{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SORA version: 1.0dev\n"
     ]
    }
   ],
   "source": [
    "## SORA package\n",
    "from sora import Occultation, Body, Star, LightCurve, Observer\n",
    "from sora.prediction import prediction\n",
    "from sora.extra import draw_ellipse\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": [
    "**Before analysing stellar occultations data, let's predict them.**\n",
    "\n",
    "To predict stellar occultation we needs the intended Solar System body ephemeris and a time window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining data for Chariklo from SBDB\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. 2021-June); 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",
      "    6.58 +/- 0 mag\n",
      "    Reference: MPO647128, \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"
     ]
    }
   ],
   "source": [
    "# First, let's consider an Solar System Body\n",
    "\n",
    "chariklo = Body(name='Chariklo', \n",
    "                ephem=['guidelines/input/bsp/Chariklo.bsp', 'guidelines/input/bsp/de438_small.bsp'])\n",
    "\n",
    "print(chariklo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ephemeris was split in 1 parts for better search of stars\n",
      "\n",
      "Searching occultations in part 1/1\n",
      "Generating Ephemeris between 2017-06-20 00:00:00.000 and 2017-06-26 23:59:00.000 ...\n",
      "Downloading stars ...\n",
      "    5 GaiaDR3 stars downloaded\n",
      "Identifying occultations ...\n",
      "\n",
      "2 occultations found.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><i>PredictionTable length=2</i>\n",
       "<table id=\"table140076456880688\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Epoch</th><th>ICRS Star Coord at Epoch</th><th>Geocentric Object Position</th><th>C/A</th><th>P/A</th><th>Vel</th><th>Dist</th><th>G</th><th>long</th><th>loct</th><th>M-G-T</th><th>S-G-T</th><th>GaiaDR3 Source ID</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th>arcsec</th><th>deg</th><th>km / s</th><th>AU</th><th>mag</th><th>deg</th><th>hh:mm</th><th>deg</th><th>deg</th><th></th></tr></thead>\n",
       "<thead><tr><th>object</th><th>object</th><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str5</th><th>float64</th><th>float64</th><th>str19</th></tr></thead>\n",
       "<tr><td>2017-06-21 09:57:43.440</td><td>18 55 36.17454 -31 31 19.03261</td><td>18 55 36.17500 -31 31 19.60516</td><td>0.573</td><td>179.41</td><td>-21.84</td><td>14.663</td><td>15.254</td><td>225</td><td>00:56</td><td>128</td><td>165</td><td>6760228702284187264</td></tr>\n",
       "<tr><td>2017-06-22 21:18:48.200</td><td>18 55 15.65251 -31 31 21.67062</td><td>18 55 15.65249 -31 31 21.62190</td><td>0.049</td><td>359.72</td><td>-22.00</td><td>14.659</td><td>14.224</td><td>53</td><td>00:50</td><td>149</td><td>166</td><td>6760223758801661440</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<PredictionTable length=2>\n",
       "         Epoch             ICRS Star Coord at Epoch    ...  GaiaDR3 Source ID \n",
       "                                                       ...                    \n",
       "         object                     object             ...        str19       \n",
       "----------------------- ------------------------------ ... -------------------\n",
       "2017-06-21 09:57:43.440 18 55 36.17454 -31 31 19.03261 ... 6760228702284187264\n",
       "2017-06-22 21:18:48.200 18 55 15.65251 -31 31 21.67062 ... 6760223758801661440"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = prediction(body=chariklo, time_beg='2017-06-20',time_end='2017-06-27',mag_lim=16)\n",
    "\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "guidelines/figures/pred_map.png generated\n"
     ]
    }
   ],
   "source": [
    "## ploting the occultation map\n",
    "\n",
    "pred['2017-06-22 21:18'].plot_occ_map(nameimg='guidelines/figures/pred_map')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='guidelines/figures/pred_map.png' style='width:600px;height:500px'/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now, let's start instantiating the Occultation**\n",
    "\n",
    "An occultation is defined by the occulting body, the occulted star, and the time of the occultation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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           ~/miniconda3/envs/sora-develop-stats/lib/python3.9/site-packages/sora_astro-1.0.dev0-py3.9.egg/sora/occultation/core.py\n",
      "\u001b[0;31mType:\u001b[0m           type\n",
      "\u001b[0;31mSubclasses:\u001b[0m     \n"
     ]
    }
   ],
   "source": [
    "Occultation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 GaiaDR3 star found band={'G': 14.223702}\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",
      "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"
     ]
    }
   ],
   "source": [
    "star_occ = Star(coord='18 55 15.65250 -31 31 21.67051')\n",
    "#star_occ = Star(code='6760223758801661440')\n",
    "\n",
    "print(star_occ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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 +/- 0.0323 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. 2021-June); 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",
      "    6.58 +/- 0 mag\n",
      "    Reference: MPO647128, \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": [
    "**After that, we instantiate the observers and their light curves**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observers**\n",
    "\n",
    "Now let's define our observers, they can be setted manually or from the MPC database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site: Tivoli\n",
      "Geodetic coordinates: Lon: 18d01m01.24s, Lat: -23d27m40.19s, height: 1.344 km\n",
      "\n",
      "\n",
      "Site: Observatorio Pico dos Dias\n",
      "Geodetic coordinates: Lon: -45d34m57.54s, Lat: -22d32m07.74756091s, height: 1.811 km\n"
     ]
    }
   ],
   "source": [
    "### User\n",
    "\n",
    "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)\n",
    "\n",
    "print(tiv)\n",
    "\n",
    "print('\\n')\n",
    "\n",
    "### MPC Database Search\n",
    "\n",
    "opd = Observer(name='Observatorio Pico dos Dias',code='874')\n",
    "\n",
    "print(opd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Light Curves**\n",
    "\n",
    "Now let's define our light curves, they can be instanciated from different way: \n",
    "- **(i)** Manually with arrays containing the flux and the times; \n",
    "- **(ii)** Read an ASCII file; \n",
    "- **(iii)** Already obtained times."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Outeniqua (Namibia)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Light curve name: Outeniqua lc\n",
      "Initial time: 2017-06-22 21:20:00.056 UTC\n",
      "End time:     2017-06-22 21:23:19.958 UTC\n",
      "Duration:     3.332 minutes\n",
      "Time offset:  0.000 seconds\n",
      "\n",
      "Exposure time:    0.1000 seconds\n",
      "Cycle time:       0.1002 seconds\n",
      "Num. data points: 2000\n",
      "\n",
      "\n",
      "There is no occultation associated with this light curve.\n",
      "\n",
      "Object LightCurve model was not fitted.\n",
      "\n",
      "Immersion and emersion times were not fitted or instantiated.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "out_lc = LightCurve(name='Outeniqua lc',file='guidelines/input/lightcurves/lc_example.dat',\n",
    "                    exptime=0.100, usecols=[0,1])\n",
    "\n",
    "print(out_lc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_lc.plot_lc()\n",
    "pl.xlim(76825,76950)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The light curve occultation model considers some physical parameters from the event:\n",
    "- Distance between the geocenter and the occulting object (AU);\n",
    "- Star diameter at the occulting object's distance (km);\n",
    "- Nominal Velocity of the event (km/s);\n",
    "\n",
    "These parameters can be automatic calculated as we connect the `LightCurve` and the `Observer` to the `Ocultation` Object.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Light curve name: Outeniqua lc\n",
      "Initial time: 2017-06-22 21:20:00.056 UTC\n",
      "End time:     2017-06-22 21:23:19.958 UTC\n",
      "Duration:     3.332 minutes\n",
      "Time offset:  0.000 seconds\n",
      "\n",
      "Exposure time:    0.1000 seconds\n",
      "Cycle time:       0.1002 seconds\n",
      "Num. data points: 2000\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rcboufleur/miniconda3/envs/sora-develop-stats/lib/python3.9/site-packages/sora_astro-1.0.dev0-py3.9.egg/sora/body/core.py:332: UserWarning: H and/or G is not defined for 10199 Chariklo. Searching into JPL Horizons service\n"
     ]
    }
   ],
   "source": [
    "occ.chords.add_chord(observer=out,lightcurve=out_lc)\n",
    "\n",
    "print(out_lc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, appart from the `LightCurve` Object having the needed parameters, also the `Occultation` object can acess the information from this `Chord`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "Site: Outeniqua\n",
      "Geodetic coordinates: Lon: 16d49m17.71s, Lat: -21d17m58.17s, height: 1.416 km\n",
      "Target altitude: 56.7 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 21:23:19.958 UTC\n",
      "Duration:     3.332 minutes\n",
      "Time offset:  0.000 seconds\n",
      "\n",
      "Exposure time:    0.1000 seconds\n",
      "Cycle time:       0.1002 seconds\n",
      "Num. data points: 2000\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"
     ]
    }
   ],
   "source": [
    "print(occ.chords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m \u001b[0mout_lc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mocc_lcfit\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",
      "Monte Carlo chi square fit for occultations lightcurve.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "tmin : `int`, `float`\n",
      "    Minimum time to consider in the fit procedure, in seconds.\n",
      "\n",
      "tmax : `int`, `float`\n",
      "    Maximum time to consider in the fit procedure, in seconds.\n",
      "\n",
      "flux_min : `int`, `float`, default=0\n",
      "    Bottom flux (only object).\n",
      "\n",
      "flux_max :`int`, `float`, default=1\n",
      "    Base flux (object plus star).\n",
      "\n",
      "immersion_time : `int`, `float`\n",
      "    Initial guess for immersion time, in seconds.\n",
      "\n",
      "emersion_time : `int`, `float`\n",
      "    Initial guess for emersion time, in seconds.\n",
      "\n",
      "opacity : `int`, `float`, default=1\n",
      "    Initial guess for opacity. Opaque = 1, Transparent = 0.\n",
      "\n",
      "delta_t : `int`, `float`\n",
      "    Interval to fit immersion or emersion time.\n",
      "\n",
      "dopacity : `int`, `float`, default=0\n",
      "    Interval to fit opacity.\n",
      "\n",
      "sigma : `int`, `float`, `array`, 'auto'\n",
      "    Fluxes errors. If None it will use the `self.dflux`. If 'auto' it\n",
      "    will calculate using the region outside the event.\n",
      "\n",
      "loop : `int`, default=10000\n",
      "    Number of tests to be done.\n",
      "\n",
      "sigma_result : `int`, `float`\n",
      "    Sigma value to be considered as result.\n",
      "\n",
      "method : `str`, default=`chisqr`\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",
      "chi2 : `sora.extra.ChiSquare`\n",
      "    ChiSquare object.\n",
      "\u001b[0;31mFile:\u001b[0m      ~/miniconda3/envs/sora-develop-stats/lib/python3.9/site-packages/sora_astro-1.0.dev0-py3.9.egg/sora/lightcurve/core.py\n",
      "\u001b[0;31mType:\u001b[0m      method\n"
     ]
    }
   ],
   "source": [
    "## We fit the modelled light curve, using chi square minimization and Monte Carlo procedures\n",
    "\n",
    "out_lc.occ_lcfit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightCurve fit: |████████████████████████████████████████|  - 100% \n",
      "\n",
      "\n",
      "Minimum chi-square: 473.931\n",
      "Number of fitted points: 496\n",
      "Number of fitted parameters: 2\n",
      "Minimum chi-square per degree of freedom: 0.959\n",
      "\n",
      "immersion:\n",
      "    1-sigma: 76880.322 +/- 0.032\n",
      "    3-sigma: 76880.353 +/- 0.131\n",
      "\n",
      "emersion:\n",
      "    1-sigma: 76890.349 +/- 0.031\n",
      "    3-sigma: 76890.347 +/- 0.108\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## An automatic version can be used for cases where the occultation is obvious!!\n",
    "## This process may take some minutes to run!!\n",
    "\n",
    "out_chi2 = out_lc.occ_lcfit(loop=1000)\n",
    "print('\\n')\n",
    "print(out_chi2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightCurve fit: |████████████████████████████████████████|  - 100% \n",
      "\n",
      "\n",
      "Minimum chi-square: 192.774\n",
      "Number of fitted points: 200\n",
      "Number of fitted parameters: 2\n",
      "Minimum chi-square per degree of freedom: 0.974\n",
      "\n",
      "immersion:\n",
      "    1-sigma: 76880.325 +/- 0.027\n",
      "    3-sigma: 76880.347 +/- 0.121\n",
      "\n",
      "emersion:\n",
      "    1-sigma: 76890.351 +/- 0.029\n",
      "    3-sigma: 76890.348 +/- 0.101\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## However, we believe that the user should set the parameters by hand!!\n",
    "## The complete description of each parameter can be seen at the function Docstring.\n",
    "## This process may take some minutes to run!!\n",
    "\n",
    "out_chi2 = out_lc.occ_lcfit(tmin=76875.0, tmax=76895.0, \n",
    "                            immersion_time=76880.3, \n",
    "                            emersion_time=76890.3, \n",
    "                            delta_t=0.2, loop=10000)\n",
    "print('\\n')\n",
    "print(out_chi2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The user can visually acess the quality of the fit by ploting the** `ChiSquare` **object.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_chi2.plot_chi2('immersion')\n",
    "pl.xlim(76880.33 - 0.20, 76880.33 + 0.20)\n",
    "pl.show()\n",
    "\n",
    "out_chi2.plot_chi2('emersion')\n",
    "pl.xlim(76890.34 - 0.20, 76890.34 + 0.20)\n",
    "pl.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Also, the user can visually acess the quality of the fit by ploting the** `LightCurve`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_lc.plot_lc()\n",
    "pl.xlim(76865, 76905)\n",
    "pl.show()\n",
    "\n",
    "out_lc.plot_model()\n",
    "pl.xlim(76890.34-0.5, 76890.34+0.5)\n",
    "pl.legend(ncol=1, fontsize=12.5, loc=2)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Light curve name: Outeniqua lc\n",
      "Initial time: 2017-06-22 21:20:00.056 UTC\n",
      "End time:     2017-06-22 21:23:19.958 UTC\n",
      "Duration:     3.332 minutes\n",
      "Time offset:  0.000 seconds\n",
      "\n",
      "Exposure time:    0.1000 seconds\n",
      "Cycle time:       0.1002 seconds\n",
      "Num. data points: 2000\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",
      "Inst. response:       0.100 seconds or 2.20 km\n",
      "Dead time effect:     0.000 seconds or 0.00 km\n",
      "Model resolution:     0.004 seconds or 0.09 km\n",
      "Modelled baseflux:    1.029\n",
      "Modelled bottomflux:  0.109\n",
      "Light curve sigma:    0.307\n",
      "\n",
      "Immersion time: 2017-06-22 21:21:20.325 UTC +/- 0.027 seconds\n",
      "Emersion time:  2017-06-22 21:21:30.351 UTC +/- 0.029 seconds\n",
      "\n",
      "Monte Carlo chi square fit.\n",
      "\n",
      "Minimum chi-square: 192.774\n",
      "Number of fitted points: 200\n",
      "Number of fitted parameters: 2\n",
      "Minimum chi-square per degree of freedom: 0.974\n",
      "\n",
      "immersion:\n",
      "    1-sigma: 76880.325 +/- 0.027\n",
      "    3-sigma: 76880.347 +/- 0.121\n",
      "\n",
      "emersion:\n",
      "    1-sigma: 76890.351 +/- 0.029\n",
      "    3-sigma: 76890.348 +/- 0.101\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(out_lc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Finally, the user can visually see the chord in the Sky-plane using the** `Chord` **Object.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "occ.chords.plot_chords(segment='positive', color='blue')\n",
    "occ.chords.plot_chords(segment='error', color='red')\n",
    "pl.legend()\n",
    "pl.xlim(+250,-250)\n",
    "pl.ylim(-250,+250)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now, let's add the other chords of this occultation.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Onduruquea (Namibia)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Chord: Onduruquea>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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=0.010,\n",
    "                    emersion ='2017-06-22 21:21:33.824', emersion_err=0.011)\n",
    "\n",
    "occ.chords.add_chord(observer=ond,lightcurve=ond_lc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\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:  0.000 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 21:21:22.213 UTC +/- 0.010 seconds\n",
      "Emersion time:  2017-06-22 21:21:33.824 UTC +/- 0.011 seconds\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(occ.chords['Onduruquea'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tivoli (Namibia)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Chord: Tivoli>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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=0.011,\n",
    "                    emersion ='2017-06-22 21:21:19.988',emersion_err=0.038)\n",
    "\n",
    "occ.chords.add_chord(observer=tiv, lightcurve=tiv_lc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Windhoek (Namibia)**\n",
    "\n",
    "When there is two chords at the same stations is important to define their names as different values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Chord: Windhoek D16 lc>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## C14\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=0.024,\n",
    "                    emersion ='2017-06-22 21:21:27.564',emersion_err=0.026)\n",
    "\n",
    "occ.chords.add_chord(name='Windhoek C14 lc', observer=whc, lightcurve=whc_c14_lc)\n",
    "\n",
    "## D16\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=0.028,\n",
    "                    emersion ='2017-06-22 21:21:27.228',emersion_err=0.034)\n",
    "\n",
    "occ.chords.add_chord(name='Windhoek D16 lc', observer=whc, lightcurve=whc_d16_lc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hakos (Namibia)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Chord: Hakos>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Also negatives chords can be added\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')\n",
    "\n",
    "occ.chords.add_chord(observer=hak, lightcurve=hak_lc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chords display and ellipse fit**\n",
    "\n",
    "After all light curves were instanciated and/or fitted, the next step is to plot the chords and fit the elipse. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "## We can add known time offsets due to camera features\n",
    "\n",
    "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\n",
    "hak_lc.dt = -0.200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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 next step is to fit an ellipse to the chords**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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=False\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      ~/miniconda3/envs/sora-develop-stats/lib/python3.9/site-packages/sora_astro-1.0.dev0-py3.9.egg/sora/occultation/core.py\n",
      "\u001b[0;31mType:\u001b[0m      method\n"
     ]
    }
   ],
   "source": [
    "## We fit a ellipse using chi square minimization and Monte Carlo procedures, the \n",
    "## The complete description of each parameter can be seen at the function Docstring.\n",
    "\n",
    "occ.fit_ellipse?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi-square: 12.130\n",
      "Number of fitted points: 10\n",
      "Number of fitted parameters: 5\n",
      "Minimum chi-square per degree of freedom: 2.426\n",
      "\n",
      "center_f:\n",
      "    1-sigma: -13.613 +/- 0.120\n",
      "    3-sigma: -13.611 +/- 0.430\n",
      "\n",
      "center_g:\n",
      "    1-sigma: -2.094 +/- 0.499\n",
      "    3-sigma: -2.089 +/- 1.626\n",
      "\n",
      "equatorial_radius:\n",
      "    1-sigma: 138.657 +/- 0.373\n",
      "    3-sigma: 138.673 +/- 1.445\n",
      "\n",
      "oblateness:\n",
      "    1-sigma: 0.086 +/- 0.003\n",
      "    3-sigma: 0.086 +/- 0.010\n",
      "\n",
      "position_angle:\n",
      "    1-sigma: 123.956 +/- 1.496\n",
      "    3-sigma: 124.121 +/- 5.140\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### This may take some minutes to run!!\n",
    "\n",
    "ellipse_chi2  = occ.fit_ellipse(center_f=-15.046, center_g=-2.495, dcenter_f=3, dcenter_g=10, \n",
    "                                equatorial_radius=138, dequatorial_radius=3, oblateness=0.093, \n",
    "                                doblateness=0.02, position_angle=126, dposition_angle=10 ,loop=10000000,\n",
    "                                dchi_min=10,number_chi=10000)\n",
    "\n",
    "\n",
    "print(ellipse_chi2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Similar, to the** `LightCurve` **fit, the user can visually acess the quality of the fit by ploting the** `ChiSquare` **object.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ellipse_chi2.plot_chi2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Also, the user can visually acess the quality of the fit by ploting the** `Chords` **and the fitted ellipses.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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)\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 resulting values can be acessed from the Dictionaries** `Occultation.fitted_params` **and** `Occultation.chi2_params`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'equatorial_radius': [138.65650796361007, 0.3729805506807651],\n",
       " 'center_f': [-13.612927096827903, 0.11996518649955146],\n",
       " 'center_g': [-2.0936385736495167, 0.4992405703706848],\n",
       " 'oblateness': [0.08573692161473534, 0.003305360393147015],\n",
       " 'position_angle': [123.95618703950413, 1.4957816035600828]}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ.fitted_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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 lc_immersion',\n",
       "  'Windhoek C14 lc_emersion',\n",
       "  'Windhoek D16 lc_immersion',\n",
       "  'Windhoek D16 lc_emersion'],\n",
       " 'radial_dispersion': array([ 0.64886238, -0.82234285, -0.20941195,  0.0507056 ,  0.14404744,\n",
       "        -2.02271261,  0.3135147 , -0.1926379 , -0.76516855,  0.53220478]),\n",
       " 'position_angle': array([301.95754708,  58.93979955, 274.10063776,  84.70868609,\n",
       "        205.80484092, 163.27224646, 238.45346645, 123.09039914,\n",
       "        238.19141651, 122.87262257]),\n",
       " 'radial_error': array([0.60995255, 0.64472971, 0.22353256, 0.24588816, 0.24592892,\n",
       "        0.8495755 , 0.53652931, 0.58124429, 0.62595044, 0.76008871]),\n",
       " 'chi2_min': 12.129614721820191,\n",
       " 'nparam': 5,\n",
       " 'npts': 10}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ.chi2_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Besides the size and shape of the body the astrometrical positions obtained using stellar occultation  is also a relevant result from the occultation and it has a precision that can be compared with space probes results (few km)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ephemeris offset (km): X = -13.6 km +/- 0.1 km; Y = -2.1 km +/- 0.5 km\n",
      "Ephemeris offset (mas): da_cos_dec = -1.280 +/- 0.011; d_dec = -0.197 +/- 0.047\n",
      "\n",
      "Astrometric object position at time 2017-06-22 21:18:48.200 for reference 'geocenter'\n",
      "RA = 18 55 15.6523911 +/- 0.034 mas; DEC = -31 31 21.622094 +/- 0.058 mas\n"
     ]
    }
   ],
   "source": [
    "occ.new_astrometric_position()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**After the instanciation of the** `Chords` **and the ellipse fit, the posfit occultation map can be plotted.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Projected shadow radius = 135.0 km\n",
      "guidelines/figures/map_posfit.png generated\n"
     ]
    }
   ],
   "source": [
    "occ.plot_occ_map(centermap_delta=[-3500,+400],zoom=20,nameimg='guidelines/figures/map_posfit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='guidelines/figures/map_posfit.png' style='width:600px;height:500px'/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Finally, the log contains all the details**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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 +/- 0.0323 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. 2021-June); 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",
      "    6.58 +/- 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 21:23:19.958 UTC\n",
      "Duration:     3.332 minutes\n",
      "Time offset:  -0.150 seconds\n",
      "\n",
      "Exposure time:    0.1000 seconds\n",
      "Cycle time:       0.1002 seconds\n",
      "Num. data points: 2000\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",
      "Inst. response:       0.100 seconds or 2.20 km\n",
      "Dead time effect:     0.000 seconds or 0.00 km\n",
      "Model resolution:     0.004 seconds or 0.09 km\n",
      "Modelled baseflux:    1.029\n",
      "Modelled bottomflux:  0.109\n",
      "Light curve sigma:    0.307\n",
      "\n",
      "Immersion time: 2017-06-22 21:21:20.175 UTC +/- 0.027 seconds\n",
      "Emersion time:  2017-06-22 21:21:30.201 UTC +/- 0.029 seconds\n",
      "\n",
      "Monte Carlo chi square fit.\n",
      "\n",
      "Minimum chi-square: 192.774\n",
      "Number of fitted points: 200\n",
      "Number of fitted parameters: 2\n",
      "Minimum chi-square per degree of freedom: 0.974\n",
      "\n",
      "immersion:\n",
      "    1-sigma: 76880.325 +/- 0.027\n",
      "    3-sigma: 76880.347 +/- 0.121\n",
      "\n",
      "emersion:\n",
      "    1-sigma: 76890.351 +/- 0.029\n",
      "    3-sigma: 76890.348 +/- 0.101\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 +/- 0.010 seconds\n",
      "Emersion time:  2017-06-22 21:21:33.634 UTC +/- 0.011 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 +/- 0.011 seconds\n",
      "Emersion time:  2017-06-22 21:21:19.838 UTC +/- 0.038 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 +/- 0.024 seconds\n",
      "Emersion time:  2017-06-22 21:21:27.189 UTC +/- 0.026 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 +/- 0.028 seconds\n",
      "Emersion time:  2017-06-22 21:21:27.228 UTC +/- 0.034 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 21:10:19.461 UTC\n",
      "End time:     2017-06-22 21:30:19.345 UTC\n",
      "Duration:     19.998 minutes\n",
      "Time offset:  -0.200 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 and emersion times were not fitted or instantiated.\n",
      "\n",
      "\n",
      "###############################################################################\n",
      "                                    RESULTS                                    \n",
      "###############################################################################\n",
      "\n",
      "Fitted Ellipse:\n",
      "equatorial_radius: 138.657 +/- 0.373\n",
      "center_f: -13.613 +/- 0.120\n",
      "center_g: -2.094 +/- 0.499\n",
      "oblateness: 0.086 +/- 0.003\n",
      "position_angle: 123.956 +/- 1.496\n",
      "polar_radius: 126.769 km \n",
      "equivalent_radius: 132.579 km \n",
      "geometric albedo (V): 0.060 (6.0%) \n",
      "\n",
      "Minimum chi-square: 12.130\n",
      "Number of fitted points: 10\n",
      "Number of fitted parameters: 5\n",
      "Minimum chi-square per degree of freedom: 2.426\n",
      "Radial dispersion: -0.232 +/- 0.797 km\n",
      "Radial error:      0.532 +/- 0.222 km\n",
      "\n",
      "Ephemeris offset (km): X = -13.6 km +/- 0.1 km; Y = -2.1 km +/- 0.5 km\n",
      "Ephemeris offset (mas): da_cos_dec = -1.280 +/- 0.011; d_dec = -0.197 +/- 0.047\n",
      "\n",
      "Astrometric object position at time 2017-06-22 21:18:48.200 for reference 'geocenter'\n",
      "RA = 18 55 15.6523911 +/- 0.034 mas; DEC = -31 31 21.622094 +/- 0.058 mas\n"
     ]
    }
   ],
   "source": [
    "print(occ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**You can find more information about each Class at their specific Jupyter-Notebook.**\n",
    "\n",
    "**The END**"
   ]
  }
 ],
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