fpraktikum/LM/auswertung/Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import util\n",
    "from scipy.stats import binned_statistic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Kennlinien PM3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131.75230566534913"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.029514066805047763"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": [
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\n"
      ],
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, ax = set_up_plot()\n",
    "calib = pd.read_excel('../messungen/vorversuch_kennlinnien.xlsx', sheet_name='Kennl')\n",
    "ax.set_xlabel('Spannung [V]')\n",
    "ax.set_ylabel('Events')\n",
    "ax.plot(calib['U/V'], calib[\"N123\"], marker='.', label='Kennlinie PM3')\n",
    "ax.axvline(2300, linestyle='dotted', color='gray', label='Gewaehlte Spannung')\n",
    "ax.legend()\n",
    "ax.set_xlim([calib['U/V'].min(), calib['U/V'].max()])\n",
    "save_fig(fig, 'kennlinie_pm3', 'vorversuch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Peakhoehen der Photomultiplier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "peaks = pd.read_excel('../messungen/vorversuch_kennlinnien.xlsx')\n",
    "peak_labels = ['P1', 'P2', 'P3']\n",
    "bin_offsets = [8, 15, 40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "peaks['dP1'] = calculate_peak_uncertainty(peaks[\"P1\"])\n",
    "peaks['dP2'] = calculate_peak_uncertainty(peaks[\"P2\"])\n",
    "peaks['dP3'] = calculate_peak_uncertainty(peaks[\"P3\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "for index, peak in enumerate(peak_labels):\n",
    "    plot_hist(peaks[peak], calculate_bins(peaks[peak]) + bin_offsets[index], save=(f'muon_{peak}_spec', 'vorversuch'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2.4\n1       3.0\n2       9.8\n3      11.2\n4     200.0\n5       3.0\n6       5.6\n7       5.4\n8       5.0\n9       3.6\n10      3.2\n11      4.2\n12      2.4\n13      2.2\n14      4.6\n15      6.4\n16      2.6\n17      3.4\n18      5.2\n19      3.4\n20      2.4\n21      3.4\n22      4.4\n23      3.0\n24      5.8\n25      9.0\n26      2.6\n27      2.0\n28      3.6\n29      3.4\n30      5.2\n31      3.6\n32      4.2\n33      2.4\n34      4.4\n35      4.2\n36      2.8\n37      3.2\n38      2.8\n39      2.6\n40      3.2\n41      2.8\n42      7.4\n43      4.4\n44      5.2\n45      9.0\n46      4.2\n47      6.2\n48      6.4\n49     11.4\nName: P2, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peaks['P2']"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "argv": [
    "/usr/bin/python3",
    "-m",
    "ipykernel_launcher",
    "-f",
    "{connection_file}"
   ],
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