diff --git a/Ferromagnetische_Hysterese/Ferromagnetische_Hysterese_Jonathan_Datenauswertung.ipynb b/Ferromagnetische_Hysterese/Ferromagnetische_Hysterese_Jonathan_Datenauswertung.ipynb
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index ee1b45f545f46cba62ae6c034649b7e694d41097..0000000000000000000000000000000000000000
--- a/Ferromagnetische_Hysterese/Ferromagnetische_Hysterese_Jonathan_Datenauswertung.ipynb
+++ /dev/null
@@ -1,942 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "885c7767-e912-4e31-b5d6-3a3443ffa58e",
-   "metadata": {},
-   "source": [
-    "# Fakultät für Physik\n",
-    "\n",
-    "## Physikalisches Praktikum P1 für Studierende der Physik\n",
-    "\n",
-    "Versuch P1-61, 62, 63 (Stand: **Oktober 2024**)\n",
-    "\n",
-    "[Raum F1-16](https://labs.physik.kit.edu/img/Klassische-Praktika/Lageplan_P1P2.png)\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Ferromagnetische Hysterese"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6cda71a9-2111-4282-a28a-821dc2202093",
-   "metadata": {},
-   "source": [
-    "Name:Reisner Vorname: Christian E-Mail: uqnwo@student.kit.edu\n",
-    "\n",
-    "\\begin{equation*}\n",
-    "\\begin{split}\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "\\end{split}\n",
-    "\\end{equation*}\n",
-    "\n",
-    "Name:Ben Tov Vorname: Jonthan E-Mail: ufoxj@student.kit.edu\n",
-    "\n",
-    "\\begin{equation*}\n",
-    "\\begin{split}\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "\\end{split}\n",
-    "\\end{equation*}\n",
-    "\n",
-    "Gruppennummer: Do06\n",
-    "\n",
-    "\\begin{equation*}\n",
-    "\\begin{split}\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "\\end{split}\n",
-    "\\end{equation*}\n",
-    "\n",
-    "\n",
-    "Betreuer: Finn Rosumek\n",
-    "\n",
-    "\\begin{equation*}\n",
-    "\\begin{split}\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "&\\\\\n",
-    "\\end{split}\n",
-    "\\end{equation*}\n",
-    "\n",
-    "Versuch durchgeführt am: 05.12.2024"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9861759e-6c07-4ec5-a750-f307ec3d8028",
-   "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": "e3e27d6b-3390-4401-8300-1dc26021fb2d",
-   "metadata": {},
-   "source": [
-    "# Durchführung"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be70efea-bff5-42d5-b5aa-0b9598c33846",
-   "metadata": {},
-   "source": [
-    "**Detaillierte Hinweise zur Durchführung der Versuche finden Sie in der Datei [Ferromagnetische_Hysterese_Hinweise.ipynb](https://gitlab.kit.edu/kit/etp-lehre/p1-praktikum/students/-/blob/main/Ferromagnetische_Hysterese/Ferromagnetische_Hysterese_Hinweise.ipynb)**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8fe74fe8-1b63-48de-832b-ad03e49b0132",
-   "metadata": {},
-   "source": [
-    "## Aufgabe 1: Induktivität und Verlustleistung einer Spule"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f05f94c-cb93-4ac6-b323-7a73a8bffbb0",
-   "metadata": {},
-   "source": [
-    "### Aufgabe 1.1: Luftgefüllte Spulte\n",
-    "\n",
-    " * Bestimmen Sie die **Spuleninduktivität** $L$, den **Verlustwiderstand** $R_{L}$ und die **elektrische Verlustleistung** $P_{L}$ (aufgrund von $R_{L}$) einer luftgefüllten Spule.\n",
-    " * Bestätigen oder Widerlegen Sie die Hypothese, dass weder $L$ noch $R_{L}$ von der effektiven Stromstärke $I_{\\mathrm{eff}}$ im Wechselstromkreis abhängen.\n",
-    " * Berechnen Sie aus den angegebenen Spulendaten im [Datenblatt](https://gitlab.kit.edu/kit/etp-lehre/p1-praktikum/students/-/blob/main/Ferromagnetische_Hysterese/Datenblatt.md) zum Versuch die erwarteten Werte für $L$ und $R_{L}$ und vergleichen Sie Ihre Erwartung mit der Messung.\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "72523276-c7ae-467f-97b4-b03495806d9e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "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",
-    "from uncertainties.umath import sin,cos\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\"${x.nominal_value:.3f} \\\\pm {x.std_dev:.3f}$\" for x in u]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7b0c0e29-f5ba-4c38-87ca-4749fca945f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "\n L\\,\\mathrm{[mH]} \\mathrm{und\\ } R \\,\\mathrm[\\Omega] \n                                     ^\nParseFatalException: Unknown symbol: \\mathrm, found '\\'  (at char 37), (line:1, col:38)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/IPython/core/formatters.py:343\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    341\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 343\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    344\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    345\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/IPython/core/pylabtools.py:170\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    168\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 170\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/backend_bases.py:2175\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2172\u001b[0m     \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[1;32m   2173\u001b[0m     \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[1;32m   2174\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2175\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2176\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2177\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/figure.py:3162\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3159\u001b[0m             \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3161\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3162\u001b[0m     \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3163\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3165\u001b[0m     renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfigure\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3166\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/axes/_base.py:3137\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3134\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m   3135\u001b[0m     _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3137\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3138\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3140\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3141\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/axis.py:1431\u001b[0m, in \u001b[0;36mAxis.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1429\u001b[0m \u001b[38;5;66;03m# Shift label away from axes to avoid overlapping ticklabels.\u001b[39;00m\n\u001b[1;32m   1430\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_label_position(renderer)\n\u001b[0;32m-> 1431\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1433\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_offset_text_position(tlb1, tlb2)\n\u001b[1;32m   1434\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moffsetText\u001b[38;5;241m.\u001b[39mset_text(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmajor\u001b[38;5;241m.\u001b[39mformatter\u001b[38;5;241m.\u001b[39mget_offset())\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/text.py:751\u001b[0m, in \u001b[0;36mText.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    748\u001b[0m renderer\u001b[38;5;241m.\u001b[39mopen_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_gid())\n\u001b[1;32m    750\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cm_set(text\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_wrapped_text()):\n\u001b[0;32m--> 751\u001b[0m     bbox, info, descent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    752\u001b[0m     trans \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\n\u001b[1;32m    754\u001b[0m     \u001b[38;5;66;03m# don't use self.get_position here, which refers to text\u001b[39;00m\n\u001b[1;32m    755\u001b[0m     \u001b[38;5;66;03m# position in Text:\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/text.py:381\u001b[0m, in \u001b[0;36mText._get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    379\u001b[0m clean_line, ismath \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preprocess_math(line)\n\u001b[1;32m    380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m clean_line:\n\u001b[0;32m--> 381\u001b[0m     w, h, d \u001b[38;5;241m=\u001b[39m \u001b[43m_get_text_metrics_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    382\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclean_line\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fontproperties\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    383\u001b[0m \u001b[43m        \u001b[49m\u001b[43mismath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    384\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    385\u001b[0m     w \u001b[38;5;241m=\u001b[39m h \u001b[38;5;241m=\u001b[39m d \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/text.py:69\u001b[0m, in \u001b[0;36m_get_text_metrics_with_cache\u001b[0;34m(renderer, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Call ``renderer.get_text_width_height_descent``, caching the results.\"\"\"\u001b[39;00m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;66;03m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[39;00m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;66;03m# the passed-in argument do not mess up the cache.\u001b[39;00m\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_text_metrics_with_cache_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     70\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweakref\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/text.py:77\u001b[0m, in \u001b[0;36m_get_text_metrics_with_cache_impl\u001b[0;34m(renderer_ref, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mlru_cache(\u001b[38;5;241m4096\u001b[39m)\n\u001b[1;32m     74\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_text_metrics_with_cache_impl\u001b[39m(\n\u001b[1;32m     75\u001b[0m         renderer_ref, text, fontprop, ismath, dpi):\n\u001b[1;32m     76\u001b[0m     \u001b[38;5;66;03m# dpi is unused, but participates in cache invalidation (via the renderer).\u001b[39;00m\n\u001b[0;32m---> 77\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrenderer_ref\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/backends/backend_agg.py:216\u001b[0m, in \u001b[0;36mRendererAgg.get_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mget_text_width_height_descent(s, prop, ismath)\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ismath:\n\u001b[1;32m    215\u001b[0m     ox, oy, width, height, descent, font_image \u001b[38;5;241m=\u001b[39m \\\n\u001b[0;32m--> 216\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmathtext_parser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    217\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m width, height, descent\n\u001b[1;32m    219\u001b[0m font \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_font(prop)\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/mathtext.py:79\u001b[0m, in \u001b[0;36mMathTextParser.parse\u001b[0;34m(self, s, dpi, prop, antialiased)\u001b[0m\n\u001b[1;32m     77\u001b[0m prop \u001b[38;5;241m=\u001b[39m prop\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m prop \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     78\u001b[0m antialiased \u001b[38;5;241m=\u001b[39m mpl\u001b[38;5;241m.\u001b[39m_val_or_rc(antialiased, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext.antialiased\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 79\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_cached\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mantialiased\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/mathtext.py:100\u001b[0m, in \u001b[0;36mMathTextParser._parse_cached\u001b[0;34m(self, s, dpi, prop, antialiased)\u001b[0m\n\u001b[1;32m     97\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parser \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:  \u001b[38;5;66;03m# Cache the parser globally.\u001b[39;00m\n\u001b[1;32m     98\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m_parser \u001b[38;5;241m=\u001b[39m _mathtext\u001b[38;5;241m.\u001b[39mParser()\n\u001b[0;32m--> 100\u001b[0m box \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    101\u001b[0m output \u001b[38;5;241m=\u001b[39m _mathtext\u001b[38;5;241m.\u001b[39mship(box)\n\u001b[1;32m    102\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvector\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/matplotlib/_mathtext.py:2173\u001b[0m, in \u001b[0;36mParser.parse\u001b[0;34m(self, s, fonts_object, fontsize, dpi)\u001b[0m\n\u001b[1;32m   2170\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_expression\u001b[38;5;241m.\u001b[39mparseString(s)\n\u001b[1;32m   2171\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ParseBaseException \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m   2172\u001b[0m     \u001b[38;5;66;03m# explain becomes a plain method on pyparsing 3 (err.explain(0)).\u001b[39;00m\n\u001b[0;32m-> 2173\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m ParseException\u001b[38;5;241m.\u001b[39mexplain(err, \u001b[38;5;241m0\u001b[39m)) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   2174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state_stack \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m   2175\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_in_subscript_or_superscript \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "\u001b[0;31mValueError\u001b[0m: \n L\\,\\mathrm{[mH]} \\mathrm{und\\ } R \\,\\mathrm[\\Omega] \n                                     ^\nParseFatalException: Unknown symbol: \\mathrm, found '\\'  (at char 37), (line:1, col:38)"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<kafe2.fit._base.plot.Plot at 0x7f29a853da90>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "UUr=0.01\n",
-    "UUl=0.005\n",
-    "UIf=0.1\n",
-    "Udelt=0.5\n",
-    "UR=np.array([ufloat(0.509,UUr),ufloat(1.316,UUr),ufloat(1.846,UUr),ufloat(3.313,UUr),ufloat(4.438,UUr)]) #mV\n",
-    "UL=np.array([ufloat(0.724,UUl),ufloat(1.866,UUl),ufloat(2.656,UUl),ufloat(4.665,UUl),ufloat(6.268,UUl)]) #mv\n",
-    "delt=np.array([ufloat(22.85-20.13,Udelt),ufloat(16.41-14.26,Udelt),ufloat(26.35-23.64,Udelt),ufloat(16.73-14.03,Udelt),ufloat(16.02-13.30,Udelt)]) #ms\n",
-    "delts=delt/1000\n",
-    "If=np.array([ufloat(34.35,UIf),ufloat(89.8,UIf),ufloat(126.0,UIf),ufloat(223.4,UIf),ufloat(301.5,UIf)]) #mA\n",
-    "omega=2*np.pi*ufloat(50,0.1)\n",
-    "R=ufloat(10,0.5)\n",
-    "L=np.array([(UL[i]/UR[i])*(R/omega)*sin(omega*delts[i]) for i in range(len(UR))])\n",
-    "RL=np.array([(UL[i]/UR[i])*R*cos(omega*delts[i]) for i in range(len(UR))])\n",
-    "PL=RL*If**2\n",
-    "L1=L*1000\n",
-    "def l(r,L2=1):\n",
-    "    return L2\n",
-    "data = kafe2.XYContainer(x_data=n(If),y_data=n(L1))\n",
-    "data.add_error(axis='x', err_val=s(If))\n",
-    "data.add_error(axis='y', err_val=s(L1))\n",
-    "data.label = '$Induktivität$ '\n",
-    "fit1=kafe2.XYFit(xy_data=data,model_function=l)\n",
-    "fit1.do_fit()\n",
-    "fit1.assign_model_function_latex_name(r\"L\")\n",
-    "def R(r,R_L=1):\n",
-    "    return R_L\n",
-    "data = kafe2.XYContainer(x_data=n(If),y_data=n(RL))\n",
-    "data.add_error(axis='x', err_val=s(If))\n",
-    "data.add_error(axis='y', err_val=s(RL))\n",
-    "data.label = '$Widerstand$ '\n",
-    "fit2=kafe2.XYFit(xy_data=data,model_function=R)\n",
-    "fit2.do_fit()\n",
-    "kafe2.plot({fit1,fit2}, x_label=r\"$I_f\\,\\mathrm{[mA]}$\", y_label=r'$ L\\,\\mathrm{[mH]} \\mathrm{und\\ } R \\,\\mathrm{[\\Omega]} $')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8bd0860c-c28b-4442-b42e-1d0f048deddb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "N=1000\n",
-    "r=0.034\n",
-    "l=0.068\n",
-    "d=0.0007\n",
-    "A=np.pi*r**2\n",
-    "mu0=4*np.pi*10**(-7)\n",
-    "rhocu=1.78*10**(-8)\n",
-    "le=N*2*np.pi*r\n",
-    "Ad=np.pi*(d/2)**2\n",
-    "Lrech=N**2*mu0*A/(l+0.91*r)\n",
-    "Rl=rhocu*(le/Ad)\n",
-    "print(f\"Rechnerisch ergibt sich L={Lrech} H und R={Rl} Ohm\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b699a264-3663-45b1-9ce4-ab2424a308d5",
-   "metadata": {},
-   "source": [
-    "**V E R S U C H S B E S C H R E I B U N G**\n",
-    "\n",
-    "*Fügen Sie Ihre Versuchsbeschreibung hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b92d6f12-dd01-47e0-af99-1a77716ee6da",
-   "metadata": {},
-   "source": [
-    "**L Ö S U N G**\n",
-    "\n",
-    "*Fügen Sie numerische Berechnungen zur Lösung dieser Aufgabe hier ein. Löschen 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. Fügen Sie ggf. weitere Code-Zellen zu.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d2e8948-810e-4240-9f99-8cb2c8bb2db2",
-   "metadata": {},
-   "source": [
-    "**D I S K U S S I O N**\n",
-    "\n",
-    "*Fügen Sie eine abschließende Diskussion und Bewertung Ihrer Lösung hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "20887c3e-9c2e-4cf2-918e-c6091ec8f900",
-   "metadata": {},
-   "source": [
-    "### Aufgabe 1.2: Spule mit Eisenkern\n",
-    "\n",
-    " * Wiederholen Sie die Messungen von **Aufgabe 1.1**, diesmal jedoch mit einem Eisenkern in der Spule.\n",
-    " * Berechnen Sie aus den Daten der Spule und den gemessenen Werten für $L$ die (mittlere) **relative Permeabilität** $\\langle\\mu_{r}\\rangle$ als Funktion von $I_{\\mathrm{eff}}$.\n",
-    " * Berechnen Sie aus den gemessenen Werten für $R_{L}$ und $I_{\\mathrm{eff}}$ die **Verlustleistung $P_{L}$ der Spule**, als Funktion von $I_{\\mathrm{eff}}$ und vergleichen Sie mit dem Ergebnis aus **Aufgabe 1.1**.\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "id": "93e1f6ca-f090-4c7b-bef2-d5276ef6412b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[526.0295006203545+/-175.41254775697024\n",
-      " 1416.8857923346143+/-145.91465445596555\n",
-      " 1670.8755744172236+/-221.3227491205514\n",
-      " 1875.7588232651378+/-241.72126639650415]\n"
-     ]
-    },
-    {
-     "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": [
-    "UUr=0.01\n",
-    "UUl=0.01\n",
-    "UIf=0.1\n",
-    "Udelt=0.5\n",
-    "UR=np.array([ufloat(0.154,UUr),ufloat(0.230,UUr),ufloat(0.297,UUr),ufloat(0.332,UUr)]) #mV\n",
-    "UL=np.array([ufloat(2.201,UUl),ufloat(4.330,UUl),ufloat(7.370,UUl),ufloat(9.163,UUl)])*10 #mv\n",
-    "delt=np.array([ufloat(36.39-34.95,Udelt),ufloat(19.68-16.16,Udelt),ufloat(28.92-25.97,Udelt),ufloat(20.95-17.96,Udelt)]) #ms\n",
-    "delts=delt/1000\n",
-    "If=np.array([ufloat(10.02,UIf),ufloat(15.04,UIf),ufloat(20.09,UIf),ufloat(22.68,UIf)]) #mA\n",
-    "omega=2*np.pi*ufloat(50,0.1)\n",
-    "R=ufloat(10,0.5)\n",
-    "L=np.array([(UL[i]/UR[i])*(R/omega)*sin(omega*delts[i]) for i in range(len(UR))])\n",
-    "RL=np.array([(UL[i]/UR[i])*R*cos(omega*delts[i]) for i in range(len(UR))])\n",
-    "PL=RL*(If/1000)**2\n",
-    "N=1000\n",
-    "r=0.034\n",
-    "l=0.48\n",
-    "d=0.0007\n",
-    "A=0.038**2\n",
-    "mu0=4*np.pi*10**(-7)\n",
-    "rhocu=1.78*10**(-8)\n",
-    "le=N*2*np.pi*r\n",
-    "Ad=np.pi*(d/2)**2\n",
-    "mur=(L*l)/(N**2*mu0*A)\n",
-    "print(mur)\n",
-    "\n",
-    "\n",
-    "plt.errorbar(n(If),n(PL),s(PL),s(If),marker='None', linestyle='None')  \n",
-    "plt.ylabel(r'$P_L\\,\\mathrm{ [W]}$')\n",
-    "plt.xlabel('$I_f \\,\\mathrm{ [mA]}$')\n",
-    "plt.title(\"Verlustleistung\")\n",
-    "plt.show()\n",
-    "plt.errorbar(n(If),n(mur),s(mur),s(If),marker='None', linestyle='None')  \n",
-    "plt.ylabel(r'$\\mu_r$')\n",
-    "plt.xlabel('$I_f \\,\\mathrm{ [mA]}$')\n",
-    "plt.title(\"Relative Permeabilität\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0722c5d3-c073-4d86-a6c0-e6dc05073693",
-   "metadata": {},
-   "source": [
-    "**V E R S U C H S B E S C H R E I B U N G**\n",
-    "\n",
-    "*Fügen Sie Ihre Versuchsbeschreibung hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa723eed-4a46-41a3-a823-7e61a1d252c7",
-   "metadata": {},
-   "source": [
-    "**L Ö S U N G**\n",
-    "\n",
-    "*Fügen Sie numerische Berechnungen zur Lösung dieser Aufgabe hier ein. Löschen 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. Fügen Sie ggf. weitere Code-Zellen zu.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "41f840e3-c7de-4945-8370-b54745a50f95",
-   "metadata": {},
-   "source": [
-    "**D I S K U S S I O N**\n",
-    "\n",
-    "*Fügen Sie eine abschließende Diskussion und Bewertung Ihrer Lösung hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3aadb392-61cb-4657-b068-9d9c1216460e",
-   "metadata": {},
-   "source": [
-    "## Aufgabe 2: Hysterese"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "e42096c8-8cb9-4937-8b94-8e0c78cdc05c",
-   "metadata": {},
-   "source": [
-    "### Aufgabe 2.1: Hysteresekurve\n",
-    "\n",
-    " * Stellen Sie die **Hysteresekurve** $B(H)$ für den Eisenkern aus **Aufgabe 1.2** für zwei geeignete effektive Stromestärken von $I_{\\mathrm{eff}}$ (im Primärkreis der Schaltung) auf dem Oszilloskop dar.\n",
-    " * Bestimmen Sie aus den aufgezeichneten Kurven $\\langle\\mu_{r}\\rangle$ und vergleichen Sie mit Ihren Ergebnissen aus **Aufgabe 1.2**.\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "id": "9a5e3128-2be6-4b8e-9c54-449e1ddff14b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Resampling by factor 13\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 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"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Wir erhalten mur=(9.0+/-0.8)e+02. Dies wurde aus 151 von 300 Werten beerchnet\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Ur ist Channel B und UC ist Channel A\n",
-    "#R2 = 10 kOhm\n",
-    "#C = 10 uF\n",
-    "N1=1000\n",
-    "N2=50\n",
-    "l=ufloat(0.48,0.01)\n",
-    "R2=ufloat(10000,500)\n",
-    "R1=ufloat(10,0.5)\n",
-    "UHfak=N1/(R1*l)\n",
-    "C=ufloat(10*10**(-6),0.5*10**(-6))\n",
-    "UHfak=N1/(R1*l)\n",
-    "UBfak=C*R2/(N2*(0.039)**2)\n",
-    "mu0=4*np.pi*10**(-7)\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from scipy import interpolate\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Read cvs file as pandas dataframe\n",
-    "#df = pd.read_csv(\"Christian_ist_ein_Schatz2_2_10_02mA2.csv\")\n",
-    "# Translate dataframe columns into native python lists\n",
-    "#t  = df[\"Frequenz\"].to_list()[1:-1:10]\n",
-    "#UB = df[\"Kanal A\"].to_list()[1:-1:10] \n",
-    "#UH = df[\"Kanal B\"].to_list()[1:-1:10] \n",
-    "#UB=np.array(UB)\n",
-    "#UH=np.array(UH)\n",
-    "data = np.genfromtxt('Christian_ist_ein_Schatz2_2_10_02mA2.csv', delimiter=\",\", skip_header=3 )\n",
-    "t, UB, UH= data[:,0], data[:,1], data[:,2]\n",
-    "t=t[1:-1:]\n",
-    "UB=UB[1:-1:]/1000\n",
-    "UH=UH[1:-1:]/1000\n",
-    "from PhyPraKit.phyTools import resample, meanFilter\n",
-    "\n",
-    "# If length is too large, resample by an appropriate factor, we are fine with \n",
-    "# 150 data points\n",
-    "il=len(UH)\n",
-    "size=300\n",
-    "if il > size:\n",
-    "    g = int(il/size)\n",
-    "    # This is an example of smoothing by averaging over n neighbors\n",
-    "    #print(\"Smoothing with window size \", n)\n",
-    "    #t  = meanFilter(vUH, width=n)\n",
-    "    #UH = meanFilter(vUH, width=n)\n",
-    "    #UB = meanFilter(vUB, width=n)\n",
-    "    # This is an example of down sampling by averaging over n neighbors\n",
-    "    print(\"Resampling by factor\", g)\n",
-    "    t  = resample(t , n=g)\n",
-    "    UH = resample(UH, n=g)\n",
-    "    UB = resample(UB, n=g)\n",
-    "\n",
-    "CALIB_UH2H = UHfak.n  # U_H -> H <-- adjust !\n",
-    "CALIB_UB2B = UBfak.n   # U_B -> B <-- adjust !\n",
-    "H = UH * CALIB_UH2H\n",
-    "B = UB * CALIB_UB2B\n",
-    "# Interpolate the points of (t,H) by spline functions; s=0 means that no extra \n",
-    "# smoothing will be applied, each point of H will be used for the spline\n",
-    "spl_Ht = interpolate.UnivariateSpline(t, H, s=0)\n",
-    "spl_Bt = interpolate.UnivariateSpline(t, B, s=0)\n",
-    "\n",
-    "# Plot hysteresis curve as Channel A vs. Channeel B\n",
-    "tplt = np.linspace(t[0], t[-1], 200)\n",
-    "unitH = \"(A/m)\"; unitB = \"(T)\"\n",
-    "fig = plt.figure(1, figsize=(6.0, 6.0))\n",
-    "ax1 = fig.add_subplot()\n",
-    "ax1.scatter(H, B, color=\"blue\", marker=\"o\", s=5.0, label=\"Data points\")\n",
-    "ax1.plot(spl_Ht(tplt), spl_Bt(tplt), color=\"red\", label=\"Spline function\")\n",
-    "ax1.set_xlabel(\"H  \" + unitH)\n",
-    "ax1.set_ylabel(\"B \" + unitB)\n",
-    "ax1.legend(numpoints=1, loc=\"best\")\n",
-    "ax1.grid(linestyle=\"dashed\")\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "stdU=0.00000005\n",
-    "Uh=np.array([ufloat(x,stdU) for x in UH])\n",
-    "Ub=np.array([ufloat(x,stdU) for x in UB])\n",
-    "b=UBfak*Ub\n",
-    "h=UHfak*Uh\n",
-    "murt=((b/(mu0*h))**2)**0.5\n",
-    "#murt2=[x for x in murt if x<2000]\n",
-    "#murt2=np.sort(murt)[49:-100]\n",
-    "plt.plot(range(len(murt)),n(murt))\n",
-    "plt.show()\n",
-    "#mur=np.mean(murt2)\n",
-    "mur=np.median(murt)\n",
-    "#plt.plot(range(len(murt2)),n(murt2))\n",
-    "#plt.plot(range(len(murt2)),np.ones(len(murt2))*mur.n)\n",
-    "#plt.show()\n",
-    "print(f\"Wir erhalten mur={mur} als Median. \")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "id": "6750644c-75af-4671-b198-8a1f1c9bb3bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Resampling by factor 13\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 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"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Wir erhalten mur=(1.71+/-0.15)e+03 als Median. \n"
-     ]
-    }
-   ],
-   "source": [
-    "#Ur ist Channel B und UC ist Channel A\n",
-    "#R2 = 10 kOhm\n",
-    "#C = 10 uF\n",
-    "N1=1000\n",
-    "N2=50\n",
-    "l=ufloat(0.48,0.01)\n",
-    "R2=ufloat(10000,500)\n",
-    "R1=ufloat(10,0.5)\n",
-    "C=ufloat(10*10**(-6),0.5*10**(-6))\n",
-    "mu0=4*np.pi*10**(-7)\n",
-    "UHfak=N1/(R1*l)\n",
-    "UBfak=C*R2/(N2*(0.039)**2)\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from scipy import interpolate\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Read cvs file as pandas dataframe\n",
-    "#df = pd.read_csv(\"Christian_ist_ein_Schatz2_2_10_02mA2.csv\")\n",
-    "# Translate dataframe columns into native python lists\n",
-    "#t  = df[\"Frequenz\"].to_list()[1:-1:10]\n",
-    "#UB = df[\"Kanal A\"].to_list()[1:-1:10] \n",
-    "#UH = df[\"Kanal B\"].to_list()[1:-1:10] \n",
-    "#UB=np.array(UB)\n",
-    "#UH=np.array(UH)\n",
-    "data = np.genfromtxt('Christian_ist_ein_Schatz2_1_24_24_02mA2.csv', delimiter=\",\", skip_header=3 )\n",
-    "t, UB, UH= data[:,0], data[:,1], data[:,2]\n",
-    "t=t[1:-1:]\n",
-    "UB=UB[1:-1:]/1000\n",
-    "UH=UH[1:-1:]/1000\n",
-    "from PhyPraKit.phyTools import resample, meanFilter\n",
-    "\n",
-    "# If length is too large, resample by an appropriate factor, we are fine with \n",
-    "# 150 data points\n",
-    "il=len(UH)\n",
-    "size=300\n",
-    "if il > size:\n",
-    "    g = int(il/size)\n",
-    "    # This is an example of smoothing by averaging over n neighbors\n",
-    "    #print(\"Smoothing with window size \", n)\n",
-    "    #t  = meanFilter(vUH, width=n)\n",
-    "    #UH = meanFilter(vUH, width=n)\n",
-    "    #UB = meanFilter(vUB, width=n)\n",
-    "    # This is an example of down sampling by averaging over n neighbors\n",
-    "    print(\"Resampling by factor\", g)\n",
-    "    t  = resample(t , n=g)\n",
-    "    UH = resample(UH, n=g)\n",
-    "    UB = resample(UB, n=g)\n",
-    "\n",
-    "CALIB_UH2H = UHfak.n  # U_H -> H <-- adjust !\n",
-    "CALIB_UB2B = UBfak.n   # U_B -> B <-- adjust !\n",
-    "H = UH * CALIB_UH2H\n",
-    "B = UB * CALIB_UB2B\n",
-    "# Interpolate the points of (t,H) by spline functions; s=0 means that no extra \n",
-    "# smoothing will be applied, each point of H will be used for the spline\n",
-    "spl_Ht = interpolate.UnivariateSpline(t, H, s=0)\n",
-    "spl_Bt = interpolate.UnivariateSpline(t, B, s=0)\n",
-    "\n",
-    "# Plot hysteresis curve as Channel A vs. Channeel B\n",
-    "tplt = np.linspace(t[0], t[-1], 200)\n",
-    "unitH = \"(A/m)\"; unitB = \"(T)\"\n",
-    "fig = plt.figure(1, figsize=(6.0, 6.0))\n",
-    "ax1 = fig.add_subplot()\n",
-    "ax1.scatter(H, B, color=\"blue\", marker=\"o\", s=5.0, label=\"Data points\")\n",
-    "ax1.plot(spl_Ht(tplt), spl_Bt(tplt), color=\"red\", label=\"Spline function\")\n",
-    "ax1.set_xlabel(\"H  \" + unitH)\n",
-    "ax1.set_ylabel(\"B \" + unitB)\n",
-    "ax1.legend(numpoints=1, loc=\"best\")\n",
-    "ax1.grid(linestyle=\"dashed\")\n",
-    "plt.show()\n",
-    "\n",
-    "stdU=0.00000005\n",
-    "Uh=np.array([ufloat(x,stdU) for x in UH])\n",
-    "Ub=np.array([ufloat(x,stdU) for x in UB])\n",
-    "b=UBfak*Ub\n",
-    "h=UHfak*Uh\n",
-    "murt=((b/(mu0*h))**2)**0.5\n",
-    "#murt2=[x for x in murt if x<2000]\n",
-    "#murt2=np.sort(murt)[49:-100]\n",
-    "plt.plot(range(len(murt)),n(murt))\n",
-    "plt.show()\n",
-    "#mur=np.mean(murt2)\n",
-    "mur=np.median(murt)\n",
-    "#plt.plot(range(len(murt2)),n(murt2))\n",
-    "#plt.plot(range(len(murt2)),np.ones(len(murt2))*mur.n)\n",
-    "#plt.show()\n",
-    "print(f\"Wir erhalten mur={mur} als Median. \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e96d00f4-6793-429d-b1f6-7287723557c8",
-   "metadata": {},
-   "source": [
-    "**V E R S U C H S B E S C H R E I B U N G**\n",
-    "\n",
-    "*Fügen Sie Ihre Versuchsbeschreibung hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "db113a38-b1df-4ab9-b89c-a24bfd10b7c8",
-   "metadata": {},
-   "source": [
-    "**L Ö S U N G**\n",
-    "\n",
-    "*Fügen Sie numerische Berechnungen zur Lösung dieser Aufgabe hier ein. Löschen 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. Fügen Sie ggf. weitere Code-Zellen zu.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eaa372be-36be-4914-9477-b3025bdee5c5",
-   "metadata": {},
-   "source": [
-    "**D I S K U S S I O N**\n",
-    "\n",
-    "*Fügen Sie eine abschließende Diskussion und Bewertung Ihrer Lösung hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "66f60b58-6aac-4a8b-bb6a-adad17f3c95a",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "### Aufgabe 2.2: Hystereseverlust \n",
-    "\n",
-    "**Diese Aufgabe ist nur für Studierende mit Hauptfach Physik verpflichtend. Studierende mit Nebenfach Physik und Lehramtstudierende können diese Aufgabe überspringen.**\n",
-    "\n",
-    " * Bestimmen Sie den **Hystereseverlust** $P_{\\mathrm{hyst}}$ und den dazu äquivalenten **Verlustwiderstand** $R_{\\mathrm{hyst}}$ aus den Magnetisierungskurven von **Aufgabe 2.1** für die verwendeten Werte von $I_{\\mathrm{eff}}$.\n",
-    " * Vergleichen Sie Ihr Ergebnis für $P_{\\mathrm{hyst}}$ mit Ihren Ergebnissen für $P_{L}$ aus **Aufgabe 1**.\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf9b0247-cd93-47a3-93af-82ddaafb1da8",
-   "metadata": {},
-   "source": [
-    "**V E R S U C H S B E S C H R E I B U N G**\n",
-    "\n",
-    "*Fügen Sie Ihre Versuchsbeschreibung hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "691ea74e-c262-436d-9f9f-a512e2f7f5b1",
-   "metadata": {},
-   "source": [
-    "**L Ö S U N G**\n",
-    "\n",
-    "*Fügen Sie numerische Berechnungen zur Lösung dieser Aufgabe hier ein. Löschen 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. Fügen Sie ggf. weitere Code-Zellen zu.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a27d59cd-4be9-49dc-b15b-d74960881da5",
-   "metadata": {},
-   "source": [
-    "**D I S K U S S I O N**\n",
-    "\n",
-    "*Fügen Sie eine abschließende Diskussion und Bewertung Ihrer Lösung hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "5aaf2521-dbac-4802-a743-f267363680fc",
-   "metadata": {},
-   "source": [
-    "## Aufgabe 3: Magnetische Härte\n",
-    "\n",
-    " * Stellen Sie die Hysteresekurve für einen **Ferrit-Schalenkern** am Oszilloskop dar und vergleichen Sie diese mit der Hysteresekurve des Eisenkerns. \n",
-    " * Ermitteln Sie hierzu zusätzlich jeweils die folgenden Größen:\n",
-    "   * **Remanenz** $B_{R}$,\n",
-    "   * **Koerzitivfeldstärke** $H_{C}$ und\n",
-    "   * **Sättigungsinduktion** $B_{S}$.\n",
-    " * Diskutieren Sie Ihre Erwartung für $P_{\\mathrm{hyst}}$ für den Ferrit-Schalenkern im Vergleich zum Eisenkern. \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c00ba658-a8fa-45d6-80d2-7fe218b65fbd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Ieff = 262.2 mA\n",
-    "#250 und 50 Windungen\n",
-    "\n",
-    "#bei FerritSchlenker\n",
-    "#Ieff = 25.30 mA\n",
-    "#250 und 50 Windungen"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf498ca8-ac2f-4d18-829e-3edd1b05b6e9",
-   "metadata": {},
-   "source": [
-    "**V E R S U C H S B E S C H R E I B U N G**\n",
-    "\n",
-    "*Fügen Sie Ihre Versuchsbeschreibung hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f5600ea-c2ea-4d77-a77e-e3b471c99146",
-   "metadata": {},
-   "source": [
-    "**L Ö S U N G**\n",
-    "\n",
-    "*Fügen Sie numerische Berechnungen zur Lösung dieser Aufgabe hier ein. Löschen 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. Fügen Sie ggf. weitere Code-Zellen zu.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3fb8b19-3453-4ee0-8be3-79e63c9d428f",
-   "metadata": {},
-   "source": [
-    "**D I S K U S S I O N**\n",
-    "\n",
-    "*Fügen Sie eine abschließende Diskussion und Bewertung Ihrer Lösung hier ein. Löschen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b669bee3-e4f2-4a1d-be44-6ef3be4f6170",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "# Beurteilung"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4ab1a4b-9cdf-43aa-a33c-ae863bd60902",
-   "metadata": {},
-   "source": [
-    " * Nach Abschluss des Versuchs haben Sie die Möglichkeit diesen Versuch individuell zu beurteilen.\n",
-    " * **Folgen Sie zur Beurteilung dieses Versuchs diesem [Link](https://www.empirio.de/s/mlNVWZpooS)**.\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 auszufüllen.  "
-   ]
-  }
- ],
- "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
-}