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Nov 30, 2023
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Add python&xml tutorial and fix typos #8

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2 changes: 1 addition & 1 deletion .devcontainer/devcontainer.json
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Expand Up @@ -8,4 +8,4 @@
"openFiles": ["tutorial/README.md"]
}
}
}
}
8 changes: 4 additions & 4 deletions tutorial/notebook/go_analysis.ipynb
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Expand Up @@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"这是一个使用CodeFuse-Query分析Go项目的教程。在教程中，你将体验到使用命令行工具对代码仓库进行数据化，然后使用Godel语言来分析这个仓库。"
"这是一个使用 CodeFuse-Query 分析 Go 项目的教程。在教程中，你将体验到使用命令行工具对代码仓库进行数据化，然后使用 Godel 语言来分析这个仓库。"
]
},
{
Expand Down Expand Up @@ -54,7 +54,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"STEP 1: 代码数据化。使用 `sparrow database create` 命令创建一个db文件，指定待分析的仓库地址（当前目录下的gorm子目录），分析的语言（go），以及db文件的存储路径（放置在当前目录下的/db/gorm）。执行该命令之后，竟会生成一份db文件，该文件存储着代码仓库的结构化数据，之后的分析就是针对这份数据进行。"
"STEP 1: 代码数据化。使用 `sparrow database create` 命令创建一个db文件，指定待分析的仓库地址（当前目录下的gorm子目录），分析的语言（go），以及db文件的存储路径（放置在当前目录下的/db/gorm）。执行该命令之后，就会生成一份db文件，该文件存储着代码仓库的结构化数据，之后的分析就是针对这份数据进行。"
]
},
{
Expand All @@ -75,7 +75,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"STEP 2: 使用Godel分析语言分析db文件。在本教程中，可以点击代码左侧的执行按钮，直接运行分析脚本。在命令行中，你可以使用 `sparrow query run` 命令来执行查询脚本，具体可以使用`sparrow query run -h` 来获取详细参数信息。"
"STEP 2: 使用Godel分析语言分析db文件。在本教程中，可以点击代码左侧的执行按钮，或使用快捷键：`Shift+Enter`，直接运行分析脚本。这里使用 `%db /path/to/db` 魔法命令来设置COREF db路径，内核会读取这个值来进行query查询。"
]
},
{
Expand Down Expand Up @@ -150,7 +150,7 @@
"collapsed": false
},
"source": [
"STEP 3: 好了，你可以针对分析生成的结果，进行进一步的代码分析了，比如你可以结合pandas库，使用刚刚生成的 query.json 实现最大函数复杂度的排序查询："
"STEP 3: 好了，你可以针对分析生成的结果，进行进一步的代码分析了，比如你可以结合pandas库，使用刚刚生成的 query.json 实现最大函数复杂度Top 10的排序查询："
]
},
{
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214 changes: 214 additions & 0 deletions tutorial/notebook/python_analysis.ipynb
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@@ -0,0 +1,214 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"这是一个使用 CodeFuse-Query 分析 Python 项目的教程。在教程中，你将体验到使用命令行工具对代码仓库进行数据化，然后使用 Godel 语言来分析这个仓库。"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"检查cli是否就绪"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!which sparrow"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"STEP 0: 克隆要分析的仓库。我们以 python 仓库 [requests](https://github.com/psf/requests.git) 项目为例。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-11-22T08:30:58.387715Z",
"start_time": "2023-11-22T08:30:44.572634Z"
}
},
"outputs": [],
"source": [
"!git clone https://github.com/psf/requests.git"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"STEP 1: 代码数据化。使用 `sparrow database create` 命令创建一个db文件，指定待分析的仓库地址（当前目录下的requests子目录），分析的语言（python），以及db文件的存储路径（放置在当前目录下的/db/requests）。执行该命令之后，就会生成一份db文件，该文件存储着代码仓库的结构化数据，之后的分析就是针对这份数据进行。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-11-23T03:46:32.220317Z",
"start_time": "2023-11-23T03:46:12.785705Z"
}
},
"outputs": [],
"source": [
"!sparrow database create --source-root requests --data-language-type python --output ./db/requests"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"STEP 2: 使用Godel分析语言分析db文件。在本教程中，可以点击代码左侧的执行按钮，或使用快捷键：`Shift+Enter`，直接运行分析脚本。这里使用 `%db /path/to/db` 魔法命令来设置COREF db路径，内核会读取这个值来进行query查询。"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<b>示例</b> 查询 [requests](https://github.com/psf/requests.git) 的文件注释率信息。\n",
"\n",
"第一行通过内核魔法命令指定分析的db路径，后面写查询文件代码注释率 Godel 脚本。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%db ./db/requests\n",
"// script\n",
"use coref::python::*\n",
"\n",
"fn default_db() -> PythonDB {\n",
" return PythonDB::load(\"coref_python_src.db\")\n",
"}\n",
"\n",
"/**\n",
" * Get cyclomatic complexity of functions\n",
" *\n",
" * @param name function name\n",
" * @param value cyclomatic complexity of function\n",
" * @param path path of file including this function\n",
" * @param sline function start line\n",
" * @param eline function end line\n",
" */\n",
"fn getCyclomaticComplexity(\n",
" name: string,\n",
" value: int,\n",
" path: string,\n",
" sline: int,\n",
" eline: int) -> bool {\n",
" // get metric function\n",
" for (c in MetricFunction(default_db())) {\n",
" if (path = c.getLocation().getFile().getRelativePath() &&\n",
" name = c.getQualifiedName() &&\n",
" value = c.getCyclomaticComplexity() &&\n",
" sline = c.getLocation().getStartLineNumber() &&\n",
" eline = c.getLocation().getEndLineNumber()) {\n",
" return true\n",
" }\n",
" }\n",
"}\n",
"\n",
"fn main() {\n",
" output(getCyclomaticComplexity())\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"保存上一次运行的 query 结果保存到一个JSON文件"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%save_to ./query.json"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"STEP 3: 好了，你可以针对分析生成的结果，进行进一步的代码分析了，比如你可以结合pandas库，使用刚刚生成的 query.json 实现最大函数复杂度Top 10的排序查询："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-11-23T03:54:56.998681Z",
"start_time": "2023-11-23T03:54:56.976694Z"
},
"collapsed": false
},
"outputs": [],
"source": [
"%%python\n",
"import pandas as pd\n",
"data = pd.read_json('./query.json')\n",
"data.sort_values('value', ascending=False, inplace=True)\n",
"top_10 = data.head(10)\n",
"print(top_10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Enjoy！"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Godel kernel",
"language": "rust",
"name": "godel-jupyter"
},
"language_info": {
"file_extension": ".gdl",
"help_links": [
{
"text": "Godel kernel Magics",
"url": "https://sparrow.alipay.com"
}
],
"mimetype": "text/rust",
"name": "rust",
"version": "0.0.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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