fpraktikum/LM/auswertung/Untitled.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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    "ein.tags": "worksheet-0",
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Kennlinien PM3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "131.75230566534913"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eta = .03\n",
    "rt = 506/60\n",
    "T = 1/eta**2*1/rt\n",
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
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    "ein.hycell": false,
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   },
   "outputs": [],
   "source": [
    "N=1148\n",
    "c=N/T\n",
    "dc=np.sqrt(N)/T\n",
    "dc/c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
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   },
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb7e89befd0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib qt5\n",
    "calib = pd.read_excel('../messungen/vorversuch_kennlinnien.xlsx', sheet_name='Kennl')\n",
    "plt.plot(calib['U/V'], calib[\"N123\"])"
   ]
  }
 ],
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  "name": "Untitled.ipynb"
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