pgp1-python-einfuehrung/IminuitExample.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8c7b32df-d0c4-4777-89b5-8684b8cb3bc4",
   "metadata": {},
   "source": [
    "# Keywords Script:\n",
    "\n",
    "* Wiederholen sie die aus dem PGP1 bekannten Konzepte:\n",
    "    * Messunsicherheiten (Messfehler), statistische und systematische Unsicherheit\n",
    "    * Korrelation und Antikorrelation\n",
    "    * Gaussverteilung (Normalverteilung)\n",
    "    * Arthmetirsches Mittel und Standardabweichung\n",
    "    * Zentraler Grenzwertsatz\n",
    "    * Wahrscheinlichkeitsverteilung\n",
    "* Binominal-Verteilung (Not sure if needed)\n",
    "* Poisson-Verteilung\n",
    "\n",
    "\n",
    "# Aufgaben zur Vorbereitung:\n",
    "\n",
    "\n",
    "* Verknüpfen des Zentralengrenzwertsatzs, der Normalverteilung und des arithmetrishen Mittels (in Python):\n",
    "    1. Nimm random nicht Gaußverteilung, e.g., exponential decay\n",
    "    2. Plotte Zerfallsverteilung.\n",
    "    3. Ziehe 2, 5, 10, 100 verschieden \"Messungen\"\n",
    "    4. Berechne Mittelwert von \"gemessenen\" Werte und plote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8f008534-a7a3-482e-9495-611866787c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# import numpy as np\n",
    "\n",
    "# import iminuit\n",
    "\n",
    "# print(iminuit.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "36435872-d6c4-4e16-8814-82f90342d84d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Truth\n",
    "# R = 10*10**3  # Ohm\n",
    "# C = 10*10**-6  # F\n",
    "# I0 = 10/R\n",
    "\n",
    "# def discharge_current(t, I0, R, C):\n",
    "#     return I0 * np.exp(-t/(R*C))\n",
    "\n",
    "\n",
    "# time_truth = np.arange(0, 1, 0.1)\n",
    "\n",
    "\n",
    "# # Make psuedo measured values:\n",
    "# sigma_time = 0.02\n",
    "# dtime = np.random.normal(0, sigma_time, len(time_truth))\n",
    "# time_mess = time_truth + dtime\n",
    "\n",
    "# sigma_current = 0.05\n",
    "# current_truth = discharge_current(time_truth, I0, R, C)/10**-3\n",
    "# current_mes = current_truth + np.random.normal(0, sigma_current, len(current_truth))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94c87544-8084-4994-8025-44e789b6d9f2",
   "metadata": {},
   "source": [
    "TODO but relastic values not floats with infinit digits add header..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e77a7824-f8fa-43b2-ad98-009e17c05c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# df = pd.DataFrame()\n",
    "# df['time'] = time_mess\n",
    "# df['current'] = current_mes\n",
    "# df['delta_current'] = sigma_current\n",
    "# df['delta_time'] = np.abs(dtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "54c27237-effd-4396-91d8-b6b4d43d589c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_csv('data/discharge_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f03f0f86-7452-4528-b0a1-7e4411dbc410",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(time_truth, discharge_current(time_truth, I0, R, C)/10**-3, marker='.')\n",
    "# plt.errorbar(time_mess, current_mes, xerr=sigma_time, yerr=np.abs(sigma_current), ls='', marker='.')\n",
    "# plt.ylabel('Current [mA]')\n",
    "# plt.xlabel('Time [s]')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9a4045f-f389-40f5-9f19-32f8bbebc75d",
   "metadata": {},
   "source": [
    "# Methode der kleinsten Quadrate\n",
    "\n",
    "Im folgenden wolllen wir die **Methode der kleinsten Quadrate (Least Squares)** näher beleuchten. Diese Methode wird oft benutzt, um eine Funktion $\\lambda(x; \\ $**$\\phi$**$)$ mit den Funktionsparametern $\\mathbf{\\phi}$ an die gemessenen Punkte **$(x,y)$** anzupassen. Um jedoch die **Methode der kleinsten Quadrate** zu verstehen, wollen wir sie erst einmal anschaulich und halb-mathematisch herleiten. Dabei stüzen wir uns im Folgenden auf eine Herleitung aus dem Buch **\"Statistical Data Analysis\"**  von **Glen Cowan**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a8f4ef9-222c-440f-8621-e612a8988fd0",
   "metadata": {},
   "source": [
    "Bevor wir dies jedoch tun, schauen wir uns das Problem des Fittens doch erst einmal anschaulich an. \n",
    "\n",
    "<figure class=\"image\">\n",
    "<img src=\"images/SketchLeastSquares.png\"  alt=\"{{ Beispiel PDF }}\" width=70%>\n",
    "</figure>\n",
    "\n",
    "Beim fitten, zum Beispiel einer Geraden (lila) an eine Reihe von Messpunkten (schwarz), wollen wir den Abstand zwischen den einzelnen Messpunkten und der Geraden (orange) möglichst klein halten. Sprich die Summe über alle $\\Delta Y_i$ \n",
    "\n",
    "$$\\sum_i \\Delta Y_i $$\n",
    "\n",
    "sollte möglichst klein sein, wobei $\\Delta Y_i$ durch \n",
    "\n",
    "$$ \\Delta Y_i = y_i – f(x_i, \\vec{\\theta})$$\n",
    "\n",
    "gegeben ist und $f(x, \\vec{\\theta})$ unsere Fitfunktion repräsentiert. Hierbei Symbolisiert $\\vec{\\theta}$ die Parameter unserer Funktion. Sprich im Fall einer Geraden die Steigung $m$ und den Offset $y_0$ ($\\vec{\\theta}=(m, y_0)$). \n",
    "\n",
    "Darüber hinaus sollte die Richtung des Abstandes, sprich ob ein Messpunkt unterhalb oder oberhalb der Fitfunktion liegt keine Rolle spielen. Daher quadrieren wir das Ganze und erhalten somit\n",
    "\n",
    "$$ LS = \\sum_i = (y_i – f(x_i, \\theta))^2$$\n",
    "\n",
    "Dies ist die allgemeinste Form der Methode der kleinsten Quadrate. Sie besagt, dass die Funktion welche die Messpunkte am besten beschreibt, sprich die optimalen Werte für $\\vec{\\theta}$ aufweist, den Ausdruck LS minimiert. \n",
    "\n",
    "Nun weisen unsere Messpunkte nicht nur Werte für X und Y aus, sondern sind noch zusätzlich durch einen Messunsicherheit (Messfehler) charakterisiert. Diese sollte wir natürlich bei der Überlegung unserer Parameter $\\vec{\\theta}$ berücksichtigen. Sprich Messwerte mit einer großen Unsicherheit sollten weniger stark berücksichtigt werden wie Messwerte mit einer kleinen Unsicherheit. Dies können wir gewährleisten sofern wir die Distanzen $\\Delta Y_i$ mit den jeweiligen Unsicherheiten $\\Delta y_i$ gewichten, sprich  \n",
    "\n",
    "$$ \\chi^2 = \\sum_i =\\frac{(y_i – f(x_i, \\theta))^2}{\\Delta y_i^2}$$\n",
    "\n",
    "berechnen. Das Quadrieren der Unsicherheiten sorgt dafür, dass der Ausdruck dimensionslos wird. Diese besondere Form der kleinsten Quadrate nennt man auch oft $\\chi^2$-Fit. Wir werden später noch einmal genauer beleuchten warum. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1153a474-8afe-44ae-8511-b403a4ad861d",
   "metadata": {},
   "source": [
    "Nun wollen wir uns erst einmal ein Beispiel angucken, wie dies in der Praxis aussieht. In der nachfolgenden Animation wird ein Ohm’schwer Widerstand an eine Reihe von Spannungs- und Strommessungen gefittet. Dies entspricht unserem obigen Geradenbeispiel. \n",
    "<figure class=\"image\">\n",
    "<img src=\"images/MaterialPythonkurs092018/Fitting_gif.gif\"  alt=\"{{ Least Square Beispiel }}\" width=100%>\n",
    "</figure>\n",
    "<span style=\"color:red\">TODO: Update animation use only LS without uncertainties?</span>\n",
    "Wie die Animation zeigt, werden so lange verschiedene Widerstände ausprobiert, bis ein Wert gefunden wurde bei dem das $\\chi^2$ klein wird. Dieses variieren der Widerstandswerte passiert nicht zufällig, sondern basiert auf einem Algorithmus für ein Optimierungsverfahren. \n",
    "\n",
    "Es gibt verschiedene Arten von Algorithmen um Minimierungsprobleme zu lösen. Wie diese genau aufgebaut sind, lernen Sie in anderen Progrmmierveranstaltungen wie zum Beispiel *Programmieren für Physiker* oder *Computer in der Wissenschaft*. Zum Glück haben uns bereits in Python andere Menschen diese Arbeit abgenommen. Im folgenden wollen wir uns das package `imnuit` etwas genauer angucken, welches bereits ein sehr umfangreiches und mächtiges Fittingtool darstellt. \n",
    "\n",
    "[iminuit](https://iminuit.readthedocs.io/en/stable/tutorials.html) verfügt auch über eine exzellente Dokumentation, mit Hilfe dessen Sie auch komplexere Probleme lösen können."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "702b9a12-539d-43b5-8314-7f8dcdf93cda",
   "metadata": {},
   "source": [
    "Um mit Hilfe von `imnuit` etwas zu fitten brauchen wir zunächst einmal ein paar Messdaten und ein Fitmodel. Im Folgenden wollen wir die Entladekurve eines Kondensators mit der Kapazität $C$ über einen Widerstand $R$ bestimmen. Die Entladekurve ist durch eine einfache Exponentialfunktion der Form \n",
    "\n",
    "$$ I = I_0 \\exp\\{-t/RC\\}$$\n",
    "\n",
    "gegeben. Die Messdaten befinden sich in einer CSV-Datei im Ordner `data`. Die CSV-Datei kann mit Hilfe des `pandas` package eingelesen werden. [pandas](https://pandas.pydata.org/) ist ähnlich wie `numpy` ein package welches eine Fülle an Funktionen zum Verarbeiten und Verwalten von Daten bereitstellt. Es gehört ähnlich wie auch `numpy`, `scipy` und `matplotlib` zu den Standardbibliotheken, welche sehr häufig in der Wissenschaft verwendet werden. Aufgrund der zeitlichen Limitierung des Versuchstages können wir leider nicht auf alle Funktionen von `pandas` eingehen und wollen uns im Folgenden lediglich auf die Basics beschränken. Für ihre zukünftigen Praktika lohnt es sich jedoch noch mehr über `pandas` in Ihrer Eigenstudienzeit zu lernen."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5215840a-1276-49c1-9181-274cd8a2b4bf",
   "metadata": {},
   "source": [
    "CSV-Datein können Sie wie folgt eingelesen werden\n",
    "<span style=\"color:red\">TODO: Add dummy file with dummy header to show things...</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f8ef1be0-a42d-4a11-b674-c2ed099fefcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data_frame = pd.read_csv('data/discharge_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15800aa8-8a7f-4d59-ab06-3edc6bb1e443",
   "metadata": {},
   "source": [
    "Dabei gibt pandas die Daten als so genannten DataFrames zurück. Dies sind Objekte welche ähnlich wie strukturierte `numpy.arrays` zu behandeln sind. DataFrames werden allgemein als Tabellen dargestellt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f14ca80f-e0d7-4447-9335-b3744f7a028f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>time</th>\n",
       "      <th>current</th>\n",
       "      <th>delta_current</th>\n",
       "      <th>delta_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000637</td>\n",
       "      <td>1.066538</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.000637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.088553</td>\n",
       "      <td>0.406316</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.011447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.194773</td>\n",
       "      <td>0.143093</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.005227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.306413</td>\n",
       "      <td>0.078141</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.006413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.405285</td>\n",
       "      <td>0.065042</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.005285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.507390</td>\n",
       "      <td>0.011885</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.007390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.613279</td>\n",
       "      <td>-0.018824</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.013279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.707501</td>\n",
       "      <td>0.044513</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.007501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.790479</td>\n",
       "      <td>0.006881</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.009521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.883672</td>\n",
       "      <td>-0.019052</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.016328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      time   current  delta_current  delta_time\n",
       "0           0  0.000637  1.066538           0.05    0.000637\n",
       "1           1  0.088553  0.406316           0.05    0.011447\n",
       "2           2  0.194773  0.143093           0.05    0.005227\n",
       "3           3  0.306413  0.078141           0.05    0.006413\n",
       "4           4  0.405285  0.065042           0.05    0.005285\n",
       "5           5  0.507390  0.011885           0.05    0.007390\n",
       "6           6  0.613279 -0.018824           0.05    0.013279\n",
       "7           7  0.707501  0.044513           0.05    0.007501\n",
       "8           8  0.790479  0.006881           0.05    0.009521\n",
       "9           9  0.883672 -0.019052           0.05    0.016328"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc24d5fa-d3c0-4866-b18a-9dd07768a222",
   "metadata": {},
   "source": [
    "Um die Daten aus einer Bestimmente Spalte zu bekommen können diese einfach mit dem Spaltennamen aufgerufen werden:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "93b7cbb1-1095-4a53-83d9-7b32f068daea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.000637\n",
       "1    0.088553\n",
       "2    0.194773\n",
       "3    0.306413\n",
       "4    0.405285\n",
       "5    0.507390\n",
       "6    0.613279\n",
       "7    0.707501\n",
       "8    0.790479\n",
       "9    0.883672\n",
       "Name: time, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame['time']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4476302c-eb65-409a-b1aa-2342ecbd9c88",
   "metadata": {},
   "source": [
    "oder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "969d8afa-5d52-4e01-8b64-ddab090891b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.066538\n",
       "1    0.406316\n",
       "2    0.143093\n",
       "3    0.078141\n",
       "4    0.065042\n",
       "5    0.011885\n",
       "6   -0.018824\n",
       "7    0.044513\n",
       "8    0.006881\n",
       "9   -0.019052\n",
       "Name: current, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame['current']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32249263-ec9f-44de-81b7-7a6c69e23332",
   "metadata": {},
   "source": [
    "Einzelne Messwerte lassen sich mit Hilfe von `.loc` bestimemn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4b44637-8e25-46c1-863d-3cd7604f52dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0118852615051639"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.loc[5, 'current']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c0c04b-be37-482d-aabc-802bfa2965d2",
   "metadata": {},
   "source": [
    "Sollten Sie eine Spalte an Messdaten in ein `numpy.array` umwandeln wollen so können Sie dies über"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f246f55e-5fc8-427c-990d-3e97799b5aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.06653795,  0.40631626,  0.1430927 ,  0.07814083,  0.06504185,\n",
       "        0.01188526, -0.01882397,  0.04451315,  0.00688072, -0.01905164])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame['current'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d2bd9ed-852d-4448-a051-a6f677ea891d",
   "metadata": {},
   "source": [
    "Die Messdaten können Sie auch wie gewohnt mit Hilfe von `matplotlib` darstellen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e3898686-3926-48a0-be4c-4d460a1792f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.errorbar(\n",
    "    data_frame['time'], \n",
    "    data_frame['current'], \n",
    "    data_frame['delta_current'], \n",
    "    ls='', \n",
    "    marker='.', \n",
    "    color='k'\n",
    ")\n",
    "plt.xlabel('Time []')\n",
    "plt.ylabel('Current []')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "100a4fe4-a5c4-4be3-a7f7-13337b97a194",
   "metadata": {},
   "source": [
    "Nun wollen wir die Messdaten mit Hilfe von `iminuit` fitten. Hierzu müssen wir zunächste zwei Module des packages importieren und eine Funktion für die Entladekurve des Kondensators definieren:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2ffe340b-cd0f-45ec-b5b8-42e7a0349d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from iminuit import Minuit, cost\n",
    "import numpy as np\n",
    "\n",
    "def discharge_current(t, I0, R, C):\n",
    "    return I0 * np.exp(-t/(R*C))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef87da8f-7af9-4e3f-af63-c28a2e1d9830",
   "metadata": {},
   "source": [
    "Nun können wir den fit selbst durchführen. Hierzu muss zuerst mittels dem `cost` Modul eine sogenannte Kostenfunktion erstellt werden. Die Kostenfunktion ist im Grunde unsere $\\chi^2$ \n",
    "\n",
    "$$ \\chi^2 = \\sum_i =\\frac{(y_i – f(x_i, \\theta))^2}{\\Delta y_i^2}$$\n",
    "\n",
    "Funktion welche minimiert werden soll. Dies ist bereits bei `iminuit` für uns vordefiniert. Anschließend können wir die genutzt Kostenfunktion über `Minuit` minimieren lassen. Hierzu müssen wir zunächst geeignete Startwerte für den Minimierungsprozess vorgeben. Diese sollten im Idealfall nicht allzu weit von den wahren Werten entfernt liegen. Wir werden an einem späteren Beispiel noch einmal genauer zeigen, wie man hier vorgehen kann. Um den Minimierungsprozess zu starten muss noch am Ende `migrad()` aufgerufen werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bf36b7b9-fb20-47b7-8538-479026b48fb2",
   "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 = 2.707 (χ²/ndof = 0.4) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 87 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 2.18e-10 (Goal: 0.0002) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total run time of algorithms\"> time = 0.5 sec </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:#FFF79A;color:black\" title=\"Is covariance matrix accurate?\"> APPROXIMATE </td>\n",
       "        <td style=\"text-align:center;background-color:#c15ef7;color:black\" title=\"Is covariance matrix positive definite?\"> NOT pos. def. </td>\n",
       "        <td style=\"text-align:center;background-color:#c15ef7;color:black\" title=\"Was positive definiteness enforced by Minuit?\"> 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> I0 </td>\n",
       "        <td> 1.07 </td>\n",
       "        <td> 0.05 </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> R </td>\n",
       "        <td> 0.03e6 </td>\n",
       "        <td> 0.05e6 </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> C </td>\n",
       "        <td> 3e-6 </td>\n",
       "        <td> 5e-6 </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> I0 </th>\n",
       "        <th> R </th>\n",
       "        <th> C </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> I0 </th>\n",
       "        <td> 0.00253 </td>\n",
       "        <td style=\"background-color:rgb(248,248,250);color:black\"> -34.3309 <strong>(-0.014)</strong> </td>\n",
       "        <td style=\"background-color:rgb(248,248,250);color:black\"> -3.459e-9 <strong>(-0.014)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> R </th>\n",
       "        <td style=\"background-color:rgb(248,248,250);color:black\"> -34.3309 <strong>(-0.014)</strong> </td>\n",
       "        <td> 2.25e+09 </td>\n",
       "        <td style=\"background-color:rgb(120,120,250);color:black\"> -224.592785048e-3 <strong>(-0.997)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> C </th>\n",
       "        <td style=\"background-color:rgb(248,248,250);color:black\"> -3.459e-9 <strong>(-0.014)</strong> </td>\n",
       "        <td style=\"background-color:rgb(120,120,250);color:black\"> -224.592785048e-3 <strong>(-0.997)</strong> </td>\n",
       "        <td> 2.25e-11 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 2.707 (χ²/ndof = 0.4)      │              Nfcn = 87               │\n",
       "│ EDM = 2.18e-10 (Goal: 0.0002)    │            time = 0.5 sec            │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│          Valid Minimum           │        No Parameters at limit        │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
       "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
       "│  Covariance   │     Hesse ok     │APPROXIMATE│NOT pos. def.│   FORCED   │\n",
       "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
       "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
       "│   │ Name │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
       "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
       "│ 0 │ I0   │   1.07    │   0.05    │            │            │         │         │       │\n",
       "│ 1 │ R    │  0.03e6   │  0.05e6   │            │            │         │         │       │\n",
       "│ 2 │ C    │   3e-6    │   5e-6    │            │            │         │         │       │\n",
       "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌────┬───────────────────────────────────────────────────────┐\n",
       "│    │                I0                 R                 C │\n",
       "├────┼───────────────────────────────────────────────────────┤\n",
       "│ I0 │           0.00253          -34.3309         -3.459e-9 │\n",
       "│  R │          -34.3309          2.25e+09 -224.592785048e-3 │\n",
       "│  C │         -3.459e-9 -224.592785048e-3          2.25e-11 │\n",
       "└────┴───────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA6eUlEQVR4nO3de3xU9Z3/8feZSTIJIQmBkJCQQASUqwKCpAHx1hRaLNVlaam6YtmqW4tdNL9tFRVptRVrlUIrlhaltbtabDG1bmFRpCIosSgYb9yEcAkhCQmXXCbkNnN+fyQZCCSQCZk5c3k9H84jmTPfM/MZz8C8+Z7v93sM0zRNAQAAWMRmdQEAACC8EUYAAIClCCMAAMBShBEAAGApwggAALAUYQQAAFiKMAIAACxFGAEAAJaKsLqAznC73Tpy5Iji4uJkGIbV5QAAgE4wTVPV1dVKS0uTzdZx/0dQhJEjR44oIyPD6jIAAEAXFBUVKT09vcPHgyKMxMXFSWp+M/Hx8RZXAwAAOqOqqkoZGRme7/GOBEUYaT01Ex8fTxgBACDIXGiIBQNYAQCApQgjAADAUoQRAABgKcIIAACwFGEEAABYijACAAAsRRgBAACWIowAAABLEUYAAIClCCMAAMBShBEAAGApwggAALAUYQQAAFgqbMOI0+mUYRgyDENOp9PqcgAACFthG0YAAEBgIIwAAABLEUYAAIClCCMAAMBShBEAAGApwggAALAUYQQAAFiKMAIAACxFGAEAAJYK2zDicrk8v2/atKnNfQAA4D9hGUby8vI0YsQIz/1p06YpMzNTeXl5FlYFAEB4CrswkpeXp5kzZ6q4uLjN9uLiYs2cOZNAAgCAn4VVGHG5XJo3b55M0zznsdZt9913H6dsAADwo7AKI5s3b9bhw4c7fNw0TRUVFWnz5s1+rAoAgPAWVmGkpKSkW9sBAICLF1ZhJDU1tVvbAQCAixdWYWTy5MlKT0+XYRjtPm4YhjIyMjR58mQ/VwYAQPgKqzBit9u1dOlSSTonkLTeX7Jkiex2u99rAwAgXIVVGJGkGTNmaPXq1UpLS2uzPT09XatXr9aMGTMsqgwAgPBkmO3Ncw0wVVVVSkhIUGVlpeLj47vtORNT0tX7K99TRM9E7V35/xQbHdUtzw0AADr//R12PSOt7Ha73HXVir7kSjkGXKGDx+usLgkAgLAUtmGkVWPFIUnSnrJqiysBACA8EUYqDkqSdhNGAACwRNiHkYby5jDyBWEEAABLhH0YaT1NQ88IAADWIIy0nKYpOn5KtQ1NFlcDAED4Cfsw4j5VpT6xkZKkL8pqLK4GAIDwE/ZhRJKG9I2VxKkaAACs4HUY2bRpk6ZPn660tDQZhqHXXnvtgvts3LhRV155pRwOh4YMGaI//OEPXSi1e8XGxso0TZmmqeFpvSQxiBUAACt4HUacTqdGjx6tZcuWdar9/v37deONN+r6669XQUGB7rvvPt1555164403vC7WV4b2i5Mk7eY0DQAAfhfh7Q5f+9rX9LWvfa3T7ZcvX65LLrlEzzzzjCRp+PDhevfdd/XLX/5SU6dO9fblfeKylJ6SpD2l9IwAAOBvPh8zkp+fr5ycnDbbpk6dqvz8fF+/dKddmtLcM1JaVafKU40WVwMAQHjxeRgpLS1VSkpKm20pKSmqqqrSqVOn2t2nvr5eVVVVbW6+FB8dqbSEaEmMGwEAwN8CcjbNokWLlJCQ4LllZGT4/DVbe0eYUQMAgH/5PIz069dPZWVlbbaVlZUpPj5eMTEx7e4zf/58VVZWem5FRUW+LtMziJW1RgAA8C+vB7B6Kzs7W2vXrm2zbf369crOzu5wH4fDIYfD4evS2ristWeEQawAAPiV1z0jNTU1KigoUEFBgaTmqbsFBQU6dKj5Gi/z58/X7NmzPe2/973vqbCwUD/60Y+0a9cuPffcc/rzn/+s+++/v3veQTcZ2hJGdpVWyTRNi6sBACB8eB1GPvzwQ40dO1Zjx46VJOXm5mrs2LF69NFHJUklJSWeYCJJl1xyidasWaP169dr9OjReuaZZ/T8888HzLTeVpem9JTNkE7UNupodb3V5QAAEDYMMwi6AaqqqpSQkKDKykrFx8f77HVyFr+jvUdr9Ps5V+n6ock+ex0AAMJBZ7+/A3I2jVWGpzb/j9pZ4tupxAAA4DTCyBmGpzaPG9lZwiBWAAD8hTByBnpGAADwP8LIGUa0hJHC8hrVNbosrgYAgPBAGDlDcpxDvWOj5DalPazECgCAXxBGzmAYhoa1rMS6i3EjAAD4BWHkLK3jRnYwbgQAAL8gjJyFQawAAPgXYeQsp6f3siw8AAD+QBg5y5DknoqwGaqqa9KRyjqrywEAIOQRRs7iiLBrSHJPSdLOI5yqAQDA1wgj7WidUcO4EQAAfI8w0o7WQay7SpneCwCArxFG2sGMGgAA/Icw0o7WMLL/mFO1DU0WVwMAQGgjjLSjb5xDST0dMk1pN6dqAADwKcJIB1rXG2ElVgAAfIsw0oFR/RMkSZ8VE0YAAPAlwkgHRqU1h5HPj1RaXAkAAKGNMNKBy1t6RnaVVKuhyW1xNQAAhC7CSAcyescoPjpCDS63vjjKIFYAAHyFMNIBwzA840Y+Z9wIAAA+Qxg5j9Yw8mkx40YAAPAVwsh5jExrXvzsMwaxAgDgM4SR82gdxLqzpEpNLgaxAgDgC4SR88jsE6vYKLvqGt3aV+60uhwAAEISYeQ8bDZDI9NaFz/jVA0AAL5AGLkAz0qsjBsBAMAnCCMXMKp/yyBWekYAAPAJwsgFeNYaOVIlt9u0uBoAAEIPYeQCBvftqehIm2obXNp/jEGsAAB0N8LIBdhthkakcqoGAABfIYx0Qut6I4QRAAC6H2GkE0Z6wgjXqAEAoLsRRjrhzJ4RBrECANC9CCOdcGlyT8VE2lVd36TCCgaxAgDQnQgjnRBht3l6Rz4uOmltMQAAhBjCSCeNzmgJI4dPWlsIAAAhhjDSSaMzekmiZwQAgO5GGOmk0em9JEk7SqpU3+SythgAAEIIYaST0hNj1Cc2So0uUztLqq0uBwCAkEEY6STDMDhVAwCADxBGvNB6qoYwAgBA9yGMeKF1Rk0BM2oAAOg2hBEvtPaMFJY7VVnbaG0xAACECMKIFxJjozSwTw9J0ifFJ60tBgCAENGlMLJs2TJlZmYqOjpaWVlZ2rp163nbL1myREOHDlVMTIwyMjJ0//33q66urksFW41xIwAAdC+vw8grr7yi3NxcLVy4UNu3b9fo0aM1depUHT16tN32L7/8sh588EEtXLhQO3fu1AsvvKBXXnlFDz300EUXb4XWGTUFRZXWFgIAQIjwOowsXrxYd911l+bMmaMRI0Zo+fLl6tGjh1auXNlu+y1btmjSpEm69dZblZmZqSlTpuiWW265YG9KoBrTOoi16KRMkyv4AgBwsbwKIw0NDdq2bZtycnJOP4HNppycHOXn57e7z8SJE7Vt2zZP+CgsLNTatWs1bdq0Dl+nvr5eVVVVbW6BYmRaguw2QxU19SqpDM5TTQAABBKvwkhFRYVcLpdSUlLabE9JSVFpaWm7+9x666167LHHdPXVVysyMlKDBw/Wddddd97TNIsWLVJCQoLnlpGR4U2ZPhUdadewfnGSmntHAADAxfH5bJqNGzfqiSee0HPPPaft27crLy9Pa9as0eOPP97hPvPnz1dlZaXnVlRU5OsyvTKmZdzIR4dOWFsIAAAhIMKbxklJSbLb7SorK2uzvaysTP369Wt3nwULFuj222/XnXfeKUm6/PLL5XQ6dffdd+vhhx+WzXZuHnI4HHI4HN6U5lfjBibqpX8e0raDhBEAAC6WVz0jUVFRGjdunDZs2ODZ5na7tWHDBmVnZ7e7T21t7TmBw263S1LQDgAdNzBRkvRZcZXqGrmCLwAAF8OrnhFJys3N1R133KHx48drwoQJWrJkiZxOp+bMmSNJmj17tvr3769FixZJkqZPn67Fixdr7NixysrK0t69e7VgwQJNnz7dE0qCzYDePZTUM0oVNQ36/Eilxg3sbXVJAAAELa/DyKxZs1ReXq5HH31UpaWlGjNmjNatW+cZ1Hro0KE2PSGPPPKIDMPQI488ouLiYvXt21fTp0/Xz372s+57F35mGIauHJCoN3eUadvBE4QRAAAugmEGwbmSqqoqJSQkqLKyUvHx8VaXI0n67Tv7tOj/dmnqyBT99vbxVpcDAEDA6ez3N9em6aLWcSPbDrL4GQAAF4Mw0kWj+ico0t68+FnR8VNWlwMAQNAijHRRdKRdo/o3Lw2/7dBxi6sBACB4EUYuwnjPqRrWGwEAoKsIIxfhzHEjAACgawgjF+HKAc1hZHdplarrGi2uBgCA4EQYuQjJ8dHK6B0jtyl9XFRpdTkAAAQlwshFGjeAcSMAAFwMwshFah038uFBZtQAANAVhJGLdGVLGPno0Em53Cx+BgCAtwgjF2lYv3jFOSJUU9+knSVVVpcDAEDQIYxcJLvN0PjM5t6Rf+7nVA0AAN4ijHSDrEF9JEn/LDxmcSUAAAQfwkg3mHBJb0nSBweOy824EQAAvEIY6QaX909QTKRdJ2obtbe8xupyAAAIKoSRbhBpt3mm+HKqBgAA7xBGuknrqRoGsQIA4B3CSDdpDSNb9x+XaTJuBACAziKMdJMxGb0UZbfpaHW9DhyrtbocAACCBmGkm0RH2jUmo5ckaet+xo0AANBZhJFu5Bk3Usi4EQAAOosw0o2yBjGIFQAAbxFGutGVAxJltxkqPnlKh08wbgQAgM4gjHSjWEeERvVPkNQ8qwYAAFwYYaSbfanlVM2WfQxiBQCgMwgj3WzS4CRJ0pa9Faw3AgBAJxBGutn4zERF2g0dqaxjvREAADqBMNLNekRFaOyA5uvUvLe3wuJqAAAIfIQRH/CcqtlHGAEA4EIIIz4waUgfSVL+vmNyuxk3AgDA+RBGfGB0Ri/FRtl1orZRO0qqrC4HAICARhjxgUi7zbM0fD5TfAEAOC/CiI9MGtI8buQ9xo0AAHBehBEfmdgyiHXr/uNqaHJbXA0AAIGLMOIjw/rFqXdslGobXPr48EmrywEAIGARRnzEZjOUPbh5Vg3rjQAA0DHCiA+dXhqeQawAAHSEMOJDreuNbD90QjX1TRZXAwBAYCKM+NDAPrEa2KeHmtwmU3wBAOgAYcTHrr2sryTpnT1HLa4EAIDARBjxsWsubQ4jG3eXyzRZGh4AgLMRRnwse3AfRdoNHT5xSvsrnFaXAwBAwCGM+FisI0JXZTYvDf/OnnKLqwEAIPAQRvzg9LgRwggAAGcjjPjBtUObw8j7hcdU1+iyuBoAAAILYcQPhqbEKSXeobpGtz44cNzqcgAACChdCiPLli1TZmamoqOjlZWVpa1bt563/cmTJzV37lylpqbK4XDosssu09q1a7tUcDAyDOP0qZrdnKoBAOBMXoeRV155Rbm5uVq4cKG2b9+u0aNHa+rUqTp6tP11NBoaGvSVr3xFBw4c0OrVq7V7926tWLFC/fv3v+jig8k1jBsBAKBdEd7usHjxYt11112aM2eOJGn58uVas2aNVq5cqQcffPCc9itXrtTx48e1ZcsWRUZGSpIyMzMvruogdPWQJNkM6YujNSo+eUr9e8VYXRIAAAHBq56RhoYGbdu2TTk5OaefwGZTTk6O8vPz293n9ddfV3Z2tubOnauUlBSNGjVKTzzxhFyujgdy1tfXq6qqqs0t2PXqEaUxGb0kcaoGAIAzeRVGKioq5HK5lJKS0mZ7SkqKSktL292nsLBQq1evlsvl0tq1a7VgwQI988wz+ulPf9rh6yxatEgJCQmeW0ZGhjdlBqzrhyZLkjbsLLO4EgAAAofPZ9O43W4lJyfrd7/7ncaNG6dZs2bp4Ycf1vLlyzvcZ/78+aqsrPTcioqKfF2mX0wZ2U+StHlvhZxcxRcAAElejhlJSkqS3W5XWVnbf9mXlZWpX79+7e6TmpqqyMhI2e12z7bhw4ertLRUDQ0NioqKOmcfh8Mhh8PhTWlB4bKUnhrYp4cOHqvVpj3l+trlqVaXBACA5bzqGYmKitK4ceO0YcMGzza3260NGzYoOzu73X0mTZqkvXv3yu12e7bt2bNHqamp7QaRUGYYhqaMaD7F9eYOTtUAACB14TRNbm6uVqxYoRdffFE7d+7UPffcI6fT6ZldM3v2bM2fP9/T/p577tHx48c1b9487dmzR2vWrNETTzyhuXPndt+7CCJfGdHcg7RhZ5kaXe4LtAYAIPR5PbV31qxZKi8v16OPPqrS0lKNGTNG69at8wxqPXTokGy20xknIyNDb7zxhu6//35dccUV6t+/v+bNm6cHHnig+95FEBk3MFG9Y6N03NmgrfuPa9KQJKtLAgDAUoZpmqbVRVxIVVWVEhISVFlZqfj4eKvLuWg/Wv2x/vzhYd2RPVA/uWmU1eUAAOATnf3+5to0FpjScqrmzR1lCoIsCACATxFGLHD1pUmKibSrpLJOnxUH/4JuAABcDMKIBaIj7Z4L5725o/3F4gAACBeEEYtMGdkyxfdzpvgCAMIbYcQiNwxLlt1maHdZtQ4ec1pdDgAAliGMWKRXjyhlXdJbkrSeBdAAAGGMMGIhz2qsnKoBAIQxwoiFvtJy4bwPDx5XRU29xdUAAGANwoiF+veK0aj+8XKb0j92HrW6HAAALEEYsdjpBdCY4gsACE+EEYu1TvHd9EWFnPVNFlcDAID/EUYsNjQlTgN691BDk1ubvyi3uhwAAPyOMGIxwzCYVQMACGuEkQAwpWVWzYZdR9XocltcDQAA/kUYCQDjBiaqd2yUKk816oP9x60uBwAAvyKMBAC7zVDO8GRJ0trPSiyuBgAA/yKMBIivX5EmSVrzSYkamjhVAwAIH4SRADFxcB/1jXPoRG2jNu1hVg0AIHwQRgJEhN2mb4xu7h35a0GxxdUAAOA/hJEA8i9j+0tqvopvVV2jxdUAAOAfhJEAMjItXpcm91RDk1vrPmV5eABAeCCMBBDDMHRzS+/IXz/iVA0AIDwQRgLMTWOax428v/+Yjpw8ZXE1AAD4HmEkwKQn9tCES3rLNKXXPz5idTkAAPgcYSQAtQ5kfY1TNQCAMEAYCUDTLk9VlN2mXaXV2llSZXU5AAD4FGEkACXEROrLLcvDM5AVABDqCCMBqnVWzd8KiuVymxZXAwCA7xBGAtR1Q/sqISZSZVX1er/wmNXlAADgM4SRAOWIsOvGK1IlcaoGABDaCCMBrHVWzbrPSnWqwWVxNQAA+AZhJICNH5io9MQY1dQ36a2dZVaXAwCATxBGAphhGKw5AgAIeYSRAHfTmOYw8s6ech2rqbe4GgAAuh9hJMANSe6pK9IT1OQ29eqHB2QYhgzDkNPptLo0AAC6BWEkCMxoOVWzenuJxZUAAND9CCNB4F+uTFdMpF17jjrlSB9pdTkAAHQrwkgQSIiJ1M1j0yRJcWOnWVwNAADdizASJP7tSwMlST2GTpQttpe1xQAA0I0II0FiZFqCxqTHy7BHKu6KqVaXAwBAtyGMBJFbxjcPZO055qtqcrstrgYAgO5BGAkiU4b3lau2UhHxffXOHi6eBwAIDYSRIBIVYVPNx29Kkv704RGLqwEAoHsQRoKIy+VS1Yd/U83nb+vtd97RntJKq0sCAOCiEUaCRF5enkaMGCF37Ukd+/szKvvTQ7pq1FDl5eVZXRoAABelS2Fk2bJlyszMVHR0tLKysrR169ZO7bdq1SoZhqGbb765Ky8btvLy8jRz5kwVF7e9WF7VsTLNnDmTQAIACGpeh5FXXnlFubm5WrhwobZv367Ro0dr6tSpOnr06Hn3O3DggP7rv/5LkydP7nKx4cjlcmnevHkyTbPdx01Tuu++++RyufxcGQAA3cPrMLJ48WLdddddmjNnjkaMGKHly5erR48eWrlyZYf7uFwu3XbbbfrJT36iQYMGXVTB4Wbz5s06fPjweVqYKioq0ubNm/1WEwAA3cmrMNLQ0KBt27YpJyfn9BPYbMrJyVF+fn6H+z322GNKTk7Wd7/73a5XGqZKSjp3cbzOtgMAINBEeNO4oqJCLpdLKSkpbbanpKRo165d7e7z7rvv6oUXXlBBQUGnX6e+vl719fWe+1VVVd6UGVJSU1O7tR0AAIHGp7Npqqurdfvtt2vFihVKSkrq9H6LFi1SQkKC55aRkeHDKgPb5MmTlZ6eLsMwOmwTEZ+kEWMn+LEqAAC6j1dhJCkpSXa7XWVlZW22l5WVqV+/fue037dvnw4cOKDp06crIiJCERER+uMf/6jXX39dERER2rdvX7uvM3/+fFVWVnpuRUVF3pQZUux2u5YuXSpJ5wSS1vuJN9ytv2xnETQAQHDyKoxERUVp3Lhx2rBhg2eb2+3Whg0blJ2dfU77YcOG6dNPP1VBQYHn9o1vfEPXX3+9CgoKOuzxcDgcio+Pb3MLZzNmzNDq1auVlpbWZnt6err+66nfqcfQifr9ewdU18iMGgBA8PFqzIgk5ebm6o477tD48eM1YcIELVmyRE6nU3PmzJEkzZ49W/3799eiRYsUHR2tUaNGtdm/V69eknTOdpzfjBkzlJOTo4SEBEnS2rVrNWXKFLlMQ+/94m0dqazTXz4s0u3ZmdYWCgCAl7wOI7NmzVJ5ebkeffRRlZaWasyYMVq3bp1nUOuhQ4dks7Gwqy/Y7XbP79dcc43sdrvskv7j2sFa+PrnWv5Oob49YYAi7fz/BwAED8PsaDWtAFJVVaWEhARVVlaG9Skbp9Opnj17SpJqamoUGxsrSaprdOnqn/9DFTUNevqbozVzXLqVZQIAIKnz39/8EzoEREfa9d2rmxeTe27jXrncAZ8vAQDwIIyEiH/70gDFR0eosNypNz4vtbocAAA6jTASIuKiI/WdiZmSpGVv7+3wWjYAAAQawkgImTPpEvWIsuvzI1XauKfc6nIAAOgUwkgISYyN0q0TBkiSlv2D3hEAQHAgjISYu64ZpCi7TR8ePKGt+49bXQ4AABdEGAkisbGxMk1Tpml6pvWeLSU+WjPHN0/tffbtvf4sDwCALiGMhKDvXTNYdpuhzV9U6JPDJ60uBwCA8yKMhKABfXroG6Obr2OzjN4RAECAI4yEqO9fN1iS9MbnZfqirNriagAA6BhhJERdmhKnr47sJ0n61T/oHQEABC7CSAj7wZeHyDCk//34iD4uOml1OQAAtIswEsJGpiXoX8b0lyT9bO1O1h0BAAQkwkiI+39Th8oRYdPW/cf11s6jVpcDAMA5CCMhrn+vGP371ZdIkhb93041utwWVwQAQFuEkTBwz3WD1Ts2SoXlTq36oMjqcgAAaIMwEgbioyM178uXSpKWvrVHNfVNFlcEAMBphJEwcWvWAF2SFKuKmgb99p19VpcDAIAHYSRMRNpteuCrwyRJKzYXqrSyzuKKAABoRhgJI1NHpmj8wETVNbr1zJu7rS4HAABJhJGwYhiGHrpxuCRp9fbD2llSZXFFAAAQRsLOlQMSdeMVqTJN6Ym1O60uBwAAwkg4emDqMEXaDW3+okKb9pRbXQ4AIMwRRsLQgD49NDs7U5L0k//9XPVNLmsLAgCENcJImPrPGy5VUk+H9pU79dzbTPUFAFiHMBKmEnpE6sffGCFJem7jXn1RVm1xRQCAcEUYCWM3Xp6qLw9LVqPL1IN5n8rt5qq+AAD/I4yEMcMw9PjNoxQbZde2gyf00tZDVpcEAAhDhJEwl9YrRj+cOlSS9PP/28XKrAAAvyOMQLdnZ2pMRi/V1Dfp0b99ZnU5AIAwQxiB7DZDT/7r5YqwGXpzR5nWfVZidUkAgDBCGIEkaVi/eH3v2sGSpEf/9rkqTzVaXBEAIFwQRuBx7w1DNCgpVker6/XzdbusLgcAECYII/CIjrTriRmXS5Je/uchbd1/3OKKAADhgDCCNr40qI++fVWGJOmHqz9WdR2nawAAvkUYwTnmTxuu/r1idPBYrR7+62cyTRZDAwD4DmEE50iIidSvbhkju83Q6x8f0V+2Hba6JABACCOMoF3jBvZW7lcukyQt/Nvn2nuUa9cAAHyDMIIO3XPtYF09JEmnGl269+WPVNfosrokAEAIIoygQzabocWzRiupZ5R2lVbribU7rS4JABCCCCM4r+S4aD3zrTGSpD/mH9S6z0qtLQgAEHIII7igay/rq/+4dpAk6UerP9bhE7UWVwQACCWEEXTKf00ZqjEZvVRV16R5qwrU5HJbXRIAIEQQRtApkXabfn3LWMU5IrTt4Ak9s36P1SUBAEIEYQSdltG7hxb9a/Ny8b/ZuE+vfVRscUUAgFBAGIFXvn5Fmufqvj9a/Yk+PMD1awAAF6dLYWTZsmXKzMxUdHS0srKytHXr1g7brlixQpMnT1ZiYqISExOVk5Nz3vYIfD+aOlRTR6aoweXW3f+9TYeOMaAVANB1XoeRV155Rbm5uVq4cKG2b9+u0aNHa+rUqTp69Gi77Tdu3KhbbrlFb7/9tvLz85WRkaEpU6aouJgu/mBlsxn65awxGtU/XsedDfr3Fz9Q5SkuqAcA6BrD9PIqaFlZWbrqqqv07LPPSpLcbrcyMjL0gx/8QA8++OAF93e5XEpMTNSzzz6r2bNnd+o1q6qqlJCQoMrKSsXHx3tTLnyorKpONz37nkqr6jT50iSt/M5VirRz5g8A0Kyz399efXM0NDRo27ZtysnJOf0ENptycnKUn5/fqeeora1VY2Ojevfu3WGb+vp6VVVVtbkh8KTER+v5O8YrJtKuzV9U6Mevf84VfgEAXvMqjFRUVMjlciklJaXN9pSUFJWWdm5lzgceeEBpaWltAs3ZFi1apISEBM8tIyPDmzLhR6P6J2jpt8fIMKSX/nlIv3/vgNUlAQCCjF/71J988kmtWrVKf/3rXxUdHd1hu/nz56uystJzKyoq8mOV8NaUkf00/2vDJEk/XbNDG3aWWVwRACCYeBVGkpKSZLfbVVbW9sumrKxM/fr1O+++Tz/9tJ588km9+eabuuKKK87b1uFwKD4+vs0Nge2uyYP07asy5Dal77+0Xfn7jnV6X6fTKcMwZBiGnE6nD6sEAAQir8JIVFSUxo0bpw0bNni2ud1ubdiwQdnZ2R3u99RTT+nxxx/XunXrNH78+K5Xi4BlGIYev3mUbhiWrPomt7774gf6gDVIAACd4PVpmtzcXK1YsUIvvviidu7cqXvuuUdOp1Nz5syRJM2ePVvz58/3tP/5z3+uBQsWaOXKlcrMzFRpaalKS0tVU1PTfe8CASHSbtNzt12pyZcmqbbBpTm//0DbD52wuiwAQIDzOozMmjVLTz/9tB599FGNGTNGBQUFWrdunWdQ66FDh1RSUuJp/5vf/EYNDQ2aOXOmUlNTPbenn366+94FAkZ0pF0rZo/XxMF9VFPfpDtWbtUnh09aXRYAIIB5vc6IFVhnJPjUNjTpOys/0NYDx5UQE6mX78rSyLSEdts6nU717NlTklRTU6PY2Fh/lgoA8BGfrDMCdFaPqAitnHOVrhzQS5WnGvVvz/9Tu0urrS4LABCACCPwmZ6OCP3h3yfoivQEnaht1G3Pv6+9RwkkAIC2CCPwqfjoSP33v2dpRGq8KmoaNOu37+vjopNWlwUACCCEEfhcQo9I/c+dWRqZFq9jzgZ9+3fv6+1d7V9YEQAQfggj8IvesVFadfeXNPnSJJ1qdOnOP36oP3/QvLKuy+XytNu0aVOb+wCA0EcYgd/ERUfqhTuu0owr+8vlNvWjVz/Rvy98ViNGjPC0mTZtmjIzM5WXl2dhpQAAfyKMwK+iImx65pujNff6wardvUW/f+wHKi4ubtOmuLhYM2fOJJAAQJhgnRFYwuVyKTktQ8ePlrT7uGEYSk9P1/79+2W32/1cHQCgO7DOCALa5s2bOwwikmSapoqKirR582Y/VgUAsAJhBJY485IB3dEOABC8CCOwRGpqare2AwAEL8IILDF58mSlp6fLMIwO2/RITNGIsRP8WBUAwAqEEVjCbrdr6dKlknRuIGm5H3vtd3XTc/nafuiEv8sDAPgRYQSWmTFjhlavXq20tLQ22zPS0/Wr5/9HoyZ9RSWVdZr123y98O5+ud0BP/ELANAFTO2F5VqPryStXbtWU6ZMkd1uV3Vdox549ROt/bRUkvSlQb311L+O1oA+Pawst0ucTqd69uwpSaqpqVFsbKzFFQGA7zG1F0HjzHVErrnmGs/9uOhILbv1Sj1+00jFRNr1fuFxfXXpJv0x/wC9JAAQQggjCGiGYej27Eytu2+yJlzSW7UNLj36t8912/P/VNHxWqvLAwB0A8IIgsLAPrFaddeX9OPpIxQTaVd+4TFNXbJJ//3+QXpJ/MzpdMowDBmGIafTaXU5AEIAYQRBw2Yz9J1Jl7TpJVnw2mf69or39cnhk1aXBwDoIsIIgs7ZvSRb9x/XN559T3Nf3q4DFfxLHQCCDWEEQam1l2R97jWaMba/DENa80mJcha/o4V/+0wVNfVWlwgA6CTCCIJaemIPLZ41Rmt+MFnXDe2rJrepF/MP6tqn3tbSt76Qs77J6hIBABdAGEFIGJEWrz/MmaCX78rSFekJcja49Mu39ujaX7yt/84/oEaX2+oSAQAdIIwgpEwcnKS/zZ2kZ28dq4F9eqiipkEL/va5vrL4Ha35pERWrfHncrk8v2/atKnNfQDnYtZWeCGMwHKxsbEyTVOmaXbLyqSGYejrV6Rp/f3X6rGbRqpPbJQOHKvV3Je36+bntih/37FuqLrz8vLyNGLECM/9adOmKTMzU3l5eX6tAwACFcvBI+TV1DdpxaZCrdhcqNqG5h6J64f21Q+nDtOINN9+nvLy8jRz5sxzemRaLw64evVqzZgxw6c1dDeWtoc/8DkLDZ39/iaMIGyUV9frVxu+0J+2HlJTy0JpEzJ767YvDdBXR/WTI8J+gWfwjsvlUmZmpg4fPtzu44ZhKD09Xfv372+zJH6g6+haQkB3IoyEBq5NA5ylb5xDj988Sutzr9X00Wmy2wxtPXBc81YVKHvRP7To/3bq4LHuOze9efPmDoOIJJmmqaKiIm3evLnbXtPXOOUEwBcIIwg7lyTF6te3jNV7D9yg+3MuU2pCtI47G/Tbdwp17S826vYX/ql1n5Wq6SJn4JSUlHRrO6u1nnIqLi5us724uFgzZ84kkADoMk7TIOw1udx6e3e5XvrnQb2zp1ytfyJS4h2addUAffuqDKX1ivH6eTdu3Kjrr7/+gu3efvttXXfddV4/vz+F6iknBC5O04QGxowAXXDoWK3+9MEh/fmDIh1zNkiSbIb05eEpui1rgK65tK9sNqNTz9X6BV5cXNzulOJg+gIPpWCF4EAYCQ2MGQG6YECfHnrgq8OUP//L+vUtY/WlQb3lNqX1O8r0nd9/oGt+8bae/ccX2nu0+oJrltjtdi1dulTS6dkzrVrvL1myJOCDiBR6p5wABBbCCNCOqAibpo9O06q7s/VW7jWaMylT8dEROnzilJ5+c49yFm/S9U9v1E//vkP/LDzW4fiSGTNmaPXq1UpLS2uzPT09Paim9aampnZru0DC4lqBiYUCwwunaYBOOtXg0t8/OaK/f1Ki/H3H1HBGAOnVI1I3DE1WzogUXXNZX/V0RLTZN9inw4bSKaezcTog8OTl5ek///M/2wyWTk9P19KlS4MmwKMZY0YAH6qpb9KmPeV6a0eZ/rH7qE7WNnoei7LblD24j3JGpOgrw1PULyE6JL7wWmfTSGoTSIJ5ATeJMBJognGhQD5DHSOMAH7S5HJr28ETWr+jTOt3lungsdo2j1/eP0HXDEnUI3NuUsPR/aqprgrav6za+xdrRkaGlixZEnBfEJ3FF0ngCNZZW3yGOkYYASxgmqb2ldfozR1lemtHmT4qOqkz/4S56526eni6sgb11fjMRI3J6KXYs07pBLpgP+V0Nr5IAkewztriM9Sxzn5/B9ffgkCAMwxDQ5LjNCQ5Tt+/bojKq+v19q6j+r9Pi7Xh0yLZHLHaUnhCWwpPSGqeNjw8NV7jByZqXGZvjR+Y2KU1TfzpzOBxzTXXBHUQQWBh1lb4IowAPtQ3zqFvXZWhG0f0Vs87Jymy70Ate2WdPi1xatvBEyo+eUqfH6nS50eq9GL+QUlSWkK0J5iMG5ioYf3iFGFn4htCXyjP2sL5EUYAfzHdajy6X7de1d/TjVtSeUofHjihbQdP6MODx7WzpFpHKut05OMj+t+Pj0iSYiLtGtQ3VoP69tQlSbEa3DdWg5J66pK+sefM2gGC2eTJk5Wenn7BWVuTJ0+2oDr4En+TARZKTYjR9NExmj66eR0SZ32TPi46qQ8PntCHB0/oo4MnVF3f5Ok9OVtKvEOXJDUHlUFJsRrcEljSE2PoTUHQaV0ocObMmTIMo91ZW8GyUCC8QxgBAkisI0IThyRp4pAkSZLLberAMacKy50qLK/R/oqW3ytqVFHToLKqepVV1ev9wuNtnifSbmhgn1gNOiOotPau9I6NsuKtwc+CdVBl60KB7a0zEsyztgJVoHxOCCOAH8TGxl5w+fj22G2GBvftqcF9e0pKafNY5anGcwJKYblT+yucqm9ya+/RGu09WiOprM1+vXpENvemJPXUoL4tp3369tSA3j0UHRl+/+I8e6XPYJ8dFApmzJihnJyckJq1hfMjjABBKiEmUmMHJGrsgMQ2291uU0cqT3l6UwornJ7AUnzylE7WNuqjQyf10aGTbfazGVL/xBhdktRTKXEO9enpUFLPKPXpGaU+sQ7PT4fR/tL3wah13ZRW06ZNY6XPABFMs7YItBePdUaAMHKqwdUcTCpqtL/cqcKKlsBS7lR1fVOnn8dVVyO386SyxoxUcnxMc1BpDS8twaX194SYyE5f6difgnGlT28ESvd7VwVL/cG+dL2v/z+z6BmATjNNU+U19dpf7tTBY7Uqr6nXsZoGHXM2/6yoqdcxZ4OOOxvkcnv3V4bdZqh3bJT6xEYpqaej+feezb/3iW0OMbEOu2Ii7YqJsis6ouVnZPO2SLtxzlWPL1awrvTpjWD5Mu9IMNQfCoE2UMJIl07TLFu2TL/4xS9UWlqq0aNH69e//rUmTJjQYfu//OUvWrBggQ4cOKBLL71UP//5zzVt2rSuvDQAHzAMQ8lx0UqOi1bWoD4dtnO7TVWeatQxZ70qaho8gaX593MDTFVdk1xuU+XV9SqvrpdU7XVtNkOeoOJoCSoxLUHFEWnrMMRER9o891u3Nbe16ZOtWzoMIlJzOCsqKtLmzZsDaqVPBA6Xy6V58+a1OxbMNE0ZhqH77rtPN910U9AGWn/yOoy88sorys3N1fLly5WVlaUlS5Zo6tSp2r17t5KTk89pv2XLFt1yyy1atGiRvv71r+vll1/WzTffrO3bt2vUqFHd8iYA+IfNZigxNkqJsVEacu4f93M0NLl1oralZ6VNUGkJL87mn84Gl041uFTf1PzzVKNLrR0wblNyNrjkbOi+S8g7d2zqVLt5K9/W4B2RnrATfUYQim4JQtFnBKGYyNbwc0ZIirQrym6T3WZ4bjaj5XfDkM0mz7aIlse7uycI3W/z5s0E2m7kdRhZvHix7rrrLs2ZM0eStHz5cq1Zs0YrV67Ugw8+eE77pUuX6qtf/ap++MMfSpIef/xxrV+/Xs8++6yWL19+keUDCGRRETalxEcrJT7aq/1M01Sjy9SpRpfqGpsDSt0ZQaW+0a1Tjafv17W2a3SpruWxujMeO9Xo0qlGt+pbfi/rm6KKTtRR1hStyrMG+vqDYaglqDQHluawopYwY5PddsbjnlBzuq3dZkimWyn/9gvJ7dZ3/viRIiMiTgeis5/bZshuNIfNiLMCk81oG6IibGfuK8/vhiEZav7Z/B4MGS3vxWi93/K72jxmnNGm+b4MqaGhXrGjbpBM6fVPShUd7TjP87d9DrW5f0a7M57/nMd0Zn1nP+fp12313idfdOpYbvl0r+IHjdaZu58dNts+duZ2o93tZ+vMPh21OXWqVpFJA5p/b3TJqpNhXoWRhoYGbdu2TfPnz/dss9lsysnJUX5+frv75OfnKzc3t822qVOn6rXXXuvwderr61VfX++5X1V17mJPAEKXYRiKijAUFWFTQkxktz+/y3WNMt9cct6VPpP7penlBXPU4FKbHpvWwOMJSY1tQ09do/uc7Q1NbrlMUy538+1Cw25MU2oyTV2w4QVE9x8uSfrgYOVFPY9Vkm5s/u6Y/7ddFldyrrpDnYmz0q/yy/W74i0+rubipH33OUnSnrIaJfWyZlymV2GkoqJCLpdLKSlt1ztISUnRrl3tf1hKS0vbbV9aWtrh6yxatEg/+clPvCkNADqtMyt9Pvfsr3TD8H4+eX3TbA4kzcGkJaSYptzu04GlNby43Tr9+xmB5px9zNNt3W5TVTU1+tbNX5fcLj204Me6fMIkybCd8Tw6z2ue8dwtbd2mqSZXB/WaptT8n0zTbPnZ9n7z42bz9jN/b2mrNvdNNblcevPNNyUZ+nJOjux2e9v9znoOnfOcbetQm/tnPoc8x//cuk7X7jl2rT97T9DJtX1VX1ne4XF2JPTVpVdcJdlOr4Z8dvbtaArJmZ9Js832s9qd8ajZTp3n7tP2CVxNTTr88bty155Uwag6ZQ252ZoxLqYXiouLTUnmli1b2mz/4Q9/aE6YMKHdfSIjI82XX365zbZly5aZycnJHb5OXV2dWVlZ6bkVFRWZkszKykpvygWA83r11VfN/v37my1/Q5uSzIyMDPPVV1+1urSL0t77Sk9PD/r3FWheffVV0zAM0zCMNv+vW7cF+v9vf3xOKisrO/X97dXFK5KSkmS321VW1nZFx7KyMvXr1/6/IPr16+dVe0lyOByKj49vcwOA7jZjxgzt2LHDc3/t2rXav39/wE/HPJ/W6aZnrnshScXFxZo5c6by8vIsqiz0tC5dn5aW1mZ7enp6wE/rDbTPiVdhJCoqSuPGjdOGDRs829xutzZs2KDs7Ox298nOzm7TXpLWr1/fYXsA8KdgWunzQi403VSS7rvvvjYrhuLiBGOgDcTPideX9czNzdWKFSv04osvaufOnbrnnnvkdDo9s2tmz57dZoDrvHnztG7dOj3zzDPatWuXfvzjH+vDDz/Uvffe233vAgDg1XRTdJ9gC7SB+DnxemrvrFmzVF5erkcffVSlpaUaM2aM1q1b5xmkeujQIdnOGKwzceJEvfzyy3rkkUf00EMP6dJLL9Vrr73GGiMA0M1KSkq6tR1CUyB+Trq0Auu9997bYc/Gxo0bz9n2zW9+U9/85je78lIAgE5KTU3t1nYITYH4OfH6NA0AIDBNnjxZ6enpHa7gahiGMjIyNHnyZD9XhkASiJ8TwggAhIjW9VOkdlb5bLm/ZMmSgB/TAN8KxM8JYQQAQkgwTzeF/wTa58Qw25vbE2A6ewliAPBWMFyqvita/96UmqebTpkyhR4RHwnmz5CvPyed/f7u0gBWAAgVsbGx7a63EOyCbboprBEonxPCCAAAFyFUA60/MWYEAABYijACAAAsRRgBAACWIowAAABLEUYAAIClCCMAAMBSTO0FgBDEdFN0RqB8TugZAQAAliKMAAAASxFGAACApQgjAADAUoQRAABgKcIIAACwFGEEAABYijACAAAsRRgBAACWIowAAABLEUYAAIClCCMAAMBShBEAAGApwggAALAUYQQAAFgqwuoCOsM0TUlSVVWVxZUAAIDOav3ebv0e70hQhJHq6mpJUkZGhsWVAAAAb1VXVyshIaHDxw3zQnElALjdbh05ckRxcXEyDKPbnreqqkoZGRkqKipSfHx8tz0vLg7HJTBxXAITxyUwcVyamaap6upqpaWlyWbreGRIUPSM2Gw2paen++z54+Pjw/rDEqg4LoGJ4xKYOC6BieOi8/aItGIAKwAAsBRhBAAAWCqsw4jD4dDChQvlcDisLgVn4LgEJo5LYOK4BCaOi3eCYgArAAAIXWHdMwIAAKxHGAEAAJYijAAAAEsRRgAAgKVCPowsW7ZMmZmZio6OVlZWlrZu3Xre9n/5y180bNgwRUdH6/LLL9fatWv9VGl48ea4rFixQpMnT1ZiYqISExOVk5NzweOIrvH2z0urVatWyTAM3Xzzzb4tMEx5e1xOnjypuXPnKjU1VQ6HQ5dddhl/l/mAt8dlyZIlGjp0qGJiYpSRkaH7779fdXV1fqo2wJkhbNWqVWZUVJS5cuVK8/PPPzfvuusus1evXmZZWVm77d977z3TbrebTz31lLljxw7zkUceMSMjI81PP/3Uz5WHNm+Py6233mouW7bM/Oijj8ydO3ea3/nOd8yEhATz8OHDfq48tHl7XFrt37/f7N+/vzl58mTzpptu8k+xYcTb41JfX2+OHz/enDZtmvnuu++a+/fvNzdu3GgWFBT4ufLQ5u1xeemll0yHw2G+9NJL5v79+8033njDTE1NNe+//34/Vx6YQjqMTJgwwZw7d67nvsvlMtPS0sxFixa12/5b3/qWeeONN7bZlpWVZf7Hf/yHT+sMN94el7M1NTWZcXFx5osvvuirEsNSV45LU1OTOXHiRPP5558377jjDsKID3h7XH7zm9+YgwYNMhsaGvxVYljy9rjMnTvXvOGGG9psy83NNSdNmuTTOoNFyJ6maWho0LZt25STk+PZZrPZlJOTo/z8/Hb3yc/Pb9NekqZOndphe3ivK8flbLW1tWpsbFTv3r19VWbY6epxeeyxx5ScnKzvfve7/igz7HTluLz++uvKzs7W3LlzlZKSolGjRumJJ56Qy+XyV9khryvHZeLEidq2bZvnVE5hYaHWrl2radOm+aXmQBcUF8rrioqKCrlcLqWkpLTZnpKSol27drW7T2lpabvtS0tLfVZnuOnKcTnbAw88oLS0tHOCI7quK8fl3Xff1QsvvKCCggI/VBieunJcCgsL9Y9//EO33Xab1q5dq7179+r73/++GhsbtXDhQn+UHfK6clxuvfVWVVRU6Oqrr5ZpmmpqatL3vvc9PfTQQ/4oOeCFbM8IQtOTTz6pVatW6a9//auio6OtLidsVVdX6/bbb9eKFSuUlJRkdTk4g9vtVnJysn73u99p3LhxmjVrlh5++GEtX77c6tLC2saNG/XEE0/oueee0/bt25WXl6c1a9bo8ccft7q0gBCyPSNJSUmy2+0qKytrs72srEz9+vVrd59+/fp51R7e68pxafX000/rySef1FtvvaUrrrjCl2WGHW+Py759+3TgwAFNnz7ds83tdkuSIiIitHv3bg0ePNi3RYeBrvx5SU1NVWRkpOx2u2fb8OHDVVpaqoaGBkVFRfm05nDQleOyYMEC3X777brzzjslSZdffrmcTqfuvvtuPfzww7LZwrtvIGTffVRUlMaNG6cNGzZ4trndbm3YsEHZ2dnt7pOdnd2mvSStX7++w/bwXleOiyQ99dRTevzxx7Vu3TqNHz/eH6WGFW+Py7Bhw/Tpp5+qoKDAc/vGN76h66+/XgUFBcrIyPBn+SGrK39eJk2apL1793rCoSTt2bNHqampBJFu0pXjUltbe07gaA2MJpeIC/2pvQ6Hw/zDH/5g7tixw7z77rvNXr16maWlpaZpmubtt99uPvjgg5727733nhkREWE+/fTT5s6dO82FCxcytdcHvD0uTz75pBkVFWWuXr3aLCkp8dyqq6utegshydvjcjZm0/iGt8fl0KFDZlxcnHnvvfeau3fvNv/+97+bycnJ5k9/+lOr3kJI8va4LFy40IyLizP/9Kc/mYWFheabb75pDh482PzWt75l1VsIKCEdRkzTNH/961+bAwYMMKOioswJEyaY77//vuexa6+91rzjjjvatP/zn/9sXnbZZWZUVJQ5cuRIc82aNX6uODx4c1wGDhxoSjrntnDhQv8XHuK8/fNyJsKI73h7XLZs2WJmZWWZDofDHDRokPmzn/3MbGpq8nPVoc+b49LY2Gj++Mc/NgcPHmxGR0ebGRkZ5ve//33zxIkT/i88ABmmSf8QAACwTsiOGQEAAMGBMAIAACxFGAEAAJYijAAAAEsRRgAAgKUIIwAAwFKEEQAAYCnCCAAAsBRhBAAAWIowAgAALEUYAQAAliKMAAAAS/1/opl9JO8gpq8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kostenfunktion:\n",
    "ls = cost.LeastSquares(\n",
    "    data_frame['time'],\n",
    "    data_frame['current'], \n",
    "    data_frame['delta_current'], \n",
    "    discharge_current,\n",
    ")\n",
    "\n",
    "# Minimierung\n",
    "mi = Minuit(ls,        # Kostenfunktion \n",
    "            I0=0.9,    # Startwerte\n",
    "            R=10*10**3, \n",
    "            C=10**-6\n",
    "           )\n",
    "mi.migrad()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5df2d60-8284-4757-96c8-7f26afc17942",
   "metadata": {},
   "source": [
    "Wie ihr seht gibt euch minuit euch vier (<span style=\"color:red\">Old iminuit version does not show plot... Check which version is used on jupyter hub.. </span>) verschiedene Objekte zurück. Für euch am wichtigsten ist die erste Tabelle, welche euch zeigt, ob euer Fit funktioniert hat. Im Allgemeinen gilt sind hier alle Felder grün hat euer Fit funktioniert, gelbe Felder können ein Problem andeuten müssen sie aber nicht und lila Felder bedeuten, dass etwas mit eurem Fit nicht in Ordnung ist. Die Bedeutungen der einzelnen Felder für unseren obigen Fit sind auch nochmal in der nachfolgenden Abbildung einzeln erklärt. Die Bedeutung der meisten Felder werden wir noch im laufe des Kurses kennen lernen. \n",
    "\n",
    "<figure class=\"image\">\n",
    "<img src=\"images/FitPerformance.png\"  alt=\"{{Fit Performance }}\" width=100%>\n",
    "</figure>\n",
    "\n",
    "Wie wir unserer Tabelle entnehmen können gibt es also ein Problem mit unserem Fit um besser verstehen zu können was das Problem sein könnte wollen wir uns auch noch die anderen Outputs angucken.\n",
    "\n",
    "Die zweite Tabelle zeigt uns die bestimmten Werte für die Parameter in der Spalte `Value` und die deren Unsicherheiten in der Spalte `Hess error`. Hierbei fällt auf das für unseren obigen Fit die Unsicherheiten der Parameter $R$ und $C$ größer sind als die bestimmten Werte selbst. \n",
    "\n",
    "Die dritte Tabelle ist die sogennnante **Kovarianzmatrix**. Die Kovarianzmatrix hat als Einträge auf ihrer **Hauptdiagonalen** die **Varianzen der entsprechenden Parameter** auf der **Nebendiagonalen** stehen die **Kovarianzen**. Die Werte in Klammern gibt die **Korrelation** zwischen den entspechenden Parameters an. Sind zwei Parameter stark **korreliert** wird das entsprechende Feld **blau** dargestellt, bei einer **antikorrelation** ist das Feld **rot**. \n",
    "\n",
    "Die letzte Ausgabe ist ein Plot unserer Messwerte zusammen mit der Fitfunktion basierend auf den Parametern des besten Fits."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72665daa-1d74-41da-8b9a-1e4c427eed07",
   "metadata": {},
   "source": [
    "Obwohl unser Fit unsere Messdaten gut Widerspiegelt scheint es ein Problem mit der Bestimmung einiger Parameter zu geben. Die große Unsicherheit in $R$ und $C$ deutet an, dass hier das Problem liegt. Um dies zu bestätigen können wir uns einmal das reduzierte $\\chi^2(x, I_0, R, C)$ als Funktion des entsprechenden Parameters von `iminuit` plotten lassen, während wir die anderen Parameter, so wie die x-Werte konstant lassen. \n",
    "\n",
    "Für $I_0$ sieht das entsprechende Profil so aus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d3230cb6-fbe3-4093-ba09-5271dc168a4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/XENONnT/anaconda/envs/XENONnT_development/lib/python3.9/site-packages/iminuit/minuit.py:2353: IMinuitWarning: Specified nsigma bound, but error matrix is not accurate\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mi.draw_profile('I0')\n",
    "plt.ylabel('$\\chi^2(I_0, x, R, C)/ndof$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b837e542-d3c9-4f61-a8d1-4f22db7d5137",
   "metadata": {},
   "source": [
    "Bei den anderen beiden Parametern ist dies nicht der Fall:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "af339c6e-f0e7-40cd-a2cf-61aaaa4df1e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjEAAAHHCAYAAABOTAltAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABU10lEQVR4nO3dd3gUVdsG8HvTdgMkgUAgiYSQQOgJJUAMXQhSA5ZPRFFCEURRUJQSfelCAgKivIiCVKUIKqJSFHjp0iFSQgsCoYQikErq7vn+wB1Y0ncmO7vJ/buuvdidPTvzzE52efac88xohBACRERERDbGTu0AiIiIiMzBJIaIiIhsEpMYIiIisklMYoiIiMgmMYkhIiIim8QkhoiIiGwSkxgiIiKySUxiiIiIyCYxiSEiIiKbxCSGiIiIbBKTGLK4ixcv4s0334S/vz90Oh1cXV3RunVrfP7550hPT7dYHLt370avXr3g4+MDnU4HT09PdO3aFfv27SvS68+dO4f3338frVq1gk6ng0ajweXLl/Ns+/333+O1115DQEAANBoNOnTooNyOPMFgMGDmzJnw8/ODTqdDUFAQVq9enW/bBQsWoEmTJnB2dkblypXRsWNH/PXXXyUWX2JiIqpWrQqNRoMffvih0PbLli2DRqPJ97Zy5Uqp7aRJk/Jso9PpSmRffvnlFzRr1gw6nQ41atTAxIkTkZOTk2fbbdu2oWPHjnBzc4OLiwuCg4Px/fffl0hcRdW5c2doNBq88847hba9fPlygcdhyJAhUtudO3fm2+7AgQOK78eZM2fQtWtXVKhQAe7u7nj99ddx586dPNtevHgRr776KqpWrQpnZ2cEBATg448/VjwmsgwHtQOgsmXjxo146aWXoNVq0b9/fzRq1AhZWVnYu3cvRo8ejdOnT2PhwoUWieX8+fOws7PDsGHD4Onpifv37+O7775Du3btsHHjRnTt2rXA1+/fvx9ffPEFGjRogPr16yMmJibftgsWLMDRo0fRokUL3L17V+E9MfXxxx8jOjoaQ4YMQYsWLbBhwwa8+uqr0Gg06Nu3r0nbQYMGYeXKlejfvz/eeecdpKWl4fjx47h9+3aJxTdhwgQ8ePCgyO3btWuHb7/9Ntfyzz77DH/99Rc6deqU67kFCxagQoUK0mN7e3vzgi3A5s2b8dxzz6FDhw6YN28eTp48iU8++QS3b9/GggULTNouXboUgwcPRufOnTF9+nTY29vj3LlzuHr1quJxFdVPP/2E/fv3F7m9h4dHnsdhy5YtWLlyJZ599tlcz40YMQItWrQwWVa7du3iB1uAa9euoV27dnBzc8P06dORmpqKWbNm4eTJkzh06BCcnJyktjExMejQoQOeeuopfPDBB6hcuTLi4+NVPQ4kkyCykL///ltUqFBB1KtXT9y4cSPX8xcuXBBz585VIbJH0tLSRLVq1USXLl0KbXv37l2RnJwshBDi008/FQDEpUuX8mwbHx8v9Hq9EEKIhg0bivbt2ysVsolr164JR0dHMXz4cGmZwWAQbdu2FdWrVxc5OTnS8u+//14AED/99FOJxJKXkydPCgcHBzFlyhQBQKxbt86s9Tx48EC4uLiIzp07myyfOHGiACDu3LmjRLgFatCggWjcuLHIzs6Wln388cdCo9GIM2fOSMsuXboknJ2dxYgRIxTZ7qVLlwQAsWPHDrPXkZ6eLmrWrCkdh8f/XoqrU6dOwtXVVaSnp0vLduzYIev4Fsdbb70lnJ2dxZUrV6RlW7duFQDE119/LS3T6/WiUaNGIiQkRDx48KDE4yLL4HASWczMmTORmpqKxYsXw8vLK9fztWvXxsiRI1WI7JFy5crBw8MDiYmJhbZ1d3eHi4tLkdbr4+MDO7uifdyuX7+OQYMGoVq1atBqtWjYsCGWLFlSpNdu2LAB2dnZePvtt6VlGo0Gb731Fq5du2byy3vOnDlo2bIlnn/+eRgMBqSlpRVpG3KMHDkSzz//PNq2bStrPb/++itSUlLQr1+/PJ8XQiA5ORlCiHzXkZmZiYkTJ6J27drQarXw8fHBmDFjkJmZWej2Y2NjERsbi6FDh8LB4VGH9ttvvw0hhMkw2VdffQW9Xo8pU6YAAFJTUwuMyxJmzpwJg8GADz/8UNZ6EhISsGPHDrzwwgv5DtmlpKTkO8Rm9N133yE4OBjOzs5wd3dH3759i9w78uOPP6Jnz56oUaOGtCwsLAx16tTB2rVrpWV//PEHTp06hYkTJ8LZ2RkPHjyAXq8v0jbIejGJIYv59ddf4e/vj1atWpm9jgcPHuCff/4p9Hb//v0irzM5ORn//PMPzp49i48++ginTp3Kc4jCEm7duoWnn34a27ZtwzvvvIPPP/8ctWvXxuDBgzF37txCX3/8+HGUL18e9evXN1nesmVL6Xng4T4fOnQILVq0wEcffQQ3NzdUqFAB/v7+Jl/8Slq3bh3+/PNPzJw5U/a6Vq5cCWdnZ7zwwgt5Pu/v7y/NPXnttddw69Ytk+cNBgN69eqFWbNmITw8HPPmzcNzzz2Hzz77DC+//HKh2ze+j82bNzdZ7u3tjerVq0vPAw/nwtSrVw+bNm1C9erV4eLigsqVK2P8+PEwGAzF3XXZ4uPjER0djRkzZsDZ2VnWutasWQODwZBvMjlw4EC4urpCp9PhmWeewZEjR3K1mTZtGvr374+AgADMmTMH7733HrZv34527doV+mPi+vXruH37dq7jADz8m3/yOACAVqtF8+bNUb58eZQrVw59+/bFvXv3irHXZFXU7QiisiIpKUkAEL1795a1HuNwQWE3X1/fIq+zS5cu0uucnJzEm2++adI1XhSFDSc9rqDhpMGDBwsvLy/xzz//mCzv27evcHNzK7QbvEePHsLf3z/X8rS0NAFAjBs3TgghxLFjxwQAUblyZVGtWjXx5ZdfipUrV4qWLVsKjUYjNm/eXOh+FMeDBw9EjRo1RGRkpBBC3nDD3bt3hZOTk+jTp0+u5+bOnSveeecdsXLlSvHDDz+IkSNHCgcHBxEQECCSkpKkdt9++62ws7MTe/bsMXn9V199JQCIffv2FRiD8XjHx8fneq5Fixbi6aeflh67urqKSpUqCa1WK8aPHy9++OEH8eqrr5ocj+KQO5z0f//3f6JVq1bSY8gYTgoODhZeXl7SUKnRvn37xIsvvigWL14sNmzYIKKiokTlypWFTqcTx44dk9pdvnxZ2Nvbi2nTppm83jjs+OTyJx0+fFgAECtWrMj13OjRowUAkZGRIYQQolevXtLffL9+/cQPP/wgxo8fLxwcHESrVq2EwWAw6z0gdXFiL1lEcnIyABR5+CU//fv3R5s2bQptV5xfmNHR0fjggw9w9epVLF++HFlZWYV2f5cEIQR+/PFH9OnTB0II/PPPP9JzXbp0wZo1a3Ds2DG0bt0633Wkp6dDq9XmWm7s6jdWf6WmpgIA7t69iwMHDiAkJAQA0KtXL/j5+eGTTz4pdGJzcURHRyM7OxsfffSR7HX98MMPyMrKyvPX/5PDkS+++CJatmyJfv364csvv8S4ceMAPOwVql+/PurVq2fyPnfs2BEAsGPHjgJ7DI3vY37vtfHvHXj4XhsMBkRHR2Ps2LFSXPfu3cPnn3+Ojz76qMDPRWpqKjIyMqTHxl7GpKQkk9gdHR3h5uaW73qM+/Xjjz/i4MGDBbYrivPnz+Po0aN4//33cw2VtmrVyuT969WrF/7v//4PQUFBiIyMxJYtWwA8nFxsMBjQp08fk33x9PREQEAAduzYUeDfTGHHwdhGq9VKf/MtWrTAd999B+DhcShXrhwiIyOxfft2hIWFmfNWkJrUzqIsYdeuXaJnz57Cy8tLABDr168v1uvT09NFRESEaNSokbC3t8+3N2HHjh2iadOmwsnJSdSqVUssXbrU5Pkvv/xSBAYGChcXF+Hi4iKefvppsWnTJvN2ysYo1RNT0jIzM0XDhg3Fiy++WKzXKdETc+vWrUJ7mIyTcBMSEkxuxh6aovbEGH/B+vn55Wo7cOBA4ejoaDJh9Um3b9822X5KSkq+bY0TW5csWSItk9MT065dO+Hu7i6ysrKK/BpPT0/RqVMn6XH9+vULfJ+Nk3Dv3r1rsp+JiYlCiOL1xJQvX14AMJl4KoQQy5cvFwDErl27Cow9IiKiSL2PhU0Wz87OFo0aNRL9+/c3WQ4ze2ImTJggAIgjR44U+TV9+/YVTk5O0gTzt956q8B9CgoKEkIIkZKSYnIcbt++LYQoXk9Mjx49BACxfPlyk3ZXrlwRAMTkyZOL/R6Q+spET0xaWhoaN26MQYMG5TuGXhC9Xg9nZ2eMGDECP/74Y55tLl26hB49emDYsGFYuXIltm/fjjfeeANeXl7o0qULAKB69eqIjo5GQEAAhBBYvnw5evfujePHj6Nhw4ay9tHaubq6wtvbG6dOnZK1ntTUVOkXVUHs7e3h4eFR7PU7OTmhV69eiI6ORnp6uuw5A8VhnB/x2muvISIiIs82QUFBAJBrYvTSpUsxYMAAeHl5YceOHRBCQKPRSM8nJCQAeDhn4/F/q1WrlmsbVatWRXZ2NtLS0vL9Zd+iRQtcuXJFejxx4kRMmjQpz7YTJkzAU089hQ4dOkjn0bl58yYA4M6dO7h8+TJq1KhRpInP8fHx2LNnD4YOHQpHR8dC2xv5+PiYzHswGAwIDAzEnDlz8m0PAC+88AJ27dolLY+IiMCyZcuk9z8hIUFqa5SQkCDNQQIevtcXLlzI9V5XrVoVAAqdvzVmzBi89tpr0uNbt27htddew6xZs9C4cWNpeaVKlQpcz4oVK3Du3Dl8/fXXuc5nlJKSgsuXL6Nq1aooV65cgesxWrVqFerWrYvg4OAitQcevq9ZWVlIS0uDq6srDAYDNBoNNm/enGcZvLFMftasWZg8ebK03NfXF5cvXzY5Dk9KSEiAu7u71EuT3998UY8DWacykcR069YN3bp1y/f5zMxMfPzxx1i9ejUSExPRqFEjzJgxQzohWfny5aXzPuzbty/PyWZfffUV/Pz8MHv2bABA/fr1sXfvXnz22WdSEhMeHm7ymmnTpmHBggU4cOBAqU9iAKBnz55YuHAh9u/fj9DQULPW8eSXWX6MX3LmSE9PhxACKSkpFk1iPDw84OLiAr1eX2i39tatW00eG/9+mjRpgm+++QZnzpxBgwYNpOeNwwdNmjQB8PAL3dPTE9evX8+17hs3bkCn0xU4xLFy5UqTExP6+/vn2zY+Ph5xcXF5tjFWUd2/fx8VK1bMdx1Gq1evhhAi34mkeRFC4PLly2jatKm0rFatWtI5Zh5P9p40e/Zsk//cjP8RGt/HI0eOmCQsN27cwLVr1zB06FBpWXBwMC5cuIDr16+bvAc3btwAgEKT7QYNGpgcS+PfdXBwcLFOmhgfH4/s7Ow8hyNXrFiBFStWYP369XjuuecKXdfBgwcRFxcnVVwV1d9//w2dTiclJ7Vq1YIQAn5+fqhTp06+r3tyGNn4uXzqqafg4eGR54ThQ4cOSccJePh+LVq0KNfffFGPA1kpNbuB1IA8hpPeeOMN0apVK7F7924RFxcnPv30U6HVasX58+dzvT4iIiLPIZG2bduKkSNHmixbsmSJcHV1zTOOnJwcsXr1auHk5CROnz5t7u7YlLi4OFG+fHnRoEEDcfPmzTyfL+w8MRcvXhRbt24t9LZ3795C47l161auZffv3xc+Pj7Cx8fHZPmVK1dMzv3xJKUm9g4YMEA4OTmJkydP5nrO2IVekKtXr+Z7npinnnrK5DwxI0eOFADEH3/8IS27c+eOcHV1Fd27dy90W0W1Z88esX79epPb1KlTBQAxZswYsX79emloKC0tTZw5cybf87wEBQWJGjVq5DsJM6/3aP78+QKAmDNnjrRs2bJluc4jYvTgwQORmppa6H7Vq1dPNG7c2OQ9/c9//iM0Go2IjY2Vlq1fv14AEB999JG0TK/XizZt2gh3d3dpuKOozJ3Ye+bMmVzHwRhb9+7dxfr1603O33TmzJlcQ2BGI0aMEABEXFxcns/ndRxiYmKEo6Oj6NWrl7QsLi5O2Nvbi1dffTXXMTUYDLkmuOdl2LBhwtnZ2WRob9u2bQKAWLBggbQsISFBaLVa0aZNG5OJyJGRkQKAOHToUKHbIutT5pOYK1euCHt7e3H9+nWTdp06dZIqKR6XXxITEBAgpk+fbrJs48aNAoBJRcmJEydE+fLlhb29vXBzcxMbN26Ut0M2ZsOGDUKn04lKlSqJkSNHikWLFon58+eLfv36CScnJzF06FCLxdKsWTPRq1cvMW3aNLFo0SIxfvx4Ub16dWFnZ5drrkb79u3Fkzl/YmKimDp1qpg6daro2rWrACA++OADMXXqVDFv3jyTtrt27ZLaVq1aVdSsWVN6/PiciJs3bwpfX19Rrlw5MXLkSPH111+LqKgo8dJLL4lKlSoVab+McwGGDh0qFi1aJM0FWLlypUm7mzdvCi8vL+Hi4iImTpwo5syZI+rUqSOcnZ1FTExMcd7KYstvToxx+cSJE3O95uTJk4VW9Dg7O4sBAwaI2bNni/nz54tXXnlFaDQa0aRJE5GWlia10+v1onv37kKj0Yi+ffuKefPmiblz54phw4YJd3d3cfjw4UL34ddffxUajUZ07NhRLFy4UIwYMULY2dmJIUOGmLQzGAyiU6dOQqPRiKFDh4r58+eLzp0755tEFUaJk909DvnMiUE+82xycnJEtWrVTOb9POmZZ54R3bt3F5988olYuHCheO+990S5cuWEm5ubSYInhBBRUVECgGjVqpWYOXOmWLBggRgzZowICAgQn376aaHxx8fHi8qVK4tatWqJL774QkyfPl1UqlRJBAYG5koQjSf369y5s5g/f74YOnSo0Gg04pVXXil0O2SdynwS89tvvwkAonz58iY3BweHPEs45SYxmZmZ4sKFC+LIkSNi3LhxokqVKmWmJ8bo/PnzYsiQIaJmzZrCyclJuLi4iNatW4t58+YV+1epHP/9739FmzZtRJUqVYSDg4Pw8PAQ4eHhYvfu3bna5pXEGP8zyev2ZIl3QaXhT/6HfevWLTF8+HDh4+MjHB0dpUmpCxcuLNJ+6fV6MX36dOHr6yucnJxEw4YNxXfffZdn24sXL4rnn39euLq6CmdnZ9GxY0eL/CI1J4kZN26cACBOnDiR73rfeOMN0aBBA+Hi4iIcHR1F7dq1xdixY6UzKz8uKytLzJgxQzRs2FBotVpRqVIlERwcLCZPnmxSjl2Q9evXiyZNmgitViuqV68u/vOf/+Q54TglJUWMHDlSeHp6CicnJxEYGJjvMSmM2knMli1bBADxxRdf5LvOzz//XLRs2VK4u7sLBwcH4eXlJV577TVx4cKFPNv/+OOPok2bNtL3b7169cTw4cPFuXPnirQPp06dEs8++6woV66cqFixoujXr1+evb0Gg0HMmzdP1KlTRzg6OgofH598jxnZBo0QKp860sI0Go3JuO/333+Pfv364fTp07kmllWoUAGenp4mywYMGIDExET8/PPPJsvbtWuHZs2amZyQbOnSpXjvvfeQlJSUbzxhYWGoVasWvv76a1n7RUREVNaUiYm9BWnatCn0ej1u374t61TooaGh2LRpk8myrVu3FjqB1WAwFOk050RERGSqTCQxqampiIuLkx5funQJMTExcHd3R506ddCvXz/0798fs2fPRtOmTXHnzh1s374dQUFB6NGjB4CH10rJysrCvXv3kJKSIl2x2Dj7fdiwYfjvf/+LMWPGYNCgQfjf//6HtWvXYuPGjdJ2IyMj0a1bN9SoUQMpKSlYtWoVdu7cid9//91i7wUREVGpofZ4liUYx9mfvEVERAghHo6NT5gwQdSsWVM4OjoKLy8v8fzzz5uMvfv6+ua5jie306RJE+Hk5CT8/f1znexu0KBB0jwFDw8P0alTJ5PKECIiIiq6MjcnhoiIiEoHXsWaiIiIbBKTGCIiIrJJpXpir8FgwI0bN+Di4lLgqcWJiIjIeoh/L/3i7e1d4HXVSnUSc+PGjVwXZyMiIiLbcPXqVVSvXj3f50t1EmO8gN3Vq1fh6uqqcjRkbY4fP652CGSmjIwMdO3aFQCwZcsW6HQ6lSMiczx+UU6ixyUnJ8PHx6fAC9ECpTyJMQ4hubq6MomhXIxX0iXb8/jZtcuXL2/Rq42Tcvi9TIUpbCoIJ/YSERGRTWISQ0RERDapVA8nEVHpZG9vj549e0r3iahsYhJDRDbHyckJkyZNUjsMIlIZh5OIiIjIJrEnhohsjhACGRkZAACdTseTWRKVUeyJISKbk5GRgbZt26Jt27ZSMkNEZQ+TGCIiIrJJTGKIiIjIJlltEqPX6zF+/Hj4+fnB2dkZtWrVwtSpUyGEUDs0IiIisgJWO7F3xowZWLBgAZYvX46GDRviyJEjGDhwINzc3DBixAi1wyMiIiKVWW0S8+eff6J3797o0aMHAKBmzZpYvXo1Dh06pHJkREREZA2sdjipVatW2L59O86fPw8A+Ouvv7B3715069Yt39dkZmYiOTnZ5EZkrW6n5eDOAz30HCIlIjKL1fbEjBs3DsnJyahXrx7s7e2h1+sxbdo09OvXL9/XREVFYfLkyRaMksh8b2/6BwLAN+EeqKTjqfOLw87ODp06dZLuE1HZZLVJzNq1a7Fy5UqsWrUKDRs2RExMDN577z14e3sjIiIiz9dERkZi1KhR0uPk5GT4+PhYKmSiIjMIAWP/iz1P1FZsWq0WM2bMUDsMIlKZ1SYxo0ePxrhx49C3b18AQGBgIK5cuYKoqKh8kxitVgutVmvJMInMYnhsBMmOOQwRkVmsth/2wYMHubqJ7e3tYTAYVIqISDmPJzH2TGKIiMxitT0x4eHhmDZtGmrUqIGGDRvi+PHjmDNnDgYNGqR2aESyPT6Z147DScWWnp6Otm3bAgD27NkDZ2dnlSMiIjVYbRIzb948jB8/Hm+//TZu374Nb29vvPnmm5gwYYLaoRHJZjKcZLX9oURE1s1qkxgXFxfMnTsXc+fOVTsUIsU9PirKOTFERObhb0AiFRgeH05SMQ4iIlvG708iFej/zWHsNICGc2KIiMzCJIZIBcY5MaxMIiIyH5MYIhUYq5NYmUREZD6rndhLVJoZHhtOouKzs7ND69atpftEVDYxiSFSgf7f6iQOJ5lHq9Xi888/VzsMIlIZf8IQqcAgDSepHAgRkQ1jEkOkAmk4iVkMEZHZmMQQqYBzYuRJT09HmzZt0KZNG6Snp6sdDhGphHNiiFTAEmv5MjIy1A6BiFTGnhgiFbDEmohIPiYxRCpgTwwRkXxMYohUoDewOomISC4mMUQqYHUSEZF8TGKIVKDncBIRkWysTiJSAUus5dFoNGjWrJl0n4jKJiYxRCowsDpJFp1Oh4ULF6odBhGpjMNJRCpgdRIRkXxMYohUYLwAJIeTiIjMxySGSAXG4SR7ZjFmSU9PR1hYGMLCwnjZAaIyjHNiiFSg58Re2RITE9UOgYhUxp4YIhWwOomISD4mMUQq4LWTiIjkYxJDpAL2xBARycckhkgFhn+rk1hiTURkPiYxRCrgye6IiORjdRKRCqST3fFnhFk0Gg0aNGgg3SeisolJDJEKWGItj06nw4oVK9QOg4hUxt+BRCqQTnbHXgQiIrMxiSFSAXtiiIjkYxJDpAKWWMuTkZGB8PBwhIeHIyMjQ+1wiEglVpvE1KxZExqNJtdt+PDhaodGJJvewOokOYQQSEhIQEJCAsS/Q3NEVPZY7cTew4cPQ6/XS49PnTqFzp0746WXXlIxKiJlsDqJiEg+q01iPDw8TB5HR0ejVq1aaN++vUoRESmHw0lERPJZbRLzuKysLHz33XcYNWpUgeeEyMzMRGZmpvQ4OTnZEuERFRurk4iI5LOJzuyff/4ZiYmJGDBgQIHtoqKi4ObmJt18fHwsEyBRMbE6iYhIPptIYhYvXoxu3brB29u7wHaRkZFISkqSblevXrVQhETFw+EkIiL5rH446cqVK9i2bRt++umnQttqtVpotVoLREUkj7E6icNJ5tFoNPD395fuE1HZZPVJzNKlS1G1alX06NFD7VCIFMOeGHl0Oh3Wrl2rdhhEpDKrHk4yGAxYunQpIiIi4OBg9fkWUZFJSYxVfwKJiKybVX+Fbtu2DfHx8Rg0aJDaoRApylidxJPdERGZz6q7N5599lmejZNKJWN1kj1zGLNkZGSgf//+AIAVK1ZAp9OpHBERqcGqkxii0opzYuQRQuDvv/+W7hNR2WTVw0lEpRVPdkdEJB+TGCIV6A0P/2VPDBGR+ZjEEKmA1UlERPLxK5RIBXqpOknlQIiIbBiTGCIVGKTqJGYxRETmYnUSkQpYnSSPRqOBl5eXdJ+IyiYmMUQqYHWSPDqdDr/++qvaYRCRyjicRKQCVicREcnHJIZIBdKcGH4CiYjMxq9QIhWwOkke42UH+vfvj4yMDLXDISKVcE4MkQoeTexlFmMOIQRiY2Ol+0RUNrEnhkgFelYnERHJxiSGSAUssSYiko9JDJEKDAaWWBMRycUkhkgF7IkhIpKPSQyRCozVSSyxJiIyH6uTiFTA6iT5KlasqHYIRKQyJjFEKnh0AUh147BVzs7O2LZtm9phEJHK2JlNpAKWWBMRycckhkgFBumMvcxiiIjMxSSGSAUGXgBSloyMDAwdOhRDhw7lZQeIyjDOiSFSAS8AKY8QAseOHZPuE1HZxK9QIhXoOZxERCQbkxgiFbA6iYhIPiYxRCpgdRIRkXxMYogszPDYHA4OJxERmY9JDJGF6R+bh8rhJCIi87E6icjCDI8lMRxOMp9Op1M7BCJSGZMYIgszPJbF2DGLMYuzszP27t2rdhhEpDIOJxFZGHtiiIiUYdVJzPXr1/Haa6+hcuXKcHZ2RmBgII4cOaJ2WESycE4MEZEyrHY46f79+2jdujWeeeYZbN68GR4eHrhw4QIqVaqkdmhEsrA6Sb7MzEyMGTMGADBz5kxotVqVIyIiNVhtEjNjxgz4+Phg6dKl0jI/Pz8VIyJShp4nupPNYDBg37590n0iKpusdjjpl19+QfPmzfHSSy+hatWqaNq0KRYtWlTgazIzM5GcnGxyI7I2Bp7ojohIEVabxPz9999YsGABAgIC8Pvvv+Ott97CiBEjsHz58nxfExUVBTc3N+nm4+NjwYiJisY4nGTPoSQiIlmsNokxGAxo1qwZpk+fjqZNm2Lo0KEYMmQIvvrqq3xfExkZiaSkJOl29epVC0ZMVDT6f0c/2BNDRCSP1SYxXl5eaNCggcmy+vXrIz4+Pt/XaLVauLq6mtyIrI00nGS1nz4iIttgtV+jrVu3xrlz50yWnT9/Hr6+vipFRKQM43ASK5OIiOSx2iTm/fffx4EDBzB9+nTExcVh1apVWLhwIYYPH652aESyGFidRESkCKstsW7RogXWr1+PyMhITJkyBX5+fpg7dy769eundmhEsuhZnSSbs7MzT3xJRNabxABAz5490bNnT7XDIFLUo+EklQMhIrJxVjucRFRaGauTWGJNRCQPkxgiC+PJ7uTLzMzE2LFjMXbsWGRmZqodDhGphEkMkYXpjcNJzGLMZjAYsH37dmzfvp2XHSAqw5jEEFkYe2KIiJTBJIbIwlhiTUSkDCYxRBbG6iQiImUwiSGyMFYnEREpg0kMkYVxTgwRkTKYxBBZmLE6yZ6fPiIiWaz6jL1EpdGjnhh2xZhLp9Nhz5490n0iKpuYxBBZGIeT5NNoNHB2dlY7DCJSGTu0iSxMz+okIiJFsCeGyMIenSeGWYy5srKyMH36dADARx99BCcnJ5UjIiI1sCeGyMKMZ8lnT4z59Ho9fvvtN/z222/Q6/Vqh0NEKmESQ2RhUk8MP31ERLLwa5TIwh7NiWFXDBGRHExiiCyM1UlERMpgEkNkYXpeAJKISBFMYogszMDhJCIiRTCJIbIwVicRESmD54khsjDOiZFPp9Nh69at0n0iKpvM6okZM2YMMjIylI6FqEx4dAFIZjHm0mg0qFSpEipVqgQNh+WIyiyzkpi5c+ciKSkJADBgwAA8ePBA0aCISjP2xBARKcOsJMbb2xsxMTEAgG+//RapqalKxkRUqrE6Sb6srCzMmDEDM2bMQFZWltrhEJFKzEpiPvjgA4SHh6Nt27YAgJUrV+LQoUNIT09XNDii0ojVSfLp9XqsW7cO69at42UHiMows5KYd999F0eOHEHXrl0hhMD8+fPRqlUruLq6on79+ujbty+io6OxefNmpeMlsnkcTiIiUobZ1UlBQUEICgrCsmXLsH//fpQvXx4nTpxATEwMYmJisGHDBkybNg0pKSlKxktk8/QssSYiUoTsEusLFy5I90NCQhASEiI9Fv92mxPRIwZWJxERKaJET3bH0kei3DicRESkDNlJTE5ODqZNm4bQ0FA0a9YMERER0kmoiCg3A6uTiIgUITuJGTduHL788kt06tQJzz33HDIzM9GzZ08MHDiQw0lEedCzOomISBGy58SsWrUKa9asQbt27aRlly5dQs+ePTFr1iyMHj3arPVOmjQJkydPNllWt25dnD17Vla8RGrjcJJ8Wq0Wv/zyi3SfiMom2T0xaWlpqF69uskyPz8/zJs3DwsXLpS17oYNGyIhIUG67d27V9b6iKyBsTqJw0nms7Ozg7e3N7y9vWFnx+vYEpVVsj/9bdq0wfLly3Mt9/Pzw40bN2St28HBAZ6entKtSpUqstZHZA14sjsiImXIHk6aMWMGWrdujfv37+Pdd99FQEAAsrOzMW/ePDRo0EDWui9cuABvb2/odDqEhoYiKioKNWrUyLd9ZmYmMjMzpcfJycmytk9UEoyXHWAHgvmys7Px5ZdfAgDefvttODo6qhwREalB9tdoo0aNsHPnTuzfvx9169aFTqdDuXLl8O2332Lu3LlmrzckJATLli3Dli1bsGDBAly6dAlt27Yt8OR5UVFRcHNzk24+Pj5mb5+opHBOjHw5OTn49ttv8e233yInJ0ftcIhIJbJ7YgCgadOmOHz4MM6ePYvY2Fi4uLggJCQErq6uZq+zW7du0v2goCCEhITA19cXa9euxeDBg/N8TWRkJEaNGiU9Tk5OZiJDVkc62R2Hk4iIZDEriQkNDUXTpk3RpEkTNGnSBEFBQdDpdKhXrx7q1aundIwAgIoVK6JOnTqIi4vLt41Wq2WlAlk99sQQESnDrCSmR48eOHHiBGbPno2LFy9Co9EgICBASmqMt6pVqyoWaGpqKi5evIjXX39dsXUSqYHVSUREyjArifnPf/4j3T906BCee+45NGrUCBqNBsuWLcPZs2eh0WhQrVo1syuUPvzwQ4SHh8PX1xc3btzAxIkTYW9vj1deecWs9RFZC1YnEREpQ/acmLfeegvz58/H888/Ly3btGkThg4dioiICLPXe+3aNbzyyiu4e/cuPDw80KZNGxw4cAAeHh5yQyZSlbE6yZ7VSUREsshOYs6cOYMmTZqYLOvevTu+/PJLfPbZZ2avd82aNTIjI7JOnBNDRKQM2b8FW7RokefJ7gIDA3Ho0CG5qycqdTicJJ9Wq8X333+P77//npP5icow2T0xc+bMQceOHXHlyhW8//77aNSoEbKysjB79myeYZcoD+yJkc/Ozg61atVSOwwiUpnsJCY4OBgHDx7E8OHD0aRJEzg6OsJgMMDBwQGLFy9WIkaiUkWaE8MkhohIFkVOdlevXj1s374d8fHxiImJgZ2dHYKDg+Hl5aXE6olKFYPBOJykciA2LDs7G0uWLAEADBo0iJcdICqjZCcxhw8fxrhx43Dnzh3Url1bOkdMdna2EvERlToGqTqJWYy5cnJysGjRIgBA//79mcQQlVGyJ/a+/vrrsLe3x9ChQ+Hn54ddu3Zh4MCBqFmzJipXrqxEjESlip5zYoiIFCG7J+bq1avYuHFjrkl2V65cQUxMjNzVE5U6j6qTVA6EiMjGyU5iQkNDcf369VxJjK+vL3x9feWunqjUeTSxl1kMEZEcsoeT3n//fUyZMgX37t1TIh6iUo8l1kREypDdExMeHg6NRoM6deqgd+/e0hWuAwMD4eTkpESMRKUKq5OIiJQhO4mJi4vDX3/9Jd2mT5+Oy5cvw9HREXXr1sWJEyeUiJOo1HjUE8MshohIDtlJjL+/P/z9/U0uAJmcnIy//vqLCQxRHngBSPmcnJyky52wx5eo7DI7iZkwYQJ69+6N4ODgXM+5urqibdu2aNu2razgiEojzomRz97eHg0bNlQ7DCJSmdm/Ba9du4Zu3bqhevXqeOutt7B582ZkZWUpGRtRqaT/t8Sa1UlERPKYncQsWbIEN2/exOrVq+Hi4oL33nsPVapUwYsvvogVK1awWokoH+yJkS87OxsrVqzAihUreHZwojJM1qi8nZ0d2rZti5kzZ+LcuXM4ePAgQkJC8PXXX8Pb2xvt2rXDrFmzcP36daXiJbJpQohHlx1gEmO2nJwcfPHFF/jiiy+Qk5OjdjhEpBJFpxbWr18fY8aMwb59+3D16lVERERgz549WL16tZKbIbJZhsfuszqJiEgeRa5inRcPDw8MHjwYgwcPLqlNENkcYy8MANixOomISBZFkpjs7GzcvHkTDx48gIeHB9zd3ZVYLVGpY3isK4ZzYoiI5DH7t2BKSgoWLFiA9u3bw9XVFTVr1kT9+vXh4eEBX19fDBkyBIcPH1YyViKbZ7z4I8DqJCIiucxKYubMmYOaNWti6dKlCAsLw88//4yYmBicP38e+/fvx8SJE5GTk4Nnn30WXbt2xYULF5SOm8gm6R8fTmIOQ0Qki1nDSYcPH8bu3bvzPdlUy5YtMWjQIHz11VdYunQp9uzZg4CAAFmBEpUGBiYxRESKMSuJKWq1kVarxbBhw8zZBFGpZDzRnQasTpLDyckJX331lXSfiMomWRN7r1y5gnPnziEoKAienp65nr9x4wa8vb3lbIKoVOGJ7pRhb2+P5s2bqx0GEanM7Im9q1evRu3atdG1a1f4+/vj22+/BQDEx8cjOjoaISEhqFGjhmKBEpUG+n+rk3iiOyIi+cxOYqZOnYp3330XJ0+eROfOnfHWW29h/PjxqFWrFpYtW4bmzZtj3bp1SsZKZPOM1UkcSpInJycHa9euxdq1a3nGXqIyzOzhpIsXL2LkyJHw9fXF/PnzUaNGDezbtw8nTpxA/fr1lYyRqNSQhpN4ojtZsrOzMXPmTABAeHg4HBxK7LydRGTFzP4qzc7OhrOzMwCgevXq0Ol0mDVrFhMYogJwTgwRkXJk/R5ctWoVzp49C+DhRLtKlSopEhRRaWWsTuKJ7oiI5DM7iWnbti0mTpyIhg0bokqVKsjIyMDnn3+OtWvXIjY2luPURHlgTwwRkXLMHkjetWsXAODChQs4evQojh07hmPHjmHFihVITEyEk5MT6tSpgxMnTigWLJGtY3USEZFyZM+GCwgIQEBAAPr27Sstu3TpEo4cOYLjx4/LXb0kOjoakZGRGDlyJObOnavYeoksidVJRETKKZEp/X5+fvDz88NLL72kyPoOHz6Mr7/+GkFBQYqsj0gtrE4iIlKO4knM/fv38ccff+D69esAAG9vb3Tp0sXsSb+pqano168fFi1ahE8++UTJUIksTs85MYpwdHSUemQdHR3VDYaIVKPo78HFixcjNDQUBw8ehMFggMFgwMGDB9GqVSssXrzYrHUOHz4cPXr0QFhYmJKhEqnCwOokRTg4OKBNmzZo06YNzxFDVIYp+umfOXMmjh07hvLly5ssnzp1Kpo1a4bBgwcXa31r1qzBsWPHcPjw4SK1z8zMRGZmpvQ4OTm5WNsjKmmsTiIiUo6iPTEajQYpKSm5lqekpEBTzF+eV69exciRI7Fy5UrodLoivSYqKgpubm7SzcfHp1jbJCppTGKUkZOTg19//RW//vorT+dAVIYp2hMza9YstG/fHo0aNcJTTz0FALh27RpOnz6N2bNnF2tdR48exe3bt9GsWTNpmV6vx+7du/Hf//4XmZmZsLe3N3lNZGQkRo0aJT1OTk5mIkNWRW/gcJISsrOzMXnyZABAWFgYh5SIyihFP/k9e/ZEt27dcOjQIdy4cQPAw4m9LVu2zJVwFKZTp044efKkybKBAweiXr16GDt2bJ7r02q10Gq15u8AUQljTwwRkXLMTmLeeOMNBAcHo1mzZmjcuLE05GNvb4/Q0FDZgbm4uKBRo0Ymy8qXL4/KlSvnWk5kK4zVSfYssSYiks3sJObChQtYt24dUlJS4ODggLp160pJTXBwMJo0aZJrgi9RWffoZHcqB0JEVAooftmBX375BYmJibCzs0OdOnUQGxurWLA7d+5UbF1Eang0nMQshohILpu57ABRacCT3RERKccmLjtAVFoYWJ1ERKQY1iUSWRCrk5Th6OiI6Oho6T4RlU1MYogsiNVJynBwcOClSIhI2TP2ElHBWJ1ERKScEk1i7Ozs0LFjRxw9erQkN0NkM/SsTlJETk4Otm3bhm3btvGyA0RlWIkOJy1ZsgSXL1/G8OHDceDAgZLcFJFNMM6JsWcOI0t2djbGjRsHANizZw8vO0BURsn+5KekpMDFxSXP5wYMGAAAmDRpktzNEJUKxuokDicREcknezipbdu2uHnzphKxEJV6PNkdEZFyZCcxTZs2RUhICM6ePWuyPCYmBt27d5e7eqJSxcDqJCIixcj+Kl26dCkGDBiANm3aYO/evTh//jz69OmD4ODgYl+5mqi007M6iYhIMYrMhps8eTK0Wi06d+4MvV6PTp06Yf/+/WjZsqUSqycqNTicRESkHNk9Mbdu3cLIkSPxySefoEGDBnB0dMSAAQOYwBDlQc/qJCIixcjuifHz80PdunWxbt069OjRA1u2bMHLL7+M+Ph4jB49WokYiUoNXnZAGY6Ojpg4caJ0n4jKJtlJzJIlS0yuYN21a1fs2LEDPXv2xOXLlzF//ny5myAqNfRSiTWzGDkcHBwQHh6udhhEpDLZw0mPJzBGzZo1w59//on//e9/cldPVKqwJ4aISDkldprLmjVr4s8//yyp1RPZJJZYKyMnJ0c6C/jTTz/NM/YSlVEl+smvVKlSSa6eyOY8ugAku2LkyM7OxnvvvQeAlx0gKsv4e5DIglidRESkHCYxRBbEOTFERMop0STGzs4OHTt2xNGjR0tyM0Q2w1idZM/hJCIi2Uo0iVmyZAnatWuH4cOHl+RmiGwGe2KIiJRTorPhBgwYAACYNGlSSW6GyGYY58TYcSCXiEg2s79KY2NjMX78eCQmJioYDlHpxuokIiLlmN0TExUVhdTUVFSsWDHXcxkZGbh8+TLq1asnJzaiUsfA6iRFODo6YsyYMdJ9IiqbzE5iDhw4gIULF+b5nE6nw5AhQ9C9e3dERkaaHRxRacM5McpwcHBAnz591A6DiFRm9nDStWvXULt27XyfHzZsGH755RdzV09UKnE4iYhIOWYnMe7u7khISMj3+ZYtWyIuLs7c1ROVSnrDw385nCSPXq/HkSNHcOTIEej1erXDISKVmJ3EtGvXDsuWLct/xXZ2yMjIMHf1RKUSh5OUkZWVhWHDhmHYsGHIyspSOxwiUonZScyHH36IRYsW5TsvZv/+/fD39zc7MKLSSP/vcJI9sxgiItnMTmKCg4Px5Zdf4u2330bnzp3x888/Iz4+Hvfu3cOGDRswduxYvPrqq0rGSmTz2BNDRKQcWafcGjJkCHbu3ImkpCS88MIL8PPzg4eHB55//nkEBgbi/fffN3vdCxYsQFBQEFxdXeHq6orQ0FBs3rxZTrhEqmMSQ0SkHNln7G3Tpg0OHTqEs2fP4tixY3jw4AEaNWqEp59+WtZ6q1evjujoaAQEBEAIgeXLl6N37944fvw4GjZsKDdsIlWwOomISDlmJTHx8fGoUaOGybJ69erle3K769ev46mnnirWNsLDw00eT5s2DQsWLMCBAweYxJDNYnUSEZFyzBpOatGiBd58800cPnw43zZJSUlYtGgRGjVqhB9//NHsAIGH5ZRr1qxBWloaQkNDZa2LSE0cTiIiUo5ZPTGxsbGYNm0aOnfuDJ1Oh+DgYHh7e0On0+H+/fuIjY3F6dOn0axZM8ycORPdu3c3K7iTJ08iNDQUGRkZqFChAtavX48GDRrk2z4zMxOZmZnS4+TkZLO2S1RSWJ2kDAcHB4wYMUK6T0Rlk0aIf79VzZCeno6NGzdi7969uHLlCtLT01GlShU0bdoUXbp0QaNGjWQFl5WVhfj4eCQlJeGHH37AN998g127duWbyEyaNAmTJ0/OtTwpKQmurq6yYqHS5+jRoxbf5jub7yAhVY9PnnFH/SpOFt8+kTUJDg5WOwSyUsnJyXBzcyv0/29ZSYylhYWFoVatWvj666/zfD6vnhgfHx8mMZQnNZKYtzbdwe00PaI6uqNOZSYxVLYxiaH8FDWJsal+WIPBYJKkPEmr1UKr1VowIqLiYXWSMvR6Pc6ePQvgYVGBvb29yhERkRqKncSkp6fj3r17uaqNTp8+rWjVUGRkJLp164YaNWogJSUFq1atws6dO/H7778rtg0iSzP8W53EKTHyZGVlISIiAgCwZ88eODs7qxwREamhWNVJP/zwAwICAtCjRw8EBQXh4MGD0nOvv/66ooHdvn0b/fv3R926ddGpUyccPnwYv//+Ozp37qzodogsidVJRETKKVZPzCeffIKjR4+iWrVqOHr0KCIiIvDRRx/h1VdfhdJTaxYvXqzo+oisAauTiIiUU6wkJjs7G9WqVQPwcELW7t278fzzzyMuLg4ajvETFYo9MUREyinWcFLVqlVx4sQJ6bG7uzu2bt2KM2fOmCwnorwxiSEiUk6xkphvv/0WVatWNVnm5OSE1atXY9euXYoGRlQaScNJTGKIiGQrVhJTvXp1eHp6mizbtm0bAKB169bKRUVUSj3qiWEWQ0Qkl1nXTnpcjx49MGrUKGRlZSkRD1GpZuAFIBXh4OCAIUOGYMiQIbzsAFEZJjuJ2b17N3777Tc0b94cp06dyrNNQkICXnzxRbmbIrJpQgj8m8NwToxMjo6OePPNN/Hmm2/C0dFR7XCISCWyk5iQkBAcO3YMzZs3R4sWLTBnzhzpOYPBgNjYWEyYMAF79uyRuykim2Z47CwEdsxiiIhkU6QftkKFCpg9ezbKlSuH0aNHY/Xq1VICk5mZCV9fX0RFRSmxKSKbZZLEMIeRxWAw4NKlSwAAPz8/2NnJ/j1GRDZIdhLzzTffYMqUKbh+/TqcnZ3RsmVLAMDBgwcxfPhwTJ06FRUrVpS7GSKbp3/shJCcEyNPZmYmXn75ZQC87ABRWSb758tHH32EHj16IDY2FikpKdi/fz/279+P2bNn45tvvsGoUaPw4MEDJWIlsmmmPTHMYoiI5JKdxHTo0AGTJk1C3bp1Tc7a+/777+PQoUM4cuRIrussEZVF+seSGPbEEBHJJzuJWbt2rXQpgicFBgbi8OHD6NmzJ9q1ayd3U0Q2jXNiiIiUVeInWNBqtZg7dy569OhR0psismqGf7MYO4DXGiMiUoDFpvR37tzZUpsiskq8bhIRkbJYl0hkIcbqJFYDExEpg+frJrIQXjdJOQ4ODnj99del+0RUNvHTT2QhHE5SjqOjI0aOHKl2GESkMnZsE1mIscSa5dVERMpgTwyRhRiMc2I4nCSbwWDAzZs3AQCenp687ABRGcUkhshC9P9ewpo9MfJlZmaiV69eAHjZAaKyjD9fiCyEc2KIiJTFJIbIQqThJGYxRESKYBJDZCHsiSEiUhaTGCILYXUSEZGymMQQWQirk4iIlMUkhshCWJ1ERKQsllgTWQjnxCjH3t4eL730knSfiMomJjFEFmK8AKQ9sxjZnJycMHbsWLXDICKVcTiJyELYE0NEpCz2xBBZCJMY5QghkJiYCACoWLEiNJwsTVQmMYkhshBWJyknIyMDnTt3BsDLDhCVZVY7nBQVFYUWLVrAxcUFVatWxXPPPYdz586pHRaR2YzVSeyJISJShtUmMbt27cLw4cNx4MABbN26FdnZ2Xj22WeRlpamdmhEZjHwZHdERIqy2uGkLVu2mDxetmwZqlatiqNHj6Jdu3YqRUVkPg4nEREpy2p7Yp6UlJQEAHB3d1c5EiLzSJcdsJlPHRGRdbPanpjHGQwGvPfee2jdujUaNWqUb7vMzExkZmZKj5OTky0RHlGRsDqJiEhZNvGbcPjw4Th16hTWrFlTYLuoqCi4ublJNx8fHwtFSFQ4PYeTiIgUZfVJzDvvvIPffvsNO3bsQPXq1QtsGxkZiaSkJOl29epVC0VJVDj2xCjH3t4ePXv2RM+ePXnZAaIyzGqHk4QQePfdd7F+/Xrs3LkTfn5+hb5Gq9VCq9VaIDqi4jPwApCKcXJywqRJk9QOg4hUZrVJzPDhw7Fq1Sps2LABLi4uuHnzJgDAzc2NJ7Yim/SoOknlQIiISgmrHU5asGABkpKS0KFDB3h5eUm377//Xu3QiMzyqDqJWYxcQgikp6cjPT0d4t/kkIjKHqvtieEXE5U2nBOjnIyMDLRt2xYALztAVJZZbU8MUWljrE6yZ3USEZEimMQQWQh7YoiIlMUkhshCeAFIIiJlMYkhshBWJxERKYtJDJGFGFidRESkKCYxRBbCOTFERMqy2hJrotJGz+EkxdjZ2aFTp07SfSIqm5jEEFmINJzEEmvZtFotZsyYoXYYRKQy/oQhshAOJxERKYtJDJGF6A3Gk92pHAgRUSnBJIbIQh71xDCLkSs9PR3NmzdH8+bNkZ6ernY4RKQSJjFEFmK8ACTnoRIRKYNfp0QWwjkxRETKYhJDZCEGXgCSiEhRTGKILIQ9MUREymISQ2QhxgtAsjqJiEgZTGKILOTRBSCZxRARKYFn7CWyEL10AUh14ygN7Ozs0Lp1a+k+EZVNTGKILIRzYpSj1Wrx+eefqx0GEamMP2GILITDSUREymISQ2Qh7IkhIlIWkxgiCzFWJzGJkS89PR1t2rRBmzZteNkBojKMc2KILIQnu1NWRkaG2iEQkcrYE0NkIXoOJxERKYpJDJGFGFhiTUSkKH6dElkIq5OIiJTFJIbIQqST3TGHISJSBJMYIgthiTURkbJYnURkIQYDh5OUotFo0KxZM+k+EZVNTGKILIQ9McrR6XRYuHCh2mEQkco4nERkIbwAJBGRsvh1SmQhrE4iIlKWVScxu3fvRnh4OLy9vaHRaPDzzz+rHRKR2VidpJz09HSEhYUhLCyMlx0gKsOsOolJS0tD48aNMX/+fLVDIZKNc2KUlZiYiMTERLXDICIVWfXE3m7duqFbt25qh0GkCFYnEREpy6qTmOLKzMxEZmam9Dg5OVnFaIhMGTicRESkKKseTiquqKgouLm5STcfHx+1QyKSSBeALFWfOiIi9ZSqr9PIyEgkJSVJt6tXr6odEhGAh5VJ/+YwHE4iIlJIqRpO0mq10Gq1aodBlItxKAngxF4iIqWUqiSGyFo9nsRwTox8Go0GDRo0kO4TUdlk1UlMamoq4uLipMeXLl1CTEwM3N3dUaNGDRUjIyoevXiUxbAnRj6dTocVK1aoHQYRqcyqk5gjR47gmWeekR6PGjUKABAREYFly5apFBVR8Zn2xDCLISJSglUnMR06dIB47Bcska3inBgiIuWVquokImtlMHA4SUkZGRkIDw9HeHg4MjIy1A6HiFRi1T0xRKWFdMkBcCKqEoQQSEhIkO4TUdnEnhgiC+CJ7oiIlMevVCILMAjjdZNUDoSIqBRhEkNkAXrpuknMYoiIlMIkhsgCpDkxzGGIiBTDJIbIAvT/ZjE8Wy8RkXJYnURkAY96YpjFKEGj0cDf31+6T0RlE5MYIgswsDpJUTqdDmvXrlU7DCJSGb9SiSyAc2KIiJTHJIbIAvRSiTWzGCIipTCJIbIAg1RirW4cpUVGRgb69OmDPn368LIDRGUY58QQWYCxOonDScoQQuDvv/+W7hNR2cSeGCILMPBkd0REimMSQ2QBek7sJSJSHJMYIguQemL4iSMiUgy/UokswMDqJCIixTGJIbIAnieGiEh5rE4isgAmMcrSaDTw8vKS7hNR2cQkhsgCHl0Akv/hKkGn0+HXX39VOwwiUhmHk4gsgD0xRETKYxJDZAF6VicRESmOX6lEFsDqJGVlZGSgf//+6N+/Py87QFSGcU4MkQXwZHfKEkIgNjZWuk9EZRN7YogsgHNiiIiUxySGyAIMrE4iIlIckxgiC2BPDBGR8pjEEFkA58QQESmPSQyRBRirk+yZxRARKYbVSUQWwJ4Y5VWsWFHtEIhIZUxiiCzAOCfGnkmMIpydnbFt2za1wyAilVn9cNL8+fNRs2ZN6HQ6hISE4NChQ2qHRFRsxmsn8WR3RETKseok5vvvv8eoUaMwceJEHDt2DI0bN0aXLl1w+/ZttUMjKhZWJxERKc+qh5PmzJmDIUOGYODAgQCAr776Chs3bsSSJUswbtw41eK6nZKBrByDatsnZdxO01tsW6lZD/9eipvE3H2gl+bT0COZmRmYMOZ9AMCUmZ9Bq9WpHBGZ49r9B2qHQAqoUkELnaO9Ktu22iQmKysLR48eRWRkpLTMzs4OYWFh2L9/f56vyczMRGZmpvQ4OTm5RGIbuToG+/++WyLrptKtuElM5P/u4m46E+YnGbIycDXmGABgxOY7sHNiEmOTNu1QOwJSwOohTyO0VmVVtm21Scw///wDvV6PatWqmSyvVq0azp49m+droqKiMHny5BKPzdHBDloHqx6JoyIQBssmBzpHOzTz0hbrNY52GjjxTy0Xw2PviZMdYMf3yCZpeOBKBTWHya02iTFHZGQkRo0aJT1OTk6Gj4+P4ttZMail4uskyzt69KjaIRRqfncPtUOwSunp6Wg7++H9Zc9Vg7Ozs7oBkVmCg4PVDoFsnNUmMVWqVIG9vT1u3bplsvzWrVvw9PTM8zVarRZabfF+6RIREZFtstq+PCcnJwQHB2P79u3SMoPBgO3btyM0NFTFyIiIiMgaWG1PDACMGjUKERERaN68OVq2bIm5c+ciLS1NqlYiIiKissuqk5iXX34Zd+7cwYQJE3Dz5k00adIEW7ZsyTXZl4jKHp2OFUlEZZ1GCFFqz0KRnJwMNzc3JCUlwdXVVe1wyMrYwsReotKME3spP0X9/9tq58QQERERFYRJDBEREdkkJjFEZHMyMzMxcuRIjBw50uQs3URUtlj1xF4iorwYDAbs27dPuk9EZRN7YoiIiMgmMYkhIiIim8QkhoiIiGwSkxgiIiKySUxiiIiIyCaV6uok48mIk5OTVY6ErFFqaqraIZCZMjIypPtpaWnQ6/UqRkPm4ncz5cf4t1HYRQVK9WUHrl27Bh8fH7XDICIiIjNcvXoV1atXz/f5Up3EGAwG3LhxAy4uLtBoNGqHo6jk5GT4+Pjg6tWrpf66UGVpX4Gytb9laV+BsrW/3NfSyxL7K4RASkoKvL29YWeX/8yXUj2cZGdnV2AGVxq4urqWiQ8NULb2FShb+1uW9hUoW/vLfS29Snp/3dzcCm3Dib1ERERkk5jEEBERkU1iEmOjtFotJk6cCK1Wq3YoJa4s7StQtva3LO0rULb2l/taelnT/pbqib1ERERUerEnhoiIiGwSkxgiIiKySUxiiIiIyCYxiSEiIiKbxCTGSt27dw/9+vWDq6srKlasiMGDBxd6rZ8OHTpAo9GY3IYNG2bSJj4+Hj169EC5cuVQtWpVjB49Gjk5OSW5K0VS3P29d+8e3n33XdStWxfOzs6oUaMGRowYgaSkJJN2T74fGo0Ga9asKendMTF//nzUrFkTOp0OISEhOHToUIHt161bh3r16kGn0yEwMBCbNm0yeV4IgQkTJsDLywvOzs4ICwvDhQsXSnIXiqU4+7to0SK0bdsWlSpVQqVKlRAWFpar/YABA3Idw65du5b0bhRJcfZ12bJlufZDp9OZtClNxzav7yONRoMePXpIbaz12O7evRvh4eHw9vaGRqPBzz//XOhrdu7ciWbNmkGr1aJ27dpYtmxZrjbF/S6whOLu608//YTOnTvDw8MDrq6uCA0Nxe+//27SZtKkSbmOa7169UpmBwRZpa5du4rGjRuLAwcOiD179ojatWuLV155pcDXtG/fXgwZMkQkJCRIt6SkJOn5nJwc0ahRIxEWFiaOHz8uNm3aJKpUqSIiIyNLencKVdz9PXnypHjhhRfEL7/8IuLi4sT27dtFQECAePHFF03aARBLly41eU/S09NLencka9asEU5OTmLJkiXi9OnTYsiQIaJixYri1q1bebbft2+fsLe3FzNnzhSxsbHiP//5j3B0dBQnT56U2kRHRws3Nzfx888/i7/++kv06tVL+Pn5WXS/8lPc/X311VfF/PnzxfHjx8WZM2fEgAEDhJubm7h27ZrUJiIiQnTt2tXkGN67d89Su5Sv4u7r0qVLhaurq8l+3Lx506RNaTq2d+/eNdnXU6dOCXt7e7F06VKpjbUe202bNomPP/5Y/PTTTwKAWL9+fYHt//77b1GuXDkxatQoERsbK+bNmyfs7e3Fli1bpDbFff8spbj7OnLkSDFjxgxx6NAhcf78eREZGSkcHR3FsWPHpDYTJ04UDRs2NDmud+7cKZH4mcRYodjYWAFAHD58WFq2efNmodFoxPXr1/N9Xfv27cXIkSPzfX7Tpk3Czs7O5ItzwYIFwtXVVWRmZioSuznM3d8nrV27Vjg5OYns7GxpWVE+lCWpZcuWYvjw4dJjvV4vvL29RVRUVJ7t+/TpI3r06GGyLCQkRLz55ptCCCEMBoPw9PQUn376qfR8YmKi0Gq1YvXq1SWwB8VT3P19Uk5OjnBxcRHLly+XlkVERIjevXsrHapsxd3XpUuXCjc3t3zXV9qP7WeffSZcXFxEamqqtMxaj+3jivIdMmbMGNGwYUOTZS+//LLo0qWL9Fju+2cJ5n5fNmjQQEyePFl6PHHiRNG4cWPlAisAh5Os0P79+1GxYkU0b95cWhYWFgY7OzscPHiwwNeuXLkSVapUQaNGjRAZGYkHDx6YrDcwMBDVqlWTlnXp0gXJyck4ffq08jtSRHL293FJSUlwdXWFg4PpJcGGDx+OKlWqoGXLlliyZEmhl3ZXSlZWFo4ePYqwsDBpmZ2dHcLCwrB///48X7N//36T9sDDY2Rsf+nSJdy8edOkjZubG0JCQvJdp6WYs79PevDgAbKzs+Hu7m6yfOfOnahatSrq1q2Lt956C3fv3lU09uIyd19TU1Ph6+sLHx8f9O7d2+RzV9qP7eLFi9G3b1+UL1/eZLm1HVtzFPa5VeL9s1YGgwEpKSm5PrMXLlyAt7c3/P390a9fP8THx5fI9kv1BSBt1c2bN1G1alWTZQ4ODnB3d8fNmzfzfd2rr74KX19feHt748SJExg7dizOnTuHn376SVrv4wkMAOlxQestaebu7+P++ecfTJ06FUOHDjVZPmXKFHTs2BHlypXDH3/8gbfffhupqakYMWKEYvEXFJNer8/zPT979myer8nvGBnfB+O/BbVRizn7+6SxY8fC29vb5Mu+a9eueOGFF+Dn54eLFy/io48+Qrdu3bB//37Y29srug9FZc6+1q1bF0uWLEFQUBCSkpIwa9YstGrVCqdPn0b16tVL9bE9dOgQTp06hcWLF5sst8Zja478PrfJyclIT0/H/fv3ZX82rNWsWbOQmpqKPn36SMtCQkKwbNky1K1bFwkJCZg8eTLatm2LU6dOwcXFRdHtM4mxoHHjxmHGjBkFtjlz5ozZ63/8P/DAwEB4eXmhU6dOuHjxImrVqmX2es1V0vtrlJycjB49eqBBgwaYNGmSyXPjx4+X7jdt2hRpaWn49NNPLZLEUPFER0djzZo12Llzp8mE1759+0r3AwMDERQUhFq1amHnzp3o1KmTGqGaJTQ0FKGhodLjVq1aoX79+vj6668xdepUFSMreYsXL0ZgYCBatmxpsry0HNuyatWqVZg8eTI2bNhg8kO0W7du0v2goCCEhITA19cXa9euxeDBgxWNgUmMBX3wwQcYMGBAgW38/f3h6emJ27dvmyzPycnBvXv34OnpWeTthYSEAADi4uJQq1YteHp65poNf+vWLQAo1nqLyhL7m5KSgq5du8LFxQXr16+Ho6Njge1DQkIwdepUZGZmlvh1P6pUqQJ7e3vpPTa6detWvvvl6elZYHvjv7du3YKXl5dJmyZNmigYffGZs79Gs2bNQnR0NLZt24agoKAC2/r7+6NKlSqIi4tT7T86Oftq5OjoiKZNmyIuLg5A6T22aWlpWLNmDaZMmVLodqzh2Jojv8+tq6srnJ2dYW9vL/vvxdqsWbMGb7zxBtatW5drKO1JFStWRJ06daS/dSVxTowFeXh4oF69egXenJycEBoaisTERBw9elR67f/+9z8YDAYpMSmKmJgYAJC+EENDQ3Hy5EmThGHr1q1wdXVFgwYNlNnJx5T0/iYnJ+PZZ5+Fk5MTfvnll1zlqnmJiYlBpUqVLHLhMicnJwQHB2P79u3SMoPBgO3bt5v8In9caGioSXvg4TEytvfz84Onp6dJm+TkZBw8eDDfdVqKOfsLADNnzsTUqVOxZcsWk3lR+bl27Rru3r1r8h+9pZm7r4/T6/U4efKktB+l8dgCD08ZkJmZiddee63Q7VjDsTVHYZ9bJf5erMnq1asxcOBArF692qRkPj+pqam4ePFiyRxXi0wfpmLr2rWraNq0qTh48KDYu3evCAgIMCk5vnbtmqhbt644ePCgEEKIuLg4MWXKFHHkyBFx6dIlsWHDBuHv7y/atWsnvcZYYv3ss8+KmJgYsWXLFuHh4WE1JdbF2d+kpCQREhIiAgMDRVxcnEkpX05OjhBCiF9++UUsWrRInDx5Uly4cEF8+eWXoly5cmLChAkW2681a9YIrVYrli1bJmJjY8XQoUNFxYoVpQqx119/XYwbN05qv2/fPuHg4CBmzZolzpw5IyZOnJhniXXFihXFhg0bxIkTJ0Tv3r2tqgy3OPsbHR0tnJycxA8//GByDFNSUoQQQqSkpIgPP/xQ7N+/X1y6dEls27ZNNGvWTAQEBIiMjAxV9tGouPs6efJk8fvvv4uLFy+Ko0ePir59+wqdTidOnz4ttSlNx9aoTZs24uWXX8613JqPbUpKijh+/Lg4fvy4ACDmzJkjjh8/Lq5cuSKEEGLcuHHi9ddfl9obS6xHjx4tzpw5I+bPn59niXVB759airuvK1euFA4ODmL+/Pkmn9nExESpzQcffCB27twpLl26JPbt2yfCwsJElSpVxO3btxWPn0mMlbp796545ZVXRIUKFYSrq6sYOHCg9MUuhBCXLl0SAMSOHTuEEELEx8eLdu3aCXd3d6HVakXt2rXF6NGjTc4TI4QQly9fFt26dRPOzs6iSpUq4oMPPjApSVZLcfd3x44dAkCet0uXLgkhHpZpN2nSRFSoUEGUL19eNG7cWHz11VdCr9dbdN/mzZsnatSoIZycnETLli3FgQMHpOfat28vIiIiTNqvXbtW1KlTRzg5OYmGDRuKjRs3mjxvMBjE+PHjRbVq1YRWqxWdOnUS586ds8SuFElx9tfX1zfPYzhx4kQhhBAPHjwQzz77rPDw8BCOjo7C19dXDBkyRPUvfqPi7Ot7770nta1WrZro3r27ybk1hChdx1YIIc6ePSsAiD/++CPXuqz52Ob3/WLcv4iICNG+fftcr2nSpIlwcnIS/v7+JufDMSro/VNLcfe1ffv2BbYX4mF5uZeXl3BychJPPfWUePnll0VcXFyJxK8RwkL1pkREREQK4pwYIiIisklMYoiIiMgmMYkhIiIim8QkhoiIiGwSkxgiIiKySUxiiIiIyCYxiSEiIiKbxCSGiIiIsHv3boSHh8Pb2xsajQY///xziW5v0qRJ0Gg0Jrd69eoVax1MYojI5ty8eRPvvvsu/P39odVq4ePjg/Dw8FzXryGioktLS0Pjxo0xf/58i22zYcOGSEhIkG579+4t1ut5FWsisimXL19G69atUbFiRXz66acIDAxEdnY2fv/9dwwfPhxnz55VO0Qim9StWzd069Yt3+czMzPx8ccfY/Xq1UhMTESjRo0wY8YMdOjQwextOjg4yLqSN5MYIrIpb7/9NjQaDQ4dOoTy5ctLyxs2bIhBgwapGBlR6fbOO+8gNjYWa9asgbe3N9avX4+uXbvi5MmTCAgIMGudFy5cgLe3N3Q6HUJDQxEVFYUaNWoU+fW8dhIR2Yx79+6hSpUqmDZtGiIjI9UOh6jU0mg0WL9+PZ577jkAQHx8PPz9/REfHw9vb2+pXVhYGFq2bInp06cXexubN29Gamoq6tati4SEBEyePBnXr1/HqVOn4OLiUqR1sCeGiGxGXFwchBDFnvxHRPKcPHkSer0ederUMVmemZmJypUrAwDOnj2L+vXrF7iesWPHIjo6GgBMhq6CgoIQEhICX19frF27FoMHDy5SXExiiMhmsOOYSB2pqamwt7fH0aNHYW9vb/JchQoVAAD+/v44c+ZMgesxJjx5qVixIurUqYO4uLgix8UkhohsRkBAADQaDSfvEllY06ZNodfrcfv2bbRt2zbPNk5OTrJ6SVNTU3Hx4kW8/vrrRX4NS6yJyGa4u7ujS5cumD9/PtLS0nI9n5iYaPmgiEqJ1NRUxMTEICYmBgBw6dIlxMTEID4+HnXq1EG/fv3Qv39//PTTT7h06RIOHTqEqKgobNy40aztffjhh9i1axcuX76MP//8E88//zzs7e3xyiuvFHkdnNhLRDbl77//RuvWreHu7o4pU6YgKCgIOTk52Lp1KxYsWFBodzYR5W3nzp145plnci2PiIjAsmXLkJ2djU8++QQrVqzA9evXUaVKFTz99NOYPHkyAgMDi729vn37Yvfu3bh79y48PDzQpk0bTJs2DbVq1SryOpjEEJHNSUhIwLRp0/Dbb78hISEBHh4eCA4Oxvvvvy/rnBVEZFuYxBAREZFN4pwYIiIisklMYoiIiMgmMYkhIiIim8QkhoiIiGwSkxgiIiKySUxiiIiIyCYxiSEiIiKbxCSGiIiIbBKTGCIiIrJJTGKIiIjIJjGJISIiIpvEJIaIiIhs0v8Dr+psAQp/Z6gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mi.draw_profile('R')\n",
    "plt.ylabel('$\\chi^2(R, x, I_0, C)/ndof$')\n",
    "plt.show()\n",
    "\n",
    "mi.draw_profile('C')\n",
    "plt.ylabel('$\\chi^2(C, x, I_0 R)/ndof$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "499ffb9a-00a7-4c06-ada4-1d3a41a7f1d4",
   "metadata": {},
   "source": [
    "Das liegt daran, dass $R$ und $C$ vollständig korreliert sind. Reduziert `iminuit` $C$ um ein Faktor zwei so wird dies dadurch kompenziert, dass das optimale Minimum verlangt, dass $R$ um einen Faktor zwei größer sein muss. Sprich es ist ohne weitere Infromation nicht möglich $R$ und $C$ näher zu bestimmen lediglich das Produkt der beiden Größen.\n",
    "\n",
    "Sprich wir müssen in unser Fitfunktion $R$ und $C$ durch die Zerfallszeit $\\tau$ ersetzen und schreiben\n",
    "\n",
    "$$ I = I_0 \\exp\\{-t/\\tau\\}$$\n",
    "\n",
    "mit $\\tau = R \\cdot C$.\n",
    "\n",
    "Fürhen wir nun erneut den Fit durch erhalten wir ein richtiges Ergebniss..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "847419a7-d77b-4207-8607-44af9d615ffc",
   "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 = 2.707 (χ²/ndof = 0.3) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 87 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.11e-05 (Goal: 0.0002) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total run time of algorithms\">  </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> I0 </td>\n",
       "        <td> 1.07 </td>\n",
       "        <td> 0.05 </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> tau </td>\n",
       "        <td> 0.097 </td>\n",
       "        <td> 0.011 </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> I0 </th>\n",
       "        <th> tau </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> I0 </th>\n",
       "        <td> 0.00254 </td>\n",
       "        <td style=\"background-color:rgb(198,198,250);color:black\"> -0.22e-3 <strong>(-0.396)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> tau </th>\n",
       "        <td style=\"background-color:rgb(198,198,250);color:black\"> -0.22e-3 <strong>(-0.396)</strong> </td>\n",
       "        <td> 0.000116 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 2.707 (χ²/ndof = 0.3)      │              Nfcn = 87               │\n",
       "│ EDM = 1.11e-05 (Goal: 0.0002)    │                                      │\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 │ I0   │   1.07    │   0.05    │            │            │         │         │       │\n",
       "│ 1 │ tau  │   0.097   │   0.011   │            │            │         │         │       │\n",
       "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌─────┬───────────────────┐\n",
       "│     │       I0      tau │\n",
       "├─────┼───────────────────┤\n",
       "│  I0 │  0.00254 -0.22e-3 │\n",
       "│ tau │ -0.22e-3 0.000116 │\n",
       "└─────┴───────────────────┘"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from iminuit import Minuit, cost\n",
    "\n",
    "def discharge_current2(t, I0, tau):\n",
    "    return I0 * np.exp(-t/tau)\n",
    "\n",
    "ls = cost.LeastSquares(\n",
    "    data_frame['time'],\n",
    "    data_frame['current'], \n",
    "    data_frame['delta_current'], \n",
    "    discharge_current2\n",
    ")\n",
    "mi = Minuit(ls, I0=0.9, tau=0.3)\n",
    "mi.migrad()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a46c76ec-5b00-48f4-9a46-2ea083ca5dba",
   "metadata": {},
   "source": [
    "... und die Werte und Fehler lassen sich über ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "69f540a5-e89b-4c24-aa7e-b03eaedb28d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0670397937137222"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mi.values['I0']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66733c05-692d-46e3-ae82-6f84d66ef28c",
   "metadata": {},
   "source": [
    "... bzw. ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "66e6da5b-ff32-4982-a3aa-5b9b93262073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.050402330240634355"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mi.errors['I0']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c670cd3f-fcfb-4cfc-a8d4-eded75e9a669",
   "metadata": {},
   "source": [
    "... für jeden Parameter auslesen. Dies lässt sich nun auch nutzen, um unsere Messwerte samt Fit in einem etwas schöneren Plot mit Achsenbeschriftungen darzustellen. Hierbei können wir ausnutzen, dass `iminuit` die Parameter in der Reihenfolge der Argumente unser definierten Fitfunktion speichert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "45fcf856-c58e-424d-8fd7-15037cb6698e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(data_frame['time'], \n",
    "             data_frame['current'], \n",
    "             xerr=data_frame['delta_time'], \n",
    "             yerr=data_frame['delta_current'], \n",
    "             ls='', \n",
    "             marker='.',\n",
    "             label='Data'\n",
    "            )\n",
    "x = np.arange(0, 1, 0.01)\n",
    "plt.plot(x, \n",
    "         discharge_current2(x, *mi.values), # Sternchen operator zum entpacken der Werte\n",
    "         color='k',\n",
    "         label='Best fit'\n",
    "        )\n",
    "plt.legend()\n",
    "plt.ylabel('Current [mA]')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b47df6fb-7fd6-473c-b3d7-af937b5518f0",
   "metadata": {},
   "source": [
    "# <span style=\"color:red\">ADD TASK HERE </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd73609-8593-4725-a7c4-317d6a48a72f",
   "metadata": {},
   "source": [
    "# Mathematisch motivierete Herleitung des $\\chi^2$-Fits:\n",
    "\n",
    "Nach diesen anfänglichen Beispielen wollen wir uns eine semi-mathematische Herleitung des $\\chi^2$-Fits angucken um etwas besser zu verstehen, warum diese Methode für uns in der Physik so wichtig ist. In unserem Grundpraktikum haben wir bereits gelernt, dass Messwerte durch Zufallszahlen $x_i$ representiert werden und einer gewissen **Wahrscheinlichkeitsdichtefunktion (probability density function)** $f(x)$ unterliegen.\n",
    "\n",
    "<figure class=\"image\">\n",
    "<img src=\"images/MaterialPythonkurs092018/PorbDensFun.png\"  alt=\"{{ Beispiel PDF }}\" width=70%>\n",
    "</figure>\n",
    "\n",
    "\n",
    "Eine **pdf** gibt an, mit welcher **Wahrscheinlichkeit ein Wert $x_i$** innerhalb eines **infinitesimalen Intervals $\\text{d}x_i$** zu finden ist. Des Weitren gilt, dass die Gesamtwahrscheinlichkeit gegeben ist durch $\\int_S f(x) dx = 1$. \n",
    "\n",
    "Nun betrachten wir folgendes Beispiel: In unserem Labor messen wir genau drei mal die Raumtemperartur T. Auch hier gilt, dass unsere Messung der einzelnen $T_i$ einer gewissen **Wahrscheinlichkeitsdichtefunktion** folgen. Betrachten Sie nun das folgende Bild; Welche **Wahrscheinlichkeitsdichtefunktion** passt besser zu den gezeigten Daten und **Warum?**\n",
    "\n",
    "<figure class=\"image\">\n",
    "<img src=\"images/MaterialPythonkurs092018/ProbMaxTemp.png\"  alt=\"{{ Beispiel PDF }}\" width=100%>\n",
    "</figure>\n",
    "\n",
    "Die rechte Verteilung spiegelt unsere Messdaten besser wieder. Dies können wir auch mathematisch ausdrücken. Für $N$ voreinander unabhängige Zufallszahlen bzw. Messpunkte (in unserem Beispiel $N = 3$) ist die Gesamtwahrscheinlichkeit gegeben durch das Produkt der einzelnen Wahrscheinlichkeitsdichten $f(x_i, \\theta)$ multipliziert mit dem jeweiligen infinitesimalen element $dx_i$\n",
    "\n",
    "$$\\prod_{i = 1}^{N} f(x_i,\\theta) \\  dx_i \\text{   für alle } x_i \\text{ in } [x_i, x_i + dx_i]$$\n",
    "\n",
    "wobei $x_i$ in unserem Beispiel den Messpunkten $T_i$ und $f(x_i,\\theta)$ unserer Gausverteilung mit $\\theta = (\\mu, \\sigma)$ entspricht. Sprich sofern unsere Werte gut von der jeweiligen **Wahrscheinlichkeitsdichtefunktion** repräsentiert werden, d.h. wir die richtigen Parameter $\\theta$ gewählt haben (wie im rechten oberen Plot), gilt \n",
    "\n",
    "$$ \\prod_{i = 1}^{N} f(x_i,\\theta)  dx_i \\ \\ \\text{ist} \\ \\textbf{maximal.}$$\n",
    "\n",
    "Da die einzelnen $dx_i$ von unseren Parametern $\\theta$ unabhängig sind, gilt die gleiche Argumentation auch für \n",
    "\n",
    "$$ \\mathcal{L}(x_1 ... x_N; \\theta_1 ... \\theta_N) = \\prod_{i = 1}^{N} f(x_i,\\theta)$$ \n",
    "\n",
    "wobei $\\mathcal{L}(x_1 ... x_N; \\theta_1 ... \\theta_N)$ die sogenannte **\"likelihood\"** function darstellt.\n",
    "\n",
    "Wie kommen wir nun von der **likelihood function** auf unsere **Methode der kleinsten Quadrate** und das Fitten einer Funktion $\\lambda(x; \\ $**$\\phi$**$)$ an die gemessenen Punkte **$(x,y)$**? Dazu brauche wir noch einen Zwischenschritt. Oftmals ist es einfacher, statt die **likelihood function** zu maximieren, die so genannte **log likelihood function**\n",
    "\n",
    "$$ \\log( \\mathcal{L}(x_1 ... x_N; \\theta_1 ... \\theta_N)) = \\sum_{i = 1}^{N} \\log(f(x_i,\\theta))$$\n",
    "\n",
    "zu maximieren. Dies ist im Grunde das Gleiche, da der Logarithmus eine monoton-steigende Funktion ist. Auch in unserem Fall der **Methode der kleinsten Quadrate** benötigen wir die **log likelihood function**. \n",
    "\n",
    "Stellen Sie sich nun vor, wir haben eine Messung mit $N$ voneinander unabhängigen Messpunkten (x,y). Des Weiteren nehmen wir an, dass alle $x_i$ ohne Fehler sind und dass unsere $y_i$ gaußförmig um einen unbekannten Wahrenwert $\\lambda_i$ (sprich $\\lambda_i$ entspricht dem Erwartungswert $\\mu_i$ unserer Gaußverteilung) mit einer bekannten Varianz $\\Delta y_i^2$ verteilt sind (Diese Annahme lässt sich mit dem zentralen Grenzwertsatz begründen, so lange der Fehler sich aus der Summe kleiner Fehler zusammensetzt). Die dazugehörige  **likelihood function** ist dann gegeben durch:\n",
    "\n",
    "$$ \\mathcal{L}(y_1 ... y_N; \\lambda_1 ... \\lambda_N, \\Delta y_1 ... \\Delta y_N)) = \\prod_{i = 1}^{N}\\frac{1}{\\sqrt{2 \\pi \\Delta y_i^2}} \\cdot \\exp \\bigg( \\frac{ -(y_i - \\lambda_i)^2}{2 \\cdot \\Delta y_i^2}\\bigg)$$\n",
    "\n",
    "Beziehungsweise die **log likelihood function** mit $\\lambda_i = \\lambda(x_i; \\phi)$ ergibt sich zu\n",
    "\n",
    "$$ \\log(\\mathcal{L}(y, \\theta)) \\approx -\\frac{1}{2} \\sum_{i = 1}^{N}\\bigg( \\frac{ (y_i - \\lambda(x_i; \\phi))^2}{\\Delta y_i^2}\\bigg)$$\n",
    "\n",
    "wobei die konstanten Terme, welche nicht von unserer Funktion $\\lambda(x_i; \\phi)$ abhängen, vernachlässigt worden sind. Durch den Faktor $-\\frac{1}{2}$ ist das Maximieren dieser **log likelihood function** gleich dem Minimieren von\n",
    "\n",
    "$$ \\chi(\\phi_1 ... \\phi_N)^2 = \\sum_{i = 1}^{N} \\frac{ (y_i - \\lambda(x_i; \\phi))^2}{\\Delta y_i^2}$$\n",
    "\n",
    "Diese Funktion ist unsere gesuchte **Methode der kleinsten Quadrate**. Mit ihrer Hilfe kann eine beliebige Funktion $\\lambda(x; \\phi)$, welche liniear in ihren Parametern $\\phi$ ist, an unsere Messdaten $(x,y\\pm\\Delta y)$ gefittet werden. Dabei stellt der Fitprozess selbst lediglich ein Minimierungsproblem dar. Im Folgenden sind unsere Annahmen noch einmal grafisch in einem Beispiel dargestellt.\n",
    "\n",
    "<figure class=\"image\">\n",
    "<img src=\"images/MaterialPythonkurs092018/LeastSquare.png\"  alt=\"{{ Least Square Beispiel }}\" width=100%>\n",
    "</figure>\n",
    "\n",
    "\n",
    "Need to update figure above... sigma is touching best fit too often..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be4a8d21-29db-4866-9117-8746b80d5945",
   "metadata": {},
   "source": [
    "How does optimization work.... ? \n",
    "\n",
    "Use verbose mode to check steps.... \n",
    "\n",
    "=> Alternating specific value check if cost function minimizes.... If yes continue if not start changing other parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "43bfd15e-7b68-4b70-bc06-0b23f89f7bff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9, 10000.0, 1e-06) -> 80299.69058891697\n",
      "(0.9012452948646013, 10000.0, 1e-06) -> 80526.18965485664\n",
      "(0.8987547051353988, 10000.0, 1e-06) -> 80073.51136202173\n",
      "(0.9004308748479504, 10000.0, 1e-06) -> 80378.02359217647\n",
      "(0.8995691251520497, 10000.0, 1e-06) -> 80221.39587601055\n",
      "(0.9, 10013.834507037946, 1e-06) -> 79622.63978381334\n",
      "(0.9, 9986.165492962054, 1e-06) -> 80984.35435795251\n",
      "(0.9, 10001.383450703794, 1e-06) -> 80231.64462947704\n",
      "(0.9, 9998.616549296206, 1e-06) -> 80367.81267664742\n",
      "(0.9, 10000.0, 1.0013836609610407e-06) -> 79622.5374588146\n",
      "(0.9, 10000.0, 9.986163390389592e-07) -> 80984.45899720961\n",
      "(0.9, 10000.0, 1.000138366096104e-06) -> 80231.63429361269\n",
      "(0.9, 10000.0, 9.99861633903896e-07) -> 80367.8230356535\n",
      "(0.01874743961019698, 11237.265275850094, 1.1237265276308393e-06) -> 220.6722136427291\n",
      "(0.284586001978377, 10864.031952074476, 1.0864031952394525e-06) -> 2881.0841599860123\n",
      "(0.11358432817377395, 11104.115717637773, 1.110411571804675e-06) -> 424.5249883164687\n",
      "(0.018790527094992016, 11237.265275850094, 1.1237265276308393e-06) -> 220.62940218460562\n",
      "(0.018704352125401946, 11237.265275850094, 1.1237265276308393e-06) -> 220.71513473340076\n",
      "(0.01874743961019698, 11237.403620920473, 1.1237265276308393e-06) -> 220.67272516952943\n",
      "(0.01874743961019698, 11237.126930779714, 1.1237265276308393e-06) -> 220.6717021065287\n",
      "(0.01874743961019698, 11238.648726553887, 1.1237265276308393e-06) -> 220.67732848757515\n",
      "(0.01874743961019698, 11235.8818251463, 1.1237265276308393e-06) -> 220.66709785786958\n",
      "(0.01874743961019698, 11237.265275850094, 1.1237403642404498e-06) -> 220.6727252472707\n",
      "(0.01874743961019698, 11237.265275850094, 1.1237126910212289e-06) -> 220.67170202878458\n",
      "(0.01874743961019698, 11237.265275850094, 1.1238648937269433e-06) -> 220.67732926485905\n",
      "(0.01874743961019698, 11237.265275850094, 1.1235881615347354e-06) -> 220.66709708029992\n",
      "(0.02354972366532792, 11237.181373608577, 1.123718137406686e-06) -> 216.57497287202676\n",
      "(0.03559487891832495, 11236.970928846556, 1.123697092930479e-06) -> 212.29214614381652\n",
      "(0.03563637946965444, 11236.970928846556, 1.123697092930479e-06) -> 212.2922088228832\n",
      "(0.03555337836699545, 11236.970928846556, 1.123697092930479e-06) -> 212.29218519571265\n",
      "(0.03559487891832495, 11250.557499010538, 1.123697092930479e-06) -> 212.28602162487988\n",
      "(0.03559487891832495, 11223.384358682575, 1.123697092930479e-06) -> 212.29893152088437\n",
      "(0.03559487891832495, 11242.256957337766, 1.123697092930479e-06) -> 212.28968534014302\n",
      "(0.03559487891832495, 11231.684900355347, 1.123697092930479e-06) -> 212.29470697987432\n",
      "(0.03559487891832495, 11236.970928846556, 1.1250807538915197e-06) -> 212.28591504175927\n",
      "(0.03559487891832495, 11236.970928846556, 1.1223134319694383e-06) -> 212.2990626524163\n",
      "(0.03559487891832495, 11236.970928846556, 1.1242300939534861e-06) -> 212.28966528326941\n",
      "(0.03559487891832495, 11236.970928846556, 1.123164091907472e-06) -> 212.2947287082918\n",
      "(0.03559487891832495, 11236.970928846556, 1.123697092930479e-06) -> 212.29214614381652\n",
      "(0.03563637946965444, 11236.970928846556, 1.123697092930479e-06) -> 212.2922088228832\n",
      "(0.03555337836699545, 11236.970928846556, 1.123697092930479e-06) -> 212.29218519571265\n",
      "(0.03559487891832495, 11242.256957337766, 1.123697092930479e-06) -> 212.28968534014302\n",
      "(0.03559487891832495, 11231.684900355347, 1.123697092930479e-06) -> 212.29470697987432\n",
      "(0.03559487891832495, 11236.970928846556, 1.1242300939534861e-06) -> 212.28966528326941\n",
      "(0.03559487891832495, 11236.970928846556, 1.123164091907472e-06) -> 212.2947287082918\n",
      "(0.035603179028590844, 11236.970928846556, 1.123697092930479e-06) -> 212.29215054115292\n",
      "(0.03558657880805905, 11236.970928846556, 1.123697092930479e-06) -> 212.29214581571875\n",
      "(0.03559487891832495, 11238.028134544798, 1.123697092930479e-06) -> 212.29164599966447\n",
      "(0.03559487891832495, 11235.913723148315, 1.123697092930479e-06) -> 212.29265028925306\n",
      "(0.03559487891832495, 11236.970928846556, 1.1238036931350805e-06) -> 212.2916418550488\n",
      "(0.03559487891832495, 11236.970928846556, 1.1235904927258775e-06) -> 212.29265450073018\n",
      "(0.03563637946965444, 11242.256957337766, 1.123697092930479e-06) -> 212.28964759003514\n",
      "(0.03563637946965444, 11236.970928846556, 1.1242300939534861e-06) -> 212.28962669934117\n",
      "(0.03559487891832495, 11242.256957337766, 1.1242300939534861e-06) -> 212.28730364301265\n",
      "(2.2387013730650716, 153512.47970788088, 1.5321715146660597e-05) -> 800863512.0926026\n",
      "(0.1457502036256623, 18350.746367798274, 1.833597995616985e-06) -> 207.48252123940458\n",
      "(0.1457912284905751, 18350.746367798274, 1.833597995616985e-06) -> 207.4798360033104\n",
      "(0.14570917876074949, 18350.746367798274, 1.833597995616985e-06) -> 207.48521165210263\n",
      "(0.1459299849447334, 18350.746367798274, 1.833597995616985e-06) -> 207.4707922201848\n",
      "(0.1455704223065912, 18350.746367798274, 1.833597995616985e-06) -> 207.4943496708378\n",
      "(0.1457502036256623, 18355.972636896357, 1.833597995616985e-06) -> 207.48310932666232\n",
      "(0.1457502036256623, 18345.52009870019, 1.833597995616985e-06) -> 207.48193536080672\n",
      "(0.1457502036256623, 18377.34683525181, 1.833597995616985e-06) -> 207.4855372327258\n",
      "(0.1457502036256623, 18324.145900344738, 1.833597995616985e-06) -> 207.4795624641695\n",
      "(0.1457502036256623, 18350.746367798274, 1.8341249573147995e-06) -> 207.48311469203867\n",
      "(0.1457502036256623, 18350.746367798274, 1.8330710339191706e-06) -> 207.4819300358387\n",
      "(0.1457502036256623, 18350.746367798274, 1.8371014591676097e-06) -> 207.48650848378404\n",
      "(0.1457502036256623, 18350.746367798274, 1.8300945320663605e-06) -> 207.47863341106964\n",
      "(0.7597049288459635, 57573.50328070053, 5.7468089597108976e-06) -> 1195304.6375728513\n",
      "(0.17644793988667737, 20311.884213443387, 2.0292585438216807e-06) -> 205.3090326087997\n",
      "(0.17662678161462758, 20311.884213443387, 2.0292585438216807e-06) -> 205.29923976445386\n",
      "(0.17626909815872716, 20311.884213443387, 2.0292585438216807e-06) -> 205.31889673847678\n",
      "(0.17644793988667737, 20338.347528461414, 2.0292585438216807e-06) -> 205.3000128159778\n",
      "(0.17644793988667737, 20285.42089842536, 2.0292585438216807e-06) -> 205.31803195949215\n",
      "(0.17644793988667737, 20311.884213443387, 2.032743630070525e-06) -> 205.29713827751468\n",
      "(0.17644793988667737, 20311.884213443387, 2.0257734575728364e-06) -> 205.32089141942058\n",
      "(0.17644793988667737, 20311.884213443387, 2.035058399123462e-06) -> 205.2892176101183\n",
      "(0.17644793988667737, 20311.884213443387, 2.0234586885198993e-06) -> 205.32874924043455\n",
      "(27.62661499141232, 16248.151295370044, 1706130.262293866) -> 251105710597.7842\n",
      "(13.33333318096676, 18364.134039350407, 817749.488563337) -> 58488401349.6309\n",
      "(6.619666259704454, 19358.027782062527, 400470.0496509) -> 14416071655.532528\n",
      "(3.3643835536006943, 19839.941042029135, 198142.08833926904) -> 3723505862.3797007\n",
      "(1.7616745866406807, 20077.206672322078, 98527.74846861274) -> 1020775163.9700875\n",
      "(0.9666590199526081, 20194.901071874065, 49114.565853965316) -> 307268348.5349943\n",
      "(0.5708295004403338, 20253.499820849538, 24512.284903154356) -> 107107299.21801966\n",
      "(0.3733862168956244, 20282.729397823954, 12240.44843273936) -> 45804716.44149411\n",
      "(0.27480907512602976, 20297.32279447712, 6113.51141164059) -> 24799091.121010832\n",
      "(0.22557003916293816, 20304.612159635515, 3053.121681224085) -> 16701478.438160054\n",
      "(0.20096962923562423, 20308.25401345127, 1524.1144528530604) -> 13253455.27378609\n",
      "(0.17644793988667737, 20311.884213443387, 2.0292585438216807e-06) -> 205.3090326087997\n",
      "(0.18644794988667238, 20311.884213443387, 2.0292585438216807e-06) -> 204.87090665322776\n",
      "(0.16644792988668236, 20311.884213443387, 2.0292585438216807e-06) -> 205.97003457517442\n",
      "(0.17744794088667687, 20311.884213443387, 2.0292585438216807e-06) -> 205.2551905927564\n",
      "(0.17544793888667787, 20311.884213443387, 2.0292585438216807e-06) -> 205.36510338495106\n",
      "(0.17644793988667737, 20345.474497156254, 2.0292585438216807e-06) -> 205.297580051539\n",
      "(0.17644793988667737, 20278.29392973052, 2.0292585438216807e-06) -> 205.3204522310887\n",
      "(0.17644793988667737, 20311.884213443387, 0.010008301289497496) -> 10127550.783925368\n",
      "(0.17644793988667737, 20311.884213443387, -0.010004242772409851) -> 10298760.971541697\n",
      "(0.17644793988667737, 20311.884213443387, 0.001002656461639189) -> 9393352.887426605\n",
      "(0.17644793988667737, 20311.884213443387, -0.0009985979445515456) -> 11107759.034556672\n",
      "(0.17644793988667737, 20311.884213443387, 0.00010209197885335842) -> 4496959.593791175\n",
      "(0.17644793988667737, 20311.884213443387, -9.803346176571506e-05) -> 24060229.576861788\n",
      "(0.17644793988667737, 20345.474497156254, 2.0292585438216807e-06) -> 205.297580051539\n",
      "(0.17644793988667737, 20479.835632007715, 2.0292585438216807e-06) -> 205.25140775735804\n",
      "(0.17644793988667737, 20815.738469136362, 2.0292585438216807e-06) -> 205.13289457452655\n",
      "(0.17644793988667737, 21823.44698052232, 2.0292585438216807e-06) -> 204.7387032579184\n",
      "(0.17644793988667737, 24846.572514680178, 2.0292585438216807e-06) -> 202.97384781203542\n",
      "(0.17644793988667737, 33915.94911715377, 2.0292585438216807e-06) -> 189.71900804568585\n",
      "(0.17644793988667737, 53902.16792630852, 2.0292585438216807e-06) -> 112.11853240474552\n",
      "(0.17660350804040564, 53902.16792630852, 2.0292585438216807e-06) -> 112.02891844456174\n",
      "(0.1762923717329491, 53902.16792630852, 2.0292585438216807e-06) -> 112.20820219166187\n",
      "(0.17644793988667737, 53926.81183550759, 2.0292585438216807e-06) -> 111.98914229504203\n",
      "(0.17644793988667737, 53877.52401710944, 2.0292585438216807e-06) -> 112.24790543650509\n",
      "(0.17644793988667737, 53902.16792630852, 1.2035530574775356e-05) -> 749047.029338218\n",
      "(0.17644793988667737, 53902.16792630852, -7.977013487131994e-06) -> 551322055.5667844\n",
      "(0.17644793988667737, 53902.16792630852, 3.029885746917048e-06) -> 266.94117153688745\n",
      "(0.17644793988667737, 53902.16792630852, 1.0286313407263131e-06) -> 200.63772384513936\n",
      "(0.17644793988667737, 53902.16792630852, 2.1293212641312174e-06) -> 98.19360194119729\n",
      "(0.17644793988667737, 53902.16792630852, 1.929195823512144e-06) -> 125.87066857173629\n",
      "(0.17644793988667737, 53926.81183550759, 2.0292585438216807e-06) -> 111.98914229504203\n",
      "(0.17644793988667737, 54025.3874723039, 2.0292585438216807e-06) -> 111.47142213353476\n",
      "(0.17644793988667737, 54271.82656429465, 2.0292585438216807e-06) -> 110.17620040438197\n",
      "(0.17644793988667737, 55011.143840266916, 2.0292585438216807e-06) -> 106.28781862033046\n",
      "(0.17644793988667737, 57229.09566818372, 2.0292585438216807e-06) -> 94.75564832455532\n",
      "(0.17644793988667737, 63882.951151934125, 2.0292585438216807e-06) -> 67.4635526553343\n",
      "(0.17644793988667737, 77130.26013395059, 2.0292585438216807e-06) -> 166.0658152339351\n",
      "(0.17644793988667737, 70506.60564294236, 2.0292585438216807e-06) -> 74.85498866142912\n",
      "(0.17644793988667737, 65774.91669150036, 2.0292585438216807e-06) -> 64.35253460447431\n",
      "(0.1765641692483437, 65774.91669150036, 2.0292585438216807e-06) -> 64.30414002567912\n",
      "(0.17633171052501104, 65774.91669150036, 2.0292585438216807e-06) -> 64.40102621120258\n",
      "(0.17651380928933824, 65774.91669150036, 2.0292585438216807e-06) -> 64.32509655837454\n",
      "(0.1763820704840165, 65774.91669150036, 2.0292585438216807e-06) -> 64.3800038130908\n",
      "(0.17644793988667737, 65793.73073160845, 2.0292585438216807e-06) -> 64.33807470297462\n",
      "(0.17644793988667737, 65756.10265139227, 2.0292585438216807e-06) -> 64.36735084850508\n",
      "(0.17644793988667737, 65780.32187277022, 2.0292585438216807e-06) -> 64.34834382018747\n",
      "(0.17644793988667737, 65769.5115102305, 2.0292585438216807e-06) -> 64.35675480071737\n",
      "(0.17644793988667737, 65774.91669150036, 2.0392648158526344e-06) -> 64.15399394342332\n",
      "(0.17644793988667737, 65774.91669150036, 2.019252271790727e-06) -> 64.65698618113858\n",
      "(0.17644793988667737, 65774.91669150036, 2.0302591710247762e-06) -> 64.32783003130145\n",
      "(0.17644793988667737, 65774.91669150036, 2.028257916618585e-06) -> 64.37829817359554\n",
      "(0.2344777213715522, 66547.78052455382, 2.0531017871071074e-06) -> 61.585030328012934\n",
      "(0.20901253603801712, 66208.62500401438, 2.0426386663839283e-06) -> 56.72515398100699\n",
      "(0.20907444236790867, 66208.62500401438, 2.0426386663839283e-06) -> 56.71917030619342\n",
      "(0.20895062970812556, 66208.62500401438, 2.0426386663839283e-06) -> 56.73116796004825\n",
      "(0.20901253603801712, 66213.70561491995, 2.0426386663839283e-06) -> 56.74138808391011\n",
      "(0.20901253603801712, 66203.54439310881, 2.0426386663839283e-06) -> 56.70896451667243\n",
      "(0.20901253603801712, 66208.62500401438, 2.042799988069747e-06) -> 56.741862793017\n",
      "(0.20901253603801712, 66208.62500401438, 2.0424773446981096e-06) -> 56.708492452531246\n",
      "(0.20901253603801712, 66208.62500401438, 2.042761751455297e-06) -> 56.73789817783422\n",
      "(0.20901253603801712, 66208.62500401438, 2.0425155813125594e-06) -> 56.712437309677895\n",
      "(0.2438002039870601, 64855.580803911354, 2.000894814508966e-06) -> 46.021485022433374\n",
      "(0.36937377714370073, 59971.476612314334, 1.8502113986232294e-06) -> 40.327889933348494\n",
      "(0.32044449156657334, 61874.55002793656, 1.9089246429968158e-06) -> 35.61290012059835\n",
      "(0.3204914794149062, 61874.55002793656, 1.9089246429968158e-06) -> 35.606381162457645\n",
      "(0.3203975037182405, 61874.55002793656, 1.9089246429968158e-06) -> 35.61942637223192\n",
      "(0.3205171891354251, 61874.55002793656, 1.9089246429968158e-06) -> 35.60281735731878\n",
      "(0.3203717939977216, 61874.55002793656, 1.9089246429968158e-06) -> 35.62300034226913\n",
      "(0.32044449156657334, 61877.67381585722, 1.9089246429968158e-06) -> 35.60713779018709\n",
      "(0.32044449156657334, 61871.4262400159, 1.9089246429968158e-06) -> 35.618673453810274\n",
      "(0.32044449156657334, 61874.55002793656, 1.909022668684815e-06) -> 35.607039107744875\n",
      "(0.32044449156657334, 61874.55002793656, 1.9088266173088165e-06) -> 35.618772516707736\n",
      "(0.34027373143588147, 61845.32976113261, 1.908023281952575e-06) -> 33.57022654071601\n",
      "(0.36399982222101446, 61810.36711468092, 1.9069447850406015e-06) -> 32.69141328003167\n",
      "(0.36406955908866795, 61810.36711468092, 1.9069447850406015e-06) -> 32.69131128707305\n",
      "(0.36393008535336097, 61810.36711468092, 1.9069447850406015e-06) -> 32.691531161430646\n",
      "(0.36399982222101446, 61814.04559820505, 1.9069447850406015e-06) -> 32.69131234203796\n",
      "(0.36399982222101446, 61806.688631156794, 1.9069447850406015e-06) -> 32.69153484199518\n",
      "(0.36399982222101446, 61810.36711468092, 1.9070612382090942e-06) -> 32.691309980430496\n",
      "(0.36399982222101446, 61810.36711468092, 1.9068283318721088e-06) -> 32.69153829582436\n",
      "(0.3642505705772579, 61818.727695937, 1.9072027264443144e-06) -> 32.69104283789013\n",
      "(0.36421273360985085, 61817.46611621927, 1.9071638040735702e-06) -> 32.69103067341378\n",
      "(0.36428285717637465, 61817.46611621927, 1.9071638040735702e-06) -> 32.69102791322033\n",
      "(0.36414261004332704, 61817.46611621927, 1.9071638040735702e-06) -> 32.691049518377234\n",
      "(0.36421273360985085, 61820.65100788768, 1.9071638040735702e-06) -> 32.69104586238568\n",
      "(0.36421273360985085, 61814.28122455086, 1.9071638040735702e-06) -> 32.691031007928835\n",
      "(0.36421273360985085, 61817.46611621927, 1.9072639657700813e-06) -> 32.691046309818674\n",
      "(0.36421273360985085, 61817.46611621927, 1.9070636423770592e-06) -> 32.69103116757846\n",
      "(0.36421273360985085, 61817.46611621927, 1.9071638040735702e-06) -> 32.69103067341378\n",
      "(0.36428285717637465, 61817.46611621927, 1.9071638040735702e-06) -> 32.69102791322033\n",
      "(0.36414261004332704, 61817.46611621927, 1.9071638040735702e-06) -> 32.691049518377234\n",
      "(0.36421273360985085, 61820.65100788768, 1.9071638040735702e-06) -> 32.69104586238568\n",
      "(0.36421273360985085, 61814.28122455086, 1.9071638040735702e-06) -> 32.691031007928835\n",
      "(0.36421273360985085, 61817.46611621927, 1.9072639657700813e-06) -> 32.691046309818674\n",
      "(0.36421273360985085, 61817.46611621927, 1.9070636423770592e-06) -> 32.69103116757846\n",
      "(0.3642267583231556, 61817.46611621927, 1.9071638040735702e-06) -> 32.691028834593496\n",
      "(0.3641987088965461, 61817.46611621927, 1.9071638040735702e-06) -> 32.69103315562488\n",
      "(0.36421273360985085, 61818.10309455296, 1.9071638040735702e-06) -> 32.69103246906524\n",
      "(0.36421273360985085, 61816.82913788559, 1.9071638040735702e-06) -> 32.691029498701766\n",
      "(0.36421273360985085, 61817.46611621927, 1.9071838364128724e-06) -> 32.69103250996962\n",
      "(0.36421273360985085, 61817.46611621927, 1.907143771734268e-06) -> 32.69102948208074\n",
      "(0.36428285717637465, 61820.65100788768, 1.9071638040735702e-06) -> 32.69105455821643\n",
      "(0.36428285717637465, 61817.46611621927, 1.9072639657700813e-06) -> 32.691055227582524\n",
      "(0.36421273360985085, 61820.65100788768, 1.9072639657700813e-06) -> 32.691077333435835\n"
     ]
    },
    {
     "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 = 32.69 (χ²/ndof = 4.7) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 193 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.9e-05 (Goal: 0.0002) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total run time of algorithms\">  </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:#FFF79A;color:black\" title=\"Is covariance matrix accurate?\"> APPROXIMATE </td>\n",
       "        <td style=\"text-align:center;background-color:#c15ef7;color:black\" title=\"Is covariance matrix positive definite?\"> NOT pos. def. </td>\n",
       "        <td style=\"text-align:center;background-color:#c15ef7;color:black\" title=\"Was positive definiteness enforced by Minuit?\"> 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> I0 </td>\n",
       "        <td> 0.36 </td>\n",
       "        <td> 0.04 </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> R </td>\n",
       "        <td> 0.062e6 </td>\n",
       "        <td> 0.016e6 </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> C </td>\n",
       "        <td> 1.9e-6 </td>\n",
       "        <td> 0.5e-6 </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> I0 </th>\n",
       "        <th> R </th>\n",
       "        <th> C </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> I0 </th>\n",
       "        <td> 0.00128 </td>\n",
       "        <td style=\"background-color:rgb(245,245,250);color:black\"> -21.3103 <strong>(-0.038)</strong> </td>\n",
       "        <td style=\"background-color:rgb(245,245,250);color:black\"> -660.62e-12 <strong>(-0.038)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> R </th>\n",
       "        <td style=\"background-color:rgb(245,245,250);color:black\"> -21.3103 <strong>(-0.038)</strong> </td>\n",
       "        <td> 2.48e+08 </td>\n",
       "        <td style=\"background-color:rgb(121,121,250);color:black\"> -7.61159469847e-3 <strong>(-0.994)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> C </th>\n",
       "        <td style=\"background-color:rgb(245,245,250);color:black\"> -660.62e-12 <strong>(-0.038)</strong> </td>\n",
       "        <td style=\"background-color:rgb(121,121,250);color:black\"> -7.61159469847e-3 <strong>(-0.994)</strong> </td>\n",
       "        <td> 2.36e-13 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 32.69 (χ²/ndof = 4.7)      │              Nfcn = 193              │\n",
       "│ EDM = 1.9e-05 (Goal: 0.0002)     │                                      │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│          Valid Minimum           │        No Parameters at limit        │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
       "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
       "│  Covariance   │     Hesse ok     │APPROXIMATE│NOT pos. def.│   FORCED   │\n",
       "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
       "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
       "│   │ Name │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
       "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
       "│ 0 │ I0   │   0.36    │   0.04    │            │            │         │         │       │\n",
       "│ 1 │ R    │  0.062e6  │  0.016e6  │            │            │         │         │       │\n",
       "│ 2 │ C    │  1.9e-6   │  0.5e-6   │            │            │         │         │       │\n",
       "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌────┬───────────────────────────────────────────────────────┐\n",
       "│    │                I0                 R                 C │\n",
       "├────┼───────────────────────────────────────────────────────┤\n",
       "│ I0 │           0.00128          -21.3103       -660.62e-12 │\n",
       "│  R │          -21.3103          2.48e+08 -7.61159469847e-3 │\n",
       "│  C │       -660.62e-12 -7.61159469847e-3          2.36e-13 │\n",
       "└────┴───────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls = cost.LeastSquares(time_mess, current_mes, dcurrent, discharge_current, verbose=1)\n",
    "\n",
    "\n",
    "mi = Minuit(ls, I0=0.9, R=10*10**3, C=10**-6)\n",
    "mi.migrad()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c664e4f0-0226-4be0-91f4-7c28aee16a4a",
   "metadata": {},
   "source": [
    "General problem LeastSquares only accounts for uncertainty in y but not x! Needs always to be kept in mind... "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c68c1c7-5568-4fc8-a2c6-7ca6791ad2f6",
   "metadata": {},
   "source": [
    "Box yellow -> Pay attention to result think about it \n",
    "\n",
    "Box purple -> Fit did not converege cannot be used... \n",
    "\n",
    "\n",
    "Limits can also be specified only as onsided as e.g. `(lower_boundary, None)` ..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f63b27-cd52-49b5-8c97-12615b808a2e",
   "metadata": {},
   "source": [
    "Now more complex example to show other minuit features:\n",
    "\n",
    "-> Counting experiment\n",
    "\n",
    "-> Poisson statistics -> uncertainty sqrt n..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "143a2a23-0a62-439f-9d28-9f555ae85589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of counts per bin')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rnd_bkd = np.random.exponential(39.7, 5000)\n",
    "rnd_bkd += 40\n",
    "\n",
    "peak1 = np.random.normal(53.3, 2.1, 5000)\n",
    "peak2 = np.random.normal(60.5, 2.78, 12000)\n",
    "data = np.concatenate([rnd_bkd, peak1, peak2])\n",
    "\n",
    "entries, edges = np.histogram(data, bins=60, range=(40, 80))\n",
    "center = edges[:-1] + np.diff(edges)/2\n",
    "\n",
    "plt.errorbar(center, entries, np.sqrt(entries), ls='', marker='.')\n",
    "plt.xlabel('Energy [keV]')\n",
    "plt.ylabel('Number of counts per bin')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9f1ac1-fe1c-44b6-99a2-30f7bd0f6c98",
   "metadata": {},
   "source": [
    "Concept entries PER BIN ! Differential plot...\n",
    "\n",
    "-> Binnded fit problem, how to choose binning?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b582615c-9251-409d-bcfc-d19fd579e161",
   "metadata": {},
   "source": [
    "More complex fit, now use regional masking and parameter fixing...\n",
    "\n",
    "First define fit model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f84d7527-c0d2-475d-966d-5363a8e09369",
   "metadata": {},
   "outputs": [],
   "source": [
    "def peak(x, A, mu, sigma):\n",
    "    return A*np.exp(-(x-mu)**2/(2*sigma**2))\n",
    "\n",
    "def bkg(x, A, tau):\n",
    "    return A*np.exp(-x/tau)\n",
    "\n",
    "def fit_model(x, A_p1, A_p2, mu_p1, mu_p2, sigma_p1, sigma_p2, A_bkg, tau_bkg):\n",
    "    return peak(x, A_p1, mu_p1, sigma_p1) + peak(x, A_p2, mu_p2, sigma_p2) + bkg(x, A_bkg, tau_bkg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32014861-316c-4692-9d52-48f2fb71321c",
   "metadata": {},
   "source": [
    "Explain how to get some good first parameters based on plot above...\n",
    "\n",
    "Tau is not really possible to guess..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a31901cf-a0ce-4db8-a072-a661fbbb7296",
   "metadata": {},
   "outputs": [],
   "source": [
    "ls = cost.LeastSquares(center, entries, np.sqrt(entries), fit_model)\n",
    "\n",
    "mi = Minuit(ls, \n",
    "            A_p1 = 800, \n",
    "            A_p2 = 1400,\n",
    "            mu_p1 = 54,\n",
    "            mu_p2 = 60,\n",
    "            sigma_p1 = 2,\n",
    "            sigma_p2 = 2,\n",
    "            A_bkg = 100,\n",
    "            tau_bkg = 10, \n",
    "           )\n",
    "mi.limits['tau_bkg'] = (0, None)\n",
    "mi.fixed[:] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1e69a046-770f-4c38-9b91-0176bb0686a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9cb4760640>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(center, entries, np.sqrt(entries), ls='', marker='.')\n",
    "plt.xlabel('Energy [keV]')\n",
    "plt.ylabel('Number of counts per bin')\n",
    "\n",
    "x = np.arange(40, 80, 0.1)\n",
    "plt.plot(x, fit_model(x, *mi.values), color='k', label='Initial guess')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f755f4-b780-43a6-a923-49662c4c701a",
   "metadata": {},
   "source": [
    "First fit background therefore use only region outside of peaks.... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "81232354-a7b8-4e2a-9ac0-159ce0a03da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9cb4650640>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls.mask = (center < 45) | (center >= 70)\n",
    "\n",
    "\n",
    "plt.errorbar(center[ls.mask], entries[ls.mask], np.sqrt(entries[ls.mask]), ls='', marker='.', label='Not masked')\n",
    "plt.errorbar(center[~ls.mask], entries[~ls.mask], np.sqrt(entries[~ls.mask]), ls='', marker='.', label='Masked')\n",
    "plt.xlabel('Energy [keV]')\n",
    "plt.ylabel('Number of counts per bin')\n",
    "\n",
    "x = np.arange(40, 80, 0.1)\n",
    "plt.plot(x, fit_model(x, *mi.values), color='k', label='Initial guess')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4a93a1c2-17df-46c2-b38e-9a509fe16fc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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> A_p1 </td>\n",
       "        <td> 800 </td>\n",
       "        <td> 8 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> A_p2 </td>\n",
       "        <td> 1.400e3 </td>\n",
       "        <td> 0.014e3 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 2 </th>\n",
       "        <td> mu_p1 </td>\n",
       "        <td> 54.0 </td>\n",
       "        <td> 0.5 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> mu_p2 </td>\n",
       "        <td> 60.0 </td>\n",
       "        <td> 0.6 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 4 </th>\n",
       "        <td> sigma_p1 </td>\n",
       "        <td> 2.00 </td>\n",
       "        <td> 0.02 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 5 </th>\n",
       "        <td> sigma_p2 </td>\n",
       "        <td> 2.00 </td>\n",
       "        <td> 0.02 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 6 </th>\n",
       "        <td> A_bkg </td>\n",
       "        <td> 100 </td>\n",
       "        <td> 1 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 7 </th>\n",
       "        <td> tau_bkg </td>\n",
       "        <td> 10.0 </td>\n",
       "        <td> 0.1 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌───┬──────────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
       "│   │ Name     │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
       "├───┼──────────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
       "│ 0 │ A_p1     │    800    │     8     │            │            │         │         │  yes  │\n",
       "│ 1 │ A_p2     │  1.400e3  │  0.014e3  │            │            │         │         │  yes  │\n",
       "│ 2 │ mu_p1    │   54.0    │    0.5    │            │            │         │         │  yes  │\n",
       "│ 3 │ mu_p2    │   60.0    │    0.6    │            │            │         │         │  yes  │\n",
       "│ 4 │ sigma_p1 │   2.00    │   0.02    │            │            │         │         │  yes  │\n",
       "│ 5 │ sigma_p2 │   2.00    │   0.02    │            │            │         │         │  yes  │\n",
       "│ 6 │ A_bkg    │    100    │     1     │            │            │         │         │       │\n",
       "│ 7 │ tau_bkg  │   10.0    │    0.1    │            │            │    0    │         │       │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mi.fixed[['tau_bkg', 'A_bkg']] = False\n",
    "mi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3e90c2ed-c282-47c2-b0fe-3063f9545639",
   "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 = 17.73 (χ²/ndof = 0.9) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 118 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 3.54e-05 (Goal: 0.0002) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total run time of algorithms\">  </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> A_p1 </td>\n",
       "        <td> 800 </td>\n",
       "        <td> 8 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> A_p2 </td>\n",
       "        <td> 1.400e3 </td>\n",
       "        <td> 0.014e3 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 2 </th>\n",
       "        <td> mu_p1 </td>\n",
       "        <td> 54.0 </td>\n",
       "        <td> 0.5 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> mu_p2 </td>\n",
       "        <td> 60.0 </td>\n",
       "        <td> 0.6 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 4 </th>\n",
       "        <td> sigma_p1 </td>\n",
       "        <td> 2.00 </td>\n",
       "        <td> 0.02 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 5 </th>\n",
       "        <td> sigma_p2 </td>\n",
       "        <td> 2.00 </td>\n",
       "        <td> 0.02 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 6 </th>\n",
       "        <td> A_bkg </td>\n",
       "        <td> 254 </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> 7 </th>\n",
       "        <td> tau_bkg </td>\n",
       "        <td> 37.4 </td>\n",
       "        <td> 2.6 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> A_p1 </th>\n",
       "        <th> A_p2 </th>\n",
       "        <th> mu_p1 </th>\n",
       "        <th> mu_p2 </th>\n",
       "        <th> sigma_p1 </th>\n",
       "        <th> sigma_p2 </th>\n",
       "        <th> A_bkg </th>\n",
       "        <th> tau_bkg </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p1 </th>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 801 </td>\n",
       "        <td style=\"background-color:rgb(125,125,250);color:black\"> -71 <strong>(-0.962)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> tau_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(125,125,250);color:black\"> -71 <strong>(-0.962)</strong> </td>\n",
       "        <td> 6.79 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 17.73 (χ²/ndof = 0.9)      │              Nfcn = 118              │\n",
       "│ EDM = 3.54e-05 (Goal: 0.0002)    │                                      │\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 │ A_p1     │    800    │     8     │            │            │         │         │  yes  │\n",
       "│ 1 │ A_p2     │  1.400e3  │  0.014e3  │            │            │         │         │  yes  │\n",
       "│ 2 │ mu_p1    │   54.0    │    0.5    │            │            │         │         │  yes  │\n",
       "│ 3 │ mu_p2    │   60.0    │    0.6    │            │            │         │         │  yes  │\n",
       "│ 4 │ sigma_p1 │   2.00    │   0.02    │            │            │         │         │  yes  │\n",
       "│ 5 │ sigma_p2 │   2.00    │   0.02    │            │            │         │         │  yes  │\n",
       "│ 6 │ A_bkg    │    254    │    28     │            │            │         │         │       │\n",
       "│ 7 │ tau_bkg  │   37.4    │    2.6    │            │            │    0    │         │       │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬─────────────────────────────────────────────────────────────────────────┐\n",
       "│          │     A_p1     A_p2    mu_p1    mu_p2 sigma_p1 sigma_p2    A_bkg  tau_bkg │\n",
       "├──────────┼─────────────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │        0        0        0        0        0        0        0        0 │\n",
       "│     A_p2 │        0        0        0        0        0        0        0        0 │\n",
       "│    mu_p1 │        0        0        0        0        0        0        0        0 │\n",
       "│    mu_p2 │        0        0        0        0        0        0        0        0 │\n",
       "│ sigma_p1 │        0        0        0        0        0        0        0        0 │\n",
       "│ sigma_p2 │        0        0        0        0        0        0        0        0 │\n",
       "│    A_bkg │        0        0        0        0        0        0      801      -71 │\n",
       "│  tau_bkg │        0        0        0        0        0        0      -71     6.79 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mi.migrad()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4837389e-098b-43a0-ad42-2fd354fa7c6e",
   "metadata": {},
   "source": [
    "Now next step first peak..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0b435af3-73ea-42de-9ab7-6a16ae9dbceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9cb4566940>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(center, entries, np.sqrt(entries), ls='', marker='.')\n",
    "plt.xlabel('Energy [keV]')\n",
    "plt.ylabel('Number of counts per bin')\n",
    "\n",
    "x = np.arange(40, 80, 0.1)\n",
    "plt.plot(x, fit_model(x, *mi.values), color='k', label='Initial guess')\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6def3e2b-5edf-48bb-99b8-2b7fdaae51c5",
   "metadata": {},
   "source": [
    "\n",
    "-> Remove mask....\n",
    "\n",
    "-> Update initla guess\n",
    "\n",
    "-> fixe again parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "823e05a0-516c-4d30-8dc7-5381e0e2e617",
   "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 = 1290 (χ²/ndof = 22.6) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 186 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 9.24e-06 (Goal: 0.0002) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total run time of algorithms\">  </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> A_p1 </td>\n",
       "        <td> 687 </td>\n",
       "        <td> 13 </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> A_p2 </td>\n",
       "        <td> 1.400e3 </td>\n",
       "        <td> 0.014e3 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 2 </th>\n",
       "        <td> mu_p1 </td>\n",
       "        <td> 53.51 </td>\n",
       "        <td> 0.04 </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_p2 </td>\n",
       "        <td> 60.0 </td>\n",
       "        <td> 0.6 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 4 </th>\n",
       "        <td> sigma_p1 </td>\n",
       "        <td> 2.131 </td>\n",
       "        <td> 0.034 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 5 </th>\n",
       "        <td> sigma_p2 </td>\n",
       "        <td> 2.00 </td>\n",
       "        <td> 0.02 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 6 </th>\n",
       "        <td> A_bkg </td>\n",
       "        <td> 254 </td>\n",
       "        <td> 28 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 7 </th>\n",
       "        <td> tau_bkg </td>\n",
       "        <td> 37.4 </td>\n",
       "        <td> 2.6 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> A_p1 </th>\n",
       "        <th> A_p2 </th>\n",
       "        <th> mu_p1 </th>\n",
       "        <th> mu_p2 </th>\n",
       "        <th> sigma_p1 </th>\n",
       "        <th> sigma_p2 </th>\n",
       "        <th> A_bkg </th>\n",
       "        <th> tau_bkg </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p1 </th>\n",
       "        <td> 160 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(247,247,250);color:black\"> -0.0106 <strong>(-0.020)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(180,180,250);color:black\"> -0.2349 <strong>(-0.540)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p1 </th>\n",
       "        <td style=\"background-color:rgb(247,247,250);color:black\"> -0.0106 <strong>(-0.020)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td> 0.00179 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,209,209);color:black\"> 0.0004 <strong>(0.275)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(180,180,250);color:black\"> -0.2349 <strong>(-0.540)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,209,209);color:black\"> 0.0004 <strong>(0.275)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td> 0.00118 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> tau_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 1290 (χ²/ndof = 22.6)      │              Nfcn = 186              │\n",
       "│ EDM = 9.24e-06 (Goal: 0.0002)    │                                      │\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 │ A_p1     │    687    │    13     │            │            │         │         │       │\n",
       "│ 1 │ A_p2     │  1.400e3  │  0.014e3  │            │            │         │         │  yes  │\n",
       "│ 2 │ mu_p1    │   53.51   │   0.04    │            │            │         │         │       │\n",
       "│ 3 │ mu_p2    │   60.0    │    0.6    │            │            │         │         │  yes  │\n",
       "│ 4 │ sigma_p1 │   2.131   │   0.034   │            │            │         │         │       │\n",
       "│ 5 │ sigma_p2 │   2.00    │   0.02    │            │            │         │         │  yes  │\n",
       "│ 6 │ A_bkg    │    254    │    28     │            │            │         │         │  yes  │\n",
       "│ 7 │ tau_bkg  │   37.4    │    2.6    │            │            │    0    │         │  yes  │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬─────────────────────────────────────────────────────────────────────────┐\n",
       "│          │     A_p1     A_p2    mu_p1    mu_p2 sigma_p1 sigma_p2    A_bkg  tau_bkg │\n",
       "├──────────┼─────────────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │      160        0  -0.0106        0  -0.2349        0        0        0 │\n",
       "│     A_p2 │        0        0   0.0000        0   0.0000        0        0        0 │\n",
       "│    mu_p1 │  -0.0106   0.0000  0.00179   0.0000   0.0004   0.0000   0.0000   0.0000 │\n",
       "│    mu_p2 │        0        0   0.0000        0   0.0000        0        0        0 │\n",
       "│ sigma_p1 │  -0.2349   0.0000   0.0004   0.0000  0.00118   0.0000   0.0000   0.0000 │\n",
       "│ sigma_p2 │        0        0   0.0000        0   0.0000        0        0        0 │\n",
       "│    A_bkg │        0        0   0.0000        0   0.0000        0        0        0 │\n",
       "│  tau_bkg │        0        0   0.0000        0   0.0000        0        0        0 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls.mask = None\n",
    "mi.values['A_p1'] = 700\n",
    "mi.values['sigma_p1'] = 3\n",
    "mi.fixed[:] = True\n",
    "mi.fixed[['A_p1', 'mu_p1', 'sigma_p1']] = False\n",
    "mi.migrad()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34df75bf-3750-4186-ae12-4f6bb9e49931",
   "metadata": {},
   "source": [
    "Now lets do last peak:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "264a9891-423c-479a-8906-c048aac2fd2e",
   "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 = 95.17 (χ²/ndof = 1.7) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 250 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 4.52e-06 (Goal: 0.0002) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total run time of algorithms\">  </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> A_p1 </td>\n",
       "        <td> 687 </td>\n",
       "        <td> 13 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> A_p2 </td>\n",
       "        <td> 1.140e3 </td>\n",
       "        <td> 0.014e3 </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> mu_p1 </td>\n",
       "        <td> 53.51 </td>\n",
       "        <td> 0.04 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> mu_p2 </td>\n",
       "        <td> 60.599 </td>\n",
       "        <td> 0.032 </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_p1 </td>\n",
       "        <td> 2.131 </td>\n",
       "        <td> 0.034 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 5 </th>\n",
       "        <td> sigma_p2 </td>\n",
       "        <td> 2.725 </td>\n",
       "        <td> 0.028 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 6 </th>\n",
       "        <td> A_bkg </td>\n",
       "        <td> 254 </td>\n",
       "        <td> 28 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 7 </th>\n",
       "        <td> tau_bkg </td>\n",
       "        <td> 37.4 </td>\n",
       "        <td> 2.6 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> A_p1 </th>\n",
       "        <th> A_p2 </th>\n",
       "        <th> mu_p1 </th>\n",
       "        <th> mu_p2 </th>\n",
       "        <th> sigma_p1 </th>\n",
       "        <th> sigma_p2 </th>\n",
       "        <th> A_bkg </th>\n",
       "        <th> tau_bkg </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p1 </th>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 201 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,249,249);color:black\"> 0.0023 <strong>(0.005)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(174,174,250);color:black\"> -229.0e-3 <strong>(-0.587)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,249,249);color:black\"> 0.0023 <strong>(0.005)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td> 0.00104 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(226,226,250);color:black\"> -0.2e-3 <strong>(-0.187)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(174,174,250);color:black\"> -229.0e-3 <strong>(-0.587)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(226,226,250);color:black\"> -0.2e-3 <strong>(-0.187)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0.000758 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> tau_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 95.17 (χ²/ndof = 1.7)      │              Nfcn = 250              │\n",
       "│ EDM = 4.52e-06 (Goal: 0.0002)    │                                      │\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 │ A_p1     │    687    │    13     │            │            │         │         │  yes  │\n",
       "│ 1 │ A_p2     │  1.140e3  │  0.014e3  │            │            │         │         │       │\n",
       "│ 2 │ mu_p1    │   53.51   │   0.04    │            │            │         │         │  yes  │\n",
       "│ 3 │ mu_p2    │  60.599   │   0.032   │            │            │         │         │       │\n",
       "│ 4 │ sigma_p1 │   2.131   │   0.034   │            │            │         │         │  yes  │\n",
       "│ 5 │ sigma_p2 │   2.725   │   0.028   │            │            │         │         │       │\n",
       "│ 6 │ A_bkg    │    254    │    28     │            │            │         │         │  yes  │\n",
       "│ 7 │ tau_bkg  │   37.4    │    2.6    │            │            │    0    │         │  yes  │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬─────────────────────────────────────────────────────────────────────────────────┐\n",
       "│          │      A_p1      A_p2     mu_p1     mu_p2  sigma_p1  sigma_p2     A_bkg   tau_bkg │\n",
       "├──────────┼─────────────────────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │         0         0         0     0.000         0         0         0         0 │\n",
       "│     A_p2 │         0       201         0    0.0023         0 -229.0e-3         0         0 │\n",
       "│    mu_p1 │         0         0         0     0.000         0         0         0         0 │\n",
       "│    mu_p2 │     0.000    0.0023     0.000   0.00104     0.000   -0.2e-3     0.000     0.000 │\n",
       "│ sigma_p1 │         0         0         0     0.000         0         0         0         0 │\n",
       "│ sigma_p2 │         0 -229.0e-3         0   -0.2e-3         0  0.000758         0         0 │\n",
       "│    A_bkg │         0         0         0     0.000         0         0         0         0 │\n",
       "│  tau_bkg │         0         0         0     0.000         0         0         0         0 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mi.fixed[:] = True\n",
    "mi.fixed[['A_p2', 'mu_p2', 'sigma_p2']] = False\n",
    "mi.migrad()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32d67543-870f-4bd9-bba4-2d01086c671a",
   "metadata": {},
   "source": [
    "And now do everything one last time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "72d43004-cd80-418a-996a-f1e7a7133ce9",
   "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 = 50.35 (χ²/ndof = 1.0) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 525 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 2.19e-05 (Goal: 0.0002) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total run time of algorithms\">  </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> A_p1 </td>\n",
       "        <td> 609 </td>\n",
       "        <td> 15 </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> A_p2 </td>\n",
       "        <td> 1.133e3 </td>\n",
       "        <td> 0.014e3 </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> mu_p1 </td>\n",
       "        <td> 53.13 </td>\n",
       "        <td> 0.08 </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_p2 </td>\n",
       "        <td> 60.35 </td>\n",
       "        <td> 0.06 </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_p1 </td>\n",
       "        <td> 2.00 </td>\n",
       "        <td> 0.06 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 5 </th>\n",
       "        <td> sigma_p2 </td>\n",
       "        <td> 2.92 </td>\n",
       "        <td> 0.04 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 6 </th>\n",
       "        <td> A_bkg </td>\n",
       "        <td> 254 </td>\n",
       "        <td> 26 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 7 </th>\n",
       "        <td> tau_bkg </td>\n",
       "        <td> 37.0 </td>\n",
       "        <td> 2.4 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> A_p1 </th>\n",
       "        <th> A_p2 </th>\n",
       "        <th> mu_p1 </th>\n",
       "        <th> mu_p2 </th>\n",
       "        <th> sigma_p1 </th>\n",
       "        <th> sigma_p2 </th>\n",
       "        <th> A_bkg </th>\n",
       "        <th> tau_bkg </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p1 </th>\n",
       "        <td> 213 </td>\n",
       "        <td style=\"background-color:rgb(250,226,226);color:black\"> 0.03e3 <strong>(0.161)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,210,210);color:black\"> 0.297 <strong>(0.268)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,199,199);color:black\"> 0.2830 <strong>(0.343)</strong> </td>\n",
       "        <td style=\"background-color:rgb(233,233,250);color:black\"> -0.1103 <strong>(-0.133)</strong> </td>\n",
       "        <td style=\"background-color:rgb(198,198,250);color:black\"> -0.2594 <strong>(-0.400)</strong> </td>\n",
       "        <td style=\"background-color:rgb(244,244,250);color:black\"> -0.02e3 <strong>(-0.048)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,242,242);color:black\"> 2 <strong>(0.051)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,226,226);color:black\"> 0.03e3 <strong>(0.161)</strong> </td>\n",
       "        <td> 191 </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.030 <strong>(0.029)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,236,236);color:black\"> 0.0711 <strong>(0.091)</strong> </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.0264 <strong>(-0.034)</strong> </td>\n",
       "        <td style=\"background-color:rgb(194,194,250);color:black\"> -0.2656 <strong>(-0.433)</strong> </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.01e3 <strong>(-0.030)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 1 <strong>(0.027)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,210,210);color:black\"> 0.297 <strong>(0.268)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.030 <strong>(0.029)</strong> </td>\n",
       "        <td> 0.00578 </td>\n",
       "        <td style=\"background-color:rgb(250,139,139);color:black\"> 0.0032 <strong>(0.737)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,148,148);color:black\"> 0.0029 <strong>(0.678)</strong> </td>\n",
       "        <td style=\"background-color:rgb(162,162,250);color:black\"> -0.0023 <strong>(-0.674)</strong> </td>\n",
       "        <td style=\"background-color:rgb(241,241,250);color:black\"> -0.134 <strong>(-0.068)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,237,237);color:black\"> 0.016 <strong>(0.086)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,199,199);color:black\"> 0.2830 <strong>(0.343)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,236,236);color:black\"> 0.0711 <strong>(0.091)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,139,139);color:black\"> 0.0032 <strong>(0.737)</strong> </td>\n",
       "        <td> 0.0032 </td>\n",
       "        <td style=\"background-color:rgb(250,155,155);color:black\"> 0.0020 <strong>(0.632)</strong> </td>\n",
       "        <td style=\"background-color:rgb(160,160,250);color:black\"> -0.0017 <strong>(-0.695)</strong> </td>\n",
       "        <td style=\"background-color:rgb(240,240,250);color:black\"> -0.1166 <strong>(-0.079)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,239,239);color:black\"> 0.0101 <strong>(0.073)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(233,233,250);color:black\"> -0.1103 <strong>(-0.133)</strong> </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.0264 <strong>(-0.034)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,148,148);color:black\"> 0.0029 <strong>(0.678)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,155,155);color:black\"> 0.0020 <strong>(0.632)</strong> </td>\n",
       "        <td> 0.00323 </td>\n",
       "        <td style=\"background-color:rgb(182,182,250);color:black\"> -0.0013 <strong>(-0.521)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.2869 <strong>(-0.194)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,226,226);color:black\"> 0.0222 <strong>(0.160)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(198,198,250);color:black\"> -0.2594 <strong>(-0.400)</strong> </td>\n",
       "        <td style=\"background-color:rgb(194,194,250);color:black\"> -0.2656 <strong>(-0.433)</strong> </td>\n",
       "        <td style=\"background-color:rgb(162,162,250);color:black\"> -0.0023 <strong>(-0.674)</strong> </td>\n",
       "        <td style=\"background-color:rgb(160,160,250);color:black\"> -0.0017 <strong>(-0.695)</strong> </td>\n",
       "        <td style=\"background-color:rgb(182,182,250);color:black\"> -0.0013 <strong>(-0.521)</strong> </td>\n",
       "        <td> 0.00197 </td>\n",
       "        <td style=\"background-color:rgb(250,227,227);color:black\"> 0.1756 <strong>(0.152)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.0207 <strong>(-0.191)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_bkg </th>\n",
       "        <td style=\"background-color:rgb(244,244,250);color:black\"> -0.02e3 <strong>(-0.048)</strong> </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.01e3 <strong>(-0.030)</strong> </td>\n",
       "        <td style=\"background-color:rgb(241,241,250);color:black\"> -0.134 <strong>(-0.068)</strong> </td>\n",
       "        <td style=\"background-color:rgb(240,240,250);color:black\"> -0.1166 <strong>(-0.079)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.2869 <strong>(-0.194)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,227,227);color:black\"> 0.1756 <strong>(0.152)</strong> </td>\n",
       "        <td> 676 </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -61 <strong>(-0.966)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> tau_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,242,242);color:black\"> 2 <strong>(0.051)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 1 <strong>(0.027)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,237,237);color:black\"> 0.016 <strong>(0.086)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,239,239);color:black\"> 0.0101 <strong>(0.073)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,226,226);color:black\"> 0.0222 <strong>(0.160)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.0207 <strong>(-0.191)</strong> </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -61 <strong>(-0.966)</strong> </td>\n",
       "        <td> 5.97 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 50.35 (χ²/ndof = 1.0)      │              Nfcn = 525              │\n",
       "│ EDM = 2.19e-05 (Goal: 0.0002)    │                                      │\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 │ A_p1     │    609    │    15     │            │            │         │         │       │\n",
       "│ 1 │ A_p2     │  1.133e3  │  0.014e3  │            │            │         │         │       │\n",
       "│ 2 │ mu_p1    │   53.13   │   0.08    │            │            │         │         │       │\n",
       "│ 3 │ mu_p2    │   60.35   │   0.06    │            │            │         │         │       │\n",
       "│ 4 │ sigma_p1 │   2.00    │   0.06    │            │            │         │         │       │\n",
       "│ 5 │ sigma_p2 │   2.92    │   0.04    │            │            │         │         │       │\n",
       "│ 6 │ A_bkg    │    254    │    26     │            │            │         │         │       │\n",
       "│ 7 │ tau_bkg  │   37.0    │    2.4    │            │            │    0    │         │       │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬─────────────────────────────────────────────────────────────────────────┐\n",
       "│          │     A_p1     A_p2    mu_p1    mu_p2 sigma_p1 sigma_p2    A_bkg  tau_bkg │\n",
       "├──────────┼─────────────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │      213   0.03e3    0.297   0.2830  -0.1103  -0.2594  -0.02e3        2 │\n",
       "│     A_p2 │   0.03e3      191    0.030   0.0711  -0.0264  -0.2656  -0.01e3        1 │\n",
       "│    mu_p1 │    0.297    0.030  0.00578   0.0032   0.0029  -0.0023   -0.134    0.016 │\n",
       "│    mu_p2 │   0.2830   0.0711   0.0032   0.0032   0.0020  -0.0017  -0.1166   0.0101 │\n",
       "│ sigma_p1 │  -0.1103  -0.0264   0.0029   0.0020  0.00323  -0.0013  -0.2869   0.0222 │\n",
       "│ sigma_p2 │  -0.2594  -0.2656  -0.0023  -0.0017  -0.0013  0.00197   0.1756  -0.0207 │\n",
       "│    A_bkg │  -0.02e3  -0.01e3   -0.134  -0.1166  -0.2869   0.1756      676      -61 │\n",
       "│  tau_bkg │        2        1    0.016   0.0101   0.0222  -0.0207      -61     5.97 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mi.fixed[:] = False\n",
    "mi.migrad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "067fbf6f-14c4-4a46-afb3-71753d06af23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9cb42aa8b0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(center, entries, np.sqrt(entries), ls='', marker='.')\n",
    "plt.xlabel('Energy [keV]')\n",
    "plt.ylabel('Number of counts per bin')\n",
    "\n",
    "x = np.arange(40, 80, 0.1)\n",
    "plt.plot(x, fit_model(x, *mi.values), color='k', label='Best fit')\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ef19633-0947-4568-b537-a1c69e42b7c2",
   "metadata": {},
   "source": [
    "If we are happy with result we can also of course do everything in a single cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2311f135-8410-4f35-8d58-b9bcef0fed53",
   "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 = 50.35 (χ²/ndof = 1.0) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 525 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 2.19e-05 (Goal: 0.0002) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total run time of algorithms\">  </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> A_p1 </td>\n",
       "        <td> 609 </td>\n",
       "        <td> 15 </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> A_p2 </td>\n",
       "        <td> 1.133e3 </td>\n",
       "        <td> 0.014e3 </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> mu_p1 </td>\n",
       "        <td> 53.13 </td>\n",
       "        <td> 0.08 </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_p2 </td>\n",
       "        <td> 60.35 </td>\n",
       "        <td> 0.06 </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_p1 </td>\n",
       "        <td> 2.00 </td>\n",
       "        <td> 0.06 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 5 </th>\n",
       "        <td> sigma_p2 </td>\n",
       "        <td> 2.92 </td>\n",
       "        <td> 0.04 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 6 </th>\n",
       "        <td> A_bkg </td>\n",
       "        <td> 254 </td>\n",
       "        <td> 26 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 7 </th>\n",
       "        <td> tau_bkg </td>\n",
       "        <td> 37.0 </td>\n",
       "        <td> 2.4 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> A_p1 </th>\n",
       "        <th> A_p2 </th>\n",
       "        <th> mu_p1 </th>\n",
       "        <th> mu_p2 </th>\n",
       "        <th> sigma_p1 </th>\n",
       "        <th> sigma_p2 </th>\n",
       "        <th> A_bkg </th>\n",
       "        <th> tau_bkg </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p1 </th>\n",
       "        <td> 213 </td>\n",
       "        <td style=\"background-color:rgb(250,226,226);color:black\"> 0.03e3 <strong>(0.161)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,210,210);color:black\"> 0.297 <strong>(0.268)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,199,199);color:black\"> 0.2830 <strong>(0.343)</strong> </td>\n",
       "        <td style=\"background-color:rgb(233,233,250);color:black\"> -0.1103 <strong>(-0.133)</strong> </td>\n",
       "        <td style=\"background-color:rgb(198,198,250);color:black\"> -0.2594 <strong>(-0.400)</strong> </td>\n",
       "        <td style=\"background-color:rgb(244,244,250);color:black\"> -0.02e3 <strong>(-0.048)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,242,242);color:black\"> 2 <strong>(0.051)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,226,226);color:black\"> 0.03e3 <strong>(0.161)</strong> </td>\n",
       "        <td> 191 </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.030 <strong>(0.029)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,236,236);color:black\"> 0.0711 <strong>(0.091)</strong> </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.0264 <strong>(-0.034)</strong> </td>\n",
       "        <td style=\"background-color:rgb(194,194,250);color:black\"> -0.2656 <strong>(-0.433)</strong> </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.01e3 <strong>(-0.030)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 1 <strong>(0.027)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,210,210);color:black\"> 0.297 <strong>(0.268)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.030 <strong>(0.029)</strong> </td>\n",
       "        <td> 0.00578 </td>\n",
       "        <td style=\"background-color:rgb(250,139,139);color:black\"> 0.0032 <strong>(0.737)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,148,148);color:black\"> 0.0029 <strong>(0.678)</strong> </td>\n",
       "        <td style=\"background-color:rgb(162,162,250);color:black\"> -0.0023 <strong>(-0.674)</strong> </td>\n",
       "        <td style=\"background-color:rgb(241,241,250);color:black\"> -0.134 <strong>(-0.068)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,237,237);color:black\"> 0.016 <strong>(0.086)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,199,199);color:black\"> 0.2830 <strong>(0.343)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,236,236);color:black\"> 0.0711 <strong>(0.091)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,139,139);color:black\"> 0.0032 <strong>(0.737)</strong> </td>\n",
       "        <td> 0.0032 </td>\n",
       "        <td style=\"background-color:rgb(250,155,155);color:black\"> 0.0020 <strong>(0.632)</strong> </td>\n",
       "        <td style=\"background-color:rgb(160,160,250);color:black\"> -0.0017 <strong>(-0.695)</strong> </td>\n",
       "        <td style=\"background-color:rgb(240,240,250);color:black\"> -0.1166 <strong>(-0.079)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,239,239);color:black\"> 0.0101 <strong>(0.073)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(233,233,250);color:black\"> -0.1103 <strong>(-0.133)</strong> </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.0264 <strong>(-0.034)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,148,148);color:black\"> 0.0029 <strong>(0.678)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,155,155);color:black\"> 0.0020 <strong>(0.632)</strong> </td>\n",
       "        <td> 0.00323 </td>\n",
       "        <td style=\"background-color:rgb(182,182,250);color:black\"> -0.0013 <strong>(-0.521)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.2869 <strong>(-0.194)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,226,226);color:black\"> 0.0222 <strong>(0.160)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(198,198,250);color:black\"> -0.2594 <strong>(-0.400)</strong> </td>\n",
       "        <td style=\"background-color:rgb(194,194,250);color:black\"> -0.2656 <strong>(-0.433)</strong> </td>\n",
       "        <td style=\"background-color:rgb(162,162,250);color:black\"> -0.0023 <strong>(-0.674)</strong> </td>\n",
       "        <td style=\"background-color:rgb(160,160,250);color:black\"> -0.0017 <strong>(-0.695)</strong> </td>\n",
       "        <td style=\"background-color:rgb(182,182,250);color:black\"> -0.0013 <strong>(-0.521)</strong> </td>\n",
       "        <td> 0.00197 </td>\n",
       "        <td style=\"background-color:rgb(250,227,227);color:black\"> 0.1756 <strong>(0.152)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.0207 <strong>(-0.191)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_bkg </th>\n",
       "        <td style=\"background-color:rgb(244,244,250);color:black\"> -0.02e3 <strong>(-0.048)</strong> </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.01e3 <strong>(-0.030)</strong> </td>\n",
       "        <td style=\"background-color:rgb(241,241,250);color:black\"> -0.134 <strong>(-0.068)</strong> </td>\n",
       "        <td style=\"background-color:rgb(240,240,250);color:black\"> -0.1166 <strong>(-0.079)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.2869 <strong>(-0.194)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,227,227);color:black\"> 0.1756 <strong>(0.152)</strong> </td>\n",
       "        <td> 676 </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -61 <strong>(-0.966)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> tau_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,242,242);color:black\"> 2 <strong>(0.051)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 1 <strong>(0.027)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,237,237);color:black\"> 0.016 <strong>(0.086)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,239,239);color:black\"> 0.0101 <strong>(0.073)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,226,226);color:black\"> 0.0222 <strong>(0.160)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.0207 <strong>(-0.191)</strong> </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -61 <strong>(-0.966)</strong> </td>\n",
       "        <td> 5.97 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 50.35 (χ²/ndof = 1.0)      │              Nfcn = 525              │\n",
       "│ EDM = 2.19e-05 (Goal: 0.0002)    │                                      │\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 │ A_p1     │    609    │    15     │            │            │         │         │       │\n",
       "│ 1 │ A_p2     │  1.133e3  │  0.014e3  │            │            │         │         │       │\n",
       "│ 2 │ mu_p1    │   53.13   │   0.08    │            │            │         │         │       │\n",
       "│ 3 │ mu_p2    │   60.35   │   0.06    │            │            │         │         │       │\n",
       "│ 4 │ sigma_p1 │   2.00    │   0.06    │            │            │         │         │       │\n",
       "│ 5 │ sigma_p2 │   2.92    │   0.04    │            │            │         │         │       │\n",
       "│ 6 │ A_bkg    │    254    │    26     │            │            │         │         │       │\n",
       "│ 7 │ tau_bkg  │   37.0    │    2.4    │            │            │    0    │         │       │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬─────────────────────────────────────────────────────────────────────────┐\n",
       "│          │     A_p1     A_p2    mu_p1    mu_p2 sigma_p1 sigma_p2    A_bkg  tau_bkg │\n",
       "├──────────┼─────────────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │      213   0.03e3    0.297   0.2830  -0.1103  -0.2594  -0.02e3        2 │\n",
       "│     A_p2 │   0.03e3      191    0.030   0.0711  -0.0264  -0.2656  -0.01e3        1 │\n",
       "│    mu_p1 │    0.297    0.030  0.00578   0.0032   0.0029  -0.0023   -0.134    0.016 │\n",
       "│    mu_p2 │   0.2830   0.0711   0.0032   0.0032   0.0020  -0.0017  -0.1166   0.0101 │\n",
       "│ sigma_p1 │  -0.1103  -0.0264   0.0029   0.0020  0.00323  -0.0013  -0.2869   0.0222 │\n",
       "│ sigma_p2 │  -0.2594  -0.2656  -0.0023  -0.0017  -0.0013  0.00197   0.1756  -0.0207 │\n",
       "│    A_bkg │  -0.02e3  -0.01e3   -0.134  -0.1166  -0.2869   0.1756      676      -61 │\n",
       "│  tau_bkg │        2        1    0.016   0.0101   0.0222  -0.0207      -61     5.97 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls = cost.LeastSquares(center, entries, np.sqrt(entries), fit_model)\n",
    "\n",
    "mi = Minuit(ls, \n",
    "            A_p1 = 800, \n",
    "            A_p2 = 1400,\n",
    "            mu_p1 = 54,\n",
    "            mu_p2 = 60,\n",
    "            sigma_p1 = 2,\n",
    "            sigma_p2 = 2,\n",
    "            A_bkg = 100,\n",
    "            tau_bkg = 10, \n",
    "           )\n",
    "mi.limits['tau_bkg'] = (0, None)\n",
    "mi.fixed[:] = True\n",
    "ls.mask = (center < 45) | (center >= 70)\n",
    "mi.fixed[['tau_bkg', 'A_bkg']] = False\n",
    "mi.migrad()\n",
    "ls.mask = None\n",
    "mi.values['A_p1'] = 700\n",
    "mi.values['sigma_p1'] = 3\n",
    "mi.fixed[:] = True\n",
    "mi.fixed[['A_p1', 'mu_p1', 'sigma_p1']] = False\n",
    "mi.migrad()\n",
    "mi.fixed[:] = True\n",
    "mi.fixed[['A_p2', 'mu_p2', 'sigma_p2']] = False\n",
    "mi.migrad()\n",
    "mi.fixed[:] = False\n",
    "mi.migrad()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f25d0b2-aeca-45d6-ac95-00a45b4886c0",
   "metadata": {},
   "source": [
    "### Add task here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2d4c8e9-da2c-489e-9b2f-de24f042c341",
   "metadata": {},
   "source": [
    "When does a fit fit?\n",
    "\n",
    "-> Fit residuals..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "30cafddc-ea17-4158-82cc-f132dee2c8de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Residuals [$\\\\sigma$]')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkUAAAGwCAYAAACnyRH2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA9/ElEQVR4nO3de1iUdf7/8deAioAyWiAHA0WtTMXzITRN0zys303DXF2t1MwOW7seS3HL1kxxO2iXu6VtbR46bGaatSfLUNuf5SpqpG5JaSimgJExKBIa3L8/XGYdBZwZ5jzPx3XNdTn33Pfwvh2Gec3n/hxMhmEYAgAACHIh3i4AAADAFxCKAAAARCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQJNXzdgH+orKyUidOnFDjxo1lMpm8XQ4AALCDYRg6ffq0EhISFBJSe1sQochOJ06cUGJiorfLAAAATjh27JiuueaaWvchFNmpcePGki78p0ZFRXm5GgAAYI+SkhIlJiZaP8drQyiyU9Uls6ioKEIRAAB+xp6uL3S0BgAAEKEIAABAEqEIAABAEqEIAABAEqEIAABAEqEIAABAEqEIAABAEqEIAABAEqEIAABAEqEIAABAEqEIAABAEqEIACAp31KmTw8XKd9S5u1SAK9hQVgACHJrs/KUvmG/Kg0pxCRlpKVoTI8kb5cFeBwtRQAQxPItZdZAJEmVhjR3wwFajBCUCEUAEMRyi0qtgahKhWHoSNFZ7xQEeBGhCAgA9AeBs5KjIxVist0WajKpZXSEdwoCvIhQBPi5tVl56rN4i8a9vFN9Fm/R2qw8b5cEPxJvDldGWopCTReSUajJpEVpHRRvDvdyZb6PLyOBx2QYhnHl3VBSUiKz2SyLxaKoqChvlwNIuvBHuc/iLTaXP0JNJm2fM4APNTgk31KmI0Vn1TI6gt8dO9A53X848vlNSxHgx+gPAleJN4crtfXVBCI70Dk9cBGKAD9GfxDA81zxZYRLb76JUAT4MfqDAJ5X1y8j9AP0XfQpshN9iuDL6A8CeNbarDzN3XBAFYZh/TJiT58i+gF6niOf38xoDQSAeHM4f1ABDxrTI0n9rotx+MtIbZfeeA97n19ePvvXv/6ln//850pISJDJZNLGjRtr3X/btm0ymUyX3QoKCjxTMAAg4DjTOZ1+gL7NL0NRaWmpOnXqpBdeeMGh43JycpSfn2+9NWvWzE0VAgBwOfoB+ja/vHw2bNgwDRs2zOHjmjVrpiZNmri+IAAA7OTspTe4n1+2FDmrc+fOio+P16233qpPPvmk1n3Ly8tVUlJicwMAwBWYF8o3BUUoio+P14oVK7R+/XqtX79eiYmJ6t+/v/bu3VvjMRkZGTKbzdZbYmKiBysGAACe5vdD8k0mk959912NHDnSoeNuvvlmJSUl6bXXXqv28fLycpWXl1vvl5SUKDExkSH5AAD4EYbk26Fnz57avn17jY+HhYUpLCzMgxUBCAb5ljLlFpUqOToyYC6dBOI5ITgFbSjKzs5WfHy8t8sAEEQCcRHRQDwnBC+/DEVnzpzRoUOHrPdzc3OVnZ2tq666SklJSUpPT9fx48e1Zs0aSdLzzz+v5ORktW/fXj/++KNeeeUVbdmyRR9++KG3TgFAkKlpEdF+18X4betKIJ4TgptfhqLdu3drwIAB1vszZsyQJE2YMEGrVq1Sfn6+8vL+t5bMuXPnNHPmTB0/flwRERHq2LGjPvroI5vnAAB3CsSZjAPxnBDc/DIU9e/fX7X1D1+1apXN/UcffVSPPvqom6sCgJpVzWR86ZpX/jyTcSCeE4JbUAzJBwBvC8SZjAPxnBDc/H5Ivqc4MqQPAGqSbykLuJmMA/GcEDgYkg8APireHB5wwSEQzwnBictnAAAAIhQBAABIIhQFtXxLmT49XKR8S5m3SwEAwOvoUxSkmIUWAABbtBQFoZpmoaXFCAAQzAhFQai2WWgBAAhWhKIgVDUL7cWYhRYAEOwIRUGIWWgBALgcHa2D1JgeSep3XQyz0AIA8F+EoiDGLLQAAPwPl88AAABEKAIAAJBEKAIAAJBEKAJssPQJAAQvOloD/8XSJwAQ3GgpAsTSJwAAQhEgiaVPAACEIkASS58AAAhFgCSWPgEA0NEasGLpEwAIboQi4CIsfQLpQsf73KJSJUdH8vsABBFCEQBchKkZgOBFnyLARzBxpPcxNQMQ3GgpAnwArRO+obapGbiMBgQ+WooAL6N1wncwNQMQ3AhFgJcxcaTvYGoGILhx+QzwsqrWiYuDEa0T3sPUDIBzAmHUJqEI8LKq1om5Gw6owjBonfABTM0AOCZQ+kWaDMMwrrwbSkpKZDabZbFYFBUV5e1yEIDyLWW0TgDwO/mWMvVZvOWy1u7tcwb4xN8yRz6/aSkCfAStEwC8zZlLYIE0apNQBAAAnL4EFkj9Ihl9BgBAkKvL1CCBNGrTL0PRv/71L/385z9XQkKCTCaTNm7ceMVjtm3bpq5duyosLExt2rTRqlWr3F4nAAD+oK5Tg4zpkaTtcwboL1Nu1PY5A/yyk7Xkp6GotLRUnTp10gsvvGDX/rm5uRo+fLgGDBig7OxsTZs2Tffee68++OADN1cKAIDvc8XEpfHmcKW2vtovW4iq+GWfomHDhmnYsGF2779ixQolJyfrueeekyTdcMMN2r59u5YuXaohQ4ZUe0x5ebnKy8ut90tKSupWNAAAPoqpQS7wy1DkqB07dmjQoEE224YMGaJp06bVeExGRobmz5/v5soAAPANTFzqp5fPHFVQUKDY2FibbbGxsSopKVFZWfWdyNLT02WxWKy3Y8eOeaJUAAC8JhAugdVFULQUOSMsLExhYWHeLgMAAHhIULQUxcXFqbCw0GZbYWGhoqKiFB4enGkYAADYCopQlJqaqszMTJttmzdvVmpqqpcqAgAAvsYvQ9GZM2eUnZ2t7OxsSReG3GdnZysvL0/Shf5Ad999t3X/Bx54QN98840effRRHTx4UC+++KLefvttTZ8+3RvlAwAAH+SXoWj37t3q0qWLunTpIkmaMWOGunTponnz5kmS8vPzrQFJkpKTk/X3v/9dmzdvVqdOnfTcc8/plVdeqXE4PgDXyLeU6dPDRXbNigsA3mYyDMO48m5wZJVdAM6vowQAruTI57dfthQB8G11WUcJALyFUATA5eq6jhIAeAOhCIDLuWIdJQQO+pbBXxCKALhc1TpKoaYLyShY11HChb5lfRZv0biXd6rP4i1am5V35YMAL6GjtZ3oaA04Lt9SFtTrKAW7fEuZ+izeYnMpNdRk0vY5A/h9gMc48vnNMh8A3CbeHM6HXxCrrW8ZvxfwRVw+AwC4BX3L4G8IRQAAt6BvGfwNl88AAG4zpkeS+l0XQ98yH5FvKVNuUamSoyN5LapBKAIAuBV9y3yDr88y7wuBjVAEAECAq2mW+X7XxfhEYPWVwEafIgAAApwvzzLvS8sCEYoAAAhwvjwS0JcCG6EIAIAA58sjAX0psNGnCACAIOCrIwGrAtvcDQdUYRheDWyEIgAAgoSvjgT0lcBGKAIAAF7nC4GNPkUAAAAiFAEAAEgiFAEAAEgiFAEAAEgiFAEAAEgiFAEAAEgiFAEAAEgiFAEAAEgiFAEAAEgiFAEAAEgiFAEA4HL5ljJ9erhI+ZYyb5cCB7D2GQAALrQ2K0/pG/ar0pBCTFJGWorG9EjydlmwAy1FAAC4SL6lzBqIJKnSkOZuOECLkZ8gFAEA4CK5RaXWQFSlwjB0pOisdwqCQwhFAAC4SHJ0pEJMtttCTSa1jI7wTkFwCKEIbkEnQwDBKN4croy0FIWaLiSjUJNJi9I6KN4c7uXKYA86WsPl6GQIIJiN6ZGkftfF6EjRWbWMjiAQ+RFaiuBSdDIEgAstRqmtryYQ+Rm/DUUvvPCCWrZsqYYNG6pXr17atWtXjfuuWrVKJpPJ5tawYUMPVhs86GQIAPBXfhmK1q5dqxkzZuiJJ57Q3r171alTJw0ZMkQnT56s8ZioqCjl5+dbb0ePHvVgxcGDToYAAH/ll6FoyZIlmjJliiZNmqR27dppxYoVioiI0KuvvlrjMSaTSXFxcdZbbGxsrT+jvLxcJSUlNjdcGZ0MAQD+yu86Wp87d0579uxRenq6dVtISIgGDRqkHTt21HjcmTNn1KJFC1VWVqpr165atGiR2rdvX+P+GRkZmj9/vktrDxZ0MgQA+CO/aykqKipSRUXFZS09sbGxKigoqPaY66+/Xq+++qree+89vf7666qsrFTv3r317bff1vhz0tPTZbFYrLdjx4659Dzs5a9D2+lkCADwN37XUuSM1NRUpaamWu/37t1bN9xwg1566SUtWLCg2mPCwsIUFhbmqRKrxdB2AAA8x+9aiqKjoxUaGqrCwkKb7YWFhYqLi7PrOerXr68uXbro0KFD7ijRJRjaDgCAZ/ldKGrQoIG6deumzMxM67bKykplZmbatAbVpqKiQvv371d8fLy7yqwzhrYDAOBZfnn5bMaMGZowYYK6d++unj176vnnn1dpaakmTZokSbr77rvVvHlzZWRkSJKefPJJ3XjjjWrTpo2Ki4v1zDPP6OjRo7r33nu9eRq1qhrafnEwYmg7AADu45ehaMyYMfruu+80b948FRQUqHPnztq0aZO183VeXp5CQv7XCPbDDz9oypQpKigoUNOmTdWtWzd9+umnateunbdO4YqqhrbP3XBAFYbB0HYAANzMZBiGceXdUFJSIrPZLIvFoqioKI/93HxLGUPbAQS0fEuZcotKlRwdyd85uJwjn99+2VIUTOLN4fyRABCwGGULX+J3Ha0BAIGBUbbwNYQiAIBXMMoWvoZQBADwChaQhq8hFAEAvIIFpOFr6GiNoMIoF8C3sIA0fAmhCEGDUS6Ab2KULXwFl88QFBjlAgC4EkIRggKjXAAAV0IoQlBglAsA4EoIRQgKjHIBECjyLWX69HARl//dgI7WCBqMcgHg7xgw4l52h6L333/f4Se/9dZbFR7OBw98B6NcAPirmgaM9Lsuhr9rLmJ3KBo5cqRDT2wymfT111+rVatWjtYEAAAuUduAEUKRazjUp6igoECVlZV23SIi6MAKAICrMGDE/ewORRMmTHDoUtidd96pqKgop4oCAAC2GDDifibDMIwr72brzJkzatSokTvq8VklJSUym82yWCyEPQCA1+Rbyhgw4gBHPr+dGpJvNpu1fv16p4oDEFgYHuw7eC2CQ7w5XKmtryYQuYFTLUUhISEaNGiQSktLZTKZ1L17d40fP149evRwR40+gZYi4HIMD/YdvBZA9dzeUiRJn332mbp27aqbbrpJ//nPf9S3b1/NmjXL2aeDH+JbaXBjPTnfwWsBuIbTkze++eabuvXWW6339+3bpxEjRqh58+aaPn26S4qD7+JbKRge7Dt4LQDXcKql6KqrrlJiYqLNto4dO+qPf/yjli9f7pLC4Lv4VgqJ4cG+hNcCcA2nQlHnzp21cuXKy7a3adNGeXl5dS4Kvo0V5yExPNiX8FoAruHU5bOnnnpKAwYM0IkTJ/SrX/1KHTt2VGlpqRYtWqTk5GRX1wgfU/Wt9OJg5MlvpfmWMuUWlSo5OpI/+h5Q2/8368n5Dl4LoO6cCkU33nij/v3vf2vq1Knq27evqgawNWzYUOvWrXNpgfA9Vd9K5244oArD8Oi3UvoyeZY9/9+sJ+c7eC2AunFqSP7FTp48qT179qiyslK9evVSdHS0q2rzKQzJv5ynJxDLt5Spz+Itl7VQbZ8zgA8CN+D/u2a0VgL+w5HPb6dHn1Vp1qyZhg0bVtengR/y9LdSRth4lrf/v301eNBaCQQuuzta79u3T5WVlXY/8X/+8x/99NNPThUVbOoy308wzRXECBvP8ub/99qsPPVZvEXjXt6pPou3aG2W4wM43PHeYOQlENjsDkVdunTR999/b/cTp6amMhLNDnX54++KDw5/wggbz/LW/7crgoe73huMvAQCm92XzwzD0OOPP66ICPu+JZ47d87pooJFTX/8+10Xc8UPnroc688YYeNZ3vj/rutlO3e+N7w98hKAe9kdivr166ecnBy7nzg1NVXh4Xxg1aYuf/y93d/Dmxhh41me/v+ua/Bw53vDmyMvAbif3aFo27ZtbiwjONXljz/fWBGo6ho83P3eoLUSCFxOLwiLuqtLnw361yCQjemRpO1zBugvU27U9jkDHBrd5Yn3Rrw5XKmtr+b9BgSYOs9TFCzcOU9RXeb78fRcQYC/4L0BQHLs89tvW4peeOEFtWzZUg0bNlSvXr20a9euWvdft26d2rZtq4YNGyolJUX/+Mc/PFTpldXlWyffWIHq8d4A4Ci/DEVr167VjBkz9MQTT2jv3r3q1KmThgwZopMnT1a7/6effqpf/vKXmjx5sj777DONHDlSI0eO1IEDBzxcOQAAtfPl+ed8uTZXcOryWVlZmQzDsA7PP3r0qN599121a9dOgwcPdnmRl+rVq5d69OihP/7xj5KkyspKJSYm6te//rXmzJlz2f5jxoxRaWmp/va3v1m33XjjjercubNWrFhh189kmQ8A9vDVmbjhH3x5xnRfrq02br98NmLECK1Zs0aSVFxcrF69eum5557TiBEjtHz5cmee0m7nzp3Tnj17NGjQIOu2kJAQDRo0SDt27Kj2mB07dtjsL0lDhgypcX9JKi8vV0lJic0NAGoTbBOqwrV8ecZ0X67NlZwKRXv37lXfvn0lSe+8845iY2N19OhRrVmzRsuWLXNpgZcqKipSRUWFYmNjbbbHxsaqoKCg2mMKCgoc2l+SMjIyZDabrbfExMS6Fw8gYAXLhwbcx5dnTPfl2lzJqVB09uxZNW7cWJL04YcfKi0tTSEhIbrxxht19OhRlxboLenp6bJYLNbbsWPHvF0SAB8WLB8acB9fXt/Rl2tzJadCUZs2bbRx40YdO3ZMH3zwgbUf0cmTJ93e3yY6OlqhoaEqLCy02V5YWKi4uLhqj4mLi3Nof0kKCwtTVFSUzQ0AahIsHxpwH1+ef86Xa3Mlu2e0vti8efM0btw4TZ8+XQMHDlRqaqqkC61GXbp0cWmBl2rQoIG6deumzMxMjRw5UtKFjtaZmZl6+OGHqz0mNTVVmZmZmjZtmnXb5s2brXUDQF2xBAhcwZdnTPfl2lzF6ckbCwoKlJ+fr06dOikk5EKD065duxQVFaW2bdu6tMhLrV27VhMmTNBLL72knj176vnnn9fbb7+tgwcPKjY2VnfffbeaN2+ujIwMSReG5N98881avHixhg8frrfeekuLFi3S3r171aFDB7t+JqPPAN/iq6O8mDQS8C2OfH471VIkXbgkdenlp549ezr7dA4ZM2aMvvvuO82bN08FBQXq3LmzNm3aZO1MnZeXZw1qktS7d2+9+eabeuyxxzR37lxde+212rhxo92BCIBv8eWhwSxYDPgvu1uKZsyYYfeTLlmyxOmCfFUwthT56jdxBLd8S5n6LN5y2YKv2+cM4PcUwGXc0lL02Wef2bWfyWS68k7web78TRzBrbZRXoQiAHVhdyjaunWrO+uAD6lpvpV+18XwoQOvqxrldWlLEaO8ANSVX659BvdivhX4smAZGgzA85zuaC1JX3zxhfLy8nTu3Dmb7bfddludioJ38U0cvi4YhgYD8DynQtE333yj22+/Xfv375fJZFJVX+2q/kQVFRWuqxAex3wr8AeM8gLgak6FoqlTpyo5OVmZmZlKTk7Wrl279P3332vmzJl69tlnXV0jvIBv4gCAYONUKNqxY4e2bNmi6OhohYSEKCQkRDfddJMyMjL0m9/8xu6RavBtfBMHAAQTpzpaV1RUWBeEjY6O1okTJyRJLVq0UE5OjuuqAwAA8BCnWoo6dOigzz//XMnJyerVq5eefvppNWjQQH/605/UqlUrV9cIAADgdk6Foscee0ylpaWSpCeffFL/93//p759++rqq6/WW2+95dICAQAAPMHpBWEvderUKTVt2jRgZ7QOxmU+AADwd25fEPbJJ5+s9fF58+Y587QAAABe41Qoevfdd23unz9/Xrm5uapXr55at25NKAIAAH7HqVBU3ZD7kpISTZw4UbfffnudiwIAAPA0l619FhUVpfnz5+vxxx931VMCASXfUqZPDxcp31Lm7VIAANWo09pnl7JYLLJYLK58SiAgrM3KU/qG/ao0pBCTlJGWojE9krxdFgDgIk6FomXLltncNwxD+fn5eu211zRs2DCXFAYEinxLmTUQSRcW2p274YD6XRfDjOEA4EOcCkVLly61uR8SEqKYmBhNmDBB6enpLikMCBS5RaXWQFSlwjB0pOgsoQgAfIhToSg3N9fVdQABKzk6UiEm2QSjUJNJLaMjvFcUAOAyLutoDaB68eZwZaSlKPS/E5uGmkxalNaBViIA8DF2txTNmDHD7iddsmSJU8UAgWpMjyT1uy5GR4rOqmV0BIEIAHyQ3aHo0rmJ9u7dq59++knXX3+9JOmrr75SaGiounXr5toKgQARbw4nDAGAD7M7FG3dutX67yVLlqhx48ZavXq1mjZtKkn64YcfNGnSJPXt29f1VQIAALiZUwvCNm/eXB9++KHat29vs/3AgQMaPHiwTpw44bICfQULwgIA4Lx8S5lyi0qVHB3p0VZzty8IW1JSou++++6y7d99951Onz7tzFMCAIAA5S8T2Do1+uz222/XpEmTtGHDBn377bf69ttvtX79ek2ePFlpaWmurhEAAPipmiaw9cUlj5xqKVqxYoVmzZqlcePG6fz58xeeqF49TZ48Wc8884xLCwQAAP7LnyawdSoURURE6MUXX9Qzzzyjw4cPS5Jat26tyMhIlxYHAAD8mz9NYFunyRsjIyPVsWNHdezYkUAEn8Fq9ADgO/xpAluHJm9csGCBIiMjrziRI5M3wlv8pTMfAAQTf5nA1qHJG6v6D106kePFTP9NgoCnsRo9AEd4a4h4sPKHCWydmrzx4n8DvsKfOvMB8C5alVEdp/oUlZWV6ezZs9b7R48e1fPPP68PP/zQZYUBjqrqzHcxX+3MB8B7/GmIODzLqVA0YsQIrVmzRpJUXFysnj176rnnntOIESO0fPlylxYI2MufOvMB8J7aWpUR3Jwakr93714tXbpUkvTOO+8oLi5On332mdavX6958+bpwQcfdGmRgL38pTMfAO/xpyHi8CynWorOnj2rxo0bS5I+/PBDpaWlKSQkRDfeeKOOHj3q0gIvderUKY0fP15RUVFq0qSJJk+erDNnztR6TP/+/WUymWxuDzzwgFvrhPfEm8OV2vpqAhGAatGqjJo41VLUpk0bbdy4Ubfffrs++OADTZ8+XZJ08uRJty+WOn78eOXn52vz5s06f/68Jk2apPvuu09vvvlmrcdNmTJFTz75pPV+RATfCAAgWNGqjOo4FYrmzZuncePGafr06Ro4cKBSU1MlXWg16tKli0sLvNiXX36pTZs2KSsrS927d5ck/eEPf9DPfvYzPfvss0pISKjx2IiICMXFxbmtNgCAf/GHIeLwLKcun91xxx3Ky8vT7t27tWnTJuv2gQMHWvsaucOOHTvUpEkTayCSpEGDBikkJEQ7d+6s9dg33nhD0dHR6tChg9LT021Gz1WnvLxcJSUlNjd4DrNSAwA8zamWIkmKi4u7rOWlZ8+edS6oNgUFBWrWrJnNtnr16umqq65SQUFBjceNGzdOLVq0UEJCgvbt26fZs2crJydHGzZsqPGYjIwMzZ8/32W1w37MHwLAFzC5Y/Bxeu2z//f//p/uvPNOpaam6vjx45Kk1157Tdu3b3f4uebMmXNZR+hLbwcPHnS2VN13330aMmSIUlJSNH78eK1Zs0bvvvuudTHb6qSnp8tisVhvx44dc/rnw37MH+IetLwBjlmblac+i7do3Ms71WfxFq3NyvN2SfAAp1qK1q9fr7vuukvjx4/XZ599pvLyckmSxWLRokWL9I9//MOh55s5c6YmTpxY6z6tWrVSXFycTp48abP9p59+0qlTpxzqL9SrVy9J0qFDh9S6detq9wkLC1NYWJjdzwnX8OdZqX31WyUtb4BjWDIoeDkVip566imtWLFCd999t9566y3r9j59+uipp55y+PliYmIUExNzxf1SU1NVXFysPXv2qFu3bpKkLVu2qLKy0hp07JGdnS1Jio+Pd7hWuJe/zh/iq8GDP+6A4/z5yxnqxqnLZzk5OerXr99l281ms4qLi+taU41uuOEGDR06VFOmTNGuXbv0ySef6OGHH9bYsWOtI8+OHz+utm3bateuXZKkw4cPa8GCBdqzZ4+OHDmi999/X3fffbf69eunjh07uq1WOMcf5w/x5Ut+zNwLOI4lg4KXUy1FcXFxOnTokFq2bGmzffv27WrVqpUr6qrRG2+8oYcfflgDBw5USEiIRo0apWXLllkfP3/+vHJycqyjyxo0aKCPPvpIzz//vEpLS5WYmKhRo0bpsccec2udcJ6/zR/iy98q/bXlDfCmqi9nczccUIVh+MWXM7iGU6FoypQpmjp1ql599VWZTCadOHFCO3bs0KxZs/T444+7ukYbV111Va0TNbZs2VKG8b9PgMTERH388cdurQmu50/zh/hy8PD1P+6+2g8LvsNbvyP+9uUMruFUKJozZ44qKys1cOBAnT17Vv369VNYWJhmzZqlX//6166uEfBpvh48fPWPu6/2w4Lv8PbviD99OYNrmIyLm1UcdO7cOR06dEhnzpxRu3bt1KhRI5WVlSk8PPB+iUpKSmQ2m2WxWNy+lAn8U76lzOeCh6/Kt5Spz+Itl7WubZ8zIOj/72g9u4DfEbiKI5/fTk/eKF3or9OuXTtJF2aAXrJkiZ5++ulaJ1IEAhXfKu3ny/2wvMnbLSO+hN8ReINDo8/Ky8uVnp6u7t27q3fv3tq4caMkaeXKlUpOTtbSpUuti8MCQE0Y3XM5Xx7F6A38jsAbHApF8+bN0/Lly9WyZUsdOXJEo0eP1n333aelS5dqyZIlOnLkiGbPnu2uWgEECH+cesHdmD7BFr8j8AaHLp+tW7dOa9as0W233aYDBw6oY8eO+umnn/T555/LZDJd+QkA4L98tQO4t/jyKEZv4XcEnuZQR+sGDRooNzdXzZs3lySFh4dr165dSklJcVuBvoKO1gDcbW1W3mWjGIO1TxHgKm7raF1RUaEGDRr87+B69dSoUSPnqgQA2KBlBPAuh0KRYRiaOHGidaHUH3/8UQ888IAiIyNt9tuwYYPrKgSAIMIoRsB7HApFEyZMsLl/5513urQYAAAAb3EoFK1cudJddQAAAHiVQ0PyAQAAAhWhCAAAQIQiwCH5ljJ9ergoaGcZBoBAVqe1z4BgwrpUABDYaCkKcLRsuAbrUgFA4KOlKIDRsuE6rNgNAIGPlqIARcuGa7FiN4Idrc4IBoSiAMWK267Fit0IZmuz8tRn8RaNe3mn+izeorVZed4uCXALLp8FKFbcdj3WpUIwqqnVud91MbwHEHBoKQpQtGy4R7w5XKmtr+b/EUGDVmcEE1qKAhgtGwDqilZnBBNaigIcLRsA6oJWZwQTWooAALWi1RnBglAEALiieHM4YQgBj8tnAAAAIhQBAABIIhQBAABIIhQBQY/lGwDgAjpaA0GMRYMB4H9oKQKCFIsGA4AtQhEQpFi+AQBsEYqAIFW1fMPFWL4BQDAjFAFBiuUbAMAWHa2BIMbyDQDwP37XUrRw4UL17t1bERERatKkiV3HGIahefPmKT4+XuHh4Ro0aJC+/vpr9xYKuJA7h82zaDAAXOB3oejcuXMaPXq0HnzwQbuPefrpp7Vs2TKtWLFCO3fuVGRkpIYMGaIff/zRjZUCrrE2K099Fm/RuJd3qs/iLVqbleftkgAgIJkMwzCuvJvvWbVqlaZNm6bi4uJa9zMMQwkJCZo5c6ZmzZolSbJYLIqNjdWqVas0duzYao8rLy9XeXm59X5JSYkSExNlsVgUFRXlsvMAapNvKVOfxVtsRomFmkzaPmcALTsAYIeSkhKZzWa7Pr/9rqXIUbm5uSooKNCgQYOs28xms3r16qUdO3bUeFxGRobMZrP1lpiY6IlygwazKNuHYfMA4DkBH4oKCgokSbGxsTbbY2NjrY9VJz09XRaLxXo7duyYW+sMJlwOsh/D5gHAc3wiFM2ZM0cmk6nW28GDBz1aU1hYmKKiomxuqDtmUXYMw+YBwHN8Ykj+zJkzNXHixFr3adWqlVPPHRcXJ0kqLCxUfHy8dXthYaE6d+7s1HPCebVdDuKDvnoMmwcAz/CJUBQTE6OYmBi3PHdycrLi4uKUmZlpDUElJSXauXOnQyPY4BpVl4Mu7TjM5aDaxZvDCUMA4GY+cfnMEXl5ecrOzlZeXp4qKiqUnZ2t7OxsnTlzxrpP27Zt9e6770qSTCaTpk2bpqeeekrvv/++9u/fr7vvvlsJCQkaOXKkl84ieHE5CADgq3yipcgR8+bN0+rVq633u3TpIknaunWr+vfvL0nKycmRxWKx7vPoo4+qtLRU9913n4qLi3XTTTdp06ZNatiwoUdrxwVcDgIA+CK/nafI0xyZ5wAAAPgG5ikCAABwEKEIAABAhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIA8Bv5ljJ9erhI+ZYyb5cCBCS/WxAWAILR2qw8pW/Yr0pDCjFJGWkpGtMjyaHnyLeUKbeoVMnRkSzEDFSDUAQAPi7fUmYNRJJUaUhzNxxQv+ti7A43rghVQKDj8hkA+LjcolJrIKpSYRg6UnTWruNrClVchgNsEYoAwMclR0cqxGS7LdRkUsvoCLuOr2uoAoIFoQgAfFy8OVwZaSkKNV1IRqEmkxaldbD70lldQxUQLOhTBAB+YEyPJPW7LkZHis6qZXSEQx2lq0LV3A0HVGEYDocqIFgQigDAT8Sbw50OMnUJVUCwIBQBQJCoS6gCggF9igAAAEQoAgAAkEQoAgAAkEQoAhCEWEMMQHXoaA0gqLDcBYCa0FIEIGiw3AWA2hCKAAQNlrsAUBtCEYCgwXIXAGpDKAIQNOq6hhiAwEZHawBBheUuANSEUAQg6LDcBYDqcPkMAABAhCIAAABJhCIAAABJhCIAAABJfhiKFi5cqN69eysiIkJNmjSx65iJEyfKZDLZ3IYOHereQgEAgF/xu9Fn586d0+jRo5Wamqo///nPdh83dOhQrVy50no/LCzMHeUBAAA/5XehaP78+ZKkVatWOXRcWFiY4uLi3FARAAAIBH53+cxZ27ZtU7NmzXT99dfrwQcf1Pfff1/r/uXl5SopKbG5AQCAwBUUoWjo0KFas2aNMjMz9fvf/14ff/yxhg0bpoqKihqPycjIkNlstt4SExM9WDEAAPA0nwhFc+bMuawj9KW3gwcPOv38Y8eO1W233aaUlBSNHDlSf/vb35SVlaVt27bVeEx6erosFov1duzYMad/PgAA8H0+0ado5syZmjhxYq37tGrVymU/r1WrVoqOjtahQ4c0cODAavcJCwujMzYAAEHEJ0JRTEyMYmJiPPbzvv32W33//feKj4/32M8EAAC+zScunzkiLy9P2dnZysvLU0VFhbKzs5Wdna0zZ85Y92nbtq3effddSdKZM2f0yCOP6N///reOHDmizMxMjRgxQm3atNGQIUO8dRoAAMDH+ERLkSPmzZun1atXW+936dJFkrR161b1799fkpSTkyOLxSJJCg0N1b59+7R69WoVFxcrISFBgwcP1oIFC7g8BgAArEyGYRjeLsIflJSUyGw2y2KxKCoqytvlAAAAOzjy+e13l88AAADcgVAEAAAgQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAwI/lW8r06eEi5VvKvF0KAkA9bxcAAIAz1mblKX3DflUaUohJykhL0ZgeSd4uC36MliIAgN/Jt5RZA5EkVRrS3A0HaDFCnRCKAAB+J7eo1BqIqlQYho4UnfVOQQgIhCIAgN9Jjo5UiMl2W6jJpJbREd4pCAGBUAQA8Dvx5nBlpKUo1HQhGYWaTFqU1kHx5nAvVwZ/RkdrAIBfGtMjSf2ui9GRorNqGR1BIEKdEYoAAH4r3hxOGILLcPkMAABAhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJhCIAAABJfhaKjhw5osmTJys5OVnh4eFq3bq1nnjiCZ07d67W43788Uc99NBDuvrqq9WoUSONGjVKhYWFHqoaAAD4A78KRQcPHlRlZaVeeukl/ec//9HSpUu1YsUKzZ07t9bjpk+frr/+9a9at26dPv74Y504cUJpaWkeqhoAAPgDk2EYhreLqItnnnlGy5cv1zfffFPt4xaLRTExMXrzzTd1xx13SLoQrm644Qbt2LFDN954o10/p6SkRGazWRaLRVFRUS6rHwAAuI8jn99+1VJUHYvFoquuuqrGx/fs2aPz589r0KBB1m1t27ZVUlKSduzYUeNx5eXlKikpsbkBAPxLvqVMnx4uUr6lzNulwA/U83YBdXHo0CH94Q9/0LPPPlvjPgUFBWrQoIGaNGlisz02NlYFBQU1HpeRkaH58+e7qlQAgIetzcpT+ob9qjSkEJOUkZaiMT2SvF2W2+VbypRbVKrk6EjFm8O9XY5f8YmWojlz5shkMtV6O3jwoM0xx48f19ChQzV69GhNmTLF5TWlp6fLYrFYb8eOHXP5zwAAuEe+pcwaiCSp0pDmbjgQ8C1Ga7Py1GfxFo17eaf6LN6itVl53i7Jr/hES9HMmTM1ceLEWvdp1aqV9d8nTpzQgAED1Lt3b/3pT3+q9bi4uDidO3dOxcXFNq1FhYWFiouLq/G4sLAwhYWF2VU/AMC35BaVWgNRlQrD0JGiswHbelJTEOx3XUzAnrOr+UQoiomJUUxMjF37Hj9+XAMGDFC3bt20cuVKhYTU3tjVrVs31a9fX5mZmRo1apQkKScnR3l5eUpNTa1z7QAA35McHakQk2yCUajJpJbREd4rys2CMQi6mk9cPrPX8ePH1b9/fyUlJenZZ5/Vd999p4KCApu+QcePH1fbtm21a9cuSZLZbNbkyZM1Y8YMbd26VXv27NGkSZOUmppq98gzAIB/iTeHKyMtRaEmk6QLgWhRWoeADgdVQfBigR4EXc0nWorstXnzZh06dEiHDh3SNddcY/NY1cwC58+fV05Ojs6ePWt9bOnSpQoJCdGoUaNUXl6uIUOG6MUXX/Ro7QAAzxrTI0n9rovRkaKzahkdEdCBSPpfEJy74YAqDCMogqCr+f08RZ7CPEUAAH+QbykLmiBoD0c+v/2qpQgAANQu3hxOGHKSX/UpAgAAcBdCEQAAgAhFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAklj7zG5V6+aWlJR4uRIAAGCvqs/tqs/x2hCK7HT69GlJUmJiopcrAQAAjjp9+rTMZnOt+5gMe6ITVFlZqRMnTqhx48YymUx1eq6SkhIlJibq2LFjioqKclGFviUYzlEKjvPkHANHMJwn5xgYXHmOhmHo9OnTSkhIUEhI7b2GaCmyU0hIiK655hqXPmdUVFTA/kJXCYZzlILjPDnHwBEM58k5BgZXneOVWoiq0NEaAABAhCIAAABJhCKvCAsL0xNPPKGwsDBvl+I2wXCOUnCcJ+cYOILhPDnHwOCtc6SjNQAAgGgpAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQo8pjFixfLZDJp2rRp1m0//vijHnroIV199dVq1KiRRo0apcLCQu8VWUfVnWP//v1lMplsbg888ID3inTC7373u8vOoW3bttbHA+F1vNI5BsLrKEnHjx/XnXfeqauvvlrh4eFKSUnR7t27rY8bhqF58+YpPj5e4eHhGjRokL7++msvVuycK53nxIkTL3s9hw4d6sWKHdOyZcvL6jeZTHrooYckBcZ7UrryeQbC+7KiokKPP/64kpOTFR4ertatW2vBggU265R58n3JjNYekJWVpZdeekkdO3a02T59+nT9/e9/17p162Q2m/Xwww8rLS1Nn3zyiZcqdV5N5yhJU6ZM0ZNPPmm9HxER4cnSXKJ9+/b66KOPrPfr1fvfWydQXsfazlHy/9fxhx9+UJ8+fTRgwAD985//VExMjL7++ms1bdrUus/TTz+tZcuWafXq1UpOTtbjjz+uIUOG6IsvvlDDhg29WL397DlPSRo6dKhWrlxpve9Pw7uzsrJUUVFhvX/gwAHdeuutGj16tKTAeU9e6Twl/39f/v73v9fy5cu1evVqtW/fXrt379akSZNkNpv1m9/8RpKH35cG3Or06dPGtddea2zevNm4+eabjalTpxqGYRjFxcVG/fr1jXXr1ln3/fLLLw1Jxo4dO7xUrXNqOkfDMC6774+eeOIJo1OnTtU+FiivY23naBiB8TrOnj3buOmmm2p8vLKy0oiLizOeeeYZ67bi4mIjLCzM+Mtf/uKJEl3iSudpGIYxYcIEY8SIEZ4pyAOmTp1qtG7d2qisrAyY92R1Lj5PwwiM9+Xw4cONe+65x2ZbWlqaMX78eMMwPP++5PKZmz300EMaPny4Bg0aZLN9z549On/+vM32tm3bKikpSTt27PB0mXVS0zlWeeONNxQdHa0OHTooPT1dZ8+e9XCFdff1118rISFBrVq10vjx45WXlycpsF7Hms6xir+/ju+//766d++u0aNHq1mzZurSpYtefvll6+O5ubkqKCiweS3NZrN69erlV6/llc6zyrZt29SsWTNdf/31evDBB/X99997odq6O3funF5//XXdc889MplMAfWevNil51nF39+XvXv3VmZmpr766itJ0ueff67t27dr2LBhkjz/vuTymRu99dZb2rt3r7Kysi57rKCgQA0aNFCTJk1stsfGxqqgoMBDFdZdbecoSePGjVOLFi2UkJCgffv2afbs2crJydGGDRs8XKnzevXqpVWrVun6669Xfn6+5s+fr759++rAgQMB8zrWdo6NGzcOiNfxm2++0fLlyzVjxgzNnTtXWVlZ+s1vfqMGDRpowoQJ1tcrNjbW5jh/ey2vdJ7ShUtnaWlpSk5O1uHDhzV37lwNGzZMO3bsUGhoqJfPwDEbN25UcXGxJk6cKClw/rZe6tLzlALj7+ucOXNUUlKitm3bKjQ0VBUVFVq4cKHGjx8vSR5/XxKK3OTYsWOaOnWqNm/e7Dd9ERxlzzned9991n+npKQoPj5eAwcO1OHDh9W6dWtPlVonVd9YJKljx47q1auXWrRoobffflvh4eFerMx1ajvHyZMnB8TrWFlZqe7du2vRokWSpC5duujAgQNasWKFNSwEAnvOc+zYsdb9U1JS1LFjR7Vu3Vrbtm3TwIEDvVK3s/785z9r2LBhSkhI8HYpblXdeQbC+/Ltt9/WG2+8oTfffFPt27dXdna2pk2bpoSEBK+8L7l85iZ79uzRyZMn1bVrV9WrV0/16tXTxx9/rGXLlqlevXqKjY3VuXPnVFxcbHNcYWGh4uLivFO0g650jhd3EKzSq1cvSdKhQ4c8Xa7LNGnSRNddd50OHTqkuLg4v38dq3PxOVbHH1/H+Ph4tWvXzmbbDTfcYL1MWPV6XTpKyd9eyyudZ3VatWql6Ohov3o9Jeno0aP66KOPdO+991q3BeJ7srrzrI4/vi8feeQRzZkzR2PHjlVKSoruuusuTZ8+XRkZGZI8/74kFLnJwIEDtX//fmVnZ1tv3bt31/jx463/rl+/vjIzM63H5OTkKC8vT6mpqV6s3H5XOsfqmuGzs7MlXfjD7a/OnDmjw4cPKz4+Xt26dfP717E6F59jdfzxdezTp49ycnJstn311Vdq0aKFJCk5OVlxcXE2r2VJSYl27tzpV6/llc6zOt9++62+//57v3o9JWnlypVq1qyZhg8fbt0WiO/J6s6zOv74vjx79qxCQmyjSGhoqCorKyV54X3p8q7bqNGlIwUeeOABIykpydiyZYuxe/duIzU11UhNTfVegS5w8TkeOnTIePLJJ43du3cbubm5xnvvvWe0atXK6Nevn3eLdNDMmTONbdu2Gbm5ucYnn3xiDBo0yIiOjjZOnjxpGEZgvI61nWOgvI67du0y6tWrZyxcuND4+uuvjTfeeMOIiIgwXn/9des+ixcvNpo0aWK89957xr59+4wRI0YYycnJRllZmRcrd8yVzvP06dPGrFmzjB07dhi5ubnGRx99ZHTt2tW49tprjR9//NHL1duvoqLCSEpKMmbPnn3ZY4HwnqxS03kGyvtywoQJRvPmzY2//e1vRm5urrFhwwYjOjraePTRR637ePJ9SSjyoEtDUVlZmfGrX/3KaNq0qREREWHcfvvtRn5+vvcKdIGLzzEvL8/o16+fcdVVVxlhYWFGmzZtjEceecSwWCzeLdJBY8aMMeLj440GDRoYzZs3N8aMGWMcOnTI+nggvI61nWOgvI6GYRh//etfjQ4dOhhhYWFG27ZtjT/96U82j1dWVhqPP/64ERsba4SFhRkDBw40cnJyvFSt82o7z7NnzxqDBw82YmJijPr16xstWrQwpkyZYhQUFHixYsd98MEHhqRqX59AeE9Wqek8A+V9WVJSYkydOtVISkoyGjZsaLRq1cr47W9/a5SXl1v38eT70mQYF00bCQAAEKToUwQAACBCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQDYrX///jKZTDKZTNZ1prZt2yaTyXTZAqSu9rvf/c76s59//nm3/iwgWBGKALjFxIkTrR/iF9+GDh3q7dLqZMqUKcrPz1eHDh3q/FyFhYWqX7++3nrrrWofnzx5srp27SpJmjVrlvLz83XNNdfU+ecCqB6hCIDbDB06VPn5+Ta3v/zlL279mefOnXPr80dERCguLk716tWr83PFxsZq+PDhevXVVy97rLS0VG+//bYmT54sSWrUqJHi4uIUGhpa558LoHqEIgBuExYWpri4OJtb06ZNrY+bTCa98soruv322xUREaFrr71W77//vs1zHDhwQMOGDVOjRo0UGxuru+66S0VFRdbH+/fvr4cffljTpk1TdHS0hgwZIkl6//33de2116phw4YaMGCAVq9ebb3MVVpaqqioKL3zzjs2P2vjxo2KjIzU6dOnnT7ns2fPatiwYerTp4/1ktorr7yiG264QQ0bNlTbtm314osvWvefPHmyMjMzlZeXZ/M869at008//aTx48c7XQsAxxCKAHjV/Pnz9Ytf/EL79u3Tz372M40fP16nTp2SJBUXF+uWW25Rly5dtHv3bm3atEmFhYX6xS9+YfMcq1evVoMGDfTJJ59oxYoVys3N1R133KGRI0fq888/1/3336/f/va31v0jIyM1duxYrVy50uZ5Vq5cqTvuuEONGzd26lyKi4t16623qrKyUps3b1aTJk30xhtvaN68eVq4cKG+/PJLLVq0SI8//rhWr14tSfrZz36m2NhYrVq16rJa0tLS1KRJE6dqAeAEAwDcYMKECUZoaKgRGRlpc1u4cKF1H0nGY489Zr1/5swZQ5Lxz3/+0zAMw1iwYIExePBgm+c9duyYIcnIyckxDMMwbr75ZqNLly42+8yePdvo0KGDzbbf/va3hiTjhx9+MAzDMHbu3GmEhoYaJ06cMAzDMAoLC4169eoZ27Ztq/Gcbr75ZmPq1Kk227Zu3WpIMr788kujY8eOxqhRo4zy8nLr461btzbefPNNm2MWLFhgpKamWu/PmTPHSE5ONiorKw3DMIxDhw4ZJpPJ+Oijjy6roUWLFsbSpUtrrBGA82gpAuA2AwYMUHZ2ts3tgQcesNmnY8eO1n9HRkYqKipKJ0+elCR9/vnn2rp1qxo1amS9tW3bVpJ0+PBh63HdunWzec6cnBz16NHDZlvPnj0vu9++fXtri83rr7+uFi1aqF+/fk6d66233qo2bdpo7dq1atCggaQL/YIOHz6syZMn25zDU089ZVP/Pffco9zcXG3dulXShVaili1b6pZbbnGqFgDOqXtPQQCoQWRkpNq0aVPrPvXr17e5bzKZVFlZKUk6c+aMfv7zn+v3v//9ZcfFx8fb/Bxn3HvvvXrhhRc0Z84crVy5UpMmTZLJZHLquYYPH67169friy++UEpKirV+SXr55ZfVq1cvm/0v7jB97bXXqm/fvlq5cqX69++vNWvWaMqUKU7XAsA5hCIAPqtr165av369WrZs6dBor+uvv17/+Mc/bLZlZWVdtt+dd96pRx99VMuWLdMXX3yhCRMmOF3r4sWL1ahRIw0cOFDbtm1Tu3btFBsbq4SEBH3zzTdX7DA9efJkPfjgg7rtttt0/PhxTZw40elaADiHy2cA3Ka8vFwFBQU2t4tHjl3JQw89pFOnTumXv/ylsrKydPjwYX3wwQeaNGmSKioqajzu/vvv18GDBzV79mx99dVXevvtt60dmS9ufWnatKnS0tL0yCOPaPDgwXWeA+jZZ5/V+PHjdcstt+jgwYOSLnQkz8jI0LJly/TVV19p//79WrlypZYsWWJz7OjRo1W/fn3df//9Gjx4sBITE+tUCwDHEYoAuM2mTZsUHx9vc7vpppvsPj4hIUGffPKJKioqNHjwYKWkpGjatGlq0qSJQkJq/vOVnJysd955Rxs2bFDHjh21fPly6+izsLAwm30nT56sc+fO6Z577nHuJC+xdOlS/eIXv9Att9yir776Svfee69eeeUVrVy5UikpKbr55pu1atUqJScn2xwXERGhsWPH6ocffnBZLQAcYzIMw/B2EQDgbgsXLtSKFSt07Ngxm+2vvfaapk+frhMnTlg7SNekf//+6ty5s1eX2WjZsqWmTZumadOmea0GIFDRUgQgIL344ovKysrSN998o9dee03PPPOMTZ+hs2fP6vDhw1q8eLHuv//+Kwaii5+3UaNG2r9/v7tKr9aiRYvUqFGjyyZ5BOA6tBQBCEjTp0/X2rVrderUKSUlJemuu+5Senq6tcP27373Oy1cuFD9+vXTe++9p0aNGl3xOY8fP66ysjJJUlJSkt1ByhVOnTplndQyJiZGZrPZYz8bCBaEIgAAAHH5DAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQJL0/wHgkduZ2Fz1CwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals = (entries - fit_model(center, *mi.values))/np.sqrt(entries)\n",
    "\n",
    "plt.plot(center, residuals, ls='', marker='.')\n",
    "plt.xlabel('Energy [keV]')\n",
    "plt.ylabel('Residuals [$\\sigma$]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ef61ca-afc5-472d-8e8e-b4726ef2a3dd",
   "metadata": {},
   "source": [
    "Make a nice combined plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d9fbe83b-3146-4d72-89a4-084c29752e24",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/jobs/29211635/ipykernel_829/3494681880.py:7: UserWarning: The figure layout has changed to tight\n",
      "  fig_fit.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAHWCAYAAAD6oMSKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACKQElEQVR4nO3dd3wT9f8H8Ncl3SulQBedQKEte5bKkCUbQXAg6BewAmqZDsZPQUABRb+KIAgoQlVwIwIqe8mUPQRKCy1toAtKU1roSu73R78NhBbatEku4/X0kYf07nL3zuVyeeczBVEURRARERGRxZNJHQARERERGQYTOyIiIiIrwcSOiIiIyEowsSMiIiKyEkzsiIiIiKwEEzsiIiIiK8HEjoiIiMhKMLEjIiIishJ2UgdgCTQaDa5fvw53d3cIgiB1OERERGQlRFHE7du34e/vD5ms5uVtTOyq4Pr16wgMDJQ6DCIiIrJSqampCAgIqPF+mNhVgbu7O4DSk+7h4SFxNERERGQtcnNzERgYqM01aoqJXRWUVb96eHgwsSMiIiKDM1RTL3aeICIiIrISTOyIiIiIrAQTOyIiIiIrwTZ2REREVk6tVqO4uFjqMGySvb095HK5yY7HxI6IiMhKiaKI9PR05OTkSB2KTfP09ISvr69JxsJlYkdERGSlypI6b29vuLi4cJB9ExNFEXfu3EFmZiYAwM/Pz+jHZGJHRERkhdRqtTapq127ttTh2CxnZ2cAQGZmJry9vY1eLcvOE0RERFaorE2di4uLxJFQ2XtginaOTOyIiIisGKtfpWfK94CJHREREZGVYGJHRERE9BCzZ8+Gj48PBEHAhg0bMGrUKAwePFjqsB6KiR0RERGZjVGjRkEQBO2jdu3a6NOnD86cOWOwY8yePRstW7asdLsLFy5gzpw5WLFiBdLS0tC3b1989tlnWLNmjXabrl27YvLkyQaLraaY2BERGVF+fr72Cyo/P1/qcIgsQp8+fZCWloa0tDTs3LkTdnZ2GDBggMnjuHz5MgBg0KBB8PX1haOjIxQKBTw9PU0eS1UxsSMiIiKz4ujoCF9fX/j6+qJly5aYPn06UlNTkZWVpd0mNTUVzz77LDw9PeHl5YVBgwYhOTlZu37Pnj1o3749XF1d4enpiY4dO+Lq1atYs2YN5syZg9OnT2t/dN1fAldm9uzZGDhwIABAJpNpO0DcXxU7atQo7N27F5999pl2X/fHIAWOY0dERGQDRFFE8R1pphWzd7Gvds/QvLw8fPfdd2jYsKF2PL7i4mL07t0b0dHR+Pvvv2FnZ4f3339fW2Urk8kwePBgjBkzBt9//z2Kiorwzz//QBAEPPfcczh37hy2bNmCHTt2AAAUCkW547755psICQnB6NGjkZaWVmFsn332GS5duoSmTZti7ty5AIC6detW63UaChM7IiIiG1B8pxgL3BZIcuwZeTPg4OpQ5e03b94MNzc3AKXNGfz8/LB582bIZKUVjT/++CM0Gg2++uorbcK4evVqeHp6Ys+ePWjbti1UKhUGDBiABg0aAAAiIiK0+3dzc4OdnR18fX0fGoObm5u2yvVh2ykUCjg4OMDFxeWR+zIlVsUSERGRWenWrRtOnTqFU6dO4Z9//kHv3r3Rt29fXL16FQBw+vRpJCYmwt3dHW5ubnBzc4OXlxcKCgpw+fJleHl5YdSoUejduzcGDhyIzz777KGlbtaGJXZERCZy7do1NGrUSOowyEbZu9hjRt4MyY6tD1dXVzRs2FD791dffQWFQoEvv/wS77//PvLy8tCmTRusXbu23HPLqkJXr16NiRMnYsuWLfjxxx/xzjvvYPv27ejQoUPNXoyZY2JHRGREcXFx2n9HRERg5cqViImJkTAislWCIOhVHWpOBEGATCbD3bt3AQCtW7fGjz/+CG9vb3h4eDz0ea1atUKrVq0wY8YMREdHY926dejQoQMcHBygVqsNEpsh92UIrIolIjISpVKJCRMmaP/WaDQYN24clEqlhFERmb/CwkKkp6cjPT0dFy5cwIQJE5CXl6ftpTpixAjUqVMHgwYNwt9//42kpCTs2bMHEydOhFKpRFJSEmbMmIFDhw7h6tWr2LZtGxISErTt7EJCQpCUlIRTp07hxo0bKCwsrHasISEhOHLkCJKTk3Hjxg1oNBqDnIPqYmJHRGQkCQkJ5W7yarUaiYmJEkVEZBm2bNkCPz8/+Pn5ISoqCkePHsXPP/+Mrl27AgBcXFywb98+BAUFYciQIYiIiEBMTAwKCgrg4eEBFxcXXLx4EUOHDkWjRo0wduxYxMbGYty4cQCAoUOHok+fPujWrRvq1q2L77//vtqxvvnmm5DL5YiMjETdunWRkpJiiFNQfaKE9u7dKw4YMED08/MTAYi//fabdl1RUZE4depUsWnTpqKLi4vo5+cnvvjii+K1a9d09nHz5k1x+PDhoru7u6hQKMSXXnpJvH37ts42p0+fFjt16iQ6OjqKAQEB4ocffqhXnCqVSgQgqlSqar9WIrI9qampokwmEwFoH3K5XExNTZU6NLIBd+/eFc+fPy/evXtX6lBs3qPeC0PnGJKW2OXn56NFixZYunRpuXV37tzBiRMnMHPmTJw4cQLr169HfHw8nnzySZ3tRowYgX///Rfbt2/H5s2bsW/fPowdO1a7Pjc3F7169UJwcDCOHz+Ojz76CLNnz8bKlSuN/vqIyLYFBARgyZIl2r/lcjlWrFiBgIAACaMiImsmiKIoSh0EUNow8rfffnvkxLpHjx5F+/btcfXqVQQFBeHChQuIjIzE0aNH0bZtWwClxbf9+vWDUqmEv78/vvjiC7z99ttIT0+Hg0Npo9Hp06djw4YNuHjxYpViy83NhUKhgEqlemQjTSKiB+Xn52vH44qPj2evWDKZgoICJCUlITQ0FE5OTlKHY9Me9V4YOsewqDZ2KpUKgiBoBww8dOgQPD09tUkdAPTs2RMymQxHjhzRbtOlSxdtUgcAvXv3Rnx8PG7dumXS+InIttWrV0/qEIjIylnMcCcFBQWYNm0ann/+eW1Gm56eDm9vb53t7Ozs4OXlhfT0dO02oaGhOtv4+Pho19WqVavcsQoLC3V6yOTm5hr0tRAREREZg0WU2BUXF+PZZ5+FKIr44osvjH68BQsWQKFQaB+BgYFGPyYREZExmEmLK5tmyvfA7BO7sqTu6tWr2L59u079s6+vLzIzM3W2LykpQXZ2tnbONl9fX2RkZOhsU/b3w+Z1mzFjBlQqlfaRmppqyJdERERkdPb2pbM93LlzR+JIqOw9KHtPjMmsq2LLkrqEhATs3r0btWvX1lkfHR2NnJwcHD9+HG3atAEA7Nq1CxqNBlFRUdpt3n77bRQXF2tP6Pbt29G4ceMKq2EBwNHREY6OjkZ8ZURkC0SNiJzzOTj+1XFoijVQ7lQi+PFgOCl0G0/f38EiLy8Prq6uUoRLVkYul8PT01NbAOLi4gJBECSOyraIoog7d+4gMzMTnp6ekMvlRj+mpIldXl6ezkCdZaNAe3l5wc/PD08//TROnDiBzZs3Q61Wa9vNeXl5wcHBAREREejTpw/GjBmD5cuXo7i4GOPHj8ewYcPg7+8PABg+fDjmzJmDmJgYTJs2DefOncNnn32GTz/9VJLXTETWT6PW4PjK4zj08SHcuqLbSUtmJ0PzF5uj6+yuUAQpJIqQbEVZzdSDtVtkWp6eng+tJTQ0SYc72bNnD7p161Zu+ciRIzF79uxynR7K7N69Wzv6dHZ2NsaPH49NmzZBJpNh6NChWLx4sfbXLwCcOXMGsbGxOHr0KOrUqYMJEyZg2rRpVY6Tw50QUVXlXsvFz8/8DOWh0mnDHD0cUS+qHhxcHZB1Pgs3L90EADi4O+DJr55Ek2ebsMSOjE6tVqO4uFjqMGySvb39I0vqDJ1jmM04duaMiR0RVUXmuUx81+c73L52Gw7uDuj+fne0frk17F3utatRHlZi2xvbkHqwtO3uEx8/gRavtGBiR2SjDJ1jmHUbOyIiS5F9ORvfPvEt8tLzUCeiDob/MRy1Qsu34w3oEIBRe0dhx/QdOPTfQ9j+5naUqEskiJiIrJHZ94olIjJ3BTkFWNtnLfLS8+DT3Acv7X+pwqSujMxOhl4f98Ljsx8HAOyZsQchCAEAXLt2zRQhE5GVYmJHRFQDoiji99G/IzsxG4pgBV7Y+gKcvZyr9NzHZz2OVi+3wnHNcVzFVQBAREQEVq1aZcyQiciKMbEjIqqB4yuO4+KGi5A7yPHsL8/Czdet8if9jyAIaDGjBTZhE0SUNnfWaDQYN24clEqlsUImIivGxI6IqJpyknOw/a3tAICeH/aEf1t/vfdx5eoVbVJXRq1W6wwFRURUVUzsiIiq6c/xf6IorwhBnYIQNTGqWvsICwuDTKZ7K5bL5WjYsKEhQiQiG8PEjoioGi79cQkJfyRAZi/DwK8GQpBVb0T/gIAALFmyRPu3AAGvPvYqAgICDBUqEdkQJnZERHpSF6uxdcpWAMDfxX+jbnhd5OfnV3t/I0eOBADURV1MxmTUPVAXWReyDBIrEdkWJnZERHo6+fVJZCdkw7mOM/Zhn8H2m4UstBnQBqJGxM4ZOw22XyKyHUzsiIj0UHy3GPvmliZz0VOjUYhCg+6/85zOEGQC4n+PR9rJNIPum4isHxM7IiI9nFx1Erev34YiWIHmMc0Nvv/ajWuj6bCmAIC/5/1t8P0TkXVjYkdEVEWaEg0O/fcQAKDjtI6wczTOrIyd3+4MALjw6wVknWdbOyKqOiZ2RERV9O/P/yInOQcudV3QclRLnXU1mQrM1dUVoihCFEW4urqibmRdhA8OBwAcWXKkJiETkY1hYkdEVAWiKOLgwoMAgKiJUbB3tkdcXJx2vaGnAms/sT0A4Mw3Z1CQU2Cw/RKRdWNiR0RUgfz8fAiCAEEQkJ+fjyvbryD9VDrsXezR7rV2UCqVmDBhgnZ7Q08FFtI1BN5NvVF8pxgnV580yD6JyPoxsSMiqoIDCw8AAFqPaQ1nL2ckJCRAo9HobGPIqcAEQUC78e0AAEeXHoWoESt5BhEREzsiokrduHADSTuTIMgFdJjSAYBppgJr/kJzOHk64dblW0jcwrljiahyTOyIiCpxZvUZAECjAY3gGewJoPxUYHK5HCtWrDDoVGAOrg5o+VJLAMDBRQd1qoaJiCrCxI6I6BHsYIfz358HALQZ20ZnXdlUYABw/vx5xMTEGPz47WNLO1Ek70iGAgqD75+IrAsTOyKiRwhCEDJuZUARpECD3g0eul29evWMcvxa9WshpGsIIALN0MwoxyAi68HEjoioAmVDmVzBFSzCIqS1ToNMLs0ts/l/Sme4aIEWkhyfiCwHEzsiogc8OJSJCBH/3fRfgw1loq/IoZGwc7aDAxxQG7VrNBgyEVk3JnZERA8w9lAm+nL0cERqk1QswiLcxE2DD4ZMRNaDiR0R0QOqOpTJg1OBGYtSqcSXx7+EiNKx7Aw9GDIRWQ8mdkREDwgICMDMcTMhQABgnKFM9JGQkACNaD4liERkvuykDoCIyByFZ4ZjMiZjP/bju/PfoVGjRpLFUlaCeH/1sKEHQyYi68ASOyKiBxTmFuLyH5ehgAJKKI02lElVPTgYsgABSxcvlawEkYjMFxM7IqIHnP/1PEoKSpCFLKQhTepwANwbDHkohmIyJqOzT2eJIyIic8TEjojoAWe/OwsAOIMzEkdSXi5yoYACF365IHUoRGSGmNgREd0nV5mLpN1JAICzOCtxNOWdR+n0Zpc2X0JJQYnE0RCRuWFiR0R0n3M/ngNEIKhzEG6Jt4w+lIm+ruEa3APcUZRXhMvbLksdDhGZGSZ2RET3Of9TaYlY02FNJY7k4cKeDAMAnP/lvMSREJG5YWJHRPQ/Ock5uPbPNUAAIoZESB2OjvsHQ27xfOmcsfEb46EuUkscGRGZEyZ2RET/U1YCFvJ4CNx83SSO5uECHwuEm58bClWFuLLzitThEJEZYWJHRPQ/538uTewin4mUOJJHE2QCGg9qDAC4tOmSxNEQkTlhYkdEhHvVsIJMMLtq2Io0GlA6E8alzZcgiqLE0RCRuWBiR0SEe9WwwV2Czboatkxo91DYOdshNzUXGWcyAAD5+fkQBAGCICA/P1/iCIlICpImdvv27cPAgQPh7+8PQRCwYcMGnfWiKGLWrFnw8/ODs7MzevbsiYSEBJ1tsrOzMWLECHh4eMDT0xMxMTHIy8vT2ebMmTPo3LkznJycEBgYiIULFxr7pRGRhfn3p38BAJHPmnc1bBl7Z3vU71kfQGmpHRERIHFil5+fjxYtWmDp0qUVrl+4cCEWL16M5cuX48iRI3B1dUXv3r1RUFCg3WbEiBH4999/sX37dmzevBn79u3D2LFjtetzc3PRq1cvBAcH4/jx4/joo48we/ZsrFy50uivj4gsgypVhetHr5tlb9hHaTTwf9WxbGdHRP9jV50naTQaJCYmIjMzExqNRmddly5dqryfvn37om/fvhWuE0URixYtwjvvvINBgwYBAL755hv4+Phgw4YNGDZsGC5cuIAtW7bg6NGjaNu2LQBgyZIl6NevHz7++GP4+/tj7dq1KCoqwtdffw0HBwc0adIEp06dwieffKKTABKR7YrfGA8ACOoYBDcf86+GLdOof2lid+2fa8jLyIPgJkgcERFJTe8Su8OHD6Nhw4aIiIhAly5d0LVrV+2jW7duBgssKSkJ6enp6Nmzp3aZQqFAVFQUDh06BAA4dOgQPD09tUkdAPTs2RMymQxHjhzRbtOlSxc4ODhot+nduzfi4+Nx69atCo9dWFiI3NxcnQcRWa9LG0tLvBo92UjiSPTj7u8OvzZ+gAgk/KnbTOXatWsSRUVEUtI7sXvllVfQtm1bnDt3DtnZ2bh165b2kZ2dbbDA0tPTAQA+Pj46y318fLTr0tPT4e3trbPezs4OXl5eOttUtI/7j/GgBQsWQKFQaB+BgYE1f0FEZJYKVAXauWHDB4VLHI3+ynrHJmxOQFxcnHZ5REQEVq1aJVVYRCQRvRO7hIQEzJ8/HxEREfD09NRJgBQKhTFiNLkZM2ZApVJpH6mpqVKHRERGcnnrZWiKNajduDZqN6otdTh6K2tnd2LLCUyYMEG7XKPRYNy4cVAqlVKFRkQS0Duxi4qKQmJiojFi0eHr6wsAyMjI0FmekZGhXefr64vMzEyd9SUlJcjOztbZpqJ93H+MBzk6OsLDw0PnQUTW4/5hQf79tbQ3bNmAv5bGr5UfXH1ckX4nvVybZ7VabZL7NRGZD70TuwkTJuCNN97AmjVrcPz4cZw5c0bnYSihoaHw9fXFzp07tctyc3Nx5MgRREdHAwCio6ORk5OD48ePa7fZtWsXNBoNoqKitNvs27cPxcXF2m22b9+Oxo0bo1atWgaLl4gsjwwyXNlWOiVX4yctM7ETZAIa9GoAL3hBEHQ7T8jlcjRs2FCiyIhICnr3ih06dCgA4KWXXtIuEwQBoihCEASo1VWfkDovL0/n12RSUhJOnToFLy8vBAUFYfLkyXj//fcRFhaG0NBQzJw5E/7+/hg8eDCA0jYkffr0wZgxY7B8+XIUFxdj/PjxGDZsGPz9/QEAw4cPx5w5cxATE4Np06bh3Llz+Oyzz/Dpp5/q+9KJyMoEIxiFOYVwqeuCgA4BUodTbQ16N8CZb89geL3hWKtcC6A0qVuxYgUCAiz3dRGR/vRO7JKSkgx28GPHjun0pH399dcBACNHjsSaNWswdepU5OfnY+zYscjJyUGnTp2wZcsWODk5aZ+zdu1ajB8/Hj169IBMJsPQoUOxePFi7XqFQoFt27YhNjYWbdq0QZ06dTBr1iwOdUJEaIzSUrpGAxpBJrfciXgaPNEAABCmDIMznHEXd3H+/Hk0amRZvXyJqOYEkZMMVio3NxcKhQIqlYrt7YisQH5+Ptzc3DAEQxCMYIzdMNYie8Teb2WblUg7kYb1WI8zOIO8vDy4urpKHRYRVcLQOUaVSuw2btyIvn37wt7eHhs3bnzktk8++WSNgyIiMqayYUHWYz0ECGh8rTHCYdmJXYPeDZB2Ig0N0RBnYLj2zkRkWapUYieTybRjxslkD6+u0LeNnaVgiR2R9VAqlQgODtbpQSqXy5GcnGzR7dGS9yYjrmsc8pGPj/ExbufdZokdkQWQpMTu/hvgg93piYgsSUJCwkOHBbHkxC4wOhD2bvZwzXOFLyoeyomIrJ/lthYmIqqGsLCwcjUP1jAsiNxBjvrd6wMAfpz3I0vriGxUtRK7nTt3YsCAAWjQoAEaNGiAAQMGYMeOHYaOjYjI4AICAjD12akQUDrmmzUNC9Kgd2nv2MvbLkscCRFJRe/EbtmyZejTpw/c3d0xadIkTJo0CR4eHujXrx+WLl1qjBiJiAwqIjsCkzEZTdEU58+fR0xMjNQhGURoj1AAgPKQEsV3iivZmoiskd7j2M2fPx+ffvopxo8fr102ceJEdOzYEfPnz0dsbKxBAyQiMqSivCKk7EmBAgpkIAP16tWTOiSDqd2oNtzrueP2tdtIPZiK+j3rSx0SEZmY3iV2OTk56NOnT7nlvXr1gkqlMkhQRETGkrg1EeoiNbKRjSxkSR2OQQmCgNDupaV2V3ZekTgaIpKC3ondk08+id9++63c8t9//x0DBgwwSFBERMZyaeMlAMBFXJQ4EuMoq45N3pUsbSBEJIkqVcXeP0VXZGQk5s2bhz179iA6OhoAcPjwYRw4cABvvPGGcaIkIjIATYkGl/4oTeziES9xNMZRVmJ3/dh1FKgK4KRwquQZRGRNqjRAcWhoaNV2Jgi4csX6iv85QDGRdbi67yrWPL4Gzl7OeDPjTcjsrHPEpyWNliA7IRvDfh+Gxk82ljocInoESQYoTkpKqvGBiIikFr+xtJQurH+Y1SZ1QGmpXXZCNpJ2JTGxI7Ix1ntnIyK6jyiKiP+9NLFrPMi6k52ydnZJu/ijnMjWMLEjIptw4+INZCdmQ+4gR4NeDaQOx6hCuoYAADLPZiI/M1/aYIjIpJjYEZFNKCutC+0RCkd3R4mjMS7Xuq7wae4DAEjazVI7IlvCxI6IbEJZ+zpbaXOmrY7dycSOyJboldiVlJRg7ty5UCqVxoqHiMjg8jLyoDxcet9qNLCRxNGYRtmwJ2xnR2Rb9Ers7Ozs8NFHH6GkpMRY8RARGdylzZcAEfBv6w+PerYxZFFwl2AIcgG3Lt9CztUcqcMhIhPRuyq2e/fu2Lt3rzFiISIyClvpDXs/Rw9H1GtXOg8uS+2IbEeVxrG7X9++fTF9+nScPXsWbdq0gaurq876J5980mDBERHVVPGdYlzZXjpwuq20rysT2iMUysNKJO1MQqvRraQOh4hMQO/E7rXXXgMAfPLJJ+XWCYIAtVpd86iIiAzk8vbLKCkogWeIJ7ybeUsdjkmFdAvB3/P+xtW9VyGKIgRBkDokIjIyvatiNRrNQx9M6ojI3JT1hm30ZCObS2wCowMhs5MhV5mLnOQcqcMhIhOo0XAnBQUFhoqDiMjgNGoNLm26BAAIHxQucTSmZ+9iD/92/gCAq3uvShwNEZmC3omdWq3Ge++9h3r16sHNzQ1XrpS2XZk5cyZWrVpl8ACJiKrr2pFruJN1B06eTgjqHCR1OJIIfjwYAHB1HxM7Ilugd2I3b948rFmzBgsXLoSDg4N2edOmTfHVV18ZNDgiIn3l5+dDEAQIgoCzv5wFAIT1C4PcXi5xZNII7vK/xI4ldkQ2Qe/E7ptvvsHKlSsxYsQIyOX3bpQtWrTAxYsXDRocEVFNXN58GUBp+zpbFdQxCIJMwK0rt5CrzJU6HCIyMr0Tu2vXrqFhw4bllms0GhQXFxskKCKimqqDOshOyIbcQY6wvmFShyMZRw9H+LX2A8DqWCJboHdiFxkZib///rvc8l9++QWtWnGcJCIyD+Eo7SwR2iMUjh6OEkcjraAupe0Lk/cmSxsIERmd3uPYzZo1CyNHjsS1a9eg0Wiwfv16xMfH45tvvsHmzZuNESMRkd484AEVVAgfbHu9YR8U8ngIDn9yGCn7UqQOhYiMTO8Su0GDBmHTpk3YsWMHXF1dMWvWLFy4cAGbNm3CE088YYwYiYiqLC4uDgDwJ/7EIizC4bzDEkckvaBOQYAA3Lh4A3kZeVKHQ0RGJIiiKEodhLnLzc2FQqGASqWCh4dtTCBOZImUSiWCg4Oh0Wi0y+RyOZKTkxEQECBhZNJb3mI5Ms5k4OmfnkaTZ5pIHQ4R/Y+hc4xqD1B87NgxfPvtt/j2229x/PjxGgdCRFRTCQkJOkkdUDr2ZmJiokQRmQ+OZ0dkG/RuY6dUKvH888/jwIED8PT0BADk5OTgscceww8//GDzv4qJSDphYWGQyWTlSuwq6slva4K7BOOfJf9wPDsiK6d3id3LL7+M4uJiXLhwAdnZ2cjOzsaFCxeg0Wjw8ssvGyNGIqIqCQgIwIz/zICA0jlh5XI5VqxYwR+cuDdQcebZTNzNvitxNERkLHondnv37sUXX3yBxo0ba5c1btwYS5Yswb59+wwaHBGRviJvRWIyJqMVWuH8+fOIiYmROiSz4OrtijrhdQAAV/9mqR2RtdI7sQsMDKxwIGK1Wg1/f3+DBEVEVB3Fd4uRvCMZCiiQjnTUq1dP6pDMiradHatjiayW3ondRx99hAkTJuDYsWPaZceOHcOkSZPw8ccfGzQ4IiJ9XNlxBcX5xVBBhTSkSR2O2WEHCiLrp3diN2rUKJw6dQpRUVFwdHSEo6MjoqKicOLECbz00kvw8vLSPmpKrVZj5syZCA0NhbOzMxo0aID33nsP94/QIooiZs2aBT8/Pzg7O6Nnz55ISEjQ2U92djZGjBgBDw8PeHp6IiYmBnl5HMuJyNpc3FA6X3XP8T0hiiJcXV0ljsi8lLWzSz+ZjgJVgcTREJEx6N0rdtGiRUYIo2IffvghvvjiC8TFxaFJkyY4duwYRo8eDYVCgYkTJwIAFi5ciMWLFyMuLg6hoaGYOXMmevfujfPnz8PJyQkAMGLECKSlpWH79u0oLi7G6NGjMXbsWKxbt85kr4WIjEtdrEb8hngAQMRTERJHY5486nmgVoNauHX5FlIPptr0HLpE1sqsBygeMGAAfHx8sGrVKu2yoUOHwtnZGd999x1EUYS/vz/eeOMNvPnmmwAAlUoFHx8frFmzBsOGDcOFCxcQGRmJo0ePom3btgCALVu2oF+/flAqlVVqF8gBionMX+LWRKztsxYudV3wxvU3ILOr9jCdVu3X//yKc9+ew37sx4a8DSzVJJKY2QxQbAqPPfYYdu7ciUuXLgEATp8+jf3796Nv374AgKSkJKSnp6Nnz57a5ygUCkRFReHQoUMAgEOHDsHT01Ob1AFAz549IZPJcOTIkQqPW1hYiNzcXJ0HEZm38z+fBwBEDI1gUvcIAZ1Kh34JRrDEkRCRMehdFWtK06dPR25uLsLDwyGXy6FWqzFv3jyMGDECAJCeng4A8PHx0Xmej4+Pdl16ejq8vb111tvZ2cHLy0u7zYMWLFiAOXPmGPrlEJGRqIvVuLD+AgCgybOcLutRAjsFAgD84Y+i/CKW2BFZGbP+WfvTTz9h7dq1WLduHU6cOIG4uDh8/PHH2km+jWXGjBlQqVTaR2pqqlGPR0Q1k7QzCQW3CuDq7artIEAV8wjygAoqyCFH2j/sOUxkbcy6xO6tt97C9OnTMWzYMABAs2bNcPXqVSxYsAAjR46Er68vACAjIwN+fn7a52VkZKBly5YAAF9fX2RmZurst6SkBNnZ2drnP6isty8RWYZ/f/oXABDxdARkcrP+vSo5QRBwARfgAx+c3HISkQMipQ6JiAyoxnfA3NxcbNiwARcuXDBEPDru3LkDmUw3RLlcrp0HMjQ0FL6+vti5c6dOPEeOHEF0dDQAIDo6Gjk5OTh+/Lh2m127dkGj0SAqKsrgMROZi/z8fAiCAEEQkJ+fL3U4RqMuUuPib6XDnLAatnJxcXHYgi2IQxxeXPqiTuc0IrJ8eid2zz77LD7//HMAwN27d9G2bVs8++yzaN68OX799VeDBjdw4EDMmzcPf/zxB5KTk/Hbb7/hk08+wVNPPQWg9Jfn5MmT8f7772Pjxo04e/Ys/vOf/8Df3x+DBw8GAERERKBPnz4YM2YM/vnnHxw4cADjx4/HsGHDOFMGkRW4suMKCnIK4ObrhqBOQVKHY9aUSiUmTJig/VuEiHHjxkGpVEoYFREZkt6J3b59+9C5c2cAwG+//QZRFJGTk4PFixfj/fffN2hwS5YswdNPP43XXnsNERERePPNNzFu3Di899572m2mTp2KCRMmYOzYsWjXrh3y8vKwZcsW7Rh2ALB27VqEh4ejR48e6NevHzp16oSVK1caNFYiksbZtWcBsBq2KhISErQ1HmXUajUSExMlioiIDE3vceycnZ1x6dIlBAYGakvHPvjgA6SkpCAyMtIqZ3TgOHZkifLz8+Hm5gYAyMvLs8rej4W3C/Gxz8couVuCmMMxCIgKkDoks6ZUKhEcHKyT3MkEGa6mXEVAAM8dkRQkH8cuMDAQhw4dQn5+PrZs2YJevXoBAG7duqVTSkZE5uPatWtSh2AUF9ZfQMndEtRuVBv12teTOhyzFxAQgCVLlmj/FiAgpmkMkzoiK6J3Yjd58mSMGDECAQEB8PPzQ9euXQGUVtE2a9bM0PERUTXdPyxQRESEVTaSP/PNGQBA8xebQxAEiaOxDCNHjgQA1EZtTMZk1E+pD41aU8mziMhS6J3Yvfbaazh06BC+/vprHDhwQNtrtX79+gZvY0dE1fNgI3mNRmN1jeRzlblI2p0EAGj+QnOJo7E8t3ALddzqoFBViMxzmZU/gYgsQrVaGrdt2xb9+/fHtWvXUFJSAgDo378/OnbsaNDgiKh6bKGR/Jm1ZwARCOocBM8QT6nDsTgaaFAvqrT6OuXvFImjISJD0Tuxu3PnDmJiYuDi4oImTZogJaX0hjBhwgR88MEHBg+QiPQXFhZW4RiQDRs2lCgiwxJFEWe+vVcNS9UT0LG0bd3VfVcljoSIDEXvxG7GjBk4ffo09uzZo9NZomfPnvjxxx8NGhwRVc+DjeTlcjlWrFhhNY3k046nIevfLMgd5WjyDAcl1oerqytEUYQoimjYozTRT/k7BXoOkEBEZkrvxG7Dhg34/PPP0alTJ53Gyk2aNMHly5cNGhwRVV9ZI3kAOH/+PGJiYiSMxnDy8/PxartXAQCNBjeCkyd741dXvfb1IHeQIy89D9mJ2VKHQ0QGoHdil5WVBW9v73LLy6YvIiLzU69exUOBWOK0YwU5BWiKpgCAFjEtJI7Gstk52WmHiWE7OyLroHdi17ZtW/zxxx/av8uSua+++ko7PysRma+i/CIojyhxafMlKA8o4QIXqUPSy/kfzsMBDshEJupFc+y6mgrqUjoNGxM7Iutgp+8T5s+fj759++L8+fMoKSnBZ599hvPnz+PgwYPYu3evMWIkomooa0tVRnlYif0L9iNxayLUhWrt8qmYimu4hjNrzqBdTDvYOel9WzAZURRxetVpAMAxHGMtgQEEdw7GfuxnBwoiK6F3iV2nTp1w6tQplJSUoFmzZti2bRu8vb1x6NAhtGnTxhgxElENFOUV4feY37EqehXiN8ZDXaiGm58b/Fr7QRGiAADUQz1sG78Ny5osw6XNlySO+OFSD6bi5oWbuIEbOImTVjujhikFPhYIQSbg1pVbyL2WK3U4RFRD1fpp3qBBA3z55ZeGjoWIDOz29dv4rs93yDybCQhAy1EtEf16NOo2qattV+ft5o0QhGCI9xDcunIL3w/8Hq3Htkbfz/qaXendsS+O4QROYCM2AiidUWPlypVW0zFECo4ejvBt6Yu0E2lI+TsFTYc1lTokIqqBKpXY5ebm6vz7UQ8iMg+3r9/G152+RubZTLj5umHkrpEY9PUgeDf11lZhxsXF4Q7u4DzOY/6N+VD1VgECcGLlCXz7xLe4m31X4ldxjypVhYM/HsQmbNIus8YZNaRQ1s7u6t+sjiWydFVK7GrVqoXMzNIpZzw9PVGrVq1yj7LlRCS9wtuFWNd/HXKSclCrfi28dPAlhHQN0dmmomnHFu9YjB7f9YCjwhEp+1Ow6rFVuJV0y8TRV+zwp4dxo+QGROiOt2ZtM2pIIbhzMAAgZR87UBBZuirVs+zatQteXl4AgN27dxs1ICKqGVEUsTFmI9JPpcPV2xUvbn8RtULL/+h62LRjJf4leGn/S1jbby1uxt/E1x2/xqi9o1A7rLbRY8/Pz4ebmxsAIC8vD66urgCAOzfv4PjK4/CCF2SCDBrxXtzWNKOGVII6l5bYZZ7LxN3su3D2cpY4IiKqriqV2D3++OOws7NDSUkJ9u7diwYNGuDxxx+v8EFE0jq+8jjO/3weMjsZhv0+DLXqV1yS/qhpx7ybeiPmUAy8m3ojLy0P3/T4BjnJOSaIvmJHlx5FcX4xGrdsjMVLFuvEa00zakjFta4r6oTXAQCk7GepHZEl06tXrJ2dHT766COUlJQYKx4iqoGc5Bxse30bAKDHgh4I6PDwhKeyacc86nngxR0vwquRF3JTczE7dDbSL6Ub9wVUoCi/CEcWHwEAdJzeEaNGjdKus6YZNaRWVmrHdnZElk3v4U66d+/O8eqIzJAoivjj1T9QfKcYQZ2DEP165QOGVzbtmJuPG57941lkIxu1UAs/P/kz7ty8Y/DYK1I2lMmJL0/g7s27qFW/FiKHRups87AZNUh/wV3Yzo7IGug9lkHfvn0xffp0nD17Fm3atNG2gSnz5JNPGiw4Iqq6ixsuInFLIuQOcgxcORCCTL/Bex+WJLn5uSEOcXgJLwHxwLr+6/CfHf+Bg5uDIcLWERcXp/13REQEli5eitvzbwMAOk7rCJmdDCg0+GEJ90rs0k6koSivyCjvLxEZnyDePzR9FTzYJkdnZ4IAtVr90PWWKjc3FwqFAiqVCh4eHlKHQ1SOukiNpZFLcevyLXR+uzO6v9+9Ss97WGeFirbxgAdiPWPhmOOIBr0b4PmNz0PuIK/yfiqjVCoRHBys06FDJsgwSZyE+o3q49Vzr0JuL9d7v1R1i4IXQZWiwovbX0T9nvWlDofIJhg6x9C7Klaj0Tz0YY1JHZElOLrsKG5dvgU3Xzd0mt6pys8rm3ZMFMWHJmNlpWi5yMWHuR/itMNpXN56GRtGbYCo0et34SNV1EtXI2qQjWx0n9edSZ0JsJ0dkeXTO7H75ptvUFhYvi6kqKgI33zzjUGCIqKqK8ovwv4F+wEAXed0NWgVWkVj3f1e8jtuy2/j3PfnsGXyFuhZ6P9QFfXSFSCgWbtmiBgaYZBj0KOxnR2R5dM7sRs9ejRUKlW55bdv38bo0aMNEhQRVd2xL44hPzMfnqGeaDm6pUH3XWEpmkaDyBmlnRj+WfIP/p7/t0GO9WAvXQECBgoDMeLLEdqZMsi4ykrslIeVUBexBobIEumd2ImiWOFNVqlUQqFQGCQoIqqakoISHPzoIACgy8wuBq+ufNhYd73G9UKfz/oAAHa/sxunvz6tXV/Wm7U6Ro4cCUc44hk8g8mYjNcmvwbfFr7V3h/pp054HbjUcUFJQQmuH7sudThEVA1VTuxatWqF1q1bQxAE9OjRA61bt9Y+WrRogc6dO6Nnz57GjJWIHnD2+7PIz8yHR6AHmr/Q3OD7f9RYd1ETo9D5nc4AgA8nfajdJiIiAqtWrSq3r/z8fAiCAEEQkJ+fX+HxRFHEIAxCEzRBSFgIus+rWicQMgxBENjOjsjCVXm4k8GDBwMATp06hd69e2t7wAGAg4MDQkJCMHToUIMHSEQVE0URhz89DABoP6G90ToXjBw5ErGxsQBKx7pr1KiRdl23ud2QkpyCOd/N0S7TaDQYN24cevfurfeMEMcWH0MkIqGGGv2+7Ad7Z3vDvAiqsuAuwbj428XSdnbTpI6GiPRV5cTu3XffBQCEhITgueeeg5OTk9GCIqLKJe1MQubZTNi72qPNmDYmOeaDY90JgoCgUUEQv9PtQKFWq5GYmKhXYnfuh3PY+07p4OdbsAXT2jKrkEJZiV3KgRRo1BrI5Hq32CEiCen9iR05ciScnJxQVFQEpVKJlJQUnQcRmUZZaV3L0S3h5CndD61GjRuVa4cnE2QI8gt66HMebId3dt1Z/Paf3wARaPtaWxzRHKnWWHhUc74tfOHg7oBCVSEyz2ZKHQ4R6UnvxC4hIQGdO3eGs7MzgoODERoaitDQUISEhCA0NNQYMRLRA7IuZCHhzwRAADpM6iBpLBX1Zh0gDsDmfptxZecV7fIHZ5VYtWoV1EVq7Py/nVg/Yj00xRo0G94M/Zb0Yy9YCcnsZAh8LBAA29kRWSK9pxQbNWoU7OzssHnzZvj5+fEGTCSBg4tKe8JeFC/C0c/RqMcqG8T4Ue5vh7f7u904+X8ncevKLXzb81vUf6I+Ap8NLDce3tixY3F9/nVorpQOpxI1OQq9/9tb76nQyPCCuwTj8tbLSNmXgqgJUVKHQ0R60DuxO3XqFI4fP47w8HBjxENElSi+W4wLP10AAPyDfySOpry2g9siakAUds/cjWNfHMOV7Vewc/tOaFB+PLzLVy6jSZ0m6P9Ff0Q+HSlRxPSg+3vGPmyIKyIyT3ondpGRkbhx44YxYiGiKri44SIKcwqRgxwkIUnqcCrkpHBC38V90WFyBxxbfgziOhHCNQEi7pX8ySDDk9OfRP8Z/eHoYdxSR9JPvXb1IHeUIz8jH9kJ2ajdqLbUIRFRFendxu7DDz/E1KlTsWfPHty8eRO5ubk6DyIyrlNfnwIAHMZhiBBrNCCwoTxsztla9WvhiYVPYLZyNj6e97F2uVwmx8qvVmLIgiFM6syQnZMd6rUv7QHNdnZElkUQ9Zzosaz324NF82XF9Wq19U1Dk5ubC4VCAZVKBQ8PD6nDIRuWk5yDz0I/wwmcwEZsBFD6mVy5ciViYmIkju7R8vPzteNfxsfH64yHR+YlPz8fT7o9iS7ogsjhkXhm7TNSh0RktQydY+hdFbt79+4aH5SIqufUmlNQQYVN2KRdVpMBgaXy4Hh4ZH6uorSk7tpB6UuEiajq9E7sHn/8cWPEQUSVEDUiTq0+hWxk67RVA6o3IDDRo6QiFRpooEpWIVeZC48A1lYQWYJqDSn+999/44UXXsBjjz2mbd/z7bffYv/+/QYNDigdyPSFF15A7dq14ezsjGbNmuHYsWPa9aIoYtasWfDz84OzszN69uyJhIQEnX1kZ2djxIgR8PDwgKenJ2JiYpCXl2fwWImMKXlPMlQpKvh7+JcbEFgul6Nhw4YSRVY1D2uHR+apCEU4gRNQQcV2dkQWRO/E7tdff0Xv3r3h7OyMEydOoLCwEACgUqkwf/58gwZ369YtdOzYEfb29vjrr79w/vx5/Pe//0WtWrW02yxcuBCLFy/G8uXLceRI6Wj1vXv3RkFBgXabESNG4N9//8X27duxefNm7Nu3D2PHjjVorETGdvb7swCA6OeidQYElsvlWLFiBUvryGDKBpPejM1YhEX4csWXEkdERFUm6qlly5ZiXFycKIqi6ObmJl6+fFkURVE8ceKE6OPjo+/uHmnatGlip06dHrpeo9GIvr6+4kcffaRdlpOTIzo6Oorff/+9KIqieP78eRGAePToUe02f/31lygIgnjt2rUqxaFSqUQAokqlquYrIaqZksIS8YNaH4izMVu8suuKmJeXJwIQAYjx8fFSh0dWJDU1VZTJZNrrC4Aog0xMTU2VOjQiq2ToHEPvErv4+Hh06dKl3HKFQoGcnJya5JjlbNy4EW3btsUzzzwDb29vtGrVCl9+ee+XY1JSEtLT09GzZ0+dOKKionDo0CEAwKFDh+Dp6Ym2bdtqt+nZsydkMhmOHDli0HiJjOXytssouFUANz83BHcJ1lnHjghkSAkJCdBoHhhMGhqcO35OooiISB96J3a+vr5ITEwst3z//v2oX7++QYIqc+XKFXzxxRcICwvD1q1b8eqrr2LixInaaoL09HQAgI+Pj87zfHx8tOvS09Ph7e2ts97Ozg5eXl7abR5UWFjI8fnIrJz7vvRLtcmzTSCTV6tpLFGVhIWFlWvDKUCA0w0niSIiIn3o/Q0xZswYTJo0CUeOHIEgCLh+/TrWrl2LN998E6+++qpBg9NoNGjdujXmz5+PVq1aYezYsRgzZgyWL19u0OM8aMGCBVAoFNpHYGCgUY9H9CjFd4px8feLAICmzzcFwI4IZDwBAQE6bThlggwDMRBFF4okjIqIqkrvxG769OkYPnw4evTogby8PHTp0gUvv/wyxo0bpzPJtyH4+fkhMlJ3/siIiAikpKQAKC09BICMjAydbTIyMrTrfH19kZmZqbO+pKQE2dnZ2m0eNGPGDKhUKu0jNTXVIK+HqCry8/MhCAIEQUB+fj4ubb6E4vxieIZ6amcDIDKmkSNHav/9+8Lf0RqtcXUfe8YSWQK9EztBEPD2228jOzsb586dw+HDh5GVlYX33nvP4MF17NgR8fHxOssuXbqE4ODSNkahoaHw9fXFzp07tetzc3Nx5MgRREdHAwCio6ORk5OD48ePa7fZtWsXNBoNoqKiKjyuo6MjPDw8dB5EUjn3Q2k1bNNhTTkZO5lcmwFtAABpJ9JQlMdSOyJzp/cAxWUcHBzKlaYZ2pQpU/DYY49h/vz5ePbZZ/HPP/9g5cqVWLlyJYDSJHPy5Ml4//33ERYWhtDQUMycORP+/v4YPHgwgNISvj59+mircIuLizF+/HgMGzYM/v7+Ro2fqKaKbhch4c/ScRmbPNdE4mjIFnkEekARrIDqqgopB1LQsLd5j5dIZOvMuhV2u3bt8Ntvv+H7779H06ZN8d5772HRokUYMWKEdpupU6diwoQJGDt2LNq1a4e8vDxs2bIFTk73GvquXbsW4eHh6NGjB/r164dOnTppk0Mic3boh0NQF6rhFeYFn+Y+lT+ByAAebMMZ2i0UAJC8O1nawIioUoIoimLlm9k2Q0/QS/Qoy5YtQ2xsLIDS3ogDMRATpk1Azw96VvJMIuM4/c1pbBi5AfXa18PLR16WOhwiq2LoHMOsS+yIbI1SqdTphCRCxCZsgqKjQsKoyNaFdAsBAFw/fh2FuYXSBkNEj1SlxK5169a4desWAGDu3Lm4c+eOUYMislUVDQ4rQkSeG+c2JukoAhWo1aAWRLXIeWOJzFyVErsLFy4gPz8fADBnzhzk5fFLhsgYKhocVibIEBYWJlFERKXKSu3Yzo7IvFWpV2zLli0xevRodOrUCaIo4uOPP4abm1uF286aNcugARLZkrLBYe9vYzf/zfkICAiQODKydaHdQnHyq5NM7IjMXJU6T8THx+Pdd9/F5cuXceLECURGRsLOrnxOKAgCTpw4YZRApcTOE2RK+fn5iHCLQHd0h38tf7yf9T6nESPJ3b5+G5/U+wQQgKk3p8K5lrPUIRFZBUPnGFUqsWvcuDF++OEHAIBMJsPOnTvLzb9KRIbTDM0QilA0G9SMSR2ZBXd/d9RuVBs3L93E1X1XET4oXOqQiKgCen9jaDQaJnVERiSKIhqhEQCgYX8OBkvmg+3siMxftYoCLl++jAkTJqBnz57o2bMnJk6ciMuXLxs6NiKblJ+UD094ws7ZDhH9I6QOh0iLiR2R+dM7sdu6dSsiIyPxzz//oHnz5mjevDmOHDmCJk2aYPv27caIkcimXNp0CQBQv0d92DvbSxwN0T0hXUMAABlnMnDnBoe9IjJHes8VO336dEyZMgUffPBBueXTpk3DE088YbDgiGzRpc2liV2jgY0kjoRIl5uPG+pG1kXW+Swk701G5FDjzhdORPrTu8TuwoULiImJKbf8pZdewvnz5w0SFJGtys/Kh/KwEgAQ1p9j15H5YXUskXnTO7GrW7cuTp06VW75qVOn2KmCqIYS/0oERMC3lS886nFoHTI/2sRuT7KkcRBRxfSuih0zZgzGjh2LK1eu4LHHHgMAHDhwAB9++CFef/11gwdIZEvK2texGpbMVcjjIQCArH+zkJeRBzefigerJyJp6J3YzZw5E+7u7vjvf/+LGTNmAAD8/f0xe/ZsTJw40eABEtkKdZEaiVsTAQCNBjCxI/PkUscFPi18kHE6A0m7ktDs+WZSh0RE99G7KlYQBEyZMgVKpRIqlQoqlQpKpRKTJk2CIAjGiJHIJlz9+yqKbhfB1ccV/m38pQ6H6KHqP1EfADBz+EwIgqCdS5yIpFejIe3d3d3h7u5uqFiIbFpZNWxY/zAIMv5IIvPV4IkGAID6qC9xJET0IM5VRGQGRFHUJnaNBzaWOBqiRwvqFAS5gxwA4AEPXLt2TeKIiKgMEzsiM3Az/iZuXbkFuYMc9XuyFITMm72LPS6HXMYiLEIuchEREYFVq1ZJHRYRgYkdkVkoG5Q4pFsIHNwcJI6G6NGUSiW+SfgGIkQApXOIjxs3DkqlUuLIiEivxK64uBg9evRAQkKCseIhsknaYU7YG5YsQEJCAjSiRmeZWq1GYmKiRBERURm9Ejt7e3ucOXPGWLEQ2aS72XeRciAFABM7sgxhYWGQyXS/PuRyORo2bChRRERURu+q2BdeeIFtKYgMKHFrIkS1CO+m3vAM8ZQ6HKJKBQQEYMmSJRBQ2ntbJsiwYsUKBAQESBwZEek9QHFJSQm+/vpr7NixA23atIGrq6vO+k8++cRgwRHZAu0wJwM4NyxZjpEjR+KL2C/QBm0Q2TKywjnEicj09E7szp07h9atWwMALl26pLOOAxQT6UdToimdHxashiXLo4QST+NpFJwpQIGqAE4KJ6lDIrJ5eid2u3fvNkYcRDYp9WAqCnIK4FzbGQEdWI1FlsPV1RW3xFtYErYE2YnZSN6TjPBB4VKHRWTzqj3cSWJiIrZu3Yq7d+8CKB1glYj0UzbMSVi/MMjkHH2ILE/Z9GJXdlyROBIiAqqR2N28eRM9evRAo0aN0K9fP6SlpQEAYmJi8MYbbxg8QCJrxmFOyNJpE7vtTOyIzIHeid2UKVNgb2+PlJQUuLi4aJc/99xz2LJli0GDI7Jm2YnZuHHxBmR2MjTo3UDqcIiqJbRbKASZgJvxN6FKUUkdDpHN0zux27ZtGz788MNy3drDwsJw9epVgwVGZO0u/VFaWhfcJZiNzsliOXk6oV5UPQClQ/cQkbT0Tuzy8/N1SurKZGdnw9HR0SBBEdkCDnNC1iKsX+k1nPgnEzsiqemd2HXu3BnffPON9m9BEKDRaLBw4UJ069bNoMERWavC3EJc3Vtaws32dWTpyhK7KzuuoKSwROJoiGyb3sOdLFy4ED169MCxY8dQVFSEqVOn4t9//0V2djYOHDhgjBiJrM7lbZehKdGgdqPaqB1WW+pwiGrEt6Uv3HzdkJeeh5S/U1C/Z32pQyKyWXqX2DVt2hSXLl1Cp06dMGjQIOTn52PIkCE4efIkGjRgA3Ciqigb5qTRQJbWkeUTZAIa9i2dJzbhzwSJoyGybXqX2AGAQqHA22+/behYiGyCRq1Bwh+lX36shiVrEdYvDKdWn0LCnwno/UlvqcMhslnVSuxu3bqFVatW4cKFCwCAyMhIjB49Gl5eXgYNjsja5OfnI9wtHC/jZTgqHBHYMVDqkIgMov4T9SHIS4c9yb6cDa8G/D4gkoLeVbH79u1DSEgIFi9ejFu3buHWrVtYvHgxQkNDsW/fPmPESGRVGqG0lC70iVDI7eUSR0NkGE4KJwR1CgIA7fzHRGR6eid2sbGxeO6555CUlIT169dj/fr1uHLlCoYNG4bY2FhjxEhkVeqhHpKQBNcoV6lDITKost6xbGdHJB29E7vExES88cYbkMvvlTTI5XK8/vrrSEw07q+0Dz74AIIgYPLkydplBQUFiI2NRe3ateHm5oahQ4ciIyND53kpKSno378/XFxc4O3tjbfeegslJeyST6a3/NPl+A7fIQ5xGDRtEFatWiV1SEQGU5bYJe9ORvGdYomjIbJNeid2rVu31ratu9+FCxfQokULgwRVkaNHj2LFihVo3ry5zvIpU6Zg06ZN+Pnnn7F3715cv34dQ4YM0a5Xq9Xo378/ioqKcPDgQcTFxWHNmjWYNWuW0WIlqohSqcTUWVMhQgQAaDQajBs3DkqlUuLIiAyjbpO68Aj0QElBCZL3JEsdDpFNqlLniTNnzmj/PXHiREyaNAmJiYno0KEDAODw4cNYunQpPvjgA6MEmZeXhxEjRuDLL7/E+++/r12uUqmwatUqrFu3Dt27dwcArF69GhERETh8+DA6dOiAbdu24fz589ixYwd8fHzQsmVLvPfee5g2bRpmz54NBwcHo8RM9KCEhARoRI3OMrVajcTExHJT9BFZIkEQENY/DMeXH0fCnwnaEjwiMp0qJXYtW7aEIAgQRVG7bOrUqeW2Gz58OJ577jnDRfc/sbGx6N+/P3r27KmT2B0/fhzFxcXo2bOndll4eDiCgoJw6NAhdOjQAYcOHUKzZs3g4+Oj3aZ379549dVX8e+//6JVq1bljldYWIjCwkLt37m5uQZ/TWR7gnyDIEDQltgBpc0YGjZsKGFURIYV1u9/id0fCRCXiBAEQeqQiGxKlRK7pKQkY8fxUD/88ANOnDiBo0ePlluXnp4OBwcHeHp66iz38fFBenq6dpv7k7qy9WXrKrJgwQLMmTPHANET3VNwrgADMRCbsAkiRMjlcqxYsYKldWRVQruHQu4gR05yDm5cvIG6EXWlDonIplQpsQsODjZ2HBVKTU3FpEmTsH37djg5OZnsuDNmzMDrr7+u/Ts3NxeBgRxvjGrm0qZLaI3WyEEO9mEfzp8/j0aNOEAxWRcHVweEdAvB5a2XEb8xnokdkYlVa4Di69evY//+/cjMzIRGo9tmaOLEiQYJDCitas3MzETr1q21y9RqNfbt24fPP/8cW7duRVFREXJycnRK7TIyMuDr6wsA8PX1xT///KOz37Jes2XbPMjR0RGOjo4Gex1EmpJ7s018s/cbBHeR5scSkSmEDw4vTew2xKPTtE5Sh0NkU/RO7NasWYNx48bBwcEBtWvX1mk/IQiCQRO7Hj164OzZszrLRo8ejfDwcEybNg2BgYGwt7fHzp07MXToUABAfHw8UlJSEB0dDQCIjo7GvHnzkJmZCW9vbwDA9u3b4eHhgcjISIPFSvQoqYdScTf7LpxqOSHwMZb+knVr/GRj/PHqH1AeUeJ22m24+7lLHRKRzdA7sZs5cyZmzZqFGTNmQCbTe7QUvbi7u6Np06Y6y1xdXVG7dm3t8piYGLz++uvw8vKCh4cHJkyYgOjoaG2P3V69eiEyMhIvvvgiFi5ciPT0dLzzzjuIjY1lqRyZTPzGeABAo/6NILMz7ueGSGru/u6o174erv1zDZc2XUKbsW2kDonIZuj9DXPnzh0MGzbM6EldVX366acYMGAAhg4dii5dusDX1xfr16/XrpfL5di8eTPkcjmio6Pxwgsv4D//+Q/mzp0rYdRkay5tugQAaDSQberINjQe3BgAcHHDRYkjIbItgnj/GCZVMHXqVHh5eWH69OnGisns5ObmQqFQQKVSwcPDQ+pwyMLcvHQTnzf+HDJ7Gd7KegtOCtN1BCKSStaFLCyLXAa5gxxv3XgLju6sISGqiKFzDL2rYhcsWIABAwZgy5YtaNasGezt7XXWf/LJJzUOisiaxG8qrYYNeTyESR3ZjDrhdeAV5oXshGwkbklEk2eaSB0SkU2oVmK3detWNG5cWsz+YOcJItJ1aeP/qmGfZDUs2Q5BEBA+OBwHPzqI+A3xTOyITETvxO6///0vvv76a4waNcoI4RBZlzs37yDlQAoAoPHAxhJHQ2RaZYndpc2XUFJYAjvHao2wRUR60LsHhKOjIzp27GiMWIisTvzv8RDVInxb+sIzxFPqcIhMKqBDANz83FCYW4grO65IHQ6RTdA7sZs0aRKWLFlijFiIrM6FXy8AACKGRkgcCZHp3bl7BzvTdgIAznx/RuJoiGyD3uXi//zzD3bt2oXNmzejSZMm5TpP3D/UCJEtK1AV4PL2ywCAiCFM7Mg2ncEZeMMbBZsK8FTRU5A7yKUOiciq6Z3YeXp6YsiQIcaIhciqXNp8CZpiDeqE10HdSM6XSbYnLi4O13ANcYiDkCvA/S13/N9n/yd1WERWTe9x7GwRx7Gj6vhxyI+4+NtFdH67M7q/313qcIhMSqlUIjg4WGc+cZkgw9WUqwgICJAwMiLzYugcwzymjyCyMkX5RUjckgiA7evINiUkJOgkdQCgETWIvxAvUUREtkHvqtjQ0NBHjld35Qp7PhElbklEyd0SeIZ6wrelr9ThEJlcWFgYZDKZTnInQIBDmoOEURFZP70Tu8mTJ+v8XVxcjJMnT2LLli146623DBUXkUW7vzcsB+4mWxQQEIAlS5YgNjYWQGk17ABxAG7uugn8R+LgiKyYwdrYLV26FMeOHcPq1asNsTuzwjZ2pI+SwhJ8VPcjFN0uQsyhGAR0YHsisk35+flwc3MDAOxZuwd7RuyBg7sD3sx4E/bO9pU8m8g2mG0bu759++LXX3811O6ILNaV7VdQdLsI7vXcUa99PanDIZKMq6srRFGEKIroMqwLPEM8UXS7CJc2XZI6NCKrZbDE7pdffoGXl5ehdkdkscqqYcOfCocgYzUsEQAIMgFNhzcFAJz5joMVExmL3m3sWrVqpdNmSBRFpKenIysrC8uWLTNocESWRl2sRvzG0l5/kUMjJY6GyLw0H9Ec++fvR+Jfibhz4w5c6rhIHRKR1dE7sRs8eLDO3zKZDHXr1kXXrl0RHh5uqLiILNKVHVdwN/suXL1dEdQ5SOpwiMxK3ci68G3li/ST6fj353/R7tV2UodEZHX0TuzeffddY8RBZPHy8/Mxrd80tEALNBrSCDI5h4kkelCzEc2QfjIdZ9eeZWJHZAT85iEykOI7xaiHekhCEmp1qyV1OERmqdnzzQABSD2QiltXbiE/Px+CIEAQBOTn50sdHpHFq3JiJ5PJIJfLH/mws9O7AJDIavx32n+xFEsRhzh0fb4rVq1aJXVIRGbH3d8d9XvUBwCcijuls+7atWsSRERkXao8jt3vv//+0HWHDh3C4sWLodFoUFBQYLDgzAXHsaPKKJVKBAUGQcS9j5NcLkdycjLnxSR6wNnvz2L98PXwCPSAw1QHjJ8wHkBpAcLKlSsRExMjcYREpmPoHKPKRWyDBg0qtyw+Ph7Tp0/Hpk2bMGLECMydO7fGARFZonMnzukkdQCgVquRmJjIxI7oARFPRcCplhNSU1Px2cTPtMs1Gg3GjRuH3r1783NDVE3VamN3/fp1jBkzBs2aNUNJSQlOnTqFuLg4BAcHGzo+IosgXhQhQHfMOrlcjoYNG0oUEZH5snOyQ7MRzZCNbGhEjc66sh9ERFQ9eiV2KpUK06ZNQ8OGDfHvv/9i586d2LRpE5o2bWqs+IgsQuZfmRiIgdq/5XI5VqxYwVIHoodoHdMaXvDiDyIiA6tyYrdw4ULUr18fmzdvxvfff4+DBw+ic+fOxoyNyCLcunILyXuS0VpoDXe4AwDOnz/PdkJEj+Db0hfhrcP5g4jIwKrcxm769OlwdnZGw4YNERcXh7i4uAq3W79+vcGCI7IEp9acAgA0eKIBcrfmShsMkQVpFdMKaSfSoIAC3+JbnD9/Ho0aNZI6LCKLVuXE7j//+Y/OVGJEBIgaEafjTgMAWo5uKW0wRBam6fNNse3NbWhwtwGu7r+KoEacrYWopqqc2K1Zs8aIYRBZpqRdSVClqODk6YTwwZxSj0gfzrWc0Wx4M5xcdRJHPz+KoI5M7IhqijNPENXAqdWnAABNhzeFnRMH6CbSV7vY0mnFzv9yHrfTbkscDZHlY2JHVE0FOQW4sP4CAKDV6FYSR0Nkmfxa+SGwYyA0JRqc+PKE1OEQWTwmdkTVdHbdWZQUlMC7qTf82vhJHQ6RxSortTu2/BjUxWqJoyGybEzsiKpBFEUcXXYUANB6TGt2LCKqgcihkXD1cUVeWh4u/nZR6nCILBoTO6JquLrvKrL+zYK9qz1ajGwhdThEFk3uIEebcW0AAIcXHZY4GiLLxsSOqBqOfl5aWtf8heZwUjhJHA2R5Wv3ajvIHeRQHlIi5UCK1OEQWSwmdkR6yr2Wiwu/lXaaKGsbREQ14+brpi39PrjwoMTREFkuJnZEejq+8jhEtYigzkHwaeYjdThEViP6jWhAAOI3xiPrQpbU4RBZJCZ2RHpQF6lxYmXpkAwsrSMyrDqN62gH+j74MUvtiKqDiR2RHs6uO4u89Dy4+bkh4qkIqcMhsjodp3YEAJz97ixuX+eAxUT6MuvEbsGCBWjXrh3c3d3h7e2NwYMHIz4+XmebgoICxMbGonbt2nBzc8PQoUORkZGhs01KSgr69+8PFxcXeHt746233kJJSYkpXwpZAVEj4sCHBwAAHSZ3gNxBLnFERNYnoEMAgjoHQV2kxoGFB6QOh8jimHVit3fvXsTGxuLw4cPYvn07iouL0atXL+Tn52u3mTJlCjZt2oSff/4Ze/fuxfXr1zFkyBDterVajf79+6OoqAgHDx5EXFwc1qxZg1mzZknxksiCxW+Kx42LN+CocETbV9pKHQ6R1Xp81uMASgcsZqkdkX4EURRFqYOoqqysLHh7e2Pv3r3o0qULVCoV6tati3Xr1uHpp58GAFy8eBERERE4dOgQOnTogL/++gsDBgzA9evX4eNT2tB9+fLlmDZtGrKysuDg4FDpcXNzc6FQKKBSqeDh4WHU10jmKS8vD5PdJyMQgWj/Rnv0/biv1CERWS1RFLGmyxqk7E9Bu/Ht0G9JP6lDIjIaQ+cYZl1i9yCVSgUA8PLyAgAcP34cxcXF6Nmzp3ab8PBwBAUF4dChQwCAQ4cOoVmzZtqkDgB69+6N3Nxc/PvvvxUep7CwELm5uToPsm3KA0p4wAOJSITfU5w+jMiYBEFA17ldAQAnVp6AKlUlbUBEFsRiEjuNRoPJkyejY8eOaNq0KQAgPT0dDg4O8PT01NnWx8cH6enp2m3uT+rK1petq8iCBQugUCi0j8DAQAO/GrI0C6csxCIswnf4Dm26tMGqVaukDonIqoV2C0VI1xCoi9T4e/7fUodDZDEsJrGLjY3FuXPn8MMPPxj9WDNmzIBKpdI+UlNTjX5MMl+Hfz2MNRfWQERpqwWNRoNx48ZBqVRKHBmRdes6pysA4OSqk8hOzJY0FiJLYRGJ3fjx47F582bs3r0bAQEB2uW+vr4oKipCTk6OzvYZGRnw9fXVbvNgL9myv8u2eZCjoyM8PDx0HmSbRFHE+nfXa5O6Mmq1GomJiRJFRWQbgrsEo2HfhtAUa7Bj2g6pwyGyCGad2ImiiPHjx+O3337Drl27EBoaqrO+TZs2sLe3x86dO7XL4uPjkZKSgujoaABAdHQ0zp49i8zMTO0227dvh4eHByIjI03zQshiJfyZgJJ/SyBA0Fkul8vRsGFDiaIish29Pu4FQS7gwvoLuLrvqtThEJk9s07sYmNj8d1332HdunVwd3dHeno60tPTcffuXQCAQqFATEwMXn/9dezevRvHjx/H6NGjER0djQ4dOgAAevXqhcjISLz44os4ffo0tm7dinfeeQexsbFwdHSU8uWRmRM1Ina9vQsKKDCh5wTtcrlcjhUrVuiUHhORcdSNrIvmo5pDBRXGPz4e8RfjK38SkQ0z6+FOBEGocPnq1asxatQoAKUDFL/xxhv4/vvvUVhYiN69e2PZsmU61axXr17Fq6++ij179sDV1RUjR47EBx98ADs7uyrFweFObNOZtWfw2wu/wdHDEROvTET23WwkJiaiYcOGTOqITOjTDz7FGzPegAgRMkGGlV+uRExMjNRhERmEoXMMs07szAUTO9tTmFuIz8M/R15aHrrP647O/9dZ6pCIbJJSqURwcDA0Go12mVwuR3JyMn9gkVWw6XHsiExlz+w9yEvLg1dDL0S/Hi11OEQ2KyEhQSepA9h5iehRqlYXSWTF8vPz4ebmBqC0842iUIEji48AAPou6Qs7J35MiKQSFhYGmUymk9wJEOCicpEwKiLzxRI7snlxcXHaf0dERGD60OkQ1SIihkSgYR/2fCWSUkBAAJYsWaL9WybIMBADcfyd41AXqyWMjMg8MbEjm6ZUKjFhwr0erxqNBnEJcbjjdAe9P+0tYWREVOa1115Damoqdu/ejYunL6JTnU7IPJfJGSmIKsDEjmxaRe13RIgIHhMMRZBCoqiI6EEBAQHo2rUrwpqFoc/iPgCAfe/tg/JI6Qww+fn5EAQBgiDg0qVLUoZKJCkmdmTTytrv3E8GGQa+OVCiiIioMs2eb4amw5pCVIv47cXfUJRfVK5JBedzJlvFxI5s2oPtdwQIWPzxYgQGBUoYFRFVpt+yfvAI8EB2QjbWjl1brkkF53MmW8XEjmze43UfxxRMwUiMxM6VOxH7RqzUIRFRJZxrOWNw3GAAwN51ezkkCtH/MLEjm5Z2Ig0bRm6AAgo8//rz6Damm9QhEVEVhXYPRee3O8MLXpzPmeh/mNiRzcpLz8MPg35Ayd0SNOzTEE8sfELqkIhIT13ndEXr3q0xEAO1yR3ncyZbxpFXySblZ+Xj2ye+Ra4yF7Ub18bQ74dCJufvHCJLI5PLMGTtENxsexMNkhvAqY0Txv00DsH1g6UOjUgS/CYjm3Pn5h182/NbZJ7LhJufG4ZvHg4nTyepwyKianKp7YJn1z+LOi514HjcESdmnwCnQSdbxcSOrJ5SqcTu3buhVCqRl5GHb5/4FhlnMuDq44qRu0fCq6GX1CESUQ35tfLDM788A0Eu4My3Z7Dz/3ZKHRKRJJjYkVVbtmwZAgMD0b17dwQHB+PlyJeRfjIdrt6uGLlrJOo0riN1iERkIGF9wzDwy9IxKA98cAAHFh6QOCIi02NiR1arounCfsr+CUKIgNF/j0bdyLoSRkdExtBqdCt0n9cdALBj2g5OO0Y2h4kdWa2HTRcW9VkUajeqLVFURGRsnf+vM7rO7QoA2PX2LuyduxeiKHLaMbIJ7BVrJZRKJRISEhAWFsYu/v9jn2YPAQJE3GtELZfL0aR1EwmjIiJTeHzm45DZybDr/3Zhz7t7cDvtNq5GXtWuj4iIwMqVKxETEyNhlESGxxK7arq/QX5NtjGEB9uR2fociZnnMrG271rsHLGTY1sR2bDOMzqjz2d9AAHYtXwXJkzktGNk/ZjYVUNVEqmqJls1Tf4qakdmizcrURSRtCsJ3w/8Hl80/wKJWxIhs5PhlYmv4NK5S9i9ezeSk5P565zIxkRNjMJzvz2HXMdcndJ7gNOOkXUSRA72U6nc3FwoFAr8POZnFMmL8OKKF6ER77Xdkslk2PXlLgQGBcLexR5Zt7PQrl87nfZdcrkcycnJOqVFy5YtQ2xsrHYf1akW2L17N7p3717h8q5du+r5SqtWpWuqat/KjpOfn48AtwAEIQhDQ4dCTLp3KUc+HYkeC3pwKBMiAgAc+/MY2vdvX65pxoP3ZSJTK8sxVCoVPDw8arw/trHTw/EvjyMNadBAt0G+RqPB6pjVCEUoACAJSeW2UavVmB02G43dG8PexR559nmYlThLZx9jx4yFbKcMft5+sHex1z6yC7ORlpuG+iH1ERgYqLOurkNdyGSycknkg3Mk5ufnw83NDQAQHx+PRo0alXt9VUk0DZGMVkVFxxk9ajRuXb6FtJNpSN6djLXr10IFFc7gDM4mncVgh8F4eczLiJrIzhFEpKttv7b4/NPPMWHKBGiggQABMc1joJArpA6NyKBYYlcFZdn0pqmboMpR4cUvX9QZ1VwGGRa0XAB3tTuK7xQj63YW5mXO0/llKEDAZEyGAqU3kSQkIQ5x5Y41EiO1CSIAnMAJbMImiBAhQMBADERrtNZ5zoPbDHEegsdqPaaTAO7P3o81l9ZoY5nQdQL6te4HOyc72DvbI7sgG0PnDy1XErlv7T4EBgbCzskOmTmZaNOrTaUlkVXxYGmcKIoovlOMgpwCXLl4BW2eaKMbC2R40+lNuBS4AABUUGERFvHXNxHpJTUlFb+99xtSVqfAXe0OR4Ujen3cC61eagVBVtoel53RyJRYYiehLm93gYeHB1QtVNrSpLIG+Q+WWoWsCsG4ceOgVqshl8vx2cLPMHzwcBTfKUbxnWKkXE3Bt899q5u8CDIMeGMAatnXQvGdYqRlpmHO93O0yYsIEZuxGe0atoNbsZt2X63vtEYDsQGykQ0veEFxV4Hbd29r96uCSieJFCFiyZ4lkO+R6ySaFZVEfvn8l5WWRM4KmYUwlzDYOdnBztEOKo0KNzQ34O3oDS9HLwgyAYJcKP2/IGBfxj78kPHDvUTUZQiaFzaHqBYfHgs0yCjIQJhzGLybesMpyAnirxW3l+GNmIgeJjAoEBO/nIiMCRn4/aXfkXY8DZvGbMLRZUfR67+98NeFv0xSK0FkLCyxq4KKsmmlUonExEQ0bNjwkW3RHrXNqlWrdJK/BxPEqrafE0UR6kK1NtF78PH34b8xauaocvv5aNhHaOLdBCUFJUi/kY5x68fplIDJIMN74e/BvcQdJQUluJF/AwtuLXhkSWRlJYwVlbTdvw9BLqDAvQAf5HygG4tMhpO7TqJpp6aQyWVQKpUIDg42SOkhEdkmTYkGhz87jH1z96Ewt5A1ASQJQ5fYQaRKqVQqEYCoUqkMvu/U1FRx9+7dYmpqaoXrZDKZCED7kMvlFW5b2TGqsp+vvvpKlMvl2vVfffVVuX09uM2yT5eJqlSVeDPhpnhy+8nyx5HJxYM/HRSv7LoiXt5+Wfx24bc668sem3/aLBbmFYoajabasVS0DRFRZfIy88Q/Yv8QR8lGVXh/2r17t9QhkhUzdI7BxK4KjJnYVcZQyUtV9/OoRLOybXbt2lXpTVGfZLUmsRAR6evMvjOiIAg69ycBgvjTlJ/ErAtZUodHVsrQOQarYqvA4MWkeqpKta8p9/Oo/VelerSyKmgiIqncf396sDmJdzNvRD4TicinI1HgVoDExER2sKAaM3SOwcSuCqRO7CxJVZM2YyeZRETVVXZ/CgkMwe0jt3F27Vlc3nYZmpLSH633tyWWCTIs+nARxr85HoIgSBz5wxmqpy97DBseEzsJMLHTD5M2IrI2d7Pv4uLvF/H3N39j8p7J5TqAvV33bUS0j4B/W3/4tvJFnfA6qBVaC3IHuYRRlzLU+KOrVq3C2LFjodFo2GPYgJjYSYCJHRERAQ8freDBMUgBQJALqBVaC7Ub14ZXmBcUQQp4BHjAo54H3Ou5w93PvdLEr6YlZPqMIPCoY5nrSASmnC3JWKWVHMeOiIhIImFhYRXO9vPar68BqcD1Y9eRcSYDNy/dxI38G7iSeAVeiV7aIaEe5OzlDOfaznD2coZLbRedv7ee34oPf/oQGlEDmSDDvDfm4cVnXoSDm4P2Yedsh/SsdCRerri9X0JCgk6sQMVjflZWqlfV/QCmS7aqUoJojqWVxq7OZoldFbDEjoiIylSlLfHSpUsxfvx4AKWDz096YhIeq/UYcpW5uH3tNm5fv43soux7A8s/kPhVNuZnmXIzD7kOQbRHNOwc7SB3kCNXlot3Lr6jOy6oIMOy/svg7ekNmZ0M2YXZiPkhRndGJUGGHyb/AN86vpDZy3Aj7waGvDek3OxE25Zug7+PP+T2csjsZPj+z+8xa8ks7fqF7yzEi8+8CJmdDDJ7GWR2Mqxetxqv/9/r2m2++PwLxMTEQGYn087+UZmqlCBKUVpZWdJWUaL5zDPPsCrW1JjYERHR/R7VlrgqiYBO4ieTYc6rc9C/dX/cuXkHd7Pv4sjZI3jnj3fKHTfWOxZBJUEoyi9CdmF2tZK/BweON9QUl1VJRivdRkBpEmgnw23ZbWQL2ahrXxdejl7a5TI7GRILE7E4dXG5mKe1mIbIOpGQyWW4oLqA+Ufml9vmvT7voUVgC+2+/vr3LyzZtaQ0FkHAGwPfwMD2A7XrTyadxPil48vt5+uZX6N9s/ba7X7d+StmLpmpLdX7YOoHGP7UcO369BvpaP1E63LXxdG/j6L1Y61ZFUtERCSVgICAh1ajVVZtqVQqMXHiRO06jUaD2ctnY1TyKO0+w5XhmBU8q1wSMP34dO02O7bvwKe9PtU5jggRnb7shPbN2kNdqEZJYQmeL3wek65PQnJKMvxr+aOOax2UFJZAU6yBpkSDtBtp+PaT8lNcdh3eFV5OXtAUa6AuViOyJBK9c3sjTZWGuo514Wnnqd2HulgN1S0VxESxXDx3FHfgI/OBplgDVaEKYnH5bbKRXZrYiYCmWINjxccemUTKIYcAoVyCWHS6CElIAgDcxd0Kt8neko0TOAGgNNH8HJ/fi0UU8d+N/4V6o1onGa1oP2ffO4sUpGi3uT9h1Wg0mPbBNKR9kPbIqTvVajWWDlgKQ2JiR0REZEAPa4fXsGFDAFVrrxYQEICVK1eWq/K9P5kMjwiv8Djt+7Qvl3Q2QqNHxpwbkVvuWKNiRun1upVKJT4L/qxcPO+ce0enenRN8Jry25x+B/4+/tCoNUhNScXcDnMhau7Nk/6H7A/M2jwLvrV9oSkpTSa9N3rj7U/f1paQzXllDp7q+pR2vaZEA7c9bpj33bzSbQQZpj49Ff1b94dGXbr+eOJxiN+VTzTr9K+DJt5NtPu5m3AXK4+vLG3vCBlGR45GS9+W2vW3s29DvFh+P0U+RfCw94CmRIN6hfUg3CqfINZ1qKvXea6UQYY5tnJSzjxBRESW51Gz/RhyBh5DTq1oiJl8DDEdZFVmMdInZkNN3WmI/VT02nNu5XBKser6/PPPxeDgYNHR0VFs3769eOTIkSo9j4kdERHp61GJgLklZIZkymTLEKSeupNTilXTjz/+iP/85z9Yvnw5oqKisGjRIvz888+Ij4+Ht7f3I5/LzhNERGRoHMz94Uw99aSUU3dygOJqioqKQrt27fD556WNJDUaDQIDAzFhwgRMnz79kc9lYkdERGRatpL4coDiaigqKsLx48cxY8YM7TKZTIaePXvi0KFDEkZGREREFXlUz2N6OJtI7G7cuAG1Wg0fHx+d5T4+Prh48WK57QsLC1FYWKj9Ozc31+gxEhEREdWUTOoAzNGCBQugUCi0j8DAQKlDIiIiIqqUTSR2derUgVwuR0ZGhs7yjIwM+Pr6ltt+xowZUKlU2kdqaqqpQiUiIiKqNptI7BwcHNCmTRvs3LlTu0yj0WDnzp2Ijo4ut72joyM8PDx0HkRERETmziba2AHA66+/jpEjR6Jt27Zo3749Fi1ahPz8fIwePVrq0IiIiIgMwmYSu+eeew5ZWVmYNWsW0tPT0bJlS2zZsqVchwoiIiIiS2Uz49jVBMexIyIiImMwdI5hE23siIiIiGwBEzsiIiIiK8HErgrKBiu+f9BiW1ZYWIjZs2fzfIDn4n48F/fwXNzDc3EPz8U9PBf3GDrHYBu7Krh69SpCQkJw5MgR+Pv7Sx2O5G7fvo3IyEicP38e7u7uUocjKZ6Le3gu7uG5uIfn4h6ei3t4Lu65fv06oqKikJycjODg4Brvj4ldFezevRvdu3eXOgwiIiKyUrt27UK3bt1qvB+bGe6kJurXrw8ASE1NZa9YIiIiMpjc3FwEBgZqc42aYmJXBS4uLgDAWSiIiIjIKMpyjZpi54kqcHR0lDoEIiIismKGyjWY2BERERFZCSZ2RERERFaCiR0RERGRlWBiR0RERGQlmNgRERERWQmbSOwWLFiAdu3awd3dHd7e3hg8eDDi4+OlDouIiIjIoGwisdu7dy9iY2Nx+PBhbN++HcXFxejVqxfy8/OlDo2IiIjIYGxySrGsrCx4e3tj79696NKlS6Xb5+bmQqFQQKVScYBiIiIiMhhD5xg2UWL3IJVKBQDw8vKSOBIiIiIiw7G5KcU0Gg0mT56Mjh07omnTphVuU1hYiMLCQu3fubm5AIACVQEcRAeTxElERETWryC3wKD7s7nELjY2FufOncP+/fsfus2CBQswZ86ccsv//flfuDm7GTM8IiIisiF5d/MMuj+bamM3fvx4/P7779i3bx9CQ0Mful1FJXaBgYHYt2wfPGt5miBSIiIisgU5t3LQ5bUuBmtjZxMldqIoYsKECfjtt9+wZ8+eRyZ1QOlEvBVNxmvnaAcHV1bFkuVIv5GO5OvJCPEPgW8dX6nDIaoQr1Pj4zk2X3Z3DJuKGTWx27hxo97PeeKJJ+Ds7GzQOGJjY7Fu3Tr8/vvvcHd3R3p6OgBAoVAY/FhE5uKXbb9g1tJZ0IgayAQZ5sbOxdO9npY6LCIdvE6Nz5DnmAmi+TNqVaxMpl+nW0EQkJCQgPr16xs0DkEQKly+evVqjBo1qtLnl3VFPrjqILzqsictmb/0G+noHtMdGlGjXSaTybDrq128GVs5S/riNfR1akmv3VQMeY6ZhBtHdlY2Hot5zHKqYtPT0+Ht7V2lbd3d3Y0Sgw01IyQCACRfT9a5kQOlPcKvpl3lF54Vs7QvXkNep5b22k3FUOc4/Ua69vwCgEbUYNayWejUuhPvKWbGqOPYjRw5Uq+qzhdeeIEDAFdT+o10HD5zGOk30qUOhcxAiH8IZILux1smkyHYL1iiiMjYHvbFa873BENdp5b42k3FUOf4UQkimRejJnarV6/WlsLl5VXenfeLL75AnTp1jBmSVfpl2y/oHtMdo94Zhe4x3fHLtl+kDokk5lvHF3Nj52qbQ8hkMsx9bS5/WVsxS/ziNdR1aomv3VQMdY75Y9FymKxXrEKhwE8//YShQ4ea6pA2gcXjhmGNbXOe7vU0OrXuhKtpVxHsF2w1r4sqVvbF+2BbKnP/4jXEdWqpr91UDHGOyxLEWctmQaPR8MeiGTNZYieKIlasWIFPPvkEgiCgbdu2GDFiBNq1a2eqEKwS21LVnDm2zTFUoulbx5fXgRkwxQ8HS/7irel1asmv3VQMcS/gj0XLYLIBimUyGWrXro1hw4bB1dUVx48fx99//43x48fj448/NkUI1WbOvWLZ+7FmzPH8mWOiSdVn6vcz/Ua6zX7x2vJrp4cz1A8rY/1As7hesfdbt24dnnjiCe3fZ86cwaBBg1CvXj1MmTLFlKFYDf5SrRlzK/Fk1bp1keL9tOVSWlt+7VQxQ/2wsqQf3CZL7Ly8vBAYGKizrHnz5vj8888xZcoUi0jsSu6WoDi/WOowyhnUcRCiwqOQmpGKQJ9A+Nb2Ncs4zVE9z3oVts3xV/hLcg4vX7lcYaJ5JekKajvXNnk8Uki/mY6U9BQE+QbBt7Zlf0nz/TQ/1nR90aOl36z4h1VUeJRe772h9vMwJXdLaryP+5kssWvZsiVWr16NDz/8UGd5w4YNkZKSYqowakR5SAmVu0rqMB6qLuqiIKsAyUiWOhSLEtslFkv3LdX+EovtHIuCc9KcR1meDIIg6Iy9KBNkEJIEJGeZPh5T23ZhGz7f9zlEUYQgCBjfZTx6RfSSOqxqs/X309xY2/VFwI28G7iuug5/hT/quOmOqnHm2pkKf1gd3XoUzeo1q/IxDLWfh8V8+/ZtvffxKCZL7N5//31069YN169fx2uvvYbmzZsjPz8f8+fPr3TuVnMhc5TBwY1zxVqbAe0GICo8CtdvXYd/LX/Uda8rWSz+bv6Y3GsyPtv2mTbRnNRrEvx9/SWLyVSybmdh6b6l2iRIFEUs3bcUUeFRkr4nNWHL76e5scbry9ZtObul3GerT7M+2vXB/sHla2QEGYL8g/T6LjfUfh4Wc1S9KL32URmTJXYdOnTA4cOHMWnSJHTu3Fn74XJycsLPP/9sqjBqRG4vh52TSZslkon4OfnBr66f1GEA+F+i2TgK17KvoZ5XPdT1sI0vnfT09PK/ikUNMvIzzOa9qQ5bfT/NjbVeX7YqKzdLmyABpe/lZ9s+Q1TjKO1nzM/JD1P6TcGnf36qTaSm9Jui9/ttqP08LOZlw5fptZ/KmDRLadGiBfbs2YPMzEwcP34cGo0GUVFRHJSY6AF1PeraXAIQ4BVQ4a/iel71JIzKMGzx/TQ31nx9GUpWbhaU2UoEeAWY/fWqzFZWmKhfy76mE3u/Vv3QrkG7Gv+wMsR+HhZzmiqtWjE9jCTFT97e3ujbt68UhyYDsKQPP1mOuh51K/xVzGuMDIHX16P9efLPcuemX6t+Uof1UPok6ob6YVXT/TwsZj+FYUuMjTqO3ZkzZ9C0aVPtVCaV+ffff9G4cWPY2ZlXdWfZOHY/j/tZr3HsrDEBsrQPP1merNwsVluS0fD6Ki8rNwvDlwwvl3Csm7DOrM+RJX4fVRRzh4AOeGbFM5Yxjl2rVq2Qnp6OunWrdmFER0fj1KlTqF+/vjHDMglLvOAqk5WbpX1NQGkR8qd/fop2DdqZ9YefLAurLcmYqnJ9WeOP8keparWmuTFUNaspVRRzdla2QY9h1MROFEXMnDkTLi4uVdq+qKjImOGYjLUmQJb64be1mzQRVZ81/iivjBTtDw11X7bEH4LGjtmoiV2XLl0QHx9f5e2jo6Ph7OxsxIhMw1IToMpYYuNjW7xJmzMm2dbF2t5Pa/1RXhlTtz/kfdm4jJrY7dmzx5i7N1uWmACVedSN2tQf/pp+adjqTVoqlb1fvJlbF2t8P631R3lVmKpak/dl4zOvXgpGtnTpUnz00UdIT09HixYtsGTJErRv375a+zJUAmROv3ircqM21YffEF8atnyTNrXK3i9T38wN9bky5efTnO4FlbHWL2dL/lFuCKao1uR92fhsJrH78ccf8frrr2P58uWIiorCokWL0Lt3b8THx8Pb21uvfRkqATKnX7z63KiN/eE31JeGrd+kTaUq75cpb+aG+lyZ8vNZ1WOZS/JnrV/OHBLFMB51nZrrfdlcPluGYDOJ3SeffIIxY8Zg9OjRAIDly5fjjz/+wNdff43p06dXaR/qYjXSb6ZX+CXWOqh1uWlpvJy84OVfOjyKukitsy7rdsVfhhXtx1Cybt/Xzf+BY6RkpFR4o07NTIWXU9WHeDEEQ8Xi5eSFSb0n4bOt903f0nsSvJy8yr0fVH1Veb/83P0qvJn7uvsa9L3Q53P1qM+DKT+fVT3WX2f+Knct920uzXigpno/pdC7SW+0DmqtM8Wgpb8mU6rsOjXH+7LUny11sWFft00kdkVFRTh+/DhmzJihXSaTydCzZ08cOnSo3PaFhYUoLCzU/p2bmwsAOPX1KaQhDRqU/xLb9sU2hKLqc94mIckg+6mqEziBTdgEESIECBiIgWiN1tr1KqggQICIe8MaChBw/YfryEe+weN5FEPGooACkzAJ2ciGl+gFxRYFDm45aOiQbVpV368BGKBzDQ4QByDhiwQkIMFgsVT1c1XZ58GUn8+qHEsFFRZhkfYca0QNFm1ZBGwpvcalYIr3U2oJsK7XY2xVvU7N6b5sDp+tAhQYdH8mS+zu3r0LURS1Q59cvXoVv/32GyIjI9GrVy+jHvvGjRtQq9Xw8fHRWe7j44OLFy+W237BggWYM2dOhfvygleFX2Je0K9Uy1D7qQoVVNobMACIELEJm9AADbQXrgIKDMTAcl92UnxpGDoWxf/+I+Oo6vvVGq3RAA1Kb+bwMsp7UpXPVVU+D6b8fFblWNnI1llfFnc2siW7tk3xfpJl0ec6NZf7sjl+tmrKZIndoEGDMGTIELzyyivIyclBVFQU7O3tcePGDXzyySd49dVXTRVKpWbMmIHXX39d+3dubi4CAwPR8qWW8KrjBZyBYYptDbWfSpy6egrij+UvXP9h/mgR1EK77DE8huduP6dTBSEVc4rF0B5VBWipzOr9quRzVdXPg6k+n1U5VtjtMHy7/NtyVZ+9XullNdcQWT5LvE7NIebsG9nA14bbn8kSuxMnTuDTTz8FAPzyyy/w8fHByZMn8euvv2LWrFlGTezq1KkDuVyOjIwMneUZGRnw9fUtt72joyMcHR3LLZfbyyF3kGNA2wGIahRV456hhtpPZYJ8gipsDxPoHQi5g1xnW9/avvCtXf6cSMGcYjEUc+owY2jm8n5V9rmq6ufBVJ/PqhzLt7ZvhY36zeF8E5WxxOvU0DFXpxOG3F5e+UZ6MOpcsfdzcXHBxYsXERQUhGeffRZNmjTBu+++i9TUVDRu3Bh37twx6vGjoqLQvn17LFmyBACg0WgQFBSE8ePHV9p5orpzxZoTa00oLKknk6XOx2iNLPXzwHlObY8l3ePKWOJ1aoiYq3tfyc7Ktpy5Yu/XsGFDbNiwAU899RS2bt2KKVOmAAAyMzMN8kIq8/rrr2PkyJFo27Yt2rdvj0WLFiE/P1/bS9bamduceoa4WVnal7O1DhFhiczt81BVljh9ElWfpQ2DU8YSr9OaxmxOYzuaLLGbNWsWhg8fjilTpqBHjx6Ijo4GAGzbtg2tWrUy+vGfe+45ZGVlYdasWUhPT0fLli2xZcuWch0qrJm5fNgMkZCZ04fowbg4fpPxGSJmc/k8SMES33NzY+xzWNV7nKX9wLVW5vTD3WSJ3dNPP41OnTohLS0NLVrca6Dco0cPPPXUUyaJYfz48Rg/frxJjkUVM1RCZk4fojKV3WDNcfBTS/xSsMSYzQnPX82Z4hxW5R5nrj9wbZE5/XCXmfJgvr6+aNWqFWSye4dt3749wsPDTRkGSehRNyt9lH2I7mfsD1FWbhZOJp9EVm5WhesqusE+uG2/Vv2wbsI6/PeF/2LdhHWSfqFWNWZzYokxmxOev5oz1Tmsyj3OUPdTqrmyH+5l75mUP9xNmtj9/fffeOGFFxAdHY1r10ovvG+//Rb79+83ZRgW51EJhaUxVEJm6g/Rnyf/xPAlw/Hmd29i+JLh+PPknzrr9bnB1vWoi5YhLSX/RW2JXwqWGLM5seTzZy73QVOdw6rc46T4gUsPZy4/3E1WFfvrr7/ixRdfxIgRI3Dy5EntzA4qlQrz58/Hn3/+WckebJO1VZsYsjrSVA3gq1LdYU7F8FXFmG2PpZ4/c7oPmvIcVnaPM8fmHbbOHNrumqzE7v3338fy5cvx5Zdfwt7eXru8Y8eOOHHihKnCsCjWWm1iyF81pij9qsovdHMqhq8qfWI2l9ISSzzP5sQSz5+53QdNfQ4ru8eZSykRmQ+TldjFx8ejS5cu5ZYrFArk5OSYKgyLYo4dBKqiKr3FzOFXTVVV9Re6qYfQMESvvKrEbE6lJYDlDlViLizt/JnjfdDczqEl3U/J+EyW2Pn6+iIxMREhISE6y/fv34/69eubKgyLYonVJuaWBBiCPtUdprrBGvI8Pypmc+11xy+ymrGk82eu90FLOodkW0yW2I0ZMwaTJk3C119/DUEQcP36dRw6dAhvvvkmZs6caaowakRdrEZJQYnJjlfLoRYm9ZqEz7bdN4dkr0mo5VDLpHFUVdbtipOAVgGtzHaewKrqFdELrQJa6cyFKtV7YMrzfDX9aoWlJSnpKajlUMugxyKqiKXdB4n0pS5WG3R/Jkvspk+fDo1Ggx49euDOnTvo0qULHB0d8eabb2LChAmmCqNGNIUaFOUVmfSY3UO7o/mI5khTpcFP4Yc6bnVMHkNVXb3+kCTgegoU9RQSRWU4CkEBhVfp65DyPTDlefZ28IYgCLh/5kGZIENdh7pmex2SYdzIu4HrquvwV/ijjlsdSfdjSfdBIn1pCjWVb6QHkyV2giDg7bffxltvvYXExETk5eUhMjISbm5uuHv3LpydnU0VSrUFRAegVm3Tl1KEIMTkx6wOp5tOkG1+oMpEJkO73u3MehJoS2PK8xyCEMx2mY05X82BRqOBTCbDuy+/i7bd2hr0OGRe1u9ejzlr52hLyN4d8y6GdBsi2X4Ay7kPEunr1s1bwFrD7U8Q7/8pbmKFhYVYunQpFi5ciPT0dKnCqFRubi4UCgUOrjoIr7peUodj1n7Z9gtmLZulTQLmvjYXT/d6WuqwrI6pz3P6jXRcTbuKYL9g+NZhkn6/9BvpSL6ejBD/EKs4N+k30tE9pnu5Hw67vtql1+sz1H6IrF12VjYei3kMKpUKHh4eNd6f0UvsCgsLMXv2bGzfvh0ODg6YOnUqBg8ejNWrV+Ptt9+GXC7HlClTjB0GmcjTvZ5Gp9admAQYmanPs28dX76XFfhl2y+YtXSWtkRqbqzl/5BJvp5cvqpfo8HVtKt6XQOG2g8R6cfoid2sWbOwYsUK9OzZEwcPHsQzzzyD0aNH4/Dhw/jkk0/wzDPPQC6XGzsMMiEmAabB8yyt9Bvp2qQOKG3nOGvZLHRq3cmi35cQ/5DyvVBlMgT7BUuyHyLSj9EHKP7555/xzTff4JdffsG2bdugVqtRUlKC06dPY9iwYUzqiMgiPapEypL51vHF3Ni52jm9y6r69U1WDbUfItKP0UvslEol2rRpAwBo2rQpHB0dMWXKFAiCYOxDExEZjTWXSBmqqp9NM4hMz+gldmq1Gg4ODtq/7ezs4ObmZuzDEhEZlbWXSPnW8UVUs6gavx5D7ccSpd9Ix+Ezh5F+w3w7B5L1MXqJnSiKGDVqFBwdHQEABQUFeOWVV+Dq6qqz3fr1640dChGRQbFEih7GGjvWkGUweondyJEj4e3tDYVCAYVCgRdeeAH+/v7av8sexpKcnIyYmBiEhobC2dkZDRo0wLvvvouiIg5uSUQ1Z8slUlSxh3WsYckdmYLRS+xWr15t7EM80sWLF6HRaLBixQo0bNgQ586dw5gxY5Cfn4+PP/5Y0tiIiMj6VHWoF2sbA5HMg8lmnpBKnz590KdPH+3f9evXR3x8PL744gsmdkREZHBV6VjDqtrKMfGtHqtP7CqiUqng5fXwGSQKCwtRWFio/Ts3NxcAUFJYgqJ8VuESEdHDeTl7YdaYWZj71Vzt7DCzXp4FL2cvFOUXIf1mxVW17cPbc/rF/1m/ez3mfjlXm/jOGjOr2tPRmbuSwhKD7s/mErvExEQsWbLkkaV1CxYswJw5c8otL7lbggIUGDM8IiKyAv1a9kOb99pAmaVEQN0A+NTyQcGt0u+PxITECqtqLydehqfMU4JozUvGrQxtUgeUJr5zv5qLNsFt4FPLR+LoDK/krmETO0nniq2J6dOn48MPP3zkNhcuXEB4eLj272vXruHxxx9H165d8dVXXz30eRWV2AUGBiIjJcMg87gREZHtUl5TonGzxtBo7iV3crkcF89cREC9AAkjMw979+1Fnyf7lFu+ddNWdOncRYKIjCs3Nxc+QT4GmyvWYhO7rKws3Lx585Hb1K9fXzuG3vXr19G1a1d06NABa9as0Y49VRW5ublQKBQGO+lERGTbVq1ahXHjxkGtVkMul2PFihWIiYmROiyzoFQqERwcXC7xTU5ORkCA9SW+hs4xLDax08e1a9fQrVs3tGnTBt99953e05gxsSMiIkNTKpVITExEw4YNrTJhqQlbSnyZ2Onp2rVr6Nq1K4KDgxEXF6eT1Pn6Vq2RKhM7IiIi07KVxNfQOYbVd57Yvn07EhMTkZiYWO7CsPKcloiIyGIFBARYdUJnLEafeUJqo0aNgiiKFT6IiIiIrInVJ3ZEREREtoKJHREREZGVYGJHREREZCWY2BERERFZCSZ2RERERFaCiR0RERGRlbD6cewMoWxolNzcXIkjISIiImtSllsYahg2JnZVUDYnbWBgoMSREBERkTW6efMmFApFjffDxK4KvLy8AAApKSkGOemWLjc3F4GBgUhNTbX5KdZ4Lu7hubiH5+Ienot7eC7u4bm4R6VSISgoSJtr1BQTuyqQyUqbIioUCpu/AO/n4eHB8/E/PBf38Fzcw3NxD8/FPTwX9/Bc3FOWa9R4PwbZCxERERFJjokdERERkZVgYlcFjo6OePfdd+Ho6Ch1KGaB5+Menot7eC7u4bm4h+fiHp6Le3gu7jH0uRBEQ/WvJSIiIiJJscSOiIiIyEowsSMiIiKyEkzsiIiIiKwEE7uH+OCDDyAIAiZPnqxdVlBQgNjYWNSuXRtubm4YOnQoMjIypAvSRCo6F127doUgCDqPV155RbogjWj27NnlXmt4eLh2vS1dF5WdC1u6LgDg2rVreOGFF1C7dm04OzujWbNmOHbsmHa9KIqYNWsW/Pz84OzsjJ49eyIhIUHCiI2nsnMxatSoctdGnz59JIzYOEJCQsq9TkEQEBsbC8C27heVnQtbul+o1WrMnDkToaGhcHZ2RoMGDfDee+/pTCNmqPsFByiuwNGjR7FixQo0b95cZ/mUKVPwxx9/4Oeff4ZCocD48eMxZMgQHDhwQKJIje9h5wIAxowZg7lz52r/dnFxMWVoJtWkSRPs2LFD+7ed3b2Pjq1dF486F4DtXBe3bt1Cx44d0a1bN/z111+oW7cuEhISUKtWLe02CxcuxOLFixEXF4fQ0FDMnDkTvXv3xvnz5+Hk5CRh9IZVlXMBAH369MHq1au1f1tjj8ijR49CrVZr/z537hyeeOIJPPPMMwBs635R2bkAbOd+8eGHH+KLL75AXFwcmjRpgmPHjmH06NFQKBSYOHEiAAPeL0TScfv2bTEsLEzcvn27+Pjjj4uTJk0SRVEUc3JyRHt7e/Hnn3/WbnvhwgURgHjo0CGJojWuh50LURTL/W3N3n33XbFFixYVrrO16+JR50IUbeu6mDZtmtipU6eHrtdoNKKvr6/40UcfaZfl5OSIjo6O4vfff2+KEE2msnMhiqI4cuRIcdCgQaYJyIxMmjRJbNCggajRaGzufvGg+8+FKNrW/aJ///7iSy+9pLNsyJAh4ogRI0RRNOz9glWxD4iNjUX//v3Rs2dPneXHjx9HcXGxzvLw8HAEBQXh0KFDpg7TJB52LsqsXbsWderUQdOmTTFjxgzcuXPHxBGaTkJCAvz9/VG/fn2MGDECKSkpAGzzunjYuShjK9fFxo0b0bZtWzzzzDPw9vZGq1at8OWXX2rXJyUlIT09XefaUCgUiIqKsrpro7JzUWbPnj3w9vZG48aN8eqrr+LmzZsSRGs6RUVF+O677/DSSy9BEASbvF+UefBclLGV+8Vjjz2GnTt34tKlSwCA06dPY//+/ejbty8Aw94vWBV7nx9++AEnTpzA0aNHy61LT0+Hg4MDPD09dZb7+PggPT3dRBGazqPOBQAMHz4cwcHB8Pf3x5kzZzBt2jTEx8dj/fr1Jo7U+KKiorBmzRo0btwYaWlpmDNnDjp37oxz587Z3HXxqHPh7u5uU9fFlStX8MUXX+D111/H//3f/+Ho0aOYOHEiHBwcMHLkSO377+Pjo/M8a7w2KjsXQGk17JAhQxAaGorLly/j//7v/9C3b18cOnQIcrlc4ldgHBs2bEBOTg5GjRoFwPa+R+734LkAbOt7ZPr06cjNzUV4eDjkcjnUajXmzZuHESNGAIBB7xdM7P4nNTUVkyZNwvbt262q7Ut1VOVcjB07VvvvZs2awc/PDz169MDly5fRoEEDU4VqEmW/qACgefPmiIqKQnBwMH766Sc4OztLGJnpPepcxMTE2NR1odFo0LZtW8yfPx8A0KpVK5w7dw7Lly/XJjO2oirnYtiwYdrtmzVrhubNm6NBgwbYs2cPevToIUncxrZq1Sr07dsX/v7+UociuYrOhS3dL3766SesXbsW69atQ5MmTXDq1ClMnjwZ/v7+Br9fsCr2f44fP47MzEy0bt0adnZ2sLOzw969e7F48WLY2dnBx8cHRUVFyMnJ0XleRkYGfH19pQnaSCo7F/c3hi0TFRUFAEhMTDR1uCbn6emJRo0aITExEb6+vjZzXVTk/nNREWu+Lvz8/BAZGamzLCIiQls1Xfb+P9jj0RqvjcrORUXq16+POnXqWOW1AQBXr17Fjh078PLLL2uX2er9oqJzURFrvl+89dZbmD59OoYNG4ZmzZrhxRdfxJQpU7BgwQIAhr1fMLH7nx49euDs2bM4deqU9tG2bVuMGDFC+297e3vs3LlT+5z4+HikpKQgOjpawsgNr7JzUVG1yalTpwCU3uCtXV5eHi5fvgw/Pz+0adPGZq6Litx/LipizddFx44dER8fr7Ps0qVLCA4OBgCEhobC19dX59rIzc3FkSNHrO7aqOxcVESpVOLmzZtWeW0AwOrVq+Ht7Y3+/ftrl9nq/aKic1ERa75f3LlzBzKZbsoll8uh0WgAGPh+UfO+HtbrwR47r7zyihgUFCTu2rVLPHbsmBgdHS1GR0dLF6AJ3X8uEhMTxblz54rHjh0Tk5KSxN9//12sX7++2KVLF2mDNJI33nhD3LNnj5iUlCQeOHBA7Nmzp1inTh0xMzNTFEXbui4edS5s7br4559/RDs7O3HevHliQkKCuHbtWtHFxUX87rvvtNt88MEHoqenp/j777+LZ86cEQcNGiSGhoaKd+/elTByw6vsXNy+fVt88803xUOHDolJSUnijh07xNatW4thYWFiQUGBxNEbnlqtFoOCgsRp06aVW2dL9wtRfPi5sLX7xciRI8V69eqJmzdvFpOSksT169eLderUEadOnardxlD3CyZ2j/BgYnf37l3xtddeE2vVqiW6uLiITz31lJiWliZdgCZ0/7lISUkRu3TpInp5eYmOjo5iw4YNxbfeektUqVTSBmkkzz33nOjn5yc6ODiI9erVE5977jkxMTFRu96WrotHnQtbuy5EURQ3bdokNm3aVHR0dBTDw8PFlStX6qzXaDTizJkzRR8fH9HR0VHs0aOHGB8fL1G0xvWoc3Hnzh2xV69eYt26dUV7e3sxODhYHDNmjJieni5hxMazdetWEUCF77Ut3S9E8eHnwtbuF7m5ueKkSZPEoKAg0cnJSaxfv7749ttvi4WFhdptDHW/EETxvmGPiYiIiMhisY0dERERkZVgYkdERERkJZjYEREREVkJJnZEREREVoKJHREREZGVYGJHREREZCWY2BERERFZCSZ2RERERFaCiR0RkUS6du0KQRAgCIJ2nsw9e/ZAEIRyE8Ub2uzZs7XHXrRokVGPRUSmw8SOiCzCqFGjtInI/Y8+ffpIHVqNjBkzBmlpaWjatGmN95WRkQF7e3v88MMPFa6PiYlB69atAQBvvvkm0tLSEBAQUOPjEpH5YGJHRBajT58+SEtL03l8//33Rj1mUVGRUffv4uICX19f2NnZ1XhfPj4+6N+/P77++uty6/Lz8/HTTz8hJiYGAODm5gZfX1/I5fIaH5eIzAcTOyKyGI6OjvD19dV51KpVS7teEAR89dVXeOqpp+Di4oKwsDBs3LhRZx/nzp1D37594ebmBh8fH7z44ou4ceOGdn3Xrl0xfvx4TJ48GXXq1EHv3r0BABs3bkRYWBicnJzQrVs3xMXFaatM8/Pz4eHhgV9++UXnWBs2bICrqytu375d7dd8584d9O3bFx07dtRWz3711VeIiIiAk5MTwsPDsWzZMu32MTEx2LlzJ1JSUnT28/PPP6OkpAQjRoyodixEZP6Y2BGRVZkzZw6effZZnDlzBv369cOIESOQnZ0NAMjJyUH37t3RqlUrHDt2DFu2bEFGRgaeffZZnX3ExcXBwcEBBw4cwPLly5GUlISnn34agwcPxunTpzFu3Di8/fbb2u1dXV0xbNgwrF69Wmc/q1evxtNPPw13d/dqvZacnBw88cQT0Gg02L59Ozw9PbF27VrMmjUL8+bNw4ULFzB//nzMnDkTcXFxAIB+/frBx8cHa9asKRfLkCFD4OnpWa1YiMhCiEREFmDkyJGiXC4XXV1ddR7z5s3TbgNAfOedd7R/5+XliQDEv/76SxRFUXzvvffEXr166ew3NTVVBCDGx8eLoiiKjz/+uNiqVSudbaZNmyY2bdpUZ9nbb78tAhBv3boliqIoHjlyRJTL5eL169dFURTFjIwM0c7OTtyzZ89DX9Pjjz8uTpo0SWfZ7t27RQDihQsXxObNm4tDhw4VCwsLtesbNGggrlu3Tuc57733nhgdHa39e/r06WJoaKio0WhEURTFxMREURAEcceOHeViCA4OFj/99NOHxkhEloUldkRkMbp164ZTp07pPF555RWdbZo3b679t6urKzw8PJCZmQkAOH36NHbv3g03NzftIzw8HABw+fJl7fPatGmjs8/4+Hi0a9dOZ1n79u3L/d2kSRNtydl3332H4OBgdOnSpVqv9YknnkDDhg3x448/wsHBAUBpO7nLly8jJiZG5zW8//77OvG/9NJLSEpKwu7duwGUltaFhISge/fu1YqFiCxHzVvrEhGZiKurKxo2bPjIbezt7XX+FgQBGo0GAJCXl4eBAwfiww8/LPc8Pz8/neNUx8svv4ylS5di+vTpWL16NUaPHg1BEKq1r/79++PXX3/F+fPn0axZM238APDll18iKipKZ/v7O0GEhYWhc+fOWL16Nbp27YpvvvkGY8aMqXYsRGQ5mNgRkc1o3bo1fv31V4SEhOjVC7Vx48b4888/dZYdPXq03HYvvPACpk6disWLF+P8+fMYOXJktWP94IMP4Obmhh49emDPnj2IjIyEj48P/P39ceXKlUo7QcTExODVV1/Fk08+iWvXrmHUqFHVjoWILAerYonIYhQWFiI9PV3ncX+P1srExsYiOzsbzz//PI4ePYrLly9j69atGD16NNRq9UOfN27cOFy8eBHTpk3DpUuX8NNPP2k7J9xfClarVi0MGTIEb731Fnr16lXjMeI+/vhjjBgxAt27d8fFixcBlHYOWbBgARYvXoxLly7h7NmzWL16NT755BOd5z7zzDOwt7fHuHHj0KtXLwQGBtYoFiKyDEzsiMhibNmyBX5+fjqPTp06Vfn5/v7+OHDgANRqNXr16oVmzZph8uTJ8PT0hEz28NthaGgofvnlF6xfvx7NmzfHF198oe0V6+joqLNtTEwMioqK8NJLL1XvRT7g008/xbPPPovu3bvj0qVLePnll/HVV19h9erVaNasGR5//HGsWbMGoaGhOs9zcXHBsGHDcOvWLYPFQkTmTxBFUZQ6CCIiSzNv3jwsX74cqampOsu//fZbTJkyBdevX9d2eniYrl27omXLlpJO6RUSEoLJkydj8uTJksVARIbDEjsioipYtmwZjh49iitXruDbb7/FRx99pNOG7s6dO7h8+TI++OADjBs3rtKk7v79urm54ezZs8YKvULz58+Hm5tbuYGMiciyscSOiKgKpkyZgh9//BHZ2dkICgrCiy++iBkzZmg7YcyePRvz5s1Dly5d8Pvvv8PNza3SfV67dg13794FAAQFBVU5GTSE7Oxs7cDNdevWhUKhMNmxich4mNgRERERWQlWxRIRERFZCSZ2RERERFaCiR0RERGRlWBiR0RERGQlmNgRERERWQkmdkRERERWgokdERERkZVgYkdERERkJZjYEREREVmJ/wepLNUZce1GQAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_fit = plt.figure(constrained_layout=True)\n",
    "gs = fig_fit.add_gridspec(5, 5, hspace=0)\n",
    "\n",
    "\n",
    "main_axis = fig_fit.add_subplot(gs[:4, :])\n",
    "res_axis = fig_fit.add_subplot(gs[4:, :], sharex=main_axis)\n",
    "fig_fit.tight_layout()\n",
    "\n",
    "\n",
    "main_axis.errorbar(center, entries, np.sqrt(entries), ls='', marker='.', color='k')\n",
    "\n",
    "x = np.arange(40, 80, 0.1)\n",
    "main_axis.plot(x, fit_model(x, *mi.values), color='purple', label='Best fit')\n",
    "main_axis.legend()\n",
    "main_axis.set_ylabel('Number of entries per bin')\n",
    "main_axis.xaxis.set_tick_params(direction='inout')\n",
    "main_axis.tick_params(axis='x', labelcolor=(0, 0, 0, 0))\n",
    "main_axis.set_xlim(40, 80)\n",
    "\n",
    "res_axis.set_xlabel('Energy [keV]')\n",
    "res_axis.set_ylabel('Res [$\\sigma$]')\n",
    "res_axis.set_ylim(-3, 3)\n",
    "res_axis.set_yticks([-2, 0,  2])\n",
    "res_axis.fill_between((40, 80), -1, 1, alpha=0.3, color='purple')\n",
    "res_axis.fill_between((40, 80), -2, 2, alpha=0.3, color='purple')\n",
    "res_axis.axhline(0, color='purple')\n",
    "res_axis.set_xlim(40, 80)\n",
    "res_axis.plot(center, \n",
    "              residuals,\n",
    "              color='k', marker='.', ls=''\n",
    "             )\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe65a21-572e-4618-bcd8-78f13e945e8a",
   "metadata": {},
   "source": [
    "If fit is \"good\" ~ every 3rd point should be beyond 1 sigma residual band.... Normal distribution one sigma 68 % .... etc.\n",
    "\n",
    "If residuals show systematic behavior model is wrong:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "850870af-e546-4d95-b9de-8a4e7b61c241",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/jobs/29211635/ipykernel_829/4141622746.py:10: UserWarning: The figure layout has changed to tight\n",
      "  fig_fit.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pseudo_data = np.random.normal(0, 2, 5000)\n",
    "\n",
    "\n",
    "fig_fit = plt.figure(constrained_layout=True)\n",
    "gs = fig_fit.add_gridspec(5, 5, hspace=0)\n",
    "\n",
    "\n",
    "main_axis = fig_fit.add_subplot(gs[:4, :])\n",
    "res_axis = fig_fit.add_subplot(gs[4:, :], sharex=main_axis)\n",
    "fig_fit.tight_layout()\n",
    "\n",
    "\n",
    "entries1, edges1, _ = main_axis.hist(pseudo_data, bins=25, range=(-5,5), histtype='step', color='k')\n",
    "center1 = edges1[:-1] + np.diff(edges1)/2\n",
    "\n",
    "residuals1 = (entries1 - peak(center1, 400, 0.2, 2))/np.sqrt(entries1)\n",
    "\n",
    "x = np.arange(-5, 5, 0.1)\n",
    "\n",
    "main_axis.plot(x, peak(x, 400, 0.2, 2), color='purple')\n",
    "main_axis.set_ylabel('Number of entries per bin')\n",
    "main_axis.xaxis.set_tick_params(direction='inout')\n",
    "main_axis.tick_params(axis='x', labelcolor=(0, 0, 0, 0))\n",
    "main_axis.set_xlim(-5, 5)\n",
    "\n",
    "res_axis.set_xlabel('Energy [keV]')\n",
    "res_axis.set_ylabel('Res [$\\sigma$]')\n",
    "res_axis.set_ylim(-3, 3)\n",
    "res_axis.set_yticks([-2, 0,  2])\n",
    "res_axis.fill_between((-5, 5), -1, 1, alpha=0.3, color='purple')\n",
    "res_axis.fill_between((-5, 5), -2, 2, alpha=0.3, color='purple')\n",
    "res_axis.axhline(0, color='purple')\n",
    "res_axis.set_xlim(-5, 5)\n",
    "res_axis.plot(center1, \n",
    "              residuals1,\n",
    "              color='k', marker='.', ls=''\n",
    "             )\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48e95a88-0742-4221-a716-17dacfc02823",
   "metadata": {},
   "source": [
    "### Chi-Square:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1789cf-7ed3-4db3-a0ae-9e563a9dc85e",
   "metadata": {},
   "source": [
    "Need to update the following:...\n",
    "\n",
    "\n",
    "Wie Sie sehen können, ist der Wert für den Widerstand zwar gleich geblieben, jedoch die Unsicherheit des Wertes hat sich erhöht.\n",
    "\n",
    "Wie gut fittet unsere obige Funktion unsere Messdaten? Sehr gut? Gut? Befriedigend? Oder doch eher schlecht?   Wäre es nicht gut, ein Maß für die Güte des Fits zu haben? Wie könnte ein solches Maß aussehen?\n",
    "\n",
    "Sie haben das entscheidende Kriterium bereits kennengelernt, bei der Methode der kleinsten Quadrate geht es darum, das  $\\chi^2$ zu minimieren. Gucken wir uns hierzu erst noch einmal an, wie sich das $\\chi^2$ berechnet:\n",
    "\n",
    "$$ \\chi(\\phi_1 ... \\phi_N)^2 = \\sum_{i = 1}^{N} \\frac{ (y_i - \\lambda(x_i; \\phi))^2}{\\Delta y_i^2}$$\n",
    "\n",
    "\n",
    "Coming back to figure\n",
    "\n",
    "<figure class=\"image\">\n",
    "<img src=\"images/MaterialPythonkurs092018/LeastSquare.png\"  alt=\"{{ Least Square Beispiel }}\" width=80%>\n",
    "</figure>\n",
    "\n",
    "Chi-Square can be understood easily, want to minimize distance, put bigger emphsis on values with smaller uncertainty as more confident...\n",
    "\n",
    "\n",
    "\n",
    "Wie Sie sehen können, ist das $\\chi^2$ für unsere zweite Funktion etwas größer als für das klassische ohm'sche Gesetzt. Somit würden wir unseren zweiten Ansatz verwerfen.\n",
    "\n",
    "Damit man für einen gegebenen Datensatz nicht hunderte von verschiedenen Funktionen durchprobieren muss, gibt es für das $\\chi^2$ eine allgemeine Faustregel, welche den berechneten $\\chi^2$-Wert mit der Anzahl unserer Freiheitsgrade vergleicht. Die Anzahl an Freiheitsgrade ist allgemeinhin gegeben als *Anzahl der Messwerte - Anzahl der Funktionsparameter* ($m - n$).\n",
    "\n",
    "1. Sofern  $\\chi^2/\\text{ndof} >> 1$: sollte die Hypothese bzw. die Fitfunktion angezweifelt werden. Sie beschreibt in diesem Fall die Messdaten nur unzureichend. (Bzw. sollte $\\chi^2/\\text{ndof} > 1$ kann dies auch bedeuten, dass die Unsicherheiten unterschätzt sind)\n",
    "2. Sofern $\\chi^2/\\text{ndof} \\approx 1$: beschreibt die Hypothese bzw. die Fitfunktion die Daten wie erwartet und wird nicht abgelehnt. \n",
    "3. Falls $\\chi^2/\\text{ndof} << 1$ beschreibt die Hypothese bzw. die Fitfunktion die Daten wesentlich besser als erwartet. In diesem Fall heißt es nicht, dass unsere Hypothese falsch ist, aber man sollte überprüfen, ob die gemessenen Fehler nicht überschätzt worden sind (oder eine Korrelation zwischen den Messfehlern vorliegt).  \n",
    "\n",
    "Sofern Sie eine Arbeit schreiben und Ihre **Goodness-of-the-Fit** ($\\chi^2/\\text{ndof}$) angeben wollen, so geben Sie immer beides an, das $\\chi^2$ und die Anzahl an Freiheitsgraden ndof. Beide Werte getrennt haben einen größeren Informationsgehalt als der resultierende Quotient (Genaueres lernen Sie z.B. in der Vorlesung *Statistik, Datenanalyse und Simulationen* im Master).\n",
    "\n",
    "Sehen wir uns hierzu nochmal unseren Doppelpeakfit etwas genauer an. Iminuit berechnet hier für uns bereits das reuzierete $\\chi^2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fa85a19a-f066-4567-abb0-6283ae1bc90b",
   "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 = 50.35 (χ²/ndof = 1.0) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 525 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 2.19e-05 (Goal: 0.0002) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total run time of algorithms\">  </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> A_p1 </td>\n",
       "        <td> 609 </td>\n",
       "        <td> 15 </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> A_p2 </td>\n",
       "        <td> 1.133e3 </td>\n",
       "        <td> 0.014e3 </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> mu_p1 </td>\n",
       "        <td> 53.13 </td>\n",
       "        <td> 0.08 </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_p2 </td>\n",
       "        <td> 60.35 </td>\n",
       "        <td> 0.06 </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_p1 </td>\n",
       "        <td> 2.00 </td>\n",
       "        <td> 0.06 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 5 </th>\n",
       "        <td> sigma_p2 </td>\n",
       "        <td> 2.92 </td>\n",
       "        <td> 0.04 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 6 </th>\n",
       "        <td> A_bkg </td>\n",
       "        <td> 254 </td>\n",
       "        <td> 26 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 7 </th>\n",
       "        <td> tau_bkg </td>\n",
       "        <td> 37.0 </td>\n",
       "        <td> 2.4 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> A_p1 </th>\n",
       "        <th> A_p2 </th>\n",
       "        <th> mu_p1 </th>\n",
       "        <th> mu_p2 </th>\n",
       "        <th> sigma_p1 </th>\n",
       "        <th> sigma_p2 </th>\n",
       "        <th> A_bkg </th>\n",
       "        <th> tau_bkg </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p1 </th>\n",
       "        <td> 213 </td>\n",
       "        <td style=\"background-color:rgb(250,226,226);color:black\"> 0.03e3 <strong>(0.161)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,210,210);color:black\"> 0.297 <strong>(0.268)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,199,199);color:black\"> 0.2830 <strong>(0.343)</strong> </td>\n",
       "        <td style=\"background-color:rgb(233,233,250);color:black\"> -0.1103 <strong>(-0.133)</strong> </td>\n",
       "        <td style=\"background-color:rgb(198,198,250);color:black\"> -0.2594 <strong>(-0.400)</strong> </td>\n",
       "        <td style=\"background-color:rgb(244,244,250);color:black\"> -0.02e3 <strong>(-0.048)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,242,242);color:black\"> 2 <strong>(0.051)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,226,226);color:black\"> 0.03e3 <strong>(0.161)</strong> </td>\n",
       "        <td> 191 </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.030 <strong>(0.029)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,236,236);color:black\"> 0.0711 <strong>(0.091)</strong> </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.0264 <strong>(-0.034)</strong> </td>\n",
       "        <td style=\"background-color:rgb(194,194,250);color:black\"> -0.2656 <strong>(-0.433)</strong> </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.01e3 <strong>(-0.030)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 1 <strong>(0.027)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,210,210);color:black\"> 0.297 <strong>(0.268)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.030 <strong>(0.029)</strong> </td>\n",
       "        <td> 0.00578 </td>\n",
       "        <td style=\"background-color:rgb(250,139,139);color:black\"> 0.0032 <strong>(0.737)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,148,148);color:black\"> 0.0029 <strong>(0.678)</strong> </td>\n",
       "        <td style=\"background-color:rgb(162,162,250);color:black\"> -0.0023 <strong>(-0.674)</strong> </td>\n",
       "        <td style=\"background-color:rgb(241,241,250);color:black\"> -0.134 <strong>(-0.068)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,237,237);color:black\"> 0.016 <strong>(0.086)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,199,199);color:black\"> 0.2830 <strong>(0.343)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,236,236);color:black\"> 0.0711 <strong>(0.091)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,139,139);color:black\"> 0.0032 <strong>(0.737)</strong> </td>\n",
       "        <td> 0.0032 </td>\n",
       "        <td style=\"background-color:rgb(250,155,155);color:black\"> 0.0020 <strong>(0.632)</strong> </td>\n",
       "        <td style=\"background-color:rgb(160,160,250);color:black\"> -0.0017 <strong>(-0.695)</strong> </td>\n",
       "        <td style=\"background-color:rgb(240,240,250);color:black\"> -0.1166 <strong>(-0.079)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,239,239);color:black\"> 0.0101 <strong>(0.073)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(233,233,250);color:black\"> -0.1103 <strong>(-0.133)</strong> </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.0264 <strong>(-0.034)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,148,148);color:black\"> 0.0029 <strong>(0.678)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,155,155);color:black\"> 0.0020 <strong>(0.632)</strong> </td>\n",
       "        <td> 0.00323 </td>\n",
       "        <td style=\"background-color:rgb(182,182,250);color:black\"> -0.0013 <strong>(-0.521)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.2869 <strong>(-0.194)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,226,226);color:black\"> 0.0222 <strong>(0.160)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(198,198,250);color:black\"> -0.2594 <strong>(-0.400)</strong> </td>\n",
       "        <td style=\"background-color:rgb(194,194,250);color:black\"> -0.2656 <strong>(-0.433)</strong> </td>\n",
       "        <td style=\"background-color:rgb(162,162,250);color:black\"> -0.0023 <strong>(-0.674)</strong> </td>\n",
       "        <td style=\"background-color:rgb(160,160,250);color:black\"> -0.0017 <strong>(-0.695)</strong> </td>\n",
       "        <td style=\"background-color:rgb(182,182,250);color:black\"> -0.0013 <strong>(-0.521)</strong> </td>\n",
       "        <td> 0.00197 </td>\n",
       "        <td style=\"background-color:rgb(250,227,227);color:black\"> 0.1756 <strong>(0.152)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.0207 <strong>(-0.191)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_bkg </th>\n",
       "        <td style=\"background-color:rgb(244,244,250);color:black\"> -0.02e3 <strong>(-0.048)</strong> </td>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.01e3 <strong>(-0.030)</strong> </td>\n",
       "        <td style=\"background-color:rgb(241,241,250);color:black\"> -0.134 <strong>(-0.068)</strong> </td>\n",
       "        <td style=\"background-color:rgb(240,240,250);color:black\"> -0.1166 <strong>(-0.079)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.2869 <strong>(-0.194)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,227,227);color:black\"> 0.1756 <strong>(0.152)</strong> </td>\n",
       "        <td> 676 </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -61 <strong>(-0.966)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> tau_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,242,242);color:black\"> 2 <strong>(0.051)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 1 <strong>(0.027)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,237,237);color:black\"> 0.016 <strong>(0.086)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,239,239);color:black\"> 0.0101 <strong>(0.073)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,226,226);color:black\"> 0.0222 <strong>(0.160)</strong> </td>\n",
       "        <td style=\"background-color:rgb(225,225,250);color:black\"> -0.0207 <strong>(-0.191)</strong> </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -61 <strong>(-0.966)</strong> </td>\n",
       "        <td> 5.97 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 50.35 (χ²/ndof = 1.0)      │              Nfcn = 525              │\n",
       "│ EDM = 2.19e-05 (Goal: 0.0002)    │                                      │\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 │ A_p1     │    609    │    15     │            │            │         │         │       │\n",
       "│ 1 │ A_p2     │  1.133e3  │  0.014e3  │            │            │         │         │       │\n",
       "│ 2 │ mu_p1    │   53.13   │   0.08    │            │            │         │         │       │\n",
       "│ 3 │ mu_p2    │   60.35   │   0.06    │            │            │         │         │       │\n",
       "│ 4 │ sigma_p1 │   2.00    │   0.06    │            │            │         │         │       │\n",
       "│ 5 │ sigma_p2 │   2.92    │   0.04    │            │            │         │         │       │\n",
       "│ 6 │ A_bkg    │    254    │    26     │            │            │         │         │       │\n",
       "│ 7 │ tau_bkg  │   37.0    │    2.4    │            │            │    0    │         │       │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬─────────────────────────────────────────────────────────────────────────┐\n",
       "│          │     A_p1     A_p2    mu_p1    mu_p2 sigma_p1 sigma_p2    A_bkg  tau_bkg │\n",
       "├──────────┼─────────────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │      213   0.03e3    0.297   0.2830  -0.1103  -0.2594  -0.02e3        2 │\n",
       "│     A_p2 │   0.03e3      191    0.030   0.0711  -0.0264  -0.2656  -0.01e3        1 │\n",
       "│    mu_p1 │    0.297    0.030  0.00578   0.0032   0.0029  -0.0023   -0.134    0.016 │\n",
       "│    mu_p2 │   0.2830   0.0711   0.0032   0.0032   0.0020  -0.0017  -0.1166   0.0101 │\n",
       "│ sigma_p1 │  -0.1103  -0.0264   0.0029   0.0020  0.00323  -0.0013  -0.2869   0.0222 │\n",
       "│ sigma_p2 │  -0.2594  -0.2656  -0.0023  -0.0017  -0.0013  0.00197   0.1756  -0.0207 │\n",
       "│    A_bkg │  -0.02e3  -0.01e3   -0.134  -0.1166  -0.2869   0.1756      676      -61 │\n",
       "│  tau_bkg │        2        1    0.016   0.0101   0.0222  -0.0207      -61     5.97 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f464246-d333-4143-baf0-aa2a632c5be4",
   "metadata": {},
   "source": [
    "Estimate ourselves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b0ad46ce-f541-40bb-898c-154ad5f94787",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50.35308014445669 52 0.9683284643164748\n"
     ]
    }
   ],
   "source": [
    "def chi_squre_ndof(x_values, y_values, dy_values, fit_model, minuit):\n",
    "    ndof = len(x_values) - len(minuit.values)\n",
    "    chi = np.sum((y_values - fit_model(x_values, *minuit.values))**2/dy_values**2)\n",
    "    return chi, ndof\n",
    "\n",
    "\n",
    "chi_squre, ndof = chi_squre_ndof(center, entries, np.sqrt(entries), fit_model, mi)\n",
    "print(chi_squre, ndof, chi_squre/ndof)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "295031f4-6d18-411c-b5dd-a62ed97da7f1",
   "metadata": {},
   "source": [
    "### Chi-Squre hypothesis testing..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8c11bc85-4e25-4d40-8397-257414d48a1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chi2\n",
    "chi_distribution = lambda x, ndof: chi2.pdf(x, ndof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "76836863-109c-4e7c-989e-04b62ec4ca9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\chi^2(x)$')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(20, 90)\n",
    "plt.plot(x, chi_distribution(x, 52))\n",
    "\n",
    "x = np.arange(chi_squre, 90, 0.1)\n",
    "plt.fill_between(x, chi_distribution(x, 52), alpha=0.3)\n",
    "plt.ylim(0, None)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('$\\chi^2(x)$')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b30829b3-a9e8-4d93-8895-9fd9f67ab9dc",
   "metadata": {},
   "source": [
    "Explain p-value...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cfa9d88a-eada-49dd-8cb3-73c7dd345c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.538885338595922, 0.6898594516527083)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value = lambda x, ndof: 1 - chi2.cdf(x, ndof)\n",
    "p_value(chi_squre, ndof), p_value(chi_squre*10, ndof*10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cba146a-6309-42d1-92cb-8bdde2da42a2",
   "metadata": {},
   "source": [
    "Try alternative fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9b91ee55-ac17-4dd6-9827-48677f772096",
   "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 = 261.3 (χ²/ndof = 4.9) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 344 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 2.08e-05 (Goal: 0.0002) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total run time of algorithms\">  </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> A_p1 </td>\n",
       "        <td> 618 </td>\n",
       "        <td> 14 </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> A_p2 </td>\n",
       "        <td> 1.138e3 </td>\n",
       "        <td> 0.014e3 </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> mu_p1 </td>\n",
       "        <td> 53.21 </td>\n",
       "        <td> 0.09 </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_p2 </td>\n",
       "        <td> 60.44 </td>\n",
       "        <td> 0.06 </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_p1 </td>\n",
       "        <td> 2.23 </td>\n",
       "        <td> 0.07 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 5 </th>\n",
       "        <td> sigma_p2 </td>\n",
       "        <td> 2.84 </td>\n",
       "        <td> 0.04 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 6 </th>\n",
       "        <td> c </td>\n",
       "        <td> 43.7 </td>\n",
       "        <td> 1.3 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> A_p1 </th>\n",
       "        <th> A_p2 </th>\n",
       "        <th> mu_p1 </th>\n",
       "        <th> mu_p2 </th>\n",
       "        <th> sigma_p1 </th>\n",
       "        <th> sigma_p2 </th>\n",
       "        <th> c </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p1 </th>\n",
       "        <td> 198 </td>\n",
       "        <td style=\"background-color:rgb(250,229,229);color:black\"> 0.03e3 <strong>(0.142)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,212,212);color:black\"> 0.315 <strong>(0.255)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,202,202);color:black\"> 0.275 <strong>(0.319)</strong> </td>\n",
       "        <td style=\"background-color:rgb(231,231,250);color:black\"> -0.139 <strong>(-0.147)</strong> </td>\n",
       "        <td style=\"background-color:rgb(199,199,250);color:black\"> -0.2464 <strong>(-0.390)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,245,245);color:black\"> 0.6 <strong>(0.032)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,229,229);color:black\"> 0.03e3 <strong>(0.142)</strong> </td>\n",
       "        <td> 199 </td>\n",
       "        <td style=\"background-color:rgb(239,239,250);color:black\"> -0.101 <strong>(-0.082)</strong> </td>\n",
       "        <td style=\"background-color:rgb(247,247,250);color:black\"> -0.020 <strong>(-0.023)</strong> </td>\n",
       "        <td style=\"background-color:rgb(230,230,250);color:black\"> -0.144 <strong>(-0.152)</strong> </td>\n",
       "        <td style=\"background-color:rgb(207,207,250);color:black\"> -0.2100 <strong>(-0.331)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,248,248);color:black\"> 0.2 <strong>(0.010)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,212,212);color:black\"> 0.315 <strong>(0.255)</strong> </td>\n",
       "        <td style=\"background-color:rgb(239,239,250);color:black\"> -0.101 <strong>(-0.082)</strong> </td>\n",
       "        <td> 0.00773 </td>\n",
       "        <td style=\"background-color:rgb(250,131,131);color:black\"> 0.004 <strong>(0.793)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,138,138);color:black\"> 0.004 <strong>(0.746)</strong> </td>\n",
       "        <td style=\"background-color:rgb(157,157,250);color:black\"> -0.0028 <strong>(-0.712)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,244,244);color:black\"> 0.005 <strong>(0.040)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,202,202);color:black\"> 0.275 <strong>(0.319)</strong> </td>\n",
       "        <td style=\"background-color:rgb(247,247,250);color:black\"> -0.020 <strong>(-0.023)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,131,131);color:black\"> 0.004 <strong>(0.793)</strong> </td>\n",
       "        <td> 0.00377 </td>\n",
       "        <td style=\"background-color:rgb(250,146,146);color:black\"> 0.003 <strong>(0.692)</strong> </td>\n",
       "        <td style=\"background-color:rgb(156,156,250);color:black\"> -0.0020 <strong>(-0.722)</strong> </td>\n",
       "        <td style=\"background-color:rgb(245,245,250);color:black\"> -0.003 <strong>(-0.040)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(231,231,250);color:black\"> -0.139 <strong>(-0.147)</strong> </td>\n",
       "        <td style=\"background-color:rgb(230,230,250);color:black\"> -0.144 <strong>(-0.152)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,138,138);color:black\"> 0.004 <strong>(0.746)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,146,146);color:black\"> 0.003 <strong>(0.692)</strong> </td>\n",
       "        <td> 0.00449 </td>\n",
       "        <td style=\"background-color:rgb(179,179,250);color:black\"> -0.0017 <strong>(-0.549)</strong> </td>\n",
       "        <td style=\"background-color:rgb(233,233,250);color:black\"> -0.011 <strong>(-0.130)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(199,199,250);color:black\"> -0.2464 <strong>(-0.390)</strong> </td>\n",
       "        <td style=\"background-color:rgb(207,207,250);color:black\"> -0.2100 <strong>(-0.331)</strong> </td>\n",
       "        <td style=\"background-color:rgb(157,157,250);color:black\"> -0.0028 <strong>(-0.712)</strong> </td>\n",
       "        <td style=\"background-color:rgb(156,156,250);color:black\"> -0.0020 <strong>(-0.722)</strong> </td>\n",
       "        <td style=\"background-color:rgb(179,179,250);color:black\"> -0.0017 <strong>(-0.549)</strong> </td>\n",
       "        <td> 0.00202 </td>\n",
       "        <td style=\"background-color:rgb(231,231,250);color:black\"> -0.0082 <strong>(-0.142)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> c </th>\n",
       "        <td style=\"background-color:rgb(250,245,245);color:black\"> 0.6 <strong>(0.032)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,248,248);color:black\"> 0.2 <strong>(0.010)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,244,244);color:black\"> 0.005 <strong>(0.040)</strong> </td>\n",
       "        <td style=\"background-color:rgb(245,245,250);color:black\"> -0.003 <strong>(-0.040)</strong> </td>\n",
       "        <td style=\"background-color:rgb(233,233,250);color:black\"> -0.011 <strong>(-0.130)</strong> </td>\n",
       "        <td style=\"background-color:rgb(231,231,250);color:black\"> -0.0082 <strong>(-0.142)</strong> </td>\n",
       "        <td> 1.63 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 261.3 (χ²/ndof = 4.9)      │              Nfcn = 344              │\n",
       "│ EDM = 2.08e-05 (Goal: 0.0002)    │                                      │\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 │ A_p1     │    618    │    14     │            │            │         │         │       │\n",
       "│ 1 │ A_p2     │  1.138e3  │  0.014e3  │            │            │         │         │       │\n",
       "│ 2 │ mu_p1    │   53.21   │   0.09    │            │            │         │         │       │\n",
       "│ 3 │ mu_p2    │   60.44   │   0.06    │            │            │         │         │       │\n",
       "│ 4 │ sigma_p1 │   2.23    │   0.07    │            │            │         │         │       │\n",
       "│ 5 │ sigma_p2 │   2.84    │   0.04    │            │            │         │         │       │\n",
       "│ 6 │ c        │   43.7    │    1.3    │            │            │    0    │         │       │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬────────────────────────────────────────────────────────────────┐\n",
       "│          │     A_p1     A_p2    mu_p1    mu_p2 sigma_p1 sigma_p2        c │\n",
       "├──────────┼────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │      198   0.03e3    0.315    0.275   -0.139  -0.2464      0.6 │\n",
       "│     A_p2 │   0.03e3      199   -0.101   -0.020   -0.144  -0.2100      0.2 │\n",
       "│    mu_p1 │    0.315   -0.101  0.00773    0.004    0.004  -0.0028    0.005 │\n",
       "│    mu_p2 │    0.275   -0.020    0.004  0.00377    0.003  -0.0020   -0.003 │\n",
       "│ sigma_p1 │   -0.139   -0.144    0.004    0.003  0.00449  -0.0017   -0.011 │\n",
       "│ sigma_p2 │  -0.2464  -0.2100  -0.0028  -0.0020  -0.0017  0.00202  -0.0082 │\n",
       "│        c │      0.6      0.2    0.005   -0.003   -0.011  -0.0082     1.63 │\n",
       "└──────────┴────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def alternative_fit_model(x, A_p1, A_p2, mu_p1, mu_p2, sigma_p1, sigma_p2, c):\n",
    "    return peak(x, A_p1, mu_p1, sigma_p1) + peak(x, A_p2, mu_p2, sigma_p2) + c\n",
    "\n",
    "ls = cost.LeastSquares(center, entries, np.sqrt(entries), alternative_fit_model)\n",
    "\n",
    "mi = Minuit(ls, \n",
    "            A_p1 = 800, \n",
    "            A_p2 = 1400,\n",
    "            mu_p1 = 54,\n",
    "            mu_p2 = 60,\n",
    "            sigma_p1 = 2,\n",
    "            sigma_p2 = 2,\n",
    "            c = 100, \n",
    "           )\n",
    "mi.limits['c'] = (0, None)\n",
    "mi.fixed[:] = True\n",
    "ls.mask = (center < 45) | (center >= 70)\n",
    "mi.fixed[['c']] = False\n",
    "mi.migrad()\n",
    "ls.mask = None\n",
    "mi.values['A_p1'] = 700\n",
    "mi.values['sigma_p1'] = 3\n",
    "mi.fixed[:] = True\n",
    "mi.fixed[['A_p1', 'mu_p1', 'sigma_p1']] = False\n",
    "mi.migrad()\n",
    "mi.fixed[:] = True\n",
    "mi.fixed[['A_p2', 'mu_p2', 'sigma_p2']] = False\n",
    "mi.migrad()\n",
    "mi.fixed[:] = False\n",
    "mi.migrad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4aa0f3d9-1d0b-4b4c-b816-2a0cb9ae9793",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "261.25768423416054 53 4.929390268569067\n"
     ]
    }
   ],
   "source": [
    "chi_squre, ndof = chi_squre_ndof(center, entries, np.sqrt(entries), alternative_fit_model, mi)\n",
    "print(chi_squre, ndof, chi_squre/ndof)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcb62098-1e8b-4c9f-8aa3-048037f0d21e",
   "metadata": {},
   "source": [
    "Fit obviusly worse...\n",
    "\n",
    "But is p-value of 0.4 better than 0.2? No ! Hypothesis test -> need to decide on threshold before..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2aa83565-40bf-4a06-a289-772af9201699",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19e53c1c-a95b-416f-a461-55786b812a96",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d790f2a-d2ca-454b-b207-eced7c2c498a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c020ed79-cf0d-4de4-855a-ef6b5307c6c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9e474fae-192a-4067-91fc-d0b03b47d762",
   "metadata": {},
   "source": [
    "When do assumptions break? \n",
    "\n",
    "E.g. low stats. \n",
    "\n",
    "Should we add ML and unbinned fits here? Maybe as outlook as otherwise too much?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c3f1f1d4-4b84-45a1-9d23-4cbb8ba32c8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lorentzian( x, x0, a, gam ):\n",
    "    return a * gam**2 / ( gam**2 + ( x - x0 )**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c3b58808-f155-4194-b02e-e5f649cb86aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6316d5dfd0e2480aaf38be2a289a691f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "\n",
    "res_good_model = []\n",
    "res_overfitting = []\n",
    "res_wrong_model = []\n",
    "\n",
    "def peak(x, A, mu, sigma):\n",
    "    return A*np.exp(-(x-mu)**2/(2*sigma**2))\n",
    "\n",
    "def lorentzian( x, x0, a, gam ):\n",
    "    return a * gam**2 / ( gam**2 + ( x - x0 )**2)\n",
    "\n",
    "\n",
    "for i in tqdm(range(5000)):\n",
    "    \n",
    "    test_data = np.random.normal(0, 2, 5000)\n",
    "    \n",
    "    entries, edges = np.histogram(test_data, bins=25, range=(-4,4))\n",
    "    center = edges[:-1] + np.diff(edges)/2\n",
    "    \n",
    "    ls = cost.LeastSquares(center, entries, np.sqrt(entries), peak)\n",
    "    mi = Minuit(ls, \n",
    "                mu=0.1,\n",
    "                sigma=1.5,\n",
    "                A = 300\n",
    "               )\n",
    "    mi.migrad()\n",
    "    \n",
    "    chi, ndof = chi_squre_ndof(center, entries, np.sqrt(entries), peak, mi)\n",
    "    res_good_model.append(p_value(chi, ndof))\n",
    "\n",
    "\n",
    "    ls = cost.LeastSquares(center, entries, np.sqrt(entries)*1.1, peak)\n",
    "    mi = Minuit(ls, \n",
    "                mu=0.1,\n",
    "                sigma=1.5,\n",
    "                A = 300\n",
    "               )\n",
    "    mi.migrad()\n",
    "    \n",
    "    chi, ndof = chi_squre_ndof(center, entries, np.sqrt(entries)*1.1, peak, mi)\n",
    "    res_overfitting.append(p_value(chi, ndof))\n",
    "\n",
    "\n",
    "    ls = cost.LeastSquares(center, entries, np.sqrt(entries), lorentzian)\n",
    "    mi = Minuit(ls, \n",
    "                x0=0,\n",
    "                gam=3,\n",
    "                a = 300,\n",
    "               )\n",
    "    mi.migrad()\n",
    "    \n",
    "    chi, ndof = chi_squre_ndof(center, entries, np.sqrt(entries), lorentzian, mi)\n",
    "    res_wrong_model.append(p_value(chi, ndof))\n",
    "\n",
    "res_wrong_model = np.array(res_wrong_model)\n",
    "res_good_model = np.array(res_good_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f41e0b38-56b6-4f2a-bc75-075f622a2068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()\n",
    "axes.hist(res_good_model, bins=25, range=(0, 1), histtype='step', color='purple', label='Good model')\n",
    "axes.hist(res_wrong_model, bins=25, range=(0, 1), histtype='step', color='orange', label='Wrong model')\n",
    "axes.hist(res_overfitting, bins=25, range=(0, 1), histtype='step', color='firebrick', label='Too large uncertainties (10 %)')\n",
    "axes.set_xlabel('p-value')\n",
    "axes.set_ylabel('Number of fits')\n",
    "axes.legend()\n",
    "axes.axvline(0.1, color='k')\n",
    "axes2 = plt.twiny()\n",
    "axes2.set_xlabel('Red. $\\chi^2$')\n",
    "axes2.set_xticks([0.2, 0.5, 0.8], ['> 1', '1', '< 1'])\n",
    "plt.show()\n",
    "\n",
    "axes.set_yscale('log')\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "fc58ee5c-308c-4479-9236-751d7f158fe5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of wrong model fits rejected: 0.9998\n",
      "Fraction of good model fits rejected: 0.1130\n"
     ]
    }
   ],
   "source": [
    "print(f'Fraction of wrong model fits rejected: {np.sum(res_wrong_model<0.1)/len(res_wrong_model):.4f}')\n",
    "print(f'Fraction of good model fits rejected: {np.sum(res_good_model<0.1)/len(res_good_model):.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d5f5efbe-ef8f-48b0-b27b-166f21cb5a06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of wrong model fits rejected: 0.9990\n",
      "Fraction of good model fits rejected: 0.0572\n"
     ]
    }
   ],
   "source": [
    "print(f'Fraction of wrong model fits rejected: {np.sum(res_wrong_model<0.05)/len(res_wrong_model):.4f}')\n",
    "print(f'Fraction of good model fits rejected: {np.sum(res_good_model<0.05)/len(res_good_model):.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de9861f6-7870-4dd8-8366-15e0c7dd5125",
   "metadata": {},
   "source": [
    "Failed to reject model, NOT confirms model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e69f0a15-81a8-43e9-bda5-463705ad19c2",
   "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": "3.9.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}