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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Let's Explore PyNE Meshes!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from yt.config import ytcfg; ytcfg[\"yt\",\"suppressStreamLogging\"] = \"True\"\n",
      "from yt.mods import *\n",
      "from pyne.xs.channels import sigma_t\n",
      "from pyne.material import Material, from_atom_frac\n",
      "from pyne.mesh import Mesh, IMeshTag, MetadataTag, ComputedTag"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ImportError",
       "evalue": "No module named halo_catalog.api",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-2-c9a37f39ff47>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0myt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mytcfg\u001b[0m\u001b[1;33m;\u001b[0m \u001b[0mytcfg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"yt\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"suppressStreamLogging\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"True\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0myt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmods\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpyne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchannels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msigma_t\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpyne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmaterial\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMaterial\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfrom_atom_frac\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpyne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmesh\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMesh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIMeshTag\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMetadataTag\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mComputedTag\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/scopatz/.local/lib/python2.7/site-packages/yt-3.0dev-py2.7-linux-x86_64.egg/yt/mods.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     99\u001b[0m     \u001b[0mRAMSESStaticOutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mRAMSESFieldInfo\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 101\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0myt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrontends\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhalo_catalogs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[1;32mimport\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    102\u001b[0m     \u001b[0mHaloCatalogStaticOutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mHaloCatalogFieldInfo\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[0mRockstarStaticOutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mRockstarFieldInfo\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/scopatz/.local/lib/python2.7/site-packages/yt-3.0dev-py2.7-linux-x86_64.egg/yt/frontends/halo_catalogs/api.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;31m#-----------------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mhalo_catalog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[1;32mimport\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m      \u001b[0mHaloCatalogStaticOutput\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m      \u001b[0mIOHandlerHaloCatalogHDF5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mImportError\u001b[0m: No module named halo_catalog.api"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import yt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yt.__file__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "'/home/scopatz/.local/lib/python2.7/site-packages/yt-3.0dev-py2.7-linux-x86_64.egg/yt/__init__.pyc'"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, let's create a 2D structured mesh, whose grid points in x & y are [Cantor dust](http://en.wikipedia.org/wiki/Cantor_set#Cantor_dust).  Most of the mesh will have water as a coolant.  Some volume elements will have fuel, though."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def cantor(n):\n",
      "    return [0.] + cant(0., 1., n) + [1.]\n",
      "\n",
      "def cant(x, y, n):\n",
      "    if n == 0:\n",
      "        return []\n",
      "    new_pts = [2.*x/3. + y/3., x/3. + 2.*y/3.]\n",
      "    return cant(x, new_pts[0], n-1) + new_pts + cant(new_pts[1], y, n-1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c5 = cantor(5)\n",
      "coords = [c5, c5, [0.0, 1.0]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We access volume elements (`ve`) on the mesh through a volume element index (`ve_idx`).  This index is a unique integer `i` that gives a sort ordering to an otherwise unorder mesh.  The volume element index is defined on the range from 0 (inclusive) to the number of volumes in the mesh (exclusive).  You may access a volume element's material through the `mats` attribute and indexing with `i`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = Mesh(structured_coords=coords, structured=True)\n",
      "fuel = from_atom_frac({'U235': 0.045, 'U238': 0.955, 'O16': 2.0}, density=10.7)\n",
      "cool = from_atom_frac({'H1': 2.0, 'O16': 1.0}, density=1.0)\n",
      "for i in range(len(m)):\n",
      "    m.mats[i] = cool\n",
      "m.mats[len(m)/2] = fuel\n",
      "m.mats[len(m)/4] = fuel\n",
      "for i, c in enumerate(c5[:-1]):\n",
      "    m.mats[i*len(c5)] = fuel"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Tags"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`Tag`s - sometimes known as fields - are generic way of storing data on a mesh.  Tags can be accessed as attributes on the mesh class itself.  For example, the density tag may be grabbed via:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.density "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "MaterialPropertyTag(name='density', doc='the density [g/cc]')"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To get the value of the density, you have to provide the location by its index."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.density[42]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "1.0"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can also slice, mask, or fancy index tags:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print m.density[::100]  # slice"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 10.7   1.    1.    1.    1.    1.    1.    1.    1.    1.    1.    1.\n",
        "   1.    1.    1.    1.   10.7   1.    1.    1.    1.    1.    1.    1.\n",
        "   1.    1.    1.    1.    1.    1.    1.    1.   10.7   1.    1.    1.\n",
        "   1.    1.    1.    1. ]\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print m.density[m.density[:] >= 10]  # mask"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7\n",
        "  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7\n",
        "  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7\n",
        "  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7\n",
        "  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7  10.7\n",
        "  10.7  10.7  10.7  10.7]\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print m.density[[10, 0, 11, 100]]  # fancy index is fancy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  1.   10.7   1.    1. ]\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To get a list of all current tag names, uses the `tags` dictionary on the mesh:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.tags.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "['sub_act',\n",
        " 'sub_lan',\n",
        " 'number_density',\n",
        " 'density',\n",
        " 'comp',\n",
        " 'expand_elements',\n",
        " 'to_atom_frac',\n",
        " 'molecular_weight',\n",
        " 'mass_density',\n",
        " 'atoms_per_mol',\n",
        " 'sub_tru',\n",
        " 'mass',\n",
        " 'attrs',\n",
        " 'mult_by_mass',\n",
        " 'sub_ma',\n",
        " 've_idx',\n",
        " 'sub_fp']"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Analysis & Visulaization"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "PyNE meshes are supported by the [yt project](http://yt-project.org/).  Use yt's plotting infrastructure to display tags on your mesh."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pf = PyneMoabHex8StaticOutput(m)\n",
      "s = SlicePlot(pf, 'z', 'density', origin='native')\n",
      "s.display()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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\"><br>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<yt.visualization.plot_window.AxisAlignedSlicePlot at 0x6155a10>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Computed Tags"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Computed tags - also known as 'derived fields' - are a way of having a lazily evaluated 'virtual tag.'  A computed tag is defined with a function, lambda, or any other callable object.  The function must have the following interface:\n",
      "\n",
      "```python\n",
      "def f(mesh, i):\n",
      "    \"\"\"mesh is a pyne.mesh.Mesh() object and i is the volume element index\n",
      "    to compute.\n",
      "    \"\"\"\n",
      "    # ... do some work ...\n",
      "    return anything_you_want\n",
      "```\n",
      "\n",
      "Here is a somewhat silly example which squares the density.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.density2 = ComputedTag(lambda mesh, i: mesh.density[i]**2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that the bounds on the color bar have changed."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pf = PyneMoabHex8StaticOutput(m)\n",
      "s = SlicePlot(pf, 'z', 'density2', origin='native')\n",
      "s.display()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img 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\"><br>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<yt.visualization.plot_window.AxisAlignedSlicePlot at 0x6f31550>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is a more serious example which uses PyNE's cross section tools to compute the one-group total cross section $\\sigma_t$ everywhere on the mesh."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.sigma_t = ComputedTag(lambda mesh, i: sigma_t(mesh.mats[i], group_struct=[10.0, 1e-6], phi_g=[1.0])[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pf = PyneMoabHex8StaticOutput(m)\n",
      "s = SlicePlot(pf, 'z', 'sigma_t', origin='native')\n",
      "s.display()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img 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\"><br>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<yt.visualization.plot_window.AxisAlignedSlicePlot at 0x70bee50>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Yes, that was only one line of code."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}