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     "source": [
      "##Fitting\n",
      "\n",
      "A lot of time in our analysis we need to extract observable by fitting to a shape function. In this tutorial we will show you how to fit this in Python. The tools we will focus in this tutorial is [iminuit](http://iminuit.github.com/iminuit/), and [probfit](http://iminuit.github.com/probfit/).They are heavily influenced by [ROOFIT](http://roofit.sourceforge.net/) and [ROOT's MINUIT](http://root.cern.ch/root/html/TMinuit.html)(and also [PyMinuit](http://code.google.com/p/pyminuit/)).\n",
      "\n",
      "The basic idea of fitting is quite simple\n",
      "\n",
      "1. We have PDF and data.\n",
      "2. We build our cost function.\n",
      "3. We use minimizer to find shape parameters and one of those is our physics observable.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Minimizer\n",
      "\n",
      "We will start this tutorial with minimizer. Minuit is hands down(for me) the best minimizer there is for HEP.\n",
      "There are couple wrapper for Minuit in Python. The one we will show here is [iminuit](https://github.com/iminuit/iminuit). It's relatively new. It has all the function I use but might not have your favorite feature; but, you are welcome to implement it(I'll point you in the right direction).\n",
      "\n",
      "iminuit has its own [quick start tutorial](http://nbviewer.ipython.org/urls/raw.github.com/iminuit/iminuit/master/tutorial/tutorial.ipynb) that givens you an overview of its feature and [hard core tutorial](http://nbviewer.ipython.org/urls/raw.github.com/iminuit/iminuit/master/tutorial/hard-core-tutorial.ipynb) that teach you how to complex stuff like using cython, make your own costfunction fast, and even parallel computing. We will be showing here basic feature of iminuit. If you need to do advance stuff take a look at those tutorials."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Quick Start"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from iminuit import Minuit, describe"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext inumpy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#define a function to minimize\n",
      "#making x,y correlates on purpose\n",
      "def f(x,y,z):\n",
      "    return (x-2)**2 + (y-x)**2 + (z-4)**2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#iminuit relies on python introspection to read function signature\n",
      "describe(f) # one of the most useful function from iminuit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "['x', 'y', 'z']"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#notice here that it automatically knows about x,y,z\n",
      "#no RooRealVar etc. needed here\n",
      "m = Minuit(f)\n",
      "#it warns about every little thing that might go wrong"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:3: InitialParamWarning: errordef is not given. Default to 1.\n",
        "-c:3: InitialParamWarning: Parameter x does not have initial value. Assume 0.\n",
        "-c:3: InitialParamWarning: Parameter x is floating but does not have initial step size. Assume 1.\n",
        "-c:3: InitialParamWarning: Parameter y does not have initial value. Assume 0.\n",
        "-c:3: InitialParamWarning: Parameter y is floating but does not have initial step size. Assume 1.\n",
        "-c:3: InitialParamWarning: Parameter z does not have initial value. Assume 0.\n",
        "-c:3: InitialParamWarning: Parameter z is floating but does not have initial step size. Assume 1.\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#the most frequently asked question is Does my fit converge\n",
      "#also look at your console it print progress if you use print_level=2\n",
      "m.migrad();\n",
      "#bonus: see that link with a plus sign on the top left corner?\n",
      "#clicking it will give you a latex table that you can copy paste\n",
      "#to your paper/beamer slide;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
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      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = 6.57752842029e-23</td>\n",
        "                <td>NFCN = 52</td>\n",
        "                <td>NCALLS = 52</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 6.57730573206e-23</td>\n",
        "                <td>GOAL EDM = 1e-05</td>\n",
        "                <td>UP = 1.0</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
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        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#ZbNITXtQkz').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>x</td>\n",
        "                <td>2.000000e+00</td>\n",
        "                <td>1.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>y</td>\n",
        "                <td>2.000000e+00</td>\n",
        "                <td>1.414214e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>3</td>\n",
        "                <td>z</td>\n",
        "                <td>4.000000e+00</td>\n",
        "                <td>1.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"ZbNITXtQkz\" style=\"display:none;\">\n",
        "            <textarea rows=\"12\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & x & 2.000e+00 & 1.000e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & y & 2.000e+00 & 1.414e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & z & 4.000e+00 & 1.000e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
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     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Accessing Value/Error"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print m.values\n",
      "print m.errors\n",
      "print m.fval"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'y': 1.999999999999814, 'x': 1.999999999997652, 'z': 3.999999999992544}\n",
        "{'y': 1.414213562372341, 'x': 0.9999999999995464, 'z': 0.9999999999998791}\n",
        "6.57752842029e-23\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.print_param()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#SkjPtoYYPW').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>x</td>\n",
        "                <td>2.000000e+00</td>\n",
        "                <td>1.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>y</td>\n",
        "                <td>2.000000e+00</td>\n",
        "                <td>1.414214e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>3</td>\n",
        "                <td>z</td>\n",
        "                <td>4.000000e+00</td>\n",
        "                <td>1.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"SkjPtoYYPW\" style=\"display:none;\">\n",
        "            <textarea rows=\"12\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & x & 2.000e+00 & 1.000e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & y & 2.000e+00 & 1.414e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & z & 4.000e+00 & 1.000e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
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      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#correlation matrix\n",
      "#Only Chrome/safari gets the vertical writing mode right.\n",
      "m.print_matrix() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "            <table>\n",
        "                <tr>\n",
        "                    <td><a onclick=\"$('#zwAccFjbKq').toggle()\" href=\"#\">+</a></td>\n",
        "        \n",
        "            <td>\n",
        "            <div style=\"width:20px;position:relative; width: -moz-fit-content;\">\n",
        "            <div style=\"display:inline-block;-webkit-writing-mode:vertical-rl;-moz-writing-mode: vertical-rl;writing-mode: vertical-rl;\">\n",
        "            x\n",
        "            </div>\n",
        "            </div>\n",
        "            </td>\n",
        "            \n",
        "            <td>\n",
        "            <div style=\"width:20px;position:relative; width: -moz-fit-content;\">\n",
        "            <div style=\"display:inline-block;-webkit-writing-mode:vertical-rl;-moz-writing-mode: vertical-rl;writing-mode: vertical-rl;\">\n",
        "            y\n",
        "            </div>\n",
        "            </div>\n",
        "            </td>\n",
        "            \n",
        "            <td>\n",
        "            <div style=\"width:20px;position:relative; width: -moz-fit-content;\">\n",
        "            <div style=\"display:inline-block;-webkit-writing-mode:vertical-rl;-moz-writing-mode: vertical-rl;writing-mode: vertical-rl;\">\n",
        "            z\n",
        "            </div>\n",
        "            </div>\n",
        "            </td>\n",
        "            \n",
        "                </tr>\n",
        "                \n",
        "            <tr>\n",
        "                <td>x</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(228,157,137)\">\n",
        "                0.71\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                -0.00\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>y</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(228,157,137)\">\n",
        "                0.71\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                -0.00\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>z</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                -0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                -0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            </table>\n",
        "\n",
        "            <pre id=\"zwAccFjbKq\" style=\"display:none;\">\n",
        "            <textarea rows=\"14\" cols=\"50\" onclick=\"this.select()\" readonly>%\\usepackage[table]{xcolor} % include this for color\n",
        "%\\documentclass[xcolor=table]{beamer} % for beamer\n",
        "\\begin{tabular}{|c|c|c|c|}\n",
        "\\hline\n",
        " & x & y & z\\\\\n",
        "\\hline\n",
        "x & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{228,157,137} 0.71 & \\cellcolor[RGB]{163,254,186} -0.00\\\\\n",
        "\\hline\n",
        "y & \\cellcolor[RGB]{228,157,137} 0.71 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{163,254,186} -0.00\\\\\n",
        "\\hline\n",
        "z & \\cellcolor[RGB]{163,254,186} -0.00 & \\cellcolor[RGB]{163,254,186} -0.00 & \\cellcolor[RGB]{255,117,117} 1.00\\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
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      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Checking convergence\n",
      "More details on this in the tip section at the end. Basically return value of migrad tells you a bunch of fit status."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print m.migrad_ok()\n",
      "print m.matrix_accurate()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n",
        "True\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####minos"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.minos(); #do m.minos('x') if you need just 1 of them"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <span>Minos status for x: <span style=\"background-color:#92CCA6\">VALID</span></span>\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>Error</td>\n",
        "                <td>-0.999999999998</td>\n",
        "                <td>1.0</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>Valid</td>\n",
        "                <td style=\"background-color:#92CCA6\">True</td>\n",
        "                <td style=\"background-color:#92CCA6\">True</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>At Limit</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>Max FCN</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>New Min</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
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       "html": [
        "\n",
        "        <span>Minos status for y: <span style=\"background-color:#92CCA6\">VALID</span></span>\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>Error</td>\n",
        "                <td>-1.41421356237</td>\n",
        "                <td>1.41421356237</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>Valid</td>\n",
        "                <td style=\"background-color:#92CCA6\">True</td>\n",
        "                <td style=\"background-color:#92CCA6\">True</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>At Limit</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>Max FCN</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
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        "                <td>New Min</td>\n",
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       "html": [
        "\n",
        "        <span>Minos status for z: <span style=\"background-color:#92CCA6\">VALID</span></span>\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>Error</td>\n",
        "                <td>-0.999999999993</td>\n",
        "                <td>1.00000000001</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>Valid</td>\n",
        "                <td style=\"background-color:#92CCA6\">True</td>\n",
        "                <td style=\"background-color:#92CCA6\">True</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>At Limit</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>Max FCN</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>New Min</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "{'x': {'lower_new_min': False, 'upper': 1.0000000000023477, 'lower': -0.9999999999976521, 'at_lower_limit': False, 'min': 1.999999999997652, 'at_lower_max_fcn': False, 'is_valid': True, 'upper_new_min': False, 'at_upper_limit': False, 'lower_valid': True, 'upper_valid': True, 'at_upper_max_fcn': False, 'nfcn': 18L},\n",
        " 'y': {'lower_new_min': False, 'upper': 1.414213562373281, 'lower': -1.4142135623729089, 'at_lower_limit': False, 'min': 1.999999999999814, 'at_lower_max_fcn': False, 'is_valid': True, 'upper_new_min': False, 'at_upper_limit': False, 'lower_valid': True, 'upper_valid': True, 'at_upper_max_fcn': False, 'nfcn': 28L},\n",
        " 'z': {'lower_new_min': False, 'upper': 1.000000000007456, 'lower': -0.9999999999925441, 'at_lower_limit': False, 'min': 3.999999999992544, 'at_lower_max_fcn': False, 'is_valid': True, 'upper_new_min': False, 'at_upper_limit': False, 'lower_valid': True, 'upper_valid': True, 'at_upper_max_fcn': False, 'nfcn': 18L}}"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.merrors"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "{('x', -1.0): -0.9999999999976521,\n",
        " ('x', 1.0): 1.0000000000023477,\n",
        " ('y', -1.0): -1.4142135623729089,\n",
        " ('y', 1.0): 1.414213562373281,\n",
        " ('z', -1.0): -0.9999999999925441,\n",
        " ('z', 1.0): 1.000000000007456}"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Contour and Profile(Scan)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.draw_mncontour('x','y') # you can get the raw value using m.mncontour  or m.mncontour_grid;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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fNAhaZmZwWLwYnFQKJWUaR/IFFb0CeLELpeOyZRj0x6iJ75YvD8e4cb1pJ8q3\nlJtbjqVLf8OjRyW4etUb5ubarCMBQGOB11dVgZNI0FBbCwDQ0NOD5/Hj+GXsWHQfNgw6FhY0hcMj\n9JLLc9XPnuHo6NGw8/ZWmJK/cCEdV68+wtato1hHUUjHj8fDzm4fbG31EB29kDclH71nD65+8QVy\n79yBqro6jIYMQcT/bH9tOGAABq1ahetbtwKgKRw+oaLnMUldHY6PHw/j0aN5v9XwC1VV9ViyJAzf\nfz8OGhp0d6h/IpU2vapMQ6M1Ll6ch88+c+HN+viwJUuQ9OuvUFFVhWj9ekRt2YI+c+ZAz8YGJ6dP\nb3xczxEjGm84T6vn+IOKnscuf/wx2mppwXX7doUZHW3bdh0ODl3h7m7COgqvlJfX4vDh+ygpqXnl\nYz08zGBrqy+HVK+nvqoKtaWlmBUWhiEffYRBq1ahurgY0bt3Y8ovv+BpWhpEGzei6MEDPAgMxLOM\nDEjq6xXmZ7YloKLnqfQLF5Bw4gQmHjigMCe1MjKeYe/eW/jqq9Gso/BKYGAS+vX7ESdOJGD16kvY\nsycawKtH9nyhqq6O2vJy/P711wCAHs7O6DV6NIoSE5F37x5mnD4NAIj4+GMUJydjwv79vL75fEtE\nF0zxUGVREX6ws8Okw4fRa+RI1nFe29Spv8LeXh+ffDKMdRRe2bbtOjp2bIPFix1w924upkz5FRcu\nzIWZmTYvT1g21NQgaN48aJqaok3HjhiyejWyRCLE7N+PvosWofvQoagqLsatPXug0bUrHBYvBgDU\nlpWhTQd+77cjZE11p2IMFVsQjuMQ4uODPnPnKlTJX7/+GNHROVixYhDrKMxdvJiOmzfFAJ5fGZyU\nVIQuXdoDAPr164qZM63x4YfnWUZ8qdrycpyeOxftDQxgPmkS4o4cQfSePejYrRu6ODgg7sgRlIrF\nUNfWhpa5OcTXrzf+WSp5/qKi55k733+P8txcuGzaxDrKG9m+/QbWrRsGdfWW+5b9yZMqDBq0H599\ndhWrVl3Ejh03AAD29vr4/vs7jY/78suRSEwsxL17ebwbzbdSU0NDTQ36+vnBoH9/eB4/jtSQEBQm\nJMDEzQ0aeno4PXs2CuLjEXfkCDr36gVOKmUdm7wCFT2PFCUlQbR+Pab88gtUWivOipWamgZERmZh\nyhQL1lGYiosrgIWFNqKivPHtt2NRVFSFbduuY/nygYiNzcf1648BPH+LPXu2DSIiMhkn/jOO4yCt\nr4eOpSUEgRq0AAAXC0lEQVSepKaivroaejY2sPPxQfTu3VDX1obLpk3oNnQo7v7wA7RMTeG8YYPC\nnENqyeg7xBMNNTU4NXs2Rm7ZAm0zM9Zx3siNG2JYWupAS0uddRS5SkoqwvnzaSgsrAQAcBwQE5MP\nALCz08eoUb2QlvYUDx4UYdMmF3z8cQSKi6sAPN9L3t6ePytrXpwrUFVXh4a+PtLDw1GR//xYbGbN\ngrq2NkTr1wMARm7eDPddu+C+axfLyOQNUNHzxOU1a6BpbAx7X1/WUd5YcnIx+vTRYx1DbiQSKbZv\nvw5396MIDHwAD4/jyMoqgY2NLuzt9REcnAwAsLXVg6WlDoKCkrFwYV/06aOH1asvwdX1CG7ezGa+\nZ0388eO4tXcv8mJi/vT7Az/8EHWVlUg4fhzFKSkAgL5+fmin/98XJmUVFblmJe+Gip4H0s6fR1Jg\nIMb/+CPv5mxfR0bGM/Tq1XL2GVdWVsK1a2KcPTsHP/3kAScnI2zadBVVVfVwcOiKS5cyUVcngY5O\nO2hotEZBwfMR/5dfjsTy5QMwZ44NYmIWw8aG3YtjyMKFuP3ttyjPycHZ997Do6tXATy/SE9JWRnO\nGzeiTCzGlY0bcWPHDoQtWkR7ySswKnrGKgsLEeLjg0mHDkFdS4t1nLeSmVmCXr06s44hN8+e1UBf\nXwP5+RUAgM2bRyA/vwJxcQUYONAQtbUN2LXrdwCAiYkmCgqeP65du9bo00cPCxbYMcsOAAkBAair\nqIDPtWsYuWULeo4Ygbs//AAAUGndGtKGBmgaG2PYunWw8/ZGeXY2XLdvx4D332eam7w9uRe9j48P\n9PT0YGNjI++n5h2O4xDi64s+8+ah54gRrOO8tczMZ+jRQ5ijvRdl3tDw35UlGhqtUVvbgCdPqlBT\n04C2bVUxfrwpjh6NR9++XeDn1xc//HAXS5aEYd68IIwZY8Kra0NM3Nzgum1b4+e2Xl5QUlKCtKEB\nnFQK5VatwEmlaNO+PYxdXeH61VewmDKFYWLyruRe9N7e3ggPD5f30/LS7e++Q3leHlw++4x1lLfG\ncdwfUzfCGtEHByejZ89dmDIlAADQqtXzfypSKYfWrVXg4tIDYWEPkZtbDgBYutQRd+7k4t69PDg6\nGiA8fC5GjeqFq1cXYP58O15Nyal16oSO3bo1fv4kNRW15eVQbtUKSsrKqH76FNF79qAgLg4AzccL\ngdyLfujQoejcWVil8DYKExNxZcMGeCrYUsq/KiqqgrKykuBW3Fy7JsZHHw1Gp05qOHQoFsDzk7Av\n+nr+/OfTL8HByY1lP3x4d7Rt+3wTMhMTTUydaglTU/5Ox71Y/16Rnw9da2sAwP3Dh5Fz6xZMx4yB\nkZMTy3ikGfFja7y/2LBhQ+PHzs7OcHZ2ZpZFFhpqanB69myM/PJLaPVW7P3as7PL0K1bR9Yxmt36\n9cOhodEa2trq2LbtBubPt4OKijI4jkN9vQSqqipYsWIgTp16AB+fYJSW1qJjxzYK9f/ixfr31u3b\no6GmBmf/9S/kREdjZnAw2nftyjgdeRWRSASRSPRaj2Wy101WVhY8PDwQHx//90AtYK+b8ytWoFQs\nxrSTJ3n1lv5t3L2bCz+/UNy7t5h1FJmor5fA0/NXjBzZE8uXD2ws+f8VFPQAbduqKuyOnZfXrMG1\nrVsx7JNPFO6KbPJftNcNj6SFhyPp1Cl4KOhSyr9q06YVnj6thkQizMvgVVVV8N57jvD3j238/MmT\nKhw7Fofo6GwAwOTJFrwu+cqiIlz++GNI6uv/8eu9PTww5ehRKnkBo6KXo8qiIoT4+mLSoUNoq6nJ\nOk6zsLLSgYFBBxw6dJ91lHfyshcqjuPg5maCgQMN8cEH57B16zVERGSiR49OGDDAUM4p3wzHcYg7\ndgzf29hAUlsLTiL5x8cZOTnBZvZsOacj8iT3op81axacnJyQmpoKIyMj+Pv7yzsCExzHIWzRoudL\nKV1cWMdpNkpKSvjmGzesXRuB7Owy1nHeiEQihUiUhUWLQtG16048fVr9t8coKSmhvl6C/PwK/PDD\nXeTklGPyZAsMHtztH/5G/igVi3F8/Hhc37oVs0JDMXrHDrRSU2MdizAi95Oxx48fl/dT8kJqaCiK\nk5MxNSCAdZRm5+hogJUrB2HgwJ8RFDQDjo4GrCO9VF2dBJGRmTh9OhlnziSja9f2mDnTCrdv+0FT\ns+0//hl//1jo6raDWLyC+bYFr8JJpbizbx8i163DwA8/xIygIIVe1UWaB914RA7qKirwnZUVJvr7\nK/SFUa9y5kwy/PxCsXr1YCxfPuBvJy1ZKSurxW+/pSI4OAXnz6fDzEwLnp4WmDLFAsbGr55C4+PN\nQf5JcUoKQhcuBCeVwuPnn6Fj0bJ3E21pmupOKno5uPn118i+cQPTTp5kHUXm0tOfYsmS35CTU4ad\nO92YnaTMzS1HSEgKgoNTcP36Ywwd2h2TJplh/PjejTcBEQpJfT1ubN+Omzt3Yvj69ej/3nu0dXAL\nREXPkFQiwR4TE0wNCIBB//6s48gFx3EIC0vFypUX0KmTGnx87DFzpjU6dZLdHHFZWS2uXXuMS5cy\ncPlyJsTiUowZY4qJE83g7m6CDh3ayOy5Wcq9exchvr7Q0NfH+B9+QKfu3VlHIoxQ0TOUFh4O0fr1\nWBgdzTqK3DU0SHHxYjr8/WMRHp4GJycjODkZYeBAQ/Tvb/BWxV9fL0FmZglSUoqRnFyMe/fycfdu\nLnJyytG/vwFGjeqJUaN6oV+/ro3bFghRfVUVRBs24P6hQ3D96iv0mTtXIaaXiOxQ0TMUtmQJNE1M\n4LRqFesoTJWV1eLy5QxER+fg99+zcedOLtTUWsHIqCOMjDpAS0sdbdqoQE2tFVq1UkZNTQOqqxtQ\nXV2Pp0+rUVRUhcLCShQVVaJr1/YwM9OGmZkW7O310a9fV5ibawu62P9XZmQkQv38YODoCPddu9BO\nV5d1JMIDVPSMcFIpvjYygldEhMLdNUrWpFIORUWVyM4ug1hchqdPq1Fb24DaWgnq6yVQU2sFNbVW\naNtWFZqabaGr2w46OurQ09OAmhovd+6QuZrSUlz6v//Dw7NnMfa772Dm4cE6EuGRprqzZf6LkZO8\ne/fQun17Kvl/oKysBD09DejpaaBfP9pX5VUeBAXh3Pvvo/f48ViakAC1joqzpw5hj4pehlJCQmA+\ncSLrGESBlWVnI3z5chQmJMDz+HF0HzqUdSSigFrGpCYjKcHB6E1vr8lbqCkpwaWPPsI+W1voWFlh\nyf37VPLkrdGIXkZKsrJQnpcHw0GDWEchCqS+uhq3v/sO17duhdmECVhy/z46GPJ7Tx3Cf1T0MpIS\nEgLTsWPp7jzktUjq6nBv/35EffEFDBwdMT8yErpWVqxjEYGgopeRlJAQOL73HusYhOekEgnijh7F\nlY0boWVqihlBQTBwdGQdiwgMFb0M1JSUIOfWLcwMDmYdhfAUJ5UiKTAQovXroa6tjUkHD6L7sGGs\nYxGBoqKXgbTwcHQfNgyt2/F7p0MifxzH4eHZs4hcuxZKKipw++YbGI8eTVe1EpmiopeBlOBgmE2Y\nwDoG4ZnMiAhErF2L2rIyuGzaBPNJk6jgiVzQlbHNTFJXh6/09LAsKQntu3RhHYfwgPjmTUSuXYvS\nx48xfMMGWM+cSSfpSbOjK2Pl6FFUFLR696aSJ8iPjUXkp58i//59DF+3Drbz50NFVZV1LNICUdE3\ns7SzZ2E6bhzrGISh4uRkRK5bh8dRURiyZg2mBQaiVRthbpNMFANdGdvM0sLDYTJmDOsYhIFnmZk4\ns2AB/IcNQ5d+/fB+WhoGfPABlTxhTu5FHx4eDnNzc5iammLr1q3yfnqZqikpQcmjR+jSty/rKESO\nynNz8duyZfjJ0REdu3XD+w8fYsjq1bTqivCGXIteIpHgX//6F8LDw5GUlITjx4/jwYMH8owgU8Up\nKdA2M6MTbS1EZVERLqxahe9tbNC6XTv8KzkZLp99RjtLEt6Ra9HfunULJiYm6NGjB1RVVTFz5kwE\nC+iiomcZGdA0YXOPVCI/NSUliPz0U3xrbo6G6mosjY+H6/btUNfWZh2NkH8k15OxOTk5MDIyavzc\n0NAQ0f9wi70NGzY0fuzs7AxnZ2c5pHt3dRUVaNOhA+sYREZqy8oQvXs3onftQm8PDyy6exedevRg\nHYu0UCKRCCKR6LUeK9eif92LQ/636BVJQ00NVFq3Zh2DNLOa0lLc+f57/P711zAePRo+N25Ay9SU\ndSzSwv11ELxx48aXPlauRW9gYACxWNz4uVgshqGAtmDV0NdHVmQk6xikmZSKxYjetQux/v4wcXfH\n/MhI6Fhaso5FyBuTa9E7ODjg4cOHyMrKQteuXREQEIDjx4/LM4JMafXujWIBnVxuiaQSCTIuXkTM\n/v3IuHwZdgsWYHFMDDp268Y6GiFvTa5F36pVK+zduxdubm6QSCTw9fWFhYWFPCPIlK6VFWrLy1EQ\nFwe9Pn1YxyFv4Gl6Ou4fPIjYgweh0aUL7H184PHzz7SChggC7XXTzK5s2oTy7GyM/+EH1lHIK9RX\nVSHp1CnEHjiAwsRE9JkzB3be3vQiTRRSU91JRd/MKvLz8a2FBT5IT0dbTU3WcchfcByHnOhoxBw4\ngKTAQBgOHAh7X1+YeXjQiXSi0Kjo5ez03LnQt7OD06pVrKOQP1QUFCDuyBHEHDgAaUMD7H180Gfe\nPHQwMGAdjZBmQUUvZzm3biFwxgy8n5ZGV8kyJKmvx8OzZxF74AAeXb0K88mTYe/jA6PBg2kfeCI4\ntE2xnBn07492enpIDQuD+cSJrOO0OEVJSYj198f9I0egZWoKO29vTDl2DK01NFhHI4QJGtHLSNyx\nY4g9cABely+zjtIi1JSWIuHECcT6+6NMLIatlxfsvL2h1bs362iEyAVN3TAgqavDNz16YO7589Cz\nsWEdR5A4qRRZIhFi/f2REhqKXiNHws7HByZublBuRW9WSctCRc9I1ObNKEpMxJRjx1hHEZSSR49w\n/9AhxB48iDbt28POxwd95syhTcVIi0ZFz0hNaSl+cnDA0E8+gd2CBazjKLT66moknzmDWH9/5N29\nC+tZs2Dv4wN9e3s6sUoIqOiZKkpKwkFnZ8w8cwZGTk6s4ygUjuOQc+sW7h86hMSAAHTp1w923t6w\nmDwZrdTUWMcjhFeo6Bl7eO4cznh5YeSXX8Le2xtKynQHx5fhpFLk3L6NpJMnkfTHvVb7zJsHWy8v\n2m+GkCZQ0fNA/v37CFu8GJxEAvfdu2E0aBDrSLxRlp2NzMhIpJ8/j/QLF6CurQ1LT09YTp8OXWtr\nmpoh5DVQ0fMEJ5Ui/pdfcHnNGmhbWMB2/nyYT5zYYtZ3cxyHivx8FCYkoCAuDjnR0RDfuAFJbS26\nDR0KE3d3GLu5oVP37qyjEqJwqOh5pqGmBg9On0b8sWN4fP06TMeOhcWUKejl6iqY3RJrSkpQmJiI\nwvh4FCYkNP4XSkrQs7GBrrU1uvbvDyMnJ3Tu1YtG7YS8Iyp6HqssKkJSYCBSQ0Px+No1dLG3h7G7\nO0zHjIGerS3vC7C+uhrFDx6gID4eRQkJz0s9IQE1JSXQsbSE7h+lrmtjA10rK7TT0+P9MRGiiKjo\nFUR9VRWyRCKknTuHtPPnUVdeDmM3N5i4u6OXqyvUtbSYZZM2NODJw4d/Gp0XJiSgTCyGpqkp9Gxs\noGNt/bzUra3RqXt3OulMiBxR0Suop+npSAsPR3p4OLKuXIGOpSVMxoyBibs7ujo4yGTDNI7jUPro\nUWORv/j1JDUVHQwMoGttDR0rK+ja2EDPxgaapqZQUVVt9hyEkDdDRS8ADbW1eBwV9bz4z59HeV4e\neo0ahZ4jRqDniBHobGz8RlMiHMehIi8PRQ8eoDAh4b/TLomJaNOhQ+PI/MXUi46FBVTV1WV4hISQ\nd0FFL0Bl2dlIv3ABWZGRyIyIAJSUoG1ujo7duqGjkRHUOndufKy0oQHVT5+i+skTVBYVoSQrC09S\nU9G6XTtomZn9bdql7f/8WUKIYqCiFziO4/AsIwPP0tNR+vgxysRi1JSUAH+M8JWUldFWSwvqWlpo\nq6WFTj16QMvUFGqdOjFOTghpLlT0hBAicE11p1yXRZw8eRJWVlZQUVHBvXv35PnUhBDSYsm16G1s\nbBAUFIRhw4bJ82kJIaRFk+vdGczNzeX5dIQQQsDTe8Zu2LCh8WNnZ2c4Ozszy0IIIXwkEokgEole\n67HNfjLW1dUV+fn5f/v9zZs3w8PDAwDg4uKCHTt2oG/fvn8PRCdjCSHkjTXVnc0+or948WJz/5WE\nEELeAbPNSGjUTggh8iHXog8KCoKRkRF+//13jBs3DmPGjJHn0xNCSItEF0wRQogA8OaCKUIIIfJH\nRU8IIQJHRU8IIQJHRU8IIQJHRU8IIQJHRU8IIQJHRU8IIQJHRU8IIQJHRU8IIQJHRU8IIQJHRU8I\nIQJHRU8IIQJHRU8IIQJHRU8IIQJHRU8IIQJHRU8IIQJHRd9MXvdu7EJCxyx8Le14AWEeMxV9MxHi\nD8er0DELX0s7XkCYx0xFTwghAkdFTwghAsfLm4MTQgh5cy+r81ZyzvFKPHvdIYQQhUdTN4QQInBU\n9IQQInBU9IQQInBU9M0gPDwc5ubmMDU1xdatW1nHkTkfHx/o6enBxsaGdRS5EYvFcHFxgZWVFayt\nrbF7927WkWSupqYGAwYMgJ2dHSwtLbFmzRrWkeRCIpHA3t4eHh4erKM0G96tulE0EokEZmZmuHTp\nEgwMDODo6Ijjx4/DwsKCdTSZiYqKgoaGBry8vBAfH886jlzk5+cjPz8fdnZ2qKioQL9+/XDmzBlB\nf58BoKqqCurq6mhoaMCQIUPw1VdfYciQIaxjydTOnTtx9+5dlJeXIyQkhHWcZkEj+nd069YtmJiY\noEePHlBVVcXMmTMRHBzMOpZMDR06FJ07d2YdQ6709fVhZ2cHANDQ0ICFhQVyc3MZp5I9dXV1AEBd\nXR0kEgk0NTUZJ5Kt7OxsnD17FgsXLhTUCkAq+neUk5MDIyOjxs8NDQ2Rk5PDMBGRtaysLMTExGDA\ngAGso8icVCqFnZ0d9PT04OLiAktLS9aRZGrFihXYvn07lJWFVY3COhoG6AKvlqWiogJTp07Frl27\noKGhwTqOzCkrKyM2NhbZ2dm4evWqIPeBeSEsLAy6urqwt7cX1GgeoKJ/ZwYGBhCLxY2fi8ViGBoa\nMkxEZKW+vh6enp6YO3cuJk2axDqOXHXs2BHjxo3DnTt3WEeRmRs3biAkJAQ9e/bErFmzEBERAS8v\nL9axmgUV/TtycHDAw4cPkZWVhbq6OgQEBGDChAmsY5FmxnEcfH19YWlpiQ8//JB1HLkoLi5GSUkJ\nAKC6uhoXL16Evb0941Sys3nzZojFYmRmZuLEiRMYMWIEDh8+zDpWs6Cif0etWrXC3r174ebmBktL\nS8yYMUPwKzFmzZoFJycnpKamwsjICP7+/qwjydz169dx9OhRREZGwt7eHvb29ggPD2cdS6by8vIw\nYsQI2NnZYcCAAfDw8MDIkSNZx5IbIU3L0vJKQggROBrRE0KIwFHRE0KIwFHRE0KIwFHRE0KIwFHR\nE/IKt2/fhq2tLWpra1FZWQlra2skJSWxjkXIa6NVN4S8hk8//RQ1NTWorq6GkZERVq9ezToSIa+N\nip6Q11BfXw8HBwe0bdsWN2/eFNQaayJ8NHVDyGsoLi5GZWUlKioqUF1dzToOIW+ERvSEvIYJEyZg\n9uzZyMjIQF5eHvbs2cM6EiGvrRXrAITw3eHDh9GmTRvMnDkTUqkUTk5OEIlEcHZ2Zh2NkNdCI3pC\nCBE4mqMnhBCBo6InhBCBo6InhBCBo6InhBCBo6InhBCBo6InhBCB+3/b5ZeeRyJa/AAAAABJRU5E\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x103ebcd10>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.draw_profile('x') # this is 1d evaluation not minos scan;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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jEjmy9QgN2jbg1N5TVGuQ/eyxYs2KnNx7Er9uflz++zIpSSmULFeS4qWLkxiX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\nZhynvDKZcZyef/55Pv74Y4oVy7l7Lsh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       "text": [
        "<matplotlib.figure.Figure at 0x104183990>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Initial value, Limit, initial error, fixing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = Minuit(f, x=2, y=4, fix_y=True, limit_z=(-10,10), error_z=0.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:1: InitialParamWarning: errordef is not given. Default to 1.\n",
        "-c:1: InitialParamWarning: Parameter x is floating but does not have initial step size. Assume 1.\n",
        "-c:1: InitialParamWarning: Parameter z does not have initial value. Assume 0.\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.migrad();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = 2.00000011701</td>\n",
        "                <td>NFCN = 30</td>\n",
        "                <td>NCALLS = 30</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 1.17011093083e-07</td>\n",
        "                <td>GOAL EDM = 1e-05</td>\n",
        "                <td>UP = 1.0</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#zUJhrEtzMQ').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>x</td>\n",
        "                <td>3.000070e+00</td>\n",
        "                <td>7.071068e-01</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>y</td>\n",
        "                <td>4.000000e+00</td>\n",
        "                <td>1.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>3</td>\n",
        "                <td>z</td>\n",
        "                <td>3.999673e+00</td>\n",
        "                <td>9.980094e-01</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>-10.0</td>\n",
        "                <td>10.0</td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"zUJhrEtzMQ\" style=\"display:none;\">\n",
        "            <textarea rows=\"12\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & x & 3.000e+00 & 7.071e-01 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & y & 4.000e+00 & 1.000e+00 &  &  &  &  & FIXED\\\\\n",
        "\\hline\n",
        "3 & z & 4.000e+00 & 9.980e-01 &  &  & -1.000e+01 & 1.000e+01 & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Building cost fuction using probfit\n",
      "\n",
      "You could write your always own cost function(see iminuit hardcore tutorial for example). But why should you. probfit provide convenience functor for you to build a simple cost function like UnbinnedLH, BinnedLH, BinnedChi2, Chi2Regression.\n",
      "\n",
      "Let's try to fit a simple gaussian with unbinned likelihood."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from math import exp, pi, sqrt\n",
      "from probfit import UnbinnedLH\n",
      "from iminuit import Minuit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "seed(0)\n",
      "gdata = randn(1000)\n",
      "hist(gdata,bins=100, histtype='step');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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lrEq7NZ999lnd8urVq8nKyrIxjf+Ki4t56qmnWL16NZdccondcS5IOP7fWlN0\nfoO9LMti4sSJpKSkMHnyZLvj+OXgwYNUVFQAcOrUKdavX99y57TFp6V9+/Z13KiSsWPHWmlpaVZG\nRoY1ZswYq7y83O5IfunXr5/Vp08fKzMz08rMzLQmTZpkdyS/rFy50oqPj7cuueQSy+12W8OHD7c7\nkk/Wrl1rJSYmWldddZU1e/Zsu+P45Y477rB69uxpdezY0YqPj7eef/55uyP55b333rNcLpeVkZFR\n97r/85//bHcsn2zbts3KysqyMjIyrAEDBlhPPvlki88PaJIpERGxj66AIyLiMCpuERGHUXGLiDiM\niltExGFU3CIiDqPiFhFxmP8H56dCV+kKLoYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10419d8d0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#you can define your pdf manually like this\n",
      "def my_gauss(x, mu, sigma):\n",
      "    return exp(-0.5*(x-mu)**2/sigma**2)/(sqrt(2*pi)*sigma)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#probfit use the same describe magic as iminuit\n",
      "#Build your favorite cost function like this\n",
      "#Notice no RooRealVar etc. It use introspection to\n",
      "#find parameters\n",
      "#the only requirement is that the first argument is\n",
      "#in independent variable the rest are parameters\n",
      "ulh = UnbinnedLH(my_gauss, gdata)\n",
      "describe(ulh)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "['mu', 'sigma']"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = Minuit(ulh, mu=0.2, sigma=1.5)\n",
      "m.set_up(0.5) #remember up is 0.5 for likelihood and 1 for chi^2\n",
      "ulh.show(m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:1: InitialParamWarning: Parameter mu is floating but does not have initial step size. Assume 1.\n",
        "-c:1: InitialParamWarning: Parameter sigma is floating but does not have initial step size. Assume 1.\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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hQK4A2fNsMvvfuaNV3jgxkS5qbt8OVK9Op/bDhpnNTQky9z2Q1/+xbTkkzlqD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\nWUXVpMTlCbM4fZpWvz19Gmj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       "text": [
        "<matplotlib.figure.Figure at 0x104174c50>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.migrad();\n",
      "ulh.show(m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = 1405.88688927</td>\n",
        "                <td>NFCN = 67</td>\n",
        "                <td>NCALLS = 67</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 7.65781084211e-07</td>\n",
        "                <td>GOAL EDM = 5e-06</td>\n",
        "                <td>UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#uYVtOczqlw').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>mu</td>\n",
        "                <td>-4.528735e-02</td>\n",
        "                <td>3.121221e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>sigma</td>\n",
        "                <td>9.870165e-01</td>\n",
        "                <td>2.206980e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"uYVtOczqlw\" style=\"display:none;\">\n",
        "            <textarea rows=\"10\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & $\\mu$ & -4.529e-02 & 3.121e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\sigma$ & 9.870e-01 & 2.207e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "output_type": "display_data",
       "png": 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tW8s5JEotVarAmDFy3cYvdTX2RTtyA6geZ1NZ/61qvzP9BJWXLgCgzfcT+OQT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\nJYx25LczW7fKAT/A1me/0r0CNI5LxYo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       "text": [
        "<matplotlib.figure.Figure at 0x103de2fd0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Another way to fit gaussian\n",
      "probfit comes with a bunch of builtin functions so you don't have to write your own pdf. If you can't find your favorite pdf there is nothing preventing you from doing:\n",
      "\n",
      "```\n",
      "def my_secret_pdf(x,y,z):\n",
      "    return secret_formula(x,y,z)\n",
      "```\n",
      "\n",
      "But, it's better if you fork our project, implement it and submit a pull request."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from probfit import gaussian"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ulh = UnbinnedLH(gaussian, gdata)\n",
      "describe(ulh)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "['mean', 'sigma']"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = Minuit(ulh, mean=0.2, sigma =0.3)\n",
      "m.migrad()\n",
      "ulh.show(m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:1: InitialParamWarning: Parameter mean is floating but does not have initial step size. Assume 1.\n",
        "-c:1: InitialParamWarning: Parameter sigma is floating but does not have initial step size. Assume 1.\n"
       ]
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = 1405.8868885</td>\n",
        "                <td>NFCN = 84</td>\n",
        "                <td>NCALLS = 84</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 2.0619807636e-10</td>\n",
        "                <td>GOAL EDM = 5e-06</td>\n",
        "                <td>UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#byMznKMYLP').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>mean</td>\n",
        "                <td>-4.525667e-02</td>\n",
        "                <td>3.121275e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>sigma</td>\n",
        "                <td>9.870336e-01</td>\n",
        "                <td>2.207075e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"byMznKMYLP\" style=\"display:none;\">\n",
        "            <textarea rows=\"10\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & mean & -4.526e-02 & 3.121e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\sigma$ & 9.870e-01 & 2.207e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "output_type": "display_data",
       "png": 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4cSLb/uPHpSO/f192xVy9Ove6zp2TD8T792X3+q++0ve9A/rXrxXlyBW6pk4d\n6XymT4cGDfIoOGcOxMdD27ayy2Ex4UoFL463TB2dOmVKjmVq1ZL20XLZjz0myz7/vJmFKooU5cg1\noPc4m571m0N7JeMlOegHZM5xC3Y3tITtD3aaCk5OsGGDjDOZET3fO6B//VpRjlxR7Bl9eybcuSOH\nTzZvbm05Zude2SpykBBYbOi+wrZQjlwDeo+z6Vl/YbXbnY9l8N0v5cp//lN4QfnEYrYfP172L9+y\nBXbtMlu1er53QP/6taIcuaJYU3bedEqSCP37g5+fteUUHY89Bm+9JZenTlU5yx8xlCPXgN7jbHrW\nXxjt9nFncQr5FiN2OadItAAWtf3YsdKh794Nv/1mlir1fO+A/vVrRTlyRbGl/NcfY0hK4gdDP5oM\nqkOTJnIOiWJLuXLw9ttyefp062pRWBTlyDWg9zibnvUXVPvjyRcou2YxAE1+msJXX8nc5IcOmVGc\nBixu+9dfBxcXGSffvbvQ1en53gH969eKcuSKYskrt+ZgSHwAvXvj2asuTZoUeX4s26BcufQeLGkD\noBTFHuXINaD3OJue9RdI+5UrDLr9lVyeOtWsevKLVWw/ZoycKGPzZpxPFu4niJ7vHdC/fq0oR64o\nfnz6KU7iLnfbP5N7YpHiTMWKMGoUADWWq1b5o4By5BrQe5xNz/rzq73E7etyOjQgPti6rXGwou3H\njYNSpai4J5Qn7xwvcDV6vndA//q1ohy5oljhtflzuH2bXaU6ktgwwNpyrEeVKnKyZuDFcx9aWYyi\nqFGOXAN6j7PpWX9G7TduQFwcXL6cc9lSSQnUCZM5VeaXt8xQ9YsX4cKF3Pdb1fbjx5Pi4Ej7qyFw\n6lSBqtDzvQP6168VlY9coRtq1JCdMgwGaNcu+/62/yyhxJ14aNmSQ+daFbkeX1/o1Usu16tX5KfL\nP25uXO4ylKq/fAOzZ4PXYmsrUhQRD22Rh4WF4e3tjaenJx999FG2/aGhofj5+eHv70/jxo3Zvn17\nkQi1JnqPs+lZf0bt9+/D2bOyVb5iRZaCSUl0PfWpXB4/3iLaNm+WWuLi5HJOWNv25waMJwUDrFhB\nmVt5/HTIBWvrLyx616+VPB250WgkODiYsLAwIiMjWb16NSdPnsxUpkOHDhw7doyIiAiWLVvGK6lx\nOYXCovzwA5XuxnKrWh2Z5VABwP0anux+rBckJtLo9/nWlqMoIvJ05AcPHsTDwwN3d3ccHR3p378/\noaGhmcptW/qWAAAgAElEQVSUKVPGtHz79m0qVapUNEqtiN7jbHrWr0m7EDJ0AJx85m2ws51XP7Zg\n+1Wu8heK//4vKZmYkK9jbUF/YdC7fq3kGSOPi4ujRo0apnU3NzcOHDiQrdz69euZNGkSFy5cYMuW\nLTnWNXToUNzd3QFwcXGhYcOGJiOn/fxR62o9r3XIZf+cOXDsGH6lqhLT8gXuhYdz/3728hnXY2Oh\nalXrXA+EEx4OJUqYp77Tp2V9ue3/XTwgvF49Ak+coOmfSwgPb5hjfbVqpa9HRkKu9lbrRbYeHh7O\nsmXLAEz+UhMiD9auXStefvll0/qKFStEcHBwruV37dolvLy8sm1/yGlsnh07dlhbQqHQs/6M2u3t\nhUhKyqFQhw5CgFjV4EOxapXcVKuWENHRmYutXStE795y+a23hJg9uwgEZyGr7VNShEj7OuzdK0SL\nFoWrf8wYIebOzX3/7t1CtGwphFi/XggQ18vVFCIxMcey//wjhLu7XF65UogBA/R97wihf/1afWee\nv0FdXV2JjY01rcfGxuLm5pZr+VatWpGcnMy1a9e0P0kUisJw5IhM2VqmDNs8XrW2Gtule3euV/Ki\nwq2z8OOP1lajMDN5OvImTZoQFRVFTEwMiYmJhISE0KNHj0xlzpw5g0hNYn/kyBEAKlasWERyrUP6\nT2J9omf9D9U+Z478+8or3ClRocj15Bebsb2dHYdap048MXt2poknQkPhiy9g+fLsh9mM/gKid/1a\nydOROzg4sGDBAoKCgvD19aVfv374+PiwaNEiFi1aBMC6deuoX78+/v7+vPHGG6xZs8YiwhUKzp6F\nH34Ae3s5qYIiT040GkyCU2U4ehS2bTNtHzlSpve9fBneeMOKAhUF5qGv97t06cKpU6c4ffo0kyZN\nAmDkyJGMHDkSgHfeeYcTJ04QERHB7t27adq0adEqtgLpL8v0iZ7156l9wQIwGuH556FmTYtpyg+2\nZHujYyn2NhotVz75JNO+WbNkqzzr89CW9BcEvevXiu3001Io8sOdO/DNN3JZtcY1s7/ha1CqFISF\nwd9/W1uOwkwoR64BvcfZ9Kw/V+3Ll0N8PLRoAc2aWVRTfrA1298tXREGD5Yrn3/+0PK2pj+/6F2/\nVpQjV+iPlBSYJ5NjqaBuAUiz2XffwfXr1tWiMAvKkWtA73E2PevPUfuWLTKbn5sb9O5tcU35wSZt\n7+sLnTrB3bvp4alcsEn9+UDv+rWiHLlCf8ydK/++/jo4OlpXi15Je6+wYAEOIsm6WhSFRqWx1YDe\n42y2qv/+fejXD+7dk+vPPJM+b3Aa2bSfPClTDZYuDSNGZNplZwcffgjffivzhNvZQDMlJ9sbDLJB\nHB8PJUpYXhMAQUFQpw6cOkXX8j8B/XIsllX/u+9CWpaOypXh+++LVmZhsdV739woR66wGjdvws6d\nsiv43r2wfXt2R54VuwWpL+gGD5ZzU2Zg5sz0jhhTpsj85baGwQD79slrB6hVy0pC7OxkrHzUKF65\nO5fcHHlWfv0Vhg2DJ56AZ5+1fUf+qGADbRbbR+9xNlvWX7KkbJ3mNkdyRu0VxHUM36cOP8zB49es\nKevq1AnatJFO09rkZPuAgHSddepYXpOJwYOhQgWaJO3H8fD+HIvkpL9FC+jYsYi1mQlbvvfNiXLk\nCt0wXCzGcPeu9IC+vtaWo3/KlDHN6+m0eJ6VxSgKg3LkGtB7nE3P+k3ak5MZJRbIZR11ObR527/+\nOsnYU+qXH+HcuWy7bV7/Q9C7fq0oR67QBz//TE1iEV5e0LmztdUUH2rU4L8l+2IwGuUYfYUuUY5c\nA3qPs+lZv0l7apfDlNFv2EZ3FI3owfZfO6V2RVy0SPYtz4Ae9OeF3vVrRT/fCMWjy6FD8PvvxFMe\n8cJga6uxORITpf+VsyJpw2iUx9y9C4cdm5PYKABu3MhhVmuFHlCOXAN6j7PpWX9gYKBpOP5iwwhw\ndrauoHxS1LavWhXeew8qVZKmkVO0PZyXXgIXF3lccjIkB6e2yufOlSkQUtHzvQP6168V5cgVts35\n87KjuZ0dCw3B1lZjc0yalN6ybtQIbt/WdtytWxASIo+7dg2cXugDrq6yI/7WrUUrWmF2lCPXgN7j\nbHrWHz5xIiQlwbPPctZgrdEzBUc3tnd0hODUB+Vnn5k260Z/Luhdv1aUI1fYLvfvw4YNclnlHC96\nRoyQqQ82b6bceZWrXE8oR64BvcfZdKt/1SoCb96UMYOWLa2tpkDoyvYV03OVe22WqRB0pT8H9K5f\nK8qRK2wTIdKzHI4daxvj7R8FUlMfPLn7O8o8ULnK9YJy5BrQe5xNl/rDw+H4ccIrVJBzcuoU3dk+\nNVe5w4O7tD2zWH/6s6B3/VpRjlxhm6S1xnv2lJm1zMTZs7BqlcyG+ygSGSmvPzY2j0KpKRA6RaVO\nbq2weZQj14De42y603/mDPzyC5QoQeBHH5mt2gYNwMsL/vtfKF/eMmF3W7L9009DuXLy+uvUgfr1\ncynYuTO3qnlR6W4sgTqfCs6W7F+UqHzkCttj/nwZIx84UM5eYCY8PWHlSrNVpzueekp+HoqdHaeC\n3qDpstflYKznnitybYrCoVrkGtB7nE1X+m/dgqVL5fIbb+hLew7oVX90q8HccSxP+N69MkWCTtGr\n/fOLcuQK2+LbbyEhQc4M0bChtdU8siSXcmZH7dSp9OapXOW2jnLkGtB7nE0v+u2EET5PncotdQCQ\nXrTnhp71b/EKJtDOTqZIOH/e2nIKhJ7tnx+UI1fYDE0v/gL//ANPPgndu1tbziPP1TK15MScSUnw\n5ZfWlqPIA+XINaD3OJte9Hc/k9rlcMwYsLcH9KM9N3Svv00bufDVV/nLk2sj6N3+WlG9VhQ2Qfl/\nIqh3bSeULctWt2EsTp3U/epV2WWwenXr6tMDBgNMnixH2leqZKYJf+rVg8aN4fBh2QGdl8xQqcLc\nqBa5BvQeZ9OD/tr/TX2hNnw4vx0sh7099O4NcXGBnDljXW2FwZK2/+ILmfeqVy/ZgDYHgW3bps+R\nOneu7BaqI/Rw75sD5cgV1ufiRVx3ryYFA4weDYCfH/TrB48/bmVtOqJJE2kzs2c0eP55OYPF8eM0\nuR1u5soV5kA5cg3oPc5m8/q/+gr75EQOVu0pX3Rm4ObNcOtoMhM2b/uHEB4eLlMkvPYaAAMuz7Wu\noHyid/trRTlyhVUpKe6bekRsqP2mldUocuXVV6FECVrf/IUS53Qc6yqmKEeuAb3H2WxZ/7P3V8Pl\ny8Q/6U9kxVbZ9pcvH2h5UWbElm2vBZP+ypVh0CDsEFQOmW9VTflB7/bXinLkCushBK/ckz/Vzzyj\nco7bPKkvPSttWCpTKShsBuXINaD3OJut6nf8PZy6yX9ClSrEPd0vxzIqRm5dMun38+OQcyD2dxKw\n+26p1TTlB73bXysPdeRhYWF4e3vj6enJRzmkFF25ciV+fn40aNCAli1b8ueffxaJUEXxw+nr1Bdn\no0aR4phzzvEyZaBdO/m+rXRp1WjXgsEATk7SZm3bypS95mJ1ZZk6wX7hfJlSQWEbiDxITk4WtWvX\nFtHR0SIxMVH4+fmJyMjITGV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p9lxKjBiaPtP3IxjW1Yoa2akBvcfZHqb/9m2IioL4ke/I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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104197ed0>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Other Cost functions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Binned $\\chi^2$\n",
      "Just a $\\chi^2$ with symmetric poisson error assumption. Currently doesn't support unextended fit yet(easy fix, anyone wanna do it?).\n",
      "But, binned likelihood support both extended and unextended fit."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from probfit import Extended, BinnedChi2\n",
      "seed(0)\n",
      "gdata = randn(10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mypdf = Extended(gaussian)\n",
      "describe(mypdf) # just basically N*gaussian(x,mean,sigma)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "['x', 'mean', 'sigma', 'N']"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bx2 = BinnedChi2(mypdf, gdata, bound=(-3,3))#create cost function\n",
      "bx2.show(args={'mean':1.0, 'sigma':1.0, 'N':10000}) #another way to draw it"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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RuaSCg4NhYWGBtLQ0ODg4qEa858+fx/Tp0wFQb9lu3bqpJp6dnJwwfPhwAEDf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\nkydj5MiRAGh0npmZCQDIyMhA27ZtAQB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       "text": [
        "<matplotlib.figure.Figure at 0x10416a850>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = Minuit(bx2, mean=1.0, sigma=1.0, N=1000.)\n",
      "m.migrad()\n",
      "bx2.show(m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:1: InitialParamWarning: Parameter mean is floating but does not have initial step size. Assume 1.\n",
        "-c:1: InitialParamWarning: Parameter sigma is floating but does not have initial step size. Assume 1.\n",
        "-c:1: InitialParamWarning: Parameter N is floating but does not have initial step size. Assume 1.\n"
       ]
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = 33.9840496564</td>\n",
        "                <td>NFCN = 87</td>\n",
        "                <td>NCALLS = 87</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 2.21724517031e-07</td>\n",
        "                <td>GOAL EDM = 1e-05</td>\n",
        "                <td>UP = 1.0</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#vBRbXQlADV').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>mean</td>\n",
        "                <td>-1.856158e-02</td>\n",
        "                <td>1.003145e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>sigma</td>\n",
        "                <td>9.883410e-01</td>\n",
        "                <td>7.437716e-03</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>3</td>\n",
        "                <td>N</td>\n",
        "                <td>9.970810e+03</td>\n",
        "                <td>9.970433e+01</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"vBRbXQlADV\" style=\"display:none;\">\n",
        "            <textarea rows=\"12\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & mean & -1.856e-02 & 1.003e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\sigma$ & 9.883e-01 & 7.438e-03 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & N & 9.971e+03 & 9.970e+01 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "output_type": "display_data",
       "png": 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4e3vz0ksvceTIkXzZdubMGVq2bIm3tzeenp6Eh298dNDBg3QODiYGCGzUCK/O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\nJnVJkiQNkUldkiRJQ/4PxQvRFr50IMUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10417b9d0>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Binned Likelihood\n",
      "Poisson binned log likelihood with minimum subtractacted(aka likelihood ratio)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from probfit import Extended, BinnedLH\n",
      "seed(0)\n",
      "gdata = randn(10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mypdf = gaussian\n",
      "describe(mypdf) # just basically N*gaussian(x,mean,sigma)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "['x', 'mean', 'sigma']"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "blh = BinnedLH(mypdf, gdata, bound=(-3,3))#create cost function\n",
      "#it can also do extended one if you pass it an extended pdf and pass extended=True to BinnedLH\n",
      "blh.show(args={'mean':1.0, 'sigma':1.0})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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NG4HnniOJp3dvWlYRavhuqgHWyBlFsVeSMOnfrq46r3Fy/jwF8bNnScr44QeSVZTCzQ34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\nGDRoEBISEvIF8tq1a6N27dr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       "text": [
        "<matplotlib.figure.Figure at 0x104197dd0>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = Minuit(blh, mean=1.0, sigma=1)\n",
      "m.set_up(0.5)\n",
      "m.migrad()\n",
      "blh.show(m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:1: InitialParamWarning: Parameter mean is floating but does not have initial step size. Assume 1.\n",
        "-c:1: InitialParamWarning: Parameter sigma is floating but does not have initial step size. Assume 1.\n"
       ]
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = 16.9210948316</td>\n",
        "                <td>NFCN = 59</td>\n",
        "                <td>NCALLS = 59</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 1.35282725541e-06</td>\n",
        "                <td>GOAL EDM = 5e-06</td>\n",
        "                <td>UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#dgnPGPAOwC').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>mean</td>\n",
        "                <td>-1.944627e-02</td>\n",
        "                <td>1.005891e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>sigma</td>\n",
        "                <td>9.895137e-01</td>\n",
        "                <td>7.536312e-03</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"dgnPGPAOwC\" style=\"display:none;\">\n",
        "            <textarea rows=\"10\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & mean & -1.945e-02 & 1.006e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\sigma$ & 9.895e-01 & 7.536e-03 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "output_type": "display_data",
       "png": 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W9FrKhAlaRalYAzWPXLGoM2fkRvfDhuVyY6dOEBEhV3LOnm3x2KxNUhK4usKO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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x103e987d0>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####$\\chi^2$ Regression\n",
      "Some time you just want a simple line fit as opposed to fitting pdf."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from probfit import Chi2Regression"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = linspace(-10,10,30)\n",
      "y = 3*x**2 +2*x + 1\n",
      "#add some noise\n",
      "y = y+randn(30)*10\n",
      "errorbar(x,y,10, fmt='b.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "<Container object of 3 artists>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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d4INltcL3v9+9epbEvrAE+draWlJSUkhOTiYxMZE777yTysrKcJxKZMS53bB1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\nEsP+H/roQ/GQBSukAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10415f810>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#there is a poly2 builtin but just to remind you that you can do this\n",
      "def my_poly(x, a, b, c):\n",
      "    return a*x**2+ b*x+ c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "err = np.array([10]*30)\n",
      "x2reg= Chi2Regression(my_poly, x, y, error=err)\n",
      "x2reg.draw(args={'a':1,'b':2,'c':3})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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kJARxcXEAgLi4OHXwDwkJQUJCAkpLS5GRkYH09HQEBAQYqfiW79FUhS2juhBQ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\nhrw9XepCKuUdpcysmbverOVzIRajtZM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       "text": [
        "<matplotlib.figure.Figure at 0x10416aa50>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = Minuit(x2reg, a=1, b=2, c=3)\n",
      "m.migrad()\n",
      "x2reg.show(m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:1: InitialParamWarning: Parameter a is floating but does not have initial step size. Assume 1.\n",
        "-c:1: InitialParamWarning: Parameter b is floating but does not have initial step size. Assume 1.\n",
        "-c:1: InitialParamWarning: Parameter c is floating but does not have initial step size. Assume 1.\n"
       ]
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = 34.2023676505</td>\n",
        "                <td>NFCN = 52</td>\n",
        "                <td>NCALLS = 52</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 3.89079131844e-07</td>\n",
        "                <td>GOAL EDM = 1e-05</td>\n",
        "                <td>UP = 1.0</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#IiFlQAxrbk').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>a</td>\n",
        "                <td>3.011252e+00</td>\n",
        "                <td>5.738228e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>b</td>\n",
        "                <td>2.121267e+00</td>\n",
        "                <td>3.058568e-01</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>3</td>\n",
        "                <td>c</td>\n",
        "                <td>2.134416e+00</td>\n",
        "                <td>2.741159e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"IiFlQAxrbk\" style=\"display:none;\">\n",
        "            <textarea rows=\"12\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & a & 3.011e+00 & 5.738e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & b & 2.121e+00 & 3.059e-01 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & c & 2.134e+00 & 2.741e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "output_type": "display_data",
       "png": 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uuvz22285zuTzMvB5zY7v3btHfHw8oMx0d+zYkS19wqPtk5KSSElJARSjfPjw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\neSKiBsYmT0TUwNjkiYga2F+MacGwyNdSGgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1041dec10>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Let's do some physics\n",
      "Remeber the D mass?? Let's try to fit relativistic Breit-Wigner to it."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from root_numpy import root2rec"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = root2rec('data/*.root')\n",
      "bb = root2rec('data/B*.root')\n",
      "cc = root2rec('data/cc*.root')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hs = np.hstack\n",
      "hist([hs(data.DMass), hs(bb.DMass), hs(cc.DMass)], bins=50, histtype='step');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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bps3gFy4ATz0FTDGZNjEvr3mTaJAy/OY3wJ9MmmVM+9211I4d5u9HJSc3b8EQ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104217ad0>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Simple fit\n",
      "First lets fit bb's DMass alone with a Breit-Wigner."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from probfit import UnbinnedLH, draw_compare_hist,\\\n",
      "                    vector_apply, Normalized, rtv_breitwigner,\\\n",
      "                    linear, rename, AddPdfNorm\n",
      "from iminuit import Minuit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bb_dmass = hs(bb.DMass)\n",
      "print bb.DMass.size"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3219\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#you can compare them like this\n",
      "draw_compare_hist(rtv_breitwigner, {'m':1.87, 'gamma':0.01}, bb_dmass, normed=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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hRj/pgK0xJY69GN96SyZssdkgKwuSkqSrd2EH3O++k3c3DNiON57Tp+UV93dv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\nNCZBB2yNRqMxCf8PVkQCDrtsQFQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10419d3d0>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Our DMass is a truncated one so we need to have it normalized properly\n",
      "#this is easy with Normalized functor which normalized your pdf\n",
      "#this might seems a bit unusual if you never done functinal programming\n",
      "#but normalize just wrap the function around and return a new function\n",
      "signalpdf = Normalized(rtv_breitwigner,(1.83,1.91))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ulh = UnbinnedLH(signalpdf, bb_dmass)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = Minuit(ulh, m=1.875, gamma=0.01)#I shift it on purpose\n",
      "m.set_up(0.5)\n",
      "ulh.show(m) #you can see it before the fit begins;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:1: InitialParamWarning: Parameter m is floating but does not have initial step size. Assume 1.\n",
        "-c:1: InitialParamWarning: Parameter gamma is floating but does not have initial step size. Assume 1.\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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wae1oDY5j2yQs2BKjxDiaVMjKVzrB1miktaMFtFotC7YRyOp6MhIWbIYxlaIi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\nFBQkXL58WRAEQSgoKBCCgoJ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       "text": [
        "<matplotlib.figure.Figure at 0x1042134d0>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit -n1 -r1 m.migrad();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:257: SmallIntegralWarning: (1.8630708456039429, 1.875, -0.05716159358979792)\n"
       ]
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = -10458.6241946</td>\n",
        "                <td>NFCN = 53</td>\n",
        "                <td>NCALLS = 53</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 6.52822663213e-07</td>\n",
        "                <td>GOAL EDM = 5e-06</td>\n",
        "                <td>UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#qeCWwlXFzN').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>m</td>\n",
        "                <td>1.869087e+00</td>\n",
        "                <td>1.344898e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>gamma</td>\n",
        "                <td>1.151186e-02</td>\n",
        "                <td>3.003198e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"qeCWwlXFzN\" style=\"display:none;\">\n",
        "            <textarea rows=\"10\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & m & 1.869e+00 & 1.345e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\gamma$ & 1.151e-02 & 3.003e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 1: 142 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ulh.show(m) #looks good"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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9xVtvvUVBpWZIVlYWPhWdNT4+PmRlZVV5b2JiIgEBAQB4eHgQFRVl/hliclZT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\nYfFiUKO0GhUqhq3QN3/+KcX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       "text": [
        "<matplotlib.figure.Figure at 0x103e98950>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.minos();#do m.minos('m') if you need just 1 parameter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <span>Minos status for m: <span style=\"background-color:#92CCA6\">VALID</span></span>\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>Error</td>\n",
        "                <td>-0.000134384231326</td>\n",
        "                <td>0.000134627933923</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>Valid</td>\n",
        "                <td style=\"background-color:#92CCA6\">True</td>\n",
        "                <td style=\"background-color:#92CCA6\">True</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>At Limit</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>Max FCN</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>New Min</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <span>Minos status for gamma: <span style=\"background-color:#92CCA6\">VALID</span></span>\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>Error</td>\n",
        "                <td>-0.000295920358877</td>\n",
        "                <td>0.000304715369101</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>Valid</td>\n",
        "                <td style=\"background-color:#92CCA6\">True</td>\n",
        "                <td style=\"background-color:#92CCA6\">True</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>At Limit</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>Max FCN</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>New Min</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "{'gamma': {'lower_new_min': False, 'upper': 0.00030471536910134004, 'lower': -0.00029592035887724555, 'at_lower_limit': False, 'min': 0.01151186448673394, 'at_lower_max_fcn': False, 'is_valid': True, 'upper_new_min': False, 'at_upper_limit': False, 'lower_valid': True, 'upper_valid': True, 'at_upper_max_fcn': False, 'nfcn': 12L},\n",
        " 'm': {'lower_new_min': False, 'upper': 0.00013462793392262257, 'lower': -0.00013438423132646268, 'at_lower_limit': False, 'min': 1.8690871087182035, 'at_lower_max_fcn': False, 'is_valid': True, 'upper_new_min': False, 'at_upper_limit': False, 'lower_valid': True, 'upper_valid': True, 'at_upper_max_fcn': False, 'nfcn': 14L}}"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.print_param()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#VcwnpvPZSn').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>m</td>\n",
        "                <td>1.869087e+00</td>\n",
        "                <td>1.344898e-04</td>\n",
        "                <td>-1.343842e-04</td>\n",
        "                <td>1.346279e-04</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>gamma</td>\n",
        "                <td>1.151186e-02</td>\n",
        "                <td>3.003198e-04</td>\n",
        "                <td>-2.959204e-04</td>\n",
        "                <td>3.047154e-04</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"VcwnpvPZSn\" style=\"display:none;\">\n",
        "            <textarea rows=\"10\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & m & 1.869e+00 & 1.345e-04 & -1.344e-04 & 1.346e-04 &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\gamma$ & 1.151e-02 & 3.003e-04 & -2.959e-04 & 3.047e-04 &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###More ComplexPDF\n",
      "\n",
      "Now let's add the background and fit it. Looks like a job for linear + breitwigner."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bound = (1.83,1.91)\n",
      "bgpdf = Normalized(linear,bound)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "describe(bgpdf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "['x', 'm', 'c']"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#remember our breit wigner also has m argument which means different thing\n",
      "describe(rtv_breitwigner)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "['x', 'm', 'gamma']"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#renaming is easy\n",
      "signalpdf = Normalized(rename(rtv_breitwigner,['x','mass','gamma']),(1.83,1.91))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "describe(signalpdf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "['x', 'mass', 'gamma']"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#now we can add them\n",
      "total_pdf = AddPdfNorm(signalpdf,bgpdf)\n",
      "#if you want to just directly add them up with out the factor(eg. adding extended pdf)\n",
      "#use AddPdf\n",
      "describe(total_pdf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "['x', 'mass', 'gamma', 'm', 'c', 'f_0']"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ulh = UnbinnedLH(total_pdf, np.hstack(data.DMass))\n",
      "m = Minuit(ulh, mass=1.87, gamma=0.01, m=0., c=1, f_0=0.7)\n",
      "ulh.show(m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:2: InitialParamWarning: Parameter mass is floating but does not have initial step size. Assume 1.\n",
        "-c:2: InitialParamWarning: Parameter gamma is floating but does not have initial step size. Assume 1.\n",
        "-c:2: InitialParamWarning: Parameter m is floating but does not have initial step size. Assume 1.\n",
        "-c:2: InitialParamWarning: Parameter c is floating but does not have initial step size. Assume 1.\n",
        "-c:2: InitialParamWarning: Parameter f_0 is floating but does not have initial step size. Assume 1.\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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0kPM2NmkC991X85m8PDxKWtdt2pRTwIYPHlWdsgw9+kn1ElHUb7KzaXLqKJdd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\nmnD5MpEfy0Ey7zb/t3zQpij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       "text": [
        "<matplotlib.figure.Figure at 0x1002e5c10>"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.migrad();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:1: SmallIntegralWarning: (1.8630708456039429, 1.87, -0.09079225177349604)\n",
        "-c:1: SmallIntegralWarning: (1.8630708456039429, 0.0, -0.007922517734960222)\n"
       ]
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = -21505.9979204</td>\n",
        "                <td>NFCN = 248</td>\n",
        "                <td>NCALLS = 248</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 4.16787410874e-09</td>\n",
        "                <td>GOAL EDM = 5e-06</td>\n",
        "                <td>UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#vtnAehbhFD').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>mass</td>\n",
        "                <td>1.869398e+00</td>\n",
        "                <td>1.414321e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>gamma</td>\n",
        "                <td>1.081224e-02</td>\n",
        "                <td>3.908496e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>3</td>\n",
        "                <td>m</td>\n",
        "                <td>-4.677901e-01</td>\n",
        "                <td>3.999174e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>4</td>\n",
        "                <td>c</td>\n",
        "                <td>1.041123e+00</td>\n",
        "                <td>8.896113e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>5</td>\n",
        "                <td>f_0</td>\n",
        "                <td>5.347971e-01</td>\n",
        "                <td>1.279253e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"vtnAehbhFD\" style=\"display:none;\">\n",
        "            <textarea rows=\"16\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & mass & 1.869e+00 & 1.414e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\gamma$ & 1.081e-02 & 3.908e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & m & -4.678e-01 & 3.999e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "4 & c & 1.041e+00 & 8.896e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "5 & $f_{0}$ & 5.348e-01 & 1.279e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ulh.show(m, parts=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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if/zxB/v372fNmjXs3r1bXfjs378/CQkJJCcn07p1a2bNmgUoW2KysrLYsWMH06dPp2/f\nvrzzzjscPHiQAwcOsH//fgBu3LhBZGQkf//9N3Xq1OGjjz4iLi6OpUuX8tFHHwGKu2f9+vXs2bOH\nBQsW8OabbxqUs1OnTgYjI0sGiIB+/o5q1apRq1YtLl++XKpO8chEZ2dn0tLSTOrq3Llzem10eUEA\nvv32Wx5//HFcXV2ZN28e7777rsm+QImyDA4O5qWXXiI2Nlb9TrrcHua4xYzlKjHV3tT3ACUs38nJ\niRo1atCzZ08AgoKC1MRYS5cuJScnh8uXL5erTuYmzSctJw2vuv54FPQlJweGBg3lUZdHuXLzCosP\nLS5TpxLJ/XJXkY46Yzl+/HjWr1+vRq0ZCuIoD7Zt20a/fv2wt7endu3a9OnTR134PHDgAOHh4QQG\nBjJ//nwOHTqkttNtP/T396dp06b4+fmh0Wjw8/NT81nY29ur+TgCAgLo0qULtra2ejlK8vLyGDly\nJIGBgQwcOFDvGcXZvHmzwchIQyHYJalRo8a9qqdMdPs5hwwZwtq1azlz5gzDhw9nzJgxZbbduXMn\nSUlJ/Pzzz/Tt21f9TlFRUQafYyzXSHHEPeYk0ZVFRETw559/kp6ezq1bt9RJw9SpU/nrr78ICQlh\n8+bNODs7q3lXDPV1Lzr5eJ0yu76+fhxRURo++EDpa2TISABm75ttsn1lYo05MiyBNerJbIPduXNn\nYmNjAWjQoAEbNmzg2LFjrFu3DkdHxwoRruQG8uK/8NHR0Xz//ffs37+fiRMnqi4RQC/fSMlcJLr8\nI8XdAMZylEyfPp1mzZqxf/9+du/eTV5enkE5dQcTlPzoMvIVx9nZWT2goKCggKtXr9KgQYNSdc6e\nPateF8/lUVI/xduUzB/i7OzMhQsXyMvLo23btgAMHDiQ7du3G/wexjBkTM1xi91LThJTbXRUr16d\n/v37q+l1mzVrxpIlS9i7dy//+c9/AKhXr1656eRI5hHO5v+NfWF9Ulc8x4cfQn6+UvaM7zPUtKvJ\n5lObSbmcYrQPiaQ8qNK5RDp06MCKFSu4desW165dU1OigpJjuWnTpuTn5zNv3rwKCV/Ozs6madOm\nAMydO9foiS9btmwxOMPW5bguTnF30uLFi/VOYNHRrFkz6taty65duxBC8NtvvxnM2VH8ZdajRw/W\nrVtHVlYWV65cYf369fTo0YPGjRtz48YNjh8/DsD69evx9fUt1ZcxOnfuzC+//GLyexhzi91LTpK+\nffuyYMEC8vLySE1N5fjx47Rr147r16+rbo+CggJWrlxJcHAwAJcuXaKoqAhQdpiMGDECgO7du9+z\nToqz/IjiE3fNfQJ7W3u9sjrV69C/dX8A5u2fZ7SPysQafbOWwCr1ZImVzrshJiZGeHt7i/DwcNG/\nf3/x888/CyGE+OGHH4S7u7to166dGDVqlBg+fLgQQojo6GixZMkSIYQQqampeiecFC+rU6eO3jOm\nTZumXuvKjh8/LgIDA0VQUJB499139drcKzdv3hTPPPOM8PT0FGFhYeL3339Xy7Rarfrz7t27hb+/\nv/Dw8BCjRo1S7yckJAgXFxdRq1Yt0bBhQ+Hv76+W/fLLL8LT01N4enqK2bNnq/fXrFkjtFqtCAoK\nEl26dBGpqalCCCHS09OFi4uLqFu3rnB0dBSurq7qToywsDCh1WpLfdatWyeEUHZgREZGCi8vLxEV\nFSWuXLkihBAiLS1N9OrVS3326tWrhbe3t/Dw8BCTJk1S7xtrL4QQn332mfDw8BCtWrUSa9euFUII\ncf78eeHj4yMCAwNFQECAGDt2rLp7ZPHixcLLy0t4e3uLl156SeTl5d2zTgzR/uf2ghhE19eVsfPd\nd0Lc3ugjhBBi5dGVghhEu5/aGe2jMtm0aZOlRbAKqqqeTNnOKp9L5Pr169SqVYsbN27QuXNnfvrp\nJ7RabTlIKJGUTXpOOs7/daYa1XnuTCZzf67F99/D33/D998rda7nXaf+F/UpKCog851MGtRoYLpT\nicQE951LxJL861//Ijg4mDZt2jBgwABprCWVyurjqxEIWjt0w07UMlinln0tOj7SEYFg44nS6xYS\nSXlR5Q32/PnzSUpK4vDhw2ZtR7M2rNKPZiEsoav4U8oz/Ry6m6zX3UMp/zPlz4oWqUzkmDIPa9RT\nlTfYEomlEELw18m/APC272yybg8PZYvoupR1Mq2wpMKQBrsEutwT3t7e6mksxliyZAk2NjZ6p9ac\nPn2a7t274+vri5+fn7qFr3hwjbOzM0899RSg7BXv2bMnWq0Wf39/Zs+ebbas5qa51eXz8PLy4osv\nvlDvf/jhhwQFBaHVaomMjFS3wOXl5TF8+HACAwPRarX89ddfapuFCxcSFBSEv7+/3v77U6dOERkZ\nSVBQkJrPo7yp7H2zJ7NOcib7DA1qNMDZzt9k3aCmQTSq2Ygz2Wc4ceVEJUloGGvcX2wJrFJPlljp\nrKoYyj1RfPdCcbKzs0V4eLh49NFHxe7du9X7nTt3Fhs2bBBCCHH9+nVx48aNUm2L58GYOHGiGD9+\nvBBCiIsXL4oGDRqI/Px8vfoVlc8jOztbrffNN9+IESNGCCGE+Pbbb8WLL74ohFDyjbRp00YIIURm\nZqZ45JFHRGZmphBCiGHDhomNGzcKIYzn87Bmfk36VRCDePL3J8XPPwtxWyWldono6Pt7X0EM4rd9\nv1WuoJIHClO2s0rPsE+ePImPjw/Dhw+nVatWPP/886xbt44OHTrg7e2tBk6UF4ZyTxjK9QHK7HT8\n+PFUr15d3QN+6NAhCgsL1f3XNWvWLBXJmJ2dTVxcnLrvOCcnh+zsbLWsYcOGVKtm3slt95vPo06d\nOmq9a9eu6eXm6NKlC6DkG3F0dCQxMZETJ07g5eVFw4YNAYiMjFT3RhvL51GeVLbP8a9Tyl8WnVp0\nMlqnoABOnFA+PjUfA2D7mbsLTCpvrNE3awmsUU9V2mADpKSkMHbsWI4cOcLRo0dZuHAh27ZtY+rU\nqUyaNKlU/WPHjhmMOgwJCVENozHKymOhY+/evaSlpdGrV69Sz3Z0dKR///6EhITwzjvvqAEdOpYt\nW0a3bt2oXbs2AL179+bgwYM0b96coKAgvYN+KzqfB8D777/PI488wuzZs9X0o0FBQcTGxlJYWEhq\naip79uzh7NmzeHl5cfToUU6dOkVBQQHLli1TIzIN5fO4cuWKSX1Xdbac2gJA5xbG/dc//QRaLXTr\nBt+/VzUMtuTBpcobbHd3d71cIN26dQPQy/lRHG9vb4NRh3v37qVu3br3LU9RURFjxoxh6tSp6j1x\ne5GpoKCALVu2MG3aNHVGWtIn/fvvvzNo0CD1evv27Wi1Ws6dO0dycjKvv/46OTlK5reKyOdRks8+\n+4zTp08zfPhw3n77bQBefPFFXFxcCA0NZfTo0Tz22GPY2tri6OjIDz/8wLPPPkunTp1wd3dXswbe\nTT6Pe6UyfY6ZNzJJuZJCTbuaBDUNMlrv1i148UVlhv3uC6HYiGocuHCA7FumJwcViVX6Zi2ANeqp\nyhvskrlADOX8KM7Ro0cNzrCDg4PVdJo6EhIS1LIVK1YYzD1RfMYNigvj4MGDRERE4O7uzs6dO3ny\nySfZu3cvrq6uaLVa3NzcsLW1pV+/fnoLkpmZmSQmJtK7d2/13vbt23nmmWcA8PDwwN3dnaNHj5b6\nXuWVz8PQdwIYPHiw6mKytbXlv//9L0lJSSxbtoysrCy8vb0BeOKJJ9i5cyfbt2/H29ubVq1aAYbz\neZTHC9JSJKYpumjTrA3VbMxzUTlUq4HtxRCKRBGPPZPAH39UpISSh5Eqb7DvllatWhmcYSclJVGv\nXj29uu3atVPL+vTpYzQfR3Hq1avHxYsXSU1NJTU1lfbt2xMbG0tISAihoaFkZWWRmZkJwMaNG/Hz\n81PbLl68mD59+qgvHVDycutOTjl//jxHjx6lZcuWes+sqHweulwaoOSh1uXmyM3N5fp15eir9evX\nY2dnh4+PDwAXLlwA4MqVK/zwww+MHKlkqzOWz6M8qUyf4640JQd3O+d2Zrd5+WV4qo3iFrH32E5y\ncoWIVibW6Ju1BNaoJ/OmDhak5Myy+HV5J3yqX78+H374oZrFbeLEiWomwokTJxIaGqqmbjWEra0t\nU6dOJTIyEiEEoaGhvPTSS2r5woUL9Y6pAnj++ef5+eefCQoKoqioiC+//FLN3te+fXtu3bpV6jlf\nfvklUVFRjB8/noEDBzJr1izc3NxYtGgRoPjiX3rpJVatWkW1atX49ttv6dGjB4WFhYwYMYLWrVsD\nMGHCBI4ePYqtrS0eHh788MMPgPLi6NmzJzY2Nri4uPDbb7+pz3777bfZt2+fqhNPT0/gzhFlGo2G\nzp078913392F5qseCWkJwN0Z7Dp14Kl2YSw6Azcc94DhXGESyT1T5XOJSCSVjRCCxlMacyn3Eqlv\npeLm6MasWbB9O8yahV4uka++gpMnlX8BjmYexec7H+riwluFZyh27KZEYhZWnUtEIqlsUrNSuZR7\niSa1mtCiXguDdfLz4cYNKJki3auhF7Xta5PNWa5zsRKklTxMmDTYN2/eJCwsDK1Wi6+vr/rnfExM\nDC4uLuqCnbG9ypKysUY/mqWoLF3p3CFtm7c16HZr0gTmz4dGjSAmBm6nTAfARmNDkJOyqySDpMoQ\ntxRyTJmHNerJpA/bwcGBTZs2UbNmTQoKCujYsSNbt25Fo9EwZswYs46akkisjaQMxdCGNg81WD5g\ngPIxRkizELad2UaGJgkwnTRKIrkbynSJ1KxZE1DySxQWFlK/fn3A9CklVRldoEjxKD9DfP7553h5\neeHj48O6detKlfft21fvtJjNmzcTEhKCnZ2dGkBiDhEREaSmphIWFoaXlxfPPfcc+brzp0pgLM/J\n6tWr0Wq1BAcHEx4eTkqKclRVZmam0TwlX3/9NQEBAfj7++sF6wDMmDGD1q1b4+/vX6UyJFbWvtmk\ndMVga5veWyrf4KbKbpsM9pZRs2Kwxv3FlsAa9VTmLpGioiJCQkJISUnh1Vdfxc/Pj8WLFzNjxgzm\nzp1LaGgo06ZNM3iuY3R0NG5ubgA4Ojqi1WpVJen+HKns6yeffJJRo0bRsmVL4uPjDdY/dOgQs2bN\nYubMmXh6etKtWzf+97//YWNjQ0REBH/88Qc3btzgxo0b6nc9d+4cb7zxhpooydDz165dS40aNZg4\ncaJe+bvvvkvPnj3p0qULCxcuZNasWeo2Ol372NhY3nvvPQ4ePAiAn58fDRo04IknnuC1117jk08+\n4ZFHHuHw4cP85z//YdiwYcyePZvg4GA+//xzli9fztChQ3nhhRc4cuQIX3/9NTNnziQyMpKePXvS\nqFEjnJ2dEUIQGxvLjBkzqFatmrot0VL/X5V93blzZ5IzkuEk3PrnFij/DRw9Gk96OkDZ/YU0C4GT\ncEZsQ0dV+X7yuupdx8fHq5Mpnb00irkJSbKyskRYWJjYtGmTOH/+vCgqKhJFRUXi/fffVxMFmZvA\n5G6YM2eOekxXeSYUql27ttGySZMmicmTJ6vXPXr0EDt27BBCCJGTkyM6duwoDh06pHc8l47o6Gix\nePFig/3++uuvIiYmRu9eXFycaNSokSgsLBRCCLFjxw7Ro0ePUm3/3//7f+KVV15Rr19++WX1eLH2\n7duLXbt2qbK///77QgghZs6cKV577TUhhBApKSnCy8tLFBUViUWLFqmJnoQQ4tNPPxVffvmlEEKI\nZ555Rk3oVNWojCOdzl49K4hB1J9cX1y9WiSWLRNi2TIhXn/9TvKnssgryBO2MfaCGMTVm1crVmAD\nVNWjr6oaVVVPpmyn2fuw69WrR+/evdm9e7f6lgAYOXKkyb3J98PBgwf57LPP2LFjBw0aNDCYmyI+\nPp7Ro0eXul+rVi22bt16T889d+4c7du3V69dXFw4d+4coCR9Gjt2rOoqKotLly6p4fSXL18mLy9P\nTdI0b948srOzcXR0VEO8nZ2dDeYvMZXn5Ntvv6V79+7UrFmTunXrsnPnTgBeeuklunbtSvPmzcnJ\nyWHRokVoNBoCAgL44IMPuHz5Mg4ODqxatYp27ZT9xsePH2fz5s289957ODg4MHXqVEJDDftyH0SS\nM5RoF20fG9qaAAAgAElEQVRTLStWaHj3XWjTRil79lnz+rCztcOJAM6xh30Z+whvEV5B0koeNkwa\n7MzMTKpVq4ajoyO5ubmsX7+eiRMnkpGRoZ4mvnTpUoMnf5cHcXFxDBw4UA0k0fnPixMREUFSUsWv\nxgshSE5O5sSJE0yfPt1gHhNDNGzYUJVvzpw5nDx5kokTJ6rluuRN94Juv+aQIUNYu3Ytbdu2ZerU\nqYwZM4affvqJSZMmodVqiY+PJyUlhaioKPbv34+Pjw/vvvsu3bt3p1atWgQHB6t5PwoKCrhy5Qo7\nd+4kMTGRgQMHcuKEZfM76yg+UShPDh2Ct94CISDVNRncFIMtLkJEBMwreRj6qVOwdy906gS3MxeW\npCnBnGMPe9P3VrrBrig9PWhYo55MLjqmp6fTtWtXtFotYWFh9OnTh8jISN555x0CAwMJCgrir7/+\nYvr06RUinDnBN5s2bTKYN6RDhw73/NyS+TfOnj2Li4sLO3fuZPfu3bi7uxMeHs6xY8fUlKIl5Tb1\nnYrTsGFDsrKy1LDus2fP4uzsXKZMZ86cwdnZmQsXLpCXl6dGZw4cOJDt25VscYbylBw5cgRQEjzt\n3r2bv/76C0dHRzVXiIuLC08//TQAbdu2xcbGhkuXLpWhMevm+HG4fh3Gjwf7R5SXq27hsBRHj0KH\nDvDNN+DhAbt2GazWnBAA4o8mceQI3LxZIaJLHjYs4Ycxl4MHDwpvb29x6dIlIYRQ/y0PTPmwDx48\nKIKCgsStW7fEiRMnRMuWLUVRUZFenZMnTxr0YQ8bNsyoD9sQmzZtEs8884xYsGCBEELxTf/www+l\n6l2+fFm4u7uLK1eu6P1cWFgomjVrJo4dOyaEEOLnn38WAwYMEEIIMXr0aNVnnpGRIZydnVUdnj9/\nXgghxKlTp4SPj4+4elXxtc6cOVN89NFHQgghjh49KlxdXc3+LhVNRfkcly0Tom9f5WenzzwEMYj9\nGfvFb78J8fzzxSrevClEixZCzJqlXC9cKISPjxC5uaX6/OSXHYIYRPW3A0SDBkLcPmeiUqiqvtmq\nRlXVkynbWaUNthDKoqO/v78ICgoSw4cPv+/+xo0bJ1xcXIStra1wcXERH3/8sRBCiNjYWNVQCSHE\nZ599Jjw8PESrVq3E2rVrS/WTmpoqAgIC1OuEhATh4uIiatWqJRo2bKga88zMTKHVag1+/v77b7Fp\n0yZx4sQJ0a5dO+Hp6SkGDhwo8vLyhBBC7N69W4wcOVJ9xi+//CI8PT2Fp6enmD17tnp/zZo1QqvV\niqCgINGlSxeRmpoqhFBOsHniiSdEYGCg8Pf3F/Pnz1fbhIeHC19fXxEUFCTi4uLU+3l5eeKFF14Q\n/v7+IiQkpEoN6oo22Fm5WYIYhM1H1UVeQV5pg/3zz0L07KnfeMAAIUosJAshxPW868LmYxth+7Gt\nePf9XPHppxUiukGq0v9ZVaaq6smU7ZS5RCQPPcuXwy+/wNhvttBpdidsz7chOHE3mZnQuTPMng0U\nFYGvL/zwA9w+jQdQHODdusHp01DipCC/7/04dPEQwwsSaFm9LR98UKlfS2KlyFwiEokZ6CIcnwjV\n8v33sGgRqOdUrF4NtWsrq5DF8fUFV1cwEFwV0kzxY1sqRF3y4CENtoXRbaCXlE1F60q3pS/KP5i2\nbaFtWyVfCAALFsCIEWBoQXn4cPj111K31YhHTeVGPMoxZR7WqCdpsCWS2+hm2KVC0vPzlRn27YMf\nSvHcc8oMOytL77YuCdQFzb5yl1XycCINtoWxxr2glqIidVWkyePghYNo0BDoFKhfuGWLsoXPwHZL\nABwd4dFHYeNGvdu6syAvaA5QJIoMtawQ5JgyD2vUU5U32N988w2+vr4MGTLEaJ0333wTLy8vgoKC\nyi2Ixpw+o6Ojadmypbr3e//+/YBy3FZQUBDBwcG0adOGuLg4vXaFhYUEBwfrRYgmJCTQrl07goOD\nadu2rXq+ojns2bOHgIAAvLy8eOutt4zWM5bQylhCrP/+97/4+fkRFBREt27dOH36NFB673uNGjWI\njY0FlGCnNm3aEBAQQHR0NIWF1nHsSo7DYfKL8vFs4Emd6iUSgy1bBgaOX9OjRw/480+9W41qNsK5\njjP5mutcIaWcJZY8lFhia8rd4OPjI9LS0oyWr1q1Sjz++ONCCCF27twpwsLC7vuZ5vYZHR0tlixZ\nUur+tWvX1J/3798vPDw89MqnTZsmBg8eLPr06aNuLercubO6fXD16tUiIiKiVL8TJ07U286no23b\ntmoukccff1ysWbOmVB3d3vK8vDyRmpoqPDw81L3lu3btEunp6aX2pm/atEnk3t5j/MMPP4hnn322\nVL+XL18WDRo0ELm5uaKwsFC4urqK48ePCyGE+Oijj8Qs3Z7lcqAit/Vpo38VxCCeWfRM6QpubkLs\n32+6k4MHlT3aJfbr95rfSxCDeO6T/ys/gcugqm5Xq2pUVT2Zsp1Veob9yiuvcOLECXr27MlXujOY\nShAbG8uwYcMACAsLIysri/Pnz9/Xc++mT2Fg+02tWrXUn69du0YjdeVKiWRcvXo1I0eO1GvbrFkz\n9VT3rKwsg9GOhkhPTycnJ0fNBTJ06FA1V0lxli9fzqBBg7Czs8PNzQ1PT0923Y7Sa9eunZpqoDgR\nERE4ODgAih7Onj1bqs7//d//0atXLxwcHLh06RL29vbqOY/dunW7q1SzliS7huJnLuW/PnkScnPB\n3990B61bQ2EhHDumd1vnx04X0o8tuX+q9CG8M2fO5M8//yQ+Pl7NJ1KStLQ0XF1d1WsXFxfOnj1b\nKkfHc889x9GjR0u1//e//80LL7xwT32CcpDtJ598QmRkJJMnT1ZPRF+2bBkTJkwgPT1dz/0wevRo\npkyZQnZ2NnDHjzZ58mQ6duzI2LFjKSoqYseOHQAcOHCAoUOHApCRkYG9vb368tq4cSNpaWl6SaFM\nJY8qmdDKUD1jzJo1i169epW6v2DBAsaOHQtAo0aNKCgoYM+ePbRp04bFixfrhdPfLxXpc7xa807S\nJz02b1ZyhpR14LNGc8ct0qqVels9fUZU3hHq1uibtQTWqKcqPcM2l5KzXEO5PBYsWEBSUlKpT0lj\nfTd9fv755xw7dozExEQuX77MF198oZb169ePw4cPs2LFCoYMGYIQgpUrV9KkSROCg4NL9T9ixAi+\n+eYbTp8+zfTp03nxxRcBCAgIUGV95ZVX+PTTT9VrYy8xczH31Pl58+axd+9exo0bp3c/PT2dv//+\nmx49eqj9LViwgNGjRxMWFkbdunXVpFJVGSGE8Rn2X38pBtscunZV6hdDt/CYgZxhS+4fqzfYhhI1\nGXInPPvsswaTRP3222/33KfOjWBvb8/w4cNJSEgoVSc8PJyCggIuXbrE9u3biY2Nxd3dnUGDBhEX\nF6cau4SEBJ566ikABgwYYLAvY9+/uKvC3ORRxuqVZMOGDUyaNInY2Fjs7Oz0yhYtWsTTTz+tZ5Tb\nt2/P5s2b2bVrF+Hh4bQqNtu8X+533+zFi/DuuzB2rPL54w/lfmb+GfKrXaFRzUY0q91Mv9HmzUq4\nozl06KAcrV7sZezVwItqogZXOcPl3Mv3Jb+5WOP+YktglXqyhOP8bnBzczOZ9Kn4AuGOHTvKfdHR\nVJ/nzp0TQghRVFQk3nrrLTFhwgQhhBD//POPuqC3Z88e0bJly1Jt4+PjxRNPPKEufAQHB4v4+Hgh\nhBAbNmwQoaGhZsvbrl07sXPnTlFUVFTmoqOphFYlFx337t0rPDw8xD///GPwuWFhYarMOi5cuCCE\nEOLmzZsiMjKyXBd27revuDghWrYUYsoUIUaMEEJ3TsR7c5YLYhDd5nbTb5CWJkSDBkLcPlyiTIqK\nhGjeXIiUFL3bzT9qJ4hBbEq9P/nNpaouplU1qqqeTNnOKm+w3d3dy8zS9/rrrwsPDw8RGBgo9uzZ\nUy7PNdZnr169RHp6uhBCiK5du4qAgADh7+8vhgwZIq5fvy6EEOKLL74Qfn5+QqvVio4dO4qEhIRS\n/cfHx4s+ffqo14mJiaJdu3YiKChItG/fXuzdu1cIoewyMZY86vLly0IIJUmUv7+/8PDwEKNGjVL7\nNDehlbGEWN26dRNNmzZVn/fkk0+qbVJTU4WLi0up7zVu3DjRunVr0apVK/H111/fhcYrnrg4IXSb\nb9asuWOwB8/8RBCD+Pef/9Zv8Pvvd9L4mcuAAUL89pvereCPXhLEIKbvmH6PkkseJkzZTpn8SfLQ\nsGkTfPKJ8u/atfDVV8q/j33Vnx1X/+C3p37jhcBiaxqjR0PTpoofxVymT1cSbH//vXqr50ff8aft\nG0Rro/n1ydIh7BJJcWTypyqMVfrRLERF6So1V1kQ1O3oUElIgNvbJc3msccUP3YxnITS776Myll4\nlGPKPKxRTyYN9s2bNwkLC0Or1eLr68uECRMA5WzCqKgovL296d69O1klcihUBLoTwIt/Ro0aVeHP\nlTzYZN/KJiMvBZsie3wa+dwpyM+H5OQ7BzqaS3CwMsPOyVFvNRFKqPvBiwfJK8jn1i3Uj0RyN5g0\n2A4ODmzatInk5GT279/Ppk2b2Lp1K5MnTyYqKopjx46p+48rmujo6FJb8mbMmFHhz61orHEvqKWo\nCF0dOH8AgDo3/bGzLbYL5u+/wc0N6ta9uw7t7ZUgm313ZtPVqUt9WpJXmMe/Pz9KzZpKtzVrwpw5\n5fAlSiDHlHlYo57KdInoTgfPy8ujsLCQ+vXr60UCDhs2zGBknURiDehSqta9UQ7uEB0hIcohvcVo\nitL/iRvJTJ6szK5ffx1uB7dKJGZRZqRjUVERISEhpKSk8Oqrr+Ln58f58+fVqD8nJyejYdvR0dG4\nubkB4OjoiFarVd9qOv/Rw36tu1dV5KnK18nJybz99tv30R5Aud6/P57LlyH5vGKwq52oRXx8/J36\ny5dDq1a3a9/l84KDif/jDwgMVMsdTtYD4GLzfcALxMfHc/YseHqWv75Kjq3y7v9Bub7f8VRe1/Hx\n8cyePRtAtZdGMXerSVZWlggLCxNxcXHC0dFRr6x+/fp3tTVFcoequhe0KlIe+7BLbutr+7+2ghjE\nY4P195OLgAAhEhPv7UGJiUIEBqqX778vxOBPlgliEC0+iBJffqncHzVKiIrY+SjHlHlUVT2Zsp1m\n7xKpV68evXv3Zs+ePTg5OZGRkQEo4clNmjQxtxtJCXRvXEnZlLeuiijgwAXFh10vt5hLJDcX/vkH\nAgONtCwDf39l4fHmTfWWziVywSa5wre7yjFlHtaoJ5MGOzMzU90Bkpuby/r16wkODqZv377Mub1a\nMmfOHPqVlStYIqmC3KhxnJsFN2li3wK7Qsc7BQcPgre3soB4Lzg4KO0PHFBvOdKCetXrkWtzkRwy\n7lNyycOKSYOdnp5O165d0Wq1hIWF0adPHyIjIxk/fjzr16/H29ubuLg4xo8fX1nyPnAU9zdKTFPe\nusq5naHPvUaJhE9JSaDVGmhxFwQHK/0ANjYwZ44GzQVlxp5RwalW5ZgyD2vUk8lFx4CAAPbuLX2A\naIMGDdiwYUOFCSWRVAaX7RXD2SC/xA6R5OT7N9jFdoqMGqXkhfo+NYjY81twDtkH9ATgwoU7KbRb\ntoRqVTrhscTSyEhHC2ONfjRLUZ66at4cLtsrM+zkNVr98wnK2WA3bqykyu7bTunzWLbyovDxgUWL\n4IknlBPa5869v0fqkGPKPKxRT/J9LnkoCQyEut77uHENti3R4l7/dkFREezff/8GOyhI8YXn58Pt\ntLS63Nj7zisG+7XXlA/AK69AXt79PVLy4CNn2BbGGv1olqI8dZVxLYOMaxnUrV4XN0e3OwUpKdCo\nkXIS+v1Quza4usKRI+otv8Z+2GhsOJJ5hNz83Pvr3wRyTJmHNepJGmzJQ4kuEVOQU5D+yTvl4Q7R\nUSLisYZdDVo1bEWRKOLgxYPl8wzJQ4U02BbGGv1olqI8daVzS+jcFCrlsUNEh4EQddUtUoGZ++SY\nMg9r1JM02JKHEl0OEa1TCeOcnKxsySsPQkLUrX06dM/TvTAkkrtBGmwLY41+NEtRnrrSGexSM+zy\ndIlotUp/RUXqLd3zdM+vCOSYMg9r1JPcJSJ54CgshC1boKBAuW7RAry87pTn3MrhSOYR7GzsCGgS\ncKfg/HklnNzVtXwEadBA+Zw4AZ6ewJ1DEvaf348QwuyT6yUSkAbb4lijH81SmKurffugTx8IC4Os\nLKhRQzHgOpIykhAIApwCqF6tun5DrRbK04gGBSmz7NsGu2ntpjSu2ZiLNy5y6uop/R0q5YQcU+Zh\njXqSLhHJA0dREbRqBRs2wNdf63kkAEhMSwSgbfO2+gXlueCoQ6vVO8xAo9FUysKj5MFEGmwLY41+\nNEtRXrranb4bgNDmofoF5bngqEM3wy6GtqnyUqgoP7YcU+ZhjXqSBlvy0GF0hp2crBjY8qTEDBsg\npGkIcOfFIZGYizTYFsYa/WiWojx0lV/tCilXUnCo5oBvY987BTduwKlT0Lr1fT9DDzc3xZF++bJ6\nq62z8qJITEuskNzYckyZhzXqSRpsyUNFTh1lVhvcNLj0obs+Pmrej3LDxkZJXFJslu1R34P6DvU5\nf/08Z7LPlO/zJA800mBbGGv0o1mK8tCVzmDrZrkqFeEO0WFg4bH4LLu8kWPKPKxRT9JgSx4qcuoo\nBjK0WYkFR92WvorAwMKjzn+eeK78DbbkwcWkwT5z5gxdunTBz88Pf39/vvnmGwBiYmJwcXEhODiY\n4OBg1q5dWynCPohYox/NUpSHrozOsPftq7QZNlSswZZjyjysUU8mA2fs7OyYPn06Wq2Wa9eu0aZN\nG6KiotBoNIwZM4YxY8ZUlpwSyT1Rrdqd3XpX8s9z65kz1LGvg3dD7zuVdDmwK8pg+/vD0aNKwuvb\n50TqXhi7z+2mSBQh/9iVmIPJUdK0aVO0t/9MrF27Nq1btyYtLQ2gwk9+fliwRj+apbgXXbVtCzt2\nwC+/wOgpymy2TfM22GiKDf3UVKhfX/lUBDVqKPHxxXJjN6/THOc6zmTfyubYpWPl+jg5pszDGvVk\ndmj6yZMnSUpKon379mzbto0ZM2Ywd+5cQkNDmTZtGo4GEr5HR0fj5uYGgKOjI1qtVv0zRKesh/1a\nR1WRpypfJycnm10/Jyee+HjlOjBQKU86uhhQAmb06icnE+/sDPHxFSd/8+awYAERgYFquftVd9Js\n0khMS+TcuQxq1gSoOP3J63sfTxV5HR8fz+zZswFUe2kMjTBjqnzt2jUiIiL44IMP6NevHxcuXKBx\n48YAfPjhh6SnpzNr1iz9jjUaOQuXWITdu5Ujt3aXiEvp/f96s/r4ahYOWMhAv4F3Cj76CISATz+t\nOKEmT4aLF2HaNPXWpC2TeD/ufUa1G0Xe8m/QahW5JQ83pmxnmY6z/Px8+vfvzwsvvEC/fv0AaNKk\nCRqNBo1Gw8iRI0lISChfiSWSckYIwa6zuwADEY4VuUNERyUvPEoeTEwabCEEI0aMwNfXl7ffflu9\nn56erv68dOlSAgICDDWXmEFJ14jEOPejq6OXjnIp9xLNajcrnSGvIvdg69Bt7Ss2c9LlMklKT6KQ\n/HJ7lBxT5mGNejLpw962bRvz5s0jMDCQ4NtJcSZNmsTvv/9OcnIyGo0Gd3d3fvzxx0oRViK5V7ae\n3gpAx0c66uegvnJF+bRsWbECNG2qRD2eOwfOzgDUr1EfrwZeHL98nMvVDgAhFSuDxOoxabA7duxI\nUcnclMDjjz9eYQI9bOgWISRlcz+6Km6w9di3DwICFGNakWg0d9witw02KNv7jl8+zkX7RMrLYMsx\nZR7WqCe5+VPyUGDSYFe0O0SHgYjHMOcwADJrbOff/4aGDZWPjEWTGEIabAtjjX40S3GvukrPSSfl\nSgq17WsT6BSoX1gZC446goJKLTzqXiA3m2zh9Gk4dgy6d4diy0R3jRxT5mGNepIGW/LAs+3MNgAe\ndXmUajYlvICVseCoQ3cobzGCnIKoY1+H1KxUbtql0bAhODhUjjgS60MabAtjjX40S3GvujLqDsnP\nV6IPK2uXU6tWcOYMXL+u3rK1seUx18f05Lxf5JgyD2vUkzTYkgceowb7yBF45BFuhxhWPHZ2ygEJ\nf/+tdzv8kXAAtpzeYqiVRKIiDbaFsUY/mqW4F13l3MohKSMJW42tusCnUpkLjjoMLDyGtyhfgy3H\nlHlYo56kwZY80OxK20WRKCKkWQi17GvpF1bmgqMOAwuP7ZzbYW9rz4HzB8i6mVW58kisCmmwLYw1\n+tEsxb3oyqg7BCp3wVGHgYVHh2oOtG3eFoFg2+lt9/0IOabMwxr1JA225IHGqMEWwjIz7MBAOHBA\nycFdjPJ2i0geTKTBtjDW6EezFHerq5sFN9l+ZjtgwGCnpytGu1mzcpLOTOrXVyJjTpzQu12eC49y\nTJmHNepJGmzJA8uOMzvILcgl0CmQJrWa6BfqFhyL5xWpLAwsPD7m+hgaNCSmJVKgya18mSRWgTTY\nFsYa/WiW4m51tTF1IwCR7pGlC/fuVc4NswQGFh4dHRwJdAokvyifTPv7S1csx5R5WKOepMGWPLCY\nNNiJicr5YZbAwMIj3PFjZzj8VdkSSawEabAtjDX60SzF3eiqsNpVEtISqGZTjU4tOpWusHu35Qy2\ngRk2QFe3rgCk19h4X93LMWUe1qgnabAlDyQ5DeMpEkW0c25Hnep19AvT0yE3F8o4P6/CcHeHq1ch\nM1Pvdhf3LthobLhYfQc3i65ZRjZJlUYabAtjjX40S3E3urraRMlP2sOjR+nCPXsgNNQyC46g5N4O\nDVXcMsVwdHCkbfO2FGnyOXrr3t0ickyZhzXqyaTBPnPmDF26dMHPzw9/f3+++eYbAC5fvkxUVBTe\n3t50796drCwZnSWpOgghyG6yBoDHPQ0ctpGYqBhMS9K2bSmDDRDlEQXAoZsbKlsiiRVg0mDb2dkx\nffp0Dh48yM6dO/nuu+84fPgwkydPJioqimPHjhEZGcnkyZMrS94HDmv0o1kKc3WVeu0weTVP0bhm\nY9o0b1O6giX91zratQMDh1d3c+8GwKGb6++5azmmzMMa9WTSYDdt2hTt7Uiw2rVr07p1a9LS0oiN\njWXYsGEADBs2jGXLllW8pBKJmWy/qMyue3j2wEZTYogLoRjsqjLDLnYoL8Cjro9SragWafkHCex4\nhrZtYdcuC8koqXKYPNOxOCdPniQpKYmwsDDOnz+Pk5MTAE5OTpw/f95gm+joaNxuL+w4Ojqi1WpV\nv5Hu7Sav5fXdXOswVX/7hTVwElq4tihd38MDNBrijx+Hf/6x3Pf55x/Izyfi9Glo0UKvPMqjG2s2\nLSew+1dc3DaNlBTIzTW//4iIiCrz/1XVr3VYUp74+Hhmz54NoNpLY2iEKPGKN8C1a9fo3LkzH374\nIf369aN+/fpcuXJFLW/QoAGXL1/W71ijwYyuJZJ7Jj8ffv4Z8vKUa29veLRLFo2+bExhURGZ71yg\nYc2G+o3++AN++QVWrqx8gUvSty8MHQoDBujd/mnPT/xr5b/o26ovtWKX88QTMHiwhWSUVDqmbGeZ\nu0Ty8/Pp378/Q4YMoV+/foAyq87IyAAgPT2dJk2amOpCYoKSb3qJcUrq6sQJmDBB+TchAd55B1Yf\nX02hKKD2pU6ljTVUDf+1DiMLj497KQulG05soFBz6667lWPKPKxRTyYNthCCESNG4Ovry9tvv63e\n79u3L3PmzAFgzpw5qiGXSCobJyf4+msYP165Xn50OQCOGUbGZFXwX+swsvDoUteFIKcgbuTf4EIN\nGfUouYNJg71t2zbmzZvHpk2bCA4OJjg4mLVr1zJ+/HjWr1+Pt7c3cXFxjNf9tkjuGp1PS1I2Zemq\nyOYWq4+vBsAx48nSFXQLjm0M7ByxBKGhyp7wwsJSRb29ewOQVnvFXXcrx5R5WKOeTC46duzYkaIS\neXt1bNgg94lKqhbXG8dxLe8a3nWDqJ7rVrrCiRNQqxY0bVrpshmkYUNo0gSOHgVfX72iJ1s9yaQt\nkzhbeylF4mtkjJsE5CiwONboR7MUZenqqutCALo0fdpwhaoQMFMSI37s0OahuNR1IdcujdRbu++q\nSzmmzMMa9SQNtuSBIK/wFtnNlXiAQJtnDVfatg06dKhEqczAiB/bRmNDPx/FD594bSkA48YpE/Im\nTRTfvYFmkgccabAtjDX60SyFKV0duLGOIvur2F8OYsr4Vvj7G6i0dWvVM9hGZtgAT/k8BcDua38g\nhCA1FT7/HP7+W0n4ZyT8QY4pM7FGPUmDLXkg2JChuEMmDhhIaircjkO4Q3Y2HD8OISGVLptJQkLg\n4EG4ebNUUacWnahe0Ij0/GMcuHAAgHr1lBl29eqVLaikKiANtoWxRj+apTCmq2t511h2RHGHPOtn\nxB2yc6eyO6SqWbqaNZUFRwOz7Go21XC9pgTV/P7372Z3KceUeVijnqTBllg9fxz+g+v513nM9TE8\nGngYrlQV3SE6wsNhi+HDd30LnwNg8soFLPlDUK9eZQomqWpIg21hrNGPZimM6WrOPiWIa1jQMOON\nt26Fjh2Nl1uSTp1g82aDRYv/25HmtZuD40k2p+wiKqrs7uSYMg9r1JM02BKrJr/maTalbqK6bXUG\n+g00UilfcTk8+mjlCmcuHTvCjh0GA2jsqtnyrL/i5ll4eF5lSyapYkiDbWGs0Y9mKQzpKtvjFwSC\np1o/haODo+GGycnKcWD161eofPdMo0bg7GzwnEeAoUFDAcWPfaug7NwickyZhzXqSRpsidVSUFTA\nVY+fAPhXyL+MV6zK7hAdJtwi2qZagpyCuJx7mRXH7j5UXfLgIA22hbFGP5qlKKmrv86toqDmObwb\nehPhFmGwDaAEzFR1g21i4RFguHY4AL8m/1pmV3JMmYc16kkabInV8vs/PwDwcpuX0Rg7UFeIqr1D\nREk0M0cAACAASURBVIfOYBvJg/x84PPY2dix9p+1nL56upKFk1QVpMG2MNboR7MUxXV1+OJhtmb8\niaaghundISkpUK0atGhhvE5V4JFHlD3Zx44ZLG5UsxH9fftTJIr4ae9PJruSY8o8rFFP0mBLrJKv\ndn0FQN3UoYYPKtCxZYviDjE2A69KhIcb9WMDvBr6KgA/7/2ZIk1eZUklqUJIg21hrNGPZil0usq8\nkcncfXMBqH/0bRMtgI0boWvXCpasnCjDjx3+SDh+jf3IuJZBhuNSo/XkmDIPa9RTmQb7xRdfxMnJ\niYCAAPVeTEwMLi4ueocaSCSVxYyEGdwsuEmnZo9jn+1jvKIQisGOjKw84e4HEztFQDnr77W2rwFw\nwumrypJKUoUo02APHz68lEHWaDSMGTOGpKQkkpKS6NmzZ4UJ+KBjjX40SxEfH0/2rWy+2fUNAP/y\nnWC6weHDSu6Qli0rQbpyoFUrJQlUaqrRKsOChlHfoT5ZtXdy+Np2g3XkmDIPa9RTmQY7PDyc+gYC\nDuSJ6BJL8H3i92TdzCL8kXBCG4ebrqybXVuD/xoUObt1g/XrjVapZV+LV9sqvuxlF6ZVlmSSKoLJ\nI8JMMWPGDObOnUtoaCjTpk3D0bF0lFl0dDRubm4AODo6otVqVb+R7u0mr+W1udfX868zNWEqAIWr\n+/DG/4sHTLRfuJCI116rMvKbdR0VBStXEu/tbbT+G23f4PM5X7LD5g/+vvA3/k389cojIiKqzvep\n4tc6LClPfHw8s2/nA9bZS2NohBlT5ZMnT9KnTx8OHFBy8l64cIHGjRsD8OGHH5Kens6sWbP0O9Zo\n5CxcUq7ExMfw8V8fY5vWgf/6b0Gj0eDhAb16Gaicnw+NGyvnJTo5Vbqs90xaGgQGwoULYGtrtJr7\n629wssl3DPAdwP8983+VKKCkojFlO+9pl0iTJk3QaDRoNBpGjhxJgjyr6J4p+aaXGObi9Yt8Of9L\nABrt+5w339QwapQRYw1KMiVPT+sy1qDkFGnaFPbuNVnNI30CdprqLD60mOSMZL0yOabMwxr1dE8G\nOz09Xf156dKlejtIJJKKYGL8RHILcolwfhyH82X4rgHWrAFrXQyPioJ160xWqZHvzOONFV92THxM\nJQglqQqU6RIZNGgQf/31F5mZmTg5OfHxxx8THx9PcnIyGo0Gd3d3fvzxR5xKzGSkS0RSXhw4fwDt\nj1o0aFjTdz8v9fPl5MkyGgUHw7ffVv2QdEP8+Sd88omSA8UIffrAM8MzeOVwS3ILckl8KZHQ5lXs\nRHjJPWHKdprlwy7vh0ok5iKEoOvcrsSfjOeNdm/w79YziIjAtMFOTwc/P8UPXO2e19Utx82biisn\nJUVJvWqAPn3gX/+CLQ7vMGX7FHp69mTN82sqWVBJRVDuPmxJ+WGNfrTK5Ke9PxF/Mp7GNRsTpTHj\nuBVQ3CHdulmnsQZwcIAuXcCMgLRxj42jtn1t1v6zllXHVgFyTJmLNepJGmxJleVs9lnGrhsLwIzH\nZ1DXoa55DZcvhyefrEDJKoHevWHVqjKrNa7VmI8jPgbgzbVvkpufW9GSSSyIdIlIqiRCCPr83odV\nx1fxZKsnWfrsUjQaDSdPYtolcuOGssvi5Elo0KDS5C130tIgIADOnwc7u1LFfftCRobyVbHJ52iX\nYI5lHSSmcwwTIyZWvrySckO6RCRWx+9//86q46uoV70e3/f+3ni+65KsXw+hodZtrEHZ3ufhYTS3\nyJQp8MEHMHIkZF6w49xP3wMQs/Fz1iakVKakkkpEGmwLY41+tIrmn8v/8OoqZcvatO7TaF6nOWCm\nrpYts353iI6nn4alhrPytWqlzLL79lUi8I9v6MQAryFQ7RZj5g2Wf92agTX+7kmDLalS3Mi/Qf9F\n/cm+lc3TrZ/mxeAXyc+Hjz6CmTNh0iQTjfPzYeVK6Nev0uStUHQGu6jIZLUaNRTXyIy+X1It35HD\nOQn8b8//KklISWUiDbaF0eUWkCh+69dXv87+8/vxauDFr0/+ikajITMTpk+HNm0i8PKCqVONdLBp\nk+JGqOqny5hLq1ZQrx6YGUnctHZTPI/OBDcY/edojmQeqVj5rBxr/N2TBltSZfgh4SdmJ8+G/Bqc\nmrKEDqF3doXUrg3jximfAQOMdLBoEQwcWCmyVhpPPw2LF5tdvcmFZ+nhNJTcglwGLxlMXqE8meZB\nQhpsC2ONfrSKYNWxVbz5p5JZ76cnZ5KyPYBz5/TrmNRVfr7ivzZqza2U556DhQvLdIsUJ+LGQNwd\n3UnKSOL9uPcrUDjrxhp/96w0skBi7UyYAJcuKT/nOe1gkcMzFIpC7Ha8x8iJQ9Uys9mwAby9lcNs\nHyT8/aF+feXk906dzGpSw7YW85+eT/iv4UzdPpWQpiEMChhUwYJKKgM5w7Yw1uhHKw+++AJCQqCp\n/2Hm5D1BbkEuQ/xfxG7rfwAlSPHqVdBqlaBFO7sydPXbb/DCC5UjfGUzaBD8/rvZ1bXaCB51fZRp\n3ZUDDoYvH86us7sqSjqrxRp/92TgjMQi2NjA3rT99JzfnfPXz9PHuw9ze/+BS/NqXLum1Dl2TImD\nAWVbtdHJc3a2UpiSAg1NnKBuraSmQrt2SjCNvb3Jqp07K3mjOndWFnFfWfUK/9vzP5rWbkrCyARc\n67lWktCSe0UGzlRhrNGP9v/bO/P4KIrsgX97JpMhJ0mQBEgiCYGEI5AAIocgQUBQAUEQb5ZD1gsV\nRBd0T/Un4A2KK+qiwMKutyu4HIoYQRCQI8ASbhJICAmBJOQ+p35/PCYH5L5mkvT38+lPT1/V1W+6\nX1W9evWqPlC+u7hlVQRJWUlop0fw6aRPcTCUtdAFB0sNOzxc9HGFsvriC5kZvTkqa4DAQOjWrVpD\n1QGioiIB+fCX3raUiIAIEjMTGfPvMaTkpDRgRpsWTfHb0xW2TqPz4+kfYcpwUnNTGRd8J4bP1uFs\ncq59gitXwkMP1V8G7ZHp0+Hjj2t8mclo4su7vyS4TTAHkw4yavUoLudeboAM6jQGuklEp9FQSrF0\n91LmbJpDkSrigZ4P8uHtH9PazURBAWRmygAQq0mkWhw9KsFF4uLKjbnRbMjMBH9/mQm+XbsKTytt\nEilNfHo8Q1cM5XTqaQb4DeD7B7/HzezWwJnWqQ26SUTH5uQV5jFj7Qye2vgURaoIfpnHijtXYjLW\nUcl+9BFMm9a8lTWII/pdd0lrohb4ufuxZcoWrm99PTvjdzJ6zWjdPNIEqVRhT58+HR8fnzJTgKWk\npDBy5EiCg4O59dZbSUtLa/BMNmeaoh2tpkQnRzNw+UA+ifoEJwcn/j3x32g/LsKg1ay+cI2scnNh\n1SqYObP+MmvPPPIIfPBBlT7ZVhv21XT06MhPv/sJP3c/dsTtYPDHgzmTdqYBMto0aIrfXqVfzLRp\n09h4VRD1RYsWMXLkSI4fP87w4cNZtGhRg2ZQp+liURbe2fUOfT/sy/7E/QR6BLJ9+nbuDb23fm7w\nxRcyFVinTvWTnr3Tr5+4y1RjYoOK6OTZiV9n/EqodyhHLh5h4PKB10ziq2PHqCqIiYlRoaGhxdsh\nISEqMTFRKaXU+fPnVUhISLnXVSNpnWbEkCFKeXrK4uur1K6YQ+rmT25W/A3F31DTv52uuoalF5/j\n5KRUUZFS+flKOThIGhkZSrm4VPOGFotSffoo9d13DfZMdsny5UrdcUeFh2+7TSlXV5Fx27ZKHT1a\n/nmpOakqYkWE4m8o51ec1T8P/LOBMqxTUyrTnTUe6ZiUlFQ84a6Pjw9JSUkVnjt16lQCAgIA8PDw\nIDw8vNhZ3doc0bebx/aRI5G88QYMv70vXR95kYEvvo0FC227t+XDsR/ikejBP6P3EhsbQatWsHt3\nJFu3wk03laSXkwNQzfsvXQpJSUTcdptdPH+jbd97L8ybR+Tq1eDnd83xb76JICsLfvklkueeg5SU\na9PbsQPWr4+iv3oB1bYjP19eyUNvP8QXIV/w+bOfY3Yw28/ztoDtyMhIVqxYAVCsLyuiSi+R2NhY\nxo4dy6FDhwDw9PQkNTW1+LiXlxcpKdd2XuheItUjMjKy+E9syvh2zGXmB8t479ArXMy+CEqjZ95j\njDD8H6+/5InRKFMVpqXJ2kpBATg7Uy0vkTKymjhRfK+feKLBn83u+POfZVz/3/9e7mGrnAYOFMeS\n9u1l5Ohf/iLB/0JDJaChhwd8u1Yx//OPeHnvk+QX5dO7XW9Wjl9JT5+e5aZta5KTYeFCKCqS7UGD\n4J57apeWvX579eol4uPjQ2JiIgDnz5/H29u7brnTadLkFOSwbM8ykiYH8+KuOVzMvkiI0yDmuv3G\njHbv8fc3PWvmplcdjh6Fbdvgd7+r54SbCLNmyVD15ORKT3v5ZbjpJjHxf/aZDAQFUApeew3WrIH2\n7TTu6fx7dkzfURwwqu+HfVmwbQGFlsJGeJiacfgwrF0rz5SfD8uX2zpHjUuNFfa4ceNYecW1aOXK\nlYxvBsHiY2Kk1mc0ynL77Y13b3ss4atDclYyL//8Mh0Xd+Sx/z5GkWsc3bx6se6+dRx57hfemNuX\np5+WGrOnp8jVZJJ1bSmW1auvwpNPiqtbS8THR6ISvvtuuYetchoxAp5+Wpaq6lV9O/TlwKMHePSG\nRymwFPDHLX+k30f92H52ez1nvu74+ckzTZhQt3Sa4rdXqcK+7777GDRoEMeOHcPf359PPvmE+fPn\n88MPPxAcHMyWLVuYP39+Y+W1wUhPl2HQ+fkSFO3iRVvnyD6xKAvfn/qeyV9MxvctX/4S+ReSs5Pp\n274vXps/Z+P4/YwJHlNm/sXTp8XckZ8vwZzKc5cuKpLW/YfVmSTlzBmZFX3WrPp7sKbICy/Ae+/V\n68vqZnbj/Tve54eHfqBj645EJUYx+JPBPPTNQyRkJFSdgE6DU2mn478riBC2efPmBslMQ5CXJ6Vx\nbq5sh4RIaM+r0bSSGnZjYq92NCtKKea/Fc2muC846bqCLJP47Ro0A2OCx/DMgGeICIjA/yUNQznz\n5BqqaMM5OMD8+fC//8n2vHkVnxsZGUnEmjXij+zpWcsnaiYEBspkDa+/Li2OUtT1nRrRaQTRT0Sz\n6JdFvLb9NVYfXM2X0V8y68ZZzLtpHtc5X1fHzNsH9v7tlUezj4d98SJ8+iksXgwXLsi8gOUp7IZk\ny5ayFcJZs+Dxxxs3DzVBKcX+xP18deQrvor+imOZx+CKfjSmB/LEoBn8YeRUfN1963wvTatinsbS\nxMfLHIfHj9f5vs2CP/4RwsJgzpxKh6vXBmeTMy8Ne4lp4dN47ofn+OrIV7yx4w0+2PMBT/Z/kln9\nZtHerX2t0r58WaIJ5OVde6x/f/jkk7rlvTnT7BU2gIsLTJ0qzfNlyxrnntnZ8Ouv8nv9egm29vLL\n8K9/SceJFVuU8LGxJR1QBgMMHgyX8hJZ8+tmNp38gX1pP3Ap/3zx+ca8NoztcieP33wvT40dziMz\nDPi6l592QxKxfj3Mni2DR3TEmDtlirhNLFlSvLs+36lAz0C+nPwlexL28Kctf2LTqU0s2LaA1395\ng1G+97NgzDP09OnJb7+JaRHk7+ndu+I0L1+GxESZ7b00J05IGdRYNLXaNbQQhW0Lvv4ann0WevSQ\n7QcfhO7dxcVq82b5xgBGj5aX+9gxucbKuHEl19Y3s560cCL1GIbrd3I6/1d89/xKTPb/ypyjZbZj\nYo8JPHrzRJ6ZOJS/TnYgPAjKsXo0Drt3w88/V9PQ3YKYP19erGefFR++BmDlSkhIuIGhbGTLqh20\nvu0NLl73H76LX8F3y1YwPHAEW177PUPbjUMVmtm7FzIyKk/TZJJsl6YGs6C1WHSF3UBYLHDrrRLq\nojQjRkjLPj1ddNC+fZF88UUEX38t7koREeKxlpsLL75Y93wUWYo4lXqKQ0mHOHjhILvP7WZz+E4K\nHUpiwMRkg9HihOHsUH4/YiSPjBjJjHGhPDtJo38n0GztTq8UzJlD5AMPENFSPUMqwscHHn1U7Hyr\nVwPl22Y1DebOFd/rs2dlu7rMni0elE5O8OLDg5g//2tOpZ6ky0NLcL7pY36M2Qx3b+agkxeTu97P\nzqenoVTvMp3P9ohuw9apkpCQktr1W2+VmE1AlPXChaKoq6ptvPUWvPFGyfb7H+YR3P80J1JOcOLS\nCaIvRnMw6SCHLxwmpzCn7MUO4GXqwLDOAwk0DcQzawBBTn1x6tuKUaPAbLZhTbo81qyBnBxpjuhc\ny/PPS3Psp59g2LByT/nHP8QUBjBjhryHNeGvfy3bz9vZqzOGje8S+9mLrD70T55Z9Qkp7Q6wbP9S\nmLoUxzldaRUzkaD8u9i/3nbKWylISJD19u1i7rcydqx8c00JXWFXQl6exNmxjqoqTdeu1zbpaoO/\nf0S5+48cKTGR3HQTuHvlEJ8eT1x6HHGX4/g8MQ7fR+Nw6nCafbEnmLD3LGpv+VVhP3c/ihJ6knq0\nJ06X+9AqeSBr/+nPDTfYlVoun+Rkae6vW0dEv362zo194uoqNuzHH4cDB8qtNfbuXbFdedMmOHRI\nCurbb69Z7dvLyYunbnyaZwc/zd5zUayIWsHqA2u45HmUTM9XOMArBC7pyIRuE7it820MuX4I4FRh\nepcvl7z3gYGV28KrwzffwAMPlExGNG2aDI5dtQpiYyNqlWZ8vLSOrdx8M1x3nRSI+/aV7L/lFmnR\n1Ce6wq6EPXtkoo+rg8EnJkpto5ozNlVIgcolQ0tmb8IFjlkucNFwgde3X+BwhwvsSrnA1u3JpBUm\nYjwURzZX+du6AAo4B5hAw0Anz0A6e3Wmi1cXul7XlV4+vejp3RNPJ0/uugsenCYhleuL5culowjk\nQ2sQ5syRL05X1pVz553yh7z5Zo3coO69V7yYADZsEAec2prCw9uFs3j0Yl4f+Tpbz2zlX1Ff8fGv\n33CGMyzeuZjFOxfTyqEVN3rfTEboKKISb6Gnd0+MBvGl9fWVoearV0NqqpgN9+6t3r3Pn4d33pGa\nNMg3e9ttYlqcMEE6+0tTF0W6dCmsWyetlAMH4KmnxHX4tddgxw4Zhbl3r7RKpk+v/X3Ko0Uo7Pwu\nl5l69Dx5GRqX7jcw96SGo8GA2WDAUdO4lGvg4hCNDxIMnCs0cClc46tkAyeLNNqNNfDUYjnPbDBg\nwMLWn3P59LNcfknMoagoh/zCbAoKsskryCK7IIuM/Ay2pKUR7XWZx/6bRlpu+UtuYS6kwD8+upJR\nR/iv1cX9KkcIk8GEr7sv/u7++Lf25+gufzpd58/UOwNYtbgLQ8MCePyRyidorW8WLJACoE0b8YBp\nXzsvr4rZsEG+gCtxbJqizbHR0DTRWP36ERkQQMR991XrspdfLvl9/fUlCq86GI3SJ2P9bcVkNDG8\n03BuCRxOr/ilHM3cyYHcdfyWsonctvvZmvA99P+e3h+Au9mdQf6DGOw/mCEdh7BidT+cTE7s2wcP\nPyzpxcdLzbioSBR5eU5Ce/ZIbXrqVHldli8XhV0ZCQmRWION1QSlxDln3jyx71tlppQMEXjsMTE7\nNUQopRahsPOu+4r0pEIu5xSRbrGweI0FHBTOrRXXBykycxXJveDlfZBToHF5iMa07VBgMZI/RmPU\nNgNFGLBoBpTmAAYHeMDEkAMm+W1wBM0BDB5gcQaLJ3RrDyEFHFaF4JQP5kJwLwBLAajCK+siDJYY\nWvcOoTDbhEORiY7tzLg5tMLd0YnWjs4c3eeCh4MH40e6YzYYiwuZlGMGvNoaMF2nke1l4IxTDnsz\n8oqPl1kbDFgMGgqN6lqn3dyklmI0imnIxaVkf9++4g6YlwfPPNMAihokAtRjj8mMMtab61ROYCD8\n4Q8ykGby5FqNAjt+XDw8nJyqDjO+c6cE84Lya6yaBk8/ZQAGkZg4iPDwhRw4mcRnv23m+eXf491v\nK7FpsWw8uZGNJyXGt8lgIqxdGNc79OFix77sSehD1pmenDtnZulSSbeigHZduojTzKZNYtpxcYHC\nQlHizYUWobCzO77CN5GnZcMMFoBCyLwE0ZdKzjtX4npMaa+k/FK/DZoBs+ZCUa4zfj4uuJhccHGU\ntbOjC04mN5wc3TkX60VinAeTJ3rgbHbHydGdViY3Wjm6YjK5YHZw4T9rHbjgCvf1t/DNOguZeYrb\nh1rIV4o8i4V8i4UYo+JsmoX/7MulEAvefhaMrRTHr7cQ56Y4HWfhfz0tHHZX/HCs7LV5SsnaYiHn\nMcW3RoXjzyVKvDzFbtak9WF6Q2M4Bhw1A2aDxoIiA47HNPqtMjBAyXlmo4GVBQYc47Qy11rTLG9f\nmWNX7XPQNOmcUkrsscOHw8iRxbLXa9fVYO5cIjZskN7rP/2pRpcOGCBNexA30wsXKnd579On5tnz\ncfVhfNADvLH1AWJWy1yT289u55e4X/jl7C8cSDzAnoQ97GEPhEO/j8CoOWC+LZSV6b3ofl13sgq6\nQ2oPLCqA8qJrjBolhY61hls6OmRpOnSIqPkD2JgWobBdoh9l1nNpmI1mzEYzjkZHUi+a+fB9M28s\nMnM+3sx77ziybKkcNzvIOdbfrRxaFStms9HMhg0aS5fKgJiKWLUKNv8Gf+5f8Tkn0yH+PDzgA2eT\nxWb3lF/ZcyJir7gGHpSOjnHTxT3ryfcl/smT4+D378kHZu0EnT37Wi+Au+6C+x9UjBlfosTzrQpd\nld1X5lgV+9IKC4sLiNLnVbbPer+r91mUkoKksBDz3Xfj6OWFeefOCguXygqc6u6rrHC5ep8R7N5V\nDaNRjMB9+kiBN3BgtS/9/POS3z4+EgOmPrl8WRpNpX20/dz9uCf0Hu4JlRip6Xnp7D+/n7V79vHJ\nxr20672PoxePku0exaoDZWfGMRuccAzryoNfd0e72I1znkHsPteJIM8gvJy87P6/+vxzcewBabG+\n8ILY8SujRShs14PP8cotZfcdPQqfn4YHe8EBBavPw+jOjZ+3uLhIKrOjDRtW4qk1d2755zz5pASt\nAhnksHNn+W5bBjRaGTRaVRXgw0YUKUVBVBR548eTv2EDeUFBZQqIX3/+mdDBg2tU4GQVFFRZCFW3\nwLEoVWkLobx9lRUu9V3gOFxRUJEnThDx4Ydw//0QFSVBsGuIg4PYp00mUbB1jbHj4wPvv8+VSSqg\nIhO7u9mdoQFDcUsZypa98Nsy+HFbJnNfO8BT/3eYw8mHiU6OJjo5moSMBPJc97Pm0H65OAj6/6Mk\nnSDPIDp5diLIK4ggzyACPALwd/fHz90PcKu1Dbs0Dg7iXrtqlfi3W1sdDg7invvee2IaWrpUys5X\nX5VQtyCjjadMkYrWu++Km6GusFsAPXvKAqKsmyrG9HSMkyfTauHCcn0mU11cGFwL5VNfFF1VQFSn\n1VBV4ZJVUFCnFo51nWexoAGOBgPGQ4dw7tMHx/few/zTTzi2b4/ZaKyycCm9767vDBTmajhiYJSj\ngdUZGuasuhU4U6dWv8br7i6urWLOcOX222/i931vKnPO1+vTWLQ8mt//MZr1u47x67FT+HQ9zanU\nU1JTT9zP/sT95abfSnNHc/Wk/9IQvM3+dPfzo4uPH14OfmSd98XL3I7WpjaE9jDg4SGtA2tIiXPn\nSkxFf/6zlItWrK/twoXSAQlimTp1ShT2nj0y4cLIkVKrDg0V5f7tt9WTi66wbUxFftj1wa5dJQNz\nTp5ssNvUD/n5MovM6NFlv4BS2NqGbdQ0nIzGSryIbUuRVYkPGiTr/Hzyp04lr1s38ufNq1bhUrzP\n07qvgGyLhUNZ5Rc4NTGpaVAjk9ToPWX3PX68bOES52ggrYcfWd7X4xN4G95nDTw7wYAJyCvI5FJW\nIhezzpOUfo7EzHgSL58lOSuBxPR4cgsyIfQyu9O2gyrgu4QCxE+2FBYjrj+0JaidDxmJ3iSe9KG1\ngw8mizfe7X3YeNIHbxdvfDr70Ma5Da0cSozlXl4lSv3qfoBOnWpn/4cWrLA1TUrK++4TV6HamrtO\nnpSpl6wdHHfcIXFDGgJNE/Pknj2ylGf20DSJLb1xo3jE9eghtu7hw+3YldliEYdVNzcJq6hTK4ya\nhrPRiLN1h6OjeNkMHAgdOogB2UYopSiCOpmkrilcNAv5zhZO51iINShSO1jYmFL6PDfyDS7kuXci\n301R4GPBSSl8LRZyigrJsxSRZ7GQW6SwGAxoygKWQlnIRxXlkakKOWDJB99C6FNA9hUvr7ct53k7\n6qx4e13Z54CildEBZ6MDTkYTriYzrg5mztzoxDHNiS27nDnQywWDciXplBttWrnhaXbDzdSKHHcz\nUEHvaClarMLu3BlWrJCKHYhHVHUxmyEyEoKCICtL1rNmSW123bqaKexTpyK5dEkmTq2KWbPEpQ4k\nOJTVB7Y0zz9fMtrqzjvFF9WGVoTq8cILEkrxxx8rNZbqftjVo4ycrCO8brpJHK3vuMMmedI0DQfA\nwWjEuZ6Czn9/HC5ugSXzYd1R+PB7WP1k9a8XOQ1jyRI4dVrx5tuK195WnL9k4U9/U7y6OI+UgouM\neeAi//j8EnmmVLrekMbF3GxScjO4lJdBWkEGl/OzySzMoxCNTIMjmZoDGEygXXH79TKRqJnYE2OC\nYBMnNBOf779yjsGEZnDEMGwXWYXvApV/rHVS2AEBAbi7u2M0GjGZTOwuPV7TzjEapQVeG265RexZ\n1ngf3t5SOTQYykbcqwpfX+klDg6W7Zdeqvz8gICKfVCthITUPE6ETVmwAP7zHwn04GSvxoYmTlCQ\nyHjcOOkd02OyXMOZWI3NmzROR4OXl5F2jtAm3xGXXDfuDgjkYIY0WP48pPzrlVJkFWRxKfsSKTkp\nXMq5ss6+xKdrU9h96BIWcxLK6RIhYSkUmC6RmptKak4qBZYCioBWAR9Xmc86KWxN04iMjMSrhcUn\n1rSa1cgr4p574J57IuqeUFNEKbElffWVlFrWYA+VoNeuq0e5chowQJT2+PFiV7v11kbPl71hTdfM\nqgAADblJREFUlVOfPmJCfOcd2T9mTM3T0jQNV0dXXB1d6ejRscyxxyoxRSqlyCnM4fa70jD2r1od\n19kkUtF07Do6FaIUPPecBAaPjKx6hlid+mHQIBm/PWGC2AMbc7bpBsBgEPPf7bdDUpLM51AbhgyR\nKAjlpf/vf8s9jh+X4fH1jaZpOJucaVXgXPXJ1EMNe8SIERiNRh555BFmzpxZ5vjUqVMJuNKG9/Dw\nIDw8vLhUi4yMBGjw7S5dGu9+0dGQmRnB6dPw66+RJCaC1c+zouut+xpLHjbfHjAAZs4k8rff4LXX\niLiirKtzfVRUFLNnz7av57HD7avfrWvOX7uWyDvugN/9joi33rJ5fmu7rRSsXh2BxQIHD0ZeCVpV\n/eurep969IB335Xtgwcj6datZulXdzsyMpJDh1bw5puwc2cAlaLqQEJCglJKqQsXLqiwsDC1devW\n4mN1TLreiI9XqkOHxrnX9u1KdeqkVGCgLK+8UvU1P/30U4Pny244f16pAQOUuvtupbKyanx5i5JV\nHaiWnE6eVCo4WKlnn1WqqKjB82SP2NP7NGqUUhs2yO/KdKd25YQ68+KLL+Lq6srcK8PxNE2zC3PJ\nuXNw442y1rEhu3ZJQKLp08V2befDhlsEly6JecTNTYbIXtc8ZkNvioweLSElRo+uXHfWeoxydnY2\nGVeCAmRlZfH999/T0zrcTkfHSlERvPKKeCgsXixBgnVlbR+0aSOulN27S8+bNb6Bjt1Sa4WdlJTE\nkCFDCA8Pp3///owZM4Zb9Z7nGlPa3tjsiIkRZ/EffpCRPhMm1Cm5Zi2reqRGcjKZ4PXXJdDHpElS\noObnV31dM6Apvk+17nQMDAwkKiqq6hN1Wh4FBfD22zIFx3PPyRRf9TRYQqeBuOMOmSblscektv3x\nx2JL1LEr6s2GfU3Cug27ZRIZKUGV27WTMfJBQbbOkU5NUErCyc2eLSPLXnxRt203Ag1uw9bRKUN0\ntMSHnD5dhppv3Kgr66aIpslEj4cPiyNyt24yT2Renq1zpoOusG1OU7SjleHwYQmeEhEhY/aPHJEh\nnA3QsdjkZdVI1Iuc2rSRIM3btkmrqUsXsXM3I8XdFN8nXWHr1Byl5EOeOFHCAIaGStjCOXMkMpZO\n86FrV4lo9sUXsu7SBd56q2QyR51GRbdh61SfjAyJQ/H3v0vH4hNPyPTQztUbVqvTDPjtN3HPXL9e\nWlKzZkmBrVMndBu2Tv1gsUiT+JFHoGNH8dtdskRMH08+qSvrlka/frBmjfRZtG8vQaQiImD5cr3W\n3QjoCtvG2KUdTSmpST3zDPj7S9HfqRMcOgRffim2ahsMfrFLWdkhjSKn9u3FZzs2Vgru//5XCvRJ\nkyTAlHXyRjumKb5PLXYCA52ryMiQ6Hnr18vi6ipN3s2buRL1RkfnWhwdpS9j4kSZuumrr6SzcupU\nqXmPHSvxStu1s3VOmwW6Dbulkp0t8T22boWff5Ya9YABMoDijjukc0lHp7akpEjM0nXrYNMmcfG8\n5RZZBg+WCoFOMdW1Yes17JZCYqKMZNu+XZR0VJRMtT50qIxEHDJEggDp6NQHXl7wwAOyFBTI/Hk/\n/QSLFkmYgl69ZNqy/v2lRuXvr8eYqQZ6DdvGRNb3PIVKycNGRYmCti45OTIh5MCBoqQHDAAXl/q7\nbyNQ77Jqpti9nHJyZIbonTullbdrlwzSufFGUeDh4VKZ8PNrUCVuT3LSa9jNncJCOHsWjh2THvvo\naBnEEh0tnhu9eomCnjJFvDoCAvQajI594OQk/vvDh8u2UvIu79oFu3dLHJpDh2SQTmioKO+ePWWy\n0i5dZDJUQ8v0l9Br2PaKUhKvOD4eTp2SWcVLr+PjpSMnOBh69JAQmd27SwdhNeZH1NGxe5KTRXFb\nl+PHZYBWWpp4LXXpAp07yzooSMwqfn5N0tVUr2HbK0pBVpa8jElJUpKcOycK2LqOj4eEBKmJ+PnJ\ny9mpk9Qyxo+X3wEB0kOvo9Ncadu2pKOyNJmZUmk5cUKWXbtk8sW4OPl2XF1FeV+9+PqCj4/MIerp\n2SRr6brCrgtW5ZuWJi5NaWmyXLoEFy6IUr56nZwspglvb2jblkizmYjwcHmZevWStZ+frJtgTaEh\nsSeboz3T7OXk6gphYbJcjVLyjcXFiZklLk6WqCipEF24IEtmJpHu7kT4+5coceva21s6TT09S9ae\nnlKBsjEtU2EXFUkpnZkp/sel1+X9Tk8vq5RLK2dHR/DwkD/UuvbyKlbI9Ogha+t227ZlOvuiFi8m\n4spEoDqVExUV1bwVUT3RouVkrQx5e0sfTkXk5RG1cCER48ZJS/fChZL1//4nbompqSVLSoqkbVXe\nVytzN7eKF1fXstt1iA1fa4W9ceNGZs+eTVFREQ8//DDz5s2rdSYampG5a6HT7BIlnJcnQrQuVqGW\n97ttWzFBWP+Y0srZw6POZok0fThvtdFlVT10OVUDs5k0kMkaqoNS4t1SWomXXjIypGUdGyu/y1us\n+sdsLtEzLi7g4sLt6VOBR6rMRq0UdlFREbNmzWLz5s34+vrSr18/xo0bRzc7HRG303GojNizKmIn\nJ91jQkdHp/pompgonZ3FXFlblJJBa1YFnpUFWVnsn+9DcDUur5XC3r17N507dyYgIACAe++9l2+/\n/dZuFXaGoTV0am3rbJRLbGysrbPQZNBlVT10OVUPm8hJ04pr1aU5X83uqlq59X355Zds2rSJjz76\nCIDVq1eza9cu3n333VL50muwOjo6OrWhXt36qqOM7cEHW0dHR6c5UStHRF9fX+Li4oq34+Li8PPz\nq7dM6ejo6OhcS60U9g033MCJEyeIjY0lPz+fzz77jHHjxtV33nR0dHR0SlErk4iDgwNLly5l1KhR\nFBUVMWPGDLvtcNTR0dFpLtS4hj19+nR8fHz4wx/+wLFjxzh58iTPP/988fGLFy8yevRowsPDCQ0N\nZcWKFYCYTYYNG0aPHj0IDQ3lnXfeqbeHsEescurZs2e5xyuSk5WioiJ69+7N2LFjGyG3tqUuskpL\nS2PSpEl069aN7t27s3PnzkbKdeNTFzktXLiQHj160LNnT+6//37ymtHs5+VRlaxSU1OZMGECYWFh\n9O/fn8OHDxcf27hxI127dqVLly68+uqrjZXl6qFqyNatW9W+fftUaGhoucf/+te/qvnz5yullEpO\nTlZeXl6qoKBAnT9/Xu3fv18ppVRGRoYKDg5W0dHRNb19k6G2crLy5ptvqvvvv1+NHTu2UfJrS+oi\nqylTpqjly5crpZQqKChQaWlpjZNpG1BbOcXExKjAwECVm5urlFJq8uTJasWKFY2Wb1tQlayeffZZ\n9dJLLymllDp69KgaPny4UkqpwsJCFRQUpGJiYlR+fr4KCwuzKz1V4xr2kCFD8PT0rPB4+/btSU9P\nByA9PZ02bdrg4OBAu3btCA8PB8DV1ZVu3bqRkJBQy2LG/qmtnADi4+NZv349Dz/8cIvwtqmtrC5f\nvsy2bduYPn06IKa61q3t09++PqitnNzd3TGZTGRnZ1NYWEh2dja+dRn80QSoSlZHjhxh2LBhAISE\nhBAbG8uFCxfKjDExmUzFY0zshXoPVzVz5kwOHz5Mhw4dCAsLY8mSJdecExsby/79++nfv399377J\nUJmc5syZw+uvv46hCUYTawgqklVMTAxt27Zl2rRp9OnTh5kzZ5KdnW3j3NqOiuTk5eXF3Llzuf76\n6+nQoQMeHh6MGDHCxrm1LWFhYXz99deADAQ8c+YM8fHxnDt3Dn9//+Lz/Pz8OGdHsZnrXSMsWLCA\n8PBwEhISiIqK4oknniAjI6P4eGZmJpMmTWLJkiW4tuB53SqS03fffYe3tze9e/duEbXr6lCRrAoL\nC9m3bx+PP/44+/btw8XFhUWLFtk6uzajPDllZmZy6tQpFi9eTGxsLAkJCWRmZrJmzRpbZ9emzJ8/\nn7S0NHr37s3SpUvp3bs3RqPR7gf81bvC3rFjB3fffTcAQUFBBAYGcuzYMQAKCgqYOHEiDz74IOPH\nj6/vWzcpypPT0aNH2bFjB2vXriUwMJD77ruPLVu2MGXKFBvn1rZU9E75+fnh5+dHv379AJg0aRL7\n9u2zZVZtSnlyOnLkCHv37mXQoEHFJpK77rqLHTt22Di3tsXNzY2PP/6Y/fv3s2rVKpKTkwkKCrL7\nMSb1rrC7du3K5s2bAUhKSuLYsWN06tQJpRQzZsyge/fuzNbDiZYrp6CgIBYsWEBcXBwxMTF8+umn\n3HLLLaxatcrGubUtFb1T7dq1w9/fn+PHjwOwefNmevToYcus2pSK3qmQkBB27txJTk4OSik2b95M\n9+7dbZxb23L58mXy8/MB+Oijjxg6dCiurq72P8akpr2U9957r2rfvr0ymUzKz89PLV++XC1btkwt\nW7ZMKSW902PGjFG9evVSoaGhas2aNUoppbZt26Y0TVNhYWEqPDxchYeHqw0bNtRf96mdUVs5lSYy\nMrJFeInURVZRUVHqhhtuUL169VITJkxo1l4idZHTq6++qrp3765CQ0PVlClTVH5+vq0eo1GoSlY7\nduxQwcHBKiQkRE2cOLHMe7N+/XoVHBysgoKC1IIFC2z1COXSYHM66ujo6OjUL7obgo6Ojk4TQVfY\nOjo6Ok0EXWHr6OjoNBF0ha2jo6PTRNAVto6Ojk4TQVfYOjo6Ok2E/wcsezzdgmVy7AAAAABJRU5E\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10416fed0>"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.print_matrix()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "            <table>\n",
        "                <tr>\n",
        "                    <td><a onclick=\"$('#laAdHmGLBf').toggle()\" href=\"#\">+</a></td>\n",
        "        \n",
        "            <td>\n",
        "            <div style=\"width:20px;position:relative; width: -moz-fit-content;\">\n",
        "            <div style=\"display:inline-block;-webkit-writing-mode:vertical-rl;-moz-writing-mode: vertical-rl;writing-mode: vertical-rl;\">\n",
        "            mass\n",
        "            </div>\n",
        "            </div>\n",
        "            </td>\n",
        "            \n",
        "            <td>\n",
        "            <div style=\"width:20px;position:relative; width: -moz-fit-content;\">\n",
        "            <div style=\"display:inline-block;-webkit-writing-mode:vertical-rl;-moz-writing-mode: vertical-rl;writing-mode: vertical-rl;\">\n",
        "            gamma\n",
        "            </div>\n",
        "            </div>\n",
        "            </td>\n",
        "            \n",
        "            <td>\n",
        "            <div style=\"width:20px;position:relative; width: -moz-fit-content;\">\n",
        "            <div style=\"display:inline-block;-webkit-writing-mode:vertical-rl;-moz-writing-mode: vertical-rl;writing-mode: vertical-rl;\">\n",
        "            m\n",
        "            </div>\n",
        "            </div>\n",
        "            </td>\n",
        "            \n",
        "            <td>\n",
        "            <div style=\"width:20px;position:relative; width: -moz-fit-content;\">\n",
        "            <div style=\"display:inline-block;-webkit-writing-mode:vertical-rl;-moz-writing-mode: vertical-rl;writing-mode: vertical-rl;\">\n",
        "            c\n",
        "            </div>\n",
        "            </div>\n",
        "            </td>\n",
        "            \n",
        "            <td>\n",
        "            <div style=\"width:20px;position:relative; width: -moz-fit-content;\">\n",
        "            <div style=\"display:inline-block;-webkit-writing-mode:vertical-rl;-moz-writing-mode: vertical-rl;writing-mode: vertical-rl;\">\n",
        "            f_0\n",
        "            </div>\n",
        "            </div>\n",
        "            </td>\n",
        "            \n",
        "                </tr>\n",
        "                \n",
        "            <tr>\n",
        "                <td>mass</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(165,252,185)\">\n",
        "                -0.02\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(168,247,182)\">\n",
        "                -0.05\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(168,247,183)\">\n",
        "                -0.05\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(165,251,185)\">\n",
        "                -0.02\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>gamma</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(165,252,185)\">\n",
        "                -0.02\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(219,171,144)\">\n",
        "                0.61\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>m</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(168,247,182)\">\n",
        "                -0.05\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(241,137,127)\">\n",
        "                -0.85\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                -0.00\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>c</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(168,247,183)\">\n",
        "                -0.05\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(241,137,127)\">\n",
        "                -0.85\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                -0.00\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>f_0</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(165,251,185)\">\n",
        "                -0.02\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(219,171,144)\">\n",
        "                0.61\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                -0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                -0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            </table>\n",
        "\n",
        "            <pre id=\"laAdHmGLBf\" style=\"display:none;\">\n",
        "            <textarea rows=\"18\" cols=\"50\" onclick=\"this.select()\" readonly>%\\usepackage[table]{xcolor} % include this for color\n",
        "%\\documentclass[xcolor=table]{beamer} % for beamer\n",
        "\\begin{tabular}{|c|c|c|c|c|c|}\n",
        "\\hline\n",
        " & mass & $\\gamma$ & m & c & $f_{0}$\\\\\n",
        "\\hline\n",
        "mass & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{165,252,185} -0.02 & \\cellcolor[RGB]{168,247,182} -0.05 & \\cellcolor[RGB]{168,247,183} -0.05 & \\cellcolor[RGB]{165,251,185} -0.02\\\\\n",
        "\\hline\n",
        "$\\gamma$ & \\cellcolor[RGB]{165,252,185} -0.02 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{163,253,186} -0.01 & \\cellcolor[RGB]{163,253,186} -0.01 & \\cellcolor[RGB]{219,171,144} 0.61\\\\\n",
        "\\hline\n",
        "m & \\cellcolor[RGB]{168,247,182} -0.05 & \\cellcolor[RGB]{163,253,186} -0.01 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{241,137,127} -0.85 & \\cellcolor[RGB]{163,253,186} -0.00\\\\\n",
        "\\hline\n",
        "c & \\cellcolor[RGB]{168,247,183} -0.05 & \\cellcolor[RGB]{163,253,186} -0.01 & \\cellcolor[RGB]{241,137,127} -0.85 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{163,254,186} -0.00\\\\\n",
        "\\hline\n",
        "$f_{0}$ & \\cellcolor[RGB]{165,251,185} -0.02 & \\cellcolor[RGB]{219,171,144} 0.61 & \\cellcolor[RGB]{163,253,186} -0.00 & \\cellcolor[RGB]{163,254,186} -0.00 & \\cellcolor[RGB]{255,117,117} 1.00\\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Note on complex PDF\n",
      "There is nothing preventing you from doing something like this:\n",
      "```\n",
      "def mypdf(x,mass, gamma, m, c, f_0):\n",
      "    return brietwigner(x, mass, gamma) + f_0*(m*x+c)/normalization\n",
      "ulh=UnbinnedLH(mypdf, data)\n",
      "m=Minuit(ulh, **initial_values)\n",
      "m.migrad()\n",
      "```\n",
      "\n",
      "If your PDF is more complicated than what you can do with AddPDF and AddPDFNorm, it might be\n",
      "easier to write it out manually."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Simultaneous Fit\n",
      "Let try to simultaneous fit 2 gaussians with the same width but different mean."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from probfit import SimultaneousFit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data1 = randn(10000)\n",
      "data2 = randn(10000)+10\n",
      "hist([data1,data2], histtype='step', bins=100);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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EPwj88pcwdqzRKUQOyJyZybwz5hkdQ/pJBS8ig9K+/fv4zZrfAHDK2FO4Lek2gxMNPZqi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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10421c4d0>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#note here that they share the same sigma\n",
      "g1 = rename(gaussian, ['x','mu1','sigma'])\n",
      "g2 = rename(gaussian, ['x','mu2','sigma'])\n",
      "print describe(g1)\n",
      "print describe(g2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['x', 'mu1', 'sigma']\n",
        "['x', 'mu2', 'sigma']\n"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#make two likelihood and them up\n",
      "ulh1 = UnbinnedLH(g1,data1)\n",
      "ulh2 = UnbinnedLH(g2,data2)\n",
      "sim = SimultaneousFit(ulh1,ulh2)\n",
      "print describe(sim) #note the sigma merge"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['mu1', 'sigma', 'mu2']\n"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.draw(args=(0.5, 1.5, 10.5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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xQQsc342jFh+a6mTs2QM88wxPIxw/Hjh4EHj6acDDgwOcAwOBr77iC0LNmvBc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\nzfj8+TNsNhsePHggagEEAENDQ5DJZAAAmUyGoZmLEBYQT2r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       "text": [
        "<matplotlib.figure.Figure at 0x1041e38d0>"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = Minuit(sim, mu1=0.5, sigma=1.5, mu2=10.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:1: InitialParamWarning: errordef is not given. Default to 1.\n",
        "-c:1: InitialParamWarning: Parameter mu1 is floating but does not have initial step size. Assume 1.\n",
        "-c:1: InitialParamWarning: Parameter sigma is floating but does not have initial step size. Assume 1.\n",
        "-c:1: InitialParamWarning: Parameter mu2 is floating but does not have initial step size. Assume 1.\n"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.migrad();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = 28276.585769</td>\n",
        "                <td>NFCN = 81</td>\n",
        "                <td>NCALLS = 81</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 1.53288894679e-08</td>\n",
        "                <td>GOAL EDM = 1e-05</td>\n",
        "                <td>UP = 1.0</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#TgucmwOQqC').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>mu1</td>\n",
        "                <td>1.023630e-02</td>\n",
        "                <td>1.407006e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>sigma</td>\n",
        "                <td>9.949032e-01</td>\n",
        "                <td>7.035022e-03</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>3</td>\n",
        "                <td>mu2</td>\n",
        "                <td>9.994690e+00</td>\n",
        "                <td>1.407006e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"TgucmwOQqC\" style=\"display:none;\">\n",
        "            <textarea rows=\"12\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & mu1 & 1.024e-02 & 1.407e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\sigma$ & 9.949e-01 & 7.035e-03 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & mu2 & 9.995e+00 & 1.407e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.show(m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ThaGD8NRVwy+/2E+HA22oOXtlfA72sGFue7frhfSrPkqNWxsye9xslo1zQbcD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\nV4PWzwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104182b50>"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Note on simultaneous fit\n",
      "Again there is nothing preventing you from doing\n",
      "<code>\n",
      "def my_cost_function(mu1, mu2, sigma):\n",
      "    return ulh1(mu1,sigma)+ulh2(mu2.sigma)\n",
      "\n",
      "m=Minuit(my_cost_function, **initial_values)\n",
      "m.migrad()\n",
      "</code>\n",
      "\n",
      "If your cost function is more complex than adding them together this is the to do it."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Toy generation.\n",
      "This is invert CDF implementation (not accept reject). Large overhead but fast element-wise. Anyone want to signup for accept/reject?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from probfit import gen_toy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "toy = gen_toy(total_pdf, 1000, (1.83,1.91), mass=1.87, gamma=0.01, c=1.045, m=-0.43, f_0=0.5, quiet=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['x', 'mass', 'gamma', 'm', 'c', 'f_0']\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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Lp4+Rl+cbYOBPTxjY/Gkmh/MNLF44gO0bj7f53pmWzTQ+Z/BQu1+D74DQYA9t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\nSlqaZLOEEAIQ+ANyQ96Na8wUrTUjBNRWq6mvU7Dw6RjyChoYd0pDMMW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       "text": [
        "<matplotlib.figure.Figure at 0x10418b810>"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hist(toy, bins=100, histtype='step');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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9xAj7Z+hOVF/Y/f07P7g/kZrcd59YHGnYsOYHWGRbbGMnIlIZFnYiIpVRfVMM\nEdmHJImLFObMEesVFeI+e5NlY4bsbCAmxv4ZJAl46SXRNyU/H5gyxb777/aFXa/XO/0k23q9HoDO\nwSk61hXeS4A5lWbIGR4uemkaemw/9ljz3uH20KMHkJ5uHFYhOtrY+dGe7+emTcCZM8b1MWPssttG\nFjfFpKenY9iwYbj77ruxZs0aJTPZlSiazq0rZASYU2ldLecvfgHMmiWOlufMAaZPd8wR+5Qpxgyz\nZ4ue5E1z2sOoUcYMc+YAd91lt10DsLCw19fX45lnnkF6ejpOnjyJ7du349SpU0pnIyIiC1hU2LOz\ns/HLX/4Svr6+cHV1xdy5c/HZZ58pnY2IiCwgybJhLEHzffLJJ/jnP/+JLVu2AAC2bduGf//730hK\nSjJu2BHfwYiIVMCCstyMRSdPzSna1gYjIiLLWNQUc8cdd6C4uLhxvbi4GD4+PoqFIiIiy1lU2MPD\nw/Hdd9+hqKgItbW1+OijjzB9+nSlsxERkQUsaorp2bMnNm3ahClTpqC+vh5Lly7F8OHDlc5GREQW\n6PQR+5IlS6DRaPDiiy/i9OnTOHPmDFauXNn4eHl5OR544AGEhYUhKCgI77//PgDg+vXriIiIQFhY\nGAIDA5u9xhYMOYODg9t8vL2cBvX19dBqtZhm4zFOrcnp6+uLkJAQaLVajB492ikzVlRUYNasWRg+\nfDgCAwNx5MgRp8t5+vRpaLXaxmXAgAHYuHGj0+UEgNdffx0jRoxAcHAw5s+fj59//tkpc27YsAHB\nwcEICgrChg0bbJbRnJw//fQTZs6cidDQUERERODEiRONj9mzP441OTt6bStyJx08eFDOzc2Vg4KC\n2nz81VdflePj42VZluVLly7JAwcOlG/cuCHLsixXV1fLsizLN27ckCMiIuRDhw51dvd2ySnLsrx+\n/Xp5/vz58rRp02yW0dqcvr6+8o8//mjTfNZmXLRokZycnCzLsvi9V1RUOGVOg/r6etnLy0v+4Ycf\nnC5nYWGh7OfnJ1+/fl2WZVmePXu2/P777ztdzm+++UYOCgqSr127JtfV1cmTJk2Sz5w547Ccv/3t\nb+U//elPsizLcn5+vhwZGSnLsizX1dXJ/v7+cmFhoVxbWyuHhobKJ0+edLqc5ry2pU4fsY8fPx7u\n7u7tPu7t7Y3KykoAQGVlJQYNGoSePUWLT5+bs+3W1taivr4eAwcO7Ozu7ZKzpKQEe/bsQWxsrM2v\n7rEmJ2CGVG9SAAAEy0lEQVSfq48szXjlyhUcOnQIS27ONN6zZ08MGDDA6XI2lZGRAX9/fwwePNjp\nct56661wdXVFTU0N6urqUFNTgzvuuMOpcrq4uODUqVOIiIhA79694eLigvvvvx+ffvqpw3KeOnUK\nE24OQj906FAUFRXh4sWLdu+PY0nOSzdnB+rotS0pPrrjsmXLcOLECdx+++0IDQ1t9jWsoaEBYWFh\n0Gg0mDBhAgIDA5XevSI5V6xYgXXr1qFHD8cPfmkqpyRJmDRpEsLDwxv7FDhTxsLCQnh4eGDx4sUY\nOXIkli1bhpqaGqfL2dSOHTswf/58B6Qzai/nwIED8cILL+DOO+/E7bffDjc3N0zq7CzwNs4pSRKC\ng4Nx6NAhXL58GTU1Nfjiiy9QUlLisJyhoaGNf1iys7Px/fffo6SkBOfOnWv2B9zHxwfnzp1zVMx2\nc1pC8cq1evVqhIWF4fz588jLy8PTTz+Nq1evip316IG8vDyUlJTg4MGDDh0Lo72cu3fvhqenJ7Ra\nrVNci2/q/czMzMSxY8fw5Zdf4u2338ahQ4ecKmNdXR1yc3Pxm9/8Brm5uejbty8SExMdktFUToPa\n2lp8/vnn+PWvf+2wjEDbOauqqnD27Fm89dZbKCoqwvnz51FVVYUPP/zQ6XIOGzYMv//97zF58mQ8\n+OCD0Gq1Dj1Iio+PR0VFBbRaLTZt2gStVgsXFxen60TZXk5LKP5uZ2VlNf7H8Pf3h5+fH06fPt3s\nOQMGDMDUqVORk5Oj9O7N1lbO/Px8ZGVlIS0tDX5+fpg3bx7279+PRYsWOVVOw/vp7e0NAPDw8MDM\nmTORnZ3tVBl9fHzg4+ODe+65BwAwa9Ys5ObmOiSjqZwGX375JUaNGgUPew9J2EJbOU+dOoWjR49i\n7NixjU0zjz76KLKyspwqZ35+PgBxsi8nJwdfffUV3NzcMHToUIfl7N+/P7Zu3Ypjx44hNTUVly5d\ngr+/v9P1x2kr55AhQyzaluKFfdiwYcjIyAAAlJWV4fTp0xgyZAjKy8tRcXMszWvXrmHfvn3QarVK\n796qnP7+/li9ejWKi4tRWFiIHTt2YOLEiUhNTXWqnEOGDEFNTU3j0WZ1dTX27t1r/hlzO2X08vLC\n4MGDUVBQAEC0X49w4GSX7eU02L59O+bNm+eoeI3a+2wOHToUR44cwbVr1yDLMjIyMhzanGnq/bx4\n8SIA4IcffsDOnTsd2rx15coV1NbWAgC2bNmC+++/H/369XO6/jjt5bRIZ8/szp07V/b29pZdXV1l\nHx8fOTk5WX733Xfld999V5ZlcXb84YcflkNCQuSgoCD5ww8/lGVZlo8fPy5rtVo5NDRUDg4Olteu\nXdvZXdslZ1N6vd7mV8VYmvPs2bNyaGioHBoaKo8YMUJevXq102WUZVnOy8uTw8PD5ZCQEHnmzJk2\nvSrGmpxVVVXyoEGD5MrKSpvlUyLnmjVr5MDAQDkoKEhetGiRXFtb65Q5x48fLwcGBsqhoaHy/v37\nbZbRnJxZWVlyQECAPHToUDk6OrrZZ3DPnj1yQECA7O/vb9P/Q9bmNLy2V69eso+Pj7x161aT+7Jo\nEDAiInJejr/sg4iIFMXCTkSkMizsREQqw8JORKQyLOxERCrDwk5EpDL/D2H/nSNEpKluAAAAAElF\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10418d650>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ulh = UnbinnedLH(total_pdf, toy)\n",
      "m = Minuit(ulh, mass=1.87, gamma=0.01, c=1.045, m=-0.43, f_0=0.5)\n",
      "m.migrad();\n",
      "ulh.show(m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:2: InitialParamWarning: Parameter mass is floating but does not have initial step size. Assume 1.\n",
        "-c:2: InitialParamWarning: Parameter gamma is floating but does not have initial step size. Assume 1.\n",
        "-c:2: InitialParamWarning: Parameter m is floating but does not have initial step size. Assume 1.\n",
        "-c:2: InitialParamWarning: Parameter c is floating but does not have initial step size. Assume 1.\n",
        "-c:2: InitialParamWarning: Parameter f_0 is floating but does not have initial step size. Assume 1.\n"
       ]
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = -2756.9986626</td>\n",
        "                <td>NFCN = 472</td>\n",
        "                <td>NCALLS = 472</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 3.10277679189e-07</td>\n",
        "                <td>GOAL EDM = 5e-06</td>\n",
        "                <td>UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#NfsqKLtAQk').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>mass</td>\n",
        "                <td>1.870038e+00</td>\n",
        "                <td>3.294864e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>gamma</td>\n",
        "                <td>9.423832e-03</td>\n",
        "                <td>9.778754e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>3</td>\n",
        "                <td>m</td>\n",
        "                <td>4.031586e+03</td>\n",
        "                <td>1.414212e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>4</td>\n",
        "                <td>c</td>\n",
        "                <td>6.188177e+03</td>\n",
        "                <td>1.414212e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>5</td>\n",
        "                <td>f_0</td>\n",
        "                <td>5.332493e-01</td>\n",
        "                <td>3.463870e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"NfsqKLtAQk\" style=\"display:none;\">\n",
        "            <textarea rows=\"16\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & mass & 1.870e+00 & 3.295e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\gamma$ & 9.424e-03 & 9.779e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & m & 4.032e+03 & 1.414e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "4 & c & 6.188e+03 & 1.414e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "5 & $f_{0}$ & 5.332e-01 & 3.464e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
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       "output_type": "display_data",
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nl19+4cGDB7i4uDBq1CgmT57Mxo0bOXToEJ07d87zOnIrL3nM229D5mMPtm+H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\n2BUKheIaQwV2hUKhuMb4f9ezgSQMAVgzAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104184a50>"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Tips\n",
      "\n",
      "A lot of time you want to generate toy from fitted parameters. Retyping/Repeating yourself is not the best use of time. Use python dictionary expansion."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.fitarg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "{'c': 6188.177096551795,\n",
        " 'error_c': 1.414212148161654,\n",
        " 'error_f_0': 0.03463870349452257,\n",
        " 'error_gamma': 0.0009778753714724175,\n",
        " 'error_m': 1.4142121450350984,\n",
        " 'error_mass': 0.0003294864038653029,\n",
        " 'f_0': 0.533249323216673,\n",
        " 'fix_c': False,\n",
        " 'fix_f_0': False,\n",
        " 'fix_gamma': False,\n",
        " 'fix_m': False,\n",
        " 'fix_mass': False,\n",
        " 'gamma': 0.009423831866177658,\n",
        " 'limit_c': None,\n",
        " 'limit_f_0': None,\n",
        " 'limit_gamma': None,\n",
        " 'limit_m': None,\n",
        " 'limit_mass': None,\n",
        " 'm': 4031.5859291021125,\n",
        " 'mass': 1.8700381598984956}"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "toy = gen_toy(total_pdf, 1000, (1.83,1.91), quiet=False, **m.fitarg)#note the double star"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['x', 'mass', 'gamma', 'm', 'c', 'f_0']\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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ERqdH9vgl3Y0pOYkrMQFXrySfus/8Tz/efuUKCr7S+uxGbE+nXmvwyw8JUGTJ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\no7x4QQGMHavE6rBYWuNOV1cr4U1HjOg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/5s7tymvmBSy3WOCGG1qXQF0obfX9938rI4X/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fAAAD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\niBDHCSGFHSJEiBDHCf8PwqCwxSikZ0MAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1041a9fd0>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Saving/Loading your toy\n",
      "using np.save"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.save('mytoy.npy', toy)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loaded_toy = np.load('mytoy.npy')\n",
      "hist(loaded_toy, bins=100, histtype='step');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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0NiIiaoM29Tz98ssv8a9//Qtr164FAKxfvx6HDh3C6tWrLQeWJOVSEhF1IKr0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\npDEs7EREGsPCTkSkMSzsREQa8/+E7tJW0Vt5nAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1041aca10>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Recipe\n",
      "I won't cover it but just something you may find useful.\n",
      "\n",
      "- Using Cython to write pdf for speed\n",
      "- Checking convergence programatically\n",
      "- Saving and reusing fit argument"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Using Cython\n",
      "\n",
      "Skip this part if you don't have cython\n",
      "\n",
      "A much more comprehensive example of how to use cython is provided in iminuit [hard core tutorial](http://nbviewer.ipython.org/urls/raw.github.com/iminuit/iminuit/master/tutorial/hard-core-tutorial.ipynb). We will show a simple example here."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext cythonmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "from libc.math cimport sqrt\n",
      "cimport cython\n",
      "\n",
      "cdef double pi = 3.14159265358979323846264338327\n",
      "\n",
      "@cython.embedsignature #you need this or experimental @cython.binding.\n",
      "cpdef double cython_bw(double x, double m, double gamma):\n",
      "    cdef double mm = m*m\n",
      "    cdef double xm = x*x-mm\n",
      "    cdef double gg = gamma*gamma\n",
      "    cdef double s = sqrt(mm*(mm+gg))\n",
      "    cdef double N = (2*sqrt(2)/pi)*m*gamma*s/sqrt(mm+s)\n",
      "    return N/(xm*xm+mm*gg)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cython_bw(1,2,3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "0.2585302502852219"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from probfit import describe\n",
      "describe(cython_bw)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "['x', 'm', 'gamma']"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ulh = UnbinnedLH(cython_bw, bb_dmass)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = Minuit(ulh, m=1.875, gamma = 0.01)\n",
      "ulh.show(m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:1: InitialParamWarning: Parameter m is floating but does not have initial step size. Assume 1.\n",
        "-c:1: InitialParamWarning: Parameter gamma is floating but does not have initial step size. Assume 1.\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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elNcQhaOo68lEjJZXbWxsxMiRI3HixAk88MADCAkJQUlJCbRaLQBAq9WipKSk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\nVbip6Xnb91BV1W6vNwtAjInm1bh6orJ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       "text": [
        "<matplotlib.figure.Figure at 0x10e469910>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit -r1 -n1 m.migrad()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = -10170.1470088</td>\n",
        "                <td>NFCN = 56</td>\n",
        "                <td>NCALLS = 56</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 9.31305324182e-07</td>\n",
        "                <td>GOAL EDM = 5e-06</td>\n",
        "                <td>UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#yMQUCilCFe').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>m</td>\n",
        "                <td>1.869105e+00</td>\n",
        "                <td>1.236187e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>gamma</td>\n",
        "                <td>9.609371e-03</td>\n",
        "                <td>2.250473e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"yMQUCilCFe\" style=\"display:none;\">\n",
        "            <textarea rows=\"10\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & m & 1.869e+00 & 1.236e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\gamma$ & 9.609e-03 & 2.250e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 1: 68.8 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Building Cost Functions Manually\n",
      "\n",
      "See iminuit hardcore tutorial."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Checking convergence programatically"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "status, param = m.migrad()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = -10170.1470097</td>\n",
        "                <td>NFCN = 10</td>\n",
        "                <td>NCALLS = 66</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 4.27228260843e-13</td>\n",
        "                <td>GOAL EDM = 5e-06</td>\n",
        "                <td>UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#aiQbpHRHFH').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>m</td>\n",
        "                <td>1.869105e+00</td>\n",
        "                <td>1.236193e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>gamma</td>\n",
        "                <td>9.609357e-03</td>\n",
        "                <td>2.250544e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"aiQbpHRHFH\" style=\"display:none;\">\n",
        "            <textarea rows=\"10\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & m & 1.869e+00 & 1.236e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\gamma$ & 9.609e-03 & 2.251e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print status.has_covariance\n",
      "print status.is_valid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n",
        "True\n"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#or an umbrella call\n",
      "m.migrad_ok()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#minos\n",
      "results = m.minos('m')\n",
      "print results['m']\n",
      "print results['m'].upper_valid\n",
      "print results['m'].lower_valid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <span>Minos status for m: <span style=\"background-color:#92CCA6\">VALID</span></span>\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>Error</td>\n",
        "                <td>-0.000123642360121</td>\n",
        "                <td>0.000123614309471</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>Valid</td>\n",
        "                <td style=\"background-color:#92CCA6\">True</td>\n",
        "                <td style=\"background-color:#92CCA6\">True</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>At Limit</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>Max FCN</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>New Min</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "                <td style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'lower_new_min': False, 'upper': 0.0001236143094705069, 'lower': -0.00012364236012110086, 'at_lower_limit': False, 'min': 1.8691050836421013, 'at_lower_max_fcn': False, 'is_valid': True, 'upper_new_min': False, 'at_upper_limit': False, 'lower_valid': True, 'upper_valid': True, 'at_upper_max_fcn': False, 'nfcn': 12L}\n",
        "True\n",
        "True\n"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Saving/Reuse fit argument\n",
      "Some time we want to resue fitting argument."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.fitarg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "{'error_gamma': 0.0002250543924548983,\n",
        " 'error_m': 0.000123619287240136,\n",
        " 'fix_gamma': False,\n",
        " 'fix_m': False,\n",
        " 'gamma': 0.00960935659431392,\n",
        " 'limit_gamma': None,\n",
        " 'limit_m': None,\n",
        " 'm': 1.8691050836421013}"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "old_fitarg = m.fitarg\n",
      "ulh2 = UnbinnedLH(cython_bw, bb_dmass)\n",
      "m2 = Minuit(ulh2, **old_fitarg)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m2.print_param()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#KQdKDjYAFf').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>m</td>\n",
        "                <td>1.869105e+00</td>\n",
        "                <td>1.236193e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>gamma</td>\n",
        "                <td>9.609357e-03</td>\n",
        "                <td>2.250544e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"KQdKDjYAFf\" style=\"display:none;\">\n",
        "            <textarea rows=\"10\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & m & 1.869e+00 & 1.236e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\gamma$ & 9.609e-03 & 2.251e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#since fitarg is just a dictionary you can dump it via pickle\n",
      "import pickle\n",
      "out = open('my_fitarg.pck','w')\n",
      "pickle.dump(old_fitarg,out)\n",
      "out.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fin = open('my_fitarg.pck','r')\n",
      "loaded_fitarg = pickle.load(fin)\n",
      "fin.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print loaded_fitarg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'fix_m': False, 'm': 1.8691050836421013, 'limit_m': None, 'error_m': 0.000123619287240136, 'fix_gamma': False, 'error_gamma': 0.0002250543924548983, 'limit_gamma': None, 'gamma': 0.00960935659431392}\n"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m3 = Minuit(ulh2, **loaded_fitarg)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m3.migrad();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td>FCN = -10170.1470097</td>\n",
        "                <td>NFCN = 29</td>\n",
        "                <td>NCALLS = 29</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td>EDM = 1.23520523748e-10</td>\n",
        "                <td>GOAL EDM = 5e-06</td>\n",
        "                <td>UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\">Valid</td>\n",
        "                <td align=\"center\">Valid Param</td>\n",
        "                <td align=\"center\">Accurate Covar</td>\n",
        "                <td align=\"center\">PosDef</td>\n",
        "                <td align=\"center\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\">Hesse Fail</td>\n",
        "                <td align=\"center\">HasCov</td>\n",
        "                <td align=\"center\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#NEtBfODIFA').toggle()\">+</a></td>\n",
        "                <td>Name</td>\n",
        "                <td>Value</td>\n",
        "                <td>Parab Error</td>\n",
        "                <td>Minos Error-</td>\n",
        "                <td>Minos Error+</td>\n",
        "                <td>Limit-</td>\n",
        "                <td>Limit+</td>\n",
        "                <td>FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>m</td>\n",
        "                <td>1.869105e+00</td>\n",
        "                <td>1.236185e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>gamma</td>\n",
        "                <td>9.609347e-03</td>\n",
        "                <td>2.250465e-04</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"NEtBfODIFA\" style=\"display:none;\">\n",
        "            <textarea rows=\"10\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & m & 1.869e+00 & 1.236e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\gamma$ & 9.609e-03 & 2.250e-04 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}