GLEAM_Vs_TGSS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import astropy.units as u\n",
    "import astropy.coordinates as coord\n",
    "from astroquery.vizier import Vizier\n",
    "%matplotlib inline\n",
    "from astropy.io import fits\n",
    "from astroquery.simbad import Simbad\n",
    "import matplotlib\n",
    "import aplpy\n",
    "from astroquery.sdss import SDSS\n",
    "from astroquery.skyview import SkyView\n",
    "from astropy.coordinates import Angle\n",
    "from astropy.table import Table\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Region of Sky:\n",
    "\n",
    "RA=240.0\n",
    "DE=12.0\n",
    "Radius=3\n",
    "\n",
    "#Catalogues:\n",
    "\n",
    "First_Catalogue_Name='GLEAM'\n",
    "Second_Catalogue_Name='TGSS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Name of Flux in Catalogue\n",
    "\n",
    "First_Flux='Fint151'\n",
    "Second_Flux='Stotal'\n",
    "\n",
    "#And their error\n",
    "First_Flux_error='e_'+First_Flux\n",
    "Second_Flux_error='e_'+Second_Flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# First_Catalogue_Data= Table.read(First_Catalogue_Name+'.fit', format='fits')\n",
    "# #print First_Catalogue_Data\n",
    "\n",
    "# Second_Catalogue_Data= Table.read(Second_Catalogue_Name+'.fit', format='fits')\n",
    "# #print Second_Catalogue_Data\n",
    "\n",
    "# CV_Data= Table.read('Downes.fit', format='fits')\n",
    "# #print CV_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# start_time = time.time()\n",
    "# #Getting data from first catalogue\n",
    "\n",
    "# Vizier.ROW_LIMIT = -1\n",
    "# First_Catalog_List = Vizier.find_catalogs(First_Catalogue_Name)\n",
    "# First_Catalogs = Vizier.get_catalogs(First_Catalog_List.keys())\n",
    "# First_Catalogue_Data=First_Catalogs[0]\n",
    "\n",
    "\n",
    "# #Getting data from second catalogue\n",
    "\n",
    "# Second_Catalog_List = Vizier.find_catalogs(Second_Catalogue_Name)\n",
    "# Second_Catalogs = Vizier.get_catalogs(Second_Catalog_List.keys())\n",
    "# Second_Catalogue_Data=Second_Catalogs['J/A+A/598/A78/table3']\n",
    "\n",
    "# #Getting CV data\n",
    "\n",
    "# CV_Catalog_List = Vizier.find_catalogs('Downes+ 2001-2006')\n",
    "# CV_Catalogs = Vizier.get_catalogs(CV_Catalog_List.keys())\n",
    "# CV_Catalogue_Data=Second_Catalogs[0]\n",
    "# print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Making array for first catalogue in region of the sky chosen \n",
    "\n",
    "n=0\n",
    "RA_First=[]\n",
    "DE_First=[]\n",
    "Lum_First=[]\n",
    "RA_First_e=[]\n",
    "DE_First_e=[]\n",
    "Lum_First_e=[]\n",
    "while n< len(First_Catalogue_Data):\n",
    "    if RA-Radius<First_Catalogue_Data[\"_RAJ2000\"][n] and First_Catalogue_Data[\"_RAJ2000\"][n]<RA+Radius and DE-Radius<First_Catalogue_Data[\"_DEJ2000\"][n] and First_Catalogue_Data[\"_DEJ2000\"][n]<DE+Radius:\n",
    "        RA_First.append(First_Catalogue_Data[\"_RAJ2000\"][n])\n",
    "        DE_First.append(First_Catalogue_Data[\"_DEJ2000\"][n])\n",
    "        Lum_First.append(First_Catalogue_Data[First_Flux][n])\n",
    "        RA_First_e.append(First_Catalogue_Data[\"e_RAJ2000\"][n])\n",
    "        DE_First_e.append(First_Catalogue_Data[\"e_DEJ2000\"][n])\n",
    "        Lum_First_e.append(First_Catalogue_Data[First_Flux_error][n])\n",
    "    n=n+1\n",
    "\n",
    "#Making array for second catalogue in region of the sky chosen \n",
    "    \n",
    "n=0\n",
    "RA_Second=[]\n",
    "DE_Second=[]\n",
    "Lum_Second=[]\n",
    "RA_Second_e=[]\n",
    "DE_Second_e=[]\n",
    "Lum_Second_e=[]\n",
    "while n< len(Second_Catalogue_Data):\n",
    "    if RA-Radius<Second_Catalogue_Data[\"_RAJ2000\"][n] and Second_Catalogue_Data[\"_RAJ2000\"][n]<RA+Radius and DE-Radius<Second_Catalogue_Data[\"_DEJ2000\"][n] and Second_Catalogue_Data[\"_DEJ2000\"][n]<DE+Radius:\n",
    "        RA_Second.append(Second_Catalogue_Data[\"_RAJ2000\"][n])\n",
    "        DE_Second.append(Second_Catalogue_Data[\"_DEJ2000\"][n])\n",
    "        Lum_Second.append(Second_Catalogue_Data[Second_Flux][n]/1000)\n",
    "        RA_Second_e.append(Second_Catalogue_Data[\"e_RAJ2000\"][n]/3600)\n",
    "        DE_Second_e.append(Second_Catalogue_Data[\"e_DEJ2000\"][n]/3600)\n",
    "        Lum_Second_e.append(Second_Catalogue_Data[Second_Flux_error][n]/1000)\n",
    "    n=n+1\n",
    "\n",
    "#Making array for CV catalogue in region of the sky chosen \n",
    "    \n",
    "n=0\n",
    "RA_CV=[]\n",
    "DE_CV=[]\n",
    "while n< len(CV_Data):\n",
    "    if RA-Radius<CV_Data[\"_RAJ2000\"][n] and CV_Data[\"_RAJ2000\"][n]<RA+Radius and DE-Radius<CV_Data[\"_DEJ2000\"][n] and CV_Data[\"_DEJ2000\"][n]<DE+Radius:\n",
    "        RA_CV.append(CV_Data[\"_RAJ2000\"][n])\n",
    "        DE_CV.append(CV_Data[\"_DEJ2000\"][n])\n",
    "    n=n+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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WrHPdeO219AmOo7cB+l9uN31y7VoNU6wONcHDcRw98cQTGX+bO3cuTZ8+naqqquj73/9+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\nbSQdxZ95ngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111851f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting coords of two arrays to compare\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.scatter(RA_First, DE_First, s=10, c='b', marker=\"s\", label=First_Catalogue_Name)\n",
    "ax1.scatter(RA_Second,DE_Second, s=10, c='r', marker=\"o\", label=Second_Catalogue_Name)\n",
    "plt.legend(loc='upper left');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#This loop finds common stars between catalogues:\n",
    "\n",
    "#Creating new arrays:\n",
    "\n",
    "#Luminosities of stars in common(and error)\n",
    "Lum_First_Relevant=[]\n",
    "Lum_Second_Relevant=[]\n",
    "Lum_First_Relevant_e=[]\n",
    "Lum_Second_Relevant_e=[]\n",
    "\n",
    "#Coordinates of stars in common(and error)\n",
    "RA_First_Relevant=[]\n",
    "RA_Second_Relevant=[]\n",
    "DE_First_Relevant=[]\n",
    "DE_Second_Relevant=[]\n",
    "RA_First_Relevant_e=[]\n",
    "RA_Second_Relevant_e=[]\n",
    "DE_First_Relevant_e=[]\n",
    "DE_Second_Relevant_e=[]\n",
    "\n",
    "#Creating new variables\n",
    "\n",
    "#index for first catalogue:\n",
    "f=0\n",
    "#index for second catalogue:\n",
    "s=0\n",
    "#number of iterations:\n",
    "n=0\n",
    "#Number of stars in both catalogues\n",
    "corr=1\n",
    "\n",
    "#While loop goes through all of first catalogue..\n",
    "while f<len(RA_First):\n",
    "    #If it compares a source in first catalogue to every source in second without finding one in common then..\n",
    "    if s==len(RA_Second):\n",
    "        #It moves on to next source in first and..\n",
    "        f=f+1\n",
    "        #Starts comparing it to the first source in the second catalogue\n",
    "        s=0\n",
    "    #This is a formula for overlapping circles with the radii being the error\n",
    "    #If this is true catalogues have that star in common\n",
    "    elif RA_First_e[f]+RA_Second_e[s]>= abs(RA_First[f]-RA_Second[s]) and DE_First_e[f]+DE_Second_e[s]>= abs(DE_First[f]-DE_Second[s]):\n",
    "        #print coords and luminosity\n",
    "        '''print corr,\":\"\n",
    "        print First_Catalogue_Name,\" Coords: (\",RA_First[f],\",\",DE_First[f],\")\"\n",
    "        print First_Catalogue_Name,\" error: (\",RA_First_e[f],\",\",DE_First_e[f],\")\"\n",
    "        print Second_Catalogue_Name,\" Coords: (\",RA_Second[s],\",\",DE_Second[s],\")\"\n",
    "        print Second_Catalogue_Name,\" error: (\",RA_Second_e[s],\",\",DE_Second_e[s],\")\"\n",
    "        print First_Catalogue_Name,\" Flux:\",Lum_First[f]*.001\n",
    "        print Second_Catalogue_Name,\" Flux:\",Lum_Second[s]*.001\n",
    "        print \"\"'''\n",
    "        #Making arrays of flux of sources in common (and error)\n",
    "        Lum_First_Relevant.append((Lum_First[f]))\n",
    "        Lum_Second_Relevant.append(Lum_Second[s])\n",
    "        Lum_First_Relevant_e.append((Lum_First_e[f]))\n",
    "        Lum_Second_Relevant_e.append(Lum_Second_e[s])\n",
    "        \n",
    "        #Making arrays of coordinates of sources in common \n",
    "        RA_First_Relevant.append(RA_First[f])\n",
    "        RA_Second_Relevant.append(RA_Second[s])\n",
    "        RA_First_Relevant_e.append(RA_First_e[f])\n",
    "        RA_Second_Relevant_e.append(RA_Second_e[s])\n",
    "        DE_First_Relevant.append(DE_First[f])\n",
    "        DE_Second_Relevant.append(DE_Second[s])\n",
    "        DE_First_Relevant_e.append(DE_First_e[f])\n",
    "        DE_Second_Relevant_e.append(DE_Second_e[s])\n",
    "        \n",
    "        #Number of common sources increased\n",
    "        corr=corr+1\n",
    "        #Move onto next source in first catalogue\n",
    "        f=f+1\n",
    "        #Starts comparing it to the first source in the second catalogue\n",
    "        s=0\n",
    "    #If neither of these are true then..\n",
    "    else:\n",
    "        #Try next source in second catalogue\n",
    "        s=s+1\n",
    "    #Before going onto next loop increase number of iterations\n",
    "    n=n+1\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of iterations: 132950\n",
      "Number of stars corrolated: 122\n"
     ]
    }
   ],
   "source": [
    "print \"Number of iterations:\",n\n",
    "print \"Number of stars corrolated:\",corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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NDaW+P3NosVJBbsjh4w6xsoYm1euTKFGQU5CrmkoHuUKz+PK9b2hoyJcvf5fD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\n/0D52bynXRk3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ad8550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Luminosities (unit Jy, log scale)\n",
    "\n",
    "if corr>0:\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(111)\n",
    "    ax1.scatter(Lum_First_Relevant, Lum_Second_Relevant)\n",
    "    plt.xlabel(First_Catalogue_Name)\n",
    "    plt.ylabel(Second_Catalogue_Name)\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\n",
    "    plt.xlim(0.01,10)\n",
    "    plt.ylim(0,10)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.048303 0.1509\n"
     ]
    }
   ],
   "source": [
    "#This finds source with biggest fractional change\n",
    "\n",
    "#Defining new variables\n",
    "Fractional_Diff=0\n",
    "n=0\n",
    "Current_Max_Diff=0\n",
    "Max_Diff_Index=0\n",
    "m=0\n",
    "\n",
    "#This loop goes through the whole lists of common stars\n",
    "while n<corr-1:\n",
    "    #Calculates nth fractional change\n",
    "    Fractional_Diff=(abs(Lum_First_Relevant[n]-Lum_Second_Relevant[n]))/Lum_First_Relevant[n]\n",
    "    #If the fractional change is larger than the combined error (i.e is relevant) then..\n",
    "    if abs(Lum_First_Relevant[n]-Lum_Second_Relevant[n])>Lum_First_Relevant_e[n]+Lum_Second_Relevant_e[n]:\n",
    "        #..if this fractional change is greater than the biggest change found before it then..\n",
    "        if Fractional_Diff>Current_Max_Diff:\n",
    "            #This is the biggest fractional change\n",
    "            Max_Diff_Index=n\n",
    "            Current_Max_Diff=Fractional_Diff\n",
    "        else:\n",
    "            m=m+1\n",
    "    n=n+1\n",
    "if m==n:\n",
    "    print \"no result\"\n",
    "else:\n",
    "    print Lum_First_Relevant[Max_Diff_Index],Lum_Second_Relevant[Max_Diff_Index]\n",
    "#When loop is finished it will have found the biggest change in the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coordinates of source with largest change\n",
      "238.28566 12.7667711111\n",
      "CV data:\n",
      "238.234166667 12.9131111111\n",
      "240.298054137 9.28685543111\n"
     ]
    }
   ],
   "source": [
    "print \"Coordinates of source with largest change\"\n",
    "print RA_First_Relevant[Max_Diff_Index],DE_First_Relevant[Max_Diff_Index]\n",
    "print \"CV data:\"\n",
    "n=0\n",
    "while n<len(RA_CV):\n",
    "    print RA_CV[n],DE_CV[n]\n",
    "    n=n+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV: 238.234166667 12.9131111111\n",
      "Correlated Source: 237.938675 12.7234888889\n",
      "Change in Luminosity: 1.244772 0.048\n",
      "\n",
      "CV: 240.298054137 9.28685543111\n",
      "Correlated Source: 239.983138889 9.78688111111\n",
      "Change in Luminosity: 0.171105 0.0259\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Checking if correlated sources are in CV catalogue:\n",
    "\n",
    "#Set CV error:\n",
    "RA_CV_e=.5#10.0/3600.0\n",
    "DE_CV_e=.5#10.0/3600.0\n",
    "\n",
    "#New variables for comparing:\n",
    "c=0\n",
    "f=0\n",
    "\n",
    "\n",
    "#While loop goes through all of CV catalogue and compares them in similar way to previous loop\n",
    "while c<len(RA_CV):\n",
    "    if f==len(RA_First_Relevant):\n",
    "        c=c+1\n",
    "        f=0\n",
    "    elif RA_First_Relevant_e[f]+RA_CV_e>= abs(RA_First_Relevant[f]-RA_CV[c]) and DE_First_Relevant_e[f]+DE_CV_e>= abs(DE_First_Relevant[f]-DE_CV[c]):\n",
    "        print \"CV:\",RA_CV[c],DE_CV[c]\n",
    "        print \"Correlated Source:\",RA_First_Relevant[f],DE_First_Relevant[f]\n",
    "        print \"Change in Luminosity:\",Lum_First_Relevant[f],Lum_Second[f]\n",
    "        print \"\"\n",
    "        c=c+1\n",
    "        f=0\n",
    "    else:\n",
    "        #Try next source in second catalogue\n",
    "        f=f+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object has no attribute '__getitem__'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-42ac3db4ab74>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m#Find image from SDSS:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mimages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSDSS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mCV\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maplpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFITSFigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m#Gives in grid and makes it grayscale (can also be made colorscale)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object has no attribute '__getitem__'"
     ]
    }
   ],
   "source": [
    "#This displays image of a correlated source\n",
    "\n",
    "\n",
    "#Which source to show:\n",
    "x=Max_Diff_Index\n",
    "\n",
    "#Coordinates of this star:\n",
    "pos=coord.SkyCoord(RA_Second_Relevant[x]*u.deg, DE_Second_Relevant[x]*u.deg)\n",
    "#pos='Eta Carinae'\n",
    "ra=np.array([RA_Second_Relevant[x]])\n",
    "de=np.array([DE_Second_Relevant[x]])\n",
    "\n",
    "#Find image from DSS:\n",
    "images = SkyView.get_images(pos,survey=['DSS'])\n",
    "print images[0]\n",
    "\n",
    "CV = aplpy.FITSFigure(images[0])\n",
    "\n",
    "\n",
    "#Gives in grid and makes it grayscale (can also be made colorscale)\n",
    "CV.show_grayscale()\n",
    "CV.add_grid()\n",
    "\n",
    "#This plots a circle where the relevant source should be \n",
    "x_pix, y_pix = CV.world2pixel(ra,de)\n",
    "plt.scatter(x_pix, y_pix, s=100, c='red', alpha=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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