ortsauflösung.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "using CSV, Plots, DataFrames, Measurements, LsqFit, Roots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auslesen der Daten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25-element Vector{Measurement{Float64}}:\n",
       " 1571.0 ± 39.0\n",
       " 1325.0 ± 36.0\n",
       "  780.0 ± 27.0\n",
       "  427.0 ± 20.0\n",
       "  300.0 ± 17.0\n",
       "  264.0 ± 16.0\n",
       "  288.0 ± 16.0\n",
       "  311.0 ± 17.0\n",
       "  248.0 ± 15.0\n",
       "  276.0 ± 16.0\n",
       "        ⋮\n",
       "  307.0 ± 17.0\n",
       "  283.0 ± 16.0\n",
       "  265.0 ± 16.0\n",
       "  261.0 ± 16.0\n",
       "  231.0 ± 15.0\n",
       "  203.0 ± 14.0\n",
       "  229.0 ± 15.0\n",
       "  129.0 ± 11.0\n",
       "   88.0 ± 9.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = CSV.read(\"data/Ortsauflösung.csv\", DataFrame)\n",
    "D_dist = 0.8 #Fehler der Distanz\n",
    "\n",
    "dists = measurement.(data[:,1], D_dist)\n",
    "c_per_mins = measurement.(data[:,2], data[:,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotten der Ergebnisse - Fitten der Kurve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit-Parameter: [1281.7421585331008, 0.3197180377657868, 1.3262745820068236, 231.03508813589804]\n",
      "A = measurement(fit.param[1], param_errors[1]) = 1282.0 ± 63.0\n",
      "mu = measurement(fit.param[2], param_errors[2]) = 0.32 ± 0.073\n",
      "sigma = measurement(fit.param[3], param_errors[3]) = 1.326 ± 0.078\n",
      "b = measurement(fit.param[4], param_errors[4]) = 231.0 ± 18.0\n",
      "FWHM: 3.12 ± 0.18\n"
     ]
    },
    {
     "data": {
      "image/png": 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Definiere die Fit-Funktion\n",
    "fit_model(x, p) = @. p[1] * exp(-1/2 * ((x - p[2]) / p[3])^2) + p[4]\n",
    "\n",
    "# Extrahiere die Werte aus den Messungen\n",
    "x_data = [dists[i].val for i in 1:length(dists)]\n",
    "y_data = [c_per_mins[i].val for i in 1:length(c_per_mins)]\n",
    "\n",
    "# Initiale Schätzungen für die Parameter A, mu, sigma, b\n",
    "p0 = [2000.0, 0.0, 2.0, 200.0]  # Erhöhe den Wert für A\n",
    "\n",
    "# Führe den Fit durch\n",
    "fit = curve_fit(fit_model, x_data, y_data, p0)\n",
    "\n",
    "# Berechne die Unsicherheiten der Fit-Parameter\n",
    "param_errors = standard_errors(fit)\n",
    "\n",
    "# Zeige die Fit-Parameter an \n",
    "println(\"Fit-Parameter: \", fit.param)\n",
    "\n",
    "# Plotte die Daten zusammen mit dem Fit und der Halbwertsbreite\n",
    "fig = plot(\n",
    "    dists, c_per_mins,\n",
    "    seriestype=\"scatter\",\n",
    "    label=\"Messwerte\",\n",
    "    xlabel=\"Distanz [mm]\",\n",
    "    ylabel=\"Counts pro Minute [1/min]\",\n",
    "    title=\"Ortsauflösung\"\n",
    ")\n",
    "\n",
    "plot!(\n",
    "    x -> fit_model(x, fit.param),\n",
    "    minimum(x_data):0.1:maximum(x_data),\n",
    "    label=\"Fit\"\n",
    ")\n",
    "\n",
    "#Finde und Plotte FWHM\n",
    "FWHM = measurement(2 * sqrt(2 * log(2)) * fit.param[3], 2 * sqrt(2 * log(2)) * param_errors[3])\n",
    "c_per_min_max_half = maximum(y_data) / 2\n",
    "\n",
    "plot!(\n",
    "    [-22.0,22.0], [c_per_min_max_half,c_per_min_max_half],\n",
    "    label=\"FWHM\",\n",
    "    line=:dash\n",
    ")\n",
    "\n",
    "@show A = measurement(fit.param[1], param_errors[1])\n",
    "@show mu = measurement(fit.param[2], param_errors[2])\n",
    "@show sigma = measurement(fit.param[3], param_errors[3])\n",
    "@show b = measurement(fit.param[4], param_errors[4])\n",
    "\n",
    "\n",
    "println(\"FWHM: \", FWHM)\n",
    "\n",
    "savefig(\"plots/Ortsauflösung.png\")\n",
    "\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"output.csv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Erstelle ein DataFrame mit Beispieldaten\n",
    "df = DataFrame(FWHM = FWHM)\n",
    "\n",
    "# Schreibe das DataFrame in eine CSV-Datei\n",
    "CSV.write(\"output.csv\", df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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