TLEFit - EQN - GPS FD - GPS Sat.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 sys\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "successful-tobacco",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tlefit_equinoctial_eph_fd import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec08023f-14d7-40cd-a498-2aa867b6cec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import coord_skyfield\n",
    "from coord_skyfield import ITRF2TEME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9b2b016-9a05-4346-8c0b-7beda379564b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_826628/2830078750.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": 7,
   "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): 88.984\n",
      "Condition number (ATWA_acc): 248580326496728.1\n",
      "Lambda:  0.1\n",
      "Residuals after/before 2.56e+04 < 6.88e+09\n",
      "Covariance a: 0.034 m\n",
      "dx  [ 1.73695617e+00 -1.69568580e-04 -2.25813922e-04  1.89943210e-04\n",
      " -2.19749692e-04  2.79962368e-05 -1.34090463e-01]\n",
      "COE elements = [26560.405167980836, 0.005235142290253865, 0.9537381165740815, 5.586316636743829, 4.617518301381834, 0.08796207642409204, -0.13408946323670282]\n",
      "EQN elements = (26560.405167980836, -0.00372520386262617, -0.003678256513768384, 4.008611707370168, -0.33158822536864135, 0.3961866137808042, -0.13408946323670282)\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): 89.031\n",
      "Condition number (ATWA_acc): 2.905195589892679e+17\n",
      "Lambda:  0.01\n",
      "Residuals after/before 2.53e+04 < 2.56e+04\n",
      "Covariance a: 0.034 m\n",
      "dx  [ 5.17038556e-05  1.53680567e-07  1.44584083e-08  7.90900795e-08\n",
      "  5.59810316e-08  4.78653520e-07 -2.33605006e-15]\n",
      "COE elements = [26560.405219684693, 0.0052350227770928594, 0.95373863930629, 5.586317314468845, 4.617536284328352, 0.0879434948426372, -0.13408946323670515]\n",
      "EQN elements = (26560.405219684693, -0.003725050182059452, -0.003678242055360125, 4.008611786460248, -0.33158816938760977, 0.3961870924343243, -0.13408946323670515)\n",
      "Residual (b) = [ 1.53442726e-01 -1.25732767e-01 -5.94065392e-02 -1.68431149e-06\n",
      "  7.47311415e-06  1.79897416e-07]\n",
      "Residuals (b) r = 0.207, v = 7.66e-06\n",
      "\n",
      "Convergence test: 0.99807, sigma_new(5.35) < sigma_old(2.77e+03)\n",
      "\n",
      "#################### ITERATION 3 ####################\n",
      "\n",
      "Condition number (A): 89.031\n",
      "Condition number (ATWA_acc): 3.5485946141672986e+17\n",
      "Lambda:  0.001\n",
      "Residuals after/before 2.53e+04 < 2.53e+04\n",
      "Covariance a: 0.034 m\n",
      "dx  [-6.62030883e-09 -1.97747203e-14 -5.30932857e-13  3.65659377e-12\n",
      "  1.66833164e-12 -1.40351631e-12 -1.27170954e-19]\n",
      "COE elements = [26560.405219678072, 0.005235022777479975, 0.9537386393029006, 5.586317314469578, 4.617536284397132, 0.08794349477678232, -0.13408946323670515]\n",
      "EQN elements = (26560.405219678072, -0.0037250501820792255, -0.0036782420558910587, 4.008611786463906, -0.3315881693859414, 0.3961870924329208, -0.13408946323670515)\n",
      "Residual (b) = [ 1.56258281e-01 -1.39070312e-01 -5.11207691e-02 -2.04973750e-06\n",
      "  6.03300635e-06  2.46323463e-06]\n",
      "Residuals (b) r = 0.215, v = 6.83e-06\n",
      "\n",
      "Convergence test: 0.00747152, sigma_new(5.31) < sigma_old(5.35)\n",
      "\n",
      "Stopped due to residual convergence\n",
      "\n",
      "#################### SOLUTION IN 3 ITERATIONS ####################\n",
      "\n",
      "Solution 26560.405219678072 0.0052350 54.6452 320.0724 264.5653 5.0388 -1.3409e-01\n",
      "Original 26558.66821181454 0.0049560 54.6305 320.0891 264.0673 5.5093 +1.0000e-06\n",
      "Residuals (b) r = 0.215, v = 6.83e-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   2.153e-01 km 6.831e-06 km/s\n"
     ]
    }
   ],
   "source": [
    "last_obs = 300\n",
    "obs_stride = 1\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": null,
   "id": "c74616e7-21b8-4f7e-b9d2-fe813bfcab9d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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 -13409+0 0  9991',\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 0x7fba24112980>]"
      ]
     },
     "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 0x7fba04cd2b00>]"
      ]
     },
     "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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F1K0bMGcOAGDwbbcBkZEX9lweFFY3Xd2NJhfcfPjhh7jzzjuxYsUKjB49usbrLRYLLMo+wypms7ne/gLV572bI7anvtie+mJ76ovt6SXFbCTT+fNi+4UqxvJymBT7QRkqKmDQdOkY5OEaLVqo7mVUBCeGsDCYFD0cprAwVfASEBcHdOwIvP020KIFzK1aXehTeVSbnwufBjfFxcU4dOiQo5yZmYmMjAxER0cjKSkJs2bNwokTJ7B8+XIAoitq0qRJeO211zBw4EBkZ2cDAIKDgxGhHARFRETkz5TryGjXtXE3O8rTIn3KadvKHo3QUPUA46gooFMnZ1k+vuuu2tW9Afh0nZsdO3agb9++jmncqamp6Nu3L2bPng0AOHXqFLKyshzXL168GJWVlZg2bRpat27t+JoxY4ZP6k9EROQTyuClpOTCghuZyeRcpwYQwU1UlLMcEwO0awe88w7w0Ufq9xoZn2ZuhgwZAkmRStNatmyZqrx+/fr6rRAREVFToOxmKi11nfqtnVnkTXCjXddGG9y0bCle77yzbnVuQNxbioiIqCnYuBFYulQce8rcaBfxA6pfVE8Z3GjXrWnRAlCOo0lIqFu9faDJDSgmIiJqlq6+Wrx26eKaqdGjWyo0VB3cREcDvXsDcXFip/GLL76w+jcgBjdERESNnXKF4X371EGJmxWJDdoVib3J3LRooR5z06qVyObs2yf2mQoOrnv9GxiDGyIiosZOmXmpqPC8l1RFheuu3JWV4jUsDCgqcp5Xjqlp0ULd9SR3SdXj2jX1hWNuiIiIGjtlcFNU5HkvKfkad7SL1yoDl9BQICnJWVZO+25imLkhIiJq7JTBjTaYcTeAuLoxNuHhwIkT4jggQAQ0shYtgMRE4LHHxAys/v31qbsPMLghIiJq7IqLncdeLNpn8BTcyIKC1AOIQ0PF2JoXXtChwr7FbikiIqLGztO6NnXtlgoOFgvzuXuviWNwQ0RE1Nh5Wsemtt1SsuBgID7eWVYeN3EMboiIiBqjnBxA3n+xtpmbqqnfknazSeXsqOBgoHVrZzk2VodKNw4MboiIiBobSRKL9vXsCRw9qs7MaMfclJWp18EBYLDbxYG2qykuznkcHAy0aeMs9+unU+V9jwOKiYiIGpuyMmD/fnG8bZsIdmTazI0kqXf2VgoPB86edZaVXU/BwUBgILB9O3D4MHDZZfrV38cY3BARETU22qnfJpOzrM3caK9XCgtTl5WZG3k14ksuEV9+hN1SREREjY0yWCksrHnRvmq2V5C0wY1yI0x5l28/xOCGiIiosVEGN9puqLpujBkUpB5QrDz2MwxuiIiIGhtlsFJSUvPU7+o2xtQu2peYqC77KQY3REREjY1y9lNJSc1Tv+VgSLNzt6QMbiwWIDraGdR066ZjhRsXDigmIiJqbJSZGXfBjTZzI+/6HR4u3pcpx9XIQc1//gN8/z1w++361rkRYXBDRETU2GgX7dPuJaXN3MjCwsTifzLl7Cg5uLnpJvHlx9gtRURE1BhkZwMHDohjZfCizdycP++yaJ+DZtE+SbnqsMWiU0UbP2ZuiIiIfE2SgCuuADIzgYMHXbultN1Q3myMCagzN6Gh+tS1CWBwQ0RE5GsFBcCff4rjXbs87yUFeLcxJgBJOeZGOS3cz7FbioiIyNeUmZj8/JoX7fMiuLGZzeq1bBjcEBERUYNRBitFRTXPjpKvl7dQkClWJLZrgxubTafKNn4MboiIiHxNmbkpLq55XRt50T7tGBtFdsYeEKBeqC8wUKfKNn4MboiIiHxNG9zUtCKxPFtKG9woxtjYzWZx8OCDIshJTdWvvo0cgxsiIiJfKy11HrvrlqpuXRvtOJqYGMehPaBqztCrr4purH79dKps48fghoiIyNeUmRl3mZvqghtt5kax67dNua6NnMVpJhjcEBER+ZoyeNFmbioqxMJ97ngIbir9eGPMmjC4ISIi8gXlisSe9pICqp/67aFbqlKziWZzwkX8iIiIGpokAT16AGfPAllZrtsrVDf1W0ubuYmOdn4Lk0mnyjY9zNwQERE1tLw8EdgAwJYtNW+M6U1wExioWufG0IzWtdFicENERNTQysqcx+7WtdFmbqpb10ZZNpsBRbbGrJyB1cwwuCEiImpoyuCloMD7zI2n4EZepG/oUADAqYEDdaps08MxN0RERA1NGczk5wPKadvupn7b7eJV0e0EQD2gWJ7u/eabsH31FQ61b49OulW4aWHmhoiIqKHJKwwDrhtluhtQLNPOjlJmbkJCxGvXrrA/+CCkgOabv2BwQ0RE1NA8Tf121y0l89QtJQc35NvgZuPGjRgzZgzatGkDg8GAVatW1fiZ9evXo1+/frBYLOjYsSOWLVtW7/UkIiLSlTJzU1pa815SMk/BTTNe10bLp8FNSUkJevfujYULF3p1fWZmJkaPHo2hQ4ciIyMDDz74IO6++25899139VxTIiIiHXnaXsFmU8+mUlIGMwYDEBrqLDNz4+DTDrlRo0Zh1KhRXl+/aNEitGvXDq+88goAoFu3bti0aRNeffVVpKSk1Fc1iYiILpwkiVeDQR3caDM3gHcrEpvN6uBGni1FTWu21NatWzFs2DDVuZSUFDz44IPVfqa8vBzlivRfYdUPjNVqhbW6Ps06ku+n932bK7anvtie+mJ76qs5tKfpr3+FYd8+VG7bBkNJieMXsL2kBCgvV3WlSIWFMACQwsJgKCpynLeFhEBeyUYym1EZFgZ5S0x7aSlsmnb0p/aszbM0qeAmOzsbcXFxqnNxcXEoLCxEWVkZgt30N6alpWHu3Lku59euXYuQekrhpaen18t9myu2p77Ynvpie+rLX9vTVF6Ov3zzDQDg13nzIAUE4JKq9wqzs1EUGIhExfXWs2cRCOB8YCCUv9n2njiBHvI1BgO+/e473KC4z4bVq1Xf15/as7QWixI2qeCmLmbNmoXU1FRHubCwEImJiRgxYgTCtQOzLpDVakV6ejqGDx8OczPbXr4+sD31xfbUF9tTX37fnkeOOA77tm8PSbFeTYTZjPDYWNXl5qoxN0Gxsc5tGgB0HTgQqJpIYw4JwXXXXed4Lzw62lH2x/YsrK6rzo0mFdzEx8cjJydHdS4nJwfh4eFuszYAYLFYYFEujlTFbDbX2x94fd67OWJ76ovtqS+2p778tj3lRfgAmIqKVIN/DWVlMFRWqi6X94UyaP4THhAV5bwmMFC01ejRwDffwDh1KoyatvOn9qzNczSp4GbQoEFY7SblNmjQIB/ViIiIyAvKqd/nzgGKIMXtisQyT4v2yQOIly8Hvv8euPFGferqB3w6Fby4uBgZGRnIyMgAIKZ6Z2RkICsrC4DoUpo4caLj+nvvvReHDx/Go48+in379uHNN9/Exx9/jJkzZ/qi+kRERN7RbrdQ02wpmTcrEkdHAzfdBBi5Lq/Mpy2xY8cO9O3bF3379gUApKamom/fvpg9ezYA4NSpU45ABwDatWuHb775Bunp6ejduzdeeeUVvP3225wGTkREjZsyc1NQ4HkRPyVvghty4dNuqSFDhkCS5/274W714SFDhmDXrl31WCsiIiKdeVqBWJKA4mL3n1MGM0YjF+3zEnNYRERE9a2mTE1+vvvPKTM3gYHqsTrshqoWW4aIiKg+SJIziNGOsVEGO4DoqgIAk0l9Xpm5MZvVZe09yIHBDRERUX248UYgKQk4cUIdiJSUVL/dgqeNMc1mdbbm/Hl96+tHGNwQERHpzWoFVq0CcnKATz+teXaUHKhogxvtXlJK7JaqFluGiIhIb8eOOY/Ly13H3FTXpeQpcyOva9O2rXi97LILr6efYnBDRESkt5IS53Ftpn4rtmUA4NotBQBLlwKTJwNPPqlPXf1Qk1qhmIiIqEnQrkjcqpWzXNd1beTMzdCh4ouqxcwNERGR3pTBy7lz6mCnokKd2VFSBjcBAYBy38Rq9lAkVwxuiIiI9KZdkdjbdW20mRoGN3XC4IaIiEhvyuCmrKz6dW20tGNslAENZ0d5jS1FRESkNz1WJNYGNwaDbtXzdwxuiIiI9LB/P/Dll2Jl4pqmfsvBTU1Tv7Vr25BXOFuKiIhID3//O5CRAXz2mWu3VHUrEkdEOI/lskwb2DBz4zVmboiIiC5UebkIbABg0ybvF+2LjFSXPQU35DUGN0RERBfqzBnnscnkfXDjzbo2Fot47dLlwuvZTDC4ISIiulDKTSy169p4u2ifyQSEhDjLcubm/feBlBTgiSf0q6+f45gbIiKiC6VdkVgZzFRUiHE37niaHSUf/+1v4ou8xswNERHRhfKUuQG8m/qtXbRPmcWhWqlz5iYrKwtHjx5FaWkpWrVqhYsvvhgWuV+QiIioOalpjM25c+4/503mhmqtVsHNkSNH8NZbb2HFihU4fvw4JElyvBcYGIgrr7wS99xzD/72t7/ByJUUiYiouVBmbjytaxMcrO6iUs6W0gY3TBjUmdcRyAMPPIDevXsjMzMT//znP/HHH3+goKAAFRUVyM7OxurVq3HFFVdg9uzZ6NWrF37++ef6rDcREZFv/fkn8Mknrov2udtuQV7LxtPUb223FJMEdeZ15qZFixY4fPgwWrZs6fJebGwsrrnmGlxzzTWYM2cO1qxZg2PHjuGSSy7RtbJERESNxt//DuzYAXz4oTrL4i5zU1QkXiMigFOnnOe13VLKcTbKbBDVitfBTVpamtc3HTlyZJ0qQ0RE1CTY7SKwAYB164Brr3W+5y5zI9NmbrTdUspViKubYUU1Ys6LiIiotrSzn5RZFk/BjTeL9sm6dq1z9Zq7OgU3Z8+exbRp09C9e3fExMQgOjpa9UVEROTXcnOdx9pgxtvMjckEtGjhLMuL9v3f/wFJScDUqbpVt7mp01Tw22+/HYcOHcJdd92FuLg4GLiZFxERNSfKqd35+a7jYwoK3H9OGdxoBxDLmZv77xdfVGd1Cm5+/PFHbNq0Cb1799a7PkRERI1faanzuKDA+0X7lMGNxcJF++pJnbqlunbtijIOdCIiouZK+TuwqMg1c+NNcKPN3HBdG93UKbh588038eSTT2LDhg04e/YsCgsLVV9ERER+x253TulWZm7cjbGRu6202Riua9Mg6tQtFRkZicLCQlxzzTWq85IkwWAwwGaz6VI5IiKiRuPBB4F//xv44Qd15ub8+erH3EREqAMhT91SHL+qmzoFNxMmTIDZbMYHH3zAAcVERNQ8vP66eH31VWD4cOf5mqZ+e1q0z2Rylvm7VDd1Cm727NmDXbt2oUuXLnrXh4iIqPFR7KWIoiJ15qasrPrVhD0t2qfdGJPBjW7q1ME3YMAAHDt2TO+6EBERNU7KrqWAANfgxptF+4xGIDTUWQ4KUl8bUKd8A7lRp5a8//77MWPGDDzyyCPo2bMnzPLCQ1V69eqlS+WIiIgahbw853FlpTq4sdmA4mL3n/M0O0ob3Fx11QVXk4Q6BTfjxo0DANx5552OcwaDgQOKiYjIPymDm6IidSYHqH7qtzJzY7GoZ0/Jwc3vvwPffgv84x+6VJXqGNxkZmbqXQ8iIqLGSxm8FBS4bmpZl3Vt5G6o7t3FF+mmTsFN27Zt9a4HERFR41JRARw+LDawVAYzpaWuwY1yOwYlbeZGGdwoBymTrrweULxt2zavb1paWorff//dq2sXLlyI5ORkBAUFYeDAgdi+fbvH6xcsWIAuXbogODgYiYmJmDlzJs5XN0qdiIioru65B+jWDVi7Vj0b6vz56jM3YWHq89pF+5TjbBjc1Buvg5vbb78dKSkpWLlyJUpKStxe88cff+CJJ55Ahw4dsHPnzhrv+dFHHyE1NRVz5szBL7/8gt69eyMlJQWnT592e/0HH3yAxx9/HHPmzMHevXvxzjvv4KOPPsITTzzh7WMQERF55733xOv8+TUHN/Kifdqp39rMjXK6t92uW1VJzevg5o8//sDo0aPx1FNPITIyEhdffDGGDx+OMWPG4IorrkBMTAz69euHzMxMrF27FhMnTqzxnvPnz8eUKVMwefJkdO/eHYsWLUJISAjeffddt9dv2bIFgwcPxq233ork5GSMGDEC48ePrzHbQ0REVGcVFa5Tv6vbX1Eb3ISHO4/lXb9lnPpdb7xuWbPZjAceeAAPPPAAduzYgU2bNuHo0aMoKytD7969MXPmTAwdOhTR0dFe3a+iogI7d+7ErFmzHOeMRiOGDRuGrVu3uv3M5Zdfjv/+97/Yvn07Lr30Uhw+fBirV6/G7bffXu33KS8vR7li/QF57yur1Qqr1epVXb0l30/v+zZXbE99sT31xfbUV6Nrz/JyyIuc2I1GSCUlcKwlfP487GVlbrMD9ogIx3kpMBA2s9nxi9YeGAib1eq8r9kMWz09b6NrTx3U5lnqFDYOGDAAAwYMqMtHHXJzc2Gz2RAXF6c6HxcXh3379rn9zK233orc3FxcccUVkCQJlZWVuPfeez12S6WlpWHu3Lku59euXYuQetpePj09vV7u21yxPfXF9tQX21NfjaU9LXl5GFl1nJubi5xdu9BT8X7h8eOIdPO5nPJytK46rjSZsC0jA1dWlfNKS7F59WrcUFU+aDJh3+rV9VB7p8bSnnoo1U6/96BJ5cTWr1+PefPm4c0338TAgQNx6NAhzJgxA8899xyefvppt5+ZNWsWUlNTHeXCwkIkJiZixIgRCFemC3VgtVqRnp6O4cOHuyxsSLXH9tQX21NfbE99Nbr2VEyKiQkMRMvkZNXbEdVslRDXuTPw888AgIAWLTBo6FDHe9EXXYTrrrsOtnnzYFy+HO3/9S+0v+gi/euORtieOpB7Xrzhs+AmJiYGJpMJOTk5qvM5OTmIj493+5mnn34at99+O+6++24AQM+ePVFSUoJ77rkHTz75JIxutou3WCywWCwu581mc739gdfnvZsjtqe+2J76Ynvqq9G0p2I4g7GkBNB0iRjkAcQaRsXQDIPFggDF7CljcDCMZjMwaxYwaxYa4ikbTXvqoDbPUae9pfQQGBiI/v37Y926dY5zdrsd69atw6BBg9x+prS01CWAMVXtqCpxSh0REV2I+fOBdu2A335TDxg+f951Y0w5uNH2ACgHFFss6qnf2u0WqN74LLgBgNTUVCxZsgTvvfce9u7di6lTp6KkpASTJ08GAEycOFE14HjMmDF46623sGLFCmRmZiI9PR1PP/00xowZ4whyiIiI6uShh4AjR4AXXnCd+q0NbuRMjnKqN+Aa3CgX7dPuAk71Rrduqfz8fERqp8DVYNy4cThz5gxmz56N7Oxs9OnTB2vWrHEMMs7KylJlap566ikYDAY89dRTOHHiBFq1aoUxY8bg+eef1+sxiIiouTtwwDVzU93U79pkbjj1u8HUqaX/9a9/ITk52bGB5i233IJPP/0U8fHxWL16NXr37u31vaZPn47p06e7fW/9+vXqygYEYM6cOZgzZ05dqk1EROSecmhDQEDNmRuZNnPD7RYahTp1Sy1atAiJiYkAxDSz9PR0fPvttxg1ahQeeeQRXStIRERU75TTjAMDvc/cKIOZgACgRQtn2WIRX8oyNYg6ZW6ys7Mdwc3XX3+NW265BSNGjEBycjIGDhyoawWJiIjq3dmz6rIyU2O3A0VF7j+n7JZyN8ZGOQmG3VINpk6Zm6ioKBw7dgwAsGbNGgwbNgyAmLFks9n0qx0REVFDKC52HhcV1W3X76Ag9Rgb7QDizp0vrI7ktTqFkTfeeCNuvfVWdOrUCWfPnsWoUaMAALt27ULHjh11rSAREVG9+PRTYPlyYOFCdTBTVOQ6xkbe9VvL0xgb+XjrVmD9euAf/9Cj1uSFOgU3r776KpKTk3Hs2DG8+OKLCA0NBQCcOnUK9913n64VJCIiqhcPPQQcPQrExwPKzZ7djbGRgxuDQT0w2FPmRj6+7DLxRQ2mTsGN2WzGww8/7HJ+5syZF1whIiKiBnH0qHj944+aF+2Tu6UiI9VdVIoViLmuTePhdXDz5Zdfen3T66+/vk6VISIiahB2u/M4JEQdzJSXu2ZuKivFa1SUM7gJDFQHMNVlbqjBeR3cjB071qvrDAYDBxUTEVHjpgxeDAbvp357WqRPW3az3yE1DK+DG7syyiUiImrKlMGL1aouV1YCJSXuP1fT3lHslmoUGFYSEVHzo+yGKivzfnaUMrgJDHQNbpRr2VRUXGgtqY7qvKJQSUkJNmzYgKysLFRo/gAfeOCBC64YERGRrhYuBF57Dfj6a9EVJXO3ro23U7+VwU1IiHj9+9+Br74C7rpLl2pT7dUpuNm1axeuu+46lJaWoqSkBNHR0cjNzUVISAhiY2MZ3BARUeMj72P4738DkyY5z7ubHeVN5qa64Gb5chEsaTfVpAZTp26pmTNnYsyYMTh37hyCg4Oxbds2HD16FP3798fLL7+sdx2JiIj0427qtx6ZG3mMjdnMwMbH6hTcZGRk4KGHHoLRaITJZEJ5eTkSExPx4osv4oknntC7jkRERBdGufCewVDzrt9ycKPcCBNQBzfaMTdy5oZ8rk7BjdlshrFqiltsbCyysrIAABEREY49p4iIiBoNq9V5XFmpztS4W9dGHksaFeU8ZzIBVSvyA6i+W4p8rk5jbvr27Yuff/4ZnTp1wtVXX43Zs2cjNzcX//nPf9CjRw+960hERHRhtFO9a1qRWBYZCRw/Lo7drWujXcSPGoU6ZW7mzZuH1q1bAwCef/55REVFYerUqThz5gwWL16sawWJiIgumKdMjdVa/bo2ysyNdgViiwVo1UrfepIu6pS5GTBggOM4NjYWa9as0a1CREREunjxReD114GNG9VTv0tKXDM1BQXu71HTon0Wi7PMFYkbDf5JEBGRf3rsMdGl9MorNY+xUW6GqeQpcyMPNp4+HejUCbj7bn3qTResTpmbdu3awaCMgjUOHz5c5woRERHp6uTJuk/99pS5kYOb11/Xo5akozoFNw8++KCqbLVasWvXLqxZswaPPPKIHvUiIiLSR2Wl69Rvb4MbT5kbzo5qtOoU3MyYMcPt+YULF2LHjh0XVCEiIqILZrM5j72Z+i13SwUGqveE0mZulLOjtGvgUKOh65ibUaNG4dNPP9XzlkRERLWnzNR4M/W7tFS8KjM1ZrM6gLFY1GVmbhotXYObTz75BNHR0XrekoiIyDslJcCvv4pj7VTvmsbcyDx1QwUFAXFxzrKHsafkW3VexE85oFiSJGRnZ+PMmTN48803dascERGR1/7yF2D9emDrViAhwXm+rEydqZEkoLDQ/T2UwU1wsOdF+9gt1WjVKbgZO3asqmw0GtGqVSsMGTIEXbt21aNeREREtbN+vXhduRL4xz+c5/Wa+i0fP/aYWDtnwoQLrjLVjzoFN3PmzNG7HkRERHWnHASs7XbSa3aUvK/UCy9cUFWp/nkd3BRWl8JzI5xbvRMRUUNSdjvZbDXv+l2XzI1y00xq1LwObiIjIz0u3KdkU07BIyIiqm+epnq7y9x4E9xox9wwuGkyvA5ufvjhB8fxkSNH8Pjjj+OOO+7AoEGDAABbt27Fe++9h7S0NP1rSURE5IkyM1NUVPO6NnK3lHZdG23mRjndm8FNk+F1cHP11Vc7jp999lnMnz8f48ePd5y7/vrr0bNnTyxevBiTJk3St5ZERERap08Dhw4Bl1/uObhxl7kpLxev0dFAdrY4NhqBsDDnNUFB6l2/lTOlqFGr0zo3W7duVe0MLhswYAC2b99+wZUiIiKqUb9+wODBwM8/q4OXsrK6rWsTHKwOYIKDgZYtnWXu+t1k1OlPKjExEUuWLHE5//bbbyMxMfGCK0VERFSjEyfE62efed47ytt1bYKC1MFNUBBgMgG9e4sF+0aO1K/uVK/qNBX81Vdfxd/+9jd8++23GDhwIABg+/btOHjwILdfICKi+me1qo/rujGmclV97QBiOdDZsAE4cwbo2PGCqkwNp06Zm+uuuw4HDhzAmDFjkJeXh7y8PIwZMwYHDhzAddddp3cdiYiI1JTBjDfbK3g79VuZuZGXNYmIYGDTxNQpcwOIrql58+bpWRciIiLvKIOXioq6Z248jbnhmm1NlteZm99++w12u91x7OmrNhYuXIjk5GQEBQVh4MCBNQ5Izs/Px7Rp09C6dWtYLBZ07twZq1evrtX3JCKiJk4ZvBQX1xzcVJe5UQ4YDgpSBzsRERdeT/IJrzM3ffr0QXZ2NmJjY9GnTx8YDAZIkuRyncFg8HoRv48++gipqalYtGgRBg4ciAULFiAlJQX79+9HbGysy/UVFRUYPnw4YmNj8cknnyAhIQFHjx5FZGSkt49BRERN1ddfi12/Z81SBy/upn5XtyJxZKQ6i6PN3Ch/9wQG6lVzamBeBzeZmZloVTXfPzMzU5dvPn/+fEyZMgWTJ08GACxatAjffPMN3n33XTz++OMu17/77rvIy8vDli1bYDabAQDJycm61IWIiBq5MWPEa8+eQFKS83xJiTqY0U4FB8SWDIAYQCwHN2azemG+4GAR/MiUO4tTk+J1cNO2bVu3x3VVUVGBnTt3YtasWY5zRqMRw4YNw9atW91+5ssvv8SgQYMwbdo0fPHFF2jVqhVuvfVWPPbYYzCZTBdcJyIiaqTkRfcAYO9e9eJ62uCmslJ0VbmjzNSEhLhO/TYYgKeeArZu5dTvJqxOA4rfe+89xMTEYPTo0QCARx99FIsXL0b37t3x4YcfehX85ObmwmazIS4uTnU+Li4O+/btc/uZw4cP4/vvv8eECROwevVqHDp0CPfddx+sVmu1O5WXl5ejXPGXQt4A1Gq1wqqcSqgD+X5637e5Ynvqi+2pL7anvmpsz+PHYa46tNlskIqKHL/ApJIS2IuLofwvrj0vz+2gUntkpOO8FBwMW0CA4z72Fi1gs1qB2bOVFavT8/iaP/581uZZ6hTczJs3D2+99RYAsVrxG2+8gQULFuDrr7/GzJkz8dlnn9XltjWy2+2IjY3F4sWLYTKZ0L9/f5w4cQIvvfRStcFNWloa5s6d63J+7dq1CFHuGaKj9PT0erlvc8X21BfbU19sT31V157hmZkYWnV8KCMD58rKcFlVuSQ3Fyf27EEXxfX5mZmIhqtT589D7mwqlST8umcPLq8qHz13Dr/52QQVf/r5LC0t9fraOgU3x44dQ8eqOf+rVq3CTTfdhHvuuQeDBw/GkCFDvLpHTEwMTCYTcnJyVOdzcnIQHx/v9jOtW7eG2WxWdUF169YN2dnZqKioQKCbwV+zZs1Camqqo1xYWIjExESMGDEC4TpP87NarUhPT8fw4cMdY4Ko7tie+mJ76ovtqa+a2tOgmEnbMS4OUo8ejnILAB01q+NHuZnwAgDx3bsDmzcDAEJiYnCpYt/EpIsvxkV+slabP/58Fla3yrQbdQpuQkNDcfbsWSQlJWHt2rWO4CEoKAhl1e3foREYGIj+/ftj3bp1GDt2LACRmVm3bh2mT5/u9jODBw/GBx98ALvdDmPVHh8HDhxA69at3QY2AGCxWGCxWFzOm83mevsDr897N0dsT32xPfXF9tRXte1ZWek4NBUXq7qLDKWlMCl39gZgqGZdG5NirI4hJAQBitlRpqgomPzsz9Kffj5r8xx1WqF4+PDhuPvuu3H33XerViX+/fffazV7KTU1FUuWLMF7772HvXv3YurUqSgpKXHMnpo4caJqwPHUqVORl5eHGTNm4MCBA/jmm28wb948TJs2rS6PQUREjdlrrwH33SdmOnna9bukpPp1bbT/uVVutxASoh6YrNwRnJq0OmVuFi5ciKeeegrHjh3Dp59+ipZViyDt3LkT48eP9/o+48aNw5kzZzB79mxkZ2ejT58+WLNmjWOQcVZWliNDA4hVkb/77jvMnDkTvXr1QkJCAmbMmIHHHnusLo9BRESNVWUl8OCD4viGG9TBjXZ2lNUqAh4lObPTsiVw8qTzvHKqd3CwOtiJidGj5tQI1Cm4iYyMxBtvvOFy3t3A3ZpMnz692m6o9evXu5wbNGgQtm3bVuvvQ0RETYgyIDl9Wr2gnrtMTV6e+/sog5vgYJGtkYWEAAEBwJQpwC+/ACkp+tSdfK5O3VIA8OOPP+K2227D5ZdfjhNV287/5z//waZNm3SrHBERNVMlJc7j/HzXzI02uDl71v19lNsraNe1adFCvC5eDOzYoc7iUJNWp+Dm008/RUpKCoKDg/HLL7841pEpKCjgZppERHThlMFMYWHNwY2nzI1MuzEmt+7xW3UKbv75z39i0aJFWLJkiWr08uDBg/HLL7/oVjkiImqmlMGLu+BGu3eUt5kbZXZGuVox+ZU6BTf79+/HVVdd5XI+IiIC+dVtK09ERFQTu128egpuSktdMzfVbbegDGaCg9XBDjM3fqtOwU18fDwOHTrkcn7Tpk1o3779BVeKiIian0HPPIOA3r1FoKLtlqpp6rdMm43RZm6UZXnMDfmdOgU3U6ZMwYwZM/DTTz/BYDDg5MmTeP/99/Hwww9j6tSpeteRiIj8XWEhYjMyYNi/H9i0yTWYUQY75eXVZ2qUwQvgmrkJDwc6dxazpIYOBfmnOk0Ff/zxx2G323HttdeitLQUV111FSwWCx5++GHcf//9eteRiIj83bFjzuOzZ53dU0Dtx9jIPQvBwUBoqPO9kBCx6/fWrWJ6eadO+tSdGp06BTcGgwFPPvkkHnnkERw6dAjFxcXo3r07QkNDUVZWhmDlaHQiIqIaGJTBSmGhyKzItN1UQPXBjbJbKiREva5NRIR4jY7mtG8/V+d1bgCxP1T37t1x6aWXwmw2Y/78+WjXrp1edSMiouZCO4DYU7cUAOTmilfFRsoA1KsMt2jBqd/NVK2Cm/LycsyaNQsDBgzA5ZdfjlWrVgEAli5dinbt2uHVV1/FzJkz66OeRETkz5TBjLu9o6oLbrRjbLQDiJXdUnLmhvxerbqlZs+ejX//+98YNmwYtmzZgptvvhmTJ0/Gtm3bMH/+fNx8880waaNoIiIid44cEXtIdezoOjtKuQO0u24pm028xsSI8TMybeYmPt5ZZnDTbNQquFm5ciWWL1+O66+/Hnv27EGvXr1QWVmJX3/9FQaDob7qSERE/qa0FOjXD6ioALKyXHf9Vo6VcZe5kdWUuVEGN9wYs9moVbfU8ePH0b9/fwBAjx49YLFYMHPmTAY2RERUO8eOAefOicBl714YalrXprTU/X2UwYzFou6GatFCnBs2TAw0Hj5c32egRqtWmRubzYZAxc6sAQEBCFX+IBEREXlD2ZV08qQ6mHHXDXXunPv7aPeOUi7MJ2d/Vq8W3V+cydts1Cq4kSQJd9xxBywWCwDg/PnzuPfee9FCs8rjZ599pl8NiYjI/yiDG282xpQHEAcFqa/VBjfK7ix5WrjZrB7DQ36vVsHNpEmTVOXbbrtN18oQEVEzUVTkPC4ocM3caIMbeV2bli2BEyec57VjbJSDhrXjcajZqFVws3Tp0vqqBxERNSe13fXbahWv0dHVBzfBweoBxFyor9m6oEX8iIiIvCJJwNSpwPTp4lgZ3BQUqIMZd5kbWW1mRzG4abYY3BARUf37809g0SJg4UJxrMncGLzd9Vs7Oyo83FkODhZfY8eK89ddp+sjUNNRp72liIiIakXZlZSZ6Zq5Ue4l5Sm4Ua5Vo907Sp7c8umnYpE/DiJuthjcEBFR/cvOdh7n5bkOIK6ahQtA7Aieny+OTSbnasSAa3CjnK0r7x1lNIovarb4p09ERPVPuU5Nfr5rcFNerr7+zBnxql1V2FPmRrkjODVrDG6IiKj+abuhapr6XVwsXlu1Up9Xjrlp0YLBDbnF4IaIiOrHd98BmzeLY+X2CdrMjXYXcCVlpsZsBsLCnGVtt5RmQVlqvjjmhoiI9JeVBYwcKVYU1o6xcdMtZaguMFEGN+62VwgLA9q2BY4eBQYO1PURqOlicENERPrbv1+8nj8P7NvnGtxodwFXTulWqim4MRiAb78V08uHDtWt+tS0MbghIiL9Kad+nz6t7pbSdkOVlTnel0wmGKqbHaUNbuQxNt26iS+iKhxzQ0RE+lPOjjp92nW7heo2xvQ0O0ob3HDvKKoGgxsiItKfMlOTm6suuwluDBUV4kAZsAQGqjfCZHBDXmJwQ0RE+vjmG2DbNnFcUuI8rw1m3GVuqkieFukLDlbvF8XghqrBMTdERHTh9u8H/vIXEYy4y9Roy9VtjaDd5Ts01FkOCRErGT/+OPDDD8BNN+n7DOQ3GNwQEdGF27tXvJaUAAcPqoMZ7aJ9hYXqxfcUJO0u3+4GEKel6VRp8lfsliIiogt3/Ljz+NQpz2NsKipEwAMAsbHq+yi7nUJC1Jkbee8oohowuCEiogsnb3QJACdPuo65UQY7ACBJ4lUR3FQGBqpXIK5u6jdRDRjcEBHRhVMGM+fOee6WUlLsHWW3WFzH2ChnS3F7BfISgxsiIqo9mw2YMgV45RVR9hTcuMvcyBTBjS0wEJJ2dlRcnNjCAQC6dNGp8uTvOKCYiIhqb9064O23xfGUKergJj+/+jE3gYFizA0ABASoxtHYLBaYlcFNZKTYXmHdOmDXLuBvf6uPJyE/1CgyNwsXLkRycjKCgoIwcOBAbN++3avPrVixAgaDAWPHjq3fChIRkVpmpvP48GHXzI2yXFDgDHbi4pzng4NVY2wqLRb1oGF5cPHllwPTpgEmk371J7/m8+Dmo48+QmpqKubMmYNffvkFvXv3RkpKCk6fPu3xc0eOHMHDDz+MK6+8soFqSkREDqdOOY+zsjxnboqKnMfa4EYxxsZmsUBKSHC+zwHEVEc+D27mz5+PKVOmYPLkyejevTsWLVqEkJAQvPvuu9V+xmazYcKECZg7dy7at2/fgLUlIiIAQHGx8zg72/OYG6X4eOexJnNjs1iAxETn+9WshUNUE5+OuamoqMDOnTsxa9Ysxzmj0Yhhw4Zh69at1X7u2WefRWxsLO666y78+OOPHr9HeXk5ysvLHeXCwkIAgNVqhdVqvcAnUJPvp/d9myu2p77Ynvpqdu0pSTAsXw4kJUEaOhTGwkLInUS206dhKC52/G9ZyssDSkth0N7CZIIUFeW8zmKBPTjYeZ/AQFiDghDQsSMMhw7Bes01QHNpX535489nbZ7Fp8FNbm4ubDYb4pRpSgBxcXHYt2+f289s2rQJ77zzDjIyMrz6HmlpaZg7d67L+bVr1yKknv5XkJ6eXi/3ba7Ynvpie+qrubRn9O+/48onnwQAfLVyJfrs3w85x5K5Ywdic3IQXlUuz85GkJup3zazGVl5eZDz7QVWK/48dAj9q8qVwcFIT09H0KxZCDp7FvnZ2cDq1fX5WH7Pn34+S6vLBrrRpGZLFRUV4fbbb8eSJUsQo9xczYNZs2YhNTXVUS4sLERiYiJGjBiB8PBwD5+sPavVivT0dAwfPhzm6vZNIa+xPfXF9tRXc2tPw5kzjuORyckwKf79bB8RAYPROcoh6Nw5x7EUHw9DdjYAwBQWhrY9e4oNNgGEx8Wh9/DhwKuvAgCsYWHNpj3rmz/+fMo9L97waXATExMDk8mEnJwc1fmcnBzEK/tlq/z55584cuQIxowZ4zhnt9sBAAEBAdi/fz86dOig+ozFYoHFYnG5l9lsrrc/8Pq8d3PE9tQX21NfzaY9Ff9rNmdnq8pG7ewoBUNCghiTA8AQHAyTYpCwMSQExrZtHeWKsLDm054NxJ/aszbP4dMBxYGBgejfvz/WrVvnOGe327Fu3ToMGjTI5fquXbti9+7dyMjIcHxdf/31GDp0KDIyMpCoHIhGRET6Uf6vOStLPaD47Fn3wY3F4rrLtzJjHhYGXHSRo1gpL9ZHdIF83i2VmpqKSZMmYcCAAbj00kuxYMEClJSUYPLkyQCAiRMnIiEhAWlpaQgKCkKPHj1Un4+sWhNBe56IiC6A1SrWlhkwALjnHvV07pMn1cFNbq772VEhIerp3CEh6uAmIkKU+/SB9NtvON2nD7gGMenB58HNuHHjcObMGcyePRvZ2dno06cP1qxZ4xhknJWVBaPR5zPWiYial9WrgSVLxNff/67O3OTmqoObY8ecx3FxgDzUIDhYHdxoMzfygn3ff4/KrCwUHTmi91NQM+Xz4AYApk+fjunTp7t9b/369R4/u2zZMv0rRETU3P3+u/N43z515ubMGXVwo5wZddFFzuBGm7kJDgbatHGW5U0xo6LEYn4MbkgnTIkQEZErZabm0CHPmRtZUBCgnMnqLrhp185ZVu4ATqQjBjdERCSsWAFs2SKOlcFMZqbrmBt580ulkBDnflByWRncRESIcuvWonzJJfrVnUihUXRLERGRj23ZAowfL2Y45eaqg5vsbHVZ2X3Upo0IdgCgRQvX4EZZlgOdb74BDh4EhgzR+ymIADC4ISIiAJC3sikvB3bscA1ulJmbykrxajaLvaLk4EYbzGgHFMsDiPv2FV9E9YTdUkREJAYJy7QDiLWZG1mLFup1bNx1SynWsXEEN0T1jMENEVFzVFIC3Hkn8PzzoqwMXo4e9Zy5kYWEqIMbbbdURASQnOwsJyXpUnWimrBbioioOfrmG2DpUnE8fjxQUOB8TxvcHD8OnD8vjiMjgfx8cVxT5iYqSnRdzZsH7NwJjB5dH09C5ILBDRFRc/Trr87jbdvUwcyxY+qyHNgAYir3rl3i2JvgBgBmzdKv3kReYLcUEVFzUFkJPPQQ8MILonz2rPO9gwfVmZszZ9yPsQkMBBISnGV3wY1yjI1yNWKiBsTMDRFRc/Ddd8D8+eI4JUUdzBw+rA5mTp1yZmuCg50rEIeFAa1aOa/TBjcREUBionMLht696+dZiGrAzA0Rkb8qLnZO296923l+61bnuBlA7PKtDHaUgU779s7jsDD1CsTa4CY6GjAYgPR04MsvgcGDdXkMotpicENE5I/OnAG6dRNjZAoLgQMHnO8dPKgObs6cUQc3suBg52rCgOhm0m6voA1uAKBnT2DMGBHoEPkAu6WIiPxFQYGYnRQSImZDHT8uzn/3nXodmz//VAczOTnup3qHh6u7obTdUqGhQGyss8x1bKiRYOaGiMgfnDsH9Ojh3JV7/37ne7//7jpgWJm5yc11HmsHBCuDGW3mJjpajLFp3RowmYArrtDtcYguBDM3RERN1TffiDE1N9wgxrnImZo1a0R2Rvbnn951QwUGiuBGvo82U6Mtx8QARqPYlyo/H+jcWa8nI7ogDG6IiJqKEycAu11kSw4dEuNaJAn44w/xJTt4UD3VOztbHcycPOmcAaWkzcxoMzeRker35fE2ylWIiRoBdksRETVWZWXOKdl5eWJq9cUXi26n9etFYAOIRfiUmZojR0Q3lUzbDaUMbJRbIkRE1BzctGnjLDNTQ40UMzdERI1FRoboGureXcxw6t1bjGX57Tfg+++d2ZgtW9RjarSZmtxcdTCTk+N+Ub7QULEmTVaWKNcU3ERFicHKX34pgqnLLrvQJyaqFwxuiIgaitUKBASIKdKSBPz732Iw7g03iC0PBg4U7588KcbQHDkiPrd9O7B3r/M++/cDmZnO8unT6kyNNrjJznYeh4U5Z0ZFRqqncmu7pSIiXIMbQHSHETVi7JYiItLLmTPObiS7HXjnHWDTJlE+eRJo2xYYOlQENj/+CEydCtx4owhM/vc/oKICKC0Vm0zu2+e874EDYhVhWU6O6KaSaYOb06fVwY3MYlF3K0VEuK4wrAxuoqLUwY1yzRuiRozBDRFRdcrKnONabDZg8WIxrRoA8vMx4MUXYVi2TJQzMsRAX3nn6y+/BO6+G7j+ejGj6dtvxbYGGzaILM0vv4jr7HYRyBw65Py+x46py6dOuXY7KYObM2fUwc2xY856K2kzNTVlbqKjxVffvuL8oEHVtxVRI8LghoiaD6vVeVxRAfznPyLLAYjMyJAhwH//K8pbtogunJkzRfntt4F//AP4+98BAMZFi5CwZQsC7rlHBBIffwyUl4uxMUVFItgBRNBx/LgYFyM7ckTdzXTkiLqb6fhxkZ2R5eaqgxttMHPmjDrYkVksQHy8sxwRod61W5upcZe5MRjEdg1Hj6qzOESNGIMbIvIf8j5KgAgOnnrKue3A6tUiM/Gvf4nyiy8CEycC06aJ8ty5Iqty++2i/MorIlvz2muivHateN2zB6ishEEZnBQUiG4n2YkTrpkYZbfSsWPqcTBnzqiDmbw89YrCZ8+6ZmqU5WPH1M8ui4pSBzM1ZW5athQDjGXyTCqLRQwkJmoiGNwQUdNUVia2FZDHuPznP0BQkBikCwDPPgs8/zxw112i/Nhj4trHHxflVavE6yefiFflOjGSpA4+ysvFrCVZTo7YlFJ24oQzAwSIzMupU87yqVPqVYDz89XBS16e5/fPnlVnbrTfv6JCvJrNzkG/gGvwoh1jo30/Ohro0AG48kqgY0dgwAAQNUUMboioaTh5EkhLcwYRzzwDjBwpMi4A8OijItPy0EOi/Oab4lUe0CsHQTLlmBS7XQQGsvx8cS/ZqVPqTMnJk+pgJDtbXdYGKwUF6vK5czUHN8qydozNiRNwS5upcZe58ZTJadlSdENt3CgyXuHh7r8PUSPH4IaIGqezZ4F33wVKSkQ5NRV44gnR1QSIbiUAeOEF8SpnWuTrjZp/3gIDncfnz6szMWfOqDMh2dnqTMnp0+pgJD8fBk/BizY4qamsDW5OnhSzpmRZWepgSyavO6MsewpmtJmbli3V42gSEpzH3NGbmjAGN0TUeCgDiIcfFl1Kzz0nyh99JF6XLHH/WYvFeWy3A8HBznJRkTo4yM523WtJOxtJWXYXvCjf1wYr586p3z93Tv2+dsXgrCxn1xKgHnws1wcQa+AoybOZqitrg5uWLV2Dm+7dgU6dxMDjfv1A5A8Y3BBR4zB3LhAb6wxe5CnWCxbU/Fm7XR3cnDunDha0wcrZs67dPspyTo56NpKbMTAG7QBfeWE8QHQblZc7y8ePq2dqKQcbuyvLddVmT+LigBYtnOXoaPUYm5oyNy1bqsutWonuuF9/ZTcU+RUGN0TkG5mZwLx5zl/kzz8vXuVMjUwZJMi0XTR5eepupSNH1MHE6dPqMSunTqmDlyNHPGdOsrLU91POfALU+zq5K2uDF3kGl7I+7nTooC5rgxNtpkYb3Li7vn17Z7lrV/EaHCymvRP5CW6/QES+cd99wJo1Ish46SVn8OBuATptgHP8uLr8558ieyPTBg8HD3p+X7kaMKDetwlwDU5qG8xU182kFRurnnWVkKDuspIDlWPHxGtUlHqgdFSUyNbI3HVDtWsHvPqqaG9loEPkR5i5IaKG8fPPYg0Zeb+kNWvE6xtvqAMPwDkoWKZcAwZwDSa0wUpNwUttyzV1I9U1mFEO4AVcMzXughVlN1RNY25iYtRlefuEBx8EHnnEfZ2I/AAzN0TUMMaMEUFKUJDYxkBmNqu7lADXYKCmbiBtpqWmsnIBPqD2mRttWd5VWyZnnoxGdeB20UXqrFP79upp3QkJoj3kLJY8pkbO5kRHq++nzdy0aqXuXmrZEujTR+z+bTAAF18MouaAmRsiqh/Hj4vuDzkLI2dfPvxQnZkpKxOzkZS0wYM2uNGWa5u50QY32mBGu+mknG2SKadpK0VEqMvaTEzHjupyu3bqcnS0OlOjzdxoBxC3auU5UxMfLwKcX34Rg4ZDQ93Xm8jPMLghovpx881ibZqXXlIP1jUY1MFMZaXrgNqagpvaZm60wY1y5pSSci0cd5TTywGUKcezAK7Bi7asDXZiY9UBh7vZT8qytluqVSt1OSYG6N9fZMfCw50Dhjt1cg2kiPwYgxsi0ockiSyMbNs28fruu0BhofN8RYVrpka5aSRQ+8yNNpjRdnPJ+y4pVyEG1JtKAs5gQNa2rbrcpYv628hjWGSdOqnL2mAmIUE9Zb1lS9fMjKeyu0yNMriJjwfatAG2bwd27nQJxoiaCwY3RKSPuXPFeI81a9RBjjaYqahQ79sE1Bzc1DQ7SZ5NpV0XRht8aIITl2Cmc2d1WZt50bxf0qaN5+u1s5HcBTPKrixtN5S2HBvrmrnp1Us8t9nsDK569nStC1EzwuCGiPQxd65Yf2buXPWYlfPn1ZkbwDU4qW1wI99fO8ZFG5zUFMx066Yud+9eq88XXXSR+n1tpiY+3nWAr3a2k6dMjbZbqlUrka2RtWsn6vzdd8DWreqsEFEz1iiCm4ULFyI5ORlBQUEYOHAgtm/fXu21S5YswZVXXomoqChERUVh2LBhHq8nonpSXAz88IOYvSN3+wBi8K0yU6PdugCofXBT3RgZbfBRU3BT22CnRw+P17tkbuLi1Kv8xsS4TuWuKZhRlhMS1PeLjwdSUoCBA4HJk53XDh8uxtoQEYBGENx89NFHSE1NxZw5c/DLL7+gd+/eSElJwWnlQlYK69evx/jx4/HDDz9g69atSExMxIgRI3Ciul1yiah+3HEHcM01wKJF6uBFklzH1NQ0ZkYb3LhblRgAEhPV5dpmatxlZpQbaCYkqLNB2qnTHTuq9nc6Hx0NSZmZadXKczCjLcfEqL9fmzbqRfcuuggYMkRdDg8X45nefRdE5J7Pg5v58+djypQpmDx5Mrp3745FixYhJCQE71bzF/f999/Hfffdhz59+qBr1654++23YbfbsW7dugauOVEz9+mn4vX119XdUGfPuk6l1mZqtMHN0aPiVTvgNy5OXdYGM9qyNvOifT8pST07SRtMxMWpy0lJ6r2c4uJUwUl5RIRrsKLMtMTGumZelF1HSUnqXb0TE4HBg8Vx27Yi8LnsMmDFCmDHDu7UTeQlny7iV1FRgZ07d2LWrFmOc0ajEcOGDcPWrVu9ukdpaSmsViuilTMIFMrLy1Gu+F9gYVXfv9VqhVW5V4wO5Pvpfd/miu2prwtuz1OnYPzqK9gnTgQqKiCHIVJZGWy5uc5/TCorUXn4sOofF/uBA6r/SUl//gnVr+mqOknt28OgmPlk79gRRsXqxLYOHWBS/Eemsl071fepjIuDKTQUhqrZUtaWLREQFQVD1T5S1qgoBEREON+PiUFAcLCjLtboaARUVjrLrVohwGBwlqOiEGC1OsrlkZGQ7Hb19YCzHBMDU0CA49mtrVrBGBsLOVdkDQmB4aqrEPD225A6dECl2QwMHw7Du+9C6tXL2d13442qdvJH/PuuL39sz9o8i0+Dm9zcXNhsNsRp/ncWFxeHfdp1Karx2GOPoU2bNhg2bJjb99PS0jB37lyX82vXrkWI8n9MOkpPT6+X+zZXbE991bU9Bz3zDGIzMnBw0yYcv+oqyH/jrLm52JmejkGKazPXroVyUnTRr79COfTXoM3sVMkJDYVycvbxwEAkKcp/VFaip6L80+HDuCQsDIFVO3Jv2rcPlwYHI6QqePnht99wlSQhqOr6tbt3I0URiK3etg3XnT7tCNRWb96Ma2w2yB1Nq//3P1xntzvf//57XBsSgtCqbjfJZEKxyYRwxfUDIiKQABG4rP7uO3SPikIniGnj6777DhHduuHyFi1wdPhw/PHtt0BYGBLvvx/5nTqhaPVqcaPoaLEIonYPrWaAf9/15U/tWVrd4pluGCRJu0Ndwzl58iQSEhKwZcsWDBrk/Kfx0UcfxYYNG/DTTz95/PwLL7yAF198EevXr0evXr3cXuMuc5OYmIjc3FyEK9PFOrBarUhPT8fw4cNh1qbXqdbYnvq60PY0Vy1wJyUno3LFCpgvu8zxXuXSpQiYPNlRto8ZA+NXXznKUkQEDNpxOACkhAQYFOPlbNOmwbRwobP8zDMwPfOM8/t89hkC5CwGAOu2bQiYOBGGqhWJrQcOIGDIEBhOnhTl0lIEJCXBUDUmyFpRAdMNN8D47beQEhJQmZkJ0113wfif/8B+7bWwffstjC+8ANPs2bD94x+wv/46jC+9BNOTT8J+xx2wLV4Mw0cfwTRpEioffRSrL7sMI4uKEDhxIuwzZsD+4ovAoUMwzZgB++TJkG66CSgogDEtDdL110O6/PKqB5fYxaTBv+/68sf2LCwsRExMDAoKCmr8/e3TzE1MTAxMJhNyNJvi5eTkIF67uJbGyy+/jBdeeAH/+9//qg1sAMBiscDiZnqk2Wyutz/w+rx3c8T21JfX7XnuHJCeDowdq1oUz1BWBrNmY8sAeZfqKkZ5DI38GTmwCQsDqrIsAGBITlbtrWTSrM1i0gwQDmjTRoxhqepeNsfFqfZaMrdtKwYBVwU35uBgYMYM4OmngVtvFc/9+uvAI4/A8OijovzCC0Dr1jDeeSeMZjPw1FPAyJEw9ewJk9ksNpgcMADGq64S7992G/CXv4ixMt9+C+O4cTCMHAlTdDRMRqMY97N2rbMbLiYGeOUVj01NTvz7ri9/as/aPIdPBxQHBgaif//+qsHA8uBgZSZH68UXX8Rzzz2HNWvWYMCAAQ1RVaLmZ8IEYNw48Yu5KlgAIAId7bo12qnb2tlPsuRkdVm7yF3btmKzSWVZOZspMlI96FgOdmSBgcDzz4tZUu+8I8498gjw9dfAW2+JcocOwGefiYG68j3+9S/nTCuDAbjkErGFASC+34gRzrJcD2XmJSZGXW8i8imf/21MTU3FkiVL8N5772Hv3r2YOnUqSkpKMLkqxT1x4kTVgON//etfePrpp/Huu+8iOTkZ2dnZyM7ORrF2uXUiqjtJAr79VhwvXqye2l1S4roXlHY2lJyd0S6yV1NwExmpnq0UGaleBC8mRkyHlgUGir2rLBYR1AAiMNm7F7jzTlG2WIDRo9VBEBH5NZ92SwHAuHHjcObMGcyePRvZ2dno06cP1qxZ4xhknJWVBaPif0RvvfUWKioqcNNNN6nuM2fOHDyj6JsnolrKzxcBTdU4EQd3e0EdPKgue8rU/PqruqykDW7CwkQAc+aMKEdEqKdOR0cDDz0ETJwI3H+/OHfNNWK7B45hIaIqPg9uAGD69OmYPn262/fWr1+vKh85cqT+K0TUHN1zD7ByJfDHH86px4AIerSzm7TdUHK3VUgIoJzRUFNwoy0rszSAc++k9HSxZozBANx+OzBypDrDw8CGiBR83i1FRI3EypXi9Y03xGBiWWmpazdUdf/J0O6iXVMwExOjHssSE6PedNNsBl5+WWw3sGCB83yrVhzjQkTV4r8ORM1Vfj7w+ediY0tlMGM0qsuA65ia6oKbmoKZxER1liUsTIzvkUVFAdddJ46HDxevvXqJ7QbGj6/+WYiIFBpFtxQR+cA//gF8/DEwezZwww3O88XFNQc38oDh6GggL895vqbMTUSE2JtJXmlUu7K40QikpYlBw7fcUqvHISKSMXND1Fx9/LF4XbxYvfFlRQWQlaW+VjvGRqYNXrTBjbYcFgbYbM5yixbArbeKY3mBu/BwYNYsMWWbiKgOmLkhai5OnECnlSuB/v3VmzWWl6uzLwBQteKvg9wNZbGod+xOTgZ++UVdVoqKEt1QctdTRIRq0T0AwJNPivfvu6+WD0RE5B4zN0TNhGnqVHR//30Yn31WnZkpLnYNbrRTveWApF079fmaMjehoeoxNSEhwPXXi2N5DZwOHYClS8X6NEREOmBwQ9RMGNesAQCYlixRd0NZrYBm+wSXMTaymoKbhAR12d3CeWlpwLBhwKpVNdaZiKguGNwQ+autW8VA4d9+U22XIFks6uAGcM3UyIv2xcSoz9cU3GiDmcBA0TWl1L27WLdmyJAaH4GIqC4Y3BD5q/HjgS+/BJ55RpWJMZSXA9nZ6murGzCsDWZqCm5CQ13v8cQT4vXpp2usMhGRHhjcEPmLykqRpZEk8SXvzP3558Dp0+prtcFMdcGNdnsEbXCjHWMT4GaOQmoqkJEhgiwiogbA4IbIXzz7LNC7N/Dii+pup5AQ515NMm03VEmJeNVOv9ZmZpTBjcnkul0CAEyaJF5vu028Go2iXlxRmIgaCKeCE/mL554Tr08/7VzlFxDbJ8h7P8m0wY2sQwf1YGJP3VJGo3q1YTl4efFFoEsX4O67a1d/IiKd8L9SRE3Vrl3A9OmuC+5Zra5jarTr1si7eCv3dQJcMzfKYEY7OFieHm4yiVd5I8vYWLEIX6tW3j0HEZHOGNwQNVV33QUsXAjMmOHcDkGmDW727XN/j06d1GXtGBtlt5S8Lo1MDozeeQcIDhYrHRMRNQIMboiaCuVGl4DI3ABivRhtt9P+/Z7LMk/BTWioOvsSHKy+Vp72PWmSWAhw7NgaHoCIqGEwuCFqKmbMAG68EXjhBdf3tMHNb7+py1UDjM9HRqrPd+yoLiu7oSIj1dkai0W8Tp0qXpWznzhYmIgaEf6LRNRULF8uXl99VbUoHwDntG+ZNripUqxdQVgZ3ISHqzM14eHqoEUeW7NgAbB5MzBlivd1JyJqQAxuiBqrhQvF4Nzvv1fvz1Ra6rpdwq+/qsvaYKeKKrgxGl0zNcoBw/KCfPJAYXnX7sBAcaycKUVE1IgwuCFqrKZPF+vTPPOMet0am801uFHuzK108cWqoiq4iYpSb68QFaXeLVzuhlq/Hpg5E5g/v9aPQETkCwxuiBqLH38Exo0TU7uVmZo9e4Djx51lSQJ271Z/1ovgRjIaUdK6tfO96GjxJYuNFdkYeWG+K64Qrz16iMBGO1uKiKiRYnBD1FiMHQt8/LFYaTg/33m+okId3ADA9u3qcnGxeO3aVX2+Rw/ncXQ0ypUbW0ZHqzM3ctZm9WrgvvuAxx6ry1MQEfkcgxsiXzl9WmxsKS+Gl5cnXj//XB3MlJSoVw0GgJ9/dn/PPn2cx0ajWClY1rIlKpTbJbRure6GkgOjK64Q432YqSGiJorBDZGv3HYbcMMNwKJFYpCwrLLSNVOjDWbkAcNxcerzyuAmNlb1vtSqFcqV3VAtWojXFSuACROARx+t23MQETUyDG6IGorN5lw5WJKA9HRx/J//qIOZ0lLX2U4//eT+ngMGqMu9ezuP4+LU3U6tW6MyOBiSPP176FDxOm4c8N//qsffEBE1YQxuiBrKhAmiK2jtWvXsp4IC9eynykpg5071Z+Vuqfh49XllcBMbCyQmOstxcaqyVDWlu/L994F//xuYPPlCnoaIqNFicEPUEOx24KOPxPG//63OzJw86Tq1e+tW9/fRZmr693ceX3SRCHBkiYliIb5LLgEASDfdJM737Qvccw9XFSYiv8V/3Yjqy7x5IpA4fBjIyXGeP3FCvZN3QYHr1O7ffxev2q6iSy91HsfHqze2TEhQXy8PEP72W2D3bkhXXVXnRyEiakoY3BDVB5sNePJJICMDeOMNdabm6FF1cAMAmza5v89ll6nLysxNu3bqAcUdOogtEl55BRg5ErjzTnG+ZUv1lHAiIj/H4IZIL6++Cvz1r2JVYeUA4YMHgSNHnOXsbNdduuV1a7QbWyqDm1at1Jma9u3FgGF5Yb5rrxWvqakiW8MBwkTUTAX4ugJEfsFmE0EFAHTvDgwb5nxv3z7X2U8bN7q/z5VXAl995Swru6G6dhVdT7L+/cW4me+/Bw4cAEaPvrBnICLyE8zcENXVggViBlRBgbqb6fffxTgbWWYmcOiQ+rN//CFe27dXn5e3PADEAGHl+927iwHCs2aJadwTJ4rzXbsC11/PjSyJiKowc0PkLXm/J4NBTOWeOVOUBwwAunVzXrdnj3rDSpsN2LDB/T2vuMIZCBmNzp23AaBXL9ENFR8vurLk2U7z5unyOERE/oqZGyJv2O1iPE3HjqKLSbli8Pffi64n2eHDIsBROnhQvCoX2QPUmZqOHdV7Q111FWA2i2nhW7aou7qIiKhazNwQVWf/fpGhGTxY7Lr9xRfi/DvvAEFBzuu2bVOPhZEk4JtvxLHJJDI3sqFDgV9/FceRkeoBwwMHigHC774rgpmpU8X55GT1QGIiIvKImRsimSQ5N7E8dUqsUXPFFcAPP6hXDP7uO3WmJjdX7KStJAc0ykG+JpNzywNA7AN18cUiqAkJETtxA2Ll4CVLxPgaIiKqNQY3RABQXAz06yfWjjlxQmyRUFYm3luyRGRuZDt2iGyNkrzCsHLFYAD4y1+cxxdfDIwY4Vyb5u9/F+NsNmwQU8e1a9oQEVGdNIrgZuHChUhOTkZQUBAGDhyI7fKaH9VYuXIlunbtiqCgIPTs2ROrtf9rJqrJ+fMiuBg7FigsBD77TCy4l5UFvPSSeqr211+LbiKZ3e4cQzN8uPO8wQCMH+8sd+okxulYLKKckiK6szZsAD7/HJgyRZy3WICoqPp4SiKiZsnnwc1HH32E1NRUzJkzB7/88gt69+6NlJQUnD592u31W7Zswfjx43HXXXdh165dGDt2LMaOHYs92gGcRAUFYodtACgpAa6+WoyfOXkSWL5c7PX0xRfAP/+pXlvmvfdE15OsqMg5QHjkSOf5gADg9tud5c6dxdTwwEBRHjNGjKH54gvgiSeA2bPF+S5dRFDFvZ2IiOqFz/91nT9/PqZMmYLJkyeje/fuWLRoEUJCQvDuu++6vf61117DyJEj8cgjj6Bbt2547rnn0K9fP7zxxhsNXHPyuaIiQA6CS0uB554TXwUFwObNYp2YVq3E4N4FC0Q2ZssWkbF55x3nfV59FfjkE2c5P190TRkMwB13OM9HRIgNJ2WXXQaMGuUcG/O3v4lp219+CcyZAzz7rDifkgI8/zwQGloPjUBERFo+nS1VUVGBnTt3YtasWY5zRqMRw4YNw9ZqdkXeunUrUuWVYKukpKRg1apV9VnVGpVXluNYwTGcrjiNowVHYQ4wX/A9JYh1VSRJfrUj4KgY25EUnqi4UHJ/rGQwOBd5kwfOSpL62G53fhmNIjNhtQIVFc77BgeL8/J1Npt4tVjEa1mZyFwEBoqun8JCMVjWaHR+JiBATHHOzhZjTeQtBE6cEN09bdsC8fEwrlmDvj//DOORIyJIefttsbXB+PHi87Nni+8xfry4z48/ijouXQqcOyfG0QDqcS+A8zqTScxC+vNPUY6JEasMP/GEKPfrJwb5LlsmymPGANddJ9a1+f134NFHxWd27BDTtceNE9elpIgvIiLyCZ8GN7m5ubDZbIhTbv4HIC4uDvuUs1EUsrOz3V6fnZ3t9vry8nKUl5c7yoWFhQAAq9UKq9V6IdVX+fnEz7jyvStF4Q/dbqtisgGVz9XPvRsjE4AkQKwjo/Tkk+ryhx8CACSjEQgJgSEzU5Tj4yF17gxj1fgZe79+sN9/PwImTxblm26CLTUVAUOHwlBaCtuUKbDfey9M334Lw++/wzZ7NqQ+fWBYvhzG9ethe+YZEaRt3CgyReHhIvhTTtXW8WdKb/LPu54/980Z21NfbE99+WN71uZZ/H6dm7S0NMydO9fl/Nq1axESEqLb9zlQcgCBhkDd7iczVGVbDDDAJEkoChSBWrBRrLMiaZfcr24J/qrMi0GSxGcMBser8thxTpJgsNkgBQTAHhAAyWCAQZJgrKhQ38NoBAwGGCsrIQGwBwbCYLPBWFkJu9mMyuBgmKqCS8lohFR1rdFmQ0VYGMpiYhBYVISgvDyUR0aiuE0btMjORlBeHvK6dEFxQgKi9+9HSE4OTg0ciJLWrZG4fj3MxcXIHD0aeZ07o+uHHyLo3DnsnTAB+R07ovvy5aJ8220oTEpC59at0SInB3vHj0dZVBRaP/ooQk+dQuaoUag8dQoh8+cjJCcHuT17isAlNRUGu11kzFavFkHM9derZ0w1Yenp6b6ugl9he+qL7akvf2rPUnkMpRcMklRdP0b9q6ioQEhICD755BOMHTvWcX7SpEnIz8/HF/KiaQpJSUlITU3Fgw8+6Dg3Z84crFq1Cr/Ki6MpuMvcJCYmIjc3F+E6ryNitVqRnp6O4cOHw2y+8G6p5o7tqS+2p77Ynvpie+rLH9uzsLAQMTExKCgoqPH3t08zN4GBgejfvz/WrVvnCG7sdjvWrVuH6dOnu/3MoEGDsG7dOlVwk56ejkGDBrm93mKxwCJPxVUwm8319gden/dujtie+mJ76ovtqS+2p778qT1r8xw+75ZKTU3FpEmTMGDAAFx66aVYsGABSkpKMLlqXMTEiRORkJCAtLQ0AMCMGTNw9dVX45VXXsHo0aOxYsUK7NixA4sXL/blYxAREVEj4fPgZty4cThz5gxmz56N7Oxs9OnTB2vWrHEMGs7KyoJRsR7I5Zdfjg8++ABPPfUUnnjiCXTq1AmrVq1Cjx49fPUIRERE1Ij4PLgBgOnTp1fbDbV+/XqXczfffDNuvvnmeq4VERERNUU+X8SPiIiISE8MboiIiMivMLghIiIiv8LghoiIiPwKgxsiIiLyKwxuiIiIyK8wuCEiIiK/wuCGiIiI/AqDGyIiIvIrDG6IiIjIrzSK7RcakiRJAMTW6XqzWq0oLS1FYWGh3+zC6ktsT32xPfXF9tQX21Nf/tie8u9t+fe4J80uuCkqKgIAJCYm+rgmREREVFtFRUWIiIjweI1B8iYE8iN2ux0nT55EWFgYDAaDrvcuLCxEYmIijh07hvDwcF3v3RyxPfXF9tQX21NfbE99+WN7SpKEoqIitGnTBkaj51E1zS5zYzQacdFFF9Xr9wgPD/ebH6bGgO2pL7anvtie+mJ76svf2rOmjI2MA4qJiIjIrzC4ISIiIr/C4EZHFosFc+bMgcVi8XVV/ALbU19sT32xPfXF9tRXc2/PZjegmIiIiPwbMzdERETkVxjcEBERkV9hcENERER+hcGNThYuXIjk5GQEBQVh4MCB2L59u6+r1Chs3LgRY8aMQZs2bWAwGLBq1SrV+5IkYfbs2WjdujWCg4MxbNgwHDx4UHVNXl4eJkyYgPDwcERGRuKuu+5CcXGx6prffvsNV155JYKCgpCYmIgXX3yxvh+twaWlpeGSSy5BWFgYYmNjMXbsWOzfv191zfnz5zFt2jS0bNkSoaGh+Nvf/oacnBzVNVlZWRg9ejRCQkIQGxuLRx55BJWVlapr1q9fj379+sFisaBjx45YtmxZfT9eg3vrrbfQq1cvxzoggwYNwrfffut4n215YV544QUYDAY8+OCDjnNsU+8988wzMBgMqq+uXbs63mdb1kCiC7ZixQopMDBQevfdd6Xff/9dmjJlihQZGSnl5OT4umo+t3r1aunJJ5+UPvvsMwmA9Pnnn6vef+GFF6SIiAhp1apV0q+//ipdf/31Urt27aSysjLHNSNHjpR69+4tbdu2Tfrxxx+ljh07SuPHj3e8X1BQIMXFxUkTJkyQ9uzZI3344YdScHCw9O9//7uhHrNBpKSkSEuXLpX27NkjZWRkSNddd52UlJQkFRcXO6659957pcTERGndunXSjh07pMsuu0y6/PLLHe9XVlZKPXr0kIYNGybt2rVLWr16tRQTEyPNmjXLcc3hw4elkJAQKTU1Vfrjjz+k119/XTKZTNKaNWsa9Hnr25dffil988030oEDB6T9+/dLTzzxhGQ2m6U9e/ZIksS2vBDbt2+XkpOTpV69ekkzZsxwnGebem/OnDnSxRdfLJ06dcrxdebMGcf7bEvPGNzo4NJLL5WmTZvmKNtsNqlNmzZSWlqaD2vV+GiDG7vdLsXHx0svvfSS41x+fr5ksVikDz/8UJIkSfrjjz8kANLPP//suObbb7+VDAaDdOLECUmSJOnNN9+UoqKipPLycsc1jz32mNSlS5d6fiLfOn36tARA2rBhgyRJou3MZrO0cuVKxzV79+6VAEhbt26VJEkEm0ajUcrOznZc89Zbb0nh4eGO9nv00Ueliy++WPW9xo0bJ6WkpNT3I/lcVFSU9Pbbb7MtL0BRUZHUqVMnKT09Xbr66qsdwQ3btHbmzJkj9e7d2+17bMuasVvqAlVUVGDnzp0YNmyY45zRaMSwYcOwdetWH9as8cvMzER2draq7SIiIjBw4EBH223duhWRkZEYMGCA45phw4bBaDTip59+clxz1VVXITAw0HFNSkoK9u/fj3PnzjXQ0zS8goICAEB0dDQAYOfOnbBarar27Nq1K5KSklTt2bNnT8TFxTmuSUlJQWFhIX7//XfHNcp7yNf488+zzWbDihUrUFJSgkGDBrEtL8C0adMwevRol+dmm9bewYMH0aZNG7Rv3x4TJkxAVlYWALalNxjcXKDc3FzYbDbVDxAAxMXFITs720e1ahrk9vHUdtnZ2YiNjVW9HxAQgOjoaNU17u6h/B7+xm6348EHH8TgwYPRo0cPAOJZAwMDERkZqbpW2541tVV11xQWFqKsrKw+Hsdndu/ejdDQUFgsFtx77734/PPP0b17d7ZlHa1YsQK//PIL0tLSXN5jm9bOwIEDsWzZMqxZswZvvfUWMjMzceWVV6KoqIht6YVmt3EmkT+YNm0a9uzZg02bNvm6Kk1aly5dkJGRgYKCAnzyySeYNGkSNmzY4OtqNUnHjh3DjBkzkJ6ejqCgIF9Xp8kbNWqU47hXr14YOHAg2rZti48//hjBwcE+rFnTwMzNBYqJiYHJZHIZpZ6Tk4P4+Hgf1appkNvHU9vFx8fj9OnTqvcrKyuRl5enusbdPZTfw59Mnz4dX3/9NX744QfVDvfx8fGoqKhAfn6+6npte9bUVtVdEx4e7nf/qAYGBqJjx47o378/0tLS0Lt3b7z22mtsyzrYuXMnTp8+jX79+iEgIAABAQHYsGED/u///g8BAQGIi4tjm16AyMhIdO7cGYcOHeLPpxcY3FygwMBA9O/fH+vWrXOcs9vtWLduHQYNGuTDmjV+7dq1Q3x8vKrtCgsL8dNPPznabtCgQcjPz8fOnTsd13z//few2+0YOHCg45qNGzfCarU6rklPT0eXLl0QFRXVQE9T/yRJwvTp0/H555/j+++/R7t27VTv9+/fH2azWdWe+/fvR1ZWlqo9d+/erQoY09PTER4eju7duzuuUd5DvqY5/Dzb7XaUl5ezLevg2muvxe7du5GRkeH4GjBgACZMmOA4ZpvWXXFxMf7880+0bt2aP5/e8PWIZn+wYsUKyWKxSMuWLZP++OMP6Z577pEiIyNVo9Sbq6KiImnXrl3Srl27JADS/PnzpV27dklHjx6VJElMBY+MjJS++OIL6bfffpNuuOEGt1PB+/btK/3000/Spk2bpE6dOqmmgufn50txcXHS7bffLu3Zs0dasWKFFBIS4ndTwadOnSpFRERI69evV00PLS0tdVxz7733SklJSdL3338v7dixQxo0aJA0aNAgx/vy9NARI0ZIGRkZ0po1a6RWrVq5nR76yCOPSHv37pUWLlzoN9NDlR5//HFpw4YNUmZmpvTbb79Jjz/+uGQwGKS1a9dKksS21INytpQksU1r46GHHpLWr18vZWZmSps3b5aGDRsmxcTESKdPn5YkiW1ZEwY3Onn99delpKQkKTAwULr00kulbdu2+bpKjcIPP/wgAXD5mjRpkiRJYjr4008/LcXFxUkWi0W69tprpf3796vucfbsWWn8+PFSaGioFB4eLk2ePFkqKipSXfPrr79KV1xxhWSxWKSEhATphRdeaKhHbDDu2hGAtHTpUsc1ZWVl0n333SdFRUVJISEh0l//+lfp1KlTqvscOXJEGjVqlBQcHCzFxMRIDz30kGS1WlXX/PDDD1KfPn2kwMBAqX379qrv4S/uvPNOqW3btlJgYKDUqlUr6dprr3UENpLEttSDNrhhm3pv3LhxUuvWraXAwEApISFBGjdunHTo0CHH+2xLz7grOBEREfkVjrkhIiIiv8LghoiIiPwKgxsiIiLyKwxuiIiIyK8wuCEiIiK/wuCGiIiI/AqDGyIiIvIrDG6IiIjIrzC4ISJdJCcnY8GCBb6uhm7Wr18Pg8HgsjkhETV+DG6IqEbHjh3DnXfeiTZt2iAwMBBt27bFjBkzcPbsWV9XTRdDhgzBgw8+qDp3+eWX49SpU4iIiPBNpYiozhjcEJFHhw8fxoABA3Dw4EF8+OGHOHToEBYtWoR168TO93l5eT6pl81mg91ur7f7BwYGIj4+HgaDod6+BxHVDwY3ROTRtGnTEBgYiLVr1+Lqq69GUlISRo0ahf/97384ceIEnnzySce1RUVFGD9+PFq0aIGEhAQsXLjQ8Z4kSXjmmWeQlJQEi8WCNm3a4IEHHnC8X15ejocffhgJCQlo0aIFBg4ciPXr1zveX7ZsGSIjI/Hll1+ie/fusFgsePvttxEUFOTSdTRjxgxcc801AICzZ89i/PjxSEhIQEhICHr27IkPP/zQce0dd9yBDRs24LXXXoPBYIDBYMCRI0fcdkt9+umnuPjii2GxWJCcnIxXXnlF9X2Tk5Mxb9483HnnnQgLC0NSUhIWL17seL+iogLTp09H69atERQUhLZt2yItLa1Ofy5E5IGPN+4kokbs7NmzksFgkObNm+f2/SlTpkhRUVGS3W6X2rZtK4WFhUlpaWnS/v37pf/7v/+TTCaTY6ftlStXSuHh4dLq1aulo0ePSj/99JO0ePFix73uvvtu6fLLL5c2btwoHTp0SHrppZcki8UiHThwQJIkSVq6dKlkNpulyy+/XNq8ebO0b98+qbi4WIqLi5Pefvttx30qKytV544fPy699NJL0q5du6Q///zTUa+ffvpJkiRJys/PlwYNGiRNmTJFOnXqlHTq1CmpsrLSsaP9uXPnJEmSpB07dkhGo1F69tlnpf3790tLly6VgoODVbsot23bVoqOjpYWLlwoHTx4UEpLS5OMRqO0b98+SZIk6aWXXpISExOljRs3SkeOHJF+/PFH6YMPPtDnD4uIHBjcEFG1tm3bJgGQPv/8c7fvz58/XwIg5eTkSG3btpVGjhypen/cuHHSqFGjJEmSpFdeeUXq3LmzVFFR4XKfo0ePSiaTSTpx4oTq/LXXXivNmjVLkiQR3ACQMjIyVNfMmDFDuuaaaxzl7777TrJYLI6gxJ3Ro0dLDz30kKN89dVXSzNmzFBdow1ubr31Vmn48OGqax555BGpe/fujnLbtm2l2267zVG22+1SbGys9NZbb0mSJEn333+/dM0110h2u73auhHRhWO3FBHVSJIkr64bNGiQS3nv3r0AgJtvvhllZWVo3749pkyZgs8//xyVlZUAgN27d8Nms6Fz584IDQ11fG3YsAF//vmn436BgYHo1auX6ntMmDAB69evx8mTJwEA77//PkaPHo3IyEgAYmzOc889h549eyI6OhqhoaH47rvvkJWVVas22Lt3LwYPHqw6N3jwYBw8eBA2m81xTlk/g8GA+Ph4nD59GoDoAsvIyECXLl3wwAMPYO3atbWqAxF5h8ENEVWrY8eOMBgMjgBFa+/evYiKikKrVq1qvFdiYiL279+PN998E8HBwbjvvvtw1VVXwWq1ori4GCaTCTt37kRGRobja+/evXjttdcc9wgODnYZ4HvJJZegQ4cOWLFiBcrKyvD5559jwoQJjvdfeuklvPbaa3jsscfwww8/ICMjAykpKaioqKhjq3hmNptVZYPB4Bj43K9fP2RmZuK5555DWVkZbrnlFtx00031Ug+i5izA1xUgosarZcuWGD58ON58803MnDkTwcHBjveys7Px/vvvY+LEiY6AY9u2barPb9u2Dd26dXOUg4ODMWbMGIwZMwbTpk1D165dsXv3bvTt2xc2mw2nT5/GlVdeWet6TpgwAe+//z4uuugiGI1GjB492vHe5s2bccMNN+C2224DANjtdhw4cADdu3d3XBMYGKjKvrjTrVs3bN68WXVu8+bN6Ny5M0wmk9d1DQ8Px7hx4zBu3DjcdNNNGDlyJPLy8hAdHe31PYjIM2ZuiMijN954A+Xl5UhJScHGjRtx7NgxrFmzBsOHD0dCQgKef/55x7WbN2/Giy++iAMHDmDhwoVYuXIlZsyYAUDMdnrnnXewZ88eHD58GP/9738RHByMtm3bonPnzpgwYQImTpyIzz77DJmZmdi+fTvS0tLwzTff1FjHCRMm4JdffsHzzz+Pm266CRaLxfFep06dkJ6eji1btmDv3r34xz/+gZycHNXnk5OT8dNPP+HIkSPIzc11O8X8oYcewrp16/Dcc8/hwIEDeO+99/DGG2/g4Ycf9rot58+fjw8//BD79u3DgQMHsHLlSsTHxzu60IhIHwxuiMijTp06YceOHWjfvj1uueUWdOjQAffccw+GDh2KrVu3qjIODz30EHbs2IG+ffvin//8J+bPn4+UlBQAQGRkJJYsWYLBgwejV69e+N///oevvvoKLVu2BAAsXboUEydOxEMPPYQuXbpg7Nix+Pnnn5GUlFRjHTt27IhLL70Uv/32m6pLCgCeeuop9OvXDykpKRgyZAji4+MxduxY1TUPP/wwTCYTunfvjlatWrkdj9OvXz98/PHHWLFiBXr06IHZs2fj2WefxR133OF1W4aFheHFF1/EgAEDcMkll+DIkSNYvXo1jEb+U0ykJ4Pk7UhBIiIioiaA/10gIiIiv8LghoiIiPwKgxsiIiLyKwxuiIiIyK8wuCEiIiK/wuCGiIiI/AqDGyIiIvIrDG6IiIjIrzC4ISIiIr/C4IaIiIj8CoMbIiIi8isMboiIiMiv/D/CNXyhs4rKnAAAAABJRU5ErkJggg==",
      "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 0x7fba04917010>]"
      ]
     },
     "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(object_name=op.like('~~GPS~~'), orderby='TLE_LINE1', format='json'))\n",
    "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",
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       "  'OBJECT_TYPE': 'PAYLOAD',\n",
       "  'RCS_SIZE': 'LARGE',\n",
       "  'COUNTRY_CODE': 'US',\n",
       "  'LAUNCH_DATE': '2015-03-25',\n",
       "  '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))\n",
    "\n",
    "    # print(f'{sat[\"TLE_LINE0\"][2:]} {solution_residuals(t_is, solve_sat, candidate_sat.model)}')"
   ]
  },
  {
   "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>2.667961</td>\n",
       "      <td>1.014868</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>2.685144</td>\n",
       "      <td>0.915504</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>2.798877</td>\n",
       "      <td>0.941792</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>3.076632</td>\n",
       "      <td>1.175188</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>13655.398480</td>\n",
       "      <td>1040.530089</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>13655.433784</td>\n",
       "      <td>1036.763553</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>13655.490270</td>\n",
       "      <td>1039.200601</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>14799.212873</td>\n",
       "      <td>10944.562958</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>14799.342119</td>\n",
       "      <td>10945.147727</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>14800.350223</td>\n",
       "      <td>10948.427374</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>14800.359758</td>\n",
       "      <td>10948.958294</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>14800.381537</td>\n",
       "      <td>10949.078937</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>18644.708753</td>\n",
       "      <td>14855.129969</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>18644.913252</td>\n",
       "      <td>14855.396853</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>18645.506308</td>\n",
       "      <td>14856.178753</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>18645.562382</td>\n",
       "      <td>14857.247391</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>26341.980232</td>\n",
       "      <td>26707.480312</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>26342.162794</td>\n",
       "      <td>26707.791902</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>26342.280045</td>\n",
       "      <td>26707.958536</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>26342.820966</td>\n",
       "      <td>26708.138379</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...      2.667961   \n",
       "567  2 28129 054.6464 320.0627 0052109 264.7328 094...      2.685144   \n",
       "565  2 28129 054.6471 320.1034 0052084 264.7458 094...      2.798877   \n",
       "566  2 28129 054.6470 320.1034 0052057 264.7173 094...      3.076632   \n",
       "97   2 11690 063.8937 271.2930 0273259 182.2802 177...  13655.398480   \n",
       "95   2 11690 063.8947 271.3546 0272966 182.2597 177...  13655.433784   \n",
       "96   2 11690 063.8945 271.3237 0273183 182.2697 177...  13655.490270   \n",
       "64   2 10684 063.4960 271.4665 0061292 174.6670 185...  14799.212873   \n",
       "66   2 10684 063.4948 271.4050 0061264 174.7138 185...  14799.342119   \n",
       "67   2 10684 063.4948 271.4053 0061462 174.4244 185...  14800.350223   \n",
       "63   2 10684 063.4961 271.4673 0061451 174.3758 185...  14800.359758   \n",
       "65   2 10684 063.4956 271.4366 0061450 174.3827 185...  14800.381537   \n",
       "501  2 26605 056.5661 018.0814 0029663 239.6383 120...  18644.708753   \n",
       "502  2 26605 056.5665 018.0427 0029595 239.6489 120...  18644.913252   \n",
       "504  2 26605 056.5669 017.9849 0029655 239.4496 120...  18645.506308   \n",
       "503  2 26605 056.5665 018.0235 0029766 239.8189 119...  18645.562382   \n",
       "524  2 27704 053.9573 259.2051 0117859 194.4817 165...  26341.980232   \n",
       "526  2 27704 053.9567 259.1640 0117891 194.5129 165...  26342.162794   \n",
       "525  2 27704 053.9567 259.1641 0117886 194.5152 165...  26342.280045   \n",
       "523  2 27704 053.9576 259.2453 0117897 194.3381 165...  26342.820966   \n",
       "\n",
       "     residual_epoch  \n",
       "568        1.014868  \n",
       "567        0.915504  \n",
       "565        0.941792  \n",
       "566        1.175188  \n",
       "97      1040.530089  \n",
       "95      1036.763553  \n",
       "96      1039.200601  \n",
       "64     10944.562958  \n",
       "66     10945.147727  \n",
       "67     10948.427374  \n",
       "63     10948.958294  \n",
       "65     10949.078937  \n",
       "501    14855.129969  \n",
       "502    14855.396853  \n",
       "504    14856.178753  \n",
       "503    14857.247391  \n",
       "524    26707.480312  \n",
       "526    26707.791902  \n",
       "525    26707.958536  \n",
       "523    26708.138379  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_candidates.sort_values('residual').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "26605381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " (10668.296087647044, 7292.796722440949, -31842.335182255498),\n",
       " (-2.501846359599823, 1.0696316015728493, 0.8648788164327018))"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "id": "444ccbc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-20613.52012712, -18917.25585364,  10292.77859863])"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "id": "b5321b55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " (-9945.224039473405, -11624.459131194762, -21549.556583621394),\n",
       " (2.9762057453190787, -2.509765457002418, -0.021943379755239682))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve_sat.model.sgp4_tsince(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8956fb7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " (10668.296167702032, 7292.796688214704, -31842.335209930596),\n",
       " (-2.5018463562478765, 1.0696316038636393, 0.8648788064260986))"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "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": 28,
     "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": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eph_is[0], t_is[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2e80597d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " (30657.74437934246, -6623.393156897523, -25793.922031972415),\n",
       " (-0.8113843357046191, 1.142564036944282, -1.6191111000462464))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidate_sat.model.sgp4(*jday_datetime(t_is[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0d9e8ca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61871.72970205591"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "id": "13ca5515-3de9-43c3-a1a0-7bff462aecc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_tle = candidate_sat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fbf1cfe4-0c37-43bd-b9f6-c3e88f86a2ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2006, 10, 20, 17, 18, 13, 250016, tzinfo=UTC)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat_epoch_datetime(ref_tle.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5085ef0a-be34-463d-9def-88d7bef57e1e",
   "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(t, ephemeris, last_obs=last_obs, obs_stride=obs_stride, lamda=1e-3, rms_epsilon=0.0001, debug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "22bcdf33-0ee9-4b65-a3bd-5b88c757dbd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# exporter.export_tle(aligned_solve_sat.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1c3e0bfa-3b9b-4c70-8731-aa165c2ed27d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ajd, ajdf = jday_datetime(sat_epoch_datetime(ref_tle.model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 38,
   "id": "1a59ceca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2006, 10, 20, 17, 18, 13, 250016, tzinfo=UTC)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat_epoch_datetime(ref_tle.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a424100d",
   "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": [
    "tt[aligned_epoch_obs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 41,
   "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 264.4272  85.1870  2.00562788    16')"
      ]
     },
     "execution_count": 41,
     "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": 42,
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exporter.export_tle(ref_tle.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c74616e7-21b8-4f7e-b9d2-fe813bfcab9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Position Residual (m)')"
      ]
     },
     "execution_count": 43,
     "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": "1c0a2b26",
   "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
}