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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qKalUhBveQJOIiMiyGG7MpFGjRpDL5bh9+zauXLkidTlEREQ1BsONmSgUCt5Ak4iISAIMN2bEcTdERESWx3BjRq1atQIAnD59WuJKiIiIag6GGzNiuCEiIrI8hhszCgkJAQCcOnWKV0wRERFZCMONGTVv3hwymQw3bty4755YREREZB4MN2bk7OyMRo0aAbjbekNERETmx3BjZvd2TREREZH5MdyYGQcVExERWRbDjZmx5YaIiMiyGG7M7N6WG14xRUREZH4MN2bWtGlTODg4oKCgAJmZmVKXQ0REZPcYbsxMoVDobsPAcTdERETmx3BjARVdUxx3Q0REZH4MNxbAQcVERESWw3BjAbwcnIiIyHIYbiygouXmr7/+glarlbgaIiIi+8ZwYwGNGjWCUqlEcXEx0tPTpS6HiIjIrjHcWIBcLkeLFi0AcNwNERGRuTHcWEhF1xTH3RAREZkXw42F8HJwIiIiy2C4sRBeDk5ERGQZDDcWUhFuUlNTUVZWJnE1RERE9ovhxkLq168PFxcXlJaWIi0tTepyiIiI7BbDjYXIZDJO5kdERGQBDDcWxEHFRERE5sdwY0G8HJyIiMj8GG4siC03RERE5sdwY0EVLTfnzp1DcXGxxNUQERHZJ4YbC6pbty68vb2h0WjYNUVERGQmDDcWJAgCwsLCAADHjh2TuBoiIiL7xHBjYaGhoQCA5ORkSesgIiKyVww3FsZwQ0REZF4MNxZW0S11/PhxaDQaiashIiKyPww3Fta0aVM4OzujsLAQ58+fl7ocIiIiu8NwY2FyuRytW7cGwK4pIiIic2C4kUDFuBteMUVERGR6DDcSqBh3w5YbIiIi02O4kQCvmCIiIjIfhhsJtG7dGoIgQK1WQ61WS10OERGRXWG4kYCLiwuaNWsGgK03REREpsZwIxF2TREREZkHw41EGG6IiIjMg+FGIryBJhERkXlIHm6WLFmCoKAgODk5ITw8HIcPH37o9p9++imaNWsGZ2dnBAYGYvLkybhz546FqjWdtm3bAgDOnTuH27dvS1wNERGR/ZA03GzYsAExMTGYMWMGkpKS0LZtW0RFReHq1auVbr927VpMmTIFM2bMQEpKClasWIENGzZg6tSpFq68+nx9feHv7w9RFHHy5EmpyyEiIrIbkoabBQsW4NVXX8WoUaPQsmVLLFu2DCqVCitXrqx0+4MHD6JLly4YOnQogoKC0KtXLwwZMuSRrT3Wil1TREREpidZuCktLcXRo0cRGRn5dzEyGSIjI5GYmFjpPp07d8bRo0d1YebChQvYtm0b+vbt+8DXKSkpQX5+vt5iLTiomIiIyPQcpHrh3NxcaDQa+Pr66q339fXFmTNnKt1n6NChyM3NRdeuXSGKIsrLyzF27NiHdkvNmTMHM2fONGntpsJwQ0REZHqSDyg2xr59+zB79mx8/vnnSEpKwqZNm7B161bMmjXrgfvExsYiLy9Pt2RmZlqw4oer6JY6efIkysvLJa6GiIjIPkjWcuPt7Q25XI6cnBy99Tk5OfDz86t0n+nTp2PYsGF45ZVXANy9jUFhYSFee+01vPfee5DJ7s9qSqUSSqXS9G/ABIKDg+Hq6orbt28jNTUVrVq1krokIiIimydZy41CoUD79u2RkJCgW6fVapGQkICIiIhK9ykqKrovwMjlcgCAKIrmK9ZMZDKZ7pJwdk0RERGZhqTdUjExMVi+fDlWr16NlJQUjBs3DoWFhRg1ahQAYPjw4YiNjdVtP2DAACxduhTr169Heno6du3ahenTp2PAgAG6kGNrKrqmkpKSJK6EiIjIPkjWLQUA0dHRuHbtGuLi4qBWqxEaGor4+HjdIONLly7ptdRMmzYNgiBg2rRpyMrKQp06dTBgwAD8+9//luotVFuHDh0AAH/++afElRAREdkHQbTF/pxqyM/Ph4eHB/Ly8uDu7i51OUhJSUHLli2hUqmQl5cHBwdJ8yYREZFVMub726aulrJHzZo1g5ubG4qKivDXX39JXQ4REZHNY7iRmEwmY9cUERGRCTHcWIFOnToBgM3eRoKIiMiaMNxYgY4dOwJgyw0REZEpMNxYgYqWmxMnTqC4uFjiaoiIiGwbw40VCAgIgK+vLzQaDe8QTkREVE0MN1ZAEARd6w27poiIiKqH4cZKVIy74aBiIiKi6mG4sRJsuSEiIjINhhsrUdFyc+7cOdy8eVPiaoiIiGwXw42V8PLyQqNGjQAAR44ckbgaIiIi28VwY0U4mR8REVH1MdxYEQ4qJiIiqj6GGytyb8tNDbtZOxERkckw3FiRsLAwyOVyqNVqZGVlSV0OERGRTWK4sSIqlQohISEAeEk4ERFRVTHcWBkOKiYiIqoehhsrw0HFRERE1cNwY2UqWm6OHDkCrVYrcTVERES2h+HGyrRq1QoqlQr5+flISUmRuhwiIiKbw3BjZRwcHBAeHg4A+P333yWuhoiIyPYw3FihLl26AGC4ISIiqgqGGyvUtWtXAMCBAwckroSIiMj2MNxYocceewyCIODChQvIzs6WuhwiIiKbwnBjhTw8PNCmTRsA7JoiIiIyFsONleK4GyIioqphuLFSFeGG426IiIiMw3BjpSoGFR87dgyFhYUSV0NERGQ7HAzZaNGiRUYfeNSoUXBzczN6P7qrfv36CAgIwOXLl3H48GH06NFD6pKIiIhsgkHhZtKkSQgICIBcLjfooJmZmejfvz/DTTV17doV69evx4EDBxhuiIiIDGRQuAHu3uvIx8fHoG0ZakyjS5cuWL9+PQcVExERGcGgMTczZsyAq6urwQedOnUqvLy8qlwU3VUx7ubgwYPQaDQSV0NERGQbBFEURamLsKT8/Hx4eHggLy8P7u7uUpfzUBqNBrVq1UJBQQGSk5PRtm1bqUsiIiKShDHf3ya5Wio/Px9Lly5Fhw4dTHE4+h+5XI7HHnsMAC8JJyIiMlS1ws3evXsxbNgw+Pv7Y9asWbq7WZPpVHRNcdwNERGRYQweUFwhKysLq1atwtdff41bt27h5s2bWLt2LZ5//nkIgmCOGms0zlRMRERkHINbbn744Qf07dsXzZo1Q3JyMj755BNcuXIFMpkMrVu3ZrAxk/DwcMjlcly6dAmZmZlSl0NERGT1DA430dHRCAsLQ3Z2NjZu3IiBAwdCoVCYszYC4OrqitDQUABsvSEiIjKEweFm9OjRWLJkCXr37o1ly5bh5s2b5qyL7lEx7ua3336TuBIiIiLrZ3C4+eKLL5CdnY3XXnsN69atg7+/PwYOHAhRFKHVas1ZY433xBNPAAD2798vcSVERETWz6irpZydnTFixAjs378fJ0+eRKtWreDr64suXbpg6NCh2LRpk7nqrNG6desGQRBw+vRp5OTkSF0OERGRVavypeBNmjTB7NmzkZmZie+++w5FRUUYMmSIKWuj/6ldu7ZuAr+9e/dKXA0REZF1q/YkfjKZDAMGDMDmzZt5NY8Z9ezZEwCwZ88eiSshIiKybkbPcwMAV65cwYEDB3D16lW98TaCIGDChAkmK47+1rNnTyxYsIDhhoiI6BGMvrfUqlWrMGbMGCgUCtSuXVtvfhtBEHDhwgWTF2lKtnRvqXvl5+fDy8sLGo0GGRkZaNCggdQlERERWYxZ7y01ffp0xMXFIS8vDxkZGUhPT9ct1h5sbJm7uzs6duwIgONuiIiIHsbocFNUVIQXXngBMplJ7rmJJUuWICgoCE5OTggPD8fhw4cfuv2tW7cwfvx4+Pv7Q6lUomnTpti2bZtJarF2PXr0AMBwQ0RE9DBGJ5TRo0dj48aNJnnxDRs2ICYmBjNmzEBSUhLatm2LqKgoXL16tdLtS0tL8dRTTyEjIwP//e9/kZqaiuXLl6NevXomqcfa3Tuo2MjeRCIiohrD6DE3Go0G/fv3R3FxMVq3bg1HR0e95xcsWGDwscLDw9GxY0csXrwYAKDVahEYGIgJEyZgypQp922/bNkyfPzxxzhz5sx9r/sgJSUlKCkp0T3Oz89HYGCgzY25Ae62mtWqVQulpaU4e/YsmjRpInVJREREFmHWMTdz5szBjh07kJOTg5MnT+LYsWO6JTk52eDjlJaW4ujRo4iMjPy7GJkMkZGRSExMrHSfn3/+GRERERg/fjx8fX0REhKC2bNnQ6PRPLReDw8P3RIYGGhwjdZGpVIhIiICAC8JJyIiehCjLwX/5JNPsHLlSowcObJaL5ybmwuNRgNfX1+99b6+vjhz5kyl+1y4cAF79uzBiy++iG3btiEtLQ2vv/46ysrKMGPGjEr3iY2NRUxMjO5xRcuNrerZsyf279+PPXv2YMyYMVKXQ0REZHWMbrlRKpXo0qWLOWp5JK1WCx8fH3z55Zdo3749oqOj8d5772HZsmUP3EepVMLd3V1vsWUV42727t3Le3oRERFVwuhwM3HiRHz22WfVfmFvb2/I5fL77pWUk5MDPz+/Svfx9/dH06ZNIZfLdetatGgBtVqN0tLSatdkCzp16gSVSoVr167h9OnTUpdDRERkdYwON4cPH8bq1asRHByMAQMGYPDgwXqLoRQKBdq3b4+EhATdOq1Wi4SEBN24kn/q0qUL0tLS9Foszp49C39/fygUCmPfik1SKBTo2rUrAF4STkREVBmjw42npycGDx6Mbt26wdvbW2+wroeHh1HHiomJwfLly7F69WqkpKRg3LhxKCwsxKhRowAAw4cPR2xsrG77cePG4caNG5g4cSLOnj2LrVu3Yvbs2Rg/fryxb8Om8T5TRERED2b0gOKvv/7aZC8eHR2Na9euIS4uDmq1GqGhoYiPj9cNMr506ZLeZIGBgYHYsWMHJk+ejDZt2qBevXqYOHEi3n33XZPVZAsqws2+ffug0Wj0uumIiIhqOqPnubF1tnpvqXuVl5fD29sbeXl5OHToEDp16iR1SURERGZl8nlu2rVrh5s3bxpcQNeuXZGVlWXw9mQcBwcHPPnkkwCA7du3S1wNERGRdTGoWyo5ORnHjx+Hl5eXQQdNTk7WmxWYTK9Pnz7YtGkTtm/f/sA5foiIiGoig8fcPPnkkwbfz0gQhCoXRIbp3bs3gLtXr+Xm5sLb21viioiIiKyDQeEmPT3d6AMHBAQYvQ8ZLiAgAK1bt8bJkyexc+dODB06VOqSiIiIrIJB4aZBgwbmroOqoE+fPjh58iS2b9/OcENERPQ/Rs9zQ9ajT58+AIAdO3bwVgxERET/w3Bjw7p06QI3Nzdcu3YNR48elbocIiIiq8BwY8McHR0RGRkJgJeEExERVWC4sXEVXVMMN0RERHcZHW6Cg4Nx/fr1+9bfunULwcHBJimKDFcRbg4dOlTp50JERFTTGB1uMjIyoNFo7ltfUlLCWYklEBAQgJCQEIiiiJ07d0pdDhERkeQMnsTv559/1v2+Y8cOvTuAazQaJCQkICgoyKTFkWH69OmDU6dOYfv27RgyZIjU5RAREUnK4BtnVtydWxCE+2YqdnR0RFBQED755BP079/f9FWakD3cOPOf9u7di549e6JOnTpQq9V6d1InIiKyB8Z8fxvcclMxj0rDhg3x559/crp/K9KlSxe4urri2rVrSEpKQocOHaQuiYiISDJG/y9+eno6g42VUSgUvCSciIjofwxuublXQkICEhIScPXq1ftmxl25cqVJCiPj9O3bF5s3b8bWrVsxffp0qcshIiKSjNEtNzNnzkSvXr2QkJCA3Nxc3Lx5U28hafTv3x+CIODQoUO8ao2IiGo0o1tuli1bhlWrVmHYsGHmqIeqyN/fH4899hgSExPx008/4fXXX5e6JCIiIkkY3XJTWlqKzp07m6MWqqZnnnkGAPDjjz9KXAkREZF0jA43r7zyCtauXWuOWqiaKsLNvn372EVIREQ1ltHdUnfu3MGXX36J3bt3o02bNnB0dNR7fsGCBSYrjozTuHFjhISE4NSpU9iyZQu7DomIqEYyOtycOHECoaGhAIBTp07pPScIgkmKoqp75plncOrUKfz4448MN0REVCMZPEOxvbDHGYrvdezYMbRr1w7Ozs7Izc2FSqWSuiQiIqJqM+b7u8rz9KelpWHHjh0oLi4GgPtuyUDSCA0NRYMGDVBcXMwbaRIRUY1kdLi5fv06nnzySTRt2hR9+/ZFdnY2AGD06NH417/+ZfICyTiCIPCqKSIiqtGMDjeTJ0+Go6MjLl26pNflER0djfj4eJMWR1VTEW5++eUXlJWVSVwNERGRZRkdbnbu3ImPPvoIAQEBeuubNGmCixcvmqwwqrouXbqgTp06uHnzJn799VepyyEiIrIoo8NNYWFhpYNUb9y4AaVSaZKiqHrkcjmefvppAOyaIiKimsfocPP444/jm2++0T0WBAFarRbz5s1Djx49TFocVV1F19TmzZvvu7kpERGRPTN6npt58+bhySefxJEjR1BaWop33nkHp0+fxo0bN/D777+bo0aqgieffBKurq7IysrCn3/+ifDwcKlLIiIisgijW25CQkJw9uxZdO3aFQMHDkRhYSEGDx6MY8eOoVGjRuaokarAyckJ/fv3BwCsX79e4mqIiIgsx6hJ/MrKytC7d28sW7YMTZo0MWddZmPvk/jd65dffsHTTz8Nf39/ZGZmQi6XS10SERFRlZhtEj9HR0ecOHGiWsWR5URFRaFWrVrIzs7G/v37pS6HiIjIIozulnrppZewYsUKc9RCJqZQKPDcc88BAL757jsETdmKoClbUVRaLnFlRERE5mP0gOLy8nKsXLkSu3fvRvv27eHi4qL3PO8Kbl2GDh2K5cuXY/OmTfB45WkIDo6P3omIiMiGGR1uTp06hXbt2gEAzp49q/cc7wpufR5//HHUq1cPWVlZUKQfharJY1KXREREZFZGhRuNRoOZM2eidevWqFWrlrlqIhOSy+WIjo7GggULUPjXfoYbIiKye0aNuZHL5ejVqxdu3bplpnLIHIYOHQoAKE47BG1JkcTVEBERmVeV5rm5cOGCOWohM2nXrh2aNGkKsbwURef+kLocIiIiszI63Hz44Yd46623sGXLFmRnZyM/P19vIesjCAL+LzoaAFCYwkvCiYjIvhk1iR8AyGR/56F7BxCLoghBEKDRaExXnRnUpEn87pV88jTC2oQAggwZmZfRoJ6/1CUREREZzJjvb6Ovltq7d2+VCyPpNG3WDAq/xihVp+HHH/6LSW9OkLokIiIiszA63HTr1s0cdZAFuLTohlJ1GjasX8dwQ0REdsvocPPrr78+9PknnniiysWQealaPIGb+77GH4mJSE1NRbNmzaQuiYiIyOSMDjfdu3e/b929Y2+sfcxNTebgVhvu4c+i8PQerFixAvPmzZO6JCIiIpMz+mqpmzdv6i1Xr15FfHw8OnbsiJ07d1apiCVLliAoKAhOTk4IDw/H4cOHDdpv/fr1EAQBgwYNqtLr2rui0nLdsvbQJQBArW4jUG/s1/j24AXkFRbzPlNERGR3jL5a6kH279+PmJgYHD161Kj9NmzYgOHDh2PZsmUIDw/Hp59+io0bNyI1NRU+Pj4P3C8jIwNdu3ZFcHAwvLy8sHnzZoNeryZdLRU0ZesDnxO1GtzJOAbn4A7ImNvPglUREREZz5jvb6Nbbh7E19cXqampRu+3YMECvPrqqxg1ahRatmyJZcuWQaVSYeXKlQ/cR6PR4MUXX8TMmTMRHBxcnbJrLEEmx53MU1KXQUREZHJGj7k5ceKE3mNRFJGdnY25c+ciNDTUqGOVlpbi6NGjiI2N1a2TyWSIjIxEYmLiA/f74IMP4OPjg9GjR+O333576GuUlJSgpKRE97gmTTT41wdRAAB13h1ELtgP7T1tdKJWg+K/9uPo2vkSVUdERGQeRrfchIaGIiwsDKGhobrf+/bti9LSUnz11VdGHSs3NxcajQa+vr566319faFWqyvd58CBA1ixYgWWL19u0GvMmTMHHh4euiUwMNCoGm2ZSuEAlcIBwXVcMfPpVrr1MgGonb4TZfnXsGHtdxJWSEREZHpGh5v09HRcuHAB6enpSE9Px8WLF1FUVISDBw+iefPm5qhRp6CgAMOGDcPy5cvh7e1t0D6xsbHIy8vTLZmZmWat0Vo92z5A9/vumG6YNKATAGDFihXQarVSlUVERGRyRndLNWjQwGQv7u3tDblcjpycHL31OTk58PPzu2/78+fPIyMjAwMGDNCtq/hidnBwQGpqKho1aqS3j1KphFKpNFnN9sDPwwnPPfccJkyYgPT0dOzbtw89e/aUuiwiIiKTMLjlZs+ePWjZsmWlY1by8vLQqlWrR45/+SeFQoH27dsjISFBt06r1SIhIQERERH3bd+8eXOcPHkSycnJuuXpp59Gjx49kJycXKO6nKpLpVLhxRdfBACjuxOJiIismcHh5tNPP8Wrr75a6eVXHh4eGDNmDBYsWGB0ATExMVi+fDlWr16NlJQUjBs3DoWFhRg1ahQAYPjw4boBx05OTggJCdFbPD094ebmhpCQECgUCqNfvyYbPXo0AGDTpk24ceOGxNUQERGZhsHh5vjx4+jdu/cDn+/Vq5fRc9wAQHR0NObPn4+4uDiEhoYiOTkZ8fHxukHGly5dQnZ2ttHHpUdr164dQkNDUVJSgm+++UbqcoiIiEzC4En8nJyccOrUKTRu3LjS59PS0tC6dWsUFxebtEBTq0mT+N2rqLQcLeN2ALh7ibhKcXe41RdffIGxY8eiUaNGSE1NhVwul7JMIiKiSpllEr969erh1KkHT/p24sQJ+Pv7G14lWYWXXnoJtWrVwvnz57F9+3apyyEiIqo2g8NN3759MX36dNy5c+e+54qLizFjxgz079/fpMWR+bm4uOCVV14BACxcuFDiaoiIiKrP4G6pnJwctGvXDnK5HG+88QaaNWsGADhz5gyWLFkCjUaDpKSk+ybkszbsltLvlgKAixcvIjg4GFqtFqdPn0bLli2lKpOIiKhSZumW8vX1xcGDBxESEoLY2Fg888wzeOaZZzB16lSEhITgwIEDVh9sqHINGjTQ3Vn9s88+k7YYIiKiaqrSXcFv3ryJtLQ0iKKIJk2aoFatWuaozSzYcnN/yw1w967u3bt3h0qlwuXLl23qMyUiIvtnzPd3lcKNLaup4eZRRFFEaGgoTpw4gY8//hhvvfWW1CURERHpmKVbiuybIAiYOHEiAGDx4sXQaDQSV0RERFQ1DDekM2TIENSuXRsXL17Ezz//LHU5REREVcJwQzrOzs4YM2YMAF4WTkREtovhhvSMGzcODg4O2L9/Pw4dOiR1OUREREZjuCE9AQEBeOmllwAAs2fPlrgaIiIi4zHc0H2mTJkCQRDw888/4+TJk1KXQ0REZBSGG7pPs2bN8NxzzwEA5s6dK3E1RERExmG4oUrFxsYCANavX4+0tDSJqyEiIjIcww1VKiwsDH379oVWq8W8efOkLoeIiMhgDDf0QFOnTgUArFq1CpcvX5a4GiIiIsMw3NADdenSBU888QTKysrwySefSF0OERGRQRhu6KHee+89AMAXX3yBa9euSVwNERHRozHc0EM99dRTaN++PYqLi9l6Q0RENoHhhh5KEATExcUBABYtWoTs7GyJKyIiIno4hht6pAEDBiAiIgLFxcWYNWuW1OUQERE9FMMNPZIgCLrJ/JYvX47z589LXBEREdGDMdyQQZ544gn07t0b5eXlum4qIiIia8RwQwaruJHm2rVrcfz4cYmrISIiqhzDDRksLCwML7zwAoC/LxEnIiKyNgw3ZJRZs2bBwcEBW7duxW+//SZ1OURERPdhuCGjNG7cGKNHjwZw9+aaoihKXBEREZE+hhsyWlxcHJydnfH777/jv//9r9TlEBER6WG4IaPVrVsX7777LgDgX//6FwoLCyWuiIiI6G8MN1Ql77zzDho0aIDMzEx89NFHUpdDRESkw3BDVeLs7IwFCxYAAObNm4cLFy5IXBEREdFdDDdUZc888wwiIyNRUlKCf/3rX1KXQ0REBIDhhqpBEAQsXLgQDg4O2Lx5M3bu3Cl1SURERAw3VD0tW7bEhAkTAABvvvkmSktLJa6IiIhqOoYbqrYZM2bAx8cHqampWLhwodTlEBFRDcdwQ9Xm4eGhu2JqxowZSEtLk7giIiKqyRhuyCRGjBiBJ598EsXFxXj11Veh1WqlLomIiGoohhsyCUEQsHz5cqhUKuzbtw/Lly+XuiQiIqqhGG7IZBo2bIjZs2cDAN5++21kZmZKXBEREdVEDDdkUm+88QYiIiJQUFCAcePG8caaRERkcQw3ZFJyuRwrVqyAQqHA1q1bsXbtWqlLIiKiGobhhkyuRYsWiIuLAwBMnDgR2dnZEldEREQ1CcMNmcU777yDsLAwXL9+HSNGjODVU0REZDEMN2QWjo6OWLNmDZydnbFr1y7dTTaJiIjMjeGGzKZFixa6GYunTp2Ko0ePSlwRERHVBAw3ZFavvPIKBg8ejLKyMgwZMgS3b9+WuiQiIrJzVhFulixZgqCgIDg5OSE8PByHDx9+4LbLly/H448/jlq1aqFWrVqIjIx86PYkrYrJ/QICAnDu3DndTTaJiIjMRfJws2HDBsTExGDGjBlISkpC27ZtERUVhatXr1a6/b59+zBkyBDs3bsXiYmJCAwMRK9evZCVlWXhyslQXl5eWLNmDWQyGVatWoV169ZJXRIREdkxQZR4lrXw8HB07NgRixcvBgBotVoEBgZiwoQJmDJlyiP312g0qFWrFhYvXozhw4c/cvv8/Hx4eHggLy8P7u7u1a6fDBcXF4dZs2bBxcUFf/zxB0JCQqQuiYiIbIQx39+SttyUlpbi6NGjiIyM1K2TyWSIjIxEYmKiQccoKipCWVkZvLy8Kn2+pKQE+fn5egtJIy4uDk8++SQKCwsxaNAg3Lx5U+qSiIjIDkkabnJzc6HRaODr66u33tfXF2q12qBjvPvuu6hbt65eQLrXnDlz4OHhoVsCAwOrXTdVjYODA9avX4+goCCcP38eQ4YMgUajkbosIiKyM5KPuamOuXPnYv369fjxxx/h5ORU6TaxsbHIy8vTLbyZo7S8vb2xefNmODs7Y8eOHZg2bZrUJRERkZ2RNNx4e3tDLpcjJydHb31OTg78/Pweuu/8+fMxd+5c7Ny5E23atHngdkqlEu7u7noLSatt27ZYuXIlgLsBdePGjQbtV1RajqApWxE0ZSuKSsvNWSIREdkwScONQqFA+/btkZCQoFun1WqRkJCAiIiIB+43b948zJo1C/Hx8ejQoYMlSiUTe+GFF/DWW28BAEaOHIljx45JXBEREdkLybulYmJisHz5cqxevRopKSkYN24cCgsLMWrUKADA8OHDERsbq9v+o48+wvTp07Fy5UoEBQVBrVZDrVZzcjgbNGfOHPTq1QtFRUXo27cvMjIypC6JiIjsgOThJjo6GvPnz0dcXBxCQ0ORnJyM+Ph43SDjS5cu6d1VeunSpSgtLcVzzz0Hf39/3TJ//nyp3gJVkYODA77//nu0adMGarUavXv3xvXr16Uui4iIbJzk89xYGue5sT5ZWVmIiIhAZmYmOnfujN27d8PZ2fm+7YpKy9EybgcA4K8PoqBSOFi6VCIikojNzHNDBAD16tVDfHw8PD09cfDgQbz44ou8RJyIiKqM4YasQsuWLfHzzz9DqVTixx9/xBtvvIEa1qhIREQmwnBDVuPxxx/Hd999B0EQsGzZMkyePJkBh4iIjMZwQ1blueeew1dffQUAWLhwId566y0GHCIiMgrDDVmdl19+GV988QUAYMGCBZgyZQoDDhERGYzhhqzSa6+9hs8//xzA3Ukbp02bxoBDREQG4bW0ZLXGjRuH8vJyvPnmm5g9ezYKCosgKntCEGRQ591BcB1XqUskIiIrxJYbsmoTJkzAp59+CgD4eu9p3frIBfux4c9LElVFRETWjJP4kU2oMzAWqmadIcj+zuNyQcCBKT3g73H/hH9ERGRfOIkf2R2XFl31gg0AaEQRGblFElVERETWiuGGbMKef3WDTPjHSq0W8iLei4qIiPQx3JBNCK7jiplPt9I9FrUaXN/xGfr26Iz9+/dLWBkREVkbhhuyGc+2D9D9vu6lFmjmeAPXr19HZGQkvvzySwkrIyIia8JwQzYptHlD/Prrr4iOjkZ5eTnGjBmDN998E+Xl5VKX9khFpeUImrIVQVO2oqjU+uslIrI1DDdks1QqFdatW4dZs2YBAD777DP06NEDly9flrgyIiKSEsMN2TRBEDBt2jRs2rQJbm5uOHDgAEJDQ7Ft2zapSyMiIokw3JBdeOaZZ5CUlIR27drh+vXr6NevH9555x2UlZVJXRoREVkYww3ZjcaNG+PgwYOYMGECAODjjz9G165dkZqaKnFlto/jhIjIljDckF1RKpVYtGgRfvjhB3h4eODw4cMIDQ3Ff/7zH2g0GqnLIyIiC2C4Ibs0ePBgnDhxAk899RTu3LmDmJgYdO/eHWlpaVKXRkREZsZwQ3arfv362LFjB7744gu4urriwIEDaNOmDebPn8+xOEREdozhhuyaIAh47bXXcPLkSfTs2RPFxcV4++23ERYWhl9//VXq8oiIyAwYbqhGCAoKwq5du7BixQp4e3vj9OnT6NatG4YPH46cnBypyyMiIhNiuCGboVI4IGNuP2TM7QeVwsHo/WUyGV5++WWkpqZizJgxEAQB3377LZo2bYq5c+eiuLjYDFUT1Ry8qo6sBcMN1TheXl5YtmwZ/vjjD7Rv3x75+fmIjY1F06ZNsWrVKoteVaXOu2Ox1yIiqikYbqjG6tSpEw4fPoxvv/0W9evXx+XLlzFq1CiEhYXhl19+gSiKJn/NotJyrD10Sfc4csF+fJuYYfLXISKqyRhuqEaTyWR46aWXkJqaio8//hienp44efIknn76abRv3x6bN2+GVqs12eu1jNuBD7em6B5rRWD6T6eRnccuMSIiU2G4IQLg5OSEt956C+fPn8e7774LFxcXHDt2DM888wzCwsKwceNGs3ZXZeQWme3YREQ1DcMN0T28vLwwd+5cZGRk4L333oObmxtOnDiB559/Hk2bNsWiRYtQUFBQ5ePv+Vc3yAT9dTIBCPJWVbNyy+E4ISKydgw3RJXw9vbGhx9+iIyMDMTFxcHLywsXLlzAxIkTERgYiLfffhsXL140+rjBdVwx8+lWuscyAZgzuDX8PZxNWb7JcZwQEdkShhuih/Dy8sLMmTORmZmJpUuXomnTpsjLy8P8+fPRsGFD9OvXDz/99BPKyw2/7PXZ9gG633fHdEN0x/rmKN1ksvOKOU6IiGwKww2RAVQqFcaOHYuUlBRs2bIFkZGREEUR27Ztw6BBgxAUFIS4uDicP3/eqOP6eTiZqWLTSc8trHQ9xwkRkbViuCEygkwmQ79+/bBr1y6cPXsW77zzDurUqYOsrCzMmjULjRs3RufOnbF06VJcv35d6nJNoqG3i02PE+LEckQ1D8MNURU1adIEH330ETIzM7F+/XpERUVBJpMhMTERr7/+Ovz9/TFgwACsXr0aN2/elLrcKvP3cLbJcUJEFRhwax6GG6JqUiqViI6ORnx8PC5fvowFCxYgLCwMZWVl2LJlC0aOHAkfHx9ERUXhyy+/RHZ2ttQlG83WxgmR9HhVHUmJ4YbIhPz9/TF58mQkJSXh9OnTmDlzJlq3bo3y8nLs3LkTY8aMQeOg+shePQm3fluDI38eNukkgZZgC+OEyPI4+zZZE4YbIjNp2bIl4uLicOLECaSmpmL27Nno2LEjAKBUnYa8g+vQrWsX+Pr64oUXXsBXX32FjIwMaYsmqiLOvk3WhOGGyAKaNm2K2NhYHD58GOcvZqJ2n4lQNe0MNzc35ObmYsOGDXj11VfRsGFDNG7cGKNHj8bq1atx4cIFs9zjishSeFUdScFB6gKIaho/Pz+4tnkKrm2eQvK0HjiVnITdu3dj9+7d+OOPP3D+/HmcP38eK1euBADUq1cPXbt2RXh4OMLDw9GuXTs4ObFriKzLnn91Q+SC/dDek8Vt6ao6si8MN0QSUigU6Nq1K7p27Yr3338f+fn5+O233/Drr7/it99+w5EjR5CVlYUNGzZgw4YNAABHR0e0bdsWHTp0QLt27dCuXTuEhIRAqVRK/G6oJquYfXv6T6cB8Ko6khbDDZEVcXd3R79+/dCvXz8AQFFREQ4dOoTExET88ccfOHToEK5evYojR47gyJEjuv0cHBzQsmVLtG7dWm8JCAiAIAgPejkik3q2fYAu3OyO6YbgOq4SV0Q1FcMNkYWpFA7ImNvPsG1VKvTo0QM9evQAAIiiiIsXL+LQoUNISkrCsWPHkJSUhOvXr+PEiRM4ceKE3v7u7u5o3rw5mjdvjhYtWqB58+Zo0qQJgoOD4ezM/6O2BUWl5WgZtwMA8NcHUVApbOPPNq+qM56tftbWiGeOyIYIgoCgoCAEBQUhOjoawN3Ak5mZiePHj+PkyZM4ceIETp48idTUVOTn5+Pw4cM4fPjwfccJCAjQBZ2GDRvqLT4+PmzxISKbxXBDZOMEQUD9+vVRv359DBgwQLe+pKQE58+fR0pKCs6cOYOUlBSkpqbi3LlzyMvLQ2ZmJjIzM7Fnz577jqlUKhEYGKg7rq9/XRQcuwW5W20cO1YHjYMawNvbG3K53JJvtdrUeXfYVUJUAzDcENkppVKJli1bomXLlnrrRVHE9evXce7cOZw7dw4XLlxAenq6bsnKykJJSQnS0tKQlpZ233G7/jALACCXy+Hj4wM/Pz/dUqdOHfj4+OgWb29v1K5dG97e3nBxcbF4a1BlE8vNfLoVhkUEWbQOsh4MuDUDww1RDSMIAry9veHt7Y2IiIj7ni8tLUVWVhYyMzNx6dIlXLx4EVlZWcjKysKVK1eQlZUFtVoNjUaD7Oxsg28noVQqUbt2bXh5ecHLywu1atXS/fT09NRb3N3d4e7uDg8PD7i7u8PNza1KV4NVjF+oUDGxXGRLX17FUwNU3EeqsoD7bPsAjmmxY1bxyS5ZsgQff/wx1Go12rZti88++wydOnV64PYbN27E9OnTkZGRobt5Yd++fS1YMZH9UigUurE3D1JeXo5r165BrVYjOzsbarUaarUa165dw7Vr13D16lVcvXoVubm5yM3NRUlJCUpKSnDlyhVcuXKlynW5ubnpFldXV7i6usLFxUX308XFBSqVSvezKO0GVI073nesvX+eQqegWnB2doaTkxOUSiWcnJzg4GAVfxLJRP4ZboG/A+70n04bPLCfbI/k/yVv2LABMTExWLZsGcLDw/Hpp58iKioKqamp8PHxuW/7gwcPYsiQIZgzZw769++PtWvXYtCgQUhKSkJISIgE74Co5nFwcIC/vz/8/f0RFhb20G1FUURRUZEu6Ny8eRM3b97EjRs3cOPGDdy6deu+JT8/H3l5ecjPz0dhYSGAuy1K169fx/Xr1w2uU+5WG87BX0OQ/T0Zu6jVYPjgPtAU3H8cuVwOpVJZ6aJQKHQ/710cHR11Px+0ODg4PPBnxSKXy+/7KZfLUS4KuJN5ChDk+POwJ1yclLrn5HI5ZDLZfb/f+7Ni+efjfy6CIHAQOdkNQZR4bvfw8HB07NgRixcvBgBotVoEBgZiwoQJmDJlyn3bR0dHo7CwEFu2bNGte+yxxxAaGoply5bdt33F/zFWyM/PR2BgIPLy8uDu7m6Gd0REplReXo6CggIUFBTg9u3ber/fvn0bhYWFKCgoQGFhIYqKivR+FhcX44pTQ1wLirwbcLRaiH+uRfHpBNy5cwfFxcUoLy+X+i1ajYqAUxF2HvXzn78LMhlyb5cCAHw9nCH75/P3BKh/rjPk+XvXV/xe2bqK30UAGqUbbnSeBAj33G1I1MLn0GI4lt2u9FiVnRdTbvOgdRqtiIPn74buLo29IZdVfixDQqiptqnqsZo3b46PP/7YoOMbKj8/Hx4eHgZ9f0vaclNaWoqjR48iNjZWt04mkyEyMhKJiYmV7pOYmIiYmBi9dVFRUdi8eXOl28+ZMwczZ840Wc1EZFkODg6oVasWatWqVaX97507ZM/bPRBcZ4De8xqNBiUlJbhz547ez3uX0tLS+36WlZWhtLRUt1Q8Lisr01vKy8v1fr93KSsrg0aj0T3WaDS6dRVLWVk50q7mA1ot6nkoodX+/ZxWq9XbVqvV6q0z9o7zoihCFEWT3Kn+yu1qH8JkXPPl8Ip6A4JMDlGrwY0di3HxxC6py3qohAypK6geY1pYzUHScJObmwuNRgNfX1+99b6+vjhz5kyl+6jV6kq3V6vVlW4fGxurF4YqWm6IqOapbGI5uVwOlUoFlco674FU3YndKsLKveHn3hB0b6D55+OKpeLxvesrtrv3uaLSMgxechCAiI1jH4OTg1zveUOWipr/+Xtl21S27b3vu+LxndJyvPH9cQiOTpjZJxi+nV8H8Hqlx7p3/3+uq8o2xqy7U1aOd384CQD46NnWUP7v/FXl2FJuA6DSYSWWJPmYG3Or6C8nIqqJBEHQjcmxhCtft7fI6xijqLQcb/+pAAAM+z/rnfm3qLQcs87cDbJDhlpvnbZA9uhNzKdiErCcnBy99Tk5OfDz86t0Hz8/P6O2JyKyF+q8O1KXQBbCz7p6JA03CoUC7du3R0JCgm6dVqtFQkJCpfNvAEBERITe9gCwa9euB25PRGRLikrL9ZZ/ztHybWKG3vNkm/75ORvyWfMzN5zkbV4xMTEYMWIEOnTogE6dOuHTTz9FYWEhRo0aBQAYPnw46tWrhzlz5gAAJk6ciG7duuGTTz5Bv379sH79ehw5cgRffvmllG+DiMgkKpubpcK9c7RU4FwttulhnzNQ+WddgZ/5o0kebqKjo3Ht2jXExcVBrVYjNDQU8fHxukHDly5dguyeOSo6d+6MtWvXYtq0aZg6dSqaNGmCzZs3c44bIiIiAmAF89xYmjHXyROR7avu1UaWdm+3gzrvDiIX7If2nr/SMgHYHdNNd+WXtb8fa2CN/wb+2b1kyGddwRrql4Ix39+SjrkhIiJ9KoWDbgmu44qZT7fSPScTgDmDWyO4jqtuG7JN937Ohn7W/MwNx3BDRGTFnm0foPt9d0w3RHesL2E1ZE78rE2H4YaIyEZUNgkh2Sd+1tXD9i0ismsqhQOvLiGqYdhyQ0RERHaFLTdERGTX2HpX87DlhoiIiOwKww0RERHZFYYbIiIisisMN0RERGRXGG6IiIjIrjDcEBERkV3hjTOJiIjI6vHGmURERFRjMdwQERGRXWG4ISIiIrvCcENERER2heGGiIiI7ArDDREREdkVhhsiIiKyKww3REREZFcYboiIiMiuMNwQERGRXWG4ISIiIrvCcENERER2heGGiIiI7ArDDREREdkVhhsiIiKyKw5SF2BpoigCAPLz8yWuhIiIiAxV8b1d8T3+MDUu3BQUFAAAAgMDJa6EiIiIjFVQUAAPD4+HbiOIhkQgO6LVanHlyhW4ublBEASTHjs/Px+BgYHIzMyEu7u7SY9N+niuLYfn2nJ4ri2H59pyTHWuRVFEQUEB6tatC5ns4aNqalzLjUwmQ0BAgFlfw93dnf+xWAjPteXwXFsOz7Xl8FxbjinO9aNabCpwQDERERHZFYYbIiIisisMNyakVCoxY8YMKJVKqUuxezzXlsNzbTk815bDc205UpzrGjegmIiIiOwbW26IiIjIrjDcEBERkV1huCEiIiK7wnBDREREdoXhxkhLlixBUFAQnJycEB4ejsOHDz90+40bN6J58+ZwcnJC69atsW3bNgtVavuMOdfLly/H448/jlq1aqFWrVqIjIx85GdDfzP233WF9evXQxAEDBo0yLwF2hFjz/WtW7cwfvx4+Pv7Q6lUomnTpvw7YiBjz/Wnn36KZs2awdnZGYGBgZg8eTLu3LljoWpt16+//ooBAwagbt26EAQBmzdvfuQ++/btQ7t27aBUKtG4cWOsWrXKtEWJZLD169eLCoVCXLlypXj69Gnx1VdfFT09PcWcnJxKt//9999FuVwuzps3T/zrr7/EadOmiY6OjuLJkyctXLntMfZcDx06VFyyZIl47NgxMSUlRRw5cqTo4eEhXr582cKV2x5jz3WF9PR0sV69euLjjz8uDhw40DLF2jhjz3VJSYnYoUMHsW/fvuKBAwfE9PR0cd++fWJycrKFK7c9xp7rNWvWiEqlUlyzZo2Ynp4u7tixQ/T39xcnT55s4cptz7Zt28T33ntP3LRpkwhA/PHHHx+6/YULF0SVSiXGxMSIf/31l/jZZ5+JcrlcjI+PN1lNDDdG6NSpkzh+/HjdY41GI9atW1ecM2dOpds///zzYr9+/fTWhYeHi2PGjDFrnfbA2HP9T+Xl5aKbm5u4evVqc5VoN6pyrsvLy8XOnTuLX331lThixAiGGwMZe66XLl0qBgcHi6WlpZYq0W4Ye67Hjx8v9uzZU29dTEyM2KVLF7PWaW8MCTfvvPOO2KpVK7110dHRYlRUlMnqYLeUgUpLS3H06FFERkbq1slkMkRGRiIxMbHSfRITE/W2B4CoqKgHbk93VeVc/1NRURHKysrg5eVlrjLtQlXP9QcffAAfHx+MHj3aEmXahaqc659//hkREREYP348fH19ERISgtmzZ0Oj0ViqbJtUlXPduXNnHD16VNd1deHCBWzbtg19+/a1SM01iSW+G2vcjTOrKjc3FxqNBr6+vnrrfX19cebMmUr3UavVlW6vVqvNVqc9qMq5/qd3330XdevWve8/INJXlXN94MABrFixAsnJyRao0H5U5VxfuHABe/bswYsvvoht27YhLS0Nr7/+OsrKyjBjxgxLlG2TqnKuhw4ditzcXHTt2hWiKKK8vBxjx47F1KlTLVFyjfKg78b8/HwUFxfD2dm52q/BlhuyO3PnzsX69evx448/wsnJSepy7EpBQQGGDRuG5cuXw9vbW+py7J5Wq4WPjw++/PJLtG/fHtHR0XjvvfewbNkyqUuzO/v27cPs2bPx+eefIykpCZs2bcLWrVsxa9YsqUujKmDLjYG8vb0hl8uRk5Ojtz4nJwd+fn6V7uPn52fU9nRXVc51hfnz52Pu3LnYvXs32rRpY84y7YKx5/r8+fPIyMjAgAEDdOu0Wi0AwMHBAampqWjUqJF5i7ZRVfl37e/vD0dHR8jlct26Fi1aQK1Wo7S0FAqFwqw126qqnOvp06dj2LBheOWVVwAArVu3RmFhIV577TW89957kMnYFmAqD/pudHd3N0mrDcCWG4MpFAq0b98eCQkJunVarRYJCQmIiIiodJ+IiAi97QFg165dD9ye7qrKuQaAefPmYdasWYiPj0eHDh0sUarNM/ZcN2/eHCdPnkRycrJuefrpp9GjRw8kJycjMDDQkuXblKr8u+7SpQvS0tJ0ARIAzp49C39/fwabh6jKuS4qKrovwFSESpG3YDQpi3w3mmxocg2wfv16UalUiqtWrRL/+usv8bXXXhM9PT1FtVotiqIoDhs2TJwyZYpu+99//110cHAQ58+fL6akpIgzZszgpeAGMvZcz507V1QoFOJ///tfMTs7W7cUFBRI9RZshrHn+p94tZThjD3Xly5dEt3c3MQ33nhDTE1NFbds2SL6+PiIH374oVRvwWYYe65nzJghurm5ievWrRMvXLgg7ty5U2zUqJH4/PPPS/UWbEZBQYF47Ngx8dixYyIAccGCBeKxY8fEixcviqIoilOmTBGHDRum277iUvC3335bTElJEZcsWcJLwaX22WefifXr1xcVCoXYqVMn8Y8//tA9161bN3HEiBF623///fdi06ZNRYVCIbZq1UrcunWrhSu2Xcac6wYNGogA7ltmzJhh+cJtkLH/ru/FcGMcY8/1wYMHxfDwcFGpVIrBwcHiv//9b7G8vNzCVdsmY851WVmZ+P7774uNGjUSnZycxMDAQPH1118Xb968afnCbczevXsr/ftbcX5HjBghduvW7b59QkNDRYVCIQYHB4tff/21SWsSRJHtbURERGQ/OOaGiIiI7ArDDREREdkVhhsiIiKyKww3REREZFcYboiIiMiuMNwQERGRXWG4ISIiIrvCcENERER2heGGiCQ3cuRIDBo0yOKvu2rVKgiCAEEQMGnSJIP2GTlypG6fzZs3m7U+Iqoa3hWciMxKEISHPj9jxgwsXLhQspsTuru7IzU1FS4uLgZtv3DhQsydOxf+/v5mroyIqorhhojMKjs7W/f7hg0bEBcXh9TUVN06V1dXuLq6SlEagLvhy8/Pz+DtPTw84OHhYcaKiKi62C1FRGbl5+enWzw8PHRhomJxdXW9r1uqe/fumDBhAiZNmoRatWrB19cXy5cvR2FhIUaNGgU3Nzc0btwY27dv13utU6dOoU+fPnB1dYWvry+GDRuG3Nxco2v+/PPP0aRJEzg5OcHX1xfPPfdcdU8DEVkQww0RWaXVq1fD29sbhw8fxoQJEzBu3Dj83//9Hzp37oykpCT06tULw4YNQ1FREQDg1q1b6NmzJ8LCwnDkyBHEx8cjJycHzz//vFGve+TIEbz55pv44IMPkJqaivj4eDzxxBPmeItEZCbsliIiq9S2bVtMmzYNABAbG4u5c+fC29sbr776KgAgLi4OS5cuxYkTJ/DYY49h8eLFCAsLw+zZs3XHWLlyJQIDA3H27Fk0bdrUoNe9dOkSXFxc0L9/f7i5uaFBgwYICwsz/RskIrNhyw0RWaU2bdrofpfL5ahduzZat26tW+fr6wsAuHr1KgDg+PHj2Lt3r24Mj6urK5o3bw4AOH/+vMGv+9RTT6FBgwYIDg7GsGHDsGbNGl3rEBHZBoYbIrJKjo6Oeo8FQdBbV3EVllarBQDcvn0bAwYMQHJyst5y7tw5o7qV3NzckJSUhHXr1sHf3x9xcXFo27Ytbt26Vf03RUQWwW4pIrIL7dq1ww8//ICgoCA4OFTvT5uDgwMiIyMRGRmJGTNmwNPTE3v27MHgwYNNVC0RmRNbbojILowfPx43btzAkCFD8Oeff+L8+fPYsWMHRo0aBY1GY/BxtmzZgkWLFiE5ORkXL17EN998A61Wi2bNmpmxeiIyJYYbIrILdevWxe+//w6NRoNevXqhdevWmDRpEjw9PSGTGf6nztPTE5s2bULPnj3RokULLFu2DOvWrUOrVq3MWD0RmZIgSjUtKBGRxFatWoVJkyZVaTyNIAj48ccfJbltBBE9HFtuiKhGy8vLg6urK959912Dth87dqykMyoT0aOx5YaIaqyCggLk5OQAuNsd5e3t/ch9rl69ivz8fACAv7+/wfekIiLLYbghIiIiu8JuKSIiIrIrDDdERERkVxhuiIiIyK4w3BAREZFdYbghIiIiu8JwQ0RERHaF4YaIiIjsCsMNERER2ZX/B5xNVSGnTeOEAAAAAElFTkSuQmCC",
      "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": 105,
   "id": "143a2a23-0a62-439f-9d28-9f555ae85589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of counts per bin')"
      ]
     },
     "execution_count": 105,
     "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=120, 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": 106,
   "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": 109,
   "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 = 400, \n",
    "            A_p2 = 700,\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": 110,
   "id": "1e69a046-770f-4c38-9b91-0176bb0686a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbb14f5cdc0>"
      ]
     },
     "execution_count": 110,
     "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": 111,
   "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": 112,
   "id": "d1d06116-d726-4163-b414-6ccde6a19027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 120)"
      ]
     },
     "execution_count": 112,
     "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": 113,
   "id": "f24d19d8-3483-45b5-aee9-1d3f8755da22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True, 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",
       "        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",
       "        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,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True]),\n",
       " array([40.16666667, 40.5       , 40.83333333, 41.16666667, 41.5       ,\n",
       "        41.83333333, 42.16666667, 42.5       , 42.83333333, 43.16666667,\n",
       "        43.5       , 43.83333333, 44.16666667, 44.5       , 44.83333333,\n",
       "        70.16666667, 70.5       , 70.83333333, 71.16666667, 71.5       ,\n",
       "        71.83333333, 72.16666667, 72.5       , 72.83333333, 73.16666667,\n",
       "        73.5       , 73.83333333, 74.16666667, 74.5       , 74.83333333,\n",
       "        75.16666667, 75.5       , 75.83333333, 76.16666667, 76.5       ,\n",
       "        76.83333333, 77.16666667, 77.5       , 77.83333333, 78.16666667,\n",
       "        78.5       , 78.83333333, 79.16666667, 79.5       , 79.83333333]))"
      ]
     },
     "execution_count": 113,
     "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": 114,
   "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": 115,
   "id": "81232354-a7b8-4e2a-9ac0-159ce0a03da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbb14dee3a0>"
      ]
     },
     "execution_count": 115,
     "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": 119,
   "id": "4a93a1c2-17df-46c2-b38e-9a509fe16fc7",
   "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 = 35.17 (χ²/ndof = 0.8) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 77 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 4.56e-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> 400 </td>\n",
       "        <td> 4 </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> 700 </td>\n",
       "        <td> 7 </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> 108 </td>\n",
       "        <td> 12 </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> 39.9 </td>\n",
       "        <td> 3.0 </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> 152 </td>\n",
       "        <td style=\"background-color:rgb(125,125,250);color:black\"> -36 <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\"> -36 <strong>(-0.963)</strong> </td>\n",
       "        <td> 9.16 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 35.17 (χ²/ndof = 0.8)      │              Nfcn = 77               │\n",
       "│ EDM = 4.56e-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     │    400    │     4     │            │            │         │         │  yes  │\n",
       "│ 1 │ A_p2     │    700    │     7     │            │            │         │         │  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    │    108    │    12     │            │            │         │         │       │\n",
       "│ 7 │ tau_bkg  │   39.9    │    3.0    │            │            │    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      152      -36 │\n",
       "│  tau_bkg │        0        0        0        0        0        0      -36     9.16 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 119,
     "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[['tau_bkg', 'A_bkg']] = False\n",
    "mi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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 = 35.17 (χ²/ndof = 0.8) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 91 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 2.29e-12 (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> 400 </td>\n",
       "        <td> 4 </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> 700 </td>\n",
       "        <td> 7 </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> 108 </td>\n",
       "        <td> 12 </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> 39.9 </td>\n",
       "        <td> 3.0 </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> 152 </td>\n",
       "        <td style=\"background-color:rgb(125,125,250);color:black\"> -36 <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\"> -36 <strong>(-0.963)</strong> </td>\n",
       "        <td> 9.15 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 35.17 (χ²/ndof = 0.8)      │              Nfcn = 91               │\n",
       "│ EDM = 2.29e-12 (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     │    400    │     4     │            │            │         │         │  yes  │\n",
       "│ 1 │ A_p2     │    700    │     7     │            │            │         │         │  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    │    108    │    12     │            │            │         │         │       │\n",
       "│ 7 │ tau_bkg  │   39.9    │    3.0    │            │            │    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      152      -36 │\n",
       "│  tau_bkg │        0        0        0        0        0        0      -36     9.15 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 120,
     "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": 121,
   "id": "0b435af3-73ea-42de-9ab7-6a16ae9dbceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbb14a6cd00>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(center, entries, np.sqrt(entries), ls='', marker='.')\n",
    "plt.xlabel('Energy [keV]')\n",
    "plt.ylabel('Number of counts per bin')\n",
    "\n",
    "x = np.arange(40, 80, 0.1)\n",
    "plt.plot(x, fit_model(x, *mi.values), color='k', label='Initial guess')\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6def3e2b-5edf-48bb-99b8-2b7fdaae51c5",
   "metadata": {},
   "source": [
    "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": 122,
   "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": 123,
   "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": 124,
   "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 = 1416 (χ²/ndof = 12.1) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 169 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 3.27e-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> 345 </td>\n",
       "        <td> 6 </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> 700 </td>\n",
       "        <td> 7 </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> 53.47 </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> 2.138 </td>\n",
       "        <td> 0.034 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 5 </th>\n",
       "        <td> sigma_p2 </td>\n",
       "        <td> 2.00 </td>\n",
       "        <td> 0.02 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 6 </th>\n",
       "        <td> A_bkg </td>\n",
       "        <td> 108 </td>\n",
       "        <td> 12 </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> 39.9 </td>\n",
       "        <td> 3.0 </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> 40 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.0064 <strong>(0.024)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(179,179,250);color:black\"> -0.1170 <strong>(-0.544)</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(250,246,246);color:black\"> 0.0064 <strong>(0.024)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td> 0.00171 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,219,219);color:black\"> 0.0003 <strong>(0.207)</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(179,179,250);color:black\"> -0.1170 <strong>(-0.544)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td style=\"background-color:rgb(250,219,219);color:black\"> 0.0003 <strong>(0.207)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.0000 </td>\n",
       "        <td> 0.00116 </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 = 1416 (χ²/ndof = 12.1)      │              Nfcn = 169              │\n",
       "│ EDM = 3.27e-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     │    345    │     6     │            │            │         │         │       │\n",
       "│ 1 │ A_p2     │    700    │     7     │            │            │         │         │  yes  │\n",
       "│ 2 │ mu_p1    │   53.47   │   0.04    │            │            │         │         │       │\n",
       "│ 3 │ mu_p2    │   60.0    │    0.6    │            │            │         │         │  yes  │\n",
       "│ 4 │ sigma_p1 │   2.138   │   0.034   │            │            │         │         │       │\n",
       "│ 5 │ sigma_p2 │   2.00    │   0.02    │            │            │         │         │  yes  │\n",
       "│ 6 │ A_bkg    │    108    │    12     │            │            │         │         │  yes  │\n",
       "│ 7 │ tau_bkg  │   39.9    │    3.0    │            │            │    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 │       40        0   0.0064        0  -0.1170        0        0        0 │\n",
       "│     A_p2 │        0        0   0.0000        0   0.0000        0        0        0 │\n",
       "│    mu_p1 │   0.0064   0.0000  0.00171   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 │  -0.1170   0.0000   0.0003   0.0000  0.00116   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": 124,
     "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": 125,
   "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 = 133.6 (χ²/ndof = 1.1) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 232 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 3.64e-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> 345 </td>\n",
       "        <td> 6 </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> 572 </td>\n",
       "        <td> 7 </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> 53.47 </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> 60.652 </td>\n",
       "        <td> 0.032 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 4 </th>\n",
       "        <td> sigma_p1 </td>\n",
       "        <td> 2.138 </td>\n",
       "        <td> 0.034 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 5 </th>\n",
       "        <td> sigma_p2 </td>\n",
       "        <td> 2.710 </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> 108 </td>\n",
       "        <td> 12 </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> 39.9 </td>\n",
       "        <td> 3.0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td> yes </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> A_p1 </th>\n",
       "        <th> A_p2 </th>\n",
       "        <th> mu_p1 </th>\n",
       "        <th> mu_p2 </th>\n",
       "        <th> sigma_p1 </th>\n",
       "        <th> sigma_p2 </th>\n",
       "        <th> A_bkg </th>\n",
       "        <th> tau_bkg </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p1 </th>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 50 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.0065 <strong>(0.029)</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\"> -112.0e-3 <strong>(-0.586)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,246,246);color:black\"> 0.0065 <strong>(0.029)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td> 0.00103 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(222,222,250);color:black\"> -0.2e-3 <strong>(-0.217)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(174,174,250);color:black\"> -112.0e-3 <strong>(-0.586)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(222,222,250);color:black\"> -0.2e-3 <strong>(-0.217)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0.00073 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> tau_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.000 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0 </td>\n",
       "        <td> 0 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 133.6 (χ²/ndof = 1.1)      │              Nfcn = 232              │\n",
       "│ EDM = 3.64e-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     │    345    │     6     │            │            │         │         │  yes  │\n",
       "│ 1 │ A_p2     │    572    │     7     │            │            │         │         │       │\n",
       "│ 2 │ mu_p1    │   53.47   │   0.04    │            │            │         │         │  yes  │\n",
       "│ 3 │ mu_p2    │  60.652   │   0.032   │            │            │         │         │       │\n",
       "│ 4 │ sigma_p1 │   2.138   │   0.034   │            │            │         │         │  yes  │\n",
       "│ 5 │ sigma_p2 │   2.710   │   0.027   │            │            │         │         │       │\n",
       "│ 6 │ A_bkg    │    108    │    12     │            │            │         │         │  yes  │\n",
       "│ 7 │ tau_bkg  │   39.9    │    3.0    │            │            │    0    │         │  yes  │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬─────────────────────────────────────────────────────────────────────────────────┐\n",
       "│          │      A_p1      A_p2     mu_p1     mu_p2  sigma_p1  sigma_p2     A_bkg   tau_bkg │\n",
       "├──────────┼─────────────────────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │         0         0         0     0.000         0         0         0         0 │\n",
       "│     A_p2 │         0        50         0    0.0065         0 -112.0e-3         0         0 │\n",
       "│    mu_p1 │         0         0         0     0.000         0         0         0         0 │\n",
       "│    mu_p2 │     0.000    0.0065     0.000   0.00103     0.000   -0.2e-3     0.000     0.000 │\n",
       "│ sigma_p1 │         0         0         0     0.000         0         0         0         0 │\n",
       "│ sigma_p2 │         0 -112.0e-3         0   -0.2e-3         0   0.00073         0         0 │\n",
       "│    A_bkg │         0         0         0     0.000         0         0         0         0 │\n",
       "│  tau_bkg │         0         0         0     0.000         0         0         0         0 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 125,
     "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": 126,
   "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 = 110.2 (χ²/ndof = 1.0) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 490 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 7.62e-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> 314 </td>\n",
       "        <td> 7 </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> 568 </td>\n",
       "        <td> 7 </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> 53.27 </td>\n",
       "        <td> 0.08 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> mu_p2 </td>\n",
       "        <td> 60.53 </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> 2.13 </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> 2.81 </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> 109 </td>\n",
       "        <td> 12 </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> 39.7 </td>\n",
       "        <td> 2.8 </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> 48.4 </td>\n",
       "        <td style=\"background-color:rgb(250,228,228);color:black\"> 10 <strong>(0.147)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,212,212);color:black\"> 0.138 <strong>(0.254)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,203,203);color:black\"> 0.1230 <strong>(0.316)</strong> </td>\n",
       "        <td style=\"background-color:rgb(231,231,250);color:black\"> -0.0575 <strong>(-0.144)</strong> </td>\n",
       "        <td style=\"background-color:rgb(202,202,250);color:black\"> -0.1117 <strong>(-0.373)</strong> </td>\n",
       "        <td style=\"background-color:rgb(245,245,250);color:black\"> -0 <strong>(-0.039)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,244,244);color:black\"> 1 <strong>(0.040)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,228,228);color:black\"> 10 <strong>(0.147)</strong> </td>\n",
       "        <td> 49.5 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.001 </td>\n",
       "        <td style=\"background-color:rgb(250,241,241);color:black\"> 0.0227 <strong>(0.058)</strong> </td>\n",
       "        <td style=\"background-color:rgb(242,242,250);color:black\"> -0.0262 <strong>(-0.065)</strong> </td>\n",
       "        <td style=\"background-color:rgb(197,197,250);color:black\"> -0.1235 <strong>(-0.407)</strong> </td>\n",
       "        <td style=\"background-color:rgb(247,247,250);color:black\"> -0 <strong>(-0.020)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0 <strong>(0.018)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,212,212);color:black\"> 0.138 <strong>(0.254)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.001 </td>\n",
       "        <td> 0.00612 </td>\n",
       "        <td style=\"background-color:rgb(250,138,138);color:black\"> 0.0033 <strong>(0.749)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,147,147);color:black\"> 0.0031 <strong>(0.684)</strong> </td>\n",
       "        <td style=\"background-color:rgb(161,161,250);color:black\"> -0.0023 <strong>(-0.684)</strong> </td>\n",
       "        <td style=\"background-color:rgb(242,242,250);color:black\"> -0.057 <strong>(-0.064)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,238,238);color:black\"> 0.018 <strong>(0.080)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,203,203);color:black\"> 0.1230 <strong>(0.316)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,241,241);color:black\"> 0.0227 <strong>(0.058)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,138,138);color:black\"> 0.0033 <strong>(0.749)</strong> </td>\n",
       "        <td> 0.00313 </td>\n",
       "        <td style=\"background-color:rgb(250,153,153);color:black\"> 0.0021 <strong>(0.646)</strong> </td>\n",
       "        <td style=\"background-color:rgb(159,159,250);color:black\"> -0.0017 <strong>(-0.696)</strong> </td>\n",
       "        <td style=\"background-color:rgb(239,239,250);color:black\"> -0.0529 <strong>(-0.082)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,239,239);color:black\"> 0.0118 <strong>(0.074)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(231,231,250);color:black\"> -0.0575 <strong>(-0.144)</strong> </td>\n",
       "        <td style=\"background-color:rgb(242,242,250);color:black\"> -0.0262 <strong>(-0.065)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,147,147);color:black\"> 0.0031 <strong>(0.684)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,153,153);color:black\"> 0.0021 <strong>(0.646)</strong> </td>\n",
       "        <td> 0.00331 </td>\n",
       "        <td style=\"background-color:rgb(181,181,250);color:black\"> -0.0013 <strong>(-0.528)</strong> </td>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.1336 <strong>(-0.203)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,225,225);color:black\"> 0.0273 <strong>(0.167)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(202,202,250);color:black\"> -0.1117 <strong>(-0.373)</strong> </td>\n",
       "        <td style=\"background-color:rgb(197,197,250);color:black\"> -0.1235 <strong>(-0.407)</strong> </td>\n",
       "        <td style=\"background-color:rgb(161,161,250);color:black\"> -0.0023 <strong>(-0.684)</strong> </td>\n",
       "        <td style=\"background-color:rgb(159,159,250);color:black\"> -0.0017 <strong>(-0.696)</strong> </td>\n",
       "        <td style=\"background-color:rgb(181,181,250);color:black\"> -0.0013 <strong>(-0.528)</strong> </td>\n",
       "        <td> 0.00186 </td>\n",
       "        <td style=\"background-color:rgb(250,229,229);color:black\"> 0.0695 <strong>(0.141)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.0218 <strong>(-0.177)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_bkg </th>\n",
       "        <td style=\"background-color:rgb(245,245,250);color:black\"> -0 <strong>(-0.039)</strong> </td>\n",
       "        <td style=\"background-color:rgb(247,247,250);color:black\"> -0 <strong>(-0.020)</strong> </td>\n",
       "        <td style=\"background-color:rgb(242,242,250);color:black\"> -0.057 <strong>(-0.064)</strong> </td>\n",
       "        <td style=\"background-color:rgb(239,239,250);color:black\"> -0.0529 <strong>(-0.082)</strong> </td>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.1336 <strong>(-0.203)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,229,229);color:black\"> 0.0695 <strong>(0.141)</strong> </td>\n",
       "        <td> 131 </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -32 <strong>(-0.966)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> tau_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,244,244);color:black\"> 1 <strong>(0.040)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0 <strong>(0.018)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,238,238);color:black\"> 0.018 <strong>(0.080)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,239,239);color:black\"> 0.0118 <strong>(0.074)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,225,225);color:black\"> 0.0273 <strong>(0.167)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.0218 <strong>(-0.177)</strong> </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -32 <strong>(-0.966)</strong> </td>\n",
       "        <td> 8.11 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 110.2 (χ²/ndof = 1.0)      │              Nfcn = 490              │\n",
       "│ EDM = 7.62e-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     │    314    │     7     │            │            │         │         │       │\n",
       "│ 1 │ A_p2     │    568    │     7     │            │            │         │         │       │\n",
       "│ 2 │ mu_p1    │   53.27   │   0.08    │            │            │         │         │       │\n",
       "│ 3 │ mu_p2    │   60.53   │   0.06    │            │            │         │         │       │\n",
       "│ 4 │ sigma_p1 │   2.13    │   0.06    │            │            │         │         │       │\n",
       "│ 5 │ sigma_p2 │   2.81    │   0.04    │            │            │         │         │       │\n",
       "│ 6 │ A_bkg    │    109    │    12     │            │            │         │         │       │\n",
       "│ 7 │ tau_bkg  │   39.7    │    2.8    │            │            │    0    │         │       │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬─────────────────────────────────────────────────────────────────────────┐\n",
       "│          │     A_p1     A_p2    mu_p1    mu_p2 sigma_p1 sigma_p2    A_bkg  tau_bkg │\n",
       "├──────────┼─────────────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │     48.4       10    0.138   0.1230  -0.0575  -0.1117       -0        1 │\n",
       "│     A_p2 │       10     49.5    0.001   0.0227  -0.0262  -0.1235       -0        0 │\n",
       "│    mu_p1 │    0.138    0.001  0.00612   0.0033   0.0031  -0.0023   -0.057    0.018 │\n",
       "│    mu_p2 │   0.1230   0.0227   0.0033  0.00313   0.0021  -0.0017  -0.0529   0.0118 │\n",
       "│ sigma_p1 │  -0.0575  -0.0262   0.0031   0.0021  0.00331  -0.0013  -0.1336   0.0273 │\n",
       "│ sigma_p2 │  -0.1117  -0.1235  -0.0023  -0.0017  -0.0013  0.00186   0.0695  -0.0218 │\n",
       "│    A_bkg │       -0       -0   -0.057  -0.0529  -0.1336   0.0695      131      -32 │\n",
       "│  tau_bkg │        1        0    0.018   0.0118   0.0273  -0.0218      -32     8.11 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 126,
     "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": 127,
   "id": "067fbf6f-14c4-4a46-afb3-71753d06af23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbb14cdd190>"
      ]
     },
     "execution_count": 127,
     "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": 128,
   "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 = 110.2 (χ²/ndof = 1.0) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 536 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 3.81e-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> 314 </td>\n",
       "        <td> 7 </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> 568 </td>\n",
       "        <td> 7 </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> 53.27 </td>\n",
       "        <td> 0.08 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> mu_p2 </td>\n",
       "        <td> 60.53 </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> 2.13 </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> 2.81 </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> 109 </td>\n",
       "        <td> 12 </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> 39.7 </td>\n",
       "        <td> 2.8 </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> 48.4 </td>\n",
       "        <td style=\"background-color:rgb(250,228,228);color:black\"> 10 <strong>(0.147)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,212,212);color:black\"> 0.138 <strong>(0.254)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,203,203);color:black\"> 0.1230 <strong>(0.316)</strong> </td>\n",
       "        <td style=\"background-color:rgb(231,231,250);color:black\"> -0.0575 <strong>(-0.144)</strong> </td>\n",
       "        <td style=\"background-color:rgb(202,202,250);color:black\"> -0.1117 <strong>(-0.373)</strong> </td>\n",
       "        <td style=\"background-color:rgb(245,245,250);color:black\"> -0 <strong>(-0.039)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,244,244);color:black\"> 1 <strong>(0.040)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,228,228);color:black\"> 10 <strong>(0.147)</strong> </td>\n",
       "        <td> 49.5 </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.001 </td>\n",
       "        <td style=\"background-color:rgb(250,241,241);color:black\"> 0.0227 <strong>(0.058)</strong> </td>\n",
       "        <td style=\"background-color:rgb(242,242,250);color:black\"> -0.0262 <strong>(-0.065)</strong> </td>\n",
       "        <td style=\"background-color:rgb(197,197,250);color:black\"> -0.1235 <strong>(-0.407)</strong> </td>\n",
       "        <td style=\"background-color:rgb(247,247,250);color:black\"> -0 <strong>(-0.020)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0 <strong>(0.018)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p1 </th>\n",
       "        <td style=\"background-color:rgb(250,212,212);color:black\"> 0.138 <strong>(0.254)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,250,250);color:black\"> 0.001 </td>\n",
       "        <td> 0.00611 </td>\n",
       "        <td style=\"background-color:rgb(250,138,138);color:black\"> 0.0033 <strong>(0.749)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,147,147);color:black\"> 0.0031 <strong>(0.684)</strong> </td>\n",
       "        <td style=\"background-color:rgb(161,161,250);color:black\"> -0.0023 <strong>(-0.684)</strong> </td>\n",
       "        <td style=\"background-color:rgb(242,242,250);color:black\"> -0.057 <strong>(-0.064)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,238,238);color:black\"> 0.018 <strong>(0.080)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu_p2 </th>\n",
       "        <td style=\"background-color:rgb(250,203,203);color:black\"> 0.1230 <strong>(0.316)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,241,241);color:black\"> 0.0227 <strong>(0.058)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,138,138);color:black\"> 0.0033 <strong>(0.749)</strong> </td>\n",
       "        <td> 0.00313 </td>\n",
       "        <td style=\"background-color:rgb(250,153,153);color:black\"> 0.0021 <strong>(0.646)</strong> </td>\n",
       "        <td style=\"background-color:rgb(159,159,250);color:black\"> -0.0017 <strong>(-0.696)</strong> </td>\n",
       "        <td style=\"background-color:rgb(239,239,250);color:black\"> -0.0529 <strong>(-0.082)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,239,239);color:black\"> 0.0118 <strong>(0.074)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p1 </th>\n",
       "        <td style=\"background-color:rgb(231,231,250);color:black\"> -0.0575 <strong>(-0.144)</strong> </td>\n",
       "        <td style=\"background-color:rgb(242,242,250);color:black\"> -0.0262 <strong>(-0.065)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,147,147);color:black\"> 0.0031 <strong>(0.684)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,153,153);color:black\"> 0.0021 <strong>(0.646)</strong> </td>\n",
       "        <td> 0.00331 </td>\n",
       "        <td style=\"background-color:rgb(181,181,250);color:black\"> -0.0013 <strong>(-0.528)</strong> </td>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.1338 <strong>(-0.203)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,225,225);color:black\"> 0.0273 <strong>(0.167)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma_p2 </th>\n",
       "        <td style=\"background-color:rgb(202,202,250);color:black\"> -0.1117 <strong>(-0.373)</strong> </td>\n",
       "        <td style=\"background-color:rgb(197,197,250);color:black\"> -0.1235 <strong>(-0.407)</strong> </td>\n",
       "        <td style=\"background-color:rgb(161,161,250);color:black\"> -0.0023 <strong>(-0.684)</strong> </td>\n",
       "        <td style=\"background-color:rgb(159,159,250);color:black\"> -0.0017 <strong>(-0.696)</strong> </td>\n",
       "        <td style=\"background-color:rgb(181,181,250);color:black\"> -0.0013 <strong>(-0.528)</strong> </td>\n",
       "        <td> 0.00186 </td>\n",
       "        <td style=\"background-color:rgb(250,229,229);color:black\"> 0.0696 <strong>(0.141)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.0218 <strong>(-0.178)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A_bkg </th>\n",
       "        <td style=\"background-color:rgb(245,245,250);color:black\"> -0 <strong>(-0.039)</strong> </td>\n",
       "        <td style=\"background-color:rgb(247,247,250);color:black\"> -0 <strong>(-0.020)</strong> </td>\n",
       "        <td style=\"background-color:rgb(242,242,250);color:black\"> -0.057 <strong>(-0.064)</strong> </td>\n",
       "        <td style=\"background-color:rgb(239,239,250);color:black\"> -0.0529 <strong>(-0.082)</strong> </td>\n",
       "        <td style=\"background-color:rgb(224,224,250);color:black\"> -0.1338 <strong>(-0.203)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,229,229);color:black\"> 0.0696 <strong>(0.141)</strong> </td>\n",
       "        <td> 131 </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -32 <strong>(-0.966)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> tau_bkg </th>\n",
       "        <td style=\"background-color:rgb(250,244,244);color:black\"> 1 <strong>(0.040)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,247,247);color:black\"> 0 <strong>(0.018)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,238,238);color:black\"> 0.018 <strong>(0.080)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,239,239);color:black\"> 0.0118 <strong>(0.074)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,225,225);color:black\"> 0.0273 <strong>(0.167)</strong> </td>\n",
       "        <td style=\"background-color:rgb(227,227,250);color:black\"> -0.0218 <strong>(-0.178)</strong> </td>\n",
       "        <td style=\"background-color:rgb(124,124,250);color:black\"> -32 <strong>(-0.966)</strong> </td>\n",
       "        <td> 8.12 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 110.2 (χ²/ndof = 1.0)      │              Nfcn = 536              │\n",
       "│ EDM = 3.81e-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     │    314    │     7     │            │            │         │         │       │\n",
       "│ 1 │ A_p2     │    568    │     7     │            │            │         │         │       │\n",
       "│ 2 │ mu_p1    │   53.27   │   0.08    │            │            │         │         │       │\n",
       "│ 3 │ mu_p2    │   60.53   │   0.06    │            │            │         │         │       │\n",
       "│ 4 │ sigma_p1 │   2.13    │   0.06    │            │            │         │         │       │\n",
       "│ 5 │ sigma_p2 │   2.81    │   0.04    │            │            │         │         │       │\n",
       "│ 6 │ A_bkg    │    109    │    12     │            │            │         │         │       │\n",
       "│ 7 │ tau_bkg  │   39.7    │    2.8    │            │            │    0    │         │       │\n",
       "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬─────────────────────────────────────────────────────────────────────────┐\n",
       "│          │     A_p1     A_p2    mu_p1    mu_p2 sigma_p1 sigma_p2    A_bkg  tau_bkg │\n",
       "├──────────┼─────────────────────────────────────────────────────────────────────────┤\n",
       "│     A_p1 │     48.4       10    0.138   0.1230  -0.0575  -0.1117       -0        1 │\n",
       "│     A_p2 │       10     49.5    0.001   0.0227  -0.0262  -0.1235       -0        0 │\n",
       "│    mu_p1 │    0.138    0.001  0.00611   0.0033   0.0031  -0.0023   -0.057    0.018 │\n",
       "│    mu_p2 │   0.1230   0.0227   0.0033  0.00313   0.0021  -0.0017  -0.0529   0.0118 │\n",
       "│ sigma_p1 │  -0.0575  -0.0262   0.0031   0.0021  0.00331  -0.0013  -0.1338   0.0273 │\n",
       "│ sigma_p2 │  -0.1117  -0.1235  -0.0023  -0.0017  -0.0013  0.00186   0.0696  -0.0218 │\n",
       "│    A_bkg │       -0       -0   -0.057  -0.0529  -0.1338   0.0696      131      -32 │\n",
       "│  tau_bkg │        1        0    0.018   0.0118   0.0273  -0.0218      -32     8.12 │\n",
       "└──────────┴─────────────────────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 128,
     "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": 129,
   "id": "30cafddc-ea17-4158-82cc-f132dee2c8de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Residuals [$\\\\sigma$]')"
      ]
     },
     "execution_count": 129,
     "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": 130,
   "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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",
      "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 Residuen in ein Histogramm eintragen. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "05e24224-66f7-45ed-99c6-f6d257e2c779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(residuals, bins=10, range=(-3, 3), histtype='step')\n",
    "plt.xlabel('Residual [$\\sigma$]')\n",
    "plt.ylabel('#Entries per bin')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24ce04cc-5234-4326-9a28-7624b9c7d23e",
   "metadata": {},
   "source": [
    "Bzw. den Anteil an Residuen berechnen welcher innerhalb der 1 $\\sigma$ Umgebeung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "39009321-41f4-49f4-820a-717be277b1b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.675"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.abs(residuals) < 1)/len(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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",
      "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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",
      "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
}