From 75b8e9864d9e2647bd2c887762acc431f53c77a3 Mon Sep 17 00:00:00 2001 From: "Peter J. Holzer" Date: Fri, 11 Feb 2022 13:20:58 +0100 Subject: [PATCH] Add jupyter notebook --- test.ipynb | 98 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 98 insertions(+) create mode 100644 test.ipynb diff --git a/test.ipynb b/test.ipynb new file mode 100644 index 0000000..f0df957 --- /dev/null +++ b/test.ipynb @@ -0,0 +1,98 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello, jupyter!\n" + ] + } + ], + "source": [ + "print(\"hello, jupyter!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Überschrift\n", + "\n", + "Ein Absatz mit `markdown` Markup: Das ist *wichtig*.\n", + "\n", + "| Kann | das |\n", + "|------|-----|\n", + "| auch | Tabellen? |" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'sine')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import math\n", + "%matplotlib inline\n", + "x = np.arange(0, math.pi*2, 0.05)\n", + "y = np.sin(x)\n", + "fig = plt.figure()\n", + "ax = fig.add_axes([0, 0, 1, 1])\n", + "ax.plot(x, y)\n", + "ax.set_title(\"sine wave\")\n", + "ax.set_xlabel(\"angle\")\n", + "ax.set_ylabel(\"sine\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}