diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index cf953372..48d0ec20 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -8,4 +8,4 @@
"openFiles": ["tutorial/README.md"]
}
}
- }
\ No newline at end of file
+}
\ No newline at end of file
diff --git a/tutorial/notebook/go_analysis.ipynb b/tutorial/notebook/go_analysis.ipynb
index ba1078d7..aa03e4ee 100644
--- a/tutorial/notebook/go_analysis.ipynb
+++ b/tutorial/notebook/go_analysis.ipynb
@@ -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。"
]
},
{
@@ -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。"
]
},
{
@@ -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。"
]
},
{
@@ -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："
]
},
{
diff --git a/tutorial/notebook/python_analysis.ipynb b/tutorial/notebook/python_analysis.ipynb
new file mode 100644
index 00000000..79d88848
--- /dev/null
+++ b/tutorial/notebook/python_analysis.ipynb
@@ -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": [
+ "示例 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
+}
diff --git a/tutorial/notebook/xml_analysis.ipynb b/tutorial/notebook/xml_analysis.ipynb
new file mode 100644
index 00000000..c81efd2f
--- /dev/null
+++ b/tutorial/notebook/xml_analysis.ipynb
@@ -0,0 +1,280 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "这是一个使用CodeFuse-Query分析 XML 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 [sofa-boot](https://github.com/sofastack/sofa-boot.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/sofastack/sofa-boot.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sofa-boot子目录），分析的语言（xml），以及db文件的存储路径（放置在当前目录下的/db/sofa-boot）。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 sofa-boot --data-language-type xml --output ./db/sofa-boot"
+ ]
+ },
+ {
+ "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": [
+ "示例 查询 [sofa-boot](https://github.com/sofastack/sofa-boot.git) 的POM信息（如文件路径，引用的jar资源，版本信息）。\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POM信息Godel脚本。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%db ./db/sofa-boot\n",
+ "// script\n",
+ "use coref::xml::*\n",
+ "\n",
+ "schema DependencyElement extends XmlElement {}\n",
+ "\n",
+ "impl DependencyElement {\n",
+ " @data_constraint\n",
+ " pub fn __all__(db: XmlDB) -> *DependencyElement {\n",
+ " for(e in XmlElement(db)) {\n",
+ " if (e.getElementName() = \"dependency\") {\n",
+ " yield DependencyElement {\n",
+ " id: e.id,\n",
+ " location_id: e.location_id,\n",
+ " parent_id: e.parent_id,\n",
+ " index_order: e.index_order\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "schema GroupElement extends XmlElement {}\n",
+ "\n",
+ "impl GroupElement {\n",
+ " @data_constraint\n",
+ " pub fn __all__(db: XmlDB) -> *GroupElement {\n",
+ " for(e in XmlElement(db)) {\n",
+ " if (e.getElementName() = \"groupId\") {\n",
+ " yield GroupElement {\n",
+ " id: e.id,\n",
+ " location_id: e.location_id,\n",
+ " parent_id: e.parent_id,\n",
+ " index_order: e.index_order\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "schema VersionElement extends XmlElement {}\n",
+ "\n",
+ "impl VersionElement {\n",
+ " @data_constraint\n",
+ " pub fn __all__(db: XmlDB) -> *VersionElement {\n",
+ " for(e in XmlElement(db)) {\n",
+ " if (e.getElementName() = \"version\") {\n",
+ " yield VersionElement {\n",
+ " id: e.id,\n",
+ " location_id: e.location_id,\n",
+ " parent_id: e.parent_id,\n",
+ " index_order: e.index_order\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "schema ArtifactElement extends XmlElement {}\n",
+ "\n",
+ "impl ArtifactElement {\n",
+ " @data_constraint\n",
+ " pub fn __all__(db: XmlDB) -> *ArtifactElement {\n",
+ " for(e in XmlElement(db)) {\n",
+ " if (e.getElementName() = \"artifactId\") {\n",
+ " yield ArtifactElement {\n",
+ " id: e.id,\n",
+ " location_id: e.location_id,\n",
+ " parent_id: e.parent_id,\n",
+ " index_order: e.index_order\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "schema PomFile extends XmlFile {}\n",
+ "\n",
+ "impl PomFile {\n",
+ " @data_constraint\n",
+ " pub fn __all__(db: XmlDB) -> *PomFile {\n",
+ " for(f in XmlFile(db)) {\n",
+ " if (f.getFileName() = \"pom.xml\") {\n",
+ " yield PomFile {\n",
+ " id: f.id,\n",
+ " file_name: f.file_name,\n",
+ " relative_path: f.relative_path\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// output relative path of the file, referenced jar name and version\n",
+ "fn out(fileName: string, m1: string, m2: string, m3: string) -> bool {\n",
+ " let (db = XmlDB::load(\"coref_xml_src.db\")) {\n",
+ " for (f in PomFile(db),\n",
+ " e1 in GroupElement(db),\n",
+ " e2 in VersionElement(db),\n",
+ " e3 in ArtifactElement(db),\n",
+ " c1 in XmlCharacter(db),\n",
+ " c2 in XmlCharacter(db),\n",
+ " c3 in XmlCharacter(db),\n",
+ " p in DependencyElement(db)) {\n",
+ " if (f.key_eq(p.getLocation().getFile()) &&\n",
+ " fileName = f.getRelativePath() &&\n",
+ " p.key_eq(e1.getParent()) &&\n",
+ " e1.key_eq(c1.getBelongedElement()) &&\n",
+ " m1 = c1.getText() &&\n",
+ " p.key_eq(e2.getParent()) &&\n",
+ " e2.key_eq(c2.getBelongedElement()) &&\n",
+ " m2 = c2.getText() &&\n",
+ " p.key_eq(e3.getParent()) &&\n",
+ " e3.key_eq(c3.getBelongedElement()) &&\n",
+ " m3 = c3.getText()) {\n",
+ " return true\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "fn main() {\n",
+ " output(out())\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "保存上一次运行的 query 结果保存到一个 JSON/CSV 文件"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%save_to ./query.csv"
+ ]
+ },
+ {
+ "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。"
+ ]
+ },
+ {
+ "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
+}