alexpearce/histogram_sampling.ipynb

## histogram_sampling.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import probfit\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cfb86aa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nsample = 10000\n",
    "nbins = 20\n",
    "range = (-5, 5)\n",
    "xs = np.random.normal(0, 1, size=nsample)\n",
    "hist, edges = np.histogram(xs, bins=nbins, range=range)\n",
    "probfit.plt.hist(xs, bins=nbins, range=range);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pdf = probfit.HistogramPdf(hist, edges, xname='x')\n",
    "toy_data = probfit.gen_toy(pdf, nsample=nsample, bound=(-5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that the generated data looks like the template. Generate more data to see the convergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IiASi0CAiIiKBKDSIiIhIIAoNIiIiEohCg4iIiASi0CAiIiKBKDSIiIhIIAoN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\n/wcHbonwdRl2oAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cfadc9ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probfit.plt.hist(toy_data, bins=nbins, range=range, color='b', normed=True, label='Toy data')\n",
    "probfit.plt.hist(xs, bins=nbins, range=range, color='r', normed=True, histtype='step', label='Template data')\n",
    "probfit.plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "binned_lh = probfit.BinnedLH(pdf, toy_data, bins=nbins, bound=range)\n",
    "minuit = probfit.Minuit(binned_lh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "This call works, but does generate a LaTeX-related warning. I think it's because the fit has no independent parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:1: LogWarning: x is really small return 0\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<hr>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td title=\"Minimum value of function\">FCN = 3.5785412931267584</td>\n",
       "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 1</td>\n",
       "                <td title=\"Number of call in last migrad\">NCALLS = 1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td title=\"Estimated distance to minimum\">EDM = 3.5785412931267584</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:#FF7878\">False</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"
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-91-bca49e3bc183>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mminuit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmigrad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32miminuit/_libiminuit.pyx\u001b[0m in \u001b[0;36miminuit._libiminuit.Minuit.migrad (iminuit/_libiminuit.cpp:5434)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32miminuit/_libiminuit.pyx\u001b[0m in \u001b[0;36miminuit._libiminuit.Minuit.print_fmin (iminuit/_libiminuit.cpp:9740)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32miminuit/_libiminuit.pyx\u001b[0m in \u001b[0;36miminuit._libiminuit.Minuit.print_param (iminuit/_libiminuit.cpp:8668)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.5/site-packages/iminuit/frontends/html.py\u001b[0m in \u001b[0;36mprint_param\u001b[0;34m(self, mps, merr, float_format, smart_latex, latex_map)\u001b[0m\n\u001b[1;32m    209\u001b[0m         ltable = LatexFactory.build_param_table(mps, merr,\n\u001b[1;32m    210\u001b[0m                                                 \u001b[0mfloat_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat_format\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msmart_latex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msmart_latex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m                                                 latex_map=latex_map)\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;31m# rows = str(ltable).count('\\n')+1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.5/site-packages/iminuit/latex.py\u001b[0m in \u001b[0;36mbuild_param_table\u001b[0;34m(cls, mps, merr, float_format, smart_latex, latex_map)\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0malignment\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'|c|r|r|r|r|r|r|r|c|'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         ret = LatexTable(data, headers=headers, alignment=alignment,\n\u001b[0;32m--> 191\u001b[0;31m                          smart_latex=smart_latex, latex_map=latex_map)\n\u001b[0m\u001b[1;32m    192\u001b[0m         \u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat_format\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat_format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.5/site-packages/iminuit/latex.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, headers, smart_latex, escape_under_score, alignment, rotate_header, latex_map)\u001b[0m\n\u001b[1;32m     34\u001b[0m                  latex_map=None):\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mheaders\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m             \u001b[0;32massert\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "minuit.migrad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nZvGx1m8AZwAXAa8FXgn8S2wdhwPrgEeAqcBCYImZXZWiXhlEd3d30SWUkpe5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\nUFzkaal8PXQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cfb407ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "binned_lh.draw(minuit);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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