diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000..f45c693
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,6 @@
+.~lock*
+**.mypy_cache/
+**.venv/
+**__pycache__/
+*.docx
+data*.csv
\ No newline at end of file
diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000..b0084e1
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,5 @@
+REFETCH_PERIOD_H=6
+POSTGRES_USER=rosseti
+POSTGRES_PASSWORD=rosseti
+POSTGRES_DB=rosseti
+POSTGRES_HOST=db
\ No newline at end of file
diff --git a/.vscode/launch.json b/.vscode/launch.json
new file mode 100644
index 0000000..1b42d6f
--- /dev/null
+++ b/.vscode/launch.json
@@ -0,0 +1,15 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Module",
+            "type": "python",
+            "request": "launch",
+            "module": "parser",
+            "justMyCode": true,
+        }
+    ]
+}
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..5b86b9d
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,7 @@
+FROM python:3-slim
+WORKDIR /srv
+COPY requirements.txt .
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+COPY ./parser_api ./parser_api
+COPY ./rosseti_parser ./rosseti_parser
+CMD python -m uvicorn parser_api.main:app
diff --git a/README.md b/README.md
index da1655b..56fd61b 100644
--- a/README.md
+++ b/README.md
@@ -1,82 +1,29 @@
-**СПБ ГУП «ИАЦ»**
+# СПБ ГУП «ИАЦ»
 
-**Тестовое задание для Python-разработчика (дата аналитика)**
+## Тестовое задание для Python-разработчика (дата аналитика)
 
-О компании:
+Задание: [Task.md](https://git.dm1sh.ru/dm1sh/iac_test/src/branch/main/Task.md)
 
-Государственное унитарное предприятие, работающее в области
-информатизации и информационного обеспечения органов государственной
-власти Санкт-Петербурга и других организаций, а также предоставления
-услуг в сфере создания и использования современных информационных и
-телекоммуникационных систем, средств и технологий 
+- Скрипт для парсинга и работа с данными: [rosseti_parser](https://git.dm1sh.ru/dm1sh/iac_test/src/branch/main/rosseti_parser)
 
-Более подробно о нас: <https://iac.spb.ru/>
+    Модуль можно запустить командой `python -m rosseti_parser`, или импортировать из него необходимые методы и использовать где-то ещё
 
-Мы на Хабре: <https://career.habr.com/companies/iac-spb>
+- Анализ данных: [main.ipynb](https://git.dm1sh.ru/dm1sh/iac_test/src/branch/main/main.ipynb) 
+    
+    Необходимо скачать (платформа пока не поддерживает отображение ноутбука). Для запуска, установить библиотеки из `requirements.dev.txt`
 
-**Текст задания:**
+- Визуализация: [Yandex DataLens](https://datalens.yandex/kq0ymfuy5w7e9)
+    
+    Простенький дашборд с картой точек, хитмапом и несколькими графиками
 
-**- Парсинг**
+- Создание базы данных, API и docker: [parser_api](https://git.dm1sh.ru/dm1sh/iac_test/src/branch/main/parser_api)
 
-Считывание таблицы с сайта <https://rosseti-lenenergo.ru/planned_work/>
+    FastAPI приложение, запускающее в дополнительном потоке периодическое обновление данных в базе.
 
-![](media/image1.png){width="6.818295056867892in"
-height="2.37117125984252in"}
-
-Фильтр по времени - период текущей недели (текущий день и неделя вперед)
-
--   Успешное чтение необходимых полей на сайте и их сохранение в
-    pandas.DataFrame
-
--   Переключение между страницами и совершение полной выгрузки
-    ![](media/image2.png){width="2.468441601049869in"
-    height="0.49993766404199474in"}
-
--   Настройка автоматического запуска скрипта по расписанию
-
-**- Работа с данными**
-
--   Парсинг столбца Улица (разбиение строки на отдельные адреса)
-
--   Геокод адресов через
-    <https://petersburg.ru/mainPortal/api_services/view/2223> и
-    сохранение building_id найденных зданий
-
--   Запись результата в csv файл
-
-**- Анализ данных и Визуализация**
-
--   Выполнить в свободной форме на основе данных, полученных ранее
-
-В дополнение к скрипту можно сделать дашборд на [Yandex DataLens
-/Grafana](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiCrdStlbKBAxViSPEDHcw0B4kQFnoECB0QAQ&url=https%3A%2F%2Fdatalens.yandex.ru%2F&usg=AOvVaw3gsVaz_KTvGMRtFZrsXAGk&opi=89978449)
-
-**\*Создание базы данных**
-
--   Вместо сохранения результата в csv, развернуть базу данных и
-    сохранять результаты в неё (PostgreSQL, Clickhouse, MongoDB)
-
--   (Можно сделать скелет с подключением и записью в бд на локалхосте)
-
-**\*API**
-
--   Написать API к базе данных или csv-файлу на FastAPI
-
-**\*\*Docker**
-
--   Оборачивание всей сделанной работы в docker-compose
-
--   При первоначальной настройке и запуске компоуза парсер начнет
-    работать и собирать данные в БД / csv. Доступ к данным
-    осуществляется по API.
-
-Результаты проделанной работы залить на Github и прислать на
-[tg:Faneagain](https://t.me/faneagain)
-
-//При проблемах с парсингом для выполнения остальных задач можно
-попросить готовый набор данных
-
-Задания помеченные «**\***, **\*\***» будут оцениваться как
-дополнительные.
-
-Удачи!
+    Доступные методы:
+    
+    - GET /api/list - Поиск по каждому полю в отдельности
+    - GET /api/search - Поиск по всем полям сразу
+    - GET /api/check - Проверка, является ли отключение в вашем доме сейчас официальным и если да, то когда сеть снова включат.
+    - PUT /api/create - Отладочное поле для добавления записей в БД
+    - GET / - Healthcheck
diff --git a/Task.md b/Task.md
new file mode 100644
index 0000000..da1655b
--- /dev/null
+++ b/Task.md
@@ -0,0 +1,82 @@
+**СПБ ГУП «ИАЦ»**
+
+**Тестовое задание для Python-разработчика (дата аналитика)**
+
+О компании:
+
+Государственное унитарное предприятие, работающее в области
+информатизации и информационного обеспечения органов государственной
+власти Санкт-Петербурга и других организаций, а также предоставления
+услуг в сфере создания и использования современных информационных и
+телекоммуникационных систем, средств и технологий 
+
+Более подробно о нас: <https://iac.spb.ru/>
+
+Мы на Хабре: <https://career.habr.com/companies/iac-spb>
+
+**Текст задания:**
+
+**- Парсинг**
+
+Считывание таблицы с сайта <https://rosseti-lenenergo.ru/planned_work/>
+
+![](media/image1.png){width="6.818295056867892in"
+height="2.37117125984252in"}
+
+Фильтр по времени - период текущей недели (текущий день и неделя вперед)
+
+-   Успешное чтение необходимых полей на сайте и их сохранение в
+    pandas.DataFrame
+
+-   Переключение между страницами и совершение полной выгрузки
+    ![](media/image2.png){width="2.468441601049869in"
+    height="0.49993766404199474in"}
+
+-   Настройка автоматического запуска скрипта по расписанию
+
+**- Работа с данными**
+
+-   Парсинг столбца Улица (разбиение строки на отдельные адреса)
+
+-   Геокод адресов через
+    <https://petersburg.ru/mainPortal/api_services/view/2223> и
+    сохранение building_id найденных зданий
+
+-   Запись результата в csv файл
+
+**- Анализ данных и Визуализация**
+
+-   Выполнить в свободной форме на основе данных, полученных ранее
+
+В дополнение к скрипту можно сделать дашборд на [Yandex DataLens
+/Grafana](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiCrdStlbKBAxViSPEDHcw0B4kQFnoECB0QAQ&url=https%3A%2F%2Fdatalens.yandex.ru%2F&usg=AOvVaw3gsVaz_KTvGMRtFZrsXAGk&opi=89978449)
+
+**\*Создание базы данных**
+
+-   Вместо сохранения результата в csv, развернуть базу данных и
+    сохранять результаты в неё (PostgreSQL, Clickhouse, MongoDB)
+
+-   (Можно сделать скелет с подключением и записью в бд на локалхосте)
+
+**\*API**
+
+-   Написать API к базе данных или csv-файлу на FastAPI
+
+**\*\*Docker**
+
+-   Оборачивание всей сделанной работы в docker-compose
+
+-   При первоначальной настройке и запуске компоуза парсер начнет
+    работать и собирать данные в БД / csv. Доступ к данным
+    осуществляется по API.
+
+Результаты проделанной работы залить на Github и прислать на
+[tg:Faneagain](https://t.me/faneagain)
+
+//При проблемах с парсингом для выполнения остальных задач можно
+попросить готовый набор данных
+
+Задания помеченные «**\***, **\*\***» будут оцениваться как
+дополнительные.
+
+Удачи!
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000..29e3e82
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,31 @@
+version: "3"
+services:
+  db:
+    image: postgres
+    ports:
+      - "5432:5432"
+    env_file:
+      - .env
+    volumes:
+      - db_storage:/var/lib/postgresql
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U $POSTGRES_USER"]
+      interval: 5s
+      timeout: 5s
+      retries: 5
+    hostname: "db"
+
+  app:
+    build: .
+    depends_on:
+      db:
+        condition: service_healthy
+    links:
+      - db
+    ports:
+      - "8000:8000"
+    env_file:
+      - .env
+
+volumes:
+  db_storage:
\ No newline at end of file
diff --git a/main.ipynb b/main.ipynb
new file mode 100644
index 0000000..9e04159
--- /dev/null
+++ b/main.ipynb
@@ -0,0 +1,1684 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parsing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from rosseti_parser import RossetiParser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Parse Rosseti site or load data from csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Read from \"./data.csv\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "file_path = './data.csv'\n",
+    "\n",
+    "if os.path.isfile(file_path):\n",
+    "    parser = RossetiParser(file_path=file_path)\n",
+    "else:\n",
+    "    parser = RossetiParser()\n",
+    "    parser.save_df(file_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "From 2023-09-21 for 7 days with 251 records\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(parser)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = parser.df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Parse street dataframe column and split row by it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>Регион РФ (область, край, город фед. значения, округ)</th>\n",
+       "      <th>Административный район</th>\n",
+       "      <th>Населённый пункт</th>\n",
+       "      <th>Улица</th>\n",
+       "      <th>Плановая дата начала отключения электроснабжения</th>\n",
+       "      <th>Плановое время начала отключения электроснабжения</th>\n",
+       "      <th>Плановая дата восстановления отключения электроснабжения</th>\n",
+       "      <th>Плановое время восстановления отключения электроснабжения</th>\n",
+       "      <th>Филиал</th>\n",
+       "      <th>РЭС</th>\n",
+       "      <th>Комментарий</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Ленинградская область</td>\n",
+       "      <td>Тосненский район</td>\n",
+       "      <td>п. Рябово</td>\n",
+       "      <td>Мысленская,Заречная</td>\n",
+       "      <td>28-09-2023</td>\n",
+       "      <td>17:00</td>\n",
+       "      <td>28-09-2023</td>\n",
+       "      <td>18:00</td>\n",
+       "      <td>Гатчинские ЭС</td>\n",
+       "      <td>Тосненский РЭС</td>\n",
+       "      <td>В связи с производством оперативных переключен...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Санкт-Петербург</td>\n",
+       "      <td>Фрунзенский район</td>\n",
+       "      <td>г. Санкт-Петербург</td>\n",
+       "      <td>ул. Димитрова  д. 12</td>\n",
+       "      <td>28-09-2023</td>\n",
+       "      <td>11:00</td>\n",
+       "      <td>28-09-2023</td>\n",
+       "      <td>17:00</td>\n",
+       "      <td>Кабельная сеть</td>\n",
+       "      <td>Невский РЭС</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Санкт-Петербург</td>\n",
+       "      <td>Фрунзенский район</td>\n",
+       "      <td>г. Санкт-Петербург</td>\n",
+       "      <td>ул. Димитрова  к.1</td>\n",
+       "      <td>28-09-2023</td>\n",
+       "      <td>11:00</td>\n",
+       "      <td>28-09-2023</td>\n",
+       "      <td>17:00</td>\n",
+       "      <td>Кабельная сеть</td>\n",
+       "      <td>Невский РЭС</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Санкт-Петербург</td>\n",
+       "      <td>Фрунзенский район</td>\n",
+       "      <td>г. Санкт-Петербург</td>\n",
+       "      <td>ул. Димитрова  лит.А</td>\n",
+       "      <td>28-09-2023</td>\n",
+       "      <td>11:00</td>\n",
+       "      <td>28-09-2023</td>\n",
+       "      <td>17:00</td>\n",
+       "      <td>Кабельная сеть</td>\n",
+       "      <td>Невский РЭС</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Санкт-Петербург</td>\n",
+       "      <td>Фрунзенский район</td>\n",
+       "      <td>г. Санкт-Петербург</td>\n",
+       "      <td>д. 16  к.1</td>\n",
+       "      <td>28-09-2023</td>\n",
+       "      <td>11:00</td>\n",
+       "      <td>28-09-2023</td>\n",
+       "      <td>17:00</td>\n",
+       "      <td>Кабельная сеть</td>\n",
+       "      <td>Невский РЭС</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1835</th>\n",
+       "      <td>250</td>\n",
+       "      <td>Ленинградская область</td>\n",
+       "      <td>Гатчинский район</td>\n",
+       "      <td>Антропшино, г. Коммунар, д. Покровское, Пудомя...</td>\n",
+       "      <td>Гамболовский проезд Парковая д.3</td>\n",
+       "      <td>21-09-2023</td>\n",
+       "      <td>00:00</td>\n",
+       "      <td>21-09-2023</td>\n",
+       "      <td>07:00</td>\n",
+       "      <td>Южные электрические сети</td>\n",
+       "      <td>Пушкинский РЭС</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1836</th>\n",
+       "      <td>250</td>\n",
+       "      <td>Ленинградская область</td>\n",
+       "      <td>Гатчинский район</td>\n",
+       "      <td>Антропшино, г. Коммунар, д. Покровское, Пудомя...</td>\n",
+       "      <td>Гамболовский проезд Садоводство Славяночка-2</td>\n",
+       "      <td>21-09-2023</td>\n",
+       "      <td>00:00</td>\n",
+       "      <td>21-09-2023</td>\n",
+       "      <td>07:00</td>\n",
+       "      <td>Южные электрические сети</td>\n",
+       "      <td>Пушкинский РЭС</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1837</th>\n",
+       "      <td>250</td>\n",
+       "      <td>Ленинградская область</td>\n",
+       "      <td>Гатчинский район</td>\n",
+       "      <td>Антропшино, г. Коммунар, д. Покровское, Пудомя...</td>\n",
+       "      <td>Гамболовский проезд Славяночка-3</td>\n",
+       "      <td>21-09-2023</td>\n",
+       "      <td>00:00</td>\n",
+       "      <td>21-09-2023</td>\n",
+       "      <td>07:00</td>\n",
+       "      <td>Южные электрические сети</td>\n",
+       "      <td>Пушкинский РЭС</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1838</th>\n",
+       "      <td>250</td>\n",
+       "      <td>Ленинградская область</td>\n",
+       "      <td>Гатчинский район</td>\n",
+       "      <td>Антропшино, г. Коммунар, д. Покровское, Пудомя...</td>\n",
+       "      <td>Гамболовский проезд КП Павловские Дачи</td>\n",
+       "      <td>21-09-2023</td>\n",
+       "      <td>00:00</td>\n",
+       "      <td>21-09-2023</td>\n",
+       "      <td>07:00</td>\n",
+       "      <td>Южные электрические сети</td>\n",
+       "      <td>Пушкинский РЭС</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1839</th>\n",
+       "      <td>250</td>\n",
+       "      <td>Ленинградская область</td>\n",
+       "      <td>Гатчинский район</td>\n",
+       "      <td>Антропшино, г. Коммунар, д. Покровское, Пудомя...</td>\n",
+       "      <td>Гамболовский проезд</td>\n",
+       "      <td>21-09-2023</td>\n",
+       "      <td>00:00</td>\n",
+       "      <td>21-09-2023</td>\n",
+       "      <td>07:00</td>\n",
+       "      <td>Южные электрические сети</td>\n",
+       "      <td>Пушкинский РЭС</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1840 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      index Регион РФ (область, край, город фед. значения, округ)  \\\n",
+       "0         0                              Ленинградская область      \n",
+       "1         1                                    Санкт-Петербург      \n",
+       "2         1                                    Санкт-Петербург      \n",
+       "3         1                                    Санкт-Петербург      \n",
+       "4         1                                    Санкт-Петербург      \n",
+       "...     ...                                                ...      \n",
+       "1835    250                              Ленинградская область      \n",
+       "1836    250                              Ленинградская область      \n",
+       "1837    250                              Ленинградская область      \n",
+       "1838    250                              Ленинградская область      \n",
+       "1839    250                              Ленинградская область      \n",
+       "\n",
+       "     Административный район  \\\n",
+       "0          Тосненский район   \n",
+       "1         Фрунзенский район   \n",
+       "2         Фрунзенский район   \n",
+       "3         Фрунзенский район   \n",
+       "4         Фрунзенский район   \n",
+       "...                     ...   \n",
+       "1835       Гатчинский район   \n",
+       "1836       Гатчинский район   \n",
+       "1837       Гатчинский район   \n",
+       "1838       Гатчинский район   \n",
+       "1839       Гатчинский район   \n",
+       "\n",
+       "                                       Населённый пункт  \\\n",
+       "0                                             п. Рябово   \n",
+       "1                                    г. Санкт-Петербург   \n",
+       "2                                    г. Санкт-Петербург   \n",
+       "3                                    г. Санкт-Петербург   \n",
+       "4                                    г. Санкт-Петербург   \n",
+       "...                                                 ...   \n",
+       "1835  Антропшино, г. Коммунар, д. Покровское, Пудомя...   \n",
+       "1836  Антропшино, г. Коммунар, д. Покровское, Пудомя...   \n",
+       "1837  Антропшино, г. Коммунар, д. Покровское, Пудомя...   \n",
+       "1838  Антропшино, г. Коммунар, д. Покровское, Пудомя...   \n",
+       "1839  Антропшино, г. Коммунар, д. Покровское, Пудомя...   \n",
+       "\n",
+       "                                             Улица  \\\n",
+       "0                              Мысленская,Заречная   \n",
+       "1                             ул. Димитрова  д. 12   \n",
+       "2                               ул. Димитрова  к.1   \n",
+       "3                             ул. Димитрова  лит.А   \n",
+       "4                                       д. 16  к.1   \n",
+       "...                                            ...   \n",
+       "1835              Гамболовский проезд Парковая д.3   \n",
+       "1836  Гамболовский проезд Садоводство Славяночка-2   \n",
+       "1837              Гамболовский проезд Славяночка-3   \n",
+       "1838        Гамболовский проезд КП Павловские Дачи   \n",
+       "1839                          Гамболовский проезд    \n",
+       "\n",
+       "     Плановая дата начала отключения электроснабжения  \\\n",
+       "0                                          28-09-2023   \n",
+       "1                                          28-09-2023   \n",
+       "2                                          28-09-2023   \n",
+       "3                                          28-09-2023   \n",
+       "4                                          28-09-2023   \n",
+       "...                                               ...   \n",
+       "1835                                       21-09-2023   \n",
+       "1836                                       21-09-2023   \n",
+       "1837                                       21-09-2023   \n",
+       "1838                                       21-09-2023   \n",
+       "1839                                       21-09-2023   \n",
+       "\n",
+       "     Плановое время начала отключения электроснабжения  \\\n",
+       "0                                                17:00   \n",
+       "1                                                11:00   \n",
+       "2                                                11:00   \n",
+       "3                                                11:00   \n",
+       "4                                                11:00   \n",
+       "...                                                ...   \n",
+       "1835                                             00:00   \n",
+       "1836                                             00:00   \n",
+       "1837                                             00:00   \n",
+       "1838                                             00:00   \n",
+       "1839                                             00:00   \n",
+       "\n",
+       "     Плановая дата восстановления отключения электроснабжения  \\\n",
+       "0                                            28-09-2023         \n",
+       "1                                            28-09-2023         \n",
+       "2                                            28-09-2023         \n",
+       "3                                            28-09-2023         \n",
+       "4                                            28-09-2023         \n",
+       "...                                                 ...         \n",
+       "1835                                         21-09-2023         \n",
+       "1836                                         21-09-2023         \n",
+       "1837                                         21-09-2023         \n",
+       "1838                                         21-09-2023         \n",
+       "1839                                         21-09-2023         \n",
+       "\n",
+       "     Плановое время восстановления отключения электроснабжения  \\\n",
+       "0                                                 18:00          \n",
+       "1                                                 17:00          \n",
+       "2                                                 17:00          \n",
+       "3                                                 17:00          \n",
+       "4                                                 17:00          \n",
+       "...                                                 ...          \n",
+       "1835                                              07:00          \n",
+       "1836                                              07:00          \n",
+       "1837                                              07:00          \n",
+       "1838                                              07:00          \n",
+       "1839                                              07:00          \n",
+       "\n",
+       "                        Филиал             РЭС  \\\n",
+       "0                Гатчинские ЭС  Тосненский РЭС   \n",
+       "1               Кабельная сеть     Невский РЭС   \n",
+       "2               Кабельная сеть     Невский РЭС   \n",
+       "3               Кабельная сеть     Невский РЭС   \n",
+       "4               Кабельная сеть     Невский РЭС   \n",
+       "...                        ...             ...   \n",
+       "1835  Южные электрические сети  Пушкинский РЭС   \n",
+       "1836  Южные электрические сети  Пушкинский РЭС   \n",
+       "1837  Южные электрические сети  Пушкинский РЭС   \n",
+       "1838  Южные электрические сети  Пушкинский РЭС   \n",
+       "1839  Южные электрические сети  Пушкинский РЭС   \n",
+       "\n",
+       "                                            Комментарий  \n",
+       "0     В связи с производством оперативных переключен...  \n",
+       "1                                                   NaN  \n",
+       "2                                                   NaN  \n",
+       "3                                                   NaN  \n",
+       "4                                                   NaN  \n",
+       "...                                                 ...  \n",
+       "1835                                                NaN  \n",
+       "1836                                                NaN  \n",
+       "1837                                                NaN  \n",
+       "1838                                                NaN  \n",
+       "1839                                                NaN  \n",
+       "\n",
+       "[1840 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from rosseti_parser import split_addresses\n",
+    "\n",
+    "df = split_addresses(df)\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fetch `Building_ID` and coordinates from Geocoder API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rosseti_parser import fetch_builing_ids\n",
+    "\n",
+    "file_path = 'data_with_building_id.csv'\n",
+    "\n",
+    "if os.path.isfile(file_path):\n",
+    "    df = pd.read_csv(file_path)\n",
+    "else:\n",
+    "    df = fetch_builing_ids(df)\n",
+    "    df.to_csv('./data_with_building_id.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1840 entries, 0 to 1839\n",
+      "Data columns (total 15 columns):\n",
+      " #   Column                                                     Non-Null Count  Dtype  \n",
+      "---  ------                                                     --------------  -----  \n",
+      " 0   index                                                      1840 non-null   int64  \n",
+      " 1   Регион РФ (область, край, город фед. значения, округ)      1840 non-null   object \n",
+      " 2   Административный район                                     1840 non-null   object \n",
+      " 3   Населённый пункт                                           1840 non-null   object \n",
+      " 4   Улица                                                      1741 non-null   object \n",
+      " 5   Плановая дата начала отключения электроснабжения           1840 non-null   object \n",
+      " 6   Плановое время начала отключения электроснабжения          1840 non-null   object \n",
+      " 7   Плановая дата восстановления отключения электроснабжения   1840 non-null   object \n",
+      " 8   Плановое время восстановления отключения электроснабжения  1840 non-null   object \n",
+      " 9   Филиал                                                     1839 non-null   object \n",
+      " 10  РЭС                                                        1839 non-null   object \n",
+      " 11  Комментарий                                                675 non-null    object \n",
+      " 12  ID здания                                                  576 non-null    float64\n",
+      " 13  Широта                                                     576 non-null    float64\n",
+      " 14  Долгота                                                    576 non-null    float64\n",
+      "dtypes: float64(3), int64(1), object(11)\n",
+      "memory usage: 215.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run dataframe preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rosseti_parser import preprocess_df, COL_NS, ICOL_NS, preprocess_read_df\n",
+    "\n",
+    "file_path = 'data_preprocessed.csv'\n",
+    "\n",
+    "if os.path.isfile(file_path):\n",
+    "    df = pd.read_csv(file_path)\n",
+    "    df = preprocess_read_df(df)\n",
+    "else:\n",
+    "    df = preprocess_df(df)\n",
+    "    df.to_csv('./data_preprocessed.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1840 entries, 0 to 1839\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column       Non-Null Count  Dtype         \n",
+      "---  ------       --------------  -----         \n",
+      " 0   index        1840 non-null   int64         \n",
+      " 1   region       1840 non-null   object        \n",
+      " 2   area         1840 non-null   object        \n",
+      " 3   town         1840 non-null   object        \n",
+      " 4   street       1741 non-null   object        \n",
+      " 5   branch       1839 non-null   object        \n",
+      " 6   res          1839 non-null   object        \n",
+      " 7   comment      675 non-null    object        \n",
+      " 8   building_id  576 non-null    float64       \n",
+      " 9   lat          576 non-null    float64       \n",
+      " 10  lng          576 non-null    float64       \n",
+      " 11  start        1840 non-null   datetime64[ns]\n",
+      " 12  finish       1840 non-null   datetime64[ns]\n",
+      "dtypes: datetime64[ns](2), float64(3), int64(1), object(7)\n",
+      "memory usage: 187.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Group dataframe rows by Rosseti table indexes "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rosseti_parser import group_by_index\n",
+    "\n",
+    "igr_df = group_by_index(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>region</th>\n",
+       "      <th>area</th>\n",
+       "      <th>town</th>\n",
+       "      <th>street</th>\n",
+       "      <th>branch</th>\n",
+       "      <th>res</th>\n",
+       "      <th>comment</th>\n",
+       "      <th>building_id</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>start</th>\n",
+       "      <th>finish</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ленинградская область</td>\n",
+       "      <td>Тосненский район</td>\n",
+       "      <td>п. Рябово</td>\n",
+       "      <td>[Мысленская,Заречная]</td>\n",
+       "      <td>Гатчинские ЭС</td>\n",
+       "      <td>Тосненский РЭС</td>\n",
+       "      <td>В связи с производством оперативных переключен...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2023-09-28 17:00:00</td>\n",
+       "      <td>2023-09-28 18:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Санкт-Петербург</td>\n",
+       "      <td>Фрунзенский район</td>\n",
+       "      <td>г. Санкт-Петербург</td>\n",
+       "      <td>[ул. Димитрова  д. 12, ул. Димитрова  к.1, ул....</td>\n",
+       "      <td>Кабельная сеть</td>\n",
+       "      <td>Невский РЭС</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[46292.0, nan, nan, nan, nan, nan, nan, nan]</td>\n",
+       "      <td>[59.847309449, nan, nan, nan, nan, nan, nan, nan]</td>\n",
+       "      <td>[30.380589538, nan, nan, nan, nan, nan, nan, nan]</td>\n",
+       "      <td>2023-09-28 11:00:00</td>\n",
+       "      <td>2023-09-28 17:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Санкт-Петербург</td>\n",
+       "      <td>Приморский район (СПб)</td>\n",
+       "      <td>Санкт-Петербург</td>\n",
+       "      <td>[пр. Комендантский д. 7 к. 1 лит. А , пр. Коме...</td>\n",
+       "      <td>Кабельная сеть</td>\n",
+       "      <td>Западный РЭС</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[112146.0, 126063.0, nan]</td>\n",
+       "      <td>[60.005013183, 60.004879653, nan]</td>\n",
+       "      <td>[30.266799897, 30.264780597, nan]</td>\n",
+       "      <td>2023-09-28 10:00:00</td>\n",
+       "      <td>2023-09-28 17:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Санкт-Петербург</td>\n",
+       "      <td>Выборгский район (СПб)</td>\n",
+       "      <td>Санкт-Петербург</td>\n",
+       "      <td>[ул. Композиторов д.10 д.6]</td>\n",
+       "      <td>Кабельная сеть</td>\n",
+       "      <td>Северный РЭС</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[56457.0]</td>\n",
+       "      <td>[60.049447721]</td>\n",
+       "      <td>[30.314760352]</td>\n",
+       "      <td>2023-09-28 10:00:00</td>\n",
+       "      <td>2023-09-28 17:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Санкт-Петербург</td>\n",
+       "      <td>Приморский район (СПб)</td>\n",
+       "      <td>Санкт-Петербург</td>\n",
+       "      <td>[ул. Маршала Новикова д. 6 к. 1 лит. А , ул. М...</td>\n",
+       "      <td>Кабельная сеть</td>\n",
+       "      <td>Западный РЭС</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[4020.0, 25834.0, 85754.0, 63976.0, 46929.0, 4...</td>\n",
+       "      <td>[60.010836015, 60.009947245, 60.010262878, 60....</td>\n",
+       "      <td>[30.265892945, 30.265490881, 30.266135371, 30....</td>\n",
+       "      <td>2023-09-28 10:00:00</td>\n",
+       "      <td>2023-09-28 17:00:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      region                    area                town  \\\n",
+       "index                                                                      \n",
+       "0      Ленинградская область        Тосненский район           п. Рябово   \n",
+       "1            Санкт-Петербург       Фрунзенский район  г. Санкт-Петербург   \n",
+       "2            Санкт-Петербург  Приморский район (СПб)     Санкт-Петербург   \n",
+       "3            Санкт-Петербург  Выборгский район (СПб)     Санкт-Петербург   \n",
+       "4            Санкт-Петербург  Приморский район (СПб)     Санкт-Петербург   \n",
+       "\n",
+       "                                                  street          branch  \\\n",
+       "index                                                                      \n",
+       "0                                  [Мысленская,Заречная]   Гатчинские ЭС   \n",
+       "1      [ул. Димитрова  д. 12, ул. Димитрова  к.1, ул....  Кабельная сеть   \n",
+       "2      [пр. Комендантский д. 7 к. 1 лит. А , пр. Коме...  Кабельная сеть   \n",
+       "3                            [ул. Композиторов д.10 д.6]  Кабельная сеть   \n",
+       "4      [ул. Маршала Новикова д. 6 к. 1 лит. А , ул. М...  Кабельная сеть   \n",
+       "\n",
+       "                  res                                            comment  \\\n",
+       "index                                                                      \n",
+       "0      Тосненский РЭС  В связи с производством оперативных переключен...   \n",
+       "1         Невский РЭС                                                NaN   \n",
+       "2        Западный РЭС                                                NaN   \n",
+       "3        Северный РЭС                                                NaN   \n",
+       "4        Западный РЭС                                                NaN   \n",
+       "\n",
+       "                                             building_id  \\\n",
+       "index                                                      \n",
+       "0                                                    NaN   \n",
+       "1           [46292.0, nan, nan, nan, nan, nan, nan, nan]   \n",
+       "2                              [112146.0, 126063.0, nan]   \n",
+       "3                                              [56457.0]   \n",
+       "4      [4020.0, 25834.0, 85754.0, 63976.0, 46929.0, 4...   \n",
+       "\n",
+       "                                                     lat  \\\n",
+       "index                                                      \n",
+       "0                                                    NaN   \n",
+       "1      [59.847309449, nan, nan, nan, nan, nan, nan, nan]   \n",
+       "2                      [60.005013183, 60.004879653, nan]   \n",
+       "3                                         [60.049447721]   \n",
+       "4      [60.010836015, 60.009947245, 60.010262878, 60....   \n",
+       "\n",
+       "                                                     lng               start  \\\n",
+       "index                                                                          \n",
+       "0                                                    NaN 2023-09-28 17:00:00   \n",
+       "1      [30.380589538, nan, nan, nan, nan, nan, nan, nan] 2023-09-28 11:00:00   \n",
+       "2                      [30.266799897, 30.264780597, nan] 2023-09-28 10:00:00   \n",
+       "3                                         [30.314760352] 2023-09-28 10:00:00   \n",
+       "4      [30.265892945, 30.265490881, 30.266135371, 30.... 2023-09-28 10:00:00   \n",
+       "\n",
+       "                   finish  \n",
+       "index                      \n",
+       "0     2023-09-28 18:00:00  \n",
+       "1     2023-09-28 17:00:00  \n",
+       "2     2023-09-28 17:00:00  \n",
+       "3     2023-09-28 17:00:00  \n",
+       "4     2023-09-28 17:00:00  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "igr_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 251 entries, 0 to 250\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column       Non-Null Count  Dtype         \n",
+      "---  ------       --------------  -----         \n",
+      " 0   region       251 non-null    object        \n",
+      " 1   area         251 non-null    object        \n",
+      " 2   town         251 non-null    object        \n",
+      " 3   street       152 non-null    object        \n",
+      " 4   branch       250 non-null    object        \n",
+      " 5   res          250 non-null    object        \n",
+      " 6   comment      81 non-null     object        \n",
+      " 7   building_id  69 non-null     object        \n",
+      " 8   lat          69 non-null     object        \n",
+      " 9   lng          69 non-null     object        \n",
+      " 10  start        251 non-null    datetime64[ns]\n",
+      " 11  finish       251 non-null    datetime64[ns]\n",
+      "dtypes: datetime64[ns](2), object(10)\n",
+      "memory usage: 25.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "igr_df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Building fetching visualisation "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_percent(n):\n",
+    "    print(np.round(n * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Percent of rows with street specified"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "61.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "igr_not_nan_street_df = igr_df[~pd.isnull(igr_df['street'])].copy()\n",
+    "\n",
+    "not_nan_street = len(igr_not_nan_street_df)/len(igr_df)\n",
+    "\n",
+    "print_percent(not_nan_street)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Percent of rows with at least one building found"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "45.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "@np.vectorize\n",
+    "def has_fetched_building_id(cell):\n",
+    "    return len(np.array(cell)[~pd.isnull(np.array(cell))]) > 0\n",
+    "\n",
+    "\n",
+    "not_nan_building_rows = len(igr_not_nan_street_df[\n",
+    "    has_fetched_building_id(igr_not_nan_street_df['building_id'])\n",
+    "]) / len(igr_not_nan_street_df)\n",
+    "\n",
+    "print_percent(not_nan_building_rows)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Percent of street entities with found buildings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "32.0\n",
+      "71.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "def count_found_building_percent(row: pd.Series):\n",
+    "    streets = np.array(row['street'])\n",
+    "    buildings = np.array(row['building_id'])\n",
+    "\n",
+    "    buildings = buildings[~pd.isnull(buildings)]\n",
+    "\n",
+    "    return len(buildings) / len(streets)\n",
+    "\n",
+    "\n",
+    "found_buildings_per_row = igr_not_nan_street_df[['street', 'building_id']].apply(\n",
+    "    count_found_building_percent, axis=1)\n",
+    "\n",
+    "found_buildings = found_buildings_per_row.mean()\n",
+    "found_buildings_not_nan_street = found_buildings_per_row[found_buildings_per_row != 0].mean()\n",
+    "\n",
+    "print_percent(found_buildings)\n",
+    "\n",
+    "print_percent(found_buildings_not_nan_street)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def full_pair(n):\n",
+    "    return np.array([n, 1 - n])\n",
+    "\n",
+    "\n",
+    "percents_df = pd.DataFrame({\n",
+    "    'Найдено зданий всего': full_pair(found_buildings) * not_nan_street,\n",
+    "    'Найдено хотя бы одно здание': full_pair(found_buildings_not_nan_street) * not_nan_street,\n",
+    "    'Непустое поле \"Улица\"': full_pair(not_nan_street),\n",
+    "}, index=['Да', 'Нет']).T\n",
+    "\n",
+    "percents_df.plot(kind='barh', stacked=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "area\n",
+       "Приморский район (СПб)     9.681592\n",
+       "Курортный район            6.953731\n",
+       "Красногвардейский район    6.571429\n",
+       "Фрунзенский район          6.304798\n",
+       "Пушкинский район           5.096774\n",
+       "Name: found_building_percent, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "region\n",
+       "Санкт-Петербург          47.958324\n",
+       "Ленинградская область     0.893617\n",
+       "Name: found_building_percent, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "town\n",
+       "г. Санкт-Петербург    34.892893\n",
+       "г. Пушкин              3.000000\n",
+       "г.Зеленогорск          3.000000\n",
+       "п.Лисий Нос            1.253731\n",
+       "п.Молодежное           1.100000\n",
+       "Name: found_building_percent, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "res\n",
+       "Западный РЭС      8.666667\n",
+       "Курортный РЭС     6.953731\n",
+       "Пушкинский РЭС    5.990391\n",
+       "Южный РЭС         5.333333\n",
+       "Восточный РЭС     4.571429\n",
+       "Name: found_building_percent, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "igr_not_nan_street_df['found_building_percent'] = found_buildings_per_row\n",
+    "\n",
+    "fb_area = igr_not_nan_street_df.groupby('area')['found_building_percent'].sum().sort_values(ascending=False)\n",
+    "fb_region = igr_not_nan_street_df.groupby('region')['found_building_percent'].sum().sort_values(ascending=False)\n",
+    "fb_town = igr_not_nan_street_df.groupby('town')['found_building_percent'].sum().sort_values(ascending=False)\n",
+    "fb_res = igr_not_nan_street_df.groupby('res')['found_building_percent'].sum().sort_values(ascending=False)\n",
+    "\n",
+    "fb_town.loc['г. Санкт-Петербург'] += fb_town['Санкт-Петербург']\n",
+    "fb_town.drop('Санкт-Петербург', inplace=True)\n",
+    "\n",
+    "fb_area = fb_area[~(fb_area == 0)]\n",
+    "fb_town = fb_town[~(fb_town == 0)]\n",
+    "fb_res = fb_res[~(fb_res == 0)]\n",
+    "\n",
+    "for series in (fb_area, fb_region, fb_town, fb_res):\n",
+    "    display(series.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_found_building_percent(df, name):\n",
+    "    fb_area_df = pd.DataFrame(df).rename(columns={\n",
+    "        'found_building_percent': 'Найдено зданий в ' + name\n",
+    "    })\n",
+    "\n",
+    "    ax = fb_area_df.T.plot(kind='barh', stacked=True)\n",
+    "\n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "    ax.spines['bottom'].set_visible(False)\n",
+    "    ax.spines['left'].set_visible(False)\n",
+    "\n",
+    "    ax.set_xticks([])\n",
+    "\n",
+    "    return ax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7ff44cef9510>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fb_area_ax = plot_found_building_percent(fb_area, 'районах')\n",
+    "fb_area_ax.legend(ncol=3, loc=(-0.46, -0.25), framealpha=1, title='Район')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7ff44d91f290>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fb_region_ax = plot_found_building_percent(fb_region, 'регионах РФ')\n",
+    "fb_region_ax.legend(loc=(0.05, 0), title='Регион')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7ff44cdbeed0>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fb_town_ax = plot_found_building_percent(fb_town, 'населённых пунктах')\n",
+    "fb_town_ax.legend(ncol=2, loc=(-0.8, -0.25), framealpha=1, title='Населённый пункт')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7ff44cdfef90>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fb_res_ax = plot_found_building_percent(fb_res, 'РЭС')\n",
+    "fb_res_ax.legend(ncol=3, loc=(-0.245, -0.1), framealpha=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outage statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 576 entries, 0 to 575\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column       Non-Null Count  Dtype         \n",
+      "---  ------       --------------  -----         \n",
+      " 0   index        576 non-null    int64         \n",
+      " 1   region       576 non-null    object        \n",
+      " 2   area         576 non-null    object        \n",
+      " 3   town         576 non-null    object        \n",
+      " 4   street       576 non-null    object        \n",
+      " 5   branch       576 non-null    object        \n",
+      " 6   res          576 non-null    object        \n",
+      " 7   comment      274 non-null    object        \n",
+      " 8   building_id  576 non-null    float64       \n",
+      " 9   lat          576 non-null    float64       \n",
+      " 10  lng          576 non-null    float64       \n",
+      " 11  start        576 non-null    datetime64[ns]\n",
+      " 12  finish       576 non-null    datetime64[ns]\n",
+      "dtypes: datetime64[ns](2), float64(3), int64(1), object(7)\n",
+      "memory usage: 58.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "has_geo_df = df[pd.notnull(df['building_id'])].reset_index(drop=True)\n",
+    "\n",
+    "has_geo_df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Distribution of outage by observation day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dates_df = pd.DataFrame(index=pd.to_datetime(np.unique(np.concatenate([has_geo_df[a].dt.date.unique() for a in ('start', 'finish')]))))\n",
+    "\n",
+    "start_day_count = has_geo_df.groupby(has_geo_df['start'].dt.day).size()\n",
+    "finish_day_count = has_geo_df.groupby(has_geo_df['finish'].dt.day).size()\n",
+    "\n",
+    "dates_df = dates_df.join(pd.Series(start_day_count, name='Количество начавшихся'), on=dates_df.index.day)\n",
+    "\n",
+    "dates_df = dates_df.join(pd.Series(finish_day_count, name='Количество закончившихся'), on=dates_df.index.day)\n",
+    "\n",
+    "ax = plt.subplot()\n",
+    "\n",
+    "dates_df.plot(kind='bar', ax=ax)\n",
+    "\n",
+    "ax.set_xticklabels(dates_df.index.strftime(\"%m-%d\\n%a\"))\n",
+    "ax.get_xaxis().set_tick_params(labelrotation=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Distribution of outage time by hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "start_n = has_geo_df.groupby(has_geo_df['start'].dt.hour).size().rename('Начавшиеся')\n",
+    "finish_n = has_geo_df.groupby(has_geo_df['finish'].dt.hour).size().rename('Закончившиеся')\n",
+    "\n",
+    "hrs_df = pd.concat([start_n, finish_n], axis=1, sort=True)\n",
+    "\n",
+    "hrs_df.fillna(0, inplace=True)\n",
+    "\n",
+    "hrs_df.plot()\n",
+    "\n",
+    "xlim = plt.xlim()\n",
+    "\n",
+    "plt.ylabel('Количество')\n",
+    "plt.gca().get_xaxis().set_major_formatter('{x:.0f}:00')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Number of outages in time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Количество')"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "start_series = has_geo_df.resample('1H', on='start').size()\n",
+    "finish_series = has_geo_df.resample('1H', on='finish').size()\n",
+    "\n",
+    "start_series.plot(label='Начавшиеся')\n",
+    "finish_series.plot(label='Закончившиеся')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.xlabel('')\n",
+    "plt.ylabel('Количество')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Frequency representation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7ff44a7fd410>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for series in (start_series, finish_series):\n",
+    "    plt.plot(abs(np.fft.rfft(series)))\n",
+    "\n",
+    "plt.legend(('Начало', 'Конец'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Number of simultanious outages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Количество одновременно отключённых')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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MUaI0ZcYMEREZj6D2XmYMiChEsgOi5ORkKTUpCAK6d+8ufc+iavWIGaKz9SECgqfMmCEiIuNRa5UZGzNSuGQHRD/++KMa46BzkGqIWFRNRBFM7T5ErCGiUMkOiAYMGKDGOOgsfD4BJe76TZlxPzMiMjL1tu6oeXwiuUIqql69ejVuueUW9O3bF0ePHgUAvP/++/jpp58UHRz5Fbs9EF/j55wyq76fGSIiMiLxWmZTOCKSaoiYIaIQ1SsgevPNN1FcXAwA+PTTTzF8+HDExsZi8+bNcLvdAICioiL89a9/VW+kJiauGHNEWc+5HQc3eCUiIwssu1dpLzPGQxSiegVEf//731FYWAgAeP755/Hmm2/i3//+N6KjA9M3/fr1w+bNm1UZpNnVZ6d7EWuIiMjI1JoyEzNO3MuMQlWvGqIdO3ZI3+/Zswf9+/ev9Rin0ykFTaSsQFPGc/+62JiRiIwssHWHSnuZccqMQlSvDNFll10m1QplZGRg3759tR7z008/oW3btsqOjgAEN2Wsf4aIjRmJyIjU6kNks3DZPYWnXgHRiBEjEB8fDwCYMGEC7rvvPmzYsAEWiwU5OTn48MMP8eCDD2LSpEmqDtas6tuUMfgxFVU+uD3sCUVExqLasnsrGzNSeOo1ZTZjxgzp+0cffRQ+nw+DBw9GWVkZ+vfvD4fDgQcffBD33nuvagM1s/o2ZQSAhKDHFFd44Eg4exE2EZGWxCkt5afM2IeIwiO7D5HFYsHjjz+Ohx56CPv27UNJSQk6d+6MhIQENcZHqH9TRsD/KSnREYVitweu8io0SXCoPTwionpTr6i65vGJ5JIdEInsdjs6d+6s5FjoDKRVZvWYMgP8gVOx28PCaiIyHEGlomr2IaJwyQ6IsrKyznr/Z599FvJgqG7FFfXb6V6UyOaMRGRQgT5Eyh7XyqJqCpPsTtVOp7PG16JFi2C1WqW/k/ICy+7rnyEK/jkiIqNQbXNXqTEjIyIKjewM0fz582v8/X//+x9mz57NJfcqktOYEQjevoNTZkRkLGr1IZJqiJgiohCFtJdZMJ/Pp/jySapJTmNG/+OYISIiYxL7ENnCfvepycpl9xQm2Rkil8sFACgvL8eCBQvg9XrRrFkzxQdGAXJWmQU/js0Zicho1OpDJE2Z+RQ9LJmI7IAoOTlZ+o9st9vx6quvwuHg0m41BVaZ1TdDxCkzIjImtWqIAp2qmSGi0MgOiH788UcAQGxsLM477zw0atRI8UFRwPr9J1FUXgWLBfXuKcSiaiIyqkANkbLHFY/HgIhCJTsgGjBggBrjoDpUenx4/PPtAICberVEcpy9Xj/HHe+JyKgEtVaZsYaIwhRSWduiRYvQv39/NGnSBKmpqRgwYAC+/fZbpcdmev9a9Qf+OF6KJgl2PDK8Y71/jjveE5FRiVtrKL0Wx8YaIgqT7IDo7bffRlZWFjp16oRXX30Vr7zyCjp06ICsrCy88847aozRlA6eLMUby/cBAJ68pjOccfUrqAaYISIi41Jr2b2UIeKyewqR7Cmz2bNn47XXXsPdd98t3Xbrrbeie/fueOmllzBu3DhFB2hWb6/Ohtvjw2Xtm2DkhfJW8SVy2T0RGZRqU2YsqqYwyc4QHTlyBEOGDKl1+9ChQ3Hw4EFFBkXA/hMlAICsHs1lL0+Vpsy4yoyIDEatzV3F47FTNYVKdkDUtm1bLFq0qNbtX3/9NVq2bKnIoAg4cqocANA8OVb2z4pTZuVVXlRxQp2IDEStPkS26oiI8RCFSvaU2YMPPog777wT69evR9++fQEAa9aswaeffoq33npL8QGakc8nIKfQHxC1SImT/fOJQf2Kiis8SImv3+o0IiK1qZUhskhF1YyIKDSyA6Lbb78dMTExmDt3Lj7++GM0a9YMnTt3xjfffIPhw4erMUbTyS92o8orIMpqQXqi/KaXUTYr4u02lFZ64SqvYkBERIYhZnBsCkdENk6ZUZhkB0QAMHbsWIwdOxaJiYlYuXIlN3ZV2JFTZQCApskxiApxw5+k2Gh/QMTCaiIyEDFDpN6UGQMiCo3sgCgrK0v6vqKiApMmTUJ8fLx022effabMyEwsnPohUVJMNI4VVaCIS++JyEA4ZUZGJTsgcjqd0ve33HKLooMhPzFD1KKR/PohUYzdBgCoqGJRNREZh6/6kqT4XmZSp2pFD0smIjsgmj9/vhrjoCBihqhFo9AzRFFW8dMSAyIiMg71+hD5/2RjRgpVaAUqpKqj4gqzMDJE4qclDy8ORGQgXkGdrTvYmJHCJTtDlJKSctb7CwoKQh4M+SlRQxTIEPHiQETGodrWHWINES95FCLZGaLCwkLMnDkTc+bMqfNLjlWrVuHaa69Fs2bNYLFY8MUXX9S4//bbb4fFYqnxdeWVV9Z4TEFBAf785z8jKSkJycnJGD9+PEpKSmo8Ztu2bbj88ssRExODzMxMzJ49W+5pa8bnE3BUgSkzKUPEqwMRGYg0Zabw/ISNe5lRmEJedp+Wlhb2k5eWluLCCy/EuHHjaqxeC3bllVfWqFtyOGr25fnzn/+MY8eOYenSpaiqqsIdd9yBiRMnYsGCBQAAl8uFYcOGYciQIXjzzTexfft2jBs3DsnJyZg4cWLY56C04yVuVHp9sFktaOqMCfk4kZwhqvT48Lclu5Hnctfr8Y3iovHg8A7SHm5EZFyqb+7KKTMKkeyAyGKxoLi4GImJiYiNDT2DAQBXXXUVrrrqqrM+xuFwICMjo877du3ahcWLF+OXX37BxRdfDAB44403cPXVV+Pll19Gs2bN8OGHH6KyshLvvPMO7HY7unTpgq1bt+LVV181ZEAkrjDLSAq9BxEA2Ko/fkViDdGSnbn49+psWT/jFQQ8P6qrSiMiIqWovpdZBF7zyBhkB0SCIOD8888HAFitVqSnp6N79+4YN24cRo8erfgAV6xYgbS0NDRq1AiDBg3C888/j8aNGwMA1q1bh+TkZCkYAoAhQ4bAarViw4YNGD16NNatW4f+/fvDbg90ax4+fDheeuklnDp1Co0aNar1nG63G253IDvhcrkUP68zkeqHwpguAyJ7ldnWw4UAgL7tGmNwp/SzPraovAqvL9uLDzccwpgeLdC9Ze3fJxEZhzilpXhjRgv3MqPwyA6IfvzxRwiCgKqqKrhcLuTk5OCXX37BDTfcgH/961+44447FBvclVdeiaysLLRp0wZ//PEHHnvsMVx11VVYt24dbDYbcnNza03dRUVFISUlBbm5uQCA3NxctGnTpsZj0tPTpfvqCohmzZqFZ555RrHzkEOJJfcAYLNF7iqzbUcKAQBZPVrg+p4tzvn4I6fK8Nnmo3js8x34ekq/sDJrRKQuaesOhQMiqTEjIyIKkeyAaMCAAXXe3qNHD7z66quKBkRjx46Vvu/atSu6deuGdu3aYcWKFRg8eLBiz3O6GTNmYPr06dLfXS4XMjMzVXu+YIGAKPQl90Dk1hB5fQJ2HPVn5C5s4TzHo/0ev7oTlu/Ox65jLsxfcwAT+nMrGSKj8qnUh8gWodc8Mg7FPkpPmDABzz33nFKHq1Pbtm3RpEkT7Nu3DwCQkZGB/Pz8Go/xeDwoKCiQ6o4yMjKQl5dX4zHi389Um+RwOJCUlFTjSyuBLtXhTplFZg3RvvwSlFd5EW+3oW1qQr1+pnGCAzOu6ggAeHHxbsxfk839jIgMSrwkKd2HSEwM87VPoZIdEFVV1b03Vnx8PBIS6vcGFqojR47g5MmTaNq0KQCgT58+KCwsxKZNm6THLF++HD6fD71795Yes2rVqhrjXrp0KTp06FDndJnepKaMYfQgAiI3Q/Rr9XTZBc2dsnbD/lPPTFzfswW8PgHPfP0bHvrfNlRUeVUaJRGFSsoQKVxVzSkzCpfsgGjEiBEoLy+vcVtJSQkmTJiAUaNGyTpWSUkJtm7diq1btwIAsrOzsXXrVhw6dAglJSV46KGHsH79ehw4cADLli3Dddddh/bt22P48OEAgE6dOuHKK6/EhAkT8PPPP2PNmjWYMmUKxo4di2bNmgEAbr75ZtjtdowfPx47d+7EwoUL8dprr9WYEjMKQQjuQRTelJlUQxRhfYjE+qELM5Nl/ZzVasHfru+GJ0Z0gtUC/G/TEfznJ3kr1YhIfYK07F7Z44o1SRG4joQMQnZA5PP5MHjwYBQVFQHwZ1u6dOmC3bt3Y8uWLbKOtXHjRnTv3h3du3cHAEyfPh3du3fHU089BZvNhm3btmHkyJE4//zzMX78ePTs2ROrV6+u0Yvoww8/RMeOHTF48GBcffXVuOyyy/Cvf/1Lut/pdOL7779HdnY2evbsiQceeABPPfWUIZfcHy9xw+3xwWoBMsLoQQRE7iqzbUf8/6+61bN+KJjFYsGdl7fFlIHtAQD7j5cqOjYiCp9Xtb3M2IeIwiO7qPrbb7/FjTfeiP79++OSSy7Bf//7Xzz33HOYNm2a7GWUV1xxxVnne5csWXLOY6SkpEhNGM+kW7duWL16tayx6UEsqE5PioE9Krzyrkjcy8zt8WLXMbGgOjnk4yTF+hs0RlowSGQGPrX2MrPWPD6RXLIDIrvdjk8//RS333475s+fj++++w7Dhg1TY2ymU1hWCQBokuA4xyPPLRJriHYfK0aVV0CjuOiwisqjIjAYJDIDQRCCpsxU2suMr3sKkeyAaNu2bQCABx54AMeOHcOdd96J999/XypQ7tatm7IjNJFSt78IOM5uC/tYkdipWqwf6tYiOaymbbbq5SaRVj9F1NAFJ2/UWnYfQZc8MhjZAdFFF10kvVmJ010DBw4E4K/h8Hq5sidUpW4PACDBEdIWczVEYobo1+r6ofr2HzoTZoiIjCl4Okv5rTtYQ0Thkf3Om53NlTtqKakOiOIVCIgCNUSRU0cTnCEKR6QWlBM1dMGfUZTeuoN7mVG4ZL/zHjx4EH379kVUVPhv2lRTWaU/uxbvCH/KLBIzRGJRefu08PpZRUXwtiVEDVlw9kZOn7H6EI/HBBGFSvZSpoEDB6KgoECNsZieOGUWb1cgQxRhfYgEQUB5dSPFuDADQql+KkLOncgsatYQKXtsFlVTuGQHRGyLrh4lp8wiLUPk9viki2VsdHgBUXSEnTuRWdSsIVJplRnfoyhEIb3zrlu37ozbXvTv3z+sAZmZkkXVkbbKzF0VqPeJCTMgisT6KSIzCA6I1OpDxA/tFKqQ3nlHjx5d5+1cZRae0kplpoyAyMsQidNlUVYLom3hNaVkDRGRMfnUXHbPKTMKU0jvPLm5ufD5fLW+GAyFR9kMUWRlScSNWMOdLgNYQ0RkVD6filNm7ENEYZIdECm9VJIClCyqjtQMkUOBgIg1RETGpEkfIr7uKUQsqjYQJafMIm0vMzEgirWHN10GBM69KkKyY0RmoWYfIhsbM1KYZKcifHyTUY2inaptkZUlEafMYqIUqJ+KsHMnMgtB2ule+WOL8RVXmVGoQnrn/eOPPzB37lzs2rULANC5c2fcd999aNeunaKDMxtlO1VHVh2NVEOkwD5uURF27kRmIX5GUbp+CAjay4yf2SlEsucnlixZgs6dO+Pnn39Gt27d0K1bN2zYsAFdunTB0qVL1RijKQiCIHWqNuNeZuWV/qtYuEvugcCFMVLOncgsfFKGSMWAiBkiCpHsd95HH30U06ZNw4svvljr9kceeQRDhw5VbHBm4vb4pDdwZXa7j8xVZkoERIFl95Fx7kRmIQYraqzN4ZQZhUt2hmjXrl0YP358rdvHjRuH3377TZFBmZE4XQaYe5VZbHT4RdVREdaUksgsxFhF6X3MgEBRtSBw8Q+FRva7T2pqKrZu3Vrr9q1btyItLU2JMZmSWFAdZ7dJ/TTCEWmrzBTNEInBIGuIiAxFzSmz4GNGyGWPDEZ2KmLChAmYOHEi9u/fj759+wIA1qxZg5deegnTp09XfIBmUequXnKvQHYICGRJIiVDpGxjRi67JzIi8XKkxpRZ8AdJr09QJQtFDZvsd98nn3wSiYmJeOWVVzBjxgwAQLNmzTBz5kxMnTpV8QGaRWmluOQ+/IAAiLwMUbkKNUSREgwSmYX4mlQnQxT4noXVFArZAZHFYsG0adMwbdo0FBcXAwASExMVH5jZKLnkHoi8oKCiSrlVZqwhIjImNfsQBWeEGBBRKMJ692UgpJyy6ikzpQKiSFtlVq7glJlYQyQITJ0TGYmafYhYQ0ThCn9JDykisI+ZMlNmkVZYXFGp4NYdtsCFMVICQiIzCCy7VzcgipTMOBkLAyKDUHrKLNJqiCo8ytUQRVsD/615YSQyDp9GU2Zcdk+hYEBkEGWVyu1jBkTeKrPySuUCouALY6QEhERmIKg6ZRb4PlKue2QsDIgMokThZfcRlyFStKg6KCCKkClDIjNQM0NksVjYrZrCElJAtHLlSlx77bVo37492rdvj5EjR2L16tVKj81UAjvdK1xDFCEBkZJF1VZr4MLIGiIi4wj0IVJnoYM1qFs1kVyyA6IPPvgAQ4YMQVxcHKZOnYqpU6ciNjYWgwcPxoIFC9QYoymUqlZDFBkBgZKNGYFAHVGkBIREZiBmiNRa+Slu38HXPYVC9rvvCy+8gNmzZ2PatGnSbVOnTsWrr76K5557DjfffLOiAzQLsTFjnGn7EIk1RMrM4tqsFsDLKTMiI1GzDxEAWK0AvOxDRKGR/e6zf/9+XHvttbVuHzlyJLKzsxUZlBmJW3ewU7WyU4aRcv5EZqBmH6Lg40ZIYpwMRnZAlJmZiWXLltW6/YcffkBmZqYigzIjadm9wnuZCQLgi4CgQMmiaiA4Q8YrI5FRiBlrleKhwJQZM0QUAtnvvg888ACmTp2KrVu31tjc9d1338Vrr72m+ADNQull96cvPbcbvFuzVFStUGNKG7fvIDIcNXe7BwKBFqfMKBSy330nTZqEjIwMvPLKK/j4448BAJ06dcLChQtx3XXXKT5As5B2u1esD1HkdG31+gRUevyZHKWKqqUpM9YQERmGmn2IgMAHwUjIipPxhPTuO3r0aIwePVrpsZhaicLL7oMzRFU+H2KhzHHV4K7uUg0oXFQNZoiIjCSwdYc6x5cCIr7sKQRszGgQ4pSZYrvdB2eIDJ4lEbtUA0BMlELL7llDRGQ4ahdVW7jsnsIg+923UaNGZ22qVVBQENaAzMjt8aKqOmhRulM1YPwsiVg/5IiywqpQrZN4/lUGDwaJzESqIVLpo7hYVM0aIgqF7HffuXPnAvD3k5g0aRKeffZZpKWlKT0uUxHrhwDldru3WCywWS3w+gTDf1pSeoUZEHl7uRGZgaByUbWVRdUUBtkB0W233SZ9f++992LMmDFo27atooMyG7FLdUy0FVE25T46iQGR0btVK92lGggsuzd6dozITMRLkWpbd0TYlkVkLKwhMoBShZfci6Ij5OKgdJdqIHgvN2MHg0RmIm3doVanaguLqil0Yb8DqRXpm4mYIVKqfkgUKSutlO5SDbCGiMiI1C6qDqwy4+ue5JP9DpyVlSV9X1FRgbvvvhvx8fHSbZ999pkyIzORkuoaIqVWmInE6TejZ4jEVWZKNWUEWENEZESa1RDxdU8hkP0O7HQ6pe9vueUWRQdjVmUK9yAS2SKkOWFFdVNGpZbcA6whIjIir8p9iKzcuoPCIDsgmj9/vhrjMLUSlabMoiKlhkiFDFEgGGQNEZFRaDZlxpc9hYBF1QZQ6lanqDpQQ2Tsq0OFR4VVZhFSP0VkJoLKfYgs7ENEYZD9Dty9e/ezFlJv3rw5rAGZUWmlWEOk7JRZpGSIxBoih5KrzCKkforITNTe3FXsWsIpMwqF7IBo1KhRAPyR/qxZs3D33XcjJSVF6XGZipghUrqoOtJWmamSIeKUGZFhqN6HqPq4AgMiCoHsd+Cnn35a+v6VV17Bfffdx8aMYZICIsVriCIjS6JGp+pICQaJzCSQIVLn+FJRNT8HUQhYQ2QAai27j5SgQI1O1dGcMiMyHIF9iMjAGBAZQFmlOsvuoyJkx3c1+hBFSjBIZCbqZ4iqn4evewqB7JTE9OnTpe8rKyvxwgsv1OhN9OqrryozMhNRa9l95PQhCux2rxTWEBEZj9rL7tmHiMIh+x14y5Yt0vd9+/bF/v37pb9zG4/QqFVUHWmrzJghImrY1F5lxr3MKByy34F//PFHNcZhamXVAYF6fYiMfXVQo1M1a4iIjEftPkSBxox83ZN8If+33LdvH5YsWYLy8nIAXOYYjhIpQ6R0H6LICArU7FTNzV2JjEO8Fqm27J5F1RQG2QHRyZMnMXjwYJx//vm4+uqrcezYMQDA+PHj8cADDyg+QDNwV2dIHApmSIDIyRCp2YfI6AXlRGaifg2R/0+jfwgkY5IdEE2bNg3R0dE4dOgQ4uLipNtvvPFGLF68WNHBmYX44hVXhSklUoICcdm9sp2qIyMYJDITtVeZ2bh1B4VBdtHK999/jyVLlqBFixY1bj/vvPNw8OBBxQZmJuJKKJvCVwkzZ4hsETJdSGQmavchsrComsIg+yN5aWlpjcyQqKCgAA6HQ5FBmY344o1SOCAK9CEy9tVB7FStZA1RFGuIiAxHzNyotSBZ2svM4Nc8MibZAdHll1+O9957T/q7xWKBz+fD7NmzMXDgQEUHZxbibvRKf2oSsySG70NUnSFScpWZLUKmC4nMRKs+RFzkQ6GQPWU2e/ZsDB48GBs3bkRlZSUefvhh7Ny5EwUFBVizZo0aY2zw1K8hMvbFQZoyUzBDFM0aIiLDUb1TdYRc88iYZGeILrjgAvz++++47LLLcN1116G0tBRZWVnYsmUL2rVrp8YYGzzxxWvGGqIqr086f2UzRJGRHSMyE0Hlxow21hBRGELqBOh0OvH4448rPRZT8vkE6cVrU/giEQmrzMTsEADE2JXfuoOfFImMQ3w5qtaHSNzLjFNmFALZAdGqVavOen///v1DHowZBe+5E6Vw+9ZIyBCJTRmtFsBuU2PZvXGDQSKzEQMVBV/qNXDKjMIhOyC64oorpOj+9MI1i8UCr9db14/RGQS/cG0mrCESV5jFRNsU/dQYCedOZDZaFVXzZU+hkB0QXXjhhThx4gTGjx+PW2+9FY0bN1ZjXKZRIyBSa5WZga8OavQgAgLnzmX3RMYh7jGmfg0RX/ckn+zE5ZYtW/DZZ5/h6NGj6N27N+655x5s3boVTqcTTqdTjTE2aMHBitJF1ZHQh0hacq9wQMQMEZHxqN2HSKw64OauFIqQZnIvueQS/Pvf/8b+/fvRt29fXHfddZg7d67CQzOH4Beu0o0ZpRoiA2dJyqWASNmiAtYQERmPVlNmXmaIKAQhrTIDgMOHD+Ptt9/GO++8gx49euCyyy5TclymEZwhsiqdIYqgVWZK9iACAudu5GCQyGwEtfcyE3e7Z4aIQiD7Y/kXX3yBq6++Gr169UJ5eTmWL1+O5cuX4+KLL1ZjfA2e1JRRhStEJKwyc6vQpRqIjPopIrPxqdyHiEXVFA7ZGaKsrCy0aNECY8aMgcfjwT//+c8a97/66quKDc4MvNIyVOUvEJFQR6NahigC6qeIzEb9PkScMqPQyQ6I+vfvD4vFgp07d9a6T63/5A2Z16teQBQJWZLgZfdKCmzuatzpQiKzUX3rDjZmpDDIDohWrFihwjDMy/QZokp1VpnZIuDcicxGULmomjVEFI6Ql/bs27cPS5YsQXl5OYDQdhdetWoVrr32WjRr1gwWiwVffPFFjfsFQcBTTz2Fpk2bIjY2FkOGDMHevXtrPKagoAB//vOfkZSUhOTkZIwfPx4lJSU1HrNt2zZcfvnliImJQWZmJmbPni17rGoRC57VyRAZv4Yo0IdI4VVm1dkxBkRExqHV5q582VMoZL8LnTx5EoMHD8b555+Pq6++GseOHQMAjB8/Hg888ICsY5WWluLCCy/EvHnz6rx/9uzZeP311/Hmm29iw4YNiI+Px/Dhw1FRUSE95s9//jN27tyJpUuX4ptvvsGqVaswceJE6X6Xy4Vhw4ahVatW2LRpE/72t79h5syZ+Ne//iX31FXhUbGoOlBHY9xpI7dafYi42z2R4UgBkUoRkXhYfhCiUMgOiKZNm4bo6GgcOnQIcXFx0u033ngjFi9eLOtYV111FZ5//nmMHj261n2CIGDu3Ll44okncN1116Fbt2547733kJOTI2WSdu3ahcWLF+Ptt99G7969cdlll+GNN97ARx99hJycHADAhx9+iMrKSrzzzjvo0qULxo4di6lTp561+NvtdsPlctX4UotaO90HH9PIS8/V6lQdWHZv3GCQyGzU7kPETtUUDtkB0ffff4+XXnoJLVq0qHH7eeedh4MHDyo2sOzsbOTm5mLIkCHSbU6nE71798a6desAAOvWrUNycnKNJf9DhgyB1WrFhg0bpMf0798fdrtdeszw4cOxZ88enDp1qs7nnjVrltR52+l0IjMzU7HzOp0UEKlwgYiEGiK3xx+wOKLMt7EtkdkEtu5Q5/gWBkQUBtnvQqWlpTUyQ6KCggI4HA5FBgUAubm5AID09PQat6enp0v35ebmIi0trcb9UVFRSElJqfGYuo4R/BynmzFjBoqKiqSvw4cPh39CZyC+YSu9sSsQGavMpClDhbe/jraxhojIaAJbd6hbVM3EMIVC9rvQ5Zdfjvfee0/6u8Vigc/nw+zZszFw4EBFB6cXh8OBpKSkGl9q8Uk1RMoGBP5jGj9DJE5pKT1laOOyeyLDUX/rDv+foSzyIZK97H727NkYPHgwNm7ciMrKSjz88MPYuXMnCgoKsGbNGsUGlpGRAQDIy8tD06ZNpdvz8vJw0UUXSY/Jz8+v8XMejwcFBQXSz2dkZCAvL6/GY8S/i4/Rk0fFFHJg2si4QYFaReWREAwSmY1Wq8z4uqdQyE5LXHDBBfj9999x2WWX4brrrkNpaSmysrKwZcsWtGvXTrGBtWnTBhkZGVi2bJl0m8vlwoYNG9CnTx8AQJ8+fVBYWIhNmzZJj1m+fDl8Ph969+4tPWbVqlWoqqqSHrN06VJ06NABjRo1Umy8ofKaPEPkVWnKjDVERMajeh8idqqmMIS0uavT6cTjjz8e9pOXlJRg37590t+zs7OxdetWpKSkoGXLlrj//vvx/PPP47zzzkObNm3w5JNPolmzZhg1ahQAoFOnTrjyyisxYcIEvPnmm6iqqsKUKVMwduxYNGvWDABw880345lnnsH48ePxyCOPYMeOHXjttdcwZ86csMevBE1WmRk4KFArQ8QaIiLjCdQQqXN8MdBiPEShCCkgOnXqFP7zn/9g165dAIDOnTvjjjvuQEpKiqzjbNy4sUbd0fTp0wEAt912G9599108/PDDKC0txcSJE1FYWIjLLrsMixcvRkxMjPQzH374IaZMmYLBgwfDarVizJgxeP3116X7nU4nvv/+e0yePBk9e/ZEkyZN8NRTT9XoVaQnNQOiSAgK1Nq6JDgYFASB28oQGYDqNUQRkBUn45IdEIndpZ1Op7Tc/fXXX8ezzz6Lr7/+Gv3796/3sa644oqzFr9ZLBY8++yzePbZZ8/4mJSUFCxYsOCsz9OtWzesXr263uPSksfkfYjUriEC/BfHKBVW8RGRPNzLjIxMdkA0efJk3HjjjfjnP/8Jm83fTM/r9eKee+7B5MmTsX37dsUH2ZB5VQoIgo9p5E9Lam1dElyT5PEJiFK27yMRhUBQuVO1tJcZAyIKgexK1n379uGBBx6QgiEAsNlsmD59eo16IKofMVhR4wIRUavMFM7gBAeYRq6hIjIT8VKk1pSZ1JjRuJc8MjDZAVGPHj2k2qFgu3btwoUXXqjIoMxEDFbU3cvMuAGBR6ohUmeVGRCoUyIifQWmzLjKjIxH9pTZ1KlTcd9992Hfvn249NJLAQDr16/HvHnz8OKLL2Lbtm3SY7t166bcSBso8QKhTg2R8TtVqzVlWDNDxI+LREagdg2ROFPuM/A1j4xLdkB00003AQAefvjhOu+zWCzSqh6v1xv+CBs4j0qrrIDIqCHyqFRDZLFYYLNa4PUJhj5/IjNRe5UZ9zKjcMgOiLKzs9UYh2mpWVQdCX2IxPOPVmUvN39AVGXg8ycyE7X7EAWmzNQ5PjVssgOiVq1aqTEO0/KqOKceJS27N+6UUaDtgDqduivBGiIio1C/D5H/T+5lRqGQ/S5UWVmJt956CwsXLgQAzJkzB/3798ddd92FwsJCpcfX4HlVWmUFRFaGSM22A6whIjKGwLJ7dY4vBlqcJqdQyM4Q3XPPPVi4cCEcDgcWLVqEn376CWPGjMHXX3+NqVOn4r333lNjnA2WWqusgMD+aEa+OKjZmFLsRWTkgJDITFRfZRYBdZNkXLIDoq+//hqff/45WrdujfPPPx9Lly7F4MGDcf3112PkyJFqjLFBk1aZqbHbvc34GSJxOk/VGipOmREZgpisVWsrHe5lRuGQHRCdOHECXbt2RXp6OuLi4tCmTRsAQOvWrXHixAnFB9jQqV1DAxj705KaGaLoCDh/IjNRf+sO9iGi0MkOiARBQElJCWJiYmC1WlFSUgKXywWXy6XG+Bo8LVaZeQ28wWng/JUPCMUMWRVriIgMQVC7qJp7mVEYQtrt/vzzzwfgD466d+8ufW/EN1yjU3PrjkjY4FTVGqIIqKEiMhOtaojYmJFCITsg+vHHH9UYh2mptds7UDPIMOoGp2r3IQJYQ0RkFFpNmTEeolDIDogGDBigxjhMy6dBhgQwbpZELKo2a6duIjPxqj1lxtc8hSGkKbPCwkL85z//kTZ57dKlC8aNGwen06no4MxAzSmj0zNERqRmDVEUa4iIDEXtPkQ2bt1BYZD933Ljxo1o164d5syZg4KCAhQUFODVV19Fu3btsHnzZjXG2KB51dzt/rQaIiOSAkJVpsyqa4g4ZUZkCIGtO1hUTcYjO0M0bdo0jBw5Ev/+978RFeX/cY/HgzvvvBP3338/Vq1apfggGzJxVw01MkRWqwUWi39lh1G7NatZQxUtdarmxZHICMTLEKfMyIhkB0QbN26sEQwBQFRUFB5++GFcfPHFig7ODLwq7fYuirJaUOU15o7vghAYl5pThkYNBonMRquiaiaIKBSyp8ySkpJw6NChWrcfPnwYiYmJigzKTNSsIQo+rhFXWgUHaapMGdr4aZHISNTuQ1S9Ww8bM1JIZAdEN954I8aPH4+FCxfi8OHDOHz4MD766CPceeeduOmmm9QYY4MmfmJSIyDwH9e4vXiCp7LUyRBV72VmwGCQyIwCNUTqHJ+bu1I4ZE+Zvfzyy7BYLLj11lvh8XgAANHR0Zg0aRJefPFFxQfY0Ilv1mo0ZgSMveN98EUr2qb8shNu3UFkLGo3ZuSUGYVDdkBkt9vx2muvYdasWfjjjz8AAO3atUNcXJzigzMDNbfuCD6uEYMC9TNEXHZPZCTqT5kZ93pHxhdSHyIAiIuLQ9euXZUciymJc91qbO7qP65xC4uDL1o2FS6QrCEiMha1i6rFywiX3VMoZAdEWVlZZ73/s88+C3kwZiQVVat0gTB2hsgfpFktau3lxhoiIiMRL0NqlwgwIKJQyE5LOJ1O6WvRokWwWq01biN5xKaBNhVqaPzHNW4NkRioqNGl2n9c42bHiMxI2sxa5RoiA17uKALIzhDNnz9f+v5///sfZs+ejbZt2yo6KDORpsxUukAYeZWZmj2Igo9rxGCQyIwEjfoQGfF6R8an0o4yVF9qF1UbuQ+Rml2qgaAaIgOeO5EZ+TQqqvYxIKIQMCDSmdqNGY1cQyR16VapgEqqITLguROZkfp9iGo+D5EcsqfMXn/9del7j8eDd999F02aNJFumzp1qjIjMwmfZtNGxqujCWSIzLfCjsiM1M4QSVNmDIgoBLIDojlz5kjfZ2Rk4P3335f+brFYGBDJ5NFgLzPAmBmiQFG1uufODBGRMQhqN2a0sqiaQic7IMrOzlZjHKYlJi9UryEy4BVC7aLqqOqVe6whIjIGtfsQiYtTWENEoQi5MePpBEHA4cOHAQA2mw3NmzdX6tANmtSLx8R7mUWpVkNk3GCQyIzEl6JFtSkz/5+cMqNQyA6Itm3bVuftJ0+exJAhQ3DhhReiSZMm+P7778MenBlotsrMgEGBx6vudCFriIiMRe0MkfjBUhD8H9LVCryoYZIdEF100UWwWCzSXDAA6e8WiwWbN29WdIANXWDrDpWXnhswKFA7GIzm1h1EhqL6XmZBx/UJ6u0AQA1TSFNmGzZsQGpqao3b8vPzcemllyoyKDMRC4tVz5IYsI4m0HJArVVm/uNWGfDciczIp/IHQGuNgEiADYyIqP5CCohatmyJtLS0GrfFxMQoMiCzUb2w2MCrzNTOEBn53InMSHwtqtaHKOizldcnINqmzvNQwxRSQLRkyRI0adIESUlJaNOmDZo1a6b0uExDnDJTvxeP8YICtYuqjXzuRGak9pRZ8HFZV01yhRQQ3XbbbdL3FosFrVu3xp/+9CfFBmUmgQyROsc38iozsa5J/Roi49VPEZmRT+U+RMGZdq40I7lkB0S+6jeXyspKnDx5Evv378eKFSvwj3/8Q/HBmUGghsi8GSL16qdYQ0RkJKqvMgsKtIz4IZCMLeQ+RHa7HU2bNkXTpk3Rr18/jBgxAj169IDNZkN6ejpycnKUHGeD5RO0qqMxXpbEq/LWHawhIjIWrfoQAaixEpqoPhRrzHjRRRdJ2SOqPzFLolYKWazPMWKGqErlFXZGPnciswkOUFTrVG1lhohCF3JAtGnTJuzatQsA0LlzZ/To0UOxQZmJV/XCYuNuX6F2DVGg5QADdSK9Bccnan0AtJzWh4hIDtkBUX5+PsaOHYsVK1YgOTkZAFBYWIiBAwfio48+qtWfiM5Oq2X3RsySqF1DJE7FGfHciczGVyNDpF5/IJvVAq9PqPF8RPUhOyC69957UVxcjJ07d6JTp04AgN9++w233XYbpk6div/+97+KD7IhkwIilVddGDF9rH52zLjnTlRf320/hs2HTtW6PS0xBrdc2gqx9shothMcoFhUWlUL+KfjvODrnuSTHRAtXrwYP/zwgxQMAf4ps3nz5mHYsGGKDs4MxH22TJkh8qpbVB3NGiKKcIVllZjy3y1nfHP/YutR/OvWi9E8OVbjkcknaDBlFjg2M0QkX0jL7qOjo2vdHh0dzaLqEIj/ZKotPTdwLx7NNrZlDRFFqOwTpfD6BCTFROGmXi2l232CgE83H8XOHBdGvvETJl3RDvYoK2Kibbi6a1MkOBRbL6OY4KBOrYw4EHjdG/CSRwYn+1UzaNAg3Hffffjvf/8rdag+evQopk2bhsGDBys+wIbOo3JhsaEzRBrVEDF1TpHq4MkyAEDHpkmYcXWnGvfd1rc17np/E3bmuPD8ol3S7YcLyvDAsA6ajrM+akyZqbjFmJh9YoaI5JI9V/H3v/8dLpcLrVu3Rrt27dCuXTu0adMGLpcLb7zxhhpjbLAEQZBWQqjdnNCIQYG0ykylGiIuu6dIJwZErVLiat3XolEc/nd3X9w7qD1GdG2Krs2dAIA/jpdoOsb60mKVmf/Y/j/ZqZrkkp0hyszMxObNm/HDDz9g9+7dAIBOnTphyJAhig+uoauRQjZhhkj1PkScMqMId7CgFADQukl8nffH2m1SNujb7cdwz4ebkedyazY+ObToQwQA1uqDszEjyRXSRLPFYsHQoUMxdOhQpcdjKh4NAiJppZUh+xCZd2NbovoQM0Qt68gQnS49KQYAkFtUoeqYQqVVhkisT+LnIJJLxcWPdC7BGSK1t68wYlDAGiKis5OmzBrXJyByAADyiysMmR3RqobIIgVExvs3IGNjQKSj4DluleKhoF48xvu4pHanatYQUSQrdXtwosQ//dUqpe4ps2Bpif4MUZVXQEFppapjC4UYEFks6u1lBgA2a83nI6ovBkQ6Cp7GYoZIeawhokgmZoeS46LhjKvd6uR09igrGsfbAcCQdURifKLmdBkQmDJjQERyMSDSkVeLzQ5txp02CnSqZg0R0ekOVRdU17XC7EzEOqI8l/HqiMQARc2CaiCQfeLLnuQKqaja6/Xiiy++kDZ37dKlC0aOHAmbLTJayBtF8D5maqWQIyFDpNaUWbSBg0GicwnUD517ukyUnuTAb8eMGhD5/1Rzugzglj0UOtkB0b59+zBixAgcOXIEHTr4l3vOmjULmZmZWLRoEdq1a6f4IBsqtaeMgo9txIuDV+Vl94FO1cY7d6JzOVhQ/4JqUYazeqWZEQMinzYZIvH4nDIjuWTPVUydOhVt27bF4cOHsXnzZmzevBmHDh1CmzZtMHXqVDXG2GD5VM6QBB/biBmiKs26dLOGiCLPwZP+KbP6LLkXiYXVRqwhEgMUNbftAAJ9iHwGvOaRscnOEK1cuRLr169HSkqKdFvjxo3x4osvol+/fooOrqGTMkQa7OtjzFVm2mSIfIL/4mhV+6MpkYJCmTITM0RGnjLTqqianapJLtkZIofDgeLi4lq3l5SUwG63KzIosxCDFJtKW1cAgdVrRpw2UruGKLhYmxdHiiSVHh9yCssBAK1lTJmJvYiMGRAFlt2rSQy4+JInuWQHRNdccw0mTpyIDRs2QBAECIKA9evX4+6778bIkSPVGGODJa4GV3PKLCJqiFRaZRb872rEgJDoTI6cKoNPAGKjbUhNdNT754y8ykxsFql2ptZq4GseGZvsd6LXX38d7dq1Q58+fRATE4OYmBj069cP7du3x2uvvabGGBsssbZFzRSyGBRUGfDioHaGKHgqjnVEFEnEguqWKXGyVmWJAdGJkkpUGaz/llZTZiyqplDJriFKTk7Gl19+ib1799bY3LV9+/aKD66h82pQVC1Oxxmzhkjdouro4CkzAwaERGdySMaWHcFS4uyItllQ5RWQX+xG8+RYNYYXEq36EAVqB/maJ3lC6kMEAOeddx7OO+88AP6+RCSf+CatZgo5ysBLz6UMkUo1VMH/rFUGPH+iYD6fgO925OJUWSV+2JUHQH5AZLVakJYYg6OF5chzVRgrIKr+TKZ2HyILN3elEMmeMsvOzsZNN92ESZMm4dSpUxg5ciQcDgc6dOiAbdu2qTHGBkuTDJGB59MDq8zUqSGyWCzSv60Rz58o2DtrsjF5wWY88cUOrN57AgDQukn9V5iJpMJqg+16r1mGiFNmFCLZGaK77roL+fn5SExMxKBBg5Ceno4vv/wSb7/9Nu6//34sX75cjXEawuq9xxGfEP5FpntmIzjjojVpzGjkHd/FrJXaAaHHJ7CGiAztaGE5Xl36OwCgX/vGSHREo1G8Hdde2Ez2sYxaWK3ZXmbsQ0Qhkh0QbdiwAatXr0arVq2QkpKCX375BT169ED79u3Ru3dvNcZoGJM+2AyrQ14Kuy5dmzvx9b2XSS9YLTpVG7ExoxikqHn+0TYr3B6fIacMiUQzv9qJskovLm7VCO+P6x3WNLoYEOUarDljIEOkzZSZAS95ZHCyA6Li4mI0bdoUTqcTcXFxSE5OBuAvtq6rP1FD0rFpIqJj5KewRZUeH/bml0gdaD0qTxkBMPSUkZZThkYMCIkA4PuduVj6Wx6irBb8Natr2DWFYkCUb7AMkVZ9iNiYkUIVUlH14sWL4XQ64fP5sGzZMuzYsQOFhYUKD814/nd3XyQlJYX880dOleGyl35EZXW1n7YBgfGmjLSZMjRuQEgkCAJe+Na/SfaE/m1xfnpi2McUa4iMtp+ZVhki8fMlp8xIrpACottuu036/q677pK+V3v1QKSzR/lfqW6PD4IgaLPKzGbcgCAQEKqYIbMZNyAkcpV7pC067rlCmY2xMwxaQyRegtT8AAQEAi4WVZNcsgMiH99YQuaIsgHwFxf6C32129zViMvO1V52Dxh76xKiw6f8wVCTBDsSY6IVOWZakjE3eBUzNlpt3WHED4FkbLI/mr/33ntwu7V5oc2cORMWi6XGV8eOHaX7KyoqMHnyZDRu3BgJCQkYM2YM8vLyahzj0KFDGDFiBOLi4pCWloaHHnoIHo9Hk/GfzhEV+Od2e3yqb24KAPEOf8xb6vYYLoXMGiIyuyOn/PuVNW8U/mINkbjBa4nbgxK3Pte6umi2uauVe5lRaGQHRHfccQeKiorUGEudunTpgmPHjklfP/30k3TftGnT8PXXX+OTTz7BypUrkZOTg6ysLOl+r9eLESNGoLKyEmvXrsX//d//4d1338VTTz2l2fiD2YM6J1d6fFLRn5q73TeO99cTeHwCCsurVHueUGixyszIU4ZER6s3cG3RSLkGigmOKMTb/dloI02bCRr1IbKyqJpCJDsgEjT+TxYVFYWMjAzpq0mTJgCAoqIi/Oc//8Grr76KQYMGoWfPnpg/fz7Wrl2L9evXAwC+//57/Pbbb/jggw9w0UUX4aqrrsJzzz2HefPmobKy8ozP6Xa74XK5anwpwWq1ILr6Ddrt8Qa2rlBxysgeZUVKvB0AkF9snIsjENyHSL0aInEaoqDUWNMHRIB/oQUAtFC4o3TLxv7VsLuPGWflL/cyI6MLqaj6448/PuNqq1tvvTWsAZ1u7969aNasGWJiYtCnTx/MmjULLVu2xKZNm1BVVYUhQ4ZIj+3YsSNatmyJdevW4dJLL8W6devQtWtXpKenS48ZPnw4Jk2ahJ07d6J79+51PuesWbPwzDPPKHoeIkeUDVVeDyqDeuOoXWSYmuBAQWkljhe70TFD1aeSRYtVZu2axOPXw4X443ipas9BFCpxykzJDBEA9G6Tgl3HXNiQfRIjujVV9NihCiy7Z2NGMqaQAqLZs2fDZrPVut1isSgaEPXu3RvvvvsuOnTogGPHjuGZZ57B5Zdfjh07diA3Nxd2u13qgyRKT09Hbm4uACA3N7dGMCTeL953JjNmzMD06dOlv7tcLmRmZipyTvYoK+D21xD5NJgyA4DURAf25BUj32BFll4NiqrbpSUAAP44XqLacxCF6qgUEClXQwT4A6J31x7A+v0nFT1uOLTauoNF1RSqkAKijRs3Ii0tTemx1HLVVVdJ33fr1g29e/dGq1at8PHHHyM2Vr1NCx0OBxwOhzrHFpfeV/k0yZAAQFqi/1yOlxgrIPJ41a8halu9FxQzRGRE0pSZwhmiXm1SAAC/55XgZIkbjRPUuZ7JodXWHWIbE8ZDJJd6xRsqSE5Oxvnnn499+/YhIyMDlZWVtRpC5uXlISPDPy+UkZFRa9WZ+HfxMVoTA6JKr1dK6aqZIQH8GSIAxs0QqTllVp0h2n+8RPP6N6KzKSqvgqvCvwqsucIBUeMEBzpUN3n8ObtA0WOHipu7ktHJDohatWpV53SZFkpKSvDHH3+gadOm6NmzJ6Kjo7Fs2TLp/j179uDQoUPo06cPAKBPnz7Yvn078vPzpccsXboUSUlJ6Ny5s+bjB4KaMwZliNT+xJRq1AyRFBCqF5e3ahwHqwUorvAY7vzJ3MTpspR4O+LsISXrz6p3W3+WaINhAiL/n2rXELExI4VK9jtRdnY2GjdurMZYannwwQexcuVKHDhwAGvXrsXo0aNhs9lw0003wel0Yvz48Zg+fTp+/PFHbNq0CXfccQf69OmDSy+9FAAwbNgwdO7cGX/5y1/w66+/YsmSJXjiiScwefJk1abEzkVszuj2+jTJkADBGSJjrTLT4vwdUTZkpvjrM/7I57QZGYcaS+6DXdrWf502Sh2R1Jlf7RoiabsedZ+HGh7ZAdHUqVPx+uuv17r973//O+6//34lxiQ5cuQIbrrpJnTo0AE33HADGjdujPXr1yM1NRUAMGfOHFxzzTUYM2YM+vfvj4yMDHz22WfSz9tsNnzzzTew2Wzo06cPbrnlFtx666149tlnFR2nHMEZokBjRnVnLtMS/Y3ajJQhEQRBsxqqdqksrCbjEeuHmiu85F4k1hHtzi3GqdIztxnRijhlrf7WHf4/mSEiuWTnaT/99FN89dVXtW7v27cvXnzxRcydO1eJcQEAPvroo7PeHxMTg3nz5mHevHlnfEyrVq3w7bffKjamcAVqiIKLqtV9TmnKrNg4AVHwChC1M2TtUuOxfDewn4XVZCBqLbkXNUlw4Ly0BOzNL8GG7AJceYG+PTe0mjLjsnsKley34pMnT8LpdNa6PSkpCSdOnFBkUA1ZIEPk1SxDJAZExRUeVFR5VX2u+greSkPtT4xtmSEiAwqsMFN2yX0wsY7om205WL33OLYeLtRtcYFWRdUWdqqmEMl+J27fvj0WL15c6/bvvvsObdu2VWRQDZkjaMd7r0YZoqSYKOl5jZIlqpkhUvcfgFNmZERq1xABgTqib7Ydw1/+8zNGzVuDzzYfVe35ziYQEKmcIbJw2T2FRvaU2fTp0zFlyhQcP34cgwYNAgAsW7YMr7zyiqLTZQ2VWFRd6QkuqlY3ILBYLEhNdODIqXLkF1dIRcZ60jJD1C7V34voaGE5Kqq8iInWZ5UkUbDAxq7qBUSDO6ZjaOd0HC4oQ56rAqfKqnDgpD5Tx1r1IRKvJwWlbhw4EThXq8WCzJRY1afsKHLJDojGjRsHt9uNF154Ac899xwAoHXr1vjnP/+p+LYdDZE9KEOkVVEx4G/OeORUuUEzROqef0q8Hc7YaBSVVyH7RCk6Na172xkirZS4PSgs82+2rFZRNQDE2m34960XAwBmfbcLb63cj/JKfabNA1t3qPs8YsD1wfpD+GD9oRr3Xd+zBV7+04XqDoAiVkipiUmTJuHIkSPIy8uDy+XC/v37GQzVk1RUHbx1hwYBkbT03iABkbjTvdUSWCarFovFImWJOG1GRiD2IEqOi5Y2IFZbbHVmtFynOkKtNncd0ikN6UkOJDqipC/x3LcdKVT1uSmyhdQNzOPxYMWKFfjjjz9w8803AwBycnKQlJSEhIQERQfY0AQyRF7NNncFjLfSTKvpQlG71ARsPlTIXkRkCGpt2XE2+gdE2hRV923fBBseG1Ljts2HTiHrH2tRUcXmRHRmsgOigwcP4sorr8ShQ4fgdrsxdOhQJCYm4qWXXoLb7cabb76pxjgbjJo1RP4Xp9pTRkBQLyKDBERaBoNAYKXZ/hPMEJH+pPohFafLThdr91979FppKmhUVF2XmCh9g0GKDLIDovvuuw8XX3wxfv311xodq0ePHo0JEyYoOriGKLiGSIB2FwjjTZlp06VbJE6Zfbc9Fz9nL0Oc3YbZ11+Inq0aafL8RMG0WHJ/OnExgX41RP4/9ShqloJBnc79XBZsOIR5P+4Lq5lkZkoc3r7tYiRpNAXbEMkOiFavXo21a9fCbrfXuL1169Y4elSf5ZyRxBE0ZSZmR7TJEBltyqx6p3uVN7YVXdQyGTHRVlRU+XCsyL+Fyde/5jAgIl2cLPF3jhZfl1rQe8pMq6076iKee4XHeAHRgROlmPn1TlR6wpvOO1ZUgY9/OYw7L2f7m1DJDoh8Ph+83tr/qY4cOYLExERFBtWQBRdVR1c3INIiKAhkiIyxn5nWGaK0xBiseWQQjhVVYNH2Y/jnij+QZ7C93cg8xKAkzq5dC4hAQKRPHY1WW3fUJSbaf62t8gqo8gauvXoTBAFPfrkDlR4f+rVvjBlXdQrpOMt35+PVpb/jww2HMK5fG9UXqjRUsgOiYcOGYe7cufjXv/4FwJ/+LCkpwdNPP42rr75a8QE2NMGNGcX/tDYNUshiDdGJkkr4fILuLxita4gAoHGCA40THNJ0RS4DItJJWfXUTawKu9yfid7TRlqtMqtLcO+xiiqvYQKir7cdw+q9J2CPsuKFUV3Rukl8SMdp0yQe/1q1H9knSrH2j5O47LwmCo/UHGT/r3jllVewZs0adO7cGRUVFbj55pul6bKXXnpJjTE2KHU1ZtQiKGic4J/i9PoEFJTpv9Gj1qvMgqUl+YPDfJcxpg/JfMQMUayGTUJjDLLKTI++iI4oq/S8RllpdrzYjWe//g0AMGVg+5CDIQCId0Qhq0dzAMD76w8oMTxTkv3xpEWLFvj111/x0UcfYdu2bSgpKcH48ePx5z//GbGx2q2YiFTBRdUxGk4bRdusSIm3o6C0EseL3WiSoF3tQl2kKTONaoiCZVQHRHmuCkNky8h8xJVesXbtPhBIdTQNvA9RXSwWC2KibCiv8hpiP8ftR4pw1/sbcaLEjbap8bhrQPh1P7dc2grvrTuIpb/l4VhROZo6+X4sV0j52qioKNxyyy1Kj8UUajRm1DBDBPgLOAtKK5Ff7Eanppo85RlpmR07XWqiAxaLPygrKKvUPTgk8xFXemm5jYw4ZaZXhkjQqA/RmcTajREQfbf9GO5fuBVujw9tm8Tj37deLM0chOP89ET0bpOCDdkFeOyz7ejavOYm7J2bOXHlBRlhP09DJjsg+uqrr856/8iRI0MejBnUaMyo0W73otREB3bnFhtipZlHwx5Mp4u2WdE43oETJW7kFlUwICLNiTVEcVrWEOmeIdKvDxEAxFRfe/XuRTTj8+1we3wY2CEVc8d2hzNWuWXyf+nTChuyC/DjnuP4cc/xGvdZLMDPjw2RFthQbbJfjaNGjarxd4vFIkX+FoulzhVoFCB+EnDX2NxVmwuEkbpVB4qq9SluTE/yB0T+VXfOcz6eSEkVutQQ6bvSSs8+RAAQY9e3DxPgnxkQ97Cbe6OywRAAXH1BUzx2dbm0NYxo4cbDqKjy4VRZJQOiswhp2X2wxMRE/Prrr2jblr0P6sMeNGUm9eXQKCASa2c+3ngYw7qko12qftusaB0Mni4jKQY7c1zILdI/OCTz0bOoGtBnpZVWW3ecSaAXkX5F1WWVHun7OIfyv3ur1YKJ/dvVuv2HXfk4WliOUrenjp8iUdivCL2i/UgVvOxe66Dgpl4t0dQZg+wTpRj19zVYtitPk+eti0fHGiIgsNKMvYhIa4IgSAFRjIZF1cErrfSYNhJ0LKoG9O/UDQSmSu02q6YBaZwBsmORIKzfyIEDB1BaWsqGjDI4omsHRFpliDJT4vDVlMtwSetGKHZ7cPcHm3C0sPzcP6gCLfdxq4uYLTNKo0oyD7fHJwUHWtYQWSyWQJakUvssiU+63mn+1AD0r6ECgvtPaZcZBAIBURkDorOS/WrMysoCAJSXl2P9+vUYPHgwUlNTFR9YQ2W3BYqq9Zg2Sk104MM7L8WI11djb34Jth8p0nSDSZHeGaL0JP88em4RAyLSVvCndLHQVyux0TaUVXp1yRB5g2pN9SDWUOkbEPmnrOI1D4j8b/WllZwyOxvZAZHT6S9AzcjIwLXXXotx48YpPqiGzBEdaMworrTSOiiwR1nRsWkS9uaX4FBBqabPLfLq2IcIANKd4pQZa4hIW2IwYrdZEaVxHY+ezRnFomotOvPXRe/GlID+GSJOmZ2d7IBo/vz5aozDNAIZoqBO1TpcIFql+HfZPnCyTPPnBgKrzPToVA0A6YmsISJ9SPVD0dr/34/V8Y1R9z5E0pSZfkXV5Tq0WwACv3dOmZ2d7N+Ky+U66/1JSUkhD8YMHEEXQfGFqdWO78FaNfYHRId0Coh0X2VWnSE6WVqJSo9PWv1HpLZynbIEgL51ND7dp8z0zxCJU1ZabuoLAPHVAVgZp8zOSnZAlJycXOd/aEEQ2IeoHuxBKXLxxaFHUNCqsX/fnIM6TZlV6TRdKGoUFw27zYpKrw/5xRVo0ShOl3GQ+QR2utc2SwAE73iv35SZXqvMpM1tDTBlpnVAxAxR/ch+RbZt2xb5+fl49NFH0a9fPzXG1KA5gjIR4idFXabMqjNER0+V65Ih0buGyGKxIC3JgSOnypHncjMgIs3osW2HSM/mhHr3IYoxwiozt5gh0jYY5iqz+pH9W9m1axfeeOMNvPDCC9iyZQtmz56NNm3aqDG2BsliscAeZUWlxyf959QjS5KW6EBMtBUVVT4cLSxHmzB2Wg6F3p2qASA9KaY6IGIdEWkn0JRRhxqiaP22r5D6EOkUEYk1W7r2IarSJ0MU7+CUWX3IfkVGR0dj+vTp2Lt3L5o3b45u3brhgQceQGFhoQrDa5gctpoXJT2yJBaLBa1SqqfNTmo/baZ3DRFQc9d7Iq2YtobIJ9YQaf7UAPSdLhSV6zVlFs0MUX2E/BElJSUFc+fOxZYtW3DgwAG0b98ec+fOVXBoDZfjtE+Ges2pt6yeNjuoQ2G13n2IACBN7EXEgIg0FMgQ6VBDpGMdje41RAZYZVbqFoNhfabMuOz+7GT/Vrp3716rqFoQBLjdbjzwwAO4//77lRpbg2U/rfeIXkvPxaX3egREeneqBoK6VbMXEWlIzwyRvn2IWENUXqVTY0YHGzPWR9i73ZN8jtOKKfXKkrSqrhvSozmjR+eiasBfQwSwWzVpS98aIjFToH2WJNCHyLzL7nVrzGiAfdwigeyA6Omnn1ZjHKZyeoZIt4BIx+aMgRoifYuqASCP+5mRhqQMkR6rzHQMCrh1R2DKjKvMjImNGXVweg2RbgGR2JyxoAw+n6Dp6o8qr/41ROJ+ZnnMEJGGpAyRjn2I9K0h0vypARikqFqcMnPoM2XGgOjs2JhRB46o02uI9LlCNE+ORZTVgkqPD3nFFWjq1G6TVyPUEIkZotJKL0rcHiQ4tH+DIvMJTJmZqw+ROGWm115m4jSVW8ei6jKdsoOBDBFriM4mpHeA//3vf0hJSVF6LKZxehNEvbIkUTYrmjeKxcGTZThwokzTgMgIq8ziHVFIdESh2O1BnqsCCakJuo2FzKNCqiPRsYZIl2X3/j/160Okf4aoTKcpMy67r5+Qfiv9+vVDWlqa0mMxDUeUMYqqAaBlShwOnizDoYJS9GnXWLPnNUIfIgBISbCj2O3BqdJKIFXXoZBJ6JUlCH5OPVeZ6d6HSNfGjNWdqjWeMhMbM4qbiuv5nnM6QRCwN7/Efw0OUbwjCl2aJYVdnxZSQPTbb7/h5MmTiI+PR0ZGBux2e1iDMJvay+71+8/ZunE8Vu89ofnS+0CGSN9NVZPj7Dh4sgynyqp0HQeZh641RHb9Cov17kMkLbv3eKUSD63p1Zgx+PnKKj1IjInW9PnPxO3x4qkvdmLhxsNhH+v5URfglktbhXWMkF6RgwcPlv5DWa1WdOzYEePGjcO0adPCGoxZ1GrMqGNAJBZWHyzQNiDyevVfdg8AybH+C0NhWeifTojk0LWGSMcsiaB7HyJr9Tj8mRI99pKTVplp3JTTEWWFxeI/9/JKryECojxXBe7+YBO2HCqE1QK0bhKPUP5rFJZV4WRpJXbmnH3BV33I/q1kZ2dDEARUVVXB5XIhJycHP//8M5588kl4PB489NBDYQ+qoTNShqil1JxR215EHoNMmTWKEwMiZohIGxVVJq0hMkgfIsD/O9A6IPL5BOnfXespM4vFgnh7FErcHpQaoI7I5xPwl/9swO95JUiKicIbN/fAgPNDq1n4cMNBPP75DhxXoH2K7ICoVauaKamePXvi2muvxfnnn49nn32WAVE9GClD1CzZX0itdbdmcZWZ3nPZyXH+6d5TzBCRRsp03O3eCFt36NWHKNpmRZTVAo9P0GX7juAgVOspM8D/uy9xewyx0uxkaSV+zyuBxQJ8NeUytA5jc/G0RP9q4ePF4b+HKZa3Gzt2LLp06aLU4Ro0u63mi0HPLElKvD8gKCit1HRevcogGaJkMUNUzgwRaSNQR6JfHyI9psz03roD8J9/sdujS4YseIVXTJT2AVG83YbjMEa3anFD7SYJjrCCIQBITfT3k8vXMyDatGkTdu3aBQDo3LkzevTogR49eoQ9IDMwSmNGIBAQeXwCXOUeOOO0mVsWa4hsNn2LqhtVZ4hYQ0RaqdCxhkhqzOjxaV5YLOhcVA34+zAVuz26ZMiCC6r1mBUQi/iNMGUmBkRic9xwpFUHRCdK3GE3GJYdEOXn52Ps2LFYsWIFkpOTAQCFhYUYOHAgPvroI6Smcu3yuZzemFGvRmWAP20fb7ehtNKLk6VuzQIio9QQiRmiU6XMEJE2jNCY0esTUOUVYI/S7vUnttrQ8yUvFlbrkSESN1bVY7os+HnLDTBlllddoiFusB2OJgn+gKjKK6CwvEr6kB8K2R/P7733XhQXF2Pnzp0oKChAQUEBduzYAZfLhalTp4Y8EDMxSmNGUePq/1AFYfSBkIs1RGRGghAorI3Rsaga0D4o8Om8lxkQlCHTIUui18auIjEgEle66Sm3OkOUpkBAZI+ySotjwq0jkv2KXLx4Mf7xj3+gU6dO0m2dO3fGvHnz8N1334U1GLMIbsxos1p0vUAAgWmzkxoGREbJEIkvpCLWEJEG3B6fNHWkRw2RWFgMaF9YLRZV6/khKDaoF5HWxCmzeB1+70DQ9h06duoW5VcHREpkiIBAHZHmAZHP50N0dO1plejoaPh8+u0RE0mCM0R6TpeJGosBUYmWGSL9t+4AAjVEzBCRFsprFNbqUz+nV2G13n2IAMAhnbv271XilJl+GSJ/IGaEKbNcBWuIgMBKs/wwl97LfkUOGjQI9913H3JycqTbjh49imnTpmHw4MFhDcYsgmuI9A4IgOCVZtotvRczRNE6F1WLNVMVVT5dCi3JXMRpKrvNiiid/u87dOpFZKQpMz1qiIySITLClJlYQ6TElBmgY4bo73//O1wuF1q3bo127dqhXbt2aNOmDVwuF954442wBmMWwQGR3lNGgH8/L0DbKTOjZIgSHVHS74DNGUltgR5E+n0QEBtCah8Q+f/UdZVZtH5blxilhkjPzW1FeQpPmaUptPRedqiamZmJzZs344cffsDu3bsBAJ06dcKQIUPCGoiZBAdEejZlFDUO6kWkFY/Xn7LWOyC0WCxIjovGiZJKnCqrRIZTmRcoUV3EN2I96odEehUWG6UPEaBXQKTvKjNx2b3ejRndHq/0XpNusAxRvV+VxcXFSExMBOB/Exk6dCiGDh1a4zG//PILLrnkkrAGZAbBRdV6BwQA0Dhe+1VmHoNkiADAGRsIiIjUFNjYVZ83RUC/aSMj9CHSs1N3mY4NOQF/Y0YAKNN5ykzcFcFuC6wOC1egOaNGNUTDhg1DSUlJnfd5PB488cQT6NevX1iDMQu70WqIEvQrqo7Sebd7IFBYXcQpM1JZuY7bdohidK8h0vRpaxA/jOrZqVrvPkRlOjdmFIOWtCSHYvVkmtcQFRcXY8iQIXC5au4ou2PHDlxyySV499138cUXX4Q1GLMwWlG1tMpMh6JqI5x/oBcRAyJSl1RHomsNkT6rzPTe3BUIPnftV5kZZspM5xqi3CLlmjKKlKohqver8scff0RpaSmGDh0Kl8sFQRDw0ksv4eKLL0anTp2wfft2XH311WENxiwMlyE6bT8zLUgZIpv+5y91q+aUGanMUDVEJiyq1rMPkXGmzPStIQps26FcQJRavey+uCK8bVnq/ZtJTU3F8uXLMWTIEAwaNAgOhwN79+7FBx98gOuvvz7kAZiRUWuIqrwCit0eJMWov32HxyCdqgE2ZyTtSF2qdZwy06uGyGegrTv06VStd4bIGFNmagRESTFRsEdZUenx4XixG5kpcSEdR1beNjU1FcuWLYPH48GmTZuwatUqBkMhsBtslVms3Sa9SAs0qiMSN3eNNkANkTRlpmFROZlTuc5Lr4HAfmZaTxuZvQ+R/svuqxsz6jxlpuTGriKLxaLItJnsd6MmTZpg+fLl6Ny5M26++WacOnUq5Cc3K6P1IQK0377DWDVE4pQZM0SkrsDGrjrWEOlWVO3/U8/XvEPXZfdGacyo75SZ2KVa6RYnShRW1/s3k5WVVePvSUlJWLVqFXr16oWuXbtKt3/22WchD8YsavQhMsDWHYC/sPrIqXKcLNGmsNpjoBoicZVZIWuISGXlOteRAPrVEBlh6w59M0RG2e3eGMvuxe02lJImBUShL72v96vS6XTW+nubNm1CfmIzC54yM0JAANQsrNaC2JjRSBmiQtYQkcoMUUOkUy8eQxVVV+mxyswYU2ZlVV4IgqDL1KUgCA0jQzR//vyQn4Rqqrnbvf41NACQUl1YrdWUmdcgu90DQHIsM0SkjcCUGfsQ6SFGxykz3fcyc/jP3esT4Pb4dAnKS9weKTBUsoYIAFITxA1eNawhovBFB2WFDJIgQuMEjTNEBqohahRfnSEqq9Ks7QCZU6Co2gA1RJr3IfL/qW8fIn32cQMCtTu6ZYiCAiC9ps3EgurEmCjFp43TksLPEDEg0oHFYpHqiIzQqRnQfj8zI3aq9vgElOhccEgNWyAg0rGGSKegQDBAY0ZdM0RSDyp9AqIomxV2m/93r1dzRnGXeyWX3ItSE3RYZUbKEOuIjJAhAbRdZSYIgqEyRDHRNqk/CXe8JzUZYsosSq8aIv2LqmN0yo5Venyoqm41oteUGRCYNtOrOaPSu9wHY4Yogol1REYICIDAlJkWq8x8QbNS0QaZMxTriNitmtRkiIDIru+yeyP0IdK6qDo4ANOzB5U4baZXc0axoDpN4fohIFBUfaLELTUBlUu/UNXkHIbLEGm3473YpRowzvknx0Uj11XBDBGpytQ1RIboVO0/90qvD16foNn1p6zKn5GJslpqrDLWmt7dqvNVnDJrUj1l5vEJePjTbYiunh50l9W9KX1dGBDpJFBDZIyAoHHQlJnaSzI93kD0boQaIiBQR8QMEakpkCEyQh8ifTpV69mdPzgzV1HlRbxDm99DqVvf+iGReL5iTyStZZ8oBQA0U3jJPQBE26xonhyLo4Xl+N+mI9LtPndZvY/BgEgn4qcEI2zdAQSmzCo9PpRWepGg4oXCE5TONFKGCGANEanLCFt3xOo8ZabnSz64KW65hgGRERpyAoGAUI8Mkcfrw6aD/p0turdspMpzvHlLT/y4J7/GbRWlJXhkbv1+ngGRToyWIYqzRyEm2oqKKh8KSipVDYi8vuAMkTHOP5kZItJAhQFqiPRbdq//KjOr1b/C1+3xaVpUrneXapGe3ap35rhQ4vYgKSYKnZomqfIcXVs40bVFzSbSLpcLj9Tz540xX2FCRiuqBgK73p8oVbewWqwhsliMkyFrxAwRaUDqVmyQxoxa9t0SDNCHCNCnU7f4exdXeeklrvqDbqkOU2Ybsk8CAHq1STHU+14wBkQ6MdqyeyBo+w6Vd7w3UpdqUWDKjBkiUocgCIEaIgNMmQGA26NdHZERMkSAPjVUUkCkY+2Y//n1mzLbsL8AAHBp28aaP3d9ccpMJ0ZbZQYEAqJfjxQipbqmSGli8yzAWOcuTpkdKijD5kOnVHkOu82Kzk2TDJMVM5IDJ0pRoFAwmp4Ug+bJsYocS0nBwYeeAVFMcB1NpVezLRyMsHUHoM/WJeKUmZ6/d0C/KTOvT8DP2f6AqHcbBkR0GrvBaoiAQGH1G8v34Y3l+1R5jiirBX/7UzcAQLRBVpgBgVVmmw8VIusfa1V7nikD2+PB4R1UO34kWvn7cdw+/2coNXtjsQAPDuuAe65op2vPm9MFvwnF6Lj0WuxYXOn14VhRBRrFq/Ph53RG2LoD0Kc5o5iRiTfplNmuYy4Uuz1IdEShczN16oeUYKqAaN68efjb3/6G3NxcXHjhhXjjjTfQq1cvXcZixAzR9T1bYMfRItU+OZW5vThZWonnvtkFALAZpCkjAFzaNgW926Qgp6hcleP7fMDRwnK8ufIPXHthM3TISFTleSJNeaUXT3yxHYLg7yMSbn8e8d/5b0v2YGdOEZ68pnOt1g7O2GhdesGI2yXYbVZE2fT9MHDZeU2wfHc+Zn23C++N66VJ4Cht3aHz5yCxK70eNUR6tlsAAlNmWmeI1u/31w9dYuD6IcBEAdHChQsxffp0vPnmm+jduzfmzp2L4cOHY8+ePUhLS9N8PEYsqu7brgm+nzZAteMXlFZi0CsrpOaPRsqOJcZEY+FdfVR9jrve34glO/Pw2Ofb8cldfTh1BuD15XtxuKAczZwxWDp9gCLLoBdsOISnv9qBb7fn4tvtubXubxQXjVduuBCDOqaH/VxyGGHJveipazrjp30nsHrvCXz1aw6uu6i56s9plAxRrA5TZuUGWWWmV2PG9VL9UIqmzyuXceYsVPbqq69iwoQJuOOOO9C5c2e8+eabiIuLwzvvvKPLeOwG29xVCynxdjx2VSfp70YKBrXw9LVdEG+3YdPBU1i48bDew9Hdntxi/HvVfgDAzJFdFOsJc3PvlvjvhEvRqnEcLBbU+AKAU2VVGP9/GzHvx32arrIywpJ7Uesm8bh3YHsAwHPf7EJRufqrK42wlxkQXFStXVBQapBVZno0ZvTXD/kzREauHwJMkiGqrKzEpk2bMGPGDOk2q9WKIUOGYN26dbUe73a74XYHlp67XC7FxyROmen9aUlrf7q4Bf636Qh+PlBgqmAQAJolx2La0PPx/KJdeOzz7Zj51U69h6Qrj0+A1ydgaOd0DOuSoeixL26dgpUPDax1e6XHh5lf78SCDYfwtyV78NqyvdDqFSjGXkbIEAHAxAFt8fnWo9h/vBQXP79U9WuRWFSud12XWEP0+Oc78NSX2rwGq7z+c9d9lVn1/71lu/PR4YnvNHlOAf7XXYIjCl0MXD8EmCQgOnHiBLxeL9LTa6bI09PTsXv37lqPnzVrFp555hlVx3RRy2TYrBZcmOk894MbEIvFgr9mXYAb3lqPHq3U6VZqZLf3bY1vtx/D5kOFmi55NqrkuGjMHNlFs+ezR1nx19Fd0aVZEp75+jdU6vA76KFSl165HFE2vJjVDX9+e331TuzqZ8tSEx1o5tR3BWCPVo2waPsxeHxCja75arNaoPv1vkszJ+xRVlR6fJpff67umqF77dy5WAQtc8Y6ycnJQfPmzbF27Vr06ROoE3n44YexcuVKbNiwocbj68oQZWZmoqioCElJykW45ZVew3xa1FpFlReOKKvunxb14PH6pF2fzS4l3q7bdgYlbo/mfaesFguaOmMM9f++qLwKxRXaNCRtkuDQbJn/2eQXV2geDCc4oqT2HnoqrqjSZIo0mJ7/710uF5xOZ73ev02RIWrSpAlsNhvy8vJq3J6Xl4eMjNqpeofDAYfDUet2pZk1GAJgiIuiXqJsVrRoFKf3MEwvwRGl6hY1kcIZGw1nbLTew9BUWqLym4tGisSYaCTGmOv3XV/Gzl8pxG63o2fPnli2bJl0m8/nw7Jly2pkjIiIiMicTPPxaPr06bjttttw8cUXo1evXpg7dy5KS0txxx136D00IiIi0plpAqIbb7wRx48fx1NPPYXc3FxcdNFFWLx4ca1CayIiIjIfUxRVh0tOURYREREZg5z3b1PUEBERERGdDQMiIiIiMj0GRERERGR6DIiIiIjI9BgQERERkekxICIiIiLTY0BEREREpseAiIiIiEyPARERERGZnmm27giH2Mzb5XLpPBIiIiKqL/F9uz6bcjAgqoeTJ08CADIzM3UeCREREcl18uRJOJ3Osz6GAVE9pKSkAAAOHTp0zn/QhuqSSy7BL7/8ovcwdMFzN+e5A+Y+f567Oc8daFjnX1RUhJYtW0rv42fDgKgerFZ/qZXT6TTt5q42m43nbkJmPnfA3OfPczfnuQMN8/zF9/GzPkaDcVADMHnyZL2HoBueu3mZ+fx57uZl1vO3CPWpNDI5l8sFp9OJoqKiBhc1ExERNVRy3r+ZIaoHh8OBp59+Gg6HQ++hEBERUT3Jef9mhoiIiIhMjxkiIiIiMj0GRERERGR6DIgIADBr1ixccsklSExMRFpaGkaNGoU9e/ZI9xcUFODee+9Fhw4dEBsbi5YtW2Lq1KkoKirScdTKONe5A8Bdd92Fdu3aITY2Fqmpqbjuuuuwe/dunUasrPqcv0gQBFx11VWwWCz44osvtB2oCupz7ldccQUsFkuNr7vvvlunESunvr/3devWYdCgQYiPj0dSUhL69++P8vJyHUasrHOd/4EDB2r93sWvTz75RMeRh68+v/vc3Fz85S9/QUZGBuLj49GjRw98+umnOo1YGwyICACwcuVKTJ48GevXr8fSpUtRVVWFYcOGobS0FACQk5ODnJwcvPzyy9ixYwfeffddLF68GOPHj9d55OE717kDQM+ePTF//nzs2rULS5YsgSAIGDZsGLxer44jV0Z9zl80d+5cWCwWHUapjvqe+4QJE3Ds2DHpa/bs2TqNWDn1Ofd169bhyiuvxLBhw/Dzzz/jl19+wZQpU+rV08XoznX+mZmZNX7nx44dwzPPPIOEhARcddVVOo8+PPX53d96663Ys2cPvvrqK2zfvh1ZWVm44YYbsGXLFh1HrjKBJH/961+Fiy++WEhISBBSU1OF6667Tti9e3eNx7z11lvCgAEDhMTERAGAcOrUKX0Gq7L8/HwBgLBy5cozPubjjz8W7Ha7UFVVpeHI1Fefc//1118FAMK+ffs0HJk2znT+W7ZsEZo3by4cO3ZMACB8/vnn+gxQRXWd+4ABA4T77rtPv0FppK5z7927t/DEE0/oOCrt1Od1f9FFFwnjxo3TcFTaqOvc4+Pjhffee6/G41JSUoR///vfWg9PM5Ef5iuoPlFzWVkZrrzySjz22GM6jlR94lTY2dqdi30doqIaVsPzc517aWkp5s+fjzZt2jTI/e3qOv+ysjLcfPPNmDdvHjIyMvQamurO9Lv/8MMP0aRJE1xwwQWYMWMGysrK9Bieqk4/9/z8fGzYsAFpaWno27cv0tPTMWDAAPz00096DlM153rdb9q0CVu3bm0QWfHT1XXuffv2xcKFC1FQUACfz4ePPvoIFRUVuOKKK3QapQb0jsiM7GyfGH788ccGmyHyer3CiBEjhH79+p3xMcePHxdatmwpPPbYYxqOTH1nO/d58+YJ8fHxAgChQ4cODTI7dKbznzhxojB+/Hjp72iAGaIznftbb70lLF68WNi2bZvwwQcfCM2bNxdGjx6t0yjVUde5r1u3TgAgpKSkCO+8846wefNm4f777xfsdrvw+++/6zha5dXnmjdp0iShU6dOGo5KG2c691OnTgnDhg0TAAhRUVFCUlKSsGTJEp1GqQ0GRGexd+9eAYCwffv2Wvc15IDo7rvvFlq1aiUcPny4zvuLioqEXr16CVdeeaVQWVmp8ejUdbZzLywsFH7//Xdh5cqVwrXXXiv06NFDKC8v12GU6qnr/L/88kuhffv2QnFxsXRbQwyIzvX/XrRs2bIGN11a17mvWbNGACDMmDGjxmO7du0qPProo1oPUVXn+t2XlZUJTqdTePnllzUemfrOdO5TpkwRevXqJfzwww/C1q1bhZkzZwpOp1PYtm2bTiNVHwOiMzjXJ4aGGhBNnjxZaNGihbB///4673e5XEKfPn2EwYMHN7hg4FznHsztdgtxcXHCggULNBiZNs50/vfdd59gsVgEm80mfQEQrFarMGDAAH0GqzA5v/uSkhIBgLB48WINRqa+M537/v37BQDC+++/X+P2G264Qbj55pu1HKKq6vO7f++994To6GghPz9fw5Gp70znvm/fPgGAsGPHjhq3Dx48WLjrrru0HKKmGlbxh4ImT56MHTt2NNj58tMJgoB7770Xn3/+OVasWIE2bdrUeozL5cLw4cPhcDjw1VdfISYmRoeRKq8+517XzwiCALfbrcEI1XWu83/00Udx55131rita9eumDNnDq699loth6q4UH73W7duBQA0bdpU5dGp61zn3rp1azRr1qzWcuzff/894ldZAfJ+9//5z38wcuRIpKamajhC9Zzr3MUaudNXE9psNvh8Ps3GqTndQjEDq88nhoaWIZo0aZLgdDqFFStWCMeOHZO+ysrKBEHwT5P17t1b6Nq1q7Bv374aj/F4PDqPPjznOvc//vhD+Otf/yps3LhROHjwoLBmzRrh2muvFVJSUoS8vDydRx++c51/XdBApszOde779u0Tnn32WWHjxo1Cdna28OWXXwpt27YV+vfvr/PIw1ef3/ucOXOEpKQk4ZNPPhH27t0rPPHEE0JMTEyDmC6s7//7vXv3ChaLRfjuu+90GqnyznXulZWVQvv27YXLL79c2LBhg7Bv3z7h5ZdfFiwWi7Bo0SKdR68eBkRBfD6fMHnyZKFZs2bnLBpsaAERgDq/5s+fLwhC4Hzr+srOztZ17OE617kfPXpUuOqqq4S0tDQhOjpaaNGihXDzzTfXaskQqc51/mf6mYYQEJ3r3A8dOiT0799fSElJERwOh9C+fXvhoYceEoqKivQduALq+3ufNWuW0KJFCyEuLk7o06ePsHr1an0GrLD6nv+MGTOEzMxMwev16jNQFdTn3H///XchKytLSEtLE+Li4oRu3brVWobf0HBz1yD33HMPFixYgC+//BIdOnSQbnc6nYiNjQXg796Zm5uLjRs3YsKECVi1ahUSExPRsmXLsy5RJyIiIuNiQBTkTB1458+fj9tvvx0AMHPmTDzzzDNnfQwRERFFFgZEREREZHrsVE1ERESmx4CIiIiITI8BEREREZkeAyIiIiIyPQZEREREZHoMiIiIiMj0TB0QHT9+HJMmTULLli3hcDiQkZGB4cOHY82aNXoPjYiIiDRk6s1dx4wZg8rKSvzf//0f2rZti7y8PCxbtgwnT57Ue2hERESkIdNmiAoLC7F69Wq89NJLGDhwIFq1aoVevXphxowZGDlypPSYO++8E6mpqUhKSsKgQYPw66+/SseYOXMmLrroIrz11lvIzMxEXFwcbrjhBhQVFel1WkRERBQC0wZECQkJSEhIwBdffAG3213nY/70pz8hPz8f3333HTZt2oQePXpg8ODBKCgokB6zb98+fPzxx/j666+xePFibNmyBffcc49Wp0FEREQKMPXWHZ9++ikmTJiA8vJy9OjRAwMGDMDYsWPRrVs3/PTTTxgxYgTy8/PhcDikn2nfvj0efvhhTJw4ETNnzsTzzz+PgwcPonnz5gCAxYsXY8SIETh69CgyMjL0OjUiIiKSwbQZIsBfQ5STk4OvvvoKV155JVasWIEePXrg3Xffxa+//oqSkhI0btxYyiYlJCQgOzsbf/zxh3SMli1bSsEQAPTp0wc+nw979uzR45SIiIgoBKYuqgaAmJgYDB06FEOHDsWTTz6JO++8E08//TTuueceNG3aFCtWrKj1M8nJyZqPk4iIiNRj+oDodJ07d8YXX3yBHj16IDc3F1FRUWjduvUZH3/o0CHk5OSgWbNmAID169fDarWiQ4cOGo2YiIiIwmXaKbOTJ09i0KBB+OCDD7Bt2zZkZ2fjk08+wezZs3HddddhyJAh6NOnD0aNGoXvv/8eBw4cwNq1a/H4449j48aN0nFiYmJw22234ddff8Xq1asxdepU3HDDDawfIiIiiiCmzRAlJCSgd+/emDNnDv744w9UVVUhMzMTEyZMwGOPPQaLxYJvv/0Wjz/+OO644w4cP34cGRkZ6N+/P9LT06XjtG/fHllZWbj66qtRUFCAa665Bv/4xz90PDMiIiKSy9SrzMI1c+ZMfPHFF9i6daveQyEiIqIwmHbKjIiIiEjEgIiIiIhMj1NmREREZHrMEBEREZHpMSAiIiIi0zNNQDRr1ixccsklSExMRFpaGkaNGlVre42KigpMnjxZ2q5jzJgxyMvLk+7/9ddfcdNNNyEzMxOxsbHo1KkTXnvttRrH+Omnn9CvXz80btwYsbGx6NixI+bMmaPJORIREVFoTNOHaOXKlZg8eTIuueQSeDwePPbYYxg2bBh+++03xMfHAwCmTZuGRYsW4ZNPPoHT6cSUKVOQlZWFNWvWAAA2bdqEtLQ0fPDBB8jMzMTatWsxceJE2Gw2TJkyBQAQHx+PKVOmoFu3boiPj8dPP/2Eu+66C/Hx8Zg4caJu509ERERnZtqi6uPHjyMtLQ0rV65E//79UVRUhNTUVCxYsADXX389AGD37t3o1KkT1q1bh0svvbTO40yePBm7du3C8uXLz/hcWVlZiI+Px/vvv6/KuRAREVF4TDNldrqioiIAQEpKCgB/9qeqqgpDhgyRHtOxY0e0bNkS69atO+txxGPUZcuWLVi7di0GDBig0MiJiIhIaaaZMgvm8/lw//33o1+/frjgggsAALm5ubDb7bV2sk9PT0dubm6dx1m7di0WLlyIRYsW1bqvRYsWOH78ODweD2bOnIk777xT8fMgIiIiZZgyIJo8eTJ27NiBn376KeRj7NixA9dddx2efvppDBs2rNb9q1evRklJCdavX49HH30U7du3x0033RTOsImIiEglpguIpkyZgm+++QarVq1CixYtpNszMjJQWVmJwsLCGlmivLy8WjvX//bbbxg8eDAmTpyIJ554os7nadOmDQCga9euyMvLw8yZMxkQERERGZRpaogEQcCUKVPw+eefY/ny5VLAIurZsyeio6OxbNky6bY9e/bg0KFD6NOnj3Tbzp07MXDgQNx222144YUX6vXcPp8PbrdbmRMhIiIixZkmQzR58mQsWLAAX375JRITE6W6IKfTidjYWDidTowfPx7Tp09HSkoKkpKScO+996JPnz7SCrMdO3Zg0KBBGD58OKZPny4dw2azITU1FQAwb948tGzZEh07dgQArFq1Ci+//DKmTp2qw1kTERFRfZhm2b3FYqnz9vnz5+P2228H4G/M+MADD+C///0v3G43hg8fjn/84x/SlNnMmTPxzDPP1DpGq1atcODAAQDAG2+8gbfeegvZ2dmIiopCu3btMGHCBNx1112wWk2TkCMiIooopgmIiIiIiM6EKQsiIiIyPQZEREREZHoMiIiIiMj0GBARERGR6TEgIiIiItNjQERERESmx4CIiIiITI8BEREREZkeAyIiIiIyPQZEREREZHoMiIiIiMj0/h8/PgLHYtxYTQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "@np.vectorize\n",
+    "def count_inclusions(now):\n",
+    "    return has_geo_df[(has_geo_df['start'] <= now) & (now <= has_geo_df['finish'])].size\n",
+    "\n",
+    "\n",
+    "hours = pd.date_range(has_geo_df['start'].min(), has_geo_df['finish'].max(), freq='1H')\n",
+    "\n",
+    "outages = count_inclusions(hours)\n",
+    "\n",
+    "pd.DataFrame(outages, index=hours).plot()\n",
+    "\n",
+    "plt.legend().set_visible(False)\n",
+    "plt.ylabel('Количество одновременно отключённых')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7ff44a6dbcd0>]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(abs(np.fft.rfft(outages)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Outages, that will last for more than a day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>region</th>\n",
+       "      <th>area</th>\n",
+       "      <th>town</th>\n",
+       "      <th>street</th>\n",
+       "      <th>branch</th>\n",
+       "      <th>res</th>\n",
+       "      <th>comment</th>\n",
+       "      <th>building_id</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>start</th>\n",
+       "      <th>finish</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [index, region, area, town, street, branch, res, comment, building_id, lat, lng, start, finish]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "has_geo_df[has_geo_df['finish'] - has_geo_df['start'] > pd.Timedelta(\"1d\")]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Outages lengths distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Частота')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "durations = (has_geo_df['finish'] - has_geo_df['start']) / pd.Timedelta('1H')\n",
+    "\n",
+    "durations.plot(kind='hist', density=True)\n",
+    "plt.xlabel(\"Часы\")\n",
+    "plt.ylabel(\"Частота\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Number of buildings where outages occur more than once"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "repeating_buildings = has_geo_df[has_geo_df.duplicated('building_id', keep=False)].drop_duplicates(['index', 'building_id']).groupby('building_id')\n",
+    "\n",
+    "dups = repeating_buildings.size().value_counts()\n",
+    "\n",
+    "dups.rename({1: 'Повторно\\nв одном запросе'}, inplace=True)\n",
+    "\n",
+    "dups.plot(kind='bar', rot=0)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/parser/__init__.py b/parser/__init__.py
deleted file mode 100644
index 8a8e72c..0000000
--- a/parser/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .rosseti import RossetiParser
-from .address import split_addresses
-from .building_id import fetch_builing_ids
-from .preprocess import preprocess_df
\ No newline at end of file
diff --git a/parser/building_id.py b/parser/building_id.py
deleted file mode 100644
index 3aeb998..0000000
--- a/parser/building_id.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from __future__ import annotations
-from typing import Optional, Tuple, Any
-
-import requests
-import pandas as pd
-
-
-def get_building_id(row: pd.Series[Any]) -> Optional[Tuple[int, float, float]]:
-    r = requests.get('https://geocode.gate.petersburg.ru/parse/eas', params={
-        'street': row['Улица']
-    })
-
-    res = r.json()
-
-    if 'error' not in res:
-        return (res['Building_ID'], res['Longitude'], res['Latitude'])
-
-    return None
-
-
-def fetch_builing_ids(df: pd.DataFrame) -> pd.DataFrame:
-    df[['Building_ID', 'lng', 'lat']] = df.apply(
-        get_building_id, axis=1, result_type='expand')
-
-    return df
diff --git a/parser/preprocess.py b/parser/preprocess.py
deleted file mode 100644
index f5dea3f..0000000
--- a/parser/preprocess.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import pandas as pd
-
-
-def preprocess_df(df: pd.DataFrame) -> pd.DataFrame:
-    df['start'] = df['Плановая дата начала отключения электроснабжения'] + \
-        ' ' + df['Плановое время начала отключения электроснабжения']
-
-    df['finish'] = df['Плановая дата восстановления отключения электроснабжения'] + \
-        ' ' + df['Плановое время восстановления отключения электроснабжения']
-
-    df = df.drop(columns=[
-        'Улица',
-        'Плановая дата начала отключения электроснабжения',
-        'Плановая дата восстановления отключения электроснабжения',
-        'Плановое время начала отключения электроснабжения',
-        'Плановое время восстановления отключения электроснабжения'
-    ])
-
-    return df
diff --git a/parser_api/__init__.py b/parser_api/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/parser_api/config.py b/parser_api/config.py
new file mode 100644
index 0000000..ddd8d08
--- /dev/null
+++ b/parser_api/config.py
@@ -0,0 +1,9 @@
+import os
+
+REFETCH_PERIOD_H = int(os.environ.get('REFETCH_PERIOD_H', '4'))
+
+POSTGRES_USER = os.environ.get('POSTGRES_USER', 'rosseti')
+POSTGRES_PASSWORD = os.environ.get('POSTGRES_PASSWORD', 'rosseti')
+POSTGRES_DB = os.environ.get('POSTGRES_DB', 'rosseti')
+POSTGRES_HOST = os.environ.get('POSTGRES_HOST', 'localhost')
+POSTGRES_PORT = int(os.environ.get('POSTGRES_PORT', '5432'))
diff --git a/parser_api/controller.py b/parser_api/controller.py
new file mode 100644
index 0000000..885be51
--- /dev/null
+++ b/parser_api/controller.py
@@ -0,0 +1,111 @@
+from typing import List, Optional
+from functools import reduce
+import datetime
+
+from fastapi import HTTPException
+from sqlalchemy import func, True_
+from sqlalchemy.orm import Session
+from sqlalchemy.sql import operators
+from sqlalchemy.sql.expression import BinaryExpression
+
+from . import models, schemas
+
+
+def create_record(db: Session, record: schemas.Record):
+    db_record = models.Record(
+        region=record.region,
+        area=record.area,
+        town=record.town,
+        street=record.street,
+        start=record.start,
+        finish=record.finish,
+        branch=record.branch,
+        res=record.res,
+        comment=record.comment,
+        building_id=record.building_id,
+        lat=record.lat,
+        lng=record.lng,
+    )
+    db.add(db_record)
+    db.commit()
+    db.refresh(db_record)
+
+    return db_record
+
+
+def contains_lower(name, val):
+    if type(val) == str:
+        return getattr(models.Record, name).icontains(val)
+    else:
+        return getattr(models.Record, name) == val
+
+
+def and_if_can(a: BinaryExpression, b: Optional[BinaryExpression]):
+    if b is not None:
+        return a & b
+    else:
+        return a
+
+
+def search_each(db: Session, filters: schemas.RecordRequest) -> List[schemas.Record]:
+    query = None
+
+    if filters.start:
+        query = (models.Record.start <= filters.start)
+    if filters.finish:
+        query = and_if_can(models.Record.finish >= filters.finish, query)
+
+    filters = list(
+        filter(lambda x: x[1] is not None and x[0] not in ('start, finish'), filters))
+
+    query = reduce(lambda acc, ftr: and_if_can(
+        contains_lower(*ftr), acc), filters, query)
+
+    if query is None:
+        res = db.query(models.Record).all()
+
+    res = db.query(models.Record).filter(query).all()
+
+    return res
+
+
+def search_all(db: Session, prompt: str) -> List[schemas.Record]:
+    prompt = prompt.strip()
+
+    query = reduce(lambda acc, name: acc | contains_lower(name, prompt), (
+        'region',
+        'area',
+        'town',
+        'street',
+        'branch',
+        'res'
+    ), contains_lower('comment', prompt))
+
+    res = db.query(models.Record).filter(query).all()
+
+    return res
+
+
+def check_outage(db: Session, building_id: int) -> schemas.CheckResponse:
+    building_query = db.query(models.Record).filter(
+        (models.Record.building_id == building_id))
+
+    if building_query.count() == 0:
+        raise HTTPException(404, 'No such building')
+
+    now = datetime.datetime.now()
+
+    res = building_query.filter(
+        (models.Record.start <= now) &
+        (now <= models.Record.finish)
+    ).first()
+
+    if res is None:
+        return {
+            'is_outage': False
+        }
+
+    return {
+        'is_outage': True,
+        'when_finish': res.finish
+    }
diff --git a/parser_api/database.py b/parser_api/database.py
new file mode 100644
index 0000000..85b163f
--- /dev/null
+++ b/parser_api/database.py
@@ -0,0 +1,31 @@
+from typing import Generator
+import os
+
+from sqlalchemy import create_engine, URL
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker, Session
+
+from .config import POSTGRES_USER, POSTGRES_PASSWORD, POSTGRES_HOST, POSTGRES_PORT, POSTGRES_DB
+
+engine = create_engine(
+    URL.create(
+        "postgresql+psycopg",
+        username=POSTGRES_USER,
+        password=POSTGRES_PASSWORD,
+        host=POSTGRES_HOST,
+        port=POSTGRES_PORT,
+        database=POSTGRES_DB,
+    ),
+    client_encoding='utf8',
+)
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+Base = declarative_base()
+
+# Dependency
+def get_db() -> Generator[Session, None, None]:
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
diff --git a/parser_api/job.py b/parser_api/job.py
new file mode 100644
index 0000000..bb7a66a
--- /dev/null
+++ b/parser_api/job.py
@@ -0,0 +1,31 @@
+from rosseti_parser import pipeline, preprocess_read_df
+import pandas as pd
+import numpy as np
+from datetime import datetime
+import logging
+
+from .database import get_db
+from . import models
+
+from io import StringIO
+
+
+def job():
+    fetch_start = datetime.now()
+    print("Starting refetch job: " + fetch_start.isoformat())
+
+    db = next(get_db())
+
+    parser = pipeline()
+
+    db.query(models.Record).delete()
+    db.commit()
+
+    print("Rewriting db: " + datetime.now().isoformat())
+
+    for i, row in parser.df.iterrows():
+        row = row.where((pd.notnull(row)), None)
+        db.add(models.Record(**row.to_dict()))
+        db.commit()
+
+    print(f"Fetched in {datetime.now() - fetch_start}\n{parser}")
diff --git a/parser_api/main.py b/parser_api/main.py
new file mode 100644
index 0000000..1141a30
--- /dev/null
+++ b/parser_api/main.py
@@ -0,0 +1,37 @@
+from contextlib import asynccontextmanager
+import datetime
+
+from fastapi import FastAPI
+import schedule
+
+from . import models, router
+from .database import engine
+from .scheduler import run_continuously, run_threaded
+from .job import job
+from .config import REFETCH_PERIOD_H
+
+models.Base.metadata.create_all(bind=engine)
+
+start_stamp = datetime.datetime.now()
+
+
+async def lifespan(app: FastAPI):
+    schedule.every(REFETCH_PERIOD_H).hours.do(job)
+    stop_run_continuously = run_continuously()
+
+    run_threaded(job)
+
+    yield
+
+    stop_run_continuously()
+
+app = FastAPI(lifespan=lifespan)
+
+app.include_router(router.router)
+
+
+@app.get('/')
+def root():
+    return {
+        "up_since": start_stamp
+    }
diff --git a/parser_api/models.py b/parser_api/models.py
new file mode 100644
index 0000000..8518a81
--- /dev/null
+++ b/parser_api/models.py
@@ -0,0 +1,23 @@
+from sqlalchemy import Boolean, Column, Integer, String, DateTime, Float
+from sqlalchemy.orm import relationship
+
+from .database import Base
+
+
+class Record(Base):
+    __tablename__ = 'records'
+
+    id = Column(Integer, primary_key=True, index=True)
+    index = Column(Integer)
+    region = Column(String, nullable=True)
+    area = Column(String, nullable=True)
+    town = Column(String, nullable=True)
+    street = Column(String, nullable=True)
+    start = Column(DateTime)
+    finish = Column(DateTime)
+    branch = Column(String, nullable=True)
+    res = Column(String, nullable=True)
+    comment = Column(String, nullable=True)
+    building_id = Column(Integer, nullable=True)
+    lat = Column(Float, nullable=True)
+    lng = Column(Float, nullable=True)
diff --git a/parser_api/router.py b/parser_api/router.py
new file mode 100644
index 0000000..2c1b13e
--- /dev/null
+++ b/parser_api/router.py
@@ -0,0 +1,33 @@
+from fastapi import HTTPException, Depends
+from sqlalchemy.orm import Session
+from typing import List, Annotated
+
+from fastapi import APIRouter
+
+from . import models, schemas, controller
+from .database import SessionLocal, get_db
+
+router = APIRouter(prefix='/api')
+
+
+@router.get('/list', response_model=List[schemas.Record])
+def list_rows(
+    filters: Annotated[schemas.RecordRequest, Depends()],
+    db: Session = Depends(get_db)
+):
+    return controller.search_each(db, filters)
+
+
+@router.get('/search', response_model=List[schemas.Record])
+def search_rows(query: str, db: Session = Depends(get_db)):
+    return controller.search_all(db, query)
+
+
+@router.get('/check', response_model=schemas.CheckResponse)
+def check(building_id: int, db: Session = Depends(get_db)):
+    return controller.check_outage(db, building_id)
+
+
+@router.put('/create', response_model=schemas.Record)
+def create_record(record: schemas.RecordCreate, db: Session = Depends(get_db)):
+    return controller.create_record(db, record)
diff --git a/parser_api/scheduler.py b/parser_api/scheduler.py
new file mode 100644
index 0000000..48501fd
--- /dev/null
+++ b/parser_api/scheduler.py
@@ -0,0 +1,25 @@
+import threading
+import time
+
+import schedule
+
+
+def run_continuously(interval=1):
+    cease_continuous_run = threading.Event()
+
+    class ScheduleThread(threading.Thread):
+        @classmethod
+        def run(cls):
+            while not cease_continuous_run.is_set():
+                schedule.run_pending()
+                time.sleep(interval)
+
+    continuous_thread = ScheduleThread()
+    continuous_thread.start()
+
+    return cease_continuous_run.set
+
+
+def run_threaded(job):
+    job_thread = threading.Thread(target=job)
+    job_thread.start()
diff --git a/parser_api/schemas.py b/parser_api/schemas.py
new file mode 100644
index 0000000..bbc72a3
--- /dev/null
+++ b/parser_api/schemas.py
@@ -0,0 +1,39 @@
+import datetime
+from typing import Optional
+
+from pydantic import BaseModel
+
+
+class BaseRecord(BaseModel):
+    index: Optional[int] = None
+    region: Optional[str] = None
+    area: Optional[str] = None
+    town: Optional[str] = None
+    street: Optional[str] = None
+    branch: Optional[str] = None
+    res: Optional[str] = None
+    comment: Optional[str] = None
+    building_id: Optional[int] = None
+    lat: Optional[float] = None
+    lng: Optional[float] = None
+
+
+class Record(BaseRecord):
+    id: int
+    start: datetime.datetime
+    finish: datetime.datetime
+
+
+class RecordRequest(BaseRecord):
+    start: Optional[datetime.datetime] = None
+    finish: Optional[datetime.datetime] = None
+
+
+class RecordCreate(BaseRecord):
+    start: datetime.datetime
+    finish: datetime.datetime
+
+
+class CheckResponse(BaseModel):
+    is_outage: bool
+    when_finish: Optional[datetime.datetime] = None
diff --git a/requirements.dev.txt b/requirements.dev.txt
new file mode 100644
index 0000000..95c505a
--- /dev/null
+++ b/requirements.dev.txt
@@ -0,0 +1,72 @@
+annotated-types==0.5.0
+anyio==3.7.1
+asttokens==2.4.0
+autopep8==2.0.4
+backcall==0.2.0
+beautifulsoup4==4.12.2
+certifi==2023.7.22
+charset-normalizer==3.2.0
+comm==0.1.4
+contourpy==1.1.1
+cycler==0.11.0
+debugpy==1.8.0
+decorator==5.1.1
+executing==1.2.0
+fastapi==0.103.1
+fonttools==4.42.1
+greenlet==2.0.2
+idna==3.4
+ipykernel==6.25.2
+ipython==8.15.0
+jedi==0.19.0
+jupyter_client==8.3.1
+jupyter_core==5.3.1
+kiwisolver==1.4.5
+lxml==4.9.3
+matplotlib==3.8.0
+matplotlib-inline==0.1.6
+mypy==1.5.1
+mypy-extensions==1.0.0
+nest-asyncio==1.5.8
+numpy==1.26.0
+packaging==23.1
+pandas==2.1.0
+pandas-stubs==2.0.3.230814
+parso==0.8.3
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==10.0.1
+platformdirs==3.10.0
+prompt-toolkit==3.0.39
+psutil==5.9.5
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pycodestyle==2.11.0
+pydantic==2.3.0
+pydantic_core==2.6.3
+Pygments==2.16.1
+pyparsing==3.1.1
+python-dateutil==2.8.2
+pytz==2023.3.post1
+pyzmq==25.1.1
+requests==2.31.0
+schedule==1.2.0
+scipy==1.11.2
+seaborn==0.12.2
+six==1.16.0
+sniffio==1.3.0
+soupsieve==2.5
+SQLAlchemy==2.0.21
+stack-data==0.6.2
+starlette==0.27.0
+tornado==6.3.3
+traitlets==5.10.0
+types-beautifulsoup4==4.12.0.6
+types-html5lib==1.1.11.15
+types-pytz==2023.3.1.0
+types-requests==2.31.0.2
+types-urllib3==1.26.25.14
+typing_extensions==4.8.0
+tzdata==2023.3
+urllib3==2.0.4
+wcwidth==0.2.6
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..6c8fd79
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,32 @@
+annotated-types==0.5.0
+anyio==3.7.1
+beautifulsoup4==4.12.2
+bs4==0.0.1
+certifi==2023.7.22
+charset-normalizer==3.2.0
+click==8.1.7
+fastapi==0.103.1
+greenlet==2.0.2
+h11==0.14.0
+idna==3.4
+lxml==4.9.3
+numpy==1.26.0
+pandas==2.1.1
+psycopg==3.1.10
+psycopg-binary==3.1.10
+psycopg-pool==3.1.7
+pydantic==2.3.0
+pydantic_core==2.6.3
+python-dateutil==2.8.2
+pytz==2023.3.post1
+requests==2.31.0
+schedule==1.2.0
+six==1.16.0
+sniffio==1.3.0
+soupsieve==2.5
+SQLAlchemy==2.0.21
+starlette==0.27.0
+typing_extensions==4.8.0
+tzdata==2023.3
+urllib3==2.0.5
+uvicorn==0.23.2
diff --git a/rosseti_parser/__init__.py b/rosseti_parser/__init__.py
new file mode 100644
index 0000000..7559a4f
--- /dev/null
+++ b/rosseti_parser/__init__.py
@@ -0,0 +1,5 @@
+from .rosseti import RossetiParser
+from .address import split_addresses
+from .building_id import fetch_builing_ids
+from .preprocess import preprocess_df, COL_NS, ICOL_NS, preprocess_read_df, group_by_index
+from .util import pipeline
diff --git a/parser/__main__.py b/rosseti_parser/__main__.py
similarity index 64%
rename from parser/__main__.py
rename to rosseti_parser/__main__.py
index cc6f312..02291f6 100644
--- a/parser/__main__.py
+++ b/rosseti_parser/__main__.py
@@ -2,20 +2,11 @@ import sys
 import schedule
 import time
 
-from . import RossetiParser, split_addresses, fetch_builing_ids, preprocess_df
+from . import pipeline
 
 
-def job() -> None:
-    parser = RossetiParser()
-
-    print(parser)
-
-    parser.df = split_addresses(parser.df)
-
-    parser.df = fetch_builing_ids(parser.df)
-
-    parser.df = preprocess_df(parser.df)
-
+def job():
+    parser = pipeline()
     parser.save_df(f'./data_{parser.today.strftime("%d-%m-%y_%H:%M")}.csv')
 
 
diff --git a/parser/address.py b/rosseti_parser/address.py
similarity index 79%
rename from parser/address.py
rename to rosseti_parser/address.py
index fe9d4a9..9f0cbe7 100644
--- a/parser/address.py
+++ b/rosseti_parser/address.py
@@ -6,9 +6,9 @@ import re
 
 T = TypeVar('T')
 
-street_prefixes = ('ул.', 'бул.', 'пр.', 'ул', 'бул',
+STREET_PREFIXES = ('ул.', 'бул.', 'пр.', 'ул', 'бул',
                    'пр', 'ш.', 'ш', 'пер.', 'пер')
-houses_prefixes = ('д.', 'д')
+HOUSES_PREFIXES = ('д.', 'д')
 
 
 def unfold_house_ranges(token: str) -> str:
@@ -56,8 +56,8 @@ def split_address(address: str) -> List[str]:
         accumulator = ''
 
         for i in range(len(tokens)):
-            if (any_of_in(street_prefixes, tokens[i].lower()) and
-                    any_of_in(street_prefixes, accumulator.lower())):
+            if (any_of_in(STREET_PREFIXES, tokens[i].lower()) and
+                    any_of_in(STREET_PREFIXES, accumulator.lower())):
                 res += unfold_houses_list(accumulator)
                 accumulator = ''
 
@@ -71,17 +71,18 @@ def split_address(address: str) -> List[str]:
 
 
 def process_row(row: pd.Series[str]) -> pd.Series[str]:
-    if pd.isnull(row['Улица']):
-        return row
-
-    addresses = split_address(row['Улица'])
-
     row = row.copy()
 
-    row['Улица'] = addresses
+    if pd.isnull(row['Улица']):
+        row['Улица'] = [None]
+    else:
+        addresses = split_address(row['Улица'])
+        row['Улица'] = addresses
 
     return row
 
 
 def split_addresses(df: pd.DataFrame) -> pd.DataFrame:
-    return df.apply(process_row, axis=1).explode('Улица', ignore_index=True)
+    merged_df = df.apply(process_row, axis=1).reset_index()
+
+    return merged_df.explode('Улица', ignore_index=True)
diff --git a/rosseti_parser/building_id.py b/rosseti_parser/building_id.py
new file mode 100644
index 0000000..e5a7d34
--- /dev/null
+++ b/rosseti_parser/building_id.py
@@ -0,0 +1,31 @@
+from __future__ import annotations
+from typing import Optional, Tuple, Any, List
+
+import requests
+import pandas as pd
+import numpy as np
+
+GeoTupleType = Tuple[Optional[int], Optional[float], Optional[float]]
+
+
+def get_building_id(street: str) -> GeoTupleType:
+    if pd.isnull(street):
+        return None, None, None
+
+    r = requests.get('https://geocode.gate.petersburg.ru/parse/eas', params={
+        'street': street
+    }, timeout=10)
+
+    res = r.json()
+
+    if 'error' in res:
+        return None, None, None
+
+    return res['Building_ID'], res['Latitude'], res['Longitude']
+
+
+def fetch_builing_ids(df: pd.DataFrame) -> pd.DataFrame:
+    df[['ID здания', 'Широта', 'Долгота']] = df.apply(
+        lambda row: get_building_id(row['Улица']), axis=1, result_type='expand')
+
+    return df
diff --git a/rosseti_parser/preprocess.py b/rosseti_parser/preprocess.py
new file mode 100644
index 0000000..533d24b
--- /dev/null
+++ b/rosseti_parser/preprocess.py
@@ -0,0 +1,62 @@
+from __future__ import annotations
+from typing import Any, List
+
+import pandas as pd
+
+COL_NS = {
+    'region': 'Регион РФ (область, край, город фед. значения, округ)',
+    'area': 'Административный район',
+    'town': 'Населённый пункт',
+    'street': 'Улица',
+    'start_date': 'Плановая дата начала отключения электроснабжения',
+    'start_time': 'Плановое время начала отключения электроснабжения',
+    'finish_date': 'Плановая дата восстановления отключения электроснабжения',
+    'finish_time': 'Плановое время восстановления отключения электроснабжения',
+    'branch': 'Филиал',
+    'res': 'РЭС',
+    'comment': 'Комментарий',
+    'building_id': 'ID здания',
+    'lat': 'Широта',
+    'lng': 'Долгота'
+}
+
+ICOL_NS = dict(map(reversed, COL_NS.items()))
+
+
+def preprocess_df(df: pd.DataFrame) -> pd.DataFrame:
+    df.rename(columns=ICOL_NS, inplace=True)
+
+    for a in ('start', 'finish'):
+        df[f'{a}'] = pd.to_datetime(
+            df[f'{a}_date'].astype(str) + ' ' + df[f'{a}_time'].astype(str),
+            dayfirst=True
+        )
+        df.drop(columns=[f'{a}_date', f'{a}_time'], inplace=True)
+
+    return df
+
+
+def preprocess_read_df(df: pd.DataFrame) -> pd.DataFrame:
+    for name in ('start', 'finish'):
+        df[name] = pd.to_datetime(df[name])
+
+    return df
+
+
+def join_columns(col: pd.Series[Any]) -> List[Any] | Any:
+    first = col.iloc[0]
+
+    if col.name in ('street', 'building_id', 'lat', 'lng') and pd.notnull(first):
+        return list(col)
+
+    return first
+
+
+def group_by_index(df: pd.DataFrame) -> pd.DataFrame:
+    groupped = df.groupby('index')
+
+    res_df = groupped.apply(
+        lambda index_df: index_df.apply(join_columns)
+    ).drop(columns='index')
+
+    return res_df
diff --git a/parser/rosseti.py b/rosseti_parser/rosseti.py
similarity index 100%
rename from parser/rosseti.py
rename to rosseti_parser/rosseti.py
diff --git a/rosseti_parser/util.py b/rosseti_parser/util.py
new file mode 100644
index 0000000..4c17f7e
--- /dev/null
+++ b/rosseti_parser/util.py
@@ -0,0 +1,18 @@
+from typing import Optional
+
+from . import RossetiParser, split_addresses, fetch_builing_ids, preprocess_df
+
+
+def pipeline(parser: Optional[RossetiParser] = None) -> RossetiParser:
+    if parser is None:
+        parser = RossetiParser()
+
+    print(parser)
+
+    parser.df = split_addresses(parser.df)
+
+    parser.df = fetch_builing_ids(parser.df)
+
+    parser.df = preprocess_df(parser.df)
+
+    return parser