fits_viz2-Copy2.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import yt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ds = yt.load(\"fits/grs-50-cube.fits\", nan_mask=0.0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [WARNING  ] 2014-04-29 13:17:07,408 Cannot find time\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,409 Detected these axes: GLON-CAR GLAT-CAR VELOCITY \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [WARNING  ] 2014-04-29 13:17:07,662 No length conversion provided. Assuming 1 = 1 cm.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,676 Parameters: current_time              = 0.0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,676 Parameters: domain_dimensions         = [325 357 424]\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,677 Parameters: domain_left_edge          = [  5.00000000e-01   5.00000000e-01  -5.06022453e+03]\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,678 Parameters: domain_right_edge         = [   325.5           357.5         85054.97932435]\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,679 Parameters: cosmological_simulation   = 0.0\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dd = ds.all_data()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "extrema = dd.quantities.extrema(\"temperature\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [WARNING  ] 2014-04-29 13:17:07,887 Guessing this is a temperature field based on its units of K.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,888 Adding field temperature to the list of fields.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,915 Loading field plugins.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,915 Loaded angular_momentum (8 new fields)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,916 Loaded astro (14 new fields)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,916 Loaded cosmology (20 new fields)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,917 Loaded fluid (56 new fields)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,919 Loaded fluid_vector (88 new fields)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,920 Loaded geometric (103 new fields)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,920 Loaded local (103 new fields)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,921 Loaded magnetic_field (109 new fields)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "yt : [INFO     ] 2014-04-29 13:17:07,922 Loaded species (109 new fields)\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "extrema"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "(-41.0505065918 K, 79.6376800537 K)"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fc = dd.cut_region([\"obj['temperature'] > 0\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print fc[\"temperature\"].max()\n",
      "print len(fc[\"temperature\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "79.6376800537 K\n",
        "26849446\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pplot = yt.ProfilePlot(fc, \"temperature\", [\"ones\"], weight_field=None)\n",
      "pplot.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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\"><br>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<yt.visualization.profile_plotter.ProfilePlot at 0x10c7511d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print fc[\"temperature\"].max()\n",
      "print len(fc[\"temperature\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.873809456825 K\n",
        "48690\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fc._cond_ind.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "26849446"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    }
   ],
   "metadata": {}
  }
 ]
}