diff options
Diffstat (limited to 'sem5/sig/mm11')
-rw-r--r-- | sem5/sig/mm11/Opgaver.ipynb | 93 |
1 files changed, 93 insertions, 0 deletions
diff --git a/sem5/sig/mm11/Opgaver.ipynb b/sem5/sig/mm11/Opgaver.ipynb new file mode 100644 index 0000000..d8077e0 --- /dev/null +++ b/sem5/sig/mm11/Opgaver.ipynb @@ -0,0 +1,93 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Opgave 1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import cmath\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def f(k):\n", + " part1 = (2 + 2j * k * math.pi)\n", + " tope = (1 + k * 1j * math.pi)\n", + " part2 = np.exp(tope) - np.exp(-tope)\n", + " return part2 / part1\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.collections.PathCollection at 0x7f78484dd610>" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = np.arange(-30, 30, dtype=np.complex)\n", + "y = abs(f(x))\n", + "plt.scatter(x, y)\n" + ] + } + ], + "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": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} |