wehub-resource-sync
6188617037
Environment Corruption Check / test-python-versions (3.12.8) (push) Failing after 2s
Environment Corruption Check / test-python-versions (3.11.11) (push) Failing after 1s
Pre-commit checks / pre-commit-check (push) Failing after 1s
Environment Corruption Check / test-python-versions (3.13.2) (push) Failing after 4s
147 lines
4.4 KiB
Markdown
147 lines
4.4 KiB
Markdown
|
|
|
|
# Chart Visualization Tool
|
|
|
|
The chart visualization tool generates data processing code through Python and ultimately invokes [@visactor/vmind](https://github.com/VisActor/VMind) to obtain chart specifications. Chart rendering is implemented using [@visactor/vchart](https://github.com/VisActor/VChart).
|
|
|
|
## Installation (Mac / Linux)
|
|
|
|
1. Install node >= 18
|
|
|
|
```bash
|
|
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
|
|
```
|
|
|
|
2. Install dependencies
|
|
|
|
```bash
|
|
# 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](https://github.com/coreybutler/nvm-windows?tab=readme-ov-file#readme) and install it.
|
|
|
|
2. Use nvm to install node
|
|
|
|
```powershell
|
|
# 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
|
|
```
|
|
|
|
3. Install dependencies
|
|
|
|
```bash
|
|
# 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
|
|
```typescript
|
|
{
|
|
// 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
|
|
```typescript
|
|
{
|
|
// 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
|
|
```typescript
|
|
{
|
|
// 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](https://www.visactor.io/vchart/guide/tutorial_docs/Theme/Theme_Extension).
|
|
|
|
## 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:
|
|
```bash
|
|
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:
|
|
```bash
|
|
python -m app.tool.chart_visualization.test.report_demo
|
|
```
|
|
The results are also located under `workspace\visualization`.
|