GMAT OPM Prop JAX Example.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e082734",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "afb57ee7-116e-4a81-a2c2-e06b18265a72",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:jax._src.lib.xla_bridge:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import warnings\n",
    "\n",
    "import datetime as dt\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the GMAT Python API\n",
    "sys.path.append(f'{Path.home()}/Downloads/GMAT-src-R2020a/GMAT-R2020a/GMAT-R2020a-Linux-x64/api/')\n",
    "from load_gmat import *\n",
    "\n",
    "from skyfield.api import EarthSatellite, load\n",
    "from sgp4.api import Satrec, WGS72\n",
    "from sgp4.model import wgs72, wgs84\n",
    "from sgp4.conveniences import jday_datetime, UTC, sat_epoch_datetime, dump_satrec\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import coord_skyfield\n",
    "from coord_skyfield import ITRF2TEME, TEME2ITRF\n",
    "\n",
    "from tlefit_equinoctial_eph_fd import *\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "483b38ce",
   "metadata": {},
   "source": [
    "## Mock up an example OPM\n",
    "This block of code will take an existing satellite from a TLE, propagate it 100 minutes from its epoch, and print the osculating orbit state vector (with some random noise added) in the ITRF (ECEF) coordinate frame. We also print the epoch for that state. If you already have an OPM from your launch provider, you can skip this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68db91ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We'll use an existing satellite and fudge it a bit to fake a pre or post-launch OPM from a launch provider\n",
    "\n",
    "ts = load.timescale()\n",
    "\n",
    "line1 = '1 40019U 14033K   21064.48089419  .00000027  00000-0  13123-4 0  9994'\n",
    "line2 = '2 40019  97.7274 245.3630 0083155 314.3836  45.0579 14.67086574359033'\n",
    "satellite = EarthSatellite(line1, line2, 'APRIZESAT 10', ts)\n",
    "\n",
    "# Propagate TLE 100 minutes from epoch\n",
    "err, r, v = satellite.model.sgp4_tsince(100) # Propagate 100 minutes from epoch, just to make it spicy\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Add some random offsets\n",
    "r += np.random.randn(3) * 10 / 1000\n",
    "v += np.random.randn(3) * 0.001 / 1000\n",
    "\n",
    "# Update the epoch\n",
    "epoch_dt = sat_epoch_datetime(satellite.model) + dt.timedelta(minutes=100)\n",
    "epoch = epoch_dt.strftime('%d %b %Y %H:%M:%S.%f')[:-3]\n",
    "\n",
    "# Convert the new state vector from TEME (SGP4) to ITRF (ECEF) coordinate frame\n",
    "r, v = TEME2ITRF([epoch_dt], [(r, v)])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "70104dc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "05 Mar 2021 13:12:29.258\n",
      "[-3173.91430404 -6203.94723041   802.14005485] [-0.99114021  1.42336564  7.44539408]\n"
     ]
    }
   ],
   "source": [
    "print(epoch)\n",
    "print(r, v)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a0156bba",
   "metadata": {},
   "source": [
    "## OPM Input\n",
    "If you have a pre or post-launch OPM from your launch provider, enter the epoch and state vector in the cell below. This script expects a UTC timestamp and a state vector in the rotating ITRF (ECEF) coordinate frame. This is usually what is provided, but if you have something else, you can usually do some extra work to get to ECEF.\n",
    "\n",
    "If you don't have an OPM or you're just experimenting, use the dummy OPM data we generated above.\n",
    "\n",
    "Since we're using python lists, you can enter multiple epochs and state vectors to represent multiple satellites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5b398658",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = [\"05 Mar 2021 13:12:29.258\"]\n",
    "\n",
    "states = [([-3173.91430404, -6203.94723041, 802.14005485], [-0.99114021, 1.42336564, 7.44539408])]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3f8eb7fd",
   "metadata": {},
   "source": [
    "Loop through OPM satellites and use GMAT to propagate the spacecraft forward by 3 days using a high precision orbit propagator (Special Perturbations) to obtain an ephemeris we can later use to  fit a TLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3400f15d",
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      "0\n"
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   "source": [
    "for idx, (t, state) in enumerate(zip(epochs, states)):\n",
    "    print(idx)\n",
    "\n",
    "    r, v = state[0], state[1]\n",
    "\n",
    "    gmat.LoadScript(\"gmat/prelaunch_opm.script\")\n",
    "\n",
    "    sat = gmat.GetObject(\"Sat\")\n",
    "    sat.SetField(\"Epoch\", t)\n",
    "    sat.SetField(\"X\", r[0])\n",
    "    sat.SetField(\"Y\", r[1])\n",
    "    sat.SetField(\"Z\", r[2])\n",
    "    sat.SetField(\"VX\", v[0])\n",
    "    sat.SetField(\"VY\", v[1])\n",
    "    sat.SetField(\"VZ\", v[2])\n",
    "\n",
    "    # You will want to set appropriate values for drag and mass specific to your staellites\n",
    "    sat.SetField(\"DragArea\", 0.087) # m^2\n",
    "    sat.SetField(\"DryMass\", 15) # kg\n",
    "    \n",
    "    eph = gmat.GetObject(\"EphemerisFile1\")\n",
    "    eph.SetField(\"Filename\", f'/home/dan/GIT/tle-tailor/output/EphemerisFile_Sat{idx}.e')\n",
    "\n",
    "    gmat.RunScript()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "56a69b24",
   "metadata": {},
   "source": [
    "Read the GMAT ephemeris files and save them as feather files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e684104",
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx, t in enumerate(epochs):\n",
    "\n",
    "    df = pd.read_fwf(f'{Path.home()}/GIT/tle-tailor/output/EphemerisFile_Sat{idx}.e', widths=(21, 24, 24, 24, 24, 24, 24), names=('time', 'x', 'y', 'z', 'xdot', 'ydot', 'zdot'), skiprows=15, skipfooter=4)\n",
    "    df['timestamp'] = pd.Timestamp(epochs[idx], tz='UTC') + pd.to_timedelta(df.time, unit='s')\n",
    "    df.to_feather(f'sat{idx}.fth')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "successful-tobacco",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4f62c340",
   "metadata": {},
   "source": [
    "Loop over the satellites' ephemeris we generated from GMAT and use it to fit a TLE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f9b2b016-9a05-4346-8c0b-7beda379564b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2021-03-05 13:17:31.498125+00:00\n",
      "\n",
      "1 99999U 1800100  21064.55383678  .00000000  00000-0  12449-3 0  9996\n",
      "2 99999  97.7289 245.4295 0082987 313.9440  70.5114 14.67088174    13\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for idx in range(len(states)):\n",
    "\n",
    "    df = pd.read_feather(f'sat{idx}.fth')\n",
    "\n",
    "    # Convert state vectors from ECEF to TEME\n",
    "    t = df.timestamp\n",
    "    ephemeris = [((row['x'], row['y'], row['z']), (row['xdot'], row['ydot'], row['zdot'])) for idx, row in df.iterrows()]\n",
    "\n",
    "    ephemeris_teme = ITRF2TEME(t, ephemeris)\n",
    "    ephemeris = ephemeris_teme\n",
    "    \n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter(\"ignore\")\n",
    "        t = np.array([_t.to_pydatetime() for _t in t])\n",
    "\n",
    "    # Run the fitter\n",
    "    last_obs = 4320\n",
    "    obs_stride = 1\n",
    "    epoch_obs = 0\n",
    "    lamda = 1e-3 * 0 + 1 # Interesting.The smaller number works, but diverges. This is better\n",
    "    rms_epsilon = 0.002\n",
    "    iterations, solve_sat, elements_coe, sigma, sigmas, dxs, bs, lamdas, b_epoch, b_new_epoch, b, P, A = \\\n",
    "    test_tle_fit_normalized_equinoctial(t, ephemeris, last_obs=last_obs, obs_stride=obs_stride, epoch_obs=epoch_obs, lamda=lamda, rms_epsilon=rms_epsilon, debug=False)\n",
    "\n",
    "    # Optionally thin the observations\n",
    "    tt = t[::obs_stride]\n",
    "    tephemeris = ephemeris[::obs_stride]\n",
    "\n",
    "    if last_obs:\n",
    "        tt = tt[:last_obs]\n",
    "        tephemeris = tephemeris[:last_obs]\n",
    "\n",
    "    print(f'Epoch: {tt[epoch_obs]}\\n')\n",
    "\n",
    "    print('\\n'.join(exporter.export_tle(solve_sat.model)))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c96268b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "attachments": {},
   "cell_type": "markdown",
   "id": "f9d20823",
   "metadata": {},
   "source": [
    "## Find the distance to TLEs with the same launch and proximate epoch\n",
    "\n",
    "Note: It only makes sense to do this if your satellite has already launched and 18 SDS is tracking objects from your launch. Or you can use it with the example satellite as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a7a3aa4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "\n",
    "import json\n",
    "\n",
    "from sgp4.conveniences import sat_epoch_datetime, jday_datetime\n",
    "\n",
    "from spacetrack import SpaceTrackClient\n",
    "import spacetrack.operators as op\n",
    "\n",
    "st = SpaceTrackClient(os.environ['SPACETRACK_USER'], os.environ['SPACETRACK_PWD'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b5da5852",
   "metadata": {},
   "source": [
    "Search for objects in the same launch group. For your own satellite, you'll want to update the `object_id_like` variable to match the launch year and launch number portions of your launch's international designator. In the example, the satellite was part of the 33rd launch in 2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfea155f",
   "metadata": {},
   "outputs": [],
   "source": [
    "object_id_like = \"2014-033~~\" \n",
    "launch_objects = json.loads(st.gp(object_id=op.like(object_id_like), orderby='TLE_LINE1', format='json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c031073a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tles = []\n",
    "\n",
    "sat_ids = [sat['NORAD_CAT_ID'] for sat in launch_objects]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9d5192af",
   "metadata": {},
   "source": [
    "Get the General Perturbations (GP) aka TLEs for objects in the launch group and centered in time around the epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5918816f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sat_gps = json.loads(st.gp_history(norad_cat_id=','.join(sat_ids),  epoch=op.inclusive_range((sat_epoch_datetime(solve_sat.model) - dt.timedelta(days=1)).date(), (sat_epoch_datetime(solve_sat.model) + dt.timedelta(days=+2)).date()), orderby='TLE_LINE1', format='json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b276c8ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def solution_residuals(t, solve_sat, comp_sat):\n",
    "    \n",
    "    bs = []\n",
    "    \n",
    "    offset_idxs = range(len(t))\n",
    "    \n",
    "    for offset_idx in offset_idxs:\n",
    "\n",
    "        # Obs - Nom\n",
    "        jd, jdf = jday_datetime(t[offset_idx])\n",
    "        b = np.ravel(np.array(np.array(solve_sat.sgp4(jd, jdf)[1:] - np.array(comp_sat.sgp4(jd, jdf)[1:]))))[:3]\n",
    "\n",
    "        bs.append(b.T @ b)\n",
    "        \n",
    "    return np.array(bs)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7b40b17b",
   "metadata": {},
   "source": [
    "Loop over objects in the launch group and calculate the residuals between the SpaceTrack object and the TLE we solved for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df38cc26",
   "metadata": {},
   "outputs": [],
   "source": [
    "candidates = []\n",
    "\n",
    "for sat in sat_gps:\n",
    "    candidate_sat = EarthSatellite(sat['TLE_LINE1'], sat['TLE_LINE2'], sat['TLE_LINE0'], ts)\n",
    "\n",
    "    res = solution_residuals(t, solve_sat.model, candidate_sat.model)\n",
    "    res_epoch = np.sqrt(res[epoch_obs])\n",
    "    res = np.sqrt(np.mean(res))\n",
    "\n",
    "    candidates.append((sat[\"NORAD_CAT_ID\"], sat[\"OBJECT_ID\"], sat[\"EPOCH\"], sat[\"TLE_LINE0\"], sat[\"TLE_LINE1\"], sat[\"TLE_LINE2\"], res, res_epoch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "08bff3f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_candidates = pd.DataFrame(candidates, columns=['norad_cat_id', 'object_id', 'epoch', 'tle_line0', 'tle_line1', 'tle_line2', 'residual', 'residual_epoch'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b439b8e6",
   "metadata": {},
   "source": [
    "If we're lucky, when we sort the results by RMS residual, the top (mimimum residual) will correspond to our object. Ideally, the next closest object will have a much larger residual. Note you will likely have several TLEs for each object because we search over several days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dc90d850",
   "metadata": {},
   "outputs": [
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       "      <th>tle_line0</th>\n",
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       "      <td>3.590873</td>\n",
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       "      <td>40019</td>\n",
       "      <td>2014-033K</td>\n",
       "      <td>2021-03-04T01:10:01.639776</td>\n",
       "      <td>0 APRIZESAT 10</td>\n",
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       "      <td>2 40019  97.7273 244.0120 0083241 319.0193  40...</td>\n",
       "      <td>3.629966</td>\n",
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       "      <td>2014-033K</td>\n",
       "      <td>2021-03-04T17:32:09.071520</td>\n",
       "      <td>0 APRIZESAT 10</td>\n",
       "      <td>1 40019U 14033K   21063.73066055  .00000024  0...</td>\n",
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       "      <td>2014-033K</td>\n",
       "      <td>2021-03-04T17:32:09.071520</td>\n",
       "      <td>0 APRIZESAT 10</td>\n",
       "      <td>1 40019U 14033K   21063.73066055  .00000024  0...</td>\n",
       "      <td>2 40019  97.7274 244.6553 0083197 316.8128  42...</td>\n",
       "      <td>3.732681</td>\n",
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       "      <th>116</th>\n",
       "      <td>40019</td>\n",
       "      <td>2014-033K</td>\n",
       "      <td>2021-03-06T21:54:56.929824</td>\n",
       "      <td>0 APRIZESAT 10</td>\n",
       "      <td>1 40019U 14033K   21065.91315891  .00000034  0...</td>\n",
       "      <td>2 40019  97.7276 246.7139 0083072 309.7504  49...</td>\n",
       "      <td>3.741146</td>\n",
       "      <td>0.396120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>40019</td>\n",
       "      <td>2014-033K</td>\n",
       "      <td>2021-03-06T23:33:09.677664</td>\n",
       "      <td>0 APRIZESAT 10</td>\n",
       "      <td>1 40019U 14033K   21065.98136201  .00000035  0...</td>\n",
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       "      <td>3.742400</td>\n",
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       "      <td>0 APRIZESAT 10</td>\n",
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       "      <td>3.742950</td>\n",
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       "      <th>109</th>\n",
       "      <td>40019</td>\n",
       "      <td>2014-033K</td>\n",
       "      <td>2021-03-05T00:05:00.048192</td>\n",
       "      <td>0 APRIZESAT 10</td>\n",
       "      <td>1 40019U 14033K   21064.00347278  .00000025  0...</td>\n",
       "      <td>2 40019  97.7274 244.9126 0083182 315.9314  43...</td>\n",
       "      <td>3.743111</td>\n",
       "      <td>0.420300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>40019</td>\n",
       "      <td>2014-033K</td>\n",
       "      <td>2021-03-05T18:05:20.238144</td>\n",
       "      <td>0 APRIZESAT 10</td>\n",
       "      <td>1 40019U 14033K   21064.75370646  .00000028  0...</td>\n",
       "      <td>2 40019  97.7275 245.6203 0083134 313.5003  45...</td>\n",
       "      <td>3.743621</td>\n",
       "      <td>0.404995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>40019</td>\n",
       "      <td>2014-033K</td>\n",
       "      <td>2021-03-05T18:05:20.238144</td>\n",
       "      <td>0 APRIZESAT 10</td>\n",
       "      <td>1 40019U 14033K   21064.75370646  .00000028  0...</td>\n",
       "      <td>2 40019  97.7275 245.6203 0083134 313.5003  45...</td>\n",
       "      <td>3.743621</td>\n",
       "      <td>0.404995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>40019</td>\n",
       "      <td>2014-033K</td>\n",
       "      <td>2021-03-06T12:05:40.445376</td>\n",
       "      <td>0 APRIZESAT 10</td>\n",
       "      <td>1 40019U 14033K   21065.50394034  .00000031  0...</td>\n",
       "      <td>2 40019  97.7275 246.3279 0083098 311.0743  48...</td>\n",
       "      <td>3.744374</td>\n",
       "      <td>0.398414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>40019</td>\n",
       "      <td>2014-033K</td>\n",
       "      <td>2021-03-06T12:05:40.445376</td>\n",
       "      <td>0 APRIZESAT 10</td>\n",
       "      <td>1 40019U 14033K   21065.50394034  .00000031  0...</td>\n",
       "      <td>2 40019  97.7275 246.3279 0083098 311.0743  48...</td>\n",
       "      <td>3.744374</td>\n",
       "      <td>0.398414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>40019</td>\n",
       "      <td>2014-033K</td>\n",
       "      <td>2021-03-06T02:16:23.967840</td>\n",
       "      <td>0 APRIZESAT 10</td>\n",
       "      <td>1 40019U 14033K   21065.09472185  .00000026  0...</td>\n",
       "      <td>2 40019  97.7275 245.9420 0083118 312.3991  47...</td>\n",
       "      <td>3.780917</td>\n",
       "      <td>0.406406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>40034</td>\n",
       "      <td>2014-033AA</td>\n",
       "      <td>2021-03-04T03:15:40.659264</td>\n",
       "      <td>0 ANTELSAT</td>\n",
       "      <td>1 40034U 14033AA  21063.13588726  .00000344  0...</td>\n",
       "      <td>2 40034  97.6882 271.9813 0057274 238.6148 120...</td>\n",
       "      <td>4558.820972</td>\n",
       "      <td>8261.488479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>40034</td>\n",
       "      <td>2014-033AA</td>\n",
       "      <td>2021-03-04T13:02:02.023008</td>\n",
       "      <td>0 ANTELSAT</td>\n",
       "      <td>1 40034U 14033AA  21063.54307897  .00000325  0...</td>\n",
       "      <td>2 40034  97.6882 272.3679 0057293 237.2686 122...</td>\n",
       "      <td>4558.829767</td>\n",
       "      <td>8261.547223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>40034</td>\n",
       "      <td>2014-033AA</td>\n",
       "      <td>2021-03-04T13:02:02.023008</td>\n",
       "      <td>0 ANTELSAT</td>\n",
       "      <td>1 40034U 14033AA  21063.54307897  .00000325  0...</td>\n",
       "      <td>2 40034  97.6882 272.3679 0057293 237.2686 122...</td>\n",
       "      <td>4558.829767</td>\n",
       "      <td>8261.547223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>40034</td>\n",
       "      <td>2014-033AA</td>\n",
       "      <td>2021-03-05T11:50:11.864832</td>\n",
       "      <td>0 ANTELSAT</td>\n",
       "      <td>1 40034U 14033AA  21064.49319288  .00000323  0...</td>\n",
       "      <td>2 40034  97.6882 273.2699 0057317 234.1334 125...</td>\n",
       "      <td>4558.845567</td>\n",
       "      <td>8261.565773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>40034</td>\n",
       "      <td>2014-033AA</td>\n",
       "      <td>2021-03-05T19:58:49.659744</td>\n",
       "      <td>0 ANTELSAT</td>\n",
       "      <td>1 40034U 14033AA  21064.83251921  .00000328  0...</td>\n",
       "      <td>2 40034  97.6882 273.5920 0057333 233.0096 126...</td>\n",
       "      <td>4558.850980</td>\n",
       "      <td>8261.556216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>40034</td>\n",
       "      <td>2014-033AA</td>\n",
       "      <td>2021-03-05T19:58:49.659744</td>\n",
       "      <td>0 ANTELSAT</td>\n",
       "      <td>1 40034U 14033AA  21064.83251921  .00000328  0...</td>\n",
       "      <td>2 40034  97.6882 273.5920 0057333 233.0096 126...</td>\n",
       "      <td>4558.850980</td>\n",
       "      <td>8261.556216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    norad_cat_id   object_id                       epoch       tle_line0  \\\n",
       "105        40019   2014-033K  2021-03-04T10:59:18.094848  0 APRIZESAT 10   \n",
       "106        40019   2014-033K  2021-03-04T10:59:18.094848  0 APRIZESAT 10   \n",
       "104        40019   2014-033K  2021-03-04T01:10:01.639776  0 APRIZESAT 10   \n",
       "107        40019   2014-033K  2021-03-04T17:32:09.071520  0 APRIZESAT 10   \n",
       "108        40019   2014-033K  2021-03-04T17:32:09.071520  0 APRIZESAT 10   \n",
       "116        40019   2014-033K  2021-03-06T21:54:56.929824  0 APRIZESAT 10   \n",
       "117        40019   2014-033K  2021-03-06T23:33:09.677664  0 APRIZESAT 10   \n",
       "110        40019   2014-033K  2021-03-05T11:32:29.258016  0 APRIZESAT 10   \n",
       "109        40019   2014-033K  2021-03-05T00:05:00.048192  0 APRIZESAT 10   \n",
       "112        40019   2014-033K  2021-03-05T18:05:20.238144  0 APRIZESAT 10   \n",
       "111        40019   2014-033K  2021-03-05T18:05:20.238144  0 APRIZESAT 10   \n",
       "114        40019   2014-033K  2021-03-06T12:05:40.445376  0 APRIZESAT 10   \n",
       "115        40019   2014-033K  2021-03-06T12:05:40.445376  0 APRIZESAT 10   \n",
       "113        40019   2014-033K  2021-03-06T02:16:23.967840  0 APRIZESAT 10   \n",
       "251        40034  2014-033AA  2021-03-04T03:15:40.659264      0 ANTELSAT   \n",
       "252        40034  2014-033AA  2021-03-04T13:02:02.023008      0 ANTELSAT   \n",
       "253        40034  2014-033AA  2021-03-04T13:02:02.023008      0 ANTELSAT   \n",
       "254        40034  2014-033AA  2021-03-05T11:50:11.864832      0 ANTELSAT   \n",
       "256        40034  2014-033AA  2021-03-05T19:58:49.659744      0 ANTELSAT   \n",
       "255        40034  2014-033AA  2021-03-05T19:58:49.659744      0 ANTELSAT   \n",
       "\n",
       "                                             tle_line1  \\\n",
       "105  1 40019U 14033K   21063.45784832  .00000031  0...   \n",
       "106  1 40019U 14033K   21063.45784832  .00000031  0...   \n",
       "104  1 40019U 14033K   21063.04863009  .00000026  0...   \n",
       "107  1 40019U 14033K   21063.73066055  .00000024  0...   \n",
       "108  1 40019U 14033K   21063.73066055  .00000024  0...   \n",
       "116  1 40019U 14033K   21065.91315891  .00000034  0...   \n",
       "117  1 40019U 14033K   21065.98136201  .00000035  0...   \n",
       "110  1 40019U 14033K   21064.48089419  .00000027  0...   \n",
       "109  1 40019U 14033K   21064.00347278  .00000025  0...   \n",
       "112  1 40019U 14033K   21064.75370646  .00000028  0...   \n",
       "111  1 40019U 14033K   21064.75370646  .00000028  0...   \n",
       "114  1 40019U 14033K   21065.50394034  .00000031  0...   \n",
       "115  1 40019U 14033K   21065.50394034  .00000031  0...   \n",
       "113  1 40019U 14033K   21065.09472185  .00000026  0...   \n",
       "251  1 40034U 14033AA  21063.13588726  .00000344  0...   \n",
       "252  1 40034U 14033AA  21063.54307897  .00000325  0...   \n",
       "253  1 40034U 14033AA  21063.54307897  .00000325  0...   \n",
       "254  1 40034U 14033AA  21064.49319288  .00000323  0...   \n",
       "256  1 40034U 14033AA  21064.83251921  .00000328  0...   \n",
       "255  1 40034U 14033AA  21064.83251921  .00000328  0...   \n",
       "\n",
       "                                             tle_line2     residual  \\\n",
       "105  2 40019  97.7273 244.3980 0083219 317.6953  41...     3.590873   \n",
       "106  2 40019  97.7273 244.3980 0083219 317.6953  41...     3.590873   \n",
       "104  2 40019  97.7273 244.0120 0083241 319.0193  40...     3.629966   \n",
       "107  2 40019  97.7274 244.6553 0083197 316.8128  42...     3.732681   \n",
       "108  2 40019  97.7274 244.6553 0083197 316.8128  42...     3.732681   \n",
       "116  2 40019  97.7276 246.7139 0083072 309.7504  49...     3.741146   \n",
       "117  2 40019  97.7276 246.7783 0083069 309.5300  49...     3.742400   \n",
       "110  2 40019  97.7274 245.3630 0083155 314.3836  45...     3.742950   \n",
       "109  2 40019  97.7274 244.9126 0083182 315.9314  43...     3.743111   \n",
       "112  2 40019  97.7275 245.6203 0083134 313.5003  45...     3.743621   \n",
       "111  2 40019  97.7275 245.6203 0083134 313.5003  45...     3.743621   \n",
       "114  2 40019  97.7275 246.3279 0083098 311.0743  48...     3.744374   \n",
       "115  2 40019  97.7275 246.3279 0083098 311.0743  48...     3.744374   \n",
       "113  2 40019  97.7275 245.9420 0083118 312.3991  47...     3.780917   \n",
       "251  2 40034  97.6882 271.9813 0057274 238.6148 120...  4558.820972   \n",
       "252  2 40034  97.6882 272.3679 0057293 237.2686 122...  4558.829767   \n",
       "253  2 40034  97.6882 272.3679 0057293 237.2686 122...  4558.829767   \n",
       "254  2 40034  97.6882 273.2699 0057317 234.1334 125...  4558.845567   \n",
       "256  2 40034  97.6882 273.5920 0057333 233.0096 126...  4558.850980   \n",
       "255  2 40034  97.6882 273.5920 0057333 233.0096 126...  4558.850980   \n",
       "\n",
       "     residual_epoch  \n",
       "105        0.475438  \n",
       "106        0.475438  \n",
       "104        0.487599  \n",
       "107        0.433772  \n",
       "108        0.433772  \n",
       "116        0.396120  \n",
       "117        0.404267  \n",
       "110        0.416184  \n",
       "109        0.420300  \n",
       "112        0.404995  \n",
       "111        0.404995  \n",
       "114        0.398414  \n",
       "115        0.398414  \n",
       "113        0.406406  \n",
       "251     8261.488479  \n",
       "252     8261.547223  \n",
       "253     8261.547223  \n",
       "254     8261.565773  \n",
       "256     8261.556216  \n",
       "255     8261.556216  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_candidates.sort_values('residual').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0372f531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_ix = df_candidates.sort_values('residual').index[0]\n",
    "obj_ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ecef5add",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('1 99999U 1800100  21064.55383678  .00000000  00000-0  12449-3 0  9996', '2 99999  97.7289 245.4295 0082987 313.9440  70.5114 14.67088174    13')\n",
      "('1 40049U 14033AR  21065.75113626  .00000369  00000-0  45874-4 0  9993', '2 40049  97.9297 354.7348 0013281 153.7451 206.4441 14.88346192363927')\n"
     ]
    }
   ],
   "source": [
    "print(exporter.export_tle(solve_sat.model))\n",
    "print((sat['TLE_LINE1'], sat['TLE_LINE2']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "26605381",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 40019U 14033K   21063.45784832  .00000031  00000-0  13645-4 0  9995\n",
      "2 40019  97.7273 244.3980 0083219 317.6953  41.7853 14.67086494358887\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('1 40019U 14033K   21063.45784832  .00000031  00000-0  13645-4 0  9995',\n",
       " '2 40019  97.7273 244.3980 0083219 317.6953  41.7853 14.67086494358887')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat = sat_gps[obj_ix]\n",
    "print(sat['TLE_LINE1'])\n",
    "print(sat['TLE_LINE2'])\n",
    "candidate_sat = EarthSatellite(sat['TLE_LINE1'], sat['TLE_LINE2'], sat['TLE_LINE0'], ts)\n",
    "exporter.export_tle(candidate_sat.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "444ccbc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.41068154, -0.2083052 , -0.11828184])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jd, jdf = solve_sat.model.jdsatepoch, solve_sat.model.jdsatepochF\n",
    "(np.ravel(solve_sat.model.sgp4(jd, jdf)[1:]) - np.ravel(candidate_sat.model.sgp4(jd, jdf)[1:]))[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0d9e8ca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Position Residual (m)')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.sqrt(solution_residuals(t, solve_sat.model, candidate_sat.model)) * 1000)\n",
    "plt.title(\"Solution vs. Candidate NORAD Object Residuals\")\n",
    "plt.xlabel(\"Observation number\")\n",
    "plt.ylabel(\"Position Residual (m)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1009161",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ce81ed56-c64b-4c88-a185-b862d3607c6e",
   "metadata": {},
   "source": [
    "## Calculate new TLE at same epoch as Reference / Truth TLE\n",
    "\n",
    "* Grab the the epoch from the reference (truth) TLE\n",
    "* Fit all ephemeris so epoch is in the sweet spot of the bathtub\n",
    "    * This is probably bullshit, since we wouldn't have the future ephemeris yet\n",
    "* Calculate the predicted state vector at the reference epoch using the fitted model\n",
    "* add the predicted state vector to our ephemeris\n",
    "* Re-fit up to the predicted state vector to get the mean elements for the reference epoch\n",
    "* Compare fitted TLE to reference TLE\n",
    "\n",
    "### Notes\n",
    "* Probably better to interpolate ephemeris or use EKF to reference epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "13ca5515-3de9-43c3-a1a0-7bff462aecc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_tle = candidate_sat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fbf1cfe4-0c37-43bd-b9f6-c3e88f86a2ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 3, 4, 10, 59, 18, 94847, tzinfo=UTC)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat_epoch_datetime(ref_tle.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1c3e0bfa-3b9b-4c70-8731-aa165c2ed27d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ajd, ajdf = jday_datetime(sat_epoch_datetime(ref_tle.model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1aab5431-18bf-418b-8a5e-48243d50ab57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We'll do the thinning outside so we can preserve the new epoch\n",
    "\n",
    "if obs_stride:\n",
    "    tt = t[::obs_stride]\n",
    "    et = ephemeris[::obs_stride]\n",
    "\n",
    "if last_obs:\n",
    "    tt = tt[:last_obs]\n",
    "    et = et[:last_obs]\n",
    "\n",
    "aligned_epoch_obs = np.searchsorted(tt, sat_epoch_datetime(ref_tle.model))\n",
    "# ii = np.searchsorted(t, sat_epoch_datetime(ref_tle.model))\n",
    "tt = np.insert(t, aligned_epoch_obs, sat_epoch_datetime(ref_tle.model))\n",
    "et = ephemeris[:aligned_epoch_obs] + [tuple(np.array(x) for x in ref_tle.model.sgp4(ajd, ajdf)[1:])] + ephemeris[aligned_epoch_obs:] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1a59ceca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 3, 4, 10, 59, 18, 94847, tzinfo=UTC)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat_epoch_datetime(ref_tle.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a424100d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 3, 4, 10, 59, 18, 94847, tzinfo=UTC)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt[aligned_epoch_obs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0e62fca3-46fc-4aae-88a6-e62373d1c5d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "iterations, aligned_solve_sat, elements_coe, sigma, sigmas, dxs, bs, lamdas, b_epoch, b_new_epoch, b, P, A = test_tle_fit_normalized_equinoctial(tt, et, last_obs=None, obs_stride=None, epoch_obs=aligned_epoch_obs, lamda=1e-3, rms_epsilon=0.0001, debug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "88224b20-67ca-4904-8b83-3e3299c44146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1 99999U 14033K   21063.45784832  .00000000  00000-0  13844-3 0  9992',\n",
       " '2 99999  97.7289 244.3959 0082968 317.4242  42.0541 14.67086119    11')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aligned_solve_sat.model.intldesg = ref_tle.model.intldesg\n",
    "# aligned_solve_sat.model.satnum = ref_tle.model.satnum # FIXME: assign this in satrec at creation\n",
    "aligned_solve_sat.model.classification = ref_tle.model.classification\n",
    "# aligned_solve_sat.model.ndot = ref_tle.model.ndot # Useless, but pretty\n",
    "# aligned_solve_sat.model.nddot = ref_tle.model.nddot# Useless, but pretty\n",
    "\n",
    "exporter.export_tle(aligned_solve_sat.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0512d433-60d7-4de9-945a-b498f449c894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1 40019U 14033K   21063.45784832  .00000031  00000-0  13645-4 0  9995',\n",
       " '2 40019  97.7273 244.3980 0083219 317.6953  41.7853 14.67086494358887')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exporter.export_tle(ref_tle.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c74616e7-21b8-4f7e-b9d2-fe813bfcab9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Position Residual (m)')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.sqrt(solution_residuals(t, aligned_solve_sat.model, ref_tle.model)) * 1000)\n",
    "plt.title(\"Solution vs. Candidate NORAD Object Residuals\")\n",
    "plt.xlabel(\"Observation number\")\n",
    "plt.ylabel(\"Position Residual (m)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7744e542",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.10.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "422d96f55d2b279cc3694c0d7101cd7135f83d32ba2b51a1c4e4fd4874965f43"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}