{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a49afec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from iminuit import Minuit\n",
    "from scipy.stats import norm\n",
    "\n",
    "# Using the lognormal from numba_stats\n",
    "# instead of scipy_stats speeds things\n",
    "# up by a big factor.\n",
    "# Be careful however because the call sequences\n",
    "# are not exactly the sameand not all\n",
    "# functions are implemented.\n",
    "# from scipy.stats import lognorm\n",
    "from numba_stats import lognorm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa49c280",
   "metadata": {},
   "source": [
    "### This is fake data with 3 point\n",
    "We are going to fit Data = mu*S + B with some uncertainty on B <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e1da7c51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is some fake data in 3 bins\n",
    "data = np.array([12, 7, 4])\n",
    "sig  = np.array([ 2,  2, 2])  # signal predicted with mu=1\n",
    "bg   = np.array([10,  5, 2])  # bg predicted\n",
    "err  = 0.2*bg                 # bg uncertainty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef904097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check that lognormal makese sense, compare with gaussian\n",
    "color = ('red', 'green', 'blue')\n",
    "ax=plt.subplot(111)\n",
    "for b,e,c in zip(bg, err, color):\n",
    "    x = np.linspace(0.01, 20., 400)\n",
    "    ax.plot(x, lognorm.pdf(x, np.log(1+e/b), 0, b), color=c, \n",
    "            label='numba logn  $\\mu$ = '+str(b) + '   $\\sigma$ = ' + str(e))\n",
    "    ax.plot(x, norm.pdf(x,    scale=e, loc=b), color=c, linestyle='dashed', \n",
    "            label='gaus  $\\mu$ = '+str(b) + '   $\\sigma$ = ' + str(e))\n",
    "\n",
    "ax.grid()\n",
    "ax.legend()\n",
    "ax.set_xlim([0, 20.])\n",
    "_ = ax.set_ylim(0,1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8db9aeea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot it\n",
    "x = np.array([1.,2.,3.])\n",
    "b = np.array([0.5, 1.5, 2.5, 3.5, 4.5])\n",
    "ax = plt.subplot(111)\n",
    "ax.hist(x, b, weights=bg,  alpha=0.1, color='blue', label='BG', histtype='stepfilled')\n",
    "\n",
    "n1,b1,p1 = ax.hist(x, b, weights=bg+err, histtype='stepfilled',\n",
    "                   facecolor='none', edgecolor='none', linestyle='--')\n",
    "n2,b2,p2 = ax.hist(x, b, weights=bg-err, histtype='stepfilled', \n",
    "                   facecolor='none', edgecolor='none', linestyle='--')\n",
    "ax.bar(x=b1[:-1], height=n2-n1, bottom=n1, width=np.diff(b1),\n",
    "       align='edge', linewidth=0, edgecolor='blue', color='none', zorder=-1, \n",
    "       label='bg uncertainty', hatch='\\\\\\\\\\\\')\n",
    "\n",
    "ax.hist(x, b, weights=sig, color='red', label='sig $\\mu$=1', histtype='step',\n",
    "        linestyle='dotted')\n",
    "ax.errorbar(x, data, yerr=np.sqrt(data), fmt='o', color='black', label='data')\n",
    "# ax.bar(x, sig, align='center',width=1, color='None', edgecolor='red', label='sig ($\\mu$=1)')\n",
    "\n",
    "ax.set_xlim((0.5, 3.5))\n",
    "ax.legend()\n",
    "_ = ax.set_ylim((0,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a0d6cb03",
   "metadata": {},
   "outputs": [],
   "source": [
    "class myFit:\n",
    "    \"\"\"\n",
    "    Simplest example of wrapper around a minuit function\n",
    "    NLL fit to bins of data = mu*signal + background\n",
    "    including sigma of background (absolute)\n",
    "    \n",
    "    Sample usage:\n",
    "    Example of 3 bins, np.arrays are to be passed.\n",
    "    The fit parameters ordered for pinit are\n",
    "       mu, 1st bin bg, 2bd bin bg, 3rd bin bg\n",
    "    \n",
    "    f = myFit(data, signal, background, sigma)\n",
    "    m = Minuit(f.myNLL, pinit, name=(\"mu\", \"b1\", \"b2\", 'b3'))\n",
    "    m.errordef = Minuit.LIKELIHOOD\n",
    "    m.simplex()\n",
    "    m.migrad()\n",
    "    m.minos()\n",
    "    \n",
    "    After defining \"f\" can change data members as\n",
    "    f.d  = newData\n",
    "    f.b  = newBG\n",
    "    f.s  = newSignal\n",
    "    f.eb = newBGsigma\n",
    "    \"\"\"\n",
    "    def __init__(self, d, s, b, eb):\n",
    "        self.d     = d\n",
    "        self.b     = b\n",
    "        self.eb    = eb\n",
    "        self.s     = s\n",
    "        self.blah  = np.log(1+eb/b)\n",
    "\n",
    "    # This should really protect against logs of negative numbers\n",
    "    def myNLL(self, p):\n",
    "        mu = p[0]\n",
    "        fitbg = p[1:]\n",
    "        temp1 = (-self.d * np.log(mu*self.s + fitbg) + mu*self.s + fitbg).sum()\n",
    "        #temp2 = 0\n",
    "        #for bb,ee,fit in zip(self.b, self.eb, fitbg):\n",
    "        #    pdf = lognorm.pdf(fit, np.log(1+ee/bb), scale=bb)\n",
    "        #   temp2 = temp2 - np.log(pdf)\n",
    "        #return temp1+temp2\n",
    "        return temp1 - (np.log(lognorm.pdf(fitbg, self.blah, 0, self.b))).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88d5277f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize a myFit object that contains the\n",
    "# data, the signal model (mu=1), the pre-fit bg\n",
    "# prediction, its uncertainty, as well as the\n",
    "# negative log-likelihood function\n",
    "thisFit = myFit(data, sig, bg, err)\n",
    "\n",
    "# initialize a Minuit object with pinit as the initial guess\n",
    "pinit     = np.array([1, bg[0], bg[1], bg[2]])\n",
    "mData = Minuit(thisFit.myNLL, pinit, name=(\"mu\", \"b1\", \"b2\", 'b3'))\n",
    "mData.errordef = Minuit.LIKELIHOOD\n",
    "mData.limits['mu'] = (-0.5, None)   # force mu>-0.5\n",
    "#mData.simplex()\n",
    "#mData.migrad()\n",
    "#mData.minos()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e95e1994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th colspan=\"5\" style=\"text-align:center\" title=\"Minimizer\"> Migrad </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Minimum value of function\"> FCN = -23.78 </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"No. of function evaluations in last call and total number\"> Nfcn = 59 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.84e-05 (Goal: 0.0001) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"No. of gradient evaluations in last call and total number\">  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> No Parameters at limit </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below EDM threshold (goal x 10) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below call limit </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Covariance </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Hesse ok </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix accurate?\"> Accurate </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix positive definite?\"> Pos. def. </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Was positive definiteness enforced by Minuit?\"> Not forced </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th title=\"Variable name\"> Name </th>\n",
       "        <th title=\"Value of parameter\"> Value </th>\n",
       "        <th title=\"Hesse error\"> Hesse Error </th>\n",
       "        <th title=\"Minos lower error\"> Minos Error- </th>\n",
       "        <th title=\"Minos upper error\"> Minos Error+ </th>\n",
       "        <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
       "        <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
       "        <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 0 </th>\n",
       "        <td> mu </td>\n",
       "        <td> 1.1 </td>\n",
       "        <td> 0.8 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> -0.5 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> b1 </td>\n",
       "        <td> 9.7 </td>\n",
       "        <td> 1.6 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 2 </th>\n",
       "        <td> b2 </td>\n",
       "        <td> 4.8 </td>\n",
       "        <td> 0.9 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> b3 </td>\n",
       "        <td> 1.93 </td>\n",
       "        <td> 0.35 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> mu </th>\n",
       "        <th> b1 </th>\n",
       "        <th> b2 </th>\n",
       "        <th> b3 </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu </th>\n",
       "        <td> 0.579 </td>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.246 <strong>(-0.201)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.117 <strong>(-0.180)</strong> </td>\n",
       "        <td style=\"background-color:rgb(234,234,250);color:black\"> -0.0337 <strong>(-0.127)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b1 </th>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.246 <strong>(-0.201)</strong> </td>\n",
       "        <td> 2.59 </td>\n",
       "        <td style=\"background-color:rgb(250,245,245);color:black\"> 0.0495 <strong>(0.036)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.0143 <strong>(0.025)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b2 </th>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.117 <strong>(-0.180)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,245,245);color:black\"> 0.0495 <strong>(0.036)</strong> </td>\n",
       "        <td> 0.725 </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0.00679 <strong>(0.023)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b3 </th>\n",
       "        <td style=\"background-color:rgb(234,234,250);color:black\"> -0.0337 <strong>(-0.127)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.0143 <strong>(0.025)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0.00679 <strong>(0.023)</strong> </td>\n",
       "        <td> 0.122 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = -23.78                     │              Nfcn = 59               │\n",
       "│ EDM = 1.84e-05 (Goal: 0.0001)    │                                      │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│          Valid Minimum           │        No Parameters at limit        │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
       "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
       "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
       "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
       "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
       "│   │ Name │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
       "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
       "│ 0 │ mu   │    1.1    │    0.8    │            │            │  -0.5   │         │       │\n",
       "│ 1 │ b1   │    9.7    │    1.6    │            │            │         │         │       │\n",
       "│ 2 │ b2   │    4.8    │    0.9    │            │            │         │         │       │\n",
       "│ 3 │ b3   │   1.93    │   0.35    │            │            │         │         │       │\n",
       "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌────┬─────────────────────────────────┐\n",
       "│    │      mu      b1      b2      b3 │\n",
       "├────┼─────────────────────────────────┤\n",
       "│ mu │   0.579  -0.246  -0.117 -0.0337 │\n",
       "│ b1 │  -0.246    2.59  0.0495  0.0143 │\n",
       "│ b2 │  -0.117  0.0495   0.725 0.00679 │\n",
       "│ b3 │ -0.0337  0.0143 0.00679   0.122 │\n",
       "└────┴─────────────────────────────────┘"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The standard minimizer\n",
    "mData.migrad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "138bf496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th colspan=\"5\" style=\"text-align:center\" title=\"Minimizer\"> Migrad </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Minimum value of function\"> FCN = -23.78 </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"No. of function evaluations in last call and total number\"> Nfcn = 519 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.84e-05 (Goal: 0.0001) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"No. of gradient evaluations in last call and total number\">  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> No Parameters at limit </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below EDM threshold (goal x 10) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below call limit </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Covariance </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Hesse ok </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix accurate?\"> Accurate </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix positive definite?\"> Pos. def. </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Was positive definiteness enforced by Minuit?\"> Not forced </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th title=\"Variable name\"> Name </th>\n",
       "        <th title=\"Value of parameter\"> Value </th>\n",
       "        <th title=\"Hesse error\"> Hesse Error </th>\n",
       "        <th title=\"Minos lower error\"> Minos Error- </th>\n",
       "        <th title=\"Minos upper error\"> Minos Error+ </th>\n",
       "        <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
       "        <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
       "        <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 0 </th>\n",
       "        <td> mu </td>\n",
       "        <td> 1.1 </td>\n",
       "        <td> 0.8 </td>\n",
       "        <td> -0.7 </td>\n",
       "        <td> 0.8 </td>\n",
       "        <td> -0.5 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> b1 </td>\n",
       "        <td> 9.7 </td>\n",
       "        <td> 1.6 </td>\n",
       "        <td> -1.5 </td>\n",
       "        <td> 1.7 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 2 </th>\n",
       "        <td> b2 </td>\n",
       "        <td> 4.8 </td>\n",
       "        <td> 0.9 </td>\n",
       "        <td> -0.8 </td>\n",
       "        <td> 0.9 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> b3 </td>\n",
       "        <td> 1.93 </td>\n",
       "        <td> 0.35 </td>\n",
       "        <td> -0.32 </td>\n",
       "        <td> 0.38 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> mu </th>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> b1 </th>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> b2 </th>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> b3 </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"Lower and upper minos error of the parameter\"> Error </th>\n",
       "        <td> -0.7 </td>\n",
       "        <td> 0.8 </td>\n",
       "        <td> -1.5 </td>\n",
       "        <td> 1.7 </td>\n",
       "        <td> -0.8 </td>\n",
       "        <td> 0.9 </td>\n",
       "        <td> -0.32 </td>\n",
       "        <td> 0.38 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"Validity of lower/upper minos error\"> Valid </th>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"Did scan hit limit of any parameter?\"> At Limit </th>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"Did scan hit function call limit?\"> Max FCN </th>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"New minimum found when doing scan?\"> New Min </th>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> mu </th>\n",
       "        <th> b1 </th>\n",
       "        <th> b2 </th>\n",
       "        <th> b3 </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu </th>\n",
       "        <td> 0.579 </td>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.246 <strong>(-0.201)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.117 <strong>(-0.180)</strong> </td>\n",
       "        <td style=\"background-color:rgb(234,234,250);color:black\"> -0.0337 <strong>(-0.127)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b1 </th>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.246 <strong>(-0.201)</strong> </td>\n",
       "        <td> 2.59 </td>\n",
       "        <td style=\"background-color:rgb(250,245,245);color:black\"> 0.0495 <strong>(0.036)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.0143 <strong>(0.025)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b2 </th>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.117 <strong>(-0.180)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,245,245);color:black\"> 0.0495 <strong>(0.036)</strong> </td>\n",
       "        <td> 0.725 </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0.00679 <strong>(0.023)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b3 </th>\n",
       "        <td style=\"background-color:rgb(234,234,250);color:black\"> -0.0337 <strong>(-0.127)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.0143 <strong>(0.025)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0.00679 <strong>(0.023)</strong> </td>\n",
       "        <td> 0.122 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = -23.78                     │              Nfcn = 519              │\n",
       "│ EDM = 1.84e-05 (Goal: 0.0001)    │                                      │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│          Valid Minimum           │        No Parameters at limit        │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
       "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
       "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
       "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
       "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
       "│   │ Name │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
       "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
       "│ 0 │ mu   │    1.1    │    0.8    │    -0.7    │    0.8     │  -0.5   │         │       │\n",
       "│ 1 │ b1   │    9.7    │    1.6    │    -1.5    │    1.7     │         │         │       │\n",
       "│ 2 │ b2   │    4.8    │    0.9    │    -0.8    │    0.9     │         │         │       │\n",
       "│ 3 │ b3   │   1.93    │   0.35    │   -0.32    │    0.38    │         │         │       │\n",
       "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬───────────────────────┬───────────────────────┬───────────────────────┬───────────────────────┐\n",
       "│          │          mu           │          b1           │          b2           │          b3           │\n",
       "├──────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┤\n",
       "│  Error   │   -0.7    │    0.8    │   -1.5    │    1.7    │   -0.8    │    0.9    │   -0.32   │   0.38    │\n",
       "│  Valid   │   True    │   True    │   True    │   True    │   True    │   True    │   True    │   True    │\n",
       "│ At Limit │   False   │   False   │   False   │   False   │   False   │   False   │   False   │   False   │\n",
       "│ Max FCN  │   False   │   False   │   False   │   False   │   False   │   False   │   False   │   False   │\n",
       "│ New Min  │   False   │   False   │   False   │   False   │   False   │   False   │   False   │   False   │\n",
       "└──────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┘\n",
       "┌────┬─────────────────────────────────┐\n",
       "│    │      mu      b1      b2      b3 │\n",
       "├────┼─────────────────────────────────┤\n",
       "│ mu │   0.579  -0.246  -0.117 -0.0337 │\n",
       "│ b1 │  -0.246    2.59  0.0495  0.0143 │\n",
       "│ b2 │  -0.117  0.0495   0.725 0.00679 │\n",
       "│ b3 │ -0.0337  0.0143 0.00679   0.122 │\n",
       "└────┴─────────────────────────────────┘"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Aymmetric Errors\n",
    "mData.minos()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bcea63d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu = 1.066 +/- 0.754\n",
      "Minos errors:\n",
      "-0.696 +0.826\n"
     ]
    }
   ],
   "source": [
    "# You can get the results of the fit from the \n",
    "# Minuit object.  For example, the for the 0th parameter, ie, mu\n",
    "print(\"mu =\", round(mData.values[0],3), \"+/-\", round(mData.errors[0],3))\n",
    "print(\"Minos errors:\")\n",
    "print(round(mData.merrors[\"mu\"].lower,3), \" +\", \n",
    "      round(mData.merrors[\"mu\"].upper,3), sep=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "067318e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show a profile log likelihood scan (this is just for fun)\n",
    "mumin = mData.values[0] - 2*mData.errors[0]\n",
    "mumax = mData.values[0] + 3*mData.errors[0]\n",
    "mu, loglik= mData.draw_mnprofile(\"mu\",size=100, bound=[mumin,mumax], subtract_min=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fa1a9acd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\Delta$log-lik')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It does not look so pretty, but we can redraw it\n",
    "ax = plt.subplot(111)\n",
    "ax.plot(mu,loglik)\n",
    "ax.set_xlim(mu[0], mu[-1])\n",
    "ax.set_ylim(bottom=0)\n",
    "ax.grid()\n",
    "ax.plot([0,0],[0,ax.get_ylim()[1]], color='black',linestyle='dotted')\n",
    "ax.plot([mu[0], mu[-1]], [0.5, 0.5], color='red', linestyle='dashed')\n",
    "ax.plot([mu[0], mu[-1]], [2.0, 2.0], color='red', linestyle='dashed')\n",
    "ax.set_xlabel(\"Signal Strength\", size=15)\n",
    "ax.set_ylabel(\"$\\Delta$log-lik\", size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1782dab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.ContourSet at 0x13a8a7040>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# an example of a countour plot\n",
    "mData.draw_mncontour(\"mu\",\"b1\", cl=[0.68,0.95])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5561b4cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13a8ea400>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make it prettier\n",
    "p68    = mData.mncontour(\"mu\",\"b1\",cl=0.68)\n",
    "p95    = mData.mncontour(\"mu\",\"b1\",cl=0.95)\n",
    "# close the boundaries\n",
    "x68 = p68[:,0]\n",
    "y68 = p68[:,1]\n",
    "x68 = np.append(x68,x68[0])\n",
    "y68 = np.append(y68,y68[0])\n",
    "x95 = p95[:,0]\n",
    "y95 = p95[:,1]\n",
    "x95 = np.append(x95,x95[0])\n",
    "y95 = np.append(y95,y95[0])\n",
    "\n",
    "ax = plt.subplot(111)\n",
    "ax.plot(x68,y68, label=\"68%\")\n",
    "ax.plot(x95,y95, label=\"95%\")\n",
    "ax.grid()\n",
    "ax.scatter(mData.values[0], mData.values[1], color='black', label=\"fit value\")\n",
    "ax.set_xlabel(\"Signal Strength\", size=15)\n",
    "ax.set_ylabel(\"Background, first bin\", size=15)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ad978b9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}