From 8cc7589468c31e3657b70fcc59e81612ca62af7a 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: Wed, 11 Dec 2024 20:11:26 +0000
Subject: [PATCH] Delete Ferromagnetische_Hysterese_Jonathan_final.ipynb

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 ...magnetische_Hysterese_Jonathan_final.ipynb | 1293 -----------------
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 delete mode 100644 Ferromagnetische_Hysterese/Ferromagnetische_Hysterese_Jonathan_final.ipynb

diff --git a/Ferromagnetische_Hysterese/Ferromagnetische_Hysterese_Jonathan_final.ipynb b/Ferromagnetische_Hysterese/Ferromagnetische_Hysterese_Jonathan_final.ipynb
deleted file mode 100644
index 8c75063..0000000
--- a/Ferromagnetische_Hysterese/Ferromagnetische_Hysterese_Jonathan_final.ipynb
+++ /dev/null
@@ -1,1293 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "885c7767-e912-4e31-b5d6-3a3443ffa58e",
-   "metadata": {},
-   "source": [
-    "# FakultÃ¤t fÃ¼r Physik\n",
-    "\n",
-    "## Physikalisches Praktikum P1 fÃ¼r 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 durchgefÃ¼hrt 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": [
-    "# DurchfÃ¼hrung"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be70efea-bff5-42d5-b5aa-0b9598c33846",
-   "metadata": {},
-   "source": [
-    "**Detaillierte Hinweise zur DurchfÃ¼hrung 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: InduktivitÃ¤t und Verlustleistung einer Spule"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f05f94c-cb93-4ac6-b323-7a73a8bffbb0",
-   "metadata": {},
-   "source": [
-    "### Aufgabe 1.1: LuftgefÃ¼llte Spulte\n",
-    "\n",
-    " * Bestimmen Sie die **SpuleninduktivitÃ¤t** $L$, den **Verlustwiderstand** $R_{L}$ und die **elektrische Verlustleistung** $P_{L}$ (aufgrund von $R_{L}$) einer luftgefÃ¼llten Spule.\n",
-    " * BestÃ¤tigen oder Widerlegen Sie die Hypothese, dass weder $L$ noch $R_{L}$ von der effektiven StromstÃ¤rke $I_{\\mathrm{eff}}$ im Wechselstromkreis abhÃ¤ngen.\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 fÃ¼r $L$ und $R_{L}$ und vergleichen Sie Ihre Erwartung mit der Messung.\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "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\"${x.nominal_value:.2f} \\\\pm {x.std_dev:.2f}$\" for x in u]"
-   ]
-  },
-  {
-   "attachments": {
-    "7cd64ca7-6bdc-45d3-a85b-d703b5a97f03.png": {
-     "image/png": 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"
-    }
-   },
-   "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",
-    "![1_1FH.png](attachment:7cd64ca7-6bdc-45d3-a85b-d703b5a97f03.png)   \n",
-    "Quelle: Hinweise fÃ¼r den Versuch Ferromagnetische Hysterese-Impedanz der Spule \n",
-    "\n",
-    "\n",
-    "Es wurde eine Spule mit dem der Windungszahl $N=1000$ der LÃ¤nge $l=6.8\\,\\mathrm{cm}$, einer Drahtdicke $d=0.7\\,\\mathrm{mm}$ und einer mittleren Wicklungsradius $r=3.4 \\,\\mathrm{cm}$  und einer QuerschnitsflÃ¤che von $A\\approx 15.21 \\,\\mathrm{cm^2}$ mit dem Wiederstand $R_1=10 \\pm 0.5 \\,\\mathrm{\\Omega}$ in Reihe geschaltet. Diese Schaltung wurde mit einer sinusfÃ¶rmigen Spannung der Frequenz $f= 50 \\pm 0.1 \\,\\mathrm{Hz}$ und einer Ampitude $U_0=12 \\,\\mathrm{V}$. Mit einem Ampermeter wurde der Efektiv Strom durch die Schaltung gemessen. Desweiteren wurden die Spannungen an $R_1$ und $L$ gemessen. Aufgezeichnet wurden fÃ¼r verschiedene Werte fÃ¼r $I_eff$ die jewieligen Pickspannungen $U_{L,0}$ und $U_{L,0}$, sowie die Zeitdifferenz der NulldurchlÃ¤ufe von $U_R$ und $U_L$. \n",
-    "FÃ¼r die Impedanz einer reelen Spule gilt:   \n",
-    "$Z=R_L+ i\\omega L=|Z|\\cdot e^{i\\Delta \\varphi}$   \n",
-    "und somit nach der Aufspalltung in einen Real und ImaginÃ¤rteil sowie mit $|Z|=\\dfrac{U_{L,0}}{U_{R,0}}R$ und $\\Delta\\varphi = \\omega\\,\\Delta t$:   \n",
-    "$$\n",
-    "\\begin{equation*}\n",
-    "\\begin{split}\n",
-    "&L = \\frac{U_{L,0}}{U_{R,0}}\\,\\frac{R}{\\omega}\\,\\sin(\\omega\\Delta t);\\\\\n",
-    "&\\\\\n",
-    "&R_{L} = \\frac{U_{L,0}}{U_{R,0}}\\,R\\,\\cos(\\omega\\Delta t).\n",
-    "\\end{split}\n",
-    "\\end{equation*}\n",
-    "$$ \n",
-    "\n",
-    "sowie aus weiteren Ãœberlegungen:   \n",
-    "$$\n",
-    "\\begin{equation*}\n",
-    "\\begin{split}\n",
-    "&L \\approx N^{2}\\,\\frac{\\mu_{0}\\,\\,A}{\\ell+0.91r}.\n",
-    "&\\\\\n",
-    "&R_{L} =\\rho\\,\\frac{l_{Draht}}{A_{Draht}}.\n",
-    "\\end{split}\n",
-    "\\end{equation*}\n",
-    "$$ \n",
-    "\n",
-    "Wobei $l_{Draht}$ die LÃ¤nge des Drahtes ist und $A_{Draht}$ der Querschnitt des Drahtes ist. \n",
-    "\n",
-    "Die Verlustleistung an $L$ lÃ¤sst sich Ã¼ber $P_{ver}=R_L\\cdot I_{eff}^2$\n",
-    "\n",
-    "Desweiteren kann Ã¼ber eine Anpassung von $\\mu_r$ und $R_L$ als konstante Werte gegen $I_{eff}$ kann gezeigt werden, dass die beiden Werte von $I_{eff}$ unbhÃ¤ngig sind. \n",
-    "\n",
-    "Die Fehler wurden aus den Unsicherheiten beim Ablesen und den Begerentzheit der Anzeigen, von den wir ausgehen, dass sie NÃ¤herungsweise den gesamten Fehler verantworten, wie follgt abgeschÃ¤zt:   \n",
-    "$\\Delta\\omega=0.1\\,\\mathrm{Hz}$   \n",
-    "die Restlichen Unsicherheiten kÃ¶nnen der darstellung der Messwerte entnommen werden.   \n",
-    "Die Berechnung der weiteren Unsicherheiten erfollgte mitels der Pythonbibliothek *uncertainties* mithilfe liniarer Fehlerpfortpflanzung berechnet. \n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b92d6f12-dd01-47e0-af99-1a77716ee6da",
-   "metadata": {},
-   "source": [
-    "**L Ã– S U N G**\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "8e80a26f-775b-44f1-a90e-21ad5f95ce46",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Es wurde gemessen:\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>$I_{eff}\\mathrm{[mA]} $</th>\n",
-       "      <th>$U_{R,0} \\,\\mathrm{mV} $</th>\n",
-       "      <th>$U_{L,0} \\,\\mathrm{mV} $</th>\n",
-       "      <th>$\\Delta t \\,\\mathrm{ms} $</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>$34.35 \\pm 0.10$</td>\n",
-       "      <td>$0.51 \\pm 0.01$</td>\n",
-       "      <td>$0.72 \\pm 0.01$</td>\n",
-       "      <td>$2.72 \\pm 0.30$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>$89.80 \\pm 0.10$</td>\n",
-       "      <td>$1.32 \\pm 0.01$</td>\n",
-       "      <td>$1.87 \\pm 0.01$</td>\n",
-       "      <td>$2.15 \\pm 0.30$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>$126.00 \\pm 0.10$</td>\n",
-       "      <td>$1.85 \\pm 0.01$</td>\n",
-       "      <td>$2.66 \\pm 0.01$</td>\n",
-       "      <td>$2.71 \\pm 0.30$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>$223.40 \\pm 0.10$</td>\n",
-       "      <td>$3.31 \\pm 0.01$</td>\n",
-       "      <td>$4.67 \\pm 0.01$</td>\n",
-       "      <td>$2.70 \\pm 0.30$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>$301.50 \\pm 0.10$</td>\n",
-       "      <td>$4.44 \\pm 0.01$</td>\n",
-       "      <td>$6.27 \\pm 0.01$</td>\n",
-       "      <td>$2.72 \\pm 0.30$</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  $I_{eff}\\mathrm{[mA]} $ $U_{R,0} \\,\\mathrm{mV} $ $U_{L,0} \\,\\mathrm{mV} $  \\\n",
-       "0        $34.35 \\pm 0.10$          $0.51 \\pm 0.01$          $0.72 \\pm 0.01$   \n",
-       "1        $89.80 \\pm 0.10$          $1.32 \\pm 0.01$          $1.87 \\pm 0.01$   \n",
-       "2       $126.00 \\pm 0.10$          $1.85 \\pm 0.01$          $2.66 \\pm 0.01$   \n",
-       "3       $223.40 \\pm 0.10$          $3.31 \\pm 0.01$          $4.67 \\pm 0.01$   \n",
-       "4       $301.50 \\pm 0.10$          $4.44 \\pm 0.01$          $6.27 \\pm 0.01$   \n",
-       "\n",
-       "  $\\Delta t \\,\\mathrm{ms} $  \n",
-       "0           $2.72 \\pm 0.30$  \n",
-       "1           $2.15 \\pm 0.30$  \n",
-       "2           $2.71 \\pm 0.30$  \n",
-       "3           $2.70 \\pm 0.30$  \n",
-       "4           $2.72 \\pm 0.30$  "
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "UUr=0.01\n",
-    "UUl=0.005\n",
-    "UIf=0.1\n",
-    "Udelt=0.3\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",
-    "print(\"Es wurde gemessen:\")\n",
-    "pd.DataFrame({\"$I_{eff}\\mathrm{[mA]} $\":utl(If),\n",
-    "              \"$U_{R,0} \\,\\mathrm{mV} $\":utl(UR),\n",
-    "              \"$U_{L,0} \\,\\mathrm{mV} $\":utl(UL),\n",
-    "              \"$\\Delta t \\,\\mathrm{ms} $\":utl(delt)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "7b0c0e29-f5ba-4c38-87ca-4749fca945f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>$I_{eff}\\mathrm{[mA]} $</th>\n",
-       "      <th>$P_{verl} \\,\\mathrm{W} $</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>$34.35 \\pm 0.10$</td>\n",
-       "      <td>$0.01 \\pm 0.00$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>$89.80 \\pm 0.10$</td>\n",
-       "      <td>$0.08 \\pm 0.00$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>$126.00 \\pm 0.10$</td>\n",
-       "      <td>$0.15 \\pm 0.01$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>$223.40 \\pm 0.10$</td>\n",
-       "      <td>$0.49 \\pm 0.02$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>$301.50 \\pm 0.10$</td>\n",
-       "      <td>$0.89 \\pm 0.04$</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  $I_{eff}\\mathrm{[mA]} $ $P_{verl} \\,\\mathrm{W} $\n",
-       "0        $34.35 \\pm 0.10$          $0.01 \\pm 0.00$\n",
-       "1        $89.80 \\pm 0.10$          $0.08 \\pm 0.00$\n",
-       "2       $126.00 \\pm 0.10$          $0.15 \\pm 0.01$\n",
-       "3       $223.40 \\pm 0.10$          $0.49 \\pm 0.02$\n",
-       "4       $301.50 \\pm 0.10$          $0.89 \\pm 0.04$"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\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 = '$InduktivitÃ¤t$ '\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 R2(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=R2)\n",
-    "fit2.do_fit()\n",
-    "RL=ufloat(fit2.parameter_values[0],fit2.parameter_errors[0])\n",
-    "kafe2.plot({fit1,fit2}, x_label=r\"$I_f\\,\\mathrm{[mA]}$\", y_label=r'$ L\\,\\mathrm{[mH]} \\mathrm{und\\ } R \\,\\mathrm{[\\Omega]} $')\n",
-    "Pver=RL*(If/1000)**2\n",
-    "pd.DataFrame({\"$I_{eff}\\mathrm{[mA]} $\":utl(If),\n",
-    "              \"$P_{verl} \\,\\mathrm{W} $\":utl(Pver)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "8bd0860c-c28b-4442-b42e-1d0f048deddb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Rechnerisch ergibt sich L=0.04612598620440388 H und R=9.880816326530613 Ohm\n"
-     ]
-    }
-   ],
-   "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": "04620cbe-dd13-479c-b3ef-5a7ef7f31455",
-   "metadata": {},
-   "source": [
-    "Aus den Daten von $U_{R,0},U_{R,0}$ und $\\Delta t$ follgt also:   \n",
-    "$R_L=9.78\\pm 0.75 \\,\\mathrm{\\Omega}$   \n",
-    "$L=33.1\\pm 2.3 \\,\\mathrm{mH}$  \n",
-    "\n",
-    "FÃ¼r die Verlustleistung an der Spule lÃ¤sst sich berechnen:   \n",
-    "$P_{verl}=$\n",
-    "Beide Fits mit einem konstanten Wert beschreiben die Daten im Rahmen der Unsicherheiten sehr gut, wie sich aus $\\chi^2/ndf < 1$ und $chi^2 \\ \\mathrm{proability}\\approx 0.7$ follgern lÃ¤sst. Sie sind also nicht von $I_{eff}$ abhÃ¤ngig.\n",
-    "\n",
-    "\n",
-    "Eine Berechnung von $R_L$ und $L$ aus den Dimensionierungen der Spule ergibt:   \n",
-    "$R_L\\approx9.88\\,\\mathrm{\\Omega}$   \n",
-    "$L\\approx 46.1\\,\\mathrm{mH}$ \n",
-    "\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",
-    "Beide Werte von $R_L$ sind im Rahmen der Messunsicherheit vertrÃ¤glich, die beiden Werte fÃ¼r $L$ nicht. Ein Vergleich mit den Herstellerangben ($L=44\\,\\mathrm{mH}$) legt nahe, dass der Wert fÃ¼r L aus der Berechnung von mithilfe von $U_{R,0},U_{R,0}$ und $\\Delta t$ warscheinlich zu klein ist. Es kÃ¶nnte eine Unsicherheit zu klein AbgeschÃ¤tz worden sein, oder BeschÃ¤digung in der Spule fÃ¼hert zu einer faktischen Ã„nderung der Daten der Spule von denn Herstellerangaben.    \n",
-    "\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 fÃ¼r $L$ die (mittlere) **relative PermeabilitÃ¤t** $\\langle\\mu_{r}\\rangle$ als Funktion von $I_{\\mathrm{eff}}$.\n",
-    " * Berechnen Sie aus den gemessenen Werten fÃ¼r $R_{L}$ 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",
-    "---"
-   ]
-  },
-  {
-   "attachments": {
-    "61b1d54c-4a46-4ef5-b20b-d113f03ded4e.png": {
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"
-    }
-   },
-   "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",
-    "![1_2FH.png](attachment:61b1d54c-4a46-4ef5-b20b-d113f03ded4e.png)   \n",
-    "Quelle: Hinweise fÃ¼r den Versuch Ferromagnetische Hysterese-Impedanz der Spule \n",
-    "\n",
-    "\n",
-    "Der Versuch wurde wie in Aufgabe 1.1 Wiederholt und in soweit VerÃ¤ndert als das ein geschlossenen Eisenkern, welcher durch Schichten von islierdem Matrial WirbelstrÃ¶me weitestgehend unterdrÃ¼ckt, in die Spule eingefÃ¼gt wurde. In diesem ist die mittler FeldlinienlÃ¤nge $l=48 \\,\\mathrm{cm}$. Zur Messbereichserweiterung wurde Das zur Messung von $U_L$ durch das zuschalten eines Wiederstnad $R_S=9 \\,\\mathrm{M\\Omega}$ verzehnfacht. \n",
-    "Aus:   \n",
-    "\n",
-    "\\begin{equation*}\n",
-    "\\begin{split}\n",
-    "&L = N^{2}\\,\\frac{\\mu_{0}\\cdot \\mu_r\\cdot A}{\\ell}\n",
-    "\\end{split}\n",
-    "\\end{equation*}\n",
-    "follgt:   \n",
-    "$\\mu_r = \\dfrac {\\ell\\cdot L}{N^{2}\\cdot\\mu_{0}\\cdot A}$.\n",
-    "\n",
-    "wobei $\\mu_r$ in dem Fall ein Zeitlicher Mittelwert darstellt.\n",
-    "\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa723eed-4a46-41a3-a823-7e61a1d252c7",
-   "metadata": {},
-   "source": [
-    "**L Ã– S U N G**\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "93e1f6ca-f090-4c7b-bef2-d5276ef6412b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    .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>$I_{eff}\\mathrm{[mA]} $</th>\n",
-       "      <th>$U_{R,0} \\,\\mathrm{[mV]} $</th>\n",
-       "      <th>$U_{L,0} \\,\\mathrm{[mV]} $</th>\n",
-       "      <th>$\\Delta t \\,\\mathrm{[ms]} $</th>\n",
-       "      <th>$\\mu_r $</th>\n",
-       "      <th>$P_{verl} \\,\\mathrm{[W]} $</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>$10.02 \\pm 0.10$</td>\n",
-       "      <td>$0.15 \\pm 0.01$</td>\n",
-       "      <td>$22.01 \\pm 0.10$</td>\n",
-       "      <td>$1.44 \\pm 0.50$</td>\n",
-       "      <td>$526.03 \\pm 175.41$</td>\n",
-       "      <td>$0.13 \\pm 0.01$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>$15.04 \\pm 0.10$</td>\n",
-       "      <td>$0.23 \\pm 0.01$</td>\n",
-       "      <td>$43.30 \\pm 0.10$</td>\n",
-       "      <td>$3.52 \\pm 0.50$</td>\n",
-       "      <td>$1416.89 \\pm 145.91$</td>\n",
-       "      <td>$0.19 \\pm 0.06$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>$20.09 \\pm 0.10$</td>\n",
-       "      <td>$0.30 \\pm 0.01$</td>\n",
-       "      <td>$73.70 \\pm 0.10$</td>\n",
-       "      <td>$2.95 \\pm 0.50$</td>\n",
-       "      <td>$1670.88 \\pm 221.32$</td>\n",
-       "      <td>$0.60 \\pm 0.13$</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>$22.68 \\pm 0.10$</td>\n",
-       "      <td>$0.33 \\pm 0.01$</td>\n",
-       "      <td>$91.63 \\pm 0.10$</td>\n",
-       "      <td>$2.99 \\pm 0.50$</td>\n",
-       "      <td>$1875.76 \\pm 241.72$</td>\n",
-       "      <td>$0.84 \\pm 0.19$</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  $I_{eff}\\mathrm{[mA]} $ $U_{R,0} \\,\\mathrm{[mV]} $  \\\n",
-       "0        $10.02 \\pm 0.10$            $0.15 \\pm 0.01$   \n",
-       "1        $15.04 \\pm 0.10$            $0.23 \\pm 0.01$   \n",
-       "2        $20.09 \\pm 0.10$            $0.30 \\pm 0.01$   \n",
-       "3        $22.68 \\pm 0.10$            $0.33 \\pm 0.01$   \n",
-       "\n",
-       "  $U_{L,0} \\,\\mathrm{[mV]} $ $\\Delta t \\,\\mathrm{[ms]} $  \\\n",
-       "0           $22.01 \\pm 0.10$             $1.44 \\pm 0.50$   \n",
-       "1           $43.30 \\pm 0.10$             $3.52 \\pm 0.50$   \n",
-       "2           $73.70 \\pm 0.10$             $2.95 \\pm 0.50$   \n",
-       "3           $91.63 \\pm 0.10$             $2.99 \\pm 0.50$   \n",
-       "\n",
-       "               $\\mu_r $ $P_{verl} \\,\\mathrm{[W]} $  \n",
-       "0   $526.03 \\pm 175.41$            $0.13 \\pm 0.01$  \n",
-       "1  $1416.89 \\pm 145.91$            $0.19 \\pm 0.06$  \n",
-       "2  $1670.88 \\pm 221.32$            $0.60 \\pm 0.13$  \n",
-       "3  $1875.76 \\pm 241.72$            $0.84 \\pm 0.19$  "
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "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",
-    "pd.DataFrame({\"$I_{eff}\\mathrm{[mA]} $\":utl(If),\n",
-    "              \"$U_{R,0} \\,\\mathrm{[mV]} $\":utl(UR),\n",
-    "              \"$U_{L,0} \\,\\mathrm{[mV]} $\":utl(UL),\n",
-    "              \"$\\Delta t \\,\\mathrm{[ms]} $\":utl(delt),\n",
-    "              \"$\\mu_r $\":utl(mur),\n",
-    "              \"$P_{verl} \\,\\mathrm{[W]} $\":utl(PL)\n",
-    "             })\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "b7500752-447c-4130-9711-a14b88d21c0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.errorbar(n(If),n(PL),s(PL),s(If),marker='None', linestyle='None')  \n",
-    "plt.ylabel(r'$P_L\\,\\mathrm{ [W]}$')\n",
-    "plt.xlabel('$I_{eff} \\,\\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_{eff} \\,\\mathrm{ [mA]}$')\n",
-    "plt.title(\"Relative PermeabilitÃ¤t\")\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "41f840e3-c7de-4945-8370-b54745a50f95",
-   "metadata": {},
-   "source": [
-    "**D I S K U S S I O N**\n",
-    "\n",
-    "Die Wert von $\\mu_r$ sind mit den Angaben auf Wikipedia fÃ¼r die magnetische PermeabilitÃ¤t von Eisen (300 bis 10000) (https://de.wikipedia.org/wiki/Magnetische_Permeabilit%C3%A4t) vertrÃ¤glich. Dies ist aber aufgrun des sehr weiten Intervalls nur begernzt AusagekrÃ¤ftig. \n",
-    "\n",
-    "Es lÃ¤sst sich feststellen, dass die Verlustleistung tendenziell viel grÃ¶ÃŸer ist wenn der Eisenkern in die Spule eingefÃ¼hrt ist. ES gilt also $P_{verlustLuft}<P_{verlustLuft}$ wenn nicht $I_{effLuft}\\gg I_{effEisen}$. Es liegt nahe dass die (Um-)Magnetesierung Eisens im Fall mit einem Eisenkern einen erheblichen Teil der Verlustleistung ausmacht. \n",
-    "\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** $B(H)$ fÃ¼r den Eisenkern aus **Aufgabe 1.2** fÃ¼r zwei geeignete effektive StromestÃ¤rken von $I_{\\mathrm{eff}}$ (im PrimÃ¤rkreis 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",
-    "---"
-   ]
-  },
-  {
-   "attachments": {
-    "9d8bfa84-6f03-4d9c-81b6-460e11615910.png": {
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"
-    }
-   },
-   "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",
-    "![Hysteresemessung.png](attachment:9d8bfa84-6f03-4d9c-81b6-460e11615910.png)\n",
-    "Quelle: Hinweise fÃ¼r den Versuch Ferromagnetische Hysterese-Magnetisierung und Polarisation\n",
-    "\n",
-    "Es wurde die Spule aus der Aufgabe 1.1 und 1.2 wie berits in den Aufgaben 1.2 geschaltet. Es wird zusÃ¤tzlich eine Spule mit den gleich Spezifikationen aber einer Windungszahl von $N_2=50$ um den Eisenkern gestÃ¼lpt und die Anordung so zu einem Transformator ergÃ¤nzt. Diese zweite Spule wird mit einem Wiederstand $R_2=10 \\pm 0.5 \\,\\mathrm{k\\Omega}$ und einem Kondsator mit $C= 10 \\pm 0.5 \\,\\mathrm{\\mu F}$ in Reihe geschaltet. Gemessen wurde die Spannung $U_H$ am Wiederstand $R_1$ und die Spannung $U_H$ an der Spule $C$. \n",
-    "\n",
-    "Es gilt:   \n",
-    "$H=N_1\\dfrac{I_1}{l}=N_1\\dfrac{U_H}{l\\cdot R_1}$\n",
-    "\n",
-    "sowie im hier vorliegenden Fall $R_2\\cdot 2\\cdot \\pi \\cdot f \\cdot C \\gg 1$:   \n",
-    "$$\n",
-    "\\begin{equation*}\n",
-    "U_{B} = \\frac{1}{C\\,R_{2}}\\int U_{i}\\,\\mathrm{d}t = \\frac{N_{2}\\,A}{C\\,R_{2}}\\int \\dot{B}\\,\\mathrm{d}t = \\frac{N_{2}\\,A}{C\\,R_{2}}\\,B.\n",
-    "\\end{equation*}\n",
-    "$$    \n",
-    "also\n",
-    "$$\n",
-    "\\begin{equation*}\n",
-    "B = \\frac{C\\,R_{2}}{N_{2}\\,A}\\,U_{B}\n",
-    "\\end{equation*}\n",
-    "$$\n",
-    "\n",
-    "Die so erhalten Werte fÃ¼r $B$ kÃ¶nnen gegen $H$ aufgetragen werden um die Hysterkurve zu erhalten, dabei wurden die Messwerte auf 300 Daten ($H,B,t$) runtergesampelt und mittels des Phyenbibliothek *PhryPraKit* geglÃ¤ttet. Des weitern wurden mithilfe von Spliens zwischen den Dten interpoliert um eine Stetige Hysteriekurve zu erhalten. \n",
-    "\n",
-    "Des weiteren lÃ¤sst sich $\\mu_r$ als zeitlicher Mittelwert wie follgt berechnen:   \n",
-    "$\\mu_{r} = \\frac{B_{S}}{\\mu_{0}\\,H_{S}}$   \n",
-    "Da fÃ¼r $H\\approx 0$ zu sehr hohen Werten fÃ¼r $mu_r$ auftreten wurde der Median anstelle des arithmetischen Mittels betrachtet.\n",
-    "\n",
-    "Die Fehler wurden aus den Unsicherheiten beim Ablesen und den Begerenzheit der Anzeigen bzw. aus den Herstellerangben wie follgt abgeschÃ¤tzt:   \n",
-    "$\\Delta U_H=\\Delta U_B=0.05\\,\\mathrm{\\mu V}$   \n",
-    "$\\Delta l=0.01\\,\\mathrm{m}$      \n",
-    "$\\Delta R_2=500\\,\\mathrm{\\Omega}$    \n",
-    "$\\Delta R_1=0.5\\,\\mathrm{\\Omega}$     \n",
-    "$\\Delta C=0.5\\,\\mathrm{\\mu F}$        \n",
-    "Die Berechnung der weiteren Unsicherheiten erfollgte mitels der Pythonbibliothek *uncertainties* mithilfe liniarer Fehlerpfortpflanzung berechnet. \n",
-    "\n",
-    "Die Messungen wurden bei $I_{eff}=10.02 \\pm 0.05 \\,\\mathrm{mA}$ und $I_{eff}=24.24 \\pm 0.05 \\,\\mathrm{mA}$ durchgefÃ¼hrt.\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "db113a38-b1df-4ab9-b89c-a24bfd10b7c8",
-   "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": 56,
-   "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"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Wir erhalten mur=(9.0+/-0.8)e+02 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",
-    "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",
-    "# 350 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.title(r\"Hysteresekurve bei $I_{eff}=10.02 \\pm 0.05 \\,\\mathrm{mA}$\")\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(murt)\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": 59,
-   "id": "6750644c-75af-4671-b198-8a1f1c9bb3bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Resampling by factor 9\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Wir erhalten mur=(1.72+/-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=400\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.title(r\"Hysteresekurve bei $I_{eff}=24.24 \\pm 0.05 \\,\\mathrm{mA}$\")\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": "d3814593-e9d4-428c-8ca9-2c980d56bfd3",
-   "metadata": {},
-   "source": [
-    "Es lÃ¤sst sich aus den Daten errechen: \n",
-    "FÃ¼r $I_{eff}=10.02 \\pm 0.05 \\,\\mathrm{mA}$:   \n",
-    "$\\mu_r=900 \\pm 80$   \n",
-    "FÃ¼r $I_{eff}=24.24 \\pm 0.05 \\,\\mathrm{mA}$:   \n",
-    "$\\mu_r=1720 \\pm 150$   "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eaa372be-36be-4914-9477-b3025bdee5c5",
-   "metadata": {},
-   "source": [
-    "**D I S K U S S I O N**\n",
-    "\n",
-    "Der Vergleich mit den Werten aus fÃ¼r $\\mu_r$ aus der Aufgabe 1.2 legt nahe, dass die Werte miteinader weitestgehen VertrÃ¤glich sind.   \n",
-    "Zwar ist der Pull fÃ¼r die Werte mit $I_{eff}=10.02 \\pm 0.05 \\,\\mathrm{mA}$, mit 1.94 zwar grÃ¶ÃŸer als 1 da aber der Wert in der 1.2 aus nur einer Einzigen Messung beruht und somit stastistische Ausgen nur eine begerenzt Ausgekraft haben und der Wert aus der 2.1 aufgrund seiner Bestimmung Ã¼ber den Median auch Unsicher ist, kann von einer groben VertrÃ¤glichkeit beider Messungen Augegangen werden. \n",
-    "\n",
-    "FÃ¼r den Wert bei $I_{eff}=24.24 \\pm 0.05 \\,\\mathrm{mA}$ gibt es keine direckten Vergleichswerte in der Aufgabe 1.2 zwar ist $\\mu_r$ hier kleiner als $\\mu_r$ fÃ¼r $I_{eff}=22.68 \\pm 0.10 \\,\\mathrm{mA}$, was dem Ablesbaren Trend von $\\mu_r$ fÃ¼r steigenden $I_{eff}$ wiederspricht. Dies bewegt sich aber im Rahmen der Unsicherheiten.  \n",
-    "\n",
-    "\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 fÃ¼r Studierende mit Hauptfach Physik verpflichtend. Studierende mit Nebenfach Physik und Lehramtstudierende kÃ¶nnen diese Aufgabe Ã¼berspringen.**\n",
-    "\n",
-    " * Bestimmen Sie den **Hystereseverlust** $P_{\\mathrm{hyst}}$ und den dazu Ã¤quivalenten **Verlustwiderstand** $R_{\\mathrm{hyst}}$ aus den Magnetisierungskurven von **Aufgabe 2.1** fÃ¼r die verwendeten Werte von $I_{\\mathrm{eff}}$.\n",
-    " * Vergleichen Sie Ihr Ergebnis fÃ¼r $P_{\\mathrm{hyst}}$ mit Ihren Ergebnissen fÃ¼r $P_{L}$ 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",
-    "*FÃ¼gen Sie Ihre Versuchsbeschreibung hier ein. LÃ¶schen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "691ea74e-c262-436d-9f9f-a512e2f7f5b1",
-   "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": "markdown",
-   "id": "a27d59cd-4be9-49dc-b15b-d74960881da5",
-   "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",
-    "---"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "5aaf2521-dbac-4802-a743-f267363680fc",
-   "metadata": {},
-   "source": [
-    "## Aufgabe 3: Magnetische HÃ¤rte\n",
-    "\n",
-    " * Stellen Sie die Hysteresekurve fÃ¼r einen **Ferrit-Schalenkern** am Oszilloskop dar und vergleichen Sie diese mit der Hysteresekurve des Eisenkerns. \n",
-    " * Ermitteln Sie hierzu zusÃ¤tzlich jeweils die folgenden GrÃ¶ÃŸen:\n",
-    "   * **Remanenz** $B_{R}$,\n",
-    "   * **KoerzitivfeldstÃ¤rke** $H_{C}$ und\n",
-    "   * **SÃ¤ttigungsinduktion** $B_{S}$.\n",
-    " * Diskutieren Sie Ihre Erwartung fÃ¼r $P_{\\mathrm{hyst}}$ fÃ¼r 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",
-    "*FÃ¼gen Sie Ihre Versuchsbeschreibung hier ein. LÃ¶schen Sie hierzu diesen kursiv gestellten Text aus dem Dokument.* \n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f5600ea-c2ea-4d77-a77e-e3b471c99146",
-   "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": "markdown",
-   "id": "a3fb8b19-3453-4ee0-8be3-79e63c9d428f",
-   "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": "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 MÃ¶glichkeit 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 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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