solutions/project_03_solution_take-home_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d32ef79-8c37-48a5-a61b-4248b5d19840",
   "metadata": {},
   "source": [
    "# Project 03 -- Take-Home Test\n",
    "\n",
    "## Due Tuesday, March 12, 2024, 12:00 noon\n",
    "\n",
    "1. Write a small python function to propagate two-body orbits using Kepler's equation (not numerical integration).<br>\n",
    "`def orbit(a,e,i,w,raan,i):`<br>\n",
    "    `stuff`<br>\n",
    "    `return t, p`<br>\n",
    "\n",
    "2. Apply this function to the satellite of your choosing by obtaining a current TLE for that satellite.\n",
    "3. Create a ground track for that satellite for two orbit periods.\n",
    "4. Make a sky plot for that satellite to observe it from the Clarkson campus (Potsdam, NY).\n",
    "\n",
    "You can confer with each other, but please submit you own work.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "16a69078-dd31-4825-b4ec-74bbec1cfb2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure Jupyter so figures appear in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Configure Jupyter to display the assigned value after an assignment\n",
    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a01ae9d7-151f-48af-8e83-4c09ef5ee3c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from modsim import *\n",
    "except ImportError:\n",
    "    print(\"Download modsim from the course repository at https://github.com/jeffwalton/AE470\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "695bc2d6-871c-48f9-bf66-83ad210c1734",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "af663270-c667-4b94-b179-04d355254324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.2921159e-05"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu_earth = 398600.4418 # km^3 / s ^2\n",
    "r_earth = 6378.135 # km\n",
    "w_earth = 7.2921159e-5 # radians/second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "609cf673-2b3a-4928-b1fe-8c8b4ac52404",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "import configparser\n",
    "\n",
    "def elset_spacetrack(sat_catno='25544', print_els=True):\n",
    "    \"\"\"\n",
    "    requires space-track.ini file in current directory with these parameters\n",
    "        [configuration]\n",
    "        username = myUserName\n",
    "        password = MyPa55W0rd    \n",
    "    \"\"\"\n",
    "    uriBase                = \"https://www.space-track.org\"\n",
    "    requestLogin           = \"/ajaxauth/login\"\n",
    "    requestCmdAction       = \"/basicspacedata/query\" \n",
    "    \n",
    "    config = configparser.ConfigParser()\n",
    "    config.read(\"./space-track.ini\")\n",
    "    configUsr = config.get(\"configuration\",\"username\")\n",
    "    configPwd = config.get(\"configuration\",\"password\")\n",
    "    siteCred = {'identity': configUsr, 'password': configPwd}\n",
    "    \n",
    "    request_by_catno = f\"/class/gp/NORAD_CAT_ID/{sat_catno}/orderby/TLE_LINE1%20ASC/format/json\"\n",
    "\n",
    "    class MyError(Exception):\n",
    "        def __init___(self,args):\n",
    "            Exception.__init__(self,\"my exception was raised with arguments {0}\".format(args))\n",
    "            self.args = args\n",
    "\n",
    "    with requests.Session() as session:\n",
    "        # need to log in first. note that we get a 200 to say the web site got the data, not that we are logged in\n",
    "        resp = session.post(uriBase + requestLogin, data = siteCred)\n",
    "        if resp.status_code != 200:\n",
    "            raise MyError(resp, \"POST fail on login\")\n",
    "    \n",
    "        resp = session.get(uriBase + requestCmdAction + request_by_catno)\n",
    "        if resp.status_code != 200:\n",
    "            print(resp)\n",
    "            raise MyError(resp, \"GET fail on request for elset by catno\")\n",
    "    \n",
    "        retData = json.loads(resp.text)\n",
    "    \n",
    "        if print_els:\n",
    "            for e in retData:\n",
    "                print(e.keys())\n",
    "                for k in e.keys():\n",
    "                    print(f\"{k} = {e[k]}\")\n",
    "\n",
    "        session.close()\n",
    "    return retData[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "930e4134-c26d-4922-b1bb-bd26717943ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 10.  200.    5.  350.  180.   44.7]\n"
     ]
    }
   ],
   "source": [
    "def convert_angle_to_0_2pi_interval(angle):\n",
    "    new_angle = np.arctan2(np.sin(angle), np.cos(angle))\n",
    "    if new_angle < 0:\n",
    "        new_angle = abs(new_angle) + 2 * (np.pi - abs(new_angle))\n",
    "    return new_angle\n",
    "\n",
    "angles = [10, 200, 365, -10, -180, 404.7]\n",
    "print(np.rad2deg([convert_angle_to_0_2pi_interval(np.deg2rad(a)) for a in angles]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6ac0e6d0-a33c-49d2-9f85-14c12c64e5cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  10.  -160.     5.   -10.  -180.    44.7]\n"
     ]
    }
   ],
   "source": [
    "def convert_angle_to_plusminus_pi_interval(angle):\n",
    "    new_angle = np.arctan2(np.sin(angle), np.cos(angle))\n",
    "    if new_angle < -np.pi:\n",
    "        new_angle = abs(new_angle) + 2*np.pi\n",
    "    elif new_angle > np.pi:\n",
    "        new_angle = abs(new_angle) - 2*np.pi\n",
    "    return new_angle\n",
    "\n",
    "angles = [10, 200, 365, -10, -180, 404.7]\n",
    "print(np.rad2deg([convert_angle_to_plusminus_pi_interval(np.deg2rad(a)) for a in angles]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44941d12-81f8-412e-b3ba-9b5b4137d740",
   "metadata": {},
   "outputs": [],
   "source": [
    "def orbital_elements(epoch, a, incl, ecc, Omega, w, M, n, T):\n",
    "    return pd.Series(dict(epoch=epoch,\n",
    "                          semimajor_axis=a,\n",
    "                          inclination=incl,\n",
    "                          eccentricity=ecc,\n",
    "                          raan=Omega,\n",
    "                          arg_perigee=w, \n",
    "                          mean_anomaly=M, \n",
    "                          mean_motion=n, \n",
    "                          orbit_period=T\n",
    "                         ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e664cc47-c95b-4092-9415-cf1797c4e1ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['CCSDS_OMM_VERS', 'COMMENT', 'CREATION_DATE', 'ORIGINATOR', 'OBJECT_NAME', 'OBJECT_ID', 'CENTER_NAME', 'REF_FRAME', 'TIME_SYSTEM', 'MEAN_ELEMENT_THEORY', 'EPOCH', 'MEAN_MOTION', 'ECCENTRICITY', 'INCLINATION', 'RA_OF_ASC_NODE', 'ARG_OF_PERICENTER', 'MEAN_ANOMALY', 'EPHEMERIS_TYPE', 'CLASSIFICATION_TYPE', 'NORAD_CAT_ID', 'ELEMENT_SET_NO', 'REV_AT_EPOCH', 'BSTAR', 'MEAN_MOTION_DOT', 'MEAN_MOTION_DDOT', 'SEMIMAJOR_AXIS', 'PERIOD', 'APOAPSIS', 'PERIAPSIS', 'OBJECT_TYPE', 'RCS_SIZE', 'COUNTRY_CODE', 'LAUNCH_DATE', 'SITE', 'DECAY_DATE', 'FILE', 'GP_ID', 'TLE_LINE0', 'TLE_LINE1', 'TLE_LINE2'])\n",
      "CCSDS_OMM_VERS = 3.0\n",
      "COMMENT = GENERATED VIA SPACE-TRACK.ORG API\n",
      "CREATION_DATE = 2024-03-14T10:16:17\n",
      "ORIGINATOR = 18 SPCS\n",
      "OBJECT_NAME = ISS (ZARYA)\n",
      "OBJECT_ID = 1998-067A\n",
      "CENTER_NAME = EARTH\n",
      "REF_FRAME = TEME\n",
      "TIME_SYSTEM = UTC\n",
      "MEAN_ELEMENT_THEORY = SGP4\n",
      "EPOCH = 2024-03-14T09:53:09.051648\n",
      "MEAN_MOTION = 15.49907257\n",
      "ECCENTRICITY = 0.00061680\n",
      "INCLINATION = 51.6400\n",
      "RA_OF_ASC_NODE = 65.1219\n",
      "ARG_OF_PERICENTER = 5.0268\n",
      "MEAN_ANOMALY = 148.9614\n",
      "EPHEMERIS_TYPE = 0\n",
      "CLASSIFICATION_TYPE = U\n",
      "NORAD_CAT_ID = 25544\n",
      "ELEMENT_SET_NO = 999\n",
      "REV_AT_EPOCH = 44385\n",
      "BSTAR = 0.00026068000000\n",
      "MEAN_MOTION_DOT = 0.00014407\n",
      "MEAN_MOTION_DDOT = 0.0000000000000\n",
      "SEMIMAJOR_AXIS = 6795.134\n",
      "PERIOD = 92.909\n",
      "APOAPSIS = 421.190\n",
      "PERIAPSIS = 412.808\n",
      "OBJECT_TYPE = PAYLOAD\n",
      "RCS_SIZE = LARGE\n",
      "COUNTRY_CODE = ISS\n",
      "LAUNCH_DATE = 1998-11-20\n",
      "SITE = TTMTR\n",
      "DECAY_DATE = None\n",
      "FILE = 4224556\n",
      "GP_ID = 251617018\n",
      "TLE_LINE0 = 0 ISS (ZARYA)\n",
      "TLE_LINE1 = 1 25544U 98067A   24074.41191032  .00014407  00000-0  26068-3 0  9999\n",
      "TLE_LINE2 = 2 25544  51.6400  65.1219 0006168   5.0268 148.9614 15.49907257443850\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "epoch             2024-03-14 09:53:09.051648\n",
       "semimajor_axis                      6795.134\n",
       "inclination                         0.901288\n",
       "eccentricity                        0.000617\n",
       "raan                                1.136592\n",
       "arg_perigee                         0.087734\n",
       "mean_anomaly                        2.599867\n",
       "mean_motion                         0.001127\n",
       "orbit_period                         5574.54\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve orbital elements for Space Station from www.space-track.org\n",
    "e = elset_spacetrack('25544',True)\n",
    "\n",
    "epoch = pd.to_datetime(e['EPOCH'])\n",
    "semimajor_axis = float(e['SEMIMAJOR_AXIS']) # km\n",
    "inc = np.deg2rad(float(e['INCLINATION'])) # rad\n",
    "ecc = float(e['ECCENTRICITY']) # non-dimensional\n",
    "raan = np.deg2rad(float(e['RA_OF_ASC_NODE'])) # rad\n",
    "arg_perigee = np.deg2rad(float(e['ARG_OF_PERICENTER'])) # rad\n",
    "mean_anomaly = np.deg2rad(float(e['MEAN_ANOMALY'])) # rad\n",
    "mean_motion = float(e['MEAN_MOTION']) * 2*np.pi / 24 / 60 / 60 # rad/second\n",
    "orbit_period = float(e['PERIOD']) * 60 # seconds \n",
    "\n",
    "# print(epoch, semimajor_axis, inc, ecc, raan, arg_perigee, mean_anomaly, mean_motion, orbit_period)\n",
    "\n",
    "init_orb_ele = orbital_elements(epoch, semimajor_axis, inc, ecc, raan, arg_perigee, mean_anomaly, mean_motion, orbit_period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bd6aada7-a4a3-44d9-be46-d7f701d0d688",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-03-14 09:53:09.051648 6795.134 0.9012880257298718 0.0006168 1.1365915701544953 0.08773421083925095 2.5998668883802813 0.0011271243639668302 5574.54\n"
     ]
    }
   ],
   "source": [
    "print(epoch, semimajor_axis, inc, ecc, raan, arg_perigee, mean_anomaly, mean_motion, orbit_period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e864c62-9104-4fbb-94e9-c3331229afe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fixed values for testing\n",
    "init_orb_ele = orbital_elements(pd.to_datetime('2024-03-11 12:10:02.989056'), 6795.393, 0.9012950070468797, 0.000615, 1.3877636481382514, 6.187565698779825, 2.454901586038884, 0.0011270600202222941, 5574.84)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "532a930f-462e-4645-b638-dde7930bc105",
   "metadata": {},
   "outputs": [],
   "source": [
    "# solve Kepler's equation\n",
    "def keplers_equation(ecc, M, err_tol=1.0e-6, max_iter=10):\n",
    "    if M < np.pi:\n",
    "        E = M + ecc/2\n",
    "    else:\n",
    "        E = M - ecc/2\n",
    "    ratio = 0.1\n",
    "    # print(f\"init: {ecc=}, {M=}, {err_tol=}, {max_iter=}, {E=}, {ratio=}\")\n",
    "    for iter in range(0,max_iter+1):\n",
    "        # print(f\"{iter=}, {E=}\" )\n",
    "        f = E - ecc*np.sin(E) - M\n",
    "        f_prime = 1 - ecc*np.cos(E)\n",
    "        ratio = f/f_prime\n",
    "        E -= ratio\n",
    "        # print(f\"{f=}, {f_prime=}, {ratio=}, {E=}\")\n",
    "        if np.abs(ratio) < err_tol:\n",
    "            break\n",
    "    return E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1141f5a-c311-4978-8c69-3f15b78dd761",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tbp_rv_from_elements(mu, orbital_elements, epoch_t0, epoch_t):\n",
    "    \"\"\"\n",
    "    find r, v at epoch_t\n",
    "    \"\"\"\n",
    "    # get elements\n",
    "    epoch, a, incl, ecc, raan, w, M, n, _ = orbital_elements\n",
    "    T = 2*np.pi/n\n",
    "\n",
    "    # at epoch_t0, seconds since periapsis passage\n",
    "    tp = M/n\n",
    "    \n",
    "    # time of new elements since periapsis passage\n",
    "    t = ((epoch_t - epoch_t0) % T) + tp\n",
    "\n",
    "    # find elements at t seconds since periapsis\n",
    "    M_t = 2*np.pi*t/T\n",
    "    M_t = convert_angle_to_0_2pi_interval(M_t)\n",
    "    E_t = keplers_equation(ecc, M_t, err_tol=1e-8)\n",
    "    E_t = convert_angle_to_0_2pi_interval(E_t)\n",
    "    \n",
    "    theta = 2 * np.arctan( np.sqrt((1+ecc)/(1-ecc))*np.tan(E_t/2) )\n",
    "    theta = convert_angle_to_0_2pi_interval(theta)     \n",
    "    theta_deg = np.rad2deg(theta)\n",
    "\n",
    "    #define variable for sin and cos\n",
    "    ct = np.cos(theta)\n",
    "    st = np.sin(theta)\n",
    "    craan = np.cos(raan)\n",
    "    sraan = np.sin(raan)\n",
    "    ci = np.cos(incl)\n",
    "    si = np.sin(incl)\n",
    "    com = np.cos(w)\n",
    "    som = np.sin(w)\n",
    "    #calculations\n",
    "    p = a*(1-ecc**2)\n",
    "    h = np.sqrt(p*mu)\n",
    "    r = p/(1+ecc*np.cos(theta))\n",
    "    rp_vec = np.matrix([r*ct, r*st, 0])\n",
    "    vp_vec = np.matrix([((-mu/h)*st), (mu/h*(ecc+ct)), 0])\n",
    "    Cgp = np.matrix([[(com*craan-sraan*ci*som), (-craan*som-sraan*ci*com), sraan*si],\n",
    "                     [(sraan*com+craan*ci*som), (-sraan*som+craan*ci*com), -craan*si],\n",
    "                     [si*som, si*com, ci]])\n",
    "    r_vec = Cgp*np.transpose(rp_vec)\n",
    "    v_vec = Cgp*np.transpose(vp_vec)\n",
    "    # print(r_vec, r_vec.T, r_vec[0,0], r_vec[1,0])\n",
    "    return t, np.rad2deg(M_t), theta_deg, np.rad2deg(E_t), r_vec[0,0], r_vec[1,0], r_vec[2,0], v_vec[0,0], v_vec[1,0], v_vec[2,0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e3fa9a6e-6381-4732-ad0f-9ee433ff7827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2306.637112545632,\n",
       " 148.9614,\n",
       " 148.99781967425332,\n",
       " 148.97961224239364,\n",
       " -4247.726444498908,\n",
       " -4767.348940922894,\n",
       " 2334.925472730547,\n",
       " 2.4624601815432423,\n",
       " -4.839980677239864,\n",
       " -5.3952371858220145)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test at t=0\n",
    "tbp_rv_from_elements(mu_earth, init_orb_ele, 0.0, 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7052c0d6-b8d8-4ede-bbd9-90d2cfa0330b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def orbit(init_orb_ele, num_orbits=2, num_steps=100):\n",
    "    time_0 = init_orb_ele.epoch\n",
    "    t0 = 0.0 # seconds from epoch\n",
    "    orbit_period = init_orb_ele.orbit_period  # seconds\n",
    "    time_f = time_0 + pd.to_timedelta(num_orbits*orbit_period, unit='seconds')\n",
    "    time_step = (time_f - time_0)/(num_orbits*num_steps)\n",
    "    print(f\"{time_0=}, {time_f=}, {time_step=}\")\n",
    "\n",
    "    # initialize orbit DataFrame\n",
    "    orbit = pd.DataFrame(dict(t=np.arange(t0,num_orbits*orbit_period,time_step.total_seconds())), \n",
    "                            index=pd.date_range(start=time_0, end=time_f, periods=(num_orbits*num_steps+1)))\n",
    "\n",
    "    orbit['tp'], orbit['M'], orbit['theta'], orbit['E'], orbit['r_x'], orbit['r_y'], orbit['r_z'], orbit['v_x'], orbit['v_y'], orbit['v_z'] = zip(*orbit.apply(lambda r: tbp_rv_from_elements(mu_earth, init_orb_ele, t0, r.t), axis=1))\n",
    "    \n",
    "    return orbit\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "fff27357-c832-436a-8fbd-c310f934c696",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time_0=Timestamp('2024-03-14 09:53:09.051648'), time_f=Timestamp('2024-03-15 01:22:14.451648'), time_step=Timedelta('0 days 00:00:55.745400')\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>tp</th>\n",
       "      <th>M</th>\n",
       "      <th>theta</th>\n",
       "      <th>E</th>\n",
       "      <th>r_x</th>\n",
       "      <th>r_y</th>\n",
       "      <th>r_z</th>\n",
       "      <th>v_x</th>\n",
       "      <th>v_y</th>\n",
       "      <th>v_z</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-14 09:53:09.051648</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>2306.637113</td>\n",
       "      <td>148.961400</td>\n",
       "      <td>148.997820</td>\n",
       "      <td>148.979612</td>\n",
       "      <td>-4247.726444</td>\n",
       "      <td>-4767.348941</td>\n",
       "      <td>2334.925473</td>\n",
       "      <td>2.462460</td>\n",
       "      <td>-4.839981</td>\n",
       "      <td>-5.395237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-14 09:54:04.797048</th>\n",
       "      <td>55.7454</td>\n",
       "      <td>2362.382513</td>\n",
       "      <td>152.561408</td>\n",
       "      <td>152.593955</td>\n",
       "      <td>152.577684</td>\n",
       "      <td>-4102.177257</td>\n",
       "      <td>-5027.586149</td>\n",
       "      <td>2029.763283</td>\n",
       "      <td>2.757748</td>\n",
       "      <td>-4.493590</td>\n",
       "      <td>-5.549591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-14 09:55:00.542448</th>\n",
       "      <td>111.4908</td>\n",
       "      <td>2418.127913</td>\n",
       "      <td>156.161417</td>\n",
       "      <td>156.189963</td>\n",
       "      <td>156.175692</td>\n",
       "      <td>-3940.465115</td>\n",
       "      <td>-5268.014221</td>\n",
       "      <td>1716.603631</td>\n",
       "      <td>3.042155</td>\n",
       "      <td>-4.129514</td>\n",
       "      <td>-5.682072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-14 09:55:56.287848</th>\n",
       "      <td>167.2362</td>\n",
       "      <td>2473.873313</td>\n",
       "      <td>159.761425</td>\n",
       "      <td>159.785858</td>\n",
       "      <td>159.773643</td>\n",
       "      <td>-3763.227956</td>\n",
       "      <td>-5487.686888</td>\n",
       "      <td>1396.680735</td>\n",
       "      <td>3.314563</td>\n",
       "      <td>-3.749185</td>\n",
       "      <td>-5.792160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-14 09:56:52.033248</th>\n",
       "      <td>222.9816</td>\n",
       "      <td>2529.618713</td>\n",
       "      <td>163.361433</td>\n",
       "      <td>163.381656</td>\n",
       "      <td>163.371546</td>\n",
       "      <td>-3571.164723</td>\n",
       "      <td>-5685.739603</td>\n",
       "      <td>1071.255297</td>\n",
       "      <td>3.573902</td>\n",
       "      <td>-3.354101</td>\n",
       "      <td>-5.879424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15 01:18:31.470048</th>\n",
       "      <td>55522.4184</td>\n",
       "      <td>7658.311651</td>\n",
       "      <td>134.569700</td>\n",
       "      <td>134.620025</td>\n",
       "      <td>134.594865</td>\n",
       "      <td>-4657.649252</td>\n",
       "      <td>-3550.656200</td>\n",
       "      <td>3451.519923</td>\n",
       "      <td>1.197015</td>\n",
       "      <td>-6.021950</td>\n",
       "      <td>-4.572983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15 01:19:27.215448</th>\n",
       "      <td>55578.1638</td>\n",
       "      <td>7714.057051</td>\n",
       "      <td>138.169709</td>\n",
       "      <td>138.216820</td>\n",
       "      <td>138.193267</td>\n",
       "      <td>-4581.786349</td>\n",
       "      <td>-3879.134524</td>\n",
       "      <td>3189.962841</td>\n",
       "      <td>1.523850</td>\n",
       "      <td>-5.759129</td>\n",
       "      <td>-4.807919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15 01:20:22.960848</th>\n",
       "      <td>55633.9092</td>\n",
       "      <td>7769.802451</td>\n",
       "      <td>141.769717</td>\n",
       "      <td>141.813429</td>\n",
       "      <td>141.791576</td>\n",
       "      <td>-4487.866042</td>\n",
       "      <td>-4192.324705</td>\n",
       "      <td>2915.833698</td>\n",
       "      <td>1.844655</td>\n",
       "      <td>-5.473632</td>\n",
       "      <td>-5.023888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15 01:21:18.706248</th>\n",
       "      <td>55689.6546</td>\n",
       "      <td>7825.547851</td>\n",
       "      <td>145.369725</td>\n",
       "      <td>145.409866</td>\n",
       "      <td>145.389798</td>\n",
       "      <td>-4376.259801</td>\n",
       "      <td>-4488.993651</td>\n",
       "      <td>2630.213730</td>\n",
       "      <td>2.158168</td>\n",
       "      <td>-5.166585</td>\n",
       "      <td>-5.220045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15 01:22:14.451648</th>\n",
       "      <td>55745.4000</td>\n",
       "      <td>2306.766155</td>\n",
       "      <td>148.969734</td>\n",
       "      <td>149.006144</td>\n",
       "      <td>148.987941</td>\n",
       "      <td>-4247.408637</td>\n",
       "      <td>-4767.973455</td>\n",
       "      <td>2334.229232</td>\n",
       "      <td>2.463155</td>\n",
       "      <td>-4.839200</td>\n",
       "      <td>-5.395619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     t           tp           M       theta  \\\n",
       "2024-03-14 09:53:09.051648      0.0000  2306.637113  148.961400  148.997820   \n",
       "2024-03-14 09:54:04.797048     55.7454  2362.382513  152.561408  152.593955   \n",
       "2024-03-14 09:55:00.542448    111.4908  2418.127913  156.161417  156.189963   \n",
       "2024-03-14 09:55:56.287848    167.2362  2473.873313  159.761425  159.785858   \n",
       "2024-03-14 09:56:52.033248    222.9816  2529.618713  163.361433  163.381656   \n",
       "...                                ...          ...         ...         ...   \n",
       "2024-03-15 01:18:31.470048  55522.4184  7658.311651  134.569700  134.620025   \n",
       "2024-03-15 01:19:27.215448  55578.1638  7714.057051  138.169709  138.216820   \n",
       "2024-03-15 01:20:22.960848  55633.9092  7769.802451  141.769717  141.813429   \n",
       "2024-03-15 01:21:18.706248  55689.6546  7825.547851  145.369725  145.409866   \n",
       "2024-03-15 01:22:14.451648  55745.4000  2306.766155  148.969734  149.006144   \n",
       "\n",
       "                                     E          r_x          r_y          r_z  \\\n",
       "2024-03-14 09:53:09.051648  148.979612 -4247.726444 -4767.348941  2334.925473   \n",
       "2024-03-14 09:54:04.797048  152.577684 -4102.177257 -5027.586149  2029.763283   \n",
       "2024-03-14 09:55:00.542448  156.175692 -3940.465115 -5268.014221  1716.603631   \n",
       "2024-03-14 09:55:56.287848  159.773643 -3763.227956 -5487.686888  1396.680735   \n",
       "2024-03-14 09:56:52.033248  163.371546 -3571.164723 -5685.739603  1071.255297   \n",
       "...                                ...          ...          ...          ...   \n",
       "2024-03-15 01:18:31.470048  134.594865 -4657.649252 -3550.656200  3451.519923   \n",
       "2024-03-15 01:19:27.215448  138.193267 -4581.786349 -3879.134524  3189.962841   \n",
       "2024-03-15 01:20:22.960848  141.791576 -4487.866042 -4192.324705  2915.833698   \n",
       "2024-03-15 01:21:18.706248  145.389798 -4376.259801 -4488.993651  2630.213730   \n",
       "2024-03-15 01:22:14.451648  148.987941 -4247.408637 -4767.973455  2334.229232   \n",
       "\n",
       "                                 v_x       v_y       v_z  \n",
       "2024-03-14 09:53:09.051648  2.462460 -4.839981 -5.395237  \n",
       "2024-03-14 09:54:04.797048  2.757748 -4.493590 -5.549591  \n",
       "2024-03-14 09:55:00.542448  3.042155 -4.129514 -5.682072  \n",
       "2024-03-14 09:55:56.287848  3.314563 -3.749185 -5.792160  \n",
       "2024-03-14 09:56:52.033248  3.573902 -3.354101 -5.879424  \n",
       "...                              ...       ...       ...  \n",
       "2024-03-15 01:18:31.470048  1.197015 -6.021950 -4.572983  \n",
       "2024-03-15 01:19:27.215448  1.523850 -5.759129 -4.807919  \n",
       "2024-03-15 01:20:22.960848  1.844655 -5.473632 -5.023888  \n",
       "2024-03-15 01:21:18.706248  2.158168 -5.166585 -5.220045  \n",
       "2024-03-15 01:22:14.451648  2.463155 -4.839200 -5.395619  \n",
       "\n",
       "[1001 rows x 11 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orb = orbit(init_orb_ele, num_orbits=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b65ec056-340f-4111-9339-b094531fb587",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c55add67-1a49-4e06-bb95-23fbd2f7bab0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "                            \n",
       "var gd = document.getElementById('d2583b32-ebe9-4400-97e4-3eb9cba9edf1');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
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       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure(\n",
    "    data=[go.Scatter3d(\n",
    "        x=orb.r_x,\n",
    "        y=orb.r_y,\n",
    "        z=orb.r_z,\n",
    "        mode='markers',\n",
    "        marker=dict(\n",
    "            size=2\n",
    "        )\n",
    "    )\n",
    "         ]\n",
    "    )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4cb82bc5-353f-4cfe-bf31-f25fcef769c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import copysign, trunc\n",
    "\n",
    "def julian_date( year, month, day, ut ):\n",
    "    jd = 367*year - trunc((7*(year+trunc((month+9)/12)))/4) + trunc((275*month)/9) + day + 1721013.5 + ut/24 - \\\n",
    "        0.5*copysign(1.0,100*year+month-190002.5) + 0.5\n",
    "    return jd\n",
    "\n",
    "def decimal_hour(h, m, s):\n",
    "    return h + m/60 + s/60/60\n",
    "\n",
    "def hms(dh):\n",
    "    mm,ss = divmod(abs(dh)*3600, 60)\n",
    "    hh,mm = divmod(mm, 60)\n",
    "    return hh,mm,ss\n",
    "\n",
    "def gmst(jd0, h):\n",
    "    dut = jd0 - 2451545.0 \n",
    "    # gmst = (18.697375 + 24.065709824279*dut) % 24\n",
    "    gmst = (6.697375 + 0.065709824279*dut + 1.0027379*h) % 24\n",
    "    return gmst\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "04a0696f-3abb-40ad-82cd-a77f15fad193",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-03-14 09:53:09.051648 <class 'pandas._libs.tslibs.timestamps.Timestamp'>\n"
     ]
    }
   ],
   "source": [
    "print(epoch, type(epoch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "91a4313e-add5-4141-966a-38e7649db6c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024 3 14 9 53 9.051648\n"
     ]
    }
   ],
   "source": [
    "print(epoch.date().year, epoch.date().month, epoch.date().day, epoch.hour, epoch.minute, epoch.second+(epoch.microsecond/1000000.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "25160437-2670-458d-8ada-d3ebb0e2083c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t1=9.88584768, t2=(9.0, 53.0, 9.051648000000569)\n"
     ]
    }
   ],
   "source": [
    "t1 = decimal_hour(epoch.hour,epoch.minute,epoch.second+(epoch.microsecond/1000000.0))\n",
    "t2 = hms(t1)\n",
    "\n",
    "print(f\"{t1=}, {t2=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "0d6bd268-b871-45a6-8114-234f06823bb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "julian_date(2024,3,11,0.0)=2460380.5\n",
      "decimal_hour(12,10,2.989056)=12.16749696\n",
      "g0=23.47733776703126, hms(g0)=(23.0, 28.0, 38.41596131253755)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(23.0, 28.0, 38.41596131253755)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EPOCH = 2024-03-11T12:10:02.989056\n",
    "# g0 is the the GMST at EPOCH\n",
    "print(f\"{julian_date(2024,3,11,0.0)=}\")\n",
    "print(f\"{decimal_hour(12,10,2.989056)=}\")\n",
    "g0 = gmst(julian_date(2024,3,11,0.0),decimal_hour(12,10,2.989056))\n",
    "print(f\"{g0=}, {hms(g0)=}\")\n",
    "hms(g0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "167422ef-b8aa-44d4-8029-c66e7e9ec4ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.386571032304573"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g0 = gmst(julian_date(epoch.date().year, epoch.date().month, epoch.date().day,0.0),decimal_hour(epoch.hour, epoch.minute, epoch.second+(epoch.microsecond/1000000.0)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "0faba027-7650-4b93-b4e3-f235c93263ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "320.7985654845686"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g0*15.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "eb710a0f-4584-4d51-ab11-06c1dfc18b13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.59899120338036"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.deg2rad(g0*15.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1da97501-effb-4cc9-9749-f62ea54f7c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "def earth_rot(gmst0, t):\n",
    "    \"\"\"\n",
    "    returns earth rotation angle since epoch\n",
    "    parameters:\n",
    "        gmst0: the the GMST at EPOCH\n",
    "        t: time in seconds since EPOCH\n",
    "    \"\"\"\n",
    "    return np.deg2rad(gmst0*15.0) + w_earth*(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5c2417a6-de3a-4169-8413-6b89143c1b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "orb['theta_earth'] = orb.apply(lambda r: convert_angle_to_0_2pi_interval(earth_rot(g0, r.t)), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2c5d8140-a47e-4418-970a-0bd65da9f8e8",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>tp</th>\n",
       "      <th>M</th>\n",
       "      <th>theta</th>\n",
       "      <th>E</th>\n",
       "      <th>r_x</th>\n",
       "      <th>r_y</th>\n",
       "      <th>r_z</th>\n",
       "      <th>v_x</th>\n",
       "      <th>v_y</th>\n",
       "      <th>v_z</th>\n",
       "      <th>theta_earth</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>2024-03-14 09:53:09.051648</th>\n",
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       "      <th>2024-03-14 09:54:04.797048</th>\n",
       "      <td>55.7454</td>\n",
       "      <td>2362.382513</td>\n",
       "      <td>152.561408</td>\n",
       "      <td>152.593955</td>\n",
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       "      <td>-4102.177257</td>\n",
       "      <td>-5027.586149</td>\n",
       "      <td>2029.763283</td>\n",
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       "      <th>2024-03-14 09:55:00.542448</th>\n",
       "      <td>111.4908</td>\n",
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       "      <td>3.042155</td>\n",
       "      <td>-4.129514</td>\n",
       "      <td>-5.682072</td>\n",
       "      <td>5.607121</td>\n",
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       "    <tr>\n",
       "      <th>2024-03-14 09:55:56.287848</th>\n",
       "      <td>167.2362</td>\n",
       "      <td>2473.873313</td>\n",
       "      <td>159.761425</td>\n",
       "      <td>159.785858</td>\n",
       "      <td>159.773643</td>\n",
       "      <td>-3763.227956</td>\n",
       "      <td>-5487.686888</td>\n",
       "      <td>1396.680735</td>\n",
       "      <td>3.314563</td>\n",
       "      <td>-3.749185</td>\n",
       "      <td>-5.792160</td>\n",
       "      <td>5.611186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-14 09:56:52.033248</th>\n",
       "      <td>222.9816</td>\n",
       "      <td>2529.618713</td>\n",
       "      <td>163.361433</td>\n",
       "      <td>163.381656</td>\n",
       "      <td>163.371546</td>\n",
       "      <td>-3571.164723</td>\n",
       "      <td>-5685.739603</td>\n",
       "      <td>1071.255297</td>\n",
       "      <td>3.573902</td>\n",
       "      <td>-3.354101</td>\n",
       "      <td>-5.879424</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2024-03-15 01:18:31.470048</th>\n",
       "      <td>55522.4184</td>\n",
       "      <td>7658.311651</td>\n",
       "      <td>134.569700</td>\n",
       "      <td>134.620025</td>\n",
       "      <td>134.594865</td>\n",
       "      <td>-4657.649252</td>\n",
       "      <td>-3550.656200</td>\n",
       "      <td>3451.519923</td>\n",
       "      <td>1.197015</td>\n",
       "      <td>-6.021950</td>\n",
       "      <td>-4.572983</td>\n",
       "      <td>3.364565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15 01:19:27.215448</th>\n",
       "      <td>55578.1638</td>\n",
       "      <td>7714.057051</td>\n",
       "      <td>138.169709</td>\n",
       "      <td>138.216820</td>\n",
       "      <td>138.193267</td>\n",
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       "      <td>-3879.134524</td>\n",
       "      <td>3189.962841</td>\n",
       "      <td>1.523850</td>\n",
       "      <td>-5.759129</td>\n",
       "      <td>-4.807919</td>\n",
       "      <td>3.368630</td>\n",
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       "    <tr>\n",
       "      <th>2024-03-15 01:20:22.960848</th>\n",
       "      <td>55633.9092</td>\n",
       "      <td>7769.802451</td>\n",
       "      <td>141.769717</td>\n",
       "      <td>141.813429</td>\n",
       "      <td>141.791576</td>\n",
       "      <td>-4487.866042</td>\n",
       "      <td>-4192.324705</td>\n",
       "      <td>2915.833698</td>\n",
       "      <td>1.844655</td>\n",
       "      <td>-5.473632</td>\n",
       "      <td>-5.023888</td>\n",
       "      <td>3.372695</td>\n",
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       "    <tr>\n",
       "      <th>2024-03-15 01:21:18.706248</th>\n",
       "      <td>55689.6546</td>\n",
       "      <td>7825.547851</td>\n",
       "      <td>145.369725</td>\n",
       "      <td>145.409866</td>\n",
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       "      <td>-4488.993651</td>\n",
       "      <td>2630.213730</td>\n",
       "      <td>2.158168</td>\n",
       "      <td>-5.166585</td>\n",
       "      <td>-5.220045</td>\n",
       "      <td>3.376760</td>\n",
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       "    <tr>\n",
       "      <th>2024-03-15 01:22:14.451648</th>\n",
       "      <td>55745.4000</td>\n",
       "      <td>2306.766155</td>\n",
       "      <td>148.969734</td>\n",
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       "      <td>-4247.408637</td>\n",
       "      <td>-4767.973455</td>\n",
       "      <td>2334.229232</td>\n",
       "      <td>2.463155</td>\n",
       "      <td>-4.839200</td>\n",
       "      <td>-5.395619</td>\n",
       "      <td>3.380825</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     t           tp           M       theta  \\\n",
       "2024-03-14 09:53:09.051648      0.0000  2306.637113  148.961400  148.997820   \n",
       "2024-03-14 09:54:04.797048     55.7454  2362.382513  152.561408  152.593955   \n",
       "2024-03-14 09:55:00.542448    111.4908  2418.127913  156.161417  156.189963   \n",
       "2024-03-14 09:55:56.287848    167.2362  2473.873313  159.761425  159.785858   \n",
       "2024-03-14 09:56:52.033248    222.9816  2529.618713  163.361433  163.381656   \n",
       "...                                ...          ...         ...         ...   \n",
       "2024-03-15 01:18:31.470048  55522.4184  7658.311651  134.569700  134.620025   \n",
       "2024-03-15 01:19:27.215448  55578.1638  7714.057051  138.169709  138.216820   \n",
       "2024-03-15 01:20:22.960848  55633.9092  7769.802451  141.769717  141.813429   \n",
       "2024-03-15 01:21:18.706248  55689.6546  7825.547851  145.369725  145.409866   \n",
       "2024-03-15 01:22:14.451648  55745.4000  2306.766155  148.969734  149.006144   \n",
       "\n",
       "                                     E          r_x          r_y          r_z  \\\n",
       "2024-03-14 09:53:09.051648  148.979612 -4247.726444 -4767.348941  2334.925473   \n",
       "2024-03-14 09:54:04.797048  152.577684 -4102.177257 -5027.586149  2029.763283   \n",
       "2024-03-14 09:55:00.542448  156.175692 -3940.465115 -5268.014221  1716.603631   \n",
       "2024-03-14 09:55:56.287848  159.773643 -3763.227956 -5487.686888  1396.680735   \n",
       "2024-03-14 09:56:52.033248  163.371546 -3571.164723 -5685.739603  1071.255297   \n",
       "...                                ...          ...          ...          ...   \n",
       "2024-03-15 01:18:31.470048  134.594865 -4657.649252 -3550.656200  3451.519923   \n",
       "2024-03-15 01:19:27.215448  138.193267 -4581.786349 -3879.134524  3189.962841   \n",
       "2024-03-15 01:20:22.960848  141.791576 -4487.866042 -4192.324705  2915.833698   \n",
       "2024-03-15 01:21:18.706248  145.389798 -4376.259801 -4488.993651  2630.213730   \n",
       "2024-03-15 01:22:14.451648  148.987941 -4247.408637 -4767.973455  2334.229232   \n",
       "\n",
       "                                 v_x       v_y       v_z  theta_earth  \n",
       "2024-03-14 09:53:09.051648  2.462460 -4.839981 -5.395237     5.598991  \n",
       "2024-03-14 09:54:04.797048  2.757748 -4.493590 -5.549591     5.603056  \n",
       "2024-03-14 09:55:00.542448  3.042155 -4.129514 -5.682072     5.607121  \n",
       "2024-03-14 09:55:56.287848  3.314563 -3.749185 -5.792160     5.611186  \n",
       "2024-03-14 09:56:52.033248  3.573902 -3.354101 -5.879424     5.615251  \n",
       "...                              ...       ...       ...          ...  \n",
       "2024-03-15 01:18:31.470048  1.197015 -6.021950 -4.572983     3.364565  \n",
       "2024-03-15 01:19:27.215448  1.523850 -5.759129 -4.807919     3.368630  \n",
       "2024-03-15 01:20:22.960848  1.844655 -5.473632 -5.023888     3.372695  \n",
       "2024-03-15 01:21:18.706248  2.158168 -5.166585 -5.220045     3.376760  \n",
       "2024-03-15 01:22:14.451648  2.463155 -4.839200 -5.395619     3.380825  \n",
       "\n",
       "[1001 rows x 12 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c6ff7581-4233-458d-a283-b6b1efb9225e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def R3(theta):\n",
    "    c = np.cos(theta)\n",
    "    s = np.sin(theta)\n",
    "    return np.matrix([[ c, s, 0],\n",
    "                      [-s, c, 0],\n",
    "                      [0, 0, 1]])\n",
    "\n",
    "def eci2ecef(vec_eci,gst):\n",
    "    r_vec = R3(gst)@np.transpose(vec_eci)\n",
    "    return r_vec[0,0], r_vec[1,0], r_vec[2,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "af0006e7-cb51-4d64-af57-4a3fef086e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "orb['r_x_ecef'], orb['r_y_ecef'], orb['r_z_ecef'] = zip(*orb.apply(lambda r: eci2ecef(np.matrix([r.r_x, r.r_y, r.r_z]), r.theta_earth), axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1fc73874-d130-485b-8dd2-9ca5c393cbbb",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2024-03-15 01:21:18.706248</th>\n",
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       "      <td>149.006144</td>\n",
       "      <td>148.987941</td>\n",
       "      <td>-4247.408637</td>\n",
       "      <td>-4767.973455</td>\n",
       "      <td>2334.229232</td>\n",
       "      <td>2.463155</td>\n",
       "      <td>-4.839200</td>\n",
       "      <td>-5.395619</td>\n",
       "      <td>3.380825</td>\n",
       "      <td>5256.247627</td>\n",
       "      <td>3625.729152</td>\n",
       "      <td>2334.229232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     t           tp           M       theta  \\\n",
       "2024-03-14 09:53:09.051648      0.0000  2306.637113  148.961400  148.997820   \n",
       "2024-03-14 09:54:04.797048     55.7454  2362.382513  152.561408  152.593955   \n",
       "2024-03-14 09:55:00.542448    111.4908  2418.127913  156.161417  156.189963   \n",
       "2024-03-14 09:55:56.287848    167.2362  2473.873313  159.761425  159.785858   \n",
       "2024-03-14 09:56:52.033248    222.9816  2529.618713  163.361433  163.381656   \n",
       "...                                ...          ...         ...         ...   \n",
       "2024-03-15 01:18:31.470048  55522.4184  7658.311651  134.569700  134.620025   \n",
       "2024-03-15 01:19:27.215448  55578.1638  7714.057051  138.169709  138.216820   \n",
       "2024-03-15 01:20:22.960848  55633.9092  7769.802451  141.769717  141.813429   \n",
       "2024-03-15 01:21:18.706248  55689.6546  7825.547851  145.369725  145.409866   \n",
       "2024-03-15 01:22:14.451648  55745.4000  2306.766155  148.969734  149.006144   \n",
       "\n",
       "                                     E          r_x          r_y          r_z  \\\n",
       "2024-03-14 09:53:09.051648  148.979612 -4247.726444 -4767.348941  2334.925473   \n",
       "2024-03-14 09:54:04.797048  152.577684 -4102.177257 -5027.586149  2029.763283   \n",
       "2024-03-14 09:55:00.542448  156.175692 -3940.465115 -5268.014221  1716.603631   \n",
       "2024-03-14 09:55:56.287848  159.773643 -3763.227956 -5487.686888  1396.680735   \n",
       "2024-03-14 09:56:52.033248  163.371546 -3571.164723 -5685.739603  1071.255297   \n",
       "...                                ...          ...          ...          ...   \n",
       "2024-03-15 01:18:31.470048  134.594865 -4657.649252 -3550.656200  3451.519923   \n",
       "2024-03-15 01:19:27.215448  138.193267 -4581.786349 -3879.134524  3189.962841   \n",
       "2024-03-15 01:20:22.960848  141.791576 -4487.866042 -4192.324705  2915.833698   \n",
       "2024-03-15 01:21:18.706248  145.389798 -4376.259801 -4488.993651  2630.213730   \n",
       "2024-03-15 01:22:14.451648  148.987941 -4247.408637 -4767.973455  2334.229232   \n",
       "\n",
       "                                 v_x       v_y       v_z  theta_earth  \\\n",
       "2024-03-14 09:53:09.051648  2.462460 -4.839981 -5.395237     5.598991   \n",
       "2024-03-14 09:54:04.797048  2.757748 -4.493590 -5.549591     5.603056   \n",
       "2024-03-14 09:55:00.542448  3.042155 -4.129514 -5.682072     5.607121   \n",
       "2024-03-14 09:55:56.287848  3.314563 -3.749185 -5.792160     5.611186   \n",
       "2024-03-14 09:56:52.033248  3.573902 -3.354101 -5.879424     5.615251   \n",
       "...                              ...       ...       ...          ...   \n",
       "2024-03-15 01:18:31.470048  1.197015 -6.021950 -4.572983     3.364565   \n",
       "2024-03-15 01:19:27.215448  1.523850 -5.759129 -4.807919     3.368630   \n",
       "2024-03-15 01:20:22.960848  1.844655 -5.473632 -5.023888     3.372695   \n",
       "2024-03-15 01:21:18.706248  2.158168 -5.166585 -5.220045     3.376760   \n",
       "2024-03-15 01:22:14.451648  2.463155 -4.839200 -5.395619     3.380825   \n",
       "\n",
       "                               r_x_ecef     r_y_ecef     r_z_ecef  \n",
       "2024-03-14 09:53:09.051648  -278.488257 -6379.125345  2334.925473  \n",
       "2024-03-14 09:54:04.797048   -27.592429 -6488.737889  2029.763283  \n",
       "2024-03-14 09:55:00.542448   222.615724 -6574.928242  1716.603631  \n",
       "2024-03-14 09:55:56.287848   471.346357 -6637.350725  1396.680735  \n",
       "2024-03-14 09:56:52.033248   717.817967 -6675.746376  1071.255297  \n",
       "...                                 ...          ...          ...  \n",
       "2024-03-15 01:18:31.470048  5327.501072  2432.814899  3451.519923  \n",
       "2024-03-15 01:19:27.215448  5337.368205  2748.263351  3189.962841  \n",
       "2024-03-15 01:20:22.960848  5328.808874  3052.920574  2915.833698  \n",
       "2024-03-15 01:21:18.706248  5301.766254  3345.741835  2630.213730  \n",
       "2024-03-15 01:22:14.451648  5256.247627  3625.729152  2334.229232  \n",
       "\n",
       "[1001 rows x 15 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "c2a0bf85-2495-4364-8566-7283e8f833d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vec_to_latlon(vec):\n",
    "    r = vector_mag(vec)\n",
    "    l = vec.x/r\n",
    "    m = vec.y/r\n",
    "    n = vec.z/r\n",
    "    lat = np.arcsin(n)\n",
    "    if m>0:\n",
    "        lon = np.arccos(l/np.cos(lat))\n",
    "    else:\n",
    "        lon = convert_angle_to_plusminus_pi_interval(2*np.pi - np.arccos(l/np.cos(lat)))\n",
    "    \n",
    "    return np.rad2deg(lon), np.rad2deg(lat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6918da02-37a9-47ae-bfe3-fb11bdafd3c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-161.61629962451386 33.12454287112769\n"
     ]
    }
   ],
   "source": [
    "# Curtis, Example 4.1, page 187\n",
    "r_vec = Vector(-5368,-1784, 3691)\n",
    "lon, lat = vec_to_latlon(r_vec)\n",
    "print(lon,lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "54f3255e-4df6-4226-9236-2a2cdf244afc",
   "metadata": {},
   "outputs": [],
   "source": [
    "orb['gt_lon'], orb['gt_lat'] = zip(*orb.apply(lambda r: vec_to_latlon(Vector(r.r_x_ecef, r.r_y_ecef, r.r_z_ecef)), axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a97a139f-b7e0-4214-a34e-2785711ef51d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>tp</th>\n",
       "      <th>M</th>\n",
       "      <th>theta</th>\n",
       "      <th>E</th>\n",
       "      <th>r_x</th>\n",
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       "      <th>theta_earth</th>\n",
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       "      <th>2024-03-14 09:53:09.051648</th>\n",
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       "      <th>2024-03-14 09:54:04.797048</th>\n",
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       "      <th>2024-03-14 09:55:00.542448</th>\n",
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       "      <td>-5.682072</td>\n",
       "      <td>5.607121</td>\n",
       "      <td>222.615724</td>\n",
       "      <td>-6574.928242</td>\n",
       "      <td>1716.603631</td>\n",
       "      <td>-88.060805</td>\n",
       "      <td>14.624316</td>\n",
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       "    <tr>\n",
       "      <th>2024-03-14 09:55:56.287848</th>\n",
       "      <td>167.2362</td>\n",
       "      <td>2473.873313</td>\n",
       "      <td>159.761425</td>\n",
       "      <td>159.785858</td>\n",
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       "      <td>1071.255297</td>\n",
       "      <td>-83.862780</td>\n",
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       "      <th>2024-03-15 01:18:31.470048</th>\n",
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       "    <tr>\n",
       "      <th>2024-03-15 01:19:27.215448</th>\n",
       "      <td>55578.1638</td>\n",
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       "      <td>-3879.134524</td>\n",
       "      <td>3189.962841</td>\n",
       "      <td>1.523850</td>\n",
       "      <td>-5.759129</td>\n",
       "      <td>-4.807919</td>\n",
       "      <td>3.368630</td>\n",
       "      <td>5337.368205</td>\n",
       "      <td>2748.263351</td>\n",
       "      <td>3189.962841</td>\n",
       "      <td>27.244384</td>\n",
       "      <td>27.984480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15 01:20:22.960848</th>\n",
       "      <td>55633.9092</td>\n",
       "      <td>7769.802451</td>\n",
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       "      <td>141.813429</td>\n",
       "      <td>141.791576</td>\n",
       "      <td>-4487.866042</td>\n",
       "      <td>-4192.324705</td>\n",
       "      <td>2915.833698</td>\n",
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       "      <td>-5.473632</td>\n",
       "      <td>-5.023888</td>\n",
       "      <td>3.372695</td>\n",
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       "    <tr>\n",
       "      <th>2024-03-15 01:21:18.706248</th>\n",
       "      <td>55689.6546</td>\n",
       "      <td>7825.547851</td>\n",
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       "      <td>145.389798</td>\n",
       "      <td>-4376.259801</td>\n",
       "      <td>-4488.993651</td>\n",
       "      <td>2630.213730</td>\n",
       "      <td>2.158168</td>\n",
       "      <td>-5.166585</td>\n",
       "      <td>-5.220045</td>\n",
       "      <td>3.376760</td>\n",
       "      <td>5301.766254</td>\n",
       "      <td>3345.741835</td>\n",
       "      <td>2630.213730</td>\n",
       "      <td>32.254456</td>\n",
       "      <td>22.760299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15 01:22:14.451648</th>\n",
       "      <td>55745.4000</td>\n",
       "      <td>2306.766155</td>\n",
       "      <td>148.969734</td>\n",
       "      <td>149.006144</td>\n",
       "      <td>148.987941</td>\n",
       "      <td>-4247.408637</td>\n",
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       "      <td>2334.229232</td>\n",
       "      <td>2.463155</td>\n",
       "      <td>-4.839200</td>\n",
       "      <td>-5.395619</td>\n",
       "      <td>3.380825</td>\n",
       "      <td>5256.247627</td>\n",
       "      <td>3625.729152</td>\n",
       "      <td>2334.229232</td>\n",
       "      <td>34.597687</td>\n",
       "      <td>20.080089</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     t           tp           M       theta  \\\n",
       "2024-03-14 09:53:09.051648      0.0000  2306.637113  148.961400  148.997820   \n",
       "2024-03-14 09:54:04.797048     55.7454  2362.382513  152.561408  152.593955   \n",
       "2024-03-14 09:55:00.542448    111.4908  2418.127913  156.161417  156.189963   \n",
       "2024-03-14 09:55:56.287848    167.2362  2473.873313  159.761425  159.785858   \n",
       "2024-03-14 09:56:52.033248    222.9816  2529.618713  163.361433  163.381656   \n",
       "...                                ...          ...         ...         ...   \n",
       "2024-03-15 01:18:31.470048  55522.4184  7658.311651  134.569700  134.620025   \n",
       "2024-03-15 01:19:27.215448  55578.1638  7714.057051  138.169709  138.216820   \n",
       "2024-03-15 01:20:22.960848  55633.9092  7769.802451  141.769717  141.813429   \n",
       "2024-03-15 01:21:18.706248  55689.6546  7825.547851  145.369725  145.409866   \n",
       "2024-03-15 01:22:14.451648  55745.4000  2306.766155  148.969734  149.006144   \n",
       "\n",
       "                                     E          r_x          r_y          r_z  \\\n",
       "2024-03-14 09:53:09.051648  148.979612 -4247.726444 -4767.348941  2334.925473   \n",
       "2024-03-14 09:54:04.797048  152.577684 -4102.177257 -5027.586149  2029.763283   \n",
       "2024-03-14 09:55:00.542448  156.175692 -3940.465115 -5268.014221  1716.603631   \n",
       "2024-03-14 09:55:56.287848  159.773643 -3763.227956 -5487.686888  1396.680735   \n",
       "2024-03-14 09:56:52.033248  163.371546 -3571.164723 -5685.739603  1071.255297   \n",
       "...                                ...          ...          ...          ...   \n",
       "2024-03-15 01:18:31.470048  134.594865 -4657.649252 -3550.656200  3451.519923   \n",
       "2024-03-15 01:19:27.215448  138.193267 -4581.786349 -3879.134524  3189.962841   \n",
       "2024-03-15 01:20:22.960848  141.791576 -4487.866042 -4192.324705  2915.833698   \n",
       "2024-03-15 01:21:18.706248  145.389798 -4376.259801 -4488.993651  2630.213730   \n",
       "2024-03-15 01:22:14.451648  148.987941 -4247.408637 -4767.973455  2334.229232   \n",
       "\n",
       "                                 v_x       v_y       v_z  theta_earth  \\\n",
       "2024-03-14 09:53:09.051648  2.462460 -4.839981 -5.395237     5.598991   \n",
       "2024-03-14 09:54:04.797048  2.757748 -4.493590 -5.549591     5.603056   \n",
       "2024-03-14 09:55:00.542448  3.042155 -4.129514 -5.682072     5.607121   \n",
       "2024-03-14 09:55:56.287848  3.314563 -3.749185 -5.792160     5.611186   \n",
       "2024-03-14 09:56:52.033248  3.573902 -3.354101 -5.879424     5.615251   \n",
       "...                              ...       ...       ...          ...   \n",
       "2024-03-15 01:18:31.470048  1.197015 -6.021950 -4.572983     3.364565   \n",
       "2024-03-15 01:19:27.215448  1.523850 -5.759129 -4.807919     3.368630   \n",
       "2024-03-15 01:20:22.960848  1.844655 -5.473632 -5.023888     3.372695   \n",
       "2024-03-15 01:21:18.706248  2.158168 -5.166585 -5.220045     3.376760   \n",
       "2024-03-15 01:22:14.451648  2.463155 -4.839200 -5.395619     3.380825   \n",
       "\n",
       "                               r_x_ecef     r_y_ecef     r_z_ecef     gt_lon  \\\n",
       "2024-03-14 09:53:09.051648  -278.488257 -6379.125345  2334.925473 -92.499728   \n",
       "2024-03-14 09:54:04.797048   -27.592429 -6488.737889  2029.763283 -90.243641   \n",
       "2024-03-14 09:55:00.542448   222.615724 -6574.928242  1716.603631 -88.060805   \n",
       "2024-03-14 09:55:56.287848   471.346357 -6637.350725  1396.680735 -85.938003   \n",
       "2024-03-14 09:56:52.033248   717.817967 -6675.746376  1071.255297 -83.862780   \n",
       "...                                 ...          ...          ...        ...   \n",
       "2024-03-15 01:18:31.470048  5327.501072  2432.814899  3451.519923  24.543912   \n",
       "2024-03-15 01:19:27.215448  5337.368205  2748.263351  3189.962841  27.244384   \n",
       "2024-03-15 01:20:22.960848  5328.808874  3052.920574  2915.833698  29.808767   \n",
       "2024-03-15 01:21:18.706248  5301.766254  3345.741835  2630.213730  32.254456   \n",
       "2024-03-15 01:22:14.451648  5256.247627  3625.729152  2334.229232  34.597687   \n",
       "\n",
       "                               gt_lat  \n",
       "2024-03-14 09:53:09.051648  20.086337  \n",
       "2024-03-14 09:54:04.797048  17.370227  \n",
       "2024-03-14 09:55:00.542448  14.624316  \n",
       "2024-03-14 09:55:56.287848  11.854228  \n",
       "2024-03-14 09:56:52.033248   9.065138  \n",
       "...                               ...  \n",
       "2024-03-15 01:18:31.470048  30.512084  \n",
       "2024-03-15 01:19:27.215448  27.984480  \n",
       "2024-03-15 01:20:22.960848  25.397662  \n",
       "2024-03-15 01:21:18.706248  22.760299  \n",
       "2024-03-15 01:22:14.451648  20.080089  \n",
       "\n",
       "[1001 rows x 17 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "96a581a5-3dee-4334-a3f3-8c84e2962ea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.basemap import Basemap\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def ground_track(ground_tracks, plot_title='Ground Track'):\n",
    "    \"\"\"\n",
    "    ground_tracks is a list of DataFrames of lon lat ground tracks\n",
    "    For example, [[orbit1.lon, orbit1.lat], [orbit2.lon, orbit2.lat]]\n",
    "    lon and lat are in degrees\n",
    "    \"\"\"\n",
    "    plt.rcParams['figure.figsize'] = [12, 10]\n",
    "    \n",
    "    # llcrnrlat,llcrnrlon,urcrnrlat,urcrnrlon\n",
    "    # are the lat/lon values of the lower left and upper right corners\n",
    "    # of the map.\n",
    "    # lat_ts is the latitude of true scale.\n",
    "    # resolution = 'c' means use crude resolution coastlines.\n",
    "    m = Basemap(projection='merc',llcrnrlat=-85,urcrnrlat=85,\\\n",
    "                llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='l')\n",
    "    m.drawcoastlines()\n",
    "    m.fillcontinents(color='coral',lake_color='aqua',alpha=0.6)\n",
    "    # draw parallels and meridians.\n",
    "    m.drawparallels(np.arange(-90.,91.,30.))\n",
    "    m.drawmeridians(np.arange(-180.,181.,60.))\n",
    "    m.drawmapboundary(fill_color='aqua') \n",
    "    plt.title(plot_title)\n",
    "\n",
    "    for gt in ground_tracks:\n",
    "        lon, lat = m(gt.lon, gt.lat)\n",
    "        m.scatter(lon, lat, marker = 'o', color='r' )\n",
    "\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d61d2e91-12f6-49d6-88b4-9489f51d9b53",
   "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>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-14 09:53:09.051648</th>\n",
       "      <td>-92.499728</td>\n",
       "      <td>20.086337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-14 09:54:04.797048</th>\n",
       "      <td>-90.243641</td>\n",
       "      <td>17.370227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-14 09:55:00.542448</th>\n",
       "      <td>-88.060805</td>\n",
       "      <td>14.624316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-14 09:55:56.287848</th>\n",
       "      <td>-85.938003</td>\n",
       "      <td>11.854228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-14 09:56:52.033248</th>\n",
       "      <td>-83.862780</td>\n",
       "      <td>9.065138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15 01:18:31.470048</th>\n",
       "      <td>24.543912</td>\n",
       "      <td>30.512084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15 01:19:27.215448</th>\n",
       "      <td>27.244384</td>\n",
       "      <td>27.984480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15 01:20:22.960848</th>\n",
       "      <td>29.808767</td>\n",
       "      <td>25.397662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15 01:21:18.706248</th>\n",
       "      <td>32.254456</td>\n",
       "      <td>22.760299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15 01:22:14.451648</th>\n",
       "      <td>34.597687</td>\n",
       "      <td>20.080089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  lon        lat\n",
       "2024-03-14 09:53:09.051648 -92.499728  20.086337\n",
       "2024-03-14 09:54:04.797048 -90.243641  17.370227\n",
       "2024-03-14 09:55:00.542448 -88.060805  14.624316\n",
       "2024-03-14 09:55:56.287848 -85.938003  11.854228\n",
       "2024-03-14 09:56:52.033248 -83.862780   9.065138\n",
       "...                               ...        ...\n",
       "2024-03-15 01:18:31.470048  24.543912  30.512084\n",
       "2024-03-15 01:19:27.215448  27.244384  27.984480\n",
       "2024-03-15 01:20:22.960848  29.808767  25.397662\n",
       "2024-03-15 01:21:18.706248  32.254456  22.760299\n",
       "2024-03-15 01:22:14.451648  34.597687  20.080089\n",
       "\n",
       "[1001 rows x 2 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt = orb[['gt_lon', 'gt_lat']]\n",
    "gt = gt.rename({'gt_lon':'lon', 'gt_lat':'lat'}, axis='columns')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68e874db-ba4f-46c6-9c4a-bb742fa0008d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "81befa29-ebae-4d18-8e15-b22edcaff4da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2024-03-14 09:53:09.051648')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epoch = init_orb_ele.epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0fa78ab9-8fc9-409e-9fd4-2a92ccd5f673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ground_track([gt], f'Ground Tracks - Space Station {epoch=}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33f48598-35c7-42bb-9a22-4677f04765cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "762691d1-ca76-4b84-920c-9cb3b20d93ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4896075.528078086, -2838980.2935610227, 2931226.2884184243)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See Algorithms for Global Positioning, Kai Borre and Gilbert Strang, Wellesley-Cambridge Press, 2012.\n",
    "\n",
    "def ecef2enu(x_vec, lon_obs, lat_obs):\n",
    "    clon = np.cos(lon_obs)\n",
    "    clat = np.cos(lat_obs)\n",
    "    slon = np.sin(lon_obs)\n",
    "    slat = np.sin(lat_obs)\n",
    "    F = np.matrix([[ -slon, -slat*clon, clat*clon],\n",
    "                   [  clon, -slat*slon, clat*slon],\n",
    "                   [     0,       clat,      slat]])\n",
    "    return F.transpose @ x_vec\n",
    "\n",
    "def azi_ele(enu_vec):\n",
    "    E = enu_vec.x\n",
    "    N = enu_vec.y\n",
    "    U = enu_vec.z\n",
    "    azi = convert_angle_to_0_2pi_interval(np.arctan2(E,N))\n",
    "    ele = np.arctan2(U,np.sqrt(N**2 + E**2))\n",
    "    return azi,ele\n",
    "\n",
    "def cart2geo(lat, lon, h):\n",
    "    a = 6378137. # meters\n",
    "    f = 0.003_352_810_681_183_637_418 # non-dimensional\n",
    "    N = a/np.sqrt(1.0-f*(2-f)*np.sin(lat)**2)\n",
    "    X = (N+h)*np.cos(lat)*np.cos(lon)\n",
    "    Y = (N+h)*np.cos(lat)*np.sin(lon)\n",
    "    Z = (((1-f)**2)*N + h)*np.sin(lat)\n",
    "    return X, Y, Z\n",
    "\n",
    "# test\n",
    "cart2geo( 57.02929569, 9.950248114, 56.950)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faf52451-f77f-44fd-860a-e6a7f089276d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate vector from observer to satellite, then determine azimuth and elevation of satelite\n",
    "def satellite_azi_ele("
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6069722-2f24-4c59-a52a-884c00cff279",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e83aff6-8fb6-4bce-a7de-44d91ec6f559",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d1c1b3a-b00d-4218-8102-f1d43119d81a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fa252938-7953-4084-8736-76cc136ff3a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0033528106811836376"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = 0.003_352_810_681_183_637_418"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1391bd02-8a98-4471-a18d-2b6ebc25d28e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}