TLEFit - EQN - GPS JAX - GPS Sat.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "likely-hearing",
   "metadata": {},
   "outputs": [],
   "source": [
    "%config Completer.use_jedi = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "indirect-brighton",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "import datetime as dt\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a103c735",
   "metadata": {},
   "outputs": [],
   "source": [
    "import coord_skyfield\n",
    "from coord_skyfield import ITRF2TEME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "successful-tobacco",
   "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": [
    "from tlefit_equinoctial_eph_jax import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38e3e17b-058f-4d23-9bef-1415c8a2c27b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d6255d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_825819/2830078750.py:8: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
      "  t = np.array([_t.to_pydatetime() for _t in t])\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_feather('ephemeris/gpsd6n22short.fth')\n",
    "\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",
    "t = np.array([_t.to_pydatetime() for _t in t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "persistent-command",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial semi-major axis (a) = 26558.668 km\n",
      "COE elements (original) = [26558.66821181454, 0.004955996580964593, 0.9534815917054646, 5.5866081275021084, 4.608844240039538, 0.09615470379843861, 1e-06]\n",
      "Residuals at epoch time [-1.15872338e+01 -1.88040707e+00 -6.43374342e-01 -6.90100427e-04\n",
      "  6.81057778e-04 -1.47135401e-03]\n",
      "Residual magnitudes at epoch time 11.7564, 0.00176209\n",
      "\n",
      "\n",
      "#################### ITERATION 1 ####################\n",
      "\n",
      "Condition number (A): 552434.741\n",
      "Condition number (ATWA_acc): 3890524036554012.5\n",
      "Lambda:  0.1\n",
      "Residuals after/before 2.62e+04 < 6.88e+09\n",
      "Covariance a: 217.159 m\n",
      "dx  [ 1.73672797e+00 -1.69973201e-04 -2.25566410e-04  1.90197416e-04\n",
      " -2.19695833e-04  2.84561044e-05  1.44184906e-04]\n",
      "COE elements = [26560.404939789303, DeviceArray(0.00523526, dtype=float64), DeviceArray(0.95373862, dtype=float64), DeviceArray(5.58631729, dtype=float64), DeviceArray(4.61742971, dtype=float64), DeviceArray(0.08805028, dtype=float64), 0.00014518490602093316]\n",
      "EQN elements = (26560.404939789303, DeviceArray(-0.00372561, dtype=float64), DeviceArray(-0.00367801, dtype=float64), DeviceArray(4.00861196, dtype=float64), DeviceArray(-0.33158817, dtype=float64), DeviceArray(0.39618707, dtype=float64), 0.00014518490602093316)\n",
      "Residual (b) = [ 1.09155863e+01  2.53668061e+00 -5.53713248e-01  7.69550083e-04\n",
      " -7.70229194e-04  1.20066612e-03]\n",
      "Residuals (b) r = 11.2, v = 0.00162\n",
      "\n",
      "Convergence test: 0.944616, sigma_new(2.77e+03) < sigma_old(5e+04)\n",
      "\n",
      "#################### ITERATION 2 ####################\n",
      "\n",
      "Condition number (A): 552734.267\n",
      "Condition number (ATWA_acc): 1.095203317807251e+16\n",
      "Lambda:  0.01\n",
      "Residuals after/before 2.52e+04 < 2.62e+04\n",
      "Covariance a: 217.178 m\n",
      "dx  [ 2.69115600e-04  5.59099421e-07 -2.32151777e-07 -1.72114719e-07\n",
      "  2.06740397e-09  1.82016188e-08  2.23363178e-08]\n",
      "COE elements = [26560.405208904904, DeviceArray(0.00523502, dtype=float64), DeviceArray(0.95373864, dtype=float64), DeviceArray(5.58631731, dtype=float64), DeviceArray(4.61753627, dtype=float64), DeviceArray(0.08794351, dtype=float64), 0.000145207242338703]\n",
      "EQN elements = (26560.405208904904, DeviceArray(-0.00372505, dtype=float64), DeviceArray(-0.00367824, dtype=float64), DeviceArray(4.00861179, dtype=float64), DeviceArray(-0.33158817, dtype=float64), DeviceArray(0.39618709, dtype=float64), 0.000145207242338703)\n",
      "Residual (b) = [ 1.29016494e-01 -1.17593801e-01 -5.33305616e-02 -4.08801649e-06\n",
      "  5.80431077e-06  2.10368211e-07]\n",
      "Residuals (b) r = 0.183, v = 7.1e-06\n",
      "\n",
      "Convergence test: 0.998047, sigma_new(5.41) < sigma_old(2.77e+03)\n",
      "\n",
      "#################### ITERATION 3 ####################\n",
      "\n",
      "Condition number (A): 552734.099\n",
      "Condition number (ATWA_acc): 1.337978962834096e+16\n",
      "Lambda:  0.001\n",
      "Residuals after/before 2.52e+04 < 2.52e+04\n",
      "Covariance a: 217.178 m\n",
      "dx  [ 2.52160498e-08 -8.90850223e-10 -9.61740590e-10 -2.53554107e-11\n",
      " -1.29898594e-11 -2.87640178e-11  2.09290545e-12]\n",
      "COE elements = [26560.40520893012, DeviceArray(0.00523502, dtype=float64), DeviceArray(0.95373864, dtype=float64), DeviceArray(5.58631731, dtype=float64), DeviceArray(4.61753628, dtype=float64), DeviceArray(0.0879435, dtype=float64), 0.00014520724443160845]\n",
      "EQN elements = (26560.40520893012, DeviceArray(-0.00372505, dtype=float64), DeviceArray(-0.00367824, dtype=float64), DeviceArray(4.00861179, dtype=float64), DeviceArray(-0.33158817, dtype=float64), DeviceArray(0.39618709, dtype=float64), 0.00014520724443160845)\n",
      "Residual (b) = [ 1.56209421e-01 -1.38988230e-01 -5.11015624e-02 -2.05177143e-06\n",
      "  6.02745507e-06  2.45040309e-06]\n",
      "Residuals (b) r = 0.215, v = 6.82e-06\n",
      "\n",
      "Convergence test: 0.0187563, sigma_new(5.31) < sigma_old(5.41)\n",
      "\n",
      "Stopped due to residual convergence\n",
      "\n",
      "#################### SOLUTION IN 3 ITERATIONS ####################\n",
      "\n",
      "Solution 26560.40520893012 0.0052350 54.6452 320.0724 264.5653 5.0388 +1.4521e-04\n",
      "Original 26558.66821181454 0.0049560 54.6305 320.0891 264.0673 5.5093 +1.0000e-06\n",
      "Residuals (b) r = 0.215, v = 6.82e-06\n",
      "Stopped in 3 iterations\n",
      "Residual at epoch     1.176e+01 km 1.762e-03 km/s\n",
      "Residual at new epoch 2.172e-01 km 0.000e+00 km/s\n",
      "Residual at the end   2.152e-01 km 6.822e-06 km/s\n"
     ]
    }
   ],
   "source": [
    "last_obs = 300\n",
    "obs_stride = 1\n",
    "lamda = 1e-3 * 0 + 1\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, lamda=lamda, rms_epsilon=0.0001, debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c717148b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.047166  0.        0.       -0.        0.       -0.        0.      ]\n",
      " [ 0.        0.        0.       -0.        0.        0.        0.      ]\n",
      " [ 0.        0.        0.       -0.       -0.       -0.        0.      ]\n",
      " [-0.       -0.       -0.        0.        0.        0.       -0.      ]\n",
      " [ 0.        0.       -0.        0.        0.        0.        0.      ]\n",
      " [-0.        0.       -0.        0.        0.        0.       -0.      ]\n",
      " [ 0.        0.        0.       -0.        0.       -0.        0.      ]]\n"
     ]
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    {
     "data": {
      "text/plain": [
       "217.1783061666334"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with np.printoptions(precision=6, suppress=True):\n",
    "    print(P)\n",
    "np.sqrt(P[0][0]) * 1000\n",
    "np.sqrt(P[0][0]) * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a6260cf7-0cbe-47ae-860f-77133cbe1b1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2006-10-19 02:44:46.999968+00:00\n"
     ]
    }
   ],
   "source": [
    "# Split the data into in-spampe and out-of-sample\n",
    "\n",
    "# Optionally thin the observations\n",
    "t_is = tt = t[::obs_stride]\n",
    "eph_is = teph = ephemeris[::obs_stride]\n",
    "\n",
    "if last_obs:\n",
    "    t_os = t_is[last_obs:]\n",
    "    t_is = t_is[:last_obs]\n",
    "    eph_os = eph_is[last_obs:]\n",
    "    eph_is = eph_is[:last_obs]\n",
    "\n",
    "jd, jdf = solve_sat.model.jdsatepoch, solve_sat.model.jdsatepochF\n",
    "print(sat_epoch_datetime(solve_sat.model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a67e566c-9e3a-4a79-bafd-354a98290077",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1 99999U 1800100  06292.11443287  .00000000  00000-0  14521-3 0  9990',\n",
       " '2 99999  54.6452 320.0724 0052350 264.5653   5.0388  2.00563044    16')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exporter.export_tle(solve_sat.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "separate-sleeping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f98844ad1e0>]"
      ]
     },
     "execution_count": 10,
     "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.semilogy(range(len(sigmas)), sigmas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "soviet-defensive",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f986c668400>]"
      ]
     },
     "execution_count": 11,
     "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.semilogy(bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d697421-7793-4e32-b3e8-666f1e949908",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43408e31-2415-4bd6-ba26-ceb0b1fe9f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def solution_residuals(t, ephemeris, solve_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",
    "        res = np.array(ephemeris[offset_idx]) - np.array(solve_sat.model.sgp4(jd, jdf)[1:])\n",
    "        \n",
    "        b = np.concatenate((res[0], res[1]))\n",
    "\n",
    "        #bs.append(np.linalg.norm(b.T @ W @ b))\n",
    "        bs.append(np.linalg.norm(b.T @ b))\n",
    "        \n",
    "    return np.array(bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "014de72f-8599-4dfa-9581-5fa4d8d298af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(solution_residuals(t_is, eph_is, solve_sat) * 1000, c='g')\n",
    "plt.plot(range(last_obs, len(tt)), solution_residuals(t_os, eph_os, solve_sat) * 1000, c='r')\n",
    "\n",
    "plt.xlabel('Observations')\n",
    "plt.ylabel('Residuals (m)')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cbfa9383-5927-4fe0-b10f-e6e68adb9a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f986c743160>]"
      ]
     },
     "execution_count": 14,
     "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.semilogy(solution_residuals(t_is, eph_is, solve_sat) * 1000, c='g')\n",
    "plt.semilogy(range(last_obs, len(tt)), solution_residuals(t_os, eph_os, solve_sat) * 1000, c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcdcc86e-87b0-4977-b8f5-195b16f64dd5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f9d20823",
   "metadata": {},
   "source": [
    "## Find the distance to TLEs with the same launch and proximate epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cfea155f",
   "metadata": {},
   "outputs": [],
   "source": [
    "launch_objects = json.loads(st.gp(mean_motion=op.inclusive_range(1.95, 2.05), orderby='TLE_LINE1', format='json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d348d48b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'CCSDS_OMM_VERS': '2.0',\n",
       "  'COMMENT': 'GENERATED VIA SPACE-TRACK.ORG API',\n",
       "  'CREATION_DATE': '2022-12-18T17:49:27',\n",
       "  'ORIGINATOR': '18 SPCS',\n",
       "  'OBJECT_NAME': 'OPS 5111 (NAVSTAR 1)',\n",
       "  'OBJECT_ID': '1978-020A',\n",
       "  'CENTER_NAME': 'EARTH',\n",
       "  'REF_FRAME': 'TEME',\n",
       "  'TIME_SYSTEM': 'UTC',\n",
       "  'MEAN_ELEMENT_THEORY': 'SGP4',\n",
       "  'EPOCH': '2022-12-18T02:42:08.339328',\n",
       "  'MEAN_MOTION': '1.98072313',\n",
       "  'ECCENTRICITY': '0.00810510',\n",
       "  'INCLINATION': '63.4576',\n",
       "  'RA_OF_ASC_NODE': '90.9769',\n",
       "  'ARG_OF_PERICENTER': '212.2486',\n",
       "  'MEAN_ANOMALY': '147.2602',\n",
       "  'EPHEMERIS_TYPE': '0',\n",
       "  'CLASSIFICATION_TYPE': 'U',\n",
       "  'NORAD_CAT_ID': '10684',\n",
       "  'ELEMENT_SET_NO': '999',\n",
       "  'REV_AT_EPOCH': '31114',\n",
       "  'BSTAR': '0.00000000000000',\n",
       "  'MEAN_MOTION_DOT': '-0.00000012',\n",
       "  'MEAN_MOTION_DDOT': '0.0000000000000',\n",
       "  'SEMIMAJOR_AXIS': '26782.595',\n",
       "  'PERIOD': '727.007',\n",
       "  'APOAPSIS': '20621.536',\n",
       "  'PERIAPSIS': '20187.385',\n",
       "  'OBJECT_TYPE': 'PAYLOAD',\n",
       "  'RCS_SIZE': 'MEDIUM',\n",
       "  'COUNTRY_CODE': 'US',\n",
       "  'LAUNCH_DATE': '1978-02-22',\n",
       "  'SITE': 'AFWTR',\n",
       "  'DECAY_DATE': None,\n",
       "  'FILE': '3708942',\n",
       "  'GP_ID': '220806935',\n",
       "  'TLE_LINE0': '0 OPS 5111 (NAVSTAR 1)',\n",
       "  'TLE_LINE1': '1 10684U 78020A   22352.11259652 -.00000012  00000-0  00000-0 0  9995',\n",
       "  'TLE_LINE2': '2 10684  63.4576  90.9769 0081051 212.2486 147.2602  1.98072313311148'},\n",
       " {'CCSDS_OMM_VERS': '2.0',\n",
       "  'COMMENT': 'GENERATED VIA SPACE-TRACK.ORG API',\n",
       "  'CREATION_DATE': '2022-12-18T17:49:27',\n",
       "  'ORIGINATOR': '18 SPCS',\n",
       "  'OBJECT_NAME': 'OPS 5112 (NAVSTAR 2)',\n",
       "  'OBJECT_ID': '1978-047A',\n",
       "  'CENTER_NAME': 'EARTH',\n",
       "  'REF_FRAME': 'TEME',\n",
       "  'TIME_SYSTEM': 'UTC',\n",
       "  'MEAN_ELEMENT_THEORY': 'SGP4',\n",
       "  'EPOCH': '2022-12-18T04:34:56.052192',\n",
       "  'MEAN_MOTION': '2.01626461',\n",
       "  'ECCENTRICITY': '0.05110230',\n",
       "  'INCLINATION': '63.5848',\n",
       "  'RA_OF_ASC_NODE': '313.8237',\n",
       "  'ARG_OF_PERICENTER': '45.6525',\n",
       "  'MEAN_ANOMALY': '45.2481',\n",
       "  'EPHEMERIS_TYPE': '0',\n",
       "  'CLASSIFICATION_TYPE': 'U',\n",
       "  'NORAD_CAT_ID': '10893',\n",
       "  'ELEMENT_SET_NO': '999',\n",
       "  'REV_AT_EPOCH': '32798',\n",
       "  'BSTAR': '0.00000000000000',\n",
       "  'MEAN_MOTION_DOT': '-0.00000034',\n",
       "  'MEAN_MOTION_DDOT': '0.0000000000000',\n",
       "  'SEMIMAJOR_AXIS': '26466.925',\n",
       "  'PERIOD': '714.192',\n",
       "  'APOAPSIS': '21441.311',\n",
       "  'PERIAPSIS': '18736.269',\n",
       "  'OBJECT_TYPE': 'PAYLOAD',\n",
       "  'RCS_SIZE': 'MEDIUM',\n",
       "  'COUNTRY_CODE': 'US',\n",
       "  'LAUNCH_DATE': '1978-05-13',\n",
       "  'SITE': 'AFWTR',\n",
       "  'DECAY_DATE': None,\n",
       "  'FILE': '3708942',\n",
       "  'GP_ID': '220808201',\n",
       "  'TLE_LINE0': '0 OPS 5112 (NAVSTAR 2)',\n",
       "  'TLE_LINE1': '1 10893U 78047A   22352.19092653 -.00000034  00000-0  00000-0 0  9994',\n",
       "  'TLE_LINE2': '2 10893  63.5848 313.8237 0511023  45.6525  45.2481  2.01626461327984'},\n",
       " {'CCSDS_OMM_VERS': '2.0',\n",
       "  'COMMENT': 'GENERATED VIA SPACE-TRACK.ORG API',\n",
       "  'CREATION_DATE': '2022-12-15T18:29:43',\n",
       "  'ORIGINATOR': '18 SPCS',\n",
       "  'OBJECT_NAME': 'OPS 5117 (NAVSTAR 5)',\n",
       "  'OBJECT_ID': '1980-011A',\n",
       "  'CENTER_NAME': 'EARTH',\n",
       "  'REF_FRAME': 'TEME',\n",
       "  'TIME_SYSTEM': 'UTC',\n",
       "  'MEAN_ELEMENT_THEORY': 'SGP4',\n",
       "  'EPOCH': '2022-12-15T12:19:16.474368',\n",
       "  'MEAN_MOTION': '2.00556358',\n",
       "  'ECCENTRICITY': '0.04830510',\n",
       "  'INCLINATION': '63.8963',\n",
       "  'RA_OF_ASC_NODE': '88.0047',\n",
       "  'ARG_OF_PERICENTER': '202.9089',\n",
       "  'MEAN_ANOMALY': '157.2961',\n",
       "  'EPHEMERIS_TYPE': '0',\n",
       "  'CLASSIFICATION_TYPE': 'U',\n",
       "  'NORAD_CAT_ID': '11690',\n",
       "  'ELEMENT_SET_NO': '999',\n",
       "  'REV_AT_EPOCH': '32806',\n",
       "  'BSTAR': '0.00000000000000',\n",
       "  'MEAN_MOTION_DOT': '-0.00000008',\n",
       "  'MEAN_MOTION_DDOT': '0.0000000000000',\n",
       "  'SEMIMAJOR_AXIS': '26560.988',\n",
       "  'PERIOD': '718.003',\n",
       "  'APOAPSIS': '21465.884',\n",
       "  'PERIAPSIS': '18899.821',\n",
       "  'OBJECT_TYPE': 'PAYLOAD',\n",
       "  'RCS_SIZE': 'MEDIUM',\n",
       "  'COUNTRY_CODE': 'US',\n",
       "  'LAUNCH_DATE': '1980-02-09',\n",
       "  'SITE': 'AFWTR',\n",
       "  'DECAY_DATE': None,\n",
       "  'FILE': '3702025',\n",
       "  'GP_ID': '220467840',\n",
       "  'TLE_LINE0': '0 OPS 5117 (NAVSTAR 5)',\n",
       "  'TLE_LINE1': '1 11690U 80011A   22349.51338512 -.00000008  00000-0  00000-0 0  9994',\n",
       "  'TLE_LINE2': '2 11690  63.8963  88.0047 0483051 202.9089 157.2961  2.00556358328066'},\n",
       " {'CCSDS_OMM_VERS': '2.0',\n",
       "  'COMMENT': 'GENERATED VIA SPACE-TRACK.ORG API',\n",
       "  'CREATION_DATE': '2022-12-16T20:15:47',\n",
       "  'ORIGINATOR': '18 SPCS',\n",
       "  'OBJECT_NAME': 'OPS 5118 (NAVSTAR 6)',\n",
       "  'OBJECT_ID': '1980-032A',\n",
       "  'CENTER_NAME': 'EARTH',\n",
       "  'REF_FRAME': 'TEME',\n",
       "  'TIME_SYSTEM': 'UTC',\n",
       "  'MEAN_ELEMENT_THEORY': 'SGP4',\n",
       "  'EPOCH': '2022-12-16T07:33:10.890432',\n",
       "  'MEAN_MOTION': '2.03456197',\n",
       "  'ECCENTRICITY': '0.05340760',\n",
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       "  'SITE': 'AFETR',\n",
       "  'DECAY_DATE': None,\n",
       "  'FILE': '3708942',\n",
       "  'GP_ID': '220819875',\n",
       "  'TLE_LINE0': '0 NAVSTAR 73 (USA 260)',\n",
       "  'TLE_LINE1': '1 40534U 15013A   22352.20029153  .00000003  00000-0  00000-0 0  9995',\n",
       "  'TLE_LINE2': '2 40534  53.5946 257.0807 0075496  22.2140 338.1642  2.00570744 56211'},\n",
       " {'CCSDS_OMM_VERS': '2.0',\n",
       "  'COMMENT': 'GENERATED VIA SPACE-TRACK.ORG API',\n",
       "  'CREATION_DATE': '2022-12-18T02:46:40',\n",
       "  'ORIGINATOR': '18 SPCS',\n",
       "  'OBJECT_NAME': 'NAVSTAR 74 (USA 262)',\n",
       "  'OBJECT_ID': '2015-033A',\n",
       "  'CENTER_NAME': 'EARTH',\n",
       "  'REF_FRAME': 'TEME',\n",
       "  'TIME_SYSTEM': 'UTC',\n",
       "  'MEAN_ELEMENT_THEORY': 'SGP4',\n",
       "  'EPOCH': '2022-12-17T20:38:30.753888',\n",
       "  'MEAN_MOTION': '2.00568588',\n",
       "  'ECCENTRICITY': '0.00795470',\n",
       "  'INCLINATION': '55.0609',\n",
       "  'RA_OF_ASC_NODE': '319.2792',\n",
       "  'ARG_OF_PERICENTER': '8.5509',\n",
       "  'MEAN_ANOMALY': '351.5853',\n",
       "  'EPHEMERIS_TYPE': '0',\n",
       "  'CLASSIFICATION_TYPE': 'U',\n",
       "  'NORAD_CAT_ID': '40730',\n",
       "  'ELEMENT_SET_NO': '999',\n",
       "  'REV_AT_EPOCH': '5437',\n",
       "  'BSTAR': '0.00000000000000',\n",
       "  'MEAN_MOTION_DOT': '-0.00000052',\n",
       "  'MEAN_MOTION_DDOT': '0.0000000000000',\n",
       "  'SEMIMAJOR_AXIS': '26559.908',\n",
       "  'PERIOD': '717.959',\n",
       "  'APOAPSIS': '20393.049',\n",
       "  'PERIAPSIS': '19970.497',\n",
       "  'OBJECT_TYPE': 'PAYLOAD',\n",
       "  'RCS_SIZE': 'LARGE',\n",
       "  'COUNTRY_CODE': 'US',\n",
       "  'LAUNCH_DATE': '2015-07-15',\n",
       "  'SITE': 'AFETR',\n",
       "  'DECAY_DATE': None,\n",
       "  'FILE': '3707070',\n",
       "  'GP_ID': '220749129',\n",
       "  'TLE_LINE0': '0 NAVSTAR 74 (USA 262)',\n",
       "  'TLE_LINE1': '1 40730U 15033A   22351.86007817 -.00000052  00000-0  00000-0 0  9994',\n",
       "  'TLE_LINE2': '2 40730  55.0609 319.2792 0079547   8.5509 351.5853  2.00568588 54374'},\n",
       " {'CCSDS_OMM_VERS': '2.0',\n",
       "  'COMMENT': 'GENERATED VIA SPACE-TRACK.ORG API',\n",
       "  'CREATION_DATE': '2022-12-18T17:49:27',\n",
       "  'ORIGINATOR': '18 SPCS',\n",
       "  'OBJECT_NAME': 'NAVSTAR 75 (USA 265)',\n",
       "  'OBJECT_ID': '2015-062A',\n",
       "  'CENTER_NAME': 'EARTH',\n",
       "  'REF_FRAME': 'TEME',\n",
       "  'TIME_SYSTEM': 'UTC',\n",
       "  'MEAN_ELEMENT_THEORY': 'SGP4',\n",
       "  'EPOCH': '2022-12-18T09:52:17.316192',\n",
       "  'MEAN_MOTION': '2.00561839',\n",
       "  'ECCENTRICITY': '0.00845740',\n",
       "  'INCLINATION': '55.9472',\n",
       "  'RA_OF_ASC_NODE': '80.5255',\n",
       "  'ARG_OF_PERICENTER': '219.2268',\n",
       "  'MEAN_ANOMALY': '140.1741',\n",
       "  'EPHEMERIS_TYPE': '0',\n",
       "  'CLASSIFICATION_TYPE': 'U',\n",
       "  'NORAD_CAT_ID': '41019',\n",
       "  'ELEMENT_SET_NO': '999',\n",
       "  'REV_AT_EPOCH': '5222',\n",
       "  'BSTAR': '0.00000000000000',\n",
       "  'MEAN_MOTION_DOT': '-0.00000035',\n",
       "  'MEAN_MOTION_DDOT': '0.0000000000000',\n",
       "  'SEMIMAJOR_AXIS': '26560.504',\n",
       "  'PERIOD': '717.983',\n",
       "  'APOAPSIS': '20407.001',\n",
       "  'PERIAPSIS': '19957.736',\n",
       "  'OBJECT_TYPE': 'PAYLOAD',\n",
       "  'RCS_SIZE': 'LARGE',\n",
       "  'COUNTRY_CODE': 'US',\n",
       "  'LAUNCH_DATE': '2015-10-31',\n",
       "  'SITE': 'AFETR',\n",
       "  'DECAY_DATE': None,\n",
       "  'FILE': '3708942',\n",
       "  'GP_ID': '220821169',\n",
       "  'TLE_LINE0': '0 NAVSTAR 75 (USA 265)',\n",
       "  'TLE_LINE1': '1 41019U 15062A   22352.41131153 -.00000035  00000-0  00000-0 0  9991',\n",
       "  'TLE_LINE2': '2 41019  55.9472  80.5255 0084574 219.2268 140.1741  2.00561839 52225'},\n",
       " {'CCSDS_OMM_VERS': '2.0',\n",
       "  'COMMENT': 'GENERATED VIA SPACE-TRACK.ORG API',\n",
       "  'CREATION_DATE': '2022-12-18T17:49:27',\n",
       "  'ORIGINATOR': '18 SPCS',\n",
       "  'OBJECT_NAME': 'NAVSTAR 76 (USA 266)',\n",
       "  'OBJECT_ID': '2016-007A',\n",
       "  'CENTER_NAME': 'EARTH',\n",
       "  'REF_FRAME': 'TEME',\n",
       "  'TIME_SYSTEM': 'UTC',\n",
       "  'MEAN_ELEMENT_THEORY': 'SGP4',\n",
       "  'EPOCH': '2022-12-18T11:57:16.812864',\n",
       "  'MEAN_MOTION': '2.00562057',\n",
       "  'ECCENTRICITY': '0.00651400',\n",
       "  'INCLINATION': '54.9495',\n",
       "  'RA_OF_ASC_NODE': '140.1700',\n",
       "  'ARG_OF_PERICENTER': '230.1197',\n",
       "  'MEAN_ANOMALY': '129.2963',\n",
       "  'EPHEMERIS_TYPE': '0',\n",
       "  'CLASSIFICATION_TYPE': 'U',\n",
       "  'NORAD_CAT_ID': '41328',\n",
       "  'ELEMENT_SET_NO': '999',\n",
       "  'REV_AT_EPOCH': '5023',\n",
       "  'BSTAR': '0.00000000000000',\n",
       "  'MEAN_MOTION_DOT': '0.00000088',\n",
       "  'MEAN_MOTION_DDOT': '0.0000000000000',\n",
       "  'SEMIMAJOR_AXIS': '26560.484',\n",
       "  'PERIOD': '717.982',\n",
       "  'APOAPSIS': '20355.364',\n",
       "  'PERIAPSIS': '20009.334',\n",
       "  'OBJECT_TYPE': 'PAYLOAD',\n",
       "  'RCS_SIZE': 'LARGE',\n",
       "  'COUNTRY_CODE': 'US',\n",
       "  'LAUNCH_DATE': '2016-02-05',\n",
       "  'SITE': 'AFETR',\n",
       "  'DECAY_DATE': None,\n",
       "  'FILE': '3708942',\n",
       "  'GP_ID': '220819452',\n",
       "  'TLE_LINE0': '0 NAVSTAR 76 (USA 266)',\n",
       "  'TLE_LINE1': '1 41328U 16007A   22352.49811126  .00000088  00000-0  00000-0 0  9994',\n",
       "  'TLE_LINE2': '2 41328  54.9495 140.1700 0065140 230.1197 129.2963  2.00562057 50237'},\n",
       " {'CCSDS_OMM_VERS': '2.0',\n",
       "  'COMMENT': 'GENERATED VIA SPACE-TRACK.ORG API',\n",
       "  'CREATION_DATE': '2022-12-18T17:49:27',\n",
       "  'ORIGINATOR': '18 SPCS',\n",
       "  'OBJECT_NAME': 'NAVSTAR 77 (USA 289)',\n",
       "  'OBJECT_ID': '2018-109A',\n",
       "  'CENTER_NAME': 'EARTH',\n",
       "  'REF_FRAME': 'TEME',\n",
       "  'TIME_SYSTEM': 'UTC',\n",
       "  'MEAN_ELEMENT_THEORY': 'SGP4',\n",
       "  'EPOCH': '2022-12-18T03:29:42.815616',\n",
       "  'MEAN_MOTION': '2.00557069',\n",
       "  'ECCENTRICITY': '0.00224090',\n",
       "  'INCLINATION': '55.1276',\n",
       "  'RA_OF_ASC_NODE': '142.6430',\n",
       "  'ARG_OF_PERICENTER': '187.8287',\n",
       "  'MEAN_ANOMALY': '167.9260',\n",
       "  'EPHEMERIS_TYPE': '0',\n",
       "  'CLASSIFICATION_TYPE': 'U',\n",
       "  'NORAD_CAT_ID': '43873',\n",
       "  'ELEMENT_SET_NO': '999',\n",
       "  'REV_AT_EPOCH': '2945',\n",
       "  'BSTAR': '0.00000000000000',\n",
       "  'MEAN_MOTION_DOT': '0.00000095',\n",
       "  'MEAN_MOTION_DDOT': '0.0000000000000',\n",
       "  'SEMIMAJOR_AXIS': '26560.925',\n",
       "  'PERIOD': '718.000',\n",
       "  'APOAPSIS': '20242.310',\n",
       "  'PERIAPSIS': '20123.269',\n",
       "  'OBJECT_TYPE': 'PAYLOAD',\n",
       "  'RCS_SIZE': 'LARGE',\n",
       "  'COUNTRY_CODE': 'US',\n",
       "  'LAUNCH_DATE': '2018-12-23',\n",
       "  'SITE': 'AFETR',\n",
       "  'DECAY_DATE': None,\n",
       "  'FILE': '3708942',\n",
       "  'GP_ID': '220822696',\n",
       "  'TLE_LINE0': '0 NAVSTAR 77 (USA 289)',\n",
       "  'TLE_LINE1': '1 43873U 18109A   22352.14563444  .00000095  00000-0  00000-0 0  9993',\n",
       "  'TLE_LINE2': '2 43873  55.1276 142.6430 0022409 187.8287 167.9260  2.00557069 29456'},\n",
       " {'CCSDS_OMM_VERS': '2.0',\n",
       "  'COMMENT': 'GENERATED VIA SPACE-TRACK.ORG API',\n",
       "  'CREATION_DATE': '2022-12-18T17:49:27',\n",
       "  'ORIGINATOR': '18 SPCS',\n",
       "  'OBJECT_NAME': 'NAVSTAR 78 (USA 293)',\n",
       "  'OBJECT_ID': '2019-056A',\n",
       "  'CENTER_NAME': 'EARTH',\n",
       "  'REF_FRAME': 'TEME',\n",
       "  'TIME_SYSTEM': 'UTC',\n",
       "  'MEAN_ELEMENT_THEORY': 'SGP4',\n",
       "  'EPOCH': '2022-12-18T14:12:09.824832',\n",
       "  'MEAN_MOTION': '2.00559977',\n",
       "  'ECCENTRICITY': '0.00298310',\n",
       "  'INCLINATION': '55.7519',\n",
       "  'RA_OF_ASC_NODE': '21.7355',\n",
       "  'ARG_OF_PERICENTER': '186.0103',\n",
       "  'MEAN_ANOMALY': '32.4687',\n",
       "  'EPHEMERIS_TYPE': '0',\n",
       "  'CLASSIFICATION_TYPE': 'U',\n",
       "  'NORAD_CAT_ID': '44506',\n",
       "  'ELEMENT_SET_NO': '999',\n",
       "  'REV_AT_EPOCH': '2446',\n",
       "  'BSTAR': '0.00000000000000',\n",
       "  'MEAN_MOTION_DOT': '-0.00000060',\n",
       "  'MEAN_MOTION_DDOT': '0.0000000000000',\n",
       "  'SEMIMAJOR_AXIS': '26560.668',\n",
       "  'PERIOD': '717.990',\n",
       "  'APOAPSIS': '20261.766',\n",
       "  'PERIAPSIS': '20103.300',\n",
       "  'OBJECT_TYPE': 'PAYLOAD',\n",
       "  'RCS_SIZE': 'LARGE',\n",
       "  'COUNTRY_CODE': 'US',\n",
       "  'LAUNCH_DATE': '2019-08-22',\n",
       "  'SITE': 'AFETR',\n",
       "  'DECAY_DATE': None,\n",
       "  'FILE': '3708942',\n",
       "  'GP_ID': '220823740',\n",
       "  'TLE_LINE0': '0 NAVSTAR 78 (USA 293)',\n",
       "  'TLE_LINE1': '1 44506U 19056A   22352.59178038 -.00000060  00000-0  00000-0 0  9992',\n",
       "  'TLE_LINE2': '2 44506  55.7519  21.7355 0029831 186.0103  32.4687  2.00559977 24468'},\n",
       " {'CCSDS_OMM_VERS': '2.0',\n",
       "  'COMMENT': 'GENERATED VIA SPACE-TRACK.ORG API',\n",
       "  'CREATION_DATE': '2022-12-17T18:13:04',\n",
       "  'ORIGINATOR': '18 SPCS',\n",
       "  'OBJECT_NAME': 'NAVSTAR 79 (USA 304)',\n",
       "  'OBJECT_ID': '2020-041A',\n",
       "  'CENTER_NAME': 'EARTH',\n",
       "  'REF_FRAME': 'TEME',\n",
       "  'TIME_SYSTEM': 'UTC',\n",
       "  'MEAN_ELEMENT_THEORY': 'SGP4',\n",
       "  'EPOCH': '2022-12-17T08:49:20.828352',\n",
       "  'MEAN_MOTION': '2.00567616',\n",
       "  'ECCENTRICITY': '0.00293150',\n",
       "  'INCLINATION': '55.6659',\n",
       "  'RA_OF_ASC_NODE': '79.0549',\n",
       "  'ARG_OF_PERICENTER': '183.1129',\n",
       "  'MEAN_ANOMALY': '171.8085',\n",
       "  'EPHEMERIS_TYPE': '0',\n",
       "  'CLASSIFICATION_TYPE': 'U',\n",
       "  'NORAD_CAT_ID': '45854',\n",
       "  'ELEMENT_SET_NO': '999',\n",
       "  'REV_AT_EPOCH': '1840',\n",
       "  'BSTAR': '0.00000000000000',\n",
       "  'MEAN_MOTION_DOT': '-0.00000035',\n",
       "  'MEAN_MOTION_DDOT': '0.0000000000000',\n",
       "  'SEMIMAJOR_AXIS': '26559.994',\n",
       "  'PERIOD': '717.962',\n",
       "  'APOAPSIS': '20259.719',\n",
       "  'PERIAPSIS': '20103.998',\n",
       "  'OBJECT_TYPE': 'PAYLOAD',\n",
       "  'RCS_SIZE': 'LARGE',\n",
       "  'COUNTRY_CODE': 'US',\n",
       "  'LAUNCH_DATE': '2020-06-30',\n",
       "  'SITE': 'AFETR',\n",
       "  'DECAY_DATE': None,\n",
       "  'FILE': '3705911',\n",
       "  'GP_ID': '220702456',\n",
       "  'TLE_LINE0': '0 NAVSTAR 79 (USA 304)',\n",
       "  'TLE_LINE1': '1 45854U 20041A   22351.36760218 -.00000035  00000-0  00000-0 0  9998',\n",
       "  'TLE_LINE2': '2 45854  55.6659  79.0549 0029315 183.1129 171.8085  2.00567616 18409'},\n",
       " {'CCSDS_OMM_VERS': '2.0',\n",
       "  'COMMENT': 'GENERATED VIA SPACE-TRACK.ORG API',\n",
       "  'CREATION_DATE': '2022-12-18T17:49:27',\n",
       "  'ORIGINATOR': '18 SPCS',\n",
       "  'OBJECT_NAME': 'NAVSTAR 80 (USA 309)',\n",
       "  'OBJECT_ID': '2020-078A',\n",
       "  'CENTER_NAME': 'EARTH',\n",
       "  'REF_FRAME': 'TEME',\n",
       "  'TIME_SYSTEM': 'UTC',\n",
       "  'MEAN_ELEMENT_THEORY': 'SGP4',\n",
       "  'EPOCH': '2022-12-18T02:56:32.415360',\n",
       "  'MEAN_MOTION': '2.00564995',\n",
       "  'ECCENTRICITY': '0.00248910',\n",
       "  'INCLINATION': '54.4531',\n",
       "  'RA_OF_ASC_NODE': '262.6405',\n",
       "  'ARG_OF_PERICENTER': '190.0218',\n",
       "  'MEAN_ANOMALY': '326.4475',\n",
       "  'EPHEMERIS_TYPE': '0',\n",
       "  'CLASSIFICATION_TYPE': 'U',\n",
       "  'NORAD_CAT_ID': '46826',\n",
       "  'ELEMENT_SET_NO': '999',\n",
       "  'REV_AT_EPOCH': '1589',\n",
       "  'BSTAR': '0.00000000000000',\n",
       "  'MEAN_MOTION_DOT': '-0.00000007',\n",
       "  'MEAN_MOTION_DDOT': '0.0000000000000',\n",
       "  'SEMIMAJOR_AXIS': '26560.225',\n",
       "  'PERIOD': '717.972',\n",
       "  'APOAPSIS': '20248.201',\n",
       "  'PERIAPSIS': '20115.979',\n",
       "  'OBJECT_TYPE': 'PAYLOAD',\n",
       "  'RCS_SIZE': 'LARGE',\n",
       "  'COUNTRY_CODE': 'US',\n",
       "  'LAUNCH_DATE': '2020-11-05',\n",
       "  'SITE': 'AFETR',\n",
       "  'DECAY_DATE': None,\n",
       "  'FILE': '3708942',\n",
       "  'GP_ID': '220824719',\n",
       "  'TLE_LINE0': '0 NAVSTAR 80 (USA 309)',\n",
       "  'TLE_LINE1': '1 46826U 20078A   22352.12259740 -.00000007  00000-0  00000-0 0  9995',\n",
       "  'TLE_LINE2': '2 46826  54.4531 262.6405 0024891 190.0218 326.4475  2.00564995 15894'},\n",
       " {'CCSDS_OMM_VERS': '2.0',\n",
       "  'COMMENT': 'GENERATED VIA SPACE-TRACK.ORG API',\n",
       "  'CREATION_DATE': '2022-12-18T17:49:27',\n",
       "  'ORIGINATOR': '18 SPCS',\n",
       "  'OBJECT_NAME': 'NAVSTAR 81 (USA 319)',\n",
       "  'OBJECT_ID': '2021-054A',\n",
       "  'CENTER_NAME': 'EARTH',\n",
       "  'REF_FRAME': 'TEME',\n",
       "  'TIME_SYSTEM': 'UTC',\n",
       "  'MEAN_ELEMENT_THEORY': 'SGP4',\n",
       "  'EPOCH': '2022-12-18T04:10:36.038208',\n",
       "  'MEAN_MOTION': '2.00563024',\n",
       "  'ECCENTRICITY': '0.00091540',\n",
       "  'INCLINATION': '55.2621',\n",
       "  'RA_OF_ASC_NODE': '23.6621',\n",
       "  'ARG_OF_PERICENTER': '214.8076',\n",
       "  'MEAN_ANOMALY': '163.4264',\n",
       "  'EPHEMERIS_TYPE': '0',\n",
       "  'CLASSIFICATION_TYPE': 'U',\n",
       "  'NORAD_CAT_ID': '48859',\n",
       "  'ELEMENT_SET_NO': '999',\n",
       "  'REV_AT_EPOCH': '1114',\n",
       "  'BSTAR': '0.00000000000000',\n",
       "  'MEAN_MOTION_DOT': '-0.00000062',\n",
       "  'MEAN_MOTION_DDOT': '0.0000000000000',\n",
       "  'SEMIMAJOR_AXIS': '26560.399',\n",
       "  'PERIOD': '717.979',\n",
       "  'APOAPSIS': '20206.577',\n",
       "  'PERIAPSIS': '20157.951',\n",
       "  'OBJECT_TYPE': 'PAYLOAD',\n",
       "  'RCS_SIZE': 'LARGE',\n",
       "  'COUNTRY_CODE': 'US',\n",
       "  'LAUNCH_DATE': '2021-06-17',\n",
       "  'SITE': 'AFETR',\n",
       "  'DECAY_DATE': None,\n",
       "  'FILE': '3708942',\n",
       "  'GP_ID': '220826194',\n",
       "  'TLE_LINE0': '0 NAVSTAR 81 (USA 319)',\n",
       "  'TLE_LINE1': '1 48859U 21054A   22352.17402822 -.00000062  00000-0  00000-0 0  9995',\n",
       "  'TLE_LINE2': '2 48859  55.2621  23.6621 0009154 214.8076 163.4264  2.00563024 11149'}]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[lo for lo in launch_objects if 'NAVSTAR' in lo['OBJECT_NAME']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c031073a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tles = []\n",
    "\n",
    "sat_ids = [sat['NORAD_CAT_ID'] for sat in launch_objects]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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_is, solve_sat.model, candidate_sat.model)\n",
    "    res_epoch = np.sqrt(res[-1])\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": 22,
   "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'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dc90d850",
   "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>norad_cat_id</th>\n",
       "      <th>object_id</th>\n",
       "      <th>epoch</th>\n",
       "      <th>tle_line0</th>\n",
       "      <th>tle_line1</th>\n",
       "      <th>tle_line2</th>\n",
       "      <th>residual</th>\n",
       "      <th>residual_epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>28129</td>\n",
       "      <td>2003-058A</td>\n",
       "      <td>2006-10-20T05:39:53.175168</td>\n",
       "      <td>0 NAVSTAR 53 (USA 175)</td>\n",
       "      <td>1 28129U 03058A   06293.23603212 -.00000058  0...</td>\n",
       "      <td>2 28129 054.6457 320.0220 0052136 264.7011 094...</td>\n",
       "      <td>2.667964</td>\n",
       "      <td>1.014833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>28129</td>\n",
       "      <td>2003-058A</td>\n",
       "      <td>2006-10-19T05:43:58.315295</td>\n",
       "      <td>0 NAVSTAR 53 (USA 175)</td>\n",
       "      <td>1 28129U 03058A   06292.23886939 -.00000059 +0...</td>\n",
       "      <td>2 28129 054.6464 320.0627 0052109 264.7328 094...</td>\n",
       "      <td>2.685148</td>\n",
       "      <td>0.915499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>28129</td>\n",
       "      <td>2003-058A</td>\n",
       "      <td>2006-10-18T05:48:03.299040</td>\n",
       "      <td>0 NAVSTAR 53 (USA 175)</td>\n",
       "      <td>1 28129U 03058A   06291.24170485 -.00000059  0...</td>\n",
       "      <td>2 28129 054.6471 320.1034 0052084 264.7458 094...</td>\n",
       "      <td>2.798881</td>\n",
       "      <td>0.941797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>28129</td>\n",
       "      <td>2003-058A</td>\n",
       "      <td>2006-10-18T05:48:03.325823</td>\n",
       "      <td>0 NAVSTAR 53 (USA 175)</td>\n",
       "      <td>1 28129U 03058A   06291.24170516 -.00000059  0...</td>\n",
       "      <td>2 28129 054.6470 320.1034 0052057 264.7173 094...</td>\n",
       "      <td>3.076637</td>\n",
       "      <td>1.175152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>11690</td>\n",
       "      <td>1980-011A</td>\n",
       "      <td>2006-10-20T04:50:54.165120</td>\n",
       "      <td>0 OPS 5117 (NAVSTAR 5)</td>\n",
       "      <td>1 11690U 80011A   06293.20201580 -.00000012 +0...</td>\n",
       "      <td>2 11690 063.8937 271.2930 0273259 182.2802 177...</td>\n",
       "      <td>13655.398467</td>\n",
       "      <td>1040.530041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>11690</td>\n",
       "      <td>1980-011A</td>\n",
       "      <td>2006-10-18T04:59:07.223135</td>\n",
       "      <td>0 OPS 5117 (NAVSTAR 5)</td>\n",
       "      <td>1 11690U 80011A   06291.20772249 -.00000006  0...</td>\n",
       "      <td>2 11690 063.8947 271.3546 0272966 182.2597 177...</td>\n",
       "      <td>13655.433771</td>\n",
       "      <td>1036.763506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>11690</td>\n",
       "      <td>1980-011A</td>\n",
       "      <td>2006-10-19T04:55:00.855263</td>\n",
       "      <td>0 OPS 5117 (NAVSTAR 5)</td>\n",
       "      <td>1 11690U 80011A   06292.20487101 -.00000011 +0...</td>\n",
       "      <td>2 11690 063.8945 271.3237 0273183 182.2697 177...</td>\n",
       "      <td>13655.490257</td>\n",
       "      <td>1039.200554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>10684</td>\n",
       "      <td>1978-020A</td>\n",
       "      <td>2006-10-18T05:30:29.613023</td>\n",
       "      <td>0 OPS 5111 (NAVSTAR 1)</td>\n",
       "      <td>1 10684U 78020A   06291.22950941 -.00000008 +0...</td>\n",
       "      <td>2 10684 063.4960 271.4665 0061292 174.6670 185...</td>\n",
       "      <td>14799.212858</td>\n",
       "      <td>10944.563035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>10684</td>\n",
       "      <td>1978-020A</td>\n",
       "      <td>2006-10-20T05:58:32.334239</td>\n",
       "      <td>0 OPS 5111 (NAVSTAR 1)</td>\n",
       "      <td>1 10684U 78020A   06293.24898535 -.00000013 +0...</td>\n",
       "      <td>2 10684 063.4948 271.4050 0061264 174.7138 185...</td>\n",
       "      <td>14799.342105</td>\n",
       "      <td>10945.147804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>10684</td>\n",
       "      <td>1978-020A</td>\n",
       "      <td>2006-10-20T05:58:32.596032</td>\n",
       "      <td>0 OPS 5111 (NAVSTAR 1)</td>\n",
       "      <td>1 10684U 78020A   06293.24898838 -.00000013  0...</td>\n",
       "      <td>2 10684 063.4948 271.4053 0061462 174.4244 185...</td>\n",
       "      <td>14800.350209</td>\n",
       "      <td>10948.427452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>10684</td>\n",
       "      <td>1978-020A</td>\n",
       "      <td>2006-10-18T05:30:29.595744</td>\n",
       "      <td>0 OPS 5111 (NAVSTAR 1)</td>\n",
       "      <td>1 10684U 78020A   06291.22950921 -.00000008  0...</td>\n",
       "      <td>2 10684 063.4961 271.4673 0061451 174.3758 185...</td>\n",
       "      <td>14800.359743</td>\n",
       "      <td>10948.958372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>10684</td>\n",
       "      <td>1978-020A</td>\n",
       "      <td>2006-10-19T05:44:31.917120</td>\n",
       "      <td>0 OPS 5111 (NAVSTAR 1)</td>\n",
       "      <td>1 10684U 78020A   06292.23925830 -.00000012 +0...</td>\n",
       "      <td>2 10684 063.4956 271.4366 0061450 174.3827 185...</td>\n",
       "      <td>14800.381522</td>\n",
       "      <td>10949.079014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>26605</td>\n",
       "      <td>2000-071A</td>\n",
       "      <td>2006-10-18T07:49:12.945792</td>\n",
       "      <td>0 NAVSTAR 49 (USA 154)</td>\n",
       "      <td>1 26605U 00071A   06291.32584428 -.00000108 +0...</td>\n",
       "      <td>2 26605 056.5661 018.0814 0029663 239.6383 120...</td>\n",
       "      <td>18644.708749</td>\n",
       "      <td>14855.129987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>26605</td>\n",
       "      <td>2000-071A</td>\n",
       "      <td>2006-10-19T07:45:09.124991</td>\n",
       "      <td>0 NAVSTAR 49 (USA 154)</td>\n",
       "      <td>1 26605U 00071A   06292.32302228 -.00000104  0...</td>\n",
       "      <td>2 26605 056.5665 018.0427 0029595 239.6489 120...</td>\n",
       "      <td>18644.913248</td>\n",
       "      <td>14855.396871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>26605</td>\n",
       "      <td>2000-071A</td>\n",
       "      <td>2006-10-20T19:39:03.671136</td>\n",
       "      <td>0 NAVSTAR 49 (USA 154)</td>\n",
       "      <td>1 26605U 00071A   06293.81879249 -.00000096  0...</td>\n",
       "      <td>2 26605 056.5669 017.9849 0029655 239.4496 120...</td>\n",
       "      <td>18645.506304</td>\n",
       "      <td>14856.178772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>26605</td>\n",
       "      <td>2000-071A</td>\n",
       "      <td>2006-10-19T19:43:07.401216</td>\n",
       "      <td>0 NAVSTAR 49 (USA 154)</td>\n",
       "      <td>1 26605U 00071A   06292.82161344 -.00000102  0...</td>\n",
       "      <td>2 26605 056.5665 018.0235 0029766 239.8189 119...</td>\n",
       "      <td>18645.562378</td>\n",
       "      <td>14857.247410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>27704</td>\n",
       "      <td>2003-010A</td>\n",
       "      <td>2006-10-19T02:44:05.757791</td>\n",
       "      <td>0 NAVSTAR 52 (USA 168)</td>\n",
       "      <td>1 27704U 03010A   06292.11395553 -.00000038  0...</td>\n",
       "      <td>2 27704 053.9573 259.2051 0117859 194.4817 165...</td>\n",
       "      <td>26341.980230</td>\n",
       "      <td>26707.480248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>27704</td>\n",
       "      <td>2003-010A</td>\n",
       "      <td>2006-10-20T02:40:00.590015</td>\n",
       "      <td>0 NAVSTAR 52 (USA 168)</td>\n",
       "      <td>1 27704U 03010A   06293.11111794 -.00000035  0...</td>\n",
       "      <td>2 27704 053.9567 259.1640 0117891 194.5129 165...</td>\n",
       "      <td>26342.162792</td>\n",
       "      <td>26707.791837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>27704</td>\n",
       "      <td>2003-010A</td>\n",
       "      <td>2006-10-20T02:40:00.503615</td>\n",
       "      <td>0 NAVSTAR 52 (USA 168)</td>\n",
       "      <td>1 27704U 03010A   06293.11111694 -.00000035  0...</td>\n",
       "      <td>2 27704 053.9567 259.1641 0117886 194.5152 165...</td>\n",
       "      <td>26342.280043</td>\n",
       "      <td>26707.958472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>27704</td>\n",
       "      <td>2003-010A</td>\n",
       "      <td>2006-10-18T02:48:11.539872</td>\n",
       "      <td>0 NAVSTAR 52 (USA 168)</td>\n",
       "      <td>1 27704U 03010A   06291.11680023 -.00000037 +0...</td>\n",
       "      <td>2 27704 053.9576 259.2453 0117897 194.3381 165...</td>\n",
       "      <td>26342.820964</td>\n",
       "      <td>26708.138315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    norad_cat_id  object_id                       epoch  \\\n",
       "568        28129  2003-058A  2006-10-20T05:39:53.175168   \n",
       "567        28129  2003-058A  2006-10-19T05:43:58.315295   \n",
       "565        28129  2003-058A  2006-10-18T05:48:03.299040   \n",
       "566        28129  2003-058A  2006-10-18T05:48:03.325823   \n",
       "97         11690  1980-011A  2006-10-20T04:50:54.165120   \n",
       "95         11690  1980-011A  2006-10-18T04:59:07.223135   \n",
       "96         11690  1980-011A  2006-10-19T04:55:00.855263   \n",
       "64         10684  1978-020A  2006-10-18T05:30:29.613023   \n",
       "66         10684  1978-020A  2006-10-20T05:58:32.334239   \n",
       "67         10684  1978-020A  2006-10-20T05:58:32.596032   \n",
       "63         10684  1978-020A  2006-10-18T05:30:29.595744   \n",
       "65         10684  1978-020A  2006-10-19T05:44:31.917120   \n",
       "501        26605  2000-071A  2006-10-18T07:49:12.945792   \n",
       "502        26605  2000-071A  2006-10-19T07:45:09.124991   \n",
       "504        26605  2000-071A  2006-10-20T19:39:03.671136   \n",
       "503        26605  2000-071A  2006-10-19T19:43:07.401216   \n",
       "524        27704  2003-010A  2006-10-19T02:44:05.757791   \n",
       "526        27704  2003-010A  2006-10-20T02:40:00.590015   \n",
       "525        27704  2003-010A  2006-10-20T02:40:00.503615   \n",
       "523        27704  2003-010A  2006-10-18T02:48:11.539872   \n",
       "\n",
       "                  tle_line0  \\\n",
       "568  0 NAVSTAR 53 (USA 175)   \n",
       "567  0 NAVSTAR 53 (USA 175)   \n",
       "565  0 NAVSTAR 53 (USA 175)   \n",
       "566  0 NAVSTAR 53 (USA 175)   \n",
       "97   0 OPS 5117 (NAVSTAR 5)   \n",
       "95   0 OPS 5117 (NAVSTAR 5)   \n",
       "96   0 OPS 5117 (NAVSTAR 5)   \n",
       "64   0 OPS 5111 (NAVSTAR 1)   \n",
       "66   0 OPS 5111 (NAVSTAR 1)   \n",
       "67   0 OPS 5111 (NAVSTAR 1)   \n",
       "63   0 OPS 5111 (NAVSTAR 1)   \n",
       "65   0 OPS 5111 (NAVSTAR 1)   \n",
       "501  0 NAVSTAR 49 (USA 154)   \n",
       "502  0 NAVSTAR 49 (USA 154)   \n",
       "504  0 NAVSTAR 49 (USA 154)   \n",
       "503  0 NAVSTAR 49 (USA 154)   \n",
       "524  0 NAVSTAR 52 (USA 168)   \n",
       "526  0 NAVSTAR 52 (USA 168)   \n",
       "525  0 NAVSTAR 52 (USA 168)   \n",
       "523  0 NAVSTAR 52 (USA 168)   \n",
       "\n",
       "                                             tle_line1  \\\n",
       "568  1 28129U 03058A   06293.23603212 -.00000058  0...   \n",
       "567  1 28129U 03058A   06292.23886939 -.00000059 +0...   \n",
       "565  1 28129U 03058A   06291.24170485 -.00000059  0...   \n",
       "566  1 28129U 03058A   06291.24170516 -.00000059  0...   \n",
       "97   1 11690U 80011A   06293.20201580 -.00000012 +0...   \n",
       "95   1 11690U 80011A   06291.20772249 -.00000006  0...   \n",
       "96   1 11690U 80011A   06292.20487101 -.00000011 +0...   \n",
       "64   1 10684U 78020A   06291.22950941 -.00000008 +0...   \n",
       "66   1 10684U 78020A   06293.24898535 -.00000013 +0...   \n",
       "67   1 10684U 78020A   06293.24898838 -.00000013  0...   \n",
       "63   1 10684U 78020A   06291.22950921 -.00000008  0...   \n",
       "65   1 10684U 78020A   06292.23925830 -.00000012 +0...   \n",
       "501  1 26605U 00071A   06291.32584428 -.00000108 +0...   \n",
       "502  1 26605U 00071A   06292.32302228 -.00000104  0...   \n",
       "504  1 26605U 00071A   06293.81879249 -.00000096  0...   \n",
       "503  1 26605U 00071A   06292.82161344 -.00000102  0...   \n",
       "524  1 27704U 03010A   06292.11395553 -.00000038  0...   \n",
       "526  1 27704U 03010A   06293.11111794 -.00000035  0...   \n",
       "525  1 27704U 03010A   06293.11111694 -.00000035  0...   \n",
       "523  1 27704U 03010A   06291.11680023 -.00000037 +0...   \n",
       "\n",
       "                                             tle_line2      residual  \\\n",
       "568  2 28129 054.6457 320.0220 0052136 264.7011 094...      2.667964   \n",
       "567  2 28129 054.6464 320.0627 0052109 264.7328 094...      2.685148   \n",
       "565  2 28129 054.6471 320.1034 0052084 264.7458 094...      2.798881   \n",
       "566  2 28129 054.6470 320.1034 0052057 264.7173 094...      3.076637   \n",
       "97   2 11690 063.8937 271.2930 0273259 182.2802 177...  13655.398467   \n",
       "95   2 11690 063.8947 271.3546 0272966 182.2597 177...  13655.433771   \n",
       "96   2 11690 063.8945 271.3237 0273183 182.2697 177...  13655.490257   \n",
       "64   2 10684 063.4960 271.4665 0061292 174.6670 185...  14799.212858   \n",
       "66   2 10684 063.4948 271.4050 0061264 174.7138 185...  14799.342105   \n",
       "67   2 10684 063.4948 271.4053 0061462 174.4244 185...  14800.350209   \n",
       "63   2 10684 063.4961 271.4673 0061451 174.3758 185...  14800.359743   \n",
       "65   2 10684 063.4956 271.4366 0061450 174.3827 185...  14800.381522   \n",
       "501  2 26605 056.5661 018.0814 0029663 239.6383 120...  18644.708749   \n",
       "502  2 26605 056.5665 018.0427 0029595 239.6489 120...  18644.913248   \n",
       "504  2 26605 056.5669 017.9849 0029655 239.4496 120...  18645.506304   \n",
       "503  2 26605 056.5665 018.0235 0029766 239.8189 119...  18645.562378   \n",
       "524  2 27704 053.9573 259.2051 0117859 194.4817 165...  26341.980230   \n",
       "526  2 27704 053.9567 259.1640 0117891 194.5129 165...  26342.162792   \n",
       "525  2 27704 053.9567 259.1641 0117886 194.5152 165...  26342.280043   \n",
       "523  2 27704 053.9576 259.2453 0117897 194.3381 165...  26342.820964   \n",
       "\n",
       "     residual_epoch  \n",
       "568        1.014833  \n",
       "567        0.915499  \n",
       "565        0.941797  \n",
       "566        1.175152  \n",
       "97      1040.530041  \n",
       "95      1036.763506  \n",
       "96      1039.200554  \n",
       "64     10944.563035  \n",
       "66     10945.147804  \n",
       "67     10948.427452  \n",
       "63     10948.958372  \n",
       "65     10949.079014  \n",
       "501    14855.129987  \n",
       "502    14855.396871  \n",
       "504    14856.178772  \n",
       "503    14857.247410  \n",
       "524    26707.480248  \n",
       "526    26707.791837  \n",
       "525    26707.958472  \n",
       "523    26708.138315  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_candidates.sort_values('residual').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "26605381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " (10668.296087647044, 7292.796722440949, -31842.335182255498),\n",
       " (-2.501846359599823, 1.0696316015728493, 0.8648788164327018))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat = sat_gps[6]\n",
    "candidate_sat = EarthSatellite(sat['TLE_LINE1'], sat['TLE_LINE2'], sat['TLE_LINE0'], ts)\n",
    "candidate_sat.model.sgp4(jd, jdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "444ccbc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-20613.52006192, -18917.25591717,  10292.77861932])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.ravel(solve_sat.model.sgp4(jd, jdf)[1:]) - np.ravel(candidate_sat.model.sgp4(jd, jdf)[1:]))[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b5321b55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " (-9945.223974273918, -11624.45919472531, -21549.556562930655),\n",
       " (2.976205749750984, -2.5097654537063634, -0.02194336742101166))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve_sat.model.sgp4_tsince(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8956fb7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " (10668.296167702032, 7292.796688214704, -31842.335209930596),\n",
       " (-2.5018463562478765, 1.0696316038636393, 0.8648788064260986))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidate_sat.model.sgp4_tsince((sat_epoch_datetime(solve_sat.model) - sat_epoch_datetime(candidate_sat.model)).total_seconds() / 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4546ac6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2006-10-19 02:44:46.999968+00:00 2006-10-20 17:18:13.250016+00:00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-138806.250048"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(sat_epoch_datetime(solve_sat.model), sat_epoch_datetime(candidate_sat.model))\n",
    "(sat_epoch_datetime(solve_sat.model) - sat_epoch_datetime(candidate_sat.model)).total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "84f6ffad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((array([-20421.04220671,  16961.05759437,    -48.200446  ]),\n",
       "  array([-1.41399684, -1.74163053, -3.16097417])),\n",
       " datetime.datetime(2006, 10, 15, 23, 59, 47, tzinfo=<UTC>))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eph_is[0], t_is[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2e80597d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " (30657.74437934246, -6623.393156897523, -25793.922031972415),\n",
       " (-0.8113843357046191, 1.142564036944282, -1.6191111000462464))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidate_sat.model.sgp4(*jday_datetime(t_is[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0d9e8ca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61871.72970205591"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.norm(np.ravel(eph_is[0])[:3] - np.ravel(candidate_sat.model.sgp4(*jday_datetime(t_is[0]))[1:])[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1440dc6",
   "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": 33,
   "id": "13ca5515-3de9-43c3-a1a0-7bff462aecc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_tle = candidate_sat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fbf1cfe4-0c37-43bd-b9f6-c3e88f86a2ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2006, 10, 20, 17, 18, 13, 250016, tzinfo=UTC)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat_epoch_datetime(ref_tle.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1c3e0bfa-3b9b-4c70-8731-aa165c2ed27d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ajd, ajdf = jday_datetime(sat_epoch_datetime(ref_tle.model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 39,
   "id": "1a59ceca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2006, 10, 20, 17, 18, 13, 250016, tzinfo=UTC)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat_epoch_datetime(ref_tle.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a424100d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2006, 10, 20, 17, 18, 13, 250016, tzinfo=UTC)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt[aligned_epoch_obs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 42,
   "id": "88224b20-67ca-4904-8b83-3e3299c44146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1 99999U 72072A   06293.72098669  .00000000  00000-0 -10000+1 0  9992',\n",
       " '2 99999  54.6451 320.0064 0052539 264.4273  85.1870  2.00562788    17')"
      ]
     },
     "execution_count": 42,
     "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": 43,
   "id": "0512d433-60d7-4de9-945a-b498f449c894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1 06192U 72072A   06293.72098669  .00000791  00000-0  10000-3 0  9994',\n",
       " '2 06192  69.9404 150.1133 5399813  47.6269 347.1851  2.01337587250640')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exporter.export_tle(ref_tle.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c74616e7-21b8-4f7e-b9d2-fe813bfcab9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Position Residual (m)')"
      ]
     },
     "execution_count": 44,
     "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": "c74616e7-21b8-4f7e-b9d2-fe813bfcab9d",
   "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
}