docs/guidelines/occultation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Occultation Class\n",
    "\n",
    "The `Occultation` Class within SORA was created to reduce and analyze stellar occultations, and manage all the other Objects in a SORA occultation. Here we have some useful tasks that allow converting the times for each observatory in positions in the sky plane ($\\xi$, $\\eta$), fit an ellipse to the points, obtain the astrometrical position resulting, among others.\n",
    "\n",
    "The documentation <a href=https://sora.readthedocs.io/>here</a> contains the details about every step.  \n",
    "\n",
    "This Jupyter-Notebook was designed as a tutorial for how to work with the `Occultation` Class. More information about the other classes, please refer to their specif Jupyter-Notebook. Any further question, please contact the core team: Altair Ramos Gomes Júnior, Bruno Eduardo Morgado, Gustavo Benedetti Rossi, and Rodrigo Carlos Boufleur.\n",
    "\n",
    "**The** `Occultation` **Docstring was designed to help the users. Also, each function has its Docstring containing its main purpose and the needed parameters (physical description and formats). Please, do not hesitate to use it.**\n",
    "\n",
    "## 0. Index\n",
    "\n",
    "1. [Instantiating an Occultation Object and adding observations](#section_1)\n",
    "\n",
    "2. [Projecting the times in the sky plane and the Chords](#section_2)\n",
    "\n",
    "3. [Ellipse fit](#section_3)\n",
    "\n",
    "4. [Viewing and saving the results](#section_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SORA version: 0.3\n"
     ]
    }
   ],
   "source": [
    "## SORA package\n",
    "from sora import Occultation, Body, Star, LightCurve, Observer\n",
    "\n",
    "## Other main packages\n",
    "#from astropy.time import Time\n",
    "import astropy.units as u\n",
    "\n",
    "## Usual packages\n",
    "import numpy as np\n",
    "import matplotlib.pylab as pl\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"section_1\"></a>\n",
    "\n",
    "## 1. Instantiating an Occultation Object and adding observations\n",
    "\n",
    "The `Occultation` Class can be instantiated in only one way. For this it is needed a `Star` Object, an `Body` Object with an `Ephemeris` Object (`EphemKernel`, `EphemPlanet` or `EphemHorizons`), and the occultation time (within 60 minutes of the correct value)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mOccultation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mstar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mephem\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mreference_center\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'geocenter'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Instantiates the Occultation object and performs the reduction of the\n",
       "occultation.\n",
       "\n",
       "Attributes\n",
       "----------\n",
       "star : `sora.Star`, `str`, required\n",
       "    the coordinate of the star in the same reference frame as the ephemeris.\n",
       "    It must be a Star object or a string with the coordinates of the object\n",
       "    to search on Vizier.\n",
       "\n",
       "body : `sora.Body`, `str`\n",
       "    Object that will occult the star. It must be a Body object or its name\n",
       "    to search in the Small Body Database.\n",
       "\n",
       "ephem : `sora.Ephem`, `list`\n",
       "    Object ephemeris. It must be an Ephemeris object or a list.\n",
       "\n",
       "time : `str`, `astropy.time.Time`, required\n",
       "    Reference time of the occultation. Time does not need to be exact, but\n",
       "    needs to be within approximately 50 minutes of the occultation closest\n",
       "    approach to calculate occultation parameters.\n",
       "\n",
       "reference_center : `str`, `sora.Observer`, `sora.Spacecraft`\n",
       "    A SORA observer object or a string 'geocenter'.\n",
       "    The occultation parameters will be calculated in respect\n",
       "    to this reference as center of projection.\n",
       "\n",
       "\n",
       "Important\n",
       "---------\n",
       "When instantiating with \"body\" and \"ephem\", the user may define the\n",
       "Occultation in 3 ways:\n",
       "\n",
       "1. With `body` and `ephem`.\n",
       "\n",
       "2. With only \"body\". In this case, the \"body\" parameter must be a Body\n",
       "object and have an ephemeris associated (see Body documentation).\n",
       "\n",
       "3. With only `ephem`. In this case, the `ephem` parameter must be one of the\n",
       "Ephem Classes and have a name (see Ephem documentation) to search for the\n",
       "body in the Small Body Database.\n",
       "\u001b[0;31mFile:\u001b[0m           ~/Documentos/códigos/SORA/sora/occultation/core.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Occultation?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**First let's instantiate the `Star` and the `Body`**\n",
    "\n",
    "SORA will automatically search for the star information. A warning will raise when any information is missing. In this example, there is no star radius available in Gaia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining data for Chariklo from SBDB\n",
      "1 GaiaDR3 star found band={'G': 14.223701}\n",
      "star coordinate at J2016.0: RA=18h55m15.65210s +/- 0.0197 mas, DEC=-31d31m21.6676s +/- 0.018 mas\n",
      "\n",
      "Downloading star parameters from I/297/out\n"
     ]
    }
   ],
   "source": [
    "chariklo = Body(name='Chariklo', spkid='2010199',\n",
    "                ephem=['input/bsp/Chariklo.bsp', 'input/bsp/de438_small.bsp'])\n",
    "\n",
    "star_occ  = Star(coord='18 55 15.65250 -31 31 21.67051') # Occ Chariklo 22-06-2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now, we can instantiate the** `Occultation`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stellar occultation of star GaiaDR3 6760223758801661440 by 10199 Chariklo (1997 CU26).\n",
      "\n",
      "Geocentric Closest Approach: 0.049 arcsec\n",
      "Instant of CA: 2017-06-22 21:18:48.200\n",
      "Position Angle: 359.72 deg\n",
      "Geocentric shadow velocity: -22.00 km / s\n",
      "Sun-Geocenter-Target angle:  166.42 deg\n",
      "Moon-Geocenter-Target angle: 149.11 deg\n",
      "\n",
      "\n",
      "No observations reported\n",
      "\n",
      "###############################################################################\n",
      "                                     STAR                                      \n",
      "###############################################################################\n",
      "GaiaDR3 star Source ID: 6760223758801661440\n",
      "ICRS star coordinate at J2016.0:\n",
      "RA=18h55m15.65210s +/- 0.0197 mas, DEC=-31d31m21.6676s +/- 0.0180 mas\n",
      "pmRA=3.556 +/- 0.025 mas/yr, pmDEC=-2.050 +/- 0.020 mas/yr\n",
      "GaiaDR3 Proper motion corrected as suggested by Cantat-Gaudin & Brandt (2021) \n",
      "Plx=0.2121 +/- 0.0228 mas, Rad. Vel.=-40.49 +/- 3.73 km/s \n",
      "\n",
      "Magnitudes: G: 14.224, B: 14.320, V: 13.530, R: 14.180, J: 12.395, H: 11.781,\n",
      "            K: 11.627\n",
      "\n",
      "Apparent diameter from Kervella et. al (2004):\n",
      "    V: 0.0216 mas, B: 0.0216 mas\n",
      "Apparent diameter from van Belle (1999):\n",
      "    sg: B: 0.0238 mas, V: 0.0244 mas\n",
      "    ms: B: 0.0261 mas, V: 0.0198 mas\n",
      "    vs: B: 0.0350 mas, V: 0.0315 mas\n",
      "\n",
      "Geocentric star coordinate at occultation Epoch (2017-06-22 21:18:48.200):\n",
      "RA=18h55m15.65251s +/- 0.0327 mas, DEC=-31d31m21.6706s +/- 0.0341 mas\n",
      "\n",
      "###############################################################################\n",
      "                          10199 Chariklo (1997 CU26)                           \n",
      "###############################################################################\n",
      "Object Orbital Class: Centaur\n",
      "Spectral Type:\n",
      "    SMASS: D  [Reference: EAR-A-5-DDR-TAXONOMY-V4.0]\n",
      "       Relatively featureless spectrum with very steep red slope.\n",
      "Discovered 1997-Feb-15 by Spacewatch at Kitt Peak\n",
      "\n",
      "Physical parameters:\n",
      "Diameter:\n",
      "    302 +/- 30 km\n",
      "    Reference: Earth, Moon, and Planets, v. 89, Issue 1, p. 117-134 (2002), \n",
      "Rotation:\n",
      "    7.004 +/- 0 h\n",
      "    Reference: LCDB (Rev. 2023-February); Warner et al., 2009, [Result based on less than full coverage, so that the period may be wrong by 30 percent or so.]  REFERENCE LIST:[Fornasier, S.; Lazzaro, D.; Alvarez-Candal, A.; Snodgrass, C.; et al. (2014) Astron. Astrophys. 568, L11.], [Leiva, R.; Sicardy, B.; Camargo, J.I.B.; Desmars, J.; et al. (2017) Astron. J. 154, A159.]\n",
      "Absolute Magnitude:\n",
      "    6.55 +/- 0 mag\n",
      "    Reference: MPO719217, \n",
      "Albedo:\n",
      "    0.045 +/- 0.01 \n",
      "    Reference: Earth, Moon, and Planets, v. 89, Issue 1, p. 117-134 (2002), \n",
      "\n",
      "Ellipsoid: 151.0 x 151.0 x 151.0\n",
      "\n",
      "----------- Ephemeris -----------\n",
      "\n",
      "EphemKernel: CHARIKLO/DE438_SMALL (SPKID=2010199)\n",
      "Ephem Error: RA*cosDEC: 0.000 arcsec; DEC: 0.000 arcsec\n",
      "Offset applied: RA*cosDEC: 0.0000 arcsec; DEC: 0.0000 arcsec\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "occ = Occultation(star=star_occ, body=chariklo, time='2017-06-22 21:18')\n",
    "\n",
    "print(occ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the occultation was observed by a spacecraft, the Occultation parameters should be refered to this observer. For that, it is necessary to pass the observer as the center of reference for the `Occultation`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sora import Spacecraft\n",
    "# nh = Spacecraft(name='New Horizons', spkid='-98', ephem='horizons')\n",
    "# nh_occ = Occultation(star=star, body=body, time=time, reference_center=nh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note that at the** `print(Occultation)` **there are no observations added to this** `Occultation` **yet.**\n",
    "\n",
    "It is needed one `Observer` and one `LightCurve` to define an `Chord`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's instanciate the** `Observers`\n",
    "\n",
    "Here we will give 5 locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = Observer(name='Outeniqua'  ,lon='+16 49 17.710', lat='-21 17 58.170', height =1416)\n",
    "ond = Observer(name='Onduruquea' ,lon='+15 59 33.750', lat='-21 36 26.040', height =1220)\n",
    "tiv = Observer(name='Tivoli'     ,lon='+18 01 01.240', lat='-23 27 40.190', height =1344)\n",
    "whc = Observer(name='Windhoek'   ,lon='+17 06 31.900', lat='-22 41 55.160', height =1902)\n",
    "hak = Observer(name='Hakos'      ,lon='+16 21 41.320', lat='-23 14 11.040', height =1843)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a spacecraft observed the occultation, a `Spacecraft` object could be defined and added to the `Occultation` normally."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's instanciate the** `LightCurves`\n",
    "\n",
    "Here we give 6 light curves. Note that 2 are for the same site observed with different telescopes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#THE ERRORS ARE INCREASED FOR BETTER VISUALIZATION\n",
    "\n",
    "out_lc = LightCurve(name='Outeniqua lc',\n",
    "                    initial_time='2017-06-22 21:20:00.056',\n",
    "                    end_time ='2017-06-22 21:29:59.963',\n",
    "                    immersion='2017-06-22 21:21:20.329',immersion_err=0.320,\n",
    "                    emersion ='2017-06-22 21:21:30.343',emersion_err=0.340)\n",
    "\n",
    "ond_lc = LightCurve(name='Onduruquea lc',\n",
    "                    initial_time='2017-06-22 21:11:52.175',\n",
    "                    end_time ='2017-06-22 21:25:13.389',\n",
    "                    immersion='2017-06-22 21:21:22.213',immersion_err=0.100,\n",
    "                    emersion ='2017-06-22 21:21:33.824',emersion_err=0.110)\n",
    "\n",
    "tiv_lc = LightCurve(name='Tivoli lc',\n",
    "                    initial_time='2017-06-22 21:16:00.094',\n",
    "                    end_time ='2017-06-22 21:28:00.018',\n",
    "                    immersion='2017-06-22 21:21:15.628',immersion_err=0.700,\n",
    "                    emersion ='2017-06-22 21:21:19.988',emersion_err=0.700)\n",
    "\n",
    "whc_c14_lc = LightCurve(name='Windhoek C14 lc',\n",
    "                    initial_time='2017-06-22 21:12:48.250',\n",
    "                    end_time ='2017-06-22 21:32:47.963',\n",
    "                    immersion='2017-06-22 21:21:17.609',immersion_err=0.240,\n",
    "                    emersion ='2017-06-22 21:21:27.564',emersion_err=0.260)\n",
    "\n",
    "whc_d16_lc = LightCurve(name='Windhoek D16 lc',\n",
    "                    initial_time='2017-06-22 21:20:01.884',\n",
    "                    end_time ='2017-06-22 21:22:21.894',\n",
    "                    immersion='2017-06-22 21:21:17.288',immersion_err=0.280,\n",
    "                    emersion ='2017-06-22 21:21:27.228',emersion_err=0.340)\n",
    "\n",
    "hak_lc = LightCurve(name='Hakos lc',\n",
    "                    initial_time='2017-06-22 21:10:19.461',\n",
    "                    end_time ='2017-06-22 21:30:19.345')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To add the observation to the** `Occultation` **just use the** `Occultation.chords.add_chord()`\n",
    "\n",
    "During this step the `LightCurve` model parameters are automaticaly updated using the `Occultation` parameters: the Shadow's velocity, the star diameter and the distance to the occulting body. This means that is no need for the user to do the `LightCurve.set_dist()`, `LightCurve.set_vel()` and `LightCurve.set_star_diam()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/altair/Documentos/códigos/SORA/sora/body/core.py:337: UserWarning: H and/or G is not defined for 10199 Chariklo. Searching into JPL Horizons service\n",
      "  warnings.warn('H and/or G is not defined for {}. Searching into JPL Horizons service'.format(self.shortname))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Chord: Tivoli>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ.chords.add_chord(observer=out, lightcurve=out_lc)\n",
    "occ.chords.add_chord(observer=ond, lightcurve=ond_lc)\n",
    "occ.chords.add_chord(observer=tiv, lightcurve=tiv_lc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that a warning comes up if the flux drop is not calculated automatically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The same goes for two observation at with the same** `Observer` **, however is important to define their names as different values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Chord: Windhoek D16>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ.chords.add_chord(name='Windhoek C14', observer=whc, lightcurve=whc_c14_lc)\n",
    "occ.chords.add_chord(name='Windhoek D16', observer=whc, lightcurve=whc_d16_lc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Also, the same goes for negative observations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Chord: Hakos>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ.chords.add_chord(observer=hak, lightcurve=hak_lc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If an spacecraft observed the occultation, just define the `Spacecraft` object and add it to the `Occutation` as usual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sora import Spacecraft\n",
    "# nh = Spacecraft(name='New Horizons', spkid='-98', ephem='horizons')\n",
    "# occ.chords.add_chord(observer=nh, lightcurve=lightcurve)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To check which observers were added to the** `Occultation` **just use** `Occultation.chords`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ChordList:\n",
       "    0: Chord(Outeniqua)\n",
       "    1: Chord(Onduruquea)\n",
       "    2: Chord(Tivoli)\n",
       "    3: Chord(Windhoek C14)\n",
       "    4: Chord(Windhoek D16)\n",
       "    5: Chord(Hakos)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ.chords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**If any of them was wrongly added just remove it using** `Occultation.chords.remove_chord()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "occ.chords.remove_chord(name='Outeniqua')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ChordList:\n",
       "    0: Chord(Onduruquea)\n",
       "    1: Chord(Tivoli)\n",
       "    2: Chord(Windhoek C14)\n",
       "    3: Chord(Windhoek D16)\n",
       "    4: Chord(Hakos)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ.chords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**If all the chords were wrongly added the user can clear the** `Occultation` **using** `Occultation.chords.clear()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "occ.chords.clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ChordList:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ.chords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Chord: Hakos>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ.chords.add_chord(observer=out, lightcurve=out_lc)\n",
    "occ.chords.add_chord(observer=ond, lightcurve=ond_lc)\n",
    "occ.chords.add_chord(observer=tiv, lightcurve=tiv_lc)\n",
    "occ.chords.add_chord(name='Windhoek C14', observer=whc, lightcurve=whc_c14_lc)\n",
    "occ.chords.add_chord(name='Windhoek D16', observer=whc, lightcurve=whc_d16_lc)\n",
    "occ.chords.add_chord(observer=hak, lightcurve=hak_lc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"section_2\"></a>\n",
    "\n",
    "## 2. Projecting the times in the sky plane and the Chords\n",
    "\n",
    "This step is done automatically without the user having to ask for it. The user can see it automatically using `Occultation.chords.summary()` that contains all positions for all observations added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Name      Longitude      Latitude      status              time              f        g   \n",
      "------------ ------------ ------------- ------------ ----------------------- --------- -------\n",
      "   Outeniqua 16d49m17.71s -21d17m58.17s Initial Time 2017-06-22 21:20:00.056  -1912.82   45.76\n",
      "                                           Immersion 2017-06-22 21:21:20.329   -118.28   65.39\n",
      "                                            Emersion 2017-06-22 21:21:30.343    105.60   67.83\n",
      "                                            End Time 2017-06-22 21:29:59.963  11501.40  190.23\n",
      "  Onduruquea 15d59m33.75s -21d36m26.04s Initial Time 2017-06-22 21:11:52.175 -12876.38 -135.47\n",
      "                                           Immersion 2017-06-22 21:21:22.213   -138.25    7.19\n",
      "                                            Emersion 2017-06-22 21:21:33.824    121.28   10.05\n",
      "                                            End Time 2017-06-22 21:25:13.389   5029.49   63.76\n",
      "      Tivoli 18d01m01.24s -23d27m40.19s Initial Time 2017-06-22 21:16:00.094  -7123.66 -202.63\n",
      "                                           Immersion 2017-06-22 21:21:15.628    -70.56 -126.77\n",
      "                                            Emersion 2017-06-22 21:21:19.988     26.91 -125.73\n",
      "                                            End Time 2017-06-22 21:28:00.018   8971.45  -31.61\n",
      "Windhoek C14  17d06m31.9s -22d41m55.16s Initial Time 2017-06-22 21:12:48.250 -11505.42 -198.50\n",
      "                                           Immersion 2017-06-22 21:21:17.609   -121.72  -73.52\n",
      "                                            Emersion 2017-06-22 21:21:27.564    100.82  -71.11\n",
      "                                            End Time 2017-06-22 21:32:47.963  15314.98   90.02\n",
      "Windhoek D16  17d06m31.9s -22d41m55.16s Initial Time 2017-06-22 21:20:01.884  -1814.42  -91.87\n",
      "                                           Immersion 2017-06-22 21:21:17.288   -128.89  -73.59\n",
      "                                            Emersion 2017-06-22 21:21:27.228     93.31  -71.19\n",
      "                                            End Time 2017-06-22 21:22:21.894   1315.35  -58.00\n",
      "       Hakos 16d21m41.32s -23d14m11.04s Initial Time 2017-06-22 21:10:19.461 -14875.34 -316.83\n",
      "                                            End Time 2017-06-22 21:30:19.345  11939.87  -23.52\n"
     ]
    }
   ],
   "source": [
    "occ.chords.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's give a look to an specific** `Chord`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "chord = occ.chords['Outeniqua']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The user can easily acess the f and g for the immersion and emersion times using the** `Chord.get_fg()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-118.27875072,  105.59793973]), array([65.3915666 , 67.83395663]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chord.get_fg()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To see the f and g projection for the immersion and emersion times used in the fitting process, the user can use the following function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Outeniqua_immersion' 'Outeniqua_emersion' 'Onduruquea_immersion'\n",
      " 'Onduruquea_emersion' 'Tivoli_immersion' 'Tivoli_emersion'\n",
      " 'Windhoek C14_immersion' 'Windhoek C14_emersion' 'Windhoek D16_immersion'\n",
      " 'Windhoek D16_emersion']\n",
      "[[-118.27875072   65.3915666 ]\n",
      " [ 105.59793973   67.83395663]\n",
      " [-138.25381491    7.1933912 ]\n",
      " [ 121.275169     10.05237144]\n",
      " [ -70.56164003 -126.77190052]\n",
      " [  26.91022935 -125.73348183]\n",
      " [-121.71711407  -73.51645459]\n",
      " [ 100.81847461  -71.10987823]\n",
      " [-128.89276574  -73.59407832]\n",
      " [  93.30744752  -71.19108221]]\n",
      "[[ 7.15400526  0.07806865]\n",
      " [ 7.60120413  0.08290053]\n",
      " [ 2.23518846  0.02463189]\n",
      " [ 2.45872898  0.0270767 ]\n",
      " [15.64912805  0.16673645]\n",
      " [15.64918323  0.16669294]\n",
      " [ 5.36497525  0.05803441]\n",
      " [ 5.81210573  0.06283513]\n",
      " [ 6.25913618  0.06770901]\n",
      " [ 7.60044325  0.08217062]]\n"
     ]
    }
   ],
   "source": [
    "names, fg, error = occ.chords.get_limb_points()\n",
    "print(names)\n",
    "print(fg)\n",
    "print(error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The user can also set a specific time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-28724.98453460884, -258.35753813600354)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chord.get_fg(time='2017-06-22 21:00:00.000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Finally, the user can visually see the chord in the Sky-plane using the** `Chord.plot_chord()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chord.plot_chord(segment='positive', color='blue')\n",
    "chord.plot_chord(segment='error', color='red')\n",
    "\n",
    "pl.xlim(+250,-250)\n",
    "pl.ylim(-250,+250)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's define a** `LightCurve` **with times and fluxes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "chord2 = occ.chords['Hakos']\n",
    "\n",
    "chord2.lightcurve.set_flux(file='input/lightcurves/lc_example_5.dat', exptime=1.000, usecols=[0,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The** `Chord.plot_chord()` **can also be used with an** `linestyle = \"exposure\"` **to see the times where the the chord in fact holds information and the readout time (without information).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chord2.plot_chord(segment='negative', linestyle='exposure', color='green')\n",
    "\n",
    "pl.xlim(+250,-250)\n",
    "pl.ylim(-250,+250)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now let's consider all the chords**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ.chords.plot_chords()\n",
    "occ.chords.plot_chords(segment='error', color='red')\n",
    "\n",
    "pl.legend(loc=1)\n",
    "pl.xlim(+170,-330)\n",
    "pl.ylim(-250,+250)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**If there are known time shifts, this can be easily solved using** `LightCurve.dt`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_lc.dt = -0.150\n",
    "ond_lc.dt = -0.190\n",
    "tiv_lc.dt = -0.150\n",
    "whc_c14_lc.dt = -0.375\n",
    "whc_d16_lc.dt = +0.000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ.chords.plot_chords()\n",
    "occ.chords.plot_chords(segment='error', color='red')\n",
    "\n",
    "pl.legend(loc=1)\n",
    "pl.xlim(+170,-330)\n",
    "pl.ylim(-250,+250)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The user can save these positions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "occ.to_file()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"section_3\"></a>\n",
    "\n",
    "## 3. Ellipse fit\n",
    "\n",
    "The next step is the fitting of an ellipse. The five parameters that describe the ellipse are:\n",
    "\n",
    "**1.** and **2.** The centre position ($f_0$, $g_0$)\n",
    "\n",
    "**3.** The apparent equatorial radius, semi-major axix ($a'$)\n",
    "\n",
    "**4.** The oblatness ($\\epsilon' = \\frac{a' - b'}{a'}$) \n",
    "\n",
    "**5.** The position angle of the pole, semi-minor axis ($P$)\n",
    "\n",
    "The result of the fit is a `ChiSquare` Object, and its functions can be found at its specific Jupyter-Notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here, there is only the manual method, the user should provide the parameters to the fit and also the region for searching each parameters.**\n",
    "\n",
    "The equation to be minimize is:\n",
    "\n",
    "$\\chi^2 = \\sum_{i}^{N} \\frac{(r_{i} - r'_{i})^2}{\\sigma_i^2 + \\sigma_{model}^2}$\n",
    "\n",
    "where:\n",
    "- $r_i$ is the radial distance between the $i^{th}$ observed point and the ellipse centre;\n",
    "- $r'_i$ is the radial distance between the modelled ellipse's $i^{th}$ point and the ellipse centre;\n",
    "- $\\sigma_i$ is the unceartainty of the $i^{th}$ observed point\n",
    "- $\\sigma_{model}$ is the model uncertainty, that is releated to the difference between the real apparent shape of the occultating object and the ellipse model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mocc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_ellipse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Fits an ellipse to given occultation using given parameters.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "center_f : `int`, `float`, default=0\n",
       "    The coordinate in f of the ellipse center.\n",
       "\n",
       "center_g : `int`, `float`, default=0\n",
       "    The coordinate in g of the ellipse center.\n",
       "\n",
       "equatorial_radius : `int`, `float`\n",
       "    The Equatorial radius (semi-major axis) of the ellipse.\n",
       "\n",
       "oblateness : `int`, `float`, default=0\n",
       "    The oblateness of the ellipse.\n",
       "\n",
       "position_angle : `int`, `float`, default=0\n",
       "    The pole position angle of the ellipse in degrees.\n",
       "    Zero is in the North direction ('g-positive'). Positive clockwise.\n",
       "\n",
       "dcenter_f : `int`, `float`\n",
       "    Interval for coordinate f of the ellipse center.\n",
       "\n",
       "dcenter_g : `int`, `float`\n",
       "    Interval for coordinate g of the ellipse center.\n",
       "\n",
       "dequatorial_radius `int`, `float`\n",
       "    Interval for the Equatorial radius (semi-major axis) of the ellipse.\n",
       "\n",
       "doblateness : `int`, `float`\n",
       "    Interval for the oblateness of the ellipse\n",
       "\n",
       "dposition_angle : `int`, `float`\n",
       "    Interval for the pole position angle of the ellipse in degrees.\n",
       "\n",
       "loop : `int`, default=10000000\n",
       "    The number of ellipses to attempt fitting.\n",
       "\n",
       "dchi_min : `int`, `float`\n",
       "    If given, it will only save ellipsis which chi square are smaller than\n",
       "    chi_min + dchi_min. By default `None` when used with `method='chisqr'`, and\n",
       "    `3` for other methods.\n",
       "\n",
       "number_chi : `int`, default=10000\n",
       "    In the `chisqr` method if `dchi_min` is given, the procedure is repeated until\n",
       "    `number_chi` is reached.\n",
       "    In other methods it is the number of values (simulations) that should lie within\n",
       "    the provided `sigma_result`.\n",
       "\n",
       "verbose : `bool`, default=True\n",
       "    If True, it prints information while fitting.\n",
       "\n",
       "ellipse_error : `int`, `float`\n",
       "    Model uncertainty to be considered in the fit, in km.\n",
       "\n",
       "sigma_result : `int`, `float`\n",
       "    Sigma value to be considered as result.\n",
       "\n",
       "method : `str`, `default='least_squares'`\n",
       "    Method used to perform the fit. Available methods are:\n",
       "    `chisqr` : monte carlo computation method used in versions of SORA <= 0.2.1.\n",
       "    `fastchi` : monte carlo computation method, allows multithreading .\n",
       "    `least_squares` or `ls`: best fit done used levenberg marquardt convergence algorithm.\n",
       "    `differential_evolution` or `de`: best fit done using genetic algorithms.\n",
       "    All methods return a Chisquare object.\n",
       "\n",
       "threads : `int`\n",
       "    Number of threads/workers used to perform parallel computations of the chi square\n",
       "    object. It works with all methods except `chisqr`, by default 1.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "chisquare : `sora.ChiSquare`\n",
       "    A ChiSquare object with all parameters.\n",
       "\n",
       "Important\n",
       "---------\n",
       "Each occultation is added as the first argument(s) directly.\n",
       "\n",
       "Mandatory input parameters: 'center_f', 'center_g', 'equatorial_radius',\n",
       "'oblateness', and 'position_angle'.\n",
       "\n",
       "Parameters fitting interval: 'dcenter_f', 'dcenter_g', 'dequatorial_radius',\n",
       "'doblateness', and 'dposition_angle'. Default values are set to zero.\n",
       "Search done between (value - dvalue) and (value + dvalue).\n",
       "\n",
       "\n",
       "Examples\n",
       "--------\n",
       "To fit the ellipse to the chords of occ1 Occultation object:\n",
       "\n",
       ">>> fit_ellipse(occ1, **kwargs)\n",
       "\n",
       "To fit the ellipse to the chords of occ1 and occ2 Occultation objects together:\n",
       "\n",
       ">>> fit_ellipse(occ1, occ2, **kwargs)\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Documentos/códigos/SORA/sora/occultation/core.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ.fit_ellipse?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ellipse fit: |████████████████████████████████████████|  - 100% \n",
      "Total elapsed time: 3.069 seconds.\n"
     ]
    }
   ],
   "source": [
    "ellipse_chi2  = occ.fit_ellipse(center_f=-15.046, center_g=-2.495, dcenter_f=10, dcenter_g=20, \n",
    "                                equatorial_radius=138, dequatorial_radius=50, oblateness=0.093, \n",
    "                                doblateness=0.20, position_angle=90, dposition_angle=90 ,loop=100000,\n",
    "                                dchi_min=10,number_chi=20000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that here we only used 100000 loops, but it is recommended at least 10 millions for a good sample for each parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi-square: 0.062\n",
      "Number of fitted points: 10\n",
      "Number of fitted parameters: 5\n",
      "Minimum chi-square per degree of freedom: 0.012\n",
      "\n",
      "center_f:\n",
      "    1-sigma: -13.668 +/- 1.124\n",
      "    3-sigma: -13.676 +/- 4.350\n",
      "\n",
      "center_g:\n",
      "    1-sigma: -2.175 +/- 4.183\n",
      "    3-sigma: 0.512 +/- 17.236\n",
      "\n",
      "equatorial_radius:\n",
      "    1-sigma: 138.894 +/- 5.872\n",
      "    3-sigma: 151.854 +/- 24.419\n",
      "\n",
      "oblateness:\n",
      "    1-sigma: 0.087 +/- 0.041\n",
      "    3-sigma: 0.138 +/- 0.138\n",
      "\n",
      "position_angle:\n",
      "    1-sigma: 127.631 +/- 19.996\n",
      "    3-sigma: 125.103 +/- 89.946\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(ellipse_chi2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkEAAAHDCAYAAAAjlM7jAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACGPUlEQVR4nO3deXhTVfoH8O+9F2QpiwICxdAKQxV3GSgUBOlUpW7gBhQUrGOAIqhIaaGI/phRpKUtIKIipRVQBrAouODCwBARpbV1QWXGpSAtDaI4LoVWREjO74/OvWS5N0u3NMn38zx5tMm5JyehJC/nvOe8khBCgIiIiCjMyIEeABEREVEgMAgiIiKisMQgiIiIiMISgyAiIiIKSwyCiIiIKCwxCCIiIqKwxCCIiIiIwhKDICIiIgpLDIKIiIgoLDEIIiIiorAUlEHQe++9h5EjR6JHjx6QJAmvvvqq0+NCCPztb39Djx490KZNG8THx+Pf//53YAZLREREzVKLQA+gLmpqanDFFVfgr3/9K+644w63x7Ozs7FkyRKsWbMGF1xwARYsWIDrrrsOX3/9Ndq3b+/Tc9jtdnz33Xdo3749JElq6JdAREREjUAIgePHj6NHjx6QZS9zPSLIARBbtmzRfrbb7aJ79+4iKytLu+/3338XHTt2FM8995zP/VZWVgoAvPHGG2+88cZbEN4qKyu9ftcH5UyQJwcPHsT333+PESNGaPe1atUKw4cPx549e5CSkqJ73cmTJ3Hy5Ent59r4CqisrESHDh0ad9BEHlx22WU4dOhQoIdBDUiWZQwePBgffPCB13b79u3Deeedh8OHD+Pbb79F7969ceDAAYwcObJOz718+XLcfffdAODU53nnnefxusOHD2PFihVYvny517ESBdKxY8fQs2dPn1Z+Qi4I+v777wEA3bp1c7q/W7duqKioMLwuMzMTf//7393u79ChA4MgCqjevXszCAohiqJg5cqVSExMRM+ePQ3bybKMvLw8XHTRRQBqP4vU/2/fvj1kWYbdbvf4HJdffjkGDRqk/aMOAB588EHceuutMJlMTn16UlBQgClTpsBut0OWZYwePRqFhYVObex2O3744Qef+iNqCr6ksgRlYrQvXF+8EMLjGzJ37lxUVVVpt8rKysYeIpFXVqsV7777bqCHQQ0oIyMDZrMZR44c0X1clmWkpaWhuLgYvXv3htVqdWtjMpmQl5cHRVEA1AY9aWlpKCkpgcViQXl5OcxmMw4ePOgUAAG1n4VFRUU+j9dqtWoBEFAb7Lzyyitu7RRFQZ8+fXzul6g5CLmZoO7duwOonRGKjIzU7j969Kjb7JCjVq1aoVWrVo0+PiJ/7NmzJ9BDoAaWmZmJqVOnYvfu3bqPP/roo+jZsyfi4uJgt9shSRJmzZqFGTNmwGQyae3MZjMSExOxf/9+9OnTx+kxq9UKi8WCn376qd7jLSsrc5txstlskCRJC7AkScLKlSudxkAUDEJuJqhXr17o3r07tm/frt33xx9/YNeuXRgyZEgAR0bkv4b4EqPGZTab/Wpvt9uxf/9+wz/bAQMGOM28CCGQm5uLqKgoTJgwAYWFhdrskMlkQnx8vFPwUVBQgOjoaCQkJOD+++/XfY7zzz/f7T41cHKdeYqJidHdYeM4wyTLMhITEz2/cKJmKCiDoOrqauzduxd79+4FUJsMvXfvXhw6dAiSJOGhhx7CwoULsWXLFuzbtw/33HMP2rZtizvvvDOwAyfyU+fOnQM9BPKif//+frVXFAURERHIyspye+ymm27Cb7/9ppvrI4TAP/7xDyQlJSEqKgoFBQVubfSWrvTU1NQ4/ewYOEVHRzv17br0phcQ2Ww27N+/38OrJmqmfN4z3oxYLBbd7XDJyclCiNpt8vPnzxfdu3cXrVq1EldffbX44osv/HqOqqoqAUBUVVU1wisg8g2Pamj+t4kTJ/rcVlEUkZ+fL3bu3Fnv51UUxW0LsC/9ul5XWVkpJElyaiPLslvflZWVwmKxiJKSEiHLstexEAWKP9/fQRkENQUGQdRcjB07NuBf9KF+kyRJpKSkNFr/siyLwsJCLVCorKx0CyTqcluyZIlbQOOpXzUIc/TSSy/pti0sLNT63Llzp9Pz5OfnC0VRDPskCiR/vr+DcjmMKJz8/vvvgR5CSJMkCatWrcIjjzzSaP3n5eVh8ODBKCsrg9Vq1ZaY6nsafWpqqtvyVWpqquEpuRs2bEBiYqJu7o8eo2Uys9mM8vJyp51oRMEo5HaHEYWaK664Aq+//nqghxGyhBD46quv8O233zZK/5Ik4eeff0Z0dLR2zk5eXh4SExOddljVld1uR0pKCn755RfMmTNH21HmSlEUVFRUYNy4cT6N47PPPkNmZqZTflFKSgoSExNhMpm0G1Ewk0R9/waGqGPHjqFjx46oqqriYYkUUEuWLMGsWbMCPQyqB9cgQ5ZlzJs3D48//rhP13744YcoLy/HTz/9hCNHjuCxxx5za+d6eKIaCAkhoCgKMjMzkZGR4dRGURSUl5djxYoVWLhwocf+VBaLBfHx8V7HTRQo/nx/cyaIqJm74IILAj0EAnDzzTdj69atfl+nF0zY7XbdAEivrRACNTU1GDNmDIDaHWALFixwamd0nfpYVlYW+vfvr3vez6xZs7Bp0ya3saizRa5Bk9GBiFarFWVlZYiJieEMEQUN5gQRNXMRERGBHgIBdQqAJEnC+PHjfWqrKAoWLVrkls/jGniYTCZMnDjRqc31119v2K/dbkdGRgbatWunmytUWFiouySnjsfxVGqjAxE9bbEnas64HGaAy2HUXJSWlmLgwIGBHgb56NFHH8Vll12GnTt3Ii8vz/CsHkd333034uLiMHLkSGzbtg0pKSmw2Wxa4OGYeGy1WrX8IpWnOmKqwsJCVFRUaHlD3uTn58NsNsNqteqeSu1pPOoyG2eEKBD8+f7mTBBRM/fRRx8FegjkhwULFqCiosLnAAgAXnjhBUybNg1RUVEA4HHnlV4ZC6NkaEdjx47F7NmzfRqT4wnQeqdSexsPD0+kYMEgiKiZYxAUXIQQPs+26F07ZcoUAHALPNSyFkbLWnfccYe2dCVJkm5Q5OvEv1rawxd6ZTVYTJWCBYMgomauvmfJUNPzJQAy+nPVC0Acc27i4uJwxx13uF23ZcsWFBUVwWKx4NChQ9i4caPXMRidJ+RPEKNX0Z7FVClYMAgiaubOOuusQA8hbKWkpBgGCp5IkoQHHnjAYxshhGEgVF1drc38bN261a0e2CuvvOJ2jc1mQ01NjTaD1KtXL6/jfO2112CxWJCdne1UG2zmzJler3XEwxMpWDEIImrmDh48GOghhK0+ffogIyPD7+uEEHj66acxevRoj7Mt6tKXq5EjRyIqKgoJCQkYOXKkTzlArrM31dXVXsfZrl07xMfHIz4+Hg8//DAmTJgAAMjNzdV2eRlVl3flLXeIqFlq+KodoYG1w6i5SEpKCnhtrXC9ybLsVlzUn5uiKKKkpERYLBYxevRop8eSk5NFSUlJnfu+8cYbtTphevW7fKkjVllZKZKTkw3bSJKkvX5ZllkjjIICa4cRhZDLL7880EMIW3a7vV5lLdQlqj59+rgtYb344ov1Ovrgrbfe0g40zMrKcluCcs3VkWVZmz1S83aOHDmCtWvXGj6HqC2yDeBM2Qxfao4RBQueGE1E1EjUJaply5a5BVN12T2mRz0Mcdy4cdpSVGlpKXbv3o1hw4ahvLxcO+cHgNP/u5bK8Ebd+s4lLwoVDIKImrmjR48GeghUB+psC1Bb/60xOQYn99xzj9PsTnJyMtasWaP9bDKZUFBQ4JRs7StufadQw+UwomauVatWgR4C+WnatGkoKiqC2WzGnj17GmzWx4ganJSWlrotb61duxalpaXaz1arFZMnT/Z7TLIsc+s7hRwGQUTN3E8//RToIZCfnn32WcTFxeGee+7BuHHj3B6XZRnZ2dkNcgaUmhNkMpmwe/du3TYffPCB9v96S3MAMH36dLzxxhsex6SeIk0UKhgEETVz7dq1C/QQqA7sdjvWrl2rG3CoszDPPPOMT32NGTPGcKu93W7HnDlzUFBQgGHDhum2ueqqqwDUzgLpLc3JsoyMjAzcfPPNWLVqle5z+XOKNFGwYBBE1MzdddddgR4CNYLZs2c7zdAYmTdvHgoLC1FcXOzxlOmUlBRERkYiOTnZ6bHk5GTExsYC0K/zBQCpqanaMpfZbEZxcbFbG0mSmA9EIYdBEFEz9+9//zvQQ6BG8o9//MPj4w8//DAWLFgAAIiNjcWqVau0Le+u1OToNWvWoKSkBEuXLkVJSQnWrFnjVHdML5Dq2rWr08+RkZFu7XxZuvP1YEWi5oJBEFEzt2XLlkAPgQLkuuuuc/pZLU9RWFjo8cTo2NhYPPTQQ4iMjER6erpT3TG95bm5c+c6BS5lZWW6W/o9LYc51jdTT5smau4YBBE1c7fddlugh0ABYLQd3WQyYcyYMU6zQrIsIzMz02nnVkFBAXr27Inc3FynumN61Fkklb+V4a1Wq1t9Mx6sSMGAQRBRM3fJJZcEegjkhSRJuO+++xq0T2/b0c1mM7KysiDLsnZgojr7UlpaismTJ/v8XK4Bjr+V4fVyjVwDK6LmiIclelNTA+itwSsK0Lq1czsjsgy0aVO3tr/9Bhgd2y9JQNu2dWt74gTg6ZyQiIi6tf39d8Bma5i2bdvWjhsATp4ETp9umLZt2tS+zwDwxx/AqVMN07Z16zO/K/60PXWqtr2B9999V/v/FgA81ZQ/CUB9RxUAnk4Y+gPA6Tq0lQG09tD21P9u/raVALRpoLanUTtmVVujhn62taH2PXZrKwR+PXzY6VrDtjrsAH53+PnF557DhHHj9D8r/vcZYbVaMWfOHLSy2yEBgN2OmVOm4Pj332PeI4+gDWoLgJ1wuLQNat87RxKAZ556CqZOnZzuN995J64fNgwHDhzAn/70J5x33nnO4/nf32Wr1YqfDx9GO0mC3eHzR5FlxPToUXsNPyNqNdJnBFq1Alq08L/t6dO174WRs84CWrb0v63NVvtnZ6Rly9r2/ra122u/j3xp66tGrGEW1LQCbLVhhfvtxhudL2jbVr8dIMTw4c5tu3QxbjtggHPb6Gjjthdf7Nz24ouN20ZHO7cdMMC4bZcuzm2HDzdu27atc9sbbzRu6/rrNnq057bV1WfaJid7bnv06Jm206Z5bnvw4Jm2aWme2+7bd6bt/Pme25aUnGmbne25rcVypu3TT3ts++H//Z9W0DLZU5+AGO1Q/HK0l7bJDm1v9NJ2mkPb4V7apjm0HeCl7XyHthd7aZvt0DbaS9unHdp28dJ2tUPbtl7aFsK5wKintltd2lZ7aGtxaDdkyBCfPiN27twpAIiDHvrd5zKGfZ7GXIfPiPz8fCHLsrB46pefEWdujfQZIbZuPdN29WrPbQsLz7QtLPTcdvXqM223bvXc9umnz7S1WDy3zc4+07akxHPb+fPPtN23z3PbtDQhBAuoEoWU1q09zaVQKJEkCU8++aTXdlarFV999VXjD8jLGOpSeoOoOZGEECLQg2iOjh07ho4dO6Lqu+/QoUMH9wZcDtNvy6lu/9t6mb62/vgjevbqBYDLYc12OayebR2XwyRJwp233ILXXnsNdiEgSxKefvpp7fyfNS+8APP992vBh+sSl4TaaR/877+OiwdtAcj/W7ZSZBnLly8/c66QwWfE2rVrcf/997uNxVJSgoSEBAC1f8bqv6jffustXH311c4vkJ8RtbgcVquRl8O07++qKv3vbwcMggz48yYSNbZevXqhvLw80MMgH0iSBF8+Vn1tB9QmJqt//tHR0YazL4qiIDMzE3PnzoXNZoMsy0hNTcXYsWNRU1PjVkneWx0wq9Xq9nyexqI+xvpiFEj+fH9zOYyombNarQyAgkhKSorXgwUlScLrr7/ucy0udaeV0YnPqqysLHTq1ElrI4RA3759ERsbiz59+qCsrAwAEB8f71Og4mnXl787yIiaI+4OI2rm1C8uCoyEhAS8++67PuW+SJKEm266CStXrvTYTgiBW265xeeZIFmWfSpZER0djXHjxmn9CiGQkpKCX375BXPmzIHdbocsy8jLy4PZbPban3pekOtsjzoWs9mMxMREn2eWiJobzgQRNXMxMTGBHkJYO+ecc/D444/71FYIgZEjR/oU3Njtdp+DICEEtm3bhiNHjhi2URQFQgjdmRs1AFKf1/EgQ8dSF65lL3yZ7TGZTD7PLBE1N8wJMsCcIGpOLr30UtYQC3OKouDhhx/WDcgkScKqVauQmJjolqfjOpOjslgsOHDggLbDS13CE0K4zRZZrVbO9lDQYE4QUYjp3bt3oIdAAWaz2dCjRw/dx55//nmYzWbdmRv1VGlHiqIgIiLCaYu7EEKbmXKdLeJsD4UqBkFEQaBtW08brCkcKIqCm2+++cyWdgdms1krmaEWWbVYLCgvL0d6erpuYLR7926PeU4se0HhgInRREGge/fugR4CBdiECRNgMpmwZs0adOjQAcuXL9ceU2du1N1mZWVliImJ0WZuHBOYS0tLnXKEjHgqmEoUKjgTRBQErr322kAPgQJs3bp1WvLyM8884/a4zWbDggULEBUVhYSEBERHR2uzQ0DtklafPn2QkZGhGwDJsqzlBcmyzO3uFBYYBBEFgcrKykAPgQJMXZ7as2ePbhAjSRJWrlxpmNcD6J/7AwBLly5FVlaW1/ONiEINgyCiIPD2228HeggUYIqioLS0FOPGjfP5Gte8HvXcH9d+r7rqKqcZIr0AiigUMQgiCgI//PBDoIdAASTLMjIyMpCRkeF2tpAsy0hJSdE9c8j1kEWjc38OHjxoeDI0UShjEEQUBPr37x/oIVAA2e12LFy4UHcpa+PGjXjkkUfcZniA2jIarnk9rrvHAOjOLkmSxMRoCnkMgoiCQPv27QM9BAowvZkeRVEwePBgtxkeWZaRk5OD9PR0ra3jadDquT8AMGXKFN2+mR9E4YBBEFEQ+PzzzwM9BEJtYJCdna0769JU1Od2LWHhOMNTUVGBtLQ07ZqCggJER0e77RrzVJDVbrc7LYe5ltQgCgUsm2GAZTOoObnlllvw+uuvB3oYYUmSJDzxxBP4/fffERsbi4iICHz88cc+nbXT0BRFQVFREWpqanwuYWG1Wt1KaSiKoi2FRUVFGc4ylZeXw2QyoaCgQDtd2p8CrESB4M/3Nw9LJAoCbdq0CfQQwpYQAvPmzXMKFNRE5aysrHoFQkOGDMGePXsMH5ckCZIkwW63azM/sbGxHvu0Wq3aYYkAUFhYaJj0HB8fj1mzZiE3N9etnxtuuEHrz7G8huPBjDxHiIIdZ4IMcCaImpNRo0bhjTfeCPQwyIEkSbj//vvxzDPPNOqMUGFhIc4991yfZn4cZ2wcC6K6cpzl0ZspUkmSZBgkWSwWLa+IqDlhAVWiEHP8+PFAD4FcCCGwfPlyDB8+vNGeQ0189qV4qeuMjWNBVNc+HXOJ1KRqvTwnIQQWL16se7YQd45RKGAQRBQEuDus+bJYLE4/1ydpWpIkw8RnbzwlOauWLl2K8vJyt3wes9mMDRs26F4jhEBqaqrb2UJcCqNQwOUwA1wOo+aEy2HBQZIk3dkXXyxduhSjR48GAOzfv9+n5S/X/B+jZS3AORm6rKwM7dq1w8GDBwHU5iYZXS/LMioqKvwaF1EgMTGaKMR89913gR4C+aA+/6b8+eefceTIEcTGxvoUZOjt2Lr99tvx8ssvu7WVZRmTJk3CggULsGrVKrdAR5IkrFq1Cnl5eU5LapIkIS8vz2npjCiUcCbIAGeCqDnp168f9u7dG+hhUANQFAW33XabbrACAMnJyVizZo3HPvSSmWVZrleCtuNMUVFREQBoBzESBRMmRhOFmBEjRgR6CGFFXZZypShKnU5SVhQFOTk5WqmKQYMGGbZdu3YtSktL3e53PKxQL/+nvjvUbDYbioqKUFZWhsGDB2PMmDEMgCjkMQgiCgJGX8rUOIxmacaNG+f3kpcsyygqKkJaWhri4+Nx5MgRp3IWej744AOnn11PfP7Xv/7V4KdWS5KEpKQkJCQkICoqCjk5OQ3aP1FzxCCIKAh89NFHgR4CAVi/fr3fM0F2ux01NTUAgJycHAwcONDrNVdddZX2/3qHFT7xxBNeZ35kWcbDDz/sU7DkeqaQEAKzZ8/WPR+IKJQwMZooCHz99deBHgKhNjhITEzE9u3bfV5+Us/Uyc3NxezZs722T05OdjoV2pet767Pt2HDBi2fp3fv3m7JzlOmTMEVV1wBSZLQuXNnCCGQlJTk1tecOXMwbtw4LotRyGIQRBQELrzwwkAPgf5n+/btePrppzFt2jSvbdUzdQB4DYDmz5+Pm266ya0sRkxMjNekZ/Vx9fnGjBmjPWY2m5GYmKglO1dUVGh1z9RdZYmJibrb+9UiqgyCKFRxOYwoCAwYMCDQQ6D/sdvt6NKli9dlMUmSkJ+fj969e2PBggWGuUSyLCM/Px+TJk1CdXW1W5V29URno+dTFAXFxcVa0rXZbHar+G4ymTBmzBgMHjzYqfCrWgcMABYtWqTbN0+GplAWkkHQ6dOn8cgjj6BXr15o06YNevfujccee6zJKz4TNZTq6upAD4H+Ry1lkZ2d7TEQEkLgr3/9KxISErTZIFcpKSnaQYSOic/qTjI1iPnll190gyhZlpGVlYXq6mrtEEPXJOqCggKtvd7SmlpMNT09HTk5OXU+sZooKIkQtGDBAtG5c2exdetWcfDgQbFp0ybRrl078eSTT/rcR1VVlQAgqqqqGnGkRL6prKwUkiQJALwF8KYoisjPzxf5+flCluV69TV16lTtz9aoL1mWRUpKiuGffUpKinatLMsiOzvbrS9FUURlZaWorKwUL730kltf6uOOv2sWi8XpPvX+nTt3ut1P1Nz48/0dkkHQTTfdJO69916n+26//XYxYcIEn/tgEETNTU5OTsCDgHC+/d///Z8oKSnxGLT4epMkSQtMFi9eXKc+ZFl2C2iMxjV16lTtMUmStOvUoM4bx6BPlmWfriEKlLAPgjIzM0V0dLT4+uuvhRBC7N27V3Tt2lWsX7/e8Jrff/9dVFVVabfKykqf30SipnDXXXcFPBAI95ssy4Z/Dv7O1Hma4fH1eqMxehuXoiiisLDQp1kdvaDPdfaIqDnxJwgKyZygOXPmYPz48ejbty9atmyJfv364aGHHsL48eMNr8nMzETHjh21W8+ePZtwxESeWa1WrF+/PtDDCHt2ux3/+Mc/3O5XFAWvv/66X2cIrVy5sk61xmRZRk5Ojm7CsqIomDx5slPFd73nsNlsOPfcc2EymbQk6tLSUqc8JJWnPCKioNfoIVkAbNiwQZhMJrFhwwbx+eefixdeeEF06tRJrFmzxvAazgRRc7Zz586Az4LwZnzLzs4WQtQuGymKUud+li5dKsaMGaP7mOPszRtvvOFxFkmSJJGWliZeeukl3cdlWRaVlZW6uU2Oy11qHhFngiiYhP1ymMlkEk8//bTTfY8//ri48MILfe6DOUHUnDAxuuFujjkxDXWzWCzan1VOTk6d+lcURZSUlOjm9aiBSWVlpUhMTKx3fzk5OR5zmxRFETk5OU55ROr/+5pHRBQoYb8c9ttvv7kdFa8oCrfIU9AymUy46aabAj2MoCbLMqZOnQoAdVqG8tSvujRVWlqK2bNnG/a/cOFC3TIWsixj5cqVqK6u1v2c2rhxIwCgZ8+e2LZtm0/jstlsqKmpQV5enrY8JssysrOzkZaW5vEkapvN5nSekPp6CgsLtbOIiEJCY0dkgZCcnCzOO+88bYv85s2bRZcuXcTs2bN97oMzQdTcrF69OuCzKMF+q++uLqPb6NGjRVpamuEMkCzLIicnR+zcuVPk5ORoS2aKooi0tDRtackoCdloRke9GSU/O/bruu3d29Z8vfsdZ7yImquwXw47duyYmDFjhoiKihKtW7cWvXv3FvPmzRMnT570uQ8GQdTczJgxI+BBRCjfHAMJx/9viO3wDz/8sNt5Pnpn8eTn57s9d35+vtecsNtvv91t/GqekieOS17qTVEUj+cNETV3YR8ENQQGQdTcjBo1KuCBQijebrzxRt37HROR09LSGvQ59QIKvZkZNYG5pKTEY396gZokSR5zdxyTotVAzTEwc0zyZh4QBZOwzwkiCkXHjx8P9BBC0ttvv617v+M28rFjxzboc9psNhQVFWlb0q1WKwoLC91ydOx2O5YtW4a4uDjDvu666y7d3B4hBFJSUty2vAO1Ry44VpYXQmDRokVa6Q2gtvBqeXm5U00yolDDKvJEQaJr166BHkLI8VadvbS0FPHx8Y1Suy0pKQlCCO1sIWFQG2zJkiWGYxw9ejSysrKwYcMG3TbqeT6u9b88nf3j2NZkMrF2GIU0zgQRBYnLL7880EMIKZIkYfLkyR7bzJ07F1arFTExMbq7uupDDXpEbVqC2+OKomDo0KGGAdC8efOwadMmrcq8ugPMtQ+9QxX1Xg8rxlM4YhBERGFD3SavbpU3qu6uUpetysrKkJWVpRto+HNKtK9tp02bhmnTpuG9994zbJOVlaUtdalLV2lpaT5VgXcNnFgxnsKVJPT+CUI4duwYOnbsiKqqKnTo0CHQwyHCQw89hGXLlgV6GEFPPTPMl48+SZIgSRLsdjtkWcaiRYuwf/9+5OXlQQgBWZYNZ3KagsViQZ8+fVBWVoaYmBitDMb+/fud8nuM+NOWKFj48/3NnCCiINGqVatADyEk2Gw23fv18oMcAxy73Y45c+Zo98myjMmTJ3udTfKFJEl+B1KKoqC0tBTXXHONFqTl5eXBbDb7FPyogVN8fHw9Rk4U3LgcRhQkTp48GeghhAS9JSlFUVBcXIzCwkKPS1aOM0h2ux35+flu7X3NHVJnmVT+5HwpioKsrCxkZGRogZvdbseUKVN0d4M5KigoQHR0NBISEhAdHY2CggKfn5co1DAIIgoSF154YaCHEBL0ZlwmTJiA2NhYdOnSxa8ZGZvNhlmzZjnl1kycONFr7o/rMpoQAp9//rnX51MURStd0b9/f8Mt9a4cK8U7bo232+1ISUkxrCBPFOqYE2SAOUHU3GzduhUjR44M9DBCkqIoKC8vBwBER0f7XGfQ8br9+/cjIiICcXFxDVancPjw4di9ezfsdruWvKye11NaWoqBAwe6XSPLMioqKrQlsYKCAi3wMToSQF2Oc1xSIwpW/nx/cyaIKEjs2LEj0EMIWY5n5CxatMjn69Qt9mVlZejTp49hAdS6kCQJ69atQ0VFhe6BhUZnF9ntdhQVFQFwPxTRaGyOS3xGBywShSImRhNR2HM8I6d///4+X1ddXa3NHEmShPvvv7/BxrRo0SJtNkcv0TkmJsYwoXrs2LHIzs7GgAEDdAMfdUZIb2bI6IBFolDEmSCiIBEVFRXoIYQkSZKczsiJiYnx+dp169Y5lZ5Yvnx5ncaQkpKi5RXJsoycnBykp6d7vMZkMmHVqlWG+UezZ8/Gli1b3B5Xk8AtFguKi4t5aCKFNQZBREFi//79gR5CSJJlGYmJidrPR44c8esAxIZ4fsc6XRUVFUhLS/PpWrPZjDvuuMPw8eXLlzvNFKkBX2RkJIQQiIyM5KGJFNYYBBEFCb0kWKo/m82GTZs2wWq1oqCgAIMGDWqwww8VRUF2drbHbfN2ux1xcXHYuHEj4uPjnQIQdVeXUY5OaWkpXn75ZZ/HI4TAO++847RFHgALpVL4qnfN+hBVVVUlAIiqqqpAD4VICCHEzp07BQDeGukmy7KQJMljG2+Pu/ZXUlIihBAiPz9fyLLs9Zrs7Gztz9vxGlmWRX5+vtvvxOLFi+v9uhVFEZWVlU32e0zU2Pz5/uZMEFGQ8CdXhfznSykNSZKQkpLi04GIdrvd6SBCb30DQEZGBqxWq+6uLr1dW8OGDfPapzdqIjRROGIQREQhrSHze+x2O6655hoUFxdjyZIlSEhI8Nh+5cqVeOSRRzB58mSfgiC73Y79+/ejrKzMcNeWo9jYWCQnJzvd179/fy1I8+W1MxGawhm3yBMFiT179gR6CEFJCOFzbS5f2o0dO9avWl9PPPGET+2AMwHJkSNHdB+PiIhwu2/BggUYNGgQfvjhB9x0002IjY11Koy6YcMGrbyGoiiYMGEC1q1bB5vNxkRoCnsMgoiCBJcs6s6XgEWWZcTHx2Pnzp0N0p+/HAOSsrIy3TY1NTVOP7ueBt2zZ0/ExsbCZDJpgU16ejrGjx/vVC1+wYIF2gnX1dXVsFqtDIQoLHE5jChI/P7774EeQshxXC6y2+0+BUD+8iV/SJZlFBUVaTuzYmJivJ7fY1QHTM0bctxZZjKZnHaemUwmHDhwAHFxcSykSmGNQRBRkGDdsIY1f/78RpnRcSTLMrKysrye+2O3251meUwmk9P5PbIsY+bMmdrj6lZ+o7whb5XifU28Jgp1DIKIgkRsbCwiIyMDPYyQIEkSLr744kZ9DlmWMXfuXGRkZCA3NxeyLGPq1KlYsWKFx1kedQYnMTER5eXlWgCVm5uL6Oho5ObmYsqUKboBnKIoiIiI8Brg+Jp4TRTqGAQRBQmr1WqYMEv+EULgs88+a7STodUZoMzMTKdgZNWqVejfvz9SU1O1QMgxF8h1BmfDhg1YsmSJUx9z5swxrAe2cuVK3SKurgGOL8ttROGAQRBRkODusIa1cOFCjB492vDxvn371qnfpKQkFBcXQ1EU3WAkLi4Oubm5AIC0tDTtlGa9JSp1V5cjtVirI1mWUVxcDLPZrBVWdaQGOOosEwCWyyACgyAiCmOvvPIK7rzzTt3Hvvrqqzr12adPH8TFxWHWrFm6jzsGOUuXLtXu11uiUnd9ubrjjjucApi8vDzExsYCALZt2+bUVq0Xtm3bNpbLIHIhicbODAxSx44dQ8eOHVFVVYUOHToEejhEsFqt6NmzZ6CHQQ3MYrEgPj4eVqsV0dHRToGQLMuYPn26W3V6RVFQVFSEmpoabds7AN0+1LZxcXFu95eXl3P2h0KOP9/fnAkiChImkwnXX399oIcRUpqyWjzgvl3eMQ/HdUcYUDsT5BoAAbXLajU1NW4FV40Snt9//30mQhPpYBBEFEQaolZUuDr//PPd7mvKiXBFUbBo0SKnZSzHbe8AYDabUVRU5FNfeknMRgnPQ4cOZSI0kQ4GQURB5Ntvvw30EIJWeXl5wJ5bkiTMnDkT48aN07a92+12bdu74zk+Bw8eNOwD8JzErM4mOQY8drsdn3/+OROhiXQwCCIKIq5lEyg4CCG0gEfd9q7OQvl6UKEQAlOnTnVLYnY8GRoAEhMTnWa4hBBISUnRzh1iIjTRGQyCiILIf//730APoUncdtttgR5CozDa9u6YnzNkyBDD6/Py8px+1jsZuqyszG2ZT+3ftXwGUbhjEEQURI4dOxboITSJLVu2GD7W1MnMDU1v27trgnR2drbhtWqwZFT6ol27dsz/IfIRgyCiIMJ/wTdtMnNj8CVBOj09HfPmzdO9Vg1mjHaC1dTUMP+HyEcMgoiImoiiKMjMzET//v1RVFTkMUF6wYIFyMnJ0S2vAXgufWE2m5n/Q+QDHpZogIclUnM0Z84cw6USqr8JEybgz3/+M2JiYtCuXTuUlpbq5vD4S5IkzJo1C926ddNqf8myDCGE08yW3gGGVqsV+/fvdzsUsaysDB999BHmzp0Lm82mBUkMeCjc+fP9zSDIAIMgao5WrFiBadOmBXoYIUuWZS1AycvL02p6LViwACtXrqxX32otMW8fueoJ0sCZYCcmJkYLgAoKCjB58mQIISBJErKzszFgwACnIIkonPHEaCKiOnBNMrZarTCZTHjuuec8Flv1hc1m8xoAOeb8qEtk6s6v3NxcWK1WLQACavOjZs+ezQCIqI4YBBFRUFuxYgWys7MbfNeY47Z1q9WKzZs316s/SZJ0q7/r5fzk5OQgPT3dKShLT09HRkaGWyAlhPDplGkicscgiCiIdO7cOdBDaFaSk5MxdepUXHTRRY2ya6y0tBSA/k4sQH+7/m233Ya77rpLtz/XXWF5eXmoqKhwSmC2Wq2YM2eO7vX/+Mc/6vpSiEhHi0APgIh816tXr0APoVlZt24dTCYTFi5c2Cj9z549G+PHj9d2YrlWYb/99tuxadMmp2tee+013YBJCIHY2FiUl5e7JTq7FkH1N6DTq4tGRN5xJogoiHz00UeBHkKzYrPZ8MQTTzTq2UFz5851q/AuSRKSkpLw8ssvu7U32kkmy7IW+Hg6tVlv67tjH3pYToWobhgEEQURBkFNb926dcjJyYHZbEZWVhYkSYIQAuvXr/cr+Fq0aJFPycuuAZdKURRkZWXxNGiiBsTlMCJq9iRJwp133ul34NFQMjIyEB8fj9mzZ/v9/JIkYe7cuejfv7+228wbs9mMxMRE7N+/HxEREaipqdFmkTp16oSUlBSns4G4M4yobnhOkAGeE0TNEc8JCpyxY8eisLDQ7+umTp2KvLw8t/OHAP1zgHyhd4CiXpu69E0U7HhYYgNgEETNUWlpKQYOHBjoYTSZa6+9Fjt27Aj0MKAoCmw2m+5jrgnTrte5HpAoyzIqKiqwbds2rQCqa3BUXwUFBY3WN1Fzx8MSiUJUdXV1oIfQZCRJahYBEAC3AqcqSZKwaNEipKWlueXwyLKMSZMmuS2f2e12PPHEE7oV4K1Wa73HalRdviH6Jgo1zAkiCiIxMTGBHkKjUROOVc1pkvqSSy5xGx8A3HnnnVotMEmSkJaWhrPOOguZmZmw2+2GpTbU5TFH6uGM9V26Mqou3xB9E4UaLocZ4HIYNVcdO3bEsWPHAj2MsKYXEAG+1wfT60OveGpdWK1WREdHu51p1BB9EwUDLocRhTD+uyWwjAIgwLf6YEBtUOJ4erQsy8jMzHSrHm+xWPxexnLdYi/LsuFyHlG4YxBEFGRatmwZ6CGENU9BjizLXmuYSZKElStXIj09XTv3x263IyMjAwUFBQBqE5ujoqKQkJCAqKgo5ObmegyIXAMms9mM8vJypKWlAThTjFXtn4hqcTnMAJfDqLnq3Lkzfv7550APIySp5+5cfvnl+OCDD9CnTx/ccssthru/9AwfPhzvvfeeYbBUUlKC2NhY3WUrWZZRXFyMQYMG6V6vt9PLaCcYl8UoXHE5jCiEtWvXLtBDCFnqNvjY2Fg89NBD+OGHH/wKgABg165dhgGQJEmIjIwEAOzZs8etb7vdjscff9zwetedXp52gnlKkCaiWgyCiIJMcnJyoIcQ0tQgwmq1YvLkyQ3atxAC+/fvR0FBAcaPH6/b5o033vDYh2Mg4ynQ0atBxhIbRM4YBBEFGXUmgRqHGkTs2bOnwZPQZVlGdXW10+yNvxwDGU+BjmuCNEtsELljEEQUZFhEVZ8sy5g3b169+1GDiJ9++qkBRgWnQxTtdjtGjRrldwCkJlu7BjLeAh01QdpisaC8vJynRhO5YGK0ASZGU3M1atQor0sm4ejhhx/Gfffdh549e9a5D7VS+w8//IAlS5bUebZGJcsypk+fjuXLl9drTEVFRU5FVF35UkuMKFwwMRrA4cOHMWHCBHTu3Blt27bFlVdeiY8//jjQwyKqN26R15eZmYlJkybV+XpJknDbbbdh9uzZyM3NrXcABNTO/BgFQK7LWEZsNhvef/993QBH3RoPAPHx8QyAiPwUkkHQL7/8gquuugotW7bE22+/jf/85z9YvHgxzj777EAPjajeRo4cGeghNEtCCGzbtq1e17/88ssNmgfk6cygZ555BoWFhV7PFQKA1NRUREdHIz09XdsZVlBQgOjoaCQkJPAMIKI6CsnlsIyMDHzwwQfYvXt3nfvgchg1VxaLBQkJCYEeBnnhqYSGJEk4dOgQysrKdP8sPVWmlyQJc+fORVZWFs8AItIR9sthr7/+OgYMGIAxY8aga9eu6NevH1atWhXoYRE1iFAuotqcPfTQQ17bKIqCnJwcWCwWrF+/3nBWadWqVTCZTIa7u4qLi7FkyRLda4UQWLhwIc8AImoAIRkEffvtt1ixYgViYmKwbds2TJ06FQ8++CBeeOEFw2tOnjyJY8eOOd2IKHgoioIRI0Y0Wt933nmnx6WrpUuXory8HOPGjYMQAr169dJtL8syEhMTARjv7oqNjcWYMWN8zhtSr+UZQET+CckgyG63489//jMWLlyIfv36ISUlBZMnT8aKFSsMr8nMzETHjh21W312mBA1Ju4Mc3bbbbehsLAQRUVFuPXWWxvlOdQCpJ6yB06dOoVt27ZpeTpxcXG47rrr3NrZ7XanGRujbeyuAZInsizzDCCiOgjJnKDo6Ghcd911yM/P1+5bsWIFFixYgMOHD+tec/LkSZw8eVL7+dixY+jZsydzgqjZGT9+PDZu3BjoYYQNWZaxaNEizJkzx+OOMUmSIEmSWy0wAPXK3bFarVi2bJnhln213lhsbKyvL4kopIV9TtBVV12Fr7/+2um+b775BtHR0YbXtGrVCh06dHC6ETVHv/76a6CHEFIeeOABw2UnWZaRlZXlNQACameJ9GqBpaam1vnUZrUG2IwZM1BRUQGLxYKcnByn/vLy8hgAEdVRSAZBM2fORHFxMRYuXIj9+/dj/fr1yMvLw/Tp0wM9NKJ6u/zyywM9hJDSu3dvFBcXu+XvqDMsAwYM8PnMINc+FEXBjBkztOWuoqIi9O7dW9vm7onrFvht27YhPj4eaWlpPAWaqIGEZBAUGxuLLVu2YMOGDbj00kvx+OOP48knn8Rdd90V6KER1VvHjh0DPYQGJ0lSo2z7v/baa722adWqFSIjI7Fq1Sq3GZbIyEh89dVXPp3lI0kSHn74YW1WyXHWx2Qy4cCBA4iLi/PpXB+j6vClpaU8HJGoIQnSVVVVJQCIqqqqQA+FyMmMGTMEgJC7KYoi5s2bJyRJavLnlmVZZGdni5deekkUFhaKyspKkZ+frzsWRVFETk6OSEtL0x6XZVkkJycLWZYFACFJksjOztb+zEpKStz6UhRFVFZWuv35VlZWisWLFxuOU/1vfn5+U/7aEQUNf76/GQQZYBBEzdWzzz4b8IClsW7Dhw+vVxA0cODABgmI5s2bpwUcjjdJkkRJSYn2Z1FZWSksFosoKSlxa68GOfn5+bp9ARAWi8Xpz9ZTW71gTC+IIgp3/nx/h+RyGFEoC+WyGbt27apX2YqSkpJ6j8Fut+OJJ57QzQMSQqCmpkb72WQyIT4+HtXV1bqHFxYVFTktazmSZRkRERHaz65LYK5tXfFwRKL6YxBEFGRMJhOuv/76QA8jLEmS5BS4qPROfgaAf/3rX4ZJ1Xa7HXFxcVpuUFlZmW7bpUuXori4WPdkaR6OSFQ/DIKIgtCoUaMCPYSwJIRwClxUJpMJWVlZbu3z8/M9JlWrCc9Wq9WwhMbo0aMRGxure7I0E6OJ6odBEFEQKi8vD/QQGsS5554b6CF4NXPmTKdAxjFwcTRgwAC3a202G2bNmuUxEFKXtYxKaKiBjtHJ0kRUdwyCiILQF198EeghNIgff/wx0EPwSFEUxMXFueUpqYGL1WqFxWKB1WrFRx99pHv92LFjvT6HuqzlS6BTn5wpInLWItADICL/VVdXB3oIQUOSpDoFDupMzJAhQyDLslvpix07duCaa66B3W6HLMu6z5GVlYXq6mrD53ed7VFPiI6JiXFb6srJycHs2bO117Rq1SrOBhHVE2eCiIKQY507MibLMmbNmuXXNYqioLCwUJuJ0Vumuu2225x2kNntdt1AZ8CAAbq5PrIsa0Vf1ROkXU+Idsw7ys3N1QIgoHY2aPLkyT6dPE1ExkKygGpD8KcAG1FTu+OOO7B58+ZAD6PZW7FiBfr374+4uDifSl8oioKZM2di7NixqK6udpqRKS0txfvvv49zzjkHf/3rX33qSy2UWlBQgJSUFNhsNm325+eff8acOXMghNCdrVKvB4CoqCjdIKuwsBBjxozx4Z0gCh/+fH9zOYyIQtbu3bsxbdo0n5bDxo4di02bNiE3Nxe5ubkAamdsUlNT0a1bN7+qyDsuc1mtVvTu3RtFRUWoqalBnz59sHHjRreZHVdq3pGoPdS2Dq+eiLzhTJABzgRRc3b33XfjxRdfDPQwAq6u+T56/agBTF3l5ORg+PDheP/99zF06FDExsaioKBAOwBRlmXk5eUhMTER0dHRXp/L20yQJEk4dOgQt8kTufDn+5s5QURBqLnvqmoqDfVvuDFjxtQ5AJIkCdnZ2TjnnHMQFxeH1NRUxMXFITc3V7cI6p49e3wKgByLr65atcppm72aGM0AiKh+uBxGFITOPvvsQA8hZEiShLS0NLz88st+B0KSJOHDDz9EZGSk0+yO3W7XXT6z2WyQJMltt5lKlmVs3LgRgwcPdgpwzGYzEhMTUVRUBABOj3vaUUZEnnEmiCgIXX311YEeQshYtGgRYmNjsWjRojpfq1fywm63ux2SqCgKBg8ejNTUVN3+UlNTMWbMGN1gxmQyYcyYMU6Pe9pRRkTeMQgiCkJ6pxOT/2666Sakp6cDANLS0pCdna1bAwwA+vfvrz0myzJycnKQnp4Oq9WKH3/8UbfkxaJFi3RPgJ4xY4ZbgCRJEmJjY33e9u5acNXoJGsiMsYgiCgIBdthif369Qv0EHS9/fbbTkFDeno6iouL8dBDD7m13bt3L4qLi2GxWFBRUYG0tDRtJiYpKQlCCC0QUhQFWVlZGDBgAIqKitxOgFbzfNQASU3wTkpKQlRUlE8zOnqzT6wsT+SfeuUE1dTUYMeOHThw4ABkWUbfvn3xl7/8Ba1atfJ67XfffYdHHnkEkiRxCpfIT+3atQv0EPzy6aefBnoIuux2u1a3C6hdXpo0aZJuW5vNhpqaGsTHxwNwn4lRz/tRD1pUc4LUXWHqdSrHPJ+kpCTtfvUgxMTERI85Pka/A3pV7olIX52DoPz8fMyePRtVVVVO93fp0gX/93//h+nTp3u8/pdffsGaNWsYBBHVQbDNBDVXjnW7rFarYQDk2hbQn4lRf87IyHBbptILakwmk+45QEIIFBUVeTwI0eh3oKamxvAaInJWp+Wwp556CikpKaiqqtL+Aqu3H3/8EQ8++CCuu+46HD16tKHHS0QIvpmg5kiSJKxcuRIAYLFYMGfOHI/tZ86cqbW1Wq2G5TA++OCDJlmm0nt+10CNiDzzOwj69ttvMXv2bAgh0Lp1a8yaNQtbt27FK6+8ggcffBDt27eHEAI7d+7EkCFDcPDgwcYYN1FY40xQ/cmyjJ9//lnbXbV+/XqP7U+ePImoqChtJ9a2bducaooBtbM+y5Ytc7vWKDgxSmKWZRmDBw/2OB69mmaOxViJyAfCT2lpaUKSJNG2bVvx4Ycfuj1+5MgRMWrUKCFJkpBlWfTo0UPs27fPrd2+ffu0Ns1RVVWVACCqqqoCPRQiN5WVlQIAb/W8SZJU52sVRRGVlZWipKTEYz+Kooj8/Hy3P8OcnBwhy7I2DrUPo/aefhcsFouorKxsyF8xoqDlz/e33zNBO3fuhCRJeOCBBzBw4EC3x7t3747XXnsNjz/+OADgyJEjGD58OD766CN/n4qIDJhMJrdEW6o9RNB167nRwZL1Lblhs9mwatUqvPHGG4b9zJ8/32lXmErdXu+YVK1Wltdr74n6u8AZICL/1Wk5DABuuOEGj+3mzZuHF154AS1btsTPP/+Ma665Brt3767bKInITe/evQM9hGbn7rvvxqFDh7BixQrMnz8fDzzwAH799Vfdtp4CIEmSMHLkSK/P99hjj2n/4NPz+OOPY9u2bU73Wa1W3fwjm82Gc889l8EMURPye3eYuvOgY8eOXtveddddOPvsszFmzBgcP34c119/PTZv3ozExET/R0pETn777bdAD6HZ6dy5M0wmE6ZOnQqr1YqePXv6fK2iKJg0aRKuueYaLR/nzTffrFdRVb2dYWVlZboBmCzLTGomamJ+zwSpU8s//PCDT+1vuukmvPXWW2jXrh1OnDiBW265BVu2bPH3aYnIxX//+99AD6FZkSTJKZl4z549Pl977bXXwm63Y+XKlUhKStKSm10Tn9Xn8YfrzjC9XV0AcMcdd3AWiKiJ+R0E9e3bFwDw4Ycf+nxNfHw8duzYgXPOOQd//PEHkpKSsG7dOn+fmogcHDt2LNBDaDCyLBsGF+pnjjdCCCxbtqxOZSN27Nihzc4IIZCbm4vo6GgAQHl5OSwWC9544w0sWbIEr7/+ul+BkOvOMJPJhKysLLd2mzdvZskLoibmdxAUFxcHIQRee+01v64bOHAgLBYLunXrhtOnTyM7O9vfpyYiBxdeeGGgh9Bg7HY7pkyZ4nb/ZZddhq+++srnftTgpaCgAL169dJt88ADD/g8ppSUFBw5cgRvvvkmbrnlFqSmpuKWW27B3Xff7XMglJmZCeDM+UKAfu03dcbIarU6tSWixuN3EDRixAgAtXV0SktL/br28ssvx65du7RTUomo7jp16hToITQYx4MLHX3xxRd+96UGL0ZnlPm6lA/UBiYDBw5Ebm6u0wnQ69atw4cffogVK1Z47eOjjz5yq/RudNDi9u3bWRWeqAn5HQT95S9/QadOnSCEwKOPPur3E15wwQV4//33mQBIVE++1OgLFg39jyKbzYadO3fqPrZp0ya/83r0+q+pqcHUqVORlpbmsW1hYaFbCQ3APd/Ibrdj4cKFrApP1IT8DoIURcFXX32FyspKPP/883V60qioKK0as9EHFRF5FkpBUEOTJAl5eXm6jwkhtECkrhzzfGbMmOFXUKUue5nNZhQVFXm8llXhiRpXnWqHdenSBeeddx569OhR5yfu1KkThg8fjuHDh9e5D6JwZnQIYKiQZRmDBg2q07VCpyipY7+XX355fYaGO+64Q/t/k8mERYsW+XW9mkpQXV3tcRaMtcCIGledgiAiCrxQnyGw2+1+5x362u/9999frz4KCwudcnbS09ORk5Pj84zQ3LlzDYuwqlgLjKjxMQgiClJdunQJ9BAaXX0OKnTkugW/Ifp1zdkZN26cz9eqy1x6RVBzcnJgsVj8Lp9BRP7z68ToQ4cOaf8fFRWle39dOPZFRL4pLy8P9BCaPVmW8cwzz+Crr77Sre5eX47BjNFJ0Hocl7nMZjMSExOxf/9+9OnThzM/RE3IryBIPXdDkiScPn3a7f66cO2LiHwTGRkZ6CE0a4qiYMKECZg2bVqjHcmhKAoiIiJgsVjQrl07yLLsdZZJb5nLZDIx+CEKAL+CIKMPEp75Q9T0+vfvH+ghNLq6VHpfsWIFTpw4gZiYGIwaNapRP58GDRqEuLg42O12yLKMiRMnYt26dbDZbLrt58+fj0mTJjHgIWom/AqCVq9e7df9RNR4hgwZEughNKqkpCS89NJLfl0jSZI28yPLcqP/A82xPpl6iGJRURG2bt2Kxx57zK39JZdcwgCIqBmRBKdxdB07dgwdO3ZEVVUVOnToEOjhEOmKjIzE999/H+hhNLi6zAA1FxaLBX369EFUVJTTa5BlGRUVFQyCiBqZP9/f3B1GFMQuu+yyQA+hwdUlAGouZ+moCc8mkwmrVq1y2vWVl5fHAIiomWEQRBTE+vXrF+ghNLi6zAAF6sykMWPGOAU6jgnPZrNZq0DP7e5EzZNfOUFE1Lz897//DfQQQkZdZqCmTZuGJUuWGG5v564vouatwYOgzz77DLt378a3336L48ePG+6SUEmSxErJRHXEIKjh+BsAKYqCo0ePok+fPoiPj2+cQRFRo2qwIOjrr7/Gvffei+LiYp+vEUIwCCKqhxYtOJnrypezeup7jSzLsNlsSEpKAgCkpaVhxowZnPUhCjINkhN0+PBhXH311SguLtYKF0ZERMBkMiEqKsrwFh0dzdOiierhkksuCfQQmhVJkgxnZSRJ0kpnSJKk1exSFAWLFi0yrOHlaN68eSgsLHQLmHJzc51qiRFRcGiQf0Y+8cQT+PHHHyFJEiZNmoS0tDRccMEFDdE1EXmwe/fuQA+hWWjTpg1OnDgBIQR27typ2+all17C4MGDtfwdAE65POeccw5SUlJgs9kgyzKuu+46bN++XTsIMSsrC+np6SgsLNTtX60llpiYyBkhoiDRIEHQO++8A0mScPfddyMvL68huiQiH/z666+BHkKT01u6OnHihNdrBg8erCUql5aWYvfu3Rg2bJjTbi7XGl5Wq9Wvml6OtcSIqPlrkCDou+++AwDcfffdDdEdEfmod+/e2Lt3b6CH0WTGjh2LCy+8EI8//rhf191+++1aYHLPPfdg7dq12mPJyclYs2YNAPfdXHq7u4YMGeJxJ1lpaSkTpYmCRIPkBJ1zzjkAgLPPPrshuiMiH0VERAR6CE3qlVdewcCBA/2+bsuWLbBarSgtLXUKgABg7dq1KC0t9bkv14MQXc2dOxdWq9XvMRJR02uQIGjAgAEAgG+++aYhuiMiH1155ZWBHkKTstlsaNeuHZKTk/2+bv/+/YY5VB988IFf/ZnNZhQVFXl8LiJq/hokCHrwwQchhGA+EFETO3ToUKCH0OQ2btyIBQsW4I033tB2e6kkScLq1avd7pdlGX369MGwYcN0+7zqqqtgtVphsVh8nsWprq7WvV99LiJq/hokCLruuuswe/ZsWCwW3HfffTh16lRDdEtE5GblypWIiorCrl273PJy1OM5XKntYmNj3WaRkpOT8fnnnyM6OhoJCQk+b3WPiYnR3VY/d+5cJkYTBQm/qsi/8MILHh/Py8tDUVERIiMjMXr0aPTt2xdt27b12m9zTKhmFXkKBqWlpXXKkQkFagDiuFNMURSsX79eO8TQkcVi0RKWS0tL8cEHH+Cqq65CZGQkoqOj3fopLy8HAJSVlSEmJkY3sCkoKNC21TuOKy8vj7XCiALEn+9vv4IgWZbdppnrS5IknD59ukH7bAgMgihY/PnPf8ann34a6GEERGJiIrZt26b9PHr0aHTq1MltaV4NavQCGYvFgoSEBLf709LSsGTJEu2cIKPAprS0FHFxcbpBFGeEiJqeP9/ffi+HqSdCN+SNiOquS5cugR5CvVx66aV1uk5RFPzzn/90uu/ll1/WzU3MyspyCkgc83/0lrVkWdYCIODMQYh6+ULV1dVu5xYxOZooOPh1TtDBgwcbaxxEVEfBvitz3759fl+jKApmzpyJ3Nxcn9oPGDAAVqsVZWVl+PjjjzFnzhynGZ68vDxtWcuob6ODENUgynUmiMnRRM2fX0FQdHR0Y42DiOooMjISFRUVgR5Gg5AkCbfeeiteffVVw1litVjpkSNHfAqCFEXBjh07cM0117jN2KgzPOXl5SgvL3cqqeE4E6T24xjYqEFVTEyMWxCVlZWFsrIyAOCSGFEz1iC7w4gocFJSUgI9hAYjhMBrr73mcZl8yZIl2LhxI+Li4rz2J8sybrvtNjzxxBOGVeIdl67U5zWZTMjLy9MORFQUBStXrtQCmoKCAqfdZABQXl4Oi8WCzMxMzJkzx6+dZkQUIKIO3nrrLdGvXz/Rr18/8Y9//MOva9etW6ddu3379ro8vd8WLlwoAIgZM2b4fE1VVZUAIKqqqhpvYEQN4NlnnxUAwuomy7LXNlOnThUlJSVCkiSP7RRFEdnZ2VqfsiyL/Px8IYQQlZWVwmKxiMrKSu39rqysdHt+WZZFZWWl7mOKojhdT0SNy5/v7zolRs+cOROfffYZOnfujDvvvNOv6++880507twZe/fuxaxZs/x9er+VlpYiLy8Pl19+eaM/F1EgfP3114EeQpOSJMlwVsdRQkICdu/e7XFWSa0On5GRoZsEbTKZEB8f77SkVVZWprustmzZMt3HHGea/D2QkYgal99B0M6dO/HNN99AlmU8+eSTfj+hJElYtmwZFEXBvn378O677/rdh6+qq6tx1113YdWqVVp9M6JQc+GFFwZ6CA1O3a0lSZLTsRySJGHu3Lk+HdUxbtw4r//Q2rhxI/r37+/X7q6YmBjd51+6dCnatWvnttNMzSVyXULjMhlR4PkdBL3yyisAak+JvuSSS+r0pBdffDESExOd+msM06dPx0033YRrr73Wa9uTJ0/i2LFjTjeiYKDW7gsldrsdaWlpOHToEA4dOoTCwkKsWLECU6ZMQVZWlk9Ha3ibLZJlGYMHD0ZNTY3u40bFaU0mk25wZbPZUFNT45RLJMsyMjMzAQBTpkzxacs9ETUdv4OgkpISSJKEkSNH1uuJb775ZgghUFxcXK9+jGzcuBGffPKJ9gHkTWZmJjp27Kjdevbs2SjjImpoRjWsgt3SpUsB1AYdx44dw7Rp07By5UqflsJ8MXHiRGzbtg233HKL7uNGwREAzJgxw3DGx2w2IysrS9s2n5GRgWXLlvEsIaJmyO8gSN2KW98p+AsuuAAAtKPpG1JlZSVmzJiBdevWoXXr1j5dM3fuXFRVVWm3ysrKBh8XUWOIiYkJ9BAahRokWK1WTJkypV4Hq+rV+HrxxRedZmdcGc0EAZ53j1mtVu0cIqB21mfp0qVuS2g8S4go8PwOgqqqqgAAnTp1qtcTq9c3xrLTxx9/jKNHj6J///5o0aIFWrRogV27duGpp55CixYtnOr8qFq1aoUOHTo43YiCgclkwvXXXx/oYTQ4SZLQp08f3WRjf/vRC6DsdrvHfj3NBFmtVvTu3RtFRUWwWCwoLy/XSmoYJUfPmjXLcMs9EQWG30GQGhz8+uuv9Xpi9fr27dvXqx8911xzDb744gvs3btXuw0YMAB33XUX9u7dq30QEYWKUaNGBXoIjcaoWrvefa6Pr1ixwjAI8sTTLI1jgnNcXBwOHDjgFMwYjbdr167aWUKOQRMRBY7fQVDXrl0BAP/5z3/q9cRffvmlU38NqX379rj00kudbhEREejcuXOd6xQRUdMSQmDWrFk4cuQIUlNTnZKN09LSsGHDBsNrJUlCVlYWfvvtN79nkTzN0qhLc54SnE0mExYtWuR27dy5cwHAbcs9EQWOX2UzAGDgwIH46quv8Prrr2PatGl1fuLXXnsNkiQhNja2zn0QUWgrLCxEYWEhgNrARi2ZoebeuNbscjR79my/n0+SJBQVFSEyMhIWiwUxMTFawGK1WlFYWGiY4OwY2PTv39+tb6PaY0QUOH7PBN1www0AgO3bt+O9996r05O+9957WvVntb/G9u6779bpXCOiYNC5c+dADwEAGjU3SQih7RgDziQn6y091TWJWgiBwsJCp/N80tPTkZubi+joaN2t8XpLZ3pLYkyEJmp+JOHnp8Xp06fRt29ffPvtt+jatSt27drl106xb775BldffTV+/PFHnH/++fj666/RooXfE1KN7tixY+jYsSOqqqqYJE3NntVqDZtjHSwWC+Lj47UCpjU1NRg1apTfgY9erpAsyxBC+NyXLMtITU3VZqccFRQUOBVVXblyJfOAiJqAP9/ffs8EtWjRAosXL4YkSfjxxx8xYMAALF261OtZJdXV1XjyyScxYMAAHD16FACwePHiZhkAEQUbk8kUElvlJUnymPCsKAoiIiKQnp6uzdaMGjUK5513nt/Pk52d7fRckiQhNTXV5wAoKSkJALRZItcToM1mMxOhiZo5v2eCVJmZmZg3b5529kVERASGDRuGP//5z+jWrRsiIiJQU1ODH374AZ988gl2796Nmpoa7QPmsccewyOPPNJwr6SBcSaIgs0dd9yBzZs3B3oYdabOqowdOxaFhYVYunSp03EWsizj9ttvxyuvvFKvM4PUvtQzz4qKigAAgwcPBgBER0d7TaZWy3k4tlMUBeXl5cz5IQowv76/61Op9YUXXhDt2rUTkiQJSZKELMuGN7VNRESEWL16dX2etkmwijwFm9WrVwe8unt9bmq1d7WKu1rBfd68eV4rwdflZrFYdN/H/Px8oSiKx2vHjh3rV59E1HT8+f6u80yQ6siRI1iyZAleeOEF/Pjjj4btunTpgnvuuQcPPfQQevToUZ+nbBKcCaJgY7FYkJCQEOhhNAh1VgUAoqKi6j3zY9S/0axNaWkpBg4caHhtUVER4uLiOBNE1Az58/1d74ScyMhI5OTkICcnB//5z3/w2Wef4b///S+OHz+O9u3bo0uXLrjiiitw8cUX1/epiMiDUMgJUqnbyY8ePdrgARAAzJw5U9tmX1ZW5rQVHjCuxybLMlauXInY2Fjk5eW5JT4zACIKLg2alXzxxRcz2CGiepNl2WPtrvqQJAljx45FQUGBdvChLMvIy8vTkpfVLe6OMz2yLKO4uFg728xsNiMxMRH79+/Xtr67ni1ERM2b37vDiKh5KisrC/QQGozdbkdcXBw+//xzt8Kj9SWEwKBBgzB58mTDk59dC6TKsoysrCy3w11NJhPi4+Oxbds2p7OFXHeKEVHzxCCIKETExMQ0eMAQSHa7HU888QRuvPFGv1/X1Vdf7fFxoXMWkLoEpzKbzcjKytJmhDIyMnSDG19KaRBR88QgiIiaBaPzgd58802/84J2797t9/O7nuhstVoxZ84cp+BmypQpbsGNUdV4x4CKiJonBkFEIaKsrKxRkoibgizLWLRokdfK8L7y5X2QJElb7tJLbNYLbux2O5YtW6b9bLVa8eOPP7rNVLFEBlFw4HHNRCFCL5k3WGRkZCAtLQ0AkJ6e3iB96iU2A7WBjBr0OCY2uyYzt2vXTrffJUuWYMaMGdi2bZu2DKYeniiE4E4xoiDCmSCiEOGazBtMFi1aBKvVirS0NLdyFnXVr18/rR9FUZCXl4eKigpYLBYUFRWhd+/eAID4+HjdgMVom7zdbkdRUZFTHpAQArIso7CwkCUyiIIIgyCiEOJYr+qNN94I9HDcGCU422w2rXxFenq6Fqz4GhDp9fvxxx9rszRZWVlITExEWVkZSktLERcX53Unl1GiuaIoEELo5gGde+65nAEiCiL1PjE6VPHEaAp2Y8aMwcsvvxzoYfhMzQvq37+/01k7VqsVRUVF+OmnnzB9+vQ6LfepwYzex52nk54LCgowefJk7TpJkrBq1SokJia61RjjidFEzUOjVpEnouavtLQ0qAIgoHaZKT093W2GxmQyYcyYMZg6darTcp8/2+b1tsSrPO3kMpvNyM7O1p5L/a/r0iPzgIiCE2eCDHAmiIKV1WrFwoULsWLFikAPpV70ZlasViv27NmjJSEnJSU1yvM4Pp+nGR+r1WqYWE1EgcGZIKIwVVBQgOjo6KAPgADnPCHgzGtLSkrCuHHjcOjQoXonUCuKgqysLJSVlekebujtDCD1xGgGQETBiTNBBjgTRMFGb9Yi2HnLwcnMzMTcuXNhs9n87nvp0qU4deoUMjIydOuHAd5ngoio+eFMEFEY0pu1CHZCCEyePBl5eXm6MzKxsbEoLy/HkiVL/O67T58+WgAE6Je70Mv98TRzRETBhYclEoWIYD4s0RMhBB5//HG3+xVFQUREBMrKyjB06FAtT8hXpaWlhktdjrM8jtXiS0tLtVIaejNHRBRcuBxmgMthFIwKCgqcDvELVYqiYMKECXjxxRe1gGTixIl44YUXfC6ZIUmS2/tUnyRpImoeuBxGFKbMZjOefvrpQA/DbwkJCT4nOc+cOROvvvoqXnjhBaelrBdffBHPPPMMVqxYgalTpxper25zdw2AZFk23OZutVpRWFioO3PkmLxNRMGFM0EGOBNEwaigoACTJk0K9DD8tmTJEqSmpvrU1tPBh54el2UZqampiI2N1d1aX1hYiDFjxrjd7212jctiRM0LZ4KIwpDVasWUKVMCPQy/ybKMEydO+Hz4oaeDDz09vnHjRuTk5GDIkCFus06KomDw4MFu16jvqaflRb2EaiIKDgyCiEJEMO4OU/Ny5s2b51dSs78cgxx/Tnv29T31dOo0ETVf3B1GFCICvTvM3+dOSUlBXl5eI47ojKysLLcdX5dffjnef/99DB06FLGxsbrXqUVUHQM0veU2RVHQp0+fRho9ETUWzgQRhQh1hqO+pyjXlb/BV15eXoPN/kiS5PF1DxgwwOnngoICxMXFITU1FXFxcYaV5I1kZ2ezbhhRCOBMEFEIMZvNaN++fYPU1Gps9Q2AJEnC66+/jhMnTkAIgV69eqG8vBxJSUmGszRq7THHPB81pycxMdEtkCkrK3MbpxACAwYMQHl5OeuGEQU5zgQRhRi9xN+68KdKe12NHDnS7Xnuuecen64VQmDr1q0YN24ckpKSEBcXh2PHjmHVqlW6szSOtcc81QNzpC4xOlKDKtYNIwp+3CJvgFvkKZjdc889WLt2bb36kCQJN910E7Zu3dpAozJ+npiYGHzzzTfafX/6059w4MABv/tSDy8E4DRL462umuuhh6Wlpdi9ezeGDRuGzz//HCkpKbDZbFpQxe3wRM2XP9/fXA4jCjFWqxUvvvhivftRZ1oamxDCKQACgAMHDmD16tX49ddfceDAAZ8PgFRndFxnaDzt8nLN6XENIEePHs2lL6IQxSCIKMQE41Z5Pb/++itGjx6NqKgon68x2qWlt8tLtWHDBu2QxNLSUrcZtJdffhkXXnghFixY4OcrIKLmjjlBRCFG/cIPdldddRUWLFjgVwL1hAkTdGdqTCYT7rjjDrf7ZVmGEEI76HD37t26/S5cuJCHIRKFIAZBRNTs9O/fH7t27cLKlSv9um7dunVOwYrVaoXFYkFpaSk2b97s1l4IgaSkJERHR6OgoADDhg3T7VcIwcMQiUIQgyCiEKO3rTvYfPLJJ0hPT/f7OsddXupusISEBMTFxekuEarvk7pNPjIyEqNHj3ZrpygKIiIiYLFYOCNEFEIYBBGFGL1t3c2B45i8LdfVNYiTZRkRERFuNb/8KX2xadMmzJs3TxujoiiYMGEC4uLikJCQoM0aEVHwa36flERUL+rJ0c2FLMvIzs7G+PHjtfsaa6bKbrdj0KBBWLZsmW7gowY2ekGYY1L1ggULcOjQIVgsFhQVFeHFF190O1yRM0JEwY+7w4hC0M8//xzoIWh69+6NOXPmGAY+KSkp6Ny5MxYuXOi1L6MdXo6EEMjNzdWtZSaEMAyAXEtfmEwmmEwmWCwWw8MVuV2eKLhxJogoxFitVsyePbtR+p45cyamTp3q1zX79+/3GLjk5+fj+PHjPvU1a9Ysn5/XqCiqEMJpPLIso6ioyPAARE+nRhNRcGMQRBRi9uzZ02h9Hzt2DM8991yD9mmz2bB8+XKv7UpKStC1a1ef+/3www99ame321FTU2P4uLq8yIKpRKGHy2FE5LNAJARLkoRVq1YhMjISGRkZDd6/L7M6ZrMZiYmJPDWaKMQwCCIKMUOGDPEpdyZYSJKE9u3bY8+ePQ1yErYkSZAkCXa73a9ZHTVHiIhCB5fDiEKMyWTCokWLAj0Mn3nbLm+325GUlITx48frtpVlGYmJiVrejizLhn2mpaXh0KFDqKiogMViQXl5OYuhEoUxBkFEIWjAgAGBHoJPFEXBokWLtHwbT4xmgex2O3bs2IHi4mJYLBZUVFRg1apVWp+yLGPq1KmorKxETk6ONqPjWmSViMIPl8OIQpC6o6k5F1JVl6LMZjPi4+MxaNAgn7a/67HZbKipqUF8fDwA5vAQkW84E0QUglx3NDVHGzZs0JaiPvroo3rlMOklN6uzPQA8lrtQ64vx8EOi8MMgiChEmc1mFBUVNcsSGoqiYPDgwQBqd5xNmzatXn1lZWWhrKzMLZBxrB+mV+7C2+NEFNokESpbSBrYsWPH0LFjR1RVVaFDhw6BHg5RnVgsFiQkJAR6GE4cl8GsViuio6P9WrZTl/lkWUZqaiq6du2KjIwM7b68vDzDvhVFQXl5OUwmk9fHiSg4+fP9zZwgohDW3HKD5s+fj0mTJmlBhtG2d0mSMGvWLOTm5ro9tnHjRpx77rna8pdjIGO32zFlyhS0b99e+9mRY7mLsrIylsMgCnPNb56ciBqMmhvU1EtiRlvUS0tLtQCjoKDAqaiqSpZlfPjhhxg7dqxuuYrBgwdrO7v0CqV62lLvmDvEchhExCCIKMSZzWYUFxd7PY+nIRmtsr/11ltYsWIFSktLMWXKFN1ZoIkTJ+Lzzz9HXFyc0+OyLGPlypUAapf5SktLsXjxYsMx2O12SJKkBTquByOyHAYRMSfIAHOCKJSUlpYiOTkZX375ZaCHAsBzNXhFUWC3290elyQJN9xwA95++20IIXxe5issLNSWz/QCHKvVyq30RCHEn+9vBkEGGARRqLjnnnuwdu3aQA8jINREZwAoKytDTEwMAx2iEOfP9zeXw4hCWGlpadAFQP7mLxm1V5e3tm3bpm2Dj4qKQnp6Os8EIiIA3B1GFNLeeOONQA/Bb2oujy+T1LIso7i4GDU1NYiIiHD6r5rgHBUVpfUlhEBubi6WLFmibaUnovAVkkFQZmYmNm/ejK+++gpt2rTBkCFDsGjRIlx44YWBHhpRk4qMjAz0EOpECAFFUfCXv/wFO3bsMGw3d+5cxMbGGj6enp6uG0zZ7XakpKQgMTGRy2NEYSwkl8N27dqF6dOno7i4GNu3b8fp06cxYsQI1NTUBHpoRE1q5MiRuvfX9x8ETbHl3mazYcqUKR7bLFy40PCUZ6vViiVLlnjsf//+/fUaIxEFt5AMgt555x3cc889uOSSS3DFFVdg9erVOHToED7++ONAD42oSZlMJiQnJzvdd9NNN+Hrr7+uc5+yLGP69On1HZpX6plA+fn5htv7hRBISUnRzfHROwzRtX+eCUQU3kIyCHJVVVUFAOjUqZNhm5MnT+LYsWNON6JgZ7Va8eKLLzrd99Zbb9Wrz5tuugnLly/3qa0sy3U6n8jxzB6z2YwPP/zQcPbJaEZH7zBEvf6JKHyFfBAkhEBqaiqGDh2KSy+91LBdZmYmOnbsqN169uzZhKMkahx6syH1PRXD12RrRVGQmppq+Hx6wZEkSSgsLER5eblT0nJsbKzTwYauz6M3o6N3GGJOTg4sFotb/0QUnkIyMdrR/fffj88//xzvv/++x3Zz585Famqq9vOxY8cYCFHQ06sd1lS1xOx2O7p27ar7/EII3eBICIFjx47pztAkJiZi/fr12LlzJ1atWgW73e51RsdsNiMxMZGHIRKRrpA+LPGBBx7Aq6++ivfeew+9evXy61oelkihoqCgACkpKbDZbFAUBTabrcmeW5ZlTJ48Gfn5+drzz5w5U7cwquM1FRUVTgFLQUGBVmZDlmUsWrQIAwYMYGBDRG7C/sRoIQQeeOABbNmyBe+++y5iYmL87oNBEIUStTTE0aNHkZSU1OTPr1aFnzFjBgDnyu96LBYL4uPjAdSO3bW9ehI0AyAichX2J0ZPnz4d69atw/r169G+fXt8//33+P7773HixIlAD40oIEwmE+Lj4zFkyJCAPL8QAkuXLtXGYpTfA9QGTEePHtV2fOnlNdlsNhQVFXl9XqvVCovFwhOiiUhXSAZBK1asQFVVFeLj4xEZGandXnrppUAPjSigtm3b5paQLEkSEhMTdXdSJScnu22xrys1cLFYLEhMTER5eTnS0tLc2gkhkJSUhOjoaBQUFKBdu3a6/Y0bN87wjCCgdglNLZeh9kVE5Cgkl8MaApfDKNSUlpZi0KBBugnJemUqHnjgATz11FOwWq144YUXMG/evAYbiyzLyMvLQ2JiolNZC1eKomD9+vWGS3hGy2JcQiMKX2G/HEZEzgoKCjBw4EDDYEPv/meeeQY5OTmIjo5u0AAIOFO2Ys+ePR637NtsNkiSZHjWkNEZQUZLaDwhmogcMQgiCnFWq9Vr+Qk9drsdc+bMabTt9N4CHKB29qaiosLj43pnBOkdlMgToonIFYMgohDnrXyEEV8rudeVoig4//zzPT6emZmJOXPm6I7D0xlBegcl8oRoInIV8oclEoU7vQMTA00NSqqrqw0DLbvdjqNHj+qOe/78+Zg0aZLHoIYHJRKRN0yMNsDEaAoljocN1ldDzBAVFhZizJgxugnMjtQlLdfHJUnCqlWrWPqCiNwwMZqInJjNZhQXFxsWFNVj1FYIgalTp/rUl1G+z/nnnw+r1Yo9e/Zg8uTJhmcG2e12pKamuj3uqXo8EZGvGAQRhQlPRUj1eJo18mUmSFEUjBgxQvexwsJCREVFISkpCStXroTNZsPUqVPdgiZFUTBjxgysX7/erQ/u9iKi+mIQRBRGzGYzMjMzPe7I8oVawNST/Px8/POf/3S7X5ZlLF682C2QysvLw9y5c7UZJsdk5iFDhnC3FxE1OAZBRGHEarUiIyOjXjk9V199tU+5RX/96191n2f06NG699vtdmRmZmpFUrOyspxyfhyXxbjbi4gaAhOjDTAxmkKRxWJBQkJCvfowSlb2haIoKCoqQlxcnNfr1ROet23bpiV1OxZiZQBERHqYGE1EuvQOEfSXUbKyN7IsY+XKlVpukuM49JbnbDYbtm7d6rSrTQiBJUuWYM+ePUyKJqJ640yQAc4EUagqKCjA5MmTDZfEvG2Bl2UZFRUVOHLkiGEtMr1riouLERsbq91ntVq1SvDnn3++2+yQGhgZ9a/WH+M2eSJyxJkgIjKUmJjo8fFbb73V4+PDhg2DyWTyeNChq0WLFjkFQEDtqc5jxozBmDFj3HauqbNEnvpX649xRoiI6opBEFGY8Va09LLLLvN4/a5du5CTk+PX0tqAAQO8tjGbzSgvL4fFYsGGDRt8CrC4TZ6I6oNBEBE56dWrl9ct9BkZGQDg07lDiqIgIiICFovF66yNyWRCfHw8hgwZ4tM2fm6TJ6L6YBBEFGa8BRj33nsv7r77bo+zPHa7Hfv379dmbwoLC3Xby7KMCRMmIC4uDgkJCYiOjsa8efOwZMkSlJaWGvZvMpkwd+5ct/slSXLaJj9z5kxPL5WIyDNBuqqqqgQAUVVVFeihEDW4/Px8IcuyAKB7UxRFvPHGG4ZtFEURlZWVbn0qiiIACFmWRVpamigpKfH4PMnJyT6PT1EUkZ+fLyorK0VaWpqQJEl7rvz8/CZ414goGPjz/c2ZIKIwZDabUVFRgbFjx+o+brPZ0K5dO93lLseDCq1WKwoLC1FYWIiff/7ZaSt73759UVhY6PE8oLVr17rNCFmtVrdir7Iso6ioSNsJtmTJEi1niAnSRFRXLQI9ACIKDJPJhMWLF2PTpk1uSchqrk18fDwSExOxf/9+VFdXo6ysDEOHDkVsbKzHrfbifwVObTab13G8+eabqK6uRkxMDEwmE8rKytwCJ7vdjpqaGgDQfVxNkOYBikTkDwZBRGHMZDJh1apVTsGMeqihGlCYTCa3U5vnzp2LrKwsjzu4fAmAAODvf/+79rx5eXlITEyELMtOgY5jArS6K83ocSIiX/GwRAM8LJHCiePBhYMHD3aaUbFarYiOjva7TIaiKLDb7X7VKXMslaHOJKnLb46HIhYUFHh8nIjClz/f3wyCDDAIIqpVl3pjsiwjNTUVXbt2xdy5c51mhSRJwpgxYxAfH49p06bpPl98fDysViv279+PPn366C5zeXuciMITg6AGwCCIqJbVakVUVJTXGR1JkvDwww/j5MmTWLJkiVYNfu7cuVp1eJWiKHj11VcxcuRIt35KSkrcTpcmIvKVP9/fzAnypqYG0DsMTlGA1q2d2xmRZaBNm7q1/e03wOjLR5KAtm3r1vbECcDT8kZERN3a/v474CkXxJ+2bdvWjhsATp4ETp9umLZt2tS+zwDwxx/AqVMN07Z16zO/K/60PXWqtr2RVq2AFi38b3v6dO17YeSss4CWLb22NZ1zDnIWLkTa/87tkQE4/OZj/Lhx2LhxI4QQePKJJ3AKgPbbYrdj2cKFaO36e2mz4eC+fWgJQH2XJABtAJz473/1/460bFk75v/1ixMnjF9bixa17wVQ+3fit98apq0/f+/5GaHflp8R/rdt5p8Rbm1ttto/OyOOf5f9aevt771jW181whb9kKCdM1D7keF+u/FG5wvattVvBwgxfLhz2y5djNsOGODcNjrauO3FFzu3vfhi47bR0c5tBwwwbtuli3Pb4cON27Zt69z2xhuN27r+uo0e7bltdfWZtsnJntsePXqm7bRpntsePHimbVqa57b79p1pO3++57YlJWfaZmd7bmuxnGn79NOe227deqbt6tWe2xYWnmlbWOi57erVZ9pu3eqx7c+PPy5uu+02AUAM99QnINJw5myfAV7a/s2h7cVe2oq0tDPjPXjQc9tp0860PXrUc1vHs4qqqz23HT3a+XfYU1t+RtTe+Blx5hbCnxHi6afPtLVYPLfNzj7TtqTEc9v588+03bfPc9v/fUbwnCAialCPPvootmzZ0uD9jhw50mvZDSKixsKcIAPamuJ33+mvKXKqW78tp7r9bxuAqe7Dhw+jrKICfS66qDap+H9tDx8+jGeeeQZPPfUUHH+T/gCgvqOOy2Fq8Q3HtqcBnP7fFnbHtrIkYe3atRg0aBDOO++82jtbtkTpZ59h0KBBgBBQf/MVWcaXX355pt3/2nI5TKctPyNq/5+fEf63DdHlMOYENaSICOe/lJ7a+dOnrxw/lBqyreOHaEO2dfzQb8i2rVqd+aJqyLZnneX7GnJjtW3Z8syHR0O2bdHizIedg4KCAu3MH/VsHrPZjJylSzFnzhx4+3eRHcBvOHOuDwC37ert27dHUlKS1hYAIAQ69eyJ8y64wKm/6upq7Tm1tnY7yr77zq2tRpZ9/3skSY3TFmgebfkZUYufEf63NfiMqHdbRfH9d9iftv78vfcRgyCiMOJakkItOXHw4EE88cQTfvU1d+5c7Wwe9VRpdbu61Wr1+UBDvcMPZVnG0aNHYbVauf2diBoNc4KIwohRyYmFCxf63VdWVpZWr8tkMiE+Pt7plGnHumOO9cZcubaVJAlCCCQlJSE6OhoFBQV+j42IyBfMCTLAc4IoFOmd/uw6C+MP9WBDx/7LysoQExMDANizZw8kSXI7hdpobEVFRRg3bpzbDFJ5eTlnhIjIJ/58f3MmiCiM6M3QZGVlQZb9/yhwXd4qKChAdHQ0EhISEBUVhaioKCQlJWHcuHHYtm2bT2Pr0qWLYXFUIqKGxpkgA5wJolDmWnLCMVnaV9nZ2UhPT9f681RfzNfZHL1+OBNERP7gTBAReeSaw5OYmOi2K0yWZaxYscKwD8fSFnq5Ro5sNhuKiopgsVi0PCKjcfmaS0REVF8MgogIZWVlbkGQ3W7HgQMHkJ2d7dbedSlM3eFlRJIkjBs3DgkJCV6Tnc1mM8rLy2GxWFBeXs7q8ETUaBgEERFiYmIgqQfJOVi6dCnGjx+PnJwcLcjRm53R2+Gltlf/67ot39uMkONMFRFRY2BOkAHmBFG4SU9PR25urtv96g6w0tJSvP/++xg6dKhhlXfHXCMA2L9/P44ePYqkpCS3toWFhejSpQtiYmIY7BBRg/Hn+5tBkAEGQRRuPCUlb9u2TfeU6br2K8syhBAQQvjdHxGRJ0yMJiK/GSUlA9A9ZdrTcpa3ftUAqC79ERE1FJbNICKN2Wx2K4FhsVh0z+4pKiryeTkrMTER69evdzoN2rW//fv3c1mMiJoUgyAicmIymZyCEaPaXklJST4tZ7kWbJ08ebLPdcWIiBoTc4IMMCeI6IyCggKtUrw6m+NIzR0CoJXNUAupGh2iqAZC6rIbc4KIqCEwJ4iIGpTZbMbtt98OAG4BEFC7nLVs2TKtbIZ6FpC3QxQLCwt5FhARBQyDICJyY7VanU53Li0txaZNmwzby7KMJUuWuCVPt2vXzvAQRbvdjnPPPZd5QEQUMAyCiMiJYyFUdUZn9+7dhu0VRUFqaqpu8nRNTQ3y8vJ0AyHmARFRoDEIIiKN1WrV3Q5/wQUX6LZfsWIFioqKcPz4cbfHHIMcvRwi1gQjokDj7jAi0ujl8NhsNrRr1w7JyclYu3atdn9ycjJatmyJuLg43byfrKwsALVnDDkGQbIso6ioyPDUaSKipsIgiIg0etvh1RmdNWvWYPr06fjggw9w1VVXITIy0nDnFwAMGDBAN6iy2+2oqalp1NdBROQLLocRkcbo1Gh1u3t1dTVGjx6N2NhYjzu/1MBJr7o8c4GIqLlgEERETsxmM8rLy1FYWIj169cjMTFRN1laL8ABape71MDJU1BFRBRoPCzRAA9LpHDmesqzY60vwLmwqnqIoqIomDlzJmbMmOEW5DhWl2cARESNiVXkGwCDIApXnk55dmSxWBAfH88Ah4iaFZ4YTUR15u2UZ6B2JigiIgIWiwUAEB8fzwCIiIIOgyAicqKX6yPLsnafoiiYMGEC4uLinHKEiIiCDYMgInKil8ycl5eHiooKWCwWFBUV4cUXX3Q7UFEtsUFEFCx4ThARuTGbzUhMTHTL9TGZTEhPT9c9UHH//v1cEiOioBLSM0HPPvssevXqhdatW6N///4e6x8RkTOTyeSW62O1WrF48WK3tpIk8ewfIgo6IRsEvfTSS3jooYcwb948fPrppxg2bBhuuOEGHDp0KNBDIwpaZWVlbnXAiIiCVcgGQUuWLIHZbMakSZNw0UUX4cknn0TPnj2xYsWKQA+NKGgZHZAohMD+/fsDMCIioroLySDojz/+wMcff4wRI0Y43T9ixAjs2bNH95qTJ0/i2LFjTjcicmYymbBo0SK3+1kKg4iCUUgGQf/9739hs9nQrVs3p/u7deuG77//XveazMxMdOzYUbv17NmzKYZKFHTS0tKQnZ3ttGWepTCIKBiFZBCkkiTJ6WchhNt9qrlz56Kqqkq7VVZWNsUQiYJSenq6tmW+vLwcZrM50EMiIvJbSG6R79KlCxRFcZv1OXr0qNvskKpVq1Zo1apVUwyPKCSoBVKJiIJVSM4EnXXWWejfvz+2b9/udP/27dsxZMiQAI2KiIiImpOQnAkCgNTUVEycOBEDBgzA4MGDkZeXh0OHDmHq1KmBHhoRERE1AyEbBCUlJeGnn37CY489hiNHjuDSSy/FW2+9hejo6EAPjYiIiJoBSfDkM13Hjh1Dx44dUVVVhQ4dOgR6OEREROQDf76/QzIniIiIiMgbBkFEREQUlhgEERERUVhiEERERERhiUEQERERhSUGQURERBSWGAQRERFRWGIQRERERGGJQRARERGFJQZBREREFJYYBBEREVFYYhBEREREYYlBEBEREYUlBkFEREQUlhgEERERUVhiEERERERhiUEQERERhSUGQURERBSWGAQRERFRWGIQRERERGGJQRARERGFJQZBREREFJYYBBEREVFYYhBEREREYalFoAfQXAkhAADHjh0L8EiIiIjIV+r3tvo97gmDIAPHjx8HAPTs2TPAIyEiIiJ/HT9+HB07dvTYRhK+hEphyG6347vvvkP79u0hSRKA2uiyZ8+eqKysRIcOHQI8wsDie+GM74czvh/O+H6cwffCGd8PZw3xfgghcPz4cfTo0QOy7DnrhzNBBmRZhslk0n2sQ4cO/GX9H74Xzvh+OOP74Yzvxxl8L5zx/XBW3/fD2wyQionRREREFJYYBBEREVFYYhDkh1atWmH+/Plo1apVoIcScHwvnPH9cMb3wxnfjzP4Xjjj++Gsqd8PJkYTERFRWOJMEBEREYUlBkFEREQUlhgEERERUVhiEERERERhiUGQi9OnT+ORRx5Br1690KZNG/Tu3RuPPfYY7Ha71kYIgb/97W/o0aMH2rRpg/j4ePz73/8O4KgbznvvvYeRI0eiR48ekCQJr776qtPjvrz2kydP4oEHHkCXLl0QERGBUaNGwWq1NuGraBie3otTp05hzpw5uOyyyxAREYEePXrg7rvvxnfffefUR6i8F4D33w1HKSkpkCQJTz75pNP94fZ+fPnllxg1ahQ6duyI9u3bIy4uDocOHdIeD6f3o7q6Gvfffz9MJhPatGmDiy66CCtWrHBqEyrvR2ZmJmJjY9G+fXt07doVt956K77++munNuHyWertvQj0ZymDIBeLFi3Cc889h6effhpffvklsrOzkZOTg+XLl2ttsrOzsWTJEjz99NMoLS1F9+7dcd1112n1xoJZTU0NrrjiCjz99NO6j/vy2h966CFs2bIFGzduxPvvv4/q6mrcfPPNsNlsTfUyGoSn9+K3337DJ598gkcffRSffPIJNm/ejG+++QajRo1yahcq7wXg/XdD9eqrr+LDDz9Ejx493B4Lp/fjwIEDGDp0KPr27Yt3330Xn332GR599FG0bt1aaxNO78fMmTPxzjvvYN26dfjyyy8xc+ZMPPDAA3jttde0NqHyfuzatQvTp09HcXExtm/fjtOnT2PEiBGoqanR2oTLZ6m39yLgn6WCnNx0003i3nvvdbrv9ttvFxMmTBBCCGG320X37t1FVlaW9vjvv/8uOnbsKJ577rkmHWtjAyC2bNmi/ezLa//1119Fy5YtxcaNG7U2hw8fFrIsi3feeafJxt7QXN8LPSUlJQKAqKioEEKE7nshhPH7YbVaxXnnnSf27dsnoqOjxdKlS7XHwu39SEpK0j439ITb+3HJJZeIxx57zOm+P//5z+KRRx4RQoT2+3H06FEBQOzatUsIEd6fpa7vhZ6m/CzlTJCLoUOH4l//+he++eYbAMBnn32G999/HzfeeCMA4ODBg/j+++8xYsQI7ZpWrVph+PDh2LNnT0DG3FR8ee0ff/wxTp065dSmR48euPTSS0P+/amqqoIkSTj77LMBhN97YbfbMXHiRKSnp+OSSy5xezyc3g+73Y4333wTF1xwARITE9G1a1cMGjTIaYkonN4PoPaz9fXXX8fhw4chhIDFYsE333yDxMREAKH9flRVVQEAOnXqBCC8P0td3wujNk31WcogyMWcOXMwfvx49O3bFy1btkS/fv3w0EMPYfz48QCA77//HgDQrVs3p+u6deumPRaqfHnt33//Pc466yycc845hm1C0e+//46MjAzceeedWtG/cHsvFi1ahBYtWuDBBx/UfTyc3o+jR4+iuroaWVlZuP766/HPf/4Tt912G26//Xbs2rULQHi9HwDw1FNP4eKLL4bJZMJZZ52F66+/Hs8++yyGDh0KIHTfDyEEUlNTMXToUFx66aUAwvezVO+9cNXUn6WsIu/ipZdewrp167B+/Xpccskl2Lt3Lx566CH06NEDycnJWjtJkpyuE0K43Req6vLaQ/n9OXXqFMaNGwe73Y5nn33Wa/tQfC8+/vhjLFu2DJ988onfry0U3w91I8Utt9yCmTNnAgCuvPJK7NmzB8899xyGDx9ueG0ovh9AbRBUXFyM119/HdHR0Xjvvfcwbdo0REZG4tprrzW8Ltjfj/vvvx+ff/453n//fbfHwu2z1NN7AQTms5QzQS7S09ORkZGBcePG4bLLLsPEiRMxc+ZMZGZmAgC6d+8OAG7R59GjR92i+lDjy2vv3r07/vjjD/zyyy+GbULJqVOnMHbsWBw8eBDbt2/X/uUChNd7sXv3bhw9ehRRUVFo0aIFWrRogYqKCsyaNQvnn38+gPB6P7p06YIWLVrg4osvdrr/oosu0naHhdP7ceLECTz88MNYsmQJRo4cicsvvxz3338/kpKSkJubCyA0348HHngAr7/+OiwWC0wmk3Z/OH6WGr0XqkB9ljIIcvHbb79Blp3fFkVRtH/Z9erVC927d8f27du1x//44w/s2rULQ4YMadKxNjVfXnv//v3RsmVLpzZHjhzBvn37Qu79Uf/SlpWVYceOHejcubPT4+H0XkycOBGff/459u7dq9169OiB9PR0bNu2DUB4vR9nnXUWYmNj3bZFf/PNN4iOjgYQXu/HqVOncOrUKY+fraH0fgghcP/992Pz5s3YuXMnevXq5fR4OH2WensvgAB/ltYrrToEJScni/POO09s3bpVHDx4UGzevFl06dJFzJ49W2uTlZUlOnbsKDZv3iy++OILMX78eBEZGSmOHTsWwJE3jOPHj4tPP/1UfPrppwKAWLJkifj000+1LH1fXvvUqVOFyWQSO3bsEJ988olISEgQV1xxhTh9+nSgXladeHovTp06JUaNGiVMJpPYu3evOHLkiHY7efKk1keovBdCeP/dcOW6O0yI8Ho/Nm/eLFq2bCny8vJEWVmZWL58uVAURezevVvrI5zej+HDh4tLLrlEWCwW8e2334rVq1eL1q1bi2effVbrI1Tej/vuu0907NhRvPvuu06fDb/99pvWJlw+S729F4H+LGUQ5OLYsWNixowZIioqSrRu3Vr07t1bzJs3z+kPw263i/nz54vu3buLVq1aiauvvlp88cUXARx1w7FYLAKA2y05OVkI4dtrP3HihLj//vtFp06dRJs2bcTNN98sDh06FIBXUz+e3ouDBw/qPgZAWCwWrY9QeS+E8P674UovCAq396OgoED06dNHtG7dWlxxxRXi1VdfdeojnN6PI0eOiHvuuUf06NFDtG7dWlx44YVi8eLFwm63a32Eyvth9NmwevVqrU24fJZ6ey8C/Vkq/W+QRERERGGFOUFEREQUlhgEERERUVhiEERERERhiUEQERERhSUGQURERBSWGAQRERFRWGIQRERERGGJQRARhZT4+HhIkoT4+PhGew5JkiBJEv72t7812nM0J+effz4kScI999zj9ti7776rvR/vvvtuk4+NqD4YBBEREVFYYhBERH4Lt5kQIgpNLQI9ACKihsQlmaYVHx8PVl+iYMWZICIiIgpLDIKIiIgoLDEIIvJDSUkJJk+ejAsuuADt2rVDREQE+vbti+nTp6OsrMzjtSdOnMATTzyBK664AhEREejcuTOuuuoqrFq1Cna73esuG087dBzdc889kCQJ559/vu7jv/zyC1avXo0JEybg4osvRrt27XDWWWehe/fuSExMRF5eHv744w/da9UxqP7+979rY1ZvRuN74403MHr0aJhMJrRq1QqdO3fG4MGDkZWVherqasPXs2bNGq3v8vJynDx5Ek8++STi4uLQpUsXt9wkb7vD6vP6G9Pf/vY37XUCQFVVFR5//HH069cPZ599NiRJwpo1a7T2NTU1eOmllzBp0iRceeWV6NixI1q2bIlzzz0Xw4cPR25ursf31dFbb72FG264Aeeeey7atm2LCy64AKmpqfjuu++8Xuvt99bX3Xqur1/Pli1bcOutt2q/Q+3bt0fv3r0xbNgwPProoygpKfE6XiIngoi8OnXqlLjvvvsEAMNby5YtRV5enu71hw8fFn379jW89vrrrxfbtm3TfrZYLG59REdHCwAiOTnZ41iTk5MFABEdHa37uNqPp1u/fv3EkSNH6nSt6/hOnDghbrvtNo/X9OjRQ3z66ae64129erXWrrS0VFx55ZVu18+fP19rP3z4cAFADB8+vMFfv0rveetr/vz5Wr/ffPONOP/8893GtXr1arfX6enWq1cv8eWXX3p83hkzZhhe37VrV/HRRx95/N2zWCwef2+9/XnovX5Xp0+fFmPGjPH6evv37+/xOYhcMTGayAdmsxkvvPACAOCGG27AXXfdhQsuuACSJGHv3r148skn8e9//xtTpkxB9+7dMXLkSO3a06dP4+abb8ZXX30FABgxYgTuu+8+9OzZE4cOHcKzzz6Ld955Bz/99FOTvBabzYZBgwbh5ptvRr9+/dCtWzf88ccfOHjwINatW4d33nkHn376KcaNG+f2L/t//vOf+OOPP3DZZZcBAO677z5MmzbNqc0555zj9HNycjK2bNkCALjiiiswa9YsXHTRRfj555+xceNGrFmzBt999x2uueYafP755zjvvPMMx242m/HFF1/g7rvvRlJSErp3745Dhw6hVatWTfL6m8ro0aNx+PBhPPDAAxg1ahTOOecclJWVITo6Wmtz+vRpXHbZZRg1ahQGDBiAHj16QAiBiooKbNmyBYWFhTh48CBuvfVW7N27F61bt3Z7nsWLF2PZsmUAgB49emDu3LkYOHAgfv/9d7z55pt48sknMXr0aPz2229N9tr1rFixAps2bQIADB06FJMmTcKf/vQntGvXDj///DP27duHt99+Gz///HNAx0lBKNBRGFFz9/LLL2v/0ly1apVumxMnToiEhAQBQJx//vni1KlT2mNPPfWUdv2UKVN0r7/33nud/kXbmDNB33zzjcfrn3/+eW0cO3bs0G2jPu5tJmTr1q1a22uuuUacPHnSrU1eXp7WZuzYsW6PO84EARAFBQUen9PbzENTvn5/OM6EyLIs/vnPf3ps7+11bN++XciyLACI/Px8t8e///570bZtW+13RW/m61//+pdo0aKF4SyfEE0zEzRs2DABQAwaNMjp75arn376yeNzELliThCRF5mZmQCA2267DZMmTdJt07p1azz99NMAgPLycqcZhBUrVgAAunXrhqVLl+pev2zZMpx77rkNOGpjMTExHh//61//in79+gEAXn311Xo91zPPPAMAaNmyJVavXo2zzjrLrc3kyZNx7bXXAgA2b96MI0eOGPaXkJCAe++9t15jasrXX1f33HMPrrvuOo9tvL2Oa6+9FqNGjQKg/zrWrl2rzfAsXrwY3bt3d2uTkJCAyZMn+zjqxvP9998DAIYMGYIWLYwXMDp16tRUQ6IQwSCIyIPDhw/j448/BgCMHTvWY9uLLroIXbp0AQAUFRUBAL777jt8+eWX2vVt27bVvbZdu3Ze+28MQgh8//33+Oabb7Bv3z7t1qNHDwDAZ599Vue+T58+jV27dgEArrvuOvTs2dOwrfpFe/r0aY9LUHfddVedx6OnMV9/fdTldf74448oKytzeh1qYK33Onbs2AGgdvnylltuMey3vkFnQ4iMjARQm1z/3//+N8CjoVDCnCAiDz766CPt/8ePH4/x48f7dJ36L9cvvvhCuy82NtbjNQMHDtRmThrbm2++iRUrVuC9997D8ePHDdvV5wvn22+/1WYaBg0a5LGt4+P79u0zbHf55ZfXeTyOmuL114evr/ODDz7AU089hR07dnjMh9F7HervZr9+/TzOrlx55ZU466yzArJjTpWcnIz33nsP+/fvR58+fXD77bfjuuuuw7Bhw2AymQI2Lgp+DIKIPDh69GidrlO//H/55Rftvq5du3q8plu3bnV6Ln8IITB58mQUFBT41P7EiRN1fi7HL2Vvr81xKcbTl7lr0rW/mvL114cvr/Nvf/sb/v73v/vUn97rUH83vf1etmjRAp06ddIC+0C49957ceDAAWRnZ6OqqgqrV6/G6tWrAQB/+tOfcOutt2LatGno3bt3wMZIwYnLYUQe2Gw27f//8Y9/4IsvvvDptmDBAgBwKifg6fwT17aN5fnnn9cCgCuvvBJr1qzBl19+iWPHjuH06dMQQkAIgYkTJzbomLy9dl8pilKv6wP1+v3l7XX+61//0gKg3r1749lnn8Xnn3+OX3/91el1PProo16fy5c/m0C9D46eeOIJ7N+/H0888QQSEhK0peUDBw5g8eLF6Nu3L5577rkAj5KCDWeCiDzo3Lmz9v+SJOHSSy/163rHRM0ffvjBY1tvs06yXPtvFrvd7rFdTU2N4WOrVq0CUPuv5z179qBNmza67RxnsOrK8bV7m0VwfLwxk1ub8vU3JvV1nH322SgqKjKczfH0Os455xx8//33Xn8vT58+Xa/3oyF+b1XR0dF4+OGH8fDDD+PUqVMoKSnBpk2bsHLlSvz++++YNm0aBg0apCW2E3nDmSAiDxw/TP/5z3/6fb16ng4AlJaWemzr7fH27dsD8P4F/fXXXxs+9u9//xsAcMsttxgGAEIIfPLJJx6fwxe9e/fW/rX+4YcfemzreNKvv4GmP5ry9Tcm9XUkJCR4XM5yzGlzpf5u7t27F6dPnzZs99lnn9UrH6ghfm/1tGzZEldddRWefPJJrF+/HkDtn93LL79ct4FSWGIQRORBnz59cPHFFwMANm7ciEOHDvl1fY8ePXDRRRcBADZt2mSYY1JTU4PCwkKPffXq1QsA8MknnxguT+zbt88pGduV+mXn6fC7119/3Wu5BPXgvZMnTxq2adGiBYYPHw4A2L59OyorKw3b5ufnA6hdBvJWXqE+Gur1B5ovr2Pv3r0oLi42fFw9luDnn3/GG2+8Ydju+eefr+Moa6m/t998841hEvqPP/6o7Vari2uuuUb7f+4eI38wCCLy4pFHHgEA/P7777j99tvx448/GrY9efIknn32Wfz+++/afffddx+A2iWfWbNm6V43c+ZMr8thakDx3XffYcOGDW6PHz9+3Ot2ZvVsmTfeeEP3X+YHDhxwOwFaj7pl+cCBAx7bTZ8+HQBw6tQp3HvvvbozCs8//7w2y3bHHXdofTeGhnr9gaa+jvfffx/ffvut2+M//vgjJkyY4LGP5ORkbTYsNTVVd1ls165dyMvLq9dY1d/bP/74A8uXL3d7/NSpUzCbzR6T0NetW+dxtspxllYNuoh80sSHMxIFJfUUZgCiS5cuYt68eeKf//yn+PTTT8X7778v1q5dKyZNmiQ6deokAIjjx49r1546dUr069dPu/76668Xr776qvj444/Fq6++KkaMGCEAiNjYWI8n7x49elR06NBBABCtW7cWf//730VxcbH48MMPxTPPPCP+9Kc/idatW2vPpXdidE5OjvYcffv2Fc8//7z48MMPxa5du8T8+fNFx44dRevWrcWf//xnj6dO33XXXQKAaNWqlXjuuefEF198IcrKykRZWZn44YcfnNo61ny68sorxYsvvig++ugjsX37dmE2m4UkSQKA6NSpk7BarW7P5Xhi9MGDB73+WXk6obihXr/aR2OdGO3Npk2btLYmk0ksX75c7NmzR3zwwQciJydHREZGCkmSxODBgz32mZubqz1+3nnniaefflqUlJSI9957T2RkZIhWrVqJ6Ohoce6559b5xOiTJ09qp53Lsixmzpwpdu/eLUpLS8Xq1atFv379hCRJYtCgQYZjBSC6desm7rvvPvHiiy+KPXv2iE8++US8/fbbIjU1VbRp00YAEO3atROVlZVe3z8iFYMgIh+cPn1azJ49WyiK4rWIY0REhPjtt9+crj98+LC48MILDa8ZMWKE1wKqQghRWFhoOIbWrVuLwsJCj2Uz/vjjDy3o0ru1adPGax9CCPHpp5+KVq1a6fbRmAVU6xsENdTrD3QQJIQQf/3rXw1fh6Io4sknn/SpzwcffNCwny5duojS0tJ6FVAVQojdu3eLiIgIw7EuXbrU41i9/Z0DIM4++2yxbds2n947IhWXw4h8oCgKFi1ahP/85z+YNWsW+vXrh3POOQeKoqB9+/a45JJLcNddd2Ht2rU4cuSIW9Jtjx498Omnn2LBggW49NJL0aZNG5x99tmIi4vDs88+i7ffflu3pISrMWPGYM+ePbjttttw7rnn4qyzzkLPnj2RnJyMjz76CGPGjPF4fcuWLfHmm2/iqaeewoABA9C2bVu0adMGffr0wdSpU/HJJ5947QOo3V5eVFSE8ePHIyoqymMB09atW2Pz5s14/fXXcfvtt6NHjx4466yzcM4552DQoEHIzMzE119/jSuvvNLr89ZXQ73+5uD555/Hiy++iGHDhqF9+/Zo1aoVoqOjMXHiROzZswczZszwqZ9ly5bhzTffRGJiIjp16oTWrVujT58+ePDBB/Hpp59iwIAB9R7r0KFD8fHHH2PixIno0aMHWrZsicjISNxxxx1477338NBDD3m8/quvvsLy5ctx66234uKLL0bnzp3RokULnHPOOYiLi8Pf/vY3fP311xgxYkS9x0rhRRKiGRwAQUR499138Ze//AUAYLFYGjVBmIiImBhNREREYYpBEBEREYUlBkFEREQUllg2g4ionk6dOuX3iceqXr16ISIiooFHRES+YBBERFRPhw8fdiqR4g8mwRMFDoMgomYiPj6+WVTrJiIKF9wiT0RERGGJidFEREQUlhgEERERUVhiEERERERhiUEQERERhSUGQURERBSWGAQRERFRWGIQRERERGGJQRARERGFJQZBREREFJb+Hzdrm3y3XSd7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ellipse_chi2.plot_chi2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now, besides the chords we can plot the fitted ellipse**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sora.extra import draw_ellipse\n",
    "\n",
    "occ.chords.plot_chords()\n",
    "occ.chords.plot_chords(segment='error', color='red')\n",
    "\n",
    "#plotting the best fitted ellipse, in black\n",
    "draw_ellipse(**ellipse_chi2.get_values())\n",
    "\n",
    "pl.legend(loc=1)\n",
    "pl.xlim(+170,-330)\n",
    "pl.ylim(-250,+250)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Not just the fitted ellipse, but the user can also plot all the ellipses inside an sigma region, for instance within $3\\sigma$**\n",
    "\n",
    "This step will, possible, plot large number of ellipses, this can spend some CPU time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk8AAAGwCAYAAACw64E/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACM5ElEQVR4nO3dd1gU1/4G8Hd26b13VEBU7N1YEXtiN7ZoLImJN9YYY3KvyS+JMc00441ekxiNvSUxthgLGrFXbKjYQaSJAi7SYXd+fyATFhbYhV2W8n7y7MPsmTMz350gfDnnzDmCKIoiiIiIiEgrMmMHQERERFSTMHkiIiIi0gGTJyIiIiIdMHkiIiIi0gGTJyIiIiIdMHkiIiIi0gGTJyIiIiIdmBg7gJpIpVIhPj4etra2EATB2OEQERGRFkRRxNOnT+Hl5QWZrOLtR0yeKiA+Ph6+vr7GDoOIiIgq4MGDB/Dx8anw8UyeKsDW1hZAwc23s7MzcjRERESkjbS0NPj6+kq/xyuKyVMFFHbV2dnZMXkiIiKqYSo75IYDxomIiIh0wOSJiIiISAdMnoiIiIh0wDFPRERkNEqlEnl5ecYOg2oJU1NTyOVyg1+HyRMREVU5URSRmJiIJ0+eGDsUqmUcHBzg4eFh0HkYmTwREVGVK0yc3NzcYGVlxQmHqdJEUURmZiaSkpIAAJ6enga7FpMnIiKqUkqlUkqcnJ2djR0O1SKWlpYAgKSkJLi5uRmsC48DxomIqEoVjnGysrIyciRUGxV+XxlyLB2TJyIiMgp21ZEhVMX3FZMnIiIiIh0weSIiIiLSAZMnIiKiWmTNmjVwcHAwdhi1GpMnIiIiHTx48ABTpkyBl5cXzMzMUL9+fbz55ptITk7W+hzR0dEQBAGXLl3Se3xjxozBrVu39H5e+geTJyIiIi3du3cP7du3x61bt7B582bcuXMHP/74Iw4dOoTOnTsjJSXF2CHC0tISbm5uxg6jVmPyREREpKUZM2bAzMwMBw4cQHBwMOrVq4fnn38eBw8eRFxcHN5//30ABU987dixQ+1YBwcHrFmzBgDg5+cHAGjTpg0EQUDPnj2leqtXr0ZQUBAsLCzQpEkTLF++XNpX2GL1xx9/ICQkBFZWVmjVqhVOnTol1dHUbbdo0SK4u7vD1tYWU6ZMwX/+8x+0bt1a2t+zZ0/MmTNH7Zhhw4Zh8uTJ0vsNGzagffv2sLW1hYeHB8aNGydNSFnXcJJMIiIyusFLj+PR05wqv66rrTl2z+qmVd2UlBTs378fn332mTQZYyEPDw+MHz8eW7duVUt2SnP27Fl07NgRBw8eRLNmzWBmZgYA+Pnnn/HRRx9h2bJlaNOmDS5evIjXX38d1tbWmDRpknT8+++/j2+++QaBgYF4//338dJLL+HOnTswMSn5a/3XX3/FRx99hP/973/o3r071q9fj++//x7+/v5afe5Cubm5+OSTT9C4cWMkJSXhrbfewuTJk/HXX3/pdJ7agMkTEREZ3aOnOUhMyzZ2GGW6ffs2RFFEUFCQxv1BQUFITU3Fo0ePyj2Xq6srAMDZ2RkeHh5S+SeffIJvv/0WI0aMAFDQQnX9+nX89NNPasnTvHnzMHDgQADAxx9/jGbNmuHOnTto0qRJiWstWbIEr776Kl577TUAwKeffoqDBw8iO1u3+/3qq69K2/7+/vj+++/RsWNHpKenw8bGRqdz1XRMnoiIyOhcbc1r/HVFUQRQ8UkaHz16JA1Gf/3116Xy/Px82Nvbq9Vt2bKltF24hltSUpLG5CkyMhJvvPGGWlnnzp1x+PBhneK7ePEiFixYgEuXLiElJQUqlQoAEBMTg6ZNm+p0rpqOyRMRERmdtl1nxtSwYUMIgoDr169j2LBhJfbfuHEDjo6OcHFxgSAIUjJVqLzlQgqTkZ9//hmdOnVS21d8jTZTU1NpuzBZKzy+ImQyWZnxZmRkoF+/fujXrx82bNgAV1dXxMTEoH///sjNza3wdWsqDhgnIiLSgrOzM/r27Yvly5cjKytLbV9iYiI2btyIMWPGQBAEuLq6IiEhQdp/+/ZtZGZmSu8LxzgplUqpzN3dHd7e3rh37x4aNmyo9iocYF4RQUFBOH36tFpZ8ffF41Uqlbh69ar0/saNG3j8+DEWLVqE7t27o0mTJnV2sDjA5ImIiEhry5YtQ05ODvr374+jR4/iwYMH2LdvH/r27Qtvb2989tlnAIBevXph2bJluHDhAs6fP4833nhDrbXIzc0NlpaW2LdvHx4+fAiFQgEAWLBgAb744gv897//xa1btxAREYHVq1dj8eLFFY75zTffxC+//IJffvkFt27dwkcffYRr166p1enVqxf27NmDPXv24MaNG5g+fTqePHki7a9Xrx7MzMywdOlS3Lt3D7t27cInn3xS4ZhqOiZPREREWgoMDMT58+cREBCAMWPGICAgAFOnTkVISAhOnToFJycnAMC3334LX19f9OjRA+PGjcO8efNgZWUlncfExATff/89fvrpJ3h5eWHo0KEAgNdeew0rV67EmjVr0KJFCwQHB2PNmjWVankaM2YMPvzwQ/z73/9Gu3btcP/+fUybNk2tzquvvopJkyZh4sSJCA4Ohp+fH0JCQqT9rq6uWLNmDX777Tc0bdoUixYtwjfffFPhmGo6QSzeyUnlSktLg729PRQKBezs7IwdDhFRjZKdnY2oqCj4+fnBwsLC2OHUSQsWLMCOHTsMMsO5sZX1/aWv399seSIiIiLSAZMnIiIiIh0weSIiIqpjCudrooph8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDqoMcnTF198gQ4dOsDW1hZubm4YNmwYbt68qVZHFEUsWLAAXl5esLS0RM+ePUvMopqTk4NZs2bBxcUF1tbWGDJkCGJjY6vyoxAREVENVmOSpyNHjmDGjBk4ffo0QkNDkZ+fj379+iEjI0Oq89VXX2Hx4sVYtmwZzp07Bw8PD/Tt2xdPnz6V6syZMwfbt2/Hli1bcPz4caSnp2PQoEFq6wsRERFVtejoaAiCwKfgaoAakzzt27cPkydPRrNmzdCqVSusXr0aMTExCA8PB1DQ6rRkyRK8//77GDFiBJo3b461a9ciMzMTmzZtAgAoFAqsWrUK3377Lfr06YM2bdpgw4YNiIiIwMGDB4358YiIqIZ48OABpkyZAi8vL5iZmaF+/fp48803kZycbOzQqIrUmOSpuMJFFAvXEYqKikJiYiL69esn1TE3N0dwcDBOnjwJAAgPD0deXp5aHS8vLzRv3lyqo0lOTg7S0tLUXkREVPfcu3cP7du3x61bt7B582bcuXMHP/74Iw4dOoTOnTsjJSXFqPHl5eUZ9fp1RY1MnkRRxNy5c9GtWzc0b94cAJCYmAgAcHd3V6vr7u4u7UtMTISZmRkcHR1LraPJF198AXt7e+nl6+urz49DREQ1xIwZM2BmZoYDBw4gODgY9erVw/PPP4+DBw8iLi4O77//PgCgQYMG+Pzzz/Hqq6/C1tYW9erVw4oVK9TOdfbsWbRp0wYWFhZo3749Ll68qLZ/zZo1cHBwUCvbsWMHBEGQ3i9YsACtW7fGL7/8An9/f5ibm0MURTRo0ABLlixRO7Z169ZYsGCB9P727dvo0aMHLCws0LRpU4SGhkIQBOzYsQMAEBYWBkEQ8OTJE+mYS5cuQRAEREdHS2UnT55Ejx49YGlpCV9fX8yePVttSM2GDRvQvn172NrawsPDA+PGjUNSUpKWd7x6qpHJ08yZM3HlyhVs3ry5xL6i31RAQaJVvKy48urMnz8fCoVCej148KBigRMRUY2VkpKC/fv3Y/r06bC0tFTb5+HhgfHjx2Pr1q0QRREA8O2330pJ0fTp0zFt2jTcuHEDAJCRkYFBgwahcePGCA8Px4IFCzBv3rwKxXXnzh38+uuv2LZtm9bjpVQqFUaMGAG5XI7Tp0/jxx9/xL///W+drx0REYH+/ftjxIgRuHLlCrZu3Yrjx49j5syZUp3c3Fx88sknuHz5Mnbs2IGoqChMnjxZ52tVJybGDkBXs2bNwq5du3D06FH4+PhI5R4eHgAKWpc8PT2l8qSkJKk1ysPDA7m5uUhNTVVrfUpKSkKXLl1Kvaa5uTnMzc31/VGIiKjQT8FAuhFaI2zcgH8d0arq7du3IYoigoKCNO4PCgpCamoqHj16BAB44YUXMH36dADAv//9b3z33XcICwtDkyZNsHHjRiiVSvzyyy+wsrJCs2bNEBsbi2nTpun8EXJzc7F+/Xq4urpqfczBgwcRGRmJ6Oho6Xfp559/jueff16na3/99dcYN24c5syZAwAIDAzE999/j+DgYPzwww+wsLDAq6++KtX39/fH999/j44dOyI9PR02NjY6Xa+6qDHJkyiKmDVrFrZv346wsDD4+fmp7ffz84OHhwdCQ0PRpk0bAAXfUEeOHMGXX34JAGjXrh1MTU0RGhqK0aNHAwASEhJw9epVfPXVV1X7gYiI6B/pScDTeGNHUSmFLU6FPRktW7aU9gmCAA8PD6m7KjIyEq1atYKVlZVUp3PnzhW6bv369XVKnAqvX69ePbVGiIpcPzw8HHfu3MHGjRulMlEUoVKpEBUVhaCgIFy8eFFaSy8lJQUqlQoAEBMTg6ZNm+p8zeqgxiRPM2bMwKZNm7Bz507Y2tpKY5Ts7e1haWkJQRAwZ84cfP755wgMDERgYCA+//xzWFlZYdy4cVLdKVOm4O2334azszOcnJwwb948tGjRAn369DHmxyMiqtts3Kr9dRs2bAhBEHD9+nUMGzasxP4bN27A0dERLi4uAABTU1O1/YIgSIlDYaJVFplMVqKepgHh1tbWOh+r6frFh6/IZLISdYtfX6VS4V//+hdmz55d4nz16tVDRkYG+vXrh379+mHDhg1wdXVFTEwM+vfvj9zc3BLH1BQ1Jnn64YcfAAA9e/ZUK1+9erXUd/ruu+8iKysL06dPR2pqKjp16oQDBw7A1tZWqv/dd9/BxMQEo0ePRlZWFnr37o01a9ZALpdX1UchIqLitOw6MyZnZ2f07dsXy5cvx1tvvaU27ikxMREbN27ExIkTyx1nCwBNmzbF+vXrkZWVJZ3n9OnTanVcXV3x9OlTZGRkSAmStmOaXF1dkZCQIL1PS0tDVFSU2vVjYmIQHx8PLy8vAMCpU6dKnAMo6KEpHOpS/Ppt27bFtWvX0LBhQ41xRERE4PHjx1i0aJH0sNX58+e1+gzVWY0ZMC6KosZX0UFngiBgwYIFSEhIQHZ2No4cOSI9jVfIwsICS5cuRXJyMjIzM7F7924+PUdUCqVSidzcXLUXJ5SlumzZsmXIyclB//79cfToUTx48AD79u1D37594e3tjc8++0yr84wbNw4ymQxTpkzB9evX8ddff+Gbb75Rq9OpUydYWVnhvffew507d7Bp0yasWbNGq/P36tUL69evx7Fjx3D16lVMmjRJrZGgT58+aNy4MSZOnIjLly/j2LFj0pOChRo2bAhfX18sWLAAt27dwp49e/Dtt9+q1fn3v/+NU6dOYcaMGbh06RJu376NXbt2YdasWQAKWp/MzMywdOlS3Lt3D7t27cInn3yi1WeozmpMyxMR6Y8oikhLS8PVq1cRGRmJ+vXrIy0tDQqFAhkZGcjOzkZubi7y8/M1Hm9iYgJzc3NYWFjAysoK9vb2sLOzg729PRwdHeHm5gY7Ozut/gInqkkCAwNx/vx5LFiwAGPGjEFycjI8PDwwbNgwfPTRR9Lcg+WxsbHB7t278cYbb6BNmzZo2rQpvvzyS7z44otSHScnJ2zYsAHvvPMOVqxYgT59+mDBggWYOnVqueefP38+7t27h0GDBsHe3h6ffPKJWsuTTCbD9u3bMWXKFHTs2BENGjTA999/jwEDBkh1TE1NsXnzZkybNg2tWrVChw4d8Omnn2LUqFFSnZYtW+LIkSN4//330b17d4iiiICAAIwZMwZAQevVmjVr8N577+H7779H27Zt8c0332DIkCFa3afqShC16XglNWlpabC3t4dCoYCdnZ2xwyEqV1paGmJjYxETE4P79+/j8ePHaolR0fER5f1IkMvlkMlkMDExgampqfQ+NzcXmZmZUsuUubk5XF1d4enpCR8fH/j6+sLBwYEJFSE7OxtRUVHw8/ODhYWFscOhIgRBwPbt2zWO6aopyvr+0tfvb7Y8EdVCubm5uHfvHiIiIhAdHY3MzMwy6xcOYtWGUqmEUqlEXl4esrKyNNYRBAGiKCI9PR03b97EuXPnABT8td2gQQM0bNgQAQEBNfYxZSKq25g8EdUSOTk5CA8Px6VLl6R5ZoxFFEVpjFRRmZmZuHXrFq5evQoA8PT0ROPGjdGsWTPpCSUiouqOyRNRDSaKIsLDw3H69OkasSipSqVSS6gSEhKQkJCAsLAwuLq6okWLFmjZsiXs7e2NGCVR3cWRPNph8kRUAyUlJSE0NBR3796tNT/sHj16hL///ht///03PD090b17dzRu3Fiaa4aIqLpg8kRUg9y4cQOhoaFGX7nd0BISEvDrr79CJpOhadOmeP7559VmYiYiMiYmT0TVnCiKOHfuHP7++2/k5OQYO5wqpVKpcPXqVVy9ehV2dnYYPHhwqZPxERFVFSZPRNWUKIq4cOEC9u7dy4kpUfCI8caNG2FiYoJOnTqhV69e7NIjIqNg8kRUDV29ehU7duxg0qRBfn4+Tpw4gRMnTqBZs2YYPHgwzM3NjR0WEdUhTJ6IqpGEhASsXbu2znXPVdS1a9dw7do1+Pv7Y9iwYWrrWBIRGQrbvImqgaysLCxduhQrVqxg4lQB9+7dw+LFi7F582ZkZ2cbOxyqoxYsWIDWrVvr9ZwNGjTAkiVLpPeCIGDHjh16vQbpji1PREYkiiL27duHs2fPGjuUWuHWrVv48ssv0bJlSwwePBgmJvwRR/pR3rJCkyZNwrJly6QFcQ0lISEBjo6OBr0GlY8/WYiMJCYmBuvXry918V2quCtXruDatWvo27cvOnXqZOxwqBZISEiQtrdu3YoPP/wQN2/elMosLS1hY2Nj8CWHPDw8DHp+0g677YiqWF5eHjZu3IjVq1czcTIgpVKJffv24bvvvkNcXJyxw6EazsPDQ3rZ29tDEIQSZUW77fbv3w8LCws8efJE7TyzZ89GcHCw9H7btm1o1qwZzM3N0aBBA3z77bdlxsFuu+qByRNRFYqKisKXX36JO3fuGDuUOiMtLQ0rV67E77//zqcXqcr06dMHDg4O2LZtm1SmVCrx66+/Yvz48QCA8PBwjB49GmPHjkVERAQWLFiADz74AGvWrDFS1KQtdtsRVQGlUok9e/bg4sWLxg6lThIEAdeuXcOdO3cwcuRITrRZDY35cwweZz2u8uu6WLpg66Ctej+vXC7HmDFjsGnTJkyZMgUAcOjQIaSmpmLUqFEAgMWLF6N379744IMPAACNGjXC9evX8fXXX2Py5Ml6j4n0h8kTkYGlpqZi7dq1UCgUxg6lzipc/y8nJwcbN25E48aNMWrUKMjlciNHRoUeZz1GUmaSscPQq/Hjx6Nz586Ij4+Hl5cXNm7ciBdeeEEa8B0ZGYmhQ4eqHdO1a1csWbIESqWS35/VGJMnIgO6efMmtm7dWmsW760N5HI5bt68iW+//RYTJ07kANxqwsXSpdZdt2PHjggICMCWLVswbdo0bN++HatXr5b2i6JY4ik+/qyoGZg8ERmAKIoICwvD0aNHjR0KFaNUKiEIArKysvDTTz+hZ8+eagN4yTgM0XVWHYwbNw4bN26Ej48PZDIZBg4cKO1r2rQpjh8/rlb/5MmTaNSoEVudqjkOGCfSs9zcXGzcuJGJUzVW+Ne9iYkJwsLCsGrVKj75SAYxfvx4XLhwAZ999hlGjhwJCwsLad/bb7+NQ4cO4ZNPPsGtW7ewdu1aLFu2DPPmzTNixKQNJk9EepSZmYkVK1bg7t27xg6FtJCfnw9LS0vExsZi8eLFSE1NNXZIVMsEBgaiQ4cOuHLlivSUXaG2bdvi119/xZYtW9C8eXN8+OGHWLhwIQeL1wCCyA5WnaWlpcHe3h4KhQJ2dnbGDoeqiSdPnmDlypXIyMgwdiikIwsLC2RnZ0Mmk+HFF19E06ZNjR1SrZadnY2oqCj4+fmptcQQ6UNZ31/6+v3NliciPUhKSsL//vc/Jk41VHZ2tjTG5LfffsPff/9t5IiIqDpj8kRUSQ8fPsSKFSs4ZqaGUyqVUKlUMDMzw7Fjx7Bt2zY++UREGjF5IqqEwsSJM1fXHrm5ubCxscHVq1exatUq/r8lohKYPBFVUFJSElasWAGVSmXsUEjP0tPT4ezsjLi4OPzvf/9Dbm6usUMiomqEyRNRBSgUCqxcuZKJUy2WnJwMd3d3pKamYtmyZcjJyTF2SERUTTB5ItJR4XQEeXl5xg6FDOzhw4fw9PTE06dP8f333yMrK8vYIRFRNcDkiUgHeXl5+OWXX5CZmWnsUKiKJCQkwMvLC5mZmVi6dCmfqCQiJk9E2hJFEZs3b0ZycrKxQ6EqFh8fD29vb2RlZeF///sfW6CI6jgmT0RaOnToEKKioowdBhlJXFwc6tWrh6ysLCxfvpzdtkR1GJMnIi1ERkbixIkTxg6DjCwmJgYNGjRAeno6li9fzrm9qFxhYWEQBAFPnjyp1HkmT56MYcOGSe979uyJOXPmVOqc2hAEATt27DD4dWoaJk9E5Xj06BF+/fVXY4dB1UR0dDTq16+PJ0+eYMWKFZxIs4748ccfYWtrq5Ywp6enw9TUFN27d1ere+zYMQiCgFu3bqFLly5ISEiAvb19VYdsNLm5ufjqq6/QqlUrWFlZwcXFBV27dsXq1aulFtujR49i8ODB8PLy0ipB+9e//gVBELBkyRLDfwAtMHkiKkN+fj5Wrlxp7DComrl//z7q1auHR48eYcOGDcYOh6pASEgI0tPTcf78eans2LFj8PDwwLlz59QeIgkLC4OXlxcaNWoEMzMzeHh4QBAEY4Rd5XJzc9G/f38sWrQIU6dOxcmTJ3H27FnMmDEDS5cuxbVr1wAAGRkZaNWqFZYtW1buOXfs2IEzZ87Ay8vL0OFrjckTURk2bdrECRJJo5iYGLi7u+PevXvYu3evscMhA2vcuDG8vLwQFhYmlYWFhWHo0KEICAjAyZMn1cpDQkKk7aLddmvWrIGDgwP279+PoKAg2NjYYMCAAUhISJCOVyqVmDt3LhwcHODs7Ix3331XYwunSqXCu+++CycnJ3h4eGDBggVq+2NiYjB06FDY2NjAzs4Oo0ePxsOHD9Xq7N69G+3atYOFhQX8/f3x8ccfl9kdvXDhQri7u+PSpUsa9y9ZsgRHjx7FoUOHMGPGDLRu3Rr+/v4YN24czpw5g8DAQADA888/j08//RQjRowo9VpAwVjDmTNnYuPGjTA1NS2zblVi8kRUivDwcA4QpzI9fPgQtra2OHv2LM6ePWvscMjAevbsicOHD0vvDx8+jJ49eyI4OFgqz83NxalTp6TkSZPMzEx88803WL9+PY4ePYqYmBjMmzdP2v/tt9/il19+wapVq3D8+HGkpKRg+/btJc6zdu1aWFtb48yZM/jqq6+wcOFChIaGAih4OnjYsGFISUnBkSNHEBoairt372LMmDHS8fv378fLL7+M2bNn4/r16/jpp5+wZs0afPbZZyWuJYoi3nzzTSmm1q1ba/xsGzduRJ8+fdCmTZsS+0xNTWFtbV3qfSlOpVJhwoQJeOedd9CsWTOtj6sKJsYOgKg6evLkCf78809jh0E1QGZmJszMzLB37164urrCz8/P2CHVSFEvjkT+48dVfl0TFxf4bftdq7o9e/bEW2+9hfz8fGRlZeHixYvo0aMHlEolvv/+ewDA6dOnkZWVVWbylJeXhx9//BEBAQEAgJkzZ2LhwoXS/iVLlmD+/Pl48cUXARSMt9q/f3+J87Rs2RIfffQRACAwMBDLli3DoUOH0LdvXxw8eBBXrlxBVFQUfH19AQDr169Hs2bNcO7cOXTo0AGfffYZ/vOf/2DSpEkAAH9/f3zyySd49913pfMCBcMXJk6ciPPnz+PEiRPw8fEp9bPdvn0bPXv21OZ2luvLL7+EiYkJZs+erZfz6ROTJ6JiRFHEzz//bOwwqIZQKpWwsrJCfn4+Nm7ciDlz5sDGxsbYYdU4+Y8fI79Yl1J1ExISgoyMDJw7dw6pqalo1KgR3NzcEBwcjAkTJiAjIwNhYWGoV68e/P39Sz2PlZWVlDgBgKenJ5KSkgAULP2UkJCAzp07S/tNTEzQvn37El13LVu2VHtf9DyRkZHw9fWVEicAaNq0KRwcHBAZGYkOHTogPDwc586dU2tpUiqVyM7ORmZmJqysrAAAb731FszNzXH69Gm4uLiUeY9EUdTL+K7w8HD897//xYULF6rleDEmT0TFHDhwgDOIk06ePn0KT09PJCQk4Oeff8abb74JmYyjInRhUs4v5epw3YYNG8LHxweHDx9GamoqgoODAQAeHh7w8/PDiRMncPjwYfTq1avM8xQfuyMIQoWe2tR0nsL1NktLYoqWq1QqfPzxxxrHHVlYWEjbffv2xebNm7F//36MHz++zJgaNWqEyMhInT9LcceOHUNSUhLq1asnlSmVSrz99ttYsmQJoqOjK32NymDyRFREcnIyTp8+bewwqAZKSEhAvXr1EBMTgy1btmDcuHHGDqlG0bbrzNhCQkIQFhaG1NRUvPPOO1J5cHAw9u/fj9OnT+OVV16p8Pnt7e3h6emJ06dPo0ePHgAKus3Cw8PRtm1brc/TtGlTxMTE4MGDB1Lr0/Xr16FQKBAUFAQAaNu2LW7evImGDRuWea4hQ4Zg8ODBGDduHORyOcaOHVtq3XHjxuG9997DxYsXS4x7ys/PR05OjlbjniZMmIA+ffqolfXv3x8TJkyo1P3VFyZPREVwWoJ/iKIo/bDTNJu2TCaDubk5zMzM2MryTExMDFxcXHD79m2cPHkSXbp0MXZIpGchISGYMWMG8vLypJYnoCB5mjZtGrKzs8sc76SNN998E4sWLUJgYCCCgoKwePFinSfZ7NOnD1q2bInx48djyZIlyM/Px/Tp0xEcHIz27dsDAD788EMMGjQIvr6+GDVqFGQyGa5cuYKIiAh8+umnaucbPnw41q9fjwkTJsDExAQjR47UeN05c+Zgz5496N27Nz755BN069YNtra2OH/+PL788kusWrUKrVu3Rnp6Ou7cuSMdFxUVhUuXLsHJyQn16tWDs7MznJ2d1c5tamoKDw8PNG7cWKd7YQhMnoieOXjwILKzs40dRpXIzs5GSkoKkpOTkZqaiqdPn0qv9PR0ZGdnIzc3V+uuBFNTU5iZmcHa2hq2trZqLycnJzg7O8Pe3r5OJFmPHz+GmZkZDh48iMDAQLi6uho7JNKjkJAQZGVloUmTJnB3d5fKg4OD8fTpUwQEBKiNM6qIt99+GwkJCZg8eTJkMhleffVVDB8+HAqFQutzFE48OWvWLPTo0QMymQwDBgzA0qVLpTr9+/fHn3/+iYULF+Krr76CqakpmjRpgtdee03jOUeOHCk9ASeTyTR295mbmyM0NBTfffcdfvrpJ8ybNw9WVlYICgrC7Nmz0bx5cwDA+fPn1ZLMuXPnAgAmTZqENWvWaP05jUUQOT2uztLS0mBvbw+FQgE7Oztjh0N6kJ2djS+//NLYYeidSqVCamoq4uPjkZCQgISEBCQlJSEjI6PKY5HL5XB2doaLiwu8vLzg6ekJLy8vWFpaVnkshmZmZobc3FxYW1vjrbfeglwuN3ZI1Up2djaioqLg5+enNraGSB/K+v7S1+9vtjwRAVi1apWxQ9ALlUqFxMREREdH4/79+7h//77OrWlWVlawtLSEmZmZ1C1nampaYvCpUqlETk4OcnNzkZOTg5ycHGRkZECpVGo8r1KpRFJSEpKSknD9+nWp3NHRET4+PmjQoAH8/Pzg6OhYLZ+u0UVubi6cnJyQkpKC33//XW1uHSKq+Zg8UZ1369YtPDbC/DL6kpmZiTt37uDmzZu4e/duucmStbW1NJ7A2dkZTk5OsLOzg62tLWxsbCrVSiKKIrKysqQuwLS0NCQnJ+Px48dITk5GSkqK9DRQodTUVKSmpiIiIgIAYGdnhwYNGqBhw4YIDAyssS1TKSkpsLOzw40bNxAREYEWLVoYOyQi0hMmT1SniaJYIxf9zcjIwLVr13Dt2jXExMSUOjbJysoKvr6+UheZp6cnbG1tDRaXIAiwsrKClZWV2niQQkqlEsnJyYiPj5deiYmJastBpKWl4cqVK7hy5QpkMhnq16+Pxo0bo3HjxnB0dDRY7IaQlpYGmUyGnTt3IjAwkF1URLUEkyeq044cOVJqN1N1k5eXh8jISERERODu3bslWnCAgrlZ/Pz84Ofnh/r168PV1bVaDdKWy+Vwc3ODm5ubtLyDUqlEfHw8oqKiEB0djZiYGCmZUqlUiIqKQlRUFPbt2wcfHx+0bNkSzZo102mZB2NTKpXYtGkTXn31VWOHQkR6wOSJ6qz8/HwcOXLE2GGUKzk5GefPn8fFixc1dsk5OTlJLTO+vr41bnCyXC6XZkLu0aMH8vPzERMTg1u3buHGjRtqj2jHxsYiNjYW+/btQ0BAAFq3bo0mTZpU68+sUqlgbm6OBw8e4PLly2jVqpWxQyKiSmLyRHXWb7/9ZuwQSiWKIu7cuYNTp07h3r17Jfbb2dmhRYsWaNGiBTw8PIwQoeGYmJjA398f/v7+6N+/Px49eoQbN27g6tWr0tITKpUKt2/fxu3bt2FjY4N27dqhbdu2sLe3N3L0muXk5EAQBOzevRtNmjSBubm5sUMiokpg8kR1UkZGBm7dumXsMEpQKpW4fv06jh8/jofF1vmSy+Vo1qwZ2rZti3r16lWr7jhDEQRB6ubr0aMHEhMTERERgStXruDp06cAgPT0dBw5cgRHjx5FkyZN0KVLl0rPs2MIoihCqVRiy5Yt0kKsRFQzMXmiOmnjxo3GDkGNSqVCRESEtOxDUY6Ojmjfvj3atGkjLdRZV3l4eMDDwwO9e/dGVFQUzp07h5s3b0IURYiiiMjISERGRqJ+/fro1q0bGjZsWK2mPZDJZIiOjsbdu3fVFoYlopqFyRPVOWlpaUhISDB2GAAKWiNu3bqFQ4cOSV1Shby8vNC9e3c0bty4TrQy6UImkyEgIAABAQFQKBS4cOECwsPDkZ6eDgDSHFfu7u7o0aMHgoKCqsU9LBzkv23bNrzzzjvVKrEjIu0Z/6cJURVbu3atsUMAAMTHx+OXX37B5s2b1RInPz8/TJw4Ea+//nq1+aVfndnb2yMkJARz5szB0KFD1dbDevjwIX777Tf8/PPPuHPnToVWrjeErKwsHDhwwNhhUBUICwuDIAg6r01X3OTJkzFs2DDpfc+ePTFnzpxKnVMbhcu8kDr+VKY6RaFQICUlxagxZGZm4s8//8SKFSvw4MEDqdzb2xuTJk3CpEmT4O/vz1YJHZmYmKBNmzaYMWMGxowZA29vb2lfQkICNmzYgDVr1qjdc2M6c+aMNG6Lqr8ff/wRtra2anOSpaenw9TUFN27d1ere+zYMQiCgFu3bqFLly5ISEiotg8z6NuaNWsgCAIEQYBcLoejoyM6deqEhQsXllib7+jRoxg8eDC8vLzKTNIiIyMxZMgQ2Nvbw9bWFs899xxiYmKq4NOUjskT1Snr1q0z2rVFUcTFixexdOlSnD9/Xip3dnbGmDFj8Nprr8HPz89o8dUWMpkMQUFBeO211zB+/Hi1pxHv37+PVatWYdu2bUhLSzNilAXfD9X5iU9SFxISgvT0dLV/u8eOHYOHhwfOnTuHzMxMqTwsLAxeXl5o1KgRzMzM4OHhUaf+GLKzs0NCQgJiY2Nx8uRJTJ06FevWrUPr1q0RHx8v1cvIyECrVq2wbNmyUs919+5ddOvWDU2aNEFYWBguX76MDz74wOgTzjJ5ojrj6dOnRmt1UigU2LBhA3bu3ImsrCwAgKmpKfr27Ytp06YhKCioTv1wrQqCICAwMBBTp07FyJEj4eTkJO2LiIjA0qVLcfz4cbWWhKr24MEDxMbGGu36pL3GjRvDy8sLYWFhUllYWBiGDh2KgIAAnDx5Uq08JCRE2i7abbdmzRo4ODhg//79CAoKgo2NDQYMGKA2DlOpVGLu3LlwcHCAs7Mz3n33XY1dziqVCu+++y6cnJzg4eGBBQsWqO2PiYnB0KFDYWNjAzs7O4wePbrEU7y7d+9Gu3btYGFhAX9/f3z88cdl/ptYuHAh3N3dcenSpVLrCIIADw8PeHp6IigoCFOmTMHJkyeRnp6Od999V6r3/PPP49NPP8WIESNKPdf777+PF154AV999RXatGkDf39/DBw4EG5ubqUeUxWYPFGdsWHDhiq/pkqlwk8//YTvvvsOd+/elcqbN2+OWbNmoWvXrjAx4XMbhiSTydC8eXPMmDEDAwcOlNbKy8vLw8GDB7F8+XKNc2lVlT/++MNo1ybd9OzZE4cPH5beHz58GD179kRwcLBUnpubi1OnTknJkyaZmZn45ptvsH79ehw9ehQxMTGYN2+etP/bb7/FL7/8glWrVuH48eNISUnB9u3bS5xn7dq1sLa2xpkzZ/DVV19h4cKFCA0NBVDQsjls2DCkpKTgyJEjCA0Nxd27d9UWqd6/fz9efvllzJ49G9evX8dPP/2ENWvW4LPPPitxLVEU8eabb0oxFa4QoC03NzeMHz8eu3bt0npVB5VKhT179qBRo0bo378/3Nzc0KlTp2oxBos/talOyM7OLvE0m6Glp6fjm2++USuztbXF4MGD0ahRoyqNhQrmyerQoQOaNWuGw4cP4/z58xBFESkpKVi3bh3atm2Lfv36VXl3QGpqKq5cuYKWLVtW6XWrm18/P4fMtNwqv66VnRlGv9dBq7o9e/bEW2+9hfz8fGRlZeHixYvo0aMHlEolvv/+ewDA6dOnkZWVVWbylJeXhx9//FGarmLmzJlYuHChtH/JkiWYP38+XnzxRQAF4632799f4jwtW7bERx99BAAIDAzEsmXLcOjQIfTt2xcHDx7ElStXEBUVJc17tn79ejRr1gznzp1Dhw4d8Nlnn+E///mPNO+Yv78/PvnkE7z77rvSeYGC1RgmTpyI8+fP48SJE/Dx8dHqfhXXpEkTPH36FMnJyVq1HCUlJSE9PR2LFi3Cp59+ii+//BL79u3DiBEjcPjwYQQHB1coDn1g8kR1wtatW6v0ejExMRrHs0yfPl1q+SDjsLKywsCBA9G2bVv89ddf0gDyCxcu4Pbt2xg0aBAaN25cpTHt2bMHLVq0qNNdt5lpuch4kmPsMMoUEhKCjIwMnDt3DqmpqWjUqBHc3NwQHByMCRMmICMjA2FhYahXrx78/f1LPY+VlZXaPF+enp7SH3cKhQIJCQno3LmztN/ExATt27cv0XVXPOEuep7IyEhp2aNCTZs2hYODAyIjI9GhQweEh4fj3Llzai1NSqUS2dnZyMzMlOaVe+utt2Bubo7Tp0/DxcVF19smKYxf2+/zwqk9hg4dirfeegsA0Lp1a5w8eRI//vgjkyciQ1IqlYiOjq6Sa4miiDNnzuDAgQNqC/d26NABAwcOrJIYSDuenp545ZVXcP78eRw8eBC5ubl4+vQpNm/ejNatW+P555+vsmVUCrt6unTpUiXXq46s7Myq/XUbNmwIHx8fHD58GKmpqdIvbw8PD/j5+eHEiRM4fPgwevXqVeZ5TE1N1d4LglChaTQ0nafw544oihqTlKLlKpUKH3/8scYxR0VbYPv27YvNmzdj//79GD9+vM5xFoqMjISdnZ3adCJlcXFxgYmJCZo2bapWHhQUhOPHj1c4Dn1g8kS1XlXNp5Obm4tdu3bh6tWrUln9+vUxcuRI2NraVkkMpBuZTIaOHTuiUaNG2L17tzQu7dKlS4iJicHIkSPh5eVVJbGEhYWhc+fOdbb1SduuM2MLCQmRVgJ45513pPLg4GDs378fp0+fxiuvvFLh89vb28PT0xOnT59Gjx49ABR0m4WHh6Nt27Zan6dp06aIiYnBgwcPpNan69evQ6FQICgoCADQtm1b3Lx5Ew0bNizzXEOGDMHgwYMxbtw4yOVyjB07VufPlZSUhE2bNmHYsGFaz11nZmaGDh064ObNm2rlt27dQv369XWOQZ+YPFGtJoqi2qPFhvL06VNs2rRJ7YmZLl26oHfv3pDL5Qa/PlWOg4MDXn75ZVy8eBF79+5FXl4eUlJSsHLlSvTu3RudO3c2+GSleXl5OHHiBLp162bQ61DlhISEYMaMGcjLy1PrNgoODsa0adOQnZ1d5ngnbbz55ptYtGgRAgMDERQUhMWLF+s8yWafPn3QsmVLjB8/HkuWLEF+fj6mT5+O4OBgtG/fHgDw4YcfYtCgQfD19cWoUaMgk8lw5coVRERE4NNPP1U73/Dhw7F+/XpMmDABJiYmGDlyZKnXFkURiYmJEEURT548walTp/D555/D3t4eixYtkuqlp6fjzp070vuoqChcunQJTk5OqFevHgDgnXfewZgxY9CjRw+EhIRg37592L17t9pTj8ZQo562K29CLVEUsWDBAnh5ecHS0hI9e/bEtWvX1Ork5ORg1qxZcHFxgbW1NYYMGcJHhWuxq1evqnWfGUJiYiJ+/vlnKXEyMzPD6NGj0a9fPyZONYggCGjbti3eeOMNqbVJpVIhNDQUmzdvlqaYMKSwsDCDf79S5YSEhCArKwsNGzaEu7u7VB4cHIynT58iICCg0gtTv/3225g4cSImT56Mzp07w9bWFsOHD9fpHIW/Ix0dHdGjRw/06dMH/v7+auM/+/fvjz///BOhoaHo0KEDnnvuOSxevLjUVp2RI0di7dq1mDBhQplPiaalpcHT0xPe3t7o3LkzfvrpJ0yaNAkXL16Ep6enVO/8+fNo06YN2rRpAwCYO3cu2rRpgw8//FCqM3z4cPz444/46quv0KJFC6xcuRLbtm0z+h8Zglhd1ivQwt69e3HixAm0bdsWL774IrZv3642Xf2XX36Jzz77DGvWrEGjRo3w6aef4ujRo7h586bUbTJt2jTs3r0ba9asgbOzM95++22kpKQgPDxc6190aWlpsLe3h0KhgJ2dnSE+KunJd999Z9DJEO/evYutW7ciN7fgKSF7e3uMGzdO7Ycq1Tz5+fkICwtTG1fh6OiIsWPHGvz/bUhIiNRdU1tlZ2cjKioKfn5+Rp/skGqfsr6/9PX7u0a1PJU1oZYoiliyZAnef/99jBgxAs2bN8fatWuRmZmJTZs2ASh4imHVqlX49ttv0adPH7Rp0wYbNmxAREQEDh48WNUfhwxMoVAYNHGKjIzEpk2bpMTJy8sLr732GhOnWsDExAR9+vTBhAkTpKcjU1NTsXLlSkRERBj02seOHTPo+Ymo8mpU8lSWqKgoJCYmol+/flKZubk5goODpZlfw8PDkZeXp1bHy8sLzZs3V5sdtricnBykpaWpvaj6K5wszhAuX76MX3/9VZrsrXHjxpg8eTIHhtcyAQEBmDp1qrTES15eHrZt24ZDhw4ZbJHh/Pz8MmdvJiLjqzXJU2JiIgCU+Kvf3d1d2peYmAgzMzM4OjqWWkeTL774Avb29tKrsv3ZZHiiKJYY76Yv4eHh2L59u/TLs2XLlhg9ejTMzIzzqDUZlqOjI6ZMmYJWrVpJZceOHcMff/xhsKVd9u7da5DzEpF+1JrkqVDxx3xLm+tClzrz58+HQqGQXtVlVXYq3Y0bNwxy3suXL2P37t3S+/bt22PYsGEcGF7LmZqaYtiwYRgwYIBUFhERgfXr1xtkIHlubq7acj5EVL3UmuSpsFm9eAtSUlKS1Brl4eGB3NxcpKamllpHE3Nzc9jZ2am9qHozxARq169fV3vCs3Pnzhg4cKDBH2GvaoIgwNTUFJaWlrC3t4eLiws8PDzg7e0tzVjs5eUFd3d3ODs7w97eHtbW1jAzM6vVSaQgCHjuuecwduxYaT3C+/fvY9WqVVAoFHq/Hte8I6q+as08T35+fvDw8EBoaKj02GNubi6OHDmCL7/8EgDQrl07mJqaIjQ0FKNHjwYAJCQk4OrVq/jqq6+MFjvpV35+PuLj4/V6ztu3b+P333+Xuuo6dOiAfv361cgJDc3NzeHq6gp/f3/4+vrCwcEBdnZ2eu92zMnJQXJyMpKSkqRXSkoKnj59arDurqrQpEkTvPLKK9i0aRMyMjLw+PFj/PLLL5g4caLWMydrIzMzEwqFAvb29no7JxHpR41KnsqbUGvOnDn4/PPPERgYiMDAQHz++eewsrLCuHHjABQ8Rj5lyhS8/fbbcHZ2hpOTE+bNm4cWLVqgT58+xvpYpGeXL1/W6/ni4+Px66+/SvPvFC7dUd0TJ5lMBkdHRwQGBqJNmzZwdXWt0pjNzc3h5eWlcYZuURShUCgQFRWF6OhoxMXFITU1tcbMceTt7Y3XXnsN69evR0pKChQKBVavXo2JEydqteCptjZt2oRp06bp7XxEpB81Knk6f/682sytc+fOBQBMmjQJa9aswbvvvousrCxMnz4dqamp6NSpEw4cOKD2BNR3330HExMTjB49GllZWejduzfWrFlTq7sb6hp9zij+5MkTbNq0CXl5eQAKljwYMmRIteyqMzExgaenJzp27IhmzZpV6+ROEAQ4ODioTZAHAFlZWbh79y5u3ryJ6OhopKenGzHKsjk6OuKVV17B+vXrpdXfV69ejQkTJuhtSZekpCSoVKpq+f1GVJfVqEkyqwtOkll95efnq60QXhnZ2dn45ZdfpFXKfX19MXHixBKLcRqTvb092rVrh86dO0vjcGoTpVKJBw8e4PLly7h9+zYyMjKMHVIJmZmZ2LBhg9RVbGFhgcmTJ0vjMCurRYsWGue2q8k4SSYZUlVMkln7ftpSnVZ8AcmKUqlU+OOPP6TEycnJCWPHjq0WiZObmxt69OiBoKCgWt8iIZfL0aBBAzRo0ABAQcvUzZs3cfHiRTx48MBgcy3pwsrKChMnTsSmTZsQExOD7OxsrFu3DpMnT9ZLF15EREStS54I6NmzJ1q3bo0lS5YYOxSqACZPVKuEh4fr5TzHjh3DrVu3AACWlpYYP348rK2t9XLuirC3t0e3bt3Qpk2bOt3FbGlpidatW6N169ZQqVS4f/8+zpw5g9u3bxt1vJSFhQXGjx+P9evXIzY2FpmZmVi3bh1eeeUVvQwiv3HjBpo0aaKHSKmyJk+ejCdPnpRYWzUsLAwhISFITU2Fg4ODUWKjqsPkiWoNURQRFRVV6fPcvn0bhw8flt6/+OKLen2KSltyuRytWrVCz549OXO5BjKZDH5+fvDz84MoioiJicHRo0cRFRVllBYpc3NzjB8/HuvWrUNCQgLS09Oxbt06vPrqq5V+Ym7btm14//339RQpEVVW7W7zpzrl4cOHlT6HQqFQm1+nV69eaNiwYaXPqwt7e3sMHToU7733HgYPHszESQuCIKB+/fqYMGEC3nvvPQwfPrzESgJVwdLSEhMmTJC66xQKBTZu3Ijs7OxKnTc/P19aQ5Gqv+TkZLz00kvw8fGBlZUVWrRogc2bN5d5zL59+2Bvb49169YBKOiu7dWrFywtLeHs7IypU6eqPUARFhaGjh07wtraGg4ODujatSvu379v0M9F/2DLE9UalV2ORaVSYfv27dKM0Y0bN0a3bt30EZpWPD098cILL8DHx6fKrlkbmZiYoGXLlmjZsiWePn2KsLAwXLp0qcq69QrHQK1atQqpqalISkrC1q1bMX78+EoN6l+/fj2mTJmix0irlw3z5yDjSWr5FfXM2sERL3+xRK/nzM7ORrt27fDvf/8bdnZ22LNnDyZMmAB/f3906tSpRP0tW7Zg6tSpWL9+PYYOHYrMzEwMGDAAzz33HM6dO4ekpCS89tprmDlzJtasWYP8/HwMGzYMr7/+OjZv3ozc3FycPXu2Wj9hW9sweaJaIzIyslLHnzp1CtHR0QAAOzs7DBs2rEoGZNevXx8DBgzQ29NZ9A9bW1sMHjwYL7zwAiIiIhAaGorMzEyDX9fGxgYvv/wyVq5ciaysLERFRWH37t0YNmxYhX/BxcbG6jnK6iXjSSrSU5KNHYZW/vzzT9jY2KiVFS4SDhTMAzZv3jzp/axZs7Bv3z789ttvJZKn5cuX47333sPOnTulqXg2btyIrKwsrFu3ThpruWzZMgwePBhffvklTE1NoVAoMGjQIAQEBAAAgoKCDPJZSTMmT1Qr5OfnIzm54j94ExIScOjQIen9iBEjYGlpqY/QSuXt7Y3BgweXuTQQ6YdcLpcGmsfFxWHHjh14/PixQa/p7OyMcePGYe3atcjPz8fly5fh6OiInj17Vvic169fR9OmTfUXZDVi7VD13awVvW5ISAh++OEHtbIzZ87g5ZdfBlCQSC1atAhbt25FXFwccnJykJOTU+Khk23btuHhw4c4fvw4OnbsKJVHRkaiVatWavW7du0KlUqFmzdvokePHpg8eTL69++Pvn37ok+fPhg9ejQ8PT11/ixUMUyeqFaIi4ur8LFKpRLbt2+XunW6du0qPRpvCA4ODhg8eDD8/f0Ndg0qnbe3N2bMmIHHjx9jx44dlfreKY+vry9GjBiBX3/9FUDBOBUPD48KPzm3ffv2Wps86bvrzJCsra1LjIUs2jL47bff4rvvvsOSJUvQokULWFtbY86cOSXGrbVu3RoXLlzA6tWr0aFDB6lVsqzF6gvLV69ejdmzZ2Pfvn3YunUr/u///g+hoaF47rnn9PlRqRQcME61QmWesjt58qQ0n5O7u7vaLPb6ZGJigsGDB2P27NlMnKoBFxcXvPbaa3jrrbcMOs6sadOmass//fHHH3j06FGFzpWfny/Ndk/V17FjxzB06FC8/PLLaNWqFfz9/XH79u0S9QICAnD48GHs3LkTs2bNksqbNm2KS5cuqU0Ke+LECchkMjRq1Egqa9OmDebPn4+TJ0+iefPm2LRpk2E/GEmYPFGtUNHxTsnJyQgLCwNQ8Bfd0KFDDTJTd5s2bTB37ly0bduWgzqrGTs7O0yZMgWzZs2Cq6urQa7RtWtXNGvWDEDBguVbtmyp8BN4+/bt02doZAANGzZEaGgoTp48icjISPzrX/9CYmKixrqNGjXC4cOHsW3bNsyZMwcAMH78eFhYWGDSpEm4evUqDh8+jFmzZmHChAlwd3dHVFQU5s+fj1OnTuH+/fs4cOAAbt26xXFPVYjJE9V4oihKLUe6Hrd7925poOdzzz2ntzXJCjk6OmLKlCkYMmSIwcdQUeU4OTlh+vTpmDp1KszNzfV67sLEvHAKg+TkZOzYsaNC81FduHBBr7GR/n3wwQdo27Yt+vfvj549e8LDwwPDhg0rtX7jxo3x999/Y/PmzXj77bdhZWWF/fv3IyUlBR06dMDIkSPRu3dvLFu2DEDBE503btzAiy++iEaNGmHq1KmYOXMm/vWvf1XRJySubVcBXNuuenny5An++9//6nzc9evXpbEoDg4OmD59OszMzPQWV5cuXRASElIr15yrCyIiItTm/NKHlJQUrFixQmp1euGFF9QGCmtrzpw5lZ5405i4th0ZUlWsbceWJ6rxKjLgNy8vDwcOHJDeDxgwQG+Jk42NDV599VX07duXiVMN1qJFC/zf//2fXrtCnJyc1Fog9u/fX2p3Tlm2b9+ut5iISHdMnqjGq8j8N6dPn8aTJ08AAH5+fmjcuLFeYmnevDlmzZoFX19fvZyPjEsul2P06NF466239JYIN2nSRGptUiqV+P3333WePZwzSRMZF5MnqvHu3LmjU/309HQcO3YMQMFYlAEDBuhlEPeQIUPw4osv6rXrj6oHOzs7vP/++5Wao6movn37SpOiPn78GPv379f5HJWZ14yIKod9ClTjpaSkaCy3T01Fn4MHoZLJIApCwUsmw5dxidJf+v1dvND3/B2IsrtQyQRAkCHVxg57OvSGABEC8Ozrs22haFnBV3MzM3Tv1hXRsEPMpTjIBAFymQCZIEAmoGBbJkD+rFwQIG0XlssEATJZQV25oFt54fXkQsG5+TSf4QQHB6NTp074+uuvK7Xci6mpKUaOHImffvoJeXl5CA8PR7NmzXSawuKPP/7A66+/XuEYiKjimDxRjZabm1vqLzHLrCw0L7beXSKAXc+2LQCsehQHr0fqY6ZuuNTHZ20maB9EHnB4f8XnmdI3KWEThCKJ3LMETCZAEIQiyRsK6hRNzIrUL3q8lKQVTwyLJosyAXIBxZI8AXIZniV36ueQP7t++eeGWnxyoUjsUuJYeI7yy9W/QsNneJacSgnvP+eTmZjiP++9j927diHiyuUK/39ycXFB37598ddffwEAdu7cienTp2v9pF98fHyFr11d8HklMoSq+L5i8kQ1WllLbMg0/AP6EkDWs+03AGiamEBVBevZGZJKBFRKEQB/MRmWKYD26i2TJbaffRUA2bP/H71vncGcsA1QCQLGCgIem5rjbF4OFAoF7v13OT50coG1LE9qMVXJZFDJZNg8fnyJCDIzM2FlZVWVH1ovTE1NARTEzyk8SN8K168s/D4zBCZPVKOV1mUHAA/d3bHkzTchE0UIKhWepqdj2fr1gFIJU7kJbMZMxNcW1hBUKggiIIgqQCUi19QM/cxuQQUBBSlIka9iwdemzZrDv2FDiKIIpQpQiuKz7YKX6lm5ShShUolQPvuqEiFtK5+ViyKk7aLlmuqrxMKvkLYLy1XPzlO0XCx6vPisngrFrvHPOf851z9lVLZ/0qUyK0nMsnLgl/JPq9FmAC0AZALYmqnAtEwFii/Aoiwlod+5cydeeumlioRtVHK5HA4ODtL8bFZWVuxupkoTRRGZmZlISkqCg4MD5HK5wa7F5IlqtLIe81aamEDh+M+in4cuX0b+swkx23fsAJNG9ZBZyrHeSCv1vMOGDUOrVq0qFG9NI4rqiZpa8lY04ROLlElJ4bPEUu24fxLLguSuoG5pCaeUAJZxrbISynLLi51X0/VUogilxs/2T7lKFPE4ORk5ufklE24AoihI24IMSDO3gkxUQa5SwVtU4XOVEnOetZROB3AJBe1a0v+HUhKLW7duGfJ/v0EVDpivyAS3RGVxcHCQvr8MhckT1WjaPrKdm5uL8+fPAwBkMhk6d+5coeuNHTtWb9Ma1AQF46MAOQSYGu6PuFrjzJkz5S+f0sER33V4V61IpVLBe9UqxMXF4TqAIX36oFunThBUqoKW0zLGcCiVSoP+hW0ogiDA09MTbm5uXK+P9MbU1LRK/j0weaIaTdvHtS9fvoysrILRTs2bN6/QzLKTJk1CgwYNdD6O6o5OnTrB3d0da9eu1ek4mUyGF154AT///DMA4O+jR9G0ZUutvk9PnjyJ7t27Vyje6kAul9fI5I/qtpo9MpbqPG0WV1WpVDh9+rT0viKtThMmTGDiRFpp0KABZs6cqfNx3t7eaN++PYCCltKiM+CXpXBhayKqOkyeqEbT5pHUqKgoqYWqQYMG8PT01OkaL730kk7z7xA5Ozvj7bff1vm4Xr16SU+fXb16FQ8ePCj3mMrMN0VEFcPkiWosbcdJXLx4Udru0KGDTtcYPnw4GjVqpNMxREDBGofz5s3T6RgrKyv06tVLeh8aGqrVHwhPnz7VOT4iqjgmT1Rjpaenl1snMzMTkZGRAAp+Meky2Pu5555Dy5YtKxwfkbW1tc4JVNu2beHs7AwAiImJ0eqJut9//71C8RFRxTB5ohorIyOj3DoRERFQPpueoGXLllov7urp6Yn+/ftXKj4ioCCB0qULTy6Xo3fv3tL7gwcPlts1FxMTU+H4iEh3TJ6oxnry5Em5dS5duiRtt2nTRqvzmpiYcM0w0isbGxvMmTNH6/pBQUHw9vYGADx69AhXrlwxUGREVBFMnqjGKi95SklJQUJCAoCCliR3d3etzjtv3jzOdkx6Z29vj9dee02ruoIgoG/fvtL7Y8eOldv6VNaEsUSkX0yeqMYqb8zT9evXpe1mzZppdc6xY8dqvTArka68vb0xevRoreo2aNAA9evXB1Awn1nR72dNdu7cWen4iEg7TJ6oxlIoFGXuL/rLJigoqNzz+fj41KnZw8k4goKC1FqVytKjRw9p++jRo2W2PrHliajqMHmiGqusbrsnT54gPr5g8VUPDw/p6aWyTJo0SV+hEZWpS5cuCAwMLLeev7+/NPYpKSmpRq9lR1SbMHmiGqusuW2K/pLRptXp5Zdf1vpJPCJ9GDduHKytrcusIwiCWuvTqVOnyqxfXmssEekHkyeqscpamuXu3bvSdnmTXNrZ2SEgIEBvcRFp68033yy3TqNGjeDi4gKgYCHswocgNCl3UWIi0gsmT1RjFc7fpKk8KioKQMHEmOU9ZTd58mR9h0akFVNT03LXwRMEAZ06dZLenz17ttS6N2/e1FtsRFQ6Jk9U68TGxiI3NxcAEBAQAJms9G/zoKAgODo6VlVoRCU4OzurLcmiScuWLaWnQK9cuVLqBLHaLOVCRJXH5IlqnXv37knb5XXHDRkyxNDhEJWre/fucHBwKHW/ubk52rZtC6CgZfXChQtVFBkRacLkiWqdoktV+Pn5lVrP29sbFhYWVRESUblmzJhR5v6ii1pfunSp1FYmTllAZHhMnqhWUalUiIuLAwDY2trC3t6+1LoTJ06sqrCIymViYlLmdBlOTk5o0KABgIJJM2NjYzXW++uvvwwRHhEVweSJapVHjx5J4518fX3LrGtmZlYVIRFprUGDBmU+4NC6dWtp++LFixrrPHjwQN9hEVExTJ6oVin6i8PHx6fUev/5z3+qIhwinZW1KHXTpk2lpP/atWvSHwpEVLWYPFGNVNp4j8JZxYGykyeuX0fVlVwux8iRIzXuMzMzQ9OmTQEAOTk5pc44zqfuiAyLyRPVSHl5eRrLk5KSpO3Suj+KDrwlqo6aNWtWaoLfsmVLabu0xYI50ziRYTF5ohopJyenRJkoilLy5ODgUOovnxdeeMGgsRHpQ2lP39WvXx+WlpYAgNu3b2vsurt69apBYyOq65g8UY2kKXlSKBTSLxJXV9eqDolIr2xtbeHp6VmiXC6XS+s15uXlqS1FVOjQoUMGj4+oLmPyRDWSpm6JR48eSdtubm4aj+vbt6/BYiLStylTpmgsL7rYdWldd0RkOEyeqEZ68uRJibLk5GRpu7SWpy5duhgqJCK9k8vlGmfJ9/PzkyZ4vX37NlQqVVWHRlSnmeh6QHR0NI4dO4bo6GhkZmbC1dUVbdq0QefOnTlbM1UZTSvLF02oylrqgqgmGT9+PBYuXKhWZmJiAn9/f1y/fh3Z2dmIj48v8XSpKIoQBKEqQyWqM7ROnjZt2oTvv/8eZ8+ehZubG7y9vWFpaYmUlBTcvXsXFhYWGD9+PP7973+jfv36hoyZCA8fPixRVrQrT9PM4tbW1gaNicgQBEGAq6urWrc0ULBuY2GX3Z07d0okT8nJyXBxcamyOInqEq267dq2bYvFixfj5ZdfRnR0NBITExEeHo7jx4/j+vXrSEtLw86dO6FSqdC+fXv89ttvho6b6rjiv0iAf1qeBEGAnZ1dif1z5841dFhEBqFp4syi3XmaBo0vX77coDER1WVatTx98sknGDhwYKn7zc3N0bNnT/Ts2ROffvopoqKi9BYgkSalPW0HFDylJJfLS+yXyTjEj2omU1PTEmUODg5wcXHB48ePERsbi+zsbLWhE5wok8hwtPptUlbiVJyLiwsnIaQqp1QqkZmZCaAgeSKqbcaMGVOizN/fH0BBosQ17Yiqjs4DxgslJSUhKSmpxFMeRWe/Jaoq2dnZ0nbhBIJFaWqJIqpJmjRpUqKsXr16OHv2LICCdR0DAwOrOiyiOknn5Ck8PByTJk1CZGSk1CwsCIL0ZIdSqdR7kETlycrKkratrKxK7C9trTCimszX11fa1tTyxCfuiAxD50Egr7zyCho1aoSTJ0/i3r17iIqKUvtKZAxFkydNLU8NGzasynCIDKJ41529vb30cERsbGyJP17j4uKqLDaiukTnlqeoqCj88ccf/GVE1UrR5EnTfGMmJhXuoSaqNjR13fn6+uLatWvIy8tDUlKS2pIuZ86cKTGFARFVns4tT71798bly5cNEQtRheXn50vbZmZmRoyEqGp5e3tL24mJiWr7uEAwkWHo/Of4ypUrMWnSJFy9ehXNmzcv8QjtkCFD9BYckbaKdldwcDjVZn5+fmrTwbi7u0vbmiaPJSL90zl5OnnyJI4fP469e/eW2McB41TVAm7fxpBdu7A1LxvbnpUNOnYA/zp/CKmTPaG0NoEIGbDiMCDIAEEOyOTPvso0lMmflcmKlckBQdBQJnt2nuJl8lLOoaF+RePRqb6syDGlxMqBxTXCyJEj8fXXX0vvmTwRVT2dk6fZs2djwoQJ+OCDD9T+0RIZg1leHuyePlUrs8xQwSRDBVcxBVLPdHxS1QdX0xQmWGqJl6YyHRNM2bPyCid5xbZLJH7lJIVax6OPBFlTrBqS9koo/jSpjY0NrK2tkZGRgYcPH/IJO6IqoHPylJycjLfeeouJE1UL+SYmUNjZISsnE8gpGPckNwVEmYA8mSkEADKIkAsCILJVtEyiquAFALxVhqWxxVD7hO5fYhJUEGB6Pxt2Bx/jaH4u/gaQmZmJgatXQenpjf3PPw+A0xUQGYLOydOIESNw+PBhtXWViIzldqNGWDJ3Ls6fPw/8+ScAYPsLQxHdpo1avY8++qhgQ6UqSKJUyoKvourZtuqfssL3Rbc17it2Lmlf8fqF59D12qpy4il+fZWGsrJiEg0XZ2ESRppJ/0/yKnS4R+FGZh4Qm40gAH8/K8qLiYWP+E+y9PDhQ3h4eICI9Efn5KlRo0aYP38+jh8/jhYtWpQYMD579my9BUekraKDxMscdyeTAZAVNE+R4YiiHpM8DQldWfVVzxLDEmVFEj2NiaCWCWap59d0Dm0T4eIxaYrzn+OUyjxAVEEmAgIA/yK3PgqAd5GWpps3bzJ5ItKzCj1tZ2NjgyNHjuDIkSNq+wRBqDHJ0/Lly/H1118jISEBzZo1w5IlS9C9e3djh0UVVDR5KjptARlJ0S4o0rt0hQJLliwBmgEIUuFG5HXgt98BAKu7d8f9Ij/LIiMjERwcbJxAiWqpCk2SWdNt3boVc+bMwfLly9G1a1f89NNPeP7553H9+nXUq1fP2OFRBRSdBJNPfFJtVzirOABAJoODs4v0NvnpU+QVmeuMT+AR6Z/Oj31cuXKl1H07duyoTCxVZvHixZgyZQpee+01BAUFYcmSJfD19cUPP/xg7NCogtjyRHVJ8QHgDg4O0vaTJ0+qNhiiOkjn5Kl///4a17Dbtm0bxo8fr5egDCk3Nxfh4eHo16+fWnm/fv1w8uRJjcfk5OQgLS1N7UXVS9H17Iou1UJUF1hYWEjjTzMyMowcDVHtp3PyNG3aNPTu3RsJCQlS2datWzFx4kSsWbNGn7EZxOPHj6FUKktMteDu7l5iaYNCX3zxBezt7aVX0ZXMqXooL3kSRbEqwyGqcoXzPzF5IjI8nZOnDz/8EEOGDEGfPn2QkpKCTZs24ZVXXsG6deswatQoQ8RoEMWbvcuaC2X+/PlQKBTS68GDB1URIpWh+BIs5SVP6enpBo+JyJisra0BFMz1pFJxqggiQ6rQUvP//e9/MWHCBDz33HOIi4vD5s2bMXToUH3HZhAuLi6Qy+UlWpmSkpJKnfjT3Nwc5ubmVREeacnS0lItISoveUpNTYWtrW2VxEZkDIXJE1Dwb6Do+9zcXC6YTaRHWiVPu3btKlE2bNgwHDlyBC+99BIEQZDqVPeFgc3MzNCuXTuEhoZi+PDhUnloaGiNSQCpYIxH0eRJLpfD3NwcOTk5GluZkpKS+CQl1WpFl23JzMxUS55SU1O5KgSRHmmVPA0bNqzUfb/88gt++eUXADVnYeC5c+diwoQJaN++PTp37owVK1YgJiYGb7zxhrFDIy3Z2tri8ePHamX29vZISkpCWloaVCoVZEXWEDt//jzat29f1WESVZmiExbn5anPXP748WMmT0R6pFXyVNv6z8eMGYPk5GQsXLgQCQkJaN68Of766y/Ur1/f2KGRlpydnUvMOVaYPCmVSmRkZKh103GuG6rtykqeUlJSqjocolqtQmOeaoPp06dj+vTpxg6DKsjV1bVEmb29vbStUCg4xonqlKLJU/G5zkp7kpiIKkarp+22bNmi9QkfPHiAEydOVDggIm3Y2NiUKCs6UaBCoSixn5NnUm1WVstTcnJyVYdDVKtplTz98MMPaNKkCb788ktERkaW2K9QKPDXX39h3LhxaNeuHZuIyeCKPl1XqGjypOmXRfExUkS1SVmLY3NiXyL90qrb7siRI/jzzz+xdOlSvPfee7C2toa7uzssLCyQmpqKxMREuLq64pVXXsHVq1fh5uZm6Lipjiv6ZFGhol15jx49KrH/+vXrXF2eaq2iE8EWn7MuJyenqsMhqtW0HvM0aNAgDBo0CMnJyTh+/Diio6ORlZUFFxcXtGnTBm3atFF7uonIkDSNZ3J2doYgCBBFUWPydOLECfTq1asqwiOqckUf7CmePNW2h36IjE3nAePOzs6cD4mMTlO3nYmJCZycnJCcnIzHjx+XmK6Av0CoNiva8sQ/ZIkMi//CqEYqbSmdwi7j/Px8pKamlthfE+YhI6qIslqeiEi/mDxRrVJ0IsD4+PgS+2/fvl2V4RBVmaJPk5qY1NlZaIiqBJMnqrE0rdXl7e0tbcfFxZXYv2PHDkOGRGQ0RQeFcy1OIsNi8kQ1lq+vb4myoslTbGxsif186ohqA03rN2ZnZ0vbFhYWVRkOUZ3D5IlqrFatWpUos7KygpOTEwAgISFB48SYRQfWEtVEFy9eLFFWNHliyxORYencMT537lyN5YIgwMLCAg0bNsTQoUOlX2BEhuLp6amx3MfHBykpKVAqlUhMTISPj4/a/h07dmD48OFVESKRQVy+fLlEWdFWVbY8ERmWzsnTxYsXceHCBSiVSjRu3BiiKOL27duQy+Vo0qQJli9fjrfffhvHjx9H06ZNDREzEQCUmqD7+vriypUrAIDo6OgSydOVK1eYPFGNpmkG/cKuPDMzMw4YJzIwnbvthg4dij59+iA+Ph7h4eG4cOEC4uLi0LdvX7z00kuIi4tDjx498NZbbxkiXiJJaXPZ+Pv7S9v37t3TWIdzPlFtIoqitASLnZ2dkaMhqv10Tp6+/vprfPLJJ2r/QO3s7LBgwQJ89dVXsLKywocffojw8HC9BkqkLScnJ9jb2wMAYmJiSiySCgB//PFHVYdFZDDZ2dnS+D5NyRPnfSLSL52TJ4VCgaSkpBLljx49kv7ycXBwQG5ubuWjIyqHn59fiTJBEKTy/Px8PHjwoESda9euGTw2IkPQ9MBD0YV/NSVP7MYj0q8Kddu9+uqr2L59O2JjYxEXF4ft27djypQpGDZsGADg7NmzaNSokb5jJSphwIABGsuLdt3dvXtXYx1Nj3sTVXeXLl0qUaZQKKRtTes+cgA5kX7pnDz99NNP6N27N8aOHYv69eujXr16GDt2LHr37o0ff/wRANCkSROsXLlS78ESFVe4HEtxAQEBUlfFjRs3NP61vmTJEkOGRmQQR44cKVFWdAC5pgcpHBwcDBkSUZ2jc1uujY0Nfv75Z3z33Xe4d+8eRFFEQEAAbGxspDqtW7fWZ4xEOrO2tka9evVw//59aaFgV1dXtTpKpRKiKHI8CNUoRVuZCj1+/FjadnFxKbG/fv36Bo2JqK6p8CSZNjY2aNmyJVq1aqWWOBFVF02aNJG2b9y4obHO9u3bqyocIoMpL3ny8vKqynCIaj3OME41XosWLTSWF02eIiMjNdaJiIgwSExEhlB0FvGiCpMnGxsbWFpalthfWvc2EVUMkyeq8QofVCjO0dER7u7uAID4+HikpKRorFc4oSZRdadpYev09HRkZGQA0NzqBHDuJyJ9Y/JENV5pk2UC6q1SpSVJ7LqjmuLmzZslyuLj46Xt0pYsMjU1NVhMRHURkyeq1YomT5cvXy51UeDo6OgqiohIv+Li4qRtb29vI0ZCVHcweaJaobRuCXt7e2nCzNTUVMTGxmqst3btWoPFRqQPRVuYSitn8kRUNZg8Ua3w5ptvlrqvZcuW0ramCQYLRUVF6TMkIr36+eefS5SJoii1PFlaWmqcz8na2trQoRHVOUyeqFYoa9xT06ZNpTEfERERpT6xtG7dOoPERmQojx8/RmZmJgDAx8dH45xlTZs2reqwiGo9Jk9U65mbm0utT7m5uWU+XVdWyxSRsdy7d6/cck3rPAJA586dDRITUV3G5Ilqjb59+5a6r0OHDtL22bNnSx04vnPnTr3HRVRZ69ev11hetKu5tOTJ0dHRIDER1WVMnqjW6NKlS6n7PDw8UK9ePQAFXR1lPV2naS4doupGpVJJ38eWlpbSnGZEZHhMnqjOKNr6dOrUqVLrXb58GSqVqipCIirXxx9/rLE8Pj5eGr/n5+encdxfWWMBiaji+C+LapXSZlgGgKCgIGlKg1u3buHhw4el1v3vf/+r99iI9Knoeo0NGzbUWKfok6ZEpD9MnqhWmTFjRqn7TExM1AbPnjhxotS6aWlpZSZXRFVh5cqVpe4rXK9REAQ0btxYY51u3boZJC6iuo7JE9Upbdu2lRZOjYiIQGpqaql1f/zxx1IHlhNVhaKzhxf16NEjJCcnAwDq1atX6lxOTk5OBouNqC5j8kS1jpeXV6n7zM3N0bFjRwAFEwyW1foEAH/88YdeYyPSVllrLhbtsmvSpEmp9TTN+0RElcfkiWqd119/vcz9nTp1kibNvHDhQpmtT1evXoVCodBrfETaKG0+MlEU1faVljyVlVQRUeUweaI6x8rKCs899xyAgse9w8LCyqy/ZMkSdt9RlVq1alWp++Lj4/Ho0SMABV12pc3jxPFORIbD5IlqpREjRpS5v0uXLrCwsABQMDVBUlJSmfXZfUdVqbQFrIGC79dCrVq1KrWep6enXmMion8weaJaqUWLFmXut7S0VPvL/O+//y6z/tWrV5GQkKCX2IjKUtq8TgCQn5+PiIgIAAVPjzZr1kxjPUEQOMcTkQHxXxfVWR07doSNjQ2AggG4RZe60GTFihXIzc2titCojsrPzy9z/40bN5CVlQWgYExTYetpcd27d9d7bET0DyZPVGt98MEHZe43MzNDr169pPd79+6FUqks85ilS5dy/BMZzGeffVbm/jNnzkjbbdu2LbVe0dn0iUj/mDxRraVNt0Xr1q2lqQ2SkpIQHh5eZv309HQcOnRIL/ERFXXhwoUy98fFxeHBgwcAADc3t1IXAgYgtagSkWEweaJabebMmWXul8lkeP7556X3f//9NzIzM8s85sSJE4iJidFLfESFdu/eXeb+s2fPStudOnUqdQ4nf39/vcZFRCUxeaJazdnZudw6vr6+0hpg2dnZOHDgQLnHrF69GmlpaZWOjwgoe5A4ULBc0NWrVwEAFhYWZT4Q8cILL+g1NiIqickT1XpDhgwpt06fPn1gbm4OALh06RLu3r1b7jHfffcd8vLyKh0f1W3R0dHl1jlx4oQ0Hq9du3YwMzMrta42fzAQUeWYGDsAIkNr06YNdu3apVbWJzQUrS5dglluLkyePeHUEMD0Z/uPr1+PpYIc1jIZAAEigHxTc5zu8DKi63eAABUAERtfXQsrKwvIAAgCAAEQAAgyETIIgOzZewEoHIIlyACZIECQAQKefZUJEISCR8wFmVCkjvpLJggQ5IAgkxW8lwkF2/KCrzK5UHAOuQCZXP7sqwwQZJCZPKtjIoMgkxfUlcsgyGXP6sogK3xvIoNgIodMVvBVkMsgM5VDkMshl8sAEznkJvKCOnIZBBMTCCYyPh5fAWvXri1z/9OnT6WxeMUXty6uQYMG+gyNiErB5InqhB49euDo0aPSe/OcHNhkZKjV+ReArQCOAIgC8LGoxOIiT9+ZKnMhV8mRaV1f7Ti1sxQ+iKcs9tVoxGdBVFEgogqCKBYkl6IIASIEUfUs/Szc988LUH//TzkgCGJB4gnxn+3C5FQQi2wXvsSCxLHE18KkFNK2TFa07FliKkNBklmYzBZNWot8RWGSWpjAFm7LZQX15LJ/EtkiCew/ieuzr3I5fv39V9hBhCgAJnl5sMzOgHlOdsHdksuQbyLD1nOnpSkMnmvdGk4mJlDm5UEpkwFyudrtL29yWCLSDyZPVCeEhISoJU+ZlpZIdXCAVWYmTJ91vQmiiBUAWgHIBrAEwDAAhTPmCABEgS0rZRJkEAVAhLz8uvoi4p+ktVpQPXsBQNnduqboKW03vHMY/Q8tU9ufCGDOs20LAL+ePw/P8+cBAPkyGT778EO1+ra2thUNmoh0wOSJ6ozu3bvj2LFjAIDDvXvjcO/emuudOIHQ0FCIAIbY2+ONN96ApaUlkK981hJyBIBQ0IqiAgQRkImAf/0G6N+vH1T5Koj5SqiUKohKZcF7pargvUoFVb4SorKwTISoelZHJT47pqCeqBQL6hd+VYnP9okQlSJUKhVEFQrqqsSC4599FcWi7wGIz7ZFPNuPZ3UA1bOvAKAqaDD6Zz8AUfWsjaiwHIAoCs+2C574EsUi7UiiensSULJ9CZAVbAuFZQXNQqJQUF6wXYUJWDUgFP5PKOIjAIXPfk4FoLbgSrGn7Tp27GigyIioOCZPVGf06tVLSp7K0rlzZ9y6dQv379+HQqHAnj178OKLL0IwkRdr5FD/5RWZFAPx/HGMGTNG36HXWap8JVRKJcR8FVT5+VDlqSAq8wveK1UQ8/OffVVKiacqXwmVSgU8q1OQrBZNUoskrkqllJxKiWphgqqWuEI6rmhSqp64Qkpa/0lgCxJOVeH7Z8lrUtIjPOt0ROH3kVKeiaj6beGUEgvT/BxcV+ZjZW5Bp7AtgHflcmSZmkJ4lsVmWFur3au+fftW6f8borpMEDldss7S0tJgb28PhUIBOzs7Y4dDOrh+/Tp+++23cus9efIEP/74I7KzswEAw4cPL3MR1qICAgIwfvz4UufhobqtvGkJCm3cuBG3b98GAPTu3bvMJVesrKzwzjvv6CU+otpMX7+/OYCD6pSmTZtqVc/BwQGDBg2S3u/ZswePHz/W6ti7d+/il19+KWj9ICpC28Tp9u3bUuJkZ2eH5557rsz6o0aNqnRsRKQ9Jk9U50yfPr38SgCaN28utTbl5uZi69atyMnJ0erY2NhYLFu2TOv6VPtt3bpVq3q5ubnYs2eP9L53794wNTUt8xhOUUBUtZg8UZ3j6uqqdd2BAwfCzc0NAPDo0SPs2rVL64WBU1NT8e2330KhUFQoTqo9Ll26hBs3bmhV9+jRo3jy5AmAgqSocPb70nTt2rWy4RGRjpg8UZ30f//3f1rVMzMzw5gxY6TZx69du4ZTp05pfZ28vDwsWbIEcXFxFYqTar7Y2Fjs3LlTq7pJSUk4efIkAEAul2PQoEHljp3r1atXpWMkIt0weaI6SS6Xo0mTJlrVdXZ2xvDhw6X3oaGhuHPnjk7XW7lyJS5fvqzTMVTzPXr0CKtWrdKqrlKpxPbt26Wxct26dYOLi0uZx/j7+3NWdyIj4L86qrNGjx6tdd0mTZpITzuJoohff/0VDx8+1Ol6O3bswPbt26U1yqh2S0pKwvLly7Wuf+TIESQkJAAAXFxc0K1bt3KP4UBxIuNg8kR1liAImDp1qtb1Q0JCpNaq3NxcbNq0CU+fPtXpmleuXMHSpUs5DqqWu3v3Ln744Qet68fGxkpzkMlkMowYMaLcQeLe3t6wsLCoVJxEVDFMnqhO8/T0hI2NjVZ1C3+peXl5AQAUCgU2b96M3Nxcna6pUCiwZMkS3Lp1S+d4qfo7f/48NmzYoHX97Oxs/PHHH9KDCMHBwdL3WFk4GSuR8TB5ojrvrbfe0rqumZkZXnrpJdjb2wMA4uPjsXXrVmnhVl1s3rwZv//+O/Lyyl7/jGqO7du3q00zUB5RFLFz506kpKQAKGhN0qa7zsvLi+vYERkRkyeq82QyGcaNG6d1fVtbW4wbN056Au/u3bvYtm1bhcYyXbt2Dd988w3i4+N1PpaqD5VKhe+++w5XrlzR6bgzZ84gMjISAGBhYYGRI0dCLi9/TT9dvl+JSP+YPBEBCAwMhHWxtcLK4u7ujnHjxsHEpGB5yMjISOzevbtCs4rn5ubi559/xv79+zkreQ309OlTfPLJJ0hLS9PpuJiYGBw4cEB6P2zYMDg6OpZ7XKNGjXT6XiUi/WPyRPTM22+/rVP9+vXrY+zYsdKj4pcuXcK+ffu0nkSzuNOnT+Orr75iK1QNcvnyZSxevFjn41JTU7F161YpWe7atavWU2e8+OKLOl+PiPSLyRPRM4IgYMqUKTod07BhQ7z44ovSRIZnz57Fn3/+WeEWpJycHPz888/YvHkzx0JVY7m5uVi+fDl27Nih87HZ2dnYvHkzMjIyAAB+fn5aT3TZtWtXmJmZ6XxNItKvGpM8ffbZZ+jSpQusrKzg4OCgsU5MTAwGDx4Ma2truLi4YPbs2SWehIqIiEBwcDAsLS3h7e2NhQsXVrilgGofHx8feHh46HRMs2bNMHToUOl9eHg4du3aVakuuFu3buGLL77A5cuX+f1Zzdy7dw9ffPEFHj16pPOxSqUS27ZtQ1JSEoCCCVhHjx6t1TgnmUzG2cSJqokakzzl5uZi1KhRmDZtmsb9SqUSAwcOREZGBo4fP44tW7Zg27Ztal0xaWlp6Nu3L7y8vHDu3DksXboU33zzTYWa3an2+te//qXzMa1bt8aIESOkFqhLly5VekJMURSxY8cOfP3110hMTKzweUg/srOzsWLFCqxfv75Cx6tUKuzevRu3b98GUDBAfNy4cbC0tNTq+HHjxnE2caJqQhBr2J+1a9aswZw5c6SFMwvt3bsXgwYNwoMHD6Q5UrZs2YLJkycjKSkJdnZ2+OGHHzB//nw8fPhQelJq0aJFWLp0KWJjY8tdQ6pQWloa7O3toVAoYGdnp9fPR9VDVlYWvvrqK52Pu3btGrZt2ya1OgUGBmLUqFF66Wpxc3PDpEmTYGVlVelzkfZEUcTp06fVBndX5Bz79+/H6dOnARQsD/Tyyy/Dz89Pq+O9vLzw+uuvV/j6RFRAX7+/a82fMadOnULz5s3VJpfr378/cnJyEB4eLtUJDg6WEqfCOvHx8YiOji713Dk5OUhLS1N7Ue1maWmJkJAQnY9r1qyZWjfM7du3sWbNGqSnp1c6pqSkJHz99ddYt24dcnJyKn0+Kt+DBw/w6aefVipxAgqWXilMnARBwIsvvqh14gRwQkyi6qbWJE+JiYlwd3dXK3N0dISZmZnU5aGpTuH7srpFvvjiC9jb20svX19fPUdP1VGPHj20nn28qCZNmuDll1+WkvT4+HisWrUKycnJeokrKioKixYtwrp16zio3EBSUlKwePFi/PLLL5WePuLIkSMICwuT3g8ePBhNmzbV+vgXXniBLdxE1YxRk6cFCxZAEIQyX+fPn9f6fJq63URRVCsvXqew17KsLrv58+dDoVBIrwcPHmgdE9Vsusw+XpSfnx9effVVaRbo1NRUrFq1ClFRUXqLLSoqCp9//jlWrlyp8xIxpJlCocD333+PpUuX6rxuYXGiKOLvv//G4cOHpbJ+/fqhbdu2Wp/D1tYW7du3r1QcRKR/Jsa8+MyZMzF27Ngy6zRo0ECrc3l4eODMmTNqZampqcjLy5Nalzw8PEq0MBU+9VK8Raooc3Nzta4+qjtkMhnefPNN/Pe//9X5WHd3d7z22mvYsGEDHj16hMzMTKxbtw4DBgxAx44dtR5jV564uDh88cUXsLa2xsSJE+Hm5qaX89YlCQkJ2LRpk166V4GCxCk0NBQnT56Uyvr164cuXbrodJ5XX31Vb98nRKQ/Rk2eXFxc4OLiopdzde7cGZ999hkSEhLg6ekJADhw4ADMzc3Rrl07qc57772H3NxcaQDvgQMH4OXlpXWSRnWPg4MDBg8ejN27d+t8rL29PV599VVs27YNd+7cgSiK2Lt3LxITEzFw4EBphnJ9yMjIwA8//ABBENC/f3906tRJb+eujURRxIULF7Bnzx69TgeRn5+P3bt34/Lly1LZ888/r/P/j+eff77UaVmIyLhqzNN2MTExSElJwa5du/D111/j2LFjAAomKbSxsYFSqUTr1q3h7u6Or7/+GikpKZg8eTKGDRuGpUuXAihokm/cuDF69eqF9957D7dv38bkyZPx4Ycf6jS7NJ+2q5vWr1+Pe/fuVehYlUqFgwcPqrVEeHt7Y+TIkVotyVFRtra2eOWVVwx6jZrm0aNH2LNnD+7fv6/3c2dnZ+PXX39V+z4ZNGiQzl1vtra2eOutt9jqRKRn+vr9XWOSp8mTJ2Pt2rUlyg8fPoyePXsCKEiwpk+fjr///huWlpYYN24cvvnmG7Uut4iICMyYMQNnz56Fo6Mj3njjDXz44Yc6/ZBi8lR3ff7555UapH3lyhXs2rUL+fn5AAq6hIcMGYJmzZrpK8RSeXp64uWXX65zUx2IooikpCScO3cOFy5cMNiko0+ePMHmzZvx8OFDAICJiQlGjBih0+DwQv/5z384VIDIAOpc8lSdMHmqu5RKJT799NNKnSM+Ph6//fYbUlNTpbL27dujf//+MDU1rWyIWnFwcMALL7yAhg0b1srWjZycHNy/fx/Xr1/HlStXDD5Le1RUFH777TdkZmYCKJjq4qWXXkK9evV0PteoUaMqlHARUfmYPBkRk6e67enTp5WelT47Oxt//vknrl69KpW5uLhg+PDh8Pb2rmyIOpHL5QgMDETHjh1Rr149rZYKqW6ys7MRFxeH+/fv49q1a0hJSamS64qiiDNnzmD//v1Sgubo6Ijx48dXaDynp6cnpk6dqu8wiegZJk9GxOSJ4uLisHLlykqdQxRFXLx4EX/99ZfUjScIArp27YqePXvqdTC5LmQyGTw8PBAYGAh/f3+4ubnBwsLCKLFokpGRgUePHiEpKQkPHjxAdHS03p6S00VWVhb+/PNPXLt2TSoLCAjAiy++WOGu0Q8++IBLsBAZEJMnI2LyRABw9epVbNu2rdLnSUpKwvbt25GQkCCVubq6YtiwYVXeClUaQRBga2sLd3d3uLq6ws3NDQ4ODrCzs4OdnZ1eW6uUSiXS09Px5MkTKBQKPHnyBE+ePEFSUhKSkpKqxcSg9+/fxx9//AGFQiGVdevWDb169apw8vOvf/1L50WpiUg3TJ6MiMkTFQoLC8ORI0cqfR6lUonjx4/jyJEjajNat2/fHr1799Z68VhjEQQBZmZmMDc3h5WVFaysrGBubg4TExPpJZPJIIoilEollEol8vPzkZ+fj+zsbGRkZCAzMxPZ2dmVWkzZ0PLz83Hs2DEcPXpU6qazsLDAkCFDKjVOqW3bthg8eLC+wiSiUjB5MiImT1TUb7/9huvXr+vlXImJidixY4faZK5WVlbo27cvWrVqxS4dI4qNjcWuXbukiXUBoF69ehgxYkSl5mOytLTEvHnz+P+WqAoweTIiJk9U3KpVqxAbG6uXcymVSpw+fRphYWFqXVQ+Pj7o169fhZ7goorLzc3F4cOHcfr0abXlnHr27Inu3btXOumZPXs25+EiqiJMnoyIyRNpsmzZMr0t/gsUTOp64MABtQHJQMHCw71794arq6verkUliaKIq1evIjQ0FGlpaVK5h4cHhg4dKq1kUBlDhw5F69atK30eItIOkycjYvJEmoiiiMWLF+v9ya+7d+9i7969ePz4sVQmCALatm2L7t27cwkPA4iLi8O+ffvUFgE3MTFBz5490blzZ70MkA8KCsKoUaNq5TxbRNUVkycjYvJEpVGpVFi8eDEyMjL0el6lUolLly7h8OHDasmZTCZDq1at0K1bNzg7O+v1mnXRo0ePcPjw4RJj2AIDAzFgwAC93WMHBwdMnz69yiZFJaICTJ6MiMkTlSU/Px+LFy9GVlaW3s+dm5uL06dP4/jx48jNzZXKBUFA8+bN0bVrVz7uXgEpKSkICwtDRESE2mzkzs7OGDBgAAIDA/V2LblcjpkzZ7LFkMgImDwZEZMnKk9OTg6WLFmC7OxsvZ53yM6dEFQqpKpU2PToEbY8eoT0Yo/2t7azwwgvL7Rzs0GmRQZEQQaVTIAok0GEAJVMgEomA1DwVZQJEAUZRKHga76JCc62CYAAARABofA/UVDbBqCxvMS2DnUBqJfDsF1asbGxOHnyJCIjI9WSJmtra3Tv3h0dOnTQ+4zrL7/8MgICAvR6TiLSDpMnI2LyRNrIzMzE//73P2m9M334v4ULIS8yD5QCwP8ALAZQfKi6D4A3AEx6tq2tp5YydPmh+qytpksStvqTa7DNUkIlA1SCUPCSCRAFQCkr2M4BcCQrHxsUubiQpZ542stkmOLkhJecnWEpl0MUniWYglDwksmgksnwZwXnZBo0aBDatWtX2VtCRBWkr9/fxln/gagOsLKywsyZM7F8+XK9DSIXiv2tYw/gPQBvAlgDYBmAG8/2xQL4PwAfAOgDYDKAYQDKWzhEJateA5hFQYRY0ARWLu/H2bDP0DzJ5j0AKwGsBpBYbJ87gFkAZqlUsHv8GCgyOL84lSBUKHnq1q0bEyeiWoLJE5EBWVpaYvr06fjpp5/UlvKoqP/NnAlBpYJMFCGI4j/bKhXMRRHzVCpExsXh0PXruBwbCxGACCD02cvCVI6WPq5o6+OCpp4OMJfJIBNVgFh4HhFKuYDWyQEQIUqJi/T12TYAjeUinu3TUK7reSpSJ83aBCIAmQjIVCKSlSJ2KUX8rgKOarifQQDeBjAegLar94kVeDquadOm6NWrl87HEVH1xG67CmC3HekqKysLq1at0us8UOVJTU3F5cuXcenSJTx58qTEfhMTEzRs2BBNmjRBQEAAbG1tqyw2Q3ry5Alu376NyMhIREVFofiPOJlMhsaNAtGuTSsE+DWADCqYKAWYiIJ6QlrGdpK7u9bx+Pv746WXXjLaQs9E9A+OeTIiJk9UEbm5uVi/fr3eZiLXlkqlQkxMDC5fvozr168jJydHYz03Nzf4+/vD398f9evXh7m5eZXGWVE5OTmIjY3F3bt3cfv2bTx69EhjPScnJ7Rp0watW7euskSxfv36GD9+PKckIKommDwZEZMnqiilUonff/8dN27cKL+yAeTn5yMqKgqRkZG4ceNGmYPZXV1d4ePjA29vb/j4+MDFxcXorScqlQopKSlITEzEgwcPEBMTg8TExBKtS4UcHBzQvHlzNGvWDB4eHlU6IaWPjw8mTJgAMzOzKrsmEZWNyZMRMXmiyhBFEfv27cPZs2eNGodKpcKDBw9w584d3Lt3D/Hx8aUmIUDBXFLOzs5wdXWFq6srXFxcYG9vDzs7O9jZ2en1kf7s7GykpqYiNTUVKSkpSElJwcOHD5GUlKS23p8mPj4+CAwMRGBgIDw9PY0yg7evry/Gjx9fY1rviOoKJk9GxOSJ9OHcuXPYu3dvmQlLVcrKykJ0dDTu3buH2NhYPHz4EKoi0yKUx8bGBjY2NrCwsICFhQUsLS1hbm4OExMTCIIAmUwmLaKbn5+PvLw86ZWdnY2MjAzpVV6CVJSbmxvq1auH+vXrIyAgAFZW5T1PaFiBgYEYNWoUu+qIqiEmT0bE5In0JSoqClu3bi11HJIx5eXlISEhAbGxsYiPj8ejR4/w+PFjKJWapwKoCo6OjvDw8IC7uzu8vLzg6+sLS0tLo8VTXIsWLTB06FC9T6xJRPrB5MmImDyRPqWkpGDTpk1V+iReRalUKqSmpuLx48dITk5GWloaFAqF9DUzM1On1qriLC0tYW1tDRsbGzg4OMDJyQmOjo5wdHSEq6trte4Ge+6559CvXz8u9EtUjTF5MiImT6Rvubm5+PPPPxEREWHsUCpFFEWpG67wpVQqoVKpIIqi9NXU1BSmpqYwMTGBqakpzM3NYWVlVWNbbAYPHoy2bdsaOwwiKgdnGCeqRczMzDB8+HA0aNAAf/31l1G7xipDEASYmZnBzMysTvxhYW5ujrFjx6JBgwbGDoWIqhCTJ6JqQhAEtG3bFt7e3vj999/xuIwlQsj4PDw8MHr0aDg6Oho7FCKqYjJjB0BE6tzd3TF16lQ899xzxg6FStGxY0dMmTKFiRNRHcWWJ6JqyNTUFP3790eTJk2wY8cOKBSKajOlQV1mamqK4cOHIygoyNihEJERccB4BXDAOFWlnJwcHD58GGfOnDF2KHWav78/hg4dyn/zRDUYB4xXAxf374GtjQ0EWcEEgMKzl0wmhyAIRd4/2yeU877Ydon3Qhl1heLHCgXn52PTNZ65uTkGDBiA1q1bY8+ePVW+Nl5dJ5PJMHjwYLRq1Yr/nogIAFueKqQwc/10eD9YVPNZhIsmaFBL8uTSrM9qyZigITkrkeQJRd7Ln9URyk0MNSd/gnQOWZHzSEloGecuM9kUCs6rfn31z1/yeqWdq/ykt6qSVVEUceHCBRw6dAhZWVkGvRYVtDYNGzasyhYSJiLDYssTaUUUVRCVKqhq5pPvNU6lk1WhWDJZJDkrmqz6i8CTjCdITk0BREAUAEAACpO3Il9FCEDh/mL7AKHIscXrFJSpHV/s/KKULJZ27SLn0ubaRa4jFq1fRgyGYGZmhlGjRqFhw4YGuwYR1VxseaqAwsz13P49sLa0/Gfyv2evgm1lwfsS+/4p/6duYX0N5xGLvxelc0t1n51LVCmLHF/s/KKq2PUKytTfi2qxq1QqoGhMYsVnjiYyhJIJlqbEDSiaDKolbRr22drZwcnZWXMXuVCYGP9TJpPJAK1aNeVSC6Uu3fMaW0yLJtWCTEPCXdhaK1e7xj9xyjXEXVi/lCEH7LKkWoAtT9VAo+e61anBo6IoPkvCxLITPI3JmlgsuSuZrGlMQjWdu8S1Sk8oqyZZFTXEVLFktWjiy2S1fAIASH//ic+yqSL7KiAzKx2ZD+MrF1htVDTR0tgtr946KrWWqrXGatGVLyWdRRJFofhQAUHD9bXoXi8W4z9DB4okj9qMVy0t9lLe63IejletGZg8kdYEQYAgyDk7WBX5J1kt2ZJYPCks3Pc0TYHTp04j8kYkhMKkQhTxT2JR8FVQe188+RCLHVvyq1D0uGLXEUT1cxU/v1AsluJ1hOJlRb4KRa4jHVs0Lg2ftei5NF1b+hXFRviyiSJUSiVQQ2e/r2nq7HhVTQmlLi275ZwrNytTL/9/mDwRVVP/JKvar/fm4O4B38DGyM7OxvHjx3Hq1KlKLdRbm1lYWGD48OFo1KgRAPVkVb2VU/wnWS3RilnQ+ohirabFz1FWi6TU+liidVTL1lepNbj8ll31BFwFlHMNja2vYvGWVs2xl9ZCXPieysbxqoaRnZenl/MweSKqhSwsLNCnTx/07t0bFy9exMGDB/l03jPW1tYYOHAgmjRpotY1UjRZrZnLE9cs2nXdK7XrFi8l6RVVSqiK7PtnDGclhgqUkqyWmyBzvGqtwuSJqBYrXC+vbdu2SE5Oxh9//IH4+Lo5nsfZ2Rm9evVCUFAQx5NUA4JMBgGATC4HUL2nfKnp9D1eVasWSX2NVy2R5Go5XrWUxDM9MwvYfqDS95RP21UAZxinmkylUuHIkSM4c+YMcnJyjB2OwTVq1Ag9evSAt7e3sUMhIiPT1+9vJk8VwOSJaouUlBSEhobi1q1btWpslI2NDTp37oxWrVrB2tra2OEQUTXBqQqIqNKcnJwwZswYAEBsbCyOHj2Ke/fuQVkDn6iytLREs2bN0K5dO3h4eBg7HCKqxdjyVAFseaLaLjU1FadPn0ZkZCSePn1q7HBK5eTkhKZNm6JNmzZwcnIydjhEVM2x286ImDxRXSKKIh48eICrV6/izp07UCgURunik8vlcHR0RP369dGyZUv4+PgUzIFDRKQldtsRUZUQBAH16tVDvXr1pLLs7GzEx8cjKioK8fHxePz4MTIzM5Gfn1/p68nlclhbW8PFxQVubm6oX78+AgICYFrNF+EmorqDyRMR6czCwgL+/v7w9/dXKxdFEdnZ2cjMzERWVhYyMzORl5cHpbLgUefCsVSmpqYwMzODqakprKysYGNjAysrK8jlnGGJiKo/Jk9EpDeCIMDS0hKWlpbGDoWIyGA4YICIiIhIB0yeiIiIiHTA5ImIiIhIB0yeiIiIiHTA5ImIiIhIB0yeiIiIiHTA5ImIiIhIB0yeiIiIiHTA5ImIiIhIB0yeiIiIiHTA5ImIiIhIB0yeiIiIiHTA5ImIiIhIB0yeiIiIiHTA5ImIiIhIB0yeiIiIiHTA5ImIiIhIB0yeiIiIiHTA5ImIiIhIBzUieYqOjsaUKVPg5+cHS0tLBAQE4KOPPkJubq5avZiYGAwePBjW1tZwcXHB7NmzS9SJiIhAcHAwLC0t4e3tjYULF0IUxar8OERERFSDmRg7AG3cuHEDKpUKP/30Exo2bIirV6/i9ddfR0ZGBr755hsAgFKpxMCBA+Hq6orjx48jOTkZkyZNgiiKWLp0KQAgLS0Nffv2RUhICM6dO4dbt25h8uTJsLa2xttvv23Mj0hEREQ1hCDW0GaXr7/+Gj/88APu3bsHANi7dy8GDRqEBw8ewMvLCwCwZcsWTJ48GUlJSbCzs8MPP/yA+fPn4+HDhzA3NwcALFq0CEuXLkVsbCwEQdDq2mlpabC3t4dCoYCdnZ1hPiARERHplb5+f9eIbjtNFAoFnJycpPenTp1C8+bNpcQJAPr374+cnByEh4dLdYKDg6XEqbBOfHw8oqOjS71WTk4O0tLS1F5ERERUN9XI5Onu3btYunQp3njjDaksMTER7u7uavUcHR1hZmaGxMTEUusUvi+so8kXX3wBe3t76eXr66uvj0JEREQ1jFGTpwULFkAQhDJf58+fVzsmPj4eAwYMwKhRo/Daa6+p7dPU7SaKolp58TqFvZZlddnNnz8fCoVCej148EDnz0pERES1g1EHjM+cORNjx44ts06DBg2k7fj4eISEhKBz585YsWKFWj0PDw+cOXNGrSw1NRV5eXlS65KHh0eJFqakpCQAKNEiVZS5ublaVx8RERHVXUZNnlxcXODi4qJV3bi4OISEhKBdu3ZYvXo1ZDL1RrPOnTvjs88+Q0JCAjw9PQEABw4cgLm5Odq1ayfVee+995CbmwszMzOpjpeXl1qSRkRERFSaGjHmKT4+Hj179oSvry+++eYbPHr0CImJiWqtSP369UPTpk0xYcIEXLx4EYcOHcK8efPw+uuvSyPqx40bB3Nzc0yePBlXr17F9u3b8fnnn2Pu3LlaP2lHREREdVuNmOfpwIEDuHPnDu7cuQMfHx+1fYVjluRyOfbs2YPp06eja9eusLS0xLhx46R5oADA3t4eoaGhmDFjBtq3bw9HR0fMnTsXc+fOrdLPQ0RERDVXjZ3nyZg4zxMREVHNU+fneSIiIiIyBiZPRERERDpg8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDpg8kRERESkAxNjB1ATiaIIAEhLSzNyJERERKStwt/bhb/HK4rJUwU8ffoUAODr62vkSIiIiEhXT58+hb29fYWPF8TKpl91kEqlQnx8PGxtbSEIgrHD0SgtLQ2+vr548OAB7OzsjB1OtcR7pB3eJ+3wPmmH90k7vE/a0fU+iaKIp0+fwsvLCzJZxUcuseWpAmQyGXx8fIwdhlbs7Oz4D68cvEfa4X3SDu+TdniftMP7pB1d7lNlWpwKccA4ERERkQ6YPBERERHpgMlTLWVubo6PPvoI5ubmxg6l2uI90g7vk3Z4n7TD+6Qd3iftGOs+ccA4ERERkQ7Y8kRERESkAyZPRERERDpg8kRERESkAyZPRERERDpg8lRDfPHFF+jQoQNsbW3h5uaGYcOG4ebNm2p1RFHEggUL4OXlBUtLS/Ts2RPXrl1Tq5OTk4NZs2bBxcUF1tbWGDJkCGJjY6vyoxjc0aNHMXjwYHh5eUEQBOzYsUNtP++TbpYvXw4/Pz9YWFigXbt2OHbsmLFDMrrPPvsMXbp0gZWVFRwcHDTWiYmJweDBg2FtbQ0XFxfMnj0bubm5anUiIiIQHBwMS0tLeHt7Y+HChZVec6u6iI6OxpQpU+Dn5wdLS0sEBATgo48+KnEP6vp9AoAhQ4agXr16sLCwgKenJyZMmID4+Hi1OrxPBXJyctC6dWsIgoBLly6p7avSeyRSjdC/f39x9erV4tWrV8VLly6JAwcOFOvVqyemp6dLdRYtWiTa2tqK27ZtEyMiIsQxY8aInp6eYlpamlTnjTfeEL29vcXQ0FDxwoULYkhIiNiqVSsxPz/fGB/LIP766y/x/fffF7dt2yYCELdv3662n/dJe1u2bBFNTU3Fn3/+Wbx+/br45ptvitbW1uL9+/eNHZpRffjhh+LixYvFuXPnivb29iX25+fni82bNxdDQkLECxcuiKGhoaKXl5c4c+ZMqY5CoRDd3d3FsWPHihEREeK2bdtEW1tb8ZtvvqnCT2I4e/fuFSdPnizu379fvHv3rrhz507Rzc1NfPvtt6U6vE8FFi9eLJ46dUqMjo4WT5w4IXbu3Fns3LmztJ/36R+zZ88Wn3/+eRGAePHiRam8qu8Rk6caKikpSQQgHjlyRBRFUVSpVKKHh4e4aNEiqU52drZob28v/vjjj6IoiuKTJ09EU1NTccuWLVKduLg4USaTifv27avaD1BFiidPvE+66dixo/jGG2+olTVp0kT8z3/+Y6SIqpfVq1drTJ7++usvUSaTiXFxcVLZ5s2bRXNzc1GhUIiiKIrLly8X7e3txezsbKnOF198IXp5eYkqlcrgsRvDV199Jfr5+UnveZ8027lzpygIgpibmyuKIu9Tob/++kts0qSJeO3atRLJU1XfI3bb1VAKhQIA4OTkBACIiopCYmIi+vXrJ9UxNzdHcHAwTp48CQAIDw9HXl6eWh0vLy80b95cqlPb8T5pLzc3F+Hh4Wr3AQD69etXp+5DRZw6dQrNmzeHl5eXVNa/f3/k5OQgPDxcqhMcHKw2uV///v0RHx+P6Ojoqg65SigUCulnFsD7pElKSgo2btyILl26wNTUFADvEwA8fPgQr7/+OtavXw8rK6sS+6v6HjF5qoFEUcTcuXPRrVs3NG/eHACQmJgIAHB3d1er6+7uLu1LTEyEmZkZHB0dS61T2/E+ae/x48dQKpVl3ivSLDExscR9c3R0hJmZmdr3maZ7W7ivtrl79y6WLl2KN954QyrjffrHv//9b1hbW8PZ2RkxMTHYuXOntK+u3ydRFDF58mS88cYbaN++vcY6VX2PmDzVQDNnzsSVK1ewefPmEvsEQVB7L4piibLitKlT2/A+aa8i96omWrBgAQRBKPN1/vx5rc+n6R4Vv3ea7m1px1YXFblP8fHxGDBgAEaNGoXXXntNbR/vU4F33nkHFy9exIEDByCXyzFx4kS1gcy18T5pe4+WLl2KtLQ0zJ8/v8zzVeU9MtGpNhndrFmzsGvXLhw9ehQ+Pj5SuYeHB4CC7NnT01MqT0pKkjJrDw8P5ObmIjU1Va1VJSkpCV26dKmiT2BcvE/ac3FxgVwuL/EXWdF7VZvMnDkTY8eOLbNOgwYNtDqXh4cHzpw5o1aWmpqKvLw8te8zTfcWKNkyWp3oep/i4+MREhKCzp07Y8WKFWr1eJ8aSNsuLi5wcXFBo0aNEBQUBF9fX5w+fRqdO3eutfdJ23v06aef4vTp0yXWr2vfvj3Gjx+PtWvXVv090m24FhmLSqUSZ8yYIXp5eYm3bt3SuN/Dw0P88ssvpbKcnByNA6G3bt0q1YmPj6/VA6FRyoBx3iftdOzYUZw2bZpaWVBQEAeMP1PegPH4+HipbMuWLSUGrzo4OIg5OTlSnUWLFtWqAb6xsbFiYGCgOHbsWI1PqvI+aRYTEyMCEA8fPiyKIu/T/fv3xYiICOm1f/9+EYD4+++/iw8ePBBFservEZOnGmLatGmivb29GBYWJiYkJEivzMxMqc6iRYtEe3t78Y8//hAjIiLEl156SeMj+D4+PuLBgwfFCxcuiL169ap1j+A/ffpUvHjxonjx4kURgLh48WLx4sWL0uP1vE/aK5yqYNWqVeL169fFOXPmiNbW1mJ0dLSxQzOq+/fvixcvXhQ//vhj0cbGRvp+e/r0qSiK/zw23bt3b/HChQviwYMHRR8fH7XHpp88eSK6u7uLL730khgRESH+8ccfop2dXa15tDwuLk5s2LCh2KtXLzE2Nlbt51Yh3idRPHPmjLh06VLx4sWLYnR0tPj333+L3bp1EwMCAqSnwnif1EVFRZU6VUFV3SMmTzUEAI2v1atXS3VUKpX40UcfiR4eHqK5ubnYo0cPMSIiQu08WVlZ4syZM0UnJyfR0tJSHDRokBgTE1PFn8awDh8+rPFeTZo0SRRF3idd/e9//xPr168vmpmZiW3btpWmx6jLJk2apPF7rLClQBQLEqyBAweKlpaWopOTkzhz5ky1R6RFURSvXLkidu/eXTQ3Nxc9PDzEBQsW1PhWgkKrV68u9edWUXX9Pl25ckUMCQkRnZycRHNzc7FBgwbiG2+8IcbGxqrVq+v3qShNyZMoVu09EkSxlk0/SkRERGRAfNqOiIiISAdMnoiIiIh0wOSJiIiISAdMnoiIiIh0wOSJiIiISAdMnoiIiIh0wOSJiIiISAdMnoiIiIh0wOSJiGo8URQxdepUODk5QRAEXLp0qdS6EyZMwOeffy69b9CgAZYsWaLXeCIiIuDj44OMjAy9npeIqgcmT0RU4+3btw9r1qzBn3/+iYSEBDRv3lxjvStXrmDPnj2YNWuWQeNp0aIFOnbsiO+++86g1yEi42DyREQ13t27d+Hp6YkuXbrAw8MDJiYmGustW7YMo0aNgq2trcFjeuWVV/DDDz9AqVQa/FpEVLWYPBFRjTZ58mTMmjULMTExEAQBDRo00FhPpVLht99+w5AhQ8o83+rVq2Fvb4/Q0FAAQM+ePTFr1izMmTMHjo6OcHd3x4oVK5CRkYFXXnkFtra2CAgIwN69e9XO079/fyQnJ+PIkSN6+ZxEVH0weSKiGu2///0vFi5cCB8fHyQkJODcuXMa6125cgVPnjxB+/btSz3XN998g3nz5mH//v3o27evVL527Vq4uLjg7NmzmDVrFqZNm4ZRo0ahS5cuuHDhAvr3748JEyYgMzNTOsbMzAytWrXCsWPH9PdhiahaYPJERDWavb09bG1tIZfL4eHhAVdXV431oqOjIZfL4ebmpnH//PnzsXjxYoSFheG5555T29eqVSv83//9HwIDAzF//nxYWlrCxcUFr7/+OgIDA/Hhhx8iOTkZV65cUTvO29sb0dHRevmcRFR9aB4YQERUy2RlZcHc3ByCIJTY9+233yIjIwPnz5+Hv79/if0tW7aUtuVyOZydndGiRQupzN3dHQCQlJSkdpylpaVaaxQR1Q5seSKiOsHFxQWZmZnIzc0tsa979+5QKpX49ddfNR5ramqq9l4QBLWywoRMpVKp1UtJSSm1JYyIai4mT0RUJ7Ru3RoAcP369RL7OnbsiH379uHzzz/H119/rbdrXr16FW3atNHb+YioemC3HRHVCa6urmjbti2OHz8uJVJFde7cGXv37sWAAQNgYmKCt956q1LXi46ORlxcHPr06VOp8xBR9cOWJyKqM6ZOnYqNGzeWur9r167Ys2cPPvjgA3z//feVutbmzZvRr18/1K9fv1LnIaLqRxBFUTR2EEREVSE7OxuNGzfGli1b0LlzZ4NdJycnB4GBgdi8eTO6du1qsOsQkXGw5YmI6gwLCwusW7cOjx8/Nuh17t+/j/fff5+JE1EtxZYnIiIiIh2w5YmIiIhIB0yeiIiIiHTA5ImIiIhIB0yeiIiIiHTA5ImIiIhIB0yeiIiIiHTA5ImIiIhIB0yeiIiIiHTA5ImIiIhIB/8Pt60CF3XpShYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ.chords.plot_chords()\n",
    "occ.chords.plot_chords(segment='error', color='red')\n",
    "\n",
    "#plotting the best fitted ellipse, in black\n",
    "draw_ellipse(**ellipse_chi2.get_values())\n",
    "\n",
    "# ploting all the ellipses within 3-sigma, in gray\n",
    "draw_ellipse(**ellipse_chi2.get_values(sigma=3),alpha=1.0,lw=1)\n",
    "\n",
    "pl.legend(loc=1)\n",
    "pl.xlim(+170,-330)\n",
    "pl.ylim(-250,+250)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**As can be seen, there are some ellipses that cross the Hakos negative chord. They are impossible solutions as the Hakos lightcurve show no evidence of an occultation. The User can filter this solutions using the** `filter_negative_chord()` **function.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sora.occultation import filter_negative_chord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mfilter_negative_chord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchord\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchisquare\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Get points for the ellipse with the given input parameters.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "chord : `sora.observer.Chord`\n",
       "    Chord object, must be associated to an Occultation to work.\n",
       "\n",
       "chisquare : `sora.extra.ChiSquare`\n",
       "    Resulted ChiSquare object of fit_ellipse.\n",
       "\n",
       "sigma : `int`, `float`\n",
       "    Uncertainty of the expected ellipse, in km.\n",
       "\n",
       "step : `int`, `float`, `str`\n",
       "    If a number, it corresponds to the step, in seconds, for each point of\n",
       "    the chord path. The step can also be equal to ``'exposure'``. In this\n",
       "    case, the chord path will consider the lightcurve individual times and\n",
       "    exptime.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Documentos/códigos/SORA/sora/occultation/utils.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter_negative_chord?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter chord: Hakos |█████...................................|  - 14% \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter chord: Hakos |██████████████..........................|  - 36% \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter chord: Hakos |████████████████████████................|  - 61% \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter chord: Hakos |█████████████████████████████████.......|  - 84% \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter chord: Hakos |████████████████████████████████████████|  - 100% \n",
      "Minimum chi-square: 0.062\n",
      "Number of fitted points: 10\n",
      "Number of fitted parameters: 5\n",
      "Minimum chi-square per degree of freedom: 0.012\n",
      "\n",
      "center_f:\n",
      "    1-sigma: -13.668 +/- 1.124\n",
      "    3-sigma: -13.676 +/- 4.350\n",
      "\n",
      "center_g:\n",
      "    1-sigma: -2.175 +/- 4.183\n",
      "    3-sigma: 0.512 +/- 17.236\n",
      "\n",
      "equatorial_radius:\n",
      "    1-sigma: 138.894 +/- 5.872\n",
      "    3-sigma: 149.149 +/- 21.714\n",
      "\n",
      "oblateness:\n",
      "    1-sigma: 0.087 +/- 0.041\n",
      "    3-sigma: 0.132 +/- 0.132\n",
      "\n",
      "position_angle:\n",
      "    1-sigma: 127.631 +/- 19.996\n",
      "    3-sigma: 125.103 +/- 89.946\n",
      "\n"
     ]
    }
   ],
   "source": [
    "filter_chi2 = filter_negative_chord(chord=occ.chords['Hakos'], chisquare=ellipse_chi2, step=0.5)\n",
    "\n",
    "print(filter_chi2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ.chords.plot_chords()\n",
    "occ.chords.plot_chords(segment='error', color='red')\n",
    "\n",
    "#plotting the best fitted ellipse, in black\n",
    "draw_ellipse(**filter_chi2.get_values())\n",
    "\n",
    "# ploting all the ellipses within 3-sigma, in gray\n",
    "draw_ellipse(**filter_chi2.get_values(sigma=3),alpha=1.0,lw=1)\n",
    "\n",
    "pl.legend(loc=4,ncol=2)\n",
    "pl.xlim(+90,-90)\n",
    "pl.ylim(-180,-100)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The user can set** `step = \"exposure\"` **and consider only the region where data was acquired, allowing ellipses that crosses the negative chord during its readout time.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter chord: Hakos |█████...................................|  - 14% \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter chord: Hakos |███████████████.........................|  - 39% \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter chord: Hakos |████████████████████████................|  - 61% \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter chord: Hakos |██████████████████████████████████......|  - 86% \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter chord: Hakos |████████████████████████████████████████|  - 100% \n",
      "Minimum chi-square: 0.062\n",
      "Number of fitted points: 10\n",
      "Number of fitted parameters: 5\n",
      "Minimum chi-square per degree of freedom: 0.012\n",
      "\n",
      "center_f:\n",
      "    1-sigma: -13.668 +/- 1.124\n",
      "    3-sigma: -13.676 +/- 4.350\n",
      "\n",
      "center_g:\n",
      "    1-sigma: -2.175 +/- 4.183\n",
      "    3-sigma: 0.512 +/- 17.236\n",
      "\n",
      "equatorial_radius:\n",
      "    1-sigma: 138.894 +/- 5.872\n",
      "    3-sigma: 149.421 +/- 21.986\n",
      "\n",
      "oblateness:\n",
      "    1-sigma: 0.087 +/- 0.041\n",
      "    3-sigma: 0.132 +/- 0.132\n",
      "\n",
      "position_angle:\n",
      "    1-sigma: 127.631 +/- 19.996\n",
      "    3-sigma: 125.103 +/- 89.946\n",
      "\n"
     ]
    }
   ],
   "source": [
    "filter_2_chi2 = filter_negative_chord(chord = occ.chords['Hakos'], chisquare = ellipse_chi2, step='exposure')\n",
    "\n",
    "print(filter_2_chi2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ.chords.plot_chords(segment='positive')\n",
    "occ.chords.plot_chords(segment='error', color='red')\n",
    "\n",
    "chord2.plot_chord(segment='negative', linestyle='exposure', color='m', label='Hakos')\n",
    "\n",
    "#plotting the best fitted ellipse, in black\n",
    "draw_ellipse(**filter_2_chi2.get_values())\n",
    "\n",
    "# ploting all the ellipses within 3-sigma, in gray\n",
    "draw_ellipse(**filter_2_chi2.get_values(sigma=3),alpha=1.0,lw=1)\n",
    "\n",
    "pl.legend(loc=4,ncol=2)\n",
    "pl.xlim(+90,-90)\n",
    "pl.ylim(-180,-100)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The radial velocity issue**\n",
    "\n",
    "During the `LightCurve` fitting, the user has to add the velocity for that light curve. In the initial process, only the geocentric velocity was determined, and only after the fit, a radial velocity can be correctly calculated.\n",
    "\n",
    "Usually, the light curves' features are dominated by the exposure time. However, in the cases that the fresnel diffraction plays a significant role in it, we suggest that the `LightCurve` fitting procedure should be redone with the correct velocities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outeniqua - Velocity used: 22.004\n",
      "    Immersion Radial Velocity: 17.737\n",
      "    Emersion Radial Velocity: 18.251\n",
      "Onduruquea - Velocity used: 22.004\n",
      "    Immersion Radial Velocity: 22.247\n",
      "    Emersion Radial Velocity: 22.241\n",
      "Tivoli - Velocity used: 22.004\n",
      "    Immersion Radial Velocity: 8.578\n",
      "    Emersion Radial Velocity: 5.214\n",
      "Windhoek C14 - Velocity used: 22.004\n",
      "    Immersion Radial Velocity: 18.148\n",
      "    Emersion Radial Velocity: 17.475\n",
      "Windhoek D16 - Velocity used: 22.004\n",
      "    Immersion Radial Velocity: 18.084\n",
      "    Emersion Radial Velocity: 17.531\n"
     ]
    }
   ],
   "source": [
    "occ.check_velocities()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"section_4\"></a>\n",
    "\n",
    "## 4. Viewing and saving the results\n",
    "\n",
    "In the end, we can see the results using Python dictionaries. The `Occultation.fitted_params` will have fitted parameters and their $1\\sigma$ uncertainties. The `Occultation.chi2_params` will have some information about the fit and its quality.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'equatorial_radius': [138.89352413223418, 5.87211142064497],\n",
       " 'center_f': [-13.667768439201584, 1.1244130991054],\n",
       " 'center_g': [-2.1752330636486708, 4.183227210281122],\n",
       " 'oblateness': [0.08691476665827529, 0.04137195723191982],\n",
       " 'position_angle': [127.63060055201066, 19.995835521096133]}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ.fitted_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'chord_name': ['Outeniqua_immersion',\n",
       "  'Outeniqua_emersion',\n",
       "  'Onduruquea_immersion',\n",
       "  'Onduruquea_emersion',\n",
       "  'Tivoli_immersion',\n",
       "  'Tivoli_emersion',\n",
       "  'Windhoek C14_immersion',\n",
       "  'Windhoek C14_emersion',\n",
       "  'Windhoek D16_immersion',\n",
       "  'Windhoek D16_emersion'],\n",
       " 'radial_dispersion': array([ 0.42225914, -1.71388408, -0.98985022, -0.75811248,  0.11545787,\n",
       "        -1.476433  , -0.57528982, -0.30934502, -1.65000735,  0.4106766 ]),\n",
       " 'position_angle': array([302.0253906 ,  58.88192531, 274.13847747,  84.6754296 ,\n",
       "        205.7991068 , 163.23850876, 238.47059825, 123.04590315,\n",
       "        238.20850598, 122.82838325]),\n",
       " 'radial_error': array([ 7.15442539,  7.60165495,  2.23532561,  2.45888165, 15.65002209,\n",
       "        15.65007494,  5.36529313,  5.81244286,  6.25950441,  7.60088706]),\n",
       " 'chi2_min': 0.06220013599688508,\n",
       " 'nparam': 5,\n",
       " 'npts': 10}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ.chi2_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The astrometrical positions obtained can be seen using** `Occultation.new_astrometric_position()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ephemeris offset (km): X = -13.7 km +/- 1.1 km; Y = -2.2 km +/- 4.2 km\n",
      "Ephemeris offset (mas): da_cos_dec = -1.286 +/- 0.106; d_dec = -0.205 +/- 0.393\n",
      "\n",
      "Astrometric object position at time 2017-06-22 21:18:48.200 for reference 'geocenter'\n",
      "RA = 18 55 15.6523907 +/- 0.111 mas; DEC = -31 31 21.622102 +/- 0.395 mas\n"
     ]
    }
   ],
   "source": [
    "occ.new_astrometric_position()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**If the user wants the position at a specific time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ephemeris offset (km): X = -13.7 km +/- 1.1 km; Y = -2.2 km +/- 4.2 km\n",
      "Ephemeris offset (mas): da_cos_dec = -1.286 +/- 0.106; d_dec = -0.205 +/- 0.393\n",
      "\n",
      "Astrometric object position at time 2017-06-22 21:21:00.000 for reference 'geocenter'\n",
      "RA = 18 55 15.6310578 +/- 0.111 mas; DEC = -31 31 21.623497 +/- 0.395 mas\n"
     ]
    }
   ],
   "source": [
    "occ.new_astrometric_position('2017-06-22 21:21:00.000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Also a 'post-fit' occultation map can be created using** `Occultation.plot_occ_map()`\n",
    "\n",
    "The function that plots the map contains a large number of _kwargs_. They can be used to completely control the map and its tutorial can be found at SORA documentation <a href=https://sora.readthedocs.io/>here</a>.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Projected shadow radius = 134.5 km\n",
      "output/map_posfit.png generated\n"
     ]
    }
   ],
   "source": [
    "occ.plot_occ_map(centermap_delta=[-3500,+400],zoom=20,nameimg='output/map_posfit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='output/map_posfit.png' style='width:600px;height:500px'/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**All this information can also be seen using** `print(Occultation)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stellar occultation of star GaiaDR3 6760223758801661440 by 10199 Chariklo (1997 CU26).\n",
      "\n",
      "Geocentric Closest Approach: 0.049 arcsec\n",
      "Instant of CA: 2017-06-22 21:18:48.200\n",
      "Position Angle: 359.72 deg\n",
      "Geocentric shadow velocity: -22.00 km / s\n",
      "Sun-Geocenter-Target angle:  166.42 deg\n",
      "Moon-Geocenter-Target angle: 149.11 deg\n",
      "\n",
      "\n",
      "5 positive observations\n",
      "1 negative observations\n",
      "\n",
      "###############################################################################\n",
      "                                     STAR                                      \n",
      "###############################################################################\n",
      "GaiaDR3 star Source ID: 6760223758801661440\n",
      "ICRS star coordinate at J2016.0:\n",
      "RA=18h55m15.65210s +/- 0.0197 mas, DEC=-31d31m21.6676s +/- 0.0180 mas\n",
      "pmRA=3.556 +/- 0.025 mas/yr, pmDEC=-2.050 +/- 0.020 mas/yr\n",
      "GaiaDR3 Proper motion corrected as suggested by Cantat-Gaudin & Brandt (2021) \n",
      "Plx=0.2121 +/- 0.0228 mas, Rad. Vel.=-40.49 +/- 3.73 km/s \n",
      "\n",
      "Magnitudes: G: 14.224, B: 14.320, V: 13.530, R: 14.180, J: 12.395, H: 11.781,\n",
      "            K: 11.627\n",
      "\n",
      "Apparent diameter from Kervella et. al (2004):\n",
      "    V: 0.0216 mas, B: 0.0216 mas\n",
      "Apparent diameter from van Belle (1999):\n",
      "    sg: B: 0.0238 mas, V: 0.0244 mas\n",
      "    ms: B: 0.0261 mas, V: 0.0198 mas\n",
      "    vs: B: 0.0350 mas, V: 0.0315 mas\n",
      "\n",
      "Geocentric star coordinate at occultation Epoch (2017-06-22 21:18:48.200):\n",
      "RA=18h55m15.65251s +/- 0.0327 mas, DEC=-31d31m21.6706s +/- 0.0341 mas\n",
      "\n",
      "###############################################################################\n",
      "                          10199 Chariklo (1997 CU26)                           \n",
      "###############################################################################\n",
      "Object Orbital Class: Centaur\n",
      "Spectral Type:\n",
      "    SMASS: D  [Reference: EAR-A-5-DDR-TAXONOMY-V4.0]\n",
      "       Relatively featureless spectrum with very steep red slope.\n",
      "Discovered 1997-Feb-15 by Spacewatch at Kitt Peak\n",
      "\n",
      "Physical parameters:\n",
      "Diameter:\n",
      "    302 +/- 30 km\n",
      "    Reference: Earth, Moon, and Planets, v. 89, Issue 1, p. 117-134 (2002), \n",
      "Rotation:\n",
      "    7.004 +/- 0 h\n",
      "    Reference: LCDB (Rev. 2023-February); Warner et al., 2009, [Result based on less than full coverage, so that the period may be wrong by 30 percent or so.]  REFERENCE LIST:[Fornasier, S.; Lazzaro, D.; Alvarez-Candal, A.; Snodgrass, C.; et al. (2014) Astron. Astrophys. 568, L11.], [Leiva, R.; Sicardy, B.; Camargo, J.I.B.; Desmars, J.; et al. (2017) Astron. J. 154, A159.]\n",
      "Absolute Magnitude:\n",
      "    6.54 +/- 0 mag\n",
      "    Reference: JPL Horizons, \n",
      "Phase Slope:\n",
      "    0.15 +/- 0 \n",
      "    Reference: JPL Horizons, \n",
      "Albedo:\n",
      "    0.045 +/- 0.01 \n",
      "    Reference: Earth, Moon, and Planets, v. 89, Issue 1, p. 117-134 (2002), \n",
      "\n",
      "Ellipsoid: 151.0 x 151.0 x 151.0\n",
      "\n",
      "----------- Ephemeris -----------\n",
      "\n",
      "EphemKernel: CHARIKLO/DE438_SMALL (SPKID=2010199)\n",
      "Ephem Error: RA*cosDEC: 0.000 arcsec; DEC: 0.000 arcsec\n",
      "Offset applied: RA*cosDEC: 0.0000 arcsec; DEC: 0.0000 arcsec\n",
      "\n",
      "\n",
      "###############################################################################\n",
      "                             POSITIVE OBSERVATIONS                             \n",
      "###############################################################################\n",
      "\n",
      "-------------------------------------------------------------------------------\n",
      "Site: Outeniqua\n",
      "Geodetic coordinates: Lon: 16d49m17.71s, Lat: -21d17m58.17s, height: 1.416 km\n",
      "Target altitude: 56.6 deg\n",
      "Target azimuth:  115.3 deg\n",
      "\n",
      "Light curve name: Outeniqua lc\n",
      "Initial time: 2017-06-22 21:20:00.056 UTC\n",
      "End time:     2017-06-22 21:29:59.963 UTC\n",
      "Duration:     9.998 minutes\n",
      "Time offset:  -0.150 seconds\n",
      "\n",
      "Object LightCurve was not instantiated with time and flux.\n",
      "\n",
      "Bandpass:             0.700 +/- 0.300 microns\n",
      "Object Distance:      14.66 AU\n",
      "Used shadow velocity: 22.004 km/s\n",
      "Fresnel scale:        0.040 seconds or 0.87 km\n",
      "Stellar size effect:  0.010 seconds or 0.23 km\n",
      "\n",
      "Object LightCurve model was not fitted.\n",
      "\n",
      "Immersion time: 2017-06-22 21:21:20.179 UTC +/- 0.320 seconds\n",
      "Emersion time:  2017-06-22 21:21:30.193 UTC +/- 0.340 seconds\n",
      "\n",
      "\n",
      "-------------------------------------------------------------------------------\n",
      "Site: Onduruquea\n",
      "Geodetic coordinates: Lon: 15d59m33.75s, Lat: -21d36m26.04s, height: 1.220 km\n",
      "Target altitude: 56.1 deg\n",
      "Target azimuth:  114.7 deg\n",
      "\n",
      "Light curve name: Onduruquea lc\n",
      "Initial time: 2017-06-22 21:11:52.175 UTC\n",
      "End time:     2017-06-22 21:25:13.389 UTC\n",
      "Duration:     13.354 minutes\n",
      "Time offset:  -0.190 seconds\n",
      "\n",
      "Object LightCurve was not instantiated with time and flux.\n",
      "\n",
      "Bandpass:             0.700 +/- 0.300 microns\n",
      "Object Distance:      14.66 AU\n",
      "Used shadow velocity: 22.004 km/s\n",
      "Fresnel scale:        0.040 seconds or 0.87 km\n",
      "Stellar size effect:  0.010 seconds or 0.23 km\n",
      "\n",
      "Object LightCurve model was not fitted.\n",
      "\n",
      "Immersion time: 2017-06-22 21:21:22.023 UTC +/- 0.100 seconds\n",
      "Emersion time:  2017-06-22 21:21:33.634 UTC +/- 0.110 seconds\n",
      "\n",
      "\n",
      "-------------------------------------------------------------------------------\n",
      "Site: Tivoli\n",
      "Geodetic coordinates: Lon: 18d01m01.24s, Lat: -23d27m40.19s, height: 1.344 km\n",
      "Target altitude: 58.5 deg\n",
      "Target azimuth:  112.4 deg\n",
      "\n",
      "Light curve name: Tivoli lc\n",
      "Initial time: 2017-06-22 21:16:00.094 UTC\n",
      "End time:     2017-06-22 21:28:00.018 UTC\n",
      "Duration:     11.999 minutes\n",
      "Time offset:  -0.150 seconds\n",
      "\n",
      "Object LightCurve was not instantiated with time and flux.\n",
      "\n",
      "Bandpass:             0.700 +/- 0.300 microns\n",
      "Object Distance:      14.66 AU\n",
      "Used shadow velocity: 22.004 km/s\n",
      "Fresnel scale:        0.040 seconds or 0.87 km\n",
      "Stellar size effect:  0.010 seconds or 0.23 km\n",
      "\n",
      "Object LightCurve model was not fitted.\n",
      "\n",
      "Immersion time: 2017-06-22 21:21:15.478 UTC +/- 0.700 seconds\n",
      "Emersion time:  2017-06-22 21:21:19.838 UTC +/- 0.700 seconds\n",
      "\n",
      "\n",
      "-------------------------------------------------------------------------------\n",
      "Site: Windhoek\n",
      "Geodetic coordinates: Lon: 17d06m31.9s, Lat: -22d41m55.16s, height: 1.902 km\n",
      "Target altitude: 57.4 deg\n",
      "Target azimuth:  113.4 deg\n",
      "\n",
      "Light curve name: Windhoek C14 lc\n",
      "Initial time: 2017-06-22 21:12:48.250 UTC\n",
      "End time:     2017-06-22 21:32:47.963 UTC\n",
      "Duration:     19.995 minutes\n",
      "Time offset:  -0.375 seconds\n",
      "\n",
      "Object LightCurve was not instantiated with time and flux.\n",
      "\n",
      "Bandpass:             0.700 +/- 0.300 microns\n",
      "Object Distance:      14.66 AU\n",
      "Used shadow velocity: 22.004 km/s\n",
      "Fresnel scale:        0.040 seconds or 0.87 km\n",
      "Stellar size effect:  0.010 seconds or 0.23 km\n",
      "\n",
      "Object LightCurve model was not fitted.\n",
      "\n",
      "Immersion time: 2017-06-22 21:21:17.234 UTC +/- 0.240 seconds\n",
      "Emersion time:  2017-06-22 21:21:27.189 UTC +/- 0.260 seconds\n",
      "\n",
      "\n",
      "-------------------------------------------------------------------------------\n",
      "Site: Windhoek\n",
      "Geodetic coordinates: Lon: 17d06m31.9s, Lat: -22d41m55.16s, height: 1.902 km\n",
      "Target altitude: 57.4 deg\n",
      "Target azimuth:  113.4 deg\n",
      "\n",
      "Light curve name: Windhoek D16 lc\n",
      "Initial time: 2017-06-22 21:20:01.884 UTC\n",
      "End time:     2017-06-22 21:22:21.894 UTC\n",
      "Duration:     2.333 minutes\n",
      "Time offset:  0.000 seconds\n",
      "\n",
      "Object LightCurve was not instantiated with time and flux.\n",
      "\n",
      "Bandpass:             0.700 +/- 0.300 microns\n",
      "Object Distance:      14.66 AU\n",
      "Used shadow velocity: 22.004 km/s\n",
      "Fresnel scale:        0.040 seconds or 0.87 km\n",
      "Stellar size effect:  0.010 seconds or 0.23 km\n",
      "\n",
      "Object LightCurve model was not fitted.\n",
      "\n",
      "Immersion time: 2017-06-22 21:21:17.288 UTC +/- 0.280 seconds\n",
      "Emersion time:  2017-06-22 21:21:27.228 UTC +/- 0.340 seconds\n",
      "\n",
      "\n",
      "###############################################################################\n",
      "                             NEGATIVE OBSERVATIONS                             \n",
      "###############################################################################\n",
      "\n",
      "-------------------------------------------------------------------------------\n",
      "Site: Hakos\n",
      "Geodetic coordinates: Lon: 16d21m41.32s, Lat: -23d14m11.04s, height: 1.843 km\n",
      "Target altitude: 56.8 deg\n",
      "Target azimuth:  112.5 deg\n",
      "\n",
      "Light curve name: Hakos lc\n",
      "Initial time: 2017-06-22 21:10:19.961 UTC\n",
      "End time:     2017-06-22 21:30:16.955 UTC\n",
      "Duration:     19.950 minutes\n",
      "Time offset:  0.000 seconds\n",
      "\n",
      "Exposure time:    1.0000 seconds\n",
      "Cycle time:       2.9998 seconds\n",
      "Num. data points: 400\n",
      "\n",
      "Bandpass:             0.700 +/- 0.300 microns\n",
      "Object Distance:      14.66 AU\n",
      "Used shadow velocity: 22.004 km/s\n",
      "Fresnel scale:        0.040 seconds or 0.87 km\n",
      "Stellar size effect:  0.010 seconds or 0.23 km\n",
      "\n",
      "Object LightCurve model was not fitted.\n",
      "\n",
      "Immersion and emersion times were not fitted or instantiated.\n",
      "\n",
      "\n",
      "###############################################################################\n",
      "                                    RESULTS                                    \n",
      "###############################################################################\n",
      "\n",
      "Fitted Ellipse:\n",
      "equatorial_radius: 138.894 +/- 5.872\n",
      "center_f: -13.668 +/- 1.124\n",
      "center_g: -2.175 +/- 4.183\n",
      "oblateness: 0.087 +/- 0.041\n",
      "position_angle: 127.631 +/- 19.996\n",
      "polar_radius: 126.822 km \n",
      "equivalent_radius: 132.720 km \n",
      "geometric albedo (V): 0.062 (6.2%) \n",
      "\n",
      "Minimum chi-square: 0.062\n",
      "Number of fitted points: 10\n",
      "Number of fitted parameters: 5\n",
      "Minimum chi-square per degree of freedom: 0.012\n",
      "Radial dispersion: -0.652 +/- 0.811 km\n",
      "Radial error:      7.579 +/- 4.655 km\n",
      "\n",
      "Ephemeris offset (km): X = -13.7 km +/- 1.1 km; Y = -2.2 km +/- 4.2 km\n",
      "Ephemeris offset (mas): da_cos_dec = -1.286 +/- 0.106; d_dec = -0.205 +/- 0.393\n",
      "\n",
      "Astrometric object position at time 2017-06-22 21:18:48.200 for reference 'geocenter'\n",
      "RA = 18 55 15.6523907 +/- 0.111 mas; DEC = -31 31 21.622102 +/- 0.395 mas\n"
     ]
    }
   ],
   "source": [
    "print(occ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**And this can be saved to an ASCII file using** `Occultation.to_log`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output/LC_test_1a.log  output/Test_occ.log\n"
     ]
    }
   ],
   "source": [
    "occ.to_log('output/Test_occ.log')\n",
    "\n",
    "!ls output/*.log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This Jupyter-Notebook was designed as a tutorial for how to work with the** `Occultation` **Class. More information about the other classes, please refer to their specif Jupyter-Notebook. Any further question, please contact the core team: Altair Ramos Gomes Júnior, Bruno Eduardo Morgado, Gustavo Benedetti Rossi, and Rodrigo Carlos Boufleur.**\n",
    "\n",
    "**The End**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}