{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Nuclear Data\n",
      "\n",
      "PyNE provides a top-level interface for a variety of basic nuclear data needs. This aims to provide quick access to very high fidelity data. Values are taken from the nuc_data.h5 library.  The basic suite of data comes from public sources.  However if you have access to proprietary or export controlled data, such as CINDER cross sections, then PyNE will also attempt to provide an interface to this as well.\n",
      "\n",
      "All functionality may be found in the data module:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pyne import data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The usual suspects follow."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Atomic Mass [amu]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.atomic_mass('U235')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "235.043930131"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Natural Abundance Ratios"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.natural_abund('U235')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "0.007204"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.natural_abund('Pu-239')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Half Lives (s) & Decay Constants (1/s)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.half_life('U-238')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "1.409993568e+17"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.decay_const('U-238')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "4.915959875924379e-18"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Decay Children"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.decay_children('Rb86')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "set([360860000, 380860000])"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data.branch_ratio('Rb86', 360860000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "5.2e-05\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Neutron Scattering Lengths [cm]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.b('H1')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "2.554930849083787e-12"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.b_coherent('H1')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(-3.7406e-13+0j)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.b_incoherent('H1')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "(2.5274000000000003e-12+0j)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Half-life Plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib\n",
      "matplotlib.rc('font', family='serif', size=14)\n",
      "import matplotlib.pyplot as plt\n",
      "from pyne import nucname\n",
      "\n",
      "anums = map(nucname.anum, data.half_life_map.keys())\n",
      "\n",
      "fig = plt.figure(figsize=(7,7))\n",
      "plt.semilogy(anums, data.half_life_map.values(), 'ko')\n",
      "plt.xlabel('A')\n",
      "plt.ylabel('Half-life [s]')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.text.Text at 0x29f5350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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2dgiFQlnbuK2GwylsuEbLPpgubGtra6GmpoYYy7Rjxw64+uqrs7bV19fDq6++mv47kUhA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\ncn6za2pPFl7Pcu8Qi6QNZiTzz8SoWbVRAJggbD/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Ghr2Qzu33+6k8es36mKSuK1cXpYGB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       "text": [
        "<matplotlib.figure.Figure at 0x29e6f50>"
       ]
      }
     ],
     "prompt_number": 12
    }
   ],
   "metadata": {}
  }
 ]
}