{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An introduction into the tools and tutorials available for the analysis of TESS data\n",
    "\n",
    "Welcome everyone to this *TESS* Lightkurve tutorial!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n",
    "\n",
    "[Rebekah Hounsell](https://heasarc.gsfc.nasa.gov/docs/tess/helpdesk.html) - Support scientist for *TESS* in the NASA GSFC GI Office. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning Goals\n",
    "\n",
    "In this tutorial, we will teach the user how to access, analyze, and manipulate data from the NASA Exoplanet mission *TESS* (this can also be applied to *Kepler* & *K2*). All tools presented will teach the user how to work with time series data for the purpose of scientific research. \n",
    "\n",
    "The tutorial assumes a basic knowledge of python and astronomy, and will walk the user through several of the concepts outlined below:\n",
    "\n",
    "1. How to obtain *TESS* data products from the MAST archive\n",
    "2. How to use *Lightkurve* to access the various data products and create time series\n",
    "3. How to analyze and assess various data anomalies and how you might visualize them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "\n",
    "This tutorial requires the use of specific packages:\n",
    "- [**Lightkurve**](https://docs.lightkurve.org/index.html) to work with *TESS* data (v2.0.1)\n",
    "- [**Matplotlib**](https://matplotlib.org/) for plotting.\n",
    "- [**Numpy**](https://numpy.org) for manipulating the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightkurve as lk\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First time users\n",
    "\n",
    "If you are not that experienced with *Python*, or cannot download *Lightkurve*, you can run this notebook as a [Colab notebook](https://colab.research.google.com/notebooks/intro.ipynb?utm_source=scs-index). Colaboratory allows users to write and execute *Python* in your browser with zero configuration required. \n",
    "\n",
    "All you need is a Google account and to copy and paste in the following command at the top of your colab notebook:\n",
    "\n",
    "`!pip install lightkurve`\n",
    "\n",
    "This downloads the *Lightkurve* package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to *TESS*: \n",
    "\n",
    "The *Transiting Exoplanet Survey Satellite (TESS)* is a NASA-sponsored Astrophysics Explorer-class mission that is performing a near all-sky survey to search for planets transiting nearby stars. *TESS* completed its primary mission in July of 2020, and has now entered its extended mission. The current extended mission will last until September 2022, and will continue to scan the sky for exoplanets and transient events. The *TESS* mission is now more community focused with a larger guest investigator (GI) program.\n",
    "\n",
    "Over the last three years *TESS* has observed both the northern and southern hemispheres, with each hemisphere being split into ~13 sectors. Each sector is observed for ~27 days by *TESS's* four cameras.\n",
    "\n",
    "The main data products collected by the *TESS* mission are described below. \n",
    "\n",
    "- [Full Frame Images (FFIs)](https://heasarc.gsfc.nasa.gov/docs/tess/data-products.html#full-frame-images): The full sector images, with a cadence of 30-min (years 1 & 2) or 10-min (years 3 & 4).\n",
    "- [Target Pixel Files (TPFs)](https://heasarc.gsfc.nasa.gov/docs/tess/data-products.html#target-pixel-files-tpfs): Postage stamp cut outs from the FFIs, focused on a selected target of interest. Each stamp has a cadence of 2-min or 20-sec. \n",
    "- [Light Curve Files (LCFs)](https://heasarc.gsfc.nasa.gov/docs/tess/data-products.html#light-curve-files): The time series data produced for each 2-min or 20-sec TPF object. \n",
    "\n",
    "To learn more about the *TESS* mission and its data products, please visit the [*TESS* GI pages](https://heasarc.gsfc.nasa.gov/docs/tess/data-products.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. How to obtain *TESS* data products from the MAST archive\n",
    "\n",
    "You can access *TESS*, *Kepler*, and *K2* data via the [ Mikulksi Archive for Space Telescopes (MAST)](https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html) archive or via the *Lightkurve* package (see Section 2).\n",
    "\n",
    "Here, we are focusing on obtaining your data via the MAST portal. Using the portal, you can enter the name of your object, its TIC number, or position (i.e., R.A and Dec). If listed in the archive, the table containing each observation will be returned."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. How to use *Lightkurve* to access the various data products and create a time series\n",
    "\n",
    "\n",
    "[*Lightkurve*](https://docs.lightkurve.org/tutorials/index.html) offers a user-friendly way to analyze time series data obtained by telescopes, in particular *NASA’s Kepler* and *TESS* exoplanet missions. You can search for the various data products for *TESS* on MAST using the following *Lightkurve* functions:\n",
    "\n",
    "- To look for your object in a full frame image: [`search_tesscut()`](https://docs.lightkurve.org/reference/api/lightkurve.search_tesscut.html?highlight=search_tesscut)\n",
    "\n",
    "- To look for target pixel files:  [`search_targetpixelfile()`](https://docs.lightkurve.org/reference/api/lightkurve.search_targetpixelfile.html?highlight=search_targetpixelfile) \n",
    "\n",
    "- To obtain light curve files for your object of interest: [`search_lightcurve()`](https://docs.lightkurve.org/reference/api/lightkurve.search_lightcurve.html?highlight=search_lightcurve) \n",
    "\n",
    "For the purpose of this tutorial, we will be examining [L 98-59](https://arxiv.org/pdf/1903.08017.pdf), a bright M dwarf star at a distance of 10.6 pc. This star is host to three terrestrial-sized planets and is also known in the *TESS* system as TIC 307210830. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Accessing the data products\n",
    "\n",
    "Let's go through each one of the above functions and see what data is available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_ffi = lk.search_tesscut('L 98-59')\n",
    "search_tpf = lk.search_targetpixelfile('L 98-59')\n",
    "search_lcf = lk.search_lightcurve('L 98-59')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "SearchResult containing 15 data products.\n",
       "\n",
       "<table id=\"table140208156282192\">\n",
       "<thead><tr><th>#</th><th>mission</th><th>year</th><th>author</th><th>exptime</th><th>target_name</th><th>distance</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th>s</th><th></th><th>arcsec</th></tr></thead>\n",
       "<tr><td>0</td><td>TESS Sector 01</td><td>2018</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>1426</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>TESS Sector 02</td><td>2018</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>1426</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>2</td><td>TESS Sector 05</td><td>2018</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>1426</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>3</td><td>TESS Sector 08</td><td>2019</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>1426</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>4</td><td>TESS Sector 09</td><td>2019</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>1426</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>5</td><td>TESS Sector 10</td><td>2019</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>1426</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>6</td><td>TESS Sector 11</td><td>2019</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>1426</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>7</td><td>TESS Sector 12</td><td>2019</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>1426</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>8</td><td>TESS Sector 28</td><td>2020</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>475</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>9</td><td>TESS Sector 29</td><td>2020</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>475</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>10</td><td>TESS Sector 32</td><td>2020</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>475</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>11</td><td>TESS Sector 35</td><td>2021</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>475</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>12</td><td>TESS Sector 36</td><td>2021</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>475</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>13</td><td>TESS Sector 37</td><td>2021</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>475</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "<tr><td>14</td><td>TESS Sector 38</td><td>2021</td><td><a href='https://mast.stsci.edu/tesscut/'>TESScut</a></td><td>475</td><td>L 98-59</td><td>0.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "SearchResult containing 15 data products.\n",
       "\n",
       " #     mission     year  author exptime target_name distance\n",
       "                                   s                 arcsec \n",
       "--- -------------- ---- ------- ------- ----------- --------\n",
       "  0 TESS Sector 01 2018 TESScut    1426     L 98-59      0.0\n",
       "  1 TESS Sector 02 2018 TESScut    1426     L 98-59      0.0\n",
       "  2 TESS Sector 05 2018 TESScut    1426     L 98-59      0.0\n",
       "  3 TESS Sector 08 2019 TESScut    1426     L 98-59      0.0\n",
       "  4 TESS Sector 09 2019 TESScut    1426     L 98-59      0.0\n",
       "  5 TESS Sector 10 2019 TESScut    1426     L 98-59      0.0\n",
       "  6 TESS Sector 11 2019 TESScut    1426     L 98-59      0.0\n",
       "  7 TESS Sector 12 2019 TESScut    1426     L 98-59      0.0\n",
       "  8 TESS Sector 28 2020 TESScut     475     L 98-59      0.0\n",
       "  9 TESS Sector 29 2020 TESScut     475     L 98-59      0.0\n",
       " 10 TESS Sector 32 2020 TESScut     475     L 98-59      0.0\n",
       " 11 TESS Sector 35 2021 TESScut     475     L 98-59      0.0\n",
       " 12 TESS Sector 36 2021 TESScut     475     L 98-59      0.0\n",
       " 13 TESS Sector 37 2021 TESScut     475     L 98-59      0.0\n",
       " 14 TESS Sector 38 2021 TESScut     475     L 98-59      0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_ffi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above table provides several important pieces of information: \n",
    "- The sector in which the object was observed.\n",
    "- The year in which the object was observed.\n",
    "- The author of the data. This has multiple options and each is a hyperlink that when clicked will provide you with more information. \n",
    "- The cadence of the observation. \n",
    "- The name of the target.\n",
    "- The distance of the observation from your target of interest. This is useful if you conduct a cone search around your objects co-ordinates. \n",
    "\n",
    "The table above indicates that our object was observed in multiple sectors. Note that in years 1 and 2 (2018 & 2019) that the cadence of the FFI data was 30-min, but in year 3 (2020/2021) it is 10-min.\n",
    "\n",
    "Let's see if any other data exists - i.e., was it observed as a target of interest and does it have a Target Pixel File. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "SearchResult containing 28 data products.\n",
       "\n",
       "<table id=\"table140208682983824\">\n",
       "<thead><tr><th>#</th><th>mission</th><th>year</th><th>author</th><th>exptime</th><th>target_name</th><th>distance</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th>s</th><th></th><th>arcsec</th></tr></thead>\n",
       "<tr><td>0</td><td>TESS Sector 02</td><td>2018</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>TESS Sector 02</td><td>2018</td><td><a href='https://archive.stsci.edu/hlsp/tess-spoc'>TESS-SPOC</a></td><td>1800</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>2</td><td>TESS Sector 05</td><td>2018</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>3</td><td>TESS Sector 05</td><td>2018</td><td><a href='https://archive.stsci.edu/hlsp/tess-spoc'>TESS-SPOC</a></td><td>1800</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>4</td><td>TESS Sector 08</td><td>2019</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>5</td><td>TESS Sector 09</td><td>2019</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>6</td><td>TESS Sector 10</td><td>2019</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>7</td><td>TESS Sector 11</td><td>2019</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>8</td><td>TESS Sector 12</td><td>2019</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>9</td><td>TESS Sector 28</td><td>2020</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>20</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>18</td><td>TESS Sector 35</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>20</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>19</td><td>TESS Sector 35</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>20</td><td>TESS Sector 36</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>20</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>21</td><td>TESS Sector 36</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>22</td><td>TESS Sector 37</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>20</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>23</td><td>TESS Sector 37</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>24</td><td>TESS Sector 38</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>20</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>25</td><td>TESS Sector 38</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>26</td><td>TESS Sector 39</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>20</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>27</td><td>TESS Sector 39</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "</table>\n",
       "Length = 28 rows"
      ],
      "text/plain": [
       "SearchResult containing 28 data products.\n",
       "\n",
       " #     mission     year   author  exptime target_name distance\n",
       "                                     s                 arcsec \n",
       "--- -------------- ---- --------- ------- ----------- --------\n",
       "  0 TESS Sector 02 2018      SPOC     120   307210830      0.0\n",
       "  1 TESS Sector 02 2018 TESS-SPOC    1800   307210830      0.0\n",
       "  2 TESS Sector 05 2018      SPOC     120   307210830      0.0\n",
       "  3 TESS Sector 05 2018 TESS-SPOC    1800   307210830      0.0\n",
       "  4 TESS Sector 08 2019      SPOC     120   307210830      0.0\n",
       "  5 TESS Sector 09 2019      SPOC     120   307210830      0.0\n",
       "  6 TESS Sector 10 2019      SPOC     120   307210830      0.0\n",
       "  7 TESS Sector 11 2019      SPOC     120   307210830      0.0\n",
       "  8 TESS Sector 12 2019      SPOC     120   307210830      0.0\n",
       "  9 TESS Sector 28 2020      SPOC      20   307210830      0.0\n",
       "...            ...  ...       ...     ...         ...      ...\n",
       " 18 TESS Sector 35 2021      SPOC      20   307210830      0.0\n",
       " 19 TESS Sector 35 2021      SPOC     120   307210830      0.0\n",
       " 20 TESS Sector 36 2021      SPOC      20   307210830      0.0\n",
       " 21 TESS Sector 36 2021      SPOC     120   307210830      0.0\n",
       " 22 TESS Sector 37 2021      SPOC      20   307210830      0.0\n",
       " 23 TESS Sector 37 2021      SPOC     120   307210830      0.0\n",
       " 24 TESS Sector 38 2021      SPOC      20   307210830      0.0\n",
       " 25 TESS Sector 38 2021      SPOC     120   307210830      0.0\n",
       " 26 TESS Sector 39 2021      SPOC      20   307210830      0.0\n",
       " 27 TESS Sector 39 2021      SPOC     120   307210830      0.0\n",
       "Length = 28 rows"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_tpf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Our object was observed as a target of interest and has 2-min and 20-sec cadenced data. This means that there should be light curve files already on the archive. Let's check those out. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "SearchResult containing 38 data products.\n",
       "\n",
       "<table id=\"table140208156281744\">\n",
       "<thead><tr><th>#</th><th>mission</th><th>year</th><th>author</th><th>exptime</th><th>target_name</th><th>distance</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th>s</th><th></th><th>arcsec</th></tr></thead>\n",
       "<tr><td>0</td><td>TESS Sector</td><td>2018</td><td>DIAMANTE</td><td>1800</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>TESS Sector 02</td><td>2018</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>2</td><td>TESS Sector 02</td><td>2018</td><td><a href='https://archive.stsci.edu/hlsp/tess-spoc'>TESS-SPOC</a></td><td>1800</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>3</td><td>TESS Sector 02</td><td>2018</td><td><a href='https://archive.stsci.edu/hlsp/qlp'>QLP</a></td><td>1800</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>4</td><td>TESS Sector 02</td><td>2018</td><td><a href='https://archive.stsci.edu/hlsp/tasoc'>TASOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>5</td><td>TESS Sector 02</td><td>2018</td><td><a href='https://archive.stsci.edu/hlsp/tasoc'>TASOC</a></td><td>1800</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>6</td><td>TESS Sector 05</td><td>2018</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>7</td><td>TESS Sector 05</td><td>2018</td><td><a href='https://archive.stsci.edu/hlsp/tess-spoc'>TESS-SPOC</a></td><td>1800</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>8</td><td>TESS Sector 05</td><td>2018</td><td><a href='https://archive.stsci.edu/hlsp/qlp'>QLP</a></td><td>1800</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>9</td><td>TESS Sector 08</td><td>2019</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>28</td><td>TESS Sector 35</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>20</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>29</td><td>TESS Sector 35</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>30</td><td>TESS Sector 36</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>20</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>31</td><td>TESS Sector 36</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>32</td><td>TESS Sector 37</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>20</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>33</td><td>TESS Sector 37</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>34</td><td>TESS Sector 38</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>20</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>35</td><td>TESS Sector 38</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>36</td><td>TESS Sector 39</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>20</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>37</td><td>TESS Sector 39</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "</table>\n",
       "Length = 38 rows"
      ],
      "text/plain": [
       "SearchResult containing 38 data products.\n",
       "\n",
       " #     mission     year   author  exptime target_name distance\n",
       "                                     s                 arcsec \n",
       "--- -------------- ---- --------- ------- ----------- --------\n",
       "  0   TESS Sector  2018  DIAMANTE    1800   307210830      0.0\n",
       "  1 TESS Sector 02 2018      SPOC     120   307210830      0.0\n",
       "  2 TESS Sector 02 2018 TESS-SPOC    1800   307210830      0.0\n",
       "  3 TESS Sector 02 2018       QLP    1800   307210830      0.0\n",
       "  4 TESS Sector 02 2018     TASOC     120   307210830      0.0\n",
       "  5 TESS Sector 02 2018     TASOC    1800   307210830      0.0\n",
       "  6 TESS Sector 05 2018      SPOC     120   307210830      0.0\n",
       "  7 TESS Sector 05 2018 TESS-SPOC    1800   307210830      0.0\n",
       "  8 TESS Sector 05 2018       QLP    1800   307210830      0.0\n",
       "  9 TESS Sector 08 2019      SPOC     120   307210830      0.0\n",
       "...            ...  ...       ...     ...         ...      ...\n",
       " 28 TESS Sector 35 2021      SPOC      20   307210830      0.0\n",
       " 29 TESS Sector 35 2021      SPOC     120   307210830      0.0\n",
       " 30 TESS Sector 36 2021      SPOC      20   307210830      0.0\n",
       " 31 TESS Sector 36 2021      SPOC     120   307210830      0.0\n",
       " 32 TESS Sector 37 2021      SPOC      20   307210830      0.0\n",
       " 33 TESS Sector 37 2021      SPOC     120   307210830      0.0\n",
       " 34 TESS Sector 38 2021      SPOC      20   307210830      0.0\n",
       " 35 TESS Sector 38 2021      SPOC     120   307210830      0.0\n",
       " 36 TESS Sector 39 2021      SPOC      20   307210830      0.0\n",
       " 37 TESS Sector 39 2021      SPOC     120   307210830      0.0\n",
       "Length = 38 rows"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_lcf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wonderful! Light curves for our object of interest have already been created. \n",
    "\n",
    "### 2.2 Creating a light curve using a Light Curve File: \n",
    "\n",
    "Now on to getting the light curve for our object of interest. From the above table, it looks like there are multiple authors for our target. For the purpose of this tutorial, let's stick to \"SPOC\" data products which have a 2-min cadence. We can return only these results using the following commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "SearchResult containing 15 data products.\n",
       "\n",
       "<table id=\"table140208683094800\">\n",
       "<thead><tr><th>#</th><th>mission</th><th>year</th><th>author</th><th>exptime</th><th>target_name</th><th>distance</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th>s</th><th></th><th>arcsec</th></tr></thead>\n",
       "<tr><td>0</td><td>TESS Sector 02</td><td>2018</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>TESS Sector 05</td><td>2018</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>2</td><td>TESS Sector 08</td><td>2019</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>3</td><td>TESS Sector 09</td><td>2019</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>4</td><td>TESS Sector 10</td><td>2019</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>5</td><td>TESS Sector 11</td><td>2019</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>6</td><td>TESS Sector 12</td><td>2019</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>7</td><td>TESS Sector 28</td><td>2020</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>8</td><td>TESS Sector 29</td><td>2020</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>9</td><td>TESS Sector 32</td><td>2020</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>10</td><td>TESS Sector 35</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>11</td><td>TESS Sector 36</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>12</td><td>TESS Sector 37</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>13</td><td>TESS Sector 38</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "<tr><td>14</td><td>TESS Sector 39</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>307210830</td><td>0.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "SearchResult containing 15 data products.\n",
       "\n",
       " #     mission     year author exptime target_name distance\n",
       "                                  s                 arcsec \n",
       "--- -------------- ---- ------ ------- ----------- --------\n",
       "  0 TESS Sector 02 2018   SPOC     120   307210830      0.0\n",
       "  1 TESS Sector 05 2018   SPOC     120   307210830      0.0\n",
       "  2 TESS Sector 08 2019   SPOC     120   307210830      0.0\n",
       "  3 TESS Sector 09 2019   SPOC     120   307210830      0.0\n",
       "  4 TESS Sector 10 2019   SPOC     120   307210830      0.0\n",
       "  5 TESS Sector 11 2019   SPOC     120   307210830      0.0\n",
       "  6 TESS Sector 12 2019   SPOC     120   307210830      0.0\n",
       "  7 TESS Sector 28 2020   SPOC     120   307210830      0.0\n",
       "  8 TESS Sector 29 2020   SPOC     120   307210830      0.0\n",
       "  9 TESS Sector 32 2020   SPOC     120   307210830      0.0\n",
       " 10 TESS Sector 35 2021   SPOC     120   307210830      0.0\n",
       " 11 TESS Sector 36 2021   SPOC     120   307210830      0.0\n",
       " 12 TESS Sector 37 2021   SPOC     120   307210830      0.0\n",
       " 13 TESS Sector 38 2021   SPOC     120   307210830      0.0\n",
       " 14 TESS Sector 39 2021   SPOC     120   307210830      0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_lcf_refined = lk.search_lightcurve('L 98-59', author=\"SPOC\", exptime=120)\n",
    "search_lcf_refined "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now see five search results. Let's download these and see what the light curve looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lcf = search_lcf_refined.download_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LightCurveCollection of 15 objects:\n",
       "    0: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=2 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    1: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=5 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    2: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=8 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    3: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=9 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    4: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=10 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    5: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=11 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    6: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=12 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    7: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=28 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    8: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=29 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    9: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=32 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    10: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=35 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    11: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=36 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    12: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=37 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    13: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=38 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "    14: <TessLightCurve LABEL=\"TIC 307210830\" SECTOR=39 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lcf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This has downloaded the light curve for each sector, and stored the data in arrays. You can look at the data for a specific sector by specifying an array number as indicated below. This displays the data for sector 2 as a table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>TessLightCurve length=18300 LABEL=&quot;TIC 307210830&quot; SECTOR=2 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux</i>\n",
       "<table id=\"table140208421568400\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>time</th><th>flux</th><th>flux_err</th><th>timecorr</th><th>cadenceno</th><th>centroid_col</th><th>centroid_row</th><th>sap_flux</th><th>sap_flux_err</th><th>sap_bkg</th><th>sap_bkg_err</th><th>pdcsap_flux</th><th>pdcsap_flux_err</th><th>quality</th><th>psf_centr1</th><th>psf_centr1_err</th><th>psf_centr2</th><th>psf_centr2_err</th><th>mom_centr1</th><th>mom_centr1_err</th><th>mom_centr2</th><th>mom_centr2_err</th><th>pos_corr1</th><th>pos_corr2</th></tr></thead>\n",
       "<thead><tr><th></th><th>electron / s</th><th>electron / s</th><th>d</th><th></th><th>pix</th><th>pix</th><th>electron / s</th><th>electron / s</th><th>electron / s</th><th>electron / s</th><th>electron / s</th><th>electron / s</th><th></th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th><th>pix</th></tr></thead>\n",
       "<thead><tr><th>object</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th><th>float64</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th></tr></thead>\n",
       "<tr><td>1354.1074113410245</td><td>2.4635420e+04</td><td>1.8856627e+01</td><td>-8.0586493e-04</td><td>91190</td><td>664.04462</td><td>338.97644</td><td>2.3127123e+04</td><td>1.7658133e+01</td><td>1.8465968e+03</td><td>5.2003989e+00</td><td>2.4635420e+04</td><td>1.8856627e+01</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.04462</td><td>6.2346959e-04</td><td>338.97644</td><td>6.9568102e-04</td><td>3.1294446e-02</td><td>1.5483069e-01</td></tr>\n",
       "<tr><td>1354.1088002024744</td><td>2.4656008e+04</td><td>1.8861403e+01</td><td>-8.0589182e-04</td><td>91191</td><td>664.05609</td><td>338.96900</td><td>2.3150639e+04</td><td>1.7662607e+01</td><td>1.8428802e+03</td><td>5.1911125e+00</td><td>2.4656008e+04</td><td>1.8861403e+01</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.05609</td><td>6.2315754e-04</td><td>338.96900</td><td>6.9629494e-04</td><td>4.3172963e-02</td><td>1.4587776e-01</td></tr>\n",
       "<tr><td>1354.110189063866</td><td>2.4635619e+04</td><td>1.8864876e+01</td><td>-8.0591877e-04</td><td>91192</td><td>664.07351</td><td>338.95814</td><td>2.3137189e+04</td><td>1.7665859e+01</td><td>1.8525369e+03</td><td>5.2004828e+00</td><td>2.4635619e+04</td><td>1.8864876e+01</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.07351</td><td>6.2400498e-04</td><td>338.95814</td><td>6.9669099e-04</td><td>6.0803384e-02</td><td>1.3428329e-01</td></tr>\n",
       "<tr><td>1354.1129667867635</td><td>2.4621027e+04</td><td>1.8853863e+01</td><td>-8.0597255e-04</td><td>91194</td><td>664.05132</td><td>338.94885</td><td>2.3098303e+04</td><td>1.7655546e+01</td><td>1.8542960e+03</td><td>5.2071209e+00</td><td>2.4621027e+04</td><td>1.8853863e+01</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.05132</td><td>6.2639196e-04</td><td>338.94885</td><td>6.9927127e-04</td><td>3.7734102e-02</td><td>1.2694269e-01</td></tr>\n",
       "<tr><td>1354.1143556482134</td><td>2.4617400e+04</td><td>1.8859161e+01</td><td>-8.0599944e-04</td><td>91195</td><td>664.09017</td><td>338.97538</td><td>2.3127893e+04</td><td>1.7660507e+01</td><td>1.8433275e+03</td><td>5.1999226e+00</td><td>2.4617400e+04</td><td>1.8859161e+01</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.09017</td><td>6.2417402e-04</td><td>338.97538</td><td>6.9604575e-04</td><td>7.8965843e-02</td><td>1.5301819e-01</td></tr>\n",
       "<tr><td>1354.1157445097215</td><td>2.4630531e+04</td><td>1.8860582e+01</td><td>-8.0602628e-04</td><td>91196</td><td>664.08357</td><td>338.96449</td><td>2.3136076e+04</td><td>1.7661839e+01</td><td>1.8441443e+03</td><td>5.1992383e+00</td><td>2.4630531e+04</td><td>1.8860582e+01</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.08357</td><td>6.2411965e-04</td><td>338.96449</td><td>6.9649977e-04</td><td>7.2042428e-02</td><td>1.4030553e-01</td></tr>\n",
       "<tr><td>1354.117133371171</td><td>2.4625502e+04</td><td>1.8855038e+01</td><td>-8.0605317e-04</td><td>91197</td><td>664.08138</td><td>338.96244</td><td>2.3130492e+04</td><td>1.7656647e+01</td><td>1.8393002e+03</td><td>5.1891294e+00</td><td>2.4625502e+04</td><td>1.8855038e+01</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.08138</td><td>6.2480610e-04</td><td>338.96244</td><td>6.9642899e-04</td><td>6.8586096e-02</td><td>1.3917884e-01</td></tr>\n",
       "<tr><td>1354.118522232678</td><td>2.4619252e+04</td><td>1.8856379e+01</td><td>-8.0608000e-04</td><td>91198</td><td>664.07300</td><td>338.95776</td><td>2.3123014e+04</td><td>1.7657902e+01</td><td>1.8428878e+03</td><td>5.1969514e+00</td><td>2.4619252e+04</td><td>1.8856379e+01</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.07300</td><td>6.2365801e-04</td><td>338.95776</td><td>6.9719343e-04</td><td>6.0448773e-02</td><td>1.3230386e-01</td></tr>\n",
       "<tr><td>1354.1199110941275</td><td>2.4591127e+04</td><td>1.8846928e+01</td><td>-8.0610689e-04</td><td>91199</td><td>664.07806</td><td>338.96029</td><td>2.3098383e+04</td><td>1.7649052e+01</td><td>1.8459741e+03</td><td>5.1905088e+00</td><td>2.4591127e+04</td><td>1.8846928e+01</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.07806</td><td>6.2481815e-04</td><td>338.96029</td><td>6.9739192e-04</td><td>6.4667158e-02</td><td>1.3584568e-01</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>1381.5000762208806</td><td>nan</td><td>nan</td><td>-1.1857160e-03</td><td>110913</td><td>664.02023</td><td>338.82238</td><td>2.3102398e+04</td><td>1.8364481e+01</td><td>3.0264915e+03</td><td>6.2652044e+00</td><td>nan</td><td>nan</td><td>1000000000000000</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.02023</td><td>6.5423414e-04</td><td>338.82238</td><td>7.4187893e-04</td><td>5.3329854e-03</td><td>-1.7557999e-02</td></tr>\n",
       "<tr><td>1381.5014650890794</td><td>nan</td><td>nan</td><td>-1.1857362e-03</td><td>110914</td><td>664.02570</td><td>338.81828</td><td>2.3131156e+04</td><td>1.8370392e+01</td><td>3.0202869e+03</td><td>6.2575917e+00</td><td>nan</td><td>nan</td><td>1000000000000000</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.02570</td><td>6.5429986e-04</td><td>338.81828</td><td>7.4093667e-04</td><td>1.0951885e-02</td><td>-1.8822383e-02</td></tr>\n",
       "<tr><td>1381.5028539571613</td><td>nan</td><td>nan</td><td>-1.1857564e-03</td><td>110915</td><td>664.02563</td><td>338.82131</td><td>2.3093904e+04</td><td>1.8351555e+01</td><td>3.0234182e+03</td><td>6.2496614e+00</td><td>nan</td><td>nan</td><td>1000000000000000</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.02563</td><td>6.5500144e-04</td><td>338.82131</td><td>7.4103329e-04</td><td>9.7870119e-03</td><td>-1.7654052e-02</td></tr>\n",
       "<tr><td>1381.50424282536</td><td>nan</td><td>nan</td><td>-1.1857765e-03</td><td>110916</td><td>664.01844</td><td>338.82636</td><td>2.3070465e+04</td><td>1.8338472e+01</td><td>3.0037410e+03</td><td>6.2505035e+00</td><td>nan</td><td>nan</td><td>1000000000000000</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.01844</td><td>6.5486954e-04</td><td>338.82636</td><td>7.4021460e-04</td><td>2.8580690e-03</td><td>-1.0282305e-02</td></tr>\n",
       "<tr><td>1381.5056316934429</td><td>nan</td><td>nan</td><td>-1.1857968e-03</td><td>110917</td><td>664.02351</td><td>338.81538</td><td>2.3084883e+04</td><td>1.8339640e+01</td><td>3.0044412e+03</td><td>6.2367158e+00</td><td>nan</td><td>nan</td><td>1000000000000000</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.02351</td><td>6.5468432e-04</td><td>338.81538</td><td>7.4014551e-04</td><td>8.9326696e-03</td><td>-2.2021463e-02</td></tr>\n",
       "<tr><td>1381.507020561642</td><td>nan</td><td>nan</td><td>-1.1858169e-03</td><td>110918</td><td>664.02287</td><td>338.81223</td><td>2.3056941e+04</td><td>1.8327822e+01</td><td>3.0007908e+03</td><td>6.2351022e+00</td><td>nan</td><td>nan</td><td>1000000000000000</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.02287</td><td>6.5470359e-04</td><td>338.81223</td><td>7.4105512e-04</td><td>7.0573296e-03</td><td>-2.6359776e-02</td></tr>\n",
       "<tr><td>1381.5084094298413</td><td>nan</td><td>nan</td><td>-1.1858371e-03</td><td>110919</td><td>664.02458</td><td>338.81035</td><td>2.3082803e+04</td><td>1.8332623e+01</td><td>2.9834062e+03</td><td>6.2297935e+00</td><td>nan</td><td>nan</td><td>1000000000000000</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.02458</td><td>6.5470277e-04</td><td>338.81035</td><td>7.4060517e-04</td><td>9.5733264e-03</td><td>-2.9673917e-02</td></tr>\n",
       "<tr><td>1381.5097982979241</td><td>nan</td><td>nan</td><td>-1.1858573e-03</td><td>110920</td><td>664.01752</td><td>338.82169</td><td>2.3091609e+04</td><td>1.8332087e+01</td><td>2.9773435e+03</td><td>6.2250428e+00</td><td>nan</td><td>nan</td><td>1000000000000000</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.01752</td><td>6.5375940e-04</td><td>338.82169</td><td>7.3996367e-04</td><td>3.0533469e-03</td><td>-1.5633952e-02</td></tr>\n",
       "<tr><td>1381.5111871661225</td><td>nan</td><td>nan</td><td>-1.1858775e-03</td><td>110921</td><td>664.02862</td><td>338.81318</td><td>2.3086258e+04</td><td>1.8320450e+01</td><td>2.9649575e+03</td><td>6.2088137e+00</td><td>nan</td><td>nan</td><td>1000000000000000</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.02862</td><td>6.5425027e-04</td><td>338.81318</td><td>7.3958829e-04</td><td>1.3605391e-02</td><td>-2.5300540e-02</td></tr>\n",
       "<tr><td>1381.5125760342053</td><td>nan</td><td>nan</td><td>-1.1858977e-03</td><td>110922</td><td>664.01887</td><td>338.81982</td><td>2.3105682e+04</td><td>1.8324867e+01</td><td>2.9604985e+03</td><td>6.2097011e+00</td><td>nan</td><td>nan</td><td>1000000000000000</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>664.01887</td><td>6.5310486e-04</td><td>338.81982</td><td>7.3841790e-04</td><td>3.2073301e-03</td><td>-1.8903004e-02</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<TessLightCurve length=18300 LABEL=\"TIC 307210830\" SECTOR=2 AUTHOR=SPOC FLUX_ORIGIN=pdcsap_flux>\n",
       "       time             flux      ...   pos_corr1      pos_corr2   \n",
       "                    electron / s  ...      pix            pix      \n",
       "      object          float32     ...    float32        float32    \n",
       "------------------ -------------- ... -------------- --------------\n",
       "1354.1074113410245  2.4635420e+04 ...  3.1294446e-02  1.5483069e-01\n",
       "1354.1088002024744  2.4656008e+04 ...  4.3172963e-02  1.4587776e-01\n",
       " 1354.110189063866  2.4635619e+04 ...  6.0803384e-02  1.3428329e-01\n",
       "1354.1129667867635  2.4621027e+04 ...  3.7734102e-02  1.2694269e-01\n",
       "1354.1143556482134  2.4617400e+04 ...  7.8965843e-02  1.5301819e-01\n",
       "1354.1157445097215  2.4630531e+04 ...  7.2042428e-02  1.4030553e-01\n",
       " 1354.117133371171  2.4625502e+04 ...  6.8586096e-02  1.3917884e-01\n",
       " 1354.118522232678  2.4619252e+04 ...  6.0448773e-02  1.3230386e-01\n",
       "1354.1199110941275  2.4591127e+04 ...  6.4667158e-02  1.3584568e-01\n",
       "               ...            ... ...            ...            ...\n",
       "1381.5000762208806            nan ...  5.3329854e-03 -1.7557999e-02\n",
       "1381.5014650890794            nan ...  1.0951885e-02 -1.8822383e-02\n",
       "1381.5028539571613            nan ...  9.7870119e-03 -1.7654052e-02\n",
       "  1381.50424282536            nan ...  2.8580690e-03 -1.0282305e-02\n",
       "1381.5056316934429            nan ...  8.9326696e-03 -2.2021463e-02\n",
       " 1381.507020561642            nan ...  7.0573296e-03 -2.6359776e-02\n",
       "1381.5084094298413            nan ...  9.5733264e-03 -2.9673917e-02\n",
       "1381.5097982979241            nan ...  3.0533469e-03 -1.5633952e-02\n",
       "1381.5111871661225            nan ...  1.3605391e-02 -2.5300540e-02\n",
       "1381.5125760342053            nan ...  3.2073301e-03 -1.8903004e-02"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lcf[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this table, you are given the time and the flux for your object of interest.\n",
    "There does however seem to be three entries for flux: flux, sap_flux, and pdcsap_flux. \n",
    "By default the flux = pdcsap_flux, but what do these entries mean?\n",
    "\n",
    "- **Simple Aperture Photometry (SAP)**:  The SAP light curve is calculated by summing together the brightness of pixels that fall within an aperture set by the *TESS* mission. This is often referred to as the *optimal aperture*, but in spite of its name, it can sometimes be improved upon! Because the SAP light curve is a sum of the brightness in chosen pixels, it is still subject to systematic artifacts of the mission.\n",
    "\n",
    "- **Pre-search Data Conditioning SAP flux (PDCSAP) flux**: SAP flux from which long term trends have been removed using so-called Co-trending Basis Vectors (CBVs). PDCSAP flux is usually cleaner data than the SAP flux and will have fewer systematic trends.\n",
    "\n",
    "\n",
    "You can switch between fluxes using the following commands,\n",
    "\n",
    "    pdcsap = lcf[0].pdcsap_flux\n",
    "    \n",
    "    sapflux = lcf[0].sap_flux\n",
    "\n",
    "Let's now plot both the pdcsap and sap light curves and see what they look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = lcf[0].plot(column='sap_flux', normalize=True, label=\"SAP\");\n",
    "lcf[0].plot(ax=ax, column='pdcsap_flux', normalize=True, label=\"PDCSAP\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some big differences between these two light curves, specifically the dips in the SAP light curve and its overall gradent. These differences are caused by scattered light and other noise issues. For more information refer to [these tutorials](https://docs.lightkurve.org/tutorials/index.html#removing-instrumental-noise). For now, let's think about how we can manipulate the light curves.\n",
    "\n",
    "#### 2.2.1 Manipulating a light curve:\n",
    "\n",
    "There are a set of useful functions in *Lightkurve* which you can use to work with the data. \n",
    "These include:\n",
    "\n",
    "- [flatten()](https://docs.lightkurve.org/reference/api/lightkurve.LightCurve.flatten.html?highlight=flatten#lightkurve.LightCurve.flatten): Remove long term trends using a Savitzky–Golay filter\n",
    "- [remove_outliers()](https://docs.lightkurve.org/reference/api/lightkurve.LightCurve.remove_outliers.html?highlight=remove_outliers): Remove outliers using simple sigma clipping\n",
    "- [remove_nans()](https://docs.lightkurve.org/reference/api/lightkurve.LightCurve.remove_nans.html?highlight=remove_nans): Remove infinite or NaN values (these can occur during thruster firings)\n",
    "- [fold()](https://docs.lightkurve.org/reference/api/lightkurve.LightCurve.fold.html?highlight=fold): Fold the data at a particular period\n",
    "- [bin()](https://docs.lightkurve.org/reference/api/lightkurve.LightCurve.bin.html?highlight=bin): Reduce the time resolution of the array, taking the average value in each bin.\n",
    "\n",
    "We can use these simply on a light curve object. For this tutorial lets stick with the PDCSAP flux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PDCSAP light curve of  L 98-59')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = lcf[0].plot() \n",
    "ax.set_title(\"PDCSAP light curve of  L 98-59\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Flattening "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j6N69O5YsWYIGDRrwuPSAhmf83evXryMkJKTETCJ60LrHa9euRWxsLBdzpE2bNqFLly6m6+EF7/ehqKjI732wAzNmzMBbb72lWS47O9sHvQlumjdvbptY9lKMjt92FIp88Z26XC7k5eUhPDycW53PP/88N2WNVSg9b6mvh5gRI0ZwvU9GcTqdcDgcCAsL83dXuBKQmn2Hw2H4Q928eTPCwsLw1FNPeRxv1qwZsrOzPUxS9JSVCvoAEBYWhurVq6s6LtmVwsJCr3CZOTk5tgmh2bp1a0XTq2DFn0Lk+fPnddtRNm/ePCjtbbXMxa5du4ZVq1Zh5syZPuqRMjzfmX///VezTM+ePbm1V5LR89yCcXEZLOzbtw9NmjTRdQ6vOdaqBZZV9RYUFJiaL5RMf3bt2oVdu3Yx1zNq1CiMGjWKqawdF7FKBKSwb4YTJ04gKirKrZEXiI6Odv9upKwc165dw99//43q1aurlnM6nUz/fOlA0q1bN69treeee842iVEuX77s3vIrSfBcbOm5f+3atVPMwqokbFy6dMmQZlyJQYMGaZaxevA9e/Ysk+3mgQMH8NVXX3kdb9asmep5evqfl5en+T6w1FdUVMRUrkOHDiRY2pxz587h2rVrfmmb3g1v9CpIrl27hscee0zxd9bxwepnYaZ+q8ZoJd/BuXPnYt68ecz15OfnIy8vT/a3S5cu4a+//grIhHclTtjPzc1FRESE13HhmBAnVm9ZOZKSkpCfn49XX31VtVxCQgI6dOig+W/JkiWa18eLs2fPIicnx+OYy+VC3759TdUbSCthMVOmTPF3F+ByuZicUaX3+NixY/jhhx+8yunROKkJlUJ7WVlZzPUZ4bffflP93RfChlkHbRbtOCtxcXFMjmxa9+X7779niirkdDoVJ0GCP3JjpXRMltKpUye/JGRS4p133sGFCxf83Q1VpKFJ/blo4akcMctnn32m+b7pxei91Rs1T4oeuUOtj82bN8fChQttsWurlxIn7PuK+fPnY/369ejatatm7Nbk5GSkpqZq/nvllVd81HtrED6ic+fO2cLmUM8AkJKS4vH3sWPHeHfHMrZs2YJJkyZ5HLNi0SUW9vVqPliczpQIJo2inmu5ePGil/DdqFEjnD17lrkOPSaGLpcLgwcPZi5P8OfZZ5+1vA2xo6RZtm7dqujHYReBSY/W1yhFRUV+N2mUzgHAzXCTSiYuixYtMhx1ifeYrDdqnhkcDgeKi4tx+PBhnDlzRrWsMI9+/PHHvuiaKUqcsB8RESGrkReOiTX5esqKWbRoEVJTU9G5c2e0atVKs0/h4eFM/0JCQpiukYULFy5wEfiuXr3KHPJSaG/FihUYMmSI6bal9foCoS0rFl579uzRfc6YMWO8Yl4bzWdw/PhxTJs2TXcfBC5duoS9e/cCAEaOHKnr3Oeff95wu8GI0jt96dIlnDp1SvG8oqIi9wLA5XLh6tWrqt+HnqhBLpdLVmD5999/kZeXZyutpFU4nU7ZXS4rQvL6ehdUMBMdNmwYxo0bJ1uGpxAnZ+ZmB7744gtD56k9r5SUFLz77rtGu2QIaX8WL17sVebIkSMezuA///yzpYsSNQdd4Xdf0atXL3eUMTFCIJFRo0bhm2++Yarrp59+4tw7/pQ4YT86OhqnT5/2mpgE+/saNWoYKiuwaNEiLFy4EJ06ddI03/EnLVu2VI3Dyxo5Jycnx7R50YYNGwwNMEL/Zs2aZahdu2mEtaKdyPX32LFjOH36NJf2Dx06hLlz5xo+/5dfflE18yooKDBs6mNG86+HoqIiVVMbo5PR3LlzuTjqz549Gx999JHHseLiYg9t/uHDh7F27VqcPXtWM5wcD959913Mnj3bcMr5QCIuLg4rV670Oi4OAT1hwgT06tVLd93//vsv2rZtC8C3Y5MghD7++OMAbn6nPP3DhIy7evCFFlypT1bs2ubk5Pg0UIfw/ixevBgzZ87E5cuXmc47cOAAc1neWPXOK9W7bds22fnKbnIBL0qcsB8bG4u8vDwvR9O1a9eiQoUKqF27tqGywM0Pa+HChejQoYNssp2SjHhFLx1kBwwYoMv0QIqw1SjUO27cuICMgCTFaGhMcbbTc+fOefx29OhRTe0KD4T6xe2sWLECL730kqH6jGr+WTUzAr///rsluwzTpk3D0aNHudcL3FyktWnTxv332rVrmX1MeGjSrl+/rqrVX716NT799FPT7fgL8T26dOmS5qLm22+/xbZt23S3c+3aNQ+zAaVn88MPP3B1ENyyZQu3usSYSbwll0gL8L4ndhLM/NWXRYsWyR53uVz4888/8ccff3DZdeNtv+9L9I5zgepbqEbACvuZmZnIyMjA9u3bAQAnT55ERkYGMjIy3IPx3r170bZtW4+PISYmBg899BBmzJiBtLQ07NmzB9OnT8fOnTuRkJDgEXlHT9lly5ZhwYIFeOSRRxATE4MDBw54/LM7SUlJPnXssmpgvHz5MpYuXYrz58+7ty15xJOX46+//rKkXl6cPn0arVu39jiWnJxsqC69ORLknm9xcTGTucOoUaO4ZVCcPn26rvKFhYW2HOhXrVqlOI74qr9G2zl8+LBtonj5m+PHjzMlO3I4HB4mW/n5+e4dp3nz5uH33393l5V7LsXFxbozivOmRYsWltXN4523Wji/du2a18LwwoULmjvqevnss88061GqV7iPRUVFmpGDnn32WQ9FkpXwejbr1q3TLX+xzHV2nCO0CMikWsBN5x6xSYAg6APAnDlzEBoaCuDmoCd9MEOGDEFKSgoWLFiA3NxcREVFYdCgQbLRSVjLCpqcnTt3YufOnV712N2ma9u2bYiOjsbLL7/sPqbnhX7xxRcxdepURWdk6cfL82MROyn+97//df//pEmT0LFjR27tSOncubMtHI2VkBuYhR0Pufsv7K6cP38eVatWZW6H96S5detWVKpUyf23nEnEiy++6F7MBeLAq5fp06ejZcuWhs5Vej75+fm6IqVI73Pz5s3dizK1Z8AzmV6gc+LECfz4448YPny4ajnhma1YsQI9evTA3LlzmZNHulwuZGRk4P333zfdX7m61Vi/fj2OHTuGhIQEj+NWC9ZJSUm2y/EwePBgr92NIUOGwOl0KmrjeSJ+Vlr3X2y6o1b24sWLHr/fuHFDc9dgy5YtbkuIq1evYvXq1bJJ48T95TFeXL582R1Q4L777mM+TxivXC6XbDhPO+0m6SFghX0W55569erJCtlhYWHo3r07unfvrlkHa1kz29T9+/dHqVKemywtW7Y0PLnzQO8LnZWV5eXMHBMTg3bt2nnUacWkn5aW5q7fSmbNmoVOnTqpllm1ahWaN2/Orc1Dhw6hbNmysv4hehDujZotrKAFb9WqlaFFjNazdTgcKCoqQlZWFsLDw1GuXDmmeuVMIrKysvDFF1/gtttu09UHswST0Lp8+XJdIe2k165mKicVNMR/jxs3Dh988IGOngYmOTk5Xu+nGmrvVmFhoaYdfXFxsfv7NpugSAuHw4H8/Hy3Uk1gx44d2Lp1q5ewb3WCpzlz5jAJ+2+99Ra+/vpr0+3269cPn3/+OYCbpoJnz571msOdTqeXtnz37t26BE81hEyvcvhKIO3bt6+mWduUKVPw8MMPY/DgwSgoKFA1Z+TZb6VIOsuXL8eDDz6o2gfhvfrzzz+59cffBKwZTzCRmJiIGTNmePzzhaBvxqHuyJEjTI6hcvHdAfZtRjXMTiB6z2dxBB46dKj7/5XsTvUwYcIExMXFYeDAgYpl/C2AyrWv9jw7duyoGHYvLy+POUKM0K6dNS16MjcC8rsx0t9Yj7O2JUYteo/ccRZ7b+nzWbp0KQoLCwMyeo+eey0XJlO4F3qjVSnx22+/uceldevWuYVLsfnam2++6XGOGX8yof9FRUVemeVZz502bRomTJhguA9mMBLxTA6xGdXJkydx5MgR1fJq/gv9+vUzNBePHDkSY8eOVS0jhFCV+k7xWgga9ctQMq01M6aznvPZZ5+pjlsOhwO7d++2vZmuXkjYL8EY3UosLi7GqFGjdGlI5EybeONvoVcKT83aqVOnZAczo4Ku1iRhBNaQn3KmdQJ6Mk7qXWT4AyVTijVr1ngtalj6zvKOm3HAbtq0qa5dnYULFzKVKywsxPr1691CUf/+/bloWP0Bj3dMLhu1noWh0Iddu3bh119/BeCZEVusiNm3b5/HuSwJ1MzYLGstFk+fPq2oKDpy5Ig7fK8cSkEe7IJSTgG1cLm///67IWE/Pz9f87wNGzZg586dHu/s8OHD3SbQeu+jleOr2bqNvJNKSOchpZDUdn0P5SBhvwQjjRettvUuZs6cOfjzzz+9Pgi1j1VuYv/tt990ZRS1IpY1C1qJNXwFy8By5MgRDBs2TPF3XwnDWu0I1/Liiy9yac+ukSLEtrDi5/fBBx/ojoLicDjw66+/Kjr4HT9+XHd9crz99tu6M/2qhVd0OBy4fPkyBg4c6Da5u3DhAq5cuaKrDbtgZoJX+y54afvNwjJG6DEfkd6v3NxcxXu4YMECy7OVjxkzxuuYmfwiAi6XCxcuXJDNw8AqzLOOmyWdwsJCXYE3cnNzmeUHuWcgDkmtRyFlJ0jYL+G4XC7k5eXpztwJmE/tPWjQIF0h5KQ2qw6Hw9QCgPWaxdpZnoPtoEGDNMsotTdixAhZE6lTp05h8+bNHucK/128eLGqhokXSpEdhElWfN+zsrKwe/dur7IFBQW6hGFfZBb1Ny6XC7m5uYqTDc8kRWZj5ttth4UXZq+L9Xy5coIDvR38UuT6JzZhVMNIWFKeLFu2zOuYNESxGfxhkqpVJ6/v0S7O9nJjoNo17t271yPCm1ZAATFS/6K1a9fC5XLh9ddft8W9YIWE/RLO9u3b8cwzz2D37t3Yv3+/rNbDSszaXauFMTMLD7ti6WCwdOlSt9b0t99+M1zvnj173MndWHA4HJg0aRLXhClKDoOpqam6MrOuWbPG69ihQ4c0M07yGminTp2KBQsWcKnLDlgVi1zPBCnw7bffBrzg788J3eFwYOnSparPVBw+Ufy3Ufbv3y/7TYox0obceyCnnWU1VTMLz/dyz549qqaqWm0dPHiQq8ZYyydOsEeX8xHasmWL5rwn1GdV7hAeSK9ZmK+cTqfiTrDc+6j0LRw4cMDv4W31QMJ+CSc/P9+tHf/7779ltR48UBrshNBYRrFyIu7bt6/huPQCK1as8Ph73Lhxms5cYpRsVOUme1/zyy+/ePwt9EM8AC5btsxSTR6Pa9+zZw8OHTpkWT4GXvASTi5dumRoktJjEyv8PWHChIAX9sUYfd+EzLisiO/ZuHHjmPx/tAR0gC0TNcu7oXYfpBlorUhoJFemsLDQy5TN5XKp+gfo5dKlS17jhNAXqbPquXPn3Cag4v6KQ0ULvPvuu+6cQXLImQaZ4cMPP0RRURGuXbvm9dvo0aM9IutZkWTQ34j9W6ToGa9YcmbYBRL2CVl4OQsK7NixA5MmTeJq75qTk+PhEMVT6J09ezaysrK87IqlwrsW0nCkUgoLCz0iO+jB5XJ5mEH99ttvzA6TAmYyDQvaH7V3ZfHixYavzwinT5/WbW/uK/QKvYWFhR65RPRE6FGjV69e+PLLL3WfJ4cZ+27g5jdsZ+3Y+fPnvUwFjSxeBKGPNZSfnjbEEXK0kItvbgap4Mvab7X3liVijpzwu3PnTsTFxXkcu3btmkf4ZzFGHMRZtO/CtYnHVvH1CosPl8vl8cz69u2reF9WrlzpdUwrUZ147pK7X6tWrcI777wDwPu5jRgxwt1HlgWiFKMLfLnr5/XOiq0WBJM4OVj6rjfKmh0gYd8G9O/fH7169fL4J9Wa+hojQsT8+fMVoxEYRc0c5LPPPjO1M6B2jUrCkJ645CycP38e/fr1U/xdye5SbkDasmWLrA/E9u3bFTUZM2bMUO2fHqdBM/bIeusAlHc7zNqbS/njjz+8TJb0fB9azsNy99jlcmHNmjVuB2Yz2nFei2A1B9w//viDyf9GajYwdOhQUzlKrKZVq1amzO2k6F34yoX7VdpFYUErVr8YI3bscuZwwbSzYxS5e7Bt2za0adPG45iSEKlnQbx9+3b88MMPHgnz5Gsxp3AAACAASURBVN4R8Tgp/V26G8KSfK9Pnz7MfVTi5MmTAP5nFw943pPr169zGd83btyo+JuebySQIGHfBvgrzj5v1q9f77bh0/tBOhwOWYdTuUg4gpZCqhkJFHr27In9+/ebrkfq76Bkp5menm64DbkQgdL6pf3RgpeTF8+dHLW6unXrZso3RGt3R+kei/ukJmizHGM9Vy9CHQcOHMD8+fN1n1tYWMjdRIEHP/zwg3vSt5sT3rVr12THDxb/JzWNppTWrVsr/mZmUW4GoS7BBCdQFhJy/czNzfUSonnMZxs3bjSlnBAj9JtFHsnIyHD//5UrVwy1KYyz6enpsvciMTFRNdqcQGpqquF3g8UcLhAhYZ8wzNq1axV/05twBQAmT57sdUxOKyrEiHc4HLoEBb2aB5fLhfPnz1syoUgXMUrhPdUEeamWVFqmffv2XPqqhRXCkC8ncSNtnTp1yhJbZCNI65VOkuLrY9HQSc9Ro6CgQNd12U1wVmL06NFuMwgzfY6JieESMlj8PP755x+3H4zR7+TMmTOmItD44zkWFha6n4nwHsstpD/66CPs2LHDZ/2Sc3KVYvQ5ZWdn6/ItOHLkiKzTrNbzUlLcsDr7S+evlJQUHDx4EMDNvA5GTIHkOHfuHFNdSjvwgagc5AUJ+yUcPfGSeaFnohg9erTXMcG0x+FweHy8Wn0Wax5YKCoq4rKFL2eKJF2kKDnwie+V2ETKau34r7/+6uVopwVPO00lxBorl8t48igx+fn5un0x2rdv77HVXVhYyNV8aOXKldiwYYP7b4fD4bX1XFxcjNTUVK9z1UxFeO8Ybt++XTMiR6BoX6XwSuAk5wTJ2rYW8+fP96qfpb/jx49HYmKi7n7paUOgqKgIJ0+eNB1UYPXq1V5J6uTqSUtLYw4xzBo17KOPPgIg/1xYxg65fkpDOgLeTr6pqano27ev++9Dhw6ptvPFF19g69atXsd57Z7J+Q4AwJIlS7yOiedmI5mL5e5ZRkYGdu3aZdinUO/uox7sbv5Dwj6hG19qdcSD1JkzZ+B0OlGq1M3X1qgZCaCe0ZAn586dQ6tWrbyOs2iDpOXFGkK5azejzZJGzBkxYgR+/PFHADd3cMQDt5ppj9W89dZbXsekWqXZs2dj1KhRzHUaDXknfgZff/21od0sJZYvX+61cyan4RML2lrvklhosJrXXnvN/f+BKuxLMZoRe/r06ap18kouZtf7rGUOtX//fqZ7oOf+ly5dmqlcUlKS6u9Dhw7FmTNn3Mng5K5BLTmlXsaPH89U7rvvvtNVr1yfWBZf0ndK79gvzLN22dHTMqk0Aw+fBSshYZ+QRW2rzMyHq3SuOGZ8fn6+bHiy+Ph4LF++3K2NMbMl99JLL3kNZGZDjMk5UVllj3z27FmPBUNBQYGpmMe9evXy+Pv69etISUkBcDNWupj169cz16tHA/PGG2+4zbaE86STsdT0QM6u/cyZM7qzybKi9P6qaW/VkoMJC5XCwkLD5h5dunQBAI+kMXKoxQGXYuQbF58jdghXegf0Lnh9jbRfly9fhsvl8tK+aqH2XBctWuR+fmrwjo6mBKu5hZFoQWLEfe3SpYumnbSayShrm3KRx8T9OHDggNfvq1atMmTuJA26wHshJuez8c8//yjmXuFlvqJn99LlcqFDhw6qZYYNGya7O8lSt90wE9nOF5CwXwLp3bs3APUBSE2LYkVCE7HWcfz48Rg+fLhXW7wTeUi3hKUTnRAZQI38/Hw8+eSTANQndSliJ2Mx0ogmSs8oPz/fLwlNkpKS3H0ysjUrIPcO7d+/Hy+99JLHMb2mRICyidOCBQtkBW+5e/zhhx96HcvJyVHc9lf7ltSizQhaz9jYWI+FjdY3psfRUgkWAURNK22mHRZnUrsgXbBLF79muHHjhqxig3cWVlYh3oqkikqRaARcLpd7t1aMOCLdzz//bEgoFCO32ye+Z8uXL9ddp1IWdKvDDct9PwsWLFCcs3jZ7CvlLFBCkCOU3ueDBw+66zSzUy+H3BhekiFh3wb4OvTm5s2b3f8v9xEKYSeVPjY1bfWoUaNMb5U5nU7k5eV5aSmMfPxyGg292VKFbINyFBYWus1AhHv5zTffeJWTal2VriUzM1O2nJzQu3v3buVOW8ScOXPc/y9s6Yptm6Xb8UajxbDYS+uJUAPc3PqWS/AlLi9sO8vFQ09OTnYvlFnbFCMnrEjjggtovevx8fGa7QkI76daIhklfv31V93nqCF+n0+ePGlLDZ0Us31Uezd++uknU3WzYnTXsqioSFbxo+eeKJldipELRCDEegduOuQePnyYuU095pF6kF4Dz7CsSrhcLsV+CsdPnTrl3omVQ0uzb+Qd52USw9q2nL19Xl4e5s6d63FMUOLpyeJeEiBh3wbYLfSmYJ+ohNrAcebMGW6e9y+//LLXMb0ar0mTJgHw1EhItaLdunUz0DtvhL6Jk7UIpj3i5EiAsubESGQTuwhMehyUtGxOjWg2eWiJv/jiCwDe91TLrl9tohUQtt7VtF287LfFfgzjxo0zVMc///yjGCWKB4JJQCBo982gFiNdKwqXGtJdF733UW7ckJroLVmyRDapkRBpRU87LNFqjCiY1BDGpEaNGhk63why13Do0CEPrTtreEfWMXXfvn2q87Jw/8S+auL3RSkRodqunJacwIKed1bOROnixYuYNm2ahwmNmsmXHcP8+goS9gndmPlgWARTvRpbtfME+1pxFkVp/1mSAelB3A8lzZ2g6RdrrwD5JDos7fgSrefAeg1DhgwBYGyBo3We9LdLly6hoKDA8EKUh4ZbmJC++uorAPZZpMnhcDgMO6TK1VWSWbdune5zWKKGSHcQ9bxPrM8kJydH1kdBKyrMu+++y9wXPf1RQ+76hR2NoqIi2d9ZxxPg5q5r586ddbWvhFwkHimFhYWYN28eU1usEZ+kZpICUh8DFsdasyFlBXt+l8uFCxcumFJ8NW/enKkchd4kSiRGhGfAnLAvDBBazixymh5ewpFaPXLRXljr0hOqT7Ad15o01Z4Rj6Q/PM0ItBwxpaxevVqzTrVrU5rgcnJyvM5r3rw5Lly4IHu9clFuWO6p0L7e70HQaPsqIpQcxcXF3LVcSg7ASlE/eMSftwKhj6+//rrH32L+/PNPPPnkk6a+PeH9kftueGci9zWsvjHS35TKWOX8yPL8hJj+a9asUd1588fiXbhfcuE2tTD6LQptmh0//v77b11Ju+T6IL4Gll0QuWck7P6bxc7KG4CEfUIGrUWAkse/gJbZgL8/CrX29+7dq0sIk/N/kItYoEcQFjt3mdkJ8RVa7UttO3k5Yq1fv142SdSBAwe43xO5+gTt5WOPPWaoTqmtqRTxfZLeMyOx2wWOHj2K1NRUt8mS1Si9+3v27MHVq1fduyZ2iVMtmKhIBUzxTsfFixcNh2wV0DPOsCZD04Ovxg2WdrTmHCUzEyU+/vhjJpM4wRRFTfEg3X1VgvcOlvj9kL6LvBfK/ppDjhw54hWBzSgsiw+569STtCyQIWGf8EL6QYhjZgPa2mg5J0hfo2aCoDUoKG11ymEmIg0gL+CIt1RZBSBfDtbSQVktioXL5eLubC5eTCkJvVYI+9I8BkYFbt6RVowiddj1hTZZ6tsgtmcXMmP7G6VvTpzhWy56jJWwmJHpCa0K3FxA2CVc4JkzZ7gLsLzfZ70RbXi2JzXZEhLsCWWMOApLszJrlbEK1lC2Wv5Dixcv5tGdoIWEfcILafguPc5YvLEyEgaP8gL//vuv6rlChCO9yMVT9jfSZyJkVeSZQVepritXrnBJXqUnV4DQH+mz4GXTLse+ffvc/z916lTL2hHTvXt39///9ddf3BYYau+FeKzh5ZxsFiXbYfHiTilpk1QxYmcOHDiAadOmaZZzuVxo3bo1AOD777+3pC+zZ8/GunXrVJ2ZtdAafzZt2qSrPmlIVK3vwSqFi1X1sgjZLE7VSrz55ptMZaUR6JTgoURctGiR1zFfjHN2gIT9EkxaWpqqZsdfW3tmt8e14HldQl1yCVnE6ElGYneUHA6N3le586x+BwYOHGhp/WYpLi52Tx7z5s0zlNhHL2KNttb9CXSbcj0IO4GC5vzKlSuK+Qf0KEYEn4BLly5xDxKgRUZGBlM54R08d+4c1qxZYyi3B+siLjEx0ZAzs4CR0LJqSJ1ojdjF80AayQ3gM4fpCQZhFit3PexUl50p4+8OEDfj7Eu3hVu2bOmT8Jta27j+sKM9evQoatasCcD3q2Uz7Ym3Qn3pfLh//36fmE6pRUlSigUtl4SF5R6zanuUMDuAWzkBsNpf23kSGj16NPc6N27ciB9//BFt2rThXrceWKKCaPkt6eXy5cvc6tKb+EiN4uJi9xzBEkFGDiHx1Ny5cxXzVADmnXDNLkClz1nqa2RlGFq9SHO7aGF0LLGrtloIXWrnMdJuGBb2zazApTz77LPc6gpEEhMTER4e7u9uyCIMKr52YtEKkyaHENIQsF4zLCCOIiEO7ymYtviCM2fOGDYT8gd6HHS1bFHloiEB6hpWVhtRMWazd4rhnajK7ugRGNauXet3YZ8FuwpBgHxCOCW0xlTeSovt27dzrY8n0twfeq/dF0EBBHwVQtKfJi5qikYhGzKPRbcvovzZAcPC/uTJk7kNeHqFfafTidTUVBw9ehRHjx5FTk4O4uPj0alTJ6bz8/LyMH/+fKSnpyM3NxdRUVGIi4tDkyZNDJc12yd/oRUOTXiBd+3a5asueSD9gNT6K9ZuG7X91DuICv2TRov4+OOPDbVvFCGigRVIHVN5Y8YOWJwlUfyuqDlht2jRwnB7hH7sLBizUJJjc/Pm7bfftqzumTNncq1PT8ZegH+SQ5YMtaxtGe2TmtmW1d/10qVLNcvs3bvXdDt2F9J5YcqMJzo6GrGxsYbP37JlC44fP677vNzcXKSlpbnbZ4nXLWbs2LE4fPgwunTpgsjISGzYsAETJ05EcXExmjZtaqis2T75C7UPVmya4WtzHrMDia8/YF6xeo1SUlODiydkacQKuTKsWBHqsKQSaMK+dOz4/PPPvcoI19SjRw93cqBgJDk5WbNMTEyMD3qijTSijJk5oEuXLj6PuCSFJbGa1QiJD8UIixCr5zwWmWPZsmWW9iGYMCXs33PPPYiPjzd8/oULFwwJ+1WqVMGiRYvgcDhw5coVXYJ1ZmYmdu3ahYEDB+Lpp58GANSvXx9ZWVlITk5G48aN3ZEW9JQ10yc7IwyYPLdzrciYaqYNM2glgxEjje4QLAj3mtXpz9f40hHt2LFjPmsrGAmUhYHQzxMnTuDvv//2c28IOTZs2GD43D///BOhoaGqZXbv3u3xN+8gDCzO0P6IEW+VIkQa0ptlDucRwStQxhyzGF66hoeH45ZbbjHVeNmyZREWFqb7PIfDYfgBbd68GWFhYV7h+5o1a4bs7GyPF05PWTN9siuHDh3ivjXJgsvl0h0mTa4OX6DnmT/33HPMZQNJU2/3bVAjMaiN8sorr/isLaspKSHpjCAs3O14bXp8Uuz+7ZrBbNI4Lc2+mcUEC9IQ2HIEyg4ky3ciddD2RQSykoRhzT6PBAa9evVCr169TNejhxMnTiAqKsorTnJ0dLT797p16+ouawbWkGEhISEICQkx3Z4YtY/Q6XS6JwOek9rs2bOZy/pKG242bCRLJAg9cdntGKpTK5kawRc7CpICwWzHrncs4LFzZCa+vBQ92Wbt/hx5BgLRi52/PwG7Pz+B3NxczYhTYh9BAPjpp5+s7laJosSF3szNzUXVqlW9jkdERLh/N1LWDAkJCUzlrHD4ZbXZ92eGVl9g1ASFZ8g8Mf62F5WDl32kr57v5s2bfdJOsKDHGV6P+aWexb0d8Ie224pQpizY3dx08ODBfmvbaIbsYN4tEdA7hufk5GDAgAGqZfx130rC8wJKoLBvR5KTk5lCb/LW6mvhcrncuw6+NIVQQ+8gw9rvX375xUh3LAuj2L59e0vqLUmoxfQmvDl79qwl9dolM64RpNrGQMaOu4WEb7DDO6wWIY2wnhIn7EdERMhq5IVjgtZeb1kzhIeH2zLO/q5du9yx43mEuGJFbWCyKsRkSYt9ThBaTJ06VfE3OyUYspJgEvZ5Jtsi7CFA26EPrNjV5CgQzLV4wN1W4NSpU+jatSvvarkRHR2N06dPe714QnKGGjVqGCpL+IakpCSmcoE0CBKEHTlw4IC/u+AXxL41JUUQIAIT1nmOt6BtxGfTrnOyXfvFG+7CfmFhoaEMlb4iNjYWeXl5XtFe1q5diwoVKqB27dqGyhL84PHxlZQPOBDxVXZjf3H77bf7uwtcKKnfkJ7rlkYQsTMl9Xn6Gl9HrmPBLma4doQ1QEqgo9uMZ9GiRaq/SxNbWEVmZiYKCgrc0VpOnjzpdrJs2LAhQkNDsXfvXgwdOhQdO3Z05wOIiYnBQw89hBkzZsDpdKJatWrYuHEjdu7ciQEDBnhE3tFTlrVPhDY8tGk0sdkXXo7thLWcOnXK522mp6f7vE0p4rHDrqYHBKEHnnlygg1esoLdZQ5Dwn758uVRpoz8qb56qWbOnImsrCz33xkZGW7Bes6cOW7Buri42OshDBkyBCkpKViwYAFyc3MRFRWFQYMGoUmTJl7t6CnL2ic7MX36dH93wYtAijFPEFIC2SGVsP+kTRB6scM7bdfQzSVlQa9b2K9cuTISEhK8Ek0JHD16FP369TPdMS2++uorzTL16tWTjdUaFhaG7t27o3v37pp16CnL0ifCN6Slpfm7CwRBBCB2EIys4MaNG/7uQonAju+PnhwvJY2SEiVIt7Bfs2ZNHD16VFHYD6boBb6if//+XnHVW7ZsiZYtW/qpR4GP2Qy8BOFvSorGSUpBQYHp7OxmCNb564033vB3F4IKpVCmPBOk8ULL/LokU1IsCXQL+y+99JJqVtNq1aphzJgxpjpV0khMTLRl6E2CIAhf8/3337t9rAjCruzatUv2+IIFC3zcE8IMvAJG2D1yl+5oPA888ABiYmIUfw8NDUW9evVMdYogCIIomWzfvt2v7QerZp/wDSkpKf7uAqGDkvK9cw+9SRAEQRAEEcyUFCGRYMPu74NpYf/KlSuYMmUKj74QBEEQJZyNGzf6tX27T9oEQRB6MS3sO51OrFu3rsR4NBMEQRDBCwn7BAt2t9EmCDFkxkMQBEEQBKEDWhQSgYTuaDwEfyj0JkEQhD2gXWqCIIINEvZtAIXeJAiCsAeksSVYoPeECCTIjIcgCIIg/j8kxBEs0HtCBBIk7BMEQRAEQRBEkELCPkEQBEEQhA4oGg8RSHAR9mk7iyAIguBFSkqK3+YVms8IFug9IQIJ08J+REQE4uPjvaLJEARBEIQRSpUqhREjRiA/P9/nbZMQRxBEsGE6Gk+5cuUQHx/Poy8lFgq9SRAE8T9ee+01bN++Hf3798dHH32EyMhIn7VNwj5BEMEGhd60ARR6kyAIwpNHH30UUVFRGD16NDp37ozY2FiftEvCPkEQerH7uMFN2P/nn39w5swZREZGonz58u7j58+fR0pKCo4fP44qVaogPj4etWvX5tUsQRAEEaRUq1YNn332GcaNG4dDhw6hc+fOljtG2n3SJgiC0As3Q/ulS5fio48+wrVr19zH8vLy8P777yM9PR2nTp3Cjh07MHToUJw/f55XswRBEEQQExoaihEjRqBMmTIYPnw48vLyLG2PhH2CBXpPiECCm7C/b98+REVFISoqyn1s7dq1+Oeff9CkSRMkJSWha9euyM/Px/fff8+rWYIgCCLIcTgc6NSpE9q2bYsBAwbgzJkz/u4SQRCEG7uHYuUm7F++fBlVq1b1OLZ9+3aULl0a3bp1w1133YU2bdqgZs2a2Lt3L69mCYIgiBJCTEwMhg0bhjFjxmDLli2WtEEaW4Iggg1uwn5eXh7CwsLcf7tcLhw8eBD33nsvbrvtNvfxqKgoXL58mVezBEEQRAmiWrVqSExMRFpaGubNm8ddOCdhn2DBH2FhCfty/Phxf3dBFW7CfsWKFXHhwgX334cPH4bT6US9evU8yhUVFaFMGQoCRBAEQRgjNDQUw4cPR0hIiE/s+AlCSnZ2tr+7QBDMcJO669Spg40bN2LLli2oX78+vv32WzgcDjz22GMe5U6dOoWKFSvyajYooDj7BEEQ+nA4HIiPj0dmZiYGDBiAIUOGePiMGYU0+wQLBQUF/u4CQTDDTdh/9dVXsXnzZnz66acAbg6Y9erVQ926dd1lLly4gFOnTuH555/n1WxQQHH2CYIgjBETE4PIyEhu8fhJ2CcIItjgJuzffffdGD9+PH788Ufk5OTg3nvvRfv27T3K7Ny5EzVr1jQ1GDudTqSmpuLo0aM4evQocnJyEB8fj06dOjGdn5eXh/nz5yM9PR25ubmIiopCXFwcmjRp4rOyBEEQBD+EePzjx4/HwYMH8cYbbxiOjkHCPkEQwQZX4/l7770X/fr1U/y9RYsWaNGihak2cnNzkZaWhujoaMTGxmL16tW6zh87diwOHz6MLl26IDIyEhs2bMDEiRNRXFyMpk2b+qQsQRAEwRfBjj81NRXDhw/HkCFDPIJGsELCPkEQwUbAecpWqVIFixYtgsPhwJUrV3QJ+5mZmdi1axcGDhyIp59+GgBQv359ZGVlITk5GY0bN0bp0qUtLUsQBEFYg8PhQMeOHU3Z8ZOwTxBEsMEtGo+vcDgchrdnN2/ejLCwMDz11FMex5s1a4bs7GwcOnTI8rIEQRCEtcTExGD48OH49NNPdcfjJ2GfIIhgI+CEfTOcOHECUVFRXlr26Oho9+9Wl5XD6XQy/btx44aeyyUIgiixVK1aFZ999hnS0tIwd+5cZiGehH2CIIKNgDPjMUNubq5Xll8AiIiIcP9udVk5EhISGHoPXY7IBEEQJR0jdvwk7BMEEWyUKGHfriQnJzOF3gwJCfFBbwiCIIIHwY5/x44dXOPxEwRBBAqmzXiuXLmCKVOm8OiL5URERMhq2YVjgibeyrJyhIeHM/0jYZ8gCMIYDRs2dNvxb968WbEcafYJggg2TAv7TqcT69atQ3FxMY/+WEp0dDROnz6NoqIij+OCTX2NGjUsL0sQBEH4B8GOf/Xq1fjmm29kBXsS9gmCCDZKlINubGws8vLysGnTJo/ja9euRYUKFVC7dm3LyxIEQRD+Q7DjDw0NxbBhw+B0Oj1+J2GfIIhgIyBt9jMzM1FQUIC8vDwAwMmTJ5GRkQHg5lZtaGgo9u7di6FDh6Jjx46Ij48HcDMc20MPPYQZM2bA6XSiWrVq2LhxI3bu3IkBAwZ4RNOxqixBEAThX8R2/AMHDvSw4ydhnyCIYCMghf2ZM2ciKyvL/XdGRoZb2J8zZw5CQ0MBAMXFxV4D95AhQ5CSkoIFCxYgNzcXUVFRGDRoEJo0aeLVjlVlCYIgCP/TsGFDREZGYvTo0Xjttdfw+OOPk7BPEETQ4XCZHNnOnTuHnj17YtmyZShVqkRZBZnG6XSiQ4cOSE1NZYrGw4uYmBiftUUQBKGXzMxMn7aXn5+PCRMmoHr16ggJCcHnn3/u0/YJggh8fD1u6ZEhA1KzH2z079/fa6HUsmVLtGzZ0k89IgiCKDkI9vupqamYNGmSv7tDEATBFRL2bUBiYqJPNfsEQRCEJ4Id/9WrV5GUlOTv7hAEQXCDi90N2TgSBEEQwcAjjzzi7y4QBEFwxbSwHxERgfj4eLLXJwiCIAIeUl4RBBFsmJbQy5Ur5w5tSRAEQRCBDAn7BEEEG6SOJwiCIAiCIIgghbuDblFREbZt24bDhw8jJycHderUwfPPPw8AuHz5MnJyclC9enVKNEUQBEEQBEEQFsNV2N+7dy8SExORnZ0Nl8sFh8OBoqIit7C/e/duTJkyBYMHD8aTTz7Js+mAhkJvEgRB2AMy4yEIItjgJuwfP34cH3/8MYqLi9G6dWvUrVsX48eP9yjzxBNPICkpCZs2bSJhXwSF3iQIgrAHJOwTBBFscBP2Fy9ejBs3buCTTz5BgwYNZMuEhoYiKioKR48e5dUsQRAEQRAEQRAKcHPQ3bdvH+rUqaMo6AtUrlwZ2dnZvJolCIIgCG6QZp8giGCDm7DvdDpRsWJFzXI3btxAcXExr2YJgiAIgiAIglCAm7Bfvnx5nD59WrPciRMnULlyZV7NEgRBEAQ3SLNPEESwwU3Yf/jhh3Hy5Els3rxZscyaNWtw8eJFPProo7yaJQiCIAiCIAhCAW4Ouq+++ip+//13TJw4Ee3bt0ejRo0AAAUFBThx4gS2bNmCb7/9FhEREWjXrh2vZoMCCr1JEARBEARBWAE3Yb9KlSoYPnw4xo8fj2+//RZLliyBw+FARkYGMjIy4HK5cPvtt2PIkCEoX748r2aDAgq9SRAEYQ8iIiL83QWCIAiucE2q9cADDyApKQm//vordu3ahaysLBQXF6NSpUpo0KABmjdvjnLlyvFskiAIgiC48cADD/i7CwRBEFzhKuwDQHh4ONq2bYu2bdvyrpogCIIgCIIgCB1wc9AlCIIgCIIgCMJekLBPEATBmeeee87fXSAIgiAIACaEfafTievXr5tq/Pr163A6nabqIAiCsBvjx4/3dxcIgiAIAoAJYT8+Ph5JSUmmGp8xYwbi4+NN1UEQBEEQBEEQhDyGHXRdLhdlGuQExdknCIIgCMLONGrUCFu3bvV3NwgDmIrGs3//fkyZMsXU+QTF2SeIQGL69Ol49913/d0NgiA0mDVrFnr37o2CggJ/d8XNrbfeimvXrvm7G4YICQnxdxcIg5gS9s+dO4dz586Z6oDD4dB9Tl5eHubPn4/09HTk5uYiKioKcXFxaNKkiea5hw4dwvz583HgwAG4XC7cd999eP3113H//ff7rCyhj7CwMOTl5fm7GwQBALjrrrv83QWCIBgxImOo8cgjj2Dnzp2GC4VmsQAAIABJREFUz+fdH1/iD2uONm3a4Mcff/R5u8GGYWF/zJgxPPuhi7Fjx+Lw4cPo0qULIiMjsWHDBkycOBHFxcVo2rSp4nmHDh3CBx98gNq1a6Nfv34AgO+++w5Dhw7F2LFj8Z///MfysgRBEARBBB4PPvggbr31VlN1BLKw7w/ofvHBsLBfr149nv1gJjMzE7t27cLAgQPx9NNPAwDq16+PrKwsJCcno3HjxihdurTsuQsWLMCtt96KkSNHIjQ0FADQoEEDdOvWDV9//TUmTJhgeVmCIKxh0qRJGDhwoOXtBKuv0mOPPYZt27Yp/t6wYUPs2LFDV53NmzfHqlWrzHaNIGxBhQoVvL7/smXL6opMGKzjB3DTzOfGjRv+7gYhQ8DF2d+8eTPCwsLw1FNPeRxv1qwZsrOzcejQIcVz//rrL9SrV88tkAM3M/4+8MAD+Ouvv5CdnW152UBn5MiRPm+TVvaEmAoVKvi1fT2TddmyZS3sCV+qVavGvU4KMmCOSZMmuf//vvvu82NPAhcz8weLjbo0uEZJRknRaga6v3wIuLt44sQJREVFeb1U0dHR7t+VuHHjhuzHKxw7fvy45WXlcDqdTP9K6oqZhH3f4Cthon379qbOV3offPWeFBUVMZcNpHdXyzxB77X07NnT65yOHTvq7hdxk0WLFvm7CwGJmW9QbmEfSN80b1hCpdeuXdsHPSH0EnDCfm5uLiIiIryOC8dyc3MVz61evToOHjyI4uJi97GioiL3boD4XKvKypGQkIAOHTpo/luyZIlqPb5Aa6C74447vI4lJCSYajMQtz27devm7y7oRs3fhSdlypiKC+B3rNBeqeGr+/Xqq69yra9Dhw5ex3xhZkVYw9SpU/3dBUPwFs7NzkfPPPMMp574lkceeQSxsbGqZRwOBwYNGsS13ZK8uOJJwAn7ZmjVqhXOnDmDpKQkXL58GRcvXsQXX3yBrKwsAJ7bRVaVlSM5ORmpqama/1555RXet0Q3dv/wHn/8cZ+1JTUlExMMW48jRoywpF6z94aXZt9opKywsDDcfvvths41wpYtW3zSjtYiRu/9DcRFuhp2GH/9SZUqVfzdBUPwfA95zH88EonWqVPHdB16Ubp2sZLICvnA7jJHoBBwEklERISsplw4Jqf1F3j++efRpUsXrF+/Hm+++SbeeustnDp1Ci+99BIAT1tgq8rKER4ezvTPDjFuy5Urx6We//u//2Muq+djDwkJcd93I7zwwgsAbu7WaDF58mTF33w1QFnpKG/VdqxV9+bRRx/VVb5BgwaG2wqGxZwd6NGjh7+7wIzZKCx2wkj42HvvvdeCnliL2bHG5XJ5aOJ5LBx41HHbbbeZrkMvSgoOsV+JFe+IkWcoZ2FQ0gm4GSs6OhqnT5/2spsVbPVr1Kihen5cXBwWLFiA6dOn46uvvsLEiRNx9epVhIaGolatWj4pG8gYmfDkBjeWnAhGaNOmjeFze/bsiXfeeYdLP3wpDD722GOW1GuVZtZsveLBX2wWEhYWhk2bNpmqW0BrITBgwADNOj7//HPT/gl2YtSoUbrPEZ6V0u5Et27dEBUVZapf/qB+/fpex5o3b255uyxKCDFKiqbKlSvrbpvHIl0Y91988UXTdUkZOnSo7PG7776b6fyuXbsCACIjIz2O33LLLR5/20HT7I8+sAjQYWFh3Ns1cq16FT8lgYAT9mNjY5GXl+c1qa9duxYVKlRg0kaGhISgRo0aqFKlCrKyspCeno4XXnjB66O2smxJRDxR8bZ7FibfRx55xHAdzzzzDLdB9IknntA9MRvB5XLhwQcftLwdHvCyPVd7Rryi32iZ6bD4NzRu3Bgvv/wyU3tqO5K+Quvdr1Spkm6be2Fhp/bsA3F8/Prrr72O/fe//7W8XSWNvK/9SIwivGNCf3v27MmtbqVvv3fv3kzny+1au1wuWwj3UvR8M76Yhwj7w03Yv3DhAnNZtVjOWsTExOChhx7CjBkzkJaWhj179mD69OnYuXMnEhIS3IPI3r170bZtW48IBidOnMCiRYuwfft27Nq1C8uWLUO/fv1QrVo1vP766x7tWFXWTlStWtXS+tWcefTYHLJoyT/66CPm+lhp0aKF4XPvvPNOfP/996pl1AbsDz74wHDbWnTu3Nl0HUZ2LnhlnhVPvmYmYrFiQEtQe+KJJwy1IUQJ0+Khhx7SVa8vIl60bt3a1PmszyYQzUP8hdKumNIOkl39JgTtuS9C0/KOxsOzfqPIJeqU22265ZZb8OSTT3Jpk9d1su60CJjdJf/hhx9MnR8scAvz0KdPH3Tv3h3PPvusYpnr169jzpw5SEtLw/Llyw23NWTIEKSkpGDBggXIzc1FVFQUBg0a5GUaUlxc7PGxlilTBrt378ZPP/2EvLw8VK5cGS1atEBcXJxHjHwry9oJqwcptW0/PW0LWsHevXtj2rRpqmV5Duz+1OiwasEdDge3CV3Pu1qtWjWcOXPGUDvS+3r77bfjypUrhs6vWbOmoT4Anv4Ozz33HNLS0hTLlkQbUB7vfyBq7ZUw+p3dc889OHr0KHP56tWr4+TJk4p1bd26lbkupT77a2xj2ekxipJQaGZ8tKtm36o+ZWZmIiYmxpK6jSI1q9JLIJoJWgE3zf6NGzcwZcoUTJgwAVevXvX6/ciRI+jTpw9WrVqFO++801RbYWFh6N69O+bNm4dly5Zh2rRpXoJ+vXr18NNPP6FTp07uY5GRkRg3bhwWLlyIZcuWYdasWXj99ddlhRyrytqJUqVKue0UpfAYjHllzhT60qVLF8Uy/hqQH3jgAaZylSpV8jqmV5sLePtMaE1kenwYWHd6+vTpo/r7ww8/zNymnnYFxJO62bFEQOn9EZy9pbsJ/taYGvEF4BHBQ+9121FQsjtqwpbSt6f0XPz1nj733HOmzhfP26yIzVVYorIpmeqpvbPNmjVjvjaju4ElHampIOu7MG7cOCu6EzRwE/YnT56M6OhopKeno3fv3tizZ4/7tyVLlmDw4ME4c+YMmjVrFrDxeq2if//+6NWrl8e/X375xfJ2HQ6HosZSacDTmjzUfjc68ffq1UvxNzOmNnKIFzks/Z07dy5TvXLJhNRMR1wulylnY4Hhw4czl2UVDNq1a6f6u1bEJqtCb/KoU4h4JdwL4fsQPwteApSZkHVxcXG62zMTfchK1HaD/Yn029F67rzs6dXeBSUljL8Xn1LUFDMsGMnmLL5vLOazSs7LavfyxRdfZM4K3a9fP9xzzz1MZY0irt9sAjDB7FbpvggmV2b84gDr3tVmzZq5/5+UDN5wE/bvvvtuJCYmon379sjOzsawYcMwe/ZsfPDBB0hJSUFYWBg+/PBDvPfee7bXePuaxMREzJgxw+OflWnmGzVqBEB7QFWLI6+H8uXLa5ZRiz2sFG2mb9++Xh+13Ef+7rvvarYvYFUsaSMDHEssdzsOauI+hYeHe/1uJjSqtH41YmJimBcWWu96w4YNmeqRQ2mnQ8kp1yp/DbV3cPv27Za0yYJ4krYTer8tpUhlarukciEUtcYKI1GRfImahpX1nhoZ12rUqIH3338fgOc99NcYWbNmTcvDF/P0dxHqnDVrluzvVuQWMfJs9Nr8A/I+DnoI1ERoYrhG4yldujTefPNNjB49GmFhYfj555/x119/oUGDBpg+fbpPEx4RyggDoZz5hHjy4bUNKSxc5D5sYQAxYlf3+uuvu+sU/iudKEeOHKk5ecppMhITE3U5soq1k6VLlzadE0FpcJJeixFzK6GOL774gqmcFK0BWun3/v37A/C249a7EBIL8Gp9qVu3rjtvghbCfbRCMJBb8ADKEX2MaO3NYlYIGzx4sNcxu9o7G8UKjaSRHVw9kZus0qJqjdesz12wx2Y1h1QjPDyca+IzIcACy7X07dtXV91KY4IamzZt8lKcievh9awLCgpkj7/55ptc6hej1Gc1006t3WorIlMFQ44N7qE3nU4nVq9eDafTCZfLBZfLhdOnTys6HBH+RTqQ1a9fH+vXrweg/CGuXr2aW/uCzXXdunW51FeuXDmPfrNoduU+5OjoaFSsWJG5XfEEvHXrVqbkY2qDc926dWUFeek5Xbt2NWxHLd3FYJ2gtdpTipYjaM/lYjHLRZJgwcikKSB3HcIxnkKqUl3iKBksz7Bbt26aZdasWcPeMQ3U7o+R40uXLuXTsRKO3PtkVtAbMmSIrvK9evUybc4BAPfddx8Afb5IcjuDH374oem+SBF8AFjGAr1R98qWLas7P0rZsmW98ghpZQE3Mo4pCftWOFUrwdup1uz3YTczOSNwFfb37duH3r17Y8OGDbjnnnswdepUxMXFITs7G8OHD8ecOXNw48YNnk0SBlAbACZNmoRy5cqpltHKCKyXyZMnGxb2pLBMhG3btvWw9f/000+Z61KiQoUKqqEl5ZLIyDntimFJxmYk2glrQh09A5x4snvvvfdUy8r5iRgNxap1DwVYQ2CywMtBV+9EbHQb3ewWNuu901qwi+sZO3asiR4FFnqfs7S8WghjAbXFltzzE9r49NNP0aZNG0MO3y6Xy5IkSlrIadHVlDqs36rSQt/Mwl9pPnI4HLKO2HrNSFu1auU2m+IlkMolJlVKiieHGb8+4H8LQDkWL16smTU+2HYUecFN2J87dy6GDh2KS5cuoX379pg0aRKio6PxxhtvYOzYsahUqRJ++ukn9O/fH8ePH+fVLGES6UfBY/WuZJ4gNyDfcccdXHwD9HzclStX9pikeMV6VuuDXNQYLYdJOQdePdpWJRwOB3e/GbGWz+owlUYcfJcsWeJ1zAoNqVFY3t+YmBh88sknuuvmFWdbDaUFkNJ1yS3S9DiTBwpqwQWUEN8zHoKLmhKibt26TPddKeqV0DfBRE96XAsj1ya3E8vTx83o/dYSQllYsWKFrvKlSpUyHZpSiqBUEO8a8JAL5BZpclmnGzZsqPgMatWqpdkXHnMkK4G0W8lN2P/uu+9Qvnx5jB49Gm+++abHA3nggQfc4TFPnDjBlGqesA65F19O28v7A5ET0t566y3F8kbtL6UDhfg67rzzTrhcLg+b+qKiIkPtKLVhloyMDEvbqVatGho0aGB4UpOe16RJE48FG6+kVzzh2Q8ez2DGjBke34OQL+D//u//FM8JDQ3V3AGzaqJLT09napfV30CuTyxZif2FUafyevXqqb574kW3FaEaldrWm7FZySbdbChW4Xyz76iSwkbqCyAIsmXLllW8N+LjU6dOZV4sOxwO5ug7eq9X2AkTFsniXQihv3JzqZlxLzw83PSuoBg5i4B+/fpxq1+M9LqtivjFc8fYargJ+0899RSmTZumuLoNDw/HgAEDMHDgQJ9kzQsk/BV6E/hfpBypKYueQULqHa9nIFNzphEmQrW+CL9Vr14d06dP92rf5XK5NUF33323u/w777zjLlNYWChbN6tTzujRo5nKsaLXNEevWVXTpk01nXMB9ud4++23Gw6nmZSUxFz2m2++AcCeAVhOA1SjRg1VZYOcyZdeWATDxx57zOO9Fhafb7/9tu72tBDa0RJExP1JTEz0+Fv4FtUcz80uqFwul60i84ivRytD9y+//KKo/e7evTtTG0rzopn7GhUV5XX+3XffrVtIkRPSfb2oNxIRRXpPheg1arkMGjRo4I71/sQTTzCbCgI3FxcDBw5kGsP13DMh0MDo0aMVx/unn36auT67IPWLW7lypWy5SZMmmWpHLp+QIC+wQDb7IgYPHszklNikSRPNLKglDV+H3hQjREgykuBJCbkPo2fPntzql6NUqVKKtq2CtlE8sISHh7sHZCVhnzWijngrctu2bZrl9TpziZG7t7wcp6yMX69EmTJlmAfSiIgIZGZmMvkyKFG6dGl3Aj6tdjMzM2UdxcTaNHEiH/HvelHbtuaFlgAi3I/atWt7JSlU4pNPPnELVHrMeJSOG7EdtwN33nknkyM2C0beH7l737ZtW1024NJ3XWweoscpXw4lYU0pippcfa+99ppqGyxERkaiQoUKqiExIyMjPfKiaJnn+HL3skyZMpZEm5HDH7uySu+Z3l0/lr7ruT61999OCgo1uEfjYUHPSpmwP1KhWO4jUsrUy0JxcbHhc4H/CcNSu1Lgpk0tq5PZe++9p7j9LQwGLBpuM4OoHTQM4ucdGhrKJZa0VROL1CyG1/3TkzzOzLUtXrzY8LlShH5oKWWkWmy1axW0jHLOjTwXPb5m2bJlhiZxJc2kGD0+SrydQ4GbCa8aN27sdVwcWvHRRx9VteUXfDRYhXUlEyVp6GQ1jMoN4h3a4cOHu8d7l8sl688g7Qtv4To8PBw9evRw57uxEj3vj9Xfnp76zZgPaY3xvCKWCclhAyVzLzdhf9++fbr+Efamf//+ePTRR5nKJiUlcdkZUAp1qVdAU0qvLSeIh4WFMWtP69Spo5qxV24wW7RokWI/5K5LMFWRsnHjRsV2rVgAKIXVi4yM9NAQ9+7d250QTe76WQb45s2be2yr80zeUrNmTY9n5nK5cOeddypu4VoRZccXCzS1cLjC+yb0SxBIpY7UvHw4zGAXYf/uu+/2cmIXnuO6desUz5MupOSe/eTJk2WVH0JZIzk69Lxj7dq1U0z0JjBz5kyPRHJaOTfM2kR36tTJHddeitlgAuLxXWlxonRMDyzO1Bs3bkT16tXxyiuvaIbNtAP+8nXq06cPt3al8AogYYWPjZVwC5w6ZMgQXS/G8uXLeTVN6ITlo3v55ZeZ66tQoQK6du3qzlRrdIBYsWKFrLZDr2af1UlKrwa2UaNGaNSoEb799luv35TuqTiMWP369bFnzx5VYf/BBx/0+Pv7779H+/bt3fHkhXOSkpIsMY1KTk5GQkICQkNDZfunpl3TG6O+adOmuPPOO9GlSxeP4zVq1MCePXsUz9P7fokzjrpcLpQpUwaxsbE4duyYrnrEdUhZunSp21xMry2unKD+1ltv4euvv2ZqG/DWsIsRhEe9IQh5cO+993L3afE3cllvBUJCQtCsWTNF7aGwkO3Zsyd69OghW4bnzpAezDx31oVj586d3XlcxDRu3FgxUMLTTz+NtLQ0w30LCQnBI488gp07d6r2TY3hw4cbioQlhcezVKvj8ccfx+bNm5nqeeaZZ/Dbb7+5/zbid8U7EpAv0fO+22FH3SzcNPvPPPOM7L+mTZviwQcfRFhYGFwuFx599NGgSD1MeCJotPR+FOLyctulTz75pO7IEf5CaRD+7rvvAABff/01IiMjZcvVq1fPQ5MmUL16dVmHUrHQLc02KMSs15Mop1u3bqhTp45qJsu6detixowZAKCqzRejFnN/0qRJqFatmvtvIYyZVQPr66+/bjoGtBJmgw64XC5s3brV/TfPhZzcNYkd1I2cD9xcVDds2BD//e9/ce+996Jdu3Z44okn3O+d4BwvF14vEGF5N8qUKaO6rS9WcrEIfqyhN3kIkaxRyUaOHMm8kysd04Vww6yZrdu2bet2yA8LCzO061emTBnMmjXL/bcR/wO1nQsejrHihb24/VdffVXzXHF5Pf6QEydOdI9bL7zwAj7++GPmcwWMKG1dLpfbAVqK2nv83HPPuf9f6XyWeuSQKtnM1mdHuAn7/fr1Q9++fb3+9evXD2PGjMHs2bMRGxuL06dPm7LfJuyJ2sfC4ritdM6UKVNUNWm8bBJfeOEFXRlzpbz44ouKUV6kSUrkNPsfffSRYhgyQbAWkG7BN23a1COakhANRs7ZUcleuEePHrLJVMTExMS4TXg6duzI5KvRunVr1TqNUKtWLaSkpOg+T9o/OU2W8EzETtRSx0Ctd076+8svv4zJkydr9k/LNliPM7MaDocDCQkJputp1KgRQkJCMHz4cDz66KOIi4tD48aNmUw69Dru2pG6deuayuAsIF2sG7kHRqNhiZELVCC3K9SqVStNx1/hGpQUNYKtvNK1CtGC7rzzTveuVcWKFbF27VrVdlkR2r3jjjtQsWJFxMXFqZo2qS3mxTuHPBBMGletWqVYhlemb6GNiIgILuaTYv+Iu+66y73bDyjnalBCupAYP368rvO1kvyJ0bJkIM2+DiIiItC/f384nU7MnTvXV80SFtOhQwfNMoKDrJ4PpkKFCkzleUy2wM04xnK2fNKwokrUqlWLyfFuzpw5sqEja9WqxeSUpJYpU0At1rlcWnnWEKNSO8qFCxd6ZAZWe148nfLLlCmjayAXI+6jXCQdAbHPgtwiTI+DbuPGjRXfDUHAZ5m0pUKhHHL9EjRiWrbg0j4IAp2vBPBAEvSBmwvn2267TdXhj+Wafv75Z1P9iIyMlM2TwqKpF/dPrjyrSQiPZ1eqVCm35l9s/hgREcG8E6CXhx56CGlpafjggw9Qr149lC9fXvZaypYt67HzxoqRbPO1atXCPffcwzxmyvVXeKe0nouSIq1z586GxmzxwqdixYoeZrmCMo01OpUQFtwodsut4m98Go1HiNzBEp6wJOHrOPt2fnF///13WYFUCTUzETlGjRqFNm3aALiZoVbLvn/ZsmW66teicuXKbuHayHNQ2tYX6srMzPT6TU+EFD0DZM2aNZl3bebMmQMAsva6UrScAQXE2Sblsg1LadGihceCxSrhUk+977//vq4Fq5F3RtCSKoVo/eqrr2SPC/Hh7Txe+BIlp049Dn8s95IlDKWYMWPGyO6Wi3eKWEz62rZti8TERKY+saJ351Xw7xC3FxYW5k4qxRs94WK1dt5Kly7tVaZ3794eJnl676ParrYaLIoBMdJr7tOnjyFFGosz9fvvv2967NUKHiLUr5Tz5JZbblFUcrVq1Uqz/aFDh2qWsRs+D72Zl5eHq1ev+rpZW+PPOPu8BR6zgkFYWJgu52DW2OECDz/8sNupaNiwYT4Jf8YTIxknt2/f7v5/PWHkqlatasgMp2zZsqhbty6WLFniPibE8DZi0iUgvWaxKQGLtqh27do+ydKq55sqV66c4QmdBbFPhIBwH6XPVjpRlypVSjNjrxmkC1NW+/SSTpUqVTyeizjvg8Ctt97q8a2JNeVKlCtXDk2aNFGNNsSKnpCa/kJNacKKWOicNGmS1+5nmTJlTJktq0VtAm5eQ40aNTTzGPgbufDMcqZRrP0VkqPJMWDAAI8wsnJs2LBBMX+C3Bzhcrk8Fj/t2rVj6qed8Kmwv23bNvz555+ysW0J3yHNMGsGo+EWtdq3yyAF3HRkshNGw8Zt3LhRl19CpUqVMGLECObywjMrW7YsUlJSULNmTeZzxRh5J83mYtCDnCbXbHx5tfrEqN2b9u3be2mrJkyYoFheaoInt+gQzCr0oPX8KleurGjLbafvXoqZsVLLH0YN6T257777ZCM1yXHXXXcpzrejRo2SNf+Rew/U/FtYKF26tDuBYzAhfjZ33HGHpjJD7/uttBsnrueWW24xrcTguXsn1PXiiy8q+jqwtKd1r+TMYSdPnox27dppzj1GElEGen4obqE3p0yZovhbXl4ezp49ixMnTsDlcjGlkycCF6UkVZ07d8bUqVORnZ3t4x7dxKggYVX0KCMD7IABA/D888+7hSU9dfDyb1BCz5a43nrU6oqKijK1Y6CXsmXL4saNG4q/azlL/vTTTx5/85po5Uw1WJ6JrwVstcRTdhb2pdx6662aTo3C/f/uu+8QExPDRcjRg5BIcO/evV6/qeUMEbNixQpDtudiSpcu7RUpRm6eEMY1Pffg7bff9jqmdp/Hjh2LihUryuYu0fstKmVftxLWe9O2bVukp6d7HBs1ahSGDRtmqp0XXnjBHcZUjVdeeQXATbOYQ4cOMdfPSp8+fbwCNYh9o1599VUPAf3+++93R6pTQ9yvBx98MGjyQnET9lm85CtXroz4+HjTCTgIfrB8cJ07d1aMfiI3YdSqVQtbtmxBbGysx/FWrVohPT2dOYOd2sAr3sbzRXILceg2fxIRESGrnb///vstibtvF+rUqaOoDV66dKnPUsgLqAn0St+UkDNBzrSGlSpVqng9Zx4ZjI1Q0mz5pSaDHTt2lI14xYv27du7/S3UYNk1MCNYaUXeMcrvv/+OM2fOeBwzsmhXujYlu2618MJq9cmhtuhnhTVAghiWaDzDhg3zCKAA6I+II0dcXJyurLFC2HVfw2J7X6dOHRw8eFCzXDCMddyE/TFjxij+FhISgvLly3N50QjfsmrVKlSsWFFR2JcOnM2aNcO9996ruE3G66NJTU11/7+QttpKlDLKmuG2227D559/rhhyUw4lrWz16tWDOqTtggULFH8zsiVrhA8++MA9yYWFhaFt27b4448/vMopTcBq0X9YCQ8P93rOCxcuZDrXiGkQ8T+6du3qkeG6TJkyut89tUWp9Dmw5slQEpCtfq7i90l6Xaw2+zx2MZR2LGfOnGm6bi3M3uMnnngCU6dO9UgiGB4e/v/au++4KK71f+CfpfcqKFJERRQQREVEVOwFS4xeEFFz1Ws0yU2+MdFobIl6VaLhXhRjLPFGY5RYMUZMFCwRgoqKBRtSFbGBAkqXsvv7g9/OZdnC7rLLzC7P+/XKKzJ7ZubslN1nz5zzHJU9qZT3+MoqJynpgzrqAEjvFaAuMTExIrO3azOVfUtKG+xAuK25DytF+ql99913Ku2XqUmP9JWhq6uLwYMHY+nSpXKvM2rUKJXXo+k10NygZ203cuRIicubtmg17tus6j77qqLOgK+596noFzeXjpsk8mQaEVqwYIFY11Z5gya2u1kpolevXhg3bpxIsN8a9d27dy9mzZoFXV3dVvuxrw6SGqrmzp0rNqu4ImRdZ6oYT6csS0tLiZOxnT17lvnMVeYph7w2bdrU4skPNZnm3iVaZOHChWLdAsaPH99qGXlURZ5Av/EHyE8//SQzS4SqP2y4+qUpKy9+U83lSm8pHo+HwMBAvH37VuF1O3TogBcvXrRo3wD7Lc2KPKIyvJOkAAAgAElEQVQGgPj4eJGuVaqY4EgbqPtabS2XLl1SeJ333ntP5ji2plJTU5n+9U0p87llZmam9s87YdpRSTMkq3vfzXXFaQl11l2eH4wGBgYqC0ptbGyaDaC7desm9TNLFcdC+HlubW3NpGBuTJHUtdJIa6BprLnkFDt37sQHH3wg8TW2v5NUgYJ9DoiKilL74Elp2AyAZc26y9XAnGu49CHUpUuXFn0Ju7i4IC4uDosXLwbQkDK0uXzKqqbIUxbhsW/6JaLotavOa13ZuRzUte3mcPG+bxx4KfLDHFBvZiZpunTpIteEWqqgjvP18ccfy11W0jU4bNgwpcfFKHpNs32vN7e9Xbt2obq6mgmy2fq+UCbNqTzH6r///S88PDxa/DRaIBCgb9++EpcL/8/FzyZFKB3st6R/MI/Hw65du5Rev6qqCvv370dycjLKysrg5OSEkJAQBAUFNbtuZmYm9u/fjwcPHkAgEKBbt26YOXMmPD09W1Q2JycHBw4cQFZWFsrLy2FnZ4chQ4Zg8uTJCj3+bQ1cChCl6dKli8ITgxDFcO3Di8fjiXxJs1E/eQZ1Nac1671///5W21drcHZ2lphSjysU+THIJVy714Uk1WvOnDkoKiqSa31JQVhLUiUrGtTJM+u5PNT1NNDIyAg1NTUiywIDA0WO75QpU1SaDr1jx45iT3uaxhzvvvsuhgwZ0uJ9SeoWpAotfUrNRUoH+4WFhaqsh0IiIiKQlZWFWbNmwdHREYmJiYiMjASfz5eZbzYzMxNLly6Fu7s7MygyNjYWK1euREREhMiNq0jZx48fY8mSJXB0dMT7778PCwsL3Lt3DwcPHkROTg6nZ1tr7Swm8pKVH1wep0+flvh4mfyPqn/0KZv/XxXrsEGZAa+SgoOWvF95M6Vs3rxZ5pM0WVrzfNjY2IhNTKTJFDl2M2fORFJSEoKCgqQmROA6Q0NDGBoatuqkWsruY+PGjRIzfCmyPXkngJw+fbrMgfSenp44efKkXNtqyTHl8XiwtbUVmdhUmCJTVRwcHJiZkIX7bMrIyKhFWclam6Z8J8midLB/4sQJVdZDbqmpqbh16xa++OIL5pehj48PCgsLsWfPHgwePFhqABsTEwNTU1OsXr2aaW3v1asX5s2bh927d4sEmIqUTUxMRE1NDZYtW8ZcwL169UJxcTHi4+NRXl7eqnnAFWFkZISUlJRW219r3TTqnABDmEKRqAdbH6xHjx5V++BkVbfEy3usGuefbkpW8KBMYGFgYKDUrMDa8IXaEn369MHAgQPRpUsXudc5duyYXOVUFXSvWbOG+XdISIhYsBwfHw9TU1M8evRIru21dHBtS64ZSRMvybs9Yf51Sf3NAwICxAan9+/fX2awr6OjQ0+yOaLpvXLs2DHExsYiJyeHpRqphsaNJLt8+TKMjY3FvrxGjhyJ4uJiiZM3CKWnp8Pb21ukW42JiQm8vLyQnp4uMtmTImWFH1hN+92bmZlBR0eH89kCuF4/rlFFCkVVCA8Ph5OTk9LrSxsQ2/RvS0tLubrIcbWrgLxcXV01/j0oQl2pOE1NTXH+/PkWbaOtio6OFpuoS9Y12dqfRY2TRowdOxYDBw4Ueb3pwODm7ic7OztcuHChRXVi454VdruR1Kjk6uqKSZMmtXaVpFI2u5ODg0OLZn7WVE27crm4uMh1zLg+w67Swf6KFSsQGxsr8bXKykqxfmKqkpeXBycnJ7HWe+HkI3l5eVLXra2tlZghQriscWuEImWHDx8OU1NTbN++HS9evEBlZSWuXr2K06dPY9y4cc322a+srJTrP1VM4NFSwvcvT+YdR0dHdVenTVu0aJHE6e5VofGHW/v27anVqZWpK4BZsWKFQpk+ZsyYgaioKLXUhchH2rUgKcUiWzn3m1Iks5akOqszBaMs8h6nttAoMHv2bKxdu1Yl2+L6U7vmzqc89f/tt99UVR21ULpJ986dO1L7i4aHh2PEiBH49NNPla6YNGVlZRIDD+HjxLKyMqnruri4ICMjA3w+n/llXl9fzzwNaLyuImXbt2+PyMhIREREYN68eczyiRMnivwtzZw5c5otAzQc1+nTp8tVVhZlb7zNmzfDx8cHQEMGoeYC/sOHD4u1/BDNJM+Xm62trdyD0SVNcc8GHo/H+S8iZXXr1k0sU9DkyZMV2oatrW2zKetUhYsB1Pfff892FRQyYcIEXL16FQD3AyxZjIyM5G7IUOV1065dO9Z+aLQGNu4xLt7X8nB0dMSwYcOYv5t7H1yfn0Yt/TcEAgEnP2gmTJiALVu2YMeOHQgLCwOfz8eBAweYwcaNR8QrUragoABr166FlZUVli5dCktLS2RmZuLQoUOorq5u9kfPnj175Eq9yWbe6tjYWDg6Oip040q6+FvzuvDy8lLq0RqXs4Fw2YoVK8SW6ejoYPXq1WLL586dK3U7rXmNaGuwP336dHzyySdqnUSmLdwn/fv3Z7sKCn3mtubAWFmsrKwQFBSE9PR0VuuhqJiYGM7NDyFPgyGXadrnq/DeGTNmDGbPni3ymre3t1LjkbhA4zprm5ubS2y9Fy6TNLpeaNSoUXjz5g0OHz6MU6dOAWjIjjF58mTExsbCxsZGqbJ79+5FVVUVtmzZwrRs9uzZExYWFoiOjsawYcNkzjBsYmLCWp59eamq715rpiHdu3evyN/yfgFqUzYQeVhbW4sta64fvySSxn7weDyF01na2toiKytLoXWUpWhQ1PiLd8CAAWjfvr2qq6QQaeNtpE3Q1Ji09y7PGJ6OHTuy/t7bCrYDd2VYWVkhKioKwcHBat3PgQMHVJrVhWtpsgFInehJWWwE35p4DTcmPGY8Hk8k9tMkGhfsu7q6IikpCfX19SL99oV99ZsLSkNCQjBp0iQ8e/YMxsbGsLe3x9atW2FkZAQ3Nzelyubm5sLZ2Vnsg0I4O+zjx49lBvuaSNmbd8WKFTh9+rSKa0Na4pNPPhH5m+0P5qioKAQGBjJ/JyYmqm1fnTt3RnZ2ttzlG3/x+vv7w9/fXx3VksuVK1ckZh47e/asQttpnP3l3LlzcrVc/fvf/1ZoH20V2/eStpM1A7s6KXpe7ezsJM7PoyyBQCD3zM50DTZPVWmjuUzjsvEEBASgqqpK7EI/d+4cbGxs4O7u3uw29PX10alTJ9jb26OwsBDJyckYPXq0xG4n8pS1tbXF48ePUVVVJbLugwcPmNe5RJ6ppZuj7I3QNCUZYY/wS0BdE7ooy8DAQO5xLC0VGhqqsi9DWekt1UFaimFFp59vPEDS0tKy2eOhq6sr1+cs0b6AQVHaGmgq+r7c3d3x888/q7QOLemi19rnRZn7gI1rJzg4GM7Ozpyoi6ppXMu+n58ffH19sW3bNlRWVsLBwQFJSUm4ceMGFi1axHwB3rlzBytXrsS0adMQHh4OoKH1/9KlS3Bzc4O+vj4ePnyIo0ePwsHBATNnzhTZjyJl33nnHaxfvx5fffUVJk2aBAsLC2RkZODo0aNwdnaWOA0zm8LCwhAfH892NQjLuByIfPzxx62yH3V8iHP5uDZmYWGBqVOnsl0NogZNr0GuzvMiy8cffyz2tJ0rtCH4ay3KHitDQ0OmC3VraZx9qHG9NeUzXZYWBfvnz5+XmE+Zx+NJfU2oJWmKli9fjn379iEmJgZlZWVwcnLC4sWLxXKB8/l8kZOkp6eHtLQ0xMXFoaqqCnZ2dggODkZISIhYFxxFyvbv3x/r1q3D0aNHsWvXLlRUVMDOzg5jxoxBaGgo5wb8EPZJSptHNJumfSEYGRlhyZIlCq1jYGAgc1wU4Y7GwQpb+dJbEhQrOtaHaB91pZaWR9PPc01P5tCiYJ+tN25sbIz58+dj/vz5Ust4e3sjLi5OZJmjoyM2bNgg1z4UKQs0zOIrTEupCb7++mu1Zuog3KdILmxNM2vWLJWkqVUEVzKhqNO4ceMwdOhQlW931apVnE9SwBZlsvEQ7abMeZ4wYYJKBzNrM1XPKs4FSgf7J06cUGU92rSFCxeK9ZseP368yGyFqiachIwQZTLvtJZ9+/YpHAQGBQUhMDBQYpYhdfL390dycrJKB+JxjZ6enlpSz02cOFHl29QWigQXXLp3penduzfbVWix7t2748aNG2xXQy4mJibYsGGDSsbqKUsTrktZNL3+gAb22ddGUVFRGteqpam/btsqSQOjhd0xJH2QceX8enh4KLwOWzO+CscLqarlW9GBtkQ7KXsvciFAkZT0YteuXSzURLW4lnRDFh0dHVYDfU3k4OAg9vnLle9EZXErDQchaqTKmzU0NFRl22oNf/31l9gyYcs3F4ICIk7RFJpEO2lKJhNJli9fznYVtFJLP7Nb+/rgyvUor++//55J7NKYubm5xs6wTC37hCjhyy+/ZLsKhBAOGzZsGDp06NDi7Whyn31NzAJESNOJBd977z1MmjQJjo6OUlMecx0F+0Qp1BqsPehcsq+5IK0l2csIOyIjI1WyHW3rs08Il5ibmzebrMTe3h729vatVCP1oGCfqNXBgwfZrgJDU3+RC505c0busteuXZO7LAUI3Ofo6Mh2FQjhlCNHjrC6f0tLS1b3zzZFxhly+TvmxIkTGjdmUhkU7BPGggUL5C4rb2sTVyZFSUhIaPXsLKqmSP1b2hrI5Q9nLlM2nS0dbyKNJnfjUafOnTsrVF5VT1pIw6BfeeMFrl+TbWXeEAr2OYCN1JuSvPfee626v9ZkY2PDdhU4S9LkIYQQbmjrLciqMmzYMLarwAkzZsxAVVUV29UgrYyCfQ7QxNSbRDtMnz6dM09f2EKt6oTLFixYgDlz5jRbbsOGDbh9+3Yr1IiwqaUzubb1z/u2ilJvEtKGLVy4ED169BBZRsEvIdyhp6cn15NJyqVO2NK/f3+pr1lbW6N9+/atWBsiCbXsk1Z35coVtqtAmtG4K8/SpUvVMmsqV6iy2xL9UCKEaLtPP/0Uvr6+zN+yPvfmzJkDPp/fGtUiMlCwT5TC4/Hg4uKi1LqanhWnrfHx8WG7ChqhJT8aaJwEUaXZs2dTBieiNjNnzhT5W9bnl46OjtiYRNL6KNgnStHV1cWxY8fYrgYhhJAmJkyYwHYVANCPWEK4gn5uEUKIClAXHsI2Cq4JIZJQsE8IadNUGaRTsEXYRD84CRva+nUXHR3NdhWaRd14OIArefYJaWsoOCdENc6ePct2FdqElqbeJKo3cOBAtqvQLAr2OYDy7BPSdtnb27eZWRyJ9rKyshJbRskYVI8CfaIMCvYJIW0WF744d+7cSUERUQmuPanq2rUrzpw5w3Y1VI7tzw2unWd3d3dcvXqV7WoQGajPPiFEhLW1NaytrdmuRqth+4vTwMCAgn2iEs0FocuWLWulmjTg8Xht6rOkrRo3bhzbVSDNoJZ9QoiIL7/8kvWWK0KI6tGPStVgu4GAa+j7gvso2CeEiNDTa1sfC6r8oqIvPUIIIVxD3XiIRklJSWG7CkSLqLKFjlr7CNvoGiRsoOuO+9pWEx5HUepN+bW1VmdCCFEVCso0HxdTb7a0Pr1798bNmzdVVBsiCUVOHECpNwkhhLSUvb09XFxcpL7etWvXVqyN9uJasM22lh4PMzMzFdWESKORwX5VVRX279+P5ORklJWVwcnJCSEhIQgKCmp23czMTOzfvx8PHjyAQCBAt27dMHPmTHh6eipddtOmTTh//rzUfUZGRqJHjx6Kv1FCiMagAICwbcaMGZgxY4bU13v27NmKtdFu9JREdfT19dmugtbTyGA/IiICWVlZmDVrFhwdHZGYmIjIyEjw+XwMHTpU6nqZmZlYunQp3N3d8fnnnwMAYmNjsXLlSkRERIgE5IqUnTZtGoKDg8X2t3btWujr66Nbt24qeucEAMLCwtiuAtESqg7QKQAghBDFUJYo9dO4YD81NRW3bt3CF198gSFDhgAAfHx8UFhYiD179mDw4MFSL5yYmBiYmppi9erVMDIyAgD06tUL8+bNw+7du/Htt98qVdbBwQEODg4i+7pz5w5KS0sRFhZGF7KKLV68mO0qEEIIIUQFqJFE/TQuG8/ly5dhbGyMQYMGiSwfOXIkiouLkZmZKXXd9PR0eHt7M8E7AJiYmMDLywvp6ekoLi5WqqwkZ86cAY/Hw8iRIxV9i4SQVkJfMoQQ0jItfUJKDaLqp3HBfl5eHpycnMQuDldXV+Z1aWprayX2DRMue/TokVJlm6qoqMDFixfRq1cvdOjQQWo5ocrKSrn+q62tbXZbhBBCNFvT7GyENKZt44NmzZrFdhW0nsZ14ykrK5MYQJubmzOvS+Pi4oKMjAzw+Xzmw7S+vp55GtB4XUXKNpWUlISamhqMGjVKrvc0Z84cucqFh4dj+vTpcpUlhLQ+bfsSJuy4evUq21UgMvj7+8PY2JiVfWvj08jOnTuzXQWtp3HBfktMmDABW7ZswY4dOxAWFgY+n48DBw6gsLAQgGhriiJlm0pISIC5uTkGDBggV7327NkjV+pNGrFOCHdp45cwIUScl5cXvLy82K4GZ7S0kYOeZKmfxgX75ubmElvVhcuELfySjBo1Cm/evMHhw4dx6tQpAECPHj0wefJkxMbGwsbGRqmyjT18+BDZ2dl455135A7OTUxMKM9+M+zt7dmuAiGEEEKaaGmwz+PxYGFhoaLaEEk0Lth3dXVFUlIS6uvrRfrtC/vqd+rUSeb6ISEhmDRpEp49ewZjY2PY29tj69atMDIygpubm9Jlhc6cOQMAGD16dEveJmnijz/+YLsKRAuZmJjQD0lCCGGZrLmKSMtp3LOTgIAAVFVV4dKlSyLLz507BxsbG7i7uze7DX19fXTq1An29vYoLCxEcnIyRo8eDUNDwxaVra2txYULF+Du7t7sjw5CCPtGjRqFY8eOsV0NQgjRWDRWifs0rmXfz88Pvr6+2LZtGyorK+Hg4ICkpCTcuHEDixYtYlr779y5g5UrV2LatGkIDw8H0ND6f+nSJbi5uUFfXx8PHz7E0aNH4eDggJkzZ4rsR5GyQikpKSgrK6OR5YRoCB0dHZH0uoQQQoi20bhgHwCWL1+Offv2ISYmBmVlZXBycsLixYsRFBQkUo7P54v84tTT00NaWhri4uJQVVUFOzs7BAcHIyQkROwLX5GyQgkJCTAyMsLgwYNV/6YJIYQQ0qatWbMGdnZ2bFeDaBiNDPaNjY0xf/58zJ8/X2oZb29vxMXFiSxzdHTEhg0b5NqHImWF1q5dq1B5Qoj2oEfZhBB16927N9tVIBpII4N9bbNw4UKx1FPjx4/H+PHjWaoRIUQZlH6TENLWUEMH91GwzwFRUVGUepMQLUBfeoQQQrhG47LxEEIIF1GrPiGkLaJGDu6jYJ8QQgghhCiFgn3uo2CfEEIIIYQQLUXBPiGEEEIIUQq17HMfBfuEEEIIIUQpVlZWcHJyYrsaRAbKxsMBlHqTEEIIIZrI1dUVx48fZ7saRAYK9jmAUm8SQgghhBB1oG48hBBCCCGEaCkK9gkhRAUcHBxoKntCCCGcQ914CCFEBQYPHozBgwezXQ1CCCFEBLXsE0IIIYQQoqUo2CeEEEIIIURLUbBPCCGEEEKIlqI++xxAefYJIYQQQog6ULDPAc3l2a+srERJSQn4fH4r1oqQ5uno6MDa2prmiSCEEEI4ioJ9DuPz+Thy5AhSUlLYrgohMgUEBCA0NFTsCRUhhBBC2EXBPocJA/2JEyeiS5cu0NXVZbtKhIior69Hbm4u4uLiAABhYWEs14gQQgghjVGwz1GVlZVMoD98+HC2q0OIVK6urgCAuLg4TJw4kbr0EEIIIRxCz9w5qqSkBADQpUsXlmtCSPOE16nwuiWEEEIIN1Cwz1HCwbjUdYdoAuF1SoPICSGEEG6hbjwcICn1ZmBgIEu1IYQQQggh2oKCfQ6QlHozPz8f169fZ6lGqjNx4sRmyyxYsADe3t54//33sXTpUgwcOJB5TSAQ4MKFC4iPj8ejR4/w9u1b2NnZITAwEJMmTYK1tbXU7W7atAnp6ekoLi6Gnp4eXF1dMXXqVPTp00ekXGVlJXbv3o1Lly6htrYWPj4++OCDD2Bvb8+UWbZsGe7evStxP4sXL0ZQUBAqKytx/PhxXL9+HU+fPoWuri7c3Nzw3nvvwc3NTWSdgwcP4t69e8jMzERlZSWioqLQrVs3sW0/ffoUP/zwA+7duwcjIyMEBQVh1qxZMDQ0ZMrU19fj119/xdmzZ/Hy5UtYWVmhf//+mD59OszMzJj3GB0djezsbLx+/RpGRkZwc3PDjBkz4O7uLrLPkpIS/PDDD7h+/Tp0dHTg7++PefPmwdzcXOqxJoQQQgg3aWSwX1VVhf379yM5ORllZWVwcnJCSEgIgoKCml03MzMT+/fvx4MHDyAQCNCtWzfMnDkTnp6eLSoLAPfu3cORI0fw4MED1NbWwtbWFsOHD8e0adNa/J41VWRkpMjfixcvxoQJEzBkyBBmmYODA6qrq8XWFQgEiIqKQlJSEoYPH47JkyfDxMQET58+RXx8PDIzMxERESF133V1dZgyZQocHBzw9u1bnDlzBmvWrEFERAS8vLxE6pibm4sPPvgAJiYmiImJwcqVK/Hdd98xQfVHH32EyspKke2fOHECly5dQq9evQAAL1++xOnTpzFy5EjMmDEDdXV1iIuLw5IlS/Dtt9+KBPynT59Ghw4d4Ovri0uXLkmsf3l5OVasWAF7e3ssW7YMb968wX//+1+UlZVh0aJFTLkDBw7g6NGjmD59Onr06IH8/Hzs27cPBQUF+Oqrr5hjYWBggOnTp8POzg4VFRX47bffsHLlSmzatAmOjo4AGn44rFq1CnV1dVi4cCHq6+uxZ88erFu3Dhs2bACPx5N6vAkhhBDCPRoZ7EdERCArKwuzZs2Co6MjEhMTERkZCT6fj6FDh0pdLzMzE0uXLoW7uzs+//xzAEBsbCxWrlyJiIgI9OjRQ6myAHDhwgVs2rQJgwYNwsKFC2FkZITnz5+juLhY9QdAgzQ9TgBgZ2cntlxSsH/69GlcuHABH3/8McaOHcss9/b2xpgxY3Dt2jWZ+168eLHI33379sX777+PP//8kwn2MzIykJqailWrVsHPzw8A0KlTJ8yfPx/nz59HcHAwAMDFxUVs+//+97/Ru3dvWFpaAgDat2+PH374AUZGRkwZX19fzJs3DydPnsRnn33GLN+9ezd0dHRw584dqcH+6dOnUV5ejujoaGYfOjo6+M9//oOpU6fC2dkZAJCUlIQhQ4Zg6tSpAAAfHx9UV1fj559/RnV1NYyMjGBhYSHyA0FYt+nTp+PixYvMupcuXcLDhw+xdetWdOrUCQBgY2ODJUuW4MaNG+jbt6/MY04IIYQQbtG4Abqpqam4desWPvroIwQHB8PHxwf/93//B19fX+zZswf19fVS142JiYGpqSlWr16NAQMGYMCAAfjXv/4FY2Nj7N69W+myRUVF+P777zFmzBgsXrwY/v7+8PHxwZgxYxAeHq6W49AW/Prrr+jSpYtIoC/E4/Hg7++v0PZ0dXVhZmaGuro6ZllqaipMTU1Fglh7e3t4enrK/DGRnp6OgoICkScURkZGIoE+ABgYGMDJyUnsR588k0+lpqbC19eXCfQBYODAgdDX10dqaiqzrK6uTqwbmKmpKQQCAQQCgdTtGxkZwcDAQOSeSU1NhaurKxPoA4CHhwfs7e1F9kkIIYQQzaBxwf7ly5dhbGyMQYMGiSwfOXIkiouLkZmZKXXd9PR0eHt7iwRkJiYm8PLyYvp2K1M2ISEB1dXVCAkJUcVbJABevXqF58+fM63tyhIIBKivr0dpaSmOHTuGZ8+eifx4yM/Ph5OTk1j3FGdnZzx58kTqdhMTE2FoaIiAgACZ+6+urkZubi7TCq+IJ0+ewMnJSWSZvr4+OnTogPz8fGbZ2LFj8eeff+LWrVuoqqpCVlYWfv31V4wYMQLGxsYi6/P5fNTX16O4uBg//vgjeDwehg0bxryen58vsa4uLi4i+ySEEEKIZtC4bjx5eXlwcnISS0kpnNgnLy8PHh4eEtetra2Fvr6+2HLhskePHsHGxkbhsnfv3oW5uTmePHmCdevWIS8vD+bm5hgwYADmzJlDkwwpoaioCADQrl27Fm0nISEBW7duBQAYGxtjyZIlIl2IKioqYGpqKraemZkZysrKJG6zvr4eFy9eRP/+/cVa8pvat28f3r59i/Hjxytc9/Lycql1Ky8vZ/6eOnUq6uvr8fXXXzMt+QMGDMDHH38stm5MTAwOHz4MALCyssLq1avRoUMHufb5+PFjhd8DIYQQQtilccF+WVmZSHAiJMwUIi1AAxpaJzMyMsDn85luFPX19czTgMbrKlK2qKgIb9++xYYNGxAaGop58+YhKysLMTExyMvLw8aNG2UObGw68BNoaBHm8/lMSyzQ0HVFWvePb775BoWFhVL3oQzhwFA2CIPWlg4IDQgIQJcuXVBaWoq//voLGzduxPLly5t9YiAQCKTu++bNm3j9+rVIFx5JLly4gBMnTuDDDz9Ex44dlaq/PO//5MmT+O233/CPf/wDbm5uePr0Kfbv34/vvvuOGW8iNG7cOAQEBKCkpASnT5/G6tWrsW7dOpHBw5L2Ket4EEIIIYS7NC7Yb4kJEyZgy5Yt2LFjB8LCwsDn83HgwAEmSG4cSCtSViAQoKamBn//+98RGhoKoGEQqZ6eHnbt2oW0tDT4+vpKrdecOXMkLrewsEB+fj5qamoANAyUtLW1lViWraBcXYQt+i9fvmzRdiwtLZk+73379kVpaSn27NnDBPumpqZ49eqV2HoVFRVM2sqmEhMTYW5uLpbCs7GbN28iOjoaU6ZMUapVHxBvwRcqLy9nuveUlpZi9+7dmD17Nt555x0AQM+ePWFpaYn169dj4sSJIoG8ra0tcw35+fnhs88+Q0xMDFatWiVzn7KOByGEENzFZTMAACAASURBVEK4S+OCfXNzc4mt98JlsnKBjxo1Cm/evMHhw4dx6tQpAA3ZYiZPnozY2FimW46iZYX7bBr89e3bF7t27UJOTo7MYH/Pnj1iXX2ePn2KrVu3wtnZmQns2lLLart27eDg4IDr16/jvffeU9l23dzccOvWLeZvZ2dn3Lp1S6zlWtiXv6m3b9/iypUrGDp0KPT0JN8+mZmZ+OabbzBo0CDMnj1b6bo6OTmJjRuora3FixcvMGrUKADAixcvUFtbiy5duoiUE/794sULsRz/Qjo6OujcuTMyMjKYZc7OzsjNzRUr+/jxY4UHRBNCCCGEfRo3QNfV1RVPnjwRy7qTl5cHACJZRCQJCQlBTEwMtm7dih9//BGRkZEoLy9nJhlSpqxwvEBT8nZFMTExEfvPyMgIOjo60NHRga6uLnR1deXK4KJNJk+ejJycHCQkJIi9JhAImk29KUl6erpINzA/Pz9UVFTgxo0bzLKXL1/i/v376Nevn9j6V69eRVVVldQuPPn5+Vi9ejU8PDywYMGCFv1A8/PzQ1paGkpLS5llly9fRm1tLfNkws7ODgCQnZ0tsm5WVhYAiEwM1lRdXR2ysrLEjkdeXp7IYNwHDx6gsLCwxYOlCSGEENL6NK5lPyAgAPHx8bh06RIGDx7MLD937hxsbGzEZgOVRF9fn/lRUFhYiOTkZIwePVpkVlJFygYGBiI+Ph7Xr19H165dmeXCVIXdu3dX7s22cWPHjsX9+/fx/fff48GDBwgICGAm1UpISIChoaHEgBwArl27hvPnz6Nfv36ws7NDWVkZLly4gFu3bonk3+/evTv8/PywZcsWzJ07l5lUy97eHsOHDxfbbmJiIuzs7CROrPb69Wt8/fXX0NPTw5QpU0QCcH19fZFr486dOygtLWUGvd6+fRuFhYWwt7dnZtIdO3YsTp48iXXr1mHatGl4/fo1fvzxRwwdOpTJmGNtbY3AwED88ssv4PP5cHNzw5MnT/DLL7/Aw8OD+VF6+vRpZGZmwtfXF9bW1kyf/efPn4sM5A0MDISrqyu++eYbzJo1i5lUy9PTU2a3JUIIIYRwk8YF+35+fvD19cW2bdtQWVkJBwcHJCUl4caNG1i0aBGTpefOnTtYuXIlpk2bxuS6z8vLw6VLl+Dm5gZ9fX08fPgQR48ehYODA2bOnCmyH0XK9unTB/7+/jh48CAEAgG6d++OrKwsHDx4EP369ROZrZXIj8fjYeHChejTpw/i4+Pxn//8BzU1NbCzs0O/fv3w7rvvSl23Q4cOqK2txd69e1FaWgoLCwu4uroiIiIC3t7eImUXL16M3bt3Y/v27airq4OPjw+WLVsm9uOvvLwc169fx6RJkyS22Ofn5zP9/1euXCnymr29PX788Ufm719++QV3795l/v7pp58AAMOHD2cG1ZqZmWH9+vXYuXMnIiIiYGhoiKCgILGuQQsWLMDhw4dx+vRpFBUVwcrKCoGBgZgxYwbzNMjFxQWXL1/Grl27UF5eDmtra3Tr1g1RUVHo3Lkzsy1dXV2sXr0aP/zwA/7zn/8w8xnMmzevTXUjI4QQQrQFTyBr1h2Oqqqqwr59+5CcnIyysjI4OTkhNDQUQUFBTJk7d+5g+fLlCA8Px/Tp0wE09IP/7rvv8PjxY1RVVcHOzg5BQUEICQkRS6GoSFmgoS/3gQMHkJiYiJKSEtjY2GDo0KEIDw+XmMITaMjCExYWhkOHDon12c/Pz0dUVBQWLlyoVI52QloTXa+EcJufnx9NjEeIFpEVQzalcS37QEO+9Pnz52P+/PlSy3h7eyMuLk5kmaOjIzZs2CDXPhQpCwCGhoaYPXt2iwZkEkIIIYQQokoaGexrm4ULF4oNvg0MDGSpNoQQQgghRFtQsM8BUVFRErvxXL9+naUaEUII0Sbnzp1juwqEEJa0rVyOhBBCSBsknFyQENL2ULBPCCGEEEKIlqJgn6OEffibTh7GRXw+H0VFReDz+WxXhSippedQeJ22tYnfuKa2tha//PILamtr2a4KURKdQ81H51A7aNN5pG9mjrK2tgYA5ObmslyT5gkEAhQXF0MDs7iS/6+l51B4nQqvW8KO2tpaHDhwQCu+nNoqOoeaj86hdtCm80gDdDnKxMQEAQEBTPrQLl26MBOGcQ2fz0dBQQEMDAyoZVdDKXsO6+vrkZubi7i4OGaGY0IIIYRwBwX7HCAp9eb48eMRGhoKAGLzBXANn8/Hq1ev0K5dOwr2NVRLz2FAQABzvRJCCCGEOyjY5wBJqTeFwsLCMHHiRJSUlHC2T3x1dTUWLlyI1atXS5xdmHCfsudQR0cH1tbW1KJPCCGEcJWAsKaiokIwYcIEQUVFBbPs5MmTatufurYt6X2oiiYeD3VuWxPPoUCgecdDU7dN92LrbVsTz6FAoHnHQxO3rannUJ3b1sQ6c/3zVJH6UZ8Ljvn99981ctvqoqnHQ13b1sRzCGjm8dDUbauLph4PTbz21EkTj4embltdNPF4aGKd1am160zBPiGEEEIIIVqKgn1CCCGEEEK0FAX7hBBCCCGEaCnKxsMiwf+fwKiyspJZxufzRf5WJXVtW7hNdWxbE4+HOretiecQ0Lzjoanbpnux9batiecQ0LzjoYnb1tRzqM5ta2Kduf55KlxfIMdkmDyBPKWIWrx69Qpz5sxhuxqEEEIIIUQD7dmzB+3atZNZhoJ9FvH5fBQXF8PY2Bg8Ho/t6hBCCCGEEA0gEAhQVVUFGxubZifDpGCfEEIIIYQQLUUDdAkhhBBCCNFSFOwTQgghhBCipSjYJ4QQQgghREtR6s02qrKyEocOHUJubi5yc3NRWlqK8PBwTJ8+XaRcbm4u9u3bh0ePHqG0tBQGBgZwdHTE+PHjMWzYMJGyd+7cwfLlyyXuLzIyEj169FCoHJFNHedQ6N69ezhy5AgePHiA2tpa2NraYvjw4Zg2bRpTpqqqCvv370dycjLKysrg5OSEkJAQBAUFqfV9axs2zyPdi6qhjnO4adMmnD9/Xuo+G58fuhdVg83zSPeiaqjr8zQnJwcHDhxAVlYWysvLYWdnhyFDhmDy5MkwMjJiynH1XqRgv40qKytDfHw8XF1dERAQgISEBInlKioq0K5dOwQFBcHW1hbV1dVITExEVFQUCgsLERYWJrbO3//+d3h7e4ss69Spk9LliGTqOocXLlzApk2bMGjQICxcuBBGRkZ4/vw5iouLRcpFREQgKysLs2bNgqOjIxITExEZGQk+n4+hQ4eq621rHbbPI0D3Ykup4xxOmzYNwcHBYttYu3Yt9PX10a1bN2YZ3YuqwfZ5BOhebCl1nMPHjx9jyZIlcHR0xPvvvw8LCwvcu3cPBw8eRE5ODlauXMmU5eq9SMF+G2Vvb48DBw6Ax+PhzZs3Um8Ib29vsQ8ef39/FBQU4PTp0xKD/Y4dO8rVCiFvOSKZOs5hUVERvv/+e4wZMwb//Oc/meU+Pj4i66empuLWrVv44osvMGTIEKZMYWEh9uzZg8GDB0NXV1dVb1WrsXkehehebBl1nEMHBwc4ODiIlL1z5w5KS0sRFhbG3F90L6oOm+dRiO7FllHHOUxMTERNTQ2WLVvGnMtevXqhuLgY8fHxKC8vh5mZGafvReqz30bxeLwW5fY3NzenLxCWqeMcJiQkoLq6GiEhITLXvXz5MoyNjTFo0CCR5SNHjkRxcTEyMzOVrldbw+Z5JKrRWp+nZ86cAY/Hw8iRI5lldC+qDpvnkaiGOs6hnl5Du7iJiYnIcjMzM+jo6DCvc/lepJZ9Ihc+nw+BQIDy8nIkJyfj5s2b+OCDDySW3bFjB7799lsYGhqiR48eCAsLg5eXl9LliGrIcw7v3r0Lc3NzPHnyBOvWrUNeXh7Mzc0xYMAAzJkzh/mwy8vLg5OTk9iHoqurK/O6h4dHq7yvtkaV51GI7sXWpcjnqVBFRQUuXryIXr16oUOHDsxyuhfZo8rzKET3YuuS5xwOHz4cv/32G7Zv347Zs2fDwsICd+/exenTpzFu3Dimzz6X70UK9olctm/fjtOnTwNo+JU7f/58sX6IJiYmeOedd9CzZ09YWFjg+fPnOHbsGJYvX45Vq1ahT58+CpUjqiXPOSwqKsLbt2+xYcMGhIaGYt68ecjKykJMTAzy8vKwceNG8Hg8lJWVSfyiMjc3B9DQb5KohyrPI92L7JDnHDaVlJSEmpoajBo1SmQ53YvsUeV5pHuRHfKcw/bt2yMyMhIRERGYN28es3zixIkif3P5XqRgn8glNDQUo0ePxps3b3D16lXs3LkT1dXVmDJlClOma9eu6Nq1K/O3l5cXAgIC8H//93/Ys2cP82ElbzmiWvKcQ4FAgJqaGvz9739HaGgogIa+jXp6eti1axfS0tLg6+vL1lsgUO15pHuRHfKcw6YSEhKYpzOEG1R5HuleZIc857CgoABr166FlZUVli5dCktLS2RmZuLQoUOorq7Gp59+yuI7kA8F+0Qu9vb2sLe3BwD4+fkBAH7++WeMGDEClpaWUtczMzNDv379cOrUKbx9+xaGhoYtKkeUJ885FLZANP1i6du3L3bt2oWcnBz4+vrC3NxcYiuFcJlwO0T1VHkeJaF7Uf0U/Tx9+PAhsrOz8c4770BfX1/kNboX2aPK8ygJ3YvqJ8853Lt3L6qqqrBlyxamy47wCUx0dDSGDRsGb29vTt+LNECXKMXd3R319fV48eJFs2UFAgEANDtoRt5yRDUknUNh38Kmmp4bV1dXPHnyBPX19SLl8vLyAFCquNbUkvMoDd2Lrau5z9MzZ84AAEaPHi32Gt2L3NGS8ygN3YutS9I5zM3NhbOzs0g+fQBM2tTHjx8D4Pa9SME+Ucrt27eho6MjsX9aY+Xl5bh27Rq6dOkCAwODFpcjqiPpHAYGBgIArl+/LlI2NTUVANC9e3cAQEBAAKqqqnDp0iWRcufOnYONjQ3c3d3VWXXSSEvOoyR0L7Y+WZ+ntbW1uHDhAtzd3SUGC3QvckdLzqMkdC+2Pknn0NbWFo8fP0ZVVZVI2QcPHjCvA9y+F6kbTxuWmpqKt2/fMhfw48ePcfHiRQANj/uNjIywdetWGBsbw93dHVZWVigtLcXFixfx119/YcqUKSKPKiMjI2FnZ4du3brBwsICz549w6+//orXr1/js88+U7gcaZ6qz2GfPn3g7++PgwcPQiAQoHv37sjKysLBgwfRr18/JiuEn58ffH19sW3bNlRWVsLBwQFJSUm4ceMGFi1aRGlZFcTWeaR7UXVUfQ6FUlJSUFZWhlmzZkncL92LqsXWeaR7UXVUfQ7feecdrF+/Hl999RUmTZoECwsLZGRk4OjRo3B2dkbfvn0BcPte5AmEz4hImzN37lwUFhZKfO2///0v2rdvj7Nnz+Ls2bPIz89HRUUFjIyM0LlzZ4wePVpsSukjR44gOTkZBQUFqKqqgrm5OTw9PRESEiLyi1becqR5qj6HAPD27VscOHAAiYmJKCkpgY2NDYYOHYrw8HCRfqZVVVXYt2+fyLTgoaGhrE8LronYOo90L6qOOs4hAHz11Vd48OAB9u7dK5YyVYjuRdVh6zzSvag66jiHt2/fxtGjR/Ho0SNUVFTAzs4O/fr1Q2hoKCwsLJhyXL0XKdgnhBBCCCFES1GffUIIIYQQQrQUBfuEEEIIIYRoKQr2CSGEEEII0VIU7BNCCCGEEKKlKNgnhBBCCCFES1GwTwghhBBCiJaiYJ8QQgghhBAtRTPoEkJUbuLEiQqVt7e3x48//oiCggK8//776NmzJ7755hs11Y478vPzceXKFdy8eRPPnj3D69evYWZmBg8PD0yaNImZ6bY5d+/exfLlyyEQCDB27Fh8/PHHYmWWLVuGu3fvSt3G6tWrmZkgheQ5jz4+Pli/fr3Y8vT0dBw6dAgZGRmoq6uDs7Mzxo8fjxEjRkjd1qtXrxATE4MbN26grKwMdnZ2GDx4MKZOnQoDAwOJ69TU1ODIkSNISkrCy5cvYW5ujj59+mDGjBlo165ds/UXOnv2LKKjo5m/TU1NcfDgQZEyko6Hnp4erKys4OXlhZCQELi6ujKvNXfMJRFO+iOcGEj4t1DTCYN0dHRgYmICS0tLdO3aFX369MHgwYOlHi9lCfcbFxen0u2qUnl5OcLDw0WWRUREwNvbm6UaEcINFOwTQlRu+PDhYsvS09Px/PlzdO7cGZ07dxZ5rfEMhG3JV199haKiIpiYmMDd3R09evRAfn4+Ll++jJSUFMydOxeTJk2SuY3a2lps3bpV7n0GBgbCyMhIbLmtra3YMknnUSg1NRWlpaXw9PQUe+3y5cvYsGEDBAIBvLy8YGFhgbS0NGzevBkPHz7E+++/L7bO8+fPsXjxYrx58wadOnWCp6cnsrOzcejQIaSlpSEiIkJkBmegIdBfuXIl0tPTYWNjg/79+6OwsBBnz57FtWvXEBkZCQcHB3kOC0N4fRoaGkot0/i4VFZWIjs7G4mJibh48SLWrFkDHx8fAECfPn1gb28v9j7T09NhZWWFPn36iG1b0rmRpPF5rKysREFBAZKTk5GUlISffvoJCxYsgJ+fn1zb0hZ6enrMuRF+3hBCKNgnhKjB559/LrZs06ZNeP78OQICAjB9+nSJ69na2mLbtm0yAy1t4uzsjDlz5iAwMFAkkD116hS2bduG3bt3o3fv3nBxcZG6jUOHDuHZs2cYNWoUEhISmt3nP/7xD5GWYlkknUegoQX1r7/+AgCxqeXLy8sRHR0NPp+PZcuWITAwEABQUlKCL7/8Er/99hv8/f2ZgFgoOjoab968wcSJEzF//nwAQH19PTZu3IjLly/j8OHDmDFjhsg6hw8fRnp6Onr06IF//etfMDY2BgAcP34cP/74I6Kjo7Fhwwa53quQrOtTqOlxqaurw5YtW/Dnn39i165d+O677wAAoaGhYuuePXsW6enpcHJyknp85SHpPJaUlODQoUP4/fffsXbtWnz99ddiT2u0mZGREXNMhZ83hBDqs08I4RA9PT04OzuLtYZqq7Vr12LIkCFiLdbBwcHo3bs3+Hw+Ll68KHX9x48fIzY2FqNGjYKHh4e6q8u4ePEiamtr0b17d3Ts2FHktYSEBFRUVKB///5MoA8A1tbWmDNnDoCGYLyxzMxM3Lt3D1ZWVkwZANDV1cVHH30EPT09nDx5EnV1dcxrdXV1OHnyJADgww8/ZAJ9AHj33Xfh6uqKe/fuITs7W3VvXAo9PT3mB8KjR49QXl6u9n1KYm1tjQ8//BAzZ84En89HdHQ0amtrWakLIYQ7qGWfEMIZ0vrsC/tTh4eHY9iwYdi7dy9u376Nuro6eHh4YO7cuXBxcUF9fT1iY2Nx7tw5vHz5Eu3atcOkSZMwfvx4qfs7cuQIbt68ieLiYpiYmKBnz56YNm2aWFej1ta5c2fcvHkTRUVFEl8XCATYunUrTE1NMXv2bFy5cqXV6vbnn38CEG/VB4CrV68CAAYOHCj2mp+fHwwMDJCWloaamhqmX/m1a9cAAP369RP74WNtbQ1PT0/cvn0b6enpTP/r+/fvo6KiAg4ODujatavYvgYOHIhHjx7h6tWrcHNza8G7lY+VlRXz7/r6erXvT5aQkBAkJCSgsLAQycnJEs+TJPX19Th27BjOnDmDV69ewcbGBkOHDkVYWJjE8gKBAElJSbhy5QpycnJQVFQEHR0dODk5YcSIEQgODoaOzv/aFLdv344//vgDn3zyCcaMGSNxe/Pnz0dhYSF27drF/OjPz8/H4cOH8eDBAxQVFcHY2Bi2trbw9vbG3/72N9jY2ChxlAhpO6hlnxCiMQoKCrBo0SLk5OSgZ8+eaN++PW7cuIHly5ejpKQE33zzDY4ePYr27dvD29sbL1++xI4dOxAfHy+2rXv37uHTTz9FfHw8jIyM0L9/f3Ts2BGXL1/GF198gdu3b7PwDv/nxYsXABqCXUn++OMPpKen4x//+AfMzc3l3u6ZM2ewbds27NixAydOnBAZ7CmPwsJC3L9/H3p6ehg8eLDY648ePQIAiQG4vr4+XFxcUFNTg6dPnzLLHz58KHWdxsuF5Rr/u0uXLnKvo07CJwgWFhawtLRslX1Ko6ury/zYunPnjtzrRUZG4ueff8br16/Rp08fuLq64vjx44iIiIBAIBArX1tbi3//+9+4efMmLC0t4e/vD3d3dzx+/Bg7duwQGfAMNDyxAiDxfgSA27dv48WLF/D19WUC/ezsbHz22WdITEyEpaUlBgwYAHd3d9TV1eHEiRMi1xEhRDJq2SeEaIzz58/j3XffxZw5c6CjowOBQIDo6GicO3cOK1asgI6ODrZt28ZkYUlLS8PKlStx+PBhkZbEyspKbNy4ETU1NVi6dKlIK/StW7ewZs0aREVFYdeuXWItza3h+fPnTGt3//79xV4vKirCzz//DB8fH5mDaCU5dOiQyN979uxBWFgYpk2bJtf6iYmJEAgE6Nu3r9jA6srKSlRUVACA1Ew47dq1Q3Z2Nl6+fMk8PXn58mWz6zQup+w66lBRUYHMzEzs3LkTgOR++mwQ/gjKz8+Xq7xwgHGHDh2wYcMGZsD2ixcvsGzZMrx69UpsHV1dXSxbtkzsicybN2+wevVqnD9/HqNGjULPnj0BAK6urvDw8EB6ejoePnwo9vRM+COg8b0aFxeHmpoakfEfQvn5+TA1NZXr/RHSllHLPiFEYzg4OGD27NlM1wAej4d3330XQMMX//z580WCv169eqFLly4oLCxEQUEBs/zMmTMoKSnB5MmTxbqb+Pr6Yty4cSgqKmIC7tZUX1+PzZs3o7a2FoMHD5bYBWXHjh2oqanBRx99JPd2vby8sHDhQuzatQtHjx7Fjh078N5770FHRwcxMTE4ceKEXNuR1YWnqqqK+be0QdbCDDKNy1ZXV8tcR7hcmXWE5VRp4sSJzH/Tpk3D119/jYqKCnzxxRfM9cg24Q8xeccPnDp1CgAwY8YMkcxMHTp0kNqNR1dXV2xwOQBYWlpi1qxZACDWvWzs2LEAIDaYvLS0FCkpKbCysoK/vz+z/M2bNwAgNqAbaBjgTl14CGkeBfuEEI3Rs2dP6Orqiizr0KEDgIZBksIWxMaEqRdLSkqYZbdu3QLQkHlFEmE6yaysrJZXWkE7d+7E/fv30aFDB4nB/KVLl5CSkoKQkBA4OTnJvd2ZM2di2LBh6NChAwwNDeHo6IipU6dixYoVAIBffvkFb9++lbmN7OxspjW1cUCmCEndQYTLeDyewttRZB1VGT58OPNfUFAQevTogdLSUvz0008KdZtRJ0WOT11dHTIyMqCjoyNxrEVQUJDM9XNzcxEbG4vt27dj8+bN2LRpE/744w8AwLNnz0TKDho0CObm5vjzzz9Frrfz58+jtrYWI0aMgJ7e/zodCH/sbtq0CZmZmeDz+c2+H0KIKOrGQwjRGJJywQtbiq2trUUGAwoJW3gbZyURtvIvWrRI5v5KS0ubrVN8fDzu378vsszCwgJz585tdt2mDhw4gFOnTsHKygpr1qwR64tfWVmJnTt3omPHjirrLtKnTx+4ubkhOzsbGRkZEltQhS5cuACgIWCT1L2pcUact2/fwsTERKyMMMBrXFb4b2mt8C1ZR9689YqQlDIzJycHy5Ytw6pVq7Bt2zbmRyhbhNeumZlZs2XLyspQV1cHGxsbiefVxMQEpqamTBctodraWmzevBlJSUlSt934aQwAGBgYYNiwYThx4gQuXrzIdENLSEgAj8fD6NGjRcpPmTIF9+/fx9WrV3H16lWYmprC3d0d/fr1w4gRIyReY4QQURTsE0I0hqxWSkVaeIWtgwMHDpSZ09/d3b3Zbd2/fx/nz58XWWZvb69wsP/777/jl19+gampKdasWSOW0hJoCCiLi4thb2+PVatWibwmfHJx9epVPHnyBA4ODvj000/l2nfHjh2RnZ2N4uJiqWXq6+uZ3PpDhw6VWKZxUPjq1SuJ8wMI+37b2dkxy+zs7JCbmyuxX7isdRq/Js866tS1a1eMHTsWv/76K37//XelfuypUm5uLgDInKNBSNLTFnkcP34cSUlJ6NSpE+bMmYOuXbvCzMwMenp6ePr0KT788EOJ2w4ODsaJEyeQkJCA4cOHIz09Hfn5+fDx8RG77k1MTLB+/Xrcv38f165dw507d5CWloabN2/i6NGj2LBhg8ITpxHS1lCwTwhpc9q1a4enT58iLCysxSk2P//88xZNjgQ0tJjv3LkThoaG+Prrr6VmmBEqLCyUmkWnuLgYxcXFYq2wsgj7dTduOW8qLS2N+aHh5eUltZwwv31OTo5YoFlXV4fHjx9DX18fjo6OzPLOnTsz6RslES53dXUVWQf4X1ArzzrqJpzk6smTJ622T0nq6+uZ+RmEqUplsbCwgJ6eHl6/fo3a2lqx1v3GA68bS0lJAQAsXrwYnTp1EnlNmE1KEicnJ3h7e+POnTt48uQJMzC3aau+EI/Hg5eXF3PdvXnzBj/88AOSkpLw888/48svv2z2PRLSllGffUJIm9OrVy8A/wtW2JSamorNmzdDV1cXy5cvZ8YLSOLt7Y24uDiJ/y1YsABAwwDIuLg4bNmyRa79v3nzhumGJC31JfC/LjxDhw6V+RSlX79+ACBxMrBr166hpqYGvXr1YnLsN17n2rVrYpNAlZSU4P79+zA1NRU5Nh4eHjA1NcXz588l/kgQ7l+47dYgDHDV0XVIEUePHsXLly9ha2srlsFGEj09Pbi7u4PP5+PSpUtirwuf6DQl/JEoKSNScnKyzH0KB+oeP34cycnJMDc3l6uuQMMAYOEkZnl5eXKtQ0hbRsE+IaTNCQ4OhqWlJY4cOYKzZ8+KdTWorq7G+fPnpXYRUZX79+8zk4ctWbIEffr0Uct+Hjx4gNu3b4u9z4KCAqxfvx7V1dXo37+/1DSW1dXVzA8jaV14hEaPANxR3QAABTxJREFUHg0TExNcuXJFJHB8/fo19uzZAwBiGWvc3d3h4eGB169f46effmKW19fXY/v27airq8OECRNEBm7q6+szk6Xt3LlTpO/+8ePH8ejRI3h6esrVFUsVcnJymBZqPz+/VtlnUyUlJdi5cyf2798PHR0dLFiwQO7UscLgOyYmRqQ7V2FhIQ4ePChxHWGXG2EmH6GLFy+KdW1rasCAAbC0tER8fDzevn2LYcOGSazrqVOnJD4luH79OoDW66ZFiCajbjyEkDbHzMwMy5cvx7p16xAdHY0DBw7AxcUF+vr6ePnyJZ48eYLq6mpER0dLDYBV4V//+hdqamrQvn17pKSkSHzS4OnpKXG2UUU8efIE0dHRsLGxQceOHWFtbY1Xr14hJycHNTU1cHFxwSeffCJ1/ZSUFFRVVaFbt25wdnaWuS9zc3N8+umn+Pbbb7Fhwwb07NkTFhYWuHXrFioqKjBx4kTmyUpjn332Gb744gucOHECt2/fhrOzM7KysvDixQt0794dU6dOFVsnLCwMaWlpSE9PxwcffABPT0+8fPkSGRkZMDc3Z552qNqmTZuYf9fV1TH75PP58Pf3l3vGWnlIe4qye/dukTSmBQUFePToEfh8PqytrfHZZ5+hd+/ecu9n6NChuHz5Mi5fvowPP/wQvXr1gkAgQFpaGnr27Akejyc2Z8Hf/vY33LhxA3v37sXFixfRsWNHPHv2DNnZ2Zg8eTJ+/fVXqfvT19fHyJEjERsbC0B6F55Tp05h27ZtcHZ2hrOzM3R1dfH06VPk5ubCwMAA4eHhcr9HQtoqCvYJIW2Sp6cnvvvuOxw/fhypqam4ffs2dHV1YWNjg379+mHAgAHNBrYtJewHXVBQIDIPQFMtDfa7d++O4OBgZGZmIj8/H+np6TAyMkLnzp0xaNAgBAcHyxyo3LgLjzwGDhyIb775BocOHUJGRgbq6urg7OyM8ePHY+TIkRLX6dixI6KjoxETE4MbN27gyZMnsLOzQ1hYGEJDQ0W6/QgZGBhg/fr1OHLkCJKSkpCSkgIzMzMMHz4cM2fOVFurb+NWax0dHZiamsLLywvDhg3DiBEjJGaFUlRNTQ0A6V2ChE9NdHR0YGxsDCsrKwwaNAh9+/bFoEGDJB4vWXg8HpYsWYJjx47hzJkzSE1NhY2NDSZMmIDp06fjww8/FFunZ8+e2LhxI/bt24fc3Fw8ffoUrq6uWLZsGbp27Soz2AcacufHxsbCw8NDrM+/0MyZM5GSkoKMjAykpaWhrq4O7dq1w5gxYzBlyhSJA9kJIaJ4AmWH4RNCCCFa4uzZs4iOjkZ4eDjTH5wtVVVVCAsLg6GhIQ4fPszKXAKtYevWrYiPj8eCBQuk/ghU1qZNm3D+/HlERETINUiZEG1GLfuEEELI/5eSkoKCggIYGhrin//8Jyt1+P333yEQCJjuM9qosLAQf/75JywsLDB48GCVbLO6uhrbt28HAKSnp6tkm4RoAwr2CSGEkP/v4cOHePjwIUxNTVs92I+KisKzZ8+QkZEBPT09ieMUNN2xY8fw8OFD3Lp1CzU1NZg1a5bMLmSKqKura3ZgMCFtEXXjIYQQQjhg4sSJMDExgZubG6ZNm6aV3U+WLVuGu3fvwtbWFmPGjMG0adO09ukFIVxBwT4hhBBCCCFaivLsE0IIIYQQoqUo2CeEEEIIIURLUbBPCCGEEEKIlqJgnxBCCCGEEC1FwT4hhBBCCCFaioJ9QgghhBBCtBQF+4QQQgghhGgpCvYJIYQQQgjRUhTsE0IIIYQQoqX+H8kKk+cPIdq4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flat_lc = lcf[0].flatten(window_length=401)\n",
    "flat_lc.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Folding the light curve\n",
    "From the [L 98-59 System](https://arxiv.org/pdf/1903.08017.pdf) paper, we know that planet c has a period of 3.690621 days. We can use the `fold()` function to find the transit in our data as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "folded_lc = flat_lc.fold(period=3.690621)\n",
    "folded_lc.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Binning the light curve\n",
    "Often, to see a trend, it can be beneficial to bin the data, this can be achieved via the `bin()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "binned_lc = folded_lc.bin(time_bin_size=0.01)\n",
    "binned_lc.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, we can now see our transit very clearly! Note that we can achieve the same plot from our data using one line of code instead of several, see below.\n",
    "\n",
    "`lcf[0].flatten(window_length=401).fold(period=3.690621).bin(time_bin_size=0.01).plot();`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interact with your light curve\n",
    "\n",
    "There is also an interactive tool for light curves called `.interact_bls`. Box Least Squares (BLS), is a method for identifying transit signals in a light curve.\n",
    "\n",
    "The `.interact_bls` method allows you to identify periodic transit signals in light curves by manually selecting the period and duration of the signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(null);\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error(url) {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < css_urls.length; i++) {\n",
       "      const url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\": \"dM3QQsP+wXdHg42wTqW85BjZQdLNNIXqlPw/BgKoExPmTG7ZLML4EGqLMfqHT6ON\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\": \"8x57I4YuIfu8XyZfFo0XVr2WAT8EK4rh/uDe3wF7YuW2FNUSNEpJbsPaB1nJ2fz2\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\": \"3QTqdz9LyAm2i0sG5XTePsHec3UHWwVsrOL68SYRoAXsafvfAyqtQ+h440+qIBhS\"};\n",
       "\n",
       "    for (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\": \"dM3QQsP+wXdHg42wTqW85BjZQdLNNIXqlPw/BgKoExPmTG7ZLML4EGqLMfqHT6ON\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\": \"8x57I4YuIfu8XyZfFo0XVr2WAT8EK4rh/uDe3wF7YuW2FNUSNEpJbsPaB1nJ2fz2\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\": \"3QTqdz9LyAm2i0sG5XTePsHec3UHWwVsrOL68SYRoAXsafvfAyqtQ+h440+qIBhS\"};\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.bokehjs_exec.v0+json": "",
      "text/html": [
       "\n",
       "<script id=\"1002\">\n",
       "  var xhr = new XMLHttpRequest()\n",
       "  xhr.responseType = 'blob';\n",
       "  xhr.open('GET', \"http://localhost:65219/autoload.js?bokeh-autoload-element=1002&bokeh-absolute-url=http://localhost:65219&resources=none\", true);\n",
       "  \n",
       "  xhr.onload = function (event) {\n",
       "    var script = document.createElement('script'),\n",
       "    src = URL.createObjectURL(event.target.response);\n",
       "    script.src = src;\n",
       "    document.body.appendChild(script);\n",
       "  };\n",
       "xhr.send();\n",
       "</script>"
      ]
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "server_id": "f4060b0b8c1d457ab61d3bb880846748"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lcf[0].interact_bls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The light curve in the top right panel is phase-folded with the highest power period. When you zoom in on a region of period space in the BLS periodogram, it will automatically update the phase plot with the new period-at-max-power. Changing the duration using the slider in the bottom left will also update the BLS periodogram and phase-folded light curve. Finally, the parameters of the BLS model can be found in the bottom right panel.\n",
    "\n",
    "What if your object is not a target of interest but simply observed within the full framed images? You can still extract the data and create a 30-min or 10-min cadenced light curve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Creating a light curve using FFI data:\n",
    "\n",
    "In our previous FFI search, we found that *L 98-59* was observed in Sector 2 with a 30-min cadence. This data is stored as the 2nd argument of the *search_ffi* array. \n",
    "\n",
    "To create the light curve from the FFI data, we must first download the relevant images. Note that we do not want the entirety of the Sector 2 FFI, only a small region surrounding our object of interest. We can specify the size of the region we want to cut out using the commands below; in this case we want a 10x10 pixel region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ffi_data = search_ffi[1].download(cutout_size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now see what this cut out looks like and also check that our object is at the center of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f84b36fd0d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ffi_data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above figure indicates the pixels on the CCD camera, with which *L 98-59* was observed. The color indicates the amount of flux in each pixel, in electrons per second. The y-axis shows the pixel row, and the x-axis shows the pixel column. The title tells us the *TESS* Input Catalogue ([TIC](https://tess.mit.edu/science/tess-input-catalogue/)) identification number of the target, and the observing cadence of this image. By default, `plot()` shows the first observation cadence in the Sector.\n",
    "\n",
    "It looks like our star is isolated, so we can extract a light-curve by simply summing up all the pixel values in each image. To do this, we need to first define an **aperture mask**. \n",
    "\n",
    "Many decisions go into the choice of aperture mask, including the significant blending of the large *TESS* pixels. In this tutorial, we are going to define an aperture by defining a median flux value and only selecting pixels at a certain sigma above that threshold. \n",
    "\n",
    "In most situations, a threshold mask will be the best choice for custom aperture photometry, as it doesn’t involve trial and error beyond finding the best sigma value. You can define a threshold mask using the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_mask = ffi_data.create_threshold_mask(threshold=15, reference_pixel='center')\n",
    "n_target_pixels = target_mask.sum()\n",
    "n_target_pixels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This indicates that there are 9 pixels which are above our threshold and in our mask. We can now check to make sure that our target is covered by this mask using plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ffi_data.plot(aperture_mask=target_mask, mask_color='r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice! We see our target mask centered on the 9 brightest pixels in the center of the image. Let’s see what the light curve looks like. Note that this light curve will be uncorrected for any anomalies or noise, and that the flux is therefore based upon “Simple Aperture Photometry” (SAP).\n",
    "\n",
    "To create our light curve we will pass our **aperture_mask** to the [`to_lightcurve`](https://docs.lightkurve.org/reference/api/lightkurve.KeplerTargetPixelFile.to_lightcurve.html?highlight=to_lightcurve) function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ffi_lc = ffi_data.to_lightcurve(aperture_mask=target_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, we can examine the light curve data as a table, but note this time that there is only one flux value and that as default this is the SAP flux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>TessLightCurve length=1196 LABEL=&quot;&quot; SECTOR=2</i>\n",
       "<table id=\"table140208687476304\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>time</th><th>flux</th><th>flux_err</th><th>centroid_col</th><th>centroid_row</th><th>cadenceno</th><th>quality</th></tr></thead>\n",
       "<thead><tr><th></th><th>electron / s</th><th>electron / s</th><th>pix</th><th>pix</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>object</th><th>float32</th><th>float32</th><th>float64</th><th>float64</th><th>int64</th><th>int32</th></tr></thead>\n",
       "<tr><td>1354.1355100037465</td><td>20954.431640625</td><td>3.968478202819824</td><td>664.053236257685</td><td>338.870953330744</td><td>0</td><td>0</td></tr>\n",
       "<tr><td>1354.1563430385859</td><td>20953.640625</td><td>3.9688515663146973</td><td>664.0529987132587</td><td>338.87003750094146</td><td>1</td><td>0</td></tr>\n",
       "<tr><td>1354.177176075171</td><td>20948.37890625</td><td>3.9678900241851807</td><td>664.0535754626561</td><td>338.8696240269748</td><td>2</td><td>0</td></tr>\n",
       "<tr><td>1354.1980091135024</td><td>20953.16796875</td><td>3.9682953357696533</td><td>664.053397969705</td><td>338.86938462421125</td><td>3</td><td>0</td></tr>\n",
       "<tr><td>1354.218842153522</td><td>20949.62109375</td><td>3.9680519104003906</td><td>664.05334777157</td><td>338.86842105447164</td><td>4</td><td>0</td></tr>\n",
       "<tr><td>1354.239675195171</td><td>20950.841796875</td><td>3.9680023193359375</td><td>664.0529491917277</td><td>338.8680324715659</td><td>5</td><td>0</td></tr>\n",
       "<tr><td>1354.260508238421</td><td>20944.640625</td><td>3.9673573970794678</td><td>664.0522733076061</td><td>338.86667562350004</td><td>6</td><td>0</td></tr>\n",
       "<tr><td>1354.2813412832716</td><td>20952.73046875</td><td>3.9680874347686768</td><td>664.0519973612013</td><td>338.86666190722457</td><td>7</td><td>0</td></tr>\n",
       "<tr><td>1354.302174329665</td><td>20949.45703125</td><td>3.9677042961120605</td><td>664.0511057724311</td><td>338.8659224181862</td><td>8</td><td>0</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>1381.3018854391335</td><td>21803.31640625</td><td>4.048139572143555</td><td>664.009622243243</td><td>338.7804066840283</td><td>1186</td><td>0</td></tr>\n",
       "<tr><td>1381.3227185149694</td><td>21763.5703125</td><td>4.044528007507324</td><td>664.0098234495485</td><td>338.7783433746617</td><td>1187</td><td>0</td></tr>\n",
       "<tr><td>1381.3435515902245</td><td>21740.970703125</td><td>4.0420989990234375</td><td>664.010488493472</td><td>338.7803301985909</td><td>1188</td><td>0</td></tr>\n",
       "<tr><td>1381.364384664897</td><td>21700.6015625</td><td>4.038733005523682</td><td>664.0101843813644</td><td>338.77844460947045</td><td>1189</td><td>0</td></tr>\n",
       "<tr><td>1381.385217739045</td><td>21676.36328125</td><td>4.036615371704102</td><td>664.0108564056399</td><td>338.7785207357921</td><td>1190</td><td>0</td></tr>\n",
       "<tr><td>1381.4060508126108</td><td>21656.921875</td><td>4.034541606903076</td><td>664.0106157420802</td><td>338.777296648174</td><td>1191</td><td>0</td></tr>\n",
       "<tr><td>1381.4268838857115</td><td>21613.62890625</td><td>4.0302863121032715</td><td>664.0110574507974</td><td>338.77740512578055</td><td>1192</td><td>0</td></tr>\n",
       "<tr><td>1381.447716958347</td><td>21571.404296875</td><td>4.026115417480469</td><td>664.0115646734967</td><td>338.77744780257865</td><td>1193</td><td>0</td></tr>\n",
       "<tr><td>1381.468550030574</td><td>21527.71875</td><td>4.021993160247803</td><td>664.0113081777426</td><td>338.77612574703835</td><td>1194</td><td>0</td></tr>\n",
       "<tr><td>1381.4893831023946</td><td>21476.515625</td><td>4.017423152923584</td><td>664.0124305558461</td><td>338.7753083946345</td><td>1195</td><td>0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<TessLightCurve length=1196 LABEL=\"\" SECTOR=2>\n",
       "       time              flux           flux_err      ... cadenceno quality\n",
       "                     electron / s     electron / s    ...                  \n",
       "      object           float32          float32       ...   int64    int32 \n",
       "------------------ --------------- ------------------ ... --------- -------\n",
       "1354.1355100037465 20954.431640625  3.968478202819824 ...         0       0\n",
       "1354.1563430385859    20953.640625 3.9688515663146973 ...         1       0\n",
       " 1354.177176075171  20948.37890625 3.9678900241851807 ...         2       0\n",
       "1354.1980091135024  20953.16796875 3.9682953357696533 ...         3       0\n",
       " 1354.218842153522  20949.62109375 3.9680519104003906 ...         4       0\n",
       " 1354.239675195171 20950.841796875 3.9680023193359375 ...         5       0\n",
       " 1354.260508238421    20944.640625 3.9673573970794678 ...         6       0\n",
       "1354.2813412832716  20952.73046875 3.9680874347686768 ...         7       0\n",
       " 1354.302174329665  20949.45703125 3.9677042961120605 ...         8       0\n",
       "               ...             ...                ... ...       ...     ...\n",
       "1381.3018854391335  21803.31640625  4.048139572143555 ...      1186       0\n",
       "1381.3227185149694   21763.5703125  4.044528007507324 ...      1187       0\n",
       "1381.3435515902245 21740.970703125 4.0420989990234375 ...      1188       0\n",
       " 1381.364384664897   21700.6015625  4.038733005523682 ...      1189       0\n",
       " 1381.385217739045  21676.36328125  4.036615371704102 ...      1190       0\n",
       "1381.4060508126108    21656.921875  4.034541606903076 ...      1191       0\n",
       "1381.4268838857115  21613.62890625 4.0302863121032715 ...      1192       0\n",
       " 1381.447716958347 21571.404296875  4.026115417480469 ...      1193       0\n",
       " 1381.468550030574     21527.71875  4.021993160247803 ...      1194       0\n",
       "1381.4893831023946    21476.515625  4.017423152923584 ...      1195       0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ffi_lc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now plot this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f84b08b7550>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ffi_lc.plot(label=\"SAP FFI\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the above light curve, we can see two dominant peaks and observe that the flux in the aperture is dominated by what is known as scattered light. We can tell this because *TESS* orbits Earth twice in each sector, thus patterns which appear twice within a sector are typically related to *TESS’* orbit (such as the scattered light effect).\n",
    "\n",
    "We will discuss this issue in more detail below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. How to analyze and assess various data anomalies and how you might visualize them\n",
    "\n",
    "Lets take a look at the SAP light curves derived from our FFI data and the PDCSAP light curve derived from our Light Curve File.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f849105ccd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = lcf[0].plot(column='pdcsap_flux', normalize=True, label=\"PDCSAP\");\n",
    "ffi_lc.plot(ax=ax, normalize=True, label=\"SAP FFI\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the figure above, you can see that the SAP light curve has a long-term change in brightness that has been removed in the PDCSAP light curve, while keeping the transits at the same depth. For most inspections, a PDCSAP light curve is what you want to use, but when looking at astronomical phenomena that aren't planets (e.g. long-term variability), the SAP flux may be preferred. \n",
    "\n",
    "The primary source of noise removed from the SAP light curve is that of scattered light. Each of TESS's cameras has a lens hood to reduce the scattered light from the Earth and the Moon. Due to TESS's wide field of view and the physical restrictions of the Sun shade, the lens hood is not 100% efficient. The effect of the scattered light on the CCD's can be seen in this [video](https://www.youtube.com/watch?v=SP4QSF9G6FA)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive inspection:\n",
    "\n",
    "By interactively inspecting the area around your object of interest, you can see when scattered light comes into play, and also how it effects the light curve. To do this, we use the `interact()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "(function(root) {\n",
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       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
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       "\n",
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       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
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       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
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       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
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       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
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       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
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       "      render(props, toinsert[toinsert.length - 1]);\n",
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       "\n",
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       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
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       "    events.on('output_added.OutputArea', handleAddOutput);\n",
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       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
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       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
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       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
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       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
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       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
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       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
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       "\n",
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       "\n",
       "    for (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
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       "      Bokeh.set_log_level(\"info\");\n",
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       "    function(Bokeh) {\n",
       "    \n",
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       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
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       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
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       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
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Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; 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i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
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    {
     "data": {
      "application/vnd.bokehjs_exec.v0+json": "",
      "text/html": [
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       "<script id=\"1003\">\n",
       "  var xhr = new XMLHttpRequest()\n",
       "  xhr.responseType = 'blob';\n",
       "  xhr.open('GET', \"http://localhost:65233/autoload.js?bokeh-autoload-element=1003&bokeh-absolute-url=http://localhost:65233&resources=none\", true);\n",
       "  \n",
       "  xhr.onload = function (event) {\n",
       "    var script = document.createElement('script'),\n",
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  {
   "cell_type": "markdown",
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   "source": [
    "You can move the large bottom left slider to change the location of the vertical red bar, which indicates which cadence is being shown in the TPF postage stamp image. The slider beneath the TPF postage stamp image controls the screen stretch, which defaults to logarithmic scaling initialized to 1% and 95% lower and upper limits respectively.\n",
    "\n",
    "You can move your cursor over individual data points to show hover-over tooltips indicating additional information about that datum. Currently, the tooltips list the cadence, time, flux, and quality flags. The tools on the right hand side of the plots enable zooming and pixel selection.\n",
    "\n",
    "Interaction modes:\n",
    "\n",
    "- Clicking on a single pixel shows the time series light curve of that pixel alone.\n",
    "- Shift-clicking on multiple pixels shows the light curve using that pixel mask.\n",
    "- Shift-clicking on an already selected pixel will deselect that pixel.\n",
    "- Clicking and dragging a box will make a rectangular aperture mask — individual pixels can be deselected from this mask by shift-clicking (box deselecting does not work).\n",
    "- The screen stretch high and low limits can be changed independently by clicking and dragging each end, or simultaneously by clicking and dragging in the middle.\n",
    "- The cadence slider updates the postage stamp image at the position of the vertical red bar in the light curve.\n",
    "- Clicking on a position in the light curve automatically seeks to that cadence number.\n",
    "- The left and right arrows can be clicked to increment the cadence number by one.\n",
    "- The interact() tool works for *TESS* data and *Kepler/K2*. \n",
    "\n",
    "This tool can also be used to see how crowded the field of your sources is and if anything else unusual happened during observation."
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    "### Interact Sky:\n",
    "\n",
    "*Lightkurve* has an additional tool to interactively inspect target pixel files — `.interact_sky`. This method brings up a single frame of the target pixel file with targets identified by Gaia marked by red circles. The size of the circle scales with the magnitude of the target, where brighter sources are larger and fainter sources are smaller. Using your cursor, you can hover over the red circles to display useful information from Gaia, including its Gaia ID, G band magnitude, and coordinates."
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      "/Users/rhounsel/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/lightkurve/interact.py:517: LightkurveWarning: Proper motion correction cannot be applied to the target, as none is available. Thus the target (the cross) might be noticeably away from its actual position, if it has large proper motion.\n",
      "  category=LightkurveWarning)\n"
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    "ffi_data.interact_sky()"
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    "This tool is useful for crowded sources. "
   ]
  },
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   "source": [
    "### Cadence Quality Flags:\n",
    "\n",
    "The *TESS* pipeline populates a series of quality flags to indicate when a cadence may have been taken during an anomalous event.  These flags are available in the Light Curve Files, the Target Pixel Files, and a subset are available for the FFIs. \n",
    "\n",
    "### Aperture Mask Image Flags:\n",
    "\n",
    "The Light Curve Files and Target Pixel Files contain an image in the **APERTURE FITS** extension that describes how each pixel was used in the processing. \n",
    "\n",
    "Tables of these flags can be found [here](https://outerspace.stsci.edu/display/TESS/2.0+-+Data+Product+Overview#id-2.0DataProductOverview-Table:CadenceQualityFlags), where a description of each flag is provided."
   ]
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    "## Additional Resources \n",
    "\n",
    "In this tutorial, we have covered the basics of how to obtain, reduce and analyze *TESS* data using *Lightkurve*. We have, however, only skimmed the surface of what *Lightkurve* can do and how to investigate the data. For more detailed tutorials as well as other useful tools, please visit the following pages.\n",
    "\n",
    "- [*Lightkurve Tutorials page*](https://docs.lightkurve.org/tutorials/index.html): A set of 21 tutorials dealing with Kepler/K2 and TESS data\n",
    "- [TESS GI data products page](https://heasarc.gsfc.nasa.gov/docs/tess/data-analysis-tools.html): A set of 7 TESS specific tutorials.\n",
    "- [STScI Kepler K3 notebooks](https://github.com/spacetelescope/notebooks/tree/master/notebooks/MAST/Kepler): A set of notebooks produced by a collaboration between NumFocus, MAST, *Lightkurve*, and TESS GI office. They make use of python astronomical data packages to demonstrate how to analyze time series data from these NASA missions. New tools are presented here and techniques for the advanced user.\n"
   ]
  }
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