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Chart Visualization Tool

The chart visualization tool generates data processing code through Python and ultimately invokes @visactor/vmind to obtain chart specifications. Chart rendering is implemented using @visactor/vchart.

Installation (Mac / Linux)

  1. Install node >= 18
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash
# Activate nvm, for example in Bash
source ~/.bashrc
# Then install the latest stable release of Node
nvm install node
# Activate usage, for example if the latest stable release is 22, then use 22
nvm use 22
  1. Install dependencies
# Navigate to the appropriate location in the current repository
cd app/tool/chart_visualization
npm install

Installation (Windows)

  1. Install nvm-windows

    Download the latest version nvm-setup.exe from the official GitHub page and install it.

  2. Use nvm to install node

# Then install the latest stable release of Node
nvm install node
# Activate usage, for example if the latest stable release is 22, then use 22
nvm use 22
  1. Install dependencies
# Navigate to the appropriate location in the current repository
cd app/tool/chart_visualization
npm install

Tool

python_execute

Execute the necessary parts of data analysis (excluding data visualization) using Python code, including data processing, data summary, report generation, and some general Python script code.

Input

{
  // Code type: data processing/data report/other general tasks
  code_type: "process" | "report" | "others"
  // Final execution code
  code: string;
}

Output

Python execution results, including the saving of intermediate files and print output results.

visualization_preparation

A pre-tool for data visualization with two purposes,

Data -> Chart

Used to extract the data needed for analysis (.csv) and the corresponding visualization description from the data, ultimately outputting a JSON configuration file.

Chart + Insight -> Chart

Select existing charts and corresponding data insights, choose data insights to add to the chart in the form of data annotations, and finally generate a JSON configuration file.

Input

{
  // Code type: data visualization or data insight addition
  code_type: "visualization" | "insight"
  // Python code used to produce the final JSON file
  code: string;
}

Output

A configuration file for data visualization, used for the data_visualization tool.

data_visualization

Generate specific data visualizations based on the content of visualization_preparation.

Input

{
  // Configuration file path
  json_path: string;
  // Current purpose, data visualization or insight annotation addition
  tool_type: "visualization" | "insight";
  // Final product png or html; html supports vchart rendering and interaction
  output_type: 'png' | 'html'
  // Language, currently supports Chinese and English
  language: "zh" | "en"
}

VMind Configuration

LLM

VMind requires LLM invocation for intelligent chart generation. By default, it uses the config.llm["default"] configuration.

Generation Settings

Main configurations include chart dimensions, theme, and generation method:

Generation Method

Default: png. Currently supports automatic selection of output_type by LLM based on context.

Dimensions

Default dimensions are unspecified. For HTML output, charts fill the entire page by default. For PNG output, defaults to 1000*1000.

Theme

Default theme: 'light'. VChart supports multiple themes. See Themes.

Test

Currently, three tasks of different difficulty levels are set for testing.

Simple Chart Generation Task

Provide data and specific chart generation requirements, test results, execute the command:

python -m app.tool.chart_visualization.test.chart_demo

The results should be located under workspace\visualization, involving 9 different chart results.

Simple Data Report Task

Provide simple raw data analysis requirements, requiring simple processing of the data, execute the command:

python -m app.tool.chart_visualization.test.report_demo

The results are also located under workspace\visualization.