3XfP/c/zgAED+O53v7vA7/H73/+ec845h5NOOomTTz758+37778/Tz/9NKussgpz5szh7LPPnufYYcOGse666y7we0uSVKkMe9TuImJf4H3gnsa3YEVEf75YIv3B5s6Rme9HxDbAX4FdgJsiYrfMnNHC2x9BMTHzG83N9yNJkjrHG28UC3HOnj2ba665psk2m2222UKFPS2997hx4zjnnHOabDNgwADDHklSTXKCZrW7iDgPOI5iDp6/U0ykDLAKMAxYArgZ2LXRBM0PZObgRufqRbEc+xbAvcBOmflx4wmaW1GTEzRLktQOFnSCZierLR/7vjzs9/JxgmbJkT3qGCOAsRTLqK8LbE8xh85k4H7gauDq1oy8ycwPI2IHinBoO+DOiPhOB9UtSZIkSVLVM+xRu8vMt4ALS4/WtL+fZubvKe3/BPh2o83T5ndME+fYH2jdeq6SJEmSJFWxRcpdgCRJkiRJktqPYY8kSZIkSVINMeyRJEmSJEmqIYY9kiRJkiRJNcSwR5IkSZIkqYYY9kiSJEmSJNUQwx5JkiRJkqQaYtgjSZIkSZJUQwx7JEmSJEmSasii5S5AqnRTp9aVu4QuY+zYsQAMGjSozJV0LfZ7edjv5WPfS5KkWufIHkmSJEmSpBpi2CNJkiRJklRDDHskSZIkSZJqiGGPJEmSJElSDTHskSRJkiRJqiGGPZIkSZIkSTXEsEeSJEmSJKmGGPZIkiRJkiTVEMMeSZIkSZKkGmLYI0mSJEmSVEMMeyRJkiRJkmqIYY8kSZIkSVINMeyRJEmSJEmqIYY9kiRJkiRJNcSwR5IkSZIkqYYY9kiSJEmSJNUQwx5JkiRJkqQaYtgjSZIkSZJUQwx7JEmSJEmSaohhjyRJkiRJUg0x7JEkSZIkSaohhj2SJEmSJEk1xLBHkiRJkiSphhj2SJIkSZIk1RDDHkmSJEmSpBpi2CNJkiRJklRDDHskSZIkSZJqiGGPJEmSJElSDTHskSRJkiRJqiGGPZIkSZIkSTXEsEeSJEmSJKmGGPZIkiRJkiTVEMMeSZIkSZKkGmLYI0mSJEmSVEMMeyRJkiRJkmrIouUuQKp0ffr0LncJXciG5S6gS5g6ta7cJUiSJEnqQI7skSRJkiRJqiGGPZIkSZIkSTXEsEeSJEmSJKmGGPZIkiRJkiTVEMMeSZIkSZKkGmLYI0mSJEmSVEMMeyRJkiRJkmqIYY8kSZIkSVINMeyRJEmSJEmqIYY9kqT5ykxGjRrFtttuyworrMBXvvIVBg8ezKWXXsqcOXNafZ7Zs2dz3XXXMXToUFZffXWWX355NthgA4466iheeumlDvwEkiRJUteyaLkLkCRVtiOOOILrr7+efv36sfvuu7PEEktw//33c+KJJ/LPf/6T3/3ud606z2GHHcbo0aNZYYUV2HHHHenZsycvvvgi11xzDTfccAPXX389W221VQd/GkmSJKn2GfZogURENto0B/gAeA74Q2Ze3Yr2daX2lwGjMrNxGyJiceAYYE9gTWAJYDLwDvAIcH1mPtCg/cHAnxqdZmap/QPALzPzxdZ+TqmrGzNmDNdffz0rr7wy9957L3379gVg5syZHHDAAVx77bUMGzaMnXbaab7neeqppxg9ejRrrbUW99xzD0suueTn+6688kqOOeYYfv3rXxv2SJIkSe3AsEcL6/TS82IUYczOwNYRsWFmnthC+68CuwJbARtShDqfi4ieFAHNN4D3gBtLzz2B9YAjgD6lNo09C9xU+nNvYDBwELBXRGyTmY+29YNKXdGtt94KwDHHHPN50APQvXt3fvKTn3DnnXdyySWXtBj2jB8/HoAtt9xyrqAHYNiwYRxzzDFMnjy5fYuXJEmSuijDHi2UzDyt4euI2Ba4Gzg+Iv43M8e30H4z4EHgqIgYkZnjGuw+niLouQvYMTNnNjp2KWCtZkp7puF7RURQjPg5CDgb2Lp1n1Dq2iZOnAjAwIED59lXv+2RRx5h5syZdO/evdnzrLnmmgA89NBDTJ8+nSWWWOLzfX/9618BHNUjSZIktRPDHrWrzLwnIl6mCGG+CYxvof3DpfZrAxsADcOeTUvPFzcOekrHfgD8o5V1ZURcRBH2bNSaYyTx+WieN954Y5599aN1Zs2axfjx41l99dWbPc/aa6/NUUcdxUUXXcQ3v/lNdthhB3r27MlLL73EPffcw+67785Pf/rTDvkMkiRJUlfjalzqCFF6nmcOnhZ81uh1/T0dzf8E2TYLWpfUZQ0ZMgSACy+8kA8++ODz7Z999hlnn33256+nTp3a4rnOOusszj33XCZPnswf/vAHzjvvPO68806+9rWvse+++9KjR4/2/wCSJElSF+TIHrWriNgOWIMiUHm8Fe23pJjrZybwWKPd1wL7A2dGxEDgNuCpzHx3AeoK4KjSy3+29XiplowdO7bV29ddd1022WQTHn30UTbYYAO22morunfvzmOPPcbkyZPp378/7733Hm+//Xaz54Vi+fYRI0Zwww03cOSRRzJ06FB69erFK6+8wm9+8xv22GMPfvSjH7HXXnu12+esFvPrN3Us+7487Pfyse/Lw37vOIMGDSp3CVLFiiYWQJJa1GB1rYYTLq8B7AJ0A85tOEFzM+3rJ2heFDguM0c28T7DgTMoJlmu9x5wL/C7zHywUfuDKebmaWqC5vWB6UCTEzTX1dU1+c3Qp0/vpjZLVevxx59oU/tZs2Zx1VVXcccdd/DWW2/RvXt3NthgA4455hhOOukkXn/9dW666SZWWGGFZs8xZswYzjjjDPbbbz9OOOGEufa9//777LLLLnTr1o077rhjngmcJUmSmtJc2NO7d+9ocofUhRj2aIE0sZR6AlMpQpY/ZuaVLbRveNyhmdl4ufSGxy4JfJtiDp//Kj3X3+9xZmae0qDtwcy79PpnwLsUq3b9orml1w171FVMnVo31+v63zi29bdjM2bMYMCAAfTq1YvXXnttvm0PPPBAbrnlFq655hqGDh06z/4tt9yS5557jvvvv5/111+/TXVUqwXtdy08+7487Pfyse/Lw34vH8Meydu4tJAys01/kda3j4gewLeAPwK/jYg3MvPeZo75BLi59CAiugOHA+cDP4uIv2TmM40OG5WZB7elNkltc+ONNzJz5kx23333Ftt++umnQDGKpyn1y64vtthi7VegJEmS1EU5QbPKIjM/zsy/ATtS3PY1qjSCpzXHzszMC4FrSpu26aAyJQHTpk2bZ9tzzz3HKaecQp8+fea6LeuTTz7hlVde4a233pqr/aabFovrXXTRRdTVzT2y6NJLL+Xtt99mueWW+3yJdkmSJEkLzpE9KqvMfC4iLgGOBE4A/qcNh39YenaYptSBdt11VxZffHHWXnttevbsyb///W/uuusullhiCa655hqWX375z9s++eST7Ljjjmy22Wbcdtttn28/9NBDue666/jXv/7FhhtuyNChQ+nduzfPPvssDz74IN26deNXv/oV3bp1K8dHlCRJkmqKYY8qwc+BQ4AfRsRFmfkBQEQcCTzT1GTKEbEmsGfp5YON90tqPzvvvDM33ngj1157LTNmzGD55Zfn4IMP5oQTTpjvpMwN9ezZkzvvvJMLL7yQMWPGcMMNNzBz5kyWWWYZdtllF4499lg22GCDDv4kkiRJUtdg2KOyy8y3I+K3wHHAj4GTS7t2AC6OiPHAw8BbwJeAQcD2FCt6/W9mtrjEu6QFN3z4cIYPH96qtltssQVTp05tcl/Pnj056aSTOOmkk9qzPEmSJEmNOGePKsXZwCfA8IhYrrTtx8APgZeBTYDhwNHAesCtwI6ZeVwZapUkSZIkqWI5skcLZEFX4ZrP/gl8sZx6/bZXgBGlR2vf5zLgsrbUJkmSJElSLXFkjyRJkiRJUg0x7JEkSZIkSaohhj2SJEmSJEk1xLBHkiRJkiSphhj2SJIkSZIk1RDDHkmSJEmSpBpi2CNJkiRJklRDDHskSZIkSZJqiGGPJEmSJElSDVm03AVIlW7q1Lpyl9BljB07FoBBgwaVuRJJkiRJql6O7JEkSZIkSaohhj2SJEmSJEk1xLBHkiRJkiSphhj2SJIkSZIk1RDDHkmSJEmSpBpi2CNJkiRJklRDDHskSZIkSZJqiGGPJEmSJElSDTHskSRJkiRJqiGGPZIkSZIkSTXEsEeSJEmSJKmGGPZIkiRJkiTVEMMeSZIkSZKkGmLYI0mSJEmSVEMMeyRJkiRJkmqIYY8kSZIkSVINicwsdw1SRairq/ObQZIkSapyvXv3jnLXIJWbI3skSZIkSZJqiGGPJEmSJElSDTHskSRJkiRJqiGGPZIkSZIkSTXEsEeSJEmSJKmGuBqXJEmSJElSDXFkjyRJkiRJUg0x7JEkSZIkSaohhj2SJEmSJEk1xLBHkiRJkiSphhj2SCURsWJEXBoR70TEpxExPiLOi4ilyl1bLSv1czbzeK/c9VWziNgjIkZGxEMRMa3Up1e2cMymEXF7REyJiOkR8VxEHB8R3Tqr7lrQlr6PiIHz+R7IiPhzZ9dfjSKib0QcFhGjI+LV0vVbFxF/j4hDI6LJ//N4zS+8tva913z7iYhzIuKeiHir1O9TIuLpiDg1Ivo2c4zXfDtoS997zUsqB1fjkoCIWA34B7AscDPwMrARsDXwb2CzzJxcvgprV0SMB/oA5zWx+6PM/HXnVlQ7IuIZYD3gI+A/wJrAVZm5fzPtdwZuBGYA1wJTgB2BNYAbMnPPzqi7FrSl7yNiIDAOeBa4qYnTvZCZN3RYsTUiIo4ELgbeBe4D3gSWA3YDelNc23tmg//4eM23j7b2vdd8+4mImcBTwIvARKAHsAmwIfAOsElmvtWgvdd8O2lL33vNSyoHwx4JiIg7gSHA8Mwc2WD7b4ATgN9l5pHlqq+WlcIeMnNgeSupPRGxNUXQ8CqwFcUPYc0FDl8utetNEW4+Udq+OHAv8C1g38z0t4+t0Ma+H0jxQ8CozDy486qsLRGxDcUPW7dl5pwG2/sDjwErAXtk5o2l7V7z7WQB+n4gXvPtIiIWz8wZTWz/H+D/ARdn5lGlbV7z7aiNfT8Qr3lJnczbuNTllUb1DAHGAxc22n0q8DFwQET06OTSpIWSmfdl5thsXaq/B9AP+HP9DwClc8wAflp6+f0OKLMmtbHv1Q4y897MHNMwbChtfw/4benl4Aa7vObbyQL0vdpJU2FDyXWl50ENtnnNt6M29r0kdbpFy12AVAG2Lj3f1cR/VD+MiIcpwqBNgHs6u7gu4ksRsT8wgCJcew54MDNnl7esLmWb0vNfm9j3IPAJsGlEfCkzP+28srqUr0TE94C+wGTgkcx8rsw11YrPSs+zGmzzmu8cTfV9Pa/5jrNj6blhf3rNd46m+r6e17ykTmPYIxX3qQO80sz+sRRhz+oY9nSU/sAVjbaNi4hDMvOBchTUBTX7fZCZsyJiHLAOsCrwUmcW1oV8u/T4XETcDxyUmW+WpaIaEBGLAgeWXjb8IddrvoPNp+/rec23k4j4IdCT4hatDYHNKcKGXzRo5jXfAVrZ9/W85iV1Gm/jkop/nAHqmtlfv71PJ9TSFf0J2JYi8OkBfB34HTAQuCMi1itfaV2K3wfl8wlwJrABsFTpUT/Pz2DgHm8jXSi/AL4G3J6ZdzbY7jXf8Zrre6/59vdDilvPj6cIG/4KDMnMSQ3aeM13jNb0vde8pE5n2COprDLz9NJ8DxMy85PMfKE0GfZvgCWA08pbodSxMnNiZp6SmU9l5tTS40GKEYX/BL4KHFbeKqtTRAwHfkCxwuIBZS6nS5lf33vNt7/M7J+ZQfGLk90oRuc8HRHfKG9lta81fe81L6kcDHukL36T1buZ/fXbp3ZCLfpC/aSeW5a1iq7D74MKk5mzgD+UXvp90EYRcQxwPsWyyFtn5pRGTbzmO0gr+r5JXvMLr/SLk9EUIUJf4PIGu73mO1ALfd/cMV7zkjqMYY8E/y49r97M/vrVFJqb00cdo374s8OaO0ez3weleTdWoZhg9fXOLEp+HyyIiDgeGAm8QBE2vNdEM6/5DtDKvp8fr/l2kJlvUIRt60TEMqXNXvOdoJm+nx+veUkdwrBHKu6XBhgSEXN9T0REL2AzinutH+3swrq4TUrP/qezc9xbet6hiX1bAksC/3CFlk7n90EbRcRJwLnAMxRhw8RmmnrNt7M29P38eM23n6+UnutXtvSa7zyN+35+vOYldQjDHnV5mfkacBfFhMBHN9p9OsVvWq7IzI87ubSaFxFrNTUhYUQMBC4ovbyyM2vqwm4A3gf2iYgN6zdGxOLAz0svLy5HYbUuIr7ROGgubd8WOKH00u+DVoiIn1FMCvwksG1mvj+f5l7z7agtfe813z4iYvWImOeWrIhYJCL+B1iWIrz5oLTLa76dtLXvveYllUNkZrlrkMouIlYD/kHxj/PNFEuObgxsTXH71qaZObl8FdamiDiNYgLPB4E3gA+B1YBhwOLA7cCumTmzXDVWs4jYBdil9LI/sD3Fbw4fKm17PzN/2Kj9DcAM4M/AFGAniuV6bwD2Sv/RaJW29H1p2d1BFH8H/ae0f11gm9Kff5aZ9T+IqRkRcRBwGcVv0kfS9IpD4zPzsgbHeM23g7b2vdd8+yjdMnc28HdgHDAZWI5iladVgfcogrcXGxzjNd8O2tr3XvOSysGwRyqJiJWAMyiGN/cF3gVGA6c3+K2Y2lFEbAUcCfwXXyy9PpXiFoArKEZU+ZfUAiqFaafOp8kbmTmw0TGbAT+2CEiUAAAM2ElEQVQBvkURuL0KXAr8b2a2Zji6aFvfR8ShwK4US1QvAywGTAAeAS7IzIeaO4m+0Io+B3ggMwc3Os5rfiG1te+95ttHRHyN4t/QzYEVKZZM/5jil1S3UVzD80yO7TW/8Nra917zksrBsEeSJEmSJKmGOGePJEmSJElSDTHskSRJkiRJqiGGPZIkSZIkSTXEsEeSJEmSJKmGGPZIkiRJkiTVEMMeSZIkSZKkGmLYI0mSJEmSVEMMeyRJkiRJkmqIYY8kSZIkSVINMeyRJOn/t3fnsVKVZxzHv7+ogIIICu4iLm1c6lIX1FgDFdS67xrXklSb1mpt65LaamvVVozaxq2mpqkYcddUrbuAuKERra1L3RWptqKAgMii4NM/3ne8p+PMvXPnzp3rHX+fZHJm5pzzvu8z5yZwnrznec3MzMzMWoiTPWZmZmZmZmZmLcTJHjMzs24iKfJreAPaGpXbmt7lgfUSksbmmKdU2Dcl7xvb/JE1l6TpOdZRPT0WMzMz6x2c7DEzM6ugkKjp7GtKT4/degdJW0s6+6uQsCqStIWkuyXNl/SxpIck7dzBOddLWiZpu2aN08zMrDdbvqcHYGZm9iU1s8r3qwIrAIuBeRX2zym8fyVvP23guKx1bA38GngYGN/OcW+Q/t4WNmFM3UrS14HHgZWBpcAyYBQwWdLoiHiswjm7AkcAV0bE000crpmZWa/lZI+ZmVkFEbFmpe/zzJ2RwE0RMbaDNjZp/MjsqyYiRvf0GBrobFKiZzxwAinhcx5wOjAO+FbxYEl9gCuAD4BfNnGcZmZmvZof4zIzMzOzZhlNms1zckQsiohPSUmcmcBOklYqO/5UYBPg9Ij4sLlDNTMz672c7DEzM+smHRVoltRf0qmSpkqaI2mxpDcl3SnpKEkrdKKvMZIW5P7GVdg/QNIvJE2TNC/39ZqkSyWtV6XNz4sgSxok6QJJL0taKGluJ8a2jaRxkh6TNEPSEkmzc/vHSVqu1rY60efAXA/nn/l3WSDpOUm/kbRKB+d26rrUE5+kAK7OH0dWqP00qnBsuwWaJa0h6eLCtZkn6SlJp0jqW+Wc8bnNsyUtJ+kn+bdamGO+q5vq46wGzIqI+aUvImIp8Dbp/6WDC2McTkoEPQZc0w1jMTMza1l+jMvMzKwHSNoMuBsYnr9aCswH1gM2APYl1TaZXkNbBwI3AH2BMyJiXNn+TYF7gfULfS0BNgZOAo6WtG9EPF6li6HAM8CG+bxPaomx4AHSTT6kujMLSbWPRubXgZL2zzf9XSZpY2AibfGWat1skV9jJY2JiNcqnFvPdaknvpnAisBAUk2nYq0nqPE3ljSCdG1XzV99BPQBts+vYyTtHhHvV2li+RzvHnkcS0gJl72B0ZJ2jYgnahlLjWYDQyQNLCV8cjJsfeAzoDh759IcywkREQ0cg5mZWcvzzB4zM7Mmk7QqcB8pofAWcADQPyJWA1Yi1S25mpRo6KitY4FbaLspLk/0rALcQ7qZvgXYCugXEQOAjYDrSTf3t0kaVKWbX5GKUu8JrBQRA4HOzPp4gFRgd62I6B8Rg4EBwDHAe8BewE870V5VucbLbaR4/w3snvsaAIwBZgDDgL+Wz3rpwnXpdHy5JtTJ+ePUiFiz7DW1hlgHA7eTEj3PAyPytRkAHEpKnGwFXNdOMz8iJYUOBwZExMr5nBeAfsAlHY2jkyYDywGXSOonaXlSzZ41gCcjYmGObT9SYu2yiHi+wWMwMzNreZ7ZY2Zm1nw/J80UmQXsEhHvlnbkGiaP51e7JJ1EuhlfBhwbERMqHHYaKXlxQ0QcWdwREW8CR+Ukx3eA44CLKrTRF9grIl4onPt6R+MrHHtkhe8+BiZIeht4hFSs98Ja22zH4cCWpFkq/zdmYJKkvYBngc2Bo4C/FPbXdV2aHF/RicBawFxg94h4L/e9DLhV0nzgfmBMnqEzuUIbg0ixfr4KVkQ8p7Qc/NPA9pKGRcSMBo35HGAfYCxwNOlvty/pep0BIGlF0t/1f0irlZmZmVkneWaPmZlZ8x2btxcVEwqdIeks0mMunwCHVEn0AHw3by9up7nr83a3KvvvLUuaNExEPEpKVgyXtHYDmjwkb++oNOaIeBG4NX88rGx3l69Lhf4aHV9RKdY/lxI9ZX0/AJQewSqPteTRSsudR8QzwDv54ze6OtBCuy8Bu5BmUC0mPbr1CDAmIh7Jh51FSlD+LCI+krS6pGtyHaSFkiZL2rZRYzIzM2tFntljZmbWRLno7Br54z31NaHfkx4L+hjYPyImVTlwPWDdUl+5KHAlffK2YqFm2hIGdZN0KGkmzTakGkD9Khy2Nmk2R1dsk7cPtXPMZNJjV6Vju3xdmhhfqb8+tCVhOop1JwqxlpnWzrnvkv5+BrdzTKdFxD9IjwR+gaRNgFOAiRFxk6R+pBg2ByaRZl0dCEyRNCInj8zMzKyMkz1mZmbNtUbhfT2Pxgyjrf7LD6slerK1Cu9Xr6Ht8mWvSz6oZWCV5JosN5Nu0EuWkG7al+XPQ0mzjfvX20/B0Lxtb2ZOacbKapKUi//WdV16IL6SVWmboV1LrEOr7P+onXMX523Nq8I1wBV5e2LeHk9K9FwZEScASDoauJZU6+fgJo7NzMys1/BjXGZmZr3Le8DD+f35kjZq59jiv/ODI0IdvIZXaWdZle9rcTwpEbIQ+DGwXkT0i4ihpWLEtM12URf6KVdpZk136Kn4ipoVa7eSdCSwK+kxulfy1/vk7eWFQ68nJSD3qLSsvZmZmTnZY2Zm1mwzC+/Xr3pUdUtIN8CPA+sAkyVVa6fY17A6+mqEQ/P23Ii4LCLeKe7MN+tDGthfaRZSe/GWHm2bXVjSu97r0uz4SuaQ6t1AbbHWPTurGSQNJNWVmg78trCrdC3eKn0REZ/lz/3pnt/WzMys13Oyx8zMrIkiYjppdg6kJbnraWNBPvcp0o3+ZEnrVjjuLdqSGBVrpDRBaVzPVtm/M42dmfL3vP12O8fsWnZsV65LV+IrJWs6PeMnIj4hLY8OnYz1S+o8YE3g5NLy62XKf8MVu39IZmZmvZeTPWZmZs13bd6eImmdehqIiPnAHqSb+A1JCZ+1Khw6Pm9Pba8vJYPqGUsH5uXtFhX6XJ50k99IpZW29pT0zQp9bk7bKlY3l+2u57p0Jb75eVvv716KdWylay9pd1JxZvhirF8a+TqdANwVEXeW7X47b7ctHD8I2JhUoHxWUwZpZmbWyzjZY2Zm1nwXkIrqDgEelbRfXl0JSStIGinpxkqzdYoiYi5pufTngK8BkySVF+IdB7yZ+5oq6TBJn8+KkDRM0vdJSaMDGhRf0YN5e5ak/Us1VvKqS38DRpBu2hvlJtLvAXC7pDGSlPscTVppawXgReC6snPruS5die/FvN1M0g51xHo58F/SLJf7JG2X+15O0sHAjfm4iRExuY72q5I0XVJIGt/FdgRcCXxCqnlUrrQy2vl5Cfa+wEWkmO+PiK7UkzIzM2tZTvaYmZk1WUTMJj1W9Q6wAXAHsEDSLFKh3ynA4dSwamZEzAHGAP8CNgUmSlqtsH8uaQbQS6RHvm4CPpI0S9JC0syJPwFbA9WWZu+Ki4A3gIHA7cAiSfPyeHYDfkADZ2fkx5sOJsU1jJSMWSDpY2Bi/m4GcFBELCk7t57rUnd8EfEa8Ehu70lJs3MSZbqkHWuI9UNSgu5DYEtgmqT5wALSrJ/BpMTXUR211YOOB3YAfpcfOyx3FfAysB3pMbt5wPdIMZ7ZrEGamZn1Nk72mJmZ9YCIeJ60pPSZwNPAIlLB2RmkpMERtC2b3VFbHwCjgVdIN/0PFh/JiojXgdKjMg+RkgOrAEtJyYCrgL2BCQ0IrXxsc4AdSbM3SvEsIsU4MiLGd0OfrwNbAefQVteG/P5cYMuIeLXKuZ26Lg2I7yDgj6SCwwNIBYnXp8Y6RhHxFLAZ8AfgVdKspaV57KcBO0TE+7W0Vav8eFqpMPK0LrQzBDgfeA24sNIxEbGIVJNoAjCXVOfoIWBURLxUb99mZmatTm2LUJiZmZmZtS/POnqC9MjbRuUzpMzMzKzneWaPmZmZmXXGyLy9wIkeMzOzLyfP7DEzMzOzmkm6m/RY4IYRsbinx2NmZmZf5GSPmZmZmZmZmVkL8WNcZmZmZmZmZmYtxMkeMzMzMzMzM7MW4mSPmZmZmZmZmVkLcbLHzMzMzMzMzKyFONljZmZmZmZmZtZCnOwxMzMzMzMzM2shTvaYmZmZmZmZmbUQJ3vMzMzMzMzMzFqIkz1mZmZmZmZmZi3EyR4zMzMzMzMzsxbiZI+ZmZmZmZmZWQtxssfMzMzMzMzMrIU42WNmZmZmZmZm1kKc7DEzMzMzMzMzayFO9piZmZmZmZmZtZD/AfuQeUclDmq8AAAAAElFTkSuQmCC\n", "text/plain": "<Figure size 432x288 with 1 Axes>"}, "metadata": {"image/png": {"height": 277, "width": 573}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "15 out of 20 tickers were removed\n{'PRSP': 41, 'SKM': 101, 'EIX': 12, 'FAST': 121, 'DLR': 16}\nFunds remaining: $18.14\n"}]}}, "97a776adcf08471ebdab45d0c54439f7": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "97afa325baaf4161a5a2abfcf910074d": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "DescriptionStyleModel", "state": {"description_width": ""}}, "97b863f6bc5f41dc9552a739aee72424": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {"height": "40px", "width": "50%"}}, "97bc22ffbdac4e5c945e687147f8ee25": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "SliderStyleModel", "state": {"description_width": ""}}, "98632c3584fa4ea78dc63355bdb30c63": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_afb4cf9f5b1448b095b6ce3fcfe142de", "outputs": [{"data": {"image/png": 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3VVzb1efx51z6U3Kvb6AIaJat+3FJ6zTZHwcoxq5v9BkelvTOLp2DE5qkzf8WjS5Js5JaK3v/Kmm1gvdf0sJ7XdLmZcdNyXYNVdz4bZTna5K+2uI53HF5qAiYZnlc0yTth3Jpz+xgXavn3n9jk7TL5tI+MIBj6shcPud3mg+P7j1oQYsFlrtfLenH6el2qhsrJt0tvERRuXtNUSmYrnIflnSSomJ+mOJu4taKC5tZisrdqWZWOJmNxWQAf1J0dZqjmExgH0VlagtFAfqIYuzoM8xs/5J8dlUEspZT/Oheoriztknapn0ULa2ez73tSUX3l3zLp+PSa/lHnwnZurXNiv12SPr/WcUFSJbHVxR3986R1I2WgAsrgqmXKCpz26bt317R9TgbX2031cZvamTDlNd/FWP3bKHYz19RrTXIMdakVZbiOLtWUVk6RdIHFF3j9pOUzaq5jaJbd68tpBgn8zhJuyhmg91C8d1epjiuVpb0GzMbMQjbI0XFfyfFeEwfV4zz+UFFMC/vF4quSEMU46Aepji/N1Z0F7wqpdtB0uUW3dIzxyq+z2wG6afU/xzYUMVjf3bTbopy42HFMbq5YnbYLytuVtT7tCKg+0fFxfAmiuDDjxUVrqGSxpnZW3q83d0yM/f/8NJU/Zev34Ntyct3/XqwYPn6TZarZHn9eLbZUAp7mdkb9bT0/151aWRmb5X0HcV5eYg36dLWBdnvxMuSLs29ng1zsLJqY042081y72eK8/pCxU2zjSTtrCgzpJjJeDDGx11Yse3fVZRV71eUV7srxuudqTgnf2El3RK74GrVfn8+1STtVqq1qus3m7yZHacoX4YoWg8dq+iCvrniNz7rRnuIaq3+2jVEcfzeoBimY0fFcbB12v4bUrp1JV1l/VtVzytld2Z79e1SfHXJ9mTOTn9XVvNWV9k4po+r8QRY84qrFPU6STrUyluyH69az84zu7Tu0xX1vX8q6uYzUmvML1t73Y+3UNy4flxxbj9v0UPl52ZWNh55Jiur+7QUNLNlVSsn8+X5uxV1yRmSPt/GNrZqkmqTo21i0SOqG/GAwxX13lsk7aso87ZV7dheTNL5DVqiLqyYxOg7kvZUlJubKFou/lAx2dFiks6zmEizmdMVdb/fpvzel57/siT9uYrJkn6t+P3YWFFmZ60UV1GM2VlYJzGzQ1MeS0r6t2JC0J1TPqMVZeMcRbDvhvS73Wv5Y/OxkjQLKW6MTFBce+2g2OZtFBOk3pzSbagYj79+/qIvp2XZGMmPqbisu7/NbT9XUQ+QouX95xS/OVspyooX07afYmZfaDPvdnWrbteKTuu/axd8N4VSy/K3plbmZynqJVL8Brdy3YteqzpCzINHuw/1bdVwl+KuZ7NHv7t+Ka9FVbtjOkfSZrllP86t55gWtsUVP0DDCtK9R7UWllMlLV2Q5rq0/N8qubuq+OG/LaX7b30+iqDs9LT8JUnbN9iPi0taru61UbnPMqaF76Ib27yOIgDuih/htxXk8XbFReAb+3oAx8+KRfs/t3yIaq1x5qqgtU5Kl//e/yJp2YI02+TSXFGSz8RcmumS3lWQZjnFBY6nv/1a2Ki7LWjXapLHdqq1dPlGSZqxufWM7PC7GlO3n09tkn6fXNrDGqT7fC7dJxt8J1Na2MaWP2cu3bgm34unbWjUgntUXfpzJQ0pSJdv1XlkC3lN7OS7amMfjWoh/adz6X/cJO3fc2kv7Pa259aTb3l/T0maP+XSLNckvz1zaU+uW7ZFbtk16XzLLjSz1/O/VReow9YaHeyHpVVrEXZR3bIlFBdNriYtv9S9ci9/bBX+binK9BtzaYrWNabVY1TNW9A2Kz9XVbTmdUk3l6QZ1egztfhdZS3RX5f0jgbpfp5L9/a6Ze9U1I1cEfTsl0/6nvLnYb/912z/KgLWb2/yeQ7M5bF/k+NqSgv7p+E+1gBa0La6PJduGdXqiKUttBSB8WbbNE+1oE3v21wxe3h2Ln9V0dIsazRwU+4YLKxvt7GufOu8Ro9HJb2nSV7PtJDPHElHtXjcnqO46bCraj0lXsq+J0VZ9Wc1+L3u0vd4Vt1n+LcigLifIqjUsBV3g319lop7I56cS3NwSV7Nys135I6h35ekObRue05okucpdenLrvW+m0tzRsl5MTst/7mkRUry2VrSK2qhbtPiOVjagjYdY1m6+xqkW1zSKk3W+dlcXv1agqc02XH7UJvHTb8WtIqbVG9su1LPsYL98HxKM1vl1/ndaEGbv2Zo1oPVVPvNfLSDdZlqPWBnS3pLg7Rj6o7fVZvk3ag8e0UNevXwGNxH5RvAg0e7D7XeHT//uKpBfmur1iXlMUnD1Hf4gxtUUOEo2ZZNGqzna7l0h9Yt2zS3rF+Xtbq0G+TSHlC37ITcsi90sG9H5d4/pknabm3zD3LLdmuQRz745j0+xt6iWtD4iyVp8t97aSVfMUOxS3q+ZPnEXD6fb5BPvuvYhgXLx7Wyb9TCRWeL++iKlMf9JcvH5tYzssN1jMnlMVlNum4r7ly7pMtayPuulPaWBt/JlBbyaflz5tKNa/K9lN4YyKXPn6tPS1qsJN0yila3LunyFvKa2Okx0eI+GtVC+pVVu9iZJWndknQH5fJ1Sb/p9ran9ayo2g2i1yV9sCTdpNy2FH4fubSjc2mLLvhOq/ts+cfJuXQfzB0D/W4O9mBffCa3Hf26qypaqHr6/kqD1OpeuZc/tq5scX8fUbB8TKvHqJoEaFvcj1mXwtdVcAGm7gRo88G840vSLKbakDY3FyzPD+tReGGe0m2ZS9evvtXO/m3yme5NeVzd5Lia0kJeDfexBjFAm9JmAfXXVHCjOqUZp9pvROHFuObBAG1670qK3lKzcnnkH9dK+p8ubOMOilacn1P0ulpK0iKKFpCfVLT0y9b5rBrfvLhHtZb9yytaeQ5XBNqycbezvAoDN4qAy9Uln/n1/LGXttkV9ZN+N1y7+D0urr43/OofMxQNMD4jackG+eQDbf+SNLQk3QjVbup3fCNV0UI3O0eWKlieD9D+rdk+VN8A7d0qv9ZbRLVheGbUr1txg9wVPQYLg7O5tFnjn5nN0rZwDk6oW7a4orXnSYoeLq4IvH2gC8dMVr8eX7K8mwHa/NAU726QT74OeEpJmm4EaI/L5TGmhfRZQ6nnOlzfz3LrO6skzTBFz4D8ebtek3zLArS/UoNykMfgPxjiAAs8j5kQP5eejpR0keLHVorK236eSrYmJrl7o+5z2YWrFN3f8rLBx6er76DfRds7SdEKV4qLorxd099sSIBe6tY275D+PivpNw2y+bXiQrKr0qQAq5rZuma2QZrIYGXVtrfZBGWT3L1woqokOyZGWPPJWi5ssCx/bPV0gpm81BVmBTNbO9s/aR9lQ2Ssb2aLDMKmXOINum6nyQLWTU8vaiG/W9LfzeuGOZgX/NFj4qVW/drdXyla4DFRVdZVvPC4cfeJ7m7pMaq9Te0+d39K0aVRilaZt5jZp81seTNbxGJCvpMVZVx+yIf6ybYGzGJSoqsUZYIkne7u/1eSfPHc/0VDUeTNLnmfJMndj1Z07b5T0cLqJcUF0Cfc/etp2xZVtFiS4obc9PT6KqnrbTZZ3BSLSbK6McRFNszNU6pN0JaXDXMwVNInWsyzW+Xer7qUT1eZ2bJmtrrFxIVZ+flytljRw6br3P02RStBqdZVtN5HFBd6UgSc6mV1lamSLm+wrtslPZCefmCgZarFxKMrm9k76353skmVujpx6Dwiq7MtpLrhpCQp1R/2TE8nuPvj9Wkkyd1XzJXnz/RmU9uTJtn5uOJGe1k5PUrSQV3o+n29u2/j7j9x9/vdfaa7z3H3J9z9V4pu81emtG9VdIUv8z53P8bdb3L3Z939NXef5u5/cPdPpc+TTY54ahr2q490/bCHoiv43xRl/4uK8nM7dx8nSWa2kmJW9rmKoWpeT69vaDH5Zzbp4UNm9r9WMmFeK9z9ZUX3+z0UXdhfr0uylOLc/6mkR8xstxayvdQLZptP65uqWhf7lspfM3tL+q3Pl5uz0uKFJL2rSRYXZfuwRb8su9bzmPwpKx+XUvRyybZziKKHjRR1sTlqbGL6u6QGPmzbDtZ30sGXFDeKj1PcfLtfMfbsTa1maGYLlZS92VA2PS17U71m6/T0DndvNDTChYqAudT/ulpS18rDdup2Uq1+169u16JvqTZx16FmdpGZvdfMhprZMmkowz8qxtZupw68jWK4iXel/49SDKW2r2IYtEGZdA3NtTRWBTAPu6UbAQV3P8/MtlNcUH44e1nR3L/VAv3OJut41mLG6neof8UiG09pmKTXrf8E82VWyv5JY89k+d6RKmC91I1tXlTRhVKS7m5UmXL3OWb2F/UdB7IjaQypz0v6mCKo1+hicrkm2T3UZPl/c/8vo9q4gPWed/fnS5YV5dNTZvYxRVfzLRUV0jILKQaqf7ZBmm5oNn5VfkyyK9o4HocqWkw/18lG9Ui7Y3W1egz2/Ljpoq8ruoLvrbiI/ll65M1SXPRmQcoZ6qJUpl6m2uzEv5N0TIO35IPkQ+ue18vPiFxYVrv7BSoYEzTnWMWENde5+/i0zWsoxo9eQXGx9rCil8gXJI02s/e7+wsN8iyVKu/vT09/5e5zC5LdqrgQf4ciyPTDgjR53Sz3Gp0Hg11+vk/RSnZ7xXfRSLPfmIG4QNI3JK1pZlu4+x/rlmfj076kuAn6hvT7nM0mfVejG2TJHxUXf0srvv9/trOhKag7Jm3TJmp8cdvLfVYJd7/HzO5WjJV5oJl9qy5gtK9qF+D1ZeE8K32vlyjqW1LciP+eojX0q4qA3X6KsYfHSBplZtu5e1vHT6ZZgwp3n20xH8JWiuNoVzNbsaiu30Je481sW8X4y0sojt1TCtK9pvjM32uQ3Q8V9ekfuvu9kmQxRvWElPc0xTm1jmKM+m3NbPsWzsuybXdFT6grLMbD3UJx7G2kaH2f3dBbIaXZ091Lb9KotXrIGmpQ/prZ5oq5ILZT/O430qwMaLce1fAaTtEbLvMuRStPSVpTUQeWpK+YWZ/5TJpYqXmSjs2W9HN3v7lZwlTXOUhxHm6sCO6W6XXZu7Zq9aM/NUro7q+Y2b1KgUczsxYbVLWrvm7XTLb9HV2Hu/sUM/uo4qboMMWNoH0Kkl6qCPRn45Y3rAO7+8N1L/3BzH6suLG+t6Q7zGxHd2+439F7tKAFaj6rvoNzn+Xu7UzA8J820tRPrFQ2aUIz+btlI1Q7p58qSNtt3djmZRUtiKT29l/HzOxdivHyxiqGXmjW0qfZHdBZTZbng86N1tWtfAYk3aG9QhGY2kGNg7OZTu8St6NZUKnT41HqQcvLAWo3gNbqsTOvtRQulVoq7aNodXW7oktj5hVFxfTdivGfM/kg3ICkoMJFilZGkvR/kvZo0jomXzludt7kl7cdWE7B0q8qLgA+m1t0luKC+nZJK7v7uxQXxQ8rboZ9u9115eQnkSxqbZld9GfL3m1mGzXJs5vlXmledTf/enoemNlXFRf7+6l5cFbqbfmZzYIt1U0WZjFZU9bq6Cp3rz8O87/Prdyozqdpa/LI1Dr0D4rxG7dW830yGL85Vcha0b5DMUZr3mfS36cVwwHML45ULTj7C3f/SGqBOtPdX3X3h9z9WNVaB49UrRdbT6RjPbshYaq12OvEL3P/d5SPmY1WfP4nFBPyZb9B5ynqJxdLWt7dN1AE0KYqGisc3vlm17j7C+7+W3c/wd0/oqhP7aZaq1eT9BMza3TeDageYmbHK27y7KvmwVmpeRnQbj2q2fVFfnm+fKuy7nm7+k7E9QHFMBBPKIKEZ5jZNxtlkHrW/FExmduWahyclXpf9ub3bTu/OwurdzdfW67bpd4C2ffacaOB1FPrXYrvpb4BzIOSDlYEVZfMvd52HTi1ev90Wscyks61Nlq4oDdoQQvUHKy+Be82Zrb4ILRElWrn4pNqffZrqXmFqJfmu21OXfEvU4wpKUXl92JFwPZZSbOzu69m9m9FC74F7Yfqa4qZa6UYq+0Hiq7VT0qalbWaM7MTlS4kNDj7qKi1Xl7+9+wA1WbzbsVg3NBoR7PPusBw90slXZouDLNg11NZV0qy5pfUAAAgAElEQVQz2yKX/G/dWGfqsnieagGDP0jatWwYiZx/q9badlXVhgEpsmrd+9p1tqIlx9ez4TDMbBVFyyMpJpiZLknu/qSZHasISOxnZke00A2zjxQsyHeTn9RiHf5A5WYof7Mzs21Ua0H3nGJymZsUgY4ZueP2A4qgv9TD8tPdHzWz2xUt4vYysyNz3ZD3Va3cPK8wg8HzA9W6Dd8q6UzF+J9PS3op1937fJUP1/BmcLGileUyiovmGyQp3ejIuhb/stNWkxXJAsuvK3pGFHL3q9OxuqWkrcxsbY8hyHoln/eKpal6nI+ZLaE43qUot7OgzraKQP0rinGz50iSu99vZqdL+qaifO36rOupnne1mU1STNK0lOL3d2vF2LRdZWY7KObQkCLg9l3FUABT1Lfc/LBi7Fypebk5WPWofN3zdLV3c6FwmJI2zPQYQi7vZjP7paL16ZqSvm5mN3v50Ew/UfRYkGK4i7MUN76flvRyruy9VFEnWtCuiaS+dbRVS1OFlVQ7Jjqp273B3f8t6TBJh5nZCorWtM+7+xuBWDPLerk85+4d9QR091lmdp3it3UdxbBLf2n8LvQSAVpAb3RHPDk9fVFROV5f8WN7aIvZtNJSJkszte715xStm4ZJ+luHXTSmKirAQ1QbL7GXurHNLyha95ja23+d2la1Lpvf9jSWY4llGyybF71RGTWzIQ2Gi1iy5PXMIenvI5I2b3CDYl7bP/mKyayCSmuv5C8CSnulmFmz/Y4G0nE4pWDRJrn//zzQ9aTg7C9VGz/1j4pJEF9q4e1/Uy2ou54aV3DXy/3/YJvbeKCiS9+DigvZTBbAeTnrIptze/q7hKLcbvf8+LA6C2Lsa2ZHufvs5kkr1dJ5nDQ6l7Pyc66kbdz97yXpBrP8PF8RoF1W0i6qjSWbBTrLxhP+r2q/z6189/k09XWcUma2tCJYLMVxOqrB79e89rvTVelC+QLFvAi7mdlyaQiQLMjpilbG84VUnmbDWD3u7s2GQrpHtXkK1lXf4Ge35VtyDiTgPdB8xipaDV/j7lfmXs/K84fSGK55WXm+gZkN9ZKxXwfK3R9JgZs90ktrqwcBWtXKzTmStmowvEUvz/8V1Diglr/+yH8f+bqnD2Lds5S7P29m+ynqLybph2b27vphiVLr2azOcpOkDzW4lhussje/b9v53XlNtXFbuy1/83+90lT9l7dVt2vE3f+julbeZvY21a73B1r/zTcoGCkCtJViiAMs8NLFwcWKWTpfVlQOs7GFDknjwLRi00YLU3fCkenpX+sW35P+LqUY/6ltqUVFNubSZk26IpVm00babmzzbEXXW0l6X6rMF0qtXwc6OP2Guf8vabCud6q1rv3zknxXmkYVqXXLFpjZCNXGxPpNk9bj72tj2wbDPbn/B9JdUWrvPBjwfkdn0rhp2YXjFA2wgpq6df1Mta7gdygm2JhZ/q4+bsn9P6pJ2mz5S2qjtbeZLSfpO4pj9JC6lrDZZE9FFynTCtK1IxveYK5i/+zT5JGNtbisahOozMtaOo/T/m80Bl/2G/PXBsFZaXDLz0tVG0PvU5JkZuur9rt9YVFANAV98r/PzYaGyFrAzlCta3Qr1lZtXL9Ly4Kz6fzcuElevRh/cLBlwxwMlfSp1MIyG3/wRndvZ99WzVXr2t7KZKL5NG218u9AfoKmJ0tT9TAfM9tQ0hcVPcvqhyvIyumieQuy8twUYz730hO5/3t1fmXl5t1Nxh7uZbnZ8BpOtd4xUt9ruH+oNjzeQOueXePuf1aU/VI0OPpUQbL8HBzjy4Kzqexvdv3VrWPjH6pNsrV5o4RpnPTsd+yvPRp/NtumbCiFLa3xxMj5eVL+0KPtyeyV+//iAeb1ttz/XZ3PAe0jQAtEd4410/9fTHc/P6Xanaqfm9lqLeSzgZlt0mD5Qap1Dbm+btkVuf+PbmFdZa5Of5dRDNnQrnxAbtHSVKFb25zdjV9e0q4N0n1M0vABrEfq22ug0dhPn22wbF71aO7/RsfhJxssa2n/pO6WDStOFbhftX3wqXRDpFPZedDsHJC6s9/RmcNVaz3wkwat7ppKwZ+zVQtE3iVpB3dvp0XGraq1ptnLzArHQzOzUYpxYSXp9y22zs18TzFG27nuflvdsuxC/q3pwiVvtYJ0LUnnUjYJxUR3v8DdL2n0kHSSarML95uRfh7UrfM4K0MblZ9LStq/xe0asDTURVY3GJ2CzPlhAgrHE06yuspykkpvVpvZ+1ULsvxffUutJlr9Xd5NzSfVaafsHgxtb4+7P6Bo+SbFMAd7qRas+2n3Nq33UsAkCyivbGZrlqVNZfA2uZceLUs7UGa2smrH82vqe3OtXfnAalk38qJtMMX3ubCksak7c15WThd1qc7Kc1ebwZQOxpfMB0UfafO9rWql3FxGva1HHVC2b1JALgtwzlTt/FS6SXpNevq+9Ps+rzhBtRskx6Wb2nmtlr17qfm44l0pe1PDnex83DzNG1JmX9VuUNRfV3dNKseya95lVfJbmI6T7Lf1FdWG4+i6NG77V9PTp1TrGdNJXssq5hyR4nipvBX4go4ALRZoFjO5Zl1ZL3f3c6Q3uhLsr6j8LCvpwhZaj0jSz1KhWb+e96g29tYLki7ML3f3Pyi6l0jSx83suCbbPdTMDkpj0uT9WLU76982s+1UwswWTxdqefmxONdWA13c5rNV6176w1Rxrn/vaurblbdT+e5yY0q2c3d1aeKFQTYx9/9RRa2RzWyMpI80yOM51Y6fXVL3p/o8VpD0q843szdSBWpserqMYvy0hhNNmNmmaUyzetl5sHzR+Vznj6q19DnCzPpNsJDOw881yacyZjbKzDw9Jla9PVJUGFPLsbLlu0k6NT39q2IG7KJ0I3OfbUqDVZ6h2k2teyRtn43h2qoUlDotPV1GUZ71ueBLPTbOyN6S+wxNWcwW/inFeVo0U3TWJW2Iar9rmeyiYZZqrSJb9UnVWraNb+UNad9lN9+2sxgfd172oGpd/A4o+G3MJpg8of71OtlvzFrWd3zkLI+FFWMU9nL27iJZEHYRxbGRHR/3NumS+xPVum3/wMzeXp8g9bzId7tvd0zMf6oWRNi74OZCNs7emfWvF2in7B4M2fY0rE8VyFrRrqva8FvPqhZoL2Vmz+TKvIGMrdot+W0+s+j7Tb6sWvfgf7j7Q/UJzOyU3Gc7pmD5W1M5WSrdcLpStYDUOHefVpBulya/QWYxsVXWQ+B5SRc0WnedQxU3uu9X8TmTDVOzekFZkpXnkzoY3mAjM5tgZts0C9aa2eGqDTnxX8UYpb2QlZvrWww5V78dQxVlWKPeCwO1sWoBr3onq9aQZ1xBr5pvqlYPvKhJUDGrl4zpdENblXpxZK1oV1f/VrSTc//vW9QyNPW2+FELq8vKurc1Om9alK/PjSu62Z1+E7L606uKxlb9dLE8/L5q3/FpJY1AvqnaDZWfeP+JN2Vmi+W2p3ReA4vhC8qWDZf0G9WG3TisqBwwsx2tNkZtWV5LKybEzX4vf+/urUzOhh5iDFrM75Y0sw1aTPtEvhJmZmspAppSjDv06Xxid7/OzL6nqDT+j6TjJX2jQf53KVre3Gtm31FUrhZVzMT7ZdXGrTuyqNBWXATfoSjcT0oBiHGKAfpnKu4Srq3oRribpLdIWku5MWncfWoKOl+pmGlzgpmNV9z5m6JowTtS0b12L8W4Zlfl3v9ECmKMlHSQmf1NEazIupu8VHeXvxvb/JCZnSLpWEWrgHvN7FTFAPdS7PuvKgIe9ykGL+/UdYpuKisqhq9YVlGhfkpxwbynouL7T0VgvpWZZOcJ7v5XM/uDoovVhyT91szOUAz0/zZJH1dclGeTcBTl8brFJCx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615wPpBvB9I8rKL68PvSLqrolyfZVdbfW2pLprSnMatul60ejrq6qF7TWvjSyzusQLPOYfrk0yTfThbN3qKr56aZ7+1W/asb6jykOZpe5/XLxJNvH1m8+A3WBdYV+BVNzUro3KOe01s4bWa8PweSOTzcN1YvThbOfTTJv5E19og/BRP4pySOTHNlau3UFZafah+ZOsh3WRe9Lsn+6kHbTJA9P8p4kOyU5t6p2GynrdQiW2aZfvjRJS/InSe6eZNckn0vyhCQfGyk/Y/1HQAsA67mqOi7JS9LdhOV5A1cH1hqtte1aa5XuD+Snpxs98c2qetSwNYPZq6r2TDdq9i1u7AWrprX2qv6+Ate11pa01r7T30z8X5LcNcmJw9YQZq2xHPS3SZ7SWvtKa+3m1trlSQ5JdzPyvavqcUNVjNlhRZ/+jq1fNAN1gXWFfgXLUVXHJnlbku8l2be1dsO4IvoQrED/B/In001FtVWS949s1oeg109t8P50XxV95RR3m2ofmmx0E6xPxm74+oSRdV6HYJmxf+ffHL3xZJL00+SMfZNwj345Y/1HQDu7XNEvHzTJ9rE7Ak82Ry1wZ5P2q/6PhPul+/TsqpmsFMwGVfXiJCcn+U66cPbaCYrpQzBFrbWfpPuw42FVdc9+tT4Ey2yWri88JMnSqmpjj3TThSTJe/t1b+2fL68P3Svd17t/bv5ZSJKMTbGz6cg6r0OwzFh/mCxQ/XW/vOu48mu8/whoZ5cv9st5VfUH16aq7p7k8enuEPfVma4YrMUu6Jd/OsG2JyS5W5KL3bGU9U1VvTzJvyb5Vrpw9peTFNWHYOXcu1+O3UVbH4Jlbkvy75M8vtmX+Ur/fGz6g+X1oSeNKwPru8f2y9GwyOsQLHN+urlnHzo+d+uN3TTs6n45Y/1HQDuLtNZ+nG5S4p2S/M24za9K9ynYB1prt8xw1WBt9vEk1yd5dlU9emxlVW2S5LX903cPUTEYSlW9Mt1Nwb6eZP/W2vXLKa4PwYiqelBV3elrblV1l6p6XbqbT1zcWhsbgaEPQa+1dmtr7YUTPZKc1Rc7vV93Rv/8femC3WOraqexY1XVFunmsk2Wfa0b1nlV9ZCq2nSC9TsleUf/9IMjm7wOQa//ttN/JdkxyYtGt1XVvCQHphtd+9l+9Yz1n2qtTcdxmCZVtXOSi9O9uf90ku8n2TPJvummNtirtbZwuBrC8KrqaUme1j/dLt1/olcl+XK/7vrW2vHjyn88ydIkH0lyQ5KnJHlwv/5ZzX+GrCeq6ogkp6Ub3XdyJp6zb0Fr7bSRffQh6PVTg7wh3Si/q5MsTLJtkr3T3STs2nQffHxvZB99CFagqk5MN83BUa21U8dt+9skb0/X385IcnuSZybZPt3Nxo4PrCf6vvKSJPOT/CTJTUl2TnJQkk2SnJPkkNba7SP7eB2CXlVtny532yHdiNpvppuq4GnpRtc+u7V25kj5Gek/AtpZqKp2SPLqdEOot0ryiySfTPKqkdEYsN4aeQM/mZ+01nYat8/jk7wiyePSvXH5UZL/SPL21trv7nQEWEdNof8kyZdaa/uM208fgiRV9UdJjk7yx+nCoc2T3JLug/Sz0/WJ8Tfb04dgBZYX0PbbD05yfJJHpfsm6PeSvKO1dvpM1hOGVlV7p3sdemS6wSqbphvx960kH0j3rds7BT1eh2CZqto6yT+lC1rvleTGdAO+3tBau3SC8mu8/whoAQAAAAAGYg5aAAAAAICBCGgBAAAAAAYioAUAAAAAGIiAFgAAAABgIAJaAAAAAICBCGgBAAAAAAYioAUAAAAAGIiAFgAAAABgIAJaAAAAAICBCGgBAAAAAAYioAUAAAAAGIiAFgBgHVFVrX/sNA3H2qc/1oLVrthaoqqO7Nt84QTbLuy3HTnzNZtZVbWgb+s+Q9cFAGB9IKAFAJgFRsLVlX1cOHTdWTtU1SOq6sT1IWQeVVUPr6qzq+rGqrqlqr5YVY9fwT4frqrfVdWjZ6qeAMD6a4OhKwAAQJLkuknWb5lkwyRLkyyeYPsNIz9f0S9/M431Yt3xiCQnJPlSktOWU+7H6f69LZmBOq1RVfWgJBcluXuS3yb5XZJ9klxQVfu31r4ywT77JfnzJO9urX1tBqsLAKynBLQAALNAa227idb3I2T3TnJGa+3IFRxjl+mvGeub1tr+Q9dhGp2YLpw9Lckx6ULa1yZ5WZKTkvzxaOGq2ijJO5P8KskrZrCeAMB6zBQHAADAumr/dKNmX9Rau7W19pt0wet1SR5XVXcbV/74JLskeVlr7dczW1UAYH0loAUAWEes6CZhVbVpVR1fVRdX1Q1VtbSqrqqqs6rquVW14Uqc64Cqurk/30kTbN+sqv6xqi6rqsX9ua6sqrdX1Q6THPOOG3FV1eZV9caq+kFVLamqRStRt0dV1UlV9ZWq+mlV3VZVC/vjv7Cq5kz1WCtxznv087v+T/97ubmqvl1Vr6qquSvYd6Wuy6q0r6pakvf1T/eeYC7jfUbKLvcmYVW1bVW9ZeTaLK6qS6vqJVW18ST7nNYf88SqmlNVL+5/V0v6Nn9mDc33ulWS61trN46taK39NslP0v0ttMVIHXdKF95+Jcnpa6AuAAATMsUBAMB6oKoemuTsJDv1q36b5MYkOyS5X5KD083VuWAKxzokyX8m2TjJP7TWThq3/SFJzk1y35Fz3ZbkAUn+NsnhVXVwa+2iSU6xdZKvJ7l/v9/tU2njiM+lC+aSbh7VJenm8t27fxxSVU/tg7rVVlUPSPKFLGvv2NytD+8fR1bVAa21KyfYd1Wuy6q077okd01yj3RzFI/OXZxM8XdcVXuku7Zb9qtuSrJRksf0j+dV1bzW2i8nOcQGfXsP7OtxW7qQ9KAk+1fVfq21S6ZSlylamOSeVXWPsZC2D7Dvm+T3SUZHyb69b8sxrbU2jXUAAFguI2gBANZxVbVlks+mCwGvTvK0JJu21rZKcrd083C+L104uKJjPT/Jx7IsyBofzs5Nck66AOxjSXZLsklrbbMkOyf5cLpA7syq2nyS0/xTuhujPSnJ3Vpr90iyMqMrP5fuJk/3aq1t2lrbIslmSZ6X5NokT07ydytxvEn1c5aema69P0syrz/XZkkOSPLTJDsm+eT40aWrcV1Wun39HMcv6p9e3Frbbtzj4im0dYskn0oXzl6eZI/+2myW5NB0YeduST60nMP8Tbog97Akm7XW7t7v850kmyR524rqsZIuSDInyduqapOq2iDdHLTbJvlqa21J37anpAvDT26tXT7NdQAAWC4jaAEA1n3/N92IzOuT/Elr7X/HNvRzcl7UP5arqv42XYD2uyTPb619cIJiL00XOP5na+05oxtaa1cleW4fTP5pkhcmefMEx9g4yZNba98Z2fdHK6rfSNnnTLDuliQfrKqfJJmf7oZRb5rqMZfjsCS7phsN+gd1TnJ+VT05yTeTPCzJc5P8x8j2VbouM9y+UccmuVeSRUnmtdau7c/9uyQfr6obk5yX5IB+JOwFExxj83Rt/cpI3b9dVUcm+VqSx1TVjq21n05TnV+d5M+SHJnk8HT/djdOd73+IUmq6q7p/l1fk+SEaTovAMCUGUELALDue36/fPNoCLgyquqV6b4CfnuSZ04SzibJEf3yLcs53If75RMn2X7uuKBz2rTWvpwuYNypqu49DYd8Zr/89ER1bq19N8nH+6fPGrd5ta/LBOeb7vaNGmvrqWPh7Lhzfy7J2PQE49s65suj4ezIvl9P8vP+6R+tbkVHjvv9JH+SbqTy0nTTGsxPckBrbX5f7JXpPlT4+9baTVW1TVWd3s/ru6SqLqiq3aerTgAA4xlBCwCwDutvfLRt//ScVTtE/Uu6r8zfkuSprbXzJym4Q5Ltx87V35hqIhv1ywlvFpZlId8qq6pD041YfVS6OW03maDYvdONmlwdj+qXX1xOmQvSTUkwVna1r8sMtm/sfBtlWXC6orY+LiNtHeey5ez7v+n+/WyxnDIrrbX2rXTTZdxJVe2S5CVJvtBaO6OqNknXhoclOT/d6OZDklxYVXv0gS8AwLQS0AIArNu2Hfl5Vb42vmOWzWf615OFs717jfy8zRSOfbdJ1v9qKhWbSD/H6EfThWpjbksXtP2uf751um+Sbbqq5xmxdb9c3gjYsZGhW1VV9TegWqXrMkD7xmyZZd++m0pbt55k+03L2Xdpv9xwJeq1ut7ZL4/tl0elC2ff3Vo7Jkmq6vAkH0g3d+0zZrBuAMB6whQHAAAsz7VJvtT//Iaq2nk5ZUffW27RWqsVPHaa5Di/m2T9VByVLrxckuS4JDu01jZprW09dkOsLBtVWqtxnvEmGsG6JgzVvlEz1dY1qqqek2S/dFNMXNGv/rN++Y6Roh9O96HBgVU1ZwarCACsJwS0AADrtutGfr7vKux/W7rQ6qIk90lyQVVNdpzRc+24CueaDof2y9e01k5urf18dGMfsN1zGs83Ntp3ee0dm/ZhYT96Nln16zLT7RtzQ7r5W5OptXWVR0HPhKq6R7p5khcked3IprFrcfXYitba7/vnm2bN/G4BgPWcgBYAYB3WWluQbhRskjx5FY9xc7/vpenCuQuqavsJyl2dZcHjhHN+zoCxen1zku2Pz/SOAP1Gv9x3OWX2G1d2da7L6rRvLGBd6ZG1rbXbk4zdBG2l2jpLvTbJdkle1FpbMsH28b/Du675KgEA6ysBLQDAuu8D/fIlVXWfVTlAa+3GJAemC97uny6kvdcERU/rl8cv71zV2XxV6rICi/vlwyc45wbpgrnp9PF++aSqeuQE53xYkmf2Tz86bvOqXJfVad+N/XJVf+9jbT1yomtfVfPS3SAsuXNbZ43+Oh2T5DOttbPGbf5Jv9x9pPzmSR6Q7iZ5189IJQGA9YqAFgBg3ffGdDd2umeSL1fVU6pqoySpqg2rau+q+shEo2JHtdYWJXlikm8neWCS86tq/M2gTkpyVX+ui6vqWVV1x+jDqtqxqv4yXdD7tGlq36jP98tXVtVTx+YMrapdkvxXkj3SBW3T5Yx0v48k+VRVHVBV1Z9z/yTnpLvp1XeTfGjcvqtyXVanfd/tlw+tqj1Xoa3vSPKLdKNJP1tVj+7PPaeqnpHkI325L7TWLliF40+qqhZUVauq01bzOJXk3UluTzeH73jn9Ms3VNU2VbVxkjena/N5rbXVmR8ZAGBCAloAgHVca21huikHfp7kfkk+neTmqro+3c2mLkxyWJINpnCsG5IckOR7SR6S5AtVtdXI9kXpRtp+P910CGckuamqrq+qJelGKL4nySOStEy/Nyf5cZJ7JPlUkluranFfnycmOTrTOAqy/+r/M9K1a8d0AerNVXVLki/0636a5OmttdvG7bsq12WV29dauzLJ/P54X62qhX3wuaCqHjuFtv46Xaj+6yS7Jrmsqm5McnO60bVbpAurn7uiYw3oqCR7Jnl9PyXHeP+W5AdJHp1uCorFSf4iXRv/30xVEgBYvwhoAQDWA621y5M8LF3I9LUkt6a76dFP0wV9f54uKJzKsX6VZP8kV6QL6j4/Ol1Ba+1HSca+Rv7FdIHe3CS/TRfg/VuSg5J8cBqaNr5uNyR5bLpRkmPtuTVdG/durZ22Bs75oyS7JXl1ls3Tmv7n1yTZtbX2w0n2XanrMg3te3qSd6W76dVm6W6Kdd9McV7e1tqlSR6a5F+T/DDd6ODf9nV/aZI9W2u/nMqxpqqfumHs5lyXrcZx7pnkDUmuTPKmicq01m5NN8fuB5MsSjdv7xeT7NNa+/6qnhsAYHlq2Y1kAQAAZpd+dO8l6aaD2Hn8SGQAgLWdEbQAAMBstne/fKNwFgBYFxlBCwAAzFpVdXa6KTPu31pbOnR9AACmm4AWAAAAAGAgpjgAAAAAABiIgBYAAAAAYCACWgAAAACAgQhoAQAAAAAGIqAFAAAAABiIgBYAAAAAYCACWgAAAACAgQhoAQAAAAAGIqAFAAAAABiIgBYAAAAAYCACWgAAAACAgQhoAQAAAAAGIqAFAAAAABiIgBYAAAAAYCACWgAAAACAgfx/HuD7zsrkJbYAAAAASUVORK5CYII=\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 692}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "15 out of 20 tickers were removed\n{'PRSP': 324, 'SKM': 479, 'EIX': 142, 'FAST': 1828, 'DLR': 112}\nFunds remaining: $20.22\n"}]}}, "9879cde325f74c31b9eca2a14a9df476": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_2a0b3edbc9274100abbad18b36fa21a1", "outputs": [{"data": {"image/png": 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NHWReJ1fyOrRu2iLULj6ea7QS6spSvsXln+QKZ1PyE9uj6g7+jzQpy6rkKH8iBzd+QK48NydXzp9i7rcLfLRJPu+u7AQvFBv3/eSBRzct8jydHAwqgz0LkSu6vSv5H178r/qpHz+iV2U+ppLmQXJzuTKPg4FHyBfqL1XEg9jmu5EvWs8hP10aT27S+jbyWw9ur5TljBb5lGluJj/dvo/cR3aLYj0fzNxBk/FN8plaTH+IfBJ4kty8eDty096PUBsbI9H8QnlyJ+uGzi66nwCuLfaBd5HHIdmi2LbnVfavf9P8TRYTK8sZN8BtNaGSR3ksXkYeBG0j8kC1BzRbD8BN5GPxrcW63JVaX9VEHnBxTGXeV5L385sq36/+GFgfWGIg37OSbnKb7VJ+178X++hmwP+QB5VcvEg/vpL+2srPPcjH+rbk+q08ic8Alm1Srmpeg6pTO9gXGh4HXeS1OLn1yifJAfgy39MGkee5lXze3CbtQZW0+9VN278yreEYDJW0G1bSTqmbtkfx/9nUjZMAfLeYdmflf0FtXIiDe739GpS9ep54W+X/a1T2t3luQlvsE9cW851FvgF7M/mJWPXiv+F+2avjgN4Ge75APr/+oFhXWxXfaUfyeeFflWUd1mI5TeuLDrfTr4v5n6ISGGxyTJXBuysaTN+WWuDoeXKQ6q3kOvjD1MZqKOuueR5IdLJ+yQPp/hn4KvmhzKbFZzfge9TGS3gCWLtu3rHkuvk7leW8nXnr7hXr5mu5jhl4sKercwl5UM+yLP+vzXY9rZL2tQ2mj+RgT/m2xjuBRRvk2Ytgz2bUjvdf0XzMnuuoXXstPYjl/aayvrdtMP2aYtov6v6/DDl4Odf5Angj+Z7jceCVQ7D9XkG+Xi/LfCgDHO+OuW/ay/p3KnncuY3I9XN1f32QJmO6ka/Hyhca7Ea+B9iYPGbUN6kFzeYAmzbJoxrsKa+jLi7y24hcb+1bSV8N9pT3cD8jn4c2JNdDl1TS3E+TMZzqln0fcAj5PLZhsZ5Oqaz3f9EkMN7BOu8m2POPStqGgSpy4OC54lg5iFxvbkh+eLUXueVamccfqRtPsZh/ffKLNBL5HrhRXbdYZZ5Ogj1nVdJMAw4gH9tbkR+GzqpM/2yLddCLYM97K3mc2CbtqpW0U3t9/BbLqL6t9qsdzvPuIv2z1X2BURLsuanJTlX/mScaWeS1CLUAwnPAZpVpp1SW88UOypKKnX2ekwbwJmoXUzNo8ASa2oXZv2gS9SU/US0r1Efr8yEP+FYeAE8Db2+xHhcDlq/73/jKd5nQwbboRZnXoXYheR8NTm7k1kP/rq7rQew/Kzda/5XpC1C7mH+BJq/Qq9vufwaWaZBm20qaC5rkM7WSZhawQYM0y5MvSMoLk0YXL2WZW64bOrvoXrtNHm+jFvA5skmaiZXljBvgtppQt57bVbK7V9LO84Stkq46cN08wVe6GFSzm+9ZSTe5zXZJRRlatSwbX5f+hzS4YGPuViuf7iCvqQPZVl2so/GDnL/R50IGMRgxcw/UuXybtO+vpD2ubtrxlWm7tclnhUra6+qmLUk+TyTy21/eRx6M+mvUbmIOqaT/OLXzT9uBrXuwPcublwfq9znya+ETxZvTutimExqkWYB8A1CmaVQ39uQ4oLfBnjVo0gKzmL4ItRvEWc323Up5Jg9wO3241fqtpKu2MturbtoYakHVF4B3NtlO1YDpPOuvk/VL+/POBtQenP2og/1qXAfrqOU6Zj6+eh24ukj7CA0G+i3SLE7tGq9ZmUZksId88/4CuQ7btkmevXr1+ieoDd5/K7mO3Ip8A7s/tRbUs6gErAewnC2o1cn30aDOI3e3KLfHGUUZ3k2tRfzTFDehxbFUBk8bnq97tH6qAZhEvoY/FdiTHHic5xqzST471uVzGo17SRxXSfOJJnm1O/5fTQ4WJeBXTdLsV1eeo9rkWf+K62b3el+rpDm5wfR1qAWjv0/xcooG6bYh32wn4JQBbrtOB2h+dyXdLS3SLUaD1px1aQ6o5DVPC7UiTc9evU4Okr1UdhoE2Ir18EiRZjbN7/N7Eeyp3jO061kT1Oqefw5keR2Up2xE8SIdvOad/DCkbJhR35J8VAR7Ov38skV+r6V2AXEveRyEahev39BkFPMGZdmkxXKqbwiofyK8aWXaPBdTdWnXr6Tdu27aUZVpTSOdLfIeX5l/Qpu0vSrzNyvTdmmRR/VGPvVqh2yyrGWpBaCavSWhut3f1CKvG4o0jzSZPrWSz2da5FNtHvuGBtMnd7Ju6OKmpk0+FxR5/KXJ9ImV5Ywb4DImVPK4izY3stRaZf2sg7zLJ65Xtdgm0zrIp+PvWUk3uc12aRpkrKSvHqv/pfKUtC7dUtSeJp3fQV5TB7pPdLiOxg9y/urnXnLLs0G9ZYLcoqDMs+F6rKTdqZL25Lpp1QcE87zdpS7tYpW0tzaYvgu1C4b6z1RqbzpbgRwYepEenqhblHvtSjnm6Z5Kvlkop7+7w236ixbpquv7U0N1HNDDYE+H63GDyvLe2yRN0/qiw2UsRq3r1ZUt0pUPbZ6g0mqxmLZrpRxNW8+Rr5vKruMPU3ez0836bfOd/l+Rx2ONjntGd7CnGpxrdkM1oZKmYZdTRmCwhxzgLM/P322RZ0+CPUWeb6Z5N5DnyV3n2t4otch/KXIwvszzw03SBfmBRKNyvEjlWps8YGoiX58M+O2SHZR9MeZusVL/eaKoF/63vk6oy6d6034fzYOUy1F7QHj2IMp9SGX7zdNakbmDPX9rtw6ZO9jzx0Z1SpFuIWoBwifql01+yJDIY/o1DPRU0pbXCU+2S9tk/qbBnmK7rkduHflMeUwBb+nBPlMev+c2md7LYE+1+90bW+Tz8Uq6E5qk6UWwp9qSZkIH6cuA/MODXe8N8t62UpbfdThPuX/eVn+M8nJ5G1dK6R/URqQeB/yU2mBHDwF7pmKNtHFbSummFtN/QF6ZkJvJVZUDP80CLm1T3tvIF/eQn/ZWvbv4+Tj5CcJQ6lWZdyh+PgRc1CKbn5O7RfVUMSDb6hGxbkSsXwwityq18rYbHPq2lFLDQYIL5T6xXLQf1PjsFtOq+9Zr2uTTM8UAdytFxGvL9VOso0eKJOtFxELzoShTUos3NBUDtZVvY/ppB/mVg+duHvltTCPJdSmlf3aR/ucppWcbTUh5MOBy4NyG+01KaWpKKYrP+O6KOl98h/wmiTdQ64p6HrnF3xnkJ7WDsVjl9zlt0s5uMl8v8yGl9EvyU8BLyfXeHPKNxRHkQFL5prOvk4PTZ6SUroeXBro8MvJg/LOLQSLPi4jXtylTJ/au/P7jBtMvIF/E1qdt5awW07qp9wZ1HAyViFgiIl4VEa+v1J/Va6GevoCglFJ6hnzeBBgfEas3KNsqwPbFnxeklJ6qS1K9Vml6TZFSmkXuGg25Jeqgv1PkFwWsFXmw/XK9zSomjyU/6e8n51M7r/5vkzTl/x+ltm3nklL6UKU+n9LjMg7UkeTz83TyDfuQiohXkOufZgOkjiF37dmzGES52/zHkPf31xX/+mlKqeF1R3H/8D5y986/kev+x8mtFt+WirdhFcfiseSgyL6pGLg6It4QecD5cuD6OyLiK5HfvjggRd3wrqJcvyMHnaqWJB/73wXuiYhdOsj2vJRSw/Neym8Kurf4s6P6NyKWjfzik+rxX9ZPY8gB81Z+mrob/PtHze71ivPtmcWfS5K71ZflXID8cAbyOajdW0inFj+XIHfZG4wdqoMgk1uJ3UYOTixKbu27U0rpylaZVEXEmIhYNSJeV3fNX77Za0jOV5XlL0K+9gG4IaX0lxbJz6b2ZtOGLyJIKa1cqQ+nD7BY3VzbQe36bp5rux6oXle1HZg58gsa9iYf4//b7Bjtta4r1Tau6sXNSUrpxxHxNnI//3eU/yaPMdPpznFjm2U8FPmNDa9m3kpq0+Ln0sCL0flLLVYpfylOWGW+NxSV+VDqRZkXoXay/GOrijml9FxE/Jk8ls2gRH6j0WfIJ/t1ySeOZpZvk90dbaY/Wvl9KWoXq/UeSflVnJ3mM6QiYjdgH3JwrtXrA8eQ+543eg1hL7Wq8KG2PwJc0MX+uDD5ZvnhgRRqiLT7rvU63QeHfL8ZCim/4rK6f90EnBsRPyHfIJ0aEeuklD49wEVUAwQL1/1dr/pGivo6tj6fVlrlA0BK6Q+0eJtDRLyF3JLmQXLLUSK/mec35OP2BfK+8Upy97OdIuKtKaUb2pSt2fLGkPvwQx7Yc579LqX0VET8nNwC4Z0RsWJq/IrSqlb7bzf13og5DiJiVeBz5JuANclP95tpd44ZjDPJF3pB3leOq5v+EWrnvzOZV/m63qfIA3S2ch15nD7I1yItr4kaiYi3koO321F7rWwzy5PHh+gLKaXZkd8K9gXgLRHxmmrQPyLWpXaT+ZOUXx8+4kXEhuSxqiC3zuv5Q7u65a1KrgNfT64Dv05ujXcX+R5kA/Ig6h8gtwTbKiJ2bhYobpB/kMeQKu8Vrid3G2uqeFD19eLTzLfI19PfSin9qVjW1uSxyxYnB/3vJrfsOBrYLiLe3uohWJsyJXJw/oKIWIa8b21MHq9lK/J1EeQXIVwQEe9Prd/y2En9uyYt6t/ijU2fIg8VsEKb/NrVm91eR7Wrr6rnzQ3IrU8gjyFY1lWHREQ3wcxV2icZsNnA91NKv2uXsLh//Dj5HLERrd9sOpTnK8i9bcrroz+0SphSejYi/kRu7fKGiIgOG2d0q5trO6iVv6f34UUQe7fiz8dpEvCvpF+SHLCF3DK35frspRHXsqfiAGpPJCGvmMu6mP/BLtIsV/f/pq+WbaP66t/lqK3foXudWk0vyrwMtYvgbtbfgEXEBuRB4CaSu5e1a9XRLjJb/yS0XjWA1WpZvcpnUCJi4Yi4gDxI3Q60DvSUhiJ6Xe+xNtMHuj/C3PvkSNDuu9brdN8ZaS2YBiWldDG110h+KiIGGgh+ovJ7u/29Ov2Jumm9yqetIlB+evHn5yo3UF8gB3oeJDd/Xp98sX5+sczJ0UUktM4O5MARNA4MlMoWPwuRgwntNN1/6x4AtNt/R8RxUATh7iBvi7VoHeiBoa0/r6L2+uk9G0wv/3c/+Ql/vfJa5eEOnpJXH4zVX+O0VLQgPYV8k74r7QM9MH/OO/NbOQB7kG++qv63Lt2IV9xE/pAcZLkwpdTy5qRHTiMHeiB3ufhCSum2lNLslNJTKaU/pJQ+SB5CAPKgvV/qIv9TqD1d/xO55US7uqeliNiJHJB/gDz4bBlc/zH5+uQc8uDi65NvxmeQA6KfHMxySymlx1JKl6SUjkopvYd8PbULtdY4QX6o0uqYG1T9GxFHkAPGH6Z9oAfaH//dXke1u7+oTq/Wb8N57fl7aq2e30B+ackh5P1oEeDkiDimVQYRsSx5vZ9OvnZo12JsqOvd6rrtpLFFmWZBhu5BTsfXdsX1Vbldu762a+MD5BZhkHs7tAsmnUhuAf8AxQPB+aXXLXt66RPMvRG3jYjF5kMLGaitl3+T+zJ2alAnmEEadWUuuhv9jDxIM+QT6Tnk4M9DwOwyKhwR/wJWp/2Fer85jDzyPOQnud8kP7n6N/BUSukFgIg4muKihPmzjl5oM71at+xN7n/dqfkRHO1Gu++qmgvIbymD3L2r7VOsBv5FfssD5GO+VQu7aleYfzXIp1G6bvPpxOHk8XMuTymdU/n/x4qfJ6WU/gaQUpoTEZ8id/Ndhzyo6HUDWGa1+fApxc15J/P8vwEsa1QqLpzPI7/x5nnyoKe/JHchm1G2xiia/pfH+ZDVnymlVLSA+zKwTkRsUnY3j4g3UWu5c1aXXR56bS9q3envJbeAuJYcqHqq7BoRER8jd4mHPjw3p5TuiogryV3rJkTEESmlF4oWe2Vg7rqU0u3DV8qu7Eytq8o1EfGhJunKG8wFKmmeSSld2M3CImKlYpmQu9i36iL6FXIrskXIrZiP6CD/b5AfDEO+PnrbYFsqRcTi5K7KkAdlLm8QtyP3BHiW3CLqOYCU0l+KchxDru+/SY8V13kXRsRt5AFylyQ/NNiGPJZPT0XEDuQxRyHfvH+N3N1pGvBE2fUkIt5BHmsI2h//8+s6qnopQX2oAAAgAElEQVTt+Q066FpTcf8gl/1kMUxG1e8i4kfkVjFrAV+KiN+llH7bJI9TyW/chXz9dBr5pTP/JR+DZXfC88gByb6rdzvQzbXdKtT2iYFc27XyscrvLfez4lqkHOLgcnJL60ZJ16j8/taIGFf8fkmlLuraiAz2RMTG1Jo3P06ODq5HPnD36zCblbpIM6Pu/w+TuzMtDfxtgM3QykE6FyCPOTPUelHmx6g9xepm/Q3UduQmgpAHF231NKeTJ4sjyUsntohYoMWF+xJN/l/at/h5D3mwrmbBzpG2fqrdsJ5qcAIcKtULiqYtFyOi3XrXwFQDM+MGmMffyBcxkJ8I/7lF2uq4N/U3XH9rkq7bfFqKPD7VoeSbgAMq/38FuZk85Cd+L0kp/Tci/kmuszeky2BPRCxHbUy4bqxfDTCMYB0dx4VWx/Ju1J5MHphSatYCY37Wn2eSgz2QgyrlttizLk0j5bXKCm3OK1B7iFKdr1PleWcmsEVKqdlT9pF23hkKp5ODPauSu3FeRH4AU3afGBWtegrV7qpf6yD9QtTGfnqQPLBxN9aldjN6c6uEKaUnI+JOcpecVSJi6WLsqYYiYhK5+xfkuv6tKaVHm6XvwkTyuev/Ukq/qPy/HBvljmLMm6qyfl8/IhYeqnE4Ukr3RMSvyWP7QL5+7nmwh9rx/xywdUrp7ibphvL4X4nWN+fV+4/q9qhee6b5eO3ZVErpkYjYk3yeD+BbEfHG8mFtqQgIlNc+V5L36Wb3cvOr7q2u25Wbppo3zfPk+/ehMF+u7VqJiNdS68b7tw664y9MrS78GHMHipr5auX3dWnfNbOpEdeNq7hAPod8knmG3Iyt7Iu5b0Ts2mzeOpu2mhgRK1K7Ganv+16elJYkX4h3rei3W/ZR3axNc8um2XSRthdlng3cWfy5cfG0s6GiVc5gBwZ7Q+X3pgMXRsTr6Kz70khSjcC2qpTXbTahuKEr+xBf1KZV28ZdlG1+qF7YbdM0VWe6OQ4Gvd41KK+s/D7QpxBXVX4f3yZtOf1p5m09dhO1Ptrt8ql2Obu6Tdp6p5NP5MeklO6p/L86AHyjm5aZDdJ16iPU+qofS347YqvPPpV5O7nIGG6dHsfQ+lju6BzDfKw/U0p3kVtnAnwoIhYquoh8uPjfTY3GXyqU1ypLMPd3a+R/Kr93O15GmffvWgR6oP16G4rxGgZjIOW5kFrXhH3qfs4itxxTY9Xxazp5cUQ1TdOBdSPiOHK3TMgtwbdPKQ16nL+IeAN5bK+nmLdLVllPt6rLg9yKcCg9UPl9qI6v8vj/Y4tADwxtvdnyHo5a61+Y+x7uH9SGABnstWfPpPzChrKuWI/aeHtV1TFLz20W6CnOF+3uv3q1b/yD2gDHm7dKWHRnL+8//zpE4/WUZSrr5C2j9UtpBnNt10r1OupHPcx3SIy4YA+5ydpaxe+fK6Kye1Hrn/n9iFij4ZxzWz8iNmkx/ePUomyX1027oPL7wQxc+RRkKdoMGNdE9eZ+kaapsl6VuXxKsCKtnxzvRn4Dx2BUW5a16it7QItpI1V1oMpW+2GrMTQ6Wj/FgIstK+Fh8Bdq62CvIrg6UOVx0O4YgN6sdw3c7pXf2w0g28w11J7OfSAiGvb7jojx1FrO/Cql9HR1evH3r4o/14qIbZvksxS1QfYeKpbfkYjYmzwY4d+BSXWTqzcFjZoar94gXafKLlxPkoNMU9p8fkBtwMsPxSDeHDM/FF0xyqf0TY/jouvTei2yGqnnmHIcpeXJA8u+jdoT0VbjL1WvVZpeUxT7dHksPkzr1nGNlOut1XlnVWpvvWmmm2uY+aGbcwnw0pt/yib674iILam9Me2s+TS0QE8UdUG0+1C73p5d+X8nT/XrVc/HW0WLN21GfvtV+YKQB+vr80q6o6mNd3En+TXWvRg/MsittBYEJqaU6luVlPV0o7q8vCcpXwXe7XK7UQ2w3NM01eB0cvwvxdBeR+3dbN0UN/dlsORJKi1ji+P1/4o/Ny6uE0aKo6iNlfTlmPfNc52erz5A+3HYuq7rGikaAVTfltvqrWsfphbsrL+v7pkiiFTe8y5D7W3Ucyn2k7LF7LPUuhwOSlGPlfk+B/yk3Twppekd1r3nVmbbojJtwK16YIQFeyLio+Q3cAGcn1I6A6CoyD9KrkiXAc5uddKo+F40eL12cYFYdhl6jLpXbKeUriY3oQP4YER8mRaKQXQ/XvRPrjqFWsT/+MhvGGuWx2IRUT+qenXsktfSQg/LfDq1JvTfKi7o6uddg86aALfzj8rvE5qU8730aNC7+Wxq5ffPN2olFRETgPe0yONhavvPzkUTz/o8VqL1q5KHRVEZTyz+XIrc37zlIH8RsWnRB7xeeRys2Oh4rnMdtSeCn2p0U1schwfW/3+kiIjxUXt959QRUJ7lI+JD7S5Ki/q7HMR0DnX1aiXdtMr3G1c/vWjaXAZOliLXQ3Mtu2gBWg4GncgD3zVS/f/JxXzVfAL4NrWBBCfVN61upqivJxXL37e+6X7Rv7p8Krpn3bxltxDIg4p2rAjuluNuXJQ6fGsNtYuIsTS5OBphygvMzSJinie0kd9Y0+6JWifnmE8zsC5xg3Eutaele1G7cZlDrdtMIxdRG6R134iYZ3y+4lxzOrVuRqem7t8QVK63rSJirfqJkd8qMoX2g4d2fA0zn3RzLqn6HvkmbQx5nMGo/L+liJhSqe+ajZEzKkXEfpXvdnr99JTSf6i1uFyDJuPwFDdlp1G7J2nYXay4ri3HJryLHOgZ6Oub6+1Hfmj2FxqPu1PW06+JiP+pm1bW77cNoAvXhhFxWURs28E59pPk3g6Qg+EDGROvE+Xxv17kYTXqy7EwOSg9lG+C2ojcPbqR46g1CpicUnqybvox1K4Df9omQEFEjCuux4dUSunv1Fr3vIZ5W/fcVfn9w41arETEeuRrlnbKuu6VkcehGoxvVX6f3OgBXORuTeX11hzy8TyPiJheqTMGEkAu/T9q23hSkwfKx1ALzp7aaMybiFi0Up5Or6V2pHb9dklq/4bT4ZdSGtSH3BUqFZ+byG9U6uQzti6ftckR8UQeBHBsg2VNqizrqDZlubH4eQ+5/+km5NcXTiRHgst0H2nyvVYh9xct0/2RHHTYCngTsDX5xuYH5D6NCVirQT7vJgdPUvHzp+SnyBsXZXo/eUCuh4FdGsx/bzHvTHKzsTeSB/VcB1hjiMp8TCWP6eRmrZsXny8UZZ1NflqYKO7tB7DvLE4edKxc1rnAu8jNAN9Jfj3nC+SnNw8VaaY2yavMY3KbZU6spB3XYPrUYtq0NvmMr+QzoUmaqyppfkV+gvvm4jv+hHzxeG0lzcQGeXyrMv3OYvttRj7hf5H8BO4Fcp/xVt+r5ffucHtNqOQxvsN5vleZZwb5ZLBTsR42I499cAy5FUgit1Koz+OjlTzOJ7ekWJfacbB4XfofV9JfT76xfTPwdvKxNqduvc+zzzB3XTLPdhnI/tDpPlaXV8P9vYtttmSx3aqfX1byP6F+eot1cR/5BPshcheRN5LrlP3IgeZU+RzUokzT2u2L5JvImyvpLiUfNxuTL5Bur0w7tc06OLWS9vZi/o3JA4deWpl2M7BoF+u23M9+0CLNlyr5f4NcF+9FrT67HYgut+kplTzf08V8q5PrnAT8pm7axHbbpJK21XHTy+PgLZW8ZpIH/t6EfB76dLEfPV3dTxrksSr5CWcidyk5nXyhtiG5XiiPhWtafa9233uAx+bPi/xmF98jAb/oYL5ti++SyBe7p5DfYLQR+di8rlLWW4BFWhzTzc47n6tM/3exvrcs1v2nyNdV9ettfJPllPvcreTrofWo1d3Ld7OOqdUdUwe4T3V1Lqmbt1pXJOCGDrfzlMo8HxrkPrMR89bns4q8/9tg2hYDXM70Is9n26Tbr/LdTm+SZrvK/prI10IfJB+Dm5LfanZLZfqjwOoN8jmokuYx8tsI291nLN/h912ZXMe8AGzWJM0Yckulcl1/hFyfn1wp12cGsK43rsx/L/lGfo8i7zcWP/clB3aq+99HG+S1Y6f7GvnaKJHHIKqftnslnxnkoMs2le11G/Me//Msr27/2LyDdXFCJf0Nxc/zyPcDG5Ifjl5cSXM/De4Vi7z2qaSbTW6dV957bVysq0OL9foCcMUAj5V1Ksu5rIP061K7J7wHWLBu+q8r+f2BXKdvTD6OTiDfvz5J7f6r4TFa7DNlPlOK7Vet6xatpG2735AfKpdp/kkebHhT8nnhcPLxU07/bAd1SwJWHsg6r+T1lUpe08itXTch1w3nVqbdDSzTJI9FK+la1neVeX5emWfnwXyHBnlXzxdtj5mO8+1BwcZVCtbNZ0Ilj4WpXbA9D2zZZFkLUQvivABs06IsE4sd8MUmy38BOKTNd1uVeSvYZp9naXCCKvJ5J7XgSqtPo2DPhBbppw5FmclPV85oMd9s8g3L5PJ/g9h/3sLcwbf6zz3kimlas+9c5FOmn9xmeRMracc1mD61mDatTT7jK/lMaJJmTfKFcrPvdhm58n1pn22Qxyuo7fONPnPIrVTafa+W0zvcVtV9cXyH8wT5ad6cDvfJgxvksRhz39zXf8bXpV+O2sVIo88fya8SbbrP0D/Bnur36OgzyDweAz7epkzTOtkXycHrP7ZZ3mRgTJvlla/MbZXPTXRx4UG+8ErkwPdyLdItzNyBxerncWCTLrfnIuSboUS+yZvnRr7N/GUg4AUqDwsYgcGeIk314r/+8wQ5eDC52b5b5PERclCkWT5/Ig/22fR7tfveAzw2392gLO/tcN73FPtPq336OmCFJvOPq6Sb2GD6guSuEM3yfhE4ng7OCeTWV83ymViXtt02mEaLerHdPkWX55IG67yadp8Ot1Uvgz2tjodGn4YBmA6W07NgT5Hu/cx9M9jscy9N6kRqwYluPl/s8PuWN4bfaZNua2qB2frPb6i7ce9w2a+j9nKUTj4zgb2b5NWTYE8x/YdtyvF98oO7pstjcMGe9ch1c7Pl3w+s0ya/91E7X7b7nD/AY6WrYE8xT7VO+HjdtDWY+xqp/vM4+cFXmUezYM+S5JZCzfLZvJK2k2DPwuSH1K3W4fO0OeboYbCnyO/rNL/PT+Qu9vM0aqjM31Wwh9yabXaR/r8M4JjvYt/oWbBnpHTjOpHaoE5HpZR+3yhRyn0xdydf5C1A7s41T9eWSvpjyX2rLyA3aZtD3jjnAf+TUjqpVaFSSv9JKW1H7k//Q/JI2I+TL5RnkZ9UnU0eP2HllFLD1/allC4hN9k7hNza42HyQfE0ubXGT8iV0jz9CVNKk8kH4oXkwEHLJqK9KHNK6cWU0r7kINUl5DfszCa3GjqT/OSj1bgCHUspXUne9j8s8n+OXDnfTH5jyZvTIPsqDpeUB2vdkNwk+G7yOnyM3ApnH/KJsmV//5SbHW5N3nf+TN5nniFH1n9A3hanDtFXGLSUHU3e/79K/u4Pkff/Z8itRX5N3tZvSCnVj3tCymMibEm+ufgzeX9u+haalN+UsQVwNPnC/hlynXEzuXXA/6QeDOb4MvIv8vo/klwf/I1aHfYkeV/8JXnck9ekPD7MoKWU/ktuSbAv+SbuYXL99wD5qfxOKaUJqU23q5TSCymlj5KPt/OL+ecU+U0t8t8iddgdoBiEsOyy8IU075tZqsueQ25RdjT5wmsOOfD/M/Kx2+1bsXahNmDxRal4dXgXyq5cCzD3q9tHpJTSF8ktcH5Lrjtnky+EvwtsmFK6qIM8ziLXB+eSz//Pkbf974HPkC+oBj3mxwD8irnfHDODDscUSPkV2GuRW0XeTL4BfI78/S6maBkw0Hou5W5f7yEfG38g15/lNcAUcveZw5rnMJd9yHXD1eTv2G2Xsp7p9lxS52Jqbxt8gtbd7VSRUvoZuRvfl8h17kPk/fVZcn18CTkwsP4A6sRBKbpCfoB8E9pyn04pXUM+J11Avk6dQ+7y9BXgXan77pKklO4kj5G5Pfka6TLyOfUp8nX7E8XfF5Fb1a2ZUvpRt8sZQLk+Rh6DZSr53qE89/6S3JqhbDkzVGaQWxAfTK7jZpGv5f5GXk/rtbs3SCmdD7yK/Na2y8j3gbPJ32U6uWXSJPJDit0a5zIkjqZW7xxeHbsn5bGiNiQHvu4gHyNPFr9/A3hjSunidgtIuWvb5sBJ5K6JZc+ZAUkpzUkp7Ul+0HU2+TxcLdt3yMfvCQNdxgDL9Xlyq6WzyfcTs8n7znXkFqpvTq0HGe/WHtRejnHmQI754RBFJKkvFOM/3Fv8eVRKaeKwFUaSJEmjXkSsTr7BWQD4bvFATFKPRMQJ1MbpWaXTBzCSWhspLXskSZKkkejj1K6ZzxjOgkiS1CmDPZIkSVIDxdtnDij+vD6l1NUb9CRJGi4Ltk8iSZIkvTxExCvJAzqvRn7JwArFpK8OW6EkSeqSwR5JkiSp5mzyq9mrzkkpXTochZEkaSAM9kiSJEnzepb8NqTJwLeGtyiSJHWnr97GJUmSJEmS9HLnAM2SJEmSJEl9xGCPJEmSJElSH3HMHqkwa9Ys+zRKkiRJo9zSSy8dw10GabjZskeSJEmSJKmPGOyRJEmSJEnqIwZ7JEmSJEmS+ojBHkmSJEmSpD5isEeSJEmSJKmPGOyR1Nfuuusu7rrrruEuhnrIbdpf3J79xe3ZX9ye/cdtKr18GOyRJEmSJEnqIwZ7JEmSJEmS+ojBHkmSJEmSpD5isEeSJEmSJKmPGOyRJEmSJEnqIwZ7JEmSJEmS+ojBHkmSJEmSpD5isEeSJEmSJKmPGOyRJEmSJEnqIwZ7JEmSJEmS+ojBHkmSJEmSpD5isEeSJEmSJKmPGOyRJEmSJEnqIwZ7JEmSJEmS+ojBHkmSJEmSpD6y4HAXQBrpxo5deriLoMLMmbOGuwiSJEmSNOLZskeSJEmSJKmPGOyRJEmSJEnqIwZ7JEmSJEmS+ojBHkmSJEmSpD5isEeSJEmSJKmPGOyRJEmSJEnqIwZ7JEmSJEmS+ojBHkmSJEmSpD5isEeSJEmSJKmPGOyRJEmSJEnqIwZ7JL1snHvuuYwdO5axY8dy5plnDjifSZMmvZTP1KlTe1dASZIkSeoBgz2SXhYeeOABDj74YJZccslB5XPLLbdw0kknDTofSZIkSRoqBnvUlYhYJyJOjojbImJWRMyJiP9ExCUR8fGIWKSSNjX4zI6IaRHx44hYt0H+4xvMMyciHoiIn0XEFk3KFRGxW0RcWJRnTkTMiIhrI+KgiFh8KNeLRraUEgceeCDLLrsse++994DzefbZZ9lvv/3YcMMNeec739nDEkqSJElS7yw43AXQ6BERRwBHkoOEfwB+DDwJrASMB74P7A9sXDfrUZXflwY2BfYC3hcRW6WUbmmwuPuAycXvSwCbA7sBu0bEbimlX1TKNRY4D3gbMAu4FJgGLAvsAHwd+FREvCul9LcBfHWNclOmTOHqq6/m4osv5uqrrx5wPkcddRT33Xcf1157LV/72td6WEJJkiRJ6h2DPepIRHyJHLS5H3h/SumGBmneBXy+/v8ppYkN0p4MfBL4LDChwSKn1c8XEUcBR5CDN78o/rcA8DPgrcCvgT1SSjMq8ywIHA0cBlweERumlB5s+4XVN+69915OPfVU9ttvP7bccssBB3uuuuoqTj/9dI477jjWXHPNHpdSkiRJknrHblxqKyLGAROB54B3NAr0AKSULgZ27DDby4ufK3RRlFOLn6+OiHK+D5MDPfcAu1YDPUWZnk8pfQk4F1gVOKaL5WmUe/755znyyCNZaaWVOOKIIwacz6xZszjwwAPZYost2G+//XpYQkmSJEnqPVv2qBN7AwsBU1JKt7VKmFKa3WGeby1+/rGLckR1UcXP/y1+fj2l9HSLeY8GPgjsGRGfSik928VyNULcddddXaU//fTTufPOO/ne977HAw88AMCjjz4KwIMPPthxfkceeSQzZszg5JNP5u677wbg8ccfB/LAz92WS73heu8vbs/+4vbsL27P/tMv23Tttdce7iJII5bBHnViq+Lnbwcyc0RMrPy5FLAJsCVwMdDNwCcHFj//mVJ6pOiitXnxvytazZhSuj0i/kNu3bMxcG0Xy9UodNtttzF58mT22GMPNthggwHnc+WVV3LppZdyyCGHsNpqq/WwhJIkSZI0NAz2qBOrFD8fGOD8Rzb43+3AOSmlJ5rMM64SJFoC2AzYGngR+ELx/2WBhYvf7++gHPeTgz2rdpBWI1CnT2+ef/55dt99d9Zaa62Xul2V8y677LIArLTSSm3ze+yxxzjppJPYdtttOeyww4ioNS5baqmlAFhttdV8qjSflU8jXe/9we3ZX9ye/cXt2X/cptLLh8EeDbmU0kt3yBGxBLAecAJwdkSsl1I6vMFsr6IWJHoeeBi4gNxd67ohLrJGuSeffPKl7lZbbrllwzSf/vSn+fSnP81+++3HCSec0DDN/fffz4wZM7jqqqtYZpllGqbZZZddADjuuOM44IADelB6SZIkSRocgz3qxH+BdYFXDjajlNJTwI0RsSu5pdAhEXF6Sqm+Zc5VKaXxbbJ7FJhDbt2zOtCu8/Hqxc//dFdqjTaLLLIIe+65J1AbW6dsifOXv/yFv/71r2yxxRastdZabLrppk3zWXbZZV/Kp951113HPffcw9ve9jZWXnllXv/61/f4W0iSJEnSwBjsUSeuBd4CbA/8oBcZppRmRsSdwIbFp5NuWPV5PB8RN5C7d72VFsGeiFiX3H1rNt0NCq1RaLHFFuPkk08G5m2ufPzxx/PXv/6V3Xffnb322uuleZ5++mkeeOABFltsMVZfPccFV1tttZfyqbf//vtzzz33cOCBBzJ+/Pgh/DaSJEmS1B1fva5O/Ij82vX3RUTL5gsRsUgX+Zb9YgazH36/+HlQRCzWIt2Xi58/8U1cauTmm29m00039dXqkiRJkkY9gz1qK6U0DZhI7i51SURs3ChdROwI/KqTPCNiF+DV5CDSYMbgORv4HbAW8POImGtglYgYExFHAx8md0f7yiCWJUmSJEnSiGc3LnUkpXRc8arzI4GbIuI6cneoJ4GVgG2AtWnQRaru1etLAK8Hdir+/lJK6cFBlOuFiHgf8HPgHcA/I+IS4D7y27p2IAeVpgE7p5SmD3RZ6g+HHXYYhx122Dz/33rrrZk5c2bH+Zx22mmcdtppvSyaJEmSJPWEwR51LKV0dET8DDgA2A7YG1gUmAHcApwInNVg1uqr118gv1nr/4BTUkq/6UG5HouItwLvB/Ykjy20HDkQ9XfgVOC0lNLTg12WJEmSJEkjncEedSWl9HfgUx2mjfap5plnKjCQ+RJwXvGRJEmSJOllyzF7JEmSJEmS+ojBHkmSJEmSpD5isEeSJEmSJKmPGOyRJEmSJEnqIwZ7JEmSJEmS+ojBHkmSJEmSpD5isEeSJEmSJKmPGOyRJEmSJEnqIwsOdwGkkW7mzFnDXQRJkiRJkjpmyx5JkiRJkqQ+YrBHkiRJkiSpjxjskSRJkiRJ6iMGeyRJkiRJkvqIwR5JkiRJkqQ+YrBHkiRJkiSpjxjskSRJkiRJ6iMGeyRJkiRJkvqIwR5JkiRJkqQ+YrBHkiRJkiSpjxjskSRJkiRJ6iMGeyRJkiRJkvqIwR5JkiRJkqQ+YrBHkiRJkiSpjxjskSRJkiRJ6iMGeyRJkiRJkvqIwR5JkiRJkqQ+YrBHkiRJkiSpjxjskSRJkiRJ6iMGeyRJkiRJkvqIwR5JkiRJkqQ+YrBHkiRJkiSpjxjskSRJkiRJ6iMGeyRJkiRJkvqIwR5JkiRJkqQ+YrBHkiRJkiSpjxjskSRJkiRJ6iMGeyRJkiRJkvqIwR5JkiRJkqQ+YrBHkiRJkiSpjxjskSRJkiRJ6iMGeyRJkiRJkvqIwR5JkiRJkqQ+YrBHkiRJkiSpjxjskSRJkiRJ6iMLDncBpJFu7Nilh7sIGpSNh7sA6jm3aX9xe/aTm24a7hJIkiSwZY8kSZIkSVJfMdgjSZIkSZLURwz2SJIkSZIk9RGDPZIkSZIkSX3EYI8kSZIkSVIfMdgjSZIkSZLURwz2SJIkSZIk9RGDPZIkSZIkSX3EYI8kSZIkSVIfMdgjSZIkSZLURwz2SJIkaVhceOGFHHzwwey0006svvrqjB07lk984hNN0z/xxBN89atfZZNNNmGllVbiVa96FbvuuitXXXVV18t+6KGHOPjgg9lggw1YccUVWXPNNdljjz245ZZbBvOVJEkaERYc7gJIkiTp5WnSpEncdtttLLnkkqy66qo88cQTTdPOnDmTHXfckTvuuIN1112Xvffem6eeeopLL72U97znPXz7299mr7326mi59913HzvssAPTp09no402Yuedd+aRRx7h4osv5vLLL2fKlClsv/32vfqakiTNd7bs0ZCLiFT3mR0RD0fEnyLi+xGxU0SMaTLv5GKeCR0sZ2KDZT0bEXdHxHcjYlyPv5okSRqE4447jptvvpn777+fr3/96y3THn/88dxxxx3svPPOXHPNNZxwwgmcfPLJXH/99ay22moceuih/Pvf/+5ouV/84heZPn06++67L1dccQXHHnssZ5xxBldddRWLLrooBx54IE899VQvvqIkScPCYI/mp6OKz0nAFGAmsCdwKXB9RLy2R8u5qrKs7wGzgf8F/hQRa/doGZIkaZC22WYb1lxzTSKibdpLLrkEgC996UssuGCtcfoKK6zAAQccwDPPPMNZZ53VNp9nn32WK664ggUWWIAvf/nLcy17rbXWYo899mD69OlcdNFFA/hGkiSNDAZ7NN+klCYWn6+klD6VUnoLsAbwM2Bj4IqIWLEHi5paWdangDeQA0rLAF/qQf6SJGk+e/DBBwEYN27cPNPK/3Uyds9jjz3Gc889x3LLLccrXvGKQeUlSdJIZbBHwyql9CDwIWAqsHI8KoEAACAASURBVDpDEIxJKb0ITC7+3KTX+UuSpKG33HLLAXm8nXrTpk0D4O67726bz9ixYxkzZgwzZszgySefHFRekiSNVA7QrGGXUnoxIo4BxgO7R8TnUkppiBb33BDlK0mSCnfddVfX85Tj7Tz++OMN599ss8248MILOfzwwzn22GMZMyYP9/fYY4/x7W9/+6XfO1n2RhttxI033sghhxzC5z73uZf+f//99/OTn/wEyG/rGsj36Eeuh/7TL9t07bUdoUFqxmCPRoprgeeBFYFxwL29yrgY/PnjleVIkqRRZr/99uP666/nt7/9LdOmTWOTTTbhmWee4eqrr2aFFVZg+vTpLLBAZ43WDzroIPbZZx9++tOfcuutt7LBBhswc+ZMrrzySlZffXX+8Y9/dJyXJEkjkcEejQgppdkRMQNYCViBwQV7xkfExOL3ZYG3AesAtwNfHUw5JUlSewN52j59+nQAllpqqYbzr7322lxzzTVMmjSJyy67jPPPP5/llluO3Xbbjf33358NN9yQFVZYoaNll3mdeOKJTJ06lfPOO4+VV16ZAw88kO23356ddtqJVVdd9WXfaqBs/fFyXw/9xG0qvXwY7NFIUr4OY7BduLYtPlW3AONTSrMGmbckSRomK664IpMmTWLSpElz/b8cTHnDDTfsOK9Xv/rVnH766fP8v+zG1U1ekiSNNLZP1YgQEYuSW+EAPDzI7I5KKQUwhvy2r28DbwLOiwj3eUmS+syUKVMA2G233Qad17nnntuzvCRJGi7e+Gqk2Irc0uzBlNK0XmSYUnoxpXR/SukzwM+BtwOf7EXekiRp/nrxxRcbvj1rypQpTJkyhc0224x3vetdc02bMWMG//jHP5gxY8Zc/589ezazZ8+e638pJb72ta9x7bXXsuuuu/KmN72p919CkqT5xG5cGnZFa5vDiz9/OkSL+TywM3BERExOKT0+RMuRJEkduvjii7nkkkuA/PYrgBtvvJH9998fyK9bP+aYYwB4+umnee1rX8v48eN59atfzQILLMANN9zAjTfeyOte9zomT548z6DK3/3udznxxBM59NBDOeyww176/z333MNOO+3EdtttxxprrMFzzz3HVVddxe23384WW2zBN7/5zfnx9SVJGjIGezSsImJF4BTya9f/BRw3FMtJKf0rIr5HbtnzeeDIoViOJEnq3K233so555wz1/+mTZvGtGnTAFh99dVfCvYsssgi7Lrrrlx//fVMnToVgNe85jV85StfYf/992fxxRfveLkrrrgib3/727nxxhu57LLLWGihhXjd617HpEmT2HvvvVlwQS+RJUmjW6Q02LFwpdYiotzJjip+LgCMBdYjd99aGLgR2COldHfdvJOBjwK/B+aaVvHTlNLlxRu4jiSP2TOxQTlWAe4hv+L9NSmlR6rTZ82a1fBgGDt26dZfUJIkAXDTTX8EfNNPv/DNTf3n5bJNl1566WifSupvPrbQ/FS2ppkDPAHcB5wJnA9cnlJ6scW8WxafRm4BLm+38JTSfyPiNOAg4DByCx9JkiRJkvqKwR4NueLNWAOddwIwocO0E4GJbdJ8HoM8kiRJkqQ+5tu4JEmSJEmS+ojBHkmSJEmSpD5isEeSJEmSJKmPGOyRJEmSJEnqIwZ7JEmSJEmS+ojBHkmSJEmSpD5isEeSJEmSJKmPLDjcBVDvRMT6wFbAIsBvUkq3D3ORJEmSJEnSfGawZxSJiB2AI4FrU0qH1E37IvBVaq21UkQcnlI6cT4Xs+/MnDlruIugQbjrrrsAWHvttYe5JOoVt2l/cXv2l2JzSpKkYWY3rtHlA8BmwK3Vf0bEm4BjgTHAv4Fp5G17XERsOZ/LKEmSJEmShpHBntFls+Ln5XX//wQQwAXAuJTSmsApxf8OmH/FkyRJkiRJw81gz+iyIjAnpfRg3f93BBJwfErpxeJ/xxQ/bdkjSZIkSdLLiMGe0WUs8Ez1HxGxCjAOmJFSurn8f0rpIeAJYKX5WUBJkiRJkjS8DPaMLo8DS0fEEpX/vaX4eW2D9AmYPeSlkiRJkiRJI4bBntHlr8XPjwFERJDH60nA76oJI2IZYCngv/OzgJIkSZIkaXgZ7BldziQPuvz1iLgEuBHYmty1a0pd2m2Kn3+ff8WTJEmSJEnDzWDP6PJj4BxgQWAnYCNgDvDJlNLDdWk/Uvz87fwrniRJkiRJGm4LDncB1LmUUgL2iIjTgc3JY/j8NqV0dzVdRCwETAO+BVw0v8spSZIkSZKGj8GeUSQilip+vS6ldE2zdCml54CD50+pJEmSJEnSSGI3rtFlJvAosOpwF0SSJEmSJI1MtuwZXZ4Enk8p3T/cBZEkSZIkSSOTLXtGl3uBxSPCIJ0kSZIkSWrIYM/och6wELDLcBdEkiRJkiSNTAZ7RpdJwB+BMyJi++EujCRJkiRJGnnsDjS6fBG4ElgXuDwi/gr8AXgYeKHZTCmlo+dP8SRJkiRJ0nAz2DO6TAQSEMXfbwQ2aJE+ivQGeyRJkiRJepkw2DO6nEkO3kiSJEmSJDVksGcUSSlNGO4ySJIkSZKkkc0BmiVJkiRJkvqIwR5JkiRJkqQ+YrBnFIqIV0fEtyPi7xHxZEQ8Xzd9bEQcERFfiYiFhquckiRJkiRp/nPMnlEmIt5LHqh5cWpv5Zpr0OaU0syIeAuwNXA7cP58LaQkSZIkSRo2tuwZRSJiHeBsYAngu8A2wCNNkn+PHAx61/wpnSRJkiRJGgls2TO6HAwsCnwjpfR5gIh4oUnaK4qfm86PgkmSJEmSpJHBlj2jy/bkLlsntUuYUnoQeApYfagLJUmSJEmSRg6DPaPLysATRSCnE7OBhYewPJIkSZIkaYQx2DO6PAUsERFj2iWMiFcAY4FHh7xUkiRJkiRpxDDYM7r8jbzNNuog7QeLtDcPaYkkSZIkSdKIYrBndDmP/Iatr0ZE020XEW8ATiCP73P2fCqbJEmSJEkaAQz2jC5nAH8F3gr8NiLeS/FGtYh4Q0S8KyJOBa4HlgV+D5w7XIWVJEmSJEnzn69eH0VSSs9FxI7ARcC2wDaVybdUfg9ywGfXlFKaj0WUJEmSJEnDzJY9o0xKaTrwP8AngOuA58jBnQBeBG4E9ge2SSk9MlzllCRJkiRJw8OWPaNQSul54PvA94s3cy1LDtzNKKZJkiRJkqSXKVv2jCKNBmVOKb2QUno4pfRgo0BPRKw2f0onSZIkSZJGgnBIl9EjIs5MKe3VRfpxwJUppdcMWaH6yKxZsxoeDGPHLj2/iyJJkiSNaDNnzhruIjS19NJLx3CXQRputuwZXT4SESd3kjAi1gSuBl41tEWSJEmSJEkjicGe0eVh4ICIOPb/s3fn4XqN9/7H398miDE7UqRqSFXQOjG1VRUhMTtExKxU5KdH6UB1OKetU5RSrZOilFKtxNSGFkFQ1KxqKkUpoWKOkDQ7SCLT9/fHesLOzrOTvbOHZ9jv13U918pzr3ut9V1dVpP92Wvd95I6RcTGwN3AOhTTr0uSJEmSpG7CsKe27ArMAL4XEd8t1yEiNgXuAtYuLffoquIkSZIkSVLlGfbUkMz8O7AnMBs4IyKOaro+IjYH7gTWAm4H9szM97q8UEmSJEmSVDGGPTUmM/8CjADmAudHxCEAEfE54A7go8DNwLDMnFWxQiVJkiRJUkUY9tSgzLwV+CKQwJiIOAG4FegDXA+MyMz3K1iiJEmSJEmqEMOeGpWZ1wBHAT2BU4DewDXA/pk5p5K1SZIkSZKkyjHsqWGZeQnwLSCA3wEHZua8ylYlSZIkSZIqqWelC1B5ETG/Dd0TOBg4OCIWW5eZXmdJkiRJkroJn+ypXtFBH6+xJEmSpIo66aST2Hvvvdl0003p168f/fv3Z/DgwZxxxhlMmzat7DYPPvggBxxwAP3796dfv35su+22nH/++cyf35bfi0NE9IiIQyPi3oiYHBEzI+K5iLgkIjbtiPOTqk1kZqVrUBkRsX5H7SszX+qofdWzxsbGsjdDQ0Pvri5FkiRJqmrTpze2qf8aa6zB5ptvzsYbb8waa6zBe++9xyOPPMJjjz3Gxz72MW677TbWWWedD/pPmDCBww8/nF69ejFixAj69OnDLbfcwsSJExk+fDhjx45t8Vi9e/de5HWHiBgHHAi8CtwAvAMMBHanmOV4j8y8o00nJFU5wx4ts4hY2n88ozJzTJntVgFeB1YFLsvMw5dynIOA/wdsBTQAjcAU4FHg1sy8LCI2BCa28RQGZ+Z9C78Y9kiSJEmt09awZ/bs2fTq1Wux9lNPPZXRo0dz5JFHMnr0aABmzJjBVlttxYwZM/jTn/7Elltu+cE+9t57bx566CF+85vfsN9++5U9VtOwJyI+BzwE/APYOjNnNlk3CvgtcGdm7timE5KqnGO5qCP8qIX2x1toP4Qi6Elg/4g4LjP/Xa5jRPwWGAXMBG4EJgE9gI2BvYHBwGXAtDJ1fAT4Yek4p5TZ/cst1CdJkiSpA5ULegD22WcfRo8ezQsvvPBB2/jx43n77bc5+OCDPwh6Fu7jhBNOYPjw4UsMe5rZoLT8c9OgZ+GhSss1WnseUq0w7KkhEbEi8DlgVmY+vJS+nwNWBB7KzNmdWVdmntzGTY4C5gM/B74LfAn4RfNOETGEIuh5Cdg2M19vtn55YIdSDdOAk5ut70kR9ixYhholSZIkdbJbbrkFgE03/XDonHvvvReAnXfeebH+gwYNYqWVVuKhhx7i/fffZ4UVVljaIf5RWu4YEStm5qwm6/YqLW9fpuKlKmbYU1sOA34FnA0sMewB/gs4svQZ07lltV5EbAF8FrgZOBP4JkWti4U9wLal5R+aBz0AmTkHuK2TSpUkSZLUwc4991zeffddZsyYweOPP84DDzzApptuyvHHH/9Bn4kTi9EZNtxww8W279mzJ+uvvz7PPPMMkyZNYuONN17i8TLzqYg4Czge+GdE3EgxZs+mFGP2/B743w46PalqGPbUlv1Ly8ta0fci4MsUA5GN6ayClsFRpeWYzHwrIm4ChkfEFzLzgWZ9p5aWG3VdeZIkSZI6y7nnnsuUKVM++L7zzjtz/vnn89GPfvSDthkzZgCw2mqrld3HwvbGxtaNG5SZ34qIZ4GzgK82WfUoMDYz32vLOUi1wLCntmwMzAH+3oq+fyv13aRTKwIi4uQyzZOaD84cESsBhwL/5sP3Y8cAwylCoOZhz80UqfuwiLgOGEfxRNML6cjikiRJUsUsfPqmrSZMmADA1KlTeeKJJzjvvPPYdtttOeuss9hkk+JHl7lz5wIwadKkstOsz5o1a7G2lkREAOdQhDz/C1wOTAe2oAh/bo6Ir2fmL5fphKQq9ZFKF6A26Qe825qgIzMXUAQl/Tq9KjipzOeIMv0OAlYDfpeZ75faJgBvAQdGxCLTXmXmy8C+wIsUgdCVFDNuTY+ImyPiixHhf8OSJElSjenbty9Dhw7lvPPOo7GxkZNOOumDdSuvvDIA7777btltF7b37t2qWXNHAt8AfpGZZ2Tmq5n5bmlW3mHALOCM0ozBUt3wyZ7aMgPoU2ZgscWUBnNuKG3TqTIzlt4L+PAVrkuabDs3Iq4EjqN46uf8Zvu+vTSt+nbA9hTTrw+ieL92d+DwiNi7NH6PJEmSpC4wYMCADtvPJptswpNPPsnqq69O3759GThwIM888wxz5sxZ7Djz5s1j8uTJ9OzZk/79+7fmEAsHYb6z+YrMnBwR/wS2pHiL4tH2nY1UPXwqorY8QXHN9m1F3/0opih/qlMraqWIGAhsAzyVmY80Wz2mtPyvcttm5oLMvCczf5yZ+1I8rbQHMAXYjQ9DJEmSJEk1ZvLkyQD06NEDgMGDBwNw++2LT5J1//33M3PmTLbeeuvWzMQFsLBTS9OrL2z3l8eqK4Y9teUqIICfR8SmLXWKiP+gmNY8S9tUg4WBzH9ERDb9AI+V1m0REVsvbUdZuAU4sdS0YyfUK0mSJKkDPP/882UHU16wYAGnnnoqb731Fp///OdpaGgAYPjw4fTt25drrrmGxx577IP+s2fP5rTTTgPgyCOPXGRfM2fO5LnnnuOVV15pfph7S8tvNR82IiKOBtYBJgNPt+MUparja1y15bfAMcDmwMMR8VuKQYxfLq1fH/hPivFyegFPUszKVVGlV8oOA+bT8sxg6wK7Ujzd81Ard/3OwkO0pz5JkiRJnefWW2/llFNOYZtttmH99ddn9dVXZ8qUKdx///1MmjSJtdZai3POOeeD/qutthrnnHMOI0eOZK+99mLfffelT58+3HzzzUycOJHhw4ez776Lvuzw6KOPMmzYMAYNGsR9993XdNX5FMNFbAY8FxHXUwzQvBXFL43nA1/LzMVHgpZqmGFPDcnMeRGxJ3Ajxejxx5Q+zQXwOLB3Zs7twhJbcgDF+EE3ZOaXy3WIiAbgdeDgiPhWZr4TEf8JLA/cmJnzmvVflWKcH4B7Oq90SZIkSe0xZMgQXnzxRR544AGeeOIJGhsbWXnllfnkJz/JQQcdxNFHH02fPn0W2WavvfZiwoQJjB49muuvv57333+fDTbYgNNOO42jjz6aYpKtpcvMdyNiEPAtiuEwvkjxM8ZbwNXA/2Vma3/ZLNWMcAbr2hMRywNfBr4EfIYPQ7t5FIOKXQr8prMHLS69grXUAZoj4j6KQZWHZ+b1S+h3GcUTQEdn5oUR8R3gTGAaxeOXz1Oc47rAnkBv4C/ATpk5u8z+egJzgfmZudRgs7GxsezN0NDQqlH+JUmSpG5j+vTFX8uqFr179/bJf3V7hj01rhRorF76Oq35EzCdfOylhj0R8SmK91/fANZd0uOREbE9cDfwt8z8TESsAewN7ELx2OXawMrAvyleUbuaItQq+/SSYY8kSZLUOQx7pOpm2COVGPZIkiRJrWPYI1U3Z+OSJEmSJEmqIw7QXKVKrzQBzMzMR5q1tUlmOoCxJEmSJEndhGFP9boLSOBZ4NPN2toi8TpLkiRJktRtGAJUr5cpgprXy7RJkiRJkiSVZdhTpTKzf2vaJEmSJEmSmnKAZkmSJEmSpDpi2FNDImL7iNimDf23XtZBnSVJkiRJUm3yNa7achfwBvDxVvYfB6yL11mSJEmSpG7DJ3tqT3Ryf0mSJEmSVMN84qO+rQrMqXQRtW769MZKl6B2mDhxIgADBgyocCXqKF7T+uL1rC9ez/ri9aw/XlOp+/DJnjoVEVsDqwOvVboWSZIkSZLUdXyyp4pFxEhgZLPm1SPijiVtBjQAnwYSuLmTypMkSZIkSVXIsKe69QeGNGtbvkxbS+4BTuy4ciRJkiRJUrUz7Klu1wGTSn8O4LdAI/DNJWyzAJgB/CMzn+/U6iRJkiRJUtUx7Klimfl34O8Lv0fEb4FZmTm2clVJkiRJkqRqZthTQzLTAbUlSZIkSdISGR5IkiRJkiTVEcMeSZIkSZKkOmLYI0mSJEmSVEcMeyRJkiRJkuqIYY8kSZIkSVIdMeyRJEmSJEmqI4Y9kiRJkiRJdcSwR5IkSZIkqY70rHQBWnYRsSawFbBGqekt4G+ZOaVyVUmSJEmSpEoy7KlBEbEd8GNgcAvr7wH+NzPv79LCJEmSJElSxfkaV42JiKOBOymCngAWAFNKn/mlth2AuyLiK5WqU5IkSZIkVYZhTw2JiC2B84AewP3AbsAqmfmxzPwYsCqwe2ldD+C80jaSJEmSJKmbMOypLd+muGZXAUMy87bMfH/hysx8PzNvpXiy5w8Ugc+3KlKpJEmSJEmqCMOe2rIDkMDxmbmgpU6ldd8s9R3SNaVJkiRJkqRqYNhTW9YApmfmG0vrmJmvA9P5cKYuSZIkSZLUDRj21JYZwKoRsfLSOpb6rFbaRpIkSZIkdROGPbXlbxTj8Bzbir7Hlfo+2qkVSZIkSZKkqmLYU1suopha/dSI+HFE9G7eISI+FhE/B06hGLPnoi6uUZIkSZIkVVDPSheg1svMayLiMuBLwPeBb0fE34HXgF7AesAAYDmKUGhsZl5bqXolSZIkSVLXM+ypPUcAzwDfoxiTZ+syfWYApwP/13VlSZIkSZKkamDYU2MyM4EzIuJcYBdgKz6ccestinF9bs3MmRUqUZIkSZIkVZBhT43KzPeA60ofSZIkSZIkwAGaJUmSJEmS6ophjyRJkiRJUh3xNa4qFRF3lP74UmaOatbWFpmZO3VcZZIkSZIkqZoZ9lSvIaXlP8u0tUW2uxJJkiRJklQzDHuq16jSsrFMmyRJkiRJUlmGPVUqM8e2pk2SJEmSJKkpB2iWJEmSJEmqIz7ZIy1FQ0PvSpegdvlspQvoMtOnNy69kyRJkqS6Z9hTpSJivY7aV2a+3FH7kiRJkiRJ1c2wp3q92EH7SbzOkiRJkiR1G4YA1SuqbD+SJEmSJKkGGPZUqcx08GxJkiRJktRmBgqSJEmSJEl1xLBHkiRJkiSpjvgaVw2LiDWBrYA1Sk1vAX/LzCmVq0qSJEmSJFWSYU8NiojtgB8Dg1tYfw/wv5l5f5cWJkmSJEmSKs7XuGpMRBwN3EkR9ASwAJhS+swvte0A3BURX6lUnZIkSZIkqTIMe2pIRGwJnAf0AO4HdgNWycyPZebHgFWB3UvregDnlbaRpEVcccUVNDQ0LPGz+uqrt2pfAwcObHEfG220USefiSRJkqTmfI2rtnybIqC7CvhiZi5oujIz3wdujYjbgd8D+wPfAr7U1YVKqm4DBw7kf/7nf8que+CBB7jnnnvYZZddWr2/1VZbjWOOOWax9lVWWWWZa5QkSZK0bAx7assOQALHNw96msrMBRHxTWA/YEgX1bbMIiJb0W1oZt5V6n8EcAkwNjOPKLV9hOL1tu0pgrDflTnOBsDfgbnAZpn5akfUL9WizTbbjM0226zsuoUhz8iRI1u9v969e/P973+/Q2qTJEmS1D6GPbVlDWB6Zr6xtI6Z+XpETOfDmbpqwY+WsG7SkjYsBVwjKcKcX0bEPZn52sL1EdEDuBxYBTjYoEcq7x//+AcPP/wwa6+9Nrvttluly5EkSZK0DAx7assMoCEiVs7M95bUMSJWBlYD/t0llXWAzDy5ndtPiojjKJ76GRMRu2bmwqeGvg98AbgyM8e1r1Kpfo0ZMwaAww47jB49erR6uzlz5jBu3DheffVVVlppJTbddFMGDRrUpn1IkiRJ6hiGPbXlb8AuwLHAT5bS9ziKQZof7eyiqklmjomIvYERFP8bnB0RnwVOBF4BvlbJ+qRqNmvWLK666ip69OjB4Ycf3qZt33zzTb7ylUUnAFx//fX55S9/yXbbbdeRZUqSJElaCmfjqi0XUUytfmpE/DgiejfvEBEfi4ifA6dQjO9zURfXWA2OAt4EflIKei6nCDZHZub0ilYmVbFrr72WxsZGdt55Z9ZZZ51Wb3fooYcyfvx4nnvuOV5//XX+8pe/MGrUKF5++WUOOOAAnnzyyU6sWpIkSVJz8eFbLqoFETGWYnatBOZQjFHzGtALWA8YACxHEQqNzcxRFSq11ZoM0NzSmD2zM/OMJv2PoNkAzWX2uRdwA/A+sALw88z89pLqaGxsLHszNDQslqlJVenhhx9p1/ZHHnkkTzzxBKNHj2b77bdvdz1nn302V1xxBUOGDOHMM89s9/4kSZKaGjBgQNn23r17RxeXIlUdX+OqPUcAzwDfoxiTZ+syfWYApwP/13VldYiTWmhvBM5oYV1ZmXljRNxNMYPZy8AP2lmbVNdeeOEFnnjiCdZcc00GDRrUIfvcb7/9uOKKK3jsscc6ZH+SJEmSWsewp8aUBhw+IyLOpRi/Zys+nHHrLYpxfW7NzJkVKnGZZWaHJfARsSPFNOwA6wCfB+7pqP1L1ail3261xsUXXwzAqFGj2GSTTTqknjXXXBOA2bNnt6u25iZOnAi073xVPbye9cXrWV+8nvXHayp1H4Y9VSoiTgTezcyfl1tfmo3rutJHTUREAzAGmEcxmPV5wNiI2Cwz36lkbVI1mj17NuPGjaNHjx586Utf6rD9PvJI8VpZ//79O2yfkiRJkpbOAZqr18nAd5o2RMS/IuKvlSmnppwPrAuclJm/An4K9AfOqmRRUrW67rrrmD59+hIHZp47dy7PPfccL7744iLtzz77LO+9995i/V966SW++93vAnDggQd2fNGSJEmSWuSTPdUrWTyM608xELNaEBEHAYcA91OEPFAEZ/8JHBkR12XmjRUqT6pKY8eOBeCII45osc/rr7/O1ltvzbrrrrvI7FrXXHMNv/zlL9l2221Zd911WWWVVXjxxRe59dZbmT17Nrvuuivf+MY3OvsUJEmSJDVh2FO9pgF9I2JVXz1qnYj4OHAB8C5weGYuAMjMuRHxJeAR4NcRMTAz365gqVLVePbZZ3nggQf4+Mc/zq677trm7QcPHszzzz/PE088wV//+ldmzpxJ79692WabbTjooIM4+OCDiXBCDEmSJKkrGfZUr79SPI1yfURcTRFgAKwYEYe3ZUeZeWlHF9cZIuLkJay+LjMfX8K2QTFOTx/gy5n5r6brM/OpiPgh8DOKQOiAdhcs1YGNN96Y6dOnL7Xf+uuvX7bfdtttx3bbbdcZpUmSJElaRoY91esUYCjF1OHbN2lfDbikjfuqibCHlqdeB5gEtBj2UAzEvDMwPjN/00Kf0cAwYP+IOCwzL1+mKiVJkiRJqmKGPVUqMx+OiC2Ao4BNgRWBIcBc4IEKltbh2jrlemaOoXiKp2nbOcA5S9luAYsGZ5IkSZIk1R3DniqWmc8D/73we0QsAKZl5tDKVSVJkiRJkqqZYU9teRl4s9JFSJIkSZKk6mXYU0Mys3+la5AkSZIkSdXtI5UuQK0XEfMj4rU29H8xIuZ1Zk2SJEmSJKm6GPbUlih92rqNJEmSJEnqJgx76tsKwPxKFyFJkiRJkrqOYU+dioh+wJrA25WuRZIkSZIkdR0HnZsJXQAAIABJREFUaK5iEbE9MKRZ8yoRceKSNgMagN1Lf76/c6qTJEmSJEnVyLCnug0FTgKySdvKpbYlWThOzzTgR51QlyRJkiRJqlKGPdXtcWBsk+8jgdnAVUvYZgEwA/gHcG1mTu288rqH6dMbK12C2mHixIkADBgwoMKVSJIkSVLXMOypYpk5Hhi/8HtEjAQaM3NU5aqSJEmSJEnVzLCntowA3oyIHpnpLFuSJEmSJGkxhj215VqK17Q+AbxS4VokSZIkSVIVMuypLe8C8zLToEeSJEmSJJX1kUoXoDZ5EVgpIgzpJEmSJElSWYY9teUqYDlgn0oXIkmSJEmSqpNhT205E3gEuDAidqp0MZIkSZIkqfr4OlBt+R5wB/Ap4NaIeAJ4AHgLaHF2rsw8pWvKkyRJkiRJlWbYU1tOBhKI0vfNgc2W0D9K/Q17JEmSJEnqJgx7asulFOGNJEmSJElSWYY9NSQzj6h0DZIkSZIkqbo5QLMkSZIkSVIdMeyRJEmSJEmqI77GVaMiYghwILAVsEap+S3gb8BVmXlXZSqTJEmSJEmVZNhTYyLio8AVwM4Lm5qs/gTwOeArEXEbcFhmvt3FJUqSJEmSpAoy7KkhEbE8cBvFdOsBPADcAbxa6rIOsCPwBWAX4NaI2CYz51SgXEmSJEmSVAGGPbXl68DmwDTgkMy8rUyfH0bErsDvSn2/BpzVdSVKkiRJkqRKcoDm2nIQkMBRLQQ9AGTmrcBRFE//HNxFtUmSJEmSpCpg2FNbNgZmA9e2ou+1pb6bdGpFkiRJkiSpqhj21JblgLmZmUvrmJkLgLn4qp4kSZIkSd2KYU9teRlYNSK2WlrHiPgMsGppG0mSJEmS1E0Y9tSWmyjG4flNRKzRUqeIWAv4DcX4PhO6qDZJkiRJklQFfMWntvwUGEkx9fo/I+LXwF3Aa0AvYD1gKHAEsBLFrF0/q0ShkiRJkiSpMgx7akhmTomI/wSuA/oB3y19mgvgDWCfzJzShSVKkiRJkqQK8zWuGpOZDwGfBk4CnqR4VStKnyy1nQhsmpkPV6pOSZIkSZJUGT7ZU4MyczpwKnBqRCwHrF5aNS0z51auMkmSJEmSVGmGPTWuFO68Wek6JEmSJElSdTDsqQERsQKwD/AZYDVgOvAgcENmzqtkbZIkSZIkqboY9lS5iNgWuJpiQObmJkXEPpn5ZBeXJUmSJEmSqpQDNFexiPg4cCNF0LNwAOa3Fq4GPgHcFBG9K1OhJEmSJEmqNoY91e04oIHita3DgZUysx+wMnAsMAtYGziyYhVKkiRJkqSqYthT3XaheJrn2My8PDPnAGTm7Mw8j2L69QB2rWCNkiRJkiSpihj2VLcNKMKeP7aw/uom/SRJkiRJkgx7qtyqwFuZObvcysx8qfTHlbuuJEmSJEmSVM0Me6pftqJPdHoVkiRJkiSpJjj1urQUDQ1OdlbbPlvpAtThvKb1xetZX7ye9aU+r+f06Y2VLkGSOp1hT/VbPSLuaEefzMydOrooSZIkSZJUnQx7qt/ywJB29GnNa2CSJEmSJKlOGPZUt7GVLkCSJEmSJNUWw54qlpmjKl2DJEmSJEmqLc7GJUmSJEmSVEcMeyRJkiRJkuqIYY8kSZIkSVIdMeyRJEmSJEmqI4Y9kiRJkiRJdcSwR5IkSZLKmDZtGpdeeimHHnooW265Jf369WO99dZj991359JLL2XBggWL9D/mmGNoaGhY4mfvvfdu1bFfffVVvv3tb7PTTjux0UYbseaaa7LJJpuwxx57cPnllzN37tzOOGVJdcKp1yVJkiSpjOuuu45vfetb9OvXj8GDB7POOuswZcoUbrjhBo499lhuv/12xo4dS0QAsOeee7LeeuuV3de4ceOYNGkSu+yyS6uO/eKLL3L11Vfzmc98hj333JM+ffowbdo0br/9dr7+9a8zbtw4rr32Wnr29Ec6SYuLzKx0DapDEdED+H/AYcBAYFXg38Bk4CHg+sy8vtR3CHAncHdmDimzrz2BqyieRDs4M8dHRH/gxVKX94CPZeY7ZbYN4Hlgg1LT0My8q1zNjY2NZW+GhobeSztdSZIk1Yjp0xtb3ffuu+9m5syZ7LbbbnzkIx++FPHmm2+y00478eqrrzJ27FiGDx++lGNO51Of+hTz58/nmWeeoW/fvks99pw5c+jZs+cixwWYO3cuI0aM4L777uOSSy5hxIgRrT6fiRMnAjBgwIBWb1OLevfuHZWuQao0X+NShysFPTcCFwGbATcBo4HLgTeALwL/3cp9jQKuA94Hds7M8c26zANWBg5pYRc7UQQ989p2FpIkSerudthhB/bYY4/FApe11lqLUaNGAXDfffctdT/jxo1j1qxZDBs2rFVBD8Dyyy+/2HEBlltuOfbcc08AXnjhhVbtS1L34zN/6gyHALsDfwd2yMxFfn0SESsBn1/aTiLi+8DpwKvA7pn5jzLdHgXWB/6LIlxq7r8ogqI7gD3acA6SJElSi5ZbbjmAVr1GNXbsWABGjhzZ7uPOnz+f2267DYBNN9203fuTVJ8Me9QZti0txzQPegAycybFa1tllV69Ohs4FniaIuh5pYXu84BLgO9HxOaZ+fcm+/kosA/wB8D3FSVJktQh5s2bx+9//3sAdt555yX2feihh3j66afZcMMN2X777dt8rKlTp3LRRReRmUydOpU777yTf/3rXxxwwAHssYe/y5RUnmGPOsPU0nKjtm4YEcsDlwIHAX8BhmXmtKVsdjHwPYqneL7epH0ksDzwa+DLba1FkiRJKufkk0/m6aefZtddd2WnnXZaYt8xY8YAy/5Uz9SpU/npT3/6wfeI4Bvf+AYnnnjiMu1PUvfgAM3qcBGxJfAgRZh4BXAt8GhmvtRC/yEUT/o8SjGI887ADcBBmTmrhW36UwzQfH9mbhcRtwOfAdZeuE1EPAP0yMyNIuJy4FAcoFmSJKlbe/jhR9q1/e9//3tGjx5N//79ufjii+ndu+V/K7777rvssccezJ8/n5tuuomGhoZlPu78+fN56623uPPOO7nwwgvZYIMNOOuss5Z4/HrX0kDTDtAsOUCzOkFmPkYxC9ebpeUfgUkRMTUiro2IYS1s+hmKoOdZYN+Wgp4W/BpoAA4AiIjBwCYUT/1IkiRJ7XbVVVcxevRoPvGJT3DBBRcsNWi56aabmD17NkOHDm1X0APQo0cP+vXrxyGHHMIPfvADnnzySS688MJ27VNS/fI1LnWKzLwqIq4FhgLbAVuWlvsA+0TEpcARueijZc8CKwAbA7+IiK9l6x89uxZ4m+JVrkuBo4C5wJgOOB1JkiTViWWddvz888/nzDPP5NOf/jTjx49njTXWWOo2N998MwDf+MY3OnS68zXXXJMTTjiBp556qk377S5Tr0vyyR51osycm5m3ZuaJmTkM+CjFWDzvAYcDw5ttMhnYHpgIHAP8NiJa9d9oZs6hCHm2i4gvAPsD12fmlI45G0mSJHVXZ599Nj/4wQ8YOHAgN9xwQ6uCnkceeYSnnnqKDTfckMGDB3doPW+88QZQPO0jSeUY9qjLZOb8zLwKOKvUtGOZPq9QBD5PA0cAV0REa59A+3VpeRXQi/JTsUuSJEmt9rOf/YyTTz6ZLbbYguuvv56+ffu2arvWDszc2NjIc889x+TJkxdpf/zxx5k/f/5i/d99912+973vAbDbbru1qhZJ3Y+vcakS3iktyw6clpmTI2IH4DbgYKBXRBxUenqnRZn5z4i4FxgMTCptL0mSJC2TK6+8ktNPP50ePXrwhS98gV/96leL9VlvvfU49NBDF2mbMWMG1157LSussAJf/OIXl3iMG2+8ka997WsccsghXHDBBR+0/+xnP+PBBx9k6623Zp111mGllVbitdde47bbbqOxsZHPf/7zHH/88R1zopLqjmGPOlxEHEIxfs6fM3NBs3X9KMbVAbinpX1k5tsRsSNwC8U4P9dFxL6ZOXsphz+KYmDml9ow3o8kSZK0mJdeKiaTnT9//iJBTFODBg1aLOy5+uqree+999hvv/1a/SRQcyNHjmSVVVbh0Ucf5f7772fmzJk0NDSwxRZbMGLECA477DB69vTHOUnlOfW6OlxEnA0cRzEGz30UU6QDfALYE1gRGA+MyMxsMvX63Zk5pNm+VgUmUDytcwewd2a+13zq9VbU5NTrkiRJYvr0xkqXUDHdZYBmp16XfLJHnWM0xSDLOwObAbtRjKEzFbgLuBK4sjVP3mTmOxGxO0U4tDPwp4j4z06qW5IkSZKkmueTPVKJT/ZIkiTVP5/s8ckeqTtwNi5JkiRJkqQ6YtgjSZIkSZJURwx7JEmSJEmS6ohhjyRJkiRJUh0x7JEkSZIkSaojhj2SJEmSJEl1xLBHkiRJkiSpjhj2SJIkSZIk1RHDHkmSJEmSpDrSs9IFSNVu+vTGSpegdpg4cSIAAwYMqHAl6ihe0/ri9awvXs/64vWUpNrlkz2SJEmSJEl1xLBHkiRJkiSpjhj2SJIkSZIk1RHDHkmSJEmSpDpi2CNJkiRJklRHDHskSZIkSZLqiGGPJEmSJElSHTHskSRJkiRJqiOGPZIkSZIkSXXEsEeSJEmSJKmOGPZIkiRJkiTVEcMeSZIkSZKkOmLYI0mSJEmSVEcMeyRJkiRJkuqIYY8kSZIkSVIdMeyRJEmSJEmqI4Y9kiRJkiRJdcSwR5IkSZIkqY4Y9kiSJEmSJNURwx5JkiRJkqQ6YtgjSZIkSZJURwx7JEmSJEmS6ohhjyRJkiRJUh0x7JEkSZIkSaojhj2SJEmSJEl1xLBHkiRJkiSpjhj2SJIkSZIk1RHDHkmSJEmSpDpi2CNJkiRJklRHDHskSZIkSZLqiGGPJEmSJElSHTHskSRJkiRJqiOGPZIkSZIkSXXEsEeSJEmSJKmOGPZIkiRJkiTVkZ6VLkCqdg0NvStdgtrls5UuQB1u8Ws6fXpjBeqQJEmSqpNP9kiSJEmSJNURwx5JkiRJkqQ6YtgjSZIkSZJURwx7JEmSJEmS6ohhjyRJkiRJUh0x7JEkSZIkSaojhj2SJEmSJEl1xLBHkiRJkiSpjhj2SJIkSZIk1RHDHklStzN+/Hi++93vsscee7DuuuvS0NDAUUcdtcRtHnzwQQ444AD69+9Pv3792HbbbTn//POZP39+q4/7+uuvc+GFF7L//vszcOBA1lxzTT7xiU+wzz77cP3117f3tCRJkiQAela6AEmSutqZZ57JU089xSqrrMLaa6/NO++8s8T+EyZM4PDDD6dXr16MGDGCPn36cMstt/CDH/yABx98kLFjx7bquBdddBFnn30266+/PoMHD2attdbilVde4YYbbuCuu+7iq1/9KqeffnpHnKIkSZK6McMeLZOIyGZNC4B/A08AF2fmla3o31jqPwYYm5nN+xARvYCvAwcAmwArAlOB14EHgKsz8+4m/Y8ALmm2mzml/ncDP8vMp1t7npLq0+mnn87HP/5xNthgA+677z6GDRvWYt8ZM2Zw3HHH0aNHD2688Ua23HJLAE444QT23ntvxo8fzx//+Ef222+/pR53q6224sYbb2S77bZbpP3ZZ59ll1124fzzz+fAAw9kiy22aN8JSpIkqVsz7FF7/ai0XI4ijBkODI2Iz2bmt5bSf0NgBLAD8FmKUOcDEbEKRUCzFTAZ+GNpuQqwOXAU0FDq09zfgetKf+4NDAFGAgdGxI6Z+de2nqik+rH99tu3uu/48eN5++23Ofjggz8IegB69erFCSecwPDhw/nNb37TqrBn7733Ltu+8cYbM2LECMaOHcu9995r2CNJkqR2MexRu2TmyU2/R8ROwG3ANyPiF5k5aSn9BwH3AF+NiNGZ+WKT1d+kCHpuBYZl5pxm2/YBPtVCaY83PVZEBMUTPyOBnwBDW3eGkrq7e++9F4Cdd955sXWDBg1ipZVW4qGHHuL9999nhRVWWObjLLfccgD07OlfzZIkSWofB2hWh8rMPwP/BAL4XCv639+k/2eard62tLygedBT2vbfmfmXVtaVwPmlr1u3ZhtJApg4cSIAG2644WLrevbsyfrrr8+8efOYNGnSMh9jxowZXH/99UQEO+644zLvR5IkSQLDHnWOKC0XG4NnKeY2+z61tNyofeV8YFnrktSNzZgxA4DVVlut7PqF7Y2Njcu0/8zk2GOPZcqUKRx55JFsvPHGy1aoJEmSVOKz4upQEbEzsDFFoPJwK/pvTzHWzxzgoWarxwGHAadGRH9gAvC3zHxjGeoK4Kulrw+2dXtJ1W3h0zfL4rXXXgOKUKfcfubOLXLoSZMmlZ1mfdasWQC88sor9OnTp83HP+uss7juuuvYcsstGTVqVLvOpZZ11/OuV17P+uL1rD/1ck0HDBhQ6RKkqmXYo3aJiJNLf1yOIuTZh+IJmrMy86Wl9F84QHMA32ke4mTmjRFxHHAKcEzpQ0RMBu4ALszMe1oobYsmx1o4QPMWwCzghDaepqRubOWVVwbg3XffLbt+Yfuqq67a5n3/4he/4Morr2TLLbfk7LPPZvnll1/2QiVJkqQSwx6110mlZQLTgXuB32Tm5Uvpv1ACR2Zm8+nSi5WZv4iIi4FdKMbw2bK0/CLwxYg4NTNPLLPp5qUPFK+HvQFcBpzh1OtS/WnPb/YmT54MFK9jldvPwIEDeeaZZ5gzZ85i6+fNm8fkyZPp2bMnO+ywQ5sGaP7+97/PZZddxuDBgxk3bhwrrbTSMp9DLVv422V/O1sfvJ71xetZf7ymUvfhmD1ql8yM0ucjmbl6Zg5dQtDzQX+K6dN3AV4BfhURLY5ImpkzM3N8Zv5PZu4KrE4xTft84IcRUW6O4rFNals+M9fPzMMNeiS11eDBgwG4/fbbF1t3//33M3PmTLbeeutWBz2ZyXe+8x0uuOAChg4dylVXXdVtgx5JkiR1DsMeVURmvpeZtwPDgB7A2Iho1U87mTknM38J/K7U5NQ1kjrN8OHD6du3L9dccw2PPfbYB+2zZ8/mtNNOA+DII49cZJuZM2fy3HPP8corryzSnpkcd9xxXHzxxeyyyy787ne/Y8UVV+z8k5AkSVK34mtcqqjMfCIifg0cDRwPnNaGzd8pLWOJvSSpmRtvvJEJEyYAMGXKFAAeeughjjnmGAD69u3Lj3/8Y6B4veucc85h5MiR7LXXXuy777706dOHm2++mYkTJzJ8+HD23XffRfb/6KOPMmzYMAYNGvTBcQB++tOfcumll7LiiisycOBAzjrrrMVqGzhwIHvttVennLckSZK6B8MeVYMfA6OA70TE+Zn5b4CIOBp4PDP/2nyDiNgEOKD0taVBmiWprCeffJLf/e53i7RNmjSJSZMmAbDuuut+EPYA7LXXXkyYMIHRo0dz/fXX8/7777PBBhtw2mmncfTRR1NM+Ld0L71UjFs/a9Ysfv7zn5ftc8ghhxj2SJIkqV0iMytdg2pQRCQUY/B0RP+IOBs4jmIA5e+X2q4DhgOTgPspxvdZARgA7EYxo9cvMvO4Jvs5AriEYsyeI9pyTo2NjWVvhoaG3m3ZjaQKmD69sdIlaBk5WGh98XrWF69n/eku17R3794++a9uzzF7VC1+AswEjo2ItUpt/w18B/gnsA1wLPA1ilm2bgSGNQ16JEmSJEmSr3FpGbX2iZ7W9s/MN4GVm7U9B4wufVp7nDHAmLbUJkmSJElSPfHJHkmSJEmSpDpi2CNJkiRJklRHDHskSZIkSZLqiGGPJEmSJElSHTHskSRJkiRJqiOGPZIkSZIkSXXEsEeSJEmSJKmOGPZIkiRJkiTVEcMeSZIkSZKkOtKz0gVI1W769MZKl6B2mDhxIgADBgyocCXqKF5TSZIkacl8skeSJEmSJKmOGPZIkiRJkiTVEcMeSZIkSZKkOmLYI0mSJEmSVEcMeyRJkiRJkuqIYY8kSZIkSVIdMeyRJEmSJEmqI4Y9kiRJkiRJdcSwR5IkSZIkqY4Y9kiSJEmSJNURwx5JkiRJkqQ6YtgjSZIkSZJURwx7JEmSJEmS6ohhjyRJkiRJUh0x7JEkSZIkSaojhj2SJEmSJEl1JDKz0jVIVaGxsdGbQZIkSapxvXv3jkrXIFWaT/ZIkiRJkiTVEcMeSZIkSZKkOmLYI0mSJEmSVEcMeyRJkiRJkuqIYY8kSZIkSVIdcTYuSZIkSZKkOuKTPZIkSZIkSXXEsEeSJEmSJKmOGPZIkiRJkiTVEcMeSZIkSZKkOmLYI5VExDoR8duIeD0i3o+ISRFxdkT0qXRtapvStcsWPpMrXZ/Ki4j9I+LciLg3ImaUrtflS9lm24i4KSKmRcSsiHgiIr4ZET26qm6V15brGRH9l3DPZkT8vqvr16Iiom9EfDkiro2I50v3W2NE3BcRR0ZE2X9Teo9Wp7ZeT+/R6hcRP42IP0fEK6XrOS0iHouIkyKibwvbeH9KdaxnpQuQqkFEfBL4C7AmMB74J7A1cBywe0QMysypFSxRbdcInF2m/d2uLkSt9r/A5hTX6FVgkyV1jojhwB+B2cA4YBowDDgLGAQc0JnFaqnadD1L/g5cV6b9qQ6sS8vmAOAC4A3gTuBlYC1gX+BiYI+IOCCbTPPqPVrV2nw9S7xHq9fxwN+A24ApwMrANsDJwFERsU1mvrKws/enVP+cel0CIuJPwK7AsZl5bpP2n1P85XlhZh5dqfrUNhExCSAz+1e2ErVFRAylCAWeB3ag+AHkisw8rEzf1Ur9egODMvORUnsv4A7gC8AhmelvmyukjdezP/AiMDYzj+i6KtVaEbEjxQ+PEzJzQZP2fsBDwLrA/pn5x1K792gVW4br2R/v0aoWEb0yc3aZ9tOAHwAXZOZXS23en1I34Gtc6vZKT/XsCkwCftls9UnAe8CXImLlLi5N6lYy887MnFjmN8nl7A+sAfx+4T9SS/uYTfFECcAxnVCmWqmN11NVLjPvyMwbmgYDpfbJwK9KX4c0WeU9WsWW4XqqypULekquKi0HNGnz/pS6AV/jkmBoaXlrmX/0vBMR91OEQdsAf+7q4rTMVoiIw4D1KAK7J4B7MnN+ZctSB9mxtLylzLp7gJnAthGxQma+33VlqZ3WjoivAH2BqcADmflEhWvS0s0tLec1afMerV3lrudC3qO1Z1hp2fQ6eX9K3YBhjwQbl5bPtbB+IkXYsxGGPbWkH3BZs7YXI2JUZt5diYLUoVq8bzNzXkS8CGwKbAA805WFqV12KX0+EBF3ASMz8+WKVKQlioiewOGlr01/cPQerUFLuJ4LeY9WuYj4DrAKxStanwW2owh6zmjSzftT6gZ8jUsq/jKEYkDfcha2N3RBLeoYlwA7UQQ+KwMDgQuB/sDNEbF55UpTB/G+rS8zgVOBzwB9Sp+F4/wMAf7sq7RV6wzgP4CbMvNPTdq9R2tTS9fTe7R2fIdiGIJvUgQ9twC7ZuZbTfp4f0rdgGGPpLqTmT8qjUfwZmbOzMynSgNs/xxYkWJmCklVIjOnZOaJmfm3zJxe+txD8VTlg8CGwJcrW6Wai4hjgW9TzGD5pQqXo3Za0vX0Hq0dmdkvM4PiF177Ujyd81hEbFXZyiR1NcMe6cPfXvRuYf3C9uldUIs618JBJ7evaBXqCN633UBmzqOYBhq8b6tKRHwdOAd4GhiamdOadfEerSGtuJ5leY9Wr9IvvK6lCOT6Apc2We39KXUDhj0SPFtabtTC+oWzF7Q0po9qx8JHmH3UvPa1eN+Wxpz4BMXgov/qyqLUKbxvq0xEfBM4F3iKIhiYXKab92iNaOX1XBLv0SqWmS9RhHibRsRHS83en1I3YNgjFe+bA+waEYvcExGxKjCI4l31v3Z1Yepw25SW/uOl9t1RWu5eZt32wErAX5xFpC5431aRiPgf4CzgcYpgYEoLXb1Ha0AbrueSeI9Wv7VLy4Uzknp/St2AYY+6vcx8AbiVYvDerzVb/SOK31RdlpnvdXFpWgYR8alyg0RGRH/gvNLXy7uyJnWKPwBvAwdHxGcXNkZEL+DHpa8XVKIwtV1EbNU8bC+17wQcX/rqfVthEfFDigF8HwV2ysy3l9Dde7TKteV6eo9Wt4jYKCIWeyUrIj4SEacBa1KEN/8urfL+lLqByMxK1yBVXER8EvgLxV+G4ymmmfw8MJTi9a1tM3Nq5SpUa0XEyRQDTN4DvAS8A3wS2BPoBdwEjMjMOZWqUeVFxD7APqWv/YDdKH5TfG+p7e3M/E6z/n8AZgO/B6YBe1NMKfsH4MD0L7mKacv1LE3dPIDi/4dfLa3fDNix9OcfZubCH0BUARExEhhD8WTAuZSfxWdSZo5pso33aJVq6/X0Hq1upVfxfgLcB7wITAXWopgxbQNgMkWg93STbbw/pTpn2COVRMS6wCkUj7T2Bd4ArgV+1OQ3IapyEbEDcDSwJR9OvT6d4hH1yyie0vL/+KpQKag7aQldXsrM/s22GQScAHyBIsx7Hvgt8IvMnL/YHtRl2nI9I+JIYATFlM8fBZYD3gQeAM7LzHtb2om6RiuuJ8DdmTmk2Xbeo1WordfTe7S6RcR/UPzbZztgHYop09+j+IXlBIr7bbFBt70/pfpm2CNJkiRJklRHHLNHkiRJkiSpjhj2SJIkSZIk1RHDHkmSJEmSpDpi2CNJkiRJklRHDHskSZIkSZLqiGGPJEmSJElSHTHskSRJkiRJqiOGPZIkSZIkSXXEsEeSJEmSJKmOGPZIkiRJkiTVEcMeSZIkSZKkOmLYI0lSJ4mILH36d8C+hpT2NandhdWIiDiidM53lVl3V2ndEV1fWdeKiEmlcx1S6VokSVJtMOyRJKmMJkFNWz93Vbp21YaI2CIiTu4OgVVTETEwIiZExIyIeC8i7oyIQUvZ5sqImB8Rn+2qOiVJqmU9K12AJElV6s0W2lcHlgNmA41l1k9r8udnS8u5HViX6scWwEnA3cCYJfR7geK/t5ldUFOniv/f3p0XE42RAAALU0lEQVTHylmVcRz//lJoCy2lpS1QoAuLhkVA2YmSYluK7DuEVRLBaAVRWSIKBkGlhKJhk0iMlLBvEZCdcsFCC6EgyiJLEUoFZWmhG91sefzjnOG+DjP3zp07nXLH3yeZvDPznvcs896kfZ+c8xzpi8A0YB1gBbAS2BNokzQ2Ip6ocM0Y4Gjgqoh4pondNTMz67Ec7DEzM6sgIjas9H2euTMauCUiTuykji0b3zP7fxMRY1d3HxroPFKgZzIwgRTw+QVwFjAR+FqxsKTewJXAB8BPm9hPMzOzHs3LuMzMzMysWcaSZvOcFhFLIuI/pCDOe8DuktYuK38GsCVwVkR81NyumpmZ9VwO9piZma0inSVoltRP0hmSpkv6UNJSSW9IulvSsZLW7EJb4yQtyu1NrHC+v6SfSJohaX5ua6akyyQNr1Lnp0mQJQ2UdJGkVyQtljSvC33bQdJESU9Imi1pmaS5uf6TJPWqta4utDkg58P5W/5dFkl6XtLPJa3bybVdui/1jE9SANfkj6Mr5H7as1C2wwTNkjaQdEnh3syX9LSk0yX1qXLN5FzneZJ6SfpB/q0W5zHfs4ry4wwG5kTEgtIXEbECeIv0/9JBhT6OIgWCngCuXQV9MTMza1lexmVmZrYaSNoauBcYlb9aASwAhgObAgeQcpvMqqGuQ4CbgD7A2RExsez8VsD9wMhCW8uALYBTgeMkHRAR06o0MRR4FtgsX7e8ljEWPER6yIeUd2YxKffR6Pw6RNJB+aG/2yRtAUyhfbylXDfb5teJksZFxMwK19ZzX+oZ33vAWsAAUk6nYq4nqPE3lrQL6d6ul79aCPQGds6v4yWNj4j3q1SxRh7v3rkfy0gBl/2AsZLGRMSTtfSlRnOBIZIGlAI+ORg2EvgEKM7euSyPZUJERAP7YGZm1vI8s8fMzKzJJK0HPEAKKLwJHAz0i4jBwNqkvCXXkAINndV1AnAb7Q/F5YGedYH7SA/TtwHbA30joj+wOXAj6eH+DkkDqzTzM1JS6n2AtSNiANCVWR8PkRLsDouIfhExCOgPHA+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"text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 685}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "15 out of 20 tickers were removed\n{'PRSP': 34, 'SKM': 62, 'EIX': 14, 'FAST': 168, 'DLR': 12}\nFunds remaining: $3.62\n"}]}}, "9cd5f87312a14063a03d21dfd1ef4224": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_b0a56948048649cfb53b5a80aff0a201", "outputs": [{"ename": "NameError", "evalue": "name 'opt_obj' is not defined", "output_type": "error", "traceback": ["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/home/pavel/Repo/cryptocurrency-analysis/venv/lib/python3.6/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in 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"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/home/pavel/Repo/cryptocurrency-analysis/venv/lib/python3.6/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 252\u001b[0m \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 253\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m<ipython-input-429-6a75fe793ab3>\u001b[0m in \u001b[0;36mf\u001b[0;34m(opt_obj, x, tr, tri)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopt_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtri\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mdraw_portfolio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtri\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m opt_obj_widget = Dropdown(options=['target_return', 'target_risk', 'max_sharpe', 'min_volatility'],\n", "\u001b[0;32m<ipython-input-423-363b4ce4956f>\u001b[0m in \u001b[0;36mdraw_portfolio\u001b[0;34m(prices_df, opt_obj, total_v, target_return, target_risk)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdraw_portfolio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprices_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_v\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_return\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_risk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mweight_bounds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mEFmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_analysis_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprices_df\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m<ipython-input-232-2dfe506dcf8a>\u001b[0m in \u001b[0;36mgenerate_analysis_model\u001b[0;34m(pricing_data, weight_bounds)\u001b[0m\n\u001b[1;32m 8\u001b[0m \"\"\"\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# Annualised mean (daily) historical return from input (daily) asset prices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mmu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpected_returns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean_historical_return\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpricing_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrequency\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m252\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;31m# Annualised sample covariance matrix of (daily) asset returns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrisk_models\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_cov\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpricing_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrequency\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m252\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/pavel/Repo/cryptocurrency-analysis/venv/lib/python3.6/site-packages/pypfopt/expected_returns.py\u001b[0m in \u001b[0;36mmean_historical_return\u001b[0;34m(prices, frequency)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"prices are not in a dataframe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRuntimeWarning\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m \u001b[0mprices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 50\u001b[0m \u001b[0mdaily_returns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreturns_from_prices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdaily_returns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfrequency\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/pavel/Repo/cryptocurrency-analysis/venv/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m 420\u001b[0m dtype=values.dtype, copy=False)\n\u001b[1;32m 421\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 422\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'DataFrame constructor not properly called!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 424\u001b[0m \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: DataFrame constructor not properly called!"]}]}}, "a90e9f689e174a529f96a20ee78ba68a": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_66c0f5137e3a4c5381b879c0259a4a23"}}, "a95458864c7f43dba2ee0c55e71a8759": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {"height": "60px", "width": "50%"}}, "a95b709c09924caeb40c60fda913dfd9": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {"align_items": "flex-start", "display": "flex", "flex_flow": "row wrap", "width": "70%"}}, "a97d3ebfdff7447393953747f497c82e": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": 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\n", 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\n", 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\n", 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"text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 695}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "16 out of 20 tickers were removed\n{'PRSP': 22, 'EIX': 17, 'FAST': 249, 'DLR': 4}\nFunds remaining: $18.07\n"}]}}, "bb64b7de05664a9892d6295c5e7ea022": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "FloatSliderModel", "state": {"continuous_update": false, "description": "Target return:", "layout": "IPY_MODEL_0ea762fddc534d37aae055e3b7924f0c", "max": 0.2687043293906299, "readout_format": ".1%", "step": 0.001, "style": "IPY_MODEL_5271e467277a472680a102b4c89dd7ac", "value": 0.165}}, "bb83e4e3953241afa9077125ad3006aa": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "VBoxModel", "state": {"_dom_classes": ["widget-interact"], "children": 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\n", 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6pPMKph2Rpj3R9vlu6fPH1MDgjwXLnZFbz6WDLdbIp083AoqbjS9XbO9frsjr+Jr7zLdU0sWE4prttW1f0TK46LxoqZR+59xn+ylugpYtd5qkvTqsjy3V95HsotdLqjgfq7neO3YjkNIdnVvuGRXpHuhQZlc8qv6egnkPr/m7HZ+bp1b3E2nfKjueZ6+fqqKuUN8B8/YdwDp/OuUxRdXdbiydK/N9PS7r8TT/qzXS/jP3/VbqcXmr5fLoufsDXiXrd6gLwItXty812GerogPqLK/j2qYtqWgh5IoT2206lOXm9PeRdPDZStHK6GT1Pfn4eElZVstV5jMVd8f2VVzkvFNxAf5QLp9PlOTzIbUCTbMlXabohH/LVKZ9FSdFk9QKti6uGHTlk7n8v5Q+y7/WHKQyfyWXZoKik/Esj2MV/SnOUHTe72q7sOnyN99LcbJzmaTDFBdpb5f0fkX/WPflyvK9inyyNLcpLiQeU3Q0/s60no9V36DlyJJ8xqXpExX9mr2sGFBnR8WjZR9X3/7ORpfkM7bOulG9YOtUSdelbeCDir6f3pl+25/mtq+nVHJwV/PB1mxf/J3iDu4WiiDNp8rWg2Kk3cMV/Y1trnh07pe56X+UtGhu3jcptvNshN6n1H8f2Ei5i59uvmcu3dgOv0v2Xe9P2+jWkrZVDAj1upR+ZC79dbm/H1Ps6zso6rfsQuN5FfTpVJDXgOrUGttC4X5QUo9l81T12Zr1ofWgpCUGUMbsIr9un61FffPtkpv+7Q7LWzGX9qa2advlpm3dNi3rG/VlpT5x0+cXp8//r+nfr6DsO+bKd0ju89cp6lZXQRCoLY9RBdvvbxQX65sr6uNLcmkeVHEftfn95gZF3XSPpP9W1MPbSjpVreDCDJWMGp7Lp9/+WbE999vn1dwxZvxA9klFlxLZctbqkPbhbD0XTFtffc9hLlEEg7I69ZrctCdVfkyoXL/p9/+XpK8r6vhtFPX8HoobaNkNzVclvbtt3mUUdXO+L85Pqn/d/aa2+SrXsXoPtq6SlpcPIBcdS7KbZyuq1dfzLzr8Vsfl8vxAwfR5Ktia5smPql3WZ+sWagUpxjRd7oLl/VsV+3HFfKa4ubWNpPPS9uiKumfzgvSnpunPqC0oJOlvadqVuc9el9suBxSUq/gON+a++xWSlusxn3zQ7BZJ0xV1yafT+tlaEfTM9t05kt5ZkteJivP/MYrjw7sU9eYuKY+ncsv6bEke+WBrdh51t1rHg+0Uj0xn/cnng63ZNdytivPuLRRd5Zyb+40LrwVTXpur1ThnqmJAo/9SnI9tr6ibJqp1vdSvz88u1nvdYGv+hkLVjcYH0/c+WdEQaav02+2teFog2y8nt+8rikfYN1K09syWtYP613WvL9luCoOt6TfL0rysuE7cXnE9cqj67r+/rPhuAw62pu+Y5dExGJkr20zlztO6WF7+HGHZDmkfy6Xdvsfvl79R/sNet0teJet3qAvAi1e3L/W9uLqloEIvehUOFKIIqGYBvFnKXdimA2zlCav6twj9p9pay6V0m6l1svG8Ck5sJP0+TX9c0noly1tGrQvTF9rzUYwoOCVNf0UFJ+K5tEvnD37ps5G57zKqxm/RRJnXV+tE5jEVdwT+FvU90fIBbD9vLFr/uemLqHUBNVslF6ltv/sdklYoSLNDLk3hBZRaF3OefrtNCtK8Xq2TtInKBQhzabIyV64b1Qu2Vg5wowgaZAHXL5ekGZ1bzogef6tRbeu59M58Sr9fLu0RFemOyqXrd/NDXQyK0s33zKUb2+F38VSGqlZvI9vSX6iCu+2STsqlObJGXuN6+a26WEcju5jvS2oFi29SjJa6jSLY91lFYMcVF2jvGGAZX69Wq9WZkr6rCPhumf5+T63WZL9R20AnKY/Dct/zmBrLzC7Mnm773NRqafJ0+t7vTOsju+j5bi79e3Np+x1/BuH3zAK709qXp9ZgMTNU0cqiYN8eXZIu3+L1QzX2m9+poPW/pANzab5ZsqzS/bNiex5RML2pY8z4geyTikBz5fpN6fKtrE8qmP733PTCC3ZFgLRy/dWY3um4s7pa+/xfamxXI2uso8p1rB6DrXWnt6W9SK1z0TdUpMsu5B9R8RNZ82KwdVm1WnjOTt/1I4r6dTdFMD0LNl+nkhuDDX6HfXPr6Pqa81S17J6u8sYE66p17PibIni4o2Igr351W25fqgy6D/D7f7St/FNSeT6lCLKVnnu05bNUWz7/UEFLWUV/oVmaS0vyeosqbpgqrln+qtZ1VNExeP228lyrkqfBUvqd29L/RsU39T6k1rnIXQXTF1erYcmtklYuWd6qaj0VclfR/tvltlg1QNab1boenK6KQavUue7dQq3W6f1aaKc0tQfIUodgq+JmRnZ+NEXSpgVp8gNEuUoCqWom2LppLo+f10j/p1z6rgcqlfTz3PylT2yp75NOLumAHpa1uOKGRJbH+3tZR7wq1vFQF4AXr25fqv/Ie/71q4r81lWr1cajkoapbxcDfyg7IBaUpfSCX9IJuXSHt03bKjdttw7ff6Nc2k+2Tcs/Cnh0D+t2ZG7+UR3SNlXms3PT9qjIIx9I80HexlZUKwBcdgc9/7uXjoKuOPl0FTx2nKaPy+VzVEU+Z+TS9RtZUg0GW2uuo1+kPP5ZMn10bjkjelzGqFwehS3b2tJnd4MrW9WltFnr1b9W/Cbja+RT+3vm0o3t8LuUBmBy6fP7ar8WM7l0y6sVoLuiRl7jet0maq6jkV3Ou536tkbOv6YrBjpZraFyLqFoMfFwyfLuVwTtCh8hk3RMLm1pK5Jc+gkp7dSCaVurvGuS+9RqEbeUWsGXfZr+7Uq2p+yi67KC6e/OlbMwuJ/S5fft21V+jN0gl+4bHfabaSoJUikCnM+kdLeVpCndPyu25xE9rsc6x5jxA90n1eqW5uGKNFkL2Dnt30fRWiv7rtdU5LGYWvXvTEmr9Lp+O3yfI3NlLRp9Ob9djayRX+U61twNtuaD48eVpBmZS/OlkjTzXLA1zbeIosVg/sI+/3pM8SRKaXCsofKvrlbdO1s1WxiqPNj6Y7U99VUw7+dL5nXlupFRBHVmKVrG92t40PB6GK3yrodmKc5dT5T05oo88kGzOZI2qEibPe781ADKnL/u6Nf1g/oGW2dVlT2lzwdbX1H1TY58FwMj26ZlN/Nmq3Pgcq9cPoWtfLvYFqe3fb6YYmC5g9V66tAlfbGB7eW7Ka9nS6Y3GWz9Ym561XXRumo1/LipJE0Twdb8DcmxNdJfmUu/YQ/Ly7c0vV0F5/eKm/I/bdtvP9XDsk7LzX/tQLcTXv1fDJCFhZ67/1utUfxGKO7uXpj+n6i4U+Q1srrHY/THMlmLH6n/qJDZyKBTJP22Q3nvUdzVlSIQkZcNtPKS4gJqMDVV5p3S34mSfl2Rzc8Vj7A0KnXUvrqZbWBmG5nZRoruEbLydhqc6x53LxykKcm2iZWs86BShaP0tuUjSaUDxjTNwhvSoAcb5dbRcynJhjbAgVFqutwrRgY2s/UVgRkp9uFOspF1t7EOg/kMgRvc/ZEu0v/c3acXTXD3lxTBOKlku3H3ce5u6TWyu6IOnjQgw0GKbiCKLKloofRRs/4DnvVge0UwYM2S6esqLqreUTJ96dz7mTWWN6NgPkmSu/9DEXD9qWJfm6UIDH1D0admVhd+UdHX8+/c/aeSZGaLWgyO+M80mMRkM/ttp8FFato3V94fFUy/TtFyR4rHuOu4pOwY6+73K26GSp3rvT+6+4SiCR6jbd9WM59GNXCMGYiL09+1zOxdBWVbUq2BNf7u7uPbkuTPVUrPKVLdnA0asrgiKDggZraCma1lMdhhtt6yEeZN8cTQAsPdb1AEIqUIlhQ5JP19Va3z1PZ8js/V519ruJgDsaWilf7bSqavkaZvP1gFMLNlFF07ZIMUfc0LRhUvsYOkjSVtkt5/XtHtxf6SxqZzkELu/g1Fdxh/V9Rn0xVPRB2h9Fungau+pwiY/a+nEcgtBiw9x1oD/j2VBmxarYuvXlSm0Ypj2eWKQGPeYorA5qmSHjKzk2ocY29L9XWZ7Bx2tVTvVDKzZczsLWb2ttz+n9ep3hznMXBjXVeXHT+S/KBIZddwt7r7g53KlXvffj3UrSXzg1ApzhMeUpR1VcUNxk+7+2ndZGpmK5vZOm3n/Nk5xxsGuu3VkK3fmeo76GEf6fo9G5hzq6JrLHffN1cfXt5jeXo9t2uft67LFfWDFNv5381sJ4uBr5eyGHD714puHvLleV03CzGz/RQNwaToWnBUD2VFB4sNdQGAAfprE8EBd/+Rmb1fcTdp1+xjxWNBz9bM5uYOy5hoMeL8moqTtbyt0t9hkuZ0ETdYNXtjZovl8v2Hu08rnqUxTZR5SUnrpX9vTRfEhdx9lpndoXj8akAsRnQ/SnGHeQPFoEplXt8huwc6TH8h9355RXC6yHPu/lzJtKJ8BpWZ7aXoM2k7xSOAZRZV9Gc0cZCL1Gk03q1y73/Rxfa4hKKV2aReCjVIuh15uO42OOjbTVPMbF3FI4BvUVyYjlb0gTleEWTdQtF34c6SvqkImu9XVYd0WN6nJJ2j2J7vT8v7i2KQoTek5ZyieGT/XWb2MXe/oi2bfMB7iRqLzS44C+tqd79P8bhnWZnXV6yDaWrdMJRa/XS7ItC+ouLR1feb2V7ufmWNspU5KP19RvHUR3uZ3cwuUqy/zczs7e5+R3u6Np223xcVdVCn7Xee2Q8aPsYMxMWK38IUNwraA0sfUmsk9IsK5t849/7GDsu6Ifd+E8WNgq6Y2ZaK1qsfUOx3VQZzvQ2V7ym6sFrHzEa6+7hsgpmtoFZQ52p3f2YIytcTM9tHsX0tqeh26iRF/T5J0kqK/tdPUbTuvcbM/sfdG20wYGZLKJ7I2Tx99KtUjlrc/V9tH/3NzM5V3HTaV9I/zGxndy/cT1K9W1X3Hq64wXa7YhuQxcj1NypuEM1QBHfXVgTddzWzbd398brfoaBMt0naL52LvyMt/+2K874RKdmSiqfmXq/YN8t0ey7c75zLzFZXdBH0YcW1UtWJXKf9v9vzqMprOEX3AHMULbTLruG2SkHPulbtnGRAfqOKYGWeme2s2AZ3UOuYUOb1ipazgyU77tyXGgxUuUHRtZml+erePOlGr+d2Usn5XRV3n21meyi6RdpAcaPqdwVJH1A8+ZUFTKfWXYaZfUDx5IUUN1v+y90H8zddaNGyFWj5lFotaKTol6aocitTdUe0Pc1KbZ+v0p6wpvxdrJXU2qfnRoXZRJlXUOtkqpv11zMz20StYMpGqr4IljrflfxPh+n54E/VsprKZ0DMbAkz+4WknylaHVcFWjO93Lnt1osdpve6PUpd3g2eCzp913Z1t515rQVvlYsVgVZX9Gd3srv/291nuvvU1Bp3F0VgUYrWeYf3sqDUaiMfaN3a3X/q7pPc/VV3f8rdL1D0FztJcSI9Nl0I5+VPdOvsN1ma2ifIbc5XnPifmrWENrP9FYHWVxSPOq6vCFqdo7jBfmFq3dU1M3ub4mJcitaos0uSZn1PSvVatza1/dbNZ1DPfQfhGNMzd39U0ZpOkvY2s6XakhyQ/k5T1PntsnOVOep8Qy1/Y7r9HKcjMztOEfA4QJ0DrdLcOe7MbT9Wazs+pG3aAYrHb6UY+Ga+YGZvUlzUL6k4N93K3X/k7s+k+nWCu1+mqFseUewv55rZeqWZdl+GxRXB/6zF3DWSPlpRh9Xi7jMVN6UnKgKIF/bylIWZrap4nHeOpMNy5TpLEWi9R9HFxyaKbhBuVgzoec5Ayp9x9xnufp27f8PdP+7uayqCr3/JJfuMmW1ekoU0wHPYFOy7XxFsXUvVgVap8/7f7XlU5fVFasCSBf7a67eVu1xWZqDnnjMVAcbstZ3ihuitafohkq5OjXEKmdkiZvZ9xT7xYXUOtEqDWPemFt4rpH/rNHYa0HGnpl7P7drnrS3dRHmHoq/+f7dNnqjoWm5L9W1F+4JqMLMdFEHaJdL8e7j736vnQq8ItgIth6pvBbmDmc2tk/nsQPiU+h44O70O6pfT3DPflTmdcP9MMYCJFC0SdlYEdZZW9MVo7m6Snshmm+sFHVonKAZ2poYZAAAgAElEQVSukKID/4MUj/0NU/SZmq2fU3PzzI111OmiKH8y+Ul1t03Oa3dzB3QBOL9Lwaqspcif3L1fC8qc43Lv/7vHRR6k1sXfV9298OTY3Z9Q6+J2WUVrprx8C6PVqxZoZiuqdaHVdcskMztI0QLlXsUFeSarXy9w97+lcrtiPU1StHL9cLfLS/KB02Pyjy+2Pcr4iFp1wv6pRdlCYR49xmQtVodL2j1X1pVT2aTo175TC6JBky7+vqZYF5MU2+s7FC2olsyts/fmZ5vrBR1k7j5F8QipJO2ZWrNmsvrtCRW3cppXfUyt4Mw3K7r6eEHS6enfxSR9oomFp0DTpWrVe3+UtGcKlA6Yu/9HMVisFH2G9tK9xbcV51jnuvutkmRmr1PryYbjsqfs0nr6XPr8Q2Y2KC28Uzl2lpTvJmvvwVhWunF5uWLQo5mK7nJGKgLKS+X2//yNwk77/1w5j0rBwSyecp26O/c8tT2/Lrm735N73eDuP1TcGP55SjNS1S24D1GrbnlI0bXFJoqA5xK5dZ9/emaBq3s7qH1u15ZmluIpoJ64+3/c/TR3X0/xe6yr2CdWTd3F/Cd9lrm3U55mtp3SIK+KfW3PDufYGCC6EQD02qNrWZ82LynuUG8o6Vuq31qqTkuMLM3zbZ9PUjxOP0zSvWX913XwvFqPuAx2fzpSM2V+UdECytTd+uvVjmodmE539y9WpF2hYtq86LUTSzNbpOJx6k6t2g5Lfx+WtE1FdxTz2vrJP5L2H49+gueG/Al96Q3MXlsTLqTyffrdVppKEQA1s4mKls0bVKVtYnlt09uXlz/RLeuXsGj6fR3S9pEurs+UXhuIa1ZuctaP3fX5edx9upndrmitvrnq9WucX+ZiarWC7MZKiiBHUavJeU12/OzUEKFqX54XjzE/k/QdRcDrQLV+i30V/atKxV0ISK1zlUUU+1hV66835t63n+N0kh13ZkvaoaLvx3ntuDMYzlf047mUYp87J/XRlz1ae0Gv3aUMkabq165Z9Md+saI7Dyn6y/ywl/RxPgD5LqBGqNXfYkdmtosiiPmUYkCqzIZqPbJ8fdtsNyn2lUUVwd0/dlfcetx9ppldoKg/pL6BnSbtq7iWkKRD3L2sPhrM/b/y+iI1vsm6oHmtfnP3OWb2guLG0Ovm4rlnqfQY+n8rBnZ6o6QvmNkF7v5YQfKs7n1Occ5fVnfPlbo3rc8XFTeG39gpvQZ23KlbphfM7Nm0rPWqrrHSdjIi/ftQ2/nZQMowWcXjlmRjCExRh648zGwbRQvmZRWB4H3cvXLMFQwcLVux0DOz5RSPoi6ueJRuO0VfUpJ0mJntWTZvm62qJqY7tyPSv3e1Tc5OMJdVqz+prngMUJH1UbR1j61yuwmYNlHmrA8qSdoy3SEulFoMDXQgkXz/c6UdpafH1+o8KjIvybfGqzopKr2AMbOV1OpD6tcd+v3dsouyzQ35i7SBDrDRzX4w4PWOfvIDodUZfC1L0+tJbTfLy0/vs7x0IZNdzGxj1YOA5Pue/lvHEvb1DUUQ8wLvP7hLdsFa1Df05LY03dhNrYvRMZL2q/HKHisdyicwupHty50uKqv25XnuGJNarGZ9Re6cWrRKEXiVSvrfTfLnKtt0WNS2uffd9peYrbe7KgKtUufjTi83fQdT1+VJLQpvT//+d9vf2SoZGGse1kj92q10Ppn1pypFdxofdPf2waCa8Kbc+276TXydYpR3KUZdz8+b1dNz2vNM3Qy83JZusOQHmRqs/Svb/13STyrSDeZ5Z+U1XFp2do1Sdg23SdEgTUMhtZI/Of27pKJP5CLZwGN/qAi0SnO37s3W79vSNXqV7LgzR60BBgdDNqjuMFVfi75LrXqs23O7rpjZ2xUDpErST6puwpnZOxRPRCynqJP39YH134+aCLYC0nmS3prefzbdlTxQrRYcPzCzNWrks1GqzMocrNajF9e2TftF7v2xNZZVJqs4l1d0i9CtfHCt02ihTZU5e/xqFcVgHWX2Ur2+hKrkW/NX9ZX0qQEuZyjkR6+v2g4/XjGt1vpJ/XZ1uvCe2/6p1jo4sKA/zW5k+0HHEXPVzHpHX/l1ukNVwtTlQBYce6QqbRPLU99R1ouWlw2aVdTNgKTXWltlj8jOUowqW4uZ7ag4PmWPWrfLgqxFj7qt0ZamG1nA1CV9xd0v7/SSdHWa5wOp38Z5XfZ7blHW76KZvVHS+yrymFePMT9KfxdTdO2wvloXz5dW9F2ZP1cpPadI2/TB6d+Z6jvidh3Zeqs67iyjzo+Wd3MOMze8Vp4ON1/anZ/+bmxm71WrLvld6s5kftJk/VpLCrReqOjCQIqWobumx24blbp62Cn9O0fRv2pdX1Y0wrja+w+4mNXTi6hvMFdmtryqb6xV6rJf2XyQ7eFul1VTtv+bqvsDHcx6czczq2rdmu+mqOwabjFFn7PzigvUegT+Y2a2Tn5i2k+yLpSq6t63KG64Vmmy7s3W7xKSRpUlMrO3KgbXk6SbU4B5sOT3z6ouqw7Ovf95aapmZE/kzpH0f2WJ0jXbtYo6Y7ak/d39F2Xp0SyCrViomdkn1DoZu8LT6KepT6lPKC4sV5B0SbqY6OT7RXc1zWwzSdnjhC9KuiQ/PfWt9+f070fNLP8oUVG5lzCzgwtODM5Vq/XS6Wb2/oo8li7o6ynfd2Xl40INlvl8tR7F/raZ9esCIQW7z2r/vAf5TsZHlZTzI5I+3cCy5rZxufefL2olbGajVN1f4yS1tp/dU7+S7Xm8QTGIxzwldWMxOv27vKQrcy24CpnZVma2a8GkbD9YpUYrhRvUaoHzmYIBaJT2w/9p/3xeYWYjc31ujhvq8igewcwuELYwsyOKEqVWQfkTzMK79GY2Pvf9RhQkyc93opmtWZLPVmp1KzNH0e9Vu7PVGrn2dIuBT9qdpBhRWpLGpD74OkrBmiwI87mS+bIWcX0e+U8XWVu3pakl7fPZfnJjF8GerIXSImqo/8VBNi79XVUFXSak9T9W1ReS8+ox5g9q9R13oFqtWqVWILYfd79DrQG2dk2Pphb5qlqPi1/q7v1GGu8gW2/rmNm27RNTNxYXqvPo3bXPYeaSXstzmVqtGS9WqxX09zvNaGZfy9V3x3exzMHya7VavR1pZpsWJUqtvfM3kPrV52a2fu67FT4ymwKJ31OrzrlR0i7u/nJR+jJmtrOZVf5mqdXdpWoFPq/J+latkf/Gir5XX1Hx+cG9ihsXUv/6KPt/jvr2qVrXTWb2sU43ANJjx0fnPmoPCDelTr35BbUGOBsMSysa1/TrYtHMdldrnd/t7uPakoyV9Gh6f6KZFd5ozeW3vJl9fmDF7Sw9wp4F5BaV9L9t0+co+mmVpB1TULWPdA58uTq3Sm+y7v2BWk/GnGIxiGl7uV6n6P4muzY/uygjM7s8V2dU/i4d/FKt7fQQM3tXwbJ2VQzWKsUgZX8qKdNNuTIVNlwxs9UqbvouambfVKvP9W+7e3tr6yztporj/3DFtfbH3X1+6NZpgUGfrZjfLVNUCZd4MvV5Ium1i89z07+Pq+1Olbv/3sy+IekYxWMBJynuQpe5RdGy7XYzO1NxQbukohXMMWr183ZkyQAsH5f0D0WLpFPNbA/FAfxOxeNCyykOYNtK2kPRn806yvWh5u7PpwDyLxUnDr8zs58o7rqOV9w1HqFoPbCPomP0X+Xmf9LMxqc0B5vZvYrHY7LRDl9JIyQ2WeYHzOxrihEX10jr7wzFCbIU6/44RQDtTvU2+EDm94qRK9+o6CJiBcWFzNOKi7i9FSdUDymC7L2OMDrXuftdZvY3xSP071OMQPodxQX2mxQDLXxM0cJju5I85pjZRZKOVPT7e2Palu9RHC/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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 685}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "15 out of 20 tickers were removed\n{'PRSP': 33, 'SKM': 51, 'EIX': 14, 'FAST': 177, 'DLR': 12}\nFunds remaining: $10.62\n"}]}}, "bdf31489bcf6493ab1525373237a0540": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "HBoxModel", "state": {"children": ["IPY_MODEL_3da53705995e48068a666576c34050c1", "IPY_MODEL_1742d71dc3b04f8e920eb2b10d6baa65", "IPY_MODEL_72de63de305043da9a5b58c30987f29d"], "layout": "IPY_MODEL_b302496ada174c8f9522eb5b5c51459c"}}, "be0980af6b724661ab1bc4a86f1256b8": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_d4dddc5aad24419e9bce4d10d0aa981d", "outputs": [{"data": {"image/png": 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\n", 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tttoqG220Ud70pjflc5/7XJ555plltHoAAABgWWrsAi8AkmOOOSaXXHJJNthgg7zrXe/K6quvnuuvvz4f//jH88tf/jLf/va3BzTPr371qxx00EF5/vnnc/DBB2fTTTfN5MmT84UvfCGTJ0/O5ZdfnlGjRi3jTwMAAAA0SRi7iFJKHcCwPWqt17fHj0/y3SQTa63j220rJbkuyW5JDqu1XtBPnS2T3JHk+STb1VofaGL9wPL14x//OJdcckle8YpX5Nprr82YMWOSJHPmzMn73//+XHTRRTnggANy0EEHLXGeefPm5bjjjsvs2bPzgx/8IPvvv3+SZP78+Rk/fnyuuOKKnH322TnhhBOW+WcCAAAAmuOYgv59egmvaUt6sNY6P8lRSWYlOauUsumi/aWUlZN8P8laSY4VxELvuPLKK5Mkxx9//MIgNklWW221/Mu//EuS5Nxzz13qPDfeeGPuueee7LzzzguD2CRZaaWV8pnPfCZJct5556XWgfz9CAAAAOgWdsb2o9Z66jCfn1ZK+Vhau2YnlFL2qf+TmnwyyU5JflBrvWh4KwW6ySOPPJIkGTt27Iv6FrTdcsstmTNnTlZbbbXFzvOLX/wiSbLXXnv1O89WW22Ve++9N9OmTcsWW2wx/IUDAAAAHbHMdsaWUjYspexXSnl/+7VfKWXDZVWv29RaJyS5LMleST6WJKWUNyU5Jcmfkhy33BYHLBMLdsPed999L+qbNm1akmTu3LkLf16cqVOnJkle+cpX9tu/oP3ee+8d4koBAACA5aHxMLaUsksp5fokf0kyKcmE9mtSkr+UUq4rpbyl6bpd6pgkDyc5vR3Efj+t3chH1VpnLNeVAY3bZ599kiRnnXVWnnjiiYXtzz//fE4//fSFv8+YseSv/6xZs5Ik6667br/966yzTpJk5syZw1ovAAAA0FmNHlNQSvlwkq+nFfKWJPOSPNbuHtOut3uS60spx9daB3ateIeVUk5dTNeztdYzBjpPrfWxUsoHk/w4yY1JRiX5cq31uuGvEuiEBbtUB2K77bbLjjvumFtvvTVvfOMbs/vuu2e11VbLlClTMn369LzsZS/LQw89lAcffPBF8y76++zZs5MkDzzwQL/1F4S1Dz300KDWByw/vqvQ+3zPobf5jkPvmzp1arbeeutlXqexnbGllNcn+UaSlZPclGTfJGvVWjeutW6cZO0k+7X7Vk7yjfYz3ehTi3l9YrAT1VqvTHJDWkHs/Un+ubllAt1k5ZVXzle+8pUcf/zxWW+99TJp0qRMmjQpm2++eb7zne9kjTXWSJKst956S5xnrbXWSpI8/fTT/fYvaF8wDgAAABgZmtwZ+49phbsXJzms1jp/0c5a63NJri6l/CzJhUneneTjSd7f4BoaUWstTc1VSnlrkt3av748yZuTTG5qfmDZGspfxU477bScdtppL2h79tln88ADD2TMmDEZN27cwvYFf2FftM7rX//6/PznP8/TTz/db/2HHnooSbL77ru7wAu6XH/fcaC3+J5Db/Mdh97X6e95k2fG7p6kJjmhbxC7qHbfP7THjmuwftcppYxO67zcuUmOTeszTyylrL081wV03g9/+MPMmTMn73rXu5Y6dtddd02S/OxnP3tR37Rp03Lvvfdms802y9ixY5teJgAAALAMNRnGbpBkRq31L0sbWGv9c5IZ7Wd62dlJNkvyqVrrt5J8PsnYJF9ZnosClp0F57ku6s4778wpp5yS0aNH54QTTljYPnv27EybNm3hTtcFdtlll2yzzTa5+eabc9VVVy1snz9/fj71qU8lST7wgQ+klMY28QMAAAAd0OQxBbOSjC6lrFlr7f+gw7ZSyppJ1knyxJLGjWSllPcmOTStM3I/324+Ncn+Sf5XKeVH7fNkgR7yzne+My95yUuy7bbbZq211so999yTq6++OquvvnouuOCCbLzxxgvH3n777XnPe96TN7zhDbn22msXtq+88so566yzctBBB+Woo47KwQcfnJe//OW54YYb8pvf/CY77rhjPvKRjyyPjwcAAAAMQ5M7Y3+d1sVcHx3A2I+1x97eYP2uUUrZNMk3kzyV5MgFxzbUWp9P64zc55KcW0p56fJbJbAsHHzwwXnqqady0UUX5ayzzsrvfve7jB8/Prfeemt22WWXAc/zpje9Kddee23233//XHvttTn77LMza9as/NM//VMuu+yyjBo1ahl+CgAAAGBZaHJn7DlJ9kny2fbO1y/WWmcuOqCUsnGSE9MKbGv7ma5TSjl1Cd0/qrX+5xKeLWmdE7tekg/WWv+waH+t9a5SyslJvpBWYPueYS8Y6Bof/ehH89GPDuRvUq2zYW+77bbF9r/qVa/KxIkTm1oaAAAAsJw1FsbWWi8tpXwvrZ2fn0zyj6WUO5I8mOQlSTZPsnWSVZOUJBNrrZc1Vb9hn1pC37Qkiw1j0wqa90pyea31O4sZc2aSA5O8u5RyRK31+0NaJQAAAAAwYjS5MzZJxif5rySfSOtM2B36GTMryeeSfKnh2sNWax3UbTi11glp7YJdtO2rSb66lOfmJ9ltkMsDAAAAAEawRsPYWmtNckYp5etJ9k7yhiQbtLsfTetc2atrrbObrAsAAAAA0O2a3hmbJKm1Pp3kR+0XAAAAAMAKb6XlvQAAAAAAgBWBMBYAAAAAoAOGdExBKeXa9o/31VqP7tM2GLXWuudQ1gAAAAAAMJIM9czYce33u/tpG4w6xPoAAAAAACPKUMPYo9vvM/tpAwAAAACgjyGFsbXWiQNpAwAAAACgxQVeAAAAAAAdIIwFAAAAAOiAIR1TUErZvKkF1Frvb2ouAAAAAIBuNdQLvP7YUP06jDUAAAAAAIwYQw1CS0P1m5oHAAAAAKCrDSmMrbU6axYAAAAAYBCEqgAAAAAAHSCMBQAAAADogGV2eVYpZcMkb0iyQbvp0SS/rrU+sqxqAgAAAAB0q8bD2FLKLklOS7LrYvonJ/k/tdabmq4NYYw7cQAAIABJREFUAAAAANCtGj2moJTy4STXpRXEliTzkzzSfs1rt+2e5PpSyt81WRsAAAAAoJs1FsaWUl6f5BtJVk5yU5J9k6xVa9241rpxkrWT7NfuWznJN9rPAAAAAAD0vCZ3xv5je76Lk4yrtV5Ta31uQWet9bla69Vp7Yz9j7QC2Y83WB8AAAAAoGs1GcbunqQmOaHWOn9xg9p9/9AeO67B+gAAAAAAXavJMHaDJDNqrX9Z2sBa65+TzGg/AwAAAADQ85oMY2clWbuUsubSBrbHrNN+BgAAAACg5zUZxv46rXNgPzqAsR9rj729wfoAAAAAAF2ryTD2nCQlyWdLKaeVUtbtO6CUsnEp5ctJPpPWmbHnNFgfAAAAAKBrrdLURLXWS0sp30vy/iSfTPKPpZQ7kjyY5CVJNk+ydZJV0wptJ9ZaL2uqPgAAAABAN2ssjG0bn+S/knwirTNhd+hnzKwkn0vypYZrAwAAAAB0rUbD2FprTXJGKeXrSfZO8oYkG7S7H03rXNmra62zm6wLAAAAANDthhTGllJOSfJUrfXL/fXXWp9O8qP2CwAAAABghTfUnbGnJnkoycIwtpTyhySP1Fp3bGBd9GPGjJnLewkAAAAAwBANNYytSVbq0zY2rYu6AAAAAADoo2+gOlCPJxlTSlm7ycUAAAAAAPSqoe6MvTXJ/kmuKKVckuSpdvvqpZQjBzNRrfX8Ia4BAAAAAGDEGGoY+5kkeyTZPclui7Svk+S7g5xLGAsAAAAA9LwhhbG11ttKKX+T5Jgkr0myepJxSZ5PcktjqwMAAAAA6BFD3RmbWuu9Sf5pwe+llPlJHq+17tHEwgAAAAAAesmQw9h+3J/k4QbnAwAAAADoGY2FsbXWsU3NBQAAAADQa1ZqaqJSyrxSyoODGP/HUsrcpuoDAAAAAHSzxsLYJKX9GuwzAAAAAAA9r8kwdrBGJZm3HOsDAAAAAHTMcgljSykvS7JhkseWR30AAAAAgE4b8gVepZTdkozr07xWKeWUJT2WZHSS/do/3zTU+gAAAAAAI8mQw9gkeyT5VJK6SNua7bYlWXBO7ONJPj2M+gAAAAAAI8Zwwtj/TDJxkd+PSvJskouX8Mz8JLOS/C7JZbXW6cOoDwAAAAAwYgw5jK21Xp7k8gW/l1KOSjKz1np0EwsDAAAAAOglw9kZ29c7kzxcSlm51jqvwXkBAAAAAEa8JsPYy9I6hmCLJH9qcF4AAAAAgBGvyTD2qSRza62CWAAAAACAPlZqcK4/JlmjlNJkwAsAAAAA0BOaDGMvTrJqknc0OCcAAAAAQE9oMoz9YpJfJfl2KWXPBucFAAAAABjxmjxS4BNJrk3y6iRXl1LuTHJLkkeTzFvcQ7XWzzS4BgAAAACArtRkGHtqkpqktH9/XZLtljC+tMcLYwEAAACAntdkGHt+WuEqAAAAAAB9NBbG1lrHNzUXAAAAAECvafICLwAAAAAAFkMYCwAAAADQAU2eGbtQKWVckr9N8oYkG7SbH03y6yQX11qvXxZ1AQAAAAC6VaNhbCnlpUn+PcleC5oW6d4iyfZJ/q6Uck2SI2qtjzVZHwAAAACgWzUWxpZSVktyTZLt0gphb0lybZIH2kNenuStSXZKsneSq0spO9Za5zS1BgAAAACAbtXkztjjk7wuyeNJDq21XtPPmJNLKfskuaA99rgkX2lwDQAAAAAAXanJC7zem6QmOWYxQWySpNZ6dZJj0to9+74G6wMAAAAAdK0mw9htkjyb5LIBjL2sPfZVDdYHAAAAAOhaTYaxqyZ5vtZalzaw1jo/yfNp+AIxAAAAAIBu1WQYe3+StUspb1jawFLKG5Os3X4GAAAAAKDnNRnGXpXWObDfKaVssLhBpZSNknwnrfNlJzVYHwAAAACgazV5TMDnkxyVZLskd5dSzk1yfZIHk7wkyeZJ9kgyPskaSR5P8oUG6wMAAAAAdK3Gwtha6yOllP2T/CjJy5Kc2H71VZL8Jck7aq2PNFUfAAAAAKCbNXlMQWqtU5Jsm+RTSX6b1lEEpf2q7bZTkrym1npbk7UBAAAAALpZk8cUJElqrTOSfDbJZ0spqyZZv931eK31+abrAQAAAACMBI2HsYtqh68PL8saAAAAAAAjwbDD2FLKqCTvSPLGJOskmZHkl0l+XGudO9z5AQAAAAB6wbDC2FLKzkkuSevCrr6mlVLeUWv97XBqAAAAAAD0giFf4FVK2TTJlWkFsQsu6Hp0QXeSLZJcVUpZd7iLBAAAAAAY6YYcxib5WJLRaR1LcGSSNWqtL0uyZpKPJnkmySZJ/tdwFwkAAAAAMNINJ4zdO63dsB+ttX6/1jonSWqtz9Zav5HkU2ntkN1n+MsEAAAAABjZhhPGbplWGPvDxfRfssg4AAAAAIAV2nDC2LWTPFprfba/zlrrfe0f1xxGDQAAAACAnjCcMDZp7YxdmjLMGgAAAAAAI95ww1gAAAAAAAZglWE+v34p5dphjKm11j2HuQYAAAAAgK433DB2tSTjhjFmIMccAAAAAACMeMMJYyc2tgoAAAAAgB435DC21np0kwsBAAAAAOhlLvACAAAAAOgAYSwAAAAAQAcIYwEAAAAAOkAYCwAAAADQAcJYAAAAAIAOEMYCAAAAAHSAMBYAAAAAoAOEsQAAAAAAHSCMBQAAAADoAGEsAAAAAEAHCGMBAAAAADpAGAsAAAAA0AHCWAAAAACADhDGAgAAAAB0gDAWAAAAAKADhLEAAAAAAB0gjAUAAAAA6ABhLAAAAABABwhjAQAAAAA6QBgLAAAAANABqyzvBTBwo0evu7yXADTuTct7AdBVZsyYubyXAAAAsMzYGQsAAAAA0AHCWAAAAACADhDGAgAAAAB0gDAWAAAAAKADhLEAAAAAAB0gjAUAAAAA6ABhLAAAAABABwhjAQAAAAA6QBgLAAAAANABwlgAAAAAgA4QxgIAAAAAdIAwFgAAAACgA4SxAAAAAAAdIIwFAAAAAOgAYSwAAAAAQAcIYwEAAAAAOkAYCwAAAADQAcJYAAAAAIAOEMYCAAAAAHSAMBYAAAAAoAOEsQAAAAAAHSCMBQAAAADoAGEsAAAAAEAHCGMBAAAAADpAGAsAAAAA0AHCWAAAAACADhDGAgAAAAB0gDAWAAAAAKADhLEAAAAAAB0gjAUAAAAA6ABhLAAwol1++eU58cQT87a3vS2bbbZZRo8enWOOOabfsccee2xGjx69xNdBBx004NpPPvlkPvvZz2b77bfPRhttlFe84hU55JBDcsMNNzT18QAAgB6yyvJeAADAcHzxi1/MXXfdlbXWWiubbLJJnnzyycWOPeCAA7L55pv323fRRRdl2rRp2XvvvQdUd8aMGdlvv/1y991359WvfnWOPvroPP3007nqqqty8MEH52tf+1p22mmnIX0mAACgN/V8GFtKWTnJB5IckeSvk6yd5IkkDyWZkuSKWusV7bHjklyX5IZa67h+5jogycVp7Sh+X6318lLK2CR/bA95OsnGtdYX/b/AUkpJcm+SLdtNe9Rar2/iMwLAiuxzn/tcNt1002y55Za58cYbc+CBBy527Nvf/va8/e1vf1H7jBkz8rWvfS2rrbZaDjvssAHVPf3003P33XfnwAMPzHe/+92sskrrX6tOOeWU7LHHHjnppJNyySWXZKONNhraBwMAAHpOTx9T0A5ir0xyTpLtklyV5Mwk30/ylySHJfmnAc51dJIfJXkuyV611sv7DJmbZM0khy5mij3TCmLnDu5TAABLsttuu+WVr3xlWn/3HJqLLroozzzzTA488MCMGTNmQM9MmjQpSfLP//zPC4PYJNlggw3ykY98JM8880yuuOKKIa8JAADoPT0dxqYVjO6X5I4kY2utR9RaP1Fr/Xitdd8kL01y8tImKaV8Msl5ae2m3bXWelM/w25v939oMdN8KK0g95rBfwwAYFmaOHFikuSoo44a8DMPP/xwkmTs2LEv6lvQdttttw17bQAAQO/o9TB25/b7hFrrzL6dtdbZtdbrFvdwaflqks8l+X2SnWutv1vM8LlJvpvkTaWU1/WZ56VJ3pHkh0keH/zHAACWlSlTpuT3v/99ttpqq+y2224Dfm7BDtr77rvvRX3Tpk1bbB8AALDi6vUwdnr7/a8G+2ApZbUkFyT5aJKb09oR+6elPPZvSWpevDv2qCSrJTl3sOsAAJatCRMmJBncrtgk2WeffZK0zo6dN2/ewvbHHnssZ599dpIs8TIxAABgxdPrF3hdmuSkJB8upayd5LIkt9dal7ZNZa0kk5LsleTHSd5ba31macVqrX8opVyb5PBSyomLPPPBJFNrrdeXUj441A8DAL1u6tSpw3r+wQcfTJLMmjVrQHM99dRTufTSS7Pqqqtmxx13HFT9Qw89ND/96U9z+eWXZ4cddsj222+fZ555JpMnT84GG2yQJFlppdbfvYf7uYDu53sOvc13HHrf1KlTs/XWWy/zOj29M7bW+pskRyR5uP3+wyTTSinTSymXlVIWd93yG9MKYu9JcshAgthFnJtkdJL3JEkpZdckr0pr1ywA0EWuuuqqPPvss9ljjz0yevToQT370pe+NBMnTsx73vOezJ49O//xH/+Rm266KXvvvXfOOOOMJMl66623LJYNAACMUL2+Mza11otLKZcl2SPJLkle335/R5J3lFLOTzK+1loXeeyeJKOSbJPka6WU4/r0L8llSR5L66iC85Mck+T5JBMa+DgA0NOG+5fohx56KEmyzjrrDGiun/zkJ0mSv//7vx9S7a233jo77rjji9pvuOGGJMm22267cBzQmxbslvM9h97kOw69r9Pf857eGbtArfX5WuvVtdZTaq0HJnlpkvcmeTrJkUkO7vPIQ0l2SzI1ybFJziulDOh/q1rrnLRC2F1KKTsleXeSK2qtjzTzaQCAJvzqV7/KXXfdla222iq77rpro3NfeOGFSZJ999230XkBAICRbYUIY/uqtc6rtV6c5Cvtprf2M+ZPaQWyv08yPsm/l1IGupN4wUVdFyd5SZJzhrVgAKBxA724a+bMmfnv//7vhbtuF5g/f36eeuqpF42/8MILc+GFF+bNb35zxo0b19RyAQCAHtDzxxQsxYIrjkt/nbXWh0opuye5Jsn7kryklPLe9u7Xxaq13l1K+UWSXZNMaz8PACwDV155ZSZNmpQkeeSR1n+IMmXKlBx77LFJkjFjxuS00057wTOzZs3KZZddllGjRuWwww5b6vzHHXdcDj300Hzzm99c2D579uz81V/9VcaNG5ctttgiK620Un75y19mypQp2WabbTJhwoR+w1oAAGDF1dNhbCnl0LTOb/15rXV+n76XpXWua5JMXtwctdbHSilvTfL/0jpn9kellENqrc8upfwxaV3cdd8gzpsFAAbpt7/9bS644IIXtE2bNi3Tpk1Lkmy22WYvCmMvueSSPP3003nXu96VMWPGDKnuqFGjcsghh+TWW2/N9ddfnyTZcsstc/LJJ+fYY4/NGmus4eZlAADgBUov54SllP+b5GNpnQF7Y5I/tru2SHJAktWTXJ7knbXWWkoZl+S6JDfUWsf1mWvtJJPS2u16bZKDaq1Pl1LGtue9qda6ywDW9P0khyfZo9Z6fX9jZs6c2e8/lNGj113a9AAwos2YMXN5L6FRLv2A3ud7Dr3Ndxx630C+5+uuu26//1X9UPT0ztgkZ6Z1CddeSbZLsm9aZ7hOT3J9kh8k+cFAdq7WWp8speyXVni7V5KfllL2X0brBgAAAAB6TE+Hse1LuM5qvwYy/vos5vzYdv/sJHv3aZ61pGf6meOIJEcMdDwAAAAA0BtWWt4LAAAAAABYEQhjAQAAAAA6QBgLAAAAANABwlgAAAAAgA4QxgIAAAAAdIAwFgAAAACgA4SxAAAAAAAdIIwFAAAAAOgAYSwAAAAAQAcIYwEAAAAAOkAYCwAAAADQAcJYAAAAAIAOEMYCAAAAAHSAMBYAAAAAoAOEsQAAAAAAHSCMBQAAAADoAGEsAAAAAEAHCGMBAAAAADpAGAsAAAAA0AHCWAAAAACADhDGAgAAAAB0gDAWAAAAAKADhLEAAAAAAB0gjAUAAAAA6ABhLAAAAABABwhjAQAAAAA6QBgLAAAAANABwlgAAAAAgA4QxgIAAAAAdMAqy3sBDNyMGTOX9xKAhk2dOjVJsvXWWy/nlQAAAADLmp2xAAAAAAAdIIwFAAAAAOgAYSwAAAAAQAcIYwHg/2/vzsMsqeq7gX9/DpuCDjtqAFFUUCOuAUUjqxAliguIURR8IwkBgiYSfRNjwB1fNRFRIdFEcIuoiBIBUUEcBY24EHdEZVyCoAwwLMPict4/qhqubfdMT09P9cLn8zz3qb5Vp06d03fO3O5vn3sKAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABrDObDeAqdt448Wz3QRYcK67bvlsNwEAAAC4kzAzFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABrDPbDQCYb84999ycfPLJufTSS3PNNddkq622ysMf/vAceeSR2XnnnVd5/vvf//4ceeSRKy1zl7vcJddcc81MNRkAAACYA4SxAKvh2GOPzQknnJBNN900++23XzbbbLP86Ec/ytlnn50zzzwzJ598cg466KCV1vHQhz40L3vZy5Lk9sB10003TZJ88YtfzJIlS/LEJz5x7XYEAAAAGNy8D2Orqo3b9dsk1yb5RpJ3tdY+MIXyy/vypyQ5tbU2vkyqaoMkRyU5MMmOSe6aZFmSK5J8McmHW2ufGyl/aJJ3j6vmtr7855L8v9bad6baT2D2XXXVVTnxxBOz5ZZb5sILL8wWW2xx+7ElS5bkqU99al73utetMozdaaedstNOOyVJLrvssiTJAx7wgCS5PYQ95JBD1kYXAAAAgFk078PYEa/st+umC0v3T7JHVT26tfa3qyh//yRPT7JbkkenC11vV1UbpQtQH5nkyiSn99uNkjwsyV8k2bgvM97/JPlY//XiJLsnOSTJs6pqz9bal1a3o8Ds+OlPf5rf/va3edSjHvU7QWySPOEJT8jd7373LFu2bNr1f/vb387FF1+ce9/73tl3333XtLkAAADAHLNgwtjW2nGjz6tqrySfTvLiqnpra23pKso/LsmSJEdU1Ztba5ePHH5xuiD2U0me0lq7bdy5myR50CRNu2T0WlVV6WbMHpLk9Un2mFoPgdm2/fbbZ7311svXvva1LFu2LJttttntxy688MLccMMN2W+//aZd/ymnnJIkOfjgg7No0aI1bS4AAAAwxyyYMHa81tp5VfW9dCHpHyVZuoryF/blH5zkUUlGw9hd++1J44PY/txrk1w0xXa1qnpHujB21Xf6AeaMTTbZJMcdd1xe/vKXZ5dddsl+++2XTTfdNJdffnnOOeec7LHHHnnLW94yrbpvvvnmfOhDH8qiRYvy/Oc/f4ZbDgAAAMwFCzaM7VW//b01YFfhV+Oej33u+IFr1pzbTbddwCw74ogjsu222+aoo47Kqaeeevv++93vfnnOc57ze8sXTNUZZ5yR5cuXZ999983WW289U80FAAAA5pAFG8ZW1d5JdkgXeF48hfJPSLfW7G1Jvjzu8GlJDk7y6qraLslZSb7WWvv5NNpVSY7on/736p4PzKyxG2hN1Xve85684x3vyEEHHZQDDzwwm2++eZYuXZq3v/3tOeyww7JkyZIcffTRq92Ok08+OUmyzz77rHabgLnPuIaFzziHhc0Yh4Xvsssuu/3m2mtTtTa/J2dW1VgHRm/ItUOSpyVZlORfRm/gNUn5sRt4rZPkRa21Eye4ztFJXpXuJlxjrkxyfpJ/ba0tGVf+0HRrw050A6+HJ7k5yYQ38Fq+fPmEL8rGGy+eaDewBi6++CtTLvvVr341hx9+eHbfffe88Y1v/J1jt9xyS575zGfm6quvzumnn75as1t/+MMf5tnPfna23HLLnHnmmdaLBQAAgFkwWRi7ePHimvDANCykmbHH9tuW5Lokn0/y7621962i/JiW5M9ba++eqHBr7a1V9a4kT0y3huwj+u1zkjynql7dWvunCU59WP9IuuUPfp7kvUmOb619Z0o9A9aa1fmr19iyBE960pMmPG/nnXfOJz7xiVx//fVTrveyyy7LGWeckSR5wQtekB133HHK7QHmvrFZNEP8hR2YHcY5LGzGOCx8Q4/zBRPGttZWK6EeK19VGyZ5bJJ/T3JyVf24tXb+JOesSPLx/pGqWi/JYUlOSPKKqvpoa+2Scaed2lo7dHXaBsxNt956a5Lk6quvnvD4smXd8tLrrbfeatV5zjnnZNGiRXne85635o0EAAAA5qy7zHYDZltr7abW2meSPCXdsganVtXdpnjuba21tyf5z37XnmupmcAcsOuuuybpZsheccUVv3Ps05/+dL70pS9lgw02yC677JIk+dWvfpXvf//7ufzyyyet87zzzsv111+fvffe2427AAAAYIFbMDNj11Rr7RtV9c4khyf5mySvXY3Tb+i3M7Z+BDD37L///tl9991zwQUXZJdddsl+++2XrbbaKpdeemnOPffctNZy7LHHZtNNN02SXHHFFdl5552zzTbb5Jvf/OaEdY4tUXDooYcO1Q0AAABglghjf9drkrwgyTFV9Y7W2rVJUlWHJ7lkopttVdWOSQ7sny4ZfxxYOO5yl7vkwx/+cN75znfmox/9aM4666ysWLEim2yySfbZZ5/85V/+Zfbcc+oT5C+99NJccskl2XLLLbPPPvusxZYDAAAAc4EwdkRr7X+r6uQkL0ry0iR/3x/6kyQnVdXSJBcm+WmS9ZM8IMm+SdZN8tbW2sWDNxoY1LrrrpsjjjgiRxxxxCrL3uc+98l111036fEddtghF1/c/bexaNGiGWsjAAAAMDfd6deMncDrk6xIcnRVbdXve2mSY5J8L8ljkhyd5MgkD0vyiSRPaa29aBbaCgAAAADME/N+ZmxrbbXWaV1V+dbaVUk2HLfv+0ne3D+mep1TkpyyOm0DAAAAABYuM2MBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAawzmw3gKm77rrls90EAAAAAGCazIwFAAAAABiAMBYAAAAAYADCWAAAAACAAQhjAQAAAAAGIIwFAAAAABiAMBYAAAAAYADCWAAAAACAAQhjAQAAAAAGIIwFAAAAABiAMBYAAAAAYADCWAAAAACAAQhjAQAAAAAGIIwFAAAAABiAMBYAAAAAYADCWAAAAACAAQhjAQAAAAAGIIwFAAAAABiAMBYAAAAAYADCWAAAAACAAQhjAQAAAAAGIIwFAAAAABiAMBYAAAAAYADCWAAAAACAAQhjAQAAAAAGIIwFAAAAABiAMBYAAAAAYADCWAAAAACAAQhjAQAAAAAGIIwFAAAAABiAMBYAAAAAYADCWAAAAACAAVRrbbbbwDjLly/3ogAAAADAHLB48eKaqbrMjAUAAAAAGIAwFgAAAABgAMJYAAAAAIABCGMBAAAAAAYgjAUAAAAAGEC11ma7DQAAAAAAC56ZsQAAAAAAAxDGAgAAAAAMQBgLAAAAADAAYSwAAAAAwACEsXNQVW1dVf9RVVfRdxwaAAAUZ0lEQVRU1a1VtbSq3lJVm8x224CpqaoDqurEqvp8VV1fVa2q3reKc3atqrOr6pqqurmqvlFVL66qRUO1G5iaqtqsql5YVWdU1Q/6Mbu8qr5QVX9eVRP+jGWcw/xRVW+oqvOq6qf9eL2mqr5eVcdW1WaTnGOMwzxWVQf3P7e3qnrhJGX+tKou6N/3b6yq/66qQ4ZuKzA1fabWJnlcOck5a/X9vFprM1EPM6Sqtk9yUZItk3w8yfeS7JxkjySXJnlca23Z7LUQmIqquiTJw5LcmORnSXZM8v7W2sGTlN8/yelJbklyWpJrkjwlyQ5JPtJaO3CIdgNTU1WHJzkpyc+TfDbJT5JsleQZSRanG88HtpEftIxzmF+q6rYkX0vynSS/SLJhksckeXSSK5I8prX205HyxjjMY1W1TZJvJlmUZKMkh7XW3jWuzFFJTkyyLN04vy3JAUm2TvLm1toxgzYaWKWqWppk4yRvmeDwja21N40rv9bfz4Wxc0xVnZtknyRHt9ZOHNn/z0n+Jsm/ttYOn632AVNTVXukC2F/kGS3dGHNhGFsVd2jL7c43R9cvtLv3yDJ+Ukem+TPWmsfHKj5wCpU1Z7pgpmzWmu/Hdl/zyRfTrJNkgNaa6f3+41zmGeqaoPW2i0T7H9tkn9IclJr7Yh+nzEO81hVVZJPJ7lvko8mOSbjwtiq2i7dZKmbkjyqtba0379JkouTbJ9k19baF4dsO7ByfRib1tp2Uyg7yPu5ZQrmkH5W7D5JliZ5+7jDx6b7T/95VbXhwE0DVlNr7bOttcva1P7idUCSLZJ8cOw/+76OW5L8Y//0r9ZCM4Fpaq2d31r7r9Egtt9/ZZKT+6e7jxwyzmGemSiI7X2o3z5gZJ8xDvPb0Un2TPKCdL93T+T/JFk/ydvGgtgkaa1dm+R1/VMTp2B+G+T9XBg7t+zRbz81wS93NyS5MMnd0n08Clg49uy3n5zg2JIkK5LsWlXrD9ckYA38qt/+emSfcQ4Lx1P67TdG9hnjME9V1YOSHJ/khNbakpUUXdk4P2dcGWBuWb9fE/ofqupFVbXHJOu/DvJ+vs6anMyM26Hffn+S45elmzn7wCTnDdIiYAiTjv3W2q+r6vIkD0lyvyTfHbJhwOqpqnWSPL9/OvpDnHEO81RVHZNu/cjF6daLfXy6IPb4kWLGOMxD/fv2e9Ot/f4Pqyi+snH+86q6KcnWVXW31tqKmW0psIbumW6sj7q8ql7QWvvcyL5B3s+FsXPL4n67fJLjY/s3HqAtwHCMfVg4jk/yh0nObq2dO7LfOIf565h0N+gb88kkh7bWfjmyzxiH+emfkjwiyeNbazevouxUxvmGfTlhLMwd707y+STfTnJDuiD1qCR/keScqnpsa+1/+rKDvJ9bpgAAYAZU1dFJXpLu5h7Pm+XmADOktXbP1lqlm1XzjHS/xH29qh45uy0D1kRV7ZJuNuyb3XQLFq7W2iv7+z1c1Vpb0Vr7Vmvt8CT/nOSuSY4buk3C2LllLGFfPMnxsf3XDdAWYDjGPsxzVXVUkhOSfCfJHq21a8YVMc5hnut/iTsj3bJhmyV5z8hhYxzmkX55gvek+yjyK6Z42lTH+WQz6oC5Zeymu08Y2TfI+7kwdm65tN8+cJLjY3dsnWxNWWB+mnTs9z8o3jfdjYB+NGSjgKmpqhcnOTHJt9IFsVdOUMw4hwWitfbjdH94eUhVbd7vNsZhftko3Xh9UJJbqqqNPZIc25d5Z7/vLf3zlY3ze6VbouBn1ouFeWNsuaENR/YN8n4ujJ1bPttv96mq33ltquruSR6Xbu2ZLw3dMGCtOr/f/skEx56Q5G5JLmqt3Tpck4CpqKqXJfmXJJekC2J/MUlR4xwWlnv329/0W2Mc5pdbk/z7JI+v92W+0D8fW8JgZeP8SePKAHPfY/rtaLA6yPu5MHYOaa39MMmnkmyX5Mhxh1+ZLq1/b2vtpoGbBqxdH0lydZJnV9Wjx3ZW1QZJXtM/PWk2GgZMrqpeke6GXV9Nsldr7eqVFDfOYR6pqgdW1e99RLGq7lJVr02yZbpfxq7tDxnjMI+01m5urb1wokeSM/tip/b7TuufvztdiHtUVW03VldVbZJu7dnkjo89A3NAVT2oqjacYP92Sd7WP33fyKFB3s+rtbamdTCDqmr7JBel+wHv40m+m2SXJHukW55g19bastlrITAVVfW0JE/rn94zyb7p/uL2+X7f1a21Y8aV/0iSW5J8MMk1SZ6aZId+/7Oa/7BhzqiqQ5Kckm5W3ImZeH24pa21U0bOMc5hnuiXH3l9uplxlydZlmSrJLulu4HXlen+CPOdkXOMcVgAquq4dEsVHNZae9e4Y3+d5K3p/k84LcltSQ5IsnW6G4EdE2DO6MfzS5IsSfLjJDck2T7Jfkk2SHJ2kqe31m4bOWetv58LY+egqtomyavSTYveLMnPk5yR5JUjf30H5rCRH+Im8+PW2nbjznlckpcneWy6N4YfJPmPJG9trf3m92oAZs0UxniSfK61tvu484xzmAeq6g+THJ7k8elClo2T3JRucsRZ6cbs+Bv1GeOwAKwsjO2PPyXJMUkeme7Txt9J8rbW2qlDthNYtaraLd37+SPSTZLaMN3Nty5J8t50nz7/vWB0bb+fC2MBAAAAAAZgzVgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAAAAAGAAwlgAAAAAgAEIYwEAAAAABiCMBQAAAAAYgDAWAGCBqKrWP7abgbp27+tausYNmyeq6tC+zxdMcOyC/tihw7dsWFW1tO/r7rPdFgCAhUYYCwAwB4wEqav7uGC22878UFUPr6rj7gyB8qiqemhVnVVV11fVTVX12ap63CrO+UBV/aaqHj1UOwGAO4d1ZrsBAAAkSa6aZP+mSdZNckuS5RMcv2bk60v77a9msF0sHA9PcmySzyU5ZSXlfpju39uKAdq0VlXVA5NcmOTuSX6d5DdJdk9yflXt1Vr7wgTn7Jnkz5Kc1Fr7yoDNBQDuBISxAABzQGvtnhPt72e+7pbktNbaoauoY8eZbxl3Nq21vWa7DTPouHRB7ClJjkgXyL4myUuTHJ/k8aOFq2q9JG9P8sskLx+wnQDAnYRlCgAAgIVqr3SzYV/UWru5tfardCHrVUkeW1V3G1f+mCQ7Jnlpa+3aYZsKANwZCGMBABaIVd3Aq6o2rKpjquqiqrqmqm6pqh9V1ZlV9dyqWnc1rrV3Vd3YX+/4CY5vVFX/UFUXV9Xy/lqXVdVbq2qbSeq8/SZZVbVxVb2hqr5XVSuq6rrVaNsjq+r4qvpCVf2kqm6tqmV9/S+sqkVTrWs1rnmPfj3W/+m/LzdW1Teq6pVVtXgV567W6zKd/lVVS/Lu/uluE6w9vPtI2ZXewKuqtqqqN4+8Nsur6stV9ZKqWn+Sc07p6zyuqhZV1Yv779WKvs+fWEvrs26W5OrW2vVjO1prv07y43S/C20y0sbt0gW1X0hy6lpoCwCAZQoAAO4MqurBSc5Ksl2/69dJrk+yTZL7JnlKurU1l06hrqcn+c8k6yf5+9ba8eOOPyjJOUnuM3KtW5PcP8lfJzm4qp7SWrtwkktskeSrSe7Xn3fbVPo44lPpQrikW/d0Rbq1d3frH0+vqv37UG6NVdX9k3wmd/R3bK3Vh/aPQ6tq79baZROcO53XZTr9uyrJXZPcI92awqNrDSdT/B5X1c7pXttN+103JFkvyR/1j+dV1T6ttV9MUsU6fX/37dtxa7pAdL8ke1XVnq21L06lLVO0LMnmVXWPsUC2D6vvk+S3SUZnv76178sRrbU2g20AALidmbEAAAtcVW2a5JPpAr/LkzwtyYattc2S3C3dupnvThcErqqu5yf5cO4IrcYHsYuTnJ0u7Ppwkocl2aC1tlGS7ZN8IF34dnpVbTzJZf4p3U3LnpTkbq21eyRZnVmTn0p3A6Z7tdY2bK1tkmSjJM9LcmWSJyf5m9Wob1L9GqOnp+vvT5Ps019royR7J/lJkm2TnDF+1ugavC6r3b9+TeIX9U8vaq3dc9zjoin0dZMkH0sXxH4zyc79a7NRkgPTBZsPS/L+lVRzZLrQ9qAkG7XW7t6f860kGyQ5YVXtWE3nJ1mU5ISq2qCq1km3ZuxWSb7UWlvR9+2p6YLvE1tr35zhNgAA3M7MWACAhe//pptpeXWSP26t/e/YgX4NzQv7x0pV1V+nC8t+k+T5rbX3TVDs79KFi//ZWnvO6IHW2o+SPLcPIf8kyQuTvGmCOtZP8uTW2rdGzv3Bqto3UvY5E+y7Kcn7qurHSZaku5nTG6da50oclGSndLM8f6fNSc6rqicn+XqShyR5bpL/GDk+rddl4P6NOirJvZJcl2Sf1tqV/bV/k+QjVXV9knOT7N3PcD1/gjo2TtfXL4y0/RtVdWiSryT5o6ratrX2kxlq86uS/GmSQ5McnO7f7vrpXq+/T5Kqumu6f9dXJDl2hq4LADAhM2MBABa+5/fbN40Gfqujql6R7mPctyU5YJIgNkkO6bdvXkl1H+i3T5zk+DnjQs0Z01r7fLowcbuquvcMVHlAv/34RG1urX07yUf6p88ad3iNX5cJrjfT/Rs11td3jQWx4679qSRjSwyM7+uYz48GsSPnfjXJz/qnf7imDR2p97tJ/jjdDORb0i1NsCTJ3q21JX2xV6T7A8LfttZuqKotq+rUfh3eFVV1flU9aqbaBADcuZkZCwCwgPU3Jdqqf3r29Kqof073sfebkuzfWjtvkoLbJNl67Fr9TaMmsl6/nfBGXrkj0Ju2qjow3UzUR6Zbg3aDCYrdO91syDXxyH772ZWUOT/dsgJjZdf4dRmwf2PXWy93hKSr6utjM9LXcS5eybn/m+7fzyYrKbPaWmuXpFvy4vdU1Y5JXpLkM62106pqg3R9eEiS89LNWn56kguqauc+3AUAmDZhLADAwrbVyNfT+ej3trlj/dG/miyI7d1r5Ostp1D33SbZ/8upNGwi/ZqgH0oXoI25NV2o9pv++RbpPiG24XSvM2KLfruyma1jMz43q6rqbw41rddlFvo3ZtPc8am6qfR1i0mO37CSc2/pt+uuRrvW1Nv77VH99rB0QexJrbUjkqSqDk7y3nRrzT5zwLYBAAuQZQoAAFiZK5N8rv/69VW1/UrKjv5suUlrrVbx2G6Sen4zyf6pOCxdULkiydFJtmmtbdBa22LsZlW5Y7ZorcF1xptoZuraMFv9GzVUX9eqqnpOkj3TLRNxab/7T/vt20aKfiDdHwj2rapFAzYRAFiAhLEAAAvbVSNf32ca59+aLqC6MMkfJDm/qiarZ/Ra207jWjPhwH776tbaia21n40e7MO0zWfwemOzeFfW37GlG5b1s2KT6b8uQ/dvzDXp1ltNptbXac9uHkJV3SPdusZLk7x25NDYa3H52I7W2m/75xtm7XxvAYA7EWEsAMAC1lpbmm52a5I8eZp13Nif++V0Qdz5VbX1BOUuzx0h44RrdA5grF1fn+T44zKzMzu/1m/3WEmZPceVXZPXZU36NxamrvaM2dbabUnGblC2Wn2do16T5J5JXtRaWzHB8fHfw7uu/SYBAHcGwlgAgIXvvf32JVX1B9OpoLV2fZJ904Vs90sXyN5rgqKn9NtjVnat6mw8nbaswvJ++9AJrrlOuhBuJn2k3z6pqh4xwTUfkuSA/umHxh2ezuuyJv27vt9O9/s+1tdDJ3rtq2qfdDfvSn6/r3NG/zodkeQTrbUzxx3+cb991Ej5jZPcP90N7K4epJEAwIIljAUAWPjekO6mS5sn+XxVPbWq1kuSqlq3qnarqg9ONNt1VGvtuiRPTPKNJA9Icl5Vjb9R0/FJftRf66KqelZV3T6rsKq2raq/SBfqPm2G+jfq0/32FVW1/9gan1W1Y5L/SrJzulBtppyW7vuRJB+rqr2rqvpr7pXk7HQ3pPp2kvePO3c6r8ua9O/b/fbBVbXLNPr6tiQ/TzdL9JNV9ej+2ouq6plJPtiX+0xr7fxp1D+pqlp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"text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 689}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "15 out of 20 tickers were removed\n{'PRSP': 36, 'SKM': 72, 'EIX': 13, 'FAST': 153, 'DLR': 14}\nFunds remaining: $9.49\n"}]}}, "c0240e0b58aa4b21a1d5d3eff11325a5": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "FloatSliderModel", "state": {"continuous_update": false, "description": "Target return:", "layout": "IPY_MODEL_523f9d9bbf5f4fcc9aa742a86ec548a0", "max": 0.2687043293906299, "step": 0.001, "style": "IPY_MODEL_2af4364f5d7a41a188121c7405834b93", "value": 0.2418338964515669}}, "c03b173e51f441368f0b87c4966c496a": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "SliderStyleModel", "state": {"description_width": "initial"}}, "c04ac4c0d23943c697a95f3b5dc5b018": {"model_module": 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\n", 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\n", "text/plain": "<Figure size 432x288 with 1 Axes>"}, "metadata": {"image/png": {"height": 277, "width": 573}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "14 out of 20 tickers were removed\n{'PRSP': 42, 'SKM': 113, 'EIX': 11, 'FAST': 98, 'DLR': 16, 'CBPO': 5}\nFunds remaining: $22.24\n"}]}}, "c2820fce5b584947a74727224277afb0": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "HBoxModel", "state": {"children": ["IPY_MODEL_5b7f7df38f1b44909108fc22cb98d87a", "IPY_MODEL_9a9458e724e24736a7f1b2d55edc5986", "IPY_MODEL_72a96e177e634c78945e68673895c0a3"], "layout": "IPY_MODEL_fc1ed93abaaa4b9380743339a8b4a0c4"}}, "c28efc935e324e6198db862103e4162d": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "SliderStyleModel", "state": {"description_width": ""}}, "c2964e0cd6ea4d7fb579cbe8cbfc8a97": {"model_module": "@jupyter-widgets/base", 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"text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 695}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "16 out of 20 tickers were removed\n{'PRSP': 21, 'EIX': 17, 'FAST': 254, 'DLR': 3}\nFunds remaining: $7.97\n"}, {"ename": "NameError", "evalue": "name 'draw_portfolio_with_securities' is not defined", "output_type": "error", "traceback": ["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/home/pavel/Repo/cryptocurrency-analysis/venv/lib/python3.6/site-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mtri\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mdraw_portfolio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtri\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m opt_obj_widget = Dropdown(options=['target_return', 'target_risk', 'max_sharpe', 'min_volatility'],\n", "\u001b[0;32m<ipython-input-23-c4afb85c9e3d>\u001b[0m in \u001b[0;36mdraw_portfolio\u001b[0;34m(prices_df, opt_obj, total_v, target_return, target_risk)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mdraw_cleaned_weights_with_perfomance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcleaned_weights\u001b[0m\u001b[0;34m,\u001b[0m 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Jzm/YHwAAAACgZ5qFrbXWOaWUvZJMTPK6dILXo/opLUluS7JPrXV2q/4AAAAAAL3U8hiB1FofSLJdkmOSTEnybDrhaul+PyXJ0Um2q7Xe37I3AAAAAEAvtTxGIElSa30myblJzi2ljEqyVnfo0VrrnNb9AAAAAACGg+Zh6/y64eq0pdkDAAAAAGA4aHqMAAAAAADAimpQO1tLKTt1v32q1vrLBa4tkVrr5MHcBwAAAAAwnAz2GIFrk9Qkv02yxQLXlkQdwhoAAAAAAIaNwQad96YTlD7QzzUAAAAAgBXOoMLWWuvGA7kGAAAAALCi8IIsAAAAAIAGmoWtpZSdSinbLUH9NoN9qRYAAAAAwHDT8uVU1yZ5MMmGA6y/NMm4xmsAAAAAAOiJ1scIlKVcDwAAAAAwLPXyzNYxSZ7pYX8AAAAAgGZ6EraWUrZJslaSP/aiPwAAAABAa4M+L7WU8u4k717g8lqllKsXdVuSviRbJKlJrhxsfwAAAACA4WQoL6faOMmEBa6N7ufawkxOcvIQ+gMAAAAADBtDCVu/n2Rq9/uS5KtJHkvywUXcMzfJ40l+U2v93RB6AwAAAAAMK4MOW2uttye5fd6fSylfTfKXWutFLRYGAAAAALAsGcrO1ueptfbkZVsAAAAAAMOBgBQAAAAAoAFhKwAAAABAA8JWAAAAAIAGhK0AS8lPf/rT7Lffftliiy2ywQYb5LWvfW3e/e53Z8qUKQOeo9aaiy66KLvttls23HDDjB07NhMmTMhXv/rVzJ07dymuHgAAAFhSwlaApeATn/hEDjzwwNx+++3ZbbfdcuSRR+a1r31tfvzjH2ePPfbIpZdeOqB5jjjiiBx33HG59957c8ABB+TQQw/NX/7ylxx//PE56qijlvJTAAAAAEtiVK8XMJyUUuoAynaptV7brT8sydeSXFRrPax7bUSSa5LslOTgWusl/fTZJMntSWYn2bLWen+L9QPDw8MPP5yzzz476623Xm644Yasu+66z41Nnjw5++yzTz796U/nwAMPXOQ8P/rRj/Ltb387L3vZy3L11Vdn7bXXTpI888wzOfTQQ3PppZdmr732yj777LNUnwcAAAAYGGFr/z65iLGpi7qx1jq3lPLudMLUc0opk2utf5w3XkoZmeQbSVZP8g5BKyx/7rvvvsydOzfjx49/XtCaJDvttFPGjBmTGTNmLHaeiRMnJkmOOeaY54LWJBk9enQ+9rGP5ac//WkuuOACYSsAAAAME8LWftRaTxni/VNLKcels+v1wlLK7rXWebtmT0qyfZJ/q7UO7PeIgWXKK17xiowePTq33nprZsyY8byg9IYbbsgTTzyRvfbaa7HzTJs2LUmy8cYbv2Bs3rUbb7wxzzzzTEaPHt1k7QAAAMDgLbWwtZSyXpKtkszb1vVIkltrrdOWVs/hpNZ6YSllnyT7JTkuyVmllP+T5OQk9yU5upfrA5aeNddcM6eccko+9rGPZdttt81ee+2VtdZaK3/4wx9y5ZVXZpdddslZZ5212HnmhbT33HPPC8amTp2aJJkzZ06mTp2azTffvOkzAAAAAEuuedhaSnlDktOS7LiQ8clJ/qHWekPr3sPQEUl2SPKZUsr16RwfMCrJu2utM3u6MmCp+sAHPpCXvvSlOeaYY3LRRRc9d32TTTbJwQcf/ILjBfqz++675zvf+U7OOeecHHDAAVlzzTWTJLNnz85nPvOZ5+pmzvQ/JwAAADAcNA1bSylHJjk7yYgkJcmzSaZ3h9fu9ts5ybWllGNqrV9u2b+VUsopCxl6utZ6+kDnqbVOL6W8L8mPklyfZOUkX6i1XjP0VQJ/TXffffcS1V988cU599xzc+CBB+Ztb3tb1llnnUydOjXnnHNODj/88EyePDnHHnvsIufYcssts9122+Wmm27K+PHjs/POO2f06NGZMmVKZsyYkQ022CAPPfRQ/vjHPy7x+oDhyc8yrJj87MOKx889DE+bbbbZkOco/3uU6BAnKuXvktycTtB6fZJPJZlca53VHV85naD140len04Qu02t9VdNFtBAKWVx/zAeq7X2zVd/WDrnsl5Uaz1sEfNem86z35tk83n/TBba5LHH+l1HX98ai1kesLTcfPMvB1x7yy235Mgjj8yECRNyxhlnPG/s6aefzgEHHJDp06fnu9/9bjbaaKNFzjVnzpx885vfzJVXXpn77rsvo0ePzvjx43PMMcfkxBNPzO9///t8//vfz4Ybbjio5wIAAAA6Fha2rrHGGmWgc7Tc2frhdILWy5IcXGudO/9gN2CcVEq5Ksm3krw1yfFJDm24hiZqrQP+B7g4pZRdk+zU/eNGSbZNMrnV/MBfx5L87da8YwP23HPPfu/bZpttMnHixDz++OP9js/7W+55Y6eddlpOO+2059U8/fTTuf/++7P22mtnwoQJA14bMDwt+HMPrBj87MOKx889LP9GNJxr5yQ1yYcWDFrn1x37YLd2QsP+w04ppS/JhUnmJDkqnWe+qJQyppfrApauWbM6m9enT5/e7/iMGTOSJKNHjx50j+9+97t55plncsABBwx6DgAAAKCtlmHruklm1lofXFxhrfWBJDO79yzPzk0yLsknaq3nJflsko2T/FMvFwUsXTvssEOSzg7XBx544HljP/vZz3LTTTdllVVWybbbbpuk88Kr//7v/84f/vCHF8z1+OOPv+DaHXfckZNPPjl9fX350Ic+tBSeAAAAABiMlscIPJ6kr5SyWq31z4sqLKWsluTFSf7UsP+wUko5MMlBSW5IJ2RNklOSvDnJ/yulfL/WOrFHywOWon333TcTJkzItddem2233TZ77bVX1l9//fz2t7/NT3/609Ra84lPfCJrrbVWkuSBBx7INttsk3HjxuXOO+983lz77bdfVllllWyxxRZZffXV89vf/jaTJk3KqquumksuuSQveclLevGIAAAAQD9ahq23Jvn7JMcm+cxiao9LMjLJLQ37DxullA2TfCnJgwNtYwAAIABJREFUk0neNe9YhVrr7FLKoUl+meSCUspraq39/54xsMwaMWJEvv3tb+eCCy7I9773vVxxxRV56qmnsuaaa2b33XfP+9///uy6664DmmvffffNd7/73Vx66aV5+umn85KXvCSHHXZYPvShD3kpFgAAAAwzLcPW85PsnuRT3Z2rZ9RaH5u/oJTykiQnpBPI1u49w04p5ZRFDH+/1nrbIu4t6ZzTumaS99Vafz//eK3116WUjyf5XDqB7NuGvGBg2FlppZXygQ98IB/4wAcWW/uyl70sM2fO7Hfs2GOPzbHHHtt6eQAAAMBS0CxsrbV+r5Ty9SSHJjkpyYdLKbcn+WOSVZK8NMlmSVZKUpJcVGu9vFX/xj6xiLGpSRYatqYTJL8xyQ9qrf+6kJozk+yd5K2llENqrd8Y1CoBAAAAgGGj5c7WJDksyX8m+Wg6Z7Ju00/N40k+neTzjXsPWa21LGH9hensYp3/2heTfHEx981NstMSLg8AAAAAGMaahq211prk9FLK2emc37pVknW7w4+kc67rpFrrUy37AgAAAAD0WuudrUmSWuufk3y/+wUAAAAAsNwb0esFAAAAAAAsD4StAAAAAAANDOoYgVLK1d1v76m1vmeBa0ui1lp3G8waAAAAAACGk8Ge2Tqh+/lf/VxbEnWQ/QEAAAAAhpXBhq3v6X4+1s81AAAAAIAVzqDC1lrrRQO5BgAAAACwovCCLAAAAACABoStAAAAAAANDOoYgVLKS1stoNZ6b6u5AAAAAAB6ZbAvyPpDo/51CGsAAAAAABg2Bht0lkb9W80DAAAAANBTgwpba63OegUAAAAAmI/QFAAAAACgAWErAAAAAEADS+3lVKWU9ZJslWTd7qVHktxaa522tHoCAAAAAPRK87C1lPKGJKcl2XEh45OT/EOt9YbWvQEAAAAAeqXpMQKllCOTXJNO0FqSzE0yrfv1bPfazkmuLaW8v2VvAAAAAIBeaha2llL+Lsm/JBmZ5IYkeyRZvdb6klrrS5KMSfKm7tjIJP/SvQcAAAAAYJnXcmfrh7vzXZZkQq31Z7XWWfMGa62zaq2T0tnZ+p10AtfjG/YHAAAAAOiZlmHrzklqkg/VWucurKg79sFu7YSG/QEAAAAAeqZl2Lpukpm11gcXV1hrfSDJzO49AAAAAADLvJZh6+NJxpRSVltcYbfmxd17AAAAAACWeS3D1lvTOYf12AHUHtetvaVhfwAAAACAnmkZtp6fpCT5VCnltFLKGgsWlFJeUkr5QpJT0zmz9fyG/QEAAAAAemZUq4lqrd8rpXw9yaFJTkry4VLK7Un+mGSVJC9NslmSldIJZS+qtV7eqj8AAAAAQC81C1u7Dkvyn0k+ms6ZrNv0U/N4kk8n+Xzj3gAAAAAAPdM0bK211iSnl1LOTvL3SbZKsm53+JF0znWdVGt9qmVfAAAAAIBeG1TYWko5OcmTtdYv9Ddea/1zku93vwAAAAAAlnuD3dl6SpKHkjwXtpZSfp9kWq11uwbroh8zZz7W6yUAAAAAAAsx2LC1JhmxwLWN03kRFgAAAADACmfBwHSgHk2ydillTMvFAAAAAAAsqwa7s/WmJG9O8sNSyreTPNm9vmop5V1LMlGt9eJBrgEAAAAAYNgYbNh6apJdkuycZKf5rr84ydeWcC5hKwAAAACwzBtU2FprvbmU8rokRyT52ySrJpmQZHaSG5utDgAAAABgGTHYna2ptf4uyUfm/bmUMjfJo7XWXVosDAAAAABgWTLosLUf9yZ5uOF8AAAAAADLjGZha61141ZzAQAAAAAsa0a0mqiU8mwp5Y9LUP+HUsqcVv0BAAAAAHqpWdiapHS/lvQeAAAAAIBlXsuwdUmtnOTZHvYHAAAAAGimJ2FrKWWDJOslmd6L/gAAAAAArQ36BVmllJ2STFjg8uqllJMXdVuSviRv6n5/w2D7AwAAAAAMJ4MOW5PskuQTSep811brXluUeee0Pprkk0PoDwAAAAAwbAwlbL0tyUXz/fndSZ5Octki7pmb5PEkv0lyea11xhD6AwAAAAAMG4MOW2utP0jyg3l/LqW8O8ljtdb3tFgYAAAAAMCyZCg7Wxe0X5KHSykja63PNpwXAAAAAGDYaxm2Xp7OMQEvT3Jfw3kBAAAAAIa9lmHrk0nm1FoFrQAAAADACmdEw7n+kORFpZSWAS4AAAAAwDKhZdh6WZKVkryl4ZwAAAAAAMuElmHrGUl+meTLpZTdGs4LAAAAADDstfyV/48muTrJ3ySZVEq5I8mNSR5J8uzCbqq1ntpwDQAAAAAAPdEybD0lSU1Sun9+bZItF1FfuvXCVgAAAABgmdcybL04nfAUAAAAAGCF0yxsrbUe1mouAAAAAIBlTcsXZAEAAAAArLCErQAAAAAADbQ8s/U5pZQJSd6eZKsk63YvP5Lk1iSX1VqvXRp9AQAAAAB6pWnYWkpZJ8k3k7xx3qX5hl+eZOsk7y+l/CzJIbXW6S37AwAAAAD0SrOwtZQyOsnPkmyZTsh6Y5Krk9zfLdkoya5Jtk/y90kmlVK2q7U+02oNAAAAAAC90nJn6zFJXpvk0SQH1Vp/1k/Nx0spuye5pFt7dJJ/argGAAAAAICeaPmCrAOT1CRHLCRoTZLUWiclOSKd3a/vaNgfAAAAAKBnWoatr0zydJLLB1B7ebf2VQ37AwAAAAD0TMuwdaUks2utdXGFtda5SWan8Qu6AAAAAAB6pWXYem+SMaWUrRZXWEoZn2RM9x4AAAAAgGVey7D1x+mcw/qvpZR1F1ZUSlk/yb+mc77rFQ37AwAAAAD0TMtf4/9skncn2TLJf5VSLkhybZI/JlklyUuT7JLksCQvSvJoks817A8AAAAA0DPNwtZa67RSypuTfD/JBklO6H4tqCR5MMlbaq3TWvUHAAAAAOillscIpNY6JckWST6R5M50jgoo3a/avXZykr+ttd7csjcAAAAAQC+1PEYgSVJrnZnkU0k+VUpZKcla3aFHa62zW/cDAAAAABgOmoet8+uGqw8vzR4AAAAAAMPBkMPWUsrKSd6SZHySFyeZmeQ/kvyo1jpnqPMDAAAAACwLhhS2llJ2SPLtdF6ItaCppZS31FrvHEoPAAAAAIBlwaBfkFVK2TDJxHSC1nkvwHpk3nCSlyf5cSlljaEuEgAAAABguBt02JrkuCR96Rwb8K4kL6q1bpBktSTHJvlLkrFJ/t9QFwkAAAAAMNwNJWz9+3R2sx5ba/1GrfWZJKm1Pl1r/Zckn0hnh+vuQ18mAAAAAMDwNpSwdZN0wtbvLmT82/PVAQAAAAAs14YSto5J8kit9en+Bmut93S/XW0IPQAAAAAAlglDCVuTzs7WxSlD7AEAAAAAMOwNNWwFAAAAACDJqCHev1Yp5eoh1NRa625DXAMAAAAAQM8NNWwdnWTCEGoGcgwBAAAAAMCwN5Sw9aJmqwAAAAAAWMYNOmyttb6n5UIAAAAAAJZlXpAFAAAAANCAsBUAAAAAoAFhKwAAAABAA8JWAAAAAIAGhK0AAAAAAA0IWwEAAAAAGhC2AgAAAAA0IGwFAAAAAGhA2AoAAAAA0ICwFQAAAACgAWErAAAAAEADwlYAAAAAgAaErQAAAAAADQhbAQAAAAAaELYCAAAAADQgbAUAAAAAaEDYCgAAAADQgLAVAAAAAKABYSsAAAAAQAPCVgAAAACABkb1egEMXF/fGr1eAvBX8X96vQBYrs2c+VivlwAAACyn7GwFAAAAAGhA2AoAAAAA0ICwFQAAAACgAWErAAAAAEADwlYAAAAAgAaErQAAAAAADQhbAQAAAAAaELYCAAAAADQgbAUAAAAAaEDYCgAAAADQgLAVAAAAAKABYSsAAAAAQAPCVgAAAACABoStAAAAAAANCFsBAAAAABoQtgIAAAAANCBsBQAAAABoQNgKAAAAANCAsBUAAAAAoAFhKwAAAABAA8JWAAAAAIAGhK0AAAAAAA0IWwEAAAAAGhC2AgAAAAA0IGwFAAAAAGhA2AoAAAAA0ICwFQAAAACgAWErAAAAAEADwlYAAAAAgAaErQAAAAAADQhbAQAW4wc/+EFOOOGE7Lnnnhk3blz6+vpyxBFHLLR+1qxZueCCC7Lrrrtmk002yYYbbphtttkmH/nIR3LvvfcOuO8999yTvr6+hX69973vbfF4AABAI6N6vQAAgOHujDPOyK9//eusvvrqGTt2bJ544omF1s6ZMyf77rtvbrrppmy++eY54IADsvLKK+fWW2/N+eefn29961uZNGlSXvWqVw24/6tf/erstddeL7i+xRZbDOp5AACApWO5D1tLKSOTvDfJIUlek2RMkj8leSjJlCQ/rLX+sFs7Ick1Sa6rtU7oZ669klyWzo7gd9Raf1BK2TjJH7olf07yklrrC/4LrJRSkvwuySbdS7vUWq9t8YwAwNL16U9/OhtuuGE22WSTXH/99dl7770XWjtx4sTcdNNN2XnnnXP55ZdnxIgRz5vnc5/7XM4+++ycc845A+7/mte8JieddNKQngEAAFj6lutjBLpB68Qk5yfZMsmPk5yZ5BtJHkxycJKPDHCu9yT5fpJZSd5Ya/3BAiVzkqyW5KCFTLFbOkHrnCV7CgCg13baaae84hWvSOfvThdt6tSpSZLdd9/9eUFrkrz5zW9OkkyfPr35GgEAgN5b3ne2HpTkTUluT7JzrfWx+QdLKS9Ksu3iJimlnJTk00nuT/KmWutv+im7JcnLkhyeTri7oMPTCWqvTrLnEjwDALAMmXc8wFVXXZWjjjrqeYHrT3/60yTJhAkTlmjOhx56KF/72tfy6KOPZq211srWW2+dV7/61c3WDAAAtLG8h607dD8vXDBoTZJa61PpHBvQr+6v/p+V5Ngkd6UTtN63kPI5Sb6W5KRSymtrrbfPN886Sd6S5DtJ6mAeBABYNuyxxx7Ze++986Mf/Sg77LBDdt5554wePTq33XZbbrrpphxxxBE5/PDDl2jOa665Jtdc8/z/y/KGN7whX/rSlzJu3LiWywcAAIZguT5GIMmM7ufmS3pjKWV0kkvSCVp/kWTHRQSt83wlnTB1wf+CeneS0UkuWNJ1AADLllJKLr744px44om5++678+Uvfzlnn312fv7zn2eHHXbI2972towaNbC/737Ri16UE044Iddee22mTp2aqVOn5oorrsiOO+6Y66+/Pvvuu2/+/Oc/L+UnAgAABqrUuvxutCyl/F2S/0hnB+83k1ye5JZa6z0LqZ+Qzk7XW9J5idYbk/woyYG11r8s5J6N03lB1g211jeUUq5KMj7J2Hn3lFL+M8nIWuvmpZRvJHlnFvGCrMcee6zffyl9fWss/qEBgEW6+eZfDun+W265JUceeWTe9KY35VOf+tQLxmfNmpVTTjklv/jFL3Lcccdlp512yiqrrJLbb789Z555Zh588MGcfvrp2XnnnQe9hjlz5uTwww/Pr3/96xx//PE56KCFHRkPAAAM1Gabbdbv9TXWWGPxL2/oWq53ttZaf5XkkCQPdz+/m2RqKWVGKeXyUsrCXiU8Pp2g9bdJ9l9Y0LoQFyTpS/K2JCml7JjkVensegUAlnMXXXTRc+e17r///llnnXWy+uqr5/Wvf31OP/30zJkzJ2eeeeaQeowaNSr77rtvkuRXv/pVi2UDAAANLO9ntqbWelkp5fIkuyR5Q5K/636+JclbSikXJzmsPn+L72+TrJzklUn+uZRydB34FuDLk0xP5yiBi5MckWR2kgsbPA4AMEQL+9vqgXrooYeSJC9+8Yv7nevmm29Okuy///4vGN9ss83S19eXBx98MGuvvXbWWmutJMndd9+9xGvbYostkiQjRowY8jMBvTGYn31g2ebnHpZ/y/XO1nlqrbNrrZNqrSfXWvdOsk6SA5P8Ocm7kuy7wC0PJdkpyd1Jjkry1VLKgP5Z1VqfSSdkfUMpZfskb03yw1rrtDZPAwAMZ88880ySZPr06S8YmzVrVp588skkyUorrTSkPr/8Zec4hI033nhI8wAAAO2sEGHrgmqtz9ZaL0vyT91Lu/ZTc186getdSQ5L8s1SykB3As97EdZlSVZJcv6QFgwALDO23377JMkXvvCFzJo163lj844R2GqrrTJmzJjnrj/55JOZOnXqc7tm57ntttsyd+7cF/S47rrrcu655yZJ3v72t7d+BAAAYJCW+2MEFuOJ7me/h9zWWh8qpeyc5GdJ3pFklVLKgd3dqwtVa/2vUsrPk+yYZGr3fgBgGTVx4sRcccUVSZJp0zq/rDJlypQcddRRSZK11147p512WpLkwx/+cH7yk5/kuuuuy9Zbb503vvGNWWWVVfIf//EfueWWW7Lqqqvm9NNPf97811xzTU499dQcdNBB+dKXvvTc9Y997GP5/e9/n2222SZjx45NkvzmN7/J5MmTnxvfdtttl+7DAwAAA7Zch62llIPSOT/132utcxcY2yCdc1WTZPLC5qi1Ti+l7JrkJ+mc8/r9Usr+tdanF9P+iHRejHXPEpz3CgAMQ3feeWcuueSS512bOnVqpk6dmiQZN27cc2Hr2LFjc9111+Wss87KpEmT8s1vfjNz587N+uuvn4MPPjgf/OAHs/nmmw+o74EHHpgrrrgit956a6666qrMnj076623Xvbbb78cfvjh2WGHHZo+JwAAMDRlec4BSylnJTkunTNYr0/yh+7Qy5PslWTVJD9Isl+ttZZSJiS5Jsl1tdYJC8w1JskV6exWvTrJPrXWP5dSNu7Oe0Ot9Q0DWNM3/v/27jzet6quG/jni4Ak6L1MgsaURI5FKYFDySBiSs4iT2hJT1JEhFbU85QZOCU9aorkUFqCmjmbJhgOiAym4Gyaisl1CKW4cC/oZXBYzx97H/l5POfec89dnOm+36/X77XPb++1117rbNbh3M9Zv7WTPCnJ4a21C2cqs379+hlvyurVqzZVPQCwCevWrV/sJvwYD8uArZOxD1sf4x6Wp1WrVs34qfiZrOiZrUlelOEhV0cm+bkkD8uwhuraJBcmeUOSN8xl5mlr7Yaq+pUM4eyRSc6vqkfcRu0GAAAAAJaZFR22jg+5etn4mkv5CzPL+q3j8Q1JHjpt9/UbO2eGOp6c5MlzLQ8AAAAALA/bLHYDAAAAAABWAmErAAAAAEAHwlYAAAAAgA6ErQAAAAAAHQhbAQAAAAA6ELYCAAAAAHQgbAUAAAAA6EDYCgAAAADQgbAVAAAAAKADYSsAAAAAQAfCVgAAAACADoStAAAAAAAdCFsBAAAAADoQtgIAAAAAdCBsBQAAAADoQNgKAAAAANCBsBUAAAAAoANhKwAAAABAB8JWAAAAAIAOhK0AAAAAAB0IWwEAAAAAOhC2AgAAAAB0IGwFAAAAAOhA2AoAAAAA0IGwFQAAAACgA2ErAAAAAEAHwlYAAAAAgA6ErQAAAAAAHQhbAQAAAAA62HaxG8DcrVu3frGbACyAK664IklywAEHLHJLAAAAgM1hZisAAAAAQAfCVgAAAACADoStAAAAAAAdCFsBAAAAADoQtgIAAAAAdCBsBQAAAADoQNgKAAAAANCBsBUAAAAAoANhKwAAAABAB8JWAAAAAIAOhK0AAAAAAB0IWwEAAAAAOhC2AgAAAAB0IGwFAAAAAOhA2AoAAAAA0IGwFQAAAACgA2ErAAAAAEAHwlYAAAAAgA6ErQAAAAAAHQhbAQAAAAA6ELYCAAAAAHQgbAUAAAAA6EDYCgAAAADQgbAVAAAAAKADYSsAAAAAQAfCVgAAAACADoStAAAAAAAdCFsBAAAAADoQtgIAAAAAdCBsBQAAAADoQNgKAAAAANCBsBUAAAAAoANhKwAAAABAB8JWAAAAAIAOhK0AAAAAAB0IWwEAAAAAOhC2AgAAAAB0IGwFAAAAAOhA2AoAAAAA0IGwFQAAAACgA2ErAAAAAEAHwlYAAAAAgA6ErQAAAAAAHQhbAQAAAAA6ELYCAAAAAHQgbAUAAAAA6EDYCgAAAADQgbAVAAAAAKADYSsAAAAAQAfCVgAAAACADoStAAAAAAAdCFsBAAAAADoQtgIAAAAAdCBsBQAAAADoQNgKAAAAANCBsBUAAAAAoANhKwAAAABAB8JWAAAAAIAOhK0AAAAAAB0IWwEAAAAAOhC2AgAAAAB0IGwFAAAAAOhA2AoAAAAA0IGwFQAAAACgA2ErAAAAAEAHwlYAAAAAgA6ErQAAAAAAHQhbAQAAAAA6ELYCAAAAAHQgbAUAAAAA6EDYCgAAAADQgbAVAAAAAKADYSsAAAAAQAfCVgAAAACADoStAAAAAAAdCFsBAAAAADrYdrEbwNytXr1qsZsAW5V169YvdhMAAACAZcTMVgAAAACADoStAAAAAAAdCFsBAAAAADoQtgIAAAAAdCBsBQAAAADoQNgKAAAAANCBsBUAAAAAoANhKwAAAABAB8JWAAAAAIAOhK0AAAAAAB0IWwEAAAAAOhC2AgAAAAB0IGwFAAAAAOhA2AoAAAAA0IGwFQAAAACgA2ErAAAAAEAHwlYAAAAAgA6ErQAAAAAAHQhbAQAAAAA6ELYCAAAAAHQgbAUAAAAA6EDYCgAAAADQgbAVAAAAAKADYSsAAAAAQAfCVgAAAACADoStAAAAAAAdCFsBAAAAADoQtgIAAAAAdCBsBQAAAADoQNgKAAAAANCBsBWgo/PPPz+Pfexjc6973St77rlnDjzwwDzlKU/JZZddtln1XHLJJV3qAQAAABbOtovdAICV4rTTTsuZZ56ZXXbZJUcffXR23XXXfOUrX8l5552Xd73rXXnlK1+ZY489dpP1nHXWWXnta1+7xfUAAAAAC6taa4vdhi1SVdM78IMk1yX5TJJXt9beMIfy68fyZyc5p83wTamqHZKcnOSYJPdI8hNJ1ia5Ksm/JXlLa+1DE+WPT/KaadXcMpb/UJL/11r7/Ex9Wr9+/Yw3ZfXqVTPtBm4j69atn3PZq6++Ove85z2z22675dJLL83uu+/+w2MXXXRRHvWoR2XffffNpz/96TnVs3r16nz0ox+ddz3A8nLFFVckSQ444IBFbgmwkIx92PoY97A8rVq1quZadiXNbH3WuN0uQxj66CSHV9VBrbU/3ET5n07y2CSHJjkoQ6j6Q1W1U4aA9L5JvpXkbeN2pyQHJvntJKvHMtN9Osk/j1+vSnJYkqckeWJVHdFa+8jmdhRYer7+9a/nBz/4Qe53v/v9SECaJA9+8INzxzveMWvXrp1zPfe5z322qB4AAABg4a2YsLW1dvrk+6p6SJL3JXl6Vb20tbZmE+UflOSiJCdV1Ytaa1dOHH56hqD1vUke2Vq7Zdq5Oye55yxN+9TktaqqMsx4fUqS5yc5fG49BJay/fffP9tvv30+8YlPZO3atdl1111/eOzSSy/NDTfckKOPPnpO9Wy33Xb53Oc+t0X1AAAAAAtvxYSt07XWPlBVX8gQgv5ikjWbKH/pWP5eSe6XZDJsfeC4fcX0oHU897okH55ju1pVvTxD2HrwXM4Blr6dd945p59+ep7xjGfkkEMOydFHH51ddtklV155Zd7znvfk8MMPz0te8pI51XPyySfnJS95yRbVAwAAACy8FRu2jqbWU9jchWm/O+391Gd2f2bLmvND820XsISddNJJ2WeffXLyySfnnHPO+eH+u93tbjnuuON+bFmA2Rx33HG5613vmuc973lbVA8AAACwsFZs2FpVRya5e4ZA8/I5lH9whrVeb0ly2bTDb0ry5CTPqar9kpyb5BOttW/Oo12V5KTx7Uc393xg4UwtXj9Xr33ta/Pyl788xx57bI455pjstttuWbNmTV72spflhBNOyEUXXZRTTjllweoBlp/N/bkDrAzGPmx9jHtYmno8vK5aW96TK6tqqgOTD7y6e5LHJLldkhdPPiBrlvJTD8jaNsnTWmtnzXCdU5I8O8NDrqZ8K8kFSf62tXbRtPLHZ1ibdaYHZP18khuTzPiArPXr1894U1avXjXTbuA2cvnlH5tz2Y9//OM58cQTc9hhh+UFL3jBjxy76aab8vjHPz7XXHNN3va2t2Wvvfa6zesBAAAANs9sYeuqVatqxgMzWEkzW08bty3JuiQXJ/n71trrN1F+SkvyW62118xUuLX20qp6dZKHZljD9RfG7XFJjquq57TW/mKGUw8cX8mwPME3k7wuyRmttc/PqWfAoticv2hNfdz/4Q9/+IznHXzwwXn3u9+d66+/fqP1TtVz0EEHbVE9wPIyNbvFuIati7EPWx/jHla+FRO2ttbmnDBPlq+qHZM8IMnfJ3llVX21tXbBLOdsSPLO8ZWq2j7JCUnOTPLMqnp7a+1T0047p7V2/Oa0DVh+br755iTJNddcM+PxtWuHpZ+33377OdVz3XXXbVE9AAAAwMLbZrEbsNhaa99prb0/ySMzLDtwTlXdYY7n3tJae1mSfxp3HXEbNRNY4h74wAcmGWamXnXVVT9y7H3ve18+8pGPZIcddsghhxySJPnud7+bL33pS7nyyitnrOcd73jHnOoBAAAAlo4VM7N1S7XWPlNVr0pyYpI/SPK8zTj9hnG7WbNrgZXj0Y9+dA477LBceOGFOeSQQ3L00Udnjz32yBe/+MWcf/75aa3ltNNOyy677JIkueqqq3LwwQdn7733zmc/+9kfqefggw/OZZddNqd6AAAAgKVD2PqjnpvkN5OcWlUvb61dlyRVdWKST830MKuqukeSY8a3F00/Dmwdttlmm7zlLW/Jq171qrz97W/Pueeemw0bNmTnnXfOUUcdld/5nd/JEUdsevL7NttskzPPPDNvfvObc/HFF8+7HgAAAGDhCVsntNb+q6pemeRpSf4kyZ+Oh34lySuqak2SS5N8PcntkxyQ5GFJtkvy0tba5QveaGDJ2G677XLSSSflpJNO2mTZfffdN+vWrZvx2LbbbpvLCzjnAAAWXUlEQVTjjjsup502/Tl+AAAAwFK21a/ZOoPnJ9mQ5JSq2mPc9ydJTk3yhST3T3JKkt9LcmCSdyd5ZGvtaYvQVgAAAABgiVj2M1tba5u1TuqmyrfWrk6y47R9X0ryovE11+ucneTszWkbAAAAALB8mdkKAAAAANCBsBUAAAAAoANhKwAAAABAB8JWAAAAAIAOhK0AAAAAAB0IWwEAAAAAOhC2AgAAAAB0IGwFAAAAAOhA2AoAAAAA0IGwFQAAAACgA2ErAAAAAEAHwlYAAAAAgA6ErQAAAAAAHQhbAQAAAAA6ELYCAAAAAHQgbAUAAAAA6EDYCgAAAADQgbAVAAAAAKADYSsAAAAAQAfCVgAAAACADoStAAAAAAAdCFsBAAAAADoQtgIAAAAAdCBsBQAAAADoQNgKAAAAANCBsBUAAAAAoANhKwAAAABAB8JWAAAAAIAOhK0AAAAAAB1su9gNYO7WrVu/2E0AAAAAAGZhZisAAAAAQAfCVgAAAACADoStAAAAAAAdCFsBAAAAADoQtgIAAAAAdCBsBQAAAADoQNgKAAAAANCBsBUAAAAAoANhKwAAAABAB8JWAAAAAIAOhK0AAAAAAB0IWwEAAAAAOhC2AgAAAAB0IGwFAAAAAOhA2AoAAAAA0IGwFQAAAACgA2ErAAAAAEAHwlYAAAAAgA6ErQAAAAAAHQhbAQAAAAA6ELYCAAAAAHQgbAUAAAAA6EDYCgAAAADQgbAVAAAAAKADYSsAAAAAQAfCVgAAAACADoStAAAAAAAdCFsBAAAAADoQtgIAAAAAdCBsBQAAAADoQNgKAAAAANBBtdYWuw1Ms379ejcFAAAAAJaAVatW1VzLmtkKAAAAANCBsBUAAAAAoANhKwAAAABAB8JWAAAAAIAOhK0AAAAAAB1Uax58DwAAAACwpcxsBQAAAADoQNgKAAAAANCBsBUAAAAAoANhKwAAAABAB8LWJaiq9qqqf6iqq6rq5qpaU1UvqaqdF7ttwPxV1ROq6qyquriqrq+qVlWv38Q5D6yq86rq2qq6sao+U1VPr6rbLVS7gfmrql2r6qlV9Y6q+vI4jtdX1SVV9VtVNePvYsY+LG9V9VdV9YGq+vo4hq+tqk9W1WlVtess5xj3sMJU1ZPH3/lbVT11ljK/WlUXjr8ffLuqPlpVT1notgL9VGttsdvAhKraP8mHk9w5yTuTfCHJwUkOT/LFJA9qra1dvBYC81VVn0pyYJJvJ/lGknsk+cfW2pNnKf/oJG9LclOSNyW5Nskjk9w9yVtba8csRLuB+auqE5O8Isk3k3wwydeS7JHkcUlWZRjjx7SJX8iMfVj+quqWJJ9I8vkk/51kxyT3T3JQkquS3L+19vWJ8sY9rDBVtXeSzya5XZKdkpzQWnv1tDInJzkrydoMY/+WJE9IsleSF7XWTl3QRgNdCFuXmKo6P8lRSU5prZ01sf+vk/xBkr9trZ24WO0D5q+qDs8Qsn45yaEZgpcZw9aqutNYblWGP7J8bNy/Q5ILkjwgya+11t64QM0H5qGqjsgQspzbWvvBxP49k1yWZO8kT2itvW3cb+zDClBVO7TWbpph//OS/FmSV7TWThr3GfewwlRVJXlfkp9K8vYkp2Za2FpV+2WYXPWdJPdrra0Z9++c5PIk+yd5YGvt3xay7cCWs4zAEjLOaj0qyZokL5t2+LQMP4R/vap2XOCmAR201j7YWruize2vXE9IsnuSN079o2us46Ykfz6+/d3boJlAR621C1pr/zIZtI77v5XklePbwyYOGfuwAswUtI7ePG4PmNhn3MPKc0qSI5L8ZoZ/x8/kfye5fZK/mQpak6S1dl2SvxzfmmgFy5CwdWk5fNy+d4Z/lN2Q5NIkd8jwESRgZTti3P7rDMcuSrIhyQOr6vYL1ySgs++O2+9N7DP2YWV75Lj9zMQ+4x5WkKq6Z5IzkpzZWrtoI0U3NvbfM60MsIwIW5eWu4/bL81y/Ipx+zML0BZgcc3686C19r0kVybZNsndFrJRQB9VtW2S3xjfTv4jy9iHFaSqTq2q06vqxVV1cZLnZAhaz5goZtzDCjH+//11GdZo/7NNFN/Y2P9mhhmxe1XVHbo2ErjNbbvYDeBHrBq362c5PrV/9QK0BVhcfh7AynZGkvskOa+1dv7EfmMfVpZTMzwUb8q/Jjm+tfY/E/uMe1g5/iLJLyT5pdbajZsoO5exv+NYbkOf5gELwcxWAIAFVFWnJPmjDA/F+PVFbg5wG2qt7dlaqyR7Jnlchtmpn6yq+y5uy4DequqQDLNZX+ShVrB1E7YuLVN/0Vo1y/Gp/esWoC3A4vLzAFagqjo5yZlJPp/k8NbatdOKGPuwArXWrm6tvSPDw3B3TfLaicPGPSxz4/IBr82wJMAz53jaXMf+bDNfgSVK2Lq0fHHczrYm69RTS2db0xVYOWb9eTD+MvdTGR6q85WFbBQwf1X19CRnJfn3DEHrt2YoZuzDCtZa+2qGP7bcu6p2G3cb97D87ZRhDN8zyU1V1aZeSU4by7xq3PeS8f3Gxv5dMiwh8I3WmiUEYJkRti4tHxy3R1XVj9ybqrpjkgdlWKvlIwvdMGDBXTBuf2WGYw9OcockH26t3bxwTQLmq6r+T5IXJ/lUhqD1v2cpauzDynfXcfv9cWvcw/J3c5K/n+X1ybHMJeP7qSUGNjb2Hz6tDLCMCFuXkNbafyZ5b5L9kvzetMPPyvCXrde11r6zwE0DFt5bk1yT5H9V1UFTO6tqhyTPHd++YjEaBmyeqnpmhgdifTzJQ1pr12ykuLEPy1xV/UxV/djHgqtqm6p6XpI7ZwhPrxsPGfewzLXWbmytPXWmV5J3jcXOGfe9aXz/mgwh7clVtd9UXVW1c4a1X5PklQvUBaCjaq0tdhuYUFX7J/lwhl/C3pnkP5IckuTwDMsHPLC1tnbxWgjMV1U9Jsljxrd7JnlYho8EXjzuu6a1duq08m9NclOSNya5Nsmjktx93P/E5oc4LGlV9ZQkZ2eYwXZWZl53bU1r7eyJc4x9WMbGJUOen2EW25VJ1ibZI8mhGR6Q9a0Mf3j5/MQ5xj2sUFV1eoalBE5orb162rHfT/LSDD8n3pTkliRPSLJXhgdtnRpg2RG2LkFVtXeSZ2f4OMGuSb6Z5B1JnjXxF3BgmZn4RWs2X22t7TftnAcleUaSByTZIcmXk/xDkpe21r7/YzUAS8ocxn2SfKi1dti084x9WKaq6j5JTkzySxkCk9VJvpNh4sS5Gcbx9IfjGfewQm0sbB2PPzLJqUnum+HTx59P8jettXMWsp1AP8JWAAAAAIAOrNkKAAAAANCBsBUAAAAAoANhKwAAAABAB8JWAAAAAIAOhK0AAAAAAB0IWwEAAAAAOhC2AgAAAAB0IGwFAAAAAOhA2AoAAAAA0IGwFQAAAACgA2ErAAAAAEAHwlYAgBWiqtr42q9DXYeNda3Z4oYtE1V1/NjnC2c4duF47PiFb9nCqqo1Y18PW+y2AAAsN8JWAIAlYCIo3dzXhYvddpaHqvr5qjp9awiMJ1XVz1bVuVV1fVV9p6o+WFUP2sQ5b6iq71fVQQvVTgBgZdh2sRsAAECS5OpZ9u+SZLskNyVZP8Pxaye+/uK4/W7HdrFy/HyS05J8KMnZGyn3nxn+e9uwAG26TVXVzyS5NMkdk3wvyfeTHJbkgqp6SGvtkhnOOSLJryV5RWvtYwvYXABgBRC2AgAsAa21PWfaP85cPTTJm1prx2+ijnv0bxlbm9baQxa7DR2dniFoPTvJSRkC1+cm+ZMkZyT5pcnCVbV9kpcl+Z8kz1jAdgIAK4RlBAAAgJXqIRlmsz6ttXZja+27GULUq5M8oKruMK38qUnukeRPWmvXLWxTAYCVQNgKALBCbOoBWVW1Y1WdWlUfrqprq+qmqvpKVb2rqp5UVdttxrWOrKpvj9c7Y4bjO1XVn1XV5VW1frzWFVX10qrae5Y6f/gQqqpaXVV/VVVfqKoNVbVuM9p236o6o6ouqaqvVdXNVbV2rP+pVXW7uda1Gde807ge6qfH78u3q+ozVfWsqlq1iXM3677Mp39V1ZK8Znx76Axr/x42UXajD8iqqj2q6kUT92Z9VV1WVX9UVbef5ZyzxzpPr6rbVdXTx+/VhrHP776N1kfdNck1rbXrp3a01r6X5KsZ/i2080Qb98sQxF6S5JzboC0AwFbAMgIAAFuBqrpXknOT7Dfu+l6S65PsneSnkjwyw9qWa+ZQ12OT/FOS2yf509baGdOO3zPJe5LsO3Gtm5P8dJLfT/Lkqnpka+3SWS6xe5KPJ7nbeN4tc+njhPdmCNmSYd3RDRnWvj10fD22qh49hm5brKp+Osn7c2t/p9Y6/dnxdXxVHdlau2KGc+dzX+bTv6uT/ESSO2VY03dyrd9kjt/jqjo4w73dZdx1Q5Ltk/zi+Pr1qjqqtfbfs1Sx7djfh43tuDlD4Hl0kodU1RGttX+bS1vmaG2S3arqTlOB6xhG75vkB0kmZ6++dOzLSa211rENAMBWxMxWAIAVrqp2SfKvGQK9K5M8JsmOrbVdk9whw7qVr8kQ9G2qrt9I8pbcGkpND1pXJTkvQ5j1liQHJtmhtbZTkv2TvCFDuPa2qlo9y2X+IsNDwR6e5A6ttTsl2ZxZj+/N8ICju7TWdmyt7ZxkpyS/nuRbSR6R5A82o75ZjWt8vi1Df7+e5KjxWjslOTLJ15Lsk+Qd02d9bsF92ez+jWsCP218++HW2p7TXh+eQ193TvLPGYLWzyY5eLw3OyU5JkNweWCSf9xINb+XIZQ9NslOrbU7juf8e5Idkpy5qXZspguS3C7JmVW1Q1Vtm2HN1j2SfKS1tmHs26MyBNtntdY+27kNAMBWxMxWAICV7/9mmCl5TZJfbq3919SBcQ3LS8fXRlXV72cIw76f5Ddaa6+fodgfZwgP/6m1dtzkgdbaV5I8aQwZfyXJU5O8cIY6bp/kEa21f58498ubat9E2eNm2PedJK+vqq8muSjDw5JeMNc6N+LYJD+XYZbmj7Q5yQeq6hFJPpnk3kmelOQfJo7P674scP8mnZzkLknWJTmqtfat8drfT/LWqro+yflJjhxnqF4wQx2rM/T1kom2f6aqjk/ysSS/WFX7tNa+1qnNz07yq0mOT/LkDP/t3j7D/frTJKmqn8jw3/VVSU7rdF0AYCtlZisAwMr3G+P2hZOB3uaoqmdm+Jj1LUmeMEvQmiRPGbcv2kh1bxi3D53l+HumhZbdtNYuzhAW7ldVd+1Q5RPG7TtnanNr7XNJ3jq+feK0w1t8X2a4Xu/+TZrq66ungtZp135vkqklAKb3dcrFk0HrxLkfT/KN8e19trShE/X+R5JfzjCD+KYMSwdclOTI1tpFY7FnZvgDwR+21m6oqjtX1TnjOrgbquqCqrpfrzYBACubma0AACvY+NCfPca3582vivrrDB9L/06SR7fWPjBLwb2T7DV1rfGhTDPZftzO+KCs3BrYzVtVHZNhJul9M6wBu8MMxe6aYTbjlrjvuP3gRspckOFj/1Nlt/i+LGD/pq63fW4NQTfV1wdkoq/TXL6Rc/8rw38/O2+kzGZrrX0qw5IUP6aq7pHkj5K8v7X2pqraIUMf7p3kAxlmHT82yYVVdfAY3gIAzErYCgCwsu0x8fV8Ppq9T25d//N3ZwtaR3eZ+PrOc6j7DrPs/5+5NGwm45qcb84QkE25OUNo9v3x/e4ZPuG143yvM2H3cbuxmalTMzZ3raoaH740r/uyCP2bsktu/VTcXPq6+yzHb9jIuTeN2+02o11b6mXj9uRxe0KGoPUVrbWTkqSqnpzkdRnWen38ArYNAFiGLCMAAMDGfCvJh8avn19V+2+k7OTvlju31moTr/1mqef7s+yfixMyBJEbkpySZO/W2g6ttd2nHgaVW2d71hZcZ7qZZpbeFharf5MWqq+3qao6LskRGZZx+OK4+1fH7d9MFH1Dhj8APKyqbreATQQAliFhKwDAynb1xNf7zuP8mzMEUJcm+ckkF1TVbPVMXmufeVyrh2PG7XNaa2e11r4xeXAMy3breL2pWbgb6+/U0gprx1mtyfzvy0L3b8q1GdY7TebW13nPTl4IVXWnDOsKr0nyvIlDU/fiyqkdrbUfjO93zG3zvQUAVhBhKwDACtZaW5NhdmqSPGKedXx7PPeyDEHbBVW11wzlrsytIeKMa2QugKl2fXKW4w9K35mZnxi3h2+kzBHTym7JfdmS/k2FpZs947W1dkuSqQeAbVZfl6jnJtkzydNaaxtmOD79e/gTt32TAICVQNgKALDyvW7c/lFV/eR8KmitXZ/kYRlCtLtlCFzvMkPRs8ftqRu7Vg1Wz6ctm7B+3P7sDNfcNkPI1tNbx+3Dq+oXZrjmvZM8YXz75mmH53NftqR/14/b+X7fp/p6/Ez3vqqOyvBwrOTH+7pkjPfppCTvbq29a9rhr47b+02UX53kpzM8IO6aBWkkALBsCVsBAFa+v8rwUKPdklxcVY8any6fqtquqg6tqjfONFt1UmttXZKHJvlMkgOSfKCqpj8I6YwkXxmv9eGqemJV/XBWYFXtU1W/nSG0fUyn/k1637h9ZlU9emqNzfGp8/+S5OAMoVkvb8rw/UiSf66qI6uqxms+JMl5GR749Lkk/zjt3Pncly3p3+fG7b2q6pB59PVvknwzwyzPf62qg8Zr366qHp/kjWO597fWLphH/bOqqjVV1arq7C2sp5K8IsktGda8ne68cfv8qrpzVd0+yQsz9Pn81tqWrCcMAGwFhK0AACtca21tho/1fyPJTyV5Z5JvV9U1GR60dGGSY5NsO4e6rk1yZJLPJ7lnkvdX1a4Tx9dlmAH7HxmWHHhTkhuq6pqq2pBh5uDfJvn5JC39vTDJfya5U5J/TnJjVa0f2/PQJCem4+zE8eP1j8/Qr30yhKHfrqrvJHn/uO9rSR7XWrt52rnzuS/z7l9r7YokF431faSq1o4h5pqquv8c+npdhoD8uiQ/l+Tyqro+ybczzHrdOUPw/KRN1bWITkhySJK/HJe9mO7vknwhyUEZlnlYn+S3MvTxzxeqkQDA8iVsBQDYCrTWPpvk3hkCo48luTHDA3++liG0+7UMod9c6vqfJA9J8sUModv7JpcEaK19OcnUR7U/mCGcW5XkexnCuL9LcnSS13fo2vS2XZvk/hlmL07158YMfTy0tXb2bXDNLyc5MMmzc+u6phm/fk6Sn2utfWmWczfrvnTo3+OSvDzDA592yvBAqH0zx3VsW2uXJblXkhcn+VKGWbvfG9v+x0kOaa3991zqmqtxeYSpB1NdvgX17Jbk+UmuSPKCmcq01m7MsCbt65Osy7DO7QeTHNZa+4/5XhsA2HrUrQ9EBQAAWFrGWbf/lmHJhf2nzxAGAFhKzGwFAACWskPH7V8JWgGApc7MVgAAYMmqqnMzLEtxt9baTYvdHgCAjRG2AgAAAAB0YBkBAAAAAIAOhK0AAAAAAB0IWwEAAAAAOhC2AgAAAAB0IGwFAAAAAOhA2AoAAAAA0IGwFQAAAACgA2ErAAAAAEAHwlYAAAAAgA6ErQAAAAAAHQhbAQAAAAA6ELYCAAAAAHQgbAUAAAAA6EDYCgAAAADQgbAVAAAAAKCD/w8QTnK80AdwMAAAAABJRU5ErkJggg==\n", 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\n", 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N+bzUUsphSQ5brnlqKeWyFT2WZHKSVyWpSb4z1PoAAAAAACPJcC6nmp5k5nJt6/TTNpArk3x0GPUBAAAAAEaM4YStFyaZ1/25JPlKkr4kf72CZ55OsiDJz2qttw+jNgAAAADAiDLksLXW+pMkP1n6eynlK0meqLWe1WJhAAAAAACjyXB2tj5LrbUnl20BAAAAAIwEAlIAAAAAgAaErQAAAAAADQhbAQAAAAAaELYCo9JFF12UY445Jm9961szbdq0TJ48OUcccUS/YxctWpQvfOEL+Yu/+IvMmDEjG2+8cSZPnpyvfvWrQ65/7bXXZv/998/06dOz2WabZccdd8zpp5+eJUuWDHlOAAAAYHRrdkEWwPPpxBNPzE9/+tNssMEG2XzzzfPII48MOPaxxx7LsccemyTZZJNNsummm+aee+4Zcu1Zs2bl0EMPzYQJE7LPPvtkypQp+e53v5sPfehDufbaa3PWWWcNeW4AAABg9LKzdRmllDqI18xlxh/ebTtzmba1SilXdNsPGqDOS0spj5RSHi6lbLH6PxmMPZ/61Kdyww035O67785JJ520wrHrrbdevvnNb+bnP/95brvtthx88MFDrrtgwYIcffTRGTduXC655JKcdtppOf7443PVVVdlu+22y0UXXZRvfetbQ54fAAAAGL3sbO3fx1fQN29FD9Zany6lHJbkJ0k+X0q5stb6q6X9pZRxSf49yQZJDqy1Dn17HazBdtlll0GPXWeddbL77rs3qXvRRRflwQcfzIEHHphtt932mfYJEybkwx/+cPbaa6+cccYZ2W+//ZrUAwAAAEYPYWs/aq0fG+bz80opRyf5tyRnllL2qLXWbvexSXZI8vVa67nDWynwfLvqqquSJLvttttz+nbaaaest956mTNnTp588smsu+66z/fyAAAAgB5abccIlFI2KaW8pZTynu7rLaWUTVZXvZGm1npmkguS7Jbk6CQppfxhko8muTvJ+3u2OGDI5s6dmyR5+ctf/py+8ePHZ8stt8zixYszb96853llAAAAQK8139laSpmR5JNJdh6g/8ok/7vWenXr2iPQEUl2TPKPpZQfpnN8wPgkh9Va5/d0ZcCQLFiwIEkyceLEfvuXtvf19T1vawIAAABGhqZhaynlyCSnprNjtiRZkuTBbvdG3Xq7Jrm8lPKBWuu/tKzfSinlYwN0Lay1fnqw89RaHyyl/FmS/0zywyTrJvlcrfUHw18ljC1Ld4wOxa9+1TkWecGCBYOa5+GHH06S3Hfffatcd9GiRUmSefPmZcmSJc/pf+KJJ5Ikd999d6ZMmbJKc7N6Dec7Bs8H31FGA99TRjrfUUYD31NGujX5O7rVVlsNe45mYWspZdskp6UTtP4wyfFJrqy1PtntXzedoPUjSXZKclopZU6t9cZWa2jouAHa+5IMOmxNklrrJaWUK9L57Hcl+dAw1wb00Prrr58kefTRR/vtX9q+4YYbPm9rAgAAAEaGljtb/y6doPW8JH9ca3162c5u6Dq7lHJpkm8keVeSv03ynoZraKLWWlrNVUp5Y5Kl16ZvkeT1Sa5sNT+MFcP516N77703SedP+Aczz9SpU5Mkm2666SrX3WabbXLrrbfmqaeees6zixcvzr333pvx48dn1113dUHWCLH0X2Vb/AslrA6+o4wGvqeMdL6jjAa+p4x0vqNttLwga9ckNcnfLB+0Lqvb99fdsTMb1h9xSimTk5yZZHGSo9L5zGeVUmx5g1Fq5507x1Ffeumlz+m7+uqr8/jjj2e77bYTtAIAAMAaqGXYunGS+bXW36xsYK3110nmd58Zy05PMi3JcbXWLyb5P0mmJzm5l4sCVq6vry+33XbbM7tml9prr72y0UYb5fzzz8+NN/7/U1AWLlyYE044IUnyp3/6p8/rWgEAAICRoeUxAguSTC6lrF9rfWxFA0sp6yeZmOS3DeuPKKWUA5IclOTqdELWJPlYkrcl+dNSyoW11kt6tDwY9S655JLMmjUrSXL//fcnSebMmZOjjjoqSbLRRhvlk5/85DPjTz755Nx2221JkltuuSVJcvbZZ+eaa65Jkuywww459NBDnzX/+9///hx00EH5whe+8Ez7xIkTc8opp+Swww7LnnvumX333TdTpkzJd77zncydOzd77bVX9t1339X4yQEAAICRqmXY+uMkuyf5qyT/uJKxRycZl+SGhvVHjFLKi5N8IcmjSQ5deqxCrXVRKeU9Sa5P8uVSyja11gd7uFQYtW655Zacc845z2qbN29e5s2blySZNm3as8LWSy+9NFdfffWzxl977bW59tprn/l92bB1Rfbcc8/MmjUrJ510Ui6++OI8+eSTeelLX5oTTjghRx55ZEppduwzAAAAMIq0DFu/lGSPJMd3d66eWGvtW3ZAKeVFSY5JJ5Ct3WdGnFLKx1bQfWGt9aYVPFvSOad1SpI/q7X+32X7a60/LaV8JMln0glk9x/2gmENdOyxx+bYY48d9Pilu2AH6+CDD87BBx88YP/222+fb37zm6s0JwAAADC2NQtba63nl1K+luQ9SY5N8nellJ8k+VWSCUl+J8lWSdZOUpKcVWu9oFX9xo5bQd+8JAOGrekEybsluajWesYAY05K8o4k7yqlHFJr/fchrRIAAAAAGDFa7mxNksOT3Jrkg+mcybpdP2MWJPlUks82rj1stdZV+tvfWuuZ6exiXbbtlCSnrOS5p5PssorLAwAAAABGsKZha621Jvl0KeXUdM5vfW2SjbvdD6RzruvsWuvjLesCAAAAAPRa652tSZJa62NJLuy+AAAAAADGvLV6vQAAAAAAgLFA2AoAAAAA0MCQjhEopVzW/fHOWut7l2tbFbXW+qahrAEAAAAAYCQZ6pmtM7vvP++nbVXUIdYHAAAAABhRhhq2vrf73tdPGwAAAADAGmdIYWut9azBtAEAAAAArClckAUAAAAA0ICwFQAAAACggSEdI1BK+Z1WC6i13tVqLgAACBAJkwAAIABJREFUAACAXhnqBVl3NKpfh7EGAAAAAIARY6hBZ2lUv9U8AAAAAAA9NaSwtdbqrFcAAAAAgGUITQEAAAAAGhC2AgAAAAA0sNoupyqlbJLktUk27jY9kOTHtdb7V1dNAAAAAIBeaR62llJmJPlkkp0H6L8yyf+utV7dujYAAAAAQK80PUaglHJkkh+kE7SWJE8nub/7WtJt2zXJ5aWU97WsDQAAAADQS83C1lLKtklOSzIuydVJ3pxkg1rri2qtL0qyYZK3dPvGJTmt+wwAAAAAwKjXcmfr33XnOy/JzFrr92utTy7trLU+WWudnc7O1v9IJ3D924b1AQAAAAB6pmXYumuSmuRvaq1PDzSo2/fX3bEzG9YHAAAAAOiZlmHrxknm11p/s7KBtdZfJ5nffQYAAAAAYNRrGbYuSLJhKWX9lQ3sjpnYfQYAAAAAYNRrGbb+OJ1zWP9qEGOP7o69oWF9AAAAAICeaRm2filJSXJ8KeWTpZRJyw8opbyolPK5JJ9I58zWLzWsDwAAAADQM+NbTVRrPb+U8rUk70lybJK/K6X8JMmvkkxI8jtJtkqydjqh7Fm11gta1QcAAAAA6KVmYWvX4UluTfLBdM5k3a6fMQuSfCrJZxvXBgAAAADomaZha621Jvl0KeXUJLsneW2SjbvdD6RzruvsWuvjLesCAAAAAPTakMLWUspHkzxaa/1cf/211seSXNh9AQAAAACMeUPd2fqxJPcmeSZsLaX83yT311q3b7Au+jF/fl+vlwDPMXfu3CTJVltt1eOVAAAAAPTWUMPWmmSt5dqmp3MRFgAAAADAGmf5wHSwHk6yUSllw5aLAQAAAAAYrYa6s/W/k7wtycWllG8mebTb/oJSyqGrMlGt9atDXAMAAAAAwIgx1LD1E0nekGTXJLss0z4xyb+t4lzCVgAAAABg1BtS2Fprva6U8gdJjkiydZIXJJmZZFGSa5qtDgAAAABglBjqztbUWm9P8vdLfy+lPJ3k4VrrG1osDAAAAABgNBly2NqPu5Lc13A+AAAAAIBRo1nYWmud3mouAAAAAIDRZq1WE5VSlpRSfrUK4+8opSxuVR8AAAAAoJeaha1JSve1qs8AAAAAAIx6LcPWVbVukiU9rA8AAAAA0ExPwtZSymZJNknyYC/qAwAAAAC0NuQLskopuySZuVzzBqWUj67osSSTk7yl+/PVQ60PAAAAADCSDDlsTfKGJMclqcu0rd9tW5Gl57Q+nOTjw6gPAAAAADBiDCdsvSnJWcv8fliShUnOW8EzTydZkORnSS6otT40jPoAAAAAACPGkMPWWutFSS5a+nsp5bAkfbXW97ZYGAAAAADAaDKcna3L2yfJfaWUcbXWJQ3nBQAAAAAY8VqGrRekc0zAS5Lc3XBeAAAAAIARr2XY+miSxbVWQSsAAAAAsMZZq+FcdyRZr5TSMsAFAAAAABgVWoat5yVZO8neDecEAAAAABgVWoatJya5Psm/lFLe1HBeAAAAAIARr+Wf/H8wyWVJXplkdinl5iTXJHkgyZKBHqq1fqLhGgAAAAAAeqJl2PqxJDVJ6f7+miSvXsH40h0vbAUAAAAARr2WYetX0wlPAQAAAADWOM3C1lrr4a3mAgAAAAAYbVpekAUAAAAAsMYStgIAAAAANNDyzNZnlFJmJnl3ktcm2bjb/ECSHyc5r9Z6+eqoCwAAAADQK03D1lLKC5OcnWS3pU3LdL8kyR8leV8p5ftJDqm1PtiyPgAAAABArzQLW0sp6yT5fpJXpxOyXpPksiT3dIdskeSNSXZIsnuS2aWU7WutT7VaAwAAAABAr7Tc2fqBJK9J8nCSg2qt3+9nzEdKKXskOac79v1JTm64BgAAAACAnmh5QdYBSWqSIwYIWpMktdbZSY5IZ/frgQ3rAwAAAAD0TMuw9feSLExywSDGXtAd+4qG9QEAAAAAeqZl2Lp2kkW11rqygbXWp5MsSuMLugAAAAAAeqVl2HpXkg1LKa9d2cBSyuuSbNh9BgAAAABg1GsZtn47nXNYzyilbDzQoFLKpknOSOd811kN6wMAAAAA9EzLP+P/P0kOS/LqJD8vpXw5yeVJfpVkQpLfSfKGJIcnWS/Jw0k+07A+AAAAAEDPNAtba633l1LeluTCJJslOab7Wl5J8pske9da729VHwAAAACgl1oeI5Ba65wkr0pyXJJb0jkqoHRftdv20SRb11qva1kbAAAAAKCXWh4jkCSptc5PcnyS40spayeZ2u16uNa6qHU9AAAAAICRoHnYuqxuuHrf6qwBAAAAADASDDtsLaWsm2TvJK9LMjHJ/CTXJvnPWuvi4c4PAAAAADAaDCtsLaXsmOSb6VyItbx5pZS9a623DKcGAAAAAMBoMOQLskopL05ySTpB69ILsB5Y2p3kJUm+XUqZNNxFAgAAAACMdEMOW5McnWRyOscGHJpkvVrrZknWT/JXSZ5IsnmSPx3uIgEAAAAARrrhhK27p7Ob9a9qrf9ea30qSWqtC2utpyU5Lp0drnsMf5kAAAAAACPbcMLWl6YTtn5rgP5vLjMOAAAAAGBMG07YumGSB2qtC/vrrLXe2f1x/WHUAAAAAAAYFYYTtiadna0rU4ZZAwAAAABgxBtu2AoAAAAAQJLxw3x+ainlsmGMqbXWNw1zDQAAAAAAPTfcsHWdJDOHMWYwxxAAAAAAAIx4wwlbz2q2CgAAAACAUW7IYWut9b0tFwIAAAAAMJq5IAsAAAAAoAFhKwAAAABAA8JWAAAAAIAGhK0AAAAAAA0IWwEAAAAAGhC2AgAAAAA0IGwFAAAAAGhA2AoAAAAA0ICwFQAAAACgAWErAAAAAEADwlYAAAAAgAaErQAAAAAADQhbAQAAAAAaELYCAAAAADQgbAUAAAAAaEDYCgAAAADQgLAVAAAAAKABYSsAAAAAQAPCVgAAAACABoStAAAAAAANjO/1Ahi8yZMn9XoJq938+X29XgIAAAAADImdrQAAAAAADQhbAQAAAAAaELYCAAAAADQgbAUAAAAAaEDYCgAAAADQgLAVAAAAAKABYSsAAAAAQAPCVgAAAACABoStAAAAAAANCFsBAAAAABoQtgIAAAAANCBsBQAAAABoQNgKAAAAANCAsBUAAAAAoAFhKwAAAABAA8JWAAAAAIAGhK0AAAAAAA0IWwEAAAAAGhC2AgAAAAA0IGwFAAAAAGhA2AoAAAAA0ICwFQAAAACgAWErAAAAAEADwlYAAAAAgAaErQAAAAAADQhbAQAAAAAaELYCAAAAADQgbAUAAAAAaEDYCgAAAADQgLAVAAAAAKCB8b1eAAzHcccdlxtvvDG//OUv89BDD2XChAmZNm1a3v72t+eII47I1KlTBzXPNttsk7vvvrvfvk022SS33XZby2UDAAAAMAYJWxnVTj/99LzmNa/JzJkzs/HGG+exxx7L9ddfn09/+tM566yz8v3vfz9bbLHFoOaaOHFijjrqqOe0b7DBBq2XDQAAAMAYNObD1lLKuCR/kuSQJNsk2TDJb5Pcm2ROkotrrRd3x85M8oMkV9RaZ/Yz19uTnJfO8QsH1lovKqVMT3JHd8hjSV5Ua32kn2dLktuTvLTb9IZa6+UtPuOa7O67786ECROe03788cfnpJNOysknn5yTTjppUHNNmjQpxx57bOslAgAAALCGGNNntnaD1kuSfCnJq5N8O8lJSf49yW+S/HGSvx/kXO9NcmGSJ5PsVmu9aLkhi5Osn+SgAaZ4UzpB6+JV+xSsSH9Ba5LsvffeSZJf/vKXz+dyAAAAAFiDjfWdrQcleUuSnyTZtdbat2xnKWW9JK9f2SSllGOTfCrJPUneUmv9WT/DbkiyZZI/TyfcXd6fpxPUXpbkravwGRiC7373u0mSrbfeetDPPPXUUzn33HNzzz33ZL311svWW2+dnXbaKePGjVtdywQAAABgDBnrYeuO3fczlw9ak6TW+ng6xwb0q/un//+U5K+S/E86QWv/tyh1dqz+W5JjSymvqbX+ZJl5Xphk7yT/kaQO5YOwYqeeemoeffTRLFiwIDfddFOuueaabL311vmbv/mbQc9x33335X3ve9+z2rbccst8/vOfz4wZM1ovGQAAAIAxZqyHrQ913393VR8spayT5KtJDkjyoyTvqLU+vJLH/jXJB9PZxfqBZdoPS7JOki8n+bNVXQsrd+qpp+b+++9/5vfddtstp59+el74whcO6vmDDz44O+ywQ175yldmgw02yLx58/LlL385Z555Zvbff//Mnj0722yzzepaPgAAAABjQKl17G60LKVsm+TadELls5NckOSGWuudA4yfmc5O1xvSuURrtyT/meSAWusTAzwzPZ0Lsq6utc4opVya5HVJNl/6TCnl1iTjaq2/W0r59yQHZwUXZPX19fX7P2Xy5Ekr/9Cj3HXXXT+s5x966KHcfPPNOe200/L444/n5JNPzite8Yohz/dP//RPOfvsszNz5syceOKJw1obAAAAACPXVltt1W/7pEmTymDnGNMXZNVab0xySJL7uu/fSjKvlPJQKeWCUso7Bnj0dekErb9Isu9AQesAvpxkcpL9k6SUsnOSV6Sz65XVbKONNsob3vCGnHbaaenr68txxx03rPn222+/JMmNN97YYnkAAAAAjGFj/RiB1FrPK6VckOQNSWYk2bb7vneSvUspX01yeH32Ft9fJFk3ye8l+edSyvvr4LcAX5DkwXSOEvhqkiOSLEpyZoOPM+YN9C8IQ5nnFa94RW655ZZMnTo1G2200ZDm2WSTTZIkCxcubLa2sWbu3LlJ2v2/g9Z8RxnpfEcZDXxPGel8RxkNfE8Z6XxH2xjTO1uXqrUuqrXOrrV+tNb6jiQvTOcs1seSHJpkr+UeuTfJLknmJjkqyVdKKYP6b1VrfSqdkHVGKWWHJO9KcnGt9f4VP0lr9957b5Jk3LhxQ57j+us7xxpMnz69xZIAAAAAGMPWiLB1ebXWJbXW85Kc3G16Yz9j7k4ncP2fJIcnObuUMtidwF/uvp+XZEKSLw1rwfTr9ttvT19f33Pan3766Rx//PF54IEH8vrXvz6TJ09OkixatCi33XZb7rjjjmeN/8UvfpHHHnvsOfPceeedOeaYY5Ik7373u1fDJwAAAABgLBnzxwisxCPd934Pua213ltK2TXJ95McmGRCKeWA7u7VAdVaf15KuSrJzknmdZ+nsdmzZ+cTn/hEtt9++2y55ZaZOnVq7r///lx99dWZN29eNt1005xyyinPjP/1r3+d7bbbLtOmTcstt9zyTPv555+fz3/+89lxxx0zbdq0bLDBBrnjjjsye/bsLFy4MHvssUf+8i//shcfEQAAAIBRZEyHraWUg9I5P/W/aq1PL9e3WTrnqibJlQPNUWt9sJTyxiTfTeec1wtLKfvWWheupPwR6VyMdecqnPfKKpg5c2buuOOOXHPNNbn55pvT19eX9ddfPy972ctywAEH5Mgjj8yUKVNWOs/OO++c22+/PTfffHP++7//O48//ngmTZqU7bffPgcccEAOPPDAlDLoS+cAAAAAWEON6bA1yeuTHJ3k3lLKD5Ms/fvxlyR5e5IXJLkoyX+saJJa629LKbslmZXkrUlmlVLeWWt97t+e//9nfp7k58P/CAzkVa96VU488cRBj99yyy0zf/7857TPmDEjM2bMaLk0AAAAANZAYz1sPSmdS652S/LqJG9O5wzVh5JcnuTrSb4+mJ2ntdZHSilvSSec3S3J90opb1tN6wYAAAAARpkxHbZ2L7n6fPc1mPGXZ4DzW7v9jyfZfbnmBSt6pp85DklyyGDHAwAAAACjw1q9XgAAAAAAwFggbAUAAAAAaEDYCgAAAADQgLAVAAAAAKABYSsAAAAAQAPCVgAAAACABoStAAAAAAANCFsBAAAAABoQtgIAAAAANCBsBQAAAABoQNgKAAAAANCAsBUAAAAAoAFhKwAAAABAA8JWAAAAAIAGhK0AAAAAAA0IWwEAAAAAGhC2AgAAAAA0IGwFAAAAAGhA2AoAAAAA0ICwFQAAAACgAWErAAAAAEADwlYAAAAAgAaErQAAAAAADQhbAQAAAAAaELYCAAAAADQgbAUAAAAAaEDYCgAAAADQgLAVAAAAAKABYSsAAAAAQAPje70ABm/+/L5eLwEAAAAAGICdrQAAAAAADQhbAQAAAAAaELYCAAAAADQgbAUAAAAAaEDYCv+vvTsPs6WqzwX8/WQ6CnpAEByYnFEjDigKJjIp4ogaNHEmN+KjxoiJaG7idSBqwKiJHDVoohFwiESJIyLILKBRI0QUUFRAEVAPo8yD6/5R1bJpu8/podjd9Hnf59lP9a5atWqtsxeb7m+vvQoAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAEIWwEAAAAABiBsBQAAAAAYgLAVAAAAAGAAwlYAAAAAgAGsvdANYOY23HD5QjeBWbriiisXugkAAAAAjImZrQAAAAAAAxC2AgAAAAAMQNgKAAAAADAAYSsAAAAAwACErQAAAAAAAxC2AgAAAAAMQNgKAAAAADAAYSsAAAAAwACErQAAAAAAAxC2AgAAAAAMQNgKAAAAADAAYSsAAAAAwACErQAAAAAAAxC2AgAAAAAMQNgKAAAAADAAYSsAAAAAwACErQAAAAAAAxC2AgAAAAAMQNgKAAAAADAAYSsAAAAAwACErQAAAAAAAxC2AgAAAAAMQNgKAAAAADAAYSsAAAAAwACErQAAAAAAAxC2AgAAAAAMQNgKAAAAADAAYSsAAAAAwACErQAAAAAAAxC2AgAAAAAMQNgKi9BJJ52UF73oRXnQgx6UTTfdNNtss02e+9zn5phjjlmQegAAAABYvbUXugHAbb3lLW/JihUrcp/73CdPfepTs/HGG2flypU544wzcsopp2T33Xcfaz0AAAAAzMwdPmytqjZp12+TXJ7ke0k+0lr71AzKX9mXPyTJoa21yWVSVcuSvCbJ85Jsk+TOSS5NclGSbyT5TGvtpJHyeyf52KRqbuzLn5TkH1trZ820n6wZDj300KxYsSIveMELctBBB2Xddde9zfGbbrpprPUAAAAAMHN3+LB1xP79dp10YeieSXapqse01v56NeUfkOQ5SXZK8ph0oervVNUG6QLSRye5JMkR/XaDJI9I8ookG/ZlJvvfJJ/vf16eZOckL0vy/KratbX2zdl2lKXphhtuyNvf/vZsvvnmUwakSbLOOuuMrR4AAAAAZmfJhK2ttbeNPq+q3ZJ8LcnrqmpFa+381ZR/QpKTk7y6qt7bWjtv5PDr0gWtxyR5ZmvtxknnbpTkIdM07YzRa1VVpZvx+rIkByTZZWY9ZKk74YQTsnLlyrzqVa/Kne50pxx99NE5++yzs95662W77bbL9ttvP9Z6AAAAAJidJRO2TtZaO66qzkkXgj42yfmrKX9qX/6hSbZLMhq27thvD54ctPbnXp7ktBm2q1XVv6QLW6Ve/M53v/vdJMmyZcvyxCc+MWedddtVJnbcccccdthh2WSTTcZSDwAAAACzc6eFbsDtrPrt763BuhqTF7S8tN8+aH7N+Z25toslbOXKlUmSFStWJEmOOuqoXHjhhTn11FOz66675rTTTsvLXvaysdUDAAAAwOws2ZmtVfWkJA9OF2h+ewbln5hurdcbk3xr0uHDk7w4ydurauskRyb5bmvt4jm0q5K8un/637M9nzuWc889d8ZlL7/88iTJWmutlQMOOCCbbLJJLr744qy77rrZf//98/3vfz+nnnpqjjjiiGy77ba3ez2zNZu+wkIwRlnsjFHuCIxTFjtjlDsC45TFbk0eow984APnXceSmdlaVW/rH++sqs8m+Wq6GaTva61dsJryhyc5ti+/3+QQtbX25ST7JrkuyauSfDnJRVV1cVV9sg9qp/PIkWv9c5LvJnlpX9eb5t9zloq73vWuSZIHP/jBufe9732bY8uWLcsOO+yQJPnBD34wlnoAAAAAmJ2lNLP1rf22JbkiydeTfLS19onVlJ/Qkvx5a+1jUxVura2oqo8keXK6NVwf1W9fmOSFVfX21tpbpjj1Ef0j6ZYnuDjJx5Mc2Fo7a4ryLCGz+UTksY99bA477LBsttlmU5635ZZbJunC1FXVO1Q9MzXxidcQdcHtwRhlsTNGuSMwTlnsjFHuCIxTFjtjdBhLJmxtrdXqS/1++apaP8kOST6a5ENVdUFr7fhpzrk2yRf6R6pq3ST7JDkoyZur6r9aa2dMOu3Q1tres2kba6addtopVZVzzjknv/3tb3OnO9124vnZZ5+dJNlqq63GUg8AAAAAs7NklhGYq9baNa21Y5M8M8laSQ6tqrvM8NwbW2sfTPIf/a5db6dmsgbYcssts8cee+TCCy/MwQcffJtjxx9/fI477rgsX748u+22W5Lkpptuyo9+9KOcd95586oHAAAAgGEsmZmt89Va+15V/VuSVyb5qyTvnMXpv+m3s5pdC5O95z3vyZlnnpk3velNOeaYY7LtttvmggsuyJFHHpm11lorK1asyPLly5MkF110UbbffvtsscUWOfPMM+dcDwAAAADDWONntk7yjiQ3JNmvqjaa2FlVr6yqx091QlVtk+R5/dOTb/8mspTd5z73yYknnph99tknP/nJT/KhD30op5xySvbYY48cffTR2XPPPcdaDwAAAAAzZ2briNbaL6rqQ0n2TfLGJH/bH9ojycFVdX6SU5P8PMl6SR6Y5ClJ1kmyorX27bE3miVnk002ybvf/e68+93vXmW5rbbaKldcccW86wEAAABgGGa2/r4Dklyb5LVVtVm/741J9ktyTpLHJ3ltkr9I8ogkX07yzNbavgvQVgAAAABgkbjDz2xtrc1qndTVlW+t/TLJ+pP2/SjJe/vHTK9zSJJDZtM2AAAAAOCOy8xWAAAAAIABCFsBAAAAAAYgbAUAAAAAGICwFQAAAABgAMJWAAAAAIABCFsBAAAAAAYgbAUAAAAAGICwFQAAAABgAMJWAAAAAIABCFsBAAAAAAYgbAUAAAAAGICwFQAAAABgAMJWAAAAAIABCFsBAAAAAAYgbAUAAAAAGICwFQAAAABgAMJWAAAAAIABCFsBAAAAAAYgbAUAAAAAGICwFQAAAABgAMJWAAAAAIABCFsBAAAAAAYgbAUAAAAAGICwFQAAAABgAMJWAAAAAIABCFsBAAAAAAYgbAUAAAAAGICwFQAAAABgAMJWAAAAAIABrL3QDWDmrrjiyoVuAgAAAAAwDTNbAQAAAAAGIGwFAAAAABiAsBUAAAAAYADCVgAAAACAAQhbAQAAAAAGIGwFAAAAABiAsBUAAAAAYADCVgAAAACAAQhbAQAAAAAGIGwFAAAAABiAsBUAAAAAYADCVgAAAACAAQhbAQAAAAAGIGwFAAAAABiAsBUAAAAAYADCVgAAAACAAQhbAQAAAAAGIGwFAAAAABiAsBUAAAAAYADCVgAAAACAAQhbAQAAAAAGIGwFAAAAABiAsBUAAAAAYADCVgAAAACAAQhbAQAAAAAGIGwFAAAAABiAsBUAAAAAYADCVgAAAACAAQhbAQAAAAAGIGwFAAAAABiAsBUAAAAAYADVWlvoNjDJlVde6UUBAAAAgEVg+fLlNdOyZrYCAAAAAAxA2AoAAAAAMABhKwAAAADAAIStAAAAAAADELYCAAAAAAygWnPjewAAAACA+TKzFQAAAABgAMJWAAAAAIABCFsBAAAAAAYgbAUAAAAAGICwdRGqqs2r6t+r6qKquqGqzq+q91XVRgvdNtYcVbVXVb2/qr5eVVdVVauqT6zmnB2r6itVdVlVXVdV36uq11XVWuNqN2uOqtq4ql5eVZ+rqh/3Y+7Kqjqlqv68qqb8f5xxyjhV1buq6riq+nk/3i6rqtOr6q1VtfE05xijLKiqenH///1WVS+fpswzqurE/n336qr676p62bjbypqh/3uoTfO4ZJpzvJcydlW1W/+76SX93/IXVdXRVfW0Kcoao4xNVe29ivfRicctU5xnnM5BtdYWug2MqKr7JzktyaZJvpDknCTbJ9klyQ+TPKG1dunCtZA1RVWdkeQRSa5OcmGSbZJ8srX24mnK75nkiCTXJzk8yWVJnpnkwUk+21p73jjazZqjql6Z5OAkFyc5IcnPkmyW5LlJlqcbj89rI/+jM04Zt6q6Mcl3k5yV5FdJ1k/y+CSPSXJRkse31n4+Ut4YZUFV1RbACZL+AAAVGUlEQVRJzkyyVpINkuzTWvvIpDKvSfL+JJemG6c3JtkryeZJ3tta22+sjWbJq6rzk2yY5H1THL66tfaeSeW9lzJ2VfWPSd6Q7m+no5KsTHKPJNslOba19saRssYoY1VVj0zy7GkO/1GSXZMc2Vp7xsg5xukcCVsXmao6OsnuSV7bWnv/yP5/SvJXST7cWnvlQrWPNUdV7ZLuF4UfJ9kpXZg1ZdhaVXfryy1P94HAd/r9y5Icn2SHJC9orX16TM1nDVBVu6YLro5srf12ZP89k3wryRZJ9mqtHdHvN04Zu6pa1lq7for970zyd0kObq29ut9njLKgqqqSfC3JfZP8V5L9Milsraqt000GuCbJdq218/v9GyX5dpL7J9mxtfaNcbadpa0PW9Na23oGZb2XMnZVtU+Sf01yaJJXtNZunHR8ndbaTf3PxiiLSlV9I91kgD1ba1/s9xmn82AZgUWkn9W6e5Lzk3xw0uG3pvul9iVVtf6Ym8YaqLV2Qmvt3DazT2T2Svep7acn3oT7Oq5P8v/6p6+6HZrJGqy1dnxr7UujQWu//5IkH+qf7jxyyDhl7KYKWnv/2W8fOLLPGGWhvTbdzJY/S/d751T+T5L1knxgImhNktba5Un+oX9qYgALyXspY1VV6yV5Z7pvWf1e0JokE0Frzxhl0aiqh6cLWn+R5MiRQ8bpPAhbF5dd+u0xU4QHv0lyapK7pPsPARaTXfvtV6c4dnKSa5Ps2P8iAuMw8QvtzSP7jFMWk2f22++N7DNGWTBV9ZAkByY5qLV28iqKrmqcHjWpDAxpvX494b+rqn2rapdp1gz0Xsq4PTldKPVfSX5bVU+vqr/px+kOU5Q3RllMXtFvP9paG12z1Tidh7UXugHcxoP77Y+mOX5uupmvD0py3FhaBDMz7dhtrd1cVecleViS+yU5e5wNY81TVWsneWn/dPSXA+OUBVNV+6Vb/3J5uvVa/zBd0HrgSDFjlAXRv29+PN2srL9bTfFVjdOLq+qaJJtX1V1aa9cO21LWcPdMN05HnVdVf9ZaO2lkn/dSxu2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r6k+T/EqSk5O8sKouSHJ5kjsluWeS+yTZIwtB9vTu/uBU8wMAAAAAzMtkoXXmmUm+kuSkLOzBethmxlyb5DVJ3jTx3AAAAAAAczFpaO3uTvK6qvr9JI9J8uAk+89Or8nCPq5ndfd/TDkvAAAAAMA8LSq0VtXLklzf3adu7nx3r0vyodkPAAAAAMDt2mJfhvWKJP9t4wNV9a9V9Y/DKwIAAAAAWGYWu3VA5/sj7UFZeOkVAAAAAMAuZbFPtF6d5K5VtXLKxQAAAAAALEeLfaL1H5Mcm+TDVfXnSa6fHb9zVf3q9tyou9+zyDUAAAAAACwJiw2tr0ryqCRHJjlio+P7JPmT7byX0AoAAAAALGuLCq3d/bmqemCS45McmuTOSY5KclOST0+2OgAAAACAZWCxT7Smu7+a5L9v+Luq1ie5ursfNcXCAAAAAACWi0WH1s24LMkVE94PAAAAAGBZmCy0dvdBU90LAAAAAGA52W2qG1XVLVV1+XaMv6Sqbp5qfgAAAACAeZkstCap2c/2XgMAAAAAsKxNGVq31x2T3DLH+QEAAAAAJjGX0FpVd09ytyRXzWN+AAAAAIApLfplWFV1RJKjNjm8d1W97LYuS7IqyWNnv5+32PkBAAAAAJaKRYfWJI9K8vIkvdGxvWbHbsuGfVmvTvLKgfkBAAAAAJaEkdD6xSSnb/T3M5LckOSM27hmfZJrk/xzkg9297cH5gcAAAAAWBIWHVq7+6+S/NWGv6vqGUmu6e7jplgYAAAAAMByMfJE66aemOSKqtq9u2+Z8L4AAAAAAEvalKH1g1nYGuDgJP824X0BAAAAAJa0KUPr9Ulu7m6RFQAAAADYpew24b0uSbJnVU0ZbwEAAAAAlrwpQ+sZSfZI8oQJ7wkAAAAAsORNGVrfmOT8JH9YVY+e8L4AAAAAAEvalP/M/6Qkq5P8eJKzqurCJJ9OsibJLVu6qLtfNeEaAAAAAAB2uilD6yuSdJKa/f2AJPe/jfE1Gy+0AgAAAADL2pSh9T1ZCKcAAAAAALuUyUJrdz9zqnsBAAAAACwnU74MCwAAAABglyS0AgAAAAAMmnKP1u+pqqOSPDnJg5PsPzu8JskXkpzR3R/bEfMCAAAAAMzDpKG1qn4gyZ8l+ZkNhzY6fXCShyV5TlWdneTp3X3VlPMDAAAAAMzDZKG1qu6Q5Owk989CYP10ktVJvj4bco8kRyf5qSSPSXJWVR3e3TdOtQYAAAAAgHmY8onW30zygCRXJ3lad5+9mTEvrapjkrxvNvaEJG+ZcA0AAAAAADvdlC/DekqSTnL8FiJrkqS7z0pyfBaeen3qhPMDAAAAAMzFlKH1kCQ3JPngNoz94GzsfSecHwAAAABgLqYMrXskuam7e2sDu3t9kpsy8cu4AAAAAADmYcrQelmSlVX14K0NrKqHJFk5uwYAAAAAYFmbMrT+bRb2Xf3jqtp/S4Oq6oAkf5yF/Vz/ZsL5AQAAAADmYsp/uv/6JM9Icv8k/1JV70zysSSXJ7lTknsmeVSSZybZM8nVSd4w4fwAAAAAAHMxWWjt7iur6tgkH0py9yQvmv1sqpJ8M8kTuvvKqeYHAAAAAJiXKbcOSHd/NslPJHl5ki9lYXuAmv307NjLkhza3Z+bcm4AAAAAgHmZcuuAJEl3r03y6iSvrqo9kuw3O3V1d9809XwAAAAAAPM2eWjd2CysXrEj5wAAAAAAmLfh0FpVd0zyhCQPSbJPkrVJPpPkr7v75tH7AwAAAAAsdUOhtaoekeTPs/Dyq01dWlVP6O4vjcwBAAAAALDULfplWFX1Q0k+koXIuuFlV2s2nE5ycJK/rap9RxcJAAAAALCULTq0JnlBklVZ2CrgV5Ps2d13T7JXkucn+U6SH0zya6OLBAAAAABYykZC62Oy8BTr87v7vd19Y5J09w3d/dYkL8/Ck63HjC8TAAAAAGDpGgmtP5KF0PqXWzj/5xuNAwAAAAC43RoJrSuTrOnuGzZ3sru/Nvt1r4E5AAAAAACWvJHQmiw80bo1NTgHAAAAAMCSNhpaAQAAAAB2eSsGr9+vqlYPjOnufvTgGgAAAAAA5mo0tN4hyVEDY7Zl6wEAAAAAgCVtJLSePtkqAAAAAACWsUWH1u4+bsqFAAAAAAAsV16GBQAAAAAwSGgFAAAAABgktAIAAAAADBJaAQAAAAAGCa0AAAAAAIOEVgAAAACAQUIrAAAAAMAgoRUAAAAAYJDQCgAAAAAwSGgFAAAAABgktAIAAAAADBJaAQAAAAAGCa0AAAAAAIOEVgAAAACAQUIrAAAAAMAgoRUAAAAAYJDQCgAAAAAwSGgFAAAAABgktAIAAAAADBJaAQAAAAAGrZj3Ath2q1bt+73f1669Zo4rAQAAAAA25olWAAAAAIBBQisAAAAAwCChFQAAAABgkNAKAAAAADBIaAUAAAAAGCS0AgAAAAAMEloBAAAAAAYJrQAAAAAAg4RWAAAAAIBBQisAAAAAwCChFQAAAABgkNAKAAAAADBIaAUAAAAAGCS0AgAAAAAMEloBAAAAAAYJrQAAAAAAg4RWAAAAAIBBQisAAAAAwCChFQAAAABgkNAKAAAAADBIaAUAAAAAGCS0AgAAAAAMEloBAAAAAAYJrQAAAAAAg4RWAAAAAIBBQisAAAAAwCChFQAAAABgkNAKAAAAADBIaAUAAAAAGCS0AgAAAAAMEloBAAAAAAYJrQAAAAAAg4RWAAAAAIBBQisAAAAAwCChFQAAAABgkNAKAAAAADBIaAUAAAAAGCS0AgAAAAAMEloBAAAAAAYJrQAAAAAAg4RWAAAAAIBBQisAAAAAwCChFQAAAABgkNAKAAAAADBIaAUAAAAAGCS0AgAAAAAMEloBAAAAAAYJrQAAAAAAg4RWAAAAAIBBQisAAAAAwCChFQAAAABgkNAKAAAAADBIaAUAAAAAGCS0AgAAAAAMEloBAAAAAAYJrQAAAAAAg4RWAAAAAIBBQisAAAAAwCCh4/zMNwAAIABJREFUFQAAAABgkNAKAAAAADBIaAUAAAAAGCS0AgAAAAAMEloBAAAAAAYJrQAAAAAAg4RWAAAAAIBBQisAAAAAwCChFQAAAABgkNAKAAAAADBIaAUAAAAAGCS0AgAAAAAMEloBAAAAAAYJrQAAAAAAg4RWAAAAAIBBQisAAAAAwCChFQAAAABgkNAKAAAAADBIaAUAAAAAGCS0AgAAAAAMEloBAAAAAAYJrQAAAAAAg4RWAAAAAIBBQisAAAAAwCChFQAAAABgkNAKAAAAADBIaAUAAAAAGCS0AgAAAAAMEloBAAAAAAYJrQAAAAAAg4RWAAAAAIBBQisAAAAAwCChFQAAAABgkNAKAAAAADBIaAUAAAAAGCS0AgAAAAAMEloBAAAAAAYJrQAAAAAAg4RWAAAAAIBBQisAAAAAwCChFQAAAABgkNAKAAAAADBIaAUAAAAAGCS0AgAAAAAMEloBAAAAAAYJrQAAAAAAg4RWAAAAAIBBQisAAAAAwCChFQAAAABgkNC6C7n88stzwgkn5L73vW/udre75X73u19OOumkrF27dpvv8bjHPS6rVq3a4s8NN9ywA/8LAAAAAGBpWjHvBbBzXHLJJTnmmGOyZs2aHHvssfmxH/uxfP7zn8/b3/72nHPOOfn7v//77Lffftt8vxe/+MWbPb5ihf9JAQAAALDrUcVmqqq3Ydijuvtjs/HPTPInSU7v7mfOju2W5NwkRyT55e5+32bm+ZEkFyS5Kcn9u/vrU6x/a174whdmzZo1ef3rX5/nPOc53zv+kpe8JKeddlpe/epX5y1vecs23+/kk0/eEcsEAAAAgGVJaP1+r7yNc5fe1oXdvb6qnpGFkPoHVfWJ7r58w/mq2j3Je5PsneSpOyuyXnLJJVm9enXuec975tnPfvatzp188sk5/fTT84EPfCCnnHJK9tprr52xJAAAAAC4XRFaN9Hdrxi8/tKqekEWnnZ9d1Ud090bnpY9OclPJfnf3f2BsZVuu09+8pNJkqOPPjq77XbrbXlXrlyZhz/84Vm9enXOP//8HHnkkdt0zzPPPDNf+9rXsscee+SQQw7JEUcckTve8Y6Trx0AAAAAlgOhdQfo7ndX1S8meWKSFyT5n1X10CQvS/JvSU7Ymeu5+OKLkyT3vve9N3v+3ve+d1avXp2vfvWr2xxan/WsZ93q7/333z9vetOb8vjHP35ssQAAAACwDO229SEs0vFJrkjy2llkfW8WwvYzunvtzlzItddemyTZd999N3t+n332SZJcc801W73Xsccem/e///358pe/nG9961v53Oc+lxNPPDHXXHNNjjvuuHz0ox+dbuEAAAAAsEx4onUTVfWKLZy6obtft6336e6rqurXk/x1kn9Icsckp3b3ueOr/P9PqW6LDQH1iiuu2Ox1V199dZLkqquu2up9jznmmCTJunXrsm7duiTJ0572tOy+++554xvfmJe85CW5173utc1rg9u77fmuAkuH7y4sX76/sHz5/sLydXv4/t7nPvcZvofQ+v1evoXj1yTZ5tCaJN39kar6eJIjk1yW5CWDa1uUvffeO0ly/fXXb/b8huMrV65c9ByPf/zjc+qpp+aiiy7KunXrvFQLAAAAgF2K0LqJ7q6p7lVVRyc5YvbnPZI8PMknprj39lT2ww47LO9973tzzTXXbPa6b3/720mSRzziEUP1fuXKlVm7dm0OPPDAHHDAAYu+D9webPh/86b4f8SAncd3F5Yv319Yvnx/Yfny/b01e7TuIFW1Ksm7k9yc5HlJOsnpVbX4x0YX6ZGPfGSSZPXq1Vm/fv2tzl133XX5zGc+kz333DMPfehDFz3HxRdfnLVr12blypW5613vOrReAAAAAFhuhNYd57QkP5zk5d399iSvT3JQkrfs7IUcfPDBOfroo3PZZZflne98563Ovfa1r826devylKc85Vb/3P+iiy7KRRdddKuxl156af793//9++5/1VVX5YQTTkiSPOlJT8qKFR6UBgAAAGDXoojtAFX1lCRPS3JeFgJrkrwiybFJfq2qPtTdH9mZa3rzm9+cY445Ji9+8Yvz8Y9/PIccckjOP//8fPKTn8yP/uiP5qUvfemtxh922GFJkrVr137v2HnnnZcTTzwxhx9+eA466KDc5S53yde//vWcddZZufbaa/OgBz0or3zlK3fmfxYAAAAALAlC68Sq6oeSvC3J9Ul+tbvXJ0l331RVv5Lk/CTvrKr7dfdVO2tdBx98cM4999y85jWvyTnnnJOzzz47BxxwQJ773OfmpJNOyqpVq7Z6jwc+8IF50pOelAsuuCAXXnhhrrvuuuy999459NBD84QnPCHHHXdc7nCHO+yE/xoAAAAAWFqE1k1U1Stu4/SHuvuLt3FtZWFf1rsk+fXu/teNz3f3P1XVS5O8IQsx9r8ML3g73OMe98hpp522TWM3fpJ1g0MPPTRve9vbpl4WAAAAACx7Quv3e/ltnLs0yRZDa5LnJ/mZJH/V3X+8hTFvTvILSX6pqp7e3e9d1CoBAAAAgCVDaJ3p7trO8e/OwtOrGx/7vSS/t5Xr1ic5YjuXBwAAAAAsYbvNewEAAAAAAMud0AoAAAAAMEhoBQAAAAAYJLQCAAAAAAwSWgEAAAAABgmtAAAAAACDhFYAAAAAgEFCKwAAAADAIKEVAAAAAGCQ0AoAAAAAMEhoBQAAAAAYJLQCAAAAAAwSWgEAAAAABgmtAAAAAACDhFYAAAAAgEFCKwAAAADAIKEVAAAAAGCQ0AoAAAAAMEhoBQAAAAAYJLQCAAAAAAwSWgEAAAAABgmtAAAAAACDhFYAAAAAgEFCKwAAAADAIKEVAAAAAGCQ0AoAAAAAMEhoBQAAAAAYJLQCAAAAAAwSWgEAAAAABgmtAAAAAACDhFYAAAAAgEFCKwAAAADAIKEVAAAAAGCQ0AoAAAAAMEhoBQAAAAAYJLQCAAAAAAwSWgEAAAAABgmtAAAAAACDhFYAAAAAgEFCKwAAAADAIKEVAAAAAGCQ0AoAAAAAMEhoBQAAAAAYJLQCAAAAAAwSWgEAAAAABgmtAAAAAACDhFYAAAAAgEFCKwAAAADAIKEVAAAAAGCQ0AoAAAAAMEhoBQAAAAAYJLQCAAAAAAwSWgEAAAAABgmtAAAAAACDhFYAAAAAgEFCKwAAAADAIKEVAAAAAGCQ0AoAAAAAMEhoBQAAAAAYJLQCAAAAAAwSWgEAAAAABgmtAAAAAACDhFYAAAAAgEFCKwAAAADAIKEVAAAAAGCQ0AoAAAAAMEhoBQAAAAAYJLQCAAAAAAwSWgEAAAAABgmtAAAAAACDhFYAAAAAgEFCKwAAAADAIKEVAAAAAGCQ0AoAAAAAMEhoBQAAAAAYJLQCAAAAAAwSWgEAAAAABgmtAAAAAACDhFYAAAAAgEFCKwAAAADAIKEVAAAAAGCQ0AoAAAAAMEhoBQAAAAAYJLQCAAAAAAwSWgEAAAAABgmtAAAAAACDhFYAAAAAgEFCKwAAAPy/9u48zLKqPvf4930AQUAGEZQIiGLiPCsOXGUUI84KzgO5Ea8xTomomRxjAiaaqOgl8XoVIxpRjDMKYoOIaFCD11lBaAkqKlMzNA2Cv/vHWsc+llXV1bWrTxVV38/z1LPr7L323mvV7tX71FvrrC1J0kAGrZIkSZIkSZI0kEGrJEmSJEmSJA1k0CpJkiRJkiRJAxm0SpIkSZIkSdJABq2SJEmSJEmSNJBBqyRJkiRJkiQNZNAqSZIkSZIkSQMZtEqSJEmSJEnSQAatkiRJkiRJkjSQQaskSZIkSZIkDWTQKkmSJEmSJEkDGbRKkiRJkiRJ0kAGrZIkSZIkSZI0kEGrJEmSJEmSJA1k0CpJkiRJkiRJAxm0SpIkSZIkSdJABq2SJEmSJEmSNJBBqyRJkiRJkiQNtPliV0Bzd8UVaxa7CpIkSZIkSZKm4YhWSZIkSZIkSRrIoFWSJEmSJEmSBjJolSRJkiRJkqSBDFolSZIkSZIkaSCDVkmSJEmSJEkayKBVkiRJkiRJkgYyaJUkSZIkSZKkgQxaJUmSJEmSJGkgg1ZJkiRJkiRJGsigVZIkSZIkSZIGMmiVJEmSJEmSpIEMWiVJkiRJkiRpIINWSZIkSZIkSRrIoFWSJEmSJEmSBjJolSRJkiRJkqSBDFolSZIkSZIkaSCDVkmSJEmSJEkayKBVkiRJkiRJkgYyaJUkSZIkSZKkgQxaJUmSJEmSJGkgg1ZJkiRJkiRJGsigVZIkSZIkSZIGMmiVJEmSJEmSpIEMWiVJkiRJkiRpIINWSZIkSZIkSRrIoFWSJEmSJEmSBjJolSRJkiRJkqSBDFolSZIkSZIkaSCDVkmSJEmSJEkayKBVkiRJkiRJkgYyaJUkSZIkSZKkgVJVi10HTbFmzRoviiRJkiRJkrTItt9++8y1rCNaJUmSJEmSJGkgg1ZJkiRJkiRJGsigVZIkSZIkSZIGMmiVJEmSJEmSpIEMWiVJkiRJkiRpoFT5gHtJkiRJkiRJGsIRrZIkSZIkSZI0kEGrJEmSJEmSJA1k0CpJkiRJkiRJAxm0SpIkSZIkSdJABq1LUJLdkrw7yU+TXJdkdZK3JNlxsesmrWRJdkry3CQfTXJekmuTrElyZpI/TjLt/6lJHpLkpCSX9X2+meSlSTabdBsk/bYkz0xS/eu5M5R5dJLTe3+/Osl/JnnOpOsqCZIc2O/DF/f3yT9NcnKSQ6Yp6/1XWiKSPCrJKUku6v3x/CQfTvLgGcrbf6UJSXJokmOSfDHJlf198fEb2Gej++hKeU+dqlrsOmhMkr2As4BdgI8D3wf2BvYHfgDsU1WXLl4NpZUryfOBY4GfAacBFwK3Bp4IbA98BDisxv5jTfK4vn4dcAJwGfAY4E7AiVV12CTbIGm9JLsD3wI2A7YFjqiqd00p80LgGOBSWh++HjgU2A14c1UdOdFKSytYkn8AXg5cBHwGuATYGbgfcGpVvWKsrPdfaYlI8kbgFbR76cdoffeOwGOBzYFnV9XxY+Xtv9IEJfkGcC/gato99s7A+6vqmTOU3+g+upLeUxu0LjFJTgYOBl5cVceMrf8n4M+Af62q5y9W/aSVLMkBwDbAp6vq12PrbwOcDewOHFpVH+nrtwPOo4Ww+1TV1/r6rYBVwIOBp1XVByfaEEkkCfA54PbAfwBHMiVoTbIn7Q+e1wD3q6rVff2OwFeBvYCHVNWXJ1l3aSVKcgTwTuC9wPOq6vop27eoql/1773/SktEf5/8E+CXwD2r6hdj2/an9ckLquoOfZ39V5qw3hcvovW9fWmDiqYNWufTR1fae2qnDlhC+mjWg4HVwDumbH4N7R/ls5JsM+GqSQKqalVVfXI8ZO3rLwb+pb/cb2zTobSRNh8c3YB6+XXA3/SXf7LpaixpFi8GDgD+iHZ/nc7/BLYE3j56QwhQVZcDf99f+sdPaRNLsiXwd7RPkvxOyAowClk777/S0nE7Wu7wn+MhK0BVnQZcReuvI/ZfacKq6rSqOnf8k5mzmE8fXVHvqQ1al5b9+/KUaYKcq4AvAVsDD5p0xSRt0OgXvBvG1h3Ql5+dpvwZwFrgIf0XSEkTkuQuwNHAW6vqjFmKztaHPzOljKRN5+G0X+r+A/h1n+vxlUleMsP8jt5/paXjXNpHhPdOcqvxDUkeBtwCOHVstf1XWtrm00dX1Htqg9al5U59+cMZtp/bl38wgbpImqMkmwPP7i/Hbx4z9umqugG4gDYv1R02aQUl/Ubvr++jjYz7qw0Un60P/4w2Ena3JFsvaCUlTfWAvlwHnAN8ivbHkrcAZyX5QpLxEXHef6UloqouA15Je67Bd5O8M8lRST4EnEKbxud/je1i/5WWtvn00RX1ntqgdWnZvi/XzLB9tH6HCdRF0twdDdwdOKmqTh5bb5+Wlp5XA/cBDq+qazdQdq59ePsZtktaGLv05cuBAh5KGwV3T1pQ8zDgw2Plvf9KS0hVvYX28NjNgSOAvwAOA/4bOG7KlAL2X2lpm08fXVHvqQ1aJWmAJC8GXkab3PtZi1wdSbNI8kDaKNY3L5fJ9qUVYvQ7yw3AY6vqzKq6uqq+BTyB9gCPfWeYRkDSIkvyCuBE4DjaQ2+2Ae4HnA+8P8k/LF7tJGlhGbQuLRtK8Ufrr5hAXSRtQJIXAm8Fvgvs3z8aNc4+LS0RfcqAf6N9ZOlVc9xtrn14pr/OS1oYo/vkOeMP0QCoqrXA6NMke/el919piUiyH/BG4BNV9edVdX5Vra2q/6L9oeQnwMuSjD5mbP+Vlrb59NEV9Z7aoHVp+UFfzjQH6+/35UxzuEqakCQvBY4Bvk0LWS+eptiMfbqHPrenjc45f1PVU9JvbEvri3cB1iWp0Rfwml7m//R1b+mvZ+vDu9JG5FzUgx5Jm86oL84UrFzelzefUt77r7T4Ht2Xp03d0O+fZ9Nyifv01fZfaWmbTx9dUe+pDVqXltHN5+Akv3VtktwC2If2BLevTLpiktZL8krgn4Fv0ELWX8xQdFVf/uE02x4GbA2cVVXXLXwtJU1xHfB/Z/g6p5c5s78eTSswWx9+5JQykjadz9PmZr3r1PfI3d378oK+9P4rLR2jJ4/vPMP20frr+9L+Ky1t8+mjK+o9tUHrElJVP6JN6L8n8KdTNr+OlvK/r6qumXDVJHVJXkV7+NXXgQOr6pJZip8IXAI8Ncn9x46xFfCG/vLYTVVXSetV1bVV9dzpvoBP9GLv7etO6K/fQwtoX5hkz9GxkuxIm+sV4F8m1ARpxaqqHwOfBPYAXjK+LcnBwCNoo10/21d7/5WWji/25fOS3HZ8Q5JH0gYTrQPO6qvtv9LSNp8+uqLeU6eqFrsOGpNkL9pNZhfg48D3gAcC+9OmDHhIVV26eDWUVq4kz6FN4n8jbdqA6eaQWV1Vx43t83jazWgd8EHgMuCxwJ36+ieX/xFLiyrJa2nTBxxRVe+asu1FwNuAS4ETaCNuDgV2oz1U68jJ1lZamZLsRnuPvDtthOs5tI8nPp422vWpVfWRsfLef6UloI9CPxk4CLgK+ChwMW0qn0cDAV5aVW8d28f+K01Q73OP7y9vQ/sD5vms/0PJJePveefTR1fSe2qD1iUoye7A62nDqncCfka7Ib2uqi6fbV9Jm85YGDObL1TVflP22wf4a+DBwFbAecC7gbdV1Y0LX1NJG2O2oLVvfwxwJHBf2qeBvgu8vareO8l6Sitdkp2BV9N+mdsVuJL2S+BRVXX2NOW9/0pLQJItaJ/YfCpwV9pHiy+jzc/6tqo6ZZp97L/ShMzh99wfV9WeU/bZ6D66Ut5TG7RKkiRJkiRJ0kDO0SpJkiRJkiRJAxm0SpIkSZIkSdJABq2SJEmSJEmSNJBBqyRJkiRJkiQNZNAqSZIkSZIkSQMZtEqSJEmSJEnSQAatkiRJkiRJkjSQQaskSZIkSZIkDWTQKkmSJEmSJEkDGbRKkiRJkiRJ0kAGrZIkSZIkSZI0kEGrJEnSMpCk+teeC3Cs/fqxVg+u2E1EksN7m0+fZtvpfdvhk6/ZZCVZ3du632LXRZIk6abGoFWSJGmRjYWkG/t1+mLXXTcNSe6d5LUrISwel+QeST6d5Mok1yQ5Lck+G9jnA0luTHL/SdVTkiQtD5svdgUkSZLEz2dYf0tgC2AdsGaa7ZeNff+DvvzVAtZLy8e9gdcAXwCOm6Xcj2j/3tZOoE6bVJI/AL4E3AK4AbgR2A9YleTAqjpzmn0OAJ4GHFtVX5tgdSVJ0jJg0CpJkrTIquo2063vI1b3BU6oqsM3cIw7L3zNtNJU1YGLXYcF9FpayHoc8AJa2PoG4BXA0cD/GC+c5GbAO4BfAn89wXpKkqRlwqkDJEmSJC1HB9JGsb6kqq6tql/RAtSfAw9OsvWU8kcCdwZeUVWXT7aqkiRpOTBolSRJWgY29DCsJNskOTLJWUkuS7IuyflJPpHkGUm22IhzHZTk6n6+o6fZvm2Sv0ry1SRr+rnOTfK2JLvPcMzfPHAqyQ5J3pjk+0nWJrliI+p23yRHJzkzyYVJrktyaT/+c5NsNtdjbcQ5t+vzn/6//nO5Osk3k7wuyfYb2Hejrst82pekgPf0l/tOM9fvfmNlZ30YVpJbJ3nz2LVZk+TsJC9LsuUM+xzXj/naJJsleWn/Wa3tbf7UJpoPdSfgkqq6crSiqm4Afkz7PWjHsTruSQthzwTeuwnqIkmSVgCnDpAkSVrmktwV+DSwZ191A3AlsDtwe+AxtLksV8/hWE8A/h3YEvjLqjp6yva7AJ8Bbjd2ruuAOwIvAp6Z5DFV9aUZTrEz8HXgDn2/6+fSxjGn0AI2aPOMrqXNdbtv/3pCksf1wG2wJHcETmV9e0dzm96jfx2e5KCqOneafedzXebTvp8DNwe2o83hOz63L8zxZ5xkb9q1vWVfdRVwM+AB/etZSQ6uql/McIjNe3sf0etxHS3sfBRwYJIDqurLc6nLHF0K3CrJdqOwtQfRtwN+DYyPWn1bb8sLqqoWsA6SJGkFcUSrJEnSMpbklsBnaWHeBcDjgW2qaidga9o8le+hhXwbOtazgQ+zPpCaGrJuD5xEC7I+DNwL2KqqtgX2Aj5AC9Y+kmSHGU7zatoDwB4JbF1V2wEbM9rxFNrDjHatqm2qakdgW+BZwMXAIcCfbcTxZtTn9PwIrb3/DRzcz7UtcBBwIbAH8NGpoz0HXJeNbl+fA/gl/eVZVXWbKV9nzaGtOwIfo4Ws3wL27tdmW+AwWmh5L+D9sxzmT2mB7FOAbavqFn2fbwNbAW/dUD020ipgM+CtSbZKsjltjtZbA1+pqrW9bY+lhdrHVNW3FrgOkiRpBXFEqyRJ0vL2F7QRkpcAD62qn4w29Dkrv9S/ZpXkRbQg7Ebg2VV1/DTFXk4LDv+9qp4+vqGqzgee0QPGPwSeC7xpmmNsCRxSVd8e2/e8DdVvrOzTp1l3DXB8kh8DZ9AejPSPcz3mLJ4C3JM2OvO36gx8PskhwDnA3YBnAO8e2z6v6zLh9o17IbArcAVwcFVd3M99I3BikiuBk4GD+sjUVdMcYwdaW88cq/s3kxwOfA14QJI9qurCBarz64FHA4cDz6T9292Sdr3+EiDJzWn/rn8KvGaBzitJklYoR7RKkiQtb8/uyzeNh3kbI8mraB+tvh44dIaQFeA5ffnmWQ73gb58+AzbPzMlsFwwVfVFWlC4Z5LfW4BDHtqXH5+uzlX1HeDE/vLJUzYPvi7TnG+h2zdu1NZ3jULWKec+BRh97H9qW0e+OB6yju37deCi/vLuQys6dtzvAQ+ljRxeR5su4AzgoKo6oxd7Fe2PA39eVVcl2SXJe/u8t2uTrEpyv4WqkyRJWt4c0SpJkrRM9Qf83Lq/PGl+h8g/0T6Kfg3wuKr6/AwFdwd2G52rP4BpOjfry2kfisX6sG7ekhxGG0F6X9qcr1tNU+z3aKMYh7hvX542S5lVtI/6j8oOvi4TbN/ofDdjfQC6obY+mLG2TvHVWfb9Ce3fz46zlNloVfUN2jQUvyPJnYGXAadW1QlJtqK14W7A52mjjZ8AnJ5k7x7cSpIkzcigVZIkafm69dj38/k49h6sn+/zT2YKWbtdx77fZQ7H3nqG9b+cS8Wm0+fg/BAtHBu5jhaY3dhf70z7VNc28z3PmJ37crYRqaORmjslSX/Q0ryuyyK0b+SWrP8k3FzauvMM26+aZd91fbnFRtRrqHf05Qv78ghayHpsVb0AIMkzgffR5nZ90gTrJkmSboKcOkCSJEkzuRj4Qv/+qCR7zVJ2/H3ljlWVDXztOcNxbpxh/VwcQQsh1wIvBnavqq2qaufRg59YP8ozA84z1XQjSjeFxWrfuEm1dZNK8nTgANrUDT/oqx/dl28fK/oBWvj/iCSbTbCKkiTpJsigVZIkafn6+dj3t5vH/tfRwqcvAbcFViWZ6Tjj59pjHudaCIf15d9W1TFVddH4xh6U3WoBzzcafTtbe0fTKVzaR7PC/K/LpNs3chltflOYW1vnPSp5EpJsR5tHeDXwd2ObRtfigtGKqvp1f70Nm+ZnK0mSlhGDVkmSpGWqqlbTRqUCHDLPY1zd9z2bFrKtSrLbNOUuYH2AOO2cmBMwqtc5M2zfh4Udkflffbn/LGUOmFJ2yHUZ0r5RULrRI12r6npg9LCvjWrrEvUG4DbAS6pq7TTbp/4Mb77pqyRJkpYDg1ZJkqTl7X19+bIkt53PAarqSuARtADtDrSwdddpih7Xl0fOdq40O8ynLhuwpi/vMc05N6cFbAvpxL58ZJL7THPOuwGH9pcfmrJ5PtdlSPuu7Mv5/txHbT18umuf5GDag7Dgd9u6ZPTr9ALgU1X1iSmbf9yX9xsrvwNwR9rD4C6ZSCUlSdJNlkGrJEnS8vZG2gOMbgV8Mclj+1PkSbJFkn2TfHC6UarjquoK4OHAN4HfBz6fZOpDj44Gzu/nOivJk5P8ZjRgkj2SPI8W2D5+gdo37nN9+aokjxvNqdmfLv9JYG9aYLZQTqD9PAA+luSgJOnnPBA4ifZwp+8A75+y73yuy5D2facv75rkgfNo69uBn9FGd342yf37uTdL8iTgg73cqVW1ah7Hn1GS1UkqyXEDjxPgWOB62hy3U53Ul0cl2SXJlsCbaG0+uaqGzB8sSZJWAINWSZKkZayqLqV9lP8i4PbAx4Grk1xCe6jS6cBTgM3ncKzLgIOA7wJ3AU5NstPY9itoI1+/R5tm4ATgqiSXJFlLGzH4r8C9gWLhvQn4EbAd8DHg2iRren0eDjyfBRyV2D9S/yRau/agBaFXJ7kGOLWvuxB4YlVdN2Xf+VyXebevqs4FzujH+0qSS3uAuTrJg+bQ1stp4fjlwD2Brya5EriaNtp1R1ro/IwNHWsRHQE8EPj7PtXFVO8Evg/cnza1wxrgj2lt/JtJVVKSJN10GbRKkiSJFwSXAAABwklEQVQtc1X1LeButLDoa8C1tIf7XEgL7J5GC/zmcqxfAgcCP6AFbp8bnwagqs4DRh/PPo0WzG0P3EAL4t4JPAo4fgGaNrVulwEPoo1aHLXnWlob962q4zbBOc8D7gW8nvXzmNK//1vgnlX1wxn23ajrsgDteyLwv2kPd9qW9vCn2zHHeWur6mzgrsA/Az+kjda9odf95cADq+oXcznWXPUpEUYPofrqgOPcCjgKOBf4x+nKVNW1tDlojweuoM1rexqwX1V9b77nliRJK0fWP/xUkiRJkpaOPtr2y7RpFvaaOjJYkiRpKXFEqyRJkqSlat++fKMhqyRJWuoc0SpJkiRpSUryadpUFHeoqnWLXR9JkqTZGLRKkiRJkiRJ0kBOHSBJkiRJkiRJAxm0SpIkSZIkSdJABq2SJEmSJEmSNJBBqyRJkiRJkiQNZNAqSZIkSZIkSQMZtEqSJEmSJEnSQAatkiRJkiRJkjSQQaskSZIkSZIkDWTQKkmSJEmSJEkDGbRKkiRJkiRJ0kAGrZIkSZIkSZI0kEGrJEmSJEmSJA1k0CpJkiRJkiRJAxm0SpIkSZIkSdJABq2SJEmSJEmSNND/B1mgmmhEwISiAAAAAElFTkSuQmCC\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 685}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "19 out of 20 tickers were removed\n{'FAST': 319}\nFunds remaining: $31.25\n"}]}}, "e9ecea2d3b9e486e9be31026510f1cd7": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntTextModel", "state": {"description": "Total value:", "layout": "IPY_MODEL_729d5c5539eb4b3cb9e8658feb1e7a9b", "step": 1, "style": "IPY_MODEL_3509fc9639bb4fdc95adf5e7f7fefe76", "value": 10000}}, "e9f22b48ca264322ab01b30ebe9a3595": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {"height": "40px", "width": "50%"}}, "e9f435599ab14ad59fa9d13e55fb52fd": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_e7cdeb2bb8d548c8b8081ccb3d0305aa", "outputs": [{"data": {"image/png": 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 257\u001b[0m \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 258\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m 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\u001b[0mlatest_prices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiscrete_allocation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_latest_prices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprices_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m da = discrete_allocation.DiscreteAllocation(weights=cleaned_weights,\n\u001b[1;32m 4\u001b[0m \u001b[0mlatest_prices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlatest_prices\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtotal_portfolio_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_portfolio_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Repo/stock-market-portfolio-optimisation/venv/lib/python3.7/site-packages/pypfopt/discrete_allocation.py\u001b[0m in \u001b[0;36mget_latest_prices\u001b[0;34m(prices)\u001b[0m\n\u001b[1;32m 20\u001b[0m \"\"\"\n\u001b[1;32m 21\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"prices not in a dataframe\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mprices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mffill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: prices not in a dataframe"]}]}}, "ea15ad42eab94a118fa8b03472f6274e": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "DropdownModel", "state": {"_options_labels": ["target_risk", "target_return", "max_sharpe", "min_volatility"], "description": "Otimisation objective:", "index": 1, "layout": "IPY_MODEL_690c5080e21d43a7b2926742c56b75fa", "style": "IPY_MODEL_944f85702b4144cfbdb1ff808f93d503"}}, "ea521b475162487fb2e3ed0159b8737f": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "VBoxModel", "state": {"_dom_classes": ["widget-interact"], "children": ["IPY_MODEL_27018a2690bd4fe0bf508b3a85aed511", "IPY_MODEL_b7e129b99bd7493a8fb9b74088e4c29d", "IPY_MODEL_bc14550154f847bb8aa0caa3e8cc6076", "IPY_MODEL_198d80ed587e4e5a8ee1ad29ee7a0fac"], "layout": "IPY_MODEL_ce1e7759f9494c8aa94f21309eb110b0"}}, "ea564b0b9008453c8d114f43810eefee": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntTextModel", "state": {"description": "Total value:", "layout": "IPY_MODEL_643a5a5d18d1429baa781ca597807072", "step": 1, "style": "IPY_MODEL_6d66d7fc1b084852a41a3510f2ce5320", "value": 10000}}, "ea643017b7eb47509d22b41fd1913c3b": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_213cdf54f0ff43b5a43b51feecf44642", "outputs": [{"data": {"image/png": 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"text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 685}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "16 out of 20 tickers were removed\n{'PRSP': 19, 'EIX': 16, 'FAST': 258, 'DLR': 3}\nFunds remaining: $0.65\n"}]}}, "ea81dede9e734df39f440f28597b80b1": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "DescriptionStyleModel", "state": {"description_width": ""}}, "ea8a042b146b420d9e970c55371fccf2": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": {"_model_module_version": "1.5.0", "_view_module_version": "1.2.0", "description_width": "initial"}}, "ea9fb7423a50412cb3689e276033c9a7": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "FloatSliderModel", "state": {"continuous_update": false, "description": 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\n", 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"text/plain": "<Figure size 432x288 with 1 Axes>"}, "metadata": {"image/png": {"height": 277, "width": 573}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "15 out of 20 tickers were removed\n{'PRSP': 35, 'SKM': 64, 'EIX': 13, 'FAST': 164, 'DLR': 13}\nFunds remaining: $4.05\n"}]}}, "ed9e4a3675c84118bbbdfecf48d5ead4": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntTextModel", "state": {"description": "Total value:", "layout": "IPY_MODEL_335c9464c72c4a1a8af9e1e7dbd60ee7", "step": 1, "style": "IPY_MODEL_a4d525c0552549689d82f54b8b5ba964", "value": 10000}}, "edd21684b57e462f93f10ae29ef909cd": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "SliderStyleModel", "state": {"description_width": ""}}, "edd4c2bfbbd940118b4a40ee10e17abb": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": 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9KMkXk0xJ8oettXOq6rlJvp5kuyRfS/LK1tp9nTZmrZYsWbLG/5FnzJjxaE8FAAAAADJrp51y/bXXTvY0JsW2225bY63tPLxNkqp6ZZLPJlmR5F1J3pZk2wxC3Ve31u7vvClrtbbwFsbjhhtuSJLssssukzwTftH5LdEFvyO64HdEF/yO6ILfEV3xW6ILfkcbz4aEt13uefug1tp5SY7IYPXtuzIIbs9L8nuCWwAAAACA9dso4W2StNb+IcmfJ6kk52Sw4vaBjdUPAAAAAGCUTBnPTVW1fAPKW5LfT/L7VY9YEdxaa+OaAwAAAADAKBtvcDrmfRkepXEAAAAAAEbKeMPbJ3c6CwAAAAAAHmZc4W1r7b+7nggAAAAAAA/ZaA8sAwAAAABg/Dp7WFhVPSbJc5P8vLX2vfXUPjfJY5J8t7V2b1dzAAAAAAAYFV2uvD00yUVJfn8MtX+0AbUAAAAAAJucLsPb3xu+f2oMtWckqSSv7rA/AAAAAMDI6DK8/eUk9ye5egy1Vw5rn95hfwAAAACAkdFleLtDkrtba219ha21FUnuGt4DAAAAAMBqugxvlyaZMXxw2ToNa2Yk+XmH/QEAAAAARkaX4e01w/FeOYba302yeZIFHfYHAAAAABgZXYa3n8vgIWQfqKpnrq2oqn41yQeStOE9AAAAAACsZkqHY/19kiOTPCvJ96rq75N8LcmPhteflOR3krwmyRZJ5ic5o8P+AAAAAAAjo7PwtrX2QFXNSfLlJLtlEOQeuYbSSnJVkpe11pZ11R8AAAAAYJR0uW1CWmu3JvmNJG9K8t0kyzMIa2v4+btJjkryG621m7vsDQAAAAAwSrrcNiFJ0lq7P8npSU6vqilJZg4vLWqtPdB1PwAAAACAUdR5eLuqYVh7x8bsAQAAAAAwijrdNgEAAAAAgG6Ma+VtVe09/Piz1tq/rXZug7TWLh7PfQAAAAAAo2y82ybMTdKSXJfkGaud2xBtAnMAAAAAABhZ4w1Of5RB8HrrGs4BAAAAADBB4wpvW2s7j+UcAAAAAADj44FlAAAAAAA91Fl4W1V7V9VvbED9HuN9yBkAAAAAwKjr8mFhc5PcluRxY6z/bJIndDwHAAAAAICR0PW2CbWR6wEAAAAANgmTueft1knun8T+AAAAAAC9NSnhbVXtkWRmklsmoz8AAAAAQN+Ne7/Zqjo8yeGrnZ5ZVd9e121JZiR5RpKW5Gvj7Q8AAAAAMMom8rCwnZPss9q5aWs4tzYXJ3n7BPoDAAAAAIysiYS3/5LkpuHnSvL3SZYk+bN13LMiydIk/9Fa+/4EegMAAAAAjLRxh7ettauTXL3yuKr+PsnPW2tndTExAAAAAIBN2URW3j5Ma21SHn4GAAAAADCKBK4AAAAAAD0kvAUAAAAA6CHhLQAAAABADwlvAQAAAAB6SHgLAAAAANBDwlsAAAAAgB4S3gIAAAAA9JDwFgAAAACgh6ZsrIGralaS3ZNsPzx1Z5IrW2t3bKyeAAAAAACjovPwtqp+M8l7kuy1lusXJ/mr1tq8rnsDAAAAAIyKTrdNqKo/TnJRBsFtJVmR5I7ha/nw3AuSzK2qN3TZGwAAAABglHQW3lbVs5N8KMnmSeYleXGSrVprO7bWdkyydZLfHl7bPMmHhvcAAAAAALCaLlfevnU43ueS7NNau6C1dt/Ki621+1pr38xg5e3nMwhw/7zD/gAAAAAAI6PL8PYFSVqSt7TWVqytaHjtz4a1+3TYHwAAAABgZHQZ3m6fZHFr7bb1FbbWbk2yeHgPAAAAAACr6TK8XZpk66racn2Fw5pthvcAAAAAALCaLsPbKzPYx/ZPxlD7p8PaKzrsDwAAAAAwMroMb89IUkneXVXvqaptVy+oqh2r6gNJ3pXBnrdndNgfAAAAAGBkTOlqoNbaeVX1qSR/mOTYJG+tqquT3JJkiyRPTLJLkqkZhLxntdbO76o/AAAAAMAo6Sy8HXpNkv9MckwGe9rusYaapUlOSPL+jnsDAAAAAIyMTsPb1lpLcmJVnZZk/yS7J9l+ePnODPbF/WZr7Wdd9gUAAAAAGDVdr7xNkrTW7knyL8MXAAAAAAAbqMsHlgEAAAAA0BHhLQAAAABAD41r24Sq+vbw43+31l672rkN0Vpr+41nDgAAAAAAo2y8e97uM3z/rzWc2xBtnP0BAAAAAEbaeMPb1w7fl6zhHAAAAAAAEzSu8La1dtZYzgEAAAAAMD4eWAYAAAAA0EPCWwAAAACAHhrXtglV9cSuJtBa+1FXYwEAAAAAjIrxPrDsxo76twnMAQAAAABgZI03OK2O+nc1DgAAAADASBlXeNtas1cuAAAAAMBGJIQFAAAAAOgh4S0AAAAAQA9ttIeFVdWsJLsn2X546s4kV7bW7thYPQEAAAAARkXn4W1V/WaS9yTZay3XL07yV621eV33BgAAAAAYFZ1um1BVf5zkogyC20qyIskdw9fy4bkXJJlbVW/osjcAAAAAwCjpLLytqmcn+VCSzZPMS/LiJFu11nZsre2YZOskvz28tnmSDw3vAQAAAABgNV2uvH3rcLzPJdmntXZBa+2+lRdba/e11r6Zwcrbz2cQ4P55h/0BAAAAAEZGl+HtC5K0JG9pra1YW9Hw2p8Na/fpsD8AAAAAwMjoMrzdPsni1tpt6ytsrd2aZPHwHgAAAAAAVtNleLs0ydZVteX6Coc12wzvAQAAAABgNV2Gt1dmsI/tn4yh9k+HtVd02B8AAAAAYGR0Gd6ekaSSvLuq3lNV265eUFU7VtUHkrwrgz1vz+iwPwAAAADAyJjS1UCttfOq6lNJ/jDJsUneWlVXJ7klyRZJnphklyRTMwh5z2qtnd9VfwAAAACAUdJZeDv0miT/meSYDPa03WMNNUuTnJDk/R33BgAAAAAYGZ2Gt621luTEqjotyf5Jdk+y/fDynRnsi/vN1trPuuwLAAAAADBqxhXeVtXbk9zdWvvAmq631u5J8i/DFwAAAAAAwgjt1wAAIABJREFUG2i8Dyw7PsnbVj1RVT+squ9MeEYAAAAAAIx724SWRwa/O2fwYDIAAAAAACZovCtvFyV5bFVt3eVkAAAAAAAYGO/K2+8k+Z0kX6yqc5PcPTz/mKo6bEMGaq19cpxzAAAAAAAYWeMNb9+VZN8kL0iy9yrnt0nyDxs4lvAWAAAAAGA14wpvW2vfq6rdkhyR5JlJHpNknyTLklzW2ewAAAAAADZR4115m9ba95P8xcrjqlqRZFFrbd8uJgYAAAAAsCkbd3i7Bj9KcnuH4wEAAAAAbLI6C29bazt3NRYAAAAAwKZus64GqqrlVXXLBtTfWFUPdNUfAAAAAGCUdLltQg1fG3oPk2TGjBmTPQVIkszaaadcf+21kz0NAAAAgF7pMrzdUP8ryfJJ7M/xx0/2DCBJcoffIgAAAMAjdLZtwoaoqh2SzEry08noDwAAAADQd+NeeVtVeyfZZ7XTW1XV29d1W5IZSX57+HneePsDAAAAAIyyiWybsG+SdyRpq5zbcnhuXVbuc7soyTsn0B8AAAAAYGRNJLy9KslZqxwfnuTeJJ9bxz0rkixN8h9Jzm+tLZxAfwAAAACAkTXu8La19oUkX1h5XFWHJ1nSWnttFxMDAAAAANiUTWTl7eoOTHJ7VW3eWlve4bgAAAAAAJucLsPb8zPYFuHJSX7c4bgAAAAAAJucLsPbu5M80FoT3AIAAAAATNBmHY51Y5LpVdVlIAwAAAAAsEnqMrz9XJKpSV7R4ZgAAAAAAJukLsPbk5P8W5KPVdV+HY4LAAAAALDJ6XKLg2OSfDvJryT5ZlVdk+SyJHcmWb62m1pr7+pwDgAAAAAAI6HL8Pb4JC1JDY+fleTX1lFfw3rhLQAAAADAaroMbz+ZQRgLAAAAAMAEdRbettZe09VYAAAAAACbui4fWAYAAAAAQEeEtwAAAAAAPdTlnrcPqqp9krw6ye5Jth+evjPJlUk+11qbuzH6AgAAAACMik7D26r6pSRnJ/mtladWufzkJM9N8oaquiDJoa21n3bZHwAAAABgVHQW3lbVtCQXJPm1DELby5J8O8nNw5LHJ3lhkucl2T/JN6vqN1pr93c1BwAAAACAUdHlyts3JXlWkkVJDm6tXbCGmr+uqhclOWdYe1SSUzqcAwAAAADASOjygWUHJWlJjlhLcJskaa19M8kRGazO/f0O+wMAAAAAjIwuw9tfTnJvkvPHUHv+sPbpHfYHAAAAABgZXYa3U5Msa6219RW21lYkWZaOH5gGAAAAADAqugxvf5Rk66rafX2FVfWcJFsP7wEAAAAAYDVdhrdfzWAf209U1fZrK6qq2Uk+kcH+uF/psD8AAAAAwMjoctuCk5IcnuTXkvxXVX08ydwktyTZIskTk+yb5DVJpidZlOR9HfYHAAAAABgZnYW3rbU7qup3kvxLkh2SHD18ra6S3JbkFa21O7rqDwAAAAAwSrrcNiGtte8meUaSdySZn8HWCDV8teG5tyd5Zmvte132BgAAAAAYJV1um5Akaa0tTvLuJO+uqqlJZg4vLWqtLeu6HwAAAADAKOo8vF3VMKy9fWP2AAAAAAAYRRMOb6vqfyV5RZLnJNkmyeIklyf5UmvtgYmODwAAAACwKZpQeFtVz09ybgYPKFvdTVX1itba/In0AAAAAADYFI37gWVV9bgkX84guF35QLI7V15O8uQkX62qbSc6SQAAAACATc24w9skf5pkRgbbJByWZHprbYckWyb5kyQ/T7JTktdPdJIAAAAAAJuaiYS3+2ew2vZPWmufbq3dnySttXtbax9K8o4MVuC+aOLTBAAAAADYtEwkvH1KBuHtP6/l+rmr1AEAAAAAsAEmEt5uneTO1tq9a7rYWvvv4cctJ9ADAAAAAGCTNJHwNhmsvF2fmmAPAAAAAIBNzkTD201KVe1TVW0DX/+y2hj/uMq1nVe7tmNV/c/w2n9X1VZjmNP7VxnvTd1+YwAAAABgskyZ4P0zq+rbE6hprbX9JjiHkdFau62q3pbkzCRPTHJikrUGslW1R5I/Gx5emuTDG32S0ANf+MIXcumll2bBggVZsGBB7rrrrrz61a/OGWec8YjaZcuW5cwzz8z8+fNzzTXX5LrrrsuyZcty6qmn5rDDDtugvjfffHNOOeWUXHXVVfnxj3+cxYsXZ+bMmXnyk5+cQw45JAcddFCmTp3a1dcEAAAANnETDW+nJdlnAjVj2Xahrz6S5PQx1C3dkEFba5+oqoOT7JfkjVV1Tmtt3up1VTUtyd8n2TzJvUle31r7Rf57wpidfPLJWbBgQbbaaqvstNNOueuuu9Zae8899+TYY49NksyaNSuzZ8/OzTffPK6+N954Y84999w85znPyZw5c7Lddttl0aJFufDCC/OmN70pn/3sZ3P++ednypSJ/qcVAAAAYGLh7VmdzeIX0x2ttQUbaew/SrIgyfQkn6iqZ7XW7lut5q+SPHP4+Z2ttes30lygd0444YQ87nGPy1Oe8pRceumleelLX7rW2unTp+fcc8/Nrrvumh122CHvfe97c9JJJ42r75577pmbbropm2328B1nli1blgMPPDCXXHJJvvSlL+XAAw8c1/gAAAAAqxp3eNtae22XE+EhrbUbq+qvknwgyS8neUeSv1x5vaqeleSY4eGVSd7/qE8SJtHee+895tpp06Zl//3376TvtGnT1nh+6tSpmTNnTi699NL84Ac/6KQXAAAAgAeW9dffJfnO8PPRVbVbklTVlAy2S5ia5IEkr2utPTA5UwSSZPny5bnggguSJM985jPXUw0AAAAwNjZm7KnW2oqqen2Sf89g3+BPVNWeSd6WZPdh2Umttasna46wqVq4cGHOOOOMtNaycOHCXHTRRfnhD3+YV73qVTnggAMme3oAAADAiBDe9lhr7dqq+psk78wgsP1wksOGl/8zybsna26wKVu4cOHD9s2tqrz5zW/O29/+9kmcFQAAADBqhLfjN6uqfnUMdTe21u6ZQJ/3Jvm9JLsmOWJ4bkWS16/hIWbwC+uGG24Y13233HJLkmTp0qVjGmPRokVJkttvv33cPasq3/ve97J8+fLceeedueiii/Kxj30sc+fOzSmnnJJtt912XONuqsb7vwOsyu+ILvgd0QW/I7rgd0RX/Jbogt/R+O2yyy4THsOet+N3ZJL5Y3g9dyJNWmvL8lBou9JprbXLJjIuMHGbb755dthhhxx88MH5y7/8y8yfPz8f+9jHJntaAAAAwIiw8vYXw16rHd86KbOAjWi8/xr1k5/8JEmyzTbbjGmMmTNnJklmz57dyb+ArTRr1qwcd9xxWbBgQafjjrKV/3rr78VE+B3RBb8juuB3RBf8juiK3xJd8DvqBytvx++drbUaw2vuRJpU1S4Z7Hm7quOr6ikTGRfo1m233ZZksBoXAAAAoAvC2x6rqkpyZpLHJFme5G3DS49JcsZkzQtG3ZIlS3L99dc/uKp3pauuuirLly9/RP3dd9+dY445Jkny4he/+FGZIwAAADD6bJvQb0cm2Xv4+W9ba39bVU9P8v8n2a+qXtta+4fJmx5Mji9/+cv5yle+kiS54447kiTf/e53c+SRRyZJHvvYx+Y973nPg/WnnHJKrr/++iTJ/PnzkyRnn312LrtssHX08573vBx22GEPG/+oo47KwQcfnI985CMPnn/f+96Xyy+/PHvssUce//jHZ/r06bnllltywQUXZMmSJdlzzz3zlre8ZSN+cwAAAGBTIrztqap6QpITh4c3JDl++PnoJHOS7Jjk/VX1ldbaHY/+DGHyzJ8/P+ecc87Dzt1000256aabkiRPeMITHhbeXnjhhZk3b97D6i+//PJcfvnlDx6vGt6uzeGHH56tttoqV1xxRebNm5ef/exnmTFjRnbbbbcceOCBOfTQQzNliv+sAgAAAN2QMvTXx5JsnaQl+aPW2s+TpLW2uKrelOSfk8xMclqSgyZtljAJjj322Bx77LFjrl+5SnesDjnkkBxyyCGPOP/iF7/YtggAAADAo8aetz1UVYclOWB4eEZr7f+uer21dl6S84aHr66qlz6a8wMAAAAANj7hbc9U1ewkpwwPb0nyF2spfVOSxcPPp1fV1ht7bgAAAADAo8e2CeM3q6p+dQx197fWrt+AcT+UwXYISXJka23pmopaa7dV1dFJPp7k8Rnsj3vUBvQBAAAAAHpMeDt+Rw5f6/PfSXYey4BV9cokvzc8/Gxr7Uvrqm+tnVlVf5Bk3yRHVtXZrbV/HUsvAAAAAKDfbJvQE1W1XZIPDw8XJnnzGG89IsnPk1SSM6tq2kaYHgAAAADwKLPydgO01uZmEJJOZIzXJHnNGs7/T5IdxzHe95NMn8icAAAAAID+sfIWAAAAAKCHhLcAAAAAAD0kvAUAAAAA6CHhLQAAAABADwlvAQAAAAB6SHgLAAAAANBDwlsAAAAAgB4S3gIAAAAA9JDwFgAAAACgh4S3AAAAAAA9JLwFAAAAAOgh4S0AAAAAQA8JbwEAAAAAekh4CwAAAADQQ8JbAAAAAIAeEt4CAAAAAPSQ8BYAAAAAoIeEtwAAAAAAPSS8BQAAAADoIeEtAAAAAEAPCW8BAAAAAHpIeAsAAAAA0EPCWwAAAACAHhLeAgAAAAD0kPAWAAAAAKCHhLcAAAAAAD0kvAUAAAAA6CHhLQAAAABADwlvAQAAAAB6SHgLAAAAANBDwlsAAAAAgB4S3gIAAAAA9JDwFgAAAACgh4S3AAAAAAA9JLwFAAAAAOgh4S0AAAAAQA9NmewJMImOP36yZwBJklk77TTZUwAAAADoHeHtJmzx4sWTPQV+Ad1www1Jkl122WWSZwIAAAAw2mybAAAAAADQQ8JbAAAAAIAeEt4CAAAAAPSQ8BYAAAAAoIeEtwAAAAAAPSS8BQAAAADoIeEtAAAAAEAPCW8BAAAAAHpIeAsAAAAA0EPCWwAAAACAHhLeAgAAAAD0kPAWAAAAAKCHhLcAAAAAAD0kvAUAAAAA6CHhLQAAAABADwlvAQAAAAB6SHgLAAAAANBDwlsAAAAAgB4S3gIAAAAA9JDwFgAAAACgh4S3AAAAAAA9JLwFAAAAAOgh4S0AAAAAQA8JbwEAAAAAekh4CwAAAADQQ8JbAAAAAIAeEt4CAAAAAPSQ8BYAAAAAoIeEtwAAAAAAPSS8BQAAAADoIeEtAAAAAEAPCW8BAAAAAHpIeAsAAAAA0EPCWwAAAACAHhLeAgAAAAD0kPAWAAAAAKCHhLcAAAAAAD0kvAUAAAAA6CHhLQAAAABADwlvAQAAAAB6SHgLAAAAANBDwlsAAAAAgB4S3gIAAAAA9JDwFgAAAACgh4S3AAAAAAA9JLwFAAAAAOgh4S0AAAAAQA8JbwEAAAAAekh4CwAAAADQQ8JbAAAAAIAeEt4CAAAAAPSQ8BYAAAAAoIeEtwAAAAAAPSS8BQAAAADoIeEtAAAAAEAPCW8BAAAAAHpIeAsAAAAA0EPCWwAAAACAHhLeAgAAAAD0kPAWAAAAAKCHhLcAAAAAAD0kvAUAAAAA6CHhLQAAAABADwlvAQAAAAB6SHgLAAAAANBDwlsAAAAAgB4S3gIAAAAA9JDwFgAAAACgh4S3AAAAAAA9JLwFAAAAAOgh4S0AAAAAQA8JbwEAAAAAekh4CwAAAADQQ8JbAAAAAIAeEt4CAPy/9u48TrKqvhv/5xtAtpHFBZSwCe6AisomEnDDuICIK0EJzxMXXBLNI5qfRlwSEXxcHhV1TGIUlSQiGkXjhoiAiomoKBIRMYgKCAoIyO7g+f1xb0vRU93T3dMzfafn/X696nWr7j333nO6ztRUferUuQAAAAMkvAUAAAAAGCDhLQAAAADAAAlvAQAAAAAGSHgLAAAAADBAwlsAAAAAgAES3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABgg4S0AAAAAwAAJbwEAAAAABkh4CwAAAAAwQMJbAAAAAIABEt4CAAAAAAyQ8BYAAAAAYICEtwAAAAAAAyS8BQAAAAAYIOEtAAAAAMAACW8BAAAAAAZIeAsAAAAAMEDCWwAAAACAARLeAgAAAAAM0LoLXQEWzmabbbbQVVhwW2y1VX78wx8udDUAAAAAYDnC27XZG96w0DVYcL/yNwAAAABgoEybAAAAAAAwQMJbAAAAAIABEt4CAAAAAAyQ8BYAAAAAYICEtwAAAAAAAyS8BQAAAAAYIOEtAAAAAMAACW8BAAAAAAZIeAsAAAAAMEDCWwAAAACAARLeAgAAAAAMkPAWAAAAAGCAhLcAAAAAAAMkvAUAAAAAGCDhLQAAAADAAAlvAQAAAAAGSHgLAAAAADBAwlsAAAAAgAES3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABgg4S0AAAAAwAAJbwEAAAAABkh4CwAAAAAwQMJbAAAAAIABEt4CAAAAAAyQ8BYAAAAAYICEtwAAAAAAAyS8BQAAAAAYIOEtAAAAAMAACW8BAAAAAAZIeAsAAAAAMEDCWwAAAACAARLeAgAAAAAMkPAWAAAAAGCAhLcAAAAAAAMkvAUAAAAAGKBFEd5W1X5V1aa43VRVv6iq/6iq51XVBmP2P3ya/W+oqour6uSqOqyq7jTDOlVVPamqTqiqH1fVb6tqWVVdW1XnV9Wnq+pvq2rPqlrueVjZNgEAAAAAa7ZFEd6uwAZJtk7ypCT/lOR7VXW/Wey/UZLtkhyY5MNJzq6qbafboaq2SPLVJP+R5NAk90myJMk6STZJcv8kT0nypiTfTLL/LOqTrHybAAAAAICBW4zh7dIku4zc9kjywiTn99vvl+QLVbXhFPu/dtL+j0vy10l+2W9/UJLPVNU643buR+aekmTfftX3k7y8f7xrkn2SPC/JB5P8ejW1iVXoxBNPzGabbZbNNtssH/nIR2a9/8knn5yDDz44O+ywQ7bccsvsvPPOOeSQQ3L22WevgtoCAAAAsKZYd6ErsAr8qrV23qR136qqjyY5PcnuSe6V5C+SvGfM/pdO2v+8JKdW1QfTjZJ9YJIHJzkoySfH7P+8fnvSjdT93621308q8/Uk/9wHwAcl+cUqbhOryCWXXJJXvvKVWbJkSa6//vpZ7bts2bK86EUvykknnZQdd9wxBx98cDbZZJNcccUVOfvss/O9730vu+222yqqOQAAAABDtxjD27FaazdV1d8m+XK/6gmZRdDZWruuqo5NMjG08nEZH94e1C+XJXn5mOB29Ji3TXGMmdZppdrEymmt5SUveUnucpe75IADDshxxx03q/2POeaYnHTSSTnyyCPzmte8Jn/0R3ccCP+73/1uPqsLAAAAwBpmMU6bMJ3/HLm/3Rz2/+7I/W2mKDMxH+5VrbVr5nCO2VrZNjFH73//+3PmmWfmve99bzbaaKNZ7XvFFVfkuOOOy2677ZbXvva1ywW3SbLeeuvNV1UBAAAAWAOtNSNve6NDGcfOWbsCt01xrFG39MstquourbWr53Ce2VjZNjEHF1xwQd74xjfmiCOOyN57750zzzxzVvuffPLJufXWW3PwwQfnpptuyimnnJKLLrooS5YsyZ577plddtllFdUcAAAAgDXF2hbePmjk/mVz2P+BI/cvnqLMd/vzVJIPVtVzW2u/ncO5Zmpl28QsLVu2LC984Quz9dZb53Wve92cjvHd73aDuG+66abstttuueSSS+6w/cADD8z73//+WY/oBQAAAGDxWNumTXjNyP2vzmbH/uJifz2y6uNTFH1vbh+h+5Qkl1TVCVX1wqratarm+7fwc24Tc/OWt7wl5557bt73vvdlww03nNMxrrzyyiTJ0UcfnW233TZnnHFGLr300px66qnZdddd85nPfCaveMUr5rPaAAAAAKxhFv3I26raMMlDkrw6yQH96uuS/MMM998y3ejWv0uyZ7/6Y621s8aVb619u6qen+T9Se6UZJMkh/a3JLmpqv4ryaeSfGQu8+KubJu4owsvvHDGZc8777y84x3vyKGHHprNN9/8D/tefXU3O8YVV1wxo+Ndf/31SZJNNtkkRx99dDbaaKNcdtll2XTTTfPmN785T3va03LiiSfmOc95TrbYYos5tGrVm83fDaajLzEf9CPmg37EfNCPmA/6EfNFX2I+6Edzd5/73Gelj7EYR96+vqraxC3JjUnOyh1Dzqe11n49xf4fmrT/5UlOSRfc3pDkrUmeO10FWmsfSrJzkn9KMjmc3TDJfkneleR/qurQrNjKtol5sGzZsrz+9a/PtttumyOOOGKljrVkyZIkyW677faH+xPudre7Zaeddsrvf//7nH/++St1HgAAAADWXIt+5O2IXyT5dJK3tdZ+PsdjfCfJca21ZSsq2Fq7MMkLqurFSR6aLvx9aJJ9kuzQF7tLkhOqap3W2kfmUJ/5aNNab6bfglxzzTX5+c+7P/Pee+89tszRRx+do48+OkcccUSOPfbYKY+166675tRTT83WW2899vxbbbVVkmTzzTefl29p5tPEN25DqxdrHn2J+aAfMR/0I+aDfsR80I+YL/oS80E/GobFGN4uTfK+kcc3J7mqtfabGe7/2iQn9/fXS7JtkmcleXaSP0lyZlXtPtNRrn3Q+63+liSpqt2TvD3JI/tV76yqf2+tXT/FYVa2TcyD9ddfP8997vhB19///vdz7rnnZq+99sq9733v7L777tMea7/99stb3/rWKUfW/uhHP0qSbLfdditXaQAAAADWWIsxvP1Va+28ldj/0kn7n5Pk5Kr6WroAdfskH0h3MbI5aa19q6r+NMn3ktw7yeZJHptuFO04K9sm5sGGG26Y4447buy2Y445Jueee24OOeSQHHbYYX9Yf+ONN+aSSy7JhhtumG222eYP6x/xiEdkl112yTe/+c189rOfzQEHHPCHbR/+8IdzwQUXZIcddsiuu+666hoEAAAAwKAtxjlvV4nW2tIkn+8fHlhVj17J492Q5N9GVhmDvgh95zvfye67777cHLlVlaVLl2bTTTfNYYcdlkMOOSRHHXVUnvGMZ+RlL3tZNt544yxdujTrrLPOAtUcAAAAgIUmvJ2dv0nS+vtvnofjXTZyv01ZikVp5513zhlnnJFnP/vZOeecc7J06dKce+65eeYzn5nTTz89e+yxx0JXEQAAAIAFtBinTVhlWmvnVdWnkhycZI+qelxr7cujZaqqWmszDWIfPnL/ovmqJ6vfq1/96rz61a9ebv0+++yTa665Zsr9tt9++yxdunRVVg0AAACANZSRt7P3ppH7R43Z/u9V9dKqWjLdQarq8Un+vH94fZJT56l+AAAAAMAiILydpdbaOUk+1z/cp6r2nVRkmyTHJbm8qj7WB7mPrqqHVNXuVXVoVX08yRdy+8jn17TWrls9LQAAAAAA1gSmTZibv0/ypP7+UUnOGNl2SZKHJdk4ybP621RuShfcHrcqKgkAAAAArLmEt3PQWvuvqvpykscleUxV7dVa+2a/7aCqum+SxyfZO8lOSbZOcucktyS5OskPk3wlyQmttcvGnQMAAAAAWLstivC2tXZ6klqJ/Y9Pcvws99l/mm0/TvLjdNMnzLVOp2cl2gQAAAAArNnMeQsAAAAAMEDCWwAAAACAARLeAgAAAAAMkPAWAAAAAGCAhLcAAAAAAAMkvAUAAAAAGCDhLQAAAADAAAlvAQAAAAAGSHgLAAAAADBAwlsAAAAAgAES3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABgg4S0AAAAAwAAJbwEAAAAABkh4CwAAAAAwQMJbAAAAAIABEt4CAAAAAAyQ8BYAAAAAYICEtwAAAAAAAyS8BQAAAAAYIOEtAAAAAMAACW8BAAAAAAZIeAsAAAAAMEDCWwAAAACAARLeAgAAAAAMkPAWAAAAAGCAhLcAAAAAAAMkvAUAAAAAGCDhLQAAAADAAAlvAQAAAAAGSHgLAAAAADBAwlsAAAAAgAES3gIAAAAADJDwFgAAAABggIS3AAAAAAADtO5CV4AF9IY3LHQNFtwWW2210FUAAAAAgLGEt2uxa665ZqGrAAAAAABMwbQJAAAAAAADJLwFAAAAABgg4S0AAAAAwAAJbwEAAAAABkh4CwAAAAAwQMJbAAAAAIABEt4CAAAAAAyQ8BYAAAAAYICEtwAAAAAAAyS8BQAAAAAYIOEtAAAAAMAACW8BAAAAAAZIeAsAAAAAMEDCWwAAAACAARLeAgAAAAAMkPAWAAAAAGCAhLcAAAAAAAMkvAUAAAAAGCDhLQAAAADAAAlvAQAAAAAGSHgLAAAAADBAwlsAAAAAgAES3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABgg4S0AAAAAwAAJbwEAAAAABkh4CwAAAAAwQMJbAAAAAIABqtbaQteBVezaa6/1JAMAAADAAGy66aY107JG3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABgg4S0AAAAAwABVa22h6wAAAAAAwCRG3gIAAAAADJDwFgAAAABggIS3AAAAAAADJLwFAAAAABgg4e1aoKq2rqoPVtVlVXVLVV1cVe+sqs0Xum4MS1U9vaqOq6qvVdV1VdWq6oQV7POIqvp8VV1dVTdV1blV9fKqWmd11ZvhqKq7VtXzqupTVfWTvk9cW1Vfr6q/qKqx/+/oR4xTVW+pqq9U1S/6fnF1VZ1TVa+vqrtOsY++xLSq6jn9/2+tqp43RZknV9Xp/evX9VX1X1X156u7rgxH//65TXG7fIp9vB4xVlU9pn+vdHn/+eyyqvpSVT1xTFn9iD+oqsOneS2auN02Zj/9iOVU1ZOq6pSquqTvFxdV1UlVtdcU5fWjBVKttYWuA6tQVe2Y5KwkWyQ5OcmPkuye5FFJLkiyd2vtqoWrIUNSVd9L8uAk1ye5JMn9k/xLa+1R+0aEAAAWyElEQVQ5U5R/SpJPJrk5yYlJrk5yQJL7JflEa+0Zq6PeDEdVHZFkaZJfJvlqkp8n2TLJwUk2TddfntFG/vPRj5hKVd2a5LtJfpjkV0k2TrJnkocnuSzJnq21X4yU15eYVlVtk+QHSdZJsiTJ81trH5hU5qVJjktyVbp+dGuSpyfZOsnbW2tHrtZKMwhVdXGSzZK8c8zm61trb5tU3usRY1XV/03yynTvtb+Q5Mokd0/ysCSnttZeNVJWP+IOquohSQ6aYvM+SR6d5HOttSeP7KMfsZyqekuSV6V7v/PpdK9F905yYJJ1kxzWWjthpLx+tICEt4tcVX0pyf5J/qq1dtzI+nck+esk/9BaO2Kh6sewVNWj0r2R/EmSfdOFb2PD26rapC+3abovAb7dr98gyWlJ9kpySGvtY6up+gxAVT06XcD2udba70fW3yPJt5Jsk+TprbVP9uv1I6ZUVRu01m4es/7oJK9JsrS19uJ+nb7EtKqqknw5yb2S/HuSIzMpvK2q7dN90X1Dkoe11i7u12+e5OwkOyZ5RGvtm6uz7iy8PrxNa237GZT1esRYVfX8JP+Y5MNJXtBau3XS9vVaa7/r7+tHzEpVfTPdl9xPaa19pl+nH7Gc/rPZpUl+neRBrbVfjWx7VLq+8dPW2g79Ov1ogZk2YRHrR93un+TiJO+dtPn16T6YPLeqNl7NVWOgWmtfba1dODoqchpPTzdK4GMTL979MW5O8tr+4YtWQTUZsNbaaa21z44Gt/36y5O8v3+438gm/YgpjQtuex/vl/cZWacvsSJ/lW5E0v9K9x5onP+dZP0k75kIbpOktfabJG/uH/rSmxXxesRyqmr9JEen+1XScsFtkkwEtz39iBmrql3SBbeXJvncyCb9iHG2S5cH/tdocJt0mUCS36brNxP0owUmvF3cHtUvTxkTpPw2yTeSbJTuRR5m69H98otjtp2Z5MYkj+jfqEKSTHwgWTayTj9iLg7ol+eOrNOXmFJVPSDJsUne1Vo7c5qi0/WjL0wqw9pn/X7O5NdU1cuq6lFTzPPn9YhxHpcu/Pj3JL/v55r8m74vjZtfUj9iNl7QL/+5tTY6561+xDgXppsWavequtvohqr6kyR3TnLqyGr9aIGtu9AVYJW6X7/88RTbL0w3Mve+Sb6yWmrEYjJl/2qtLauqnybZKckOSc5fnRVjeKpq3SSH9Q9H/9PXj1ihqjoy3fykm6ab7/aR6YLbY0eK6UuM1b/+fDTdaLfXrKD4dP3ol1V1Q5Ktq2qj1tqN81tT1gD3SNeXRv20qv5Xa+2MkXVejxhnt355c5Jzkuw8urGqzkw3tdSv+1X6ETNSVRsmeU6S25J8YNJm/YjltNaurqq/SfKOJD+sqk+nm/t2x3Rz3n45yQtHdtGPFpjwdnHbtF9eO8X2ifWbrYa6sPjoX8zGsek+pHy+tfalkfX6ETNxZLoL3034YpLDRz7gJvoSU3tdkl2TPLK1dtMKys6kH23clxPerl0+lORrSf473c9Jd0jy0nSj3b5QVXu11r7fl/V6xDhb9MtXprsQ5z5JvpduHu63pRtUc1Jun15KP2KmnpmuH3xu9EKuPf2IsVpr7+znc/9gkuePbPpJkuMnTaegHy0w0yYAsEpV1V8leUW6iwA9d4GrwxqotXaP1lqlG/V2cLrQ5JyqeujC1oyhq6o90o22fbuLjLEyWmtv7Od1v6K1dmNr7bz+or/vSLJhkjcsbA1ZA0x89l6W5MDW2tdba9e31n6Q5KnpLhq87xRTKMB0JqZM+IcFrQVrlKp6VZJPJDk+3YjbjZM8LMlFSf6lqv7vwtWOyYS3i9vEtx+bTrF9Yv01q6EuLD76FytUVS9N8q50I0we1Vq7elIR/YgZ60OTT6UbnXTXJB8Z2awvcQf9dAkfSfcTv6NmuNtM+9FUI09Y+0xcjPNPRtZ5PWKcief7nNELIiZJPw3LxC+Tdu+X+hErVFU7JXlEuvD/82OK6Ecsp6r2S/KWJJ9prf2f1tpF/ReT3033ZdKlSV5RVTv0u+hHC0x4u7hd0C/vO8X2iat0TzUnLkxnyv7Vf2C+V7qRBRetzkoxHFX18iTHJTkvXXB7+Zhi+hGz1lr7WbovBHYauciCvsRkS9L1hwckubmq2sQtyev7Mv/Ur3tn/3i6fnTPdKNSLjHfLSMmpm/ZeGSd1yPGmegXU4Ubv+mXG04qrx8xnakuVDZBP2KcJ/fLr07e0L/H+Va6vHDXfrV+tMCEt4vbxD/E/avqDs91Vd05yd7p5mv7z9VdMRaF0/rln47Z9idJNkpyVmvtltVXJYainwD//6Wby+1Rk+ZMGqUfMVdb9cuJDyr6EpPdkuSfp7id05f5ev94YkqF6frREyaVgSTZs1+OfmD1esQ4X0nSkjxw8mez3sQFzH7aL/UjplVVG6Sbkuy2dP+XjaMfMc76/fLuU2yfWH9rv9SPFpjwdhFrrf1PklOSbJ/kJZM2vzHdCIGPttZuWM1VY3H4RJIrkzy7qh4+sbJ/E/Gm/uHShagYC6uqjkp3gbLvJHlMa+3KaYrrR4xVVfetquV+mlVVf1RVR6e78MtZrbWJkUr6EnfQWruptfa8cbckn+mLfbhfd2L/+EPpQt+XVtX2E8eqqs3TzZ2b3P4zedYSVfWAqtp4zPrtk7ynf3jCyCavRyyn/9XIZ5Nsm+Rlo9uqav8kj083KveL/Wr9iBV5RpLNk3xhzIXKJuhHjPO1fvmCqvrj0Q1V9YR0A/1uTnJWv1o/WmDVWlvoOrAKVdWO6f7BbZHk5CTnJ9kjyaPSTZfwiNbaVQtXQ4akqg5KclD/8B7p3kRelNtf3K9srR05qfwn0r2wfyzJ1UkOTHK/fv0zmxeZtUpV/Xm6Se9vSzdlwrh5IS9urR0/so9+xHL6aTeOSTcy8qdJrkqyZZJ9012w7PJ0Xw78cGQffYkZqao3pJs64fmttQ9M2vaXSd6drs+dmG7UydOTbJ3uwmdHhrVK319ekeTMJD9L8tt0F3d5UpIN0s0z+dTW2q0j+3g9YjlVtXW6z2bbpBuJe066nxsflG5U7rNba58cKa8fMaWq+lqSR6a7AN5npymnH3EH/ej/LyV5bLr/0z6V7r31A9JNqVBJXt5ae9fIPvrRAhLergWqapskf5duiPtdk/wy3T/ON46MWILRD7NT+VlrbftJ++yd5G+T7JXuA8xPknwwybunmHeJRWwGfShJzmit7TdpP/2IO6iqnZMcke5DydZJNktyQ7ovHj+Xrm9MvgCevsSMTBfe9tsPSHJkkoem+6XaD5O8p7X24dVZT4ahqvZN93q0a7ovtzdON0Lye0k+mu6XbMt9qPJ6xDhVdfckr0sXetwzyXXpBkoc01r71pjy+hHLqaoHpPu/6ZIk26+oL+hHTFZV66X7hfazkzww3dQHV6eb7/bdrbVTxuyjHy0Q4S0AAAAAwACZ8xYAAAAAYICEtwAAAAAAAyS8BQAAAAAYIOEtAAAAAMAACW8BAAAAAAZIeAsAAAAAMEDCWwAAAACAARLeAgAAAAAMkPAWAAAAAGCAhLcAAAAAAAMkvAUAAAAAGCDhLQAAY1VV62/bz8Ox9uuPdfFKV2wNUVWH920+fcy20/tth6/+mq1eVXVx39b9FrouAABrGuEtAMAiNBK8zvZ2+kLXnTVDVT2kqt6wNgTQo6pql6r6XFVdV1U3VNVXq2rvFezzr1V1W1U9fHXVEwBYHNZd6AoAALBKXDHF+rskWS/JzUmuHbP96pH7F/TL381jvVg8HpLk9UnOSHL8NOX+J11/u3E11GmVqqr7JvlGkjsnWZbktiT7JTmtqh7TWvv6mH0eneSQJEtba99ejdUFABYB4S0AwCLUWrvHuPX9yNp9k5zYWjt8Bce4//zXjLVNa+0xC12HefSGdMHt8UlenC7AfVOSVyU5NskjRwtX1Z2SvDfJr5P87WqsJwCwSJg2AQAAYGYek2607ctaaze11n6XLpS9IsleVbXRpPJHJrl/kle11n6zeqsKACwGwlsAAMZa0QXLqmrjqjqyqs6qqqur6uaquqiqPlNVh1bVerM412Or6vr+fMeO2b6kql5TVWdX1bX9uS6sqndX1TZTHPMPFwWrqs2q6i1V9aOqurGqrplF3R5aVcdW1der6udVdUtVXdUf/3lVtc5MjzWLc27Szyf7/f7vcn1VnVtVb6yqTVew76yel7m0r6pakg/1D/cdM3fyfiNlp71gWVVtWVVvH3lurq2qb1XVK6pq/Sn2Ob4/5huqap2qenn/t7qxb/N/rKL5Ze+a5MrW2nUTK1pry5L8LN1nq81H6rh9umD360k+vArqAgCsBUybAADArFXVA5N8Lsn2/aplSa5Lsk2SeyU5IN3coBfP4FhPTfJvSdZP8urW2rGTtj8gyReSbDdyrluS3DvJXyZ5TlUd0Fr7xhSnuHuS7yTZod/v1pm0ccQp6UK7pJu39cZ0cwfv29+eWlVP6UO8lVZV905yam5v78Rcsbv0t8Or6rGttQvH7DuX52Uu7bsiyYZJNkk3J/LoXMnJDP/GVbV7uuf2Lv2q3ya5U5Ld+ttzq2r/1tqvpjjEun17H9/X45Z0AeqTkjymqh7dWvvmTOoyQ1cluVtVbTIR4Pbh9nZJfp9kdHTtu/u2vLi11uaxDgDAWsTIWwAAZqWq7pLki+kCwp8mOSjJxq21uybZKN28nx9KFxyu6FiHJTkpt4dck4PbTZN8Pl04dlKSByfZoLW2JMmOSf41XVj3yarabIrTvC7dRdqekGSj1tomSWYzKvOUdBecumdrbePW2uZJliR5bpLLkzwxyV/P4nhT6udI/WS69v4iyf79uZYkeWySnyfZNsmnJo9KXYnnZdbt6+dUfln/8KzW2j0m3c6aQVs3T/LpdMHtD5Ls3j83S5I8I10Q+uAk/zLNYV6SLuR9VpIlrbU79/ucl2SDJO9aUT1m6bQk6yR5V1VtUFXrppvzdssk/9lau7Fv24HpgvLjWms/mOc6AABrESNvAQCYrf8v3UjOK5Ps01q7dGJDPwfoN/rbtKrqL9OFa7clOay1dsKYYq9MF0b+W2vtz0Y3tNYuSnJoH1r+aZLnJXnbmGOsn+SJrbXzRvb9yYrqN1L2z8asuyHJCVX1syRnprt41VtnesxpPCvJg9KNIr1DnZN8paqemOScJDslOTTJB0e2z+l5Wc3tG/XSJPdMck2S/Vtrl/fnvi3JJ6rquiRfSvLYfgTtaWOOsVm6tn59pO7nVtXhSb6dZLeq2ra19vN5qvPfJXlyksOTPCdd310/3fP16iSpqg3T9evLkrx+ns4LAKyljLwFAGC2DuuXbxsNCGejqo5K97PyW5M8fYrgNkn+vF++fZrD/Wu/fNwU278wKQSdN621r6ULH7evqq3m4ZBP75cnj6tza+2/k3yif/jMSZtX+nkZc775bt+oibZ+YCK4nXTuU5JMTHkwua0TvjYa3I7s+50kl/QPd17Zio4c9/wk+6Qb4XxzuqkSzkzy2NbamX2xo9J94fB/Wmu/raotqurD/TzCN1bVaVX1sPmqEwCwuBl5CwDAjPUXYdqyf/j5uR2i3pHuZ/g3JHlKa+0rUxTcJsnWE+fqL5I1zp365dgLl+X2AHDOquoZ6Ua6PjTdHLobjCm2VbrRlivjof3yq9OUOS3dNAcTZVf6eVmN7Zs4351ye6i6orbulZG2TnL2NPtemq7/bD5NmVlrrX0v3RQcy6mq+yd5RZJTW2snVtUG6dqwU5KvpBsV/dQkp1fV7n0YDAAwJeEtAACzseXI/bn8FH3b3D5/6oumCm579xy5v8UMjr3RFOt/PZOKjdPPafrxdIHbhFvShXC39Y/vnu4XbRvP9Twj7t4vpxs5OzGi9K5VVf3FsOb0vCxA+ybcJbf/CnAmbb37FNt/O82+N/fL9WZRr5X13n750n75/HTB7dLW2ouTpKqek+Sj6ebKfdpqrBsAsAYybQIAAKvT5UnO6O8fU1U7TlN29L3q5q21WsFt+ymOc9sU62fi+emCzRuT/FWSbVprG7TW7j5xca7cPhq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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 695}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "15 out of 20 tickers were removed\n{'PRSP': 30, 'SKM': 32, 'EIX': 15, 'FAST': 203, 'DLR': 9}\nFunds remaining: $23.33\n"}]}}, "f4cab51efa97438bbac2f2194d481148": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "VBoxModel", "state": {"children": ["IPY_MODEL_1d6ec95ad87340c79393550d0fe63890", "IPY_MODEL_9cc454b8fa92482793d8232ab5aad08a"], "layout": "IPY_MODEL_899b111e6d5b474e900b86d1a6c2f90b"}}, "f4deea9ffa6d40a7a0de68784ef98664": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "VBoxModel", "state": {"_dom_classes": ["widget-interact"], "children": ["IPY_MODEL_890896638a3540b98bac687438bd644f", "IPY_MODEL_3e059470a7f44839bd5531398878de86", 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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 685}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "15 out of 20 tickers were removed\n{'PRSP': 36, 'SKM': 76, 'EIX': 13, 'FAST': 150, 'DLR': 14}\nFunds remaining: $7.16\n"}]}}, "f880569ea65a4416b89bddc19e991d68": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "FloatSliderModel", "state": {"description": "tr", "layout": "IPY_MODEL_6d0b30a9f42e4d778e8706f388237ebb", "max": 0.2687043293906299, "step": 0.01, "style": "IPY_MODEL_e43b607b587d495fab9e778771d4db87", "value": 0.13}}, "f8f43e34885f4bfc991d83c73380014f": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_75b873aa8bf641048a6d347f4be61c42", "outputs": [{"data": {"image/png": 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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 688}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "14 out of 20 tickers were removed\n{'PRSP': 42, 'SKM': 113, 'EIX': 11, 'FAST': 98, 'DLR': 16, 'CBPO': 5}\nFunds remaining: $22.24\n"}]}}, "f90779634d704b03a69cf37625ec7637": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "DropdownModel", "state": {"_options_labels": ["target_return", "target_risk", "max_sharpe", "min_volatility"], "description": "Otimisation objective:", "index": 0, "layout": "IPY_MODEL_0ee9512f8a06432b8cad1f131cbaa689", "style": "IPY_MODEL_83c3b9f60e0f448184b0243bdfbe272e"}}, "f90cd44d4ef04e40b8ebc3c3b3f58334": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "IntTextModel", "state": {"description": "Total value:", "layout": 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\n", "text/plain": "<Figure size 720x504 with 1 Axes>"}, "metadata": {"image/png": {"height": 440, "width": 688}, "needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": "12 out of 20 tickers were removed\n{'PRSP': 42, 'BA': 1, 'SKM': 125, 'F': 14, 'EIX': 9, 'FAST': 66, 'DLR': 16, 'CBPO': 9}\nFunds remaining: $1.80\n"}]}}, "f970eaca12e848a9b89eda9b0529dd7c": {"model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "HBoxModel", "state": {"children": ["IPY_MODEL_fda8e71db7684f22b6b5124cbbc1cdb0", "IPY_MODEL_5d466c8ed9ea4a8aa4256af399e33515", "IPY_MODEL_ca5cae3b69154f8a86d8ac3ff40843c7"], "layout": "IPY_MODEL_478cb74ed14446bc845e9a5ae7d1fa7c"}}, "f97c46a481ab4fd3b5ff4c79d60ecdd6": {"model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {}}, "f997798b51504723b97f9e494f899375": {"model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": {"layout": "IPY_MODEL_c4b2a86015a34ec1974f5d85443fd9b5", "outputs": [{"data": {"image/png": 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Stock performance analysis using python Financial Functions2019-07-11T15:00:00+03:002019-07-11T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2019-07-11:/stock-perfomance-analysis.html<p>Continuing short series on data analysis applied to finance, this article is a step further into exploring Python’s many functionalities towards stock market diagnosis.</p><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<h2 id="Introduction-to-ffn">Introduction to ffn<a class="anchor-link" href="#Introduction-to-ffn">¶</a></h2><p>Data related works in Python are usually done using libraries such as Numpy, Pandas or Scipy, giving that these libraries are not only resourceful and versatile, but they’re well known to the data science community as fundamental Python skills. Having said that, quantitative finance professionals can count on a free open-source library built over the ones mentioned before, which provide straight-forward and easy solutions for finance, check out: <a href="http://pmorissette.github.io/ffn/">ffn</a></p>
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<p>f.fn() gives a vast array of utilities, from performance measurement and evaluation to graphing and common data transformations.</p>
<p>The easiest way to install ffn is from the Python Package Index using pip:</p>
<p><code>pip install ffn</code></p>
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<h2 id="Putting-it-to-use">Putting it to use<a class="anchor-link" href="#Putting-it-to-use">¶</a></h2><p>In our workflow, we’ll use ffn for all data retrieval and manipulation, as well as matplotlib for the graphical approach.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">ffn</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># Plotting on Jupyter Notebook</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
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<span class="o">%</span><span class="k">pylab</span> inline
<span class="c1"># Change the size of plots</span>
<span class="n">pylab</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.figsize'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">9</span><span class="p">)</span>
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<pre>Populating the interactive namespace from numpy and matplotlib
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<p>%matplotlib inline is a ‘magic function’ in iPython, called with a command line style syntax. With this back-end, the output of plotting commands is displayed inline within front-ends like the Jupyter notebook, directly below the code cell that produced it. The resulting plots will then also be stored in the notebook document.</p>
<p>Analogously, %pylab inline is used to control the default size of figures in the notebook.</p>
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<p>We’ll start using ffn while importing data for analysis. By default, ffn.get function uses Yahoo Finance to gather historical data on securities and futures negotiated in Capital Markets from all over the world.</p>
<p>We will restrain our analysis example on four companies: Google, Apple, Facebook and Amazon.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Import prices (adj. close) and define data frame variable</span>
<span class="n">prices</span> <span class="o">=</span> <span class="n">ffn</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'amzn,aapl,googl,fb'</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="s1">'2018-07-11'</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">'2019-07-11'</span><span class="p">)</span>
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<p>This should give us a data frame containing a ‘date’ indexed column, already in datetime format, along with four other columns, each providing the adjusted closing price of the chosen stocks.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">prices</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<th>amzn</th>
<th>aapl</th>
<th>googl</th>
<th>fb</th>
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<th>Date</th>
<th></th>
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<th>2018-07-11</th>
<td>1755.000000</td>
<td>45.491234</td>
<td>1171.459961</td>
<td>202.539993</td>
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<th>2018-07-12</th>
<td>1796.619995</td>
<td>46.253941</td>
<td>1201.260010</td>
<td>206.919998</td>
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<th>2018-07-13</th>
<td>1813.030029</td>
<td>46.326584</td>
<td>1204.420044</td>
<td>207.320007</td>
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<th>2018-07-16</th>
<td>1822.489990</td>
<td>46.224888</td>
<td>1196.510010</td>
<td>207.229996</td>
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<th>2018-07-17</th>
<td>1843.930054</td>
<td>46.355640</td>
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<p>As a start, a simple line chart can tell us how all these securities performed over the period.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Relative performance graphically using rebase for a common starting point</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">prices</span><span class="o">.</span><span class="n">rebase</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
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<p>The .rebase() function allows us to plot the time series from a common starting point, so that we can see how each stock performed relative to each other.</p>
<p>Our next step should be focused on the returns of each security. Tackling frequency distribution, histograms are powerful tools for distinguishing shape, identifying central points, variation, amplitude and symmetry of the returns.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Returns of each security and plot histogram </span>
<span class="n">returns</span> <span class="o">=</span> <span class="n">prices</span><span class="o">.</span><span class="n">to_returns</span><span class="p">()</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">returns</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">figsize</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
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<p>Since we’re looking at four companies from the technology sector, naturally, they follow a similar return pattern. This may be verified by finding the correlation between them, which can be observed by plotting a heat map, as follows:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Finding correlation with a heat map</span>
<span class="n">returns</span><span class="o">.</span><span class="n">plot_corr_heatmap</span><span class="p">()</span>
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<pre><module 'matplotlib.pyplot' from '/home/nekrasovp/.local/lib/python3.8/site-packages/matplotlib/pyplot.py'></pre>
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<p>Now, for ffn’s main performance measurement, we can calculate and display a wide array of statistics using the calc_stats() method.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Performance metrics</span>
<span class="n">perf</span> <span class="o">=</span> <span class="n">prices</span><span class="o">.</span><span class="n">calc_stats</span><span class="p">()</span>
<span class="n">perf</span><span class="o">.</span><span class="n">display</span><span class="p">();</span>
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<pre>Stat amzn aapl googl fb
------------------- ---------- ---------- ---------- ----------
Start 2018-07-11 2018-07-11 2018-07-11 2018-07-11
End 2019-07-11 2019-07-11 2019-07-11 2019-07-11
Risk-free rate 0.00% 0.00% 0.00% 0.00%
Total Return 14.02% 9.02% -2.34% -0.65%
Daily Sharpe 0.55 0.43 0.05 0.18
Daily Sortino 0.88 0.67 0.07 0.26
CAGR 14.03% 9.02% -2.34% -0.65%
Max Drawdown -34.10% -38.52% -23.40% -42.96%
Calmar Ratio 0.41 0.23 -0.10 -0.02
MTD 5.67% 1.94% 5.66% 4.26%
3m 8.51% 1.80% -5.42% 13.36%
6m 21.97% 33.56% 7.48% 39.94%
YTD 33.23% 28.94% 9.49% 53.51%
1Y 14.02% 9.02% -2.34% -0.65%
3Y (ann.) - - - -
5Y (ann.) - - - -
10Y (ann.) - - - -
Since Incep. (ann.) 14.03% 9.02% -2.34% -0.65%
Daily Sharpe 0.55 0.43 0.05 0.18
Daily Sortino 0.88 0.67 0.07 0.26
Daily Mean (ann.) 19.42% 13.51% 1.30% 6.83%
Daily Vol (ann.) 35.39% 31.06% 27.09% 38.12%
Daily Skew -0.01 -0.49 -0.37 -1.73
Daily Kurt 2.61 4.09 2.97 17.19
Best Day 9.45% 7.04% 6.42% 10.82%
Worst Day -7.82% -9.96% -7.50% -18.96%
Monthly Sharpe 0.50 0.37 -0.29 0.57
Monthly Sortino 0.82 0.64 -0.45 1.71
Monthly Mean (ann.) 18.28% 14.28% -5.41% 21.63%
Monthly Vol (ann.) 36.52% 38.58% 18.43% 38.26%
Monthly Skew -0.81 -0.22 -0.29 1.26
Monthly Kurt -0.07 -0.41 -0.63 1.18
Best Month 14.43% 20.04% 7.74% 27.16%
Worst Month -20.22% -18.12% -9.65% -8.24%
Yearly Sharpe - - - -
Yearly Sortino - - - -
Yearly Mean 33.23% 28.94% 9.49% 53.51%
Yearly Vol - - - -
Yearly Skew - - - -
Yearly Kurt - - - -
Best Year 33.23% 28.94% 9.49% 53.51%
Worst Year 33.23% 28.94% 9.49% 53.51%
Avg. Drawdown -8.78% -6.10% -11.29% -14.64%
Avg. Drawdown Days 69.00 41.50 88.75 118.67
Avg. Up Month 8.94% 8.62% 3.13% 10.21%
Avg. Down Month -8.86% -9.22% -5.47% -6.61%
Win Year % 100.00% 100.00% 100.00% 100.00%
Win 12m % 50.00% 50.00% 0.00% 100.00%
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<p>Although the result interpretation requires a bit of statistical knowledge, this large set of metrics can provide us simple conclusions, such as: Amazon stocks had the best performance between the Big Techs analysed, since, in the given period of the time series (12 months), the company provided a 14.02% return to it’s share-holders. Apple presented a 9.02% return, followed by Google and Facebook’s negative results.
From there, we can also access the underlying performance stats for each series, including monthly returns, drawback charts and more.</p>
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<h2 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">¶</a></h2><p>ffn for Python provides a huge set of useful information regarding stock performance in a very simple and straight-forward way. Combining it with matplotlib, we can intuitively detect stock trends or patterns, as well as technically interpret statistical insights to tell a enlightening story.</p>
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Stock market candlestick charts with plotly and python2019-07-10T20:00:00+03:002019-07-10T20:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2019-07-10:/candlestick-charts-plotly.html<p>This article is the first of a short series on data analysis applied to finance, in which I’ll seek to demonstrate a few useful artifacts used by stock market professionals, data analysts and traders. The whole approach is done using Python.</p><h2>Candlesticks Interpretation</h2>
<p>Perhaps the most common graphical way of displaying commodities price behavior in a giving time frame, candlestick graphs allows us to get a quick and intuitive perception on the stock’s performance. Each candle represents a specific period of analysis (this article will work with daily periods) and informs the opening and closing prices, as well as it’s highs and lows.</p>
<p><img alt="Candle description" src="images/candle-desc.png"></p>
<p>The so called “body” of the candle, represented by the thicker area between the opening and closing values, is shown in two different colors — as usual approach: green/white signals a bullish behavior, with the closing price being higher the opening, and red/black demonstrates a bearish behavior, where the price fell lower then it’s opening mark. The third possible case is a “flat-body” type called “doji candles”. This shape of candlestick appears when the active closes at the same (or very near) price that it opened.</p>
<p>At last, the “shadow” of the candle is a line that may be shown in the upper and/or lower part of the candle’s body. When it appears above the body, it represents the period’s highest achieved price. If below the body of the candlestick, the shadow indicates the lowest price stated in that period.</p>
<h2>Building the chart</h2>
<p>First things first, we’ll import pandas to set our data frame and plotly for the chart-building work.</p>
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">pandas.testing</span> <span class="kn">import</span> <span class="n">assert_frame_equal</span>
<span class="kn">from</span> <span class="nn">pandas_datareader</span> <span class="kn">import</span> <span class="n">data</span> <span class="k">as</span> <span class="n">web</span>
<span class="kn">import</span> <span class="nn">plotly.graph_objects</span> <span class="k">as</span> <span class="nn">go</span>
<span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>
</code></pre></div>
<p>Note that using plotly chart-studio’s library may be useful if you wish to save your graph in plotly’s dash (also sharing and embledding your graph gets easier by doing so).
Next, we’ll gather our chosen stock’s data from yahoo finance using pandas “datareader” function. For this exercise, we’ll work with AAPL stock, why not?!</p>
<div class="highlight"><pre><span></span><code><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
<span class="n">stock</span> <span class="o">=</span> <span class="s1">'AAPL'</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">web</span><span class="o">.</span><span class="n">DataReader</span><span class="p">(</span><span class="n">stock</span><span class="p">,</span> <span class="n">data_source</span><span class="o">=</span><span class="s1">'yahoo'</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="s1">'01-01-2010'</span><span class="p">)</span>
</code></pre></div>
<p>We can see that the ‘date’ column is already set as our database index and it’s already on datetime format, so we’re good to go!
Regarding our chart’s plot and subplots, we’ll need to define traces. The first trace should establish the main settings of our plot, as follows:</p>
<div class="highlight"><pre><span></span><code><span class="n">trace1</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'x'</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span>
<span class="s1">'open'</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">Open</span><span class="p">,</span>
<span class="s1">'close'</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">Close</span><span class="p">,</span>
<span class="s1">'high'</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">High</span><span class="p">,</span>
<span class="s1">'low'</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">Low</span><span class="p">,</span>
<span class="s1">'type'</span><span class="p">:</span> <span class="s1">'candlestick'</span><span class="p">,</span>
<span class="s1">'name'</span><span class="p">:</span> <span class="s1">'AAPL'</span><span class="p">,</span>
<span class="s1">'showlegend'</span><span class="p">:</span> <span class="kc">False</span>
<span class="p">}</span>
</code></pre></div>
<p>From there, our subsequent traces will plot all the analysis tools that we choose. One of the most common metrics are moving averages, which in our example will be set for 30 and 50 periods.</p>
<div class="highlight"><pre><span></span><code><span class="n">avg_30</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">Close</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">min_periods</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">avg_50</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">Close</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">min_periods</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
</code></pre></div>
<p>It’s important to notice that the averages are calculated over the closing price, and the ‘window’ attribute takes the number of periods that we want to sum to extract the mean value.
From there, we can define the subplots for both the moving averages in traces 2 and 3, using a simple scatter-type line graph and differentiating each other by color. Also, legends will be shown by default.</p>
<div class="highlight"><pre><span></span><code><span class="n">trace2</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'x'</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span>
<span class="s1">'y'</span><span class="p">:</span> <span class="n">avg_30</span><span class="p">,</span>
<span class="s1">'type'</span><span class="p">:</span> <span class="s1">'scatter'</span><span class="p">,</span>
<span class="s1">'mode'</span><span class="p">:</span> <span class="s1">'lines'</span><span class="p">,</span>
<span class="s1">'line'</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">'width'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s1">'color'</span><span class="p">:</span> <span class="s1">'blue'</span>
<span class="p">},</span>
<span class="s1">'name'</span><span class="p">:</span> <span class="s1">'Moving Average of 30 periods'</span>
<span class="p">}</span>
<span class="n">trace3</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'x'</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span>
<span class="s1">'y'</span><span class="p">:</span> <span class="n">avg_50</span><span class="p">,</span>
<span class="s1">'type'</span><span class="p">:</span> <span class="s1">'scatter'</span><span class="p">,</span>
<span class="s1">'mode'</span><span class="p">:</span> <span class="s1">'lines'</span><span class="p">,</span>
<span class="s1">'line'</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">'width'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
<span class="s1">'color'</span><span class="p">:</span> <span class="s1">'red'</span>
<span class="p">},</span>
<span class="s1">'name'</span><span class="p">:</span> <span class="s1">'Moving Average of 50 periods'</span>
<span class="p">}</span>
</code></pre></div>
<p>By now, we have all we need to plot a candlestick chart that gives us the opening, closing, highest and lowest prices of each daily candle, with also both moving averages lines in it.
We’re almost there! The next step is to aggregate all of the traces into one ‘data’ variable, which will incorporate all of the subplots. Then, we can set our chart’s layout as we please.</p>
<div class="highlight"><pre><span></span><code><span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">trace1</span><span class="p">,</span> <span class="n">trace2</span><span class="p">,</span> <span class="n">trace3</span><span class="p">]</span>
<span class="n">layout</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Layout</span><span class="p">({</span>
<span class="s1">'title'</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">'text'</span><span class="p">:</span> <span class="s1">'AAPL'</span><span class="p">,</span>
<span class="s1">'font'</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">'size'</span><span class="p">:</span> <span class="mi">15</span>
<span class="p">}</span>
<span class="p">}</span>
<span class="p">})</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Figure</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">write_html</span><span class="p">(</span><span class="s2">"./AAPL-web.html"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
<!-- # TODO Add chart-studio links -->Testing hypothesis on football data-set from kaggle2019-05-10T15:00:00+03:002019-05-10T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2019-05-10:/testing-hypothesis-on-football-data-set.html<p>In this notebook we will use the data-set, International football results from 1872 to 2019. We will try ANOVA hypothesis testing technique.</p><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>One way ANOVA (Analysis of Variance) is a technique for hypothesis testing. It is used to test whether the means of different group is really different.</p>
<p>In this notebook we will use the data-set, International football results from 1872 to 2019, which is available from the <a href="https://www.kaggle.com/martj42/international-football-results-from-1872-to-2017">Kaggle website</a>.</p>
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<li>Load libraries</li>
<li>Load and explore data-set</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sm</span>
<span class="kn">from</span> <span class="nn">statsmodels.formula.api</span> <span class="kn">import</span> <span class="n">ols</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="n">matplotlib</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">'fivethirtyeight'</span><span class="p">)</span>
<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">201910</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">reload_ext</span> watermark
<span class="o">%</span><span class="k">watermark</span> -v -m --iversions
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<pre>seaborn 0.9.0
statsmodels.api 0.10.1
scipy 1.3.1
numpy 1.17.3
matplotlib 3.1.1
pandas 0.25.2
CPython 3.7.2rc1
IPython 7.8.0
compiler : MSC v.1916 64 bit (AMD64)
system : Windows
release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
CPU cores : 8
interpreter: 64bit
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<p>We are ready to load mentioned above data-set and explore it.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'results.csv'</span><span class="p">)</span>
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<p>Getting sense of The data-set.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>date</th>
<th>home_team</th>
<th>away_team</th>
<th>home_score</th>
<th>away_score</th>
<th>tournament</th>
<th>city</th>
<th>country</th>
<th>neutral</th>
</tr>
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<tr>
<th>0</th>
<td>1872-11-30</td>
<td>Scotland</td>
<td>England</td>
<td>0</td>
<td>0</td>
<td>Friendly</td>
<td>Glasgow</td>
<td>Scotland</td>
<td>False</td>
</tr>
<tr>
<th>1</th>
<td>1873-03-08</td>
<td>England</td>
<td>Scotland</td>
<td>4</td>
<td>2</td>
<td>Friendly</td>
<td>London</td>
<td>England</td>
<td>False</td>
</tr>
<tr>
<th>2</th>
<td>1874-03-07</td>
<td>Scotland</td>
<td>England</td>
<td>2</td>
<td>1</td>
<td>Friendly</td>
<td>Glasgow</td>
<td>Scotland</td>
<td>False</td>
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<th>3</th>
<td>1875-03-06</td>
<td>England</td>
<td>Scotland</td>
<td>2</td>
<td>2</td>
<td>Friendly</td>
<td>London</td>
<td>England</td>
<td>False</td>
</tr>
<tr>
<th>4</th>
<td>1876-03-04</td>
<td>Scotland</td>
<td>England</td>
<td>3</td>
<td>0</td>
<td>Friendly</td>
<td>Glasgow</td>
<td>Scotland</td>
<td>False</td>
</tr>
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<pre><class 'pandas.core.frame.DataFrame'>
RangeIndex: 40945 entries, 0 to 40944
Data columns (total 9 columns):
date 40945 non-null object
home_team 40945 non-null object
away_team 40945 non-null object
home_score 40945 non-null int64
away_score 40945 non-null int64
tournament 40945 non-null object
city 40945 non-null object
country 40945 non-null object
neutral 40945 non-null bool
dtypes: bool(1), int64(2), object(6)
memory usage: 2.5+ MB
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<p>Rebuild index as datetime from datetime column</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">date</span><span class="p">)</span>
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<pre>DatetimeIndex(['1872-11-30', '1873-03-08', '1874-03-07', '1875-03-06',
'1876-03-04', '1876-03-25', '1877-03-03', '1877-03-05',
'1878-03-02', '1878-03-23',
...
'2019-09-10', '2019-09-10', '2019-09-10', '2019-09-10',
'2019-09-10', '2019-09-10', '2019-09-10', '2019-09-18',
'2019-09-29', '2019-09-30'],
dtype='datetime64[ns]', name='date', length=40945, freq=None)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span><span class="o">.</span><span class="n">shape</span>
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<pre>(40945, 9)</pre>
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<p>There are some types of tournament presented in this data-set.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span><span class="o">.</span><span class="n">tournament</span><span class="o">.</span><span class="n">unique</span><span class="p">()[:</span><span class="mi">40</span><span class="p">]</span>
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<pre>array(['Friendly', 'British Championship', 'Copa Lipton', 'Copa Newton',
'Copa Premio Honor Argentino', 'Copa Premio Honor Uruguayo',
'Copa Roca', 'Copa América', 'Copa Chevallier Boutell',
'Nordic Championship', 'International Cup', 'Baltic Cup',
'Balkan Cup', 'FIFA World Cup', 'Copa Rio Branco',
'FIFA World Cup qualification', 'CCCF Championship',
'NAFU Championship', 'Copa Oswaldo Cruz',
'Pan American Championship', 'Copa del Pacífico',
"Copa Bernardo O'Higgins", 'AFC Asian Cup qualification',
'Atlantic Cup', 'AFC Asian Cup', 'African Cup of Nations',
'Copa Paz del Chaco', 'Merdeka Tournament',
'UEFA Euro qualification', 'UEFA Euro',
'Windward Islands Tournament',
'African Cup of Nations qualification', 'Vietnam Independence Cup',
'Copa Carlos Dittborn', 'CONCACAF Championship',
'Copa Juan Pinto Durán', 'UAFA Cup', 'South Pacific Games',
'CONCACAF Championship qualification', 'Copa Artigas'],
dtype=object)</pre>
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<h1 id="The-Hypothesis">The Hypothesis<a class="anchor-link" href="#The-Hypothesis">¶</a></h1>
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<p>Getting the sense of the score distribution in world cup related tournaments</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fifa_wc_data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">data</span><span class="o">.</span><span class="n">tournament</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="s1">'FIFA World Cup'</span><span class="p">,</span> <span class="s1">'FIFA World Cup qualification'</span><span class="p">])]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fifa_wc_data</span><span class="o">.</span><span class="n">tournament</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
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<pre>array(['FIFA World Cup', 'FIFA World Cup qualification'], dtype=object)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fifa_wc_data</span> <span class="o">=</span> <span class="n">fifa_wc_data</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">score</span><span class="o">=</span><span class="n">fifa_wc_data</span><span class="o">.</span><span class="n">home_score</span> <span class="o">-</span> <span class="n">fifa_wc_data</span><span class="o">.</span><span class="n">away_score</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fifa_wc_data</span><span class="o">.</span><span class="n">dtypes</span>
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<pre>date object
home_team object
away_team object
home_score int64
away_score int64
tournament object
city object
country object
neutral bool
score int64
dtype: object</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fifa_wc_data</span><span class="o">.</span><span class="n">index</span>
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<pre>DatetimeIndex(['1930-07-13', '1930-07-13', '1930-07-14', '1930-07-14',
'1930-07-15', '1930-07-16', '1930-07-17', '1930-07-17',
'1930-07-18', '1930-07-19',
...
'2018-07-03', '2018-07-03', '2018-07-06', '2018-07-06',
'2018-07-07', '2018-07-07', '2018-07-10', '2018-07-11',
'2018-07-14', '2018-07-15'],
dtype='datetime64[ns]', name='date', length=8000, freq=None)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fifa_wc_data</span><span class="p">[</span><span class="s1">'score'</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">fifa_wc_data</span><span class="p">[</span><span class="s1">'score'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="s1">'ro'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">'Home/Away `score` in match'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Scores"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Home-Away score in match"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="prompt input_prompt">In [114]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Home-Away score distributions'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'pdf'</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">fifa_wc_data</span><span class="o">.</span><span class="n">score</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<div class="prompt input_prompt">In [115]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">fifa_wc_data</span><span class="p">[</span><span class="n">fifa_wc_data</span><span class="o">.</span><span class="n">neutral</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span><span class="o">.</span><span class="n">score</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Neutral venue'</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">fifa_wc_data</span><span class="p">[</span><span class="n">fifa_wc_data</span><span class="o">.</span><span class="n">neutral</span> <span class="o">==</span> <span class="kc">False</span><span class="p">]</span><span class="o">.</span><span class="n">score</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Home venue'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Score distribution for each type of venue'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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>
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<div class="prompt input_prompt">In [116]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fifa_wc_data</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'neutral'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span>
<span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">]</span>
<span class="p">)</span><span class="o">.</span><span class="n">score</span>
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<div class="prompt output_prompt">Out[116]:</div>
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<tr style="text-align: right;">
<th></th>
<th>mean</th>
<th>median</th>
<th>count_nonzero</th>
<th>std</th>
</tr>
<tr>
<th>neutral</th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>False</th>
<td>0.790199</td>
<td>1</td>
<td>5238</td>
<td>2.461279</td>
</tr>
<tr>
<th>True</th>
<td>0.119819</td>
<td>0</td>
<td>1044</td>
<td>2.591020</td>
</tr>
</tbody>
</table>
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<div class="prompt input_prompt">In [117]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fifa_wc_data</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'neutral'</span><span class="p">,</span> <span class="s1">'tournament'</span><span class="p">])</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span>
<span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">]</span>
<span class="p">)</span><span class="o">.</span><span class="n">score</span>
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<div class="prompt output_prompt">Out[117]:</div>
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<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>mean</th>
<th>median</th>
<th>count_nonzero</th>
<th>std</th>
</tr>
<tr>
<th>neutral</th>
<th>tournament</th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
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<tr>
<th rowspan="2" valign="top">False</th>
<th>FIFA World Cup</th>
<td>0.915254</td>
<td>1</td>
<td>96</td>
<td>1.972357</td>
</tr>
<tr>
<th>FIFA World Cup qualification</th>
<td>0.787948</td>
<td>1</td>
<td>5142</td>
<td>2.469257</td>
</tr>
<tr>
<th rowspan="2" valign="top">True</th>
<th>FIFA World Cup</th>
<td>0.214834</td>
<td>0</td>
<td>605</td>
<td>2.036136</td>
</tr>
<tr>
<th>FIFA World Cup qualification</th>
<td>-0.016514</td>
<td>0</td>
<td>439</td>
<td>3.221838</td>
</tr>
</tbody>
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<p>We can see difference in score on home venue against neutral.</p>
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<h1 id="ANOVA">ANOVA<a class="anchor-link" href="#ANOVA">¶</a></h1>
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<p>The 1 way anova’s null hypothesis is $μ_{score_{neutral}} = μ_{score_{nome}}$</p>
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<p>and this tests tries to see if it is true or not true</p>
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<p>let’s assume that we have initially determine our confidence level of 99.99%, which means that we will accept 0.01% error rate.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mod</span> <span class="o">=</span> <span class="n">ols</span><span class="p">(</span><span class="s1">'score ~ neutral'</span><span class="p">,</span> <span class="n">fifa_wc_data</span><span class="p">[</span><span class="n">fifa_wc_data</span><span class="o">.</span><span class="n">tournament</span><span class="o">==</span><span class="s1">'FIFA World Cup'</span><span class="p">])</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="n">anova_table</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">anova_lm</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">typ</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'ANOVA table for FIFA World Cup'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'----------------------'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">anova_table</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="n">mod</span> <span class="o">=</span> <span class="n">ols</span><span class="p">(</span><span class="s1">'score ~ neutral'</span><span class="p">,</span> <span class="n">fifa_wc_data</span><span class="p">[</span><span class="n">fifa_wc_data</span><span class="o">.</span><span class="n">tournament</span><span class="o">==</span><span class="s1">'FIFA World Cup qualification'</span><span class="p">])</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="n">anova_table</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">anova_lm</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">typ</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'ANOVA table for FIFA World Cup qualification'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'----------------------'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">anova_table</span><span class="p">)</span>
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<pre>ANOVA table for FIFA World Cup
----------------------
sum_sq df F PR(>F)
neutral 50.299529 1.0 12.230771 0.000493
Residual 3693.060471 898.0 NaN NaN
ANOVA table for FIFA World Cup qualification
----------------------
sum_sq df F PR(>F)
neutral 325.628045 1.0 50.677575 1.195318e-12
Residual 45608.099279 7098.0 NaN NaN
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<p>There are two p-values(PR(>F)) that we can see here, world cup itself and qualification games.</p>
<p>For World Cup, we cannot accept the null hypothesis under 99.99% confident level, because the p-value is greater than our alpha (0.0001 < 0.000493). So home venue is not enough factor at final round.</p>
<p>For qualification, since the p-value PR(>F) is less than our error rate (0.0001 > 1.195318e-12), we could reject the null hypothesis. This means we are quite confident that there is a different in score for qualification games by venue status.</p>
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Getting stock data with pandas-datareader2019-03-01T15:00:00+03:002019-03-01T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2019-03-01:/stock-data-with-pandas-datareader.html<p>The goal of this article is to provide an easy introduction to stock market analysis using Python. In this notebook we will use pandas_datareader module. We will walk through a simple Python script to retrieve, analyze, and visualize data from different markets.</p><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>The goal of this article is to provide an easy introduction to stock market analysis using Python. In this notebook we will use pandas_datareader module. We will walk through a simple Python script to retrieve, analyze, and visualize data from different markets.</p>
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<h1 id="Project-Setup">Project Setup<a class="anchor-link" href="#Project-Setup">¶</a></h1>
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<p>Once we've got a blank Jupyter notebook open, the first thing we'll do is import the required dependencies.</p>
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<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
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<p>We will use <a href="https://pydata.github.io/pandas-datareader/stable/index.html">pandas_datareader</a> to get data from the market.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas_datareader</span> <span class="k">as</span> <span class="nn">pdr</span>
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<p>Making the same request repeatedly can use a lot of bandwidth, slow down your code and may result in your IP being banned.</p>
<p>pandas_datareader allows you to cache queries using requests_cache</p>
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<span class="n">expire_after</span> <span class="o">=</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">session</span> <span class="o">=</span> <span class="n">requests_cache</span><span class="o">.</span><span class="n">CachedSession</span><span class="p">(</span><span class="n">cache_name</span><span class="o">=</span><span class="s1">'cache'</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">'sqlite'</span><span class="p">,</span> <span class="n">expire_after</span><span class="o">=</span><span class="n">expire_after</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">reload_ext</span> watermark
<span class="o">%</span><span class="k">watermark</span> -v -m --iversions
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<pre>requests_cache0.5.0
pandas_datareader0.7.0
IPython 5.0.0
pandas 0.23.4
CPython 3.6.7
IPython 5.0.0
compiler : GCC 5.4.0 20160609
system : Linux
release : 4.15.0-47-generic
machine : x86_64
processor : x86_64
CPU cores : 4
interpreter: 64bit
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<h1 id="Pulling-Data">Pulling Data<a class="anchor-link" href="#Pulling-Data">¶</a></h1>
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<p>We will try MOEX GAZP ticker.
You can explore securities secid with this link <a href="http://iss.moex.com/iss/securities.xml?q=Gazprom">http://iss.moex.com/iss/securities.xml?q=Gazprom</a></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">gazp</span> <span class="o">=</span> <span class="n">pdr</span><span class="o">.</span><span class="n">DataReader</span><span class="p">(</span><span class="s1">'GAZP'</span><span class="p">,</span> <span class="s1">'moex'</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2018</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2019</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">session</span><span class="o">=</span><span class="n">session</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">gazp</span><span class="p">[</span><span class="s1">'OPEN'</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
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<pre>TRADEDATE
2018-01-03 131.46
2018-01-03 131.03
2018-01-04 NaN
2018-01-04 135.42
2018-01-04 132.50
2018-01-05 NaN
2018-01-05 136.18
2018-01-05 135.60
2018-01-09 NaN
2018-01-09 139.01
Name: OPEN, dtype: float64</pre>
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<p>We will clear rows with NaN with dropna as is and plot OPEN price</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">gazp</span><span class="p">[</span><span class="s1">'OPEN'</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
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<p>We can use other data provider, lets try Yahoo finance and Chevron Corporation (CVX) ticker</p>
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<div class="prompt input_prompt">In [29]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cvx</span> <span class="o">=</span> <span class="n">pdr</span><span class="o">.</span><span class="n">DataReader</span><span class="p">(</span><span class="s1">'CVX'</span><span class="p">,</span> <span class="s1">'yahoo'</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2018</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2019</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">session</span><span class="o">=</span><span class="n">session</span><span class="p">)</span>
</pre></div>
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<div class="prompt input_prompt">In [30]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cvx</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<div class="prompt output_prompt">Out[30]:</div>
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<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>High</th>
<th>Low</th>
<th>Open</th>
<th>Close</th>
<th>Volume</th>
<th>Adj Close</th>
</tr>
<tr>
<th>Date</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2018-01-02</th>
<td>127.739998</td>
<td>125.540001</td>
<td>125.709999</td>
<td>127.580002</td>
<td>5626000.0</td>
<td>121.618179</td>
</tr>
<tr>
<th>2018-01-03</th>
<td>128.940002</td>
<td>126.900002</td>
<td>127.459999</td>
<td>128.509995</td>
<td>5805500.0</td>
<td>122.504707</td>
</tr>
<tr>
<th>2018-01-04</th>
<td>128.350006</td>
<td>127.220001</td>
<td>127.949997</td>
<td>128.110001</td>
<td>4598300.0</td>
<td>122.123413</td>
</tr>
<tr>
<th>2018-01-05</th>
<td>128.100006</td>
<td>127.099998</td>
<td>127.970001</td>
<td>127.900002</td>
<td>4189200.0</td>
<td>121.923218</td>
</tr>
<tr>
<th>2018-01-08</th>
<td>128.630005</td>
<td>127.629997</td>
<td>127.860001</td>
<td>128.529999</td>
<td>4826100.0</td>
<td>122.523773</td>
</tr>
</tbody>
</table>
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<p>Next, we'll generate a simple chart as a quick visual verification that the data looks correct.</p>
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<div class="prompt input_prompt">In [31]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cvx</span><span class="p">[</span><span class="s1">'Open'</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
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<div class="prompt output_prompt">Out[31]:</div>
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<pre><matplotlib.axes._subplots.AxesSubplot at 0x7f4bc1e4ef60></pre>
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<h1 id="Retrive-index-data">Retrive index data<a class="anchor-link" href="#Retrive-index-data">¶</a></h1><p>Now we wanna compare stock price with some index</p>
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<p>We will download stock prices for European Energy Companies as listed at <a href="https://www.nasdaq.com/screening/companies-by-industry.aspx?industry=Energy&region=Europe">https://www.nasdaq.com/screening/companies-by-industry.aspx?industry=Energy&region=Europe</a></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">securities</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'BP'</span><span class="p">,</span> <span class="s1">'TOT'</span><span class="p">,</span> <span class="s1">'EQNR'</span><span class="p">,</span> <span class="s1">'SLB'</span><span class="p">,</span> <span class="s1">'E'</span><span class="p">,</span> <span class="s1">'PHG'</span><span class="p">]</span>
<span class="n">sec_data</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">security</span> <span class="ow">in</span> <span class="n">securities</span><span class="p">:</span>
<span class="n">sec_df</span> <span class="o">=</span> <span class="n">pdr</span><span class="o">.</span><span class="n">DataReader</span><span class="p">(</span><span class="n">security</span><span class="p">,</span>
<span class="s1">'yahoo'</span><span class="p">,</span>
<span class="n">start</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2018</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="n">end</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2019</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="n">session</span><span class="o">=</span><span class="n">session</span><span class="p">)</span>
<span class="n">sec_data</span><span class="p">[</span><span class="n">security</span><span class="p">]</span> <span class="o">=</span> <span class="n">sec_df</span><span class="p">[</span><span class="s1">'Open'</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sec_data</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
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<pre>dict_keys(['BP', 'TOT', 'EQNR', 'SLB', 'E', 'PHG'])</pre>
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<p>Now we have a dictionary of 6 dataframes, each containing the historical daily average exchange prices.</p>
<p>We can preview TOT Open price to make sure it looks ok.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sec_data</span><span class="p">[</span><span class="s1">'TOT'</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
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<pre><matplotlib.axes._subplots.AxesSubplot at 0x7f4bc1cd7128></pre>
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<h2 id="Correlations-in-2018">Correlations in 2018<a class="anchor-link" href="#Correlations-in-2018">¶</a></h2><p>Calculate the pearson correlation coefficients for Energy companies in 2018</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sec_data_2018</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">sec_data</span><span class="p">)</span>
<span class="n">sec_data_2018</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<div class="prompt output_prompt">Out[35]:</div>
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<th></th>
<th>BP</th>
<th>TOT</th>
<th>EQNR</th>
<th>SLB</th>
<th>E</th>
<th>PHG</th>
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<th>Date</th>
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<th>2018-01-02</th>
<td>42.060001</td>
<td>55.450001</td>
<td>NaN</td>
<td>68.070000</td>
<td>33.189999</td>
<td>37.880001</td>
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<th>2018-01-03</th>
<td>42.430000</td>
<td>55.970001</td>
<td>NaN</td>
<td>69.970001</td>
<td>33.369999</td>
<td>37.700001</td>
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<th>2018-01-04</th>
<td>43.009998</td>
<td>57.279999</td>
<td>NaN</td>
<td>71.760002</td>
<td>34.380001</td>
<td>38.950001</td>
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<th>2018-01-05</th>
<td>43.049999</td>
<td>57.810001</td>
<td>NaN</td>
<td>72.949997</td>
<td>34.709999</td>
<td>39.400002</td>
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<th>2018-01-08</th>
<td>42.990002</td>
<td>57.730000</td>
<td>NaN</td>
<td>73.349998</td>
<td>34.660000</td>
<td>39.630001</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sec_data_2018</span><span class="o">.</span><span class="n">pct_change</span><span class="p">()</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">'pearson'</span><span class="p">)</span>
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<th></th>
<th>BP</th>
<th>TOT</th>
<th>EQNR</th>
<th>SLB</th>
<th>E</th>
<th>PHG</th>
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<th>BP</th>
<td>1.000000</td>
<td>0.801044</td>
<td>0.832809</td>
<td>0.630966</td>
<td>0.739382</td>
<td>0.443860</td>
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<th>TOT</th>
<td>0.801044</td>
<td>1.000000</td>
<td>0.775889</td>
<td>0.606954</td>
<td>0.790225</td>
<td>0.521720</td>
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<th>EQNR</th>
<td>0.832809</td>
<td>0.775889</td>
<td>1.000000</td>
<td>0.660128</td>
<td>0.757535</td>
<td>0.466831</td>
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<th>SLB</th>
<td>0.630966</td>
<td>0.606954</td>
<td>0.660128</td>
<td>1.000000</td>
<td>0.623800</td>
<td>0.348697</td>
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<th>E</th>
<td>0.739382</td>
<td>0.790225</td>
<td>0.757535</td>
<td>0.623800</td>
<td>1.000000</td>
<td>0.490694</td>
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<th>PHG</th>
<td>0.443860</td>
<td>0.521720</td>
<td>0.466831</td>
<td>0.348697</td>
<td>0.490694</td>
<td>1.000000</td>
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<p>These correlation coefficients are all over the place. Coefficients close to 1 or -1 mean that the series' are strongly correlated or inversely correlated respectively, and coefficients close to zero mean that the values tend to fluctuate independently of each other.</p>
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<h2 id="Heatmap-Visualization">Heatmap Visualization<a class="anchor-link" href="#Heatmap-Visualization">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">corr</span> <span class="o">=</span> <span class="n">sec_data_2018</span><span class="o">.</span><span class="n">pct_change</span><span class="p">()</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">'pearson'</span><span class="p">)</span>
<span class="n">corr</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">'binary'</span><span class="p">,</span> <span class="n">low</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
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<table id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07d" >
<thead> <tr>
<th class="blank level0" ></th>
<th class="col_heading level0 col0" >BP</th>
<th class="col_heading level0 col1" >TOT</th>
<th class="col_heading level0 col2" >EQNR</th>
<th class="col_heading level0 col3" >SLB</th>
<th class="col_heading level0 col4" >E</th>
<th class="col_heading level0 col5" >PHG</th>
</tr></thead>
<tbody> <tr>
<th id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07dlevel0_row0" class="row_heading level0 row0" >BP</th>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow0_col0" class="data row0 col0" >1</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow0_col1" class="data row0 col1" >0.801044</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow0_col2" class="data row0 col2" >0.832809</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow0_col3" class="data row0 col3" >0.630966</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow0_col4" class="data row0 col4" >0.739382</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow0_col5" class="data row0 col5" >0.44386</td>
</tr> <tr>
<th id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07dlevel0_row1" class="row_heading level0 row1" >TOT</th>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow1_col0" class="data row1 col0" >0.801044</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow1_col1" class="data row1 col1" >1</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow1_col2" class="data row1 col2" >0.775889</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow1_col3" class="data row1 col3" >0.606954</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow1_col4" class="data row1 col4" >0.790225</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow1_col5" class="data row1 col5" >0.52172</td>
</tr> <tr>
<th id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07dlevel0_row2" class="row_heading level0 row2" >EQNR</th>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow2_col0" class="data row2 col0" >0.832809</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow2_col1" class="data row2 col1" >0.775889</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow2_col2" class="data row2 col2" >1</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow2_col3" class="data row2 col3" >0.660128</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow2_col4" class="data row2 col4" >0.757535</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow2_col5" class="data row2 col5" >0.466831</td>
</tr> <tr>
<th id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07dlevel0_row3" class="row_heading level0 row3" >SLB</th>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow3_col0" class="data row3 col0" >0.630966</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow3_col1" class="data row3 col1" >0.606954</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow3_col2" class="data row3 col2" >0.660128</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow3_col3" class="data row3 col3" >1</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow3_col4" class="data row3 col4" >0.6238</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow3_col5" class="data row3 col5" >0.348697</td>
</tr> <tr>
<th id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07dlevel0_row4" class="row_heading level0 row4" >E</th>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow4_col0" class="data row4 col0" >0.739382</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow4_col1" class="data row4 col1" >0.790225</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow4_col2" class="data row4 col2" >0.757535</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow4_col3" class="data row4 col3" >0.6238</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow4_col4" class="data row4 col4" >1</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow4_col5" class="data row4 col5" >0.490694</td>
</tr> <tr>
<th id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07dlevel0_row5" class="row_heading level0 row5" >PHG</th>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow5_col0" class="data row5 col0" >0.44386</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow5_col1" class="data row5 col1" >0.52172</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow5_col2" class="data row5 col2" >0.466831</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow5_col3" class="data row5 col3" >0.348697</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow5_col4" class="data row5 col4" >0.490694</td>
<td id="T_8fcafb1e_621b_11e9_bfb5_e82aeae2e07drow5_col5" class="data row5 col5" >1</td>
</tr></tbody>
</table>
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<p>Here, the dark red values represent stronger correlations (note that each currency is, obviously, strongly correlated with itself), and values with light background represent strong inverse correlations.</p>
</div>
</div>
</div>
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<p>In this notebook, we tested pandas_datareader library, got fresh data on stocks of European Energy Companies for 2018 year and visualized the heatmap correlation matrix.</p>
</div>
</div>
</div>
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Upgrading OpenSSL on Ubuntu 18.042019-02-05T15:00:00+03:002019-02-05T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2019-02-05:/upgrading-openssl-ubuntu.html<p>To avoid openssl and TLS version issue we will update openSSL on server and desktop version of Ubuntu 18.0. We will download it manually, install and make necessary file permission changes.</p><p>To avoid openssl and TLS version issue we will update openSSL on server and desktop version of Ubuntu 18.04</p>
<p>We will download it manually, install and make necessary file permission changes.</p>
<p>Let's begin with fetching the tarball from official site.</p>
<div class="highlight"><pre><span></span><code>➜ wget https://www.openssl.org/source/openssl-1.1.1b.tar.gz
</code></pre></div>
<p>Next we unpack the tarball with tar and navigate to newly created folder</p>
<div class="highlight"><pre><span></span><code>➜ tar -zxf openssl-1.1.1b.tar.gz
➜ <span class="nb">cd</span> openssl-1.1.1b/
</code></pre></div>
<p>Issue the command</p>
<div class="highlight"><pre><span></span><code>➜ ./config
</code></pre></div>
<p>Issue the command make while you have gcc installed properly </p>
<div class="highlight"><pre><span></span><code>➜ make
</code></pre></div>
<p>Run make test to check for possible errors</p>
<div class="highlight"><pre><span></span><code>➜ make <span class="nb">test</span>
</code></pre></div>
<p>Backup current openssl binary</p>
<div class="highlight"><pre><span></span><code>➜ sudo mv /usr/bin/openssl ~/tmp
</code></pre></div>
<p>Issue the command</p>
<div class="highlight"><pre><span></span><code>➜ sudo make install
</code></pre></div>
<p>Create symbolic link from newly install binary to the default location:</p>
<div class="highlight"><pre><span></span><code>➜ sudo ln -s /usr/local/bin/openssl /usr/bin/openssl
</code></pre></div>
<p>Run the command sudo ldconfig to update symlinks and rebuild the library cache</p>
<div class="highlight"><pre><span></span><code>➜ sudo ldconfig
</code></pre></div>
<p>Assuming that there were no errors in executing steps 4 through 10, you should have successfully installed the new version of OpenSSL.</p>
<p>Again, from the terminal issue the command:</p>
<div class="highlight"><pre><span></span><code>➜ OpenSSL openssl version
OpenSSL <span class="m">1</span>.1.1b <span class="m">26</span> Feb <span class="m">2019</span>
</code></pre></div>
<p>Finally we have to check files and folders permission in /etc/ssl/certs/ and /usr/share/ca-certificates
and change newly created file permission:</p>
<div class="highlight"><pre><span></span><code>➜ sudo chmod o+rx ca-certificates.crt
</code></pre></div>
<p>We can check openssl functionality with </p>
<div class="highlight"><pre><span></span><code>➜ openssl s_client -connect <remote_server_addresss>
</code></pre></div>Add theme and plugins to Pelican installation2019-02-02T16:00:00+03:002019-02-02T16:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2019-02-02:/add-theme-and-plugins-to-pelican.html<p>The standard theme that comes with Pelican has one huge disadvantage: it does not display properly on mobile devices.</p><p>To accomplish this we will change our website to use the excellent pelican-bootstrap3 theme.</p>
<p>The standard theme that comes with Pelican has one huge disadvantage: it does not display properly on mobile devices.
To accomplish this we will change our website to use the excellent <a href="https://github.com/getpelican/pelican-themes/tree/master/pelican-bootstrap3">pelican-bootstrap3</a> theme.
To view the full listing of Pelican themes click <a href="https://github.com/getpelican/pelican-themes">here</a>.</p>
<h4>Goals:</h4>
<ul>
<li>[x] Setup the pelican-bootstrap3 theme</li>
<li>[x] Setup necessary plugins. </li>
<li>[x] Make a config file.</li>
</ul>
<p>Start by creating a new folder called theme in the source folder.</p>
<div class="highlight"><pre><span></span><code>blog
└── <span class="nb">source</span>
├── content
└── theme <span class="o">(</span>new<span class="o">)</span>
</code></pre></div>
<p>Now we need to tell Pelican where it can find the custom theme we will be using.
Open pelicanconf.py and add the following line:</p>
<div class="highlight"><pre><span></span><code><span class="n">THEME</span> <span class="o">=</span> <span class="s1">'theme'</span>
</code></pre></div>
<p>Next, go to GitHub and download the <a href="https://github.com/getpelican/pelican-themes/tree/master/pelican-bootstrap3">pelican-bootstrap3</a> theme.
Place all pelican-bootstrap3 files into the blog/source/theme folder so it looks like this:</p>
<div class="highlight"><pre><span></span><code>blog
└── <span class="nb">source</span>
├── content
└── theme
├── static
├── templates
├── translations
<span class="p">|</span> ...<span class="o">(</span>more files<span class="o">)</span>
</code></pre></div>
<p>To avoid errors we have to install missing plugin i18n_subsites which is used by pelican-bootstrap3 to provide support for
internationalization (i.e. i18n).
We must download the <a href="https://github.com/getpelican/pelican-plugins/tree/master/i18n_subsites">i18n_subsistes plugin</a> from Github and in include it in our project.
To view the full listing of available Pelican plugins click <a href="https://github.com/getpelican/pelican-plugins/tree/master/i18n_subsites">here</a>.</p>
<p>Add another folder inside the source called plugins. Place the i18n_subsites folder and files you downloaded from Github into the plugins folder:</p>
<div class="highlight"><pre><span></span><code>blog
└── <span class="nb">source</span>
├── content
├── plugins <span class="o">(</span>new<span class="o">)</span>
<span class="p">|</span> └── i18n_subsites <span class="o">(</span>new<span class="o">)</span>
└── theme
</code></pre></div>
<p>We must tell Pelican where the plugins folder is located just as we did for the theme folder.
Open pelicanconf.py and add the following line.</p>
<div class="highlight"><pre><span></span><code><span class="n">PLUGIN_PATHS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'plugins/'</span><span class="p">,</span> <span class="p">]</span>
</code></pre></div>
<p>A typical Pelican website will utilize many different plugins to extend its capabilities.
Each plugin must be setup individually within pelicanconf.py.
The PLUGINS variable contains all plugins being used by the website.
Open pelicanconf.py and add the following line.</p>
<div class="highlight"><pre><span></span><code><span class="n">PLUGINS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'i18n_subsites'</span><span class="p">,</span> <span class="p">]</span>
</code></pre></div>
<p>The i18n_subsites plugin relies on a language called Jinja2. To properly configure the i18n_subsites plugin we must also add the JINJA_ENVIRONMENT variable to pelicanconf.py as shown below.</p>
<div class="highlight"><pre><span></span><code><span class="n">JINJA_ENVIRONMENT</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'extensions'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'jinja2.ext.i18n'</span><span class="p">],</span>
<span class="p">}</span>
</code></pre></div>
<p>View the latest version of our website at <a href="http://127.0.0.1:8000">http://127.0.0.1:8000</a>.
Notice when you expand and shrink the width of the web browser window the website layout changes to fit?
By selecting a theme that incorporates responsive web design principles our website will be viewable on screens of all shapes and sizes.
Now that we have properly configured the bootstrap3 theme and its dependencies we can try to generate our site.</p>
<div class="highlight"><pre><span></span><code>pelican content
pelican --listen
</code></pre></div>
<p>The pelican-bootstrap3 theme comes with several options to change the style of our website.
To view the full list of available themes go the folder blog/source/theme/css.
You will see several files with the filename bootstrap.(some name).min.css.
We will be using the the bootstrap.flatly.min.css file to style our website.
Open pelicanconf.py* and add the following line.
Take a moment to try out some other options if you'd like to see what's available.</p>
<div class="highlight"><pre><span></span><code><span class="n">BOOTSTRAP_THEME</span> <span class="o">=</span> <span class="s1">'flatly'</span>
</code></pre></div>
<p>At this time we can also change the style for code blocks we may write in our blog posts.
Pelican displays code blocks using the Pygments code highlighter.
To view a full list of the available Pygments styles go to the folder blog/source/theme/css/pygments.
Our website will use monokai.css to style code blocks. Add the following line to pelicanconf.py.</p>
<div class="highlight"><pre><span></span><code><span class="n">PYGMENTS_STYLE</span> <span class="o">=</span> <span class="s1">'monokai'</span>
</code></pre></div>
<p>Let's update the sidebar too with our own preferred links and social pages.
You can use the websites shown below or choose your own.
Update the following line in pelicanconf.py</p>
<div class="highlight"><pre><span></span><code><span class="n">LINKS</span> <span class="o">=</span> <span class="p">((</span><span class="s1">'Pelican'</span><span class="p">,</span> <span class="s1">'http://getpelican.com/'</span><span class="p">),</span>
<span class="p">(</span><span class="s1">'Python.org'</span><span class="p">,</span> <span class="s1">'http://python.org/'</span><span class="p">),</span>
<span class="p">(</span><span class="s1">'Wikipedia'</span><span class="p">,</span> <span class="s1">'https://wikipedia.org'</span><span class="p">),)</span>
<span class="n">SOCIAL</span> <span class="o">=</span> <span class="p">((</span><span class="s1">'Facebook'</span><span class="p">,</span> <span class="s1">'https://www.facebook.com/nekrasovp'</span><span class="p">),</span>
<span class="p">(</span><span class="s1">'BitBucket'</span><span class="p">,</span> <span class="s1">'https://bitbucket.org/Nekrasovp/'</span><span class="p">),)</span>
</code></pre></div>
<p>Regenerate the website to see the impact of changing the style.
Not all of the changes we made to the style are apparent at this time because our website does not yet have any content.
In the next postsl we will learn how to create articles and pages for our blog.</p>Deploying a static website to bitbucket2019-02-02T16:00:00+03:002019-02-02T16:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2019-02-02:/deploying-a-static-website-to-bitbucket.html<p>We will use BitBucket for the task of hosting a small static website. The service is free and I already use BitBucket repositories to store the source code for my software projects so the workflow comes naturally.</p><p>We will use BitBucket for the task of hosting a small static website.
The service is free and I already use BitBucket repositories to store the source code for my software projects
so the workflow comes naturally.</p>
<h4>Goals:</h4>
<ul>
<li>[x] Create BitBucket repositories for your static website and its source code.</li>
<li>[x] Go-live with your website on BitBucket.</li>
</ul>
<h4>Deployment To BitBucket</h4>
<p>When building a static website it is a good idea to keep your website and its source code in two separate folders.
To make things simple let's call the folders output and source just like the website in previous posts.</p>
<div class="highlight"><pre><span></span><code>blog
└── output
└── <span class="nb">source</span>
</code></pre></div>
<p>We are going to create two BitBucket repositories: one to hold the static website and another to hold its source code.
The repository for the output folder must follow the pattern username.bitbucket.io.
This is how BitBucket knows how to display the repo's contents as a website.
The repository for the source folder can have any name we would like.
We will use the repo name blog_source.</p>
<div class="highlight"><pre><span></span><code>blog
└── output <span class="o">(</span>repo: username.bitbucket.io<span class="o">)</span>
└── <span class="nb">source</span> <span class="o">(</span>repo: blog_source<span class="o">)</span>
</code></pre></div>
<p>Login to BitBucket and create a new repository called username.bitbucket.io.
Replace the word username with your actual BitBucket user name.
Make the website public and do not initialize it with a readme file.</p>
<p>Immediately create a second repository called blog_source as well using the same settings as the first.
Now we have two empty BitBucket repositories.</p>
<p>Open up the command line. If you are following along from a previous posts we should generate the website prior to deployment.
Generate the website using the command below. We append -s publishconf.py to use settings that should only be applied when the website is being published.</p>
<div class="highlight"><pre><span></span><code>pelican content -s publishconf.py
</code></pre></div>
<p>Now its time to use Git.
While in the blog/output folder run the following git commands in order to push our files to the nekrasovp.bitbucket.io repository.</p>
<div class="highlight"><pre><span></span><code>git init
git remote add origin git@bitbucket.org:Nekrasovp/nekrasovp.bitbucket.io.git
git add -A
git commit -m <span class="s2">"initial commit"</span>
git push --set-upstream origin master
</code></pre></div>
<p>Next, navigate to the blog/source folder run the following git commands in order to push our files to the blog_source repository:</p>
<div class="highlight"><pre><span></span><code>git init
git remote add origin git@bitbucket.org:Nekrasovp/blog_source.git
git add -A
git commit -m <span class="s2">"initial commit"</span>
git push --set-upstream origin master
</code></pre></div>
<p>After running both sequences of Git commands we should be able to see our files loaded inti their respective repositories on BitBucket.
To view our website type https://username.bitbucket.io into your web browser.</p>Install Pelican and create project skeleton2019-02-01T16:00:00+03:002019-02-01T16:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2019-02-01:/install-pelican-and-create-project-skeleton.html<p>Building your own static website has several advantages over a traditional website that uses a database to store content. Static websites are faster because pages are rendered in advance and deliver the same content to all visitors. They are cheaper to host and are more secure since the website has no database.</p><p>Building your own static website has several advantages over a traditional website that uses a database to store content.
Static websites are faster because pages are rendered in advance and deliver the same content to all visitors.
They are cheaper to host and are more secure since the website has no database.</p>
<p>Creating each page in a static website one-by-one and updating them when something changes would be very time consuming.
Static site generators take a website's content, apply it to HTML-based templates and generate all of the required files ready-to-upload.
There are many static website generators available on the web, but i chose Pelican because of my preference for Python.</p>
<p>This tutorial is part of a series which will walkthrough all the steps from installing Pelican to deployment on Bitcuket.io pages (for free).
I decided to create these tutorials after making my own website with Pelican.</p>
<h4>Goals:</h4>
<ul>
<li>[x] Install Pelican</li>
<li>[x] Generate a static website</li>
<li>[x] Creating A Project Skeleton</li>
</ul>
<p>Let's begin by creating a new folder called blog with two subfolders inside the blog folder called output and source as shown below.
Our Pelican project will be stored in the source folder.
The static website generated by Pelican will be placed in the output folder.</p>
<div class="highlight"><pre><span></span><code>blog
└── output <span class="o">(</span>folder<span class="o">)</span>
└── <span class="nb">source</span> <span class="o">(</span>folder<span class="o">)</span>
</code></pre></div>
<p>Next we need to install Pelican and Markdown.
Markdown is a plain-text markup language we will be using to write the content for our website. </p>
<div class="highlight"><pre><span></span><code>pip install pelican
pip install markdown
</code></pre></div>
<p>With our basic file structure in-place and Pelican/Markdown installed we are now ready to create our Pelican project.
Navigate to the source folder using the command line and type:</p>
<div class="highlight"><pre><span></span><code>pelican-quickstart
</code></pre></div>
<p>Pelican starts by asking us a series of interview questions to customize our website.
After answering the pelican-quickstart interview questions your source folder will look like this:</p>
<div class="highlight"><pre><span></span><code>blog
├── output
└── <span class="nb">source</span>
├── content <span class="o">(</span>folder<span class="o">)</span>
├── output <span class="o">(</span>folder<span class="o">)</span>
├── pelicanconf.py
└── publishconf.py
</code></pre></div>
<p>Here's what each of the newly created files do:
* content (folder) - where website content will be stored
* output (folder) - default location where static website files are generated to
* pelicanconf.py - website settings file
* publishconf.py - additional website settings used only when the website is published</p>
<p>Now that our basic project skeleton is setup we should generate our website and preview what it looks like.
First, we must tell Pelican which folder to output the website to.
By default it will be generated to blog/source/output but we wanted them to appear in blog/output instead.
Delete the folder blog/source/output. Now open pelicanconf.py and add the following line:</p>
<div class="highlight"><pre><span></span><code><span class="n">OUTPUT_PATH</span> <span class="o">=</span> <span class="s1">'../output'</span>
</code></pre></div>
<p>Generate the website by using the following command while in the source folder.
Ignore the warning that appears. It displays because we haven't written any articles for our website yet:</p>
<div class="highlight"><pre><span></span><code>pelican content
</code></pre></div>
<p>Finally, run the following command while in the source folder. It will setup an http server on our local machine that will allow us to preview the website.</p>
<div class="highlight"><pre><span></span><code>pelican --listen
</code></pre></div>
<p>To view our website open your web browser and go to the the address: <a href="http://127.0.0.1:8000">http://127.0.0.1:8000</a>.</p>
<p>We have now successfully setup our basic Pelican project.</p>Using XGBoost regressor on Indicators, ARIMA and FFT of ohlcv data2018-12-01T15:00:00+03:002018-12-01T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2018-12-01:/feature-engineering-on-ohlcv-candles-wit-xgboost.html<p>The goal of this notebook is to apply XGBoost to set of features from ohlcv indicators, fft results and arima predictions to test feature importance.</p><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<h1 id="Using-XGBoost-regressor-on-Indicators,-ARIMA-and-FFT-of-ohlcv-data">Using XGBoost regressor on Indicators, ARIMA and FFT of ohlcv data<a class="anchor-link" href="#Using-XGBoost-regressor-on-Indicators,-ARIMA-and-FFT-of-ohlcv-data">¶</a></h1><p>The goal of this notebook is to apply XGBoost to set of fetures from ohlcv indicators, fft results and arima predictions to test feature importance.</p>
<h2 id="Table-of-content">Table of content<a class="anchor-link" href="#Table-of-content">¶</a></h2><ul>
<li><a href="#project-setup">Project Setup</a><ul>
<li><a href="#dependencies">Dependencies</a></li>
<li><a href="#ccxt-fetch-ohlcv">ccxt-fetch-ohlcv</a></li>
<li><a href="#caching-helper">caching-helper</a></li>
<li><a href="#pull-data">pull-data</a></li>
</ul>
</li>
<li><a href="#collect-features">Collect features</a><ul>
<li><a href="#fft">FFT</a></li>
<li><a href="#arima">ARIMA</a></li>
<li><a href="#ti">Technical indicators</a></li>
<li><a href="#features-eng">Features overview</a></li>
</ul>
</li>
<li><a href="#features-importance">Features importance with XGBoost regressor</a></li>
<li><a href="#conclusion">Conclusion</a></li>
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<h1 id="Project-Setup">Project Setup<a class="anchor-link" href="#Project-Setup">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">deque</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">statsmodels.tsa.arima_model</span> <span class="kn">import</span> <span class="n">ARIMA</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span>
<span class="kn">import</span> <span class="nn">xgboost</span> <span class="k">as</span> <span class="nn">xgb</span>
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<span class="o">%</span><span class="k">matplotlib</span> inline
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<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">'ignore'</span><span class="p">)</span>
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<p>After importing lets create a watermark</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">load_ext</span> watermark
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">watermark</span> -v -m -p ccxt,matplotlib,pandas,numpy,statsmodels,sklearn,xgboost,watermark
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<pre>CPython 3.6.7
IPython 5.0.0
ccxt 1.17.529
matplotlib 3.0.2
pandas 0.23.4
numpy 1.15.4
statsmodels 0.9.0
sklearn 0.20.2
xgboost 0.81
watermark 1.8.0
compiler : GCC 5.4.0 20160609
system : Linux
release : 4.15.0-43-generic
machine : x86_64
processor : x86_64
CPU cores : 4
interpreter: 64bit
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<h2 id="Define-ccxt-fetch_ohlcv-function">Define ccxt fetch_ohlcv function<a class="anchor-link" href="#Define-ccxt-fetch_ohlcv-function">¶</a></h2><p>Get an ohlcv data from public api endpoint.
Conmvert it to pandas dataframe with columns and human readable timestamp</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_ohlcv</span><span class="p">(</span><span class="n">exchange</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">timeframe</span><span class="p">):</span>
<span class="sd">'''Get an ohlcv data from an exchange</span>
<span class="sd"> and return it as pandas dataframe.</span>
<span class="sd"> timeframe = 1m 5m 15m 30m 1h 2h... etc.'''</span>
<span class="n">raw_data</span> <span class="o">=</span> <span class="n">exchange</span><span class="o">.</span><span class="n">fetch_ohlcv</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">timeframe</span><span class="p">)</span>
<span class="n">ohlcv_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">raw_data</span><span class="p">)</span>
<span class="n">ohlcv_data</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"date"</span><span class="p">,</span> <span class="s2">"open"</span><span class="p">,</span> <span class="s2">"high"</span><span class="p">,</span> <span class="s2">"low"</span><span class="p">,</span> <span class="s2">"close"</span><span class="p">,</span> <span class="s2">"volume"</span><span class="p">]</span>
<span class="n">ohlcv_data</span><span class="p">[</span><span class="s1">'date'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">ohlcv_data</span><span class="p">[</span><span class="s1">'date'</span><span class="p">],</span> <span class="n">unit</span><span class="o">=</span><span class="s1">'ms'</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ohlcv_data</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">'date'</span><span class="p">)</span>
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<h2 id="Define-CCXT-Helper-Function">Define CCXT Helper Function<a class="anchor-link" href="#Define-CCXT-Helper-Function">¶</a></h2><p>To assist with this data retrieval we'll define a function to download and cache datasets from an exchanges.
It will use sub-folder whick name we can change below.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_ccxt_data</span><span class="p">(</span><span class="n">exchange</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">timeframe</span><span class="p">):</span>
<span class="sd">'''Download and cache ccxt dataseries'''</span>
<span class="n">local_folder</span> <span class="o">=</span> <span class="n">Path</span><span class="o">.</span><span class="n">cwd</span><span class="p">()</span>
<span class="n">data_folder</span> <span class="o">=</span> <span class="s2">"raw_data"</span>
<span class="n">file_name</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">.pkl'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">exchange</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">timeframe</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'/'</span><span class="p">,</span><span class="s1">'-'</span><span class="p">)</span>
<span class="n">cache_path</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">local_folder</span><span class="p">,</span> <span class="n">data_folder</span><span class="p">,</span> <span class="n">file_name</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">cache_path</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Loaded </span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1"> from cache'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">exchange</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">timeframe</span><span class="p">))</span>
<span class="k">except</span> <span class="p">(</span><span class="ne">OSError</span><span class="p">,</span> <span class="ne">IOError</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Downloading </span><span class="si">{}</span><span class="s1"> </span><span class="si">{}</span><span class="s1"> from </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">timeframe</span><span class="p">,</span> <span class="n">exchange</span><span class="o">.</span><span class="n">name</span><span class="p">,))</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">get_ohlcv</span><span class="p">(</span><span class="n">exchange</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">timeframe</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">to_pickle</span><span class="p">(</span><span class="n">cache_path</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Cached </span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1"> at </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">exchange</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">timeframe</span><span class="p">,</span> <span class="n">cache_path</span><span class="p">))</span>
<span class="k">return</span> <span class="n">df</span>
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<h2 id="Pull-Binance-Exchange-Pricing-Data">Pull Binance Exchange Pricing Data<a class="anchor-link" href="#Pull-Binance-Exchange-Pricing-Data">¶</a></h2>
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<p>For this notebook we will take 1d candles but you can try other precisions offered by your data provider.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">exchange</span> <span class="o">=</span> <span class="n">ccxt</span><span class="o">.</span><span class="n">binance</span><span class="p">()</span>
<span class="n">symbol</span> <span class="o">=</span> <span class="s1">'BTC/USDT'</span>
<span class="n">timeframe</span> <span class="o">=</span> <span class="s1">'1d'</span>
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<p>While we prepare all wrapping and http calls in above functions we just say:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">btc_usdt_price_binance</span> <span class="o">=</span> <span class="n">get_ccxt_data</span><span class="p">(</span><span class="n">exchange</span><span class="p">,</span><span class="n">symbol</span><span class="p">,</span><span class="n">timeframe</span><span class="p">)</span>
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<pre>Loaded Binance_BTC/USDT_1d from cache
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<p>Lets explore what we got.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">btc_usdt_price_binance</span><span class="p">[</span><span class="s1">'close'</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
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<pre>date
2017-09-09 4258.81
2017-09-10 4130.37
2017-09-11 4208.47
Name: close, dtype: float64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">'There are </span><span class="si">{}</span><span class="s1"> number of days in the dataset.'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">btc_usdt_price_binance</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
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<pre>There are 500 number of days in the dataset.
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<p>Next, we'll generate a simple chart as a quick visual verification that the data looks correct.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">btc_usdt_price_binance</span><span class="p">[</span><span class="s1">'close'</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
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<pre><matplotlib.axes._subplots.AxesSubplot at 0x7f21ba0e35f8></pre>
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<h1 id="Collect-features">Collect features<a class="anchor-link" href="#Collect-features">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dataset_total_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">btc_usdt_price_binance</span><span class="p">[</span><span class="s1">'close'</span><span class="p">])</span>
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<h2 id="FFT">FFT<a class="anchor-link" href="#FFT">¶</a></h2>
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<p>We will use Fourier transforms to extract global and local trends, and also to denoise it a little. So let's see how it works.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">close_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">btc_usdt_price_binance</span><span class="p">[</span><span class="s1">'close'</span><span class="p">])</span>
<span class="n">close_data</span><span class="p">[</span><span class="s1">'datetime'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">close_data</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s1">'d'</span><span class="p">)</span>
<span class="n">data_FT</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">close_data</span><span class="p">[[</span><span class="s1">'datetime'</span><span class="p">,</span> <span class="s1">'close'</span><span class="p">]]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="n">data_FT</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Date'</span><span class="p">,</span> <span class="s1">'Close'</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">close_fft</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">data_FT</span><span class="p">[</span><span class="s1">'Close'</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()))</span>
<span class="n">fft_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'fft'</span><span class="p">:</span><span class="n">close_fft</span><span class="p">})</span>
<span class="n">fft_df</span><span class="p">[</span><span class="s1">'absolute'</span><span class="p">]</span> <span class="o">=</span> <span class="n">fft_df</span><span class="p">[</span><span class="s1">'fft'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">fft_df</span><span class="p">[</span><span class="s1">'angle'</span><span class="p">]</span> <span class="o">=</span> <span class="n">fft_df</span><span class="p">[</span><span class="s1">'fft'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">dpi</span> <span class="o">=</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">fft_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">fft_df</span><span class="p">[</span><span class="s1">'fft'</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
<span class="k">for</span> <span class="n">num_</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">9</span><span class="p">]:</span>
<span class="n">fft_list_m10</span><span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">fft_list</span><span class="p">);</span> <span class="n">fft_list_m10</span><span class="p">[</span><span class="n">num_</span><span class="p">:</span><span class="o">-</span><span class="n">num_</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">dataset_total_df</span><span class="p">[</span><span class="s1">'fft</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_</span><span class="p">)]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span><span class="n">fft_list_m10</span><span class="p">)</span>
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<pre><Figure size 1400x700 with 0 Axes></pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dataset_total_df</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
<span class="n">dataset_total_df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
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<pre><class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 500 entries, 2017-09-09 to 2019-01-21
Data columns (total 4 columns):
close 500 non-null float64
fft3 500 non-null float64
fft6 500 non-null float64
fft9 500 non-null float64
dtypes: float64(4)
memory usage: 19.5 KB
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<pre><matplotlib.axes._subplots.AxesSubplot at 0x7f21ba06b9e8></pre>
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<p>As you see the more components from the Fourier transform we use the closer the approximation function is to the real stock price (the 9 components transform is closer to the original function - the red and the blue lines almost overlap). We use Fourier transforms for the purpose of extracting long- and short-term trends so we will use the transforms with 3, 6, and 9 components. You can infer that the transform with 3 components serves as the long term trend.</p>
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<p>Another technique used to denoise data is call wavelets. Wavelets and Fourier transform gave similar results so we will only use Fourier transforms.</p>
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<div class="prompt input_prompt">In [69]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">items</span> <span class="o">=</span> <span class="n">deque</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">fft_df</span><span class="p">[</span><span class="s1">'absolute'</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()))</span>
<span class="n">items</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">fft_df</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">)))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">80</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">stem</span><span class="p">(</span><span class="n">items</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Components of Fourier transforms'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h2 id="ARIMA">ARIMA<a class="anchor-link" href="#ARIMA">¶</a></h2>
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<p>ARIMA is a technique for predicting time series data. We will show how to use it, and althouth ARIMA will not serve as our final prediction, we will use it as a technique to denoise the stock a little and to (possibly) extract some new patters or features.</p>
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<div class="prompt input_prompt">In [70]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">series</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">close_data</span><span class="p">[</span><span class="s1">'close'</span><span class="p">])</span>
<span class="n">series</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">series</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s1">'D'</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">ARIMA</span><span class="p">(</span><span class="n">series</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">missing</span><span class="o">=</span><span class="s1">'drop'</span><span class="p">)</span>
<span class="n">model_fit</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">disp</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model_fit</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
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<pre> ARIMA Model Results
==============================================================================
Dep. Variable: D.close No. Observations: 499
Model: ARIMA(5, 1, 0) Log Likelihood -3762.616
Method: css-mle S.D. of innovations 455.442
Date: Mon, 21 Jan 2019 AIC 7539.232
Time: 21:30:50 BIC 7568.720
Sample: 09-10-2017 HQIC 7550.804
- 01-21-2019
=================================================================================
coef std err z P>|z| [0.025 0.975]
---------------------------------------------------------------------------------
const -1.6730 22.743 -0.074 0.941 -46.248 42.902
ar.L1.D.close 0.0818 0.044 1.841 0.066 -0.005 0.169
ar.L2.D.close 0.0006 0.044 0.013 0.989 -0.086 0.087
ar.L3.D.close 0.0166 0.044 0.375 0.708 -0.070 0.103
ar.L4.D.close -0.1059 0.044 -2.394 0.017 -0.193 -0.019
ar.L5.D.close 0.1109 0.044 2.497 0.013 0.024 0.198
Roots
=============================================================================
Real Imaginary Modulus Frequency
-----------------------------------------------------------------------------
AR.1 -1.0949 -0.9335j 1.4388 -0.3876
AR.2 -1.0949 +0.9335j 1.4388 0.3876
AR.3 0.7071 -1.4206j 1.5868 -0.1765
AR.4 0.7071 +1.4206j 1.5868 0.1765
AR.5 1.7303 -0.0000j 1.7303 -0.0000
-----------------------------------------------------------------------------
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">output</span> <span class="o">=</span> <span class="n">model_fit</span><span class="o">.</span><span class="n">forecast</span><span class="p">()</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Next predicted value </span><span class="si">{0:.2f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">yhat</span><span class="p">)))</span>
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<pre>Next predicted value 3527.31
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">pandas.plotting</span> <span class="kn">import</span> <span class="n">autocorrelation_plot</span>
<span class="n">autocorrelation_plot</span><span class="p">(</span><span class="n">series</span><span class="p">)</span>
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<pre><matplotlib.axes._subplots.AxesSubplot at 0x7f21b9cb9f98></pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">series</span><span class="o">.</span><span class="n">values</span>
<span class="n">size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.66</span><span class="p">)</span>
<span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">size</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">size</span><span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)]</span>
<span class="n">history</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train</span><span class="p">]</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">test</span><span class="p">)):</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">ARIMA</span><span class="p">(</span><span class="n">history</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span> <span class="n">missing</span><span class="o">=</span><span class="s1">'drop'</span><span class="p">)</span>
<span class="n">model_fit</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">disp</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model_fit</span><span class="o">.</span><span class="n">forecast</span><span class="p">()</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">yhat</span><span class="p">)</span>
<span class="n">obs</span> <span class="o">=</span> <span class="n">test</span><span class="p">[</span><span class="n">t</span><span class="p">]</span>
<span class="n">history</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">obs</span><span class="p">)</span>
<span class="n">error</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Test MSE: </span><span class="si">%.3f</span><span class="s1">'</span> <span class="o">%</span> <span class="n">error</span><span class="p">)</span>
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<pre>Test MSE: 29642.647
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<p>Plot the predicted (from ARIMA) and real prices</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Real'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Predicted'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Days'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'USDT'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'ARIMA model on BTC/USDT'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<h2 id="Technical-indicators">Technical indicators<a class="anchor-link" href="#Technical-indicators">¶</a></h2>
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<p>We will create technical indicators for BTC/USDT on Binance.</p>
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<div class="prompt input_prompt">In [76]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_technical_indicators</span><span class="p">(</span><span class="n">dataset</span><span class="p">):</span>
<span class="c1"># Create 7 and 21 days Moving Average</span>
<span class="n">dataset</span><span class="p">[</span><span class="s1">'ma7'</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s1">'close'</span><span class="p">]</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">dataset</span><span class="p">[</span><span class="s1">'ma21'</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s1">'close'</span><span class="p">]</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="mi">21</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="c1"># Create MACD</span>
<span class="n">dataset</span><span class="p">[</span><span class="s1">'26ema'</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s1">'close'</span><span class="p">]</span><span class="o">.</span><span class="n">ewm</span><span class="p">(</span><span class="n">span</span><span class="o">=</span><span class="mi">26</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">dataset</span><span class="p">[</span><span class="s1">'12ema'</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s1">'close'</span><span class="p">]</span><span class="o">.</span><span class="n">ewm</span><span class="p">(</span><span class="n">span</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">dataset</span><span class="p">[</span><span class="s1">'MACD'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="s1">'12ema'</span><span class="p">]</span><span class="o">-</span><span class="n">dataset</span><span class="p">[</span><span class="s1">'26ema'</span><span class="p">])</span>
<span class="c1"># Create Bollinger Bands</span>
<span class="n">dataset</span><span class="p">[</span><span class="s1">'20sd'</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s1">'close'</span><span class="p">]</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
<span class="n">dataset</span><span class="p">[</span><span class="s1">'upper_band'</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s1">'ma21'</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="s1">'20sd'</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="p">)</span>
<span class="n">dataset</span><span class="p">[</span><span class="s1">'lower_band'</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s1">'ma21'</span><span class="p">]</span> <span class="o">-</span> <span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="s1">'20sd'</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="p">)</span>
<span class="c1"># Create Exponential moving average</span>
<span class="n">dataset</span><span class="p">[</span><span class="s1">'ema'</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s1">'close'</span><span class="p">]</span><span class="o">.</span><span class="n">ewm</span><span class="p">(</span><span class="n">com</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="k">return</span> <span class="n">dataset</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dataset_total_df</span> <span class="o">=</span> <span class="n">get_technical_indicators</span><span class="p">(</span><span class="n">series</span><span class="p">)</span>
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<h2 id="Features-overview">Features overview<a class="anchor-link" href="#Features-overview">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">'Total dataset has </span><span class="si">{}</span><span class="s1"> samples, and </span><span class="si">{}</span><span class="s1"> features.'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dataset_total_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">dataset_total_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
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<pre>Total dataset has 500 samples, and 10 features.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dataset_total_df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
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<pre><matplotlib.axes._subplots.AxesSubplot at 0x7f21de24a908></pre>
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<h1 id="Feature-importance-with-XGBoost">Feature importance with XGBoost<a class="anchor-link" href="#Feature-importance-with-XGBoost">¶</a></h1>
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<p>Having our features we have to consider whether all of them are really indicative of the direction BTC will take. There are many ways to test feature importance, but the one we will apply uses XGBoost, because it gives one of the best results in both classification and regression problems.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_feature_importance_data</span><span class="p">(</span><span class="n">data_income</span><span class="p">):</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data_income</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">'close'</span><span class="p">]</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span>
<span class="n">train_samples</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mf">0.665</span><span class="p">)</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="n">train_samples</span><span class="p">]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_samples</span><span class="p">:]</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="n">train_samples</span><span class="p">]</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_samples</span><span class="p">:]</span>
<span class="k">return</span> <span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="p">(</span><span class="n">X_train_FI</span><span class="p">,</span> <span class="n">y_train_FI</span><span class="p">),</span> <span class="p">(</span><span class="n">X_test_FI</span><span class="p">,</span> <span class="n">y_test_FI</span><span class="p">)</span> <span class="o">=</span> <span class="n">get_feature_importance_data</span><span class="p">(</span><span class="n">dataset_total_df</span><span class="p">)</span>
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<p>Build XGB regressor</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">regressor</span> <span class="o">=</span> <span class="n">xgb</span><span class="o">.</span><span class="n">XGBRegressor</span><span class="p">(</span><span class="n">gamma</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span><span class="n">base_score</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span><span class="n">colsample_bytree</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
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<p>Traing the regressors and check result</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">xgbModel</span> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_FI</span><span class="p">,</span><span class="n">y_train_FI</span><span class="p">,</span> \
<span class="n">eval_set</span> <span class="o">=</span> <span class="p">[(</span><span class="n">X_train_FI</span><span class="p">,</span> <span class="n">y_train_FI</span><span class="p">),</span> <span class="p">(</span><span class="n">X_test_FI</span><span class="p">,</span> <span class="n">y_test_FI</span><span class="p">)],</span> \
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">eval_result</span> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">evals_result</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">training_rounds</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">eval_result</span><span class="p">[</span><span class="s1">'validation_0'</span><span class="p">][</span><span class="s1">'rmse'</span><span class="p">]))</span>
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<p>Let's plot the training and validation errors in order to observe the training and check for overfitting (there isn't overfitting).</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">training_rounds</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">eval_result</span><span class="p">[</span><span class="s1">'validation_0'</span><span class="p">][</span><span class="s1">'rmse'</span><span class="p">],</span><span class="n">label</span><span class="o">=</span><span class="s1">'Training Error'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">training_rounds</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">eval_result</span><span class="p">[</span><span class="s1">'validation_1'</span><span class="p">][</span><span class="s1">'rmse'</span><span class="p">],</span><span class="n">label</span><span class="o">=</span><span class="s1">'Validation Error'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Iterations'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'RMSE'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Training Vs Validation Error'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="prompt input_prompt">In [100]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="s1">'vertical'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">xgbModel</span><span class="o">.</span><span class="n">feature_importances_</span><span class="p">))],</span> <span class="n">xgbModel</span><span class="o">.</span><span class="n">feature_importances_</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">tick_label</span><span class="o">=</span><span class="n">X_test_FI</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Feature importance.'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<h1 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">¶</a></h1>
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<p>We construct nice aproximations with some popoular methods such as Fourier Free Trnasform and so called Technical indicators(popular among traders mean aproximations) into pandas dataframe, fit and evaluate results from XGBoost regressor, we can see that popularity of this methods are correlated with our results.
Dont forget that we do that for pothethic "1d" rounded candles while you can use this method to evaluate any kind of time-series data features.</p>
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Arbitrage2018-06-06T15:00:00+03:002018-06-06T15:00:00+03:00Nekrasov Paveltag:nekrasovp.github.io,2018-06-06:/arbitrage.html<p>Here, we will give some detail on how to do real trading using arbitrage. There are always many ways to do so, and the method below is just one of them. But the key ideas are same.</p><p>This post is based on <a href="https://scribd.com/document/346123211/High-Frequency-Trading-and-Probability-Theory-Zhaodong-Wang">High Frequency Trading and Probability Theory - Zhaodong Wang</a></p>
<p>"Here, we will give some detail on how to do real trading using arbitrage.
There are always many ways to do so, and the method below is just one of
them. But the key ideas are same."</p>
<ol>
<li>
<p>Because arbitrage usually traded on several different instruments, the
first thing to do is to define a pseudo instrument, which is a combination of
instruments. Then, we calculate the market data of this pseudo instrument.
Lets go to the calendar spread example for XBT Futures on BitMEX. Because we hope to
trade on the difference of XBTH18 and XBTM18( XBTH18 refers to XBT(Bitcoin),
Expiration month — H (march), M(june) <a href="https://www.bitmex.com/app/seriesGuide/XBT">https://www.bitmex.com/app/seriesGuide/XBT</a>),
we use this definition of the combination. Suppose that the data below is the
current market data of these two instruments.</p>
<table>
<thead>
<tr>
<th>Instrument ID</th>
<th align="center">Last Price</th>
<th align="right">Bid Price</th>
<th align="right">Bid Volume</th>
<th align="right">Ask Price</th>
<th align="right">Ask Volume</th>
</tr>
</thead>
<tbody>
<tr>
<td>1. (XBTM18)</td>
<td align="center">8300.5</td>
<td align="right">8288.0</td>
<td align="right">33</td>
<td align="right">8300.0</td>
<td align="right">5000</td>
</tr>
<tr>
<td>2. (XBTH18)</td>
<td align="center">8130.0</td>
<td align="right">8101.5</td>
<td align="right">150</td>
<td align="right">8103.0</td>
<td align="right">13212</td>
</tr>
</tbody>
</table>
<p>There we use top of order book data for example.</p>
<p>Now we should consider the suitable market data for the combination, lets call it C.
For the last price, it should be the trade price of the last trade, and for the
combination, it should be the difference of the last trade between these two
instruments. Therefore, it should be 8300.5 - 8130.0 = 170.5</p>
<p><code>combined_instr_last_price = instr[1].last_price - instr[2].last_price</code></p>
<p>Now we should consider the bid price and ask price. If we want to buy
a lot of the combination, we should buy 1 lot of XBTM18 and sell 1 lot of
XBTH18. To sell 1 lot of combination means to sell 1 lot of XBTM18 and buy
1 lot of XBTH18. So we need to create pseudo lot function for each trade.</p>
<p>The bid price of the combination should be the price of
selling 1 lot of combination using the counterparty price, which means to
sell 1 lot of XBTM18 and to buy 1 lot of XBTH18 both using the counterparty
prices, and these prices should be the bid price of XBTM18 and the ask price of
XBTH18. Therefore, the bid price of the combination should be the difference
between them, that is: 8288.0 - 8103.0 = 185.0</p>
<p><code>combined_instr_bid_price = instr[1].bid_price - instr[2].ask_price</code></p>
<p>In the same way, the ask price of the combination should be 8300.0 - 8101.5 = 198.5</p>
<p><code>combined_instr_ask_price = instr[1].ask_price - instr[2].bid_price</code></p>
<p>Then we should calculate the bid volume and ask volume. The bid
volume should be the maximum volume we can sell on the counterparty
price at that time. Consider the combination: How many lots can we
sell at 186.5? It should be the minimal of bid volume of XBTM18 and ask
volume of XBTH18, that is min{33, 13212} = 33. And the ask volume should be
min{5000, 150} = 150.</p>
<p><code>combined_instr_bid_vol = min(instr[1].bid_volume, instr[2].ask_volume)</code></p>
<p><code>combined_instr_ask_vol = min(instr[1].ask_volume, instr[2].bid_volume)</code></p>
<p>Therefore, the current market data of combination should be as follows:</p>
<table>
<thead>
<tr>
<th>Last Price</th>
<th align="center">Bid Price</th>
<th align="right">Bid Volume</th>
<th align="right">Ask Price</th>
<th align="right">Ask Volume</th>
</tr>
</thead>
<tbody>
<tr>
<td>170.5</td>
<td align="center">185.0</td>
<td align="right">33</td>
<td align="right">198.5</td>
<td align="right">150</td>
</tr>
</tbody>
</table>
<p>Based on this calculation, we have a new market data for the combination:
all the fields have a similar meaning for a single instrument. Then, we can
do analysis and trading based on this market data, just as if it were a normal
single instrument. </p>
<p>We can scale with the same technique into depth of order book. </p>
</li>
<li>
<p>Arbitrage combination and price averaging.
There are some ways to find a suitable arbitrage
combination. </p>
<p>The first is using some knowledge of economics or industry
to find a potential combination, like the calendar spread, cross-market and
cross-product arbitrage we have mentioned.</p>
<p>The other way is just to hunt through the numerous market data and find a stable combination, and then
try to make an explanation of it.</p>
<p>The second technique is now a typical one for modern trading.
After finding a combination, we should make a decision about the buy or
sell price for this combination. The most popular way to decide is to utilize
some kind of average history of the prices of the combination market data.
We can base our decision on the average of the last prices, or the counterparty
price in the different market. We can use a short period market data to
calculate, or use a longer period one. A short period may be helpful for not
relying so much on a stable arbitrage, such as cross-product combination,
and a long period can be used for more stable arbitrage, such as calendar
spread. And there are still several average methods we can choose to
use: arithmetic average, value-added average or moving average. All these
methods may be effective. The best method can be found only through deep
analysis on real market data.</p>
</li>
<li>
<p>Trading processes types are, removal and passive.</p>
<p><strong>Removal</strong> means to always take the counterparty price for all the legs.
One tries to buy on ask price, and sell at bid price. It is similar to using
combination market data. The problem is that the real orders are based on
each leg; we cannot ensure that these legs are all traded simultaneously. If
some legs are traded and some are not, normally we may use some deep
price to trade for these non-traded legs, though we know that it may cause a
miss, it is still better than keeping unbalanced positions for a long time. For
some legs, their order book is thick, which means there are many orders in
their order book continuously. We will not lose a lot if we have a miss on
them. But legs with thin order books will be very dangerous. There may be
a large gap behind the best bid or ask price, and a miss will cause serious
loss. Therefore, one of the most popular practices in the area is to find one
leg with the thinnest order book, and try to make a trade in this leg first. If
the trade is made, then we shall try to trade on all other legs with thicker
order books. In this case, we will not have a very large miss. If this leg
cannot be traded, we can just cancel the order and do nothing with the other
legs. We will just lose this opportunity to trade with anything, without any
miss at all. This will be a safer way. Normally, we can predict the thickness
of the order book by studying the market, and in most cases, the difference
is very obvious. In some cases, we can detect the thickness in trading time,
and make the decision on the first trading leg based on it.</p>
<p><strong>Passive</strong> trading is to place orders at the desired price in order book, and
wait for counterparties to trade with them. While participating in arbitrage,
we can only do passive trading in one leg, and after trading, remove all
other legs. We can do so at any leg of the combination, but similar to the
consideration of removal, we would better be passive in the leg with thinnest
order book. There are many kinds of refinement for passive trading. Some
are focused on getting a better price, some on placing multi-layer orders to
minimize the time gap between order cancelling, and some on getting better
time priority for its price level. Traders often give new ideas for these kinds
of processes based on the understanding of the market microstructure.
Both removal and passive trading are important processes for arbitrage.
Removal trading is simple and safe compared with passive trading.
But passive trading may get a better price than removal, the difference is the
bid-ask spread of the market data of the leg performing passive trading.And
passive trading will have much more trading opportunities. But since you
always put some orders in the exchange, any system problem may cause
serious result.</p>
</li>
<li>
<p><strong>Calendar spread</strong> requires one to trade on different delivery time periods
with the same product. It is based on the assumption that the expected
futures prices for a different delivery time will share the same movement.
This is true in most cases. For example, index futures are normally quite
stable for the calendar spread. Below is a graph of
XBTM19, XBTU19 and XBT based on 15m market data.
To get something usefull from this data we manipulate the data
to get pseudo instrument derivatives.</p>
<p><img alt="Calendar spread" src="images/calendar-spread-img.png" title="Calendar spread"></p>
<p>We can see that the range of the price difference is very narrow, but
does not stay the same. Therefore, this pair can
be chosen for generating a good calendar spread strategy. It means that its
range is not large, so we can predict the price difference in some way. Since
it still will change some, we can have some opportunities to trade.
There is a special kind of calendar spread, called spot-futures spread.
This type of spread trades spot market versus futures market. For example,
we can trade some CSI 300 related spot market instruments against IF
futures, such as some CSI 300 ETFs. Recently, there have been several other ways to trade ETF similar
to the T + 0 rule, but the high cost is still a problem.</p>
</li>
<li>
<p><strong>Cross-market arbitrage</strong> refers to the technique in which one finds the
same or similar products in different markets, and trades between them.
For example BTC-USD pairs on different exchanges is a cross-market arbitrage. Here, we
also give a price graph showcasing this arbitrage. We use the market
data from may 2019, and select BTCUSD from the CEX.io and BTCUSD
from Bittrex. The following is the result.</p>
<p><img alt="Cross-market-arbitrage" src="images/cross-market-img.png"></p>
<p>We can see that this graph is very similar to the graph of the XBT19 calendar
spread, demonstrating a stable price with a small price movement range.
This is a typical arbitrage market.</p>
</li>
<li>
<p><strong>Cross-product arbitrage</strong> refers to using the internal relationship
between several products, and trading among them. Cross-product arbitrage
is widely used in the securities market in the US and Europe. It assumes that
they should have a similar performance for similar stocks. This might occur
because both companies are working in same industry. One may also assume
that stocks for corporations in one supply chain will have the opposite
performance if the final market has no change. This kind of arbitrage is
called alpha arbitrage. In the futures market, cross-product arbitrage is often
used for products in one step of manufacturing. For example, we know that
most parts of soybeans are crushed into soybean oil, and the remaining
part is soybean meal. All these three products are traded in the Dalian
Commodity Exchange (DCE). Below, we have a graph displaying such a
combination. According to some domain knowledge of the industry, about
5 units of soybean can be crushed to 1 unit of soybean oil, and the remaining
are 4 units of soybean meals. We use the price of soybean oil (y1305), plus
the price of soybean meal (m1305) multiplied by 4, and subtract the price
of soybean (a1305) multiplied by 5.</p>
</li>
</ol>