Files
jupyter-collection/scientific-computing-2/auto_examples_jupyter_2/exercises/plot_exercise_5.ipynb
2025-10-21 11:20:44 +08:00

43 lines
1.3 KiB
Plaintext

{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Exercise 5\n\nExercise 5 with matplotlib.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(8, 5), dpi=80)\nplt.subplot(111)\n\nX = np.linspace(-np.pi, np.pi, 256)\nS = np.sin(X)\nC = np.cos(X)\n\nplt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\nplt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\")\n\nplt.xlim(X.min() * 1.1, X.max() * 1.1)\nplt.xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])\n\nplt.ylim(C.min() * 1.1, C.max() * 1.1)\nplt.yticks([-1, 0, +1])\n\nplt.show()"
]
}
],
"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": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}