article.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:aacb37651b9187f273cffe63a56f90be62a1950e70dbb01a3a5a1e7db7a10df1"
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# import magics here\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Full Title\n",
      "## Full Subtitle"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Authors"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "authors =      [ { 'name' : 'Author One',   'affiliation' : 0 },\n",
      "                 { 'name' : 'Author Two',   'affiliation' : 1 },\n",
      "                 { 'name' : 'Author Three', 'affiliation' : 0 } ]\n",
      "\n",
      "affiliations = [ 'Some University',\n",
      "                 'Some Other University' ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Abstract\n",
      "\n",
      "This is the abstract of the article."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Introduction\n",
      "\n",
      "Cells after this section will be assembled into the Introduction section"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# A figure \n",
      "\n",
      "x = linspace(-10,10,100)\n",
      "plot(x,sin(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f6769a9fc50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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Y/0QmYxTMxGy96Hu1pk31UBoaRPQBERQbMH2MNm2SMRo7lu9VP1gv+oDZXuTu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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6769b5b590>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Methods\n",
      "\n",
      "Cells after this will be assembled into the Methods section"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Results\n",
      "\n",
      "Cells after this will be assembled into the Results section"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Conclusion\n",
      "\n",
      "Cells after this will be assembled into the Conclusion section"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# References\n",
      "\n",
      "This is the References section"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}