{
"nbformat": 4,
"nbformat_minor": 2,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sample Notebook\n",
"\n",
"This is a sample Jupyter notebook for testing anytomd conversion."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"def greet(name):\n",
" # CJK comment: \ud55c\uad6d\uc5b4 \u4e2d\u6587 \u65e5\u672c\u8a9e\n",
" return f\"Hello, {name}! \ud83d\ude80\""
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "'Hello, World! \ud83d\ude80'"
},
"metadata": {},
"execution_count": 1
}
],
"execution_count": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Processing\n",
"\n",
"Below we process some data."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"data = [1, 2, 3, 4, 5]\n",
"total = sum(data)\n",
"print(f\"Total: {total}\")"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Total: 15\n"
]
}
],
"execution_count": 2
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"This is raw text that should be in a plain fenced block."
]
}
]
}