paulromano/LinearData.ipynb

## LinearData.ipynb
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "from fudge.legacy.converting.endfFileToGND import endfFileToGND"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "eval, cov = endfFileToGND('n-094_Pu_239.endf', toStdOut=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "       WARNING: distributions for MT=3 (nonelastic) are not supported and have been ignored\n",
        "       WARNING: have prompt fission nu_bar so not including total\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "       WARNING: multiple resolved energy intervals are deprecated!\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "eval.reconstructResonances()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total = eval.getReaction('total')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xs = total.getCrossSection()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linearData = xs.getFormByToken('linear')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(linearData)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "fudge.gnd.reactionData.crossSection.linear"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To access the data, you need to use the `__getitem__` or `__getslice__` methods, e.g."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x, y = zip(*linearData[:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.loglog(x, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "[<matplotlib.lines.Line2D at 0x43fefd0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PX73am7QaS/pt28qdtBqa6XtuxOKrZUvvugHgbfdw++3y8cMPg39NWouM9N6c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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2b01ed0>"
       ]
      }
     ],
     "prompt_number": 10
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": ""
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"%matplotlib inline"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 1
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"import matplotlib.pyplot as plt\n",
	"from fudge.legacy.converting.endfFileToGND import endfFileToGND"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 2
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"eval, cov = endfFileToGND('n-094_Pu_239.endf', toStdOut=False)"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stderr",
	"text": [
	" WARNING: distributions for MT=3 (nonelastic) are not supported and have been ignored\n",
	" WARNING: have prompt fission nu_bar so not including total\n"
	]
	},
	{
	"output_type": "stream",
	"stream": "stderr",
	"text": [
	" WARNING: multiple resolved energy intervals are deprecated!\n"
	]
	}
	],
	"prompt_number": 3
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"eval.reconstructResonances()"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 4
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"total = eval.getReaction('total')"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 5
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"xs = total.getCrossSection()"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 6
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"linearData = xs.getFormByToken('linear')"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 7
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"type(linearData)"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 8,
	"text": [
	"fudge.gnd.reactionData.crossSection.linear"
	]
	}
	],
	"prompt_number": 8
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"To access the data, you need to use the `__getitem__` or `__getslice__` methods, e.g."
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"x, y = zip(*linearData[:])"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 9
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"plt.loglog(x, y)"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 10,
	"text": [
	"[<matplotlib.lines.Line2D at 0x43fefd0>]"
	]
	},
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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w6RMROcj/A/GaRHYO9At0AAAAAElFTkSuQmCC\n",
	"text": [
	"<matplotlib.figure.Figure at 0x2b01ed0>"
	]
	}
	],
	"prompt_number": 10
	}
	],
	"metadata": {}
	}
	]
	}