Workshop 5 - TimescaleDB with Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TimescaleDB with Python\n",
    "\n",
    "In this workshop, we'll explore working with TimescaleDB using Python. If you've used PostgreSQL and Python before, it should be very straight forward since the PostgreSQL driver, `psycopg2` is also used for working with TimescaleDB.\n",
    "\n",
    "## Installing \n",
    "\n",
    "In order to work with this lab, you must install TimescaleDB. Please see the [installation documentation](https://docs.timescale.com/latest/getting-started/installation/macos/installation-homebrew) for your particular system. If you have [Docker](https://www.docker.com/products/docker-desktop) available, I highly recommend using the container method as this is the simplest and most straight forward method to getting started.\n",
    "\n",
    "You can start a TimescaleDB instance, pulling the Docker image from Docker Hub as follows:\n",
    "\n",
    "```\n",
    "$ docker run -d --name timescaledb -p 5433:5432 -e POSTGRES_PASSWORD=password \\\n",
    "    -v $PWD/data/tsdb:/var/lib/postgresql/data timescale/timescaledb:1.7.4-pg12\n",
    "```\n",
    "\n",
    "Note that I've used the port binding `-p 5433:5432` instead of the default PostgreSQL port to avoid conflicts with any PostgreSQL database running on your system. Also note the use of the `POSTGRES_PASSWORD` environment variable to simplify connection from your localhost to the docker image. \n",
    "\n",
    "\n",
    "You'll also have to install the [psycopg2](https://pypi.org/project/psycopg2/) drivers from PyPI. The simplest thing to do is to install the binary package so that you don't require a compiler or external library:\n",
    "\n",
    "```\n",
    "$ pip install psycopg2-binary\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connecting\n",
    "\n",
    "To connect to the database, we'll use `psycopg2` and a connection string that specifies the host, port, username, password, and database for the location that we installed it to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import psycopg2 as pg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Database\n",
    "\n",
    "Normally a database is likely already created for you if you're going to be connecting to it from Python. However, to simplify things, we'll create our database using `psycopg2` directly. Note that this connects using the Docker username and password above, please supply your own credentials if you installed TimescaleDB separately. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from psycopg2.extensions import ISOLATION_LEVEL_AUTOCOMMIT\n",
    "\n",
    "DBSQL = (\n",
    "    \"CREATE DATABASE nyc_energy WITH OWNER postgres\",\n",
    "    \"CREATE EXTENSION IF NOT EXISTS timescaledb CASCADE\"\n",
    ")\n",
    "\n",
    "with pg.connect(\"user=postgres password=password port=5433 host=localhost sslmode=disable\") as conn:\n",
    "    # This isolation level is required to create databases.\n",
    "    conn.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT)\n",
    "    cur = conn.cursor()\n",
    "    for sql in DBSQL:\n",
    "        cur.execute(sql)\n",
    "    conn.commit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connecting to the Database\n",
    "\n",
    "In the above example we used a context manager to automatically connect to the database and close the connection when we were done. In this example we'll connect directly using a database URL string and keep the connection around for other notebook operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATABASE_URL = \"postgres://postgres:password@localhost:5433/nyc_energy?sslmode=disable\"\n",
    "\n",
    "conn = pg.connect(DATABASE_URL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Hypertable\n",
    "\n",
    "TimescaleDB is just a PostgreSQL database, so we can create any standard relational tables that we'd like. For example if we were going to create a database to ingest stock prices we might create a `stocks` table that had the name, the stock symbol, description, currency, and security type. \n",
    "\n",
    "In order to work with time series data, we'll create a _hypertable_ - an abstraction of a single continuous table across all space and time intervals which can be queried using TimescaleDB functions. A hypertable can be defined with a standard schema using column names and types, but at least one column should sepcify a time value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: define a regular table\n",
    "create_energy_table = (\n",
    "    \"CREATE TABLE IF NOT EXISTS energy_usage (\"\n",
    "    \"  time TIMESTAMPTZ NOT NULL,\"\n",
    "    \"  demand DOUBLE PRECISION,\"\n",
    "    \"  precip DOUBLE PRECISION,\"\n",
    "    \"  temp DOUBLE PRECISION\"\n",
    "    \");\"\n",
    ")\n",
    "\n",
    "# Step 2: create hypertable\n",
    "create_energy_hypertable = \"SELECT create_hypertable('energy_usage', 'time')\"\n",
    "\n",
    "\n",
    "with conn.cursor() as cur:\n",
    "    cur.execute(create_energy_table)\n",
    "    cur.execute(create_energy_hypertable)\n",
    "    conn.commit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inserting Data\n",
    "\n",
    "Inserting data is as simple as using `INSERT` SQL statements into the correct table. However, TimescaleDB is about speed, so it's nice to be able to quickly copy a whole CSV file using `pgcopy`. If you haven't already, make sure you pip install `pgcopy`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import gzip\n",
    "import pytz\n",
    "\n",
    "from datetime import datetime\n",
    "from pgcopy import CopyManager\n",
    "\n",
    "\n",
    "ET = pytz.timezone(\"America/New_York\")\n",
    "DF = \"%Y-%m-%d %H:%M:%S\"\n",
    "\n",
    "def parse_row(row):\n",
    "    row[0] = ET.localize(datetime.strptime(row[0], DF))\n",
    "    row[1] = float(row[1]) if row[1] else None\n",
    "    row[2] = float(row[2]) if row[2] else None\n",
    "    row[3] = float(row[3]) if row[3] else None\n",
    "    return row\n",
    "    \n",
    "\n",
    "\n",
    "def fast_insert(data=\"datasets/energy/nyc_energy.csv.gz\"):\n",
    "    with gzip.open(data, \"rt\") as f:\n",
    "        # open the reader and get the headers\n",
    "        reader = csv.reader(f)\n",
    "        cols = list(next(reader))\n",
    "        cols[0] = \"time\" # rename the first column\n",
    "        \n",
    "        # create the copy manager into the hypertable\n",
    "        mgr = CopyManager(conn, 'energy_usage', cols)\n",
    "        \n",
    "        # load the csv into memory, parsing the record\n",
    "        records = [\n",
    "            parse_row(row) for row in reader\n",
    "        ]\n",
    "        \n",
    "        # copy the records into the database\n",
    "        mgr.copy(records)\n",
    "        \n",
    "        # don't forget to commit!\n",
    "        conn.commit()\n",
    "        \n",
    "\n",
    "# Make sure you only run this once!\n",
    "fast_insert()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting Data\n",
    "\n",
    "We can fetch data using normal SQL methods as well. For example, to get the total number of records in 2012, we would execute the following query:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8780,)\n"
     ]
    }
   ],
   "source": [
    "query = \"SELECT count(time) as n_records FROM energy_usage WHERE time BETWEEN '2012-01-01' AND '2013-01-01'\"\n",
    "\n",
    "with conn.cursor() as cur:\n",
    "    cur.execute(query)\n",
    "    print(cur.fetchone())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting data is fun, but we're here to use TimescaleDB's time series analytics specific functionality. PostgreSQL provides `EXTRACT` and `date_trunc` functions to handle standard time intervals. TimescaleDB provides a more powerful version, `time_bucket` that handles arbitrary time intervals of any size. We could reformat our query to get the number of records per 2 weeks as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-12-19 19\n",
      "2012-01-02 336\n",
      "2012-01-16 336\n",
      "2012-01-30 336\n",
      "2012-02-13 336\n",
      "2012-02-27 337\n",
      "2012-03-12 336\n",
      "2012-03-26 336\n",
      "2012-04-09 336\n",
      "2012-04-23 336\n"
     ]
    }
   ],
   "source": [
    "query = (\n",
    "    \"SELECT time_bucket('2 weeks', time) AS year, count(*)\"\n",
    "    \"  FROM energy_usage\"\n",
    "    \"  GROUP BY year\"\n",
    "    \"  ORDER BY year\"\n",
    "    \"  LIMIT 10\"\n",
    ")\n",
    "\n",
    "with conn.cursor() as cur:\n",
    "    cur.execute(query)\n",
    "    for row in cur.fetchall():\n",
    "        print(row[0].strftime(\"%Y-%m-%d\"), row[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downsampling in Queries\n",
    "\n",
    "We can combine our database with our Pandas data analysis by querying via downsampling and then visualizing the resulting data using a Pandas series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
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uFv9Q4xwazZhCrtyLtuvZ8usRVV6jGnJTaW4KtzDNFx3kFE2iKHbY2cUn675CvIQ4KXwSJhcSmaSJfIzV/8H+EqbmUgCAqbkkuvK+DyTok9CaxHijrpBgjD0CoFpPqXMjtmcAPlTlWDcDuDlifBWAEyPGD0adQ6MZa8iQ00LZiS0k4k7cgKJJSHOTG6FJJC1vG7li39XdoJCQ5iaDC6Nc0qobAlsoO+gv2pjaxLO9TYMCGpWjfRLjGp1xrdEMA1KTaGQSbKT3s9Qk0sLcpGZdM8Z4dFOq0iex2xMSjUU3WUKTyCbr+yRkZNNUURLEMiggxJyAT0Kbm8YbWkhoNMOAr0nEnwR7Y07cQKUmoSaoFW1XrNZVnwSf2PuFFtCouckyooXEhr29eHZXT2AfKSTahZAwxL5Sm9DmpvGNFhIazTCgmpviIqujxkGt3QQEW5jKSTznhcByTYIx5u3XX7JjOdVtL5mOnyebsgJ5Ehd882G87tuPBPaRDY4yCX5+KWAcxsAYA2NAVmg5WpMYf2ghodEMA9L804iQaMTcZIfyJFTHtYw+Ckc3SUHSlk2AMS4o6p4nFN2UjRECKy1fUjhITcJxfSElBcihdMzTHB60kNBoDhHXZd4kPhRzk4wkqnmOGnkSsmietPtLn4QUJFNEt7pw6Ywoyl6eRPwQWD9Lm+9jki8kVM3ENEh3pxuHaCGh0RwiJWXia8RxLfssyIm/FpUhsL6Q8DUJJePadjxNQvoK4vglHHGihIhuyiTNupVbpbbgCQnxv+0yT7iZBiFhUsBMphkfaCGh0Rwialnu4hDMTUZ9RSIimc4/p++TEJqEaaBku942su91nAgnKXxMqUkkTZQdVrPTnV1FSLiKuckk8q5LM76Il6uv0Wiqok58QzE3lWKYYHxNojJPwtMkpOM6YaBou942U3K8blMcTcKv3SQc114lWBsM0RqP4wRzK1THteH6foqkFd0wSTO20UJCo6nB83v7kLIMLJiWq7qNOvENJbqpaPNIJNFrKxLHdUHkRx3ZEdFNXu0msWKX1yXNTXFCbp1QnoTUXEq2i2rBUQ4LahKq49ogP6Q2oTWJcYk2N2k0Nfi3X6/BtXetr7lNUJNo3NzEGOra6h3GYBJ5k7daBVb2oPbyJBLS3MSP2SoqwMYp1FcO5UnI/8suq+r4dkLO7qDjml+nIYSE1iTGH1pIaDQ16CuU69ryg47roSXT1QsNtV3Gnb9GpSZRFueUXelSlgnbZZ6vpFmExsYpSW67LkyDPK1GmpAch6Gv6F+v6hORZi2Dgj4Jx2WQsswkaW7SjuvxhhYSGk0NirZb189QCjiu4wuJfNGftOv1pHaFkPAnYH97b/UvVvJSWEgNoznNNYk4/aVtl3nag3rMsusGNAlVMHomKqNSSPimKG4qi+N/0YwttJDQaGpQKNdvJqRO8I2EwKrb1hMSniYhJ21VkxCTdNKUmgT/XwqhbNKEQfFMYbbDPL8HoGgSLvNKfABBYSi1mnB0k8OYl+VtGgaSps6TGI9oIaHR1KBoO3Un8KE6rgtlx5vQ64XOSk1CNgNSM67D9ZakJiEn9YRpIJMw45mbHNeb5AF/wi87blBIKN+JzIXwfBKG6pMIaRLacT3u0EJCo6lBMYYmMdQQ2ELZ9ZzKcTQJyyBPEKg+iXApDRkmK2suWSbFSoqT51EzwOVrx2WBEFr1M9tKLoT6v1qWwyAdAjte0UJCo6mC67JYvatLQ0ymK5SdCiHx7K4eLPjUnXh2Vw/W7farrbqMwVCim9Q8ibKY2KWz2fdJ8GuxDAPpREwh4TDPxASomkTI3KSYyqplXDsVGdcGStpxPe7QeRIaTRWKMct/D6UsB2M8+sgTEmICf2ADb+MuK60++/nz0ZSy4HiaRGXGte24gYnd90lIcxMhm4xnbiq7QXOT9E84oRBYVfOxQ8l0Ace1omUkTMOLxNKMH7QmodFUQa6WC7YDxqqvgKUm0ZSyYpub5CTbEtIk5rRlgtsJ4WG7DIZqblI1CSdoIvI0iaI0NwmfRAxNwgmZm7w6TDV8Ep4gMEPJdEqpctMgr4S5ZnyhhYRGUwU54TNWu3SGfK85bcV2XMvtwuamhBV8JKUwcKUmYVb6JMqOG4hISlWYm4ibm2I5rpnnHAd8n4Qd8kkEzE0sOgEvULvJK/CnhcR4QwsJjaYK6kRYS0OQmkRLOhFrta4ezxcSfL+wOUYeW2oSUhiUQ9FNlrL6T4U0iYRpIJM0YwmwsuMG8iRMw4+mUveP0iSMkOPaVvIkvIxrbW4ad2ghodFUQRUMtRzSciJvzSZirdYBXyh45qayFAbBSdTTJFgwuskJ5UkEfRI8usnTJExqyNykChw1mkrVAkqRPomgucl1lTwJIiQs7bgej2ghodFUIbYm4fhawdA1CVccKziJSge17fDoJpl1rZ7HruOTSBjxfRJlNxjdpEZT2S5DWhT8C2oSvPigETI3yX3kGC88qDvTjTe0kNBoqqAKhlpRS54mkYmvSVT6JKqYm4SQcBlf4RMRmkLd4sqOG/AjhM1NlklIJ00MluqbeuyQucmLphITvuxZEc6TUPdRHddSk/BLhWtNYryhhYRGU4WgJlF98i87fCXdlLIa0CSiHdfS3HT9Zafwv8WkarvMs/U3pSz0K3WfyqFSGuHaTdLcFKssR1VzE29ilBPFAsOOa4Mq93EDtZu043q8ooWERlOFgCZRx3GdNI3YuQiAXy22NeSTkCvtbMIUf/NxRziuASCXMtGvVmR13YC5ycu4FoJENTfVCuUFuDAI1G5SzU0O83pWqLWbHCekSaiOa8WpnTAN3tK0WmMKzZhECwmNpgpxNYmi7SJp8YnYdlms1bI8XjZpwjLIM2dJM46cjKXQcBSTDjc3BX0S6iQdrt0ky3I4IoO8FrKQoMRSSpOXXRdNQpNQjxPeJ6p9qSXMTeF9NWOfukKCiG4mov1E9KwydjIRPUpEq4loFRGdJsaJiK4nok1EtIaIliv7XE5EG8W/y5XxFUS0VuxzPYnaAkQ0hYjuFdvfS0Ttw/vRNZraBDWJGj4Jh2sSGTGxxzE5yeOlEyZyio/Bdt3AhCrNT7zLm9QkrEBiWzWfhGwylDB5WQ4AKNTxS4TLclhe7SYXjsu8zxjQJNxgboXavlTNk5BVarXJaXwRR5P4MYALQmNfAfB5xtjJAD4r/gaACwEsEv+uBvA9gE/4AK4BcDqA0wBco0z63wPwPmU/ea5PAbiPMbYIwH3ib41m1AhoEjXi+x3hE5ATaCGGyUlOsumEgfZsAl15bj4qi5wHKzShqqGpzemgkAgX5bMMApGiSRjkaSb1BFjYdOV1pnMYyg5DyuKaj/rdhDUJtX2prN0kzU3yWJrxQ10hwRh7CEBneBhAi3jdCmC3eH0RgJ8wzqMA2ohoNoDzAdzLGOtkjHUBuBfABeK9FsbYo4wbS38C4GLlWLeI17co4xrNqBBXk5D1jjIJWX01hiZh+5pEey6JroESP5bwCaiTMz+H75zOJSOim5TVPxF52gSAwLXVFRKhjGtLqd3E/RWivIZaKlxxqgPhKrD+NSS0JjEuGWqBv48BuIeIrgMXNC8V43MB7FC22ynGao3vjBgHgJmMsT3i9V4AM6tdDBFdDa654Igjjmj802g0Eair5VrJdHKVH3ciBoLmpvZsEvt6CwD4BJo0Dc/cJCfUsu07lJvSVqDYXji6CeANiApl16sOK81N9RzrFZ3pvB7Xroh84tdWtOv7JGyXwXT9MuaeT0JnXY8rhuq4/gCAjzPG5gP4OICbhu+SKhFaRlUdlTF2A2NsJWNs5fTp00fyUjSTiLjRTXJibcQn4ZmbLAPtWUWTsIW5KdQ3QgoPQITAlmwvUkmu8FVSQihIDSPutVXmSfgZ3tJfkrLMUKlwNxA2qzqu3VAILKAd1+ONoQqJywH8Rrz+JbifAQB2AZivbDdPjNUanxcxDgD7hDkK4v/9Q7xWjWZIFG3Hm/BqmZsc4ezNxFytA9zcZIlOcwGfhMs1hrBppqwIgqaUBcZ8s5YdchwDfitTOXnLa6uXK1EOHcvrJyFCYKVTPZxMZ0YJFqaYm0g7rscrQxUSuwG8XLx+JYCN4vUdAN4topzOANAjTEb3ADiPiNqFw/o8APeI93qJ6AwR1fRuAL9XjiWjoC5XxjWaUaFYdpFLmjCVENUo5CTprdbjCImy65mA2nNJDJYdFMqOZzoKO3lVk5JMaJN+ibLjImGENQkhJMR4e5bnY3T0F2telxMyNxFxrcZxXeFUN5CyjIBm5bKgTyLguPYyrv3eFGVbO67HE3V9EkR0K4BzAEwjop3gUUrvA/AtIrIAFCD8AQDuAvAaAJsA5AG8BwAYY51E9AUAT4jt/osxJp3hHwSPoMoAuFv8A4AvA7idiK4CsB3AW4f8KTWaIVC0HaQSPL+glrlJmlviRhABfEUv6yC1Z5MAgK58yTMd+UlssqaT65URb07zx7a/aGMGKqvAAqomwf8/cmoOlkF4YV+ft82qbZ1YvaMbV555lDex83Da4LFMg2A7jH9Oo7IVqu2EfBKK49qv3WToPIlxSl0hwRi7rMpbKyK2ZQA+VOU4NwO4OWJ8FYATI8YPAji33vVpNCMFX+0bcN3aJS2kJhHXOSyPLTOj5Sq/a6DsRTfJVbc068isbgBe/aR+RZMIm5ukIJEaRtIycNS0HF7Y1+9t85V7nsfjWztxoK+IT7/mBP5ZIpzgMlNaCqNwdFW4cqxp+kLCLxWOis+kGR/ojGuNpgpF20HKMpFOmLUd1w7zSl8AMTUJW9EkclyT6M6XUBImHbXZDxDySaQrhUQyNLG3pLngUYXHsTObA5qEjNi6c+0eb8wJ+RcAqUm4nr+kKSJPw1T7YlOluckkQtLytRXN+EELCY2mClKTSCWMmj4JObFmxQo/XnST4/skhLmpU5ibkqafU2AHHNd+dBMALww2HLYK+H0q1BX+oplNeLEz72k6u7oHAQD7e4tepFTZrfRvJEwKOK6bUpZXPFB+/mCjomoZ18F6VJrxgRYSGk0VPE3CMmvmSdjCJxEuh1H72K5no2/PCXNTvlyRTFdyGBhjAce1FBJyog4nwAFAi2du8scXzWgGY8CWjn4Mlhx09JcwrSmJkuOic6CEsuOCMVSYm6QmIYVRLmUGakc54WQ6JWw2EAKrNYlxiRYSGk0VpCaRThgYKDp43bcfxl+e21exndQkDCN+SW7HZd4ELk1DvYNlL4JIRhXZjutFOEmhIivHbj+Y5wLErcyTkJqEajqS++VLDnZ15wEAK47k1XH29ha8XA1p/pJYhh/yapkGrx1VCGoSkY5r5juu1YzrovZJjCu0kNBoqqD6JDr6i3h2Vy/W7uqp2E4192RilgtXcwtSlgGDuAmqLMxNAF/Rlx3XW3lLQdCeS+KsY6bh54+9iEKZr/7VshyAL3jUpDc143lHFzc1rTxyCgBgX28BB0R47LSmkJAw/U54lkloSlooOa7SfzsYEWWI2lFqFVgjkCcRDIH9x+aDePlXH8BHbn267vemGX20kNBoquBrEia68nyVnVds8RI1BDSTMGOZm9SIICK/B7Vah8kySRTWk0LCf1yvetlR2N9XxP0b9nvbqkitQRVYqpDYKYXEAqFJ9BTR0c8/47SmVOBYlkGe4z5hGJ7jXEY4RTq7iQK9I2qV5fjd07uw/WAef31e58uORYZau0mjmfBITQIAukVGdJQA4Ctp31/QM1iu2KZyn+DEmhZCwnaYlw+RNA3YruvlFahCYtGMJgDcTCS3VWnJ8Ec7r5i+kp65x8HenkGYBmHJnFYQAf/+27We6alSSBieCc00yEvm6y/aaM8lA13zJIZB3HHNlOgm5fwq8vtKWnrNOhbRQkKjqYLUJAh+KGqUkFCjexZOz2H9nt66x3ZcNzCxphO8B3VJCXW1TELZZr5PQhEE0nktJ9iwJiHNTflipSZRtF0Uyi4yCRNJy4BsVvfk9i4AwNQIc5MslZ4wqcJxHqVJWAbBcRgck4GICw2vp0UonLh7sBQ5rhkbaNGt0VShWOaahCyWByCQRCZRtYLjZjVjuxJmWo1wlnImaYqyHK7n0LYMA2XXRVlO0Ja/vVzN9wgzWEV0kzA3qdnNKcXco+ZdqKQswxMCEssgr0eGdFwDfghuOJkO4JqDDIGVwjBlVdMk+HHitFfVjD5aSGg0VSjYLlIiuklST5M4biYPM920v79iu4p9lIk1EzA3+VnS1XwSCVGyWxYGDOc2SE1CJaWUxSg7fgjuXR99Gd54Cq/Qn06YoJDpyDL9PBGZJwH4yXxOKJkO4JqDKzKuZckPQ/glwhqDFHSOyyqc2prDjxYSGk0EjDGUbBdpEd0kiXRcK5PksbOaAQAb9vomp709BXzstqcD+zosOLFyh7ctzE1+cT7bifZJANzk1O2Zm6J9Eiqq47io9KdYPKcFF5w4CwDQW6j0p5gGeT4JSzU3FZUqtBHmJum4Vt9LW0ZFiLDqw4mTiKgZXbSQ0GgikLH8qYSBtKUKidqaxIKpOSQtAxsVTeI7D2zC71bvxq+f2hW5DwCkkyYGy26gdhIPgY32SQBALmV6q/Cw6ShsMgKCQqLssMDxjp6eAwBEWXsSph/dZBkGcin+fajRTUaE49oVeRJh34tqbio7LgZKDma1pAHUbu4k+dDPn8LSz91TdzvN8KCFhEYTgd8UyAyYmwYiNAm1cqppEFrSFvqUZLM2UcDvQJ9fprvCJ5EwUCg5AV9BwqRQnkRISCR9TSL8XlizANToJjfQ6Q4AjpiSi/4iAJiG4a3wVcd1nyIkojQJWbvJUN5LJYLmJqlFzGzhEVVxNIk71+xBb6Hyd9CMDDq6SaOJQNrgUwkjMJmr0UKS8CRpGYZXcwnwK7Ie6CtU3SeTMJEv28J045f5tlXHdYS2sKenIM5Z6YQGgNecNMu/LpMn7ZVsbsJSQ06TloHPvOYEnHJEW8UxEgZ5uQ2mYVT0s7Bd5lV+lRgiT4Kb1VRzUzAjXQqJWa1pPLOzR5ubxiBaSGg0EaiahG34E36UJhGugmqZ5IXMAr6Jan9vMbCPEYpuktqHnLwTIgRW+iTCeQQ5JScjrEkAwLYvv7ZiLGkZnuM6LHTed/bCiu2BYGkPSxQfTFmGYm5yK4SUKR3XLgKmqHSobInMP5HmpnphsDr6afTR5iaNJgJVk1Ad14Wy65WakIS1gqQopyGRQkJmOfN9ghNrOmGi15vwlbIcrl+7KcpxLQmHoFYjZZlckwiZm2oRiKoy/KTBPkWTCPskpOO68nMGzU3yM88QQqJe6HCnqC+lGT20kNBMClZt68RPH90ee3tVk0iFVvCqSYTJXIDQattWQjnlint754BXpiKccZ1JmJCyR5qbEqYBWwmBrdQkfOEVrt1UjaRlcJ9EyNxUi/BnA7ifpVsJXQ1rEtJx7bioyCxXy65LTWh2q9QkagsJWd5cM3poIaGZFPzgoS249s7nYpsrVE1CTaYDUNGVDQj6CyzD8NqOqtsXyi4ODkRPrBnlHLIsh2XUcVwrmkRUYlwUSdPwfRIxNQkr8Nn462lNKXT0+Z8l7JPwHNeMQZVfKSvYwEkKGt/cVEdIdGkhMdpoIaGZFDy3uxeFshvbXOFpEgkzEAILAKf/9314dMtBAFBKYauJbhRIChtQTChyEgwnoGWS/jnCVWCLNRzX/jnjPcopy0DRdlC2K9uUViPglBf7TGtOoUNUjY3UJIgLiXCviXTCCITAyiilGTGjm7QmMfpoIaGZ8HTnS97kEneSkZN5ygpmXEv+sTkoJMITqapJqEl0coKsyJNIVJqOpLDxzE01NIm4PomkZfhlOWKam9RwWk+TyCXR0c872oUd9wA3MXlCQg2BtUxPAAPcX5M0DTSLDPF6QmK/EkbsutqJPRpoIaGZ8DynFNyLa66Qq/d0IphxLZnTxs0jjuOXwpZwM5E/gfUrYbNF21Um1jrmJpOH0vohsNWFRLhyazVkdFPRbsDcpFynvIZpTSn0FmzPdBSuAmsaBIdVFv/jjuugZqUmLNaLblKT7Rwd6TQqaCGhmfA8t1sREkPSJPgEpk6qasMdILiST5jBPIl80faEQNF2PQd1uMCft7/hm5tKjh8CG175NwnHNREwU9j06yF9EtxxHU/7UJ3i8pqnNXOhtF/kflQU+DMIjuvy2k01QmALZQeZhIl00vD+roVasDAcZaYZGbSQ0Ex49vYUkEmYaEpZgTDUWgQ1Cf6YTFHaeg4qvgWgMgIonCfRLrKuS7brCZZqmoSaJ9HRX8R/37XB+1tFrr6XzmuL9ZkA7oj3q8A27riW1yA1l70imS+q6ZDMuK7QJJSmQ4Wyg3TCRFIk+tULgVVNVbYWEqOCFhJjgH+5fTWuu+f5w30Z44I4/aPD9BVsNKctzG3LDE2TEJOx2vt5sCQ1CRHdpCbTGUbI3GR7+xZtP8+imk9i0YzmwDVIElXCXM89fkaszwQITcJpLE8i0nEtek7sEz6CqGQ6x2UVxf/SlimqvfLvb7Ds8J4dRBVaRhRFRZNQtTXNyKGFxBjgye1deGZn9+G+jDHPXWv34Pj//BOe39vX0H59xTKa0xbmtKWxO6aQiPJJnDC7GT941woAviZhR/gkEiYFzU0l29NCuCZRuY9qbpo/JQMA2HJgIHBNRmgiPm/JLHzr0pPxoVccE+szAarjmsXOk7BC/hbA1yT2CU0inEwnhYTLgpnlfuMhR/zvelqULJdeC7X1qdYkRoe6dwkR3UxE+4no2dD4R4hoAxGtI6KvKOOfJqJNRPQ8EZ2vjF8gxjYR0aeU8aOI6DEx/gsiSorxlPh7k3h/wbB84jFI72C5rpqtAf64ZjcA4IV9DQqJgo3mdAItmURk06AoVE1CJtNlEibOXzILrZkEXuwcwH/94TkvWimQS2Aa3gQmJ+T2rNQkHM/ZXS1PQvZz+PxFS3Dy/Laq12gahItOnlth6qlF0jJRtN1ASfJ6BKKoQkJCtk+N9ElIx3UoBBbwHdTccW2K9xoTEtonMTrEuUt+DOACdYCIXgHgIgDLGGNLAFwnxhcDuBTAErHPd4nIJCITwHcAXAhgMYDLxLYA8D8AvsEYOwZAF4CrxPhVALrE+DfEdhMOxhh6C3agBPXOrryuUROBFKSZiGijWvQKc1OclaqkaPOMZCKCYRCaU5bX7S2TMHHX2r24+W9b8XcRChvQJEQSHOAn0kVqEsokLQXRqxfP9MaWzGnFNa9fjOEkafo1l5Ixw2ZntfpOcWluyiRNZJOmF5Ja4ZOQjutwFVgrrEk43u+ZThgBn0MUWpMYfeoKCcbYQwA6Q8MfAPBlxlhRbLNfjF8E4DbGWJExthXAJgCniX+bGGNbGGMlALcBuIj4kumVAH4l9r8FwMXKsW4Rr38F4FwKt8yaAORLDhyXebH02zoGcNb/PIDvPrj5MF/Z2EMK0rjx/ZK+Qhkt6YToIx1PSBTKDtLKeX585Wm48syjAADZZKWQssKOa6EtyIKAvibhwmWVmsSCaTl89x3Lcf2lpwSOq07Qw0FSKcwXV5OYpUROqc7z1kwCXSI5MTqZrjIfJJUItjCVfcQBLnjqCXE1Ec/RXexGhaH6JI4F8DJhBvorEZ0qxucC2KFst1OMVRufCqCbMWaHxgPHEu/3iO0bpmughB/8dfOYTL6RncC8SqFiZXbf+n2H7ZrGKvI7KtuNOSyl4zrOJCQp2m6gHMeKI9sxXYR9pgOmIf6/FagC6yfTyWtuz6nRTUKTCK15XnPS7IBvAoif/xCXlGV4GeBxfRKqoFI1htZMwiszElXgT7YvDdduAhRzk+14Y5kYQlwNgVUTFjUjx1CFhAVgCoAzAPw/ALcfzlU+EV1NRKuIaNWBAwcq3v/S3evxpbs34KGNle8dbnplE3jxcMjnabCO2j0ZkdpWqcGolr5C2TM3qRnM9c4VlWkNBJ3M8ndTaxcllGQ62Qc6yicRx5cQd7UfF7VYYdxjqzkYaoRVSyaBnZ15AH7PDIlpEGzXFZqC/32lvXwR/r0NlnxzUyZpRbaHVdE+idFnqHfgTgC/YZzHAbgApgHYBWC+st08MVZt/CCANiKyQuNQ9xHvt4rtK2CM3cAYW8kYWzl9+vSK9+WNpdbzHytITWKgZIMx5q0847RxnGzICVk1OdSj7PCJqjmd8CajOGG0L3bmMa8tG/me6hORppuKshxCEMkmRUGfRGUC3mgRaDQUU0iok7zqX2jLJLxy4VNyQY2HV4H18yC8Y1mVjmv5flPK9IRqNUq2X71W+yRGh6EKid8BeAUAENGxAJIAOgDcAeBSEZl0FIBFAB4H8ASARSKSKQnu3L6Dce/sAwAuEce9HMDvxes7xN8Q79/PhujNlQ7HsVgcTNbTdxk3cUghoTt0VZIX30mpAXOTbOTTkraQFhpAve+WMYbN+/tx9Izolp7qpCdNN+FkurIb9Em0ZhIgCuZJNBKVNFyogiGuuakareK5AoCpTcnAe5bQJLim4J+nIgTWdj0/RS5pYSCi859K0XY9n5CtfRKjQt3OdER0K4BzAEwjop0ArgFwM4CbRVhsCcDlYgJfR0S3A3gOgA3gQ4wxRxznwwDuAWACuJkxtk6c4t8A3EZEXwTwNICbxPhNAH5KRJvAHeeXDvVDytXJlo6BOluOPlKTAPhKWarbxQbt7pMBKUAbExL8+5UF5ACgUKq9f0d/Cb0FG0dPb4p8XzU3yd9L9UkklPalUtPIpSyvJEZUUcBaPPkfrxq2+yE5BHNTNVQhMS2sSRDBdblAVjUv+d3JgI2S7edJNKWtuiHKJdvlvb3zZe2TGCXqCgnG2GVV3npnle2vBXBtxPhdAO6KGN8CHv0UHi8AeEu964uDLA+95UD/cBxuWJE+CYCvOuVEOJTM4omOFA6NTJhSk2hOW97kXE+T2Czuk6pCQlkZ56toEi7jVUqlppFLmaJMt6pJxJukpw6j8zooJOJrMuHy54AvJCyD0JIJTiVJUZJ8sOx4Ghzg+y76CrZnNvTNTRb6hdm1mouzZLue6U77JEaHSdHjWobpbe0YqHkDDgc3PrwFB/qL+PSFJ9Tc7vYnduC+Dftw4pxWb0zVJLS5KYjqh2jEcd2raBJS8Nb7bmWm89EzooVENuk/NtI8kggV+AOAsusiLzWJpOUlsjWqSQwnKaU3RiOhxH/9f6/Ai8JJLWkT9aim5JIVz1Quyf0LqqYA+BpdX6Hs+SWknyKXssAYF7xqAp9K0VHMTVpIjAqToixHp+h+lS85XrvE4aRoO97q84t3rscP/rolcrtnd/V4r3/55A7cs25foOBcvuR4K1PGGnPQTnTU361ewpWKqknIFWu9MMvtBweQsgzMrlJZVfVJSKEeLhUOcJu5NJ9kEqbX8Odw+iTUiK1UA+amOW0ZnLEwGIEufX1q4UNJNmX5QkD5vnJJEwbx30UKa2mCkk2UqpmcGBPmKbG91iRGh0khJLoGymiWN+AIlL/43B3rcO7X/uppLFE8/WIXXvftR7B6RzcKZQfP7OACY9V2P09RNTfJ69ZwevL+d9GIJuE7rhPe5FLPlNdf5HkV4VpJkujopmCeBCCERMnhE6NBSIm6SYdTSMhcD6DxpMQw0twUlcuRU0xM4ZIjTSlLaBKV5iYAXsRUGPm754QmpzWJ0WHCC4mi7aC/aGNuOy+alo9Zu6cRZJeyWtFTe0QhtAN9Raze0e3d8JuVIm6quQkADg6MvZDdw4Xq4G/Ecd0v9msSeRJAfXNToewGzDJhMkn/sYmKbpKmp5LjIl+ykRWTnyyuF1UqfLSY3apmTw+PkAhHNgHBek/hMirN6QT6CnaFkMjV0STk7+5rEtpxPRpMeCEhV+PzhJAYCU0iI1Y2tYRElzB5DRRtPP1iNwA/HFGq6/mS48XVA1ygaDglmymv408O0tmatAxfSNTrWWA7XlhmFOqkl4/KkzBkHL+L/qLjrarDjuvD4ZOYWaXExlBorWFuyqWU3IhkWEhYoqtdtCZRLVdC/u7SJxF2pGtGhgkvJGRk07x2nhilahIfu+1pfOeBTYd8DjkJbD9YPcS2O+8nzfUMlr26/gDwwXOO5tcmzE2zW9MwiJcQ13BU+3MjvhrVSSy7n8XRJNI1NImoPIlgFVjfJ5Ev2t4K2dckDp+5SQ0FTh2iuUlmkUeZm1TnfliTaEknIh3Xvk8i+veRz4s8tvZJjA6TSEgIc5OyinxoYwceemH4SnU8u8tvkxm+gaXjdaBoY7BkI5M0ce0bT8Rlp83Hm5bP865toGRjZksay+a34eGNHVXP9esnd2LD3t6q70801Jj4RjQJma9gGRQ747quJlEvT0IIibLjYqBkezb0lGWGSoUf3sfvUM1N7bkk/vuNJ+HN4v5VydUQEs1pK2Bukt+n1D76i9G+OBmwoKObRpcJLyS2dvCoo+NntQDwM2DLjouufGlYsrClKenZ3X70Ungikk7t/iKPYMokTLzj9CPxpTct9W76fMnBYMlBLmXiZcdMw+od3fj0b9ZUCBzGGD7xy2dw4bcePuRrHy+o2bWNOK7VVXvc6KZiHU1CnQDLEXWYfHMTw0DR8Sa/pMW1x8OpSagMR12ot59+RGSl2qxqbgoJ3Oa0hb5i2dPo0koyHcCfkSg8x3VKahLaJzEaTHgh8cK+fjSnLK/EgtQkOgdKYIz36G1Ebe0rlHH9fRuxs8uPGe8SpiS1k1hYSHQLTSJftJEvO4Fy0ymL9/fNl2wMlBxkEhZes3Q2AODWx3dUmLGkdjSZWk7IidWgBjUJ14VlEIgICdNAwqT65ibbqVrcDwBecvRU/L/zj8Pctow3ZkU4rqUmIR3XKYv3S/B8EoehdpPKoZblqIUqSNNhc1NGOq6luSleCKznuE7oshyjyYQXEhv39+GYmU2eHVPegNIpbLsM+/sKsY/3zhsfw9fvfQE/e+xFADyrtjtfGfpaCE1kPYpPolByAiYLIkI2aQlNwkYuZeL4WS246fKVAPwwTonsBjaZkBNrLmk1lHFtu5WlqvN1NIlC2akZ3ZROmPjQK47xJjUgWAXW0yQchrziuPY1CX794fLao4VcoIRLlY/EOQBUlD+X5iZfk/A7/xkE9BeihUQx5LjWPonRYeILiX39OHZGc8CkAwQjh3Z1xTM5OS7DMzu5SUn2Su4tlOEy4OjpwWJwhbIDxhi+dPd6rN7R7ZmkpLkp3Lgmm+S19AeU9/zsVP7Q/O7pXfjR37Z61WxzyeoT2URDTqyZpNmYkHBYwKySSZgxfBJuTU1Covotwk2H5DUPKI5rqUlENR0aTW593xl4y4p5gdpLw029EFjHZdgvFjsyKY+IkEtZVaObZMBCRvskRpUJXZbjYH8RBwdKWDSzCQnT4F25SkFNAuChqytjHE+9eWWJAmn6edXimdisZFoXyg62dAzgB3/dgqdf7K4wN4UfUNkQZ7DkeFqPX+eG7/v9v25GR38RnzjvOPH+yD3kYw1pWsilrIbMTU5Ik4jTeChc3roaanSQ6oSWoc1lhwUc154m0UA/iZFg2fw2LKvRO3s4yFZJpgP8+/rFzjxSlhH4rptS1Yv8yd/dS6ZrsK+IZmhMaE1ih9AQFkzlq/xc0u98daDfFxI7Y2oScrI2CNghhIT0R5x+1JTAtoWy6/U/fnxrpyeU+kV0Uzb04GSEGWSgZHsPmJqBmi/Z2Li/Hx39JTy3m0c1qbHoEx1pWsgmzQbzJNxAPkCc7mc8mS6GJqGYpML9JABuNnGZ78RNWSaKZWfM+CRGkpRleEIwLHDl4mZHZ94Lo5XwbOw6eRIprUmMJhNaSOSVMs0Aj6+WMdgH+opoTlmY1pTEtpglxKUmsWROKzr6Sxgo2l7U0tRQqeRi2cGjmw9iWlMqWMZB5EKE7bTphInuPHemS02iRTE3rdvd600ufxVhu412aBvPlF3fHt3I5w5rEumEiX19xZqr0KLdmCZBFGzGIyd/GfbclAppEmMkumkk4X42P4lQxdckBr0igZKpTUl0KAs4xhg27O2F6zKdJ3GYmNhCQinTLP+Xce0H+ouY3pzC0nlteHpHd6zjSYfa4tk8nHZHV97zNbRlE7jt6jPw1pU8ZrxgO3hiWyfOOmYq3rxirneMfJGblMJCIpMwvX7B8nqbFHPTM8o1SlNXvk6DlomEr0k0Zm4qOyxgCnrFcTPwzI5ufPWe5yO3Z4xxTSKGkJDRQeFwWdniU5oYs0nfJ1F2mHf9hztPYqTJJS2kE0ZFDaypIkO7o79YISRmtqSxTwkk+Y/fPYsLvvkwHt/WWZFxrTWJ0WFC36Wyk5m8qTJJCwMl7lDetK8f05tTWH5EGzbt7w8UkKuGVIOXzOVCYvvBvKddNKcTOGPhVFx51lEAuMmit1DGjJY0rjzzKLRnEzhuZjP6i1yTqDA3JU0c7OdCQmoepkHIJU30FWy8sK8P05pSaFceqnqtHicS0o6fTZoNZVw7rhsw6/zzqxZh2fy2QE6LinSKxzM38W1mtwXzBOT5pHnS6+Es/pc294msSQDcLBT2RwDALKU0SFsmWfHevt4iGGPY21Pwogh7Bss6uukwMaGFxKDQGmRtpVzSRL5o4661e/H8vj68efk8LD+iHQDw1I7aJTD+8tw+/GX9PgDASXN5D4jNB/qVzmP8xpWrykLZQdHmtu2F05vw1H++GmceM437JMqV0U2ZhOmZJ9TIkOZ0Av0FG935Mqbmkrj4lLlYOD2Ho6fneG+CSWJysl3f1NCQJuGyiiiiGc0pTyCHkRNRPHMT30bNlwD8PAnp+0gq/RIA3ww10YVELmlFCompTSnvs4c1iRktaZRsF935Mnb3+L7CsuNW5kloITEqTGghIc1NctWeFZrEb5/ehflTMnjzinlelMezO6NXlpL3/mSVt6qZ05bB3LYMNuzpQ3/RQdI0vAlDTi59BRuMqXZrQlPKj9HPJIOBZeqkpJqimkR2al+Bl6++5vVLcP8nzsFlpx3BP+MkaU4kJ4RcqjHHtRMyNwHAtKakZ9oLUwzF7tdChsCGhYQ8n/ytpdCQCwlZ0fZwhcCOFtmkWVHcD+DCcYYoWd4WclzPbOHj+/oK6FQEecn2hUTSMmAZpDOuR4lJISTU2jD5ko2O/iIWTM1xc07KQmsmEXCWPburB1f/ZFVkkhzAHZEnzG7Ghr296C+WgxUvxcQhV4tqBIyqIVRoEkr5aTVbVSYe9RXLnsOP789fTxa/hKx3lIlwXJ/7tQfxlu//PXI/O2RuAnjV0s6BEtyIlajMAq6VTBdmTlhIiPPJcGtPkxC/mWxZO9E1iWnNKUzJVlaIBfxqtGFNQpqi9vUWvWZhgBAS4ndPmDxySmdcjw4TWkgMlhwY5K/mZVZz50ApUN54Si64svzVkzvx5+f24SO3Pl3hqzCIT/DHz2rB5gMD6Booew5mwNcIZNVXdUWarZFgpP6tCpDmdAK9BVtoEv4DJQXTQGly+CXKYkLPJEyUHRaY4DcfGMAT26LNhXaEuWlqLgXHZZFdCv2+y/UfDWmyqjQ3iWqz0txkBquceuamw5RxPVpc8/rF+NZlp0S+J4VBe4TjGgD29RQCTbxKjouyIiQsg7S5aZSY0EIiLxLTZP9d6ZMIC4n2bMKLUgL8bOpHNnXg7Tc+GjimKeoAHT+7GY7LsHpHd2DlLwVSlCbRlIo2KQG1hISF/oJvbpLkJpsmIWowye8zbhis7TAvb0Eim+REmZzC9YRqIXNtwgXupFAa8MxNYpGiCAkjFDY7EZnRnK4QoBL5nbWGHNeyc96+3oKXqApwTcJ2GAziz6BpkHZcjxITWkgMlu3AZNyWTWCgxDvVTQ1oEil0Kq1CN+7vxwVLZuF9L1uI5/f2gSmV9GTVz4XTmgDwbG118ibibSo9IaGsSGc2+5NJ2Nyk2m4DjmuRXNRXKAc0iaxXVnlyaBK2y2CZ5Jlu4pbmsF23wqwj+x8c7K9s6iQ1iVqlwiUyJDrcmc3yNImgualJ8UlM9PDXekghEdYk0gkT7dkEdnUPBhZzRdtF2XW97zZhGoHy8ZqRY0LfqeEaSbKFKcAFg/864am2hbKD7QcHcOysZrRlE7BdFjkhTVMmBnVSB/iN3utpEv5XfOysZu91RTKdFa1lNKctdPQXUXZYtCYxScxNtnBAywk3ynkdVc6B126q9EkAdTSJGNFN33jbyXj3S47EohnNgXF5vnxIk1Cjmya6P6Ie80UTMLXntuSkeW14cnsXuvIlz/wkNQlputOaxOgx4YWEasaZ25b1XgfMTcKRyRjD5gP9cBlw7Mwmb+XeG2G7blf2b6oQEqom4Z9f7eCVDUU3qYJBzaFoTicgn4UWVUh4PonJYm7imdOyi1lUkb6osFZeBbYRc5PwScQwNx03qxn/ddGJFRN+OLpJ9YkBPPJtsguJ85fMxG1Xn4GF05sq3nvJwqnYuL8fL+zrx7SmJBImeT4JGRRgacf1qDHBhYQdWOUHNQnF3JRNouS4GCg52NbBs5mPnt6EZrFv1GSSMA0vMqNSSJjoHuT7VEvKisqTAER4n2JDV00ZLRnVcS19EpNDk5A1mOREK4v0qXkiByLMR7brIhGakGXETbS5SUQ3xTA3VUNqElKz8TQJ5Tef7ELCMg2csXBq5HsvOZqPvyhqOyVN3vZVzZ43Ta1JjBYTXEgEzU0zm/0knnB0E8C7x3UO8IljWlPKm/yrJV5Jv0aFkLDMSMc14E8U4ZWqNG+Ey39LtRxAZAhsXE1iX28BH731aTy7i+eDMMZq9uQ+nDy/tw/P7+0LjElNQv6ecgJW+3ZETfq2wypX+kLAdx6iJlENIu47kf4iaSKzTMOLmproORKHwolzWrxnckou6fcGV4o1WobhRbxpRpa6QoKIbiai/UT0bMR7nyAiRkTTxN9ERNcT0SYiWkNEy5VtLyeijeLf5cr4CiJaK/a5nkQoEhFNIaJ7xfb3ElF7ox9uMGRuskzDC72bGiEkOgdKntbQlk14oa0HB/zJ59MXHu+9lhUsK30ShhJvH/yK/0WU+W7LVZYKByrNUEdMUYWE4riW/TFiaBKFsoM3/O8juOOZ3bh/w34AwLV3rsfLv/pgoMPeWOH8bz6E87/5UGCMh7Ia3ueW4aWq2amjirkpqk1nLhndt6DQQAhsLVKWUZFMJ88LaE2iFpZp4MozFwDgprmkZaAszE2JgE9CO65HgzhPwo8BXBAeJKL5AM4D8KIyfCGAReLf1QC+J7adAuAaAKcDOA3ANcqk/z0A71P2k+f6FID7GGOLANwn/m6IqOY+c9szMAiBfg7tipDoGiihNZNAwjQ8DUFOPl+9ZCne//Kjvf3kxB7WJNSJPjzZXHXWUdj836/xKrx6x/KywoPXqyZqqZqE7I/RH8Nx/ZundmGfaFRUsl0Uyg5ufGQrAOCZHbUzzccKtrBHhzUotex3lCYRrgIrkU2ewhSHkEwXher4VoWUXFBoTaI2V551FF51wgy8/fT5SEhzk4hwA8aGT2Kw5ASScCcqdYUEY+whAJ0Rb30DwCcBqL/URQB+wjiPAmgjotkAzgdwL2OskzHWBeBeABeI91oYY48yHmf6EwAXK8e6Rby+RRmPDS/JHZzAF0zNYnpzKhCjPlUREp35sqdZyElZTj7hyqBSS1CT6cJ/R002UZNWNSGh9iEONxnieR/1zU23/H0bls5r5TkXRRuPbjnovbdmV3fd/Q8XsnwF4LchlaG/MqorqElUPrBlx42ckLPJ6DamhQZCYGuhLg6SEULCnMC9JIaDbNLCjZefihVHTkHSMlB0hLnJkKa7w++TeMsP/o6VX/zLYb2G0WBITwIRXQRgF2PsmdBbcwHsUP7eKcZqje+MGAeAmYyxPeL1XgAza1zP1US0iohWHThwwBsfVBr4SP7l1cfhh+8O9qGTmkRXnmsS7SGHtPRJhE1HUgBU67wFxJ9sZFmOsLmp2nEBPuHUy7h2XYYtHf0485hpaEpZyJdsr6dGyjI8H8VY5MWDvinMEZnT4Ta00qwHALt7Knt/O8rqUyVTRZMo23ziSUaYqBpB3huWQYEFifQ5jWTr0ImGdFzbDkPC4t+laRhVM66f2NaJJZ/9UyBjeyR4dlfviB5/rNDwk0BEWQD/DuCzw3850Qgto+qygTF2A2NsJWNs5fTp0+UY8hHVVme1prF0XltgrDllwTKIaxJKAk/YJ5GsEBIiZj+U/ducqq1JROE5rmt0m2sKCZBc0qqrSXTlSyg7DDOaU8gmTQwUHS9h7NQFU7B2Z08gWfBwo5bb2H4wj3vW7cXu7kEvssWrWSXNTUo5+KjmUWWnMgQWEJ0Ay5UCVmoeh5oNLTWJ8D0js67D96CmOinhuC45rhfdZNXIk/j6n1/AQMnBc3smxyQ+0gxluXQ0gKMAPENE2wDMA/AUEc0CsAvAfGXbeWKs1vi8iHEA2CfMURD/72/kIou2C8Yqk9aiICK055LoynMhIR3SKctE0jI8n0RYk5BCJHyzBs1NMTUJ2XMgQpP46VWn4fKXHFkxcWVTJnZ25/HSL92HRzZ2RB53v2ibOrMl7WkeMszz1AVT0FuwsfnA2IlyUn0sT27vwvt/+iQ+/ovVXl+IsMNempuOn9WM7Z35iqJ9jutWJNMBfh2vMKpz9FCQ0VHhY8k2tidrIREbP7rJT4w0DaqacS1L7IR9hSPFRC/X3/DTwBhbyxibwRhbwBhbAG4iWs4Y2wvgDgDvFlFOZwDoESajewCcR0TtwmF9HoB7xHu9RHSGiGp6N4Dfi1PdAUBGQV2ujMciXCa8HlNzSRzsL6EzH6zr1JyyPE0irBV8/FXH4rLTjsDFJ88NjKu+g9hCQlaqjRBqL1s0HZ+/6MSK8VzSwrO7erG7p4DLf/Q4AOBrf34eCz51pzdZ7uvlJpgZzSnkkrzJvCyHfeYxPB79ye1RLqfDg9rf+P8e3Q6AtweVPomEaSBhklciXWoSJ8xuQcl2sac3aHKKCoEFqpubSk60UGmUVBVNYvP+fgDwStRr6uO3fQ1qEtUc11JIlEdw8lYXhoUGStePR+KEwN4K4B8AjiOinUR0VY3N7wKwBcAmAD8E8EEAYIx1AvgCgCfEv/8SYxDb3Cj22QzgbjH+ZQCvJqKNAF4l/o6NdGzWsvGrtGeT2Nk1iJLtBoREU9ry6tqHJ/z2XBJfetNJFdqKuoKhmJU+5cozjuYjUU1pjsvw4sE8vn3/JgD+5BnUJKS5id/UJ8xuQXs2gVVVKqgeDmQ3N4N8M97C6U18FWko1XxDmsQJoqVs2ORULQS2muO67LgVE/tQkL9n2Ldx+sIpAIBjZlRmGmuikT6JksOQsPwQ2Go+iS5RgVne5y/s6wsEazTCe295Ar97elfFuBpJF5X9H5ei7Ywpc28UdWdQxthldd5foLxmAD5UZbubAdwcMb4KQMUymTF2EMC59a6vGnKVGHfSnZJL4qkX+WQZLrmxXThQ48bOhx3McTAMwqkL2rF0XmvsfcL5GVs6+r3XA0Webb5frKynN6cqzE0py8CKI6fgiW2+JiFv2LjCbbiRmsQXLj4Rn/ktT80p224glFWd4H0hwesnbe0YwJnHTPOOF1XgD6jtuB4Wc1NCmpuC5/7eO1ega6Ck8yQaQJqbLJO87PlaBf5kXS/5/4XfehiOy7Dx2gsb+m07+ov4y/r9mN6cwsWnBK0FMqQcGLqQKNoOjvuPP+HDrzgG/3r+cYH3ugZK2NdXwPGzWho+7g8f2oKjZ+TwyuOrxvo0xITNuJaTSC1HsMqUXNKbPNVGKU1DcEIPRUgAwC//6aV44ynz6m8oCH+2HV1+u8e+oo0/r9uLHz68Fa2ZBNIJk3fmE45ryyBYpoGzj52GbQfzXpTTu29+HEuuuQev+dbD+NumaD/HSCI1icWzW3DnR89Cc8ribVqV5kGqkJAT/YKpOSQtAzs6g8mBtssqynIAQDZhcRNGyCQxXD4JqXWGtZKmlIX5SoKkpj5JyxS/lR+p1pK2vOZN1ZDPszQNNaoxb9jDs/67Biprt+3v882aaoRdI+zu5sf43wc2Vbz3+T+swwXffBg//tvWwHjXQAmv+vpfcdWPn6hSYcDFtXetx5U/XuUt+Dbs7cV//G5tZP+UGx7ajN+vrtSUVCa8kMgkYpqbFO2hWgJbXP9COJ9hpMiFTGmqSj1QtHH1T59Ez2AZUiloSpkYKNooid7bAHDRyXORsgz8/HGeE/nk9i7kRWTIXWv3YLSRmkRzOoElc1oxtz2DQtkJNA/iTme+3WDZ7309JZsMlNpwXAbGEBnd5DnAQ6vA4fNJRDuuNY3j1W5yfQEui3KGUasiS3PlkVO5UJY96uOyXkRHyTpsKsOhSci+NVE8vaMbAHDdn18IVDx+dMtBbNrfj/s27McPH95asZ8ahPLEti509BdxwTcfxv89+iL+8Mzuiu3/+64N+OfbVtf8DBPuDt7fV8Sf1+1VfBIxNQmlrv2imb69WM2MjmurHq2oimyoxMOTykqpv2h7VWNll7xs0sJg2UG+5HiTWGsmgVctnokHRbkOAs8KP3VBOzbu881XKtsPDoxYfoUUEvLaUwmTaxJKKGuUuSll8XpM6mpJmiOq5UkAqDA5DVt0UxXHtaZxkpbB+0kov82UbBL9RdsL55aok3fRKwIpNYnoAI3+oo0/rqmcQD0hka9cgavlbMLXUI1HNnbgZ49t9/7epQiJvT2qZuJgZ9cgTpjdgv6ijXfe+Ji3YFu9oxtJ08CrTpiJnz+2PZBw6roMzwjhAgD3bdiHrYqP7p51ewPXo/pC7lhd+fklE+4OPtBXxC+f3OlHN8UUEu1KsT51klDr3Y+0ualRpLlpRnMKSdPAXiWyp79gY4aoU/WjK04NbN+dLwccqrNa0ugZLMN1eW5JLmli0cxmPL+vL9Kp9rk71uGKHz0e2SP6UFE1CYBP/kXb8ZLpgEohkU4YMAxCayaBblVIiMmhWsY1gArnddlhwzKxp6qEwGoah+dJOKKnCP8t5fOqTuCMMfzsUX8SlpqELPW/q8rK/QP/9yQ+/POnsWl/sKjkelFkMspMo5piizHNTV+6ez0+/4fnvIWNqkms3uEv8Dbs7YPjMrz/7IUAgMe3deLzf1gHgGsYi+e04J/PXYSBkoNP/3qtt99Hb3san/z1GgB8TujsL3mFMM9YOAX/2HwQ3UoHzkFFe3i4hml5wt3BLmPY2TU4JMc1AMxpC7aiVIVE3MmjOTU65iapSbRmEmjJBAXTQMnGYMnBm5fPwyuOnwHAd3R3DpQCmeDNaYt37CvZYIxvd9zMZvQMlr3oKAljvGVrR38Ja0ZAm+grlGEZ5K3EUxYvllgO+CRUc5PjOYnbsolAT3IZ/RJuX8qPESzvIRluTSKuiVJTHRkCW1Za0aqldCRrdvbgxke24lxxv5dEwENf0YZlEDr6SxVmlULZwcMix0jVnEu26wkNtbUxwBeiz+zswatO4I7hQgxNYkdnHut296Jku16AzK6uQc8KcEApTim19JUL2vGO048AwH1utuNi7c4enDy/DSfNa8U/vXwh7ly7B/t6CyjaDv64hmsblkGY2pRCV77sLYLetHwebJfhL+v9dDP1u9u4LyggVSbkHbyzK99wCKycaJYfESw2qwqJuBEp4VpOI4XUDFoyCc8sJss99BcdDIYyzqUPo3OgFJi85KpdRkJlU5ZncguX7N7ZNeiFGD6woaH8xljIXt4yuiotzE21NImMYjoLmJvESjJKk5BJi2FzU8keHp9EWvskho2kaaDsMB6ebAY1CbX0hly0fPo1vFJzyXY9LWLxHB4lFNYmvnXfRu/1C4qQ2NLRj7LDcPysZhTKbkC4PPQCL/1zwYmzAPiO6z88sxvP7urBdfc8HzDzAMDdz/IJnAj4x2buO9zdM4iF03L8GMp9uKt7EAmTMLctg2vfeBLOOW46BssOOgdKGCw7OHo63+c4EfnUVyh7pubXL5uDm684Fe3ZBHoGS16l45csnIrZrWn86Vnf5CSFxNHTc9hSI6F2Qt7BfQUbe4VtMq65aeWR7fjKm5fimtcvCYxPb6psr1iP0QpvzCmaRLMQDrK0+ECRaxKqJuVpEvlSwHQmzWN7hF00lzS9OP7NB4J+iTU7+YPYlLLw+NbhT8IL9/JOWQaKZafCJ7Gnp4BbH38Rg2XXExJt2WTAyeh4mkQj5qbhjW4aDoEz2UlaBhyX8ag86ZOIaEH73O5etGYSWDCVT6Il2/Vs9otFHs0uJQJw1bZOfO/BzbjstCMwf0oGGxVzk/RHyMZI6uLjgef3Y1pTCiuO5AvKQpnnOnzk1qfxum8/gv99YFPA7MUYw2+e2oVl89uwdG6r99zs7i7gaNGZTzX9FMsu0pbpLZRyKV6Y0zeh8+e1yetz7+ChjR1ImIQvv+kknH3sdLRlE1yTEEIil7Jw/pJZeGjjAc+HIoXE6QunVpQWUpmQQgLg6hNRfHWfiPDWU+dXmKeievCOFWRF1NZMwnP0zmlLwyA+2aqmGMDP5g5rEnJf6TzLJi1Mb0ohaRme4JCs2cUdZ8vmt9YtLjgUwuXdpSZhK+U15CT+6d+sxWDJ/4ytmURg1Seb0kRqEoloIVFy3EMu7ievG+Dhm5pDQ5p5C2Xf5ChL56imoOf29GLx7BZYpgGDeAhsTw1N4v8e3Y7mlIX/fN0JOHZGMzbt9xdE6/f0IWkaWC4EgTyP7bh46IUDOOe46d49VCi7FX6LxxUn+dpdPdiwtw9vXTkPs1sz3rH29RYwtz2DhEkBTaVgOwFzcJOolBD2s3p1zIo2dnTlMX9K1lsItmWT6M6XvZL6uZSJhdNzQrviz60nJI6aUvP7n7hCYn8/sgnzkJPChiokFk7L4S0r4uc8DAVVk5DmpqlNPGlOVq7NRmgSjssCN6HcV5bwaEpxc8/ctkyFer5mRw9OmN2M5lTikDJNq1EKZTyrjmupoak24IMDRc/sJk1t8oF1PMd1dZ/EYKjI3/Al02lNYrhQhbbMupeVmuVEZzsuNuzp9YSB9GPIe2HRjGaYBnmaRG+hjLue3Ys3Lp+LbNLCopnN2HJgwDNRrt/Ti0UzmzAt5CB/ekc3egs2XnHcDO83LpSdiufk2V09nqnnT8/uhWUQXrd0jmcqZYwhX+JBIumEWaFJqJp+LsXzm+Q2fgkfv7fKYGhx1ZZJoDvPHdeWQUiahlcFQD638rs7dcEkFRIvduYji+U1ylBLOt//r+fgq29Zdsjnr4U6OUrH9dRcEk0py+utoJYxV5PvguYm/hml1iA1lDlt6UAEhusyPLurByfNa0U6YQRu7Djs6h4MRFdEETb3pCyTO66VyJaPnrsIZ4jyFjs6B73fSPYclxNDuUYIrFpNtuy4+N3Tu/zuZ8MY3aQd14eOumiQ94ZlGmjNJDyfxPbOPIq265VnkbkVctU8JZfErJa0V5Vgb08BJdvFaWIVPa1J9Lkv8gl83e5eLJnTglZxT0khIRPyXnr0VE9bLNgO9ojEuONmNuP9L18IlwFPbefbPv0ij0hqzSSQTXEhIRP90kkTmYQZWHAVw5pEysRAya7ws8rn2TMtK896ezYJ22XY31dENskXy/KY0tzUlS/BMgizW9P4z9ctrvr9T7g7OGEaVRv4DIXDVZ4iDnJyb8/6mkR7NolcyvKqjao3TkuVwoPNIXOTXKHMac0EhMTWgwPoK9pYOq8N6YQZyDTtGSzXrEHDGMOZX74fl97waGAsHFVRsoPmnnRCCYEV4zOa03j76UcC4KUTWqSQyARXfZ5PIqpUuLg3PvPbZ/Humx7Hx36xGg8+f2DYkul8TWLCPWKjjiokVIE/JZdEp/itZWTSsSLgQubXyAVDS8bCK4+fgT+v24cXD+Yrepl7i4ayjd09BXQOlHDS3FbPrCUXN2t3deOIKVm055LeM1Qou9jdw5+T/3vv6XjXGfze3NtbgOMyrNnZjZNFQUcZmSfPn0kITaKkColKTYIxVFgHpGVgoGRXmJalcNvVNejlbaUV8xjANYn2XBJEhKvOOqrq9z/h7mDLIMxo4Sai4RASY5mZLWlc95ZleMOyud5EObWJaxKekFC+g5ZMHSEho5uSUpPIYH9f0cv4XLOzGwCwdF6rEBL8xu7Jl/HSL93nVW1VcVyGku16kSMblGip+zfsx6u/8VDAAV6yw+YmE2WHH0P1LbQoEWRSk5D/d4eqgFZrXyr5h8hUz5dsEUEzcgX+NI2TCmgS/m/ZlvU1CRmuKh3BSZMvLqSQaM0k8OFXHgPDIPzkH9v8+mUJPxgC4JrlWnGfnzi31dNOZf7Nmp1ckwb4AlIGVuzqHkTSMjxNHuC5Shv392Gg5OCUI9q88xTKrmeKSie4JqFq5YWyE/jMUhiEn2m/bL6DQrnS3ARw7T1bISR8c5NagqgaE+4OTpgGZjQPr5D44DlH42qR2DLWuGTFPLRmfcf1FHGTHogwN6UTphK/X2luUn0SADC3LQPG/PFnd/UiZRk4ZnoTdyiLFclTO7owUHLwyyd34vm9fbjke39HT55rFh+97Wms+OK9eP9PVwGAF74HwCsT8MhGv5tg0Q6am+T1lpxgoT5V4IXNTR/82VPY1jGgaBKVQiJhGnj3S47EFy4+EacJm2zPYBllZ3h8EnLyGQ7T1WQn4JMwVTOM5U22G/f3Y25bxptQZaOiXpF3k0mYmNmSxsyWFA4O+PkScuJUM/DX7uqBaRBOmN2CTMKEQdyk0zlQws6uQSyd6xfhlIul3d0FzG5NwzDIX+EXba97nWwyJbV0WQ8qkzCRTppeeRmAPwPpRPBzAvCe6bDjWkY+BcxNwpeyu3vQC1hJK5oPwKvltmXrm9NHJ6B/FJk/JYMZzTwhLm6ORD0+ecHxw3KckaRVrAimNaWQS/HVN1ApKNsySewtFwKr9aRlIKU0V/J9EryG1a7uQcyfksUL+/pw7MxmWKaBdII7Bh2X4ekXuwHwVdYl3/87+go2Vu/sxmDJxp1r9uCoaTkvbrxNXGfRdnCfSOx5VNEkyo4bWEWlAqaGyogsQNEkxA1vuwwPbTyAJXNaxX7R5qP/Ej06Lj11PhZ95m5058so28MVAqvzJIYL1T5vhYSENJFu3NcfKKcjK8f2FcpoySQ8s3EmYQpzj18JGQhqEs/v7cfR03OeAJGFMWXOkHSOA3wRUyi72N09iDmt/HlJmAaSloH+ku1pMtNyfOEqhZHsUZNOGMgkjJBPwvU0AaBSk8gm/HI88vMMloPh7nJ/22XePBjWJAaKNma1BJOHo5hwd7BB5EUkNdKbYbzz6hNm4iuXLMXxs5oDJcTToe9ATqhhh6rUJmQkBOBHdkkn+Ia9XEgA/g1XtB08/WIXZremYRnkldVgjOGxrZ3IJk38+eNn4wsXLYFpkFcm4G+bOtBXtHHczGas3tHt3bgV0U3K6ihobqrUJFrSCXzgnKMB8PIatXwSKgnTQC5pomewzH0S1vAl02nH9aGj/tZqRV8e9WPDdRk2H+jHMdNDQsJxMVhyAyvsjOhIKJ236ZD/Ml+y0Vcoe4sZ+d5g2XcctwbMtiYKtoOugRKmNgWblXGHsi3OK/0Ifhi6PH+F47rC3MT3kUIinQy+N1ByUCg5gWKm6vXnwuYm26+gHGeOnJB3sPRJTCYySRNvXTkfRBR4qNQHBPBX26lQbwwZHZVLWUoSj2/z7Bwo4UBfEcfPEkJCUV2f2dGNc46bgdctne0dTzY3yiZ5Lax3vWQBXr90tmd7vXvtXjSnLPzr+cehZLt48HmuVYQznlW1u5q5Sf28H3vVInFdjp9xHcMRLePKh8snoZPphg/1tw5rEn1F3h+laLuYppbQEdFNhXIwUigrnMRSk/CafSX8DPyKSgUpK9CsS13EpIUWELVPf4GbgSyDvH3keaSQyCTMit4m3NykmINFmZ+O/iJMZREHSC3HRr7sIKMIj/ZswltUyedYPkvSTMxDcOtbWyamkBDmJrlqnWzMavVVyLC5qdmz2YbGxUSrtk/1HHBF21O1j50V1CQGijZ6CzZmt6bx2dcvwVmi4c9A0a5wwGXFw2Y7Lu5dvw/nnjADrzhuOma3pnHr4zsAVBbYU69TnXBTluE9LK2KXTVpGiDiqzG7hk8iTEsmgc6BIlw2PCYiuXobjjDsyY66clfvgSaxWpeBFYHQ6QSvHKuWbQGkVuBrEmHHtaySrD43PLfBP496T8oov8HQeXiWdOWx5IQtTbvphIm0Fc6TqK5JhHO/skkT3fkyHJcFzm+ZhlcivcLcJD77QMmexJqEWFH0T1IhMVsREmFNQpqfwmYQaePPKqYqP5fA9spzLBLlOuQNJ7NHMwkTU3JJfOftywFAlHF2K1Zx+ZKNPT0FdOfLOH3hVFimgTcvn4eHNh5AX6EsQmDVfA5Vk/BfE5Gn/aiTCBEhbZkoiHpPQHSBvzCtGct7cIdDSEzJJfH9d67AG5bNOeRjTXaCQiIY9eMyeKU3kooA8TQJOxgaKlftYU1C9UkMhkw3OaVZFxDSJCxuKhosOQHTruzdki/ZAd9o1quf5kcqpZNBc1Mh9NzIxdrBgVJkq+SD3rGCCxIZ6SXLd/jJdPyzhxPwqjExhYQwN/UXJqeQUJ1RYZ9EpoqtXEb47FHyIpJitd5fdDxfg4yGkKqrLPYXtrn2F20Uy453YwJcAMmVGuDf/EdMyYIxoLdgV4TApqv4JABUFDX09+EmgHKNAn9h2jJJz+Y7XCaiC06cNeRkTI1PIHjBUDWJ4EJFFSDScR1OMpMZz14fEnEfq9FNfGJX79uwJhHUWLhm4lZoEjwBzvECQeT5AcUnYYkQ2FJQk0iH8iTC+/vX5ldXCC8IF8pwYHG9KSVDvGS7wqk9SYWELP4VJ7xrIjK71e+sF75xwn9L3i5KEg+UglnU8gGRDjh580qHcnc+eINaJo+UGojSJJImGAtqH4D/gOaLtqidFDQrScK+BZnfUSkk+ENXq8BfmNZMwnPQ60ZBYwvVvKKGFMtqy3KhEhQSvOVpoRwMJ5XJbEU7HN3kZ+Dnw4Uxk7yUfrRPwlSaegVNtdInEVWJWRYmTCd58m/Bdr1k1KjnRn4FYW0hlzT96grJ4H07V7Q96OgreZ9VmmL9Vgr1zaET0mA6ozmNr71lGc5aNO1wX8phQXXch00nXr0ZpSUiwGs+XfeWZRUNk6SqnRSZ7IZYyUlh4Tngwg9I0faqWfrHEuF/FZmj/H8ZLhhbk8gkkLSMwDZyn4Lt1izwF6Ytm/B8GDpsdeySUEyOcsKVC5VAWLcwNxEFI+RknSRp95cCyBTO5QEhQCq0j2KlYOHXYHq9ptV95DOQL9leyCrgh5eHHdeOy1B2GAxisF0W8HsQEXJJfrzwyj+Xsrxw93Cr5nnt3Cchze4y+a9gu8iX43funJBCAgDePMLF9cYy4UlTxa9cWVl36ZKI7ywnbKvphBGqzhoyN4VV7aKNgu14Wh3gr1q8GPFQUpA8VrjAnyQcytqSTkSac3ijIsczD6g+jmq0VLF7a8YWVshxDfgTbqXjmtdhCk/4ZYehv2hXmFyzSTOyMGY2aQY1iZBfRGrf6YhnIF8KPgPZ0EIpnTC96xgsO96CJnxt05tT0UJC+Tvsrzhr0TRcddZRuFIpuSGT/xrp3DlhhYQmGjkxxy3Ol01y22q2ZFY4AAHF3FRFkwivugBU2FCzoWOFC/x5r0Nhu29ZOQ8rFwSbRAHqgyBr6dd/EFTTpA5bHXsYBBF5pgiJdHhxEXRcF20XLkPI3CTvtXLFYiqbML0FTNAnYWGw5Hj+MtX8pTYYC/duGSg56C/amNfum395nwi+urcM4rXmkv7CTd774Ws7enoTtnYMVF6z4q8Im5ITplFRuE862vPFYG+KWugl0yTjNSfyXIaLls2NtX2TsiIKaBJhc1OEqh0VWQKg4kGUN2q0ucl//fJjpweu7ZzjZuA9Z1YWJksnDBTLvKInEHT8VUMWBwR0vaWxiLxHwtFNgN+dLri48PMkMhH3YLiFr3zPW8Ak1egmU1SItZEK3RtNyehJWjrVO/qKgYnYEFnS6vby/0HVoR7SJGQTsLC/TDUPx9EKZIa4X1FWaxKTlrv/+WWBHraSBdNy2Pbl18Y+TlY4xvLlymZAgOKEDsWCd/SXKjUJGcoX0iRyoSgVdZKWZqD3nnVUoGNdLdIJE50DvJa+EbPx1DQlW1abm8YemaQpVt+VoaGd1aKbHBdwgqtyeQ935UsBfxl/z/L8C0Fzk+XtExYsuSor+SaRANdbqDQRZUXWdyosJMoOZB3l8Hlk6HlHqOf8fOF3AGqbmdVtCmUH+VBvilrUfRqI6GYi2k9EzypjXyWiDUS0hoh+S0RtynufJqJNRPQ8EZ2vjF8gxjYR0aeU8aOI6DEx/gsiSorxlPh7k3h/Qd1Po/E4YXYLzjzm0B33TSKUrxCK+PB8EgPVfRLFsCaRCNljpSaRkA7IaMf1hi9cgP+oUe8+jFSpB0o2ckkrVrn3GUrYsC7KN/aQpkrVJyEn6CgzZdI0wBjAGEL3oH+vhSfVTNJER0QghrqICWuZqrkpHbFP+FjqezIaKa2Ym7xyISEBJjWJcHOjo6b5BTPjTPgpEdQx2IBPIs7T8GMAF4TG7gVwImNsKYAXAHwaAIhoMYBLASwR+3yXiEwiMgF8B8CFABYDuExsCwD/A+AbjLFjAHQBuEqMXwWgS4x/Q2ynGWWyKRMDRQf5sl1RURao7ZMoVNEkOgaC0SCef0OamyoisurfyMHthUpdDMao12JGsxoRpn0SYw1p/ikpUXk8+9hfqCRDmoQkSpPgfd4rHdfy+NnAPn7l1lToXmyqqkkEk/ECn0Vs55cE8e9/WTIjrEkcLYSE6gQHuGXAu844moQI6vAc14lh8Ekwxh4C0Bka+zNjTGaqPQpAhsVcBOA2xliRMbYVwCYAp4l/mxhjWxhjJQC3AbiI+BLvlQB+Jfa/BcDFyrFuEa9/BeBcGssdgCYouaTqk/BvKPmAdeYrfRKycXvRdgKOZzWyI2GSt/KTSXs9EZrEUJAqtdQk4qCaDbRPYuxx9rFcK1Zt8IbBQ0O9ZDrFcZ0OLGj831MuSEqh+khAtIkJqKNJxBAS4dW6LJsjr0Xu954fPYG1u3oAVJbNaUpZ+OG7V+KGd62sfv5YPglT5EkECw/WYjiehisB3C1ezwWwQ3lvpxirNj4VQLcicOR44Fji/R6xvWYUyaUsFG0XfYVgnRcv5lqsfMIPSL7kVI0s6RmMVvW7B4enLIYa5hfHaR1G+yTGHp88/3j85V/OxpFTc4HxXMqMzLhWo9VU002mivDg70VPuLF9Eso+6nWGI4gWixarhljznjC7Ba8X5Vv++gLvrRLlR3v14pmBumxh4vjepJY9MMzmpqoQ0WcA2AB+dijHOVSI6GoiWkVEqw4cOFB/B01s/NV/seKGUsthG0Zl/Dp/L3p1FhXv7ZkNDlGTSCV4wtBARFx5HLSQGHuYBuGYGc0V47mk5S1U1FX+1JxvPgxO+EpIdYXjOnq7rOczqKwQrGo26sJnppLQGr4HZZ+THZ15ft2WgesvPRnt2QSeFH2xGzWxAvFaLfNEU9/cVK0Cg8qQnwYiugLA6wC8g/nNjXcBmK9sNk+MVRs/CKCNiKzQeOBY4v1WsX0FjLEbGGMrGWMrp0+fHrWJZojICd9lEcUCQ3VvJIF+FqFS33K1U1EuRPRzAIbB3GRx23J/0W5Ik5ClyJPD0E9CMzqok6l636i9HcJlOSRhrUDVPqqZnuJGNxEp5cFDz4dsWnRQiT4kIpw4t9WLSGykD8m9Hz8b37r05Fjb+gUJ7UAFhVoM6WkkogsAfBLAGxhjeeWtOwBcKiKTjgKwCMDjAJ4AsEhEMiXBndt3COHyAIBLxP6XA/i9cqzLxetLANyvCCPNKJGtYfP0Wj+GJvx25WELr9ak462iBo3SivJQfQJeeO5AqSFNQja9r9ekSDN2UO/JRECTUIVEdKRR+N48YXZL5HZNNfxV8j21Z4RknujsGPaLHTkliyhOVNqihq+tFotmNuOik+PlPaW8PIl4FWCBeCGwtwL4B4DjiGgnEV0F4H8BNAO4l4hWE9H3AYAxtg7A7QCeA/AnAB9ijDnCp/BhAPcAWA/gdrEtAPwbgH8hok3gPoebxPhNAKaK8X8B4IXNakaPXBUVnL8neyZECwKgcuUlQ00zoXH12IfuuOb7HxwoxXZcA36JdVk9VjP2UbUENSqtvYqQiCotI1mitCUN5upEm08B//mIMtvI9r8lJ1jdwDAI33zbyfjNB18aGH/JQt/lGr624ULWrorblQ6IkUzHGLssYvimiDG5/bUAro0YvwvAXRHjW8Cjn8LjBQBvqXd9mpFFbYMYXv0fOTWL5/b0VjwgASEReqhmVmktq6r0w6VJFG03dggsAHz/XSvwk79v8+rwa8Y+6r2nahLqa9VxnTANtGcT6IrIk1AT01T7fiZh8oKBoda6AK96nEmYFSX5AeCD5xyNRzZ14PhZLRXvXXxK5cr/ZYum4fhZzdiwt29IARdxOGZ6E0q2i79vPhi7SrbWqzU1CSTrhB4qGaMdfnBqaRIzPU0i+BCoK7xDTWZTV2GNaBJz2zL49GtOiGWn1YwN0lWEhEp4QSLLu4SrA1f73XmDq+je8AA3lUZpEi89Zhq2ffm1nkZRDyLCHz5yFv7yLy8fMSFx/omzkDQN7O0t4DUnza6/A7SQ0NShvYozD/AFSLj8h6p9hDNHZdRH+HnMDacmUaVhi2biIYWEaVCgB3pwm+D9dLYQEjInQeX6y07BR195TMW4XHVHmUKb09FCYigkTMPLrh4JWjMJvHrxTDSlLFz+kgWx9tFPkKYmAbU7JCQWCiGxp6cQGFcf1mo+ib5Q18DMsPokossjaCYecnKulSUfnsBftXgm2rIJXP7SBRXbVms32+ZpEpX3Uy5lBlrrjnW+ePGJODhQCvSGr4UWEpq6zGhOYb9owq4iNYmSXd3RG1bPpbmpr1gOjKuT+aFqEqkqIY+aiYdcXNS6Z8K+h5Z0Aqs/e15D55F9S6IWMOEEv7FOey4ZcOzXQz9BmrrMbEljf1+xwmYrfQ9za9hcww+oNDf1DgY1iaOm+Sr2sGoSQ0im04wf0pZf2iXMmcdMxd82HWwo56AarTV8El9/67JDPv5YRgsJTV3eeMpcrN3VU1FcjIhw10dfhmnN1VclFR22mriQkP2nJUvn+THi1WzLcZmnCK24YX6a8YmMKopyWv/w3Suxp6cQKxM57nmihFEjOQ3jES0kNHV5z5kL8Nqlsz1TkcriOZXhfSphTWJKLomPvPIYvHZpMLJiOMNOZ7Sk8fbTj8DPH3uxQrBpJha+T6Jy8s4mrWG7r+R54vRLn2hoIaGpCxFFCohapCzePjL88BIRPnHecRXbH6r2EOaLF52It62cj5OULFbNxCOO43o4zyPrRE0mxo9LXjOuuOnyU/GK46ajuYEQ1Pe/fKFXIfNQMQzCsvltw2Jq0Ixd0jU0ieFEmi3j9oafSGhNQjMinLVoGs5a1FhnvE9feAI+feEIXZBmQiKFxKEGO8Q9T2ESCgmtSWg0mnFLpobjejhp9qohT74ao1qT0Gg04xYZAjvSPomLTpmD9Xt78dFXLhrR84xFtJDQaDTjFi+ZboTDUFOWiWtev2REzzFW0eYmjUYzbpFRR8kR1iQmM1pIaDSacctoRTdNZvQ3q9Foxi1aSIw8+pvVaDTjltGKbprM6G9Wo9GMW/wCf9onMVJoIaHRaMYtlmkgYZLWJEYQ/c1qNJpxzfGzWrwGWJrhR+dJaDSacc0fPnLW4b6ECY3WJDQajUZTFS0kNBqNRlMVLSQ0Go1GUxUtJDQajUZTFS0kNBqNRlMVLSQ0Go1GUxUtJDQajUZTFS0kNBqNRlMVYhOsHR8R9QF4XhlqBdATsWm18Ub3mQagPEzHGs3zxDn/NAAdDe4zlPMgdK7hPE+tY4U/X5x9hvo5ExHnGo17s2MUziM5AsCLw3SsOPvEvT+H83NW+4wjcf6oe2YkziPHj2OMNVdswRibUP8ArAr9fUOV7SLHG90HwKrhOtZonifOseJ+l8PxOdVzjeLnXDWEfYb0OaPONRr35micR3nvwEjcG3HumdH6nNU+40icv9H7c7i/T/lvMpib/tDg+FD2Gc5jjdY+h/v8o7VPrWM1eo6hnmey/Dbdh/n8o7FP92E+/2juA2BimptWMcZWTrTzjfbnGu1z6s83/s850c832uccK9/nRNQkbpig5xvtzzXa59Sfb/yfc6Kfb7TPOSa+zwmnSWg0Go1m+JiImoRGo9FohgktJDQajUZTlXErJIiofxTP5RDRauXfghrbPkhEDTubiIgR0f8pf1tEdICI/jjEy4573ovFuY8fwXMcls+mnG/U7pW45xzqfRI6xoj/dhHn/AwRrSOiNeJZOH2EzzePiH5PRBuJaDMRfYuIkjW2/xgRZYd4LkZEX1P+/lci+txQjhXjXHJOWUdEzxDRJ4hoTM7HY/KixiCDjLGTlX/bRuAcAwBOJKKM+PvVAHY1cgAiGkqnwcsAPCL+b+RcZgObH/Jn00QypN9uqBDRSwC8DsByxthSAK8CsGMEz0cAfgPgd4yxRQCOBdAE4Noau30MwJCEBIAigDcR0bQh7t8Ick5ZAv48XAjgmlE4b8OMayFBRE1EdB8RPUVEa4noIjG+gIjWE9EPhaT+szJBDde5VxDRX4noSSK6h4hmK2+/S6wSniWi0xo47F0AXiteXwbgVuV8pxHRP4joaSL6OxEdJ8avIKI7iOh+APc1+BmaAJwF4CoAl4qxc4joISK6k4ieJ6LvyxUOEfUT0deI6BkAL2nkXEP8bA8R0cnKdo8Q0bIGzyv3PUfVXIjof4noCvF6GxF9XrmPhmVlXuucw3Dsar9dtc/4GiLaIO7X64eoxc0G0MEYKwIAY6yDMba72rMgtKVvDfFZAIBXAigwxn4kzucA+DiAK4koR0TXieOuIaKPENFHAcwB8AARPTCEz2eDR/h8PPyGmFPuF+e6j4iOIKJWItquPB85ItpBRIlGTsoY2w/gagAfJo5JRF8loifE+d6vXMe/iXv0GSL68hA+Y8OMayEBoADgjYyx5QBeAeBrYvUBAIsAfEdI6m4Abz6E82TINzX9VtwE3wZwCWNsBYCbEVzdZBljJwP4oHgvLrcBuJSI0gCWAnhMeW8DgJcxxk4B8FkA/628t1xcy8sb/FwXAfgTY+wFAAeJaIUYPw3ARwAsBnA0gDeJ8RyAxxhjyxhjjzR4rqF8tpsAXAEARHQsgDRj7JkGzxuXDnEffQ/Av47QOYaTar9dBeI7/wGAC8X9On2I5/wzgPlE9AIRfZeIXj6CzwIALAHwpDrAGOsFL4vxXgALAJwstJqfMcauB7AbwCsYY69o+NNxvgPgHUTUGhr/NoBb5LkAXM8Y6wGwGoB87l4H4B7GWLnRkzLGtgAwAcwAF/w9jLFTAZwK4H1EdBQRXQj+u5/OGFsG4CsNf7ohMBTzxFiCAPw3EZ0NwAUwF8BM8d5Wxthq8fpJ8BtqqAyKG52flOhEACcCuFfIJBPAHmX7WwGAMfYQEbUQURtjrLveSRhja4j7Oy4DX3mrtAK4hYgWAWDgdV0k9zLGOhv9UOI83xKvbxN//xHA4+KmBRHdCr5i/RUAB8Cvh3CeoX62XwL4TyL6fwCuBPDjoZw7Jr8R/z8JXyiOZar9dlEcD2ALY2yr+PtW8JVrQzDG+oUwehn4ouwXAL6IEXgWYnAOgO8yxmxx/KHc/xUwxnqJ6CcAPgpgUHnrJfDvi5/Cn6B/AeBtAB4A1+i+OwyXcR6ApUR0ifi7FXzR+yoAP2KM5cW1Dstnrsd4FxLvAF8VrWCMlYloG4C0eK+obOcAGE5zEwFYxxirZnIJJ580koxyB4DrwB+Cqcr4FwA8wBh7o5hsH1TeG2jg+AAAIpoCrs6fREQM/OFmAO6MuF75d0Go/EOloc/GGMsT0b3gq6e3Aqi6Wo6BjaDmnA69L+8XB8P3XNQ755Co8dv9fiTOpyJ+/wcBPEhEawF8CCP3LDwH4BJ1gIhawIvsbWvgOI3yTQBPAfhRjG3vAF+oTgG/P+8fygmJaCH4vbcffH75CGPsntA25w/l2IfKeDc3tQLYLwTEKwAcOUrnfR7AdOKOPBBRgoiWKO+/TYyfBa42VqvMGMXNAD7PGFsbGm+F7+y9YkhXHeQSAD9ljB3JGFvAGJsPYCv4KvE0od4a4J+lUdNSNYby2W4EcD2AJxhjXYdw7u0AFhNRiojaAJx7CMc63Oes9tsZVc73PICF5EflvW0oJyWi44S2JzkZwHqM3LNwH4AsEb1bHMME8DVwjfIeAO8nEawhJmkA6ANQWcm0AcQK/XZws4/k7xC+H/DF6cNi234AT4BrdX8cyiKKiKYD+D6A/2U8u/keAB+Qvg0iOpaIcgDuBfAeEtFbymceUcalJiFujCK4bfAPYkWzCty2PeIwxkpCFbxe2C4t8NXHOrFJgYieBjebXNngsXeCT4phvgJukvkP8NX+oXIZgP8Jjf0awAfAb/r/BXAMuBr922E435A+G2PsSSLqRbxVXQXyXmGM7SCi2wE8Cz6hPj2U442Rc1b77S4Fn9wC52OMDRLRBwH8iYgGwH/fodAE4NtCANkANoGbrW7AyDwLjIjeCOC7RPSf4ELwLgD/Dr7qPhbAGiIqA/gh+D17g/icuw/BLwFwYfRh5e+PAPiRMH0eAPAe5b1fgJtGz2ng+BkiWg3+vdjgJqyvi/duBDePPyV8rAcAXMwY+xPxQI5VRFSC/12MKOOyLAfxCJcfMsYajZbQ1IGIzgHwr4yx1x3mSwEAENEccPPG8Ywxdwj7j/q9MhbvTyJqEj4FAnfObmSMfWOEz/kg+L20aiTPoxlZxp25iYj+CdwZ9h+H+1o0I4swMzwG4DNDFBCjfq+M4fvzfWLlug7cvPeDw3s5mvHCuNQkNBqNRjM6jHlNgojmE9EDRPQc8cS4fxbjU4joXuLp+vcSUbsYP554YlaRiP41dKxtxBNRVhORVoE1Go2mDmNekyCevTmbMfYUETWDx7FfDB4F08kY+zIRfQpAO2Ps34hoBniU08UAuhhj1ynH2gZgJWMsqm+sRqPRaEKMeU2CMbaHMfaUeN0HHnI3Fzx2/hax2S3gQgGMsf2MsScANJz1qNFoNJogY15IqIg471PAnZkzGWMys3Mv/EzrWjAAfyZeY6bhjFONRqOZbIybPAniBc1+DeBjInXee0/EU8exm53FGNslTFL3EtEGxthDI3TJGo1GM+4ZF5qEyDz8NXgRL1ljZx/51SZng6ez14Qxtkv8vx88QWzMxLFrNBrNWGTMCwmR/HMTgPWMsa8rb90B4HLx+nLwujW1jpMTjm+IFPfzwDNTNRqNRlOF8RDddBZ4nZS14JVeAZ6K/hh4CYIjwGvkvJUx1klEs8BLdLSI7fvBS15Pg19ewgLwc8ZYreYlGo1GM+kZ80JCo9FoNIePMW9u0mg0Gs3hQwsJjUaj0VRFCwmNRqPRVEULCY1Go9FURQsJjUaj0VRFCwmNZoQgos+FKxFrNOMNLSQ0Go1GUxUtJDSaYYSIPkNELxDRIwCOE2PvI6IniOgZIvo1EWWJqJmItirN7lvUvzWasYIWEhrNMEFEKwBcCuBkAK8BcKp46zeMsVMZY8vAS91fJcrePwjgtWKbS8V2usS9ZkyhhYRGM3y8DMBvGWN5xlgveH0xADiRiB4morUA3gFgiRi/EcB7xOv3APjRqF6tRhMDLSQ0mpHnxwA+zBg7CcDnAaQBgDH2NwALiOgcACZjTBec1Iw5tJDQaIaPhwBcTEQZUXH49WK8GcAe4W94R2ifnwD4ObQWoRmj6AJ/Gs0wQkSfAS9dvx/AiwCeAjAA4JMADoBXL25mjF0htp8FYCt4H/fuw3DJGk1NtJDQaA4jRHQJgIsYY+863Nei0UQxbtqXajQTDSL6NoALwSOhNJoxidYkNBqNRlMV7bjWaDQaTVW0kNBoNBpNVbSQ0Gg0Gk1VtJDQaDQaTVW0kNBoNBpNVbSQ0Gg0Gk1V/j82OI9qBwnZvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "query = (\n",
    "    \"SELECT time_bucket('24h', time) AS day, sum(demand) AS demand\"\n",
    "    \"  FROM energy_usage\"\n",
    "    \"  WHERE EXTRACT(year FROM time)='2015'\"\n",
    "    \"  GROUP BY day\"\n",
    "    \"  ORDER BY day\"\n",
    ")\n",
    "\n",
    "df = pd.read_sql(query, conn)\n",
    "df.set_index('day', inplace=True)\n",
    "_ = df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting Weekly Demand\n",
    "\n",
    "One of TimescaleDB's primary use cases is to export data into R or Python for forecasting purposes. The following is a sample using Holt-Winters implemented with statsmodels about how to extract data from the database for this type of analysis.\n",
    "\n",
    "### Step 1: Create Views\n",
    "\n",
    "Since we have a database we can create views for our training and testing datasets. Views are not tables but are materialized from a `SELECT` query. In our case we'll try to forecast the weekly demand across years where our training data is demand from before 2017 and our test data is demand from after 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_view = (\n",
    "    \"CREATE VIEW energy_usage_train AS \"\n",
    "    \"SELECT * from energy_usage \"\n",
    "    \"WHERE time <= '2016-12-31 23:59:59'\"\n",
    ")\n",
    "\n",
    "test_view = (\n",
    "    \"CREATE VIEW energy_usage_test AS \"\n",
    "    \"SELECT * from energy_usage \"\n",
    "    \"WHERE time >= '2017-01-01 00:00:00'\"\n",
    ")\n",
    "\n",
    "with conn.cursor() as cur:\n",
    "    cur.execute(train_view)\n",
    "    cur.execute(test_view)\n",
    "    conn.commit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Select Data into Python\n",
    "\n",
    "Next, we'll want to apply our downsampling and select the data from both the train and test datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_sql = \"SELECT time_bucket('1w', time) AS week, sum(demand) as demand FROM {} GROUP BY week ORDER BY week\"\n",
    "\n",
    "def select_dataset(view):\n",
    "    # Note this is safe because we are specifying the table\n",
    "    # if this was user input we should specify parameters!\n",
    "    stmt = forecast_sql.format(view)\n",
    "    df = pd.read_sql(stmt, conn)\n",
    "    df.set_index(\"week\", inplace=True)\n",
    "    return df\n",
    "\n",
    "\n",
    "train = select_dataset(\"energy_usage_train\")\n",
    "test = select_dataset(\"energy_usage_test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Train Holt-Winters Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/benjamin/Workspace/georgetown/time-series-analytics/venv/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning: No frequency information was provided, so inferred frequency W-MON will be used.\n",
      "  % freq, ValueWarning)\n",
      "/Users/benjamin/Workspace/georgetown/time-series-analytics/venv/lib/python3.7/site-packages/statsmodels/tsa/holtwinters/model.py:429: FutureWarning: After 0.13 initialization must be handled at model creation\n",
      "  FutureWarning,\n",
      "/Users/benjamin/Workspace/georgetown/time-series-analytics/venv/lib/python3.7/site-packages/statsmodels/tsa/holtwinters/model.py:922: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
      "  ConvergenceWarning,\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.api import ExponentialSmoothing\n",
    "\n",
    "model = ExponentialSmoothing(train, seasonal_periods=4, trend = 'add', seasonal = 'add').fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on our test dataset\n",
    "test[\"forecast\"] = model.forecast(len(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Visualize Forecast vs. Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = test.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Your Turn!\n",
    "\n",
    "Adjust the hyperparameters of the Holt-Winters model and make predictions for 2016 (trained on data up to 2016) instead of 2017. Can you create a better seasonal forecasting model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continuous Aggregates\n",
    "\n",
    "Assume that you're routinely querying the daily energy usage statistics. Every time you run the command the entire table has to be scanned. To optimize this process, we can create a _continuous aggregate query_ which caches the results in a materialized view that is refreshed at a specific interval. We can create this query as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"CREATE VIEW daily_demand WITH \n",
    "  (timescaledb.continuous, timescaledb.refresh_interval='24h') \n",
    "AS\n",
    "SELECT time_bucket('24h', time) AS day, \n",
    "       sum(demand) AS total_demand,\n",
    "       avg(precip) AS avg_precip,\n",
    "       sum(precip) AS total_precip,\n",
    "       min(temp) AS min_temp,\n",
    "       max(temp) AS max_temp\n",
    "FROM energy_usage\n",
    "GROUP BY time_bucket('24h', time)\n",
    "\"\"\"\n",
    "\n",
    "with conn.cursor() as cur:\n",
    "    cur.execute(query)\n",
    "    conn.commit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can then select from the view on demand as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-08-12: day=0.2f avg_precip=0.00 total_precip=0.00 min_temp=67.33 max_temp=72.21\n",
      "2017-08-11: day=0.2f avg_precip=0.00 total_precip=0.00 min_temp=66.85 max_temp=80.21\n",
      "2017-08-10: day=0.2f total_demand=61467.54 avg_precip=0.05 total_precip=1.16 min_temp=67.06 max_temp=83.69\n",
      "2017-08-09: day=0.2f total_demand=162130.30 avg_precip=0.00 total_precip=0.03 min_temp=63.79 max_temp=84.61\n",
      "2017-08-08: day=0.2f total_demand=153494.73 avg_precip=0.00 total_precip=0.03 min_temp=66.17 max_temp=79.24\n",
      "2017-08-07: day=0.2f total_demand=152933.22 avg_precip=0.02 total_precip=0.48 min_temp=65.66 max_temp=72.94\n",
      "2017-08-06: day=0.2f total_demand=138004.61 avg_precip=0.00 total_precip=0.00 min_temp=60.96 max_temp=78.62\n",
      "2017-08-05: day=0.2f total_demand=165217.20 avg_precip=0.01 total_precip=0.14 min_temp=71.04 max_temp=80.39\n",
      "2017-08-04: day=0.2f total_demand=186135.12 avg_precip=0.00 total_precip=0.12 min_temp=72.82 max_temp=81.13\n",
      "2017-08-03: day=0.2f total_demand=182077.85 avg_precip=0.00 total_precip=0.00 min_temp=67.86 max_temp=84.04\n",
      "2017-08-02: day=0.2f total_demand=190775.72 avg_precip=0.02 total_precip=0.42 min_temp=71.49 max_temp=85.70\n",
      "2017-08-01: day=0.2f total_demand=185653.80 avg_precip=0.00 total_precip=0.08 min_temp=68.17 max_temp=88.32\n",
      "2017-07-31: day=0.2f total_demand=166543.66 avg_precip=0.00 total_precip=0.03 min_temp=63.68 max_temp=86.85\n",
      "2017-07-30: day=0.2f total_demand=135480.32 avg_precip=0.00 total_precip=0.00 min_temp=59.32 max_temp=80.50\n"
     ]
    }
   ],
   "source": [
    "from psycopg2.extras import DictCursor\n",
    "\n",
    "query = \"SELECT * FROM daily_demand ORDER BY day DESC LIMIT 14\"\n",
    "\n",
    "with conn.cursor(cursor_factory=DictCursor) as cur:\n",
    "    cur.execute(query)\n",
    "    for row in cur.fetchall():\n",
    "        day = row[\"day\"].strftime(\"%Y-%m-%d\")\n",
    "        stats = [f\"{key}={val:0.2f}\" for key, val in row.items() if val is not None]\n",
    "        print(f\"{day}: {' '.join(stats)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get meta information about the continuous aggregates view by querying the `timescaledb_information` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "view_name: daily_demand\n",
      "refresh_lag: 2 days\n",
      "refresh_interval: 1 day, 0:00:00\n",
      "max_interval_per_job: 20 days\n",
      "ignore_invalidation_older_than: None\n",
      "materialization_hypertable: _timescaledb_internal._materialized_hypertable_2\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT view_name, refresh_lag, refresh_interval, max_interval_per_job,\n",
    "       ignore_invalidation_older_than, materialization_hypertable\n",
    "FROM timescaledb_information.continuous_aggregates\n",
    "\"\"\".strip()\n",
    "\n",
    "with conn.cursor(cursor_factory=DictCursor) as cur:\n",
    "    cur.execute(query)\n",
    "    row = cur.fetchone()\n",
    "    for key, val in row.items():\n",
    "        print(f\"{key}: {val}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Exceptions\n",
    "\n",
    "If you run into an error all your queries may return the following error:\n",
    "\n",
    "```\n",
    "InFailedSqlTransaction: current transaction is aborted, commands ignored until end of transaction block\n",
    "```\n",
    "\n",
    "You can fix this error by rolling back the last transaction as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.rollback()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning Up\n",
    "\n",
    "Close the connection to the database to ensure that everything is cleaned up correctly:"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {},
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   "source": [
    "conn.close()"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "\n",
    "Once you are done with the workshop you can stop the Docker container running the database as follows:\n",
    "\n",
    "```\n",
    "$ docker stop timescaledb\n",
    "```\n",
    "\n",
    "You can restart the database with `docker start timescaledb`, or to clean up and delete all stopped containers, run:\n",
    "\n",
    "```\n",
    "$ docker container prune\n",
    "```"
   ]
  }
 ],
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