From 4c1911c76dfa445368d3b1e2a326b93c323c1db6 Mon Sep 17 00:00:00 2001 From: Roger Wolf <roger.wolf@kit.edu> Date: Thu, 24 Oct 2024 10:14:42 +0200 Subject: [PATCH] add new file to support new lecture --- tools/work_with_data.ipynb | 111 +++++++++++++++++++++++++++++++++++++ 1 file changed, 111 insertions(+) create mode 100644 tools/work_with_data.ipynb diff --git a/tools/work_with_data.ipynb b/tools/work_with_data.ipynb new file mode 100644 index 0000000..888c73a --- /dev/null +++ b/tools/work_with_data.ipynb @@ -0,0 +1,111 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4abb1785-91d0-4c14-8b61-a04e2823a405", + "metadata": {}, + "source": [ + "# Aufnahme der Daten von Hand\n", + "\n", + "Um Daten manuell (d.h. per Hand) aufzunehmen und ins Jupyter-notebook einzutragen empfiehlt es sich **übersichtlich und gut organisiert** zu arbeiten. Die Aufnahme der Daten ist dadurch schneller und weniger Fehleranfällig. Gehen Sie dabei wie folgt vor:\n", + "\n", + " * Machen Sie sich **bereits in der Vorbereitung zum Versuch** klar, was Sie messen möchten. \n", + " * Legen Sie geeignete Tabellen in Code-Zellen an. \n", + " * Richten Sie die grundlegende Weiterverarbeitung der Messdaten ein, indem Sie z.B. **Code-Snipets zur Berechnung systematischer Unsicherheiten in einer Code-Zelle hinterlegen**. \n", + " * Richten Sie die Möglichkeit ein die aufgenommenen **Daten sofort zu visualisieren**. So können Sie missglückte Messungen (sog. *outlier*) leicht identifizieren und ggf. neu messen. \n", + " * Speichern Sie das Jupyter-notebook während der Datennahme in regelmäßigen Zeitabständen, damit Ihnen keine Daten verloren gehen.\n", + "\n", + "In der folgenden Code-Zelle sehen Sie ein Beispiel für eine vorgefertigte Tabelle, wie man Sie bereits in der Vorbereitung zum Versuch einrichten kann, und in die Sie nur noch Ihre Messwerte eintragen müssen. Nach Ausführen der Code-Zelle können Sie die aufgenommenen Daten zu jedem Zeitpunkt **sofort visualisieren**: " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c8b97a8a-fbfb-4b35-aa9c-a0fb933adfb7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example for the visualization of measurement points\n", + "import matplotlib.pyplot as plt\n", + "# You may use the numpy.nan object as a placeholder for values that you have \n", + "# not measured, yet\n", + "from numpy import nan, array\n", + "\n", + "import numpy as np, matplotlib.pyplot as plt\n", + "# die Messdaten\n", + "x_data = [1, 2, 3, 4, 5]\n", + "x_err = [0.3, 0.3, 0.2, 0.2, 0.1] \n", + "y_data = [1, 4, 9, 16, 25]\n", + "y_err = [0.75, 0.8, 1.2, 2.5, 3.5] \n", + "# grafische Darstellung\n", + "plt.errorbar(x_data, y_data, xerr=x_err, yerr=y_err, color='darkblue', marker=\"o\")\n", + "plt.ylabel(\"y-Achse\", size='x-large')\n", + "plt.xlabel(\"x-Achse\")\n", + "plt.title(\"Beispiel zur Visualisierung von Daten mit Unsicherheiten\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5dcc2735-37bb-4bcc-be21-1ca7bcfb63f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gewichteter Mittelwert: 4.5402900284048435 +/- 0.03668095690941965\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# Werte\n", + "vx = np.array([1, 2, 3, 4, 6, 5, 8, 7])\n", + "dx = np.array([0.1, 0.2, 0.1, 0.1, 0.05, 0.75, 1.5, 0.25]) \n", + "\n", + "# Berechnung eines gewichteten Mittelwerts\n", + "w = 1./dx**2\n", + "sumw = np.sum(w)\n", + "mean = np.sum(w*vx)/sumw\n", + "sigma = np.sqrt(1./sumw)\n", + "\n", + "print(\"Gewichteter Mittelwert:\", mean, \"+/-\", sigma)" + ] + } + ], + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} -- GitLab