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

82 lines
27 KiB
Plaintext

{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# A simple, good-looking plot\n",
"\n",
"Demoing some simple features of matplotlib\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 360x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib\n",
"\n",
"#matplotlib.use(\"Agg\")\n",
"import matplotlib.pyplot as plt\n",
"\n",
"fig = plt.figure(figsize=(5, 4), dpi=72)\n",
"axes = fig.add_axes((0.01, 0.01, 0.98, 0.98))\n",
"X = np.linspace(0, 2, 200)\n",
"Y = np.sin(2 * np.pi * X)\n",
"plt.plot(X, Y, lw=2)\n",
"plt.ylim(-1.1, 1.1)\n",
"plt.grid()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 4
}