16.01.01__Fit_picco_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from iminuit import Minuit\n",
    "from iminuit.cost import LeastSquares\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "evt_x = np.loadtxt(\"data/fit-data-1.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.05734290236616"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evt_x.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9,6),dpi=100)\n",
    "n_bins = 50\n",
    "x_range = (0, 100)\n",
    "data_y, bins, patches = ax.hist(evt_x, n_bins, range=x_range, density=False, \n",
    "    histtype=u'step', edgecolor='b', linewidth=2, label='histogram')\n",
    "ax.hist(evt_x, n_bins, range=x_range, density=False, color='b', alpha=0.2)\n",
    "ax.scatter(evt_x, np.full_like(evt_x, 0.5), marker='o', color='r', s=10, alpha=0.2, linewidth=0, label='entries')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('$\\Delta N$')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_x = (bins+(bins[1]-bins[0])/2)[:-1]\n",
    "#print(data_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_yerr = np.sqrt(data_y)\n",
    "#print(data_y)\n",
    "#print(data_yerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6),dpi=100)\n",
    "plt.errorbar(data_x, data_y, data_yerr, fmt=\"o\")\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('$\\Delta N$')\n",
    "plt.ylim(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bkg_pdf(x, par):\n",
    "    return np.exp(-x/par[0])/par[0]\n",
    "def sig_pdf(x, par):\n",
    "    return np.exp(-((x-par[0])/par[1])**2/2)/math.sqrt(2*math.pi*par[1]**2)\n",
    "def bkg_model(x, par):\n",
    "    return par[0]*bkg_pdf(x, par[1:])\n",
    "def sig_model(x, par):\n",
    "    return par[0]*sig_pdf(x, par[1:])\n",
    "def fit_model(x, par):\n",
    "    return bkg_model(x, par)+sig_model(x, par[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "least_squares_mode = LeastSquares(data_x[data_y>0], data_y[data_y>0], data_yerr[data_y>0], fit_model)\n",
    "m = Minuit(least_squares_mode, (500,5,100,10,2), name=(\"B\", \"$\\lambda$\", \"S\", \"$\\mu$\", \"$\\sigma$\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Minimum value of function\"> FCN = 39.17 </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"No. of function evaluations in last call and total number\"> Nfcn = 200 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 4.6e-06 (Goal: 0.0002) </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 style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Parameters </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> B </td>\n",
       "        <td> 377 </td>\n",
       "        <td> 28 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> $\\lambda$ </td>\n",
       "        <td> 13.1 </td>\n",
       "        <td> 1.1 </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> S </td>\n",
       "        <td> 129 </td>\n",
       "        <td> 22 </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> $\\mu$ </td>\n",
       "        <td> 10.14 </td>\n",
       "        <td> 0.34 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 4 </th>\n",
       "        <td> $\\sigma$ </td>\n",
       "        <td> 1.9 </td>\n",
       "        <td> 0.4 </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> B </th>\n",
       "        <th> $\\lambda$ </th>\n",
       "        <th> S </th>\n",
       "        <th> $\\mu$ </th>\n",
       "        <th> $\\sigma$ </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> B </th>\n",
       "        <td> 811 </td>\n",
       "        <td style=\"background-color:rgb(235,235,250);color:black\"> -3.65 <strong>(-0.114)</strong> </td>\n",
       "        <td style=\"background-color:rgb(182,182,250);color:black\"> -333 <strong>(-0.524)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,219,219);color:black\"> 2.01 <strong>(0.210)</strong> </td>\n",
       "        <td style=\"background-color:rgb(194,194,250);color:black\"> -4.47 <strong>(-0.428)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> $\\lambda$ </th>\n",
       "        <td style=\"background-color:rgb(235,235,250);color:black\"> -3.65 <strong>(-0.114)</strong> </td>\n",
       "        <td> 1.26 </td>\n",
       "        <td style=\"background-color:rgb(250,218,218);color:black\"> 5.26 <strong>(0.210)</strong> </td>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.0767 <strong>(-0.203)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,221,221);color:black\"> 0.0789 <strong>(0.192)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> S </th>\n",
       "        <td style=\"background-color:rgb(182,182,250);color:black\"> -333 <strong>(-0.524)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,218,218);color:black\"> 5.26 <strong>(0.210)</strong> </td>\n",
       "        <td> 496 </td>\n",
       "        <td style=\"background-color:rgb(214,214,250);color:black\"> -2.09 <strong>(-0.278)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,167,167);color:black\"> 4.51 <strong>(0.552)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> $\\mu$ </th>\n",
       "        <td style=\"background-color:rgb(250,219,219);color:black\"> 2.01 <strong>(0.210)</strong> </td>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.0767 <strong>(-0.203)</strong> </td>\n",
       "        <td style=\"background-color:rgb(214,214,250);color:black\"> -2.09 <strong>(-0.278)</strong> </td>\n",
       "        <td> 0.114 </td>\n",
       "        <td style=\"background-color:rgb(200,200,250);color:black\"> -0.0478 <strong>(-0.387)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> $\\sigma$ </th>\n",
       "        <td style=\"background-color:rgb(194,194,250);color:black\"> -4.47 <strong>(-0.428)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,221,221);color:black\"> 0.0789 <strong>(0.192)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,167,167);color:black\"> 4.51 <strong>(0.552)</strong> </td>\n",
       "        <td style=\"background-color:rgb(200,200,250);color:black\"> -0.0478 <strong>(-0.387)</strong> </td>\n",
       "        <td> 0.134 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌──────────────────────────────────┬──────────────────────────────────────┐\n",
       "│ FCN = 39.17                      │              Nfcn = 200              │\n",
       "│ EDM = 4.6e-06 (Goal: 0.0002)     │                                      │\n",
       "├───────────────┬──────────────────┼──────────────────────────────────────┤\n",
       "│ Valid Minimum │ Valid Parameters │        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 │ B         │    377    │    28     │            │            │         │         │       │\n",
       "│ 1 │ $\\lambda$ │   13.1    │    1.1    │            │            │         │         │       │\n",
       "│ 2 │ S         │    129    │    22     │            │            │         │         │       │\n",
       "│ 3 │ $\\mu$     │   10.14   │   0.34    │            │            │         │         │       │\n",
       "│ 4 │ $\\sigma$  │    1.9    │    0.4    │            │            │         │         │       │\n",
       "└───┴───────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌───────────┬───────────────────────────────────────────────────┐\n",
       "│           │         B $\\lambda$         S     $\\mu$  $\\sigma$ │\n",
       "├───────────┼───────────────────────────────────────────────────┤\n",
       "│         B │       811     -3.65      -333      2.01     -4.47 │\n",
       "│ $\\lambda$ │     -3.65      1.26      5.26   -0.0767    0.0789 │\n",
       "│         S │      -333      5.26       496     -2.09      4.51 │\n",
       "│     $\\mu$ │      2.01   -0.0767     -2.09     0.114   -0.0478 │\n",
       "│  $\\sigma$ │     -4.47    0.0789      4.51   -0.0478     0.134 │\n",
       "└───────────┴───────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.migrad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BbW3qP3lPpxwAAACA/JVvCfzTki6UNFfSAklTJf3LzAZKyr8hqaHbz0aPY8wqujvntsrRJRktBwAAACB/5VUC75x7wDn3e+fcC865ByW9X9IYSecN8G83Sirv9nOA54ECcbOnVqiqvETWz/smqaq8RLOnVmQzLAAAAAA5KK8S+N6cc7skvSpp2gBl2pxzjYkfSU3Zii9dXFXPPL+ewR4OmRbOmyFJ+yTxidcL581QONRfig8/8BkEAACAH/I6gTezUZIOkcRzuPJIppMnv5/BPndmlZbOn6XKsuIe0yeWl2jp/FmaO7MqK3EAAAAAyG15lcCb2c1mdpqZTTGzEyX9WVJE0m99Dg05KleewT53ZpUevuK0rtd3XHScHr/yDJJ3AEDa6C0EAPknrxJ4xe5f/62kVyT9n6Qdkk5wzm3zNSrkpFx7Bnv3bvKzp1bQbR4AAABADwV+B5BJzrlP+B0DgiOVZ7DPOWRc9gID8kRze6dmXPugJGnd9e/ViKK8+soBAADIuny7Ag8kjWewAwAAAAgSEngMWzyDHQAAAECQkMBj2OIZ7MBeDHblL9Y/AABIRkYTeDN7ycwimawT8ArPYAcAAAAQJJm+An+1pM9muE7AMzyDHQCQr7o/RWVlTX3WnqoCpIpeSKwDJC+jQwI75/6cyfqAbJg7s0onTRuvo657SFLsGeynTJ/AlXcAQGCtqK7VwvvWdr2+8PZVqiov0cJ5Mzg5DQABltYVeDMrNbMR3V4fZGaXm9l7MhcakD08gz15XNEBgNy2orpWC5at0ZbGth7T6xpatWDZGq2orh1S/VwpBAD/pNuF/l5Jn5YkMxsj6WlJX5V0r5ktyExowNBxkJFZK6prddaSx7peX3j7Kp180yNDPhgMGk5iAMNXrn+vRKJOi+5fp772Solpi+5fx34LAAIq3QR+lqR/xf/+mKQtkg5SLKm/LANxAcgxXl/RCQpOYiSPEx1A9q2sqVdtQ2u/7ztJtQ2tWllTn72gAAAZk24CP0JSU/zv90j6k3MuKukpxRJ5BMyIogJtWPwBbVj8AY0oyujQCMgDXNGJ4SRG8jjRkRpOdiBTtjb1n7ynUw7IBvaBrAMkL90E/nVJHzazyZLeK+mh+PRKSY2ZCAxA7uCKDicxUsGJjtRwsgOZVDm6JKPlAK+xD2QdIDXpJvDXS7pZ0gZJK51zT8anv0fSsxmIC8MMZx1zG1d08v8kRqY+g5zoSA0nO5Bps6dWqKq8RP0NxWqSqspLNHtqRdrz4DsbmcI+kHWA1KWVwDvn/iDpQEnvUuwKfMLfJX0lA3EhTyTzJc9Zx9yXS1d0/BpAKp9PYmTyM5jvJzqkzLVBTnYEU64nr+GQaeG8GZK0TxKfeL1w3oy0n7bCdzYyhX0g6wDpSfcKvJxzdYrdB3+2mZXGJ69yzr2ckcgQeMl8yQfxrGOuH7x5IRtXdHJdLp3ESFYyiWamP4P5fKIj04bDyY58E5Tkde7MKi2dP0uVZcU9pk8sL9HS+bPSfg58EL+zkbvYB7IOkJ50nwM/zsz+LulVSX+VlPgm+KWZfT9TwSG4kvmSD+JZx6AcvGWa11d0giAfT2J48RnMtRMdufzIL052BEvQkte5M6v08BWndb2+46Lj9PiVZ6SdvAfxOxu5Ld19YC7v11OVS98DXqzXVOrMp+3qtXSvwN8iqUOxbvTN3ab/TtLcoQaFYEv2S/6p9TsCddYxaAdvmebVFZ2EXN9x5+NJDC/O/OfjiQ6v5NrJDvQvqMlr9/3R7KkVQ9o/pbO/8OrgPdmyQUoevJq/30nZQNgHsg6QnnQT+PdIutI5t7HX9NfEY+Ty3mBdyJP9kn/yjR1JzS8Xrj7l0sGbnwcZmb6iEzRensTwY7t6ceY/3RMdfh88+4GTHcGR7smuXE6eUpVLVwqRH9LdB+bTrYy59D3gxXpNpc582q5eSzeBH6meV94TKiS19TEdeSKZLuTJf3kn98HMhbOO3KO0Vyav6HjNiwPdfDqJ4dWZf697a/gtUwcZ+dirI9dkah9A8pre/sKrg/dkywYpefBq/n4nZQNJZx+Yb7cy5sr3gBfrNZU68227ei3dBP5fkj7d7bUzs5Ckr0v6x5CjQk5Ktgt5sl/ycw4enzNnHQfDwRu6C9JJjIF4eeY/n050dJfpg4x8P9kRpC7MA0n3ZFcuJ0+pSnV/4dXBe7Jlg5Q8eDV/v5OyZKSyD8zXWxn9/h7wYr2mUme+blcvpZvAf03S583sAUlFkr4rqVrSqZKuzFBsyCGpdCFP9kv+hEPG5cRZx2QMh3uU8qmrJ5Lj9Zn/XDjRkclkx6uDjFw42ZGvn9VMbf90TnYFIXlKRSr7C68O3pMtG6Tkwav5+52UpSKZfWAu3croBb++B7xYr6nUme/b1SspJ/BmVijph5LmSXpc0r2Kdan/k6R3OufeyGiEyAmpdCFP5Uve77OOycqle5SAZCWTvATlM5iOTCY7Xh9kpHKyI0hXtf3swpzJ7Z/qya4gJU+pSGZ/4dXBe7Jl2zujgUkevJq/30lZOgbbBw6HWxn9OOntxXpNpc7hsF29kHIC75zrkHS0pJ3Ouf92zp3nnHu/c+4a5xx9HPJUql3IU0kKcuHq02CycY/SiKICbVj8AW1Y/AGNKCpIux5ASi15yYXPoBfJWyaTnXw/yPCiW7afXZi9SHaT/V7LpeTJi++VwfYXXh28J1v2N09uCEzy4NX8/U7KvMCtjN7wYr2mUifbNT3pdqFfJulzmQwEuS2dLuSpJAW50NV2MPl8pRL5JZ3kxc/PYKaTNy8SqFx6XnGmr2p71dXbry7MXl4pTOZ7LR+Tp94G2l94dfCebNk36/saY3no8/eCV/P3OynzwnC4ldEPXqzXVOpku6Yn3QS+QNICM1ttZj8zsyXdfzIZIIbGz/v/pGAk5qnIhSuVw53fIwV7JVPL5XeX0FR5caXUi2QnVw4yMn1V24v17/f9j14nu4N9r+Vj8pQKrw7eky17UMUIT+bvBa/m73dS5oWhPHLuyTd26N7nNunJN3YMesIzmbJe1JmKTM4/nfWayTq93q5erP9ckG5/qpmS1sT/PrTXe76vGTO7RLGB9iZKel7Spc65lf5GlX0rqmu18L61Xa8vvH2VqspLtHDejJQTzkQX8gXL1sjUcyPn2oBzXsu3kxJBksk2nUtSXa7m9k7NuPZBSdK669/bo2tsKsnLnEPGZW4h0jBY8maKJW9nz5iY0ufMi2QncZBR19DaZ7ymWG+cwZ5XfMr0CWnvMxLJdu/5J5Lt7j2Bkil79oyJnqz/VBPoTLdXv5PdfEyeUpHqZyXTZS+YM0X/83iNJ/NPiESdVtbUa2tTqypHlwx4LDBQ2aHsVwaafzr1elFnKusqcbtHf9I5Dl1RXatF96/rsY/p77s12bJe1JnsOvBi/qmuVy/q9Gq7prr+gyStK/DOudMH+Dkj00Gmwsw+LmmJpEWSZimWwD9oZpV+xpVtft7/B3ghFwZw8kKml8vv5CUV6V4pHaxbuhfJjt/PK/biqvZT63d4cqXa7/sf/U52vbiilI2BVFO5UjXQvfWpfFa8KFtUEPJk/gkrqmt18k2P6JO/eEpfvvs5ffIXTw3YC2agsl7NP51BFzNdZ6rrKhnpPHKu9z5uoNt4BivrRZ2p8Gr+ya5XL+pMtawX2yqI8nGkrCsk/cI5d7skmdnFkj4g6bOSFvsZWMY4p1LFG2T7HvXejJGo06L71g58VeW+tTp72uiUrwTNPbRMJ33pXTr2O/+Qk/SL+bN0yrRxsXra9+z7D+2dA8aacjmvyno0/+b2Th377YclSc9cc1ZmBhFKJVYv6vVoXUVaO1SkdjlJK1/dtLddKf02PVCdQ5KhbZD2Z3WA+VeWJtcJqrLU9fzMetGuBqlz686GpKrZurNBau+WbA1S7+z9i1VVVqy6xrb+rxSVFWv2/sV977f6MffQMi39xJFauPw1bWlq75o+saxYC98/TXMPLeuqb8W6bVpw977btusK+CeO1NwZE7qmj5C04fp3x1+1Se09T+isrNmZXLL92iZJyV3VfvK15A5g9ln/g0ipDaZSZ5LbKt3tH4k6rXxzl7Y2tatydJFmHzSm7/3FIO0vLGnh+w7RgrvX9n9F6X2HKNy5917tFeu2adFfX1dttxN5VYl2NWNCWnWmYrD5pyqVz4oXZb2afyqf62TLejX/ZOv1os5UY01FMsehqXy3Kv73YGXPOHhUxutM5Tjci2XqPv/B1ms6xyup5AyZ3K5Jbas0epflEnPO9x7vGWNmRZKaJX3MOXdPt+l3ShrjnDunj/8pltT9lM9oSRsbGhpUVlbmccTpaWrcpervzdVWjVGldml26GWFbe92fDJyhD7Z8a1B6/lt4Q2aE37Jy1CBpKyIHKdFHZ9WrfZ2ka3SDi0s/LXmhlel1aYHqzMXePFZjTjTyW0/VJ3GyvXRycoU1UTV6/HiL/fYb/gh3eWPONPK6OH97gOl2PZf0HG5JNdjPZiikkxLC29Nux0MNv/ENqhVhfa9VpXeNrg3Mkdf7rh00HI/KPyRJCVV9kvhP+nHkY8MWi7V74pU2qCklNurF9vfi/1F33Vu18LC3+wz7wUdl8cPNve2l75iTbbOVONMdv6pSmZbeVk2k3Wm8rmWlPI+wKv9ykD1elHnUOrNlFS+WyQlVfZb4V/rhsinM1pnKvtWL5bJq/l7lVskG0PS2+o/T/D9VsLeGhsbVV5eLknlzrnG/srl2xX48Yqd/N7Sa/oWSYf38z/fkLTQy6AyaUV1rRbeW60t3Rpw74OMrRqTVF3JlgO81PPgca86jdWCjsu1VLeqLcldVaJNJ1NnLiTxXnxWw+a0sPDXWtBxuUzRPpOXhYW/8T15l6TZoZdVpR2DJm+zQy93TUs20ZobXqWlunWfshNVP6RkR4qt44EOUFZGD+8xz96cQqrVeK2MHp70gU6ldmW0nCTNsXX6o05Laf1Lgx+8p9oGUynrxfb3an8xN7xKZ4dWD5roLOr49D7JsxRrJ6aoFnVcoLNDqxU2l1SdqUh1/qka7LPiddlM1pnK51pSyvsAr/YrA9XrRZ1DqTdTvPhufVPJ3YmbSp1elA3S/FOVbN1Jb6scuJUwXfmWwKfjRsXumU8YLWmjT7EMqN9BiTROCzq+oqUfjXVHqqzZKd3+/KD1Vc7/H2nq2JTjSKVbeLJlvagzSPNPhSfd8lOoN5PLH4k6LVrylFxH2z7/Gzt4lBaVXqWbzz1cujO5Nh05aExSdZ59xQmed50abPnT/aw2tXb03c0sbq6kpeu29dHNsTTeJfbelGNNt+xAwpIWxrtZ9t0tOKSFnzhN4RmxbuH9dsnstQ9MmCvppD7X1cfSijdhsOXf+sIW6Q+DH5RuPed/paP3S2qes6NOVUueGrxb+BWxuJIpe8IVf9PCl7cnvf6l5Ltap9IGky3rxfZPdh+U7v6irb1Tn+3WVsK92srKmp2qHWAf0JXonF+tOfF9wGB1piKd+Q9XKX2upYzvA7zYr3hRp5f1Jiul71YpqbIHnX2JtOKNjNaZynG4F8vk2fw92lckG0PS2yoHBv1MV74l8NslRST13hvsJ6mur39wzrVJ6vrmNsvNeyGSGq35gTd09tEHafb0Eaoqf2XwkUKn7y+llcB0qkXxRl80Uhrw4CHZssnXGYl2dJVdualNp0wvG+DAKvPz965ssryoM5V6M7etVr6xo0ci0JuTYu8XlCQ3+u30/WMDoyVR58pNbVnoOjXwukrns5oYsb5dRZKkC5dV9zmq6txjRuqkw/fXUdc9JCn2yMOBRkAfUSS9tPijGVmuVMw9ZqSWFhRr4X1rewzkN7HXMkWiToseeCOpfWD3ZQyrs2tdzT50/yElO3sNvPyVY8uTqqVybHns/5MQlrTwQ0cOPFLvh45UuGRU19/JlJ17zKik1r8UP4ncVwLd2KYFd6/dd2CiFNrgYGW92v7J7oPS318M3Fa2tuxKqpatLdbVVlL7rA5Wb+rzH65S+lynUmeS69WL/YoXdXpZb7JS+m6Vkip7wcmH6n+e3JTROlM5DvdimTybv0cXR5KNIeltNYRBP/2W7nPgc5Jzrl3SM5LOTEwzs1D89ZN+xZUJqYzWnM5IoUGRyVGdgyooz0FPZlsl231p+562pNt0kEZhT2ek4FRGrPfqkYeZboNzZ1bp4StO63p9x0XH6fErz+iRDHr9bO9M8mrEcK9G9U1m/af7vPZU2uBAZdPd/gONli55/9SGwebv94j5fs8/SLLxbOtMzd/POr2sN1lBfLqBH8vk1fy94sW2Cqq8SuDjlkj6TzP7jJkdIWmppJGSbvc3rKFJ9SAjHx/5lq+PEUuFlycwkk3KBjsgTcSZzLZK5eAx2TYdtAPSZJcr3QQq07xqg4Mlel4nWpl6hJbk7YFOMsl2OmUHW/9eJdDJlvVq+/u9v/A70fF7/kGSjwmUV/uqXEj2Et+tE8t7fnYHOuE5WFkv6vR7mbyav1e82FZBlG9d6OWc+52ZTZB0vaSJkp6TNNc513tgu0BJ5yBj7swqnTRtfNJdaHNZUrcQBPyREIPpdwyExCNZ+tghNbd3asa1D0qS1l3/3gET7oX3re16feHtq/rslp2MVLZV4uAx2W5OybTpVOtMSHZdpaL3SZF+uw8nsVypJFBe3RqQThvMFC8TrRXVtVp0/7oe6zfd9p+QOHhIpmt6qjJ1Vbu7RPLcH797tni1/dPdX2RKItEZ8HYHDxMdv+cfNInPde/9RV+f61TKejF/P+v0st5UYzh7xkStrKnX1qZWVY4u6Xc/mGxZL+r0e5m8mr9XvNhWQZN3CbwkOed+LOnHfseRSekeZHjVhTbbciF58ZOXJzAynZSluq1SPXgcrE3nygFpqidF/L4CPRi/T6Kluw8cLDH18qSEVydRB1umdMsOJFeuVGc60c6F/YXfiY7f8w+afEygvEp0ciGBCocs6ePCZMt6UWcqgjR/r3ixrYIkLxP4fJQLBxl+8jt58ZtXJzC8SMrSvd0jk1cqvbz6mQwvkkK/Eyi/T6J5sQ/MxkmJfDmJms9XqnMhgfU70fF7/kGTjwmUV4lOviZQgJ9I4APE76TET34nL37z6gSGF0lZrtzu4dctJF4lhX4nUNka7Gsgmd4HZuOkRKaugPstF04ie5lo50IC63ei4/f8AQDJIYEPmHy6rz0VficvfvPqBIYXSVku3e7hx9VPr5LCdBKoTCaPuXISLZP7wOHesydV+X6lmgQWABAEJPABlC9dMlORC1d//OTVCQwvkrKgbqtkB5wbjJdJoZ+9cHLpJFqm9oG5clIiSLhSDQCAv/LxMXLIU/n4aLxkefVIllx4XnUuyOSj0bxOClN5NFgm5cJjgRJSeTTZQHiEVnoSCfQ5x+yvOYeMy7mTcQAA5DMSeASKX8lLLvAiKc6V51X7Kdln1icrG0mhX71wgnZiZjC5dFICAAAgGSTwCJzheAtBghdJsZdJWa5vq8EGnJNiA851714/mHSTwkxdVfZaUE7MJCvR/ieW9+wREdSTEgAAIL/l7lEigD55kRQP18ERvRpwLt+fGJHrJ2ZSlQv3dQMAACSDBB6ApOAkZZkcWd3rAefy9aRIvjwarTsGRgMAAEFAAo+8lqmRxYNquC//YLwecM6rkyL5mEADAABgcNwDj7yVyZHFg2i4L38yGIUcAAAAQUICj7yU6sjiQRlALFmZHlk9qAbbroxCDgAAgCAhgUfe8WJk8SAJ6vL7dRIl3x6NBgAAgPwV/EuNQC9ejSyeDj/uQc+l5Q8Krwac4151AAAAZBJX4JF3vBxZPBV+3YOeK8sfNEEZhR8AAADDF1fg4Sk/rkB6PbJ4MhL3oPfupJ64B93Lrtm5sPwAAAAAMo8EHjkhk4l+YmTxuobWPu8DN8Xub/ZqZPHB7kE3xe5BP3vGRE+u8vq9/N3RhRwAAADIHLrQI+/4PbJ4Kvege8Hv5QcAAADgDRJ45CU/RxbPhXvQGVk9dfn2KEEAAADkH45SkbKgdIv2amTxweTKPeipLn9QtisAAAAwXHEFHnnNj5HFE/eg9zcnk1SVpXvQGVkdAAAAyB8k8ECGeX0POl29AQAAgOGJBB5IUTIJNPegAwAAAMg0Lt8BHvHrHnwAAAAA+SmvrsCb2QYzc71+rvI7Lgxf3IMOAAAAIFPy8Qr8tZJ+0e11k1+BAAAAAACQKfmYwDc55+r8DgIAAAAAgEzKqy70cVeZ2Q4ze9bMvmZmA56kMLNiMytL/EganaU4AQAAAABIWr5dgf+hpDWS6iWdKOlGSVWSrhjgf74haaH3oQEAAAAAkL6cT+DNbLGkKwcpdoRz7mXn3JJu014ws3ZJPzOzbzjn2vr53xsldf+/0ZI2ph9xbkk88gwAAAAAEGw5n8BL+r6kOwYps76f6U8rtoxTJL3SV4F4Yt+V3JsxSjjyBydwAAAAgPyR8wm8c26bpG1p/vsxkqKStmYsIAAAAAAAfJDzCXyyzGyOpOMl/UOxR8fNkXSLpGXOuZ1+xgYAAAAAwFDlTQKvWDf4T0i6TlKxpBrFEvglA/wPAAAAAACBkDcJvHNujaQT/I4jG7ivGQAAAACGn3x8DjwAAAAAAHmHBB4AAAAAgAAggQcAAAAAIADy5h54DB+MAQAAAABgOOIKPAAAAAAAAUACDwAAAABAAJDAAwAAAAAQACTwAAAAAAAEAAk8AAAAAAABQAIPAAAAAEAA8Bg5wEM88g4AAABApnAFHgAAAACAACCBBwAAAAAgAEjgAQAAAAAIABJ4AAAAAAACgAQeAAAAAIAAIIEHAAAAACAASOABAAAAAAgAEngAAAAAAAKABB4AAAAAgAAggQcAAAAAIABI4AEAAAAACABzzvkdQ04xszJJDQ0NDSorK/M7HAAAAABAnmtsbFR5ebkklTvnGvsrxxV4AAAAAAACIDAJvJl908yeMLNmM9vVT5kDzWx5vMxWM/uemRVkOVQAAAAAADIuSMltkaTfS3pS0ud6v2lmYUnLJdVJOlFSlaRfS+qQdHX2wgQAAAAAIPMCcwXeObfQOXeLpBf7KfIeSTMkzXfOPeece0DStyRdYmZF2YoTAAAAAAAvBCaBT8IcSS8657Z0m/agpDJJR/oTEgAAAAAAmRGkLvSDmShpS69pW7q91yczK5ZU3G3S6AzHBQAAAADAkPmawJvZYklXDlLsCOfcyx6G8Q1JC3tPbGzsd+R+AAAAAAAyJtn80+8r8N+XdMcgZdYnWVedpNm9pu3X7b3+3ChpSbfXVZJenjx5cpKzBQAAAAAgI0ZL6jeb9zWBd85tk7QtQ9U9KembZlbpnNsan3a2Ygu/boAY2iS1JV6bWZOkAyQ1ZSiuoRotaaNyKyagP7RXBAVtFUFBW0VQ0FYRJLnaXkdL2jxQAb+vwCfNzA6UVCHpQElhMzsm/tbrzrndkh5SLFH/jZl9XbH73r8t6bZ4kp4U55yTtCmTsQ+FmSX+bHLO0a8fOY32iqCgrSIoaKsICtoqgiSH2+ugsQQmgZd0vaTPdHv9bPz36ZIedc5FzOyDkpYqdjV+j6Q7JV2b1SgBAAAAAPBAYBJ459yFki4cpMybkt6fjXgAAAAAAMimfHoOfL5qk7RI3e7TB3IY7RVBQVtFUNBWERS0VQRJYNurxW75BgAAAAAAuYwr8AAAAAAABAAJPAAAAAAAAUACDwAAAABAAJDAAwAAAAAQACTwOc7MLjGzDWbWamZPm9lsv2PC8GZm3zCzVWbWZGZbzeweMzusV5kSM7vNzHaY2W4z+6OZ7edXzIAkmdlVZubM7NZu02iryAlmtr+ZLYu3xRYze9HM3tXtfTOz682sNv7+w2Y23c+YMTyZWdjMbjCzmnhbfMPMvmVm1q0M7RVZZ2anmtn9ZrY5/n3/4V7vD9ouzazCzO4ys0Yz22VmvzSzUVldkEGQwOcwM/u4pCWKPeJglqTnJT1oZpW+Bobh7jRJt0k6QdLZkgolPWRmI7uVuUXSPEn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      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "data_x_f = np.arange(0, bins[-1], 0.1)\n",
    "data_y_f = fit_model(data_x_f, m.values)\n",
    "bkg_y_f = bkg_model(data_x_f, m.values)\n",
    "sig_y_f = sig_model(data_x_f, m.values[2:])\n",
    "\n",
    "plt.figure(figsize=(12,10),dpi=100)\n",
    "gs = gridspec.GridSpec(8, 1)\n",
    "ax0 = plt.subplot(gs[:5, :])\n",
    "ax1 = plt.subplot(gs[6:, :])\n",
    "ax0.errorbar(data_x, data_y, data_yerr, fmt=\"o\")\n",
    "ax0.plot(data_x_f, data_y_f, label=\"fit\")\n",
    "ax0.plot(data_x_f, sig_y_f, label=\"signal\")\n",
    "ax0.plot(data_x_f, bkg_y_f, label=\"background\")\n",
    "ax0.set_xlabel('x')\n",
    "ax0.set_ylabel('$\\Delta N$')\n",
    "ax1.errorbar(data_x, (data_y-fit_model(data_x, m.values)), data_yerr, fmt=\"o\")\n",
    "ax1.plot([data_x[0],data_x[-1]], [0,0])\n",
    "ax1.set_xlabel('x')\n",
    "ax1.set_ylabel('res.')\n",
    "ax0.set_ylim(0)\n",
    "ax.legend()\n",
    "\n",
    "fit_info = [ f\"$\\\\chi^2$ / $n_\\\\mathrm{{dof}}$ = {m.fval:.1f} / {len(data_x) - m.nfit}\" ]\n",
    "for p, v, e in zip(m.parameters, m.values, m.errors):\n",
    "    fit_info.append(f\"{p} = ${v:.3f} \\\\pm {e:.3f}$\")\n",
    "ax0.legend(title=\"\\n\".join(fit_info))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unbinned (meno conveniente)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def neg_log_like_fun(par):\n",
    "    b, l, s, mu, sigma = par\n",
    "    lik = s + b\n",
    "    for x in evt_x:\n",
    "        fs = sig_pdf(x, [mu, sigma])\n",
    "        fb = bkg_pdf(x, [l])\n",
    "        lik -= math.log(s*fs + b*fb)\n",
    "    return lik\n",
    "neg_log_like_fun.errordef = Minuit.LIKELIHOOD \n",
    "g = Minuit(neg_log_like_fun, (500,5,100,10,2), name=(\"B\", \"$\\lambda$\", \"S\", \"$\\mu$\", \"$\\sigma$\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Minimum value of function\"> FCN = -633.2 </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"No. of function evaluations in last call and total number\"> Nfcn = 216 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 3.3e-07 (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 style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Parameters </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> B </td>\n",
       "        <td> 345 </td>\n",
       "        <td> 25 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> $\\lambda$ </td>\n",
       "        <td> 16.6 </td>\n",
       "        <td> 1.0 </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> S </td>\n",
       "        <td> 95 </td>\n",
       "        <td> 20 </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> $\\mu$ </td>\n",
       "        <td> 10.09 </td>\n",
       "        <td> 0.33 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 4 </th>\n",
       "        <td> $\\sigma$ </td>\n",
       "        <td> 1.7 </td>\n",
       "        <td> 0.5 </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> B </th>\n",
       "        <th> $\\lambda$ </th>\n",
       "        <th> S </th>\n",
       "        <th> $\\mu$ </th>\n",
       "        <th> $\\sigma$ </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> B </th>\n",
       "        <td> 640 </td>\n",
       "        <td style=\"background-color:rgb(217,217,250);color:black\"> -6.21 <strong>(-0.253)</strong> </td>\n",
       "        <td style=\"background-color:rgb(173,173,250);color:black\"> -296 <strong>(-0.591)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,211,211);color:black\"> 2.14 <strong>(0.260)</strong> </td>\n",
       "        <td style=\"background-color:rgb(179,179,250);color:black\"> -6.4 <strong>(-0.546)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> $\\lambda$ </th>\n",
       "        <td style=\"background-color:rgb(217,217,250);color:black\"> -6.21 <strong>(-0.253)</strong> </td>\n",
       "        <td> 0.938 </td>\n",
       "        <td style=\"background-color:rgb(250,201,201);color:black\"> 6.21 <strong>(0.324)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.0616 <strong>(-0.196)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,204,204);color:black\"> 0.138 <strong>(0.308)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> S </th>\n",
       "        <td style=\"background-color:rgb(173,173,250);color:black\"> -296 <strong>(-0.591)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,201,201);color:black\"> 6.21 <strong>(0.324)</strong> </td>\n",
       "        <td> 391 </td>\n",
       "        <td style=\"background-color:rgb(207,207,250);color:black\"> -2.15 <strong>(-0.334)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,145,145);color:black\"> 6.4 <strong>(0.700)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> $\\mu$ </th>\n",
       "        <td style=\"background-color:rgb(250,211,211);color:black\"> 2.14 <strong>(0.260)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.0616 <strong>(-0.196)</strong> </td>\n",
       "        <td style=\"background-color:rgb(207,207,250);color:black\"> -2.15 <strong>(-0.334)</strong> </td>\n",
       "        <td> 0.106 </td>\n",
       "        <td style=\"background-color:rgb(198,198,250);color:black\"> -0.0596 <strong>(-0.396)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> $\\sigma$ </th>\n",
       "        <td style=\"background-color:rgb(179,179,250);color:black\"> -6.4 <strong>(-0.546)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,204,204);color:black\"> 0.138 <strong>(0.308)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,145,145);color:black\"> 6.4 <strong>(0.700)</strong> </td>\n",
       "        <td style=\"background-color:rgb(198,198,250);color:black\"> -0.0596 <strong>(-0.396)</strong> </td>\n",
       "        <td> 0.214 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌──────────────────────────────────┬──────────────────────────────────────┐\n",
       "│ FCN = -633.2                     │              Nfcn = 216              │\n",
       "│ EDM = 3.3e-07 (Goal: 0.0001)     │                                      │\n",
       "├───────────────┬──────────────────┼──────────────────────────────────────┤\n",
       "│ Valid Minimum │ Valid Parameters │        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 │ B         │    345    │    25     │            │            │         │         │       │\n",
       "│ 1 │ $\\lambda$ │   16.6    │    1.0    │            │            │         │         │       │\n",
       "│ 2 │ S         │    95     │    20     │            │            │         │         │       │\n",
       "│ 3 │ $\\mu$     │   10.09   │   0.33    │            │            │         │         │       │\n",
       "│ 4 │ $\\sigma$  │    1.7    │    0.5    │            │            │         │         │       │\n",
       "└───┴───────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌───────────┬───────────────────────────────────────────────────┐\n",
       "│           │         B $\\lambda$         S     $\\mu$  $\\sigma$ │\n",
       "├───────────┼───────────────────────────────────────────────────┤\n",
       "│         B │       640     -6.21      -296      2.14      -6.4 │\n",
       "│ $\\lambda$ │     -6.21     0.938      6.21   -0.0616     0.138 │\n",
       "│         S │      -296      6.21       391     -2.15       6.4 │\n",
       "│     $\\mu$ │      2.14   -0.0616     -2.15     0.106   -0.0596 │\n",
       "│  $\\sigma$ │      -6.4     0.138       6.4   -0.0596     0.214 │\n",
       "└───────────┴───────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.migrad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_x_f = np.arange(0, bins[-1], 0.1)\n",
    "data_y_f = fit_model(data_x_f, g.values)\n",
    "bkg_y_f = bkg_model(data_x_f, g.values)\n",
    "sig_y_f = sig_model(data_x_f, g.values[2:])\n",
    "\n",
    "plt.figure(figsize=(9,6),dpi=100)\n",
    "gs = gridspec.GridSpec(8, 1)\n",
    "ax0 = plt.subplot(gs[:5, :])\n",
    "ax1 = plt.subplot(gs[6:, :])\n",
    "ax0.errorbar(data_x, data_y, data_yerr, fmt=\"o\")\n",
    "ax0.plot(data_x_f, data_y_f, label=\"fit\")\n",
    "ax0.plot(data_x_f, sig_y_f, label=\"signal\")\n",
    "ax0.plot(data_x_f, bkg_y_f, label=\"background\")\n",
    "ax0.set_xlabel('x')\n",
    "ax0.set_ylabel('$\\Delta N$')\n",
    "ax1.errorbar(data_x, data_y-fit_model(data_x, g.values), data_yerr, fmt=\"o\")\n",
    "ax1.plot([data_x[0],data_x[-1]], [0,0])\n",
    "ax1.set_xlabel('x')\n",
    "ax1.set_ylabel('res.')\n",
    "ax0.set_ylim(0)\n",
    "fit_info = [ f\"$\\\\chi^2$ / $n_\\\\mathrm{{dof}}$ = {g.fval:.1f} / {len(data_x) - g.nfit}\" ]\n",
    "for p, v, e in zip(g.parameters, g.values, g.errors):\n",
    "    fit_info.append(f\"{p} = ${v:.3f} \\\\pm {e:.3f}$\")\n",
    "ax0.legend(title=\"\\n\".join(fit_info))\n",
    "plt.show()"
   ]
  }
 ],
 "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",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}