{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Orbit determination example\n",
    "This notebook does the following:\n",
    "* Download an orbit first guess from SpaceTrack\n",
    "* Download laser ranging data\n",
    "* Feed the data to Orekit\n",
    "* Estimate the orbit\n",
    "* Propagate and compare the orbit to the first guess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two types of laser ranging data can be chosen (see below):\n",
    "\n",
    "* Normal point data: https://ilrs.cddis.eosdis.nasa.gov/data_and_products/data/npt/index.html\n",
    "* Full rate data: https://ilrs.cddis.eosdis.nasa.gov/data_and_products/data/frt/index.html\n",
    "    * This will improve the orbit estimation\n",
    "    * Caution, this format involves large quantities of data\n",
    "    * Caution 2, this data is unfiltered, therefore there can be a superposition of two range curves if two retro-reflectors on the satellite are visible by the station at the same time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OD parameters\n",
    "First, some parameters need to be defined for the orbit determination:\n",
    "* Satellite ID in NORAD, COSPAR and SIC code format. These IDs can be found here: https://edc.dgfi.tum.de/en/satellites/\n",
    "* Spacecraft mass: important for the drag term\n",
    "* Measurement weights: used to weight certain measurements more than others during the orbit estimation. Here, we only have range measurements and we do not know the confidence associated to these measurements, so all weights are identical\n",
    "* OD date: date at which the orbit will be estimated. \n",
    "* Data collection duration: for example, if equals 2 days, the laser data from the 2 days before the OD date will be used to estimate the orbit. This value is an important trade-off for the quality of the orbit determination:\n",
    "    * The longer the duration, the more ranging data is available, which can increase the quality of the estimation\n",
    "    * The longer the duration, the longer the orbit must be propagated, and the higher the covariance because of the orbit perturbations such as the gravity field, drag, Sun, Moon, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Satellite parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat_list = {    \n",
    "    'envisat': {\n",
    "        'norad_id': 27386,  # For Space-Track TLE queries\n",
    "        'cospar_id': '0200901',  # For laser ranging data queries\n",
    "        'sic_id': '6179',  # For writing in CPF files\n",
    "        'mass': 8000.0, # kg; TODO: compute proper value\n",
    "        'cross_section': 100.0, # m2; TODO: compute proper value\n",
    "        'cd': 2.0, # TODO: compute proper value\n",
    "        'cr': 1.0  # TODO: compute proper value\n",
    "    },    \n",
    "    'lageos2': {\n",
    "        'norad_id': 22195,\n",
    "        'cospar_id': '9207002',\n",
    "        'sic_id': '5986',\n",
    "        'mass': 405.0, # kg\n",
    "        'cross_section': 0.2827, # m2\n",
    "        'cd': 2.0, # TODO: compute proper value\n",
    "        'cr': 1.0  # TODO: compute proper value\n",
    "    },\n",
    "    'technosat': {\n",
    "        'norad_id': 42829,\n",
    "        'cospar_id': '1704205',\n",
    "        'sic_id': '6203',\n",
    "        'mass': 20.0, # kg\n",
    "        'cross_section': 0.10, # m2,\n",
    "        'cd': 2.0, # TODO: compute proper value\n",
    "        'cr': 1.0  # TODO: compute proper value\n",
    "    },\n",
    "    'snet1': {\n",
    "        'norad_id': 43189,\n",
    "        'cospar_id': '1801410',\n",
    "        'sic_id': '6204',\n",
    "        'mass': 8.0, # kg\n",
    "        'cross_section': 0.07,\n",
    "        'cd': 2.0, # TODO: compute proper value\n",
    "        'cr': 1.0  # TODO: compute proper value\n",
    "    }\n",
    "}\n",
    "\n",
    "sc_name = 'technosat'  # Change the name to select a different satellite in the dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "range_weight = 1.0 # Will be normalized later (i.e divided by the number of observations)\n",
    "range_sigma = 1.0 # Estimated covariance of the range measurements, in meters\n",
    "\n",
    "from datetime import datetime\n",
    "odDate = datetime(2020, 2, 12) # Beginning of the orbit determination\n",
    "collectionDuration = 2 # days\n",
    "from datetime import timedelta\n",
    "startCollectionDate = odDate + timedelta(days=-collectionDuration)\n",
    "\n",
    "# Orbit propagator parameters\n",
    "prop_min_step = 0.001 # s\n",
    "prop_max_step = 300.0 # s\n",
    "prop_position_error = 10.0 # m\n",
    "\n",
    "# Estimator parameters\n",
    "estimator_position_scale = 1.0 # m\n",
    "estimator_convergence_thres = 1e-2\n",
    "estimator_max_iterations = 25\n",
    "estimator_max_evaluations = 35"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API credentials\n",
    "The following sets up accounts for SpaceTrack (for orbit data) and the EDC API (for laser ranging data).\n",
    "* A SpaceTrack account is required, it can be created for free at: https://www.space-track.org/auth/createAccount\n",
    "* An EDC account is required, it can be created for free at: https://edc.dgfi.tum.de/en/register/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Space-Track\n",
    "identity_st = input('Enter SpaceTrack username')\n",
    "import getpass\n",
    "password_st = getpass.getpass(prompt='Enter SpaceTrack password for account {}'.format(identity_st))\n",
    "import spacetrack.operators as op\n",
    "from spacetrack import SpaceTrackClient\n",
    "st = SpaceTrackClient(identity=identity_st, password=password_st)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EDC API\n",
    "username_edc = input('Enter EDC API username')\n",
    "password_edc = getpass.getpass(prompt='Enter EDC API password for account {}'.format(username_edc)) # You will get prompted for your password\n",
    "url = 'https://edc.dgfi.tum.de/api/v1/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up models\n",
    "Initializing Orekit and JVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import orekit\n",
    "orekit.initVM()\n",
    "\n",
    "# Modified from https://gitlab.orekit.org/orekit-labs/python-wrapper/blob/master/python_files/pyhelpers.py\n",
    "from java.io import File\n",
    "from org.orekit.data import DataProvidersManager, DirectoryCrawler\n",
    "from orekit import JArray\n",
    "\n",
    "orekit_filename = 'orekit-data'\n",
    "DM = DataProvidersManager.getInstance()\n",
    "datafile = File(orekit_filename)\n",
    "if not datafile.exists():\n",
    "    print('Directory :', datafile.absolutePath, ' not found')\n",
    "\n",
    "crawler = DirectoryCrawler(datafile)\n",
    "DM.clearProviders()\n",
    "DM.addProvider(crawler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import station data from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yzokras/miniconda3/envs/laserod/lib/python3.8/site-packages/pandas/core/indexing.py:1761: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  return self._getitem_tuple(key)\n"
     ]
    }
   ],
   "source": [
    "stationFile = 'SLRF2014_POS+VEL_2030.0_180504.snx'\n",
    "stationEccFile = 'ecc_xyz.snx'\n",
    "import slrDataUtils\n",
    "stationData = slrDataUtils.parseStationData(stationFile, stationEccFile, startCollectionDate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The orbit determination needs a first guess. For this, we use Two-Line Elements. Retrieving the latest TLE prior to the beginning of the orbit determination. It is important to have a \"fresh\" TLE, because the newer the TLE, the better the orbit estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 42829U 17042E   20042.65175483  .00000094  00000-0  14859-4 0  9990\n",
      "2 42829  97.5382 290.2442 0014032 353.8494   6.2550 14.91054974140399\n"
     ]
    }
   ],
   "source": [
    "rawTle = st.tle(norad_cat_id=sat_list[sc_name]['norad_id'], epoch='<{}'.format(odDate), orderby='epoch desc', limit=1, format='tle')\n",
    "tleLine1 = rawTle.split('\\n')[0]\n",
    "tleLine2 = rawTle.split('\\n')[1]\n",
    "print(tleLine1)\n",
    "print(tleLine2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up Orekit frames and models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from org.orekit.utils import Constants as orekit_constants\n",
    "\n",
    "from org.orekit.frames import FramesFactory, ITRFVersion\n",
    "from org.orekit.utils import IERSConventions\n",
    "tod = FramesFactory.getTOD(IERSConventions.IERS_2010, False) # Taking tidal effects into account when interpolating EOP parameters\n",
    "gcrf = FramesFactory.getGCRF()\n",
    "itrf = FramesFactory.getITRF(IERSConventions.IERS_2010, False)\n",
    "#itrf = FramesFactory.getITRF(ITRFVersion.ITRF_2014, IERSConventions.IERS_2010, False)\n",
    "# Selecting frames to use for OD\n",
    "eci = gcrf\n",
    "ecef = itrf\n",
    "\n",
    "from org.orekit.models.earth import ReferenceEllipsoid\n",
    "wgs84Ellipsoid = ReferenceEllipsoid.getWgs84(ecef)\n",
    "from org.orekit.bodies import CelestialBodyFactory\n",
    "moon = CelestialBodyFactory.getMoon()\n",
    "sun = CelestialBodyFactory.getSun()\n",
    "\n",
    "from org.orekit.time import AbsoluteDate, TimeScalesFactory\n",
    "utc = TimeScalesFactory.getUTC()\n",
    "mjd_utc_epoch = AbsoluteDate(1858, 11, 17, 0, 0, 0.0, utc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up the propagator from the initial TLEs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from org.orekit.propagation.analytical.tle import TLE\n",
    "orekitTle = TLE(tleLine1, tleLine2)\n",
    "\n",
    "from org.orekit.attitudes import NadirPointing\n",
    "nadirPointing = NadirPointing(eci, wgs84Ellipsoid)\n",
    "\n",
    "from org.orekit.propagation.analytical.tle import SGP4\n",
    "sgp4Propagator = SGP4(orekitTle, nadirPointing, sat_list[sc_name]['mass'])\n",
    "\n",
    "tleInitialState = sgp4Propagator.getInitialState()\n",
    "tleEpoch = tleInitialState.getDate()\n",
    "tleOrbit_TEME = tleInitialState.getOrbit()\n",
    "tlePV_ECI = tleOrbit_TEME.getPVCoordinates(eci)\n",
    "\n",
    "from org.orekit.orbits import CartesianOrbit\n",
    "tleOrbit_ECI = CartesianOrbit(tlePV_ECI, eci, wgs84Ellipsoid.getGM())\n",
    "\n",
    "from org.orekit.propagation.conversion import DormandPrince853IntegratorBuilder\n",
    "integratorBuilder = DormandPrince853IntegratorBuilder(prop_min_step, prop_max_step, prop_position_error)\n",
    "\n",
    "from org.orekit.propagation.conversion import NumericalPropagatorBuilder\n",
    "from org.orekit.orbits import PositionAngle\n",
    "propagatorBuilder = NumericalPropagatorBuilder(tleOrbit_ECI,\n",
    "                                               integratorBuilder, PositionAngle.MEAN, estimator_position_scale)\n",
    "propagatorBuilder.setMass(sat_list[sc_name]['mass'])\n",
    "propagatorBuilder.setAttitudeProvider(nadirPointing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding perturbation forces to the propagator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Earth gravity field with degree 64 and order 64\n",
    "from org.orekit.forces.gravity.potential import GravityFieldFactory\n",
    "gravityProvider = GravityFieldFactory.getConstantNormalizedProvider(64, 64)\n",
    "from org.orekit.forces.gravity import HolmesFeatherstoneAttractionModel\n",
    "gravityAttractionModel = HolmesFeatherstoneAttractionModel(ecef, gravityProvider)\n",
    "propagatorBuilder.addForceModel(gravityAttractionModel)\n",
    "\n",
    "# Moon and Sun perturbations\n",
    "from org.orekit.forces.gravity import ThirdBodyAttraction\n",
    "moon_3dbodyattraction = ThirdBodyAttraction(moon)\n",
    "propagatorBuilder.addForceModel(moon_3dbodyattraction)\n",
    "sun_3dbodyattraction = ThirdBodyAttraction(sun)\n",
    "propagatorBuilder.addForceModel(sun_3dbodyattraction)\n",
    "\n",
    "# Solar radiation pressure\n",
    "from org.orekit.forces.radiation import IsotropicRadiationSingleCoefficient\n",
    "isotropicRadiationSingleCoeff = IsotropicRadiationSingleCoefficient(sat_list[sc_name]['cross_section'], sat_list[sc_name]['cr']);\n",
    "from org.orekit.forces.radiation import SolarRadiationPressure\n",
    "solarRadiationPressure = SolarRadiationPressure(sun, wgs84Ellipsoid.getEquatorialRadius(),\n",
    "                                                isotropicRadiationSingleCoeff)\n",
    "propagatorBuilder.addForceModel(solarRadiationPressure)\n",
    "\n",
    "# Atmospheric drag\n",
    "from org.orekit.models.earth.atmosphere.data import MarshallSolarActivityFutureEstimation\n",
    "msafe = MarshallSolarActivityFutureEstimation(\n",
    "    MarshallSolarActivityFutureEstimation.DEFAULT_SUPPORTED_NAMES,\n",
    "    MarshallSolarActivityFutureEstimation.StrengthLevel.AVERAGE)\n",
    "#DM.feed( MarshallSolarActivityFutureEstimation.DEFAULT_SUPPORTED_NAMES, msafe) # Feeding the F10.7 bulletins to Orekit's data manager\n",
    "\n",
    "from org.orekit.models.earth.atmosphere import NRLMSISE00\n",
    "atmosphere = NRLMSISE00(msafe, sun, wgs84Ellipsoid)\n",
    "#from org.orekit.forces.drag.atmosphere import DTM2000\n",
    "#atmosphere = DTM2000(msafe, sun, wgs84Ellipsoid)\n",
    "from org.orekit.forces.drag import IsotropicDrag\n",
    "isotropicDrag = IsotropicDrag(sat_list[sc_name]['cross_section'], sat_list[sc_name]['cd'])\n",
    "from org.orekit.forces.drag import DragForce\n",
    "dragForce = DragForce(atmosphere, isotropicDrag)\n",
    "propagatorBuilder.addForceModel(dragForce)\n",
    "\n",
    "# Relativity\n",
    "from org.orekit.forces.gravity import Relativity\n",
    "relativity = Relativity(orekit_constants.EIGEN5C_EARTH_MU)\n",
    "propagatorBuilder.addForceModel(relativity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up the estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from org.hipparchus.linear import QRDecomposer\n",
    "matrixDecomposer = QRDecomposer(1e-11)\n",
    "from org.hipparchus.optim.nonlinear.vector.leastsquares import GaussNewtonOptimizer\n",
    "optimizer = GaussNewtonOptimizer(matrixDecomposer, False)\n",
    "\n",
    "from org.orekit.estimation.leastsquares import BatchLSEstimator\n",
    "estimator = BatchLSEstimator(optimizer, propagatorBuilder)\n",
    "estimator.setParametersConvergenceThreshold(estimator_convergence_thres)\n",
    "estimator.setMaxIterations(estimator_max_iterations)\n",
    "estimator.setMaxEvaluations(estimator_max_evaluations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetching range data\n",
    "Looking for laser ranging data prior to the OD date.\n",
    "\n",
    "The API only allows to look for data using the date formats 2018-07-1%, 2018-07-14% or 2018-07-14 0% for example. As a consequence, the search must be split into several days. The results are then sorted, and the observations which are outside of the date range are deleted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <td>valid</td>\n",
       "      <td>00</td>\n",
       "      <td>532.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2131926</th>\n",
       "      <td>2020-02-11 13:21:59</td>\n",
       "      <td></td>\n",
       "      <td>2020-02-11 17:27:09</td>\n",
       "      <td>nasa_202002111700.npt</td>\n",
       "      <td>5</td>\n",
       "      <td>1704205</td>\n",
       "      <td>2020-02-11 13:20:09</td>\n",
       "      <td>70900513</td>\n",
       "      <td>valid</td>\n",
       "      <td>00</td>\n",
       "      <td>532.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2131900</th>\n",
       "      <td>2020-02-11 14:39:11</td>\n",
       "      <td></td>\n",
       "      <td>2020-02-11 14:46:26</td>\n",
       "      <td>7237_technosat_crd_20200211_14_00.npt</td>\n",
       "      <td>9</td>\n",
       "      <td>1704205</td>\n",
       "      <td>2020-02-11 14:36:46</td>\n",
       "      <td>72371901</td>\n",
       "      <td>valid</td>\n",
       "      <td>00</td>\n",
       "      <td>532.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2132259</th>\n",
       "      <td>2020-02-11 14:47:00</td>\n",
       "      <td></td>\n",
       "      <td>2020-02-12 09:02:38</td>\n",
       "      <td>7819_technosat_crd_20200211_14_00.npt</td>\n",
       "      <td>3</td>\n",
       "      <td>1704205</td>\n",
       "      <td>2020-02-11 14:39:59</td>\n",
       "      <td>78198201</td>\n",
       "      <td>valid</td>\n",
       "      <td>00</td>\n",
       "      <td>532.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               end_data_date errors        incoming_date  \\\n",
       "2131135  2020-02-10 02:30:18         2020-02-10 06:27:11   \n",
       "2131289  2020-02-10 09:18:18         2020-02-10 09:56:55   \n",
       "2131463  2020-02-10 14:29:59         2020-02-10 14:58:36   \n",
       "2131501  2020-02-10 14:49:37         2020-02-10 17:27:09   \n",
       "2131557  2020-02-10 20:55:37         2020-02-10 21:00:58   \n",
       "2131568  2020-02-10 20:58:42         2020-02-10 22:16:30   \n",
       "2131728  2020-02-11 02:38:24         2020-02-11 06:27:11   \n",
       "2131926  2020-02-11 13:21:59         2020-02-11 17:27:09   \n",
       "2131900  2020-02-11 14:39:11         2020-02-11 14:46:26   \n",
       "2132259  2020-02-11 14:47:00         2020-02-12 09:02:38   \n",
       "\n",
       "                               incoming_filename observations satellite  \\\n",
       "2131135                    nasa_202002100600.npt           13   1704205   \n",
       "2131289    7840_technosat_crd_20200210_09_00.npt            6   1704205   \n",
       "2131463    7237_technosat_crd_20200210_14_00.npt            3   1704205   \n",
       "2131501                    nasa_202002101700.npt            5   1704205   \n",
       "2131557    7811_technosat_crd_20200210_20_00.npt           14   1704205   \n",
       "2131568  7941_technosat_crd_20200210_2057_00.npt            3   1704205   \n",
       "2131728                    nasa_202002110600.npt            9   1704205   \n",
       "2131926                    nasa_202002111700.npt            5   1704205   \n",
       "2131900    7237_technosat_crd_20200211_14_00.npt            9   1704205   \n",
       "2132259    7819_technosat_crd_20200211_14_00.npt            3   1704205   \n",
       "\n",
       "             start_data_date   station status version wavelength  \n",
       "2131135  2020-02-10 02:25:17  70900513  valid      00    532.000  \n",
       "2131289  2020-02-10 09:16:50  78403501  valid      00    532.080  \n",
       "2131463  2020-02-10 14:29:18  72371901  valid      00    532.000  \n",
       "2131501  2020-02-10 14:46:11  70900513  valid      00    532.000  \n",
       "2131557  2020-02-10 20:51:09  78113802  valid      00    532.000  \n",
       "2131568  2020-02-10 20:57:46  79417701  valid      00    532.000  \n",
       "2131728  2020-02-11 02:36:01  70900513  valid      00    532.000  \n",
       "2131926  2020-02-11 13:20:09  70900513  valid      00    532.000  \n",
       "2131900  2020-02-11 14:36:46  72371901  valid      00    532.000  \n",
       "2132259  2020-02-11 14:39:59  78198201  valid      00    532.000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import slrDataUtils\n",
    "nptDatasetList = slrDataUtils.querySlrData(username_edc, password_edc, url, 'NPT',\n",
    "                                         sat_list[sc_name]['cospar_id'], startCollectionDate, odDate)\n",
    "frdDatasetList = slrDataUtils.querySlrData(username_edc, password_edc, url, 'FRD',\n",
    "                                         sat_list[sc_name]['cospar_id'], startCollectionDate, odDate)\n",
    "display(nptDatasetList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Downloading the list of observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station-id</th>\n",
       "      <th>range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-10 02:25:25.509469</th>\n",
       "      <td>70900513</td>\n",
       "      <td>1.333754e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10 02:25:41.708771</th>\n",
       "      <td>70900513</td>\n",
       "      <td>1.229854e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10 02:25:53.308286</th>\n",
       "      <td>70900513</td>\n",
       "      <td>1.157047e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10 02:26:08.107685</th>\n",
       "      <td>70900513</td>\n",
       "      <td>1.066592e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10 02:26:17.407314</th>\n",
       "      <td>70900513</td>\n",
       "      <td>1.011464e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11 14:38:36.777613</th>\n",
       "      <td>72371901</td>\n",
       "      <td>9.911642e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11 14:38:51.264889</th>\n",
       "      <td>72371901</td>\n",
       "      <td>1.032485e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11 14:45:08.573249</th>\n",
       "      <td>78198201</td>\n",
       "      <td>1.260055e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11 14:45:21.269703</th>\n",
       "      <td>78198201</td>\n",
       "      <td>1.306059e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11 14:45:36.866300</th>\n",
       "      <td>78198201</td>\n",
       "      <td>1.368869e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           station-id         range\n",
       "2020-02-10 02:25:25.509469   70900513  1.333754e+06\n",
       "2020-02-10 02:25:41.708771   70900513  1.229854e+06\n",
       "2020-02-10 02:25:53.308286   70900513  1.157047e+06\n",
       "2020-02-10 02:26:08.107685   70900513  1.066592e+06\n",
       "2020-02-10 02:26:17.407314   70900513  1.011464e+06\n",
       "...                               ...           ...\n",
       "2020-02-11 14:38:36.777613   72371901  9.911642e+05\n",
       "2020-02-11 14:38:51.264889   72371901  1.032485e+06\n",
       "2020-02-11 14:45:08.573249   78198201  1.260055e+06\n",
       "2020-02-11 14:45:21.269703   78198201  1.306059e+06\n",
       "2020-02-11 14:45:36.866300   78198201  1.368869e+06\n",
       "\n",
       "[70 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from orekit.pyhelpers import *\n",
    "from org.orekit.estimation.measurements import Range\n",
    "import slrDataUtils\n",
    "nptDataFrame = slrDataUtils.dlAndParseSlrData(username_edc, password_edc, url, 'NPT', nptDatasetList)\n",
    "# Comment out to download Full-Rate data\n",
    "# frdDataFrame = slrDataUtils.dlAndParseSlrData(username_edc, password_edc, url, 'FRD', frdDatasetList)\n",
    "display(nptDataFrame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding the measurements to the estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "slrDataFrame = nptDataFrame\n",
    "# Comment out to use Full-Rate data\n",
    "# slrDataFrame = frdDataFrame\n",
    "\n",
    "\n",
    "from org.orekit.estimation.measurements import ObservableSatellite \n",
    "observableSatellite = ObservableSatellite(0) # Propagator index = 0\n",
    "\n",
    "for receiveTime, slrData in slrDataFrame.iterrows():\n",
    "    if slrData['station-id'] in stationData.index: # Checking if station exists in the SLRF2014_POS+VEL, because it might not be up-to-date\n",
    "        orekitRange = Range(stationData.loc[slrData['station-id'], 'OrekitGroundStation'], \n",
    "                            True, # Two-way measurement\n",
    "                            datetime_to_absolutedate(receiveTime),\n",
    "                            slrData['range'],\n",
    "                            range_sigma,\n",
    "                            range_weight,\n",
    "                            observableSatellite\n",
    "                           ) # Uses date of signal reception; https://www.orekit.org/static/apidocs/org/orekit/estimation/measurements/Range.html\n",
    "        estimator.addMeasurement(orekitRange)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performing the OD\n",
    "Estimate the orbit. This step can take a long time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimatedPropagatorArray = estimator.estimate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Propagating the estimated orbit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = 300.0\n",
    "date_start = datetime_to_absolutedate(startCollectionDate)\n",
    "date_start = date_start.shiftedBy(-86400.0)\n",
    "date_end = datetime_to_absolutedate(odDate)\n",
    "date_end = date_end.shiftedBy(86400.0) # Stopping 1 day after OD date\n",
    "\n",
    "# First propagating in ephemeris mode\n",
    "estimatedPropagator = estimatedPropagatorArray[0]\n",
    "estimatedInitialState = estimatedPropagator.getInitialState()\n",
    "actualOdDate = estimatedInitialState.getDate()\n",
    "estimatedPropagator.resetInitialState(estimatedInitialState)\n",
    "estimatedPropagator.setEphemerisMode()\n",
    "\n",
    "# Propagating from 1 day before data collection\n",
    "# To 1 week after orbit determination (for CPF generation)\n",
    "estimatedPropagator.propagate(date_start, datetime_to_absolutedate(odDate).shiftedBy(7 * 86400.0))\n",
    "bounded_propagator = estimatedPropagator.getGeneratedEphemeris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Covariance analysis\n",
    "Creating the LVLH frame, computing the covariance matrix in both TOD and LVLH frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the LVLH frame \n",
    "# It must be associated to the bounded propagator, not the original numerical propagator\n",
    "from org.orekit.frames import LocalOrbitalFrame\n",
    "from org.orekit.frames import LOFType\n",
    "lvlh = LocalOrbitalFrame(eci, LOFType.LVLH, bounded_propagator, 'LVLH')\n",
    "\n",
    "# Getting covariance matrix in ECI frame\n",
    "covMat_eci_java = estimator.getPhysicalCovariances(1.0e-10)\n",
    "\n",
    "# Converting matrix to LVLH frame\n",
    "# Getting an inertial frame aligned with the LVLH frame at this instant\n",
    "# The LVLH is normally not inertial, but this should not affect results too much\n",
    "# Reference: David Vallado, Covariance Transformations for Satellite Flight Dynamics Operations, 2003\n",
    "eci2lvlh_frozen = eci.getTransformTo(lvlh, actualOdDate).freeze() \n",
    "\n",
    "# Computing Jacobian\n",
    "from org.orekit.utils import CartesianDerivativesFilter\n",
    "from orekit.pyhelpers import JArray_double2D\n",
    "jacobianDoubleArray = JArray_double2D(6, 6)\n",
    "eci2lvlh_frozen.getJacobian(CartesianDerivativesFilter.USE_PV, jacobianDoubleArray)\n",
    "from org.hipparchus.linear import Array2DRowRealMatrix\n",
    "jacobian = Array2DRowRealMatrix(jacobianDoubleArray)\n",
    "# Applying Jacobian to convert matrix to lvlh\n",
    "covMat_lvlh_java = jacobian.multiply(\n",
    "    covMat_eci_java.multiply(jacobian.transpose()))\n",
    "\n",
    "# Converting the Java matrices to numpy\n",
    "import numpy as np\n",
    "covarianceMat_eci = np.matrix([covMat_eci_java.getRow(iRow) \n",
    "                              for iRow in range(0, covMat_eci_java.getRowDimension())])\n",
    "covarianceMat_lvlh = np.matrix([covMat_lvlh_java.getRow(iRow) \n",
    "                              for iRow in range(0, covMat_lvlh_java.getRowDimension())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computing the position and velocity standard deviation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Position std: cross-track 1.577e-01 m, along-track 8.466e-01 m, out-of-plane 3.273e-01 m\n",
      "Velocity std: cross-track 8.043e-04 m/s, along-track 1.724e-04 m/s, out-of-plane 4.568e-04 m/s\n"
     ]
    }
   ],
   "source": [
    "pos_std_crossTrack = np.sqrt(covarianceMat_lvlh[0,0])\n",
    "pos_std_alongTrack = np.sqrt(covarianceMat_lvlh[1,1])\n",
    "pos_std_outOfPlane = np.sqrt(covarianceMat_lvlh[2,2])\n",
    "print(f'Position std: cross-track {pos_std_crossTrack:.3e} m, along-track {pos_std_alongTrack:.3e} m, out-of-plane {pos_std_outOfPlane:.3e} m')\n",
    "\n",
    "vel_std_crossTrack = np.sqrt(covarianceMat_lvlh[3,3])\n",
    "vel_std_alongTrack = np.sqrt(covarianceMat_lvlh[4,4])\n",
    "vel_std_outOfPlane = np.sqrt(covarianceMat_lvlh[5,5])\n",
    "print(f'Velocity std: cross-track {vel_std_crossTrack:.3e} m/s, along-track {vel_std_alongTrack:.3e} m/s, out-of-plane {vel_std_outOfPlane:.3e} m/s')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CCSDS OPM\n",
    "Writing a CCSDS OPM message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat_properties = {\n",
    "     'mass': sat_list[sc_name]['mass'],\n",
    "     'solar_rad_area': sat_list[sc_name]['cross_section'],\n",
    "     'solar_rad_coeff': sat_list[sc_name]['cd'],\n",
    "     'drag_area': sat_list[sc_name]['cross_section'],\n",
    "     'drag_coeff': sat_list[sc_name]['cr']\n",
    "}\n",
    "\n",
    "from ccsdsUtils import Ccsds\n",
    "ccsds_writer = Ccsds(originator='GOR', object_name=sc_name, object_id=sat_list[sc_name]['norad_id'], sat_properties=sat_properties)\n",
    "\n",
    "pv_eci_init = estimatedInitialState.getPVCoordinates()\n",
    "pos_eci_init = np.array(pv_eci_init.getPosition().toArray())\n",
    "vel_eci_init = np.array(pv_eci_init.getVelocity().toArray())\n",
    "\n",
    "ccsds_writer.write_opm('OPM.txt', absolutedate_to_datetime(actualOdDate), pos_eci_init, vel_eci_init, covarianceMat_eci, 'EARTH', 'GCRF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing residuals\n",
    "Getting the estimated and measured ranges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "propagatorParameters   = estimator.getPropagatorParametersDrivers(True)\n",
    "measurementsParameters = estimator.getMeasurementsParametersDrivers(True)\n",
    "\n",
    "lastEstimations = estimator.getLastEstimations()\n",
    "valueSet = lastEstimations.values()\n",
    "estimatedMeasurements = valueSet.toArray()\n",
    "keySet = lastEstimations.keySet()\n",
    "realMeasurements = keySet.toArray()\n",
    "\n",
    "from org.orekit.estimation.measurements import EstimatedMeasurement\n",
    "from org.orekit.estimation.measurements import Range\n",
    "\n",
    "import pandas as pd\n",
    "observedRangeSeries = pd.Series(dtype='float64')\n",
    "estimatedRangeSeries = pd.Series(dtype='float64')\n",
    "\n",
    "for estMeas in estimatedMeasurements:\n",
    "    estMeas = EstimatedMeasurement.cast_(estMeas)\n",
    "    observedValue = estMeas.getObservedValue()\n",
    "    estimatedValue = estMeas.getEstimatedValue()\n",
    "    pyDateTime = absolutedate_to_datetime(estMeas.date)\n",
    "    observedRangeSeries[pyDateTime] = observedValue[0]\n",
    "    estimatedRangeSeries[pyDateTime] = estimatedValue[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up Plotly for offline mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.io as pio\n",
    "#pio.renderers.default = 'jupyterlab+notebook+png'  # Uncomment for interactive plots\n",
    "pio.renderers.default = 'png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "\n",
    "trace = go.Scattergl(\n",
    "    x=observedRangeSeries.index, y=observedRangeSeries-estimatedRangeSeries,\n",
    "    mode='markers'\n",
    ")\n",
    "\n",
    "data = [trace]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = 'Range residuals',\n",
    "    xaxis = dict(\n",
    "        title = 'Datetime UTC'\n",
    "    ),\n",
    "    yaxis = dict(\n",
    "        title = 'Range residual (m)'\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = dict(data=data, layout=layout)\n",
    "\n",
    "pio.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with CPF\n",
    "The EDC API also provides Consolidated Prediction Files, which contain spacecraft position/velocity in ITRF frame as generated by their orbit determination system. We can compare our orbit determination with the one from the latest CPF prior to the first ranging data used in our orbit determination."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Requesting CPF data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>end_data_date</th>\n",
       "      <th>eph_seq</th>\n",
       "      <th>errors</th>\n",
       "      <th>incoming_date</th>\n",
       "      <th>incoming_filename</th>\n",
       "      <th>provider</th>\n",
       "      <th>satellite</th>\n",
       "      <th>start_data_date</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>682126</th>\n",
       "      <td>2020-02-16 00:00:00</td>\n",
       "      <td>5401</td>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 05:15:00</td>\n",
       "      <td>technosat_cpf_200209_5401.dlr</td>\n",
       "      <td>DLR</td>\n",
       "      <td>1704205</td>\n",
       "      <td>2020-02-09 00:00:00</td>\n",
       "      <td>valid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              end_data_date eph_seq errors        incoming_date  \\\n",
       "682126  2020-02-16 00:00:00    5401         2020-02-09 05:15:00   \n",
       "\n",
       "                    incoming_filename provider satellite      start_data_date  \\\n",
       "682126  technosat_cpf_200209_5401.dlr      DLR   1704205  2020-02-09 00:00:00   \n",
       "\n",
       "       status  \n",
       "682126  valid  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from slrDataUtils import queryCpfData, dlAndParseCpfData\n",
    "cpfList = queryCpfData(username_edc, password_edc, url, \n",
    "                       sat_list[sc_name]['cospar_id'], startCollectionDate - timedelta(days=1))\n",
    "display(cpfList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Downloading and parsing CPF data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from importlib import reload\n",
    "reload(slrDataUtils)\n",
    "from slrDataUtils import queryCpfData, dlAndParseCpfData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-09 00:00:00</th>\n",
       "      <td>6014850.308</td>\n",
       "      <td>-3499568.141</td>\n",
       "      <td>604116.673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-09 00:01:00</th>\n",
       "      <td>5990627.571</td>\n",
       "      <td>-3589772.933</td>\n",
       "      <td>155782.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-09 00:02:00</th>\n",
       "      <td>5940554.505</td>\n",
       "      <td>-3664594.643</td>\n",
       "      <td>-293209.976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-09 00:03:00</th>\n",
       "      <td>5864983.805</td>\n",
       "      <td>-3723498.345</td>\n",
       "      <td>-740963.758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-09 00:04:00</th>\n",
       "      <td>5764379.949</td>\n",
       "      <td>-3766019.979</td>\n",
       "      <td>-1185588.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12 23:56:00</th>\n",
       "      <td>-5604865.884</td>\n",
       "      <td>3554827.519</td>\n",
       "      <td>2110517.183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12 23:57:00</th>\n",
       "      <td>-5428720.465</td>\n",
       "      <td>3550688.717</td>\n",
       "      <td>2534482.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12 23:58:00</th>\n",
       "      <td>-5229723.060</td>\n",
       "      <td>3529891.001</td>\n",
       "      <td>2947650.652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12 23:59:00</th>\n",
       "      <td>-5008866.456</td>\n",
       "      <td>3492330.022</td>\n",
       "      <td>3348263.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13 00:00:00</th>\n",
       "      <td>-4767237.089</td>\n",
       "      <td>3437982.088</td>\n",
       "      <td>3734618.464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5761 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               x            y            z\n",
       "2020-02-09 00:00:00  6014850.308 -3499568.141   604116.673\n",
       "2020-02-09 00:01:00  5990627.571 -3589772.933   155782.425\n",
       "2020-02-09 00:02:00  5940554.505 -3664594.643  -293209.976\n",
       "2020-02-09 00:03:00  5864983.805 -3723498.345  -740963.758\n",
       "2020-02-09 00:04:00  5764379.949 -3766019.979 -1185588.367\n",
       "...                          ...          ...          ...\n",
       "2020-02-12 23:56:00 -5604865.884  3554827.519  2110517.183\n",
       "2020-02-12 23:57:00 -5428720.465  3550688.717  2534482.790\n",
       "2020-02-12 23:58:00 -5229723.060  3529891.001  2947650.652\n",
       "2020-02-12 23:59:00 -5008866.456  3492330.022  3348263.362\n",
       "2020-02-13 00:00:00 -4767237.089  3437982.088  3734618.464\n",
       "\n",
       "[5761 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpfDataFrame = dlAndParseCpfData(username_edc, password_edc, url, \n",
    "                                 [cpfList.index[0]], # If several ephemerides are available for this day, only take the first\n",
    "                                 startCollectionDate - timedelta(days=1),\n",
    "                                 odDate + timedelta(days=1))\n",
    "display(cpfDataFrame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Propagating the solution \n",
    "Propagating the solution and:\n",
    "* Saving the PV coordinates from both the solution and the initial TLE guess.\n",
    "* Computing the difference in LVLH frame between the solution and the initial TLE guess.\n",
    "* Computing the difference in LVLH frame between the solution and the CPF file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Propagating the bounded propagator to retrieve the intermediate states\n",
    "\n",
    "from slrDataUtils import orekitPV2dataframe\n",
    "\n",
    "PV_eci_df = pd.DataFrame()\n",
    "PV_ecef_df = pd.DataFrame()\n",
    "PV_tle_eci_df = pd.DataFrame()\n",
    "deltaPV_tle_lvlh_df = pd.DataFrame(columns=['x', 'y', 'z', 'pos_norm', 'vx', 'vy', 'vz', 'vel_norm'])\n",
    "deltaPV_cpf_lvlh_df = pd.DataFrame(columns=['x', 'y', 'z', 'norm'])\n",
    "\n",
    "# Saving all intermediate\n",
    "from java.util import ArrayList\n",
    "states_list = ArrayList()\n",
    "\n",
    "from org.hipparchus.geometry.euclidean.threed import Vector3D\n",
    "\n",
    "date_current = date_start\n",
    "while date_current.compareTo(date_end) <= 0:\n",
    "    datetime_current = absolutedate_to_datetime(date_current)    \n",
    "    spacecraftState = bounded_propagator.propagate(date_current)\n",
    "    \n",
    "    states_list.add(spacecraftState)\n",
    "    \n",
    "    PV_eci = spacecraftState.getPVCoordinates(eci)\n",
    "    PV_eci_df = PV_eci_df.append(orekitPV2dataframe(PV_eci, datetime_current))\n",
    "    \n",
    "    PV_ecef = spacecraftState.getPVCoordinates(ecef)\n",
    "    PV_ecef_df = PV_ecef_df.append(orekitPV2dataframe(PV_ecef, datetime_current))\n",
    "    \n",
    "    PV_tle_eci = sgp4Propagator.getPVCoordinates(date_current, eci)\n",
    "    PV_tle_eci_df = PV_tle_eci_df.append(orekitPV2dataframe(PV_tle_eci, datetime_current))\n",
    "    \n",
    "    '''\n",
    "    When getting PV coordinates using the SGP4 propagator in LVLH frame, \n",
    "    it is actually a \"delta\" from the PV coordinates resulting from the orbit determination\n",
    "    because this LVLH frame is centered on the satellite's current position based on the orbit determination\n",
    "    '''\n",
    "    deltaPV_lvlh = sgp4Propagator.getPVCoordinates(date_current, lvlh)\n",
    "    deltaPV_tle_lvlh_df.loc[datetime_current] = [deltaPV_lvlh.getPosition().getX(),\n",
    "                                                 deltaPV_lvlh.getPosition().getY(), \n",
    "                                                 deltaPV_lvlh.getPosition().getZ(),\n",
    "                                                 deltaPV_lvlh.getPosition().getNorm(),\n",
    "                                                 deltaPV_lvlh.getVelocity().getX(),\n",
    "                                                 deltaPV_lvlh.getVelocity().getY(), \n",
    "                                                 deltaPV_lvlh.getVelocity().getZ(),\n",
    "                                                 deltaPV_lvlh.getVelocity().getNorm()]\n",
    "    \n",
    "    pos_cpf_ecef = cpfDataFrame.loc[datetime_current]\n",
    "    ecef2lvlh = ecef.getTransformTo(lvlh, date_current)\n",
    "    delta_pos_cpf_lvlh_vector = ecef2lvlh.transformPosition(Vector3D(float(pos_cpf_ecef[0]), float(pos_cpf_ecef[1]), float(pos_cpf_ecef[2])))\n",
    "    deltaPV_cpf_lvlh_df.loc[datetime_current] = [delta_pos_cpf_lvlh_vector.getX(),\n",
    "                                                 delta_pos_cpf_lvlh_vector.getY(), \n",
    "                                                 delta_pos_cpf_lvlh_vector.getZ(),\n",
    "                                                 delta_pos_cpf_lvlh_vector.getNorm()]\n",
    "    \n",
    "    date_current = date_current.shiftedBy(dt)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting difference between estimated orbit and CPF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting position difference. The grey area represents the time window where range measurements were used to perform the orbit determination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# Rectangles to visualise time window for orbit determination.\n",
    "\n",
    "od_window_rectangle =  {\n",
    "    'type': 'rect',\n",
    "    # x-reference is assigned to the x-values\n",
    "    'xref': 'x',\n",
    "    # y-reference is assigned to the plot paper [0,1]\n",
    "    'yref': 'paper',\n",
    "    'x0': startCollectionDate,\n",
    "    'y0': 0,\n",
    "    'x1': odDate,\n",
    "    'y1': 1,\n",
    "    'fillcolor': '#d3d3d3',\n",
    "    'opacity': 0.3,\n",
    "    'line': {\n",
    "        'width': 0,\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "\n",
    "traceX = go.Scattergl(\n",
    "    x = deltaPV_cpf_lvlh_df.index,\n",
    "    y = deltaPV_cpf_lvlh_df['x'],\n",
    "    mode='lines',\n",
    "    name='Cross-track'\n",
    ")\n",
    "\n",
    "traceY = go.Scattergl(\n",
    "    x = deltaPV_cpf_lvlh_df.index,\n",
    "    y = deltaPV_cpf_lvlh_df['y'],\n",
    "    mode='lines',\n",
    "    name='Along track'\n",
    ")\n",
    "\n",
    "traceZ = go.Scattergl(\n",
    "    x = deltaPV_cpf_lvlh_df.index,\n",
    "    y = deltaPV_cpf_lvlh_df['z'],\n",
    "    mode='lines',\n",
    "    name='Out-of-plane'\n",
    ")\n",
    "\n",
    "data = [traceX, traceY, traceZ]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = 'Delta position between CPF and estimation in LVLH frame',\n",
    "    xaxis = dict(\n",
    "        title = 'Datetime UTC'\n",
    "    ),\n",
    "    yaxis = dict(\n",
    "        title = 'Position difference (m)'\n",
    "    ),\n",
    "    shapes=[od_window_rectangle]\n",
    ")\n",
    "\n",
    "fig = dict(data=data, layout=layout)\n",
    "\n",
    "pio.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing own CPF file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A CPF file usually contains 7 days of orbit prediction in ECEF frame with a sample time of 5 minutes, to allow the laser stations to track the satellite.\n",
    "\n",
    "Therefore we have to propagate for 7 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to compute MJD days and seconds of day\n",
    "def datetime_to_mjd_days_seconds(le_datetime):\n",
    "    apparent_clock_offset_s = datetime_to_absolutedate(le_datetime).offsetFrom(\n",
    "        mjd_utc_epoch, utc)\n",
    "    days_since_mjd_epoch = int(np.floor(apparent_clock_offset_s / 86400.0))\n",
    "    seconds_of_day = apparent_clock_offset_s - days_since_mjd_epoch * 86400.0\n",
    "    return days_since_mjd_epoch, seconds_of_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_end_cpf = datetime_to_absolutedate(odDate).shiftedBy(7 * 86400.0)\n",
    "\n",
    "from slrDataUtils import orekitPV2dataframe\n",
    "\n",
    "PV_ecef_cpf_df = pd.DataFrame()\n",
    "\n",
    "dt = 300.0\n",
    "date_current = datetime_to_absolutedate(odDate)\n",
    "while date_current.compareTo(date_end_cpf) <= 0:\n",
    "    datetime_current = absolutedate_to_datetime(date_current)    \n",
    "    spacecraftState = bounded_propagator.propagate(date_current)\n",
    "        \n",
    "    PV_ecef_cpf = spacecraftState.getPVCoordinates(ecef)\n",
    "    PV_ecef_cpf_df = PV_ecef_cpf_df.append(orekitPV2dataframe(PV_ecef_cpf, datetime_current))\n",
    "        \n",
    "    date_current = date_current.shiftedBy(dt)   \n",
    "    \n",
    "PV_ecef_cpf_df['mjd_days'], PV_ecef_cpf_df['seconds_of_day'] = zip(*PV_ecef_cpf_df['DateTimeUTC'].apply(lambda x: \n",
    "                                                                         datetime_to_mjd_days_seconds(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from slrDataUtils import write_cpf\n",
    "write_cpf(cpf_df=PV_ecef_cpf_df, \n",
    "          cpf_filename='cpf_out.ass', \n",
    "          ephemeris_source='ASS',  # Absolutely Serious Society\n",
    "          production_date=odDate, \n",
    "          ephemeris_sequence=(odDate-datetime(2020, 1, 1)).days*10 + 5011, \n",
    "          target_name=sc_name, \n",
    "          cospar_id=sat_list[sc_name]['cospar_id'],\n",
    "          sic=sat_list[sc_name]['sic_id'], \n",
    "          norad_id=str(sat_list[sc_name]['norad_id']), \n",
    "          ephemeris_start_date=odDate, \n",
    "          ephemeris_end_date=absolutedate_to_datetime(date_end_cpf), \n",
    "          step_time=int(dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with TLE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the components of the position different between the TLE and the estimation, in LVLH frame. The grey area represents the time window where range measurements were used to perform the orbit determination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "\n",
    "traceX = go.Scattergl(\n",
    "    x = deltaPV_tle_lvlh_df['x'].index,\n",
    "    y = deltaPV_tle_lvlh_df['x'],\n",
    "    mode='lines',\n",
    "    name='Cross-Track'\n",
    ")\n",
    "\n",
    "traceY = go.Scattergl(\n",
    "    x = deltaPV_tle_lvlh_df['y'].index,\n",
    "    y = deltaPV_tle_lvlh_df['y'],\n",
    "    mode='lines',\n",
    "    name='Along-Track'\n",
    ")\n",
    "\n",
    "traceZ = go.Scattergl(\n",
    "    x = deltaPV_tle_lvlh_df['z'].index,\n",
    "    y = deltaPV_tle_lvlh_df['z'],\n",
    "    mode='lines',\n",
    "    name='Out-Of-Plane'\n",
    ")\n",
    "\n",
    "data = [traceX, traceY, traceZ]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = 'Delta position between TLE and estimation in LVLH frame',\n",
    "    xaxis = dict(\n",
    "        title = 'Datetime UTC'\n",
    "    ),\n",
    "    yaxis = dict(\n",
    "        title = 'Position difference (m)'\n",
    "    ),\n",
    "    shapes=[od_window_rectangle]\n",
    ")\n",
    "\n",
    "fig = dict(data=data, layout=layout)\n",
    "\n",
    "pio.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting an \"enhanced\" TLE\n",
    "Let's fit a TLE to the estimated propagator. This requires the original TLE. If no TLE is available, then a first guess can be built by:\n",
    "\n",
    "* Computing the Keplerian orbital elements from the propagator, for example using RV2COE at one instant.\n",
    "* Writing these elements to the TLE. Although these elements are not mean elements, they will be fitted within a certain range.\n",
    "* Write the BSTAR coefficient equal to zero. It is a free parameter in the fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting\n",
    "Fitting the TLE, based on great example by RomaricH on the Orekit forum: https://forum.orekit.org/t/generation-of-tle/265/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from org.orekit.propagation.conversion import TLEPropagatorBuilder, FiniteDifferencePropagatorConverter\n",
    "from org.orekit.propagation.analytical.tle import TLEPropagator\n",
    "threshold = 1.0  # \"absolute threshold for optimization algorithm\", but no idea about its impact\n",
    "tle_builder = TLEPropagatorBuilder(orekitTle, PositionAngle.MEAN, 1.0)\n",
    "fitter = FiniteDifferencePropagatorConverter(tle_builder, threshold, 1000)\n",
    "fitter.convert(states_list, False, 'BSTAR')  # Setting BSTAR as free parameter\n",
    "tle_propagator = TLEPropagator.cast_(fitter.getAdaptedPropagator())\n",
    "tle_fitted = tle_propagator.getTLE()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare both the original and the \"enhanced\" TLE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 42829U 17042E   20042.65175483  .00000094  00000-0  14859-4 0  9990\n",
      "2 42829  97.5382 290.2442 0014032 353.8494   6.2550 14.91054974140399\n",
      "\n",
      "1 42829U 17042E   20042.65175483  .00000000  00000-0  14711-4 0  9994\n",
      "2 42829  97.5381 290.2444 0014127 354.4677   5.6311 14.91055530140392\n"
     ]
    }
   ],
   "source": [
    "print(orekitTle)\n",
    "print('')\n",
    "print(tle_fitted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us propagate again to save the PV from this new TLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up yet another SGP4 propagator\n",
    "sgp4Propagator_fitted = SGP4(tle_fitted, nadirPointing, sat_list[sc_name]['mass'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "PV_tle_fitted_eci_df = pd.DataFrame()\n",
    "deltaPV_tle_fitted_lvlh_df = pd.DataFrame() # SGP4 PV from SLROD origin\n",
    "\n",
    "date_current = date_start\n",
    "while date_current.compareTo(date_end) <= 0:    \n",
    "    datetime_current = absolutedate_to_datetime(date_current)\n",
    "    \n",
    "    PV_tle_fitted_eci = sgp4Propagator_fitted.getPVCoordinates(date_current, eci)\n",
    "    PV_tle_fitted_eci_df = PV_tle_fitted_eci_df.append(orekitPV2dataframe(PV_tle_fitted_eci, datetime_current))\n",
    "    \n",
    "    deltaPV_tle_fitted_lvlh = sgp4Propagator_fitted.getPVCoordinates(date_current, lvlh)\n",
    "    deltaPV_tle_fitted_lvlh_df = deltaPV_tle_fitted_lvlh_df.append(orekitPV2dataframe(deltaPV_tle_fitted_lvlh, datetime_current))\n",
    "    \n",
    "    date_current = date_current.shiftedBy(dt)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing with estimated propagator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The individual components in LVLH frame are now more centered around zero. The fitting helped. In some cases, the fitted TLE is much better, in some cases not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "\n",
    "traceX = go.Scattergl(\n",
    "    x = deltaPV_tle_fitted_lvlh_df['x'].index,\n",
    "    y = deltaPV_tle_fitted_lvlh_df['x'],\n",
    "    mode='lines',\n",
    "    name='Cross-Track'\n",
    ")\n",
    "\n",
    "traceY = go.Scattergl(\n",
    "    x = deltaPV_tle_fitted_lvlh_df['y'].index,\n",
    "    y = deltaPV_tle_fitted_lvlh_df['y'],\n",
    "    mode='lines',\n",
    "    name='Along-Track'\n",
    ")\n",
    "\n",
    "traceZ = go.Scattergl(\n",
    "    x = deltaPV_tle_fitted_lvlh_df['z'].index,\n",
    "    y = deltaPV_tle_fitted_lvlh_df['z'],\n",
    "    mode='lines',\n",
    "    name='Out-Of-Plane'\n",
    ")\n",
    "\n",
    "data = [traceX, traceY, traceZ]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = 'Delta position between fitted TLE and estimation in LVLH frame',\n",
    "    xaxis = dict(\n",
    "        title = 'Datetime UTC'\n",
    "    ),\n",
    "    yaxis = dict(\n",
    "        title = 'Position difference (m)'\n",
    "    ),\n",
    "    shapes=[od_window_rectangle]\n",
    ")\n",
    "\n",
    "fig = dict(data=data, layout=layout)\n",
    "\n",
    "pio.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}