TLEFit - EQN - GPS FD - Icesat.ipynb

{
 "cells": [
  {
   "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 os\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "successful-tobacco",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tlefit_equinoctial_eph_fd import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec08023f-14d7-40cd-a498-2aa867b6cec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import coord_skyfield\n",
    "from coord_skyfield import ITRF2TEME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f9b2b016-9a05-4346-8c0b-7beda379564b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_821344/3202393328.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/icesat.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": 7,
   "id": "persistent-command",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial semi-major axis (a) = 6964.748 km\n",
      "COE elements (original) = [6964.747920163459, 0.0008532659302083011, 1.6406707565495493, 3.510973090900842, 0.0219135748417416, 1.9295921006576968, 1e-06]\n",
      "Residuals at epoch time [ 2.17569220e+00  5.78270795e-01 -7.90739972e+00  1.39443640e-03\n",
      "  6.33258238e-04 -6.85567605e-04]\n",
      "Residual magnitudes at epoch time 8.22162, 0.00167794\n",
      "\n",
      "\n",
      "#################### ITERATION 1 ####################\n",
      "\n",
      "Condition number (A): 11.640\n",
      "Condition number (ATWA_acc): 837733.0464943255\n",
      "Lambda:  0.1\n",
      "Residuals after/before 3.06e+09 < 2.43e+13\n",
      "Covariance a: 0.011 m\n",
      "dx  [ 1.01750888e+01  9.19475347e-04  2.33653445e-04 -3.56839801e-04\n",
      "  1.05800825e-04  2.97603646e-05 -8.12654430e-03]\n",
      "COE elements = [6974.923008962788, 0.00015970199364974494, 1.6406094112810417, 3.510891104099522, 2.1601439519581467, 6.074272177720742, -0.008125544299055461]\n",
      "EQN elements = (6974.923008962788, 0.0001307023977437938, -9.176933038726119e-05, 5.462121926598822, -0.3870835374682497, -1.0000703914822022, -0.008125544299055461)\n",
      "Residual (b) = [-2.07444778e+00 -5.31457441e-01  7.88840949e+00 -1.96212797e-03\n",
      " -9.23684329e-04 -7.43416182e-05]\n",
      "Residuals (b) r = 8.17, v = 0.00217\n",
      "\n",
      "Convergence test: 0.137989, sigma_new(5.69e+04) > sigma_old(5e+04)\n",
      "\n",
      "#################### ITERATION 2 ####################\n",
      "\n",
      "Condition number (A): 11.667\n",
      "Condition number (ATWA_acc): 961385.9779367545\n",
      "Lambda:  0.01\n",
      "Residuals after/before 4.79e+06 < 3.06e+09\n",
      "Covariance a: 0.011 m\n",
      "dx  [-2.72467229e-01 -5.59540260e-05 -1.02516573e-04  9.78712973e-04\n",
      "  3.37054074e-06 -1.49395242e-05  8.04375477e-03]\n",
      "COE elements = [6974.65054173375, 0.0002081689974831975, 1.640621239916679, 3.51088314434588, 1.5687829004173404, 0.3834345948089153, -8.178953129047047e-05]\n",
      "EQN elements = (6974.65054173375, 7.474837169812955e-05, -0.0001942859038675677, 5.4631006395721355, -0.3870801669275111, -1.00008533100642, -8.178953129047047e-05)\n",
      "Residual (b) = [ 7.26799133e+00  2.88517401e+00 -3.14257207e+00 -1.88645398e-03\n",
      " -1.00652260e-03 -7.94517936e-03]\n",
      "Residuals (b) r = 8.43, v = 0.00823\n",
      "\n",
      "Convergence test: 0.98878, sigma_new(638) < sigma_old(5.69e+04)\n",
      "\n",
      "#################### ITERATION 3 ####################\n",
      "\n",
      "Condition number (A): 11.668\n",
      "Condition number (ATWA_acc): 959031.237491664\n",
      "Lambda:  0.001\n",
      "Residuals after/before 4.74e+06 < 4.79e+06\n",
      "Covariance a: 0.011 m\n",
      "dx  [-1.00138655e-03 -8.34392882e-07 -2.48958568e-06  3.10553261e-06\n",
      " -2.13860777e-08 -1.57174437e-08  2.94596228e-05]\n",
      "COE elements = [6974.649540347198, 0.00021019959455962375, 1.6406212607326796, 3.5108831576536783, 1.560825151955572, 0.39139543549549316, -5.232990849172612e-05]\n",
      "EQN elements = (6974.649540347198, 7.391397881614978e-05, -0.0001967754895524236, 5.4631037451047435, -0.38708018831358854, -1.000085346723864, -5.232990849172612e-05)\n",
      "Residual (b) = [-4.71345238e-02  2.24515330e-01  7.37189246e-03 -1.53107694e-04\n",
      "  3.53021452e-04  2.85425597e-05]\n",
      "Residuals (b) r = 0.23, v = 0.000386\n",
      "\n",
      "Convergence test: 0.960428, sigma_new(25.3) < sigma_old(638)\n",
      "\n",
      "#################### ITERATION 4 ####################\n",
      "\n",
      "Condition number (A): 11.668\n",
      "Condition number (ATWA_acc): 959019.3164485394\n",
      "Lambda:  0.001\n",
      "Residuals after/before 4.74e+06 < 4.74e+06\n",
      "Covariance a: 0.011 m\n",
      "dx  [-1.41680463e-06  8.26669027e-11 -1.00384422e-12  3.94811542e-09\n",
      " -1.33872346e-11 -4.08064155e-11  6.87838131e-08]\n",
      "COE elements = [6974.649538930394, 0.00021019962456812408, 1.6406212607725748, 3.510883157651585, 1.5608255184402748, 0.3913950729609992, -5.226112467858216e-05]\n",
      "EQN elements = (6974.649538930394, 7.391406148305248e-05, -0.00019677549055626783, 5.463103749052859, -0.3870801883269753, -1.0000853467646704, -5.226112467858216e-05)\n",
      "Residual (b) = [-9.11598354e-02  2.09286212e-01  3.38856949e-02 -1.52373920e-04\n",
      "  3.56362266e-04  7.00188811e-05]\n",
      "Residuals (b) r = 0.231, v = 0.000394\n",
      "\n",
      "Convergence test: 0.00500715, sigma_new(25.1) < sigma_old(25.3)\n",
      "\n",
      "Stopped due to residual convergence\n",
      "\n",
      "#################### SOLUTION IN 4 ITERATIONS ####################\n",
      "\n",
      "Solution 6974.649538930394 0.0002102 94.0007 201.1588 89.4287 22.4253 -5.2261e-05\n",
      "Original 6964.747920163459 0.0008533 94.0035 201.1639 1.2556 110.5575 +1.0000e-06\n",
      "Residuals (b) r = 0.231, v = 0.000394\n",
      "Stopped in 4 iterations\n",
      "Residual at epoch     8.222e+00 km 1.678e-03 km/s\n",
      "Residual at new epoch 2.538e-01 km 0.000e+00 km/s\n",
      "Residual at the end   2.308e-01 km 3.938e-04 km/s\n"
     ]
    }
   ],
   "source": [
    "last_obs = 2500\n",
    "obs_stride = 2\n",
    "lamda = 1e-3 * 0 + 1 # Interesting.The smaller number works, but diverges. This is better\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, central_diff=True, last_obs=last_obs, obs_stride=obs_stride, lamda=lamda, rms_epsilon=0.0001, debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a6260cf7-0cbe-47ae-860f-77133cbe1b1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2003-02-21 14:38:46.999679+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  03052.61026620  .00000000  00000-0 -52261-4 0  9999',\n",
       " '2 99999  94.0007 201.1588 0002102  89.4287  22.4253 14.90456728    10')"
      ]
     },
     "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 0x7fde357e34c0>]"
      ]
     },
     "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 0x7fde2e19d450>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,3))\n",
    "plt.semilogy(bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcdcc86e-87b0-4977-b8f5-195b16f64dd5",
   "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 0x7fde2d8db460>]"
      ]
     },
     "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": "215bcf85",
   "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": 24,
   "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": 26,
   "id": "cfea155f",
   "metadata": {},
   "outputs": [],
   "source": [
    "launch_objects = json.loads(st.gp(object_id=op.like('2003-002~~'), orderby='TLE_LINE1', format='json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c031073a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tles = []\n",
    "\n",
    "sat_ids = [sat['NORAD_CAT_ID'] for sat in launch_objects]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 19,
   "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": 20,
   "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": 21,
   "id": "dc90d850",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>4</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-21T22:12:10.555488</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03052.92512217 +.00000334 +0...</td>\n",
       "      <td>2 27642 093.9974 201.3200 0002012 082.5177 277...</td>\n",
       "      <td>0.449454</td>\n",
       "      <td>0.481396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-21T10:55:25.107744</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03052.45515171  .00001751  0...</td>\n",
       "      <td>2 27642 093.9975 201.0818 0002009 083.8037 276...</td>\n",
       "      <td>0.491112</td>\n",
       "      <td>0.817666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-22T11:05:36.259872</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03053.46222523  .00000675  0...</td>\n",
       "      <td>2 27642 093.9973 201.5925 0001977 080.0368 280...</td>\n",
       "      <td>0.491160</td>\n",
       "      <td>0.508716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-22T22:22:21.200447</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03053.93218982 +.00000782 +0...</td>\n",
       "      <td>2 27642 093.9986 201.8315 0001994 086.8594 273...</td>\n",
       "      <td>0.531606</td>\n",
       "      <td>0.301987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-21T10:55:25.021343</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03052.45515071  .00000069  0...</td>\n",
       "      <td>2 27642 093.9989 201.0818 0002120 093.1553 266...</td>\n",
       "      <td>2.318565</td>\n",
       "      <td>4.382716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-20T22:01:59.166623</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03051.91804591 +.00000836 +0...</td>\n",
       "      <td>2 27642 093.9987 200.8086 0001693 075.4900 284...</td>\n",
       "      <td>4.443557</td>\n",
       "      <td>9.185398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-20T10:45:14.378975</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03051.44808309  .00001254  0...</td>\n",
       "      <td>2 27642 093.9987 200.5699 0002091 092.9221 267...</td>\n",
       "      <td>6.712634</td>\n",
       "      <td>12.526059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27643</td>\n",
       "      <td>2003-002B</td>\n",
       "      <td>2003-02-20T20:33:57.017087</td>\n",
       "      <td>0 CHIPSAT</td>\n",
       "      <td>1 27643U 03002B   03051.85690992  .00003345  0...</td>\n",
       "      <td>2 27643 094.0108 200.8645 0014256 102.5331 257...</td>\n",
       "      <td>4075.204171</td>\n",
       "      <td>2717.656738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27643</td>\n",
       "      <td>2003-002B</td>\n",
       "      <td>2003-02-20T22:10:24.127104</td>\n",
       "      <td>0 CHIPSAT</td>\n",
       "      <td>1 27643U 03002B   03051.92389036 +.00003321 +0...</td>\n",
       "      <td>2 27643 094.0108 200.8988 0014209 102.2272 258...</td>\n",
       "      <td>4075.418993</td>\n",
       "      <td>2717.932791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27643</td>\n",
       "      <td>2003-002B</td>\n",
       "      <td>2003-02-21T20:40:42.670272</td>\n",
       "      <td>0 CHIPSAT</td>\n",
       "      <td>1 27643U 03002B   03052.86160498  .00002930  0...</td>\n",
       "      <td>2 27643 094.0114 201.3792 0014474 100.7745 259...</td>\n",
       "      <td>4077.190646</td>\n",
       "      <td>2721.672327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>27643</td>\n",
       "      <td>2003-002B</td>\n",
       "      <td>2003-02-21T22:17:09.764735</td>\n",
       "      <td>0 CHIPSAT</td>\n",
       "      <td>1 27643U 03002B   03052.92858524 +.00002875 +0...</td>\n",
       "      <td>2 27643 094.0114 201.4134 0014349 100.3282 259...</td>\n",
       "      <td>4077.575230</td>\n",
       "      <td>2722.118206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>27643</td>\n",
       "      <td>2003-002B</td>\n",
       "      <td>2003-02-22T20:47:28.185216</td>\n",
       "      <td>0 CHIPSAT</td>\n",
       "      <td>1 27643U 03002B   03053.86629844  .00002525  0...</td>\n",
       "      <td>2 27643 094.0119 201.8938 0014331 097.2831 262...</td>\n",
       "      <td>4078.678934</td>\n",
       "      <td>2724.075052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>27643</td>\n",
       "      <td>2003-002B</td>\n",
       "      <td>2003-02-22T22:23:55.258080</td>\n",
       "      <td>0 CHIPSAT</td>\n",
       "      <td>1 27643U 03002B   03053.93327845 +.00002480 +0...</td>\n",
       "      <td>2 27643 094.0119 201.9280 0014258 096.9517 263...</td>\n",
       "      <td>4078.874126</td>\n",
       "      <td>2724.307944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>27645</td>\n",
       "      <td>2003-002D</td>\n",
       "      <td>2003-02-20T01:30:16.955136</td>\n",
       "      <td>0 DELTA 2 R/B</td>\n",
       "      <td>1 27645U 03002D   03051.06269624  .00336756  4...</td>\n",
       "      <td>2 27645 097.5309 230.1304 0145406 291.1089 067...</td>\n",
       "      <td>9137.625697</td>\n",
       "      <td>6894.729635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>27645</td>\n",
       "      <td>2003-002D</td>\n",
       "      <td>2003-02-20T12:07:37.487999</td>\n",
       "      <td>0 DELTA 2 R/B</td>\n",
       "      <td>1 27645U 03002D   03051.50529500  .00333719  3...</td>\n",
       "      <td>2 27645 097.5309 230.6159 0144156 289.3694 069...</td>\n",
       "      <td>9138.048111</td>\n",
       "      <td>6883.316350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>27645</td>\n",
       "      <td>2003-002D</td>\n",
       "      <td>2003-02-21T13:54:57.718944</td>\n",
       "      <td>0 DELTA 2 R/B</td>\n",
       "      <td>1 27645U 03002D   03052.57983471  .00340332  4...</td>\n",
       "      <td>2 27645 097.5263 231.7972 0142034 285.1852 073...</td>\n",
       "      <td>9138.128598</td>\n",
       "      <td>6878.314347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>27645</td>\n",
       "      <td>2003-002D</td>\n",
       "      <td>2003-02-20T22:44:50.674271</td>\n",
       "      <td>0 DELTA 2 R/B</td>\n",
       "      <td>1 27645U 03002D   03051.94780873 +.00333901 +4...</td>\n",
       "      <td>2 27645 097.5308 231.1011 0143465 287.6686 070...</td>\n",
       "      <td>9138.178311</td>\n",
       "      <td>6879.957990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27645</td>\n",
       "      <td>2003-002D</td>\n",
       "      <td>2003-02-21T00:15:52.132319</td>\n",
       "      <td>0 DELTA 2 R/B</td>\n",
       "      <td>1 27645U 03002D   03052.01102005  .00332812  3...</td>\n",
       "      <td>2 27645 097.5309 231.1704 0143338 287.3942 071...</td>\n",
       "      <td>9138.218876</td>\n",
       "      <td>6878.633994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>27645</td>\n",
       "      <td>2003-002D</td>\n",
       "      <td>2003-02-21T23:00:54.703008</td>\n",
       "      <td>0 DELTA 2 R/B</td>\n",
       "      <td>1 27645U 03002D   03052.95896647 +.00339560 +4...</td>\n",
       "      <td>2 27645 097.5265 232.2135 0141486 283.7548 074...</td>\n",
       "      <td>9138.329278</td>\n",
       "      <td>6877.542184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27645</td>\n",
       "      <td>2003-002D</td>\n",
       "      <td>2003-02-22T02:02:52.753920</td>\n",
       "      <td>0 DELTA 2 R/B</td>\n",
       "      <td>1 27645U 03002D   03053.08533280  .00338809  4...</td>\n",
       "      <td>2 27645 097.5266 232.3523 0141240 283.2678 075...</td>\n",
       "      <td>9138.422158</td>\n",
       "      <td>6877.413186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   norad_cat_id  object_id                       epoch      tle_line0  \\\n",
       "4         27642  2003-002A  2003-02-21T22:12:10.555488       0 ICESAT   \n",
       "3         27642  2003-002A  2003-02-21T10:55:25.107744       0 ICESAT   \n",
       "5         27642  2003-002A  2003-02-22T11:05:36.259872       0 ICESAT   \n",
       "6         27642  2003-002A  2003-02-22T22:22:21.200447       0 ICESAT   \n",
       "2         27642  2003-002A  2003-02-21T10:55:25.021343       0 ICESAT   \n",
       "1         27642  2003-002A  2003-02-20T22:01:59.166623       0 ICESAT   \n",
       "0         27642  2003-002A  2003-02-20T10:45:14.378975       0 ICESAT   \n",
       "7         27643  2003-002B  2003-02-20T20:33:57.017087      0 CHIPSAT   \n",
       "8         27643  2003-002B  2003-02-20T22:10:24.127104      0 CHIPSAT   \n",
       "9         27643  2003-002B  2003-02-21T20:40:42.670272      0 CHIPSAT   \n",
       "10        27643  2003-002B  2003-02-21T22:17:09.764735      0 CHIPSAT   \n",
       "11        27643  2003-002B  2003-02-22T20:47:28.185216      0 CHIPSAT   \n",
       "12        27643  2003-002B  2003-02-22T22:23:55.258080      0 CHIPSAT   \n",
       "20        27645  2003-002D  2003-02-20T01:30:16.955136  0 DELTA 2 R/B   \n",
       "21        27645  2003-002D  2003-02-20T12:07:37.487999  0 DELTA 2 R/B   \n",
       "24        27645  2003-002D  2003-02-21T13:54:57.718944  0 DELTA 2 R/B   \n",
       "22        27645  2003-002D  2003-02-20T22:44:50.674271  0 DELTA 2 R/B   \n",
       "23        27645  2003-002D  2003-02-21T00:15:52.132319  0 DELTA 2 R/B   \n",
       "25        27645  2003-002D  2003-02-21T23:00:54.703008  0 DELTA 2 R/B   \n",
       "26        27645  2003-002D  2003-02-22T02:02:52.753920  0 DELTA 2 R/B   \n",
       "\n",
       "                                            tle_line1  \\\n",
       "4   1 27642U 03002A   03052.92512217 +.00000334 +0...   \n",
       "3   1 27642U 03002A   03052.45515171  .00001751  0...   \n",
       "5   1 27642U 03002A   03053.46222523  .00000675  0...   \n",
       "6   1 27642U 03002A   03053.93218982 +.00000782 +0...   \n",
       "2   1 27642U 03002A   03052.45515071  .00000069  0...   \n",
       "1   1 27642U 03002A   03051.91804591 +.00000836 +0...   \n",
       "0   1 27642U 03002A   03051.44808309  .00001254  0...   \n",
       "7   1 27643U 03002B   03051.85690992  .00003345  0...   \n",
       "8   1 27643U 03002B   03051.92389036 +.00003321 +0...   \n",
       "9   1 27643U 03002B   03052.86160498  .00002930  0...   \n",
       "10  1 27643U 03002B   03052.92858524 +.00002875 +0...   \n",
       "11  1 27643U 03002B   03053.86629844  .00002525  0...   \n",
       "12  1 27643U 03002B   03053.93327845 +.00002480 +0...   \n",
       "20  1 27645U 03002D   03051.06269624  .00336756  4...   \n",
       "21  1 27645U 03002D   03051.50529500  .00333719  3...   \n",
       "24  1 27645U 03002D   03052.57983471  .00340332  4...   \n",
       "22  1 27645U 03002D   03051.94780873 +.00333901 +4...   \n",
       "23  1 27645U 03002D   03052.01102005  .00332812  3...   \n",
       "25  1 27645U 03002D   03052.95896647 +.00339560 +4...   \n",
       "26  1 27645U 03002D   03053.08533280  .00338809  4...   \n",
       "\n",
       "                                            tle_line2     residual  \\\n",
       "4   2 27642 093.9974 201.3200 0002012 082.5177 277...     0.449454   \n",
       "3   2 27642 093.9975 201.0818 0002009 083.8037 276...     0.491112   \n",
       "5   2 27642 093.9973 201.5925 0001977 080.0368 280...     0.491160   \n",
       "6   2 27642 093.9986 201.8315 0001994 086.8594 273...     0.531606   \n",
       "2   2 27642 093.9989 201.0818 0002120 093.1553 266...     2.318565   \n",
       "1   2 27642 093.9987 200.8086 0001693 075.4900 284...     4.443557   \n",
       "0   2 27642 093.9987 200.5699 0002091 092.9221 267...     6.712634   \n",
       "7   2 27643 094.0108 200.8645 0014256 102.5331 257...  4075.204171   \n",
       "8   2 27643 094.0108 200.8988 0014209 102.2272 258...  4075.418993   \n",
       "9   2 27643 094.0114 201.3792 0014474 100.7745 259...  4077.190646   \n",
       "10  2 27643 094.0114 201.4134 0014349 100.3282 259...  4077.575230   \n",
       "11  2 27643 094.0119 201.8938 0014331 097.2831 262...  4078.678934   \n",
       "12  2 27643 094.0119 201.9280 0014258 096.9517 263...  4078.874126   \n",
       "20  2 27645 097.5309 230.1304 0145406 291.1089 067...  9137.625697   \n",
       "21  2 27645 097.5309 230.6159 0144156 289.3694 069...  9138.048111   \n",
       "24  2 27645 097.5263 231.7972 0142034 285.1852 073...  9138.128598   \n",
       "22  2 27645 097.5308 231.1011 0143465 287.6686 070...  9138.178311   \n",
       "23  2 27645 097.5309 231.1704 0143338 287.3942 071...  9138.218876   \n",
       "25  2 27645 097.5265 232.2135 0141486 283.7548 074...  9138.329278   \n",
       "26  2 27645 097.5266 232.3523 0141240 283.2678 075...  9138.422158   \n",
       "\n",
       "    residual_epoch  \n",
       "4         0.481396  \n",
       "3         0.817666  \n",
       "5         0.508716  \n",
       "6         0.301987  \n",
       "2         4.382716  \n",
       "1         9.185398  \n",
       "0        12.526059  \n",
       "7      2717.656738  \n",
       "8      2717.932791  \n",
       "9      2721.672327  \n",
       "10     2722.118206  \n",
       "11     2724.075052  \n",
       "12     2724.307944  \n",
       "20     6894.729635  \n",
       "21     6883.316350  \n",
       "24     6878.314347  \n",
       "22     6879.957990  \n",
       "23     6878.633994  \n",
       "25     6877.542184  \n",
       "26     6877.413186  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_candidates.sort_values('residual').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "33b7ca72",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>4</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-21T22:12:10.555488</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03052.92512217 +.00000334 +0...</td>\n",
       "      <td>2 27642 093.9974 201.3200 0002012 082.5177 277...</td>\n",
       "      <td>0.449454</td>\n",
       "      <td>0.481396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-21T10:55:25.107744</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03052.45515171  .00001751  0...</td>\n",
       "      <td>2 27642 093.9975 201.0818 0002009 083.8037 276...</td>\n",
       "      <td>0.491112</td>\n",
       "      <td>0.817666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-22T11:05:36.259872</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03053.46222523  .00000675  0...</td>\n",
       "      <td>2 27642 093.9973 201.5925 0001977 080.0368 280...</td>\n",
       "      <td>0.491160</td>\n",
       "      <td>0.508716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-22T22:22:21.200447</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03053.93218982 +.00000782 +0...</td>\n",
       "      <td>2 27642 093.9986 201.8315 0001994 086.8594 273...</td>\n",
       "      <td>0.531606</td>\n",
       "      <td>0.301987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-21T10:55:25.021343</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03052.45515071  .00000069  0...</td>\n",
       "      <td>2 27642 093.9989 201.0818 0002120 093.1553 266...</td>\n",
       "      <td>2.318565</td>\n",
       "      <td>4.382716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-20T22:01:59.166623</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03051.91804591 +.00000836 +0...</td>\n",
       "      <td>2 27642 093.9987 200.8086 0001693 075.4900 284...</td>\n",
       "      <td>4.443557</td>\n",
       "      <td>9.185398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27642</td>\n",
       "      <td>2003-002A</td>\n",
       "      <td>2003-02-20T10:45:14.378975</td>\n",
       "      <td>0 ICESAT</td>\n",
       "      <td>1 27642U 03002A   03051.44808309  .00001254  0...</td>\n",
       "      <td>2 27642 093.9987 200.5699 0002091 092.9221 267...</td>\n",
       "      <td>6.712634</td>\n",
       "      <td>12.526059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  norad_cat_id  object_id                       epoch tle_line0  \\\n",
       "4        27642  2003-002A  2003-02-21T22:12:10.555488  0 ICESAT   \n",
       "3        27642  2003-002A  2003-02-21T10:55:25.107744  0 ICESAT   \n",
       "5        27642  2003-002A  2003-02-22T11:05:36.259872  0 ICESAT   \n",
       "6        27642  2003-002A  2003-02-22T22:22:21.200447  0 ICESAT   \n",
       "2        27642  2003-002A  2003-02-21T10:55:25.021343  0 ICESAT   \n",
       "1        27642  2003-002A  2003-02-20T22:01:59.166623  0 ICESAT   \n",
       "0        27642  2003-002A  2003-02-20T10:45:14.378975  0 ICESAT   \n",
       "\n",
       "                                           tle_line1  \\\n",
       "4  1 27642U 03002A   03052.92512217 +.00000334 +0...   \n",
       "3  1 27642U 03002A   03052.45515171  .00001751  0...   \n",
       "5  1 27642U 03002A   03053.46222523  .00000675  0...   \n",
       "6  1 27642U 03002A   03053.93218982 +.00000782 +0...   \n",
       "2  1 27642U 03002A   03052.45515071  .00000069  0...   \n",
       "1  1 27642U 03002A   03051.91804591 +.00000836 +0...   \n",
       "0  1 27642U 03002A   03051.44808309  .00001254  0...   \n",
       "\n",
       "                                           tle_line2  residual  residual_epoch  \n",
       "4  2 27642 093.9974 201.3200 0002012 082.5177 277...  0.449454        0.481396  \n",
       "3  2 27642 093.9975 201.0818 0002009 083.8037 276...  0.491112        0.817666  \n",
       "5  2 27642 093.9973 201.5925 0001977 080.0368 280...  0.491160        0.508716  \n",
       "6  2 27642 093.9986 201.8315 0001994 086.8594 273...  0.531606        0.301987  \n",
       "2  2 27642 093.9989 201.0818 0002120 093.1553 266...  2.318565        4.382716  \n",
       "1  2 27642 093.9987 200.8086 0001693 075.4900 284...  4.443557        9.185398  \n",
       "0  2 27642 093.9987 200.5699 0002091 092.9221 267...  6.712634       12.526059  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_candidates[df_candidates['object_id'] == '2003-002A'].sort_values('residual')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "26605381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " (2261.219320997998, 1358.9466664175704, 6448.0213138740355),\n",
       " (6.61651017471882, 2.3507524816299354, -2.8093566278164706))"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "id": "444ccbc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.29629112,  0.035315  ,  0.04648425])"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "id": "b5321b55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " (2260.92302987888, 1358.9819814220282, 6448.067798125239),\n",
       " (6.616808293645903, 2.3505005636228646, -2.809007196821325))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve_sat.model.sgp4_tsince(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8956fb7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " (2261.2171970966665, 1358.9459118258458, 6448.0222156774935),\n",
       " (6.616511026517783, 2.3507529935451235, -2.8093541922207974))"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "id": "4546ac6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2003-02-21 14:38:46.999679+00:00 2003-02-22 22:22:21.200448+00:00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-114214.200769"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "id": "84f6ffad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((array([6323.75444035, 2457.64159476, 1620.790872  ]),\n",
       "  array([ 1.83967051,  0.1336331 , -7.33335782])),\n",
       " datetime.datetime(2003, 2, 19, 20, 59, 47, tzinfo=<UTC>))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eph_is[0], t_is[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2e80597d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " (6323.681277340811, 2457.63594728703, 1620.725171072416),\n",
       " (1.8395344991665756, 0.1336229347932113, -7.33348417411234))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidate_sat.model.sgp4(*jday_datetime(t_is[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0d9e8ca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09849533567316587"
      ]
     },
     "execution_count": 30,
     "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": "9dce9c9c",
   "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": 31,
   "id": "13ca5515-3de9-43c3-a1a0-7bff462aecc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_tle = candidate_sat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fbf1cfe4-0c37-43bd-b9f6-c3e88f86a2ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2003, 2, 22, 22, 22, 21, 200448, tzinfo=UTC)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat_epoch_datetime(ref_tle.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1c3e0bfa-3b9b-4c70-8731-aa165c2ed27d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ajd, ajdf = jday_datetime(sat_epoch_datetime(ref_tle.model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 37,
   "id": "1a59ceca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2003, 2, 22, 22, 22, 21, 200448, tzinfo=UTC)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat_epoch_datetime(ref_tle.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a424100d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2003, 2, 22, 22, 22, 21, 200448, tzinfo=UTC)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt[aligned_epoch_obs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 40,
   "id": "88224b20-67ca-4904-8b83-3e3299c44146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1 99999U 03002A   03053.93218982  .00000000  00000-0  61849-4 0  9995',\n",
       " '2 99999  94.0006 201.8297 0002077  85.6245 274.5221 14.90460538    15')"
      ]
     },
     "execution_count": 40,
     "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": 41,
   "id": "0512d433-60d7-4de9-945a-b498f449c894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1 27642U 03002A   03053.93218982  .00000782  00000-0  81529-4 0  9995',\n",
       " '2 27642  93.9986 201.8315 0001994  86.8594 273.2860 14.90460451  6097')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exporter.export_tle(ref_tle.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c74616e7-21b8-4f7e-b9d2-fe813bfcab9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Position Residual (m)')"
      ]
     },
     "execution_count": 42,
     "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 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
  },
  "vscode": {
   "interpreter": {
    "hash": "422d96f55d2b279cc3694c0d7101cd7135f83d32ba2b51a1c4e4fd4874965f43"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}