TLEFit - EQN - FD.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": "64bc9830-6300-455e-be44-82d34867346e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tlefit_equinoctial_fd import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "final-resolution",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "disabled-juvenile",
   "metadata": {},
   "outputs": [],
   "source": [
    "line1 = '1 25544U 98067A   14020.93268519  .00009878  00000-0  18200-3 0  5082'\n",
    "line2 = '2 25544  51.6498 109.4756 0003572  55.9686 274.8005 15.49815350868473'\n",
    "satellite = EarthSatellite(line1, line2, 'ISS (ZARYA)', ts)\n",
    "\n",
    "line1 = '1 40019U 14033K   21064.48089419  .00000027  00000-0  13123-4 0  9994'\n",
    "line2 = '2 40019  97.7274 245.3630 0083155 314.3836  45.0579 14.67086574359033'\n",
    "satellite = EarthSatellite(line1, line2, 'APRIZESAT 10', ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "persistent-command",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial semi-major axis (a) = 7054.852 km\n",
      "COE elements (original) = [7048.523352797561, 0.0083155, 1.7056648994135064, 4.282392212570847, 5.487028934272845, 0.786408709034353, 1.3123000000000001e-05]\n",
      "Residuals at epoch time [ 5.60046126e-01 -3.55208784e+00 -1.46641965e+01 -2.73377804e-03\n",
      " -7.52656544e-03  1.66212876e-04]\n",
      "Residual magnitudes at epoch time 15.0987, 0.00800939\n",
      "\n",
      "\n",
      "#################### ITERATION 1 ####################\n",
      "\n",
      "Condition number (A): 11.957\n",
      "Condition number (ATWA_acc): 95915723.74617699\n",
      "Lambda:  0.001\n",
      "Residuals after/before 12.7 < 8.2e+06\n",
      "Covariance a: 12.329 m\n",
      "dx  [-6.33044180e+00 -7.70065162e-04  8.50590616e-04 -2.03292856e-06\n",
      " -9.39080855e-05 -4.11743467e-05 -9.29380112e-04]\n",
      "COE elements = [7048.521091254834, 0.008329122738225081, 1.7056649730359073, 4.282393715943094, 5.487692180698019, 0.7857471580982693, -0.0009283801118204214]\n",
      "EQN elements = (7048.521091254834, -0.007837465160169397, -0.0028192952047540127, 4.272647747559796, -1.040637690589159, -0.4772523582256966, -0.0009283801118204214)\n",
      "\n",
      "Current Solution 7048.521091254834 0.0083291 97.7274 245.3631 314.4216 45.0200 -9.2838e-04\n",
      "        Original 7048.523352797561 0.0083155 97.7274 245.3630 314.3836 45.0579 +1.3123e-05\n",
      "Residual (b) = [-7.14673731e-01  3.11148753e+00  1.46442084e+01  2.40917232e-03\n",
      "  7.13957301e-03  8.32241505e-04]\n",
      "Residuals (b) r = 15, v = 0.00758\n",
      "\n",
      "Convergence test: 0.998346, sigma_new(82.7) < sigma_old(5e+04)\n",
      "\n",
      "#################### ITERATION 2 ####################\n",
      "\n",
      "Condition number (A): 11.937\n",
      "Condition number (ATWA_acc): 87328978.73029904\n",
      "Lambda:  0.001\n",
      "Residuals after/before 1.06e-07 < 12.7\n",
      "Covariance a: 12.309 m\n",
      "dx  [ 2.26166391e-03  1.09495305e-05  9.81324995e-06 -3.19900019e-06\n",
      "  7.94824666e-07 -1.52899854e-06  9.41307343e-04]\n",
      "COE elements = [7048.5233529187435, 0.008315499729816064, 1.70566489939542, 4.282392212570328, 5.487028935216614, 0.7864087079522459, 1.2927230914372835e-05]\n",
      "EQN elements = (7048.5233529187435, -0.007826515629718499, -0.002809481954799333, 4.272644548559601, -1.0406368957644931, -0.4772538872242321, 1.2927230914372835e-05)\n",
      "\n",
      "Current Solution 7048.5233529187435 0.0083155 97.7274 245.3630 314.3836 45.0579 +1.2927e-05\n",
      "        Original 7048.523352797561 0.0083155 97.7274 245.3630 314.3836 45.0579 +1.3123e-05\n",
      "Residual (b) = [-3.81196189e-02 -8.62172670e-02 -8.71993011e-02 -4.30851210e-06\n",
      " -4.39750904e-05 -1.07993681e-04]\n",
      "Residuals (b) r = 0.128, v = 0.000117\n",
      "\n",
      "Convergence test: 0.998754, sigma_new(0.103) < sigma_old(82.7)\n",
      "\n",
      "#################### ITERATION 3 ####################\n",
      "\n",
      "Condition number (A): 11.936\n",
      "Condition number (ATWA_acc): 87369590.1955162\n",
      "Lambda:  0.001\n",
      "Residuals after/before 1.85e-18 < 1.06e-07\n",
      "Covariance a: 12.310 m\n",
      "dx  [-1.21178686e-07 -2.56947509e-10 -8.39030499e-11  1.38857525e-10\n",
      " -1.92418199e-11 -8.17051741e-12  1.95767538e-07]\n",
      "COE elements = [7048.523352797565, 0.008315500000001606, 1.7056648994135064, 4.282392212570848, 5.487028934272882, 0.7864087090343155, 1.31229984518877e-05]\n",
      "EQN elements = (7048.523352797565, -0.00782651588666601, -0.0028094820387023793, 4.272644548698459, -1.0406368957837349, -0.47725388723240225, 1.31229984518877e-05)\n",
      "\n",
      "Current Solution 7048.523352797565 0.0083155 97.7274 245.3630 314.3836 45.0579 +1.3123e-05\n",
      "        Original 7048.523352797561 0.0083155 97.7274 245.3630 314.3836 45.0579 +1.3123e-05\n",
      "Residual (b) = [8.60104592e-08 1.34860056e-06 3.65234030e-06 6.48942011e-10\n",
      " 2.29095099e-09 1.68001080e-09]\n",
      "Residuals (b) r = 3.89e-06, v = 2.91e-09\n",
      "\n",
      "Convergence test: 0.999909, sigma_new(9.39e-06) < sigma_old(0.103)\n",
      "\n",
      "#################### ITERATION 4 ####################\n",
      "\n",
      "Condition number (A): 11.936\n",
      "Condition number (ATWA_acc): 87369079.07205085\n",
      "Lambda:  0.001\n",
      "Residuals after/before 1.36e-19 < 1.85e-18\n",
      "Covariance a: 12.310 m\n",
      "dx  [-4.44275855e-12  1.39549659e-15  8.08947532e-16  2.70378388e-16\n",
      "  4.27954214e-16 -9.27970013e-16  1.69766474e-12]\n",
      "COE elements = [7048.523352797561, 0.00831550000000002, 1.7056648994135064, 4.282392212570846, 5.487028934272848, 0.786408709034351, 1.3123000149552443e-05]\n",
      "EQN elements = (7048.523352797561, -0.007826515886664618, -0.0028094820387015657, 4.272644548698459, -1.040636895783734, -0.47725388723240414, 1.3123000149552443e-05)\n",
      "\n",
      "Current Solution 7048.523352797561 0.0083155 97.7274 245.3630 314.3836 45.0579 +1.3123e-05\n",
      "        Original 7048.523352797561 0.0083155 97.7274 245.3630 314.3836 45.0579 +1.3123e-05\n",
      "Residual (b) = [-6.82121026e-12 -4.54747351e-12 -5.91171556e-12  1.33226763e-15\n",
      "  1.20736754e-15 -6.21724894e-15]\n",
      "Residuals (b) r = 1.01e-11, v = 6.47e-15\n",
      "\n",
      "Convergence test: 0.999996, sigma_new(3.93e-11) < sigma_old(9.39e-06)\n",
      "\n",
      "Stopped due to dx convergence\n",
      "\n",
      "#################### SOLUTION IN 4 ITERATIONS ####################\n",
      "\n",
      "Solution 7048.523352797561 0.0083155 97.7274 245.3630 314.3836 45.0579 +1.3123e-05\n",
      "Original 7048.523352797561 0.0083155 97.7274 245.3630 314.3836 45.0579 +1.3123e-05\n",
      "Residuals (b) r = 1.01e-11, v = 6.47e-15\n",
      "Stopped in 4 iterations\n",
      "Residual at epoch     1.510e+01 km 8.009e-03 km/s\n",
      "Residual at new epoch 9.493e-12 km 0.000e+00 km/s\n",
      "Residual at the end   1.011e-11 km 6.472e-15 km/s\n"
     ]
    }
   ],
   "source": [
    "iterations, sigma, sigmas, dxs, bs, lamdas, b_epoch, b_new_epoch, b, P, A = test_tle_fit_normalized_equinoctial(satellite, central_diff=True, lamda=1e-3, rms_epsilon=0.0001, debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "separate-sleeping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9c8ebd92a0>]"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "id": "soviet-defensive",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9c8c9e0e20>]"
      ]
     },
     "execution_count": 6,
     "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": "markdown",
   "id": "a6f063c8-f1ae-4c8f-b1f2-c915ef7004ee",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Play with fit span"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "caca3364-07b2-4222-ab85-0f0fdedabd64",
   "metadata": {},
   "outputs": [],
   "source": [
    "debug = False\n",
    "\n",
    "fits = []\n",
    "its = []\n",
    "sigs = []\n",
    "\n",
    "# FIXME: Try timing the function as well\n",
    "\n",
    "for x in range(1, 5):\n",
    "    iterations, sigma, sigmas, dxs, bs, lamdas, b_epoch, b_new_epoch, b, P, A = test_tle_fit_normalized_equinoctial(satellite, central_diff=True, fit_span=x, lamda=1e-3, rms_epsilon=0.0001, debug=debug)\n",
    "    if debug:\n",
    "        print()\n",
    "        print('#' * 80)\n",
    "        print()\n",
    "    fits.append(x)\n",
    "    its.append(iterations)\n",
    "    sigs.append(sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "11d13fe2-657c-41a9-bba8-7d976dd6f0ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Iterations')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "ax1.semilogy(fits, sigs)\n",
    "ax2.plot(fits, its)\n",
    "\n",
    "ax1.set_xlabel(\"Fit Span\")\n",
    "ax1.set_ylabel(\"Sigma (m)\")\n",
    "ax2.set_ylabel(\"Iterations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a19bf6a-dae4-41a4-a5c3-33561f218349",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "rising-adrian",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tles/22335.txt    tles/boom.txt         tles/gps-ops.txt  tles/spire.txt\n",
      "tles/active.txt   tles/cubesat.txt      tles/planet.txt   tles/stations.txt\n",
      "tles/analyst.txt  tles/engineering.txt  tles/ses.txt\n"
     ]
    }
   ],
   "source": [
    "ls tles/*.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "swedish-projection",
   "metadata": {},
   "outputs": [],
   "source": [
    "tle_filename = 'tles/TWOLINE.TXT'\n",
    "# tle_filename = 'tles/22335.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "studied-government",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "head: cannot open 'tles/tles/TWOLINE.TXT' for reading: No such file or directory\n"
     ]
    }
   ],
   "source": [
    "!head tles/$tle_filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "silver-allah",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[19], line 9\u001b[0m\n\u001b[1;32m      5\u001b[0m tles \u001b[39m=\u001b[39m []\n\u001b[1;32m      7\u001b[0m \u001b[39mfor\u001b[39;00m cnt, ix \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(\u001b[39mrange\u001b[39m(\u001b[39m0\u001b[39m, \u001b[39mlen\u001b[39m(tle_lines), \u001b[39m3\u001b[39m)):\n\u001b[1;32m      8\u001b[0m     \u001b[39m#print(cnt, ix, tle_lines[ix])\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m     tles\u001b[39m.\u001b[39mappend((tle_lines[ix]\u001b[39m.\u001b[39mstrip(), tle_lines[ix \u001b[39m+\u001b[39;49m \u001b[39m1\u001b[39;49m]\u001b[39m.\u001b[39mstrip(), tle_lines[ix \u001b[39m+\u001b[39m \u001b[39m2\u001b[39m]\u001b[39m.\u001b[39mstrip()))\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "with open(tle_filename, 'r') as f:\n",
    "    \n",
    "    tle_lines = f.readlines()\n",
    "    \n",
    "    tles = []\n",
    "    \n",
    "    for cnt, ix in enumerate(range(0, len(tle_lines), 3)):\n",
    "        #print(cnt, ix, tle_lines[ix])\n",
    "        tles.append((tle_lines[ix].strip(), tle_lines[ix + 1].strip(), tle_lines[ix + 2].strip()))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4e74db8b-b4c7-4623-a8dd-92dafcafe377",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "manufactured-sword",
   "metadata": {},
   "outputs": [],
   "source": [
    "if tle_filename.split('/')[-1] == 'TWOLINE.TXT':\n",
    "    with open(tle_filename, 'r') as f:\n",
    "\n",
    "        tle_lines = f.readlines()\n",
    "\n",
    "        tles = []\n",
    "        tle = []\n",
    "\n",
    "        for line in tle_lines:\n",
    "            if line.startswith('#'):\n",
    "                continue\n",
    "                \n",
    "            tle.append(line.strip())\n",
    "            \n",
    "            if line.startswith('2 '):\n",
    "                tles.append((tle[0][2:9], tle[0], tle[1]))\n",
    "                tle = []\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "willing-sight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('00005U ',\n",
       "  '1 00005U 58002B   00179.78495062  .00000023  00000-0  28098-4 0  4753',\n",
       "  '2 00005  34.2682 348.7242 1859667 331.7664  19.3264 10.82419157413667'),\n",
       " ('04632U ',\n",
       "  '1 04632U 70093B   04031.91070959 -.00000084  00000-0  10000-3 0  9955',\n",
       "  '2 04632  11.4628 273.1101 1450506 207.6000 143.9350  1.20231981 44145'),\n",
       " ('06251U ',\n",
       "  '1 06251U 62025E   06176.82412014  .00008885  00000-0  12808-3 0  3985',\n",
       "  '2 06251  58.0579  54.0425 0030035 139.1568 221.1854 15.56387291  6774')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tles[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "inside-timothy",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fifty-freeware",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TLE                       Iter    Cov (m)   StdDev (m)   Res @ Epoch (m)   Res @ End (m)\n",
      "00005U                       5    236.099     1.38e-08          4.11e-08        3.73e-08\n",
      "04632U                       4     47.433     9.26e-04          2.19e-03        2.19e-03\n",
      "06251U                       4    136.803     7.89e-05          3.85e-05        3.58e-05\n",
      "08195U                       7    758.035     3.00e-08          8.34e-08        8.09e-08\n",
      "09880U                       6    757.837     6.73e-08          1.14e-07        1.17e-07\n",
      "09998U                       3     43.933     8.65e-02          2.67e-01        2.67e-01\n",
      "14128U                       4     44.486     1.18e-03          3.03e-03        3.04e-03\n",
      "16925U                       5    415.364     1.02e-03          1.61e-03        1.62e-03\n",
      "20413U                       8     27.231     6.62e-02          7.49e-01        7.47e-01\n",
      "21897U                       5    785.649     2.34e-06          4.51e-06        4.49e-06\n",
      "22312U                       5    153.545     1.76e-02          8.31e-04        9.76e-04\n",
      "22674U                       5    796.955     3.94e-05          1.08e-04        1.10e-04\n",
      "23177U                       5    702.070     6.55e-04          2.96e-03        3.03e-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dan/GIT/tle-tailor/tlefit_equinoctial_fd.py:194: RuntimeWarning: overflow encountered in multiply\n",
      "  P = np.linalg.pinv(ATWA_acc + lamda * ATWA_acc, hermitian=hermitian)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23333U                   Failed\n",
      "23599U                       4    502.982     1.23e-03          4.91e-03        4.86e-03\n",
      "24208U                       4     44.463     8.05e-04          1.65e-03        1.64e-03\n",
      "25954U                       2      0.445     9.43e+03          1.65e+04        1.65e+04\n",
      "26900U                       9      1.408     1.81e+02          5.42e+02        5.44e+02\n",
      "26975U                       4     41.658     1.02e-01          1.97e-01        1.96e-01\n",
      "28057U                       5    192.057     9.85e-07          2.08e-06        1.97e-06\n",
      "28129U                       3     44.183     6.17e-02          1.73e-01        1.74e-01\n",
      "28350U                       5    158.165     1.36e-04          2.12e-05        2.33e-05\n",
      "28623U                       4    104.222     2.16e-02          1.20e-02        1.17e-02\n",
      "28626U                       7      0.000     1.46e+03          4.02e+03        4.04e+03\n",
      "28872U                       4    118.151     5.22e-02          3.57e-03        3.69e-03\n",
      "29141U                       4    113.842     4.39e-03          3.80e-03        3.82e-03\n",
      "29238U                       4     99.761     2.60e-04          6.08e-05        6.05e-05\n",
      "00058U                       5    123.282     4.76e-08          9.40e-08        9.21e-08\n",
      "01317U                       3     44.502     6.06e-02          1.95e-01        1.95e-01\n",
      "00836U                       5     44.557     5.72e-03          8.83e-03        8.82e-03\n",
      "06893U                       7     48.824     1.09e-02          1.75e-02        1.75e-02\n",
      "09931U                       4     31.597     4.39e-01          1.64e+00        1.62e+00\n",
      "11416U                       4    158.949     3.63e-05          2.89e-05        2.96e-05\n",
      "11801U                       6    740.412     9.47e-02          2.14e-02        2.11e-02\n",
      "11871U                       6    127.258     1.34e-08          2.29e-08        1.55e-08\n",
      "12032U                       5    105.850     2.77e-06          2.90e-06        2.88e-06\n",
      "13446U                       5    423.688     1.63e-03          5.13e-04        5.11e-04\n",
      "19483U                       3     44.500     3.91e-02          1.01e-01        1.01e-01\n",
      "19484U                       9     44.509     4.09e-02          1.22e-01        1.22e-01\n",
      "19508U                       5      0.000     4.18e+03          1.37e+04        1.37e+04\n",
      "19541U                       6    780.862     2.08e-06          1.02e-05        1.03e-05\n",
      "19548U                       4     44.508     9.15e-04          2.93e-03        2.94e-03\n",
      "19596U                       4     44.508     1.10e-03          3.52e-03        3.52e-03\n",
      "19621U                       3     44.497     7.89e-02          1.29e-01        1.29e-01\n",
      "19683U                       3     44.519     6.28e-02          1.44e-01        1.44e-01\n",
      "19765U                       3     44.525     7.17e-02          1.75e-01        1.75e-01\n",
      "20338U                      25    130.908     8.93e-07          4.54e-06        4.40e-06\n",
      "20547U                       5    129.054     3.01e-08          3.52e-08        3.62e-08\n",
      "22490U                       5    136.923     4.35e-08          9.65e-08        9.26e-08\n",
      "23246U                       4    424.758     1.85e-05          3.96e-05        3.92e-05\n",
      "23581U                       3     44.516     6.45e-02          8.21e-02        8.21e-02\n",
      "23632U                      25   1338.951     2.30e-01          3.42e+00        3.47e+00\n",
      "23866U                       4    656.891     8.61e-03          2.35e-02        2.41e-02\n",
      "23949U                      14     44.503     5.37e-02          1.85e-01        1.85e-01\n",
      "24640U                       5    734.400     1.23e-05          4.55e-05        4.57e-05\n",
      "24729U                      25     38.420     3.27e-07          1.37e-06        1.29e-06\n",
      "24792U                       4    158.972     1.78e-04          5.81e-05        5.35e-05\n",
      "24808U                      25     85.332     8.01e-07          2.77e-06        2.68e-06\n",
      "25068U                       7    908.346     1.27e-05          1.16e-04        1.17e-04\n",
      "25237U                      12      0.000     1.59e+02          2.28e+02        2.28e+02\n",
      "25989U                       4     32.187     1.01e-02          8.27e-02        8.25e-02\n",
      "26536U                       5    162.100     5.91e-08          6.09e-08        5.71e-08\n",
      "27453U                       5    156.977     4.85e-08          8.54e-08        8.66e-08\n",
      "28358U                       2      0.000     4.73e+03          1.16e+04        1.17e+04\n",
      "28893U                       5    152.988     3.76e-08          5.31e-08        5.09e-08\n",
      "30582U                      25    140.074     2.62e-06          1.24e-05        1.23e-05\n",
      "88888U                       4    145.351     3.85e-05          3.59e-05        3.58e-05\n"
     ]
    }
   ],
   "source": [
    "# LM Test\n",
    "\n",
    "limit = 100#9*1000000\n",
    "failed_tles = []\n",
    "\n",
    "print(f'{\"TLE\":24s} {\"Iter\":>5s} {\"Cov (m)\":>10s}   {\"StdDev (m)\":>10s}   {\"Res @ Epoch (m)\":>13s}   {\"Res @ End (m)\":>13s}')\n",
    "\n",
    "for idx, tle in enumerate(tles):\n",
    "\n",
    "    line1 = tle[1]\n",
    "    line2 = tle[2]\n",
    "    satellite = EarthSatellite(line1, line2, tle[0], ts)\n",
    "    \n",
    "    try:\n",
    "        iterations, sigma, sigmas, dxs, bs, lamdas, b_epoch, b_new_epoch, b, P, A = test_tle_fit_normalized_equinoctial(satellite, central_diff=True, fit_span=1, max_iter=25, lamda=1e-3, rms_epsilon=0.0001, debug=False)\n",
    "        \n",
    "        print(f'{tle[0]:24s} {iterations:5d} {np.sqrt(np.diag(P)[0]) * 1000:10.3f} {sigma * 1000:12.2e} {np.linalg.norm(b_new_epoch[0:3]) * 1000:17.2e} {np.linalg.norm(b[0:3]) * 1000:15.2e}')\n",
    "    except:\n",
    "        print(f'{tle[0]:24s} Failed')\n",
    "    \n",
    "    if idx == limit:\n",
    "        break\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e79ac75e-80e3-48df-ac04-65d3e98770b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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