{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "probfit Basic Tutorial"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[probfit](http://iminuit.github.io/probfit/) is a modeling / fitting package to be used together with [iminuit](http://iminuit.github.com/iminuit/).\n",
      "\n",
      "This tutorial is a fast-paced introduction to the probfit features:\n",
      "\n",
      "* built-in common models: polynomial, gaussian, ...\n",
      "* build-in common fit statistics: chi^2, binned and unbinned likelihood\n",
      "* tools to get your fits to converge and check the results: try_uml, draw, draw_residuals, ...\n",
      "* tools to help you implement your own models and fit statistics: Normalize, Extended, integrate_1d, ...\n",
      "\n",
      "Please start this notebook with the ``ipython --pylab=inline`` option to get inline plots."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We assume you have executed this cell in all the following examples\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import iminuit\n",
      "import probfit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In your own code you can explicitly import what you need to save\n",
      "typing in interactive sessions, e.g.\n",
      "\n",
      "    from iminuit import Minuit, describe\n",
      "    from probfit import gaussian, BinnedLH\n",
      "\n",
      "We don't do this here, we only import `iminuit` and `probfit` into our\n",
      "namespace so that it is clear to you which functions and classes come\n",
      "from which package while reading the code below."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Chi^2 straight line fit\n",
      "\n",
      "We can't really call this a fitting package without being able to fit a straight line, right?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's make a straight line with gaussian(mu=0, sigma=1) noise\n",
      "np.random.seed(0)\n",
      "x = np.linspace(0, 10, 20) \n",
      "y = 3 * x + 15 + np.random.randn(len(x))\n",
      "err = np.ones(len(x))\n",
      "plt.errorbar(x, y, err, fmt='.');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10813ec50>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's define our line.\n",
      "# First argument has to be the independent variable,\n",
      "# arguments after that are shape parameters.\n",
      "def line(x, m, c): # define it to be parabolic or whatever you like\n",
      "    return m * x + c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "iminuit.describe(line)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "['x', 'm', 'c']"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Define a chi^2 cost function\n",
      "chi2 = probfit.Chi2Regression(line, x, y, err)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Chi2Regression is just a callable object; nothing special about it\n",
      "iminuit.describe(chi2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "['m', 'c']"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# minimize it\n",
      "# yes, it gives you a heads up that you didn't give it initial value\n",
      "# we can ignore it for now\n",
      "minuit = iminuit.Minuit(chi2) # see iminuit tutorial on how to give initial value/range/error\n",
      "minuit.migrad(); # MIGRAD is a very stable robust minimization method\n",
      "# you can look at your terminal to see what it is doing;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:4: InitialParamWarning: Parameter m does not have initial value. Assume 0.\n",
        "-c:4: InitialParamWarning: Parameter m is floating but does not have initial step size. Assume 1.\n",
        "-c:4: InitialParamWarning: Parameter c does not have initial value. Assume 0.\n",
        "-c:4: 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 title=\"Minimum value of function\">FCN = 12.0738531135</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 36</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 36</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 1.10886029888e-21</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 1e-05</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 1.0</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">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\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">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=\"$('#TJjdDPQkvo').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>m</td>\n",
        "                <td>2.886277e+00</td>\n",
        "                <td>7.367884e-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>c</td>\n",
        "                <td>1.613795e+01</td>\n",
        "                <td>4.309458e-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=\"TJjdDPQkvo\" 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 & 2.886e+00 & 7.368e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & c & 1.614e+01 & 4.309e-01 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The output above is a pretty-printed summary of the fit results from\n",
      "# minuit.print_fmin()\n",
      "# which was automatically called by iminuit.Minuit.migrad() after running MIGRAD.\n",
      "\n",
      "# Let's see our results as Python dictionaries ...\n",
      "print(minuit.values)\n",
      "print(minuit.errors)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'c': 16.137947520534624, 'm': 2.8862774144823855}\n",
        "{'c': 0.4309458211385722, 'm': 0.07367884284273937}\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Parabolic error\n",
      "is calculated using the second derivative at the minimum\n",
      "This is good in most cases where the uncertainty is symmetric not much correlation\n",
      "exists. Migrad usually got this accurately but if you want ot be sure\n",
      "call `minuit.hesse()` after calling `minuit.migrad()`.\n",
      "\n",
      "#### Minos Error\n",
      "is obtained by scanning the chi^2 or likelihood profile and find the point\n",
      "where chi^2 is increased by `minuit.errordef`. Note that in the Minuit documentation\n",
      "and output `errordef` is often called `up` ... it's the same thing.\n",
      "\n",
      "#### What `errordef` should I use?\n",
      "\n",
      "As explained in the Minuit documentation you should use:\n",
      "\n",
      "* `errordef = 1` for chi^2 fits\n",
      "* `errordef = 0.5` for likelihood fits\n",
      "\n",
      "`errordef=1` is the default, so you only have to set it to `errordef=0.5`\n",
      "if you are defining a likelihood cost function (if you don't your HESSE and MINOS errors will be incorrect).\n",
      "`probfit` helps you by defining a `default_errordef()` attribute on the\n",
      "cost function classes, which is automatically detected by the `Minuit` constructor\n",
      "and can be used to set `Minuit.errordef` correctly, so that users can't forget.\n",
      "Classes used in this tutorial:\n",
      "\n",
      "* `probfit.Chi2Regression.get_errordef()` and `probfit.BinnedChi2.get_errordef()` return 1.\n",
      "* `probfit.BinnedLH.get_errordef()` and `probfit.UnbinnedLH.get_errordef()` return 0.5."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's visualize our line\n",
      "chi2.draw(minuit)\n",
      "# looks good;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
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4cIEtW7YwduxY46fE5s2b8fHxAfQq8t99913xtrKUk1yoicTCRGJhYlYslIKf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\nIF3nnwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1086f8410>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Sometimes we want the error matrix (a.k.a. covariance matrix)\n",
      "print('error matrix:')\n",
      "print(minuit.matrix())\n",
      "# or the correlation matrix\n",
      "print('correlation matrix:')\n",
      "print(minuit.matrix(correlation=True))\n",
      "# or a pretty html representation\n",
      "# Note that `print_matrix()` shows the correlation matrix, not the error matrix\n",
      "minuit.print_matrix()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "error matrix:\n",
        "((0.005428571882645087, -0.027142859751431703), (-0.027142859751431703, 0.18571430075679826))\n",
        "correlation matrix:\n",
        "((1.0, -0.8548504260481388), (-0.8548504260481388, 1.0))\n"
       ]
      },
      {
       "html": [
        "\n",
        "            <table>\n",
        "                <tr>\n",
        "                    <td><a onclick=\"$('#OVlshZXilM').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",
        "            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",
        "                </tr>\n",
        "                \n",
        "            <tr>\n",
        "                <td>m</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(242,137,127)\">\n",
        "                -0.85\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>c</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(242,137,127)\">\n",
        "                -0.85\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=\"OVlshZXilM\" style=\"display:none;\">\n",
        "            <textarea rows=\"13\" cols=\"50\" onclick=\"this.select()\" readonly>%\\usepackage[table]{xcolor} % include this for color\n",
        "%\\usepackage{rotating} % include this for rotate header\n",
        "%\\documentclass[xcolor=table]{beamer} % for beamer\n",
        "\\begin{tabular}{|c|c|c|}\n",
        "\\hline\n",
        "\\rotatebox{90}{} & \\rotatebox{90}{m} & \\rotatebox{90}{c}\\\\\n",
        "\\hline\n",
        "m & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{242,137,127} -0.85\\\\\n",
        "\\hline\n",
        "c & \\cellcolor[RGB]{242,137,127} -0.85 & \\cellcolor[RGB]{255,117,117} 1.00\\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Binned Poisson likelihood fit of a Gaussian distribution\n",
      "In high energy physics, we usually want to fit a distribution to a histogram. Let's look at simple Gaussian distribution."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# First let's make some example data\n",
      "np.random.seed(0)\n",
      "data = np.random.randn(10000) * 4 + 1\n",
      "# sigma = 4 and mean = 1\n",
      "plt.hist(data, bins=100, histtype='step');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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v4dy55i/l8509wrdtMzNn27ZLP/bevfDjH5uuAPjud82ZtsEgvPGG2VdRAceO\nwYgRl36c4mL4+9/N9vjxZvZbJOvT5YK3b9/O//7v//KTn/wEgC1btvD666+zYcOGc0+ohaRFRHrE\n0eWCu1LeWgteRKR/9OmLrF6vl+rq6rb3q6urietogriIiIRcnxb8tGnTqKqq4uDBgzQ1NbF161ay\nsrL68ilERKSL+nSIJioqiqeeeorbbruNlpYWFi9eHHYzaEREBoo+nwd/++2389e//pW3336blStX\ntu1/9tlnSUlJYfDgwRw4cKBt/8GDB7niiitIT08nPT2dZcuW9XWkPnGp/ABr1qwhMTGR5ORkSkpK\nHErYdXl5ecTFxbX9mxcXFzsd6bIi7fyKC8XHxzN58mTS09PJyMhwOs5lLVq0CI/HQ+p509EOHz5M\nZmYmSUlJzJkzhyNHjjiYsHMd5Y+kn/vq6mpmzpxJSkoKkyZNYv369UAPvgd2P3nrrbfsv/71r7bf\n77fffPPNtv3BYNCeNGlSf8XosUvl//Of/2xPmTLFbmpqsoPBoD1hwgS7paXFwaSXl5eXZ69bt87p\nGF3W3NxsT5gwwQ4Gg3ZTU5M9ZcoUu6KiwulY3RIfH29/+OGHTsfospdfftk+cOBAu9/N++67z167\ndq1t27adn59vP/DAA07Fu6yO8kfSz319fb1dVlZm27ZtHz161E5KSrIrKiq6/T3otwt+JCcnk3T2\nfP8IdKn8RUVF5OTkEB0dTXx8PAkJCZSWljqQsHvsCJrNdP75FdHR0W3nV0SaSPo3nzFjBmPGjGm3\nb9euXeTm5gKQm5vLzp07nYjWJR3lh8j5HowbN460tDQARowYwcSJE6mtre329yAsrugUDAZJT0/H\n7/fz6quvOh2nW+rq6trNFIqLi6P2wgWxw9CGDRuYMmUKixcvDus/tQFqa2sZf97ZY5Hyb3w+y7KY\nPXs206ZNaztPJNI0Njbi8XgA8Hg8NDY2Opyo+yLp5/6sgwcPUlZWxs0339zt70GfFnxmZiapqakX\n3Z6/1FWogWuvvZbq6mrKysp44oknWLBgAUePHu3LWF3Wk/wdCYeTuS71tezatYulS5cSDAYpLy8n\nNjaWFStWOB23U+Hw79lb+/fvp6ysjN27d/OjH/2IV155xelIvWJZVsR9XyLt5x7g2LFjzJ8/nyef\nfJKRI0e2+1hXvgd9Ootmz5493b7PkCFDGDJkCABTp05lwoQJVFVVMXXq1L6M1iU9yX/h3P+amhq8\nYbAATVdczvAGAAAB1UlEQVS/liVLlvDlCxcBCTNuOL8iNjYWgLFjxzJv3jxKS0uZMWOGw6m6x+Px\n0NDQwLhx46ivrycmwi5AfH7eSPi5P336NPPnz2fhwoXMPbMeeXe/B44M0Zw/Dnbo0CFaWloAePfd\nd6mqquL6ML8EzPn5s7KyKCwspKmpiWAwSFVVVdjPkqivr2/b3rFjR7uZBuEo0s+vOHHiRNtfpceP\nH6ekpCTs/807kpWVRUFBAQAFBQVtpRMpIunn3rZtFi9ejM/n4957723b3+3vQQhfCG7nueees+Pi\n4uxhw4bZHo/H/vznP2/btm1v377dTklJsdPS0uypU6fa//M//9NfkbrlUvlt27a/+93v2hMmTLBv\nuOEGu7i42MGUXbNw4UI7NTXVnjx5sv3P//zPdkNDg9ORLuuFF16wk5KS7AkTJtiPPfaY03G65d13\n37WnTJliT5kyxU5JSYmI/HfeeacdGxtrR0dH23FxcfbPfvYz+8MPP7RnzZplJyYm2pmZmfZHH33k\ndMxLujD/pk2bIurn/pVXXrEty7KnTJlip6Wl2Wlpafbu3bu7/T3o08XGREQkfITFLBoREel7KngR\nEZdSwYuIuJQKXkTEpVTwIiIupYIXEXGp/wfL/2Djdt4RmQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108704690>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Define your PDF / model\n",
      "def gauss_pdf(x, mu, sigma):\n",
      "    \"\"\"Normalized Gaussian\"\"\"\n",
      "    return 1 / np.sqrt(2 * np.pi) / sigma * np.exp(-(x - mu) ** 2 / 2. / sigma ** 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Build your cost function\n",
      "# Here we use binned likelihood\n",
      "binned_likelihood = probfit.BinnedLH(gauss_pdf, data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create the minuit\n",
      "# and give an initial value for the sigma parameter\n",
      "minuit = iminuit.Minuit(binned_likelihood, sigma=3)\n",
      "# Remember: minuit.errordef is automatically set to 0.5\n",
      "# as required for likelihood fits (this was explained above)\n",
      "binned_likelihood.draw(minuit);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:3: InitialParamWarning: Parameter mu does not have initial value. Assume 0.\n",
        "-c:3: InitialParamWarning: Parameter mu is floating but does not have initial step size. Assume 1.\n",
        "-c:3: InitialParamWarning: Parameter sigma is floating but does not have initial step size. Assume 1.\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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mApCamsrQoUMBSEhIYMmSJRQXF5OTk0N2djZdunRx0FdwDyyPYmbEYdot/v2e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\nqGWI4RcEQahl/H+iUyCjG3hcdwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1086f3d50>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "minuit.migrad()\n",
      "# Like in all binned fit with long zero tail. It will have to do something about the zero bin\n",
      "# probfit.BinnedLH does handle them gracefully but will give you a warning;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:1: LogWarning: x is really small return 0\n"
       ]
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td title=\"Minimum value of function\">FCN = 20.9368166553</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 46</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 46</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 1.45381812456e-06</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 5e-06</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">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\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">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=\"$('#XlneHvbAXy').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>mu</td>\n",
        "                <td>9.258754e-01</td>\n",
        "                <td>3.962599e-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>3.952381e+00</td>\n",
        "                <td>2.826741e-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=\"XlneHvbAXy\" 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 & $\\mu$ & 9.259e-01 & 3.963e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\sigma$ & 3.952e+00 & 2.827e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Visually check if the fit succeeded by plotting the model over the data\n",
      "binned_likelihood.draw(minuit) # uncertainty is given by symmetric Poisson;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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NmQwdCtu3K5Ogrk5+loZrV8PTT8MHH5hamqQeYkjslAFeUvtkZoKDg9LJmpAA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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1086dd9d0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's see the result\n",
      "print('Value: {}'.format(minuit.values))\n",
      "print('Error: {}'.format(minuit.errors))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Value: {'mu': 0.9258754454758255, 'sigma': 3.9523813236078955}\n",
        "Error: {'mu': 0.039625990354239755, 'sigma': 0.028267407263212106}\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# That printout can get out of hand quickly\n",
      "minuit.print_fmin()\n",
      "# Also print the correlation matrix\n",
      "minuit.print_matrix()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td title=\"Minimum value of function\">FCN = 20.9368166553</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 46</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 46</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 1.45381812456e-06</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 5e-06</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">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\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">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=\"$('#bpbpIknPGG').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>mu</td>\n",
        "                <td>9.258754e-01</td>\n",
        "                <td>3.962599e-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>3.952381e+00</td>\n",
        "                <td>2.826741e-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=\"bpbpIknPGG\" 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 & $\\mu$ & 9.259e-01 & 3.963e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\sigma$ & 3.952e+00 & 2.827e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "            <table>\n",
        "                <tr>\n",
        "                    <td><a onclick=\"$('#KEkawGmLUq').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",
        "            mu\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",
        "            sigma\n",
        "            </div>\n",
        "            </div>\n",
        "            </td>\n",
        "            \n",
        "                </tr>\n",
        "                \n",
        "            <tr>\n",
        "                <td>mu</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>sigma</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=\"KEkawGmLUq\" style=\"display:none;\">\n",
        "            <textarea rows=\"13\" cols=\"50\" onclick=\"this.select()\" readonly>%\\usepackage[table]{xcolor} % include this for color\n",
        "%\\usepackage{rotating} % include this for rotate header\n",
        "%\\documentclass[xcolor=table]{beamer} % for beamer\n",
        "\\begin{tabular}{|c|c|c|}\n",
        "\\hline\n",
        "\\rotatebox{90}{} & \\rotatebox{90}{$\\mu$} & \\rotatebox{90}{$\\sigma$}\\\\\n",
        "\\hline\n",
        "$\\mu$ & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{163,254,186} -0.00\\\\\n",
        "\\hline\n",
        "$\\sigma$ & \\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": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Looking at a likelihood profile is a good method\n",
      "# to check that the reported errors make sense\n",
      "minuit.draw_mnprofile('mu');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:3: LogWarning: x is really small return 0\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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XXnqJLVu28Oijj9rOzc3NBdQ9usaNU5dWl6c0mpCQQFpaGrNnz7Y95u3tXeL3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\ncDBJBOZg6mRgxLqeswjsupc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       "text": [
        "<matplotlib.figure.Figure at 0x1086f6e50>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot a 2d contour error\n",
      "# You can notice that it takes some time to draw\n",
      "# We will this is because our PDF is defined in Python\n",
      "# We will show how to speed this up later\n",
      "minuit.draw_mncontour('mu', 'sigma');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/deil/Library/Python/2.7/lib/python/site-packages/iminuit/_plotting.py:85: LogWarning: x is really small return 0\n",
        "  sigma=this_sig)\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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nT/7H7YqKihAWFgaFQgGFQoGZM2dixIgR2LBhAwBg7ty5yMnJQXh4OHg8Hry9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\nREQob79I2odcJsPlX39FRlQUxCdPwmP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       "text": [
        "<matplotlib.figure.Figure at 0x10870f6d0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Chi^2 fit of a Gaussian distribution\n",
      "\n",
      "Let's explore another popular cost function chi^2.\n",
      "Chi^2 is bad when you have bin with 0.\n",
      "ROOT just ignore.\n",
      "ROOFIT does something I don't remember.\n",
      "But it's best to avoid using chi^2 when you have bin with 0 count."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We will use the same data as in the previous example\n",
      "np.random.seed(0)\n",
      "data = np.random.randn(10000) * 4 + 1\n",
      "# sigma = 4 and mean = 1\n",
      "plt.hist(data, bins=100, histtype='step');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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v4dy55i/l8509wrdtMzNn27ZLP/bevfDjH5uuAPjud82ZtsEgvPGG2VdRAceO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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1086d74d0>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We will use the same PDF as in the previous example\n",
      "def gauss_pdf(x, mu, sigma):\n",
      "    \"\"\"Normalized Gaussian\"\"\"\n",
      "    return 1 / np.sqrt(2 * np.pi) / sigma * np.exp(-(x - mu) **2 / 2. / sigma ** 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Binned chi^2 fit only makes sense (for now) for extended PDFs\n",
      "# probfit.Extended adds a norm parameter with name 'N'\n",
      "extended_gauss_pdf = probfit.Extended(gauss_pdf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Describe the function signature\n",
      "iminuit.describe(extended_gauss_pdf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "['x', 'mu', 'sigma', 'N']"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Chi^2 distribution fit is really bad for distribution with long tail\n",
      "# since when bin count=0... poisson error=0 and blows up chi^2\n",
      "# so give it some range\n",
      "chi2 = probfit.BinnedChi2(extended_gauss_pdf, data, bound=(-7,10))\n",
      "# This time we use the pedantic=False option to tell Minuit\n",
      "# that we don't want warnings about parameters without initial\n",
      "# value or step size.\n",
      "# And print_level=0 means that no output is generated\n",
      "minuit = iminuit.Minuit(chi2, sigma=1, pedantic=False, print_level=0)\n",
      "minuit.migrad();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now let's look at the results\n",
      "minuit.print_fmin()\n",
      "minuit.print_matrix()\n",
      "chi2.draw(minuit);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td title=\"Minimum value of function\">FCN = 36.5025994291</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 247</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 247</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 3.00607928198e-08</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 1e-05</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 1.0</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">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\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">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=\"$('#NAJmXiVDsZ').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>mu</td>\n",
        "                <td>9.064759e-01</td>\n",
        "                <td>4.482536e-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>3.959036e+00</td>\n",
        "                <td>4.112828e-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>3</td>\n",
        "                <td>N</td>\n",
        "                <td>9.969691e+03</td>\n",
        "                <td>1.033724e+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=\"NAJmXiVDsZ\" 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 & $\\mu$ & 9.065e-01 & 4.483e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\sigma$ & 3.959e+00 & 4.113e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & N & 9.970e+03 & 1.034e+02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "            <table>\n",
        "                <tr>\n",
        "                    <td><a onclick=\"$('#wtxFcCXdZo').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",
        "            mu\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",
        "            sigma\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",
        "            N\n",
        "            </div>\n",
        "            </div>\n",
        "            </td>\n",
        "            \n",
        "                </tr>\n",
        "                \n",
        "            <tr>\n",
        "                <td>mu</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(172,240,179)\">\n",
        "                -0.10\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(167,247,183)\">\n",
        "                -0.05\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>sigma</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(172,240,179)\">\n",
        "                -0.10\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(180,229,173)\">\n",
        "                0.18\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>N</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(167,247,183)\">\n",
        "                -0.05\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(180,229,173)\">\n",
        "                0.18\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=\"wtxFcCXdZo\" style=\"display:none;\">\n",
        "            <textarea rows=\"15\" cols=\"50\" onclick=\"this.select()\" readonly>%\\usepackage[table]{xcolor} % include this for color\n",
        "%\\usepackage{rotating} % include this for rotate header\n",
        "%\\documentclass[xcolor=table]{beamer} % for beamer\n",
        "\\begin{tabular}{|c|c|c|c|}\n",
        "\\hline\n",
        "\\rotatebox{90}{} & \\rotatebox{90}{$\\mu$} & \\rotatebox{90}{$\\sigma$} & \\rotatebox{90}{N}\\\\\n",
        "\\hline\n",
        "$\\mu$ & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{172,240,179} -0.10 & \\cellcolor[RGB]{167,247,183} -0.05\\\\\n",
        "\\hline\n",
        "$\\sigma$ & \\cellcolor[RGB]{172,240,179} -0.10 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{180,229,173} 0.18\\\\\n",
        "\\hline\n",
        "N & \\cellcolor[RGB]{167,247,183} -0.05 & \\cellcolor[RGB]{180,229,173} 0.18 & \\cellcolor[RGB]{255,117,117} 1.00\\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
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      },
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ly0hISCAiIoLTp0/TuHHjvH8XioJPSgoMGwZz5mi+7KtWQd++OR6WNn6rfPkc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\nwItoLca1a6FVK22S6CJF7C3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       "text": [
        "<matplotlib.figure.Figure at 0x1086e99d0>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Fast unbinned likelihood fit Cython\n",
      "\n",
      "Unbinned likelihood is computationally very very expensive if you have a lot of data.\n",
      "It's now a good time that we talk about how to speed things up with [Cython](http://cython.org)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We will use the same data as in the previous example\n",
      "np.random.seed(0)\n",
      "data = np.random.randn(10000) * 4 + 1\n",
      "# sigma = 4 and mean = 1\n",
      "plt.hist(data, bins=100, histtype='step');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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v4dy55i/l8509wrdtMzNn27ZLP/bevfDjH5uuAPjud82ZtsEgvPGG2VdRAceO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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1087010d0>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We want to speed things up with Cython\n",
      "%load_ext cythonmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "# Same gaussian distribution but now written in Cython\n",
      "# The %%cython IPython does the following:\n",
      "# * Call Cython to generate C code for a Python C extension.\n",
      "# * Compile it into a Python C extension (a shared library)\n",
      "# * Load it into the current namespace\n",
      "# If you don't understand these things, don't worry, it basically means:\n",
      "# * Get full-metal speed easily\n",
      "cimport cython\n",
      "from libc.math cimport exp, M_PI, sqrt\n",
      "@cython.binding(True) # IMPORTANT: this tells Cython to dump the function signature\n",
      "def gauss_pdf_cython(double x, double mu, double sigma):\n",
      "    return 1 / sqrt(2 * M_PI) / sigma * exp(-(x - mu) ** 2 / 2. / sigma ** 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Define the unbinned likelihood cost function \n",
      "unbinned_likelihood = probfit.UnbinnedLH(gauss_pdf_cython, data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "minuit = iminuit.Minuit(unbinned_likelihood, sigma=2, pedantic=False, print_level=0)\n",
      "# Remember: minuit.errordef is automatically set to 0.5\n",
      "# as required for likelihood fits (this was explained above)\n",
      "minuit.migrad() # yes: amazingly fast\n",
      "unbinned_likelihood.show(minuit)\n",
      "minuit.print_fmin()\n",
      "minuit.print_matrix() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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45HeU7f/8h1PxtuTnl948OzualSsrRkpurvJOAEDYVmNdy1Cl8N57SqjNA2Jx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\nVquERWo0UG/pe9iKPHbUGymNe1WlY0c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       "text": [
        "<matplotlib.figure.Figure at 0x1087d0e50>"
       ]
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td title=\"Minimum value of function\">FCN = 27927.1139471</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 69</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 69</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 5.05909350517e-09</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 5e-06</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">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\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">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=\"$('#OAPuxIqobo').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>mu</td>\n",
        "                <td>9.262679e-01</td>\n",
        "                <td>3.950226e-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>3.950224e+00</td>\n",
        "                <td>2.793227e-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=\"OAPuxIqobo\" 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 & $\\mu$ & 9.263e-01 & 3.950e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\sigma$ & 3.950e+00 & 2.793e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "            <table>\n",
        "                <tr>\n",
        "                    <td><a onclick=\"$('#PjSOetiEHT').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",
        "            mu\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",
        "            sigma\n",
        "            </div>\n",
        "            </div>\n",
        "            </td>\n",
        "            \n",
        "                </tr>\n",
        "                \n",
        "            <tr>\n",
        "                <td>mu</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>sigma</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=\"PjSOetiEHT\" style=\"display:none;\">\n",
        "            <textarea rows=\"13\" cols=\"50\" onclick=\"this.select()\" readonly>%\\usepackage[table]{xcolor} % include this for color\n",
        "%\\usepackage{rotating} % include this for rotate header\n",
        "%\\documentclass[xcolor=table]{beamer} % for beamer\n",
        "\\begin{tabular}{|c|c|c|}\n",
        "\\hline\n",
        "\\rotatebox{90}{} & \\rotatebox{90}{$\\mu$} & \\rotatebox{90}{$\\sigma$}\\\\\n",
        "\\hline\n",
        "$\\mu$ & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{163,254,186} 0.00\\\\\n",
        "\\hline\n",
        "$\\sigma$ & \\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": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Remember how slow draw_mnprofile() was in the last example?\n",
      "# Now it's super fast (even though the unbinned\n",
      "# likelihood computation is more compute-intensive).\n",
      "minuit.draw_mnprofile('mu');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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W6PtBX8pXLl/g75Dt6sWrZKZn4h2gtleCOgSxZOySgveNeoR7dMtRjm07Rsat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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1087c6690>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "But you really don't have to write your own gaussian, there are tons of builtin functions written in Cython for you."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Here's how you can list them\n",
      "import probfit.pdf\n",
      "print(dir(probfit.pdf))\n",
      "print(iminuit.describe(probfit.pdf.gaussian))\n",
      "print(type(probfit.pdf.gaussian))\n",
      "# But actually they are always all imported into the main probfit\n",
      "# namespace, so we'll keep using the simpler probfit.gaussian instead of\n",
      "# probfit.pdf.gaussian here."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['HistogramPdf', 'MinimalFuncCode', 'Polynomial', '_Linear', '__builtins__', '__doc__', '__file__', '__name__', '__package__', '__pyx_capi__', '__test__', 'argus', 'cauchy', 'cruijff', 'crystalball', 'describe', 'doublegaussian', 'gaussian', 'linear', 'novosibirsk', 'np', 'poly2', 'poly3', 'rtv_breitwigner', 'ugaussian']\n",
        "['x', 'mean', 'sigma']\n",
        "<type 'builtin_function_or_method'>\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unbinned_likelihood = probfit.UnbinnedLH(probfit.gaussian, data)\n",
      "minuit = iminuit.Minuit(unbinned_likelihood, sigma=2, pedantic=False)\n",
      "# Remember: minuit.errordef is automatically set to 0.5\n",
      "# as required for likelihood fits (this was explained above)\n",
      "minuit.migrad() # yes: amazingly fast\n",
      "unbinned_likelihood.draw(minuit, show_errbars='normal') # control how fit is displayed too;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td title=\"Minimum value of function\">FCN = 27927.1139471</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 69</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 69</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 5.07834778662e-09</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 5e-06</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">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\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">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=\"$('#bzpnamJMHN').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>mean</td>\n",
        "                <td>9.262679e-01</td>\n",
        "                <td>3.950226e-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>3.950224e+00</td>\n",
        "                <td>2.793227e-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=\"bzpnamJMHN\" 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 & 9.263e-01 & 3.950e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\sigma$ & 3.950e+00 & 2.793e-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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W1veGIvWrD2ngJWalaBQ9vdV6nC/9oSyzV+zFalWi1cLatcoIvgQvvURew6Z0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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1086d7f10>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Draw the difference between data and PDF\n",
      "plt.figure(figsize=(13,4))\n",
      "plt.subplot(121)\n",
      "unbinned_likelihood.draw_residual(minuit)\n",
      "plt.subplot(122)\n",
      "unbinned_likelihood.draw_residual(minuit, show_errbars=True, errbar_algo='sumw2', norm=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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amaIEG1E2ix3llizTyPrYsdHlrVBxAKzgjTfIxKlUKVpTsWYNcPRo6Oe/YgWw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\nkP8B0qylJPO9F0AAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108b724d0>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##But... We can't normalize everything analytically and how to generate toy sample from PDF\n",
      "\n",
      "When fitting distribution to a PDF, one of the common problem that we run into is normalization.\n",
      "Not all function is analytically integrable on the range of our interest.\n",
      "\n",
      "Let's look at an example: the [Crystal Ball function](http://en.wikipedia.org/wiki/Crystal_Ball_function).\n",
      "It's simply a gaussian with a power law tail ... normally found in energy deposited in crystals ...\n",
      "impossible to normalize analytically and normalization will depend on shape parameters."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numpy.random.seed(0)\n",
      "bound = (-1, 2)\n",
      "data = probfit.gen_toy(probfit.crystalball, 10000, bound=bound, alpha=1., n=2., mean=1., sigma=0.3, quiet=False)\n",
      "# quiet=False tells gen_toy to plot out original function\n",
      "# toy histogram and poisson error from both orignal distribution and toy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['x', 'alpha', 'n', 'mean', 'sigma']\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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mF+/Pi2Xvj6FuKZzuyk2SJNqFt8M/vAJJgo1x4xjPMuITjTw59yhFdUVu2c6l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\nSEhIdHEkRy8hISHRxfn/1V2QfY0AMZwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108bb06d0>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# To fit this function as a distribution we need to normalize\n",
      "# so that is becomes a PDF ober the range we consider here.\n",
      "# We do this with the probfit.Normalized functor, which implements\n",
      "# the trapezoid numerical integration method with a simple cache mechanism\n",
      "normalized_crystalball = probfit.Normalized(probfit.crystalball, bound)\n",
      "# this can also bedone with decorator\n",
      "# @probfit.normalized(bound)\n",
      "# def my_function(x, blah):\n",
      "#    return something\n",
      "pars = 1.0, 1, 2, 1, 0.3\n",
      "print('function: {}'.format(probfit.crystalball(*pars)))\n",
      "print('     pdf: {}'.format(normalized_crystalball(*pars)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "function: 1.0\n",
        "     pdf: 1.10945669814\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The normalized version has the same signature as the non-normalized version\n",
      "print(iminuit.describe(probfit.crystalball))\n",
      "print(iminuit.describe(normalized_crystalball))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['x', 'alpha', 'n', 'mean', 'sigma']\n",
        "['x', 'alpha', 'n', 'mean', 'sigma']\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can fit the normalized function in the usual way ...\n",
      "unbinned_likelihood = probfit.UnbinnedLH(normalized_crystalball, data)\n",
      "start_pars = dict(alpha=1, n=2.1, mean=1.2, sigma=0.3)\n",
      "minuit = iminuit.Minuit(unbinned_likelihood, **start_pars)\n",
      "# Remember: minuit.errordef is automatically set to 0.5\n",
      "# as required for likelihood fits (this was explained above)\n",
      "minuit.migrad() # yes: amazingly fast Normalize is written in Cython\n",
      "unbinned_likelihood.show(minuit)\n",
      "# The Crystal Ball function is notorious for its sensitivity on the 'n' parameter\n",
      "# probfit give you a heads up where it might have float overflow;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:4: InitialParamWarning: Parameter alpha is floating but does not have initial step size. Assume 1.\n",
        "-c:4: InitialParamWarning: Parameter n is floating but does not have initial step size. Assume 1.\n",
        "-c:4: InitialParamWarning: Parameter mean is floating but does not have initial step size. Assume 1.\n",
        "-c:4: InitialParamWarning: Parameter sigma is floating but does not have initial step size. Assume 1.\n",
        "-c:7: SmallIntegralWarning: (0.9689428295957161, 0.44086027281289175, -7.852819454184058, 0.9263111214440007, 0.29811644525305303)\n"
       ]
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td title=\"Minimum value of function\">FCN = 6154.37579109</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 178</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 178</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 1.09346528365e-06</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 5e-06</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">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\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">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=\"$('#YWsxkHCEHl').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>alpha</td>\n",
        "                <td>1.012962e+00</td>\n",
        "                <td>5.321735e-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>n</td>\n",
        "                <td>1.812763e+00</td>\n",
        "                <td>2.177144e-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>mean</td>\n",
        "                <td>9.982474e-01</td>\n",
        "                <td>5.583931e-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>4</td>\n",
        "                <td>sigma</td>\n",
        "                <td>2.996611e-01</td>\n",
        "                <td>4.195338e-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=\"YWsxkHCEHl\" style=\"display:none;\">\n",
        "            <textarea rows=\"14\" 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 & $\\alpha$ & 1.013e+00 & 5.322e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & n & 1.813e+00 & 2.177e-01 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & mean & 9.982e-01 & 5.584e-03 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "4 & $\\sigma$ & 2.997e-01 & 4.195e-03 &  &  &  &  & \\\\\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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bxRbGYm/dqo2IRIQak57EmRxt9e+uXfUy0dHQsSP06XNtQfBy+WY08qRBGwhw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\nw+HYOP8HnAojjskHFO8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1087c21d0>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## But what if I know the analytical integral formula for my distribution?\n",
      "\n",
      "`probfit` checks for a method called `integrate` with the signature `integrate(bound, nint, *arg)` to\n",
      "compute definite integrals for given `bound` and `nint` (pieces of integral this is normally ignored)\n",
      "and the rest will be passed as positional argument.\n",
      "\n",
      "For some `probfit` built-in distributions analytical formulae have been implemented."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def line(x, m, c):\n",
      "    return m * x + c\n",
      "\n",
      "# compute integral of line from x=(0,1) using 10 intevals with m=1. and c=2.\n",
      "# all probfit internal use this\n",
      "# no integrate method available probfit use simpson3/8\n",
      "print(probfit.integrate1d(line, (0, 1), 10, (1., 2.)))\n",
      "\n",
      "# Let us illustrate the point by forcing it to have integral that's off by\n",
      "# factor of two\n",
      "def wrong_line_integrate(bound, nint, m, c):\n",
      "    a, b = bound\n",
      "    # I know this is wrong:\n",
      "    return 2 * (m * (b ** 2 / 2. - a ** 2 / 2.) + c * (b - a))\n",
      "\n",
      "line.integrate = wrong_line_integrate\n",
      "# line.integrate = lambda bound, nint, m, c: blah blah # this works too\n",
      "print(probfit.integrate1d(line, (0, 1), 10, (1., 2.)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.5\n",
        "5.0\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "What if things go wrong?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this section we show you what happens when your distribution doesn't fit and how you can make it.\n",
      "\n",
      "We again use the Crystal Ball distribution as an example, which is notoriously sensitive to initial parameter values."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unbinned_likelihood = probfit.UnbinnedLH(normalized_crystalball, data)\n",
      "# No initial values given -> all parameters have default initial value 0\n",
      "minuit = iminuit.Minuit(unbinned_likelihood)\n",
      "# Remember: minuit.errordef is automatically set to 0.5\n",
      "# as required for likelihood fits (this was explained above)\n",
      "minuit.migrad() # yes: amazingly fast but tons of output on the console\n",
      "# Remember there is a heads up;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:3: InitialParamWarning: Parameter alpha does not have initial value. Assume 0.\n",
        "-c:3: InitialParamWarning: Parameter alpha is floating but does not have initial step size. Assume 1.\n",
        "-c:3: InitialParamWarning: Parameter n does not have initial value. Assume 0.\n",
        "-c:3: InitialParamWarning: Parameter n is floating but does not have initial step size. Assume 1.\n",
        "-c:3: InitialParamWarning: Parameter mean does not have initial value. Assume 0.\n",
        "-c:3: InitialParamWarning: Parameter mean is floating but does not have initial step size. Assume 1.\n",
        "-c:3: InitialParamWarning: Parameter sigma does not have initial value. Assume 0.\n",
        "-c:3: 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 title=\"Minimum value of function\">FCN = 10986.1228867</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 230</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 230</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 0.0</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 5e-06</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#FF7878\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#FF7878\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#FF7878\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#FF7878\">True</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=\"$('#lxHAcKWNeN').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>alpha</td>\n",
        "                <td>0.000000e+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>n</td>\n",
        "                <td>0.000000e+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>3</td>\n",
        "                <td>mean</td>\n",
        "                <td>0.000000e+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>4</td>\n",
        "                <td>sigma</td>\n",
        "                <td>0.000000e+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=\"lxHAcKWNeN\" style=\"display:none;\">\n",
        "            <textarea rows=\"14\" 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 & $\\alpha$ & 0.000e+00 & 1.000e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & n & 0.000e+00 & 1.000e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & mean & 0.000e+00 & 1.000e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "4 & $\\sigma$ & 0.000e+00 & 1.000e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This shows that we failed.\n",
      "# The parameters are still at the default initial values\n",
      "unbinned_likelihood.show(minuit);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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iL4DrXFkJJCQA6enK1Vnq0IheZJTuJyT92o7RCBw8yA2fM9IDOKPvjEagpgbI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\n4fx/jZARZ3MOryIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1087c2c90>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# These two status flags tell you if the best-fit parameter values\n",
      "# and the covariance matrix (the parameter errors) are OK.\n",
      "print(minuit.migrad_ok())\n",
      "print(minuit.matrix_accurate())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "False\n",
        "False\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To make MIGRAD converge we need start parameter values that are roughly correct. Remember that above the same fit converged when we used ::\n",
      "\n",
      "    start_pars = dict(alpha=1, n=2.1, mean=1.2, sigma=0.3)\n",
      "    minuit = iminuit.Minuit(unbinned_likelihood, **start_pars)\n",
      "    \n",
      "#### But how can we guess these initial values?\n",
      "\n",
      "This is a hard question that doesn't have one simple answer. Visualizing your data and model helps.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Try one set of parameters\n",
      "best_try = probfit.try_uml(normalized_crystalball, data, alpha=1., n=2.1, mean=1.2, sigma=0.3)\n",
      "print(best_try)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'alpha': 1.0, 'mean': 1.2, 'sigma': 0.3, 'n': 2.1}\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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UA23s4qWXDXYajFP3T6FOWMd1KEqJZoUkRMVcK7yG9rrt0dG4I9ehNGrTJsDa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\noub4/PrpwbOzxZextAR69QLsxdykKTE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       "text": [
        "<matplotlib.figure.Figure at 0x1056613d0>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Or try multiple sets of parameters\n",
      "# (too many will just confuse you)\n",
      "best_try = probfit.try_uml(normalized_crystalball, data, alpha=1., n=2.1, mean=[1.2, 1.1], sigma=[0.3, 0.5])\n",
      "# try_uml computes the unbinned likelihood for each set of parameters and returns the best\n",
      "# one as a dictionary.\n",
      "# This is actually a poor-man's optimization algorithm in itself called grid search\n",
      "# which is popular to find good start values for other, faster optimization methods like MIGRAD.\n",
      "print(best_try)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'alpha': 1.0, 'mean': 1.1, 'sigma': 0.3, 'n': 2.1}\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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iUFBQgI4dO2LlypWQPOuomT17NkJDQ3Hy5Em4u7vDzMys3WgdUloXVGZPNzWE\nILGiArP07DdIyEtAoH1gk/rAMAy6mZlB8uGHz6pQ1m/b3b474vPi0ZkvT71Qmb2mpd7grs8U8A0b\nNjSZM5Smo7XPoKQ0LckiEeyMjMA31N2NJpaI8aj4EcZ5jWtyP/zNzRGZl4dhAgFYOq5BPzs/xF2N\ng0QqAduATWX2mhhaOKydQmX22hbl5fJyvNqo1KF1caeiAt300EYFgKSCJLgJ3GBsqLs0QUOxYrPB\nNzREqlgMD0791SXNjczhxHNCcmEyutp2pTJ7TQwN7hRKK2DzZsDICGDVM8ShWzfNy6tkMqSKxQjT\nUXpXQeKTRPRy6tUIL/XD38wMtyoqdAZ3APCz9UNifiK62mofXktpHDS4UyitAKkUmDULMDVt+LYP\nRCJ0NjGBqY6yuwBQVlWG3PJceFh5NMJL/ehqZoazxcWQyGTK8fba8LL2wqmUU6isqYSJYd1hspTG\nQ6tCUihtnDsVFfDVMyVzJ/8OvKy9lGPbmwOOgQEcjYzwUCzWaWtiaAJXgSuSniY1mz+vKjS4Uyht\nGLFUiseVleiiRwoEkAd3P1vthQCbiq5mZrirowSwAkVqhtK00OBOobRh7otEcDU1hbGO9AcAFImL\nUFxVDBeBS7P75cXhIEUshkSP6fcelh7IKctBeXXjagNRNEODezth165d6N+/f5PbUlo3DUnJ3M2/\nCx9rH7CY5v+3NzMwgJOxsc7UDJfLRXZmNjysPHC/4H6z+/UqQYM7pVmgMnvNj0gqRVZVFTz17IW9\nX3Af3jbeGtfl5eVh9OjRcHJy0imzB8hLN/j5+YHNZmutf+JjZoZ7KvXMNaGQ2fO29kZSQevLuzdE\nZi8mJgbdunVT6gaEhIS0iJqYNmhwpzQbVGaveUkWi+FqaqpzRAoAlFaVoqiyCJ0tNBfiYrFYCA0N\n1btErYeHB7777ju88cYbWs+FdwNSM+6W7sgqzYJYorsTtiVpiMxe165dcerUKQiFQjx58gSBgYGY\nOXNmC3v8HBrc2xhr166Fu7s7eDweunbtij/++EOjHYvFws8//ww3NzfY2Nhg0aJFdSoVfvHFF7C0\ntISrqyuioqKUyyMjI+Hj4wMejwc3NzdludaGwGaz8dFHHyE4OBgGOoboVVRU4LfffsPXX38NDoeD\n4OBgjBkzRk1cWCAQ4PLlywDk1fc6dOiAH3/8EXZ2dnB0dFRWJ2wIX3zxBQICAsBiseDp6YkxY8Yo\ni0A1tD2F2nuiAAAgAElEQVT79+9XU+yZPn065s2bh9DQUHC5XPTv3x95eXn4+OOPIRAI4O3tjYSE\n51URS0tzMHnyBNja2sLV1RU///yzct3Vq1cRFBQEgUAAR0dHLFiwABKJBPdFInhxOGCxWNi6dSs8\nPT0hEAjUFIcU3C+4D08rTxiwNH8Xtra2mDNnDnr00K+2+9SpUzFixAhwudw615VCrUuYmwsHIyOc\nvXMHAwcOBJ/Ph42NDSZNmqS0ZbFYSEtLg5GBEaxhjRFvjICFhQV69eqFr776Si19yGKxsHnzZnh4\neIDH42H58uVITU1FUFAQ+Hw+Jk2apCyPUlxcjFGjRsHW1haWlpYICwtDdna2Xm1TZffu3Vi2bBks\nLCzg5eWFWbNmab3WbG1tlcXSZDIZWCwWHBwclOvXrl2LsLCwBvvQaBpdlaYBtNBhmozWXDjs8OHD\nJDc3lxBCyKFDh4iZmRnJzc3VqKE6ZMgQIhQKSUZGBvH09CTbt28nhMiVmNhsNtm+fTuRyWRk8+bN\nxNHRUbntiRMnSFpaGiGEkLi4OMLhcOroY2p7HThwoI7PupSL4uPjCYfDUVv2ww8/kLCwMI32MTEx\nxNDQkKxYsYLU1NSQkydPEg6HQ4qLiwkhcq1Kbf5pU+ORyWQkICBAL71YfZSYpk2bRqytrUl8fDyp\nrKwkQ4YMIZ07dyZ79uwhMpmMfPXVV2Tw4MGEEEKkUilxdOxOli//mkgkEpKWlkZcXV2VSlo3btwg\nV65cIVKplKSnpxNvb2/yw/r1ZHV6OhHV1BCGYUhYWBgpKSkhGRkZxMbGhkRFRRFCCLlw4QLh8/mE\nw+UQngWvzvlQVXAihBCJRLsSk6bCYVOmTCHh9ag+XS0pIf3GjSOrV68mhBBSVVWldkzVAnfDxwwn\nfUP7ErFYTO7du0c6duxI+vfvr2Y7duxYUlZWRu7evUuMjIzI4MGDyaNHj0hJSQnx8fEhv/zyCyGE\nkMLCQvLbb78RsVhMysrKyFtvvUXGjh2r3NfcuXO1XiP+/v6EEEKKiooIwzAkPz9fud2RI0eIn5+f\n1vYq/j9YLBbx8/MjhYWFWm1VaY7CYTS4a0BXcEdMTJO8mgIqsyenrcvs8fmdiEj0fPvaMnuqrF+/\nngwdPZrsenaTry2z9/bbb5O1a9cqP1dUV5DVF1aT6hrdbWvq4F4skZDuEyaQD57J7NWmtszewv0L\nSVWNXNaurcnsqVJUVESmTJlCRo8erdexWrwqJEUz5CWqSO3evRvr169Heno6ALkoQUFBgcZUQWNk\n9mxtbXHq1CmsXLkSDx8+hEwmg0gkQjdtc9+bgNYis3fhwoWXJrNXWpoDR8fnkpZSqVQpTJKcnIyF\nCxfixo0bEIlEqKmpgYu/P7xUxrbX/j5Vz0VyYTJcBa5gGzRd2/TFwtAQk5Ytw82ICPTq1QsCgQCf\nffYZZsyYoWankNnzcfNBalEqvG28GyWzpxCLF4lE+PTTT3H69GkIhUIA8uubPFNi0gdVmT1ra2sA\nuq9LBQKBAN9//z0cHBxQWloKHo+n1zGbEppzb0MoZPY2btyIoqIiCIVC+PpqVpIHoDbioaEye4sW\nLUJ+fj6EQiFCQ0PVZPYU4gyaXo0Z5aIqs6fgRWX2tPlX+59MIbN39uzZlyqzZ2npgpwcIYRC+au0\ntBTHjx8HIJfZ8/HxQUpKCkpKSvDNN9+goqam3olLigB24cIFBLkFYWbvmRrPh7Y+hoagK1j27NwZ\n07/7DtnZ2di6dSvmzZuHtLQ0NRuFzJ6gWoB7BfKO9KaS2SspKUFcXByIPFMBAJgzZ47Wa0QhCqMq\ns6egIdelRCIBi8WCsXHTF2jTBxrc2xC1ZfYiIyN1yuwVFxcjMzMTERERmDhxos5jaJPZU6CQ2dP2\nUtVPVUjR1X5fG1WZPZFIhIsXL+LYsWN49913lTYsFgvnz5/X6zwtXbpUq3+qvxAUMnvR0dF6y+xp\na8+uXbvg4vJ8cpC2G64mevXqBSMjLn78cR3EYjGkUinu3LmD69evA5A/cXK5XHA4cpm9nzdvhiHD\naC3vqxrEevftjeVRy1EgLNB4PoKDg5XbVVZWKtuk+l4TNTU1qKyshFQqhUQiQWVlpVIvND09XW04\nZfLJk/gnNRWEEKXMHqvWCB+FzN6hjYeQlJOEu/fuYs+ePTpvHKrnWfW9PjJ72q6RxMTns2UVMnvF\nxcVISkrC9u3bMX36dI2+/P7770hOToZMJsPTp0+xcOFChIaGKoN7eHg4Bg8eXG97mhIa3NsQVGav\n/crsTZt2HLdva5fZ279/P3g8ucxe37FjwVFJw2nar2JZSlEKOvA6wJSteyw8h8MBj8cDwzDw8vKC\nmcrkqLlz52Lx4rnKz++//z44HA4OHjyIVatWgcPhYO/evcpzoSqzl3rrFn4cPRpcHk8ps6e4mdaW\n2asoq8CqMavwztR32pzMXnZ2NkaMGAEej4fu3btDIBCo6a22Opm9pqCFDtNktObRMvpCZfZalheV\n2Vuzhqh1qGpDJpORnzIzSZ5KJ199/J70O/kn859G+6WKLpk9Bd98800dmb3jBQXkwrPRTLqIeRRD\nTqecpjJ7hHaoUto5VGbvOU8lEhAAtnp0/BJCkFKUgoGdBzbJsY2MgIICIDxcu42BAbBgwZfg89WX\nd+FwEFdcjH4WFlq3VcjseTh7YP3/1uOXnb9Qmb0XgAb3dkprmEFJaXoeisXwMDXV6/vNLc+FiaEJ\nBKYCnbb6IBAAy5bVb7N5s2ZFKWcTExyRSFAhlcJMy6Q2VZk9E74JPvz4Qyqz9wLQ4N5OoTJ77ZOH\nYjH66jmsLrkwGZ5Wnk16/MY+MxgyDNxMTJAsEiFQy1BCVZm9Px/8CVszW412FP2gHaoUShuhSiZD\nTlUVnE30Uyx6WPgQHpbNp7jUUDw4HL0EPADA08oTyYXJzexR+4YGdwqljZAmFqOTiQmM9CgUVl5d\njkJxITpZdGoBz/TD3dQUaZWVkOoxTNRV4Irs0mxU1dSjGk6pFxrcKZQ2giLfrg8pRSlwFbhqLRT2\nMjA3MIDA0BBZmpLytTAyMEJHi45IFaa2gGftExrcKZQ2ACGkQcG9taVkFLibmiKFpmZaBBrcKZQ2\nwBOJBEYMA0s9hkBKZVKkClPhYdX6gruHqWmD8u4Pix42aLYv5Tk0uLcTqMxe++ahSAQPPUWwM0sz\nYWlqCXMj82b2quF0MDZGSU0NympqAMhneCqK4NWGb8KHiaEJ8srzWtDD9gMN7pRmgcrsNS0NSckk\nFyY3+Km9OWT2NMFiGLiamChTMwqZPW24W7ojpShF6/rmpiEye7GxsWCxWGpFyFQFZ1oaGtwpzQaV\n2WsaxFIpnlRXo3MDhkB6WjZsfHtzyOxpoyF5d3eBO1KELy+4N0RmDwCcnJzUipCpFr9raXQG96io\nKHh5ecHDwwPffvttnfUFBQUYMWIEAgIC4Ovr2yi5M4r+UJm9V09m74+LF7FzwgTYWFqqyewpUJXZ\n4wv42PftPjhyG1a+uCll9rKy5DJ7WVlZAICUlBQ1mb1V77+PtMpKyAhRyuwBQGFhIcLCwtRk9maM\nnYHcslxU1VS1epk9XbQqmb2amhri5uZGHj16RKqrq4m/vz+5d+9enYI3S5YsIYQQ8vTpU2JpaUkk\nEkmTFb95GbTmwmFUZu/Vk9n7/q+/SOS5c2oyez/99JPyWKoyeyeunSA8S14dmT1tr4bI7GmithLT\nxo3yAmOqTJo0qY7M3ubsbPJYLFYrcDdx4kQyefLkOjJ7uxN2k6SnSa1eZi8mJoYYGRkROzs74uLi\nQj799FNSUVGh13ls8cJhV69ehbu7uzInNmnSJBw9elRZihUAHBwccPv2bQByxRIrKysYaqkz3V6I\nZWKbZD+DyKAGb/Pmm28q37/99ttYs2YNrl69qvGn8eLFi8Hn88Hn8/HJJ5/gwIEDyrK/nTt3Vr6f\nOnUq5s2bh/z8fNja2qqV4h0wYABCQkJw4cIFBAYGolOnTkplm6aivLy8jogGl8tFWVmZ1m3YbDaW\nL18OFouFkSNHwtzcHA8ePECvXr2wZMkSLFmypEE+hD+rhlVbIaixMAyD8ePHIzAwEAAwbtw4bN68\nGVOmTAEg/+42bNgAALh27RpEogIsWfIVDA0BFxcXvP/++zh48CCGDRsG4umJMfb2YLFY6Ny5M2bN\nmoW4uDh8/PHHyuMtWbIEPB4PFZwK9OnXBwkJCRg+fDj69evX5N9XQzEyMkJ6ejqys7Ph5OSEvn37\nQiQUqo2akUql+O2333D37l2YmJjA29sb06ZNQ2xsLNws3ZR590WLFsHc3Bw+Pj7w8/PDyJEjlfFp\n5MiRuHnzJqZOnQpLS0uMGzdOuf+lS5diyJAhys+bNm3Cpk2b6vVboWZloVLsjMfjab0uvb29cevW\nLXh5eSE9PR3Tpk3DwoULsWXLloadsCai3iicnZ2tJtXWoUMHXLlyRc3mgw8+wJAhQ+Do6IiysjL8\n+uuvGvcVrlJKbtCgQRj0EqXqXpTGBOWmgsrsyXlVZPbyJRIUP3qEd+fOVZPZq50+sbe3h4zIkCZM\ng42FTaPPRXOwbt06LFu2TE1mb8jkyYgqKlLaKGT2ascbQN6puj9xP4DWLbNnZ2en9MfZ2Rnr1q3D\nqFGjGhTcY2NjERsbq7d9fdQb3PU5CatXr0ZAQABiY2ORmpqKYcOG4datW3VOQHh9dUIpeqGQ2Tt3\n7hyCgoLAMAwCAwPrldlT/MpqqMze3r17MWbMGBgYGGDcuHFqMntdu3bVuv22bdvU1Jj0QVVmz93d\nHcCLy+ytWbNG4zqGYdRuJAqZvfPnz790mb2MjGTUHhBzuaQEx7/6CiF9+uDQoUMwMzPDTz/9pLHj\nM7csF+ZG5mAbsNVk9lR/idUmKipKTY2pMeiKE3Z2dsp+m0uXLmHo0KG4PWAAhCo3UoXMXmZmJjw8\n5CN9FDJ7NhwbyIisQT6pyuzZ2toiISEB3bt3Vwb3OXPmYN++fRq3dXZ2RmJioprM3tChQwE0/LpU\nqFPpS+0H34aMRKpNvR2qTk5OajqGmZmZdURrL1++jLfeegsA4ObmBhcXFzx48KDRDlG0Q2X2Xj2Z\nvVSxGEQsVpPZ27x5s8b9pAnT4CZwU5PZ69+/f73fV3PI7GVmqsvsHT58WNm5qpDZYxsYwEVl9I9C\nZi88PBxisRj3799XyuwxDAN3S3eNfpBWJLMXGxuLx48fgxCCzMxMLF68GGPHjlWub1Uye4oSnOnp\n6aiursahQ4fq1Ff28vLCX3/9BQB48uQJHjx4AFdX1+bz+BWGyuy9WjJ7wpISZFZV4cdaMnuTJk2q\ns28ASBWmwk3gpvFY+qBLZm/uXP1k9rKz1WX2rl+/jj59+oDL5arJ7LmamNSR2SspKYG9vT2mTZum\nJrPnJnADNDSpNcns3bx5E8HBwTA3N0dwcDACAgIQERGhtG11MnsnT54knp6exM3NTdnjvWXLFrJl\nyxZCiHyEzKhRo0i3bt2Ir68v2bdvX5P2+L4MWvNoGX2hMnstS3PI7KWKRGR7To5e21dKKsmq86tI\nVY1+8nvNxcaNhCxZUldmTxOF1dXk+4wMIpPJNK5XldkTVYvIqvOriEQq0WjbFmh1MnsjR47EyJEj\n1ZbNnj1b+d7a2hrHjh1r6nsOhaLkVZXZSxWL4abnrNTHJY/hxHWCkYGRbuNm5pNPvoRKP6dWLNls\nGDIMnkoksDUyUsrs+fn54dq1a9i5c6dSZs+UbQo7czs8Ln4MN0u3Zm5B89DSMnt0hmo7hcrstX1S\nKyvhpues1FRhapsMeq4mJkhVKUUwYcIEmJubY9KkSfj888/V0sAve7ZqW6N9D0h/haEye62HvDzg\n11+B+vpYq6sBVQ2OcqkUxTU1cDI21usYqUWpGO89/gU9bXncTE0RX16OIAsLNZk9jbaWbjj64CiG\nuw1vQQ/bLjS4UyjNTFkZwOUCKgMn6mBoCKjG8UdiMZxNTMDS4xdYSWUJRBIRHMwdmsDblsXFxARH\nCwpQQwgMdbTVkeuI8upylFaVgmesn47sqwwN7hpgGAZSqVRnTRQKRV/YbEAg0G2noCEpmTRhGlwF\nrm0yFWdqYAAbIyNkVlbCRUf/AothwVXgitSiVAQ6BLaQh82PRCJRm5DXVNCcuwYcHBxw+fJlmtqg\nvBQIIQ3qTFUMgWyruJqYILWeMfWquAnc2o30nkwmQ1FREY4cOdIsw8eZZ8NtmhWGYdqUmkppaSkO\nHjyI3NzcNuU3pXWSnQ3cuwcMG6affXFNDc4KhZhgY6PTlhCCQ3cPIcwzDGZGZjrtm5ujR4EBAxr2\nK+VJdTWulZVhlJWVTtuK6gocSz6Gib4TwWga+N6GYBgGZmZmCAwMxMCBAzXW5HqR2EmDO4XSzJw6\nBUREyP/qw/rMTNwXibC1SxedttdzrmPq71Nxb37z175XQghQWSn/y2IBKukjPz9g/375X32plslg\nc+kSUvv0gbUetX28N3pjz7g96OGoX3nitsyLxE6ac6dQWhlnhEK856Bf5+iZ1DMY5qbnT4LGUFEB\nxMQAV64A164BKSlATg4glcoDu0wGGBkBDg6Apyfm5veA+aUgwGOgWtCvDyMWCwP4fJwVCjFRpbia\nNoa7DUd0avQrEdxfBBrcKZRWRJVMhoslJdinUla7PqLTovF50OeNOpa0XIryhHKIH4lRnVuNGqFc\n15RhZGDnJcH49jlwkqLB6WkHpn8wMG8e0KUL4OgoH/4DyJ/ey8qUuSf2lRuw/e83wOK3gddfB6ZP\nB0JD5cOB6mGYQIAzegb3ELcQrLu0Dkv7L21Uu18VaHCnUFoRl0tK4GNmBoEe6Yny6nJcy76Ggc4D\n9do3qSEouViCwuOFKIoqgviRGGa+ZjB1N4WxozEMTWuAS5dArlyHyMIZQrsQiOzeQnW8FDw2D1bW\nVrDys4IpV6Wjl2EAHk/+8vZGRPgE9NkF+DkUAH/+CaxdK78pfPghMH/+85tCLUIsLfF9ZqZeJXkH\ndh6IiUcmoqyqDFxj7WWhX3VocKdQWhHRQiGG6dkbef7xefR06glzI/N67aqyqpC7PRe5O3LBtmHD\neow1ukR2gXmAOVhsFiASAT/8APznP0BYGPDzF4CPj3J7SZEEJedLUHCsAI9XPYaZjxkcZjvAZoIN\nWEZaBtxZWwMzZ8pft27Jg7ybG/D558DHH6sP6gfQxdQUBECyWIwuz/QFtGFmZIZeTr0Qmx6LsC4t\nKFvXxqBDISmUVkR0URFC9Azu0anRGOaqPd9emV6JB7Mf4Jr/NUgKJPA74Yce8T3gvMIZvJ48sAwZ\n+fCWrl2BO3eAf/4BIiPVAjsAsC3ZsB5rDa8dXgjKCoLTh07I25GHK+5XkLMlB7IqHTXL/f2BAweA\n2FjgwgWgWzdApYw0IO84VKRm9CHENQTRadG6DV9haHCnUFoJT6urkSIWow9Pv9mX0anRCHELqbO8\npqwGqYtTcaPHDRjZGKH3g97w2OAB824qT/hPnwLjxwNLlgDbtwOHDgHummumq8IyYsHmTRv4/+WP\nrke6ouBYAa56XUXBHwW6R3X4+ADHjsl/JcydC7zzDlBcrFwdYmmJaBV1pvoIcQtBdCoN7vVBgzuF\n0ko4W1yMgXw+2HrMVswqzUJ+RT4C7dVnauYfzsc172uQPJGg552ecPnGBWzrWvn7qCggIADw9AQS\nEuQdn42A14uHbie6ocv2LkhbmobE0ERYVYt1bzhqFJCYCPD5cj/i4gAAr/P5iCsuhkQP9SJ/e38U\nVxYjvTi9Ub6/CtDgTqG0Es4UFemdbz+Tegavu74OA5a8RIZEKMG9/7uH9OXp8PnVB167vGBkX6v8\nr1QK/OtfwKxZwL59wLff1sl9NwbB6wL0SOgB/mA+PkuNR+Xvekz+43CAjRuBTZuAyZOB1athw2bD\n3dQUV+oRRlfAYlgY5jqMPr3XAw3uFEorgBCCaKEQIZaWetlHp0UjxFWekim+UIzr/tfBtmbjtRuv\nwaKvRd0NSkqA0aPlefX4eKCJBepZRix0WtQJP7sEoHJfFu6OvwuJUKJ7w9BQ+fj5P/8E3noLw8zN\naWqmiaDBnUJpBdwXicAA8NSjnoyMyPBX2l8Y6joU2RuycffNu/Dc6gmPCA8YcDQUu0tJAXr3Blxd\n5R2Z1tZN34Bn5JqYwWL/azDuZIz4nvGouFOheyMnJ3lqxsICw776CmeePNHrWMNch+Hso7OokdW8\noNftEzoUkkJpBUQLhRhuaalXZceEvATYGdpB/KkYZTfK0P1yd5i6abkp3LwJvPEGsHw5MGdOE3ut\nmZ17WHBw9IDVa3ko7ZOA9LGeKPJ7XicnNFRDeQJjY2D7dgR/9x3uCoUovncP/FqjdmrjwHVAJ4tO\nuJZ9DUEdg5qhJW0b+uROobQCGjIE8mzCWYTvDIe0XIruf9cT2OPigOHDgQ0bWiywf/65PE4XFQEP\nne1xdUI3dDyaAm50FoqKgBMn5LVnNMIwMFm0CH0BnFu8WJ5C0gFNzWiHPrlTKC+ZKpkMF0pKsEeP\nkgNVOVXoNKsTeEN48In0AcPS8qR/8qR86v/Bg8CQIU3rcD1Mm1Z7CReV/w6E5YjbGE6qwB/uitLy\n+n+dDPP1xZmPPsL4sDDgt9+A/v212oa4hmBF7AqsGLTixZ1vZ9DgTqG8IP/9r1xGTxsFBfJyLNq4\n9KzkgKWOkgOiZBESQhJwquspbNi2QXtgP31aHtiPHZPn2l8yJp1NEHgxEImjE+Emvo+bw7yAesr1\nDhMIsJnHkz/ijx9fb4Dv37k/EvMTUVxZDL4Jv5la0DahwZ1CeUHOnwe6dweGDtVu41aPlka0UKgz\nJSNKFuHWkFsonlOM7M7ZMDfWUnLg7Fng3XeBP/5oFYFdAduKDf8z/sgIuAO/40kgq7zBGGoO8H5m\nZiiXSpHWrx9cdQR4E0MTBHcMxrlH59qkhmxzQoM7hdIE+PrqL8ZRm9NFRdjg4aF1veihCLdevwXn\nfztjl+0ujLQaqdkwLg6YNAn43/+Avn0b50wzYsAxwIMpvnDbdRdJ7ybBe4/mAK9aimD2sGHPn+Cj\nooDXXqtjr8i70+CuDu1QpVBeIk+qq5FeWYleWqolih7Kn9idw51hP8Mepx6ewgj3EXUNExKAt96S\nlxEYMKCZvW48MrYBbk7wRU1xDZLeTQKRap7spFZnZtgwYNs2eVGzhw/r2A53G47TqaepIFAtaHCn\nUF4iZ4RCDNJScqAqqwq3h91G5+Wd4fCeA5IKksAwDLyta3W8pqfLp/Rv2NCinaeNRWbIgu/vvqjO\nr8bDDx9qDMrDBAKcEwohVawbNw7497/lo39yc9VsfWx8UC2tRkpRSku432agwZ1CeYkcLyzEGxq0\nQyVCCW6PuA3HeY5w/EDeG3vq4SmMdB+pPha+sBAYMQL44gvg7bdbyu0XhmUiD/Bl18qQviK9znoH\nY2M4GRvjumopgvffl79GjJDPuH0GwzB0SKQGaHCnUF4S1TIZThcV1RGGloqluDP6DgQhAnT8oqNy\n+amUWikZsVheUiAsTF4jvY1hyDOE3yk/5B/KR1ZEVp31GksA/+tf8o7ViROBmuczUxWpGcpzaHCn\nUF4SF0pK4GlqCnuj5wW+SA1B0uQkGHcyhtv3bsqn9PLqclzJvoLXXZ5VcCQEmDED6NRJXgCsjWJk\nYwT/aH9kfpeJ/MP5autCLC1xpnadGYYBfvpJ/n7hQuXioa5DEfc4DtXS6uZ2uc2gM7hHRUXBy8sL\nHh4e+FbLRRQbG4vAwED4+vpiUBMXJKJQ2it/FhRgdK06LymfpkBaIYVXpJfaOPaolCj07dj3uazc\nmjVAWhqwc6dcqLoNY9LZBL5/+uLhvIcovVqqXD7AwgLx5eUoq6lVO8bQUN5x/Ndf8qqSAKw51vCw\n9MA/Wbpntb4q1HtVSKVSfPjhh4iKisK9e/dw4MABJCUlqdkUFxdj/vz5OHbsGO7cuYMjR440q8MU\nSnuAEIJjhYUIU0nJ5GzJgfAvIboe6VpHvu73+79jnNc4+Yc//5QHtd9/B/QoNNYW4AZy0WVnF9wZ\ndweVGZUAAI6BAXpyuYhTya8rsbCQT9L697+Vqk40765OvcH96tWrcHd3h7OzM9hsNiZNmoSjR4+q\n2ezfvx8TJkxAhw4dAADWzVhxjkJpL9wViSCDfMIOAAjPCZEeng6/Y34wtFCfflItrcbJhycxusto\n4O5d4L335GPZnZxegufNh3WYNTp+1hGJoxJRUyZ/WteYmlHg5iafGjxlCpCSQvPutah3ElN2djY6\ndnzeodOhQwdcuXJFzebhw4eQSCQYPHgwysrK8PHHH+Pdd9+ts6/w8HDl+0GDBtH0DeWV5lhBAcKs\nrMAwDMQpYiT9XxK8D3jD1L3uk3hcehy6WHWBY7WxvAP1hx9a1ezTpqTDpx0geiBC0uQk+P7pi2EC\nAabUyhaoMWCAvOLlhAkIuhiHBwUPUCAqgDWnbT5kxsbGIjY2tkn2VW9w16f8qEQiQXx8PM6ePQuR\nSISgoCD06dMHHrVm3KkGdwrlVedoYSG+dnZGTUkNEsMS4RzuDMFgzSUI/njwB8Z5jpGXFRgzBpg6\ntYW9bTkYhoHHBg/cGnoL6SvTERjujKcSCbKqqtBBm2rU/PnAlSswmrcAA0cOwNm0s5joO7FlHW8i\naj/4rly5stH7qjct4+TkhMzMTOXnzMxMZfpFQceOHRESEgJTU1NYWVlhwIABuHXrVqMdolDaO48r\nK5EiFmMgzwJJ7yaBP4QPxzmaK4vJiAx/3P8DM0/nA2VlbXpkjL6w2Cx0/bUr8nbmoehYIYYKBNpT\nM4B8BM3WrcDt2/j8JoemZp5Rb3Dv0aMHHj58iPT0dFRXV+PQoUMYPXq0ms2YMWNw8eJFSKVSiEQi\nXLlyBT46iuxTKK8yR54+xVhra+R+lwVJgQTu69212l7PuY5hjw1hs/OgvHyvjsqR7QUjOyN0PdwV\nDyzDlj4AACAASURBVN5/gNAis7rj3WvD4QC//YbgXWdRePYYLUUAHcHd0NAQGzZswPDhw+Hj44OJ\nEyfC29sbW7duxdatWwEAXl5eGDFiBLp164bevXvjgw8+oMGdQqmHw0+fYmKSKbIjstH117ojY1SJ\nuhCJiH1FwJ497a4DVRe8Pjy4fO0Ct1l5uJBdBJmugO3mBoOdu7B5jxDJDy63jJOtGIa0wC2OYRh6\nJ6W0W959FwgJkf/VxePKSgyLuo7IuSz47PWG4HXtpX5lkmpc9eLBZcJ7sFu3sQk9fnmsWQOUlsr/\n6gMhBA/ef4Djj59iyG/+6M7j6dzm9IQAeORJ4Hrxjjxl04Z5kdjZtmc/UChtjP9l52PVSgYdFjjV\nG9gBIOeT9yAzMoTdmogW8q71wTAMPDd6wj2HhdsRj/XapmL5v1D1JPv5TNZXFFrPnUJpQUTLMmFl\nZ4JOSzrVb/jXXzA79Duu7PwIfQ0MWsa5FiIpqX7lKicnIDj4+WeWCQuGkc6wGZmC8lHlMA/QIlTy\njMGeIeg/ToLENWvA9O8P9OjRRJ63LWhwp1BaiLv/y0GXcxIE3e2hXSIPAIRCkBkzMGs8G2v6zWw5\nB1uA4GDg5k1A20T2igr5PK30dPXlA16zw+QPU2D19l30uPEaDLnaQ5fAVACuVzckrghBt4kT5QfU\nI53T3qDBnUJpAaqyq5A9JxV3frbGW1ZaxmsrmD8fmUNeQ3rPbLhbah9J0xYZMKB+LZHHjzWv5xka\nonQcDxXpBkiekwzvvd71zsMZ7jYceyUirAsJAWbPlqs5tfH8e0OhOXcKpZkhUoJ7U+7h5AQGb4Tq\nSMccPAjEx2PNKAtM9p3cMg62EYYJBIheZIqK2xXI25lXr62yzsyPPwL37skLrL1i0OBOoTQzGWsy\nUFojxelpbPTUIqcHAMjOBj76COLI/+Jg2p+Y5Dup5ZxsAwwTCBAlLobPIR+kLUlDxZ0Krba9nHrh\ncclj5ElLgAMHgCVLgJRXS6mJBncKpRkpuVSC7A3ZOLraDO862mtPJchk8vrsH36II5x09O3YF45c\nzbNWX1V68njIqKpCuTsbrutccW/yPcgqZRptDVmGGOIyBGdSzwA+PvL6M1OmqAl8tHdocKdQmgmJ\nUIKkd5LgstUDvzCFmGJnp9140ya5dNzSpdiZsBMzAma0nKNtBEOGwWA+H2eEQthPtwfHi4O0f6Vp\ntQ9xDUF02rMSwPPny8sEr1rVQt6+fGhwp1CaAUIIkj9IhtVoK1zuC/ibm6OTiYlm4/v3gfBwYM8e\npJVl4E7+HYR5hrWov22FEEtLnC4qko9/3+qJp0eeoihac92ZELcQnEk9AxmRyQVNIiPlN9FalW3b\nKzS4UyjNQN6uPIgeiuC2zg3/zc3FTAcHzYYSiXxq69dfA56e+OXWL5jsOxnGhjpG1LyijHwW3GWE\ngG3JhtcvXrg/4z4kBZI6ti4CF/CMeUh8kihf4OgIbNwoT8+Ul7ew5y0PDe4UShMjfiRG2qI0eO/1\nRjqpwo2yMrxpY6PZ+JtvAGtrYM4cyIgMvyT8QlMy9dDZxAQ2bDaul5UBAARDBLD7Pzs8+OCBxmn6\nIW4h6lUi33xTPtj+s89ayuWXBg3uFEoTQqQE96fdR6clnWDuZ47/5ubiXTs7mGjSOb1yBdiyBdix\nA2AYRKdGw4pjhUCHwJZ3vA0RamWFUyolgF2+cUFleiXydtQdHqlRei8iAjhzRi7T146hk5goFB0c\nOQIUFmpfn5wsLxwGAJk/ZIJhMejwaQdUy2SIzMtDbEBA3Y0qKuTpmA0b5OkCABuvbcS8HvOaoQXt\ni5GWlvhXWhpWODsDAFjGLHjv80bCwARYDLAAx5OjtB3kPAjv/PYORBIROOxny3k8YPdu4K23gIQE\noL6O7jYMfXKnUHQwcSJw/ToQH6/5FRgoV70rv1WOzO8y4fWLFxgWg6MFBfDicODF4dTd6aJF8o3e\negsAkF6cjr8z/8ZkPzpxSRf9LCxwXyTC0+pq5TIzHzM4hzsj6Z0kyCTPh0fyjHno7tAdcelxtXbS\nD5g+HZgzB2inFWvpkzuFogdbtgD11e+SVcpwo1cS3L53g0ln+aiYLTk5mK2pIzUqCjh+HFBRLNt6\nYyum+k99/nRJ0YoRi4UhAgGihUK8o/LU7TjPEYUnCvH4m8dwWemiXK4YEjnSY6T6jsLD5UXF9u2T\nd7K2M+iTO4XSBDxa9gimHqawmyoPNrfLy5EkEmFC7Y7UwkLg/fflw/L4fABAZU0ldt7cibk95ra0\n222WkZaWOFkrV8YwDLrs6IKcLTkovVaqXK4x7w4Axsby9MzChfLZwe0MGtwplBekOK4YT/Y/QZet\nXZQzUH/KysKHTk4wUu1IJQSYOxd4+21gyBDl4sN3D8P//9s787ioq/WPvwdm2PcdAVFZZBHBPSuV\ncrc0TSsry5ta5nLbbv26rTdvZdpyy5uVVra4tGhZ5s0lTTH3FUUFAWWRRZB9Z4aZOb8/vqYgzAII\nCn3fr9d5zTDzzDnP4QsfDuf7nOfxjibEPeTqrmUMMNbNja0lJeiu2lKx9rUm5L8hnHn4DLoaHQB9\nffuSX5lPVllW44769IH586U/uJ1se0YWdxmZVqAt13Jm+hl6ftoTlYdU3zRfo+GnwkJmd7kqfcA3\n30j5bBcuvPySEIJ397/L0zc93Z5ud3gCbGzwtbLi8KWQyPp43eeFQ4wD6S+mA2BpYcmIHiPYlrat\n6c5eeAEKCuDzz9vS5XZHFncZmVZw9smzuI1xw/0O98uvfZyTw31eXrjXL2adlQVPPw2rV0O9k6rb\n0qQTlGOCx7Sn252CsW5ubDYQxhTyUQgF6woo2SkV1ja4NQNS0fGvv4YXX2ycSL4DI4u7jEwLKfip\ngLLdZQS9G3T5tVq9nmW5uTzl73/FUK+XIjOeekraBqjHO/ve4dnBzxrNTS7TNOPc3dlU3HTqAZWb\nitDPQkl+JBltuZZRQaPYnrYdnV7XdGeRkVIE0yOPSNerEyCLu4xMC9DkaUidk0rYqjAsHa6E0azO\nz6efo2PD8Mf//hdqayXxqEf8hXiSCpLk8McWcouTE6k1NeTXC4msj/tYd1xHu3L2qbP4O/nj7eDN\nsQvHDHf4zDOg0UhnDzoBsrjLyDQTIQTJjybj+6gvzoOdL7+uFYJF58/zz671CnIkJkqZCFeuBGXD\nyOP39r/HE4OewMrSqr1c71SoLCy43cWFrQZW7wBB7wZRGldK4cbCxqkIrsbSEr76Ssrzk5Jy7R1u\nZ2Rxl5FpJnkr8lDnqgl8JbDB69/m5+NnZcXQSyGOaDRS/PTChRAU1MA2ozSDzWc3M7vf7PZyu1Ny\nh7s7vxo5Pqx0VBL2dRgps1O4w+0O/pfyP+MdhoTAv/4F06eDzsAWTgdBFncZmWZQk1ZD2gtphK8M\nx8Lqyq+PTgjePH+eVy4diQdgwQLw85PC7K7ijT/eYE7/OTjbODd6T8Z87nR3Z2tJCWoj++QuQ1zw\nftAb70XeJBcmc6HigvFO584FW1t4991r7G37Iou7jIyZCJ3gzMNn6PpCV+wj7Ru890NBAa5KJcP/\nXLXv2yclBPvss0aFmdNK0vj5zM88M/iZ9nK90+JtZUWEnR27SkuN2nV/vTu1ybXMvzDf9OrdwkKq\nufruu3Dq1DX0tn2RxV1Gxkyy3stCoVTg/5R/g9f1QvBGZiavBAZKUS/l5VJSsE8+AR+fRv288ccb\nzB0wFzdbt/ZyvVMzwcODX4xldgMsbCwIWxVG7OpYduzfYbrTbt1g0SJ4+GEp534HRBZ3GRkzqEpo\nmBSsPusKCrCxsGCs2yWxnjcPhg+HSZMa9XO2+Cy/JP8iH1q6hkxwd+eXwsIm87nXx7GPI35/92Pg\nkoFUqs0o1jFjBvj6Njh01pGQxV1GxgRWQseZ6Un0eLvH5aRgf1Kn1/NSejqLe/SQVu2rV0spJN9/\nv8m+3vjjDeYPnI+rrWt7uP6XINzODisLC05UVZm07flyT7z0Xvzx5h+mO1YopG21jz+Go0evgaft\ni0lx37JlC2FhYYSEhLB48WKDdocPH0apVLJ+/fpr6qCMzPVmrjiLfYQ9Pn9rvMXy2YULBNnYcLur\nK5w7J51C/fZbsLdvZJuQn8Dms5vlVfs1RqFQMMHdnQ2FhaZtlQoq36rEYokF1anVpjvv0gU++EDa\nZqupuQbeth8KYeR/GZ1OR8+ePdm+fTt+fn4MGDCAb7/9lvDw8EZ2I0eOxM7OjkceeYTJkyc3HESh\nMPkvk4zM9SAnR0opYujH0+tkPk7rM7inuB/Wrg3j1Ct1OkIOHmRTVBR9bGykHOH33y+dRG2C0atH\nMz50PPMHzr/W0+g0ZGbC0KHSY3PYVVrKM2fPcrR/f9NjlGbyyt9e4cm8J+m7py8KpYnTwUJISf0D\nAuC995rnWCtpjXYaXbkfOnSI4OBgunXrhkqlYurUqWzYsKGR3YcffsiUKVPwNFQnUkbmBmXPHli7\ntun3bIuqCd5ylqpnI7ByaVz64P2sLG5zcaGPo6NUk9PLC558ssm+tp7dSnpJuhzX3kbc4uxMRm0t\n2Wq1SdtAl0BOjjpJpaqS84vOm+5coZC2Zr77DnbtMm1/g2C0WEdOTg4BAQGXv/b39+fgwYONbDZs\n2MCOHTs4fPiwwRwZr7322uXnsbGxxMbGttxrGZlrSK9eUt2G+uhqdMQPTsT33W6MmuPY6DM5ajVL\ncnI41LevtM++eTMcPtwo7BFAp9fx3LbnWDxiMSpLVaP3ZVqPUqFgnLs7GwsLmePnZ9J+fPh4fp/z\nO6OfGo3bWDcc+zW+xg3w8IBPP5VyBCUkgKMJ+xYSFxdHXFzcNenLqLibk8zoqaeeYtGiRZf/fTD0\nL8RrV//2yMjcoAghSHksBbtwO7o83qVJm/87d47Zvr70SEmR9tl37LhcfONqvjz+Jc42zkwMm9iW\nbv/lmeDuzoq8PLPEfULPCUw7PY15S+aRNC2Jfsf6YWlrpNQWwB13wE8/STloPvvsGnndkKsXvgsW\nLGhxX0bF3c/Pj6ysKwnus7Ky8PdvGON79OhRpk6dCkBhYSGbN29GpVIxYcKEFjslI3M9yX4/m6rT\nVfTZ06fJBc7u0lJ2l5XxqZcXjBwpJQaLimqyr6LqIl7a8RKbH9wsZ35sY8a4uTErOZlSrRYXpfEK\nov18+1GpqaRkZAkOvziQ9s80QpaYUSzlP/+B6Gj49VdJ7G9gjO659+/fn9TUVDIyMtBoNHz//feN\nRDstLY309HTS09OZMmUKn3zyiSzsMh2W4q3FZL2bRa+fe2Fp13glpxOCv589yzuBgdjffz/cdZd0\nE9UAL+54kXsj76Wvb9+2dFsGcFQquc3VlY3mRM0oFEwMm8j6pPWEfBRC4fpCircZTkB2GScnKbnY\nY49JJRNvYIz+eVMqlSxdupTRo0ej0+mYOXMm4eHhLF++HIDZs+WbQzKdh+rUapIeTiLyh0hsuto0\nabMsNxcXS0vufe01qQbnO+8Y7O9QziE2Jm8kcV5iG3ncOSkpgeefN/y+pSU89xy4NnFUYIqnJ+su\nXuShJk4GX809Effw1NaneHHIi/T8oifJM5Lpn9AflauJ+yLDhknRM3PmwPffN3mf5UbAaCjkNRtE\nDoWUuUH5/ntYvx5WLdUQf0s8Ac8G0OWxpvfZs2pr6Xv0KLvi44n4/HPYvdvgjTWdXsegzwfx5KAn\neSj6obacQqdCq5XSqRsLevn4Y6li4S23NH6vTKslYP9+sgcPxsnE1oxOr8PvP37smbGHYLdgUp9I\npa6wjohvIkw7WlMD/frBK68Y/c+ttbRGO43PXkbmL4BSq+PUhFN4TvE0KOxCCOakpvJEeTkRixbB\ngQNGIyY+OPABjtaOTOs9ra3c7pQolQaPCVzml18Mv+esVDLUxYX/FRXxgLe30X4sLSy5O/xufkz8\nkedvfZ4ei3pwtO9RLn53Ea+pXsadsLWVcvSPGycF5ptxE7e9kdMPyPy10QtGHEjCNtiW7m92N2j2\n3cWLZBYV8fyMGbBhA1wVWFCf5MJk3trzFismrJBvol4Hpnh68kNBgXm2EVNYl7gOAEs7S8JXh5P6\nRCrqHNPx8vTvL+URmjXL8Cm464gs7jJ/WYQQOH6Vikqro+eKngaFuECj4ekzZ1jxwgtYrV4t/Ttu\nAJ1ex4xfZvBa7Gv0cO3RVq7LGOEud3d+Lymh0oxiG0MDh3K+7DzpJekAOPZ3xG++H2ceOYPQmyHY\nL74IBQVSDPwNhizuMn9JhBCkv5COKrWcrTdHNii8cbXdzKNH+dvPPzPw+eelbI9GWHJwCUoLJXMH\nzG0Lt2XMwFWl4mZnZzaZEc2itFAyKXwSPyT+cPm1wBcD0ZZpyf041/RgKhWsWgUvvXTDleaTxV3m\nL0nmG5kU/VpEyYvRaKwM33r69PRpcs6c4d/R0TDR+CGkE3kneGvPW3wx4QssFPKv1vWkWVsz4VP4\nIemKuCuUCsJXhZPxWgZVp0xnmiQ8XKq69cADUmnFGwT5J1DmL0fWu1nkr84nels0wtFw2NuZ1FRe\nTk9njVqN1d/+ZrTPKk0VU3+cyn9G/YcgtyCjtjJtz10eHmwtLqbajK2Z2G6xpJWkkVl6JVuZXagd\nPd7uQeL9iehqzKilOneulPv91Vdb4/Y1RRZ3mb8U2R9mk/NxDtHbo7HysTJop0lL48G9e3m9rIyw\nxx832e9TW5+if5f+ctjjDYKHSsUgJyc2mrE1o7JUcXf43Xx36rsGr/s84oN9pD3nnj1nekCFQirN\nt2oV/P57S92+psjiLvOX4fzi82R/kE3079HYBDR9SAmAc+f4x4oVBLi5MfvBB032u+70OuIy4vh4\n3MfX0FuZ1jLN25s1+flm2T4Y9SBrTq5p8JpCoSB0WSjFm4op/Nn0qVc8PeHLL2H69Bvi9Kos7jKd\nHiEE6a+mk/dVHn3+6INtd1vDxsnJrHnxRbYMG8ZX48aZDGU8U3iGeZvm8e3kb3G0bptMgTItY5KH\nB3+UlVFoRg3UW7veSmltKQn5CQ1eV7ooCf8mnOTZyaizzQiPHDUKpk69IcIjZXGX6dRUVwsSHj9H\n3o9F+HwfQ5HCmtxcLrfi+ulEDh7k5LRpPPXYY/x4880mk0+Vq8uZ+N1E3hr+Fv27mC4SIdO+OCqV\njHVzY93FiyZtLRQWPNi78eodwHmwM/5P+JM0LQmhM0Ow33wTMjKue3ikLO4ynRa9Rs+qiDMcWFHO\nw0XR3DTGiv79adBefx2Cg4EtWyi9914mL17M+7160dvBwXjfQs9DPz3E7d1vZ2bfme0zIZlm86C3\nN6ubsTXzzclv0At9o/e6/rMrKOD8W2YU97C2lkotvvwyJCU11+VrhizuMp0SbZmWk+NOYqnW4fNN\nNKl5qgYr9vrtzbBV1M2YwZSVKxnXtSvTTBxbB3h91+sUVRfxwZgP2mE2Mi1ltKsrKTU1pJlR/7SX\nVy/cbN34I7Nx8WyFpYLw1eHkLM2hbF+Z6YHDwmDhQinvTG1tS1xvNbK4y3Q6arNqib81HrtwO/43\nIBKsDRRh0OvhlVcQr7zCnPXrsXVz470g02GMaxLW8OXxL/nh3h+wsjQccSNz/VFZWHCflxffmLE1\nAzAtalqTWzMA1n7WhH4aStIDSdQVm97HZ9YsCA2VirlcB2Rxl+nQ/PijlF77z/bNaxXsiYknK9KH\nvX2DycwycEO0qgruuQd27OCdTZs4qlTybUQEliZuoO5M38kzvz3Drw/8io+D6bSyMtefB728WJOf\nb1Z2xfuj7md90npqtU2vtj0meOBxtwdnppuRnkChkCo2bd8ubdO0M7K4y3RYqqqktNpxcVLLWpGH\n26IE/ugdzC82AcTtUtCnD/TufdUHs7JgyBBwcOCb77/nw7IyNkZF4WBpvMza6YunmfrjVL6b/B2R\nXpFtNS0ZE1hbw913Q2Cg4fb++1fsb3JyQqPXc6SiwmTf/k7+RHtHszF5o0GbHot7UFdcR9bbWQZt\nLuPsDOvWwRNPQHKyOdO7Zsj53GU6LFVV4OUFFaV60p5Lo+jXInr93Av7SHvDH9q5Ex58EJ5+mo2P\nPMKjKSn8Hh1NpL2RzwDpJekM+2oYC4cvlNP4XmcqKq6KcrqKr7+G/Hz46KMrr72RmUmOWs0noaEm\n+1+dsJrVCavZMm2LQRt1tpqjA44S8W0ELrFN185twKefSonqDxwAOzvT9pdolXaKdqCdhpH5i1FZ\nKUQXW7WIj40XJ8aeEJpijWFjnU6IN98UwttbiN9+E78XFwvPPXvEobIyk+NklWWJ7h90F0sPLr2G\n3su0FUuXCjF3bsPXsmprhevu3aJKqzX5+WpNtXBb7CYySzON2hX9ViT2dtkranNrTTul1wvxwANC\nzJxp2rYerdFOeVtGpsNStqOY/9YewflWZ6I2Rhkuj1ZUBOPHS0WNjxzh4KBB3JeYyNrISAY4ORkd\nI68yj+ErhzN3wFzmDZzXBrOQaQ/8ra0Z7ORkVjIxW5UtU3tN5avjXxm1cxvpRpfZXUicmojQmrH/\nvnw57N0rFfloB2Rxl7lh2bwZunRp3AJ89TzjkMb+icl86BhO99e7o7A0cCM0Lg769pUy98XFsdfR\nkfEnT/JVWBixLsb/nb5YdZERK0cwLWoaz9787LWfoEy7MsvXl88vXDDPts8svoj/osmY9/oEvhyI\nhY0FaS+lme7UwUHaf//HP+DECbP8aA2yuMvcsGRnQ2wsHDlype1fX8O6LseZOqiSAcf7seF8E1WS\nQSrC+dxz0v76smXw7rvsrKxk4qlTrA4P5w53d+Njl2cz9MuhTI6YzMtDX772k5Npd+50dyelpoaU\n6mqTtn18++Bm68bvacaTgCksFESsieDidxe5uM6McMteveC//4VJk9o8/4ws7jI3NPb20mrd11fA\nxlyyxx/D70FPBmyLomuUFc7OTXzo5EkYOBDOnZNWSGPHsrW4mPsSE1kXGckoNzejY54rPseQL4cw\nq+8sFsQukEvldRJUFhY87O3NCjNX7zP7zOTz+M9N9+uhotdPvUidm0rliUrTHd9/P0yeLD1qtWb5\n0hJkcZe54anNrCVhVAIXVlwgZlcMAc8EoLBoQnA1Gimvx+23S1WWf/wRPDxYk5/Pw0lJ/Nyrl8mt\nmFMXTzHsq2G8cOsL8lZMJ2Smry9f5+ej1hvfbgF4IOoBfjv3G/mVptMXOPZ1JOTDEE5NPEVdoRkH\nnN56S0os9sIL5rjdImRxl7lx0QuCz+RytP9RXG53oe++vthHGAhZPHhQqm26d6+0f/PIIwjgrcxM\nXkxLY0dMDDc3ucy/wrZz27j969t5Z+Q7PNbvsWs/H5nrTk87O6Ls7c26sepq68o9Effw2bHPzOrb\na6oXXlO9OH3PafR1Jv54KJXw3XfSAqSNDjjJ4i5zQ1KZUIn/O/EEJV8gZmcMgS8EolA2sVqvqIAn\nn5RK4L34ohQRExiIVgjmpKbyfUEB+/v2NRnH/unRT3nop4f48d4fuT/q/jaalcyNwN/9/PgwJ8cs\n2/kD57PsyDLqdGasxoHub3THwtaCc8+YUeDD3R1++kk64HT8uFn9NwdZ3GVuKLQVWs7+4ywnRpyg\nfLAPWyf0xb5XE8IsBHz/PURGQnk5nDol7WEqFBTW1TH6xAkyamv5IyaGLtbWBsfT6XU8t+053tv/\nHntm7GFI4JA2nJ3MjcAd7u7kazQcLi83advbuzdBbkH8dOYns/pWWCqI+CaC4t+KufC5GXv70dHS\naauJEyEvz6wxzEUWd5kbAiEEF7+/yOGIw2iLtQw4NYDyoV2k+OCriY+HYcNg0SJYs0aqfnMp+iW+\nooIBR48ywMmJX6OicDKSk72wupBx34zjSO4R9s3YR7BbcFtNT+YGwlKhYF5zVu8D5rP00FKz+1e6\nKIn6JYq0l9Io2V5i+gP33gszZ8KECWBGJI+5yOIuc924cEHaHj/4eRm7e8dz5rXzWLwaTuW8MBLO\nW5GRcdUHCgpg9mwYOxamTZM+POTKSntNfj6jEhJY3KMHi3r0MJoE7HDOYfp/2p9o72i2PbQNdzvj\noZEynYsZPj5sLCriokZj0nZi2ETSStI4kWd+bLpdTzsi10WS+EAiVaeqTH/g5ZelsxgPPSRlK70G\nyOIuc914cko1f8SeInduIqsr/fiHXT/mLXfh8cfh8cdh61bp/BHl5fDaa1KObFtbqQDCY4/BpURf\nVTodM8+c4bWMDH6PjuZeLy+DYwohWHZkGXd8cwf/Gf0f3h75NkoL4xWXZDofbioV93h6sjw316St\nylLFnP5zWHJwSbPGcBnqQvAHwSTckYA610SJPoVCyj9TWAj//GezxjFIixMXNIN2Gkamg1CbUytS\n5qeIX1V7xI5ZGUJbbSDfR02NEO+9J4SXlxAPPSREWlojk2Pl5aLnwYNielKSKK+rMzpufmW+mPDt\nBBGzLEYkFyZfi6nI3IA0lVumKRIrK4XXnj1m5ZsprCoUrotcRXZZdrP9yXgjQxzue1hoK0yPIwoL\nhQgJEeLTT4UQbZxbZsuWLYSFhRESEsLixYsbvb9mzRqio6Pp3bs3t9xyCwkJCU30IiMD6gtqUp9M\n5XCvwyhUCj4aMAAeCMTS9qpUu2q1tIoJDYVdu6R82CtXQvful010QvBeVhajEhJ4JTCQr8LCcDSy\nv/5ryq/ELIsh3COcg7MOEupuOjugTOcm3N6em52d+dKMG5nudu5Mj5nO+wfeN2l7NV1f7IpDjAOJ\n95uRg8bdHTZtgldflfJvtAZjyq/VakVQUJBIT08XGo1GREdHi8TExAY2+/btE6WlpUIIITZv3iwG\nDRrUqB8Tw8h0ctQX1CL16VSx23W3SH0y9XIWvdhYIXbsqGdYWSnE++8L4ecnxJgxQuzb12R/iZWV\n4qajR8Ww+Hhxtrra6NhltWVi9sbZIvD9QLErY9e1mpLMDYy5K3chhNhfViYC9+8XGp3OpO35zpLe\nfAAAHhxJREFU0vPCdZGrKKouarZPOo1OnBhzQiRNTxJ6nd4Mx/YL4enZdiv3Q4cOERwcTLdu3VCp\nVEydOpUNGzY0sBk8eDDOlw6HDBo0iOzs7Nb9tZHpNFSnVJM8O5lD4YcQdYIBpwYQ/EEw1r5XhSaW\nlUn1Jnv0gN27YcMGadUyeHADM60QvJWZyZDjx3nI25sd0dEE2doaHP/nMz8T+XEkWr2WE4+fYGjg\n0LaYpkwH5iYnJ7rb2LDWjENNAc4B3BV2Fx8f/rjZ41ioLIj8MZKaszWce+ac6RztN90EX3zR7HHq\nY/ROUk5ODgEBAZe/9vf35+DBgwbtV6xYwbhx45p877XXXrv8PDY2ltjY2OZ5KtNhKNtfRtY7WRTu\nLENxVxcsVg4k39WK/DSgXvI8+/w0gj9eCju+hnHjpEIaERFN9rmnrIz5qal4qVQc6dePbjY2BsfP\nKc/h75v/TmJBIqsnrWZYt2HXeIYynYl/du3Ks+fOcb+XFxYm8gj9383/R+zXsTwz+BnsVOYX3QCw\ntLMk6n9RHI89TubrmXR7tVsjm7i4OOLi4prVryGMintzEibt3LmTL774gr179zb5fn1xl+l86DV6\nCn8qJOfDHNQ5alwe9Wf8L+FEn7WE+rdqhKBP+S7uyf2A1eV7ELfOgGPHpNpoTXBBreb/0tKIKy3l\n3aAg7vX0NPhzWautZcmBJbyz7x3mDZzHN5O/wUZp+I+AjAzAKFdXrBQKfikqYqKHh1HbcM9wbgm4\nhWVHlvHM4GeaPZbSRUnvrb2JHxKP0kWJ/xP+Dd6/euG7YMGCZo9xeSxjb/r5+ZGVdaVOYFZWFv7+\n/o3sEhISePTRR9myZQuurgZSsMp0StTZanI/zeXCZxewC7PD70k/PCd5klegwOFD2LPnkmFlpZRL\n48MPQVcHbz4BD62R0j42Qa1ez9KcHBadP8+jvr4kDRxosMapEIL1Set5bttz9PbuzYFZB+QDSTJm\no1AoWNC9Oy+kpTHe3d1kkfQFsQsYsWoEj/Z9FEdrx2aPZ+VtRfS2aOKHxGPpYInvDN+Wum4cYxvy\ndXV1okePHiI9PV2o1eomb6hmZmaKoKAgsX//foP9mBhGpoOh1+pF0W9F4uSkk2K3626RMj9FVJ6u\nbGCTmyuEj7deiAMHhJg1SwgXFyEmThRiyxap5JgBtHq9+OrCBdF13z4xPiFBJFdVGfXlQNYBMezL\nYSLq4yix/dz2azI/mY5Nc26o/olerxc3HT0q1uTlmWV//w/3i9d3vd4C765QlVwl9vnvE7mf5xq0\naY12Gl25K5VKli5dyujRo9HpdMycOZPw8HCWL18OwOzZs/n3v/9NSUkJc+bMAUClUnHo0KG2+Usk\nc12pTq0m/+t88lbmoXNUcTrQl7xHw9BZK+G7K3a2NcX0PLya34s+hwerYdYsSEwEX8MrFCEE/ysq\n4sX0dJyVStZERHCrkSyO8RfieTXuVY7nHefVoa8yo88MLC2aXtnLyJhCoVCwsHt3Hk1J4R5PT1QW\nxqPEF8QuYPCKwcwbMA9X25btVtiF2hG9I5oTt59A6AVdHu3Son4Mobj016FNaVUFb5nrirZcS8Ha\nAvK+yqM6tRrvad74TPdh+TYHfvwRxoyR7JSaakJTNhKV8A2BGXGkht5B7rhHGb1wGBj5RdELwS9F\nRbyZmUmtXs/C7t25093d4L766Yun+Vfcv9iXtY9/3vpPHuv3mLyvLtOAjz6S1hIffdT8z444cYL7\nPD15tItpoX1046N42nmycPjCFnh5herUak4MP0Hgy4F0eazhuK3RTvnctUwjdJU6CjcWUrC2gJId\nJbgOdyXg+QDcxrhhobok1NvglkFaXh30u5S8a+NGqfrRCw/ApFVEOTkRZWwMIVh78SILz5/HSqHg\npcBAJnp4GIxW2Ht+L+/se4f92ft5dvCzrJy0stnRCjIypnize3cmnz7NA97e2Bu4x/Mnrw59lZjl\nMcwbMA8/J78Wj2kXYkfMjhiO334cvVqP/98b39dsCfLKXQYAXbWOol+LKPi+gOJtxTjf4ozXvV54\nTPRA6VJvDaBWw44dJCz4ie4nN+DYq5tUp/Tee8HHx+Q4FVotX+Xl8d+cHHysrHgpMJDRrq5NrtT1\nQs/G5I28ve9t8irz+Mfgf/C3mL/Joi5jlNas3AHuT0wkxNaWf9c7EW2Il3e8TGZZJqsmrWrZYPWo\nzajlxKgTeN3vRbfXuqFQKFqlnbK4/4XRFGgo3lRM0cYiSraX4DjQEa/7JEFXuauuGFZUSIeKfvoJ\ntmyByEji3Cax13MSL63oYdZYZ2tqWJqTw6q8PIa7uvKkvz+3GNhTL60t5evjX/PJkU9wtHbk/27+\nP+4Ov1veU5cxi+XLpdP7xrS5WzcpeKspsmpriTlyhKP9+xs9TwFQqakkbGkY6+5Zx+CAwUZtzUFz\nUUPCmAScbnIi5MMQLJQWsrjLmEYIQXVSNUUbiyjaWETlyUpcR7jiMd4DtzvcsPK0umJ87pwk6Js3\nS6dGb75Zqth+113g48N770FuLrz3nuHx6vR6NhUX89mFCxwsL2eWry9zu3QhwMAvzJHcI3xy5BPW\nJ61nbPBY5vSfw61db5ULVMs0i+pqMJbiqrwc7rsPSoykWv93RgYJVVX8EBlpcrzVCatZcnAJB2cd\nxELR+kS72nItp+46hcpLRa+1vWRxl2kabbmW0rhSSraVULy5GL1Gj/t4dzzGe+AS64KFzaUfxpoa\niIu7IugVFdLd0rFjYfRouKqwtDFxT6muZsWFC6zMz6eHjQ0zfX2Z6uWFXRN7mMU1xaw9vZYV8Sso\nrC5kdr/ZzOgzAy97w2l7ZWRaQ0mJlOnCmLjX6HSEHz7M5z17MsLE2R290HPrF7fyt5i/XbPau/pa\nPYkPJhK1Pkq+oSojoa/TU3GogpLtJZRsK6HyRCVOg5xwHelK5A+R2EfbSythnQ7ij0lH/n//XSos\n3aePJOhr10rlv0yEg9WnuK6O9YWFrMzLI7m6mod9fNgRHU14E4eUNDoNm1I3sSphFdvTtjMmeAwL\nYhcwOmi0vPUic0Nga2nJRyEhzE5O5uSAAU0uTP7EQmHBsjuXMXzlcO4MvZMujq0PabSwsSBybWSr\nFFpeuXdwhFZQebyS0j9KKd1VStmuMmy62+A6whXXka44D3GWUurqdNL/qjt3InbuRLtzNzVufhRF\n3UZh1G0U9B6O1uHK6lyphFGjpMemeO89yMjXMugfRXx38SK7S0sZ5ebGA15e3Onu3ihOWKvXsjtz\nN+sS17H29FoiPCN4OPphpkRMwcXGpelBZGTaAHNW7n/yYGIivtbWvBsUZNL2lZ2vcPriadbft/4a\neCkh31D9C6Gr0VFxqIKy3WWU/lFK+YFybLra4DzUGZehLrjc7oKVl5W0zXL0KOzfL63K//gDvL0h\nNpbcsNu45YVhRI3wNjjOrl3SLk2fPg1fL66rY1NxMW/vKyTZoZhRPi5M9fJigrt7o3zqGp2GHek7\n+DHpRzac2YC/kz+TwyfzQNQDdHc1HYkgI9MWNEfcCzQaoo4cYWOvXgxwcjJqq9aqiVkewxu3vcHk\niMnXxFdZ3DspQgjUmWrKD5VLgr6/jKoTVdhG2GE7yAW7m5yxH+SM0lWJRU4WysP7UR7ej+Xh/SjP\nnELXMwJt/8FoB95M3c3DED7SCdHUVHj4YUhONjx2v35SvYx+/eBcTQ2/FBbyS1ERxyoquM3VFesj\n7rif8eDjRaoGnyuuKea3c7/xa+qv/JryK2EeYUwOn8yk8En0cDUvskZGpi1pjrgDfHfxIgsyMjjS\nr5/J2Pf9Wfu5e+3dHHvsGL6Orc8ZI4t7J6GupI6KwxWUH5TEvPxQOQoLBU6DnHAc6IjTICecBjkx\nYlARbhlH6aM/RrTuKH21h7BCwyHlzRxWDmaXejBHFf1ROhmOBx80yHChlxqdjt7TyugyoYR0z2Iq\nLOrop3FngMaDqDoXrLHkl1/AwwPeeVfP8bzjbErdxOazmzmZf5Jh3YYxNngsd/W8q1WHO2Rk2oLm\nijvAw0lJ2FpYsLxnT5O2C+IWsCdrD1unbW119Iws7h0MIQSaXA2VJyqpPH6pxVeiydPg2M8Rx0GO\nOA2UBN3ashhFfLy0xXL0KBw7RlluJapBfbG7pa+0tB4wQPppbWHIoF4IjldWsq2khG0lJRwsL8e9\nxAG7JFe8M91wzXdEwZW+q1TpFDjE4Ry9k0T1b7jYuDA2ZCxjg8cyNHConA5A5oamJeJertXS9+hR\n3u7Rg7s9PY3aavVaYr+KZWLYRJ69+dlW+SqL+w2Mvk5PTUrNFRG/1AAc+jjgEHOphVpip01DkXQa\nTp6EU6ekR41GEvC+l4S8Xz9CRnVn02YFISEt86lOrye+spLdZWXsKStjd1kZnioVI11dGenqyjAX\nF5zq7Z9nlmayM2MncRlxxGXEodapie0WS2xgLCODRsrbLTIdipaIO8Ch8nLGnzzJ3r59CTZSAQyk\n35mBnw/kh3t+YEjgkBb7Kov7DYBerac6pZrqxGqqEqsuP9am1WIdYH1FxCOscHC6iNXFMyhOn7oi\n5Lm50LMn9Ooltago6TEgoNGKPCREqqFrrrhXaLUcrKiQhLy0lEMVFfSwseFWZ2eGuLhwq7Mz/tZS\n6Tu1Vk18XjwHsg9wIPsA+7P3o9ZeEvNLrad7T/lgkUyHpaXiDrA8N5cl2dkc6Nu3wQKoKbac3cKM\nDTM4OOsgAc4BRm0NIYt7OyGEQFukpTq1mprUGqqTr4i5+rwam+422EfYYxdhh713LXZWudhVp2CR\nnizdvUxOlkQ8MBAiI68IeFQUBAcbjju8CmPiXqvXc7yyksPl5RypqOBwRQWZtbX0cXRkiLMztzo7\nc7OTE64qFUII0krSOJJ7hAM5kpgn5CcQ6h7KYP/B3OR/Ezf530SIW4gs5jKdhtaIO8CclBRy1Wp+\n6tXLZFm+t/e+zdrTa9n9yG5sVcZX+00hi/s1pIGAn62hJvVSu/QcBdiG2GIbbIudTx32LqXYWWZj\nW52CxblLAp6aCg4O0kq8Z08IDb38PJ3u7D+iMu2IEZ5+Wqpw5NNdy8mqKk5WVXHskpCfqa6mp50d\nAxwdpebkRKSdHQr0JBUkEZ8XL7UL8RzPO46jtSN9ffsy2H8wg/0H069LPxysHK7Rd1NG5sajteKu\n0esZeeIEfRwdeT8oyOjCRwjBQz89RFVdFevuWYfSonmnkmRxbybaCi21GbWoM9XUZtRKLbOW2vRa\nas7VAJcEPFCJrWsVdrZF2OqyEIUpZO09g0dFOu6VmVRZuVLo1IMCxx4UOnYn3zmUUq9Qnlwaisqz\n6YM5zzwjxY+HhZnvr14hqHSqodStilK3Sso9q7AMruSiVkOEvT297e2JcXBggJMTve3sKKnO53TB\naRILEjldcJrjecdJLEjE38mfPj59pOYrPXraG785JCPT2aioAFdX44nFLCyuHNRuilKtlqHx8dzn\n5cVLBur//olGp2H8t+MJcArgs/GfNeu/YFnc6yH0grqLdahz1KizrxLvS8/1aj02gdbYeAtsnGqw\nsSnFRpGPjfo8tpXJqPJTISsL6uqkP/E9ekD37pyp68H7G3owYlZ3Kty7obNuHGr4zDNw9qx0Xqgp\nnn4aunaVHq+mSqcjpbqaM9XVJNfUSI/V1aRUV+NlZUVve3t6OzjQ296eXvZ22NQVkVJ4poGQJxYk\nYqu0JcIz4nKL9o4m2idaXpHLyFwiO1s652eImTPhhRek1EqGuKBWc2t8PP8ICGCun/GQ30pNJcNX\nDmdI1yG8M/IdswX+L1OsQ1+rl0T7UtPkaFBnS8+LktVcTFLjrNVQa2FBlaUCtYUGnaIaS0UxduTi\nYZHGLUFnUOWeQXGuDGq6SDcsG7SbwN9feu7h0eBmZvZ2OHcGlv/LsI8vv2x8DloLPfnWtWwqquFc\nTQ0p9US8oK6OYFtbwuzs6Glnxx1urjzoooSacnJLkjibd5ZTxWf5qSiVtJI0XGxcLgv4gC4DmB49\nnQjPCNzt3K/Rd1xGpnPib6IehoG67Q3wtbZmW3Q0t584gVqv5+kAwzdNHawc2PTAJkatHsWTW57k\ngzEfXJMMksa47uIudIK6gjo0+ZrLrS6/Dk2epsFrmhw1ukodVu5g7VyHtX0NVqoyrCnAqe4CTiWZ\ndLc4SxdVMtgo0Xn5ovP0Qefpi9bLF61vAPPeGs3NS2ehCA6QltZX5T+pqYG8vEtfVF5q9bhwwYz5\nIDiaoaGquJYsbQ3ntVces7W1XBynwa3OhvhsG7rb2OCvEoTY1XK3RT7VFelkF58n7VwaPxWfJa0k\nDTdbN4LdgglxCyHYLZhBUYMIcQshyC1IXonLyFxnetja8kdMDCNOnKBCp+OVwECDq3J3O3d+f/h3\nxq0Zx+z/zeaTOz5p9h58c2i3bZkfpmRiWa7BsrwOyzINyjI1lmVqLGp0YC2wtNfi7FaLlaoSlUUp\nVvoirDR5WFXnYFWeiVX1eVQeKhRdfKVCyz4+0uOl53HJvny3y5dlP/uAgRhUGxtYsgRUBu5nfvMN\nHD8Ojo5Nvy8QjJysZe6/aslSq8lWq8m61LLVarJqa8mo1EC1JaqLtlgWqbAsrUNRUYGi+iJCnYmO\ns4T2y6DGOpPzZeexsrQi0DmQQJdA6dE5kG4u3QhxDyHINQh7KzOWEDIyMteUsWPhiSeMb8vUJ0+j\nYWxCAn0cHPgkNBRrIxlVKzWVTFk7BYDvp3yPs43hQvAdYs/9d+XbuFoXYK/LxbHuPLb6i2isFdTY\n2FBp486pfE/um+8JngaaqysYyeuwfj2sXi09GuKFFyA/v/HrAoHaSkuNrYY7H9Zg00VDnuZKy6kn\n4LYWFvhbW+FhKXBGja2uEgtNEfraPGqqzlNWfpb88vPkVuRSXVdNF8cu+Dr40tW5awMBD3QJpKtz\nV5ysjScjkpGRaX+aK+4AlTodDyUlUVhXxw+RkXhbWRm01eq1PL31aX5P+52fp/5MqHtok3YdQtw/\nuO1nnnyjnlg7O1/ez9Zqwc4OvIzUZygvl3ZRHAzsRNTUSDUlvvlG+lqt11NUV0dhvVZQV9dAtP9s\n+RoN9hYWuCvBSaHDXtRipavCUluGTl2ItkYS7vLycxRWZFGuLsfbwZsujl2uNIcuDb927IKbrZsc\nHy4j0wGZNEkqQGasyt6jj8K/rrr/pheC1zIy+PzCBb4MC2O0m5vRcZYfWc7LO1/mvVHv8VDvhxrp\nRYcQ9+nTBV99ZdimqKjpu9dCCKqFnpI6LZXUUarXUqyru9xKdHUU66Xn5Yo6irUaCuvqqNXrcbIE\nB4UOW6HBWl+Dpa4KS20pQl2EpjafqsosyivSKSlPx0llg5e9F572ntKjnfRY/7mnvSeedp542nu2\n+c0QGRmZ60d1NRQXG37/xx/hyBFYZaAu9s6SEh4+c4a7PTx4o3v3Rumw63My/yRTf5xKL69eLBmz\nBB+HK4XmO0S0TI2thqSqOkq12sutpN7zUq2Wkro6iuo0FNepL72mo1IvUCnAFh3W1GEt1Kh01Vjo\nKhGaUrTqItS1F6mpzqOyKgdFXTmuSku6WtvibuuGm60brrauuNm64WbjhpujG662YXjY3XpZuD3s\nPFBZtu5gkYyMTOfBzk5qhjCxIOc2V1eO9+/Ps+fOEXH4MEuCg5nk4dHkf/JR3lEcfvQw/971b6I+\nieL1217nsX6PtT6jZHut3JWbt+JkrUGpr8VSVw3aKoS2HJ2mjDpNCeraImpri1CJWhwUAielJc5K\nS9yUVrhYO+Bk7YSzjbP0aO0sifWf4m3jevl5S474ysjIyDSHVavgt98Mr9zr80dpKXNSUvCysuLN\n7t252dnwDdST+Sd5/NfHqdJUsXD4Qu4IvePGX7mHb43jwcnO9UQ64LJQO1k7XW7yClpGRqYzMdTF\nhRMDBrAyL4/7ExOJtLfnHwEB3O7i0mglH+UdxZ5H9vDzmZ95bttzrRr3htlzl5GRkekorF4NL74I\nAwcatvHwgGXLGr6m1uv5Oi+P/+bkIIRgnp8fU728cGsiPlun16G0VHb8G6odmbi4OGJjY6+3G21G\nZ55fZ54byPNrK8rKYPt2MKSeOh088ID02BRCCOJKS/kkN5etxcUMcXHhXk9Pxri54VUvhLI1N1RN\n7thv2bKFsLAwQkJCWLx4cZM2TzzxBCEhIURHRxMfH98iRzoycXFx19uFNqUzz68zzw3k+bUVzs4w\neTJMmWK4GUOhUHCbqytrIyPJHjyY+728WF9YSMjBg/Q9coTnz51jc1FRq3w0uueu0+mYP38+27dv\nx8/PjwEDBjBhwgTCw8Mv22zatImzZ8+SmprKwYMHmTNnDgcOHGiVUzIyMjIdHb0e3n/f8PsHD0oZ\nKlUqJeANeDPMQk+JTwUbuhbzhV9Wq8Y3Ku6HDh0iODiYbt26ATB16lQ2bNjQQNx/+eUXpk+fDsCg\nQYMoLS0lPz8fb0NpEWVkZGQ6ORYWsGABnD9v2MbLC265RcpRWO+TgDPgzMGDsKg1TggjrFu3Tsya\nNevy16tWrRLz589vYHPnnXeKvXv3Xv56+PDh4siRIw1sALnJTW5yk1sLWksxunI39+j81Rv+V3+u\nHe7ZysjIyMjUw+gNVT8/P7Kyruz7ZGVl4X9VIuSrbbKzs/EzkbheRkZGRqZtMSru/fv3JzU1lYyM\nDDQaDd9//z0TJkxoYDNhwgRWrlwJwIEDB3BxcZH322VkZGSuM0a3ZZRKJUuXLmX06NHodDpmzpxJ\neHg4y5cvB2D27NmMGzeOTZs2ERwcjL29PV9++WW7OC4jIyMjY4QW79YbYe3atSIiIkJYWFiIo0eP\nGrTbvHmz6NmzpwgODhaLFi1qC1fahKKiIjFixAgREhIiRo4cKUpKSpq0CwwMFFFRUSImJkYMGDCg\nnb1sPuZcj7///e8iODhY9O7dWxw7dqydPWwdpua3c+dO4eTkJGJiYkRMTIx4/fXXr4OXLeORRx4R\nXl5eolevXgZtOuq1MzW3jnzdhBDi/PnzIjY2VkRERIjIyEixZMmSJu2ae/3aRNyTkpJEcnKyiI2N\nNSjuWq1WBAUFifT0dKHRaER0dLRITExsC3euOc8995xYvHixEEKIRYsWieeff75Ju27duomioqL2\ndK3FmHM9fv31VzF27FghhBAHDhwQgwYNuh6utghz5rdz504xfvz46+Rh6/jjjz/EsWPHDApgR752\npubWka+bEEJcuHBBxMfHCyGEqKioEKGhodfkd69NkpKHhYURGtp0ZZE/qR9Dr1KpLsfQdwTqx/ZP\nnz6dn3/+2aCt6CCRQuZcD0NnGjoC5v68dZTrdTVDhgzB1dXV4Psd+dqZmht03OsG4OPjQ0xMDAAO\nDg6Eh4eTm5vbwKYl1++6VZzIyckhoF70vr+/Pzk5OdfLnWZR/5CWt7e3wW+yQqFgxIgR9O/fn88+\n+6w9XWw25lyPpmyys7PbzcfWYM78FAoF+/btIzo6mnHjxpGYmNjebrYZHfnamaIzXbeMjAzi4+MZ\nNGhQg9dbcv1anPJ35MiR5OXlNXp94cKFjB8/3uTnb/Tyc4bm9+abbzb4WqFQGJzL3r178fX1paCg\ngJEjRxIWFsaQIUPaxN/Wcq3ONNyomONn3759ycrKws7Ojs2bNzNx4kRSUlLawbv2oaNeO1N0lutW\nWVnJlClTWLJkCQ5N1BNt7vVrsbhv27atpR8FzIuhv54Ym5+3tzd5eXn4+Phw4cIFvAwUf/X19QXA\n09OTSZMmcejQoRtW3Dv7mQZz5ufo6Hj5+dixY5k7dy7FxcW4mSq70wHoyNfOFJ3hutXV1TF58mSm\nTZvGxIkTG73fkuvX5tsyhvbCzImhv1GZMGECX3/9NQBff/11kxejurqaiooKAKqqqvjtt9+Iiopq\nVz+bQ2c/02DO/PLz8y//vB46dAghRIcSCGN05Gtnio5+3YQQzJw5k4iICJ566qkmbVp0/a7N/d6G\nrF+/Xvj7+wsbGxvh7e0txowZI4QQIicnR4wbN+6y3aZNm0RoaKgICgoSCxcubAtX2oSioiIxfPjw\nRqGQ9ed37tw5ER0dLaKjo0VkZGSHmF9T12PZsmVi2bJll23mzZsngoKCRO/evY2Gud6ImJrf0qVL\nRWRkpIiOjhaDBw8W+/fvv57uNoupU6cKX19foVKphL+/v1ixYkWnuXam5taRr5sQQuzevVsoFAoR\nHR19OZxz06ZNrb5+7VKsQ0ZGRkamfblu0TIyMjIyMm2HLO4yMjIynRBZ3GVkZGQ6IbK4y8jIyHRC\nZHGXkZGR6YTI4i4jIyPTCfl/trUsYsT6bbAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1087eb150>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Extended fit: two Gaussians with polynomial background"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we show how to create and fit a model that is the sum of several other models."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Generate some example data\n",
      "np.random.seed(0)\n",
      "data_peak1 = np.random.randn(3000) * 0.2 + 2\n",
      "data_peak2 = np.random.randn(5000) * 0.1 + 4\n",
      "data_range = (-2, 5)\n",
      "data_bg = probfit.gen_toy(lambda x : 4 + 4 * x + x ** 2, 20000, data_range)\n",
      "data_all = np.concatenate([data_peak1, data_peak2, data_bg])\n",
      "plt.hist((data_peak1, data_peak2, data_bg, data_all),\n",
      "         label=['Signal 1', 'Signal 2', 'Background', 'Total'],\n",
      "         bins=200, histtype='step', range=data_range)\n",
      "plt.legend(loc='upper left');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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2QjSzSp2On3NyOFNSQh9bWwDaq1SsDwy8ar+4khLGnzpljBBbvf+mprI4KQk/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\nG6yBa2Nnx5IpU3gmKQkbGxt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HGlmzJPvo6Gi6du2Kn58fAPfffz9bt241SLLXabVciItD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\nL4QQJkCSvRBCmABJ9kIIYQL+H6ET8pV40zScAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108b725d0>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Using a polynomial to fit a distribution is problematic, because the\n",
      "# polynomial can assume negative values, which results in NaN (not a number)\n",
      "# values in the likelihood function.\n",
      "# To avoid this problem we restrict the fit to the range (0, 5) where\n",
      "# the polynomial is clearly positive.\n",
      "fit_range = (0, 5)\n",
      "normalized_poly = probfit.Normalized(probfit.Polynomial(2), fit_range)\n",
      "normalized_poly = probfit.Extended(normalized_poly, extname='NBkg')\n",
      "\n",
      "gauss1 = probfit.Extended(probfit.rename(probfit.gaussian, ['x', 'mu1', 'sigma1']), extname='N1')\n",
      "gauss2 = probfit.Extended(probfit.rename(probfit.gaussian, ['x', 'mu2', 'sigma2']), extname='N2')\n",
      "\n",
      "# Define an extended PDF consisting of three components\n",
      "pdf = probfit.AddPdf(normalized_poly, gauss1, gauss2)\n",
      "\n",
      "print('normalized_poly: {}'.format(probfit.describe(normalized_poly)))\n",
      "print('gauss1:          {}'.format(probfit.describe(gauss1)))\n",
      "print('gauss2:          {}'.format(probfit.describe(gauss2)))\n",
      "print('pdf:             {}'.format(probfit.describe(pdf)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "normalized_poly: ['x', 'c_0', 'c_1', 'c_2', 'NBkg']\n",
        "gauss1:          ['x', 'mu1', 'sigma1', 'N1']\n",
        "gauss2:          ['x', 'mu2', 'sigma2', 'N2']\n",
        "pdf:             ['x', 'c_0', 'c_1', 'c_2', 'NBkg', 'mu1', 'sigma1', 'N1', 'mu2', 'sigma2', 'N2']\n"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Define the cost function in the usual way ...\n",
      "binned_likelihood = probfit.BinnedLH(pdf, data_all, bins=200, extended=True, bound=fit_range)\n",
      "\n",
      "# This is a quite complex fit (11 free parameters!), so we need good starting values.\n",
      "# Actually we even need to set an initial parameter error\n",
      "# for 'mu1' and 'mu2' to make MIGRAD converge.\n",
      "# The initial parameter error is used as the initial step size in the minimization.\n",
      "pars = dict(mu1=1.9, error_mu1=0.1, sigma1=0.2, N1=3000,\n",
      "            mu2=4.1, error_mu2=0.1, sigma2=0.1, N2=5000,\n",
      "            c_0=4, c_1=4, c_2=1, NBkg=20000)\n",
      "minuit = iminuit.Minuit(binned_likelihood, pedantic=False, print_level=0, **pars)\n",
      "# You can see that the model already roughly matches the data\n",
      "binned_likelihood.draw(minuit, parts=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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KiqJnz550796dGEWEAqhYx+V2uFXsdOPGjUyYMAFQ0jO3b99erkzTpk158MEH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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1087d0fd0>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This can take a while ... the likelihood is evaluated a few 100 times\n",
      "# (and each time the distributions are evaluated, including the\n",
      "# numerical computation of the normalizing integrals)\n",
      "minuit.migrad();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "binned_likelihood.show(minuit, parts=True);\n",
      "minuit.print_fmin()\n",
      "minuit.print_matrix()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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7lH7//n3k5OSgb9++7FxwcDASExMhFouxadMm+Pr6Kv3ynzBhAosvZ2ZmIj4+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       "text": [
        "<matplotlib.figure.Figure at 0x1087eb2d0>"
       ]
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td title=\"Minimum value of function\">FCN = 88.5482069321</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 331</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 331</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 2.50658801602e-06</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 5e-06</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">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\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">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=\"$('#pQMVDPBOiT').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>c_0</td>\n",
        "                <td>4.156499e+00</td>\n",
        "                <td>3.275183e+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>c_1</td>\n",
        "                <td>3.761296e+00</td>\n",
        "                <td>3.028133e+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>c_2</td>\n",
        "                <td>9.724907e-01</td>\n",
        "                <td>7.669779e-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>4</td>\n",
        "                <td>NBkg</td>\n",
        "                <td>1.960121e+04</td>\n",
        "                <td>1.745707e+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>mu1</td>\n",
        "                <td>1.990820e+00</td>\n",
        "                <td>5.813670e-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>6</td>\n",
        "                <td>sigma1</td>\n",
        "                <td>1.921099e-01</td>\n",
        "                <td>5.825608e-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>7</td>\n",
        "                <td>N1</td>\n",
        "                <td>2.923541e+03</td>\n",
        "                <td>9.135432e+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>8</td>\n",
        "                <td>mu2</td>\n",
        "                <td>3.994147e+00</td>\n",
        "                <td>2.123760e-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>9</td>\n",
        "                <td>sigma2</td>\n",
        "                <td>1.001096e-01</td>\n",
        "                <td>1.999429e-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>10</td>\n",
        "                <td>N2</td>\n",
        "                <td>4.969156e+03</td>\n",
        "                <td>9.797528e+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=\"pQMVDPBOiT\" style=\"display:none;\">\n",
        "            <textarea rows=\"26\" 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 & $c_{0}$ & 4.156e+00 & 3.275e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $c_{1}$ & 3.761e+00 & 3.028e+00 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & $c_{2}$ & 9.725e-01 & 7.670e-01 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "4 & NBkg & 1.960e+04 & 1.746e+02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "5 & mu1 & 1.991e+00 & 5.814e-03 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "6 & sigma1 & 1.921e-01 & 5.826e-03 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "7 & N1 & 2.924e+03 & 9.135e+01 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "8 & mu2 & 3.994e+00 & 2.124e-03 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "9 & sigma2 & 1.001e-01 & 1.999e-03 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "10 & N2 & 4.969e+03 & 9.798e+01 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "            <table>\n",
        "                <tr>\n",
        "                    <td><a onclick=\"$('#OKHuWoxfdj').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",
        "            c_0\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_1\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_2\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",
        "            NBkg\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",
        "            mu1\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",
        "            sigma1\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",
        "            N1\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",
        "            mu2\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",
        "            sigma2\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",
        "            N2\n",
        "            </div>\n",
        "            </div>\n",
        "            </td>\n",
        "            \n",
        "                </tr>\n",
        "                \n",
        "            <tr>\n",
        "                <td>c_0</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(253,120,118)\">\n",
        "                0.98\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",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,253,185)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,253,185)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>c_1</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(253,120,118)\">\n",
        "                0.98\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(252,121,119)\">\n",
        "                0.97\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(167,248,183)\">\n",
        "                0.04\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,253,186)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(169,245,182)\">\n",
        "                -0.06\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(171,242,180)\">\n",
        "                -0.09\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,253,185)\">\n",
        "                0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                0.01\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>c_2</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(252,121,119)\">\n",
        "                0.97\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,253,185)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                -0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(165,252,185)\">\n",
        "                0.02\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(165,251,184)\">\n",
        "                0.02\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                -0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,253,186)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>NBkg</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(167,248,183)\">\n",
        "                0.04\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,253,185)\">\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(167,248,183)\">\n",
        "                -0.05\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(189,215,166)\">\n",
        "                -0.28\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(197,204,161)\">\n",
        "                -0.37\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(185,222,170)\">\n",
        "                -0.23\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(190,213,166)\">\n",
        "                -0.30\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>mu1</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,253,186)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                -0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(167,248,183)\">\n",
        "                -0.05\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(170,243,181)\">\n",
        "                0.08\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(169,245,181)\">\n",
        "                0.07\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,252,185)\">\n",
        "                0.02\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>sigma1</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(164,253,185)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(169,245,182)\">\n",
        "                -0.06\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(165,252,185)\">\n",
        "                0.02\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(189,215,166)\">\n",
        "                -0.28\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(170,243,181)\">\n",
        "                0.08\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(209,185,151)\">\n",
        "                0.50\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,252,185)\">\n",
        "                -0.02\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(166,250,184)\">\n",
        "                0.03\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(166,249,183)\">\n",
        "                0.04\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>N1</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(164,253,185)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(171,242,180)\">\n",
        "                -0.09\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(165,251,184)\">\n",
        "                0.02\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(197,204,161)\">\n",
        "                -0.37\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(169,245,181)\">\n",
        "                0.07\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(209,185,151)\">\n",
        "                0.50\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(165,251,185)\">\n",
        "                -0.02\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(166,249,183)\">\n",
        "                0.04\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(167,247,183)\">\n",
        "                0.05\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>mu2</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,253,185)\">\n",
        "                0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                -0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,252,185)\">\n",
        "                -0.02\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(165,251,185)\">\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(166,249,184)\">\n",
        "                0.03\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(166,250,184)\">\n",
        "                0.03\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>sigma2</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(185,222,170)\">\n",
        "                -0.23\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,252,185)\">\n",
        "                0.02\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(166,250,184)\">\n",
        "                0.03\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(166,249,183)\">\n",
        "                0.04\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(166,249,184)\">\n",
        "                0.03\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(255,117,117)\">\n",
        "                1.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(198,202,160)\">\n",
        "                0.38\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>N2</td>\n",
        "            \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(163,253,186)\">\n",
        "                0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(164,253,186)\">\n",
        "                -0.01\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(190,213,166)\">\n",
        "                -0.30\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(165,251,185)\">\n",
        "                0.02\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(166,249,183)\">\n",
        "                0.04\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(167,247,183)\">\n",
        "                0.05\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(166,250,184)\">\n",
        "                0.03\n",
        "                </td>\n",
        "                \n",
        "                <td style=\"background-color:rgb(198,202,160)\">\n",
        "                0.38\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=\"OKHuWoxfdj\" style=\"display:none;\">\n",
        "            <textarea rows=\"29\" cols=\"50\" onclick=\"this.select()\" readonly>%\\usepackage[table]{xcolor} % include this for color\n",
        "%\\usepackage{rotating} % include this for rotate header\n",
        "%\\documentclass[xcolor=table]{beamer} % for beamer\n",
        "\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}\n",
        "\\hline\n",
        "\\rotatebox{90}{} & \\rotatebox{90}{$c_{0}$} & \\rotatebox{90}{$c_{1}$} & \\rotatebox{90}{$c_{2}$} & \\rotatebox{90}{NBkg} & \\rotatebox{90}{mu1} & \\rotatebox{90}{sigma1} & \\rotatebox{90}{N1} & \\rotatebox{90}{mu2} & \\rotatebox{90}{sigma2} & \\rotatebox{90}{N2}\\\\\n",
        "\\hline\n",
        "$c_{0}$ & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{253,120,118} 0.98 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{163,254,186} 0.00 & \\cellcolor[RGB]{163,254,186} 0.00 & \\cellcolor[RGB]{164,253,185} -0.01 & \\cellcolor[RGB]{164,253,185} -0.01 & \\cellcolor[RGB]{163,254,186} 0.00 & \\cellcolor[RGB]{163,253,186} 0.00 & \\cellcolor[RGB]{163,253,186} 0.00\\\\\n",
        "\\hline\n",
        "$c_{1}$ & \\cellcolor[RGB]{253,120,118} 0.98 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{252,121,119} 0.97 & \\cellcolor[RGB]{167,248,183} 0.04 & \\cellcolor[RGB]{164,253,186} -0.01 & \\cellcolor[RGB]{169,245,182} -0.06 & \\cellcolor[RGB]{171,242,180} -0.09 & \\cellcolor[RGB]{164,253,185} 0.01 & \\cellcolor[RGB]{163,253,186} 0.00 & \\cellcolor[RGB]{163,253,186} 0.01\\\\\n",
        "\\hline\n",
        "$c_{2}$ & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{252,121,119} 0.97 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{164,253,185} -0.01 & \\cellcolor[RGB]{163,254,186} -0.00 & \\cellcolor[RGB]{165,252,185} 0.02 & \\cellcolor[RGB]{165,251,184} 0.02 & \\cellcolor[RGB]{163,254,186} -0.00 & \\cellcolor[RGB]{163,253,186} -0.01 & \\cellcolor[RGB]{164,253,186} -0.01\\\\\n",
        "\\hline\n",
        "NBkg & \\cellcolor[RGB]{163,254,186} 0.00 & \\cellcolor[RGB]{167,248,183} 0.04 & \\cellcolor[RGB]{164,253,185} -0.01 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{167,248,183} -0.05 & \\cellcolor[RGB]{189,215,166} -0.28 & \\cellcolor[RGB]{197,204,161} -0.37 & \\cellcolor[RGB]{163,253,186} -0.01 & \\cellcolor[RGB]{185,222,170} -0.23 & \\cellcolor[RGB]{190,213,166} -0.30\\\\\n",
        "\\hline\n",
        "mu1 & \\cellcolor[RGB]{163,254,186} 0.00 & \\cellcolor[RGB]{164,253,186} -0.01 & \\cellcolor[RGB]{163,254,186} -0.00 & \\cellcolor[RGB]{167,248,183} -0.05 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{170,243,181} 0.08 & \\cellcolor[RGB]{169,245,181} 0.07 & \\cellcolor[RGB]{163,254,186} 0.00 & \\cellcolor[RGB]{164,252,185} 0.02 & \\cellcolor[RGB]{165,251,185} 0.02\\\\\n",
        "\\hline\n",
        "sigma1 & \\cellcolor[RGB]{164,253,185} -0.01 & \\cellcolor[RGB]{169,245,182} -0.06 & \\cellcolor[RGB]{165,252,185} 0.02 & \\cellcolor[RGB]{189,215,166} -0.28 & \\cellcolor[RGB]{170,243,181} 0.08 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{209,185,151} 0.50 & \\cellcolor[RGB]{164,252,185} -0.02 & \\cellcolor[RGB]{166,250,184} 0.03 & \\cellcolor[RGB]{166,249,183} 0.04\\\\\n",
        "\\hline\n",
        "N1 & \\cellcolor[RGB]{164,253,185} -0.01 & \\cellcolor[RGB]{171,242,180} -0.09 & \\cellcolor[RGB]{165,251,184} 0.02 & \\cellcolor[RGB]{197,204,161} -0.37 & \\cellcolor[RGB]{169,245,181} 0.07 & \\cellcolor[RGB]{209,185,151} 0.50 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{165,251,185} -0.02 & \\cellcolor[RGB]{166,249,183} 0.04 & \\cellcolor[RGB]{167,247,183} 0.05\\\\\n",
        "\\hline\n",
        "mu2 & \\cellcolor[RGB]{163,254,186} 0.00 & \\cellcolor[RGB]{164,253,185} 0.01 & \\cellcolor[RGB]{163,254,186} -0.00 & \\cellcolor[RGB]{163,253,186} -0.01 & \\cellcolor[RGB]{163,254,186} 0.00 & \\cellcolor[RGB]{164,252,185} -0.02 & \\cellcolor[RGB]{165,251,185} -0.02 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{166,249,184} 0.03 & \\cellcolor[RGB]{166,250,184} 0.03\\\\\n",
        "\\hline\n",
        "sigma2 & \\cellcolor[RGB]{163,253,186} 0.00 & \\cellcolor[RGB]{163,253,186} 0.00 & \\cellcolor[RGB]{163,253,186} -0.01 & \\cellcolor[RGB]{185,222,170} -0.23 & \\cellcolor[RGB]{164,252,185} 0.02 & \\cellcolor[RGB]{166,250,184} 0.03 & \\cellcolor[RGB]{166,249,183} 0.04 & \\cellcolor[RGB]{166,249,184} 0.03 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{198,202,160} 0.38\\\\\n",
        "\\hline\n",
        "N2 & \\cellcolor[RGB]{163,253,186} 0.00 & \\cellcolor[RGB]{163,253,186} 0.01 & \\cellcolor[RGB]{164,253,186} -0.01 & \\cellcolor[RGB]{190,213,166} -0.30 & \\cellcolor[RGB]{165,251,185} 0.02 & \\cellcolor[RGB]{166,249,183} 0.04 & \\cellcolor[RGB]{167,247,183} 0.05 & \\cellcolor[RGB]{166,250,184} 0.03 & \\cellcolor[RGB]{198,202,160} 0.38 & \\cellcolor[RGB]{255,117,117} 1.00\\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note the red upper left corner in the correlation matrix above?\n",
      "\n",
      "It shows that the three polynomial parameters `c_0`, `c_1` and `c_2` are highly correlated?\n",
      "The reason is that we put a constraint on the polynomial to be normalized over the fit range:\n",
      "\n",
      "    fit_range = (0, 5)\n",
      "    normalized_poly = probfit.Normalized(probfit.Polynomial(2), fit_range)\n",
      "    normalized_poly = probfit.Extended(normalized_poly, extname='NBkg')\n",
      "\n",
      "To resolve this problem you could simply use a non-normalized and non-extended polynomial to model the background. We won't do this here, though ..."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Custom Drawing\n",
      "\n",
      "The `draw()` and `show()` method we provide is intended to just give you a quick look at your fit.\n",
      "\n",
      "To make a custom drawing you can use the return value of `draw()` and `show()`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# You should copy & paste the return tuple from the `draw` docstring ...\n",
      "((data_edges, datay), (errorp, errorm), (total_pdf_x, total_pdf_y), parts) = binned_likelihood.draw(minuit, parts=True);\n",
      "# ... now we have everything to make our own plot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Wz1/9vUtKSsKkSZMQHBwMKysrVsfOzg7Xr19HcXExiAjnz59nWjUcHK+KMWOAjRsBa2ug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\nq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       "text": [
        "<matplotlib.figure.Figure at 0x1087d0d90>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now make the plot as pretty as you like, e.g. with matplotlib.\n",
      "plt.figure(figsize=(8, 5))\n",
      "plt.errorbar(probfit.mid(data_edges), datay, errorp, fmt='.', capsize=0, color='Gray', label='Data')\n",
      "plt.plot(total_pdf_x, total_pdf_y, color='blue', lw=2, label='Total Model')\n",
      "colors = ['orange', 'purple', 'DarkGreen']\n",
      "labels = ['Background', 'Signal 1', 'Signal 2']\n",
      "for color, label, part in zip(colors, labels, parts):\n",
      "    x, y = part\n",
      "    plt.plot(x, y, ls='--', color=color, label=label)\n",
      "plt.grid(True)\n",
      "plt.legend(loc='upper left');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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quH2qplvI9Mu2trZ88MEHzJw5EycnJzZt2sS0adNqLHunw4cPs2vXLvbu3YuD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FAAAE\nwElEQVRilOpLWfDVAsa8M6Zd1wloLBKJhI9mf4RcKuezg5+1tTjtnhb/bz18+DARERGEhYUBcPfd\nd7Np0ya6dOnS0qcSaYDk5OS2FgEAi9lCZWElFXkVVORbPyFDQlB61o2XDhkcQv+n+6PucOtUlmkv\n49wQ6nA1A58bWO++3FO5/PjXH1F3UOPewR11BzWuga6ofFW4BrreZEkbpr2PcUuRqkvlWNoxNh3f\nxOYTmxnffTzfPfHdTSlBejPG2EHuwNePfG0XLxmtTYs7en3zzTds27aNjz/+GIAvv/ySX375hffe\ne896QrHOrYiIiIjIbUibJA+5ntIV35RERERERETqp8VtyleGLgGkpaXZVUYpERERERGR1qLFlXJc\nXBwXL14kOTmZ6upq1q5dy4QJE1r6NCIiIiIiInZHiy9fy+Vyli5dypgxYzCZTDzwwAOik5eIiIiI\niEgjuKkZvbZu3crTTz+NyWRi/vz5vPjiizfr1LcN8+bN4/vvv8fHx4eTJ0+2tTh2SVpaGvfddx+5\nublIJBIeeughnnzyybYWy67Q6/XccccdVFVVUV1dzcSJE3n99dfbWiy7xGQyERcXR1BQEFqttq3F\nsUvCwsJwdXVFJpOhUCg4fPhwg21vmlI2mUx07tyZnTt3EhgYSJ8+fVi9erU4i25h9u/fj7OzM/fd\nd5+olFuJ7OxssrOziY2NpaysjN69e7Nx40bxXm5hKioqUCqVGI1GBg8ezFtvvcXgwYPbWiy7Y/Hi\nxRw5coTS0lI2b97c1uLYJeHh4Rw5cqRReTZuWprNK+OXFQqFLX5ZpGUZMmQIavWtE+t7K+Ln50ds\nrLWSmbOzM126dCEzM7ONpbI/akqQVldXYzKZxMRBrUB6ejo//PAD8+fPFyNjWpnGju9NU8oZGRkE\nBwfbvgcFBZGRkXGzTi8i0iokJydz7Ngx+vXr19ai2B1ms5nY2Fh8fX0ZPnw40dHRbS2S3fHMM8/w\n5ptvIpW2SRmE2waJRMKoUaOIi4uz5fBoiJv2lxCThojYG2VlZUydOpUlS5bg7Ozc1uLYHVKplOPH\nj5Oens6+ffvYs2dPW4tkV3z33Xf4+PjQs2dPcZbcyhw8eJBjx46xZcsW3n//ffbv399g25umlMX4\nZRF7wmAwoNFouPfee5k0aVJbi2PXuLm5cdddd/Hbb7+1tSh2xU8//cTmzZsJDw9n5syZ7Nq1i/vu\nu6+txbJL/P39AfD29mby5MnXdPS6aUpZjF8WsRcsFgsPPPAA0dHRPP30020tjl2Sn59vq15UWVnJ\njh076NmzZxtLZV/85z//IS0tjcuXL7NmzRpGjBjBihUr2losu6OiooLS0lIAysvL2b59OzExMQ22\nv2lK+cr45ejoaGbMmCF6q7YCM2fOZODAgVy4cIHg4GA+//zzthbJ7jh48CBffvklu3fvpmfPnvTs\n2ZOtW7e2tVh2RVZWFiNGjCA2NpZ+/fqRkJDAyJEj21osu0Y0MbYOOTk5DBkyxHYvjx8/ntGjRzfY\n/qbGKYuIiIiIiIg0jOhyJyIiIiIi0k4QlbKIiIiIiEg7QVTKIiIiIiIi7QRRKYuIiIiIiLQTRKUs\nIiIiIiLSThCVsoiIiIiISDvh/wMZi4wbPUhmPAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108bdf510>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Simultaneous fit to several data sets\n",
      "\n",
      "Sometimes, what we want to fit is the sum of likelihood /chi^2 of two PDFs for two different datasets that share some parameters.\n",
      "\n",
      "In this example, we will fit two Gaussian distributions where we know that the widths are the same\n",
      "but the peaks are at different places."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Generate some example data\n",
      "np.random.seed(0)\n",
      "data1 = np.random.randn(10000) + 3 # mean =  3, sigma = 1\n",
      "data2 = np.random.randn(10000) - 2 # mean = -2, sigma = 1\n",
      "plt.figure(figsize=(12,4))\n",
      "plt.subplot(121)\n",
      "plt.hist(data1, bins=100, range=(-7, 7), histtype='step', label='data1')\n",
      "plt.legend()\n",
      "plt.subplot(122)\n",
      "plt.hist(data2, bins=100, range=(-7, 7), histtype='step', label='data2')\n",
      "plt.legend();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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fmDdvXjB16tSG+48//ngwbdq0CBO1HWt20yV6zQ4C63ZTWLObLlHr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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108b686d0>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# There is nothing special about built-in cost function\n",
      "# except some utility function like draw and show\n",
      "likelihood1 = probfit.UnbinnedLH(probfit.rename(probfit.gaussian, ('x', 'mean2', 'sigma')), data1)\n",
      "likelihood2 = probfit.UnbinnedLH(probfit.gaussian, data2)\n",
      "simultaneous_likelihood = probfit.SimultaneousFit(likelihood1, likelihood2)\n",
      "print(probfit.describe(likelihood1))\n",
      "print(probfit.describe(likelihood2))\n",
      "# Note that the simultaneous likelihood has only 3 parameters, because the\n",
      "# 'sigma' parameter is tied (i.e. linked to always be the same).\n",
      "print(probfit.describe(simultaneous_likelihood))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['mean2', 'sigma']\n",
        "['mean', 'sigma']\n",
        "['mean2', 'sigma', 'mean']\n"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Ah, the beauty of Minuit ... it doesn't care what your cost funtion is ...\n",
      "# you can use it to fit (i.e. compute optimal parameters and parameter errors) anything.\n",
      "minuit = iminuit.Minuit(simultaneous_likelihood, sigma=0.5, pedantic=False, print_level=0)\n",
      "# Well, there's one thing we have to tell Minuit so that it can compute parameter errors,\n",
      "# and that is the value of `errordef`, a.k.a. `up` (explained above).\n",
      "# This is a likelihood fit, so we need `errordef = 0.5` and not the default `errordef = 1`:\n",
      "minuit.errordef = 0.5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Run the fit and print the results\n",
      "minuit.migrad();\n",
      "minuit.print_fmin()\n",
      "minuit.print_matrix()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td title=\"Minimum value of function\">FCN = 28184.0142876</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 97</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 97</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 2.24660525589e-09</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 5e-06</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">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\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">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=\"$('#iGsKVySUxS').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>mean2</td>\n",
        "                <td>2.981566e+00</td>\n",
        "                <td>9.903099e-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>2</td>\n",
        "                <td>sigma</td>\n",
        "                <td>9.903098e-01</td>\n",
        "                <td>4.951551e-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>mean</td>\n",
        "                <td>-1.989012e+00</td>\n",
        "                <td>9.903099e-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=\"iGsKVySUxS\" 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 & mean2 & 2.982e+00 & 9.903e-03 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & $\\sigma$ & 9.903e-01 & 4.952e-03 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & mean & -1.989e+00 & 9.903e-03 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "            <table>\n",
        "                <tr>\n",
        "                    <td><a onclick=\"$('#ksThmHphBC').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",
        "            mean2\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",
        "            sigma\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",
        "            mean\n",
        "            </div>\n",
        "            </div>\n",
        "            </td>\n",
        "            \n",
        "                </tr>\n",
        "                \n",
        "            <tr>\n",
        "                <td>mean2</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",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>sigma</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",
        "                <td style=\"background-color:rgb(163,254,186)\">\n",
        "                0.00\n",
        "                </td>\n",
        "                \n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>mean</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=\"ksThmHphBC\" style=\"display:none;\">\n",
        "            <textarea rows=\"15\" cols=\"50\" onclick=\"this.select()\" readonly>%\\usepackage[table]{xcolor} % include this for color\n",
        "%\\usepackage{rotating} % include this for rotate header\n",
        "%\\documentclass[xcolor=table]{beamer} % for beamer\n",
        "\\begin{tabular}{|c|c|c|c|}\n",
        "\\hline\n",
        "\\rotatebox{90}{} & \\rotatebox{90}{mean2} & \\rotatebox{90}{$\\sigma$} & \\rotatebox{90}{mean}\\\\\n",
        "\\hline\n",
        "mean2 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{163,254,186} 0.00 & \\cellcolor[RGB]{163,254,186} 0.00\\\\\n",
        "\\hline\n",
        "$\\sigma$ & \\cellcolor[RGB]{163,254,186} 0.00 & \\cellcolor[RGB]{255,117,117} 1.00 & \\cellcolor[RGB]{163,254,186} 0.00\\\\\n",
        "\\hline\n",
        "mean & \\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",
        "            "
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "simultaneous_likelihood.draw(minuit);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
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/sqt6qsXRC3pdD7ZdFSULenXqBDt2KP9NvPUWnDql/L+eqNcFvd58801iY2P5\n6KOPrGb0aqtlsnQpNGsGgwYZKLH+ARcvwn//az+91C7L0bn11luJjY3l0KFDNbbt+lz7Zfp0+Ogj\n+P57yMqynx72kGEpDunUrwf0evDwgAYVr5aloVHviI+HsDCoKIsvIQGOHYPkZMXBa5TFIcMv1ws9\nesDYsZCWBp98oryXlgY9eyr/Ncri6OGX651GjZQRuGmlvmvDL6awZLt2cPiwMvi5XrDUvlS5RqmG\nhoZGeSxdCpGRcMMN15dDrw4OHX5Ra1zXWrLS0uDjjw2sX29Z/LAq1HiM1pbl6KghfqsGHa6VkZCg\nJAzo9TBhwtWRfF3rYU8ZluLQTr2+U1AA6elw8qQWP9S4/pg3D86eVZx4RoaS8ZKcDCVm8muUgxZT\nVzFubpCTA23aKCMVvV6LqVeFFlN3DEynyTSH6s8/ITZWSdU1xdQjI2HrVuXzYcOUwU2DBjB4sNJO\nS2ksH22krmI6doSAAOXhkBY/1KhPfPwxFM+yB5SMlmsr+jZpAjqMhIZCcVUKDQtwaKeu1rhubWWd\nOwe7d4OzM/TpczVPffp0uOsuOH++ZjF2NR2jrWQ5OmqI36pBB3bs4N+HfTDizB+FvWn3d/XKHFtN\nDxXJsBSHdur1lT//hOefV9IYmzW7+n5CAvzxB+TnazF2jfqLbs9uGDwY56SjADQ+uJdOE2/GL6/y\nGiwaClpMXYVs2qQ8JNq0SXHsf/6p/Dfl6Lq6wpkzWkimPLSYumPw0Uewf7/yH+C772DWLEhLyuNo\nk2A8Lh+F++8ncPN8FrnNoHfiMo416ME9/nHcNKSBFlOvBG2k7kAsXQojR4K7u+bQNeofly7BQ3yI\nx+WjnGweAJ99RraTO1/fvJAzeh+88g9xd8Yn9lZT9Ti0U1drXNeashISrso6exZeeAGcanjV1HqM\nWkz9KmqI39pLB1ddIY+i1HR3//g1DNu3A7Dpt8Y85/Q6ABPPvMGEewosHqWr4XxaS4alOLRTry98\n8gmsXVv1fv/7HxSvj6uhUW949VVITIRHuqyiKyco7OaN29jbzJ9nZcHC87dzCH/aF6SgN5RdKF3j\nKlpMXQXMnAn+/sp/qDimPmsWuLjAwoVannpFaDF1x6BkTL1rVyVvfdnpSAYUbKXg7fdwfVy5GTp1\nUrLATpyAx3QLeEce47eW0QQfW33dhSC1mHo9oXVrxegXLoTi1cI0NBya6dPhnXeUX6dZWcoEu4KT\nafQr+JVqGjznAAAgAElEQVQ8GlI0/v5S+w8dCj4+sKrhWIpwIuL8eh6fdN5O2qsfh3bqao3r1kbW\n9Onw1FPK6DwrC1q1MvDcc8pEjdo6dbUcoy1lOTpqiN/aWoeEBCXccvIkeHkpI/E7+AEnhM26YdC8\nuVlGx47K7NIRI+Csc3u2MIQGFPBB5ApVHEtdyrAUh3bq9ZGEBOVnaUaGlouuUT9p0kT537o1XLkC\nvXrBhCYrAVjlfFepfcPCYM0aWLUKOnSAH5qMV2Ss/a5OdXYktJi6CigZUzflojdvDn//fTV1sVEj\npS5Go0ZaTL0ytJi6+snKgiFDlPUCVq+G2284x6JN7THiRGfXdJKz3Tl1CrZsga+/vlr/pVkz6Nk1\ng5iDbdC5uipTq5s2te/B1CFWiann5eURERFBWFgYAQEBPPvsswCcP3/evJr80KFDySoxZ/21117D\nx8cHf39/Nm3aVMvDuH44exZCQ5Vc9Jtugt7hhehXfq7UBZgwgcHGnwGloNGECXZWth6g2bb90OuV\n9UdN5S9Cz23BWYo41GYgWTp3QHkw+vXXV0f1bdsqtZCyXVpxObCPMq1aC9uVT1UrU+fk5IiIsrp5\nRESE/Pbbb/LUU0/J66+/LiIic+fOlVmzZomIyMGDByU0NFTy8/MlKSlJvLy8pKioqEYrYltCTExM\nvZD1r3+J/N//iXh5Kds/r7ooe1sOEQGJURIDREDebzZL/HyNkplZN3o5oqzq2Jcabdsa56K2MupC\nhw8/FJk8WcTNTWRjl6kiIF8FzZWGDUVyc0ViYkRCQ2MkM1PE31/kn/8UCQgQCQsTOTX9/0RATo+e\nqYpjqSsZltpXlSsfNSn+qszPz6eoqAh3d3fWrFnD1uLfRBMnTiQyMpK5c+eyevVqxo0bh6urK56e\nnnh7e7Nr1y769etXSuakSZPwLF6EUK/XExYWRmRkJHD1gUJdb5uwhry4uDiL97/tNgM7doBeH0lR\nETz4YAyDfpnNkPO/Qbt2xA0cCE2bMuDLr/nXxdf55WIOo0bdhcFwfZ6va7fnz59PXFyc2Z6qgxpt\nuzbnwlrXprbtLd1OSzOQmwu+KVsAWO2kp0cPAzqd8vmlS3HExcGoUZHk5kJOjoH8fLgw4B90+PRl\ntqz8gZhpd/Hww5GEhKj3fNa5bVfl9YuKiiQ0NFTc3NzkqaeeEhERvV5v/txoNJq3Z86cKUuWLDF/\nNmXKFFmxYkWNvm2uFwYNMg/EpWlTkTnhy0VALrk0Fzl82LzfWJaJgOTSULJ3Hiol45NPRFJT61hx\nlVId+9Js236YRureTsdEQM6jly4ehebPY2KUe0NE5JlnRB599OpI/c9dBVLUQi8CMv7GZNm40S6H\nUOdYal9VZr84OTkRFxdHSkoKv/76KzExMaU+1+l06EyV7suhss80rsYMO3aETq1ymXz4aQA+83kD\n/PwAJQtmOWNZ5DyZRlyh+X/+XUrGp58qq8JoVA/Ntu3PzaKM0rc3vJmwXs4V7qfTmTMdwcWFgogb\nAQjM2mZrFR0Oi1MaW7Rowa233srevXtp164dp0+fBiAtLY22bdsC4OHhwcmTJ81tUlJS8PDwsLLK\nV7Fm7qe9ZC1dquTq3ncfTMp5n3a5f5PeLoQNHlPNshISlH2fKnqNS07NYN062LvXpno5qqyaoCbb\ntsa5qK2MutDh668VM75JfgXgcr/BNGhQep+srKsy2rZVyk6bKOhrcuq/10oPS1CLDEup1KmfO3fO\n/PQ/NzeXzZs3Ex4eTnR0NIsWLQJg0aJFjBo1CoDo6GiWLVtGfn4+SUlJJCYm0rdvXxsfgmOj18M/\n/gFNGhQyMetdALZEzcWouzpqMY3mndq1ZX+EkrxeOO8dCgrqXN16g2bb9uXMGTh9GvqheOrUrgMs\najd+vLK8o2mkHlSFU78uqSw2s3//fgkPD5fQ0FAJDg6WN954Q0REMjIyZMiQIeLj4yNRUVGSWSId\n49VXXxUvLy/x8/OTDRs21DgudD3xr3+JfDN6pQjIyaa+8vJLRRIVdfXzzEwRJyeRGTNEPpyVLOLk\nJAU6F1nzUYqIiPTqJbJ7t52UVxmW2pdm2/alTx+RNqQrz4mcm8i3SwskI0P5bNo0kdBQEXd3xfaf\neUZkzpzS7dNP5EkuDaUInWxZcb7uD8AOWGpfdW6FmuGXJSBAZFvDwUraot8CefllKeXURUQaNhR5\n4gmRuXNFZPRoEZC/7p4tIppTL4k97UuzbcvJzBSZ02+VCMj+VpGyatXVz0omD4weXYFTTxf5w/VG\nEZC9L/9Up7rbC0vty6HLBKg1rmuJrG+/hfffV167nUtmwJUYcmjCgsyJeHvDa69VIqu4fkCXbUuv\nLstuJb0cXZajo4b4bV3ooNfDE/2V0MuhlqVDL6ZwY+PGBj79VCkPUPxoA1DM/4474LcipV2Lwztq\nrIclqEWGpVSZp65hG1JSlD+A6HyljsVPjORcQQveegt+/rmSxoMHk9WwHfq0xOIHpr1trq+GhrVx\n3qU49SPu/Qkp8f7SpYrTPndOcf6PPFK6XUICbN8OHegDwJmfdtMqS1sNzIyNfzGUwQ5dqpK33hJ5\n/HHl9fHWvUVAxrh+X+pnZ0lKhV9EZK33IyIgm4IeFzc3kQEDpMYzTesT9rQvzbarQWGhGJs2FQG5\nb9iZUuEXkdJ56tcyfLhyj3QlWQTkLK1k9N1GW2tsdyy1L4cOv9QLjh2j27k9XGngxmbnfwBK7ehP\nP6282e9d7gUgLGE5ly4J27drVR01HIjERHQ5OZzQdeFCwzbVarp0KURHwymXLpyhDa3J4H8vJNtG\nTwfEoZ26WuO61ZL1ww8AnOo5khbtG9O+PbzyytWfkiVlPf88PPqo8vpoy75cbulBm/xThBFHQEDV\nXwT14nzVc9QQv60THf78E4D9TuFERSkLwVxLyTz1kuj18Nln0LyFjv0NlNBj0c49NdPDAtQiw1Ic\n2qnXC9atA6DxPbfj7Aw9e4KbW/m7NmmilN6dPh0MW3Ws090KwOhGP/H++1pMUcOBiI0FIM65JzNn\nKnXTq4uTE6R1UuLq2xfstqZ2jo2Nw0BlsEOXquStt0SemZEt4uoq4uQkx3ZniJeXyIgRIj+Vk6E1\naJBIXt7V1yAyktVKSljTCC2lsRh72pdm29Xg5ptFQEY3WlPux5XF1EWUlMY2bUReCFsjAnKw3WCb\nqKkmLLUvbaRuB6ZPhw8/hPPfbYGCAujXD6O+ZaVtDIar9adNKV9J3YYgDRsSmLMLl8yztlVaQ6OW\nnDwJN9+MkgtQPFKf9lHPWsk82kxp3yUzrlrpvfUZh3bqao3rViUrIQGOHYNeZ9crbwwfXi1ZS5cq\nq6w/8WJTdJGROCE037Gx1npVB7XKcnTUEL+1lQ4FBZCcjLKkV2YmtGlD1MSOFcqoKKZekoyGHclp\n3Aq3/ExITbVIj+qiFhmW4tBO3VFRRtrCcKdiR1yJUy8PvR769y9eyWvoUACa7/7FqjpqaNiM4lE6\nPXsq5RfLoVs3ZWnHKtHpSGtTnOW+b5919HN0bBwGKoMdulQdmZkiw/2PK4Fxd3eRoiIxGkWuXKk4\npn4to0eLLF8uIn/+KQKS176LiLH+5+pWhT3tS7Ptyjl2TKRbNxF54QXF9p95psayTDH1qCiRX3s9\npsh79VUREfn7b5G1a62ktIqw1L60kbod0Ovh0VCldveWokHg5IROR5nSo1WRlAT3vxVKtrM7DU+f\nUN7Q0FA52YYSI/Ua0rAh3Hmn8to8Ut+/H4CDB+G992qjoWPj0E5drXFdS2R1Pqrss+bi4FLvv/su\n3HijZbLy8+FYkhN7mhXLuGaRh5roZSlqleXoqCF+a2sd8ncVO/Xw8BrLaNECPv4YWraEb+JDASiK\nLRt+UcP5tJYMS3Fop+6wiNDpmAGArUSW+sjLSzHY6tB9iuLU//5iixWU09CwHfqiDNrkn6KocVPo\n3r3W8k6fhvUnAijEGV1CAuTmWkFLx0ZXHKupuw51Ouq4S/Vx/Dh4eXGpUSv0V85QaKz+d+uYMRAc\nDBs2wLZP/oLgYM416UzrnBM2UNhxsKd9abZdMdOnK9GRZrG/sjl/EBcD+tLs4M5ayx0xAtavh0PO\ngfgXxcPu3aw/25t331Xer09Yal/aSN0e/Kos4XXc4yZEZ4VLEBBAfpMWtL58UkkG1tBQGQkJsHMn\n+OT/BcDl7kFWkbt0KfTpA+c8lBCMlgHj4E5drXHdKmUVL7aY1OGGGsu6444S9TKcnDjr3b+U7Brp\nVQ3UKsvRUUP81hY6mCbMhTgVO/VugVbRQ6+HmTMhtaXysHTfkv3Ex1dPhjX0qAsZluLQTt1hKXa8\nye371VjEuHFK/N3EWZ/ihQa2b6+NZhoaNmHpUiVUEu5q3ZG6iRPuykjdNX4f6elWFe14VJbveOLE\nCYmMjJSAgAAJDAyUBQsWiIjI7NmzxcPDQ8LCwiQsLEzWrVtnbjNnzhzx9vYWPz8/2bhxY41zLest\n2dkiOp0U6Fwk2PuyQM3qoE+bJhIeLqLXK+03P/OzCEiGdx957TWR116zvuqOgKX2pdl23XPsqFEy\nndxFQHatSrWa3EWLRB6+46QIyDmn1tK5s0jr1vVvfQFL7avSvdLS0iQ2NlZERC5evCi+vr4SHx8v\nL730krz11ltl9j948KCEhoZKfn6+JCUliZeXlxQVFdVIsfrGzJnKPCHZvFkEJL5ZnwoXxLCEa9dx\n/ObTC1Kkc5J8XOSFJ3LkxRetfQSOgaX2pdl23ZO8PVUE5Dzu0i/CaDWnu2iRyH3jjZJNMxGQ1pyp\n8X2lZiy1r0rDL+3btyesuCamm5sbPXr0ILW4voKU8xR29erVjBs3DldXVzw9PfH29mbXrl1W/F1R\nGrXGdcuTdeAAZGdjDr0ktOxv/qyyOugV6WWKUbq5Ka//83YzDjqH4EohHqdtV1ta7bIsRa22rYb4\nra10aJCghF4OEMSOnboqF3Wplh46HfEEABBAPC1aKPeVGs6ntWRYisVrlCYnJxMbG0u/fv3Ytm0b\n7733HosXL6Z379689dZb6PV6Tp06Rb9+V+PEnTp1Mt8oJZk0aRKenp4A6PV6wsLCiIyMBK4efF1v\nm7CGvLi4uHLkF2//9BMAt7zYn7s3wIoVBuLiKpYXFxdXbn9Ll0Zy111w6pTS/vDhSH6jPxnEkX1w\nCXgNdOjzZen2/PnziYuLM9tTTVCTbdfmXFjr2tS2fUXbuzevojkQTxD+/nD//QYMhurb/rXbCxdG\ncvgwdMSdPGBwu3hcgwYRF6eO81nntm3JcP7ixYvSq1cv+eGHH0REJD09XYxGoxiNRnn++edl8uTJ\nIiIyc+ZMWbJkibndlClTZOXKlTX6CVHfGDRIJGZLkVLrBeTyoWQpLBRxcqq5zN9/V9YmNa3Z+GDT\nr5Ta0t4jtfCLhWi2XXdk3/2ACMhTTd6XDRusJ9ffX7H/J3lDBOS3ng/LP/5hPflqwVL7qjL7paCg\ngLvuuov77ruPUaNGAdC2bVt0Oh06nY6pU6eaf4Z6eHhwskSedEpKCh4eHtX7lqnHNElJgMxM0p07\ncKZRF6vJXboU+vYFl4FKBkyX1D+02tIWoNl23WIKv1zyDKJZM+vJNQ1kTeEXXXw8BQXWk+9oVOrU\nRYQpU6YQEBDAY489Zn4/LS3N/PqHH34gODgYgOjoaJYtW0Z+fj5JSUkkJibSt29fG6mu3rhuRbKa\nx+8A4M8G/SssOWqprJLo9fDww5Dl3o0MXSvccs+hz/67RrIsRa2yLEWttm2Nc1FbGTbRwWikwVEl\ngTypSdU56tXR45tv4O67rzr1bnnxHDxYPRnW0MPWMiyl0pj6tm3bWLJkCSEhIYQXF9+ZM2cO33zz\nDXFxceh0Orp168Ynn3wCQEBAAGPGjCEgIAAXFxc+/PBDdBY6r+uBZoeVdRRjG0YUm5912blLxy7p\nw3A2cHT5HrJe9tTWLa0AzbbrmJQUnC7nUNiqLdmura0qWq+H5cvBxbkrl2lMR9KI8M0E3K3aj8Ng\n0yBQOdihS7sSHy/SpYtIixYiB5r2FQG5p+0WSU4Wq8XURUS++kqkbVuRl1FqVb/GrHqX0mUJ9rSv\n6822q0PqFxuVwPfAgdK/v8i2bdaVX1SkiD/gGi4C8niElTtQAZbalzaj1Mbk50NGBuRkF+Cdo9Sl\n+KtB7dZlrAhnZ9hDbwAGNNhTaaqkhkZdsv/bw8oLf3+b9aHTQZaH8hu4c84hm/Wjdhzaqas1rnut\nLJ0OAjlII65Q1N2bC05XYyKNG9dcLyenq4tRg1KHPc5Zcep9dHvQtyj9sNRRztf1jBrit7bQodW5\n6jv1muiR2kJx6iO94msswxp62EKGpTi0U3cUunaFwW7KhKC9ut6kp8OkSXDxIly6VHO5/fvDLyWW\nJm3QAC4178iFpu1pfCWbiNbHaqe4hoaVaF3CqU+ZAp0726afU817AOB9Jb6KPesxNg4DlcEOXdqV\nuDiRkBCRVR3+KQIyv9ObtSoPUBFffSUyfrxIy5Yi8V63iYBMavyN9TpwEOxpX9ebbVeHC806KEZ/\n/LhN5BcVieh0Iv++7bDST5cuNunHnlhqX9pIvY7wu6SM1LfmKOGRkJDKywNUl1GjYP585XVKe6WP\n8KLyywVoaNQp2dk0u5hGgUsj6GK9+RnXMno0pLt5UeTsCidO1O5nsAPj0E5drXHda2W5GPPpfklZ\nFNelTziNG8OSJViUbmipXm5u0Lo4UyylQ/lO3VHO1/WMGuK3VtfhyBEAMlr6Kk/zbaCHk5OS1mh0\ncuFiBz/lzcOHVXE+rSXDUhzaqaud6dNh6lRofOwvGkg+51r5UtCkBW3aQPPmtus3tXikHla0F4xG\n23WkoWEBC59W4ul7c/zJyrJtX507w2VPJa5earWM6wkbh4HKYIcu7YapPO40PhEBORA8TkaNUsJ9\nycm26bNlS5FZs0Qym3VSOj90yDYdqRR72tf1ZNvV4asuz4qAvMT/1c3cidmzFdufNasOOqs7LLUv\nbaRuQ0zlcSOc9wKQ1rGXzft0dlaWadyHUlY2Z7u2ZqOGfel2RRmpn9b7183ciR7KSH3ft4f5/HPY\nvLkO+lQRDu3U1RrXNclauhSGDoU+DZQSork9qj/pqLp6nTkDubmw9aIy9X3Tm3E1lmVNvepKlqOj\nhvittXXop1ecul+0f7XKVtRYj+Jc+MZ/H2bVKkOtozBquCbVwaGduto5fhwee7gIn7wDAMxdH8of\nf9g+zN2kCcQVj9Rv6xRXxd4aGjakoADn40cBuNjBt2769PUFnY7uxqM4Ga+/co264lhN3XWo05W7\nskx95IEHoJfbYWa+34PTDbvQ4YpSObFxYzh0SJmUZAuysuCZMcf5eLMXtGsHp0/bpiMVYk/7up5s\n22ISEsDPj/PNuzL/0WRefrmO+u3eHZKSmD06npY39ODRR+uoXxtiqX1pI3Ub0/60MlJOaqGMnPV6\nGDKk9PR+a6PXw+vLPcmmOaSns+en68epa6iMw0roJV1vu5ov5VIcgmmfdbhu+1UBDu3U1RrXLSnL\n5NTDJ4UxYADccAP8+CO0b29jvZyc+Ms5FIDDy+MIC3OM83W9o4b4rVV1MDn1ltV36rXSo/hh6bkT\na2suwxp6WFGGpTi0U1crubnQs/iZqMmpN4oI5cknwdW17vTY7xxeSgcNjTrH5NTd7TNSb3npRN32\nqwZsllRZAXboss7JzhZp1kxk0iSRi27tlJzZY8fk++9FRo2qGx2yskQearRQyY8PGiuhoXXTr72x\np31dD7Zdbfr3FwF5986YOl039/XbfhUB2aXrI76+IpmZdde3rbDUvrSRug1pnnsat0vpyvTRWqx2\nX1MOOCtxfG2krmEXRMwj9TY3+XPDDXXX9bbzSvjFTw6TkCBMn153fdsbh3bqao3r/vabgdxcOLNJ\nmfhTGBiqFKeoY70OOwVQqHOhVUYCjYsuqfZ8aTH1q6ghfms1Hc6cgcxMsmjBR9+3IyKi7vQoaNGa\nc7TiTy7Sq8OpWk16UsM1qQ4O7dTVjAh0yVRGyJvOhtV5/y4u0LxNQw47B6ITofvlA3Wug8Z1TvEo\n/TD+/Pqbrk5Hy0uXwhGUuPq/Rxy+vtbqrSw2c+LECYmMjJSAgAAJDAyUBQsWiIhIRkaG3HLLLeLj\n4yNRUVGSWSJgNWfOHPH29hY/Pz/ZuHFjjeNCjkx2toizs8hS7hEByXnvcxGROo2piyi1Z75gogjI\nDKcP60VcsSostS/NtuuAjz8WAfmCiRIcXPdx7UUNp4qA/HLXe3XbsY2w1L4qHam7urryzjvvcPDg\nQXbs2MEHH3zAoUOHmDt3LlFRUSQkJDBkyBDmzp0LQHx8PMuXLyc+Pp4NGzYwY8YMjNdZlcDp0+HW\nW5WRem8XJfzSpL+SWhgRAbNm1Z0uJWeWhhjjrqu4YlVotl0HFI/UjzfwZ+FCy0pNW5NEp+IMmPTr\nK1e9Uqfevn17wsIUp+Dm5kaPHj1ITU1lzZo1TJw4EYCJEyeyatUqAFavXs24ceNwdXXF09MTb29v\ndu3aZTPl1RjXTUiA33830NB4me6FRzA6OUNgIAAdO0K/fnWn19KlcKiBcv166uK4//6ay7oWNZ77\n6qBW21ZD/NZqOhQ79XOt/GtUarq2eiQ6+2MA3M/Uzqmr4ZpUBxdLd0xOTiY2NpaIiAjS09Np164d\nAO3atSM9PR2AU6dO0a+E1+rUqROpqallZE2aNAnP4mwQvV5PWFgYkZGRwNWDr+ttE7WVl5trAOII\n0TXBWYysadmN5jt21FheXFxcjfXR68G1/0UMW6Ef+8lpXKS682UwGIiLi6tx+/nz5xMXF2e2p5qg\nJtuuzbmw1rWpbfvIyEhWroS8HXE0Qhmp10RebWwfYK9kEwf0Tj9k9/NRk+0a27YlMZqLFy9Kz549\n5YcffhAREb1eX+pzd3d3ERGZOXOmLFmyxPz+lClTZOXKlTWKCzkqmZlK3HxmQ6WGemL/++yqz6RJ\nIuf1nkqu/MGDdtWlLqiufWm2bRse/+dlMep0Is7O0sM7X44cqXsdWrsXSr5zQ8X2s7PrXgErY6l9\nVZn9UlBQwF133cWECRMYNWoUoIxgThcXiUpLS6Nt27YAeHh4cPLkSXPblJQUPDw8qvct4+Do9bBo\nkRLDBjjfpe4zX67ldPtiHeK0fPWSaLZtO9pkJaAT4UJbbwp1dTiNugSj73HmYnulMuT6BUfsooM9\nqNSpiwhTpkwhICCAxx57zPx+dHQ0ixYtAmDRokXmGyI6Opply5aRn59PUlISiYmJ9O3b12bKWzNO\nZU1Zv/1mILhIeUh6vnPtnLo19EovduqGNWtqLcuEWs+9pajVtq1xLmorwxo6ZB77HoDY3JqXB6it\nHh9+CPt9WgHwx8JDdtPDWjIspdKY+rZt21iyZAkhISGEhyt1RF577TWeeeYZxowZw+eff46npyff\nfvstAAEBAYwZM4aAgABcXFz48MMP0el0tj8KtSFGgoyKU9eFhdpZmRIj9aNH7auIitBs27a0vKjU\nXEluXMc1X66lSxcAPPOunwwYrZ66DbgUm4hbT1/w8ICUFLvq8sADEKr/m8fme0KbNpCeDvXYGWn1\n1NXBHt976Z34Da90W0jExw/Qvz80a2YHRb75Bu69l1/0d3Bz5vd2UMB6aPXU7YjT/uLYdZj94+kT\nJ4LX4C5cdNbD2bPErj993a3ZqFG3TJ8ODZKVGPYxV3+GDrWTQwdzCd5ueTUPvzgaDu3U1RrX3bZu\ntfLCCk69tnpFRsLIaB3NbgzFABxeFseHH9ZaLdWee0dHDfHb2rZPPGLkVMFBAH5N97ObHgCG4ofe\nnfOOQkHNlrZTwzWpDg7t1NWKy4ljygsVjNTNFOvSTqvYqGFjOjul0ogrnKENGdKSrCz76TLv/Uac\natAVFwq5EHfcforUJTZKqawQO3RZ93h4KLmxCQn21uQqX3whAvJX8Ng6rT9T19jTvq4L27aAi99v\nEgHZyk0CIqNH20+XQYNE1vEPEZDXB6yynyJWwFL70kbq1ubsWUhNBTc38PKytzZXCVWycNqlaSN1\nDdvilqrE0w/jT9Om1KrsbW1p0kTRA+DhW66PuLpDO3VVxnX37cMAEBJS4xrqJbGaXgEBbMGZVhkJ\nNCzMqbU4VZ77eoAa4re11uHwYQxARht//PxqXsjLGudixgwDuuKl7Rr/XbO0RjVck+rg0E5djcQv\nU/LTTSNjtTD94YYk0xWdCFm//2XXOKdGPce02tENfrhYXF3KNri5wT8eVzJgTHrVd7Q8dSuzP2wC\nIfuWwCefoKZat5GRMHnr/dzPVzzIx2SOfpDieTX1Ci1PXQV07gwpKcyZfJTVf3mxc6d91YnbdIaw\nYe2UZSWzshx2noaWp24n2qepJ0e9JCVrq/dtEGfXOKdGPebiRUhJocCpAeebe9pbGwAK3duQ7ewO\nFy5AcYpjfcahnbrq4rp5ebQ6d5hf0EFQUO3lYb1jXLoUdjVWXg9osq/WCxao7tzXE9QQv61V+4QE\nADa16ohvD2f76VFShk5HcqPicgU1CMGo4ZpUB4d26qojPh5nYyEXmndWhsYqQq8HYzdvADwv7oei\nIjtrpFEvOaJkvjTw6aKqx0rJjYrj6oeugwwYmyVVVoAduqw7Pv9cBGRf4D321qRcBg4UyWreScmh\nt0eB6zrAnvZVr23bAk6cEDk2/kURkMv/fl4OHxb597/trZUyXWT1TW8odv/ww/ZWp8ZYal/aSN2K\nbHlbyXzZmBaq2uyS9A7Fsf59++yriEa9Y/9+SN2ihDeMPko647x5dlYK8PGB6KeU8EtubP3PgHFo\np662uG6rFOUh6XfnnayW+GLNY8zKMpDevvg3cS0XzFDbua8vqCF+W5v2nXIUp2nIuVQrHWqrRxkZ\nxYW9XI5WP/yihmtSHeycRVqPEME7Rxn9Zui92aTS7BLzSF1bBUnD2hQV0TEnEQCjR2c7K3MNnp7k\n69aTghgAACAASURBVBrQ4HSKkqFjt7KRtkfLU7cWycnQrRvnnNvy+uPpvPmmvRUqyzffQHc5SsR4\nH1XUercFWp66/fjl8yRuntqdVDoy6ZZUvvuu5rNJbUFCwyB88w/Cnj3Qq5e91ak2Wp56XVM88s3u\nFkajRnbWpQLGjYOIe7or0+xSU5U6NRoaVsItRQm9HMGPn39W1dw7AI43uD4yYBzaqasqrlvs1E+3\nD+Pvv2spqwRWP0YnJ6UuDdTqYamqzn09Qg3x25q0nz4dYj65WsjL29tQ6wlu1jwX06dDbJ7ysDQv\nrnoPS9VwTaqDQzt1VVHsIM0PItVMmJYBo2FdEhKgRZpppO7PK6+oLPSSAH8VKk49blk9z4CpLN/x\ngQcekLZt20pQUJD5vdmzZ4uHh4eEhYVJWFiYrFu3zvzZnDlzxNvbW/z8/GTjxo21yrV0ODw9RUA+\nnHlQXnjB3spUwSefKDm7991nb02sjqX2pdm2dfH0FIlhkAjIqCYb5fx5e2tUmuHDRcLZKwJS6B9g\nb3VqhKX2VelI/YEHHmDDhg2l3tPpdDzxxBPExsYSGxvL8OHDAYiPj2f58uXEx8ezYcMGZsyYgdFo\ntM03kdrIylIelDZqhJO/L+3a2VuhKgjTMmA027Yu4eEQ4KyEX4661HwJO1uxdCmkufkqG4mJUFho\nX4VsSKVO/aabbsLd3b3M+1LOE9jVq1czbtw4XF1d8fT0xNvbm127dllP03JQS1z36Pf7lRdBQTz4\nLxeCgmou61pscoxBQUps/fBhyMtTj151iFptWw3x25q0b0E2bYtOk+fUmFRdZ37/vXY61FSPimTo\n9dDCw41Mt844FxVAUpJd9KgLapSn/t5777F48WJ69+7NW2+9hV6v59SpU/Tr18+8T6dOnUhNTS23\n/aRJk/D09ARAr9cTFhZGZGQkcPXg63rbRE3aH162Em8gWR9G4mYDBw/GWU2/uOLRtNWP39dXWcxg\n8WLw9a3T83Xtdlxczc/X/PnziYuLM9tTbbG3bdfmXFjr2tSkfacL8RiAU408EJ1Trfq3pu2XPJ+X\nLxtY06QdEy+dVGy/+BrWpa1XZ7vGtl1VfCYpKalU3DE9PV2MRqMYjUZ5/vnnZfLkySIiMnPmTFmy\nZIl5vylTpsjKlStrHBdyJBIHPiAC8nST9+TsWXtrYxnZI+5R4uqff25vVaxKdexLs23r8XGfz0RA\ntnQcL3q9qC6mLiLi5ydiCH1EsfvXX7e3OtXGUvuqdvZL27Zt0el06HQ6pk6dav4Z6uHhwcmTJ837\npaSk4OHhUV3xDkdREbifULJI/nJRVw31yvhgm5YBcy2abdeM6dPB+Fc8AElNAklJUVfmS0nS3a+W\n4C0qgvo4V6zaTj0tLc38+ocffiA4OBiA6Oholi1bRn5+PklJSSQmJtK3b1/raVoO1oxT1VTWDX0L\naHHyLwAOOofUSlZ52EpWvGvtasCo9Rhrgxps2xrnorYyqts+IQG65x4EIK4gkKZNYevW2ulQEz2q\nknH6NHz3lzIBqfDgYQYNgm3b6l4PW1NpTH3cuHFs3bqVc+fO0blzZ/7zn/+Y41Q6nY5u3brxySef\nABAQEMCYMWMICAjAxcWFDz/8EJ2DLhtVHRocP4xLUT7Jzt25QHN7q2Mx8Q2UkXrern00EnHYJb5q\nimbb1qNJEwhEceqv/hBoZ20qxtcXfttdPAEp9hD0EaD+XUet9ksNyc+H7Gx4scuXfJz3ACu4izG6\nFdx8M6xYod6fnya6dIGD59vTLCddyQSw0oNGe6PVfql7sv/OooWnO4WujXDJvQTOtVvxyFaMGAHr\n1wtZOndaSDYj+5xm1tvtuPFGe2tmGVrtFxuzZw/cfjuEF+0FINapFyKwZYv6al5cy/TpcOYMxBqL\n4+qxsfZVSMOhaZGqxNMvePRQrUMHJVc9KEjHuTZKCKZrbv2cWerQTt3ecd1jxyC02KknNlOqvoWF\nwf3321evqmQlJMCVK7A9V3Hqb95bfadu73NfX1FD/Lba7eOLnXqnq6EXNRzHtTL0ehg7Fs60VEIw\nLkcP8eSTVLmgjVqOxVIc2qnbm4LcQkKMyoPGQ0160aCBEnpxc7OzYlVgWj71mL43AEF5e+yojYbD\nc1CJp2d3Um88vSSmDBjPvMPs3Kn+X9bVxmZJlRVghy5twrZtIv2bHRAB+dvZU0aPFmnVShwiTz0z\nU8TZWaR/++MiIOm0ETEa7a2WVbCnfdUX2642UVEiIFufXG1vTarklVdEPh6xWgRkPcPE31+5HxwB\nS+1LW/moBkyfrsTUIwqV0Eu2tzJKdxT0enBxgT9Oe5JBS9pyVlkwo7PKVqvRcAyKR+ot+qt/pL56\nNTQ84c+DQKDTId5+W/1JDdXFocMv9orrxsYqfz1yFad+pnPpVVTUGm8uKUvJyNOxV6eEYHK2Vi8E\no9ZjdHTUEL+tVvusLDh1Cho3JnRUN6vpYCsZly/DjjPdyceVzsYTtHDJsYsetsShnbq9MK0C18dJ\nceqXfHuZ1rV1GFq1UmLru0Vx6ute2cOl2q8VrHGdUXRAeUhKjx5KkTiV07UrFOHCUZ0PAI1PJthZ\nIxtg4zBQGezQpdUZNUokOKBQLjs1EQHZu/mciIgMHKjOmhfl0bmziJeXyB2sFAH5y2OovPeevbWq\nPfa0r/pg29Wl8KNPlVoqEybYWxWLyMwUufVWkbWN7xQBOfx/X9tbJYux1L7U/9WqQho0gIcGH6ax\n8TKnGnSlqEUrALZuhXKquaqSDh3gzjshw1MZqXfN2Fs/C2Fo2BRdvBJPJyDAvopYiF4P778PiQ2U\n+H+T43/ZWSPr49BO3Z5x3Q6nruanX/urU63x5pKydu6Edu2g5+2dOUMb3PIyaHb+b7vrdb2jhvht\ntdqbnHpg6YekajiOymQcdlXqNDU9vt+uetgCh3bq9mD6dIiJgexfFKc+6Ile9OpVRSM1o9PxJ8oB\ntD2h5atrWM706ZD5mzLSvdBZ/ZkvJTncQHHqLU9W7dQdDa32SzWJ/P/2zjw+pnv94+/JQhZibIlI\nEEVkT+yqVUFCKaqlVKu1Jr3uz7232t6WtrrQai7tbavaXnVxdQnV1lKKqhJL0dQSS7WGCiIiiCQi\nwcjk+f1xJKUlZs2ZifN+vfKSGfk+53POPPPMmc/5nucbr9gsW7iLu9gKa9ZA795qy7IKk0lZ1Wu6\n12Qm8xpz6j/HQ4dTXHqKl9b7peoY2OU0y7YFcJ7aJA0u4PMvXOMc8ehR6NHNxJEzteHiRcjLg3r1\n1JZ1S7TeLw7CxwfcMNFGd7VlrQufpru7Kz87UHz1kLyd1e/uOg2HEXha6cW/lxhmz3GdUtKkCfy4\nw11Z1hFg3z51BdkZ13klboAavm5qKnQPOICPlCjzoxo0cApd1sbS6eB4A+WDqaNuBx/NNu9M01n3\n0dVxBv/W3PGPxZQX9dg/fbtzhv24WQx3d2jYEIhRLBj2Vm7BOMu+mItLF3U10OvhoSY/Kg86dVJX\njB1wd4cNhiDOuAdQRwrQnzuitiQNFyHg1NWirotVWYmVXC3qpoy9TJ2qshY7onnqFnDqFPTsCTMv\njqVn5lz4979hwgS1ZdmF/SH9iDr2jfJVZNgwteVYjeapVw1paRD1aAwNTu6ji9t2tppc8AQnLQ26\nd8fUviN+B36k+NY3l6qK5qk7ABE4dw5anas+Z+rlZDbqDMCaV7errETDFRj1yGXqnPyFMnTskyi1\n5VjH1TN1twP7cROTymLsh0sXdTV8Xd+yIoILf6bMwxPatHEaXbbGygy4E4DAo9tsjmUpmqf+O87g\n35ozPvjCL3hSyiFacUF87a6hSmLUqwfBwehKSmguN7cdnWVfzMWli7oaxF35CTcEt7hY8PZWW47d\nOB7QgTJ0RFzezZqlFzlm/n1IGrch0WWKn/5LjVheeEFlMTawV6ecrbe+vPeWi2W4DJX1EBg1apT4\n+/tLVFRUxXN5eXmSkJAgrVq1ksTERMm/phnxtGnTpGXLltK6dWv59ttvbepf4GwkJYnceafIC+7T\nlF4X48erLcmulJaKXAyNFgF5qvMW+eYbtRVZh7n5peW29SQlicyqMUEE5LPI19SWYxP/C5okAjKV\nF+Shh9RWUznm5lelZ+qjRo1izZo11z2XkpJCYmIiBoOBnj17kpKSAsCBAwf4/PPPOXDgAGvWrOGv\nf/0rZWVljvkkUgGDAbZtgw6mq55zNfLTAcaNg9X5iq8emlf9fXUtt63HYIAwo3Kmvu60i858uUqG\nri0A7djJRx+pLMZOVFrUu3btSt0/dKj6+uuvGTFiBAAjRoxg2bJlACxfvpxhw4bh6elJSEgILVu2\nJD093UGyFarS11WWgBM6cfUiaefOVseyhKqKZTDA12cUXz3o+K19dWfdR3Nx1tx2Bv/2lu8FbyEO\n5eY73ztjHKKhqmJEjVDu0WjHTvR1bjyzxFn2xVwsXvkoNzeXgIAAAAICAsjNzQXg5MmTdL6m0AUH\nB5OdnX3DGCNHjiQkJAQAvV5PXFwc8fHxwO87X9WPy7nZ/6emxvPM4GP8+n0uv/r5Ed+ixU3/PiMj\nw276MjIyqmT/fXzi2U5n0oBLlzfyr5ehSxfIyLjx35djj+3bcrzeeecdMjIyKvLJFpwht+2RO+U4\nYnx+PsjRZtTnHKtq1OHQpd+ApjZtz5G5f6vjeaBEuFCjLv7GM6R98QX4+zs01y15bHVu38qfyczM\nvM531Ov11/1/3bp1RURk/Pjx8umnn1Y8P2bMGPnqq6+s9oWckXOzPlX89PvuU1uK3cnPF+l7r0nO\noRcBCea403uMN8KS/NJy23JOnhQZq18sAmJo1UdeekltRdaTlCQSGiqysWaC8r5eulRtSZVibn5Z\nPPslICCAU6dOAZCTk4O/vz8AQUFBZGVlVfzdiRMnCAoKsjS8U/L88/D113Bu+Wblia5d1RXkAPR6\neGemGzvclGsFDzbaVm08RnO5HXPbGmKvKN083Tu159VXVRZjAwaD8rP18tX+Tbt2qSvITlhc1AcM\nGMCCBQsAWLBgAQMHDqx4ftGiRRiNRjIzMzl06BAdO3a0r9o/YE+fqrJYb78NmzZBrd3mFfWq0uWI\nWPv87gJgfNyWSrs1Ous+2oIz5LY9joWtMW42PjkZBg2CiBKlqJ8Pbe8wDVURQ7lOBodqKxdL2blT\nFR32plJPfdiwYWzcuJGzZ8/SpEkTpkyZwsSJExkyZAhz584lJCSExYsXAxAREcGQIUOIiIjAw8OD\nDz74AJ2yunG1wOdiHgFnD1Dq6YVH+5sns6uzw/ceKICAg5vUluJQtNy2HIMBtm8roy1K8ausqLsC\nqamQkAD1mrWDJVC0cSe70oRu8a792mq9X8zA2xs+6L2cUcsHQrduSs+IasjZs5A69yLjJunx4Apy\nJg+3+i6yPt9VtN4vjqNvXzi82oCB1pQFNsbt5I0vFrsSs2fDrp3C7C/qQUEBq+ecoM9Y57TWtN4v\ndiI5GS5dgsKVivVyqUP189PLadAA/v6cNwf1HdGJ8ErCFrUlaTgRqamQ3EaxXtw6uPZZ+nXodNBW\nsWD0v93YgnElXLqoV4WvazAo/95pUor6mz/euqg7q99sbqyiNt0AiM6/uQXjrPvo6jiDf3uz8UuW\nQJcaV5c8vIUF6Qz7YUmMNWeV/dk//6c/tQtwln0xF5cu6lWBjw/4UExbdmHCjfGf3am2JIdz53P3\nABBdUL19dQ3LyMuDhsfNK+quQtOmSrPGLSblPoTmudtcfvUvzVO/BQUFMLju96wjgezAdgSdrP6L\nM/9tZBFvL1C89AvH8zlypjanT8O996oszAw0T91xvJlSyvjJerxKiyE3F65O+awOPNrzFJ+tD+SC\nrhalZwrQ13dXW9Kf0Dx1O6HXQ4L7BgCyW9yjspqqYd/R2uyiLR6YmPXoNtLT4eod8xq3KcnJsHnW\nHrxKiznh1aJaFXSA979qxDH35tSSC7z3xH615diESxf1qvJ1u5etA+Boy0SbY1mKGrF8fGAjiq8e\nW7iR/Hzn0HU74Az+7Y3GGwzQLPsHAA4H3OVwDVUdQ6+Ho4GKter/2/W9j5xlX8zFpYt6lZCfT3v5\niVI3T86EVd+ZL9eSmgpnIrsDEPjzOmbPhlWrqD79pjUsxscH7kIp6h2fvHVRd0UO1lOKeug58xaK\ncVY0T70SFiyA0i+WMuabB+Gee2DjRrUlVRnrlhfT7YG6uEspDThLPvV46CG4ej+O06J56o7hzs7C\n0p1NaFSaDfv3Q2Sk2pLszn2Bu/jmVDuOuLdky7xDPPIIeFjc8tBxaJ66Hbh8Ge7IVKwXEhLUFVPF\nmLx8Sfe8GzeEnnxPgwbcdr1gNH6nTuFxGpVmU1JTD+HhastxCMf1MRTjwx2mw/xz5BmMRrUVWYdL\nF/Wq8HXDsy0v6s7qN1saa517LwB6sRZfX67rBeOs++jqOIN/e6PxccWK9XKscRdwu3XZcIb9sDRG\nk+YepKP09Lnb4/eFYpxlX8zFpYu6I0lOhtSU4zQqNCC1a0OHDmpLqlLi4yEnSinqfdzX0j2+etoK\nGpWzf79yp/EdOeZfJHVVUlPheJDiq3e6soUHHnDR60h2bfhrBips0iq6dRMZxVwRkPTGA9SWowr5\neSY5o2sgAvLS0F/VlmMWauaXq+S2JezdK+LrK7KbWBGQSV3S1JbkUDa9+K0IyI90EBCnWlPA3PzS\nztRvgo8P9GE1AJFPmjeVsbqhr+fG9trKvoefWIuykoDKojSqnLrkE80+rug8efaL6v2NNS/sLox4\n0o6d3BNT4JLXkVy6qDvS102df5m+bsrCxD4P9bMpli2oHWurj1LU+7mvwcuLiotHauuqrjiDf1s+\nPjkZRo+GrqUbcKeMktgu6Bv7VIkGtWLUaexLVuPOuFPGkgmb0OudZ1/MxaWLuiM5tyQN37ILZNWL\nATusf+mqZEX1QXQ6av34Pb5yQW05GlWIwQA7dkDXy98BUHJ39f/G2r07tEjqAYDfjvUqq7EObZ76\nTfis3ngezX+flW1epN+uqWrLUZcuXWDbNgbzJRd6D2LRIipdFUlNtHnq9qNvX1i9Gg7pWtFSDnPo\nk+20Gt5JbVmOZ9Mm6NaNsqho3PbtVVtNBdo8dVsQoXvR1wAEJvVXWYwTcHVZt/tZxrff4vJd7DTM\nIzUVHut6lJZyGKNPHeomtFNbUtXQqRMX8cZt/z44fVptNRbj0kXdUb7ulMF7aFyaRZ5nI1oMtbzF\nqLP6zVbHulrU+7GSjm2v8NFHTqKrGuIM/m35eL0epvX8HoAavbrToJH5t1c6w35YHaNmTbwT71Z+\n37DBafbFXFy6qDuK4J3KWfpXV/qT/BftEBEaiql1OHUpYM2kTU5rvWjYn1rbFT+dxOrvp19HD8VX\nZ+1adXVYgdWeekhICH5+fri7u+Pp6Ul6ejrnzp1j6NChHDt2rGLhXv0fKoAr+I5H/GK5o2gv/9d0\nBa/v6acVMYDnn4c33sA0bjzuH7yntpqbYo/8qs65bRFlZUhAALqzZ+HgQQgNVVtR1bFvn7J6hr8/\n5OSYdReto3G4p67T6UhLS2P37t2kp6cDkJKSQmJiIgaDgZ49e5KSkmJtePXYv587ivZyjro8/mkv\nraCXc9WCcVu+BEwmlcU4lmqb2xby6d/TlYIeEgKtWqktp2qJioJmzRRP/WoOuAo2ffz88VPj66+/\nZsSIEQCMGDGCZQ5eWcEhvu7ChQCs0w+mdv0atsWyA04Tq317Ttdqju7kSUhLcx5dDkKt3HYG/7Z8\nfOOrNiQDBiiLM1ehBtVj6HTsbzFAiTFrlno6rMDqxpI6nY6EhATc3d154oknSEpKIjc3l4CAAAAC\nAgLIzc294diRI0cScnXut16vJy4ujvj4eOD3na/qxwCIkDZvHgDf+D1ClJXxMjIy7KYvIyNDleNx\no8f+E4aTNnUqBS/OQP/GRLvFt+V4vfPOO2RkZFTkkz1QM7ftkTvl2DI+ORm6/pRKGtC+xwBq2RBP\nzdy35Xh+UNyMIQBbt1bJ/tott63tQ3Dy5EkRETl9+rTExsbKpk2bRK/XX/c3devW/dM4GzbpeLZu\nVe6EDwqSvRkmKSlRW5CTYTCIgFyuUUukuFhtNTfEHvlVLXPbQh7peEgEJJ868vAgo9pyqpykJJGm\njS7LeTc/pSYcOaK2JMf3fgkMDASgYcOGPPDAA6SnpxMQEMCpU6cAyMnJwd/V1jFMTQVge/NhRMe6\n4e2tsh4nI3lGK3bV6EQN4wWKU5erLcdhVMvctpAehUsBSPPuy4f/9VRZTdVjMMDxUzVYVaastv7l\n419z9qzKoszEqqJeUlJCUVERAMXFxaxdu5bo6GgGDBjAggULAFiwYAEDr15ccxR//LppU6zvvuPy\nAsVPX2AcZlssJ/WbbY1lMMBc42MAfPbcO3ZQpGDPfbQVtXPbHsfC1hhpaWmM8FoEwKX+D1k1WcAZ\n9sOWGD5XW9z80OB+0oCm6V9SUlL1OqzBqqKem5tL165diYuLo1OnTvTr149evXoxceJEvvvuO0JD\nQ1m/fj0TJ060t17HsXEjNYvy+LlGHF8cbuOafZQdjI8PfM5QjHjS4twOXh6dxZo1aquyL9Uyty3l\nxAk89uziomdtTkT3UVuNKqSmQvPm4Du0P5epQUfjFp4desw16oKDbaA/ocImzeJgo3tEQJ7gQ6fr\no+ws5OeLtG4tsqv1wyIgX4VNksWL1VZ1PWrml7PmtiWUlopceWWqCMiOqMfl/ffVVqQeSUkiTz8t\n8qXnUBGQZ0lRtS6Ym19aQy+AAwcgMpIL+NKYk1z08OPMGedtWqUmsbFwR+42luZ2IU9Xn/uis1iz\n0dtpjpXW0Ms21n8vNO0dRkuTgYkxq5i4sY/TvLZVTXQ0nD8PbU6sYFnZAAxe0fif3IO+rmXTO+3F\nbdHQyx4+1dGjUPLGO6QBnzKcK15+dOpkW0F3Jh/c3rEKC2FZbmf+Q2vqSx7hexfZ3ODLmTx1tVHT\nR05Ohk+e2MIJk4FsGvPm3kSrX1tX99QB6teH48dhZZkXZ6lP6KV9XNm2o8p1WIpLF3V78NHUXGos\n+hgBPms4gQkT0Ga9VEJEBICOJTwIwDM1ZvLRbNc+O9VQMBgg/rf/AjCfUUTFerjkyj/2ovxiaQ1v\nT76qpdx4duJl5YBMnQrHjqmlrHJuW/ulrAzc3eHt2pN5sug11vrcz8zuy7j/fvDygsceU1uhc1JQ\noByb3dsvs/tcUxqWnYbvvoOEBLWlAZr9YgtDEvP537ogfLhIWI3fWJpxB+HhaqtSj4ICeOABuHwZ\nmpQc5PM9YRTrfLm7+Unc9H589BG0q8JuxLeF/WItyclKE7a6nGNU0UwAXrv4DPfdB127agW9MvR6\nmD0byjxrsjbiSeXJV1/VFi91UQoL4cQJ5fcFd32EDxf5Ud+L8/XvoE4ddbWpjV6vpLaHB+TqW5NG\nN3ylmHuO/I+DB+Hvf8cpZ8O4dFG31qcyGGDjRniat6jDeb4jgc1SyoYNEBamni5XiRUYCPPnpzF4\nw3hM+nqwZQtfjvuerCx1dVUHqtpHXrMGnn4aMBrxnqOc4LxdL4HfflNe56rQ4Mwx7roLJk9Ow8cH\n3uUfADzJ21wqLmXrVvMXjNE8dQfj4wP+5PIP3gVgMlPx9OS29g8tQaeDmjXhb8/X5n3vZwBoPmcS\nDw4sY71rLut4W5KcDFOmKKu3/fC3RXDyJGcDIvDv2x5vb4t7eFVL3N3B01OZt/69zwAMtKI5RxnM\nl0REOGnNcMB0ykpRYZPXkZsrcvasyPcho0RAMpr2l/BwkZAQVWW5JN26ifhwQU7QWATkMRaIl5cy\nn10t1MwvtXPbUrp1U9qaeGAUAy1FQCYFzlf19XNmnnpK5MWG/xEB+dktUjasK63S7ZubX7fdmXqH\nDrB//k/0ODofI57csfQtFi5UPpE1LMPHB0rw5Xmd0lv8DSbheem8toapi1A+u2OU28e04jAGWjE9\nZ7j2+t2El1+GgctGku3ZjIiyn4nck6q2pBvj4A+XP2HPTW7YsKHS/79wQWTnzt8fJyWJ1NRdlj26\nGBGQ06OeFRERo1Hkiy8qj2VPXdUlVn6+SPfuIu3bmmQ7nURA5tX8i8VnevbUpUJK23Xb9jgW5sbY\ntk2ka5siyXYLEgF5mFRp2lRkxYqq0+BqMYxGkXPv/E/5ihMSIua2crWHDnPzq9qeqScnQ3w8dOv2\n+xVqgwGel9eIkb38xh08VfASoHhmDRqop9VV0esVT7amtxv/aj2XKzpPRl3+D/pdmrHu7CQnQ79+\nMPzYazQuy+Yn2rMteAj//CfUqqW2OufF0xPqjh+urIx09Ci7H07hH/9QW9UfsPnjw0KqapPlfuG1\nfVye7bhBSnETAelfZ6PmHdqBkhKREydEsrJEdgycIgJykkYyuOspyc0VWbRI+bu8PJGVKx2vR4WU\ndoptm8uvv4r8+KPy/ohir1zGU0zo5E73H+X990XS0tRW6Br8q98mEZBL1JBI91+qpJaYm1/Vtqj3\n6aMUdG/vqxfusrPF5B8gAjK73nMyblyVyLitKMy7Irv8lMZo6+ghNd2M4u4uMmKESLt2IrVrO/4i\nqlbUb05Skoifn0jNmiK1PC7KHqKVwjR6nBw6JFJWprZC16FbN5G5KJMtfqKd1PG+LF26ODa/b4ui\nXplPlZ8v0quXSESEiBQUiMTGioCU3NldViy9Ip98Yn4se+qq7rEe7Z4tp/AXAZnLKIEyadjwz9+a\nHKXL1Yu6I33ka7+9vs84EZDzAS2Vi09VpKG6xOjTR8SPAjlCiAjIW0yotLur5qnbAb0e/vtfGNKn\nCENYf9izh988QjF+sph+Az0YPlxthdWTWUsa05+VlODNaObzNk/ROlS529THx0nn9d4mlM92GccH\n/JUPKfWoyck3U8HXV11hLkhqKjSNqsPTjVK5ggdP8TaTGs5h8mTlekWzZkoLAVXuOLX548NCj3an\nRgAADUZJREFUqnSTp0+LdOwoAnKCxtKMTK1PehWQnCwytvFKuYynCMgnXmOkru9l5VuTg1EhpZ1i\n2zcjKUmkSxeR0FCRo0dFJgV/LCZ0yun6xx+rLc+lKShQSkzWy3NEQK7gLsn6z+Wuuyr/Zmot5uZX\ntSzqSUkiff3TJduzqQhIjneINOc30evVvTHmdmLlSpGtk1fJJV1NEZAtdJERvbIdvl2tqF/PtZbL\ns94zKyYKrLhrmtrSqhXv1Z0sAlKKmzzh9pGASLNmyvUkX1+Rnj1trz23RVG/1qdKShKpW1ekgW+J\nzHB/tiJ5D9brLAUHsqVLF5G+fc2LZU9dt3usv9+ZLscJFgEx1asvMm+eiMnkMF2uXtTt7QH36SNS\ni/MVF/UEJDXq9SrVcDvE6HNvmbzMyxXHeDZJ0jW28JoP1Q02n7Wbm18u66kr82wz0OuhSxc4tP8y\nQ/M/ZGdxa54xTQfgv34TmN43jYTHG+PtDfPm3TxeRkaG3bRpsX7n1VUd+Ev7nZxrn4jbuTwYPZqf\n693NvxNW0aC+oNfD6NEZTtntTg3s8RqVx3giqYx2v37GAbcoRjOfS9Tk2caf0Gfz81Wm4XaJkbpQ\nx9bEV3gpeB6XqUEyc9hwNoqE80vQUUZQUEaVXU9ySFFfs2YNYWFhtGrVin/96192jV1+U9FXX0Fx\ncQHNC3czaNvTLNzWjA/5K03JYg8xPBG1lfBV/+ZwVk127IDvv4e//e3mcQvsWFW0WL+j18M3P/lT\nL/1bXgv7lFz8iSzcxlPf30fauWiSCmeQl3mM4GDnbGN6LY7M63JseY2Sk5XOiq9MyuXd8A/5Z2oc\nUzOH06TsOL/VaUPv+jvp8+nwW67qZY88ud1i6PWwdi08tW8UXTx+orRNe9yzs3hx9yAOesXShg08\n2q+Qvn1h5EiIi1NalhQU/F7T+va1z3vAw/YQ12MymRg/fjzr1q0jKCiIDh06MGDAAMIr6bafnKzc\n7enjo1xVLk+6Pz1vysMr/Wci9+zjL2xmJyuYwasVcfYQw7u1J9N5+oP89y/K51X5Ff/WrbWZF6qi\n07G1+aNM/7U/TzCbCbxNFD8zg2fxBR4uXs2X9bux0/tuMn0iOahrTeEVX2JiICREWXbwlvnhwLU0\nrcnrG2Gp5uRkWLECjEZo2xaCgn4/Fv7+8O234HWpgAdbZBCTt4PBp9a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       "text": [
        "<matplotlib.figure.Figure at 0x1087dfe50>"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Blinding parameters\n",
      "\n",
      "Often, an analyst would like to avoid looking at the result of the fitted parameter(s) before he/she finalized the analysis in order to avoid biases due to the prejudice of the analyst. Probfit provids a transformation function that hides the true value(s) of the parameter(s). The transformation function requires a string to set the seed of the random number generator, and a scale to smear the parameter(s) using a Gaussian."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from probfit import UnbinnedLH, BlindFunc, rename, AddPdfNorm\n",
      "from probfit import gaussian\n",
      "from iminuit import Minuit, describe\n",
      "from probfit import gen_toy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g0= rename(gaussian, ['x', 'm0', 's0'])\n",
      "g1= rename(gaussian, ['x', 'm1', 's1'])\n",
      "pdf= AddPdfNorm(g0,g1)\n",
      "describe(pdf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "['x', 'm0', 's0', 'm1', 's1', 'f_0']"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "seed(0)\n",
      "toydata = gen_toy(pdf, 1000,(-10,10), m0=-2, m1=2, s0=1, s1=1, f_0=0.3, quiet=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['x', 'm0', 's0', 'm1', 's1', 'f_0']\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nHfmzoolo6apvTUiQvLtPHF3Pj9/L3wAahyeQzFtUXGjj5+1Hjd71glzgWtrc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\nxMeRjlwikUh8nP8HwqbGIqhtpzYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11187fbd0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inipars= dict(m0=0, m1=0, s0=1, s1=1, f_0=0.5, error_m0=0.1, error_m1=0.1, error_s0=0.1, error_s1=0.1, error_f_0=0.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Normal fit\n",
      "uh1= UnbinnedLH(pdf, toydata)\n",
      "m1= Minuit(uh1, print_level=1, **inipars)\n",
      "m1.migrad();\n",
      "uh1.draw();\n",
      "print m1.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td title=\"Minimum value of function\">FCN = 1998.19075776</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 215</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 215</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 2.22895489152e-08</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 5e-06</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">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\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">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",
        "        "
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#uVfwJieDbF').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>m0</td>\n",
        "                <td>1.972029e+00</td>\n",
        "                <td>4.773567e-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>s0</td>\n",
        "                <td>1.053063e+00</td>\n",
        "                <td>3.721995e-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>3</td>\n",
        "                <td>m1</td>\n",
        "                <td>-1.994212e+00</td>\n",
        "                <td>7.552495e-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>s1</td>\n",
        "                <td>9.767748e-01</td>\n",
        "                <td>5.688912e-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>6.993030e-01</td>\n",
        "                <td>1.673062e-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=\"uVfwJieDbF\" 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 & m0 & 1.972e+00 & 4.774e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & s0 & 1.053e+00 & 3.722e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & m1 & -1.994e+00 & 7.552e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "4 & s1 & 9.768e-01 & 5.689e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "5 & $f_{0}$ & 6.993e-01 & 1.673e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'f_0': 0.6993030239680726, 's1': 0.976774802772317, 's0': 1.0530630364224147, 'm1': -1.9942116293354075, 'm0': 1.9720292675473272}\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jRlldPcboGDnuZmTbwPftGzMG7mY57N/P6Rsa8ikjHOz0NJd4NjkZaNAARo/W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\nLk57877/fu1NODdjpScXkXjW9Aqv8Bw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pwzc/N6T3ww1Zsqshey814njNaD75rinXUwMZ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\nct4KhULhhyjnrVAoFH6Ict4KhULhh/w/2aWvNNtnqFIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1118eba90>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Blind one parameter\n",
      "uh2= UnbinnedLH( BlindFunc(pdf, toblind='m1', seedstring='some_random_stuff', width=0.5, signflip=False), toydata)\n",
      "m2= Minuit(uh2, print_level=1, **inipars)\n",
      "m2.migrad();\n",
      "uh2.draw();\n",
      "print m2.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td title=\"Minimum value of function\">FCN = 1998.19075778</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 213</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 213</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 3.91220377184e-08</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 5e-06</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">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\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">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",
        "        "
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#hvugADrerr').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>m0</td>\n",
        "                <td>1.972030e+00</td>\n",
        "                <td>4.773574e-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>s0</td>\n",
        "                <td>1.053065e+00</td>\n",
        "                <td>3.722013e-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>3</td>\n",
        "                <td>m1</td>\n",
        "                <td>-2.022541e+00</td>\n",
        "                <td>7.552512e-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>s1</td>\n",
        "                <td>9.767755e-01</td>\n",
        "                <td>5.688929e-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>6.993031e-01</td>\n",
        "                <td>1.673063e-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=\"hvugADrerr\" 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 & m0 & 1.972e+00 & 4.774e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & s0 & 1.053e+00 & 3.722e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & m1 & -2.023e+00 & 7.553e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "4 & s1 & 9.768e-01 & 5.689e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "5 & $f_{0}$ & 6.993e-01 & 1.673e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'f_0': 0.6993030560128841, 's1': 0.9767755206854074, 's0': 1.0530648448606312, 'm1': -2.0225414570938822, 'm0': 1.9720304423455262}\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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99NNMnTqVefPmlbkuMTGRsIKMZsHBwcTExFgSyhQ2gL/u79mzx6f0Ufu+uQ+O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\nqtzzngbfAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111928590>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Blind more than one parameter. They will be shifted by the same amount\n",
      "uh3= UnbinnedLH( BlindFunc(pdf, ['m0','m1'], seedstring='some_random_stuff', width=0.5, signflip=False), toydata)\n",
      "m3= Minuit(uh3, print_level=1, **inipars)\n",
      "m3.migrad();\n",
      "uh3.draw();\n",
      "print m3.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<hr>"
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td title=\"Minimum value of function\">FCN = 1998.19075841</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 204</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 204</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 6.73095146289e-07</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 5e-06</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 0.5</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">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\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">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",
        "        "
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#zrhCDTFTts').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>m0</td>\n",
        "                <td>1.943670e+00</td>\n",
        "                <td>4.773484e-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>s0</td>\n",
        "                <td>1.053038e+00</td>\n",
        "                <td>3.721755e-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>3</td>\n",
        "                <td>m1</td>\n",
        "                <td>-2.022554e+00</td>\n",
        "                <td>7.552241e-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>s1</td>\n",
        "                <td>9.767610e-01</td>\n",
        "                <td>5.688624e-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>6.992934e-01</td>\n",
        "                <td>1.673073e-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=\"zrhCDTFTts\" 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 & m0 & 1.944e+00 & 4.773e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & s0 & 1.053e+00 & 3.722e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & m1 & -2.023e+00 & 7.552e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "4 & s1 & 9.768e-01 & 5.689e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "5 & $f_{0}$ & 6.993e-01 & 1.673e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'f_0': 0.6992933508150997, 's1': 0.9767610332884225, 's0': 1.0530382048563094, 'm1': -2.0225537775196876, 'm0': 1.9436696998689813}\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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h8f788885fvw4WVlZHDlyhBEjRrBgwQL27NnDzz//zLJlywgJCSkN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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111954b10>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print m1.values\n",
      "print m2.values\n",
      "print m3.values\n",
      "print \n",
      "print m1.errors\n",
      "print m2.errors\n",
      "print m3.errors"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'f_0': 0.6993030239680726, 's1': 0.976774802772317, 's0': 1.0530630364224147, 'm1': -1.9942116293354075, 'm0': 1.9720292675473272}\n",
        "{'f_0': 0.6993030560128841, 's1': 0.9767755206854074, 's0': 1.0530648448606312, 'm1': -2.0225414570938822, 'm0': 1.9720304423455262}\n",
        "{'f_0': 0.6992933508150997, 's1': 0.9767610332884225, 's0': 1.0530382048563094, 'm1': -2.0225537775196876, 'm0': 1.9436696998689813}\n",
        "\n",
        "{'f_0': 0.01673061544512028, 's1': 0.05688911570427262, 's0': 0.03721995281680518, 'm1': 0.0755249540358937, 'm0': 0.04773566594496333}\n",
        "{'f_0': 0.016730628544296994, 's1': 0.05688929215720884, 's0': 0.03722012879201778, 'm1': 0.07552511920275023, 'm0': 0.047735744463221284}\n",
        "{'f_0': 0.01673073187734163, 's1': 0.056886241374457414, 's0': 0.037217548244943825, 'm1': 0.07552240589154302, 'm0': 0.04773483971868747}\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print m3.values['m0']-m1.values['m0']\n",
      "print m3.values['m1']-m1.values['m1']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-0.0283595676783\n",
        "-0.0283421481843\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now it's your turn ...\n",
      "# try and apply probfit / iminuit and to your modeling / fitting task! "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}