pgp1-python-einfuehrung/IminuitExample.ipynb

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   "cell_type": "markdown",
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   "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": 5,
   "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": 6,
   "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": 7,
   "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": 8,
   "id": "54c27237-effd-4396-91d8-b6b4d43d589c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_csv('data/discharge_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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": 11,
   "id": "f14ca80f-e0d7-4447-9335-b3744f7a028f",
   "metadata": {},
   "outputs": [
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       "   Unnamed: 0      time   current  delta_current  delta_time\n",
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       "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",
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame"
   ]
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  {
   "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": 12,
   "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": 12,
     "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": 13,
   "id": "969d8afa-5d52-4e01-8b64-ddab090891b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.066538\n",
       "1    0.406316\n",
       "2    0.143093\n",
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       "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": 13,
     "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": 14,
   "id": "e4b44637-8e25-46c1-863d-3cd7604f52dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0118852615051639"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "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": 15,
     "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": 16,
   "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": 17,
   "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": 18,
   "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": 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": [
    "# 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": 19,
   "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": 20,
   "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": 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",
      "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": 21,
   "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": 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": [
    "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": 22,
   "id": "69f540a5-e89b-4c24-aa7e-b03eaedb28d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0670397937137222"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "id": "66e6da5b-ff32-4982-a3aa-5b9b93262073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.050402330240634355"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "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": 41,
   "id": "43bfd15e-7b68-4b70-bc06-0b23f89f7bff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9, 10000.0, 1e-06) -> 99.20912665811522\n",
      "(0.9000900000000001, 10000.0, 1e-06) -> 99.19412063461034\n",
      "(0.89991, 10000.0, 1e-06) -> 99.22413838602546\n",
      "(0.9002604809700815, 10000.0, 1e-06) -> 99.16571136645308\n",
      "(0.8997395190299186, 10000.0, 1e-06) -> 99.25258973321158\n",
      "(0.9, 10001.0, 1e-06) -> 99.2081333982375\n",
      "(0.9, 9999.0, 1e-06) -> 99.21012010104333\n",
      "(0.9, 10010.0, 1e-06) -> 99.19920228362008\n",
      "(0.9, 9990.0, 1e-06) -> 99.21906933767059\n",
      "(0.9, 10000.0, 1.0001e-06) -> 99.2081333982375\n",
      "(0.9, 10000.0, 9.999e-07) -> 99.21012010104333\n",
      "(0.9, 10000.0, 1.001e-06) -> 99.19920228362008\n",
      "(0.9, 10000.0, 9.989999999999999e-07) -> 99.21906933767059\n",
      "(1.1367992341436675, 15426.65629293471, 1.5426656292934658e-06) -> 71.48428998018905\n",
      "(1.1639942960414291, 16049.877285679564, 1.6049877285679507e-06) -> 69.75101904460233\n",
      "(1.308096945474447, 19352.234036123315, 1.9352234036123227e-06) -> 64.0373348681763\n",
      "(1.377047580024234, 20932.354834379075, 2.093235483437897e-06) -> 65.47264288596953\n",
      "(1.3083067929414927, 19352.234036123315, 1.9352234036123227e-06) -> 64.06897571595725\n",
      "(1.3078870980074015, 19352.234036123315, 1.9352234036123227e-06) -> 64.00572838249478\n",
      "(1.308096945474447, 19363.537683275055, 1.9352234036123227e-06) -> 63.99858990110189\n",
      "(1.308096945474447, 19340.930388971574, 1.9352234036123227e-06) -> 64.0760877366588\n",
      "(1.308096945474447, 19352.234036123315, 1.9365250134806992e-06) -> 63.99272090590841\n",
      "(1.308096945474447, 19352.234036123315, 1.933921793743946e-06) -> 64.08195930724722\n",
      "(1.308096945474447, 19352.234036123315, 1.9374628097120974e-06) -> 63.96058355345421\n",
      "(1.308096945474447, 19352.234036123315, 1.932983997512548e-06) -> 64.11411719500667\n",
      "(1.4518988196469205, 28189.753065444456, 2.8189750163700463e-06) -> 61.272614820982845\n",
      "(1.385496103118641, 24108.893380587735, 2.4108891818768406e-06) -> 49.65617486041412\n",
      "(1.385672043336562, 24108.893380587735, 2.4108891818768406e-06) -> 49.694760759553645\n",
      "(1.3853201629007201, 24108.893380587735, 2.4108891818768406e-06) -> 49.61761439538809\n",
      "(1.385496103118641, 24122.984567537602, 2.4108891818768406e-06) -> 49.6271750967005\n",
      "(1.385496103118641, 24094.80219363787, 2.4108891818768406e-06) -> 49.6852350578246\n",
      "(1.385496103118641, 24117.03763372327, 2.4108891818768406e-06) -> 49.63940655944544\n",
      "(1.385496103118641, 24100.7491274522, 2.4108891818768406e-06) -> 49.67296334907033\n",
      "(1.385496103118641, 24108.893380587735, 2.4128655527857186e-06) -> 49.61551806057589\n",
      "(1.385496103118641, 24108.893380587735, 2.4089128109679626e-06) -> 49.69695054357015\n",
      "(1.385496103118641, 24108.893380587735, 2.411780041418529e-06) -> 49.63783388084468\n",
      "(1.385496103118641, 24108.893380587735, 2.409998322335152e-06) -> 49.67453999473487\n",
      "(1.3346958218375284, 26342.987535930857, 2.6342989994964907e-06) -> 32.40412806990936\n",
      "(1.218308580069807, 31461.464245752708, 3.146147591686242e-06) -> 14.072342058026457\n",
      "(1.2041974408796614, 32082.043754269307, 3.2082056542278777e-06) -> 13.53676656364805\n",
      "(1.1905385220654157, 32682.73552056781, 3.2682749389683696e-06) -> 13.309190968111414\n",
      "(1.1906296495547257, 32682.73552056781, 3.2682749389683696e-06) -> 13.321746128711856\n",
      "(1.1904473945761056, 32682.73552056781, 3.2682749389683696e-06) -> 13.296643836281426\n",
      "(1.1905385220654157, 32687.064556715202, 3.2682749389683696e-06) -> 13.31564880356511\n",
      "(1.1905385220654157, 32678.40648442042, 3.2682749389683696e-06) -> 13.302737182521517\n",
      "(1.1905385220654157, 32682.73552056781, 3.2687484177659625e-06) -> 13.316254288012424\n",
      "(1.1905385220654157, 32682.73552056781, 3.2678014601707767e-06) -> 13.30213249281902\n",
      "(0.9801502312575129, 30448.619847559406, 3.0448628816425382e-06) -> 6.96972301330329\n",
      "(1.0625849715773497, 31323.995300450377, 3.1324006189341594e-06) -> 2.723232319724203\n",
      "(1.0626278234687758, 31323.995300450377, 3.1324006189341594e-06) -> 2.723145418857497\n",
      "(1.0625421196859235, 31323.995300450377, 3.1324006189341594e-06) -> 2.723320943276277\n",
      "(1.0625849715773497, 31326.53248355612, 3.1324006189341594e-06) -> 2.7233762521216955\n",
      "(1.0625849715773497, 31321.458117344635, 3.1324006189341594e-06) -> 2.7230896436266336\n",
      "(1.0625849715773497, 31323.995300450377, 3.132687245408039e-06) -> 2.7233950126392372\n",
      "(1.0625849715773497, 31323.995300450377, 3.13211399246028e-06) -> 2.72307123013582\n",
      "(1.0663872573694309, 31176.616934344333, 3.117662764856898e-06) -> 2.707996462603649\n",
      "(1.0676346425311973, 31128.267705196715, 3.112827836211991e-06) -> 2.7070251240566323\n",
      "(1.0676780500361664, 31128.267705196715, 3.112827836211991e-06) -> 2.70705000293859\n",
      "(1.0675912350262282, 31128.267705196715, 3.112827836211991e-06) -> 2.707002005340193\n",
      "(1.0676346425311973, 31130.901765918446, 3.112827836211991e-06) -> 2.707043830842127\n",
      "(1.0676346425311973, 31125.633644474983, 3.112827836211991e-06) -> 2.7070077881609484\n",
      "(1.0676346425311973, 31128.267705196715, 3.113128792280756e-06) -> 2.7070466092241365\n",
      "(1.0676346425311973, 31128.267705196715, 3.112526880143226e-06) -> 2.7070054284929457\n",
      "(1.067212269933915, 31118.34694378787, 3.1118357598238063e-06) -> 2.706823247817995\n",
      "(1.067168761630552, 31117.325013151873, 3.1117335667347323e-06) -> 2.706821465977302\n",
      "(1.0672122600973335, 31117.325013151873, 3.1117335667347323e-06) -> 2.7068223416968538\n",
      "(1.0671252631637707, 31117.325013151873, 3.1117335667347323e-06) -> 2.706822357390249\n",
      "(1.067168761630552, 31119.94657401041, 3.1117335667347323e-06) -> 2.706822163448656\n",
      "(1.067168761630552, 31114.703452293335, 3.1117335667347323e-06) -> 2.7068221260943885\n",
      "(1.067168761630552, 31117.325013151873, 3.112032662687679e-06) -> 2.7068223708425214\n",
      "(1.067168761630552, 31117.325013151873, 3.1114344707817854e-06) -> 2.7068223282441966\n",
      "(1.067168761630552, 31117.325013151873, 3.1117335667347323e-06) -> 2.706821465977302\n",
      "(1.0672122600973335, 31117.325013151873, 3.1117335667347323e-06) -> 2.7068223416968538\n",
      "(1.0671252631637707, 31117.325013151873, 3.1117335667347323e-06) -> 2.706822357390249\n",
      "(1.067168761630552, 31119.94657401041, 3.1117335667347323e-06) -> 2.706822163448656\n",
      "(1.067168761630552, 31114.703452293335, 3.1117335667347323e-06) -> 2.7068221260943885\n",
      "(1.067168761630552, 31117.325013151873, 3.112032662687679e-06) -> 2.7068223708425214\n",
      "(1.067168761630552, 31117.325013151873, 3.1114344707817854e-06) -> 2.7068223282441966\n",
      "(1.0671774613239084, 31117.325013151873, 3.1117335667347323e-06) -> 2.706821499750613\n",
      "(1.0671600619371957, 31117.325013151873, 3.1117335667347323e-06) -> 2.706821502889292\n",
      "(1.067168761630552, 31117.84932532358, 3.1117335667347323e-06) -> 2.7068214968699196\n",
      "(1.067168761630552, 31116.800700980166, 3.1117335667347323e-06) -> 2.7068214893882208\n",
      "(1.067168761630552, 31117.325013151873, 3.1117933859253217e-06) -> 2.7068215055878295\n",
      "(1.067168761630552, 31117.325013151873, 3.111673747544143e-06) -> 2.7068214970520583\n",
      "(1.0672122600973335, 31119.94657401041, 3.1117335667347323e-06) -> 2.70682365283658\n",
      "(1.0672122600973335, 31117.325013151873, 3.112032662687679e-06) -> 2.7068239467067996\n",
      "(1.067168761630552, 31119.94657401041, 3.112032662687679e-06) -> 2.7068246172709194\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 = 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\">  </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)    │                                      │\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": 41,
     "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(data_frame['time'], data_frame['current'], data_frame['delta_current'], 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": 69,
   "id": "143a2a23-0a62-439f-9d28-9f555ae85589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of counts per bin')"
      ]
     },
     "execution_count": 69,
     "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": [
    "Zunächst wollen wir das Fitmodel in der Form\n",
    "\n",
    "$$f(x) = A_1 \\cdot \\exp \\bigg\\{\\frac{-(x - \\mu_1)^2}{2 \\cdot \\sigma_1^2}\\bigg\\} + A_2 \\cdot \\exp \\bigg\\{\\frac{-(x - \\mu_2)^2}{2 \\cdot \\sigma_2^2}\\bigg\\} + A_3 \\exp\\{-x/\\tau\\}$$\n",
    "\n",
    "definieren. Hier lohnt es sich erst Funktionen für die einzelnen Komponenten zu definieren und dann das Gesamtmodel. Hierdurch lassen sich später die einzelnen Komponenten besser darstellen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": [
    "Nun wollen wir wieder die Kostenfunktion und den Minimierungsfunktion definieren. Startwerte können wir anhand unseres Plots ablesen, lediglich $\\tau$ lässt sich auf diese weise nicht gut bestimmen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1e69a046-770f-4c38-9b91-0176bb0686a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbb17914df0>"
      ]
     },
     "execution_count": 72,
     "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": [
    "Unser Startparameter sind bereits nicht schlecht, aber weichen noch stark von den Daten ab. Bei komplexeren Daten und Fitmodellen lohnt es sich den fit schrittweise durchzuführen. Bevor wir uns den beiden Peaks widmen, welche uns eigentlich interessieren, sollten wir Versuchen den Untergrund etwas besser zu beschreiben. Um den Untergrund besser fitten zu können sollten wir erst den Fitbereich auf einen Energiebereich limitieren, in welchem der Untergrund dominiert. Dem Plot können wir entnehmen, dass dies für alle Werte unterhalb von 45 keV und oberhalb von 70 keV der Fall ist. Im allgemeinen können wir Wertebereiche in python mit Hilfe von „Masken“ selektieren. Eine Maske lässt sich wie folg erstellen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d53e8386-ea7f-43fa-b4fe-65229308a2ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_outside_of_peaks = (center < 45) | (center >= 70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84cef7a6-13a0-4ba8-ac40-eb86a54411dc",
   "metadata": {},
   "source": [
    "Die Maske hat hierbei die Selbe länge wie unseren Daten…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d1d06116-d726-4163-b414-6ccde6a19027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 60)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mask_outside_of_peaks), len(mask_outside_of_peaks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80db0ae0-5cbd-4db9-b184-610d77bf1c58",
   "metadata": {},
   "source": [
    "… und beinhaltet Wahrheitswerte True und False, bzw. 1 und 0 mit welchen wir unsere Daten wie folgt selektieren können:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f24d19d8-3483-45b5-aee9-1d3f8755da22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ True,  True,  True,  True,  True,  True,  True, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True]),\n",
       " array([40.33333333, 41.        , 41.66666667, 42.33333333, 43.        ,\n",
       "        43.66666667, 44.33333333, 70.33333333, 71.        , 71.66666667,\n",
       "        72.33333333, 73.        , 73.66666667, 74.33333333, 75.        ,\n",
       "        75.66666667, 76.33333333, 77.        , 77.66666667, 78.33333333,\n",
       "        79.        , 79.66666667]))"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_outside_of_peaks, center[mask_outside_of_peaks]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5c07e7-1865-48f2-bd9e-0540661fd71e",
   "metadata": {},
   "source": [
    "Wir können auch verschieden Masken mit Hilfe von Wahrheitsoperatoren kombinieren\n",
    "\n",
    "TODO add operators and examples +/*\n",
    "\n",
    "    \n",
    "Unsere Selektion können wir an unsere Kostenfunktion direkt übergeben. Außerdem müssen wir noch alle Fitparameter festhalten, welche nicht zum Untergrund beitragen. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3034bb22-0b96-498d-9736-ed9bb2189460",
   "metadata": {},
   "outputs": [],
   "source": [
    "ls.mask = (center < 45) | (center >= 70)\n",
    "mi.fixed[:] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a664fd-513e-4c89-ba52-945b6f68512f",
   "metadata": {},
   "source": [
    "Nun können wir nochmal unsere Funktion und Messwerte für den ausgewählten Bereich plotten…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "81232354-a7b8-4e2a-9ac0-159ce0a03da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbb177cbd90>"
      ]
     },
     "execution_count": 77,
     "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[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": "markdown",
   "id": "c5a8d247-5b71-42ae-9706-d16192374686",
   "metadata": {},
   "source": [
    "… bevor wir die Minmierung starten, und das Resultat darstellen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mi.fixed[['tau_bkg', 'A_bkg']] = False\n",
    "mi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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 = 12.84 (χ²/ndof = 0.6) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 136 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 5.13e-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> 221 </td>\n",
       "        <td> 25 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 7 </th>\n",
       "        <td> tau_bkg </td>\n",
       "        <td> 41.0 </td>\n",
       "        <td> 3.1 </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> 612 </td>\n",
       "        <td style=\"background-color:rgb(125,125,250);color:black\"> -74 <strong>(-0.963)</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\"> -74 <strong>(-0.963)</strong> </td>\n",
       "        <td> 9.69 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 12.84 (χ²/ndof = 0.6)      │              Nfcn = 136              │\n",
       "│ EDM = 5.13e-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    │    221    │    25     │            │            │         │         │       │\n",
       "│ 7 │ tau_bkg  │   41.0    │    3.1    │            │            │    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      612      -74 │\n",
       "│  tau_bkg │        0        0        0        0        0        0      -74     9.69 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 80,
     "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": 81,
   "id": "0b435af3-73ea-42de-9ab7-6a16ae9dbceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbb176692b0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fBiIiMlqaCEQApNFlZ8+ebXBNRKSfGIiIyCjJ5XKVIfcNoRz48ffffze4LiLSTwxERGSUrl27htzcXNjZ2VW5FqI62EJEZPwYiIjIKClbc7p06dLgyfuULURXrlxBUVFRg2sjIv3DQERERun8+fMAgE6dOjX4XM2aNUPTpk0hl8tx7dq1Bp+PiPQPAxERGSVNBiJBEKTbbleuXGnw+YhI/zAQEZHREUVRo4EIgBSILl++rJHzEZF+YSAiIqOTmpqK+/fvQyaTISgoSCPn7NChAwAGIiJjxUBEREZH2TrUtm1b2NjYaOScbCEiMm4MRERkdJSBqHPnzho7pzIQ3bx5E4WFhRo7LxHpBwYiIjI6mu4/BAAeHh5wdnaGKIocaUZkhBiIiMjoaCMQcaQZkXFjICIio1JUVIS4uDgAmg1EAPsRERkzBiIiMiqXL1+GXC6Hq6srvLy8NHpujjQjMl4MRERkVCreLhMEQaPnZgsRkfFiICIio3Lx4kUAQMeOHTV+bmUgunXrFgoKCjR+fiLSHQYiIjIqyg7PDV3hviru7u5wcXGBKIpSPyUiMg4MRERkVJSBSNnfR5MEQZDOe/XqVY2fn4h0h4GIiIxGTk4O7ty5AwBo3769Vt6jTZs2AID4+HitnJ+IdIOBiIiMhrLVxtvbG05OTlp5DwYiIuPEQERERkObt8uUGIiIjBMDEREZjcYMRNevX4coilp7HyJqXAxERGQ0GiMQtW7dGgCQlZWFhw8fau19iKhxMRARkdFojEBka2uL5s2bA+BtMyJjwkBEREYhPz8fiYmJABoWiApKyuA3dxf85u5CQUlZlcewHxGR8WEgIiKjcO3aNQD/TJ6oTQxERMaHgYiIjEJj3C5TYiAiMj4MRERkFBiIiKghGIiIyCjoKhBx6D2RcWAgIiKjoOxDFBAQoLFzpmYXVbm9ZcuWEAQBubm5SE9P19j7EZHuMBARkcErLS3FrVu3AADt2rVr0Lm2xt6R/h2x4gg2xyRVOsba2hq+vr4AeNuMyFgwEBGRwUtISEBZWRlsbW3h7e1d7/OkZBfigx2XpecKEXh32yWkZBdWOpb9iIiMCwMRERm8uLg4AEDbtm0hk9X/ay3hQT4Uj3QJkosiEh8UVDq24hIeRGT4zHVdABFRQylDSdu2bRt0Hn9XO8gEqIQiM0GAn6stgPJJGztE7gUAvOHnDwDSrToiMmw6bSE6evQonnzySXh7e0MQBPz222/SvtLSUsyZMwfBwcGws7ODt7c3xo4di3v37qmcIyMjA6NHj4aDgwOcnJwwYcIE5OXlqRxz4cIFPP7447C2toaPjw+WL1/eGJdHRI1EGYga2n/Iy9EGC58KlJ7LBGDJ8CB4OdpUOtbPvzwQJSQkNOg9iUg/6DQQ5efno1OnTli9enWlfQUFBfj7778xf/58/P3339i2bRvi4uLw1FNPqRw3evRoXL58GVFRUdi5cyeOHj2KSZMmSftzcnIwYMAAtGjRArGxsfjkk0+wYMECfPvtt1q/PiJqHBVvmTXUiNDm0r/3z+yDkd18qzyuhZ8fALYQERkLnd4yGzx4MAYPHlzlPkdHR0RFRals++qrr/DYY48hKSkJvr6+uHr1Kvbs2YOYmBh07doVALBq1SoMGTIEn376Kby9vbFx40aUlJRg7dq1sLS0RGBgIM6dO4cVK1aoBCciMlyaumX2KE9H62r3+f3/LbOHDx8iNzcXTZo00eh7E1HjMqhO1dnZ2RAEAU5OTgCA6OhoODk5SWEIACIiIiCTyXDq1CnpmN69e8PS0lI6ZuDAgYiLi0NmZmaV71NcXIycnByVBxHpp9zcXKSkpADQfCCqiaOjI5ydnQHwthmRMTCYQFRUVIQ5c+bgxRdfhIODAwAgNTUV7u7uKseZm5vD2dkZqamp0jEeHh4qxyifK4951NKlS+Ho6Cg9fHx8NH05RKQhytYhd3d36Y+lxpCaXYSWLVsC4G0zImNgEIGotLQUL7zwAkRRxNdff63195s3bx6ys7OlR3Jystbfk4jqR1u3y6ry6KSN9h37A2ALEZEx0Pth98owdPv2bRw8eFBqHQIAT0/PStPml5WVISMjA56entIxaWlpKsconyuPeZSVlRWsrKw0eRlEpCXKDtUNHWGmZGtpjsRlQyttr2rSxkTXf8GsiQtbiIiMgF63ECnDUHx8PPbv3w8XFxeV/WFhYcjKykJsbKy07eDBg1AoFOjevbt0zNGjR1FaWiodExUVhXbt2qFp06aNcyFEpDXqthAVlJTBb+4u+M3dhYKSsjq/T1WTNoqCAHMnb7YQERkBnQaivLw8nDt3DufOnQNQ3ux87tw5JCUlobS0FM899xzOnDmDjRs3Qi6XIzU1FampqSgpKQEAtG/fHoMGDcLEiRNx+vRpHD9+HFOmTMGoUaOk6ftfeuklWFpaYsKECbh8+TI2b96ML7/8EjNnztTVZRORBmlqDqLaKCdtrEgGoCzrHgMRkRHQaSA6c+YMQkJCEBISAgCYOXMmQkJCEBkZibt372LHjh24c+cOOnfuDC8vL+lx4sQJ6RwbN25EQEAAwsPDMWTIEPTq1UtljiFHR0fs27cPCQkJCA0NxaxZsxAZGckh90RGQBRFjc5BVJOqJm2c8bgH5LkPkZCQAFEU6936RES6p9M+RH379oUoitXur2mfkrOzMzZt2lTjMR07dsSxY8fUro+I9Ftqairy8vIgk8mkEV/aNCK0Oeb/Xt6PaP/MPmjuaIlpgoDCwkKkpaXBwdlV6zUQkXbodR8iIqKaKG+X+fv7N/pACE9Ha1haWqJ58/KZrdmxmsiwMRARkcFq6O2y1OyiBtegbJliPyIiw8ZAREQGqz4dqh+dS2hzTFKDavDnIq9ERoGBiIgMlrotRFXNJfTutktIyS6sdw3KQPToLTNNtD4RUeNhICIig6XuHERVzSUkF0UkPiio0+uVkzYmLhsKW8vyMSkVb5lpuvWJiBoPAxERGaTS0lKpVaaut8yqmkvITBDg52pb7zqkW2apmRpvfSKixsNAREQGKSEhAWVlZbC1tZUmYq1NVXMJLRkeBC9Hm3rX0aJFCwDA/WKhQa1PRKRbDEREZJAq3i6Tyer+VTYitLn07/0z+2BkN98G1eHl5QVzc3MUP0jWeOsTETUeBiIiMkiaWOXe09G6wXWYmZmhefPmkOc+xJgOltJ2TbQ+EVHjYSAiIoNU0wizxl5CQ3nbzE9MlbZpovWJiBoPAxERGaTGWtS1LpSBKCnpn1Flmmh9IqLGw0BERAapsRZ1rQtf3/KWoOTbt3VcCRHVl04XdyUiqo/c3FykpKQAUD8QKecS0iSVFiIXjZ6aiBoJW4iIyODEx8cDANzd3eHk5KTbYvBPILqTnKzjSoiovurVQhQfH49Dhw4hPT0dCoVCZV9kZKRGCiMiqo46t8tSs4vQ0s1eq/Uob5klJd1G7tIhEAShllcQkb5ROxB99913ePPNN+Hq6gpPT0+VD74gCAxERKR1tXWofnQJjaXDg7U64ksZiPLz85GRkQEXF943IzI0ageiRYsWYfHixZgzZ4426iEiqlVNLUTVLeDau62b1uYEsrGxgbu7O9LT05GUlMRARGSA1O5DlJmZieeff14btRAR1UlNkzI2dAHX+lL2I7rNkWZEBkntQPT8889j37592qiFiKhWoijWeMtMGwu41oXythkDEZFhUvuWWevWrTF//nycPHkSwcHBsLCwUNk/depUjRVHRPSo1NRU5ObmQiaToWXLlpX2Kxdwnf97+W2zxlpCgy1ERIZN7UD07bffwt7eHkeOHMGRI0dU9gmCwEBERFqlbB3y9/eHlZVVlceMCG0uBaL9M/tofZQZUPVs1URkONQORAkJCdqog4ioTtSdobqxltBgCxGRYePEjERkUJSBSB/WMKuIfYiIDFudWohmzpyJjz76CHZ2dpg5c2aNx65YsUIjhRERVUVfA5Gyhej+/fsoLCyEjY12+ywRkWbVKRCdPXsWpaWl0r+rw9lZiUjb9GlR14qaNm0Ke3t75OXlISkpSe8CGxHVTBBFUaz9MNOWk5MDR0dHZGdnw8HBQdflEJmskpIS2NraQi6X486dO2jWrJmuS1IRFBSEy5cvY+/evRgwYICuyyEyeer8/m5QH6Lk5GQkczFDImokN2/ehFwuh729Pby9vXVdTiXsR0RkuNQORGVlZZg/fz4cHR3h5+cHPz8/ODo64v3335duqxERaUPF22X6eIueQ++JDJfaw+7ffvttbNu2DcuXL0dYWBgAIDo6GgsWLMDDhw/x9ddfa7xIIiJAfztUK3HoPZHhUjsQbdq0Cb/88gsGDx4sbevYsSN8fHzw4osvMhARkdYwEBGRtqh9y8zKygp+fn6Vtvv7+8PS0lITNRERVUnfA5GyDxFvmREZHrUD0ZQpU/DRRx+huLhY2lZcXIzFixdjypQpGi2OiKgifQ9EyhaiO3fuQC6X67gaIlJHnW6ZDR8+XOX5/v370bx5c3Tq1AkAcP78eZSUlCA8PFzzFRIRAXj48CEePnwIQP/mIFLy8vKCubk5ysrKcO/ePfj4+Oi6JCKqozoFIkdHR5XnI0aMUHnODz0RaZuydah58+aws7PTcTVVMzMzQ/PmzZGYmIjbt2/zu5HIgNQpEK1bt07bdRAR1Ujfb5cp+fr6IjExkXO0ERkYLu5KRAbBkAIRwI7VRIZGp4Ho6NGjePLJJ+Ht7Q1BEPDbb7+p7BdFEZGRkfDy8oKNjQ0iIiIQHx+vckxGRgZGjx4NBwcHODk5YcKECcjLy1M55sKFC3j88cdhbW0NHx8fLF++XNuXRkQaZmiBiC1ERIZFp4EoPz8fnTp1wurVq6vcv3z5cqxcuRJr1qzBqVOnYGdnh4EDB6KoqEg6ZvTo0bh8+TKioqKwc+dOHD16FJMmTZL25+TkYMCAAWjRogViY2PxySefYMGCBfj222+1fn1EpDmGFojYQkRkYEQ9AUDcvn279FyhUIienp7iJ598Im3LysoSraysxJ9//lkURVG8cuWKCECMiYmRjtm9e7coCIJ49+5dURRF8T//+Y/YtGlTsbi4WDpmzpw5Yrt27aqtpaioSMzOzpYeycnJIgAxOztbU5dLRGooLS0VLSwsRABiQkKCrsup0a5du0QAYqdOnXRdCpHJy87OrvPvb7VaiEpLSxEeHl7ptpU2JCQkIDU1FREREdI2R0dHdO/eHdHR0QDKlwxxcnJC165dpWMiIiIgk8lw6tQp6ZjevXurTBo5cOBAxMXFITMzs8r3Xrp0KRwdHaUHR4oQ6VZiYiJKS0thbW0ttcDoK94yIzJMagUiCwsLXLhwQVu1qEhNTQUAeHh4qGz38PCQ9qWmpsLd3V1lv7m5OZydnVWOqeocFd/jUfPmzUN2drb04BcbkW4pb5e1adMGMpkMBSVl8Ju7C35zd6GgpEzH1alSBqKMjIxK/RmJSH+p3Yfo5Zdfxg8//KCNWvSGlZUVHBwcVB5EpDuG0n8IgMp3Bv+YIjIcai/uWlZWhrVr12L//v0IDQ2tNEHaihUrNFKYp6cnACAtLQ1eXl7S9rS0NHTu3Fk6Jj09vVJ9GRkZ0us9PT2RlpamcozyufIYItJvhhSIgPJWokuXLiE5ORnt27fXdTlEVAdqtxBdunQJXbp0QZMmTXD9+nWcPXtWepw7d05jhfn7+8PT0xMHDhyQtuXk5ODUqVMICwsDAISFhSErKwuxsbHSMQcPHoRCoUD37t2lY44ePYrS0lLpmKioKLRr1w5NmzbVWL1EpD2GGIgAjjQjMiRqtxAdOnRIY2+el5eHGzduSM8TEhJw7tw5ODs7w9fXF9OnT8eiRYvQpk0b+Pv7Y/78+fD29sYzzzwDAGjfvj0GDRqEiRMnYs2aNSgtLcWUKVMwatQoeHt7AwBeeuklLFy4EBMmTMCcOXNw6dIlfPnll/j88881dh1EpF1Xr14FAAQEBFTal5pdhJZu9o1dUo2UAzEYiIgMR73nIbpx4wb27t2LwsJCAOWTKKrrzJkzCAkJQUhICABg5syZCAkJQWRkJABg9uzZePvttzFp0iR069YNeXl52LNnD6ytraVzbNy4EQEBAQgPD8eQIUPQq1cvlTmGHB0dsW/fPiQkJCA0NBSzZs1CZGSkylxFRKS/Hjx4IN0aV95+2hp7R9ofseIINsfoV/BgCxGR4RFENZPMw4cP8cILL+DQoUMQBAHx8fFo2bIlxo8fj6ZNm+Kzzz7TVq06k5OTA0dHR2RnZ7ODNVEjO3bsGHr37o0WLVogMTERKdmF6LnsIBQVvrnMBAF/zX0CXo42uiu0gv/+978YM2YM+vXrp3Lbn4galzq/v9VuIZoxYwYsLCyQlJQEW1tbafvIkSOxZ88e9aslIqrBlStXAAAdOnQAACQ8yFcJQwAgF0UkPiho7NKqxRYiIsOjdh+iffv2Ye/evWjevLnK9jZt2uD27dsaK4yICPgnEAUGBgIA/F3tIBNQqYXIz9W2qpfrhLIPUXJyMkRRhCAIOq6IiGqjdgtRfn6+SsuQUkZGBqysrDRSFBGR0qMtRF6ONlj4VKC0XyYAS4YH6c3tMgBo1qwZBEFAcXEx7t+/r+tyiKgO1A5Ejz/+OH788UfpuSAIUCgUWL58OZ544gmNFkdEdPnyZQD/BCIAGBH6Twv1/pl9MLKbfi3nYWlpKc2fxttmRIZB7Vtmy5cvR3h4OM6cOYOSkhLMnj0bly9fRkZGBo4fP66NGonIRGVmZiIlJQUAqp3g0NPRusrtuubj44N79+4hKSlJZb1FItJParcQBQUF4fr16+jVqxeefvpp5OfnY/jw4Th79ixatWqljRqJyEQp5x/y8fExuBGeXOSVyLCo3UIElM/t895772m6FiIiFY/2HzIkHGlGZFjqFYgyMzPxww8/SH+9dejQAePGjYOzs7NGiyMi01ZdILK1NEfisqG6KKnOOFs1kWFR+5bZ0aNH4efnh5UrVyIzMxOZmZlYuXIl/P39cfToUW3USEQmqqoO1YaCt8yIDIvaLUSTJ0/GyJEj8fXXX8PMzAwAIJfL8dZbb2Hy5Mm4ePGixoskItPEW2ZE1FjUXrrDxsYG586dq7TqdFxcHDp37iytbWZMuHQHUeNTfu6A8tv0Tk5Oui1ITenp6fDw8IAgCCgqKoKlpaWuSyIyOVpduqNLly5S36GKrl69ik6dOql7OiKiKl24cAFAeV8cQwtDAODm5gYrKyuIooi7d++ioKQMfnN3wW/uLhSUlOm6PCJ6hNq3zKZOnYpp06bhxo0b6NGjBwDg5MmTWL16NZYtWyZ9iQFAx44dNVcpEZmU8+fPAzDc7xFBEODr64v4+HgkJSXBo5mPrksiohqoHYhefPFFAMDs2bOr3CcIgrR2j1wub3iFRGSSlIHIkFueKwaibmE9dV0OEdVA7UCUkJCgjTqIiFQYQyDi0Hsiw6F2IGrRooU26iAiksjlcly6dAmA4d4yA6ofep+aXYSWbva6KImIqqF2p2oiIm27efMmCgoKYGNjgzZt2ui6nHqrOPR+a+wdaXvEiiPYHMNWIyJ9wkBERHpHebssKChImu/MEClvmSWmZ+ODHZel7QoReHfbJaRkG980JUSGioGIiPSOMfQfAv5pIUrLV0DxyIxvclFE4oMCHVRFRFVhICIivWPoQ+6VlC1E2XfjIRNU95kJAvxcbXVQFRFVRe1AlJycjDt3/rkXfvr0aUyfPh3ffvutRgsjIu05cuQIxo0bhx49emDo0KH47rvvUFRUpOuyJMbSQmRnZwdnZ2fIcx9iUqijtF0mAEuGB8HL0UaH1RFRRWoHopdeegmHDh0CAKSmpqJ///44ffo03nvvPXz44YcaL5CINKe0tBQTJ05E3759sX79epw6dQp//vknJk2ahNDQUL1YizAzM1MalWXoLUTAP7fN2lpkStv2z+yDkd18dVUSEVVB7UB06dIlPPbYYwCAX3/9FUFBQThx4gQ2btyI9evXa7o+ItIQuVyOF198Ed9//z0EQcBrr72GLVu2YNmyZfDw8MCVK1fQu3dvxMbG6rRO5Wz3LVq0MMglOx6lDER3kv8ZVebpaK2rcoioGmrPQ1RaWgorKysAwP79+/HUU08BAAICApCSkqLZ6ohIYxYtWoStW7fC0tIS27Ztw9ChQ6V948ePx9NPP43o6GgMHjwYsbGxUv+XxqYMZJ07d9bJ+2ua8v/H5ORkQPDTbTFEVC21W4gCAwOxZs0aHDt2DFFRURg0aBAA4N69e3BxcdF4gUTUcCdPnsTChQsBAD/88INKGALKFyLdu3cvOnfujPv372PEiBEoLS3VRak4deoUAKB79+46eX9Nk1qI7iTXciQR6ZLagejjjz/GN998g759++LFF1+UOj3u2LFDupVGRPpDLpdjypQpEEURL7/8Ml5++eUqj2vSpAm2b9+Opk2bIiYmBh9//HEjV1ru9OnTAGA03yfKQHTvzh0kLhuKxGVDYWupduM8EWmZIIqiWPthquRyOXJyctC0aVNpW2JiIuzs7ODm5qbRAvVBTk4OHB0dkZ2dDQcHB12XQ6SWDRs24NVXX4WDgwOuX78ODw+PGo/fuHEjXn75ZVhYWODy5cuNOlN0enq6VF9WVhYcHR1reYX+O3HiBHr27Ak/Pz+uBUnUyNT5/a12C1G/fv2Qm5urEoYAwNnZGSNHjlT3dESkRWVlZVi0aBEA4L333qs1DAHlI0kHDRqE0tJSzJ07V9slqoiJiQFQ3ifRGMIQ8E8fort370Iul+u4GiKqjtqB6PDhwygpKam0vaioCMeOHdNIUUSkGZs3b8aNGzfg4uKCt956q06vEQQBn376KWQyGbZt29aon2tju10GAF5eXjAzM0NpaSnS0tJ0XQ4RVaPON7KVQ2EB4MqVK0hNTZWey+Vy7NmzB82aNdNsdUTUIF9++SUAYPr06bC3r/vq6oGBgZg4cSK++eYbzJo1CydPnoRMpv2J7Y0xEJmbm6NZs2ZISkpCUlISvL29dV0SEVWhzoGoc+fOEAQBgiCgX79+lfbb2Nhg1apVGi2OiOrv9OnTiImJgaWlJV5//XW1X79w4UJs3LgRMTEx2Lp1K55//nktVPkPURSNMhAB5bfNkpKSkJycjB49eui6HCKqQp3/5EtISMDNmzelL62EhATpcffuXeTk5GD8+PHarJWI1PD1118DAEaOHFmvwQ4eHh6YNWsWAGDx4sWox/gLtdy6dQsZGRmwtLQ0ihmqK1KONEtKSqrlSCLSlTq3ELVo0QIAoFAotFYMEWlGfn4+tmzZAgD1ah1Smjp1Kj777DOcP38eu3btwrBhwzRVYiXK1qHOnTtLk78ai7oEooKSMnSI3AsAuPLhQA7NJ2pk9frExcfH49ChQ0hPT68UkCIjIzVSGBHV37Zt25Cfn49WrVrhX//6V73P4+zsjDfffBOffPIJFi9ejKFDh0IQhNpfWA/KCRmN7XYZ8M9IM7YQEekvtQPRd999hzfffBOurq7w9PRU+XIUBIGBiEgP/PTTTwCAsWPHNjjAzJw5EytXrsTJkydx6NChKvsQasLRo0cBAD179tTK+XVJ2UKkXLSWiPSP2sNGFi1ahMWLFyM1NRXnzp3D2bNnpcfff/+t0eLkcjnmz58Pf39/2NjYoFWrVvjoo49U+jKIoojIyEh4eXnBxsYGERERiI+PVzlPRkYGRo8eDQcHBzg5OWHChAnIy8vTaK1E+iIjIwMHDx4EUD6nUEFJGfzm7oLf3F0oKClT+3yenp547bXXAABLlizRaK1K2dnZOHfuHACgd+/eWnkPXWIfIiL9p3YgyszM1PpoE6WPP/4YX3/9Nb766itcvXoVH3/8MZYvX64ymm358uVYuXIl1qxZg1OnTsHOzg4DBw5EUVGRdMzo0aNx+fJlREVFYefOnTh69CgmTZrUKNdA1Nh27doFuVyOoKAgtG7dWiPn/Pe//w0zMzMcOHBA43/4AMDx48chiiJat24tDUtvaJDTJ8pbZvfv30dhYWGtx6dmF9V6DBFpltqB6Pnnn8e+ffu0UUslJ06cwNNPP42hQ4fCz88Pzz33HAYMGCB1vhRFEV988QXef/99PP300+jYsSN+/PFH3Lt3D7/99hsA4OrVq9izZw++//57dO/eHb169cKqVavwyy+/4N69e41yHUSNafv27QCAZ599VmPn9PPzk2ai//TTTzV2XiXl7TJjbB0CgKZNm8LOzg4AcOfOnSqP2Rr7z/aIFUewOYatSUSNSe1A1Lp1a8yfPx+vvvoqPvvsM6xcuVLloUn/+te/cODAAVy/fh0AcP78efz1118YPHgwgPKpAFJTUxERESG9xtHREd27d0d0dDQAIDo6Gk5OTujatat0TEREBGQymdSJ81HFxcXIyclReRAZgoKCAuzZswdA1YGoIS0P77zzDgDg119/1fiaXIcOHQIA9OnTR6Pn1ReCINR42ywluxAf7LgsPVeIwLvbLiElu/bWJCLSDLU7VX/77bewt7fHkSNHcOTIEZV9giBg6tSpGitu7ty5yMnJQUBAAMzMzCCXy7F48WKMHj0aAKTZsh9dn8nDw0Pal5qaCnd3d5X95ubmcHZ2Vpltu6KlS5di4cKFGrsOosYSFRWFwsJCtGjRAp07dwZQueVh6fBgjOzmq/a5O3fujP79+yMqKgqff/65xv4AysjIkNYwCw8P18g59ZGvry+uXr1aZSBKeJAPxSPTPMlFEYkPCuDlaNNIFRKZNrVbiCpOyPjo49atWxot7tdff8XGjRuxadMm/P3339iwYQM+/fRTbNiwQaPv86h58+YhOztbenBkCBkK5e2yZ555BoIgaLzlYfbs2QCA77//Hg8ePGh4wQAOHjwIURQRGBhY7fI/xtCnRtmPqKrvE39XO8geGQxoJgjwc7VtjNKICPUIRI3pnXfewdy5czFq1CgEBwdjzJgxmDFjBpYuXQqgfPQLgEoLJqalpUn7PD09kZ6errK/rKwMGRkZ0jGPsrKygoODg8qDSN+VlZXhjz/+APDP7bKaWh7qIzw8HCEhISgsLMR//vOfBtWrpOyT2L9/f5XtxtanpqZbZl6ONlj4VKD0XCYAS4YHsXWIqBGpfcustuU51q5dW+9iHlVQUFBpQUkzMzNpMkh/f394enriwIED0u2BnJwcnDp1Cm+++SYAICwsDFlZWYiNjUVoaCiA8r9IFQoFunfvrrFaiXTtzJkzyMjIQNOmTaW5fJQtDxVDUUNaHgRBwOzZs/Hiiy9i1apVeOedd2BjU/9f2qIoSn2eKgai6lq2erd1M9iQUNvQ+xGhzTH/9/Jr3j+zD1q61X0xXiJquHoNu6/4SE9Px8GDB7Ft2zZkZWVptLgnn3wSixcvxq5du5CYmIjt27djxYoV0l+/giBg+vTpWLRoEXbs2IGLFy9i7Nix8Pb2xjPPPAMAaN++PQYNGoSJEyfi9OnTOH78OKZMmYJRo0Zx1WkyKlFRUQCAfv36wdy8/G8dbbQ8PPfcc/Dz88ODBw+wfv36BtV89uxZJCcnw9bWFk888YS0XdMtW/qgpltmj/J0tNZ2OUT0CLVbiJR9FCpSKBR488030apVK40UpbRq1SrMnz8fb731FtLT0+Ht7Y3XX39dZTbs2bNnIz8/H5MmTUJWVhZ69eqFPXv2wNr6ny+UjRs3YsqUKQgPD4dMJsOIESM0PiKOSNeUgejRW0+abnkwNzfHrFmz8Pbbb+PTTz/FpEmTYGZmVq9z/f777wCAQYMGqbQ0abplSx9UbCESRVFrS6AQUf0IooaWsI6Li0Pfvn2RkpKiidPplZycHDg6OiI7O5v9iUgv5ebmwtnZGWVlZbh58yZatmwp7dPGoqH5+flo0aIFHj58iF9//bXek7V26tQJFy5cwIYNGzB27FiVfT9FJ0pBTiag3qPj9EVRUZEU+h48eAAXFxcdV0Rk/NT5/a2xTtU3b95EWZlhzyZLZKiOHj2KsrIytGzZUiUMaYudnR0mT54MoHy2+Pr8XXX16lVcuHAB5ubmGDp0aKX9I0KbS//eP7OPQYchALC2tpamAOHIVSL9o/afijNnzlR5LooiUlJSsGvXLrzyyisaK4yI6k55u6ziJKXaNmXKFCxfvhxnzpxBVFQUBgwYoNbrlQvQDh48uNbWEmPpU+Pr64v09HQkJSVJA0GISD+oHYjOnj2r8lwmk8HNzQ2fffZZrSPQiEg7qus/BAC2luZIXFa5Baah3Nzc8MYbb+CLL77A3LlzpRng60KhUGDjxo0AgDFjxmi8Nn3l6+uLM2fOcJFXIj2kdiBSTrFPRPrh7t27uHLlCgRBQL9+/Rr1vd977z2sXbsWZ8+exc8//yzNIl+b3bt3IykpCY6OjnjyySerPEZbQU6XlCPNGIiI9E+9+xDdv38ff/31F/766y/cv39fkzURkRqUf6SEhobC2dlZ7dc3ZFV5V1dXzJ07F0B5OCoqqtuM0l9++SUA4LXXXlMZEWrslCPN2IeISP+oHYjy8/Mxfvx4eHl5oXfv3ujduze8vb0xYcIEFBQY7hwhRIbq2LFjAHS3MOq0adPQrFkz3L59G1988UWtx58/fx5RUVGQyWSYMmWK9gvUI7VNzkhEuqN2IJo5cyaOHDmCP/74A1lZWcjKysLvv/+OI0eOYNasWdqokYhqoAxEjz/+uE7e39bWFkuWLAEALFiwAHFxcTUer2xRev755+Hn56ft8vQKAxGR/lI7EG3duhU//PADBg8eLK3zNWTIEHz33Xf43//+p40aiaga9+/fx9WrVwEAvXr1avD56ruI6pgxYzBgwAAUFxfjpZdeqvbW2Z9//ok9e/bA3NwcixYtakipBknZh+jevXucpoRIz6gdiAoKCuDh4VFpu7u7O2+ZETWyv/76CwAQGBhY74n+NLGIqiAI+P777+Hi4oK///4b48ePh1wuVzkmJSUF48aNAwBMnToVrVu3rle9hszDwwMWFhZQKBS4d++ersshogrUDkRhYWH44IMPVP4CLCwsxMKFCxEWFqbR4oioZg29XVbdIqop2YVqn8vHxwe//PILzMzM8PPPP2PEiBFIT08H8M9M9unp6QgODsbixYvrVa+hk8lkHGlGpKfUHnb/5ZdfYuDAgWjevDk6deoEoLyTpLW1Nfbu3avxAomoespA9FjYv+A3dxcA9ZbnqGkR1fosABsREYEtW7Zg5MiR+P3337F79260atUK169fh1wuh6+vL7Zt22ZSI8se5ePjg1u3bnGkGZGeUbuFKCgoCPHx8Vi6dCk6d+6Mzp07Y9myZYiPj0dgYGDtJyAijcjLy5MmSu3Zs379h5SLqFbU0EVUn332WURHR6NLly4oKSnB1atXIZfLMWzYMPz111/SrbKGDPc3ZOxYTaSf6rXKo62tLSZOnKjpWohIDdHR0ZDL5fDz80NzHx8AV9Q+h5ejDRY+FaiyiOqS4UH1ah2qKDQ0FGfOnMH169eRnJwMf39/tGrVqkHnNBYMRET6Se0WoqVLl2Lt2rWVtq9duxYff/yxRooiotpparh9TYuoNqQVRxAEtGvXDhEREQxDFSj7EPGWGZF+UTsQffPNNwgICKi0PTAwEGvWrNFIUURUu+joaABAz549VbbXd+g8oLtFVBtSs6FhCxGRflI7EKWmpsLLy6vSdjc3N6SkpGikKCKqmVwux6lTpwCUj/zUxND5xmaINWsCAxGRflI7EPn4+OD48eOVth8/fhze3t4aKYqIanb16lXk5ubCzs4Ozs1bNWjovHIR1cRlQ2scnabJVhxNDvc3NMpbZpmZmcjLy9NxNUSkpHYgmjhxIqZPn45169bh9u3buH37NtauXYsZM2awozVRIzl58iQA4LHHHkNSZmG1Q+cbSlutODUN9zd2Dg4OcHR0BMB+RET6RO1RZu+88w4ePnyIt956CyUlJQAAa2trzJkzB/PmzdN4gURU3rm5Q2T5PF9XPhwo9R8KCwuThs5XDBgNHToPVN+K07utW4NHoWmrZkPh6+uLixcvIikpCe3bt9d1OUSEerQQCYKAjz/+GPfv38fJkydx/vx5ZGRkIDIyUhv1EVEVlC1EPXr0kIbOK2lq6Lw2W3G0VbOhULcfkanO2UTUmNQOREr29vbo1q0bgoKCYGVlpcmaiKgGWVlZuHKlfM6h7t27A6h56Hx9aWPSxoq0UbOh4NB7Iv1T70BERLqx/3gMAKBly5Zwd3evtF9TQ+cbsxVHV8P9dYUjzYj0DwMRkQGo2Lk58pQc9h37o0ePHlp/X1NuxdGmhgQiU5qziagx1WvpDiJqPI92bgYEOA+cgiDvu9IW5dB5bdJ0K05j1Kyv1L1l9uhov6XDgxlOiTSsTi1EXbp0QWZmJgDgww8/REGB8Q+NJdIXVXVuFmRm8GwTrJuCqMGULUTJyclQKBQ1HmvKczYRNaY6BaKrV68iPz8fALBw4UJOJkbUiKrq3Cwq5OjXTfuBqK6TNpJ6mjVrBkEQUFxcjPv379d4rCnP2UTUmOr0Dde5c2eMGzcOvXr1giiK+PTTT2Fvb1/lsRx+T6RZj65ILyrkaHJtJ1q4P6Xjyqi+LCws4OXlhXv37iE5ORkeHh7VHmvqczYRNZY6tRCtX78eLi4u2LlzJwRBwO7du7F9+/ZKj99++03L5RKZJmXn5txze3B3zXj08uZ4CENX147Vpj5nE1FjqVMLUbt27fDLL78AAGQyGQ4cOFDlcF8i0q6Ca8cgz32Irl276roUaqAWLVrg5MmTSExMrPXYEaHNpRbC/TP7oKVb1S30RFR/ancKqK0DIBFphyiKKE69AQAMREbAz88PAJCQkKDW60xtziaixlKvdvebN2/i7bffRkREBCIiIjB16lTcvHlT07UR0f+ztTTHgYkBEIvzYWVlhcDAwNpfRHrN398fgPqBiIi0Q+1AtHfvXnTo0AGnT59Gx44d0bFjR5w6dQqBgYGIiorSRo1EBODMmTMAygc5WFhY6LiauuEaXNVTJxBxtB+R9qn9yZo7dy5mzJiBZcuWVdo+Z84c9O/fX2PFEdE/lIGIt8uMgzIQJSYmQhRFCIJQyyuISJvUbiG6evUqJkyYUGn7+PHjpQUniUjz9DEQsQWo/nx9fSEIAgoKCpCenq7rcohMntqByM3NDefOnau0/dy5cxx5RqQlCoUCsbGxAPQrEKmDa3CpsrKyQvPm5dMpsB8Rke6pHYgmTpyISZMm4eOPP8axY8dw7NgxLFu2DK+//jomTpyojRqJTN7169eRl5cHW1tbBAQE6LqcKlUVeB5dg2tzDFd3r4gdq4n0h9qBaP78+YiMjMSqVavQp08f9OnTB1999RUWLFiA999/X+MF3r17Fy+//DJcXFxgY2OD4OBg6dYBUD4UOTIyEl5eXrCxsUFERATi4+NVzpGRkYHRo0fDwcEBTk5OmDBhApcfIYMSExMDAAgJCYG5uf50qq0p8HANrtoxEBHpD7UDkSAImDFjBu7cuYPs7GxkZ2fjzp07mDZtmsY7BWZmZqJnz56wsLDA7t27ceXKFXz22Wdo2rSpdMzy5cuxcuVKrFmzBqdOnYKdnR0GDhyIoqJ//lodPXo0Ll++jKioKOzcuRNHjx7FpEmTNForkTbpY/+h2gIP1+CqHQMRkf5o0J+aTZo00VQdVfr444/h4+ODdevWSduUXyBAeevQF198gffffx9PP/00AODHH3+Eh4cHfvvtN4waNQpXr17Fnj17EBMTI/0yWbVqFYYMGYJPP/0U3t7eWr0GIk3Qx0BUU+DxcrThGlx1wEBEpD/0ekGkHTt2oGvXrnj++efh7u6OkJAQfPfdd9L+hIQEpKamIiIiQtrm6OiI7t27Izo6GgAQHR0NJycnlV8kERERkMlkOHXqVJXvW1xcjJycHJUHka6UlZXh7NmzAIBu3brpuJp/KANPRRUDD9fgqh0DEZH+0OtAdOvWLXz99ddo06YN9u7dizfffBNTp07Fhg0bAACpqakAUGmlaA8PD2lfampqpdFv5ubmcHZ2lo551NKlS+Ho6Cg9fHx8NH1pRHV29epVFBYWokmTJmjTpo2uy5HUJfAoF6UFytfgGtnNt1Fr1HfKQJSUlISyMk5bQKRLeh2IFAoFunTpgiVLliAkJASTJk3CxIkTsWbNGq2+77x586T+UdnZ2UhOTtbq+xHVRHm7LDQ0FDKZfn1k1Qk8XIOrMm9vb1haWqKsrAx37typ/QVEpDVqfbuWlpYiPDy80igubfHy8kKHDh1UtrVv3x5JSeUjWTw9PQEAaWlpKsekpaVJ+zw9PStNelZWVoaMjAzpmEdZWVnBwcFB5UGkK/rYf6gqDDzqk8lkaNGiBQDeNiPSNbUCkYWFBS5cuKCtWirp2bMn4uLiVLZdv35d+gLx9/eHp6cnDhw4IO3PycnBqVOnEBYWBgAICwtDVlaWNKkdABw8eBAKhQLdu3dvhKsgahhDCURV4RpctWM/IiL9oHb7+8svv4wffvhBG7VUMmPGDJw8eRJLlizBjRs3sGnTJnz77beYPHkygPIpAKZPn45FixZhx44duHjxIsaOHQtvb28888wzAMpblAYNGoSJEyfi9OnTOH78OKZMmYJRo0ZxhBnpvZKSEpw/fx6AfgYiBp6GYyAi0g9qf4OVlZVh7dq12L9/P0JDQ2FnZ6eyf8WKFRorrlu3bti+fTvmzZuHDz/8EP7+/vjiiy8wevRo6ZjZs2cjPz8fkyZNQlZWFnr16oU9e/bA2vqf5vuNGzdiypQpCA8Ph0wmw4gRI7By5UqN1UmkLZcvX0ZxcTGcnJzQsmVLXZdDWsBARKQf1A5Ely5dQpcuXQCU376qSBurNQ8bNgzDhg2rdr8gCPjwww/x4YcfVnuMs7MzNm3apPHaiLSt4u0yroZunBiIiPSD2oHo0KFD2qiDiKpgyP2HqG4YiIj0Q73H8N64cQN79+5FYWH5NP2iKNbyCiJSFwOR8VMGopSUFOn7lIgan9qB6OHDhwgPD0fbtm0xZMgQpKSkAAAmTJiAWbNmabxAIlNVVFSEixcvAmAgMmYuLi6wt7cHANy+fVvH1RCZLrUD0YwZM2BhYYGkpCTY2v6zJtHIkSOxZ88ejRZHZMouXryI0tJSuLq6wteXMzwbK0EQeNuMSA+o3Ydo37592Lt3L5o3b66yvU2bNvzrhqgBCkrK0CFyLwDgyocD2aHahPj7++PixYsMREQ6pHYLUX5+vkrLkFJGRgasrKw0UhQRsf+QKWELEZHuqR2IHn/8cfz444/Sc0EQoFAosHz5cjzxxBMaLY7IVKVmF0mBSJ9WuCftYCAi0j21b5ktX74c4eHhOHPmDEpKSjB79mxcvnwZGRkZOH78uDZqJDIJW2P/WdwzYsURPBC8AFxgC5EJUE66efPmTR1XQmS61G4hCgoKwvXr19GrVy88/fTTyM/Px/Dhw3H27Fm0atVKGzUSGb2U7EJ8sOOy9FwhAk0HvAWvVh24xIwJaN26NYDyQMQpTIh0o16LDzk6OuK9997TdC1EJivhQT4Uj/weFGRmCOjaSzcFUaPy9/eHIAjIzc3F/fv34e7uruuSiExOvQJRZmYmfvjhB1y9ehUA0KFDB4wbNw7Ozs4aLY7IVPi72kEmQCUUiQo5uga00F1R1Gisra3h4+ODpKQkxMfHMxAR6YDat8yOHj0KPz8/rFy5EpmZmcjMzMTKlSvh7++Po0ePaqNGIqPn5WiDhU8F/rNBoUDG3q/Q97HOOquJGpfyttmNGzd0XAmRaVI7EE2ePBkjR45EQkICtm3bhm3btuHWrVsYNWoUJk+erI0aiUzCiNDyub0UJUW4u2Y88i5EsUO1CWnTpg0ABiIiXVE7EN24cQOzZs2CmZmZtM3MzAwzZ87kB5lIA0pSr6Ms9wF8fX1568SEsIWISLfUDkRdunSR+g5VdPXqVXTq1EkjRRGZIltLcyQuG4p/d7EAwPmHTA0DEZFu1alT9YULF6R/T506FdOmTcONGzfQo0cPAMDJkyexevVqLFu2TDtVEpmQmJgYAJyh2tQoA1F8fDxEUeRyLUSNTBDrMOmFTCaDIAi1zo8hCALkcrnGitMXOTk5cHR0RHZ2NhwcHHRdDhm5Vq1a4datW9i/fz/Cw8N1XQ41ksLCQmlZpPv378PV1VXHFREZPnV+f9ephYjTyRM1jocPH+LWrVsAgNDQUB1XQ43JxsYGzZs3x507d3Djxg0GIqJGVqdA1KIF50IhagyxsbEAykccOTk56bYYanStW7eWApGyS0JdFJSUoUPkXgDAlQ8HwtayXlPMEZm0en1q7t27h7/++gvp6elQKBQq+6ZOnaqRwohMEfsPmbbWrVvj8OHDiI+P13UpRCZH7UC0fv16vP7667C0tISLi4tKxz9BEBiIiBqAK9ybNk2MNEvNLkJLN3tNlURkMtQedj9//nxERkYiOzsbiYmJSEhIkB7Kvg9EVD9sITJt9Z2ccWvsHenfESuOYHNMkkbrIjIFageigoICjBo1CjKZ2i8lohqkpKTg7t27kMlkCAkJ0XU5pAP1aSFKyS7EBzsuS88VIvDutktIyS7UeH1ExkztVDNhwgRs2bJFG7UQmTTl7bL27dvD3p63PExRq1atAAAZGRl4+PBhnV6T8CBfZVFgAJCLIhIfFGi6PCKjpnYfoqVLl2LYsGHYs2cPgoODYWFhobJ/xYoVGiuOyJQob5ex/5DpsrOzg4+PD5KTkxEXF4d//etftb7G39UOMgEqochMEODnaqvFSomMT70C0d69e9GuXTsAqNSpmojqR9lCxP5Dpq1du3ZITk7GtWvX6hSIvBxtsPCpQMz/vfy2mUwAlgwPgpejjbZLJTIqageizz77DGvXrsWrr76qhXKITJMoimwhIgBAQEAA9u/fj7i4uDq/ZkRocykQ7Z/Zh6PMiOpB7T5EVlZW6NmzpzZqITJZSUlJePDgAczNzdGxY0ddl0M6FBAQAAC4du1avV7v6WityXKITIbagWjatGlYtWqVNmohMlnK1qGOHTvC2pq/0ExZfQKRraU5EpcNReKyoZylmqie1P7knD59GgcPHsTOnTsRGBhYqVP1tm3bNFYckang/EOkpOyfeevWLZSWllb6jiUi7VA7EDk5OWH48OHaqIXIZHGGalJq1qwZ7OzskJ+fj5s3b0otRkSkXWoHonXr1mmjDiKTpVAoOMKMJIIgICAgALGxsbh27RoDEVEj4XTTRDoWHx+PnJwcWFtbIzAwUNflkB5Q3jZTZ6QZETWM2i1E/v7+Nc43xPXMiNQTHR0NAAgNDWV/EQLQ8JFmRKQ+tQPR9OnTVZ6Xlpbi7Nmz2LNnD9555x1N1UVkdApKytAhci8A4MqHA6XRQCdPngQAhIWF6aw20i8MRESNT+1ANG3atCq3r169WuoHQUQ1S80ukibPU7YQMRCRUsVbZqIochUAokagsT5EgwcPxtatWzV1uiotW7YMgiCotFIVFRVh8uTJcHFxgb29PUaMGIG0tDSV1yUlJWHo0KGwtbWFu7s73nnnHZSVlWm1VqJHbY29I/07YsURbI5JQm5uLi5dugQA6NGjh65KIz3Tpk0bCIKAzMxM3L9/X9flEJkEjQWi//3vf3B2dtbU6SqJiYnBN998U2kW3xkzZuCPP/7Ali1bcOTIEdy7d09lWgC5XI6hQ4eipKQEJ06cwIYNG7B+/XpERkZqrVaiR6VkF+KDHZel5woReHfbJew9dhoKhQK+vr7w9vbWYYWkT2xsbODn5weAt82IGovat8xCQkJUmm9FUURqairu37+P//znPxotTikvLw+jR4/Gd999h0WLFknbs7Oz8cMPP2DTpk3o168fgPJpAdq3b4+TJ0+iR48e2LdvH65cuYL9+/fDw8MDnTt3xkcffYQ5c+ZgwYIFsLS0rPR+xcXFKC4ulp7n5ORo5brIdCQ8yFdZjRwA5KKIQzHlrUO8XUaPat++PRISEnDlyhX07t1b1+UQGT21A9Ezzzyj8lwmk8HNzQ19+/bV2nwZkydPxtChQxEREaESiGJjY1FaWoqIiAhpW0BAAHx9fREdHY0ePXogOjoawcHB8PDwkI4ZOHAg3nzzTVy+fBkhISGV3m/p0qVYuHChVq6FTJO/qx1kAlRCkZkg4MbZEwB4u4wqCwoKwp9//indUq2v6jrzE5EqtT8ZH3zwgTbqqNYvv/yCv//+W1raoKLU1FRYWlrCyclJZbuHhwdSU1OlYyqGIeV+5b6qzJs3DzNnzpSe5+TkwMfHpyGXQSbOy9EGC58KlFYklwnA4meDMOW7AwDYQkSVBQUFAUCDAxER1Y1eT8yYnJyMadOmYePGjY264KWVlRUcHBxUHkQNNSK0ufTv/TP7ILRpMR4+fAgrK6sqWyrJtFUMRKIo1nJ03aRmF2nkPETGqM6BSCaTwczMrMaHublmm2JjY2ORnp6OLl26wNzcHObm5jhy5AhWrlwJc3NzeHh4oKSkBFlZWSqvS0tLg6enJwDA09Oz0qgz5XPlMUSNzdPRWpp/qEuXLlX2ZSPTFhAQAJlMhocPH1b6DlNHVaMbiaiyOieY7du3V7svOjoaK1euhEKh0EhRSuHh4bh48aLKtnHjxiEgIABz5syBj48PLCwscODAAYwYMQJA+bwdSUlJ0i2IsLAwLF68GOnp6XB3dwcAREVFwcHBAR06dNBovUQ1sbU0R+KyodJzzj9ENbGxsUHr1q1x/fp1XLp0qV5/wFU3urF3Wzd4Odposlwig1fnQPT0009X2hYXF4e5c+fijz/+wOjRo/Hhhx9qtLgmTZpIzcZKdnZ2cHFxkbZPmDABM2fOhLOzMxwcHPD2228jLCxM6qQ6YMAAdOjQAWPGjMHy5cuRmpqK999/H5MnT4aVlZVG6yVSBwMR1SYoKEgKRBUHj9RVdaMbEx8UMBARPaJefYju3buHiRMnIjg4GGVlZTh37hw2bNiAFi1aaLq+Wn3++ecYNmwYRowYgd69e8PT0xPbtm2T9puZmWHnzp0wMzNDWFgYXn75ZYwdO1bj4Y1IHfn5+bhw4QIAjjCj6jW0Y7VydGNFZoIAP1fbhpZGZHTU6vSTnZ2NJUuWYNWqVejcuTMOHDiAxx9/XFu1Venw4cMqz62trbF69WqsXr262te0aNECf/75p5YrI6q7mJgYKBQKNG/eHM2bN6/9BWSSGhqIqhrduGR4EFuHiKpQ5xai5cuXo2XLlti5cyd+/vlnnDhxotHDEJGx4O0yqgtlILp8+XK9+2g+OrpxZDdfjdRGZGwEsY7jOWUyGWxsbBAREQEzM7Nqj6t4u8pY5OTkwNHREdnZ2RyCTxoxZMgQ7N69G59//rnK2nxEFZWWlsLe3h4lJSVISEiQlvMgorpR5/d3nW+ZjR07lisuE2mAXC7H8ePHAYBLMlCNLCwsEBAQgAsXLuDSpUsMRERaVOdAtH79ei2WQWQ6Lly4gJycHDRp0gSdOnXSdTmk54KCgnDhwgVcvHgRw4YN03U5REZLr2eqJjJGx44dAwD07NmzxtvPRAAQHBwMANKoRCLSDgYiokZ29OhRALxdRnWjXNbl77//1nElRMaNgYioEYmiKLUQcZQm1YUyEMXHxyM3N1fH1RAZLwYiokZ0/fp1pKenw8rKCt26ddN1OWQA3N3d0axZM4iiiPPnz+u6HCKjxUBE1IiUrUM9evTg0jFUZ8pWorNnz+q4EiLjxUBE1IiU/Yd4u4zU0aVLFwDsR0SkTQxERI1EFEVp6Rl2qCZ1sIWISPsYiIgayY0bN5CcnAxLS0v07NlT1+WQAVG2EF2+fBnFxcU6robIODEQETWSAwcOAChfv8zWlquNU935+PjA2dkZZWVl9V7olYhqxkBEpEEFJWXwm7sLfnN3oaCkTGWfMhCFh4frojQyYIIgSK1EvG1GpB0MRERakppdJP1boVDg0KFDABiIqH44QSORdjEQEWnQ1tg70r8jVhzB5pgkAMD58+fx8OFD2Nvbc/4hqheONCPSLgYiIg1JyS7EBzsuS88VIvDutktIyS6Ubpf16dMHFhYWuiqRDJgySJ89e5Ydq4m0gIGISEMSHuRDIapuk4siEh8UsP8QNVjLli3h6uqKkpISnDt3TtflEBkdBiIiDfF3tYNMUN1mJgho5mAhzVDNQET1JQgCevToAQA4efKkjqshMj4MREQa4uVog4VPBUrPZQKwZHgQ4i/EID8/H56enggKCtJhhWTolIEoOjpax5UQGR8GIiINGhHaXPr3/pl9MLKbL/78808AwODBgyGT8SNH9RcWFgZAsy1ENU0VQWRKzHVdAJExsbU0R+KyoSrblIFoyJAhuiiJjEi3bt0gCAJu376NlJQUeHl5afT8qdlFaOlmr9FzEhkK/rlKpEW3bt3CtWvXYGZmhv79++u6HDJwTZo0kW67aqqVqLqpIohMDQMRkRbt3r0bANCrVy84OjrquBoyBpq8bVbTVBFEpoaBiEiLeLuMNE2TI81qmiqCyNQwEBFpSWFhIQ4ePAiAgYg0RxmIYmJiUFpa2qBzVTdVhJ8rFx8m08NARKQlhw4dQlFREZo3b47AwMDaX0BUB+3atYOzszMKCwsRGxvboHNVN1WEl6NNQ8skMjgMRERasm3bNgDAU089BUEQajmaqG5kMhn69OkDANKCwQ1R1VQRRKaIgYhIC8rKyvDbb78BAEaMGKHbYsjoPPHEEwA0E4iUU0UkLhvKIfdk0hiIiLTg6NGjePjwIVxcXNC7d29dl0NGRhmIjh8/jpKSEh1XQ2QcGIiItGDr1q0AgKeffhrm5pz/lDQrMDAQbm5uKCgowOnTp3VdDpFRYCAi0jCFQoHt27cD4O0y0g5BENC3b18AmrltRkQMREQad/LkSaSkpMDBwYGr25PWaLIfERExEBFp3JYtWwAAw4YNg5WVlY6rIWOlDEQnTpxAUVGRjqshMnwMREQaVFZWhp9//hkAMHLkSB1XQ8asXbt28PT0RHFxscbWNSMyZXodiJYuXYpu3bqhSZMmcHd3xzPPPIO4uDiVY4qKijB58mS4uLjA3t4eI0aMQFpamsoxSUlJGDp0KGxtbeHu7o533nkHZWVljXkpZCKioqKQlpYGV1dXDB48WNflkBETBAEREREA/lkzj4jqT68D0ZEjRzB58mScPHkSUVFRKC0txYABA5Cfny8dM2PGDPzxxx/YsmULjhw5gnv37mH48OHSfrlcjqFDh6KkpAQnTpzAhg0bsH79ekRGRuriksjI/fjjjwCAl156CRYWFjquhozd0KFDAQA7d+7UcSVEhk8QRVGs/TD9cP/+fbi7u+PIkSPo3bs3srOz4ebmhk2bNuG5554DAFy7dg3t27dHdHQ0evTogd27d2PYsGG4d+8ePDw8AABr1qzBnDlzcP/+fVhaWtb6vjk5OXB0dER2djYcHBy0eo1kuLKzs+Hp6YmioiLExMSga9euui6JjFxmZibc3Nwgl8tx69Yt+Pv767okIr2izu9vvW4helR2djYAwNnZGQAQGxuL0tJSqdkYAAICAuDr64vo6GgAQHR0NIKDg6UwBAADBw5ETk4OLl++XOX7FBcXIycnR+VBVJutW7eiqKgI7du3R2hoqK7LIRPQtGlT9OrVCwCwa9cuHVdDZNgMJhApFApMnz4dPXv2RFBQEAAgNTUVlpaWcHJyUjnWw8MDqamp0jEVw5Byv3JfVZYuXQpHR0fp4ePjo+GrIUNVUFIGv7m74Dd3FwpKVPuhrV+/HgAwduxYrl1GjWbYsGEAeNuMqKEMJhBNnjwZly5dwi+//KL195o3bx6ys7OlR3JystbfkwxPavY/Q50vXryIY8eOwczMDGPGjNFhVWRqlIHo0KFDyMvL03E1RIbLIALRlClTsHPnThw6dAjNm/+zMrOnpydKSkqQlZWlcnxaWho8PT2lYx4ddaZ8rjzmUVZWVnBwcFB5EAHA1tg70r8jVhzB5pgkAMDq1asBAM8++yyaNWumk9rINLVr1w6tWrVCSUkJ9u/fr+tyiAyWXgciURQxZcoUbN++HQcPHqzUYTA0NBQWFhY4cOCAtC0uLg5JSUkICwsDAISFheHixYtIT0+XjomKioKDgwM6dOjQOBdCRiEluxAf7Pin35lCBN7ddglxSWn46aefAJSHd6LGJAiC1Er022+/6bYYIgOm14Fo8uTJ+O9//4tNmzahSZMmSE1NRWpqKgoLCwEAjo6OmDBhAmbOnIlDhw4hNjYW48aNQ1hYGHr06AEAGDBgADp06IAxY8bg/Pnz2Lt3L95//31MnjyZswiTWhIe5EPxyJhMuSji259/Q0FBAYKCgriyPemEcs28bdu2Sd+PRKQevQ5EX3/9NbKzs9G3b194eXlJj82bN0vHfP755xg2bBhGjBiB3r17w9PTE9u2bZP2m5mZYefOnTAzM0NYWBhefvlljB07Fh9++KEuLokMmL+rHWSP9JU2E4DfN34HoDzAszM16ULPnj3h4+OD3Nxc/Pnnn7ouh8ggGdQ8RLrCeYhI6afoRMz/vfy2mUwARviW4NO3hsPR0RF37tyBvb29jiskUzV79mx88sknGDFiBP73v/9p7LwFJWXoELkXAHBwVh+0dON/42Q4jHYeIiJdGxPmh+h5/fDzxB74a84TiPpmIQBg6tSpDEOkUy+99BKA8uH3yjnbNKG6gQRExoaBiEhNXo42CGvlgjNH9+P8+fOwt7fH9OnTdV0WmbhOnTqhffv2KC4uxvbt2zVyzuoGEqRks58SGR8GIqJ6EEVR6oc2ZcoUafZ0Il0RBAEvvvgiAGDjxo0aOWd1AwkSHxRo5PxE+oSBiKgedu7cidjYWNja2mLmzJm6LocIADB69GgAwP79+3Hjxo0Gn6/qgQQC/FxtG3xuIn3DQESkppKSEsyaNQsA8Pbbb8PNzU3HFRGVa9myJQYNGgSgfJRuQ3k52mDhU4HSc5kALBkeBC9Hmwafm0jfcJRZHXCUGVX02Wef4d///jc8PDxw/fp1/jdBemXnzp148skn4eTkhLt378LWtuGtOSnZhUh8UAA/V1uGITIoHGVG1AAVF3C9dV91bai0tDSp79DSpUsZhkjvDB48GP7+/sjKysLPP/+skXMqBxIwDJExYyAiekRNw4xnzZqFnJwchIaG4pVXXtFFeUQ1MjMzw5tvvgmgfI093gQgqhsGIqIKahpmvGXLFmzcuBEymQyrV6+GTMaPD+mn8ePHw9raGmfPnlVZ61HTampNJTI0/EYnqqC6YcZ/X0/GG2+8AQCYN28eunfvroPqiOrGxcUFkyZNAgB88MEHWmsl4qSNZEwYiIgqqGqYsUwAPl0wFxkZGQgJCUFkZKRuiiNSw9y5c2FtbY0TJ05g3759Gj8/J20kY8NARFRBVcOMO5ZcxuE/t8PGxgY//fQTLC0tdVghUd14eXnhrbfeAgBERkZqvJWIkzaSsWEgInpExfXK3myegt9XzAEA/PTTTwgMDKzl1UT6Y/bs2bCxscHp06fx+++/a/TcnLSRjA0DEVEVvBxtcDsmCvOmlfcbWrRoEUaMGKHjqojU4+HhIa2zN23aNOTn52vs3F6ONlg6PBhmQnkqMhMETtpIBo0TM9YBJ2Y0PT/99BNeffVVKBQKjBs3Dj/88AMEQaj9hUR6Jj8/H4GBgbh9+zbeeecdLF++XKPn56SNpM84MaMBS8kuxImbD9gxUUdEUcSyZcvwyiuvQKFQ4LXXXsP333/PMEQGy87ODqtXrwYArFixAhcuXNDo+TlpIxkLBiI98lN0IsKWHsRL351Cz2UHTWIIa20BsCEBsbrXVjd3SnZ2NkaMGIF58+ZBFEVMmTIF33zzDecbIoM3dOhQjBgxAnK5HC+//DIKCtjxmehRvGVWB41xyywluxA9lx1UGbVhJgj4a+4TOv/LKyW7EAkP8uHvaqfRWn6KTsT838uH7coEYOnwYIzs5lvn/TXVVdNrH9235NlgmN0+hWnTpiElJQWWlpb46quv8Nprr7FliIxGSkoKQkJCkJaWhldeeQXr1q3jf99k9HjLzADp6xBWbbVa1TaHSW37a6qrptdWtW/u1vN4ccKbSElJQevWrXHs2DFMnDiRvyzIqHh5eeHnn3+GTCbDhg0b8P333zfK+7IbABkKc10XYMpyc3PxzDPPwNbWFmZNXAGfEUCFX8ICRBzZuQXXHKxga2ur8rCzs6u07WGhvNoWk/q08lQXLHq3dVM5R03nrm5fTQHQy9Gm1oBYU101vVaEWGkfBBls3Hwxc8ZkzJs3D9bW1nX6/4fI0DzxxBNYtGgR3n33Xbz11lvw9PTEk08+qbX3q62Vl0ifMBDpUG5uLg4ePCg9t+94F84Dp0CQmUFUyPFw71eYdyFK2m/WxAXmTb1RlnkP8tyHKuey79gfzgPfhiCTQVQoIIv9BfbpF2Bra4vSFo/hof8ACIIMEEUEF11EgGWGSpgqs7BHgZk9mjtZoVnT8m03c81qDC2AeremKu5TzmHy6C1C5RwmNe2vLUxV9VqZABzc8QsOHz4Msd1YCBX7BYkKHPh9Mx4LalPLT4zI8M2ZMweXL1/Gxo0b8dxzz2Hnzp3o37+/xt+nrn9QEekL9iGqA231IcrPz8cff/yBgoICFBQUID8/H/fzS3G/UIBZYQbE/Azk5+ejoKAA92z98dBvQPkvclGB0hM/Iu/CvvJ5RWyd0OyNdSq/5EWFHHfXjAeAavcpQ9WjYSpj7yrkXYiCWROXKl+b9ePbsEExbFy8IQ5bCAiq4aLL3d/KlwxwGVypxWuiZxLc7S1hbW2N2GwbbEuygiAIkAF4vasDhrV3ho2NDaytrfHntSwsiUqAQiwPNB8MaYPnujRDel4Jwr84USnwLH/cGmJ+Bh48eICDiUWIQWtAkEFUyJGx9yvk/X+4tO/YHy4D3wZkMv7VSiaprKwMI0eOxLZt22BtbY0ff/wRzz//vEbf48TNB3jpu1OVtv88sQfCWrkA0F7/RCIldX5/MxDVga7nIaqtw/XRa6kYuz620uveCbVAUVExVl2u3FWsr+Ic7AtS8LBQgWjXwaqhRqGA2e4FKHyYgjLfbrDpPUFqtaoYLKx8g+H54tJK507dNA8QUO2+4uSL/1xHExeYO3mjLKtyq1dN+8tD3JQq66rqtU3M5OjWrRv69++PgQMHwtW3NW4/LOTcKWSySkpK8Nxzz+GPP/4AUL4IbGRkpMZGVdb2vcXbadQYGIg0TNeBqLa/tGr64gFQ45dSXf+Ku5mWA3cbAU3M5VKLVtKDHEzZm4WK/wEJEDHBPRFFRUX4b04AgAodk0UFOidthSI/A0VFRSgqKkJhYWG1/1YoFDX+/2LWxAU2br6QFWTAWlEIFxcXuLq6wsXFBb6+vmjVqhVatmyJdu3aoXXr1uwkTfQIuVyOOXPm4LPPPgMAPP744/j+++/Rtm1bjZx/c0wS3t12CXJRlGayHtnNV69H1ZJxUef3N/sQGYDa+tsop9B/9ItH+cVS077azq08f1VfUsEA8uwf/cILxshuw8r3P/plOKITRnarWwdOURSlh0KhkB7K5+bm5rC0tOQcQUQNYGZmhk8//RRBQUGYMmUKjh07ho4dO+Lf//43Zs2ahaZNmzbo/CO7+aJ3W7dKM1nX1g+QSBfYQlQHum4hAqr/S6uimqbQr2lfXc5dk/q+LxHpj8TERLz++uvYt28fAMDBwQFvvfUWXnvtNbRq1Uqj78UWImosvGWmYfoQiADthgsGFyISRRHbt2/HggULcPHiP339evfujWeeeQZDhw5FmzZtNHL7uaF/iGkTO3sbDwYiDdOXQERE1BgUCgV+//13fPvtt9i7dy8q/ppwc3ND9+7d0b17d3Ts2BGtW7dGy5Yt6zV/V21/iNVnjrO67q+ONjt7a6tmTby3sWIg0jAGIiIyVUlJSdi+fTv++OMPHD16FKWlpZWOEQQBzZo1g4eHh8rDyckJTZo0qfJhbW0NKysrlYe5ubnU+rQ5Jgnztl2Upt2o6xxntb22Jtq8lVdbTQ1Zqqih723MGIg0jIGIiAgoKirC2bNncerUKZw+fRpxcXGIj49Hbm6uRs4vCAKsrKxg7ewFh9FfVppA1TX6S5ibWyC121uV5j8Lvfcb7GVlkFs1wV9NB1WaA22kzSU4WQEWFhYoklkjFzZwtwFcbMxgYWEBCwsLJBZYYvXVymONPnrCBSHN7GFhYYGsYiA1XwF/Vzs0c7aTXmthYQEzMzOk5hRVCi61Ba3a9jck0NQl5DWk5aohQa0xWq0YiDSMgYiIqGqiKOL+/ftITExEWlqayiM7Oxu5ubkqj7y8POTm5qK4uBjFxcWQy+WVztmQOc5qem1x8sVqJ6IFUO1ktMqJbGt6LVB5ktuS4+shJETDolkghPDplWpyOf8T7AtSUNrUH/cCKk+MGXz/IJoIRVVOcvuM2d9wshRhbm4OCwsLmJubq/xb+b9JRdb49kblsPFuD1sEe1jjwO1ifHc2D4IgQAAwq7cXngpylc6x9cJ9LN13q3wC3f9fDHvUY+VhrC5BrbrQ01itVgxEGsZARESkHXK5XApHysedjDy8vDlRZY4zGYAFXRUoKyvFR+csIUI1IIxzTYC1ohCZxcDP+R1QcQ40QRTROzsKpWVllcIFRAVanP8eYkEmSktLkesWjLz2T5a3QCkUwJlNKI07ijILe1i/8Gm1YammMAXUvGJATa81b+pd90luq1jaSRt1pX43CRYW5nB59WvVVjyFHGa7F8KyrADm5uYo830MBYHPSCssuCXuh0t2HERrR8S1e7lSK9/gsmh8/dmSStfaEJyHiIiIDIKZmZm0pqJSixbAsrIm1Y5Cs2tV/fxnANC50hxo5ftP3HyAE49ORCvIsGz1WmkiWuDRzt7lc6dVNYmtIDPDgVMXENLMHiduPsAbm69W2r9uy060dRKw/1Y+fjhfCAXKw92otjJ02/AdysrKUFZWhpPpBdiRYgcRAgSI6N/0AdpFzkVWCbAxR1QNgKKIl58ZAIvSnigrK0OSeTPEOXQtD3qiAs1TjsHhwSWUlZWhtLQUWTf3ILP1oPIAIipgdWEbWnm5oMTJD+Ijc7kJMjPYurdAQUEWzJt6qwae/98vNHFHmYBK+yAzw93sUhQnXysPU4Of+ecYQYb0FuE4u2ZjecgLeOS1ggw7Dp7A19AdthDVAVuIiIgaX0PmOKtqf0M6TTe0H1B9awZqnqKgrtdUn/8/7mbm4/HlhyutG7n11Q4oLS3DqI3XK+37MsIRjhYKXEwrwiexlTvgj/fLgYNQjC8TXCu18r3mcRvvzZhc9Q+gnnjLTMMYiIiIjEND5j+q7bXanFupurBUl+WXtHVNDQlqjTUPFQORhjEQEREZj4ZMRFvfVh5t0cRUAQ25poasgtAY/18xEFVj9erV+OSTT5CamopOnTph1apVeOyxx2p9HQMRERHpK32f9VuXqyAwEFVh8+bNGDt2LNasWYPu3bvjiy++wJYtWxAXFwd3d/caX8tARERE+kzXwUNfMRBVoXv37ujWrRu++uorAOVT0/v4+ODtt9/G3Llza3wtAxEREZHhUef3t6zGvUaipKQEsbGxiIiIkLbJZDJEREQgOjq60vHFxcXIyclReRAREZHxMolA9ODBA8jlcnh4eKhs9/DwQGpqaqXjly5dCkdHR+nh4+PTWKUSERGRDphEIFLXvHnzkJ2dLT2Sk5N1XRIRERFpkUnMVO3q6gozMzOkpaWpbE9LS4Onp2el45UrLxMREZFpMIkWIktLS4SGhuLAgQPSNoVCgQMHDiAsLEyHlREREZE+MIkWIgCYOXMmXnnlFXTt2hWPPfYYvvjiC+Tn52PcuHG6Lo2IiIh0zGQC0ciRI3H//n1ERkYiNTUVnTt3xp49eyp1tCYiIiLTYzLzEDUE5yEiIiIyPJyHiIiIiEgNDERERERk8hiIiIiIyOQxEBEREZHJM5lRZg2h7HfONc2IiIgMh/L3dl3GjzEQ1UFubi4AcE0zIiIiA5SbmwtHR8caj+Gw+zpQKBS4d+8emjRpAkEQNHrunJwc+Pj4IDk52SiH9Bv79QHGf43Gfn2A8V+jsV8fYPzXaOzXB2jnGkVRRG5uLry9vSGT1dxLiC1EdSCTydC8eXOtvoeDg4PR/kcOGP/1AcZ/jcZ+fYDxX6OxXx9g/Ndo7NcHaP4aa2sZUmKnaiIiIjJ5DERERERk8hiIdMzKygoffPABrKysdF2KVhj79QHGf43Gfn2A8V+jsV8fYPzXaOzXB+j+GtmpmoiIiEweW4iIiIjI5DEQERERkcljICIiIiKTx0BEREREJo+BSAeWLVsGQRAwffp0aVtRUREmT54MFxcX2NvbY8SIEUhLS9NdkQ1Q1fX17dsXgiCoPN544w3dFammBQsWVKo/ICBA2m8MP7/artHQf4YAcPfuXbz88stwcXGBjY0NgoODcebMGWm/KIqIjIyEl5cXbGxsEBERgfj4eB1WrL7arvHVV1+t9HMcNGiQDiuuOz8/v0q1C4KAyZMnAzCOz2Ft12jon0O5XI758+fD398fNjY2aNWqFT766COVtcZ09TnkTNWNLCYmBt988w06duyosn3GjBnYtWsXtmzZAkdHR0yZMgXDhw/H8ePHdVRp/VR3fQAwceJEfPjhh9JzW1vbxiytwQIDA7F//37pubn5Px8fY/n51XSNgGH/DDMzM9GzZ0888cQT2L17N9zc3BAfH4+mTZtKxyxfvhwrV67Ehg0b4O/vj/nz52PgwIG4cuUKrK2tdVh93dTlGgFg0KBBWLdunfTcUIZyx8TEQC6XS88vXbqE/v374/nnnwdgHJ/D2q4RMOzP4ccff4yvv/4aGzZsQGBgIM6cOYNx48bB0dERU6dOBaDDz6FIjSY3N1ds06aNGBUVJfbp00ecNm2aKIqimJWVJVpYWIhbtmyRjr169aoIQIyOjtZRteqr7vpEUaz03NB88MEHYqdOnarcZyw/v5quURQN/2c4Z84csVevXtXuVygUoqenp/jJJ59I27KyskQrKyvx559/bowSG6y2axRFUXzllVfEp59+unEK0rJp06aJrVq1EhUKhdF8Dh9V8RpF0fA/h0OHDhXHjx+vsm348OHi6NGjRVHU7eeQt8wa0eTJkzF06FBERESobI+NjUVpaanK9oCAAPj6+iI6Orqxy6y36q5PaePGjXB1dUVQUBDmzZuHgoKCRq6wYeLj4+Ht7Y2WLVti9OjRSEpKAmA8Pz+g+mtUMuSf4Y4dO9C1a1c8//zzcHd3R0hICL777jtpf0JCAlJTU1V+jo6OjujevbvB/Bxru0alw4cPw93dHe3atcObb76Jhw8f6qDahikpKcF///tfjB8/HoIgGNXnUOnRa1Qy5M/hv/71Lxw4cADXr18HAJw/fx5//fUXBg8eDEC3n0PeMmskv/zyC/7++2/ExMRU2peamgpLS0s4OTmpbPfw8EBqamojVdgwNV0fALz00kto0aIFvL29ceHCBcyZMwdxcXHYtm1bI1daP927d8f69evRrl07pKSkYOHChXj88cdx6dIlo/j5ATVfY5MmTQz+Z3jr1i18/fXXmDlzJt59913ExMRg6tSpsLS0xCuvvCL9rDw8PFReZ0g/x9quESi/XTZ8+HD4+/vj5s2bePfddzF48GBER0fDzMxMx1dQd7/99huysrLw6quvAjCO79FHPXqNgOF/l86dOxc5OTkICAiAmZkZ5HI5Fi9ejNGjRwOATj+HDESNIDk5GdOmTUNUVJRB9ENQV12ub9KkSdK/g4OD4eXlhfDwcNy8eROtWrVqrFLrTfnXCwB07NgR3bt3R4sWLfDrr7/CxsZGh5VpTk3XOGHCBIP/GSoUCnTt2hVLliwBAISEhODSpUtYs2aNFBYMXV2ucdSoUdLxwcHB6NixI1q1aoXDhw8jPDxcJ3XXxw8//IDBgwfD29tb16VoTVXXaOifw19//RUbN27Epk2bEBgYiHPnzmH69Onw9vbW+eeQt8waQWxsLNLT09GlSxeYm5vD3NwcR44cwcqVK2Fubg4PDw+UlJQgKytL5XVpaWnw9PTUTdFqqO36KnYQVOrevTsA4MaNG41drkY4OTmhbdu2uHHjBjw9PQ3651editdYFUP7GXp5eaFDhw4q29q3by/dFlT+rB4dlWRIP8farrEqLVu2hKurq8H8HAHg9u3b2L9/P1577TVpm7F9Dqu6xqoY2ufwnXfewdy5czFq1CgEBwdjzJgxmDFjBpYuXQpAt59DBqJGEB4ejosXL+LcuXPSo2vXrhg9erT0bwsLCxw4cEB6TVxcHJKSkhAWFqbDyuumtuurqhn+3LlzAMq/wA1RXl4ebt68CS8vL4SGhhr0z686Fa+xKob2M+zZsyfi4uJUtl2/fh0tWrQAAPj7+8PT01Pl55iTk4NTp04ZzM+xtmusyp07d/Dw4UOD+TkCwLp16+Du7o6hQ4dK24ztc1jVNVbF0D6HBQUFkMlUo4eZmRkUCgUAHX8Otdplm6r16EiBN954Q/T19RUPHjwonjlzRgwLCxPDwsJ0V2ADVby+GzduiB9++KF45swZMSEhQfz999/Fli1bir1799ZtkWqYNWuWePjwYTEhIUE8fvy4GBERIbq6uorp6emiKBrHz6+mazSGn+Hp06dFc3NzcfHixWJ8fLy4ceNG0dbWVvzvf/8rHbNs2TLRyclJ/P3338ULFy6ITz/9tOjv7y8WFhbqsPK6q+0ac3NzxX//+99idHS0mJCQIO7fv1/s0qWL2KZNG7GoqEjH1deNXC4XfX19xTlz5lTaZwyfQ1Gs/hqN4XP4yiuviM2aNRN37twpJiQkiNu2bRNdXV3F2bNnS8fo6nPIQKQjjwaiwsJC8a233hKbNm0q2trais8++6yYkpKiuwIbqOL1JSUlib179xadnZ1FKysrsXXr1uI777wjZmdn67ZINYwcOVL08vISLS0txWbNmokjR44Ub9y4Ie03hp9fTddoDD9DURTFP/74QwwKChKtrKzEgIAA8dtvv1XZr1AoxPnz54seHh6ilZWVGB4eLsbFxemo2vqp6RoLCgrEAQMGiG5ubqKFhYXYokULceLEiWJqaqoOK1bP3r17RQBV/lyM4XMoitVfozF8DnNycsRp06aJvr6+orW1tdiyZUvxvffeE4uLi6VjdPU5FESxwvSQRERERCaIfYiIiIjI5DEQERERkcljICIiIiKTx0BEREREJo+BiIiIiEweAxERERGZPAYiIiIiMnkMRERERGTyGIiIiOqob9++EAQBgiBIa0gdPnwYgiBUWlRU0xYsWCC99xdffKHV9yIyRQxERKQVr776qvQLvOJj0KBBui6tQSZOnIiUlBQEBQU1+FxpaWmwsLDAL7/8UuX+CRMmoEuXLgCAf//730hJSUHz5s0b/L5EVBkDERFpzaBBg5CSkqLy+Pnnn7X6niUlJVo9v62tLTw9PWFubt7gc3l4eGDo0KFYu3ZtpX35+fn49ddfMWHCBACAvb09PD09YWZm1uD3JaLKGIiISGusrKzg6emp8mjatKm0XxAEfP/993j22Wdha2uLNm3aYMeOHSrnuHTpEgYPHgx7e3t4eHhgzJgxePDggbS/b9++mDJlCqZPnw5XV1cMHDgQALBjxw60adMG1tbWeOKJJ7Bhwwbp1lZ+fj4cHBzwv//9T+W9fvvtN9jZ2SE3N7fe11xQUIDBgwejZ8+e0m2077//Hu3bt4e1tTUCAgLwn//8Rzp+woQJOHDgAJKSklTOs2XLFpSVlWH06NH1roWI6o6BiIh0auHChXjhhRdw4cIFDBkyBKNHj0ZGRgYAICsrC/369UNISAjOnDmDPXv2IC0tDS+88ILKOTZs2ABLS0scP34ca9asQUJCAp577jk888wzOH/+PF5//XW899570vF2dnYYNWoU1q1bp3KedevW4bnnnkOTJk3qdS1ZWVno378/FAoFoqKi4OTkhI0bNyIyMhKLFy/G1atXsWTJEsyfPx8bNmwAAAwZMgQeHh5Yv359pVqGDx8OJyenetVCRGoSiYi04JVXXhHNzMxEOzs7lcfixYulYwCI77//vvQ8Ly9PBCDu3r1bFEVR/Oijj8QBAwaonDc5OVkEIMbFxYmiKIp9+vQRQ0JCVI6ZM2eOGBQUpLLtvffeEwGImZmZoiiK4qlTp0QzMzPx3r17oiiKYlpammhubi4ePny42mvq06ePOG3aNJVthw4dEgGIV69eFTt27CiOGDFCLC4ulva3atVK3LRpk8prPvroIzEsLEx6PnfuXNHf319UKBSiKIrijRs3REEQxP3791eqoUWLFuLnn39ebY1EVD9sISIirXniiSdw7tw5lccbb7yhckzHjh2lf9vZ2cHBwQHp6ekAgPPnz+PQoUOwt7eXHgEBAQCAmzdvSq8LDQ1VOWdcXBy6deumsu2xxx6r9DwwMFBqqfnvf/+LFi1aoHfv3vW61v79+6N169bYvHkzLC0tAZT3A7p58yYmTJigcg2LFi1SqX/8+PFISEjAoUOHAJS3Dvn5+aFfv371qoWI1NfwXoFERNWws7ND69atazzGwsJC5bkgCFAoFACAvLw8PPnkk/j4448rvc7Ly0vlferjtddew+rVqzF37lysW7cO48aNgyAI9TrX0KFDsXXrVly5cgXBwcFS/QDw3XffoXv37irHV+wc3aZNGzz++ONYt24d+vbtix9//BETJ06sdy1EpD4GIiLSW126dMHWrVvh5+en1qiudu3a4c8//1TZFhMTU+m4l19+GbNnz8bKlStx5coVvPLKK/WuddmyZbC3t0d4eDgOHz6MDh06wMPDA97e3rh161atnaMnTJiAN998E0899RTu3r2LV199td61EJH6eMuMiLSmuLgYqampKo+KI8RqM3nyZGRkZODFF19ETEwMbt68ib1792LcuHGQy+XVvu7111/HtWvXMGfOHFy/fh2//vqr1Gm5YqtL06ZNMXz4cLzzzjsYMGBAg+f4+fTTTzF69Gj069cP165dA1DeaXzp0qVYuXIlrl+/josXL2LdunVYsWKFymuff/55WFhY4PXXX8eAAQPg4+PToFqISD0MRESkNXv27IGXl5fKo1evXnV+vbe3N44fPw65XI4BAwYgODgY06dPh5OTE2Sy6r++/P398b///Q/btm1Dx44d8fXXX0ujzKysrFSOnTBhAkpKSjB+/Pj6XeQjPv/8c7zwwgvo168frl+/jtdeew3ff/891q1bh+DgYPTp0wfr16+Hv7+/yutsbW0xatQoZGZmaqwWIqo7QRRFUddFEBFp2+LFi7FmzRokJyerbP/pp58wY8YM3Lt3T+oMXZ2+ffuic+fOOl06w8/PD9OnT8f06dN1VgORMWILEREZpf/85z+IiYnBrVu38NNPP+GTTz5R6SNUUFCAmzdvYtmyZXj99ddrDUMVz2tvb4+LFy9qq/QqLVmyBPb29pUmcCQizWALEREZpRkzZmDz5s3IyMiAr68vxowZg3nz5kmdsxcsWIDFixejd+/e+P3332Fvb1/rOe/evYvCwkIAgK+vb51DlCZkZGRIE1a6ubnB0dGx0d6byBQwEBEREZHJ4y0zIiIiMnkMRERERGTyGIiIiIjI5DEQERERkcljICIiIiKTx0BEREREJo+BiIiIiEweAxERERGZvP8DYog/B4N4W2gAAAAASUVORK5CYII=",
      "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": [
    "Das Resultat sieht bereits sehr gut aus. Nun können wir uns den eigentlichen Peaks widmen und starten im folgenden mit dem kleineren der Beiden. Zunächst sollten wir den maskierten Bereich entweder neu definieren oder komplett entfernen. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ebd77c40-6fcd-4881-bc1d-e3ca8ae0bf3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ls.mask = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7850ae53-ae2d-49aa-ac7b-dcef60a2dab7",
   "metadata": {},
   "source": [
    "Außerdem können wir dem Plot entnehmen, dass durch den höheren Unterrund unsere Anfangsstartwerte nicht mehr ganz so gut passen diese können wir wie folgt aktualisieren:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "823e05a0-516c-4d30-8dc7-5381e0e2e617",
   "metadata": {},
   "outputs": [],
   "source": [
    "mi.values['A_p1'] = 700\n",
    "mi.values['sigma_p1'] = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8648bf00-901e-40dc-ada2-9a6b684e8f31",
   "metadata": {},
   "source": [
    "Nun sollten wir alle Parameter wieder festhalten und nur die Parameter des ersten Peaks freigeben."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3c83690c-103e-47ff-b18f-13ac763ee87d",
   "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 = 1208 (χ²/ndof = 21.2) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 204 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 2.25e-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> 710 </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> 53.49 </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> 2.034 </td>\n",
       "        <td> 0.033 </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> 221 </td>\n",
       "        <td> 25 </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> 41.0 </td>\n",
       "        <td> 3.1 </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> 176 </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\"> -0.0009 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(177,177,250);color:black\"> -0.2454 <strong>(-0.558)</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(250,250,250);color:black\"> -0.0009 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td> 0.00155 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,217,217);color:black\"> 0.0003 <strong>(0.223)</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(177,177,250);color:black\"> -0.2454 <strong>(-0.558)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,217,217);color:black\"> 0.0003 <strong>(0.223)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td> 0.0011 </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 = 1208 (χ²/ndof = 21.2)      │              Nfcn = 204              │\n",
       "│ EDM = 2.25e-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     │    710    │    13     │            │            │         │         │       │\n",
       "│ 1 │ A_p2     │  1.400e3  │  0.014e3  │            │            │         │         │  yes  │\n",
       "│ 2 │ mu_p1    │   53.49   │   0.04    │            │            │         │         │       │\n",
       "│ 3 │ mu_p2    │   60.0    │    0.6    │            │            │         │         │  yes  │\n",
       "│ 4 │ sigma_p1 │   2.034   │   0.033   │            │            │         │         │       │\n",
       "│ 5 │ sigma_p2 │   2.00    │   0.02    │            │            │         │         │  yes  │\n",
       "│ 6 │ A_bkg    │    221    │    25     │            │            │         │         │  yes  │\n",
       "│ 7 │ tau_bkg  │   41.0    │    3.1    │            │            │    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 │      176        0  -0.0009        0  -0.2454        0        0        0 │\n",
       "│     A_p2 │        0        0   0.0000        0   0.0000        0        0        0 │\n",
       "│    mu_p1 │  -0.0009   0.0000  0.00155   0.0000   0.0003   0.0000   0.0000   0.0000 │\n",
       "│    mu_p2 │        0        0   0.0000        0   0.0000        0        0        0 │\n",
       "│ sigma_p1 │  -0.2454   0.0000   0.0003   0.0000   0.0011   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": 84,
     "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_p1', 'mu_p1', 'sigma_p1']] = False\n",
    "mi.migrad()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34df75bf-3750-4186-ae12-4f6bb9e49931",
   "metadata": {},
   "source": [
    "Jetzt wiederholen wir das ganze für den zweiten Peak…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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 = 75.44 (χ²/ndof = 1.3) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 268 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.88e-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> 710 </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> 1.162e3 </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> 53.49 </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> 60.615 </td>\n",
       "        <td> 0.031 </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> 2.034 </td>\n",
       "        <td> 0.033 </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> 2.671 </td>\n",
       "        <td> 0.027 </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> 221 </td>\n",
       "        <td> 25 </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> 41.0 </td>\n",
       "        <td> 3.1 </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\"> 0e-3 </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> 206 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,248,248);color:black\"> 5.0e-3 <strong>(0.011)</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\"> -223.8e-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\"> 0e-3 </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\"> 0e-3 </td>\n",
       "        <td style=\"background-color:rgb(250,248,248);color:black\"> 5.0e-3 <strong>(0.011)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0e-3 </td>\n",
       "        <td> 0.000981 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0e-3 </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.1e-3 <strong>(-0.181)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0e-3 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0e-3 </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\"> 0e-3 </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\"> -223.8e-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(227,227,250);color:black\"> -0.1e-3 <strong>(-0.181)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0.000704 </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\"> 0e-3 </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\"> 0e-3 </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 = 75.44 (χ²/ndof = 1.3)      │              Nfcn = 268              │\n",
       "│ EDM = 1.88e-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     │    710    │    13     │            │            │         │         │  yes  │\n",
       "│ 1 │ A_p2     │  1.162e3  │  0.014e3  │            │            │         │         │       │\n",
       "│ 2 │ mu_p1    │   53.49   │   0.04    │            │            │         │         │  yes  │\n",
       "│ 3 │ mu_p2    │  60.615   │   0.031   │            │            │         │         │       │\n",
       "│ 4 │ sigma_p1 │   2.034   │   0.033   │            │            │         │         │  yes  │\n",
       "│ 5 │ sigma_p2 │   2.671   │   0.027   │            │            │         │         │       │\n",
       "│ 6 │ A_bkg    │    221    │    25     │            │            │         │         │  yes  │\n",
       "│ 7 │ tau_bkg  │   41.0    │    3.1    │            │            │    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      0e-3         0         0         0         0 │\n",
       "│     A_p2 │         0       206         0    5.0e-3         0 -223.8e-3         0         0 │\n",
       "│    mu_p1 │         0         0         0      0e-3         0         0         0         0 │\n",
       "│    mu_p2 │      0e-3    5.0e-3      0e-3  0.000981      0e-3   -0.1e-3      0e-3      0e-3 │\n",
       "│ sigma_p1 │         0         0         0      0e-3         0         0         0         0 │\n",
       "│ sigma_p2 │         0 -223.8e-3         0   -0.1e-3         0  0.000704         0         0 │\n",
       "│    A_bkg │         0         0         0      0e-3         0         0         0         0 │\n",
       "│  tau_bkg │         0         0         0      0e-3         0         0         0         0 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 85,
     "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": [
    "Bevor wir ein letztes mal wieder alle Parameter freigeben und einen letzten fit durchführen. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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 = 46.04 (χ²/ndof = 0.9) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 563 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 2.48e-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> 649 </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> 1.154e3 </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> 53.25 </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> 3 </th>\n",
       "        <td> mu_p2 </td>\n",
       "        <td> 60.46 </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> 4 </th>\n",
       "        <td> sigma_p1 </td>\n",
       "        <td> 1.95 </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> 5 </th>\n",
       "        <td> sigma_p2 </td>\n",
       "        <td> 2.79 </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> 247 </td>\n",
       "        <td> 24 </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> 38.4 </td>\n",
       "        <td> 2.5 </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> 209 </td>\n",
       "        <td style=\"background-color:rgb(250,228,228);color:black\"> 0.03e3 <strong>(0.146)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,221,221);color:black\"> 0.181 <strong>(0.195)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,213,213);color:black\"> 0.1766 <strong>(0.248)</strong> </td>\n",
       "        <td style=\"background-color:rgb(219,219,250);color:black\"> -0.1726 <strong>(-0.241)</strong> </td>\n",
       "        <td style=\"background-color:rgb(210,210,250);color:black\"> -0.1778 <strong>(-0.310)</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(250,245,245);color:black\"> 1 <strong>(0.030)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,228,228);color:black\"> 0.03e3 <strong>(0.146)</strong> </td>\n",
       "        <td> 201 </td>\n",
       "        <td style=\"background-color:rgb(250,242,242);color:black\"> 0.049 <strong>(0.054)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,235,235);color:black\"> 0.0705 <strong>(0.101)</strong> </td>\n",
       "        <td style=\"background-color:rgb(249,249,250);color:black\"> -0.0074 <strong>(-0.010)</strong> </td>\n",
       "        <td style=\"background-color:rgb(189,189,250);color:black\"> -0.2640 <strong>(-0.469)</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(250,221,221);color:black\"> 0.181 <strong>(0.195)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,242,242);color:black\"> 0.049 <strong>(0.054)</strong> </td>\n",
       "        <td> 0.00413 </td>\n",
       "        <td style=\"background-color:rgb(250,148,148);color:black\"> 0.0021 <strong>(0.679)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,159,159);color:black\"> 0.0019 <strong>(0.605)</strong> </td>\n",
       "        <td style=\"background-color:rgb(168,168,250);color:black\"> -0.0016 <strong>(-0.628)</strong> </td>\n",
       "        <td style=\"background-color:rgb(245,245,250);color:black\"> -0.061 <strong>(-0.039)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,241,241);color:black\"> 0.010 <strong>(0.060)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,213,213);color:black\"> 0.1766 <strong>(0.248)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,235,235);color:black\"> 0.0705 <strong>(0.101)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,148,148);color:black\"> 0.0021 <strong>(0.679)</strong> </td>\n",
       "        <td> 0.00242 </td>\n",
       "        <td style=\"background-color:rgb(250,163,163);color:black\"> 0.0014 <strong>(0.581)</strong> </td>\n",
       "        <td style=\"background-color:rgb(168,168,250);color:black\"> -0.0012 <strong>(-0.632)</strong> </td>\n",
       "        <td style=\"background-color:rgb(243,243,250);color:black\"> -0.0656 <strong>(-0.055)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,243,243);color:black\"> 0.0058 <strong>(0.046)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(219,219,250);color:black\"> -0.1726 <strong>(-0.241)</strong> </td>\n",
       "        <td style=\"background-color:rgb(249,249,250);color:black\"> -0.0074 <strong>(-0.010)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,159,159);color:black\"> 0.0019 <strong>(0.605)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,163,163);color:black\"> 0.0014 <strong>(0.581)</strong> </td>\n",
       "        <td> 0.00245 </td>\n",
       "        <td style=\"background-color:rgb(188,188,250);color:black\"> -0.0009 <strong>(-0.473)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.2148 <strong>(-0.178)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,229,229);color:black\"> 0.0178 <strong>(0.142)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(210,210,250);color:black\"> -0.1778 <strong>(-0.310)</strong> </td>\n",
       "        <td style=\"background-color:rgb(189,189,250);color:black\"> -0.2640 <strong>(-0.469)</strong> </td>\n",
       "        <td style=\"background-color:rgb(168,168,250);color:black\"> -0.0016 <strong>(-0.628)</strong> </td>\n",
       "        <td style=\"background-color:rgb(168,168,250);color:black\"> -0.0012 <strong>(-0.632)</strong> </td>\n",
       "        <td style=\"background-color:rgb(188,188,250);color:black\"> -0.0009 <strong>(-0.473)</strong> </td>\n",
       "        <td> 0.00157 </td>\n",
       "        <td style=\"background-color:rgb(250,231,231);color:black\"> 0.1254 <strong>(0.130)</strong> </td>\n",
       "        <td style=\"background-color:rgb(228,228,250);color:black\"> -0.0171 <strong>(-0.170)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_bkg </th>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.01e3 <strong>(-0.030)</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(245,245,250);color:black\"> -0.061 <strong>(-0.039)</strong> </td>\n",
       "        <td style=\"background-color:rgb(243,243,250);color:black\"> -0.0656 <strong>(-0.055)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.2148 <strong>(-0.178)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,231,231);color:black\"> 0.1254 <strong>(0.130)</strong> </td>\n",
       "        <td> 593 </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -59 <strong>(-0.965)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> tau_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,245,245);color:black\"> 1 <strong>(0.030)</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(250,241,241);color:black\"> 0.010 <strong>(0.060)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,243,243);color:black\"> 0.0058 <strong>(0.046)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,229,229);color:black\"> 0.0178 <strong>(0.142)</strong> </td>\n",
       "        <td style=\"background-color:rgb(228,228,250);color:black\"> -0.0171 <strong>(-0.170)</strong> </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -59 <strong>(-0.965)</strong> </td>\n",
       "        <td> 6.37 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 46.04 (χ²/ndof = 0.9)      │              Nfcn = 563              │\n",
       "│ EDM = 2.48e-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     │    649    │    14     │            │            │         │         │       │\n",
       "│ 1 │ A_p2     │  1.154e3  │  0.014e3  │            │            │         │         │       │\n",
       "│ 2 │ mu_p1    │   53.25   │   0.06    │            │            │         │         │       │\n",
       "│ 3 │ mu_p2    │   60.46   │   0.05    │            │            │         │         │       │\n",
       "│ 4 │ sigma_p1 │   1.95    │   0.05    │            │            │         │         │       │\n",
       "│ 5 │ sigma_p2 │   2.79    │   0.04    │            │            │         │         │       │\n",
       "│ 6 │ A_bkg    │    247    │    24     │            │            │         │         │       │\n",
       "│ 7 │ tau_bkg  │   38.4    │    2.5    │            │            │    0    │         │       │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬─────────────────────────────────────────────────────────────────────────┐\n",
       "│          │     A_p1     A_p2    mu_p1    mu_p2 sigma_p1 sigma_p2    A_bkg  tau_bkg │\n",
       "├──────────┼─────────────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │      209   0.03e3    0.181   0.1766  -0.1726  -0.1778  -0.01e3        1 │\n",
       "│     A_p2 │   0.03e3      201    0.049   0.0705  -0.0074  -0.2640  -0.01e3        1 │\n",
       "│    mu_p1 │    0.181    0.049  0.00413   0.0021   0.0019  -0.0016   -0.061    0.010 │\n",
       "│    mu_p2 │   0.1766   0.0705   0.0021  0.00242   0.0014  -0.0012  -0.0656   0.0058 │\n",
       "│ sigma_p1 │  -0.1726  -0.0074   0.0019   0.0014  0.00245  -0.0009  -0.2148   0.0178 │\n",
       "│ sigma_p2 │  -0.1778  -0.2640  -0.0016  -0.0012  -0.0009  0.00157   0.1254  -0.0171 │\n",
       "│    A_bkg │  -0.01e3  -0.01e3   -0.061  -0.0656  -0.2148   0.1254      593      -59 │\n",
       "│  tau_bkg │        1        1    0.010   0.0058   0.0178  -0.0171      -59     6.37 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 86,
     "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": "markdown",
   "id": "1510691d-6fbc-466f-a802-647bbc5e9fd2",
   "metadata": {},
   "source": [
    "TODO: Add indivdiual components to the plot..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "067fbf6f-14c4-4a46-afb3-71753d06af23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbb155e9520>"
      ]
     },
     "execution_count": 95,
     "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.plot(x, peak(x, *mi.values['A_p1', 'mu_p1', 'sigma_p1']), color='gray', ls='--')\n",
    "plt.plot(x, peak(x, *mi.values['A_p2', 'mu_p2', 'sigma_p2']), color='gray', ls='-.')\n",
    "plt.plot(x, bkg(x, *mi.values['A_bkg', 'tau_bkg']), color='gray')\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ef19633-0947-4568-b537-a1c69e42b7c2",
   "metadata": {},
   "source": [
    "Das Ergebnis sieht sehr gut aus. Alle Kacheln sind grün und die Daten scheinen durch die Funktion gut beschrieben zu werden. Natürlich können wir auch das Gesamte Fitverfahren etwas kompakter in einer Zelle darstellen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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 = 46.04 (χ²/ndof = 0.9) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 563 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 2.48e-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> 649 </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> 1.154e3 </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> 53.25 </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> 3 </th>\n",
       "        <td> mu_p2 </td>\n",
       "        <td> 60.46 </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> 4 </th>\n",
       "        <td> sigma_p1 </td>\n",
       "        <td> 1.95 </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> 5 </th>\n",
       "        <td> sigma_p2 </td>\n",
       "        <td> 2.79 </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> 247 </td>\n",
       "        <td> 24 </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> 38.4 </td>\n",
       "        <td> 2.5 </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> 209 </td>\n",
       "        <td style=\"background-color:rgb(250,228,228);color:black\"> 0.03e3 <strong>(0.146)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,221,221);color:black\"> 0.181 <strong>(0.195)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,213,213);color:black\"> 0.1766 <strong>(0.248)</strong> </td>\n",
       "        <td style=\"background-color:rgb(219,219,250);color:black\"> -0.1726 <strong>(-0.241)</strong> </td>\n",
       "        <td style=\"background-color:rgb(210,210,250);color:black\"> -0.1778 <strong>(-0.310)</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(250,245,245);color:black\"> 1 <strong>(0.030)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,228,228);color:black\"> 0.03e3 <strong>(0.146)</strong> </td>\n",
       "        <td> 201 </td>\n",
       "        <td style=\"background-color:rgb(250,242,242);color:black\"> 0.049 <strong>(0.054)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,235,235);color:black\"> 0.0705 <strong>(0.101)</strong> </td>\n",
       "        <td style=\"background-color:rgb(249,249,250);color:black\"> -0.0074 <strong>(-0.010)</strong> </td>\n",
       "        <td style=\"background-color:rgb(189,189,250);color:black\"> -0.2640 <strong>(-0.469)</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(250,221,221);color:black\"> 0.181 <strong>(0.195)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,242,242);color:black\"> 0.049 <strong>(0.054)</strong> </td>\n",
       "        <td> 0.00413 </td>\n",
       "        <td style=\"background-color:rgb(250,148,148);color:black\"> 0.0021 <strong>(0.679)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,159,159);color:black\"> 0.0019 <strong>(0.605)</strong> </td>\n",
       "        <td style=\"background-color:rgb(168,168,250);color:black\"> -0.0016 <strong>(-0.628)</strong> </td>\n",
       "        <td style=\"background-color:rgb(245,245,250);color:black\"> -0.061 <strong>(-0.039)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,241,241);color:black\"> 0.010 <strong>(0.060)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,213,213);color:black\"> 0.1766 <strong>(0.248)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,235,235);color:black\"> 0.0705 <strong>(0.101)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,148,148);color:black\"> 0.0021 <strong>(0.679)</strong> </td>\n",
       "        <td> 0.00242 </td>\n",
       "        <td style=\"background-color:rgb(250,163,163);color:black\"> 0.0014 <strong>(0.581)</strong> </td>\n",
       "        <td style=\"background-color:rgb(168,168,250);color:black\"> -0.0012 <strong>(-0.632)</strong> </td>\n",
       "        <td style=\"background-color:rgb(243,243,250);color:black\"> -0.0656 <strong>(-0.055)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,243,243);color:black\"> 0.0058 <strong>(0.046)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(219,219,250);color:black\"> -0.1726 <strong>(-0.241)</strong> </td>\n",
       "        <td style=\"background-color:rgb(249,249,250);color:black\"> -0.0074 <strong>(-0.010)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,159,159);color:black\"> 0.0019 <strong>(0.605)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,163,163);color:black\"> 0.0014 <strong>(0.581)</strong> </td>\n",
       "        <td> 0.00245 </td>\n",
       "        <td style=\"background-color:rgb(188,188,250);color:black\"> -0.0009 <strong>(-0.473)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.2148 <strong>(-0.178)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,229,229);color:black\"> 0.0178 <strong>(0.142)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(210,210,250);color:black\"> -0.1778 <strong>(-0.310)</strong> </td>\n",
       "        <td style=\"background-color:rgb(189,189,250);color:black\"> -0.2640 <strong>(-0.469)</strong> </td>\n",
       "        <td style=\"background-color:rgb(168,168,250);color:black\"> -0.0016 <strong>(-0.628)</strong> </td>\n",
       "        <td style=\"background-color:rgb(168,168,250);color:black\"> -0.0012 <strong>(-0.632)</strong> </td>\n",
       "        <td style=\"background-color:rgb(188,188,250);color:black\"> -0.0009 <strong>(-0.473)</strong> </td>\n",
       "        <td> 0.00157 </td>\n",
       "        <td style=\"background-color:rgb(250,231,231);color:black\"> 0.1254 <strong>(0.130)</strong> </td>\n",
       "        <td style=\"background-color:rgb(228,228,250);color:black\"> -0.0171 <strong>(-0.170)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_bkg </th>\n",
       "        <td style=\"background-color:rgb(246,246,250);color:black\"> -0.01e3 <strong>(-0.030)</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(245,245,250);color:black\"> -0.061 <strong>(-0.039)</strong> </td>\n",
       "        <td style=\"background-color:rgb(243,243,250);color:black\"> -0.0656 <strong>(-0.055)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.2148 <strong>(-0.178)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,231,231);color:black\"> 0.1254 <strong>(0.130)</strong> </td>\n",
       "        <td> 593 </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -59 <strong>(-0.965)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> tau_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,245,245);color:black\"> 1 <strong>(0.030)</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(250,241,241);color:black\"> 0.010 <strong>(0.060)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,243,243);color:black\"> 0.0058 <strong>(0.046)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,229,229);color:black\"> 0.0178 <strong>(0.142)</strong> </td>\n",
       "        <td style=\"background-color:rgb(228,228,250);color:black\"> -0.0171 <strong>(-0.170)</strong> </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -59 <strong>(-0.965)</strong> </td>\n",
       "        <td> 6.37 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 46.04 (χ²/ndof = 0.9)      │              Nfcn = 563              │\n",
       "│ EDM = 2.48e-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     │    649    │    14     │            │            │         │         │       │\n",
       "│ 1 │ A_p2     │  1.154e3  │  0.014e3  │            │            │         │         │       │\n",
       "│ 2 │ mu_p1    │   53.25   │   0.06    │            │            │         │         │       │\n",
       "│ 3 │ mu_p2    │   60.46   │   0.05    │            │            │         │         │       │\n",
       "│ 4 │ sigma_p1 │   1.95    │   0.05    │            │            │         │         │       │\n",
       "│ 5 │ sigma_p2 │   2.79    │   0.04    │            │            │         │         │       │\n",
       "│ 6 │ A_bkg    │    247    │    24     │            │            │         │         │       │\n",
       "│ 7 │ tau_bkg  │   38.4    │    2.5    │            │            │    0    │         │       │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬─────────────────────────────────────────────────────────────────────────┐\n",
       "│          │     A_p1     A_p2    mu_p1    mu_p2 sigma_p1 sigma_p2    A_bkg  tau_bkg │\n",
       "├──────────┼─────────────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │      209   0.03e3    0.181   0.1766  -0.1726  -0.1778  -0.01e3        1 │\n",
       "│     A_p2 │   0.03e3      201    0.049   0.0705  -0.0074  -0.2640  -0.01e3        1 │\n",
       "│    mu_p1 │    0.181    0.049  0.00413   0.0021   0.0019  -0.0016   -0.061    0.010 │\n",
       "│    mu_p2 │   0.1766   0.0705   0.0021  0.00242   0.0014  -0.0012  -0.0656   0.0058 │\n",
       "│ sigma_p1 │  -0.1726  -0.0074   0.0019   0.0014  0.00245  -0.0009  -0.2148   0.0178 │\n",
       "│ sigma_p2 │  -0.1778  -0.2640  -0.0016  -0.0012  -0.0009  0.00157   0.1254  -0.0171 │\n",
       "│    A_bkg │  -0.01e3  -0.01e3   -0.061  -0.0656  -0.2148   0.1254      593      -59 │\n",
       "│  tau_bkg │        1        1    0.010   0.0058   0.0178  -0.0171      -59     6.37 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 88,
     "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": [
    " # Wann fittet ein Fit?\n",
    "Nach dem wir nun unser Model an unsere Daten angepasst haben stellt sich die Frage: „Spiegelt unser Model unsere Daten gut wider?“.  Um diese Frage beantworten zu könne gibt es verschiedene Möglichkeiten, welche wir uns im Folgenden etwas näher angucken wollen. \n",
    "## Fit Residuan: \n",
    "Schauen wir uns zunächst noch einmal an, wie das Chi-Quadrat definiert ist:\n",
    "$$$$\n",
    "Wir minimieren den Abstand zwischen einem Messwert und unserer Model und Gewichten diesen mit den Unsicherheiten unserer Messwerte. Fitresiduan spiegeln genau dies wider. Sie sind definiert als \n",
    "$$$$\n",
    "Für unseren Fit sehen sie wie folgt aus. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "30cafddc-ea17-4158-82cc-f132dee2c8de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Residuals [$\\\\sigma$]')"
      ]
     },
     "execution_count": 89,
     "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": [
    "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": [
    "Als einzelner Plot sind sie noch nicht sehr informative. Hilfreicher ist es bereits sofern wir die Residuanen zusammen mit unseren Daten und Fitmodel darstellen. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "d9fbe83b-3146-4d72-89a4-084c29752e24",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/jobs/29593351/ipykernel_11778/53208542.py:7: UserWarning: The figure layout has changed to tight\n",
      "  fig_fit.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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0/Y5ujvR9sC8Av33ym1UBY7BuHRRCCAtJ7IQQoh6MVUb2fGYe9zbn+XOtaIcPH+amm25i8+bN2qQIR0fHS67NqdPpmDhxInFxcRiNRpYvX261YHuvGb1QdAqG/QZ0Ouu36/NbB4UQwkISOyGEqIfD3x2m9EQpnqGexEw4l1iFhITwww8/8Pvvv5OQkFDn6+l0Om666Sb8/PwoLi5m1apV2j7vCG9ixsbgjTcPjXhI235h66AQQlhIYieEELUoLS1FURQURaG0tFTbvuvjXQD0mtkLveO59R/37t1LTk4OTk5O9O7du173cnFx4eabb0ZRFA4cOMCRI0e0fb1m9AIgfF84uv+9ZR8+fJjp06df8WsTQrRcktgJIUQdndh/gqw/stA56Og941zy5uHhwaZNmwAYNWoU3t7e9b52WFgYAweaZ8KuWrVKW7A99oZYPII9KDtZRgc6aMcKIURtJLETQog6sqwEEX9TPJ6hntr24cOHYzAYCA0NpU+fPld8/WuvvRYvLy/8/f2pqKgAzAWLe9zbA4De1K8lUAjR+khiJ4QQdVBdUc2hZYcA6D3TnGC5u7tz4sQJLZkbM2bMJSdLXI6joyP3338/d955J56e5xLHntN7AhBNNN7UvzVQCNF6SGInhBB1kPRzEhWFFXiGedJueDtt+4YNG1BVlY4dOxIZGXnV9/H09KyRHPpF+xExNAIFha50vep7CCFaLknshBDiMnJzc9n/r/0AdJ3aFZ3e/NaZn5/P4cOHAXM3qi2VlpayZs0abeJG9zu7AzC1+1Tc3d1tei8hRMshiZ0QQtTiwiW8lq1cBkD3u7pr2//44w8AOnXqRFBQkE3v/+2337Jlyxa2b99ujuHmjugcdJzYd4JTR0/Z9F5CiJZDEjshhLhATk5OjSW8fjL+hHMnZwK7BAJQVFTEwYMHARg8eLDNY+jbty/h4eFarTo3fzfaj2oPwKHlh2x+PyFEyyCJnRBCXCA5ObnGEl4qKp7Dzk1o2L59O6qq0q5dO0JCQmweQ8eOHZk+fTpxcXHats63dQYksRNCXJwkdkIIcYHY2NgaS3gpKIyaPgoAg8HAnj3mZcUGDBjQIDHUNrs2flI8eic9Jw+fJP9gfoPcVwjRvEliJ4QQFwgPD2f+/PnacwWF+zrfR4de5gLB1dXV9OnTh/DwcGJjYxs0ltLSUjZv3syxY8dw8XEhekw0AAeXH2zQ+wohmidJ7IQQohbTpk0DYAITmMtcZj82W9vn6urKqFGjmD59+lXVrauLtWvX8vvvv7Njxw7AujtWVdUGvbcQovmRxE4IIS7CDz960xsfvQ8dJnawSwy9epnXij106BDl5eV0uLEDeic9BckFnEqU2bFCCGuS2AkhxEV0ohMAkddG4ubvBsD+/ftJSUmpMbmioYSFhREUFER1dTX79+/H2dOZqJFRABz96WijxCCEaD4ksRNCiIuwJHZxk8wzU6urq1m9ejVfffUVqampjRKDoihaq92ePXtQVZUON5pbDyWxE0JcSBI7IYSoRWFGIaGEYsJEzPgYwDwbtkuXLgQGBhIdHd1osXTr1g0HBwfy8/PJzc0lbrw50czZlkNpfmmjxSGEaPoksRNCiFpk/poJQPvh7QmMMhcldnV1Zdy4cfzf//1fjXIoDcnFxYWOHTsCcODAAbzCvQjpFQKqeQ1bIYSwkMROCCFqcfhb8xqwnf7Sqca+hp4JW5suXboAcPjwYUwmE3E3mlvtkn6SxE4IcY4kdkIIcYHi3GJyt+eCAvE3xQOQnZ1NZmam3UqMREdH4+LiQklJCZmZmdo4u9TfUqmuqLZLTEKIpkcSOyGEuEDSSnMrWHj/cDxDzMuIrV+/nsWLF7N161a7xKTX67Xu2IMHDxLcIxivcC8MZQbS16XbJSYhRNMjiZ0QQlzA0r1pqV1XWlpKero5eYqPj7dbXJbu2MTERKvuWJkdK4SwkMROCCHOU1VSRdraNACtuzMxMRFVVQkJCcHPz89usbVr1w53d3fKy8tJS0sj7gZzYpeyKkVWoRBCAJLYCSGEldQ1qRgrjfhG+9KmYxvAvOoDQOfOne0ZGjqdjk6dzJM5jh49Sttr26J30lOUWcTpo6ftGpsQomlwsHcAQgjRlGjdsDd2QFEUzp49S0ZGBmD/xA6gb9++xMXFERUVhV6vp+3QtqT9nkbK6hTaxLexd3hCCDuTFjshhPgfk9GkTZzQVnc4ah6/FhYWho+Pj71C0wQEBBATE4Nerwcgeoy5UHLq6sZZCUMI0bRJYieEEP+Tsy2HslNluPi6EDEoAjiX2Nlz0sSlWBK7jPUZUvZECGHfxG7jxo1MmDCB0NBQFEXhxx9/tNqvqiovvPACISEhuLq6MmrUKJKTk62OKSgoYOrUqXh5eeHj48P06dMpKSmxOmb//v0MGTIEFxcXIiIieOuttxr6pQkhmqGkFebWutjrY9E76qmqqtJmw3bo0MGeoVkpLy9n9erVLF68mIDOAXiGelJdXk3WH1n2Dk0IYWd2TexKS0vp3r07H330Ua3733rrLT744AM+/fRTtm/fjru7O2PGjKGiokI7ZurUqRw6dIg1a9awcuVKNm7cyMyZM7X9xcXFjB49mrZt27J7927efvttXnzxRRYuXNjgr08I0bwk/2z+4GhZizU1NRWj0Yivry9t2jSd8WsODg7s3r2bzMxMTpw4QfRoc6tdyuoUO0cmhLA3u06eGDduHOPGjat1n6qqvPfeezz33HNMnDgRgKVLlxIUFMSPP/7IlClTSExMZNWqVezcuZM+ffoAMH/+fK6//nreeecdQkND+eqrr6iqquKf//wnTk5OdO7cmb179zJv3jyrBFAI0boV5xSTfzAfRadoiZKlG7ZDhw52WUbsYhwdHRk5ciReXl74+/sTPSaavYv3kroqFd62d3RCCHu6osTOZDKRkpJCfn4+JpPJat/QoUNtElh6ejp5eXmMGjVK2+bt7U3//v3ZunUrU6ZMYevWrfj4+GhJHcCoUaPQ6XRs376dm266ia1btzJ06FCcnJy0Y8aMGcObb77JmTNn8PX1rXHvyspKKisrtefFxcU2eU1CiKbL0toV1j8MVz9XTCaTNvQjLi7OnqHVqn///trX7a9rDwrkH8ynOLcYrzAvO0YmhLCneid227Zt44477qh1zURFUTAajTYJLC8vD4CgoCCr7UFBQdq+vLw8AgMDrfY7ODjg5+dndUxUVFSNa1j21ZbYvfHGG7z00ks2eR1CiOYh5VdzYhczNgaAnJwcysrKcHFxITIy0p6hXZabvxthfcPI3ZFL2po0etzTw94hCSHspN5j7P7v//6PPn36cPDgQQoKCjhz5oz2KCgoaIgYG92zzz5LUVGR9sjOzrZ3SEIIGyotLUVRFBRFobS0FKPBSNoa82oTlsTO29ub4cOH079/f620SFNz8uRJ1q9fz+HDh4kaZf4AK+vGCtG61bvFLjk5mW+//ZaYmJiGiEcTHBwMwIkTJwgJCdG2nzhxgh49emjH5OfnW51XXV1NQUGBdn5wcDAnTpywOsby3HLMhZydnXF2drbJ6xBCNH2523OpLK7E1d+VkN7m9xtvb2+bDS1pKElJSWzYsIGYmBgGjBjAH6//Qfq6dFRVbVJjAoUQjafeLXb9+/cnJaXhZ15FRUURHBzM2rVrtW3FxcVs376dAQMGADBgwAAKCwvZvXu3dsy6deswmUza+JMBAwawceNGDAaDdsyaNWvo0KFDrd2wQojWJ2WV+T0tenQ0On3zKe8ZGxsLQEZGBsF9g9E76zmbe5bTSbK8mBCtVb1b7ObMmcPjjz9OXl4eXbt2xdHR0Wp/t27d6nytkpISqyQxPT2dvXv34ufnR2RkJHPnzuXVV18lNjaWqKgonn/+eUJDQ5k0aRIAHTt2ZOzYscyYMYNPP/0Ug8HAQw89xJQpUwgNDQXgjjvu4KWXXmL69Ok8/fTTHDx4kPfff5933323vi9dCNFCWRK7mHHmnoiMjAxKSkqIjo7G1dXVnqFdUkBAAJ6enpw9e5bjJ48TMTCCjIQM0tel06ZD0ynPIoRoPPVO7CZPngzAfffdp21TFEVr+q/P5Ildu3YxfPhw7fljjz0GwLRp01i8eDFPPfUUpaWlzJw5k8LCQgYPHsyqVatwcXHRzvnqq6946KGHGDlyJDqdjsmTJ/PBBx9o+729vfntt9+YPXs2vXv3pk2bNrzwwgtS6kQIAUDy3mSO7z4OoJU52b59O0eOHGH48OFNujtWURSio6PZu3cvKSkpRI2IIiMhg4x1GfR9oK+9wxNC2IGiXji19TIyMzMvub9t27ZXFVBTVFxcjLe3N0VFRXh5SRkBIZq7jz/+mNmzZwOgU3SMV8dzQ68bmLnb/IFv/fr1JCYmMnHiRK31v6k6dOgQ3377LQEBAUzoMYF/Dvonrv6uPJn/JIpOxtkJ0dTZOseod4tdS0zchBCtR05ODnPmzNGem1QTK1jB7QNv17YNGzaMYcOG2SG6+mvfvj2KonDy5Enc49xx8nCi/HQ5J/afILhH7RPEhBAtV50Su59++olx48bh6OjITz/9dMljb7zxRpsEJoQQDSE5OblGYXUVFWLtFNBVcnV1JSwsjJycHDKyMmg7tC3JvySTvi5dEjshWqE6JXaTJk3SigFbJi7UxpYFioUQoiHExsai0+mskjsFhYE3DgQgOzub4ODgGhPDmrKYmBhycnJISUmh3Yh2WmI34LEB9g5NCNHI6jSv32QyaSs8mEymiz4kqRNCNHXh4eHMnz9fe66gMLPXTCLbRVJaWso///lP3nrrLSoqKuwYZf1ER5snfaSlpdFueDsAMjdkYjTIe7IQrU3zKdgkhBA2Mm3aNACu53rmMpeZD5gnTaSlmVef8Pf3t5p939SFhobi6upKZWUlBj8DLr4uVJVUkfdnnr1DE0I0sitK7NauXcv48eOJjo4mOjqa8ePH8/vvv9s6NiGEaDCuuNKHPnjjTfQYc4tXamoqcK4FrLnQ6XS0b98egPSMdNoOMU9yy9x46SoGQoiWp96J3ccff8zYsWPx9PTkkUce4ZFHHsHLy4vrr7+ejz76qCFiFEIIm2tPe3To8O/oj3eEN6qqkp5uXme1uSV2YF6tB8yF3iOHRgKS2AnRGtW73Mnrr7/Ou+++y0MPPaRte/jhhxk0aBCvv/66VhtKCCGashjMq0xEXWdOiAoKCiguLkav1xMREWHP0K5ITEwM1157Le3bt8ch3/zWnrUpC5PR1KyWSRNCXJ16/7YXFhYyduzYGttHjx5NUVGRTYISQoiGpKpqjcQuIyMDME+uaE4zYi28vb0ZNmwYkZGRBPcIxsnDiYrCCvIP5ts7NCFEI6p3YnfjjTfyww8/1Nj+3//+l/Hjx9skKCGEaEglqSV44omjmyNx18UBaN2w7dq1s2NktqFz0BExyNzqKN2xQrQudeqKPX/t1U6dOvHaa6+xfv16Bgww10jatm0bmzdv5vHHH2+YKIUQwoZSVqUAEDUiCgdnB1RV1VrsLGPVmiODwUBKSgqnTp2i7dC2pK5OJXNDJv3n9Ld3aEKIRlKnxO7dd9+1eu7r68vhw4c5fPiwts3Hx4d//vOfPPfcc7aNUAghbCx11f9mv441T5I4efIkpaWlODg4EBYWZs/QrkpFRQXffPMNAHcMuQMwt9ipqoqiyLqxQrQGdUrsLF0UQgjR3FWerSTrjywAYsaax9lZWusiIyNxcKj3nLImQ6fTceTIEYqLi5lx7wwcXBwoO1nG6aOnaRPfxt7hCSEaQfN9BxNCiCuQvjYdU7UJv1g//KL9zNta0Pi6ZcuWAeDt7034NeFkrM8gc2OmJHZCtBIyB14I0apYxtdZWutayvi62rS99n+FijfIBAohWgtJ7IQQrYaqqjUSu7KyMkJCQnB3dyc0NNSe4dmMXq8nNzeX4P7BAGRsyEBVVTtHJYRoDNIVK4RoNU4fPU1RZhF6Z73WmuXu7s7dd9+NyWRCp2sZn3VHjx7Np59+yt23343OQcfZ3LMUZhTiG+Vr79CEEA2sXu9i1dXVvPzyy+Tk5DRUPEII0WCSf00GoO3Qtji5O1ntawlJ3ZIlSwD49ddfee+99/jnv/5JaF9zK6R0xwrROtTrnczBwYG3336b6urqhopHCCEajKXMiaUb1mQyUVpaas+QbCYnJ4c5c+Zoz1VV5aOPPsKtpxsghYqFaC3q/RF1xIgRbNiwoSFiEUKIBmMoM5CxIQOAmHHmxO748eO88847LF682H6B2UhycjImk8lqm8lkojy4HJDETojWot5j7MaNG8czzzzDgQMH6N27N+7u7lb7b7zxRpsFJ4QQtpKxIQNjpRHvSG+t9EdeXh4Arq6u9gzNJmJjY9HpdFbJnaIohPcIJ0eXw5nUM5w9dhbPUE87RimEaGj1TuwefPBBAObNm1djn6IoGI3Gq49KCCFszDIbNnpstLYKQ+/evYmPj6eystKeodlEeHg48+fPZ/bs2YB5zOD48eMxOhgJ6hZE3t48sjZn0fmWznaOVAjRkOrdFWsymS76kKROCNFUpfxqXebEwt3dHT8/P3uEZHPTpk3Tvl6xYgW9evUiMzOTiEERAGRvzrZXaEKIRnJV08AqKipsFYcQQjSYM2lnKEguQOego/3I9vYOp1H06dMHgBMnThDUNwiQxE6I1qDeiZ3RaOSVV14hLCwMDw8P0tLSAHj++edZtGiRzQMUQoirlfyLucxJxKAInL2cAfjzzz9ZsmQJ+/bts2doDcbd3R1/f38AlEhz1/PxP49TVVplz7CEEA2s3onda6+9xuLFi3nrrbdwcjpXB6pLly58/vnnNg1OCCFswdING3t9rLYtLS2NjIwMCgsL7RRVw4uMjATglOEUXuFeqEaV3B25do5KCNGQ6p3YLV26lIULFzJ16lT0er22vXv37hw5csSmwQkhxNUylBtIX5cOnCtzApCVlQWcS35aAnd3d1RVRVVV3N3dadeuHYCMsxOiFal3Ypebm0tMTEyN7SaTCYPBYJOghBDCVjI3ZFJdUY1XuBeBXQIBKCoqori4GEVRCAsLs3OEDadtW/OyacePHyekfwgAWX9k2TMkIUQDq3di16lTJzZt2lRj+7fffkvPnj1tEpQQQtiKZXxdzLgYrcyJpbUuODjYakhJS+Pt7Y2/vz8RERH49zCPt8vZmoPJaLrMmUKI5qredexeeOEFpk2bRm5uLiaTie+//56jR4+ydOlSVq5c2RAxCiHEFdPKnJzXDZudbe6ObEndsBfz4IMPmgsXV5twdHeksriSk4dOEtQtyN6hCSEaQL1b7CZOnMiKFSv4/fffcXd354UXXiAxMZEVK1Zw3XXXNUSMQghxRU4nn6YgpQCdo3WZE0tiFxERYa/QGo1OZ36b1znoCL8mHICszdIdK0RLVe8WO4AhQ4awZs0aW8cihBA2ZWmtixwcqZU5qays5MSJE+btraDFzqKiooLwgeGkr00ne3M2fR/oa++QhBAN4IoSO4Bdu3aRmJgImMfd9e7d22ZBCSGELVjG151f5iQnJwdVVfHx8cHTs3Wsm/rll1+SmprK6LjRgMyMFaIlq3dil5OTw+23387mzZvx8fEBoLCwkIEDB7Js2TLCw8NtHaMQQtSbocxAxvoMoOWXObkcZ2dza6Up1ISiUyjMKOTssbN4hraOxFaI1qTeY+zuv/9+DAYDiYmJFBQUUFBQQGJiIiaTifvvv78hYhRCiHrLWJ+BsdKIV4QXAZ0CtO2taXydxciRI3n88ccZNGIQAV3M34trwq6htLTUzpEJIWyt3i12GzZsYMuWLXTo0EHb1qFDB+bPn8+QIUNsGpwQQlyp87thLWVOTCYTOTk5QOtqsfPz89O+Dr0mlPz9+UTSel6/EK1JvVvsIiIiai1EbDQaCQ0NtUlQ51/z+eefJyoqCldXV6Kjo3nllVdQVVU7RlVVXnjhBUJCQnB1dWXUqFEkJydbXaegoICpU6fi5eWFj48P06dPp6SkxKaxCtEclZaWoigKiqK0qNYbVVVrLXOSl5eHwWDAxcWFgICAi53eooVdYy7IHEHrabEUojWpd2L39ttvM2fOHHbt2qVt27VrF4888gjvvPOOTYN78803+eSTT/jwww9JTEzkzTff5K233mL+/PnaMW+99RYffPABn376Kdu3b8fd3Z0xY8ZQUVGhHTN16lQOHTrEmjVrWLlyJRs3bmTmzJk2jVUI0TSUlpYSqAvkTNoZdI46okZEafsCAgK4++67uf7667VWvNbiwIEDfPnll5T6l1JEERVUkJ6cbu+whBA2pqjnN3/Vga+vL2VlZVRXV+PgYO7JtXzt7u5udWxBQcFVBTd+/HiCgoJYtGiRtm3y5Mm4urry5ZdfoqoqoaGhPP744zzxxBOAeamgoKAgFi9ezJQpU0hMTKRTp07s3LmTPn36ALBq1Squv/56cnJy6tTKWFxcjLe3N0VFRXh5eV3VaxKiKSktLcXDwwOAkpKSGr/DzVFpaSljPMZwHdfRblQ7pq2ZZu+QmoSEhAQ2btxIXl4eCz5dgIqKTtGx8LOFTJ8+3d7hCdFq2TrHqPcYu/fee++qb1pXAwcOZOHChSQlJREXF8e+ffv4448/mDdvHgDp6enk5eUxatQo7Rxvb2/69+/P1q1bmTJlClu3bsXHx0dL6gBGjRqFTqdj+/bt3HTTTTXuW1lZSWVlpfa8uLi4AV+lEMLWOmAeAxx9fbSdI2k6IiMjKSoqYsECc1IHYFJNzJo1izFjxkhFAyFaiHondtOmNd6n32eeeYbi4mLi4+PR6/UYjUZee+01pk6dCpjHywAEBVkvjRMUFKTty8vLIzAw0Gq/g4MDfn5+2jEXeuONN3jppZds/XKEaNJyc3OJi4uzdxhXrexkmTZ+7PzErqioiK1bt9KuXTvi4+PtFZ7dhIeHc+bMGS7spDEajaSkpEhiJ0QLUe8xdo3pm2++4auvvuLrr79mz549LFmyhHfeeYclS5Y06H2fffZZioqKtIelPIIQLc35v0sdO3a0GvbQXKWtTqOYYnaxi7PKWW17RkYG27dvZ/PmzXaMzn6cnZ3p0KFDjbGFer2emJiYi5wlhGhumnRi9+STT/LMM88wZcoUunbtyl133cWjjz7KG2+8AUBwcDCAtjyQxYkTJ7R9wcHB5OfnW+2vrq6moKBAO+ZCzs7OeHl5WT2EaGlycnKYM2eO9txkMnfLWcqBNFeLPlnEe7zHSlZaJatt2rShT58+dO7c2c4R2k+3bt2YMGGC9lxB4e3n3pbWOiFakCteUqwxlJWVaQtYW+j1ekwmEwBRUVEEBwezdu1aevToAZjHw23fvp0HHngAgAEDBlBYWMju3bu1Zc/WrVuHyWSif//+jfdihGhikpOTtd8li+beLZeRksHn+z4/N4bMZD2GLCwszM4R2ldkZCS9evXCw8ODk1+fpAtdGBw02N5hCSFsqEm32E2YMIHXXnuNn3/+mYyMDH744QfmzZunTXhQFIW5c+fy6quv8tNPP3HgwAHuvvtuQkNDmTRpEmDuXho7diwzZsxgx44dbN68mYceeogpU6bYvO6eEM1JbGxsrR+cmnO33Kb/bNKSOgtLsirOrbYRExODSWfCG29ytjTvFlohhLWrTuyKi4v58ccfSUxMtEU8VubPn89f/vIXHnzwQTp27MgTTzzBrFmzeOWVV7RjnnrqKebMmcPMmTPp27cvJSUlrFq1ChcXF+2Yr776ivj4eEaOHMn111/P4MGDWbhwoc3jFaI5CQ8Pt6oJqdfrWbBgQbNtrQMwHjKiUHMMmb+/P1lZWVRXV9spsqbB29sbT09PdDodFQHmWp9Zm7PsHJUQwpbqXcfu1ltvZejQoTz00EOUl5fTvXt3MjIyUFWVZcuWMXny5IaK1W6kjp1oqc6vY3f06NFmPSvWZDTxbvi7bMzbyE/8BJxLVtu1a8cff/xBjx49mDhxop0jta/ly5dz5MgRNq7ZyNAtQ0GFx449hmeIp71DE6JVsnWOUe8Wu40bN2prwv7www+oqkphYSEffPABr7766lUHJISwj+Y+/ix7czYleSUM8BmA7n9vbYcPH2b69OnazHZLV2Rr1r59ewDumXUPQV3NpaKyt8jMfyFainondkVFRdqC0qtWrWLy5Mm4ublxww031FijVQghGsvhbw8DEHNDDCbMk0LCwsIwGo3k5uYC5skDrZ0luc3JySF8oLnbPXuzJHZCtBT1nhUbERHB1q1b8fPzY9WqVSxbtgyAM2fOWI1rE0I0TScTT5K+Np3CjEIc3R05uPwg0WOicXFvvr+/qkkl8TvzON+4m+Lgq3P7jh8/TnV1NW5ubvj7+9spwqYjMDAQZ2dnKisr8epi7vaRFjshWo56J3Zz585l6tSpeHh4EBkZybBhwwBzF23Xrl1tHZ8QwkZOHDjBqodXkbE+o8Y+vbOe3rN6M/S5obgHNL/1YrO3ZnP22FmcvZxpO7yt1b6sLPPkgIiIiBrFeVsjnU7H2LFj8fT0xMvoRQIJHN9zHEO5AUdXR3uHJ4S4SvVO7B588EH69etHdnY21113nVYuoX379jLGTogmaufHO1n1yCpM1SYUvUL7Ue0J6BRAVWkVWRuzOHXkFDs+2MGhZYeYuHgiseNitXPPn2BRUlKCu3vTS/ws3bAdbuyAt5+31bJZMr6uJkvdT1VV8QjxoOR4Ccd2HqPt0LaXPlEI0eRdUYHiPn360K1bN9LT04mOjsbBwYEbbrjB1rEJIWwg4YUENr6yEYD4SfGMfX8s3pHe2n5VVUlfm86quas4eegkX9/wNWPfG0v/h5tHAW/VpJL4rbkbttMtnaz3qarWYifj62pSFIXIQZEc/vYw2VuyJbETogWo9+SJsrIypk+fjpubG507d9beNOfMmcPf//53mwcohLhy2+dv15K64a8O59bvb7VK6sD8x739qPbM3DWTXjN6gQqrHlnFptc32SPkesvdkUtxTjFOHk5Ej4622ldQUEBZWRl6vZ6QkBA7Rdg0JSUlsWbNGgJ6BQAygUKIlqLeid2zzz7Lvn37WL9+vdVkiVGjRrF8+XKbBieEuHLp69JZ9cgqAEa8NoKhfx16yTFmDi4OjF8wnuGvDgdg3V/XsePDHVbHWGaXNiUHlx0EIG5CHA4u1p0Qlg+eYWFhODg06RUUG92GDRvYsmULSlvzz0T2lmxUU73KmgohmqB6J3Y//vgjH374IYMHD7b6I9G5c2dSU1NtGpwQ4sqUnSrj+zu/BxV63NODwc/WbT1QRVEY+tehDHtpGACr5q5i3rPztP0dO3Zk0aJFDRDxlTEajBz4+gAAXafWnLwl4+surnPnzvTq1Yt217TDwcWB8oJyTiedtndYQoirVO/E7uTJkwQGBtbYXlpaKjPOhGgiVj2yipLjJbSJb8O4D8fV+3dz6PND6XFPDwqNhfxt/t+07SaTiVmzZpGT0zTWF01dnUrZyTLcA91rdMMCMr7uEgYOHMiECRNo274tYf3MxalleTEhmr96J3Z9+vTh559/1p5b/mB8/vnnDBgwwHaRCSGuSOqaVA58fQBFp3DTv27Cyd2p3tdQFIUbPrkBXbwOFevuOaPRSEpKiq3CvSr7/7UfgC53dEHvqLfaV1ZWxunT5hYoabG7tIhB5u+P1LMTovmr96CT119/nXHjxnH48GGqq6t5//33OXz4MFu2bGHDhg0NEaMQoo6MBiO/zvkVgB4zexDW19wScyVlShxcHLj/X/czr+88q+ROr9cTExNju6DryVJ+xQUX/ur8VwC639291uMiIyMxGAy4uro2dpjNgtFoJC8vD99uvoBMoBCiJah3i93gwYPZu3cv1dXVdO3ald9++43AwEC2bt1K7969GyJGIUQd/fnPPzl99DRubdwY/HzdxtVdSnyfeF559BUUzC3zOp2OBQsWEB4eftXXvlqd6ISx0khgl0CCewTX2B8QEMC9997LjBkz7BBd87BixQo+//xzTruZWzZPHz1N2akyO0clhLgaVzRNLDo6ms8++8zWsQghroKhzMCGF82t5kOfH4qzt7NNrjv3lbn8+u6vxBBDqFcot467tcYx9ihi3B1zK123u7tdcgyhjP29uLCwMPbt20deUR5tOrbhVOIpsrdm02FCB3uHJoS4QnVqsSsuLrb6+lIPIYR97F64m5K8Enza+dB7lnXr+dWWKdnGNpxxxrnQmR/v+dFqZQd78MOPtrQFBbreUXM2rNFopKKiwg6RNS+WSSU5OTmEDzC3wkp3rBDNW50SO19fX/Lz8wHw8fHB19e3xsOyXQjR+Korq9ny9hYABj87GAdnB5YsWaLtv9oyJUaMfM/3OLg6kLYmjV2f7rrqmK9GF7qQTjreQ7zxCvOqsT83N5c333yTL7/80g7RNR8BAQE4OztTVVWFdxdz4WqZQCFE81anrth169bh5+cHQEJCQoMGJISov31L93H22Fk8wzzpPq07OTk5zJkzR9tvKVMyZsyYKx4fd4pTDHlxCAlPJ7DmyTXEjInBt33ND3O5ubnExcVd8Wu5nEULF7GJTWxkI//a9C88F3kyffp0q2Py8vIAcHKq/4zg1kSn0xEeHk5qaiqmcBMAx3Yew1hlRO+kv8zZQoimqE6J3bXXXgtAdXU1GzZs4L777msSg6eFEOb1UHfMN68QMeCxATg4O5CcnIzJZLI6zlKmpL6/u+7u7lrXq2pSSfs5jcyNmfz3vv8ybd00FJ1So3Vw4cKFNZItW8jJyeHRxx/VZuma1NoT1n79+tGxY0cMBoPNY2hpIiIiSE1NpUApwK2NG2Wnyji+5zjh18h7vBDNUb1mxTo4OPD2229TXV3dUPEIIeop648s8g/k4+DqQM/7egIQGxuLTmf9622LMiWKTmHiFxNxdHckc0MmOz7ccdHWwYYoYpyUlIRJrT1hvZCnp6fW0yAuzjLOLjs7m4iBUs9OiOau3uVORowYIfXqhLAzy0oviqKw7YNtgHlJLRcf8/rN4eHhzJ8/Xzter9fbrEyJb3tfrnv7OgB+f+Z3dq3bddHWQVtzO+OmlV6xsHddveYuLCwMRVEoLi6mTa82gEygEKI5q3e5k3HjxvHMM89w4MABevfuXaOswY033miz4IQQl+aBB0n/TQKg3+x+VvumTZvG7NmzATh8+LBNx731mdWHxO8SSV+bTsoHKeh0OqvkrqGSrezl2UxgAitYgYpaa8K6f/9+9u/fT7du3ejWrZvNY2hpnJycCA4O5vjx4zhEmf8kZP6RqZWJaazyNUII26h3Yvfggw8CMG/evBr7FEXBaDRefVRCiDrpRS9MBhPhA8JrLdJrERYWZtP7KjqFGxfdyCddP6F0dymP3fQY7/zwDmDb1sHzFaQUkPhdIr3oxRa2cIpTtSasaWlppKamEhoaatP7t2QREREcP36cUq9SdI46yvLL8MWXM5yxd2hCiHqqd1esyWS66EOSOiEaj4JCIIEUUUTf2X0b/f4+bX0YM28MAN6/eOOLeYbs4cOHG2TixB9//wPVpNJ+bHtOcQqoPWHNyjIvZG8ZOyYuz/K9ys3PJbS3OSGOQNbXFaI5qndit3TpUiorK2tsr6qqYunSpTYJSghxaUuWLEFF5Vu+5T3eY9vZbTWOscxmVVW1wbrSek7vSczYGIyVRiYzGR06m7cOAhRmFLJvyT4ArnnymoseV1JSwpkz5lYmmblfdxER5iQuLy+P0GvMiV0bzOPtrra4tRCicdU7sbv33nspKiqqsf3s2bPce++9NglKCHFxF85CVVF58KEHG2QW6uUoisKEzybg7O1MOOEMZGCD3CfhhQRM1SaiRkYROyL2ogmrpbUuKCgIFxeXBomlJfLy8sLPz4/Q0FD8e/izhz0kYK5ZerXFrYUQjaveiZ2qqrWuvZiTk4O3t7dNghJCXNylatTZg1e4F8PfGg7AcIZz8uBJm17/xIET7P9yPwAj3xh5yWMtiZ2lBUrU3ezZs7n//vvx7uatTU6Bhi1fI4SwvTpPnujZs6dWXmHkyJE4OJw71Wg0kp6eztixYxskSCHEObGxsegUnVU9N3uX/Og3ox9pK9NIWpHE6gdWM33LdBxc6j03qwZVVVn1yCpQodMtnQjre+lu3uxsc5kOGV9Xf5a6h8cKj2lJncWVFrcWQjS+Or/zTpo0CYC9e/cyZswYPDw8tH1OTk60a9eOyZMn2zxAIYS18PBw7o67myVHl1y05EdtSktLMZlMuLm5odfbdrkoRVEYv2A8n2z+hLw/8/j5wZ+5cdGNtbbu18fBZQfJSMjAwcWBUW+OuuSxVVVV2lJi0mJ35dq2bYuCYpXc2fuDgxCi7uqc2P3tb38DoF27dtx2220yfkUIOynOLaZdUjvmMpeFLGTP4T11qlG3YsUKjh49irOzMx06dGDYsGH4+tZc6/VKeYZ4Mvnfk/lq3Ffs/WIvIb1C6PdQv8ufeBGl+aWsnrsagCHPDcE36tKx5ubmYjKZ8PLykmEhV2jJkiVkZmbyyKRHeP/H9+v1wUEI0TTUu69k2rRpgPnTcX5+fo2xPtIFIkTDOvjvg6BCIYWUUnrRWaiFhYV4eXlpXWyxsbEkJSVRWVnJ/v37OXToEGPHjqVPnz42iy16dDSj3hzFmifXsGruKgI6BxA1PKre11FVlZ/u/4nS/FICuwYy8InLT8qwdMNGRERcdUtha6XT6VBVlesnX4/yo0I++Ty3/zniO8XbOzQhRB3VO7FLTk7mvvvuY8uWLVbbLZMqpJadEA3LMpFgP/svekxubi5ffvkl3bt318a+9u7dm169epGdnc369etJT0/n559/pqSkhGHDhtksvgGPDyDvzzwOfH2A5TctZ9q6aYT0CqnXNf544w+SViShd9Jz81c34+B8+beq8xM7cWXGjBmDs7Mzep2edawjlliczjjZOywhRD3Ue1bsPffcg06nY+XKlezevZs9e/awZ88e/vzzT/bs2dMQMQoh/ud08mlO7DuBzkHHxlMbay35cebMGb7++msqKirIzc3FYDBo+xRFITIykrvuuktL5jZs2MDevXttFqOlBErkkEgqiyr51+h/kbuj7rXQDvz7AOv+ug6AsR+MJahr0GXPMZlMMnHCBgIDA/H29kbRKWRj/n7mbpM6dkI0J/Vusdu7dy+7d+8mPl6a5oVobInfJwLQbng73Pzdauyvrq5m2bJllJWVERwczF133YWjo2ON4xRF4dprrwXMrfC2Hhjv6ObIHSvv4F/XmZO6JcOXMPGLiXS+tfMlz9v28TZ+nf0rOnT0/L+e9JlVt27ikydPUllZiZOTE0FBl08ExaW5u7sz4+UZrH9hPfm78u0djhCiHurdYtepUydOnTrVELEIIS7jyPdHAOh4c8da969bt478/Hzc3d25/fbbcXK6dDfa0KFDueeee6xmuduKs5czd6+9m+jR0RjKDHx727dMVibjo/hQWlpqdWxJXgk/3vMjq2evRoeO3exmxFsj6nwvPz8/7rzzTsaOHauNKRRXZu/evXz99dcokeZxilmbs+wckRCiPurdYvfmm2/y1FNP8frrr9O1a9carQFeXl42C04IcU5RdpG5S1OB+Ek1W8xzcnLYunUrABMmTKjT76KiKFY1Kc+ePYunp6fNYnbycOKOn+8g4W8J/PHGH3RTu9GFLnw76Vsi+kegc9BxYv8JUn5NobqiGkWnkGBKYAMbUHR1nwDh6OhIdHS0zeJuzY4dO0ZycjJeXb1Q9ArF2cUUZRfhHSEzjYVoDur90XbUqFFs27aNkSNHEhgYiK+vL76+vvj4+Ni0dIIQwpqlGzZycCQewdYtbKqq8uuvvwLQvXt3OnToUK9rm0wmfvrpJ9577z1OnDhhm4D/R+egY+RrI7lzw52kk44OHRm/Z7DptU1seGkDR344QnVFNeHXhHPbqttYz3pUVFmj1E4sk0+OnTpGcPdgALK3ZNszJCFEPdQ7sUtISCAhIYF169ZZPSzbbC03N5c777wTf39/XF1d6dq1K7t27dL2q6rKCy+8QEhICK6urowaNYrk5GSraxQUFDB16lS8vLzw8fFh+vTplJSU2DxWIRrS5bphhwwZQnBwMKNGXbqQb210Oh0VFRWYTCZWr16NqqqXP6megnsFs4QlzGMenZ/tTJ8H+tDngT6MeH0EM3bN4L4t97E2ea12fF3XKD179ixr1qwhKSnJ5jG3RpbJJ3l5eYReEwpIYidEc1LvrljLgOvGcObMGQYNGsTw4cP59ddfCQgIIDk52apl8K233uKDDz5gyZIlREVF8fzzzzNmzBgOHz6sFVGeOnUqx48fZ82aNRgMBu69915mzpzJ119/3WivRYirUXKihMxNmUDtiZ2iKMTHx9OhQ4crruE2evRokpKSSE9PJz09nfbt219VzBdasmQJAMUUc9ubt7Fw4UKmT5+u7c/JyWHOnDnac8sapWPGjLlkcdzMzEy2bNlCenp6nQo1i0vz9vbGy8uL4uJi3DqYJ+hk/yGJnRDNxRWNMt60aRN33nknAwcO1LpL/vWvf/HHH3/YNLg333yTiIgIvvjiC/r160dUVBSjR4/WxtKoqsp7773Hc889x8SJE+nWrRtLly7l2LFj/PjjjwAkJiayatUqPv/8c/r378/gwYOZP38+y5Yt49ixYzaNV4iGcvS/R0GF0L6heEdaj3U6v3Xtagrz+vj40Lt3bwDWr19v01a7iyVt5y8sn5ycXKPguWWN0kvx9fWlV69edOrUyWbxtnaW7lhjmLkuad7ePCrPVtozJCFEHdU7sfvuu+8YM2YMrq6u7Nmzh8pK8y97UVERr7/+uk2D++mnn+jTpw+33HILgYGB9OzZk88++0zbn56eTl5enlXXk7e3N/3799cGkW/duhUfHx+r6vqjRo1Cp9Oxfft2m8YrREM58qO5Gzb+JutJE0ajkc8++4xt27ZRXV191fcZPHgwDg4OZGdnk5aWdtXXs6hL0hYbG1tjRmtd1igNCwtjwoQJDB482GbxtnaWxC6/Ih+fKB9UkyrdsUI0E/VO7F599VU+/fRTPvvsM6sZsYMGDbJ5geK0tDQ++eQTYmNjWb16NQ888AAPP/yw1qVjWfD7wrpVQUFB2r68vDwCAwOt9js4OODn56cdc6HKykqKi4utHkLYS1VpFenr0gHocKP1pIiDBw9y/PhxNm/ebJN7eXp60qtXL4Aaq8tcjbokbeHh4cyfP99qv6xRah+WxC47O5u2Q9sCkLVJyp4I0RzUO7E7evQoQ4cOrbHd29ubwsJCW8SkMZlM9OrVi9dff52ePXsyc+ZMZsyYwaeffmrT+1zojTfewNvbW3vIEkXCntJ+T8NYacQnyoeATgHadlVVteSrf//+VmVLrsaAAQNQFIXc3Nwa9eauVF2TNsta1ACHDx+2GoNXmzNnzpCbmytLGdpYcHAwjo6OVFZW4tPdB4DMjZn2DUoIUSf1TuyCg4NrHfPyxx9/2HywdUhISI1xMx07diQrK0uLBahRnuHEiRPavuDgYPLzrSunV1dXU1BQoB1zoWeffZaioiLtYVmqSAh7SFppnu0ZNyHOagxdWloa+fn5ODk5WQ01uFo+Pj5MmTKFRx99tMZyZVejvklbWFjYZa+5Z88ePv/8c37++eerjk+co9PptKRbaWv+mcvdkUt1xdV39wshGla9E7sZM2bwyCOPsH37dhRF4dixY3z11Vc88cQTPPDAAzYNbtCgQRw9etRqW1JSEm3bmrsGoqKiCA4OZu3acyUSiouL2b59OwMGDADMrQ+FhYXs3r1bO2bdunWYTCb69+9f632dnZ3x8vKyeghhD6pJJXmluXxP3HjrGZ+Wsj/du3fXZoDbSlxcHM7Ozja95vkulrS5u7ujqmqta+DWxvIhT1rVbc/yPT3NadyD3DFWGsndKbUFhWjq6t1388wzz2AymRg5ciRlZWUMHToUZ2dnnnjiCatZb7bw6KOPMnDgQF5//XVuvfVWduzYwcKFC1m4cCFgngE4d+5cXn31VWJjY7VyJ6GhoUyaNAkwt/CNHTtW68I1GAw89NBDTJkyhdDQUJvGK4StHdt9jJK8Epw8nGh3bTtte1FRkfahp2/fvg12f1VVqaiowNXVtcHucaWqq6u1WfmW2mvCdiyJXU5ODm2HtuXwfw6TuTGTtkPa2jkyIcSl1DuxUxSFv/71rzz55JOkpKRQUlJCp06dGmStyb59+/LDDz/w7LPP8vLLLxMVFcV7773H1KlTtWOeeuopSktLmTlzJoWFhQwePJhVq1ZZtWB89dVXPPTQQ4wcORKdTsfkyZP54IMPbB6vELZm6YaNHhON3kmvbd+zZw+qqtKuXTsCAgIudvpVOXHiBD/88AMAs2bNuqpSKnCuNc5Wjh07htFoxN3dHT8/P5tdV5hZumLPnDlDj349OPyfwzKBQohm4IpHWzs5OTVK3ajx48czfvz4i+5XFIWXX36Zl19++aLH+Pn5STFi0SwlrTg3vu58MTExFBUV1XvpsPrw8vLi9OnTVFdXc+zYsTqNeWtMlm7YyMjIq046RU0uLi6MGTMGPz8/3IrdSCCB7M3ZmKpN6ByuqASqEKIR2GYanRDC5opzi8n7Mw8UiL0+1mpfREREg48rc3V1pVOnTuzfv5/du3c36cRONIxrrrkGAJPRhLO3M5VFleTtyyO0twxjEaKpko9dQjRRKb+aZ5+H9w/HPcB2s1Prw1LT7uDBg1ox8qbAZDJpiZ1lMpVoODq9jsjB5gRayp4I0bRJYidEE5WyypzYxYw7V8S3qqqK1atXc/z4cZuOV7uYyMhI2rRpg8Fg4NChQw1+v7rKz8+nsrISJyenGgXKhe2oqkpycjJr164lbKC5xVbG2QnRtNUpsevVqxdnzpwB4OWXX6asrKxBgxKitTMajKStMS/pFTP2XGJ35MgRtm3bxn/+859GiUNRFLp16waYW+2aivPLnFy4ooWwHUVR+Pnnn/njjz9wiTNPSMvalNUoHyqEEFemTu+IiYmJWgX6l156iZKSkgYNSojWLnd7LpXFlbj6uxLSO0Tbvm/fPsBcu66xJgx06dIFgIyMjCbzuy/j6xpP586d6dGjB2F9w3BwdaDsVBmnjpyyd1hCiIuo0+SJHj16cO+99zJ48GBUVeWdd965aHmTF154waYBCtHalJaWcs+Qe7iWa2k7oi06vfnzV3FxMWlp5lY8SytaY/D19WXIkCGEhYU1iXp2qqpKYteIrrvuOu3r8GvCyUjIIHNjJgEdG6bMjhDi6tQpsVu8eDF/+9vfWLlyJYqi8Ouvv9a6LqWiKJLYCWEDMZi7X9uNaqdts4xxi4yMxNfXt1HjGTFiRKPe71IKCws5e/YsOp2uyc3Ubekih0SSkZBB1qYs+syy3TJ2QgjbqVNi16FDB5YtWwaY1xBcu3YtgYGBDRqYEK1V2ckyPPAgnXScOjlp2xMTEwEapX5kU1ZeXk5YWBgODg44OjraO5xWwWQyceLECQJ6m1vpMjdkoqqq1A8Uogmqdx07k8nUEHEIIf7ng5c+4D3eQ0XlX9f+i4ULF3LLLbeQnZ0NmJfJs4fTp0+zd+9e/Pz86Nmzp11iAAgNDeX++++XAfyN6Mcff+TAgQNcO/BadI46inOKOZN6Br8YWfFDiKbmiqaTpaamMmfOHEaNGsWoUaN4+OGHSU1NtXVsQrQ6OTk5vLb4NVTMSYvJZGLWrFkcPHgQd3d3IiIi8PLysktsGRkZ/PHHH2zfvt0u97+QtBY1HkuXd25+LuHXmJcaS09It2dIQoiLqHdit3r1ajp16sSOHTvo1q0b3bp1Y/v27XTu3Jk1a9Y0RIxCtBpJR5O0pM7CaDRSVVXFY489xi233GKnyMwthTqdjhMnTlBQUGCXGKqrq6mqqrLLvVszSxHorKws2g4zf52RkGHHiIQQF1PvrthnnnmGRx99lL///e81tj/99NNWM6iEEPXjXeWNgmKV3On1emJiYtDpdHh6etotNjc3N9q1a0daWhpHjhxh4MCBjR5DSkoK33zzDZ07d2by5MmNfv/WKjAwEGdnZyorK/HqZm4xzkjIkHF2QjRB9W6xS0xMZPr06TW233fffRw+fNgmQQnRWpXtLWMCE1Aw/7HU6/V88MEHTWb2Z3x8PGAulGwPeXl5qKqKs7OzXe7fWul0Oq20TGVAJXpnPSV5JZw+etrOkQkhLlTvxC4gIIC9e/fW2L53716ZKSvEVUpdlUovetGVrgAcPnwYd3d35s2bp9Wws6cOHToAkJ2dbZdixcOGDWPu3LkMHjy40e/d2lm6Y3PycogYGAFA+joZZydEU1PvrtgZM2Ywc+ZM0tLStK6YzZs38+abb/LYY4/ZPEAhWovK4kqyt5hnvmZj/tfX15fMTHNpicauXVcbLy8vQkNDOXbsGEePHqV3796NHoO3t3ej31OcKwadmZlJ/+H9yUjIICMhg74P9rVzZEKI89U7sXv++efx9PTkH//4B88++yxgLj/w4osv8vDDD9s8QCFai/R16ZiqTfjH+VNw1Dw5Yd++faiqSlBQUJNI7MDcHXvmzBkMBoO9QxGNKDQ0FAcHB8rLy/Htbf5ZzFifgWpSUXQyzk6IpqLeiZ2iKDz66KM8+uijnD17FsCuA7qFaClSVqUAED02Wtt29OhR4FwXaFPQv39/Bg0ahE53RdWSrtiGDRvIzc2lf//+REdHX/4EYVN6vZ6IiAjS09Op8K/A0c2RslNl5B/KJ6hrkL3DE0L8z1W9M3t6ekpSJ4QNqKqqJXYxY8zLiVVXV5OSYt7WlBI7JyenRk/qAJKSkkhOTqa0tLTR7y3MLN2x2ceyiRxs/lrKngjRtDT+u7MQoobTR09TlFmE3llP22v/VycsIwODwYCnpychISF2jrAmVVUpKipqlHtVVFRw/PhxANq1a9co9xQ1WSZQZGZmSj07IZooSeyEaAJSVptb5toObYuTu3l9WEs3bFxcXJOrFVZYWMh7773HJ598gtFobPD7ZWVloaoqfn5+dlt5Q0B4eDh6vZ6zZ8/i1tkNgD9//JOSs40/Q1oIUbt6j7ETQthe6irzknzRY86NHevevTuOjo5NqhvWwsvLi+rqaiorK8nOzm7wVrSMjAxAWuvszdHRkcjISCoqKvCI9aCSSlxxJf9APh4DPewdnhCCerbYGQwGRo4cSXJyckPFI0SrYyg3kLE+A4CYsTHa9vDwcEaPHq11fzUlOp2OmBhzrI3xfiCJXdNx5513MnPmTMIjw0kkkXTS2b1it73DEkL8T70SO0dHR/bv399QsQjRKmVuzKS6ohqvcC8COgXYO5w6i42NBRo+sauoqCAvLw+gSSa5rY1l4sySJUv4kR9ZwhJu+/ttLFq0yM6RCSHgCsbY3XnnnfILLIQNpa7+Xzfs2GhtLN3mzZtJSUmhurranqFdUnS0Od6TJ09SWFjYYPexFGiW8XVNR05ODnPmzNGeq6jMmjWLnJwcO0YlhIArGGNXXV3NP//5T37//Xd69+6Nu7u71f558+bZLDghWoMLy5yUlJTw+++/A/D444/j4dE0xy65uroSERFBVlYWSUlJ9OvXr0HuI92wTc/XX3+NyWSy2mY0GklJSSE8PNxOUQkh4AoSu4MHD9KrVy/AXFfqfE1t5p4QTV1hZiGnEk+h6BSiRkYBkJpqbsELCQlpskmdRWxsLFlZWSQnJ0ti14rExMSgKAqqqmrbdMq5cZdCCPupd2KXkJDQEHEI0SpZumHDB4Tj6usKoBUlbg5/JOPj4ykpKSE+Pr5Brl9eXq6Nr5PErukYPXo0b731Fk8++SQACgp3trtTWuuEaAKuuI5dSkoKq1evpry8HMDqk5sQom60btj/zYY1mUxaYmeZnNCUtWnThrFjxzZY0pWVlQWAv7+/rHLThHh4ePDAAw8A4Iorc5lL+4z2lBeU2zkyIUS9E7vTp08zcuRI4uLiuP7667Vq8NOnT+fxxx+3eYBCtFRGg5G039OAc4ldTk4OFRUVuLi4EBYWZs/wmgTphm36yiknulM0qGg/z0II+6l3Yvfoo4/i6OhIVlYWbm5u2vbbbruNVatW2TQ4IVqynK05VJ2twi3AjZBe5iXDzu+Gtcd6rFfCZDKRlpZGQkKCzVvur732Wm6//XZ69+5t0+uKq1dcXMyyZcv47rvvtA8mqb+l2jkqIUS9x9j99ttvrF69usZYitjYWDIzM20WmBAt3fmzYRWdeeKRZTxZcxhfZ2E0Gvn3v/9NdXU1nTp1IigoyGbXdnFxIS4uzmbXE7ajKApHjhzBycmJySMns23eNlJ/S0VVVZlIJ4Qd1TuxKy0ttWqpsygoKMDZ2dkmQQnRGqT8ak7soseeW0bs9ttv5+TJk82qXpujoyNRUVEkJyeTlJRk08RONF1BQUG4urpSXl6OU6wTemc9xdnFnD56mjbxbewdnhCtVr37eoYMGcLSpUu154qiYDKZeOuttxg+fLhNgxOipTp7/Cx5e/NAgejR5xI7RVEIDAzExcXFjtHVX0OsQrFp0ybWrl3L6dOnbXZNYTuKomhjH7Pzsmk71LwqSMrqFDtGJYSod4vdW2+9xciRI9m1axdVVVU89dRTHDp0iIKCAjZv3twQMQrR4ljGIoX2DsU9wFzkuzl3YVkSu5ycHMrLy3F1db2q66mqyq5duyguLqZt27b4+/vbIkxhY+3atSMxMZGMjAyiR0eTtiaNtN/SuOaRa+wdmhCtVr1b7Lp06UJSUhKDBw9m4sSJlJaWcvPNN/Pnn38SHR19+QsIIUhddW4ZMTBPQJg/fz7ffvutVkKoOfHx8SEgIABVVbUCy1dDVVWGDx9Ot27dZH3YJiwqylxUOysri7bDzf9PGeszqK5sukvhCdHS1bvFDsDb25u//vWvto5FiBavtLQUTw9PnuRJ3HDTZhNmZ2dz5swZKioqmu1Y1ZiYGE6ePElKSgpdunS5qmvpdDp69OhBjx49bBOcaBBt2rTB09OTs2fPUu5djnuQO6UnSsnekk3U8Ch7hydEq3RF9RTOnDnDO++8w/Tp05k+fTr/+Mc/KCgosHVsNfz9739HURTmzp2rbauoqGD27Nn4+/vj4eHB5MmTOXHihNV5WVlZ3HDDDbi5uREYGMiTTz7ZpBdXFy1bKKG44YaztzPh/c2zy5tjmZMLWbpjL/z9Ey2XoihaT01aWpo2XtSyoooQovHV+y/Ixo0badeuHR988AFnzpzhzJkzfPDBB0RFRbFx48aGiBGAnTt3smDBArp162a1/dFHH2XFihX85z//YcOGDRw7doybb75Z2280Grnhhhuoqqpiy5YtLFmyhMWLF/PCCy80WKxCXEoEEaSTjsc1HugczL+ClkkHzanMyYUiIyOZNWsWM2fOvKrrGI1Gtm7dSl5enqxo0wxYfmZTU1OJHiOJnRD2Vu/Ebvbs2dx2222kp6fz/fff8/3335OWlsaUKVOYPXt2Q8RISUkJU6dO5bPPPsPX11fbXlRUxKJFi5g3bx4jRoygd+/efPHFF2zZsoVt27YB5rp7hw8f5ssvv6RHjx6MGzeOV155hY8++oiqqqoGiVeIi1myZAnb2c4SlvDIb4+waNEiiouLtVau5jxOVa/XExwcfNUTQHJzc/ntt9+sZt+Lpssyzi4/P5/AawJBgby9eZw9dtbOkQnROtU7sUtJSeHxxx9Hr9dr2/R6PY899pjWnWRrs2fP5oYbbmDUqFFW23fv3o3BYLDaHh8fT2RkJFu3bgVg69atdO3a1aq21pgxYyguLubQoUMNEq8QtcnJyWHOnDmomFuhTKqJWbNmabPJw8LCcHd3t2eINnM1LW1paeZlqdq3b99sZwm3Jm5ubtryd3ln8wjra/46+Rfblb4RQtRdvRO7Xr16kZiYWGN7YmIi3bt3t0lQ51u2bBl79uzhjTfeqLEvLy8PJycnfHx8rLYHBQVpFfzz8vJqFEy1PLccc6HKykqKi4utHkJcreTkZEwmk9U2o9GotS43525YC6PRyA8//MA//vEPysrKruga5yd2onmw/F+lpqYSO/5/NQ1/Tqa0tBRFUVAUhdLSUnuGKESrUadZsfv379e+fvjhh3nkkUdISUnhmmvMtYq2bdvGRx99xN///nebBpednc0jjzzCmjVrGrVg6xtvvMFLL73UaPcTrUNsbCwKitZiB+bW7h49enDq1Clt8kFzptfrycvLo7S0lNTUVLp27Vqv8ysqKsjJyQEksWtOYmJi2LRpE+np6Qy4YQDrX1hP6ppUqitkkpoQja1OiV2PHj1QFMWqe+Wpp56qcdwdd9zBbbfdZrPgdu/eTX5+Pr169dK2GY1GNm7cyIcffsjq1aupqqqisLDQqtXuxIkTBAcHAxAcHMyOHTusrmsZz2Q55kLPPvssjz32mPa8uLiYiIgIW70s0UqFhYYx2XMy3539DhUVvV7PggULuOmmm+wdmk3FxMSQn59PSkpKvRO7jIwMVFXFz8+vRku8aLrCwsK45ZZbaN++Pc7OzniGenL22Fmy/8jWjsnNzZV1f4VoBHVK7NLT0xs6jlqNHDmSAwcOWG279957iY+P5+mnnyYiIgJHR0fWrl3L5MmTATh69ChZWVkMGDAAgAEDBvDaa6+ZB/YGBgKwZs0avLy86NSpU633dXZ2bra1xETTdWL/Cbqc7UIwwXzMxxw+fLhF/qGLjY1ly5YtpKSk1Hs1DemGbZ70er3V+2nsDbHs+WwPC95doG3r2LEjCxcuZPr06fYIUYhWo06Jnb0qv3t6etYodOru7o6/v7+2ffr06Tz22GP4+fnh5eXFnDlzGDBggNZNPHr0aDp16sRdd93FW2+9RV5eHs899xyzZ8+W5E00qqSfkwAYOGEg83+aD5jHJIWHh7eon8WIiAicnZ0pKyvj2LFj2sD6urAkds15drAwJ3YJnyXw0YaPtG0mk3my0JgxYwgPD7djdEK0bFe08sSxY8f4448/yM/PrzEY/OGHH7ZJYHX17rvvotPpmDx5MpWVlYwZM4aPP/5Y26/X61m5ciUPPPAAAwYMwN3dnWnTpvHyyy83apxCJK0wJ3ZxE8ytdEVFRXz55Zc4ODjwxBNPtJjkTq/X0759exITE0lOTq5zYldQUMDp06fR6XRaCQ3RfJhMJjZu3Eh6ejp/ufEvFDoWohqsZ0cbjUZSUlIksROiAdU7sVu8eDGzZs3CyckJf39/q24WRVEaPLFbv3691XMXFxc++ugjPvroo9pPwNzi+MsvvzRoXEJcSkleCbnbcwGIG29O7CzlgYKDg1tMUmcRExNDYmIiKSkpDBs2rE7nWIo0R0ZGtrjvR2ug0+k4cOAABQUF5J7MpcfAHigbak4Wagmzv4Voyuqd2D3//PO88MILPPvss8126SMhGpulGza0TyieIZ6A9TJiLU1sbCzBwcG0b9++zuPsLN+PljA7uLUaPHgwqqoSHh7ONbdcw4QNE1jBCqvJQtJaJ0TDqndiV1ZWxpQpUySpE6IeLuyGNRqN2niylpjIeHp6MmvWrDofbzAYtElaLfH70Vr07NlT+zp+Yjy9HupFNNF8wifsO7yvRU4WEqKpqXd2Nn36dP7zn/80RCxCtEjVFdWkrTEncZbELisri6qqKtzd3QkJCbFneE1Ceno6RqMRb29v2rRpY+9whA14hXsR2jcUb7zZ9OkmSeqEaCT1brF74403GD9+PKtWraJr1644Ojpa7Z83b57NghOiJUhfl46hzIBXuBfBPcy1Ey3djtHR0S162ayqqioyMjKIiYm5ZCu/r68vAwYMwNXVtUV/P1qDoqIijhw5gqenJ/E3xXNs5zGO/HCEPrP62Ds0IVqFK0rsVq9eTYcOHQBqTJ4QQlg7+tNRwNxaZ/kdacnj6yxUVWX+/PmUlJRw3333XbLId0BAAKNHj27E6ERDOXLkCKtWraJt27aMv2k86/7fOtLXpVNRVIGLd+OtICREa1XvxO4f//gH//znP7nnnnsaIBwhWhZVVUlaaT2+DmDKlCkkJye36MROURTatm3LoUOHSElJkdVbWom4uDhWrVpFVlYW7re50ya+DaeOnCL552S63lG/lUiEEPVX7zF2zs7ODBo0qCFiEaLFyfszj7O5Z3F0dyRq+LnabL6+vvTr1w9XV1c7RtfwLImrpZRJbdLT00lNTaW6WtYVbQl8fX0JCAhAVVVSU1OJvykegCM/HLFzZEK0DvVO7B555BHmz5/fELEI0eIcXWHuho2+LhoHlyuqB96sWRK748ePU1JSUusx69ev58svv2T37t2NGZpoQJaJEklJSVpil/xrMoZygz3DEqJVqHdit2PHDpYsWUL79u2ZMGECN998s9VDCHFObWVOvv32W3bv3o3RaLRnaI3Cw8NDm/VrGVd4PlVVCQgIwMPDQxu3K5o/S2KXkpJCcK9gvMK9MJQaSP0t1c6RCdHy1Tux8/Hx4eabb+baa6+lTZs2eHt7Wz2EEGbFucUc330cFPPamWAuc3Lo0CESEhJaTS1IS6tdbYmdoiiMHz+exx57DB8fn0aOTDSU8PBwXF1dKS8vJzc3l45/6QjAoeWH7ByZEC1fvfuGvvjii4aIQ4gWx9JaF94/HI8gD+DcWLOYmJhWM4s8NjaWTZs2kZqaislkqjWhbS3fi9ZCp9MRExPDgQMHOHr0KF2mdGH7e9s5+tNRDGUGHN0cL38RIcQVaR1NBkLYQeJ3iQDaGCNoHWVOLhQWFsa4ceO4//77rZI6g8FAdnY2qqpe4mzRXFm61o8cOUJo31B82vlgKDVoy+sJIRpGvVvsoqKiLvnp2rJMkhCtWdmpMtITzEtkdZxs7oYqLCzk5MmTKIpCdHS0PcNrVDqdjn79+tXYnpaWxrJlywgLC+P++++3Q2SiIcXExKDX6ykoKODUqVN0urUTW97awqHlh+h8S2d7hydEi1XvxG7u3LlWzw0GA3/++SerVq3iySeftFVcQjRrR/57BNWoEtwjGL9oP+BcN6xl/FFrd+SIufxFaGionSMRDcHZ2Zno6GiSkpJITEyky5QubHlrC8k/J1N5thJnT2d7hyhEi1TvxO6RRx6pdftHH33Erl27rjogIZqr0tJSPDzMY+mWjF4CoA0aB3PpB6DVrpm5d+9ekpKSGDduHO7u7tr3Iz4+/jJniuYqPj6epKQkjhw5wtCZQ/GL9aMguYCjPx2l29Ru9g5PiBbJZmPsxo0bx3fffWerywnRbDniyP51+wHoNLkTYC7roaoqiqK02rIeO3fuJDExkZSUFHJycigrK8PFxYW2bdvaOzTRQDp06ICiKOTl5VFYWEjn28xdsDI7VoiGY7OKqd9++y1+fn62upwQzc6SJeZWOgMG5lXP447QO2gT3wYwz/q88847KS8vx8Wlda6XGRsby7Fjx0hJSeHEiROAufVSr9fbOTLRUNzc3Bg1ahQBAQF4eXnRZUoXNr26iZRVKZSdLsPN383eIQrR4tQ7sevZs6fV5AlVVcnLy+PkyZN8/PHHNg1OiOYiJyeHOXPmaM9VVP59/N/8PefvhIeHa9tb89i6mJgYNmzYQEpKCs7O5vFVnTp1snNUoqENHDhQ+zqwcyDBPYLJ25vHwWUH6Te75qQaIcTVqXdiN2nSJKvnOp2OgIAAhg0bJmNlRKuVnJyMyWSy2mZSTaSkpBAaGkp5eTnu7u52iq5pCA0Nxc3NjbKyMqqqqrTB9aJ16T6tO3l789i3ZJ8kdkI0gHondn/7298aIg4hmrXY2Fh0Op1VcqfX64mJiSEnJ4cvvviCuLg4br/9djtGaV86nY64uDj27t0LmMdfOTi0vvVzW6P8/Hz27t1LSEgIXe/oypon13Bs5zFOJp4koGOAvcMTokWRAsVC2EB4eDjz589HwTxMQafoWLBgAeHh4Rw9ehRA635szc6fONKxY8dLHClakpSUFLZu3cquXbtwD3QnZpy5QPe+pfvsHJkQLU+dPy7rdLrLLvujKArV1dVXHZQQzdFfxv6FZJI5wxlm/zabvqP6AufKnLTW2bDnsyS3iqJI/bpWpHPnzuTm5tKlSxcAut/dnaQVSez/135GvDoCnV7aGISwlTondj/88MNF923dupUPPvigxhgjIVqTtJ/S8MGHLtd00ZK606dPc+rUKXQ6nYwnA631skuXLnh5edk5GtFYvL29ueWWW7TncRPicPF14WzuWRJ/SaTLjeaEr6SkpNWPRRXiatU5sZs4cWKNbUePHuWZZ55hxYoVTJ06lZdfftmmwQnRnBz4+gAAXad21bZZEpl27dq12jInFqqqkphoXj9XZsO2bg7ODnSZ0oVdn+zi4L8Oattzc3NbbQFvIWzlitq/jx07xowZM+jatSvV1dXs3buXJUuWSKFR0WqdTj7NsZ3HUPQKnW89tw5ma19t4nzV1dX07t2bsLAwYmJiKCsro6Kiwt5hiUZ06tQp1q9fz5kzZ+g5vScAy75fpu3v2LEjixYtsld4QrQI9UrsioqKePrpp4mJieHQoUOsXbuWFStWaOMmhGitDnxlbq2LHh2Ne6C5K6msrIysrCxAxtcBODo6MnToUO6//37WrVvHO++8w549e+wdlmhEv/76Kxs2bODgwYOE9g7FpasL/zX+V9tvMpmYNWsWOTk5doxSiOatzondW2+9Rfv27Vm5ciX//ve/2bJlC0OGDGnI2IRoFlSTqs3uO78b1sHBgQkTJtCvXz98fHzsFF3T5OPjg6qqWle1aB06dza3Zh88aO5+9Rntg4pqdYzRaCQlJaXRYxOipajzGLtnnnkGV1dXYmJiWLJkibZ80oW+//57mwUnRHOQnpBOYXohzt7OdLzpXAkPJycnevbsacfImo6srCzOnj1LXFwcjo6OdOjQgV9//ZWsrCxKS0tlwHwr0bFjR3755Rfy8/PJy8vjuhnXofxDsUruLPUfhRBXps4tdnfffTe33norfn5+eHt7X/QhRGvz5+d/AubWOkc3RztH0zRt2bKFb7/9lk2bNgHmWZIhISEA0mrXiri6umrDEvbu3Uv7Du15cMiDWv1HvV6v1X8UQlyZOrfYLV68uAHDEKJ5KjtdRuL35pmeve7vpW1PTU3lxIkTdO7cWT7wAMHBweTl5dG167mu6vj4eI4fP86RI0fo1avXJc4WLUn37t05fPgwBw4c4LrrruP5D5/HqbsThbpCntr+FPG9ZWlKIa6GVIUU4irs/3I/xiojIb1CCOkZom3ftWsXa9asYffu3XaMrukYNmwYjzzyCAEB55aPsqwtnZaWRmVlpb1CE40sJiYGd3d3ysrKSElJIahbEF0GdqGtqS15K/LsHZ4QzZ4kdkJcIVVVtW7YnvefG0tXVVWlDf6Wem3nXLhyTUBAAH5+fhiNRq0sjGj5dDod3bp1A2DfPvOko/6P9Adg58c7MZQb7BabEC2BJHZCXKHcHbnkH8zHwdWBrref62JMTk6muroaX19fgoKC7Bih/Z0+fZojR45gNBpr7FMUhSFDhjB+/HhZlaOV6d69O2AeX1lWVkbHmzviHelN2ckyrXSQEOLKSGInxBXa+eFOADr9pRMuPudWlTh8+LB5e6dOl11fuaXbvn07y5cvZ+XKlbXu79GjB71798bNza2RIxP2FBQUREhICCaTiQMHDqBz0GmtdlvnbUVV1ctcQQhxMZLYCXEFzh47y8Hl5lpc/R/ur203GAwkJycD0g1rMBg4cMDc+mKpXyaEhaXVbs+ePaiqSs/pPXHydOJU4ilSV6faOTohmi9J7IS4Ajs/2YnJYCJiUAShfUK17UeOHMFgMODj46OV82itEhMTqaiowNvb+5JdreXl5Wzfvp01a9Y0YnTC3rp164aDgwP5+fnk5OTg4u2izSzfOm+rnaMTovlq0ondG2+8Qd++ffH09CQwMJBJkybVqHlVUVHB7Nmz8ff3x8PDg8mTJ3PixAmrY7Kysrjhhhtwc3MjMDCQJ598kurq6sZ8KaIFMZQb2P2pebbrNXOvsdpXXFyMg4MDXbt2bfXdsJblwnr27HnJ70VZWRmrVq1i69atlJWVNVZ4ws5cXV215SiPHDkCmFu/FZ1C2po0ju06Zs/whGi2mnRit2HDBmbPns22bdtYs2YNBoOB0aNHU1paqh3z6KOPsmLFCv7zn/+wYcMGjh07xs0336ztNxqN3HDDDVRVVbFlyxaWLFnC4sWLeeGFF+zxkkQLcODrA5SdKsM70pv4SdY1twYNGsQTTzzBNddcc5GzW4dTp06RmZmJoiiXXX3D39+f4OBgVFUlMTGxkSIUTcGgQYO49957GTVqFAA+7Xy0Zfk2vrLRnqEJ0Ww16cRu1apV3HPPPXTu3Jnu3buzePFisrKytNpgRUVFLFq0iHnz5jFixAh69+7NF198wZYtW9i2bRsAv/32G4cPH+bLL7+kR48ejBs3jldeeYWPPvqIqqoqe7480Qypqsr297YD0G9OP3QONX+FnJ2dW/1kgJ07zRNLYmJi8PLyuuzxF64hKlqHNm3aEBkZadWiO+SvQ0CBoz8d5fifx+0YnRDNU5NO7C5UVFQEgJ+fHwC7d+/GYDBon/bAXPQ0MjKSrVvNYzS2bt1K165drcpOjBkzhuLiYg4dOtSI0YuWIPmXZPIP5uPo7kjP6dYtUYWFhfYJqomprKxk7969APTv3//SB/+PpUsuIyNDvo+tVFVVFaqq0qZDG7pMMf88bHp1k52jEqL5aTaJnclkYu7cuQwaNEj7I5CXl4eTkxM+Pj5WxwYFBZGXl6cdc2EtMctzyzEXqqyspLi42OohhKqqbHhpAwB9HuiDq6+rtu/MmTO8//77fPbZZ7XWbGtN9u7dS1VVFW3atKF9+/Z1OsfHx4d27doBsH///gaMTjRFa9euZd68eaSmmmfDWlrtEr9P5MSBE5c5WwhxvmaT2M2ePZuDBw+ybNmyBr/XG2+8gbe3t/aIiIho8HuKpq20tJT2uvZs3rmZUpdSBj4x0Gq/pQvR2dkZvV5vjxCbBFVV2bFjBwD9+vWr1wQSS/mLffv2SR2zVqaqqorKykptjGVg50A6/cVcLmjDixvsGZoQzU6zSOweeughVq5cSUJCAuHh4dr24OBgqqqqanTdnDhxguDgYO2YC2fJWp5bjrnQs88+S1FRkfbIzs624asRzdHixYvJJJMlLOGdyndYvnK5tk9VVW1ppPMXuW+NUlJSKCgowNnZWUvU6qpTp064uLgQEBAga8e2MgMGDGDq1KmMHz9e23bt365F0Skkfp9I9lZ5Dxairpp0YqeqKg899BA//PAD69atIyoqymp/7969cXR0ZO3atdq2o0ePkpWVxYABAwDzG8aBAwfIz8/XjlmzZg1eXl4XLSDr7OyMl5eX1UO0Xjk5OTw852FUzK1Iqqoya9YscnJyAMjOzub06dM4Ojq2+qLE27ebJ5b07NkTJyenep3r5OTEY489xpQpU3Bxcbn8CaLF8PHxISYmxqqFN7BzID3u7QHAmifWSCuuEHXUpBO72bNn8+WXX/L111/j6elJXl4eeXl5lJeXA+Dt7c306dN57LHHSEhIYPfu3dx7770MGDBAKzcxevRoOnXqxF133cW+fftYvXo1zz33HLNnz8bZ2dmeL080E0lJSZhUk9U2o9FISkoKcK5eW5cuXVr1z1RpaSlZWVmAuRv2Sjg6OtoyJNEMGQwGrcV22EvDcHB1IHtLNkd+PGLfwIRoJpp0YvfJJ59QVFTEsGHDCAkJ0R7Ll5/rBnv33XcZP348kydPZujQoQQHB/P9999r+/V6PStXrkSv1zNgwADuvPNO7r77bl5++WV7vCTRDBkPG1GwHium1+uJiYmhoqJCm13dq1cve4TXZLi7uzN37lwmT56Mr6/vVV3rzJkzNYZQiJbvzz//5N1332Xz5s0AeIV5MeBxc+/L70//jtHQuicmCVEXDvYO4FLq0vTu4uLCRx99xEcffXTRY9q2bcsvv/xiy9BEK2EoM3DwrYNMYAI/8RNgTuoWLFhAeHg4u3fvprq6moCAAMLCwuwcrf25ublps9av1J49e1ixYgXt2rVj2rRpNopMNAcuLi6Ul5ezc+dOBg0ahLOzM4OeGsSuT3eRnpxOV6eu/Hj0R+Li4uwdqhBNVpNusRPC3rb8YwvF2cUMjxxOWlIaCQkJZGRkMH36dAB69OjBrbfeyogRI1r1EmJnzpyx2bUsY60yMjI4efKkza4rmr4OHTrg5+dHRUWFVuTa2dOZwtGFvMd7JJJIx44dWbRokZ0jFaLpksROiIsozi1m89/NXUKj3hpFVGwUw4YNs5qZrdfr6dixI/Hx8Re7TItXXFzMhx9+yBdffGGT2axeXl506NABgF27dl319UTzodPpGDp0KABbtmyhqqqKnJwc3lz2pjZ5yWQyWU1eEkJYk8ROiFqoqsqvc37FUGYgYmAEnW/tXOsxAq0ckKIoNps80qdPH8Bc006W/mtdunbtip+fH+Xl5ezYsYPk5GRMpotPXhJCWJPETohaHPrmEEd+OILOQcf1H19fo5u1rKyM+fPns3Hjxla/0kTnzp155JFHuOGGG2x2zfbt2+Pn50dlZSUHDhyw2XVF03d+q93WrVtp27YtOp31nyoFhciQSHuEJ0STJ4mdEBcoOVHCL7PNk22GPj+U4O41C1nv3r2bM2fOcOTIkRp/dFojLy8vAgICbHY9RVG0Vrtdu3ZJ62grY2m1Kysr49ixY8yfP1/bp6AwgQkcfvewHSMUoumSv0hCnEdVVX6Z/Qvlp8sJ7hHM4GcH1zjGaDRqA7v79+/faidNFBQUkJub22DX79GjBw4ODhQVFXH27NkGu49oenQ6HUOGDAFg8+bN3HvvvWRnZ5OQkMAfy/6gF73YvWA3yb8k2zlSIZoeSeyEOM/uhbtJ/C4RnYOOiV9MRO9Yc93Xw4cPc/bsWdzd3encuebYu9bi999/5/PPP9dqjtmaq6srd9xxB3PnzpXVX1qhbt26ERgYSEVFBRs3biQ8PJxhw4Yx8LaB9J/bH4D/3vdfyk6V2TlSIZoWSexEq1daWoqiKAQoASx7aBkAI14bQXCP2tcStiyb1bdvXxwcmnQpyAaTk5OjLdgeGxvbYPeJioqq99JkomXQ6XSMHj0agB07dlBQUKDtG/n6SAI6BVB6opSfpv+EapKueiEsJLETrd6SJUsAOMUp5lXP43iv4wx8cmCtx2ZmZpKbm4ter6d3796NGWaToaoqv/76K2DuLg0MDGyUe+bl5TX4fUTTEh0dTXR0NCaTyWpNcEdXR2768ib0TnqO/nSUP/7+B2D+wJGQkCClUESrJomdaNVycnKYM2eO9lxF5fN9n1907NjGjRsBc0Lj4eHRKDE2NXv37uXYsWM4OTkxcuTIBr9fRUUFn376KZ999hlFRUUNfj/RtFx33XUoikJubi4VFRXa9pCeIVz/0fUArHtuHS8/9DIRERGMGDGCtm3bShFj0WpJYidatSOHj9SrRtbYsWPp2rUrgwfXnFTRGpSXl/P7778DMGzYsEZJbl1cXHBzc8NkMrFt27YGv59oWoKCgrj99tuZPXs2Li4uVvt63d+LXjN6UaQW8eJHL2rbpYixaM0ksROtlsloIuWDFBSsZ7Xq9XpiYmJqPScgIICbb74ZHx+fRoiw6Vm/fj1lZWW0adOGfv36Ndp9Bw0aBJjLzJSUlDTafUXTEBsbi6OjY637xs0fh9JB0VamsJAixqK1ksROtEqGcgPfTfmOEz+fYKJ+olaLTq/Xs2DBAqtlw0BWmQDIzc3VyryMGzcOvb7mjOGGEh0dTXh4OAaDQesOF62PyWRix44dVmsTOzg7MOPLGfX6gCZESyaJnWh1yk6VsXTkUg5/exi9k57X//M6mZmZJCQkkJGRwfTp02ucs2zZMn788cdWO8arurqaH3/8EVVV6dq1K+3bt2/U+yuKwogRI4BzxaFF67N69Wp+/fVXfvnlF6sPW/F94nnzhTe15E6Hjo8//LjGBzQhWgNJ7ESrcvzP4ywasIicrTm4+Lhw52930vGmjlqNrNr+EKSmppKUlMSBAweorq62Q9T2t379ek6dOoW7uztjx461SwxRUVG0b98ek8nE+vXr7RKDsK++ffvi6upKx44da+x78qUn2fbjNu5zuo9HeASPFR5UV7TO31fRukliJ1oF1aSy+a3NfN7/cwpSCvBu6819W+6j3bXtLnmeyWTit99+A8x/VPz9/Rsh2qYlJyeHLVu2ADB+/Hjc3NzsFotlFm5BQUGrX6O3NWrTpg1z586lV69eta740m9iP/7681/xd/Un+Zdk/j3h3xjKDHaIVAj7kcTuClgK2iqKQlJSkr3DaZYas97Urp938XS3p/nu6e8wGUzE3xTPzF0zCeh4+bVN//zzT/Lz83FxceHaa69t8FibGkvNOksXbHx8vF3jCQ0NZfr06dx3332NOsZPNB3nF6yuqKioMau9/aj2TP11Ko7ujqT9nsbSUUspzS9t7DCFsBtJ7K6ApaAtQMeOHaVe0gUul7R9/PHHNqk3dbkE+0zaGR645gH6je/H24fe5j3ewzjNyK3f3Ypbm8u3OlVWVpKQkADAtddei6ur6xXF2ZwpisJf/vIX4uPjGTdunL3DASA8PLzVrs8rzsnMzOSTTz5hw4YNNfa1u7Ydd/12F87ezuRszeHz/p+TfyjfDlEK0fgksaunCwvaNqd6SY3RSna5pK0+37/LxVtbgq2qKpmbMvlm8je8Ev0KC7Yv0MogqKi8/uXrdV64fv369ZSWluLv70/fvn3rdE5L5Ovry2233dbkEtvKykrWrVtHZWWlvUMRdlBcXExxcTEbN27k6NGjNfZHDIzg/m334xvtS2FGIYsGLCLx+0Q7RCpE41JUqeNwWcXFxXh7e/OM0zPk6fJYXLG4xjH/1+H/iA+KR++ix8HFAQc3BxzdHHF0dcTJ3Qkndycc3RxxdnfG2cP8cPF04Uz5GY6fOk7Hzh2Jjo/GxcNFK71hSx9//DGzZ88GzGswLly4sNbZnzk5OSQnJxMbG3vRGWUXOyYnJ4e2bdtadY3o9XoyMjK04xISErTZjedLSEhg2LBhdY63tnvpFB3Phz6PkmtuzUknnSWcS/4udq/aVFVV8fHHH1NUVMTUqVNbXdmEnJwcKioqmvTrXrx4MZmZmVxzzTWMGTPG3uEIO/jll1/YuXMnzs7O3H///bRp06bGMWWny/jm5m/I3JgJQNupbQmbGkbHrh1l1qxoEiw5RlFREV5eXld9PUns6kBL7HiGSip5j/esimEqKMxlLt54W51XRBEFFOCHX419AHvYwwpWoKKioDCBCfSiFzgCjqA4KShOCmf1ZylQCgh0D8TP0w+9sx4HVwf0ruYk0tHNkbOms5yqOEVEaAShIaE4uTtpCWRheSHX33f9JRMuqFvyd6lj6pK01SX5u9QxYWFhlOSV8N+l/+WuZ+6qca9pTKODRwc6T+lM5JRIeo/ufdnXfTEVFRUkJibSs2fPyx7bkhQXF7NgwQLKysqYPHkyXbp0sXdItUpJSeGrr75CURTuv/9+QkND7R2SaGRGo5GlS5eSlZVFmzZtuP/++3F2dq55nMFIwvMJzH9zvvaee6kPuEI0Jlsndg42iKnVmJIwBUfVEY/vPXjt49cwmUzoFB0PTnyQQfGDqCqtwlBuwFBmIOFQAl/v+1pL2iZHTKaPex+MlUZMlSYKywtZcWaFVTfhClYQTTTeBm8wgFqmspvdtSd/57logvg/6aRjouayWc9FPkeUUxQ4QrG+mHeL3tX2m0wmZtw/g4wPMvD38EfvrKdYLeb59c9bHTNzxkzUvSohwSEUVhaiU3SY1PNa0XQ6dCd0pG1Kw9HJEb2jnteefo1n33gWAL1Oz5v/703UbJWMlAyqK6vZvHNzrct8vT3obQJPBlJdXk0RRShYV5vXKTru/uRuhtw1BEc3c5X6hQsXMmvWLIxG40WLD1+Mi4tLq0vqADw8POjUqRM5OTnExcXZO5yLiomJoUuXLhw8eJDvvvuOWbNmWQ2sFy2fXq/nlltuYeHChZw6dYrly5dzxx134OBg/adN76gn/qF4Vr61Uqt/Z3n/GtpvKLFdY+0RvhANQlrs6qC2bDonJ4eUlBRiYmJqJApX0yX5/dff07tzb8rPlpOZlsm4aeOsEyVFx4f3f4iX3gtDmYH80/k88/MzNVoQn2r3FJ5GT9QqlTMVZ3i36N1LtjJerNtyGtOIIqrOx1wuybS4VGtmEUWXbhVVwKOtBwc8DvCvQ//CpJrQ6XS8+eab3HXXXTg4OODg4ICjoyMODg7k5eWRlpZW6//VhbKyssjLy6Nv376teoC+qqpUVVXV2vrRlJSXl/Ppp59SXFxMr169mDBhgr1DEnZw7NgxlixZQlVVFZ06dWLy5Mk1hrRc7D13hscMpj0/jX4P9dM+ENZlSIoQtiItdk1EeHj4RX/hk5OTL7qwvOWc2NhYdDpdjeSv75C+2jHHqo5ZJXUAJtVExzs6al2bCQkJqD9b5+YqKmO/GGs1jqzTok5WLVfz/j6Pv0z4CxXFFVSWVpKVlcW/7vlXjSRy0OxBeDp6Ul1RjedpT5RvlBoJV3j3cFxxxVhppL+hP3FlcZyuOI0vvnjpvMCE+aGee3ir3nir3ubnDoD+f/86gLfemwlVE1hx8rwEsfsEvLt5gw/gDSUOJUQRxSPXPUJBQQF+fn6Ulpby6aef1vp/otPp2Lp1q5b0nf/Q6/U4ODigKApZWVlUV1dz4MABgoODL3rspa5zsX0NMXbSVqqrq9m6dSsDBw5Er9ejKEqTT+oAXF1dmTRpEkuXLmXPnj3ExMTUWrxWtGyhoaHcdtttfP311xw+fBhXV1duuOEGqw9ntb3n6tDhUeLB70//zvb3tzPgiQHsVHfyyOOPmPdLd61ohqTFrg7qm03XpcUOrMerWboJLzdBoD7j0WprSbxYKyPAokWLanRbXmqM3cWOuZCqqphMJgwGA9XV1VRXV9fp69zcXLKysggKCsLHx0fbXtfzm1oBW51OV6ck8ML99Tn2Yvss/+p0uhotkdXV1XzzzTckJyfTtWtXbr75Zjt9h67c77//zubNmwkODmbmzJmturW1NTt06BDffvstAL169eKGG26w+kB14XvcJ598Ql+nvqz/23qKMotq7S2oz7hcIa6ETJ6wgyv5ptclSQLbJFt1vVddXC6euh7TFJhMJoxGo5bsGQwGjEajVYJoef7nn3+SnJyMTqdjwIABODs7W+2/8OsLHxfb1xR/vfR6vVXCV15eri2V1qZNG9zc3GokhReec2HCeLFtdT3+alszjUYjq1evZujQoXh4eNji2ySaqb179/LTTz9pRbUnTZpk9fNV2/tXdWU1+5buY+krS/kg+4Ma11z5zUpuuOWGRnsNonWRxM4OrvSbbqsEqCUlW03R9u3bWbVqFYDNZ4GaTKY6J4FXkkBe7liDwdAkk8sLKYpS76TxSo69kkdtrZyiaTt06BDff/89JpOJcePG0a9fvzqdl52VTbt27ayGpFjG93bs05EOEzsQMy6G4O7B6Bya7tAK0bxIYmcHtv6mi6Zj//79/PDDDwAMGzasRS4bZmm5tCSBRqORvLw8VqxYQWlpKS4uLowYMQJfX98ax13478W21fXY8/c1J7ZIDq/2GnW9jzBLSkpix44d3H777fVafu78HhCdouOuqLuISo/ivN5ZnDycCB8QTuSQSMKvCSeoWxAeQTVbihtrEkZTm+zR1OJp6iSxswNJ7FquPXv2sHLlSvr27cvYsWNbfMuMqqrs3r2bVatWYTQa8ff3Z+rUqfj6+jZ6HJbWzLokjvVJNMvKykhMTERVVTw8PAgMDERVVask9HKP5kpRlCtKNi1fX+zfK9lW33MaomVUVVXtmkajkaysLKKioi573oU9ICV5JSStTOLoT0fJ3JhJZVFljZn97oHuBHULok3HNvhE+bA2dS1/++Rv5rJYF5mEYYsEqK7F523lcjEvWrSImTNnXvJ1N2Y8zYEkdnYgiV3LlpOTQ1hYWItP6srLy/nll184ePAgAHFxcUyaNKnJLRVmC0lJSXzzzTcYjUbi4+OZPHlyjdpmF2NJOuuTCF7p42rvc+Hs++bOVollbcdkZmZqk83i4uLqfX3L16jw8Zsf89K7LwHUWtaptkkYOnS8M+AdIiIjcA9yZ236Wv6x4h/mfYqON//fm9xz1z24eLvg7OWMg6vDZd+T6jt5rqGTyKYWT33uZc8EURI7O5DEruVQVZVt27bRqVMnvL1rrgbSEqmqyuHDh/n1118pLS1FURRGjhzJwIEDW3Qym5yczPLlyzEajYSFhXHbbbfh6elp77Bs6mqS0Orqau3cC/+91La6HFOX/c31T09RURHvvfeeVfyKojB3yly8S73hNKTnpLMk8+I1Py9bqxNABzpXHWcdz1KgKyDQNRBfN190Tjp0zjr0znpSSlOYv39+jfv8dcJf6dK2C3pHPXonPesOruPjXz5GVVV0io5Hb3+U8deOx8HJAQdnB/SOehycHDhVfIrc/Fzat29PeHg4Dk4OOLo44uDsQP7pfLr17WaTJSNt0apX1ySyLveqazy2ShAvPEbq2AlxhcrKyvjvf/9LUlISf/75JzNnzqxzK05ztn37dlavXg2YZ71OnDix2XZZ1EdsbCxTp07lm2++ITc3l4ULF3Lbbbe1qNd+ftdrc3NhUlrXhPBqtpWWluLk5KQ9P3v2rNYid7lrWR65ubk1klJVVSlwKsC7gzkp8yvyQ3lPqZH8+Y3xAxUKcgtQD9asP1rgWGBeeQjABLtKd12y4LsDDjVW4FFQKFtRxlGOAubWw4/5WDvGpJqY9/U8TF+brIrDX/EKRu2eI8ohCnTUuiKQgsIvt//CFsctoIditZi3Mt86F8//VjnKnJ+Jr5svil5B56BD0SsUGgo5XXmaIK8g/Dz80DnotMfRk0drrRf7z2f+Sffo7ugcdZwpPcOMN2dYrzYycyZ+pX6EBIWgd9Rzuug0M2fM1CbMmEwmZs2cRZfILkRERuDgZE58F3+1mCeeeQIwtw4uWLCA+++/3+r+V5pE3nLLLdhSy/+rJgSQkZHB999/r72R9+/fv0UndeePLerUqRPr16+nf//+DBkypEW/7gtFRUUxY8YMli1bxsmTJzl16lSLSuyaM3snpaWlpXz44YdUV1fTt29fBg0ahLu7+2XPy8nJYdGiRTVaiv72t78RGhqqJYDt27dn7ty5GI3mMlRvv/02U6ZM0ZLDfw34l3WxZJ2Ou369i4CAAAylBrJTs3n57pe1pERFZaWyknFzx+Hj7EN1RTXGSiPTDkxj6eal5hV4FB23dbqNdsHtMBlMqNUqeQV5qEdqJpHFQcX4O/qjGlUKqwpZcbqWJS710XibzIXk/fCrNWnzM/rB/4aleuPNBCbUSBDd89wxYAAgjzyra1jul7UvCz3nfhYul2hWUVVrPIVfFbKXvYA5Gb3wXiaTif8+8l+rFZVqJKwmI5+M/kQ7xtLCev41Zs6YyeFZh/HWe5uTWqWI9yreq5GwJj6diI+jD4peochUxDvH36lxTMonKdhS63mHF63S2bNnWbNmDQcOHADA39+fv/zlLwQHB9s5soZRWFhIQkIC5eXl3HHHHQB4eXkxd+5cXFxc7Bydffj5+TF9+nR27dpF9+7dte0lJSVS864Vq6ioICAggOzsbLZu3cquXbvo2bMn/fr1w9/f/6LnhYeH17oG9YWTMh566CEmTZpUaxmqsLCwWq8xcuRI7ZjiiuJaVx6KvzHeqltzGtN4JeeVSy5xubDtwhqJ6Iu7XrTqQv3HiH9YnaeicuPvNzJs2DBMRhPVVdXEfB7Dw48+bI5Zp+fNl97klhtvwWgwUm2oprqymsmGycw+Ppv0rHRC24TSxrsNRoMRo8GIyWAiLj+OpX9batWaqVN0DHxgID5uPpgMJk4WnmTF0hU1ktqB4wbi4+SDqdpEG2Mbbs+8nX8f/reW/N3S/hbC/MNQq1VUo0poRShKUs3kLyAoAD16VJNKG0MblMJaEla9uXUVExRQUGsyWmAqMCe+XPyYU6dP4Yl5CEg++bUntYlZ2JKMsasDGWPXPBUUFLBgwQKqqqoA6N27N6NHj25xC8Wf3zp35swZ5s+fj6qqPPTQQ5f8A9WaVVZW8v777xMeHs6IESNabKIvLk1VVVJTU0lISODYsWPa9piYGHr06EFcXByOjo61nmuL2qFXu+Z4XV2uiL0tVzCyRTx1HatXl3iutsi/qqpkZWTRPqa99fdHp2f7uu0E+gZirDaSk5vDtZOurdEK++PHP9LGy5zc5uXncdtTt9VYuvPTpz9l5t9nyuSJxiSJXfNxfiuMqqosWLAABwcHrr/+ekJDQ+0cne2UlpZy9OhRjhw5gslk4s4779T2bd++nYiIiBb1em3t6NGjLF++XGsRaN++Pf369dPWExWti6qqpKWlsX37dpKTk7Xtzs7OdOjQgdjYWKKjoxt9Bnljripky3tdbTy2TGovd6+6HmOrVaBqO+aWW26RWbFX6qOPPuLtt98mLy+P7t27M3/+/DpVJJfErmk7ffo0ycnJHDhwgJMnT/LEE09orXLFxcV4eno2+9mfpaWlZGVlaY/zWxcUReGJJ57Azc3NjhE2PwUFBSQkJGjlXwA8PT3p2LEjgwYNkt/1Vur06dPs3buXAwcOUFRUpG1XFIWhQ4fWaDFqaI25qlBTWsGosRPNurDVKlAXHiPlTq7Q8uXLufvuu/n000/p378/7733Hv/5z384evQogYGBlzxXErum5ezZs6SmppKdnU1GRgYFBQXaPp1Ox9SpU2nfvr0dI7xyRqORkydPcvr0aU6cOEF+fj4nTpygsLCwxrEhISHEx8cTHx9PQEBAs09e7eXMmTPs2rWLP//8k/LychRF4fHHH9cG0mdnZ2M0GgkKCmqRNf9E7VRVJSsri6SkJFJSUsjPz+emm26iW7duAOTm5vLDDz8QGxvLmDFjtPMsiYi4ek0p0WxIkthdof79+9O3b18+/PBDwDwbJSIigjlz5vDMM89c8lxJ7BqHqqpUV1dTXl5OSUkJZ8+epbi4mOLiYrp06UJQUBBgXuT7v//9r3aeTqejbdu2dOjQgS5dutRpZltjMRqNVFVVUVVVRWVlJb6+vtqYnbS0NI4cOUJERARdu3YFzEnGBx/UXIQcICAggMjISCIjI2nXrp38LNpYdXU1aWlpnDhxgiFDhmjbv/jiC7KyzIObvby8CAoKwsfHB29vb3x9fenUqZN27PnjHUXLUlRUhIuLC87OzgBs27aN1atXExcXx+23364d9/e//x0HBwe8vLzw9vbGy8sLT09P3NzccHFxwdXV1erh6OgoPzOtnNSxuwJVVVXs3r2bZ599Vtum0+kYNWoUW7durfN1li9fftHurvDwcO1TWkFBAcXFxbi4uGjJiKqqZGdnYzKZuP7667VB7Vu3brUa03Gh0NBQrVuxsLCQgoICnJycCAkJ0Y45duwYBoOBESNGEBkZCcDu3bvZv3+/1bXOz+GDg4O11oezZ8/+//buPS6qMv8D+GdmYIb7/TKAXEXFGwheEC94x9um5Vq2UT9UsizcRfzZatuKaam19VNr89JmkVu6lZfNti3znptZad4iFQVRFBgEkTsMyDy/P9w5MDADI5w5M2fm+369eOmceeac73nmOc98z3nOBWVlZZBKpQgKCuLKlJSUQK1WY+TIkYiKigIAZGdn44cffjA4Xz8/P+4ct9raWqhUKjDG4O/vz90U9e7du2hoaEB8fDwSEhK4eL/88kuD9VBUVMSd4F5TUwNXV1e4uroiISEBvXr1gkKhwMWLF3HixAlERkZyR+yqq6vx/fffG5xv2/i1Ro8ezSWIly9fxpUrVxAREYEBAwZw67Zv3752N4fVJnGNjY1oampq93iqZ555hvvuioqKcOrUKajVai6xc3Nzg7OzMzw8PODn5wd/f3/uj44WmZadnR169+6N3r17c9O0jyXz8PBARUUFt6Oh5eHhoZPYvffee7h79y4cHR0hl8thZ2cHOzs72Nvbw83NDQ899BBX9rvvvkNVVRX3KC2JRML938HBASNHjuTKnjt3Tme5beMeMWIE9zo7Oxt3797VKaNNHCQSic58L126pHPEu62EhATunMMrV66grKzMYNlhw4Zxt9LJy8tDSUmJwbKDBw/mEqT8/HwUFxcbLBsbG8u1/YKCAty6dctg2ejoaK7/uXXrFpeQ6zNgwADuR7SoqAjXr183WLZfv37w8PAAcL9fzMvLQ1NTE2JjYyGVSrk+pqmpCWq1Gmq1GrW1tR2uV2u/+93v4OPjg8uXL6O0tBR5eXnw9PREnz59uDL5+flQKBSQSqW4d+8eKisruXbT9s/d3R2urq7o378/nJyckJ2dzcXj4OCgc/SrqqqKq9+mpiaoVCoA0JtsandmwsLC4OXlhbNnz6KhoQEqlQp2dnY6862rq+Pm29zc3OH35uLiAm9vb/To0QNBQUH46aef0NTUhMLCQkilUu53re18AeDGjRsG5+vo6Mj1o5GRkTh58iT3aDkACAkJ4dp32/lqf6/1USgUUCqV8PLyQt++fXH69Gk0Njbixo0bYIzdv7nzf7eF2tpanYMNhYWFaGpq4l47Oztj3LhxBtehK2wisSsrK+OGUlrz9/fH5cuX25XXbpha2g712rVrBm8ZkZur/z40v/76a7tp5eXlXGJ36dIl3Lx502Ds+fn5eqfrSwZLSkq4DSA3N7fDTs3QMvVtJDdv3uQSu4KCAhQWFhqcb+tzv1rTdhaGyraub32uXbuGa9eu6Uyrrq5Gz549uR+IvLw8nDlzBg4ODlxiV1dX1y4RNcaQIUO4jbGoqAhnz56Fvb09l9hpNJoOE/K2ZDIZ5HI5d4UucL9TGTVqlE4yLZPJsHTp0geOl5iGRCLhbh7a0NCAkpISlJaWorKykjuC01pdXR0aGhrQ0NDQbl5eXl46r7Ozsw0mPy4uLjoJ2JkzZwxuswqFQiexO3v2bLttpfX6tJ7vhQsX9PaBWsOHD9eJV3vbIH1iY2O5H7OLFy/izJkzBssOGDCA225zcnLw448/Gizbp08f7gc3NzcX//nPfwyWDQ8P5xK7/Px8HDlyxGDZHj16cIldQUEBDh48aLCs9igtcP+HuaOyM2fOhL+/P6qqqnDp0iWcP3/eYFktmUyG27dv68y3urpapw/X3kT5Qfj6+sLT07NdvJcuXeL+r1AoOu1/20pKSoKjo2OH83VyckJdXd0DzXfkyJHw9/dvN9+cnBzu/y4uLqipqXmg+Q4aNAiRkZE4dOiQTh227sOdnZ1RW1tr9Dyzs7PRu3dv9O3bF8ePH0d1dbXe+XZWD1KplBI7Iaxbtw6rVq1qN93T01OnI2+9RxMYGMh1anfv3kV1dbXOETugZQ+g9aHWkJAQnS+97V6SUqnUuRCgoqICcrkcgYGBXFntHkDrW1uEhIR0uCfu5+fHrUttbS3Ky8shk8kQEhLCzbeoqAhqtVrn6srg4OB2PzCtY/b29oaTkxMkEgnq6+u5I4FRUVGQyWSws7NDYWEhampqdPZGIyIiEBcX1+6GpZ0NUbS+2W7Pnj3b7Y06OTnp/JAZ0nY5rffcIiIiYGdnp5OAOTg4YMaMGe2eL6lQKGBvbw+5XK7zp++cG+2wKhEHBwcHhIaGIjQ01GCZBQsWoL6+HnV1dWhqasK9e/e4f9veNmPw4MGorq4GY4x7CoP2X23So9WnTx/4+vpyr1sfZW57w+nIyEidx+W1ffpBa2FhYTp9Wtuj163Ltz66oU/rNt6jR492R6tba10XQUFBOvcXbKt1XSiVyg7Ltt5u/fz8Oizb+iiKr69vh2Vb3+/Qy8urw7J+fn4ICAhAQEBAhxc0McYQExMDX19fODo6orS0FDExMWhsbERDQwPs7Ox0Pu/g4ABPT09oNBpUVVVxRxi17Ye75xtj8Pb2hoeHB3x8fKBQKBATE4P6+nrcuXMHcrlc57zyxsZGODk5gTGGxsZGvTvn2nm7ubnBw8MDvr6+kMvliImJgVqtRklJCezs7HR+K9RqNffdNTc3d3hAwNnZGd7e3ggICIBMJkNMTAyamppQXFwMqVSq06e3nq92JMwQR0dH+Pr6Ijg4GMD9I7qtjx4GBwdzbbyhoUFnW7h586bBx97J5XIolUpuFKlfv35oaGjgPtM6H2g736KiIty7d497bYqL3mziHDttw929ezcefvhhbnpKSgoqKip0ztcC9B+xCw4OpnPsCCGEEMIrvs+xs4kbNsnlcgwePBiHDx/mpmk0Ghw+fJg7x6s1hUIBNzc3nT9CCCGEEEtnM0OxS5YsQUpKCoYMGYJhw4Zh48aNqK2txbx588wdGiGEEEIIL2wmsZszZw5KS0uRmZkJlUqFQYMGYf/+/e0uqCCEEEIIESubOMeuu+g+doQQQggxBTrHjhBCCCGE6EWJHSGEEEKIlaDEjhBCCCHESlBiRwghhBBiJSixI4QQQgixEpTYEUIIIYRYCUrsjKB9vNiDPijZWqnVarz88stUH6C6aI3qogXVRQuqixZUFy2oLlrwnWPQfeyMcOPGDYSFheHHH3/Uecixraqurka/fv1w8eJFuLq6mjscs6K6aEF10YLqogXVRQuqixZUFy2KiooQHx+P69evIzQ0tNvzo8TOCEePHsX48ePNHQYhhBBCrNSRI0cwbty4bs/HZh4p1h0REREAgJs3b9KTJwghhBDCm6qqKgQHB3O5RndRYmcEJycnAICbmxsldoQQQgjhnTbX6C66eMIICoXC3CEQQgghxIrxlWtQYkcIIYQQYiUosSOEEEIIsRKU2BFCCCGEWAlK7AghhBBCrAQldoQQQgghVsImErt169Zh6NChcHV1hZ+fHx5++GHk5OSYOyxCCCGEEF7ZRGL37bffIi0tDT/88AMOHjyIpqYmJCUloba21tyhEUIIIYTwxiYfKVZaWgo/Pz98++23SExM7LR8VVUV3N3dUVlZSTcoJoQQQghv+M4xbOKIXVuVlZUAAC8vLzNHQgghhBDCH5t7pJhGo8HixYsxcuRIDBgwQG8ZtVoNtVrNva6qqgIANFQ2QM7kgsRJCCGEEOvXUNXA6/xsLrFLS0tDdnY2vvvuO4Nl1q1bh1WrVrWb/uuuX+Hi6GLK8AghhBBiQ2rqa3idn02dY7do0SLs27cPx48fR3h4uMFy+o7YBQcH4/jm4/Dw9BAgUkIIIYTYgoq7FUh8PpG3c+xs4ogdYwy///3v8c9//hPHjh3rMKkD7j+IV9/DeO0UdpA701AsIYQQQvhhV8dvKmbSxO6LL7544M9MmjQJjo6OvMaRlpaGnTt3Yt++fXB1dYVKpQIAuLu7874sYhxVmQrXi64jLDAMSh+lucMhhBBCrIJJh2Kl0ge76FYikeDq1auIiIjgNQ6JRKJ3elZWFubOndvp57WXIn///vfw8qUrabtr94HdyNyUCQ3TQCqRYnXaasxOmm3usAghJkA7cYR0rLy0HCNSR4hnKFalUsHPz8+osq6uriaJwYZOI7R4qjIVl9QBgIZpkLk5E6PiRlGnT4iVoZ04QoRn0vvYpaSkPNBQ55NPPkk3ALZy14uuc0mdlkajwY3iG2aKiBBiCoZ24lRlKjNHRoh1M2lil5WVxR2Fq6np/HLeLVu2wMfHx5QhETMLCwyDVKLb7KRSKUIDQs0UEVGVqfDDhR/oB1cExPRd0U6c7RJTO7VGgj15wt3dHXv27BFqccSMOtqolT5KrE5bzZ1/KZVKsfr51TQMaya7D+zG+NTxmPvnuRifOh67D+w2d0jEALF9V7QTJxxLSqTE1k6tkWD3sZNKpZg4cSJqa2shkUgwZMgQJCcnY+jQoUIsvlvo4gnjGXtOjapMhRvFNxAaEEpJnZmoylQYnzpe56iKVCrFkW1H6DuxMGL9rnYf2I3MzZnQaDTcThydY8cvSzqP0Rzt1BouzuH74glBnxV79uxZxMXFYdSoUfj1118xevRoLF26VMgQiAk9yDk1Sh8l4gfGi3ZDtAY0VCYeYv2uZifNxpFtR7B9zXYc2XaEkjqeWdp5jEK3Uzo6qJ+gNyjeuXMnJk2axL2+cOECZs6ciaCgIGRkZAgZCjGBjjZqSuAsj3aorO3edVeHyqxhz9lS8f1dCUnpo+y0PVDb6RpL63OFbKd0hwXDBEvsvLy8EBwcrDMtOjoa77zzDjIyMkSR2N2rv4em2iZzh2GxgjyC9G7Uge6BNlNvqjsqFKgKEKIMgdLbsjsXb0dvrFywEqu2reKGylY+vRLejt4P/H3tPboXq95bxQ0HrVywErPGzTJR5LaHz+/K0lDb6TpL63OFbKd51/L0JrXX8q/B29Gb12WZ2r36e7zOT7Bz7CZOnIjBgwfj9ddf15mek5ODmJgYNDQ0CBFGl2jPsduZvNNk99qzFgcuHcCm45u4TjotMQ1JfZO6NK+ymjIUVRYh0D0QPi6Wf7X0gUsH8M7xd8AYg0QiwaLERV1edyGV1ZShuLIYAe4BXarnspoyzN8xX+d+kVKJFO8nv2/R35sx7cvS2mB3vytLI9a2Y0n47HP5IkQ7taa2U11djSd2PCGeGxRrvfrqqxg3bhyKiorw/PPPIzo6GrW1tVi7dm2nz261FFKFFHIXelZsR34z9DeIj4pH0d0iBHoGwtfVt0vz2f/Lfrx14C2us0pPSseUgVN4jpY/pdWl2HR8E9fJMMaw6fgmxEfFd7kOhBLoEohAZWCXP3+7/Ha7m4BrmAaljaUIdOn6fE3JmPZliW2wu9+VpRFj27E0fPW5xiqtLkXh3UIEeQYZXJYQ7TTQJRCLkxa320bFuH1IG/m93EGwxG748OH44YcfkJ6ejtGjR3Mbs4ODA3bt2iVUGN0is5fBzuF+lZVWleJW+S308OoBXzfL/uEWWoBDAAJ8A7r8+dKqUm5jBe539G8deAvxfeLb1bWlfA8qlar9sADToKS2pFt1IQahytD2w0ESKUKUIdz2YkmMaV8P0gatlRDbFt9tx1L6A6F1t8811ldnv8KGrzZwiVTGtAxMi51m8uUa8puhv0F8n3gUlhciyCtItN+5zF7G6/wE7XVjYmJw7Ngx3L59Gz///DM0Gg3i4+NFd1NiS2vc1uZW+S29SVJheaHOhmtJ30MPrx56f6CCvILMEo+QfN18kTEto913YamdrDHty9g2aK2E2rb4bDuW1B9Yo9KqUq5+gfvbw4avNmBoz6Fm3SZ83XxtYpt8EGbZnfbz88PUqVPNsehus9TGbQyx7M0akyRZ2vcgtuSGb9Nip2Foz6Gi2HM2pn3xnaiLZdsDhN+2+Gg7ltYfWCNb39kRE5MmdhcuXMCAAQO4pwx05tdff0WfPn1gZ2d5wzdaYm3cYtqbNSZJssTvQUzJjSmIZc/ZmPZly0eSzLFtdbft8B2zmBJxodjyqITYmDSDio2NhUqlgq+vcRtGQkICzp07h4iICFOG1S1ibNxi3JvtLEmy1O9BLMmNrTMmCbfVI0mWum11hM+YxZaIC8XaRyWsKZk3aWLHGMOKFSvg5ORkVPnGxkZThsMLMTZuSzy6ZYyOkiQxfg/EshiThFvakSQhiHHb4itmMSbiQhLrqERnSZuxybxYkj+TJnaJiYnIyckxunxCQgIcHR1NGBE/xNa4xbgHbgyxfQ98E7KTEUuHZmnEuu2JcdviI2YxJuJCE9uoRGdJm7HJvJiO5Jo0sTt27JgpZ29WYmrcYtwDNxZf34PYEhchOxkxdWiWRszbnpj6OK3uxizWRFyMhOhzjUnajEnm+T6Sa+p1t9yrFExg06ZNeOONN6BSqRATE4O//vWvGDZsmLnDEoQY98CFIrbERcjhIksdmhJTIm7stiemdbJWYk7ExUSoPteYpM2YZJ7PI7n61n14j+EPumodspnE7tNPP8WSJUuwdetWxMfHY+PGjZg8eTJycnLg5+dn7vAEYczerDE/Ltb0A2SOxKW79SfkcJElDk2JLREHOt/2xLhO1op2gk1LyD7XmKTNmGSeryO5htZ98xObu7qKetlMYrd+/XosWLAA8+bNAwBs3boV//73v/HBBx9g+fLlRs2juakZzY3NpgzTrL6+8DXe+qbV41kmp2Nq9NQHLiMmBSUFehOXm7dvwsvBi/fl8VF/Aa4BejsZpauS9/Yp5LKMUVqtv2OMC4mz+Ee3GWKN6yR2Xg5e8Aq8v/1bc59vDkL2uV4OXkifnN6uz/Vy8NL5Xif3n4y4kDidx7K1ft/Y+XTG0LoX3ins/sq2ImFtH9RnhRobG+Hk5ITdu3fj4Ycf5qanpKSgoqIC+/bt0ymvVquhVqu511VVVQgODsZyLIcDHIQKW1CVqMRGbARDS3OQQILFWAx3uBtdRmyEXCc+l3UGZ/Av/AsMDBJI8BAeQhzieI3XHMvqTD7ysR3b201PQQrCIY5nTrdljetEjFOJSpSjHF7wEm0f+qDM8TvCVz0bM5+Oyhha9+fwHDZjMyorK+Hm5tbl+LQEO2JXX18Pxhh365MbN27gn//8J/r164ekpCSTLrusrAzNzc3w9/fXme7v74/Lly+3K79u3TqsWrXKpDFZmnKU6zQ2AGBgKEc51ziNKSM27nDHQ3ioXeJiivXhs/7iEIee6CnIj4KQy+qMF7wggaRdx+gF/o+uCsUa14nc19GPvCXtMAlJyD639TL5mH9n8+nsOzW07m7ofjLXmmCJ3cyZMzFr1iwsXLgQFRUViI+Ph729PcrKyrB+/Xo899xzQoXSqRdffBFLlizhXmuP2A2aPwhePtbZ2faq7oWPtn7UbsgtaWESNxxkTBkxGoERmFM9R+cwvD6l1aUt5910YX2ttf4EdwFWdToAAOtcJxvX0WkXpdWlWL11NbQDZgwMX0q+xJyFc2yiLzC2zxUTY79TfeteXlYOfMBfLIIldmfOnMGGDRsAALt374a/vz/Onj2LPXv2IDMz06SJnY+PD2QyGUpKSnSml5SUQKlUtiuvUCigUCjaTZfZyyCTy0wWpzkpvZV6TyBVeisfqIxYKb2VHa4HHye3W3P9Cek3Q36D+N7xFnNyOx8XEwm9TtZ0AZQlKq0q5ZI64P55VG998xbie8fD180XxdXFes+1UlWrbKY/6KzPFZsH+U7brrvMnt+8QrDErq6uDq6urgCAAwcOYNasWZBKpRg+fDhu3Lhh0mXL5XIMHjwYhw8f5s6x02g0OHz4MBYtWmTSZYuJUI9ZEhs+r+KiW1/ww1Luscbn1axCrZOtX4ErxLbV2dXk5rhfHvUppmVJ90AULLGLjIzE559/jkceeQTffPMNMjIyAAC3b9/m5WTBzixZsgQpKSkYMmQIhg0bho0bN6K2tpa7SpbcJ8RjlsSG71t+0K0vrIOl3uOvI2KMWYuPxESobauzH3mh75dHfYrpWdI9EAVL7DIzM/HEE08gIyMDEyZMQEJCAoD7R+9iY2NNvvw5c+agtLQUmZmZUKlUGDRoEPbv39/uggpC2hJyT0zMP7y2xhLv8dcZMcYM8JOYCLltGfMjL9ToB/UpwrGUES3BErvZs2dj1KhRKC4uRkxMDDd9woQJeOSRRwSJYdGiRTT0aoO6u6cv5J6YWH94bZElDb0YS4wx85WYCL1tGfMjL8ToB/UpwrKEES1Bb1CsVCrbXaxgK4/0MsTWnvQgNL6GIITaExPjD++DEKotC7EcSxp6MZYYY+YrMTHHtmUJP/IPst70W2MdBE3s/vOf/+Ddd99FXl4edu/ejaCgIHz00UcIDw/HqFGjhAzFIhiTdBibmNAG2R7fQxBCdNJi/OE1llBtWcjziSxl6OVBiC1mvhIya962OmLsetN5eNZDsMRuz549eOqpp5CcnIyzZ89yT3aorKzE2rVr8dVXXwkVikUwJukwNjGhDVI/sQ5BiO2H1xhCtWVznE9kCUdlHhRfMYvtyKg1blvG6Gy96Tw86yJYYvfqq69i69at+J//+R988skn3PSRI0fi1VdfFSoMi2FM0mFMGdogDRPzsKYYk4WOCNWWxZrMi5FYj4xa27ZlrI7Wm7Yb6yIVakE5OTlITExsN93d3R0VFRVChWExtElHa22TDmPKdLRB2jrtnr62Dm1l6MUSCdWWjVkO6T5DSXhpVanJlunr5otBYYNo+zUB2m6si2CJnVKpRG5ubrvp3333HSIiIoQKw2IYk3QYU4Y2yI5Ni52Gnb/fif978v+w8/c7aYjaTIRqy5TMC4N2KK0LbTfWRbCh2AULFiA9PR0ffPABJBIJioqKcPLkSSxduhQrVqwQKoxuaW5qxr2Ge7zNL6lvEmJ7xOo8M67t/Dsr4yn3RHpSOt460OqZhEnp8JR78hqrmHnKPeGp9AQAqhMzEqotG7Ndke5ROiv1nubg7+xPdc2z0upSFN4tRJBn155RbSzabsynuamZ1/lJmPaJtSbGGMPatWuxbt061NXVAbj/TNalS5filVdeESKELquqqoK7uzt2Ju/kHotmjLKaMhRVFiHQPRA+Lj4mjPD+soorixHgHmDyZRFiStSWxeHApQPYdHwTl4SnJaYhqW+SucOyKgcuHcA7x98BYwwSiQSLEhdRHVuh6upqPLHjCVRWVvLyJC7BEjutxsZG5ObmoqamBv369YOLiwvq6+vh6OgoZBgPRJvYHX/nODy9PY36zN6je7HqvVVcp7dywUrMGjfLxJESQohwVHdUuFlyE8H+wVb1QHdLoLqjwuTfT9Y9KiqV4pu3v6G6tjJ379xF4qJE3hI7Qe9jBwByuRz9+vUDAKjVaqxfvx5/+ctfoFKphA7lgdk52sHe2b7TcqoyFZfUAffPPVm1bRXGDB8DpQ9tkIQQ6xDsHIzgkGBzh2GVCvMK25/HqNGgqLKI6tzK2NXxm4qZ/OIJtVqNF198EUOGDMGIESPw+eefAwCysrIQHh6ODRs2ICMjw9RhCOp60XW9G+SN4htmiogQQoiYhAWGtb+YSCpFaEComSIiYmHyxC4zMxNbtmxBWFgYrl+/jkcffRTPPPMMNmzYgPXr1+P69etYtmyZqcMQFG2QxFKoylT44cIPUJVZ/hFxQkgLpY8Sq9NWQyr975WqUilWP7+aRn1Ip0w+FLtr1y78/e9/x4wZM5CdnY3o6Gjcu3cP58+fh0QiMfXizUK7QWZuzoRGo6ENkpjF7gO7kbkpkzvPc3XaasxOmm3usAghRpqdNBuj4kbhRvENhAaE0m8IMYrJL56Qy+XIz89HUND9e1E5Ojrip59+wsCBA025WF5pL574/v3v4eXrZfTnVGUq2iCJWajKVBifOr7diddHth2htkgID1RlKlwvuo6wwDDapkzEVuq4vLQcI1JHiOfiiebmZsjl8pYF2tnBxcXF1Iu1CEofpVU3RmK5OjrPk9okId1DR8NNj+q460ye2DHGMHfuXCgUCgBAQ0MDFi5cCGdnZ51ye/fuNXUohNgM7XmebY/Y0XmehHSPqkzFJRzA/bseZG7OxKi4UbTTxBOq4+4x+cUTKSkp8PPzg7u7O9zd3fHkk08iMDCQe639M5Xr168jNTUV4eHhcHR0RM+ePbFy5Uo0NjaabJmEmBudeE2IadBdD0yP6rh7TH7ELisry9SL6NDly5eh0Wjw7rvvIjIyEtnZ2ViwYAFqa2vx5ptvmjU2QkyJTrwmhH90NNz0qI67x+RH7MxtypQpyMrKQlJSEiIiIjBjxgwsXbqUhn6JTVD6KBE/MJ6SOkJ4QkfDTY/quHsEf/KEJaisrISXl+GrW9VqNdRqNfe6qqoKAHBPfQ+NtTSESwghtmzGyBkYFjVM53Fq9NvAL1uq43vqe7zOz+YSu9zcXPz1r3/tcBh23bp1WLVqVbvp9+rvoQENpgyPEEKICHhIPeAR4AEAaLhLvwumYCt1fK+e38TO5PexM5Xly5fj9ddf77DMpUuXEBUVxb0uLCzEmDFjMHbsWGzbts3g5/QdsQsODkZJQQkv95ghhBBCCAHu5xj+If683cdOtIldaWkp7ty502GZiIgI7h56RUVFGDt2LIYPH44PP/yQG7s3hvYGxXxVOiGEEEIIwH+OIdqhWF9fX/j6+hpVtrCwEOPGjcPgwYORlZX1QEkdIYQQQohYiDaxM1ZhYSHGjh2L0NBQvPnmmygtLeXeUyrpChtCCCGEWA+rT+wOHjyI3Nxc5ObmokePHjrviXQUmhBCCCFEL6sfk5w7dy4YY3r/CCHE2ty6dQtHjx7FrVu3zB0KIcQMrD6xI4QQW/H+++8jNDQU48ePR2hoKN5//31zh0QIEZhor4oVEl0VSwixdLdu3UJoaCg0mpbHMMlkMly/fr3daSiEEMvBd45BR+wIIcQKXL16VSepA4Dm5mbk5uaaKSJCiDlQYkcIIVagV69e7W7lJJPJEBkZaaaICCHmQIkdIYRYgR49euBvf/sbZDIZgPtJ3bvvvkvDsITYGDrHzgh0jh0hRCxu3bqF3NxcREZGUlJHiAjQkyfMQJv7VlVVmTkSQgjpmJubG+Li4gBQn0WIGGi3U76Os1FiZwTtM2mDg4PNHAkhhBBCrNGdO3fg7u7e7flQYmcELy8vAEBBQQEvlS52VVVVCA4Oxs2bN21+aJrqogXVRQuqixZUFy2oLlpQXbSorKxESEgIl2t0FyV2RtBeaebu7m7zDbA1Nzc3qo//orpoQXXRguqiBdVFC6qLFlQXLdpe1d7l+fAyF0IIIYQQYnaU2BFCCCGEWAlK7IygUCiwcuVKKBQKc4diEag+WlBdtKC6aEF10YLqogXVRQuqixZ81wXdx44QQgghxErQETtCCCGEECtBiR0hhBBCiJWgxI4QQgghxEpQYmfAa6+9BolEgsWLF3PTGhoakJaWBm9vb7i4uOC3v/0tSkpKzBekQPTVxdixYyGRSHT+Fi5caL4gTejll19ut65RUVHc+7bULjqrC1tqFwBQWFiIJ598Et7e3nB0dMTAgQNx+vRp7n3GGDIzMxEQEABHR0dMnDgRV69eNWPEptNZXcydO7dd25gyZYoZIzaNsLCwduspkUiQlpYGwLb6i87qwpb6i+bmZqxYsQLh4eFwdHREz5498corr+g8Royv/oJuUKzHqVOn8O677yI6OlpnekZGBv79739j165dcHd3x6JFizBr1iycOHHCTJGanqG6AIAFCxZg9erV3GsnJychQxNU//79cejQIe61nV3LpmNr7aKjugBsp13cvXsXI0eOxLhx4/D111/D19cXV69ehaenJ1fmL3/5C95++21s374d4eHhWLFiBSZPnoyLFy/CwcHBjNHzy5i6AIApU6YgKyuLe22NV0SeOnUKzc3N3Ovs7GxMmjQJjz76KADb6i86qwvAdvqL119/HVu2bMH27dvRv39/nD59GvPmzYO7uzv+8Ic/AOCxv2BER3V1NevVqxc7ePAgGzNmDEtPT2eMMVZRUcHs7e3Zrl27uLKXLl1iANjJkyfNFK1pGaoLxli719Zs5cqVLCYmRu97ttYuOqoLxmyrXSxbtoyNGjXK4PsajYYplUr2xhtvcNMqKiqYQqFg//jHP4QIUTCd1QVjjKWkpLCZM2cKE5AFSU9PZz179mQajcbm+ou2WtcFY7bVX0yfPp3Nnz9fZ9qsWbNYcnIyY4zf/oKGYttIS0vD9OnTMXHiRJ3pP//8M5qamnSmR0VFISQkBCdPnhQ6TEEYqgutHTt2wMfHBwMGDMCLL76Iuro6gSMUztWrVxEYGIiIiAgkJyejoKAAgG22C0N1oWUr7eKLL77AkCFD8Oijj8LPzw+xsbF47733uPfz8/OhUql02oa7uzvi4+Otrm10Vhdax44dg5+fH/r06YPnnnsOd+7cMUO0wmlsbMTHH3+M+fPnQyKR2GR/odW2LrRspb8YMWIEDh8+jCtXrgAAzp8/j++++w5Tp04FwG9/QUOxrXzyySc4c+YMTp061e49lUoFuVwODw8Pnen+/v5QqVQCRSicjuoCAJ544gmEhoYiMDAQFy5cwLJly5CTk4O9e/cKHKnpxcfH48MPP0SfPn1QXFyMVatWYfTo0cjOzra5dtFRXbi6utpUu7h27Rq2bNmCJUuW4E9/+hNOnTqFP/zhD5DL5UhJSeG+f39/f53PWWPb6KwugPvDsLNmzUJ4eDjy8vLwpz/9CVOnTsXJkychk8nMvAam8fnnn6OiogJz584FYHu/I621rQvAtn5Hli9fjqqqKkRFRUEmk6G5uRlr1qxBcnIyAPDaX1Bi9183b95Eeno6Dh48aFXnvnSFMXXxzDPPcP8fOHAgAgICMGHCBOTl5aFnz55ChSoI7R4VAERHRyM+Ph6hoaH47LPP4OjoaMbIhNdRXaSmptpUu9BoNBgyZAjWrl0LAIiNjUV2dja2bt3KJTO2wpi6ePzxx7nyAwcORHR0NHr27Iljx45hwoQJZonb1N5//31MnToVgYGB5g7F7PTVhS31F5999hl27NiBnTt3on///jh37hwWL16MwMBA3vsLGor9r59//hm3b99GXFwc7OzsYGdnh2+//RZvv/027Ozs4O/vj8bGRlRUVOh8rqSkBEql0jxBm0hnddH6ZFit+Ph4AEBubq7Q4QrOw8MDvXv3Rm5uLpRKpc20C31a14U+1twuAgIC0K9fP51pffv25Yamtd9/2yserbFtdFYX+kRERMDHx8cq2wYA3LhxA4cOHcLTTz/NTbPV/kJfXehjzf3FCy+8gOXLl+Pxxx/HwIED8dRTTyEjIwPr1q0DwG9/QYndf02YMAG//PILzp07x/0NGTIEycnJ3P/t7e1x+PBh7jM5OTkoKChAQkKCGSPnX2d1oW/Y5Ny5cwDud/DWrqamBnl5eQgICMDgwYNtpl3o07ou9LHmdjFy5Ejk5OToTLty5QpCQ0MBAOHh4VAqlTpto6qqCj/++KPVtY3O6kKfW7du4c6dO1bZNgAgKysLfn5+mD59OjfNVvsLfXWhjzX3F3V1dZBKdVMumUwGjUYDgOf+ovvXelivtlfsLFy4kIWEhLAjR46w06dPs4SEBJaQkGC+AAXUui5yc3PZ6tWr2enTp1l+fj7bt28fi4iIYImJieYN0kT+93//lx07dozl5+ezEydOsIkTJzIfHx92+/ZtxphttYuO6sLW2sVPP/3E7Ozs2Jo1a9jVq1fZjh07mJOTE/v444+5Mq+99hrz8PBg+/btYxcuXGAzZ85k4eHhrL6+3oyR86+zuqiurmZLly5lJ0+eZPn5+ezQoUMsLi6O9erVizU0NJg5ev41NzezkJAQtmzZsnbv2VJ/wZjhurC1/iIlJYUFBQWxL7/8kuXn57O9e/cyHx8f9sc//pErw1d/QYldB9omdvX19ez5559nnp6ezMnJiT3yyCOsuLjYfAEKqHVdFBQUsMTERObl5cUUCgWLjIxkL7zwAqusrDRvkCYyZ84cFhAQwORyOQsKCmJz5sxhubm53Pu21C46qgtbaxeMMfavf/2LDRgwgCkUChYVFcX+9re/6byv0WjYihUrmL+/P1MoFGzChAksJyfHTNGaVkd1UVdXx5KSkpivry+zt7dnoaGhbMGCBUylUpkxYtP55ptvGAC937Ut9ReMGa4LW+svqqqqWHp6OgsJCWEODg4sIiKCvfTSS0ytVnNl+OovJIy1uu0xIYQQQggRLTrHjhBCCCHESlBiRwghhBBiJSixI4QQQgixEpTYEUIIIYRYCUrsCCGEEEKsBCV2hBBCCCFWghI7QgghhBArQYkdIYQQQoiVoMSOEELMZOzYsZBIJJBIJNxzMo8dOwaJRNLuQfF8e/nll7llb9y40aTLIoQIhxI7QogozJ07l0tEWv9NmTLF3KF1y4IFC1BcXIwBAwZ0e14lJSWwt7fHJ598ovf91NRUxMXFAQCWLl2K4uJi9OjRo9vLJYRYDkrsCCGiMWXKFBQXF+v8/eMf/zDpMhsbG006fycnJyiVStjZ2XV7Xv7+/pg+fTo++OCDdu/V1tbis88+Q2pqKgDAxcUFSqUSMpms28slhFgOSuwIIaKhUCigVCp1/jw9Pbn3JRIJtm3bhkceeQROTk7o1asXvvjiC515ZGdnY+rUqXBxcYG/vz+eeuoplJWVce+PHTsWixYtwuLFi+Hj44PJkycDAL744gv06tULDg4OGDduHLZv384NmdbW1sLNzQ27d+/WWdbnn38OZ2dnVFdXd3md6+rqMHXqVIwcOZIbnt22bRv69u0LBwcHREVFYfPmzVz51NRUHD58GAUFBTrz2bVrF+7du4fk5OQux0IIsXyU2BFCrMqqVavw2GOP4cKFC5g2bRqSk5NRXl4OAKioqMD48eMRGxuL06dPY//+/SgpKcFjjz2mM4/t27dDLpfjxIkT2Lp1K/Lz8zF79mw8/PDDOH/+PJ599lm89NJLXHlnZ2c8/vjjyMrK0plPVlYWZs+eDVdX1y6tS0VFBSZNmgSNRoODBw/Cw8MDO3bsQGZmJtasWYNLly5h7dq1WLFiBbZv3w4AmDZtGvz9/fHhhx+2i2XWrFnw8PDoUiyEEJFghBAiAikpKUwmkzFnZ2edvzVr1nBlALA///nP3OuamhoGgH399deMMcZeeeUVlpSUpDPfmzdvMgAsJyeHMcbYmDFjWGxsrE6ZZcuWsQEDBuhMe+mllxgAdvfuXcYYYz/++COTyWSsqKiIMcZYSUkJs7OzY8eOHTO4TmPGjGHp6ek6044ePcoAsEuXLrHo6Gj229/+lqnVau79nj17sp07d+p85pVXXmEJCQnc6+XLl7Pw8HCm0WgYY4zl5uYyiUTCDh061C6G0NBQtmHDBoMxEkLEhY7YEUJEY9y4cTh37pzO38KFC3XKREdHc/93dnaGm5sbbt++DQA4f/48jh49ChcXF+4vKioKAJCXl8d9bvDgwTrzzMnJwdChQ3WmDRs2rN3r/v37c0fOPv74Y4SGhiIxMbFL6zpp0iRERkbi008/hVwuB3D/PLm8vDykpqbqrMOrr76qE//8+fORn5+Po0ePArh/tC4sLAzjx4/vUiyEEPHo/tm6hBAiEGdnZ0RGRnZYxt7eXue1RCKBRqMBANTU1OChhx7C66+/3u5zAQEBOsvpiqeffhqbNm3C8uXLkZWVhXnz5kEikXRpXtOnT8eePXtw8eJFDBw4kIsfAN577z3Ex8frlG99EUSvXr0wevRoZGVlYezYsfj73/+OBQsWdDkWQoh4UGJHCLEZcXFx2LNnD8LCwh7oKtQ+ffrgq6++0pl26tSpduWefPJJ/PGPf8Tbb7+NixcvIiUlpcuxvvbaa3BxccGECRNw7Ngx9OvXD/7+/ggMDMS1a9c6vQgiNTUVzz33HGbMmIHCwkLMnTu3y7EQQsSDhmIJIaKhVquhUql0/lpf0dqZtLQ0lJeX43e/+x1OnTqFvLw8fPPNN5g3bx6am5sNfu7ZZ5/F5cuXsWzZMly5cgWfffYZd3FC66Ngnp6emDVrFl544QUkJSV1+x5xb775JpKTkzF+/HhcvnwZwP2LQ9atW4e3334bV65cwS+//IKsrCysX79e57OPPvoo7O3t8eyzzyIpKQnBwcHdioUQIg6U2BFCRGP//v0ICAjQ+Rs1apTRnw8MDMSJEyfQ3NyMpKQkDBw4EIsXL4aHhwekUsPdYXh4OHbv3o29e/ciOjoaW7Zs4a6KVSgUOmVTU1PR2NiI+fPnd20l29iwYQMee+wxjB8/HleuXMHTTz+Nbdu2ISsrCwMHDsSYMWPw4YcfIjw8XOdzTk5OePzxx3H37l3eYiGEWD4JY4yZOwhCCBGbNWvWYOvWrbh586bO9I8++ggZGRkoKiriLnowZOzYsRg0aJBZH+kVFhaGxYsXY/HixWaLgRDCHzpiRwghRti8eTNOnTqFa9eu4aOPPsIbb7yhcw5dXV0d8vLy8Nprr+HZZ5/tNKlrPV8XFxf88ssvpgpdr7Vr18LFxaXdjYwJIeJGR+wIIcQIGRkZ+PTTT1FeXo6QkBA89dRTePHFF7mLMF5++WWsWbMGiYmJ2LdvH1xcXDqdZ2FhIerr6wEAISEhRieDfCgvL+du3Ozr6wt3d3fBlk0IMR1K7AghhBBCrAQNxRJCCCGEWAlK7AghhBBCrAQldoQQQgghVoISO0IIIYQQK0GJHSGEEEKIlaDEjhBCCCHESlBiRwghhBBiJSixI4QQQgixEpTYEUIIIYRYif8HyvXERzBKXo0AAAAASUVORK5CYII=",
      "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",
    "main_axis.plot(x, peak(x, *mi.values['A_p1', 'mu_p1', 'sigma_p1']), color='gray', ls='--')\n",
    "main_axis.plot(x, peak(x, *mi.values['A_p2', 'mu_p2', 'sigma_p2']), color='gray', ls='-.')\n",
    "main_axis.plot(x, bkg(x, *mi.values['A_bkg', 'tau_bkg']), color='gray')\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": [
    "Sofern wir einen unser Fitmodel unsere Daten gut widerspiegelt erwarten wir, dass die Residuen sich Gaußförmig zufällig um den Wert 0 herum verteilen. Dies folgt direkt aus der Annahme, dass de Unsicherheiten unserer Messwerte sich durch eine Gaußverteilung darstellen lassen. Dies können wir direkt überprüfen, sofern wir unsere Residuanen in ein Histogramm eintragen. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "05e24224-66f7-45ed-99c6-f6d257e2c779",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: Add histogram of residuals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08579cdf-3b28-4ea2-9c61-6ae62974af51",
   "metadata": {},
   "source": [
    "Zeigen unsere Residuen eine Struktur oder ein systematisches Verhalten deutet dies auf einen ungenauen Fit oder ein falsches Fitmodel hin. Dies ist im folgenden gezeigt. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "850870af-e546-4d95-b9de-8a4e7b61c241",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/jobs/29593351/ipykernel_11778/4141622746.py:10: UserWarning: The figure layout has changed to tight\n",
      "  fig_fit.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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v+j5+f+93ADp804FyIeWMDST3VW9QPYJfCQZgcb/FnNl2xuBEIvmLoYVdmTJleP/99wkLC2PPnj2EhobSqVMnDh8+nH7MgAEDuHDhQvr24Ycfpj+XlpZG+/btSU5OZtu2bcyYMYPp06czduxYI96OiNjB6c2nWTpwKXB7AuLafWsbG0gequUHLanSqQppSWnM6TRH06CIZCNDC7sOHTrQrl07KleuzGOPPcZ7772Hu7s7O3bsSD/Gzc0NX1/f9M3DwyP9uTVr1nDkyBFmzZpF7dq1adu2Le+88w6TJ08mOTnZiLckIjYUfyaeX7r9gjnFTLVnqxH6jiYgzg0cHB3o8lMXSgaVJDE2kblPzyUlMcXoWCL5Qo65xy4tLY05c+Zw48YNgoOD0/f/9NNPeHl5Ub16dcaMGUNi4l9zJG3fvp0aNWrg4+OTvq9169YkJCRk6PUTkdwn9VYqv3T9hcRLifjW9qXTNM1Vl5s4F3Km+6LuFPIuRMz+GJYO1EhZkexg+FqxBw8eJDg4mFu3buHu7s7ChQsJDAwE4Pnnn6ds2bKUKlWKAwcO8MYbb3Ds2DEWLFgAQHR0dIaiDkh/HB0dfd9zJiUlkZSUlP44ISHB1m9LRB6BxWJhxdAVnN99noLFCvLsgmdxctO0JrmNRxkPuv3SjZktZnLwp4OUql+KhsMbGh1LJE8zvLCrUqUK+/btIz4+nvnz59OnTx82bdpEYGAgAwcOTD+uRo0alCxZkhYtWnDixAkqVqyY5XNOmjSJ8ePH2yK+iNhB2Ddh7P1+LyYHE11/7krR8kWNjiRZVK55OVr9pxWrR65mzStr8K3tS7nm5YyOJZJnGX4p1tnZmUqVKhEUFMSkSZOoVasWn3322T2PbdCgAQCRkZEA+Pr6EhMTk+GYO499fX3ve84xY8YQHx+fvp05o1FbIjnFme1nWDns9uj40PdCqdgq63/ESc7QYHgDajxfA0uahfnPzifhrK6SiNiL4YXd/zKbzRkuk/7dvn37AChZsiQAwcHBHDx4kIsXL6Yfs3btWjw8PNIv596Li4tL+hQrdzYRMd716OvM6zYPc4qZgK4BNH6jsdGRxAZMJhMdvu2ATy0fbly8wS9df9GyYyJ2YmhhN2bMGDZv3sypU6c4ePAgY8aMYePGjfTo0YMTJ07wzjvvEBYWxqlTp1iyZAm9e/emWbNm1KxZE4BWrVoRGBhIr1692L9/P6tXr+bNN99kyJAhuLi4GPnWRMRKaSlpzHtmHtfOX6NEYInbgyVMGiyRVzi5OfHcgudwLerKuV3n0ntlRcS2DC3sLl68SO/evalSpQotWrRg9+7drF69mieffBJnZ2fWrVtHq1atqFq1Kq+88gpdu3Zl6dKl6a93dHRk2bJlODo6EhwcTM+ePenduzcTJkww8F2JSFZseGsDUVuicPFw4bmFz+FSWH+c5TVFKxSl25xuYILwb8M58NMBoyOJ5DmGDp74/vvv7/ucn58fmzZtemgbZcuWZcWKFbaMJSLZLHJVJFs/2ApAx+87Uvyx4gYnEnup2Koizcc2Z9P4TSx/aTml65fW5y1iQznuHjsRyV+unb/Gwl4LAaj3cj0Cu93//ljJG5q91YxyIeVIvp7MvGfnkXpL99uJ2IoKOxExjDnVzK/P/0pi7O1JiFv/t7XRkSQb3FmZws3LjZj9Max+ZbXRkUTyDBV2ImKY498d5/Sm0zi7O9NtbjcKuBo+taZkk8KlCvP0j08DsOerPRyZf8TgRCJ5gwo7ETFEecrzx3d/APDU10/pPqt8qFKbSjQefXtKmyX9l3D1z6sGJxLJ/VTYiUi2S7qcRBe6gAXqvFiHGs/XMDqSGOSJCU/g18iPpIQk5nefT1pymtGRRHI1FXYikq0sFgt7395LYQpTuGJh2n7W1uhIYiBHJ0e6/twV16KunN99nvX/Xm90JJFcTYWdiGSrXV/u4tK2S6SQQtCkIJzcnIyOJAbz9Pek07ROAGz/73ZObjhpcCKR3EuFnYhkm4uHL7Lu9XUArGUthSsWNjiR5BRVO1Wl7oC6YIFFvRdx8+pNoyOJ5Eoq7EQkW6QmpbKw50JSb6VSolEJdrHL6EiSw7T+pDXFKhcj4WwCywcvx2KxGB1JJNdRYSci2WLD2A1E74vGzcuN2uNqGx1HciDnQs50mdUFk6OJw3MPc/Cng0ZHEsl1sjRplNlsJjIykosXL2I2mzM816xZM5sEE5G849TGU2z7aBsAHb7rQKJXosGJJKcq/XhpQt4OYcNbG1gxZAX+TfwpUq6I0bFEcg2rC7sdO3bw/PPPc/r06bu6yU0mE2lpGqouIn+5efXm7SXDLFB3QF2qdqpKeHi40bEkB2syugmRKyM5s+0MC3stpM/GPjg46gKTSGZY/ZPy0ksvUa9ePQ4dOsSVK1e4evVq+nblyhV7ZBSRXGzFkBUknE2gWKVitP5YS4bJwzkUcODpWU/jXNiZqC1RbP1wq9GRRHINqwu748ePM3HiRAICAihSpAienp4ZNhGROw7NPcShnw9hcjTR5acuOLs7Gx1Jcomi5YvS9ovbcxxuHLuR6H3RBicSyR2svhTboEEDIiMjqVSpkj3yiEgecT36OiteXgFAszebUfrx0gYnEluKiIiweZteXl74+/unP67VuxZ/LPmDiAURLOqziAG7B+Do7Gjz84rkJVYXdsOGDeOVV14hOjqaGjVq4OSUcXLRmjVr2iyciOROFouFZS8t4+aVm/jW8aXpv5saHUlsxMvLCzc3N3r27Gnztt3c3IiIiEgv7kwmE+2ntOf05tPEHIhh0zubCH0n1ObnFclLrC7sunbtCsALL7yQvs9kMmGxWDR4QkQAODDrAMcWH8PByYHOMzrj6KRelrzC39+fiIgIYmNjbdpuREQEPXv2JDY2NkOvXSHvQrSf2p553eaxZdIWqnSsQun66v0VuR+rC7uTJ7XUi4jcX8K5BFYOWwlAyNsh+NTwMTiR2Jq/v3+G4sveArsGUr17dQ7NOcSiPosYFD6IAq5Zmq1LJM+z+iejbNmy9sghInmAxWJh6YClJMUnUfrx0jR+vbHRkSSPaPtlW05uOElsRCwbxm3gyQ+eNDqSSI6UqcJuyZIltG3bFicnJ5YsWfLAYzt27GiTYCKS++z9YS+RKyNxdHGk0/ROOBTQ3GNiG27F3ejwTQfmdJrD9v9sp2rnqvgF+xkdSyTHyVRh17lzZ6Kjo/H29qZz5873PU732InkX/FR8aweuRqA0HdDKRFQwuBEktdU6ViFWr1rsX/mfhb1WcRL+17Cyc3p4S8UyUcy9ee02WzG29s7/b/vt6moE8mf7lyCTb6WjF9jPxqObGh0JMmjWn/amsKlCnPl+BV+e+s3o+OI5Di6TiIij2zf9H2cWHOCAq4F6DStk5Z/ErspWLQgHb7tAMDOT3dydudZgxOJ5CxZGla0fv16Pvnkk/QJKgMCAhgxYgQtW7a0aTgRMV5UVNQDp7a4dekWG4dvBOCxQY9x+tppToeffmCb9pjcVvKPyu0qU7NXTQ78eIAlLyxhYPhACrholKwIZKGw++qrrxg+fDjdunVj+PDhAOzYsYN27drxySefMGTIEJuHFBFjREVFERAQQGJi4n2PeY7nCCCAc5xjwmcTMH9mzlTbbm5ueHl52Sqq5DOtP2lN5KpILh25xJZJWwh5O8ToSCI5gslisViseUGZMmUYPXo0Q4cOzbB/8uTJTJw4kXPnztk0YHZISEjA09OT+Ph4PDw8jI4jkmOEh4cTFBTErFmzCAgIuOv58+vOE/ZGGCZHE81+aoZH5cz//Pzv8lGSv935fy0sLIy6detm6jWHfznM/Ofm41DAgYHhAzVnouRZ1tQpVvfYxcXF0aZNm7v2t2rVijfeeMPa5kQkFwgICLjryzbxciK//ff2zetN/92UkOdCDEgm+VngM4FU/bkqRxcdZckLS+i/vb+m2JF8z+qfgI4dO7Jw4cK79i9evJinnnrKqramTJlCzZo18fDwwMPDg+DgYFauXJn+/K1btxgyZAjFixfH3d2drl27EhMTk6GNqKgo2rdvj5ubG97e3rz22mukpqZa+7ZExEprRq3hxsUblAgsQdN/aS1YyX4mk4l2k9vh4unC+T3n2fHZDqMjiRguUz12n3/+efp/BwYG8t5777Fx40aCg4OB2/fYbd26lVdeecWqk5cpU4b333+fypUrY7FYmDFjBp06dWLv3r1Uq1aNkSNHsnz5cubNm4enpydDhw6lS5cubN26FYC0tDTat2+Pr68v27Zt48KFC/Tu3RsnJycmTpxoVRYRybzjK4+zf+Z+MEHHHzrqxnUxTOFShWn9cWuW9F/Chjc3ULVTVYpVKmZ0LBHDZOoeu/Lly2euMZOJP//885ECFStWjI8++ohu3bpRokQJZs+eTbdu3QA4evQoAQEBbN++nYYNG7Jy5Uqeeuopzp8/j4/P7Xsrpk6dyhtvvMGlS5dwdnbO1Dl1j53Ivd3rvqeka0l8Ve0rEs4k0HBkQ1p/3NrglJIXZOUeuzssFgs/PvkjJ9efpGzzsvT5rQ8mB5OdkopkP2vqlExdij158mSmtkcp6tLS0pgzZw43btwgODiYsLAwUlJSMkyhUrVqVfz9/dm+fTsA27dvp0aNGulFHUDr1q1JSEjg8OHDWc4iIvf325u/kXAmgSLli/DEO08YHUcEk8lEh2874OTmxOlNp9k7ba/RkUQMY/hdpgcPHsTd3R0XFxdeeuklFi5cSGBgINHR0Tg7O1OkSJEMx/v4+BAdHQ1AdHR0hqLuzvN3nrufpKQkEhISMmwi8nDndp1j1xe7AHjq66dwLpS5XnEReytavmj6HxprX13L9ZjrBicSMYbhhV2VKlXYt28fO3fuZPDgwfTp04cjR47Y9ZyTJk3C09MzffPz00LSIg+TlpLG0oFLwQI1e9ak4pMVjY4kkkGDfzagZN2S3Iq7lb5usUh+Y/gdz87OzlSqVAmAoKAgdu/ezWeffcZzzz1HcnIycXFxGXrtYmJi8PX1BcDX15ddu3ZlaO/OqNk7x9zLmDFjGDVqVPrjhIQEFXeS6z1shYis+PsKETs+2UHM/hgKFitIq49b2fQ8IrbgUMCBDt924Nv633Lo50PU7FWTym0rGx1LJFsZXtj9L7PZTFJSEkFBQTg5ObF+/Xq6du0KwLFjx4iKikofjRscHMx7773HxYsX8fb2BmDt2rV4eHgQGBh433O4uLjg4uJi/zcjkk0ys0JEVrm5ueGS6MLKt29PRdTqv60oVKKQzc8jYgsl65akwYgG7Ph4B8sHL+flwy/rlgHJV6wq7FJTU5k4cSIvvPACZcqUeeSTjxkzhrZt2+Lv78+1a9eYPXs2GzduZPXq1Xh6etK/f39GjRpFsWLF8PDwYNiwYQQHB9OwYUPg9qTIgYGB9OrViw8//JDo6GjefPNNhgwZosJN8pXY2FgSExPvu0LEoyhevDi/D/yd1JuplHuiHLX61LJp+yK29sT4J4j4NYL40/FsfHsjrT5SD7PkH1YVdgUKFOCjjz6id+/eNjn5xYsX6d27NxcuXMDT05OaNWuyevVqnnzySQA++eQTHBwc6Nq1K0lJSbRu3Zqvvvoq/fWOjo4sW7aMwYMHExwcTKFChejTpw8TJkywST6R3OZeK0Q8qoOzD3JizQkcXRx56uunMJk0jYTkbM7uzrT/qj2z289mx8c7qPGPGpSsW9LoWCLZwupLsaGhoWzatIly5co98sm///77Bz7v6urK5MmTmTx58n2PKVu2LCtWrHjkLCJyt5tXbrJqxCoAmr3VjOKVixucSCRzKrerTLXnqnF47mGWDlzKizte1HJjki9YXdi1bduW0aNHc/DgQYKCgihUKOO9Nh07drRZOBEx1prX1pB4KZES1UrQ+LXGRscRsUqbT9twYvUJLoRdYOcXOwkeGWx0JBG7s7qwe/nllwH4+OOP73rOZDKRlpb26KlExHCnN59m3w/7gNtz1jk6OxobSMRK7r7utPywJcsGLmPDWxsI7BaIp5+n0bFE7Mrqfmmz2XzfTUWdSN6QlpzG8sHLAag7sC7+jf0NTiSSNXX718WvkR8pN1JYPUJz20ne90g3HNy6dctWOUQkB9n+8XYuHbmEWwk3Wk5q+fAXiORQJgcT7ae2x+RoImJBBH8s/8PoSCJ2ZfWl2LS0NCZOnMjUqVOJiYnhjz/+oEKFCrz11luUK1eO/v372yOniGSTqyevsmnCJuD2nHUFixU0OJHkJ3+fFNuWqr1YjUNfH2Ll0JWUf6I8Tm5OdjmPiNGsLuzee+89ZsyYwYcffsiAAQPS91evXp1PP/1UhZ1ILmaxWFg5dOXtOetCylGzZ02jI0k+4eXlhZubGz179rRL+54FPXmz9JvEnYpj87ubaTGxhV3OI2I0qwu7mTNn8s0339CiRQteeuml9P21atXi6NGjNg0nItnr6MKjHF9xHAcnB9pPaa856yTb+Pv7ExERYfNl8eB2L2DPnj0JfCWQ3aN2s+2jbdToUQPvat42P5eI0awu7M6dO5e+tuvfmc1mUlJSbBJKRLJf0rUkVv7z9rJhjd9ojFdVL4MTSX7j7++Pv7/9Bur4NvelSqcqHFt8jOWDl9N3U1/98SJ5jtWDJwIDA/n999/v2j9//nzq1Kljk1Aikv02jtvItXPXKFqhKE3/1dToOCJ20fbztji5ORH1exT7Z+w3Oo6IzVndYzd27Fj69OnDuXPnMJvNLFiwgGPHjjFz5kyWLVtmj4wiYmfR+6LZ+dlOANpNbodTQd1YLnmTp78nIeNDWPvaWta8uobHOjyGW3E3o2OJ2IzVPXadOnVi6dKlrFu3jkKFCjF27FgiIiJYunRp+hqvIpJ7WMwWlg9ejsVsIfCZQCq1uftWC5G8pMHwBnjX8Obm5ZusH7Pe6DgiNmV1jx1A06ZNWbt2ra2ziIgB9v6wl7M7zuLs7kzrT1obHUfE7hydHGn/VXumNZ1G+Lfh1HmhDmUaljE6lohNZHmC4j179vDjjz/y448/EhYWZstMIpJNEmMTWffGOgBCxofgUdrD2EAi2cS/iT+1+9YGYPnLyzGnmo0NJGIjVvfYnT17ln/84x9s3bqVIkWKABAXF0ejRo2YM2cOZcrorx6R3GLdmHXcvHIT7xrePD7scaPjiGSrlh+25Ojio0TvjWb3lN00GNbA6Egij8zqHrsXX3yRlJQUIiIiuHLlCleuXCEiIgKz2cyLL75oj4wiYgdnd5xl73d7AWj/VXscnRwNTiSSvQqVKJQ+UfGGNzdwPfq6wYlEHp3Vhd2mTZuYMmUKVapUSd9XpUoVvvjiCzZv3mzTcCJiH+ZUM8tfXg5A7b618W9iv7nDRHKyugPqUqp+KZISklj7mu4dl9zP6sLOz8/vnhMRp6WlUapUKZuEEhH72j1lN9F7o3Et4krLD1oaHUfEMA6ODrT/qj2Y4MCsA5zaeMroSCKPxOrC7qOPPmLYsGHs2bMnfd+ePXsYPnw4//nPf2waTkRs73r0dTa8uQGAFpNaUMi7kMGJRIxVql4p6g2uB8CKIStIS04zOJFI1lld2PXt25d9+/bRoEEDXFxccHFxoUGDBoSHh/PCCy9QrFix9E1Ecp41r64hKSGJUvVKUXdAXaPjiOQIoe+G4lbCjUtHLrHj0x1GxxHJMqtHxX766ad2iCEi2eHUplMc/OkgmKDdV+1wcMzyjEcieUrBogV58qMnWdx3MZsmbKLG8zXwKKPpfyT3sbqw69Onjz1yiIidpaWksXLoSgCCBgVRun5pgxOJ5Cy1etdi73d7idoSxZpX1tBtbjejI4lYTX+ui+QTu77cxcVDFylYvCAt3mthdByRHMdkMtFucjtMjiYO/3KYP9f9aXQkEaupsBPJB66dv8bGcRsBaPlBSwoWK2hsIJEcyqemD48PvT1Z94qhGkghuY8KO5F8YO1ra0m+lkzpBqWp06+O0XFEcrSQ8SEU8inE5WOX2f7JdqPjiFhFhZ1IHndq4ykOzv7/AROT22FyMBkdSSRHc/V05cmPngRg84TNxJ+JNziRSOY9cmGXkJDAokWLiIiIsEUeEbGhtJQ0VgxdAUC9l+pRKkiTiItkRs2eNfFv4k9KYgprRq0xOo5Iplld2D377LN8+eWXANy8eZN69erx7LPPUrNmTX799VebBxSRrNv1xS4uHb6Em5cboe+GGh1HJNf4+0CKI/OPcGLNCaMjiWSK1dOdbN68mX//+98ALFy4EIvFQlxcHDNmzODdd9+la9euNg8pkpdERUURGxtr0zbv1WOuARMij8anpg+PD3ucnZ/uZOWwlbx04CUKuFj9tSmSraz+PzQ+Pj59VYlVq1bRtWtX3NzcaN++Pa+99ppVbU2aNIkFCxZw9OhRChYsSKNGjfjggw+oUqVK+jEhISFs2rQpw+sGDRrE1KlT0x9HRUUxePBgNmzYgLu7O3369GHSpEkUKKAfQMlZoqKiCAgIIDEx0eZtu7m54eXllf54zatrSL5+e8BE7b61bX4+kfwg5O0QDv18iMt/XGb7x9tpOqap0ZFEHsjqysfPz4/t27dTrFgxVq1axZw5cwC4evUqrq6uVrW1adMmhgwZQv369UlNTeVf//oXrVq14siRIxQq9Nf6lQMGDGDChAnpj93c3NL/Oy0tjfbt2+Pr68u2bdu4cOECvXv3xsnJiYkTJ1r79kTsKjY2lsTERGbNmkVAQIBN2/by8sLf3x+4PWDi0M+HNGBC5BHdGUixqPcifn/3d2r2qImnv6fRsUTuy+rCbsSIEfTo0QN3d3f8/f0JCQkBbl+irVGjhlVtrVq1KsPj6dOn4+3tTVhYGM2aNUvf7+bmhq+v7z3bWLNmDUeOHGHdunX4+PhQu3Zt3nnnHd544w3efvttnJ2drXuDItkgICCAunXts07r3wdMBA0K0oAJkUdUs2dNwr8JT1+R4pl5zxgdSeS+rB488fLLL7N9+3Z++OEHtm7dioPD7SYqVKjAu++++0hh4uNvDym/c6n3jp9++gkvLy+qV6/OmDFjMlzG2r59OzVq1MDHxyd9X+vWrUlISODw4cP3PE9SUhIJCQkZNpG8YteXtwdMaIUJEdswmUy0/bItJofbAym0IoXkZFma7qRevXq0b9+ec+fOkZqaCkD79u1p3LhxloOYzWZGjBhB48aNqV69evr+559/nlmzZrFhwwbGjBnDjz/+SM+ePdOfj46OzlDUAemPo6Oj73muSZMm4enpmb75+fllObdITnLtwt8GTLyvARMituJby5f6Q+oDsHLYSq1IITmW1YVdYmIi/fv3x83NjWrVqhEVFQXAsGHDeP/997McZMiQIRw6dCj9nr07Bg4cSOvWralRowY9evRg5syZLFy4kBMnsj70fMyYMcTHx6dvZ86cyXJbIjnJutfX3V5h4vHS1HlBK0yI2NITE56gkHchYo/GsuOzHUbHEbknqwu7MWPGsH//fjZu3JhhsETLli2ZO3dulkIMHTqUZcuWsWHDBsqUKfPAYxs0aABAZGQkAL6+vsTExGQ45s7j+92X5+LigoeHR4ZNJLc7/ftpDsw6oAETInbiWsSVlh+0BGDT+E0knNNtPJLzWF3YLVq0iC+//JImTZpgMv31xVGtWjWre9EsFgtDhw5l4cKF/Pbbb5QvX/6hr9m3bx8AJUuWBCA4OJiDBw9y8eLF9GPWrl2Lh4cHgYGBVuURya3MqWZWDLk9YKLugLqUqqcBEyL2UKt3LcoElyHlRgprX11rdByRu1hd2F26dAlvb++79t+4cSNDoZcZQ4YMYdasWcyePZvChQsTHR1NdHQ0N2/eBODEiRO88847hIWFcerUKZYsWULv3r1p1qwZNWvWBKBVq1YEBgbSq1cv9u/fz+rVq3nzzTcZMmQILi4u1r49kVxp91e7uXjwIgWLFaTFRA2YELEXk4MpvUf80JxDnNxw0uhIIhlYXdjVq1eP5cuXpz++U8x99913BAcHW9XWlClTiI+PJyQkhJIlS6Zvdy7pOjs7s27dOlq1akXVqlV55ZVX6Nq1K0uXLk1vw9HRkWXLluHo6EhwcDA9e/akd+/eGea9E8nLrsdcZ8PYDQCETgzFrbjbQ14hIo+iZJ2SBL0UBMDKoStJS9FACsk5rJ7HbuLEibRt25YjR46QmprKZ599xpEjR9i2bdtdK0Q8jMVieeDzfn5+mWqzbNmyrFixwqpzi+QV695YR1J8EiWDSlL3RfvMjSciGYW+G8qRX45w6cgldn2xi+BR1nVsiNiL1T12TZo0Yd++faSmplKjRg3WrFmDt7c327dvJygoyB4ZReQ+zmw7w/4Z+4HbAyYcHLM0g5GIWKlg0YK0eP/2bQ8b397ItQvXDE4kcluWFlOtWLEi3377ra2ziIgVzGl/DZio078OZRo8eES5SH4XERFh0/YstSyUqFOCS3svsfa1tXSZ1cWm7YtkRaYKu4SEhPQpQR62SoOmDhHJHnum7iF6XzSuRVxpMUkDJkTux8vLCzc3twyT29tKBZcK9Db15uBPBwkaGETZZmVtfg4Ra2SqsCtatCgXLlzA29ubIkWK3HP0q8ViwWQykZamm0hF7O3GpRtsePP/B0y8F0qhEoUMTiSSc/n7+xMREUFsbKxN242IiKBnz56U7VqW07+eZsXQFQwKH4RDAd0SIcbJVGH322+/pa/fumHDBrsGEpGHWzd6HbfibuFbx5egQbq3VeRh/P398ff3t0vbVV+uysUNF7l48CK7Ju+i4fCGdjmPSGZkqrBr3rw5AKmpqWzatIkXXnjhoStEiIh9nN1xln0/7AM0YEIkJ3Au4kyLSS1YNmgZG8dupPpz1XH3dTc6luRTVn0jFChQgI8++ojU1FR75RGRB/j7gInafWvjF+xncCIRgdsDmErVL0VSQhLr3lhndBzJx6z+Uz80NNTq+epExDbCvw3nQvgFXDxd0tesFBHjOTg60G5yOzDB/pn7idoSZXQkyaesnu6kbdu2jB49moMHDxIUFEShQhlv2u7YsaPNwonIXxJjE1n/r/UAPPHOExTy1oAJkZykdP3S1H2xLuHfhrNiyAoGhg3UQArJdlYXdi+//DIAH3/88V3PaVSsiP2sG7OOW1dv4VPLh/qD6xsdR0TuocXEFkT8GkHMgRh2f7WbBv9sYHQkyWes/lPCbDbfd1NRJ2IfZ3ecZe93e4H/HzChXgCRHMnNy43QiaEAbHhrA9djrhucSPIbq78dZs6cSVJS0l37k5OTmTlzpk1Cichf/nfAhH9j+0zZICK2UffFupSq9/8DKV7XQArJXlYXdv369SM+Pv6u/deuXaNfv342CSUif9GACZHcxcHRgXZf/TWQ4vTvp42OJPmI1YXdnRUm/tfZs2fx9PS0SSgRue3GpRvpAyZC3w3VgAmRXKJ0/dLUHVAXgBVDVmBONRucSPKLTA+eqFOnDiaTCZPJRIsWLShQ4K+XpqWlcfLkSdq0aWOXkCL51fox67l19Ra+tX2p91I9o+OIiBVaTGxBxPwIrUgh2SrThV3nzp0B2LdvH61bt8bd/a9ZtZ2dnSlXrhxdu3a1eUCR/OrsjrPs/V4DJkRyK7fiblqRQrJdpgu7cePGAVCuXDmee+45XF1d7RZKJL/LMGCiX238GmmFCZHcqE7/OoR/F8753edZ+9panv7xaaMjSR5ndRdAnz59cHV1JTk5mbNnzxIVFZVhE5FHF/Z1GBfCL+BaxJWW72vAhEhu9fcVKQ7MOsCpTaeMjiR5nNWF3fHjx2natCkFCxakbNmylC9fnvLly1OuXDnKly9vj4wi+cr1mOt/DZh4TwMmRHK70vVLEzQwCLg9kCItRXO+iv1YvfJE3759KVCgAMuWLaNkyZL3HCErIlm37vV1JMUnUTKoJEGDgoyOIyI2cGdFikuHL7Hzs500erWR0ZEkj7K6sNu3bx9hYWFUrVrVHnlE8rXTm0+zf+Z+MEH7r9rj4KgBEyJ5QcFiBWn5YUuWvLCEjW9vpHr36niU8TA6luRBVn9rBAYGEhsba48sIvlaWkoay19eDkDQwCBKP17a4EQiYku1+9weCJVyI4XVo1YbHUfyKKsLuw8++IDXX3+djRs3cvnyZRISEjJsIpI1Oz/fyaXDl3DzcqPFxBZGxxERGzM5mGj3VTtMDiaOzDvCiTUnjI4keZDVhV3Lli3ZsWMHLVq0wNvbm6JFi1K0aFGKFClC0aJF7ZFRJM9LOJvAprc3AdDyg5YULFbQ4EQiYg++tXx5fNjjAKwYuoLUpFSDE0leY/U9dhs2bLBHDpF8bfWo1SRfT6ZMcBlq961tdBwRsaOQ8SEcnnuYK8evsO2jbTR7s5nRkSQPsbqwa968uT1yiORbJ9ae4Mi8I5gcTLSf0h6Tg0aai+Rlrp6utPq4FQueX8Dv7/1OjR41KFpeV7zENrI05O7333+nZ8+eNGrUiHPnzgHw448/smXLFpuGE8nrUm+lpq8w8fiwx/Gt5WtwIhHJDtW7V6d8aHlSb6WycthKLBaL0ZEkj7C6sPv1119p3bo1BQsWJDw8nKSkJADi4+OZOHGiVW1NmjSJ+vXrU7hwYby9vencuTPHjh3LcMytW7cYMmQIxYsXx93dna5duxITE5PhmKioKNq3b4+bmxve3t689tprpKbqvgXJ+bZ8sIUrx6/gXtKdJyY8YXQcEckmJpPp9hrQTg4cX36co4uOGh1J8girC7t3332XqVOn8u233+Lk5JS+v3HjxoSHh1vV1qZNmxgyZAg7duxg7dq1pKSk0KpVK27cuJF+zMiRI1m6dCnz5s1j06ZNnD9/ni5duqQ/n5aWRvv27UlOTmbbtm3MmDGD6dOnM3bsWGvfmki2unz8Mlsm3e7lbvNpG1w8XAxOJCLZyauqF41fbwzAqn+uIvl6ssGJJC+wurA7duwYzZrdfaOnp6cncXFxVrW1atUq+vbtS7Vq1ahVqxbTp08nKiqKsLAw4HYv4Pfff8/HH39MaGgoQUFBTJs2jW3btrFjxw4A1qxZw5EjR5g1axa1a9embdu2vPPOO0yePJnkZP2QSM5ksVhuLy2UlEbFVhUJfCbQ6EgiYoCm/25KkfJFSDibwMa3NxodR/IAqws7X19fIiMj79q/ZcsWKlSo8Ehh4uPjAShWrBgAYWFhpKSk0LLlX4ugV61aFX9/f7Zv3w7A9u3bqVGjBj4+PunHtG7dmoSEBA4fPnzP8yQlJWn+PTHU+bXn+XPtnzi6ONJucjstzSeSTzkVdKLdl+0A2PHpDmIOxDzkFSIPZvWo2AEDBjB8+HB++OEHTCYT58+fZ/v27bz66qu89dZbWQ5iNpsZMWIEjRs3pnr16gBER0fj7OxMkSJFMhzr4+NDdHR0+jF/L+ruPH/nuXuZNGkS48ePz3JWkUfhgguH/3P7j46m/2pKsUrFDE4kIo8iIiLi0RrwhZItSnJh/QXm9ppL4+8bU8K7BP7+/rYJKPmK1YXd6NGjMZvNtGjRgsTERJo1a4aLiwuvvvoqw4YNy3KQIUOGcOjQoWwZWTtmzBhGjRqV/jghIQE/Pz+7n1cEIJRQki4nUfyx4jR+o7HRcUQki7y8vHBzc6Nnz56P3JYHHgxhCFcPXGVg/YEcdTtKRESEijuxmtWFnclk4t///jevvfYakZGRXL9+ncDAQNzd3bMcYujQoSxbtozNmzdTpkyZ9P2+vr4kJycTFxeXodcuJiYGX1/f9GN27dqVob07o2bvHPO/XFxccHHRjeqS/eIi4qhPfQDafdWOAi5W/wiKSA7h7+9PRESEzdZPP/HTCY58fITO7p15//r7xMbGqrATq2X5W8XZ2ZnAwEe74dtisTBs2DAWLlzIxo0bKV++fIbng4KCcHJyYv369XTt2hW4PXgjKiqK4OBgAIKDg3nvvfe4ePEi3t7eAKxduxYPD49HzidiS+Y0MwcmHsABB0q3Lk2FFo92T6qIGM/f399mxVftmrW5vP4yMftjeJInbdKm5D9ZmqDYVoYMGcKsWbOYPXs2hQsXJjo6mujoaG7evAncHmnbv39/Ro0axYYNGwgLC6Nfv34EBwfTsGFDAFq1akVgYCC9evVi//79rF69mjfffJMhQ4aoV05ylD1T9hB/JJ5b3CJwlP7oEJGMHAo48NTUp8AEdahD7B7b9ARK/mJoYTdlyhTi4+MJCQmhZMmS6dvcuXPTj/nkk0946qmn6Nq1K82aNcPX15cFCxakP+/o6MiyZctwdHQkODiYnj170rt3byZMmGDEWxK5p4RzCaz/13oA1rEOVy9XgxOJSE5UpmEZynYpC8CBiQdITdJk+2Idk0XrmJCQkICnpyfx8fF4eHgYHUfyoF+6/kLEggiK1ijKiIMj2BO2h7p16xodS0RyoJ2bdjIvZB6FKUzzt5sTMi7E6EhiMGvqlEz12NWtW5erV68CMGHCBBITEx89pUg+cWzJMSIWROBQwIGa/66JhXz/t5SIPIBTYSdWsQqALRO3EHtMl2Ql8zJV2EVERKQv8zV+/HiuX79u11AieUXy9WRWDF0BQPArwXhUVo+wiDzcYQ7j3dibtOQ0lr+0HF1ck8zK1KjY2rVr069fP5o0aYLFYuE///nPfac30RqtIn/ZMHYDCWcSKFKuCM3HNufg0YNGRxKRXKLG6BpsenYTpzaeYv+M/dTuW9voSJILZKqwmz59OuPGjWPZsmWYTCZWrlxJgQJ3v9RkMqmwE/l/58POs/OznQC0n9IeJzcngxOJSG7iVsqNkPEhrHt9HWteWUPl9pUpVKKQ0bEkh8tUYVelShXmzJkDgIODA+vXr0+fM05E7mZONbNs4DIsZgvVu1enUptKRkcSkVyo4YiGHJx1kJgDMax9dS2dZ3Q2OpLkcFZPd2I2m1XUiTzEri93cSH8Ai6eLrT+pLXRcUQkl3J0cuSpb27Pbbd/5n7+XP+n0ZEkh8vSPHYnTpxg2LBhtGzZkpYtW/LPf/6TEydO2DqbSK4UdzqO3978DYCWH7TE3Tfry+2JiJRpUIb6L99einDZoGWkJKYYnEhyMqsLu9WrVxMYGMiuXbuoWbMmNWvWZOfOnVSrVo21a9faI6NIrmGxWFg+eDkpN1Lwb+JP0IAgoyOJSB7QYmILCpcuzNUTV9k4fqPRcSQHs7qwGz16NCNHjmTnzp18/PHHfPzxx+zcuZMRI0bwxhtv2COjSK5x6OdDRK6MxNHZkQ7fdsDkYDI6kojkAS4eLrT/qj0A2/+7nQvhFwxOJDmV1YVdREQE/fv3v2v/Cy+8wJEjR2wSSiQ3SoxNZNXw25OKNnurGV5VvQxOJCJ5SZWOVaj2bDUsaRaWvLgEc6rZ6EiSA1ld2JUoUYJ9+/bdtX/fvn0aVCH52upRq0mMTcS7ujeNX29sdBwRyYPafN4G16KuRO+NZvsn242OIzlQpqY7+bsBAwYwcOBA/vzzTxo1agTA1q1b+eCDDxg1apTNA4rkBpGrIznw4wEwQYfvOuDo7Gh0JBHJg9x93Gn131YseWEJG8duJODpAIpVKmZ0LMlBrC7s3nrrLQoXLsx///tfxowZA0CpUqV4++23+ec//2nzgCI5XfL1ZJYNWgZAg382oEyDMgYnEpG8rHbf2hycdZCTv51k2aBl9FrXC5NJ9/PKbVZfijWZTIwcOZKzZ88SHx9PfHw8Z8+eZfjw4fofS/KlDWM3EH86Hk9/T0LfDTU6jojkcSaTiae+fooCrgU4+dtJ9k3fZ3QkyUGyNI/dHYULF6Zw4cK2yiKS65zbde6vZcOmtsfZ3dngRCKSHxSrVIyQ8SEArBm1hmsXrhmaR3KORyrsRPKz1KRUFvdbjMVsoUaPGlRuW9noSCKSjwSPCqZk3ZLcirvFipdXYLFYjI4kOYAKO5Es2vzuZi4duUQh70K0+ayN0XFEJJ9xKOBAxx864lDAgaOLjnJknqYcExV2IllyYe8FtkzaAkC7r9rhVtzN4EQikh/51vKlyb+aALBiyApuXLphcCIxmlWFXUpKCi1atOD48eP2yiOS46WlpN2+BJtmIbBbIIFdA42OJCL5WLN/N8O7uvftSdL/ucroOGIwqwo7JycnDhw4YK8sIrnC1g+2ErM/hoLFC9L2y7ZGxxGRfM7R2ZFO0zphcjRxaM4hji46anQkMZDVl2J79uzJ999/b48sIjnexUMX2TRhEwBtP2+Lu4+7wYlERKBUvVI0eu32ogHLBy/n5pWbBicSo1g9QXFqaio//PAD69atIygoiEKFCmV4/uOPP7ZZOBEjRUVFERsbm/7YnGpm6wtbMaeY8WnuQ3KVZMLDw61qMyIiwtYxRUQACBkXwrFFx4g9GsvqUavpPL2z0ZHEAFYXdocOHaJu3boA/PHHHxme0wTFkldERUUREBBAYmJi+r7GNOZJnuQmN3l90+tcq5e1eaPc3Nzw8vKyVVQREQAKuBag4w8d+aHxD+yfsZ9qz1ajcjtNw5TfWF3YbdiwwR45RHKU2NhYEhMTmTVrFgEBAVz78xqbe2zGnGym4biGPNvx2Sy37eXlhb+/vw3Tiojc5hfsR8MRDdnxyQ6WDljK4EODKVi0oNGxJBtZXdjdERkZyYkTJ2jWrBkFCxbEYrGox07ynICAAGrVqMUPg37AnGymcrvKdBrXSf+vi0iOFfpuKMeXH+fyH5dZNXwVT8982uhIko2sHjxx+fJlWrRowWOPPUa7du24cOECAP379+eVV16xeUARo215fwvn95zHtagrHb7toKJORHI0JzcnOs/ojMnBxIEfDxCxUPf25idWF3YjR47EycmJqKgo3Nz+mpT1ueeeY9UqzZ8jeUv80Xg2T9gMQLsv21G4lNZGFpGcr0zDMjR6/fYo2WWDlmni4nzE6sJuzZo1fPDBB5QpUybD/sqVK3P69Gmr2tq8eTMdOnSgVKlSmEwmFi1alOH5vn37YjKZMmxt2mRcuunKlSv06NEDDw8PihQpQv/+/bl+/bq1b0vkLo44snfcXsypZgK6BlD9H9WNjiQikmkhb4fcnrj4UiLLBy/XWrL5hNWF3Y0bNzL01N1x5coVXFxcrG6rVq1aTJ48+b7HtGnThgsXLqRvP//8c4bne/ToweHDh1m7di3Lli1j8+bNDBw40KocIvcSQgjXIq/hVsKN9lPa6xKsiOQqBVwK0HlmZxwKOBDxawSH5hwyOpJkA6sLu6ZNmzJz5sz0xyaTCbPZzIcffsgTTzxhVVtt27bl3Xff5emn739jp4uLC76+vulb0aJF05+LiIhg1apVfPfddzRo0IAmTZrwxRdfMGfOHM6fP2/tWxNJd/XgVRrTGICnpj5FoRKFHvIKEZGcp2SdkjR7qxlwey3ZaxeyNk2T5B5WF3Yffvgh33zzDW3btiU5OZnXX3+d6tWrs3nzZj744AObB9y4cSPe3t5UqVKFwYMHc/ny5fTntm/fTpEiRahXr176vpYtW+Lg4MDOnTttnkXyh5SbKewdtxcHHCjdtjQBXQKMjiQikmVNxjShZFBJbl29xdIBS3VJNo+zurCrXr06f/zxB02aNKFTp07cuHGDLl26sHfvXipWrGjTcG3atGHmzJmsX7+eDz74gE2bNtG2bVvS0tIAiI6OxtvbO8NrChQoQLFixYiOjr5vu0lJSSQkJGTYRO5Y98Y6bpy+QQIJVH9d99WJSO7m6ORI5xmdcXR25Pjy44R/Z92KOZK7ZGkeO09PT/7973/bOstdunfvnv7fNWrUoGbNmlSsWJGNGzfSokWLLLc7adIkxo8fb4uIkgP879Jfj+Litovs+mIXAEtYQg+PHjZpV0TESN7VvAmdGMraV9eyesRqyoWUo3jl4kbHEjvIUmF39epVvv/++/R1LwMDA+nXrx/FihWzabj/VaFCBby8vIiMjKRFixb4+vpy8eLFDMekpqZy5coVfH1979vOmDFjGDVqVPrjhIQE/Pz87JZb7OdeS39llRtuDGYwhSnMTnZy3u28lv4SkTwjeGQwx5cf59SGUyzsuZB+W/rh6ORodCyxMasLuztTlHh6eqbf2/b5558zYcIEli5dSrNmzWwe8o6zZ89y+fJlSpYsCUBwcDBxcXGEhYURFBQEwG+//YbZbKZBgwb3bcfFxcXqEbySM/3v0l9ZZbFY2PP6HqJ/i8a9vDvjZ43Hp4yPlv4SkTzD5GCi84zOTK05lXO7zvH7e78T8naI0bHExqwu7IYMGcJzzz3HlClTcHS8XemnpaXx8ssvM2TIEA4ePJjptq5fv05kZGT645MnT7Jv3z6KFStGsWLFGD9+PF27dsXX15cTJ07w+uuvU6lSJVq3bg3cXu6pTZs2DBgwgKlTp5KSksLQoUPp3r07pUqVsvatSS4WEBBA3bp1s/z6vdP2Ev1bNA5ODjw//3lK1i1pw3QiIjmDp58n7ae059d//MrmdzdTqU0lyjQs8/AXSq5h9eCJyMhIXnnllfSiDsDR0ZFRo0ZlKNIyY8+ePdSpU4c6deoAMGrUKOrUqcPYsWNxdHTkwIEDdOzYkccee4z+/fsTFBTE77//nqG37aeffqJq1aq0aNGCdu3a0aRJE7755htr35bkY1f/vMqqf95eNeWJd55QUScieVr17tWp0aMGljQLC3ouIPl6stGRxIas7rGrW7cuERERVKlSJcP+iIgIatWqZVVbISEhDxx2vXr16oe2UaxYMWbPnm3VeUXuMKeaWdhrIcnXk/Fv6k+jVxsZHUlExO7afdmO05tPc/XEVVaPWk2HbzoYHUlsJFOF3YEDB9L/+5///CfDhw8nMjKShg0bArBjxw4mT57M+++/b5+UInay5f0tnNl2BhcPF56e+TQOjlZ3YouI5DquRVx5eubTzAidQfi34VRuX5mqnaoaHUtsIFOFXe3atTGZTBl6115//fW7jnv++ed57rnnbJdOxI7ObDvDxrc3AtBucjuKlCtiaB4RkexULqQcjV5rxLYPt7Gk/xJK1SuFR2kPo2PJI8pUYXfy5El75xDJVrfibvHr879iSbNQ/R+37zcREclvQt8J5eT6k1wIu8DCngvpta6Xrlzkcpkq7MqWLWvvHCLZxmKxsHTgUuJPx1O0QlGemvoUJpPJ6FgiItnO0dmRrj935Zu633Bq4ym2TNpCszftN22Z2F+WJig+f/48W7Zs4eLFi5jN5gzP/fOf/7RJMBF72fv9Xo7MO4JDAQe6/twVFw/NaSgi+VfxysVp91U7FvVexMa3N1LuiXL4N9YcnrmV1YXd9OnTGTRoEM7OzhQvXjxDT4fJZFJhJznapSOXWPnPlQCEvhdK6cdLG5xIRMR4tXrV4s81f3Jg1gEWPL+AQfsGUbBoQaNjSRZYXdi99dZbjB07ljFjxuDgoOvwknuk3kplfvf5pN5MpWKripraRERytDvLdtqSl5fXfVfUafdVO87uOMuVyCssHbCUZ+Y9o9tUciGrC7vExES6d++uok5ynTWvruHiwYsU8i5E5xmdMTnoF5aI5DxeXl64ubnRs2dPm7ft5uZGRETEPYs7l8IudJ3Tle+Dvyfi1wjCvgmj3qB6Ns8g9mV1Yde/f3/mzZvH6NGj7ZFHxC4iFkawe/JuADrP7Iy7r7vBiURE7s3f35+IiAhiY2Nt2m5ERAQ9e/YkNjb2vr12pYJK0WJSC9a+upbVI1bj18gPnxo+Ns0h9mV1YTdp0iSeeuopVq1aRY0aNXBycsrw/Mcff2yzcCK2cPXPqyzutxiA4FeDqdS6ksGJREQezN/f/77Fl70Fjwzm5PqTRK6MZN4z8xiwewAuhTXILLfIUmG3evXq9CXF/nfwhEhOknorlXnPzCMpPgm/Rn60mNjC6EgiIjmaycHE0zOf5us6X3P52GWWDVxGl9ld9B2fS1hd2P33v//lhx9+oG/fvnaII2Jbq0au4kL4BQoWL0i3ud1wdHI0OpKISI7n5uVGt7ndmN58OofmHMK/qT/1X65vdCzJBKtHQLi4uNC4cWN7ZBGxqYOzDxI2NQxM0OWnLniU0VI5IiKZ5dfIj5YftARg9cjVnN9z3uBEkhlWF3bDhw/niy++sEcWEZu5FHGJpQOXAtDszWa6r05EJAsajmxI1c5VSUtOY94z87h59abRkeQhrL4Uu2vXLn777TeWLVtGtWrV7ho8sWDBApuFE8mK5BvJzOs2j5QbKZQPLU/zcc2NjiQikiuZTCY6TetE9P5o4k7GsbjfYp5b+Jzut8vBrO6xK1KkCF26dKF58+Z4eXnh6emZYRMxksViYfng5Vw6cgn3ku50md1FC1qLiDwC1yKuPDPvGRydHTm2+Bjb/7vd6EjyAFb32E2bNs0eOURsYvdXuznw4wFMDia6zemGu4/mqxMReVSlgkrR5rM2LB+8nHWj11GybknKh5Y3Opbcg7oyJM84/ftpVo9YDUDLD1tStllZgxOJiOQdQYOCqNW7FpY0C/Ofm0/c6TijI8k9WN1jV758+QdeW//zzz8fKZBIVty8eJN5fedhTjVTvXt1gkcFGx1JRCRHepQ1aEsPKs2pXaeIPxrP9DbTafx9YxxdHR+4Bq1kL6sLuxEjRmR4nJKSwt69e1m1ahWvvfaarXKJZJojjux5bQ83Ym7gU9OHDt910I29IiL/w1Zr0HriyUAGwlGY2Hgii1j0wDVoJXtZXdgNHz78nvsnT57Mnj17HjmQiLXa0pa4Q3G4FnXl2QXP4lzI2ehIIiI5ji3XoI3dFcv2Idupba5Ny94tGTpz6APXoJXsY3Vhdz9t27ZlzJgxGlwh2er0gtPUox6YoOvsrhSrWMzoSCIiOZbN1qCtC+433Fn76lou/3QZf1TQ5RQ2Gzwxf/58ihXTl6pkn7M7znLow0MAVH25KpXaaBJiEZHsEjwqmOrdq2NJs/Asz3IzRpMX5wRW99jVqVMnw/1LFouF6OhoLl26xFdffWXTcCL3Ex8Vz5zOczCnmDnCEZ7q95TRkURE8hWTyUSH7zoQFRYFx2HXyF3Ub1Zft8MYzOrCrnPnzhkeOzg4UKJECUJCQqhataqtconcV/L1ZH7u+DM3Ym7g8ZgHi/5YxBumN4yOJSKS7zgXcqb+x/VZ0mEJHINFfRbxzC/PYHLQADajWF3YjRs3zh45JI+LioqyyQ27FrOFPa/tIWZ/DM7FnCn6clGSRyTbIKGIiGSFWyk35jKXF51eJOLXCDaM20DoO6FGx8q3bDZ4QuR+oqKiCAgIIDEx8ZHbakELmtKUVFL57sp3nB1xFjc3N7y8vGyQVEREsiKKKGr+uyb73t7H7+/+TomAEtR4vobRsfKlTBd2Dg4OD50bzGQykZqamumTb968mY8++oiwsDAuXLjAwoULM1zqtVgsjBs3jm+//Za4uDgaN27MlClTqFy5cvoxV65cYdiwYSxduhQHBwe6du3KZ599hru7lpLKKWJjY0lMTGTWrFkEBARkuZ2zK86y9629ANR/pz5Pt3saQBNjiojkAH4d/HBLdGPbh9tY/MJiilYsSpkGZYyOle9kurBbuHDhfZ/bvn07n3/+OWaz2aqT37hxg1q1avHCCy/QpUuXu57/8MMP+fzzz5kxYwbly5fnrbfeonXr1hw5cgRXV1cAevTowYULF1i7di0pKSn069ePgQMHMnv2bKuyiP0FBARQt27dLL32zPYzrHhnBQBNxjShxZstbBlNRERsoMXEFlw+epljS44xp9McBuwegKefp9Gx8hfLIzh69Kilc+fOFkdHR0vv3r0tp06dynJbgGXhwoXpj81ms8XX19fy0Ucfpe+Li4uzuLi4WH7++WeLxWKxHDlyxAJYdu/enX7MypUrLSaTyXLu3LlMnzs+Pt4CWOLj47OcX+4vLCzMAljCwsKy9PorJ65YPvL+yPI2b1vmdJ5jMaeZbZxQRESy6n9/x99KuGWZUnOK5W3etkypNcVyK/6WwQlzP2vqlCzNY3f+/HkGDBhAjRo1SE1NZd++fcyYMYOyZW236PrJkyeJjo6mZcuW6fs8PT1p0KAB27dvB273FBYpUoR69eqlH9OyZUscHBzYuXOnzbKIcRIvJ/JT25+4cfEGvrV9efrHpzXaSkQkB3Mp7EL3Jd0p5FOImP0x/NLtF9JS0oyOlW9YVdjFx8fzxhtvUKlSJQ4fPsz69etZunQp1atXt3mw6OhoAHx8fDLs9/HxSX8uOjoab2/vDM8XKFCAYsWKpR9zL0lJSSQkJGTYJOdJuZnCnE5zuPzHZTz9PXl++fM4u2t+JBGRnK5I2SI8v+x5nNyc+HPtnywbuAyLxWJ0rHwh04Xdhx9+SIUKFVi2bBk///wz27Zto2nTpvbMZjeTJk3C09MzffPz8zM6kvwPi9nCwl4LObP1DK5FXHl+xfMULlXY6FgiIpJJpeqVotsv3TA5mNg3fR+bxm8yOlK+kOnBE6NHj6ZgwYJUqlSJGTNmMGPGjHset2DBApsE8/X1BSAmJoaSJUum74+JiaF27drpx1y8eDHD61JTU7ly5Ur66+9lzJgxjBo1Kv1xQkKCirscZvUrq4n4NQJHZ0eeW/Qc3tW8H/4iERHJUR5r/xjtp7Rn2aBlbBq/CQ8/D+r2z9ogOsmcTBd2vXv3fuh0J7ZUvnx5fH19Wb9+fXohl5CQwM6dOxk8eDAAwcHBxMXFERYWRlBQEAC//fYbZrOZBg0a3LdtFxcXXFxc7P4eJGt2fLqDnZ/evkey84zOlGtezthAIiKSZUEDg4g7HceWiVtYNmgZHqU9tLa3HWW6sJs+fbrNT379+nUiIyPTH588eZJ9+/ZRrFgx/P39GTFiBO+++y6VK1dOn+6kVKlS6XPdBQQE0KZNGwYMGMDUqVNJSUlh6NChdO/enVKlStk8r9jfkflHWD1qNQAtP2hJ9e62v39TRESyV+i7oSREJXBg1gHmPTOPPhv7UCpI39P2kKVRsbayZ88e6tSpQ506dQAYNWoUderUYezYsQC8/vrrDBs2jIEDB1K/fn2uX7/OqlWr0uewA/jpp5+oWrUqLVq0oF27djRp0oRvvvnGkPcjj+bE2hP8+vyvYIF6L9ej0WuNjI4kIiI2YDKZ6Ph9R8qHlif5ejI/tfmJ2KOPvsyk3M1k0TAVEhIS8PT0JD4+Hg8PD6Pj5Dnh4eEEBQURFhZ23wmKz2w/w48tfyQlMYXAZwLp+nNXHBwN/btDREQyITO/4+9ISkhixhMzuBB+AQ8/D17Y8gKe/prA+GGsqVP0zSmGizkYw+z2s0lJTKFiq4p0mdVFRZ2ISB7k4uFCj1U9KF6lOAlnEvjxyR+5cfGG0bHyFH17iqGu/nmVWa1mcevqLcoEl+HZBc/i6OxodCwREbGTQiUK0WttLzz9Pbn8x2VmtZnFrfhbRsfKM1TYiWGunb/GzJYzuR59He8a3rcnIC6kCYhFRPI6Tz9Peq3thVsJN6L3RjOn4xxSbqYYHStPUGEnhkiMTWRW61nEnYyjaMWi9Fzdk4JFCxodS0REsknxx4rTc3VPXDxcOL35NPOemUdaspYee1Qq7CTb3bxyk5ktZ3Lx0EXcS7rTa20vCpfUqhIiIvlNyTol+ceyf1DAtQDHlx9n3rPztK7sI1JhJ9nq5tXbRV3M/hgK+RSiz299KFq+qNGxRETEIGWblqX74u44ujhybPExfv3HryruHoEKO8k2KddS+PHJH4neG41bCTf6/NYHr6peRscSERGDVWxVke6LuuPo7EjErxEs6LEAc6rZ6Fi5UqZXnpD8ISoqithY204aGRERgQsu7Biyg7jDcbh53S7qSgSWsOl5REQk96rUphLPLniWuU/P5ci8Izg4OvD0j0/jUEB9UNZQYSfpoqKiCAgIIDEx0abtuuBCb4fexB2Oo2DxgvRe3xvv6t42PYeIiOR+j7V/jGd/fZZfuv7CoTmHMDma6Dyjs+Y2tYIKO0kXGxtLYmIis2bNIiAgwCZtJicks+ufu7h68CquRV3pva43PjV9bNK2iIjkPVU6VKHb3G7Mf3Y+B386iMVsofOMzjg6aY7TzFBhJ3cJCAh46LIwmXHj0g1mtZqVXtT1WtsL39q+NkgoIiI5TUREhO0aKwt1JtYh/F/hHPr5ECmJKXSb240CLipbHkb/QmIXCecS+LHlj8QejaWQz+1Zxn1qqKdORCSv8fLyws3NjZ49e9q87eou1XnO8TmOLT7GnI5zeG7hczi5Odn8PHmJCjuxuasnrzKzxUziTsbhUcaD3ut7U/yx4kbHEhERO/D39yciIsIuA+969uzJf6f8lz2v7uHEmhPMajOL55c9j4uHi03PlZeosBObij0Wy8wWM7l27hpFKxal97reFClXxOhYIiJiR/7+/vj7+9ulba/Hvei1thc/tf2JqN+jmNli5u3ViopptaJ70TATsZkL4ReY3mw6185do0RgCfpt7qeiTkREHplfsB99NvTBzcuN83vOM735dBLOJRgdK0dSYSc2cWLNCaY3n86NizfwreNLn419KFxKy4SJiIhtlKxTkr6b+lK4VGEuHrrI98Hfc+nIJaNj5Tgq7OSR7Z+5n9ntZ5N8PZnyLcrTd2NfCpUoZHQsERHJY0oEluCFrS9QvEpxEs4k8EPjH4jaEmV0rBxFhZ1kmcViYcv7W1jUZxHmVDM1nq9BjxU9dFOriIjYTZFyRXhh6wuUCS7DrbhbzGw5k4gFNpxqJZdTYSdZYk4zs/KfK1k/Zj0Awa8G8/SPT+PorAkkRUTEvtyKu9F7XW+qdKxCWlIav3T7hV2TdxkdK0dQYSdWS76ezLxu89j95W4wQetPWtPqo1aYHExGRxMRkXzCyc2JZ399lqBBQWCBlUNXsua1NZjTzEZHM5QKO7FK/Jl4pjWdxtFFR3F0dqTbnG40HNHQ6FgiIpIPORRwoP2U9jzxzhMAbP/PduZ2nkvStSSDkxlHhZ1k2tkdZ/m2/rdE74umkHch+mzoQ7VnqxkdS0RE8jGTyUSzN5vR9eeuFHAtwB/L/uCHRj8QdyrO6GiGUGEnmXJw9kGmh0znRswNfGr68OKuF/Fr5Gd0LBEREQCqd69O3019cS/pzsVDF/n28W/z5YhZFXbyQBazhd/e/I0FPRaQlpRGlU5VeGHrCxQpW8ToaCIiIhmUfrw0A3YNoGTdkiReSmRG6Az2Td9ndKxspcJO7uvmlZvMfmo2v7/3OwCNRzfmuQXP4ezubHAyERGRe/Mo40HfzX0J7BaIOcXM4n6LWT5kOalJqUZHyxYq7OSezoed5+u6XxO5MpICrgV4+senaTmppUa+iohIjudcyJluc7vRfFxzAPZ8tYfpzacTfybe4GT2V8DoAJI1UVFRxMbG2rTNiIjbEzyeXnCaFR+tIC05jaIVi/Lsr8/iW8vXpucSERGxJ5ODiZC3Qyj9eGkW9FzAuZ3n+LrO13T9uSsVn6xodDy7UWGXC0VFRREQEEBiYqJN2y1AAbo4duHAewcAqNKxCp1ndMa1iKtNzyMiIpJdKrerzMCwgfzS9Rei90Yzq/UsnnjnCZqOaZonr0Ll6MLu7bffZvz48Rn2ValShaNHjwJw69YtXnnlFebMmUNSUhKtW7fmq6++wsfHx4i42SY2NpbExERmzZpFQECATdq89uc1wv8VTsLxBEwOJkLfC6Xx643z5P/0IiKSvxQtX5T+2/qzYugK9n6/lw1vbiDq9yg6z+iMu4+70fFsKkcXdgDVqlVj3bp16Y8LFPgr8siRI1m+fDnz5s3D09OToUOH0qVLF7Zu3WpE1GwXEBBA3bp1H6kNi8VC2NdhbBm5hdRbqbiVcKPrz12p0KKCjVKKiIgYr4BrATp+15EywWVYOXQlJ1afYGrNqXSa3onKbSsbHc9mcnxhV6BAAXx9776/Kz4+nu+//57Zs2cTGhoKwLRp0wgICGDHjh00bKjVEB4m8XIiS19cytFFt3tAK7aqePuvF9+89deLiIjkXnfu/7aZOtB4RmP2j91P3LE4ZrebTYPhDWj5QUsKuOT4suihcvw7OH78OKVKlcLV1ZXg4GAmTZqEv78/YWFhpKSk0LJly/Rjq1atir+/P9u3b39gYZeUlERS0l/LjSQkJNj1PeREJ387ycJeC7l2/hoOTg60/KAlDYc31KVXERHJEby8vHBzc6Nnz552ad+joAff9v2WiOkR7PxsJ6c2nqLrz10pEVDCLufLLjm6sGvQoAHTp0+nSpUqXLhwgfHjx9O0aVMOHTpEdHQ0zs7OFClSJMNrfHx8iI6OfmC7kyZNuuvevfwiJTGF3978jR2f7gALFK9SnK4/d6VknZJGRxMREUnn7+9PRESEzWeAgNu9gD179qTSsErU7lqbxf0WE7M/hm/qfkPoxFAa/LMBDo65c0a4HF3YtW3bNv2/a9asSYMGDShbtiy//PILBQsWzHK7Y8aMYdSoUemPExIS8PPL+8tjRW2JYvELi7ly/AoAdQfUpfUnrXEupAmHRUQk5/H398ff39+u53jsqcd46cBLLO67mBNrTrBm1Boifo2g07ROFK9c3K7ntodcVY4WKVKExx57jMjISHx9fUlOTiYuLi7DMTExMfe8J+/vXFxc8PDwyLDlZSmJKawauYppzaZx5fgVPMp40GNlDzp800FFnYiI5HuFSxamx6oePPX1Uzi7O3Nm6xmm1pzK9k+2Y04zGx3PKrmqsLt+/TonTpygZMmSBAUF4eTkxPr169OfP3bsGFFRUQQHBxuYMmc5/ftpptaays5Pd4IFar9Qm8GHBlOpTSWjo4mIiOQYJpOJoIFBDD40mApPViD1ViprRq1herPpxB6z/eVge8nRl2JfffVVOnToQNmyZTl//jzjxo3D0dGRf/zjH3h6etK/f39GjRpFsWLF8PDwYNiwYQQHB2tELJAYm8ja19eyb9o+4PbaeR2+7aCCTkRE5AGKlC1Cz9U9Cf8unDWvrOHMttu9d43faEyTMU1wKuhkdMQHytGF3dmzZ/nHP/7B5cuXKVGiBE2aNGHHjh2UKHF7xMonn3yCg4MDXbt2zTBBcX5mMVvY+8Ne1r2xjptXbgK376V78qMncfXUChIiIiIPYzKZCBoQRKXWlVj20jIiV0ay+Z3NHPzpIG2/bJuj573L0YXdnDlzHvi8q6srkydPZvLkydmUKGeLORDDspeWcXb7WQB8avrQfmp7/ILz/sAQERERW/P09+T55c8TsSCCVcNXcfXPq8xuN5uArgG0+bQNHmVy3j36Obqwk8xJjE1k4/iN7JmyB0uaBWd3Z0ImhNBgWAMcCuSq2yhFRESyTaYnPy4PTX5uwrGvj3Fyzkkifo3gj5V/ULlfZSo8XwFHV8f0Q728vOw+kvdBVNjlYmnJaWz77zY2v7OZpPjbEy7n5L8iREREcoJHmfzYBx+e4in8Ev04OvkoOybvYB3rOMQhANzc3IiIiDCsuFNhZ2dRUVE2n1zxyJEjBBDAxmc2kng2EQCfWj60/rg15UPL2/RcIiIiec2jTn5sMVs4t/ocEV9EUCSmCN3oxoAaA3Dr4saA8QOIjY01rLAzWSwWiyFnzkESEhLw9PQkPj7epnPaRUVFERAQQGJios3a9MefFrSgLGUBcPd1J/S9UGr1qZVrZ8kWERHJjVISU9j+yXa2TNpCyo0UAA5zmCHzh9C8a3ObnceaOkWFHfYr7MLDwwkKCmLWrFkEBAQ8Ultxh+M4+tVRLu24BICjiyONX29M49cb4+yuSYZFRESMcu3CNTa8tYG9P+wFC+AAtXrWovm45hStUPSR27emTtGl2GwQEBBA3bp1s/TamAMxbBi7gWOLjwHgUMCBOi/Wodm/m+k+OhERkRygcMnCdPyuIx5PejC1+1QCzAHsn7mfg7MPUvuF2jR/q3m2fWersMuhzu85z5ZJW4hYGAEWMDmYqNmrJs3H2qb6FxEREdvyqOzBXOay/sf1XPjpApGrIgn/Jpz90/dTu19tGr3WiGIVi9k1gwq7HMRisXDyt5NsmbSFk+tPpu+v9mw1QsaH4FXVy7hwIiIikilFAosQujKU07+fZsObGzi9+TRhX4cR/m041Z6rRpPRTfCp6WOXc6uwywHMaWaOLT7G1g+2cm7XOQBMjiZq9qhJ4zcaUyKwhMEJRURExFplm5alz8Y+RP0exZZJW4hcFcmhnw9x6OdDVG5fmcavN8a/qT8mk8lm51RhZ6CbV2+y9/u97PpyF/Gn4wEo4FqAOv3r0OjVRhQpV8TYgCIiIvJITCYTZZuVpWyzslzYe4Gt72/l8LzDHF9+nOPLj+Nb25fH//k4Nf5RgwKuj16WqbAzwKUjl9j5+U4O/HiAlMTbw6MLFitI0KAgGgxvgLuPu8EJRUREJKsetKJFhTcq4P0Pb07MPMHZFWeJ3hfNkheWsGrUKsp2LUvZbmUp6F0ww2uuX7+e6XOrsMsmKTdTiPg1gvDvwjm96XT6fu8a3jQY3oAaz9fAqaCTgQlFRETkUVi7okVBClKXujzO43jGeXL8++Mc/f4oW9nKb/yWpQwq7OzMBx8OfniQtavXcivuFnB7hGuVTlVo8M8GlG1e1qbX1kVERMQYWV3RwpxqJnpTNCfnnORK+BWGvTWMjzp/lP58WFgYAwcOzFRbmqAY209QfO3CNQ7NOcTOb3cSHxGfvt+zrCd1+tehdt/aePp5PvJ5REREJG+J3hdN8SrFM1zF27x5M82bN9cExdbat28f7u5Zu78t5VoKFzZc4NzKc8TuiQXz7f2ppOL/pD+hr4VSoUUFTA7qnRMREZF7863t+0ivV2H3N82bW7euW0EK8hiPEUAAlahEgb/9c0YRxUEO8mfBPwn/LtywxYBFREQk/1Bh9zfffPMNQUFBDzzm1qVbRG+M5sKGC1zecxlL2l9Xst0ruFOmbRlKty6NW2k34PaNlCrqREREJDuosPubKlWq3LWmqznNzLmd5zi+8jiRKyK5EH4hw/PeNbwJ6BJAQJcAvGt4ayCEiIiIGEaF3T3ER8Vz8reTnFhzghOrT3Dzys0Mz5duUJqArgEEPB1AsUr2XfNNREREJLNU2P3Nof8cYn///VyJvJJhv2sRVyq2rkjldpWp2LqiJhAWERGRHEmF3d+cWXoGV1wxOZgoVb8U5VuUp3LbypRpWAaHAg5GxxMRERF5IBV2f1P+2fKE9ArBv6k/rp6uRscRERERsYoKu7+pOqQqjzV7zOgYIiIiIlmi64siIiIieYQKOxEREZE8QoWdiIiISB6hwk5EREQkj8gzhd3kyZMpV64crq6uNGjQgF27dhkdSURERCRb5YnCbu7cuYwaNYpx48YRHh5OrVq1aN26NRcvXjQ6moiIiEi2yROF3ccff8yAAQPo168fgYGBTJ06FTc3N3744QejoxkuKSmJt99+m6SkJKOjiJX02eVe+uxyL312uZc+u9tMFovFYnSIR5GcnIybmxvz58+nc+fO6fv79OlDXFwcixcvvus1SUlJGT74q1evUq5cOebNm0fDhg2zI3a2uXbtGoGBgRw5coTChQsbHUesoM8u99Jnl3vps8u98vJnt2PHDp555hmuXLlC0aJFH3ywJZc7d+6cBbBs27Ytw/7XXnvN8vjjj9/zNePGjbMA2rRp06ZNmzZtuWbbtWvXQ+uifLnyxJgxYxg1alT64zs9dmfOnMHDw8PAZCIiIiIZJSQk4OfnR6VKlR56bK4v7Ly8vHB0dCQmJibD/piYGHx9fe/5GhcXF1xcXO7a7+HhocJOREREciRHR8eHHpPrB084OzsTFBTE+vXr0/eZzWbWr19PcHCwgclExBbOnj3Lhg0bOHv2rNFRRERyvFxf2AGMGjWKb7/9lhkzZhAREcHgwYO5ceMG/fr1MzqaiDyC77//nrJlyxIaGkrZsmX5/vvvjY4kIpKj5fpRsXd8+eWXfPTRR0RHR1O7dm0+//xzGjRokKnXJiQk4OnpSXx8vC7FiuQQZ8+epWzZspjN5vR9jo6OnDp1ijJlyhiYTEQke1lTp+T6e+zuGDp0KEOHDjU6hojNnD17luPHj1O5cuV8WcgcP348Q1EHkJaWRmRkZL789xARyYw8cSlWJK/RJUioXLkyDg4Zf0U5OjpmalSYiEh+pcJOJIc5e/YsAwcOTO+tMpvNDBo0KN8NHihTpgzffPNN+igwR0dHvv76a/XWiYg8QK4v7CZNmkT9+vUpXLgw3t7edO7cmWPHjhkdSyTLHnQJMr/p378/p06dYsOGDZw6dYr+/fsbHUlEJEfL9YXdpk2bGDJkCDt27GDt2rWkpKTQqlUrbty4YXQ0kSzRJciMypQpQ0hIiHrqREQyIdcPnli1alWGx9OnT8fb25uwsDCaNWtmUCqRrLtzCXLQoEGkpaXpEqSIiGRari/s/ld8fDwAxYoVMziJSNb179+f1q1bExkZSaVKlVTUiYhIpuSZeezg9k3mHTt2JC4uji1bttz3uKSkJJKSktIf31mDLSYqRvPYiYiISI6SkJCAj79P/prHDmDIkCEcOnTogUUd3B5wMX78+Lv2H553GPeC7vaKl6fEXI3hzMUz+Hn74VPUx+g4YgV9diIiucv1m9czfWye6bEbOnQoixcvZvPmzZQvX/6Bx96vx27zV5spUrSInZNmr+jL0URFR+Hv649vcV+btLlgwwImfDsBs8WMg8mBsQPG0uWJLjZpW+xLn52ISO4TdzWOZi83y1SPXa4v7CwWC8OGDWPhwoVs3LiRypUrW93GnaU6tn2/jWIl8s69efPXzGfs5LHpX+IThkygW6tuj9RmdGw0of1DMVv+mo7DwcGB3777DV8v2xSOYh/67EREcqcrl67QqH8j+16KXbJkidWvefLJJylYsGBWT3lPQ4YMYfbs2SxevJjChQsTHR0NgKenp83PlZtEx0anF3UAZouZsV+NpUndJo/0JX7q/KkMhQHcvrfx9IXTKg5yOH12IiJ5X5YLu86dO1t1vMlk4vjx41SoUCGrp7ynKVOmABASEpJh/7Rp0+jbt69Nz5Wb2OtLvFypcjiYHO7q9SlbsmyW28wu0bHRnDp/inKlyuXLQiY3f3YiIpI5jzRBcXR0NGazOVObm5ubrTJnYLFY7rnltqIuOjaaHQd2EB0bbZP27nyJ/50tvsR9vXyZMGRC+gS6Dg4OTHh5Qo4vlOavmU9o/1D6vtmX0P6hzF8z3+hI2S63fnYiIpJ5We6x69Onj1WXOnv27KmpRO7DHvfC3fkSH/vVWMxms02/xLu16kaTuk04feE0ZUuWtWlhYI9eNXtdls6N7PnZiYiI8WwyeOL69eu4u+feaUKMHDxh7xvao2Ojc82XuD0KXIAdB3bQ982+d+2f8d4MGtRo8Mjti4iI2JM1gydsslasp6cnv/76qy2ayncedC+cLfh6+dKgRoMcX9Tdr1fNFpem7XVZWkREJKexSWFnsVj4+uuvady4MU2aNGHEiBHs3r3bFk3neSo6brNngat7y0REJL+wSWEHsHfvXurWrUuTJk04fPgwTZs25dVXX7VV83mWio7b7F3gdmvVjd+++40Z783gt+9+s8klXhERkZzGZkuKzZ49myeffDL98YEDB+jUqROlS5dm5MiRtjqN4exxc79uaLfvYI+/nyM//tvK/eX3KXBEJO+xyeAJLy8vtmzZQtWqVTPsX758OSNHjuSPP/541FPY1Z3BE5u/3EzR4kXve9yCDQsY/+349Jv7xw0Yp+WYbCz6cjRnYs7g5+NnsyXQRO5FP88ikltcvXyVZkMzt6SYTXrsateuzbRp0/jggw8y7K9UqRJRUVG2OEW2OLv9LPGF4+/5XOz1WN7+6W3u1MFmi5nx347HP9EfL3ev7IyZ55WgBLcu3eIUp4yOInmUfp5FJDe5du1apo+1SWH37rvv8sQTT3D+/HlefvllatasyY0bN5g4cSLly5e3xSmyhYOLA87uzvd87uKVi/xv56bZYuZS8iVKuZfKjngiYiP6eRaR3MQhOfNDImxS2DVs2JAdO3YwfPhwmjZtmv4L09XVlXnz5tniFNnC0cmRAq73/icp61v27uWYTA74+/rf9zUikjPp51lEchNHJ8dMH2uzUbG1atVi48aNnD9/nmXLlrFkyRJOnz5Nu3btbHUKQ5XwKMHIdiPTR246mBwY2W4kJTxKGJxMRKyln2cRyatsMngit7szeGLeoHkPXXniUsIlzl05R+lipfUlIJLL6edZRHKDK5eu8MzXz9h38MSBAweoXr16+vxrD3P48GGqVKlCgQK5+zJHCY8S+gIQySP08ywieU2WL8XWqVOHy5cvZ/r44ODgXDVCVkRERCS3yXL3mcVi4a233sLNzS1TxycnJ2f1VCIiIiKSCVku7Jo1a8axY8cyfXxwcDAFCxbM6ulERERE5CGyXNht3LjRhjFERMQolxIucfbKWcoUK6N7DkVyOZtNd2K0yZMnU65cOVxdXWnQoAG7du0yOpKISI63Yu8Knv/ieV6d9SrPf/E8K/auMDqSiDyCPFHYzZ07l1GjRjFu3DjCw8OpVasWrVu35uLFi0ZHExHJsS4lXOKTFZ+kT9Rstpj5ZMUnXEq4ZHAyEcmq3D33yP/7+OOPGTBgAP369QNg6tSpLF++nB9++IHRo0dnup20lDTSktPsFVNEJEeJionKsPoG3C7uzlw8QzHXB8/pKSLZJy0l87VJri/skpOTCQsLY8yYMen7HBwcaNmyJdu3b7/na5KSkkhKSkp/nJCQAMC+H/bhiqt9A4vkUfHEc4UrFKMYnngaHUcyIZ54TJiw8Nc89SZMnJ9znhvcMDCZiPzdLW5l+libXIq9efMmiYmJ6Y9Pnz7Np59+ypo1a2zR/APFxsaSlpaGj49Phv0+Pj5ER0ff8zWTJk3C09MzffPz87N7TpG8LJxwPuVTZjCDT/mUcMKNjiSZ4IknHeiACRNwu6jrQAcV5iK5mE167Dp16kSXLl146aWXiIuLo0GDBjg5OREbG8vHH3/M4MGDbXEamxkzZgyjRo1Kf5yQkICfnx+1X6hNMS9dfhCxxqVrl5gwdQJ3Vie0YGGZaRnPvfQcJQprhGVO14hGPHftOc5fPU+poqX0mYnkQFdir8APmTvWJoVdeHg4n3zyCQDz58/Hx8eHvXv38uuvvzJ27Fi7FnZeXl44OjoSExOTYX9MTAy+vr73fI2LiwsuLi537Xd0csTR2dEuOUXyqgvXLtzzPq3oa9H4Fr/3z6DkLL7FffVZieRgjk6Zr01scik2MTGRwoULA7BmzRq6dOmCg4MDDRs25PTp07Y4xX05OzsTFBTE+vXr0/eZzWbWr19PcHCwXc8tIlCmWBkcTBl/lTiYHChdrLRBiUREcr5LCZfYe2qvzUeh26Swq1SpEosWLeLMmTOsXr2aVq1aAXDx4kU8PDxscYoHGjVqFN9++y0zZswgIiKCwYMHc+PGjfRRsiJiPyU8SjCy3cj04s7B5MDIdiM10a2IyH3Yc/5Im1yKHTt2LM8//zwjR46kRYsW6T1la9asoU6dOrY4xQM999xzXLp0ibFjxxIdHU3t2rVZtWrVXQMqRMQ+2tVpR/2K9Tl35Ryli5VWUWcnWiFCJPe73/yR9SvWt8nPtU0Ku27dutGkSRMuXLhArVq10ve3aNGCp59+2haneKihQ4cydOjQbDmXyN/py/a2Eh4l8vX7t7cVe1ekfxnc6RVtV6ed0bFE8jxb/44/e+XsPe9LPnflXM4p7AB8fX3vGqzw+OOP26p5kRxJX7aSHez9F76I3Js9fsffuS/578WdLe9LttmSYr///js9e/YkODiYc+fOAfDjjz+yZcsWW51CJEfRckySXR70F76I2Ie9fsfb+75km/TY/frrr/Tq1YsePXqwd+/e9FUd4uPjmThxIitWaFFpyXvs3Z0ucoe9/8IXkbvZ83e8Pe9LtkmP3bvvvsvUqVP59ttvcXJySt/fuHFjwsM1A73kTZrmQ7KLRh6LZD97/44v4VGC2uVq2/zn2CY9dseOHaNZs2Z37ff09CQuLs4WpxDJce582f7v/Rf6shV70MhjkeyVW3/H26Sw8/X1JTIyknLlymXYv2XLFipUqGCLU4jkSPqyleykkcci2Ss3/o63SWE3YMAAhg8fzg8//IDJZOL8+fNs376dV199lbfeessWp8gWaSlppN5KNTqG5DJFnYtS1LcogP7/EZE84dK1S5y7eo7SRUvn+/WDc8Lv+LSUtEwfa5PCbvTo0ZjNZlq0aEFiYiLNmjXDxcWFV199lWHDhtniFNnCnGQm+Xqy0TFEREQMsyZiDV9u/hKLxYLJZGJos6G0CmhldKx8zZxkfvhB/89ksVgstjpxcnIykZGRXL9+ncDAQNzd3bl58yYFCxa01SnsIiEhAU9PTzZ/uZmixYsaHUdERMQQ0ZejaT2sdcYR2A4OrP58Nb7FfR/wSrGnq5ev0mxoM+Lj4x+6VKvNJigGcHZ2JjAwEICkpCQ+/vhjPvzwQ6Kjo215GrspULAAToWcHn6giIiIFaJjozl1/hTlSpXD1yvnFkjnTpy7e4oPs5nz8efx8/czKJUUSMx8ufZI050kJSUxZswY6tWrR6NGjVi0aBEA06ZNo3z58nzyySeMHDnyUU4hIiKSq81fM5/Q/qH0fbMvof1Dmb9mvtGR7qtcqXJ3T/Hh4EDZkmUNSiTWeqTCbuzYsUyZMoVy5cpx6tQpnnnmGQYOHMgnn3zCxx9/zKlTp3jjjTdslVVEJFeIjo1mx4EdRMfmjqsVYj/RsdGMnTw2w+oFY78am2P/3/D18mXCkAk4OPz/nIkODkx4eUKO7mWUjB7pUuy8efOYOXMmHTt25NChQ9SsWZPU1FT279+PyWSyVUYRkVxj/pr56V/kDiYHJgyZQLdW3YyOJQY5df7UPS9tnr5wOscWS91adaNJ3SacvnCasiXL5ticcm+P1GN39uxZgoKCAKhevTouLi6MHDlSRZ2I5Eu5rXfG3uzZc5lbekVz66VNXy9fGtRooKIuF3qkwi4tLQ1nZ+f0xwUKFMDd3f2RQ4mI5EYP6p3Jb+x5X1luumdNlzYluz3SdCcODg60bdsWFxcXAJYuXUpoaCiFChXKcNyCBQseLaWd3ZnuZNv32yhWopjRcUQkl4qOjSa0f+hdU0X89t1v+eqL3J7/Dvb+N7bX6NXo2Ghd2iT3jA7Oaa5cukKj/o0yNd3JI/XY9enTB29vbzw9PfH09KRnz56UKlUq/fGdzV5OnTpF//79KV++PAULFqRixYqMGzeO5GRNMiwi2U+9M7fZs+fSnm3bsydQlzZzV09rbvZIgyemTZtmqxxZcvToUcxmM19//TWVKlXi0KFDDBgwgBs3bvCf//zH0Gwikj/pxvO/7iv73141W9xXZq+273d/ZJO6TfLlZ2hr+vfNPo/UY2e0Nm3aMG3aNFq1akWFChXo2LEjr776ao6/9CsieVt+752xZ8+lvdrW/ZH2lR3/vrllQI292XTliZwgPj6eYsUefJ9cUlISSUlJ6Y8TEhIASE1KJfmGLuOKiDyqjo078njVxzkTcwY/Hz98i/va7PerPdouVaTUPXsCS3qW1PeCDdj733fBhgVM+HZC+jRDYweMpcsTXR653ZwiNSk108fmqcIuMjKSL7744qGXYSdNmsT48ePv2p96M5Vb3LJXPBGRfKWIQxGKlCwCwK2rtv3dauu2izgUYczzY5j08yTMZjMODg6M+ccYijgUsXn2/Mie/74xV2PSizq4fZl3wncTCCobhE9RH1vEN1zqzcwXdo80KtZeRo8ezQcffPDAYyIiIqhatWr643PnztG8eXNCQkL47rvvHvjae/XY+fn5ERMV89DRJiIiknedPXeWP//8kwoVKlCmdBmj4+Q59vj33bR5E206trlr/+qlq2nWtJlNzmG0hIQEfPx9MjUqNkcWdpcuXeLy5csPPKZChQrpc+idP3+ekJAQGjZsyPTp09PvvcisO9OdZOYfTERERHKOs2fPUrZsWczmvy7zOjo6curUKcqUyRvFuTV1So68FFuiRAlKlCiRqWPPnTvHE088QVBQENOmTbO6qBMREZHcq0yZMnzzzTcMGjSItLQ0HB0d+frrr/NMUWetHNljl1nnzp0jJCSEsmXLMmPGDBwdHdOf8/XN/Agp9diJiIjkbmfPniUyMpJKlSrluaIu1/fYZdbatWuJjIwkMjLyrg8xF9erIiIiYqUyZcrkuYIuK3L1dcu+fftisVjuuYmIiIjkN7m6sBMRERGRv6iwExEREckjVNiJiIiI5BEq7ERERETyCBV2IiIiInmECjsRERGRPCJXz2NnK3emR0lISDA4iYiIiEhGd+qTzEznpsIOuHbtGgB+fn4GJxERERG5t2vXruHp6fnAY3L1kmK2YjabOX/+PIULF8ZkMhkdx6YSEhLw8/PjzJkzWi4tl9Fnl3vps8u99NnlXnn5s7NYLFy7do1SpUrh4PDgu+jUYwc4ODjk+WVIPDw88tz/6PmFPrvcS59d7qXPLvfKq5/dw3rq7tDgCREREZE8QoWdiIiISB6hwi6Pc3FxYdy4cbi4uBgdRaykzy730meXe+mzy7302d2mwRMiIiIieYR67ERERETyCBV2IiIiInmECjsRERGRPEKFXT6UlJRE7dq1MZlM7Nu3z+g48hCnTp2if//+lC9fnoIFC1KxYkXGjRtHcnKy0dHkHiZPnky5cuVwdXWlQYMG7Nq1y+hI8hCTJk2ifv36FC5cGG9vbzp37syxY8eMjiVZ8P7772MymRgxYoTRUQyjwi4fev311ylVqpTRMSSTjh49itls5uuvv+bw4cN88sknTJ06lX/9619GR5P/MXfuXEaNGsW4ceMIDw+nVq1atG7dmosXLxodTR5g06ZNDBkyhB07drB27VpSUlJo1aoVN27cMDqaWGH37t18/fXX1KxZ0+gohtKo2Hxm5cqVjBo1il9//ZVq1aqxd+9eateubXQssdJHH33ElClT+PPPP42OIn/ToEED6tevz5dffgncXq7Qz8+PYcOGMXr0aIPTSWZdunQJb29vNm3aRLNmzYyOI5lw/fp16taty1dffcW7775L7dq1+fTTT42OZQj12OUjMTExDBgwgB9//BE3Nzej48gjiI+Pp1ixYkbHkL9JTk4mLCyMli1bpu9zcHCgZcuWbN++3cBkYq34+HgA/YzlIkOGDKF9+/YZfv7yK60Vm09YLBb69u3LSy+9RL169Th16pTRkSSLIiMj+eKLL/jPf/5jdBT5m9jYWNLS0vDx8cmw38fHh6NHjxqUSqxlNpsZMWIEjRs3pnr16kbHkUyYM2cO4eHh7N692+goOYJ67HK50aNHYzKZHrgdPXqUL774gmvXrjFmzBijI8v/y+xn93fnzp2jTZs2PPPMMwwYMMCg5CJ515AhQzh06BBz5swxOopkwpkzZxg+fDg//fQTrq6uRsfJEXSPXS536dIlLl++/MBjKlSowLPPPsvSpUsxmUzp+9PS0nB0dKRHjx7MmDHD3lHlf2T2s3N2dgbg/PnzhISE0LBhQ6ZPn46Dg/4uy0mSk5Nxc3Nj/vz5dO7cOX1/nz59iIuLY/HixcaFk0wZOnQoixcvZvPmzZQvX97oOJIJixYt4umnn8bR0TF9X1paGiaTCQcHB5KSkjI8lx+osMsnoqKiSEhISH98/vx5Wrduzfz582nQoAFlypQxMJ08zLlz53jiiScICgpi1qxZ+e4XVW7RoEEDHn/8cb744gvg9mU9f39/hg4dqsETOZjFYmHYsGEsXLiQjRs3UrlyZaMjSSZdu3aN06dPZ9jXr18/qlatyhtvvJEvL6frHrt8wt/fP8Njd3d3ACpWrKiiLoc7d+4cISEhlC1blv/85z9cunQp/TlfX18Dk8n/GjVqFH369KFevXo8/vjjfPrpp9y4cYN+/foZHU0eYMiQIcyePZvFixdTuHBhoqOjAfD09KRgwYIGp5MHKVy48F3FW6FChShevHi+LOpAhZ1Ijrd27VoiIyOJjIy8qwhXh3vO8txzz3Hp0iXGjh1LdHQ0tWvXZtWqVXcNqJCcZcqUKQCEhIRk2D9t2jT69u2b/YFEHoEuxYqIiIjkEbr7WkRERCSPUGEnIiIikkeosBMRERHJI1TYiYiIiOQRKuxERERE8ggVdiIiIiJ5hAo7ERERkTxChZ2IiIhIHqHCTkTEICEhIZhMJkwmE/v27QNg48aNmEwm4uLi7Hrut99+O/3cn376qV3PJSLZR4WdiOQKffv2TS9E/r61adPG6GiPZMCAAVy4cMEm61rGxMTg5OTEnDlz7vl8//79qVu3LgCvvvoqFy5c0FrRInmMCjsRyTXatGnDhQsXMmw///yzXc+ZnJxs1/bd3Nzw9fWlQIFHX7rbx8eH9u3b88MPP9z13I0bN/jll1/o378/AO7u7vj6+uLo6PjI5xWRnEOFnYjkGi4uLvj6+mbYihYtmv68yWTiu+++4+mnn8bNzY3KlSuzZMmSDG0cOnSItm3b4u7ujo+PD7169SI2Njb9+ZCQEIYOHcqIESPw8vKidevWACxZsoTKlSvj6urKE088wYwZM9Ivmd64cQMPDw/mz5+f4VyLFi2iUKFCXLt2LcvvOTExkbZt29K4ceP0y7PfffcdAQEBuLq6UrVqVb766qv04/v378/69euJiorK0M68efNITU2lR48eWc4iIjmfCjsRyVPGjx/Ps88+y4EDB2jXrh09evTgypUrAMTFxREaGkqdOnXYs2cPq1atIiYmhmeffTZDGzNmzMDZ2ZmtW7cydepUTp48Sbdu3ejcuTP79+9n0KBB/Pvf/04/vlChQnTv3p1p06ZlaGfatGl069aNwoULZ+m9xMXF8eSTT2I2m1m7di1FihThp59+YuzYsbz33ntEREQwceJE3nrrLWbMmAFAu3bt8PHxYfr06Xdl6dKlC0WKFMlSFhHJJSwiIrlAnz59LI6OjpZChQpl2N577730YwDLm2++mf74+vXrFsCycuVKi8VisbzzzjuWVq1aZWj3zJkzFsBy7Ngxi8VisTRv3txSp06dDMe88cYblurVq2fY9+9//9sCWK5evWqxWCyWnTt3WhwdHS3nz5+3WCwWS0xMjKVAgQKWjRs33vc9NW/e3DJ8+PAM+zZs2GABLBEREZaaNWtaunbtaklKSkp/vmLFipbZs2dneM0777xjCQ4OTn88evRoS/ny5S1ms9lisVgskZGRFpPJZFm3bt1dGcqWLWv55JNP7ptRRHIX9diJSK7xxBNPsG/fvgzbSy+9lOGYmjVrpv93oUKF8PDw4OLFiwDs37+fDRs24O7unr5VrVoVgBMnTqS/LigoKEObx44do379+hn2Pf7443c9rlatWnrP2axZsyhbtizNmjXL0nt98sknqVSpEnPnzsXZ2Rm4fZ/ciRMn6N+/f4b38O6772bI/8ILL3Dy5Ek2bNgA3O6tK1euHKGhoVnKIiK5x6PfrSsikk0KFSpEpUqVHniMk5NThscmkwmz2QzA9evX6dChAx988MFdrytZsmSG82TFiy++yOTJkxk9ejTTpk2jX79+mEymLLXVvn17fv31V44cOUKNGjXS8wN8++23NGjQIMPxfx8EUblyZZo2bcq0adMICQlh5syZDBgwIMtZRCT3UGEnIvlG3bp1+fXXXylXrpxVo1CrVKnCihUrMuzbvXv3Xcf17NmT119/nc8//5wjR47Qp0+fLGd9//33cXd3p0WLFmzcuJHAwEB8fHwoVaoUf/7550MHQfTv35/BgwfTsWNHzp07R9++fbOcRURyD12KFZFcIykpiejo6Azb30e0PsyQIUO4cuUK//jHP9i9ezcnTpxg9erV9OvXj7S0tPu+btCgQRw9epQ33niDP/74g19++SV9cMLfe8GKFi1Kly5deO2112jVqtUjzxH3n//8hx49ehAaGsrRo0eB24NDJk2axOeff84ff/zBwYMHmTZtGh9//HGG1z7zzDM4OTkxaNAgWrVqhZ+f3yNlEZHcQYWdiOQaq1atomTJkhm2Jk2aZPr1pUqVYuvWraSlpdGqVStq1KjBiBEjKFKkCA4O9/91WL58eebPn8+CBQuoWbMmU6ZMSR8V6+LikuHY/v37k5yczAsvvJC1N/k/PvnkE5599llCQ0P5448/ePHFF/nuu++YNm0aNWrUoHnz5kyfPp3y5ctneJ2bmxvdu3fn6tWrNssiIjmfyWKxWIwOISKS27z33ntMnTqVM2fOZNj/448/MnLkSM6fP58+6OF+QkJCqF27tqFLepUrV44RI0YwYsQIwzKIiO2ox05EJBO++uordu/ezZ9//smPP/7IRx99lOEeusTERE6cOMH777/PoEGDHlrU/b1dd3d3Dh48aK/o9zRx4kTc3d3vmshYRHI39diJiGTCyJEjmTt3LleuXMHf359evXoxZsyY9EEYb7/9Nu+99x7NmjVj8eLFuLu7P7TNc+fOcfPmTQD8/f0zXQzawpUrV9Inbi5RogSenp7Zdm4RsR8VdiIiIiL/154dkAAAAAAI+v+6H6ErgiasWACACWEHADAh7AAAJoQdAMCEsAMAmBB2AAATwg4AYELYAQBMCDsAgIkA+bxqB1DF8rgAAAAASUVORK5CYII=",
      "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": [
    "Zusätzlich zu den Fit Residuan bietet das $\\chi^2$ selbst einen Weg um die „goodness-of-fit“ unseres Model bestimmen zu können...\n",
    "\n",
    "### 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": 55,
   "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 = 71.34 (χ²/ndof = 1.4) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 517 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.63e-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> 627 </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> 1.142e3 </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> 53.38 </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> 60.50 </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> 2.18 </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> 2.76 </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> 222 </td>\n",
       "        <td> 23 </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> 40.1 </td>\n",
       "        <td> 2.9 </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> 196 </td>\n",
       "        <td style=\"background-color:rgb(250,230,230);color:black\"> 0.03e3 <strong>(0.134)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,209,209);color:black\"> 0.333 <strong>(0.273)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,198,198);color:black\"> 0.292 <strong>(0.344)</strong> </td>\n",
       "        <td style=\"background-color:rgb(238,238,250);color:black\"> -0.081 <strong>(-0.092)</strong> </td>\n",
       "        <td style=\"background-color:rgb(198,198,250);color:black\"> -0.2462 <strong>(-0.397)</strong> </td>\n",
       "        <td style=\"background-color:rgb(244,244,250);color:black\"> -0.01e3 <strong>(-0.046)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,243,243);color:black\"> 2 <strong>(0.046)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,230,230);color:black\"> 0.03e3 <strong>(0.134)</strong> </td>\n",
       "        <td> 202 </td>\n",
       "        <td style=\"background-color:rgb(243,243,250);color:black\"> -0.064 <strong>(-0.052)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,248,248);color:black\"> 0.010 <strong>(0.012)</strong> </td>\n",
       "        <td style=\"background-color:rgb(235,235,250);color:black\"> -0.100 <strong>(-0.112)</strong> </td>\n",
       "        <td style=\"background-color:rgb(205,205,250);color:black\"> -0.2153 <strong>(-0.342)</strong> </td>\n",
       "        <td style=\"background-color:rgb(249,249,250);color:black\"> -0 <strong>(-0.009)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,249,249);color:black\"> 0 <strong>(0.007)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,209,209);color:black\"> 0.333 <strong>(0.273)</strong> </td>\n",
       "        <td style=\"background-color:rgb(243,243,250);color:black\"> -0.064 <strong>(-0.052)</strong> </td>\n",
       "        <td> 0.00757 </td>\n",
       "        <td style=\"background-color:rgb(250,132,132);color:black\"> 0.004 <strong>(0.788)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,140,140);color:black\"> 0.004 <strong>(0.736)</strong> </td>\n",
       "        <td style=\"background-color:rgb(157,157,250);color:black\"> -0.0028 <strong>(-0.717)</strong> </td>\n",
       "        <td style=\"background-color:rgb(240,240,250);color:black\"> -0.161 <strong>(-0.081)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,236,236);color:black\"> 0.023 <strong>(0.094)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,198,198);color:black\"> 0.292 <strong>(0.344)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,248,248);color:black\"> 0.010 <strong>(0.012)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,132,132);color:black\"> 0.004 <strong>(0.788)</strong> </td>\n",
       "        <td> 0.00367 </td>\n",
       "        <td style=\"background-color:rgb(250,147,147);color:black\"> 0.003 <strong>(0.689)</strong> </td>\n",
       "        <td style=\"background-color:rgb(154,154,250);color:black\"> -0.0020 <strong>(-0.736)</strong> </td>\n",
       "        <td style=\"background-color:rgb(237,237,250);color:black\"> -0.135 <strong>(-0.097)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,237,237);color:black\"> 0.015 <strong>(0.088)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(238,238,250);color:black\"> -0.081 <strong>(-0.092)</strong> </td>\n",
       "        <td style=\"background-color:rgb(235,235,250);color:black\"> -0.100 <strong>(-0.112)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,140,140);color:black\"> 0.004 <strong>(0.736)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,147,147);color:black\"> 0.003 <strong>(0.689)</strong> </td>\n",
       "        <td> 0.00396 </td>\n",
       "        <td style=\"background-color:rgb(176,176,250);color:black\"> -0.0016 <strong>(-0.571)</strong> </td>\n",
       "        <td style=\"background-color:rgb(223,223,250);color:black\"> -0.302 <strong>(-0.208)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,224,224);color:black\"> 0.031 <strong>(0.173)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(198,198,250);color:black\"> -0.2462 <strong>(-0.397)</strong> </td>\n",
       "        <td style=\"background-color:rgb(205,205,250);color:black\"> -0.2153 <strong>(-0.342)</strong> </td>\n",
       "        <td style=\"background-color:rgb(157,157,250);color:black\"> -0.0028 <strong>(-0.717)</strong> </td>\n",
       "        <td style=\"background-color:rgb(154,154,250);color:black\"> -0.0020 <strong>(-0.736)</strong> </td>\n",
       "        <td style=\"background-color:rgb(176,176,250);color:black\"> -0.0016 <strong>(-0.571)</strong> </td>\n",
       "        <td> 0.00196 </td>\n",
       "        <td style=\"background-color:rgb(250,228,228);color:black\"> 0.1500 <strong>(0.147)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.0229 <strong>(-0.181)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_bkg </th>\n",
       "        <td style=\"background-color:rgb(244,244,250);color:black\"> -0.01e3 <strong>(-0.046)</strong> </td>\n",
       "        <td style=\"background-color:rgb(249,249,250);color:black\"> -0 <strong>(-0.009)</strong> </td>\n",
       "        <td style=\"background-color:rgb(240,240,250);color:black\"> -0.161 <strong>(-0.081)</strong> </td>\n",
       "        <td style=\"background-color:rgb(237,237,250);color:black\"> -0.135 <strong>(-0.097)</strong> </td>\n",
       "        <td style=\"background-color:rgb(223,223,250);color:black\"> -0.302 <strong>(-0.208)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,228,228);color:black\"> 0.1500 <strong>(0.147)</strong> </td>\n",
       "        <td> 530 </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -64 <strong>(-0.966)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> tau_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,243,243);color:black\"> 2 <strong>(0.046)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,249,249);color:black\"> 0 <strong>(0.007)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,236,236);color:black\"> 0.023 <strong>(0.094)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,237,237);color:black\"> 0.015 <strong>(0.088)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,224,224);color:black\"> 0.031 <strong>(0.173)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.0229 <strong>(-0.181)</strong> </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -64 <strong>(-0.966)</strong> </td>\n",
       "        <td> 8.17 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 71.34 (χ²/ndof = 1.4)      │              Nfcn = 517              │\n",
       "│ EDM = 1.63e-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     │    627    │    14     │            │            │         │         │       │\n",
       "│ 1 │ A_p2     │  1.142e3  │  0.014e3  │            │            │         │         │       │\n",
       "│ 2 │ mu_p1    │   53.38   │   0.09    │            │            │         │         │       │\n",
       "│ 3 │ mu_p2    │   60.50   │   0.06    │            │            │         │         │       │\n",
       "│ 4 │ sigma_p1 │   2.18    │   0.06    │            │            │         │         │       │\n",
       "│ 5 │ sigma_p2 │   2.76    │   0.04    │            │            │         │         │       │\n",
       "│ 6 │ A_bkg    │    222    │    23     │            │            │         │         │       │\n",
       "│ 7 │ tau_bkg  │   40.1    │    2.9    │            │            │    0    │         │       │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬─────────────────────────────────────────────────────────────────────────┐\n",
       "│          │     A_p1     A_p2    mu_p1    mu_p2 sigma_p1 sigma_p2    A_bkg  tau_bkg │\n",
       "├──────────┼─────────────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │      196   0.03e3    0.333    0.292   -0.081  -0.2462  -0.01e3        2 │\n",
       "│     A_p2 │   0.03e3      202   -0.064    0.010   -0.100  -0.2153       -0        0 │\n",
       "│    mu_p1 │    0.333   -0.064  0.00757    0.004    0.004  -0.0028   -0.161    0.023 │\n",
       "│    mu_p2 │    0.292    0.010    0.004  0.00367    0.003  -0.0020   -0.135    0.015 │\n",
       "│ sigma_p1 │   -0.081   -0.100    0.004    0.003  0.00396  -0.0016   -0.302    0.031 │\n",
       "│ sigma_p2 │  -0.2462  -0.2153  -0.0028  -0.0020  -0.0016  0.00196   0.1500  -0.0229 │\n",
       "│    A_bkg │  -0.01e3       -0   -0.161   -0.135   -0.302   0.1500      530      -64 │\n",
       "│  tau_bkg │        2        0    0.023    0.015    0.031  -0.0229      -64     8.17 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 55,
     "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": 56,
   "id": "b0ad46ce-f541-40bb-898c-154ad5f94787",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71.34036100527231 52 1.37193001933216\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": 57,
   "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": 58,
   "id": "76836863-109c-4e7c-989e-04b62ec4ca9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\chi^2(x)$')"
      ]
     },
     "execution_count": 58,
     "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": 59,
   "id": "cfa9d88a-eada-49dd-8cb3-73c7dd345c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.038725580428877526, 3.286394945067883e-08)"
      ]
     },
     "execution_count": 59,
     "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": 60,
   "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 = 247.8 (χ²/ndof = 4.7) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 296 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 5.03e-07 (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> 635 </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> 1.140e3 </td>\n",
       "        <td> 0.015e3 </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> 53.5 </td>\n",
       "        <td> 0.1 </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> 60.61 </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> 4 </th>\n",
       "        <td> sigma_p1 </td>\n",
       "        <td> 2.42 </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> 5 </th>\n",
       "        <td> sigma_p2 </td>\n",
       "        <td> 2.68 </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> 6 </th>\n",
       "        <td> c </td>\n",
       "        <td> 43.8 </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> 182 </td>\n",
       "        <td style=\"background-color:rgb(250,235,235);color:black\"> 0.02e3 <strong>(0.102)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,211,211);color:black\"> 0.357 <strong>(0.258)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,202,202);color:black\"> 0.290 <strong>(0.322)</strong> </td>\n",
       "        <td style=\"background-color:rgb(238,238,250);color:black\"> -0.095 <strong>(-0.093)</strong> </td>\n",
       "        <td style=\"background-color:rgb(200,200,250);color:black\"> -0.2364 <strong>(-0.388)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,248,248);color:black\"> 0.2 <strong>(0.014)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,235,235);color:black\"> 0.02e3 <strong>(0.102)</strong> </td>\n",
       "        <td> 217 </td>\n",
       "        <td style=\"background-color:rgb(222,222,250);color:black\"> -0.324 <strong>(-0.214)</strong> </td>\n",
       "        <td style=\"background-color:rgb(230,230,250);color:black\"> -0.151 <strong>(-0.153)</strong> </td>\n",
       "        <td style=\"background-color:rgb(215,215,250);color:black\"> -0.304 <strong>(-0.273)</strong> </td>\n",
       "        <td style=\"background-color:rgb(226,226,250);color:black\"> -0.1248 <strong>(-0.188)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,248,248);color:black\"> 0.2 <strong>(0.012)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,211,211);color:black\"> 0.357 <strong>(0.258)</strong> </td>\n",
       "        <td style=\"background-color:rgb(222,222,250);color:black\"> -0.324 <strong>(-0.214)</strong> </td>\n",
       "        <td> 0.0105 </td>\n",
       "        <td style=\"background-color:rgb(250,124,124);color:black\"> 0.006 <strong>(0.837)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,130,130);color:black\"> 0.006 <strong>(0.803)</strong> </td>\n",
       "        <td style=\"background-color:rgb(152,152,250);color:black\"> -0.0035 <strong>(-0.751)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0.002 <strong>(0.017)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,202,202);color:black\"> 0.290 <strong>(0.322)</strong> </td>\n",
       "        <td style=\"background-color:rgb(230,230,250);color:black\"> -0.151 <strong>(-0.153)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,124,124);color:black\"> 0.006 <strong>(0.837)</strong> </td>\n",
       "        <td> 0.00446 </td>\n",
       "        <td style=\"background-color:rgb(250,137,137);color:black\"> 0.004 <strong>(0.753)</strong> </td>\n",
       "        <td style=\"background-color:rgb(151,151,250);color:black\"> -0.0023 <strong>(-0.761)</strong> </td>\n",
       "        <td style=\"background-color:rgb(242,242,250);color:black\"> -0.005 <strong>(-0.058)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(238,238,250);color:black\"> -0.095 <strong>(-0.093)</strong> </td>\n",
       "        <td style=\"background-color:rgb(215,215,250);color:black\"> -0.304 <strong>(-0.273)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,130,130);color:black\"> 0.006 <strong>(0.803)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,137,137);color:black\"> 0.004 <strong>(0.753)</strong> </td>\n",
       "        <td> 0.00569 </td>\n",
       "        <td style=\"background-color:rgb(171,171,250);color:black\"> -0.0021 <strong>(-0.608)</strong> </td>\n",
       "        <td style=\"background-color:rgb(233,233,250);color:black\"> -0.013 <strong>(-0.131)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(200,200,250);color:black\"> -0.2364 <strong>(-0.388)</strong> </td>\n",
       "        <td style=\"background-color:rgb(226,226,250);color:black\"> -0.1248 <strong>(-0.188)</strong> </td>\n",
       "        <td style=\"background-color:rgb(152,152,250);color:black\"> -0.0035 <strong>(-0.751)</strong> </td>\n",
       "        <td style=\"background-color:rgb(151,151,250);color:black\"> -0.0023 <strong>(-0.761)</strong> </td>\n",
       "        <td style=\"background-color:rgb(171,171,250);color:black\"> -0.0021 <strong>(-0.608)</strong> </td>\n",
       "        <td> 0.00204 </td>\n",
       "        <td style=\"background-color:rgb(235,235,250);color:black\"> -0.0064 <strong>(-0.112)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> c </th>\n",
       "        <td style=\"background-color:rgb(250,248,248);color:black\"> 0.2 <strong>(0.014)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,248,248);color:black\"> 0.2 <strong>(0.012)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0.002 <strong>(0.017)</strong> </td>\n",
       "        <td style=\"background-color:rgb(242,242,250);color:black\"> -0.005 <strong>(-0.058)</strong> </td>\n",
       "        <td style=\"background-color:rgb(233,233,250);color:black\"> -0.013 <strong>(-0.131)</strong> </td>\n",
       "        <td style=\"background-color:rgb(235,235,250);color:black\"> -0.0064 <strong>(-0.112)</strong> </td>\n",
       "        <td> 1.62 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 247.8 (χ²/ndof = 4.7)      │              Nfcn = 296              │\n",
       "│ EDM = 5.03e-07 (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     │    635    │    14     │            │            │         │         │       │\n",
       "│ 1 │ A_p2     │  1.140e3  │  0.015e3  │            │            │         │         │       │\n",
       "│ 2 │ mu_p1    │   53.5    │    0.1    │            │            │         │         │       │\n",
       "│ 3 │ mu_p2    │   60.61   │   0.07    │            │            │         │         │       │\n",
       "│ 4 │ sigma_p1 │   2.42    │   0.08    │            │            │         │         │       │\n",
       "│ 5 │ sigma_p2 │   2.68    │   0.05    │            │            │         │         │       │\n",
       "│ 6 │ c        │   43.8    │    1.3    │            │            │    0    │         │       │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬────────────────────────────────────────────────────────────────┐\n",
       "│          │     A_p1     A_p2    mu_p1    mu_p2 sigma_p1 sigma_p2        c │\n",
       "├──────────┼────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │      182   0.02e3    0.357    0.290   -0.095  -0.2364      0.2 │\n",
       "│     A_p2 │   0.02e3      217   -0.324   -0.151   -0.304  -0.1248      0.2 │\n",
       "│    mu_p1 │    0.357   -0.324   0.0105    0.006    0.006  -0.0035    0.002 │\n",
       "│    mu_p2 │    0.290   -0.151    0.006  0.00446    0.004  -0.0023   -0.005 │\n",
       "│ sigma_p1 │   -0.095   -0.304    0.006    0.004  0.00569  -0.0021   -0.013 │\n",
       "│ sigma_p2 │  -0.2364  -0.1248  -0.0035  -0.0023  -0.0021  0.00204  -0.0064 │\n",
       "│        c │      0.2      0.2    0.002   -0.005   -0.013  -0.0064     1.62 │\n",
       "└──────────┴────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 60,
     "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": 61,
   "id": "4aa0f3d9-1d0b-4b4c-b816-2a0cb9ae9793",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "247.75609338750667 53 4.67464327146239\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": 62,
   "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": 63,
   "id": "c3b58808-f155-4194-b02e-e5f649cb86aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f30ca0a8ec7f4a79a2bcb3743b624c3e",
       "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": 64,
   "id": "f41e0b38-56b6-4f2a-bc75-075f622a2068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ERkZix44duHDhAt58800MHjwY165dq/yOJqoitR4ye+WVV6R/d+jQAT169EDTpk3x888/w8DAQG11zZ07FwEBAdL7f/75R3WhKCcJ2NsGKMmteV9ahsCr8YCRQ837IiKVuH79OoQQcHZ2VpjesGFD5OfnAwB8fX3xxRdfIDw8HHFxcUhMTIS9vT0A4IcffkDbtm1x5swZdOvWDStXrsS4cePw4YcfAgACAgJw6tQprFy5Eh4eHjh06BCuXLmCAwcOwNbWFgCwdOlShc/finTs2BHz588H8Phzcfny5WjYsCHef/99AMDChQuxadMmXLhwAT179sTGjRthb2+Pr776SrpI5s6dO5g9ezYWLlyI3NxcbNmyBT/++CMGDBgA4HHosrOzk9aZlJSEoKAgJCUlSfXOmDEDoaGhCAoKks4PJVI2tZ9D9G/m5uZo1aoVrl+/jpdffhmFhYV4+PChwihRWlqadM6RtbU1oqKiFPooG0r+d5snh5fT0tJgampaYejS09ODnp6e9D4rK6vG21ahgnuPw5Dbj4BZm+r3kxkPRI553B8DEVG9ExUVhdLSUnh5eaGgoAAAEB8fD3t7eykMAYCLiwvMzc0RHx+Pbt26IT4+HhMnTlToq3fv3li3bp1CH2XhAgDc3NwqVVOHDh2kf2tpacHS0hLt27eXppWNvqenp0vrcnNzU7gvTO/evZGdnY3bt28jIyMDhYWF6NGjhzTfwsJCIRzGxcWhpKQErVq1UqiloKAAlpaWlaqbqDrqVCDKzs7GjRs3MHbsWHTp0gU6OjoIDw/HyJEjAQAJCQlISkqSfpnd3Nzw+eefIz09HY0bNwYAhIWFwdTUVBrRcXNzw759+xTWExYWVukPhFpj1gawcFV3FUSkYk5OTtDQ0EBCQoLC9ObNmwOAWkfHn/TkYxA0NDQUpv370J6yZGdnQ0tLC9HR0dDS0lKYZ2xsrLT1ED1JrecQzZgxA0ePHsWtW7dw8uRJvP7669DS0sLo0aNhZmaGCRMmICAgAEeOHEF0dDTGjx8PNzc39OzZEwAwaNAguLi4YOzYsTh//jwOHDiA+fPnw9fXVxrhmTRpEm7evIlZs2bhypUr2LhxI37++Wf4+/urc9OJSKYsLS3x8ssv46uvvkJOTs4z27Zp0wbJyclITk6Wpl2+fBkPHz6U/uhr06aNdCuSMidOnFCYn5ycjJSUFGn+qVOnlLU5T9UbGRkJIYRCLSYmJrCzs0OLFi2go6OD06dPS/MzMjJw9epV6X3nzp1RUlKC9PR0ODk5KbzKuyKZSFnUGohu376N0aNHw9nZGW+99RYsLS1x6tQpNGr0+KqNNWvW4NVXX8XIkSPRt29fWFtbY/fu3dLyWlpa2Lt3L7S0tODm5oYxY8bA29sbS5Yskdo4OjoiJCQEYWFh6NixI1atWoXvvvuOl9wTkdps3LgRxcXF6Nq1K3bu3In4+HgkJCTgxx9/xJUrV6SRkYEDB6J9+/bw8vLCuXPnEBUVBW9vb/Tr1w9du3YF8Pgk5eDgYGzatAnXrl3D6tWrsXv3bsyYMUPqo1WrVvDx8cH58+fx119/Yd68eSrZrg8//BDJycmYMmUKrly5gt9//x2LFi1CQEAANDU1YWxsjAkTJmDmzJk4fPgwLl68iHHjxkFT839fRa1atYKXlxe8vb2xe/duJCYmIioqSrpnE5GqqPWQ2Y4dO545X19fH4GBgQgMDKywTdOmTZ86JPYkd3d3xMTEVKtGIqqf7sbfrbPradGiBWJiYrB06VLMnTsXt2/fhp6eHlxcXDBjxgzpBGkNDQ38/vvvmDJlCvr27QtNTU0MHjwYGzZskPoaPnw41q1bh5UrV2Lq1KlwdHREUFAQ3N3dAQCamprYs2cPJkyYgO7du6NZs2ZYv349Bg8erJTt/7cmTZpg3759mDlzJjp27AgLCwtMmDBBOjEbAL788ktkZ2fjtddeg4mJCaZPn47MTMU7fQcFBeGzzz7D9OnT8c8//6Bhw4bo2bMnXn31VaXXTFRGQ/x7bJPKdfv2bdjb2yM5OVnhagileHAOCO0CDI6u2TlEyurnOXJycqTj+NnZ2RVeNkxUG/Lz85GYmAhHR0fo6+sDqD93qiYi5Sjvc6BMVb6/69RJ1URENWXmYAbfeF/k3lPCrSwqybChIcMQUT3HQERELxwzBzMGFCKqErU/uoOIiIhI3RiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9ngfIiJ68eQkAQX3am99eg0BI4faW5/MjBs3Dg8fPsRvv/1WqfYRERHw8PBARkYGzM3NVVobvTgYiIjoxZKTBOxtA5TU3p2qoWUIvBpfqVC0efNmzJw5ExkZGdDWfvwRnJ2djQYNGqB3796IiIiQ2pZ9sV+/fh0tWrRQVfVEBAYiInrRFNx7HIbcfgTM2qh+fZnxQOSYx+utRCDy8PBAdnY2zp49i549ewIA/vrrL1hbW+P06dPIz8+Xnsd05MgRODg4lBuGCgsLoaurq9xtIZIxnkNERC8mszaPH3Ss6lcVQ5ezszNsbGyeGgkaNmwYHB0dcerUKYXpHh4eAB4fNho+fDg+//xz2NrawtnZGQAQFxeH/v37w8DAAJaWlpg4cSKys7OlPsqWW7lyJWxsbGBpaQlfX18UFf3v4bcpKSkYOnQoDAwM4OjoiO3bt6NZs2ZYu3ZthdtR1u/SpUthZWUFc3NzLFmyBMXFxZg5cyYsLCxgZ2eHoKAgheWeV29JSQkCAgJgbm4OS0tLzJo1C08+g7y0tBTLli2Do6MjDAwM0LFjR/zyyy+V/59AVA4GIiKiWubh4YEjR45I748cOQJ3d3f069dPmp6Xl4fTp09LgQgAwsPDkZCQgLCwMOzduxc5OTnw9PREgwYNcObMGezatQuHDh2Cn5+fwvqOHDmCGzdu4MiRI9i6dSuCg4MRHBwszff29sadO3cQERGBX3/9Fd988w3S09Ofux2HDx/GnTt3cOzYMaxevRqLFi3Cq6++igYNGuD06dOYNGkSPvjgA9y+fRsAKlXvqlWrEBwcjO+//x7Hjx/HgwcPsGfPHoX1Llu2DD/88AM2b96MS5cuwd/fH2PGjMHRo0cr/z+B6EmCnis5OVkAEMnJycrv/H60ENvw+L91oZ/nyM7OFgAEAJGdna3SdRE9T15enrh8+bLIy8v738Ra+l2oyfq+/fZbYWRkJIqKikRWVpbQ1tYW6enpYvv27aJv375CCCHCw8MFAPH3338LIYTw8fERVlZWoqCgQOrnm2++EQ0aNFD4XQwJCRGampoiNTVVWq5p06aiuLhYavPmm2+KUaNGCSGEiI+PFwDEmTNnpPnXrl0TAMSaNWsq3IayfktKSqRpzs7Ook+fPtL74uJiYWRkJH766adK12tjYyNWrFghzS8qKhJ2dnZi2LBhQggh8vPzhaGhoTh58qRCPRMmTBCjR48WQghx5MgRAUBkZGRUWD+9OMr9HPh/Vfn+5jlERES1zN3dHTk5OThz5gwyMjLQqlUrNGrUCP369cP48eORn5+PiIgING/eHA4O/zsvqX379grnDcXHx6Njx44wMjKSpvXu3RulpaVISEiAlZUVAKBt27bQ0tKS2tjY2CAuLg4AkJCQAG1tbbi6ukrznZyc0KBBg+duR9u2baGp+b8DDVZWVmjXrp30XktLC5aWltJo0/Pq1dfXR0pKCnr06CHN19bWRteuXaXDZtevX0dubi5efvllhVoKCwvRuXPn59ZMVBEGIiKiWubk5AQ7OzscOXIEGRkZ6NevHwDA1tYW9vb2OHnyJI4cOYL+/fsrLPfvIFEVOjo6Cu81NDRQWlpaveKf06+q1lWm7HyjkJAQNGnSRGGenp6e0tZD8sNziIiI1MDDwwMRERGIiIiAu7u7NL1v377Yv38/oqKiFM4fKk+bNm1w/vx55OTkSNNOnDgBTU1N6aTr53F2dkZxcTFiYmKkadevX0dGRkbVNqgSnlevmZkZbGxscPr0aWl+cXExoqOjpfcuLi7Q09NDUlISnJycFF729vZKr5nkg4GIiEgNPDw8cPz4ccTGxkojRADQr18/fP311ygsLHxuIPLy8oK+vj58fHxw8eJFHDlyBFOmTMHYsWOlw2XP07p1awwcOBATJ05EVFQUYmJiMHHiRBgYGEBDQ6NG21ideqdOnYrly5fjt99+w5UrV/Dhhx/i4cOHUh8mJiaYMWMG/P39sXXrVty4cQPnzp3Dhg0bsHXrVqXWS/LCQ2ZE9GLKjK/T6/Hw8EBeXh5at26tEF769euHR48eSZfnP4uhoSEOHDiAqVOnolu3bjA0NMTIkSOxevXqKtXyww8/YMKECejbty+sra2xbNkyXLp0SbofkrJUpt7p06cjJSUFPj4+0NTUxLvvvovXX38dmZmZUptPP/0UjRo1wrJly3Dz5k2Ym5vD1dUVH3/8sVLrJXnREOKJGzzQU27fvg17e3skJyfDzs5OuZ0/OAeEdgEGRz++p4m6+3mOnJwcGBsbA3h8LL+65zQQKUN+fj4SExPh6Oj4vy/vOn6n6vqg7DPv0KFDGDBggLrLIXqmcj8H/l9Vvr85QkRELxYjh8fhhM8yq7TDhw8jOzsb7du3R0pKCmbNmoVmzZqhb9++6i6NqNYwEBHRi8fIoV4HlNpWVFSEjz/+GDdv3oSJiQl69eqFbdu2PXXFGNGLjIGIiEjmPD094enpqe4yiNSKV5kRERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHs8bJ7Inrh5N65g8IHD2ptfboWFjC0ta219VVFREQEPDw8kJGRAXNzc3WXIxsaGhrYs2cPhg8fXqN+mjVrhmnTpmHatGlKqas84eHh8PPzw8WLF6GlpaWy9VRHaGgo5syZg3PnzkFTU7VjOAxERPRCyb1zBxGDBqEkL6/W1qllYAD3gwcrFYqe98DURYsWYfHixUqqjGri1q1bcHR0RExMDDp16lSlZVNSUtCgQYNKtw8ODsa0adMUHmQLAGfOnFH5I5JmzZqF+fPnS2EoJSUF06dPx9mzZ3H9+nV89NFHWLt27VPL7dq1CwsWLMCtW7fQsmVLfPHFFxgyZEiF64mJicG7776La9euwcPDA1u3boWFhQUAoLi4GD169MCmTZvQvXt3aZnBgwdjwYIF2LZtG8aOHavcDX8CAxERvVAKHzxASV4eOq9eDeMWLVS+vuwbNxATEIDCBw8qFYhSUlKkf+/cuRMLFy5EQkKCNK3sWYF1SWFhIXR1ddVdRq0qLCys0fLW1tZKqaNRo0ZK6acix48fx40bNzBy5EhpWkFBARo1aoT58+djzZo15S538uRJjB49GsuWLcOrr76K7du3Y/jw4Th37hzatWtX7jLvvfce+vfvj507d+K9997D0qVLsXLlSgDAqlWr0Lt3b4UwVGbcuHFYv369ygMRBD1XcnKyACCSk5OV3/n9aCG24fF/60I/z5GdnS0ACAAiOztbpesiep68vDxx+fJlkZeXJ03LiIsTfzRvLjLi4mqlhpqsLygoSJiZmUnvS0pKxCeffCKaNGkidHV1RceOHcX+/fsVlrlw4YLw8PAQ+vr6wsLCQrz//vvi0aNHFa7jyJEjAoDIyMgQQghx79498fbbbwtbW1thYGAg2rVrJ7Zv366wTL9+/YSvr6+YOnWqsLS0FO7u7kIIIX7//Xfh5OQk9PT0hLu7uwgODlboWwgh/vrrL/HSSy8JfX19YWdnJ6ZMmfLMzwofHx8xbNgwhWlTp04V/fr1U6hnypQpYubMmaJBgwbCyspKLFq0SGGZjIwMMXHiRNG4cWOhp6cn2rZtK/78889K19W0aVOxZMkSMXbsWGFiYiJ8fHykz7qyV1lNUVFRYuDAgcLS0lKYmpqKvn37iuhoxc9eAGLPnj1CCCESExMFAPHrr78Kd3d3YWBgIDp06CBOnjyp8P/o36+y7WvatKlYs2aNwnZOmDBBNGzYUJiYmAgPDw8RGxsrzY+NjRXu7u7C2NhYmJiYCFdXV3HmzJkK97+vr6944403Kpzfr18/MXXq1Kemv/XWW2Lo0KEK03r06CE++OCDCvsyMDAQ8fHxQgghNm7cKIYMGSKEEOLGjRuiZcuWIisrq9zl/v77bwFAXL9+vdz55X0OlKnK9zdPqiYiqiPWrVuHVatWYeXKlbhw4QI8PT3xn//8B9euXQMA5OTkwNPTEw0aNMCZM2ewa9cuHDp0CH5+fpVeR35+Prp06YKQkBBcvHgREydOxNixYxEVFaXQbuvWrdDV1cWJEyewefNmJCYm4o033sDw4cNx/vx5fPDBB5g3b57CMjdu3MDgwYMxcuRIXLhwATt37sTx48erVF9Ftm7dCiMjI5w+fRorVqzAkiVLEBYWBgAoLS3FK6+8ghMnTuDHH3/E5cuXsXz5cukQUGXrWrlyJTp27IiYmBgsWLBA2ieHDh1CSkoKdu/eDQB49OgRfHx8cPz4cZw6dQotW7bEkCFD8OjRo2duw7x58zBjxgzExsaiVatWGD16NIqLi9GrVy+sXbsWpqamSElJQUpKCmbMmFFuH2+++SbS09Oxf/9+REdHw9XVFQMGDMCD/z9nzsvLC3Z2djhz5gyio6MxZ86cZz6T7q+//kLXrl0r8X9AUWRkJAYOHKgwzdPTE5GRkRUu07FjR4SFhaG4uBjh4eHo0KEDAGDSpElYsWIFTExMyl3OwcEBVlZW+Ouvv6pcZ5U8NzIRR4j+hSNEVJe8aCNEtra24vPPP1do061bN/Hhhx8KIYT45ptvRIMGDRR+90JCQoSmpqZITU0tdx1PjhCVZ+jQoWL69OnS+379+onOnTsrtJk9e7Zo166dwrR58+Yp9D1hwgQxceJEhTZ//fWX0NTULPevdyEqP0L00ksvKbTp1q2bmD17thBCiAMHDghNTU2RkJBQ7joqU1fTpk3F8OHDFdqUjezExMSU22+ZkpISYWJiojAihXJGiL777jtp/qVLlwQAacTkyZ+FMv8eIfrrr7+EqampyM/PV2jTokUL8fXXXwshhDAxMRHBwcHPrPffzMzMxA8//FDh/IpGiHR0dJ4aWQwMDBSNGzeusK+LFy+Kvn37CgcHBzF69GiRmZkpfvjhBzFs2DBx+/ZtMWjQINGiRQsxb968p5bt3LmzWLx4cbn9KmuEiOcQERHVAVlZWbhz5w569+6tML137944f/48ACA+Ph4dO3ZUOMm2d+/eKC0tRUJCAqysrJ67npKSEixduhQ///wz/vnnHxQWFqKgoACGhoYK7bp06aLwPiEhAd26dVOY9uT5HufPn8eFCxewbds2aZoQAqWlpUhMTESbNm2eW19FykYTytjY2CA9PR0AEBsbCzs7O7Rq1arcZStbV2VHStLS0jB//nxEREQgPT0dJSUlyM3NRVJSUqW3wcbGBgCQnp6O1q1bV2q958+fR3Z2NiwtLRWm5+Xl4caNGwCAgIAAvPfee/jvf/+LgQMH4s0330SLZ5xLl5eXB319/Uqtv6batm2Lo0ePSu/v37+PRYsW4dixY5gyZQp69eqF3bt3o1u3bujRowdee+01qa2BgQFyc3NVWh8DERGRjHz55ZdYt24d1q5di/bt28PIyAjTpk176iTi6lzZlJ2djQ8++AAfffTRU/McHBzKXUZTUxNCCIVpRUVFT7V78rCPhoYGSktLATz+slRGXZXdZh8fH9y/fx/r1q1D06ZNoaenBzc3t+eeiP3vbSi72rBsGyojOzsbNjY2iIiIeGpe2S0VFi9ejHfeeQchISHYv38/Fi1ahB07duD1118vt8+GDRsiIyOj0jWUsba2RlpamsK0tLS0Kp1MHhAQgGnTpsHOzg4RERH47LPPYGRkhKFDhyIiIkIhED148EDlJ5gzEBER1QGmpqawtbXFiRMn0K9fP2n6iRMnpJGYNm3aIDg4GDk5OdKX94kTJ6CpqQlnZ+dKrefEiRMYNmwYxowZA+DxF/LVq1fh4uLyzOWcnZ2xb98+hWlnzpxReO/q6orLly/DycmpUrUAj6+iunjxosK02NjYZ5738qQOHTrg9u3buHr1armjRNWpC4B0ZV1JSYnC9BMnTmDjxo3SJebJycm4d+9elfoub11PrudJrq6uSE1Nhba2Npo1a1Zhu1atWqFVq1bw9/fH6NGjERQUVGEg6ty5My5fvlzlet3c3BAeHq5wf6SwsDC4ublVavnw8HDEx8cjKCgIwON9XBaEnwzE+fn5uHHjBjp37lzlOquCJ1UTEdURM2fOxBdffIGdO3ciISEBc+bMQWxsLKZOnQrg8Qmz+vr68PHxwcWLF3HkyBFMmTIFY8eOrdThMgBo2bIlwsLCcPLkScTHx+ODDz546i/98nzwwQe4cuUKZs+ejatXr+Lnn39GcHAwgP+NdsyePRsnT56En58fYmNjce3aNfz+++/PPKm6f//+OHv2LH744Qdcu3YNixYteiogPU+/fv3Qt29fjBw5EmFhYUhMTMT+/fsRGhpa7boAoHHjxjAwMEBoaCjS0tKQmZkJ4PE+/O9//4v4+HicPn0aXl5ezx2lep5mzZohOzsb4eHhuHfvXrmHhwYOHAg3NzcMHz4cBw8exK1bt3Dy5EnMmzcPZ8+eRV5eHvz8/BAREYG///4bJ06cwJkzZ555qNLT0xPHjx9/anpsbCxiY2ORnZ2Nu3fvIjY2ViE4TZ06FaGhoVi1ahWuXLmCxYsX4+zZs5U6gT4/Px9+fn745ptvpJst9u7dG4GBgTh//jx+/fVXhUPHp06dkkbhVIkjRET0Qsr+/3Mq6tN6PvroI2RmZmL69OlIT0+Hi4sL/vjjD7Rs2RIAYGhoiAMHDmDq1Kno1q0bDA0NMXLkSKxevbrS65g/fz5u3rwJT09PGBoaYuLEiRg+fLj0ZV8RR0dH/PLLL5g+fTrWrVsHNzc3zJs3D5MnT4aenh6AxyM1R48exbx589CnTx8IIdCiRQuMGjWqwn49PT2xYMECzJo1C/n5+Xj33Xfh7e2NuLi4Sm8TAPz666+YMWMGRo8ejZycHDg5OWH58uXVrgsAtLW1sX79eixZsgQLFy5Enz59EBERgS1btmDixIlwdXWFvb09li5dWuFVYZXVq1cvTJo0CaNGjZLOrXnyBp0aGhrYt28f5s2bh/Hjx+Pu3buwtrZG3759YWVlBS0tLdy/fx/e3t5IS0tDw4YNMWLECHzyyScVrtfLywuzZs1CQkKCwijjv0djoqOjsX37djRt2hS3bt2S6t2+fTvmz5+Pjz/+GC1btsRvv/1W4T2I/u2TTz7B0KFDFW52uX79erzzzjvo27cvvLy8FO6L9NNPP8HLy+up89yUTUM8efCWnnL79m3Y29sjOTkZdnZ2yu38wTkgtAswOBqwcFV/P8+Rk5Mj3TguOztb5XdQJXqW/Px8JCYmwtHRUToxtK7fqfpF8vnnn2Pz5s1ITk5WdylUAzNnzkRWVha+/vprdZfylHv37sHZ2Rlnz56Fo6NjuW3K+xwoU5Xvb44QEdELxdDWFu4HD/JZZiqwceNGdOvWDZaWljhx4gS+/PJLpdxjiNRr3rx52LhxI0pLS1X+vLCqunXrFjZu3FhhGFImBiIieuEY2trKIqDUtmvXruGzzz7DgwcP4ODggOnTp2Pu3LnqLotqyNzcHB9//LG6yyhX165dq3XjyOpgICIiokpZs2ZNhc+2Iqrv6tbYGBEREZEaMBARUb3Ha0OI5EtZv/8MRERUb5U9vPN5dwgmohdX2e9/2edBdfEcIiKqt7S1tWFoaIi7d+9CR0enzl0hQ0SqVVpairt378LQ0BDa2jWLNAxERFRvaWhowMbGBomJifj777/VXQ4RqYGmpiYcHBykO6ZXFwMREdVrurq6aNmyJQ+bEcmUrq6uUkaHGYiIqN7T1NR86g61RERVwQPuREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHt1JhAtX74cGhoamDZtmjQtPz8fvr6+sLS0hLGxMUaOHIm0tDSF5ZKSkjB06FAYGhqicePGmDlzJoqLixXaREREwNXVFXp6enByckJwcHAtbBERERHVF3UiEJ05cwZff/01OnTooDDd398ff/75J3bt2oWjR4/izp07GDFihDS/pKQEQ4cORWFhIU6ePImtW7ciODgYCxculNokJiZi6NCh8PDwQGxsLKZNm4b33nsPBw4cqLXtIyIiorpN7YEoOzsbXl5e+Pbbb9GgQQNpemZmJrZs2YLVq1ejf//+6NKlC4KCgnDy5EmcOnUKAHDw4EFcvnwZP/74Izp16oRXXnkFn376KQIDA6Xb+G/evBmOjo5YtWoV2rRpAz8/P7zxxhtYs2ZNhTUVFBQgKytLej169Ei1O4GIiIjUSu2ByNfXF0OHDsXAgQMVpkdHR6OoqEhheuvWreHg4IDIyEgAQGRkJNq3bw8rKyupjaenJ7KysnDp0iWpzZN9e3p6Sn2UZ9myZTAzM5NeLi4uNd5OIiIiqrvUGoh27NiBc+fOYdmyZU/NS01Nha6uLszNzRWmW1lZITU1VWrz7zBUNr9s3rPaZGVlIS8vr9y65s6di8zMTOl1+fLlam0fERER1Q9qe7hrcnIypk6dirCwsDr3UEY9PT3o6elJ77OystRYDREREama2kaIoqOjkZ6eDldXV2hra0NbWxtHjx7F+vXroa2tDSsrKxQWFuLhw4cKy6WlpcHa2hoAYG1t/dRVZ2Xvn9fG1NQUBgYGKto6IiIiqk/UFogGDBiAuLg4xMbGSq+uXbvCy8tL+reOjg7Cw8OlZRISEpCUlAQ3NzcAgJubG+Li4pCeni61CQsLg6mpqXTej5ubm0IfZW3K+iAiIiJS2yEzExMTtGvXTmGakZERLC0tpekTJkxAQEAALCwsYGpqiilTpsDNzQ09e/YEAAwaNAguLi4YO3YsVqxYgdTUVMyfPx++vr7SIa9Jkybhq6++wqxZs/Duu+/i8OHD+PnnnxESElK7G0xERER1ltoCUWWsWbMGmpqaGDlyJAoKCuDp6YmNGzdK87W0tLB3715MnjwZbm5uMDIygo+PD5YsWSK1cXR0REhICPz9/bFu3TrY2dnhu+++g6enpzo2iYiIiOqgOhWIIiIiFN7r6+sjMDAQgYGBFS7TtGlT7Nu375n9uru7IyYmRhklEhER0QtI7fchIiIiIlI3BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikr0qB6LQ0FAcP35ceh8YGIhOnTrhnXfeQUZGhlKLIyIiIqoNVQ5EM2fORFZWFgAgLi4O06dPx5AhQ5CYmIiAgAClF0hERESkatpVXSAxMREuLi4AgF9//RWvvvoqli5dinPnzmHIkCFKL5CIiIhI1ao8QqSrq4vc3FwAwKFDhzBo0CAAgIWFhTRyRERERFSfVHmE6KWXXkJAQAB69+6NqKgo7Ny5EwBw9epV2NnZKb1AIiIiIlWr8gjRV199BW1tbfzyyy/YtGkTmjRpAgDYv38/Bg8erPQCiYiIiFStyiNEDg4O2Lt371PT16xZo5SCiIiIiGpblUeItLS0kJ6e/tT0+/fvQ0tLSylFEREREdWmKgciIUS50wsKCqCrq1vjgoiIiIhqW6UPma1fvx4AoKGhge+++w7GxsbSvJKSEhw7dgytW7dWfoVEREREKlbpQFR2jpAQAps3b1Y4PKarq4tmzZph8+bNyq+QiIiISMUqHYgSExMBAB4eHti9ezcaNGigsqKIiIiIalOVrzI7cuSIKuogIiIiUptKBaKAgAB8+umnMDIyeu7zylavXq2UwoiIiIhqS6UCUUxMDIqKigAA586dg4aGRrntKppOREREVJdVKhCtW7cOpqamAICIiAhV1kNERERU6yp1H6LOnTvj3r17AIDmzZvj/v37Ki2KiIiIqDZVKhCZm5tLV5ndunULpaWlSln5pk2b0KFDB5iamsLU1BRubm7Yv3+/ND8/Px++vr6wtLSEsbExRo4cibS0NIU+kpKSMHToUBgaGqJx48aYOXMmiouLFdpERETA1dUVenp6cHJyQnBwsFLqJyIiohdDpQ6ZjRw5Ev369YONjQ00NDTQtWvXCh/TcfPmzUqv3M7ODsuXL0fLli0hhMDWrVsxbNgwxMTEoG3btvD390dISAh27doFMzMz+Pn5YcSIEThx4gSAxzeEHDp0KKytrXHy5EmkpKTA29sbOjo6WLp0KYDHtwsYOnQoJk2ahG3btiE8PBzvvfcebGxs4OnpWelaiYiI6MWlISp6FscTQkNDcf36dXz00UdYsmQJTExMym03derUGhVkYWGBL7/8Em+88QYaNWqE7du344033gAAXLlyBW3atEFkZCR69uyJ/fv349VXX8WdO3dgZWUFANi8eTNmz56Nu3fvQldXF7Nnz0ZISAguXrworePtt9/Gw4cPERoaWqmabt++DXt7eyQnJ8POzq5G2/eUB+eA0C7A4GjAwlX9/TxHTk6OdJfy7OxsGBkZqWxdRERENVGV7+9K34do8ODBAIDo6GhMnTq1wkBUXSUlJdi1axdycnLg5uaG6OhoFBUVYeDAgVKb1q1bw8HBQQpEkZGRaN++vRSGAMDT0xOTJ0/GpUuX0LlzZ0RGRir0UdZm2rRpFdZSUFCAgoIC6f2jR4+Ut6FERERU51T54a5BQUFKDUNxcXEwNjaGnp4eJk2ahD179sDFxQWpqanQ1dWFubm5QnsrKyukpqYCAFJTUxXCUNn8snnPapOVlYW8vLxya1q2bBnMzMykl4uLizI2lYiIiOqoKgciZXN2dkZsbCxOnz6NyZMnw8fHB5cvX1ZrTXPnzkVmZqb0Unc9REREpFpVfnSHsunq6sLJyQkA0KVLF5w5cwbr1q3DqFGjUFhYiIcPHyqMEqWlpcHa2hoAYG1tjaioKIX+yq5C+3ebJ69MS0tLg6mpKQwMDMqtSU9PD3p6etL7rKysmm0kERER1WlqHyF6UmlpKQoKCtClSxfo6OggPDxcmpeQkICkpCS4ubkBANzc3BAXF4f09HSpTVhYGExNTaXDXG5ubgp9lLUp64OIiIioUoHI1dUVGRkZAIAlS5YgNzdXKSufO3cujh07hlu3biEuLg5z585FREQEvLy8YGZmhgkTJiAgIABHjhxBdHQ0xo8fDzc3N/Ts2RMAMGjQILi4uGDs2LE4f/48Dhw4gPnz58PX11ca4Zk0aRJu3ryJWbNm4cqVK9i4cSN+/vln+Pv7K2UbiIiIqP6rVCCKj49HTk4OAOCTTz5Bdna2Ulaenp4Ob29vODs7Y8CAAThz5gwOHDiAl19+GQCwZs0avPrqqxg5ciT69u0La2tr7N69W1peS0sLe/fuhZaWFtzc3DBmzBh4e3tjyZIlUhtHR0eEhIQgLCwMHTt2xKpVq/Ddd9/xHkREREQkqdQ5RJ06dcL48ePx0ksvQQiBlStXSveiedLChQsrvfItW7Y8c76+vj4CAwMRGBhYYZumTZti3759z+zH3d0dMTExla6LiIiI5KVSgSg4OBiLFi3C3r17oaGhgf3790Nb++lFNTQ0qhSIiIiIiOqCSgUiZ2dn7NixAwCgqamJ8PBwNG7cWKWFEREREdWWKl92r6wHuxIRERHVFdW6D9GNGzewdu1axMfHAwBcXFwwdepUtGjRQqnFEREREdWGKt+H6MCBA3BxcUFUVBQ6dOiADh064PTp02jbti3CwsJUUSMRERGRSlV5hGjOnDnw9/fH8uXLn5o+e/Zs6ZJ5IiIiovqiyiNE8fHxmDBhwlPT3333XT7zi4iIiOqlKgeiRo0aITY29qnpsbGxvPKMiIiI6qUqHzJ7//33MXHiRNy8eRO9evUCAJw4cQJffPEFAgIClF4gERERkapVORAtWLAAJiYmWLVqFebOnQsAsLW1xeLFi/HRRx8pvUAiIiIiVatyINLQ0IC/vz/8/f3x6NEjAICJiYnSCyMiIiKqLdW6D1EZBiEiIiJ6EVT5pGoiIiKiFw0DEREREckeAxERERHJXpUCUVFREQYMGIBr166pqh4iIiKiWlelQKSjo4MLFy6oqhYiIiIitajyIbMxY8Zgy5YtqqiFiIiISC2qfNl9cXExvv/+exw6dAhdunSBkZGRwvzVq1crrTgiIiKi2lDlQHTx4kW4uroCAK5evaowT0NDQzlVEREREdWiKgeiI0eOqKIOIiIiIrWp9mX3169fx4EDB5CXlwcAEEIorSgiIiKi2lTlQHT//n0MGDAArVq1wpAhQ5CSkgIAmDBhAqZPn670AomIiIhUrcqByN/fHzo6OkhKSoKhoaE0fdSoUQgNDVVqcURERES1ocrnEB08eBAHDhyAnZ2dwvSWLVvi77//VlphRERERLWlyiNEOTk5CiNDZR48eAA9PT2lFEVERERUm6ociPr06YMffvhBeq+hoYHS0lKsWLECHh4eSi2OiIiIqDZU+ZDZihUrMGDAAJw9exaFhYWYNWsWLl26hAcPHuDEiROqqJGIiIhIpao8QtSuXTtcvXoVL730EoYNG4acnByMGDECMTExaNGihSpqJCIiIlKpKo8QAYCZmRnmzZun7FqIiIiI1KJagSgjIwNbtmxBfHw8AMDFxQXjx4+HhYWFUosjIiIiqg1VPmR27NgxNGvWDOvXr0dGRgYyMjKwfv16ODo64tixY6qokYiIiEilqjxC5Ovri1GjRmHTpk3Q0tICAJSUlODDDz+Er68v4uLilF4kERERkSpVeYTo+vXrmD59uhSGAEBLSwsBAQG4fv26UosjIiIiqg1VDkSurq7SuUP/Fh8fj44dOyqlKCIiIqLaVKlDZhcuXJD+/dFHH2Hq1Km4fv06evbsCQA4deoUAgMDsXz5ctVUSURERKRClQpEnTp1goaGBoQQ0rRZs2Y91e6dd97BqFGjlFcdERERUS2oVCBKTExUdR1EREREalOpQNS0aVNV10FERESkNtW6MeOdO3dw/PhxpKeno7S0VGHeRx99pJTCiIiIiGpLlQNRcHAwPvjgA+jq6sLS0hIaGhrSPA0NDQYiIiIiqneqHIgWLFiAhQsXYu7cudDUrPJV+0RERER1TpUTTW5uLt5++22GISIiInphVDnVTJgwAbt27VJFLURERERqUeVDZsuWLcOrr76K0NBQtG/fHjo6OgrzV69erbTiiIiIiGpDtQLRgQMH4OzsDABPnVRNREREVN9UORCtWrUK33//PcaNG6eCcoiIiIhqX5XPIdLT00Pv3r1VUQsRERGRWlQ5EE2dOhUbNmxQRS1EREREalHlQ2ZRUVE4fPgw9u7di7Zt2z51UvXu3buVVhwRERFRbahyIDI3N8eIESNUUQsRERGRWlQ5EAUFBamiDiIiIiK14e2miYiISPaqPELk6Oj4zPsN3bx5s0YFEREREdW2KgeiadOmKbwvKipCTEwMQkNDMXPmTGXVRURERFRrqhyIpk6dWu70wMBAnD17tsYFEREREdU2pZ1D9Morr+DXX39VVndEREREtUZpgeiXX36BhYWFsrojIiIiqjVVPmTWuXNnhZOqhRBITU3F3bt3sXHjRqUWR0RERFQbqhyIhg8frvBeU1MTjRo1gru7O1q3bq2suoiIiIhqTZUPmS1atEjhtWDBAkyaNKlaYWjZsmXo1q0bTExM0LhxYwwfPhwJCQkKbfLz8+Hr6wtLS0sYGxtj5MiRSEtLU2iTlJSEoUOHwtDQEI0bN8bMmTNRXFys0CYiIgKurq7Q09ODk5MTgoODq1wvERERvZjUemPGo0ePwtfXF6dOnUJYWBiKioowaNAg5OTkSG38/f3x559/YteuXTh69Cju3Lmj8OiQkpISDB06FIWFhTh58iS2bt2K4OBgLFy4UGqTmJiIoUOHwsPDA7GxsZg2bRree+89HDhwoFa3l4iIiOooUUkaGhpCU1PzmS8tLa3Kdleu9PR0AUAcPXpUCCHEw4cPhY6Ojti1a5fUJj4+XgAQkZGRQggh9u3bJzQ1NUVqaqrUZtOmTcLU1FQUFBQIIYSYNWuWaNu2rcK6Ro0aJTw9PStVV3JysgAgkpOTa7R95bofLcQ2PP5vXejnObKzswUAAUBkZ2erdF1EREQ1UZXv70qfQ7Rnz54K50VGRmL9+vUoLS2tUTjLzMwEAOlqtejoaBQVFWHgwIFSm9atW8PBwQGRkZHo2bMnIiMj0b59e1hZWUltPD09MXnyZFy6dAmdO3dGZGSkQh9lbZ68yWSZgoICFBQUSO8fPXpUo+0iIiKiuq3SgWjYsGFPTUtISMCcOXPw559/wsvLC0uWLKl2IaWlpZg2bRp69+6Ndu3aAQBSU1Ohq6sLc3NzhbZWVlZITU2V2vw7DJXNL5v3rDZZWVnIy8uDgYGBwrxly5bhk08+qfa2EBERUf1SrXOI7ty5g/fffx/t27dHcXExYmNjsXXrVjRt2rTahfj6+uLixYvYsWNHtftQlrlz5yIzM1N6Xb58Wd0lERERkQpVKRBlZmZi9uzZcHJywqVLlxAeHo4///xTGtGpLj8/P+zduxdHjhyBnZ2dNN3a2hqFhYV4+PChQvu0tDRYW1tLbZ686qzs/fPamJqaPjU6BAB6enowNTWVXiYmJjXaPiIiIqrbKh2IVqxYgebNm2Pv3r346aefcPLkSfTp06dGKxdCwM/PD3v27MHhw4fh6OioML9Lly7Q0dFBeHi4NC0hIQFJSUlwc3MDALi5uSEuLg7p6elSm7CwMJiamsLFxUVq8+8+ytqU9UFERETyVulziObMmQMDAwM4OTlh69at2Lp1a7ntdu/eXemV+/r6Yvv27fj9999hYmIinfNjZmYGAwMDmJmZYcKECQgICICFhQVMTU0xZcoUuLm5oWfPngCAQYMGwcXFBWPHjsWKFSuQmpqK+fPnw9fXF3p6egCASZMm4auvvsKsWbPw7rvv4vDhw/j5558REhJS6VqJiIjoxVXpQOTt7a3wyA5l2LRpEwDA3d1dYXpQUBDGjRsHAFizZg00NTUxcuRIFBQUwNPTU+ERIVpaWti7dy8mT54MNzc3GBkZwcfHR+EEb0dHR4SEhMDf3x/r1q2DnZ0dvvvuO3h6eip1e4iIiKh+0hBCCHUXUdfdvn0b9vb2SE5OVjjHSSkenANCuwCDowELV/X38xw5OTkwNjYGAGRnZ8PIyEhl6yIiIqqJqnx/q/VO1URERER1AQMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyZ5aA9GxY8fw2muvwdbWFhoaGvjtt98U5gshsHDhQtjY2MDAwAADBw7EtWvXFNo8ePAAXl5eMDU1hbm5OSZMmIDs7GyFNhcuXECfPn2gr68Pe3t7rFixQtWbRkRERPWIWgNRTk4OOnbsiMDAwHLnr1ixAuvXr8fmzZtx+vRpGBkZwdPTE/n5+VIbLy8vXLp0CWFhYdi7dy+OHTuGiRMnSvOzsrIwaNAgNG3aFNHR0fjyyy+xePFifPPNNyrfPiIiIqoftNW58ldeeQWvvPJKufOEEFi7di3mz5+PYcOGAQB++OEHWFlZ4bfffsPbb7+N+Ph4hIaG4syZM+jatSsAYMOGDRgyZAhWrlwJW1tbbNu2DYWFhfj++++hq6uLtm3bIjY2FqtXr1YITkRERCRfdfYcosTERKSmpmLgwIHSNDMzM/To0QORkZEAgMjISJibm0thCAAGDhwITU1NnD59WmrTt29f6OrqSm08PT2RkJCAjIyMctddUFCArKws6fXo0SNVbCIRERHVEXU2EKWmpgIArKysFKZbWVlJ81JTU9G4cWOF+dra2rCwsFBoU14f/17Hk5YtWwYzMzPp5eLiUvMNIiIiojqrzgYidZo7dy4yMzOl1+XLl9VdEhEREalQnQ1E1tbWAIC0tDSF6WlpadI8a2trpKenK8wvLi7GgwcPFNqU18e/1/EkPT09mJqaSi8TE5OabxARERHVWXU2EDk6OsLa2hrh4eHStKysLJw+fRpubm4AADc3Nzx8+BDR0dFSm8OHD6O0tBQ9evSQ2hw7dgxFRUVSm7CwMDg7O6NBgwa1tDVERERUl6k1EGVnZyM2NhaxsbEAHp9IHRsbi6SkJGhoaGDatGn47LPP8McffyAuLg7e3t6wtbXF8OHDAQBt2rTB4MGD8f777yMqKgonTpyAn58f3n77bdja2gIA3nnnHejq6mLChAm4dOkSdu7ciXXr1iEgIEBNW01ERER1jVovuz979iw8PDyk92UhxcfHB8HBwZg1axZycnIwceJEPHz4EC+99BJCQ0Ohr68vLbNt2zb4+flhwIAB0NTUxMiRI7F+/XppvpmZGQ4ePAhfX1906dIFDRs2xMKFC3nJPREREUk0hBBC3UXUdbdv34a9vT2Sk5NhZ2en3M4fnANCuwCDowELV/X38xw5OTkwNjYG8HiEz8jISGXrIiIiqomqfH/X2XOIiIiIiGoLAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREcmetroLICIiovor984dFD54UON+dC0sYGhrq4SKqoeBiIiIiKol984dRAwahJK8vBr3pWVgAPeDB9UWihiIiIiIqFoKHzxASV4eOq9eDeMWLardT/aNG4gJCEDhgwcMRERERFQ/GbdoAfN27dRdRo3wpGoiIiKSPQYiIiIikj0GIiIiIpI9nkNEREQkQ8q4XD77xg0lVaN+DEREREQyo+zL5XUtLJRQlXoxEBEREcmMsi6XB9R/Q0VlYSAiIiKSqRfhcnllYSAiIiKqR3juj2owEBEREdUTPPdHdRiIiIiI6gme+6M6DERERET1DM/9UT4GIiIiolrAc3/qNgYiIiIiFeO5P3UfAxEREdEzKGtkh+f+1G0MRERERBVQ9siORbduDDN1FAMRERG9kDiyQ1XBQEQqk5mUidx7uTXux7ChIcwczJRQERGpkjICiLIUPniAsx9+yJEdqjQGIlKJzKRMBLYJRFFuUY370jHUgW+8L0MRkYooI8goM4Aoi5aBAXoEBdX4BGSO7MgDA1EdcffKXRTrp1R7ee38u2ikxHpqKvdeLopyi/D6j6+jUZvqV3Y3/i72jNmD3Hu5dSYQceSrdnA/1w5lniOjqa+PlvNWQdvUvEb96Jvrw9jauMb1MMhQVTAQqdmj1EcwAbDbazdSb52qdj/Wze7gg8//v786dDVmozaNYONqo+4ylIYjX8+njCCTczcHP4/4+YXcz8oKepolmdAzKK5xP8o6RyY7NRs/vf4Hzk44X+OalPX/LDMpEynnqv+HprIpK5zzjwXVYCBSs/yH+TAB0P+z/jBu07fa/WTHHwPwDVJjUpGdr7oPgNy8//0SpsamwtDAsNx2d+PvKnW9yu6vuu7G31XqyNfff/1do36AuvUhq+wg4xXqBaNGRtXu4278Xez1+S+u7jyMBo4NalSPSVNrWHVrXaM+lBWodbSy0db+D2hq1jwQAY9Hdmp6jkxeYQpys/XqzKiwMv94URYdQx28tfutGv1MK/t3rKb1AC9OsGIgqiPMHc3RqAYjKY/0rYFY4PD8w0i9dUV5hT2hEIXSv79/6XvoQrfCtjqGOjBsWH5gqizDhobQMdTBnjF7atSPMukY6qBpn6Y1+gBQ5nbVxQ/ZmgYZQDkfspolmWhr/wdSN+9Gao16AkpLtdFqaRDMnR2r3cfd+LtAQQaGrOhVo4CWGX8Vd4KLkZj+EvILa/5FpKFrjJeKa/b/q4yyRoVr+keQsv54UZay37Ftg7fVuC9l/I4pu54afwbdvFfjOmqKgegFYWJtAgAYsW0EivU7qGw9uXm5WPrSUgDAu8ffrXCECFDOF5qZgxl8432VMjysLHVpu3Lu5mDPG99j93/W16gfANDX18Zr37wMgwYGNern8UiKU43rUQY9g2JoahbD8aOF0G/StNr9PA4gXyBs4tc1CiDaWvloa39UKQFNU18f//llGvQaWdeoH2WNViprFFfZfyzU9I8XZVLWZ5myRmSU9RmkjGBloHsfbeweH3o1V9Mj2hiIXjCNWjcCLFR3zk5OTo70b+tO1jAyUs5flc9i5mBWZz7QlEkZ25V75w5cHH5HaX6+Umq6tfz3GvehZWAA94MHa3wyqzKf+2Q3oEuNHoSZe6cVUnesg2Pj4zWqB3gcZLpt+rbOXPmk7ABS01FhZf4RVNcO5dS1zzJl1aOM/1+3w08hdXMI8h8q57OsOhiIiNREWV/4pfn5SrlpnDJk37iBmIAAPDhzBoU1qEfZ95CpafgwtLWFR1iYUu6xU9eufKqLAaSuBQd6NmX8/8q52aDGo6Y1xUBEpAYv6uMAdC0soGVggJiAgBr3VdfuIWNoa1sn9rEqMIAQMRARVRkfB1AxQ1tbuB88+EKOpBDRi42BiGSjrt2Nty6N7CjTizySQkQvLgYiUpkX+blGdelQDhER1RwDET3lWUEmJ/d/J14+vHQJRYblX1HC5xoREVF9wkD0Asl9qIXCKzcB04pvlvg8zwsy+aWl0r9PjhoFfU3NCvtSVgBRFgYZIiKqiKwCUWBgIL788kukpqaiY8eO2LBhA7p3767uspD7UAs5t5KgY3qx2n0U/h2Ls+vtUVI0u8b1PCvI5OTmAj16AAB67dwJowpGiAAGECIiqj9kE4h27tyJgIAAbN68GT169MDatWvh6emJhIQENG7cWG11Fdx7gDPr7VFStLzGfWnpAD3Wz4Nu05qFvGcFGZ1/3ZjRvG3bWrkxIxERkarJJhCtXr0a77//PsaPHw8A2Lx5M0JCQvD9999jzpw5aqurODsbJUWacJo0DjavvF79jrKuQDf2bRi66AFmhc9v/yx6ynlgJBERUX0hi0BUWFiI6OhozJ07V5qmqamJgQMHIjIy8qn2BQUFKCgokN5nZmYCAFJSlP8U+ZT7GbhXXIyG+nowMTevfke69gB08GDvmJoXpaUP9PgW0LV8alZu3v9uq/7PpYMwNNCv+fqIiEjW7qbE4V5xMVLu34X+7dtK67fse7v0X+e/VkjIwD///CMAiJMnTypMnzlzpujevftT7RctWiQA8MUXX3zxxRdfL8ArKirquVlBFiNEVTV37lwE/OvRA8XFxYiPj4e9vT00n3FVVXU8evQILi4uuHz5MkxMTJTaN/0P93Pt4H6uHdzPtYf7unaoaj+XlpYiLS0NnTt3fm5bWQSihg0bQktLC2lpaQrT09LSYG1t/VR7PT096OnpKUzr3bu3SmrLysoCADRp0gSmpqYqWQdxP9cW7ufawf1ce7iva4cq97ODg0Ol2il3uKOO0tXVRZcuXRAeHi5NKy0tRXh4ONzc3NRYGREREdUFshghAoCAgAD4+Piga9eu6N69O9auXYucnBzpqjMiIiKSL9kEolGjRuHu3btYuHAhUlNT0alTJ4SGhsLKykqtdenp6WHRokVPHaIj5eJ+rh3cz7WD+7n2cF/XjrqwnzWEEEJtayciIiKqA2RxDhERERHRszAQERERkewxEBEREZHsMRARERGR7DEQ1YLAwEA0a9YM+vr66NGjB6Kiop7ZfteuXWjdujX09fXRvn177Nu3r5Yqrd+qsp+//fZb9OnTBw0aNECDBg0wcODA5/5/oceq+vNcZseOHdDQ0MDw4cNVW+ALoqr7+eHDh/D19YWNjQ309PTQqlUrfnZUUlX39dq1a+Hs7AwDAwPY29vD398f+fn5z1xGzo4dO4bXXnsNtra20NDQwG+//fbcZSIiIuDq6go9PT04OTkhODhY5XXK4llm6rRjxw6hq6srvv/+e3Hp0iXx/vvvC3Nzc5GWllZu+xMnTggtLS2xYsUKcfnyZTF//nyho6Mj4uLiarny+qWq+/mdd94RgYGBIiYmRsTHx4tx48YJMzMzcfv27VquvH6p6n4uk5iYKJo0aSL69Okjhg0bVjvF1mNV3c8FBQWia9euYsiQIeL48eMiMTFRREREiNjY2FquvP6p6r7etm2b0NPTE9u2bROJiYniwIEDwsbGRvj7+9dy5fXHvn37xLx588Tu3bsFALFnz55ntr9586YwNDQUAQEB4vLly2LDhg1CS0tLhIaGqrROBiIV6969u/D19ZXel5SUCFtbW7Fs2bJy27/11lti6NChCtN69OghPvjgA5XWWd9VdT8/qbi4WJiYmIitW7eqqsQXQnX2c3FxsejVq5f47rvvhI+PDwNRJVR1P2/atEk0b95cFBYW1laJL4yq7mtfX1/Rv39/hWkBAQGid+/eKq3zRVGZQDRr1izRtm1bhWmjRo0Snp6eKqxMCB4yU6HCwkJER0dj4MCB0jRNTU0MHDgQkZGR5S4TGRmp0B4APD09K2xP1dvPT8rNzUVRUREsLCxUVWa9V939vGTJEjRu3BgTJkyojTLrvers5z/++ANubm7w9fWFlZUV2rVrh6VLl6KkpKS2yq6XqrOve/XqhejoaOmw2s2bN7Fv3z4MGTKkVmqWA3V9D8rmTtXqcO/ePZSUlDx1N2wrKytcuXKl3GVSU1PLbZ+amqqyOuu76uznJ82ePRu2trZP/RLS/1RnPx8/fhxbtmxBbGxsLVT4YqjOfr558yYOHz4MLy8v7Nu3D9evX8eHH36IoqIiLFq0qDbKrpeqs6/feecd3Lt3Dy+99BKEECguLsakSZPw8ccf10bJslDR92BWVhby8vJgYGCgkvVyhIhkb/ny5dixYwf27NkDfX19dZfzwnj06BHGjh2Lb7/9Fg0bNlR3OS+00tJSNG7cGN988w26dOmCUaNGYd68edi8ebO6S3vhREREYOnSpdi4cSPOnTuH3bt3IyQkBJ9++qm6S6Ma4giRCjVs2BBaWlpIS0tTmJ6WlgZra+tyl7G2tq5Se6refi6zcuVKLF++HIcOHUKHDh1UWWa9V9X9fOPGDdy6dQuvvfaaNK20tBQAoK2tjYSEBLRo0UK1RddD1fl5trGxgY6ODrS0tKRpbdq0QWpqKgoLC6Grq6vSmuur6uzrBQsWYOzYsXjvvfcAAO3bt0dOTg4mTpyIefPmQVOT4ww1VdH3oKmpqcpGhwCOEKmUrq4uunTpgvDwcGlaaWkpwsPD4ebmVu4ybm5uCu0BICwsrML2VL39DAArVqzAp59+itDQUHTt2rU2Sq3XqrqfW7dujbi4OMTGxkqv//znP/Dw8EBsbCzs7e1rs/x6ozo/z71798b169elwAkAV69ehY2NDcPQM1RnX+fm5j4VesqCqOCjQZVCbd+DKj1lm8SOHTuEnp6eCA4OFpcvXxYTJ04U5ubmIjU1VQghxNixY8WcOXOk9idOnBDa2tpi5cqVIj4+XixatIiX3VdCVffz8uXLha6urvjll19ESkqK9Hr06JG6NqFeqOp+fhKvMqucqu7npKQkYWJiIvz8/ERCQoLYu3evaNy4sfjss8/UtQn1RlX39aJFi4SJiYn46aefxM2bN8XBgwdFixYtxFtvvaWuTajzHj16JGJiYkRMTIwAIFavXi1iYmLE33//LYQQYs6cOWLs2LFS+7LL7mfOnCni4+NFYGAgL7t/UWzYsEE4ODgIXV1d0b17d3Hq1ClpXr9+/YSPj49C+59//lm0atVK6OrqirZt24qQkJBarrh+qsp+btq0qQDw1GvRokW1X3g9U9Wf539jIKq8qu7nkydPih49egg9PT3RvHlz8fnnn4vi4uJarrp+qsq+LioqEosXLxYtWrQQ+vr6wt7eXnz44YciIyOj9guvJ44cOVLu523ZfvXx8RH9+vV7aplOnToJXV1d0bx5cxEUFKTyOjWE4BgfERERyRvPISIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIqx+LFi9GpUyd1l0FEtYSBiIiIiGSPgYiIiIhkj4GIiOo8d3d3+Pn5wc/PD2ZmZmjYsCEWLFiA8h7FmJWVBQMDA+zfv19h+p49e2BiYoLc3FwAwOzZs9GqVSsYGhqiefPmWLBgAYqKip5Zw7Rp0xSmDR8+HOPGjZPeFxQUYMaMGWjSpAmMjIzQo0cPREREVHu7iaj2MBARUb2wdetWaGtrIyoqCuvWrcPq1avx3XffPdXO1NQUr776KrZv364wfdu2bRg+fDgMDQ0BACYmJggODsbly5exbt06fPvtt1izZk2NavTz80NkZCR27NiBCxcu4M0338TgwYNx7dq1GvVLRKqnre4CiIgqw97eHmvWrIGGhgacnZ0RFxeHNWvW4P3333+qrZeXF8aOHYvc3FwYGhoiKysLISEh2LNnj9Rm/vz50r+bNWuGGTNmYMeOHZg1a1a16ktKSkJQUBCSkpJga2sLAJgxYwZCQ0MRFBSEpUuXVqtfIqodHCEionqhZ8+e0NDQkN67ubnh2rVr+Pzzz2FsbCy9kpKSMGTIEOjo6OCPP/4AAPz6668wNTXFwIEDpeV37tyJ3r17w9raGsbGxpg/fz6SkpKqXV9cXBxKSkrQqlUrhXqOHj2KGzduVH/DiahWcISIiOq1SZMmYdSoUdJ7W1tbaGtr44033sD27dvx9ttvY/v27Rg1ahS0tR9/5EVGRsLLywuffPIJPD09YWZmhh07dmDVqlUVrkdTU/Opc5b+fc5RdnY2tLS0EB0dDS0tLYV2xsbGythUIlIhBiIiqhdOnz6t8P7UqVNo2bIlLC0tYWlp+VR7Ly8vvPzyy7h06RIOHz6Mzz77TJp38uRJNG3aFPPmzZOm/f33389cf6NGjZCSkiK9LykpwcWLF+Hh4QEA6Ny5M0pKSpCeno4+ffpUaxuJSH14yIyI6oWkpCQEBAQgISEBP/30EzZs2ICpU6dW2L5v376wtraGl5cXHB0d0aNHD2ley5YtkZSUhB07duDGjRtYv369wvlF5enfvz9CQkIQEhKCK1euYPLkyXj48KE0v1WrVvDy8oK3tzd2796NxMREREVFYdmyZQgJCanx9hORajEQEVG94O3tjby8PHTv3h2+vr6YOnUqJk6cWGF7DQ0NjB49GufPn4eXl5fCvP/85z/w9/eHn58fOnXqhJMnT2LBggXPXP+7774LHx8feHt7o1+/fmjevLk0OlQmKCgI3t7emD59OpydnTF8+HCcOXMGDg4O1d9wIqoVGqK8G3kQEdUh7u7u6NSpE9auXavuUojoBcURIiIiIpI9BiIiIiKSPR4yIyIiItnjCBERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyd7/AQ2HLrr4tWlYAAAAAElFTkSuQmCC",
      "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": 64,
     "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": 65,
   "id": "fc58ee5c-308c-4479-9236-751d7f158fe5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of wrong model fits rejected: 0.9996\n",
      "Fraction of good model fits rejected: 0.1010\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": 66,
   "id": "d5f5efbe-ef8f-48b0-b27b-166f21cb5a06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of wrong model fits rejected: 0.9980\n",
      "Fraction of good model fits rejected: 0.0526\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
}