From ea22e1b7f6aedccdc7d503dbcb42efa8b2e3c95b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Christian=20Paul=20Alexandre=20Reisner-S=C3=A9n=C3=A9lar?=
 <uqnwo@student.kit.edu>
Date: Thu, 16 Jan 2025 12:29:09 +0000
Subject: [PATCH] Delete Lichtgeschwindigkeit12__2_.ipynb

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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "20220dbb-496c-405d-8e0a-ba9a0bb234a7",
-   "metadata": {},
-   "source": [
-    "# FakultÃ¤t fÃ¼r Physik\n",
-    "\n",
-    "## Physikalisches Praktikum P1 fÃ¼r Studierende der Physik\n",
-    "\n",
-    "Versuch P1-131, 132, 133 (Stand: **Oktober 2024**)\n",
-    "\n",
-    "[Raum F1-12](https://labs.physik.kit.edu/img/Klassische-Praktika/Lageplan_P1P2.png)\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Lichtgeschwindigkeit"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "51a56dd6-c2bd-4819-851e-a259ac4757a0",
-   "metadata": {},
-   "source": [
-    "Name: __________________ Vorname: __________________ E-Mail: __________________\n",
-    "\n",
-    "\\begin{equation*}\n",
-    "\\begin{split}\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "\\end{split}\n",
-    "\\end{equation*}\n",
-    "\n",
-    "Name: __________________ Vorname: __________________ E-Mail: __________________\n",
-    "\n",
-    "\\begin{equation*}\n",
-    "\\begin{split}\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "\\end{split}\n",
-    "\\end{equation*}\n",
-    "\n",
-    "Gruppennummer: _____\n",
-    "\n",
-    "\\begin{equation*}\n",
-    "\\begin{split}\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "\\end{split}\n",
-    "\\end{equation*}\n",
-    "\n",
-    "\n",
-    "Betreuer: __________________\n",
-    "\n",
-    "\\begin{equation*}\n",
-    "\\begin{split}\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "\\end{split}\n",
-    "\\end{equation*}\n",
-    "\n",
-    "Versuch durchgefÃ¼hrt am: __________________"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b24689e-ccde-4d78-aebb-6eeec7e3367b",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "\n",
-    "**Beanstandungen zu Protokoll Version _____:**\n",
-    "\n",
-    "\\begin{equation*}\n",
-    "\\begin{split}\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "\\end{split}\n",
-    "%\\text{\\vspace{10cm}}\n",
-    "\\end{equation*}\n",
-    "\n",
-    "<br>\n",
-    "Testiert am: __________________ Testat: __________________"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "207064c6-e442-4b5d-a573-685125373524",
-   "metadata": {},
-   "source": [
-    "# DurchfÃ¼hrung"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a45acfd-c891-4451-a7b5-344476a55b05",
-   "metadata": {},
-   "source": [
-    "**Detaillierte Hinweise zur DurchfÃ¼hrung der Versuche finden Sie in der Datei [Lichtgeschwindigkeit_Hinweise.ipynb](https://gitlab.kit.edu/kit/etp-lehre/p1-praktikum/students/-/blob/main/Lichtgeschwindigkeit/Lichtgeschwindigkeit_Hinweise.ipynb)**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1e99f377-b6ad-41f8-9120-146819c67268",
-   "metadata": {},
-   "source": [
-    "## Aufgabe 1: Drehspiegelmethode"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "3b98a04b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: uncertainties in /opt/conda/lib/python3.11/site-packages (3.2.2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install uncertainties\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import PhyPraKit as PPK\n",
-    "import kafe2\n",
-    "from uncertainties import ufloat\n",
-    "\n",
-    "def n(a):\n",
-    "    return np.array([x.n for x in a])\n",
-    "def s(a):\n",
-    "    return np.array([x.s for x in a])\n",
-    "def utl(u):\n",
-    "    return [f\"${x.nominal_value:.4f} \\\\pm {x.std_dev:.4f}$\" for x in u]\n",
-    "def utl2(u):\n",
-    "    return [f\"${x.nominal_value:.0f} \\\\pm {x.std_dev:.0f}$\" for x in u]\n",
-    "def pull(a,b):\n",
-    "    return (((a.n-b.n)/(a.s**2+b.s**2)**0.5)**2)**0.5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7352bc5e-0ea8-407d-8ba2-784c7015aef7",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "### Aufgabe 1.1: Vorbereitung\n",
-    "\n",
-    "Diskutieren Sie zur Vorbereitung auf diesen Versuch **das Messprinzip, den Aufbau und die Eigenschaften des Strahlengangs des verwendeten Lasers**, basierend auf den Angaben zum Versuchsaufbau. \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e0497cd-a22f-4d72-b3a4-35ebe36804f1",
-   "metadata": {},
-   "source": [
-    "**V E R S U C H S B E S C H R E I B U N G**\n",
-    "\n",
-    "*FÃ¼gen Sie Ihre Versuchsbeschreibung hier ein. LÃ¶schen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d010b23-cc16-4fac-a86c-1b2f7e140f65",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "### Aufgabe 1.2: Justierung der Apparatur\n",
-    "\n",
-    "ÃœberprÃ¼fen Sie mit Hilfe Ihres:r Tutor:in die **Justierung des Strahlengangs** und protokollieren Sie die Aufgaben aller relevanten Abschnitte des Strahlengangs. \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "37dcde6e-535a-423d-a08c-35f5054a5a51",
-   "metadata": {},
-   "source": [
-    "**V E R S U C H S B E S C H R E I B U N G**\n",
-    "\n",
-    "*FÃ¼gen Sie Ihre Versuchsbeschreibung hier ein. LÃ¶schen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "378a6f57-58fb-49c3-ad6a-7cf987cefcdc",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "### Aufgabe 1.3: Messung\n",
-    "\n",
-    " * **Kalibrieren Sie den FrequenzzÃ¤hler** zur Messung der Rotationsfrequenz $\\nu$ des Drehspiegels mit Hilfe einer Stimmgabel fÃ¼r den Kammerton A bei $440\\,\\mathrm{Hz}$.\n",
-    " * Tragen Sie eine geeignete Anzahl an Messpunkten fÃ¼r den **Versatz $s$ des beobachteten Lichtpunkts fÃ¼r verschiedene Rotationsfrequenzen $\\nu$ des Drehspiegels** auf und bestimmen Sie daraus $c$.\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "db084945-729f-4175-9c15-c00f5d99714d",
-   "metadata": {},
-   "source": [
-    "**V E R S U C H S B E S C H R E I B U N G**\n",
-    "\n",
-    "*FÃ¼gen Sie Ihre Versuchsbeschreibung hier ein. LÃ¶schen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "95ff7125-6903-4de3-9bef-ba18f152ee89",
-   "metadata": {},
-   "source": [
-    "**L Ã– S U N G**\n",
-    "\n",
-    "*FÃ¼gen Sie numerische Berechnungen zur LÃ¶sung dieser Aufgabe hier ein. LÃ¶schen Sie hierzu diesen kursiv gestellten Text aus dem Dokument. Um Code-Fragmente und Skripte in [Python](https://www.python.org/), sowie ggf. bildliche Darstellungen direkt ins [Jupyter notebook](https://jupyter.org/) einzubinden verwandeln Sie diese Zelle in eine Code-Zelle. FÃ¼gen Sie ggf. weitere Code-Zellen zu.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3d60c312",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f=np.array([3070,4600,5620,6870,8450,9520,11000,12550,14750,16900,19200])*60\n",
-    "s=np.array([4.5,4.75,4.875,5,5.125,5.25,5.5,5.75,6,6.25,6.5])\n",
-    "26310 #frequenz wo ton umherlallt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2da372c7-d225-43e8-961f-a554e2f25f99",
-   "metadata": {},
-   "source": [
-    "**D I S K U S S I O N**\n",
-    "\n",
-    "*FÃ¼gen Sie eine abschlieÃŸende Diskussion und Bewertung Ihrer LÃ¶sung hier ein. LÃ¶schen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c11608de-cb00-42ef-b692-40cca3b02be1",
-   "metadata": {},
-   "source": [
-    "## Aufgabe 2: Phasenvergleichsmethode"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2528e102-32dd-4e3a-b7a4-816d048c0c10",
-   "metadata": {},
-   "source": [
-    "### Aufgabe 2.1: Vorbereitung\n",
-    "\n",
-    "Diskutieren Sie zur Vorbereitung auf diesen Versuch **das Prinzip und den Aufbau der Messung**, basierend auf den Angaben zum Versuchsaufbau. \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "29eb98b7-48f2-4a94-aa4c-704d0084205d",
-   "metadata": {},
-   "source": [
-    "**V E R S U C H S B E S C H R E I B U N G**\n",
-    "\n",
-    "Die folgende Messung beruht auf dem Prinzip, dass bei einer im Raum propagierenden Welle beim Vergleich von zwei Punkten im Raum ein Phasenunterschied messbar ist. \n",
-    "Es wird ein roter Laser mit $\\lambda \\approx 700\\,\\mathrm{nm}$, welcher auf einer Messschiene montiert ist, mithilfe einer dÃ¼nne Sammellinse ($f=0.2 m$) auf eine Photodiode fokossiert.\n",
-    "Dabei wurde der Laser mit einer Amplitudenmodulation von $cos(\\omega_1\\,t+ \\varphi)$ betrieben und mit einem Signal $cos(\\omega_2\\,t)$ multiplkativ Ã¼berlagert. Die ist Ã¼ber eine im NetzgerÃ¤t verbaute Mischfunktion mÃ¶glich. Es gilt:   \n",
-    "$cos(\\omega_1\\,t+ \\varphi)\\cdot cos(\\omega_2\\,t)=\\dfrac{1}{2}(cos((\\omega_1+\\omega_2)\\,t+ \\varphi)+cos((\\omega_1-\\omega_2)\\,t+ \\varphi))$   \n",
-    "Dabei wird er hochfrequente Terme Ã¼ber einen Tiefpass gefiltert und nur der niederigfrequente Term proportional zu $cos((\\omega_1-\\omega_2)\\,t+ \\varphi) $ betrachtet. \n",
-    "\n",
-    "\n",
-    "\n",
-    "Zwischen dem Signal am Laser und dem an der Photodiode, welche beide als Spannungen gemessen weurden, kann nun Phasenversatz $\\Delta \\varphi=(\\omega_1-\\omega_2)\\cdot \\Delta t$ beobachtet werden. Dabei ist $\\Delta t$ die Zeitfiferenz zwischen den Maxima bei der Signale und gleichzeitig die Laufzeit des Lichtes auf der Strecke.    \n",
-    "\n",
-    "Ist dabei $\\ell$ der Abstand zwischen Photdiode und Laser, welche mithilfe eines Laserentfernungsmessers mit der Genauigkeit $\\pm 1.5 \\,\\mathrm{mm}$ gemessen wurde, so gilt $\\ell=\\Delta t\\cdot c$. \n",
-    "\n",
-    "Zu beachten ist hierbei aber, dass $\\Delta t$ hier aufgrund der Ampitudenmodulation um den Faktor $\\dfrac{\\omega_1}{\\omega_1-\\omega_2}=g$ gestreckt sodass gilt:   \n",
-    "$\\ell=\\dfrac{c\\cdot \\Delta t}{g}$ \n",
-    "\n",
-    "Es wurde dabei $\\omega_1=2 \\pi \\cdot 60m\\,\\mathrm{MHz}$ und $\\omega_2=2 \\pi \\cdot 59.9 \\,\\mathrm{MHz}$ verwendet.\n",
-    "\n",
-    "Die Betrachte Frquenz konnte inerhalb der Unsicherheiten der Messung als $f=100\\,\\mathrm{kHz}$ experimentel veriviziert werden.\n",
-    "\n",
-    "\n",
-    "\n",
-    "Nun wird die AbgeschÃ¤zt welche Voraussetzungen notwendig sind um $\\Delta \\varphi=0.2 \\,\\pi$ aufzulÃ¶sen. Es gilt:\n",
-    " \n",
-    "$\\Delta \\varphi \\cdot \\dfrac{c}{\\ell}=\\omega  \\Rightarrow  f= 0.1 \\cdot \\dfrac{c}{\\ell} < 1.5 \\cdot 10^{7}$ also brauchen wir ein Frequenz von  $f=   15 MHz$   \n",
-    "\n",
-    "Es gilt dabei fÃ¼r die dafÃ¼r nÃ¶tige AuflÃ¶sung $u$ am Osziloskop um diese Differenz mit mindestens 5 mm darzustellen \n",
-    "\n",
-    "\n",
-    "$u=T \\cdot0.1=\\dfrac{1}{f}\\cdot0.1 / 5 \\,\\mathrm {mm}= 0.00666\\,\\mu \\mathrm{s} /5\\,\\mathrm{mm}=6.66 \\,\\mathrm{ns} /5\\,\\mathrm{mm}  $\n",
-    "\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "09b67329-05f0-4dce-9068-985e2dace621",
-   "metadata": {},
-   "source": [
-    "### Aufgabe 2.2: Justierung der Apparatur\n",
-    "\n",
-    "ÃœberprÃ¼fen Sie mit Hilfe Ihres:r Tutor:in **den Aufbau des Versuchs** einschlieÃŸlich Verkabelung. \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a2f8df8-b55f-4ef3-ab6f-14468c5b3978",
-   "metadata": {},
-   "source": [
-    "**V E R S U C H S B E S C H R E I B U N G**\n",
-    "\n",
-    "Es wurde die Zeiten gemessen an den die Ampitde der Spannung maximal war um die Frequenz zu verifizieren. Es ergibt sich $f=100862 \\pm 257 \\,\\mathrm{Hz}\\approx \\omega_1-\\omega_2=100\\,\\mathrm{kHz}$.   \n",
-    "Auzch der restlich Strahlgang wurde betrachtet und kontrolliert ob der Laser durch die Linse die Photodiode strahlt also ob gemessen werden kann. Besonderheiten fiehlen bei der Justierung nicht auf. \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5b5d694b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "f=100862.86582427546+-257.16757083689004\n",
-      "[2.552e-06 1.246e-05 2.237e-05 3.232e-05 4.221e-05]\n"
-     ]
-    }
-   ],
-   "source": [
-    "T=np.array([2.552,12.46,22.37,32.32,42.21])*10**(-6)#in s\n",
-    "DT=np.array([T[1]-T[0],T[2]-T[1],T[3]-T[2],T[4]-T[3]])\n",
-    "f=1/DT\n",
-    "f1=np.mean(f)\n",
-    "delf=np.std(f,ddof=1)\n",
-    "print(f\"f={f1}+-{delf}\")\n",
-    "print(T)\n",
-    "l=[1.204,1.115,0.181,1.298,1.561,1.836]#in m+- wenig\n",
-    "dt=[741.5,585.0,-37.41,928.6,1405,1963]#in ns +- mehr\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0249cf23-8976-443d-a87f-8bd6befe3168",
-   "metadata": {},
-   "source": [
-    "### Aufgabe 2.3: Messung\n",
-    "\n",
-    " * Bestimmen Sie $c$ **mit Hilfe der am Oszilloskop ausgemessenen Phasenverschiebung** zwischen Sender und EmpfÃ¤nger eines Testsignals entlang variierender vorgegebener AbstÃ¤nde $\\ell_{i}$ zwischen Sender und EmpfÃ¤nger. \n",
-    " * Bestimmen Sie in einer zweiten Messreihe die **Phasenverschiebung mit Hilfe einer geeigneten [Lissajous-Figur](https://de.wikipedia.org/wiki/Lissajous-Figur)** am Oszilloskop im XY-Modus.\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6a8745d1-07c0-454f-989c-5102b81662f5",
-   "metadata": {},
-   "source": [
-    "**V E R S U C H S B E S C H R E I B U N G**\n",
-    "\n",
-    "Es wurde wie in der Aufgabe 2.1 beschriebn $\\Delta t$ und $\\ell$ bestimmt. Und so mithilfe der Anpassung des Models:    \n",
-    "$\\ell=\\dfrac{c\\cdot \\Delta t}{g}$    \n",
-    "an die Daten c bestimmt. Dabei  wurde $\\Delta(\\Delta t)=90 \\,\\mathrm{ns}$ aus den Unsicherheiten bei dem Ablesen der Zeitdifferenz abgeschÃ¤zt. \n",
-    "\n",
-    "Des weiteren wurde mithilfe der Lissajous-Figuren $\\ell$ fÃ¼r $\\Delta \\varphi=0$ (Grade mit positiver Steigung) und $\\Delta \\varphi=0.5 \\pi$ (Kreis) abgelsen. dabei wurde aus den Unsicherheiten beim Ablesen wurde $\\Delta(\\Delta \\varphi)=0.02<\\cdot\\pi$ und $\\Delta \\ell= 0.1\\,\\mathrm{m}$ abgeschÃ¤zt.  \n",
-    "\n",
-    "\n",
-    "Mithilfe von \\ell=\\frac{c}{\\omega_1-\\omega_2}\\cdot \\Delta \\varphi$ lÃ¤sst scih aus der Differenz beid Messwerte schlieÃŸen.:\n",
-    "\n",
-    "$c=\\dfrac{(\\ell_2-\\ell_1)(\\omega_1-\\omega_2)}{\\Delta \\varphi_2-\\Delta \\varphi_2}$\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ea71caf-13ca-4d8c-a44d-169ca13c241d",
-   "metadata": {},
-   "source": [
-    "**L Ã– S U N G**\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "041e70b4-a678-4957-8d21-2fb7977aa9a9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>$\\Delta t \\mathrm{[ns]} $</th>\n",
-       "      <th>$\\ell \\,\\mathrm{[m]} $</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>$742 \\pm 50$</td>\n",
-       "      <td>$1.2040 \\pm 0.0015$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>$585 \\pm 50$</td>\n",
-       "      <td>$1.1150 \\pm 0.0015$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>$-37 \\pm 50$</td>\n",
-       "      <td>$0.8100 \\pm 0.0015$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>$929 \\pm 50$</td>\n",
-       "      <td>$1.2980 \\pm 0.0015$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>$1405 \\pm 50$</td>\n",
-       "      <td>$1.5610 \\pm 0.0015$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>$1963 \\pm 50$</td>\n",
-       "      <td>$1.8360 \\pm 0.0015$</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  $\\Delta t \\mathrm{[ns]} $ $\\ell \\,\\mathrm{[m]} $\n",
-       "0              $742 \\pm 50$    $1.2040 \\pm 0.0015$\n",
-       "1              $585 \\pm 50$    $1.1150 \\pm 0.0015$\n",
-       "2              $-37 \\pm 50$    $0.8100 \\pm 0.0015$\n",
-       "3              $929 \\pm 50$    $1.2980 \\pm 0.0015$\n",
-       "4             $1405 \\pm 50$    $1.5610 \\pm 0.0015$\n",
-       "5             $1963 \\pm 50$    $1.8360 \\pm 0.0015$"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "l=[1.204,1.115,0.81,1.298,1.561,1.836]#in m\n",
-    "lun=[ufloat(x,0.0015) for x in l]\n",
-    "dt=[741.5,585.0,-37.41,928.6,1405,1963]#in ns\n",
-    "dtun=np.array([ufloat(x*10**(-9),50*10**(-9)) for x in dt])\n",
-    "f1=60# in Mhz\n",
-    "f2=59.9# in Mhz\n",
-    "\n",
-    "def lfunc(dt2,c=1, b=1):\n",
-    "    return c*((f1-f2)/f1)*dt2+b\n",
-    "data = kafe2.XYContainer(x_data=n(dtun),y_data=n(lun))\n",
-    "data.add_error(axis='x', err_val=s(dtun))\n",
-    "data.add_error(axis='y', err_val=s(lun))\n",
-    "data.label = '$Bestimmung c$ '\n",
-    "fit2=kafe2.XYFit(xy_data=data,model_function=lfunc)\n",
-    "fit2.do_fit()\n",
-    "c=ufloat(fit2.parameter_values[0],fit2.parameter_errors[0])\n",
-    "kafe2.plot(fit2, x_label=r\"$\\Delta t\\,\\mathrm{[s]}$\", y_label=r'$ l\\,\\mathrm{[m]} $')\n",
-    "pd.DataFrame({\"$\\Delta t \\mathrm{[ns]} $\":utl2(dtun*10**9),\n",
-    "              \"$\\ell \\,\\mathrm{[m]} $\":utl(lun)})\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "b9e6ae09-c927-4fab-9b84-82f1956e29db",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(3.0+/-0.4)e+08\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>$ \\Delta \u000b",
-       "arphi $</th>\n",
-       "      <th>$\\ell \\,\\mathrm{[m]} $</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>$0.0000 \\pm 0.0628$</td>\n",
-       "      <td>$0.4240 \\pm 0.1000$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>$1.5708 \\pm 0.0628$</td>\n",
-       "      <td>$1.6820 \\pm 0.1000$</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     $ \\Delta \n",
-       "arphi $ $\\ell \\,\\mathrm{[m]} $\n",
-       "0  $0.0000 \\pm 0.0628$    $0.4240 \\pm 0.1000$\n",
-       "1  $1.5708 \\pm 0.0628$    $1.6820 \\pm 0.1000$"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dphi=[0,0.5*np.pi]\n",
-    "l2=[0.424,1.682,]\n",
-    "l2un=[ufloat(x,0.1 ) for x in l2]\n",
-    "dphiun=[ufloat(x,0.02*np.pi) for x in dphi]\n",
-    "f1=60*10**(6)# in Mhz\n",
-    "f2=59.9+10**(6)# in Mhz\n",
-    "c=(l2un[1]-l2un[0])*(f1-f2)*2*np.pi/(dphiun[1]-dphiun[0])\n",
-    "print(c)\n",
-    "pd.DataFrame({\"$ \\Delta \\varphi $\":utl(dphiun),\n",
-    "              \"$\\ell \\,\\mathrm{[m]} $\":utl(l2un)})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a2f4fe2-a8c2-4b5d-95a7-077ab51f7900",
-   "metadata": {},
-   "source": [
-    "**D I S K U S S I O N**\n",
-    "\n",
-    "Die Anpassung der Modelles an die Daten ist nach der Beurteilung nach dem $\\chi^2$-Wert sehr gut. Auch ist unser Wert von $c_0=3.088\\cdot 10^{8}\\pm 9.9 \\cdot 10^{7}\\,\\mathrm{\\frac{m}{s}}$ ist mit dem Literaturwert von  $c_0\\approx 2.997\\cdot 10^{8}\\,\\mathrm{\\frac{m}{s}}$ gut vereinbar. \n",
-    "\n",
-    "Der Mithilfe der Lissajous-Figur gemessen Wert $c_0=3.0\\cdot 10^{8}\\pm 0.4 \\cdot 10^{8}\\,\\mathrm{\\frac{m}{s}}$ ist mit dem Literaturwert von  $c_0\\approx 2.997\\cdot 10^{8}\\,\\mathrm{\\frac{m}{s}}$ gut vereinbar. Die Unsicherheiten sind aber sehr groÃŸ.  \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a3bfd6f-b8e7-41b7-bfe4-2ff44ae0be53",
-   "metadata": {},
-   "source": [
-    "## Aufgabe 3: Bestimmung des Brechungsindex $n$ von Wasser oder Plexiglas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf35158f-d1fa-4e7f-b139-13a009f24b92",
-   "metadata": {},
-   "source": [
-    "### Aufgabe 3.1: Bestimmung von $n$ mit der Apperatur von Aufgabe 2 \n",
-    "\n",
-    "Bestimmen Sie **mit dem Aufbau von Aufgabe 2** aus der Messung der Laufzeitdifferenz des Lichtstrahls im optisch dichteren Medium den Brechungsindex fÃ¼r zwei verschiedene Medien: \n",
-    "\n",
-    " * FÃ¼r Wasser ($n_{\\mathrm{H_{2}O}}$).\n",
-    " * FÃ¼r Plexiglas ($n_{\\mathrm{Plex}}$).\n",
-    "\n",
-    "FÃ¼r Wasser ersetzen Sie $1\\,\\mathrm{m}$ des Lichtwegs in Luft durch $1\\,\\mathrm{m}$ Lichtweg in Wasser. FÃ¼r Plexiglas verwenden Sie die $8$ und $30\\,\\mathrm{cm}$ langen Plexiglaszylinder.\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b6bedf8-0827-4a8d-ace8-cc9672ede95c",
-   "metadata": {},
-   "source": [
-    "**V E R S U C H S B E S C H R E I B U N G**\n",
-    "\n",
-    "Es wurde erneut wie bei der Aufgabe 2.3 $\\Delta t$ in AbhÃ¤ngigkeit von $\\ell$ bestimmt. Dabei wird 9.2 cm Wasser in einer Messung und 30 cm  Wir verwenden dabei $g \\cdot \\Delta t = \\frac{(\\ell-d+ n*d)}{c} $. wobei $d$ die Strecke ist die im Matrial ZUrÃ¼ckgelgt wird. ES werden also fÃ¼r die Laufzeit die StreckenlÃ¤ngen nach der LichgeschwindiglÂ´keit in ihnen Unterschieden. \n",
-    "Eine Anpassung dies Modelles an die daten wurde zur Bestimmung von n genutzt. \n",
-    "Dabei wurde der Phasenversatz so eingestellt dass $\\ell$ zu dem Offset $0.424\\pm0.1\\,\\mathrm{m}$ gemessen wurde. Die GroÃŸe Unsicherheit ergibt sich daraus, dass die bei dem Einstellen der Lissajous-Figuren geschah. \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5215c029-a71f-423c-8d8b-918b7fd9725f",
-   "metadata": {},
-   "source": [
-    "**L Ã– S U N G**\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "220f034c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>$\\Delta t \\mathrm{[ns]} $</th>\n",
-       "      <th>$\\ell \\,\\mathrm{[m]} $</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>$889 \\pm 90$</td>\n",
-       "      <td>$0.4160 \\pm 0.1000$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>$1374 \\pm 90$</td>\n",
-       "      <td>$0.6830 \\pm 0.1000$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>$1723 \\pm 90$</td>\n",
-       "      <td>$0.9890 \\pm 0.1000$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>$429 \\pm 90$</td>\n",
-       "      <td>$0.1500 \\pm 0.1000$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>$554 \\pm 90$</td>\n",
-       "      <td>$0.2960 \\pm 0.1000$</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  $\\Delta t \\mathrm{[ns]} $ $\\ell \\,\\mathrm{[m]} $\n",
-       "0              $889 \\pm 90$    $0.4160 \\pm 0.1000$\n",
-       "1             $1374 \\pm 90$    $0.6830 \\pm 0.1000$\n",
-       "2             $1723 \\pm 90$    $0.9890 \\pm 0.1000$\n",
-       "3              $429 \\pm 90$    $0.1500 \\pm 0.1000$\n",
-       "4              $554 \\pm 90$    $0.2960 \\pm 0.1000$"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "l=np.array([0.84,1.107,1.413,0.574,0.72])#in m+- wenig\n",
-    "dtVerpackung=[851.7,1287,1723,329.1,405]#in ns +- mehr\n",
-    "dtWasser=np.array([889,1374,1723,428.6,554.3])#in ns +- mehr\n",
-    "\n",
-    "lun=np.array([ufloat(x,0.0015) for x in l])-ufloat(0.424,0.1)#-ufloat(0.1635,0.001)\n",
-    "dtunw=np.array([ufloat(x*10**(-9),90*10**(-9)) for x in dtWasser])\n",
-    "dtunv=np.array([ufloat(x*10**(-9),50*10**(-9)) for x in dtVerpackung])\n",
-    "\n",
-    "\n",
-    "\n",
-    "f1=60# MHz\n",
-    "f2=59.9# MHz\n",
-    "def tfunc(l,n2=1,c2=1,d=1):\n",
-    "    return f1/(f1-f2)*((l-d)/c2+n2*d/c2)\n",
-    "    \n",
-    "\n",
-    "data = kafe2.XYContainer(x_data=n(lun),y_data=n(dtunw))\n",
-    "data.add_error(axis='x', err_val=s(lun))\n",
-    "data.add_error(axis='y', err_val=s(dtunw))\n",
-    "data.label = 'mit 9.2 cm Wasser'\n",
-    "fit1=kafe2.XYFit(xy_data=data,model_function=tfunc)\n",
-    "fit1.add_parameter_constraint(name=\"d\", value=0.092, uncertainty=0.001)\n",
-    "fit1.add_parameter_constraint(name=\"c2\", value=299792000, uncertainty=500)\n",
-    "#fit1.add_parameter_constraint(name=\"n2\", value=1.333, uncertainty=0.1)\n",
-    "fit1.do_fit()\n",
-    "nw=ufloat(fit1.parameter_values[0],fit1.parameter_errors[0])\n",
-    "kafe2.plot(fit1, x_label=r\"$l\\,\\mathrm{[m]}$\", y_label=r'$ \\Delta t\\,\\mathrm{[s]} $')\n",
-    "pd.DataFrame({\"$\\Delta t \\mathrm{[ns]} $\":utl2(dtunw*10**9),\n",
-    "              \"$\\ell \\,\\mathrm{[m]} $\":utl(lun)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "f84ce43b-d9d3-49f0-819d-ed1ae22ea85f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>$\\Delta t \\mathrm{[ns]} $</th>\n",
-       "      <th>$\\ell \\,\\mathrm{[m]} $</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>$1038 \\pm 90$</td>\n",
-       "      <td>$0.2770 \\pm 0.1000$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>$1319 \\pm 90$</td>\n",
-       "      <td>$0.5250 \\pm 0.1000$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>$1713 \\pm 90$</td>\n",
-       "      <td>$0.7750 \\pm 0.1000$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>$2102 \\pm 90$</td>\n",
-       "      <td>$0.8430 \\pm 0.1000$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>$2451 \\pm 90$</td>\n",
-       "      <td>$1.0380 \\pm 0.1000$</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  $\\Delta t \\mathrm{[ns]} $ $\\ell \\,\\mathrm{[m]} $\n",
-       "0             $1038 \\pm 90$    $0.2770 \\pm 0.1000$\n",
-       "1             $1319 \\pm 90$    $0.5250 \\pm 0.1000$\n",
-       "2             $1713 \\pm 90$    $0.7750 \\pm 0.1000$\n",
-       "3             $2102 \\pm 90$    $0.8430 \\pm 0.1000$\n",
-       "4             $2451 \\pm 90$    $1.0380 \\pm 0.1000$"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "l=np.array([0.701,0.949,1.199,1.267,1.462])#in m\n",
-    "dtPlexiglas=np.array([1038,1319,1713,2102,2451])#in ns \n",
-    "\n",
-    "lun=np.array([ufloat(x,0.0015) for x in l])-ufloat(0.424,0.1)#-ufloat(0.1635,0.001)\n",
-    "dtunp=np.array([ufloat(x*10**(-9),90*10**(-9)) for x in dtPlexiglas])\n",
-    "\n",
-    "\n",
-    "f1=60# MHz\n",
-    "f2=59.9# MHz\n",
-    "def tfunc(l,n2=1,c2=1,d=1):\n",
-    "    return f1/(f1-f2)*((l-d)/c2+n2*d/c2)\n",
-    "\n",
-    "data = kafe2.XYContainer(x_data=n(lun),y_data=n(dtunp))\n",
-    "data.add_error(axis='x', err_val=s(lun))\n",
-    "data.add_error(axis='y', err_val=s(dtunp))\n",
-    "data.label = 'mit 30cm  Plexiglas'\n",
-    "fit1=kafe2.XYFit(xy_data=data,model_function=tfunc)\n",
-    "fit1.add_parameter_constraint(name=\"d\", value=0.30, uncertainty=0.01)\n",
-    "fit1.add_parameter_constraint(name=\"c2\", value=299792000, uncertainty=500)\n",
-    "#fit1.add_parameter_constraint(name=\"n2\", value=1.333, uncertainty=0.1)\n",
-    "fit1.do_fit()\n",
-    "nw=ufloat(fit1.parameter_values[0],fit1.parameter_errors[0])\n",
-    "kafe2.plot(fit1, x_label=r\"$l\\,\\mathrm{[m]}$\", y_label=r'$ \\Delta t\\,\\mathrm{[s]} $')\n",
-    "pd.DataFrame({\"$\\Delta t \\mathrm{[ns]} $\":utl2(dtunp*10**9),\n",
-    "              \"$\\ell \\,\\mathrm{[m]} $\":utl(lun)})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1f96af08-9ffe-4be3-a9bf-a58bafb0291b",
-   "metadata": {},
-   "source": [
-    "**D I S K U S S I O N**\n",
-    "\n",
-    "Die $\\chi^2$_werte legen einen guten fit nahe.\n",
-    "Wir erhalten $n_{Wasser}=0.89 \\pm 0.53$ dies ist im Rahmen der shr groÃŸen Unsicherheit mit dem Litraturwert $n_{Wasser}=1.33$ vereinabr. Auch der gemessen Wert fÃ¼r $n_{Plex}=1.57 \\pm 0.16$ was mit den Litraturwerten fÃ¼r verschiedene Plexiglase von $n_{Plex}\\approx 1.50$ im Rahmen der Unsicherheit vereinabr ist. \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "709d4064-4831-4329-b6b0-7349404b44db",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "### Aufgabe 3.2: Bestimmung von $n$ mit Hilfe eines Laserentfernungsmessers\n",
-    "\n",
-    "Wiederholen Sie die Messung aus **Aufgabe 3.1** mit dem im Praktikum zur VerfÃ¼gung stehenden Laserentfernungsmesser. Es stehen Ihnen hierzu KÃ¼vetten gefÃ¼llt mit zwei Medien zur VerfÃ¼gung: \n",
-    "\n",
-    " * Wasser\n",
-    " * SilikonÃ¶l. \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6c3e0306-645e-4770-b222-eccf0bc75e8c",
-   "metadata": {},
-   "source": [
-    "**V E R S U C H S B E S C H R E I B U N G**\n",
-    "\n",
-    "*FÃ¼gen Sie Ihre Versuchsbeschreibung hier ein. LÃ¶schen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c65c1a41-aac9-4cc6-8dfa-481e8518222f",
-   "metadata": {},
-   "source": [
-    "**L Ã– S U N G**\n",
-    "\n",
-    "\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "901048d7",
-   "metadata": {},
-   "source": [
-    "l=ufloat(0.354.0.001)\n",
-    "l1wasser=ufloat(0.385,0.001)# lÃ¤ngst\n",
-    "l2wasser=ufloat(0.389,0.001)# lÃ¤ngst\n",
-    "l3wasser=ufloat(0.372,0.001)# quer\n",
-    "l4wasser=ufloat(0.372,0.001)# quer\n",
-    "l1so=ufloat(0.399,0.001)# lÃ¤ngst\n",
-    "l2so=ufloat(0.394,0.001)# lÃ¤ngst\n",
-    "l3so=ufloat(0.376,0.001)# quer\n",
-    "l4so=ufloat(0.377,0.001)# quer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "15250692",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c99c731d-17ae-4367-8d6a-e3f40a94dac5",
-   "metadata": {},
-   "source": [
-    "**D I S K U S S I O N**\n",
-    "\n",
-    "*FÃ¼gen Sie eine abschlieÃŸende Diskussion und Bewertung Ihrer LÃ¶sung hier ein. LÃ¶schen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d70f3914-2425-4256-b0d7-e0ebfaddc6cb",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "# Beurteilung"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ceaf4ad-11e8-4ad9-adce-82c0356ba183",
-   "metadata": {},
-   "source": [
-    " * Nach Abschluss des Versuchs haben Sie die MÃ¶glichkeit diesen Versuch individuell zu beurteilen.\n",
-    " * **Folgen Sie zur Beurteilung dieses Versuchs diesem [Link](https://www.empirio.de/s/HGmapQaXlp)**.\n",
-    " * Beachten Sie, dass jede:r Studierende nur einmal pro Versuch eine Beurteilung abgeben kann.\n",
-    " * Wir empfehlen die Beurteilung nach der Besprechung Ihrer Versuchsauswertung mit Ihrem:r Tutor:in auszufÃ¼llen.  "
-   ]
-  }
- ],
- "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": "3.11.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
-- 
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