{ "cells": [ { "cell_type": "code", "execution_count": 15, "id": "f7103e0f-bcae-4297-b550-31fe911dffa1", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 24, "id": "88baf7ed-203d-4960-b314-413828ce29b1", "metadata": {}, "outputs": [], "source": [ "def solution( n, t, d, K, m ):\n", " return n*t" ] }, { "cell_type": "code", "execution_count": 25, "id": "32ec6b94-fbb5-4994-8d96-e8b3d1900299", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "K, m, d = 1, 1, 0.3\n", "t = np.linspace(0, 0.06,100 )\n", "fig, ax = plt.subplots( 1,1 )\n", "for n in [1, 3, 5, 11]:\n", " # damit plotten wir die bahnkurve\n", " ax.plot( t, solution(n, t, d, K, m), label = f'n = {n}' )\n", " ax.legend()" ] } ], "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": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 5 }