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

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Aliased versus anti-aliased\n\nThe example shows aliased versus anti-aliased text.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n\nsize = 128, 16\ndpi = 72.0\nfigsize = size[0] / float(dpi), size[1] / float(dpi)\nfig = plt.figure(figsize=figsize, dpi=dpi)\nfig.patch.set_alpha(0)\nplt.axes((0, 0, 1, 1), frameon=False)\n\nplt.rcParams[\"text.antialiased\"] = True\nplt.text(0.5, 0.5, \"Anti-aliased\", ha=\"center\", va=\"center\")\n\nplt.xlim(0, 1)\nplt.ylim(0, 1)\nplt.xticks([])\nplt.yticks([])\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
}