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 "metadata": {
  "name": "",
  "signature": "sha256:c7dd76b882e45a48edddb5aa60bf8fce32579aff803b84bc1f174098dcb2732a"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The mosaic procedure demonstrated here is simmilar to the SDSS example. For more detailed information see [the SDSS example](http://nbviewer.ipython.org/github/ProfessorBrunner/rc3-sdss/blob/master/Documentation/SDSS%20Example.ipynb).\n",
      "DSS is different from SDSS in that the photographic plates downloaded are large files so most of the mosaic consists of only cropping the downloaded image data. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd ../pipeline/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Users/dorislee/Desktop/GSoC2014/rc3-sdss/pipeline\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from dss import DSS\n",
      "from rc3 import RC3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: AstropyDeprecationWarning: ConfigurationItem has been deprecated in astropy 0.4. Use ConfigItem objects as members of ConfigNamespace subclasses instead.  See ConfigNamespace for an example. [astropy.config.configuration]\n",
        "WARNING:astropy:AstropyDeprecationWarning: ConfigurationItem has been deprecated in astropy 0.4. Use ConfigItem objects as members of ConfigNamespace subclasses instead.  See ConfigNamespace for an example.\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pgc2 = RC3(0.0075,47.2744444444,0.0399805487303,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dss = DSS()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rfits = pgc2.mosaic_band('r',0.0075,47.2744444444,3*0.0399805487303,0.0399805487303,2,dss)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "------------------mosaic_band----------------------\n",
        "Now mosaic_band on 2\n",
        "(0.0075, 47.2744444444, 0.1199416461909)\n",
        "POSSIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "POSSIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "['raw_r.fits']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "dss_out: raw_r_2\n",
        "Only one field in region of interest\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "update = pgc2.source_info(rfits,dss)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1th iteration\n",
        "------------------source_info----------------------\n",
        "Source info for DSS_r_2.fits\n",
        "1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[['PGC    2', 0.0079000000000000008, 47.2744]]\n",
        "ra,dec of catalog sources\n",
        "rc3_data: [[2, 0.0079, 47.2744]]\n",
        "Source is Obvious\n",
        "Biggest Galaxy with radius 194.695274724 pixels!\n",
        "new_ra and new_dec: 0.0064810 , +47.2747672  \n",
        "Radii: 194.695274724 pixel\n",
        "Radii: 0.0214080111832 degrees\n",
        "rc3: 0.0075 , updated: 0.0064810 \n",
        "rc3: 47.2744444444 , updated: +47.2747672 \n",
        "rc3: 0.0399805487303 , updated: 0.0214080111832 \n",
        "Mosaic_all on 2 \n",
        "------------------mosaic_all_bands----------------------\n",
        "Working on 2\n",
        "------------------mosaic_band----------------------\n",
        "Now mosaic_band on 2\n",
        "('0.0064810', '+47.2747672', 0.1199416461909)\n",
        "POSSIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "POSSIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "POSSIB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "['raw_1b.fits']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "dss_out: raw_1b_2\n",
        "Only one field in region of interest\n",
        "------------------mosaic_band----------------------"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Now mosaic_band on 2\n",
        "('0.0064810', '+47.2747672', 0.1199416461909)\n",
        "POSSIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "POSSIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "POSSIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "['raw_1r.fits']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "dss_out: raw_1r_2\n",
        "Only one field in region of interest\n",
        "------------------mosaic_band----------------------"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Now mosaic_band on 2\n",
        "('0.0064810', '+47.2747672', 0.1199416461909)\n",
        "POSSIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "POSSIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "POSSIIB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "['raw_2b.fits']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "dss_out: raw_2b_2\n",
        "Only one field in region of interest\n",
        "------------------mosaic_band----------------------\n",
        "Now mosaic_band on 2\n",
        "('0.0064810', '+47.2747672', 0.1199416461909)\n",
        "POSSIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "POSSIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "POSSIIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "['raw_2r.fits']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "dss_out: raw_2r_2\n",
        "Only one field in region of interest\n",
        "------------------mosaic_band----------------------\n",
        "Now mosaic_band on 2\n",
        "('0.0064810', '+47.2747672', 0.1199416461909)\n",
        "POSSIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "POSSIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "POSSIIIR"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "['raw_2ir.fits']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "dss_out: raw_2ir_2\n",
        "Only one field in region of interest\n",
        "Completed Mosaic"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/dorislee/.virtualenvs/py27dev/lib/python2.7/site-packages/astroquery/vizier/core.py:560: UserWarning: VOTABLE parsing raised exception: \n",
        "  warnings.warn(\"VOTABLE parsing raised exception: {0}\".format(ex))\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "update"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[0.006481, 47.2747672, 0.1199416461909, 0.021408011183161203, 2]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd ../2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Users/dorislee/Desktop/GSoC2014/rc3-sdss/pipeline/2\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DSS_1b_2.fits    DSS_2_BEST.tiff  DSS_2b_2.fits    DSS_2r_2.fits\r\n",
        "DSS_1r_2.fits    DSS_2_LOW.tiff   DSS_2ir_2.fits\r\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import Image\n",
      "import os \n",
      "os.system(\"gm convert DSS_2_BEST.tiff DSS_2_BEST.jpg\") #for ipynb display purpose only\n",
      "Image(filename=\"DSS_2_BEST.jpg\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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MweRTLDMzsZhAc55qqavfQiSuy29i3Z03qI4B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\nI/vVFT2j8UiaW2NDlLo//9k=\n",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<IPython.core.display.Image at 0x104f83950>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "os.system(\"gm convert DSS_2_LOW.tiff DSS_2_LOW.jpg\") #for ipynb display purpose only\n",
      "Image(filename=\"DSS_2_LOW.jpg\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "jpeg": 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gAUdzTXbHuZNs5EiJoizC7IUhh3im67WXNagu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\nQQTtY4Bor9ySoD8Zg1m/keNeRfqemu5gN5MmDXqPpLo+k6t1NU111bS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       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<IPython.core.display.Image at 0x104f83fd0>"
       ]
      }
     ],
     "prompt_number": 18
    }
   ],
   "metadata": {}
  }
 ]
}