Plot MPC Observation Magnitudes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "08d65ff0",
   "metadata": {},
   "source": [
    "# Plot MPC Magnitudes\n",
    "\n",
    "MPC Observations Database:  https://www.minorplanetcenter.net/db_search <br>\n",
    "<br> On the MPC Database Search Page enter an obeject designation such as '9058' for this example,<br>\n",
    "then Show, then download button, <br>\n",
    "and save the generated text data to a local file, or the directory where this notebook is located.<br>\n",
    "Example '9058.txt' <br>\n",
    "\n",
    "direct link: https://www.minorplanetcenter.net/db_search/show_object?utf8=%E2%9C%93&object_id=9058\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "Referemce: Format For Optical Astrometric Observations Of Comets, Minor Planets and Natural Satellites<br> https://www.minorplanetcenter.net/iau/info/OpticalObs.html <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9461e42e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.time import Time\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib import rc\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "import warnings\n",
    "\n",
    "def getDateTimeUT(dateOfObservation):\n",
    "    yyyy = dateOfObservation[0:4]\n",
    "    mm = dateOfObservation[5:7]\n",
    "    dd = dateOfObservation[8:10]\n",
    "    s = yyyy + '-' + mm + '-' + dd\n",
    "    ff = float(dateOfObservation[10: ])\n",
    "    seconds = int(ff * 86400)\n",
    "    hrs = int(seconds/3600)\n",
    "    mins = int((seconds - hrs * 3600)/60)\n",
    "    secs = seconds  - hrs * 3600 - mins * 60\n",
    "    s = s + ' '\n",
    "    if (hrs < 10):\n",
    "        s = s + \"0\"\n",
    "    s = s + str(hrs) + \":\"\n",
    "    if (mins < 10):\n",
    "        s = s + \"0\"\n",
    "    s = s + str(mins) + \":\"\n",
    "    if (secs < 10):\n",
    "        s = s + \"0\"\n",
    "    s = s + str(secs)\n",
    "    return s\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "mpcdates = []\n",
    "Nmpcdates = []\n",
    "Tmpcdates = []\n",
    "rmpcdates = []\n",
    "Bmpcdates = []\n",
    "Rmpcdates = []\n",
    "gmpcdates = []\n",
    "Gmpcdates = []\n",
    "Vmpcdates = []\n",
    "Impcdates = []\n",
    "ompcdates = []\n",
    "cmpcdates = []\n",
    "wmpcdates = []\n",
    "impcdates = []\n",
    "ympcdates = []\n",
    "zmpcdates = []\n",
    "Cmpcdates = []\n",
    "nonempcdates = []\n",
    "\n",
    "mags = []\n",
    "Nmags = []\n",
    "Tmags = []\n",
    "rmags = []\n",
    "Bmags = []\n",
    "Rmags = []\n",
    "gmags = []\n",
    "Gmags = []\n",
    "Vmags = []\n",
    "Imags = []\n",
    "omags = []\n",
    "cmags = []\n",
    "wmags = []\n",
    "imags = []\n",
    "ymags = []\n",
    "zmags = []\n",
    "Cmags = []\n",
    "nonemags = []\n",
    "magbands = []\n",
    "observedbands = []\n",
    "observedcatalogs = []\n",
    "ccdtotaldates = []\n",
    "ccdtotalmags = []\n",
    "ccdnucleardates = []\n",
    "ccdnuclearmags = []\n",
    "radarObs = 0\n",
    "\n",
    "satelliteObs = 0\n",
    "numobs = 0\n",
    "codes=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "30c779a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9058) 1992 JB - Magnitudes from MPC Observation Database\n",
      "desig: 9058\n",
      "fullname: (9058) 1992 JB\n",
      "opened  9058.txt\n"
     ]
    }
   ],
   "source": [
    "desig = '9058'\n",
    "fullname ='(9058) 1992 JB'\n",
    "prefix = '(9058) 1992 JB'\n",
    "mpcfilename= '9058'\n",
    "\n",
    "figfile =  prefix + '-MPC-mags.png'\n",
    "title = fullname + ' - Magnitudes from MPC Observation Database'\n",
    "print(title)\n",
    "prefix + '-MPC-mags.png'\n",
    "print('desig:', desig)\n",
    "print('fullname:', fullname)\n",
    "\n",
    "f = open(mpcfilename + '.txt', 'r')\n",
    "print('opened ', mpcfilename + '.txt')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "70d844a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "for lines in f:\n",
    "    packed = lines[0:12].strip()\n",
    "    orbitType = lines[4:5].strip()\n",
    "    typeOfObservation = lines[14:15].strip()\n",
    "    year = int(lines[15:19])\n",
    "    code = lines[77:80]\n",
    "    codes.append(code)\n",
    "    magnitudeBand = lines[70:71].strip()\n",
    "\n",
    "    if (typeOfObservation == 'S'): \n",
    "        satelliteObs = satelliteObs + 1\n",
    "        numobs = numobs + 1\n",
    "    if (typeOfObservation == 'R'): \n",
    "        radarObs = radarObs + 1\n",
    "        numobs = numobs + 1\n",
    "    if ((typeOfObservation != 's') and (typeOfObservation != 'S')and (typeOfObservation != 'r') and (typeOfObservation != 'R') ):        \n",
    "        numobs = numobs + 1\n",
    "        dateOfObservation = lines[15:32].strip()\n",
    "        datetimeUT = getDateTimeUT(dateOfObservation)\n",
    "        dt = Time(datetimeUT, format='iso').datetime\n",
    "        observedRA = lines[32:44].strip()\n",
    "        observedDecl = lines[44:56].strip()\n",
    "        observedMagnitude = lines[65:70].strip(' ')\n",
    "        magnitudeBand = lines[70:71].strip()\n",
    "        magbands.append(magnitudeBand)\n",
    "        \n",
    "        # B (default if band is not indicated)\n",
    "        if ((len(magnitudeBand) == 0) and (len(observedMagnitude) > 0)):\n",
    "            magnitudeBand='B'\n",
    "        if (magnitudeBand not in observedbands):\n",
    "            observedbands.append(magnitudeBand)\n",
    "        if   ((len(observedMagnitude) > 0) and (magnitudeBand == \"N\")):\n",
    "            Nmpcdates.append(dt)\n",
    "            Nmags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"T\")):\n",
    "            Tmpcdates.append(dt)\n",
    "            Tmags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"B\")):\n",
    "            Bmpcdates.append(dt)\n",
    "            Bmags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"R\")):\n",
    "            Rmpcdates.append(dt)\n",
    "            Rmags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"r\")):\n",
    "            rmpcdates.append(dt)\n",
    "            rmags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"V\")):\n",
    "            Vmpcdates.append(dt)\n",
    "            Vmags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"G\")):\n",
    "            Gmpcdates.append(dt)\n",
    "            Gmags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"g\")):\n",
    "            gmpcdates.append(dt)\n",
    "            gmags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"o\")):\n",
    "            ompcdates.append(dt)\n",
    "            omags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"c\")):\n",
    "            cmpcdates.append(dt)\n",
    "            cmags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"w\")):\n",
    "            wmpcdates.append(dt)\n",
    "            wmags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"I\")):\n",
    "            Impcdates.append(dt)\n",
    "            Imags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"i\")):\n",
    "            impcdates.append(dt)\n",
    "            imags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"y\")):\n",
    "            ympcdates.append(dt)\n",
    "            ymags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"z\")):\n",
    "            zmpcdates.append(dt)\n",
    "            zmags.append(float(observedMagnitude))\n",
    "        elif ((len(observedMagnitude) > 0) and (magnitudeBand == \"C\")):\n",
    "            Cmpcdates.append(dt)\n",
    "            Cmags.append(float(observedMagnitude))\n",
    "        elif ( (magnitudeBand == \"\")):\n",
    "            nonempcdates.append(dt)\n",
    "            nonemags.append('')\n",
    "        elif ((len(observedMagnitude) > 0) ):\n",
    "            mags.append(float(observedMagnitude))\n",
    "            mpcdates.append(dt)\n",
    "            print('magnitudeBand', magnitudeBand, '  observedMagnitude',observedMagnitude)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6a3f687c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "font1 = {'family': 'serif',\n",
    "        'color':  'black',\n",
    "        'weight': 'normal',\n",
    "        'size': 20,\n",
    "        }\n",
    "font2 = {'family': 'serif',\n",
    "        'color':  'black',\n",
    "        'weight': 'normal',\n",
    "        'size': 18,\n",
    "        }\n",
    "\n",
    "boxprops = dict(boxstyle='round', facecolor='#ffffff', ec='#CCCCCC', alpha=0.9)\n",
    "             \n",
    "rc('text', usetex=False)\n",
    "#fig, ax = plt.subplots(constrained_layout=True)\n",
    "#plt.rcParams['figure.constrained_layout.use'] = True\n",
    "fig, ax = plt.subplots(figsize=(20, 12))\n",
    "# fig, ax = plt.subplots(figsize=(40, 12))\n",
    "\n",
    "locator = mdates.AutoDateLocator()\n",
    "# formatter = mdates.ConciseDateFormatter(locator)\n",
    "formatter = mdates.AutoDateFormatter(locator)\n",
    "\n",
    "#formatter = mdates.AutoDateFormatter(locator, tz=None, defaultfmt='%Y-%m-%d')\n",
    "ax.xaxis.set_major_locator(locator)\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "\n",
    "# ax.grid(b=True, which='major', axis='both')\n",
    "ax.grid()\n",
    "ax.set_title(title, fontdict=font1)\n",
    "# fig.autofmt_xdate()\n",
    "\n",
    "# if (len(mags) > 0):\n",
    "#     ax.plot_date(mpcdates,   mags, label='mag',  marker='$?$',  xdate=True, ydate = False)\n",
    "if (len(Tmags) > 0):\n",
    "    ax.plot_date(Tmpcdates, Tmags, label='T-mag', color='black', fmt='.', marker='$T$', xdate=True, ydate = False)\n",
    "if (len(Nmags) > 0):\n",
    "    ax.plot_date(Nmpcdates, Nmags, label='N-mag', color='grey', fmt='.', marker='$N$', xdate=True, ydate = False)\n",
    "if (len(rmags) > 0):\n",
    "    ax.plot_date(rmpcdates, rmags, label='r-mag', fmt='.', marker='$r$', xdate=True, ydate = False)\n",
    "if (len(Bmags) > 0):\n",
    "    ax.plot_date(Rmpcdates, Rmags, label='B-mag', color='blue', fmt='.', marker='$B$', xdate=True, ydate = False)\n",
    "if (len(Rmags) > 0):\n",
    "    ax.plot_date(Rmpcdates, Rmags, label='R-mag', color='red', fmt='.', marker='$R$', xdate=True, ydate = False)\n",
    "if (len(gmags) > 0):\n",
    "    ax.plot_date(gmpcdates, gmags, label='g-mag', fmt='.', marker='$g$', xdate=True, ydate = False)\n",
    "if (len(Gmags) > 0):\n",
    "    ax.plot_date(Gmpcdates, Gmags, label='G-mag', fmt='.', marker='$G$', xdate=True, ydate = False)\n",
    "if (len(Vmags) > 0):\n",
    "    ax.plot_date(Vmpcdates, Vmags, label='V-mag', color='green', fmt='.', marker='$V$', xdate=True, ydate = False)\n",
    "if (len(omags) > 0):\n",
    "    ax.plot_date(ompcdates, omags, label='o-mag', color='orange', fmt='.', marker='$o$', xdate=True, ydate = False)\n",
    "if (len(cmags) > 0):\n",
    "    ax.plot_date(cmpcdates, cmags, label='c-mag', color='cyan', fmt='.', marker='$o$', xdate=True, ydate = False)\n",
    "if (len(Imags) > 0):\n",
    "    ax.plot_date(Impcdates, Imags, label='I-mag', marker='$I$', fmt='.', xdate=True, ydate = False)\n",
    "if (len(imags) > 0):\n",
    "    ax.plot_date(impcdates, imags, label='i-mag', fmt='.', marker='$i$', xdate=True, ydate = False)\n",
    "if (len(ymags) > 0):\n",
    "    ax.plot_date(ympcdates, ymags, label='y-mag', fmt='.', marker='$y$', xdate=True, ydate = False)\n",
    "if (len(zmags) > 0):\n",
    "    ax.plot_date(zmpcdates, zmags, label='z-mag', fmt='.', marker='$z$', xdate=True, ydate = False)\n",
    "if (len(wmags) > 0):\n",
    "    ax.plot_date(wmpcdates, wmags, label='w-mag', fmt='.', marker='$w$', xdate=True, ydate = False)\n",
    "if (len(Cmags) > 0):\n",
    "    ax.plot_date(Cmpcdates, Cmags, label='C-mag', fmt='.', marker='$C$', xdate=True, ydate = False)\n",
    "\n",
    "\n",
    "plt.xlabel('Date', fontdict=font1)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.ylabel('Magnitude', fontdict=font1)\n",
    "ax.legend(loc=0)\n",
    "plt.savefig(figfile)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e97e687d",
   "metadata": {},
   "source": [
    "The plot reveals the long history of observations of this object going back to 1992 when it was discovered:<br>\n",
    "(9058) 1992 JB<br>\n",
    "Discovery date : 1992 05 01 <br>\n",
    "Discovery site : Palomar <br>\n",
    "Discoverer(s) : Alu, J., Lawrence, K. J. <br>\n",
    "Discovery magnitude: 14.5\n",
    "Station code: 675<br>\n",
    "Observed over many oppositions<br>\n",
    "Object (9058) 1992 JB was brighter during the initial opposition. <br>\n",
    "Before 2015, the magnitude bands were mostly B or blank, V, and R. <br>\n",
    "After that, additional magnitude bands appeared as more surveys started.<br>\n",
    "More followup observations were reported after CCD imaging became more mainstream thus the variation in plotted colors in later years.  <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bdbdd12",
   "metadata": {},
   "source": [
    "## Discovery and discovery asterisk * \n",
    "\n",
    "An asterisk * is used to denote discovery observation. Observations before the asterisk, if any, are called prediscovery observations.<br>\n",
    "Reference:<br>\n",
    "Format For Optical Astrometric Observations Of Comets, Minor Planets and Natural Satellites<br>\n",
    "From the detailed notes section:<br>\n",
    "<p>\n",
    "DISCOVERY ASTERISK<br>\n",
    "Discovery observations for new (or unidentified) objects should contain `*' in column 13. Only one asterisked observation per object is expected.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "608d1abe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "opened  9058.txt\n",
      "discoveryasterisk found:\n",
      " 09058J92J00B*2A1992 05 01.38438 15 23 11.07 -08 30 15.2          14.5   20100675\n",
      "\n",
      "*\n",
      "Discovery Circumstances:\n",
      "MPC dateOfObservation 1992 05 01.38438\n",
      "datetimeUT 1992-05-01 09:13:30\n",
      "observedRA 15 23 11.07\n",
      "observedDecl -08 30 15.2\n",
      "observedMagnitude 14.5\n",
      "magnitudeBand \n",
      "magnitudeBand is blank, which means the magnitude Band is B\n",
      "Station Code 675\n"
     ]
    }
   ],
   "source": [
    "f = open(mpcfilename + '.txt', 'r')\n",
    "\n",
    "print('opened ', mpcfilename + '.txt')\n",
    "\n",
    "for lines in f:\n",
    "    if '*' in lines:\n",
    "        print('discoveryasterisk found:\\n',lines)\n",
    "        discoveryasterisk = lines[12:13]\n",
    "        print(discoveryasterisk)\n",
    "        packed = lines[0:12].strip()\n",
    "        orbitType = lines[4:5].strip()\n",
    "        typeOfObservation = lines[14:15].strip()\n",
    "\n",
    "        dateOfObservation = lines[15:32].strip()\n",
    "        datetimeUT = getDateTimeUT(dateOfObservation)\n",
    "        dt = Time(datetimeUT, format='iso').datetime\n",
    "        observedRA = lines[32:44].strip()\n",
    "        observedDecl = lines[44:56].strip()\n",
    "        observedMagnitude = lines[65:70].strip(' ')\n",
    "        magnitudeBand = lines[70:71].strip()\n",
    "        code=lines[77:80]\n",
    "        print('Discovery Circumstances:')\n",
    "        print('MPC dateOfObservation', dateOfObservation)\n",
    "        print('datetimeUT', datetimeUT)\n",
    "        print('observedRA', observedRA)\n",
    "        print('observedDecl', observedDecl)\n",
    "        print('observedMagnitude', observedMagnitude)\n",
    "        print('magnitudeBand', magnitudeBand)\n",
    "        # B (default if band is not indicated),\n",
    "        if (len(magnitudeBand) == 0):\n",
    "            print('magnitudeBand is blank, which means the magnitude Band is B')\n",
    "        print('Station Code', code)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "71f66a8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "observedbands ['V', '', 'B', 'R', 'i', 'w', 'r', 'G', 'g', 'o', 'c']\n",
      "There should be no unknown bands\n",
      "unknown band mags 0\n",
      "Nmags 0\n",
      "Tmags 0\n",
      "Bmags 10\n",
      "Rmags 41\n",
      "rmags 27\n",
      "gmags 4\n",
      "Gmags 199\n",
      "Vmags 27\n",
      "omags 108\n",
      "cmags 40\n",
      "wmags 82\n",
      "Imags 0\n",
      "imags 12\n",
      "ymags 0\n",
      "zmags 0\n",
      "wmags 82\n",
      "Cmags 0\n",
      "Total number of ground based observations with no reported magnitude 181\n",
      "Number of satellite observations (no reported magnitudes) 0\n",
      "Number of radar observations  0\n",
      "Total number of ground based observations with reported magnitudes 528\n",
      "Total number of astrometric observations 731\n",
      "Station codes: ['046' '130' '246' '261' '381' '402' '413' '448' '461' '474' '589' '596'\n",
      " '608' '645' '657' '658' '675' '688' '691' '696' '703' '711' '776' '801'\n",
      " '894' 'C95' 'D29' 'F51' 'F52' 'G45' 'G96' 'H55' 'I41' 'J43' 'K19' 'K88'\n",
      " 'M22' 'Q62' 'T05' 'T08' 'U68' 'V19' 'W33' 'W68']\n"
     ]
    }
   ],
   "source": [
    "print('observedbands', observedbands)\n",
    "\n",
    "print('There should be no unknown bands')\n",
    "print( 'unknown band mags', len(mags))\n",
    "print('Nmags', len(Nmags))\n",
    "print('Tmags', len(Tmags))\n",
    "print('Bmags', len(Bmags))\n",
    "print('Rmags', len(Rmags))\n",
    "print('rmags', len(rmags))\n",
    "print('gmags', len(gmags))\n",
    "print('Gmags', len(Gmags))\n",
    "print('Vmags', len(Vmags))\n",
    "print('omags', len(omags))\n",
    "print('cmags', len(cmags))\n",
    "print('wmags', len(wmags))\n",
    "print('Imags', len(Imags))\n",
    "print('imags', len(imags))\n",
    "print('ymags', len(ymags))\n",
    "print('zmags', len(zmags))\n",
    "print('wmags', len(wmags))\n",
    "print('Cmags', len(Cmags))\n",
    "\n",
    "print('Total number of ground based observations with no reported magnitude', len(nonemags))\n",
    "#nummagobs = len(Nmags)) + len(Tmags)) + len(Rmags)) + len(rmags)) + len(gmags)) +len(Gmags)) + len(Vmags)) + len(omags)) + len(cmags)) + len(wmags)) + len(Imags))\n",
    "nummagobs = len(Nmags) + len(Tmags) + len(Rmags) + len(rmags) + len(gmags) +len(Gmags) + len(Vmags) + len(omags) + len(cmags) + len(wmags) + len(Imags)\n",
    "print('Number of satellite observations (no reported magnitudes)', satelliteObs)\n",
    "print('Number of radar observations ', radarObs)\n",
    "print('Total number of ground based observations with reported magnitudes', nummagobs)\n",
    "print('Total number of astrometric observations', numobs)\n",
    "print('Station codes:', np.unique(codes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "035539cc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "941d5d04",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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